From 75259500f80266998f232a94853b0bc08d2925cc Mon Sep 17 00:00:00 2001 From: KB Sriram Date: Wed, 28 Feb 2018 07:16:20 -0800 Subject: [PATCH 0001/1691] C++ gradients: Fractional*Pool, Soft{Plus,Sign} 1. Adds gradients for four nn ops: FractionalAvgPool FractionalMaxPool SoftPlus SoftSign 2. Update randomization to allow numeric gradient checks on max pooling algorithms with more than one pool. Resolves https://github.com/tensorflow/tensorflow/issues/17330 --- tensorflow/cc/gradients/nn_grad.cc | 47 ++++++++++++++ tensorflow/cc/gradients/nn_grad_test.cc | 84 ++++++++++++++++++++----- 2 files changed, 115 insertions(+), 16 deletions(-) diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc index 63a67f09f6f7c2..4b89dac4c049c8 100644 --- a/tensorflow/cc/gradients/nn_grad.cc +++ b/tensorflow/cc/gradients/nn_grad.cc @@ -272,6 +272,53 @@ Status LRNGradHelper(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("LRN", LRNGradHelper); +Status SoftplusGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + auto dx = internal::SoftplusGrad(scope, grad_inputs[0], op.input(0)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("Softplus", SoftplusGradHelper); + +Status SoftsignGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + auto dx = internal::SoftsignGrad(scope, grad_inputs[0], op.input(0)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("Softsign", SoftsignGradHelper); + +Status FractionalAvgPoolGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + bool overlapping; + TF_RETURN_IF_ERROR( + GetNodeAttr(op.output(0).node()->attrs(), "overlapping", &overlapping)); + auto dx = internal::FractionalAvgPoolGrad( + scope, Shape(scope, op.input(0), Shape::OutType(DT_INT64)), + grad_inputs[0], op.output(1), op.output(2), + internal::FractionalAvgPoolGrad::Overlapping(overlapping)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("FractionalAvgPool", FractionalAvgPoolGradHelper); + +Status FractionalMaxPoolGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + bool overlapping; + TF_RETURN_IF_ERROR( + GetNodeAttr(op.output(0).node()->attrs(), "overlapping", &overlapping)); + auto dx = internal::FractionalMaxPoolGrad( + scope, op.input(0), op.output(0), grad_inputs[0], op.output(1), + op.output(2), internal::FractionalMaxPoolGrad::Overlapping(overlapping)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("FractionalMaxPool", FractionalMaxPoolGradHelper); + } // anonymous namespace } // namespace ops } // namespace tensorflow diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc index c4eba7ecb017fe..b4d457a9d14eb7 100644 --- a/tensorflow/cc/gradients/nn_grad_test.cc +++ b/tensorflow/cc/gradients/nn_grad_test.cc @@ -28,6 +28,8 @@ namespace { using ops::BiasAdd; using ops::Conv2D; using ops::Elu; +using ops::FractionalAvgPool; +using ops::FractionalMaxPool; using ops::L2Loss; using ops::LogSoftmax; using ops::LRN; @@ -41,6 +43,8 @@ using ops::Relu; using ops::Relu6; using ops::Selu; using ops::Softmax; +using ops::Softplus; +using ops::Softsign; class NNGradTest : public ::testing::Test { protected: @@ -71,22 +75,30 @@ class NNGradTest : public ::testing::Test { EXPECT_LT(max_error, 1e-3); } - // Sets tensor with random values, ensuring that the max value is largest by - // a reasonable amount. - // This is an issue for MaxPool, MaxPoolV2 and MaxPool3D, in which - // perturbations by the numeric gradient computation in the gradient checker - // can change the max value if values are too close together. + // Sets tensor with random values, ensuring that every pair of elements are at + // least a reasonable amount apart. + // This is an issue for max pooling operations, in which perturbations by the + // numeric gradient computation in the gradient checker can change the max + // value if a pool has values that are too close together. template - void SetRandomValuesWithBumpedMax(Tensor* tensor) { + void SetRandomValuesForMaxPooling(Tensor* tensor) { auto tensor_flat = tensor->flat(); - tensor_flat.setRandom(); - int32 max_index = 0; - for (size_t i = 1; i < tensor->NumElements(); i++) { - if (tensor_flat(i) > tensor_flat(max_index)) { - max_index = i; - } + // First set the array to an increasing sequence of values spaced + // a reasonable amount apart + T cur = 0; + for (size_t i = 0; i < tensor->NumElements(); i++) { + tensor_flat(i) = cur; + cur += 5e-2; + } + // Fischer-Yates shuffle the array + for (size_t i = tensor->NumElements() - 1; i >= 1; i--) { + // j <- random integer 0 <= j <= i + size_t j = random::New64() % (i + 1); + // swap values at i, j + T tmp = tensor_flat(i); + tensor_flat(i) = tensor_flat(j); + tensor_flat(j) = tmp; } - tensor_flat(max_index) += 1e-2; } Scope scope_; @@ -189,7 +201,7 @@ TEST_F(NNGradTest, MaxPoolGradHelper) { const std::vector strides{1, 2, 2, 1}; auto y = MaxPool(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesWithBumpedMax(&x_init_value); + SetRandomValuesForMaxPooling(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -202,7 +214,7 @@ TEST_F(NNGradTest, MaxPoolGradV2Helper) { Tensor strides = test::AsTensor({1, 2, 2, 1}, {4}); auto y = MaxPoolV2(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesWithBumpedMax(&x_init_value); + SetRandomValuesForMaxPooling(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -215,7 +227,7 @@ TEST_F(NNGradTest, MaxPool3DGradHelper) { const std::vector strides{1, 3, 3, 3, 1}; auto y = MaxPool3D(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesWithBumpedMax(&x_init_value); + SetRandomValuesForMaxPooling(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -248,5 +260,45 @@ TEST_F(NNGradTest, LRN){ RunTest(x, x_shape, y, x_shape); } +TEST_F(NNGradTest, SoftplusGrad) { + TensorShape shape({3, 7}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); + auto y = Softplus(scope_, x); + RunTest(x, shape, y, shape); +} + +TEST_F(NNGradTest, SoftsignGrad) { + TensorShape shape({3, 7}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); + auto y = Softsign(scope_, x); + RunTest(x, shape, y, shape); +} + +TEST_F(NNGradTest, FractionalAvgPoolGradHelper) { + TensorShape x_shape({1, 3, 7, 1}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + // Force consistent pooling regions for unit testing. + auto y = FractionalAvgPool( + scope_, x, {1, 1.2, 1.9, 1}, + FractionalAvgPool::Deterministic(true).Overlapping(true).Seed(1).Seed2( + 2)); + TensorShape y_shape({1, 2, 3, 1}); + RunTest(x, x_shape, y.output, y_shape); +} + +TEST_F(NNGradTest, FractionalMaxPoolGradHelper) { + TensorShape x_shape({1, 3, 7, 1}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + // Force consistent pooling regions for unit testing. + auto y = FractionalMaxPool( + scope_, x, {1, 1.2, 1.9, 1}, + FractionalMaxPool::Deterministic(true).Overlapping(true).Seed(1).Seed2( + 2)); + Tensor x_init_value = Tensor(DT_FLOAT, x_shape); + SetRandomValuesForMaxPooling(&x_init_value); + TensorShape y_shape({1, 2, 3, 1}); + RunTest(x, x_init_value, y.output, y_shape); +} + } // namespace } // namespace tensorflow From 1da3a47287aa911287d6667dd837dc2a7ddaa8f1 Mon Sep 17 00:00:00 2001 From: Smit Shilu Date: Thu, 22 Mar 2018 10:58:51 -0400 Subject: [PATCH 0002/1691] Update BUILD exports_files(["LICENSE"]) gives error while building on Mac and Ubuntu --- tensorflow/contrib/lite/BUILD | 2 -- 1 file changed, 2 deletions(-) diff --git a/tensorflow/contrib/lite/BUILD b/tensorflow/contrib/lite/BUILD index dafe6f136ef671..1c5bc29763de6e 100644 --- a/tensorflow/contrib/lite/BUILD +++ b/tensorflow/contrib/lite/BUILD @@ -6,8 +6,6 @@ licenses(["notice"]) # Apache 2.0 load("//tensorflow/contrib/lite:build_def.bzl", "tflite_copts", "gen_selected_ops") -exports_files(["LICENSE"]) - exports_files(glob([ "testdata/*.bin", "testdata/*.pb", From e7f3ed2477c7910e68573880efd2310e149ca785 Mon Sep 17 00:00:00 2001 From: mbhuiyan Date: Wed, 4 Apr 2018 10:52:49 -0700 Subject: [PATCH 0003/1691] Fixing a unit test failure for INTEL MKL where memeory allocation check failed because of use of INTEL MKL --- .../direct_session_with_tracking_alloc_test.cc | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc index 31fb128f937ae4..0ff022a8bceff5 100644 --- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc +++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc @@ -101,11 +101,24 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) { EXPECT_EQ(2, shape.dim_size()); EXPECT_EQ(2, shape.dim(0).size()); EXPECT_EQ(1, shape.dim(1).size()); +#ifndef INTEL_MKL + // if MKL is used, it goes through various additional + // graph rewrite pass. In TF, everytime a graph pass + // happens, "constant" nodes are allocated + // and deallocated. Each allocation calls the + // (FindChunkPtr of BFCAllocator) + // , which increments the value of AllocationId. + // Thus AllocationId becomes more than 3 and 4 if + // MKL is used, they can be 10 and 11 or + // other numbers. If MKL is used + // following check will not hold. + // Thus, skipping the check if MKL is used. if (node->name() == y->name()) { EXPECT_EQ(3, cm->AllocationId(node, 0)); } else { EXPECT_EQ(4, cm->AllocationId(node, 0)); } +#endif } EXPECT_LE(0, cm->MaxExecutionTime(node)); EXPECT_GE(run_duration_micros, cm->MaxExecutionTime(node)); From 9d1aa895adda8644ddbb55b5e1dbb0797ea6cbb0 Mon Sep 17 00:00:00 2001 From: Jie Date: Wed, 11 Apr 2018 14:42:15 -0700 Subject: [PATCH 0004/1691] [tftrt update] Added support for TRT plugin during conversion - converter & shape inference are now aware of plugin factory. - each plugin does serialization of plugin type & input dimensions - wrapper for nvinfer1::IPlugin & nvinfer1::PluginFactory * compatible with TRT 3.0.4 plugin API. * future plugin API changes willl be updated. --- tensorflow/contrib/tensorrt/BUILD | 26 ++++++ .../contrib/tensorrt/convert/convert_graph.cc | 4 +- .../contrib/tensorrt/convert/convert_nodes.cc | 84 ++++++++++++++--- .../contrib/tensorrt/kernels/trt_engine_op.cc | 4 +- .../contrib/tensorrt/plugin/trt_plugin.cc | 89 +++++++++++++++++++ .../contrib/tensorrt/plugin/trt_plugin.h | 81 +++++++++++++++++ .../tensorrt/plugin/trt_plugin_factory.cc | 81 +++++++++++++++++ .../tensorrt/plugin/trt_plugin_factory.h | 83 +++++++++++++++++ .../tensorrt/plugin/trt_plugin_utils.cc | 36 ++++++++ .../tensorrt/plugin/trt_plugin_utils.h | 51 +++++++++++ .../contrib/tensorrt/shape_fn/trt_shfn.cc | 4 +- 11 files changed, 528 insertions(+), 15 deletions(-) create mode 100644 tensorflow/contrib/tensorrt/plugin/trt_plugin.cc create mode 100644 tensorflow/contrib/tensorrt/plugin/trt_plugin.h create mode 100644 tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc create mode 100644 tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h create mode 100644 tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc create mode 100644 tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index 2f316767b35e19..98f18835b06511 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -67,6 +67,7 @@ tf_cuda_library( visibility = ["//visibility:public"], deps = [ ":trt_logging", + ":trt_plugins", ] + if_tensorrt([ "@local_config_tensorrt//:nv_infer", ]) + tf_custom_op_library_additional_deps(), @@ -86,6 +87,7 @@ cc_library( visibility = ["//visibility:public"], deps = [ ":trt_logging", + ":trt_plugins", ":trt_resources", "//tensorflow/core:gpu_headers_lib", "//tensorflow/core:lib_proto_parsing", @@ -222,6 +224,7 @@ tf_cuda_library( ], deps = [ ":segment", + ":trt_plugins", ":trt_logging", ":trt_resources", "//tensorflow/core/grappler:grappler_item", @@ -272,3 +275,26 @@ tf_cc_test( "//tensorflow/core:test_main", ], ) + +# Library for the plugin factory +#cc_library( +tf_cuda_library( + name = "trt_plugins", + srcs = [ + "plugin/trt_plugin.cc", + "plugin/trt_plugin_factory.cc", + "plugin/trt_plugin_utils.cc", + ], + hdrs = [ + "plugin/trt_plugin.h", + "plugin/trt_plugin_factory.h", + "plugin/trt_plugin_utils.h", + ], + linkstatic = 1, + deps = [ + #"@protobuf_archive//:protobuf_headers", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + ]), +) + diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index b412b296e02751..899e1721e6e6fe 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/tensorrt/convert/convert_graph.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include #include @@ -75,7 +76,8 @@ bool IsTensorRTCandidate(const tensorflow::Node* node) { // TODO(ben,jie): ... }; // LINT.ThenChange(//tensorflow/contrib/tensorrt/convert/convert_nodes.h) - return candidate_ops.count(node->type_string()); + return (candidate_ops.count(node->type_string()) || + PluginFactoryTensorRT::GetInstance().IsPlugin(&node->type_string())); } void GetSubGraphIncomingEdges(const tensorflow::Graph& graph, diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 567b4af88df65b..a03c1e224ac718 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include #include @@ -246,6 +247,15 @@ class TFAttrs { return attrs_.count(key) ? this->get(key) : default_value; } + std::vector GetAllAttrKey() { + std::vector attr_list; + for (AttrMap::iterator iter = attrs_.begin(); iter != attrs_.end(); + iter++) { + attr_list.emplace_back(iter->first); + } + return attr_list; + } + private: typedef std::map AttrMap; AttrMap attrs_; @@ -262,6 +272,12 @@ std::vector TFAttrs::get>(string key) const { return std::vector(attr.begin(), attr.end()); } +template <> +std::vector TFAttrs::get>(string key) const { + auto attr = this->at(key)->list().f(); + return std::vector(attr.begin(), attr.end()); +} + template <> std::vector TFAttrs::get>(string key) const { auto attr = this->at(key)->list().s(); @@ -424,6 +440,7 @@ using OpConverter = class Converter { std::unordered_map trt_tensors_; std::unordered_map op_registry_; + OpConverter plugin_converter_; nvinfer1::INetworkDefinition* trt_network_; std::list> temp_bufs_; tensorflow::tensorrt::TRTWeightStore* weight_store_; @@ -444,8 +461,8 @@ class Converter { * remove this and annotate the edge as a control dependency. ************************************************************************/ // skip control nodes - if (input_name[0] == '^' ) continue; - string name = input_name; + if (input_name[0] == '^') continue; + string name = input_name; auto first = name.find_first_of(':'); if (first != string::npos && first + 2 == name.size() && name[first + 1] == '0') @@ -490,13 +507,17 @@ class Converter { std::vector inputs; TF_RETURN_IF_ERROR(this->get_inputs(node_def, &inputs)); string op = node_def.op(); - if (!op_registry_.count(op)) { - return tensorflow::errors::Unimplemented( - "No converter registered for op: " + op); - } - OpConverter op_converter = op_registry_.at(op); std::vector outputs; - TF_RETURN_IF_ERROR(op_converter(*this, node_def, inputs, &outputs)); + if (PluginFactoryTensorRT::GetInstance().IsPlugin(&op)) { + TF_RETURN_IF_ERROR(plugin_converter_(*this, node_def, inputs, &outputs)); + } else { + if (!op_registry_.count(op)) { + return tensorflow::errors::Unimplemented( + "No converter registered for op: " + op); + } + OpConverter op_converter = op_registry_.at(op); + TF_RETURN_IF_ERROR(op_converter(*this, node_def, inputs, &outputs)); + } for (size_t i = 0; i < outputs.size(); ++i) { TRT_TensorOrWeights output = outputs.at(i); // TODO(jie): tf protobuf seems to be omitting the :0 suffix @@ -1158,9 +1179,9 @@ tensorflow::Status BinaryTensorOpTensor( CHECK_EQ_TYPE(tensor_r->getType(), dtype); auto op_pair = ops.find(node_def.op()); if (op_pair == ops.end()) - return tensorflow::errors::Unimplemented( - "binary op: " + node_def.op() + - " not supported at: " + node_def.name()); + return tensorflow::errors::Unimplemented("binary op: " + node_def.op() + + " not supported at: " + + node_def.name()); nvinfer1::IElementWiseLayer* layer = ctx.network()->addElementWise( *const_cast(tensor_l), @@ -1173,6 +1194,43 @@ tensorflow::Status BinaryTensorOpTensor( return tensorflow::Status::OK(); } +tensorflow::Status ConvertPlugin(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + // prepare input + std::vector all_inputs; + for (auto input : inputs) { + all_inputs.emplace_back(const_cast(input.tensor())); + } + + // plugin is owned by PluginFactory + // TODO(jie): destroy plugins later (resource management) + PluginTensorRT* plugin = + PluginFactoryTensorRT::GetInstance().CreatePlugin(&node_def.op()); + + // passing attributes + // TODO(jie): support more general attribute + TFAttrs attrs(node_def); + auto attr_key_vector = attrs.GetAllAttrKey(); + for (auto attr_key : attr_key_vector) { + std::cout << attr_key << std::endl; + // TODO(jie): support only list of float for toy example here. + auto data = attrs.get>(attr_key); + size_t size_data = data.size() * sizeof(float); + plugin->SetAttribute(attr_key, static_cast(data.data()), size_data); + } + + nvinfer1::IPluginLayer* layer = + ctx.network()->addPlugin(&all_inputs[0], int(inputs.size()), *plugin); + + for (int i = 0; i < layer->getNbOutputs(); i++) { + nvinfer1::ITensor* output_tensor = layer->getOutput(i); + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + } + return tensorflow::Status::OK(); +} + tensorflow::Status ConvertPlaceholder( Converter& ctx, const tensorflow::NodeDef& node_def, const std::vector& inputs, @@ -2073,6 +2131,8 @@ void Converter::register_op_converters() { op_registry_["Reshape"] = ConvertReshape; op_registry_["FusedBatchNorm"] = ConvertFusedBatchNorm; op_registry_["FusedBatchNormV2"] = ConvertFusedBatchNorm; + + plugin_converter_ = ConvertPlugin; } } // namespace @@ -2511,7 +2571,7 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef( std::vector input_names; std::vector input_dtypes; for (const std::pair& input : s.input_inds) { - VLOG(2) << "parsing input. Node id= " << input.first ; + VLOG(2) << "parsing input. Node id= " << input.first; int node_id = input.first; int output_idx = input.second; tensorflow::Node* node = s.graph.FindNodeId(node_id); diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index b32371b642f38b..8881c48fe688ed 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/tensorrt/kernels/trt_engine_op.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorflow/core/platform/logging.h" @@ -58,7 +59,8 @@ TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) : OpKernel(context) { IRuntime* infer = nvinfer1::createInferRuntime(logger); trt_engine_ptr_.reset(infer->deserializeCudaEngine( - serialized_engine.c_str(), serialized_engine.size(), nullptr)); + serialized_engine.c_str(), serialized_engine.size(), + &PluginFactoryTensorRT::GetInstance())); trt_execution_context_ptr_.reset(trt_engine_ptr_->createExecutionContext()); // Runtime is safe to delete after engine creation infer->destroy(); diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc new file mode 100644 index 00000000000000..0e4a157d7905ee --- /dev/null +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc @@ -0,0 +1,89 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h" +#include +#include +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT + +namespace tensorflow { +namespace tensorrt { + +PluginTensorRT::PluginTensorRT(const void* serialized_data, size_t length) { + // sanity check. + assert(EncodeOpName(GetPluginName()) != + *static_cast(serialized_data)); + const char* buffer = static_cast(serialized_data) + + sizeof(input_dim_list_.size()); + + size_t count = *reinterpret_cast(buffer); + buffer += sizeof(size_t); + + for (int i = 0; i < count; i++) { + nvinfer1::Dims dim; + std::memcpy(&(dim.nbDims), buffer, sizeof(dim.nbDims)); + buffer += sizeof(dim.nbDims); + std::memcpy(dim.d, buffer, sizeof(dim.d)); + buffer += sizeof(dim.d); + std::memcpy(dim.type, buffer, sizeof(dim.type)); + buffer += sizeof(dim.type); + input_dim_list_.emplace_back(dim); + } +} + +size_t PluginTensorRT::getSerializationSize() { + nvinfer1::Dims dim; + return sizeof(size_t) + sizeof(input_dim_list_.size()) + sizeof(dim.nbDims) + + sizeof(dim.d) + sizeof(dim.type); +} + +void PluginTensorRT::serialize(void* serialized_data) { + size_t encode_op_name = EncodeOpName(GetPluginName()); + char* buffer = static_cast(serialized_data); + std::memcpy(buffer, &encode_op_name, sizeof(size_t)); + buffer += sizeof(size_t); + + auto list_size = input_dim_list_.size(); + std::memcpy(buffer, &list_size, sizeof(input_dim_list_.size())); + buffer += sizeof(input_dim_list_.size()); + + for (int i = 0; i < input_dim_list_.size(); i++) { + auto dim = input_dim_list_[i]; + std::memcpy(buffer, &(dim.nbDims), sizeof(dim.nbDims)); + buffer += sizeof(dim.nbDims); + std::memcpy(buffer, dim.d, sizeof(dim.d)); + buffer += sizeof(dim.d); + std::memcpy(buffer, dim.type, sizeof(dim.type)); + buffer += sizeof(dim.type); + } +} + +bool PluginTensorRT::StoreAttribute(const string& key, const void* ptr, + const size_t size) { + if (attr_map_.count(key) != 0) return false; + + attr_map_.emplace(key, std::vector(size)); + std::memcpy(attr_map_[key].data(), ptr, size); + return true; +} + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_CUDA +#endif // GOOGLE_TENSORRT diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin.h new file mode 100644 index 00000000000000..1bbfe62a4e6d17 --- /dev/null +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin.h @@ -0,0 +1,81 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN +#define TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN + +#include +#include +#include +#include + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { + +using std::string; +using std::unordered_map; + +class PluginTensorRT : public nvinfer1::IPlugin { + public: + PluginTensorRT(){}; + PluginTensorRT(const void* serialized_data, size_t length); + // PluginTensorRT(const void* serialized_data, size_t length, size_t + // &incremental); + virtual string GetPluginName() = 0; + virtual bool Finalize() = 0; + + virtual bool SetAttribute(const string& key, const void* ptr, + const size_t size) = 0; + virtual bool GetAttribute(const string& key, const void* ptr, + size_t& size) = 0; + + void configure(const nvinfer1::Dims* inputs, int nbInputs, + const nvinfer1::Dims* outputs, int nbOutputs, + int maxBatchSize) override { + for (int index = 0; index < nbInputs; index++) { + nvinfer1::Dims dim; + dim.nbDims = inputs[index].nbDims; + for (int i = 0; i < dim.nbDims; i++) { + dim.d[i] = inputs[index].d[i]; + dim.type[i] = inputs[index].type[i]; + } + input_dim_list_.emplace_back(dim); + } + return; + } + + virtual bool StoreAttribute(const string& key, const void* ptr, + const size_t size); + + virtual size_t getSerializationSize() override; + virtual void serialize(void* buffer) override; + + protected: + std::unordered_map > attr_map_; + + std::vector input_dim_list_; +}; + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc new file mode 100644 index 00000000000000..799c609a3ebb46 --- /dev/null +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc @@ -0,0 +1,81 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT + +namespace tensorflow { +namespace tensorrt { + +PluginTensorRT* PluginFactoryTensorRT::createPlugin(const char* layerName, + const void* serial_data, + size_t serial_length) { + size_t parsed_byte = 0; + // extract op_name from serial_data + size_t encoded_op_name = + ExtractOpName(serial_data, serial_length, parsed_byte); + + if (!IsPlugin(encoded_op_name)) { + return nullptr; + } + + // should I lock plugins here? + instance_m_.lock(); + auto plugin_ptr = + plugin_registry_[encoded_op_name].first(serial_data, serial_length); + // string op_name = "IncPluginTRT"; + // auto plugin_ptr = plugin_registry_[EncodeLayerName(&op_name)].second(); + // auto plugin_ptr = plugin_registry_.begin()->second.second(); + owned_plugins_.emplace_back(plugin_ptr); + instance_m_.unlock(); + + return plugin_ptr; +} + +PluginTensorRT* PluginFactoryTensorRT::CreatePlugin(const string* op_name) { + if (!IsPlugin(op_name)) return nullptr; + + instance_m_.lock(); + auto plugin_ptr = plugin_registry_[EncodeLayerName(op_name)].second(); + owned_plugins_.emplace_back(plugin_ptr); + instance_m_.unlock(); + + return plugin_ptr; +} + +bool PluginFactoryTensorRT::RegisterPlugin( + const string* op_name, PluginDeserializeFunc deserialize_func, + PluginConstructFunc construct_func) { + if (IsPlugin(op_name)) return false; + + // get instance_m_ first before write to registry; + instance_m_.lock(); + auto ret = plugin_registry_.emplace( + EncodeLayerName(op_name), + std::make_pair(deserialize_func, construct_func)); + instance_m_.unlock(); + + return ret.second; +} + +void PluginFactoryTensorRT::DestroyPlugins() { return; } + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_CUDA +#endif // GOOGLE_TENSORRT diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h new file mode 100644 index 00000000000000..e68f4629d0c02f --- /dev/null +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h @@ -0,0 +1,83 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_FACTORY +#define TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_FACTORY + +#include +#include +#include +#include "trt_plugin.h" +#include "trt_plugin_utils.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { + +class PluginFactoryTensorRT : public nvinfer1::IPluginFactory { + public: + // deserialization method + // virtual nvinfer1::IPlugin* createPlugin(const char* layerName, const void* + // serialData, size_t serialLength) override; + PluginTensorRT* createPlugin(const char* layerName, const void* serialData, + size_t serialLength) override; + + // construction + PluginTensorRT* CreatePlugin(const string* op_name); + + static PluginFactoryTensorRT& GetInstance() { + static PluginFactoryTensorRT factory_instance; + return factory_instance; + } + + bool RegisterPlugin(const string* op_name, + PluginDeserializeFunc deserialize_func, + PluginConstructFunc construct_func); + + bool IsPlugin(const size_t encode_name) { + return plugin_registry_.find(encode_name) != plugin_registry_.end(); + } + + bool IsPlugin(const string* op_name) { + return IsPlugin(EncodeLayerName(op_name)); + } + + size_t EncodeLayerName(const string* op_name) { + return EncodeOpName(*op_name); + } + + void DestroyPlugins(); + + protected: + std::unordered_map > + plugin_registry_; + + // TODO(jie): Owned plugin should be associated with different sessions; + // should really hand ownership of plugins to resource management; + std::vector > owned_plugins_; + std::mutex instance_m_; +}; + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_FACTORY diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc new file mode 100644 index 00000000000000..b14480cfa67c0c --- /dev/null +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc @@ -0,0 +1,36 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT + +namespace tensorflow { +namespace tensorrt { + +size_t ExtractOpName(const void* serial_data, size_t serial_length, + size_t& incremental) { + incremental = sizeof(size_t); + if (serial_length < incremental) return 0; + size_t encoded_op_name = *static_cast(serial_data); + return encoded_op_name; +} + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_CUDA +#endif // GOOGLE_TENSORRT diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h new file mode 100644 index 00000000000000..e9675d84cd333e --- /dev/null +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h @@ -0,0 +1,51 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_UTILS +#define TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_UTILS + +#include +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { + +typedef std::function + PluginDeserializeFunc; + +typedef std::function PluginConstructFunc; + +inline size_t EncodeOpName(std::string str) { + return std::hash{}(str); +} + +// TODO(jie): work on error handling here +size_t ExtractOpName(const void* serial_data, size_t serial_length, + size_t& incremental); + +// size_t Deserialize(const char* serial_data, size_t serial_length, size_t +// &incremental); + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_UTILS diff --git a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc index 8b475177bc670d..30b5616475e9bc 100644 --- a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc +++ b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/tensorrt/shape_fn/trt_shfn.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include #include @@ -33,7 +34,8 @@ tensorflow::Status TRTEngineOpShapeInference(InferenceContext* context) { TF_RETURN_IF_ERROR(context->GetAttr("serialized_engine", &serialized_engine)); nvinfer1::IRuntime* infer = nvinfer1::createInferRuntime(logger); nvinfer1::ICudaEngine* trt_engine = infer->deserializeCudaEngine( - serialized_engine.c_str(), serialized_engine.size(), nullptr); + serialized_engine.c_str(), serialized_engine.size(), + &tensorrt::PluginFactoryTensorRT::GetInstance()); int num_batch = -1; std::vector<::tensorflow::DataType> input_type; From 0eb443db1a5654168f396702cae39f5dc3fc7e2e Mon Sep 17 00:00:00 2001 From: imsheridan Date: Tue, 17 Apr 2018 20:25:51 +0800 Subject: [PATCH 0005/1691] Add deprecated_args decoration to array_ops/ sparse_ops --- tensorflow/python/ops/array_ops.py | 2 ++ tensorflow/python/ops/sparse_ops.py | 2 ++ 2 files changed, 4 insertions(+) diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index ceeabe090dff9c..06da2485c3a47b 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -2690,6 +2690,8 @@ def reverse(tensor, axis, name=None): # pylint: disable=redefined-builtin @tf_export("reverse_sequence") +@deprecation.deprecated_args(None, "Use the `seq_axis` argument instead", "seq_dim") +@deprecation.deprecated_args(None, "Use the `batch_axis` argument instead", "batch_dim") def reverse_sequence(input, seq_lengths, seq_axis=None, diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index c580052c32c8b6..73ab216f35019c 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -110,6 +110,7 @@ def _convert_to_sparse_tensors(sp_inputs): # pylint: disable=protected-access @tf_export("sparse_concat") +@deprecation.deprecated_args(None, "concat_dim is deprecated, use axis instead", "concat_dim") def sparse_concat(axis, sp_inputs, name=None, @@ -616,6 +617,7 @@ def __repr__(self): @tf_export("sparse_split") +@deprecation.deprecated_args(None, "split_dim is deprecated, use axis instead", "split_dim") def sparse_split(keyword_required=KeywordRequired(), sp_input=None, num_split=None, From d6838f52c7daea81c57cdeab8e98c4cd617e5f8b Mon Sep 17 00:00:00 2001 From: imsheridan Date: Tue, 17 Apr 2018 20:30:13 +0800 Subject: [PATCH 0006/1691] fix typo to keep consistence --- tensorflow/python/ops/array_ops.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index 06da2485c3a47b..b6a1f5a2722568 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -2690,8 +2690,8 @@ def reverse(tensor, axis, name=None): # pylint: disable=redefined-builtin @tf_export("reverse_sequence") -@deprecation.deprecated_args(None, "Use the `seq_axis` argument instead", "seq_dim") -@deprecation.deprecated_args(None, "Use the `batch_axis` argument instead", "batch_dim") +@deprecation.deprecated_args(None, "seq_dim is deprecated, use seq_axis instead", "seq_dim") +@deprecation.deprecated_args(None, "batch_dim is deprecated, use batch_axis instead", "batch_dim") def reverse_sequence(input, seq_lengths, seq_axis=None, From df0ce53aee6f4e14b3f1c9e0e772a1f7bd1bb95a Mon Sep 17 00:00:00 2001 From: Jie Date: Mon, 16 Apr 2018 17:47:00 -0700 Subject: [PATCH 0007/1691] [PR comment addressed] adding plugin test for registration updating plugin API wrapper addressing comments in the PR addressing coding style issues removing commented code --- tensorflow/contrib/tensorrt/BUILD | 15 ++- .../contrib/tensorrt/convert/convert_graph.cc | 2 +- .../contrib/tensorrt/convert/convert_nodes.cc | 10 +- .../custom_plugin_examples/inc_op_plugin.cc | 89 ++++++++++++++ .../custom_plugin_examples/inc_op_plugin.h | 114 ++++++++++++++++++ .../contrib/tensorrt/kernels/trt_engine_op.cc | 2 +- .../contrib/tensorrt/plugin/trt_plugin.cc | 37 ++++-- .../contrib/tensorrt/plugin/trt_plugin.h | 41 +++---- .../tensorrt/plugin/trt_plugin_factory.cc | 28 +++-- .../tensorrt/plugin/trt_plugin_factory.h | 33 +++-- .../tensorrt/plugin/trt_plugin_utils.cc | 18 ++- .../tensorrt/plugin/trt_plugin_utils.h | 11 +- .../tensorrt/plugin/trt_plugins_test.cc | 112 +++++++++++++++++ .../contrib/tensorrt/shape_fn/trt_shfn.cc | 2 +- 14 files changed, 423 insertions(+), 91 deletions(-) create mode 100644 tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc create mode 100644 tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h create mode 100644 tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index 98f18835b06511..751f1d3482a2d9 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -277,7 +277,6 @@ tf_cc_test( ) # Library for the plugin factory -#cc_library( tf_cuda_library( name = "trt_plugins", srcs = [ @@ -292,9 +291,21 @@ tf_cuda_library( ], linkstatic = 1, deps = [ - #"@protobuf_archive//:protobuf_headers", ] + if_tensorrt([ "@local_config_tensorrt//:nv_infer", ]), ) +tf_cuda_cc_test( + name = "trt_plugins_test", + size = "small", + srcs = ["plugin/trt_plugins_test.cc"], + deps = [ + ":trt_plugins", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ] + if_tensorrt([ + "@local_config_cuda//cuda:cuda_headers", + "@local_config_tensorrt//:nv_infer", + ]), +) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 899e1721e6e6fe..91faba7e213a14 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -77,7 +77,7 @@ bool IsTensorRTCandidate(const tensorflow::Node* node) { }; // LINT.ThenChange(//tensorflow/contrib/tensorrt/convert/convert_nodes.h) return (candidate_ops.count(node->type_string()) || - PluginFactoryTensorRT::GetInstance().IsPlugin(&node->type_string())); + PluginFactoryTensorRT::GetInstance()->IsPlugin(node->type_string())); } void GetSubGraphIncomingEdges(const tensorflow::Graph& graph, diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index a03c1e224ac718..d02c1ebf5037d5 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -249,9 +249,8 @@ class TFAttrs { std::vector GetAllAttrKey() { std::vector attr_list; - for (AttrMap::iterator iter = attrs_.begin(); iter != attrs_.end(); - iter++) { - attr_list.emplace_back(iter->first); + for (auto & attr_item : attrs_) { + attr_list.emplace_back(attr_item.first); } return attr_list; } @@ -508,7 +507,7 @@ class Converter { TF_RETURN_IF_ERROR(this->get_inputs(node_def, &inputs)); string op = node_def.op(); std::vector outputs; - if (PluginFactoryTensorRT::GetInstance().IsPlugin(&op)) { + if (PluginFactoryTensorRT::GetInstance()->IsPlugin(op)) { TF_RETURN_IF_ERROR(plugin_converter_(*this, node_def, inputs, &outputs)); } else { if (!op_registry_.count(op)) { @@ -1207,14 +1206,13 @@ tensorflow::Status ConvertPlugin(Converter& ctx, // plugin is owned by PluginFactory // TODO(jie): destroy plugins later (resource management) PluginTensorRT* plugin = - PluginFactoryTensorRT::GetInstance().CreatePlugin(&node_def.op()); + PluginFactoryTensorRT::GetInstance()->CreatePlugin(node_def.op()); // passing attributes // TODO(jie): support more general attribute TFAttrs attrs(node_def); auto attr_key_vector = attrs.GetAllAttrKey(); for (auto attr_key : attr_key_vector) { - std::cout << attr_key << std::endl; // TODO(jie): support only list of float for toy example here. auto data = attrs.get>(attr_key); size_t size_data = data.size() * sizeof(float); diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc new file mode 100644 index 00000000000000..2155079e8b9159 --- /dev/null +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc @@ -0,0 +1,89 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" +#include + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "inc_op_plugin.h" + +namespace tensorflow { +namespace tensorrt { + +const string IncOpPlugin::plugin_name_ = "IncPluginTRT"; + +IncOpPlugin* CreateIncPlugin() { + return new IncOpPlugin(); +} + + +IncOpPlugin* CreateIncPluginDeserialize(const void* buffer, size_t length) { + return new IncOpPlugin(buffer, length); +} + +bool RegisterIncOpPlugin() { + if (PluginFactoryTensorRT::GetInstance()->IsPlugin(IncOpPlugin::plugin_name_)) + return false; + return PluginFactoryTensorRT::GetInstance()->RegisterPlugin(IncOpPlugin::plugin_name_, CreateIncPluginDeserialize, CreateIncPlugin); +} + + +IncOpPlugin::IncOpPlugin(const void* serialized_data, size_t length) : + PluginTensorRT(serialized_data, length) +{ + // account for the consumed pointer. + size_t consumed_data = PluginTensorRT::getSerializationSize(); + assert(length-consumed_data >= sizeof(float)); + SetAttribute("inc", serialized_data+consumed_data, sizeof(float)); +} + +bool IncOpPlugin::SetAttribute(const string &key, const void *ptr, const size_t size) { + if (strcmp(key.c_str(), "inc")==0 && size == sizeof(float)) { + StoreAttribute(key, ptr, size); // save the attribute to own the data; + inc_ = *static_cast(ptr); + return true; + } + return false; +} + +bool IncOpPlugin::GetAttribute(const string &key, const void *ptr, size_t &size) { + if (attr_map_.find(key) != attr_map_.end()) { + ptr = attr_map_[key].data(); + size = attr_map_[key].size(); + return true; + } + return false; +} + +int IncOpPlugin::enqueue(int batchSize, const void*const *inputs, void** outputs, void*, cudaStream_t stream) { + int count = 1; + for (int i=0; i(inputs[0]); + float *output = reinterpret_cast(outputs[0]); + IncrementKernel(input, inc_, output, count, stream); + return 0; +} + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_CUDA +#endif // GOOGLE_TENSORRT diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h new file mode 100644 index 00000000000000..52b68487e65c05 --- /dev/null +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h @@ -0,0 +1,114 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_INC_OP_PLUGIN +#define TENSORFLOW_CONTRIB_TENSORRT_INC_OP_PLUGIN + +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h" +#include +#include +#include +#include +#include +#include + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { + +using std::string; +using std::unordered_map; + +class IncOpPlugin : public PluginTensorRT +{ +public: + static const string plugin_name_; + IncOpPlugin() {}; + IncOpPlugin(const void* serialized_data, size_t length); + const string GetPluginName() override {return plugin_name_;}; + bool Finalize() override {return true;}; + bool SetAttribute(const string &key, const void *ptr, const size_t size) override; + bool GetAttribute(const string &key, const void *ptr, size_t &size) override; + + // TRT IPlugin methods + int getNbOutputs() const override {return 1;} + + nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs, int nbInputDims) override { + assert(index==0); + assert(nbInputDims==1); + return inputs[0]; + } + + // no configure needed + // use configure to setup input dimensions + void configure(const nvinfer1::Dims *inputs, int nbInputs, const nvinfer1::Dims *outputs, int nbOutputs, int maxBatchSize) override { + assert(nbInputs==1); + PluginTensorRT::configure(inputs, nbInputs, outputs, nbOutputs, maxBatchSize); + return; + } + + int initialize() override { + return 0; + } + + void terminate() override { + return; + } + + size_t getWorkspaceSize(int maxBatchSize) const override { + return 0; + } + + int enqueue(int batchSize, const void*const *inputs, void** outputs, void* workspace, cudaStream_t stream) override; + + size_t getSerializationSize() override { + return PluginTensorRT::getSerializationSize() + sizeof(float); + } + + void serialize(void* buffer) override { + // serializa parent stuff + // OpName + PluginTensorRT::serialize(buffer); + + // incremented buffer after parent serialization; + buffer = static_cast(buffer) + PluginTensorRT::getSerializationSize(); + + std::memcpy(buffer, &inc_, sizeof(float)); + buffer = static_cast(buffer) + sizeof(float); + return; + } + +protected: + float inc_; + nvinfer1::Dims dim_; + // std::unordered_map > attr_map_; +}; + +IncOpPlugin* CreateIncPlugin(); +IncOpPlugin* CreateIncPluginDeserialize(const void*, size_t); +bool RegisterIncOpPlugin(); +void IncrementKernel(const float* d_input, float inc, float* d_output, int count, cudaStream_t stream); + + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CONTRIB_TENSORRT_INC_OP_PLUGIN diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 8881c48fe688ed..162301fb52f551 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -60,7 +60,7 @@ TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) : OpKernel(context) { IRuntime* infer = nvinfer1::createInferRuntime(logger); trt_engine_ptr_.reset(infer->deserializeCudaEngine( serialized_engine.c_str(), serialized_engine.size(), - &PluginFactoryTensorRT::GetInstance())); + PluginFactoryTensorRT::GetInstance())); trt_execution_context_ptr_.reset(trt_engine_ptr_->createExecutionContext()); // Runtime is safe to delete after engine creation infer->destroy(); diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc index 0e4a157d7905ee..76007037753683 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc @@ -26,10 +26,10 @@ namespace tensorrt { PluginTensorRT::PluginTensorRT(const void* serialized_data, size_t length) { // sanity check. - assert(EncodeOpName(GetPluginName()) != - *static_cast(serialized_data)); - const char* buffer = static_cast(serialized_data) + - sizeof(input_dim_list_.size()); + const char* buffer = static_cast(serialized_data); + size_t op_name_char_count = *reinterpret_cast(buffer); + buffer += sizeof(size_t); + buffer += op_name_char_count; size_t count = *reinterpret_cast(buffer); buffer += sizeof(size_t); @@ -46,18 +46,37 @@ PluginTensorRT::PluginTensorRT(const void* serialized_data, size_t length) { } } +void PluginTensorRT::configure(const nvinfer1::Dims* inputs, int num_inputs, + const nvinfer1::Dims* outputs, int num_outputs, + int max_batch_size) { + for (int index = 0; index < num_inputs; index++) { + nvinfer1::Dims dim; + dim.nbDims = inputs[index].nbDims; + for (int i = 0; i < dim.nbDims; i++) { + dim.d[i] = inputs[index].d[i]; + dim.type[i] = inputs[index].type[i]; + } + input_dim_list_.emplace_back(dim); + } + return; +} + size_t PluginTensorRT::getSerializationSize() { nvinfer1::Dims dim; - return sizeof(size_t) + sizeof(input_dim_list_.size()) + sizeof(dim.nbDims) + - sizeof(dim.d) + sizeof(dim.type); + return sizeof(size_t) + GetPluginName().size() + + sizeof(input_dim_list_.size()) + sizeof(dim.nbDims) + sizeof(dim.d) + + sizeof(dim.type); } void PluginTensorRT::serialize(void* serialized_data) { - size_t encode_op_name = EncodeOpName(GetPluginName()); + size_t op_name_size = GetPluginName().size(); char* buffer = static_cast(serialized_data); - std::memcpy(buffer, &encode_op_name, sizeof(size_t)); + std::memcpy(buffer, &op_name_size, sizeof(size_t)); buffer += sizeof(size_t); + std::memcpy(buffer, GetPluginName().data(), op_name_size); + buffer += op_name_size; + auto list_size = input_dim_list_.size(); std::memcpy(buffer, &list_size, sizeof(input_dim_list_.size())); buffer += sizeof(input_dim_list_.size()); @@ -73,7 +92,7 @@ void PluginTensorRT::serialize(void* serialized_data) { } } -bool PluginTensorRT::StoreAttribute(const string& key, const void* ptr, +bool PluginTensorRT::StoreAttribute(const std::string& key, const void* ptr, const size_t size) { if (attr_map_.count(key) != 0) return false; diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin.h index 1bbfe62a4e6d17..59b92657f637dc 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin.h +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin.h @@ -28,46 +28,37 @@ limitations under the License. namespace tensorflow { namespace tensorrt { -using std::string; -using std::unordered_map; - +// A wrapper class for TensorRT plugin +// User application should inherit from this class to write custom kernels. +// Allows user to insert custom op in TensorRT engine +// To register plugin in converter, user should also register custom +// tensorflow::tensorrt::PluginDeserializeFunc & +// tensorflow::tensorrt::PluginConstructFunc through +// tensorflow::tensorrt::PluginFactoryTensorRT class PluginTensorRT : public nvinfer1::IPlugin { public: PluginTensorRT(){}; PluginTensorRT(const void* serialized_data, size_t length); - // PluginTensorRT(const void* serialized_data, size_t length, size_t - // &incremental); - virtual string GetPluginName() = 0; + virtual const std::string& GetPluginName() = 0; virtual bool Finalize() = 0; - virtual bool SetAttribute(const string& key, const void* ptr, + virtual bool SetAttribute(const std::string& key, const void* ptr, const size_t size) = 0; - virtual bool GetAttribute(const string& key, const void* ptr, + virtual bool GetAttribute(const std::string& key, const void* ptr, size_t& size) = 0; - void configure(const nvinfer1::Dims* inputs, int nbInputs, - const nvinfer1::Dims* outputs, int nbOutputs, - int maxBatchSize) override { - for (int index = 0; index < nbInputs; index++) { - nvinfer1::Dims dim; - dim.nbDims = inputs[index].nbDims; - for (int i = 0; i < dim.nbDims; i++) { - dim.d[i] = inputs[index].d[i]; - dim.type[i] = inputs[index].type[i]; - } - input_dim_list_.emplace_back(dim); - } - return; - } - - virtual bool StoreAttribute(const string& key, const void* ptr, + void configure(const nvinfer1::Dims* inputs, int num_inputs, + const nvinfer1::Dims* outputs, int num_outputs, + int max_batch_size) override; + + virtual bool StoreAttribute(const std::string& key, const void* ptr, const size_t size); virtual size_t getSerializationSize() override; virtual void serialize(void* buffer) override; protected: - std::unordered_map > attr_map_; + std::unordered_map > attr_map_; std::vector input_dim_list_; }; diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc index 799c609a3ebb46..44b10394c87430 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc @@ -21,13 +21,13 @@ limitations under the License. namespace tensorflow { namespace tensorrt { -PluginTensorRT* PluginFactoryTensorRT::createPlugin(const char* layerName, +PluginTensorRT* PluginFactoryTensorRT::createPlugin(const char* layer_name, const void* serial_data, size_t serial_length) { size_t parsed_byte = 0; // extract op_name from serial_data - size_t encoded_op_name = - ExtractOpName(serial_data, serial_length, parsed_byte); + std::string encoded_op_name = + ExtractOpName(serial_data, serial_length, &parsed_byte); if (!IsPlugin(encoded_op_name)) { return nullptr; @@ -37,20 +37,18 @@ PluginTensorRT* PluginFactoryTensorRT::createPlugin(const char* layerName, instance_m_.lock(); auto plugin_ptr = plugin_registry_[encoded_op_name].first(serial_data, serial_length); - // string op_name = "IncPluginTRT"; - // auto plugin_ptr = plugin_registry_[EncodeLayerName(&op_name)].second(); - // auto plugin_ptr = plugin_registry_.begin()->second.second(); owned_plugins_.emplace_back(plugin_ptr); instance_m_.unlock(); return plugin_ptr; } -PluginTensorRT* PluginFactoryTensorRT::CreatePlugin(const string* op_name) { +PluginTensorRT* PluginFactoryTensorRT::CreatePlugin( + const std::string& op_name) { if (!IsPlugin(op_name)) return nullptr; instance_m_.lock(); - auto plugin_ptr = plugin_registry_[EncodeLayerName(op_name)].second(); + auto plugin_ptr = plugin_registry_[op_name].second(); owned_plugins_.emplace_back(plugin_ptr); instance_m_.unlock(); @@ -58,21 +56,27 @@ PluginTensorRT* PluginFactoryTensorRT::CreatePlugin(const string* op_name) { } bool PluginFactoryTensorRT::RegisterPlugin( - const string* op_name, PluginDeserializeFunc deserialize_func, + const std::string& op_name, PluginDeserializeFunc deserialize_func, PluginConstructFunc construct_func) { if (IsPlugin(op_name)) return false; // get instance_m_ first before write to registry; instance_m_.lock(); auto ret = plugin_registry_.emplace( - EncodeLayerName(op_name), - std::make_pair(deserialize_func, construct_func)); + op_name, std::make_pair(deserialize_func, construct_func)); instance_m_.unlock(); return ret.second; } -void PluginFactoryTensorRT::DestroyPlugins() { return; } +void PluginFactoryTensorRT::DestroyPlugins() { + instance_m_.lock(); + for (auto& owned_plugin_ptr : owned_plugins_) { + owned_plugin_ptr.release(); + } + owned_plugins_.clear(); + instance_m_.unlock(); +} } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h index e68f4629d0c02f..824efcff355de7 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h @@ -32,39 +32,34 @@ namespace tensorrt { class PluginFactoryTensorRT : public nvinfer1::IPluginFactory { public: // deserialization method - // virtual nvinfer1::IPlugin* createPlugin(const char* layerName, const void* - // serialData, size_t serialLength) override; - PluginTensorRT* createPlugin(const char* layerName, const void* serialData, - size_t serialLength) override; + PluginTensorRT* createPlugin(const char* layer_name, const void* serial_data, + size_t serial_length) override; - // construction - PluginTensorRT* CreatePlugin(const string* op_name); + // plugin construction, PluginFactoryTensorRT owns the plugin; + PluginTensorRT* CreatePlugin(const std::string& op_name); - static PluginFactoryTensorRT& GetInstance() { - static PluginFactoryTensorRT factory_instance; + static PluginFactoryTensorRT* GetInstance() { + static PluginFactoryTensorRT* factory_instance = nullptr; + if (factory_instance == nullptr) { + factory_instance = new PluginFactoryTensorRT(); + } return factory_instance; } - bool RegisterPlugin(const string* op_name, + bool RegisterPlugin(const std::string& op_name, PluginDeserializeFunc deserialize_func, PluginConstructFunc construct_func); - bool IsPlugin(const size_t encode_name) { - return plugin_registry_.find(encode_name) != plugin_registry_.end(); + bool IsPlugin(const std::string& op_name) { + return plugin_registry_.find(op_name) != plugin_registry_.end(); } - bool IsPlugin(const string* op_name) { - return IsPlugin(EncodeLayerName(op_name)); - } - - size_t EncodeLayerName(const string* op_name) { - return EncodeOpName(*op_name); - } + size_t CountOwnedPlugins() { return owned_plugins_.size(); } void DestroyPlugins(); protected: - std::unordered_map > plugin_registry_; diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc index b14480cfa67c0c..8b65e8b41c3841 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h" +#include #if GOOGLE_CUDA #if GOOGLE_TENSORRT @@ -21,12 +22,17 @@ limitations under the License. namespace tensorflow { namespace tensorrt { -size_t ExtractOpName(const void* serial_data, size_t serial_length, - size_t& incremental) { - incremental = sizeof(size_t); - if (serial_length < incremental) return 0; - size_t encoded_op_name = *static_cast(serial_data); - return encoded_op_name; +std::string ExtractOpName(const void* serial_data, size_t serial_length, + size_t* incremental) { + size_t op_name_char_count = *static_cast(serial_data); + *incremental = sizeof(size_t) + op_name_char_count; + + assert(serial_length >= *incremental); + + const char* buffer = static_cast(serial_data) + sizeof(size_t); + std::string op_name(buffer, op_name_char_count); + + return op_name; } } // namespace tensorrt diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h index e9675d84cd333e..d4da8b261e6508 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h @@ -31,16 +31,9 @@ typedef std::function typedef std::function PluginConstructFunc; -inline size_t EncodeOpName(std::string str) { - return std::hash{}(str); -} - // TODO(jie): work on error handling here -size_t ExtractOpName(const void* serial_data, size_t serial_length, - size_t& incremental); - -// size_t Deserialize(const char* serial_data, size_t serial_length, size_t -// &incremental); +std::string ExtractOpName(const void* serial_data, size_t serial_length, + size_t* incremental); } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc new file mode 100644 index 00000000000000..2856b0f87d30f0 --- /dev/null +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc @@ -0,0 +1,112 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/types.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { +namespace test { + +class StubPlugin : public PluginTensorRT { + public: + static const std::string plugin_name_; + StubPlugin(){}; + StubPlugin(const void* serialized_data, size_t length) + : PluginTensorRT(serialized_data, length){}; + const std::string& GetPluginName() override { return plugin_name_; }; + virtual bool Finalize() { return true; }; + virtual bool SetAttribute(const std::string& key, const void* ptr, + const size_t size) { + return true; + }; + virtual bool GetAttribute(const std::string& key, const void* ptr, + size_t& size) { + return true; + }; + int getNbOutputs() const override { return 1; } + nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs, + int nbInputDims) override { + return inputs[0]; + } + int initialize() override { return 0; } + void terminate() override { return; } + size_t getWorkspaceSize(int maxBatchSize) const override { return 0; } + int enqueue(int batchSize, const void* const* inputs, void** outputs, + void* workspace, cudaStream_t stream) override { + return 0; + } +}; + +const std::string StubPlugin::plugin_name_ = "StubPlugin"; + +StubPlugin* CreateStubPlugin() { return new StubPlugin(); } + +StubPlugin* CreateStubPluginDeserialize(const void* serialized_data, + size_t length) { + return new StubPlugin(serialized_data, length); +} + +class PluginTest : public ::testing::Test { + public: + bool RegisterStubPlugin() { + if (PluginFactoryTensorRT::GetInstance()->IsPlugin( + StubPlugin::plugin_name_)) + return true; + return PluginFactoryTensorRT::GetInstance()->RegisterPlugin( + StubPlugin::plugin_name_, CreateStubPluginDeserialize, + CreateStubPlugin); + } + + protected: +}; + +TEST_F(PluginTest, Registration) { + EXPECT_FALSE( + PluginFactoryTensorRT::GetInstance()->IsPlugin(StubPlugin::plugin_name_)); + EXPECT_TRUE(RegisterStubPlugin()); + + ASSERT_TRUE( + PluginFactoryTensorRT::GetInstance()->IsPlugin(StubPlugin::plugin_name_)); +} + +TEST_F(PluginTest, CreationDeletion) { + EXPECT_TRUE(RegisterStubPlugin()); + ASSERT_TRUE( + PluginFactoryTensorRT::GetInstance()->IsPlugin(StubPlugin::plugin_name_)); + + PluginFactoryTensorRT::GetInstance()->DestroyPlugins(); + ASSERT_TRUE(PluginFactoryTensorRT::GetInstance()->CreatePlugin( + StubPlugin::plugin_name_)); + ASSERT_EQ(1, PluginFactoryTensorRT::GetInstance()->CountOwnedPlugins()); + PluginFactoryTensorRT::GetInstance()->DestroyPlugins(); + ASSERT_EQ(0, PluginFactoryTensorRT::GetInstance()->CountOwnedPlugins()); +} + +} // namespace test +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA diff --git a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc index 30b5616475e9bc..f36495f6b69ecb 100644 --- a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc +++ b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc @@ -35,7 +35,7 @@ tensorflow::Status TRTEngineOpShapeInference(InferenceContext* context) { nvinfer1::IRuntime* infer = nvinfer1::createInferRuntime(logger); nvinfer1::ICudaEngine* trt_engine = infer->deserializeCudaEngine( serialized_engine.c_str(), serialized_engine.size(), - &tensorrt::PluginFactoryTensorRT::GetInstance()); + tensorrt::PluginFactoryTensorRT::GetInstance()); int num_batch = -1; std::vector<::tensorflow::DataType> input_type; From 758f25e8168bf1ff76c63a5b54dfd50ff54e4e27 Mon Sep 17 00:00:00 2001 From: Sunitha Kambhampati Date: Tue, 17 Apr 2018 15:47:33 -0700 Subject: [PATCH 0008/1691] Fix calculation of the histogram buckets and writing to the tensor and add a unit test --- .../tensorboard/db/summary_db_writer.cc | 21 +++++--- .../tensorboard/db/summary_db_writer_test.cc | 49 +++++++++++++++++++ 2 files changed, 62 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/tensorboard/db/summary_db_writer.cc b/tensorflow/contrib/tensorboard/db/summary_db_writer.cc index 6590d6f7df4f35..046a2d38849676 100644 --- a/tensorflow/contrib/tensorboard/db/summary_db_writer.cc +++ b/tensorflow/contrib/tensorboard/db/summary_db_writer.cc @@ -1182,14 +1182,19 @@ class SummaryDbWriter : public SummaryWriterInterface { // See tensorboard/plugins/histogram/summary.py and data_compat.py Tensor t{DT_DOUBLE, {k, 3}}; auto data = t.flat(); - for (int i = 0; i < k; ++i) { - double left_edge = ((i - 1 >= 0) ? histo.bucket_limit(i - 1) - : std::numeric_limits::min()); - double right_edge = ((i + 1 < k) ? histo.bucket_limit(i + 1) - : std::numeric_limits::max()); - data(i + 0) = left_edge; - data(i + 1) = right_edge; - data(i + 2) = histo.bucket(i); + for (int i = 0, j = 0; i < k; ++i) { + // From summary.proto + // Parallel arrays encoding the bucket boundaries and the bucket values. + // bucket(i) is the count for the bucket i. The range for + // a bucket is: + // i == 0: -DBL_MAX .. bucket_limit(0) + // i != 0: bucket_limit(i-1) .. bucket_limit(i) + double left_edge = (i == 0) ? std::numeric_limits::min() + : histo.bucket_limit(i - 1); + + data(j++) = left_edge; + data(j++) = histo.bucket_limit(i); + data(j++) = histo.bucket(i); } int64 tag_id; PatchPluginName(s->mutable_metadata(), kHistogramPluginName); diff --git a/tensorflow/contrib/tensorboard/db/summary_db_writer_test.cc b/tensorflow/contrib/tensorboard/db/summary_db_writer_test.cc index 29b8063218de72..cb51325d15ff91 100644 --- a/tensorflow/contrib/tensorboard/db/summary_db_writer_test.cc +++ b/tensorflow/contrib/tensorboard/db/summary_db_writer_test.cc @@ -100,6 +100,55 @@ class SummaryDbWriterTest : public ::testing::Test { SummaryWriterInterface* writer_ = nullptr; }; +TEST_F(SummaryDbWriterTest, WriteHistogram_VerifyTensorValues) { + TF_ASSERT_OK(CreateSummaryDbWriter(db_, "histtest", "test1", "user1", &env_, + &writer_)); + int step = 0; + std::unique_ptr e{new Event}; + e->set_step(step); + e->set_wall_time(123); + Summary::Value* s = e->mutable_summary()->add_value(); + s->set_tag("normal/myhisto"); + + double dummy_value = 10.123; + HistogramProto* proto = s->mutable_histo(); + proto->Clear(); + proto->set_min(dummy_value); + proto->set_max(dummy_value); + proto->set_num(dummy_value); + proto->set_sum(dummy_value); + proto->set_sum_squares(dummy_value); + + int size = 3; + double bucket_limits[] = {-30.5, -10.5, -5.5}; + double bucket[] = {-10, 10, 20}; + for (int i = 0; i < size; i++) { + proto->add_bucket_limit(bucket_limits[i]); + proto->add_bucket(bucket[i]); + } + TF_ASSERT_OK(writer_->WriteEvent(std::move(e))); + TF_ASSERT_OK(writer_->Flush()); + writer_->Unref(); + writer_ = nullptr; + + // Verify the data + string result = QueryString("SELECT data FROM Tensors"); + const double* val = reinterpret_cast(result.data()); + double histarray[] = {std::numeric_limits::min(), + -30.5, + -10, + -30.5, + -10.5, + 10, + -10.5, + -5.5, + 20}; + int histarray_size = 9; + for (int i = 0; i < histarray_size; i++) { + EXPECT_EQ(histarray[i], val[i]); + } +} + TEST_F(SummaryDbWriterTest, NothingWritten_NoRowsCreated) { TF_ASSERT_OK(CreateSummaryDbWriter(db_, "mad-science", "train", "jart", &env_, &writer_)); From 419dbc8f44efe06612845ec291b98bb49e873639 Mon Sep 17 00:00:00 2001 From: Jie Date: Wed, 18 Apr 2018 14:42:42 -0700 Subject: [PATCH 0009/1691] [PR comment addressed] Added custom plugin example registered tensorflow custom op & plugin kernel python wrapper to import custom op & register plugin clang-format --- tensorflow/contrib/tensorrt/BUILD | 1 + .../contrib/tensorrt/convert/convert_nodes.cc | 2 +- .../tensorrt/custom_plugin_examples/BUILD | 110 ++++++++++++++++++ .../custom_plugin_examples/__init__.py | 24 ++++ .../tensorrt/custom_plugin_examples/inc_op.py | 30 +++++ .../inc_op_kernel.cu.cc | 44 +++++++ .../custom_plugin_examples/inc_op_kernel.h | 34 ++++++ .../custom_plugin_examples/inc_op_plugin.cc | 55 +++++---- .../custom_plugin_examples/inc_op_plugin.h | 85 +++++++------- .../custom_plugin_examples/ops/inc_op.cc | 34 ++++++ .../custom_plugin_examples/plugin_wrap.i | 31 +++++ .../test/plugin_test.py | 93 +++++++++++++++ .../contrib/tensorrt/plugin/trt_plugin.cc | 1 - .../contrib/tensorrt/plugin/trt_plugin.h | 10 +- .../tensorrt/plugin/trt_plugin_factory.cc | 14 +-- .../tensorrt/plugin/trt_plugin_factory.h | 6 +- .../tensorrt/plugin/trt_plugin_utils.cc | 4 +- .../tensorrt/plugin/trt_plugin_utils.h | 5 +- .../tensorrt/plugin/trt_plugins_test.cc | 6 +- 19 files changed, 485 insertions(+), 104 deletions(-) create mode 100644 tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD create mode 100644 tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py create mode 100644 tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op.py create mode 100644 tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc create mode 100644 tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h create mode 100644 tensorflow/contrib/tensorrt/custom_plugin_examples/ops/inc_op.cc create mode 100644 tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_wrap.i create mode 100644 tensorflow/contrib/tensorrt/custom_plugin_examples/test/plugin_test.py diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index 751f1d3482a2d9..9c81c127055105 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -291,6 +291,7 @@ tf_cuda_library( ], linkstatic = 1, deps = [ + "//tensorflow/core:platform_base", ] + if_tensorrt([ "@local_config_tensorrt//:nv_infer", ]), diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index d02c1ebf5037d5..874be96c781e53 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -249,7 +249,7 @@ class TFAttrs { std::vector GetAllAttrKey() { std::vector attr_list; - for (auto & attr_item : attrs_) { + for (const auto& attr_item : attrs_) { attr_list.emplace_back(attr_item.first); } return attr_list; diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD new file mode 100644 index 00000000000000..5603ed0ccf5c26 --- /dev/null +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD @@ -0,0 +1,110 @@ +package(default_visibility = ["//tensorflow:__subpackages__"]) + +load( + "//tensorflow:tensorflow.bzl", + "tf_custom_op_library", + "tf_cuda_library", + "tf_gen_op_libs", + "tf_gen_op_wrapper_py", + "tf_py_wrap_cc", + "tf_copts", +) +load( + "@local_config_tensorrt//:build_defs.bzl", + "if_tensorrt", +) +load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library") +load("//tensorflow:tensorflow.bzl", "tf_kernel_library") + +tf_kernel_library( + name = "_inc_op_plugin_kernel", + srcs = [ + "inc_op_plugin.cc", + ], + hdrs = [ + ], + gpu_srcs = [ + "inc_op_kernel.cu.cc", + "inc_op_kernel.h", + "inc_op_plugin.h", + ], + deps = if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + "//tensorflow/contrib/tensorrt:trt_plugins", + ]), +) + +tf_gen_op_libs( + op_lib_names = [ + "inc_op", + ], + deps = if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + "//tensorflow/contrib/tensorrt:trt_plugins", + ]), +) + +tf_gen_op_wrapper_py( + name = "inc_op", + gen_locally = True, + deps = [ + ":inc_op_op_lib", + ], +) + +tf_py_wrap_cc( + name = "plugin_wrap", + srcs = [ + "plugin_wrap.i", + ], + copts = tf_copts(), + deps = [ + ":_inc_op_plugin_kernel", + "//tensorflow/core:framework_lite", + "//util/python:python_headers", + ], +) + +tf_custom_op_library( + name = "_inc_op.so", + srcs = ["ops/inc_op.cc"], + deps = [ + "//tensorflow/core:lib_proto_parsing", + ] + if_tensorrt([ + "//tensorflow/contrib/tensorrt:trt_plugins", + ]), +) + +tf_custom_op_py_library( + name = "inc_op_loader", + srcs = ["inc_op.py"], + dso = [ + ":_inc_op.so", + ], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:resources", + ], +) + +py_library( + name = "inc_op_py", + srcs_version = "PY2AND3", + deps = [ + ":inc_op", + ":inc_op_loader", + ], +) + +py_library( + name = "init_py", + srcs = [ + "__init__.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":inc_op_py", + ":plugin_wrap", + ], +) diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py new file mode 100644 index 00000000000000..a61d008941897c --- /dev/null +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py @@ -0,0 +1,24 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""Import custom op for plugin and register it in plugin factory registry.""" + +from ops import gen_inc_op +from plugin_wrap import inc_op_register +from inc_op import * + +# pylint: disable=unused-import,wildcard-import,g-import-not-at-top +inc_op = gen_inc_op.inc_plugin_trt +inc_op_register() +# pylint: enable=unused-import,wildcard-import,g-import-not-at-top diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op.py new file mode 100644 index 00000000000000..ef8e26fbdedfa7 --- /dev/null +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op.py @@ -0,0 +1,30 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import platform +import os + +if platform.system() != "Windows": + from tensorflow.contrib.util import loader + from tensorflow.python.platform import resource_loader + + _inc_op = loader.load_op_library( + os.path.join(os.path.dirname(os.path.realpath(__file__)),"_inc_op.so")) +else: + raise RuntimeError("Windows not supported") diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc new file mode 100644 index 00000000000000..5dd6b9bf9497c2 --- /dev/null +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc @@ -0,0 +1,44 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h" +#include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h" + +#if GOOGLE_CUDA +#define EIGEN_USE_GPU +#if GOOGLE_TENSORRT + +namespace tensorflow { +namespace tensorrt { + +__global__ void VecInc(const float* vec, float inc, float* dest, int n) { + int i = blockDim.x * blockIdx.x + threadIdx.x; + if (i < n) dest[i] = vec[i] + inc; +} + +void IncrementKernel(const float* d_input, float inc, float* d_output, + int count, cudaStream_t stream) { + int threads_per_block = 256; + int blocks_per_grid = (count + threads_per_block - 1) / threads_per_block; + + VecInc<<>>(d_input, inc, + d_output, count); +} + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_CUDA +#endif // GOOGLE_TENSORRT diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h new file mode 100644 index 00000000000000..ec269143e89553 --- /dev/null +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h @@ -0,0 +1,34 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_INC_OP +#define TENSORFLOW_CONTRIB_TENSORRT_INC_OP + +#if GOOGLE_CUDA +#define EIGEN_USE_GPU +#if GOOGLE_TENSORRT + +namespace tensorflow { +namespace tensorrt { + +__global__ void VecInc(float* vec, float inc, float* dest, int n); + +} // namespace tensorrt +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CONTRIB_TENSORRT_INC_OP diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc index 2155079e8b9159..21617fa8b59911 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc @@ -13,24 +13,19 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h" -#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h" -#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include +#include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #if GOOGLE_CUDA #if GOOGLE_TENSORRT -#include "inc_op_plugin.h" namespace tensorflow { namespace tensorrt { -const string IncOpPlugin::plugin_name_ = "IncPluginTRT"; - -IncOpPlugin* CreateIncPlugin() { - return new IncOpPlugin(); -} +const std::string IncOpPlugin::plugin_name_ = "IncPluginTRT"; +IncOpPlugin* CreateIncPlugin() { return new IncOpPlugin(); } IncOpPlugin* CreateIncPluginDeserialize(const void* buffer, size_t length) { return new IncOpPlugin(buffer, length); @@ -39,45 +34,49 @@ IncOpPlugin* CreateIncPluginDeserialize(const void* buffer, size_t length) { bool RegisterIncOpPlugin() { if (PluginFactoryTensorRT::GetInstance()->IsPlugin(IncOpPlugin::plugin_name_)) return false; - return PluginFactoryTensorRT::GetInstance()->RegisterPlugin(IncOpPlugin::plugin_name_, CreateIncPluginDeserialize, CreateIncPlugin); + return PluginFactoryTensorRT::GetInstance()->RegisterPlugin( + IncOpPlugin::plugin_name_, CreateIncPluginDeserialize, CreateIncPlugin); } - -IncOpPlugin::IncOpPlugin(const void* serialized_data, size_t length) : - PluginTensorRT(serialized_data, length) -{ +IncOpPlugin::IncOpPlugin(const void* serialized_data, size_t length) + : PluginTensorRT(serialized_data, length) { // account for the consumed pointer. size_t consumed_data = PluginTensorRT::getSerializationSize(); - assert(length-consumed_data >= sizeof(float)); - SetAttribute("inc", serialized_data+consumed_data, sizeof(float)); + assert(length - consumed_data >= sizeof(float)); + const char* buffer = reinterpret_cast(serialized_data); + SetAttribute("inc", buffer + consumed_data, sizeof(float)); } -bool IncOpPlugin::SetAttribute(const string &key, const void *ptr, const size_t size) { - if (strcmp(key.c_str(), "inc")==0 && size == sizeof(float)) { - StoreAttribute(key, ptr, size); // save the attribute to own the data; +bool IncOpPlugin::SetAttribute(const std::string& key, const void* ptr, + const size_t size) { + if (strcmp(key.c_str(), "inc") == 0 && size == sizeof(float)) { + StoreAttribute(key, ptr, size); // save the attribute to own the data; inc_ = *static_cast(ptr); return true; } return false; } -bool IncOpPlugin::GetAttribute(const string &key, const void *ptr, size_t &size) { - if (attr_map_.find(key) != attr_map_.end()) { - ptr = attr_map_[key].data(); - size = attr_map_[key].size(); +bool IncOpPlugin::GetAttribute(const std::string& key, const void** ptr, + size_t* size) const { + const auto& iter = attr_map_.find(key); + if (iter != attr_map_.end()) { + *ptr = iter->second.data(); + *size = iter->second.size(); return true; } return false; } -int IncOpPlugin::enqueue(int batchSize, const void*const *inputs, void** outputs, void*, cudaStream_t stream) { +int IncOpPlugin::enqueue(int batch_size, const void* const* inputs, + void** outputs, void*, cudaStream_t stream) { int count = 1; - for (int i=0; i(inputs[0]); - float *output = reinterpret_cast(outputs[0]); + count *= batch_size; + const float* input = reinterpret_cast(inputs[0]); + float* output = reinterpret_cast(outputs[0]); IncrementKernel(input, inc_, output, count, stream); return 0; } diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h index 52b68487e65c05..a4774d354ca843 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h @@ -16,13 +16,13 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_TENSORRT_INC_OP_PLUGIN #define TENSORFLOW_CONTRIB_TENSORRT_INC_OP_PLUGIN -#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h" -#include -#include -#include -#include #include +#include #include +#include +#include +#include +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h" #if GOOGLE_CUDA #if GOOGLE_TENSORRT @@ -31,50 +31,44 @@ limitations under the License. namespace tensorflow { namespace tensorrt { -using std::string; -using std::unordered_map; - -class IncOpPlugin : public PluginTensorRT -{ -public: - static const string plugin_name_; - IncOpPlugin() {}; +class IncOpPlugin : public PluginTensorRT { + public: + static const std::string plugin_name_; + IncOpPlugin(){}; IncOpPlugin(const void* serialized_data, size_t length); - const string GetPluginName() override {return plugin_name_;}; - bool Finalize() override {return true;}; - bool SetAttribute(const string &key, const void *ptr, const size_t size) override; - bool GetAttribute(const string &key, const void *ptr, size_t &size) override; - - // TRT IPlugin methods - int getNbOutputs() const override {return 1;} - - nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs, int nbInputDims) override { - assert(index==0); - assert(nbInputDims==1); + const std::string& GetPluginName() const override { return plugin_name_; }; + bool Finalize() override { return true; }; + bool SetAttribute(const std::string& key, const void* ptr, + const size_t size) override; + bool GetAttribute(const std::string& key, const void** ptr, + size_t* size) const override; + + int getNbOutputs() const override { return 1; } + + nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs, + int num_input_dims) override { + assert(index == 0); + assert(num_input_dims == 1); return inputs[0]; } - // no configure needed // use configure to setup input dimensions - void configure(const nvinfer1::Dims *inputs, int nbInputs, const nvinfer1::Dims *outputs, int nbOutputs, int maxBatchSize) override { - assert(nbInputs==1); - PluginTensorRT::configure(inputs, nbInputs, outputs, nbOutputs, maxBatchSize); - return; + void configure(const nvinfer1::Dims* inputs, int num_inputs, + const nvinfer1::Dims* outputs, int num_outputs, + int max_batch_size) override { + assert(nb_inputs == 1); + PluginTensorRT::configure(inputs, num_inputs, outputs, num_outputs, + max_batch_size); } - int initialize() override { - return 0; - } + int initialize() override { return 0; } - void terminate() override { - return; - } + void terminate() override {} - size_t getWorkspaceSize(int maxBatchSize) const override { - return 0; - } + size_t getWorkspaceSize(int max_batch_size) const override { return 0; } - int enqueue(int batchSize, const void*const *inputs, void** outputs, void* workspace, cudaStream_t stream) override; + int enqueue(int batch_size, const void* const* inputs, void** outputs, + void* workspace, cudaStream_t stream) override; size_t getSerializationSize() override { return PluginTensorRT::getSerializationSize() + sizeof(float); @@ -86,24 +80,23 @@ class IncOpPlugin : public PluginTensorRT PluginTensorRT::serialize(buffer); // incremented buffer after parent serialization; - buffer = static_cast(buffer) + PluginTensorRT::getSerializationSize(); + buffer = + static_cast(buffer) + PluginTensorRT::getSerializationSize(); std::memcpy(buffer, &inc_, sizeof(float)); buffer = static_cast(buffer) + sizeof(float); - return; } -protected: + protected: float inc_; nvinfer1::Dims dim_; - // std::unordered_map > attr_map_; }; -IncOpPlugin* CreateIncPlugin(); +IncOpPlugin* CreateIncPlugin(); IncOpPlugin* CreateIncPluginDeserialize(const void*, size_t); bool RegisterIncOpPlugin(); -void IncrementKernel(const float* d_input, float inc, float* d_output, int count, cudaStream_t stream); - +void IncrementKernel(const float* d_input, float inc, float* d_output, + int count, cudaStream_t stream); } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/ops/inc_op.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/ops/inc_op.cc new file mode 100644 index 00000000000000..0dfead8f57aea9 --- /dev/null +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/ops/inc_op.cc @@ -0,0 +1,34 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT + +using namespace tensorflow; + +REGISTER_OP("IncPluginTRT") + .Attr("inc: list(float)") + .Input("input: float32") + .Output("output: float32") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->input(0)); + return Status::OK(); + }); + +#endif // GOOGLE_CUDA +#endif // GOOGLE_TENSORRT diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_wrap.i b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_wrap.i new file mode 100644 index 00000000000000..9882daa8426d8b --- /dev/null +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_wrap.i @@ -0,0 +1,31 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +/* Wrap inc_op_plugin */ +%module inc_op_plugin +%{ +#include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h" +extern bool tensorflow::tensorrt::RegisterIncOpPlugin(); +%} + +%{ +bool inc_op_register() { + return tensorflow::tensorrt::RegisterIncOpPlugin(); +} +%} + +extern bool tensorflow::tensorrt::RegisterIncOpPlugin(); + +bool inc_op_register(); diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/test/plugin_test.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/test/plugin_test.py new file mode 100644 index 00000000000000..52f49ae00e8cca --- /dev/null +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/test/plugin_test.py @@ -0,0 +1,93 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Script to show usage of TensorRT custom op & plugin.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +# normally we should do import tensorflow as tf and then +# tf.placeholder, tf.constant, tf.nn.conv2d etc but +# it looks like internal builds don't like it so +# importing every module individually + +from tensorflow.contrib import tensorrt as trt +from tensorflow.core.protobuf import config_pb2 as cpb2 +from tensorflow.python.client import session as csess +from tensorflow.python.framework import dtypes as dtypes +from tensorflow.python.framework import importer as importer +from tensorflow.python.framework import ops as ops +from tensorflow.python.ops import array_ops as aops +from tensorflow.python.ops import nn as nn +from tensorflow.python.ops import nn_ops as nn_ops +import numpy as np + +# import custom_op as plugin op +# the python api handles registration to the plugin factory +from tensorflow.contrib.tensorrt import custom_plugin_examples as cpe + +def get_plugin_graph_def(): + """Create a simple graph and return its graph_def.""" + g = ops.Graph() + with g.as_default(): + a = aops.placeholder( + dtype=dtypes.float32, shape=(None, 24, 24, 2), name="input") + relu = nn.relu(a, "relu") + v = nn_ops.max_pool( + relu, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") + + # insert custom_op in the graph + v = cpe.inc_op(v, inc=[16.5], name="plugin_test") + + v = v*2.0 + v = nn.relu(v) + v = nn.relu(v) + aops.squeeze(v, name="output") + return g.as_graph_def() + +def run_graph(gdef, dumm_inp): + """Run given graphdef once.""" + gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + ops.reset_default_graph() + g = ops.Graph() + with g.as_default(): + inp, out = importer.import_graph_def( + graph_def=gdef, return_elements=["input", "output"]) + inp = inp.outputs[0] + out = out.outputs[0] + + with csess.Session( + config=cpb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess: + val = sess.run(out, {inp: dumm_inp}) + return val + +if "__main__" in __name__: + inp_dims = (5, 24, 24, 2) + dummy_input = np.ones(inp_dims).astype(np.float32) + orig_graph = get_plugin_graph_def() # graph with plugin node + + # trigger conversion. + # plugin nodes have been registered during import, converter will be able to + # create corresponding plugin layer during conversion. + trt_graph = trt.create_inference_graph( + input_graph_def=orig_graph, + outputs=["output"], + max_batch_size=inp_dims[0], + max_workspace_size_bytes=1 << 25, + precision_mode="FP32", + minimum_segment_size=2 + ) + o2 = run_graph(trt_graph, dummy_input) + print (o2) diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc index 76007037753683..82c549dbf50caf 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc @@ -58,7 +58,6 @@ void PluginTensorRT::configure(const nvinfer1::Dims* inputs, int num_inputs, } input_dim_list_.emplace_back(dim); } - return; } size_t PluginTensorRT::getSerializationSize() { diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin.h index 59b92657f637dc..772974a769b2fb 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin.h +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin.h @@ -32,20 +32,18 @@ namespace tensorrt { // User application should inherit from this class to write custom kernels. // Allows user to insert custom op in TensorRT engine // To register plugin in converter, user should also register custom -// tensorflow::tensorrt::PluginDeserializeFunc & -// tensorflow::tensorrt::PluginConstructFunc through -// tensorflow::tensorrt::PluginFactoryTensorRT +// PluginDeserializeFunc & PluginConstructFunc through PluginFactoryTensorRT class PluginTensorRT : public nvinfer1::IPlugin { public: PluginTensorRT(){}; PluginTensorRT(const void* serialized_data, size_t length); - virtual const std::string& GetPluginName() = 0; + virtual const std::string& GetPluginName() const = 0; virtual bool Finalize() = 0; virtual bool SetAttribute(const std::string& key, const void* ptr, const size_t size) = 0; - virtual bool GetAttribute(const std::string& key, const void* ptr, - size_t& size) = 0; + virtual bool GetAttribute(const std::string& key, const void** ptr, + size_t* size) const = 0; void configure(const nvinfer1::Dims* inputs, int num_inputs, const nvinfer1::Dims* outputs, int num_outputs, diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc index 44b10394c87430..776bce119df278 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc @@ -33,12 +33,10 @@ PluginTensorRT* PluginFactoryTensorRT::createPlugin(const char* layer_name, return nullptr; } - // should I lock plugins here? - instance_m_.lock(); + std::lock_guard lock(instance_m_); auto plugin_ptr = plugin_registry_[encoded_op_name].first(serial_data, serial_length); owned_plugins_.emplace_back(plugin_ptr); - instance_m_.unlock(); return plugin_ptr; } @@ -47,10 +45,9 @@ PluginTensorRT* PluginFactoryTensorRT::CreatePlugin( const std::string& op_name) { if (!IsPlugin(op_name)) return nullptr; - instance_m_.lock(); + std::lock_guard lock(instance_m_); auto plugin_ptr = plugin_registry_[op_name].second(); owned_plugins_.emplace_back(plugin_ptr); - instance_m_.unlock(); return plugin_ptr; } @@ -60,22 +57,19 @@ bool PluginFactoryTensorRT::RegisterPlugin( PluginConstructFunc construct_func) { if (IsPlugin(op_name)) return false; - // get instance_m_ first before write to registry; - instance_m_.lock(); + std::lock_guard lock(instance_m_); auto ret = plugin_registry_.emplace( op_name, std::make_pair(deserialize_func, construct_func)); - instance_m_.unlock(); return ret.second; } void PluginFactoryTensorRT::DestroyPlugins() { - instance_m_.lock(); + std::lock_guard lock(instance_m_); for (auto& owned_plugin_ptr : owned_plugins_) { owned_plugin_ptr.release(); } owned_plugins_.clear(); - instance_m_.unlock(); } } // namespace tensorrt diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h index 824efcff355de7..08fd37684455cb 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h @@ -39,10 +39,8 @@ class PluginFactoryTensorRT : public nvinfer1::IPluginFactory { PluginTensorRT* CreatePlugin(const std::string& op_name); static PluginFactoryTensorRT* GetInstance() { - static PluginFactoryTensorRT* factory_instance = nullptr; - if (factory_instance == nullptr) { - factory_instance = new PluginFactoryTensorRT(); - } + static PluginFactoryTensorRT* factory_instance = + new PluginFactoryTensorRT(); return factory_instance; } diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc index 8b65e8b41c3841..c5d3f38280e143 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc @@ -22,8 +22,8 @@ limitations under the License. namespace tensorflow { namespace tensorrt { -std::string ExtractOpName(const void* serial_data, size_t serial_length, - size_t* incremental) { +string ExtractOpName(const void* serial_data, size_t serial_length, + size_t* incremental) { size_t op_name_char_count = *static_cast(serial_data); *incremental = sizeof(size_t) + op_name_char_count; diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h index d4da8b261e6508..a94c67bba025a1 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h @@ -18,6 +18,7 @@ limitations under the License. #include #include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h" +#include "tensorflow/core/platform/types.h" #if GOOGLE_CUDA #if GOOGLE_TENSORRT @@ -32,8 +33,8 @@ typedef std::function typedef std::function PluginConstructFunc; // TODO(jie): work on error handling here -std::string ExtractOpName(const void* serial_data, size_t serial_length, - size_t* incremental); +string ExtractOpName(const void* serial_data, size_t serial_length, + size_t* incremental); } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc index 2856b0f87d30f0..9ef0fce972a6c9 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc @@ -51,9 +51,9 @@ class StubPlugin : public PluginTensorRT { return inputs[0]; } int initialize() override { return 0; } - void terminate() override { return; } + void terminate() override {} size_t getWorkspaceSize(int maxBatchSize) const override { return 0; } - int enqueue(int batchSize, const void* const* inputs, void** outputs, + int enqueue(int batch_size, const void* const* inputs, void** outputs, void* workspace, cudaStream_t stream) override { return 0; } @@ -78,8 +78,6 @@ class PluginTest : public ::testing::Test { StubPlugin::plugin_name_, CreateStubPluginDeserialize, CreateStubPlugin); } - - protected: }; TEST_F(PluginTest, Registration) { From abfbbb86295c67eb1ac7c92235dbd5fb4b707169 Mon Sep 17 00:00:00 2001 From: Haggai Date: Wed, 18 Apr 2018 22:23:35 -0700 Subject: [PATCH 0010/1691] Remove reliance on TF core in XLA CPU Fft --- tensorflow/compiler/xla/service/cpu/BUILD | 1 - .../xla/service/cpu/runtime_fft_impl.h | 18 +++--------------- 2 files changed, 3 insertions(+), 16 deletions(-) diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 246b8028618928..6428ca528c7cf9 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -513,7 +513,6 @@ cc_library( deps = [ "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/core:framework", "//tensorflow/core:framework_lite", "//third_party/eigen3", ], diff --git a/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h b/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h index 984cb0616e0247..4f6b3633645b22 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h @@ -21,8 +21,6 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/numeric_types.h" -#include "tensorflow/core/framework/tensor.h" -#include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/platform/types.h" // 'tensorflow' namespace is used so that int64 and other types don't require @@ -71,11 +69,9 @@ void EigenFftR2C(const EigenDevice& device, complex64* out, float* operand, in_dims[0] = input_batch; Eigen::DSizes out_dims; out_dims[0] = input_batch; - TensorShape temp_shape{input_batch}; for (int i = 0; i < FFTRank; i++) { in_dims[i + 1] = fft_shape[i]; out_dims[i + 1] = i == FFTRank - 1 ? fft_shape[i] / 2 + 1 : fft_shape[i]; - temp_shape.AddDim(fft_shape[i]); } const Eigen::TensorMap, Eigen::Aligned> @@ -88,8 +84,8 @@ void EigenFftR2C(const EigenDevice& device, complex64* out, float* operand, const auto axes = Eigen::ArrayXi::LinSpaced(FFTRank, 1, FFTRank); // Compute the full FFT using a temporary tensor. - Tensor temp(DataTypeToEnum::v(), temp_shape); - auto full_fft = temp.flat_inner_dims(); + Eigen::Tensor full_fft(in_dims); + const Eigen::DSizes zero_start_indices; full_fft.device(device) = input.template fft(axes); @@ -112,11 +108,9 @@ void EigenFftC2R(const EigenDevice& device, float* out, complex64* operand, in_dims[0] = input_batch; Eigen::DSizes out_dims; out_dims[0] = input_batch; - TensorShape temp_shape{input_batch}; for (int i = 0; i < FFTRank; i++) { in_dims[i + 1] = i == FFTRank - 1 ? fft_shape[i] / 2 + 1 : fft_shape[i]; out_dims[i + 1] = fft_shape[i]; - temp_shape.AddDim(fft_shape[i]); } const Eigen::TensorMap, Eigen::Aligned> @@ -129,8 +123,7 @@ void EigenFftC2R(const EigenDevice& device, float* out, complex64* operand, // region we will slice from input given fft_shape. We slice input to // fft_shape on its inner-most dimensions, except the last (which we // slice to fft_shape[-1] / 2 + 1). - Tensor temp(DataTypeToEnum::v(), temp_shape); - auto full_fft = temp.flat_inner_dims(); + Eigen::Tensor full_fft(out_dims); // Calculate the starting point and range of the source of // negative frequency part. @@ -179,7 +172,6 @@ template void EigenFftWithRank(const EigenDevice& device, void* out, void* operand, int32 fft_type, int64 input_batch, int64 fft_length0, int64 fft_length1, int64 fft_length2) { - CHECK(::xla::FftType_IsValid(fft_type)) << fft_type; switch (fft_type) { case ::xla::FftType::FFT: EigenFftC2C( @@ -203,8 +195,6 @@ void EigenFftWithRank(const EigenDevice& device, void* out, void* operand, device, static_cast(out), static_cast(operand), input_batch, fft_length0, fft_length1, fft_length2); break; - default: - LOG(FATAL) << "Unsupported FFT type: " << fft_type; } } @@ -229,8 +219,6 @@ void EigenFftImpl(const EigenDevice& device, void* out, void* operand, input_batch, fft_length0, fft_length1, fft_length2); break; - default: - LOG(FATAL) << "Unsupported FFT rank " << fft_rank; } } From 6343b8dd77ba94c74acc3c04c985a5535b2b8169 Mon Sep 17 00:00:00 2001 From: Haggai Date: Wed, 18 Apr 2018 22:26:42 -0700 Subject: [PATCH 0011/1691] Add single-threaded support for XLA CPU Fft --- tensorflow/compiler/xla/service/cpu/BUILD | 17 ++++++++++ .../compiler/xla/service/cpu/cpu_runtime.cc | 2 ++ .../compiler/xla/service/cpu/cpu_runtime.h | 1 + .../compiler/xla/service/cpu/ir_emitter.cc | 8 ++++- .../cpu/runtime_single_threaded_fft.cc | 32 +++++++++++++++++++ .../service/cpu/runtime_single_threaded_fft.h | 31 ++++++++++++++++++ .../xla/service/cpu/simple_orc_jit.cc | 2 ++ 7 files changed, 92 insertions(+), 1 deletion(-) create mode 100644 tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc create mode 100644 tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 6428ca528c7cf9..4862f9e2f9fdfd 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -176,6 +176,7 @@ cc_library( ":runtime_matmul", ":runtime_matmul_mkl", ":runtime_single_threaded_conv2d", + ":runtime_single_threaded_fft", ":runtime_single_threaded_matmul", "@llvm//:execution_engine", "@llvm//:core", @@ -574,6 +575,22 @@ cc_library( ], ) +cc_library( + name = "runtime_single_threaded_fft", + srcs = [ + "runtime_fft_impl.h", + "runtime_single_threaded_fft.cc", + ], + hdrs = ["runtime_single_threaded_fft.h"], + copts = runtime_copts(), + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/core:framework_lite", + "//third_party/eigen3", + ], +) + cc_library( name = "runtime_single_threaded_matmul", srcs = ["runtime_single_threaded_matmul.cc"], diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc index 872b0be1f8a8ec..4fcab483d69449 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc @@ -50,6 +50,8 @@ extern const char* const kEigenConvF16SymbolName = extern const char* const kEigenConvF32SymbolName = "__xla_cpu_runtime_EigenConvF32"; extern const char* const kEigenFftSymbolName = "__xla_cpu_runtime_EigenFft"; +extern const char* const kEigenSingleThreadedFftSymbolName = + "__xla_cpu_runtime_EigenSingleThreadedFft"; extern const char* const kEigenSingleThreadedMatMulF16SymbolName = "__xla_cpu_runtime_EigenSingleThreadedMatMulF16"; extern const char* const kEigenSingleThreadedMatMulF32SymbolName = diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h index e392e231b4c71b..0cc45dac613a00 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h @@ -51,6 +51,7 @@ extern const char* const kMKLSingleThreadedMatMulF64SymbolName; extern const char* const kEigenConvF16SymbolName; extern const char* const kEigenConvF32SymbolName; extern const char* const kEigenFftSymbolName; +extern const char* const kEigenSingleThreadedFftSymbolName; extern const char* const kEigenSingleThreadedMatMulF16SymbolName; extern const char* const kEigenSingleThreadedMatMulF32SymbolName; extern const char* const kEigenSingleThreadedMatMulF64SymbolName; diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 3405277d449f2d..8c2ca7104c70e6 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -1171,7 +1171,13 @@ Status IrEmitter::HandleFft(HloInstruction* fft) { {int8_ptr_type, int8_ptr_type, int8_ptr_type, int32_type, int32_type, int64_type, int64_type, int64_type, int64_type}, /*isVarArg=*/false); - const char* fn_name = runtime::kEigenFftSymbolName; + + bool multi_threaded_eigen = + hlo_module_config_.debug_options().xla_cpu_multi_thread_eigen(); + const char* fn_name = multi_threaded_eigen + ? runtime::kEigenFftSymbolName + : runtime::kEigenSingleThreadedFftSymbolName; + llvm::Function* fft_func = llvm::cast( module_->getOrInsertFunction(fn_name, fft_type)); fft_func->setCallingConv(llvm::CallingConv::C); diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc new file mode 100644 index 00000000000000..2613ddb12704ae --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc @@ -0,0 +1,32 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h" + +#include "tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h" +#include "tensorflow/core/platform/dynamic_annotations.h" +#include "tensorflow/core/platform/types.h" + +using tensorflow::int32; +using tensorflow::int64; + +TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenSingleThreadedFft( + const void* run_options_ptr, void* out, void* operand, int32 fft_type, + int32 fft_rank, int64 input_batch, int64 fft_length0, int64 fft_length1, + int64 fft_length2) { + tensorflow::xla::EigenFftImpl(Eigen::DefaultDevice(), out, operand, fft_type, + fft_rank, input_batch, fft_length0, fft_length1, + fft_length2); +} diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h new file mode 100644 index 00000000000000..dcd133d012cf07 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h @@ -0,0 +1,31 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ + +#include "tensorflow/core/platform/types.h" + +extern "C" { + +extern void __xla_cpu_runtime_EigenSingleThreadedFft( + const void* /* xla::ExecutableRunOptions* */ run_options_ptr, void* out, + void* operand, tensorflow::int32 fft_type, tensorflow::int32 fft_rank, + tensorflow::int64 input_batch, tensorflow::int64 fft_length0, + tensorflow::int64 fft_length1, tensorflow::int64 fft_length2); + +} // extern "C" + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc index b7ce5bbe474823..7bd17002e329b0 100644 --- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc +++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc @@ -37,6 +37,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/cpu/runtime_matmul.h" #include "tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h" +#include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h" #include "tensorflow/compiler/xla/service/cpu/windows_compatibility.h" #include "tensorflow/compiler/xla/types.h" @@ -190,6 +191,7 @@ bool RegisterKnownJITSymbols() { REGISTER_CPU_RUNTIME_SYMBOL(MKLSingleThreadedMatMulF64); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF32); + REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedFft); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF64); From 8149077125f6c2701713fef12fe0f0caac729e27 Mon Sep 17 00:00:00 2001 From: Krish Ravindranath Date: Thu, 19 Apr 2018 14:53:10 -0400 Subject: [PATCH 0012/1691] changes error to ValueError, notes that shuffle must be provided and should be set True for training --- tensorflow/python/estimator/inputs/numpy_io.py | 5 +++-- tensorflow/python/estimator/inputs/pandas_io.py | 5 +++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/estimator/inputs/numpy_io.py b/tensorflow/python/estimator/inputs/numpy_io.py index a6f471291008e3..5b5eb41466a2fc 100644 --- a/tensorflow/python/estimator/inputs/numpy_io.py +++ b/tensorflow/python/estimator/inputs/numpy_io.py @@ -139,8 +139,9 @@ def numpy_input_fn(x, TypeError: `x` is not a dict or array, or if `shuffle` is not bool. """ if not isinstance(shuffle, bool): - raise TypeError('shuffle must be explicitly set as boolean; ' - 'got {}'.format(shuffle)) + raise ValueError('shuffle must be provided and explicitly set as boolean ' + '(it is recommended to set it as True for training); ' + 'got {}'.format(shuffle)) def input_fn(): """Numpy input function.""" diff --git a/tensorflow/python/estimator/inputs/pandas_io.py b/tensorflow/python/estimator/inputs/pandas_io.py index bd06843021f47f..16825e09dec94e 100644 --- a/tensorflow/python/estimator/inputs/pandas_io.py +++ b/tensorflow/python/estimator/inputs/pandas_io.py @@ -75,8 +75,9 @@ def pandas_input_fn(x, 'pandas_input_fn should not be called without pandas installed') if not isinstance(shuffle, bool): - raise TypeError('shuffle must be explicitly set as boolean; ' - 'got {}'.format(shuffle)) + raise ValueError('shuffle must be provided and explicitly set as boolean ' + '(it is recommended to set it as True for training); ' + 'got {}'.format(shuffle)) x = x.copy() if y is not None: From 459d61cbe8ab9cbb86b2bb7eac602ff565d54fde Mon Sep 17 00:00:00 2001 From: Jie Date: Thu, 19 Apr 2018 13:48:14 -0700 Subject: [PATCH 0013/1691] [PR comment addressed] switched from std::string to TF string custom_plugin_examples python test added (bazel) style guide violation addressed --- .../contrib/tensorrt/convert/convert_nodes.cc | 22 ++--- .../tensorrt/custom_plugin_examples/BUILD | 42 ++++++--- .../custom_plugin_examples/__init__.py | 12 +-- .../inc_op_kernel.cu.cc | 2 - .../custom_plugin_examples/inc_op_kernel.h | 3 +- .../{inc_op_plugin.cc => inc_op_plugin.cu.cc} | 9 +- .../custom_plugin_examples/inc_op_plugin.h | 18 ++-- .../custom_plugin_examples/ops/inc_op.cc | 4 +- .../{test => }/plugin_test.py | 46 +++++----- tensorflow/contrib/tensorrt/log/trt_logger.h | 2 +- .../contrib/tensorrt/plugin/trt_plugin.cc | 3 +- .../contrib/tensorrt/plugin/trt_plugin.h | 14 +-- .../tensorrt/plugin/trt_plugin_factory.cc | 7 +- .../tensorrt/plugin/trt_plugin_factory.h | 8 +- .../tensorrt/plugin/trt_plugin_utils.cc | 2 +- .../tensorrt/plugin/trt_plugins_test.cc | 19 ++-- tensorflow/contrib/tensorrt/plugin_test.py | 88 +++++++++++++++++++ .../tensorrt/resources/trt_resources.h | 2 +- 18 files changed, 205 insertions(+), 98 deletions(-) rename tensorflow/contrib/tensorrt/custom_plugin_examples/{inc_op_plugin.cc => inc_op_plugin.cu.cc} (91%) rename tensorflow/contrib/tensorrt/custom_plugin_examples/{test => }/plugin_test.py (67%) create mode 100644 tensorflow/contrib/tensorrt/plugin_test.py diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 874be96c781e53..c8a96e5dba863e 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -241,9 +241,9 @@ class TFAttrs { return attrs_.at(key); } template - T get(string key) const; + T get(const string& key) const; template - T get(string key, const T& default_value) const { + T get(const string& key, const T& default_value) const { return attrs_.count(key) ? this->get(key) : default_value; } @@ -261,29 +261,29 @@ class TFAttrs { }; template <> -string TFAttrs::get(string key) const { +string TFAttrs::get(const string& key) const { return this->at(key)->s(); } template <> -std::vector TFAttrs::get>(string key) const { +std::vector TFAttrs::get>(const string& key) const { auto attr = this->at(key)->list().i(); return std::vector(attr.begin(), attr.end()); } template <> -std::vector TFAttrs::get>(string key) const { +std::vector TFAttrs::get>(const string& key) const { auto attr = this->at(key)->list().f(); return std::vector(attr.begin(), attr.end()); } template <> -std::vector TFAttrs::get>(string key) const { +std::vector TFAttrs::get>(const string& key) const { auto attr = this->at(key)->list().s(); return std::vector(attr.begin(), attr.end()); } template <> -nvinfer1::Dims TFAttrs::get(string key) const { +nvinfer1::Dims TFAttrs::get(const string& key) const { auto values = this->get>(key); nvinfer1::Dims dims; dims.nbDims = values.size(); @@ -293,24 +293,24 @@ nvinfer1::Dims TFAttrs::get(string key) const { } template <> -nvinfer1::DataType TFAttrs::get(string key) const { +nvinfer1::DataType TFAttrs::get(const string& key) const { nvinfer1::DataType trt_dtype(nvinfer1::DataType::kFLOAT); TF_CHECK_OK(ConvertDType(this->at(key)->type(), &trt_dtype)); return trt_dtype; } template <> -tensorflow::DataType TFAttrs::get(string key) const { +tensorflow::DataType TFAttrs::get(const string& key) const { return this->at(key)->type(); } template <> -float TFAttrs::get(string key) const { +float TFAttrs::get(const string& key) const { return this->at(key)->f(); } template <> -bool TFAttrs::get(string key) const { +bool TFAttrs::get(const string& key) const { return this->at(key)->b(); } diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD index 5603ed0ccf5c26..3b1a7fb6f33a1c 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD @@ -1,3 +1,9 @@ +# Description: +# Example for plugin support in TensorRT(http://developer.nvidia.com/tensorrt) +# through TensorFlow integration. Targeting TensorRT 3.0.4 +# APIs are meant to change while upgrading TRT. +# add init_py into pip package BUILD dependency to install it. + package(default_visibility = ["//tensorflow:__subpackages__"]) load( @@ -8,6 +14,7 @@ load( "tf_gen_op_wrapper_py", "tf_py_wrap_cc", "tf_copts", + "tf_py_test", ) load( "@local_config_tensorrt//:build_defs.bzl", @@ -18,19 +25,16 @@ load("//tensorflow:tensorflow.bzl", "tf_kernel_library") tf_kernel_library( name = "_inc_op_plugin_kernel", - srcs = [ - "inc_op_plugin.cc", - ], - hdrs = [ - ], gpu_srcs = [ "inc_op_kernel.cu.cc", "inc_op_kernel.h", + "inc_op_plugin.cu.cc", "inc_op_plugin.h", ], - deps = if_tensorrt([ - "@local_config_tensorrt//:nv_infer", + deps = [ "//tensorflow/contrib/tensorrt:trt_plugins", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", ]), ) @@ -38,9 +42,10 @@ tf_gen_op_libs( op_lib_names = [ "inc_op", ], - deps = if_tensorrt([ - "@local_config_tensorrt//:nv_infer", + deps = [ "//tensorflow/contrib/tensorrt:trt_plugins", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", ]), ) @@ -70,9 +75,8 @@ tf_custom_op_library( srcs = ["ops/inc_op.cc"], deps = [ "//tensorflow/core:lib_proto_parsing", - ] + if_tensorrt([ "//tensorflow/contrib/tensorrt:trt_plugins", - ]), + ], ) tf_custom_op_py_library( @@ -97,6 +101,22 @@ py_library( ], ) +tf_py_test( + name = "plugin_test", + size = "small", + srcs = [ + "plugin_test.py", + ], + additional_deps = [ + ":init_py", + "//tensorflow/contrib/util:util_py", + "//tensorflow/contrib/tensorrt:init_py", + "//tensorflow/python:platform", + "//tensorflow/python:client_testlib", + "//tensorflow/python:tf_optimizer", + ], +) + py_library( name = "init_py", srcs = [ diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py index a61d008941897c..e4cd0ae8a055df 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py @@ -14,11 +14,13 @@ # ============================================================================= """Import custom op for plugin and register it in plugin factory registry.""" -from ops import gen_inc_op -from plugin_wrap import inc_op_register -from inc_op import * +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.tensorrt.custom_plugin_examples.ops import gen_inc_op +from tensorflow.contrib.tensorrt.custom_plugin_examples.plugin_wrap import inc_op_register +from tensorflow.contrib.tensorrt.custom_plugin_examples import inc_op as import_inc_op_so -# pylint: disable=unused-import,wildcard-import,g-import-not-at-top inc_op = gen_inc_op.inc_plugin_trt inc_op_register() -# pylint: enable=unused-import,wildcard-import,g-import-not-at-top diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc index 5dd6b9bf9497c2..38e1e01d9546e3 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc @@ -14,10 +14,8 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h" -#include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h" #if GOOGLE_CUDA -#define EIGEN_USE_GPU #if GOOGLE_TENSORRT namespace tensorflow { diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h index ec269143e89553..13156dad8fd574 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h @@ -17,13 +17,14 @@ limitations under the License. #define TENSORFLOW_CONTRIB_TENSORRT_INC_OP #if GOOGLE_CUDA -#define EIGEN_USE_GPU #if GOOGLE_TENSORRT namespace tensorflow { namespace tensorrt { __global__ void VecInc(float* vec, float inc, float* dest, int n); +void IncrementKernel(const float* d_input, float inc, float* d_output, + int count, cudaStream_t stream); } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cu.cc similarity index 91% rename from tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc rename to tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cu.cc index 21617fa8b59911..508ced587bd566 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cu.cc @@ -13,8 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h" +#include +#include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h" #include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #if GOOGLE_CUDA @@ -23,7 +24,7 @@ limitations under the License. namespace tensorflow { namespace tensorrt { -const std::string IncOpPlugin::plugin_name_ = "IncPluginTRT"; +const string IncOpPlugin::plugin_name_ = "IncPluginTRT"; IncOpPlugin* CreateIncPlugin() { return new IncOpPlugin(); } @@ -47,7 +48,7 @@ IncOpPlugin::IncOpPlugin(const void* serialized_data, size_t length) SetAttribute("inc", buffer + consumed_data, sizeof(float)); } -bool IncOpPlugin::SetAttribute(const std::string& key, const void* ptr, +bool IncOpPlugin::SetAttribute(const string& key, const void* ptr, const size_t size) { if (strcmp(key.c_str(), "inc") == 0 && size == sizeof(float)) { StoreAttribute(key, ptr, size); // save the attribute to own the data; @@ -57,7 +58,7 @@ bool IncOpPlugin::SetAttribute(const std::string& key, const void* ptr, return false; } -bool IncOpPlugin::GetAttribute(const std::string& key, const void** ptr, +bool IncOpPlugin::GetAttribute(const string& key, const void** ptr, size_t* size) const { const auto& iter = attr_map_.find(key); if (iter != attr_map_.end()) { diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h index a4774d354ca843..87404a755c24de 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h @@ -18,10 +18,6 @@ limitations under the License. #include #include -#include -#include -#include -#include #include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h" #if GOOGLE_CUDA @@ -33,14 +29,14 @@ namespace tensorrt { class IncOpPlugin : public PluginTensorRT { public: - static const std::string plugin_name_; - IncOpPlugin(){}; + static const string plugin_name_; + IncOpPlugin() {}; IncOpPlugin(const void* serialized_data, size_t length); - const std::string& GetPluginName() const override { return plugin_name_; }; + const string& GetPluginName() const override { return plugin_name_; }; bool Finalize() override { return true; }; - bool SetAttribute(const std::string& key, const void* ptr, + bool SetAttribute(const string& key, const void* ptr, const size_t size) override; - bool GetAttribute(const std::string& key, const void** ptr, + bool GetAttribute(const string& key, const void** ptr, size_t* size) const override; int getNbOutputs() const override { return 1; } @@ -56,7 +52,7 @@ class IncOpPlugin : public PluginTensorRT { void configure(const nvinfer1::Dims* inputs, int num_inputs, const nvinfer1::Dims* outputs, int num_outputs, int max_batch_size) override { - assert(nb_inputs == 1); + assert(num_inputs == 1); PluginTensorRT::configure(inputs, num_inputs, outputs, num_outputs, max_batch_size); } @@ -95,8 +91,6 @@ class IncOpPlugin : public PluginTensorRT { IncOpPlugin* CreateIncPlugin(); IncOpPlugin* CreateIncPluginDeserialize(const void*, size_t); bool RegisterIncOpPlugin(); -void IncrementKernel(const float* d_input, float inc, float* d_output, - int count, cudaStream_t stream); } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/ops/inc_op.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/ops/inc_op.cc index 0dfead8f57aea9..7466e590901600 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/ops/inc_op.cc +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/ops/inc_op.cc @@ -19,7 +19,7 @@ limitations under the License. #if GOOGLE_CUDA #if GOOGLE_TENSORRT -using namespace tensorflow; +namespace tensorflow { REGISTER_OP("IncPluginTRT") .Attr("inc: list(float)") @@ -30,5 +30,7 @@ REGISTER_OP("IncPluginTRT") return Status::OK(); }); +} // namespace tensorflow + #endif // GOOGLE_CUDA #endif // GOOGLE_TENSORRT diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/test/plugin_test.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py similarity index 67% rename from tensorflow/contrib/tensorrt/custom_plugin_examples/test/plugin_test.py rename to tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py index 52f49ae00e8cca..9f773c66a99075 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/test/plugin_test.py +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py @@ -23,43 +23,44 @@ # it looks like internal builds don't like it so # importing every module individually -from tensorflow.contrib import tensorrt as trt -from tensorflow.core.protobuf import config_pb2 as cpb2 -from tensorflow.python.client import session as csess -from tensorflow.python.framework import dtypes as dtypes -from tensorflow.python.framework import importer as importer -from tensorflow.python.framework import ops as ops -from tensorflow.python.ops import array_ops as aops -from tensorflow.python.ops import nn as nn -from tensorflow.python.ops import nn_ops as nn_ops -import numpy as np +from tensorflow.contrib import tensorrt +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.client import session +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import importer +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops import nn_ops +from tensorflow.python.framework import errors +import numpy # import custom_op as plugin op -# the python api handles registration to the plugin factory -from tensorflow.contrib.tensorrt import custom_plugin_examples as cpe +# the python api handles registration to the plugin factory +from tensorflow.contrib.tensorrt import custom_plugin_examples def get_plugin_graph_def(): """Create a simple graph and return its graph_def.""" g = ops.Graph() with g.as_default(): - a = aops.placeholder( + a = array_ops.placeholder( dtype=dtypes.float32, shape=(None, 24, 24, 2), name="input") relu = nn.relu(a, "relu") v = nn_ops.max_pool( relu, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") # insert custom_op in the graph - v = cpe.inc_op(v, inc=[16.5], name="plugin_test") + v = custom_plugin_examples.inc_op(v, inc=[16.5], name="plugin_test") v = v*2.0 v = nn.relu(v) v = nn.relu(v) - aops.squeeze(v, name="output") + array_ops.squeeze(v, name="output") return g.as_graph_def() def run_graph(gdef, dumm_inp): """Run given graphdef once.""" - gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) ops.reset_default_graph() g = ops.Graph() with g.as_default(): @@ -68,20 +69,20 @@ def run_graph(gdef, dumm_inp): inp = inp.outputs[0] out = out.outputs[0] - with csess.Session( - config=cpb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess: + with session.Session( + config=config_pb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess: val = sess.run(out, {inp: dumm_inp}) return val if "__main__" in __name__: inp_dims = (5, 24, 24, 2) - dummy_input = np.ones(inp_dims).astype(np.float32) + dummy_input = numpy.ones(inp_dims).astype(numpy.float32) orig_graph = get_plugin_graph_def() # graph with plugin node # trigger conversion. # plugin nodes have been registered during import, converter will be able to # create corresponding plugin layer during conversion. - trt_graph = trt.create_inference_graph( + trt_graph = tensorrt.create_inference_graph( input_graph_def=orig_graph, outputs=["output"], max_batch_size=inp_dims[0], @@ -90,4 +91,7 @@ def run_graph(gdef, dumm_inp): minimum_segment_size=2 ) o2 = run_graph(trt_graph, dummy_input) - print (o2) + if o2.reshape([-1])[0] == 35: + print("pass") + else: + raise RuntimeError("contrib/tensorrt/custom_plugin_examples wrong result") diff --git a/tensorflow/contrib/tensorrt/log/trt_logger.h b/tensorflow/contrib/tensorrt/log/trt_logger.h index 7f3544f8cfda8d..3495dc63185027 100644 --- a/tensorflow/contrib/tensorrt/log/trt_logger.h +++ b/tensorflow/contrib/tensorrt/log/trt_logger.h @@ -28,7 +28,7 @@ namespace tensorrt { // Logger for GIE info/warning/errors class Logger : public nvinfer1::ILogger { public: - Logger(string name = "DefaultLogger") : name_(name){}; + Logger(string name = "DefaultLogger") : name_(name) {}; void log(nvinfer1::ILogger::Severity severity, const char* msg) override; private: diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc index 82c549dbf50caf..062f86e8bb4dc7 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin.cc @@ -25,7 +25,6 @@ namespace tensorflow { namespace tensorrt { PluginTensorRT::PluginTensorRT(const void* serialized_data, size_t length) { - // sanity check. const char* buffer = static_cast(serialized_data); size_t op_name_char_count = *reinterpret_cast(buffer); buffer += sizeof(size_t); @@ -91,7 +90,7 @@ void PluginTensorRT::serialize(void* serialized_data) { } } -bool PluginTensorRT::StoreAttribute(const std::string& key, const void* ptr, +bool PluginTensorRT::StoreAttribute(const string& key, const void* ptr, const size_t size) { if (attr_map_.count(key) != 0) return false; diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin.h index 772974a769b2fb..dca377c2d2b836 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin.h +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin.h @@ -17,9 +17,9 @@ limitations under the License. #define TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN #include -#include #include #include +#include "tensorflow/core/platform/types.h" #if GOOGLE_CUDA #if GOOGLE_TENSORRT @@ -35,28 +35,28 @@ namespace tensorrt { // PluginDeserializeFunc & PluginConstructFunc through PluginFactoryTensorRT class PluginTensorRT : public nvinfer1::IPlugin { public: - PluginTensorRT(){}; + PluginTensorRT() {}; PluginTensorRT(const void* serialized_data, size_t length); - virtual const std::string& GetPluginName() const = 0; + virtual const string& GetPluginName() const = 0; virtual bool Finalize() = 0; - virtual bool SetAttribute(const std::string& key, const void* ptr, + virtual bool SetAttribute(const string& key, const void* ptr, const size_t size) = 0; - virtual bool GetAttribute(const std::string& key, const void** ptr, + virtual bool GetAttribute(const string& key, const void** ptr, size_t* size) const = 0; void configure(const nvinfer1::Dims* inputs, int num_inputs, const nvinfer1::Dims* outputs, int num_outputs, int max_batch_size) override; - virtual bool StoreAttribute(const std::string& key, const void* ptr, + virtual bool StoreAttribute(const string& key, const void* ptr, const size_t size); virtual size_t getSerializationSize() override; virtual void serialize(void* buffer) override; protected: - std::unordered_map > attr_map_; + std::unordered_map > attr_map_; std::vector input_dim_list_; }; diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc index 776bce119df278..736a1321fe7215 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc @@ -26,7 +26,7 @@ PluginTensorRT* PluginFactoryTensorRT::createPlugin(const char* layer_name, size_t serial_length) { size_t parsed_byte = 0; // extract op_name from serial_data - std::string encoded_op_name = + string encoded_op_name = ExtractOpName(serial_data, serial_length, &parsed_byte); if (!IsPlugin(encoded_op_name)) { @@ -41,8 +41,7 @@ PluginTensorRT* PluginFactoryTensorRT::createPlugin(const char* layer_name, return plugin_ptr; } -PluginTensorRT* PluginFactoryTensorRT::CreatePlugin( - const std::string& op_name) { +PluginTensorRT* PluginFactoryTensorRT::CreatePlugin(const string& op_name) { if (!IsPlugin(op_name)) return nullptr; std::lock_guard lock(instance_m_); @@ -53,7 +52,7 @@ PluginTensorRT* PluginFactoryTensorRT::CreatePlugin( } bool PluginFactoryTensorRT::RegisterPlugin( - const std::string& op_name, PluginDeserializeFunc deserialize_func, + const string& op_name, PluginDeserializeFunc deserialize_func, PluginConstructFunc construct_func) { if (IsPlugin(op_name)) return false; diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h index 08fd37684455cb..4e4a3af4cab5f7 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h @@ -36,7 +36,7 @@ class PluginFactoryTensorRT : public nvinfer1::IPluginFactory { size_t serial_length) override; // plugin construction, PluginFactoryTensorRT owns the plugin; - PluginTensorRT* CreatePlugin(const std::string& op_name); + PluginTensorRT* CreatePlugin(const string& op_name); static PluginFactoryTensorRT* GetInstance() { static PluginFactoryTensorRT* factory_instance = @@ -44,11 +44,11 @@ class PluginFactoryTensorRT : public nvinfer1::IPluginFactory { return factory_instance; } - bool RegisterPlugin(const std::string& op_name, + bool RegisterPlugin(const string& op_name, PluginDeserializeFunc deserialize_func, PluginConstructFunc construct_func); - bool IsPlugin(const std::string& op_name) { + bool IsPlugin(const string& op_name) { return plugin_registry_.find(op_name) != plugin_registry_.end(); } @@ -57,7 +57,7 @@ class PluginFactoryTensorRT : public nvinfer1::IPluginFactory { void DestroyPlugins(); protected: - std::unordered_map > plugin_registry_; diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc index c5d3f38280e143..a8f60886c03c17 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.cc @@ -30,7 +30,7 @@ string ExtractOpName(const void* serial_data, size_t serial_length, assert(serial_length >= *incremental); const char* buffer = static_cast(serial_data) + sizeof(size_t); - std::string op_name(buffer, op_name_char_count); + string op_name(buffer, op_name_char_count); return op_name; } diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc index 9ef0fce972a6c9..b834c5511f9956 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/test.h" @@ -31,18 +30,17 @@ namespace test { class StubPlugin : public PluginTensorRT { public: - static const std::string plugin_name_; - StubPlugin(){}; + static const string plugin_name_; + StubPlugin() {}; StubPlugin(const void* serialized_data, size_t length) - : PluginTensorRT(serialized_data, length){}; - const std::string& GetPluginName() override { return plugin_name_; }; + : PluginTensorRT(serialized_data, length) {}; + const string& GetPluginName() override { return plugin_name_; }; virtual bool Finalize() { return true; }; - virtual bool SetAttribute(const std::string& key, const void* ptr, + virtual bool SetAttribute(const string& key, const void* ptr, const size_t size) { return true; }; - virtual bool GetAttribute(const std::string& key, const void* ptr, - size_t& size) { + virtual bool GetAttribute(const string& key, const void* ptr, size_t& size) { return true; }; int getNbOutputs() const override { return 1; } @@ -59,7 +57,7 @@ class StubPlugin : public PluginTensorRT { } }; -const std::string StubPlugin::plugin_name_ = "StubPlugin"; +const string StubPlugin::plugin_name_ = "StubPlugin"; StubPlugin* CreateStubPlugin() { return new StubPlugin(); } @@ -72,8 +70,9 @@ class PluginTest : public ::testing::Test { public: bool RegisterStubPlugin() { if (PluginFactoryTensorRT::GetInstance()->IsPlugin( - StubPlugin::plugin_name_)) + StubPlugin::plugin_name_)) { return true; + } return PluginFactoryTensorRT::GetInstance()->RegisterPlugin( StubPlugin::plugin_name_, CreateStubPluginDeserialize, CreateStubPlugin); diff --git a/tensorflow/contrib/tensorrt/plugin_test.py b/tensorflow/contrib/tensorrt/plugin_test.py new file mode 100644 index 00000000000000..7c3e765bff4a40 --- /dev/null +++ b/tensorflow/contrib/tensorrt/plugin_test.py @@ -0,0 +1,88 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Script to show usage of TensorRT custom op & plugin.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib import tensorrt +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.client import session +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import importer +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops import nn_ops +import numpy as np + +# import custom_op as plugin op +# the python api handles registration to the plugin factory +from tensorflow.contrib.tensorrt import custom_plugin_examples + +def get_plugin_graph_def(): + """Create a simple graph and return its graph_def.""" + g = ops.Graph() + with g.as_default(): + a = array_ops.placeholder( + dtype=dtypes.float32, shape=(None, 24, 24, 2), name="input") + relu = nn.relu(a, "relu") + v = nn_ops.max_pool( + relu, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") + + # insert custom_op in the graph + v = custom_plugin_examples.inc_op(v, inc=[16.5], name="plugin_test") + + v = v*2.0 + v = nn.relu(v) + v = nn.relu(v) + array_ops.squeeze(v, name="output") + return g.as_graph_def() + +def run_graph(gdef, dumm_inp): + """Run given graphdef once.""" + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + ops.reset_default_graph() + g = ops.Graph() + with g.as_default(): + inp, out = importer.import_graph_def( + graph_def=gdef, return_elements=["input", "output"]) + inp = inp.outputs[0] + out = out.outputs[0] + + with session.Session( + config=config_pb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess: + val = sess.run(out, {inp: dumm_inp}) + return val + +if "__main__" in __name__: + inp_dims = (5, 24, 24, 2) + dummy_input = np.ones(inp_dims).astype(np.float32) + orig_graph = get_plugin_graph_def() # graph with plugin node + + # trigger conversion. + # plugin nodes have been registered during import, converter will be able to + # create corresponding plugin layer during conversion. + trt_graph = tensorrt.create_inference_graph( + input_graph_def=orig_graph, + outputs=["output"], + max_batch_size=inp_dims[0], + max_workspace_size_bytes=1 << 25, + precision_mode="FP32", + minimum_segment_size=2 + ) + o2 = run_graph(trt_graph, dummy_input) + print (o2) diff --git a/tensorflow/contrib/tensorrt/resources/trt_resources.h b/tensorflow/contrib/tensorrt/resources/trt_resources.h index 3c85968ae7acf5..5164247f938e9b 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_resources.h +++ b/tensorflow/contrib/tensorrt/resources/trt_resources.h @@ -82,7 +82,7 @@ class TRTWeightStore : public tensorflow::ResourceBase { class TRTEngineResource : public tensorflow::ResourceBase { public: - TRTEngineResource() : runtime_(nullptr), ctx_(nullptr){}; + TRTEngineResource() : runtime_(nullptr), ctx_(nullptr) {}; string DebugString() override { return string(""); } nvinfer1::IRuntime* runtime_; nvinfer1::IExecutionContext* ctx_; From 4e9dae45b3017f13eb68603294c6c28a63656050 Mon Sep 17 00:00:00 2001 From: Koan-Sin Tan Date: Fri, 20 Apr 2018 15:35:42 +0800 Subject: [PATCH 0014/1691] change ms to us and make timestamp uint64 1. microsecond usually is denoted as us; ms is millisecond 2. make timestamp uint64 all the way --- tensorflow/contrib/lite/profiling/profile_buffer.h | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/lite/profiling/profile_buffer.h b/tensorflow/contrib/lite/profiling/profile_buffer.h index 3bfe02571ba59f..299b2a9cad161c 100644 --- a/tensorflow/contrib/lite/profiling/profile_buffer.h +++ b/tensorflow/contrib/lite/profiling/profile_buffer.h @@ -37,9 +37,9 @@ struct ProfileEvent { // Label of the event. This usually describes the event. const char* tag; // Timestamp in microseconds when the event began. - int64_t begin_timestamp_ms; + uint64_t begin_timestamp_us; // Timestamp in microseconds when the event ended. - int64_t end_timestamp_ms; + uint64_t end_timestamp_us; // The field containing the type of event. This must be one of the event types // in EventType. EventType event_type; @@ -74,13 +74,13 @@ class ProfileBuffer { if (!enabled_) { return kInvalidEventHandle; } - int64_t timestamp = NowMicros(); + uint64_t timestamp = NowMicros(); int index = current_index_ % event_buffer_.size(); event_buffer_[index].tag = tag; event_buffer_[index].event_type = event_type; event_buffer_[index].event_metadata = event_metadata; - event_buffer_[index].begin_timestamp_ms = timestamp; - event_buffer_[index].end_timestamp_ms = 0; + event_buffer_[index].begin_timestamp_us = timestamp; + event_buffer_[index].end_timestamp_us = 0; current_index_++; return index; } @@ -103,7 +103,7 @@ class ProfileBuffer { } int event_index = event_handle % max_size; - event_buffer_[event_index].end_timestamp_ms = NowMicros(); + event_buffer_[event_index].end_timestamp_us = NowMicros(); } // Returns the size of the buffer. @@ -134,7 +134,7 @@ class ProfileBuffer { } private: - static int64_t NowMicros() { + static uint64_t NowMicros() { // TODO(shashishekhar): Refactor this to a separate file. struct timeval tv; gettimeofday(&tv, nullptr); From d3b91ba5696e998ea9155a91f58b6b6ba2afd340 Mon Sep 17 00:00:00 2001 From: Koan-Sin Tan Date: Fri, 20 Apr 2018 17:05:22 +0800 Subject: [PATCH 0015/1691] add profiling mechanism build with something like: ``` bazel build --config android_arm64 \ --cxxopt=-std=c++11 \ --cxxopt=-DTFLITE_PROFILING_ENABLED \ //tensorflow/contrib/lite/examples/label_image:label_image ``` run `label_image` will get something like: ``` ./label_image -p 1 Loaded model ./mobilenet_quant_v1_224.tflite resolved reporter invoked average time: 67.227 ms 13.349, Node 0, OpCode 3, CONV_2D 6.024, Node 1, OpCode 4, DEPTHWISE_CONV_2D 11.847, Node 2, OpCode 3, CONV_2D 3.927, Node 3, OpCode 4, DEPTHWISE_CONV_2D 1.905, Node 4, OpCode 3, CONV_2D 3.573, Node 5, OpCode 4, DEPTHWISE_CONV_2D 2.344, Node 6, OpCode 3, CONV_2D 0.964, Node 7, OpCode 4, DEPTHWISE_CONV_2D 1.224, Node 8, OpCode 3, CONV_2D 1.846, Node 9, OpCode 4, DEPTHWISE_CONV_2D 2.181, Node 10, OpCode 3, CONV_2D 0.454, Node 11, OpCode 4, DEPTHWISE_CONV_2D 0.997, Node 12, OpCode 3, CONV_2D 0.865, Node 13, OpCode 4, DEPTHWISE_CONV_2D 1.844, Node 14, OpCode 3, CONV_2D 0.753, Node 15, OpCode 4, DEPTHWISE_CONV_2D 1.724, Node 16, OpCode 3, CONV_2D 0.803, Node 17, OpCode 4, DEPTHWISE_CONV_2D 1.698, Node 18, OpCode 3, CONV_2D 0.794, Node 19, OpCode 4, DEPTHWISE_CONV_2D 1.754, Node 20, OpCode 3, CONV_2D 0.798, Node 21, OpCode 4, DEPTHWISE_CONV_2D 1.704, Node 22, OpCode 3, CONV_2D 0.204, Node 23, OpCode 4, DEPTHWISE_CONV_2D 0.983, Node 24, OpCode 3, CONV_2D 0.373, Node 25, OpCode 4, DEPTHWISE_CONV_2D 1.791, Node 26, OpCode 3, CONV_2D 0.067, Node 27, OpCode 1, AVERAGE_POOL_2D 0.388, Node 28, OpCode 3, CONV_2D 0.001, Node 29, OpCode 22, RESHAPE 0.035, Node 30, OpCode 25, SOFTMAX 0.600: 458 bow tie 0.365: 653 military uniform 0.008: 835 suit 0.008: 611 jersey 0.004: 514 cornet ``` --- .../lite/examples/label_image/label_image.cc | 47 +++++++++++++++++-- .../lite/examples/label_image/label_image.h | 1 + 2 files changed, 45 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.cc b/tensorflow/contrib/lite/examples/label_image/label_image.cc index a91467d345fdce..71d24a7ea5cb45 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.cc +++ b/tensorflow/contrib/lite/examples/label_image/label_image.cc @@ -17,6 +17,7 @@ limitations under the License. #include #include #include +#include #include #include #include @@ -70,6 +71,23 @@ TfLiteStatus ReadLabelsFile(const string& file_name, return kTfLiteOk; } +void PrintProfilingInfo(const profiling::ProfileEvent* e, uint32_t op_index, + TfLiteRegistration registration) { + // output something like + // time (ms) , Node xxx, OpCode xxx, symblic name + // 5.352, Node 5, OpCode 4, DEPTHWISE_CONV_2D + + + LOG(INFO) << std::fixed << std::setw(10) << std::setprecision(3) + << (e->end_timestamp_us - e->begin_timestamp_us) / 1000.0 + << ", Node " << std::setw(3) << std::setprecision(3) << op_index + << ", OpCode " << std::setw(3) << std::setprecision(3) + << registration.builtin_code << ", " + << EnumNameBuiltinOperator( + (BuiltinOperator)registration.builtin_code) + << "\n"; +} + void RunInference(Settings* s) { if (!s->model_name.c_str()) { LOG(ERROR) << "no model file name\n"; @@ -89,7 +107,7 @@ void RunInference(Settings* s) { tflite::ops::builtin::BuiltinOpResolver resolver; - tflite::InterpreterBuilder(*model, resolver)(&interpreter); + tflite::InterpreterBuilder (*model, resolver)(&interpreter); if (!interpreter) { LOG(FATAL) << "Failed to construct interpreter\n"; exit(-1); @@ -166,6 +184,11 @@ void RunInference(Settings* s) { exit(-1); } + profiling::Profiler* profiler = new profiling::Profiler(); + interpreter->SetProfiler(profiler); + + if (s->profiling) profiler->StartProfiling(); + struct timeval start_time, stop_time; gettimeofday(&start_time, NULL); for (int i = 0; i < s->loop_count; i++) { @@ -179,6 +202,18 @@ void RunInference(Settings* s) { << (get_us(stop_time) - get_us(start_time)) / (s->loop_count * 1000) << " ms \n"; + if (s->profiling) { + profiler->StopProfiling(); + auto profile_events = profiler->GetProfileEvents(); + for (int i = 0; i < profile_events.size(); i++) { + auto op_index = profile_events[i]->event_metadata; + const auto node_and_registration = + interpreter->node_and_registration(op_index); + const TfLiteRegistration registration = node_and_registration->second; + PrintProfilingInfo(profile_events[i], op_index, registration); + } + } + const int output_size = 1000; const size_t num_results = 5; const float threshold = 0.001f; @@ -217,13 +252,14 @@ void RunInference(Settings* s) { void display_usage() { LOG(INFO) << "label_image\n" - << "--accelerated, -a: [0|1], use Android NNAPI or note\n" + << "--accelerated, -a: [0|1], use Android NNAPI or not\n" << "--count, -c: loop interpreter->Invoke() for certain times\n" << "--input_mean, -b: input mean\n" << "--input_std, -s: input standard deviation\n" << "--image, -i: image_name.bmp\n" << "--labels, -l: labels for the model\n" << "--tflite_model, -m: model_name.tflite\n" + << "--profiling, -p: [0|1], profiling or not\n" << "--threads, -t: number of threads\n" << "--verbose, -v: [0|1] print more information\n" << "\n"; @@ -241,6 +277,7 @@ int Main(int argc, char** argv) { {"image", required_argument, 0, 'i'}, {"labels", required_argument, 0, 'l'}, {"tflite_model", required_argument, 0, 'm'}, + {"profiling", required_argument, 0, 'p'}, {"threads", required_argument, 0, 't'}, {"input_mean", required_argument, 0, 'b'}, {"input_std", required_argument, 0, 's'}, @@ -249,7 +286,7 @@ int Main(int argc, char** argv) { /* getopt_long stores the option index here. */ int option_index = 0; - c = getopt_long(argc, argv, "a:b:c:f:i:l:m:s:t:v:", long_options, + c = getopt_long(argc, argv, "a:b:c:f:i:l:m:p:s:t:v:", long_options, &option_index); /* Detect the end of the options. */ @@ -276,6 +313,10 @@ int Main(int argc, char** argv) { case 'm': s.model_name = optarg; break; + case 'p': + s.profiling = strtol( // NOLINT(runtime/deprecated_fn) + optarg, (char**)NULL, 10); + break; case 's': s.input_std = strtod(optarg, NULL); break; diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.h b/tensorflow/contrib/lite/examples/label_image/label_image.h index 4de32e33fb4ef2..4b48014e1c77ec 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.h +++ b/tensorflow/contrib/lite/examples/label_image/label_image.h @@ -25,6 +25,7 @@ struct Settings { bool verbose = false; bool accel = false; bool input_floating = false; + bool profiling = false; int loop_count = 1; float input_mean = 127.5f; float input_std = 127.5f; From f0df6701d01954073e912f24f7c983de4f091a1e Mon Sep 17 00:00:00 2001 From: joel-shor Date: Fri, 20 Apr 2018 14:01:02 +0300 Subject: [PATCH 0016/1691] [tf.data] Check in a strictly faster rejection resampling transformation. This transformation is faster because it rejects fewer data. This is done by occasionally sampling from the original data distribution in an efficient way. Tested: bazel test :resample_test --- .../data/python/kernel_tests/resample_test.py | 128 +++++++-- .../contrib/data/python/ops/resampling.py | 267 ++++++++++++++---- 2 files changed, 327 insertions(+), 68 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index 5f47dcb3399911..9e1273eba1333e 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -18,6 +18,8 @@ from __future__ import print_function import numpy as np +import time +from absl.testing import parameterized from tensorflow.contrib.data.python.ops import resampling from tensorflow.python.data.ops import dataset_ops @@ -30,47 +32,70 @@ from tensorflow.python.util import compat -class ResampleTest(test.TestCase): +def _time_resampling( + test_obj, data_np, target_dist, init_dist, use_v2, num_to_sample): + dataset = dataset_ops.Dataset.from_tensor_slices(data_np).repeat() - def testInitialKnownDistribution(self): - self._testDistribution(initial_known=True) + # Reshape distribution via rejection sampling. + apply_fn = (resampling.rejection_resample_v2 if use_v2 else + resampling.rejection_resample) + dataset = dataset.apply( + apply_fn( + class_func=lambda x: x, + target_dist=target_dist, + initial_dist=init_dist, + seed=142)) - def testInitialNotKnownDistribution(self): - self._testDistribution(initial_known=False) + get_next = dataset.make_one_shot_iterator().get_next() - def _testDistribution(self, initial_known): + with test_obj.test_session() as sess: + start_time = time.time() + for _ in xrange(num_to_sample): + sess.run(get_next) + end_time = time.time() + + return end_time - start_time + + +class ResampleTest(test.TestCase, parameterized.TestCase): + + @parameterized.named_parameters( + ('InitialnDistributionKnown', True, False), + ('InitialDistributionUnknown', False, False), + ('InitialDistributionKnownV2', True, True), + ('InitialDistributionUnknownV2', False, True)) + def testDistribution(self, initial_known, use_v2): classes = np.random.randint(5, size=(20000,)) # Uniformly sampled target_dist = [0.9, 0.05, 0.05, 0.0, 0.0] initial_dist = [0.2] * 5 if initial_known else None - iterator = (dataset_ops.Dataset.from_tensor_slices(classes).shuffle( - 200, seed=21).map(lambda c: (c, string_ops.as_string(c))).apply( - resampling.rejection_resample( - target_dist=target_dist, - initial_dist=initial_dist, - class_func=lambda c, _: c, - seed=27)).make_one_shot_iterator()) - get_next = iterator.get_next() + dataset = dataset_ops.Dataset.from_tensor_slices(classes).shuffle( + 200, seed=21).map(lambda c: (c, string_ops.as_string(c))).repeat() + apply_fn = (resampling.rejection_resample_v2 if use_v2 else + resampling.rejection_resample) + get_next = dataset.apply( + apply_fn( + target_dist=target_dist, + initial_dist=initial_dist, + class_func=lambda c, _: c, + seed=27)).make_one_shot_iterator().get_next() with self.test_session() as sess: returned = [] - with self.assertRaises(errors.OutOfRangeError): - while True: - returned.append(sess.run(get_next)) + while len(returned) < 4000: + returned.append(sess.run(get_next)) returned_classes, returned_classes_and_data = zip(*returned) _, returned_data = zip(*returned_classes_and_data) self.assertAllEqual([compat.as_bytes(str(c)) for c in returned_classes], returned_data) total_returned = len(returned_classes) - # Subsampling rejects a large percentage of the initial data in - # this case. - self.assertGreater(total_returned, 20000 * 0.2) class_counts = np.array([ len([True for v in returned_classes if v == c]) for c in range(5)]) returned_dist = class_counts / total_returned self.assertAllClose(target_dist, returned_dist, atol=1e-2) + def testRandomClasses(self): init_dist = [0.25, 0.25, 0.25, 0.25] target_dist = [0.0, 0.0, 0.0, 1.0] @@ -109,5 +134,68 @@ def _remap_fn(_): self.assertAllClose(target_dist, bincount, atol=1e-2) + @parameterized.named_parameters( + ('InitialnDistributionKnown', True, False), + ('InitialDistributionUnknown', False, False), + ('InitialDistributionKnownV2', True, True), + ('InitialDistributionUnknownV2', False, True)) + def _testNewResampleIsFaster(self, target_dist, num_to_sample): + init_dist = [0.25, 0.25, 0.25, 0.25] + num_classes = len(init_dist) + num_samples = 1000 + data_np = np.random.choice(num_classes, num_samples, p=init_dist) + + fast_time = _time_resampling(self, data_np, target_dist, init_dist, + use_v2=True, num_to_sample=num_to_sample) + slow_time = _time_resampling(self, data_np, target_dist, init_dist, + use_v2=False, num_to_sample=num_to_sample) + + self.assertLess(fast_time, slow_time) + + + def testNewResampleIsFasterSmallSkewManySamples(self): + self._testNewResampleIsFaster([0.1, 0.1, 0.1, 0.7], 1000) + + def testNewResampleIsFasterBigSkewManySamples(self): + self._testNewResampleIsFaster([0.01, 0.01, 0.01, 0.97], 1000) + + def testNewResampleIsFasterSmallSkewFewSamples(self): + self._testNewResampleIsFaster([0.1, 0.1, 0.1, 0.7], 100) + + def testNewResampleIsFasterBigSkewFewSamples(self): + self._testNewResampleIsFaster([0.01, 0.01, 0.01, 0.97], 100) + + +class MapDatasetBenchmark(test.Benchmark): + + def benchmarkResamplePerformance(self): + init_dist = [0.25, 0.25, 0.25, 0.25] + target_dist = [0.0, 0.0, 0.0, 1.0] + num_classes = len(init_dist) + # We don't need many samples to test a dirac-delta target distribution + num_samples = 1000 + data_np = np.random.choice(num_classes, num_samples, p=init_dist) + + resample_time = _time_resampling( + self, data_np, target_dist, init_dist, use_v2=False, num_to_sample=1000) + + self.report_benchmark( + iters=1000, wall_time=resample_time, name="benchmark_resample") + + def benchmarkResampleAndBatchPerformance(self): + init_dist = [0.25, 0.25, 0.25, 0.25] + target_dist = [0.0, 0.0, 0.0, 1.0] + num_classes = len(init_dist) + # We don't need many samples to test a dirac-delta target distribution + num_samples = 1000 + data_np = np.random.choice(num_classes, num_samples, p=init_dist) + + resample_time = _time_resampling( + self, data_np, target_dist, init_dist, use_v2=True, num_to_sample=1000) + + self.report_benchmark( + iters=1000, wall_time=resample_time, name="benchmark_resample_v2") + + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index b465397437adbd..94e28b9a2da467 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -20,6 +20,7 @@ import numpy as np from tensorflow.contrib.data.python.ops import batching +from tensorflow.contrib.data.python.ops import interleave_ops from tensorflow.contrib.data.python.ops import scan_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes @@ -50,14 +51,15 @@ def rejection_resample(class_func, target_dist, initial_dist=None, seed=None): A `Dataset` transformation function, which can be passed to @{tf.data.Dataset.apply}. """ - def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" - dist_estimation_batch_size = 32 target_dist_t = ops.convert_to_tensor(target_dist, name="target_dist") class_values_ds = dataset.map(class_func) + + # Get initial distribution. if initial_dist is not None: - initial_dist_t = ops.convert_to_tensor(initial_dist, name="initial_dist") + initial_dist_t = ops.convert_to_tensor( + initial_dist, name="initial_dist") acceptance_dist = _calculate_acceptance_probs(initial_dist_t, target_dist_t) initial_dist_ds = dataset_ops.Dataset.from_tensors( @@ -65,55 +67,181 @@ def _apply_fn(dataset): acceptance_dist_ds = dataset_ops.Dataset.from_tensors( acceptance_dist).repeat() else: - num_classes = (target_dist_t.shape[0].value or - array_ops.shape(target_dist_t)[0]) - smoothing_constant = 10 - initial_examples_per_class_seen = array_ops.fill( - [num_classes], np.int64(smoothing_constant)) - - def update_estimate_and_tile(num_examples_per_class_seen, c): - updated_examples_per_class_seen, dist = _estimate_data_distribution( - c, num_examples_per_class_seen) - tiled_dist = array_ops.tile( - array_ops.expand_dims(dist, 0), [dist_estimation_batch_size, 1]) - return updated_examples_per_class_seen, tiled_dist - - initial_dist_ds = (class_values_ds.batch(dist_estimation_batch_size) - .apply(scan_ops.scan(initial_examples_per_class_seen, - update_estimate_and_tile)) - .apply(batching.unbatch())) + initial_dist_ds = _estimate_initial_dist_ds( + target_dist_t, class_values_ds) acceptance_dist_ds = initial_dist_ds.map( lambda initial: _calculate_acceptance_probs(initial, target_dist_t)) + return _filter_ds(dataset, acceptance_dist_ds, initial_dist_ds, + class_values_ds, seed) + + return _apply_fn + - def maybe_warn_on_large_rejection(accept_dist, initial_dist): - proportion_rejected = math_ops.reduce_sum( - (1 - accept_dist) * initial_dist) - return control_flow_ops.cond( - math_ops.less(proportion_rejected, .5), - lambda: accept_dist, - lambda: logging_ops.Print( # pylint: disable=g-long-lambda - accept_dist, [proportion_rejected, initial_dist, accept_dist], - message="Proportion of examples rejected by sampler is high: ", - summarize=100, - first_n=10)) - - acceptance_dist_ds = (dataset_ops.Dataset.zip((acceptance_dist_ds, - initial_dist_ds)) - .map(maybe_warn_on_large_rejection)) - - def _gather_and_copy(class_val, acceptance_prob, data): - return (class_val, array_ops.gather(acceptance_prob, class_val), data) - current_probabilities_and_class_and_data_ds = dataset_ops.Dataset.zip( - (class_values_ds, acceptance_dist_ds, dataset)).map(_gather_and_copy) - filtered_ds = ( - current_probabilities_and_class_and_data_ds - .filter(lambda _1, p, _2: random_ops.random_uniform([], seed=seed) < p)) - return filtered_ds.map(lambda class_value, _, data: (class_value, data)) +def rejection_resample_v2(class_func, target_dist, initial_dist=None, + seed=None): + """A transformation that resamples a dataset to achieve a target distribution. + This differs from v1 in that it will also sample from the original dataset + with some probability, so it makes strictly fewer data rejections. This + transformation is faster than the original. + + **NOTE** Resampling is performed via rejection sampling; some fraction + of the input values will be dropped. + + Args: + class_func: A function mapping an element of the input dataset to a scalar + `tf.int32` tensor. Values should be in `[0, num_classes)`. + target_dist: A floating point type tensor, shaped `[num_classes]`. + initial_dist: (Optional.) A floating point type tensor, shaped + `[num_classes]`. If not provided, the true class distribution is + estimated live in a streaming fashion. + seed: (Optional.) Python integer seed for the resampler. + + Returns: + A `Dataset` transformation function, which can be passed to + @{tf.data.Dataset.apply}. + """ + def _apply_fn(dataset): + """Function from `Dataset` to `Dataset` that applies the transformation.""" + target_dist_t = ops.convert_to_tensor(target_dist, name="target_dist") + class_values_ds = dataset.map(class_func) + + # Get initial distribution. + if initial_dist is not None: + initial_dist_t = ops.convert_to_tensor( + initial_dist, name="initial_dist") + acceptance_dist, prob_of_original = ( + _calculate_acceptance_probs_with_mixing(initial_dist_t, + target_dist_t)) + initial_dist_ds = dataset_ops.Dataset.from_tensors( + initial_dist_t).repeat() + acceptance_dist_ds = dataset_ops.Dataset.from_tensors( + acceptance_dist).repeat() + prob_of_original_ds = dataset_ops.Dataset.from_tensors( + prob_of_original).repeat() + else: + initial_dist_ds = _estimate_initial_dist_ds( + target_dist_t, class_values_ds) + acceptance_and_original_prob_ds = initial_dist_ds.map( + lambda initial: _calculate_acceptance_probs_with_mixing( + initial, target_dist_t)) + acceptance_dist_ds = acceptance_and_original_prob_ds.map( + lambda accept_prob, _: accept_prob) + prob_of_original_ds = acceptance_and_original_prob_ds.map( + lambda _, prob_original: prob_original) + filtered_ds = _filter_ds(dataset, acceptance_dist_ds, initial_dist_ds, + class_values_ds, seed) + # Prefetch filtered dataset for speed. + filtered_ds = filtered_ds.prefetch(3) + + return interleave_ops.sample_from_datasets( + [dataset_ops.Dataset.zip((class_values_ds, dataset)), filtered_ds], + weights=prob_of_original_ds.map(lambda prob: [(prob, 1.0 - prob)]), + seed=seed) return _apply_fn +def _random_interleave_datasets(ds1, ds1_classes, ds2, prob_of_ds1, seed=None): + """Randomly interleave datasets. + + We carefully combine `ds1` and 'ds2' so that we don't needlessly compute the + filtering. + + Args: + ds1: A dataset to interleave. + ds1_classes: Dataset of class values associated with ds1. + ds2: Another dataset to interleave. + prob_of_ds1: A dataset of probabilities. Each probability represents the + likelihood of drawing from `ds1`. + seed: (Optional.) Python integer seed for the resampler. + + Returns: + A single dataset, combined from `ds1` and `ds2`. + """ + num_filtered_to_prefetch = 3 + ds2 = ds2.prefetch(num_filtered_to_prefetch) + filtered_iterator = ds2.make_one_shot_iterator() + combined_ds = dataset_ops.Dataset.zip( + (ds1_classes, ds1, prob_of_ds1)).map( + lambda ds1_class, original_data, prob_of_original: + control_flow_ops.cond( + random_ops.random_uniform([], seed=seed) < prob_of_original, + lambda: (ds1_class, original_data), + filtered_iterator.get_next)) + return combined_ds + + +def _filter_ds(dataset, acceptance_dist_ds, initial_dist_ds, class_values_ds, + seed): + """Filters a dataset based on per-class acceptance probabilities. + + Args: + dataset: The dataset to be filtered. + acceptance_dist_ds: A dataset of acceptance probabilities. + initial_dist_ds: A dataset of the initial probability distribution, given or + estimated. + class_values_ds: A dataset of the corresponding classes. + seed: (Optional.) Python integer seed for the resampler. + + Returns: + A dataset of (class value, data) after filtering. + """ + def maybe_warn_on_large_rejection(accept_dist, initial_dist): + proportion_rejected = math_ops.reduce_sum((1 - accept_dist) * initial_dist) + return control_flow_ops.cond( + math_ops.less(proportion_rejected, .5), + lambda: accept_dist, + lambda: logging_ops.Print( # pylint: disable=g-long-lambda + accept_dist, [proportion_rejected, initial_dist, accept_dist], + message="Proportion of examples rejected by sampler is high: ", + summarize=100, + first_n=10)) + + acceptance_dist_ds = (dataset_ops.Dataset.zip((acceptance_dist_ds, + initial_dist_ds)) + .map(maybe_warn_on_large_rejection)) + + def _gather_and_copy(class_val, acceptance_prob, data): + return class_val, array_ops.gather(acceptance_prob, class_val), data + + current_probabilities_and_class_and_data_ds = dataset_ops.Dataset.zip( + (class_values_ds, acceptance_dist_ds, dataset)).map(_gather_and_copy) + filtered_ds = ( + current_probabilities_and_class_and_data_ds + .filter(lambda _1, p, _2: random_ops.random_uniform([], seed=seed) < p)) + return filtered_ds.map(lambda class_value, _, data: (class_value, data)) + + +def _estimate_initial_dist_ds( + target_dist_t, class_values_ds, dist_estimation_batch_size=32, + smoothing_constant=10): + num_classes = (target_dist_t.shape[0].value or + array_ops.shape(target_dist_t)[0]) + initial_examples_per_class_seen = array_ops.fill( + [num_classes], np.int64(smoothing_constant)) + + def update_estimate_and_tile(num_examples_per_class_seen, c): + updated_examples_per_class_seen, dist = _estimate_data_distribution( + c, num_examples_per_class_seen) + tiled_dist = array_ops.tile( + array_ops.expand_dims(dist, 0), [dist_estimation_batch_size, 1]) + return updated_examples_per_class_seen, tiled_dist + + initial_dist_ds = (class_values_ds.batch(dist_estimation_batch_size) + .apply(scan_ops.scan(initial_examples_per_class_seen, + update_estimate_and_tile)) + .apply(batching.unbatch())) + + return initial_dist_ds + + +def _get_target_to_initial_ratio(initial_probs, target_probs): + # Add tiny to initial_probs to avoid divide by zero. + denom = (initial_probs + np.finfo(initial_probs.dtype.as_numpy_dtype).tiny) + return target_probs / denom + + def _calculate_acceptance_probs(initial_probs, target_probs): """Calculate the per-class acceptance rates. @@ -152,13 +280,10 @@ def _calculate_acceptance_probs(initial_probs, target_probs): 0 <= t_i <= 1, sum_i(t_i) = 1 ``` - A solution for a_i in terms of the other variables is the following: ```a_i = (t_i / p_i) / max_i[t_i / p_i]``` """ - # Add tiny to initial_probs to avoid divide by zero. - denom = (initial_probs + np.finfo(initial_probs.dtype.as_numpy_dtype).tiny) - ratio_l = target_probs / denom + ratio_l = _get_target_to_initial_ratio(initial_probs, target_probs) # Calculate list of acceptance probabilities. max_ratio = math_ops.reduce_max(ratio_l) @@ -188,3 +313,49 @@ def _estimate_data_distribution(c, num_examples_per_class_seen): math_ops.reduce_sum(num_examples_per_class_seen)) dist = math_ops.cast(init_prob_estimate, dtypes.float32) return num_examples_per_class_seen, dist + + +def _calculate_acceptance_probs_with_mixing(initial_probs, target_probs): + """Calculates the acceptance probabilities and mixing ratio. + + In this case, we assume that we can *either* sample from the original data + distribution with probability `m`, or sample from a reshaped distribution + that comes from rejection sampling on the original distribution. This + rejection sampling is done on a per-class basis, with `a_i` representing the + probability of accepting data from class `i`. + + If we try to minimize the amount of data rejected, we get the following: + + M_max = max_i [ t_i / p_i ] + M_min = min_i [ t_i / p_i ] + + The desired probability of accepting data if it comes from class `i`: + + a_i = (t_i/p_i - m) / (M_max - m) + + The desired probability of pulling a data element from the original dataset, + rather than the filtered one: + + m = M_min + + See the docstring for `_calculate_acceptance_probs` for more details. + + Args: + initial_probs: A Tensor of the initial probability distribution, given or + estimated. + target_probs: A Tensor of the corresponding classes. + + Returns: + (A 1D Tensor with the per-class acceptance probabilities, the desired + probability of pull from the original distribution.) + """ + ratio_l = _get_target_to_initial_ratio(initial_probs, target_probs) + max_ratio = math_ops.reduce_max(ratio_l) + min_ratio = math_ops.reduce_min(ratio_l) + + # Target prob to sample from original distribution. + m = min_ratio + + # TODO(joelshor): Simplify fraction, if possible. + a_i = (ratio_l - m) / (max_ratio - m) + return a_i, m From b1067116c6a2351f4c597a9391b21ad0f513565b Mon Sep 17 00:00:00 2001 From: joel-shor Date: Fri, 20 Apr 2018 14:27:30 +0300 Subject: [PATCH 0017/1691] [tf.data] Clean up resampler and update BUILD files. --- .../contrib/data/python/kernel_tests/BUILD | 6 ++- .../data/python/kernel_tests/resample_test.py | 32 +++++---------- tensorflow/contrib/data/python/ops/BUILD | 2 + .../contrib/data/python/ops/resampling.py | 40 ++++--------------- 4 files changed, 23 insertions(+), 57 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index b15b9663f4c1bd..a6b46b37e77a95 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -308,13 +308,17 @@ py_test( srcs_version = "PY2AND3", tags = ["noasan"], deps = [ + "//third_party/py/absl/testing:parameterized", + "//third_party/py/numpy", "//tensorflow/contrib/data/python/ops:resampling", "//tensorflow/python:client_testlib", + "//tensorflow/python:dtypes", "//tensorflow/python:errors", + "//tensorflow/python:math_ops", + "//tensorflow/python:random_ops", "//tensorflow/python:string_ops", "//tensorflow/python:util", "//tensorflow/python/data/ops:dataset_ops", - "//third_party/py/numpy", ], ) diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index 9e1273eba1333e..97c4b68cb64e94 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -60,10 +60,10 @@ def _time_resampling( class ResampleTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( - ('InitialnDistributionKnown', True, False), - ('InitialDistributionUnknown', False, False), - ('InitialDistributionKnownV2', True, True), - ('InitialDistributionUnknownV2', False, True)) + ("InitialnDistributionKnown", True, False), + ("InitialDistributionUnknown", False, False), + ("InitialDistributionKnownV2", True, True), + ("InitialDistributionUnknownV2", False, True)) def testDistribution(self, initial_known, use_v2): classes = np.random.randint(5, size=(20000,)) # Uniformly sampled target_dist = [0.9, 0.05, 0.05, 0.0, 0.0] @@ -95,7 +95,6 @@ def testDistribution(self, initial_known, use_v2): returned_dist = class_counts / total_returned self.assertAllClose(target_dist, returned_dist, atol=1e-2) - def testRandomClasses(self): init_dist = [0.25, 0.25, 0.25, 0.25] target_dist = [0.0, 0.0, 0.0, 1.0] @@ -135,11 +134,11 @@ def _remap_fn(_): self.assertAllClose(target_dist, bincount, atol=1e-2) @parameterized.named_parameters( - ('InitialnDistributionKnown', True, False), - ('InitialDistributionUnknown', False, False), - ('InitialDistributionKnownV2', True, True), - ('InitialDistributionUnknownV2', False, True)) - def _testNewResampleIsFaster(self, target_dist, num_to_sample): + ("SmallSkewManySamples", [0.1, 0.1, 0.1, 0.7], 1000), + ("BigSkewManySamples", [0.01, 0.01, 0.01, 0.97], 1000), + ("SmallSkewFewSamples", [0.1, 0.1, 0.1, 0.7], 100), + ("BigSkewFewSamples", [0.01, 0.01, 0.01, 0.97], 100)) + def testNewResampleIsFaster(self, target_dist, num_to_sample): init_dist = [0.25, 0.25, 0.25, 0.25] num_classes = len(init_dist) num_samples = 1000 @@ -153,19 +152,6 @@ def _testNewResampleIsFaster(self, target_dist, num_to_sample): self.assertLess(fast_time, slow_time) - def testNewResampleIsFasterSmallSkewManySamples(self): - self._testNewResampleIsFaster([0.1, 0.1, 0.1, 0.7], 1000) - - def testNewResampleIsFasterBigSkewManySamples(self): - self._testNewResampleIsFaster([0.01, 0.01, 0.01, 0.97], 1000) - - def testNewResampleIsFasterSmallSkewFewSamples(self): - self._testNewResampleIsFaster([0.1, 0.1, 0.1, 0.7], 100) - - def testNewResampleIsFasterBigSkewFewSamples(self): - self._testNewResampleIsFaster([0.01, 0.01, 0.01, 0.97], 100) - - class MapDatasetBenchmark(test.Benchmark): def benchmarkResamplePerformance(self): diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index e00f2304cc415e..8cb4fa7f14916c 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -193,7 +193,9 @@ py_library( srcs_version = "PY2AND3", deps = [ ":batching", + ":interleave_ops", ":scan_ops", + "//third_party/py/numpy", "//tensorflow/python:array_ops", "//tensorflow/python:control_flow_ops", "//tensorflow/python:dtypes", diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index 94e28b9a2da467..16d851bf96408e 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -82,8 +82,12 @@ def rejection_resample_v2(class_func, target_dist, initial_dist=None, """A transformation that resamples a dataset to achieve a target distribution. This differs from v1 in that it will also sample from the original dataset - with some probability, so it makes strictly fewer data rejections. This - transformation is faster than the original. + with some probability, so it makes strictly fewer data rejections. Due to an + implementation detail it must initialize a separate dataset initializer, so + the dataset becomes stateful after this transformation is applied + (`make_one_shot_iterator` won't work; users must use + `make_initializable_iterator`). This transformation is faster than the + original, except for overhead. **NOTE** Resampling is performed via rejection sampling; some fraction of the input values will be dropped. @@ -142,36 +146,6 @@ def _apply_fn(dataset): return _apply_fn -def _random_interleave_datasets(ds1, ds1_classes, ds2, prob_of_ds1, seed=None): - """Randomly interleave datasets. - - We carefully combine `ds1` and 'ds2' so that we don't needlessly compute the - filtering. - - Args: - ds1: A dataset to interleave. - ds1_classes: Dataset of class values associated with ds1. - ds2: Another dataset to interleave. - prob_of_ds1: A dataset of probabilities. Each probability represents the - likelihood of drawing from `ds1`. - seed: (Optional.) Python integer seed for the resampler. - - Returns: - A single dataset, combined from `ds1` and `ds2`. - """ - num_filtered_to_prefetch = 3 - ds2 = ds2.prefetch(num_filtered_to_prefetch) - filtered_iterator = ds2.make_one_shot_iterator() - combined_ds = dataset_ops.Dataset.zip( - (ds1_classes, ds1, prob_of_ds1)).map( - lambda ds1_class, original_data, prob_of_original: - control_flow_ops.cond( - random_ops.random_uniform([], seed=seed) < prob_of_original, - lambda: (ds1_class, original_data), - filtered_iterator.get_next)) - return combined_ds - - def _filter_ds(dataset, acceptance_dist_ds, initial_dist_ds, class_values_ds, seed): """Filters a dataset based on per-class acceptance probabilities. @@ -358,4 +332,4 @@ def _calculate_acceptance_probs_with_mixing(initial_probs, target_probs): # TODO(joelshor): Simplify fraction, if possible. a_i = (ratio_l - m) / (max_ratio - m) - return a_i, m + return a_i, m \ No newline at end of file From 0cba8b7c66bead25ed2e6e1c6bf5a23d6cbe9557 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Fri, 20 Apr 2018 14:44:47 +0300 Subject: [PATCH 0018/1691] [tf.data] Fix `absl` build rule. --- tensorflow/contrib/data/python/kernel_tests/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index a6b46b37e77a95..f90b17e79ee0a4 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -308,7 +308,6 @@ py_test( srcs_version = "PY2AND3", tags = ["noasan"], deps = [ - "//third_party/py/absl/testing:parameterized", "//third_party/py/numpy", "//tensorflow/contrib/data/python/ops:resampling", "//tensorflow/python:client_testlib", @@ -319,6 +318,7 @@ py_test( "//tensorflow/python:string_ops", "//tensorflow/python:util", "//tensorflow/python/data/ops:dataset_ops", + "@absl_py//absl/testing:parameterized", ], ) From 8cc506f8f6c3e9071069ede1cd5c91a9f3da7c11 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Fri, 20 Apr 2018 15:00:02 +0300 Subject: [PATCH 0019/1691] [tf.data] Reorder BUILD rule deps and add `xrange` from `six`. --- tensorflow/contrib/data/python/kernel_tests/BUILD | 2 +- tensorflow/contrib/data/python/kernel_tests/resample_test.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index f90b17e79ee0a4..92c6967933872a 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -308,7 +308,6 @@ py_test( srcs_version = "PY2AND3", tags = ["noasan"], deps = [ - "//third_party/py/numpy", "//tensorflow/contrib/data/python/ops:resampling", "//tensorflow/python:client_testlib", "//tensorflow/python:dtypes", @@ -318,6 +317,7 @@ py_test( "//tensorflow/python:string_ops", "//tensorflow/python:util", "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index 97c4b68cb64e94..7f007fede8c875 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -18,6 +18,7 @@ from __future__ import print_function import numpy as np +from six.moves import xrange # pylint: disable=redefined-builtin import time from absl.testing import parameterized From a10708db0d587831cafcb2e7dbdcbbcf11aede95 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Fri, 20 Apr 2018 15:09:50 +0300 Subject: [PATCH 0020/1691] [tf.data] Second reorder BUILD rule deps. --- tensorflow/contrib/data/python/ops/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 8cb4fa7f14916c..d9a5502508051d 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -195,7 +195,6 @@ py_library( ":batching", ":interleave_ops", ":scan_ops", - "//third_party/py/numpy", "//tensorflow/python:array_ops", "//tensorflow/python:control_flow_ops", "//tensorflow/python:dtypes", @@ -204,6 +203,7 @@ py_library( "//tensorflow/python:math_ops", "//tensorflow/python:random_ops", "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", ], ) From fc6510b506731bf2ffc2520e30fba73b79e5b687 Mon Sep 17 00:00:00 2001 From: Chris Ying Date: Tue, 17 Apr 2018 15:28:12 -0700 Subject: [PATCH 0021/1691] Fix CheckpointSaverHook to properly save every save_checkpoints_steps for TPU workloads. PiperOrigin-RevId: 193266515 (cherry picked from commit 5aba07dce5b9e924183efcd05cd82f2fbb70edc8) --- .../contrib/tpu/python/tpu/tpu_estimator.py | 9 ++ .../training/basic_session_run_hooks.py | 10 +- .../training/basic_session_run_hooks_test.py | 93 +++++++++++++++++++ ...sorflow.train.-checkpoint-saver-hook.pbtxt | 2 +- 4 files changed, 111 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 1332108d04c3a4..c8c4cc6c68555b 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -2054,6 +2054,14 @@ def _model_fn(features, labels, mode, config, params): }, every_n_secs=30) ] + input_hooks + chief_hooks = [ + training.CheckpointSaverHook( + self.model_dir, + save_secs=self._config.save_checkpoints_secs, + save_steps=self._config.save_checkpoints_steps, + steps_per_run=self._config.tpu_config.iterations_per_loop, + scaffold=scaffold) + ] summary.scalar(model_fn_lib.LOSS_METRIC_KEY, loss) with ops.control_dependencies([loss]): update_ops = _sync_variables_ops() @@ -2067,6 +2075,7 @@ def _model_fn(features, labels, mode, config, params): return model_fn_lib.EstimatorSpec( mode, loss=loss, + training_chief_hooks=chief_hooks, training_hooks=hooks, train_op=train_op, scaffold=scaffold) diff --git a/tensorflow/python/training/basic_session_run_hooks.py b/tensorflow/python/training/basic_session_run_hooks.py index 094a9e886ba87b..3651291bdfcc77 100644 --- a/tensorflow/python/training/basic_session_run_hooks.py +++ b/tensorflow/python/training/basic_session_run_hooks.py @@ -391,7 +391,8 @@ def __init__(self, saver=None, checkpoint_basename="model.ckpt", scaffold=None, - listeners=None): + listeners=None, + steps_per_run=1): """Initializes a `CheckpointSaverHook`. Args: @@ -404,6 +405,9 @@ def __init__(self, listeners: List of `CheckpointSaverListener` subclass instances. Used for callbacks that run immediately before or after this hook saves the checkpoint. + steps_per_run: `int`, number of steps that occur between each invocation + of the hook. Primarily used for TPU workloads which run multiple steps + in a while loop in a single Session.run. Raises: ValueError: One of `save_steps` or `save_secs` should be set. @@ -419,6 +423,7 @@ def __init__(self, self._timer = SecondOrStepTimer(every_secs=save_secs, every_steps=save_steps) self._listeners = listeners or [] + self._steps_per_run = steps_per_run def begin(self): self._summary_writer = SummaryWriterCache.get(self._checkpoint_dir) @@ -450,7 +455,8 @@ def before_run(self, run_context): # pylint: disable=unused-argument def after_run(self, run_context, run_values): stale_global_step = run_values.results - if self._timer.should_trigger_for_step(stale_global_step+1): + if self._timer.should_trigger_for_step( + stale_global_step + self._steps_per_run): # get the real value after train op. global_step = run_context.session.run(self._global_step_tensor) if self._timer.should_trigger_for_step(global_step): diff --git a/tensorflow/python/training/basic_session_run_hooks_test.py b/tensorflow/python/training/basic_session_run_hooks_test.py index f39a5261a93c3d..25962f6bf7abf5 100644 --- a/tensorflow/python/training/basic_session_run_hooks_test.py +++ b/tensorflow/python/training/basic_session_run_hooks_test.py @@ -719,6 +719,99 @@ def test_summary_writer_defs(self): fake_summary_writer.FakeSummaryWriter.uninstall() +class CheckpointSaverHookMultiStepTest(test.TestCase): + + def setUp(self): + self.model_dir = tempfile.mkdtemp() + self.graph = ops.Graph() + self.steps_per_run = 5 + with self.graph.as_default(): + self.scaffold = monitored_session.Scaffold() + self.global_step = variables.get_or_create_global_step() + self.train_op = training_util._increment_global_step(self.steps_per_run) + + def tearDown(self): + shutil.rmtree(self.model_dir, ignore_errors=True) + + def test_save_steps_saves_in_first_step(self): + with self.graph.as_default(): + hook = basic_session_run_hooks.CheckpointSaverHook( + self.model_dir, + save_steps=2*self.steps_per_run, + scaffold=self.scaffold, + steps_per_run=self.steps_per_run) + hook.begin() + self.scaffold.finalize() + with session_lib.Session() as sess: + sess.run(self.scaffold.init_op) + mon_sess = monitored_session._HookedSession(sess, [hook]) + mon_sess.run(self.train_op) + self.assertEqual(5, + checkpoint_utils.load_variable(self.model_dir, + self.global_step.name)) + + def test_save_steps_saves_periodically(self): + with self.graph.as_default(): + hook = basic_session_run_hooks.CheckpointSaverHook( + self.model_dir, + save_steps=2*self.steps_per_run, + scaffold=self.scaffold, + steps_per_run=self.steps_per_run) + hook.begin() + self.scaffold.finalize() + with session_lib.Session() as sess: + sess.run(self.scaffold.init_op) + mon_sess = monitored_session._HookedSession(sess, [hook]) + mon_sess.run(self.train_op) + # Saved (step=5) + self.assertEqual(5, + checkpoint_utils.load_variable(self.model_dir, + self.global_step.name)) + + mon_sess.run(self.train_op) + # Not saved (step=10) + self.assertEqual(5, + checkpoint_utils.load_variable(self.model_dir, + self.global_step.name)) + + mon_sess.run(self.train_op) + # Saved (step=15) + self.assertEqual(15, + checkpoint_utils.load_variable(self.model_dir, + self.global_step.name)) + + mon_sess.run(self.train_op) + # Not saved (step=20) + self.assertEqual(15, + checkpoint_utils.load_variable(self.model_dir, + self.global_step.name)) + + mon_sess.run(self.train_op) + # Saved (step=25) + self.assertEqual(25, + checkpoint_utils.load_variable(self.model_dir, + self.global_step.name)) + + def test_save_steps_saves_at_end(self): + with self.graph.as_default(): + hook = basic_session_run_hooks.CheckpointSaverHook( + self.model_dir, + save_steps=2*self.steps_per_run, + scaffold=self.scaffold, + steps_per_run=self.steps_per_run) + hook.begin() + self.scaffold.finalize() + with session_lib.Session() as sess: + sess.run(self.scaffold.init_op) + mon_sess = monitored_session._HookedSession(sess, [hook]) + mon_sess.run(self.train_op) + mon_sess.run(self.train_op) + hook.end(sess) + self.assertEqual(10, + checkpoint_utils.load_variable(self.model_dir, + self.global_step.name)) + + class ResourceCheckpointSaverHookTest(test.TestCase): def setUp(self): diff --git a/tensorflow/tools/api/golden/tensorflow.train.-checkpoint-saver-hook.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-checkpoint-saver-hook.pbtxt index c3037baa8c951e..327799729c9e7d 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-checkpoint-saver-hook.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-checkpoint-saver-hook.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'checkpoint_dir\', \'save_secs\', \'save_steps\', \'saver\', \'checkpoint_basename\', \'scaffold\', \'listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'model.ckpt\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'checkpoint_dir\', \'save_secs\', \'save_steps\', \'saver\', \'checkpoint_basename\', \'scaffold\', \'listeners\', \'steps_per_run\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'model.ckpt\', \'None\', \'None\', \'1\'], " } member_method { name: "after_create_session" From e1cc34d34b3a811da7c7a2d7cc6c60398c50fdfb Mon Sep 17 00:00:00 2001 From: Chris Ying Date: Tue, 17 Apr 2018 20:31:30 -0700 Subject: [PATCH 0022/1691] Disable CheckpointSaverHook when both save_checkpoints_secs and save_checkpoints_steps are None PiperOrigin-RevId: 193299688 (cherry picked from commit 41e2cd187b31e9e6d88bc042e21e73f7be0ed729) --- .../contrib/tpu/python/tpu/tpu_estimator.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index c8c4cc6c68555b..8df631b475efcb 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -2054,14 +2054,16 @@ def _model_fn(features, labels, mode, config, params): }, every_n_secs=30) ] + input_hooks - chief_hooks = [ - training.CheckpointSaverHook( - self.model_dir, - save_secs=self._config.save_checkpoints_secs, - save_steps=self._config.save_checkpoints_steps, - steps_per_run=self._config.tpu_config.iterations_per_loop, - scaffold=scaffold) - ] + chief_hooks = [] + if (self._config.save_checkpoints_secs or + self._config.save_checkpoints_steps): + chief_hooks.append( + training.CheckpointSaverHook( + self.model_dir, + save_secs=self._config.save_checkpoints_secs, + save_steps=self._config.save_checkpoints_steps, + steps_per_run=self._config.tpu_config.iterations_per_loop, + scaffold=scaffold)) summary.scalar(model_fn_lib.LOSS_METRIC_KEY, loss) with ops.control_dependencies([loss]): update_ops = _sync_variables_ops() From a722cdf7a62a3ee82ca6ee1b3d33f3d03dba49ee Mon Sep 17 00:00:00 2001 From: Francois Chollet Date: Wed, 18 Apr 2018 15:04:21 -0700 Subject: [PATCH 0023/1691] Fix loss computation bug in Model training/eval methods with eager execution enabled. Fixes #18642. PiperOrigin-RevId: 193423288 --- .../_impl/keras/engine/training_eager.py | 2 +- .../_impl/keras/engine/training_eager_test.py | 25 +++++++++++++++++++ 2 files changed, 26 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py index 4cdb5f108a05bb..695669d9ee1566 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py +++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py @@ -150,7 +150,7 @@ def _model_loss(model, inputs, targets, sample_weights=None, training=False): weighted_masked_fn = training_utils.weighted_masked_objective(loss_fn) with backend.name_scope(model.output_names[i] + '_loss'): output_loss = weighted_masked_fn( - outs[i], targets[i], weights, mask=mask) + targets[i], outs[i], weights, mask=mask) loss_metrics.append(backend.mean(output_loss)) loss_weight = model.loss_weights_list[i] diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py index 6cdb6b0753fce1..ed0f91ee1e2c6b 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py +++ b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py @@ -21,6 +21,7 @@ import numpy as np from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils from tensorflow.python.platform import test @@ -625,6 +626,30 @@ def test_class_weight_invalid_use_case(self): model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np}) +class CorrectnessTest(test.TestCase): + + @tf_test_util.run_in_graph_and_eager_modes() + def test_loss_correctness(self): + # Test that training loss is the same in eager and graph + # (by comparing it to a reference value in a deterministic case) + model = keras.Sequential() + model.add(keras.layers.Dense(3, + activation='relu', + input_dim=4, + kernel_initializer='ones')) + model.add(keras.layers.Dense(2, + activation='softmax', + kernel_initializer='ones')) + model.compile(loss='sparse_categorical_crossentropy', + optimizer=RMSPropOptimizer(learning_rate=0.001)) + x = np.ones((100, 4)) + np.random.seed(123) + y = np.random.randint(0, 1, size=(100, 1)) + history = model.fit(x, y, epochs=1, batch_size=10) + self.assertEqual( + np.around(history.history['loss'][-1], decimals=4), 0.6173) + + if __name__ == '__main__': ops.enable_eager_execution() test.main() From 2ef955b6d354378a7ca19f1f3cafccfc17f79013 Mon Sep 17 00:00:00 2001 From: Haggai Date: Fri, 20 Apr 2018 18:57:12 -0700 Subject: [PATCH 0024/1691] Abort on invalid fft type or rank --- tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h b/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h index 4f6b3633645b22..0bf693edd0b985 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h @@ -195,6 +195,9 @@ void EigenFftWithRank(const EigenDevice& device, void* out, void* operand, device, static_cast(out), static_cast(operand), input_batch, fft_length0, fft_length1, fft_length2); break; + default: + // Unsupported FFT type + abort(); } } @@ -219,6 +222,9 @@ void EigenFftImpl(const EigenDevice& device, void* out, void* operand, input_batch, fft_length0, fft_length1, fft_length2); break; + default: + // Unsupported FFT rank + abort(); } } From 364f6eae07fa8f0e2f89a9f665d0af430ea96669 Mon Sep 17 00:00:00 2001 From: Filipe Filardi Date: Sat, 21 Apr 2018 14:45:30 -0300 Subject: [PATCH 0025/1691] Create pull_request_template.md --- pull_request_template.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 pull_request_template.md diff --git a/pull_request_template.md b/pull_request_template.md new file mode 100644 index 00000000000000..8b137891791fe9 --- /dev/null +++ b/pull_request_template.md @@ -0,0 +1 @@ + From ea3d7ab5455f54a67e24428f159e9170be408d71 Mon Sep 17 00:00:00 2001 From: Filipe Filardi Date: Sat, 21 Apr 2018 14:57:38 -0300 Subject: [PATCH 0026/1691] Create Pull Request Template --- PULL_REQUEST_TEMPLATE.md | 20 ++++++++++++++++++++ pull_request_template.md | 1 - 2 files changed, 20 insertions(+), 1 deletion(-) create mode 100644 PULL_REQUEST_TEMPLATE.md delete mode 100644 pull_request_template.md diff --git a/PULL_REQUEST_TEMPLATE.md b/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 00000000000000..075bbc994558ef --- /dev/null +++ b/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,20 @@ + + +##### Pull Request Checklist + +- [ ] Read [contributing guideline](CONTRIBUTING.md). +- [ ] Read [code of conduct](CODE_OF_CONDUCT.md). +- [ ] Fill [Contributor License Agreement (CLA)](https://cla.developers.google.com/). +- [ ] Check if my changes are consistent with the [guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution). +- [ ] Changes are consistent with the [Coding Style](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#c-coding-style) +- [ ] Run [Unit Tests](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#running-unit-tests). + +##### Issue Fix + +- [ ] Yes +- [ ] No + +Fixed issue: + +##### Description + diff --git a/pull_request_template.md b/pull_request_template.md deleted file mode 100644 index 8b137891791fe9..00000000000000 --- a/pull_request_template.md +++ /dev/null @@ -1 +0,0 @@ - From 955c1edb2f92871597aaf74f5684da4d22843064 Mon Sep 17 00:00:00 2001 From: zhangyaobit Date: Mon, 23 Apr 2018 13:46:26 -0700 Subject: [PATCH 0027/1691] Update layout_optimizer.cc Place data format op on CPU:0. --- tensorflow/core/grappler/optimizers/layout_optimizer.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc index 561226f94544f7..8fb30d116de8f2 100644 --- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc @@ -919,6 +919,7 @@ class NodeProcessor : public GraphProcessor { ParseNodeName(input_name, &port); if (IsHostMemory(*input, port)) { parsed_name.type = "CPU"; + parsed_name.id = 0; device = DeviceNameUtils::ParsedNameToString(parsed_name); } } From aaf1e32d53e1b473e9d1700afba71662e28150ff Mon Sep 17 00:00:00 2001 From: zhangyaobit Date: Mon, 23 Apr 2018 13:49:22 -0700 Subject: [PATCH 0028/1691] Update layout_optimizer_test.cc Place data format op on CPU:0. --- tensorflow/core/grappler/optimizers/layout_optimizer_test.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer_test.cc b/tensorflow/core/grappler/optimizers/layout_optimizer_test.cc index 260347b0e85162..b913f2b00413e0 100644 --- a/tensorflow/core/grappler/optimizers/layout_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/layout_optimizer_test.cc @@ -36,7 +36,7 @@ class LayoutOptimizerTest : public ::testing::Test { DeviceProperties device_properties; device_properties.set_type("GPU"); device_properties.mutable_environment()->insert({"architecture", "6"}); - virtual_cluster_.reset(new VirtualCluster({{"/GPU:0", device_properties}})); + virtual_cluster_.reset(new VirtualCluster({{"/GPU:1", device_properties}})); } Output SimpleConv2D(tensorflow::Scope* s, int input_size, int filter_size, From 9ad432781fce95a397d7d4a8ce506932160b83f1 Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Mon, 23 Apr 2018 14:00:28 -0700 Subject: [PATCH 0029/1691] Update install_linux.md --- tensorflow/docs_src/install/install_linux.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index f19f827e255e7d..63b8eb30e91962 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -48,7 +48,7 @@ must be installed on your system: Toolkit. * The libcupti-dev library, which is the NVIDIA CUDA Profile Tools Interface. This library provides advanced profiling support. To install this library, - issue the following command for CUDA Toolkit >= 8.0: + issue the following command for CUDA Toolkit >= 9.0: 
     $ sudo apt-get install cuda-command-line-tools

From 7ea8e98a9ecf5ad8c23a8df220126f6addbdf2af Mon Sep 17 00:00:00 2001
From: Sagi 
Date: Tue, 24 Apr 2018 17:36:49 +0800
Subject: [PATCH 0030/1691] Update README.md

Awesome and details doc!

But I wouldn't call it an "awkward" package path :)
---
 tensorflow/go/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/go/README.md b/tensorflow/go/README.md
index b1bd87eb0c3b3a..e251356ec8e973 100644
--- a/tensorflow/go/README.md
+++ b/tensorflow/go/README.md
@@ -5,7 +5,7 @@ Construct and execute TensorFlow graphs in Go.
 [![GoDoc](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go?status.svg)](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go)
 
 > *WARNING*: The API defined in this package is not stable and can change
-> without notice. The same goes for the awkward package path
+> without notice. The same goes for the package path:
 > (`github.com/tensorflow/tensorflow/tensorflow/go`).
 
 ## Quickstart

From dd9ee4a2f13c2219ebd7c6f8754b8dd32188e2a5 Mon Sep 17 00:00:00 2001
From: Amit Patankar 
Date: Tue, 24 Apr 2018 10:59:10 -0700
Subject: [PATCH 0031/1691] Update README.md

---
 tensorflow/tools/docker/README.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/tools/docker/README.md b/tensorflow/tools/docker/README.md
index f46c56e11aa72c..525f2995ceecd4 100644
--- a/tensorflow/tools/docker/README.md
+++ b/tensorflow/tools/docker/README.md
@@ -16,12 +16,12 @@ quick links here:
 
 We currently maintain two Docker container images:
 
-* `gcr.io/tensorflow/tensorflow` - TensorFlow with all dependencies - CPU only!
+* `tensorflow/tensorflow` - TensorFlow with all dependencies - CPU only!
 
-* `gcr.io/tensorflow/tensorflow:latest-gpu` - TensorFlow with all dependencies
+* `tensorflow/tensorflow:latest-gpu` - TensorFlow with all dependencies
   and support for NVidia CUDA
 
-Note: We also publish the same containers into
+Note: We store all our containers on 
 [Docker Hub](https://hub.docker.com/r/tensorflow/tensorflow/tags/).
 
 
@@ -29,12 +29,12 @@ Note: We also publish the same containers into
 
 Run non-GPU container using
 
-    $ docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow
+    $ docker run -it -p 8888:8888 tensorflow/tensorflow
 
 For GPU support install NVidia drivers (ideally latest) and
 [nvidia-docker](https://github.com/NVIDIA/nvidia-docker). Run using
 
-    $ nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu
+    $ nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:latest-gpu
 
 
 Note: If you would have a problem running nvidia-docker you may try the old method
@@ -44,7 +44,7 @@ it there and try using nvidia-docker as described above.
     $ # The old, not recommended way to run docker with gpu support:
     $ export CUDA_SO=$(\ls /usr/lib/x86_64-linux-gnu/libcuda.* | xargs -I{} echo '-v {}:{}')
     $ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
-    $ docker run -it -p 8888:8888 $CUDA_SO $DEVICES gcr.io/tensorflow/tensorflow:latest-gpu
+    $ docker run -it -p 8888:8888 $CUDA_SO $DEVICES tensorflow/tensorflow:latest-gpu
 
 
 ## More containers

From b7bf05ade772a21bc9b74aa290a4493955ff2a1f Mon Sep 17 00:00:00 2001
From: ctiijima 
Date: Tue, 24 Apr 2018 14:17:14 -0700
Subject: [PATCH 0032/1691] typo fixes

---
 tensorflow/docs_src/get_started/index.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md
index b28cb9df75d94a..578080bb592d93 100644
--- a/tensorflow/docs_src/get_started/index.md
+++ b/tensorflow/docs_src/get_started/index.md
@@ -10,13 +10,13 @@ course prior to diving into TensorFlow documentation:
 TensorFlow is a tool for machine learning. While it contains a wide range of
 functionality, TensorFlow is mainly designed for deep neural network models.
 
-The easiest way to get started with tensorflow is using Eager Execution.
+The easiest way to get started with TensorFlow is by using Eager Execution.
 
-  * @{$get_started/eager}, is for anyone new to  machine learning or TensorFlow.
+  * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow.
 
 TensorFlow provides many APIs. The remainder of this section focuses on the
 Estimator API which provide scalable, high-performance models.
-To get started with Estimators begin by reading one of the following documents:
+To get started with Estimators, begin by reading one of the following documents:
 
   * @{$get_started/get_started_for_beginners}, which is aimed at readers
     new to machine learning.

From 61c463020618ef6441392db770bdb0ec23375c73 Mon Sep 17 00:00:00 2001
From: Nick Felt 
Date: Tue, 24 Apr 2018 14:51:20 -0700
Subject: [PATCH 0033/1691] Update tensorboard dep to 1.8.x

---
 tensorflow/tools/pip_package/setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 6da3223d339057..bcf6c1e5158e41 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -38,7 +38,7 @@
     'numpy >= 1.13.3',
     'six >= 1.10.0',
     'protobuf >= 3.4.0',
-    'tensorboard >= 1.7.0, < 1.8.0',
+    'tensorboard >= 1.8.0, < 1.9.0',
     'termcolor >= 1.1.0',
 ]
 

From a8654769c1faf6327b715edae614eb48775394a1 Mon Sep 17 00:00:00 2001
From: anj-s <32556631+anj-s@users.noreply.github.com>
Date: Tue, 24 Apr 2018 16:28:41 -0700
Subject: [PATCH 0034/1691] 1.8r Cherrypick request-cherrypicks_30740: Fix for
 dropped metrics in evaluate function for Keras models. (#18799)

---
 .../keras/_impl/keras/engine/training.py      | 29 ++-------
 .../_impl/keras/engine/training_eager.py      | 39 ++++--------
 .../_impl/keras/engine/training_eager_test.py | 11 ++--
 .../keras/_impl/keras/engine/training_test.py | 26 ++++++++
 .../_impl/keras/engine/training_utils.py      | 62 +++++++++++++++++++
 5 files changed, 109 insertions(+), 58 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py
index 71de657da81b92..2b72e0e33dd909 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training.py
@@ -276,6 +276,8 @@ def compile(self,
           self.metrics_names.append(self.output_names[i] + '_loss')
       self.nested_metrics = training_utils.collect_metrics(metrics,
                                                            self.output_names)
+      with K.name_scope('metrics'):
+        training_utils.populate_metric_names(self)
       self._feed_sample_weight_modes = []
       for i in range(len(self.outputs)):
         self._feed_sample_weight_modes.append(None)
@@ -462,7 +464,6 @@ def compile(self,
         output_weighted_metrics = nested_weighted_metrics[i]
 
         def handle_metrics(metrics, weights=None):
-          metric_name_prefix = 'weighted_' if weights is not None else ''
 
           for metric in metrics:
             if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
@@ -489,39 +490,19 @@ def handle_metrics(metrics, weights=None):
                   metric_fn = metrics_module.categorical_accuracy
                 elif metric in ('crossentropy', 'ce'):
                   metric_fn = metrics_module.categorical_crossentropy
-              if metric in ('accuracy', 'acc'):
-                suffix = 'acc'
-              elif metric in ('crossentropy', 'ce'):
-                suffix = 'ce'
               weighted_metric_fn = training_utils.weighted_masked_objective(
                   metric_fn)
-              metric_name = metric_name_prefix + suffix
             else:
               metric_fn = metrics_module.get(metric)
               weighted_metric_fn = training_utils.weighted_masked_objective(
                   metric_fn)
-              # Get metric name as string
-              if hasattr(metric_fn, 'name'):
-                metric_name = metric_fn.name
-              else:
-                metric_name = metric_fn.__name__
-              metric_name = metric_name_prefix + metric_name
-
+            metric_name = training_utils.get_base_metric_name(
+                metric, weighted=weights is not None)
             with K.name_scope(metric_name):
               metric_result = weighted_metric_fn(
                   y_true, y_pred, weights=weights, mask=masks[i])
 
-            # Append to self.metrics_names, self.metric_tensors,
-            # self.stateful_metric_names
-            if len(self.output_names) > 1:
-              metric_name = '%s_%s' % (self.output_names[i], metric_name)
-            # Dedupe name
-            j = 1
-            base_metric_name = metric_name
-            while metric_name in self.metrics_names:
-              metric_name = '%s_%d' % (base_metric_name, j)
-              j += 1
-            self.metrics_names.append(metric_name)
+            training_utils.add_metric_name(self, metric_name, i)
             self.metrics_tensors.append(metric_result)
 
             # Keep track of state updates created by
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
index 695669d9ee1566..ad239d6151e02a 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
@@ -100,7 +100,7 @@ def _eager_metrics_fn(model, outputs, targets):
         metric_names.append(metric_name)
         metric_results.append(backend.mean(metric_result))
 
-  return metric_names, metric_results
+  return metric_results
 
 
 def _model_loss(model, inputs, targets, sample_weights=None, training=False):
@@ -151,7 +151,12 @@ def _model_loss(model, inputs, targets, sample_weights=None, training=False):
       with backend.name_scope(model.output_names[i] + '_loss'):
         output_loss = weighted_masked_fn(
             targets[i], outs[i], weights, mask=mask)
-      loss_metrics.append(backend.mean(output_loss))
+      # If the number of outputs is 1 then we don't append the loss metric
+      # associated with each model output. When there are multiple outputs
+      # associated with a model, each output's loss is calculated and returned
+      # as part of the loss_metrics.
+      if len(model.outputs) > 1:
+        loss_metrics.append(backend.mean(output_loss))
 
       loss_weight = model.loss_weights_list[i]
       if total_loss is None:
@@ -274,7 +279,7 @@ def train_on_batch(model, inputs, targets, sample_weights=None):
       model, inputs, targets, sample_weights=sample_weights, training=True)
   if not isinstance(outs, list):
     outs = [outs]
-  _, metrics_results = _eager_metrics_fn(
+  metrics_results = _eager_metrics_fn(
       model, outs, targets)
   if not isinstance(loss, list):
     loss = [loss]
@@ -304,7 +309,7 @@ def test_on_batch(model, inputs, targets, sample_weights=None):
       model, inputs, targets, sample_weights=sample_weights, training=False)
   if not isinstance(outs, list):
     outs = [outs]
-  _, metrics_results = _eager_metrics_fn(
+  metrics_results = _eager_metrics_fn(
       model, outs, targets)
   if not isinstance(loss, list):
     loss = [loss]
@@ -498,34 +503,12 @@ def fit_loop(
         for l, o in zip(out_labels, outs):
           batch_logs[l] = o
         # Required for Eager mode
-        metrics_names, metrics_results = _eager_metrics_fn(
-            model, outs, targets_batch)
+        metrics_results = _eager_metrics_fn(model, outs, targets_batch)
         batch_logs['loss'] = tensor_util.constant_value(backend.mean(loss))
 
-        # TODO(anjalisridhar): Move this to compile to avoid duplicate code.
-        # In graph mode we set the metric names in compile. However in
-        # Eager mode we calculate the metrics for each batch in fit_loop.
-        # We could calculate the metric names and functions in compile.
-        # This would avoid setting the callback parameters separately.
-        # We need to do this for the first iteration alone
-        for m in metrics_names:
-          if m not in callback_metrics:
-            callback_metrics.append(m)
-
-        callbacks.set_params({
-            'batch_size': batch_size,
-            'epochs': epochs,
-            'steps': steps_per_epoch,
-            'samples': num_train_samples,
-            'verbose': verbose,
-            'do_validation': do_validation,
-            'metrics': callback_metrics or [],
-        })
-
         for k, v in zip(model.metrics_names,
                         [backend.mean(loss)] + loss_metrics + metrics_results):
           batch_logs[k] = tensor_util.constant_value(v)
-
         callbacks.on_batch_end(batch_index, batch_logs)
         if callback_model.stop_training:
           break
@@ -611,7 +594,7 @@ def test_loop(model, inputs, targets,
           targets_batch,
           sample_weights=sample_weights_batch,
           training=False)
-      _, metrics_results = _eager_metrics_fn(model, loss_outs, targets_batch)
+      metrics_results = _eager_metrics_fn(model, loss_outs, targets_batch)
       batch_outs = []
       for _, v in zip(model.metrics_names,
                       [backend.mean(loss)] + loss_metrics + metrics_results):
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
index ed0f91ee1e2c6b..c45e07e08bcb51 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
@@ -212,7 +212,7 @@ def test_evaluate_predict_on_arrays(self):
     optimizer = RMSPropOptimizer(learning_rate=0.001)
     loss = 'mse'
     loss_weights = [1., 0.5]
-    metrics = ['mae']
+    metrics = ['acc', 'mae']
     model.compile(
         optimizer,
         loss,
@@ -231,20 +231,20 @@ def test_evaluate_predict_on_arrays(self):
         [input_a_np, input_b_np], [output_d_np, output_e_np],
         batch_size=5,
         verbose=0)
-    self.assertEqual(len(out), 5)
+    self.assertEqual(len(out), 7)
     out = model.evaluate(
         [input_a_np, input_b_np], [output_d_np, output_e_np],
         batch_size=5,
         verbose=1)
-    self.assertEqual(len(out), 5)
+    self.assertEqual(len(out), 7)
     out = model.evaluate(
         [input_a_np, input_b_np], [output_d_np, output_e_np],
         batch_size=5,
         verbose=2)
-    self.assertEqual(len(out), 5)
+    self.assertEqual(len(out), 7)
     out = model.test_on_batch([input_a_np, input_b_np],
                               [output_d_np, output_e_np])
-    self.assertEqual(len(out), 5)
+    self.assertEqual(len(out), 7)
 
     # Test evaluate with dictionary inputs
     model.evaluate(
@@ -625,7 +625,6 @@ def test_class_weight_invalid_use_case(self):
       bad_w_np = np.random.random((10, 2, 2))
       model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
 
-
 class CorrectnessTest(test.TestCase):
 
   @tf_test_util.run_in_graph_and_eager_modes()
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_test.py b/tensorflow/python/keras/_impl/keras/engine/training_test.py
index 08fd26dd18d5bc..47d80704cf6345 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_test.py
@@ -23,11 +23,14 @@
 
 import numpy as np
 
+from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
 from tensorflow.python.keras._impl.keras.engine.training_utils import weighted_masked_objective
 from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays
 from tensorflow.python.platform import test
+from tensorflow.python.training.rmsprop import RMSPropOptimizer
+
 
 try:
   import scipy.sparse as scipy_sparse  # pylint: disable=g-import-not-at-top
@@ -1667,6 +1670,29 @@ def test_model_custom_target_tensors(self):
       model.train_on_batch([input_a_np, input_b_np],
                            [output_a_np, output_b_np])
 
+  @tf_test_util.run_in_graph_and_eager_modes()
+  def test_metric_names_are_identical_in_graph_and_eager(self):
+    a = keras.layers.Input(shape=(3,), name='input_a')
+    b = keras.layers.Input(shape=(3,), name='input_b')
+
+    dense = keras.layers.Dense(4, name='dense')
+    c = dense(a)
+    d = dense(b)
+    e = keras.layers.Dropout(0.5, name='dropout')(c)
+
+    model = keras.models.Model([a, b], [d, e])
+
+    optimizer = RMSPropOptimizer(learning_rate=0.001)
+    loss = 'mse'
+    loss_weights = [1., 0.5]
+    metrics = ['mae', 'acc']
+    model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights)
+    reference_metric_names = ['loss', 'dense_loss', 'dropout_loss',
+                              'dense_mean_absolute_error',
+                              'dense_acc',
+                              'dropout_mean_absolute_error',
+                              'dropout_acc']
+    self.assertEqual(reference_metric_names, model.metrics_names)
 
 if __name__ == '__main__':
   # Bazel sets these environment variables to very long paths.
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_utils.py b/tensorflow/python/keras/_impl/keras/engine/training_utils.py
index a3fc8ef2a0359c..34c0738f26fa74 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_utils.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_utils.py
@@ -26,6 +26,7 @@
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras import losses
+from tensorflow.python.keras._impl.keras import metrics as metrics_module
 from tensorflow.python.ops import math_ops
 
 
@@ -553,3 +554,64 @@ def standardize_weights(y,
 def has_symbolic_tensors(ls):
   return (any(tensor_util.is_tensor(v) for v in ls)
           and not context.executing_eagerly())
+
+
+def populate_metric_names(model):
+  for i in range(len(model.outputs)):
+    metrics = model.nested_metrics[i]
+    for metric in metrics:
+      base_metric_name = get_base_metric_name(metric)
+      add_metric_name(model, base_metric_name, i)
+
+
+def get_base_metric_name(metric, weighted=False):
+  """Returns the metric name given the metric function.
+
+  Arguments:
+      metric: Metric function name or reference.
+      weighted: Boolean indicating if the metric for which we are adding
+          names is weighted.
+
+  Returns:
+      a metric name.
+  """
+  metric_name_prefix = 'weighted_' if weighted else ''
+  if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
+    if metric in ('accuracy', 'acc'):
+      suffix = 'acc'
+    elif metric in ('crossentropy', 'ce'):
+      suffix = 'ce'
+    metric_name = metric_name_prefix + suffix
+  else:
+    metric_fn = metrics_module.get(metric)
+    # Get metric name as string
+    if hasattr(metric_fn, 'name'):
+      metric_name = metric_fn.name
+    else:
+      metric_name = metric_fn.__name__
+    metric_name = metric_name_prefix + metric_name
+
+  return metric_name
+
+
+def add_metric_name(model, metric_name, index):
+  """Makes the metric name unique and adds it to the model's metric name list.
+
+    If there are multiple outputs for which the metrics are calculated, the
+    metric names have to be made unique by appending an integer.
+
+  Arguments:
+    model: Model to which we are adding metric names.
+    metric_name: Metric name that corresponds to the metric specified by the
+        user. For example: 'acc'
+    index: The index of the model output for which the metric name is being
+        added.
+  """
+  if len(model.output_names) > 1:
+    metric_name = '%s_%s' % (model.output_names[index], metric_name)
+  j = 1
+  base_metric_name = metric_name
+  while metric_name in model.metrics_names:
+    metric_name = '%s_%d' % (base_metric_name, j)
+    j += 1
+  model.metrics_names.append(metric_name)

From d1d5fc27ad8d84f1468ce459ba8fab208b174c6f Mon Sep 17 00:00:00 2001
From: Francois Chollet <>
Date: Tue, 24 Apr 2018 17:00:40 -0700
Subject: [PATCH 0035/1691] Fix critical metrics computation bug with Model in
 Eager mode.

---
 tensorflow/python/keras/_impl/keras/engine/training_eager.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
index 4cdb5f108a05bb..924f74e5b66df5 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
@@ -96,7 +96,7 @@ def _eager_metrics_fn(model, outputs, targets):
           model.metrics_names.append(metric_name)
 
       with backend.name_scope(metric_name):
-        metric_result = metric_fn(outputs[i], targets[i])
+        metric_result = metric_fn(targets[i], outputs[i])
         metric_names.append(metric_name)
         metric_results.append(backend.mean(metric_result))
 

From 7f78414776718a350b1beb612dd8b1c26ff3f6a4 Mon Sep 17 00:00:00 2001
From: Filipe Filardi 
Date: Tue, 24 Apr 2018 22:52:29 -0300
Subject: [PATCH 0036/1691] Merge PR Template to Contributing

    - Remove pull request template.
    - Add check list in contributing as a kind of TL;DR for that file.
---
 CONTRIBUTING.md          | 11 +++++++++++
 PULL_REQUEST_TEMPLATE.md | 20 --------------------
 2 files changed, 11 insertions(+), 20 deletions(-)
 delete mode 100644 PULL_REQUEST_TEMPLATE.md

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 3dad41a88c8212..2e9d8c65e25b35 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,5 +1,16 @@
 # Contributing guidelines
 
+## Pull Request Checklist
+
+Before sending your pull requests, make sure you followed this list.
+
+- [ ] Read [contributing guidelines](CONTRIBUTING.md).
+- [ ] Read [Code of Conduct](CODE_OF_CONDUCT.md).
+- [ ] Ensure you have signed the [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
+- [ ] Check if my changes are consistent with the [guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution).
+- [ ] Changes are consistent with the [Coding Style](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#c-coding-style).
+- [ ] Run [Unit Tests](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#running-unit-tests).
+
 ## How to become a contributor and submit your own code
 
 ### Contributor License Agreements
diff --git a/PULL_REQUEST_TEMPLATE.md b/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index 075bbc994558ef..00000000000000
--- a/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,20 +0,0 @@
-
-
-##### Pull Request Checklist
-
-- [ ] Read [contributing guideline](CONTRIBUTING.md).
-- [ ] Read [code of conduct](CODE_OF_CONDUCT.md).
-- [ ] Fill [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
-- [ ] Check if my changes are consistent with the [guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution).
-- [ ] Changes are consistent with the [Coding Style](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#c-coding-style)
-- [ ] Run [Unit Tests](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#running-unit-tests).
-
-##### Issue Fix
-
-- [ ] Yes
-- [ ] No
-
-Fixed issue:
-
-##### Description
-

From 7f70c7a38fc2f4aaa9ceb52240c9112886adda5c Mon Sep 17 00:00:00 2001
From: Filipe Filardi 
Date: Tue, 24 Apr 2018 23:00:05 -0300
Subject: [PATCH 0037/1691] Make more like a table of contents

---
 CONTRIBUTING.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 2e9d8c65e25b35..8669c25c452b53 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -4,12 +4,12 @@
 
 Before sending your pull requests, make sure you followed this list.
 
-- [ ] Read [contributing guidelines](CONTRIBUTING.md).
-- [ ] Read [Code of Conduct](CODE_OF_CONDUCT.md).
-- [ ] Ensure you have signed the [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
-- [ ] Check if my changes are consistent with the [guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution).
-- [ ] Changes are consistent with the [Coding Style](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#c-coding-style).
-- [ ] Run [Unit Tests](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#running-unit-tests).
+- Read [contributing guidelines](CONTRIBUTING.md).
+- Read [Code of Conduct](CODE_OF_CONDUCT.md).
+- Ensure you have signed the [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
+- Check if my changes are consistent with the [guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution).
+- Changes are consistent with the [Coding Style](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#c-coding-style).
+- Run [Unit Tests](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#running-unit-tests).
 
 ## How to become a contributor and submit your own code
 

From 7316e5af78c583d75b7e39d022a22248c9d11ab9 Mon Sep 17 00:00:00 2001
From: Jiri Simsa 
Date: Wed, 25 Apr 2018 10:25:57 -0700
Subject: [PATCH 0038/1691] Updating release notes.

---
 RELEASE.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/RELEASE.md b/RELEASE.md
index 2717c75740aeea..55923a2c9b27ce 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -6,7 +6,7 @@
 * Added Gradient Boosted Trees as pre-made Estimators: BoostedTreesClassifier, BoostedTreesRegressor.
 * Add 3rd generation pipeline config for Cloud TPUs which improves performance and usability.
 * `tf.contrib.bayesflow` is moving out to it's own repo.
-* Added `tf.contrib.{proto,rpc}` to allow generic proto parsing and RPC communication.
+* Added `tf.contrib.{proto,rpc}` to allow generic proto parsing and RPC communication[1](#rpc-issue).
 
 ## Bug Fixes and Other Changes
 * `tf.data`:
@@ -49,13 +49,14 @@
   * Fix non-uniformity of orthogonal matrices.
   * Fix bug where multi-image Estimator eval summaries were not displayed correctly.
 
+1 The cancellation logic of the RPC op contains a concurrency error. A fix has been submitted to master and will be part of the next release.
+
 ## Thanks to our Contributors
 
 This release contains contributions from many people at Google, as well as:
 
 4d55397500, Aghasy, Alan Du, Alan Lee, Alan Yee, Alex Wiltschko, Animesh Karnewar, Ankit Gupta, Anton Matosov, Aris L, Ben Barsdell, Brent Yi, Brett Koonce, Carl Thomé, cbockman, Chikanaga Tomoyuki, Chris Tava, CéDric Deltheil, Dahan Gong, Dalmo Cirne, Daniel Erenrich, David Norman, DavidNorman, Edd Wilder-James, Fanjin Zeng, Felix Abecassis, fo40225, George Sterpu, Giovanni Terlingen, Gor Baghdasaryan, Guillaume Klein, Hanchen Li, Ilya Polenov, Jakub Kolodziejczyk, Jason Sadler, Jayaram Bobba, Jerry Liu, jinghuangintel, Jiongyan Zhang (张炯衍), Joel Shor, Jong Wook Kim, Julian Eisenschlos, Karl Lessard, Krish Ravindranath, Loo Rong Jie, Lukas Geiger, Luke Iwanski, Mahmoud Abuzaina, ManHyuk, Marvin Richter, Maximilian Mitchell, Mohammad Ashraf Bhuiyan, msofka, Mustafa Kasap, Nathan Burnham, Nathan Luehr, Naveen Marri, ngc92, nio1814, Oleg Zabluda, Ou Changkun, Panos Ipeirotis, Paul Van Eck, Peter Lee, Piotr Czapla, qjivy, Rholais Lii, Rodrigo Formigone, Russell Klopfer, ryantimjohn, Sang Han, SebastiáN RamíRez, shengfuintel, Siby Jose Plathottam, Silver Chan, Stanislaw Antol, Taehoon Lee, Tarang Chugh, Ted Chang, Thomas Bastiani, Xian Xu, Xiaoming (Jason) Cui, Yan Facai (颜发才), yaox12, Yashal Shakti Kanungo, Yong Tang, Yuan (Terry) Tang, Yuxin Wu, Ziyue(Louis) Lu
 
-
 # Release 1.7.0
 
 ## Major Features And Improvements

From 36d2e178c6d7790dd78cece70056d429aea6b917 Mon Sep 17 00:00:00 2001
From: Yifei Feng 
Date: Wed, 25 Apr 2018 11:08:42 -0700
Subject: [PATCH 0039/1691] Update version string to 1.8.0.

---
 tensorflow/core/public/version.h              |  2 +-
 tensorflow/docs_src/install/install_c.md      |  2 +-
 tensorflow/docs_src/install/install_go.md     |  2 +-
 tensorflow/docs_src/install/install_java.md   | 22 +++++++++----------
 tensorflow/docs_src/install/install_linux.md  | 22 +++++++++----------
 tensorflow/docs_src/install/install_mac.md    | 10 ++++-----
 .../docs_src/install/install_sources.md       |  4 ++--
 tensorflow/tools/pip_package/setup.py         |  2 +-
 8 files changed, 33 insertions(+), 33 deletions(-)

diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index ba69efb289a42a..522a9d84fddd2e 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -24,7 +24,7 @@ limitations under the License.
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
-#define TF_VERSION_SUFFIX "-rc1"
+#define TF_VERSION_SUFFIX ""
 
 #define TF_STR_HELPER(x) #x
 #define TF_STR(x) TF_STR_HELPER(x)
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 8c165aad52499a..1abd840ab3ca3f 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -38,7 +38,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for macOS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.8.0-rc1.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 26cbcc9a9b0a99..52a2a3f8a68dd5 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.8.0-rc1.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.8.0.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 1b0bbdba7b99fa..700ae01236c193 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 
   org.tensorflow
   tensorflow
-  1.8.0-rc1
+  1.8.0
 
 ```
 
@@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                
                  org.tensorflow
                  tensorflow
-                 1.8.0-rc1
+                 1.8.0
                
              
          
@@ -123,12 +123,12 @@ instead:
 
   org.tensorflow
   libtensorflow
-  1.8.0-rc1
+  1.8.0
 
 
   org.tensorflow
   libtensorflow_jni_gpu
-  1.8.0-rc1
+  1.8.0
 
 ```
 
@@ -147,7 +147,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or macOS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0-rc1.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -166,7 +166,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.8.0-rc1.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -174,10 +174,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0-rc1.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.8.0-rc1.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.8.0.zip).
   3. Extract this .zip file.
 
 
@@ -225,7 +225,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-
javac -cp libtensorflow-1.8.0-rc1.jar HelloTF.java

+
javac -cp libtensorflow-1.8.0.jar HelloTF.java

 
 
 ### Running
@@ -239,11 +239,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and macOS X:
 
-
java -cp libtensorflow-1.8.0-rc1.jar:. -Djava.library.path=./jni HelloTF

+
java -cp libtensorflow-1.8.0.jar:. -Djava.library.path=./jni HelloTF

 
 And the following command line executes the `HelloTF` program on Windows:
 
-
java -cp libtensorflow-1.8.0-rc1.jar;. -Djava.library.path=jni HelloTF

+
java -cp libtensorflow-1.8.0.jar;. -Djava.library.path=jni HelloTF

 
 If the program prints Hello from version, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 63b8eb30e91962..42d218c4bceb8a 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -194,7 +194,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      Virtualenv environment:
 
      
(tensorflow)$ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc1-cp34-cp34m-linux_x86_64.whl

+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl
If you encounter installation problems, see [Common Installation Problems](#common_installation_problems). @@ -299,7 +299,7 @@ take the following steps: 
      $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc1-cp34-cp34m-linux_x86_64.whl
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl
If this step fails, see @@ -485,7 +485,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: 
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc1-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl ## Validate your installation @@ -659,14 +659,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -678,14 +678,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -697,14 +697,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp35-cp35m-linux_x86_64.whl
@@ -716,14 +716,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index ff6c2f5e447873..c79075b09d6352 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -119,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc1-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -242,7 +242,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc1-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc1-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any.whl @@ -524,7 +524,7 @@ The value you specify depends on your Python version. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc1-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any.whl
@@ -532,5 +532,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc1-py2-none-a 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc1-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index d48a6ee550fd5e..3d937367fc2340 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -350,10 +350,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.8.0rc1 on Linux: +for TensorFlow 1.8.0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.8.0rc1-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.8.0-py2-none-any.whl
## Validate your installation diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index bcf6c1e5158e41..3ec5ea9af5ab4f 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -29,7 +29,7 @@ # This version string is semver compatible, but incompatible with pip. # For pip, we will remove all '-' characters from this string, and use the # result for pip. -_VERSION = '1.8.0-rc1' +_VERSION = '1.8.0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', From ba8061330e024173ae2bd916eac76990aec228e5 Mon Sep 17 00:00:00 2001 From: Nick Felt Date: Wed, 25 Apr 2018 17:46:11 -0700 Subject: [PATCH 0040/1691] Update tb-nightly dep to >= 1.9.0a0, < 1.10.0a0 Synchronize tf-nightly dep on current tb-nightly. --- tensorflow/tools/pip_package/setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index b88d023cbcaa9d..937d41c36ca33a 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -69,7 +69,7 @@ if 'tf_nightly' in project_name: for i, pkg in enumerate(REQUIRED_PACKAGES): if 'tensorboard' in pkg: - REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.8.0a0, < 1.9.0a0' + REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.9.0a0, < 1.10.0a0' break # weakref.finalize and enum were introduced in Python 3.4 From c71ea1f4ccc6513b881941435d8f78b8bebb3fce Mon Sep 17 00:00:00 2001 From: "freedom\" Koan-Sin Tan" Date: Thu, 26 Apr 2018 08:58:19 +0800 Subject: [PATCH 0041/1691] remove extra whitespace remove extra whitespace shouldn't be there --- tensorflow/contrib/lite/examples/label_image/label_image.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.cc b/tensorflow/contrib/lite/examples/label_image/label_image.cc index 71d24a7ea5cb45..456c5c6dc782f4 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.cc +++ b/tensorflow/contrib/lite/examples/label_image/label_image.cc @@ -107,7 +107,7 @@ void RunInference(Settings* s) { tflite::ops::builtin::BuiltinOpResolver resolver; - tflite::InterpreterBuilder (*model, resolver)(&interpreter); + tflite::InterpreterBuilder(*model, resolver)(&interpreter); if (!interpreter) { LOG(FATAL) << "Failed to construct interpreter\n"; exit(-1); From 12129fcd000952acc909af3eb98d3b12483704b0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Apr 2018 20:11:00 -0700 Subject: [PATCH 0042/1691] Disable gather_test under ASAN since it times out. PiperOrigin-RevId: 194338928 --- tensorflow/compiler/tests/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index 0c720932568b0c..991e65c8f528ce 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -755,6 +755,7 @@ tf_xla_py_test( name = "gather_test", size = "medium", srcs = ["gather_test.py"], + tags = ["noasan"], # times out, http://b/78599043 deps = [ ":xla_test", "//tensorflow/python:array_ops", From 98d03869f73915e81216fbf7b28c3d99c847d59f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Apr 2018 20:49:52 -0700 Subject: [PATCH 0043/1691] Added metadata to the TFLite model. PiperOrigin-RevId: 194341479 --- tensorflow/contrib/lite/schema/schema.fbs | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index a65c2e0c70dcf9..20d68ceff7bcbd 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -485,6 +485,8 @@ table Model { // their buffer. buffers:[Buffer]; + // Metadata about the model. Indirects into the existings buffers list. + metadata_buffer:[int]; } root_type Model; From 0ecfb35d5df7d7b6927e906da384e7e076171549 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 00:02:19 -0700 Subject: [PATCH 0044/1691] [XLA] Redesign: migrate other xla/tests to use the new buidler. - The set_return_value_test is not migrated because XlaBuilder does not support SetReturnValue. - Delete a compute_constant_test case since ComputeConstant no longer accepts parameters. - Delete CompilationCacheTest.MutatedComputation since the case no longer exists. - Correct WhileTest.WhileWithMixedTupleElements which used an op from one builder in another builder. - Disabled all CompilationCacheTest since there is no caching in the new design right now. PiperOrigin-RevId: 194354250 --- tensorflow/compiler/xla/tests/BUILD | 3 + tensorflow/compiler/xla/tests/call_test.cc | 45 +-- .../xla/tests/check_execution_arity_test.cc | 6 +- tensorflow/compiler/xla/tests/client_test.cc | 5 +- .../xla/tests/compilation_cache_test.cc | 71 ++--- .../xla/tests/compute_constant_test.cc | 33 --- .../compiler/xla/tests/constants_test.cc | 30 +- tensorflow/compiler/xla/tests/convert_test.cc | 60 ++-- tensorflow/compiler/xla/tests/copy_test.cc | 3 +- .../compiler/xla/tests/deallocation_test.cc | 20 +- .../xla/tests/deconstruct_tuple_test.cc | 22 +- .../compiler/xla/tests/deep_graph_test.cc | 9 +- .../compiler/xla/tests/dot_operation_test.cc | 51 ++-- .../compiler/xla/tests/dynamic_ops_test.cc | 4 +- .../xla/tests/execution_profile_test.cc | 7 +- .../exhaustive_f32_elementwise_op_test.cc | 15 +- .../compiler/xla/tests/floor_ceil_test.cc | 6 +- tensorflow/compiler/xla/tests/fmax_test.cc | 4 +- tensorflow/compiler/xla/tests/fusion_test.cc | 24 +- tensorflow/compiler/xla/tests/half_test.cc | 96 +++--- tensorflow/compiler/xla/tests/log_test.cc | 6 +- .../xla/tests/scalar_computations_test.cc | 276 +++++++++--------- tensorflow/compiler/xla/tests/select_test.cc | 36 +-- .../compiler/xla/tests/transpose_test.cc | 26 +- tensorflow/compiler/xla/tests/tuple_test.cc | 7 +- .../compiler/xla/tests/unary_op_test.cc | 30 +- tensorflow/compiler/xla/tests/while_test.cc | 35 ++- .../xla/tests/xla_hlo_profile_test.cc | 17 +- 28 files changed, 439 insertions(+), 508 deletions(-) diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index c28d14ba8ac3a0..840292010d50fd 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -654,6 +654,7 @@ xla_test( deps = [ ":client_library_test_base", ":literal_test_util", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", ], @@ -1201,6 +1202,7 @@ xla_test( "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1979,6 +1981,7 @@ xla_test( name = "deep_graph_test", srcs = ["deep_graph_test.cc"], deps = [ + "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], diff --git a/tensorflow/compiler/xla/tests/call_test.cc b/tensorflow/compiler/xla/tests/call_test.cc index 5e42365ae38dcc..a43ca3d5ca2ba3 100644 --- a/tensorflow/compiler/xla/tests/call_test.cc +++ b/tensorflow/compiler/xla/tests/call_test.cc @@ -17,7 +17,8 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test_helpers.h" @@ -32,16 +33,16 @@ namespace { class CallOpTest : public ClientLibraryTestBase { protected: - Computation CreateR0F32IdentityComputation() { - ComputationBuilder builder(client_, "Identity"); + XlaComputation CreateR0F32IdentityComputation() { + XlaBuilder builder("Identity"); builder.Parameter(0, r0f32_, "x"); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); } - Computation CreateR1S0F32AdditionComputation() { - ComputationBuilder builder(client_, "Addition"); + XlaComputation CreateR1S0F32AdditionComputation() { + XlaBuilder builder("Addition"); auto x = builder.Parameter(0, r1s0f32_, "x"); auto y = builder.Parameter(1, r1s0f32_, "y"); builder.Add(x, y); @@ -50,8 +51,8 @@ class CallOpTest : public ClientLibraryTestBase { return build_status.ConsumeValueOrDie(); } - Computation CreateR1S2F32AdditionComputation() { - ComputationBuilder builder(client_, "Addition"); + XlaComputation CreateR1S2F32AdditionComputation() { + XlaBuilder builder("Addition"); auto x = builder.Parameter(0, r1s2f32_, "x"); auto y = builder.Parameter(1, r1s2f32_, "y"); builder.Add(x, y); @@ -60,8 +61,8 @@ class CallOpTest : public ClientLibraryTestBase { return build_status.ConsumeValueOrDie(); } - Computation CreateR0F32TupleComputation() { - ComputationBuilder builder(client_, "Tuple"); + XlaComputation CreateR0F32TupleComputation() { + XlaBuilder builder("Tuple"); builder.Tuple({builder.Parameter(0, r0f32_, "x")}); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); @@ -74,8 +75,8 @@ class CallOpTest : public ClientLibraryTestBase { }; XLA_TEST_F(CallOpTest, CallR0F32IdentityScalar) { - ComputationBuilder builder(client_, TestName()); - Computation callee = CreateR0F32IdentityComputation(); + XlaBuilder builder(TestName()); + XlaComputation callee = CreateR0F32IdentityComputation(); auto constant = builder.ConstantLiteral(*Literal::CreateR0(42.0)); builder.Call(callee, {constant}); @@ -83,8 +84,8 @@ XLA_TEST_F(CallOpTest, CallR0F32IdentityScalar) { } XLA_TEST_F(CallOpTest, CallR1S0F32AddArray) { - ComputationBuilder builder(client_, TestName()); - Computation callee = CreateR1S0F32AdditionComputation(); + XlaBuilder builder(TestName()); + XlaComputation callee = CreateR1S0F32AdditionComputation(); auto x = builder.ConstantLiteral(*Literal::CreateR1({})); auto y = builder.ConstantLiteral(*Literal::CreateR1({})); builder.Call(callee, {x, y}); @@ -93,8 +94,8 @@ XLA_TEST_F(CallOpTest, CallR1S0F32AddArray) { } XLA_TEST_F(CallOpTest, CallR1S2F32AddArray) { - ComputationBuilder builder(client_, TestName()); - Computation callee = CreateR1S2F32AdditionComputation(); + XlaBuilder builder(TestName()); + XlaComputation callee = CreateR1S2F32AdditionComputation(); auto x = builder.ConstantLiteral(*Literal::CreateR1({1.0f, 2.0f})); auto y = builder.ConstantLiteral(*Literal::CreateR1({2.0f, 3.0f})); builder.Call(callee, {x, y}); @@ -103,23 +104,23 @@ XLA_TEST_F(CallOpTest, CallR1S2F32AddArray) { } XLA_TEST_F(CallOpTest, CallTreeTwoDeepBranchFactorThree) { - ComputationBuilder builder(client_, "inner"); + XlaBuilder builder("inner"); { auto x = builder.Parameter(0, r0f32_, "x"); builder.Add(x, builder.ConstantR0(1.0)); } - TF_ASSERT_OK_AND_ASSIGN(Computation inner, builder.Build()); + TF_ASSERT_OK_AND_ASSIGN(XlaComputation inner, builder.Build()); - ComputationBuilder builder2(client_, "outer"); + XlaBuilder builder2("outer"); { auto x = builder2.Parameter(0, r0f32_, "x"); x = builder2.Call(inner, {x}); x = builder2.Call(inner, {x}); x = builder2.Call(inner, {x}); } - TF_ASSERT_OK_AND_ASSIGN(Computation outer, builder2.Build()); + TF_ASSERT_OK_AND_ASSIGN(XlaComputation outer, builder2.Build()); - ComputationBuilder builder3(client_, "outermost"); + XlaBuilder builder3("outermost"); { auto x = builder3.Parameter(0, r0f32_, "x"); x = builder3.Call(outer, {x}); @@ -134,8 +135,8 @@ XLA_TEST_F(CallOpTest, CallTreeTwoDeepBranchFactorThree) { } XLA_TEST_F(CallOpTest, CallR0F32Tuple) { - ComputationBuilder builder(client_, TestName()); - Computation callee = CreateR0F32TupleComputation(); + XlaBuilder builder(TestName()); + XlaComputation callee = CreateR0F32TupleComputation(); auto elem = Literal::CreateR0(42.0); auto tuple = Literal::MakeTuple({elem.get()}); builder.Call(callee, {builder.ConstantLiteral(*elem)}); diff --git a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc index f594cc10ac6496..660ff0cad56662 100644 --- a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc +++ b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc @@ -15,9 +15,9 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" @@ -35,7 +35,7 @@ using ::testing::ContainsRegex; class CheckExecutionArityTest : public ClientLibraryTestBase {}; TEST_F(CheckExecutionArityTest, TwoParamComputationNumArguments) { - ComputationBuilder builder(client_, "add_two_params"); + XlaBuilder builder("add_two_params"); auto param_literal = Literal::CreateR1({1.1f, 2.2f}); auto p0 = builder.Parameter(0, param_literal->shape(), "param0"); @@ -75,7 +75,7 @@ TEST_F(CheckExecutionArityTest, TwoParamComputationNumArguments) { } XLA_TEST_F(CheckExecutionArityTest, CheckArgumentShapes) { - ComputationBuilder builder(client_, "add_two_params"); + XlaBuilder builder("add_two_params"); auto p0 = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "param0"); auto p1 = builder.Parameter(1, ShapeUtil::MakeShape(F32, {4}), "param1"); diff --git a/tensorflow/compiler/xla/tests/client_test.cc b/tensorflow/compiler/xla/tests/client_test.cc index 1e544717967731..0b425b93bb144e 100644 --- a/tensorflow/compiler/xla/tests/client_test.cc +++ b/tensorflow/compiler/xla/tests/client_test.cc @@ -16,7 +16,6 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" @@ -39,7 +38,7 @@ namespace { class ClientTest : public ClientLibraryTestBase {}; XLA_TEST_F(ClientTest, ExecuteWithLayout) { - ComputationBuilder b(client_, TestName()); + XlaBuilder b(TestName()); std::vector> layouts = {{0, 1}, {1, 0}}; for (const std::vector& execute_layout : layouts) { @@ -71,7 +70,7 @@ XLA_TEST_F(ClientTest, ExecuteWithLayout) { } XLA_TEST_F(ClientTest, ExecuteWithTupleLayout) { - ComputationBuilder b(client_, TestName()); + XlaBuilder b(TestName()); b.Tuple({b.ConstantR2({{1, 2}, {3, 4}}), b.ConstantR2({{10, 20}, {30, 40}})}); diff --git a/tensorflow/compiler/xla/tests/compilation_cache_test.cc b/tensorflow/compiler/xla/tests/compilation_cache_test.cc index 0f780fa87ef98f..ecce599a8a3bd5 100644 --- a/tensorflow/compiler/xla/tests/compilation_cache_test.cc +++ b/tensorflow/compiler/xla/tests/compilation_cache_test.cc @@ -18,9 +18,10 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" @@ -39,7 +40,7 @@ namespace { class CompilationCacheTest : public ClientLibraryTestBase { public: void ExecuteComputationR0F32( - const Computation& computation, + const XlaComputation& computation, tensorflow::gtl::ArraySlice arguments, float expected_result, bool expect_cache_hit) { ExecutionProfile execution_profile; @@ -55,7 +56,7 @@ class CompilationCacheTest : public ClientLibraryTestBase { } void ExecuteComputationR2F32( - const Computation& computation, + const XlaComputation& computation, tensorflow::gtl::ArraySlice arguments, std::initializer_list> expected_result, bool expect_cache_hit) { @@ -74,17 +75,20 @@ class CompilationCacheTest : public ClientLibraryTestBase { ErrorSpec error_spec_{0.0001}; }; -XLA_TEST_F(CompilationCacheTest, ComputationCalledMultipleTimes) { - ComputationBuilder builder(client_, TestName()); +// TODO(b/74197823): Disabled because there is no cache in the new design. +XLA_TEST_F(CompilationCacheTest, DISABLED_ComputationCalledMultipleTimes) { + XlaBuilder builder(TestName()); builder.Neg(builder.ConstantR0(42.0)); - Computation computation = builder.Build().ConsumeValueOrDie(); + XlaComputation computation = builder.Build().ConsumeValueOrDie(); ExecuteComputationR0F32(computation, {}, -42.0, /*expect_cache_hit=*/false); ExecuteComputationR0F32(computation, {}, -42.0, /*expect_cache_hit=*/true); ExecuteComputationR0F32(computation, {}, -42.0, /*expect_cache_hit=*/true); } -XLA_TEST_F(CompilationCacheTest, ComputationCalledWithDifferentParameters) { +// TODO(b/74197823): Disabled because there is no cache in the new design. +XLA_TEST_F(CompilationCacheTest, + DISABLED_ComputationCalledWithDifferentParameters) { std::unique_ptr data_42 = client_->TransferToServer(*Literal::CreateR0(42.0f)) .ConsumeValueOrDie(); @@ -95,9 +99,9 @@ XLA_TEST_F(CompilationCacheTest, ComputationCalledWithDifferentParameters) { client_->TransferToServer(*Literal::CreateR0(456.0f)) .ConsumeValueOrDie(); - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Neg(builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "param")); - Computation computation = builder.Build().ConsumeValueOrDie(); + XlaComputation computation = builder.Build().ConsumeValueOrDie(); ExecuteComputationR0F32(computation, {data_42.get()}, -42.0, /*expect_cache_hit=*/false); @@ -109,19 +113,20 @@ XLA_TEST_F(CompilationCacheTest, ComputationCalledWithDifferentParameters) { /*expect_cache_hit=*/true); } -XLA_TEST_F(CompilationCacheTest, MultipleComputations) { - ComputationBuilder builder_neg(client_, TestName() + "_neg"); +// TODO(b/74197823): Disabled because there is no cache in the new design. +XLA_TEST_F(CompilationCacheTest, DISABLED_MultipleComputations) { + XlaBuilder builder_neg(TestName() + "_neg"); builder_neg.Neg(builder_neg.ConstantR0(42.0)); - Computation computation_neg = builder_neg.Build().ConsumeValueOrDie(); + XlaComputation computation_neg = builder_neg.Build().ConsumeValueOrDie(); - ComputationBuilder builder_exp(client_, TestName() + "_exp"); + XlaBuilder builder_exp(TestName() + "_exp"); builder_exp.Exp(builder_exp.ConstantR0(1.0)); - Computation computation_exp = builder_exp.Build().ConsumeValueOrDie(); + XlaComputation computation_exp = builder_exp.Build().ConsumeValueOrDie(); - ComputationBuilder builder_add(client_, TestName() + "_add"); + XlaBuilder builder_add(TestName() + "_add"); builder_add.Add(builder_add.ConstantR0(2.0), builder_add.ConstantR0(3.0)); - Computation computation_add = builder_add.Build().ConsumeValueOrDie(); + XlaComputation computation_add = builder_add.Build().ConsumeValueOrDie(); ExecuteComputationR0F32(computation_neg, {}, -42.0, /*expect_cache_hit=*/false); @@ -133,7 +138,8 @@ XLA_TEST_F(CompilationCacheTest, MultipleComputations) { /*expect_cache_hit=*/true); } -XLA_TEST_F(CompilationCacheTest, DifferentParameterLayouts) { +// TODO(b/74197823): Disabled because there is no cache in the new design. +XLA_TEST_F(CompilationCacheTest, DISABLED_DifferentParameterLayouts) { // Create two GlobalData arrays with the same shape but different // layouts. Use these arrays as parameters to a simple computation. If the // layout of the array changes then computation should be recompiled (cache @@ -148,9 +154,9 @@ XLA_TEST_F(CompilationCacheTest, DifferentParameterLayouts) { auto colmaj_handle = client_->TransferToServer(*colmaj_array).ConsumeValueOrDie(); - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2}), "param0"); - Computation computation = builder.Build().ConsumeValueOrDie(); + XlaComputation computation = builder.Build().ConsumeValueOrDie(); ExecuteComputationR2F32(computation, {colmaj_handle.get()}, {{1.0f, 2.0f}, {3.0f, 4.0f}}, @@ -169,32 +175,5 @@ XLA_TEST_F(CompilationCacheTest, DifferentParameterLayouts) { /*expect_cache_hit=*/true); } -XLA_TEST_F(CompilationCacheTest, MutatedComputation) { - // Build a computation, execute it, then mutate it. The mutated computation - // should not be in the cache until it is run once. This must be done through - // the stub interface because Computations built from ComputationBuilder are - // immutable. - ComputationBuilder builder(client_, TestName()); - auto neg = builder.Neg(builder.ConstantR0(42.0)); - Computation computation = builder.Build().ConsumeValueOrDie(); - - ExecuteComputationR0F32(computation, {}, -42.0, /*expect_cache_hit=*/false); - ExecuteComputationR0F32(computation, {}, -42.0, /*expect_cache_hit=*/true); - - BinaryOpRequest request; - request.set_binop(BINOP_ADD); - *request.mutable_lhs() = neg; - *request.mutable_rhs() = neg; - OpRequest op_request; - *op_request.mutable_computation() = computation.handle(); - *op_request.mutable_binary_op_request() = request; - OpResponse response; - tensorflow::Status s = client_->stub()->Op(&op_request, &response); - ASSERT_TRUE(s.ok()); - - ExecuteComputationR0F32(computation, {}, -84.0, /*expect_cache_hit=*/false); - ExecuteComputationR0F32(computation, {}, -84.0, /*expect_cache_hit=*/true); -} - } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/compute_constant_test.cc b/tensorflow/compiler/xla/tests/compute_constant_test.cc index 7ea82a791f72ea..bf4b8fb0bcf229 100644 --- a/tensorflow/compiler/xla/tests/compute_constant_test.cc +++ b/tensorflow/compiler/xla/tests/compute_constant_test.cc @@ -18,8 +18,6 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" @@ -88,17 +86,6 @@ class ComputeConstantTest : public ::testing::Test { return literal->Get({}); } - template - StatusOr ComputeConstantScalar( - Client* client, const ComputationDataHandle& operand, - ComputationBuilder* builder, - tensorflow::gtl::ArraySlice parameters = {}) { - TF_ASSIGN_OR_RETURN(auto literal, - builder->ComputeConstant( - operand, /*output_layout=*/nullptr, parameters)); - return literal->Get({}); - } - bool IsConstant(const XlaOp& operand, XlaBuilder* builder) { StatusOr result = builder->IsConstant(operand); EXPECT_TRUE(result.ok()) << result.status(); @@ -150,26 +137,6 @@ TEST_F(ComputeConstantTest, ScalarRng) { } } -TEST_F(ComputeConstantTest, Param) { - for (ClientType client_type : client_types) { - Client* client = ClientOrDie(platform_, client_type); - ComputationBuilder b(client, TestName()); - auto param = b.Parameter(0, ShapeUtil::MakeShape(F32, {}), "lhs"); - auto computation = b.Add(param, b.ConstantR0(1.5f)); - - std::vector arguments; - arguments.push_back(std::move(*Literal::CreateR0(42.5f))); - TF_ASSERT_OK_AND_ASSIGN(bool is_constant, - b.IsConstant(computation, arguments.size())); - EXPECT_TRUE(is_constant); - - TF_ASSERT_OK_AND_ASSIGN( - auto value, - ComputeConstantScalar(client, computation, &b, arguments)); - EXPECT_EQ(value, 44.0f); - } -} - TEST_F(ComputeConstantTest, DirectParamMissing) { for (ClientType client_type : client_types) { Client* client = ClientOrDie(platform_, client_type); diff --git a/tensorflow/compiler/xla/tests/constants_test.cc b/tensorflow/compiler/xla/tests/constants_test.cc index 35aa3f6d696297..4743673561a665 100644 --- a/tensorflow/compiler/xla/tests/constants_test.cc +++ b/tensorflow/compiler/xla/tests/constants_test.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" @@ -39,7 +40,7 @@ class ConstantsTest : public ClientLibraryTestBase { }; TEST_F(ConstantsTest, ZeroCellF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR1({}); ComputeAndCompareR1(&builder, {}, {}, error_spec_); @@ -48,7 +49,7 @@ TEST_F(ConstantsTest, ZeroCellF32) { TEST_F(ConstantsTest, OneCellF32) { std::vector constant = {2.0}; - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR1(constant); ComputeAndCompareR1(&builder, constant, {}, error_spec_); @@ -57,7 +58,7 @@ TEST_F(ConstantsTest, OneCellF32) { TEST_F(ConstantsTest, OneCellS32) { std::vector constant = {2}; - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR1(constant); ComputeAndCompareR1(&builder, constant, {}); @@ -66,7 +67,7 @@ TEST_F(ConstantsTest, OneCellS32) { TEST_F(ConstantsTest, OneCellU32) { std::vector constant = {2}; - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR1(constant); ComputeAndCompareR1(&builder, constant, {}); @@ -75,7 +76,7 @@ TEST_F(ConstantsTest, OneCellU32) { TEST_F(ConstantsTest, EightCells) { std::vector constant = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}; - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR1(constant); ComputeAndCompareR1(&builder, constant, {}, error_spec_); @@ -85,14 +86,14 @@ TEST_F(ConstantsTest, SixteenCells) { std::vector constant = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0}; - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR1(constant); ComputeAndCompareR1(&builder, constant, {}, error_spec_); } TEST_F(ConstantsTest, Empty_0x2) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR2FromArray2D(Array2D(0, 2)); ComputeAndCompareR2(&builder, Array2D(0, 2), {}, error_spec_); @@ -102,14 +103,14 @@ TEST_F(ConstantsTest, Small_2x2) { std::unique_ptr> constant = MakeLinspaceArray2D(100.0, 200.0, 2, 2); - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR2FromArray2D(*constant); ComputeAndCompareR2(&builder, *constant, {}, error_spec_); } TEST_F(ConstantsTest, Empty_3x0x2) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto constant = builder.ConstantLiteral( *Literal::CreateR3FromArray3D(Array3D(3, 0, 2))); @@ -117,7 +118,7 @@ TEST_F(ConstantsTest, Empty_3x0x2) { } TEST_F(ConstantsTest, Small_2x2x2) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); Array3D array3d({ // x0 x1 {{1.f, 2.f}, // y0 @@ -145,13 +146,13 @@ TEST_F(ConstantsTest, Small_3x2x1x1) { Literal::CreateR4FromArray4D(input_array); { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantLiteral(*input_literal); ComputeAndCompareR4(&builder, input_array, {}, error_spec_); } { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR4FromArray4D(input_array); ComputeAndCompareR4(&builder, input_array, {}, error_spec_); } @@ -159,12 +160,13 @@ TEST_F(ConstantsTest, Small_3x2x1x1) { // TODO(b/29263943): Support tuple constants. TEST_F(ConstantsTest, DISABLED_TupleConstant) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantLiteral( *Literal::MakeTuple({Literal::CreateR2({{1.0}, {2.0}}).get(), Literal::CreateR1({2.0, 42}).get()})); - std::unique_ptr result = ExecuteAndTransferOrDie(&builder, {}); + std::unique_ptr result = + ExecuteAndTransfer(&builder, {}).ConsumeValueOrDie(); LiteralTestUtil::ExpectR2Near( {{1.0}, {2.0}}, LiteralView::Create(*result, {0}), error_spec_); diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc index e67a30d76c2fac..4ef0a77884c90b 100644 --- a/tensorflow/compiler/xla/tests/convert_test.cc +++ b/tensorflow/compiler/xla/tests/convert_test.cc @@ -18,8 +18,8 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" @@ -44,7 +44,7 @@ class ConvertTest : public ClientLibraryTestBase { }; TEST_F(ConvertTest, ConvertR1S32ToR1S32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1({42, 64}); builder.ConvertElementType(a, S32); @@ -53,7 +53,7 @@ TEST_F(ConvertTest, ConvertR1S32ToR1S32) { } TEST_F(ConvertTest, ConvertR1F32ToR1F32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1({42.0f, 64.0f}); builder.ConvertElementType(a, F32); @@ -62,7 +62,7 @@ TEST_F(ConvertTest, ConvertR1F32ToR1F32) { } TEST_F(ConvertTest, ConvertR1S32ToR1F32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1({42, 64}); builder.ConvertElementType(a, F32); @@ -71,7 +71,7 @@ TEST_F(ConvertTest, ConvertR1S32ToR1F32) { } TEST_F(ConvertTest, ConvertR1PREDToR1S32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1({true, false, true}); builder.ConvertElementType(a, S32); @@ -80,7 +80,7 @@ TEST_F(ConvertTest, ConvertR1PREDToR1S32) { } TEST_F(ConvertTest, ConvertR1PREDToR1F32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1({true, false, true}); builder.ConvertElementType(a, F32); @@ -89,7 +89,7 @@ TEST_F(ConvertTest, ConvertR1PREDToR1F32) { } XLA_TEST_F(ConvertTest, ConvertR1S0S32ToR1S0F32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1({}); builder.ConvertElementType(a, F32); @@ -98,7 +98,7 @@ XLA_TEST_F(ConvertTest, ConvertR1S0S32ToR1S0F32) { } TEST_F(ConvertTest, ConvertR1F32ToR1S32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1({42.6, 64.4}); builder.ConvertElementType(a, S32); @@ -107,7 +107,7 @@ TEST_F(ConvertTest, ConvertR1F32ToR1S32) { } XLA_TEST_F(ConvertTest, ConvertR1S64ToR1F32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::vector arg{ -9223371216516022272, -2, @@ -160,7 +160,7 @@ XLA_TEST_F(ConvertTest, ConvertR1S64ToR1F32) { } XLA_TEST_F(ConvertTest, ConvertR1U32ToR1F32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::vector arg{0, 1, 0x1000, 0x7fffffff, 0x80000000, 0x80000001, 0x80000002, 0x80000003, 0x80000080, 0x80000081, 0x80000082, 0xFFFFFFFF}; @@ -179,7 +179,7 @@ XLA_TEST_F(ConvertTest, ConvertR1U32ToR1F32) { } XLA_TEST_F(ConvertTest, ConvertR1F32ToR1U32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::vector arg{0.0f, 1.0f, 16777216.0f, 16777218.0f, 2147483647.0f, 4294967040.0f}; std::unique_ptr arg_literal = Literal::CreateR1({arg}); @@ -197,7 +197,7 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1U32) { } XLA_TEST_F(ConvertTest, ConvertR1U32ToR1S64) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::vector arg{0, 1, 0x1000, 0x7fffffff, 0x80000082, 0xFFFFFFFF}; std::unique_ptr arg_literal = Literal::CreateR1({arg}); auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param"); @@ -214,7 +214,7 @@ XLA_TEST_F(ConvertTest, ConvertR1U32ToR1S64) { } XLA_TEST_F(ConvertTest, ConvertR1S32ToR1S64) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::vector arg{0, 1, 0x1000, -1, -0x1000}; std::unique_ptr arg_literal = Literal::CreateR1({arg}); auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param"); @@ -231,7 +231,7 @@ XLA_TEST_F(ConvertTest, ConvertR1S32ToR1S64) { } XLA_TEST_F(ConvertTest, ConvertR1F32ToR1S64) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); // Test cases from compiler_rt library. std::vector arg{0.0f, 0.5f, @@ -268,7 +268,7 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1S64) { } XLA_TEST_F(ConvertTest, ConvertR1U8ToR1F32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1({32, 64}); builder.ConvertElementType(a, F32); @@ -277,7 +277,7 @@ XLA_TEST_F(ConvertTest, ConvertR1U8ToR1F32) { } XLA_TEST_F(ConvertTest, ConvertR1U8ToR1S32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1({32, 64}); builder.ConvertElementType(a, S32); @@ -286,7 +286,7 @@ XLA_TEST_F(ConvertTest, ConvertR1U8ToR1S32) { } XLA_TEST_F(ConvertTest, ConvertR1U8ToR1U32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1({32, 64}); builder.ConvertElementType(a, U32); @@ -295,7 +295,7 @@ XLA_TEST_F(ConvertTest, ConvertR1U8ToR1U32) { } XLA_TEST_F(ConvertTest, ConvertR1F32ToR1F64) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1({32.0f, 64.0f}); builder.ConvertElementType(a, F64); @@ -304,7 +304,7 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1F64) { } XLA_TEST_F(ConvertTest, ConvertR1F64ToR1F32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1({32.0, 64.0}); builder.ConvertElementType(a, F32); @@ -313,7 +313,7 @@ XLA_TEST_F(ConvertTest, ConvertR1F64ToR1F32) { } TEST_F(ConvertTest, ConvertS32Extremes) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.ConstantR1( {std::numeric_limits::min(), std::numeric_limits::max()}); builder.ConvertElementType(a, F32); @@ -325,7 +325,7 @@ TEST_F(ConvertTest, ConvertS32Extremes) { } TEST_F(ConvertTest, ConvertMapToS32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto b = builder.CreateSubBuilder("convert"); auto param = b->Parameter(0, ShapeUtil::MakeShape(F32, {}), "in"); b->ConvertElementType(param, S32); @@ -337,7 +337,7 @@ TEST_F(ConvertTest, ConvertMapToS32) { } TEST_F(ConvertTest, ConvertMapToF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto b = builder.CreateSubBuilder("convert"); auto param = b->Parameter(0, ShapeUtil::MakeShape(S32, {}), "in"); b->ConvertElementType(param, F32); @@ -354,7 +354,7 @@ TEST_F(ConvertTest, ConvertMapToF32) { // input -> convert -> reshape // the new convert should have the same element type as the old convert. TEST_F(ConvertTest, ConvertReshape) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto input = builder.ConstantR1({42}); auto reshape = builder.Reshape(input, /*dimensions=*/{0}, /*new_sizes=*/{}); builder.ConvertElementType(reshape, F32); @@ -393,7 +393,7 @@ XLA_TEST_F(ConvertTest, ConvertR1F16ToR1F32) { std::unique_ptr dot_lhs_handle, client_->TransferToServer(*Literal::CreateR1(input))); - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConvertElementType( builder.Parameter( 0, ShapeUtil::MakeShape(F16, {static_cast(input.size())}), @@ -413,7 +413,7 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1F16) { std::unique_ptr dot_lhs_handle, client_->TransferToServer(*Literal::CreateR1(input))); - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConvertElementType( builder.Parameter( 0, ShapeUtil::MakeShape(F32, {static_cast(input.size())}), @@ -424,28 +424,28 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1F16) { } XLA_TEST_F(ConvertTest, ConvertC64ToC64) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::vector x = {{42.0f, 64.0f}}; builder.ConvertElementType(builder.ConstantR1(x), C64); ComputeAndCompareR1(&builder, x, {}, ErrorSpec(0.0001)); } XLA_TEST_F(ConvertTest, ConvertS64S64) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::vector x = {{-42, 64}}; builder.ConvertElementType(builder.ConstantR1(x), S64); ComputeAndCompareR1(&builder, x, {}); } XLA_TEST_F(ConvertTest, ConvertU64U64) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::vector x = {{42, 64}}; builder.ConvertElementType(builder.ConstantR1(x), U64); ComputeAndCompareR1(&builder, x, {}); } XLA_TEST_F(ConvertTest, ConvertU64S64) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::vector unsigned_x = {{42, UINT64_MAX}}; builder.ConvertElementType(builder.ConstantR1(unsigned_x), S64); std::vector signed_x = {{42, -1}}; @@ -453,7 +453,7 @@ XLA_TEST_F(ConvertTest, ConvertU64S64) { } XLA_TEST_F(ConvertTest, ConvertS64U64) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::vector signed_x = {{42, -1, INT64_MIN}}; builder.ConvertElementType(builder.ConstantR1(signed_x), U64); std::vector unsigned_x = { diff --git a/tensorflow/compiler/xla/tests/copy_test.cc b/tensorflow/compiler/xla/tests/copy_test.cc index ece7c3b05e7faf..155fbacf58d81c 100644 --- a/tensorflow/compiler/xla/tests/copy_test.cc +++ b/tensorflow/compiler/xla/tests/copy_test.cc @@ -17,6 +17,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/array2d.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" @@ -246,7 +247,7 @@ XLA_TEST_F(CopyOpClientTest, Copy0x0) { Shape out_shape = ShapeUtil::MakeShapeWithLayout(F32, {0, 0}, {1, 0}); auto empty = Literal::CreateFromShape(in_shape); - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto param0 = builder.Parameter(0, in_shape, "input"); auto input_data = client_->TransferToServer(*empty).ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/tests/deallocation_test.cc b/tensorflow/compiler/xla/tests/deallocation_test.cc index fe5621e8dc209d..c76e5aabf4b8a3 100644 --- a/tensorflow/compiler/xla/tests/deallocation_test.cc +++ b/tensorflow/compiler/xla/tests/deallocation_test.cc @@ -16,9 +16,10 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" @@ -36,9 +37,8 @@ class DeallocationTest : public ClientLibraryTestBase { // Build and execute the given computation then verify the results can be // transferred from the device successfully. std::unique_ptr ExecuteAndCheckTransfer( - ComputationBuilder* builder, - tensorflow::gtl::ArraySlice arguments) { - Computation computation = builder->Build().ConsumeValueOrDie(); + XlaBuilder* builder, tensorflow::gtl::ArraySlice arguments) { + XlaComputation computation = builder->Build().ConsumeValueOrDie(); auto global_data = client_->Execute(computation, arguments, &execution_options_) .ConsumeValueOrDie(); @@ -48,7 +48,7 @@ class DeallocationTest : public ClientLibraryTestBase { }; TEST_F(DeallocationTest, DeallocateScalar) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR0(42.0); auto global_data = ExecuteAndCheckTransfer(&builder, {}); @@ -66,7 +66,7 @@ TEST_F(DeallocationTest, DeallocateScalar) { } TEST_F(DeallocationTest, DeallocateVector) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); @@ -79,7 +79,7 @@ TEST_F(DeallocationTest, DeallocateVector) { } TEST_F(DeallocationTest, DeallocateEmptyVector) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR1({}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); @@ -92,7 +92,7 @@ TEST_F(DeallocationTest, DeallocateEmptyVector) { } XLA_TEST_F(DeallocationTest, DeallocateTuple) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Tuple({builder.ConstantR0(42.0), builder.ConstantR1({1.0, 2.0, 3.0})}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); @@ -106,7 +106,7 @@ XLA_TEST_F(DeallocationTest, DeallocateTuple) { } XLA_TEST_F(DeallocationTest, DeallocateTupleWithRepeatedElements) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto element = builder.ConstantR0(42.0); auto inner_tuple = builder.Tuple({builder.ConstantR0(42.0), element}); builder.Tuple({element, inner_tuple, element}); @@ -121,7 +121,7 @@ XLA_TEST_F(DeallocationTest, DeallocateTupleWithRepeatedElements) { } XLA_TEST_F(DeallocationTest, DeallocateNestedTuple) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto inner_tuple = builder.Tuple({builder.ConstantR0(42.0), builder.ConstantR1({1.0, 2.0, 3.0})}); diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc index 3ab0ea4ad48c00..d0ada247483039 100644 --- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc +++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc @@ -17,9 +17,10 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" @@ -42,9 +43,8 @@ class DeconstructTupleTest : public ClientLibraryTestBase { // Build and execute the given computation then verify the results can be // transferred from the device successfully. std::unique_ptr ExecuteAndCheckTransfer( - ComputationBuilder* builder, - tensorflow::gtl::ArraySlice arguments) { - Computation computation = builder->Build().ConsumeValueOrDie(); + XlaBuilder* builder, tensorflow::gtl::ArraySlice arguments) { + XlaComputation computation = builder->Build().ConsumeValueOrDie(); auto global_data = client_->Execute(computation, arguments, &execution_options_) .ConsumeValueOrDie(); @@ -54,7 +54,7 @@ class DeconstructTupleTest : public ClientLibraryTestBase { }; TEST_F(DeconstructTupleTest, DeconstructTuple) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto const1 = builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); auto const2 = builder.ConstantR1({2.0, 4.0, 6.0, 8.0}); builder.Tuple({const1, const2}); @@ -73,7 +73,7 @@ TEST_F(DeconstructTupleTest, DeconstructTuple) { } TEST_F(DeconstructTupleTest, DeconstructTupleTwice) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto const1 = builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); auto const2 = builder.ConstantR1({2.0, 4.0, 6.0, 8.0}); builder.Tuple({const1, const2}); @@ -103,7 +103,7 @@ TEST_F(DeconstructTupleTest, DeconstructTupleTwice) { } XLA_TEST_F(DeconstructTupleTest, DeconstructTupleRepeatedElement) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto const1 = builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); auto const2 = builder.ConstantR1({2.0, 4.0, 6.0, 8.0}); builder.Tuple({const1, const2, const2, const1}); @@ -129,7 +129,7 @@ XLA_TEST_F(DeconstructTupleTest, DeconstructTupleRepeatedElement) { } TEST_F(DeconstructTupleTest, DeconstructTupleThenDeallocate) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto const1 = builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); auto const2 = builder.ConstantR1({2.0, 4.0, 6.0, 8.0}); builder.Tuple({const1, const2, const1}); @@ -159,7 +159,7 @@ TEST_F(DeconstructTupleTest, DeconstructTupleThenDeallocate) { } TEST_F(DeconstructTupleTest, DeconstructNonTuple) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); @@ -170,7 +170,7 @@ TEST_F(DeconstructTupleTest, DeconstructNonTuple) { } XLA_TEST_F(DeconstructTupleTest, DeconstructTupleFromParam) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::unique_ptr param0_literal = Literal::CreateR1({3.14f, -100.25f}); std::unique_ptr param0_data = @@ -186,7 +186,7 @@ XLA_TEST_F(DeconstructTupleTest, DeconstructTupleFromParam) { } XLA_TEST_F(DeconstructTupleTest, DeconstructNestedTuple) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto const1 = builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); auto const2 = builder.ConstantR1({2.0, 4.0, 6.0, 8.0}); builder.Tuple({builder.Tuple({const1, const2}), const1}); diff --git a/tensorflow/compiler/xla/tests/deep_graph_test.cc b/tensorflow/compiler/xla/tests/deep_graph_test.cc index 1da7a96fe2388e..085a5105aca1c1 100644 --- a/tensorflow/compiler/xla/tests/deep_graph_test.cc +++ b/tensorflow/compiler/xla/tests/deep_graph_test.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" namespace xla { @@ -22,12 +23,12 @@ TEST_F(ClientLibraryTestBase, DeepGraph) { // intended to track, we need to set kDepth to 20000. // Unfortunately, setting it that high causes the test to time out. const int kDepth = 200; - ComputationBuilder b(client_, TestName()); - ComputationDataHandle x; - ComputationDataHandle y; + XlaBuilder b(TestName()); + XlaOp x; + XlaOp y; auto x_data = CreateR0Parameter(3, 0, "x", &b, &x); auto y_data = CreateR0Parameter(1, 1, "y", &b, &y); - ComputationDataHandle z = x; + XlaOp z = x; for (int i = 0; i < kDepth; ++i) { z = b.Add(z, y); } diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc index c4031dfee593a1..6b3efba4f80e45 100644 --- a/tensorflow/compiler/xla/tests/dot_operation_test.cc +++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc @@ -18,8 +18,8 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array3d.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -51,21 +51,20 @@ using TypesF16F32F64 = ::testing::Types; using TypesF16F32F64CF64 = ::testing::Types; #elif !defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16) && \ - defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT64) && \ + defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT64) && \ defined(XLA_BACKEND_DOES_NOT_SUPPORT_COMPLEX) using TypesF16F32 = ::testing::Types; using TypesF16F32F64 = ::testing::Types; -using TypesF16F32F64CF64 = - ::testing::Types; +using TypesF16F32F64CF64 = ::testing::Types; #else #error "Situation not handled yet" #endif // Check that we can safely pass an input tuple's elements to a dot operation. TEST_F(DotOperationTest, DotOfInputTupleElem) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); - ComputationDataHandle param; + XlaOp param; auto param_data = CreateParameterAndTransferLiteral( 0, *Literal::MakeTuple({Literal::CreateR2({{1, 2}, {3, 4}}).get(), @@ -86,7 +85,7 @@ TYPED_TEST_CASE(DotOperationTest_F16F32F64CF64, TypesF16F32F64CF64); XLA_TYPED_TEST(DotOperationTest_F16F32F64CF64, ZeroElementVectorDot) { using T = TypeParam; - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto lhs = builder.ConstantR1({}); auto rhs = builder.ConstantR1({}); @@ -102,7 +101,7 @@ TYPED_TEST_CASE(DotOperationTest_F16F32F64, TypesF16F32F64); XLA_TYPED_TEST(DotOperationTest_F16F32F64, TrivialMatrixVectorDot) { using T = TypeParam; - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto lhs = builder.ConstantR2FromArray2D({{3.0f, 4.0f}}); auto rhs = builder.ConstantFromArray({3.0f, 4.0f}); auto result = builder.Dot(lhs, rhs); @@ -113,7 +112,7 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, TrivialMatrixVectorDot) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, OneElementVectorDot) { using T = TypeParam; - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto lhs = builder.ConstantR1({static_cast(2.0f)}); auto rhs = builder.ConstantR1({static_cast(3.0f)}); auto result = builder.Dot(lhs, rhs); @@ -124,7 +123,7 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, OneElementVectorDot) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, VectorDot) { using T = TypeParam; - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto lhs = builder.ConstantFromArray({1.0f, 2.5f, 42.0f}); auto rhs = builder.ConstantFromArray({11.0f, -1.0f, 0.5f}); auto result = builder.Dot(lhs, rhs); @@ -139,7 +138,7 @@ std::vector MinorToMajorForIsRowMajor(bool row_major) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x0) { using T = TypeParam; - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); auto rhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); auto result = builder.Dot(lhs, rhs); @@ -150,7 +149,7 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x0) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x3) { using T = TypeParam; - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); auto rhs = builder.ConstantR2FromArray2D( {{7.0f, 8.0f, 9.0f}, {42.0f, 77.0f, 101.0f}}); @@ -162,7 +161,7 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x3) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_3x2_2x0) { using T = TypeParam; - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto lhs = builder.ConstantR2FromArray2D( {{7.0f, 8.0f}, {9.0f, 42.0f}, {77.0f, 101.0f}}); auto rhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); @@ -174,7 +173,7 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_3x2_2x0) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_2x0_0x2) { using T = TypeParam; - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto lhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); auto rhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); auto result = builder.Dot(lhs, rhs); @@ -185,7 +184,7 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_2x0_0x2) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, FusedDot) { using T = TypeParam; - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto param0 = builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 4}), "arg0"); auto param1 = @@ -230,7 +229,7 @@ class SquareMatrixDot : public DotOperationTest { LayoutUtil::MakeLayout( MinorToMajorForIsRowMajor(rhs_row_major)))) .ConsumeValueOrDie(); - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto prim_type = primitive_util::NativeToPrimitiveType(); auto result = builder.Dot( builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), "lhs"), @@ -315,7 +314,7 @@ void ParametricDotTest::TestImpl() { addend_handle = client_->TransferToServer(*addend_lit).ConsumeValueOrDie(); } - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto prim_type = primitive_util::NativeToPrimitiveType(); auto result = builder.Dot( builder.Parameter(0, @@ -491,7 +490,7 @@ class NonsquareMatrixDot : public DotOperationTest { MinorToMajorForIsRowMajor(rhs_row_major)))) .ConsumeValueOrDie(); - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto prim_type = primitive_util::NativeToPrimitiveType(); auto result = builder.Dot( builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 3}), "lhs"), @@ -523,7 +522,7 @@ XLA_TEST_F(DotOperationTest, MatrixVectorC64) { LayoutUtil::MakeLayout({1, 0}))) .ConsumeValueOrDie(); - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto prim_type = primitive_util::NativeToPrimitiveType(); auto result = builder.Dot( builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {1, 4}), "lhs"), @@ -538,7 +537,7 @@ XLA_TEST_F(DotOperationTest, MatrixVectorC64) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, ConcurrentMatMult) { using T = TypeParam; - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto matrix1 = builder.ConstantR2FromArray2D({{1.0f, 2.0f}, {3.0f, 4.0f}}); auto matrix2 = builder.ConstantR2FromArray2D({{5.0f, 6.0f}, {7.0f, 8.0f}}); auto matrix12 = builder.Dot(matrix1, matrix2); @@ -559,7 +558,7 @@ TYPED_TEST_CASE(DotOperationTestForBatchMatMul, TypesF16F32F64); // sync-dependent on bitcasts' operands. XLA_TYPED_TEST(DotOperationTestForBatchMatMul, Types) { using T = TypeParam; - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto x = builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 2, 2, 2}), "x"); auto y = @@ -569,7 +568,7 @@ XLA_TYPED_TEST(DotOperationTestForBatchMatMul, Types) { auto y_flat = builder.Reshape(y, {0, 1, 2, 3}, {4, 2, 2}); // Slice batches into individual matrices and multiply them. - std::vector out_slices; + std::vector out_slices; for (int i = 0; i < 4; ++i) { // Slice off individual matrices and reshape to 2D tensors. auto x_slice = builder.Slice(x_flat, {i, 0, 0}, {i + 1, 2, 2}, {1, 1, 1}); @@ -615,7 +614,7 @@ XLA_TYPED_TEST(DotOperationTestForBatchMatMul, Types) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, GeneralMatMul) { using T = TypeParam; - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto x = builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 2, 2}), "x"); auto y = @@ -677,7 +676,7 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, TransposeFolding) { MinorToMajorForIsRowMajor(row_major)))) .ConsumeValueOrDie(); - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto prim_type = primitive_util::NativeToPrimitiveType(); auto lhs_arg = builder.Parameter( 0, ShapeUtil::MakeShape(prim_type, {lhs->height(), lhs->width()}), @@ -713,7 +712,7 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, new Array2D({{1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}, {6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}})); - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array); auto rhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), "rhs_arg_0"); @@ -761,7 +760,7 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, {4.0f, 3.0f}, {2.0f, 1.0f}})); - ComputationBuilder builder(this->client_, this->TestName()); + XlaBuilder builder(this->TestName()); auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array); auto lhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 2}), "lhs_arg_0"); diff --git a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc index ff53a84588fc04..bfb83faf5222b8 100644 --- a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc +++ b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc @@ -361,9 +361,9 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase { ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); // Initialize and transfer dynamic slice start indices parameter. - ComputationDataHandle starts; + XlaOp starts; std::unique_ptr start_data = CreateR1Parameter( slice_starts, 0, "slice_starts", &builder, &starts); // Build dynamic slice computation. diff --git a/tensorflow/compiler/xla/tests/execution_profile_test.cc b/tensorflow/compiler/xla/tests/execution_profile_test.cc index c8cc8e40aa3210..a6ba6db5d3bf86 100644 --- a/tensorflow/compiler/xla/tests/execution_profile_test.cc +++ b/tensorflow/compiler/xla/tests/execution_profile_test.cc @@ -13,8 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/global_data.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" #include "tensorflow/core/platform/test.h" @@ -32,9 +33,9 @@ XLA_TEST_F(ExecutionProfileTest, ExecuteWithExecutionProfile) { client_->TransferToServer( *Literal::CreateR2F32Linspace(1e0, 1e5, 256, 256))); - ComputationBuilder b(client_, TestName() + ".add"); + XlaBuilder b(TestName() + ".add"); b.Dot(b.Parameter(0, shape, "param_0"), b.Parameter(1, shape, "param_1")); - TF_ASSERT_OK_AND_ASSIGN(Computation dot_product, b.Build()); + TF_ASSERT_OK_AND_ASSIGN(XlaComputation dot_product, b.Build()); ExecutionProfile execution_profile; TF_ASSERT_OK_AND_ASSIGN( diff --git a/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc b/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc index b28fe0c15a89a1..0a37e4d4236201 100644 --- a/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc +++ b/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -35,7 +36,7 @@ class ExhaustiveF32ElementwiseOpTest int64 input_size = end - begin; LOG(INFO) << "Checking range [" << begin << ", " << end << ")"; - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::unique_ptr input_literal = Literal::CreateFromDimensions(F32, {input_size}); @@ -78,9 +79,7 @@ XLA_TEST_P(ExhaustiveF32ElementwiseOpTest, LogF32) { #endif ExhaustivelyTestF32Op( - [](ComputationBuilder* builder, const ComputationDataHandle& input) { - builder->Log(input); - }, + [](XlaBuilder* builder, const XlaOp& input) { builder->Log(input); }, std::log, known_incorrect_range); } @@ -96,17 +95,13 @@ XLA_TEST_P(ExhaustiveF32ElementwiseOpTest, ExpF32) { #endif ExhaustivelyTestF32Op( - [](ComputationBuilder* builder, const ComputationDataHandle& input) { - builder->Exp(input); - }, + [](XlaBuilder* builder, const XlaOp& input) { builder->Exp(input); }, std::exp, known_incorrect_range); } XLA_TEST_P(ExhaustiveF32ElementwiseOpTest, TanhF32) { ExhaustivelyTestF32Op( - [](ComputationBuilder* builder, const ComputationDataHandle& input) { - builder->Tanh(input); - }, + [](XlaBuilder* builder, const XlaOp& input) { builder->Tanh(input); }, std::tanh, /*known_incorrect_range=*/{0, 0}); } diff --git a/tensorflow/compiler/xla/tests/floor_ceil_test.cc b/tensorflow/compiler/xla/tests/floor_ceil_test.cc index e75a41acacc3aa..71eb914a8e5eae 100644 --- a/tensorflow/compiler/xla/tests/floor_ceil_test.cc +++ b/tensorflow/compiler/xla/tests/floor_ceil_test.cc @@ -16,8 +16,8 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -41,7 +41,7 @@ class FloorCeilTest : public ClientLibraryTestBase { tensorflow::gtl::ArraySlice expected, Function f) { LOG(INFO) << "input: {" << tensorflow::str_util::Join(expected, ", ") << "}"; - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto c = builder.ConstantR1(input); if (f == kCeil) { builder.Ceil(c); @@ -54,7 +54,7 @@ class FloorCeilTest : public ClientLibraryTestBase { void TestR0F32(float input, float expected, Function f) { LOG(INFO) << "input: " << expected; - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto c = builder.ConstantR0(input); if (f == kCeil) { builder.Ceil(c); diff --git a/tensorflow/compiler/xla/tests/fmax_test.cc b/tensorflow/compiler/xla/tests/fmax_test.cc index f2aaf6621c1f0d..73f029b59bc56a 100644 --- a/tensorflow/compiler/xla/tests/fmax_test.cc +++ b/tensorflow/compiler/xla/tests/fmax_test.cc @@ -15,8 +15,8 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/core/platform/test.h" @@ -27,7 +27,7 @@ namespace { class FmaxSimpleTest : public ClientLibraryTestBase {}; TEST_F(FmaxSimpleTest, FmaxTenValues) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto x = builder.ConstantR1( {-0.0, 1.0, 2.0, -3.0, -4.0, 5.0, 6.0, -7.0, -8.0, 9.0}); auto y = builder.ConstantR1( diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc index 6f89e9164c8d44..b947f8208a5fa3 100644 --- a/tensorflow/compiler/xla/tests/fusion_test.cc +++ b/tensorflow/compiler/xla/tests/fusion_test.cc @@ -25,8 +25,7 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/ptr_util.h" @@ -675,21 +674,20 @@ XLA_TEST_F(FusionTest, SharedConstant) { auto builder = HloComputation::Builder(TestName()); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0}))); + HloInstruction::CreateConstant(Literal::CreateR1({0}))); auto const1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({2}))); + HloInstruction::CreateConstant(Literal::CreateR1({2}))); auto add1 = builder.AddInstruction(HloInstruction::CreateBinary( - ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, const0)); + ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, const0)); auto add2 = builder.AddInstruction(HloInstruction::CreateBinary( - ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, add1)); + ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, add1)); auto add3 = builder.AddInstruction(HloInstruction::CreateBinary( - ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, add2)); + ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, add2)); auto add4 = builder.AddInstruction(HloInstruction::CreateBinary( - ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, add3)); + ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, add3)); hlo_module->AddEntryComputation(builder.Build()) - ->CreateFusionInstruction( - {add4, add3, add2, add1, const1}, - HloInstruction::FusionKind::kLoop); + ->CreateFusionInstruction({add4, add3, add2, add1, const1}, + HloInstruction::FusionKind::kLoop); HloComputation* entry_comp = hlo_module->entry_computation(); @@ -700,7 +698,7 @@ XLA_TEST_F(FusionTest, SharedConstant) { EXPECT_EQ(entry_comp->root_instruction()->fused_instruction_count(), 6); LiteralTestUtil::ExpectEqual(*Literal::CreateR1({8}), - *ExecuteAndTransfer(std::move(hlo_module), {})); + *ExecuteAndTransfer(std::move(hlo_module), {})); } XLA_TEST_F(FusionTest, Add2D) { TestElementwise2D(HloOpcode::kAdd); } @@ -779,7 +777,7 @@ void BM_ParallelFusion(int num_iters) { const int64 param2_dim1 = 1024; // Create computation. - ComputationBuilder builder(client, "ParallelFusion"); + XlaBuilder builder("ParallelFusion"); Shape shape0 = ShapeUtil::MakeShape(F32, {param0_dim0, param0_dim1}); auto param0 = builder.Parameter(0, shape0, "param0"); Shape shape1 = ShapeUtil::MakeShape(F32, {param1_dim0, param1_dim1}); diff --git a/tensorflow/compiler/xla/tests/half_test.cc b/tensorflow/compiler/xla/tests/half_test.cc index ec2f49d43bd8ce..76bf47845ca045 100644 --- a/tensorflow/compiler/xla/tests/half_test.cc +++ b/tensorflow/compiler/xla/tests/half_test.cc @@ -16,8 +16,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" @@ -39,7 +38,7 @@ class HalfTestBase : public ClientLibraryTestBase { }; using UnaryBuildFuncTy = - std::function; + std::function; struct UnaryOpTestParam { std::function compute_func; @@ -51,8 +50,8 @@ class UnaryOpTest : public HalfTestBase, XLA_TEST_P(UnaryOpTest, Ops) { std::vector x({half(1.4), half(-2.3), half(3.2), half(-4.1)}); - ComputationBuilder builder(client_, TestName()); - ComputationDataHandle x_opnd; + XlaBuilder builder(TestName()); + XlaOp x_opnd; auto x_data = CreateR1Parameter(x, /*parameter_number=*/0, "x", &builder, &x_opnd); @@ -79,30 +78,21 @@ half round_imp(half value) { INSTANTIATE_TEST_CASE_P( half, UnaryOpTest, - ::testing::Values(UnaryOpTestParam{[](half x) { return abs(x); }, - &ComputationBuilder::Abs}, - UnaryOpTestParam{[](half x) { return round_imp(x); }, - &ComputationBuilder::Round}, - UnaryOpTestParam{[](half x) { return ceil(x); }, - &ComputationBuilder::Ceil}, - UnaryOpTestParam{[](half x) { return cos(x); }, - &ComputationBuilder::Cos}, - UnaryOpTestParam{[](half x) { return exp(x); }, - &ComputationBuilder::Exp}, - UnaryOpTestParam{[](half x) { return floor(x); }, - &ComputationBuilder::Floor}, - UnaryOpTestParam{[](half x) { return log(x); }, - &ComputationBuilder::Log}, - UnaryOpTestParam{[](half x) { return -x; }, - &ComputationBuilder::Neg}, - UnaryOpTestParam{[](half x) { return sign_imp(x); }, - &ComputationBuilder::Sign}, - UnaryOpTestParam{[](half x) { return sin(x); }, - &ComputationBuilder::Sin}, - UnaryOpTestParam{[](half x) { return tanh(x); }, - &ComputationBuilder::Tanh} + ::testing::Values( + UnaryOpTestParam{[](half x) { return abs(x); }, &XlaBuilder::Abs}, + UnaryOpTestParam{[](half x) { return round_imp(x); }, + &XlaBuilder::Round}, + UnaryOpTestParam{[](half x) { return ceil(x); }, &XlaBuilder::Ceil}, + UnaryOpTestParam{[](half x) { return cos(x); }, &XlaBuilder::Cos}, + UnaryOpTestParam{[](half x) { return exp(x); }, &XlaBuilder::Exp}, + UnaryOpTestParam{[](half x) { return floor(x); }, &XlaBuilder::Floor}, + UnaryOpTestParam{[](half x) { return log(x); }, &XlaBuilder::Log}, + UnaryOpTestParam{[](half x) { return -x; }, &XlaBuilder::Neg}, + UnaryOpTestParam{[](half x) { return sign_imp(x); }, &XlaBuilder::Sign}, + UnaryOpTestParam{[](half x) { return sin(x); }, &XlaBuilder::Sin}, + UnaryOpTestParam{[](half x) { return tanh(x); }, &XlaBuilder::Tanh} - )); + )); struct UnaryPredTestParam { std::function compute_func; @@ -115,8 +105,8 @@ class UnaryPredTest : public HalfTestBase, XLA_TEST_P(UnaryPredTest, Ops) { std::vector x({half(1.4), half(-2.3), half(3.2), half(-4.1)}); - ComputationBuilder builder(client_, TestName()); - ComputationDataHandle x_opnd; + XlaBuilder builder(TestName()); + XlaOp x_opnd; auto x_data = CreateR1Parameter(x, /*parameter_number=*/0, "x", &builder, &x_opnd); @@ -136,11 +126,11 @@ XLA_TEST_P(UnaryPredTest, Ops) { INSTANTIATE_TEST_CASE_P(half, UnaryPredTest, ::testing::Values(UnaryPredTestParam{ [](half x) { return isfinite(x); }, - &ComputationBuilder::IsFinite})); + &XlaBuilder::IsFinite})); using BinaryBuildFuncTy = std::function)>; + xla::XlaBuilder*, const xla::XlaOp& x, const xla::XlaOp& y, + tensorflow::gtl::ArraySlice)>; struct BinaryOpTestParam { std::function compute_func; @@ -153,12 +143,12 @@ class BinaryOpTest : public HalfTestBase, XLA_TEST_P(BinaryOpTest, Ops) { std::vector x({half(1.0), half(2.0), half(3.0), half(-4.0)}); std::vector y({half(0.4), half(-0.3), half(0.2), half(0.1)}); - ComputationBuilder builder(client_, TestName()); - ComputationDataHandle x_opnd; + XlaBuilder builder(TestName()); + XlaOp x_opnd; auto x_data = CreateR1Parameter(x, /*parameter_number=*/0, "x", &builder, &x_opnd); - ComputationDataHandle y_opnd; + XlaOp y_opnd; auto y_data = CreateR1Parameter(y, /*parameter_number=*/1, "y", &builder, &y_opnd); @@ -184,21 +174,21 @@ INSTANTIATE_TEST_CASE_P( half, BinaryOpTest, ::testing::Values( BinaryOpTestParam{[](half x, half y) { return x + y; }, - &ComputationBuilder::Add}, + &XlaBuilder::Add}, BinaryOpTestParam{[](half x, half y) { return atan2_imp(x, y); }, - &ComputationBuilder::Atan2}, + &XlaBuilder::Atan2}, BinaryOpTestParam{[](half x, half y) { return x / y; }, - &ComputationBuilder::Div}, + &XlaBuilder::Div}, BinaryOpTestParam{[](half x, half y) { return max(x, y); }, - &ComputationBuilder::Max}, + &XlaBuilder::Max}, BinaryOpTestParam{[](half x, half y) { return min(x, y); }, - &ComputationBuilder::Min}, + &XlaBuilder::Min}, BinaryOpTestParam{[](half x, half y) { return x * y; }, - &ComputationBuilder::Mul}, + &XlaBuilder::Mul}, BinaryOpTestParam{[](half x, half y) { return pow(x, y); }, - &ComputationBuilder::Pow}, + &XlaBuilder::Pow}, BinaryOpTestParam{[](half x, half y) { return x - y; }, - &ComputationBuilder::Sub} + &XlaBuilder::Sub} )); @@ -214,12 +204,12 @@ class BinaryPredTest XLA_TEST_P(BinaryPredTest, Ops) { std::vector x({half(1.0), half(2.0), half(0.2), half(-4.0)}); std::vector y({half(0.4), half(-0.3), half(0.2), half(0.1)}); - ComputationBuilder builder(client_, TestName()); - ComputationDataHandle x_opnd; + XlaBuilder builder(TestName()); + XlaOp x_opnd; auto x_data = CreateR1Parameter(x, /*parameter_number=*/0, "x", &builder, &x_opnd); - ComputationDataHandle y_opnd; + XlaOp y_opnd; auto y_data = CreateR1Parameter(y, /*parameter_number=*/1, "y", &builder, &y_opnd); @@ -239,17 +229,17 @@ XLA_TEST_P(BinaryPredTest, Ops) { INSTANTIATE_TEST_CASE_P( half, BinaryPredTest, ::testing::Values(BinaryPredTestParam{[](half x, half y) { return x == y; }, - &ComputationBuilder::Eq}, + &XlaBuilder::Eq}, BinaryPredTestParam{[](half x, half y) { return x != y; }, - &ComputationBuilder::Ne}, + &XlaBuilder::Ne}, BinaryPredTestParam{[](half x, half y) { return x >= y; }, - &ComputationBuilder::Ge}, + &XlaBuilder::Ge}, BinaryPredTestParam{[](half x, half y) { return x > y; }, - &ComputationBuilder::Gt}, + &XlaBuilder::Gt}, BinaryPredTestParam{[](half x, half y) { return x <= y; }, - &ComputationBuilder::Le}, + &XlaBuilder::Le}, BinaryPredTestParam{[](half x, half y) { return x < y; }, - &ComputationBuilder::Lt} + &XlaBuilder::Lt} )); diff --git a/tensorflow/compiler/xla/tests/log_test.cc b/tensorflow/compiler/xla/tests/log_test.cc index 174d433a9e1731..c0c02e584c2348 100644 --- a/tensorflow/compiler/xla/tests/log_test.cc +++ b/tensorflow/compiler/xla/tests/log_test.cc @@ -16,8 +16,8 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -29,7 +29,7 @@ namespace { class LogTest : public ClientLibraryTestBase {}; XLA_TEST_F(LogTest, LogZeroValues) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto x = builder.ConstantR3FromArray3D(Array3D(3, 0, 0)); builder.Log(x); @@ -41,7 +41,7 @@ TEST_F(LogTest, LogTenValues) { std::vector input = {-0.0, 1.0, 2.0, -3.0, -4.0, 5.0, 6.0, -7.0, -8.0, 9.0}; - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto x = builder.ConstantR1(input); builder.Log(x); diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc index 0c88bef69dfc52..f35bc43a495213 100644 --- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc +++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc @@ -17,9 +17,10 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" @@ -43,83 +44,80 @@ class ScalarComputationsTest : public ClientLibraryTestBase { protected: // A template for building and running a binary comparison test. template - void TestCompare(NativeT lhs, NativeT rhs, bool expected, - ComputationDataHandle (ComputationBuilder::*op)( - const ComputationDataHandle&, - const ComputationDataHandle&, - tensorflow::gtl::ArraySlice)) { - ComputationBuilder builder(client_, TestName()); - ComputationDataHandle lhs_op = builder.ConstantR0(lhs); - ComputationDataHandle rhs_op = builder.ConstantR0(rhs); - ComputationDataHandle result = (builder.*op)(lhs_op, rhs_op, {}); + void TestCompare( + NativeT lhs, NativeT rhs, bool expected, + XlaOp (XlaBuilder::*op)(const XlaOp&, const XlaOp&, + tensorflow::gtl::ArraySlice)) { + XlaBuilder builder(TestName()); + XlaOp lhs_op = builder.ConstantR0(lhs); + XlaOp rhs_op = builder.ConstantR0(rhs); + XlaOp result = (builder.*op)(lhs_op, rhs_op, {}); ComputeAndCompareR0(&builder, expected, {}); } template void TestMinMax(NativeT lhs, NativeT rhs, NativeT expected, - ComputationDataHandle (ComputationBuilder::*op)( - const ComputationDataHandle&, - const ComputationDataHandle&, - tensorflow::gtl::ArraySlice)) { - ComputationBuilder builder(client_, TestName()); - ComputationDataHandle lhs_op = builder.ConstantR0(lhs); - ComputationDataHandle rhs_op = builder.ConstantR0(rhs); - ComputationDataHandle result = (builder.*op)(lhs_op, rhs_op, {}); + XlaOp (XlaBuilder::*op)(const XlaOp&, const XlaOp&, + tensorflow::gtl::ArraySlice)) { + XlaBuilder builder(TestName()); + XlaOp lhs_op = builder.ConstantR0(lhs); + XlaOp rhs_op = builder.ConstantR0(rhs); + XlaOp result = (builder.*op)(lhs_op, rhs_op, {}); ComputeAndCompareR0(&builder, expected, {}); } }; XLA_TEST_F(ScalarComputationsTest, ReturnScalarF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.ConstantR0(2.1f); ComputeAndCompareR0(&builder, 2.1f, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, NegateScalarF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Neg(builder.ConstantR0(2.1f)); ComputeAndCompareR0(&builder, -2.1f, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, NegateScalarS32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Neg(builder.ConstantR0(2)); ComputeAndCompareR0(&builder, -2, {}); } XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Add(builder.ConstantR0(2.1f), builder.ConstantR0(5.5f)); ComputeAndCompareR0(&builder, 7.6f, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsS32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Add(builder.ConstantR0(2), builder.ConstantR0(5)); ComputeAndCompareR0(&builder, 7, {}); } XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsU32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Add(builder.ConstantR0(35), builder.ConstantR0(57)); ComputeAndCompareR0(&builder, 92, {}); } XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsU8) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Add(builder.ConstantR0(35), builder.ConstantR0(57)); ComputeAndCompareR0(&builder, 92, {}); } XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsU64) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); const uint64 a = static_cast(1) << 63; const uint64 b = a + 1; builder.Add(builder.ConstantR0(a), builder.ConstantR0(b)); @@ -128,7 +126,7 @@ XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsU64) { } XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsS64) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); const int64 a = static_cast(1) << 62; const int64 b = a - 1; builder.Add(builder.ConstantR0(a), builder.ConstantR0(b)); @@ -137,7 +135,7 @@ XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsS64) { } XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsF64) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Add(builder.ConstantR0(0.25), builder.ConstantR0(3.5)); @@ -145,21 +143,21 @@ XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsF64) { } XLA_TEST_F(ScalarComputationsTest, SubtractTwoScalarsF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Sub(builder.ConstantR0(2.1f), builder.ConstantR0(5.5f)); ComputeAndCompareR0(&builder, -3.4f, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, SubtractTwoScalarsS32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Sub(builder.ConstantR0(2), builder.ConstantR0(5)); ComputeAndCompareR0(&builder, -3, {}); } XLA_TEST_F(ScalarComputationsTest, CastS64ToF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto a = builder.Parameter(0, ShapeUtil::MakeShape(S64, {}), "a"); builder.ConvertElementType(a, F32); @@ -172,7 +170,7 @@ XLA_TEST_F(ScalarComputationsTest, CastS64ToF32) { } XLA_TEST_F(ScalarComputationsTest, MulThreeScalarsF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Mul(builder.Mul(builder.ConstantR0(2.1f), builder.ConstantR0(5.5f)), builder.ConstantR0(0.5f)); @@ -191,7 +189,7 @@ XLA_TEST_F(ScalarComputationsTest, MulTwoScalarsS32) { for (int32 x : data) { for (int32 y : data) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Mul(builder.ConstantR0(x), builder.ConstantR0(y)); // Signed integer overflow is undefined behavior in C++. Convert the input @@ -210,7 +208,7 @@ XLA_TEST_F(ScalarComputationsTest, MulTwoScalarsU32) { for (uint32 x : data) { for (uint32 y : data) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Mul(builder.ConstantR0(x), builder.ConstantR0(y)); uint32 expected = x * y; @@ -220,7 +218,7 @@ XLA_TEST_F(ScalarComputationsTest, MulTwoScalarsU32) { } XLA_TEST_F(ScalarComputationsTest, MulThreeScalarsS32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Mul( builder.Mul(builder.ConstantR0(2), builder.ConstantR0(5)), builder.ConstantR0(1)); @@ -229,7 +227,7 @@ XLA_TEST_F(ScalarComputationsTest, MulThreeScalarsS32) { } XLA_TEST_F(ScalarComputationsTest, MulThreeScalarsF32Params) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); std::unique_ptr a_literal = Literal::CreateR0(2.1f); std::unique_ptr b_literal = Literal::CreateR0(5.5f); std::unique_ptr c_literal = Literal::CreateR0(0.5f); @@ -241,9 +239,9 @@ XLA_TEST_F(ScalarComputationsTest, MulThreeScalarsF32Params) { std::unique_ptr c_data = client_->TransferToServer(*c_literal).ConsumeValueOrDie(); - ComputationDataHandle a = builder.Parameter(0, a_literal->shape(), "a"); - ComputationDataHandle b = builder.Parameter(1, b_literal->shape(), "b"); - ComputationDataHandle c = builder.Parameter(2, c_literal->shape(), "c"); + XlaOp a = builder.Parameter(0, a_literal->shape(), "a"); + XlaOp b = builder.Parameter(1, b_literal->shape(), "b"); + XlaOp c = builder.Parameter(2, c_literal->shape(), "c"); builder.Mul(builder.Mul(a, b), c); ComputeAndCompareR0(&builder, 5.775f, @@ -252,14 +250,14 @@ XLA_TEST_F(ScalarComputationsTest, MulThreeScalarsF32Params) { } XLA_TEST_F(ScalarComputationsTest, DivideTwoScalarsF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Div(builder.ConstantR0(5.0f), builder.ConstantR0(2.5f)); ComputeAndCompareR0(&builder, 2.0f, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, RemTwoScalarsF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Rem(builder.ConstantR0(2.5f), builder.ConstantR0(5.0f)); ComputeAndCompareR0(&builder, 2.5f, {}, error_spec_); @@ -282,7 +280,7 @@ class DivS32Test : public ClientLibraryTestBase, XLA_TEST_P(DivS32Test, DivideTwoScalarsS32) { DivS32Params p = GetParam(); - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Div(builder.ConstantR0(p.dividend), builder.ConstantR0(p.divisor)); @@ -291,7 +289,7 @@ XLA_TEST_P(DivS32Test, DivideTwoScalarsS32) { XLA_TEST_P(DivS32Test, RemainderTwoScalarsS32) { DivS32Params p = GetParam(); - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Rem(builder.ConstantR0(p.dividend), builder.ConstantR0(p.divisor)); @@ -300,9 +298,9 @@ XLA_TEST_P(DivS32Test, RemainderTwoScalarsS32) { XLA_TEST_P(DivS32Test, DivideTwoScalarsNonConstS32) { DivS32Params p = GetParam(); - ComputationBuilder builder(client_, TestName()); - ComputationDataHandle dividend; - ComputationDataHandle divisor; + XlaBuilder builder(TestName()); + XlaOp dividend; + XlaOp divisor; auto dividendd = CreateR0Parameter(p.dividend, 0, "dividend", &builder, ÷nd); auto divisord = @@ -315,9 +313,9 @@ XLA_TEST_P(DivS32Test, DivideTwoScalarsNonConstS32) { XLA_TEST_P(DivS32Test, RemainderTwoScalarsNonConstDivisorS32) { DivS32Params p = GetParam(); - ComputationBuilder builder(client_, TestName()); - ComputationDataHandle dividend; - ComputationDataHandle divisor; + XlaBuilder builder(TestName()); + XlaOp dividend; + XlaOp divisor; auto dividendd = CreateR0Parameter(p.dividend, 0, "dividend", &builder, ÷nd); auto divisord = @@ -364,13 +362,13 @@ XLA_TEST_F(ScalarComputationsTest, DivU32s) { 0, 1, 2, 17, 101, 3333, 0x7FFFFFFF, 0x80000000, UINT32_MAX - 1, UINT32_MAX}; // clang-format on - Computation div_computation; + XlaComputation div_computation; { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); - ComputationDataHandle dividend = + XlaOp dividend = builder.Parameter(0, ShapeUtil::MakeShape(U32, {}), "dividend"); - ComputationDataHandle divisor = + XlaOp divisor = builder.Parameter(1, ShapeUtil::MakeShape(U32, {}), "divisor"); builder.Div(dividend, divisor); TF_ASSERT_OK_AND_ASSIGN(div_computation, builder.Build()); @@ -405,13 +403,13 @@ XLA_TEST_F(ScalarComputationsTest, RemU32s) { 0, 1, 2, 17, 101, 3333, 0x7FFFFFFF, 0x80000000, UINT32_MAX - 1, UINT32_MAX}; // clang-format on - Computation rem_computation; + XlaComputation rem_computation; { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); - ComputationDataHandle dividend = + XlaOp dividend = builder.Parameter(0, ShapeUtil::MakeShape(U32, {}), "dividend"); - ComputationDataHandle divisor = + XlaOp divisor = builder.Parameter(1, ShapeUtil::MakeShape(U32, {}), "divisor"); builder.Rem(dividend, divisor); TF_ASSERT_OK_AND_ASSIGN(rem_computation, builder.Build()); @@ -440,7 +438,7 @@ XLA_TEST_F(ScalarComputationsTest, RemU32s) { } XLA_TEST_F(ScalarComputationsTest, RemainderTwoScalarsNonConstDividendS32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto x = builder.Parameter(0, ShapeUtil::MakeShape(S32, {}), "x"); builder.Rem(x, builder.ConstantR0(80000)); @@ -450,7 +448,7 @@ XLA_TEST_F(ScalarComputationsTest, RemainderTwoScalarsNonConstDividendS32) { } XLA_TEST_F(ScalarComputationsTest, DivideTwoScalarsU32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); // This verifies 0xFFFFFFFE / 2 = 0x7FFFFFFF. If XLA incorrectly treated U32 // as S32, it would output -2 / 2 = -1 (0xFFFFFFFF). builder.Div(builder.ConstantR0(0xFFFFFFFE), @@ -460,7 +458,7 @@ XLA_TEST_F(ScalarComputationsTest, DivideTwoScalarsU32) { } XLA_TEST_F(ScalarComputationsTest, RemTwoScalarsU32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Rem(builder.ConstantR0(11), builder.ConstantR0(3)); ComputeAndCompareR0(&builder, 2, {}); @@ -469,7 +467,7 @@ XLA_TEST_F(ScalarComputationsTest, RemTwoScalarsU32) { XLA_TEST_F(ScalarComputationsTest, AndBool) { for (bool x : {false, true}) { for (bool y : {false, true}) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.And(builder.ConstantR0(x), builder.ConstantR0(y)); ComputeAndCompareR0(&builder, x && y, {}); @@ -480,7 +478,7 @@ XLA_TEST_F(ScalarComputationsTest, AndBool) { XLA_TEST_F(ScalarComputationsTest, AndS32) { for (int32 x : {0, 8}) { for (int32 y : {1, -16}) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.And(builder.ConstantR0(x), builder.ConstantR0(y)); ComputeAndCompareR0(&builder, x & y, {}); @@ -491,7 +489,7 @@ XLA_TEST_F(ScalarComputationsTest, AndS32) { XLA_TEST_F(ScalarComputationsTest, AndU32) { for (uint32 x : {0, 8}) { for (uint32 y : {1, 16}) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.And(builder.ConstantR0(x), builder.ConstantR0(y)); ComputeAndCompareR0(&builder, x & y, {}); @@ -502,7 +500,7 @@ XLA_TEST_F(ScalarComputationsTest, AndU32) { XLA_TEST_F(ScalarComputationsTest, OrBool) { for (bool x : {false, true}) { for (bool y : {false, true}) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Or(builder.ConstantR0(x), builder.ConstantR0(y)); ComputeAndCompareR0(&builder, x || y, {}); @@ -513,7 +511,7 @@ XLA_TEST_F(ScalarComputationsTest, OrBool) { XLA_TEST_F(ScalarComputationsTest, OrS32) { for (int32 x : {0, 8}) { for (int32 y : {1, -16}) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Or(builder.ConstantR0(x), builder.ConstantR0(y)); ComputeAndCompareR0(&builder, x | y, {}); @@ -524,7 +522,7 @@ XLA_TEST_F(ScalarComputationsTest, OrS32) { XLA_TEST_F(ScalarComputationsTest, OrU32) { for (uint32 x : {0, 8}) { for (uint32 y : {1, 16}) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Or(builder.ConstantR0(x), builder.ConstantR0(y)); ComputeAndCompareR0(&builder, x | y, {}); @@ -534,7 +532,7 @@ XLA_TEST_F(ScalarComputationsTest, OrU32) { XLA_TEST_F(ScalarComputationsTest, NotBool) { for (bool x : {false, true}) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Not(builder.ConstantR0(x)); ComputeAndCompareR0(&builder, !x, {}); @@ -543,7 +541,7 @@ XLA_TEST_F(ScalarComputationsTest, NotBool) { XLA_TEST_F(ScalarComputationsTest, NotS32) { for (int32 x : {-1, 0, 1}) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Not(builder.ConstantR0(x)); ComputeAndCompareR0(&builder, ~x, {}); @@ -552,7 +550,7 @@ XLA_TEST_F(ScalarComputationsTest, NotS32) { XLA_TEST_F(ScalarComputationsTest, NotU32) { for (uint32 x : {0, 1, 2}) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Not(builder.ConstantR0(x)); ComputeAndCompareR0(&builder, ~x, {}); @@ -560,7 +558,7 @@ XLA_TEST_F(ScalarComputationsTest, NotU32) { } XLA_TEST_F(ScalarComputationsTest, SelectScalarTrue) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Select(builder.ConstantR0(true), // The predicate. builder.ConstantR0(123.0f), // The value on true. builder.ConstantR0(42.0f)); // The value on false. @@ -569,7 +567,7 @@ XLA_TEST_F(ScalarComputationsTest, SelectScalarTrue) { } XLA_TEST_F(ScalarComputationsTest, SelectScalarFalse) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Select(builder.ConstantR0(false), // The predicate. builder.ConstantR0(123.0f), // The value on true. builder.ConstantR0(42.0f)); // The value on false. @@ -580,7 +578,7 @@ XLA_TEST_F(ScalarComputationsTest, SelectScalarFalse) { // This test is an explicit version of what is happening in the following // templatized comparison tests. XLA_TEST_F(ScalarComputationsTest, CompareGtScalar) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Gt(builder.ConstantR0(2.0f), builder.ConstantR0(1.0f)); ComputeAndCompareR0(&builder, true, {}); @@ -588,157 +586,156 @@ XLA_TEST_F(ScalarComputationsTest, CompareGtScalar) { // S32 comparisons. XLA_TEST_F(ScalarComputationsTest, CompareEqS32Greater) { - TestCompare(2, 1, false, &ComputationBuilder::Eq); + TestCompare(2, 1, false, &XlaBuilder::Eq); } XLA_TEST_F(ScalarComputationsTest, CompareEqS32Equal) { - TestCompare(3, 3, true, &ComputationBuilder::Eq); + TestCompare(3, 3, true, &XlaBuilder::Eq); } XLA_TEST_F(ScalarComputationsTest, CompareNeS32) { - TestCompare(2, 1, true, &ComputationBuilder::Ne); + TestCompare(2, 1, true, &XlaBuilder::Ne); } XLA_TEST_F(ScalarComputationsTest, CompareGeS32) { - TestCompare(2, 1, true, &ComputationBuilder::Ge); + TestCompare(2, 1, true, &XlaBuilder::Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGtS32) { - TestCompare(1, 5, false, &ComputationBuilder::Gt); + TestCompare(1, 5, false, &XlaBuilder::Gt); } XLA_TEST_F(ScalarComputationsTest, CompareLeS32) { - TestCompare(2, 1, false, &ComputationBuilder::Le); + TestCompare(2, 1, false, &XlaBuilder::Le); } XLA_TEST_F(ScalarComputationsTest, CompareLtS32) { - TestCompare(9, 7, false, &ComputationBuilder::Lt); + TestCompare(9, 7, false, &XlaBuilder::Lt); TestCompare(std::numeric_limits::min(), - std::numeric_limits::max(), true, - &ComputationBuilder::Lt); + std::numeric_limits::max(), true, &XlaBuilder::Lt); } // U32 comparisons. XLA_TEST_F(ScalarComputationsTest, CompareEqU32False) { - TestCompare(2, 1, false, &ComputationBuilder::Eq); + TestCompare(2, 1, false, &XlaBuilder::Eq); } XLA_TEST_F(ScalarComputationsTest, CompareNeU32) { - TestCompare(2, 1, true, &ComputationBuilder::Ne); + TestCompare(2, 1, true, &XlaBuilder::Ne); } XLA_TEST_F(ScalarComputationsTest, CompareGeU32Greater) { - TestCompare(2, 1, true, &ComputationBuilder::Ge); + TestCompare(2, 1, true, &XlaBuilder::Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGeU32Equal) { - TestCompare(3, 3, true, &ComputationBuilder::Ge); + TestCompare(3, 3, true, &XlaBuilder::Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGtU32) { - TestCompare(1, 5, false, &ComputationBuilder::Gt); - TestCompare(5, 5, false, &ComputationBuilder::Gt); - TestCompare(5, 1, true, &ComputationBuilder::Gt); + TestCompare(1, 5, false, &XlaBuilder::Gt); + TestCompare(5, 5, false, &XlaBuilder::Gt); + TestCompare(5, 1, true, &XlaBuilder::Gt); } XLA_TEST_F(ScalarComputationsTest, CompareLeU32) { - TestCompare(2, 1, false, &ComputationBuilder::Le); + TestCompare(2, 1, false, &XlaBuilder::Le); } XLA_TEST_F(ScalarComputationsTest, CompareLtU32) { - TestCompare(9, 7, false, &ComputationBuilder::Lt); + TestCompare(9, 7, false, &XlaBuilder::Lt); TestCompare(0, std::numeric_limits::max(), true, - &ComputationBuilder::Lt); + &XlaBuilder::Lt); } // F32 comparisons. XLA_TEST_F(ScalarComputationsTest, CompareEqF32False) { - TestCompare(2.0, 1.3, false, &ComputationBuilder::Eq); + TestCompare(2.0, 1.3, false, &XlaBuilder::Eq); } XLA_TEST_F(ScalarComputationsTest, CompareNeF32) { - TestCompare(2.0, 1.3, true, &ComputationBuilder::Ne); + TestCompare(2.0, 1.3, true, &XlaBuilder::Ne); } XLA_TEST_F(ScalarComputationsTest, CompareGeF32Greater) { - TestCompare(2.0, 1.9, true, &ComputationBuilder::Ge); + TestCompare(2.0, 1.9, true, &XlaBuilder::Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGeF32Equal) { - TestCompare(3.5, 3.5, true, &ComputationBuilder::Ge); + TestCompare(3.5, 3.5, true, &XlaBuilder::Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGtF32) { - TestCompare(1.0, 5.2, false, &ComputationBuilder::Gt); + TestCompare(1.0, 5.2, false, &XlaBuilder::Gt); } XLA_TEST_F(ScalarComputationsTest, CompareLeF32) { - TestCompare(2.0, 1.2, false, &ComputationBuilder::Le); + TestCompare(2.0, 1.2, false, &XlaBuilder::Le); } XLA_TEST_F(ScalarComputationsTest, CompareLtF32) { - TestCompare(9.0, 7.2, false, &ComputationBuilder::Lt); + TestCompare(9.0, 7.2, false, &XlaBuilder::Lt); } // F32 comparisons with exceptional values. The test names encode the // left/right operands at the end, and use Minf and Mzero for -inf and -0.0. XLA_TEST_F(ScalarComputationsTest, CompareLtF32MinfMzero) { - TestCompare(-INFINITY, -0.0, true, &ComputationBuilder::Lt); + TestCompare(-INFINITY, -0.0, true, &XlaBuilder::Lt); } XLA_TEST_F(ScalarComputationsTest, CompareLtF32MzeroZero) { // Comparisons of 0.0 to -0.0 consider them equal in IEEE 754. - TestCompare(-0.0, 0.0, false, &ComputationBuilder::Lt); + TestCompare(-0.0, 0.0, false, &XlaBuilder::Lt); } XLA_TEST_F(ScalarComputationsTest, CompareLtF32ZeroInf) { - TestCompare(0.0, INFINITY, true, &ComputationBuilder::Lt); + TestCompare(0.0, INFINITY, true, &XlaBuilder::Lt); } XLA_TEST_F(ScalarComputationsTest, CompareGeF32MinfMzero) { - TestCompare(-INFINITY, -0.0, false, &ComputationBuilder::Ge); + TestCompare(-INFINITY, -0.0, false, &XlaBuilder::Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGeF32MzeroZero) { // Comparisons of 0.0 to -0.0 consider them equal in IEEE 754. - TestCompare(-0.0, 0.0, true, &ComputationBuilder::Ge); + TestCompare(-0.0, 0.0, true, &XlaBuilder::Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGeF32ZeroInf) { - TestCompare(0.0, INFINITY, false, &ComputationBuilder::Ge); + TestCompare(0.0, INFINITY, false, &XlaBuilder::Ge); } XLA_TEST_F(ScalarComputationsTest, ExpScalar) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Exp(builder.ConstantR0(2.0f)); ComputeAndCompareR0(&builder, 7.3890562, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, LogScalar) { - ComputationBuilder builder(client_, "log"); + XlaBuilder builder("log"); builder.Log(builder.ConstantR0(2.0f)); ComputeAndCompareR0(&builder, 0.6931471, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, TanhScalar) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Tanh(builder.ConstantR0(2.0f)); ComputeAndCompareR0(&builder, 0.96402758, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, TanhDoubleScalar) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Tanh(builder.ConstantR0(2.0)); ComputeAndCompareR0(&builder, 0.96402758, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, PowScalar) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Pow(builder.ConstantR0(2.0f), builder.ConstantR0(3.0f)); ComputeAndCompareR0(&builder, 8.0, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, ClampScalarHighS32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Clamp(builder.ConstantR0(-1), // The lower bound. builder.ConstantR0(5), // The operand to be clamped. builder.ConstantR0(3)); // The upper bound. @@ -747,7 +744,7 @@ XLA_TEST_F(ScalarComputationsTest, ClampScalarHighS32) { } XLA_TEST_F(ScalarComputationsTest, ClampScalarMiddleS32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Clamp(builder.ConstantR0(-1), // The lower bound. builder.ConstantR0(2), // The operand to be clamped. builder.ConstantR0(3)); // The upper bound. @@ -756,7 +753,7 @@ XLA_TEST_F(ScalarComputationsTest, ClampScalarMiddleS32) { } XLA_TEST_F(ScalarComputationsTest, ClampScalarLowS32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Clamp(builder.ConstantR0(-1), // The lower bound. builder.ConstantR0(-5), // The operand to be clamped. builder.ConstantR0(3)); // The upper bound. @@ -765,7 +762,7 @@ XLA_TEST_F(ScalarComputationsTest, ClampScalarLowS32) { } XLA_TEST_F(ScalarComputationsTest, ClampScalarHighU32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Clamp(builder.ConstantR0(1), // The lower bound. builder.ConstantR0(5), // The operand to be clamped. builder.ConstantR0(3)); // The upper bound. @@ -774,7 +771,7 @@ XLA_TEST_F(ScalarComputationsTest, ClampScalarHighU32) { } XLA_TEST_F(ScalarComputationsTest, ClampScalarMiddleU32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Clamp(builder.ConstantR0(1), // The lower bound. builder.ConstantR0(2), // The operand to be clamped. builder.ConstantR0(3)); // The upper bound. @@ -783,7 +780,7 @@ XLA_TEST_F(ScalarComputationsTest, ClampScalarMiddleU32) { } XLA_TEST_F(ScalarComputationsTest, ClampScalarLowU32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Clamp(builder.ConstantR0(1), // The lower bound. builder.ConstantR0(0), // The operand to be clamped. builder.ConstantR0(3)); // The upper bound. @@ -792,7 +789,7 @@ XLA_TEST_F(ScalarComputationsTest, ClampScalarLowU32) { } XLA_TEST_F(ScalarComputationsTest, ClampScalarHighF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Clamp(builder.ConstantR0(2.0f), // The lower bound. builder.ConstantR0(5.0f), // The operand to be clamped. builder.ConstantR0(3.0f)); // The upper bound. @@ -801,7 +798,7 @@ XLA_TEST_F(ScalarComputationsTest, ClampScalarHighF32) { } XLA_TEST_F(ScalarComputationsTest, ClampScalarMiddleF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Clamp(builder.ConstantR0(2.0f), // The lower bound. builder.ConstantR0(2.5f), // The operand to be clamped. builder.ConstantR0(3.0f)); // The upper bound. @@ -810,7 +807,7 @@ XLA_TEST_F(ScalarComputationsTest, ClampScalarMiddleF32) { } XLA_TEST_F(ScalarComputationsTest, ClampScalarLowF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Clamp(builder.ConstantR0(2.0f), // The lower bound. builder.ConstantR0(-5.0f), // The operand to be clamped. builder.ConstantR0(3.0f)); // The upper bound. @@ -819,70 +816,70 @@ XLA_TEST_F(ScalarComputationsTest, ClampScalarLowF32) { } XLA_TEST_F(ScalarComputationsTest, MinS32Above) { - TestMinMax(10, 3, 3, &ComputationBuilder::Min); + TestMinMax(10, 3, 3, &XlaBuilder::Min); } XLA_TEST_F(ScalarComputationsTest, MinS32Below) { - TestMinMax(-100, 3, -100, &ComputationBuilder::Min); + TestMinMax(-100, 3, -100, &XlaBuilder::Min); } XLA_TEST_F(ScalarComputationsTest, MaxS32Above) { - TestMinMax(10, 3, 10, &ComputationBuilder::Max); + TestMinMax(10, 3, 10, &XlaBuilder::Max); } XLA_TEST_F(ScalarComputationsTest, MaxS32Below) { - TestMinMax(-100, 3, 3, &ComputationBuilder::Max); + TestMinMax(-100, 3, 3, &XlaBuilder::Max); } XLA_TEST_F(ScalarComputationsTest, MinU32Above) { const uint32 large = std::numeric_limits::max(); - TestMinMax(large, 3, 3, &ComputationBuilder::Min); + TestMinMax(large, 3, 3, &XlaBuilder::Min); } XLA_TEST_F(ScalarComputationsTest, MinU32Below) { - TestMinMax(0, 5, 0, &ComputationBuilder::Min); + TestMinMax(0, 5, 0, &XlaBuilder::Min); } XLA_TEST_F(ScalarComputationsTest, MaxU32Above) { const uint32 large = std::numeric_limits::max(); - TestMinMax(large, 3, large, &ComputationBuilder::Max); + TestMinMax(large, 3, large, &XlaBuilder::Max); } XLA_TEST_F(ScalarComputationsTest, MaxU32Below) { - TestMinMax(0, 5, 5, &ComputationBuilder::Max); + TestMinMax(0, 5, 5, &XlaBuilder::Max); } XLA_TEST_F(ScalarComputationsTest, MinF32Above) { - TestMinMax(10.1f, 3.1f, 3.1f, &ComputationBuilder::Min); + TestMinMax(10.1f, 3.1f, 3.1f, &XlaBuilder::Min); } XLA_TEST_F(ScalarComputationsTest, MinF32Below) { - TestMinMax(-100.1f, 3.1f, -100.1f, &ComputationBuilder::Min); + TestMinMax(-100.1f, 3.1f, -100.1f, &XlaBuilder::Min); } XLA_TEST_F(ScalarComputationsTest, MinPropagatesNan) { SetFastMathDisabled(true); - TestMinMax(NAN, 3.1f, NAN, &ComputationBuilder::Min); - TestMinMax(-3.1f, NAN, NAN, &ComputationBuilder::Min); + TestMinMax(NAN, 3.1f, NAN, &XlaBuilder::Min); + TestMinMax(-3.1f, NAN, NAN, &XlaBuilder::Min); } XLA_TEST_F(ScalarComputationsTest, MaxF32Above) { - TestMinMax(10.1f, 3.1f, 10.1f, &ComputationBuilder::Max); + TestMinMax(10.1f, 3.1f, 10.1f, &XlaBuilder::Max); } XLA_TEST_F(ScalarComputationsTest, MaxF32Below) { - TestMinMax(-100.1f, 3.1f, 3.1f, &ComputationBuilder::Max); + TestMinMax(-100.1f, 3.1f, 3.1f, &XlaBuilder::Max); } XLA_TEST_F(ScalarComputationsTest, MaxPropagatesNan) { SetFastMathDisabled(true); - TestMinMax(NAN, 3.1f, NAN, &ComputationBuilder::Max); - TestMinMax(-3.1f, NAN, NAN, &ComputationBuilder::Max); + TestMinMax(NAN, 3.1f, NAN, &XlaBuilder::Max); + TestMinMax(-3.1f, NAN, NAN, &XlaBuilder::Max); } XLA_TEST_F(ScalarComputationsTest, ComplicatedArithmeticExpressionF32) { // Compute the expression (1 * (3 - 1) * (7 + 0) - 4) / 20. - ComputationBuilder b(client_, TestName()); + XlaBuilder b(TestName()); b.Div( b.Sub(b.Mul(b.ConstantR0(1), b.Mul(b.Sub(b.ConstantR0(3), b.ConstantR0(1)), @@ -895,7 +892,7 @@ XLA_TEST_F(ScalarComputationsTest, ComplicatedArithmeticExpressionF32) { XLA_TEST_F(ScalarComputationsTest, ComplicatedArithmeticExpressionS32) { // Compute the expression 1 * (3 - 1) * (7 + 0) - 4. - ComputationBuilder b(client_, TestName()); + XlaBuilder b(TestName()); b.Sub(b.Mul(b.ConstantR0(1), b.Mul(b.Sub(b.ConstantR0(3), b.ConstantR0(1)), b.Add(b.ConstantR0(7), b.ConstantR0(0)))), @@ -905,21 +902,20 @@ XLA_TEST_F(ScalarComputationsTest, ComplicatedArithmeticExpressionS32) { } XLA_TEST_F(ScalarComputationsTest, SqrtF320) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); Literal zero_literal = Literal::Zero(PrimitiveType::F32); std::unique_ptr zero_data = client_->TransferToServer(zero_literal).ConsumeValueOrDie(); - ComputationDataHandle zero = - builder.Parameter(0, zero_literal.shape(), "zero"); + XlaOp zero = builder.Parameter(0, zero_literal.shape(), "zero"); builder.SqrtF32(zero); ComputeAndCompareR0(&builder, 0.0f, {zero_data.get()}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, RoundScalar) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); builder.Round(builder.ConstantR0(1.4f)); ComputeAndCompareR0(&builder, 1.0f, {}, error_spec_); diff --git a/tensorflow/compiler/xla/tests/select_test.cc b/tensorflow/compiler/xla/tests/select_test.cc index 009e7d24c5cbfa..3d694a9c3fe894 100644 --- a/tensorflow/compiler/xla/tests/select_test.cc +++ b/tensorflow/compiler/xla/tests/select_test.cc @@ -16,9 +16,9 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -35,7 +35,7 @@ class SelectTest : public ClientLibraryTestBase { }; TEST_F(SelectTest, SelectScalarF32True) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto pred = builder.ConstantR0(true); auto on_true = builder.ConstantR0(123.0f); auto on_false = builder.ConstantR0(42.0f); @@ -45,7 +45,7 @@ TEST_F(SelectTest, SelectScalarF32True) { } TEST_F(SelectTest, SelectScalarS32True) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto pred = builder.ConstantR0(true); auto on_true = builder.ConstantR0(-42); auto on_false = builder.ConstantR0(42); @@ -55,7 +55,7 @@ TEST_F(SelectTest, SelectScalarS32True) { } TEST_F(SelectTest, SelectScalarF32False) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto pred = builder.ConstantR0(false); auto on_true = builder.ConstantR0(123.0f); auto on_false = builder.ConstantR0(42.0f); @@ -65,7 +65,7 @@ TEST_F(SelectTest, SelectScalarF32False) { } XLA_TEST_F(SelectTest, SelectR1S0F32WithConstantR1S0PRED) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto pred = builder.ConstantR1({}); auto on_true = builder.ConstantR1({}); auto on_false = builder.ConstantR1({}); @@ -75,7 +75,7 @@ XLA_TEST_F(SelectTest, SelectR1S0F32WithConstantR1S0PRED) { } TEST_F(SelectTest, SelectR1F32WithConstantR1PRED) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto pred = builder.ConstantR1({false, true, false, true, false}); auto on_true = builder.ConstantR1({-2.5f, 25.5f, 2.25f, -10.0f, 6.0f}); auto on_false = builder.ConstantR1({10.0f, 5.0f, 1.0f, 10.0f, -6.0f}); @@ -88,7 +88,7 @@ TEST_F(SelectTest, SelectR1F32WithConstantR1PRED) { XLA_TEST_F(SelectTest, SelectR1S0F32WithCmpR1S0S32s) { // Similar to SelectR1S0F32WithConstantR1S0PRED, except that the pred vector // is not a constant, but rather the result of comparing two other vectors. - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto v1 = builder.ConstantR1({}); auto v2 = builder.ConstantR1({}); auto cmp = builder.Eq(v1, v2); @@ -102,7 +102,7 @@ XLA_TEST_F(SelectTest, SelectR1S0F32WithCmpR1S0S32s) { TEST_F(SelectTest, SelectR1F32WithCmpR1S32s) { // Similar to SelectR1F32WithConstantR1PRED, except that the pred vector is // not a constant, but rather the result of comparing two other vectors. - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto v1 = builder.ConstantR1({1, 2, 3, 4, 5}); auto v2 = builder.ConstantR1({9, 2, 9, 4, 9}); auto cmp = builder.Eq(v1, v2); @@ -116,7 +116,7 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1S32s) { TEST_F(SelectTest, SelectR1F32WithCmpR1F32s) { // Similar to SelectR1F32WithCmpR1S32s, except "gt"-comparing two R1F32s. - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto v1 = builder.ConstantR1({1.0f, 2.0f, 3.0f, 4.0f, 5.0f}); auto v2 = builder.ConstantR1({-1.0f, -2.0f, 13.0f, 14.0f, 4.4f}); auto cmp = builder.Gt(v1, v2); @@ -131,9 +131,9 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1F32s) { TEST_F(SelectTest, SelectR1F32WithCmpR1F32sFromParamsSmall) { // Selects among two R1F32s, which come from parameters. v1 and v2 are // compared, and selection between them happens based on a gt-comparison mask. - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); - ComputationDataHandle v1, v2; + XlaOp v1, v2; std::unique_ptr param0_data = CreateR1Parameter( {41.0f, 2.0f, 3.0f, 84.0f}, /*parameter_number=*/0, /*name=*/"v1", /*builder=*/&builder, /*data_handle=*/&v1); @@ -151,7 +151,7 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1F32sFromParamsSmall) { TEST_F(SelectTest, SelectR1F32WithCmpR1F32sFromParamsLarge) { // Similar to SelectR1F32WithCmpR1F32sFromParamsSmall, except that the // data size passed in and out is large. - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); // Number of floats in the data passed into and out of the computation. constexpr int datalen = 15 * 1000; @@ -174,7 +174,7 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1F32sFromParamsLarge) { expected_vec.push_back(larger); } - ComputationDataHandle v1, v2; + XlaOp v1, v2; std::unique_ptr param0_data = CreateR1Parameter(v1vec, /*parameter_number=*/0, /*name=*/"v1", /*builder=*/&builder, /*data_handle=*/&v1); @@ -192,7 +192,7 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1F32sFromParamsLarge) { TEST_F(SelectTest, SelectR1F32WithCmpR1S32ToScalar) { // "gt"-compares a R1S32 with a S32 scalar, and uses the resulting R1PRED to // select between two R1F32s. - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto v = builder.ConstantR1({1, -1, 2, -2}); auto s = builder.ConstantR0(0); auto cmp = builder.Gt(v, s); @@ -209,7 +209,7 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1S32ToScalar) { TEST_F(SelectTest, SelectR1F32WithCmpR1F32ToScalar) { // "gt"-compares a R1F32 with a F32 scalar, and uses the resulting R1PRED to // select between two R1F32s. - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto v = builder.ConstantR1({1.0f, 2.0f, 3.0f, 4.0f}); auto s = builder.ConstantR0(2.5f); auto cmp = builder.Gt(v, s); @@ -225,7 +225,7 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1F32ToScalar) { XLA_TEST_F(SelectTest, SelectR1S0F32WithScalarPredicate) { for (bool which : {false, true}) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto pred = builder.ConstantR0(which); auto on_true = builder.ConstantR1({}); auto on_false = builder.ConstantR1({}); @@ -236,7 +236,7 @@ XLA_TEST_F(SelectTest, SelectR1S0F32WithScalarPredicate) { } TEST_F(SelectTest, SelectR1F32WithScalarPredicateTrue) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto pred = builder.ConstantR0(true); auto on_true = builder.ConstantR1({-2.5f, 25.5f}); auto on_false = builder.ConstantR1({10.0f, 5.0f}); @@ -246,7 +246,7 @@ TEST_F(SelectTest, SelectR1F32WithScalarPredicateTrue) { } TEST_F(SelectTest, SelectR1F32WithScalarPredicateFalse) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto pred = builder.ConstantR0(false); auto on_true = builder.ConstantR1({-2.5f, 25.5f}); auto on_false = builder.ConstantR1({10.0f, 5.0f}); diff --git a/tensorflow/compiler/xla/tests/transpose_test.cc b/tensorflow/compiler/xla/tests/transpose_test.cc index fe5a1778a2cecf..59ce23d0247b58 100644 --- a/tensorflow/compiler/xla/tests/transpose_test.cc +++ b/tensorflow/compiler/xla/tests/transpose_test.cc @@ -16,8 +16,8 @@ limitations under the License. #include #include "tensorflow/compiler/xla/array2d.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" @@ -38,7 +38,7 @@ class TransposeTest : public ClientLibraryTestBase { }; XLA_TEST_F(TransposeTest, Transpose0x0) { - ComputationBuilder builder(client_, "Transpose"); + XlaBuilder builder("Transpose"); auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 0)); auto result = builder.Transpose(lhs, {1, 0}); @@ -46,7 +46,7 @@ XLA_TEST_F(TransposeTest, Transpose0x0) { } XLA_TEST_F(TransposeTest, Transpose0x42) { - ComputationBuilder builder(client_, "Transpose"); + XlaBuilder builder("Transpose"); auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 42)); auto result = builder.Transpose(lhs, {1, 0}); @@ -54,7 +54,7 @@ XLA_TEST_F(TransposeTest, Transpose0x42) { } XLA_TEST_F(TransposeTest, Transpose7x0) { - ComputationBuilder builder(client_, "Transpose"); + XlaBuilder builder("Transpose"); auto lhs = builder.ConstantR2FromArray2D(Array2D(7, 0)); auto result = builder.Transpose(lhs, {1, 0}); @@ -62,7 +62,7 @@ XLA_TEST_F(TransposeTest, Transpose7x0) { } TEST_F(TransposeTest, Transpose2x2) { - ComputationBuilder builder(client_, "Transpose"); + XlaBuilder builder("Transpose"); auto lhs = builder.ConstantR2({ {1.0, 2.0}, {3.0, 4.0}, }); @@ -74,7 +74,7 @@ TEST_F(TransposeTest, Transpose2x2) { } XLA_TEST_F(TransposeTest, Transpose0x2x3_2x3x0) { - ComputationBuilder builder(client_, "Transpose"); + XlaBuilder builder("Transpose"); auto operand = builder.ConstantR3FromArray3D(Array3D(0, 2, 3)); auto result = builder.Transpose(operand, {1, 2, 0}); @@ -82,7 +82,7 @@ XLA_TEST_F(TransposeTest, Transpose0x2x3_2x3x0) { } TEST_F(TransposeTest, Transpose1x2x3_2x3x1) { - ComputationBuilder builder(client_, "Transpose"); + XlaBuilder builder("Transpose"); auto operand = builder.ConstantR3FromArray3D({{{1, 2, 3}, {4, 5, 6}}}); auto result = builder.Transpose(operand, {1, 2, 0}); @@ -92,7 +92,7 @@ TEST_F(TransposeTest, Transpose1x2x3_2x3x1) { } TEST_F(TransposeTest, Transpose1x2x3_3x2x1) { - ComputationBuilder builder(client_, "Transpose"); + XlaBuilder builder("Transpose"); auto operand = builder.ConstantR3FromArray3D({{{1, 2, 3}, {4, 5, 6}}}); auto result = builder.Transpose(operand, {2, 1, 0}); @@ -102,7 +102,7 @@ TEST_F(TransposeTest, Transpose1x2x3_3x2x1) { } TEST_F(TransposeTest, Transpose1x2x3_1x2x3) { - ComputationBuilder builder(client_, "Transpose"); + XlaBuilder builder("Transpose"); auto operand = builder.ConstantR3FromArray3D({{{1, 2, 3}, {4, 5, 6}}}); auto result = builder.Transpose(operand, {0, 1, 2}); @@ -116,7 +116,7 @@ TEST_F(TransposeTest, MultiTranspose3x2) { Array2D transposed({{1.0f, 3.0f, 5.0f}, {2.0f, 4.0f, 6.0f}}); for (int transposes = 0; transposes <= 10; ++transposes) { - ComputationBuilder builder(client_, "Transpose"); + XlaBuilder builder("Transpose"); auto computed = builder.ConstantR2FromArray2D(input); for (int i = 0; i < transposes; ++i) { computed = builder.Transpose(computed, {1, 0}); @@ -130,7 +130,7 @@ TEST_F(TransposeTest, MultiTranspose3x2) { TEST_F(TransposeTest, Small_1x1) { auto aoperand = MakeLinspaceArray2D(0.0, 1.0, 1, 1); - ComputationBuilder builder(client_, "transpose_1x1"); + XlaBuilder builder("transpose_1x1"); auto operand = builder.ConstantR2FromArray2D(*aoperand); builder.Transpose(operand, {1, 0}); @@ -142,7 +142,7 @@ TEST_F(TransposeTest, Small_1x1) { TEST_F(TransposeTest, Small_2x2) { auto aoperand = MakeLinspaceArray2D(0.0, 4.0, 2, 2); - ComputationBuilder builder(client_, "transpose_2x2"); + XlaBuilder builder("transpose_2x2"); auto operand = builder.ConstantR2FromArray2D(*aoperand); builder.Transpose(operand, {1, 0}); @@ -162,7 +162,7 @@ void TransposeTest::TestTransposeConstant021(size_t n1, size_t n2, size_t n3) { } } - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto operand = builder.ConstantR3FromArray3D(aoperand); builder.Transpose(operand, {0, 2, 1}); diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc index 61be1746530a19..5c287bac6a7cab 100644 --- a/tensorflow/compiler/xla/tests/tuple_test.cc +++ b/tensorflow/compiler/xla/tests/tuple_test.cc @@ -18,7 +18,6 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" @@ -287,13 +286,13 @@ XLA_TEST_F(TupleTest, SelectBetweenTuplesOnFalse) { } XLA_TEST_F(TupleTest, TuplesInAMap) { - Computation tuple_computation; + XlaComputation tuple_computation; { // tuple_computation(x) = 100 * min(x, x^2) + max(x, x^2) using tuples. // // Need to put a select in there to prevent HLO-level optimizations from // optimizing out the tuples. - ComputationBuilder b(client_, "sort_square"); + XlaBuilder b("sort_square"); auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); auto x2 = b.Mul(x, x); auto x_smaller_tuple = b.Tuple({x, x2}); @@ -307,7 +306,7 @@ XLA_TEST_F(TupleTest, TuplesInAMap) { tuple_computation = computation_status.ConsumeValueOrDie(); } - ComputationBuilder b(client_, TestName()); + XlaBuilder b(TestName()); auto input = b.ConstantR1({-1.0f, 1.0f, 2.1f}); b.Map({input}, tuple_computation, {0}); ComputeAndCompareR1(&b, {-99.0f, 101.0f, 214.41f}, {}, error_spec_); diff --git a/tensorflow/compiler/xla/tests/unary_op_test.cc b/tensorflow/compiler/xla/tests/unary_op_test.cc index 835e2d7e5594d7..50c8766f2e3976 100644 --- a/tensorflow/compiler/xla/tests/unary_op_test.cc +++ b/tensorflow/compiler/xla/tests/unary_op_test.cc @@ -16,9 +16,9 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -37,7 +37,7 @@ class UnaryOpTest : public ClientLibraryTestBase { } template void AbsSize0TestHelper() { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto arg = builder.ConstantR1({}); auto abs = builder.Abs(arg); @@ -50,7 +50,7 @@ class UnaryOpTest : public ClientLibraryTestBase { template void AbsTestHelper() { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto arg = builder.ConstantR1({-2, 25, 0, -123, inf(), -inf()}); auto abs = builder.Abs(arg); @@ -59,7 +59,7 @@ class UnaryOpTest : public ClientLibraryTestBase { template void SignTestHelper() { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto arg = builder.ConstantR1( {-2, 25, 0, static_cast(-0.0), -123, inf(), -inf()}); auto sign = builder.Sign(arg); @@ -69,7 +69,7 @@ class UnaryOpTest : public ClientLibraryTestBase { template void SignAbsTestHelper() { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto arg = builder.ConstantR1({-2, 25, 0, -123}); auto sign = builder.Sign(arg); auto abs = builder.Abs(arg); @@ -86,7 +86,7 @@ int UnaryOpTest::inf() { template <> void UnaryOpTest::AbsTestHelper() { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto arg = builder.ConstantR1({{-2, 0}, {0, 25}, {0, 0}, @@ -102,7 +102,7 @@ void UnaryOpTest::AbsTestHelper() { template <> void UnaryOpTest::SignTestHelper() { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto arg = builder.ConstantR1( {{-2, 0}, {0, 25}, {0, 0}, {static_cast(-0.0), 0}, {-1, 1}}); auto sign = builder.Sign(arg); @@ -114,7 +114,7 @@ void UnaryOpTest::SignTestHelper() { template <> void UnaryOpTest::SignAbsTestHelper() { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto arg = builder.ConstantR1({{-2, 0}, {0, 25}, {0, 0}, {-0.4, 0.3}}); auto sign = builder.Sign(arg); @@ -139,7 +139,7 @@ XLA_TEST_F(UnaryOpTest, AbsTestR1) { } XLA_TEST_F(UnaryOpTest, AbsTestR0) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto argi = builder.ConstantR0(-5); auto absi = builder.Abs(argi); auto argf = builder.ConstantR0(-3.0f); @@ -155,7 +155,7 @@ XLA_TEST_F(UnaryOpTest, AbsTestR0) { } XLA_TEST_F(UnaryOpTest, SignTestR0) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto argi = builder.ConstantR0(-5); auto sgni = builder.Sign(argi); // -1 auto argf = builder.ConstantR0(-4.0f); @@ -187,7 +187,7 @@ XLA_TEST_F(UnaryOpTest, SignAbsTestR1) { } XLA_TEST_F(UnaryOpTest, UnsignedAbsTestR1) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto arg = builder.ConstantR1( {2, 25, 0, 123, std::numeric_limits::max()}); auto abs = builder.Abs(arg); @@ -197,7 +197,7 @@ XLA_TEST_F(UnaryOpTest, UnsignedAbsTestR1) { } XLA_TEST_F(UnaryOpTest, UnsignedSignTestR1) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto arg = builder.ConstantR1( {2, 25, 0, 123, std::numeric_limits::max()}); auto sign = builder.Sign(arg); @@ -206,7 +206,7 @@ XLA_TEST_F(UnaryOpTest, UnsignedSignTestR1) { } XLA_TEST_F(UnaryOpTest, SignAbsTestR2) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto arg = builder.ConstantR2({{1.0, -2.0}, {-3.0, 4.0}}); auto sign = builder.Sign(arg); auto abs = builder.Abs(arg); @@ -216,7 +216,7 @@ XLA_TEST_F(UnaryOpTest, SignAbsTestR2) { } XLA_TEST_F(UnaryOpTest, ConvertElementTypePredToS32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto lhs = builder.ConstantR1({0, 1}); auto rhs = builder.ConstantR1({1, 1}); builder.ConvertElementType(builder.Eq(lhs, rhs), S32); @@ -225,7 +225,7 @@ XLA_TEST_F(UnaryOpTest, ConvertElementTypePredToS32) { } XLA_TEST_F(UnaryOpTest, ConvertElementTypePredToF32) { - ComputationBuilder builder(client_, TestName()); + XlaBuilder builder(TestName()); auto lhs = builder.ConstantR1({0, 1}); auto rhs = builder.ConstantR1({1, 1}); builder.ConvertElementType(builder.Eq(lhs, rhs), F32); diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc index 336fed27c6f19f..c463f3eac55e5b 100644 --- a/tensorflow/compiler/xla/tests/while_test.cc +++ b/tensorflow/compiler/xla/tests/while_test.cc @@ -957,22 +957,21 @@ TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithPrngScalarResult)) { TEST_F(WhileTest, WhileThatSwapsParameterWithTupleElement) { auto element_shape = ShapeUtil::MakeShape(F32, {2}); - ComputationBuilder outer(client_, "outer"); + XlaBuilder outer("outer"); auto p = outer.Parameter(0, element_shape, "param"); auto t = outer.Tuple({p, outer.ConstantR1({1, 1})}); - TF_ASSERT_OK_AND_ASSIGN(const std::unique_ptr tuple_shape, - outer.GetShape(t)); + TF_ASSERT_OK_AND_ASSIGN(Shape tuple_shape, outer.GetShape(t)); - ComputationBuilder cond(client_, "cond"); - auto cond_t = cond.Parameter(0, *tuple_shape, "t"); + XlaBuilder cond("cond"); + auto cond_t = cond.Parameter(0, tuple_shape, "t"); TF_ASSERT_OK(Any(cond.Eq(cond.GetTupleElement(cond_t, 0), cond.ConstantR1({42, 42})), &cond) .status()); - ComputationBuilder body(client_, "body"); - auto body_t = body.Parameter(0, *tuple_shape, "t"); + XlaBuilder body("body"); + auto body_t = body.Parameter(0, tuple_shape, "t"); auto e = body.GetTupleElement(body_t, 1); body.Tuple({e, e}); @@ -993,15 +992,15 @@ TEST_F(WhileTest, WhileThatSwapsParameterWithTupleElement) { TEST_F(WhileTest, WhileThatSwapsParameterWithBroadcast) { auto element_shape = ShapeUtil::MakeShape(F32, {2}); - ComputationBuilder outer(client_, "outer"); + XlaBuilder outer("outer"); auto p = outer.Parameter(0, element_shape, "param"); - ComputationBuilder cond(client_, "cond"); + XlaBuilder cond("cond"); auto cond_t = cond.Parameter(0, element_shape, "t"); TF_ASSERT_OK( Any(cond.Eq(cond_t, cond.ConstantR1({42, 42})), &cond).status()); - ComputationBuilder body(client_, "body"); + XlaBuilder body("body"); auto body_t = body.Parameter(0, element_shape, "t"); auto e = body.Broadcast(body.ConstantR0(1.0), {2}); @@ -1019,14 +1018,14 @@ TEST_F(WhileTest, WhileThatSwapsParameterWithBroadcast) { TEST_F(WhileTest, WhileThatTurnsScalarParameterToTupleElement) { auto element_shape = ShapeUtil::MakeShape(F32, {}); - ComputationBuilder outer(client_, "outer"); + XlaBuilder outer("outer"); auto p = outer.Parameter(0, element_shape, "param"); - ComputationBuilder cond(client_, "cond"); + XlaBuilder cond("cond"); auto cond_t = cond.Parameter(0, element_shape, "t"); cond.Eq(cond_t, cond.ConstantR0(42)); - ComputationBuilder body(client_, "body"); + XlaBuilder body("body"); auto body_t = body.Parameter(0, element_shape, "t"); auto tuple = body.Tuple({body_t, body.Add(body_t, body.ConstantR0(1))}); @@ -1055,23 +1054,23 @@ TEST_F(WhileTest, WhileWithMixedTupleElements) { auto result_shape = ShapeUtil::MakeTupleShape( {ShapeUtil::MakeShape(S32, {}), ShapeUtil::MakeShape(S32, {})}); - ComputationBuilder outer(client_, "outer"); + XlaBuilder outer("outer"); auto p = outer.Tuple({outer.ConstantR0(0), outer.Parameter(0, ShapeUtil::MakeShape(S32, {}), "t")}); - ComputationBuilder cond(client_, "cond"); + XlaBuilder cond("cond"); auto params = cond.Parameter(0, result_shape, "prev"); auto cond_t = cond.Add(cond.GetTupleElement(params, 1), cond.GetTupleElement(params, 0)); cond.Lt(cond_t, cond.ConstantR0(30)); - ComputationBuilder body(client_, "body"); + XlaBuilder body("body"); auto body_t = body.Parameter(0, result_shape, "t"); auto tuple = body.Tuple( - {body.Add(body.GetTupleElement(params, 0), body.ConstantR0(1)), - body.Add(body.GetTupleElement(params, 1), body.ConstantR0(1))}); + {body.Add(body.GetTupleElement(body_t, 0), body.ConstantR0(1)), + body.Add(body.GetTupleElement(body_t, 1), body.ConstantR0(1))}); TF_ASSERT_OK_AND_ASSIGN(auto cond_computation, cond.Build()); TF_ASSERT_OK_AND_ASSIGN(auto body_computation, body.Build()); diff --git a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc index 8354bb71cb7e88..7944b5132f3d11 100644 --- a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc +++ b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc @@ -17,8 +17,9 @@ limitations under the License. #include #include "tensorflow/compiler/xla/array2d.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/service/platform_util.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -119,7 +120,7 @@ Status ParseOneProfileOutputLine( // Returns void so that we can ASSERT. void ExecuteAndFetchProfile(string* profile_output, LocalClient* client, - const Computation& computation, + const XlaComputation& computation, const Shape& lhs_arg_shape, const Shape& rhs_arg_shape) { LocalService* service = ClientLibrary::GetXlaService(client->platform()); @@ -185,7 +186,7 @@ XLA_TEST_F(HloProfileTest, ProfileSingleComputation) { TF_ASSERT_OK_AND_ASSIGN(LocalClient * client, ClientLibrary::GetOrCreateLocalClient(platform)); - ComputationBuilder builder(client, TestName()); + XlaBuilder builder(TestName()); auto result = builder.Tanh(builder.Add( builder.Parameter(0, ShapeUtil::MakeShape(F32, {m, k}), "dot_lhs"), builder.Parameter(1, ShapeUtil::MakeShape(F32, {k, n}), "dot_rhs"))); @@ -251,18 +252,18 @@ XLA_TEST_F(HloProfileTest, DISABLED_ON_GPU(ProfileWhileComputation)) { TF_ASSERT_OK_AND_ASSIGN(LocalClient * client, ClientLibrary::GetOrCreateLocalClient(platform)); - Computation condition; + XlaComputation condition; { - ComputationBuilder builder(client, "condition"); + XlaBuilder builder("condition"); auto state = builder.Parameter(0, while_result_shape, "state"); auto iteration = builder.GetTupleElement(state, 0); builder.Gt(builder.ConstantR0(5), iteration); TF_ASSERT_OK_AND_ASSIGN(condition, builder.Build()); } - Computation body; + XlaComputation body; { - ComputationBuilder builder(client, "body"); + XlaBuilder builder("body"); auto state = builder.Parameter(0, while_result_shape, "state"); auto matrix = builder.GetTupleElement(state, 1); auto next_iteration = builder.Add(builder.GetTupleElement(state, 0), @@ -271,7 +272,7 @@ XLA_TEST_F(HloProfileTest, DISABLED_ON_GPU(ProfileWhileComputation)) { TF_ASSERT_OK_AND_ASSIGN(body, builder.Build()); } - ComputationBuilder builder(client, TestName()); + XlaBuilder builder(TestName()); auto initial_while_state = builder.Tuple({builder.ConstantR0(0), builder.Parameter(0, matrix_shape, "initial_value")}); From 521606da457c7ba9185b4742bd015fd63fe5dfd4 Mon Sep 17 00:00:00 2001 From: Pavithra Vijay Date: Thu, 26 Apr 2018 00:14:10 -0700 Subject: [PATCH 0045/1691] Support CuDNN RNN layers in tf.keras. PiperOrigin-RevId: 194355293 --- tensorflow/python/keras/BUILD | 16 + .../python/keras/_impl/keras/engine/saving.py | 103 ++-- .../keras/_impl/keras/engine/saving_test.py | 38 +- .../keras/_impl/keras/layers/__init__.py | 2 +- .../_impl/keras/layers/cudnn_recurrent.py | 522 ++++++++++++++++++ .../keras/layers/cudnn_recurrent_test.py | 436 +++++++++++++++ .../keras/_impl/keras/layers/recurrent.py | 11 +- .../keras/_impl/keras/layers/serialization.py | 1 + tensorflow/python/keras/layers/__init__.py | 10 +- ...sorflow.keras.layers.-cu-d-n-n-g-r-u.pbtxt | 193 +++++++ ...rflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt | 193 +++++++ .../api/golden/tensorflow.keras.layers.pbtxt | 8 + 12 files changed, 1496 insertions(+), 37 deletions(-) create mode 100644 tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent.py create mode 100644 tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent_test.py create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-g-r-u.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index a14a121b6e99dc..a1c9f539536333 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -188,6 +188,7 @@ py_library( "_impl/keras/layers/convolutional.py", "_impl/keras/layers/convolutional_recurrent.py", "_impl/keras/layers/core.py", + "_impl/keras/layers/cudnn_recurrent.py", "_impl/keras/layers/embeddings.py", "_impl/keras/layers/local.py", "_impl/keras/layers/merge.py", @@ -206,6 +207,7 @@ py_library( deps = [ ":engine", "//tensorflow/python:array_ops", + "//tensorflow/python:cudnn_rnn_ops_gen", "//tensorflow/python:distribute", "//tensorflow/python:dtypes", "//tensorflow/python:embedding_ops", @@ -476,6 +478,19 @@ py_test( ], ) +cuda_py_test( + name = "cudnn_recurrent_test", + size = "large", + srcs = ["_impl/keras/layers/cudnn_recurrent_test.py"], + additional_deps = [ + ":keras", + "@absl_py//absl/testing:parameterized", + "//third_party/py/numpy", + "//tensorflow/python:client_testlib", + ], + shard_count = 2, +) + py_test( name = "pooling_test", size = "small", @@ -845,6 +860,7 @@ py_test( ":keras", "//tensorflow/python:client_testlib", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/python/keras/_impl/keras/engine/saving.py b/tensorflow/python/keras/_impl/keras/engine/saving.py index 2ad06ca4fdcd55..a0b709a1a58436 100644 --- a/tensorflow/python/keras/_impl/keras/engine/saving.py +++ b/tensorflow/python/keras/_impl/keras/engine/saving.py @@ -498,34 +498,10 @@ def preprocess_weights_for_loading(layer, if layer.__class__.__name__ == 'ConvLSTM2D': weights[1] = np.transpose(weights[1], (3, 2, 0, 1)) - # Convert the weights of CuDNNLSTM so that they could be loaded into LSTM - if layer.__class__.__name__ == 'LSTM' and len(weights) == 3: - # Determine if loading a CuDNNLSTM layer from the number of bias weights: - # CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4) - # if there's no bias weight in the file, skip this conversion - units = weights[1].shape[0] - bias = weights[2] - if len(bias) == units * 8: - # reshape the kernels - kernels = np.split(weights[0], 4, axis=1) - kernels = [ - kernel.reshape(-1).reshape(kernel.shape, order='F') - for kernel in kernels - ] - weights[0] = np.concatenate(kernels, axis=1) + return _convert_rnn_weights(layer, weights) - # transpose the recurrent kernels - recurrent_kernels = np.split(weights[1], 4, axis=1) - recurrent_kernels = [kernel.T for kernel in recurrent_kernels] - weights[1] = np.concatenate(recurrent_kernels, axis=1) - # split the bias into half and merge - weights[2] = bias[:units * 4] + bias[units * 4:] - - return convert_rnn_weights(layer, weights) - - -def convert_rnn_weights(layer, weights): +def _convert_rnn_weights(layer, weights): """Converts weights for RNN layers between native and CuDNN format. Input kernels for each gate are transposed and converted between Fortran @@ -557,6 +533,7 @@ def transform_kernels(kernels, func, n_gates): kernels: Stacked array of kernels for individual gates. func: Function applied to kernel of each gate. n_gates: Number of gates (4 for LSTM, 3 for GRU). + Returns: Stacked array of transformed kernels. """ @@ -578,6 +555,7 @@ def transpose_input(from_cudnn): Arguments: from_cudnn: `True` if source weights are in CuDNN format, `False` if they're in plain Keras format. + Returns: Function that converts input kernel to the other format. """ @@ -608,22 +586,85 @@ def transform(kernel): raise ValueError('Invalid bias shape: ' + str(bias_shape)) def convert_lstm_weights(weights, from_cudnn=True): - # Transpose (and reshape) input and recurrent kernels. + """Converts the weights between CuDNNLSTM and LSTM. + + Arguments: + weights: Original weights. + from_cudnn: Indicates whether original weights are from CuDNN layer. + + Returns: + Updated weights compatible with LSTM. + """ + + # Transpose (and reshape) input and recurrent kernels kernels = transform_kernels(weights[0], transpose_input(from_cudnn), n_gates) recurrent_kernels = transform_kernels(weights[1], lambda k: k.T, n_gates) - if from_cudnn: # Merge input and recurrent biases into a single set. + if from_cudnn: + # merge input and recurrent biases into a single set biases = np.sum(np.split(weights[2], 2, axis=0), axis=0) else: - # Split single set of biases evenly to two sets. + # Split single set of biases evenly to two sets. The way of + # splitting doesn't matter as long as the two sets sum is kept. biases = np.tile(0.5 * weights[2], 2) return [kernels, recurrent_kernels, biases] if source != target_class: weights = convert_lstm_weights(weights, from_cudnn=source == 'CuDNNLSTM') - # TODO(fchollet): add feature after GRU is refactored: - # convert the weights between `CuDNNGRU` and `GRU(reset_after=True)` + # convert the weights between CuDNNGRU and GRU(reset_after=True) + if target_class in ['GRU', 'CuDNNGRU'] and len(weights) == 3: + # We can determine the source of the weights from the shape of the bias. + # If there is no bias we skip the conversion since + # CuDNNGRU always has biases. + + units = weights[1].shape[0] + bias_shape = weights[2].shape + n_gates = 3 + + def convert_gru_weights(weights, from_cudnn=True): + """Converts the weights between CuDNNGRU and GRU. + + Arguments: + weights: Original weights. + from_cudnn: Indicates whether original weights are from CuDNN layer. + + Returns: + Updated weights compatible with GRU. + """ + + kernels = transform_kernels(weights[0], transpose_input(from_cudnn), + n_gates) + recurrent_kernels = transform_kernels(weights[1], lambda k: k.T, n_gates) + biases = weights[2].reshape((2, -1) if from_cudnn else -1) + return [kernels, recurrent_kernels, biases] + + if bias_shape == (2 * units * n_gates,): + source = 'CuDNNGRU' + elif bias_shape == (2, units * n_gates): + source = 'GRU(reset_after=True)' + elif bias_shape == (units * n_gates,): + source = 'GRU(reset_after=False)' + else: + raise ValueError('Invalid bias shape: ' + str(bias_shape)) + + if target_class == 'CuDNNGRU': + target = 'CuDNNGRU' + elif layer.reset_after: + target = 'GRU(reset_after=True)' + else: + target = 'GRU(reset_after=False)' + + # only convert between different types + if source != target: + types = (source, target) + if 'GRU(reset_after=False)' in types: + raise ValueError('%s is not compatible with %s' % types) + if source == 'CuDNNGRU': + weights = convert_gru_weights(weights, from_cudnn=True) + elif source == 'GRU(reset_after=True)': + weights = convert_gru_weights(weights, from_cudnn=False) + return weights diff --git a/tensorflow/python/keras/_impl/keras/engine/saving_test.py b/tensorflow/python/keras/_impl/keras/engine/saving_test.py index edd296a281766e..709a8e9fb1e1ba 100644 --- a/tensorflow/python/keras/_impl/keras/engine/saving_test.py +++ b/tensorflow/python/keras/_impl/keras/engine/saving_test.py @@ -22,6 +22,7 @@ import shutil import tempfile +from absl.testing import parameterized import numpy as np from tensorflow.python.eager import context @@ -42,7 +43,7 @@ h5py = None -class TestWeightSavingAndLoading(test.TestCase): +class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase): def test_weight_loading(self): with self.test_session(): @@ -181,6 +182,41 @@ def test_weight_preprocessing(self): _ = keras.engine.saving.preprocess_weights_for_loading( model, model.weights, original_keras_version='1') + @parameterized.named_parameters( + ('gru', keras.layers.GRU, { + 'units': 2, + 'input_shape': (3, 5) + }), + ('gru_with_reset_after', keras.layers.GRU, { + 'units': 2, + 'input_shape': (3, 5), + 'reset_after': True + }), + ('lstm', keras.layers.LSTM, { + 'units': 2, + 'input_shape': (3, 5) + }), + ('cudnngru', keras.layers.CuDNNGRU, { + 'units': 2, + 'input_shape': (3, 5) + }), + ('cudnnlstm', keras.layers.CuDNNLSTM, { + 'units': 2, + 'input_shape': (3, 5) + })) + def test_preprocess_weights_for_loading_rnn_should_be_idempotent( + self, layer_class, layer_args): + with self.test_session(): + layer = layer_class(**layer_args) + layer.build(input_shape=layer_args.get('input_shape')) + weights1 = layer.get_weights() + weights2 = keras.engine.saving.preprocess_weights_for_loading( + layer, weights1) + _ = [ + self.assertAllClose(x, y, rtol=1e-05) + for (x, y) in zip(weights1, weights2) + ] + def test_sequential_weight_loading(self): if h5py is None: return diff --git a/tensorflow/python/keras/_impl/keras/layers/__init__.py b/tensorflow/python/keras/_impl/keras/layers/__init__.py index 81b2faf106925d..d7bc859280eeed 100644 --- a/tensorflow/python/keras/_impl/keras/layers/__init__.py +++ b/tensorflow/python/keras/_impl/keras/layers/__init__.py @@ -27,6 +27,7 @@ from tensorflow.python.keras._impl.keras.layers.convolutional import * from tensorflow.python.keras._impl.keras.layers.convolutional_recurrent import * from tensorflow.python.keras._impl.keras.layers.core import * +from tensorflow.python.keras._impl.keras.layers.cudnn_recurrent import * from tensorflow.python.keras._impl.keras.layers.embeddings import * from tensorflow.python.keras._impl.keras.layers.local import * from tensorflow.python.keras._impl.keras.layers.merge import * @@ -37,4 +38,3 @@ from tensorflow.python.keras._impl.keras.layers.serialization import deserialize from tensorflow.python.keras._impl.keras.layers.serialization import serialize from tensorflow.python.keras._impl.keras.layers.wrappers import * - diff --git a/tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent.py b/tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent.py new file mode 100644 index 00000000000000..ffb90457a85bb8 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent.py @@ -0,0 +1,522 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Recurrent layers backed by cuDNN. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections + +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import constraints +from tensorflow.python.keras._impl.keras import initializers +from tensorflow.python.keras._impl.keras import regularizers +from tensorflow.python.keras._impl.keras.engine import InputSpec +from tensorflow.python.keras._impl.keras.layers.recurrent import RNN +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_cudnn_rnn_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.util.tf_export import tf_export + + +class _CuDNNRNN(RNN): + """Private base class for CuDNNGRU and CuDNNLSTM layers. + + Arguments: + return_sequences: Boolean. Whether to return the last output + in the output sequence, or the full sequence. + return_state: Boolean. Whether to return the last state + in addition to the output. + go_backwards: Boolean (default False). + If True, process the input sequence backwards and return the + reversed sequence. + stateful: Boolean (default False). If True, the last state + for each sample at index i in a batch will be used as initial + state for the sample of index i in the following batch. + """ + + def __init__(self, + return_sequences=False, + return_state=False, + go_backwards=False, + stateful=False, + **kwargs): + # We invoke the base layer's initializer directly here because we do not + # want to create RNN cell instance. + super(RNN, self).__init__(**kwargs) # pylint: disable=bad-super-call + self.return_sequences = return_sequences + self.return_state = return_state + self.go_backwards = go_backwards + self.stateful = stateful + self.supports_masking = False + self.input_spec = [InputSpec(ndim=3)] + if hasattr(self.cell.state_size, '__len__'): + state_size = self.cell.state_size + else: + state_size = [self.cell.state_size] + self.state_spec = [InputSpec(shape=(None, dim)) for dim in state_size] + self.constants_spec = None + self._states = None + self._num_constants = None + + def _canonical_to_params(self, weights, biases): + weights = [array_ops.reshape(x, (-1,)) for x in weights] + biases = [array_ops.reshape(x, (-1,)) for x in biases] + return array_ops.concat(weights + biases, axis=0) + + def call(self, inputs, mask=None, training=None, initial_state=None): + if isinstance(mask, list): + mask = mask[0] + if mask is not None: + raise ValueError('Masking is not supported for CuDNN RNNs.') + + # input shape: `(samples, time (padded with zeros), input_dim)` + # note that the .build() method of subclasses MUST define + # self.input_spec and self.state_spec with complete input shapes. + if isinstance(inputs, list): + initial_state = inputs[1:] + inputs = inputs[0] + elif initial_state is not None: + pass + elif self.stateful: + initial_state = self.states + else: + initial_state = self.get_initial_state(inputs) + + if len(initial_state) != len(self.states): + raise ValueError('Layer has ' + str(len(self.states)) + + ' states but was passed ' + str(len(initial_state)) + + ' initial states.') + + if self.go_backwards: + # Reverse time axis. + inputs = K.reverse(inputs, 1) + output, states = self._process_batch(inputs, initial_state) + + if self.stateful: + updates = [] + for i in range(len(states)): + updates.append(state_ops.assign(self.states[i], states[i])) + self.add_update(updates, inputs) + + if self.return_state: + return [output] + states + else: + return output + + def get_config(self): + config = { + 'return_sequences': self.return_sequences, + 'return_state': self.return_state, + 'go_backwards': self.go_backwards, + 'stateful': self.stateful + } + base_config = super( # pylint: disable=bad-super-call + RNN, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + + @classmethod + def from_config(cls, config): + return cls(**config) + + @property + def trainable_weights(self): + if self.trainable and self.built: + return [self.kernel, self.recurrent_kernel, self.bias] + return [] + + @property + def non_trainable_weights(self): + if not self.trainable and self.built: + return [self.kernel, self.recurrent_kernel, self.bias] + return [] + + @property + def losses(self): + return super(RNN, self).losses + + def get_losses_for(self, inputs=None): + return super( # pylint: disable=bad-super-call + RNN, self).get_losses_for(inputs=inputs) + + +@tf_export('keras.layers.CuDNNGRU') +class CuDNNGRU(_CuDNNRNN): + """Fast GRU implementation backed by cuDNN. + + More information about cuDNN can be found on the [NVIDIA + developer website](https://developer.nvidia.com/cudnn). + Can only be run on GPU. + + Arguments: + units: Positive integer, dimensionality of the output space. + kernel_initializer: Initializer for the `kernel` weights matrix, used for + the linear transformation of the inputs. + recurrent_initializer: Initializer for the `recurrent_kernel` weights + matrix, used for the linear transformation of the recurrent state. + bias_initializer: Initializer for the bias vector. + kernel_regularizer: Regularizer function applied to the `kernel` weights + matrix. + recurrent_regularizer: Regularizer function applied to the + `recurrent_kernel` weights matrix. + bias_regularizer: Regularizer function applied to the bias vector. + activity_regularizer: Regularizer function applied to the output of the + layer (its "activation"). + kernel_constraint: Constraint function applied to the `kernel` weights + matrix. + recurrent_constraint: Constraint function applied to the + `recurrent_kernel` weights matrix. + bias_constraint: Constraint function applied to the bias vector. + return_sequences: Boolean. Whether to return the last output in the output + sequence, or the full sequence. + return_state: Boolean. Whether to return the last state in addition to the + output. + go_backwards: Boolean (default False). If True, process the input sequence + backwards and return the reversed sequence. + stateful: Boolean (default False). If True, the last state for each sample + at index i in a batch will be used as initial state for the sample of + index i in the following batch. + """ + + def __init__(self, + units, + kernel_initializer='glorot_uniform', + recurrent_initializer='orthogonal', + bias_initializer='zeros', + kernel_regularizer=None, + recurrent_regularizer=None, + bias_regularizer=None, + activity_regularizer=None, + kernel_constraint=None, + recurrent_constraint=None, + bias_constraint=None, + return_sequences=False, + return_state=False, + go_backwards=False, + stateful=False, + **kwargs): + self.units = units + cell_spec = collections.namedtuple('cell', 'state_size') + self._cell = cell_spec(state_size=self.units) + super(CuDNNGRU, self).__init__( + return_sequences=return_sequences, + return_state=return_state, + go_backwards=go_backwards, + stateful=stateful, + **kwargs) + + self.kernel_initializer = initializers.get(kernel_initializer) + self.recurrent_initializer = initializers.get(recurrent_initializer) + self.bias_initializer = initializers.get(bias_initializer) + + self.kernel_regularizer = regularizers.get(kernel_regularizer) + self.recurrent_regularizer = regularizers.get(recurrent_regularizer) + self.bias_regularizer = regularizers.get(bias_regularizer) + self.activity_regularizer = regularizers.get(activity_regularizer) + + self.kernel_constraint = constraints.get(kernel_constraint) + self.recurrent_constraint = constraints.get(recurrent_constraint) + self.bias_constraint = constraints.get(bias_constraint) + + @property + def cell(self): + return self._cell + + def build(self, input_shape): + super(CuDNNGRU, self).build(input_shape) + if isinstance(input_shape, list): + input_shape = input_shape[0] + input_dim = int(input_shape[-1]) + + self.kernel = self.add_weight( + shape=(input_dim, self.units * 3), + name='kernel', + initializer=self.kernel_initializer, + regularizer=self.kernel_regularizer, + constraint=self.kernel_constraint) + + self.recurrent_kernel = self.add_weight( + shape=(self.units, self.units * 3), + name='recurrent_kernel', + initializer=self.recurrent_initializer, + regularizer=self.recurrent_regularizer, + constraint=self.recurrent_constraint) + + self.bias = self.add_weight( + shape=(self.units * 6,), + name='bias', + initializer=self.bias_initializer, + regularizer=self.bias_regularizer, + constraint=self.bias_constraint) + + self.built = True + + def _process_batch(self, inputs, initial_state): + inputs = array_ops.transpose(inputs, perm=(1, 0, 2)) + input_h = initial_state[0] + input_h = array_ops.expand_dims(input_h, axis=0) + + params = self._canonical_to_params( + weights=[ + self.kernel[:, self.units:self.units * 2], + self.kernel[:, :self.units], + self.kernel[:, self.units * 2:], + self.recurrent_kernel[:, self.units:self.units * 2], + self.recurrent_kernel[:, :self.units], + self.recurrent_kernel[:, self.units * 2:], + ], + biases=[ + self.bias[self.units:self.units * 2], + self.bias[:self.units], + self.bias[self.units * 2:self.units * 3], + self.bias[self.units * 4:self.units * 5], + self.bias[self.units * 3:self.units * 4], + self.bias[self.units * 5:], + ], + ) + + outputs, h, _, _ = gen_cudnn_rnn_ops.cudnn_rnn( + inputs, + input_h=input_h, + input_c=0, + params=params, + is_training=True, + rnn_mode='gru') + + if self.stateful or self.return_state: + h = h[0] + if self.return_sequences: + output = array_ops.transpose(outputs, perm=(1, 0, 2)) + else: + output = outputs[-1] + return output, [h] + + def get_config(self): + config = { + 'units': self.units, + 'kernel_initializer': initializers.serialize(self.kernel_initializer), + 'recurrent_initializer': + initializers.serialize(self.recurrent_initializer), + 'bias_initializer': initializers.serialize(self.bias_initializer), + 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), + 'recurrent_regularizer': + regularizers.serialize(self.recurrent_regularizer), + 'bias_regularizer': regularizers.serialize(self.bias_regularizer), + 'activity_regularizer': + regularizers.serialize(self.activity_regularizer), + 'kernel_constraint': constraints.serialize(self.kernel_constraint), + 'recurrent_constraint': + constraints.serialize(self.recurrent_constraint), + 'bias_constraint': constraints.serialize(self.bias_constraint) + } + base_config = super(CuDNNGRU, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + + +@tf_export('keras.layers.CuDNNLSTM') +class CuDNNLSTM(_CuDNNRNN): + """Fast LSTM implementation backed by cuDNN. + + More information about cuDNN can be found on the [NVIDIA + developer website](https://developer.nvidia.com/cudnn). + Can only be run on GPU. + + Arguments: + units: Positive integer, dimensionality of the output space. + kernel_initializer: Initializer for the `kernel` weights matrix, used for + the linear transformation of the inputs. + unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate + at initialization. Setting it to true will also force + `bias_initializer="zeros"`. This is recommended in [Jozefowicz et + al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) + recurrent_initializer: Initializer for the `recurrent_kernel` weights + matrix, used for the linear transformation of the recurrent state. + bias_initializer: Initializer for the bias vector. + kernel_regularizer: Regularizer function applied to the `kernel` weights + matrix. + recurrent_regularizer: Regularizer function applied to the + `recurrent_kernel` weights matrix. + bias_regularizer: Regularizer function applied to the bias vector. + activity_regularizer: Regularizer function applied to the output of the + layer (its "activation"). + kernel_constraint: Constraint function applied to the `kernel` weights + matrix. + recurrent_constraint: Constraint function applied to the + `recurrent_kernel` weights matrix. + bias_constraint: Constraint function applied to the bias vector. + return_sequences: Boolean. Whether to return the last output. in the + output sequence, or the full sequence. + return_state: Boolean. Whether to return the last state in addition to the + output. + go_backwards: Boolean (default False). If True, process the input sequence + backwards and return the reversed sequence. + stateful: Boolean (default False). If True, the last state for each sample + at index i in a batch will be used as initial state for the sample of + index i in the following batch. + """ + + def __init__(self, + units, + kernel_initializer='glorot_uniform', + recurrent_initializer='orthogonal', + bias_initializer='zeros', + unit_forget_bias=True, + kernel_regularizer=None, + recurrent_regularizer=None, + bias_regularizer=None, + activity_regularizer=None, + kernel_constraint=None, + recurrent_constraint=None, + bias_constraint=None, + return_sequences=False, + return_state=False, + go_backwards=False, + stateful=False, + **kwargs): + self.units = units + cell_spec = collections.namedtuple('cell', 'state_size') + self._cell = cell_spec(state_size=(self.units, self.units)) + super(CuDNNLSTM, self).__init__( + return_sequences=return_sequences, + return_state=return_state, + go_backwards=go_backwards, + stateful=stateful, + **kwargs) + + self.kernel_initializer = initializers.get(kernel_initializer) + self.recurrent_initializer = initializers.get(recurrent_initializer) + self.bias_initializer = initializers.get(bias_initializer) + self.unit_forget_bias = unit_forget_bias + + self.kernel_regularizer = regularizers.get(kernel_regularizer) + self.recurrent_regularizer = regularizers.get(recurrent_regularizer) + self.bias_regularizer = regularizers.get(bias_regularizer) + self.activity_regularizer = regularizers.get(activity_regularizer) + + self.kernel_constraint = constraints.get(kernel_constraint) + self.recurrent_constraint = constraints.get(recurrent_constraint) + self.bias_constraint = constraints.get(bias_constraint) + + @property + def cell(self): + return self._cell + + def build(self, input_shape): + super(CuDNNLSTM, self).build(input_shape) + if isinstance(input_shape, list): + input_shape = input_shape[0] + input_dim = int(input_shape[-1]) + + self.kernel = self.add_weight( + shape=(input_dim, self.units * 4), + name='kernel', + initializer=self.kernel_initializer, + regularizer=self.kernel_regularizer, + constraint=self.kernel_constraint) + + self.recurrent_kernel = self.add_weight( + shape=(self.units, self.units * 4), + name='recurrent_kernel', + initializer=self.recurrent_initializer, + regularizer=self.recurrent_regularizer, + constraint=self.recurrent_constraint) + + if self.unit_forget_bias: + + def bias_initializer(_, *args, **kwargs): + return array_ops.concat([ + self.bias_initializer((self.units * 5,), *args, **kwargs), + initializers.Ones()((self.units,), *args, **kwargs), + self.bias_initializer((self.units * 2,), *args, **kwargs), + ], axis=0) + else: + bias_initializer = self.bias_initializer + self.bias = self.add_weight( + shape=(self.units * 8,), + name='bias', + initializer=bias_initializer, + regularizer=self.bias_regularizer, + constraint=self.bias_constraint) + + self.built = True + + def _process_batch(self, inputs, initial_state): + inputs = array_ops.transpose(inputs, perm=(1, 0, 2)) + input_h = initial_state[0] + input_c = initial_state[1] + input_h = array_ops.expand_dims(input_h, axis=0) + input_c = array_ops.expand_dims(input_c, axis=0) + + params = self._canonical_to_params( + weights=[ + self.kernel[:, :self.units], + self.kernel[:, self.units:self.units * 2], + self.kernel[:, self.units * 2:self.units * 3], + self.kernel[:, self.units * 3:], + self.recurrent_kernel[:, :self.units], + self.recurrent_kernel[:, self.units:self.units * 2], + self.recurrent_kernel[:, self.units * 2:self.units * 3], + self.recurrent_kernel[:, self.units * 3:], + ], + biases=[ + self.bias[:self.units], + self.bias[self.units:self.units * 2], + self.bias[self.units * 2:self.units * 3], + self.bias[self.units * 3:self.units * 4], + self.bias[self.units * 4:self.units * 5], + self.bias[self.units * 5:self.units * 6], + self.bias[self.units * 6:self.units * 7], + self.bias[self.units * 7:], + ], + ) + + outputs, h, c, _ = gen_cudnn_rnn_ops.cudnn_rnn( + inputs, + input_h=input_h, + input_c=input_c, + params=params, + is_training=True) + + if self.stateful or self.return_state: + h = h[0] + c = c[0] + if self.return_sequences: + output = array_ops.transpose(outputs, perm=(1, 0, 2)) + else: + output = outputs[-1] + return output, [h, c] + + def get_config(self): + config = { + 'units': self.units, + 'kernel_initializer': initializers.serialize(self.kernel_initializer), + 'recurrent_initializer': + initializers.serialize(self.recurrent_initializer), + 'bias_initializer': initializers.serialize(self.bias_initializer), + 'unit_forget_bias': self.unit_forget_bias, + 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), + 'recurrent_regularizer': + regularizers.serialize(self.recurrent_regularizer), + 'bias_regularizer': regularizers.serialize(self.bias_regularizer), + 'activity_regularizer': + regularizers.serialize(self.activity_regularizer), + 'kernel_constraint': constraints.serialize(self.kernel_constraint), + 'recurrent_constraint': + constraints.serialize(self.recurrent_constraint), + 'bias_constraint': constraints.serialize(self.bias_constraint) + } + base_config = super(CuDNNLSTM, self).get_config() + return dict(list(base_config.items()) + list(config.items())) diff --git a/tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent_test.py b/tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent_test.py new file mode 100644 index 00000000000000..a06943b1083057 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent_test.py @@ -0,0 +1,436 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for cudnn recurrent layers.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import time + +from absl.testing import parameterized +import numpy as np + +from tensorflow.python.framework import test_util +from tensorflow.python.keras._impl import keras +from tensorflow.python.keras._impl.keras import testing_utils +from tensorflow.python.platform import test +from tensorflow.python.training.rmsprop import RMSPropOptimizer + + +class CuDNNTest(test.TestCase, parameterized.TestCase): + + @test_util.run_in_graph_and_eager_modes() + def test_cudnn_rnn_timing(self): + if test.is_gpu_available(cuda_only=True): + with self.test_session(use_gpu=True): + input_size = 10 + timesteps = 6 + units = 2 + num_samples = 32 + + for rnn_type in ['lstm', 'gru']: + times = [] + for use_cudnn in [True, False]: + start_time = time.time() + inputs = keras.layers.Input(shape=(None, input_size)) + if use_cudnn: + if rnn_type == 'lstm': + layer = keras.layers.CuDNNLSTM(units) + else: + layer = keras.layers.CuDNNGRU(units) + else: + if rnn_type == 'lstm': + layer = keras.layers.LSTM(units) + else: + layer = keras.layers.GRU(units) + outputs = layer(inputs) + + optimizer = RMSPropOptimizer(learning_rate=0.001) + model = keras.models.Model(inputs, outputs) + model.compile(optimizer, 'mse') + + x = np.random.random((num_samples, timesteps, input_size)) + y = np.random.random((num_samples, units)) + model.fit(x, y, epochs=4, batch_size=32) + + times.append(time.time() - start_time) + self.assertGreater(times[1], times[0]) + + @test_util.run_in_graph_and_eager_modes() + def test_cudnn_rnn_basics(self): + if test.is_gpu_available(cuda_only=True): + with self.test_session(use_gpu=True): + input_size = 10 + timesteps = 6 + units = 2 + num_samples = 32 + for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]: + for return_sequences in [True, False]: + with keras.utils.CustomObjectScope( + {'keras.layers.CuDNNGRU': keras.layers.CuDNNGRU, + 'keras.layers.CuDNNLSTM': keras.layers.CuDNNLSTM}): + testing_utils.layer_test( + layer_class, + kwargs={'units': units, + 'return_sequences': return_sequences}, + input_shape=(num_samples, timesteps, input_size)) + for go_backwards in [True, False]: + with keras.utils.CustomObjectScope( + {'keras.layers.CuDNNGRU': keras.layers.CuDNNGRU, + 'keras.layers.CuDNNLSTM': keras.layers.CuDNNLSTM}): + testing_utils.layer_test( + layer_class, + kwargs={'units': units, + 'go_backwards': go_backwards}, + input_shape=(num_samples, timesteps, input_size)) + + @test_util.run_in_graph_and_eager_modes() + def test_trainability(self): + if test.is_gpu_available(cuda_only=True): + with self.test_session(use_gpu=True): + input_size = 10 + units = 2 + for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]: + layer = layer_class(units) + layer.build((None, None, input_size)) + self.assertEqual(len(layer.weights), 3) + self.assertEqual(len(layer.trainable_weights), 3) + self.assertEqual(len(layer.non_trainable_weights), 0) + layer.trainable = False + self.assertEqual(len(layer.weights), 3) + self.assertEqual(len(layer.non_trainable_weights), 3) + self.assertEqual(len(layer.trainable_weights), 0) + layer.trainable = True + self.assertEqual(len(layer.weights), 3) + self.assertEqual(len(layer.trainable_weights), 3) + self.assertEqual(len(layer.non_trainable_weights), 0) + + @parameterized.named_parameters( + ('cudnngru', keras.layers.CuDNNGRU), + ('cudnnlstm', keras.layers.CuDNNLSTM), + ) + def test_regularizer(self, layer_class): + if test.is_gpu_available(cuda_only=True): + with self.test_session(use_gpu=True): + input_size = 10 + timesteps = 6 + units = 2 + num_samples = 32 + layer = layer_class( + units, + return_sequences=False, + input_shape=(timesteps, input_size), + kernel_regularizer=keras.regularizers.l1(0.01), + recurrent_regularizer=keras.regularizers.l1(0.01), + bias_regularizer='l2') + layer.build((None, None, input_size)) + self.assertEqual(len(layer.losses), 3) + + layer = layer_class( + units, + return_sequences=False, + input_shape=(timesteps, input_size), + activity_regularizer='l2') + self.assertTrue(layer.activity_regularizer) + x = keras.backend.variable( + np.ones((num_samples, timesteps, input_size))) + layer(x) + self.assertEqual(len(layer.get_losses_for(x)), 1) + + @parameterized.named_parameters( + ('cudnngru', keras.layers.CuDNNGRU), + ('cudnnlstm', keras.layers.CuDNNLSTM), + ) + def test_return_state(self, layer_class): + if test.is_gpu_available(cuda_only=True): + with self.test_session(use_gpu=True): + input_size = 10 + timesteps = 6 + units = 2 + num_samples = 32 + num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1 + + inputs = keras.Input(batch_shape=(num_samples, timesteps, input_size)) + layer = layer_class(units, return_state=True, stateful=True) + outputs = layer(inputs) + _, state = outputs[0], outputs[1:] + self.assertEqual(len(state), num_states) + model = keras.models.Model(inputs, state[0]) + + inputs = np.random.random((num_samples, timesteps, input_size)) + state = model.predict(inputs) + np.testing.assert_allclose( + keras.backend.eval(layer.states[0]), state, atol=1e-4) + + @parameterized.named_parameters( + ('cudnngru', keras.layers.CuDNNGRU), + ('cudnnlstm', keras.layers.CuDNNLSTM), + ) + def test_specify_initial_state_keras_tensor(self, layer_class): + if test.is_gpu_available(cuda_only=True): + with self.test_session(use_gpu=True): + input_size = 10 + timesteps = 6 + units = 2 + num_samples = 32 + num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1 + + inputs = keras.Input((timesteps, input_size)) + initial_state = [keras.Input((units,)) for _ in range(num_states)] + layer = layer_class(units) + if len(initial_state) == 1: + output = layer(inputs, initial_state=initial_state[0]) + else: + output = layer(inputs, initial_state=initial_state) + self.assertIn(initial_state[0], layer._inbound_nodes[0].input_tensors) + + model = keras.models.Model([inputs] + initial_state, output) + model.compile(loss='categorical_crossentropy', optimizer='adam') + + inputs = np.random.random((num_samples, timesteps, input_size)) + initial_state = [ + np.random.random((num_samples, units)) for _ in range(num_states) + ] + targets = np.random.random((num_samples, units)) + model.fit([inputs] + initial_state, targets) + + @parameterized.named_parameters( + ('cudnngru', keras.layers.CuDNNGRU), + ('cudnnlstm', keras.layers.CuDNNLSTM), + ) + def test_statefulness(self, layer_class): + if test.is_gpu_available(cuda_only=True): + with self.test_session(use_gpu=True): + input_size = 10 + timesteps = 6 + units = 2 + num_samples = 32 + + model = keras.models.Sequential() + model.add( + keras.layers.Embedding( + 10, + input_size, + input_length=timesteps, + batch_input_shape=(num_samples, timesteps))) + layer = layer_class( + units, return_sequences=False, stateful=True, weights=None) + model.add(layer) + model.compile(optimizer='sgd', loss='mse') + out1 = model.predict(np.ones((num_samples, timesteps))) + self.assertEqual(out1.shape, (num_samples, units)) + + # train once so that the states change + model.train_on_batch( + np.ones((num_samples, timesteps)), np.ones((num_samples, units))) + out2 = model.predict(np.ones((num_samples, timesteps))) + + # if the state is not reset, output should be different + self.assertNotEqual(out1.max(), out2.max()) + + # check that output changes after states are reset + # (even though the model itself didn't change) + layer.reset_states() + out3 = model.predict(np.ones((num_samples, timesteps))) + self.assertNotEqual(out2.max(), out3.max()) + + # check that container-level reset_states() works + model.reset_states() + out4 = model.predict(np.ones((num_samples, timesteps))) + self.assertAllClose(out3, out4, atol=1e-5) + + # check that the call to `predict` updated the states + out5 = model.predict(np.ones((num_samples, timesteps))) + self.assertNotEqual(out4.max(), out5.max()) + + # TODO(psv): Add generic cross product helper function for parametrized tests. + @parameterized.named_parameters( + ('cudnnlstm_to_lstm_unidirectional_impl_1', 'LSTM', False, False, 1), + ('cudnnlstm_to_lstm_bidirectional_impl_1', 'LSTM', False, True, 1), + ('lstm_to_cudnnlstm_unidirectional_impl_1', 'LSTM', True, False, 1), + ('lstm_to_cudnnlstm_bidirectional_impl_1', 'LSTM', True, True, 1), + ('cudnngru_to_gru_unidirectional_impl_1', 'GRU', False, False, 1), + ('cudnngru_to_gru_bidirectional_impl_1', 'GRU', False, True, 1), + ('gru_to_cudnngru_unidirectional_impl_1', 'GRU', True, False, 1), + ('gru_to_cudnngru_bidirectional_impl_1', 'GRU', True, True, 1), + ('cudnnlstm_to_lstm_unidirectional_impl_2', 'LSTM', False, False, 2), + ('cudnnlstm_to_lstm_bidirectional_impl_2', 'LSTM', False, True, 2), + ('lstm_to_cudnnlstm_unidirectional_impl_2', 'LSTM', True, False, 2), + ('lstm_to_cudnnlstm_bidirectional_impl_2', 'LSTM', True, True, 2), + ('cudnngru_to_gru_unidirectional_impl_2', 'GRU', False, False, 2), + ('cudnngru_to_gru_bidirectional_impl_2', 'GRU', False, True, 2), + ('gru_to_cudnngru_unidirectional_impl_2', 'GRU', True, False, 2), + ('gru_to_cudnngru_bidirectional_impl_2', 'GRU', True, True, 2), + ) + def test_load_weights_between_noncudnn_rnn(self, rnn_type, to_cudnn, + bidirectional, implementation): + if test.is_gpu_available(cuda_only=True): + with self.test_session(use_gpu=True): + input_size = 10 + timesteps = 6 + input_shape = (timesteps, input_size) + units = 2 + num_samples = 32 + inputs = np.random.random((num_samples, timesteps, input_size)) + + rnn_layer_kwargs = { + 'recurrent_activation': 'sigmoid', + # ensure biases are non-zero and properly converted + 'bias_initializer': 'random_uniform', + 'implementation': implementation + } + if rnn_type == 'LSTM': + rnn_layer_class = keras.layers.LSTM + cudnn_rnn_layer_class = keras.layers.CuDNNLSTM + else: + rnn_layer_class = keras.layers.GRU + cudnn_rnn_layer_class = keras.layers.CuDNNGRU + rnn_layer_kwargs['reset_after'] = True + + def convert_weights(source_layer, target_layer): + weights = source_layer.get_weights() + weights = keras.engine.saving.preprocess_weights_for_loading( + target_layer, weights) + target_layer.set_weights(weights) + + input_layer = keras.layers.InputLayer(input_shape) + + layer = rnn_layer_class(units, **rnn_layer_kwargs) + if bidirectional: + layer = keras.layers.Bidirectional(layer) + + cudnn_layer = cudnn_rnn_layer_class(units) + if bidirectional: + cudnn_layer = keras.layers.Bidirectional(cudnn_layer) + + model = keras.models.Sequential([input_layer, layer]) + cudnn_model = keras.models.Sequential([input_layer, cudnn_layer]) + + if to_cudnn: + convert_weights(layer, cudnn_layer) + else: + convert_weights(cudnn_layer, layer) + + self.assertAllClose( + model.predict(inputs), cudnn_model.predict(inputs), atol=1e-4) + + @test_util.run_in_graph_and_eager_modes() + def test_cudnnrnn_bidirectional(self): + if test.is_gpu_available(cuda_only=True): + with self.test_session(use_gpu=True): + rnn = keras.layers.CuDNNGRU + samples = 2 + dim = 2 + timesteps = 2 + output_dim = 2 + mode = 'concat' + + x = np.random.random((samples, timesteps, dim)) + target_dim = 2 * output_dim if mode == 'concat' else output_dim + y = np.random.random((samples, target_dim)) + + # test with Sequential model + model = keras.Sequential() + model.add( + keras.layers.Bidirectional( + rnn(output_dim), merge_mode=mode, input_shape=(None, dim))) + model.compile( + loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.fit(x, y, epochs=1, batch_size=1) + + # test config + model.get_config() + model = keras.models.model_from_json(model.to_json()) + model.summary() + + # test stacked bidirectional layers + model = keras.Sequential() + model.add( + keras.layers.Bidirectional( + rnn(output_dim, return_sequences=True), + merge_mode=mode, + input_shape=(None, dim))) + model.add(keras.layers.Bidirectional(rnn(output_dim), merge_mode=mode)) + model.compile( + loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.fit(x, y, epochs=1, batch_size=1) + + # test with functional API + inputs = keras.Input((timesteps, dim)) + outputs = keras.layers.Bidirectional( + rnn(output_dim), merge_mode=mode)( + inputs) + model = keras.Model(inputs, outputs) + model.compile( + loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.fit(x, y, epochs=1, batch_size=1) + + # Bidirectional and stateful + inputs = keras.Input(batch_shape=(1, timesteps, dim)) + outputs = keras.layers.Bidirectional( + rnn(output_dim, stateful=True), merge_mode=mode)( + inputs) + model = keras.Model(inputs, outputs) + model.compile( + loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.fit(x, y, epochs=1, batch_size=1) + + def test_preprocess_weights_for_loading_gru_incompatible(self): + """Test loading weights between incompatible layers. + + Should fail fast with an exception. + """ + if test.is_gpu_available(cuda_only=True): + with self.test_session(use_gpu=True): + input_shape = (3, 5) + + def gru(cudnn=False, **kwargs): + layer_class = keras.layers.CuDNNGRU if cudnn else keras.layers.GRU + return layer_class(2, input_shape=input_shape, **kwargs) + + def get_layer_weights(layer): + layer.build(input_shape=input_shape) + return layer.get_weights() + + def assert_not_compatible(src, dest, message): + with self.assertRaises(ValueError) as ex: + keras.engine.saving.preprocess_weights_for_loading( + dest, + get_layer_weights(src)) + self.assertIn(message, str(ex.exception)) + + assert_not_compatible( + gru(), + gru(cudnn=True), + 'GRU(reset_after=False) is not compatible with CuDNNGRU') + assert_not_compatible( + gru(cudnn=True), + gru(), + 'CuDNNGRU is not compatible with GRU(reset_after=False)') + assert_not_compatible( + gru(), + gru(reset_after=True), + 'GRU(reset_after=False) is not compatible with ' + 'GRU(reset_after=True)') + assert_not_compatible( + gru(reset_after=True), + gru(), + 'GRU(reset_after=True) is not compatible with ' + 'GRU(reset_after=False)') + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/keras/_impl/keras/layers/recurrent.py b/tensorflow/python/keras/_impl/keras/layers/recurrent.py index f6d6e1391c834b..caf9e6f46f51c7 100644 --- a/tensorflow/python/keras/_impl/keras/layers/recurrent.py +++ b/tensorflow/python/keras/_impl/keras/layers/recurrent.py @@ -503,6 +503,7 @@ def build(self, input_shape): self.state_spec = [InputSpec(shape=(None, dim)) for dim in state_size] if self.stateful: self.reset_states() + self.built = True def get_initial_state(self, inputs): # build an all-zero tensor of shape (samples, output_dim) @@ -1417,7 +1418,15 @@ def call(self, inputs, states, training=None): if 0. < self.recurrent_dropout < 1.: h_tm1 *= rec_dp_mask[0] - matrix_inner = K.dot(h_tm1, self.recurrent_kernel[:, :2 * self.units]) + + if self.reset_after: + # hidden state projected by all gate matrices at once + matrix_inner = K.dot(h_tm1, self.recurrent_kernel) + if self.use_bias: + matrix_inner = K.bias_add(matrix_inner, self.recurrent_bias) + else: + # hidden state projected separately for update/reset and new + matrix_inner = K.dot(h_tm1, self.recurrent_kernel[:, :2 * self.units]) recurrent_z = matrix_inner[:, :self.units] recurrent_r = matrix_inner[:, self.units:2 * self.units] diff --git a/tensorflow/python/keras/_impl/keras/layers/serialization.py b/tensorflow/python/keras/_impl/keras/layers/serialization.py index 928feaadbf3554..8151ad7fdddefe 100644 --- a/tensorflow/python/keras/_impl/keras/layers/serialization.py +++ b/tensorflow/python/keras/_impl/keras/layers/serialization.py @@ -26,6 +26,7 @@ from tensorflow.python.keras._impl.keras.layers.convolutional import * from tensorflow.python.keras._impl.keras.layers.convolutional_recurrent import * from tensorflow.python.keras._impl.keras.layers.core import * +from tensorflow.python.keras._impl.keras.layers.cudnn_recurrent import * from tensorflow.python.keras._impl.keras.layers.embeddings import * from tensorflow.python.keras._impl.keras.layers.local import * from tensorflow.python.keras._impl.keras.layers.merge import * diff --git a/tensorflow/python/keras/layers/__init__.py b/tensorflow/python/keras/layers/__init__.py index b45cafed3186a0..c7be8b918c1123 100644 --- a/tensorflow/python/keras/layers/__init__.py +++ b/tensorflow/python/keras/layers/__init__.py @@ -62,9 +62,6 @@ from tensorflow.python.keras._impl.keras.layers.convolutional import Cropping2D from tensorflow.python.keras._impl.keras.layers.convolutional import Cropping3D -# Convolutional-recurrent layers. -from tensorflow.python.keras._impl.keras.layers.convolutional_recurrent import ConvLSTM2D - # Core layers. from tensorflow.python.keras._impl.keras.layers.core import Masking from tensorflow.python.keras._impl.keras.layers.core import Dropout @@ -147,6 +144,13 @@ from tensorflow.python.keras._impl.keras.layers.recurrent import GRU from tensorflow.python.keras._impl.keras.layers.recurrent import LSTM +# Convolutional-recurrent layers. +from tensorflow.python.keras._impl.keras.layers.convolutional_recurrent import ConvLSTM2D + +# CuDNN recurrent layers. +from tensorflow.python.keras._impl.keras.layers.cudnn_recurrent import CuDNNLSTM +from tensorflow.python.keras._impl.keras.layers.cudnn_recurrent import CuDNNGRU + # Wrapper functions from tensorflow.python.keras._impl.keras.layers.wrappers import Wrapper from tensorflow.python.keras._impl.keras.layers.wrappers import Bidirectional diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-g-r-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-g-r-u.pbtxt new file mode 100644 index 00000000000000..8ce4db85f8e42f --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-g-r-u.pbtxt @@ -0,0 +1,193 @@ +path: "tensorflow.keras.layers.CuDNNGRU" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "cell" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "states" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'units\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\'], varargs=None, keywords=kwargs, defaults=[\'glorot_uniform\', \'orthogonal\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'False\', \'False\', \'False\', \'False\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\', \'mask\', \'training\', \'initial_state\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_initial_state" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "reset_states" + argspec: "args=[\'self\', \'states\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt new file mode 100644 index 00000000000000..98221c11650eaf --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt @@ -0,0 +1,193 @@ +path: "tensorflow.keras.layers.CuDNNLSTM" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "cell" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "states" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'units\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'unit_forget_bias\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\'], varargs=None, keywords=kwargs, defaults=[\'glorot_uniform\', \'orthogonal\', \'zeros\', \'True\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'False\', \'False\', \'False\', \'False\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\', \'mask\', \'training\', \'initial_state\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_initial_state" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "reset_states" + argspec: "args=[\'self\', \'states\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt index affc9bd09b1124..709eb5be55ef18 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt @@ -112,6 +112,14 @@ tf_module { name: "Cropping3D" mtype: "" } + member { + name: "CuDNNGRU" + mtype: "" + } + member { + name: "CuDNNLSTM" + mtype: "" + } member { name: "Dense" mtype: "" From 59a4b484f9f98be835260825a82eb303a2ee47fd Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 03:21:43 -0700 Subject: [PATCH 0046/1691] Clarify limitation of `deps` in tf_gen_op_wrapper_py PiperOrigin-RevId: 194372273 --- tensorflow/tensorflow.bzl | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index a9ddd4fc606798..e5cc886b3251f9 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -509,7 +509,9 @@ def tf_gen_op_wrappers_cc(name, # hidden: Optional list of ops names to make private in the Python module. # It is invalid to specify both "hidden" and "op_whitelist". # visibility: passed to py_library. -# deps: list of dependencies for the generated target. +# deps: list of dependencies for the intermediate tool used to generate the +# python target. NOTE these `deps` are not applied to the final python +# library target itself. # require_shape_functions: leave this as False. # hidden_file: optional file that contains a list of op names to make private # in the generated Python module. Each op name should be on a line by From 8148895adc1cf35112fb7197a798bc825a61e4f6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 03:40:24 -0700 Subject: [PATCH 0047/1691] Support matching against shape string in HLO testing matchers After this change a test can use op::Shape("f32[7,11]") instead of the longer and harder to read op::Shape(ShapeUtil::MakeShape(F32, {7, 11})) format. PiperOrigin-RevId: 194373704 --- tensorflow/compiler/xla/service/hlo_matchers.h | 10 ++++++++++ tensorflow/compiler/xla/service/hlo_matchers_test.cc | 7 +++++++ 2 files changed, 17 insertions(+) diff --git a/tensorflow/compiler/xla/service/hlo_matchers.h b/tensorflow/compiler/xla/service/hlo_matchers.h index f2ab9b5d9b6e00..5175736a2506c8 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers.h +++ b/tensorflow/compiler/xla/service/hlo_matchers.h @@ -282,11 +282,21 @@ inline ::testing::Matcher Shape( const class Shape& shape) { return ::testing::MakeMatcher(new ::xla::testing::HloShapeMatcher(shape)); } +inline ::testing::Matcher Shape( + tensorflow::StringPiece shape) { + return ::testing::MakeMatcher(new ::xla::testing::HloShapeMatcher( + ShapeUtil::ParseShapeString(shape).ValueOrDie())); +} inline ::testing::Matcher ShapeWithLayout( const class Shape& shape) { return ::testing::MakeMatcher( new ::xla::testing::HloShapeAndLayoutMatcher(shape)); } +inline ::testing::Matcher ShapeWithLayout( + tensorflow::StringPiece shape) { + return ::testing::MakeMatcher(new ::xla::testing::HloShapeAndLayoutMatcher( + ShapeUtil::ParseShapeString(shape).ValueOrDie())); +} // Verifies the value of the HloSharing against the provided sharding object. inline ::testing::Matcher Sharding( diff --git a/tensorflow/compiler/xla/service/hlo_matchers_test.cc b/tensorflow/compiler/xla/service/hlo_matchers_test.cc index c6373b2e46af7d..f2463060b7cd65 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers_test.cc +++ b/tensorflow/compiler/xla/service/hlo_matchers_test.cc @@ -105,21 +105,28 @@ TEST(HloMatchersTest, ShapeMatcher) { 0, ShapeUtil::MakeShapeWithLayout(F32, {5, 7}, {0, 1}), "param"); EXPECT_THAT(p0.get(), op::Shape(ShapeUtil::MakeShape(F32, {5, 7}))); + EXPECT_THAT(p0.get(), op::Shape("f32[5,7]")); EXPECT_THAT( p0.get(), ::testing::Not(op::ShapeWithLayout(ShapeUtil::MakeShape(F32, {5, 7})))); + EXPECT_THAT(p0.get(), ::testing::Not(op::ShapeWithLayout("f32[5,7]"))); EXPECT_THAT(p0.get(), ::testing::Not(op::Shape(ShapeUtil::MakeShape(F32, {7, 5})))); + EXPECT_THAT(p0.get(), ::testing::Not(op::Shape("f32[7,5]"))); EXPECT_THAT( p0.get(), ::testing::Not(op::ShapeWithLayout(ShapeUtil::MakeShape(F32, {7, 5})))); + EXPECT_THAT(p0.get(), ::testing::Not(op::ShapeWithLayout("f32[7,5]"))); EXPECT_THAT(p0.get(), op::Shape(ShapeUtil::MakeShapeWithLayout(F32, {5, 7}, {0, 1}))); + EXPECT_THAT(p0.get(), op::Shape("f32[5,7]{0,1}")); EXPECT_THAT(p0.get(), op::ShapeWithLayout(ShapeUtil::MakeShapeWithLayout( F32, {5, 7}, {0, 1}))); + EXPECT_THAT(p0.get(), op::ShapeWithLayout("f32[5,7]{0,1}")); EXPECT_THAT(p0.get(), ::testing::Not(op::ShapeWithLayout( ShapeUtil::MakeShapeWithLayout(F32, {5, 7}, {1, 0})))); + EXPECT_THAT(p0.get(), ::testing::Not(op::ShapeWithLayout("f32[5,7]{1,0}"))); EXPECT_THAT(Explain(p0.get(), op::Shape(ShapeUtil::MakeShape(F32, {7, 5}))), "%param = f32[5,7]{0,1} parameter(0) has incorrect shape " From 481f229881c915fec0822f68c6ce0ebbb9983da0 Mon Sep 17 00:00:00 2001 From: James Martens Date: Thu, 26 Apr 2018 04:37:28 -0700 Subject: [PATCH 0048/1691] - Adding support for Cholesky (inverse) factor multiplications. - Refactored FisherFactor to use LinearOperator classes that know how to multiply themselves, compute their own trace, etc. This addresses the feature request: b/73356352 - Fixed some problems with FisherEstimator construction - More careful casting of damping constants before they are used PiperOrigin-RevId: 194379298 --- .../contrib/kfac/python/kernel_tests/BUILD | 1 + .../python/kernel_tests/fisher_blocks_test.py | 7 +- .../kernel_tests/fisher_factors_test.py | 106 +++--- tensorflow/contrib/kfac/python/ops/BUILD | 14 + .../contrib/kfac/python/ops/estimator.py | 69 +++- .../contrib/kfac/python/ops/estimator_lib.py | 1 + .../contrib/kfac/python/ops/fisher_blocks.py | 271 ++++++++++----- .../contrib/kfac/python/ops/fisher_factors.py | 322 +++++++++++------- .../kfac/python/ops/linear_operator.py | 95 ++++++ .../contrib/kfac/python/ops/placement.py | 7 +- tensorflow/contrib/kfac/python/ops/utils.py | 16 +- 11 files changed, 632 insertions(+), 277 deletions(-) create mode 100644 tensorflow/contrib/kfac/python/ops/linear_operator.py diff --git a/tensorflow/contrib/kfac/python/kernel_tests/BUILD b/tensorflow/contrib/kfac/python/kernel_tests/BUILD index 2477d2bfc12c2d..c2436affe27354 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/BUILD +++ b/tensorflow/contrib/kfac/python/kernel_tests/BUILD @@ -58,6 +58,7 @@ py_test( deps = [ "//tensorflow/contrib/kfac/python/ops:fisher_blocks", "//tensorflow/contrib/kfac/python/ops:layer_collection", + "//tensorflow/contrib/kfac/python/ops:linear_operator", "//tensorflow/contrib/kfac/python/ops:utils", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", diff --git a/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py b/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py index 6eda6c31e34370..566d393f453236 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py @@ -22,6 +22,7 @@ from tensorflow.contrib.kfac.python.ops import fisher_blocks as fb from tensorflow.contrib.kfac.python.ops import layer_collection as lc +from tensorflow.contrib.kfac.python.ops import linear_operator as lo from tensorflow.contrib.kfac.python.ops import utils from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed @@ -46,8 +47,9 @@ class UtilsTest(test.TestCase): def testComputePiTracenorm(self): with ops.Graph().as_default(), self.test_session() as sess: random_seed.set_random_seed(200) - left_factor = array_ops.diag([1., 2., 0., 1.]) - right_factor = array_ops.ones([2., 2.]) + diag = ops.convert_to_tensor([1., 2., 0., 1.]) + left_factor = lo.LinearOperatorDiag(diag) + right_factor = lo.LinearOperatorFullMatrix(array_ops.ones([2, 2])) # pi is the sqrt of the left trace norm divided by the right trace norm pi = fb.compute_pi_tracenorm(left_factor, right_factor) @@ -245,7 +247,6 @@ def testMultiplyInverseAgainstExplicit(self): full = sess.run(block.full_fisher_block()) explicit = np.dot(np.linalg.inv(full + damping * np.eye(3)), v_flat) - self.assertAllClose(output_flat, explicit) diff --git a/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py b/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py index 432b67e5690003..9153ddf09c89ab 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py @@ -70,35 +70,44 @@ def make_inverse_update_ops(self): def get_cov(self): return NotImplementedError - def left_multiply(self, x, damping): + def instantiate_inv_variables(self): return NotImplementedError - def right_multiply(self, x, damping): - return NotImplementedError + def _num_towers(self): + raise NotImplementedError - def left_multiply_matpower(self, x, exp, damping): - return NotImplementedError + def _get_data_device(self): + raise NotImplementedError - def right_multiply_matpower(self, x, exp, damping): - return NotImplementedError + def register_matpower(self, exp, damping_func): + raise NotImplementedError - def instantiate_inv_variables(self): - return NotImplementedError + def register_cholesky(self, damping_func): + raise NotImplementedError - def _num_towers(self): + def register_cholesky_inverse(self, damping_func): raise NotImplementedError - def _get_data_device(self): + def get_matpower(self, exp, damping_func): raise NotImplementedError + def get_cholesky(self, damping_func): + raise NotImplementedError -class InverseProvidingFactorTestingDummy(ff.InverseProvidingFactor): - """Dummy class to test the non-abstract methods on ff.InverseProvidingFactor. + def get_cholesky_inverse(self, damping_func): + raise NotImplementedError + + def get_cov_as_linear_operator(self): + raise NotImplementedError + + +class DenseSquareMatrixFactorTestingDummy(ff.DenseSquareMatrixFactor): + """Dummy class to test the non-abstract methods on ff.DenseSquareMatrixFactor. """ def __init__(self, shape): self._shape = shape - super(InverseProvidingFactorTestingDummy, self).__init__() + super(DenseSquareMatrixFactorTestingDummy, self).__init__() @property def _var_scope(self): @@ -230,13 +239,13 @@ def testMakeInverseUpdateOps(self): self.assertEqual(0, len(factor.make_inverse_update_ops())) -class InverseProvidingFactorTest(test.TestCase): +class DenseSquareMatrixFactorTest(test.TestCase): def testRegisterDampedInverse(self): with tf_ops.Graph().as_default(): random_seed.set_random_seed(200) shape = [2, 2] - factor = InverseProvidingFactorTestingDummy(shape) + factor = DenseSquareMatrixFactorTestingDummy(shape) factor_var_scope = 'dummy/a_b_c' damping_funcs = [make_damping_func(0.1), @@ -248,22 +257,25 @@ def testRegisterDampedInverse(self): factor.instantiate_inv_variables() - inv = factor.get_inverse(damping_funcs[0]) - self.assertEqual(inv, factor.get_inverse(damping_funcs[1])) - self.assertNotEqual(inv, factor.get_inverse(damping_funcs[2])) - self.assertEqual(factor.get_inverse(damping_funcs[2]), - factor.get_inverse(damping_funcs[3])) + inv = factor.get_inverse(damping_funcs[0]).to_dense() + self.assertEqual(inv, factor.get_inverse(damping_funcs[1]).to_dense()) + self.assertNotEqual(inv, factor.get_inverse(damping_funcs[2]).to_dense()) + self.assertEqual(factor.get_inverse(damping_funcs[2]).to_dense(), + factor.get_inverse(damping_funcs[3]).to_dense()) factor_vars = tf_ops.get_collection(tf_ops.GraphKeys.GLOBAL_VARIABLES, factor_var_scope) - self.assertEqual(set([inv, factor.get_inverse(damping_funcs[2])]), - set(factor_vars)) + factor_tensors = (tf_ops.convert_to_tensor(var) for var in factor_vars) + + self.assertEqual(set([inv, + factor.get_inverse(damping_funcs[2]).to_dense()]), + set(factor_tensors)) self.assertEqual(shape, inv.get_shape()) def testRegisterMatpower(self): with tf_ops.Graph().as_default(): random_seed.set_random_seed(200) shape = [3, 3] - factor = InverseProvidingFactorTestingDummy(shape) + factor = DenseSquareMatrixFactorTestingDummy(shape) factor_var_scope = 'dummy/a_b_c' # TODO(b/74201126): Change to using the same func for both once @@ -278,10 +290,13 @@ def testRegisterMatpower(self): factor_vars = tf_ops.get_collection(tf_ops.GraphKeys.GLOBAL_VARIABLES, factor_var_scope) - matpower1 = factor.get_matpower(-0.5, damping_func_1) - matpower2 = factor.get_matpower(2, damping_func_2) - self.assertEqual(set([matpower1, matpower2]), set(factor_vars)) + factor_tensors = (tf_ops.convert_to_tensor(var) for var in factor_vars) + + matpower1 = factor.get_matpower(-0.5, damping_func_1).to_dense() + matpower2 = factor.get_matpower(2, damping_func_2).to_dense() + + self.assertEqual(set([matpower1, matpower2]), set(factor_tensors)) self.assertEqual(shape, matpower1.get_shape()) self.assertEqual(shape, matpower2.get_shape()) @@ -297,7 +312,7 @@ def testMakeInverseUpdateOpsManyInversesEigenDecomp(self): with tf_ops.Graph().as_default(), self.test_session() as sess: random_seed.set_random_seed(200) cov = np.array([[1., 2.], [3., 4.]]) - factor = InverseProvidingFactorTestingDummy(cov.shape) + factor = DenseSquareMatrixFactorTestingDummy(cov.shape) factor._cov = array_ops.constant(cov, dtype=dtypes.float32) damping_funcs = [] @@ -316,7 +331,8 @@ def testMakeInverseUpdateOpsManyInversesEigenDecomp(self): sess.run(ops) for i in range(ff.EIGENVALUE_DECOMPOSITION_THRESHOLD): # The inverse op will assign the damped inverse of cov to the inv var. - new_invs.append(sess.run(factor.get_inverse(damping_funcs[i]))) + new_invs.append( + sess.run(factor.get_inverse(damping_funcs[i]).to_dense())) # We want to see that the new invs are all different from each other. for i in range(len(new_invs)): @@ -328,7 +344,7 @@ def testMakeInverseUpdateOpsMatPowerEigenDecomp(self): with tf_ops.Graph().as_default(), self.test_session() as sess: random_seed.set_random_seed(200) cov = np.array([[6., 2.], [2., 4.]]) - factor = InverseProvidingFactorTestingDummy(cov.shape) + factor = DenseSquareMatrixFactorTestingDummy(cov.shape) factor._cov = array_ops.constant(cov, dtype=dtypes.float32) exp = 2 # NOTE(mattjj): must be int to test with np.linalg.matrix_power damping = 0.5 @@ -341,7 +357,7 @@ def testMakeInverseUpdateOpsMatPowerEigenDecomp(self): sess.run(tf_variables.global_variables_initializer()) sess.run(ops[0]) - matpower = sess.run(factor.get_matpower(exp, damping_func)) + matpower = sess.run(factor.get_matpower(exp, damping_func).to_dense()) matpower_np = np.linalg.matrix_power(cov + np.eye(2) * damping, exp) self.assertAllClose(matpower, matpower_np) @@ -349,7 +365,7 @@ def testMakeInverseUpdateOpsNoEigenDecomp(self): with tf_ops.Graph().as_default(), self.test_session() as sess: random_seed.set_random_seed(200) cov = np.array([[5., 2.], [2., 4.]]) # NOTE(mattjj): must be symmetric - factor = InverseProvidingFactorTestingDummy(cov.shape) + factor = DenseSquareMatrixFactorTestingDummy(cov.shape) factor._cov = array_ops.constant(cov, dtype=dtypes.float32) damping_func = make_damping_func(0) @@ -361,12 +377,12 @@ def testMakeInverseUpdateOpsNoEigenDecomp(self): sess.run(tf_variables.global_variables_initializer()) # The inverse op will assign the damped inverse of cov to the inv var. - old_inv = sess.run(factor.get_inverse(damping_func)) + old_inv = sess.run(factor.get_inverse(damping_func).to_dense()) self.assertAllClose( sess.run(ff.inverse_initializer(cov.shape, dtypes.float32)), old_inv) sess.run(ops) - new_inv = sess.run(factor.get_inverse(damping_func)) + new_inv = sess.run(factor.get_inverse(damping_func).to_dense()) self.assertAllClose(new_inv, np.linalg.inv(cov)) @@ -411,7 +427,7 @@ def testNaiveDiagonalFactorInit(self): tensor = array_ops.ones((2, 3), name='a/b/c') factor = ff.NaiveDiagonalFactor((tensor,), 32) factor.instantiate_cov_variables() - self.assertEqual([6, 1], factor.get_cov_var().get_shape().as_list()) + self.assertEqual([6, 1], factor.get_cov().get_shape().as_list()) def testNaiveDiagonalFactorInitFloat64(self): with tf_ops.Graph().as_default(): @@ -420,7 +436,7 @@ def testNaiveDiagonalFactorInitFloat64(self): tensor = array_ops.ones((2, 3), dtype=dtype, name='a/b/c') factor = ff.NaiveDiagonalFactor((tensor,), 32) factor.instantiate_cov_variables() - cov = factor.get_cov_var() + cov = factor.get_cov() self.assertEqual(cov.dtype, dtype) self.assertEqual([6, 1], cov.get_shape().as_list()) @@ -444,7 +460,7 @@ def testInitialization(self): vocab_size = 5 factor = ff.EmbeddingInputKroneckerFactor((input_ids,), vocab_size) factor.instantiate_cov_variables() - cov = factor.get_cov_var() + cov = factor.get_cov() self.assertEqual(cov.shape.as_list(), [vocab_size]) def testCovarianceUpdateOp(self): @@ -502,7 +518,7 @@ def testInit(self): self.kernel_height * self.kernel_width * self.in_channels, self.out_channels ], - factor.get_cov_var().shape.as_list()) + factor.get_cov().shape.as_list()) def testMakeCovarianceUpdateOp(self): with tf_ops.Graph().as_default(): @@ -564,7 +580,7 @@ def testHasBias(self): self.kernel_height * self.kernel_width * self.in_channels + 1, self.out_channels ], - factor.get_cov_var().shape.as_list()) + factor.get_cov().shape.as_list()) # Ensure update op doesn't crash. cov_update_op = factor.make_covariance_update_op(0.0) @@ -654,13 +670,13 @@ def test3DConvolution(self): # Ensure shape of covariance matches input size of filter. input_size = in_channels * (width**3) self.assertEqual([input_size, input_size], - factor.get_cov_var().shape.as_list()) + factor.get_cov().shape.as_list()) # Ensure cov_update_op doesn't crash. with self.test_session() as sess: sess.run(tf_variables.global_variables_initializer()) sess.run(factor.make_covariance_update_op(0.0)) - cov = sess.run(factor.get_cov_var()) + cov = sess.run(factor.get_cov()) # Cov should be rank-8, as the filter will be applied at each corner of # the 4-D cube. @@ -685,13 +701,13 @@ def testPointwiseConv2d(self): # Ensure shape of covariance matches input size of filter. self.assertEqual([in_channels, in_channels], - factor.get_cov_var().shape.as_list()) + factor.get_cov().shape.as_list()) # Ensure cov_update_op doesn't crash. with self.test_session() as sess: sess.run(tf_variables.global_variables_initializer()) sess.run(factor.make_covariance_update_op(0.0)) - cov = sess.run(factor.get_cov_var()) + cov = sess.run(factor.get_cov()) # Cov should be rank-9, as the filter will be applied at each location. self.assertMatrixRank(9, cov) @@ -716,7 +732,7 @@ def testStrides(self): with self.test_session() as sess: sess.run(tf_variables.global_variables_initializer()) sess.run(factor.make_covariance_update_op(0.0)) - cov = sess.run(factor.get_cov_var()) + cov = sess.run(factor.get_cov()) # Cov should be the sum of 3 * 2 = 6 outer products. self.assertMatrixRank(6, cov) @@ -742,7 +758,7 @@ def testDilationRate(self): with self.test_session() as sess: sess.run(tf_variables.global_variables_initializer()) sess.run(factor.make_covariance_update_op(0.0)) - cov = sess.run(factor.get_cov_var()) + cov = sess.run(factor.get_cov()) # Cov should be rank = in_channels, as only the center of the filter # receives non-zero input for each input channel. diff --git a/tensorflow/contrib/kfac/python/ops/BUILD b/tensorflow/contrib/kfac/python/ops/BUILD index cb0917bb851cff..3c01eb65e7a687 100644 --- a/tensorflow/contrib/kfac/python/ops/BUILD +++ b/tensorflow/contrib/kfac/python/ops/BUILD @@ -35,6 +35,7 @@ py_library( srcs = ["fisher_factors.py"], srcs_version = "PY2AND3", deps = [ + ":linear_operator", ":utils", "//tensorflow/python:array_ops", "//tensorflow/python:control_flow_ops", @@ -63,6 +64,19 @@ py_library( ], ) +py_library( + name = "linear_operator", + srcs = ["linear_operator.py"], + srcs_version = "PY2AND3", + deps = [ + ":utils", + "//tensorflow/python:framework_ops", + "//tensorflow/python:math_ops", + "//tensorflow/python/ops/linalg", + "@six_archive//:six", + ], +) + py_library( name = "loss_functions", srcs = ["loss_functions.py"], diff --git a/tensorflow/contrib/kfac/python/ops/estimator.py b/tensorflow/contrib/kfac/python/ops/estimator.py index d11c9c82881074..84ebf5e2e2498d 100644 --- a/tensorflow/contrib/kfac/python/ops/estimator.py +++ b/tensorflow/contrib/kfac/python/ops/estimator.py @@ -57,8 +57,8 @@ def make_fisher_estimator(placement_strategy=None, **kwargs): if placement_strategy in [None, "round_robin"]: return FisherEstimatorRoundRobin(**kwargs) else: - raise ValueError("Unimplemented vars and ops placement strategy : %s", - placement_strategy) + raise ValueError("Unimplemented vars and ops " + "placement strategy : {}".format(placement_strategy)) # pylint: enable=abstract-class-instantiated @@ -81,7 +81,9 @@ def __init__(self, exps=(-1,), estimation_mode="gradients", colocate_gradients_with_ops=True, - name="FisherEstimator"): + name="FisherEstimator", + compute_cholesky=False, + compute_cholesky_inverse=False): """Create a FisherEstimator object. Args: @@ -124,6 +126,12 @@ def __init__(self, name: A string. A name given to this estimator, which is added to the variable scope when constructing variables and ops. (Default: "FisherEstimator") + compute_cholesky: Bool. Whether or not the FisherEstimator will be + able to multiply vectors by the Cholesky factor. + (Default: False) + compute_cholesky_inverse: Bool. Whether or not the FisherEstimator + will be able to multiply vectors by the Cholesky factor inverse. + (Default: False) Raises: ValueError: If no losses have been registered with layer_collection. """ @@ -142,6 +150,8 @@ def __init__(self, self._made_vars = False self._exps = exps + self._compute_cholesky = compute_cholesky + self._compute_cholesky_inverse = compute_cholesky_inverse self._name = name @@ -300,9 +310,54 @@ def multiply_matpower(self, exp, vecs_and_vars): A list of (transformed vector, var) pairs in the same order as vecs_and_vars. """ + assert exp in self._exps + fcn = lambda fb, vec: fb.multiply_matpower(vec, exp) return self._apply_transformation(vecs_and_vars, fcn) + def multiply_cholesky(self, vecs_and_vars, transpose=False): + """Multiplies the vecs by the corresponding Cholesky factors. + + Args: + vecs_and_vars: List of (vector, variable) pairs. + transpose: Bool. If true the Cholesky factors are transposed before + multiplying the vecs. (Default: False) + + Returns: + A list of (transformed vector, var) pairs in the same order as + vecs_and_vars. + """ + assert self._compute_cholesky + + fcn = lambda fb, vec: fb.multiply_cholesky(vec, transpose=transpose) + return self._apply_transformation(vecs_and_vars, fcn) + + def multiply_cholesky_inverse(self, vecs_and_vars, transpose=False): + """Mults the vecs by the inverses of the corresponding Cholesky factors. + + Note: if you are using Cholesky inverse multiplication to sample from + a matrix-variate Gaussian you will want to multiply by the transpose. + Let L be the Cholesky factor of F and observe that + + L^-T * L^-1 = (L * L^T)^-1 = F^-1 . + + Thus we want to multiply by L^-T in order to sample from Gaussian with + covariance F^-1. + + Args: + vecs_and_vars: List of (vector, variable) pairs. + transpose: Bool. If true the Cholesky factor inverses are transposed + before multiplying the vecs. (Default: False) + + Returns: + A list of (transformed vector, var) pairs in the same order as + vecs_and_vars. + """ + assert self._compute_cholesky_inverse + + fcn = lambda fb, vec: fb.multiply_cholesky_inverse(vec, transpose=transpose) + return self._apply_transformation(vecs_and_vars, fcn) + def _instantiate_factors(self): """Instantiates FisherFactors' variables. @@ -333,9 +388,13 @@ def made_vars(self): return self._made_vars def _register_matrix_functions(self): - for exp in self._exps: - for block in self.blocks: + for block in self.blocks: + for exp in self._exps: block.register_matpower(exp) + if self._compute_cholesky: + block.register_cholesky() + if self._compute_cholesky_inverse: + block.register_cholesky_inverse() def _finalize_layer_collection(self): self._layers.create_subgraph() diff --git a/tensorflow/contrib/kfac/python/ops/estimator_lib.py b/tensorflow/contrib/kfac/python/ops/estimator_lib.py index 33c969650615bf..9c9fef471f8033 100644 --- a/tensorflow/contrib/kfac/python/ops/estimator_lib.py +++ b/tensorflow/contrib/kfac/python/ops/estimator_lib.py @@ -25,6 +25,7 @@ _allowed_symbols = [ 'FisherEstimator', + 'make_fisher_estimator', ] remove_undocumented(__name__, allowed_exception_list=_allowed_symbols) diff --git a/tensorflow/contrib/kfac/python/ops/fisher_blocks.py b/tensorflow/contrib/kfac/python/ops/fisher_blocks.py index 00b3673a742e92..32c776cb381f1b 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_blocks.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_blocks.py @@ -83,34 +83,22 @@ def normalize_damping(damping, num_replications): def compute_pi_tracenorm(left_cov, right_cov): - """Computes the scalar constant pi for Tikhonov regularization/damping. + r"""Computes the scalar constant pi for Tikhonov regularization/damping. $$\pi = \sqrt{ (trace(A) / dim(A)) / (trace(B) / dim(B)) }$$ See section 6.3 of https://arxiv.org/pdf/1503.05671.pdf for details. Args: - left_cov: The left Kronecker factor "covariance". - right_cov: The right Kronecker factor "covariance". + left_cov: A LinearOperator object. The left Kronecker factor "covariance". + right_cov: A LinearOperator object. The right Kronecker factor "covariance". Returns: The computed scalar constant pi for these Kronecker Factors (as a Tensor). """ - - def _trace(cov): - if len(cov.shape) == 1: - # Diagonal matrix. - return math_ops.reduce_sum(cov) - elif len(cov.shape) == 2: - # Full matrix. - return math_ops.trace(cov) - else: - raise ValueError( - "What's the trace of a Tensor of rank %d?" % len(cov.shape)) - # Instead of dividing by the dim of the norm, we multiply by the dim of the # other norm. This works out the same in the ratio. - left_norm = _trace(left_cov) * right_cov.shape.as_list()[0] - right_norm = _trace(right_cov) * left_cov.shape.as_list()[0] + left_norm = left_cov.trace() * int(right_cov.domain_dimension) + right_norm = right_cov.trace() * int(left_cov.domain_dimension) return math_ops.sqrt(left_norm / right_norm) @@ -188,6 +176,16 @@ def register_matpower(self, exp): """ pass + @abc.abstractmethod + def register_cholesky(self): + """Registers a Cholesky factor to be computed by the block.""" + pass + + @abc.abstractmethod + def register_cholesky_inverse(self): + """Registers an inverse Cholesky factor to be computed by the block.""" + pass + def register_inverse(self): """Registers a matrix inverse to be computed by the block.""" self.register_matpower(-1) @@ -228,6 +226,33 @@ def multiply(self, vector): """ return self.multiply_matpower(vector, 1) + @abc.abstractmethod + def multiply_cholesky(self, vector, transpose=False): + """Multiplies the vector by the (damped) Cholesky-factor of the block. + + Args: + vector: The vector (a Tensor or tuple of Tensors) to be multiplied. + transpose: Bool. If true the Cholesky factor is transposed before + multiplying the vector. (Default: False) + + Returns: + The vector left-multiplied by the (damped) Cholesky-factor of the block. + """ + pass + + @abc.abstractmethod + def multiply_cholesky_inverse(self, vector, transpose=False): + """Multiplies vector by the (damped) inverse Cholesky-factor of the block. + + Args: + vector: The vector (a Tensor or tuple of Tensors) to be multiplied. + transpose: Bool. If true the Cholesky factor inverse is transposed + before multiplying the vector. (Default: False) + Returns: + Vector left-multiplied by (damped) inverse Cholesky-factor of the block. + """ + pass + @abc.abstractmethod def tensors_to_compute_grads(self): """Returns the Tensor(s) with respect to which this FisherBlock needs grads. @@ -275,15 +300,32 @@ def instantiate_factors(self, grads_list, damping): def register_matpower(self, exp): self._factor.register_matpower(exp, self._damping_func) - def multiply_matpower(self, vector, exp): + def register_cholesky(self): + self._factor.register_cholesky(self._damping_func) + + def register_cholesky_inverse(self): + self._factor.register_cholesky_inverse(self._damping_func) + + def _multiply_matrix(self, matrix, vector, transpose=False): vector_flat = utils.tensors_to_column(vector) - out_flat = self._factor.left_multiply_matpower( - vector_flat, exp, self._damping_func) + out_flat = matrix.matmul(vector_flat, adjoint=transpose) return utils.column_to_tensors(vector, out_flat) + def multiply_matpower(self, vector, exp): + matrix = self._factor.get_matpower(exp, self._damping_func) + return self._multiply_matrix(matrix, vector) + + def multiply_cholesky(self, vector, transpose=False): + matrix = self._factor.get_cholesky(self._damping_func) + return self._multiply_matrix(matrix, vector, transpose=transpose) + + def multiply_cholesky_inverse(self, vector, transpose=False): + matrix = self._factor.get_cholesky_inverse(self._damping_func) + return self._multiply_matrix(matrix, vector, transpose=transpose) + def full_fisher_block(self): """Explicitly constructs the full Fisher block.""" - return self._factor.get_cov() + return self._factor.get_cov_as_linear_operator().to_dense() def tensors_to_compute_grads(self): return self._params @@ -305,7 +347,47 @@ def _batch_size(self): return math_ops.reduce_sum(self._batch_sizes) -class NaiveDiagonalFB(FisherBlock): +@six.add_metaclass(abc.ABCMeta) +class DiagonalFB(FisherBlock): + """A base class for FisherBlocks that use diagonal approximations.""" + + def register_matpower(self, exp): + # Not needed for this. Matrix powers are computed on demand in the + # diagonal case + pass + + def register_cholesky(self): + # Not needed for this. Cholesky's are computed on demand in the + # diagonal case + pass + + def register_cholesky_inverse(self): + # Not needed for this. Cholesky inverses's are computed on demand in the + # diagonal case + pass + + def _multiply_matrix(self, matrix, vector): + vector_flat = utils.tensors_to_column(vector) + out_flat = matrix.matmul(vector_flat) + return utils.column_to_tensors(vector, out_flat) + + def multiply_matpower(self, vector, exp): + matrix = self._factor.get_matpower(exp, self._damping_func) + return self._multiply_matrix(matrix, vector) + + def multiply_cholesky(self, vector, transpose=False): + matrix = self._factor.get_cholesky(self._damping_func) + return self._multiply_matrix(matrix, vector) + + def multiply_cholesky_inverse(self, vector, transpose=False): + matrix = self._factor.get_cholesky_inverse(self._damping_func) + return self._multiply_matrix(matrix, vector) + + def full_fisher_block(self): + return self._factor.get_cov_as_linear_operator().to_dense() + + +class NaiveDiagonalFB(DiagonalFB): """FisherBlock using a diagonal matrix approximation. This type of approximation is generically applicable but quite primitive. @@ -333,20 +415,6 @@ def instantiate_factors(self, grads_list, damping): self._factor = self._layer_collection.make_or_get_factor( fisher_factors.NaiveDiagonalFactor, (grads_list, self._batch_size)) - def register_matpower(self, exp): - # Not needed for this. Matrix powers are computed on demand in the - # diagonal case - pass - - def multiply_matpower(self, vector, exp): - vector_flat = utils.tensors_to_column(vector) - out_flat = self._factor.left_multiply_matpower( - vector_flat, exp, self._damping_func) - return utils.column_to_tensors(vector, out_flat) - - def full_fisher_block(self): - return self._factor.get_cov() - def tensors_to_compute_grads(self): return self._params @@ -452,7 +520,7 @@ def _outputs(self): return self.__outputs -class FullyConnectedDiagonalFB(InputOutputMultiTower, FisherBlock): +class FullyConnectedDiagonalFB(InputOutputMultiTower, DiagonalFB): """FisherBlock for fully-connected (dense) layers using a diagonal approx. Estimates the Fisher Information matrix's diagonal entries for a fully @@ -497,32 +565,8 @@ def instantiate_factors(self, grads_list, damping): self._damping_func = _package_func(lambda: damping, (damping,)) - def register_matpower(self, exp): - # Not needed for this. Matrix powers are computed on demand in the - # diagonal case - pass - - def multiply_matpower(self, vector, exp): - """Multiplies the vector by the (damped) matrix-power of the block. - - Args: - vector: Tensor or 2-tuple of Tensors. if self._has_bias, Tensor of shape - [input_size, output_size] corresponding to layer's weights. If not, a - 2-tuple of the former and a Tensor of shape [output_size] corresponding - to the layer's bias. - exp: A scalar representing the power to raise the block before multiplying - it by the vector. - - Returns: - The vector left-multiplied by the (damped) matrix-power of the block. - """ - reshaped_vec = utils.layer_params_to_mat2d(vector) - reshaped_out = self._factor.left_multiply_matpower( - reshaped_vec, exp, self._damping_func) - return utils.mat2d_to_layer_params(vector, reshaped_out) - -class ConvDiagonalFB(InputOutputMultiTower, FisherBlock): +class ConvDiagonalFB(InputOutputMultiTower, DiagonalFB): """FisherBlock for 2-D convolutional layers using a diagonal approx. Estimates the Fisher Information matrix's diagonal entries for a convolutional @@ -621,17 +665,6 @@ def damping_func(): self._num_locations) self._damping_func = _package_func(damping_func, damping_id) - def register_matpower(self, exp): - # Not needed for this. Matrix powers are computed on demand in the - # diagonal case - pass - - def multiply_matpower(self, vector, exp): - reshaped_vect = utils.layer_params_to_mat2d(vector) - reshaped_out = self._factor.left_multiply_matpower( - reshaped_vect, exp, self._damping_func) - return utils.mat2d_to_layer_params(vector, reshaped_out) - class KroneckerProductFB(FisherBlock): """A base class for blocks with separate input and output Kronecker factors. @@ -651,9 +684,10 @@ def compute_damping(): else: maybe_normalized_damping = damping - return compute_pi_adjusted_damping(self._input_factor.get_cov(), - self._output_factor.get_cov(), - maybe_normalized_damping**0.5) + return compute_pi_adjusted_damping( + self._input_factor.get_cov_as_linear_operator(), + self._output_factor.get_cov_as_linear_operator(), + maybe_normalized_damping**0.5) if normalization is not None: damping_id = ("compute_pi_adjusted_damping", @@ -675,6 +709,14 @@ def register_matpower(self, exp): self._input_factor.register_matpower(exp, self._input_damping_func) self._output_factor.register_matpower(exp, self._output_damping_func) + def register_cholesky(self): + self._input_factor.register_cholesky(self._input_damping_func) + self._output_factor.register_cholesky(self._output_damping_func) + + def register_cholesky_inverse(self): + self._input_factor.register_cholesky_inverse(self._input_damping_func) + self._output_factor.register_cholesky_inverse(self._output_damping_func) + @property def _renorm_coeff(self): """Kronecker factor multiplier coefficient. @@ -687,17 +729,47 @@ def _renorm_coeff(self): """ return 1.0 - def multiply_matpower(self, vector, exp): + def _multiply_factored_matrix(self, left_factor, right_factor, vector, + extra_scale=1.0, transpose_left=False, + transpose_right=False): reshaped_vector = utils.layer_params_to_mat2d(vector) - reshaped_out = self._output_factor.right_multiply_matpower( - reshaped_vector, exp, self._output_damping_func) - reshaped_out = self._input_factor.left_multiply_matpower( - reshaped_out, exp, self._input_damping_func) - if self._renorm_coeff != 1.0: - renorm_coeff = math_ops.cast(self._renorm_coeff, dtype=reshaped_out.dtype) - reshaped_out *= math_ops.cast(renorm_coeff**exp, dtype=reshaped_out.dtype) + reshaped_out = right_factor.matmul_right(reshaped_vector, + adjoint=transpose_right) + reshaped_out = left_factor.matmul(reshaped_out, + adjoint=transpose_left) + if extra_scale != 1.0: + reshaped_out *= math_ops.cast(extra_scale, dtype=reshaped_out.dtype) return utils.mat2d_to_layer_params(vector, reshaped_out) + def multiply_matpower(self, vector, exp): + left_factor = self._input_factor.get_matpower( + exp, self._input_damping_func) + right_factor = self._output_factor.get_matpower( + exp, self._output_damping_func) + extra_scale = float(self._renorm_coeff)**exp + return self._multiply_factored_matrix(left_factor, right_factor, vector, + extra_scale=extra_scale) + + def multiply_cholesky(self, vector, transpose=False): + left_factor = self._input_factor.get_cholesky(self._input_damping_func) + right_factor = self._output_factor.get_cholesky(self._output_damping_func) + extra_scale = float(self._renorm_coeff)**0.5 + return self._multiply_factored_matrix(left_factor, right_factor, vector, + extra_scale=extra_scale, + transpose_left=transpose, + transpose_right=not transpose) + + def multiply_cholesky_inverse(self, vector, transpose=False): + left_factor = self._input_factor.get_cholesky_inverse( + self._input_damping_func) + right_factor = self._output_factor.get_cholesky_inverse( + self._output_damping_func) + extra_scale = float(self._renorm_coeff)**-0.5 + return self._multiply_factored_matrix(left_factor, right_factor, vector, + extra_scale=extra_scale, + transpose_left=transpose, + transpose_right=not transpose) + def full_fisher_block(self): """Explicitly constructs the full Fisher block. @@ -706,8 +778,8 @@ def full_fisher_block(self): Returns: The full Fisher block. """ - left_factor = self._input_factor.get_cov() - right_factor = self._output_factor.get_cov() + left_factor = self._input_factor.get_cov_as_linear_operator().to_dense() + right_factor = self._output_factor.get_cov_as_linear_operator().to_dense() return self._renorm_coeff * utils.kronecker_product(left_factor, right_factor) @@ -796,7 +868,7 @@ def instantiate_factors(self, grads_list, damping): class ConvKFCBasicFB(InputOutputMultiTower, KroneckerProductFB): - """FisherBlock for convolutional layers using the basic KFC approx. + r"""FisherBlock for convolutional layers using the basic KFC approx. Estimates the Fisher Information matrix's blog for a convolutional layer. @@ -945,10 +1017,10 @@ def __init__(self, self._filter_shape = (filter_height, filter_width, in_channels, in_channels * channel_multiplier) - def multiply_matpower(self, vector, exp): + def _multiply_matrix(self, matrix, vector): conv2d_vector = depthwise_conv2d_filter_to_conv2d_filter(vector) - conv2d_result = super(DepthwiseConvDiagonalFB, self).multiply_matpower( - conv2d_vector, exp) + conv2d_result = super( + DepthwiseConvDiagonalFB, self)._multiply_matrix(matrix, conv2d_vector) return conv2d_filter_to_depthwise_conv2d_filter(conv2d_result) @@ -1016,10 +1088,14 @@ def __init__(self, self._filter_shape = (filter_height, filter_width, in_channels, in_channels * channel_multiplier) - def multiply_matpower(self, vector, exp): + def _multiply_factored_matrix(self, left_factor, right_factor, vector, + extra_scale=1.0, transpose_left=False, + transpose_right=False): conv2d_vector = depthwise_conv2d_filter_to_conv2d_filter(vector) - conv2d_result = super(DepthwiseConvKFCBasicFB, self).multiply_matpower( - conv2d_vector, exp) + conv2d_result = super( + DepthwiseConvKFCBasicFB, self)._multiply_factored_matrix( + left_factor, right_factor, conv2d_vector, extra_scale=extra_scale, + transpose_left=transpose_left, transpose_right=transpose_right) return conv2d_filter_to_depthwise_conv2d_filter(conv2d_result) @@ -1664,3 +1740,12 @@ def gamma(x): return utils.mat2d_to_layer_params(vector, Z) # pylint: enable=invalid-name + + def multiply_cholesky(self, vector): + raise NotImplementedError("FullyConnectedSeriesFB does not support " + "Cholesky computations.") + + def multiply_cholesky_inverse(self, vector): + raise NotImplementedError("FullyConnectedSeriesFB does not support " + "Cholesky computations.") + diff --git a/tensorflow/contrib/kfac/python/ops/fisher_factors.py b/tensorflow/contrib/kfac/python/ops/fisher_factors.py index 7988a3b92bf013..30f8a2a4b8ec7e 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_factors.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_factors.py @@ -24,6 +24,7 @@ import numpy as np import six +from tensorflow.contrib.kfac.python.ops import linear_operator as lo from tensorflow.contrib.kfac.python.ops import utils from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops as tf_ops @@ -399,7 +400,7 @@ def _compute_new_cov(self, source, tower): the cov update. Returns: - Tensor of same shape as self.get_cov_var(). + Tensor of same shape as self.get_cov(). """ pass @@ -448,78 +449,43 @@ def make_inverse_update_ops(self): """Create and return update ops corresponding to registered computations.""" pass - @abc.abstractmethod def get_cov(self): - """Get full covariance matrix. - - Returns: - Tensor of shape [n, n]. Represents all parameter-parameter correlations - captured by this FisherFactor. - """ - pass - - def get_cov_var(self): - """Get variable backing this FisherFactor. - - May or may not be the same as self.get_cov() - - Returns: - Variable of shape self._cov_shape. - """ return self._cov @abc.abstractmethod - def left_multiply_matpower(self, x, exp, damping_func): - """Left multiplies 'x' by matrix power of this factor (w/ damping applied). - - This calculation is essentially: - (C + damping * I)**exp * x - where * is matrix-multiplication, ** is matrix power, I is the identity - matrix, and C is the matrix represented by this factor. - - x can represent either a matrix or a vector. For some factors, 'x' might - represent a vector but actually be stored as a 2D matrix for convenience. - - Args: - x: Tensor. Represents a single vector. Shape depends on implementation. - exp: float. The matrix exponent to use. - damping_func: A function that computes a 0-D Tensor or a float which will - be the damping value used. i.e. damping = damping_func(). + def get_cov_as_linear_operator(self): + pass - Returns: - Tensor of same shape as 'x' representing the result of the multiplication. - """ + @abc.abstractmethod + def register_matpower(self, exp, damping_func): pass @abc.abstractmethod - def right_multiply_matpower(self, x, exp, damping_func): - """Right multiplies 'x' by matrix power of this factor (w/ damping applied). + def register_cholesky(self, damping_func): + pass - This calculation is essentially: - x * (C + damping * I)**exp - where * is matrix-multiplication, ** is matrix power, I is the identity - matrix, and C is the matrix represented by this factor. + @abc.abstractmethod + def register_cholesky_inverse(self, damping_func): + pass - Unlike left_multiply_matpower, x will always be a matrix. + @abc.abstractmethod + def get_matpower(self, exp, damping_func): + pass - Args: - x: Tensor. Represents a single vector. Shape depends on implementation. - exp: float. The matrix exponent to use. - damping_func: A function that computes a 0-D Tensor or a float which will - be the damping value used. i.e. damping = damping_func(). + @abc.abstractmethod + def get_cholesky(self, damping_func): + pass - Returns: - Tensor of same shape as 'x' representing the result of the multiplication. - """ + @abc.abstractmethod + def get_cholesky_inverse(self, damping_func): pass -class InverseProvidingFactor(FisherFactor): - """Base class for FisherFactors that maintain inverses explicitly. +class DenseSquareMatrixFactor(FisherFactor): + """Base class for FisherFactors that are stored as dense square matrices. - This class explicitly calculates and stores inverses of covariance matrices - provided by the underlying FisherFactor implementation. It is assumed that - vectors can be represented as 2-D matrices. + This class explicitly calculates and stores inverses of their `cov` matrices, + which must be square dense matrices. Subclasses must implement the _compute_new_cov method, and the _var_scope and _cov_shape properties. @@ -538,7 +504,19 @@ def __init__(self): self._eigendecomp = None self._damping_funcs_by_id = {} # {hashable: lambda} - super(InverseProvidingFactor, self).__init__() + self._cholesky_registrations = set() # { hashable } + self._cholesky_inverse_registrations = set() # { hashable } + + self._cholesky_by_damping = {} # { hashable: variable } + self._cholesky_inverse_by_damping = {} # { hashable: variable } + + super(DenseSquareMatrixFactor, self).__init__() + + def get_cov_as_linear_operator(self): + assert self.get_cov().shape.ndims == 2 + return lo.LinearOperatorFullMatrix(self.get_cov(), + is_self_adjoint=True, + is_square=True) def _register_damping(self, damping_func): damping_id = graph_func_to_id(damping_func) @@ -563,8 +541,6 @@ def register_matpower(self, exp, damping_func): be the damping value used. i.e. damping = damping_func(). """ if exp == 1.0: - # We don't register these. The user shouldn't even be calling this - # function with exp = 1.0. return damping_id = self._register_damping(damping_func) @@ -572,6 +548,38 @@ def register_matpower(self, exp, damping_func): if (exp, damping_id) not in self._matpower_registrations: self._matpower_registrations.add((exp, damping_id)) + def register_cholesky(self, damping_func): + """Registers a Cholesky factor to be maintained and served on demand. + + This creates a variable and signals make_inverse_update_ops to make the + corresponding update op. The variable can be read via the method + get_cholesky. + + Args: + damping_func: A function that computes a 0-D Tensor or a float which will + be the damping value used. i.e. damping = damping_func(). + """ + damping_id = self._register_damping(damping_func) + + if damping_id not in self._cholesky_registrations: + self._cholesky_registrations.add(damping_id) + + def register_cholesky_inverse(self, damping_func): + """Registers an inverse Cholesky factor to be maintained/served on demand. + + This creates a variable and signals make_inverse_update_ops to make the + corresponding update op. The variable can be read via the method + get_cholesky_inverse. + + Args: + damping_func: A function that computes a 0-D Tensor or a float which will + be the damping value used. i.e. damping = damping_func(). + """ + damping_id = self._register_damping(damping_func) + + if damping_id not in self._cholesky_inverse_registrations: + self._cholesky_inverse_registrations.add(damping_id) + def instantiate_inv_variables(self): """Makes the internal "inverse" variable(s).""" @@ -589,6 +597,32 @@ def instantiate_inv_variables(self): assert (exp, damping_id) not in self._matpower_by_exp_and_damping self._matpower_by_exp_and_damping[(exp, damping_id)] = matpower + for damping_id in self._cholesky_registrations: + damping_func = self._damping_funcs_by_id[damping_id] + damping_string = graph_func_to_string(damping_func) + with variable_scope.variable_scope(self._var_scope): + chol = variable_scope.get_variable( + "cholesky_damp{}".format(damping_string), + initializer=inverse_initializer, + shape=self._cov_shape, + trainable=False, + dtype=self._dtype) + assert damping_id not in self._cholesky_by_damping + self._cholesky_by_damping[damping_id] = chol + + for damping_id in self._cholesky_inverse_registrations: + damping_func = self._damping_funcs_by_id[damping_id] + damping_string = graph_func_to_string(damping_func) + with variable_scope.variable_scope(self._var_scope): + cholinv = variable_scope.get_variable( + "cholesky_inverse_damp{}".format(damping_string), + initializer=inverse_initializer, + shape=self._cov_shape, + trainable=False, + dtype=self._dtype) + assert damping_id not in self._cholesky_inverse_by_damping + self._cholesky_inverse_by_damping[damping_id] = cholinv + def make_inverse_update_ops(self): """Create and return update ops corresponding to registered computations.""" ops = [] @@ -606,7 +640,8 @@ def make_inverse_update_ops(self): # We precompute these so we don't need to evaluate them multiple times (for # each matrix power that uses them) - damping_value_by_id = {damping_id: self._damping_funcs_by_id[damping_id]() + damping_value_by_id = {damping_id: math_ops.cast( + self._damping_funcs_by_id[damping_id](), self._dtype) for damping_id in self._damping_funcs_by_id} if use_eig: @@ -627,29 +662,91 @@ def make_inverse_update_ops(self): self._matpower_by_exp_and_damping.items()): assert exp == -1 damping = damping_value_by_id[damping_id] - ops.append(matpower.assign(utils.posdef_inv(self._cov, damping))) + ops.append(matpower.assign(utils.posdef_inv(self.get_cov(), damping))) + + # TODO(b/77902055): If inverses are being computed with Cholesky's + # we can share the work. Instead this code currently just computes the + # Cholesky a second time. It does at least share work between requests for + # Cholesky's and Cholesky inverses with the same damping id. + for damping_id, cholesky_inv in self._cholesky_inverse_by_damping.items(): + cholesky_ops = [] + + damping = damping_value_by_id[damping_id] + cholesky_value = utils.cholesky(self.get_cov(), damping) + + if damping_id in self._cholesky_by_damping: + cholesky = self._cholesky_by_damping[damping_id] + cholesky_ops.append(cholesky.assign(cholesky_value)) + + identity = linalg_ops.eye(cholesky_value.shape.as_list()[0], + dtype=cholesky_value.dtype) + cholesky_inv_value = linalg_ops.matrix_triangular_solve(cholesky_value, + identity) + cholesky_ops.append(cholesky_inv.assign(cholesky_inv_value)) + + ops.append(control_flow_ops.group(*cholesky_ops)) + + for damping_id, cholesky in self._cholesky_by_damping.items(): + if damping_id not in self._cholesky_inverse_by_damping: + damping = damping_value_by_id[damping_id] + cholesky_value = utils.cholesky(self.get_cov(), damping) + ops.append(cholesky.assign(cholesky_value)) self._eigendecomp = False return ops def get_inverse(self, damping_func): # Just for backwards compatibility of some old code and tests - damping_id = graph_func_to_id(damping_func) - return self._matpower_by_exp_and_damping[(-1, damping_id)] + return self.get_matpower(-1, damping_func) def get_matpower(self, exp, damping_func): + # Note that this function returns a variable which gets updated by the + # inverse ops. It may be stale / inconsistent with the latest value of + # get_cov(). + if exp != 1: + damping_id = graph_func_to_id(damping_func) + matpower = self._matpower_by_exp_and_damping[(exp, damping_id)] + else: + matpower = self.get_cov() + identity = linalg_ops.eye(matpower.shape.as_list()[0], + dtype=matpower.dtype) + matpower += math_ops.cast(damping_func(), dtype=matpower.dtype)*identity + + assert matpower.shape.ndims == 2 + return lo.LinearOperatorFullMatrix(matpower, + is_non_singular=True, + is_self_adjoint=True, + is_positive_definite=True, + is_square=True) + + def get_cholesky(self, damping_func): # Note that this function returns a variable which gets updated by the # inverse ops. It may be stale / inconsistent with the latest value of # get_cov(). damping_id = graph_func_to_id(damping_func) - return self._matpower_by_exp_and_damping[(exp, damping_id)] + cholesky = self._cholesky_by_damping[damping_id] + assert cholesky.shape.ndims == 2 + return lo.LinearOperatorFullMatrix(cholesky, + is_non_singular=True, + is_square=True) + + def get_cholesky_inverse(self, damping_func): + # Note that this function returns a variable which gets updated by the + # inverse ops. It may be stale / inconsistent with the latest value of + # get_cov(). + damping_id = graph_func_to_id(damping_func) + cholesky_inv = self._cholesky_inverse_by_damping[damping_id] + assert cholesky_inv.shape.ndims == 2 + return lo.LinearOperatorFullMatrix(cholesky_inv, + is_non_singular=True, + is_square=True) def get_eigendecomp(self): """Creates or retrieves eigendecomposition of self._cov.""" # Unlike get_matpower this doesn't retrieve a stored variable, but instead # always computes a fresh version from the current value of get_cov(). if not self._eigendecomp: - eigenvalues, eigenvectors = linalg_ops.self_adjoint_eig(self._cov) + eigenvalues, eigenvectors = linalg_ops.self_adjoint_eig(self.get_cov()) # The matrix self._cov is positive semidefinite by construction, but the # numerical eigenvalues could be negative due to numerical errors, so here @@ -660,45 +757,8 @@ def get_eigendecomp(self): return self._eigendecomp - def get_cov(self): - # Variable contains full covariance matrix. - return self.get_cov_var() - - def left_multiply_matpower(self, x, exp, damping_func): - if isinstance(x, tf_ops.IndexedSlices): - raise ValueError("Left-multiply not yet supported for IndexedSlices.") - - if x.shape.ndims != 2: - raise ValueError( - "InverseProvidingFactors apply to matrix-shaped vectors. Found: %s." - % (x,)) - - if exp == 1: - return math_ops.matmul(self.get_cov(), x) + damping_func() * x - - return math_ops.matmul(self.get_matpower(exp, damping_func), x) - - def right_multiply_matpower(self, x, exp, damping_func): - if isinstance(x, tf_ops.IndexedSlices): - if exp == 1: - n = self.get_cov().shape[0] - damped_cov = self.get_cov() + damping_func() * array_ops.eye(n) - return utils.matmul_sparse_dense(x, damped_cov) - - return utils.matmul_sparse_dense(x, self.get_matpower(exp, damping_func)) - - if x.shape.ndims != 2: - raise ValueError( - "InverseProvidingFactors apply to matrix-shaped vectors. Found: %s." - % (x,)) - if exp == 1: - return math_ops.matmul(x, self.get_cov()) + damping_func() * x - - return math_ops.matmul(x, self.get_matpower(exp, damping_func)) - - -class FullFactor(InverseProvidingFactor): +class FullFactor(DenseSquareMatrixFactor): """FisherFactor for a full matrix representation of the Fisher of a parameter. Note that this uses the naive "square the sum estimator", and so is applicable @@ -757,41 +817,51 @@ class DiagonalFactor(FisherFactor): """ def __init__(self): - self._damping_funcs_by_id = {} # { hashable: lambda } super(DiagonalFactor, self).__init__() + def get_cov_as_linear_operator(self): + assert self._matrix_diagonal.shape.ndims == 1 + return lo.LinearOperatorDiag(self._matrix_diagonal, + is_self_adjoint=True, + is_square=True) + @property def _cov_initializer(self): return diagonal_covariance_initializer + @property + def _matrix_diagonal(self): + return array_ops.reshape(self.get_cov(), [-1]) + def make_inverse_update_ops(self): return [] def instantiate_inv_variables(self): pass - def get_cov(self): - # self.get_cov() could be any shape, but it must have one entry per - # parameter. Flatten it into a vector. - cov_diag_vec = array_ops.reshape(self.get_cov_var(), [-1]) - return array_ops.diag(cov_diag_vec) + def register_matpower(self, exp, damping_func): + pass - def left_multiply_matpower(self, x, exp, damping_func): - matpower = (self.get_cov_var() + damping_func())**exp + def register_cholesky(self, damping_func): + pass - if isinstance(x, tf_ops.IndexedSlices): - return utils.matmul_diag_sparse(array_ops.reshape(matpower, [-1]), x) + def register_cholesky_inverse(self, damping_func): + pass - if x.shape != matpower.shape: - raise ValueError("x (%s) and cov (%s) must have same shape." % - (x, matpower)) - return matpower * x + def get_matpower(self, exp, damping_func): + matpower_diagonal = (self._matrix_diagonal + + math_ops.cast(damping_func(), self._dtype))**exp + return lo.LinearOperatorDiag(matpower_diagonal, + is_non_singular=True, + is_self_adjoint=True, + is_positive_definite=True, + is_square=True) - def right_multiply_matpower(self, x, exp, damping_func): - raise NotImplementedError("Only left-multiply is currently supported.") + def get_cholesky(self, damping_func): + return self.get_matpower(0.5, damping_func) - def register_matpower(self, exp, damping_func): - pass + def get_cholesky_inverse(self, damping_func): + return self.get_matpower(-0.5, damping_func) class NaiveDiagonalFactor(DiagonalFactor): @@ -1167,7 +1237,7 @@ def _get_data_device(self, tower): return self._inputs[tower].device -class FullyConnectedKroneckerFactor(InverseProvidingFactor): +class FullyConnectedKroneckerFactor(DenseSquareMatrixFactor): """Kronecker factor for the input or output side of a fully-connected layer. """ @@ -1220,7 +1290,7 @@ def _get_data_device(self, tower): return self._tensors[0][tower].device -class ConvInputKroneckerFactor(InverseProvidingFactor): +class ConvInputKroneckerFactor(DenseSquareMatrixFactor): r"""Kronecker factor for the input side of a convolutional layer. Estimates E[ a a^T ] where a is the inputs to a convolutional layer given @@ -1384,7 +1454,7 @@ def _get_data_device(self, tower): return self._inputs[tower].device -class ConvOutputKroneckerFactor(InverseProvidingFactor): +class ConvOutputKroneckerFactor(DenseSquareMatrixFactor): r"""Kronecker factor for the output side of a convolutional layer. Estimates E[ ds ds^T ] where s is the preactivations of a convolutional layer @@ -1674,6 +1744,7 @@ def make_inverse_update_ops(self): psi_var) in self._option1quants_by_damping.items(): damping = self._damping_funcs_by_id[damping_id]() + damping = math_ops.cast(damping, self._dtype) invsqrtC0 = math_ops.matmul( eigen_V * (eigen_e + damping)**(-0.5), eigen_V, transpose_b=True) @@ -1702,6 +1773,7 @@ def make_inverse_update_ops(self): mu_var) in self._option2quants_by_damping.items(): damping = self._damping_funcs_by_id[damping_id]() + damping = math_ops.cast(damping, self._dtype) # compute C0^(-1/2) invsqrtC0 = math_ops.matmul( diff --git a/tensorflow/contrib/kfac/python/ops/linear_operator.py b/tensorflow/contrib/kfac/python/ops/linear_operator.py new file mode 100644 index 00000000000000..61cb955ae85df9 --- /dev/null +++ b/tensorflow/contrib/kfac/python/ops/linear_operator.py @@ -0,0 +1,95 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""SmartMatrices definitions.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.kfac.python.ops import utils +from tensorflow.python.framework import ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops.linalg import linalg +from tensorflow.python.ops.linalg import linalg_impl +from tensorflow.python.ops.linalg import linear_operator_util as lou + + +class LinearOperatorExtras(object): # pylint: disable=missing-docstring + + def matmul(self, x, adjoint=False, adjoint_arg=False, name="matmul"): + + with self._name_scope(name, values=[x]): + if isinstance(x, ops.IndexedSlices): + return self._matmul_sparse(x, adjoint=adjoint, adjoint_arg=adjoint_arg) + + x = ops.convert_to_tensor(x, name="x") + self._check_input_dtype(x) + + self_dim = -2 if adjoint else -1 + arg_dim = -1 if adjoint_arg else -2 + self.shape[self_dim].assert_is_compatible_with(x.get_shape()[arg_dim]) + + return self._matmul(x, adjoint=adjoint, adjoint_arg=adjoint_arg) + + def matmul_right(self, x, adjoint=False, adjoint_arg=False, name="matmul"): + + with self._name_scope(name, values=[x]): + + if isinstance(x, ops.IndexedSlices): + return self._matmul_right_sparse( + x, adjoint=adjoint, adjoint_arg=adjoint_arg) + + x = ops.convert_to_tensor(x, name="x") + self._check_input_dtype(x) + + self_dim = -1 if adjoint else -2 + arg_dim = -2 if adjoint_arg else -1 + self.shape[self_dim].assert_is_compatible_with(x.get_shape()[arg_dim]) + + return self._matmul_right(x, adjoint=adjoint, adjoint_arg=adjoint_arg) + + +class LinearOperatorFullMatrix(LinearOperatorExtras, + linalg.LinearOperatorFullMatrix): + + # TODO(b/78117889) Remove this definition once core LinearOperator + # has _matmul_right. + def _matmul_right(self, x, adjoint=False, adjoint_arg=False): + return lou.matmul_with_broadcast( + x, self._matrix, adjoint_a=adjoint_arg, adjoint_b=adjoint) + + def _matmul_sparse(self, x, adjoint=False, adjoint_arg=False): + raise NotImplementedError + + def _matmul_right_sparse(self, x, adjoint=False, adjoint_arg=False): + assert not adjoint and not adjoint_arg + return utils.matmul_sparse_dense(x, self._matrix) + + +class LinearOperatorDiag(LinearOperatorExtras, # pylint: disable=missing-docstring + linalg.LinearOperatorDiag): + + def _matmul_right(self, x, adjoint=False, adjoint_arg=False): + diag_mat = math_ops.conj(self._diag) if adjoint else self._diag + x = linalg_impl.adjoint(x) if adjoint_arg else x + return diag_mat * x + + def _matmul_sparse(self, x, adjoint=False, adjoint_arg=False): + diag_mat = math_ops.conj(self._diag) if adjoint else self._diag + assert not adjoint_arg + return utils.matmul_diag_sparse(diag_mat, x) + + def _matmul_right_sparse(self, x, adjoint=False, adjoint_arg=False): + raise NotImplementedError diff --git a/tensorflow/contrib/kfac/python/ops/placement.py b/tensorflow/contrib/kfac/python/ops/placement.py index bf12dbaa9adbaa..38a0e287a73f42 100644 --- a/tensorflow/contrib/kfac/python/ops/placement.py +++ b/tensorflow/contrib/kfac/python/ops/placement.py @@ -35,7 +35,7 @@ def thunk(): class RoundRobinPlacementMixin(object): """Implements round robin placement strategy for ops and variables.""" - def __init__(self, cov_devices=None, inv_devices=None, *args, **kwargs): + def __init__(self, cov_devices=None, inv_devices=None, **kwargs): """Initializes the RoundRobinPlacementMixin class. Args: @@ -45,11 +45,10 @@ def __init__(self, cov_devices=None, inv_devices=None, *args, **kwargs): inv_devices: Iterable of device strings (e.g. '/gpu:0'). Inversion computations will be placed on these devices in a round-robin fashion. Can be None, which means that no devices are specified. - *args: - **kwargs: + **kwargs: Need something here? """ - super(RoundRobinPlacementMixin, self).__init__(*args, **kwargs) + super(RoundRobinPlacementMixin, self).__init__(**kwargs) self._cov_devices = cov_devices self._inv_devices = inv_devices diff --git a/tensorflow/contrib/kfac/python/ops/utils.py b/tensorflow/contrib/kfac/python/ops/utils.py index b6f42815e79fa5..144295f4c7e36f 100644 --- a/tensorflow/contrib/kfac/python/ops/utils.py +++ b/tensorflow/contrib/kfac/python/ops/utils.py @@ -235,6 +235,13 @@ def posdef_eig_self_adjoint(mat): } +def cholesky(tensor, damping): + """Computes the inverse of tensor + damping * identity.""" + identity = linalg_ops.eye(tensor.shape.as_list()[0], dtype=tensor.dtype) + damping = math_ops.cast(damping, dtype=tensor.dtype) + return linalg_ops.cholesky(tensor + damping * identity) + + class SubGraph(object): """Defines a subgraph given by all the dependencies of a given set of outputs. """ @@ -553,13 +560,17 @@ def is_data_format_channel_last(data_format): return data_format.endswith("C") -def matmul_sparse_dense(A, B, name=None): # pylint: disable=invalid-name +def matmul_sparse_dense(A, B, name=None, transpose_a=False, transpose_b=False): # pylint: disable=invalid-name """Computes matmul(A, B) where A is sparse, B is dense. Args: A: tf.IndexedSlices with dense shape [m, n]. B: tf.Tensor with shape [n, k]. name: str. Name of op. + transpose_a: Bool. If true we transpose A before multiplying it by B. + (Default: False) + transpose_b: Bool. If true we transpose B before multiplying it by A. + (Default: False) Returns: tf.IndexedSlices resulting from matmul(A, B). @@ -573,7 +584,8 @@ def matmul_sparse_dense(A, B, name=None): # pylint: disable=invalid-name raise ValueError("A must represent a matrix. Found: %s." % A) if B.shape.ndims != 2: raise ValueError("B must be a matrix.") - new_values = math_ops.matmul(A.values, B) + new_values = math_ops.matmul( + A.values, B, transpose_a=transpose_a, transpose_b=transpose_b) return ops.IndexedSlices( new_values, A.indices, From 4eac28aa45e853d5194eb8a12ca518ec4f95d97d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 06:15:30 -0700 Subject: [PATCH 0049/1691] Format header guards under tensorflow/core/grappler. PiperOrigin-RevId: 194387041 --- tensorflow/core/grappler/clusters/cluster.h | 6 +++--- tensorflow/core/grappler/clusters/single_machine.h | 6 +++--- tensorflow/core/grappler/clusters/utils.h | 6 +++--- tensorflow/core/grappler/clusters/virtual_cluster.h | 6 +++--- tensorflow/core/grappler/costs/cost_estimator.h | 6 +++--- tensorflow/core/grappler/costs/graph_memory.h | 6 +++--- tensorflow/core/grappler/costs/graph_properties.h | 6 +++--- tensorflow/core/grappler/costs/measuring_cost_estimator.h | 6 +++--- tensorflow/core/grappler/costs/robust_stats.h | 6 +++--- tensorflow/core/grappler/costs/utils.h | 6 +++--- tensorflow/core/grappler/devices.h | 6 +++--- tensorflow/core/grappler/graph_view.h | 6 +++--- tensorflow/core/grappler/grappler_item.h | 6 +++--- tensorflow/core/grappler/grappler_item_builder.h | 6 +++--- tensorflow/core/grappler/inputs/file_input_yielder.h | 6 +++--- tensorflow/core/grappler/inputs/input_yielder.h | 6 +++--- .../grappler/inputs/trivial_test_graph_input_yielder.h | 6 +++--- tensorflow/core/grappler/inputs/utils.h | 6 +++--- tensorflow/core/grappler/op_types.h | 6 +++--- .../core/grappler/optimizers/arithmetic_optimizer.h | 8 ++++---- tensorflow/core/grappler/optimizers/auto_parallel.h | 6 +++--- tensorflow/core/grappler/optimizers/constant_folding.h | 6 +++--- .../core/grappler/optimizers/custom_graph_optimizer.h | 6 +++--- tensorflow/core/grappler/optimizers/function_optimizer.h | 6 +++--- tensorflow/core/grappler/optimizers/graph_optimizer.h | 6 +++--- .../core/grappler/optimizers/graph_optimizer_stage.h | 6 +++--- tensorflow/core/grappler/optimizers/graph_rewriter.h | 6 +++--- tensorflow/core/grappler/optimizers/layout_optimizer.h | 6 +++--- tensorflow/core/grappler/optimizers/memory_optimizer.h | 6 +++--- tensorflow/core/grappler/optimizers/meta_optimizer.h | 6 +++--- tensorflow/core/grappler/optimizers/model_pruner.h | 6 +++--- tensorflow/core/grappler/utils.h | 6 +++--- tensorflow/core/grappler/utils/functions.h | 6 +++--- tensorflow/core/grappler/utils/grappler_test.h | 6 +++--- 34 files changed, 103 insertions(+), 103 deletions(-) diff --git a/tensorflow/core/grappler/clusters/cluster.h b/tensorflow/core/grappler/clusters/cluster.h index 5068f72b30d498..0796ba65ecc4a6 100644 --- a/tensorflow/core/grappler/clusters/cluster.h +++ b/tensorflow/core/grappler/clusters/cluster.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_CLUSTERS_CLUSTER_H_ -#define TENSORFLOW_GRAPPLER_CLUSTERS_CLUSTER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_CLUSTERS_CLUSTER_H_ +#define TENSORFLOW_CORE_GRAPPLER_CLUSTERS_CLUSTER_H_ #include #include @@ -127,4 +127,4 @@ class Cluster { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_CLUSTERS_CLUSTER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_CLUSTERS_CLUSTER_H_ diff --git a/tensorflow/core/grappler/clusters/single_machine.h b/tensorflow/core/grappler/clusters/single_machine.h index 90d6a04cab6501..0ae188e0d62e38 100644 --- a/tensorflow/core/grappler/clusters/single_machine.h +++ b/tensorflow/core/grappler/clusters/single_machine.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_CLUSTERS_SINGLE_MACHINE_H_ -#define TENSORFLOW_GRAPPLER_CLUSTERS_SINGLE_MACHINE_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_CLUSTERS_SINGLE_MACHINE_H_ +#define TENSORFLOW_CORE_GRAPPLER_CLUSTERS_SINGLE_MACHINE_H_ #include "tensorflow/cc/training/coordinator.h" #include "tensorflow/core/framework/allocator.h" @@ -85,4 +85,4 @@ class SingleMachine : public Cluster { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_CLUSTERS_SINGLE_MACHINE_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_CLUSTERS_SINGLE_MACHINE_H_ diff --git a/tensorflow/core/grappler/clusters/utils.h b/tensorflow/core/grappler/clusters/utils.h index df8e7dca44ad63..ca15c48006df13 100644 --- a/tensorflow/core/grappler/clusters/utils.h +++ b/tensorflow/core/grappler/clusters/utils.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_CLUSTERS_UTILS_H_ -#define TENSORFLOW_GRAPPLER_CLUSTERS_UTILS_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_CLUSTERS_UTILS_H_ +#define TENSORFLOW_CORE_GRAPPLER_CLUSTERS_UTILS_H_ #include "tensorflow/core/common_runtime/gpu/gpu_id.h" #include "tensorflow/core/protobuf/device_properties.pb.h" @@ -36,4 +36,4 @@ DeviceProperties GetDeviceInfo(const DeviceNameUtils::ParsedName& device); } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_CLUSTERS_UTILS_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_CLUSTERS_UTILS_H_ diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.h b/tensorflow/core/grappler/clusters/virtual_cluster.h index dde70bab7a391e..e5967bac3dcc30 100644 --- a/tensorflow/core/grappler/clusters/virtual_cluster.h +++ b/tensorflow/core/grappler/clusters/virtual_cluster.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_CLUSTERS_VIRTUAL_CLUSTER_H_ -#define TENSORFLOW_GRAPPLER_CLUSTERS_VIRTUAL_CLUSTER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_CLUSTERS_VIRTUAL_CLUSTER_H_ +#define TENSORFLOW_CORE_GRAPPLER_CLUSTERS_VIRTUAL_CLUSTER_H_ #include #include "tensorflow/core/grappler/clusters/cluster.h" @@ -53,4 +53,4 @@ class VirtualCluster : public Cluster { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_CLUSTERS_VIRTUAL_CLUSTER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_CLUSTERS_VIRTUAL_CLUSTER_H_ diff --git a/tensorflow/core/grappler/costs/cost_estimator.h b/tensorflow/core/grappler/costs/cost_estimator.h index 9e01ec5ff5b48b..fe8a876f8ac3e9 100644 --- a/tensorflow/core/grappler/costs/cost_estimator.h +++ b/tensorflow/core/grappler/costs/cost_estimator.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_COSTS_COST_ESTIMATOR_H_ -#define TENSORFLOW_GRAPPLER_COSTS_COST_ESTIMATOR_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_COSTS_COST_ESTIMATOR_H_ +#define TENSORFLOW_CORE_GRAPPLER_COSTS_COST_ESTIMATOR_H_ #include #include @@ -180,4 +180,4 @@ class CostEstimator { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_COSTS_COST_ESTIMATOR_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_COSTS_COST_ESTIMATOR_H_ diff --git a/tensorflow/core/grappler/costs/graph_memory.h b/tensorflow/core/grappler/costs/graph_memory.h index 859e4c012c84c9..a8ae4cc49f0cdc 100644 --- a/tensorflow/core/grappler/costs/graph_memory.h +++ b/tensorflow/core/grappler/costs/graph_memory.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_COSTS_GRAPH_MEMORY_H_ -#define TENSORFLOW_GRAPPLER_COSTS_GRAPH_MEMORY_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_COSTS_GRAPH_MEMORY_H_ +#define TENSORFLOW_CORE_GRAPPLER_COSTS_GRAPH_MEMORY_H_ #include "tensorflow/core/framework/node_def.pb.h" #include "tensorflow/core/grappler/clusters/cluster.h" @@ -78,4 +78,4 @@ class GraphMemory { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_COSTS_GRAPH_MEMORY_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_COSTS_GRAPH_MEMORY_H_ diff --git a/tensorflow/core/grappler/costs/graph_properties.h b/tensorflow/core/grappler/costs/graph_properties.h index 485324c46643b7..7d685b58337213 100644 --- a/tensorflow/core/grappler/costs/graph_properties.h +++ b/tensorflow/core/grappler/costs/graph_properties.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_COSTS_GRAPH_PROPERTIES_H_ -#define TENSORFLOW_GRAPPLER_COSTS_GRAPH_PROPERTIES_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_COSTS_GRAPH_PROPERTIES_H_ +#define TENSORFLOW_CORE_GRAPPLER_COSTS_GRAPH_PROPERTIES_H_ #include #include @@ -125,4 +125,4 @@ class GraphProperties { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_COSTS_GRAPH_PROPERTIES_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_COSTS_GRAPH_PROPERTIES_H_ diff --git a/tensorflow/core/grappler/costs/measuring_cost_estimator.h b/tensorflow/core/grappler/costs/measuring_cost_estimator.h index 1b3edb4c27b325..3e741c91997403 100644 --- a/tensorflow/core/grappler/costs/measuring_cost_estimator.h +++ b/tensorflow/core/grappler/costs/measuring_cost_estimator.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_COSTS_MEASURING_COST_ESTIMATOR_H_ -#define TENSORFLOW_GRAPPLER_COSTS_MEASURING_COST_ESTIMATOR_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_COSTS_MEASURING_COST_ESTIMATOR_H_ +#define TENSORFLOW_CORE_GRAPPLER_COSTS_MEASURING_COST_ESTIMATOR_H_ #include #include @@ -73,4 +73,4 @@ class MeasuringCostEstimator : public CostEstimator { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_COSTS_MEASURING_COST_ESTIMATOR_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_COSTS_MEASURING_COST_ESTIMATOR_H_ diff --git a/tensorflow/core/grappler/costs/robust_stats.h b/tensorflow/core/grappler/costs/robust_stats.h index 9d8f5bc970ad9c..f247eb940cef38 100644 --- a/tensorflow/core/grappler/costs/robust_stats.h +++ b/tensorflow/core/grappler/costs/robust_stats.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_COSTS_ROBUST_STATS_H_ -#define TENSORFLOW_GRAPPLER_COSTS_ROBUST_STATS_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_COSTS_ROBUST_STATS_H_ +#define TENSORFLOW_CORE_GRAPPLER_COSTS_ROBUST_STATS_H_ #include namespace tensorflow { @@ -39,4 +39,4 @@ class RobustStats { } // namespace grappler } // namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_COSTS_ROBUST_STATS_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_COSTS_ROBUST_STATS_H_ diff --git a/tensorflow/core/grappler/costs/utils.h b/tensorflow/core/grappler/costs/utils.h index 409f07b28b16ca..d2c7c676667c50 100644 --- a/tensorflow/core/grappler/costs/utils.h +++ b/tensorflow/core/grappler/costs/utils.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_COSTS_UTILS_H_ -#define TENSORFLOW_GRAPPLER_COSTS_UTILS_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_COSTS_UTILS_H_ +#define TENSORFLOW_CORE_GRAPPLER_COSTS_UTILS_H_ #include #include @@ -111,4 +111,4 @@ string GetStatsStringFromRunMetadata(const RunMetadata& run_metadata, } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_COSTS_UTILS_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_COSTS_UTILS_H_ diff --git a/tensorflow/core/grappler/devices.h b/tensorflow/core/grappler/devices.h index 2d6c41888d92e0..1e60117b2d1649 100644 --- a/tensorflow/core/grappler/devices.h +++ b/tensorflow/core/grappler/devices.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_DEVICES_H_ -#define TENSORFLOW_GRAPPLER_DEVICES_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_DEVICES_H_ +#define TENSORFLOW_CORE_GRAPPLER_DEVICES_H_ #include @@ -39,4 +39,4 @@ int GetNumAvailableLogicalCPUCores(); } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_DEVICES_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_DEVICES_H_ diff --git a/tensorflow/core/grappler/graph_view.h b/tensorflow/core/grappler/graph_view.h index c3baad09878777..584cb9048b64fd 100644 --- a/tensorflow/core/grappler/graph_view.h +++ b/tensorflow/core/grappler/graph_view.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_GRAPH_VIEW_H_ -#define TENSORFLOW_GRAPPLER_GRAPH_VIEW_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_GRAPH_VIEW_H_ +#define TENSORFLOW_CORE_GRAPPLER_GRAPH_VIEW_H_ #include #include @@ -124,4 +124,4 @@ class GraphView { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_GRAPH_VIEW_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_GRAPH_VIEW_H_ diff --git a/tensorflow/core/grappler/grappler_item.h b/tensorflow/core/grappler/grappler_item.h index cd165ac3d460fb..939e5fa04692fd 100644 --- a/tensorflow/core/grappler/grappler_item.h +++ b/tensorflow/core/grappler/grappler_item.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_GRAPPLER_ITEM_H_ -#define TENSORFLOW_GRAPPLER_GRAPPLER_ITEM_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_GRAPPLER_ITEM_H_ +#define TENSORFLOW_CORE_GRAPPLER_GRAPPLER_ITEM_H_ #include #include @@ -93,4 +93,4 @@ std::vector ComputeTransitiveFanin( } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_GRAPPLER_ITEM_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_GRAPPLER_ITEM_H_ diff --git a/tensorflow/core/grappler/grappler_item_builder.h b/tensorflow/core/grappler/grappler_item_builder.h index 6d181e49e67aca..aafd2fdcdaf920 100644 --- a/tensorflow/core/grappler/grappler_item_builder.h +++ b/tensorflow/core/grappler/grappler_item_builder.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_GRAPPLER_ITEM_BUILDER_H_ -#define TENSORFLOW_GRAPPLER_GRAPPLER_ITEM_BUILDER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_GRAPPLER_ITEM_BUILDER_H_ +#define TENSORFLOW_CORE_GRAPPLER_GRAPPLER_ITEM_BUILDER_H_ #include #include @@ -59,4 +59,4 @@ std::unique_ptr GrapplerItemFromMetaGraphDef( } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_GRAPPLER_ITEM_BUILDER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_GRAPPLER_ITEM_BUILDER_H_ diff --git a/tensorflow/core/grappler/inputs/file_input_yielder.h b/tensorflow/core/grappler/inputs/file_input_yielder.h index b597319261011e..f3e9ecb677fdf8 100644 --- a/tensorflow/core/grappler/inputs/file_input_yielder.h +++ b/tensorflow/core/grappler/inputs/file_input_yielder.h @@ -18,8 +18,8 @@ limitations under the License. // that may be stored in the checkpoint are not restored in order to speedup the // initialization. -#ifndef TENSORFLOW_GRAPPLER_INPUTS_FILE_INPUT_YIELDER_H_ -#define TENSORFLOW_GRAPPLER_INPUTS_FILE_INPUT_YIELDER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_INPUTS_FILE_INPUT_YIELDER_H_ +#define TENSORFLOW_CORE_GRAPPLER_INPUTS_FILE_INPUT_YIELDER_H_ #include #include @@ -53,4 +53,4 @@ class FileInputYielder : public InputYielder { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_INPUTS_FILE_INPUT_YIELDER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_INPUTS_FILE_INPUT_YIELDER_H_ diff --git a/tensorflow/core/grappler/inputs/input_yielder.h b/tensorflow/core/grappler/inputs/input_yielder.h index c9f90820a9928a..06f642c513018d 100644 --- a/tensorflow/core/grappler/inputs/input_yielder.h +++ b/tensorflow/core/grappler/inputs/input_yielder.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_INPUTS_INPUT_YIELDER_H_ -#define TENSORFLOW_GRAPPLER_INPUTS_INPUT_YIELDER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_INPUTS_INPUT_YIELDER_H_ +#define TENSORFLOW_CORE_GRAPPLER_INPUTS_INPUT_YIELDER_H_ namespace tensorflow { namespace grappler { @@ -32,4 +32,4 @@ class InputYielder { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_INPUTS_INPUT_YIELDER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_INPUTS_INPUT_YIELDER_H_ diff --git a/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h b/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h index 434b660614b426..74e5080a30f9c6 100644 --- a/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h +++ b/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_INPUTS_TRIVIAL_TEST_GRAPH_INPUT_YIELDER_H_ -#define TENSORFLOW_GRAPPLER_INPUTS_TRIVIAL_TEST_GRAPH_INPUT_YIELDER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_INPUTS_TRIVIAL_TEST_GRAPH_INPUT_YIELDER_H_ +#define TENSORFLOW_CORE_GRAPPLER_INPUTS_TRIVIAL_TEST_GRAPH_INPUT_YIELDER_H_ #include #include @@ -44,4 +44,4 @@ class TrivialTestGraphInputYielder : public InputYielder { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_INPUTS_TRIVIAL_TEST_GRAPH_INPUT_YIELDER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_INPUTS_TRIVIAL_TEST_GRAPH_INPUT_YIELDER_H_ diff --git a/tensorflow/core/grappler/inputs/utils.h b/tensorflow/core/grappler/inputs/utils.h index 00fcfa7a3f4b62..627dd5359fe181 100644 --- a/tensorflow/core/grappler/inputs/utils.h +++ b/tensorflow/core/grappler/inputs/utils.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_INPUTS_UTILS_H_ -#define TENSORFLOW_GRAPPLER_INPUTS_UTILS_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_INPUTS_UTILS_H_ +#define TENSORFLOW_CORE_GRAPPLER_INPUTS_UTILS_H_ #include #include @@ -37,4 +37,4 @@ Status ReadGraphDefFromFile(const std::string& graph_def_pbtxt_path, } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_INPUTS_UTILS_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_INPUTS_UTILS_H_ diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h index 7f5da19d905b41..b25ba1924e3b9c 100644 --- a/tensorflow/core/grappler/op_types.h +++ b/tensorflow/core/grappler/op_types.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OP_TYPES_H_ -#define TENSORFLOW_GRAPPLER_OP_TYPES_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OP_TYPES_H_ +#define TENSORFLOW_CORE_GRAPPLER_OP_TYPES_H_ #include "tensorflow/core/framework/node_def.pb.h" #include "tensorflow/core/lib/core/status.h" @@ -185,4 +185,4 @@ bool HasOpDef(const NodeDef& node); } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OP_TYPES_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OP_TYPES_H_ diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h index 375f13acc131e7..689ffd45fe7cbd 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_ARITHMETIC_OPTIMIZER_H_ -#define TENSORFLOW_GRAPPLER_OPTIMIZERS_ARITHMETIC_OPTIMIZER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_ARITHMETIC_OPTIMIZER_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_ARITHMETIC_OPTIMIZER_H_ #include #include "tensorflow/core/grappler/costs/graph_properties.h" @@ -109,7 +109,7 @@ class ArithmeticOptimizer : public GraphOptimizer { Status SimplifyArithmeticOps(bool can_use_shapes); // Tries to simplify the expression that roots at `node` and replaces the uses // of `node` to the simplified expression. Returns the name of the simplified - // tensor (e.g. "split:1") or an emtpy string if no simplification is + // tensor (e.g. "split:1") or an empty string if no simplification is // performed. // // `node_map` stores the mapping from node names to NodeDef*, and will be @@ -138,4 +138,4 @@ class ArithmeticOptimizer : public GraphOptimizer { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OPTIMIZERS_ARITHMETIC_OPTIMIZER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_ARITHMETIC_OPTIMIZER_H_ diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.h b/tensorflow/core/grappler/optimizers/auto_parallel.h index 8d1098d87755c1..63f6fe5b9db870 100644 --- a/tensorflow/core/grappler/optimizers/auto_parallel.h +++ b/tensorflow/core/grappler/optimizers/auto_parallel.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_ -#define TENSORFLOW_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_ #include "tensorflow/core/framework/variable.pb.h" #include "tensorflow/core/grappler/optimizers/graph_optimizer.h" @@ -63,4 +63,4 @@ class AutoParallel : public GraphOptimizer { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_ diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h index f8a9e90d62111e..eb06cd081f7f3e 100644 --- a/tensorflow/core/grappler/optimizers/constant_folding.h +++ b/tensorflow/core/grappler/optimizers/constant_folding.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_CONSTANT_FOLDING_H_ -#define TENSORFLOW_GRAPPLER_OPTIMIZERS_CONSTANT_FOLDING_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_CONSTANT_FOLDING_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_CONSTANT_FOLDING_H_ #include "tensorflow/core/framework/device_base.h" #include "tensorflow/core/framework/op_kernel.h" @@ -116,4 +116,4 @@ class ConstantFolding : public GraphOptimizer { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OPTIMIZERS_CONSTANT_FOLDING_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_CONSTANT_FOLDING_H_ diff --git a/tensorflow/core/grappler/optimizers/custom_graph_optimizer.h b/tensorflow/core/grappler/optimizers/custom_graph_optimizer.h index 4d7f8c98d07112..ab9af5acff413b 100644 --- a/tensorflow/core/grappler/optimizers/custom_graph_optimizer.h +++ b/tensorflow/core/grappler/optimizers/custom_graph_optimizer.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_CUSTOM_GRAPH_OPTIMIZER_H_ -#define TENSORFLOW_GRAPPLER_OPTIMIZERS_CUSTOM_GRAPH_OPTIMIZER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_CUSTOM_GRAPH_OPTIMIZER_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_CUSTOM_GRAPH_OPTIMIZER_H_ #include "tensorflow/core/grappler/optimizers/graph_optimizer.h" #include "tensorflow/core/lib/core/status.h" @@ -34,4 +34,4 @@ class CustomGraphOptimizer : public GraphOptimizer { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OPTIMIZERS_CUSTOM_GRAPH_OPTIMIZER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_CUSTOM_GRAPH_OPTIMIZER_H_ diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.h b/tensorflow/core/grappler/optimizers/function_optimizer.h index e307b4e533fc5b..4352555064c43c 100644 --- a/tensorflow/core/grappler/optimizers/function_optimizer.h +++ b/tensorflow/core/grappler/optimizers/function_optimizer.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_FUNCTION_OPTIMIZER_H_ -#define TENSORFLOW_GRAPPLER_OPTIMIZERS_FUNCTION_OPTIMIZER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_FUNCTION_OPTIMIZER_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_FUNCTION_OPTIMIZER_H_ #include "tensorflow/core/grappler/optimizers/graph_optimizer.h" #include "tensorflow/core/protobuf/rewriter_config.pb.h" @@ -55,4 +55,4 @@ class FunctionOptimizer : public GraphOptimizer { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OPTIMIZERS_FUNCTION_OPTIMIZER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_FUNCTION_OPTIMIZER_H_ diff --git a/tensorflow/core/grappler/optimizers/graph_optimizer.h b/tensorflow/core/grappler/optimizers/graph_optimizer.h index 42d9837312d25f..765dd13263f029 100644 --- a/tensorflow/core/grappler/optimizers/graph_optimizer.h +++ b/tensorflow/core/grappler/optimizers/graph_optimizer.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_GRAPH_OPTIMIZER_H_ -#define TENSORFLOW_GRAPPLER_OPTIMIZERS_GRAPH_OPTIMIZER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_GRAPH_OPTIMIZER_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_GRAPH_OPTIMIZER_H_ #include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/lib/core/status.h" @@ -50,4 +50,4 @@ class GraphOptimizer { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OPTIMIZERS_GRAPH_OPTIMIZER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_GRAPH_OPTIMIZER_H_ diff --git a/tensorflow/core/grappler/optimizers/graph_optimizer_stage.h b/tensorflow/core/grappler/optimizers/graph_optimizer_stage.h index 089cad36e9ad40..b0ec967473bbec 100644 --- a/tensorflow/core/grappler/optimizers/graph_optimizer_stage.h +++ b/tensorflow/core/grappler/optimizers/graph_optimizer_stage.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_OPTIMIZER_STAGE_H_ -#define TENSORFLOW_GRAPPLER_OPTIMIZERS_OPTIMIZER_STAGE_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_GRAPH_OPTIMIZER_STAGE_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_GRAPH_OPTIMIZER_STAGE_H_ #include #include @@ -260,4 +260,4 @@ class GraphOptimizerStagePipeline { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OPTIMIZERS_OPTIMIZER_STAGE_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_GRAPH_OPTIMIZER_STAGE_H_ diff --git a/tensorflow/core/grappler/optimizers/graph_rewriter.h b/tensorflow/core/grappler/optimizers/graph_rewriter.h index 3d48d628e203e3..4a5a150dc9234f 100644 --- a/tensorflow/core/grappler/optimizers/graph_rewriter.h +++ b/tensorflow/core/grappler/optimizers/graph_rewriter.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_GRAPH_REWRITER_H_ -#define TENSORFLOW_GRAPPLER_OPTIMIZERS_GRAPH_REWRITER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_GRAPH_REWRITER_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_GRAPH_REWRITER_H_ #include #include @@ -99,4 +99,4 @@ class GraphRewriter { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OPTIMIZERS_GRAPH_REWRITER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_GRAPH_REWRITER_H_ diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.h b/tensorflow/core/grappler/optimizers/layout_optimizer.h index 357205828ddea3..49b697bb75b6b8 100644 --- a/tensorflow/core/grappler/optimizers/layout_optimizer.h +++ b/tensorflow/core/grappler/optimizers/layout_optimizer.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_LAYOUT_OPTIMIZER_H_ -#define TENSORFLOW_GRAPPLER_OPTIMIZERS_LAYOUT_OPTIMIZER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_LAYOUT_OPTIMIZER_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_LAYOUT_OPTIMIZER_H_ #include "tensorflow/core/grappler/costs/graph_properties.h" #include "tensorflow/core/grappler/costs/virtual_placer.h" @@ -57,4 +57,4 @@ class LayoutOptimizer : public GraphOptimizer { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OPTIMIZERS_LAYOUT_OPTIMIZER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_LAYOUT_OPTIMIZER_H_ diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.h b/tensorflow/core/grappler/optimizers/memory_optimizer.h index 5c555a26746b75..653ffaec4c206c 100644 --- a/tensorflow/core/grappler/optimizers/memory_optimizer.h +++ b/tensorflow/core/grappler/optimizers/memory_optimizer.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_MEMORY_OPTIMIZER_H_ -#define TENSORFLOW_GRAPPLER_OPTIMIZERS_MEMORY_OPTIMIZER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_MEMORY_OPTIMIZER_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_MEMORY_OPTIMIZER_H_ #include "tensorflow/core/grappler/optimizers/graph_optimizer.h" #include "tensorflow/core/protobuf/rewriter_config.pb.h" @@ -53,4 +53,4 @@ class MemoryOptimizer : public GraphOptimizer { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OPTIMIZERS_MEMORY_OPTIMIZER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_MEMORY_OPTIMIZER_H_ diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.h b/tensorflow/core/grappler/optimizers/meta_optimizer.h index b8d46662489c03..e736dd174ed96c 100644 --- a/tensorflow/core/grappler/optimizers/meta_optimizer.h +++ b/tensorflow/core/grappler/optimizers/meta_optimizer.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_META_OPTIMIZER_H_ -#define TENSORFLOW_GRAPPLER_OPTIMIZERS_META_OPTIMIZER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_META_OPTIMIZER_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_META_OPTIMIZER_H_ #include "tensorflow/core/framework/device_base.h" #include "tensorflow/core/grappler/grappler_item.h" @@ -90,4 +90,4 @@ Status RunMetaOptimizer(const GrapplerItem& item, const RewriterConfig& cfg, } // namespace grappler } // namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OPTIMIZERS_META_OPTIMIZER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_META_OPTIMIZER_H_ diff --git a/tensorflow/core/grappler/optimizers/model_pruner.h b/tensorflow/core/grappler/optimizers/model_pruner.h index 3d76aebef433f1..76cc792a45404c 100644 --- a/tensorflow/core/grappler/optimizers/model_pruner.h +++ b/tensorflow/core/grappler/optimizers/model_pruner.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_MODEL_PRUNER_H_ -#define TENSORFLOW_GRAPPLER_OPTIMIZERS_MODEL_PRUNER_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_MODEL_PRUNER_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_MODEL_PRUNER_H_ #include "tensorflow/core/grappler/optimizers/graph_optimizer.h" @@ -41,4 +41,4 @@ class ModelPruner : public GraphOptimizer { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_OPTIMIZERS_MODEL_PRUNER_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_MODEL_PRUNER_H_ diff --git a/tensorflow/core/grappler/utils.h b/tensorflow/core/grappler/utils.h index b15667dca26968..54cb26bafa9c4a 100644 --- a/tensorflow/core/grappler/utils.h +++ b/tensorflow/core/grappler/utils.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_UTILS_H_ -#define TENSORFLOW_GRAPPLER_UTILS_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_UTILS_H_ +#define TENSORFLOW_CORE_GRAPPLER_UTILS_H_ #include #include @@ -254,4 +254,4 @@ class SimpleGraphView { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_UTILS_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_UTILS_H_ diff --git a/tensorflow/core/grappler/utils/functions.h b/tensorflow/core/grappler/utils/functions.h index 5e8b6c69601571..692333fa175875 100644 --- a/tensorflow/core/grappler/utils/functions.h +++ b/tensorflow/core/grappler/utils/functions.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_UTILS_FUNCTIONS_H_ -#define TENSORFLOW_GRAPPLER_UTILS_FUNCTIONS_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_UTILS_FUNCTIONS_H_ +#define TENSORFLOW_CORE_GRAPPLER_UTILS_FUNCTIONS_H_ #include #include @@ -221,4 +221,4 @@ Status MakeFunctionDef(const GrapplerFunctionItem& item, } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_UTILS_FUNCTIONS_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_UTILS_FUNCTIONS_H_ diff --git a/tensorflow/core/grappler/utils/grappler_test.h b/tensorflow/core/grappler/utils/grappler_test.h index c2ba5ee7e8a601..bd4d7f2a7e89ad 100644 --- a/tensorflow/core/grappler/utils/grappler_test.h +++ b/tensorflow/core/grappler/utils/grappler_test.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_GRAPPLER_GRAPPLER_TEST_H_ -#define TENSORFLOW_GRAPPLER_GRAPPLER_TEST_H_ +#ifndef TENSORFLOW_CORE_GRAPPLER_UTILS_GRAPPLER_TEST_H_ +#define TENSORFLOW_CORE_GRAPPLER_UTILS_GRAPPLER_TEST_H_ #include @@ -75,4 +75,4 @@ class GrapplerTest : public ::testing::Test { } // end namespace grappler } // end namespace tensorflow -#endif // TENSORFLOW_GRAPPLER_GRAPPLER_TEST_H_ +#endif // TENSORFLOW_CORE_GRAPPLER_UTILS_GRAPPLER_TEST_H_ From acb55632cfc72d952340b9bc86f821f1df8f293a Mon Sep 17 00:00:00 2001 From: Shanqing Cai Date: Thu, 26 Apr 2018 08:53:46 -0700 Subject: [PATCH 0050/1691] tfdbg: disable grpc_large_data_test on ASAN PiperOrigin-RevId: 194402869 --- tensorflow/python/debug/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD index 250b4b1b6ab983..b5760df1ed47be 100644 --- a/tensorflow/python/debug/BUILD +++ b/tensorflow/python/debug/BUILD @@ -1003,6 +1003,7 @@ cuda_py_test( tags = [ "no_oss", # Test flaky due to port collisions. "no_windows", + "noasan", # Times out due to size of test (b/73731462). "oss_serial", ], ) From e563b56c0c7ef78a9d20e5f58061b2883107bcb0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 09:44:30 -0700 Subject: [PATCH 0051/1691] Disable vector_diffeomixture_test under ASAN to avoid timeouts. PiperOrigin-RevId: 194409698 --- tensorflow/contrib/distributions/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD index 2d99e8172d220a..fad613155d8861 100644 --- a/tensorflow/contrib/distributions/BUILD +++ b/tensorflow/contrib/distributions/BUILD @@ -709,6 +709,7 @@ cuda_py_test( "//tensorflow/contrib/linalg:linalg_py", "//tensorflow/python:client_testlib", ], + tags = ["noasan"], # times out, http://b/78588814 ) cuda_py_test( From 85e4dc47ea8d68bdd98f0982ba57ceb694115742 Mon Sep 17 00:00:00 2001 From: Yanping Huang Date: Thu, 26 Apr 2018 09:56:00 -0700 Subject: [PATCH 0052/1691] Fixing issue #13258. y is the square of Mahalanobis distance actually. PiperOrigin-RevId: 194411230 --- .../contrib/distributions/python/ops/mvn_full_covariance.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py b/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py index 86fcd4db54ad85..5d06a396fe7a3b 100644 --- a/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py +++ b/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py @@ -45,7 +45,7 @@ class MultivariateNormalFullCovariance(mvn_tril.MultivariateNormalTriL): The probability density function (pdf) is, with `@` as matrix multiplication, ```none - pdf(x; loc, covariance_matrix) = exp(-0.5 ||y||**2) / Z, + pdf(x; loc, covariance_matrix) = exp(-0.5 y) / Z, y = (x - loc)^T @ inv(covariance_matrix) @ (x - loc) Z = (2 pi)**(0.5 k) |det(covariance_matrix)|**(0.5). ``` @@ -54,8 +54,7 @@ class MultivariateNormalFullCovariance(mvn_tril.MultivariateNormalTriL): * `loc` is a vector in `R^k`, * `covariance_matrix` is an `R^{k x k}` symmetric positive definite matrix, - * `Z` denotes the normalization constant, and, - * `||y||**2` denotes the squared Euclidean norm of `y`. + * `Z` denotes the normalization constant. Additional leading dimensions (if any) in `loc` and `covariance_matrix` allow for batch dimensions. From 509ffc3be3152f3e89bf6bc694c9403f269128b3 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Thu, 26 Apr 2018 10:11:14 -0700 Subject: [PATCH 0053/1691] Simplify tfe.defun capture by not using convert_to_tensor PiperOrigin-RevId: 194413685 --- tensorflow/python/eager/function.py | 108 +++++----------------- tensorflow/python/eager/graph_callable.py | 8 +- 2 files changed, 29 insertions(+), 87 deletions(-) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index bdbbe864df99f7..426ee4c215a899 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -20,8 +20,6 @@ from __future__ import print_function import collections -import contextlib -import threading import numpy as np @@ -32,7 +30,6 @@ from tensorflow.python.eager import tape from tensorflow.python.eager.graph_only_ops import graph_placeholder from tensorflow.python.framework import c_api_util -from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes as dtypes_module from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops @@ -43,25 +40,6 @@ from tensorflow.python.util import nest from tensorflow.python.util import tf_decorator -# Thread-local storage for tfe Tensors which are referenced while evaluating a -# graph-mode function. -_scoped_captures = threading.local() -# _scoped_captures.tensors is either None or a map from Tensor id to a pair -# of a tfe tensor and its corresponding placeholder to pass as a function -# argument. The value should be None unless we're in function definition -# context. -_scoped_captures.tensors = None - - -@contextlib.contextmanager -def capture_tensors(captures): - old = _scoped_captures.__dict__.get("tensors", None) - try: - _scoped_captures.tensors = captures - yield - finally: - _scoped_captures.tensors = old - def capture_value(tensor_map, value, dtype, name): """Capture a value from outside the function, to pass in as an extra arg.""" @@ -105,43 +83,6 @@ def capture_value(tensor_map, value, dtype, name): return captured_value -def _convert_to_graph_tensor(value, dtype=None, name=None, as_ref=False): - """Captures a Tensor while building a graph mode function. - - Arguments: - value: A Tensor object. - dtype: The datatype of the value produced by the node in the graph. - name: str, Name of the node in the graph. - as_ref: Ignored (required by register_tensor_conversion_function). - - Returns: - Returns a constant (the current value of the tensor) if capturing - is not enabled. A placeholder which will have the value of the - tensor at runtime otherwise. - """ - del as_ref # Unused. - - if context.executing_eagerly(): - return value - - default_graph = ops.get_default_graph() - if not default_graph.building_function: - return value - - tensor_map = _scoped_captures.tensors - if tensor_map is None: - # Capturing is not enabled. - if value.dtype == dtypes_module.resource: - return value - return constant_op.constant(value.numpy()) - if type(value) == ops.Tensor and value.graph is default_graph: - # The tensor has already been converted and captured. The type check - # is intentional: we are checking that value is a Tensor and not an - # EagerTensor. - return value - return capture_value(tensor_map, value, dtype, name) - - class CapturingGraph(ops.Graph): """Graph used when constructing eager functions.""" @@ -161,6 +102,15 @@ def _use_c_api_hack(self): def clear_resource_control_flow_state(self): self._last_op_using_resource_tensor = {} + def maybe_capture_tensor(self, tensor): + if isinstance(tensor, ops.EagerTensor): + return capture_value( + self.captures, tensor, tensor.dtype, str(ops.uid())) + if tensor.graph is not self: + return capture_value( + self.captures, tensor, tensor.dtype, tensor.op.name) + return tensor + def create_op( self, op_type, @@ -176,20 +126,12 @@ def create_op( # forward the resources such as Identity and Switch can cause serialization # to fail. for i, inp in enumerate(inputs): - if inp.graph is not self: - inputs[i] = capture_value(self.captures, inp, inp.dtype, inp.op.name) + inputs[i] = self.maybe_capture_tensor(inp) return super(CapturingGraph, self).create_op( op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device) -# TODO(apassos): it'd be really nice if we could scope this registration. -# Note that we register this at a higher priority than ops.Tensor since we want -# to handle subclass specific conversion before a superclass conversion. -ops.register_tensor_conversion_function( - ops.EagerTensor, _convert_to_graph_tensor, priority=-1) - - # pylint: disable=invalid-name class HelperContext(object): """ControlFlowContext with a customizable AddOp method.""" @@ -644,21 +586,21 @@ def convert(x): x = a.mark_as_return(x) return x - with capture_tensors(captures): - this_tape = tape.push_new_tape() - try: - func_outputs = func(*func_inputs, **kwds) - func_outputs = nest.map_structure(convert, func_outputs) - finally: - tape.pop_tape(this_tape) - variables = this_tape.watched_variables() - - # Returning a closed-over tensor as an output does not trigger a - # call to convert_to_tensor, so we manually capture all such tensors. - outputs_list = _flatten(func_outputs) - func_def_outputs = [ - _convert_to_graph_tensor(x) for x in outputs_list if x is not None - ] + this_tape = tape.push_new_tape() + try: + func_outputs = func(*func_inputs, **kwds) + func_outputs = nest.map_structure(convert, func_outputs) + finally: + tape.pop_tape(this_tape) + variables = this_tape.watched_variables() + + # Returning a closed-over tensor as an output does not trigger a + # call to convert_to_tensor, so we manually capture all such tensors. + outputs_list = _flatten(func_outputs) + func_def_outputs = [ + tmp_graph.maybe_capture_tensor(x) for x in outputs_list + if x is not None + ] ids = list(sorted(captures.keys())) if ids: diff --git a/tensorflow/python/eager/graph_callable.py b/tensorflow/python/eager/graph_callable.py index d40ea982c74659..d9ffcbd2036b9e 100644 --- a/tensorflow/python/eager/graph_callable.py +++ b/tensorflow/python/eager/graph_callable.py @@ -278,8 +278,8 @@ def _graph_callable_internal(func, shape_and_dtypes): # variables. As a side-effect this will populate the variable capturing # scope's view of which variables exist. variable_captures = _VariableCapturingScope() - with variable_captures.initializing_scope(), function.capture_tensors( - captures), function.AutomaticControlDependencies() as a: + with variable_captures.initializing_scope( + ), function.AutomaticControlDependencies() as a: func_outputs = func(*func_inputs) outputs_list = nest.flatten(func_outputs) for i, x in enumerate(outputs_list): @@ -296,8 +296,8 @@ def _graph_callable_internal(func, shape_and_dtypes): # placeholders. This assumes the variable capturing scope created above # knows about all variables. tmp_graph.clear_resource_control_flow_state() - with variable_captures.capturing_scope(), function.capture_tensors( - captures), function.AutomaticControlDependencies() as a: + with variable_captures.capturing_scope( + ), function.AutomaticControlDependencies() as a: captured_outputs = func(*func_inputs) captured_outlist = nest.flatten(captured_outputs) for i, x in enumerate(captured_outlist): From 18f1349dbc4c0aedf09084277ad1b48d7c0cefb3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 10:13:03 -0700 Subject: [PATCH 0054/1691] Disable wrappers_test under ASAN since it sometimes times out. PiperOrigin-RevId: 194413982 --- tensorflow/python/keras/BUILD | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index a1c9f539536333..a09963e062808c 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -643,7 +643,10 @@ py_test( size = "medium", srcs = ["_impl/keras/layers/wrappers_test.py"], srcs_version = "PY2AND3", - tags = ["notsan"], + tags = [ + "noasan", # http://b/78599823 + "notsan", + ], deps = [ ":keras", "//tensorflow/python:client_testlib", From f495e321026683359fac213b82a20f597d4ead2a Mon Sep 17 00:00:00 2001 From: Russell Power Date: Thu, 26 Apr 2018 10:25:04 -0700 Subject: [PATCH 0055/1691] Limit the number of single allocation memory warnings. PiperOrigin-RevId: 194415953 --- tensorflow/core/framework/allocator.cc | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/tensorflow/core/framework/allocator.cc b/tensorflow/core/framework/allocator.cc index 1a7e5219cd243a..1c62d37955b313 100644 --- a/tensorflow/core/framework/allocator.cc +++ b/tensorflow/core/framework/allocator.cc @@ -68,6 +68,9 @@ static const double kLargeAllocationWarningThreshold = 0.1; // exceeds this threshold. static const double kTotalAllocationWarningThreshold = 0.5; +static const int kMaxSingleAllocationWarnings = 5; +static const int kMaxTotalAllocationWarnings = 1; + // Cache first invocation to port::AvailableRam, as it can be expensive. static int64_t LargeAllocationWarningBytes() { static int64_t value = static_cast(port::AvailableRam() * @@ -90,14 +93,18 @@ void EnableCPUAllocatorFullStats(bool enable) { class CPUAllocator : public Allocator { public: - CPUAllocator() : total_allocation_warning_triggered_(false) {} + CPUAllocator() + : single_allocation_warning_count_(0), + total_allocation_warning_count_(0) {} ~CPUAllocator() override {} string Name() override { return "cpu"; } void* AllocateRaw(size_t alignment, size_t num_bytes) override { - if (num_bytes > LargeAllocationWarningBytes()) { + if (num_bytes > LargeAllocationWarningBytes() && + single_allocation_warning_count_ < kMaxSingleAllocationWarnings) { + ++single_allocation_warning_count_; LOG(WARNING) << "Allocation of " << num_bytes << " exceeds " << 100 * kLargeAllocationWarningThreshold << "% of system memory."; @@ -115,11 +122,11 @@ class CPUAllocator : public Allocator { std::max(stats_.max_alloc_size, alloc_size); if (stats_.bytes_in_use > TotalAllocationWarningBytes() && - !total_allocation_warning_triggered_) { + total_allocation_warning_count_ < kMaxTotalAllocationWarnings) { + ++total_allocation_warning_count_; LOG(WARNING) << "Total allocated memory " << stats_.bytes_in_use << "exceeds " << 100 * kTotalAllocationWarningThreshold << "% of system memory"; - total_allocation_warning_triggered_ = true; } } return p; @@ -154,7 +161,11 @@ class CPUAllocator : public Allocator { private: mutex mu_; AllocatorStats stats_ GUARDED_BY(mu_); - bool total_allocation_warning_triggered_ GUARDED_BY(mu_); + + // Use for single allocations to avoid mutex contention when + // statistics are disabled. + std::atomic single_allocation_warning_count_; + int total_allocation_warning_count_ GUARDED_BY(mu_); TF_DISALLOW_COPY_AND_ASSIGN(CPUAllocator); }; From f63a8d6aaf251344631272d6b38327481f54fe55 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Thu, 26 Apr 2018 10:30:54 -0700 Subject: [PATCH 0056/1691] Remove "everything matched" assertions from CuDNN object-based checkpointing tests After cl/194315742 the assertions correctly point out that there are some Python objects which aren't matched (they don't have variables). Another option would be to mark these as special/optional, which we can implement if there's a need. PiperOrigin-RevId: 194416864 --- .../contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py index 012b17cee88aec..33ddfb8dee1c44 100644 --- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py +++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py @@ -717,7 +717,7 @@ def _VerifyCheckpoint( inputs = 3. * array_ops.ones([num_applications, num_layers, input_size], dtype=dtypes.float32) cudnn_output, _ = cudnn_layer(inputs) - status.assert_consumed().run_restore_ops() + status.run_restore_ops() second_save_path = cudnn_checkpoint.save(checkpoint_prefix) restore_layer = compatible_cell_fn() restore_layer_checkpoint = checkpointable_utils.Checkpoint( @@ -728,7 +728,7 @@ def _VerifyCheckpoint( restore_layer_output, current_state = restore_layer( inputs=3. * array_ops.ones([1, input_size]), state=current_state) - status.assert_consumed().run_restore_ops() + status.run_restore_ops() self.assertTrue(restore_layer.variables) for variable, expected_value in zip( restore_layer.variables, expected_variable_values): From efa789e1a5eb055b0ac1d9610318fcbd1919e150 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Thu, 26 Apr 2018 10:31:21 -0700 Subject: [PATCH 0057/1691] Add a skeleton dispatch context object, that can be used to control the dispatch rules and pass implementation-specific information down to the specialized operators. PiperOrigin-RevId: 194416937 --- tensorflow/contrib/autograph/operators/BUILD | 1 + .../autograph/operators/dispatch_context.py | 41 +++++++++++++++++++ 2 files changed, 42 insertions(+) create mode 100644 tensorflow/contrib/autograph/operators/dispatch_context.py diff --git a/tensorflow/contrib/autograph/operators/BUILD b/tensorflow/contrib/autograph/operators/BUILD index efb8d441dd839b..18bfec5d9c6991 100644 --- a/tensorflow/contrib/autograph/operators/BUILD +++ b/tensorflow/contrib/autograph/operators/BUILD @@ -22,6 +22,7 @@ py_library( "__init__.py", "control_flow.py", "data_structures.py", + "dispatch_context.py", ], srcs_version = "PY2AND3", visibility = ["//tensorflow:__subpackages__"], diff --git a/tensorflow/contrib/autograph/operators/dispatch_context.py b/tensorflow/contrib/autograph/operators/dispatch_context.py new file mode 100644 index 00000000000000..097002465bd140 --- /dev/null +++ b/tensorflow/contrib/autograph/operators/dispatch_context.py @@ -0,0 +1,41 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Structures that allow uniform control over the dispatch process.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections + + +# TODO(mdan): This is where macro override controls fit. + + +class DispatchContext(collections.namedtuple( + 'DispatchContext', + ('options',))): + """Allows passing additional parameters to the specific implementations. + + Attributes: + options: Optional dict of extra arguments that may be required by specific + implementations. + """ + + def option(self, name): + return self.options[name] + + +NO_CTX = DispatchContext(options={}) From c7dce759f245c5d341541db61baf216f3b3c98af Mon Sep 17 00:00:00 2001 From: Mingsheng Hong Date: Thu, 26 Apr 2018 10:49:01 -0700 Subject: [PATCH 0058/1691] Updates on https://www.tensorflow.org/community/swift as part of the S4TF OSS launch on 4/26 morning. PiperOrigin-RevId: 194419822 --- tensorflow/docs_src/community/swift.md | 50 +++++++++++++------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/tensorflow/docs_src/community/swift.md b/tensorflow/docs_src/community/swift.md index 54d9960b23405b..46512f7c5dae61 100644 --- a/tensorflow/docs_src/community/swift.md +++ b/tensorflow/docs_src/community/swift.md @@ -2,34 +2,34 @@ Welcome to the Swift for TensorFlow development community! -Swift for TensorFlow is a result of first-principles thinking applied to machine -learning frameworks, and works quite differently than existing TensorFlow -language bindings. Whereas prior solutions are designed within the constraints -of what can be achieved by a (typically Python or Lua) library, Swift for -TensorFlow is based on the belief that machine learning is important enough to -deserve first-class language and compiler support. - -First-class language and compiler support allows us to innovate in areas that -have traditionally been out of bounds for machine learning libraries. Our -results provide the performance of TensorFlow graphs with the ease of use of -define-by-run models, and provides a great user experience - for example, by -catching more mistakes before you run your code. +Swift for TensorFlow is the result of first-principles thinking applied to +machine learning frameworks and aims to take TensorFlow usability to new +heights. Swift for TensorFlow is based on the belief that machine learning is +important enough for first-class language and compiler support, and thus works +very differently from normal language bindings. + +First-class language and compiler support allow us to innovate in areas that +traditionally were out of bounds for machine learning libraries. Our programming +model combines the performance of TensorFlow graphs with the flexibility and +expressivity of Eager execution, while keeping a strong focus on improved +usability at every level of the stack. + ## Open Source -As announced at the TensorFlow Developer Summit, we are planning to launch our -open source project on GitHub in April. In addition to releasing the code, we -will be using an open design model, where design discussions happen in public. +We have released Swift for TensorFlow as an open-source project on GitHub! -Between now and then, we are writing some technical white papers that explain in -detail the design approach (e.g., the core compiler partitioning technique that -underlies the whole thing, our approach to automatic differentiation, etc.), -implementation tradeoffs, and the status of this work. We can’t wait to engage -with the broader community, but prefer to start the conversation when these -white papers are ready. +Our [central repository](https://github.com/tensorflow/swift) contains project +documentation, including an +[overview and technical papers](https://github.com/tensorflow/swift/tree/master/docs) +explaining specific areas of the project in depth. This repo also includes +instructions for [installing prebuilt packages](https://github.com/tensorflow/swift/blob/master/Installation.md) +for macOS and Linux platforms, [simple usage instructions](https://github.com/tensorflow/swift/blob/master/Usage.md), +and how to build from source. -[Sign up here to join the community Google -group](https://groups.google.com/a/tensorflow.org/d/forum/swift). We will -initially use it for announcements, and then open it for general discussion when -we are ready in April. +Moving forward, we will use an open design model and all discussions will be +public. +[Sign up here to join the community Google +group](https://groups.google.com/a/tensorflow.org/d/forum/swift), which we will +use for announcements and general discussion. From f20740204f970e40e6238da5ad6507887f9bd95f Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Thu, 26 Apr 2018 11:12:23 -0700 Subject: [PATCH 0059/1691] Introducing TRTOptimizationPass Use TF allocator for allocating TensorRT memory Fix an issue in build_pip_package.sh --- tensorflow/contrib/tensorrt/BUILD | 10 +- .../contrib/tensorrt/convert/convert_graph.cc | 105 +++++-- .../contrib/tensorrt/convert/convert_graph.h | 12 + .../contrib/tensorrt/convert/convert_nodes.cc | 25 +- .../contrib/tensorrt/convert/convert_nodes.h | 15 +- .../tensorrt/convert/trt_optimization_pass.cc | 236 +++++++++++++++ .../tensorrt/convert/trt_optimization_pass.h | 70 +++++ .../contrib/tensorrt/kernels/trt_engine_op.cc | 51 +++- .../contrib/tensorrt/kernels/trt_engine_op.h | 5 +- .../tensorrt/resources/trt_allocator.cc | 57 ++++ .../tensorrt/resources/trt_allocator.h | 65 ++++ .../tensorrt/resources/trt_resources.h | 3 + .../contrib/tensorrt/segment/segment.cc | 283 ++++++++++++++---- tensorflow/contrib/tensorrt/segment/segment.h | 99 +++++- .../contrib/tensorrt/segment/segment_test.cc | 6 +- .../contrib/tensorrt/test/test_tftrt.py | 60 +++- .../tools/pip_package/build_pip_package.sh | 2 +- 17 files changed, 988 insertions(+), 116 deletions(-) create mode 100644 tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc create mode 100644 tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h create mode 100644 tensorflow/contrib/tensorrt/resources/trt_allocator.cc create mode 100644 tensorflow/contrib/tensorrt/resources/trt_allocator.h diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index f80b4f1b112dcf..f7328ff2286bcb 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -88,6 +88,7 @@ cc_library( ":trt_logging", ":trt_resources", "//tensorflow/core:gpu_headers_lib", + "//tensorflow/core:gpu_runtime", "//tensorflow/core:lib_proto_parsing", "//tensorflow/core:stream_executor_headers_lib", ] + if_tensorrt([ @@ -194,10 +195,12 @@ tf_py_wrap_cc( tf_cuda_library( name = "trt_resources", srcs = [ + "resources/trt_allocator.cc", "resources/trt_int8_calibrator.cc", "resources/trt_resource_manager.cc", ], hdrs = [ + "resources/trt_allocator.h", "resources/trt_int8_calibrator.h", "resources/trt_resource_manager.h", "resources/trt_resources.h", @@ -206,6 +209,7 @@ tf_cuda_library( ":trt_logging", "//tensorflow/core:framework_headers_lib", "//tensorflow/core:framework_lite", + "//tensorflow/core:core_cpu_lib", "//tensorflow/core:lib_proto_parsing", ] + if_tensorrt([ "@local_config_tensorrt//:nv_infer", @@ -218,10 +222,12 @@ tf_cuda_library( srcs = [ "convert/convert_graph.cc", "convert/convert_nodes.cc", + "convert/trt_optimization_pass.cc", ], hdrs = [ "convert/convert_graph.h", "convert/convert_nodes.h", + "convert/trt_optimization_pass.h", ], deps = [ ":segment", @@ -230,6 +236,7 @@ tf_cuda_library( "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:utils", "//tensorflow/core:framework", + "//tensorflow/core:gpu_runtime", "//tensorflow/core:framework_lite", "//tensorflow/core:graph", "//tensorflow/core:lib", @@ -238,8 +245,7 @@ tf_cuda_library( "//tensorflow/core/grappler:devices", "//tensorflow/core/grappler/clusters:virtual_cluster", "//tensorflow/core/grappler/costs:graph_properties", - "//tensorflow/core/grappler/optimizers:constant_folding", - "//tensorflow/core/grappler/optimizers:layout_optimizer", + "//tensorflow/core/grappler/optimizers:meta_optimizer", ] + if_tensorrt([ "@local_config_tensorrt//:nv_infer", ]) + tf_custom_op_library_additional_deps(), diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index b412b296e02751..785c33c4c407ab 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -24,15 +24,20 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" #include "tensorflow/contrib/tensorrt/segment/segment.h" +#include "tensorflow/core/common_runtime/gpu/gpu_id.h" +#include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h" +#include "tensorflow/core/common_runtime/gpu/process_state.h" #include "tensorflow/core/graph/algorithm.h" #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/graph/graph_constructor.h" +#include "tensorflow/core/grappler/clusters/utils.h" #include "tensorflow/core/grappler/clusters/virtual_cluster.h" #include "tensorflow/core/grappler/costs/graph_properties.h" #include "tensorflow/core/grappler/devices.h" #include "tensorflow/core/grappler/grappler_item.h" #include "tensorflow/core/grappler/optimizers/constant_folding.h" #include "tensorflow/core/grappler/optimizers/layout_optimizer.h" +#include "tensorflow/core/grappler/optimizers/meta_optimizer.h" #include "tensorflow/core/grappler/utils.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" @@ -115,8 +120,8 @@ std::pair ParseTensorName(string name, int default_idx = 0) { int idx = default_idx; size_t sep = name.find_last_of(':'); if (sep != string::npos) { - name = name.substr(0, sep); idx = std::stoi(name.substr(sep + 1)); + name = name.substr(0, sep); } return std::make_pair(name, idx); } @@ -141,7 +146,8 @@ struct ConvertGraphParams { size_t max_supported_batch_size, size_t max_consumed_workspace_size_bytes, const tensorflow::grappler::GraphProperties& current_graph_properties, std::unordered_map>* output_edges, - int engine_precision_mode) + int engine_precision_mode, const string& device_name, + std::shared_ptr allocator, int cuda_device_id) : graph(inp_graph), output_names(output_node_names), subgraph_node_ids(subgraph_node_id_numbers), @@ -149,7 +155,10 @@ struct ConvertGraphParams { max_workspace_size_bytes(max_consumed_workspace_size_bytes), graph_properties(current_graph_properties), output_edge_map(output_edges), - precision_mode(engine_precision_mode) {} + precision_mode(engine_precision_mode), + device_name_(device_name), + allocator_(allocator), + cuda_device_id_(cuda_device_id) {} tensorflow::Graph& graph; const std::vector& output_names; const std::set& subgraph_node_ids; @@ -158,6 +167,9 @@ struct ConvertGraphParams { const tensorflow::grappler::GraphProperties& graph_properties; std::unordered_map>* output_edge_map; int precision_mode; + string device_name_; + std::shared_ptr allocator_; + int cuda_device_id_; std::vector> subgraph_inputs; std::vector> subgraph_outputs; tensorflow::EdgeSet subgraph_incoming_edges; @@ -200,7 +212,8 @@ tensorflow::Status GetCalibNode(ConvertGraphParams* params) { params->subgraph_inputs, params->subgraph_outputs, params->max_batch_size, params->max_workspace_size_bytes, params->graph_properties, params->output_edge_map, - &trt_node_def, params->precision_mode); + &trt_node_def, params->precision_mode, params->device_name_, + params->allocator_, params->cuda_device_id_); TF_RETURN_IF_ERROR(InjectCalibrationNode(s)); tensorflow::Status status; tensorflow::Node* trt_node = params->graph.AddNode(trt_node_def, &status); @@ -214,7 +227,7 @@ tensorflow::Status GetCalibNode(ConvertGraphParams* params) { auto src_output = in_edge->src_output(); auto dst_node = in_edge->dst(); auto dst_input = in_edge->dst_input(); - VLOG(1) << " update edge " << trt_node->name() << ":" << src_output + VLOG(0) << " update edge " << trt_node->name() << ":" << src_output << " -> " << dst_node->name() << ":" << dst_input; TF_RETURN_IF_ERROR( params->graph.UpdateEdge(trt_node, src_output, dst_node, dst_input)); @@ -230,7 +243,8 @@ tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { params->subgraph_inputs, params->subgraph_outputs, params->max_batch_size, params->max_workspace_size_bytes, params->graph_properties, params->output_edge_map, - &trt_node_def, params->precision_mode); + &trt_node_def, params->precision_mode, params->device_name_, + params->allocator_, params->cuda_device_id_); TF_RETURN_IF_ERROR(ConvertSubGraphToTensorRTNodeDef(s)); tensorflow::Status status; tensorflow::Node* trt_node = params->graph.AddNode(trt_node_def, &status); @@ -348,27 +362,41 @@ tensorflow::Status ConvertGraphDefToTensorRT( int num_gpus = tensorflow::grappler::GetNumAvailableGPUs(); VLOG(2) << "cpu_cores: " << num_cpu_cores; VLOG(2) << "gpus: " << num_gpus; - - TF_RETURN_IF_ERROR(optimizer.Optimize(cluster, item, &gdef)); - + tensorflow::RewriterConfig rw_cfg; + tensorflow::grappler::MetaOptimizer meta_opt(nullptr, rw_cfg); + // TF_RETURN_IF_ERROR(optimizer.Optimize(cluster, item, &gdef)); + TF_RETURN_IF_ERROR(meta_opt.Optimize(cluster, item, &gdef)); // constant folding item.graph = gdef; - tensorflow::grappler::ConstantFolding fold(nullptr); - TF_RETURN_IF_ERROR(fold.Optimize(nullptr, item, &gdef)); + // tensorflow::grappler::ConstantFolding fold(nullptr); + // TF_RETURN_IF_ERROR(fold.Optimize(nullptr, item, &gdef)); // AJ refactoring shape inference through grappler/GraphProperties. tensorflow::grappler::GraphProperties static_graph_properties(item); - TF_RETURN_IF_ERROR(static_graph_properties.InferStatically(false)); + TF_RETURN_IF_ERROR(static_graph_properties.InferStatically(true)); // Build full graph + + return ConvertAfterShapes(gdef, output_names, max_batch_size, + max_workspace_size_bytes, new_graph_def, + precision_mode, minimum_segment_size, + static_graph_properties, nullptr); +} + +tensorflow::Status ConvertAfterShapes( + const tensorflow::GraphDef& gdef, const std::vector& output_names, + size_t max_batch_size, size_t max_workspace_size_bytes, + tensorflow::GraphDef* new_graph_def, int precision_mode, + int minimum_segment_size, + const tensorflow::grappler::GraphProperties& graph_properties, + const tensorflow::grappler::Cluster* cluster) { + // Segment the graph into subgraphs that can be converted to TensorRT + tensorflow::tensorrt::segment::SegmentOptions segment_options; tensorflow::FunctionLibraryDefinition flib(tensorflow::OpRegistry::Global(), gdef.library()); tensorflow::Graph graph(flib); TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToGraph( tensorflow::GraphConstructorOptions(), gdef, &graph)); - // Segment the graph into subgraphs that can be converted to TensorRT - tensorflow::tensorrt::segment::SegmentOptions segment_options; - // TODO(ben,jie,sami): exclude output nodes (DISCUSS IT) for (auto node : output_names) { segment_options.exclude_node_list.insert(node); @@ -378,7 +406,7 @@ tensorflow::Status ConvertGraphDefToTensorRT( segment_options.minimum_segment_size = minimum_segment_size; tensorflow::tensorrt::segment::SegmentNodesVector segments; TF_RETURN_IF_ERROR(tensorrt::segment::SegmentGraph( - gdef, IsTensorRTCandidate, segment_options, &segments)); + &graph, IsTensorRTCandidate, segment_options, &segments)); if (segments.size() > 1) { VLOG(0) << "MULTIPLE tensorrt candidate conversion: " << segments.size(); } @@ -388,9 +416,17 @@ tensorflow::Status ConvertGraphDefToTensorRT( int count = 0; float total_num_nodes_in_segments = 0.; for (auto s : segments) { - total_num_nodes_in_segments += s.size(); + total_num_nodes_in_segments += s.first.size(); + } + std::map name_to_device_map; + if (cluster) { + for (const auto dm : cluster->GetDeviceSet()->devices()) { + name_to_device_map[dm->name()] = dm; + } } - for (const std::set& subgraph_node_names : segments) { + for (const auto& segment_nodes_and_device : segments) { + const std::set& subgraph_node_names = + segment_nodes_and_device.first; std::set subgraph_node_ids; size_t max_mem_per_engine = max_workspace_size_bytes * @@ -400,10 +436,37 @@ tensorflow::Status ConvertGraphDefToTensorRT( oss << " " << node_name; subgraph_node_ids.insert(node_map.at(node_name)->id()); } - VLOG(2) << "Subgraph nodes" << oss.str(); + VLOG(1) << "Subgraph nodes at device " << segment_nodes_and_device.second + << " : " << oss.str(); + auto target_device = + name_to_device_map.find(segment_nodes_and_device.second); + std::shared_ptr allocator(0); + + int cuda_device_id = 0; + if (target_device != name_to_device_map.end()) { + tensorflow::TfGpuId tf_gpu_id(target_device->second->parsed_name().id); + CudaGpuId cuda_gpu_id; + Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id); + if (!s.ok()) { + LOG(ERROR) + << "Cuda device identification failed, using device 0. Error= " << s; + } else { + cuda_device_id = cuda_gpu_id.value(); + } + tensorflow::GPUOptions gpuoptions; + auto pm = tensorflow::ProcessState::singleton(); + // this should be instantiated by now + auto dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); + VLOG(1) << "Got an allocator for device tf_device=" << tf_gpu_id.value() + << " cuda device= " << cuda_device_id << " at " << dev_allocator; + allocator = std::make_shared(dev_allocator); + } else { // device unknown or not available + allocator = std::make_shared(); + } ConvertGraphParams p(graph, output_names, subgraph_node_ids, max_batch_size, - max_mem_per_engine, static_graph_properties, - &output_edge_map, precision_mode); + max_mem_per_engine, graph_properties, &output_edge_map, + precision_mode, segment_nodes_and_device.second, + allocator, cuda_device_id); if (precision_mode == INT8MODE) { tensorflow::Status status = GetCalibNode(&p); if (status != tensorflow::Status::OK()) { diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.h b/tensorflow/contrib/tensorrt/convert/convert_graph.h index e01e4a5328061a..23a83b50943abe 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.h +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.h @@ -17,7 +17,11 @@ limitations under the License. #include +#include "tensorflow/contrib/tensorrt/segment/segment.h" #include "tensorflow/core/framework/graph.pb.h" +#include "tensorflow/core/graph/graph.h" +#include "tensorflow/core/grappler/clusters/cluster.h" +#include "tensorflow/core/grappler/costs/graph_properties.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/types.h" @@ -43,6 +47,14 @@ tensorflow::Status ConvertGraphDefToTensorRT( size_t max_workspace_size_bytes, tensorflow::GraphDef* new_graph_def, int precision_mode, int minimum_segment_size); +// Method to call from optimization pass +tensorflow::Status ConvertAfterShapes( + const tensorflow::GraphDef& graph, const std::vector& output_names, + size_t max_batch_size, size_t max_workspace_size_bytes, + tensorflow::GraphDef* new_graph_def, int precision_mode, + int minimum_segment_size, + const tensorflow::grappler::GraphProperties& graph_properties, + const tensorflow::grappler::Cluster* cluster); } // namespace convert } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index b81ae9dc3eeed6..b37c5357367303 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -346,10 +346,11 @@ void ReorderCKtoKC(const TRT_ShapedWeights& iweights, break; } case tensorflow::DataType::DT_HALF: { - Reorder2({k, c}, static_cast(iweights.GetValues()), - istrides, static_cast( - const_cast(oweights->GetValues())), - ostrides); + Reorder2( + {k, c}, static_cast(iweights.GetValues()), + istrides, + static_cast(const_cast(oweights->GetValues())), + ostrides); break; } default: @@ -2246,8 +2247,12 @@ tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) { auto op_res = new tensorflow::tensorrt::TRTCalibrationResource(); TF_CHECK_OK(op_rmgr->Create(calib_op_name, calib_op_name, op_res)); op_res->logger_ = new tensorflow::tensorrt::Logger(); + cudaSetDevice(s.cuda_device_id_); op_res->builder_ = nvinfer1::createInferBuilder(*(op_res->logger_)); - + op_res->allocator_=s.allocator_; +#if NV_TENSORRT_MAJOR >4 + op_res->builder_->setGpuAllocator(s.allocator_.get()); +#endif if (!op_res->builder_) { return tensorflow::errors::Internal( "failed to create TensorRT builder object"); @@ -2476,13 +2481,15 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef( // Topological order is needed to build TRT network tensorflow::tensorrt::Logger trt_logger; - +cudaSetDevice(s.cuda_device_id_); auto trt_builder = infer_object(nvinfer1::createInferBuilder(trt_logger)); if (!trt_builder) { return tensorflow::errors::Internal( "Failed to create TensorRT builder object"); } - +#if NV_TENSORRT_MAJOR >3 + trt_builder->setGpuAllocator(s.allocator_.get()); +#endif auto trt_network = infer_object(trt_builder->createNetwork()); if (!trt_network) { return tensorflow::errors::Internal( @@ -2707,9 +2714,11 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef( .Attr("input_nodes", input_names) .Attr("output_nodes", output_names) .Attr("OutT", output_dtypes) + .Device(s.device_name_) .Finalize(s.trt_node); - VLOG(0) << status.ToString() << " finished op building"; + VLOG(0) << status.ToString() << " finished op building for " << engine_name + << " on device " << s.device_name_ ; return tensorflow::Status::OK(); } diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 954a1e72f86043..ecccaf36e3a524 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -22,6 +22,7 @@ limitations under the License. #include #include + #include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/grappler/costs/graph_properties.h" @@ -29,7 +30,7 @@ limitations under the License. #if GOOGLE_CUDA #if GOOGLE_TENSORRT - +#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" namespace tensorflow { namespace tensorrt { namespace convert { @@ -48,7 +49,9 @@ struct SubGraphParams { const tensorflow::grappler::GraphProperties& current_graph_properties, std::unordered_map>* output_edges, tensorflow::NodeDef* constructed_trt_node, - int engine_precision_mode = FP32MODE) + int engine_precision_mode = FP32MODE, const string& device_name = "", + std::shared_ptr allocator = 0, + int cuda_device_id = 0) : graph(inp_graph), subgraph_node_ids(subgraph_node_id_numbers), input_inds(input_indices), @@ -58,7 +61,10 @@ struct SubGraphParams { graph_properties(current_graph_properties), output_edge_map(output_edges), trt_node(constructed_trt_node), - precision_mode(engine_precision_mode) {} + precision_mode(engine_precision_mode), + device_name_(device_name), + allocator_(allocator), + cuda_device_id_(cuda_device_id) {} tensorflow::Graph& graph; const std::set& subgraph_node_ids; @@ -70,6 +76,9 @@ struct SubGraphParams { std::unordered_map>* output_edge_map; tensorflow::NodeDef* trt_node; const int precision_mode; + const string device_name_; + std::shared_ptr allocator_; + const int cuda_device_id_; }; // TODO(sami): Replace references with const reference or pointers diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc new file mode 100644 index 00000000000000..880ffe1b3a0c9f --- /dev/null +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc @@ -0,0 +1,236 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. +1;4804;0c +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h" +#include "tensorflow/contrib/tensorrt/convert/convert_graph.h" +#include "tensorflow/core/grappler/clusters/cluster.h" +#include "tensorflow/core/grappler/grappler_item.h" +#include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/public/session_options.h" + +using tensorflow::str_util::Uppercase; +using tensorflow::strings::StrAppend; +using tensorflow::strings::StrCat; +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +namespace tensorflow { +namespace tensorrt { +namespace convert { +tensorflow::Status TRTOptimizationPass::Init( + const tensorflow::RewriterConfig_CustomGraphOptimizer* config) { + VLOG(1) << "Called INIT for " << m_name_ << " with config = " << config; + if (config == nullptr) { + maximum_workspace_size_ = 2 << 30; + return tensorflow::Status::OK(); + } + const auto params = config->parameter_map(); + if (params.count("minimum_segment_size")) { + minimum_segment_size_ = params.at("minimum_segment_size").i(); + } + if (params.count("max_batch_size")) { + maximum_batch_size_ = params.at("max_batch_size").i(); + } + if (params.count("max_workspace_size_bytes")) + maximum_workspace_size_ = params.at("max_workspace_size_bytes").i(); + if (params.count("precision_mode")) { + string pm = Uppercase(params.at("precision_mode").s()); + if (pm == "FP32") { + precision_mode_ = 0; + } else if (pm == "FP16") { + precision_mode_ = 1; + } else if (pm == "INT8") { + precision_mode_ = 2; + } else { + LOG(ERROR) << "Unknown precision mode '" << pm << "'"; + return tensorflow::errors::InvalidArgument( + "Unknown precision mode argument" + pm + + " Valid values are FP32, FP16, INT8"); + } + } + return tensorflow::Status::OK(); +}; + +tensorflow::Status TRTOptimizationPass::Optimize( + tensorflow::grappler::Cluster* cluster, + const tensorflow::grappler::GrapplerItem& item, GraphDef* optimized_graph) { + VLOG(1) << "Called TRTOptimization Pass " << m_name_; + VLOG(1) << "Cluster = " << cluster; + string offset(" "); + string offset2 = StrCat(offset, offset); + string offset3 = StrCat(offset2, offset); + string offset4 = StrCat(offset2, offset2); + if (cluster) { + VLOG(1) << offset << "type = " << cluster->type(); + VLOG(1) << offset << "num warmup steps = " << cluster->NumWarmupSteps(); + const auto devNames = cluster->GetDeviceNames(); + if (devNames.size()) { + VLOG(1) << offset << " Device names:"; + for (const auto s : devNames) { + VLOG(1) << offset2 << s; + } + } + std::unordered_map peak_mem; + auto status = cluster->GetPeakMemoryUsage(&peak_mem); + if (status == tensorflow::Status::OK()) { + VLOG(1) << offset << "Peak Memory Usage :"; + for (auto s : peak_mem) { + VLOG(1) << offset2 << s.first << " = " << s.second; + } + } + + const auto dev_props = cluster->GetDevices(); + if (dev_props.size()) { + VLOG(1) << offset << "Device properties:"; + for (auto k : dev_props) { + VLOG(1) << offset2 << k.first; + const auto& dt = k.second; + VLOG(1) << offset3 << "type = " << dt.type(); + VLOG(1) << offset3 << "vendor = " << dt.vendor(); + VLOG(1) << offset3 << "model = " << dt.model(); + VLOG(1) << offset3 << "frequency = " << dt.frequency(); + VLOG(1) << offset3 << "num cores = " << dt.num_cores(); + VLOG(1) << offset3 << "num registers = " << dt.num_registers(); + VLOG(1) << offset3 << "L1 cache size = " << dt.l1_cache_size(); + VLOG(1) << offset3 << "L2 cache size = " << dt.l2_cache_size(); + VLOG(1) << offset3 << "L3 cache size = " << dt.l3_cache_size(); + VLOG(1) << offset3 << "SHMem per SMP = " + << dt.shared_memory_size_per_multiprocessor(); + VLOG(1) << offset3 << "memory size = " << dt.memory_size(); + VLOG(1) << offset3 << "bandwidth = " << dt.bandwidth(); + if (dt.environment_size()) { + VLOG(1) << offset3 << "environment :"; + for (const auto e : dt.environment()) { + VLOG(1) << offset4 << e.first << " = " << e.second; + } + } + } + } + } + VLOG(1) << "item: " << item.id; + int max_dim = -1; + if (item.feed.size()) { + VLOG(1) << offset << "Feeds :"; + for (const auto& f : item.feed) { + const auto& shape = f.second.shape(); + if (shape.dims() > 0) { + if (shape.dim_size(0) > max_dim) max_dim = shape.dim_size(0); + } + VLOG(1) << offset2 << f.first << " = shaped " + << f.second.shape().DebugString(); + } + } else { + VLOG(1) << offset << "No Feeds"; + } + if (maximum_batch_size_ < 0) { // automatic batch size from input + if (max_dim > 0) { + maximum_batch_size_ = max_dim; + VLOG(1) << "Setting maximum batch size to " << max_dim; + } else { + maximum_batch_size_ = 128; + LOG(WARNING) << "Maximum batch size is not set" + " and can't be deduced from inputs setting it to" + << maximum_batch_size_ + << ". Suggest configuring it from configuration parameters"; + } + } else { + if (max_dim > maximum_batch_size_) { + LOG(WARNING) << "Configured batch size " << maximum_batch_size_ + << " is less than input batch size " << max_dim + << " adjusting maximum batch size to match input batch size"; + } + } + if (item.fetch.size()) { + VLOG(1) << offset << "Fetches :"; + for (const auto& f : item.fetch) { + VLOG(1) << offset2 << f; + } + } else { + VLOG(1) << offset << "No Fetches"; + } + + if (item.init_ops.size()) { + VLOG(1) << offset << "init ops :"; + for (const auto& f : item.init_ops) { + VLOG(1) << offset2 << f; + } + } else { + VLOG(1) << offset << "No init ops"; + } + VLOG(1) << "Save Op = " << item.save_op; + VLOG(1) << "Restore Op = " << item.restore_op; + VLOG(1) << "save_restore_loc_tensor = " << item.save_restore_loc_tensor; + if (item.keep_ops.size()) { + VLOG(1) << offset << "keep ops :"; + for (const auto& f : item.keep_ops) { + VLOG(1) << offset2 << f; + } + } else { + VLOG(1) << offset << "No keep ops"; + } + VLOG(1) << item.graph.DebugString(); + tensorflow::grappler::GraphProperties static_graph_properties(item); + TF_RETURN_IF_ERROR(static_graph_properties.InferStatically(true)); + for (const auto dev : cluster->GetDeviceSet()->devices()) { + const auto& pname = dev->parsed_name(); + VLOG(1) << "Device name= " << dev->name() + << " parsedname job= " << pname.job << " id= " << pname.id + << " has_id: " << pname.has_id << " has_job: " << pname.has_job<< + "has_type: "< +#include +#include +#include +#include + +#include "tensorflow/core/framework/graph.pb.h" +#include "tensorflow/core/grappler/optimizers/custom_graph_optimizer.h" +#include "tensorflow/core/platform/logging.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT + +namespace tensorflow { +namespace tensorrt { +namespace convert { +class TRTOptimizationPass : public tensorflow::grappler::CustomGraphOptimizer { + public: + TRTOptimizationPass(string optName = "TRTOptimizationPass") + : m_name_(optName), + minimum_segment_size_(3), + precision_mode_(0), + maximum_batch_size_(-1), + maximum_workspace_size_(-1) { + VLOG(1) << "Constructing " << m_name_; + }; + // tensorflow::Status Run(const tensorflow::GraphOptimizationPassOptions + // &options) override; + string name() const override { return m_name_; }; + tensorflow::Status Init(const tensorflow::RewriterConfig_CustomGraphOptimizer* + config = nullptr) override; + + tensorflow::Status Optimize(tensorflow::grappler::Cluster* cluster, + const tensorflow::grappler::GrapplerItem& item, + GraphDef* optimized_graph) override; + void Feedback(tensorflow::grappler::Cluster* cluster, + const tensorflow::grappler::GrapplerItem& item, + const GraphDef& optimized_graph, double result) override; + + private: + string m_name_; + int minimum_segment_size_; + int precision_mode_; + int maximum_batch_size_; + int64_t maximum_workspace_size_; +}; +} // namespace convert +} // namespace tensorrt +} // namespace tensorflow +#endif +#endif +#endif \ No newline at end of file diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index b32371b642f38b..9c59fd973b8f88 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -18,6 +18,9 @@ limitations under the License. #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor.h" #include "tensorflow/core/platform/types.h" +#include "tensorflow/core/common_runtime/gpu/process_state.h" +#include "tensorflow/core/common_runtime/gpu/gpu_id.h" +#include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h" #if GOOGLE_CUDA #if GOOGLE_TENSORRT @@ -33,9 +36,8 @@ namespace tensorrt { TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) : OpKernel(context) { // read serialized_engine - string serialized_engine; OP_REQUIRES_OK(context, - context->GetAttr("serialized_engine", &serialized_engine)); + context->GetAttr("serialized_engine", &serialized_engine_)); // register input output node name in trt_sub_graph OP_REQUIRES_OK(context, context->GetAttr("input_nodes", &input_nodes_)); @@ -46,25 +48,43 @@ TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) : OpKernel(context) { // from resourcemanager // TODO(jie): cudaSetDevice make sure trt engine is allocated on the same // gpu where the input/output is also located. - int gpu_id = context->device()->tensorflow_gpu_device_info()->gpu_id; - cudaSetDevice(gpu_id); - int device; - cudaGetDevice(&device); - if (gpu_id != device) LOG(FATAL) << "set device failed!"; + // int gpu_id = context->device()->tensorflow_gpu_device_info()->gpu_id; + // cudaSetDevice(gpu_id); + // int device; + // cudaGetDevice(&device); + // if (gpu_id != device) LOG(FATAL) << "set device failed!"; // TODO(samikama) runtime should be taken from a resourcemanager as well. // Only engine should be in the op and context and runtime should be taken // from resourcemanager - IRuntime* infer = nvinfer1::createInferRuntime(logger); - trt_engine_ptr_.reset(infer->deserializeCudaEngine( - serialized_engine.c_str(), serialized_engine.size(), nullptr)); - trt_execution_context_ptr_.reset(trt_engine_ptr_->createExecutionContext()); + // IRuntime* infer = nvinfer1::createInferRuntime(logger); + // trt_engine_ptr_.reset(infer->deserializeCudaEngine( + // serialized_engine.c_str(), serialized_engine.size(), nullptr)); + // trt_execution_context_ptr_.reset(trt_engine_ptr_->createExecutionContext()); // Runtime is safe to delete after engine creation - infer->destroy(); + // infer->destroy(); } void TRTEngineOp::Compute(OpKernelContext* context) { + if(!trt_execution_context_ptr_){ + tensorflow::TfGpuId tf_gpu_id(context->device()->tensorflow_gpu_device_info()->gpu_id); + tensorflow::GPUOptions gpuoptions; + auto pm = tensorflow::ProcessState::singleton(); + auto dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); + IRuntime* infer = nvinfer1::createInferRuntime(logger); + if(!dev_allocator){ + LOG(FATAL)<<"Can't find device allocator for gpu device"<(dev_allocator); + infer->setGpuAllocator(allocator_.get()); + trt_engine_ptr_.reset(infer->deserializeCudaEngine( + serialized_engine_.c_str(), serialized_engine_.size(), nullptr)); + trt_execution_context_ptr_.reset(trt_engine_ptr_->createExecutionContext()); + // Runtime is safe to delete after engine creation + infer->destroy(); + serialized_engine_.clear(); + } int num_binding = context->num_inputs() + context->num_outputs(); std::vector buffers(num_binding); @@ -147,7 +167,12 @@ void TRTEngineOp::Compute(OpKernelContext* context) { VLOG(2) << "enqueue returns: " << ret; // sync should be done by TF. } - +TRTEngineOp::~TRTEngineOp(){ + // Order matters! + trt_execution_context_ptr_.reset(); + trt_engine_ptr_.reset(); + allocator_.reset(); +} REGISTER_KERNEL_BUILDER(Name("TRTEngineOp").Device(DEVICE_GPU), TRTEngineOp); } // namespace tensorrt diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h index 0964b4b18a7811..791bb6f5834534 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h @@ -26,6 +26,7 @@ limitations under the License. #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorrt/include/NvInfer.h" +#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" namespace tensorflow { namespace tensorrt { @@ -36,7 +37,7 @@ class TRTEngineOp : public OpKernel { explicit TRTEngineOp(OpKernelConstruction* context); void Compute(OpKernelContext* context) override; - + ~TRTEngineOp(); private: template struct Destroyer { @@ -51,6 +52,8 @@ class TRTEngineOp : public OpKernel { std::vector input_nodes_; std::vector output_nodes_; + std::shared_ptr allocator_; + string serialized_engine_; }; } // namespace tensorrt diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc new file mode 100644 index 00000000000000..4705f6d20f5a65 --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc @@ -0,0 +1,57 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" + +#include "tensorflow/core/platform/logging.h" +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#if NV_TENSORRT_MAJOR > 2 +#include "cuda/include/cuda_runtime_api.h" + +namespace tensorflow { +namespace tensorrt { +void* TRTCudaAllocator::allocate(uint64_t size, uint64_t alignment, + uint32_t flags) { + assert((alignment & (alignment - 1)) == 0); // zero or a power of 2. + void* memory; + cudaMalloc(&memory, size); + return memory; +} +void TRTCudaAllocator::free(void* memory) { cudaFree(memory); } + +void* TRTDeviceAllocator::allocate(uint64_t size, uint64_t alignment, + uint32_t flags) { + assert((alignment & (alignment - 1)) == 0); // zero or a power of 2. + void* mem = allocator_->AllocateRaw(alignment, size); + VLOG(2) << "Allocated " << size << " bytes with alignment " << alignment + << " @ " << mem; + return mem; +} + +TRTDeviceAllocator::TRTDeviceAllocator(tensorflow::Allocator* allocator) + : allocator_(allocator) { + VLOG(1) << "Using " << allocator->Name() << " allocator from TensorFlow"; +}; +void TRTDeviceAllocator::free(void* memory) { + VLOG(2) << "Deallocating " << memory; + allocator_->DeallocateRaw(memory); +} + +} // namespace tensorrt +} // namespace tensorflow +#endif +#endif +#endif \ No newline at end of file diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.h b/tensorflow/contrib/tensorrt/resources/trt_allocator.h new file mode 100644 index 00000000000000..8bdb0519ba3e41 --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.h @@ -0,0 +1,65 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_ + +#include +#include +#include +#include +#include +#include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include "tensorflow/core/framework/allocator.h" +#include "tensorflow/core/framework/resource_mgr.h" +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorrt/include/NvInfer.h" +#if NV_TENSORRT_MAJOR == 3 +// define interface here temporarily until TRT 4.0 is released +namespace nvinfer1 { +class IGpuAllocator { + virtual void* allocate(uint64_t size, uint64_t alignment, uint32_t flags) = 0; + virtual void free(void* memory) = 0; +}; +} // namespace nvinfer1 +#endif +namespace tensorflow { +namespace tensorrt { +class TRTCudaAllocator : public nvinfer1::IGpuAllocator { + public: + TRTCudaAllocator() {} + virtual ~TRTCudaAllocator(){}; + void* allocate(uint64_t size, uint64_t alignment, uint32_t flags) override; + void free(void* memory) override; +}; +class TRTDeviceAllocator : public nvinfer1::IGpuAllocator { + public: + TRTDeviceAllocator(tensorflow::Allocator* allocator); + virtual ~TRTDeviceAllocator(){}; + void* allocate(uint64_t size, uint64_t alignment, uint32_t flags) override; + void free(void* memory) override; + + private: + tensorflow::Allocator* allocator_; +}; +class AllocatorFactory {}; + +} // namespace tensorrt +} // namespace tensorflow + +#endif +#endif +#endif \ No newline at end of file diff --git a/tensorflow/contrib/tensorrt/resources/trt_resources.h b/tensorflow/contrib/tensorrt/resources/trt_resources.h index 3c85968ae7acf5..166ca9c3deb0cd 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_resources.h +++ b/tensorflow/contrib/tensorrt/resources/trt_resources.h @@ -28,6 +28,7 @@ limitations under the License. #if GOOGLE_TENSORRT #include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" #include "tensorrt/include/NvInfer.h" +#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" namespace tensorflow { namespace tensorrt { @@ -47,6 +48,7 @@ class TRTCalibrationResource : public tensorflow::ResourceBase { << " Network = " << std::hex << network_ << std::dec << std::endl << " Engine = " << std::hex << engine_ << std::dec << std::endl << " Logger = " << std::hex << logger_ << std::dec << std::endl + << " Allocator = " << std::hex << allocator_.get()<< std::dec << std::endl << " Thread = " << std::hex << thr_ << std::dec << std::endl; return oss.str(); } @@ -57,6 +59,7 @@ class TRTCalibrationResource : public tensorflow::ResourceBase { nvinfer1::IBuilder* builder_; nvinfer1::INetworkDefinition* network_; nvinfer1::ICudaEngine* engine_; + std::shared_ptr allocator_; tensorflow::tensorrt::Logger* logger_; // TODO(sami): Use threadpool threads! std::thread* thr_; diff --git a/tensorflow/contrib/tensorrt/segment/segment.cc b/tensorflow/contrib/tensorrt/segment/segment.cc index 8fc4697c513057..8f335f2bf15e3c 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.cc +++ b/tensorflow/contrib/tensorrt/segment/segment.cc @@ -25,18 +25,58 @@ limitations under the License. #include "tensorflow/core/graph/graph_constructor.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/types.h" namespace tensorflow { namespace tensorrt { namespace segment { - +using ::tensorflow::strings::StrAppend; namespace { -bool CanContractEdge(const tensorflow::Edge* edge, - const tensorflow::Graph& graph) { - const tensorflow::Node* src = edge->src(); - const tensorflow::Node* dst = edge->dst(); +bool check_cycles(const Graph* g, const Node* src, + const std::vector& start) { + struct Work { + Node* node; + bool leave; // Are we entering or leaving n? + }; + + std::vector stack(start.size()); + for (int i = 0; i < start.size(); ++i) { + stack[i] = Work{start[i], false}; + } + + std::vector visited(g->num_node_ids(), false); + while (!stack.empty()) { + Work w = stack.back(); + stack.pop_back(); + + auto n = w.node; + if (w.leave) { + if (n == src) { + return true; + } + continue; + } + + if (visited[n->id()]) continue; + visited[n->id()] = true; + // Arrange to call leave(n) when all done with descendants. + stack.push_back(Work{n, true}); + + auto nodes = n->in_nodes(); + for (const auto node : nodes) { + if (!visited[node->id()]) { + stack.push_back(Work{node, false}); + } + } + } + return false; +} + +bool CanContractEdge(const Edge* edge, const Graph* graph) { + const auto src = edge->src(); + const auto dst = edge->dst(); // Can't contract edge if doing so would cause a cycle in the // graph. So, if there is a directed path from 'src' to 'dst', other @@ -48,46 +88,131 @@ bool CanContractEdge(const tensorflow::Edge* edge, // 1. Get all nodes incoming to 'dst', excluding 'src' // 2. Reverse DFS from those nodes // 3. If reverse DFS reaches 'src' then we have a cycle - std::vector dfs_start_nodes; - for (tensorflow::Node* node : dst->in_nodes()) { + std::vector dfs_start_nodes; + for (Node* node : dst->in_nodes()) { if (node != src) { dfs_start_nodes.push_back(node); } } - bool is_cycle = false; - if (!dfs_start_nodes.empty()) { - tensorflow::ReverseDFSFrom(graph, dfs_start_nodes, {}, - [&is_cycle, src](tensorflow::Node* node) { - if (node == src) { - is_cycle = true; - } - }); - } + bool is_cycle = check_cycles(graph, src, dfs_start_nodes); + // if (!dfs_start_nodes.empty()) { + // tensorflow::ReverseDFSFrom(graph, dfs_start_nodes, {}, + // [&is_cycle, src](tensorflow::Node* node) { + // if (node == src) { + // is_cycle = true; + // } + // }); + // } return !is_cycle; } +} // namespace +Node::Node(const tensorflow::Node* node, const int id) : node_(node), id_(id) { + if (node_) { + in_edges_.reserve(node_->in_edges().size()); + out_edges_.reserve(node_->out_edges().size()); + } +} + +Graph::Graph(const tensorflow::Graph* g) : g_(g) { + int n_nodes = g_->num_node_ids(); + nodes_.resize(n_nodes, nullptr); + nodes_[g->kSourceId] = new Node(g->source_node(), g->kSourceId); + nodes_[g->kSinkId] = new Node(g->sink_node(), g->kSinkId); + int n_edges = g->num_edge_ids(); + edges_.resize(n_edges, nullptr); + for (int i = 2; i < n_nodes; i++) { + const auto n = g->FindNodeId(i); + if (n) { + nodes_[i] = new Node(n, i); + } else { + node_ids_.insert(i); + } + } + for (int i = 0; i < n_edges; i++) { + const auto e = g->FindEdgeId(i); + if (e) { + const auto tfsrc = e->src(); + const auto tfdst = e->dst(); + bool is_control = e->IsControlEdge(); + auto src = nodes_[tfsrc->id()]; + auto dst = nodes_[tfdst->id()]; + auto edge = + new Edge(i, src, e->src_output(), dst, e->dst_input(), is_control); + edges_[i]=edge; + src->out_edges_.push_back(edge); + dst->in_edges_.push_back(edge); + } else { + edge_ids_.insert(i); + } + } +} + +void Graph::AddEdge(Node* src, int out_port, Node* dst, int in_port) { + int i = edges_.size(); + if (edge_ids_.size()) { + auto it = edge_ids_.begin(); + i = *it; + edge_ids_.erase(it); + } else { + edges_.push_back(0); + } + bool is_control = (out_port == tensorflow::Graph::kControlSlot); + is_control |= (in_port == tensorflow::Graph::kControlSlot); + auto edge = new Edge(i, src, out_port, dst, in_port, is_control); + edges_[i] = edge; + src->out_edges_.push_back(edge); + dst->in_edges_.push_back(edge); +} + +void Graph::AddControlEdge(Node* src, Node* dst) { + AddEdge(src, tensorflow::Graph::kControlSlot, dst, + tensorflow::Graph::kControlSlot); +} -void ContractEdge(tensorflow::Edge* edge, tensorflow::Graph* graph, - std::vector* remove_edges) { +void Graph::RemoveEdge(const Edge* edge) { + auto src = edge->src(); + auto dst = edge->dst(); + for (auto it = src->out_edges_.begin(); it != src->out_edges_.end(); ++it) { + if (*it == edge) { + src->out_edges_.erase(it); + break; + } + } + for (auto it = dst->in_edges_.begin(); it != dst->in_edges_.end(); ++it) { + if (*it == edge) { + dst->in_edges_.erase(it); + break; + } + } +} + +Graph::~Graph() { + for (auto x : nodes_) delete x; + for (auto x : edges_) delete x; +} + +void ContractEdge(Edge* edge, Graph* graph, + std::vector* remove_edges) { // Transfer all inputs and outputs of 'dst' to 'src' except edges // connecting the two. - tensorflow::Node* src = edge->src(); - tensorflow::Node* dst = edge->dst(); + auto src = edge->src(); + auto dst = edge->dst(); // We can use '0' for input/output index because we don't need them // to be accurate for the way we are using the graph. - std::vector in_edges(dst->in_edges().begin(), - dst->in_edges().end()); - for (const tensorflow::Edge* in_edge : in_edges) { + std::vector in_edges(dst->in_edges().begin(), + dst->in_edges().end()); + for (const Edge* in_edge : in_edges) { if (in_edge->IsControlEdge()) { if (in_edge->src() != src) { - tensorflow::Edge* e = const_cast(in_edge); + Edge* e = const_cast(in_edge); graph->AddControlEdge(e->src(), src); } } else { if (in_edge->src() != src) { - tensorflow::Edge* e = const_cast(in_edge); + Edge* e = const_cast(in_edge); if (e->src() == graph->source_node()) { graph->AddEdge(e->src(), e->src_output(), src, tensorflow::Graph::kControlSlot); @@ -98,14 +223,14 @@ void ContractEdge(tensorflow::Edge* edge, tensorflow::Graph* graph, } } - std::vector out_edges(dst->out_edges().begin(), - dst->out_edges().end()); - for (const tensorflow::Edge* out_edge : out_edges) { + std::vector out_edges(dst->out_edges().begin(), + dst->out_edges().end()); + for (const Edge* out_edge : out_edges) { if (out_edge->IsControlEdge()) { - tensorflow::Edge* e = const_cast(out_edge); + Edge* e = const_cast(out_edge); graph->AddControlEdge(src, e->dst()); } else { - tensorflow::Edge* e = const_cast(out_edge); + Edge* e = const_cast(out_edge); if (e->dst() == graph->sink_node()) { VLOG(1) << " edge to sink node " << src->name() << " -> " << e->dst()->name(); @@ -128,8 +253,6 @@ void ContractEdge(tensorflow::Edge* edge, tensorflow::Graph* graph, } } -} // namespace - tensorflow::Status SegmentGraph( const tensorflow::GraphDef& gdef, const std::function& candidate_fn, @@ -140,17 +263,23 @@ tensorflow::Status SegmentGraph( tensorflow::Graph graph(flib); TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToGraph( tensorflow::GraphConstructorOptions(), gdef, &graph)); + return SegmentGraph(&graph, candidate_fn, options, segments); +} +tensorflow::Status SegmentGraph( + tensorflow::Graph* tf_graph, + const std::function& candidate_fn, + const SegmentOptions& options, SegmentNodesVector* segments) { // tensorflow::DumpGraph("Pre-Segment", &graph); - + Graph* graph= new Graph(tf_graph); // Use a union-find to collect the nodes that belong to the same - // segment. A node value of nullptr indicates that the node is not a - // candidate for TRT. - std::vector> node_segments; - for (int i = 0; i < graph.num_node_ids(); ++i) { - tensorflow::Node* node = graph.FindNodeId(i); + // segment. A node value of nullptr indicates that tusing + // ::tensorflow::strings::StrAppendhe node is not a candidate for TRT. + std::vector> node_segments; + for (int i = 0; i < graph->num_node_ids(); ++i) { + Node* node = graph->FindNodeId(i); if (options.exclude_node_list.count(node->name()) != 0 || - !candidate_fn(node)) { + !candidate_fn(node->tf_node())) { node = nullptr; } node_segments.emplace_back(node); @@ -164,10 +293,16 @@ tensorflow::Status SegmentGraph( // a measure of how beneficial it is to include a given node in a // TRT subgraph then we can revisit this algorithm to take advantage // of that information. - std::vector order; - tensorflow::GetPostOrder(graph, &order); - - for (const tensorflow::Node* node : order) { + std::vector tforder; + tensorflow::GetPostOrder(*tf_graph, &tforder); + // use postorder implementation from tensorflow and construct mirror in + // internal format + std::vector order; + order.reserve(tforder.size()); + for (const auto tfnode : tforder) { + order.push_back(graph->FindNodeId(tfnode->id())); + } + for (const Node* node : order) { // All output nodes of 'node' have been visited... VLOG(2) << "Trying node " << node->name() << " id=" << node->id(); @@ -181,8 +316,8 @@ tensorflow::Status SegmentGraph( // nodes. Iterate since combining two nodes may unblock other // combining. while (true) { - std::set contract_edges; - for (const tensorflow::Edge* out_edge : node->out_edges()) { + std::set contract_edges; + for (const Edge* out_edge : node->out_edges()) { VLOG(2) << "... out node " << out_edge->dst()->name() << " ( " << out_edge->dst()->id() << " <- " << node->id() << " )"; if (out_edge->IsControlEdge()) { @@ -210,9 +345,9 @@ tensorflow::Status SegmentGraph( // Contract edges and collect the adjacent nodes into the same // segment/subgraph. while (!contract_edges.empty()) { - const tensorflow::Edge* contract_edge = *contract_edges.begin(); - const tensorflow::Node* src = contract_edge->src(); - const tensorflow::Node* dst = contract_edge->dst(); + const Edge* contract_edge = *contract_edges.begin(); + const Node* src = contract_edge->src(); + const Node* dst = contract_edge->dst(); VLOG(2) << "Merge " << src->name() << " <- " << dst->name() << " (" << src->id() << " <- " << dst->id(); @@ -221,13 +356,13 @@ tensorflow::Status SegmentGraph( // Contracting the edge leaves disconnected graph edges. // Remove these from the graph and from 'contract_edges' so we // don't visit them again. - tensorflow::Edge* e = const_cast(contract_edge); - std::vector remove_edges; - ContractEdge(e, &graph, &remove_edges); + Edge* e = const_cast(contract_edge); + std::vector remove_edges; + ContractEdge(e, graph, &remove_edges); - for (const tensorflow::Edge* r : remove_edges) { + for (const Edge* r : remove_edges) { contract_edges.erase(r); - graph.RemoveEdge(r); + graph->RemoveEdge(r); } } } @@ -236,9 +371,22 @@ tensorflow::Status SegmentGraph( // Collect the segments/subgraphs. Each subgraph is represented by a // set of the names of the nodes in that subgraph. std::unordered_map> sg_map; + std::unordered_map> device_maps; for (auto& u : node_segments) { if ((u.Value() != nullptr) && (u.ParentValue() != nullptr)) { sg_map[u.ParentValue()->name()].insert(u.Value()->name()); + auto tf_node = u.Value()->tf_node(); + if (tf_node->has_assigned_device_name()) { + device_maps[u.ParentValue()->name()].insert( + tf_node->assigned_device_name()); + } else if (tf_node->requested_device().size() > 0) { + device_maps[u.ParentValue()->name()].insert( + tf_node->requested_device()); + } else { + VLOG(1) << "Node " << tf_node->name() + << " has no device assigned requested device is: " + << tf_node->requested_device(); + } } } @@ -260,10 +408,33 @@ tensorflow::Status SegmentGraph( << segment_node_names.size() << " nodes, dropping"; continue; } - - segments->emplace_back(segment_node_names); + const auto& dev_itr = device_maps.find(itr.first); + if (dev_itr == device_maps.end() || dev_itr->second.size() == 0) { + VLOG(1) << "No device assigned to segment " << segments->size(); + segments->emplace_back(std::make_pair(segment_node_names, string())); + } else if (dev_itr->second.size() > 1) { + string s("Segment "); + StrAppend(&s, segments->size(), " has multiple devices attached: "); + for (const auto& dev : dev_itr->second) { + StrAppend(&s, dev, ", "); + } + LOG(WARNING) << s << " choosing " << *(dev_itr->second.begin()); + segments->emplace_back( + std::make_pair(segment_node_names, *(dev_itr->second.begin()))); + } else { + segments->emplace_back( + std::make_pair(segment_node_names, *(dev_itr->second.begin()))); + } } - + for (const auto& d : device_maps) { + string s("Segment "); + StrAppend(&s, ": '", d.first, "' "); + for (const auto& dd : d.second) { + StrAppend(&s, dd, ", "); + } + VLOG(1) << "Devices " << s; + } + delete graph; return tensorflow::Status::OK(); } diff --git a/tensorflow/contrib/tensorrt/segment/segment.h b/tensorflow/contrib/tensorrt/segment/segment.h index 7e8685f44a8c8a..659fea1859009a 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.h +++ b/tensorflow/contrib/tensorrt/segment/segment.h @@ -29,25 +29,116 @@ namespace tensorflow { namespace tensorrt { namespace segment { -using SegmentNodesVector = std::vector>; +using SegmentNodesVector = std::vector, string>>; +class Node; +class Graph; +class Edge { + public: + Edge(int id, Node* src, int src_port, Node* dst, int dst_port, + bool is_control = false) + : id_(id), + src_(src), + src_port_(src_port), + dst_(dst), + dst_port_(dst_port), + control_(is_control){}; + Node* src() const { return src_; } + Node* dst() const { return dst_; } + int src_output() const { return src_port_; } + int dst_input() const { return dst_port_; } + int id() const { return id_; } + bool IsControlEdge() const { return control_; } + ~Edge() {} + private: + int id_; + Node* src_; + int src_port_; + Node* dst_; + int dst_port_; + bool control_; +}; +class Node { + friend class Graph; + + public: + Node(const tensorflow::Node* node, const int id); + const std::vector& in_edges() const { return in_edges_; }; + const std::vector& out_edges() const { return out_edges_; }; + std::vector in_nodes() const { + std::vector res; + res.reserve(in_edges_.size()); + for (const auto e : in_edges_) { + if (e) res.push_back(e->src()); + } + return res; + } + const string& name() const { return node_->name(); } + const tensorflow::Node* tf_node() const { return node_; } + int id() const { return id_; } + + private: + const tensorflow::Node* node_; + std::vector in_edges_; + std::vector out_edges_; + int id_; +}; + +class Graph { + public: + Graph(const tensorflow::Graph* g); + void AddControlEdge(Node* src, Node* dst); + void AddEdge(Node* src, int out_port, Node* dst, int in_port); + void RemoveEdge(const Edge*); + Node* FindNodeId(int node_id) { + if (node_id < 0 || node_id > (int)nodes_.size()) return nullptr; + return nodes_[node_id]; + } + ~Graph(); + int num_node_ids() const { return nodes_.size(); } + const Node* source_node() const { + return nodes_[tensorflow::Graph::kSourceId]; + } + const Node* sink_node() const { return nodes_[tensorflow::Graph::kSinkId]; } + + private: + const tensorflow::Graph* g_; + std::vector nodes_; + std::vector edges_; + std::set edge_ids_; + std::set node_ids_; +}; struct SegmentOptions { // Segment must contain at least this many nodes. int minimum_segment_size = 2; std::set exclude_node_list; }; +// // Get the subgraphs of a graph that can be handled by TensorRT. +// // +// // @param gdef The GraphDef describing the network +// // @param candidate_fn A function that returns true for a NodeDef if +// // that node can be handled by TensorRT. +// // @param segments Returns the TensorRT segments/subgraphs. Each entry +// // in the vector describes a subgraph by giving a set of the names of +// // all the NodeDefs in that subgraph. +// // @return the status. +tensorflow::Status SegmentGraph( + const tensorflow::GraphDef& gdef, + const std::function& candidate_fn, + const SegmentOptions& options, SegmentNodesVector* segments); + // Get the subgraphs of a graph that can be handled by TensorRT. // -// @param gdef The GraphDef describing the network -// @param candidate_fn A function that returns true for a NodeDef if +// @param graph tensorflow::Graph of the network +// @param candidate_fn A function that returns true for a Node* if // that node can be handled by TensorRT. // @param segments Returns the TensorRT segments/subgraphs. Each entry // in the vector describes a subgraph by giving a set of the names of // all the NodeDefs in that subgraph. // @return the status. tensorflow::Status SegmentGraph( - const tensorflow::GraphDef& gdef, + tensorflow::Graph* graph, const std::function& candidate_fn, const SegmentOptions& options, SegmentNodesVector* segments); diff --git a/tensorflow/contrib/tensorrt/segment/segment_test.cc b/tensorflow/contrib/tensorrt/segment/segment_test.cc index 7ddabec268d4ef..7fe824b12f1f92 100644 --- a/tensorflow/contrib/tensorrt/segment/segment_test.cc +++ b/tensorflow/contrib/tensorrt/segment/segment_test.cc @@ -35,7 +35,7 @@ class SegmentTest : public ::testing::Test { TF_Operation* Add(TF_Operation* l, TF_Operation* r, TF_Graph* graph, TF_Status* s, const char* name); - std::function MakeCandidateFn( + std::function MakeCandidateFn( const std::set& node_names); protected: @@ -60,9 +60,9 @@ bool SegmentTest::GetGraphDef(TF_Graph* graph, return ret; } -std::function SegmentTest::MakeCandidateFn( +std::function SegmentTest::MakeCandidateFn( const std::set& node_names) { - return [node_names](const Node* node) -> bool { + return [node_names](const tensorflow::Node* node) -> bool { return node_names.find(node->name()) != node_names.end(); }; } diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py index ad01bedd8fa066..aaaed0c30fa4e6 100644 --- a/tensorflow/contrib/tensorrt/test/test_tftrt.py +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -18,7 +18,9 @@ from __future__ import division from __future__ import print_function +import argparse import numpy as np + # normally we should do import tensorflow as tf and then # tf.placeholder, tf.constant, tf.nn.conv2d etc but # it looks like internal builds don't like it so @@ -26,6 +28,7 @@ from tensorflow.contrib import tensorrt as trt from tensorflow.core.protobuf import config_pb2 as cpb2 +from tensorflow.core.protobuf import rewriter_config_pb2 as rwpb2 from tensorflow.python.client import session as csess from tensorflow.python.framework import constant_op as cop from tensorflow.python.framework import dtypes as dtypes @@ -59,9 +62,12 @@ def get_simple_graph_def(): return g.as_graph_def() -def run_graph(gdef, dumm_inp): +def execute_graph(gdef, dumm_inp): """Run given graphdef once.""" + print("executing") gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + #graph_options = cpb2.GraphOptions(rewrite_options=opt_config) + sessconfig = cpb2.ConfigProto(gpu_options=gpu_options) ops.reset_default_graph() g = ops.Graph() with g.as_default(): @@ -69,15 +75,18 @@ def run_graph(gdef, dumm_inp): graph_def=gdef, return_elements=["input", "output"]) inp = inp.outputs[0] out = out.outputs[0] + # with csess.Session( + # config=cpb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess: + # val = sess.run(out, {inp: dumm_inp}) with csess.Session( - config=cpb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess: + config=sessconfig, graph=g) as sess: val = sess.run(out, {inp: dumm_inp}) return val # Use real data that is representative of the inference dataset # for calibration. For this test script it is random data. -def run_calibration(gdef, dumm_inp): +def execute_calibration(gdef, dumm_inp): """Run given calibration graph multiple times.""" gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) ops.reset_default_graph() @@ -96,7 +105,9 @@ def run_calibration(gdef, dumm_inp): return val -if "__main__" in __name__: +def user(run_graph=execute_graph, run_calibration=execute_calibration): + """ Example function that converts a graph to TFTRT graph """ + inp_dims = (100, 24, 24, 2) dummy_input = np.random.random_sample(inp_dims) orig_graph = get_simple_graph_def() # use a frozen graph for inference @@ -137,3 +148,44 @@ def run_calibration(gdef, dumm_inp): assert np.allclose(o1, o4) assert np.allclose(o1, o5) print("Pass") + +def auto(): + """ Run the conversion as an optimization pass""" + inp_dims = (100, 24, 24, 2) + dummy_input = np.random.random_sample(inp_dims) + orig_graph = get_simple_graph_def() + opt_config = rwpb2.RewriterConfig() + opt_config.optimizers.extend(["constfold", "layout"]) + custom_op = opt_config.custom_optimizers.add() + custom_op.name = "TensorRTOptimizer" + custom_op.parameter_map["minimum_segment_size"].i = 3 + custom_op.parameter_map["precision_mode"].s = "FP32" + custom_op.parameter_map["max_batch_size"].i = inp_dims[0] + custom_op.parameter_map["max_workspace_size_bytes"].i = 1 << 25 + print(custom_op) + gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + graph_options = cpb2.GraphOptions(rewrite_options=opt_config) + sessconfig = cpb2.ConfigProto(gpu_options=gpu_options, + graph_options=graph_options) + print(sessconfig) + g = ops.Graph() + ops.reset_default_graph() + with g.as_default(): + inp, out = importer.import_graph_def( + graph_def=orig_graph, return_elements=["input", "output"]) + inp = inp.outputs[0] + out = out.outputs[0] + with csess.Session(config=sessconfig, graph=g) as sess: + val = sess.run(out, {inp: dummy_input}) + print(val.shape) + +if "__main__" in __name__: + P = argparse.ArgumentParser(prog="tftrt_test", + description="Example utilization of TensorFlow-TensorRT integration") + P.add_argument("--automatic", "-a", action="store_true", + help="Do TRT conversion automatically", default=False) + flags, unparsed = P.parse_known_args() + if flags.automatic: + auto() + else: + user() diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index 8f0cf8c3d19480..3af79ee170c20b 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -24,7 +24,7 @@ function real_path() { function cp_external() { local src_dir=$1 local dest_dir=$2 - for f in `find "$src_dir" -maxdepth 1 -mindepth 1 ! -name '*local_config_cuda*' ! -name '*org_tensorflow*'`; do + for f in `find "$src_dir" -maxdepth 1 -mindepth 1 ! -name '*local_config_cuda*' ! -name '*local_config_tensorrt*' ! -name '*org_tensorflow*'`; do cp -R "$f" "$dest_dir" done mkdir -p "${dest_dir}/local_config_cuda/cuda/cuda/" From a0af3551a83ba81ddfd2b43cca75edff4c0fcdc1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 11:24:26 -0700 Subject: [PATCH 0060/1691] Automated g4 rollback of changelist 192536085 PiperOrigin-RevId: 194426650 --- tensorflow/core/grappler/op_types.cc | 8 +- tensorflow/core/grappler/op_types.h | 1 + .../grappler/optimizers/constant_folding.cc | 102 ++++++++++++++++-- .../optimizers/constant_folding_test.cc | 80 +++++++++++++- 4 files changed, 175 insertions(+), 16 deletions(-) diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc index f595cf6456322a..c02430369c0e6e 100644 --- a/tensorflow/core/grappler/op_types.cc +++ b/tensorflow/core/grappler/op_types.cc @@ -250,6 +250,10 @@ bool IsPrint(const NodeDef& node) { return node.op() == "Print"; } bool IsProd(const NodeDef& node) { return node.op() == "Prod"; } +bool IsRandomShuffle(const NodeDef& node) { + return node.op() == "RandomShuffle"; +} + bool IsReal(const NodeDef& node) { return node.op() == "Real"; } bool IsRealDiv(const NodeDef& node) { return node.op() == "RealDiv"; } @@ -299,9 +303,7 @@ bool IsShape(const NodeDef& node) { return node.op() == "Shape"; } bool IsShapeN(const NodeDef& node) { return node.op() == "ShapeN"; } -bool IsShuffle(const NodeDef& node) { - return node.op() == "Shuffle" || node.op() == "RandomShuffle"; -} +bool IsShuffle(const NodeDef& node) { return node.op() == "Shuffle"; } bool IsSigmoidGrad(const NodeDef& node) { return node.op() == "SigmoidGrad"; } diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h index b25ba1924e3b9c..3cba6b8b36076c 100644 --- a/tensorflow/core/grappler/op_types.h +++ b/tensorflow/core/grappler/op_types.h @@ -98,6 +98,7 @@ bool IsPolygamma(const NodeDef& node); bool IsPrint(const NodeDef& node); bool IsProd(const NodeDef& node); bool IsPow(const NodeDef& node); +bool IsRandomShuffle(const NodeDef& node); bool IsReal(const NodeDef& node); bool IsRealDiv(const NodeDef& node); bool IsRelu6Grad(const NodeDef& node); diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc index 45bb188e8db49e..4801f18619e672 100644 --- a/tensorflow/core/grappler/optimizers/constant_folding.cc +++ b/tensorflow/core/grappler/optimizers/constant_folding.cc @@ -1575,24 +1575,106 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph, continue; } - // Remove Shuffle or Reverse op over scalar values. - if (use_shape_info && - !properties->GetInputProperties(node->name()).empty() && - (IsShuffle(*node) || IsReverse(*node) || IsTranspose(*node))) { + // Remove Shuffle or Transpose op over dimensions of size 1. + if (use_shape_info && (IsShuffle(*node) || IsTranspose(*node)) && + properties->GetInputProperties(node->name()).size() >= 2) { const auto& shape = properties->GetInputProperties(node->name())[0].shape(); - // The node is replaceable iff - // unknown_rank == false && (dim_size == 0 || all dims have size 1) - bool replaceable = !shape.unknown_rank(); - for (int j = 0; replaceable && j < shape.dim_size(); ++j) { - replaceable &= shape.dim(j).size() == 1; + if (shape.unknown_rank()) { + // Not optimizable. + continue; } - if (replaceable) { + const auto& p = properties->GetInputProperties(node->name())[1]; + if (TensorShape::IsValid(p.shape()) && p.has_value()) { + Tensor perm(p.dtype(), p.shape()); + if (!perm.FromProto(p.value())) { + return errors::InvalidArgument("Cannot parse tensor from proto: ", + p.value().DebugString()); + } + std::vector permutation; + for (int j = 0; j < perm.NumElements(); ++j) { + if (perm.dtype() == DT_INT64) { + permutation.push_back(perm.vec()(j)); + } else { + permutation.push_back(perm.vec()(j)); + } + } + if (permutation.size() != shape.dim_size()) { + // Number of elements in perm should be same as dim_size. Skip if not. + continue; + } + // The node is replaceable iff + // dim_size == 0 || all dims have size 1 || + // all dims with > 1 size are not permuted. + bool replaceable = true; + for (int j = 0; replaceable && j < shape.dim_size(); ++j) { + replaceable &= shape.dim(j).size() == 1 || j == permutation[j]; + } + if (replaceable) { + ReplaceOperationWithIdentity(0, node, optimized_graph); + continue; + } + } + } + + // Remove RandomShuffle op if it is scalar or first dimension is of size 1. + if (use_shape_info && IsRandomShuffle(*node) && + !properties->GetInputProperties(node->name()).empty()) { + const auto& shape = + properties->GetInputProperties(node->name())[0].shape(); + // The node is replaceable iff + // unknown_rank == false && (dim_size == 0 || first dim is of size 1) + if (!shape.unknown_rank() && + (shape.dim_size() == 0 || shape.dim(0).size() == 1)) { ReplaceOperationWithIdentity(0, node, optimized_graph); continue; } } + // Remove Reverse op over dimensions with size 1. + if (use_shape_info && node->op() == "ReverseV2" && + properties->GetInputProperties(node->name()).size() >= 2) { + const auto& shape = + properties->GetInputProperties(node->name())[0].shape(); + if (shape.unknown_rank()) { + // Not optimizable. + continue; + } + const auto& a = properties->GetInputProperties(node->name())[1]; + if (TensorShape::IsValid(a.shape()) && a.has_value()) { + Tensor axis(a.dtype(), a.shape()); + if (!axis.FromProto(a.value())) { + return errors::InvalidArgument("Cannot parse tensor from proto: ", + a.value().DebugString()); + } + std::set target_axes; + for (int j = 0; j < axis.NumElements(); ++j) { + // value of axis can be negative. + if (axis.dtype() == DT_INT64) { + target_axes.insert( + (axis.vec()(j) + shape.dim_size()) % shape.dim_size()); + } else { + target_axes.insert( + (axis.vec()(j) + shape.dim_size()) % shape.dim_size()); + } + } + + // The node is replaceable iff + // unknown_rank == false && + // (dim_size == 0 || all dims have size 1 || + // all dims with > 1 size are not in target_axes) + bool replaceable = !shape.unknown_rank(); + for (int j = 0; replaceable && j < shape.dim_size(); ++j) { + replaceable &= shape.dim(j).size() == 1 || + target_axes.find(j) == target_axes.end(); + } + if (replaceable) { + ReplaceOperationWithIdentity(0, node, optimized_graph); + continue; + } + } + } + if (use_shape_info && IsSlice(*node) && properties->GetInputProperties(node->name()).size() == 3) { const auto& input = properties->GetInputProperties(node->name())[0]; diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc index 25693c5c60b6b9..306ddd22d739d4 100644 --- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc +++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc @@ -1522,8 +1522,6 @@ TEST_F(ConstantFoldingTest, SplitVRemoval) { ops::SplitV s1(scope.WithOpName("s1"), in1, size_splits1, split_dim, 1); ops::SplitV s2(scope.WithOpName("s2"), in2, size_splits2, split_dim, 2); - LOG(INFO) << s1.output.size(); - LOG(INFO) << s2.output.size(); ops::Add out(scope.WithOpName("out"), s1[0], s2[0]); GrapplerItem item; @@ -1561,7 +1559,45 @@ TEST_F(ConstantFoldingTest, SplitVRemoval) { test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-5); } -TEST_F(ConstantFoldingTest, ShuffleReverseOnScalarRemoval) { +TEST_F(ConstantFoldingTest, TransposeOnSize1DimsRemoval) { + tensorflow::Scope scope = tensorflow::Scope::NewRootScope(); + + Output in1 = ops::Variable(scope.WithOpName("in1"), TensorShape({1, 2, 4, 1}), + DT_FLOAT); + Output p1 = ops::Const(scope.WithOpName("p1"), {3, 2, 1, 0}, {4}); + Output in2 = ops::Variable(scope.WithOpName("in2"), TensorShape({1, 4, 2, 1}), + DT_FLOAT); + Output p2 = ops::Const(scope.WithOpName("p2"), {3, 1, 2, 0}, {4}); + ops::Transpose t1(scope.WithOpName("t1"), in1, p1); + ops::Transpose t2(scope.WithOpName("t2").WithControlDependencies({in1}), in2, + p2); + + ops::Add out1(scope.WithOpName("out1"), t1, t2); + + GrapplerItem item; + item.fetch = {"out1"}; + TF_CHECK_OK(scope.ToGraphDef(&item.graph)); + + ConstantFolding optimizer(nullptr /* cpu_device */); + GraphDef got; + Status status = optimizer.Optimize(nullptr, item, &got); + TF_EXPECT_OK(status); + + GraphDef want; + AddNode("in1", "VariableV2", {}, {}, &want); + AddNode("in2", "VariableV2", {}, {}, &want); + AddNode("p1", "Const", {}, {}, &want); + AddNode("p2", "Const", {}, {}, &want); + AddNode("t1", "Transpose", {"in1", "p1"}, {}, &want); + AddNode("t2", "Identity", + {"in2", AsControlDependency("in1"), AsControlDependency("p2")}, {}, + &want); + AddNode("out1", "Add", {"t1", "t2"}, {}, &want); + + CompareGraphs(want, got); +} + +TEST_F(ConstantFoldingTest, RandomShuffleOnScalarRemoval) { tensorflow::Scope scope = tensorflow::Scope::NewRootScope(); Output in1 = @@ -1606,6 +1642,44 @@ TEST_F(ConstantFoldingTest, ShuffleReverseOnScalarRemoval) { test::ExpectTensorNear(tensors_expected[i], tensors[i], 1e-5); } +TEST_F(ConstantFoldingTest, ReverseOnSize1DimsRemoval) { + tensorflow::Scope scope = tensorflow::Scope::NewRootScope(); + + Output in1 = ops::Variable(scope.WithOpName("in1"), TensorShape({1, 2, 4, 1}), + DT_FLOAT); + Output a1 = ops::Const(scope.WithOpName("a1"), {3, 2, 1, 0}, {4}); + Output in2 = ops::Variable(scope.WithOpName("in2"), TensorShape({1, 2, 4, 1}), + DT_FLOAT); + Output a2 = ops::Const(scope.WithOpName("a2"), {0, 3}, {2}); + ops::Reverse r1(scope.WithOpName("r1"), in1, a1); + ops::Reverse r2(scope.WithOpName("r2").WithControlDependencies({in1}), in2, + a2); + + ops::Add out1(scope.WithOpName("out1"), r1, r2); + + GrapplerItem item; + item.fetch = {"out1"}; + TF_CHECK_OK(scope.ToGraphDef(&item.graph)); + + ConstantFolding optimizer(nullptr /* cpu_device */); + GraphDef got; + Status status = optimizer.Optimize(nullptr, item, &got); + TF_EXPECT_OK(status); + + GraphDef want; + AddNode("in1", "VariableV2", {}, {}, &want); + AddNode("in2", "VariableV2", {}, {}, &want); + AddNode("a1", "Const", {}, {}, &want); + AddNode("a2", "Const", {}, {}, &want); + AddNode("r1", "ReverseV2", {"in1", "a1"}, {}, &want); + AddNode("r2", "Identity", + {"in2", AsControlDependency("in1"), AsControlDependency("a2")}, {}, + &want); + AddNode("out1", "Add", {"r1", "r2"}, {}, &want); + + CompareGraphs(want, got); +} + TEST_F(ConstantFoldingTest, SliceWithSameDimensionRemoval) { { // size = {3, 5} tensorflow::Scope scope = tensorflow::Scope::NewRootScope(); From 6b6976e3ba19484f893092712e4577daeb92ad3b Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Thu, 26 Apr 2018 11:24:36 -0700 Subject: [PATCH 0061/1691] Deprecate tfe.Network and associated utilities in favor of tf.keras.Model. Also throws an error rather than silently saving incorrectly with tf.train.Checkpoint. (In response to confusion over tf.train.Checkpoint with tfe.Network) PiperOrigin-RevId: 194426679 --- tensorflow/contrib/eager/python/network.py | 49 +++++++++++++++++++ .../contrib/eager/python/network_test.py | 7 +++ 2 files changed, 56 insertions(+) diff --git a/tensorflow/contrib/eager/python/network.py b/tensorflow/contrib/eager/python/network.py index 2f8721324f5fc1..44828bea50c660 100644 --- a/tensorflow/contrib/eager/python/network.py +++ b/tensorflow/contrib/eager/python/network.py @@ -28,9 +28,11 @@ from tensorflow.python.keras._impl.keras.engine import base_layer as keras_base_layer from tensorflow.python.layers import base from tensorflow.python.ops import variable_scope +from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpoint_utils from tensorflow.python.training import saver as saver_lib from tensorflow.python.training import training_util +from tensorflow.python.util import deprecation # pylint: disable=protected-access # Explanation for protected-access disable: Network has lots of same-class and @@ -52,9 +54,40 @@ def _network_name_scope_naming(current_variable_scope): return current_variable_scope.name + "/" +_NETWORK_DEPRECATION_MESSAGE = ( + "Please inherit from `tf.keras.Model`, and see its documentation for " + "details. `tf.keras.Model` should be a drop-in replacement for " + "`tfe.Network` in most cases, but note that `track_layer` is no longer " + "necessary or supported. Instead, `Layer` instances are tracked on " + "attribute assignment (see the section of `tf.keras.Model`'s documentation " + "on subclassing). Since the output of `track_layer` is often assigned to " + "an attribute anyway, most code can be ported by simply removing the " + "`track_layer` calls.\n\n`tf.keras.Model` works with all TensorFlow " + "`Layer` instances, including those from `tf.layers`, but switching to " + "the `tf.keras.layers` versions along with the migration to " + "`tf.keras.Model` is recommended, since it will preserve variable names. " + "Feel free to import it with an alias to avoid excess typing :)." +) + + class Network(base.Layer): """Represents the composition of a set of Layers. + *Deprecated*. Please inherit from `tf.keras.Model`, and see its documentation + for details. `tf.keras.Model` should be a drop-in replacement for + `tfe.Network` in most cases, but note that `track_layer` is no longer + necessary or supported. Instead, `Layer` instances are tracked on attribute + assignment (see the section of `tf.keras.Model`'s documentation on + subclassing). Since the output of `track_layer` is often assigned to an + attribute anyway, most code can be ported by simply removing the `track_layer` + calls. + + `tf.keras.Model` works with all TensorFlow `Layer` instances, including those + from `tf.layers`, but switching to the `tf.keras.layers` versions along with + the migration to `tf.keras.Model` is recommended, since it will preserve + variable names. Feel free to import it with an alias to avoid excess typing + :). + `Network` implements the `Layer` interface and adds convenience methods for managing sub-`Layer`s, such as listing variables. @@ -112,6 +145,7 @@ def call(self, inputs): # - Detect layers used in __call__ that weren't registered with track_layer. # - Convert inputs to __call__ to tensors. + @deprecation.deprecated(date=None, instructions=_NETWORK_DEPRECATION_MESSAGE) def __init__(self, name=None): """Configure the `Network`. @@ -130,6 +164,10 @@ def __init__(self, name=None): ValueError: If `name` is not valid. Note that some naming errors will instead be raised when the `Network` is called. """ + if context.executing_eagerly(): + logging.warning( + ("** tfe.Network is deprecated and will be removed in a future " + "version.\n\n%s") % _NETWORK_DEPRECATION_MESSAGE) if isinstance(name, variable_scope.VariableScope): raise ValueError("VariableScopes are not valid Network names.") if name is not None and "/" in name: @@ -152,6 +190,11 @@ def __init__(self, name=None): self._variable_scope_counts_on_init = ( variable_scope.get_variable_scope_store().variable_scopes_count) + def _gather_saveables_for_checkpoint(self): + raise NotImplementedError( + "tfe.Network does not support object-based checkpointing.\n\n%s" + % _NETWORK_DEPRECATION_MESSAGE) + def _name_scope_name(self, current_variable_scope): """Overrides Layer op naming to match variable naming.""" return _network_name_scope_naming( @@ -706,6 +749,9 @@ def _strip_variable_prefix(original_variable_name): return _strip_variable_prefix +@deprecation.deprecated(date=None, instructions=( + "Please inherit from tf.keras.Model instead of tfe.Network, and use " + "tf.keras.Model.save_weights.")) def save_network_checkpoint( network, save_path, global_step=None, map_func=None): """Save variables from the Network to a checkpoint. @@ -905,6 +951,9 @@ def _set_restore_on_create(network, save_path, map_func, user_map_func, _add_deferred_restoration(network, deferred_restoration) +@deprecation.deprecated(date=None, instructions=( + "Please inherit from tf.keras.Model instead of tfe.Network, and use " + "tf.keras.Model.load_weights.")) def restore_network_checkpoint(network, save_path, map_func=None): """Restore the Network from a checkpoint. diff --git a/tensorflow/contrib/eager/python/network_test.py b/tensorflow/contrib/eager/python/network_test.py index f43376d5d777a7..6a51d03de52914 100644 --- a/tensorflow/contrib/eager/python/network_test.py +++ b/tensorflow/contrib/eager/python/network_test.py @@ -30,6 +30,7 @@ from tensorflow.python.ops import nn_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope +from tensorflow.python.training import checkpointable_utils from tensorflow.python.training import training_util @@ -62,6 +63,12 @@ def call(self, values): class NetworkTest(test.TestCase): + def test_checkpointing_not_implemented(self): + checkpoint_directory = self.get_temp_dir() + checkpoint = checkpointable_utils.Checkpoint(net=MyNetwork()) + with self.assertRaises(NotImplementedError): + checkpoint.save(checkpoint_directory) + def _save_modify_load_network_built(self, net, global_step=None): checkpoint_directory = self.get_temp_dir() checkpoint_path = network.save_network_checkpoint( From a8481834bb881f67e7b9523480c28f5b987e62e8 Mon Sep 17 00:00:00 2001 From: Anna R Date: Thu, 26 Apr 2018 11:25:43 -0700 Subject: [PATCH 0062/1691] Removing @@ comments from core TensorFlow. They are no longer needed for exporting symbols to the TensorFlow API. PiperOrigin-RevId: 194426855 --- tensorflow/python/client/client_lib.py | 24 ---- tensorflow/python/data/__init__.py | 6 - tensorflow/python/framework/constant_op.py | 18 --- tensorflow/python/framework/framework_lib.py | 54 +------- tensorflow/python/layers/layers.py | 43 +----- tensorflow/python/lib/io/python_io.py | 5 - tensorflow/python/ops/array_ops.py | 63 --------- tensorflow/python/ops/bitwise_ops.py | 10 +- tensorflow/python/ops/check_ops.py | 23 ---- tensorflow/python/ops/confusion_matrix.py | 7 +- tensorflow/python/ops/control_flow_ops.py | 30 ---- tensorflow/python/ops/functional_ops.py | 5 - tensorflow/python/ops/histogram_ops.py | 3 - tensorflow/python/ops/image_ops.py | 57 -------- tensorflow/python/ops/io_ops.py | 47 ------- tensorflow/python/ops/losses/losses.py | 14 -- tensorflow/python/ops/losses/util.py | 11 +- tensorflow/python/ops/manip_ops.py | 5 +- tensorflow/python/ops/math_ops.py | 130 ------------------ tensorflow/python/ops/metrics.py | 38 +---- tensorflow/python/ops/nn.py | 85 ------------ tensorflow/python/ops/rnn.py | 11 +- tensorflow/python/ops/rnn_cell.py | 25 +--- tensorflow/python/ops/script_ops.py | 5 +- tensorflow/python/ops/sdca_ops.py | 4 - tensorflow/python/ops/session_ops.py | 7 +- tensorflow/python/ops/sets.py | 8 +- tensorflow/python/ops/sparse_ops.py | 28 +--- tensorflow/python/ops/special_math_ops.py | 4 +- tensorflow/python/ops/spectral_ops.py | 17 +-- tensorflow/python/ops/state_ops.py | 66 +-------- tensorflow/python/ops/string_ops.py | 12 -- tensorflow/python/ops/tensor_array_ops.py | 5 +- tensorflow/python/platform/resource_loader.py | 9 +- tensorflow/python/platform/sysconfig.py | 8 +- tensorflow/python/platform/test.py | 13 -- tensorflow/python/summary/summary.py | 15 -- .../training/basic_session_run_hooks.py | 13 +- .../python/training/session_run_hook.py | 5 - tensorflow/python/training/training.py | 82 ----------- tensorflow/python/util/compat.py | 4 - 41 files changed, 20 insertions(+), 999 deletions(-) diff --git a/tensorflow/python/client/client_lib.py b/tensorflow/python/client/client_lib.py index b9ecaa4c851c08..c94767a03c28cd 100644 --- a/tensorflow/python/client/client_lib.py +++ b/tensorflow/python/client/client_lib.py @@ -16,30 +16,6 @@ """Support for launching graphs and executing operations. See the @{$python/client} guide. - -@@Session -@@InteractiveSession -@@get_default_session -@@OpError -@@CancelledError -@@UnknownError -@@InvalidArgumentError -@@DeadlineExceededError -@@NotFoundError -@@AlreadyExistsError -@@PermissionDeniedError -@@UnauthenticatedError -@@ResourceExhaustedError -@@FailedPreconditionError -@@AbortedError -@@OutOfRangeError -@@UnimplementedError -@@InternalError -@@UnavailableError -@@DataLossError -@@exception_type_from_error_code -@@error_code_from_exception_type -@@raise_exception_on_not_ok_status """ from __future__ import absolute_import diff --git a/tensorflow/python/data/__init__.py b/tensorflow/python/data/__init__.py index 5cedb89bf8f3bc..7efe0948e7729c 100644 --- a/tensorflow/python/data/__init__.py +++ b/tensorflow/python/data/__init__.py @@ -15,12 +15,6 @@ """`tf.data.Dataset` API for input pipelines. See the @{$datasets$Importing Data} Programmer's Guide for an overview. - -@@Dataset -@@Iterator -@@FixedLengthRecordDataset -@@TextLineDataset -@@TFRecordDataset """ from __future__ import absolute_import diff --git a/tensorflow/python/framework/constant_op.py b/tensorflow/python/framework/constant_op.py index 782b505d6c1d0b..b3eb57d067ba29 100644 --- a/tensorflow/python/framework/constant_op.py +++ b/tensorflow/python/framework/constant_op.py @@ -15,24 +15,6 @@ """Operations that generate constants. See the @{$python/constant_op$constants guide}. - -@@zeros -@@zeros_like -@@ones -@@ones_like -@@fill -@@constant -@@linspace -@@range -@@random_normal -@@truncated_normal -@@random_uniform -@@random_shuffle -@@random_crop -@@multinomial -@@random_gamma -@@random_poisson -@@set_random_seed """ # Must be separate from array_ops to avoid a cyclic dependency. diff --git a/tensorflow/python/framework/framework_lib.py b/tensorflow/python/framework/framework_lib.py index 392a4f65c6e62c..fffb6488425524 100644 --- a/tensorflow/python/framework/framework_lib.py +++ b/tensorflow/python/framework/framework_lib.py @@ -14,59 +14,7 @@ # ============================================================================== # pylint: disable=unused-import,g-bad-import-order -"""Classes and functions for building TensorFlow graphs. - -## Core graph data structures - -@@Graph -@@Operation -@@Tensor - -## Tensor types - -@@DType -@@as_dtype - -## Utility functions - -@@device -@@container -@@name_scope -@@colocate_with -@@control_dependencies -@@convert_to_tensor -@@convert_to_tensor_or_indexed_slices -@@convert_to_tensor_or_sparse_tensor -@@get_default_graph -@@reset_default_graph -@@import_graph_def -@@load_file_system_library -@@load_op_library -@@make_tensor_proto -@@make_ndarray - -## Graph collections - -@@add_to_collection -@@add_to_collections -@@get_collection -@@get_collection_ref -@@GraphKeys - -## Defining new operations - -@@RegisterGradient -@@NotDifferentiable -@@NoGradient -@@TensorShape -@@Dimension -@@op_scope -@@get_seed - -## For libraries building on TensorFlow - -@@register_tensor_conversion_function -""" +"""Classes and functions for building TensorFlow graphs.""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/layers/layers.py b/tensorflow/python/layers/layers.py index c5fa0d3aba7c13..11a2ebc040f017 100644 --- a/tensorflow/python/layers/layers.py +++ b/tensorflow/python/layers/layers.py @@ -14,48 +14,7 @@ # ============================================================================== # pylint: disable=line-too-long -"""This library provides a set of high-level neural networks layers. - -@@Dense -@@Dropout -@@Flatten -@@Conv1D -@@Conv2D -@@Conv3D -@@SeparableConv1D -@@SeparableConv2D -@@Conv2DTranspose -@@Conv3DTranspose -@@AveragePooling1D -@@MaxPooling1D -@@AveragePooling2D -@@MaxPooling2D -@@AveragePooling3D -@@MaxPooling3D -@@BatchNormalization - -@@Layer -@@Input -@@InputSpec - -@@dense -@@dropout -@@flatten -@@conv1d -@@conv2d -@@conv3d -@@separable_conv1d -@@separable_conv2d -@@conv2d_transpose -@@conv3d_transpose -@@average_pooling1d -@@max_pooling1d -@@average_pooling2d -@@max_pooling2d -@@average_pooling3d -@@max_pooling3d -@@batch_normalization -""" +"""This library provides a set of high-level neural networks layers.""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/lib/io/python_io.py b/tensorflow/python/lib/io/python_io.py index d4bc8afd1e32aa..aec12ab3eaaa9c 100644 --- a/tensorflow/python/lib/io/python_io.py +++ b/tensorflow/python/lib/io/python_io.py @@ -16,11 +16,6 @@ """Python functions for directly manipulating TFRecord-formatted files. See the @{$python/python_io} guide. - -@@TFRecordWriter -@@tf_record_iterator -@@TFRecordCompressionType -@@TFRecordOptions """ from __future__ import absolute_import diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index 1ea1a48c397566..3c2593066ad347 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -16,69 +16,6 @@ """Support for manipulating tensors. See the @{$python/array_ops} guide. - -@@string_to_number -@@to_double -@@to_float -@@to_bfloat16 -@@to_int32 -@@to_int64 -@@cast -@@bitcast -@@saturate_cast -@@broadcast_dynamic_shape -@@broadcast_static_shape -@@shape -@@shape_n -@@size -@@rank -@@reshape -@@squeeze -@@expand_dims -@@unravel_index -@@meshgrid -@@slice -@@strided_slice -@@split -@@tile -@@pad -@@concat -@@stack -@@parallel_stack -@@unstack -@@reverse_sequence -@@reverse -@@reverse_v2 -@@transpose -@@extract_image_patches -@@space_to_batch_nd -@@space_to_batch -@@required_space_to_batch_paddings -@@batch_to_space_nd -@@batch_to_space -@@space_to_depth -@@depth_to_space -@@gather -@@gather_nd -@@unique_with_counts -@@scatter_nd -@@dynamic_partition -@@dynamic_stitch -@@boolean_mask -@@one_hot -@@sequence_mask -@@dequantize -@@quantize -@@quantize_v2 -@@quantized_concat -@@setdiff1d -@@guarantee_const -@@fake_quant_with_min_max_args -@@fake_quant_with_min_max_args_gradient -@@fake_quant_with_min_max_vars -@@fake_quant_with_min_max_vars_gradient -@@fake_quant_with_min_max_vars_per_channel -@@fake_quant_with_min_max_vars_per_channel_gradient """ from __future__ import absolute_import diff --git a/tensorflow/python/ops/bitwise_ops.py b/tensorflow/python/ops/bitwise_ops.py index 123380cf04acf6..a1260b95cdb47b 100644 --- a/tensorflow/python/ops/bitwise_ops.py +++ b/tensorflow/python/ops/bitwise_ops.py @@ -13,15 +13,7 @@ # limitations under the License. # ============================================================================== -"""Operations for manipulating the binary representations of integers. - -@@bitwise_and -@@bitwise_or -@@bitwise_xor -@@invert -@@left_shift -@@right_shift -""" +"""Operations for manipulating the binary representations of integers.""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/ops/check_ops.py b/tensorflow/python/ops/check_ops.py index 9cea3e91f77600..306055d2025f17 100644 --- a/tensorflow/python/ops/check_ops.py +++ b/tensorflow/python/ops/check_ops.py @@ -16,29 +16,6 @@ """Asserts and Boolean Checks. See the @{$python/check_ops} guide. - -@@assert_negative -@@assert_positive -@@assert_non_negative -@@assert_non_positive -@@assert_equal -@@assert_none_equal -@@assert_near -@@assert_less -@@assert_less_equal -@@assert_greater -@@assert_greater_equal -@@assert_rank -@@assert_rank_at_least -@@assert_rank_in -@@assert_type -@@assert_integer -@@assert_proper_iterable -@@assert_same_float_dtype -@@assert_scalar -@@is_non_decreasing -@@is_numeric_tensor -@@is_strictly_increasing """ from __future__ import absolute_import diff --git a/tensorflow/python/ops/confusion_matrix.py b/tensorflow/python/ops/confusion_matrix.py index b9a93c3bedfff1..c09154129f1a72 100644 --- a/tensorflow/python/ops/confusion_matrix.py +++ b/tensorflow/python/ops/confusion_matrix.py @@ -12,12 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Confusion matrix related utilities. - - -@@remove_squeezable_dimensions -@@confusion_matrix -""" +"""Confusion matrix related utilities.""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py index f1e068d51403a6..07d4ff7b02c70e 100644 --- a/tensorflow/python/ops/control_flow_ops.py +++ b/tensorflow/python/ops/control_flow_ops.py @@ -15,36 +15,6 @@ """Control Flow Operations. See the @{$python/control_flow_ops} guide. - -@@identity -@@identity_n -@@tuple -@@group -@@no_op -@@count_up_to -@@cond -@@case -@@while_loop -@@logical_and -@@logical_not -@@logical_or -@@logical_xor -@@equal -@@not_equal -@@less -@@less_equal -@@greater -@@greater_equal -@@where -@@is_finite -@@is_inf -@@is_nan -@@verify_tensor_all_finite -@@check_numerics -@@add_check_numerics_ops -@@Assert -@@Print -@@timestamp """ # pylint: disable=g-bad-name from __future__ import absolute_import diff --git a/tensorflow/python/ops/functional_ops.py b/tensorflow/python/ops/functional_ops.py index 765a2ef99332fb..c8a1500e769585 100644 --- a/tensorflow/python/ops/functional_ops.py +++ b/tensorflow/python/ops/functional_ops.py @@ -16,11 +16,6 @@ """Functional operations. See the @{$python/functional_ops} guide. - -@@map_fn -@@foldl -@@foldr -@@scan """ from __future__ import absolute_import diff --git a/tensorflow/python/ops/histogram_ops.py b/tensorflow/python/ops/histogram_ops.py index ec38d89a0ec044..e86a8e5a5baa56 100644 --- a/tensorflow/python/ops/histogram_ops.py +++ b/tensorflow/python/ops/histogram_ops.py @@ -16,9 +16,6 @@ """Histograms. Please see @{$python/histogram_ops} guide. - -@@histogram_fixed_width_bins -@@histogram_fixed_width """ from __future__ import absolute_import diff --git a/tensorflow/python/ops/image_ops.py b/tensorflow/python/ops/image_ops.py index 3d40c391812b1c..343531ac5549db 100644 --- a/tensorflow/python/ops/image_ops.py +++ b/tensorflow/python/ops/image_ops.py @@ -17,63 +17,6 @@ """Image processing and decoding ops. See the @{$python/image} guide. - -@@decode_bmp -@@decode_gif -@@decode_jpeg -@@decode_and_crop_jpeg -@@encode_jpeg -@@extract_jpeg_shape -@@decode_png -@@encode_png -@@is_jpeg -@@decode_image -@@resize_images -@@resize_area -@@resize_bicubic -@@resize_bilinear -@@resize_nearest_neighbor -@@resize_image_with_crop_or_pad -@@central_crop -@@pad_to_bounding_box -@@crop_to_bounding_box -@@extract_glimpse -@@crop_and_resize -@@flip_up_down -@@random_flip_up_down -@@flip_left_right -@@random_flip_left_right -@@transpose_image -@@rot90 - -@@rgb_to_grayscale -@@grayscale_to_rgb -@@hsv_to_rgb -@@rgb_to_hsv -@@rgb_to_yiq -@@yiq_to_rgb -@@rgb_to_yuv -@@yuv_to_rgb -@@convert_image_dtype -@@adjust_brightness -@@random_brightness -@@adjust_contrast -@@random_contrast -@@adjust_hue -@@random_hue -@@adjust_gamma -@@adjust_saturation -@@random_saturation -@@per_image_standardization -@@draw_bounding_boxes -@@non_max_suppression -@@sample_distorted_bounding_box -@@total_variation -@@psnr -@@ssim -@@ssim_multiscale -@@image_gradients -@@sobel_edges """ from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/ops/io_ops.py b/tensorflow/python/ops/io_ops.py index f6a25610c5a2ee..b5274ef2ed05ea 100644 --- a/tensorflow/python/ops/io_ops.py +++ b/tensorflow/python/ops/io_ops.py @@ -17,53 +17,6 @@ """Inputs and Readers. See the @{$python/io_ops} guide. - -@@placeholder -@@placeholder_with_default -@@sparse_placeholder -@@ReaderBase -@@TextLineReader -@@WholeFileReader -@@IdentityReader -@@TFRecordReader -@@LMDBReader -@@FixedLengthRecordReader -@@decode_csv -@@decode_raw -@@VarLenFeature -@@FixedLenFeature -@@FixedLenSequenceFeature -@@SparseFeature -@@parse_example -@@parse_single_example -@@parse_tensor -@@serialize_tensor -@@decode_json_example -@@QueueBase -@@FIFOQueue -@@PaddingFIFOQueue -@@RandomShuffleQueue -@@PriorityQueue -@@ConditionalAccumulatorBase -@@ConditionalAccumulator -@@SparseConditionalAccumulator -@@matching_files -@@read_file -@@write_file -@@match_filenames_once -@@limit_epochs -@@input_producer -@@range_input_producer -@@slice_input_producer -@@string_input_producer -@@batch -@@maybe_batch -@@batch_join -@@maybe_batch_join -@@shuffle_batch -@@maybe_shuffle_batch -@@shuffle_batch_join -@@maybe_shuffle_batch_join """ from __future__ import absolute_import diff --git a/tensorflow/python/ops/losses/losses.py b/tensorflow/python/ops/losses/losses.py index 81ee01a41a21f2..4681eb9b175a67 100644 --- a/tensorflow/python/ops/losses/losses.py +++ b/tensorflow/python/ops/losses/losses.py @@ -15,20 +15,6 @@ """Loss operations for use in neural networks. Note: All the losses are added to the `GraphKeys.LOSSES` collection by default. - -@@Reduction -@@absolute_difference -@@compute_weighted_loss -@@cosine_distance -@@hinge_loss -@@huber_loss -@@log_loss -@@mean_pairwise_squared_error -@@mean_squared_error -@@sigmoid_cross_entropy -@@softmax_cross_entropy -@@sparse_softmax_cross_entropy - """ from __future__ import absolute_import diff --git a/tensorflow/python/ops/losses/util.py b/tensorflow/python/ops/losses/util.py index b835d963869704..10646af8a983f1 100644 --- a/tensorflow/python/ops/losses/util.py +++ b/tensorflow/python/ops/losses/util.py @@ -12,16 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utilities for manipulating the loss collections. - - -@@add_loss -@@get_losses -@@get_regularization_loss -@@get_regularization_losses -@@get_total_loss - -""" +"""Utilities for manipulating the loss collections.""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/ops/manip_ops.py b/tensorflow/python/ops/manip_ops.py index 373585395bb1e7..6633565a649df5 100644 --- a/tensorflow/python/ops/manip_ops.py +++ b/tensorflow/python/ops/manip_ops.py @@ -12,10 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Operators for manipulating tensors. - -@@roll -""" +"""Operators for manipulating tensors.""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index 2feb88cb7bca75..b93727313711e7 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -15,136 +15,6 @@ """Basic arithmetic operators. See the @{$python/math_ops} guide. - -@@add -@@subtract -@@multiply -@@scalar_mul -@@div -@@divide -@@truediv -@@floordiv -@@realdiv -@@truncatediv -@@floor_div -@@truncatemod -@@floormod -@@mod -@@cross -@@add_n -@@abs -@@negative -@@sign -@@reciprocal -@@square -@@round -@@sqrt -@@rsqrt -@@pow -@@exp -@@expm1 -@@log -@@log1p -@@sinh -@@cosh -@@asinh -@@acosh -@@atanh -@@ceil -@@floor -@@maximum -@@minimum -@@cos -@@sin -@@lbeta -@@tan -@@acos -@@asin -@@atan -@@atan2 -@@lgamma -@@digamma -@@erf -@@erfc -@@squared_difference -@@igamma -@@igammac -@@zeta -@@polygamma -@@betainc -@@rint -@@diag -@@diag_part -@@trace -@@transpose -@@eye -@@matrix_diag -@@matrix_diag_part -@@matrix_band_part -@@matrix_set_diag -@@matrix_transpose -@@matmul -@@norm -@@matrix_determinant -@@matrix_inverse -@@cholesky -@@cholesky_solve -@@matrix_solve -@@matrix_triangular_solve -@@matrix_solve_ls -@@qr -@@self_adjoint_eig -@@self_adjoint_eigvals -@@svd -@@tensordot -@@complex -@@conj -@@imag -@@angle -@@real -@@fft -@@ifft -@@fft2d -@@ifft2d -@@fft3d -@@ifft3d -@@reduce_sum -@@reduce_prod -@@reduce_min -@@reduce_max -@@reduce_mean -@@reduce_all -@@reduce_any -@@reduce_logsumexp -@@count_nonzero -@@accumulate_n -@@einsum -@@bincount -@@cumsum -@@cumprod -@@segment_sum -@@segment_prod -@@segment_min -@@segment_max -@@segment_mean -@@to_complex128 -@@to_complex64 -@@unsorted_segment_sum -@@unsorted_segment_max -@@unsorted_segment_mean -@@unsorted_segment_min -@@unsorted_segment_prod -@@unsorted_segment_sqrt_n -@@sparse_segment_sum -@@sparse_segment_mean -@@sparse_segment_sqrt_n -@@argmin -@@argmax -@@setdiff1d -@@where -@@unique -@@edit_distance -@@invert_permutation """ from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/ops/metrics.py b/tensorflow/python/ops/metrics.py index d1a8249154edd9..54fa3aefaa678b 100644 --- a/tensorflow/python/ops/metrics.py +++ b/tensorflow/python/ops/metrics.py @@ -13,43 +13,7 @@ # limitations under the License. # ============================================================================== -"""Evaluation-related metrics. - -@@accuracy -@@auc -@@false_negatives -@@false_negatives_at_thresholds -@@false_positives -@@false_positives_at_thresholds -@@mean -@@mean_absolute_error -@@mean_cosine_distance -@@mean_iou -@@mean_per_class_accuracy -@@mean_relative_error -@@mean_squared_error -@@mean_tensor -@@percentage_below -@@precision -@@precision_at_thresholds -@@recall -@@recall_at_k -@@recall_at_top_k -@@recall_at_thresholds -@@root_mean_squared_error -@@sensitivity_at_specificity -@@sparse_average_precision_at_k -@@average_precision_at_k -@@sparse_precision_at_k -@@precision_at_k -@@precision_at_top_k -@@specificity_at_sensitivity -@@true_negatives -@@true_negatives_at_thresholds -@@true_positives -@@true_positives_at_thresholds - -""" +"""Evaluation-related metrics.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function diff --git a/tensorflow/python/ops/nn.py b/tensorflow/python/ops/nn.py index 25e4add569f714..339684122ec303 100644 --- a/tensorflow/python/ops/nn.py +++ b/tensorflow/python/ops/nn.py @@ -17,91 +17,6 @@ """Neural network support. See the @{$python/nn} guide. - -@@relu -@@relu6 -@@crelu -@@swish -@@elu -@@leaky_relu -@@selu -@@softplus -@@softsign -@@dropout -@@bias_add -@@sigmoid -@@log_sigmoid -@@tanh -@@convolution -@@conv2d -@@depthwise_conv2d -@@depthwise_conv2d_native -@@separable_conv2d -@@atrous_conv2d -@@atrous_conv2d_transpose -@@conv2d_transpose -@@conv1d -@@conv3d -@@conv3d_transpose -@@conv2d_backprop_filter -@@conv2d_backprop_input -@@conv3d_backprop_filter_v2 -@@depthwise_conv2d_native_backprop_filter -@@depthwise_conv2d_native_backprop_input -@@avg_pool -@@max_pool -@@max_pool_with_argmax -@@avg_pool3d -@@max_pool3d -@@fractional_avg_pool -@@fractional_max_pool -@@pool -@@dilation2d -@@erosion2d -@@with_space_to_batch -@@l2_normalize -@@local_response_normalization -@@sufficient_statistics -@@normalize_moments -@@moments -@@weighted_moments -@@fused_batch_norm -@@batch_normalization -@@batch_norm_with_global_normalization -@@l2_loss -@@log_poisson_loss -@@sigmoid_cross_entropy_with_logits -@@softmax -@@log_softmax -@@softmax_cross_entropy_with_logits -@@softmax_cross_entropy_with_logits_v2 -@@sparse_softmax_cross_entropy_with_logits -@@weighted_cross_entropy_with_logits -@@embedding_lookup -@@embedding_lookup_sparse -@@dynamic_rnn -@@bidirectional_dynamic_rnn -@@raw_rnn -@@static_rnn -@@static_state_saving_rnn -@@static_bidirectional_rnn -@@ctc_loss -@@ctc_greedy_decoder -@@ctc_beam_search_decoder -@@top_k -@@in_top_k -@@nce_loss -@@sampled_softmax_loss -@@uniform_candidate_sampler -@@log_uniform_candidate_sampler -@@learned_unigram_candidate_sampler -@@fixed_unigram_candidate_sampler -@@compute_accidental_hits -@@quantized_conv2d -@@quantized_relu -@@quantized_relu_x -@@quantized_max_pool -@@quantized_avg_pool """ from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index 1dd464d51d9d1b..e94ad90dfd7fa7 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -13,16 +13,7 @@ # limitations under the License. # ============================================================================== -"""RNN helpers for TensorFlow models. - - -@@bidirectional_dynamic_rnn -@@dynamic_rnn -@@raw_rnn -@@static_rnn -@@static_state_saving_rnn -@@static_bidirectional_rnn -""" +"""RNN helpers for TensorFlow models.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function diff --git a/tensorflow/python/ops/rnn_cell.py b/tensorflow/python/ops/rnn_cell.py index 3d26ffb7ae1979..79eab1854a9d7b 100644 --- a/tensorflow/python/ops/rnn_cell.py +++ b/tensorflow/python/ops/rnn_cell.py @@ -12,30 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Module for constructing RNN Cells. - -## Base interface for all RNN Cells - -@@RNNCell - -## RNN Cells for use with TensorFlow's core RNN methods - -@@BasicRNNCell -@@BasicLSTMCell -@@GRUCell -@@LSTMCell - -## Classes storing split `RNNCell` state - -@@LSTMStateTuple - -## RNN Cell wrappers (RNNCells that wrap other RNNCells) - -@@MultiRNNCell -@@DropoutWrapper -@@DeviceWrapper -@@ResidualWrapper -""" +"""Module for constructing RNN Cells.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index 96fb0247157851..9f1dd2c4fdb823 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -13,10 +13,7 @@ # limitations under the License. # ============================================================================== -"""Script Language Operators. See the @{$python/script_ops} guide. - -@@py_func -""" +"""Script Language Operators. See the @{$python/script_ops} guide.""" # pylint: disable=g-bad-name from __future__ import absolute_import diff --git a/tensorflow/python/ops/sdca_ops.py b/tensorflow/python/ops/sdca_ops.py index 24ea68892a94c7..4d5aeec59125a5 100644 --- a/tensorflow/python/ops/sdca_ops.py +++ b/tensorflow/python/ops/sdca_ops.py @@ -13,10 +13,6 @@ # limitations under the License. # ============================================================================== """A Dual Coordinate Ascent optimizer library for training fast linear models. - -@@sdca_optimizer -@@sdca_fprint -@@sdca_shrink_l1 """ # pylint: disable=g-bad-name diff --git a/tensorflow/python/ops/session_ops.py b/tensorflow/python/ops/session_ops.py index ad38845153c94e..dee84bab0ce007 100644 --- a/tensorflow/python/ops/session_ops.py +++ b/tensorflow/python/ops/session_ops.py @@ -13,12 +13,7 @@ # limitations under the License. # ============================================================================== -"""Tensor Handle Operations. See the @{$python/session_ops} guide. - -@@get_session_handle -@@get_session_tensor -@@delete_session_tensor -""" +"""Tensor Handle Operations. See the @{$python/session_ops} guide.""" # pylint: disable=g-bad-name from __future__ import absolute_import diff --git a/tensorflow/python/ops/sets.py b/tensorflow/python/ops/sets.py index 54d6e1db41e99c..41ff241beab5f1 100644 --- a/tensorflow/python/ops/sets.py +++ b/tensorflow/python/ops/sets.py @@ -12,13 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tensorflow set operations. - -@@set_size -@@set_intersection -@@set_union -@@set_difference -""" +"""Tensorflow set operations.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index c580052c32c8b6..3e398db3944025 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -14,33 +14,7 @@ # ============================================================================== # pylint: disable=g-short-docstring-punctuation -"""Sparse Tensor Representation. See the @{$python/sparse_ops} guide. - -@@SparseTensor -@@SparseTensorValue -@@sparse_to_dense -@@sparse_tensor_to_dense -@@sparse_to_indicator -@@sparse_merge -@@sparse_concat -@@sparse_reorder -@@sparse_reshape -@@sparse_slice -@@sparse_split -@@sparse_retain -@@sparse_reset_shape -@@sparse_fill_empty_rows -@@sparse_transpose -@@sparse_reduce_max -@@sparse_reduce_max_sparse -@@sparse_reduce_sum -@@sparse_reduce_sum_sparse -@@sparse_add -@@sparse_softmax -@@sparse_tensor_dense_matmul -@@sparse_maximum -@@sparse_minimum -""" +"""Sparse Tensor Representation. See the @{$python/sparse_ops} guide.""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/ops/special_math_ops.py b/tensorflow/python/ops/special_math_ops.py index 5e2146b79f08e6..6204adef3bb5dc 100644 --- a/tensorflow/python/ops/special_math_ops.py +++ b/tensorflow/python/ops/special_math_ops.py @@ -14,9 +14,7 @@ # ============================================================================== """Arithmetic Operations that don't fit into math_ops due to dependencies. -To avoid circular dependencies, some math_ops should go here. Documentation -callouts, e.g. "@@my_op" should go in math_ops. To the user, these are just -normal math_ops. +To avoid circular dependencies, some math_ops should go here. """ from __future__ import absolute_import diff --git a/tensorflow/python/ops/spectral_ops.py b/tensorflow/python/ops/spectral_ops.py index 4a4ca693dcd577..28054f50ef3b12 100644 --- a/tensorflow/python/ops/spectral_ops.py +++ b/tensorflow/python/ops/spectral_ops.py @@ -12,22 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Spectral operators (e.g. DCT, FFT, RFFT). - -@@dct -@@fft -@@ifft -@@fft2d -@@ifft2d -@@fft3d -@@ifft3d -@@rfft -@@irfft -@@rfft2d -@@irfft2d -@@rfft3d -@@irfft3d -""" +"""Spectral operators (e.g. DCT, FFT, RFFT).""" from __future__ import absolute_import from __future__ import division from __future__ import print_function diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py index f6a11ca625b46c..94d7458ec87358 100644 --- a/tensorflow/python/ops/state_ops.py +++ b/tensorflow/python/ops/state_ops.py @@ -13,71 +13,7 @@ # limitations under the License. # ============================================================================== -"""Variables. See the @{$python/state_ops} guide. - -@@AUTO_REUSE -@@IndexedSlices -@@Saver -@@Variable -@@VariableScope -@@all_variables -@@assert_variables_initialized -@@assign -@@assign_add -@@assign_sub -@@constant_initializer -@@export_meta_graph -@@fixed_size_partitioner -@@get_checkpoint_state -@@get_local_variable -@@get_variable -@@get_variable_scope -@@global_variables -@@global_variables_initializer -@@glorot_normal_initializer -@@glorot_uniform_initializer -@@import_meta_graph -@@initialize_all_tables -@@initialize_all_variables -@@initialize_local_variables -@@initialize_variables -@@is_variable_initialized -@@latest_checkpoint -@@local_variables -@@local_variables_initializer -@@make_template -@@min_max_variable_partitioner -@@model_variables -@@moving_average_variables -@@no_regularizer -@@ones_initializer -@@orthogonal_initializer -@@random_normal_initializer -@@random_uniform_initializer -@@report_uninitialized_variables -@@scatter_add -@@scatter_div -@@scatter_mul -@@scatter_nd_add -@@scatter_nd_sub -@@scatter_nd_update -@@scatter_sub -@@scatter_update -@@scatter_min -@@scatter_max -@@sparse_mask -@@tables_initializer -@@trainable_variables -@@truncated_normal_initializer -@@uniform_unit_scaling_initializer -@@update_checkpoint_state -@@variable_axis_size_partitioner -@@variable_op_scope -@@variable_scope -@@variables_initializer -@@variance_scaling_initializer -@@zeros_initializer -""" +"""Variables. See the @{$python/state_ops} guide.""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/ops/string_ops.py b/tensorflow/python/ops/string_ops.py index 5bd75b9215fdbc..9f58c6a476c34d 100644 --- a/tensorflow/python/ops/string_ops.py +++ b/tensorflow/python/ops/string_ops.py @@ -16,18 +16,6 @@ """Operations for working with string Tensors. See the @{$python/string_ops} guide. - -@@regex_replace -@@string_to_hash_bucket_fast -@@string_to_hash_bucket_strong -@@string_to_hash_bucket -@@reduce_join -@@string_join -@@string_split -@@substr -@@as_string -@@encode_base64 -@@decode_base64 """ from __future__ import absolute_import diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py index 2f6badcb532c0e..d2f45ce37bbbbb 100644 --- a/tensorflow/python/ops/tensor_array_ops.py +++ b/tensorflow/python/ops/tensor_array_ops.py @@ -12,10 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""TensorArray: a dynamically sized array of Tensors. - -@@TensorArray -""" +"""TensorArray: a dynamically sized array of Tensors.""" # Mixture of pep8 and non-pep8 names, so disable pylint bad-name # pylint: disable=g-bad-name from __future__ import absolute_import diff --git a/tensorflow/python/platform/resource_loader.py b/tensorflow/python/platform/resource_loader.py index 650a1fd85113e6..b2d95518552de3 100644 --- a/tensorflow/python/platform/resource_loader.py +++ b/tensorflow/python/platform/resource_loader.py @@ -12,14 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Resource management library. - -@@get_data_files_path -@@get_path_to_datafile -@@get_root_dir_with_all_resources -@@load_resource -@@readahead_file_path -""" +"""Resource management library.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function diff --git a/tensorflow/python/platform/sysconfig.py b/tensorflow/python/platform/sysconfig.py index 56759d1b8e1949..7b6c9d19d0bb5c 100644 --- a/tensorflow/python/platform/sysconfig.py +++ b/tensorflow/python/platform/sysconfig.py @@ -13,13 +13,7 @@ # limitations under the License. # ============================================================================== -"""System configuration library. - -@@get_include -@@get_lib -@@get_compile_flags -@@get_link_flags -""" +"""System configuration library.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function diff --git a/tensorflow/python/platform/test.py b/tensorflow/python/platform/test.py index 0a0fe68be569a7..9ffb48c4a5626d 100644 --- a/tensorflow/python/platform/test.py +++ b/tensorflow/python/platform/test.py @@ -19,19 +19,6 @@ Note: `tf.test.mock` is an alias to the python `mock` or `unittest.mock` depending on the python version. - -@@main -@@TestCase -@@test_src_dir_path -@@assert_equal_graph_def -@@get_temp_dir -@@is_built_with_cuda -@@is_gpu_available -@@gpu_device_name -@@compute_gradient -@@compute_gradient_error -@@create_local_cluster - """ from __future__ import absolute_import diff --git a/tensorflow/python/summary/summary.py b/tensorflow/python/summary/summary.py index 969cbe7d358ecb..1421d2772fe140 100644 --- a/tensorflow/python/summary/summary.py +++ b/tensorflow/python/summary/summary.py @@ -16,21 +16,6 @@ """Tensor summaries for exporting information about a model. See the @{$python/summary} guide. - -@@FileWriter -@@FileWriterCache -@@tensor_summary -@@scalar -@@histogram -@@audio -@@image -@@text -@@merge -@@merge_all -@@get_summary_description -@@PluginAsset -@@get_plugin_asset -@@get_all_plugin_assets """ from __future__ import absolute_import diff --git a/tensorflow/python/training/basic_session_run_hooks.py b/tensorflow/python/training/basic_session_run_hooks.py index 47339e057fbc08..d1cc7d8ce33ac6 100644 --- a/tensorflow/python/training/basic_session_run_hooks.py +++ b/tensorflow/python/training/basic_session_run_hooks.py @@ -12,18 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Some common SessionRunHook classes. - -@@LoggingTensorHook -@@StopAtStepHook -@@CheckpointSaverHook -@@StepCounterHook -@@NanLossDuringTrainingError -@@NanTensorHook -@@SummarySaverHook -@@GlobalStepWaiterHook -@@ProfilerHook -""" +"""Some common SessionRunHook classes.""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/training/session_run_hook.py b/tensorflow/python/training/session_run_hook.py index 89f40300650f3b..5daea931288659 100644 --- a/tensorflow/python/training/session_run_hook.py +++ b/tensorflow/python/training/session_run_hook.py @@ -84,11 +84,6 @@ def end(self, session): hooks.after_run() will not be called but hooks.end() will still be called. If sess.run() raises any other exception then neither hooks.after_run() nor hooks.end() will be called. - -@@SessionRunHook -@@SessionRunArgs -@@SessionRunContext -@@SessionRunValues """ from __future__ import absolute_import diff --git a/tensorflow/python/training/training.py b/tensorflow/python/training/training.py index 4ae7f8451003c2..427e25d0f63a80 100644 --- a/tensorflow/python/training/training.py +++ b/tensorflow/python/training/training.py @@ -16,88 +16,6 @@ """Support for training models. See the @{$python/train} guide. - -@@Optimizer -@@GradientDescentOptimizer -@@AdadeltaOptimizer -@@AdagradOptimizer -@@AdagradDAOptimizer -@@MomentumOptimizer -@@AdamOptimizer -@@FtrlOptimizer -@@ProximalGradientDescentOptimizer -@@ProximalAdagradOptimizer -@@RMSPropOptimizer -@@custom_gradient -@@gradients -@@AggregationMethod -@@GradientTape -@@stop_gradient -@@hessians -@@clip_by_value -@@clip_by_norm -@@clip_by_average_norm -@@clip_by_global_norm -@@global_norm -@@cosine_decay -@@cosine_decay_restarts -@@linear_cosine_decay -@@noisy_linear_cosine_decay -@@exponential_decay -@@inverse_time_decay -@@natural_exp_decay -@@piecewise_constant -@@polynomial_decay -@@ExponentialMovingAverage -@@Coordinator -@@QueueRunner -@@LooperThread -@@add_queue_runner -@@start_queue_runners -@@Server -@@Supervisor -@@SessionManager -@@ClusterSpec -@@replica_device_setter -@@MonitoredTrainingSession -@@MonitoredSession -@@SingularMonitoredSession -@@Scaffold -@@SessionCreator -@@ChiefSessionCreator -@@WorkerSessionCreator -@@summary_iterator -@@SessionRunHook -@@SessionRunArgs -@@SessionRunContext -@@SessionRunValues -@@LoggingTensorHook -@@StopAtStepHook -@@CheckpointSaverHook -@@CheckpointSaverListener -@@NewCheckpointReader -@@StepCounterHook -@@NanLossDuringTrainingError -@@NanTensorHook -@@SummarySaverHook -@@GlobalStepWaiterHook -@@FinalOpsHook -@@FeedFnHook -@@ProfilerHook -@@SecondOrStepTimer -@@global_step -@@basic_train_loop -@@get_global_step -@@get_or_create_global_step -@@create_global_step -@@assert_global_step -@@write_graph -@@load_checkpoint -@@load_variable -@@list_variables -@@init_from_checkpoint -@@warm_start -@@VocabInfo """ # Optimizers. diff --git a/tensorflow/python/util/compat.py b/tensorflow/python/util/compat.py index 1aba7584d18cd0..a24a52eea9710e 100644 --- a/tensorflow/python/util/compat.py +++ b/tensorflow/python/util/compat.py @@ -17,10 +17,6 @@ ## Conversion routines In addition to the functions below, `as_str` converts an object to a `str`. -@@as_bytes -@@as_text -@@as_str_any -@@path_to_str ## Types The compatibility module also provides the following types: From d66adb41874acddfd9e01f46e064965ee39850ca Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 11:40:46 -0700 Subject: [PATCH 0063/1691] Simplify, test and document logic in instruction fusion that decides whether we allow fusion when an operation needs to be duplicated. PiperOrigin-RevId: 194429279 --- tensorflow/compiler/xla/service/BUILD | 1 + .../xla/service/instruction_fusion.cc | 166 +++++++++--------- .../compiler/xla/service/instruction_fusion.h | 17 +- .../xla/service/instruction_fusion_test.cc | 156 ++++++++++++++++ 4 files changed, 249 insertions(+), 91 deletions(-) diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index d55da3686cdfdf..f39bfb8012d701 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -1206,6 +1206,7 @@ tf_cc_test( ":instruction_fusion", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", ], ) diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc index b9ccfeddb565b7..dc1a39e9fa9fd3 100644 --- a/tensorflow/compiler/xla/service/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/instruction_fusion.cc @@ -128,11 +128,11 @@ namespace xla { return false; } -// An "effectively unary" operation is one that has one "large" +// An "effectively at most unary" operation is one that has at most one "large" // input with the others being negligible in terms of memory usage. // We use "has a smaller true rank than the output" as a heuristic // for "negligible" memory usage. -bool InstructionFusion::EffectivelyUnary(HloInstruction* hlo) { +bool InstructionFusion::EffectivelyAtMostUnary(HloInstruction* hlo) { int64 output_rank = 0; ShapeUtil::ForEachSubshape( hlo->shape(), @@ -156,66 +156,91 @@ bool InstructionFusion::EffectivelyUnary(HloInstruction* hlo) { } bool InstructionFusion::CanFuseOnAllPaths( - const HloReachabilityMap& reachability_map, HloInstruction* producer, - HloInstruction* consumer, DoNotFuseSet* do_not_fuse) { - auto could_fuse_on_all_paths = [&] { - // First check to see if we have already marked this producer as infeasible - // to fuse into consumer. - if (do_not_fuse->count(producer) > 0) { + HloInstruction* producer, HloInstruction* consumer, + const HloReachabilityMap& reachability_map, + const DoNotFuseSet& do_not_fuse) { + if (consumer == producer) { + return true; + } + if (!consumer->IsFusable()) { + return false; + } + for (int64 i = 0, e = consumer->operand_count(); i < e; ++i) { + auto* consumer_operand = consumer->mutable_operand(i); + // If the operand is not on a path to the producer, it doesn't matter + // whether it's fusable. + if (!reachability_map.IsReachable(producer, consumer_operand)) { + continue; + } + if (do_not_fuse.count(consumer_operand) > 0 || !ShouldFuse(consumer, i)) { return false; } - // Make sure it is possible for producer and consumer to exist in a fusion - // node. - if (!producer->IsFusable() || !consumer->IsFusable()) { + // The producer is reachable from consumer_operand which means we need + // to be able to fuse consumer_operand into consumer in order for + // producer to be fusable into consumer on all paths. + // Perform the recursive step: make sure producer can be fused into + // consumer_operand on all paths. + if (!CanFuseOnAllPaths(producer, consumer_operand, reachability_map, + do_not_fuse)) { return false; } - // We do an upward walk of the graph from consumer towards all paths which - // lead to producer to find any unfusable paths. - for (int64 i = 0, e = consumer->operand_count(); i < e; ++i) { - auto* consumer_operand = consumer->mutable_operand(i); - if (consumer_operand == producer) { - // This is the base case: our upward crawl ends but we need to make sure - // that fusion from consumer can happen. - if (!ShouldFuse(consumer, i)) { - return false; - } - } else if (reachability_map.IsReachable(producer, consumer_operand)) { - // The reachability map told us that consumer_operand is a node on the - // path to producer. We need to further investigate from - // consumer_operand. - - // First check if we have already ruled out fusing producer into - // consumer_operand. - if (do_not_fuse->count(consumer_operand) > 0) { - return false; - } - // Make sure it is possible for consumer_operand to exist in a fusion - // node. - if (!consumer_operand->IsFusable()) { - return false; - } - // The producer is reachable from consumer_operand which means we need - // to be able to fuse consumer_operand into consumer in order for - // producer to be fusable into consumer on all paths. - if (!ShouldFuse(consumer, i)) { - return false; - } - // Perform the recursive step: make sure producer can be fused into - // consumer_operand on all paths. - if (!CanFuseOnAllPaths(reachability_map, producer, consumer_operand, - do_not_fuse)) { - return false; - } + } + return true; +} + +InstructionFusion::DoNotFuseSet InstructionFusion::ComputeGloballyUnfusable( + tensorflow::gtl::ArraySlice post_order) { + auto reachability = computation_->ComputeReachability(); + + // Forbid fusion of producers that: + // a) Need to be duplicated, unless they can be fused into all consumers + // via all paths. + // b) Are more than unary, that is, fusing them would likely lead to an + // increase in memory bandwidth use. + // + // Note that if we allow fusion by these global rules, we may still forbid + // fusing operations that require duplication later depending on + // is_expensive_(). + DoNotFuseSet do_not_fuse; + for (HloInstruction* consumer : post_order) { + for (HloInstruction* producer : consumer->operands()) { + if (do_not_fuse.count(producer) > 0) { + continue; } + + // If the producer is effectively not more than unary, duplicating it + // will not increase the number of relevant inputs read, as the fusion + // node will only need to read at most 1 relevant input (the input of + // the producer). In that case, we do not forbid fusion of the operation + // here. + if (EffectivelyAtMostUnary(producer)) { + continue; + } + // Otherwise we will forbid fusing the op unless we can fuse it into + // all of its consumers on all paths. + // + // That means, that for: + // A --> B (fusable) + // \-> C (non-fusable) + // A will be not allowed to be fused into B, as it cannot be fused into C. + // + // Similarly, for: + // A -------------> B + // \-> C -> D -/ + // If: + // - A is fusable into B and C, and D is fusable into B + // - C is *not* fusable into D + // A will be not allowed to be fused into B, as it cannot be fused via + // all paths. + if (producer->IsFusable() && + CanFuseOnAllPaths(producer, consumer, *reachability, do_not_fuse)) { + continue; + } + do_not_fuse.insert(producer); } - return true; - }; - if (could_fuse_on_all_paths()) { - return true; } - // We couldn't fuse on all paths, record this result. - do_not_fuse->insert(producer); - return false; + + return do_not_fuse; } StatusOr InstructionFusion::Run(HloModule* module) { @@ -244,36 +269,7 @@ StatusOr InstructionFusion::Run(HloModule* module) { InsertOrDie(&post_order_index, post_order[i], i); } - DoNotFuseSet do_not_fuse; - auto reachability = computation->ComputeReachability(); - - auto cheap_to_duplicate = [this](HloInstruction* producer) { - if (producer->opcode() == HloOpcode::kBroadcast) { - return true; - } - if (producer->opcode() == HloOpcode::kConstant && - ShapeUtil::IsEffectiveScalar(producer->shape())) { - return true; - } - if (EffectivelyUnary(producer)) { - return true; - } - return false; - }; - - for (HloInstruction* consumer : post_order) { - for (HloInstruction* producer : consumer->operands()) { - if (cheap_to_duplicate(producer)) { - continue; - } - if (CanFuseOnAllPaths(*reachability, producer, consumer, - &do_not_fuse)) { - CHECK_EQ(do_not_fuse.count(producer), 0); - } else { - CHECK_GT(do_not_fuse.count(producer), 0); - } - } - } + DoNotFuseSet do_not_fuse = ComputeGloballyUnfusable(post_order); // Instruction fusion effectively fuses edges in the computation graph // (producer instruction -> consumer instruction) so we iterate over all diff --git a/tensorflow/compiler/xla/service/instruction_fusion.h b/tensorflow/compiler/xla/service/instruction_fusion.h index 152d0886ee9eda..2ea1fcf937ceaf 100644 --- a/tensorflow/compiler/xla/service/instruction_fusion.h +++ b/tensorflow/compiler/xla/service/instruction_fusion.h @@ -70,11 +70,11 @@ class InstructionFusion : public HloPassInterface { virtual HloInstruction* Fuse(HloInstruction* producer, HloInstruction* consumer); - // An "effectively unary" operation is one that has one "large" + // An "effectively unary" operation is one that has at most one "large" // input with the others being negligible in terms of memory usage. // We use "has a smaller true rank than the output" as a heuristic // for "negligible" memory usage. - bool EffectivelyUnary(HloInstruction* hlo); + bool EffectivelyAtMostUnary(HloInstruction* hlo); // Returns true if fusing producer into consumer would cause producer to be // duplicated. This is the case if producer has uses other than consumer. @@ -95,11 +95,16 @@ class InstructionFusion : public HloPassInterface { // The set of producers whose consumers we cannot fuse into. using DoNotFuseSet = std::unordered_set; - // Whether or not we can fuse consumer into original_producer on all paths + // Whether or not we can fuse producer into consumer on all paths // from the producer to the consumer where nodes are HLOs and edges are uses. - bool CanFuseOnAllPaths(const HloReachabilityMap& reachability_map, - HloInstruction* producer, HloInstruction* consumer, - DoNotFuseSet* do_not_fuse); + bool CanFuseOnAllPaths(HloInstruction* producer, HloInstruction* consumer, + const HloReachabilityMap& reachability_map, + const DoNotFuseSet& do_not_fuse); + + // Computes the set of nodes that we do not want to fuse into any of their + // consumers based on a global analysis of the HLO graph. + DoNotFuseSet ComputeGloballyUnfusable( + tensorflow::gtl::ArraySlice post_order); // Used to determine if an HLO is expensive. Expensive operations will not be // duplicated. diff --git a/tensorflow/compiler/xla/service/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/instruction_fusion_test.cc index 0fa2c95fb458f8..e78b99a80cf413 100644 --- a/tensorflow/compiler/xla/service/instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/instruction_fusion_test.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" namespace xla { @@ -92,6 +93,161 @@ TEST_F(InstructionFusionTest, AvoidDuplicationIfNotAllFusable) { .ValueOrDie()); } +// Counts the number of HLO ops with a given op code in the specified module. +static int Count(const HloModule& module, HloOpcode op) { + int count = 0; + for (const auto* computation : module.computations()) { + for (const auto* instruction : computation->instructions()) { + if (instruction->opcode() == op) { + ++count; + } + } + } + return count; +} + +TEST_F(InstructionFusionTest, FuseCheapNonDuplicatableOps) { + auto module = tools::Parse(R"( + HloModule test_module + ENTRY OutputFusion { + p0 = f32[4,3]{1,0} parameter(0) + add = f32[4,3]{1,0} add(p0, p0) + ROOT root = f32[4,3]{1,0} subtract(add, add) + })") + .ValueOrDie(); + // Expect the add and subtraction to be fused. + EXPECT_TRUE( + InstructionFusion(InstructionFusion::IsExpensive, /*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()) + << module->ToString(); + EXPECT_EQ(Count(*module, HloOpcode::kFusion), 1) << module->ToString(); + + // Make sure the add hasn't been duplicated. + EXPECT_EQ(Count(*module, HloOpcode::kFusion), 1) << module->ToString(); +} + +TEST_F(InstructionFusionTest, AvoidDuplicationIfNotAllFusableRecursively) { + // Make sure we do not duplicate the add, as we cannot fuse through the rng. + // + // p0 -> add -------------------------> sub + // \-> abs1 -> rng -> abs2 -/ + auto module = tools::Parse(R"( + HloModule test_module + ENTRY OutputFusion { + p0 = f32[4,3]{1,0} parameter(0) + add = f32[4,3]{1,0} add(p0, p0) + abs1 = f32[4,3]{1,0} abs(add) + rng = f32[4,3]{1,0} rng(abs1), distribution=rng_uniform + abs2 = f32[4,3]{1,0} abs(rng) + ROOT root = f32[4,3]{1,0} subtract(abs2, add) + })") + .ValueOrDie(); + // We expect abs2 to be fused into root. + EXPECT_TRUE( + InstructionFusion(InstructionFusion::IsExpensive, /*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()) + << module->ToString(); + EXPECT_EQ(Count(*module, HloOpcode::kFusion), 1) << module->ToString(); + + // Make sure the add hasn't been duplicated. + EXPECT_EQ(Count(*module, HloOpcode::kAdd), 1) << module->ToString(); + + // Use a log node with a second consumer to break the fusion. + // + // p0 -> add -------------------------> sub + // \-> abs1 -> log -> abs2 -/ + // \-> send + module = tools::Parse(R"( + HloModule test_module + ENTRY OutputFusion { + p0 = f32[4,3]{1,0} parameter(0) + add = f32[4,3]{1,0} add(p0, p0) + abs1 = f32[4,3]{1,0} abs(add) + log = f32[4,3]{1,0} log(abs1) + send = f32[4,3]{1,0} send(log), channel_id=0 + abs2 = f32[4,3]{1,0} abs(log) + ROOT root = f32[4,3]{1,0} subtract(abs2, add) + })") + .ValueOrDie(); + + // We expect abs2 to be fused into root and abs1 to be fused into log. + EXPECT_TRUE( + InstructionFusion(InstructionFusion::IsExpensive, /*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()) + << module->ToString(); + EXPECT_EQ(Count(*module, HloOpcode::kFusion), 2) << module->ToString(); + + // Make sure the add hasn't been duplicated. + EXPECT_EQ(Count(*module, HloOpcode::kAdd), 1) << module->ToString(); + + // Make sure we still fuse ops where one operand in the chain to the producer + // can't be fused. + // + // p0 ---> add1 -----------> sub + // \ \-> add2 -/ + // \-> log -/ + // \-> send + module = tools::Parse(R"( + HloModule test_module + ENTRY OutputFusion { + p0 = f32[4,3]{1,0} parameter(0) + add1 = f32[4,3]{1,0} add(p0, p0) + log = f32[4,3]{1,0} log(p0) + send = f32[4,3]{1,0} send(log), channel_id=0 + add2 = f32[4,3]{1,0} add(log, add1) + ROOT root = f32[4,3]{1,0} subtract(add1, add2) + })") + .ValueOrDie(); + + // Expect the add1 and add2 to be fused into root. + EXPECT_TRUE( + InstructionFusion(InstructionFusion::IsExpensive, /*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()) + << module->ToString(); + EXPECT_EQ(Count(*module, HloOpcode::kFusion), 1) << module->ToString(); + + // Make sure we didn't duplicate any adds. + EXPECT_EQ(Count(*module, HloOpcode::kAdd), 2) << module->ToString(); + + // A variant of the above that allows the algorithm to put add2 into the set + // of unfusable ops to short-circuit the decision whether add1 should be fused + // into sub2. + // + // /---------------\ + // p0 ---> add1 ---> add2 ------> sub2 + // \------> sub1 + // log -/ + // \-> send + module = tools::Parse(R"( + HloModule test_module + ENTRY OutputFusion { + p0 = f32[4,3]{1,0} parameter(0) + add1 = f32[4,3]{1,0} add(p0, p0) + add2 = f32[4,3]{1,0} add(add1, add1) + log = f32[4,3]{1,0} log(add2) + send = f32[4,3]{1,0} send(log), channel_id=0 + sub1 = f32[4,3]{1,0} subtract(log, add2) + sub2 = f32[4,3]{1,0} subtract(add2, add1) + ROOT root = (f32[4,3]{1,0}, f32[4,3]{1,0}) tuple(sub1, sub2) + })") + .ValueOrDie(); + + // Expect sub1 and sub2 to be fused into root. + EXPECT_TRUE( + InstructionFusion(InstructionFusion::IsExpensive, /*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()) + << module->ToString(); + EXPECT_EQ(Count(*module, HloOpcode::kFusion), 1) << module->ToString(); + + // Make sure we didn't duplicate any adds. + EXPECT_EQ(Count(*module, HloOpcode::kAdd), 2) << module->ToString(); +} + TEST_F(InstructionFusionTest, AllowUnaryDuplication) { HloComputation::Builder builder(TestName()); auto shape = ShapeUtil::MakeShape(F32, {16, 16}); From b6adaabea73669b112e88947546e41299f89d44c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 12:10:34 -0700 Subject: [PATCH 0064/1691] Move */logging.cc into :platform_base since it already exposes the header loggging.h This also brings env_time.h and env_time.cc, because on the 'default' platform logging needs env_time. Add helpers tf_platform_srcs and tf_platform_hdrs to deal with files that are not necessarily available in all platforms. PiperOrigin-RevId: 194434322 --- tensorflow/core/BUILD | 32 +++++++++++----- .../core/platform/default/build_config.bzl | 37 +++++++++---------- 2 files changed, 39 insertions(+), 30 deletions(-) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index c1cc861ef0470c..32ef0a9b1895cf 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -100,6 +100,8 @@ load("//tensorflow:tensorflow.bzl", "tf_cuda_only_cc_test") # For platform specific build config load( "//tensorflow/core:platform/default/build_config.bzl", + "tf_platform_hdrs", + "tf_platform_srcs", "tf_proto_library", "tf_proto_library_cc", "tf_additional_all_protos", @@ -119,8 +121,6 @@ load( "tf_additional_libdevice_srcs", "tf_additional_test_deps", "tf_additional_test_srcs", - "tf_env_time_hdrs", - "tf_env_time_srcs", "tf_kernel_tests_linkstatic", "tf_additional_cloud_op_deps", "tf_additional_cloud_kernel_deps", @@ -287,6 +287,7 @@ cc_library( ) PLATFORM_BASE_HDRS = [ + "platform/env_time.h", "platform/logging.h", "platform/macros.h", "platform/types.h", @@ -302,7 +303,6 @@ PLATFORM_OTHER_HDRS = [ "platform/cpu_feature_guard.h", "platform/dynamic_annotations.h", "platform/env.h", - "platform/env_time.h", "platform/file_system.h", "platform/file_system_helper.h", "platform/fingerprint.h", @@ -324,11 +324,17 @@ PLATFORM_OTHER_HDRS = [ # Smaller platform libraries that don't depend on "lib" or "lib_internal". cc_library( name = "platform_base", - srcs = glob([ - "platform/*/integral_types.h", - "platform/*/logging.h", - ]), + srcs = tf_platform_hdrs([ + "integral_types.h", + "logging.h", + ]) + tf_platform_srcs([ + "logging.cc", + "env_time.cc", + ]) + [ + "platform/env_time.cc", + ], hdrs = PLATFORM_BASE_HDRS, + copts = tf_copts(), deps = [ ":lib_platform", "//tensorflow/core/platform/default/build_config:base", @@ -339,7 +345,7 @@ cc_library( # don't have to depend on lib/platformlib. cc_library( name = "lib_proto_parsing", - srcs = glob(tf_additional_proto_srcs()) + tf_env_time_srcs(), + srcs = glob(tf_additional_proto_srcs()), hdrs = [ "lib/core/errors.h", "lib/core/status.h", @@ -354,9 +360,10 @@ cc_library( "platform/types.h", "platform/windows/cpu_info.h", "lib/bfloat16/bfloat16.h", - ] + tf_additional_proto_hdrs() + glob(tf_env_time_hdrs()), + ] + tf_additional_proto_hdrs(), copts = tf_copts(), deps = tf_lib_proto_parsing_deps() + [ + ":platform_base", "@double_conversion//:double-conversion", ], ) @@ -1759,6 +1766,7 @@ cc_library( "platform/**/env_time.cc", "platform/**/cuda_libdevice_path.cc", "platform/**/device_tracer.cc", + "platform/**/logging.cc", "platform/abi.cc", "platform/variant_coding.cc", "platform/**/variant_cord_coding.cc", @@ -1772,6 +1780,7 @@ cc_library( "platform/**/stream_executor.h", "platform/**/env_time.cc", "platform/**/device_tracer.cc", + "platform/**/logging.cc", "platform/abi.cc", "platform/variant_coding.cc", "platform/**/variant_cord_coding.cc", @@ -2805,7 +2814,10 @@ cc_library( srcs = ["platform/test_main.cc"], copts = tf_copts(), deps = [ - ":core_stringpiece", + # TODO(ahentz): we don't want to depend on "lib" here. It used to be + # that "core_stringpiece" was enough but that recently changed and + # we now need at least "str_util". + ":lib", ":lib_platform", ":stacktrace_handler", ":test_lite", diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl index ca0587e2777fc9..107c38114b5573 100644 --- a/tensorflow/core/platform/default/build_config.bzl +++ b/tensorflow/core/platform/default/build_config.bzl @@ -433,6 +433,23 @@ def tf_proto_library(name, srcs = [], has_services = None, use_grpc_plugin = has_services, ) +# A list of all files under platform matching the pattern in 'files'. In +# contrast with 'tf_platform_srcs' below, which seletive collects files that +# must be compiled in the 'default' platform, this is a list of all headers +# mentioned in the platform/* files. +def tf_platform_hdrs(files): + return native.glob(["platform/*/" + f for f in files]) + +def tf_platform_srcs(files): + base_set = ["platform/default/" + f for f in files] + windows_set = base_set + ["platform/windows/" + f for f in files] + posix_set = base_set + ["platform/posix/" + f for f in files] + return select({ + "//tensorflow:windows" : native.glob(windows_set), + "//tensorflow:windows_msvc" : native.glob(windows_set), + "//conditions:default" : native.glob(posix_set), + }) + def tf_additional_lib_hdrs(exclude = []): windows_hdrs = native.glob([ "platform/default/*.h", @@ -488,7 +505,6 @@ def tf_additional_proto_hdrs(): def tf_additional_proto_srcs(): return [ - "platform/default/logging.cc", "platform/default/protobuf.cc", ] @@ -511,25 +527,6 @@ def tf_protos_grappler(): extra_deps=tf_protos_grappler_impl(), otherwise=["//tensorflow/core/grappler/costs:op_performance_data_cc"]) -def tf_env_time_hdrs(): - return [ - "platform/env_time.h", - ] - -def tf_env_time_srcs(): - win_env_time = native.glob([ - "platform/windows/env_time.cc", - "platform/env_time.cc", - ], exclude = []) - return select({ - "//tensorflow:windows" : win_env_time, - "//tensorflow:windows_msvc" : win_env_time, - "//conditions:default" : native.glob([ - "platform/posix/env_time.cc", - "platform/env_time.cc", - ], exclude = []), - }) - def tf_additional_cupti_wrapper_deps(): return ["//tensorflow/core/platform/default/gpu:cupti_wrapper"] From 667077cbd2cc86c4a656233a2d5f579aa4caf1f1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 12:12:06 -0700 Subject: [PATCH 0065/1691] Optimize functions in the function library. PiperOrigin-RevId: 194434546 --- .../common_runtime/graph_execution_state.cc | 24 ++- tensorflow/core/grappler/optimizers/BUILD | 4 + .../grappler/optimizers/function_optimizer.cc | 5 +- .../grappler/optimizers/meta_optimizer.cc | 70 +++++++ .../optimizers/meta_optimizer_test.cc | 172 +++++++++++++++++- 5 files changed, 267 insertions(+), 8 deletions(-) diff --git a/tensorflow/core/common_runtime/graph_execution_state.cc b/tensorflow/core/common_runtime/graph_execution_state.cc index 642d91e3282313..49b1df38dcad7c 100644 --- a/tensorflow/core/common_runtime/graph_execution_state.cc +++ b/tensorflow/core/common_runtime/graph_execution_state.cc @@ -76,7 +76,7 @@ GraphExecutionState::~GraphExecutionState() { GraphDef* graph_def, const GraphExecutionStateOptions& options, std::unique_ptr* out_state) { #ifndef __ANDROID__ - VLOG(1) << "Graph proto is " << graph_def->DebugString(); + VLOG(4) << "Graph proto is " << graph_def->DebugString(); #endif // __ANDROID__ std::unique_ptr ret( @@ -497,11 +497,24 @@ Status GraphExecutionState::OptimizeGraph( // Merge optimized graph function library with an original library. // Optimized graph might have new functions specialized for it's - // instantiation context (see Grappler function optimizer). + // instantiation context (see Grappler function optimizer), and modified + // function body for the existing functions. + optimized_flib->reset(new FunctionLibraryDefinition(*flib_def_)); + + for (const FunctionDef& fdef : new_graph.library().function()) { + const string& func_name = fdef.signature().name(); + + if ((*optimized_flib)->Find(func_name)) { + VLOG(3) << "Replace function: name=" << func_name; + TF_RETURN_IF_ERROR((*optimized_flib)->RemoveFunction(func_name)); + TF_RETURN_IF_ERROR((*optimized_flib)->AddFunctionDef(fdef)); + } else { + VLOG(3) << "Add new function: name=" << func_name; + TF_RETURN_IF_ERROR((*optimized_flib)->AddFunctionDef(fdef)); + } + } + optimized_graph->reset(new Graph(OpRegistry::Global())); - optimized_flib->reset(new FunctionLibraryDefinition(OpRegistry::Global(), - new_graph.library())); - TF_RETURN_IF_ERROR((*optimized_flib)->AddLibrary(*flib_def_)); GraphConstructorOptions opts; opts.allow_internal_ops = true; @@ -540,6 +553,7 @@ Status GraphExecutionState::BuildGraph(const BuildGraphOptions& options, Status s = OptimizeGraph(options, &optimized_graph, &optimized_flib); if (!s.ok()) { + VLOG(2) << "Grappler optimization failed. Error: " << s.error_message(); // Simply copy the original graph and the function library if we couldn't // optimize it. optimized_graph.reset(new Graph(flib_def_.get())); diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD index ad2db685fcad23..5b5e1e024e8cfa 100644 --- a/tensorflow/core/grappler/optimizers/BUILD +++ b/tensorflow/core/grappler/optimizers/BUILD @@ -518,11 +518,13 @@ cc_library( ":loop_optimizer", ":memory_optimizer", ":model_pruner", + "//tensorflow/core:core_cpu_base", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler/utils:colocation", + "//tensorflow/core/grappler/utils:functions", "//tensorflow/core/grappler/utils:topological_sort", ], ) @@ -539,9 +541,11 @@ tf_cuda_cc_test( "//tensorflow/core:tensorflow", "//tensorflow/core:test", "//tensorflow/core:test_main", + "//tensorflow/core:testlib", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:utils", "//tensorflow/core/grappler/inputs:trivial_test_graph_input_yielder", + "//tensorflow/core/grappler/utils:grappler_test", ], ) diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc index 47e7dc0a969147..3a6de9e3b29e5d 100644 --- a/tensorflow/core/grappler/optimizers/function_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc @@ -579,7 +579,10 @@ Status FunctionOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item, continue; } - if (specialize_func && IsParametrized(*func)) { + // Do not specialize if function has custom gradient. + const string grad_func = ctx.function_library().FindGradient(func_name); + + if (specialize_func && grad_func.empty() && IsParametrized(*func)) { // TODO(ezhulenev): Specialize function call if input is a Const or has // a known shape. Const input tensors can be pushed into the function // body and removed from function inputs. diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc index c98eef1a6a5cca..c42d614c15e38b 100644 --- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/core/grappler/optimizers/meta_optimizer.h" +#include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/framework/function.pb.h" #include "tensorflow/core/framework/versions.pb.h" #include "tensorflow/core/grappler/optimizers/arithmetic_optimizer.h" @@ -29,6 +30,7 @@ limitations under the License. #include "tensorflow/core/grappler/optimizers/memory_optimizer.h" #include "tensorflow/core/grappler/optimizers/model_pruner.h" #include "tensorflow/core/grappler/utils/colocation.h" +#include "tensorflow/core/grappler/utils/functions.h" #include "tensorflow/core/grappler/utils/topological_sort.h" #include "tensorflow/core/lib/core/status.h" @@ -235,7 +237,75 @@ Status MetaOptimizer::OptimizeGraph(Cluster* cluster, const GrapplerItem& item, Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item, GraphDef* optimized_graph) { optimization_results_.clear(); + + // 1. Optimize main graph TF_RETURN_IF_ERROR(OptimizeGraph(cluster, item, optimized_graph)); + + // 2. Optimize function library + FunctionLibraryDefinition flib(OpRegistry::Global(), + optimized_graph->library()); + + // Optimize each function only once. + std::unordered_set optimized_funcs; + bool optimize_function_library = true; + + while (optimize_function_library) { + optimize_function_library = false; + + for (const FunctionDef& func : optimized_graph->library().function()) { + const string& func_name = func.signature().name(); + + // Skip already optimized functions. + if (optimized_funcs.find(func_name) != optimized_funcs.end()) continue; + + // Skip parametrized functions (function type or body is defined only at + // function call time by caller node attributes). + if (IsParametrized(func)) continue; + + VLOG(3) << "Optimize function: function=" << func_name; + + // Function optimization might specialize nested function calls, so we + // have to reset the flag and do at least one more pass over the library. + optimize_function_library = true; + optimized_funcs.insert(func_name); + + // Make a GrapplerItem from a FunctionDef. + GrapplerFunctionItem func_item; + TF_RETURN_IF_ERROR(MakeGrapplerFunctionItem(func, flib, &func_item)); + + // Optimize function body graph. + GraphDef optimized_func_graph; + TF_RETURN_IF_ERROR( + OptimizeGraph(cluster, func_item, &optimized_func_graph)); + + // Function body optimization might have created new specialized + // functions for each instantiation context. Add them to the library. + for (const FunctionDef& func_def : + optimized_func_graph.library().function()) { + if (flib.Find(func_def.signature().name()) == nullptr) { + TF_RETURN_IF_ERROR(flib.AddFunctionDef(func_def)); + } + } + + // Convert optimized graph back to FunctionDef. + FunctionDef optimized_func; + func_item.SwapFunctionBody(std::move(optimized_func_graph)); + TF_RETURN_IF_ERROR(MakeFunctionDef(func_item, flib, &optimized_func)); + + // Replace optimized function with a new FunctionDef. + TF_RETURN_IF_ERROR(flib.RemoveFunction(func_name)); + TF_RETURN_IF_ERROR(flib.AddFunctionDef(optimized_func)); + } + + // If optimized at least one function, update the graph library. + if (optimize_function_library) { + *optimized_graph->mutable_library() = flib.ToProto(); + } + } + + VLOG(3) << "Optimized " << optimized_funcs.size() + << " functions: " << str_util::Join(optimized_funcs, ", "); + return Status::OK(); } diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc b/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc index 9fcf07651b0953..887a988af9afed 100644 --- a/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc @@ -16,11 +16,14 @@ limitations under the License. #include "tensorflow/core/grappler/optimizers/meta_optimizer.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/framework/function_testlib.h" +#include "tensorflow/core/framework/tensor_testutil.h" #include "tensorflow/core/grappler/grappler_item.h" #include "tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h" #include "tensorflow/core/grappler/optimizers/custom_graph_optimizer.h" #include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h" #include "tensorflow/core/grappler/utils.h" +#include "tensorflow/core/grappler/utils/grappler_test.h" #include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/test.h" @@ -28,6 +31,8 @@ namespace tensorflow { namespace grappler { namespace { +constexpr char kDevice[] = "/device:CPU:0"; + class TestOptimizer : public CustomGraphOptimizer { public: static void SetOptimized(const bool flag_value) { optimized_ = flag_value; } @@ -59,7 +64,9 @@ bool TestOptimizer::optimized_; REGISTER_GRAPH_OPTIMIZER(TestOptimizer); -TEST(MetaOptimizerTest, RunsCustomOptimizer) { +class MetaOptimizerTest : public GrapplerTest {}; + +TEST_F(MetaOptimizerTest, RunsCustomOptimizer) { TrivialTestGraphInputYielder fake_input(4, 1, 10, false, {"CPU:0"}); GrapplerItem item; CHECK(fake_input.NextItem(&item)); @@ -75,7 +82,7 @@ TEST(MetaOptimizerTest, RunsCustomOptimizer) { EXPECT_TRUE(TestOptimizer::IsOptimized()); } -TEST(MetaOptimizerTest, RunOptimizersTwice) { +TEST_F(MetaOptimizerTest, RunOptimizersTwice) { TrivialTestGraphInputYielder fake_input(4, 1, 10, false, {"CPU:0"}); GrapplerItem item; CHECK(fake_input.NextItem(&item)); @@ -89,6 +96,167 @@ TEST(MetaOptimizerTest, RunOptimizersTwice) { TF_EXPECT_OK(status); } +TEST_F(MetaOptimizerTest, OptimizeFunctionLibrary) { + using test::function::NDef; + + // Enable ony function optimization. + RewriterConfig rewriter_config; + rewriter_config.set_meta_optimizer_iterations(RewriterConfig::TWO); + rewriter_config.set_function_optimization(RewriterConfig::ON); + rewriter_config.add_optimizers("function"); + + MetaOptimizer optimizer(nullptr, rewriter_config); + + // Define function library: + // + // MyMul(x, y) = x * y + // *MySquare(x) = MyMul(x, x) + // *MyQuadratic(x) = MySquare(MySquare(x)) + // + // * - marked as noinline + + FunctionDef mul_func = FunctionDefHelper::Create( + "MyMul", {"x:T", "y:T"}, {"z:T"}, {"T: {float, double}"}, + {{{"mul"}, "Mul", {"x", "y"}, {{"T", "$T"}}}}, + /* Mapping between function returns and function node outputs. */ + {{"z", "mul:z:0"}}); + + FunctionDef square_func = FunctionDefHelper::Create( + "MySquare", {"x:T"}, {"z:T"}, {"T: {float, double}"}, + {{{"my_mul"}, "MyMul", {"x", "x"}, {{"T", "$T"}}}}, + /* Mapping between function returns and function node outputs. */ + {{"z", "my_mul:z:0"}}); + (*square_func.mutable_attr())["_noinline"].set_b(true); + + FunctionDef quadratic_func = FunctionDefHelper::Create( + "MyQuadratic", {"x:T"}, {"z:T"}, {"T: {float, double}"}, + {{{"square"}, "MySquare", {"x"}, {{"T", "$T"}}}, + {{"quadratic"}, "MySquare", {"square:z"}, {{"T", "$T"}}}}, + /* Mapping between function returns and function node outputs. */ + {{"z", "quadratic:z:0"}}); + (*quadratic_func.mutable_attr())["_noinline"].set_b(true); + + // Tensorflow graph: + // + // a = tf.Placeholder(tf.float); + // b = tf.Placeholder(tf.int32); + // + // square = MySquare(a); // a^2 + // quadratic = MyQuadratic(b); // b^4 + GrapplerItem item; + item.graph = test::function::GDef( + {NDef("a", "Placeholder", {}, {{"dtype", DT_FLOAT}}, kDevice), + NDef("b", "Placeholder", {}, {{"dtype", DT_INT32}}, kDevice), + // Calls into function library + NDef("square", "MySquare", {"a"}, {{"T", DT_FLOAT}}, kDevice), + NDef("quadratic", "MyQuadratic", {"b"}, {{"T", DT_INT32}}, kDevice), + // Forward outputs + NDef("out_s", "Identity", {"square:0"}, {{"T", DT_FLOAT}}, kDevice), + NDef("out_q", "Identity", {"quadratic:0"}, {{"T", DT_INT32}}, kDevice)}, + // FunctionLib + {mul_func, square_func, quadratic_func}); + + GraphDef output; + TF_EXPECT_OK(optimizer.Optimize(nullptr, item, &output)); + + FunctionLibraryDefinition optimized_flib(OpRegistry::Global(), + output.library()); + + // Specialized and optimized functions should be added to the graph. + EXPECT_EQ(6, optimized_flib.num_functions()); + + // MyQuadratic should be specialized once: + // 0. 'quadratic' node in the main graph + const string optimized_0 = "MyQuadratic_specialized_for_quadratic"; + + // MySquare should be specialized and optimized for 3 instantiations: + // 1. 'square' node in the main graph + // 2. 'square' node in the MyQuadratic specialization + // 3. 'quadratic' node in the MyQuadratic specialization + + const string optimized_1 = "MySquare_specialized_for_square"; + const string optimized_2 = "MySquare_specialized_for_square_1"; + const string optimized_3 = "MySquare_specialized_for_quadratic"; + + const FunctionDef* optimized_func_0 = optimized_flib.Find(optimized_0); + const FunctionDef* optimized_func_1 = optimized_flib.Find(optimized_1); + const FunctionDef* optimized_func_2 = optimized_flib.Find(optimized_2); + const FunctionDef* optimized_func_3 = optimized_flib.Find(optimized_3); + + ASSERT_NE(optimized_func_0, nullptr); + ASSERT_NE(optimized_func_1, nullptr); + ASSERT_NE(optimized_func_2, nullptr); + ASSERT_NE(optimized_func_3, nullptr); + + // Graph should call optimized function. + int count = 0; + for (const NodeDef& node : output.node()) { + if (node.name() == "square" && count++) { + EXPECT_EQ("MySquare_specialized_for_square", node.op()); + } else if (node.name() == "quadratic" && count++) { + EXPECT_EQ("MyQuadratic_specialized_for_quadratic", node.op()); + } + } + EXPECT_EQ(2, count); + + // Specialized MySquare should call specialized functions. + count = 0; + for (const NodeDef& node : optimized_func_0->node_def()) { + if (node.name() == "square" && count++) { + EXPECT_EQ(optimized_2, node.op()); + } else if (node.name() == "quadratic" && count++) { + EXPECT_EQ(optimized_3, node.op()); + } + } + EXPECT_EQ(2, count); + + const std::vector optimized_funcs = { + optimized_func_1, optimized_func_1, optimized_func_3}; + + // MyMul should be inlined into all optimized versions of MySquare. + for (const FunctionDef* optimized_func : optimized_funcs) { + count = 0; + for (const NodeDef& node : optimized_func->node_def()) { + if (node.name() == "my_mul/inlined_inputs" && count++) { + EXPECT_EQ("IdentityN", node.op()); + EXPECT_EQ(2, node.input_size()); + EXPECT_EQ("x:0", node.input(0)); + EXPECT_EQ("x:0", node.input(1)); + } else if (node.name() == "my_mul/x" && count++) { + EXPECT_EQ("Identity", node.op()); + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("my_mul/inlined_inputs:output:0", node.input(0)); + } else if (node.name() == "my_mul/y" && count++) { + EXPECT_EQ("Identity", node.op()); + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("my_mul/inlined_inputs:output:1", node.input(0)); + } else if (node.name() == "my_mul/mul" && count++) { + EXPECT_EQ("Mul", node.op()); + EXPECT_EQ(2, node.input_size()); + EXPECT_EQ("my_mul/x:output:0", node.input(0)); + EXPECT_EQ("my_mul/y:output:0", node.input(1)); + } else if (node.name() == "my_mul" && count++) { + EXPECT_EQ("IdentityN", node.op()); + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("my_mul/mul:z:0", node.input(0)); + } + EXPECT_TRUE(node.device().empty()); + } + EXPECT_EQ(5, count); + } + + item.fetch = {"out_s", "out_q"}; + item.feed.emplace_back("a", test::AsScalar(2.0f)); + item.feed.emplace_back("b", test::AsScalar(4)); + auto tensors_expected = EvaluateFetchNodes(item); + + GrapplerItem optimized(item, std::move(output)); + auto tensors = EvaluateFetchNodes(optimized); + + test::ExpectTensorEqual(tensors_expected[0], tensors[0]); + test::ExpectTensorEqual(tensors_expected[1], tensors[1]); +} + } // namespace } // namespace grappler } // namespace tensorflow From f63750645826df65b05cad505546a86f0e347674 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 12:42:54 -0700 Subject: [PATCH 0066/1691] For tf.gradients(), do not backpropagate through integer tensors. All integer tensors are now considered constant with respect to all `xs`. This fixes a bug in gradients through tf.while_loop. PiperOrigin-RevId: 194438529 --- .../compiler/tests/tensor_array_ops_test.py | 4 +- .../batching/python/ops/batch_ops_test.py | 2 +- tensorflow/contrib/compiler/jit_test.py | 10 +-- .../data/kernel_tests/iterator_ops_test.py | 15 ++--- tensorflow/python/eager/function_test.py | 2 +- .../python/framework/meta_graph_test.py | 9 +-- .../python/kernel_tests/array_ops_test.py | 13 ++-- .../kernel_tests/control_flow_ops_py_test.py | 65 +++++++++++++++++-- .../kernel_tests/dynamic_stitch_op_test.py | 13 ++-- .../kernel_tests/gradient_correctness_test.py | 65 +++++++++++++++++++ .../kernel_tests/nth_element_op_test.py | 8 +-- .../kernel_tests/tensor_array_ops_test.py | 3 +- .../python/kernel_tests/topk_op_test.py | 10 +-- tensorflow/python/ops/gradients_impl.py | 36 +++++++--- 14 files changed, 198 insertions(+), 57 deletions(-) diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py index 7624d6e4b2e2ec..f332aa2e9b97e1 100644 --- a/tensorflow/compiler/tests/tensor_array_ops_test.py +++ b/tensorflow/compiler/tests/tensor_array_ops_test.py @@ -472,7 +472,9 @@ def _testTensorArrayGradientWriteReadType(self, dtype): self.assertAllEqual(c([[-2.0, -10.0]]), grad_vals[1]) def testTensorArrayGradientWriteRead(self): - for dtype in self.numeric_types: + for dtype in self.float_types: + self._testTensorArrayGradientWriteReadType(dtype) + for dtype in self.complex_types: self._testTensorArrayGradientWriteReadType(dtype) def _testTensorArrayGradientWritePackConcatAndRead(self): diff --git a/tensorflow/contrib/batching/python/ops/batch_ops_test.py b/tensorflow/contrib/batching/python/ops/batch_ops_test.py index fac7aff29f79fa..e22f978dde6f1b 100644 --- a/tensorflow/contrib/batching/python/ops/batch_ops_test.py +++ b/tensorflow/contrib/batching/python/ops/batch_ops_test.py @@ -250,7 +250,7 @@ def worker(): def testUnbatchGrad(self): """Tests that batch and unbatch are differentiable.""" with self.test_session() as sess: - inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1]) + inp = array_ops.placeholder(dtype=dtypes.float32, shape=[1]) batched, index, id_t = batch_ops.batch( [inp], num_batch_threads=1, max_batch_size=2, batch_timeout_micros=36000000, grad_timeout_micros=1000000, diff --git a/tensorflow/contrib/compiler/jit_test.py b/tensorflow/contrib/compiler/jit_test.py index 29a593f6bcfa05..b2f678fb29cedd 100644 --- a/tensorflow/contrib/compiler/jit_test.py +++ b/tensorflow/contrib/compiler/jit_test.py @@ -175,7 +175,7 @@ class CompilationEnabledInGradientTest(test.TestCase): def testCompilationInGradient(self): with self.test_session(): - x = constant_op.constant([[3]]) + x = constant_op.constant([[3.]]) y_nc = math_ops.matmul(x, x, name="not_compiled") with jit.experimental_jit_scope(): y_c = math_ops.matmul(y_nc, y_nc, name="compiled") @@ -200,11 +200,11 @@ def testCompilationGradientScopeNames(self): with self.test_session(graph=ops.Graph()): with jit.experimental_jit_scope(): # XlaScope 0 - a1 = constant_op.constant([[1]]) + a1 = constant_op.constant([[1.]]) a1t = math_ops.matmul(a1, a1) with jit.experimental_jit_scope(): # XlaScope 1 - a2 = constant_op.constant([[1]]) + a2 = constant_op.constant([[1.]]) a2t = math_ops.matmul(a2, a2) self.assertEqual(b"jit_scope_0", a1.op.get_attr("_XlaScope")) @@ -222,11 +222,11 @@ def testCompilationSeparateGradientScopeNames(self): with self.test_session(graph=ops.Graph()): with jit.experimental_jit_scope(True, separate_compiled_gradients=True): # XlaScope 0 - a1 = constant_op.constant([[1]]) + a1 = constant_op.constant([[1.]]) a1t = math_ops.matmul(a1, a1) with jit.experimental_jit_scope(True, separate_compiled_gradients=True): # XlaScope 1 - a2 = constant_op.constant([[1]]) + a2 = constant_op.constant([[1.]]) a2t = math_ops.matmul(a2, a2) self.assertEqual(b"jit_scope_0", a1.op.get_attr("_XlaScope")) diff --git a/tensorflow/python/data/kernel_tests/iterator_ops_test.py b/tensorflow/python/data/kernel_tests/iterator_ops_test.py index 0af282a0247538..820c167b6bb9dc 100644 --- a/tensorflow/python/data/kernel_tests/iterator_ops_test.py +++ b/tensorflow/python/data/kernel_tests/iterator_ops_test.py @@ -51,18 +51,15 @@ class IteratorTest(test.TestCase): - def testAttemptingGradientsRaiseExceptions(self): - component = constant_op.constant([1]) - side = constant_op.constant(0) + def testNoGradients(self): + component = constant_op.constant([1.]) + side = constant_op.constant(0.) add = lambda x: x + side dataset = dataset_ops.Dataset.from_tensor_slices(component).map(add) value = dataset.make_one_shot_iterator().get_next() - with self.assertRaisesRegexp(LookupError, "No gradient defined"): - gradients_impl.gradients(value, component) - with self.assertRaisesRegexp(LookupError, "No gradient defined"): - gradients_impl.gradients(value, side) - with self.assertRaisesRegexp(LookupError, "No gradient defined"): - gradients_impl.gradients(value, [component, side]) + self.assertIsNone(gradients_impl.gradients(value, component)[0]) + self.assertIsNone(gradients_impl.gradients(value, side)[0]) + self.assertIsNone(gradients_impl.gradients(value, [component, side])[0]) def testCapturingStateInOneShotRaisesException(self): var = variables.Variable(37.0, name="myvar") diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 1828c987f43430..185f6d981cb36a 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -309,7 +309,7 @@ def f(x): def g(x): return backprop.gradients_function(f, [0])(x)[0] - self.assertAllEqual(2, g(constant_op.constant(2))) + self.assertAllEqual(2, g(constant_op.constant(2.))) def testGraphModeEagerGradError(self): with context.graph_mode(): diff --git a/tensorflow/python/framework/meta_graph_test.py b/tensorflow/python/framework/meta_graph_test.py index e5b157648e0830..0532ed464cc7a2 100644 --- a/tensorflow/python/framework/meta_graph_test.py +++ b/tensorflow/python/framework/meta_graph_test.py @@ -476,11 +476,12 @@ def testWhileLoopGradients(self): # Create a simple while loop. with ops.Graph().as_default(): with ops.name_scope("export"): - var = variables.Variable(0) + var = variables.Variable(0.) var_name = var.name - _, output = control_flow_ops.while_loop(lambda i, x: i < 5, - lambda i, x: (i + 1, x + i), - [0, var]) + _, output = control_flow_ops.while_loop( + lambda i, x: i < 5, + lambda i, x: (i + 1, x + math_ops.cast(i, dtypes.float32)), + [0, var]) output_name = output.name # Generate a MetaGraphDef containing the while loop with an export scope. diff --git a/tensorflow/python/kernel_tests/array_ops_test.py b/tensorflow/python/kernel_tests/array_ops_test.py index 5a20eebbc559cf..7acca0a4a09deb 100644 --- a/tensorflow/python/kernel_tests/array_ops_test.py +++ b/tensorflow/python/kernel_tests/array_ops_test.py @@ -730,7 +730,7 @@ def __getitem__(self, spec): analytic_grad2 = 2 * slice_val dy = variables.Variable( - array_ops.ones(shape=slice_var.get_shape(), dtype=dtypes.int32)) + array_ops.ones(shape=slice_var.get_shape(), dtype=dtypes.float32)) assign = dy.assign(slice_var) slice_val_grad, = gradients_impl.gradients(slice_val, self.var, grad_ys=dy) slice_val_grad2, = gradients_impl.gradients( @@ -755,7 +755,8 @@ class StridedSliceGradTest(test_util.TensorFlowTestCase): def testGradient(self): with self.test_session(use_gpu=True) as sess: var = variables.Variable( - array_ops.reshape(math_ops.range(1, 97, 1), shape=(6, 4, 4))) + array_ops.reshape( + math_ops.range(1, 97, 1, dtype=dtypes.float32), shape=(6, 4, 4))) init = variables.global_variables_initializer() sess.run(init) @@ -774,7 +775,7 @@ def testGradient(self): def testGradientZero(self): with self.test_session(use_gpu=True) as sess: - var = variables.Variable(8) + var = variables.Variable(8.) init = variables.global_variables_initializer() sess.run(init) grad = GradSliceChecker(self, sess, var, np.array(8)) @@ -782,11 +783,11 @@ def testGradientZero(self): def testInt64Indices(self): with self.test_session(use_gpu=True) as sess: - a = math_ops.range(3) + a = math_ops.range(3, dtype=dtypes.float32) index = constant_op.constant(1, dtype=dtypes.int64) - b = 2 * a[index] + b = 2. * a[index] grad, = gradients_impl.gradients(b, a) - self.assertAllEqual(sess.run(grad), [0, 2, 0]) + self.assertAllEqual(sess.run(grad), [0., 2., 0.]) class StridedSliceGradTypeTest(test_util.TensorFlowTestCase): diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py index 209411cf5195b0..77e6f5f1a0d645 100644 --- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py +++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py @@ -2222,14 +2222,14 @@ def body(i, h): def testWhileWithRefsWithGradients_1(self): with self.test_session() as sess: - x = variables.Variable(0)._ref() # pylint: disable=protected-access + x = variables.Variable(0.)._ref() # pylint: disable=protected-access i = constant_op.constant(0) c = lambda i, x: math_ops.less(i, 10) - self.assertEqual(x.dtype, dtypes.int32_ref) + self.assertEqual(x.dtype, dtypes.float32_ref) def body(i, x): - self.assertEqual(x.dtype, dtypes.int32_ref) + self.assertEqual(x.dtype, dtypes.float32_ref) return [i + 1, gen_array_ops.ref_identity(x)] r = control_flow_ops.while_loop(c, body, [i, x], parallel_iterations=5) @@ -2240,7 +2240,7 @@ def body(i, x): variables.global_variables_initializer().run() self.assertEqual(r[0].dtype, dtypes.int32) - self.assertEqual(r[1].dtype, dtypes.int32_ref) + self.assertEqual(r[1].dtype, dtypes.float32_ref) value_i, value_x, value_x_grad = sess.run(r + grad) @@ -2443,6 +2443,63 @@ def testStopGradOnWhileGrad(self): r = gradients_impl.gradients(r, y)[0] self.assertEqual(388.0, r.eval()) + def testWhileGradientWithNontrainablePath1(self): + q = variables.Variable([7., 8.]) + + def cond(_, y): + del y + return False + + def body(x, _): + return x, math_ops.cast(x, dtypes.float32) + math_ops.reduce_sum(q) + + _, y = control_flow_ops.while_loop(cond, body, (math_ops.argmin(q), 0.)) + dy_dq, = gradients_impl.gradients(y, q) + self.assertIsNotNone(dy_dq) + with self.test_session() as sess: + sess.run(q.initializer) + self.assertAllClose([0., 0.], sess.run(dy_dq)) + + def testWhileGradientWithNontrainablePath2(self): + q = variables.Variable([7., 8.]) + + def cond(_, y): + return math_ops.equal(y, 0.) + + def body(x, _): + zero = constant_op.constant(0, dtype=dtypes.int64) + return zero, math_ops.cast(x, dtypes.float32) + math_ops.reduce_sum(q) + + _, y = control_flow_ops.while_loop(cond, body, (math_ops.argmin(q), 0.)) + dy_dq, = gradients_impl.gradients(y, q) + self.assertIsNotNone(dy_dq) + with self.test_session() as sess: + sess.run(q.initializer) + self.assertAllClose([1., 1.], sess.run(dy_dq)) + + def testIssue16504(self): + c = constant_op.constant(np.arange(100), dtype=dtypes.float32) + w = variables.Variable( + initial_value=np.ones(100), dtype=dtypes.float32) / 100 + k = variables.Variable(0, dtype=dtypes.int32) + chg_w = constant_op.constant(np.inf, dtype=dtypes.float32) + + def cond(k, _, chg_w): + return math_ops.logical_and(k < 10, chg_w > 1e-3) + + def body(k, w, chg_w): + grad, = gradients_impl.gradients(-math_ops.reduce_sum(w * c), w) + w_n = w * math_ops.exp(-0.1 * grad) + w_n /= math_ops.reduce_sum(w_n) + chg_w = ( + math_ops.reduce_sum(math_ops.abs(w_n - w)) / math_ops.reduce_sum( + math_ops.abs(w))) + return k + 1, w_n, chg_w + + _, w, _ = control_flow_ops.while_loop(cond, body, [k, w, chg_w]) + grad, = gradients_impl.gradients(w, c) + self.assertIsNotNone(grad) + def testStopGradMultiFlows(self): with self.test_session(): diff --git a/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py b/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py index a4b30e4319527c..159cba5fa3d69b 100644 --- a/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py +++ b/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py @@ -113,22 +113,23 @@ def testHigherRank(self): constant_op.constant([[5, 2], [0, 3]]) ] data = [ - constant_op.constant([61, 62]), - constant_op.constant([[41, 42], [11, 12]]), - constant_op.constant([[[51, 52], [21, 22]], [[1, 2], [31, 32]]]) + constant_op.constant([61., 62.]), + constant_op.constant([[41., 42.], [11., 12.]]), + constant_op.constant([[[51., 52.], [21., 22.]], + [[1., 2.], [31., 32.]]]) ] stitched_t = self.stitch_op(indices, data) stitched_val = stitched_t.eval() - correct = 10 * np.arange(7)[:, None] + [1, 2] + correct = 10. * np.arange(7)[:, None] + [1., 2.] self.assertAllEqual(correct, stitched_val) self.assertEqual([7, 2], stitched_t.get_shape().as_list()) # Test gradients - stitched_grad = 7 * stitched_val + stitched_grad = 7. * stitched_val grads = gradients_impl.gradients(stitched_t, indices + data, stitched_grad) self.assertEqual(grads[:3], [None] * 3) # Indices have no gradients for datum, grad in zip(data, sess.run(grads[3:])): - self.assertAllEqual(7 * datum.eval(), grad) + self.assertAllEqual(7. * datum.eval(), grad) def testErrorIndicesMultiDimensional(self): indices = [ diff --git a/tensorflow/python/kernel_tests/gradient_correctness_test.py b/tensorflow/python/kernel_tests/gradient_correctness_test.py index 10fe4f509080b2..e93c6235f74e8f 100644 --- a/tensorflow/python/kernel_tests/gradient_correctness_test.py +++ b/tensorflow/python/kernel_tests/gradient_correctness_test.py @@ -40,6 +40,71 @@ def testMultipleOutputChainedGradients(self): # [dexp(x)/dx + d(log(exp(x)))/dx] @ x=1 == exp(1) + 1 self.assertAllClose(grad_vals[0], exp1_plus_one) + def testIdentityGradient(self): + x = constant_op.constant(3.) + dx_dx, = gradients_impl.gradients(x, x) + with self.test_session() as sess: + self.assertAllClose(1., sess.run(dx_dx)) + + def testIntegerIdentityGradient(self): + x = constant_op.constant(3) + dx_dx, = gradients_impl.gradients(x, x) + with self.test_session() as sess: + self.assertAllClose(1, sess.run(dx_dx)) + + def testGradientWithIntegerPath(self): + x = constant_op.constant([3.9, 4.1]) + k = math_ops.to_float(math_ops.to_int32(x)) + y = x * k + dy_dx, = gradients_impl.gradients(y, x) + with self.test_session() as sess: + self.assertAllClose([3., 4.], sess.run(dy_dx)) + + def testNoIntegerGradient1(self): + x = constant_op.constant([3.9, 4.1]) + k = math_ops.to_float(math_ops.to_int32(x)) + y = k * k + dy_dx, = gradients_impl.gradients(y, x) + self.assertIsNone(dy_dx) + + def testNoIntegerGradient2(self): + k = constant_op.constant([3, 4]) + x = math_ops.to_float(k) + y = x * x + dy_dk, = gradients_impl.gradients(y, k) + self.assertIsNone(dy_dk) + + def testNoIntegerGradient3(self): + k = constant_op.constant([3, 4]) + m = k * k + dm_dk, = gradients_impl.gradients(m, k) + self.assertIsNone(dm_dk) + + def testNoIntegerGradient4(self): + k = constant_op.constant([3, 4]) + m = k * k * k + dm_dk, = gradients_impl.gradients(m, k) + self.assertIsNone(dm_dk) + + def testNoIntegerGradient5(self): + k = constant_op.constant([3, 4]) + m = k * k + n = m * m + dn_dk, = gradients_impl.gradients(n, k) + self.assertIsNone(dn_dk) + + def testNoIntegerGradient6(self): + k = constant_op.constant(3) + x = math_ops.to_float(k) + grad_1, = gradients_impl.gradients(k * k, k) + grad_2, = gradients_impl.gradients(x * x, k) + grad_3, = gradients_impl.gradients(math_ops.square(k), k) + grad_4, = gradients_impl.gradients(math_ops.square(x), k) + self.assertIsNone(grad_1) + self.assertIsNone(grad_2) + self.assertIsNone(grad_3) + self.assertIsNone(grad_4) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/nth_element_op_test.py b/tensorflow/python/kernel_tests/nth_element_op_test.py index 58cd46d2d52079..1b8f02140fb5d5 100644 --- a/tensorflow/python/kernel_tests/nth_element_op_test.py +++ b/tensorflow/python/kernel_tests/nth_element_op_test.py @@ -154,14 +154,14 @@ def testNTooLargeAtEval(self): def testGradients(self): with self.test_session(use_gpu=False) as sess: - inputs = array_ops.placeholder(dtypes.int32, shape=[3, 5]) + inputs = array_ops.placeholder(dtypes.float32, shape=[3, 5]) values = nn_ops.nth_element(inputs, 3) grad = sess.run( gradients_impl.gradients( values, inputs, grad_ys=[[-1., 2., 5.]]), - feed_dict={inputs: [[2, -1, 1000, 3, 1000], - [1, 5, 2, 4, 3], - [2, 2, 2, 2, 2], + feed_dict={inputs: [[2., -1., 1000., 3., 1000.], + [1., 5., 2., 4., 3.], + [2., 2., 2., 2., 2.], ]}) self.assertAllClose(grad[0], [[0, 0, -0.5, 0, -0.5], [0, 0, 0, 2, 0], diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py index a834675828b67a..918bbd38edfd18 100644 --- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py +++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py @@ -615,8 +615,7 @@ def _testTensorArrayGradientWriteReadType(self, dtype): self.assertAllEqual(c(-2.0), grad_vals[1]) def testTensorArrayGradientWriteRead(self): - for dtype in (np.float32, np.float64, np.int32, np.int64, np.complex64, - np.complex128): + for dtype in (np.float32, np.float64, np.complex64, np.complex128): self._testTensorArrayGradientWriteReadType(dtype) def _testTensorArrayGradientWritePackConcatAndRead(self): diff --git a/tensorflow/python/kernel_tests/topk_op_test.py b/tensorflow/python/kernel_tests/topk_op_test.py index 6ab931fdb97a89..fa7c6a0f8a6c76 100644 --- a/tensorflow/python/kernel_tests/topk_op_test.py +++ b/tensorflow/python/kernel_tests/topk_op_test.py @@ -197,13 +197,15 @@ def testKTooLarge(self): def testTopKGradients(self): with self.test_session(use_gpu=True) as sess: - inputs = array_ops.placeholder(dtypes.int32, shape=[2, 5]) + inputs = array_ops.placeholder(dtypes.float32, shape=[2, 5]) values, _ = nn_ops.top_k(inputs, 3) grad = sess.run( gradients_impl.gradients( - values, inputs, grad_ys=[[[1, 2, 3], [4, 5, 6]]]), - feed_dict={inputs: [[2, -1, 1000, 3, 4], [1, 5, 2, 4, 3]]})[0] - self.assertEqual(grad.tolist(), [[0, 0, 1, 3, 2], [0, 4, 0, 5, 6]]) + values, inputs, grad_ys=[[[1., 2., 3.], [4., 5., 6.]]]), + feed_dict={inputs: [[2., -1., 1000., 3., 4.], + [1., 5., 2., 4., 3.]]})[0] + self.assertEqual( + grad.tolist(), [[0., 0., 1., 3., 2.], [0., 4., 0., 5., 6.]]) class TopKBenchmark(test.Benchmark): diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index 13420b7f0ee5f2..581ba7de48a02d 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -121,7 +121,8 @@ def _MarkReachedOps(from_ops, reached_ops): if not reached_ops[op._id]: reached_ops[op._id] = True for output in op.outputs: - queue.extend(output.consumers()) + if _IsBackpropagatable(output): + queue.extend(output.consumers()) def _GatherInputs(to_ops, reached_ops): @@ -163,16 +164,19 @@ def _PendingCount(graph, to_ops, from_ops, colocate_gradients_with_ops): colocate_gradients_with_ops: Python bool. See docstring of gradients(). Returns: - A tuple containing: (1) a list of integers indexed by operation id, - indicating the number of backprop inputs to this operation, and (2) - a ControlFlowState object which is not None if the ops between from_ops - and to_ops contain control flow loops. + A tuple containing: (1) the subset of to_ops ids reachable from from_ops + by a path of zero or more backpropagatable tensors, (2) a list of integers + indexed by operation id, indicating the number of backprop inputs to this + operation, and (3) a ControlFlowState object which is not None if the ops + between from_ops and to_ops contain control flow loops. """ # Mark reachable ops from from_ops. reached_ops = [False] * (graph._last_id + 1) - for op in to_ops: - reached_ops[op._id] = True _MarkReachedOps(from_ops, reached_ops) + # reached_ops[X] iff X is reachable from from_ops by a path of zero or more + # backpropagatable tensors. + + reachable_to_ops = set(op._id for op in to_ops if reached_ops[op._id]) # pylint: disable=protected-access # Mark between ops. between_ops = [False] * (graph._last_id + 1) @@ -189,6 +193,8 @@ def _PendingCount(graph, to_ops, from_ops, colocate_gradients_with_ops): reached_ops[op._id] = False for inp in op.inputs: queue.append(inp.op) + # between_ops[X] iff X is on a path of zero or more backpropagatable tensors + # between from_ops and to_ops # 'loop_state' is None if there are no while loops. loop_state = control_flow_ops.MaybeCreateControlFlowState( @@ -201,7 +207,7 @@ def _PendingCount(graph, to_ops, from_ops, colocate_gradients_with_ops): if between_ops[x.op._id]: pending_count[x.op._id] += 1 - return pending_count, loop_state + return reachable_to_ops, pending_count, loop_state def _AsList(x): @@ -294,6 +300,13 @@ def _IsTrainable(tensor): dtypes.complex64, dtypes.complex128) +def _IsBackpropagatable(tensor): + if _IsTrainable(tensor): + return True + dtype = dtypes.as_dtype(tensor.dtype) + return dtype.base_dtype in (dtypes.bfloat16, dtypes.resource, dtypes.variant) + + def _VerifyGeneratedGradients(grads, op): """Verify that gradients are valid in number and type. @@ -460,6 +473,9 @@ def gradients(ys, backpropagation stops at both `tf.stop_gradient` nodes and nodes in `stop_gradients`, whichever is encountered first. + All integer tensors are considered constant with respect to all `xs`, as if + they were included in `stop_gradients`. + Args: ys: A `Tensor` or list of tensors to be differentiated. xs: A `Tensor` or list of tensors to be used for differentiation. @@ -539,7 +555,7 @@ def _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops, to_ops = [t.op for t in ys] from_ops = [t.op for t in xs] stop_gradient_ops = [t.op for t in stop_gradients] - pending_count, loop_state = _PendingCount( + reachable_to_ops, pending_count, loop_state = _PendingCount( ops.get_default_graph(), to_ops, from_ops, colocate_gradients_with_ops) # Iterate over the collected ops. @@ -564,7 +580,7 @@ def _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops, # another output's gradient. # pylint: disable=protected-access ready = (pending_count[op._id] == 0) - if ready and op._id not in to_ops_set: + if ready and op._id not in to_ops_set and op._id in reachable_to_ops: to_ops_set.add(op._id) queue.append(op) # pylint: enable=protected-access From bb2810198f7fdd228511caf6be67956d0b364d84 Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Thu, 26 Apr 2018 13:05:55 -0700 Subject: [PATCH 0067/1691] gRPC worker cache owns a shared_ptr to the channel cache PiperOrigin-RevId: 194441794 --- .../cluster_function_library_runtime_test.cc | 3 ++- .../core/distributed_runtime/rpc/grpc_server_lib.cc | 4 ++-- .../core/distributed_runtime/rpc/grpc_worker_cache.cc | 9 ++++----- .../core/distributed_runtime/rpc/grpc_worker_cache.h | 4 ++-- 4 files changed, 10 insertions(+), 10 deletions(-) diff --git a/tensorflow/core/distributed_runtime/cluster_function_library_runtime_test.cc b/tensorflow/core/distributed_runtime/cluster_function_library_runtime_test.cc index 6f96d7cb065284..cd6e13501408a0 100644 --- a/tensorflow/core/distributed_runtime/cluster_function_library_runtime_test.cc +++ b/tensorflow/core/distributed_runtime/cluster_function_library_runtime_test.cc @@ -36,7 +36,8 @@ class ClusterFunctionLibraryRuntimeTest : public ::testing::Test { ChannelCreationFunction channel_func = ConvertToChannelCreationFunction(NewHostPortGrpcChannel); std::unique_ptr worker_cache( - NewGrpcWorkerCache(NewGrpcChannelCache(spec, channel_func))); + NewGrpcWorkerCache(std::shared_ptr( + NewGrpcChannelCache(spec, channel_func)))); worker_session_.reset(new WorkerSession( "cluster_test_session", "/job:localhost/replica:0/task:0", diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc index 488dcde9f5d31c..99b6bda6b145fa 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc @@ -296,7 +296,7 @@ Status GrpcServer::WorkerCacheFactory(const WorkerCacheFactoryOptions& options, GrpcChannelSpec channel_spec; TF_RETURN_IF_ERROR(ParseChannelSpec(options, &channel_spec)); - std::unique_ptr channel_cache( + std::shared_ptr channel_cache( NewGrpcChannelCache(channel_spec, GetChannelCreationFunction())); string name_prefix = strings::StrCat("/job:", *options.job_name, "/replica:0", @@ -316,7 +316,7 @@ Status GrpcServer::WorkerCacheFactory(const WorkerCacheFactoryOptions& options, } *worker_cache = NewGrpcWorkerCacheWithLocalWorker( - channel_cache.release(), worker_impl_.get(), name_prefix); + channel_cache, worker_impl_.get(), name_prefix); return Status::OK(); } diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc index bb14e0197b7b0e..18998bbccbb44d 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc @@ -36,7 +36,7 @@ class GrpcWorkerCache : public WorkerCachePartial { // TODO(ncteisen): consider adding a config var or flag for this static constexpr const size_t kGrpcWorkerCacheThreadCount = 8; - explicit GrpcWorkerCache(GrpcChannelCache* channel_cache, + explicit GrpcWorkerCache(std::shared_ptr channel_cache, WorkerInterface* local_worker, const string& local_target) : local_target_(local_target), @@ -48,7 +48,6 @@ class GrpcWorkerCache : public WorkerCachePartial { // Explicit destructor to control destruction order. ~GrpcWorkerCache() override { threads_.clear(); // Blocks until threads exit. - delete channel_cache_; } void ListWorkers(std::vector* workers) const override { @@ -130,7 +129,7 @@ class GrpcWorkerCache : public WorkerCachePartial { const string local_target_; WorkerInterface* const local_worker_; // Not owned. - GrpcChannelCache* channel_cache_; // Owned. + std::shared_ptr channel_cache_; WorkerCacheLogger logger_; std::vector threads_; @@ -142,12 +141,12 @@ class GrpcWorkerCache : public WorkerCachePartial { } // namespace -WorkerCacheInterface* NewGrpcWorkerCache(GrpcChannelCache* cc) { +WorkerCacheInterface* NewGrpcWorkerCache(std::shared_ptr cc) { return new GrpcWorkerCache(cc, nullptr, ""); } WorkerCacheInterface* NewGrpcWorkerCacheWithLocalWorker( - GrpcChannelCache* cc, WorkerInterface* local_worker, + std::shared_ptr cc, WorkerInterface* local_worker, const string& local_target) { return new GrpcWorkerCache(cc, local_worker, local_target); } diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.h b/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.h index 7a35fdbca08e1f..d63fca74c15a5f 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.h +++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.h @@ -22,10 +22,10 @@ limitations under the License. namespace tensorflow { // The returned WorkerCacheInterface object takes the ownership of "cc". -WorkerCacheInterface* NewGrpcWorkerCache(GrpcChannelCache* cc); +WorkerCacheInterface* NewGrpcWorkerCache(std::shared_ptr cc); WorkerCacheInterface* NewGrpcWorkerCacheWithLocalWorker( - GrpcChannelCache* cc, WorkerInterface* local_worker, + std::shared_ptr cc, WorkerInterface* local_worker, const string& local_target); } // namespace tensorflow From 38244c353a7b91563b27c816105165833f5bb462 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 13:10:05 -0700 Subject: [PATCH 0068/1691] Automated g4 rollback of changelist 194269675 PiperOrigin-RevId: 194442428 --- tensorflow/compiler/xla/shape_layout.h | 3 ++- .../compiler/xla/tools/parser/hlo_parser.cc | 16 ++++++++++------ 2 files changed, 12 insertions(+), 7 deletions(-) diff --git a/tensorflow/compiler/xla/shape_layout.h b/tensorflow/compiler/xla/shape_layout.h index a1dce758cd3ab3..4c83750f3e6f3c 100644 --- a/tensorflow/compiler/xla/shape_layout.h +++ b/tensorflow/compiler/xla/shape_layout.h @@ -48,7 +48,8 @@ class ShapeLayout { bool MatchesLayoutInShape(const Shape& shape) const; // Copies the layout from the given shape into this ShapeLayout. 'other_shape' - // must be compatible with the ShapeLayout's shape. + // must be compatible with the ShapeLayout's shape, and 'other_shape' must + // have a layout (LayoutUtil::HasLayout). tensorflow::Status CopyLayoutFromShape(const Shape& other_shape); // Clears (Layout::Clear) all the Layouts stored in this object. diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc index fdbfc0210ea63a..95d3fd28b38a59 100644 --- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc +++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc @@ -303,14 +303,18 @@ bool HloParser::ParseComputations() { // set the layouts to what the hlo text says. for (int p = 0; p < computation->num_parameters(); p++) { const Shape& param_shape = computation->parameter_instruction(p)->shape(); - TF_CHECK_OK(module_->mutable_entry_computation_layout() - ->mutable_parameter_layout(p) - ->CopyLayoutFromShape(param_shape)); + if (param_shape.has_layout()) { + module_->mutable_entry_computation_layout() + ->mutable_parameter_layout(p) + ->ResetLayout(param_shape.layout()); + } } const Shape& result_shape = computation->root_instruction()->shape(); - TF_CHECK_OK(module_->mutable_entry_computation_layout() - ->mutable_result_layout() - ->CopyLayoutFromShape(result_shape)); + if (result_shape.has_layout()) { + module_->mutable_entry_computation_layout() + ->mutable_result_layout() + ->ResetLayout(result_shape.layout()); + } } return true; From 5f06514bff4061b839ee71847a299adbef9e7e03 Mon Sep 17 00:00:00 2001 From: Guangda Lai Date: Thu, 26 Apr 2018 13:12:04 -0700 Subject: [PATCH 0069/1691] Fix build by adding op_lib dependencies to trt_engine_op_loader, and remove unnecessary dependency from the tf_gen_op_libs. PiperOrigin-RevId: 194442728 --- tensorflow/contrib/tensorrt/BUILD | 9 ++++++--- tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc | 12 ++++++++++-- tensorflow/contrib/tensorrt/tensorrt_test.cc | 8 ++++---- 3 files changed, 20 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index f80b4f1b112dcf..742be7baf0bab4 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -102,9 +102,6 @@ tf_gen_op_libs( "trt_engine_op", "trt_calib_op", ], - deps = if_tensorrt([ - "@local_config_tensorrt//:nv_infer", - ]), ) tf_cuda_library( @@ -138,6 +135,12 @@ tf_custom_op_py_library( ] + if_tensorrt([ "@local_config_tensorrt//:nv_infer", ]), + kernels = [ + ":trt_engine_op_kernel", + ":trt_engine_op_op_lib", + ":trt_calib_op_op_lib", + ":trt_shape_function", + ], srcs_version = "PY2AND3", deps = [ "//tensorflow/contrib/util:util_py", diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 53ba7badcaea15..b8f881ceb16a48 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -85,7 +85,8 @@ void TRTEngineOp::Compute(OpKernelContext* context) { LOG(FATAL) << "input data inconsistent batch size"; break; } - switch (trt_engine_ptr_->getBindingDataType(binding_index)) { + auto dtype = trt_engine_ptr_->getBindingDataType(binding_index); + switch (dtype) { case nvinfer1::DataType::kFLOAT: buffers[binding_index] = (void*)(input_tensor.flat().data()); break; @@ -95,6 +96,9 @@ void TRTEngineOp::Compute(OpKernelContext* context) { case nvinfer1::DataType::kINT8: LOG(FATAL) << "int8 is not supported yet!"; break; + default: + LOG(FATAL) << "Unknown data type: " << int(dtype); + break; } } @@ -120,7 +124,8 @@ void TRTEngineOp::Compute(OpKernelContext* context) { OP_REQUIRES_OK(context, context->allocate_output(i, output_shape, &output_tensor)); - switch (trt_engine_ptr_->getBindingDataType(binding_index)) { + auto dtype = trt_engine_ptr_->getBindingDataType(binding_index); + switch (dtype) { case nvinfer1::DataType::kFLOAT: buffers[binding_index] = reinterpret_cast(output_tensor->flat().data()); @@ -131,6 +136,9 @@ void TRTEngineOp::Compute(OpKernelContext* context) { case nvinfer1::DataType::kINT8: LOG(FATAL) << "int8 is not supported yet!"; break; + default: + LOG(FATAL) << "Unknown data type: " << int(dtype); + break; } } // copied from cuda_kernel_helper since it seems only valid in *.cu.cc files diff --git a/tensorflow/contrib/tensorrt/tensorrt_test.cc b/tensorflow/contrib/tensorrt/tensorrt_test.cc index e11522ea5bda7f..3712a9a6fe349d 100644 --- a/tensorflow/contrib/tensorrt/tensorrt_test.cc +++ b/tensorflow/contrib/tensorrt/tensorrt_test.cc @@ -95,9 +95,9 @@ nvinfer1::IHostMemory* CreateNetwork() { } // Executes the network. -void Execute(nvinfer1::IExecutionContext& context, const float* input, +void Execute(nvinfer1::IExecutionContext* context, const float* input, float* output) { - const nvinfer1::ICudaEngine& engine = context.getEngine(); + const nvinfer1::ICudaEngine& engine = context->getEngine(); // We have two bindings: input and output. ASSERT_EQ(engine.getNbBindings(), 2); @@ -118,7 +118,7 @@ void Execute(nvinfer1::IExecutionContext& context, const float* input, // could be removed. ASSERT_EQ(0, cudaMemcpyAsync(buffers[input_index], input, sizeof(float), cudaMemcpyHostToDevice, stream)); - context.enqueue(1, buffers, stream, nullptr); + context->enqueue(1, buffers, stream, nullptr); ASSERT_EQ(0, cudaMemcpyAsync(output, buffers[output_index], sizeof(float), cudaMemcpyDeviceToHost, stream)); cudaStreamSynchronize(stream); @@ -143,7 +143,7 @@ TEST(TensorrtTest, BasicFunctions) { // Execute the network. float input = 1234; float output; - Execute(*context, &input, &output); + Execute(context, &input, &output); EXPECT_EQ(output, input * 2 + 3); // Destroy the engine. From d3c18b5dcf5293e81bfd9acdce3a3b8f79ae4ade Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 13:15:55 -0700 Subject: [PATCH 0070/1691] Delay deleting RingReducer until group_size_tensor_ready_ has been notified. Otherwise this can result in a bad pointer dereference under some early abort conditions. PiperOrigin-RevId: 194443206 --- tensorflow/core/common_runtime/ring_reducer.cc | 2 ++ tensorflow/core/common_runtime/ring_reducer.h | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/common_runtime/ring_reducer.cc b/tensorflow/core/common_runtime/ring_reducer.cc index a1cd7625051c33..a17281835ea5f5 100644 --- a/tensorflow/core/common_runtime/ring_reducer.cc +++ b/tensorflow/core/common_runtime/ring_reducer.cc @@ -92,6 +92,8 @@ RingReducer::RingReducer(CollectiveExecutor* col_exec, const DeviceMgr* dev_mgr, CHECK_GT(num_subdivs_, 0); } +RingReducer::~RingReducer() { group_size_tensor_ready_.WaitForNotification(); } + string RingReducer::TensorDebugString(Tensor tensor) { const DeviceBase::GpuDeviceInfo* gpu_device_info = ctx_->device()->tensorflow_gpu_device_info(); diff --git a/tensorflow/core/common_runtime/ring_reducer.h b/tensorflow/core/common_runtime/ring_reducer.h index 8fde18dc1c083f..3e1988e78706fc 100644 --- a/tensorflow/core/common_runtime/ring_reducer.h +++ b/tensorflow/core/common_runtime/ring_reducer.h @@ -32,7 +32,7 @@ class RingReducer { const CollectiveParams& col_params, const string& exec_key, int64 step_id, const Tensor* input, Tensor* output); - virtual ~RingReducer() {} + virtual ~RingReducer(); void Run(StatusCallback done); From 2c105ace934edce193669b55b13b64283caa24d7 Mon Sep 17 00:00:00 2001 From: Benoit Steiner Date: Thu, 26 Apr 2018 13:19:39 -0700 Subject: [PATCH 0071/1691] Run 2 passes of rewrites by default PiperOrigin-RevId: 194443770 --- tensorflow/core/grappler/optimizers/meta_optimizer.cc | 7 +++++-- tensorflow/python/estimator/estimator.py | 7 ++++++- tensorflow/python/grappler/memory_optimizer_test.py | 1 + 3 files changed, 12 insertions(+), 3 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc index c42d614c15e38b..2edc4da9dcb91b 100644 --- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc @@ -39,7 +39,7 @@ namespace grappler { namespace { -constexpr int kDefaultNumberOfIterations = 1; +constexpr int kDefaultNumberOfIterations = 2; int64 NumEdges(const GraphDef& graph) { int64 num_edges = 0; @@ -63,7 +63,10 @@ int NumIterations(const RewriterConfig& cfg) { } // Check if optimizer is allowed to run only once. -bool IsRunOnceOptimizer(const string& name) { return name == "layout"; } +bool IsRunOnceOptimizer(const string& name) { + return name == "layout" || name == "memory_optimizer" || + name == "arithmetic_optimizer" || name == "loop_optimizer"; +} } // namespace diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 2f1212d5a2b319..23638451103e05 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -30,6 +30,7 @@ from google.protobuf import message from tensorflow.core.framework import summary_pb2 from tensorflow.core.protobuf import config_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.client import session as tf_session from tensorflow.python.data.ops import dataset_ops from tensorflow.python.eager import context @@ -203,7 +204,11 @@ def __init__(self, model_fn, model_dir=None, config=None, params=None, logging.info('Using config: %s', str(vars(self._config))) if self._config.session_config is None: - self._session_config = config_pb2.ConfigProto(allow_soft_placement=True) + rewrite_opts = rewriter_config_pb2.RewriterConfig( + meta_optimizer_iterations=rewriter_config_pb2.RewriterConfig.ONE) + graph_opts = config_pb2.GraphOptions(rewrite_options=rewrite_opts) + self._session_config = config_pb2.ConfigProto( + allow_soft_placement=True, graph_options=graph_opts) else: self._session_config = self._config.session_config diff --git a/tensorflow/python/grappler/memory_optimizer_test.py b/tensorflow/python/grappler/memory_optimizer_test.py index 4df959ce041693..3f9d8864a2b4b7 100644 --- a/tensorflow/python/grappler/memory_optimizer_test.py +++ b/tensorflow/python/grappler/memory_optimizer_test.py @@ -76,6 +76,7 @@ def testSimpleSwap(self): rewriter_config = rewriter_config_pb2.RewriterConfig( disable_model_pruning=True, + meta_optimizer_iterations=rewriter_config_pb2.RewriterConfig.ONE, constant_folding=rewriter_config_pb2.RewriterConfig.OFF, memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL) graph = tf_optimizer.OptimizeGraph(rewriter_config, mg) From b6189a23a5f6afa59ced097d7844d58c7fd24901 Mon Sep 17 00:00:00 2001 From: Tony Wang Date: Thu, 26 Apr 2018 13:30:15 -0700 Subject: [PATCH 0072/1691] [TF:XLA] Add INTEL MKL_DNN Conv2d method to XLA/CPU backend The INTEL MKL_DNN provides 32-bit Conv2d method. With INTEL_MKL flag set, XLA backend emits runtime call to MKL_DNN Conv2d instead of Eigen. PiperOrigin-RevId: 194445212 --- tensorflow/compiler/xla/service/cpu/BUILD | 22 +++ .../compiler/xla/service/cpu/cpu_runtime.cc | 1 + .../compiler/xla/service/cpu/cpu_runtime.h | 1 + .../compiler/xla/service/cpu/ir_emitter.cc | 20 +- .../xla/service/cpu/runtime_conv2d_mkl.cc | 183 ++++++++++++++++++ .../xla/service/cpu/runtime_conv2d_mkl.h | 39 ++++ .../xla/service/cpu/simple_orc_jit.cc | 2 + 7 files changed, 264 insertions(+), 4 deletions(-) create mode 100644 tensorflow/compiler/xla/service/cpu/runtime_conv2d_mkl.cc create mode 100644 tensorflow/compiler/xla/service/cpu/runtime_conv2d_mkl.h diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 04fda3b2df5745..cef4ebacc86e3f 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -169,6 +169,7 @@ cc_library( ":orc_jit_memory_mapper", ":runtime_fp16", ":runtime_conv2d", + ":runtime_conv2d_mkl", ":runtime_fft", ":runtime_fork_join", ":runtime_matmul", @@ -470,6 +471,27 @@ cc_library( ], ) +cc_library( + name = "runtime_conv2d_mkl", + srcs = [ + "runtime_conv2d_mkl.cc", + ], + hdrs = ["runtime_conv2d_mkl.h"], + copts = runtime_copts(), + visibility = ["//visibility:public"], + deps = [ + ":runtime_conv2d", + ":runtime_single_threaded_conv2d", + "//tensorflow/compiler/xla:executable_run_options", + "//tensorflow/core:framework_lite", + "//tensorflow/core/kernels:eigen_helpers", + "//third_party/eigen3", + ] + if_mkl([ + "@mkl_dnn", + "//third_party/mkl:intel_binary_blob", + ]), +) + cc_library( name = "runtime_fft", srcs = [ diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc index 872b0be1f8a8ec..215405f6802cf1 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc @@ -37,6 +37,7 @@ extern const char* const kEigenMatMulF32SymbolName = "__xla_cpu_runtime_EigenMatMulF32"; extern const char* const kEigenMatMulF64SymbolName = "__xla_cpu_runtime_EigenMatMulF64"; +extern const char* const kMKLConvF32SymbolName = "__xla_cpu_runtime_MKLConvF32"; extern const char* const kMKLMatMulF32SymbolName = "__xla_cpu_runtime_MKLMatMulF32"; extern const char* const kMKLMatMulF64SymbolName = diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h index e392e231b4c71b..1dce6efa5cd65e 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h @@ -44,6 +44,7 @@ namespace runtime { extern const char* const kEigenMatMulF16SymbolName; extern const char* const kEigenMatMulF32SymbolName; extern const char* const kEigenMatMulF64SymbolName; +extern const char* const kMKLConvF32SymbolName; extern const char* const kMKLMatMulF32SymbolName; extern const char* const kMKLMatMulF64SymbolName; extern const char* const kMKLSingleThreadedMatMulF32SymbolName; diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 0b08ad8da3cf17..d582b5aaae9379 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -854,6 +854,8 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { const ConvolutionDimensionNumbers& dnums = convolution->convolution_dimension_numbers(); + // TODO(tonywy): Add PotentiallyImplementedAsMKLCovolution to support + // different data layouts. if (PotentiallyImplementedAsEigenConvolution(*convolution)) { const Shape& lhs_shape = lhs->shape(); const Shape& rhs_shape = rhs->shape(); @@ -942,16 +944,26 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type}, /*isVarArg=*/false); - bool multi_threaded_eigen = + bool multi_threaded = hlo_module_config_.debug_options().xla_cpu_multi_thread_eigen(); + bool use_mkl_dnn = + hlo_module_config_.debug_options().xla_cpu_use_mkl_dnn(); + + // TODO(b/78639006) Singlethread MKL conv2d is not implemented due to the + // potential race condition by setting the omp_num_threads. const char* fn_name = primitive_type == F16 - ? (multi_threaded_eigen + ? (multi_threaded ? runtime::kEigenConvF16SymbolName : runtime::kEigenSingleThreadedConvF16SymbolName) - : (multi_threaded_eigen - ? runtime::kEigenConvF32SymbolName + : (multi_threaded + ? (use_mkl_dnn ? runtime::kMKLConvF32SymbolName + : runtime::kEigenConvF32SymbolName) : runtime::kEigenSingleThreadedConvF32SymbolName); + if (!multi_threaded && use_mkl_dnn) { + LOG(WARNING) << "Using Eigen instead of MKL-DNN for single-threaded " + "conv2d function."; + } llvm::Function* conv_func = llvm::cast( module_->getOrInsertFunction(fn_name, conv_type)); conv_func->setCallingConv(llvm::CallingConv::C); diff --git a/tensorflow/compiler/xla/service/cpu/runtime_conv2d_mkl.cc b/tensorflow/compiler/xla/service/cpu/runtime_conv2d_mkl.cc new file mode 100644 index 00000000000000..c60580d6e763c6 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/runtime_conv2d_mkl.cc @@ -0,0 +1,183 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/compiler/xla/service/cpu/runtime_conv2d_mkl.h" +#include +#include "tensorflow/compiler/xla/executable_run_options.h" +#include "tensorflow/core/platform/dynamic_annotations.h" +#include "tensorflow/core/platform/types.h" + +using tensorflow::int64; + +#ifdef INTEL_MKL +#include +#include "mkldnn.hpp" +#include "tensorflow/compiler/xla/service/cpu/runtime_conv2d.h" + +namespace { + +// Downcast an int64 to int and check if value is in range. +int ToInt(int64 input) { + int output = static_cast(input); + if (static_cast(output) != input) { + std::cerr << "Error occurred in downcasting int64 to int32: Value " << input + << " is out-of-range for type int32. \n"; + exit(1); + } + return output; +} + +using mkldnn::convolution_direct; +using mkldnn::convolution_forward; +using mkldnn::engine; +using mkldnn::memory; +using mkldnn::padding_kind; +using mkldnn::primitive; +using mkldnn::prop_kind; +using mkldnn::reorder; +using mkldnn::stream; + +template +void MKLConvImpl(const EigenDevice& device, ScalarType* out, ScalarType* lhs, + ScalarType* rhs, int64 input_batch, int64 input_rows, + int64 input_cols, int64 input_channels, int64 kernel_rows, + int64 kernel_cols, int64 kernel_channels, int64 kernel_filters, + int64 output_rows, int64 output_cols, int64 row_stride, + int64 col_stride, int64 padding_top, int64 padding_bottom, + int64 padding_left, int64 padding_right, + int64 lhs_row_dilation, int64 lhs_col_dilation, + int64 rhs_row_dilation, int64 rhs_col_dilation) { + auto cpu_engine = engine(engine::cpu, 0); + + // Create a vector primitive to hold the network. + std::vector net; + + // Since memory::dims takes int for each dimension, we downcast the int64 + // values to int using the ToInt function defined above. + memory::dims conv1_src_dim = {ToInt(input_batch), ToInt(input_channels), + ToInt(input_rows), ToInt(input_cols)}; + memory::dims conv1_weights_dim = {ToInt(kernel_filters), + ToInt(kernel_channels), ToInt(kernel_rows), + ToInt(kernel_cols)}; + memory::dims conv1_dst_dim = {ToInt(input_batch), ToInt(kernel_filters), + ToInt(output_rows), ToInt(output_cols)}; + memory::dims conv1_strides = {ToInt(row_stride), ToInt(col_stride)}; + // Note: In MKL_DNN dilation starts from 0. + memory::dims conv1_dilates = {ToInt(rhs_row_dilation - 1), + ToInt(rhs_col_dilation - 1)}; + memory::dims conv1_padding_l = {ToInt(padding_top), ToInt(padding_left)}; + memory::dims conv1_padding_r = {ToInt(padding_bottom), ToInt(padding_right)}; + + // Create memory for user data. Input and output data have format of NHWC and + // kernel data has format of HWIO. + // Note that as a convention in MKL-DNN, the dimensions of the data is always + // described in NCHW/IOHW, regardless of the actual layout of the data. + auto user_src_memory = + memory({{{conv1_src_dim}, memory::data_type::f32, memory::format::nhwc}, + cpu_engine}, + lhs); + auto user_weights_memory = memory( + {{{conv1_weights_dim}, memory::data_type::f32, memory::format::hwio}, + cpu_engine}, + rhs); + auto user_dst_memory = + memory({{{conv1_dst_dim}, memory::data_type::f32, memory::format::nhwc}, + cpu_engine}, + out); + + // Create memory descriptors for convolution data with no specified format for + // best performance. + auto conv1_src_mem_desc = memory::desc( + {conv1_src_dim}, memory::data_type::f32, memory::format::any); + auto conv1_weights_mem_desc = memory::desc( + {conv1_weights_dim}, memory::data_type::f32, memory::format::any); + auto conv1_dst_mem_desc = memory::desc( + {conv1_dst_dim}, memory::data_type::f32, memory::format::any); + + // Create a convolution. + auto conv1_desc = convolution_forward::desc( + prop_kind::forward_inference, convolution_direct, conv1_src_mem_desc, + conv1_weights_mem_desc, conv1_dst_mem_desc, conv1_strides, conv1_dilates, + conv1_padding_l, conv1_padding_r, padding_kind::zero); + auto conv1_prim_desc = + convolution_forward::primitive_desc(conv1_desc, cpu_engine); + + // Create reorders for data and weights if layout requested by convolution is + // different from NCHW/OIHW. + auto conv1_src_memory = user_src_memory; + if (memory::primitive_desc(conv1_prim_desc.src_primitive_desc()) != + user_src_memory.get_primitive_desc()) { + conv1_src_memory = memory(conv1_prim_desc.src_primitive_desc()); + net.push_back(reorder(user_src_memory, conv1_src_memory)); + } + + auto conv1_weights_memory = user_weights_memory; + if (memory::primitive_desc(conv1_prim_desc.weights_primitive_desc()) != + user_weights_memory.get_primitive_desc()) { + conv1_weights_memory = memory(conv1_prim_desc.weights_primitive_desc()); + net.push_back(reorder(user_weights_memory, conv1_weights_memory)); + } + + // Check if output need layout conversion. If yes, create memory for + // intermediate layer of conv1_dst_memory. + bool need_output_conversion = + (memory::primitive_desc(conv1_prim_desc.dst_primitive_desc()) != + user_dst_memory.get_primitive_desc()); + auto conv1_dst_memory = need_output_conversion + ? memory(conv1_prim_desc.dst_primitive_desc()) + : user_dst_memory; + + // Create convolution primitive and add it to net. + net.push_back(convolution_forward(conv1_prim_desc, conv1_src_memory, + conv1_weights_memory, conv1_dst_memory)); + if (need_output_conversion) { + net.push_back(reorder(conv1_dst_memory, user_dst_memory)); + } + stream(stream::kind::eager).submit(net).wait(); +} +} // namespace +#endif // INTEL_MKL + +TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_MKLConvF32( + const void* run_options_ptr, float* out, float* lhs, float* rhs, + int64 input_batch, int64 input_rows, int64 input_cols, int64 input_channels, + int64 kernel_rows, int64 kernel_cols, int64 kernel_channels, + int64 kernel_filters, int64 output_rows, int64 output_cols, + int64 row_stride, int64 col_stride, int64 padding_top, int64 padding_bottom, + int64 padding_left, int64 padding_right, int64 lhs_row_dilation, + int64 lhs_col_dilation, int64 rhs_row_dilation, int64 rhs_col_dilation) { +#ifdef INTEL_MKL + // Since MKL_DNN cannot handle transposed convolution, this is handled by + // Eigen. + if (lhs_row_dilation > 1 || lhs_col_dilation > 1) { + __xla_cpu_runtime_EigenConvF32( + run_options_ptr, out, lhs, rhs, input_batch, input_rows, input_cols, + input_channels, kernel_rows, kernel_cols, kernel_channels, + kernel_filters, output_rows, output_cols, row_stride, col_stride, + padding_top, padding_bottom, padding_left, padding_right, + lhs_row_dilation, lhs_col_dilation, rhs_row_dilation, rhs_col_dilation); + } else { + MKLConvImpl(nullptr, out, lhs, rhs, input_batch, input_rows, input_cols, + input_channels, kernel_rows, kernel_cols, kernel_channels, + kernel_filters, output_rows, output_cols, row_stride, + col_stride, padding_top, padding_bottom, padding_left, + padding_right, lhs_row_dilation, lhs_col_dilation, + rhs_row_dilation, rhs_col_dilation); + } +#else + std::cerr << "Attempt to call MKL Conv2D runtime library without defining " + "INTEL_MKL. Add --config=mkl to build with MKL."; + exit(1); +#endif // INTEL_MKL +} diff --git a/tensorflow/compiler/xla/service/cpu/runtime_conv2d_mkl.h b/tensorflow/compiler/xla/service/cpu/runtime_conv2d_mkl.h new file mode 100644 index 00000000000000..b239e71d231c52 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/runtime_conv2d_mkl.h @@ -0,0 +1,39 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_CONV2D_MKL_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_CONV2D_MKL_H_ + +#include +#include "tensorflow/core/platform/types.h" + +extern "C" { + +extern void __xla_cpu_runtime_MKLConvF32( + const void* /* xla::ExecutableRunOptions* */ run_options_ptr, float* out, + float* lhs, float* rhs, tensorflow::int64 input_batch, + tensorflow::int64 input_rows, tensorflow::int64 input_cols, + tensorflow::int64 input_channels, tensorflow::int64 kernel_rows, + tensorflow::int64 kernel_cols, tensorflow::int64 kernel_channels, + tensorflow::int64 kernel_filters, tensorflow::int64 output_rows, + tensorflow::int64 output_cols, tensorflow::int64 row_stride, + tensorflow::int64 col_stride, tensorflow::int64 padding_top, + tensorflow::int64 padding_bottom, tensorflow::int64 padding_left, + tensorflow::int64 padding_right, tensorflow::int64 lhs_row_dilation, + tensorflow::int64 lhs_col_dilation, tensorflow::int64 rhs_row_dilation, + tensorflow::int64 rhs_col_dilation); +} + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_CONV2D_MKL_H_ diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc index b7ce5bbe474823..ff6f0a9d4e443c 100644 --- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc +++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc @@ -31,6 +31,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/cpu/custom_call_target_registry.h" #include "tensorflow/compiler/xla/service/cpu/orc_jit_memory_mapper.h" #include "tensorflow/compiler/xla/service/cpu/runtime_conv2d.h" +#include "tensorflow/compiler/xla/service/cpu/runtime_conv2d_mkl.h" #include "tensorflow/compiler/xla/service/cpu/runtime_fft.h" #include "tensorflow/compiler/xla/service/cpu/runtime_fork_join.h" #include "tensorflow/compiler/xla/service/cpu/runtime_fp16.h" @@ -178,6 +179,7 @@ bool RegisterKnownJITSymbols() { REGISTER_CPU_RUNTIME_SYMBOL(AcquireInfeedBufferForDequeue); REGISTER_CPU_RUNTIME_SYMBOL(AcquireOutfeedBufferForPopulation); + REGISTER_CPU_RUNTIME_SYMBOL(MKLConvF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenConvF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenConvF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenFft); From 7b0e865d79d8b9bacf855779b9c3ccf73d2571ac Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 13:35:35 -0700 Subject: [PATCH 0073/1691] Adding some slightly more exhaustive strided_slice test parameters. PiperOrigin-RevId: 194446000 --- .../contrib/lite/kernels/internal/BUILD | 13 ++ .../internal/optimized/optimized_ops.h | 130 ++++------------ .../internal/reference/reference_ops.h | 145 ++++-------------- .../kernels/internal/strided_slice_logic.h | 124 +++++++++++++++ .../contrib/lite/testing/generate_examples.py | 13 ++ tensorflow/contrib/lite/toco/BUILD | 1 + .../propagate_fixed_sizes.cc | 91 +---------- .../resolve_constant_strided_slice.cc | 97 ++---------- .../resolve_strided_slice_attributes.cc | 21 +++ 9 files changed, 243 insertions(+), 392 deletions(-) create mode 100644 tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD index 67dd1884966d8a..dce14cdbbb7b12 100644 --- a/tensorflow/contrib/lite/kernels/internal/BUILD +++ b/tensorflow/contrib/lite/kernels/internal/BUILD @@ -155,6 +155,7 @@ cc_library( copts = tflite_copts(), deps = [ ":quantization_util", + ":strided_slice_logic", ":types", ":round", "//third_party/eigen3", @@ -229,6 +230,17 @@ cc_test( ], ) +cc_library( + name = "strided_slice_logic", + srcs = [], + hdrs = [ + "strided_slice_logic.h", + ], + deps = [ + ":types", + ], +) + cc_library( name = "reference_base", srcs = [], @@ -241,6 +253,7 @@ cc_library( deps = [ ":quantization_util", ":round", + ":strided_slice_logic", ":types", "//third_party/eigen3", "@gemmlowp", diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index 9e9aba0169bcd4..3d6042c31fef4c 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -32,6 +32,7 @@ limitations under the License. #include "tensorflow/contrib/lite/kernels/internal/common.h" #include "tensorflow/contrib/lite/kernels/internal/quantization_util.h" #include "tensorflow/contrib/lite/kernels/internal/round.h" +#include "tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h" #include "tensorflow/contrib/lite/kernels/internal/types.h" namespace tflite { @@ -5864,90 +5865,7 @@ inline void Pad(const T* input_data, const Dims<4>& input_dims, output_dims, 0); } -// UNOPTIMIZED COPY of StridedSlice from reference_ops.h (see comments there). - -// Use until std::clamp() is available from C++17. -inline int Clamp(const int v, const int lo, const int hi) { - TFLITE_DCHECK(!(hi < lo)); - if (hi < v) return hi; - if (v < lo) return lo; - return v; -} - -inline int StartForAxis(int begin_mask, const std::vector& start_indices, - const std::vector& strides, - const Dims<4>& input_shape, int axis) { - // Begin with the specified index - int start = start_indices[axis]; - - // begin_mask override - if (begin_mask & 1 << axis) { - if (strides[axis] > 0) { - // Forward iteration - use the first element. These values will get - // clamped below (Note: We could have set them to 0 and axis_size-1, but - // use lowest() and max() to maintain symmetry with StopForAxis()) - start = std::numeric_limits::lowest(); - } else { - // Backward iteration - use the last element. - start = std::numeric_limits::max(); - } - } - - // Handle negative indices - int axis_size = input_shape.sizes[axis]; - if (start < 0) { - start += axis_size; - } - - // Clamping - start = Clamp(start, 0, axis_size - 1); - - return start; -} - -inline int StopForAxis(int end_mask, const std::vector& stop_indices, - const std::vector& strides, - const Dims<4>& input_shape, int axis) { - // Begin with the specified index - int stop = stop_indices[axis]; - - // end_mask override - if (end_mask & (1 << axis)) { - if (strides[axis] > 0) { - // Forward iteration - use the last element. These values will get - // clamped below - stop = std::numeric_limits::max(); - } else { - // Backward iteration - use the first element. - stop = std::numeric_limits::lowest(); - } - } - - // Handle negative indices - int axis_size = input_shape.sizes[axis]; - if (stop < 0) { - stop += axis_size; - } - - // Clamping - // Because the end index points one past the last element, we need slightly - // different clamping ranges depending on the direction. - if (strides[axis] > 0) { - // Forward iteration - stop = Clamp(stop, 0, axis_size); - } else { - // Backward iteration - stop = Clamp(stop, -1, axis_size - 1); - } - - return stop; -} - -inline bool LoopCondition(int index, int stop, int stride) { - // True when we have reached the end of an axis and should loop. - return stride > 0 ? index >= stop : index <= stop; -} - +// UNOPTIMIZED COPY of StridedSlice from reference_ops.h. template inline void StridedSlice(const T* input_data, const Dims<4>& input_dims, int begin_mask, int end_mask, @@ -5958,31 +5876,35 @@ inline void StridedSlice(const T* input_data, const Dims<4>& input_dims, TFLITE_DCHECK_EQ(start_indices.size(), 4); TFLITE_DCHECK_EQ(stop_indices.size(), 4); TFLITE_DCHECK_EQ(strides.size(), 4); - const int start_b = - StartForAxis(begin_mask, start_indices, strides, input_dims, 3); - const int stop_b = - StopForAxis(end_mask, stop_indices, strides, input_dims, 3); - const int start_h = - StartForAxis(begin_mask, start_indices, strides, input_dims, 2); - const int stop_h = - StopForAxis(end_mask, stop_indices, strides, input_dims, 2); - const int start_w = - StartForAxis(begin_mask, start_indices, strides, input_dims, 1); - const int stop_w = - StopForAxis(end_mask, stop_indices, strides, input_dims, 1); - const int start_d = - StartForAxis(begin_mask, start_indices, strides, input_dims, 0); - const int stop_d = - StopForAxis(end_mask, stop_indices, strides, input_dims, 0); + const int start_b = strided_slice::StartForAxis(begin_mask, start_indices, + strides, input_dims.sizes, 3); + const int stop_b = strided_slice::StopForAxis(end_mask, stop_indices, strides, + input_dims.sizes, 3); + const int start_h = strided_slice::StartForAxis(begin_mask, start_indices, + strides, input_dims.sizes, 2); + const int stop_h = strided_slice::StopForAxis(end_mask, stop_indices, strides, + input_dims.sizes, 2); + const int start_w = strided_slice::StartForAxis(begin_mask, start_indices, + strides, input_dims.sizes, 1); + const int stop_w = strided_slice::StopForAxis(end_mask, stop_indices, strides, + input_dims.sizes, 1); + const int start_d = strided_slice::StartForAxis(begin_mask, start_indices, + strides, input_dims.sizes, 0); + const int stop_d = strided_slice::StopForAxis(end_mask, stop_indices, strides, + input_dims.sizes, 0); T* out_ptr = output_data; - for (int in_b = start_b; !LoopCondition(in_b, stop_b, strides[3]); + for (int in_b = start_b; + !strided_slice::LoopCondition(in_b, stop_b, strides[3]); in_b += strides[3]) { - for (int in_h = start_h; !LoopCondition(in_h, stop_h, strides[2]); + for (int in_h = start_h; + !strided_slice::LoopCondition(in_h, stop_h, strides[2]); in_h += strides[2]) { - for (int in_w = start_w; !LoopCondition(in_w, stop_w, strides[1]); + for (int in_w = start_w; + !strided_slice::LoopCondition(in_w, stop_w, strides[1]); in_w += strides[1]) { - for (int in_d = start_d; !LoopCondition(in_d, stop_d, strides[0]); + for (int in_d = start_d; + !strided_slice::LoopCondition(in_d, stop_d, strides[0]); in_d += strides[0]) { *out_ptr++ = input_data[Offset(input_dims, in_d, in_w, in_h, in_b)]; } diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 4c8cbe42759d0a..d41ade4c9d9ec2 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -29,6 +29,7 @@ limitations under the License. #include "tensorflow/contrib/lite/kernels/internal/common.h" #include "tensorflow/contrib/lite/kernels/internal/quantization_util.h" #include "tensorflow/contrib/lite/kernels/internal/round.h" +#include "tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h" #include "tensorflow/contrib/lite/kernels/internal/types.h" namespace tflite { @@ -3131,104 +3132,6 @@ inline void Pad(const T* input_data, const Dims<4>& input_dims, output_dims, 0); } -// STRIDED SLICE -// The functions below for StridedSlice are mirrored in a number of places: -// -// propagate_fixed_sizes.cc -// propagate_shapes.cc -// resolve_constant_strided_slice.cc -// optimized_ops.h -// -// It is designed for an arbitrary number of dimensions, even though dimensions -// here are fixed at 4. This is because we expect to eventually support -// arbitrary dimensionality. Also note that the axis orders are reversed for -// runtime ops, and so the indices and masks must be as well too. -// -// Be warned this code involves some rather subtle logic of python slicing. The -// best "ground truth" is to compare results to actual python execution. - -// Use until std::clamp() is available from C++17. -inline int Clamp(const int v, const int lo, const int hi) { - TFLITE_DCHECK(!(hi < lo)); - if (hi < v) return hi; - if (v < lo) return lo; - return v; -} - -inline int StartForAxis(int begin_mask, const std::vector& start_indices, - const std::vector& strides, - const Dims<4>& input_shape, int axis) { - // Begin with the specified index - int start = start_indices[axis]; - - // begin_mask override - if (begin_mask & 1 << axis) { - if (strides[axis] > 0) { - // Forward iteration - use the first element. These values will get - // clamped below (Note: We could have set them to 0 and axis_size-1, but - // use lowest() and max() to maintain symmetry with StopForAxis()) - start = std::numeric_limits::lowest(); - } else { - // Backward iteration - use the last element. - start = std::numeric_limits::max(); - } - } - - // Handle negative indices - int axis_size = input_shape.sizes[axis]; - if (start < 0) { - start += axis_size; - } - - // Clamping - start = Clamp(start, 0, axis_size - 1); - - return start; -} - -inline int StopForAxis(int end_mask, const std::vector& stop_indices, - const std::vector& strides, - const Dims<4>& input_shape, int axis) { - // Begin with the specified index - int stop = stop_indices[axis]; - - // end_mask override - if (end_mask & (1 << axis)) { - if (strides[axis] > 0) { - // Forward iteration - use the last element. These values will get - // clamped below - stop = std::numeric_limits::max(); - } else { - // Backward iteration - use the first element. - stop = std::numeric_limits::lowest(); - } - } - - // Handle negative indices - int axis_size = input_shape.sizes[axis]; - if (stop < 0) { - stop += axis_size; - } - - // Clamping - // Because the end index points one past the last element, we need slightly - // different clamping ranges depending on the direction. - if (strides[axis] > 0) { - // Forward iteration - stop = Clamp(stop, 0, axis_size); - } else { - // Backward iteration - stop = Clamp(stop, -1, axis_size - 1); - } - - return stop; -} - -inline bool LoopCondition(int index, int stop, int stride) { - // True when we have reached the end of an axis and should loop. - return stride > 0 ? index >= stop : index <= stop; -} - template inline void StridedSlice(const T* input_data, const Dims<4>& input_dims, int begin_mask, int end_mask, @@ -3236,34 +3139,40 @@ inline void StridedSlice(const T* input_data, const Dims<4>& input_dims, const std::vector& stop_indices, const std::vector& strides, T* output_data, const Dims<4>& output_dims) { + // Note that the axis orders are reversed for runtime ops, so the indices, + // strides and masks must be as well too. TFLITE_DCHECK_EQ(start_indices.size(), 4); TFLITE_DCHECK_EQ(stop_indices.size(), 4); TFLITE_DCHECK_EQ(strides.size(), 4); - const int start_b = - StartForAxis(begin_mask, start_indices, strides, input_dims, 3); - const int stop_b = - StopForAxis(end_mask, stop_indices, strides, input_dims, 3); - const int start_h = - StartForAxis(begin_mask, start_indices, strides, input_dims, 2); - const int stop_h = - StopForAxis(end_mask, stop_indices, strides, input_dims, 2); - const int start_w = - StartForAxis(begin_mask, start_indices, strides, input_dims, 1); - const int stop_w = - StopForAxis(end_mask, stop_indices, strides, input_dims, 1); - const int start_d = - StartForAxis(begin_mask, start_indices, strides, input_dims, 0); - const int stop_d = - StopForAxis(end_mask, stop_indices, strides, input_dims, 0); + const int start_b = strided_slice::StartForAxis(begin_mask, start_indices, + strides, input_dims.sizes, 3); + const int stop_b = strided_slice::StopForAxis(end_mask, stop_indices, strides, + input_dims.sizes, 3); + const int start_h = strided_slice::StartForAxis(begin_mask, start_indices, + strides, input_dims.sizes, 2); + const int stop_h = strided_slice::StopForAxis(end_mask, stop_indices, strides, + input_dims.sizes, 2); + const int start_w = strided_slice::StartForAxis(begin_mask, start_indices, + strides, input_dims.sizes, 1); + const int stop_w = strided_slice::StopForAxis(end_mask, stop_indices, strides, + input_dims.sizes, 1); + const int start_d = strided_slice::StartForAxis(begin_mask, start_indices, + strides, input_dims.sizes, 0); + const int stop_d = strided_slice::StopForAxis(end_mask, stop_indices, strides, + input_dims.sizes, 0); T* out_ptr = output_data; - for (int in_b = start_b; !LoopCondition(in_b, stop_b, strides[3]); + for (int in_b = start_b; + !strided_slice::LoopCondition(in_b, stop_b, strides[3]); in_b += strides[3]) { - for (int in_h = start_h; !LoopCondition(in_h, stop_h, strides[2]); + for (int in_h = start_h; + !strided_slice::LoopCondition(in_h, stop_h, strides[2]); in_h += strides[2]) { - for (int in_w = start_w; !LoopCondition(in_w, stop_w, strides[1]); + for (int in_w = start_w; + !strided_slice::LoopCondition(in_w, stop_w, strides[1]); in_w += strides[1]) { - for (int in_d = start_d; !LoopCondition(in_d, stop_d, strides[0]); + for (int in_d = start_d; + !strided_slice::LoopCondition(in_d, stop_d, strides[0]); in_d += strides[0]) { *out_ptr++ = input_data[Offset(input_dims, in_d, in_w, in_h, in_b)]; } diff --git a/tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h b/tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h new file mode 100644 index 00000000000000..ef77371bf65cc9 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h @@ -0,0 +1,124 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_ + +#include +#include +#include "tensorflow/contrib/lite/kernels/internal/compatibility.h" + +namespace tflite { + +namespace strided_slice { + +// Use until std::clamp() is available from C++17. +inline int Clamp(const int v, const int lo, const int hi) { + TFLITE_DCHECK(!(hi < lo)); + if (hi < v) return hi; + if (v < lo) return lo; + return v; +} + +// Return the index for the first element along that axis. This index will be a +// positive integer between [0, axis_size - 1] that can be used to index +// directly into the data. +template +inline int StartForAxis(int begin_mask, + std::vector const& start_indices, + std::vector const& strides, + int const* input_shape, int axis) { + // Begin with the specified index + int start = start_indices[axis]; + + // begin_mask override + if (begin_mask & 1 << axis) { + if (strides[axis] > 0) { + // Forward iteration - use the first element. These values will get + // clamped below (Note: We could have set them to 0 and axis_size-1, but + // use lowest() and max() to maintain symmetry with StopForAxis()) + start = std::numeric_limits::lowest(); + } else { + // Backward iteration - use the last element. + start = std::numeric_limits::max(); + } + } + + // Handle negative indices + int axis_size = input_shape[axis]; + if (start < 0) { + start += axis_size; + } + + // Clamping + start = Clamp(start, 0, axis_size - 1); + + return start; +} + +// Return the "real" index for the end of iteration along that axis. This is an +// "end" in the traditional C sense, in that it points to one past the last +// element. ie. So if you were iterating through all elements of a 1D array of +// size 4, this function would return 4 as the stop, because it is one past the +// "real" indices of 0, 1, 2 & 3. +template +inline int StopForAxis(int end_mask, std::vector const& stop_indices, + std::vector const& strides, + int const* input_shape, int axis) { + // Begin with the specified index + int stop = stop_indices[axis]; + + // end_mask override + if (end_mask & (1 << axis)) { + if (strides[axis] > 0) { + // Forward iteration - use the last element. These values will get + // clamped below + stop = std::numeric_limits::max(); + } else { + // Backward iteration - use the first element. + stop = std::numeric_limits::lowest(); + } + } + + // Handle negative indices + int axis_size = input_shape[axis]; + if (stop < 0) { + stop += axis_size; + } + + // Clamping + // Because the end index points one past the last element, we need slightly + // different clamping ranges depending on the direction. + if (strides[axis] > 0) { + // Forward iteration + stop = Clamp(stop, 0, axis_size); + } else { + // Backward iteration + stop = Clamp(stop, -1, axis_size - 1); + } + + return stop; +} + +inline bool LoopCondition(int index, int stop, int stride) { + // True when we have reached the end of an axis and should loop. + return stride > 0 ? index >= stop : index <= stop; +} + +} // namespace strided_slice + +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_ diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index f72a4e0d8cbc89..9c9acf64c142bd 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -1772,6 +1772,19 @@ def make_strided_slice_tests(zip_path): "shrink_axis_mask": [None, 1, 2, 3, -1], "constant_indices": [False, True], }, + # 1-D Exhaustive + { + "dtype": [tf.float32], + "index_type": [tf.int32], + "input_shape": [[4]], + "begin": [[-100], [-3], [-2], [-1], [0], [1], [2], [3], [100]], + "end": [[-100], [-3], [-2], [-1], [0], [1], [2], [3], [100]], + "strides": [-2, -1, 1, 2], + "begin_mask": [0, 1], + "end_mask": [0, 1], + "shrink_axis_mask": [0], + "constant_indices": [False], + }, # Negative strides { "dtype": [tf.float32], diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD index 3f73ef620e121b..f92e546ab8aa3c 100644 --- a/tensorflow/contrib/lite/toco/BUILD +++ b/tensorflow/contrib/lite/toco/BUILD @@ -308,6 +308,7 @@ cc_library( ":toco_port", ":tooling_util", "//tensorflow/contrib/lite/kernels/internal:quantization_util", + "//tensorflow/contrib/lite/kernels/internal:strided_slice_logic", "//tensorflow/core:lib", "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc index be6e0e07dd08ab..19037bc50385b0 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -20,6 +20,7 @@ limitations under the License. #include #include "absl/strings/str_join.h" +#include "tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h" #include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" #include "tensorflow/contrib/lite/toco/model.h" #include "tensorflow/contrib/lite/toco/tooling_util.h" @@ -1235,83 +1236,6 @@ void ProcessStackOperator(Model* model, StackOperator* op) { output_array.copy_shape(*stacked_shape); } -// These StridedSlice utility functions are essentially a COPY of those in -// reference_ops.h. See comments there. - -// Use until std::clamp() is available from C++17. -int Clamp(const int v, const int lo, const int hi) { - if (hi < v) return hi; - if (v < lo) return lo; - return v; -} - -int StartForAxis(StridedSliceOperator const& op, Shape const& input_shape, - int axis) { - // Begin with the specified index - int start = op.start_indices[axis]; - - // begin_mask override - if (op.begin_mask & 1 << axis) { - if (op.strides[axis] > 0) { - // Forward iteration - use the first element. These values will get - // clamped below (Note: We could have set them to 0 and axis_size-1, but - // use lowest() and max() to maintain symmetry with StopForAxis()) - start = std::numeric_limits::lowest(); - } else { - // Backward iteration - use the last element. - start = std::numeric_limits::max(); - } - } - - // Handle negative indices - int axis_size = input_shape.dims(axis); - if (start < 0) { - start += axis_size; - } - - // Clamping - start = Clamp(start, 0, axis_size - 1); - - return start; -} - -int StopForAxis(StridedSliceOperator const& op, Shape const& input_shape, - int axis) { - // Begin with the specified index - int stop = op.stop_indices[axis]; - - // end_mask override - if (op.end_mask & (1 << axis)) { - if (op.strides[axis] > 0) { - // Forward iteration - use the last element. These values will get - // clamped below - stop = std::numeric_limits::max(); - } else { - // Backward iteration - use the first element. - stop = std::numeric_limits::lowest(); - } - } - - // Handle negative indices - int axis_size = input_shape.dims(axis); - if (stop < 0) { - stop += axis_size; - } - - // Clamping - // Because the end index points one past the last element, we need slightly - // different clamping ranges depending on the direction. - if (op.strides[axis] > 0) { - // Forward iteration - stop = Clamp(stop, 0, axis_size); - } else { - // Backward iteration - stop = Clamp(stop, -1, axis_size - 1); - } - - return stop; -} - void ProcessStridedSliceOperator(Model* model, StridedSliceOperator* op) { CHECK_GE(op->inputs.size(), 1); CHECK_EQ(op->outputs.size(), 1); @@ -1364,18 +1288,17 @@ void ProcessStridedSliceOperator(Model* model, StridedSliceOperator* op) { << " has stride=" << op->strides[i] << "."; } - // The TensorFlow documentation is not explicit on how it handles fewer - // supplied indices than dimensions, but they are accepted. We emulate TF's - // behavior by fully iterating over each "forgotten" dimension. - op->PadIndices(num_input_axes); - // Create output shape std::vector* dims = output_array.mutable_shape()->mutable_dims(); // Compute output shape for (int axis = 0; axis < num_input_axes; ++axis) { - int start_index = StartForAxis(*op, input_array.shape(), axis); - int stop_index = StopForAxis(*op, input_array.shape(), axis); + int start_index = tflite::strided_slice::StartForAxis( + op->begin_mask, op->start_indices, op->strides, + input_array.shape().dims().data(), axis); + int stop_index = tflite::strided_slice::StopForAxis( + op->end_mask, op->stop_indices, op->strides, + input_array.shape().dims().data(), axis); int dim_size = ceil(static_cast(stop_index - start_index) / op->strides[axis]); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc index 8df3c2f1955c23..1dd52e906900e9 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include +#include "tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h" #include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" #include "tensorflow/contrib/lite/toco/model.h" #include "tensorflow/contrib/lite/toco/tooling_util.h" @@ -23,88 +24,6 @@ namespace toco { namespace { -// These StridedSlice utility functions are essentially a COPY of those in -// reference_ops.h. See comments there. - -// Use until std::clamp() is available from C++17. -int Clamp(const int v, const int lo, const int hi) { - if (hi < v) return hi; - if (v < lo) return lo; - return v; -} - -int StartForAxis(StridedSliceOperator const& op, Shape const& input_shape, - int axis) { - // Begin with the specified index - int start = op.start_indices[axis]; - - // begin_mask override - if (op.begin_mask & 1 << axis) { - if (op.strides[axis] > 0) { - // Forward iteration - use the first element. These values will get - // clamped below (Note: We could have set them to 0 and axis_size-1, but - // use lowest() and max() to maintain symmetry with StopForAxis()) - start = std::numeric_limits::lowest(); - } else { - // Backward iteration - use the last element. - start = std::numeric_limits::max(); - } - } - - // Handle negative indices - int axis_size = input_shape.dims(axis); - if (start < 0) { - start += axis_size; - } - - // Clamping - start = Clamp(start, 0, axis_size - 1); - - return start; -} - -int StopForAxis(StridedSliceOperator const& op, Shape const& input_shape, - int axis) { - // Begin with the specified index - int stop = op.stop_indices[axis]; - - // end_mask override - if (op.end_mask & (1 << axis)) { - if (op.strides[axis] > 0) { - // Forward iteration - use the last element. These values will get - // clamped below - stop = std::numeric_limits::max(); - } else { - // Backward iteration - use the first element. - stop = std::numeric_limits::lowest(); - } - } - - // Handle negative indices - int axis_size = input_shape.dims(axis); - if (stop < 0) { - stop += axis_size; - } - - // Clamping - // Because the end index points one past the last element, we need slightly - // different clamping ranges depending on the direction. - if (op.strides[axis] > 0) { - // Forward iteration - stop = Clamp(stop, 0, axis_size); - } else { - // Backward iteration - stop = Clamp(stop, -1, axis_size - 1); - } - - return stop; -} - -bool LoopCondition(int index, int stop, int stride) { - // True when we have reached the end of an axis and should loop. - return stride > 0 ? index >= stop : index <= stop; -} - template void StridedSlice(StridedSliceOperator const& op, Array const& input_array, Array* output_array) { @@ -132,7 +51,9 @@ void StridedSlice(StridedSliceOperator const& op, Array const& input_array, Buffer const& input_buffer = input_array.GetBuffer(); std::vector src_coord(op.start_indices.size()); for (int axis = 0; axis < num_input_axes; axis++) { - src_coord[axis] = StartForAxis(op, input_shape, axis); + src_coord[axis] = tflite::strided_slice::StartForAxis( + op.begin_mask, op.start_indices, op.strides, input_shape.dims().data(), + axis); } // In order to handle any number (N) of dimensions, we copy elements one by @@ -155,10 +76,14 @@ void StridedSlice(StridedSliceOperator const& op, Array const& input_array, } // Check if we've overflowed. - int stop = StopForAxis(op, input_shape, axis); - if (LoopCondition(src_coord[axis], stop, stride)) { + int stop = tflite::strided_slice::StopForAxis( + op.end_mask, op.stop_indices, op.strides, input_shape.dims().data(), + axis); + if (tflite::strided_slice::LoopCondition(src_coord[axis], stop, stride)) { // Reset axis and set carry - src_coord[axis] = StartForAxis(op, input_shape, axis); + src_coord[axis] = tflite::strided_slice::StartForAxis( + op.begin_mask, op.start_indices, op.strides, + input_shape.dims().data(), axis); carry = true; } else { carry = false; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_strided_slice_attributes.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_strided_slice_attributes.cc index 7e8b249b07ecca..021e9918f2cf22 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_strided_slice_attributes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_strided_slice_attributes.cc @@ -31,6 +31,12 @@ bool ResolveStridedSliceAttributes::Run(Model* model, std::size_t op_index) { } CHECK_EQ(op->inputs.size(), 4); + const auto& input_array = model->GetArray(op->inputs[0]); + if (!input_array.has_shape()) { + // We require the dimensionality of the input to pad the indices + return false; + } + const auto& start_array = model->GetArray(op->inputs[1]); if (!start_array.has_shape()) return false; if (toco::RequiredBufferSizeForShape(start_array.shape()) > 4) { @@ -57,6 +63,21 @@ bool ResolveStridedSliceAttributes::Run(Model* model, std::size_t op_index) { CHECK_EQ(op->stop_indices.size(), op->start_indices.size()); CHECK_EQ(op->strides.size(), op->stop_indices.size()); + // The TensorFlow documentation is not explicit on how it handles fewer + // supplied indices than dimensions, but they are accepted. We emulate TF's + // behavior by fully iterating over each omitted dimension. + int num_input_axes = input_array.shape().dimensions_count(); + CHECK_LE(op->start_indices.size(), num_input_axes) + << "StridedSlice op requires no more than " << num_input_axes + << " start indices"; + CHECK_LE(op->stop_indices.size(), num_input_axes) + << "StridedSlice op requires no more than " << num_input_axes + << " stop indices"; + CHECK_LE(op->strides.size(), num_input_axes) + << "StridedSlice op requires no more than " << num_input_axes + << " strides"; + op->PadIndices(num_input_axes); + // Ideally, we would remove the input arrays after they have been resolved. // However, we must then reconstitute these input arrays for all supported // export formats. For now, leave the arrays so we don't have to modify our From 35bf3bf44c9ebf0846a2505ca528dced455653ec Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 13:53:17 -0700 Subject: [PATCH 0074/1691] Remove unnecessary TF_NEED_GCP from build scripts. PiperOrigin-RevId: 194448612 --- tensorflow/tools/ci_build/linux/libtensorflow_docker.sh | 1 - tensorflow/tools/ci_build/osx/libtensorflow_cpu.sh | 1 - tensorflow/tools/ci_build/osx/libtensorflow_gpu.sh | 1 - 3 files changed, 3 deletions(-) diff --git a/tensorflow/tools/ci_build/linux/libtensorflow_docker.sh b/tensorflow/tools/ci_build/linux/libtensorflow_docker.sh index e5d8303c6e5534..bf992cf63d27f0 100755 --- a/tensorflow/tools/ci_build/linux/libtensorflow_docker.sh +++ b/tensorflow/tools/ci_build/linux/libtensorflow_docker.sh @@ -45,7 +45,6 @@ ${DOCKER_BINARY} run \ -v ${ROOT_DIR}:/workspace \ -w /workspace \ -e "PYTHON_BIN_PATH=/usr/bin/python" \ - -e "TF_NEED_GCP=0" \ -e "TF_NEED_HDFS=0" \ -e "TF_NEED_CUDA=${TF_NEED_CUDA}" \ -e "TF_NEED_OPENCL_SYCL=0" \ diff --git a/tensorflow/tools/ci_build/osx/libtensorflow_cpu.sh b/tensorflow/tools/ci_build/osx/libtensorflow_cpu.sh index 7d471b47034f04..9ae5fc6bea50e4 100755 --- a/tensorflow/tools/ci_build/osx/libtensorflow_cpu.sh +++ b/tensorflow/tools/ci_build/osx/libtensorflow_cpu.sh @@ -24,7 +24,6 @@ source "${SCRIPT_DIR}/../builds/libtensorflow.sh" # Configure script export PYTHON_BIN_PATH="/usr/bin/python" -export TF_NEED_GCP=0 export TF_NEED_HDFS=0 export TF_NEED_CUDA=0 export TF_NEED_OPENCL_SYCL=0 diff --git a/tensorflow/tools/ci_build/osx/libtensorflow_gpu.sh b/tensorflow/tools/ci_build/osx/libtensorflow_gpu.sh index 5a901af3e5c77e..d95fcdeb8552d5 100755 --- a/tensorflow/tools/ci_build/osx/libtensorflow_gpu.sh +++ b/tensorflow/tools/ci_build/osx/libtensorflow_gpu.sh @@ -26,7 +26,6 @@ source "${SCRIPT_DIR}/../builds/libtensorflow.sh" export TF_NEED_CUDA=1 export LD_LIBRARY_PATH="/usr/local/cuda/lib:/usr/local/cuda/extras/CUPTI/lib:${LD_LIBRARY_PATH}" export PYTHON_BIN_PATH="/usr/bin/python" -export TF_NEED_GCP=0 export TF_NEED_HDFS=0 export TF_NEED_OPENCL_SYCL=0 export TF_NEED_MKL=0 From f67a78c69ab301bdc34005a884a9dd0e56b10446 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 14:53:19 -0700 Subject: [PATCH 0075/1691] Disable densenet_test on MSAN due to flaky time outs. PiperOrigin-RevId: 194458270 --- tensorflow/python/keras/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index a09963e062808c..1b66f589397527 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -336,6 +336,7 @@ py_test( size = "large", srcs = ["_impl/keras/applications/densenet_test.py"], srcs_version = "PY2AND3", + tags = ["nomsan"], # times out, http://b/78650237 deps = [ ":keras", "//tensorflow/python:client_testlib", From 4386296d48d84aceb485c09361f7b80745806a61 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 14:59:29 -0700 Subject: [PATCH 0076/1691] Adds optimization to convert division of sqrt to multiplication of rsqrt PiperOrigin-RevId: 194459152 --- tensorflow/core/grappler/op_types.cc | 4 ++ tensorflow/core/grappler/op_types.h | 2 + .../optimizers/arithmetic_optimizer.cc | 32 ++++++++++++++ .../optimizers/arithmetic_optimizer.h | 1 + .../optimizers/arithmetic_optimizer_test.cc | 43 +++++++++++++++++++ 5 files changed, 82 insertions(+) diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc index c02430369c0e6e..7a89c263744d60 100644 --- a/tensorflow/core/grappler/op_types.cc +++ b/tensorflow/core/grappler/op_types.cc @@ -289,6 +289,8 @@ bool IsReverse(const NodeDef& node) { bool IsReverseV2(const NodeDef& node) { return node.op() == "ReverseV2"; } +bool IsRsqrt(const NodeDef& node) { return node.op() == "Rsqrt"; } + bool IsRsqrtGrad(const NodeDef& node) { return node.op() == "RsqrtGrad"; } bool IsSelect(const NodeDef& node) { return node.op() == "Select"; } @@ -317,6 +319,8 @@ bool IsSplit(const NodeDef& node) { return node.op() == "Split"; } bool IsSplitV(const NodeDef& node) { return node.op() == "SplitV"; } +bool IsSqrt(const NodeDef& node) { return node.op() == "Sqrt"; } + bool IsSqrtGrad(const NodeDef& node) { return node.op() == "SqrtGrad"; } bool IsSquare(const NodeDef& node) { return node.op() == "Square"; } diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h index 3cba6b8b36076c..976d23e52795ba 100644 --- a/tensorflow/core/grappler/op_types.h +++ b/tensorflow/core/grappler/op_types.h @@ -110,6 +110,7 @@ bool IsReshape(const NodeDef& node); bool IsRestore(const NodeDef& node); bool IsReverse(const NodeDef& node); bool IsReverseV2(const NodeDef& node); +bool IsRsqrt(const NodeDef& node); bool IsRsqrtGrad(const NodeDef& node); bool IsSelect(const NodeDef& node); bool IsSeluGrad(const NodeDef& node); @@ -123,6 +124,7 @@ bool IsSoftplusGrad(const NodeDef& node); bool IsSoftsignGrad(const NodeDef& node); bool IsSplit(const NodeDef& node); bool IsSplitV(const NodeDef& node); +bool IsSqrt(const NodeDef& node); bool IsSqrtGrad(const NodeDef& node); bool IsSquare(const NodeDef& node); bool IsSquaredDifference(const NodeDef& node); diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index c0bd0bda95cc55..18076eee96e33a 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -1515,6 +1515,36 @@ class HoistCWiseUnaryFromConcatStage : public ArithmeticOptimizerStage { } }; +// Performs the conversion: +// Div(x, Sqrt(y)) => Mul(x, Rsqrt(y)) +// TODO(srjoglekar): Generalize to optimize cases like (x / pow(y, z)). +class SqrtDivToRsqrtMulStage : public ArithmeticOptimizerStage { + public: + explicit SqrtDivToRsqrtMulStage(const GraphOptimizerContext& ctx, + const ArithmeticOptimizerContext& ctx_ext) + : ArithmeticOptimizerStage("SqrtDivToRsqrtMul", ctx, ctx_ext) {} + ~SqrtDivToRsqrtMulStage() override = default; + + bool IsSupported(const NodeDef* node) const override { + return IsAnyDiv(*node); + } + + Status TrySimplify(NodeDef* node, string* simplified_node_name) override { + NodeDef* y; + TF_RETURN_IF_ERROR(GetInputNode(node->input(1), &y)); + // Optimize only if divisor is a Sqrt whose output is not being consumed + // elsewhere. + if (IsSqrt(*y) && (NumNonControlOutputs(*y, *ctx().node_map) == 1)) { + // a / sqrt(b) = a * rsqrt(b) + node->set_op("Mul"); + y->set_op("Rsqrt"); + AddToOptimizationQueue(node); + AddToOptimizationQueue(y); + } + return Status::OK(); + } +}; + } // namespace class UniqueNodes { @@ -2172,6 +2202,8 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) { pipeline.AddStage(ctx, ctx_ext); if (options_.hoist_unary_out_of_concat) pipeline.AddStage(ctx, ctx_ext); + if (options_.convert_sqrt_div_to_rsqrt_mul) + pipeline.AddStage(ctx, ctx_ext); VLOG(1) << "Run " << pipeline.NumStages() << " arithmetic optimizer stages: " << str_util::Join(pipeline.StageNames(), ", "); diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h index 689ffd45fe7cbd..24a2a50719531c 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h @@ -66,6 +66,7 @@ class ArithmeticOptimizer : public GraphOptimizer { bool remove_redundant_cast = true; bool remove_negation = true; bool hoist_unary_out_of_concat = false; + bool convert_sqrt_div_to_rsqrt_mul = false; // Choose which arithmetic optimizer stages will be enabled for a given // optimization level by default. diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc index df10dbdf48ff2a..7485d99c3bd7ac 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc @@ -148,10 +148,16 @@ class ArithmeticOptimizerTest : public GrapplerTest { DisableAllStages(optimizer); optimizer->options_.remove_negation = true; } + void EnableOnlyHoistCWiseUnaryFromConcat(ArithmeticOptimizer* optimizer) { DisableAllStages(optimizer); optimizer->options_.hoist_unary_out_of_concat = true; } + + void EnableOnlySqrtDivToRsqrtMul(ArithmeticOptimizer* optimizer) { + DisableAllStages(optimizer); + optimizer->options_.convert_sqrt_div_to_rsqrt_mul = true; + } }; TEST_F(ArithmeticOptimizerTest, NoOp) { @@ -1936,6 +1942,43 @@ TEST_F(ArithmeticOptimizerTest, RemoveNegation) { EXPECT_EQ(5, found); } +TEST_F(ArithmeticOptimizerTest, ConvertSqrtDivToRsqrtMul) { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + auto x = ops::Const(s.WithOpName("x"), {1.0f, 2.0f}, {1, 2}); + auto y = ops::Const(s.WithOpName("y"), {3.0f, 4.0f}, {1, 2}); + Output sqrt_y = ops::Sqrt(s.WithOpName("sqrt_y"), y); + Output div_x_sqrt_y = ops::Div(s.WithOpName("output"), x, sqrt_y); + + GrapplerItem item; + item.fetch = {"output"}; + TF_CHECK_OK(s.ToGraphDef(&item.graph)); + auto tensors_expected = EvaluateNodes(item.graph, item.fetch); + EXPECT_EQ(1, tensors_expected.size()); + + GraphDef output; + ArithmeticOptimizer optimizer; + EnableOnlySqrtDivToRsqrtMul(&optimizer); + OptimizeAndPrune(&optimizer, &item, &output); + auto tensors = EvaluateNodes(output, item.fetch); + EXPECT_EQ(1, tensors.size()); + + test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6); + EXPECT_EQ(item.graph.node_size(), output.node_size()); + for (int i = 0; i < output.node_size(); ++i) { + const NodeDef& node = output.node(i); + if (node.name() == "output") { + EXPECT_EQ("Mul", node.op()); + EXPECT_EQ(2, node.input_size()); + EXPECT_EQ("x", node.input(0)); + EXPECT_EQ("sqrt_y", node.input(1)); + } else if (node.name() == "sqrt_y") { + EXPECT_EQ("Rsqrt", node.op()); + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("y", node.input(0)); + } + } +} + TEST_F(ArithmeticOptimizerTest, MinimizeBroadcasts_SimpleSwap) { tensorflow::Scope s = tensorflow::Scope::NewRootScope(); From eceb3a2e3d31f404fe207bff10759cdb928c75e4 Mon Sep 17 00:00:00 2001 From: Daniel Zheng Date: Thu, 26 Apr 2018 15:02:53 -0700 Subject: [PATCH 0077/1691] Edit tensorflow.org/community/swift page. PiperOrigin-RevId: 194459754 --- tensorflow/docs_src/community/swift.md | 23 +++++++++++------------ 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/tensorflow/docs_src/community/swift.md b/tensorflow/docs_src/community/swift.md index 46512f7c5dae61..f065b207c61001 100644 --- a/tensorflow/docs_src/community/swift.md +++ b/tensorflow/docs_src/community/swift.md @@ -9,23 +9,22 @@ important enough for first-class language and compiler support, and thus works very differently from normal language bindings. First-class language and compiler support allow us to innovate in areas that -traditionally were out of bounds for machine learning libraries. Our programming -model combines the performance of TensorFlow graphs with the flexibility and -expressivity of Eager execution, while keeping a strong focus on improved -usability at every level of the stack. - +traditionally were out of bounds for machine learning libraries. Our +programming model combines the performance of TensorFlow graphs with the +flexibility and expressivity of Eager execution, while keeping a strong focus +on improved usability at every level of the stack. ## Open Source We have released Swift for TensorFlow as an open-source project on GitHub! -Our [central repository](https://github.com/tensorflow/swift) contains project -documentation, including an -[overview and technical papers](https://github.com/tensorflow/swift/tree/master/docs) -explaining specific areas of the project in depth. This repo also includes -instructions for [installing prebuilt packages](https://github.com/tensorflow/swift/blob/master/Installation.md) -for macOS and Linux platforms, [simple usage instructions](https://github.com/tensorflow/swift/blob/master/Usage.md), -and how to build from source. +Our [documentation repository](https://github.com/tensorflow/swift) contains a +[project overview](https://github.com/tensorflow/swift/blob/master/docs/DesignOverview.md) +and [technical papers](https://github.com/tensorflow/swift/tree/master/docs) +explaining specific areas in depth. There are also instructions for [installing +pre-built packages](https://github.com/tensorflow/swift/blob/master/Installation.md) +(for macOS and Ubuntu) as well as a simple +[usage tutorial](https://github.com/tensorflow/swift/blob/master/Usage.md). Moving forward, we will use an open design model and all discussions will be public. From 5dd3d19818193be9bc59a5e802a3d70853a73df4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 15:07:43 -0700 Subject: [PATCH 0078/1691] Disable triangular_solve_test on ASAN due to flaky time outs. PiperOrigin-RevId: 194460641 --- tensorflow/compiler/tf2xla/lib/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/compiler/tf2xla/lib/BUILD b/tensorflow/compiler/tf2xla/lib/BUILD index fde1977c1b1834..12fdfb605d667b 100644 --- a/tensorflow/compiler/tf2xla/lib/BUILD +++ b/tensorflow/compiler/tf2xla/lib/BUILD @@ -91,6 +91,7 @@ cc_library( xla_test( name = "triangular_solve_test", srcs = ["triangular_solve_test.cc"], + tags = ["noasan"], # sometimes times out, http://b/78650012 deps = [ ":triangular_solve", "//tensorflow/compiler/xla:array2d", From 2ce60cd2ebe835c7dea9df990b70218e418238b6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 15:08:24 -0700 Subject: [PATCH 0079/1691] Add support for variables in tf.custom_gradient PiperOrigin-RevId: 194460752 --- tensorflow/python/BUILD | 3 + tensorflow/python/ops/custom_gradient.py | 137 ++++++++++++++++------- tensorflow/python/ops/gradients_test.py | 124 ++++++++++++++------ 3 files changed, 192 insertions(+), 72 deletions(-) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 8e7f0cadad776e..e2d86fa4f75c8b 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -1880,6 +1880,7 @@ py_library( ":platform", ":spectral_grad", ":util", + ":variable_scope", "//tensorflow/python/eager:backprop", "//tensorflow/python/eager:context", "//tensorflow/python/eager:tape", @@ -2776,6 +2777,7 @@ cuda_py_test( ":framework_test_lib", ":functional_ops", ":gradients", + ":layers", ":math_grad", ":math_ops", ":nn_grad", @@ -2785,6 +2787,7 @@ cuda_py_test( ":tensor_array_grad", ":tensor_array_ops", ":test_ops", + ":variable_scope", "//third_party/py/numpy", ], ) diff --git a/tensorflow/python/ops/custom_gradient.py b/tensorflow/python/ops/custom_gradient.py index dfa07abfc64748..c07c669b593d54 100644 --- a/tensorflow/python/ops/custom_gradient.py +++ b/tensorflow/python/ops/custom_gradient.py @@ -18,13 +18,16 @@ from __future__ import division from __future__ import print_function +from tensorflow.python.eager import backprop from tensorflow.python.eager import context -from tensorflow.python.eager import tape +from tensorflow.python.eager import tape as tape_lib from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_array_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.util import nest from tensorflow.python.util import tf_decorator +from tensorflow.python.util import tf_inspect from tensorflow.python.util.tf_export import tf_export @@ -73,17 +76,25 @@ def grad(dy): for fine grained control over the gradient computation of a sequence of operations. + Note that if the decorated function uses `Variable`s, the enclosing variable + scope must be using `ResourceVariable`s. + Args: f: function `f(x)` that returns a tuple `(y, grad_fn)` where: - `x` is a `Tensor` or sequence of `Tensor` inputs to the function. - `y` is a `Tensor` or sequence of `Tensor` outputs of applying TensorFlow operations in `f` to `x`. - - `grad_fn` is a function with the signature `g(grad_ys)` which returns + - `grad_fn` is a function with the signature `g(*grad_ys)` which returns a list of `Tensor`s - the derivatives of `Tensor`s in `y` with respect to the `Tensor`s in `x. `grad_ys` is a `Tensor` or sequence of `Tensor`s the same size as `y` holding the initial value gradients for - each `Tensor` in `y`. + each `Tensor` in `y`. If `f` uses `Variable`s (that are not part of the + inputs), i.e. through `get_variable`, then `grad_fn` should have + signature `g(*grad_ys, variables=None)`, where `variables` is a list of + the `Variable`s, and return a 2-tuple `(grad_xs, grad_vars)`, where + `grad_xs` is the same as above, and `grad_vars` is a `list` + with the derivatives of `Tensor`s in `y` with respect to the variables. Returns: A function `h(x)` which returns the same value as `f(x)[0]` and whose @@ -92,43 +103,89 @@ def grad(dy): def decorated(*args, **kwargs): """Decorated function with custom gradient.""" - if not context.executing_eagerly(): - if kwargs: - raise ValueError( - "The custom_gradient decorator currently supports keywords " - "arguments only when eager execution is enabled.") - name = "CustomGradient-%s" % ops.uid() - args = [ops.convert_to_tensor(x) for x in args] - result, grad_fn = f(*args) - flat_result = nest.flatten(result) - all_tensors = flat_result + args - - @ops.RegisterGradient(name) - def internal_grad_fn(unused_op, *result_grads): # pylint: disable=unused-variable - gradients = nest.flatten(grad_fn(*result_grads[:len(flat_result)])) - # Need to return one value per input to the IdentityN, so pad the - # gradients of the inputs of the custom_gradient function with the - # gradients of the outputs as well. - return ([None] * len(flat_result)) + gradients - - with ops.get_default_graph().gradient_override_map({"IdentityN": name}): - all_tensors = array_ops.identity_n(all_tensors) - return nest.pack_sequence_as( - structure=result, flat_sequence=all_tensors[:len(flat_result)]) - - input_tensors = [ops.convert_to_tensor(x) for x in args] - - result, grad_fn = f(*args, **kwargs) - flat_result = nest.flatten(result) - # TODO(apassos) consider removing the identity below. - flat_result = [gen_array_ops.identity(x) for x in flat_result] + if context.executing_eagerly(): + return _eager_mode_decorator(f, *args, **kwargs) + else: + return _graph_mode_decorator(f, *args, **kwargs) - def actual_grad_fn(*outputs): - return nest.flatten(grad_fn(*outputs)) + return tf_decorator.make_decorator(f, decorated) - tape.record_operation(f.__name__, flat_result, input_tensors, - actual_grad_fn) - flat_result = list(flat_result) - return nest.pack_sequence_as(result, flat_result) - return tf_decorator.make_decorator(f, decorated) +def _graph_mode_decorator(f, *args, **kwargs): + """Implement custom gradient decorator for graph mode.""" + # TODO(rsepassi): Add support for kwargs + if kwargs: + raise ValueError( + "The custom_gradient decorator currently supports keywords " + "arguments only when eager execution is enabled.") + name = "CustomGradient-%s" % ops.uid() + args = [ops.convert_to_tensor(x) for x in args] + with backprop.GradientTape() as tape: + result, grad_fn = f(*args) + # The variables that grad_fn needs to return gradients for are the set of + # variables used that are *not* part of the inputs. + variables = list(set(tape.watched_variables()) - set(args)) + grad_argspec = tf_inspect.getargspec(grad_fn) + if "variables" in grad_argspec.args: + if not variable_scope.get_variable_scope().use_resource: + raise TypeError("If using @custom_gradient with a function that " + "creates variables, the enclosing variable scope must " + "have use_resource=True.") + flat_result = nest.flatten(result) + all_tensors = flat_result + args + variables + + @ops.RegisterGradient(name) + def internal_grad_fn(unused_op, *result_grads): # pylint: disable=unused-variable + """Custom grad fn wrapper.""" + result_grads = result_grads[:len(flat_result)] + if variables: + input_grads, variable_grads = grad_fn(*result_grads, variables=variables) + if len(variable_grads) != len(variables): + raise ValueError("Must return gradient for each variable from " + "@custom_gradient grad_fn.") + else: + input_grads = grad_fn(*result_grads) + variable_grads = [] + + # Need to return one value per input to the IdentityN, so pad the + # gradients of the inputs of the custom_gradient function with the + # gradients of the outputs as well. + input_grads = nest.flatten(input_grads) + return ([None] * len(flat_result)) + input_grads + variable_grads + + with ops.get_default_graph().gradient_override_map({"IdentityN": name}): + all_tensors = array_ops.identity_n(all_tensors) + return nest.pack_sequence_as( + structure=result, flat_sequence=all_tensors[:len(flat_result)]) + + +def _eager_mode_decorator(f, *args, **kwargs): + """Implement custom gradient decorator for eager mode.""" + with backprop.GradientTape() as tape: + result, grad_fn = f(*args, **kwargs) + all_inputs = list(args) + list(kwargs.values()) + # The variables that grad_fn needs to return gradients for are the set of + # variables used that are *not* part of the inputs. + variables = list(set(tape.watched_variables()) - set(all_inputs)) + flat_result = nest.flatten(result) + # TODO(apassos) consider removing the identity below. + flat_result = [gen_array_ops.identity(x) for x in flat_result] + + def actual_grad_fn(*result_grads): + """Custom grad fn wrapper.""" + if variables: + input_grads, variable_grads = grad_fn(*result_grads, variables=variables) + if len(variable_grads) != len(variables): + raise ValueError("Must return gradient for each variable from " + "@custom_gradient grad_fn.") + else: + input_grads = grad_fn(*result_grads) + variable_grads = [] + return nest.flatten(input_grads) + variable_grads + + input_tensors = [ops.convert_to_tensor(x) for x + in list(args) + list(variables)] + tape_lib.record_operation(f.__name__, flat_result, input_tensors, + actual_grad_fn) + flat_result = list(flat_result) + return nest.pack_sequence_as(result, flat_result) diff --git a/tensorflow/python/ops/gradients_test.py b/tensorflow/python/ops/gradients_test.py index 0603d3b6706b96..f33637238c3eb2 100644 --- a/tensorflow/python/ops/gradients_test.py +++ b/tensorflow/python/ops/gradients_test.py @@ -24,6 +24,8 @@ import numpy as np from tensorflow.python.client import session +from tensorflow.python.eager import backprop +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import function @@ -31,6 +33,7 @@ from tensorflow.python.framework import test_ops from tensorflow.python.framework import test_util from tensorflow.python.framework.constant_op import constant +from tensorflow.python.layers import core as core_layers from tensorflow.python.ops import array_grad # pylint: disable=unused-import from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_grad # pylint: disable=unused-import @@ -48,6 +51,7 @@ from tensorflow.python.ops import state_grad # pylint: disable=unused-import from tensorflow.python.ops import tensor_array_grad # pylint: disable=unused-import from tensorflow.python.ops import tensor_array_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.ops.nn_ops import bias_add from tensorflow.python.platform import googletest @@ -744,6 +748,47 @@ def testWarnings(self): "of unknown shape. This may consume a large amount of memory." in str(w[0].message)) + +@test_util.with_c_api +class OnlyRealGradientsTest(test_util.TensorFlowTestCase): + + def testRealOnly(self): + x = constant_op.constant(7+3j, dtype=dtypes.complex64) + y = math_ops.square(x) + with self.assertRaisesRegexp( + TypeError, + r"Gradients of complex tensors must set grad_ys " + r"\(y\.dtype = tf\.complex64\)"): + gradients.gradients(y, x) + + +class ResourceCondTest(test_util.TensorFlowTestCase): + + def testBasic(self): + gamma = resource_variable_ops.ResourceVariable( + np.random.random((3,)), + dtype="float32", name="gamma") + + inputs = array_ops.ones(shape=(3,), dtype="float32") + + def TestFn(): + output = inputs + gamma + return output + + training = array_ops.placeholder_with_default(True, shape=()) + output = control_flow_ops.cond( + training, TestFn, lambda: inputs) + + loss = output + + grads = gradients.gradients( + loss, [gamma]) + self.assertTrue(None not in grads) + + +@test_util.with_c_api +class CustomGradientTest(test_util.TensorFlowTestCase): + def testCustomGradientTrivial(self): @custom_gradient.custom_gradient @@ -797,42 +842,57 @@ def Grad(_): with self.assertRaises(RuntimeError): gradients.gradients(y, x) + def testCustomGradientWithVariables(self): -@test_util.with_c_api -class OnlyRealGradientsTest(test_util.TensorFlowTestCase): - - def testRealOnly(self): - x = constant_op.constant(7+3j, dtype=dtypes.complex64) - y = math_ops.square(x) - with self.assertRaisesRegexp( - TypeError, - r"Gradients of complex tensors must set grad_ys " - r"\(y\.dtype = tf\.complex64\)"): - gradients.gradients(y, x) - - -class ResourceCondTest(test_util.TensorFlowTestCase): - - def testBasic(self): - gamma = resource_variable_ops.ResourceVariable( - np.random.random((3,)), - dtype="float32", name="gamma") - - inputs = array_ops.ones(shape=(3,), dtype="float32") - - def TestFn(): - output = inputs + gamma - return output + @custom_gradient.custom_gradient + def F(x): + out = core_layers.dense(x, 3, use_bias=False) - training = array_ops.placeholder_with_default(True, shape=()) - output = control_flow_ops.cond( - training, TestFn, lambda: inputs) + def Grad(out_grad, variables=None): # pylint: disable=redefined-outer-name + self.assertEqual(1, len(variables)) + grads = gradients.gradients(out, [x, variables[0]], grad_ys=out_grad) + return grads[0], [array_ops.ones((4, 3))] - loss = output + return out, Grad - grads = gradients.gradients( - loss, [gamma]) - self.assertTrue(None not in grads) + with ops.Graph().as_default(): + x = array_ops.ones((2, 4)) + with variable_scope.variable_scope("f", use_resource=True) as vs: + y = F(x) + all_vars = vs.global_variables() + assert len(all_vars) == 1 + grads = gradients.gradients(y, [x, all_vars[0]]) + for g in grads: + self.assertTrue(g is not None) + with session.Session() as sess: + sess.run(variables.global_variables_initializer()) + dw = sess.run(math_ops.reduce_sum(grads[1])) + self.assertEqual(12., dw) + + def testCustomGradientWithVariablesEager(self): + with context.eager_mode(): + layer = core_layers.Dense(4, use_bias=False) + + @custom_gradient.custom_gradient + def F(x): + out = layer(x) + + def Grad(out_grad, variables=None): # pylint: disable=redefined-outer-name + del out_grad + self.assertEqual(1, len(variables)) + return (array_ops.ones((3, 2)), + [array_ops.ones((2, 4))]) + + return out, Grad + + x = array_ops.ones((3, 2)) + 2. + with backprop.GradientTape() as tape: + tape.watch(x) + y = F(x) + w, = layer.variables + dx, dw = tape.gradient(y, [x, w]) + self.assertEqual(6., math_ops.reduce_sum(dx).numpy()) + self.assertEqual(8., math_ops.reduce_sum(dw).numpy()) if __name__ == "__main__": From c9be1f2b19972e0b10e8c96e24b3dc3aa05ea651 Mon Sep 17 00:00:00 2001 From: James Martens Date: Thu, 26 Apr 2018 15:13:48 -0700 Subject: [PATCH 0080/1691] - Default values of cov and inv variables are now 0. Zero-debiasing (as in Adam) is used for the cov matrices. Note this this requires that cov variables, then inv variables, are all updated before the first training update is made. All examples have been modified to do this. NOTE: you *may* have to increase the damping value you use at the start of optimization after this change (or throughout, if you are using a constant value). - Changed the initial default approximation used for generic registrations to "diagonal" - Convenience properties for ops and thunks have all been removed, along with "make_ops_and_vars". User should only interface with "make_vars_and_create_op_thunks" (or maybe "create_ops_and_vars_thunks"). PiperOrigin-RevId: 194461623 --- tensorflow/contrib/kfac/examples/convnet.py | 51 ++++++------ tensorflow/contrib/kfac/examples/mlp.py | 78 +++++++++++++------ .../kfac/examples/tests/convnet_test.py | 2 +- .../contrib/kfac/python/kernel_tests/BUILD | 1 + .../python/kernel_tests/estimator_test.py | 23 +++--- .../python/kernel_tests/fisher_blocks_test.py | 14 ++++ .../kernel_tests/fisher_factors_test.py | 7 ++ .../python/kernel_tests/optimizer_test.py | 15 ++++ .../contrib/kfac/python/ops/estimator.py | 38 --------- .../contrib/kfac/python/ops/fisher_factors.py | 26 +++++-- .../kfac/python/ops/layer_collection.py | 2 +- .../contrib/kfac/python/ops/optimizer.py | 58 -------------- .../contrib/kfac/python/ops/placement.py | 52 ------------- 13 files changed, 149 insertions(+), 218 deletions(-) diff --git a/tensorflow/contrib/kfac/examples/convnet.py b/tensorflow/contrib/kfac/examples/convnet.py index e8e3353091df25..b261f41bf97db1 100644 --- a/tensorflow/contrib/kfac/examples/convnet.py +++ b/tensorflow/contrib/kfac/examples/convnet.py @@ -223,26 +223,26 @@ def minimize_loss_single_machine(loss, (cov_update_thunks, inv_update_thunks) = optimizer.make_vars_and_create_op_thunks() - with tf.device(device): - train_op = optimizer.minimize(loss, global_step=g_step) - def make_update_op(update_thunks): - update_op = [thunk() for thunk in update_thunks] - return tf.group(*update_op) + update_ops = [thunk() for thunk in update_thunks] + return tf.group(*update_ops) cov_update_op = make_update_op(cov_update_thunks) - with tf.control_dependencies([train_op, cov_update_op]): + with tf.control_dependencies([cov_update_op]): inverse_op = tf.cond( - tf.equal(tf.mod(g_step + 1, _INVERT_EVERY), 0), + tf.equal(tf.mod(g_step, _INVERT_EVERY), 0), lambda: make_update_op(inv_update_thunks), tf.no_op) + with tf.control_dependencies([inverse_op]): + with tf.device(device): + train_op = optimizer.minimize(loss, global_step=g_step) tf.logging.info("Starting training.") with tf.train.MonitoredTrainingSession(config=session_config) as sess: while not sess.should_stop(): global_step_, loss_, accuracy_, _ = sess.run( - [g_step, loss, accuracy, inverse_op]) + [g_step, loss, accuracy, train_op]) - if (global_step_ + 1) % _INVERT_EVERY == 0: + if global_step_ % _INVERT_EVERY == 0: tf.logging.info("global_step: %d | loss: %f | accuracy: %s", global_step_, loss_, accuracy_) @@ -357,24 +357,25 @@ def distributed_grads_only_and_ops_chief_worker( task_id, num_worker_tasks, num_ps_tasks, layer_collection) (cov_update_thunks, inv_update_thunks) = optimizer.make_vars_and_create_op_thunks() - train_op = sync_optimizer.minimize(loss, global_step=global_step) tf.logging.info("Starting training.") hooks = [sync_optimizer.make_session_run_hook(is_chief)] def make_update_op(update_thunks): - update_op = [thunk() for thunk in update_thunks] - return tf.group(*update_op) + update_ops = [thunk() for thunk in update_thunks] + return tf.group(*update_ops) if is_chief: cov_update_op = make_update_op(cov_update_thunks) - with tf.control_dependencies([train_op, cov_update_op]): - update_op = tf.cond( - tf.equal(tf.mod(global_step + 1, invert_every), 0), + with tf.control_dependencies([cov_update_op]): + inverse_op = tf.cond( + tf.equal(tf.mod(global_step, invert_every), 0), lambda: make_update_op(inv_update_thunks), tf.no_op) + with tf.control_dependencies([inverse_op]): + train_op = sync_optimizer.minimize(loss, global_step=global_step) else: - update_op = train_op + train_op = sync_optimizer.minimize(loss, global_step=global_step) with tf.train.MonitoredTrainingSession( master=master, @@ -384,7 +385,7 @@ def make_update_op(update_thunks): stop_grace_period_secs=0) as sess: while not sess.should_stop(): global_step_, loss_, accuracy_, _ = sess.run( - [global_step, loss, accuracy, update_op]) + [global_step, loss, accuracy, train_op]) tf.logging.info("global_step: %d | loss: %f | accuracy: %s", global_step_, loss_, accuracy_) return accuracy_ @@ -577,25 +578,25 @@ def train_mnist_multitower(data_dir, num_epochs, num_towers, (cov_update_thunks, inv_update_thunks) = optimizer.make_vars_and_create_op_thunks() - train_op = optimizer.minimize(loss, global_step=g_step) - def make_update_op(update_thunks): - update_op = [thunk() for thunk in update_thunks] - return tf.group(*update_op) + update_ops = [thunk() for thunk in update_thunks] + return tf.group(*update_ops) cov_update_op = make_update_op(cov_update_thunks) - with tf.control_dependencies([train_op, cov_update_op]): + with tf.control_dependencies([cov_update_op]): inverse_op = tf.cond( - tf.equal(tf.mod(g_step + 1, _INVERT_EVERY), 0), + tf.equal(tf.mod(g_step, _INVERT_EVERY), 0), lambda: make_update_op(inv_update_thunks), tf.no_op) + with tf.control_dependencies([inverse_op]): + train_op = optimizer.minimize(loss, global_step=g_step) tf.logging.info("Starting training.") with tf.train.MonitoredTrainingSession(config=session_config) as sess: while not sess.should_stop(): global_step_, loss_, accuracy_, _ = sess.run( - [g_step, loss, accuracy, inverse_op]) + [g_step, loss, accuracy, train_op]) - if (global_step_ + 1) % _INVERT_EVERY == 0: + if global_step_ % _INVERT_EVERY == 0: tf.logging.info("global_step: %d | loss: %f | accuracy: %s", global_step_, loss_, accuracy_) diff --git a/tensorflow/contrib/kfac/examples/mlp.py b/tensorflow/contrib/kfac/examples/mlp.py index 87eed03888c894..ea2b252a05702d 100644 --- a/tensorflow/contrib/kfac/examples/mlp.py +++ b/tensorflow/contrib/kfac/examples/mlp.py @@ -105,18 +105,21 @@ def build_model(examples, labels, num_labels, layer_collection): return loss, accuracy -def minimize(loss, accuracy, layer_collection, session_config=None): +def minimize(loss, accuracy, layer_collection, num_towers, session_config=None): """Minimize 'loss' with KfacOptimizer. Args: loss: 0-D Tensor. Loss to be minimized. accuracy: 0-D Tensor. Accuracy of classifier on current minibatch. layer_collection: LayerCollection instance. Describes layers in model. + num_towers: int. Number of CPUs to split minibatch across. session_config: tf.ConfigProto. Configuration for tf.Session(). Returns: accuracy of classifier on final minibatch. """ + devices = tuple("/cpu:%d" % tower_id for tower_id in range(num_towers)) + # Train with K-FAC. We'll use a decreasing learning rate that's cut in 1/2 # every 10k iterations. tf.logging.info("Building KFAC Optimizer.") @@ -125,27 +128,38 @@ def minimize(loss, accuracy, layer_collection, session_config=None): learning_rate=tf.train.exponential_decay( 0.00002, global_step, 10000, 0.5, staircase=True), cov_ema_decay=0.95, - damping=0.0001, + damping=0.0005, layer_collection=layer_collection, - momentum=0.99) - train_op = optimizer.minimize(loss, global_step=global_step) + momentum=0.99, + placement_strategy="round_robin", + cov_devices=devices, + inv_devices=devices) + + (cov_update_thunks, + inv_update_thunks) = optimizer.make_vars_and_create_op_thunks() + + def make_update_op(update_thunks): + update_ops = [thunk() for thunk in update_thunks] + return tf.group(*update_ops) + + # TODO(b/78537047): change (some) examples to use PeriodicInvCovUpdateKfacOpt + # once that gets moved over? Could still leave more advanced examples as they + # are (e.g. train_mnist_estimator in this file) + + cov_update_op = make_update_op(cov_update_thunks) + with tf.control_dependencies([cov_update_op]): + # We update the inverses only every 20 iterations. + inverse_op = tf.cond( + tf.equal(tf.mod(global_step, 100), 0), + lambda: make_update_op(inv_update_thunks), tf.no_op) + with tf.control_dependencies([inverse_op]): + train_op = optimizer.minimize(loss, global_step=global_step) tf.logging.info("Starting training.") with tf.train.MonitoredTrainingSession(config=session_config) as sess: while not sess.should_stop(): - # K-FAC has 3 primary ops, - # - train_op: Update the weights with the minibatch's gradient. - # - cov_update_op: Update statistics used for building K-FAC's - # preconditioner matrix. - # - inv_update_op: Update preconditioner matrix using statistics. - # - # The first 2 of these are cheap and should be done with each step. The - # latter is more expensive, and should be updated ~100 iterations. - global_step_, loss_, accuracy_, _, _ = sess.run( - [global_step, loss, accuracy, train_op, optimizer.cov_update_op]) - - if global_step_ % 100 == 0: - sess.run(optimizer.inv_update_op) + global_step_, loss_, accuracy_, _ = sess.run( + [global_step, loss, accuracy, train_op]) if global_step_ % 100 == 0: tf.logging.info("global_step: %d | loss: %f | accuracy: %f", @@ -180,7 +194,7 @@ def train_mnist(data_dir, num_epochs, use_fake_data=False): loss, accuracy = build_model(examples, labels, 10, layer_collection) # Fit model. - minimize(loss, accuracy, layer_collection) + minimize(loss, accuracy, layer_collection, 1) def train_mnist_multitower(data_dir, @@ -238,7 +252,8 @@ def train_mnist_multitower(data_dir, "CPU": num_towers }) return minimize( - loss, accuracy, layer_collection, session_config=session_config) + loss, accuracy, layer_collection, num_towers, + session_config=session_config) def train_mnist_estimator(data_dir, num_epochs, use_fake_data=False): @@ -298,13 +313,26 @@ def model_fn(features, labels, mode, params): layer_collection=layer_collection, momentum=0.99) + (cov_update_thunks, + inv_update_thunks) = optimizer.make_vars_and_create_op_thunks() + + def make_update_op(update_thunks): + update_ops = [thunk() for thunk in update_thunks] + return tf.group(*update_ops) + + def make_batch_executed_op(update_thunks, batch_size=1): + return tf.group(*tf.contrib.kfac.utils.batch_execute( + global_step, update_thunks, batch_size=batch_size)) + # Run cov_update_op every step. Run 1 inv_update_ops per step. - cov_update_op = optimizer.cov_update_op - inv_update_op = tf.group( - tf.contrib.kfac.utils.batch_execute( - global_step, optimizer.inv_update_thunks, batch_size=1)) - with tf.control_dependencies([cov_update_op, inv_update_op]): - train_op = optimizer.minimize(loss, global_step=global_step) + cov_update_op = make_update_op(cov_update_thunks) + with tf.control_dependencies([cov_update_op]): + # But make sure to execute all the inverse ops on the first step + inverse_op = tf.cond(tf.equal(global_step, 0), + lambda: make_update_op(inv_update_thunks), + lambda: make_batch_executed_op(inv_update_thunks)) + with tf.control_dependencies([inverse_op]): + train_op = optimizer.minimize(loss, global_step=global_step) # Print metrics every 5 sec. hooks = [ diff --git a/tensorflow/contrib/kfac/examples/tests/convnet_test.py b/tensorflow/contrib/kfac/examples/tests/convnet_test.py index 6de775cc79953b..adecda71666ee7 100644 --- a/tensorflow/contrib/kfac/examples/tests/convnet_test.py +++ b/tensorflow/contrib/kfac/examples/tests/convnet_test.py @@ -157,7 +157,7 @@ def testTrainMnistDistributed(self): num_ps_tasks=0, master="", data_dir=None, - num_epochs=1, + num_epochs=2, op_strategy="chief_worker", use_fake_data=True) diff --git a/tensorflow/contrib/kfac/python/kernel_tests/BUILD b/tensorflow/contrib/kfac/python/kernel_tests/BUILD index c2436affe27354..6e4a8d71baa85d 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/BUILD +++ b/tensorflow/contrib/kfac/python/kernel_tests/BUILD @@ -97,6 +97,7 @@ py_test( srcs = ["optimizer_test.py"], srcs_version = "PY2AND3", deps = [ + "//tensorflow/contrib/kfac/python/ops:fisher_factors", "//tensorflow/contrib/kfac/python/ops:kfac_optimizer", "//tensorflow/contrib/kfac/python/ops:layer_collection", "//tensorflow/python:array_ops", diff --git a/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py b/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py index f22dbcf2156629..0e65d419a31838 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py @@ -81,7 +81,7 @@ def testEstimatorInitManualRegistration(self): damping=0.2, layer_collection=self.layer_collection ) - est.make_ops_and_vars() + est.make_vars_and_create_op_thunks() # Check that we throw an error if we don't include registered variables, # i.e. self.weights @@ -91,7 +91,7 @@ def testEstimatorInitManualRegistration(self): cov_ema_decay=0.1, damping=0.2, layer_collection=self.layer_collection) - est.make_ops_and_vars() + est.make_vars_and_create_op_thunks() @test.mock.patch.object(utils.SubGraph, "variable_uses", return_value=42) def testVariableWrongNumberOfUses(self, mock_uses): @@ -101,7 +101,7 @@ def testVariableWrongNumberOfUses(self, mock_uses): cov_ema_decay=0.1, damping=0.2, layer_collection=self.layer_collection) - est.make_ops_and_vars() + est.make_vars_and_create_op_thunks() def testInvalidEstimationMode(self): with self.assertRaises(ValueError): @@ -111,7 +111,7 @@ def testInvalidEstimationMode(self): damping=0.2, layer_collection=self.layer_collection, estimation_mode="not_a_real_mode") - est.make_ops_and_vars() + est.make_vars_and_create_op_thunks() def testGradientsModeBuild(self): with self._graph.as_default(): @@ -121,7 +121,7 @@ def testGradientsModeBuild(self): damping=0.2, layer_collection=self.layer_collection, estimation_mode="gradients") - est.make_ops_and_vars() + est.make_vars_and_create_op_thunks() def testEmpiricalModeBuild(self): with self._graph.as_default(): @@ -131,7 +131,7 @@ def testEmpiricalModeBuild(self): damping=0.2, layer_collection=self.layer_collection, estimation_mode="empirical") - est.make_ops_and_vars() + est.make_vars_and_create_op_thunks() def testCurvaturePropModeBuild(self): with self._graph.as_default(): @@ -141,7 +141,7 @@ def testCurvaturePropModeBuild(self): damping=0.2, layer_collection=self.layer_collection, estimation_mode="curvature_prop") - est.make_ops_and_vars() + est.make_vars_and_create_op_thunks() def testExactModeBuild(self): with self._graph.as_default(): @@ -151,7 +151,7 @@ def testExactModeBuild(self): damping=0.2, layer_collection=self.layer_collection, estimation_mode="exact") - est.make_ops_and_vars() + est.make_vars_and_create_op_thunks() def test_cov_update_thunks(self): """Ensures covariance update ops run once per global_step.""" @@ -215,8 +215,11 @@ def test_round_robin_placement(self): inv_devices=["/cpu:{}".format(i) for i in range(2)]) # Construct an op that executes one covariance update per step. - (cov_update_ops, _, inv_update_ops, _, _, - _) = fisher_estimator.make_ops_and_vars(scope="test") + (cov_update_thunks, + inv_update_thunks) = fisher_estimator.make_vars_and_create_op_thunks( + scope="test") + cov_update_ops = tuple(thunk() for thunk in cov_update_thunks) + inv_update_ops = tuple(thunk() for thunk in inv_update_thunks) self.assertEqual(cov_update_ops[0].device, "/device:CPU:0") self.assertEqual(cov_update_ops[1].device, "/device:CPU:1") self.assertEqual(inv_update_ops[0].device, "/device:CPU:0") diff --git a/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py b/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py index 566d393f453236..86ec7a095afdf4 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py @@ -21,6 +21,7 @@ import numpy as np from tensorflow.contrib.kfac.python.ops import fisher_blocks as fb +from tensorflow.contrib.kfac.python.ops import fisher_factors as ff from tensorflow.contrib.kfac.python.ops import layer_collection as lc from tensorflow.contrib.kfac.python.ops import linear_operator as lo from tensorflow.contrib.kfac.python.ops import utils @@ -35,6 +36,19 @@ from tensorflow.python.platform import test +# We need to set these constants since the numerical values used in the tests +# were chosen when these used to be the defaults. +ff.set_global_constants(init_covariances_at_zero=False, + zero_debias=False, + init_inverses_at_zero=False) + +# TODO(b/78538100): As far as I can tell, all the tests that say "Make sure our +# inverse is something other than the identity" are actually broken. They never +# run the covariance update ops and so the inverse actually is the identity +# (possible plus the damping term, which would still make it a multiple of the +# identity). + + def _make_psd(dim): """Constructs a PSD matrix of the given dimension.""" mat = np.ones((dim, dim), dtype=np.float32) diff --git a/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py b/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py index 9153ddf09c89ab..fad47cd02f372e 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py @@ -35,6 +35,13 @@ from tensorflow.python.platform import test +# We need to set these constants since the numerical values used in the tests +# were chosen when these used to be the defaults. +ff.set_global_constants(init_covariances_at_zero=False, + zero_debias=False, + init_inverses_at_zero=False) + + def make_damping_func(damping): return fb._package_func(lambda: damping, damping) diff --git a/tensorflow/contrib/kfac/python/kernel_tests/optimizer_test.py b/tensorflow/contrib/kfac/python/kernel_tests/optimizer_test.py index 9325aa1b7325fa..560a9b0b426ecc 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/optimizer_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/optimizer_test.py @@ -20,6 +20,7 @@ import numpy as np +from tensorflow.contrib.kfac.python.ops import fisher_factors as ff from tensorflow.contrib.kfac.python.ops import layer_collection as lc from tensorflow.contrib.kfac.python.ops import optimizer from tensorflow.python.framework import ops @@ -32,6 +33,13 @@ from tensorflow.python.platform import test +# We need to set these constants since the numerical values used in the tests +# were chosen when these used to be the defaults. +ff.set_global_constants(init_covariances_at_zero=False, + zero_debias=False, + init_inverses_at_zero=False) + + def dummy_layer_collection(): lcoll = lc.LayerCollection() dummy = array_ops.constant([1., 2.]) @@ -186,6 +194,11 @@ def testApplyGradients(self): layer_collection, momentum=0.5, momentum_type='regular') + (cov_update_thunks, + inv_update_thunks) = opt.make_vars_and_create_op_thunks() + cov_update_ops = tuple(thunk() for thunk in cov_update_thunks) + inv_update_ops = tuple(thunk() for thunk in inv_update_thunks) + grads_and_vars = opt.compute_gradients(output, [weights, bias]) all_vars = [grad_and_var[1] for grad_and_var in grads_and_vars] @@ -193,6 +206,8 @@ def testApplyGradients(self): sess.run(tf_variables.global_variables_initializer()) old_vars = sess.run(all_vars) + sess.run(cov_update_ops) + sess.run(inv_update_ops) sess.run(op) new_vars = sess.run(all_vars) diff --git a/tensorflow/contrib/kfac/python/ops/estimator.py b/tensorflow/contrib/kfac/python/ops/estimator.py index 84ebf5e2e2498d..854f885c26f2b4 100644 --- a/tensorflow/contrib/kfac/python/ops/estimator.py +++ b/tensorflow/contrib/kfac/python/ops/estimator.py @@ -180,44 +180,6 @@ def factors(self): def name(self): return self._name - @abc.abstractmethod - def make_ops_and_vars(self, scope=None): - """Make ops and vars with a specific placement strategy. - - For each factor, all of that factor's cov variables and their associated - update ops will be placed on a particular device. For example in case of - round robin placement a new device is chosen for each factor by cycling - through list of devices in the cov_devices argument. If cov_devices is None - then no explicit device placement occurs. - - An analogous strategy is followed for inverse update ops, with the list of - devices being given by the inv_devices argument. - - Inverse variables on the other hand are not placed on any specific device - (they will just use the current the device placement context, whatever - that happens to be). The idea is that the inverse variable belong where - they will be accessed most often, which is the device that actually applies - the preconditioner to the gradient. The user will be responsible for setting - the device context for this. - - Args: - scope: A string or None. If None it will be set to the name of this - estimator (given by the name property). All variables will be created, - and all ops will execute, inside of a variable scope of the given - name. (Default: None) - - Returns: - cov_update_ops: List of ops that compute the cov updates. Corresponds - one-to-one with the list of factors given by the "factors" property. - cov_update_op: cov_update_ops grouped into a single op. - inv_update_ops: List of ops that compute the inv updates. Corresponds - one-to-one with the list of factors given by the "factors" property. - inv_update_op: inv_update_ops grouped into a single op. - cov_update_thunks: Thunks that make the ops in cov_update_ops. - inv_update_thunks: Thunks that make the ops in inv_update_ops. - """ - pass - @abc.abstractmethod def make_vars_and_create_op_thunks(self, scope=None): """Make vars and create op thunks with a specific placement strategy. diff --git a/tensorflow/contrib/kfac/python/ops/fisher_factors.py b/tensorflow/contrib/kfac/python/ops/fisher_factors.py index 30f8a2a4b8ec7e..b43232dfafaa6d 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_factors.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_factors.py @@ -43,10 +43,14 @@ # Whether to initialize covariance estimators at a zero matrix (or the identity # matrix). -INIT_COVARIANCES_AT_ZERO = False +INIT_COVARIANCES_AT_ZERO = True # Whether to zero-debias the moving averages. -ZERO_DEBIAS = False +ZERO_DEBIAS = True + +# Whether to initialize inverse (and other such matrices computed from the cov +# matrices) to the zero matrix (or the identity matrix). +INIT_INVERSES_AT_ZERO = True # When the number of inverses requested from a FisherFactor exceeds this value, # the inverses are computed using an eigenvalue decomposition. @@ -83,6 +87,7 @@ def set_global_constants(init_covariances_at_zero=None, zero_debias=None, + init_inverses_at_zero=None, eigenvalue_decomposition_threshold=None, eigenvalue_clipping_threshold=None, max_num_outer_products_per_cov_row=None, @@ -93,6 +98,7 @@ def set_global_constants(init_covariances_at_zero=None, """Sets various global constants used by the classes in this module.""" global INIT_COVARIANCES_AT_ZERO global ZERO_DEBIAS + global INIT_INVERSES_AT_ZERO global EIGENVALUE_DECOMPOSITION_THRESHOLD global EIGENVALUE_CLIPPING_THRESHOLD global _MAX_NUM_OUTER_PRODUCTS_PER_COV_ROW @@ -105,6 +111,8 @@ def set_global_constants(init_covariances_at_zero=None, INIT_COVARIANCES_AT_ZERO = init_covariances_at_zero if zero_debias is not None: ZERO_DEBIAS = zero_debias + if init_inverses_at_zero is not None: + INIT_INVERSES_AT_ZERO = init_inverses_at_zero if eigenvalue_decomposition_threshold is not None: EIGENVALUE_DECOMPOSITION_THRESHOLD = eigenvalue_decomposition_threshold if eigenvalue_clipping_threshold is not None: @@ -122,19 +130,21 @@ def set_global_constants(init_covariances_at_zero=None, def inverse_initializer(shape, dtype, partition_info=None): # pylint: disable=unused-argument - return array_ops.diag(array_ops.ones(shape[0], dtype)) + if INIT_INVERSES_AT_ZERO: + return array_ops.zeros(shape, dtype=dtype) + return linalg_ops.eye(num_rows=shape[0], dtype=dtype) def covariance_initializer(shape, dtype, partition_info=None): # pylint: disable=unused-argument if INIT_COVARIANCES_AT_ZERO: - return array_ops.diag(array_ops.zeros(shape[0], dtype)) - return array_ops.diag(array_ops.ones(shape[0], dtype)) + return array_ops.zeros(shape, dtype=dtype) + return linalg_ops.eye(num_rows=shape[0], dtype=dtype) -def diagonal_covariance_initializer(shape, dtype, partition_info): # pylint: disable=unused-argument +def diagonal_covariance_initializer(shape, dtype, partition_info=None): # pylint: disable=unused-argument if INIT_COVARIANCES_AT_ZERO: - return array_ops.zeros(shape, dtype) - return array_ops.ones(shape, dtype) + return array_ops.zeros(shape, dtype=dtype) + return array_ops.ones(shape, dtype=dtype) @contextlib.contextmanager diff --git a/tensorflow/contrib/kfac/python/ops/layer_collection.py b/tensorflow/contrib/kfac/python/ops/layer_collection.py index 366e2a82d56602..cbbfe7212c9d94 100644 --- a/tensorflow/contrib/kfac/python/ops/layer_collection.py +++ b/tensorflow/contrib/kfac/python/ops/layer_collection.py @@ -182,7 +182,7 @@ def __init__(self, self._graph = graph or ops.get_default_graph() self._loss_dict = {} # {str: LossFunction} self._subgraph = None - self._default_generic_approximation = APPROX_FULL_NAME + self._default_generic_approximation = APPROX_DIAGONAL_NAME self._default_embedding_approximation = APPROX_KRONECKER_NAME self._default_fully_connected_approximation = APPROX_KRONECKER_NAME self._default_conv2d_approximation = APPROX_KRONECKER_NAME diff --git a/tensorflow/contrib/kfac/python/ops/optimizer.py b/tensorflow/contrib/kfac/python/ops/optimizer.py index f01c5a832212f8..45a760c9f1013d 100644 --- a/tensorflow/contrib/kfac/python/ops/optimizer.py +++ b/tensorflow/contrib/kfac/python/ops/optimizer.py @@ -18,7 +18,6 @@ from __future__ import division from __future__ import print_function -import warnings # pylint disable=long-line from tensorflow.contrib.kfac.python.ops import curvature_matrix_vector_products as cmvp from tensorflow.contrib.kfac.python.ops import estimator as est @@ -243,62 +242,6 @@ def damping(self): def damping_adaptation_interval(self): return self._damping_adaptation_interval - @property - def cov_update_thunks(self): - self._maybe_make_and_save_everything() - return self._cov_update_thunks - - @property - def cov_update_ops(self): - self._maybe_make_and_save_everything() - return self._cov_update_ops - - @property - def cov_update_op(self): - self._maybe_make_and_save_everything() - return self._cov_update_op - - @property - def inv_update_thunks(self): - self._maybe_make_and_save_everything() - return self._inv_update_thunks - - @property - def inv_update_ops(self): - self._maybe_make_and_save_everything() - return self._inv_update_ops - - @property - def inv_update_op(self): - self._maybe_make_and_save_everything() - return self._inv_update_op - - def _maybe_make_and_save_everything(self): - if not self._fisher_est.made_vars(): - warnings.warn("These convenience properties will be depcrecated soon. " - "Please use explicit op/thunk creation methods instead " - "(e.g. make_ops_and_vars, etc).", - DeprecationWarning) - (self._cov_update_ops, self._cov_update_op, self._inv_update_ops, - self._inv_update_op, self._cov_update_thunks, - self._inv_update_thunks) = self.make_ops_and_vars() - - def make_ops_and_vars(self): - """Make ops and vars with device placement `self._placement_strategy`. - - See `FisherEstimator.make_ops_and_vars` for details. - - Returns: - cov_update_ops: List of ops that compute the cov updates. Corresponds - one-to-one with the list of factors given by the "factors" property. - cov_update_op: cov_update_ops grouped into a single op. - inv_update_ops: List of ops that compute the inv updates. Corresponds - one-to-one with the list of factors given by the "factors" property. - cov_update_op: cov_update_ops grouped into a single op. - inv_update_op: inv_update_ops grouped into a single op. - """ - return self._fisher_est.make_ops_and_vars(scope=self.get_name()) - def make_vars_and_create_op_thunks(self): """Make vars and create op thunks. @@ -385,7 +328,6 @@ def apply_gradients(self, grads_and_vars, *args, **kwargs): Returns: An `Operation` that applies the specified gradients. """ - self._maybe_make_and_save_everything() # In Python 3, grads_and_vars can be a zip() object which can only be # iterated over once. By converting it to a list, we ensure that it can be # iterated over more than once. diff --git a/tensorflow/contrib/kfac/python/ops/placement.py b/tensorflow/contrib/kfac/python/ops/placement.py index 38a0e287a73f42..8a20ebe19844e6 100644 --- a/tensorflow/contrib/kfac/python/ops/placement.py +++ b/tensorflow/contrib/kfac/python/ops/placement.py @@ -21,8 +21,6 @@ import itertools from tensorflow.python.framework import ops as tf_ops -from tensorflow.python.ops import control_flow_ops -from tensorflow.python.ops import variable_scope def _make_thunk_on_device(func, device): @@ -52,56 +50,6 @@ def __init__(self, cov_devices=None, inv_devices=None, **kwargs): self._cov_devices = cov_devices self._inv_devices = inv_devices - def make_ops_and_vars(self, scope=None): - """Make ops and vars with a round-robin device placement strategy. - - For each factor, all of that factor's cov variables and their associated - update ops will be placed on a particular device. A new device is chosen - for each factor by cycling through list of devices in the - `self._cov_devices` attribute. If `self._cov_devices` is `None` then no - explicit device placement occurs. - - An analogous strategy is followed for inverse update ops, with the list of - devices being given by the `self._inv_devices` attribute. - - Inverse variables on the other hand are not placed on any specific device - (they will just use the current the device placement context, whatever - that happens to be). The idea is that the inverse variable belong where - they will be accessed most often, which is the device that actually applies - the preconditioner to the gradient. The user will be responsible for setting - the device context for this. - - Args: - scope: A string or None. If None it will be set to the name of this - estimator (given by the name property). All variables will be created, - and all ops will execute, inside of a variable scope of the given - name. (Default: None) - - Returns: - cov_update_ops: List of ops that compute the cov updates. Corresponds - one-to-one with the list of factors given by the "factors" property. - cov_update_op: cov_update_ops grouped into a single op. - inv_update_ops: List of ops that compute the inv updates. Corresponds - one-to-one with the list of factors given by the "factors" property. - inv_update_op: inv_update_ops grouped into a single op. - cov_update_thunks: Thunks that make the ops in cov_update_ops. - inv_update_thunks: Thunks that make the ops in inv_update_ops. - """ - (cov_update_thunks, - inv_update_thunks) = self.make_vars_and_create_op_thunks(scope=scope) - cov_update_ops = [thunk() for thunk in cov_update_thunks] - inv_update_ops = [thunk() for thunk in inv_update_thunks] - - scope = self.name if scope is None else scope - with variable_scope.variable_scope(scope): - cov_update_op = control_flow_ops.group(cov_update_ops, - name="cov_update_op") - inv_update_op = control_flow_ops.group(inv_update_ops, - name="inv_update_op") - - return (cov_update_ops, cov_update_op, inv_update_ops, inv_update_op, - cov_update_thunks, inv_update_thunks) - def make_vars_and_create_op_thunks(self, scope=None): """Make vars and create op thunks w/ a round-robin device placement strat. From 7ec93b497a3b45aae5c6dfd97637499e9e8011ee Mon Sep 17 00:00:00 2001 From: Shivani Agrawal Date: Thu, 26 Apr 2018 15:15:37 -0700 Subject: [PATCH 0081/1691] [tf.data] Changes description for `bytes_produced_stats` and `latency_stats` in accordance with the breaking changes in cl/193432590. PiperOrigin-RevId: 194461964 --- tensorflow/contrib/data/python/ops/stats_ops.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/stats_ops.py b/tensorflow/contrib/data/python/ops/stats_ops.py index d39172039683fe..3cbaab5affd739 100644 --- a/tensorflow/contrib/data/python/ops/stats_ops.py +++ b/tensorflow/contrib/data/python/ops/stats_ops.py @@ -136,8 +136,8 @@ def _apply_fn(dataset): def bytes_produced_stats(tag): """Records the number of bytes produced by each element of the input dataset. - To consume the statistics, associate a `StatsAggregator` with an iterator - over the output dataset. + To consume the statistics, associate a `StatsAggregator` with the output + dataset. Args: tag: String. All statistics recorded by the returned transformation will @@ -158,8 +158,8 @@ def _apply_fn(dataset): def latency_stats(tag): """Records the latency of producing each element of the input dataset. - To consume the statistics, associate a `StatsAggregator` with an iterator - over the output dataset. + To consume the statistics, associate a `StatsAggregator` with the output + dataset. Args: tag: String. All statistics recorded by the returned transformation will From 2808c3f05f7713ff1ab20f365e986a4651180376 Mon Sep 17 00:00:00 2001 From: Shivani Agrawal Date: Thu, 26 Apr 2018 15:24:44 -0700 Subject: [PATCH 0082/1691] [tf.data] Adds support for adding scalar value to `StatsAggregator`. PiperOrigin-RevId: 194463407 --- tensorflow/core/framework/stats_aggregator.h | 4 ++++ tensorflow/core/kernels/data/stats_aggregator_ops.cc | 11 +++++++++++ 2 files changed, 15 insertions(+) diff --git a/tensorflow/core/framework/stats_aggregator.h b/tensorflow/core/framework/stats_aggregator.h index a449f324e603ae..8002d9291c2e0f 100644 --- a/tensorflow/core/framework/stats_aggregator.h +++ b/tensorflow/core/framework/stats_aggregator.h @@ -47,6 +47,10 @@ class StatsAggregator { virtual void AddToHistogram(const string& name, gtl::ArraySlice values) = 0; + // TODO(shivaniagarawal): consistency in double and float usage. + // Add the given `value` as Scalar with the given `name`. + virtual void AddScalar(const string& name, float value) = 0; + // Stores a protocol buffer representation of the aggregator state in the // given `out_summary`. // TODO(mrry): Consider separating this method from the `StatsAggregator` diff --git a/tensorflow/core/kernels/data/stats_aggregator_ops.cc b/tensorflow/core/kernels/data/stats_aggregator_ops.cc index dd373115806b89..33a56b2eb567a2 100644 --- a/tensorflow/core/kernels/data/stats_aggregator_ops.cc +++ b/tensorflow/core/kernels/data/stats_aggregator_ops.cc @@ -38,6 +38,11 @@ class StatsAggregatorImpl : public StatsAggregator { } } + void AddScalar(const string& name, float value) override { + mutex_lock l(mu_); + scalars_[name] = value; + } + void EncodeToProto(Summary* out_summary) override { mutex_lock l(mu_); for (const auto& pair : histograms_) { @@ -49,11 +54,17 @@ class StatsAggregatorImpl : public StatsAggregator { histogram.EncodeToProto(value->mutable_histo(), false /* doesn't preserve zero buckets */); } + for (const auto& pair : scalars_) { + Summary::Value* value = out_summary->add_value(); + value->set_tag(pair.first); + value->set_simple_value(pair.second); + } } private: mutex mu_; std::unordered_map histograms_ GUARDED_BY(mu_); + std::unordered_map scalars_ GUARDED_BY(mu_); TF_DISALLOW_COPY_AND_ASSIGN(StatsAggregatorImpl); }; From ab5de487813b4849dfb5415ee60595654dff06be Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Thu, 26 Apr 2018 15:33:38 -0700 Subject: [PATCH 0083/1691] Remove the inter-op thread pool Forgot about this in cl/194299356. However, when I checked cl/194299356, I found that we actually (incorrectly?) used the *intra* op thread pool in the parallel CPU executable? Does that mean the inter op thread pool was always unused? PiperOrigin-RevId: 194464734 --- tensorflow/compiler/xla/executable_run_options.cc | 11 ----------- tensorflow/compiler/xla/executable_run_options.h | 7 ------- .../compiler/xla/python/local_computation_builder.cc | 3 --- tensorflow/compiler/xla/service/backend.cc | 7 ------- tensorflow/compiler/xla/service/backend.h | 7 ------- tensorflow/compiler/xla/service/hlo_runner.cc | 6 +++--- tensorflow/compiler/xla/service/service.cc | 8 +++----- .../compiler/xla/tests/local_client_test_base.cc | 2 -- 8 files changed, 6 insertions(+), 45 deletions(-) diff --git a/tensorflow/compiler/xla/executable_run_options.cc b/tensorflow/compiler/xla/executable_run_options.cc index 99b8f0558e6e39..a472747bd174e3 100644 --- a/tensorflow/compiler/xla/executable_run_options.cc +++ b/tensorflow/compiler/xla/executable_run_options.cc @@ -45,17 +45,6 @@ stream_executor::Stream* ExecutableRunOptions::stream() const { return stream_; } -ExecutableRunOptions& ExecutableRunOptions::set_inter_op_thread_pool( - tensorflow::thread::ThreadPool* inter_op_thread_pool) { - inter_op_thread_pool_ = inter_op_thread_pool; - return *this; -} - -tensorflow::thread::ThreadPool* ExecutableRunOptions::inter_op_thread_pool() - const { - return inter_op_thread_pool_; -} - ExecutableRunOptions& ExecutableRunOptions::set_intra_op_thread_pool( const Eigen::ThreadPoolDevice* intra_op_thread_pool) { intra_op_thread_pool_ = intra_op_thread_pool; diff --git a/tensorflow/compiler/xla/executable_run_options.h b/tensorflow/compiler/xla/executable_run_options.h index a306ae16ba4aee..416131be006e6e 100644 --- a/tensorflow/compiler/xla/executable_run_options.h +++ b/tensorflow/compiler/xla/executable_run_options.h @@ -65,12 +65,6 @@ class ExecutableRunOptions { ExecutableRunOptions& set_stream(stream_executor::Stream* stream); stream_executor::Stream* stream() const; - // Sets the thread pool on which to run parallel CPU backend - // computations. Does not take ownership. - ExecutableRunOptions& set_inter_op_thread_pool( - tensorflow::thread::ThreadPool* inter_op_thread_pool); - tensorflow::thread::ThreadPool* inter_op_thread_pool() const; - // Sets the thread pool device on which to run Eigen subcomputations. // Does not take ownership. ExecutableRunOptions& set_intra_op_thread_pool( @@ -93,7 +87,6 @@ class ExecutableRunOptions { int device_ordinal_ = -1; DeviceAssignment* device_assignment_ = nullptr; stream_executor::Stream* stream_ = nullptr; - tensorflow::thread::ThreadPool* inter_op_thread_pool_ = nullptr; const Eigen::ThreadPoolDevice* intra_op_thread_pool_ = nullptr; ExecutionProfile* execution_profile_ = nullptr; int rng_seed_ = 0; diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index 24e17abbe06197..7102f467373edc 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -197,8 +197,6 @@ StatusOr> CompiledLocalComputation::Execute( ExecutableRunOptions options; options.set_device_ordinal(device_ordinal); options.set_allocator(client->backend().memory_allocator()); - options.set_inter_op_thread_pool( - client->backend().inter_op_thread_pool()); options.set_intra_op_thread_pool( client->backend().eigen_intra_op_thread_pool_device()); options.set_device_assignment(&device_assignment); @@ -242,7 +240,6 @@ LocalShapedBuffer* CompiledLocalComputation::ExecuteWithShapedBuffers( // Execute ExecutableRunOptions options; options.set_allocator(client->backend().memory_allocator()); - options.set_inter_op_thread_pool(client->backend().inter_op_thread_pool()); options.set_intra_op_thread_pool( client->backend().eigen_intra_op_thread_pool_device()); ScopedShapedBuffer result_buffer = diff --git a/tensorflow/compiler/xla/service/backend.cc b/tensorflow/compiler/xla/service/backend.cc index b1d616ec3506f9..349b32451a697d 100644 --- a/tensorflow/compiler/xla/service/backend.cc +++ b/tensorflow/compiler/xla/service/backend.cc @@ -138,9 +138,6 @@ Backend::Backend( << "Service found no devices for backend " << platform_->Name() << '.'; if (platform->id() == se::host::kHostPlatformId) { - inter_op_thread_pool_.reset(new tensorflow::thread::ThreadPool( - tensorflow::Env::Default(), "xla_inter_op", - tensorflow::port::NumSchedulableCPUs())); const int num_threads = intra_op_parallelism_threads > 0 ? intra_op_parallelism_threads : tensorflow::port::NumSchedulableCPUs(); @@ -155,10 +152,6 @@ int Backend::default_device_ordinal() const { return default_stream_executor()->device_ordinal(); } -tensorflow::thread::ThreadPool* Backend::inter_op_thread_pool() const { - return inter_op_thread_pool_.get(); -} - const Eigen::ThreadPoolDevice* Backend::eigen_intra_op_thread_pool_device() const { if (intra_op_thread_pool_wrapper_ == nullptr) { diff --git a/tensorflow/compiler/xla/service/backend.h b/tensorflow/compiler/xla/service/backend.h index d32a0a400d8bd5..6546602473e338 100644 --- a/tensorflow/compiler/xla/service/backend.h +++ b/tensorflow/compiler/xla/service/backend.h @@ -140,10 +140,6 @@ class Backend { // be equivalent to an executable compiled for the other. StatusOr devices_equivalent(int device_ordinal_a, int device_ordinal_b); - // For the host platform, returns the threadpool to use when scheduling - // parallel operators. For other platforms, returns NULL. - tensorflow::thread::ThreadPool* inter_op_thread_pool() const; - // For the host platform, returns the configured eigen threadpool device to be // used for scheduling work. For other platforms, returns NULL. const Eigen::ThreadPoolDevice* eigen_intra_op_thread_pool_device() const; @@ -178,9 +174,6 @@ class Backend { // The default memory allocator to use. std::unique_ptr memory_allocator_; - // For the CPU backend, a threadpool for scheduling parallel operators. - std::unique_ptr inter_op_thread_pool_; - // For the CPU backend, an Eigen threadpool device for use by Eigen code. std::unique_ptr intra_op_thread_pool_wrapper_; }; diff --git a/tensorflow/compiler/xla/service/hlo_runner.cc b/tensorflow/compiler/xla/service/hlo_runner.cc index 81c43db292a75d..48da1a505c9bea 100644 --- a/tensorflow/compiler/xla/service/hlo_runner.cc +++ b/tensorflow/compiler/xla/service/hlo_runner.cc @@ -278,14 +278,14 @@ ServiceExecutableRunOptions HloRunner::GetServiceRunOptionsForDevice( run_options.set_device_ordinal(device); run_options.set_stream(stream); run_options.set_allocator(backend().memory_allocator()); - run_options.set_inter_op_thread_pool(backend().inter_op_thread_pool()); run_options.set_intra_op_thread_pool( backend().eigen_intra_op_thread_pool_device()); if (device_assignment != nullptr) { run_options.set_device_assignment(device_assignment); } - return ServiceExecutableRunOptions(run_options, backend().StreamBorrower(), - backend().inter_op_thread_pool()); + return ServiceExecutableRunOptions( + run_options, backend().StreamBorrower(), + /*xla_intra_op_thread_pool=*/backend().eigen_intra_op_thread_pool()); } Backend& HloRunner::backend() { diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 086bd61dd04aa1..6e0d07a12f906b 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -574,7 +574,6 @@ Service::ExecuteParallelAndRegisterResult( ExecutableRunOptions options; options.set_stream(streams.back().get()); options.set_allocator(backend->memory_allocator()); - options.set_inter_op_thread_pool(backend->inter_op_thread_pool()); options.set_intra_op_thread_pool( backend->eigen_intra_op_thread_pool_device()); options.set_device_assignment(&device_assignment); @@ -688,12 +687,12 @@ StatusOr Service::ExecuteAndRegisterResult( options.set_stream(stream.get()); options.set_device_ordinal(stream->parent()->device_ordinal()); options.set_allocator(backend->memory_allocator()); - options.set_inter_op_thread_pool(backend->inter_op_thread_pool()); options.set_intra_op_thread_pool( backend->eigen_intra_op_thread_pool_device()); options.set_device_assignment(&device_assignment); - run_options.emplace_back(options, backend->StreamBorrower(), - backend->inter_op_thread_pool()); + run_options.emplace_back( + options, backend->StreamBorrower(), + /*xla_intra_op_thread_pool=*/backend->eigen_intra_op_thread_pool()); } if (options_.number_of_replicas() == 1) { @@ -1240,7 +1239,6 @@ tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg, ExecutableRunOptions options; options.set_stream(stream.get()); options.set_allocator(execute_backend_->memory_allocator()); - options.set_inter_op_thread_pool(execute_backend_->inter_op_thread_pool()); options.set_intra_op_thread_pool( execute_backend_->eigen_intra_op_thread_pool_device()); diff --git a/tensorflow/compiler/xla/tests/local_client_test_base.cc b/tensorflow/compiler/xla/tests/local_client_test_base.cc index ca8e4cdbdb6a8f..e859b3059eea86 100644 --- a/tensorflow/compiler/xla/tests/local_client_test_base.cc +++ b/tensorflow/compiler/xla/tests/local_client_test_base.cc @@ -149,8 +149,6 @@ ExecutableBuildOptions LocalClientTestBase::DefaultExecutableBuildOptions() ExecutableRunOptions LocalClientTestBase::DefaultExecutableRunOptions() const { ExecutableRunOptions run_options; - run_options.set_inter_op_thread_pool( - local_client_->backend().inter_op_thread_pool()); run_options.set_intra_op_thread_pool(thread_pool_wrapper_->device.get()); run_options.set_allocator(GetOrCreateAllocator(local_client_->platform())); return run_options; From 3ab696e7e7e5c422acaa2fb2f3a938ce14effc9c Mon Sep 17 00:00:00 2001 From: Raghuraman Krishnamoorthi Date: Thu, 26 Apr 2018 15:40:15 -0700 Subject: [PATCH 0084/1691] Handle variations in scoping of batch norms for correct unfused batch norm folding. PiperOrigin-RevId: 194465704 --- .../quantize/python/fold_batch_norms.py | 115 ++++++++++++------ .../quantize/python/fold_batch_norms_test.py | 59 +++++---- 2 files changed, 109 insertions(+), 65 deletions(-) diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms.py b/tensorflow/contrib/quantize/python/fold_batch_norms.py index 6f41722748b475..1f286bc39a21d4 100644 --- a/tensorflow/contrib/quantize/python/fold_batch_norms.py +++ b/tensorflow/contrib/quantize/python/fold_batch_norms.py @@ -480,6 +480,43 @@ def _IsValidUnfusedBatchNorm(graph, context): return bool(add_shift.outputs[0].consumers()) +def _FindMatchingTensor(graph, match_pattern, scope): + """Finds best match of ops matching match_pattern with scope. + + Example: _FindMatchingTensor(graph,'/BatchNorm/moments/Squeeze', + 'MobilenetV1/MobilenetV1/Conv2d_0/') returns: + Tensor('MobilenetV1/Conv2d_0/BatchNorm/moments/Squeeze') + + Args: + graph: Graph to inspect. + match_pattern: Part of the name of the op that we need to match, should + be present in the op's name + scope: The scope of the op. All the elements of the scope need not be + present in the op's name. + + Returns: + Tensor from graph that provides the best match to the match_pattern and + scope + """ + + oplist = graph.get_operations() + split_context = set(scope.split('/')) + match_dict = {} + for op in oplist: + if op.name.endswith(match_pattern): + split_name = op.name.split('/') + num_matches = len(set(split_name) & split_context) + if num_matches > 0: + match_dict[op.name] = num_matches + # match_dict contains matching op names from graph with values being + # number of matches to scope. We pick the key with the most matches + if match_dict: + max_key = max(match_dict, key=match_dict.get) + return graph.get_tensor_by_name(max_key + ':0') + else: + return None + + def _GetBatchNormParams(graph, context, has_scaling): """Extracts relevant tensors for folding batch norms. @@ -500,7 +537,8 @@ def _GetBatchNormParams(graph, context, has_scaling): bn_decay_mean_tensor = None bn_decay_var_tensor = None - split_context = context.split('/') + # TODO(raghuramank) This code relies on string matching and needs to be + # updated if unfused batch norm continues to be widely used # Matching variable names is brittle and relies on scoping # conventions. Fused batch norm folding is more robust. Support for unfused # batch norms will be deprecated as we move forward. Fused batch norms allow @@ -518,49 +556,48 @@ def _GetBatchNormParams(graph, context, has_scaling): # and the names of the tensors start with a single MobilenetV2 # The moving mean for example, has the name: # MobilenetV2/expanded_conv_3/depthwise/BatchNorm/moving_mean/read - # We ignore the first string (MobilenetV1 or MobilenetV2) - # in the context to match correctly in both cases - - base_context = '/'.join(split_context[1:]) - oplist = graph.get_operations() - op_suffix_mean = base_context + '/BatchNorm/moments/Squeeze' - op_suffix_variance = base_context + '/BatchNorm/moments/Squeeze_1' - op_suffix_epsilon = base_context + '/BatchNorm/batchnorm/add/y' - op_suffix_bn_decay_mean = base_context + '/BatchNorm/AssignMovingAvg/decay' - op_suffix_bn_decay_var = base_context + '/BatchNorm/AssignMovingAvg_1/decay' + # We identify the best match for an op by checking for + # 1. The suffix of the op is exactly matched + # 2. Maximum number of matches with the context.The matching + # score is given by the number of parts of context (split by /) that + # are present in the parts of the tensor name (again split by /). + # For example: scope= MobilenetV2/MobilenetV2/expanded_conv_3 and + # op.name = MobilenetV2/expanded_conv_3/depthwise/BatchNorm/moving_mean/read + # will have 2 matches,scope with a different conv layer will have one match. + + op_suffix_mean = '/BatchNorm/moments/Squeeze' + op_suffix_variance = '/BatchNorm/moments/Squeeze_1' + op_suffix_epsilon = '/BatchNorm/batchnorm/add/y' + op_suffix_bn_decay_mean = '/BatchNorm/AssignMovingAvg/decay' + op_suffix_bn_decay_var = '/BatchNorm/AssignMovingAvg_1/decay' if variable_scope.get_variable_scope().use_resource: - op_suffix_gamma = base_context + '/BatchNorm/gamma/Read/ReadVariableOp' + op_suffix_gamma = '/BatchNorm/gamma/Read/ReadVariableOp' op_suffix_moving_variance = ( - base_context + '/BatchNorm/moving_variance/Read/ReadVariableOp') - op_suffix_moving_mean = ( - base_context + '/BatchNorm/moving_mean/Read/ReadVariableOp') + '/BatchNorm/moving_variance/Read/ReadVariableOp') + op_suffix_moving_mean = ('/BatchNorm/moving_mean/Read/ReadVariableOp') else: - op_suffix_gamma = base_context + '/BatchNorm/gamma' - op_suffix_moving_variance = base_context + '/BatchNorm/moving_variance/read' - op_suffix_moving_mean = base_context + '/BatchNorm/moving_mean/read' + op_suffix_gamma = '/BatchNorm/gamma' + op_suffix_moving_variance = '/BatchNorm/moving_variance/read' + op_suffix_moving_mean = '/BatchNorm/moving_mean/read' # Parse through list of ops to find relevant ops - for op in oplist: - if op.name.endswith(op_suffix_mean): - # This is an efficient way to check for two things: - # Is batch norm present and is it training mode? - # Batch statistics are computed only during batch norm in training - batch_mean_tensor = graph.get_tensor_by_name(op.name + ':0') - if op.name.endswith(op_suffix_variance): - batch_variance_tensor = graph.get_tensor_by_name(op.name + ':0') - if op.name.endswith(op_suffix_moving_mean): - moving_mean_tensor = graph.get_tensor_by_name(op.name + ':0') - if op.name.endswith(op_suffix_moving_variance): - moving_variance_tensor = graph.get_tensor_by_name(op.name + ':0') - if op.name.endswith(op_suffix_epsilon): - batch_epsilon = graph.get_tensor_by_name(op.name + ':0') - if op.name.endswith(op_suffix_bn_decay_mean): - bn_decay_mean_tensor = graph.get_tensor_by_name(op.name + ':0') - if op.name.endswith(op_suffix_bn_decay_var): - bn_decay_var_tensor = graph.get_tensor_by_name(op.name + ':0') - if has_scaling: - if op.name.endswith(op_suffix_gamma): - gamma_tensor = graph.get_tensor_by_name(op.name + ':0') + + batch_mean_tensor = _FindMatchingTensor(graph, op_suffix_mean, context) + batch_variance_tensor = _FindMatchingTensor(graph, op_suffix_variance, + context) + moving_mean_tensor = _FindMatchingTensor(graph, op_suffix_moving_mean, + context) + moving_variance_tensor = _FindMatchingTensor(graph, op_suffix_moving_variance, + context) + batch_epsilon = _FindMatchingTensor(graph, op_suffix_epsilon, context) + bn_decay_mean_tensor = _FindMatchingTensor(graph, op_suffix_bn_decay_mean, + context) + bn_decay_var_tensor = _FindMatchingTensor(graph, op_suffix_bn_decay_var, + context) + if batch_mean_tensor is None and moving_mean_tensor is None: + ValueError('Error folding unfused batch norms') + if has_scaling: + gamma_tensor = _FindMatchingTensor(graph, op_suffix_gamma, context) if not has_scaling: gamma_tensor = array_ops.ones(moving_mean_tensor.shape) diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms_test.py b/tensorflow/contrib/quantize/python/fold_batch_norms_test.py index 64e8142e7c6092..fa5e11b4708402 100644 --- a/tensorflow/contrib/quantize/python/fold_batch_norms_test.py +++ b/tensorflow/contrib/quantize/python/fold_batch_norms_test.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import random_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import googletest from tensorflow.python.training import saver as saver_lib @@ -157,32 +158,38 @@ def testMultipleLayerConv2d(self, out_depth = 3 stride = 1 activation_fn = relu - scope = 'network/expanded_conv_1/conv' - layer1 = conv2d( - inputs, - out_depth, [5, 5], - stride=stride, - padding='SAME', - weights_initializer=self._WeightInit(0.09), - activation_fn=activation_fn, - normalizer_fn=batch_norm, - normalizer_params=self._BatchNormParams( - scale=has_scaling, fused=fused_batch_norm), - scope=scope) - # Add another layer - scope = 'network/expanded_conv_2/conv' - - _ = conv2d( - layer1, - 2 * out_depth, [5, 5], - stride=stride, - padding='SAME', - weights_initializer=self._WeightInit(0.09), - activation_fn=activation_fn, - normalizer_fn=batch_norm, - normalizer_params=self._BatchNormParams( - scale=has_scaling, fused=fused_batch_norm), - scope=scope) + scope = 'topnet/testnet' + with variable_scope.variable_scope(scope, [inputs]): + layer1 = conv2d( + inputs, + out_depth, [5, 5], + stride=stride, + padding='SAME', + weights_initializer=self._WeightInit(0.09), + activation_fn=None, + normalizer_fn=None, + scope='testnet/layer1') + # Add bn and relu with different scope + layer1 = batch_norm( + layer1, scale=has_scaling, fused=fused_batch_norm, scope='layer1') + layer1 = activation_fn(layer1) + layer2 = conv2d( + layer1, + 2 * out_depth, [5, 5], + stride=stride, + padding='SAME', + weights_initializer=self._WeightInit(0.09), + activation_fn=activation_fn, + normalizer_fn=batch_norm, + normalizer_params=self._BatchNormParams( + scale=has_scaling, fused=fused_batch_norm), + scope='testnet/layer2') + # Add bn and relu with different scope + layer2 = batch_norm( + layer2, scale=has_scaling, fused=fused_batch_norm, scope='layer2') + _ = activation_fn(layer2) + + scope = 'topnet/testnet/testnet/layer2' fold_batch_norms.FoldBatchNorms( g, is_training=True, freeze_batch_norm_delay=freeze_batch_norm_delay) From 04a5547817ad758cc7c32cd580335fd2e8a5e1e6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 16:01:00 -0700 Subject: [PATCH 0085/1691] Internal change. PiperOrigin-RevId: 194468535 --- .../contrib/lite/kernels/bidirectional_sequence_rnn_test.cc | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn_test.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn_test.cc index 12f4ff97cfd90e..911b108eaad605 100644 --- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn_test.cc +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn_test.cc @@ -161,7 +161,7 @@ static float rnn_golden_bw_output[] = { 0, 0, 1.86126, 0, 0.728256, 0.750013, 0.011861, 0.576383, 3.38891, 1.29273, 0}; -constexpr std::initializer_list weights = { +const std::initializer_list weights = { 0.461459, 0.153381, 0.529743, -0.00371218, 0.676267, -0.211346, 0.317493, 0.969689, -0.343251, 0.186423, 0.398151, 0.152399, 0.448504, 0.317662, 0.523556, -0.323514, 0.480877, 0.333113, @@ -628,12 +628,12 @@ static float golden_endtoend_output[] = { -2.080307, 0.896140, -3.104050, 0.983158, -0.424898, -1.154270, -3.805728, 1.978917, -1.314387, 1.235096, -3.148906, 1.113173, 0.111713, 2.055213, -7.565283, 2.100342}; -constexpr std::initializer_list biases = { +const std::initializer_list biases = { 0.065691948, -0.69055247, 0.1107955, -0.97084129, -0.23957068, -0.23566568, -0.389184, 0.47481549, -0.4791103, 0.29931796, 0.10463274, 0.83918178, 0.37197268, 0.61957061, 0.3956964, -0.37609905}; -constexpr std::initializer_list recurrent_weights = { +const std::initializer_list recurrent_weights = { 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, From bcefec3d6782365510c45e08763892d478dabb07 Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Thu, 26 Apr 2018 16:11:11 -0700 Subject: [PATCH 0086/1691] Fix some flakiness in test. PiperOrigin-RevId: 194470125 --- .../contrib/lite/profiling/profiler_test.cc | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/tensorflow/contrib/lite/profiling/profiler_test.cc b/tensorflow/contrib/lite/profiling/profiler_test.cc index 7914f36a319b85..7ea1d8f7d341b6 100644 --- a/tensorflow/contrib/lite/profiling/profiler_test.cc +++ b/tensorflow/contrib/lite/profiling/profiler_test.cc @@ -82,16 +82,15 @@ TEST(ProfilingTest, ProfilesAreCollected) { EXPECT_EQ("Child", profile_events[3]->tag); EXPECT_EQ("SleepForQuarter", profile_events[4]->tag); - AssertDurationOfEventAroundMs(profile_events[0], /*expected_ms*/ 500, - /*eps_ms*/ 2); - AssertDurationOfEventAroundMs(profile_events[1], /*expected_ms*/ 250, - /*eps_ms*/ 2); - AssertDurationOfEventAroundMs(profile_events[2], /*expected_ms*/ 250, - /*eps_ms*/ 2); - AssertDurationOfEventAroundMs(profile_events[3], /*expected_ms*/ 250, - /*eps_ms*/ 2); - AssertDurationOfEventAroundMs(profile_events[4], /*expected_ms*/ 250, - /*eps_ms*/ 2); +#ifndef ADDRESS_SANITIZER + // ASAN build is sometimes very slow. + const int eps_ms = 10; + AssertDurationOfEventAroundMs(profile_events[0], /*expected_ms*/ 500, eps_ms); + AssertDurationOfEventAroundMs(profile_events[1], /*expected_ms*/ 250, eps_ms); + AssertDurationOfEventAroundMs(profile_events[2], /*expected_ms*/ 250, eps_ms); + AssertDurationOfEventAroundMs(profile_events[3], /*expected_ms*/ 250, eps_ms); + AssertDurationOfEventAroundMs(profile_events[4], /*expected_ms*/ 250, eps_ms); +#endif } } // namespace From 81a34fb835e8389dd2523335c5d186405294f95e Mon Sep 17 00:00:00 2001 From: joel-shor Date: Fri, 27 Apr 2018 02:21:44 +0300 Subject: [PATCH 0087/1691] [tf.data] Just replace old resample with new. Also, add an optimization / bug fix that shortcircuits combining the two datasets if one should always be sampled from. Tested: bazel test :resample_test --- .../data/python/kernel_tests/resample_test.py | 85 ++++----- .../contrib/data/python/ops/resampling.py | 178 +++++++----------- 2 files changed, 107 insertions(+), 156 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index 7f007fede8c875..fc84301b17b461 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -34,14 +34,12 @@ def _time_resampling( - test_obj, data_np, target_dist, init_dist, use_v2, num_to_sample): + test_obj, data_np, target_dist, init_dist, num_to_sample): dataset = dataset_ops.Dataset.from_tensor_slices(data_np).repeat() # Reshape distribution via rejection sampling. - apply_fn = (resampling.rejection_resample_v2 if use_v2 else - resampling.rejection_resample) dataset = dataset.apply( - apply_fn( + resampling.rejection_resample( class_func=lambda x: x, target_dist=target_dist, initial_dist=init_dist, @@ -61,20 +59,17 @@ def _time_resampling( class ResampleTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( - ("InitialnDistributionKnown", True, False), - ("InitialDistributionUnknown", False, False), - ("InitialDistributionKnownV2", True, True), - ("InitialDistributionUnknownV2", False, True)) - def testDistribution(self, initial_known, use_v2): + ("InitialnDistributionKnown", True), + ("InitialDistributionUnknown", False)) + def testDistribution(self, initial_known): classes = np.random.randint(5, size=(20000,)) # Uniformly sampled target_dist = [0.9, 0.05, 0.05, 0.0, 0.0] initial_dist = [0.2] * 5 if initial_known else None dataset = dataset_ops.Dataset.from_tensor_slices(classes).shuffle( 200, seed=21).map(lambda c: (c, string_ops.as_string(c))).repeat() - apply_fn = (resampling.rejection_resample_v2 if use_v2 else - resampling.rejection_resample) + get_next = dataset.apply( - apply_fn( + resampling.rejection_resample( target_dist=target_dist, initial_dist=initial_dist, class_func=lambda c, _: c, @@ -96,11 +91,39 @@ def testDistribution(self, initial_known, use_v2): returned_dist = class_counts / total_returned self.assertAllClose(target_dist, returned_dist, atol=1e-2) + @parameterized.named_parameters( + ("OnlyInitial", True), + ("NotInitial", False)) + def testEdgeCasesSampleFromInitialDataset(self, only_initial_dist): + init_dist = [0.5, 0.5] + target_dist = [0.5, 0.5] if only_initial_dist else [0.0, 1.0] + num_classes = len(init_dist) + # We don't need many samples to test that this works. + num_samples = 100 + data_np = np.random.choice(num_classes, num_samples, p=init_dist) + + dataset = dataset_ops.Dataset.from_tensor_slices(data_np) + + # Reshape distribution. + dataset = dataset.apply( + resampling.rejection_resample( + class_func=lambda x: x, + target_dist=target_dist, + initial_dist=init_dist)) + + get_next = dataset.make_one_shot_iterator().get_next() + + with self.test_session() as sess: + returned = [] + with self.assertRaises(errors.OutOfRangeError): + while True: + returned.append(sess.run(get_next)) + def testRandomClasses(self): init_dist = [0.25, 0.25, 0.25, 0.25] target_dist = [0.0, 0.0, 0.0, 1.0] num_classes = len(init_dist) - # We don't need many samples to test a dirac-delta target distribution + # We don't need many samples to test a dirac-delta target distribution. num_samples = 100 data_np = np.random.choice(num_classes, num_samples, p=init_dist) @@ -134,26 +157,8 @@ def _remap_fn(_): self.assertAllClose(target_dist, bincount, atol=1e-2) - @parameterized.named_parameters( - ("SmallSkewManySamples", [0.1, 0.1, 0.1, 0.7], 1000), - ("BigSkewManySamples", [0.01, 0.01, 0.01, 0.97], 1000), - ("SmallSkewFewSamples", [0.1, 0.1, 0.1, 0.7], 100), - ("BigSkewFewSamples", [0.01, 0.01, 0.01, 0.97], 100)) - def testNewResampleIsFaster(self, target_dist, num_to_sample): - init_dist = [0.25, 0.25, 0.25, 0.25] - num_classes = len(init_dist) - num_samples = 1000 - data_np = np.random.choice(num_classes, num_samples, p=init_dist) - - fast_time = _time_resampling(self, data_np, target_dist, init_dist, - use_v2=True, num_to_sample=num_to_sample) - slow_time = _time_resampling(self, data_np, target_dist, init_dist, - use_v2=False, num_to_sample=num_to_sample) - - self.assertLess(fast_time, slow_time) - -class MapDatasetBenchmark(test.Benchmark): +class ResampleDatasetBenchmark(test.Benchmark): def benchmarkResamplePerformance(self): init_dist = [0.25, 0.25, 0.25, 0.25] @@ -164,25 +169,11 @@ def benchmarkResamplePerformance(self): data_np = np.random.choice(num_classes, num_samples, p=init_dist) resample_time = _time_resampling( - self, data_np, target_dist, init_dist, use_v2=False, num_to_sample=1000) + self, data_np, target_dist, init_dist, num_to_sample=1000) self.report_benchmark( iters=1000, wall_time=resample_time, name="benchmark_resample") - def benchmarkResampleAndBatchPerformance(self): - init_dist = [0.25, 0.25, 0.25, 0.25] - target_dist = [0.0, 0.0, 0.0, 1.0] - num_classes = len(init_dist) - # We don't need many samples to test a dirac-delta target distribution - num_samples = 1000 - data_np = np.random.choice(num_classes, num_samples, p=init_dist) - - resample_time = _time_resampling( - self, data_np, target_dist, init_dist, use_v2=True, num_to_sample=1000) - - self.report_benchmark( - iters=1000, wall_time=resample_time, name="benchmark_resample_v2") - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index 16d851bf96408e..66eaf9b69a8887 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -58,62 +58,7 @@ def _apply_fn(dataset): # Get initial distribution. if initial_dist is not None: - initial_dist_t = ops.convert_to_tensor( - initial_dist, name="initial_dist") - acceptance_dist = _calculate_acceptance_probs(initial_dist_t, - target_dist_t) - initial_dist_ds = dataset_ops.Dataset.from_tensors( - initial_dist_t).repeat() - acceptance_dist_ds = dataset_ops.Dataset.from_tensors( - acceptance_dist).repeat() - else: - initial_dist_ds = _estimate_initial_dist_ds( - target_dist_t, class_values_ds) - acceptance_dist_ds = initial_dist_ds.map( - lambda initial: _calculate_acceptance_probs(initial, target_dist_t)) - return _filter_ds(dataset, acceptance_dist_ds, initial_dist_ds, - class_values_ds, seed) - - return _apply_fn - - -def rejection_resample_v2(class_func, target_dist, initial_dist=None, - seed=None): - """A transformation that resamples a dataset to achieve a target distribution. - - This differs from v1 in that it will also sample from the original dataset - with some probability, so it makes strictly fewer data rejections. Due to an - implementation detail it must initialize a separate dataset initializer, so - the dataset becomes stateful after this transformation is applied - (`make_one_shot_iterator` won't work; users must use - `make_initializable_iterator`). This transformation is faster than the - original, except for overhead. - - **NOTE** Resampling is performed via rejection sampling; some fraction - of the input values will be dropped. - - Args: - class_func: A function mapping an element of the input dataset to a scalar - `tf.int32` tensor. Values should be in `[0, num_classes)`. - target_dist: A floating point type tensor, shaped `[num_classes]`. - initial_dist: (Optional.) A floating point type tensor, shaped - `[num_classes]`. If not provided, the true class distribution is - estimated live in a streaming fashion. - seed: (Optional.) Python integer seed for the resampler. - - Returns: - A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. - """ - def _apply_fn(dataset): - """Function from `Dataset` to `Dataset` that applies the transformation.""" - target_dist_t = ops.convert_to_tensor(target_dist, name="target_dist") - class_values_ds = dataset.map(class_func) - - # Get initial distribution. - if initial_dist is not None: - initial_dist_t = ops.convert_to_tensor( - initial_dist, name="initial_dist") + initial_dist_t = ops.convert_to_tensor(initial_dist, name="initial_dist") acceptance_dist, prob_of_original = ( _calculate_acceptance_probs_with_mixing(initial_dist_t, target_dist_t)) @@ -133,19 +78,51 @@ def _apply_fn(dataset): lambda accept_prob, _: accept_prob) prob_of_original_ds = acceptance_and_original_prob_ds.map( lambda _, prob_original: prob_original) + prob_of_original = None filtered_ds = _filter_ds(dataset, acceptance_dist_ds, initial_dist_ds, class_values_ds, seed) # Prefetch filtered dataset for speed. filtered_ds = filtered_ds.prefetch(3) - return interleave_ops.sample_from_datasets( - [dataset_ops.Dataset.zip((class_values_ds, dataset)), filtered_ds], - weights=prob_of_original_ds.map(lambda prob: [(prob, 1.0 - prob)]), - seed=seed) + prob_original_static = _get_prob_original_static( + initial_dist, target_dist_t) if initial_dist is not None else None + if prob_original_static == 1: + return dataset_ops.Dataset.zip((class_values_ds, dataset)) + elif prob_original_static == 0: + return filtered_ds + else: + return interleave_ops.sample_from_datasets( + [dataset_ops.Dataset.zip((class_values_ds, dataset)), filtered_ds], + weights=prob_of_original_ds.map(lambda prob: [(prob, 1.0 - prob)]), + seed=seed) return _apply_fn +def _get_prob_original_static(initial_dist_t, target_dist_t): + """Returns the static probability of sampling from the original. + + For some reason, `tensor_util.constant_value(prob_of_original)` of a ratio + of two constant Tensors isn't a constant. We have some custom logic to avoid + this. + + Args: + initial_dist_t: A tensor of the initial distribution. + target_dist_t: A tensor of the target distribution. + + Returns: + The probability of sampling from the original distribution as a constant, + if it is a constant, or `None`. + """ + init_static = tensor_util.constant_value(initial_dist_t) + target_static = tensor_util.constant_value(target_dist_t) + + if init_static is None or target_static is None: + return None + else: + return np.min(target_static / init_static) + + def _filter_ds(dataset, acceptance_dist_ds, initial_dist_ds, class_values_ds, seed): """Filters a dataset based on per-class acceptance probabilities. @@ -216,16 +193,42 @@ def _get_target_to_initial_ratio(initial_probs, target_probs): return target_probs / denom -def _calculate_acceptance_probs(initial_probs, target_probs): - """Calculate the per-class acceptance rates. +def _estimate_data_distribution(c, num_examples_per_class_seen): + """Estimate data distribution as labels are seen. Args: - initial_probs: The class probabilities of the data. - target_probs: The desired class proportion in minibatches. + c: The class labels. Type `int32`, shape `[batch_size]`. + num_examples_per_class_seen: Type `int64`, shape `[num_classes]`, + containing counts. + Returns: - A list of the per-class acceptance probabilities. + num_examples_per_lass_seen: Updated counts. Type `int64`, shape + `[num_classes]`. + dist: The updated distribution. Type `float32`, shape `[num_classes]`. + """ + num_classes = num_examples_per_class_seen.get_shape()[0].value + # Update the class-count based on what labels are seen in batch. + num_examples_per_class_seen = math_ops.add( + num_examples_per_class_seen, math_ops.reduce_sum( + array_ops.one_hot(c, num_classes, dtype=dtypes.int64), 0)) + init_prob_estimate = math_ops.truediv( + num_examples_per_class_seen, + math_ops.reduce_sum(num_examples_per_class_seen)) + dist = math_ops.cast(init_prob_estimate, dtypes.float32) + return num_examples_per_class_seen, dist - This method is based on solving the following analysis: + +def _calculate_acceptance_probs_with_mixing(initial_probs, target_probs): + """Calculates the acceptance probabilities and mixing ratio. + + In this case, we assume that we can *either* sample from the original data + distribution with probability `m`, or sample from a reshaped distribution + that comes from rejection sampling on the original distribution. This + rejection sampling is done on a per-class basis, with `a_i` representing the + probability of accepting data from class `i`. + + This method is based on solving the following analysis for the reshaped + distribution: Let F be the probability of a rejection (on any example). Let p_i be the proportion of examples in the data in class i (init_probs) @@ -256,47 +259,6 @@ def _calculate_acceptance_probs(initial_probs, target_probs): A solution for a_i in terms of the other variables is the following: ```a_i = (t_i / p_i) / max_i[t_i / p_i]``` - """ - ratio_l = _get_target_to_initial_ratio(initial_probs, target_probs) - - # Calculate list of acceptance probabilities. - max_ratio = math_ops.reduce_max(ratio_l) - return ratio_l / max_ratio - - -def _estimate_data_distribution(c, num_examples_per_class_seen): - """Estimate data distribution as labels are seen. - - Args: - c: The class labels. Type `int32`, shape `[batch_size]`. - num_examples_per_class_seen: Type `int64`, shape `[num_classes]`, - containing counts. - - Returns: - num_examples_per_lass_seen: Updated counts. Type `int64`, shape - `[num_classes]`. - dist: The updated distribution. Type `float32`, shape `[num_classes]`. - """ - num_classes = num_examples_per_class_seen.get_shape()[0].value - # Update the class-count based on what labels are seen in batch. - num_examples_per_class_seen = math_ops.add( - num_examples_per_class_seen, math_ops.reduce_sum( - array_ops.one_hot(c, num_classes, dtype=dtypes.int64), 0)) - init_prob_estimate = math_ops.truediv( - num_examples_per_class_seen, - math_ops.reduce_sum(num_examples_per_class_seen)) - dist = math_ops.cast(init_prob_estimate, dtypes.float32) - return num_examples_per_class_seen, dist - - -def _calculate_acceptance_probs_with_mixing(initial_probs, target_probs): - """Calculates the acceptance probabilities and mixing ratio. - - In this case, we assume that we can *either* sample from the original data - distribution with probability `m`, or sample from a reshaped distribution - that comes from rejection sampling on the original distribution. This - rejection sampling is done on a per-class basis, with `a_i` representing the - probability of accepting data from class `i`. If we try to minimize the amount of data rejected, we get the following: @@ -312,8 +274,6 @@ def _calculate_acceptance_probs_with_mixing(initial_probs, target_probs): m = M_min - See the docstring for `_calculate_acceptance_probs` for more details. - Args: initial_probs: A Tensor of the initial probability distribution, given or estimated. From 7d3e3fd76a002cd1dd78cb7f11bab760fb5abecb Mon Sep 17 00:00:00 2001 From: Malcolm Reynolds Date: Thu, 26 Apr 2018 16:24:51 -0700 Subject: [PATCH 0088/1691] More informative error message when loading a graph_def which uses unknown ops. Fixes #17014 PiperOrigin-RevId: 194472083 --- tensorflow/core/framework/op.cc | 27 ++++++++++++------- .../core/graph/graph_constructor_test.cc | 15 +++++++++++ 2 files changed, 33 insertions(+), 9 deletions(-) diff --git a/tensorflow/core/framework/op.cc b/tensorflow/core/framework/op.cc index 5f68c59fe9ae08..0873d4e47bd406 100644 --- a/tensorflow/core/framework/op.cc +++ b/tensorflow/core/framework/op.cc @@ -91,11 +91,15 @@ Status OpRegistry::LookUp(const string& op_type_name, } } } - Status status = - errors::NotFound("Op type not registered '", op_type_name, - "' in binary running on ", port::Hostname(), ". ", - "Make sure the Op and Kernel are registered in the " - "binary running in this process."); + Status status = errors::NotFound( + "Op type not registered '", op_type_name, "' in binary running on ", + port::Hostname(), ". ", + "Make sure the Op and Kernel are registered in the " + "binary running in this process. Note that if you " + "are loading a saved graph which used ops from " + "tf.contrib, accessing (e.g.) `tf.contrib.resampler` should be done" + "before importing the graph, as contrib ops are lazily registered " + "when the module is first accessed."); VLOG(1) << status.ToString(); return status; } @@ -246,10 +250,15 @@ Status OpListOpRegistry::LookUp(const string& op_type_name, auto iter = index_.find(op_type_name); if (iter == index_.end()) { *op_reg_data = nullptr; - return errors::NotFound("Op type not registered '", op_type_name, - "' in binary running on ", port::Hostname(), ". ", - "Make sure the Op and Kernel are registered in the " - "binary running in this process."); + return errors::NotFound( + "Op type not registered '", op_type_name, "' in binary running on ", + port::Hostname(), ". ", + "Make sure the Op and Kernel are registered in the " + "binary running in this process. Note that if you " + "are loading a saved graph which used ops from " + "tf.contrib, accessing (e.g.) `tf.contrib.resampler` should be done" + "before importing the graph, as contrib ops are lazily registered " + "when the module is first accessed."); } *op_reg_data = iter->second; return Status::OK(); diff --git a/tensorflow/core/graph/graph_constructor_test.cc b/tensorflow/core/graph/graph_constructor_test.cc index c18ccf6ce44265..b513778de9c86e 100644 --- a/tensorflow/core/graph/graph_constructor_test.cc +++ b/tensorflow/core/graph/graph_constructor_test.cc @@ -3160,5 +3160,20 @@ TEST_F(GraphConstructorTest, ImportGraphDef_ValidateColationConstraints) { TF_EXPECT_OK(ImportGraphDef(options, def, &graph_, nullptr)); } +TEST_F(GraphConstructorTest, ImportGraphDef_UnknownOps) { + const string pb_ascii = "node { name: 'op_from_contrib' op: 'OpFromContrib'}"; + // Try load twice to check for two parts of the error message. We cannot check + // for the whole thing in one go because the message includes the hostname. + ExpectError(pb_ascii, {"Op type not registered 'OpFromContrib'"}); + ExpectError( + pb_ascii, + {"Make sure the Op and Kernel are registered in the " + "binary running in this process. Note that if you " + "are loading a saved graph which used ops from " + "tf.contrib, accessing (e.g.) `tf.contrib.resampler` should be done" + "before importing the graph, as contrib ops are lazily registered " + "when the module is first accessed."}); +} + } // namespace } // namespace tensorflow From a13d0e527941f6affeeb8155a819a93f8b4ee0ba Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Thu, 26 Apr 2018 16:34:59 -0700 Subject: [PATCH 0089/1691] Clang-format and version fix --- .../contrib/tensorrt/convert/convert_graph.cc | 3 ++- .../contrib/tensorrt/convert/convert_nodes.cc | 10 ++++----- .../contrib/tensorrt/convert/convert_nodes.h | 1 - .../tensorrt/convert/trt_optimization_pass.cc | 4 ++-- .../contrib/tensorrt/kernels/trt_engine_op.cc | 21 ++++++++++-------- .../contrib/tensorrt/kernels/trt_engine_op.h | 3 ++- .../contrib/tensorrt/test/test_tftrt.py | 22 ++++++++++++------- .../tensorrt/test/tf_trt_integration_test.py | 19 +++++----------- 8 files changed, 43 insertions(+), 40 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 785c33c4c407ab..b40a45ee786f7c 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -449,7 +449,8 @@ tensorflow::Status ConvertAfterShapes( Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id); if (!s.ok()) { LOG(ERROR) - << "Cuda device identification failed, using device 0. Error= " << s; + << "Cuda device identification failed, using device 0. Error= " + << s; } else { cuda_device_id = cuda_gpu_id.value(); } diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index b37c5357367303..8ed0ed7b7eb07f 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2249,8 +2249,8 @@ tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) { op_res->logger_ = new tensorflow::tensorrt::Logger(); cudaSetDevice(s.cuda_device_id_); op_res->builder_ = nvinfer1::createInferBuilder(*(op_res->logger_)); - op_res->allocator_=s.allocator_; -#if NV_TENSORRT_MAJOR >4 + op_res->allocator_ = s.allocator_; +#if NV_TENSORRT_MAJOR > 3 op_res->builder_->setGpuAllocator(s.allocator_.get()); #endif if (!op_res->builder_) { @@ -2481,13 +2481,13 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef( // Topological order is needed to build TRT network tensorflow::tensorrt::Logger trt_logger; -cudaSetDevice(s.cuda_device_id_); + cudaSetDevice(s.cuda_device_id_); auto trt_builder = infer_object(nvinfer1::createInferBuilder(trt_logger)); if (!trt_builder) { return tensorflow::errors::Internal( "Failed to create TensorRT builder object"); } -#if NV_TENSORRT_MAJOR >3 +#if NV_TENSORRT_MAJOR > 3 trt_builder->setGpuAllocator(s.allocator_.get()); #endif auto trt_network = infer_object(trt_builder->createNetwork()); @@ -2718,7 +2718,7 @@ cudaSetDevice(s.cuda_device_id_); .Finalize(s.trt_node); VLOG(0) << status.ToString() << " finished op building for " << engine_name - << " on device " << s.device_name_ ; + << " on device " << s.device_name_; return tensorflow::Status::OK(); } diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index ecccaf36e3a524..8e1d7c99b6db15 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -22,7 +22,6 @@ limitations under the License. #include #include - #include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/grappler/costs/graph_properties.h" diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc index 880ffe1b3a0c9f..5c08d5afdfd680 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc @@ -189,8 +189,8 @@ tensorflow::Status TRTOptimizationPass::Optimize( const auto& pname = dev->parsed_name(); VLOG(1) << "Device name= " << dev->name() << " parsedname job= " << pname.job << " id= " << pname.id - << " has_id: " << pname.has_id << " has_job: " << pname.has_job<< - "has_type: "<device()->tensorflow_gpu_device_info()->gpu_id); + if (!trt_execution_context_ptr_) { + IRuntime* infer = nvinfer1::createInferRuntime(logger); +#if NV_TENSORRT_MAJOR > 3 + tensorflow::TfGpuId tf_gpu_id( + context->device()->tensorflow_gpu_device_info()->gpu_id); tensorflow::GPUOptions gpuoptions; auto pm = tensorflow::ProcessState::singleton(); auto dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); - IRuntime* infer = nvinfer1::createInferRuntime(logger); - if(!dev_allocator){ - LOG(FATAL)<<"Can't find device allocator for gpu device"<(dev_allocator); infer->setGpuAllocator(allocator_.get()); +#endif trt_engine_ptr_.reset(infer->deserializeCudaEngine( serialized_engine_.c_str(), serialized_engine_.size(), nullptr)); trt_execution_context_ptr_.reset(trt_engine_ptr_->createExecutionContext()); @@ -167,7 +170,7 @@ void TRTEngineOp::Compute(OpKernelContext* context) { VLOG(2) << "enqueue returns: " << ret; // sync should be done by TF. } -TRTEngineOp::~TRTEngineOp(){ +TRTEngineOp::~TRTEngineOp() { // Order matters! trt_execution_context_ptr_.reset(); trt_engine_ptr_.reset(); diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h index 791bb6f5834534..38ceec4704295e 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h @@ -23,10 +23,10 @@ limitations under the License. #if GOOGLE_CUDA #if GOOGLE_TENSORRT #include "cuda/include/cuda_runtime_api.h" +#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorrt/include/NvInfer.h" -#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" namespace tensorflow { namespace tensorrt { @@ -38,6 +38,7 @@ class TRTEngineOp : public OpKernel { void Compute(OpKernelContext* context) override; ~TRTEngineOp(); + private: template struct Destroyer { diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py index aaaed0c30fa4e6..229532011734a8 100644 --- a/tensorflow/contrib/tensorrt/test/test_tftrt.py +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -78,8 +78,7 @@ def execute_graph(gdef, dumm_inp): # with csess.Session( # config=cpb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess: # val = sess.run(out, {inp: dumm_inp}) - with csess.Session( - config=sessconfig, graph=g) as sess: + with csess.Session(config=sessconfig, graph=g) as sess: val = sess.run(out, {inp: dumm_inp}) return val @@ -149,6 +148,7 @@ def user(run_graph=execute_graph, run_calibration=execute_calibration): assert np.allclose(o1, o5) print("Pass") + def auto(): """ Run the conversion as an optimization pass""" inp_dims = (100, 24, 24, 2) @@ -165,8 +165,8 @@ def auto(): print(custom_op) gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) graph_options = cpb2.GraphOptions(rewrite_options=opt_config) - sessconfig = cpb2.ConfigProto(gpu_options=gpu_options, - graph_options=graph_options) + sessconfig = cpb2.ConfigProto( + gpu_options=gpu_options, graph_options=graph_options) print(sessconfig) g = ops.Graph() ops.reset_default_graph() @@ -179,11 +179,17 @@ def auto(): val = sess.run(out, {inp: dummy_input}) print(val.shape) + if "__main__" in __name__: - P = argparse.ArgumentParser(prog="tftrt_test", - description="Example utilization of TensorFlow-TensorRT integration") - P.add_argument("--automatic", "-a", action="store_true", - help="Do TRT conversion automatically", default=False) + P = argparse.ArgumentParser( + prog="tftrt_test", + description="Example utilization of TensorFlow-TensorRT integration") + P.add_argument( + "--automatic", + "-a", + action="store_true", + help="Do TRT conversion automatically", + default=False) flags, unparsed = P.parse_known_args() if flags.automatic: auto() diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py index 7a4732876286a9..a5c00dd6333183 100644 --- a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py +++ b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py @@ -45,8 +45,7 @@ def setUp(self): inp_dims = (100, 24, 24, 2) self._input = np.random.random_sample(inp_dims) self._original_graph = self.get_simple_graph_def() - self._gpu_options = cpb2.GPUOptions( - per_process_gpu_memory_fraction=0.50) + self._gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) self._config = cpb2.ConfigProto(gpu_options=self._gpu_options) self._reference = self.run_graph(self._original_graph, self._input) @@ -61,11 +60,7 @@ def get_simple_graph_def(self): name="weights", dtype=dtypes.float32) conv = nn.conv2d( - input=a, - filter=e, - strides=[1, 2, 2, 1], - padding="SAME", - name="conv") + input=a, filter=e, strides=[1, 2, 2, 1], padding="SAME", name="conv") b = cop.constant( [4., 1.5, 2., 3., 5., 7.], name="bias", dtype=dtypes.float32) t = nn.bias_add(conv, b, name="biasAdd") @@ -86,8 +81,7 @@ def run_graph(self, gdef, dumm_inp): inp = inp.outputs[0] out = out.outputs[0] with self.test_session( - graph=g, config=self._config, use_gpu=True, - force_gpu=True) as sess: + graph=g, config=self._config, use_gpu=True, force_gpu=True) as sess: val = sess.run(out, {inp: dumm_inp}) return val @@ -105,15 +99,14 @@ def run_calibration(self, gdef, dumm_inp): # run over real calibration data here, we are mimicking a calibration # set of 30 different batches. Use as much calibration data as you want with self.test_session( - graph=g, config=self._config, use_gpu=True, - force_gpu=True) as sess: + graph=g, config=self._config, use_gpu=True, force_gpu=True) as sess: for _ in range(30): val = sess.run(out, {inp: dumm_inp}) return val def get_trt_graph(self, mode): """Return trt converted graph.""" - if mode in ["FP32", "FP16", "INT8"]: + if mode in ["FP32", "FP16", "INT8"]: return trt.create_inference_graph( input_graph_def=self._original_graph, outputs=["output"], @@ -121,7 +114,7 @@ def get_trt_graph(self, mode): max_workspace_size_bytes=1 << 25, precision_mode=mode, # TRT Engine precision "FP32","FP16" or "INT8" minimum_segment_size=2 # minimum number of nodes in an engine - ) + ) return None def testFP32(self): From 236120d32d1c720ff72f617792d268ec2c82d9e6 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Thu, 26 Apr 2018 16:40:16 -0700 Subject: [PATCH 0090/1691] Split out SaveableObjects into their own file Pulls a couple build rules out of tensorflow/python:training. I'd like to use a SaveableObject in :checkpointable (for saving some Python state by default), which means the file with SaveableObject has to be essientially dependency-free. PiperOrigin-RevId: 194473987 --- tensorflow/python/BUILD | 14 ++- tensorflow/python/training/saveable_object.py | 99 +++++++++++++++++++ tensorflow/python/training/saver.py | 81 +-------------- 3 files changed, 115 insertions(+), 79 deletions(-) create mode 100644 tensorflow/python/training/saveable_object.py diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index e2d86fa4f75c8b..105fcbadb307d5 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -2967,7 +2967,11 @@ py_library( ["training/**/*.py"], exclude = [ "**/*test*", - "training/training_util.py", # See :training_util + # The following targets have their own build rules (same name as the + # file): + "training/checkpointable.py", + "training/saveable_object.py", + "training/training_util.py", ], ), srcs_version = "PY2AND3", @@ -2975,6 +2979,7 @@ py_library( ":array_ops", ":array_ops_gen", ":checkpoint_ops_gen", + ":checkpointable", ":client", ":control_flow_ops", ":data_flow_ops", @@ -2998,6 +3003,7 @@ py_library( ":random_ops", ":resource_variable_ops", ":resources", + ":saveable_object", ":sdca_ops", ":sparse_ops", ":state_ops", @@ -3043,6 +3049,12 @@ py_test( ], ) +py_library( + name = "saveable_object", + srcs = ["training/saveable_object.py"], + srcs_version = "PY2AND3", +) + py_library( name = "device_util", srcs = ["training/device_util.py"], diff --git a/tensorflow/python/training/saveable_object.py b/tensorflow/python/training/saveable_object.py new file mode 100644 index 00000000000000..4b19294b6545de --- /dev/null +++ b/tensorflow/python/training/saveable_object.py @@ -0,0 +1,99 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Types for specifying saving and loading behavior.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +class SaveSpec(object): + """Class used to describe tensor slices that need to be saved.""" + + def __init__(self, tensor, slice_spec, name, dtype=None): + """Creates a `SaveSpec` object. + + Args: + tensor: the tensor to save or callable that produces a tensor to save. + slice_spec: the slice to be saved. See `Variable.SaveSliceInfo`. + name: the name to save the tensor under. + dtype: The data type of the Tensor. Required if `tensor` is callable. + Used for error checking in the restore op. + """ + self._tensor = tensor + self.slice_spec = slice_spec + self.name = name + if callable(self._tensor): + if dtype is None: + raise AssertionError( + "When passing a callable `tensor` to a SaveSpec, an explicit " + "dtype must be provided.") + self.dtype = dtype + else: + self.dtype = tensor.dtype + + @property + def tensor(self): + return self._tensor() if callable(self._tensor) else self._tensor + + +class SaveableObject(object): + """Base class for saving and restoring saveable objects.""" + + def __init__(self, op, specs, name): + """Creates a `SaveableObject` object. + + Args: + op: the "producer" object that this class wraps; it produces a list of + tensors to save. E.g., a "Variable" object saving its backing tensor. + specs: a list of SaveSpec, each element of which describes one tensor to + save under this object. All Tensors must be on the same device. + name: the name to save the object under. + """ + self.op = op + self.specs = specs + self.name = name + self._device = None + + @property + def device(self): + """The device for SaveSpec Tensors.""" + # Note that SaveSpec.tensor runs Tensor-gathering ops when executing + # eagerly, making this call potentially very expensive. + # + # TODO(allenl): Consider another way to gather device information. Lower + # priority since this property isn't part of the normal save()/restore() + # workflow, but does come up when some alternative builders are passed to + # the Saver. + if self._device is None: + self._device = self.specs[0].tensor.device + return self._device + + def restore(self, restored_tensors, restored_shapes): + """Restores this object from 'restored_tensors'. + + Args: + restored_tensors: the tensors that were loaded from a checkpoint + restored_shapes: the shapes this object should conform to after + restore, or None. + + Returns: + An operation that restores the state of the object. + + Raises: + ValueError: If the object cannot be restored using the provided + parameters. + """ + # pylint: disable=unused-argument + raise ValueError("Calling an abstract method.") diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index a74d629a8f81ba..53e821c995900c 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -54,6 +54,7 @@ from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpointable +from tensorflow.python.training import saveable_object from tensorflow.python.training import training_util from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState from tensorflow.python.util import compat @@ -91,84 +92,8 @@ class BaseSaverBuilder(object): Can be extended to create different Ops. """ - class SaveSpec(object): - """Class used to describe tensor slices that need to be saved.""" - - def __init__(self, tensor, slice_spec, name, dtype=None): - """Creates a `SaveSpec` object. - - Args: - tensor: the tensor to save or callable that produces a tensor to save. - slice_spec: the slice to be saved. See `Variable.SaveSliceInfo`. - name: the name to save the tensor under. - dtype: The data type of the Tensor. Required if `tensor` is callable. - Used for error checking in the restore op. - """ - self._tensor = tensor - self.slice_spec = slice_spec - self.name = name - if callable(self._tensor): - if dtype is None: - raise AssertionError( - "When passing a callable `tensor` to a SaveSpec, an explicit " - "dtype must be provided.") - self.dtype = dtype - else: - self.dtype = tensor.dtype - - @property - def tensor(self): - return self._tensor() if callable(self._tensor) else self._tensor - - class SaveableObject(object): - """Base class for saving and restoring saveable objects.""" - - def __init__(self, op, specs, name): - """Creates a `SaveableObject` object. - - Args: - op: the "producer" object that this class wraps; it produces a list of - tensors to save. E.g., a "Variable" object saving its backing tensor. - specs: a list of SaveSpec, each element of which describes one tensor to - save under this object. All Tensors must be on the same device. - name: the name to save the object under. - """ - self.op = op - self.specs = specs - self.name = name - self._device = None - - @property - def device(self): - """The device for SaveSpec Tensors.""" - # Note that SaveSpec.tensor runs Tensor-gathering ops when executing - # eagerly, making this call potentially very expensive. - # - # TODO(allenl): Consider another way to gather device information. Lower - # priority since this property isn't part of the normal save()/restore() - # workflow, but does come up when some alternative builders are passed to - # the Saver. - if self._device is None: - self._device = self.specs[0].tensor.device - return self._device - - def restore(self, restored_tensors, restored_shapes): - """Restores this object from 'restored_tensors'. - - Args: - restored_tensors: the tensors that were loaded from a checkpoint - restored_shapes: the shapes this object should conform to after - restore, or None. - - Returns: - An operation that restores the state of the object. - - Raises: - ValueError: If the object cannot be restored using the provided - parameters. - """ - # pylint: disable=unused-argument - raise ValueError("Calling an abstract method.") + SaveSpec = saveable_object.SaveSpec + SaveableObject = saveable_object.SaveableObject class VariableSaveable(SaveableObject): """SaveableObject implementation that handles Variables.""" From 8838e2a84f98bd210147dc1a79e1037f2545dff9 Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Thu, 26 Apr 2018 17:36:20 -0700 Subject: [PATCH 0091/1691] Remove some commented code and add a TODO --- .../contrib/tensorrt/convert/convert_graph.cc | 6 +----- .../tensorrt/convert/trt_optimization_pass.cc | 1 + .../tensorrt/convert/trt_optimization_pass.h | 4 +--- .../contrib/tensorrt/resources/trt_allocator.cc | 2 +- .../contrib/tensorrt/resources/trt_allocator.h | 2 +- tensorflow/contrib/tensorrt/segment/segment.cc | 17 ++++------------- 6 files changed, 9 insertions(+), 23 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 9d79c084eecec0..44b1a8f94cc9d7 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -230,7 +230,7 @@ tensorflow::Status GetCalibNode(ConvertGraphParams* params) { auto src_output = in_edge->src_output(); auto dst_node = in_edge->dst(); auto dst_input = in_edge->dst_input(); - VLOG(0) << " update edge " << trt_node->name() << ":" << src_output + VLOG(1) << " update edge " << trt_node->name() << ":" << src_output << " -> " << dst_node->name() << ":" << dst_input; TF_RETURN_IF_ERROR( params->graph.UpdateEdge(trt_node, src_output, dst_node, dst_input)); @@ -367,12 +367,8 @@ tensorflow::Status ConvertGraphDefToTensorRT( VLOG(2) << "gpus: " << num_gpus; tensorflow::RewriterConfig rw_cfg; tensorflow::grappler::MetaOptimizer meta_opt(nullptr, rw_cfg); - // TF_RETURN_IF_ERROR(optimizer.Optimize(cluster, item, &gdef)); TF_RETURN_IF_ERROR(meta_opt.Optimize(cluster, item, &gdef)); - // constant folding item.graph = gdef; - // tensorflow::grappler::ConstantFolding fold(nullptr); - // TF_RETURN_IF_ERROR(fold.Optimize(nullptr, item, &gdef)); // AJ refactoring shape inference through grappler/GraphProperties. tensorflow::grappler::GraphProperties static_graph_properties(item); diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc index 5c08d5afdfd680..999ad1274c3b33 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc @@ -31,6 +31,7 @@ using tensorflow::strings::StrCat; namespace tensorflow { namespace tensorrt { namespace convert { +// TODO(sami): Remove VLOG messages once the code matures tensorflow::Status TRTOptimizationPass::Init( const tensorflow::RewriterConfig_CustomGraphOptimizer* config) { VLOG(1) << "Called INIT for " << m_name_ << " with config = " << config; diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h index 5b1462f573502b..81e3462a617145 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h @@ -42,8 +42,6 @@ class TRTOptimizationPass : public tensorflow::grappler::CustomGraphOptimizer { maximum_workspace_size_(-1) { VLOG(1) << "Constructing " << m_name_; }; - // tensorflow::Status Run(const tensorflow::GraphOptimizationPassOptions - // &options) override; string name() const override { return m_name_; }; tensorflow::Status Init(const tensorflow::RewriterConfig_CustomGraphOptimizer* config = nullptr) override; @@ -67,4 +65,4 @@ class TRTOptimizationPass : public tensorflow::grappler::CustomGraphOptimizer { } // namespace tensorflow #endif #endif -#endif \ No newline at end of file +#endif diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc index 4705f6d20f5a65..9d40fea06b19b0 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc @@ -54,4 +54,4 @@ void TRTDeviceAllocator::free(void* memory) { } // namespace tensorflow #endif #endif -#endif \ No newline at end of file +#endif diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.h b/tensorflow/contrib/tensorrt/resources/trt_allocator.h index 8bdb0519ba3e41..3001224b8d4a0d 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.h +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.h @@ -62,4 +62,4 @@ class AllocatorFactory {}; #endif #endif -#endif \ No newline at end of file +#endif diff --git a/tensorflow/contrib/tensorrt/segment/segment.cc b/tensorflow/contrib/tensorrt/segment/segment.cc index 8f335f2bf15e3c..ac0d782a2b9be9 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.cc +++ b/tensorflow/contrib/tensorrt/segment/segment.cc @@ -96,15 +96,6 @@ bool CanContractEdge(const Edge* edge, const Graph* graph) { } bool is_cycle = check_cycles(graph, src, dfs_start_nodes); - // if (!dfs_start_nodes.empty()) { - // tensorflow::ReverseDFSFrom(graph, dfs_start_nodes, {}, - // [&is_cycle, src](tensorflow::Node* node) { - // if (node == src) { - // is_cycle = true; - // } - // }); - // } - return !is_cycle; } } // namespace @@ -140,7 +131,7 @@ Graph::Graph(const tensorflow::Graph* g) : g_(g) { auto dst = nodes_[tfdst->id()]; auto edge = new Edge(i, src, e->src_output(), dst, e->dst_input(), is_control); - edges_[i]=edge; + edges_[i] = edge; src->out_edges_.push_back(edge); dst->in_edges_.push_back(edge); } else { @@ -271,10 +262,10 @@ tensorflow::Status SegmentGraph( const std::function& candidate_fn, const SegmentOptions& options, SegmentNodesVector* segments) { // tensorflow::DumpGraph("Pre-Segment", &graph); - Graph* graph= new Graph(tf_graph); + Graph* graph = new Graph(tf_graph); // Use a union-find to collect the nodes that belong to the same - // segment. A node value of nullptr indicates that tusing - // ::tensorflow::strings::StrAppendhe node is not a candidate for TRT. + // segment. A node value of nullptr indicates that the node is not a candidate + // for TRT. std::vector> node_segments; for (int i = 0; i < graph->num_node_ids(); ++i) { Node* node = graph->FindNodeId(i); From 0b02fd4fad3cf034e8f65a518445f3e9aa5beb2c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 17:56:08 -0700 Subject: [PATCH 0092/1691] Implements linear no-offset (aka symmetric) quantizer. PiperOrigin-RevId: 194482547 --- .../contrib/lite/kernels/internal/BUILD | 2 + .../internal/optimized/neon_tensor_utils.cc | 80 ++++++++++++++++++- .../internal/optimized/neon_tensor_utils.h | 7 ++ .../internal/optimized/tensor_utils_impl.h | 8 ++ .../reference/portable_tensor_utils.cc | 24 ++++++ .../reference/portable_tensor_utils.h | 11 +++ .../lite/kernels/internal/tensor_utils.h | 8 ++ .../kernels/internal/tensor_utils_test.cc | 49 ++++++++++++ 8 files changed, 188 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD index dce14cdbbb7b12..c5539afb9c84d0 100644 --- a/tensorflow/contrib/lite/kernels/internal/BUILD +++ b/tensorflow/contrib/lite/kernels/internal/BUILD @@ -289,6 +289,7 @@ cc_library( "reference/portable_tensor_utils.h", ], deps = [ + ":round", "//tensorflow/contrib/lite:builtin_op_data", "//tensorflow/contrib/lite/kernels:activation_functor", "//tensorflow/contrib/lite/kernels:op_macros", @@ -310,6 +311,7 @@ cc_library( deps = [ ":cpu_check", ":portable_tensor_utils", + ":round", ":types", "//tensorflow/contrib/lite:builtin_op_data", "//tensorflow/contrib/lite/kernels:activation_functor", diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc index 780401e052733c..47dfcbeb01a046 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc @@ -12,13 +12,14 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include #include #include "tensorflow/contrib/lite/builtin_op_data.h" -#include "tensorflow/contrib/lite/kernels/internal/common.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" #include "tensorflow/contrib/lite/kernels/internal/common.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h" +#include "tensorflow/contrib/lite/kernels/internal/round.h" #ifdef USE_NEON @@ -248,6 +249,83 @@ void NeonClipVector(const float* vector, int v_size, float abs_limit, } } +void NeonSymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* min, + float* max, float* scaling_factor) { + // TODO(raziel): vectorize min/max calculation. + auto minmax = std::minmax_element(values, values + size); + *min = *minmax.first; + *max = *minmax.second; + const int kScale = 127; + const float range = std::max(std::abs(*min), std::abs(*max)); + if (range == 0) { + memset(quantized_values, 0, size * sizeof(int8_t)); + *scaling_factor = 1; + return; + } + *scaling_factor = kScale / range; + + const int postamble_start = + size - (size & (2 * kFloatWeightsPerNeonLane - 1)); + + // Vectorized constants. + const float32x4_t q_factor_f32x4 = vmovq_n_f32(*scaling_factor); + const float32x4_t point5_f32x4 = vmovq_n_f32(0.5); + const float32x4_t zero_f32x4 = vmovq_n_f32(0.0); + const int32x4_t scale_i32x4 = vmovq_n_s32(kScale); + const int32x4_t neg_scale_i32x4 = vmovq_n_s32(-kScale); + + for (int i = 0; i < postamble_start; i += 2 * kFloatWeightsPerNeonLane) { + // Implements the vectorized version of the following: + // const int32 quantized_value = static_cast( + // std::round(*scaling_factor * values[i])); + // Since the vectorized round intrinsics (vrndqa_f32) is not supported + // on all Neon flavors, we use the following method for rounding: if (x + // < 0) (int)(x - 0.5) if (x >= 0) (int)(x + 0.5) + float32x4_t value0_f32x4 = vld1q_f32(&values[i]); + float32x4_t value1_f32x4 = vld1q_f32(&values[i + kFloatWeightsPerNeonLane]); + float32x4_t mul0_f32x4 = vmulq_f32(value0_f32x4, q_factor_f32x4); + float32x4_t mul1_f32x4 = vmulq_f32(value1_f32x4, q_factor_f32x4); + + int32x4_t cmp_with_zero0_ui32x4 = + (int32x4_t)vcltq_f32(mul0_f32x4, zero_f32x4); // NOLINT + int32x4_t cmp_with_zero1_ui32x4 = + (int32x4_t)vcltq_f32(mul1_f32x4, zero_f32x4); // NOLINT + + float32x4_t cmp_with_zero0_f32x4 = vcvtq_f32_s32(cmp_with_zero0_ui32x4); + float32x4_t cmp_with_zero1_f32x4 = vcvtq_f32_s32(cmp_with_zero1_ui32x4); + cmp_with_zero0_f32x4 = vaddq_f32(cmp_with_zero0_f32x4, point5_f32x4); + cmp_with_zero1_f32x4 = vaddq_f32(cmp_with_zero1_f32x4, point5_f32x4); + + mul0_f32x4 = vaddq_f32(mul0_f32x4, cmp_with_zero0_f32x4); + mul1_f32x4 = vaddq_f32(mul1_f32x4, cmp_with_zero1_f32x4); + + int32x4_t f2i0_i32x4 = vcvtq_s32_f32(mul0_f32x4); + int32x4_t f2i1_i32x4 = vcvtq_s32_f32(mul1_f32x4); + + // Implements the vectorized version of the folowing block: + // quantized_values[i] = std::min(kScale, std::max(-kScale, + // quantized_value)); + int32x4_t max0_i32x4 = vmaxq_s32(f2i0_i32x4, neg_scale_i32x4); + int32x4_t max1_i32x4 = vmaxq_s32(f2i1_i32x4, neg_scale_i32x4); + int32x4_t min0_i32x4 = vminq_s32(max0_i32x4, scale_i32x4); + int32x4_t min1_i32x4 = vminq_s32(max1_i32x4, scale_i32x4); + + int16x4_t min0_16x4 = vmovn_s32(min0_i32x4); + int16x4_t min1_16x4 = vmovn_s32(min1_i32x4); + + int16x8_t min_16x8 = vcombine_s16(min0_16x4, min1_16x4); + int8x8_t min_s8x8 = vqmovn_s16(min_16x8); + vst1_s8(&quantized_values[i], min_s8x8); + } + + for (int i = postamble_start; i < size; ++i) { + const int32 quantized_value = + static_cast(TfLiteRound(*scaling_factor * values[i])); + quantized_values[i] = std::min(kScale, std::max(-kScale, quantized_value)); + } +} + float NeonVectorVectorDotProduct(const float* vector1, const float* vector2, int v_size) { // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h index b7e317dc60e2c6..3b6f4bd583a85d 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h @@ -97,6 +97,13 @@ void ClipVector(const float* vector, int v_size, float abs_limit, NEON_OR_PORTABLE(ClipVector, vector, v_size, abs_limit, result); } +void SymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* min, float* max, + float* scaling_factor) { + NEON_OR_PORTABLE(SymmetricQuantizeFloats, values, size, quantized_values, min, + max, scaling_factor); +} + void VectorShiftLeft(float* vector, int v_size, float shift_value) { NEON_OR_PORTABLE(VectorShiftLeft, vector, v_size, shift_value); } diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h b/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h index ff15f3e3b10324..19220470f4ef73 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h @@ -117,6 +117,14 @@ void PortableZeroVector(float* vector, int v_size); // Limit a float input f between +abs_limit and -abs_limit. float PortableClip(float f, float abs_limit); +// Symmetric quantizer. +void PortableSymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* min, + float* max, float* scaling_factor); +void NeonSymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* min, + float* max, float* scaling_factor); + // Shift left a vector in place with v_size size. void PortableVectorShiftLeft(float* vector, int v_size, float shift_value); void NeonVectorShiftLeft(float* vector, int v_size, float shift_value); diff --git a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc index c5b0bccc9da5fa..5e7586eeda7f21 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc @@ -12,10 +12,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include #include #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" +#include "tensorflow/contrib/lite/kernels/internal/round.h" #include "tensorflow/contrib/lite/kernels/op_macros.h" namespace tflite { @@ -27,6 +29,28 @@ float PortableClip(float f, float abs_limit) { return result; } +void PortableSymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* min, + float* max, float* scaling_factor) { + auto minmax = std::minmax_element(values, values + size); + *min = *minmax.first; + *max = *minmax.second; + const int kScale = 127; + const float range = std::max(std::abs(*min), std::abs(*max)); + if (range == 0) { + memset(quantized_values, 0, size * sizeof(int8_t)); + *scaling_factor = 1; + return; + } + *scaling_factor = kScale / range; + for (int i = 0; i < size; ++i) { + const int32_t quantized_value = + static_cast(TfLiteRound(*scaling_factor * values[i])); + // Clamp: just in case some odd numeric offset. + quantized_values[i] = std::min(kScale, std::max(-kScale, quantized_value)); + } +} + void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, int m_cols, const float* vector, diff --git a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h index c05c21b472b05f..478cda8e193971 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h @@ -25,6 +25,10 @@ namespace tensor_utils { // Limit a float input f between +abs_limit and -abs_limit. float PortableClip(float f, float abs_limit); +void PortableSymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* min, + float* max, float* scaling_factor); + // Multiply a matrix by a batch vector, and store results in a batch-size // vector. void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix, @@ -103,6 +107,13 @@ void PortableReductionSumVector(const float* input_vector, float* output_vector, float Clip(float f, float abs_limit) { return PortableClip(f, abs_limit); } +void SymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* min, float* max, + float* scaling_factor) { + return PortableSymmetricQuantizeFloats(values, size, quantized_values, min, + max, scaling_factor); +} + void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, int m_cols, const float* vector, int n_batch, float* result, diff --git a/tensorflow/contrib/lite/kernels/internal/tensor_utils.h b/tensorflow/contrib/lite/kernels/internal/tensor_utils.h index 40d144979b2f96..997dc4425d31e8 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/tensor_utils.h @@ -23,6 +23,14 @@ namespace tensor_utils { // Limit a float input f between +abs_limit and -abs_limit. float Clip(float f, float abs_limit); +// Quantizes a buffer of floating point values using a symmetric quantization +// (i.e. linear quantization without an offset) to 8-bit signed integers. +// It also outputs the range (min, max) of the floating point buffer, and the +// scaling factor used to quantize the values. +void SymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* min, float* max, + float* scaling_factor); + // Multiply a matrix by a batch vector, and store results in a batch-size // vector using a stride value provided in result_stride. 'result_stride' shows // how the number of elements between consecutive result values. For example diff --git a/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc b/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc index 588f1a428b8c84..22b016746fe0fb 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc @@ -32,6 +32,55 @@ TEST(uKernels, ClipTest) { {0.0, -0.5, 1.0, -1.5, 2.0, -2.0, 2.0, -2.0, 2.0, -2.0}))); } +TEST(uKernels, SymmetricQuantizeFloatsTest) { + constexpr int kVectorSize = 9; + static float input[kVectorSize] = {-640, -635.0, -630, 10.0, 2.0, + -5.0, -10.0, 0.0, 1000.0}; + + int8 output[kVectorSize]; + float min, max, scaling_factor; + SymmetricQuantizeFloats(input, kVectorSize, output, &min, &max, + &scaling_factor); + + EXPECT_EQ(min, -640); + EXPECT_EQ(max, 1000); + EXPECT_NEAR(scaling_factor, 0.127, 1e-6); // EQ won't work due to fpoint. + EXPECT_THAT(output, + testing::ElementsAreArray({-81, -81, -80, 1, 0, -1, -1, 0, 127})); +} + +TEST(uKernels, SymmetricQuantizeFloatsAllZerosTest) { + constexpr int kVectorSize = 9; + static float input[kVectorSize] = {0, 0, 0, 0, 0, 0, 0, 0, 0}; + + int8 output[kVectorSize]; + float min, max, scaling_factor; + SymmetricQuantizeFloats(input, kVectorSize, output, &min, &max, + &scaling_factor); + + EXPECT_EQ(min, 0); + EXPECT_EQ(max, 0); + EXPECT_EQ(scaling_factor, 1); + EXPECT_THAT(output, testing::ElementsAreArray({0, 0, 0, 0, 0, 0, 0, 0, 0})); +} + +TEST(uKernels, SymmetricQuantizeFloatsAllAlmostZeroTest) { + constexpr int kVectorSize = 9; + static float input[kVectorSize] = {-1e-5, 3e-5, -7e-6, -9e-5, 1e-6, + 4e-5, 9e-6, 2e-4, 0}; + + int8 output[kVectorSize]; + float min, max, scaling_factor; + SymmetricQuantizeFloats(input, kVectorSize, output, &min, &max, + &scaling_factor); + + EXPECT_NEAR(min, -9e-05, 1e-6); + EXPECT_NEAR(max, 0.0002, 1e-6); + EXPECT_EQ(scaling_factor, 635000); + EXPECT_THAT(output, + testing::ElementsAreArray({-6, 19, -4, -57, 1, 25, 6, 127, 0})); +} + TEST(uKernels, MatrixBatchVectorMultiplyAccumulateTest) { constexpr int kRow = 3; constexpr int kCol = 4; From 0a1d311a40009f0663aa3d904fb8574ff13fa672 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 18:03:50 -0700 Subject: [PATCH 0093/1691] Free scratch memory in ~BaseGPUDevice. PiperOrigin-RevId: 194483351 --- tensorflow/core/common_runtime/gpu/gpu_device.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc index 1fa33991f7768f..944f0c82e706ca 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_device.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc @@ -266,6 +266,7 @@ BaseGPUDevice::BaseGPUDevice(const SessionOptions& options, const string& name, BaseGPUDevice::~BaseGPUDevice() { delete gpu_device_info_; + for (auto sb : scratch_) gpu_allocator_->DeallocateRaw(sb); for (auto ctx : device_contexts_) ctx->Unref(); } From 84b3322931fd6fd73ce4ab250a1bd3cdd6e138f6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 18:24:48 -0700 Subject: [PATCH 0094/1691] Automated g4 rollback of changelist 194442428 PiperOrigin-RevId: 194485227 --- tensorflow/compiler/xla/shape_layout.h | 3 +-- .../compiler/xla/tools/parser/hlo_parser.cc | 16 ++++++---------- 2 files changed, 7 insertions(+), 12 deletions(-) diff --git a/tensorflow/compiler/xla/shape_layout.h b/tensorflow/compiler/xla/shape_layout.h index 4c83750f3e6f3c..a1dce758cd3ab3 100644 --- a/tensorflow/compiler/xla/shape_layout.h +++ b/tensorflow/compiler/xla/shape_layout.h @@ -48,8 +48,7 @@ class ShapeLayout { bool MatchesLayoutInShape(const Shape& shape) const; // Copies the layout from the given shape into this ShapeLayout. 'other_shape' - // must be compatible with the ShapeLayout's shape, and 'other_shape' must - // have a layout (LayoutUtil::HasLayout). + // must be compatible with the ShapeLayout's shape. tensorflow::Status CopyLayoutFromShape(const Shape& other_shape); // Clears (Layout::Clear) all the Layouts stored in this object. diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc index 95d3fd28b38a59..fdbfc0210ea63a 100644 --- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc +++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc @@ -303,18 +303,14 @@ bool HloParser::ParseComputations() { // set the layouts to what the hlo text says. for (int p = 0; p < computation->num_parameters(); p++) { const Shape& param_shape = computation->parameter_instruction(p)->shape(); - if (param_shape.has_layout()) { - module_->mutable_entry_computation_layout() - ->mutable_parameter_layout(p) - ->ResetLayout(param_shape.layout()); - } + TF_CHECK_OK(module_->mutable_entry_computation_layout() + ->mutable_parameter_layout(p) + ->CopyLayoutFromShape(param_shape)); } const Shape& result_shape = computation->root_instruction()->shape(); - if (result_shape.has_layout()) { - module_->mutable_entry_computation_layout() - ->mutable_result_layout() - ->ResetLayout(result_shape.layout()); - } + TF_CHECK_OK(module_->mutable_entry_computation_layout() + ->mutable_result_layout() + ->CopyLayoutFromShape(result_shape)); } return true; From 09fc850e988e71983e9d0eb4e874f998b3a480e6 Mon Sep 17 00:00:00 2001 From: Yifei Feng <1192265+yifeif@users.noreply.github.com> Date: Thu, 26 Apr 2018 18:47:07 -0700 Subject: [PATCH 0095/1691] Update build_pip_package.sh --- tensorflow/tools/pip_package/build_pip_package.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index 8f0cf8c3d19480..3af79ee170c20b 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -24,7 +24,7 @@ function real_path() { function cp_external() { local src_dir=$1 local dest_dir=$2 - for f in `find "$src_dir" -maxdepth 1 -mindepth 1 ! -name '*local_config_cuda*' ! -name '*org_tensorflow*'`; do + for f in `find "$src_dir" -maxdepth 1 -mindepth 1 ! -name '*local_config_cuda*' ! -name '*local_config_tensorrt*' ! -name '*org_tensorflow*'`; do cp -R "$f" "$dest_dir" done mkdir -p "${dest_dir}/local_config_cuda/cuda/cuda/" From e41e70ed9827b81a07c42f68def80f3f61b70375 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 19:35:10 -0700 Subject: [PATCH 0096/1691] Implement floor operator PiperOrigin-RevId: 194490433 --- tensorflow/contrib/lite/builtin_ops.h | 1 + .../lite/g3doc/tf_ops_compatibility.md | 12 ++- tensorflow/contrib/lite/kernels/BUILD | 14 ++++ tensorflow/contrib/lite/kernels/floor.cc | 58 +++++++++++++ tensorflow/contrib/lite/kernels/floor_test.cc | 83 +++++++++++++++++++ tensorflow/contrib/lite/kernels/register.cc | 2 + tensorflow/contrib/lite/model.cc | 1 + tensorflow/contrib/lite/nnapi_delegate.cc | 3 + tensorflow/contrib/lite/schema/schema.fbs | 2 +- .../contrib/lite/schema/schema_generated.h | 22 ++--- tensorflow/contrib/lite/testing/BUILD | 1 + .../contrib/lite/testing/generate_examples.py | 27 ++++++ .../testing/generated_examples_zip_test.cc | 7 +- .../contrib/lite/toco/tflite/operator.cc | 2 + 14 files changed, 220 insertions(+), 15 deletions(-) create mode 100644 tensorflow/contrib/lite/kernels/floor.cc create mode 100644 tensorflow/contrib/lite/kernels/floor_test.cc diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h index 859bc7ab70dc36..21e0e04ef6bc5b 100644 --- a/tensorflow/contrib/lite/builtin_ops.h +++ b/tensorflow/contrib/lite/builtin_ops.h @@ -33,6 +33,7 @@ typedef enum { kTfLiteBuiltinDepthwiseConv2d = 4, kTfLiteBuiltinDequantize = 6, kTfLiteBuiltinEmbeddingLookup = 7, + kTfLiteBuiltinFloor = 8, kTfLiteBuiltinFullyConnected = 9, kTfLiteBuiltinHashtableLookup = 10, kTfLiteBuiltinL2Normalization = 11, diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index 203924f03d3101..aa28f8d050944e 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -132,7 +132,6 @@ TensorFlow operation not listed above are likely unsupported. Notably, the following common ops are not supported at the moment: * [tf.depth_to_space](https://www.tensorflow.org/api_docs/python/tf/depth_to_space) -* [tf.floor](https://www.tensorflow.org/api_docs/python/tf/floor) * [tf.gather](https://www.tensorflow.org/api_docs/python/tf/gather) * [tf.image.resize_bilinear](https://www.tensorflow.org/api_docs/python/tf/image/resize_bilinear) * [tf.slice](https://www.tensorflow.org/api_docs/python/tf/slice) @@ -254,6 +253,17 @@ Outputs { } ``` +**FLOOR** + +``` +inputs { + 0: tensor +} +outputs: { + 0: result of computing element-wise floor of the input tensor +} +``` + **FULLY_CONNECTED** ``` diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index 80cefe83b29192..689f9bfa7151eb 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -145,6 +145,7 @@ cc_library( "embedding_lookup.cc", "embedding_lookup_sparse.cc", "exp.cc", + "floor.cc", "fully_connected.cc", "gather.cc", "hashtable_lookup.cc", @@ -437,6 +438,19 @@ tf_cc_test( ], ) +tf_cc_test( + name = "floor_test", + size = "small", + srcs = ["floor_test.cc"], + tags = ["tflite_not_portable_ios"], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + tf_cc_test( name = "unidirectional_sequence_lstm_test", size = "small", diff --git a/tensorflow/contrib/lite/kernels/floor.cc b/tensorflow/contrib/lite/kernels/floor.cc new file mode 100644 index 00000000000000..4b4395f711614a --- /dev/null +++ b/tensorflow/contrib/lite/kernels/floor.cc @@ -0,0 +1,58 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace floor { + +constexpr int kInputTensor = 0; +constexpr int kOutputTensor = 0; + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TfLiteTensor* input = GetInput(context, node, kInputTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32); + output->type = input->type; + TfLiteIntArray* output_size = TfLiteIntArrayCopy(input->dims); + return context->ResizeTensor(context, output, output_size); +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + TfLiteTensor* input = GetInput(context, node, kInputTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + + optimized_ops::Floor(GetTensorData(input), GetTensorDims(input), + GetTensorData(output), GetTensorDims(output)); + return kTfLiteOk; +} +} // namespace floor + +TfLiteRegistration* Register_FLOOR() { + static TfLiteRegistration r = {/*init=*/nullptr, + /*free=*/nullptr, floor::Prepare, floor::Eval}; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/floor_test.cc b/tensorflow/contrib/lite/kernels/floor_test.cc new file mode 100644 index 00000000000000..b71e0400b6dc92 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/floor_test.cc @@ -0,0 +1,83 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +class FloorOpModel : public SingleOpModel { + public: + FloorOpModel(std::initializer_list input_shape, TensorType input_type) { + input_ = AddInput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_FLOOR, BuiltinOptions_NONE, 0); + BuildInterpreter({ + input_shape, + }); + } + + int input() { return input_; } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int output_; +}; + +TEST(FloorOpTest, SingleDim) { + FloorOpModel model({2}, TensorType_FLOAT32); + model.PopulateTensor(model.input(), {8.5, 0.0}); + model.Invoke(); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({8, 0})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2})); +} + +TEST(FloorOpTest, MultiDims) { + FloorOpModel model({2, 1, 1, 5}, TensorType_FLOAT32); + model.PopulateTensor(model.input(), { + 0.0001, + 8.0001, + 0.9999, + 9.9999, + 0.5, + -0.0001, + -8.0001, + -0.9999, + -9.9999, + -0.5, + }); + model.Invoke(); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({0, 8, 0, 9, 0, -1, -9, -1, -10, -1})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 1, 1, 5})); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc index b07e7b6ff32e9e..f91d188ffa45fc 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -80,6 +80,7 @@ TfLiteRegistration* Register_MAXIMUM(); TfLiteRegistration* Register_MINIMUM(); TfLiteRegistration* Register_ARG_MAX(); TfLiteRegistration* Register_LESS(); +TfLiteRegistration* Register_FLOOR(); BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_RELU, Register_RELU()); @@ -141,6 +142,7 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_MINIMUM, Register_MINIMUM()); AddBuiltin(BuiltinOperator_ARG_MAX, Register_ARG_MAX()); AddBuiltin(BuiltinOperator_LESS, Register_LESS()); + AddBuiltin(BuiltinOperator_FLOOR, Register_FLOOR()); // TODO(andrewharp, ahentz): Move these somewhere more appropriate so that // custom ops aren't always included by default. diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index f45f39d1e6f874..6fd3d9f2ca4c3b 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -347,6 +347,7 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, case BuiltinOperator_LOG_SOFTMAX: case BuiltinOperator_DEQUANTIZE: case BuiltinOperator_PRELU: + case BuiltinOperator_FLOOR: break; case BuiltinOperator_CAST: { TfLiteCastParams* params = MallocPOD(); diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index eab82ea8ef2354..6a78f30fd1dba5 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -278,6 +278,9 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, case tflite::BuiltinOperator_TANH: nn_op_type = ANEURALNETWORKS_TANH; break; + case tflite::BuiltinOperator_FLOOR: + nn_op_type = ANEURALNETWORKS_FLOOR; + break; case tflite::BuiltinOperator_LOGISTIC: nn_op_type = ANEURALNETWORKS_LOGISTIC; break; diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index 20d68ceff7bcbd..b16baf02dcfa12 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -78,7 +78,7 @@ enum BuiltinOperator : byte { // DEPTH_TO_SPACE = 5, DEQUANTIZE = 6, EMBEDDING_LOOKUP = 7, - // FLOOR = 8, + FLOOR = 8, FULLY_CONNECTED = 9, HASHTABLE_LOOKUP = 10, L2_NORMALIZATION = 11, diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index 0b9961d606d609..25ed9abd9f8ded 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -221,6 +221,7 @@ enum BuiltinOperator { BuiltinOperator_DEPTHWISE_CONV_2D = 4, BuiltinOperator_DEQUANTIZE = 6, BuiltinOperator_EMBEDDING_LOOKUP = 7, + BuiltinOperator_FLOOR = 8, BuiltinOperator_FULLY_CONNECTED = 9, BuiltinOperator_HASHTABLE_LOOKUP = 10, BuiltinOperator_L2_NORMALIZATION = 11, @@ -275,7 +276,7 @@ enum BuiltinOperator { BuiltinOperator_MAX = BuiltinOperator_LESS }; -inline BuiltinOperator (&EnumValuesBuiltinOperator())[57] { +inline BuiltinOperator (&EnumValuesBuiltinOperator())[58] { static BuiltinOperator values[] = { BuiltinOperator_ADD, BuiltinOperator_AVERAGE_POOL_2D, @@ -284,6 +285,7 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[57] { BuiltinOperator_DEPTHWISE_CONV_2D, BuiltinOperator_DEQUANTIZE, BuiltinOperator_EMBEDDING_LOOKUP, + BuiltinOperator_FLOOR, BuiltinOperator_FULLY_CONNECTED, BuiltinOperator_HASHTABLE_LOOKUP, BuiltinOperator_L2_NORMALIZATION, @@ -348,7 +350,7 @@ inline const char **EnumNamesBuiltinOperator() { "", "DEQUANTIZE", "EMBEDDING_LOOKUP", - "", + "FLOOR", "FULLY_CONNECTED", "HASHTABLE_LOOKUP", "L2_NORMALIZATION", @@ -1485,8 +1487,8 @@ struct Conv2DOptionsT : public flatbuffers::NativeTable { stride_w(0), stride_h(0), fused_activation_function(ActivationFunctionType_NONE), - dilation_w_factor(0), - dilation_h_factor(0) { + dilation_w_factor(1), + dilation_h_factor(1) { } }; @@ -1513,10 +1515,10 @@ struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } int32_t dilation_w_factor() const { - return GetField(VT_DILATION_W_FACTOR, 0); + return GetField(VT_DILATION_W_FACTOR, 1); } int32_t dilation_h_factor() const { - return GetField(VT_DILATION_H_FACTOR, 0); + return GetField(VT_DILATION_H_FACTOR, 1); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && @@ -1549,10 +1551,10 @@ struct Conv2DOptionsBuilder { fbb_.AddElement(Conv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } void add_dilation_w_factor(int32_t dilation_w_factor) { - fbb_.AddElement(Conv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 0); + fbb_.AddElement(Conv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); } void add_dilation_h_factor(int32_t dilation_h_factor) { - fbb_.AddElement(Conv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 0); + fbb_.AddElement(Conv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); } explicit Conv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { @@ -1572,8 +1574,8 @@ inline flatbuffers::Offset CreateConv2DOptions( int32_t stride_w = 0, int32_t stride_h = 0, ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - int32_t dilation_w_factor = 0, - int32_t dilation_h_factor = 0) { + int32_t dilation_w_factor = 1, + int32_t dilation_h_factor = 1) { Conv2DOptionsBuilder builder_(_fbb); builder_.add_dilation_h_factor(dilation_h_factor); builder_.add_dilation_w_factor(dilation_w_factor); diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD index bd888a415b0359..a1162cef38693e 100644 --- a/tensorflow/contrib/lite/testing/BUILD +++ b/tensorflow/contrib/lite/testing/BUILD @@ -28,6 +28,7 @@ gen_zipped_test_files( "depthwiseconv.zip", "div.zip", "exp.zip", + "floor.zip", "fully_connected.zip", "fused_batch_norm.zip", "gather.zip", diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index 9c9acf64c142bd..2f8f7a1a795629 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -2034,6 +2034,33 @@ def build_inputs(parameters, sess, inputs, outputs): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + +def make_floor_tests(zip_path): + """Make a set of tests to do floor.""" + + test_parameters = [{ + "input_dtype": [tf.float32], + "input_shape": [[1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]], + }] + + def build_graph(parameters): + """Build the floor op testing graph.""" + input_value = tf.placeholder( + dtype=parameters["input_dtype"], + name="input1", + shape=parameters["input_shape"]) + out = tf.floor(input_value) + return [input_value], [out] + + def build_inputs(parameters, sess, inputs, outputs): + input_value = create_tensor_data(parameters["input_dtype"], + parameters["input_shape"]) + return [input_value], sess.run( + outputs, feed_dict={inputs[0]: input_value}) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + + # Toco binary path provided by the generate rule. bin_path = None diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc index 9da8bd7a28891f..34abb213c937cc 100644 --- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc +++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc @@ -251,23 +251,25 @@ INSTANTIATE_TESTS(conv) INSTANTIATE_TESTS(depthwiseconv) INSTANTIATE_TESTS(div) INSTANTIATE_TESTS(exp) +INSTANTIATE_TESTS(floor) INSTANTIATE_TESTS(fully_connected) INSTANTIATE_TESTS(fused_batch_norm) INSTANTIATE_TESTS(gather) INSTANTIATE_TESTS(global_batch_norm) INSTANTIATE_TESTS(l2_pool) INSTANTIATE_TESTS(l2norm) +INSTANTIATE_TESTS(less) INSTANTIATE_TESTS(local_response_norm) INSTANTIATE_TESTS(log_softmax) -INSTANTIATE_TESTS(maximum) INSTANTIATE_TESTS(max_pool) +INSTANTIATE_TESTS(maximum) INSTANTIATE_TESTS(mean) INSTANTIATE_TESTS(minimum) INSTANTIATE_TESTS(mul) INSTANTIATE_TESTS(pad) +// INSTANTIATE_TESTS(prelu) INSTANTIATE_TESTS(relu) INSTANTIATE_TESTS(relu1) -// INSTANTIATE_TESTS(prelu) INSTANTIATE_TESTS(relu6) INSTANTIATE_TESTS(reshape) INSTANTIATE_TESTS(resize_bilinear) @@ -280,7 +282,6 @@ INSTANTIATE_TESTS(squeeze) INSTANTIATE_TESTS(strided_slice) INSTANTIATE_TESTS(sub) INSTANTIATE_TESTS(transpose) -INSTANTIATE_TESTS(less) } // namespace testing } // namespace tflite diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index d2e14ac5e0d7b0..fce3bad3266e85 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -901,6 +901,8 @@ std::vector> BuildOperatorList() { "MINIMUM", OperatorType::kTensorFlowMinimum)); ops.emplace_back(new SimpleOperator( "LESS", OperatorType::kTensorFlowLess)); + ops.emplace_back( + new SimpleOperator("FLOOR", OperatorType::kFloor)); return ops; } From 7c845cb25ee44d52810c0f06e7843d5b14f8b6b4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Apr 2018 22:06:36 -0700 Subject: [PATCH 0097/1691] Reenable factorization_ops_test on ASAN after adding shard_count = 4. Tests now finish with these stats: "max = 150.6s, min = 27.4s, avg = 66.3s, dev = 19.5s" over 1000 runs and this runtime distribution should be fairly safe for deadline of 300s. PiperOrigin-RevId: 194500204 --- tensorflow/contrib/factorization/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/factorization/BUILD b/tensorflow/contrib/factorization/BUILD index f28d95401c3a5d..effec42f028fe4 100644 --- a/tensorflow/contrib/factorization/BUILD +++ b/tensorflow/contrib/factorization/BUILD @@ -215,7 +215,7 @@ tf_py_test( "//tensorflow/python:platform_test", "//tensorflow/python:sparse_tensor", ], - tags = ["noasan"], # times out b/78588193 + shard_count = 4, ) # Estimators tests From f88add45446cf5fa94256a63d49fe0f62c31937e Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Fri, 27 Apr 2018 00:07:07 -0700 Subject: [PATCH 0098/1691] Automated g4 rollback of changelist 194306629 PiperOrigin-RevId: 194507274 --- tensorflow/contrib/lite/kernels/test_util.h | 4 +--- tensorflow/contrib/lite/model.cc | 6 +++++- tensorflow/contrib/lite/model.h | 1 + 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/test_util.h b/tensorflow/contrib/lite/kernels/test_util.h index a5f345e98a9d4f..a9064d54e7704d 100644 --- a/tensorflow/contrib/lite/kernels/test_util.h +++ b/tensorflow/contrib/lite/kernels/test_util.h @@ -88,9 +88,7 @@ struct TensorData { class SingleOpResolver : public OpResolver { public: SingleOpResolver(const BuiltinOperator op, TfLiteRegistration* registration) - : op_(op), registration_(registration) { - registration_->builtin_code = op; - } + : op_(op), registration_(registration) {} TfLiteRegistration* FindOp(BuiltinOperator op) const override { if (op == op_) { return registration_; diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index 6fd3d9f2ca4c3b..e15f1be7d38802 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -194,6 +194,7 @@ TfLiteStatus InterpreterBuilder::BuildLocalIndexToRegistrationMapping() { builtin_code); status = kTfLiteError; } else if (builtin_code != BuiltinOperator_CUSTOM) { + flatbuffer_op_index_to_registration_types_.push_back(builtin_code); registration = op_resolver_.FindOp(builtin_code); if (registration == nullptr) { error_reporter_->Report("Didn't find op for builtin opcode '%s'\n", @@ -207,6 +208,8 @@ TfLiteStatus InterpreterBuilder::BuildLocalIndexToRegistrationMapping() { } else { const char* name = opcode->custom_code()->c_str(); registration = op_resolver_.FindOp(name); + flatbuffer_op_index_to_registration_types_.push_back( + BuiltinOperator_CUSTOM); if (registration == nullptr) { error_reporter_->Report("Didn't find custom op for name '%s'\n", name); status = kTfLiteError; @@ -700,7 +703,8 @@ TfLiteStatus InterpreterBuilder::ParseNodes( continue; } - BuiltinOperator op_type = static_cast(reg->builtin_code); + auto op_type = + flatbuffer_op_index_to_registration_types_[op->opcode_index()]; if (op_type != BuiltinOperator_CUSTOM && op->custom_options()) { error_reporter_->Report( "Found builtin operator %s with custom options.\n", diff --git a/tensorflow/contrib/lite/model.h b/tensorflow/contrib/lite/model.h index a7d7f3ea109679..5a55b031a8c280 100644 --- a/tensorflow/contrib/lite/model.h +++ b/tensorflow/contrib/lite/model.h @@ -188,6 +188,7 @@ class InterpreterBuilder { ErrorReporter* error_reporter_; std::vector flatbuffer_op_index_to_registration_; + std::vector flatbuffer_op_index_to_registration_types_; const Allocation* allocation_ = nullptr; }; From f1e00684f14a9a2c50ca0e05710a1bd2bc2e734f Mon Sep 17 00:00:00 2001 From: joel-shor Date: Fri, 27 Apr 2018 13:01:10 +0300 Subject: [PATCH 0099/1691] [tf.data] Make documentation changes, and add correct import. --- .../contrib/data/python/kernel_tests/resample_test.py | 2 +- tensorflow/contrib/data/python/ops/resampling.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index fc84301b17b461..b556525ce444b7 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -59,7 +59,7 @@ def _time_resampling( class ResampleTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( - ("InitialnDistributionKnown", True), + ("InitialDistributionKnown", True), ("InitialDistributionUnknown", False)) def testDistribution(self, initial_known): classes = np.random.randint(5, size=(20000,)) # Uniformly sampled diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index 66eaf9b69a8887..982ff66c139780 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -25,6 +25,7 @@ from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import logging_ops @@ -102,9 +103,8 @@ def _apply_fn(dataset): def _get_prob_original_static(initial_dist_t, target_dist_t): """Returns the static probability of sampling from the original. - For some reason, `tensor_util.constant_value(prob_of_original)` of a ratio - of two constant Tensors isn't a constant. We have some custom logic to avoid - this. + `tensor_util.constant_value(prob_of_original)` returns `None` if it encounters + an Op that it isn't defined for. We have some custom logic to avoid this. Args: initial_dist_t: A tensor of the initial distribution. From 4f693319008a3c287042b72d96523d3403b5a0ca Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Fri, 27 Apr 2018 05:31:38 -0700 Subject: [PATCH 0100/1691] [TF:XLA] Bump open source llvm revision to r330926 PiperOrigin-RevId: 194530610 --- tensorflow/workspace.bzl | 8 ++++---- third_party/llvm/llvm.BUILD | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 8b26a32eac14bf..74590723d218cd 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -452,11 +452,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/c1e9b6f826c86c87a7e7173f1baf7e7df9f43e32.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/c1e9b6f826c86c87a7e7173f1baf7e7df9f43e32.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/185e3b301589256077081c88db6674c91d2db176.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/185e3b301589256077081c88db6674c91d2db176.tar.gz", ], - sha256 = "92b7c01074f694a77b4d664951d1ec071e30ef19c61e673158e95fbb6e447b54", - strip_prefix = "llvm-c1e9b6f826c86c87a7e7173f1baf7e7df9f43e32", + sha256 = "bf48d588d1a8e5b73299fdf0a00b28c7b78f96e640f048ac5fe6e70d63d69486", + strip_prefix = "llvm-185e3b301589256077081c88db6674c91d2db176", build_file = clean_dep("//third_party/llvm:llvm.BUILD"), ) diff --git a/third_party/llvm/llvm.BUILD b/third_party/llvm/llvm.BUILD index cbb1b2fe429e90..35a1ce36e47584 100644 --- a/third_party/llvm/llvm.BUILD +++ b/third_party/llvm/llvm.BUILD @@ -264,7 +264,7 @@ genrule( # Rules that apply the LLVM tblgen tool. gentbl( name = "intrinsics_gen", - tbl_outs = [("-gen-intrinsic", "include/llvm/IR/Intrinsics.gen")], + tbl_outs = [("-gen-intrinsic", "include/llvm/IR/Intrinsics.inc")], tblgen = ":llvm-tblgen", td_file = "include/llvm/IR/Intrinsics.td", td_srcs = glob([ @@ -275,7 +275,7 @@ gentbl( gentbl( name = "attributes_gen", - tbl_outs = [("-gen-attrs", "include/llvm/IR/Attributes.gen")], + tbl_outs = [("-gen-attrs", "include/llvm/IR/Attributes.inc")], tblgen = ":llvm-tblgen", td_file = "include/llvm/IR/Attributes.td", td_srcs = ["include/llvm/IR/Attributes.td"], From ec56b5325106c71b3cbff66883187410e6d9b339 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Apr 2018 07:21:37 -0700 Subject: [PATCH 0101/1691] Fix bug in @custom_gradient in Eager mode with numpy inputs PiperOrigin-RevId: 194538828 --- tensorflow/python/BUILD | 1 + tensorflow/python/ops/custom_gradient.py | 7 ++++++- tensorflow/python/ops/gradients_test.py | 16 ++++++++++++++++ 3 files changed, 23 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 105fcbadb307d5..44d9147bb63598 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -1878,6 +1878,7 @@ py_library( ":math_grad", ":math_ops", ":platform", + ":resource_variable_ops", ":spectral_grad", ":util", ":variable_scope", diff --git a/tensorflow/python/ops/custom_gradient.py b/tensorflow/python/ops/custom_gradient.py index c07c669b593d54..446ad1b8776523 100644 --- a/tensorflow/python/ops/custom_gradient.py +++ b/tensorflow/python/ops/custom_gradient.py @@ -24,6 +24,7 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_array_ops +from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope from tensorflow.python.util import nest from tensorflow.python.util import tf_decorator @@ -166,7 +167,11 @@ def _eager_mode_decorator(f, *args, **kwargs): all_inputs = list(args) + list(kwargs.values()) # The variables that grad_fn needs to return gradients for are the set of # variables used that are *not* part of the inputs. - variables = list(set(tape.watched_variables()) - set(all_inputs)) + variable_inputs = [ + arg for arg in all_inputs + if isinstance(arg, resource_variable_ops.ResourceVariable) + ] + variables = list(set(tape.watched_variables()) - set(variable_inputs)) flat_result = nest.flatten(result) # TODO(apassos) consider removing the identity below. flat_result = [gen_array_ops.identity(x) for x in flat_result] diff --git a/tensorflow/python/ops/gradients_test.py b/tensorflow/python/ops/gradients_test.py index f33637238c3eb2..9d296174df59ec 100644 --- a/tensorflow/python/ops/gradients_test.py +++ b/tensorflow/python/ops/gradients_test.py @@ -894,6 +894,22 @@ def Grad(out_grad, variables=None): # pylint: disable=redefined-outer-name self.assertEqual(6., math_ops.reduce_sum(dx).numpy()) self.assertEqual(8., math_ops.reduce_sum(dw).numpy()) + def testWithNumpyInputs(self): + with context.eager_mode(): + + @custom_gradient.custom_gradient + def F(x): + out = x + + def Grad(_): + return (None, None) + + return out, Grad + + x = np.ones((3, 2), dtype=np.float32) + # Smoke test to ensure numpy inputs are accepted + F(x) + if __name__ == "__main__": googletest.main() From 4dee7b57a47817ec8c972cbb117868463ef15cdf Mon Sep 17 00:00:00 2001 From: Yifei Feng <1192265+yifeif@users.noreply.github.com> Date: Fri, 27 Apr 2018 07:46:49 -0700 Subject: [PATCH 0102/1691] Update tf_tests.cmake --- tensorflow/contrib/cmake/tf_tests.cmake | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake index 92f2ab6dea8e7d..5942ff3363a96d 100644 --- a/tensorflow/contrib/cmake/tf_tests.cmake +++ b/tensorflow/contrib/cmake/tf_tests.cmake @@ -267,6 +267,8 @@ if (tensorflow_BUILD_PYTHON_TESTS) "${tensorflow_source_dir}/tensorflow/python/kernel_tests/variable_scope_test.py" "${tensorflow_source_dir}/tensorflow/python/kernel_tests/functional_ops_test.py" "${tensorflow_source_dir}/tensorflow/python/kernel_tests/py_func_test.py" + # Flaky on Windows cpu with py36 (b/73556968) + "${tensorflow_source_dir}/tensorflow/python/kernel_tests/sparse_reshape_op_test.py" # Windows file management related issues. "${tensorflow_source_dir}/tensorflow/python/training/evaluation_test.py" # training tests From f7f02482f486a7c430fe030d62f756685cd8d9d0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Apr 2018 09:25:52 -0700 Subject: [PATCH 0103/1691] Added string conversion operator to tensorflow::StringPiece. Marked ToString method as deprecated. This will allow tensorflow::StringPiece to be replaced with absl::string_view (once the deprecated method is removed) as absl::string_view does not contain the ToString method. PiperOrigin-RevId: 194551042 --- tensorflow/core/lib/core/stringpiece.h | 8 ++++++++ tensorflow/core/lib/core/stringpiece_test.cc | 5 +++++ 2 files changed, 13 insertions(+) diff --git a/tensorflow/core/lib/core/stringpiece.h b/tensorflow/core/lib/core/stringpiece.h index 0cf6c248509aa0..d7ecc44e507e25 100644 --- a/tensorflow/core/lib/core/stringpiece.h +++ b/tensorflow/core/lib/core/stringpiece.h @@ -92,6 +92,7 @@ class StringPiece { StringPiece substr(size_t pos, size_t n = npos) const; // Return a string that contains the copy of the referenced data. + // DEPRECATED: use std::string(sv) instead. std::string ToString() const { return std::string(data_, size_); } // Three-way comparison. Returns value: @@ -100,6 +101,13 @@ class StringPiece { // > 0 iff "*this" > "b" int compare(StringPiece b) const; + // Converts to `std::basic_string`. + template + explicit operator std::basic_string, A>() const { + if (!data()) return {}; + return std::basic_string, A>(data(), size()); + } + private: const char* data_; size_t size_; diff --git a/tensorflow/core/lib/core/stringpiece_test.cc b/tensorflow/core/lib/core/stringpiece_test.cc index de35d6eac6e854..952b9eaaaae43a 100644 --- a/tensorflow/core/lib/core/stringpiece_test.cc +++ b/tensorflow/core/lib/core/stringpiece_test.cc @@ -55,4 +55,9 @@ TEST(StringPiece, Ctor) { } } +TEST(StringPiece, ConversionToString) { + EXPECT_EQ("", std::string(StringPiece(""))); + EXPECT_EQ("foo", std::string(StringPiece("foo"))); +} + } // namespace tensorflow From 5e0f151d885b0d5b25573f7300dee31a5bd9e6d6 Mon Sep 17 00:00:00 2001 From: Patrick Nguyen Date: Fri, 27 Apr 2018 10:28:50 -0700 Subject: [PATCH 0104/1691] Fix merge conflict manual merge error. --- tensorflow/docs_src/install/install_java.md | 39 --------------------- 1 file changed, 39 deletions(-) diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 6a4ac290881e51..05b28787017487 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -65,11 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow -<<<<<<< HEAD 1.8.0-rc1 -======= - 1.8.0-rc0 ->>>>>>> 43a7072882196c7ac2d9429050a3140b1ecb52db @@ -128,20 +124,12 @@ instead: org.tensorflow libtensorflow -<<<<<<< HEAD 1.8.0-rc1 -======= - 1.8.0-rc0 ->>>>>>> 43a7072882196c7ac2d9429050a3140b1ecb52db org.tensorflow libtensorflow_jni_gpu -<<<<<<< HEAD 1.8.0-rc1 -======= - 1.8.0-rc0 ->>>>>>> 43a7072882196c7ac2d9429050a3140b1ecb52db ``` @@ -160,11 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download -<<<<<<< HEAD [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0-rc1.jar), -======= - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0-rc0.jar), ->>>>>>> 43a7072882196c7ac2d9429050a3140b1ecb52db which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -183,11 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ -<<<<<<< HEAD "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.8.0-rc1.tar.gz" | -======= - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.8.0-rc0.tar.gz" | ->>>>>>> 43a7072882196c7ac2d9429050a3140b1ecb52db tar -xz -C ./jni ### Install on Windows @@ -195,17 +175,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: Take the following steps to install TensorFlow for Java on Windows: 1. Download -<<<<<<< HEAD [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0-rc1.jar), which is the TensorFlow Java Archive (JAR). 2. Download the following Java Native Interface (JNI) file appropriate for [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.8.0-rc1.zip). -======= - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0-rc0.jar), - which is the TensorFlow Java Archive (JAR). - 2. Download the following Java Native Interface (JNI) file appropriate for - [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.8.0-rc0.zip). ->>>>>>> 43a7072882196c7ac2d9429050a3140b1ecb52db 3. Extract this .zip file. @@ -254,11 +227,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -<<<<<<< HEAD 
javac -cp libtensorflow-1.8.0-rc1.jar HelloTF.java
-======= -
javac -cp libtensorflow-1.8.0-rc0.jar HelloTF.java
->>>>>>> 43a7072882196c7ac2d9429050a3140b1ecb52db ### Running @@ -272,19 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -<<<<<<< HEAD 
java -cp libtensorflow-1.8.0-rc1.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: 
java -cp libtensorflow-1.8.0-rc1.jar;. -Djava.library.path=jni HelloTF
-======= -
java -cp libtensorflow-1.8.0-rc0.jar:. -Djava.library.path=./jni HelloTF
- -And the following command line executes the `HelloTF` program on Windows: - -
java -cp libtensorflow-1.8.0-rc0.jar;. -Djava.library.path=jni HelloTF
->>>>>>> 43a7072882196c7ac2d9429050a3140b1ecb52db If the program prints Hello from version, you've successfully installed TensorFlow for Java and are ready to use the API. If the program From 899ee329a3018ce43e0bf0eef607ed37f8b822ca Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Fri, 27 Apr 2018 10:58:43 -0700 Subject: [PATCH 0105/1691] Add DeviceSet to Cluster (#18838) * Add DeviceSet to Cluster so we can access memory allocators during grappler optimizations * Fix review comments * Add missing dependency to :virtual_cluster and fix clang format. --- tensorflow/core/BUILD | 4 ++-- tensorflow/core/common_runtime/graph_execution_state.cc | 2 +- tensorflow/core/grappler/clusters/BUILD | 2 ++ tensorflow/core/grappler/clusters/cluster.h | 6 ++++++ tensorflow/core/grappler/clusters/virtual_cluster.cc | 8 ++++++++ tensorflow/core/grappler/clusters/virtual_cluster.h | 4 ++++ 6 files changed, 23 insertions(+), 3 deletions(-) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index acca47e9a3569c..a6747bb1a5c0ee 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -2296,7 +2296,9 @@ tf_cuda_library( CORE_CPU_BASE_HDRS = GRAPH_HDRS + [ "common_runtime/device.h", + "common_runtime/device_factory.h", "common_runtime/device_mgr.h", + "common_runtime/device_set.h", "common_runtime/eval_const_tensor.h", "common_runtime/graph_runner.h", "common_runtime/shape_refiner.h", @@ -2354,9 +2356,7 @@ CORE_CPU_LIB_HEADERS = CORE_CPU_BASE_HDRS + [ "common_runtime/copy_tensor.h", "common_runtime/costmodel_manager.h", "common_runtime/debugger_state_interface.h", - "common_runtime/device_factory.h", "common_runtime/device_resolver_local.h", - "common_runtime/device_set.h", "common_runtime/dma_helper.h", "common_runtime/eigen_thread_pool.h", "common_runtime/executor.h", diff --git a/tensorflow/core/common_runtime/graph_execution_state.cc b/tensorflow/core/common_runtime/graph_execution_state.cc index 642d91e3282313..adf7ae294f652b 100644 --- a/tensorflow/core/common_runtime/graph_execution_state.cc +++ b/tensorflow/core/common_runtime/graph_execution_state.cc @@ -490,7 +490,7 @@ Status GraphExecutionState::OptimizeGraph( cpu_device = device; } } - grappler::VirtualCluster cluster(device_map); + grappler::VirtualCluster cluster(device_map, device_set_); GraphDef new_graph; TF_RETURN_IF_ERROR(grappler::RunMetaOptimizer( item, rewrite_options, cpu_device, &cluster, &new_graph)); diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD index 9ecf5a6cf789fe..30c6126fbb58c1 100644 --- a/tensorflow/core/grappler/clusters/BUILD +++ b/tensorflow/core/grappler/clusters/BUILD @@ -56,6 +56,7 @@ cc_library( ], visibility = ["//visibility:public"], deps = [ + "//tensorflow/core:core_cpu_base", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", @@ -73,6 +74,7 @@ cc_library( visibility = ["//visibility:public"], deps = [ ":cluster", + "//tensorflow/core:core_cpu_base", "//tensorflow/core:framework", "//tensorflow/core:protos_all_cc", "//tensorflow/core/grappler/costs:op_level_cost_estimator", diff --git a/tensorflow/core/grappler/clusters/cluster.h b/tensorflow/core/grappler/clusters/cluster.h index 5068f72b30d498..b16950ade4c94d 100644 --- a/tensorflow/core/grappler/clusters/cluster.h +++ b/tensorflow/core/grappler/clusters/cluster.h @@ -21,6 +21,7 @@ limitations under the License. #include #include +#include "tensorflow/core/common_runtime/device_set.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/grappler/grappler_item.h" #include "tensorflow/core/lib/core/status.h" @@ -92,6 +93,10 @@ class Cluster { // sorted alphabetically. const std::vector GetDeviceNames() const; + // The DeviceSet is not always available, but when it is it contains a + // superset of the devices listed in GetDevices/GetDeviceNames(). + const DeviceSet* GetDeviceSet() const { return device_set_; } + // Enables collecting the allocator stats. Call with enable=true must be made // before Provision(). virtual Status EnablePeakMemoryStats(bool enable) { @@ -119,6 +124,7 @@ class Cluster { protected: std::unordered_map devices_; + const DeviceSet* device_set_ = nullptr; // Not owned const int timeout_s_; SessionOptions options_; RunOptions run_options_; diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.cc b/tensorflow/core/grappler/clusters/virtual_cluster.cc index abfa7bc48e6e24..5c9b2320b5bbf4 100644 --- a/tensorflow/core/grappler/clusters/virtual_cluster.cc +++ b/tensorflow/core/grappler/clusters/virtual_cluster.cc @@ -37,6 +37,14 @@ VirtualCluster::VirtualCluster( : Cluster(0), node_estimator_(node_estimator), node_manager_(node_manager) { devices_ = devices; } + +VirtualCluster::VirtualCluster( + const std::unordered_map& devices, + const DeviceSet* device_set) + : VirtualCluster(devices) { + device_set_ = device_set; +} + VirtualCluster::~VirtualCluster() {} Status VirtualCluster::Provision() { return Status::OK(); } diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.h b/tensorflow/core/grappler/clusters/virtual_cluster.h index dde70bab7a391e..48a46a8b591a24 100644 --- a/tensorflow/core/grappler/clusters/virtual_cluster.h +++ b/tensorflow/core/grappler/clusters/virtual_cluster.h @@ -17,6 +17,8 @@ limitations under the License. #define TENSORFLOW_GRAPPLER_CLUSTERS_VIRTUAL_CLUSTER_H_ #include + +#include "tensorflow/core/common_runtime/device_set.h" #include "tensorflow/core/grappler/clusters/cluster.h" #include "tensorflow/core/grappler/costs/op_level_cost_estimator.h" #include "tensorflow/core/grappler/costs/virtual_scheduler.h" @@ -34,6 +36,8 @@ class VirtualCluster : public Cluster { VirtualCluster(const std::unordered_map& devices, OpLevelCostEstimator* node_estimator, ReadyNodeManager* node_manager); + VirtualCluster(const std::unordered_map& devices, + const DeviceSet* device_set); ~VirtualCluster() override; From d1e0a73577b226d2a865a96f1b4ea9f463f3f4ed Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Apr 2018 11:41:21 -0700 Subject: [PATCH 0106/1691] Internally rewrite @recompute_grad to use @custom_gradient PiperOrigin-RevId: 194571125 --- .../layers/python/layers/rev_block_lib.py | 98 ++++++++++++------- .../python/layers/rev_block_lib_test.py | 48 ++++++--- tensorflow/python/ops/custom_gradient.py | 42 ++++++-- tensorflow/python/ops/gradients_test.py | 34 +++++++ 4 files changed, 167 insertions(+), 55 deletions(-) diff --git a/tensorflow/contrib/layers/python/layers/rev_block_lib.py b/tensorflow/contrib/layers/python/layers/rev_block_lib.py index 02d294c68f1e10..1a439f0a4deb32 100644 --- a/tensorflow/contrib/layers/python/layers/rev_block_lib.py +++ b/tensorflow/contrib/layers/python/layers/rev_block_lib.py @@ -33,6 +33,7 @@ from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib.framework.python import ops as contrib_framework_ops +from tensorflow.python.eager import backprop from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops as framework_ops @@ -40,6 +41,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import control_flow_util +from tensorflow.python.ops import custom_gradient from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope @@ -50,6 +52,13 @@ LAYER_RE = re.compile(".*revlayer_([0-9]*)/([fg])/.*") _USE_DEFAULT = "__rev_block_lib_default" +_WRONG_VARS_ERR = """\ +The variables used on recompute were different than the variables originally +used. The function wrapped with @recompute_grad likley creates its own variable +scope with a default name and has been called twice in the same enclosing scope. +To fix, ensure each call to the function happens in its own unique variable +scope. +""" def _acc_grads(*lists_of_grads): @@ -432,6 +441,10 @@ def new_dec(*args, **kwargs): def recompute_grad(fn, use_data_dep=_USE_DEFAULT, tupleize_grads=False): """Decorator that recomputes the function on the backwards pass. + To use this function, you must use `ResourceVariable`s (i.e. + `variable_scope(name, use_resource=True), which are the default in Eager mode + and when running on TPU. + Args: fn: a function that takes Tensors (all as positional arguments) and returns a tuple of Tensors. @@ -472,44 +485,55 @@ def _recompute_grad(fn, args, use_data_dep=_USE_DEFAULT, tupleize_grads=False): if use_data_dep_ == _USE_DEFAULT: use_data_dep_ = _is_on_tpu() - cached_vs = [] - cached_arg_scope = [] - - def grad_fn(inputs, variables, outputs, output_grads): - """Recompute outputs for gradient computation.""" - del outputs - # Recompute outputs - with framework_ops.control_dependencies(output_grads): - if use_data_dep_: - inputs = _force_data_dependency(output_grads, inputs) - with contrib_framework_ops.arg_scope(cached_arg_scope[0]): - with variable_scope.variable_scope(cached_vs[0], reuse=True): - outputs = fn(*inputs) - - if not (isinstance(outputs, list) or isinstance(outputs, tuple)): - outputs = [outputs] - outputs = list(outputs) - grads = gradients_impl.gradients(outputs, inputs + variables, output_grads) - - if tupleize_grads: - if use_data_dep_: - grads = _tuple_with_data_dep(grads) - else: - grads = control_flow_ops.tuple(grads) - - grad_inputs = grads[:len(inputs)] - grad_vars = grads[len(inputs):] - return grad_inputs, grad_vars - - @_fn_with_custom_grad(grad_fn) + @custom_gradient.custom_gradient def fn_with_recompute(*args): - cached_vs.append(variable_scope.get_variable_scope()) - # TODO(rsepassi): Rm conditional in TF 1.4 - if hasattr(contrib_framework_ops, "current_arg_scope"): - cached_arg_scope.append(contrib_framework_ops.current_arg_scope()) - else: - cached_arg_scope.append({}) - return fn(*args) + """Wrapper for fn.""" + # Forward pass + vs = variable_scope.get_variable_scope() + arg_scope = contrib_framework_ops.current_arg_scope() + with backprop.GradientTape() as tape: + outputs = fn(*args) + original_vars = set(tape.watched_variables()) + + # Backward pass + def grad_fn(*output_grads, **kwargs): + """Recompute outputs for gradient computation.""" + variables = [] + if original_vars: + variables = kwargs["variables"] + if set(variables) != original_vars: + raise ValueError(_WRONG_VARS_ERR) + del kwargs + inputs = list(args) + # Recompute outputs + with framework_ops.control_dependencies(output_grads): + if use_data_dep_: + inputs = _force_data_dependency(output_grads, inputs) + with contrib_framework_ops.arg_scope(arg_scope): + with variable_scope.variable_scope(vs, reuse=True): + with backprop.GradientTape() as tape: + outputs = fn(*inputs) + recompute_vars = set(tape.watched_variables()) + if original_vars != recompute_vars: + raise ValueError(_WRONG_VARS_ERR) + + if not (isinstance(outputs, list) or isinstance(outputs, tuple)): + outputs = [outputs] + outputs = list(outputs) + grads = gradients_impl.gradients(outputs, inputs + variables, + output_grads) + + if tupleize_grads: + if use_data_dep_: + grads = _tuple_with_data_dep(grads) + else: + grads = control_flow_ops.tuple(grads) + + grad_inputs = grads[:len(inputs)] + grad_vars = grads[len(inputs):] + return grad_inputs, grad_vars + + return outputs, grad_fn return fn_with_recompute(*args) diff --git a/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py b/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py index 8c118402a4c85d..8107486d7d9a12 100644 --- a/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py +++ b/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py @@ -278,7 +278,7 @@ def fn_both(x): ] outputs_and_vars = [] for name, wrapped_fn in names_and_fns: - with variable_scope.variable_scope(name) as vs: + with variable_scope.variable_scope(name, use_resource=True) as vs: out = math_ops.reduce_sum(wrapped_fn(x)) outputs_and_vars.append((out, vs.trainable_variables())) @@ -304,19 +304,45 @@ def fn_both(x): self.assertAllClose(current, g) current = g - def testResourceVariable(self): - @rev_block_lib.recompute_grad(tupleize_grads=True) + def testDoubleCallInSameScopeFails(self): + + @rev_block_lib.recompute_grad + def layer_with_recompute(inputs): + return core_layers.dense(inputs, 2) + + with variable_scope.variable_scope("layer", use_resource=True): + inputs = array_ops.ones((2, 4), dtypes.float32) + out1 = layer_with_recompute(inputs) + out2 = layer_with_recompute(inputs) + out1 + out = math_ops.reduce_sum(out2) + + tvars = variables.trainable_variables() + assert len(tvars) == 4 + with self.assertRaisesWithPredicateMatch( + ValueError, "called twice in the same enclosing scope"): + gradients_impl.gradients(out, [inputs] + tvars) + + def testDoubleCallInUniqueScope(self): + + @rev_block_lib.recompute_grad def layer_with_recompute(inputs): - var = variable_scope.get_variable("var", ()) - return var * inputs + with variable_scope.variable_scope("inner", use_resource=True): + return core_layers.dense(inputs, 2) - inputs = array_ops.ones((), dtypes.float32) with variable_scope.variable_scope("layer", use_resource=True): - outputs = layer_with_recompute(inputs) - loss = math_ops.square(outputs) - grads = gradients_impl.gradients(loss, variables.trainable_variables()) - self.assertEqual(1, len(grads)) - self.assertTrue(grads[0] is not None) + inputs = array_ops.ones((2, 4), dtypes.float32) + + with variable_scope.variable_scope("layer1", use_resource=True): + out1 = layer_with_recompute(inputs) + with variable_scope.variable_scope("layer2", use_resource=True): + out2 = layer_with_recompute(inputs) + out1 + out = math_ops.reduce_sum(out2) + + tvars = variables.trainable_variables() + assert len(tvars) == 4 + grads = gradients_impl.gradients(out, [inputs] + tvars) + for grad in grads: + self.assertTrue(grad is not None) class FnWithCustomGradTest(test.TestCase): diff --git a/tensorflow/python/ops/custom_gradient.py b/tensorflow/python/ops/custom_gradient.py index 446ad1b8776523..d934f27cb96f4a 100644 --- a/tensorflow/python/ops/custom_gradient.py +++ b/tensorflow/python/ops/custom_gradient.py @@ -26,6 +26,7 @@ from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope +from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import nest from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect @@ -121,17 +122,42 @@ def _graph_mode_decorator(f, *args, **kwargs): "arguments only when eager execution is enabled.") name = "CustomGradient-%s" % ops.uid() args = [ops.convert_to_tensor(x) for x in args] + + # Checking global and local variables attempts to ensure that no non-resource + # Variables are added to the graph. + current_var_scope = variable_scope.get_variable_scope() + before_vars = set(current_var_scope.global_variables() + + current_var_scope.local_variables()) with backprop.GradientTape() as tape: result, grad_fn = f(*args) + after_vars = set(current_var_scope.global_variables() + + current_var_scope.local_variables()) + new_vars = after_vars - before_vars + for v in new_vars: + if not isinstance(v, resource_variable_ops.ResourceVariable): + raise TypeError( + "All variables used by a function wrapped with @custom_gradient must " + "be `ResourceVariable`s. Ensure that no `variable_scope` is created " + "with `use_resource=False`.") # The variables that grad_fn needs to return gradients for are the set of # variables used that are *not* part of the inputs. variables = list(set(tape.watched_variables()) - set(args)) grad_argspec = tf_inspect.getargspec(grad_fn) - if "variables" in grad_argspec.args: + variables_in_signature = ("variables" in grad_argspec.args or + grad_argspec.keywords) + if variables and not variables_in_signature: + raise TypeError("If using @custom_gradient with a function that " + "uses variables, then grad_fn must accept a keyword " + "argument 'variables'.") + if variables_in_signature and not variables: + # User seems to intend to use variables but none were captured. if not variable_scope.get_variable_scope().use_resource: raise TypeError("If using @custom_gradient with a function that " - "creates variables, the enclosing variable scope must " + "uses variables, the enclosing variable scope must " "have use_resource=True.") + else: + logging.warn("@custom_gradient grad_fn has 'variables' in signature, but " + "no ResourceVariables were used on the forward pass.") flat_result = nest.flatten(result) all_tensors = flat_result + args + variables @@ -167,11 +193,13 @@ def _eager_mode_decorator(f, *args, **kwargs): all_inputs = list(args) + list(kwargs.values()) # The variables that grad_fn needs to return gradients for are the set of # variables used that are *not* part of the inputs. - variable_inputs = [ - arg for arg in all_inputs - if isinstance(arg, resource_variable_ops.ResourceVariable) - ] - variables = list(set(tape.watched_variables()) - set(variable_inputs)) + variables = [v for v in set(tape.watched_variables()) if v not in all_inputs] + grad_argspec = tf_inspect.getargspec(grad_fn) + if (variables and + not ("variables" in grad_argspec.args or grad_argspec.keywords)): + raise TypeError("If using @custom_gradient with a function that " + "uses variables, then grad_fn must accept a keyword " + "argument 'variables'.") flat_result = nest.flatten(result) # TODO(apassos) consider removing the identity below. flat_result = [gen_array_ops.identity(x) for x in flat_result] diff --git a/tensorflow/python/ops/gradients_test.py b/tensorflow/python/ops/gradients_test.py index 9d296174df59ec..5e8b8822efd606 100644 --- a/tensorflow/python/ops/gradients_test.py +++ b/tensorflow/python/ops/gradients_test.py @@ -894,6 +894,40 @@ def Grad(out_grad, variables=None): # pylint: disable=redefined-outer-name self.assertEqual(6., math_ops.reduce_sum(dx).numpy()) self.assertEqual(8., math_ops.reduce_sum(dw).numpy()) + def testCustomGradientErrorsWithNonResourceVariables(self): + + def F(x, use_resource=False): + with variable_scope.variable_scope("f", use_resource=use_resource): + out = core_layers.dense(x, 4, use_bias=False) + + def Grad(out_grad, variables=None): # pylint: disable=redefined-outer-name + del out_grad + self.assertEqual(1, len(variables)) + return (array_ops.ones((3, 2)), [array_ops.ones((2, 4))]) + + return out, Grad + + @custom_gradient.custom_gradient + def FResource(x): + return F(x, use_resource=True) + + @custom_gradient.custom_gradient + def FNonResource(x): + return F(x, use_resource=False) + + x = array_ops.ones((3, 2)) + 2. + + # Wrapping scope has use_resource=True but inner scope sets to False. Fails. + with variable_scope.variable_scope("vs1", use_resource=True): + with self.assertRaisesWithPredicateMatch(TypeError, + "must be `ResourceVariable`s"): + FNonResource(x) + + # Wrapping scope has use_resource=False but inner scope sets to True. + # Passes. + with variable_scope.variable_scope("vs2", use_resource=False): + FResource(x) + def testWithNumpyInputs(self): with context.eager_mode(): From bad891b351ef319d7fa8fc1ee77d02e35f39897c Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Fri, 27 Apr 2018 11:47:42 -0700 Subject: [PATCH 0107/1691] Docs: fix typo --- tensorflow/docs_src/get_started/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md index b28cb9df75d94a..746126c7206905 100644 --- a/tensorflow/docs_src/get_started/index.md +++ b/tensorflow/docs_src/get_started/index.md @@ -10,7 +10,7 @@ course prior to diving into TensorFlow documentation: TensorFlow is a tool for machine learning. While it contains a wide range of functionality, TensorFlow is mainly designed for deep neural network models. -The easiest way to get started with tensorflow is using Eager Execution. +The easiest way to get started with TensorFlow is using Eager Execution. * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow. From 6d793e177ce377d52772574a3eb90af88e780f97 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Apr 2018 12:46:46 -0700 Subject: [PATCH 0108/1691] Replace GrapplerFunctionItem input with a constant. PiperOrigin-RevId: 194579253 --- tensorflow/core/grappler/utils/functions.cc | 63 +++++++++++++++- tensorflow/core/grappler/utils/functions.h | 9 ++- .../core/grappler/utils/functions_test.cc | 75 +++++++++++++++++++ 3 files changed, 143 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/grappler/utils/functions.cc b/tensorflow/core/grappler/utils/functions.cc index 790809bc6709f0..79b823fa2da129 100644 --- a/tensorflow/core/grappler/utils/functions.cc +++ b/tensorflow/core/grappler/utils/functions.cc @@ -566,6 +566,60 @@ Status RegisterGrapplerFunctionConnectivity( return Status::OK(); } +Status ReplaceInputWithConst(const NodeDef& input_const, int input_position, + GrapplerFunctionItem* item) { + if (!IsConstant(input_const)) { + return errors::InvalidArgument("Input node ", input_const.name(), + " is not a constant"); + } + + auto& inputs = item->input_arg_expansions_; + + // Find input arg expansion and input placeholder position in it for the + // given function input position. + InputArgExpansion* input_arg_expansion = nullptr; + int placeholder_idx = input_position; + + for (InputArgExpansion& input : inputs) { + if (placeholder_idx < input.placeholders.size()) { + input_arg_expansion = &input; + break; + } + placeholder_idx -= input.placeholders.size(); + } + + if (input_arg_expansion == nullptr) { + return errors::InvalidArgument( + "Input placeholder not found: input_position=", input_position, + " function=", item->id); + } + + // Delete placeholder from input expansion. + string placeholder_name = input_arg_expansion->placeholders[placeholder_idx]; + item->input_arg_placeholders_.erase(placeholder_name); + input_arg_expansion->placeholders.erase( + input_arg_expansion->placeholders.begin() + placeholder_idx); + + // Delete empty input expansions. + inputs.erase(std::remove_if(inputs.begin(), inputs.end(), + [](const InputArgExpansion& input) { + return input.placeholders.empty(); + }), + inputs.end()); + + // Replace placeholder node in the function body with a const node. + for (NodeDef& node : *item->graph.mutable_node()) { + if (node.name() == placeholder_name) { + node = input_const; + node.set_name(placeholder_name); + node.clear_input(); // remove potential control inputs + node.clear_device(); // device placement is defined by instantiating node + } + } + + return Status::OK(); +} + Status MakeFunctionDef(const GrapplerFunctionItem& item, const FunctionLibraryDefinition& flib, FunctionDef* func) { @@ -579,6 +633,9 @@ Status MakeFunctionDef(const GrapplerFunctionItem& item, // Add function input arguments. for (const InputArgExpansion& input_arg : item.inputs()) { + CHECK(input_arg.placeholders.size() == 1) // do some sanity checking + << "Inputs of tensor sequences are not supported"; + OpDef::ArgDef arg_def; arg_def.set_name(input_arg.input_name); arg_def.set_type(input_arg.data_type); @@ -588,15 +645,15 @@ Status MakeFunctionDef(const GrapplerFunctionItem& item, // Add function output arguments. for (const OutputArgExpansion& output_arg : item.outputs()) { + CHECK(output_arg.output_tensors.size() == 1) // do some sanity checking + << "Outputs of tensor sequences are not supported"; + OpDef::ArgDef arg_def; arg_def.set_name(output_arg.output_name); arg_def.set_type(output_arg.data_type); arg_def.set_is_ref(output_arg.is_ref); *func->mutable_signature()->add_output_arg() = arg_def; - CHECK(output_arg.output_tensors.size() == 1) // do some sanity checking - << "Outputs of tensor sequences are not supported"; - string ret; for (const string& output_tensor : output_arg.output_tensors) { TF_RETURN_IF_ERROR(connectivity.AsFunctionDefInput(output_tensor, &ret)); diff --git a/tensorflow/core/grappler/utils/functions.h b/tensorflow/core/grappler/utils/functions.h index 692333fa175875..d9d71b80ebcf4d 100644 --- a/tensorflow/core/grappler/utils/functions.h +++ b/tensorflow/core/grappler/utils/functions.h @@ -162,6 +162,9 @@ class GrapplerFunctionItem : public GrapplerItem { GrapplerFunctionItem& SwapFunctionBody(GraphDef&& other); private: + friend Status ReplaceInputWithConst(const NodeDef&, int, + GrapplerFunctionItem*); + AttrValueMap func_attr_; // Attributes specific to function definition that // produced this item (FuncDef.attr field). @@ -189,12 +192,16 @@ bool HasParametrizedBody(const FunctionDef& func); bool IsParametrized(const FunctionDef& func); // Register GrapplerFunctionItem input arg expansion and function body outputs -// in the GrapplerFunctionConnectivity. Use function library definition to +// in the GrapplerFunctionConnectivity. Use function library definition to // lookup function body nodes output names and ranges. Status RegisterGrapplerFunctionConnectivity( const GrapplerFunctionItem& item, const FunctionLibraryDefinition& flib, GrapplerFunctionConnectivity* connectivity); +// Replace one of the function inputs with a constant. +Status ReplaceInputWithConst(const NodeDef& input_const, int input_position, + GrapplerFunctionItem* item); + // Make a GrapplerFunctionItem from the function definition and function // instantiation attributes (caller node attributes). Returns error if the given // function def cannot be converted (e.g. not all attributes are defined). diff --git a/tensorflow/core/grappler/utils/functions_test.cc b/tensorflow/core/grappler/utils/functions_test.cc index 6dfd49b9438081..fa6fec70ff9744 100644 --- a/tensorflow/core/grappler/utils/functions_test.cc +++ b/tensorflow/core/grappler/utils/functions_test.cc @@ -573,6 +573,81 @@ TEST_F(FunctionsTest, MakeFunctionDef) { EXPECT_EQ(2, count); } +TEST_F(FunctionsTest, ReplaceInputWithConst) { + FunctionDef func = FunctionDefHelper::Create( + "MyMul", {"x:T", "y:T"}, {"z:T"}, {"T: {float, double}"}, + {{{"output"}, "Mul", {"x", "y"}, {{"T", "$T"}}}}, + /* Mapping between function returns and function node outputs. */ + {{"z", "output:z:0"}}); + + std::unordered_map func_attr; + func_attr["T"].set_type(DT_FLOAT); + FunctionLibraryDefinition flib(OpRegistry::Global(), FunctionDefLibrary()); + + GrapplerFunctionItem item; + TF_EXPECT_OK(MakeGrapplerFunctionItem(func, func_attr, flib, &item)); + + EXPECT_EQ(2, item.input_size()); + EXPECT_EQ(1, item.output_size()); + + ASSERT_EQ(3, item.function_body().node_size()); + + const NodeDef &input_x = item.function_body().node(0); + const NodeDef &input_y = item.function_body().node(1); + + // Initially inputs added to the graph as placeholders. + EXPECT_EQ("Placeholder", input_x.op()); + EXPECT_EQ("Placeholder", input_y.op()); + + // Replace inputs x and y with constants. + NodeDef const_input_x; + const_input_x.set_op("Const"); + AddNodeAttr("Tag", "const_input_x", &const_input_x); + + NodeDef const_input_y; + const_input_y.set_op("Const"); + AddNodeAttr("Tag", "const_input_y", &const_input_y); + + // Replace input x. + TF_EXPECT_OK(ReplaceInputWithConst(const_input_x, 0, &item)); + + EXPECT_EQ(1, item.input_size()); + EXPECT_EQ("Const", input_x.op()); + EXPECT_EQ("const_input_x", input_x.attr().at("Tag").s()); + + // Replace input y. + TF_EXPECT_OK(ReplaceInputWithConst(const_input_y, 0, &item)); + + EXPECT_EQ(0, item.input_size()); + EXPECT_EQ("Const", input_y.op()); + EXPECT_EQ("const_input_y", input_y.attr().at("Tag").s()); + + // Make a function from const-specialized function item. + FunctionDef specialized; + TF_EXPECT_OK(MakeFunctionDef(item, flib, &specialized)); + + EXPECT_EQ(0, specialized.signature().input_arg_size()); + EXPECT_EQ(1, specialized.signature().output_arg_size()); + EXPECT_EQ(3, specialized.node_def_size()); + + // Check that graph has const nodes pushed into function body. + int count = 0; + for (const NodeDef &node : specialized.node_def()) { + if (node.name() == "x" && count++) { + EXPECT_EQ("Const", node.op()); + EXPECT_EQ("const_input_x", node.attr().at("Tag").s()); + } else if (node.name() == "y" && count++) { + EXPECT_EQ("Const", node.op()); + EXPECT_EQ("const_input_y", node.attr().at("Tag").s()); + } else if (node.name() == "output" && count++) { + EXPECT_EQ("Mul", node.op()); + EXPECT_EQ("x:output:0", node.input(0)); + EXPECT_EQ("y:output:0", node.input(1)); + } + } + EXPECT_EQ(3, count); +} + TEST_F(FunctionsTest, SwapFunctionBodyAndMakeFunctionDef) { using test::function::NDef; From de26d3e7d5cda6c3c43f644f77d935a0e3db3d6d Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Fri, 27 Apr 2018 12:59:20 -0700 Subject: [PATCH 0109/1691] eager: Improve error message when GradientTape is used incorrectly. PiperOrigin-RevId: 194580654 --- tensorflow/python/eager/pywrap_tfe_src.cc | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc index 2bfa1f052cfe65..4ecba1a46be8ff 100644 --- a/tensorflow/python/eager/pywrap_tfe_src.cc +++ b/tensorflow/python/eager/pywrap_tfe_src.cc @@ -1402,8 +1402,12 @@ PyObject* TFE_Py_TapeGradient(PyObject* tape, PyObject* vspace, auto* tape_set = GetTapeSet(); if (tape_set->find(tape_obj) != tape_set->end()) { PyErr_SetString(PyExc_RuntimeError, - "Trying to call tape.gradient on a non-persistent tape " - "while it is still active."); + "gradient() cannot be invoked within the " + "GradientTape context (i.e., while operations are being " + "recorded). Either move the call to gradient() to be " + "outside the 'with tf.GradientTape' block, or " + "use a persistent tape: " + "'with tf.GradientTape(persistent=true)'"); return nullptr; } } From 69465b017eb76d210bda3b752aabf792ce52609e Mon Sep 17 00:00:00 2001 From: Sourabh Bajaj Date: Fri, 27 Apr 2018 13:01:44 -0700 Subject: [PATCH 0110/1691] Remove scope name from bfloat16 PiperOrigin-RevId: 194580957 --- tensorflow/contrib/tpu/python/tpu/bfloat16.py | 2 +- tensorflow/contrib/tpu/python/tpu/bfloat16_test.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/bfloat16.py b/tensorflow/contrib/tpu/python/tpu/bfloat16.py index 5e49af6408e8aa..fa74f651aa63c7 100644 --- a/tensorflow/contrib/tpu/python/tpu/bfloat16.py +++ b/tensorflow/contrib/tpu/python/tpu/bfloat16.py @@ -73,5 +73,5 @@ def bfloat16_scope(): This enables variables to be read as bfloat16 type when using get_variable. """ with variable_scope.variable_scope( - 'bfloat16', custom_getter=_get_custom_getter()) as varscope: + '', custom_getter=_get_custom_getter()) as varscope: yield varscope diff --git a/tensorflow/contrib/tpu/python/tpu/bfloat16_test.py b/tensorflow/contrib/tpu/python/tpu/bfloat16_test.py index 48a01c7308fbf1..26fd3768278cac 100644 --- a/tensorflow/contrib/tpu/python/tpu/bfloat16_test.py +++ b/tensorflow/contrib/tpu/python/tpu/bfloat16_test.py @@ -32,7 +32,7 @@ def testScopeName(self): """Test if name for the variable scope is propogated correctly. """ with bfloat16.bfloat16_scope() as bf: - self.assertEqual(bf.name, "bfloat16") + self.assertEqual(bf.name, "") def testRequestedDType(self): """Test if requested dtype is honored in the getter. From 3e582feec7b7e1b71d5a4b590edc1e4d4e4a3126 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Fri, 27 Apr 2018 13:07:16 -0700 Subject: [PATCH 0111/1691] Roll forward the custom optimizers change (#18742) --- .../core/grappler/optimizers/meta_optimizer.cc | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc index 2edc4da9dcb91b..5230177dcab296 100644 --- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc @@ -160,13 +160,26 @@ Status MetaOptimizer::InitializeOptimizersByName( VLOG(2) << "Can't register an optimizer by name: " << optimizer_name; } } + for (const auto& optimizer_config : cfg_.custom_optimizers()) { + auto custom_optimizer = CustomGraphOptimizerRegistry::CreateByNameOrNull( + optimizer_config.name()); + if (custom_optimizer) { + VLOG(2) << "Registered custom configurable graph optimizer: " + << optimizer_config.name(); + TF_RETURN_IF_ERROR(custom_optimizer->Init(&optimizer_config)); + optimizers->push_back(std::move(custom_optimizer)); + } else { + VLOG(2) << "Can't register an optimizer by name: " + << optimizer_config.name(); + } + } return Status::OK(); } Status MetaOptimizer::OptimizeGraph(Cluster* cluster, const GrapplerItem& item, GraphDef* optimized_graph) { std::vector> optimizers; - if (cfg_.optimizers().empty()) { + if (cfg_.optimizers().empty() && cfg_.custom_optimizers().empty()) { TF_RETURN_IF_ERROR(InitializeOptimizers(&optimizers)); } else { TF_RETURN_IF_ERROR(InitializeOptimizersByName(&optimizers)); @@ -337,7 +350,7 @@ bool MetaOptimizerEnabled(const RewriterConfig& cfg) { cfg.auto_parallel().enable() || cfg.memory_optimization() != RewriterConfig::NO_MEM_OPT || cfg.debug_stripper() == RewriterConfig::ON || - !cfg.optimizers().empty(); + !cfg.optimizers().empty() || !cfg.custom_optimizers().empty(); } Status RunMetaOptimizer(const GrapplerItem& item, const RewriterConfig& cfg, From dd24a090971a68a42925b2d1276af165434c9913 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Fri, 27 Apr 2018 23:46:22 +0300 Subject: [PATCH 0112/1691] [tf.data] Pass a Tensor to `tensor_util.constant_value` instead of possible a python list. --- tensorflow/contrib/data/python/ops/resampling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index 982ff66c139780..f7ea44bec0a376 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -86,7 +86,7 @@ def _apply_fn(dataset): filtered_ds = filtered_ds.prefetch(3) prob_original_static = _get_prob_original_static( - initial_dist, target_dist_t) if initial_dist is not None else None + initial_dist_t, target_dist_t) if initial_dist is not None else None if prob_original_static == 1: return dataset_ops.Dataset.zip((class_values_ds, dataset)) elif prob_original_static == 0: From ec580e61b9b02c34a079834cab6d07ff61733016 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Fri, 27 Apr 2018 13:55:35 -0700 Subject: [PATCH 0113/1691] [TF:XLA] Bump open source llvm revision to r330950 PiperOrigin-RevId: 194588403 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 74590723d218cd..5f57485d74630d 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -452,11 +452,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/185e3b301589256077081c88db6674c91d2db176.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/185e3b301589256077081c88db6674c91d2db176.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/3b2f0b2c7e66d226a9342be5163da4240e2951a8.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/3b2f0b2c7e66d226a9342be5163da4240e2951a8.tar.gz", ], - sha256 = "bf48d588d1a8e5b73299fdf0a00b28c7b78f96e640f048ac5fe6e70d63d69486", - strip_prefix = "llvm-185e3b301589256077081c88db6674c91d2db176", + sha256 = "49bb3cbb7c8e9af091c5a743fa7ae749656994408438f38c9b6ac6a052fdce56", + strip_prefix = "llvm-3b2f0b2c7e66d226a9342be5163da4240e2951a8", build_file = clean_dep("//third_party/llvm:llvm.BUILD"), ) From 6da711a50c3ef98aebacd6a909596a0f74b783e1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Apr 2018 14:05:46 -0700 Subject: [PATCH 0114/1691] Remove whitespaces from tags in saved_model_cli. This currently causes tags mismatch because a leading whitespace is added within the saved_model_cli when doing ', '.join(tag_set). PiperOrigin-RevId: 194590154 --- tensorflow/python/tools/saved_model_cli.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/tools/saved_model_cli.py b/tensorflow/python/tools/saved_model_cli.py index 73ea85ab0c4c58..5b9d25d449d43d 100644 --- a/tensorflow/python/tools/saved_model_cli.py +++ b/tensorflow/python/tools/saved_model_cli.py @@ -195,14 +195,14 @@ def _show_all(saved_model_dir): """ tag_sets = reader.get_saved_model_tag_sets(saved_model_dir) for tag_set in sorted(tag_sets): - tag_set = ', '.join(tag_set) - print('\nMetaGraphDef with tag-set: \'' + tag_set + - '\' contains the following SignatureDefs:') + print("\nMetaGraphDef with tag-set: '%s' " + "contains the following SignatureDefs:" % ', '.join(tag_set)) + tag_set = ','.join(tag_set) signature_def_map = get_signature_def_map(saved_model_dir, tag_set) for signature_def_key in sorted(signature_def_map.keys()): print('\nsignature_def[\'' + signature_def_key + '\']:') - _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key, + _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key, indent=1) From ac2416120ddd13891486cce6135160cc2f412f92 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Sat, 28 Apr 2018 00:20:53 +0300 Subject: [PATCH 0115/1691] [tf.data] Fix indentation. --- tensorflow/contrib/data/python/ops/resampling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index f7ea44bec0a376..1194b8447a568d 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -86,7 +86,7 @@ def _apply_fn(dataset): filtered_ds = filtered_ds.prefetch(3) prob_original_static = _get_prob_original_static( - initial_dist_t, target_dist_t) if initial_dist is not None else None + initial_dist_t, target_dist_t) if initial_dist is not None else None if prob_original_static == 1: return dataset_ops.Dataset.zip((class_values_ds, dataset)) elif prob_original_static == 0: From a4dbc33512adb3705345b093a0aafec151e7e32d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Apr 2018 14:28:12 -0700 Subject: [PATCH 0116/1691] If two identical functions are given different grad func, they should be named differently. Otherwise, tf.gradients gets confused. PiperOrigin-RevId: 194593519 --- tensorflow/python/framework/function.py | 37 +++--- tensorflow/python/framework/function_test.py | 114 ++++++++++++++----- tensorflow/python/ops/gradients_impl.py | 32 +++--- 3 files changed, 129 insertions(+), 54 deletions(-) diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py index 2432ab378c8ed8..e7f9e590af8421 100644 --- a/tensorflow/python/framework/function.py +++ b/tensorflow/python/framework/function.py @@ -353,8 +353,10 @@ def _create_definition_if_needed_impl(self): raise ValueError("Function can not return None.") # Ensures each output is a Tensor in the function graph. outputs = [ops.convert_to_tensor(t) for t in outputs] - outputs = [temp_graph.capture(t) if t.graph is not temp_graph else t - for t in outputs] + outputs = [ + temp_graph.capture(t) if t.graph is not temp_graph else t + for t in outputs + ] self._extra_inputs = temp_graph.extra_inputs inputs.extend(temp_graph.extra_args) # pylint: disable=protected-access @@ -362,9 +364,13 @@ def _create_definition_if_needed_impl(self): # pylint: enable=protected-access # Extra kwargs are treated as attrs on the function def. - base_func_name = self._func_name or _get_func_name(self._func) - kwargs_attr = _parse_kwargs_as_attrs(base_func_name, - **self._extra_kwargs) + if self._func_name: + base_func_name = self._func_name + else: + base_func_name = _get_func_name(self._func) + if self._grad_func: + base_func_name += ("_%s" % self._grad_func.name) + kwargs_attr = _parse_kwargs_as_attrs(base_func_name, **self._extra_kwargs) if not temp_graph._c_graph: # pylint: disable=protected-access # Build the FunctionDef @@ -503,6 +509,12 @@ def __call__(self, *args, **kwargs): self.add_to_graph(ops.get_default_graph()) args = [ops.convert_to_tensor(_) for _ in args] + self._extra_inputs ret, op = _call(self._signature, *args, **kwargs) + + # Set a hidden attr in 'op' so that gradients_impl can refer back + # to this _DefinedFunction instance to access python_grad_func. + assert isinstance(op, ops.Operation) + setattr(op, "__defun", self) + if self._shape_func is not None: shapes = self._shape_func(op) if len(shapes) != len(op.outputs): @@ -591,12 +603,11 @@ def instantiate(self, input_types): # _OverloadedFunction. We need to instantiate it with the # right input types. output_types = [ - dtypes.DType(_.type) - for _ in defined._signature.output_arg # pylint: disable=protected-access + dtypes.DType(_.type) for _ in defined._signature.output_arg # pylint: disable=protected-access ] # pylint: disable=protected-access - defined._grad_func = self._grad_func.instantiate( - input_types + output_types) + defined._grad_func = self._grad_func.instantiate(input_types + + output_types) # pylint: enable=protected-access self._overload[key] = defined return defined @@ -833,8 +844,8 @@ def _call(sig, *inputs, **kwargs): ValueError: if the arguments are invalid. """ if len(inputs) != len(sig.input_arg): - raise ValueError("Expected number of arguments: %d, received: %d" % - (len(sig.input_arg), len(inputs))) + raise ValueError("Expected number of arguments: %d, received: %d" % (len( + sig.input_arg), len(inputs))) name = kwargs.pop("name", None) g = ops.get_default_graph() func_name = sig.name @@ -950,8 +961,8 @@ def _from_library(lib): fdef for fdef in lib.function if func_to_grad[fdef.signature.name] is None ] if not ready: - raise ValueError("FunctionDefLibrary contains cyclic gradient functions!\n" - + str(lib)) + raise ValueError( + "FunctionDefLibrary contains cyclic gradient functions!\n" + str(lib)) # function name -> _DefinedFunction initialized = {} diff --git a/tensorflow/python/framework/function_test.py b/tensorflow/python/framework/function_test.py index 594596ec1e195d..a5c19f189ea5c4 100644 --- a/tensorflow/python/framework/function_test.py +++ b/tensorflow/python/framework/function_test.py @@ -136,7 +136,8 @@ def MyIdentityFunc(a): def testTooManyOutputNames(self): @function.Defun( - dtypes.float32, func_name="MyIdentity", + dtypes.float32, + func_name="MyIdentity", out_names=["my_result1", "my_result2"]) def MyIdentityFunc(a): return a @@ -239,10 +240,11 @@ def Forward(x): inp = np.array([-1, 1, 2, -2], dtype=np.float32) feed = {x: inp} - cfg = config_pb2.ConfigProto(graph_options=config_pb2.GraphOptions( - optimizer_options=config_pb2.OptimizerOptions( - opt_level=config_pb2.OptimizerOptions.L1, - do_function_inlining=True))) + cfg = config_pb2.ConfigProto( + graph_options=config_pb2.GraphOptions( + optimizer_options=config_pb2.OptimizerOptions( + opt_level=config_pb2.OptimizerOptions.L1, + do_function_inlining=True))) with session.Session(graph=g, config=cfg) as sess: out, = sess.run(dx, feed) self.assertAllClose(1 - np.square(np.tanh(inp)), out) @@ -334,18 +336,20 @@ def Foo(x): y = Foo(x) dx, = gradients_impl.gradients(y, [x]) - cfg = config_pb2.ConfigProto(graph_options=config_pb2.GraphOptions( - optimizer_options=config_pb2.OptimizerOptions( - opt_level=config_pb2.OptimizerOptions.L0, - do_common_subexpression_elimination=True, - do_function_inlining=True, - do_constant_folding=True))) + cfg = config_pb2.ConfigProto( + graph_options=config_pb2.GraphOptions( + optimizer_options=config_pb2.OptimizerOptions( + opt_level=config_pb2.OptimizerOptions.L0, + do_common_subexpression_elimination=True, + do_function_inlining=True, + do_constant_folding=True))) with self.test_session(graph=g, config=cfg): self.assertAllClose(y.eval(), 6.) self.assertAllClose(dx.eval(), 2.) def _testZNoDepOnY(self, use_const_grad_ys): + @function.Defun(dtypes.float32, dtypes.float32) def Foo(x, y): # pylint: disable=unused-argument return x * 2 @@ -775,9 +779,9 @@ def testCaptureInWhileLoop(self): @function.Defun() def Foo(): - return control_flow_ops.while_loop(lambda i: i < 10, - lambda i: i + x, + return control_flow_ops.while_loop(lambda i: i < 10, lambda i: i + x, [0]) + y = Foo() with self.test_session(graph=g) as sess: @@ -790,9 +794,8 @@ def testCaptureInCond(self): @function.Defun(dtypes.bool) def Foo(pred): - return control_flow_ops.cond(pred, - lambda: x, - lambda: x + 1) + return control_flow_ops.cond(pred, lambda: x, lambda: x + 1) + y = Foo(True) z = Foo(False) @@ -945,6 +948,7 @@ def testTwoInputsSameOp(self): self.assertEqual(len(f.signature.input_arg), 3) def testGradientWithIntegerFunctionArgument(self): + @function.Defun(dtypes.int32, dtypes.float32) def Foo(t, x): return x[t] @@ -959,8 +963,7 @@ def Foo(t, x): x = np.zeros((2,)).astype(np.float32) with session.Session(graph=g) as sess: self.assertAllClose( - np.array([1.0, 0.0]).astype(np.float32), - sess.run(dinp, {inp: x})) + np.array([1.0, 0.0]).astype(np.float32), sess.run(dinp, {inp: x})) def testFunctionMarkedStateful(self): @@ -1073,6 +1076,60 @@ def CapturesGuaranteedConst(): sess.run(var.initializer) _ = sess.run(CapturesGuaranteedConst(), {also_not_const: 1.0}) + def testSameFunctionDifferentGrads(self): + + def PartOne(x): + + # Default grad is dx = dy * 2 + @function.Defun(dtypes.float32) + def Foo(x): + return x * 2 + + return Foo(x) + + def PartTwo(x): + + @function.Defun(dtypes.float32, dtypes.float32) + def Bar(x, dy): + return x + dy # crazy backprop + + @function.Defun(dtypes.float32, grad_func=Bar) + def Foo(x): + return x * 2 + + return Foo(x) + + def PartThree(x): + + def Bar(op, dy): + return op.inputs[0] * dy / 2 # crazy backprop + + @function.Defun(dtypes.float32, python_grad_func=Bar) + def Foo(x): + return x * 2 + + return Foo(x) + + g = ops.Graph() + with g.as_default(): + x = constant_op.constant(100.) + x0 = x + y0 = PartOne(x0) + dx0, = gradients_impl.gradients(ys=[y0], xs=[x0]) + x1 = x + y1 = PartTwo(x1) + dx1, = gradients_impl.gradients(ys=[y1], xs=[x1]) + x2 = x + y2 = PartThree(x2) + dx2, = gradients_impl.gradients(ys=[y2], xs=[x2]) + + with self.test_session(graph=g) as sess: + v0, v1, v2 = sess.run([dx0, dx1, dx2]) + + self.assertAllEqual(v0, 2.) + self.assertAllEqual(v1, 101.) + self.assertAllEqual(v2, 50.) + @test_util.with_c_shapes class FunctionsFromProtos(test.TestCase): @@ -1271,9 +1328,10 @@ def testExperimentalAttrs(self): @function.Defun(dtypes.int32, experimental_tag="tag_value") def FunctionWithAttr(i): return array_ops.identity(i) + self.assertTrue("experimental_tag" in FunctionWithAttr.definition.attr) - self.assertEqual( - FunctionWithAttr.definition.attr["experimental_tag"].s, b"tag_value") + self.assertEqual(FunctionWithAttr.definition.attr["experimental_tag"].s, + b"tag_value") @test_util.with_c_shapes @@ -1401,7 +1459,8 @@ def Loop(cell, w, i): return Loop(cell, weights, inp) cell = function.Defun(dtypes.float32, dtypes.float32, dtypes.float32, - dtypes.float32)(cell) + dtypes.float32)( + cell) if mode == "cell": # Just represent the LSTM as a function. return Loop(cell, weights, inp) @@ -1500,12 +1559,13 @@ class FunctionInlineControlTest(test.TestCase): def testFoo(self): dtype = dtypes.float32 - cfg = config_pb2.ConfigProto(graph_options=config_pb2.GraphOptions( - optimizer_options=config_pb2.OptimizerOptions( - opt_level=config_pb2.OptimizerOptions.L0, - do_common_subexpression_elimination=True, - do_function_inlining=True, - do_constant_folding=True))) + cfg = config_pb2.ConfigProto( + graph_options=config_pb2.GraphOptions( + optimizer_options=config_pb2.OptimizerOptions( + opt_level=config_pb2.OptimizerOptions.L0, + do_common_subexpression_elimination=True, + do_function_inlining=True, + do_constant_folding=True))) cell_func_call_pattern = re.compile(r"Cell[^/]*\(") for noinline in [False, True]: diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index 581ba7de48a02d..1448151fef4aab 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -256,21 +256,21 @@ def _DefaultGradYs(grad_ys, continue if y.dtype.is_floating or y.dtype.is_integer: if not grad_y.dtype.is_floating and not grad_y.dtype.is_integer: - raise TypeError("Gradient type %s generated for real or " - "integer-valued tensor %s with type %s must be " - "real or integer" % - (dtypes.as_dtype(grad_y.dtype).name, y, - dtypes.as_dtype(y.dtype).name)) + raise TypeError( + "Gradient type %s generated for real or " + "integer-valued tensor %s with type %s must be " + "real or integer" % (dtypes.as_dtype(grad_y.dtype).name, y, + dtypes.as_dtype(y.dtype).name)) elif y.dtype.is_complex: if not grad_y.dtype.is_complex: - raise TypeError("Gradient type %s generated for complex-valued " - "tensor %s with type %s must be real" % - (dtypes.as_dtype(grad_y.dtype).name, y, - dtypes.as_dtype(y.dtype).name)) + raise TypeError( + "Gradient type %s generated for complex-valued " + "tensor %s with type %s must be real" % (dtypes.as_dtype( + grad_y.dtype).name, y, dtypes.as_dtype(y.dtype).name)) else: - raise TypeError("Tensor %s with type %s must be numeric " - "to obtain a default gradient" % - (y, dtypes.as_dtype(y.dtype).name)) + raise TypeError( + "Tensor %s with type %s must be numeric " + "to obtain a default gradient" % (y, dtypes.as_dtype(y.dtype).name)) # Create a grad_y tensor in the name scope of the gradient. # Required for TensorArrays to identify which gradient call a # grad_y value is coming from. @@ -605,15 +605,19 @@ def _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops, loop_state.ExitGradWhileContext(op, before=True) grad_fn = None - # pylint: disable=protected-access func_call = None + # pylint: disable=protected-access is_func_call = ops.get_default_graph()._is_function(op.type) + # pylint: enable=protected-access has_out_grads = any(isinstance(g, ops.Tensor) or g for g in out_grads) if has_out_grads and (op._id not in stop_ops): if is_func_call: func_call = ops.get_default_graph()._get_function(op.type) + # Note that __defun is not set if the graph is + # imported. If it's set, we prefer to access the original + # defun. + func_call = getattr(op, "__defun", func_call) grad_fn = func_call.python_grad_func - # pylint: enable=protected-access else: # A grad_fn must be defined, either as a function or as None # for ops that do not have gradients. From 8477e7cdd0dafb2e9f9f1c1ad3929b15a29a5ada Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Fri, 27 Apr 2018 14:36:24 -0700 Subject: [PATCH 0117/1691] [XLA:CPU] Implement fusion for the Gather HLO PiperOrigin-RevId: 194594759 --- tensorflow/compiler/xla/service/cpu/BUILD | 1 + .../compiler/xla/service/cpu/cpu_compiler.cc | 3 +- .../xla/service/cpu/cpu_instruction_fusion.cc | 1 + .../cpu/cpu_instruction_fusion_test.cc | 149 +++++++++++++++ .../xla/service/elemental_ir_emitter.cc | 86 +++++++++ .../compiler/xla/service/llvm_ir/ir_array.h | 4 + .../xla/tests/gather_operation_test.cc | 178 ++++++++++++++++++ 7 files changed, 421 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index cef4ebacc86e3f..2fc6c6bd551575 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -624,6 +624,7 @@ tf_cc_test( "//tensorflow/compiler/xla/service:transpose_folding", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", "//tensorflow/core:lib", ], ) diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index 3c0c367df30639..150c12eeace5b7 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -258,7 +258,6 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) { /*rewrite_inference_op=*/true, /*rewrite_grad_op=*/true, /*use_fusion=*/false); - pipeline.AddPass(); pass.AddPass( /*is_layout_sensitive=*/false, [](const Shape&, const Shape&) { return false; }, @@ -287,6 +286,8 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) { pipeline.AddPass(/*is_layout_sensitive=*/false); pipeline.AddPass(); + pipeline.AddPass(); + ReducePrecisionInsertion::AddPasses( &pipeline, module->config().debug_options(), ReducePrecisionInsertion::PassTiming::AFTER_FUSION); diff --git a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc index 0fc5a746bbbc76..b40d264c03aba6 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc @@ -34,6 +34,7 @@ bool CanBeLoopFused(const HloInstruction& hlo) { hlo.opcode() == HloOpcode::kConcatenate || hlo.opcode() == HloOpcode::kDynamicSlice || hlo.opcode() == HloOpcode::kDynamicUpdateSlice || + hlo.opcode() == HloOpcode::kGather || hlo.opcode() == HloOpcode::kPad || hlo.opcode() == HloOpcode::kReshape || hlo.opcode() == HloOpcode::kReverse || diff --git a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc index 6ed1cd31b18f63..a98e85a151ffb7 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/service/transpose_folding.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" #include "tensorflow/core/lib/gtl/array_slice.h" namespace op = xla::testing::opcode_matchers; @@ -697,6 +698,154 @@ TEST_F(OpcodeFusionTest, DotAddOutputFusion_19x50x1_multi_use) { Not(op::Fusion())); } +struct GatherLoopFusionTestSpec { + string test_name; + string hlo_computation_text; + + static string Name( + const ::testing::TestParamInfo& info) { + return info.param.test_name; + } +}; + +class GatherLoopFusionTest + : public OpcodeFusionTest, + public ::testing::WithParamInterface {}; + +TEST_P(GatherLoopFusionTest, GatherLoopFusion) { + const GatherLoopFusionTestSpec& spec = GetParam(); + string hlo_string = tensorflow::strings::StrCat( + "HloModule ", spec.test_name, "\n\n", spec.hlo_computation_text); + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + tools::Parse(hlo_string)); + + RunFusionAndCheckOpcodesWereFused( + module.get(), + {HloOpcode::kGather, HloOpcode::kAdd, HloOpcode::kBroadcast, + HloOpcode::kParameter, HloOpcode::kParameter, HloOpcode::kParameter}); +} + +std::vector GetGatherLoopFusionTestSpecs() { + std::vector result; + + result.push_back({"FusedTensorFlowGatherV2", R"( +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + gather = s32[3,2] gather(operand, indices), + output_window_dims={0}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=1, + window_bounds={3, 1} + one = s32[] constant(1) + one_broadcasted = s32[3,2] broadcast(one), dimensions={} + ROOT result = s32[3,2]{1,0} add(gather, one_broadcasted) +} +)"}); + + result.push_back({"FusedTensorFlowGatherMultipleBatchDims", R"( +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2] parameter(1) + gather = s32[2,3,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=2, + window_bounds={3, 1} + one = s32[] constant(1) + one_broadcasted = s32[2,3,2] broadcast(one), dimensions={} + ROOT result = s32[2,3,2]{2,1,0} add(gather, one_broadcasted) +} +)"}); + + result.push_back({"FusedTensorFlowGatherNdMultipleBatchDims", R"( +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2,2] parameter(1) + gather = s32[2,2] gather(operand, indices), + output_window_dims={}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=2, + window_bounds={1, 1} + one = s32[] constant(1) + one_broadcasted = s32[2,2] broadcast(one), dimensions={} + ROOT result = s32[2,2]{1,0} add(gather, one_broadcasted) +} +)"}); + + result.push_back({"FusedTensorFlowGatherNd_0", R"( +ENTRY main { + operand = s32[3,3,2] parameter(0) + indices = s32[2,2] parameter(1) + gather = s32[2,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=1, + window_bounds={1,1,2} + one = s32[] constant(1) + one_broadcasted = s32[2,2] broadcast(one), dimensions={} + ROOT result = s32[2,2]{1,0} add(gather, one_broadcasted) +} +)"}); + + result.push_back({"FusedTensorFlowGatherNd_1", R"( +ENTRY main { + operand = s32[3,3,2] parameter(0) + indices = s32[2,2] parameter(1) + gather = s32[2,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1,2} + one = s32[] constant(1) + one_broadcasted = s32[2,2] broadcast(one), dimensions={} + ROOT result = s32[2,2]{1,0} add(gather, one_broadcasted) +} +)"}); + + result.push_back({"FusedDynamicSlice", R"( +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + gather = s32[1,1] gather(operand, indices), + output_window_dims={0,1}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1} + one = s32[] constant(1) + one_broadcasted = s32[1,1] broadcast(one), dimensions={} + ROOT result = s32[1,1]{1,0} add(gather, one_broadcasted) +} +)"}); + + result.push_back({"FusedBatchDynamicSlice", R"( +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2] parameter(1) + gather = s32[2,1,1] gather(operand, indices), + output_window_dims={1,2}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1} + one = s32[] constant(1) + one_broadcasted = s32[2,1,1] broadcast(one), dimensions={} + ROOT result = s32[2,1,1]{2,1,0} add(gather, one_broadcasted) +} +)"}); + + return result; +} + +INSTANTIATE_TEST_CASE_P(GatherLoopFusionTestInstantiation, GatherLoopFusionTest, + ::testing::ValuesIn(GetGatherLoopFusionTestSpecs()), + GatherLoopFusionTestSpec::Name); } // namespace } // namespace cpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index 38b5efa9fb2cdb..4b01c878fbc077 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -1587,6 +1587,92 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( } return operand_to_generator.at(input_hlo)(input_index); }; + + case HloOpcode::kGather: + return [this, hlo, &operand_to_generator]( + const IrArray::Index& index) -> StatusOr { + const Shape& operand_shape = hlo->operand(0)->shape(); + const Shape& indices_shape = hlo->operand(1)->shape(); + const Shape& output_shape = hlo->shape(); + + const GatherDimensionNumbers& dim_numbers = + hlo->gather_dimension_numbers(); + + const llvm_ir::ElementGenerator& operand_generator = + operand_to_generator.at(hlo->operand(0)); + const llvm_ir::ElementGenerator& indices_generator = + operand_to_generator.at(hlo->operand(1)); + + // This is the index into `operand` that holds the element we want to + // generate. This index "unsafe" as in the components in here may be + // out of bounds. + IrArray::Index unsafe_operand_index; + + // First copy in the window indices to unsafe_operand_index. + for (int64 i = 0, e = operand_shape.dimensions_size(), + unsafe_operand_index_dim = 0; + i < e; i++) { + if (c_binary_search(dim_numbers.elided_window_dims(), i)) { + unsafe_operand_index.push_back(ir_builder_->getInt64(0)); + } else { + unsafe_operand_index.push_back(index[dim_numbers.output_window_dims( + unsafe_operand_index_dim++)]); + } + } + + // This is the index of the index vector in the gather_indices tensor. + IrArray::Index gather_index_index; + { + std::vector gather_index_index_components; + for (int64 i = 0, e = output_shape.dimensions_size(); i < e; i++) { + if (!c_binary_search(dim_numbers.output_window_dims(), i)) { + gather_index_index.push_back(index[i]); + } + } + + if (gather_index_index.size() != indices_shape.dimensions_size()) { + gather_index_index.InsertAt(dim_numbers.index_vector_dim(), + nullptr); + } + } + + auto add_to_unsafe_operand_index = [&](llvm::Value* index_component, + int64 dim) { + llvm::Value* gather_dim_component_extended = + ir_builder_->CreateSExtOrTrunc(index_component, + ir_builder_->getInt64Ty()); + unsafe_operand_index[dim_numbers.gather_dims_to_operand_dims(dim)] = + ir_builder_->CreateAdd( + unsafe_operand_index[dim_numbers.gather_dims_to_operand_dims( + dim)], + gather_dim_component_extended); + }; + + if (indices_shape.dimensions_size() == dim_numbers.index_vector_dim()) { + TF_ASSIGN_OR_RETURN(llvm::Value * gather_dim_component, + indices_generator(gather_index_index)); + add_to_unsafe_operand_index(gather_dim_component, 0); + } else { + int64 index_vector_size = + indices_shape.dimensions(dim_numbers.index_vector_dim()); + for (int64 i = 0; i < index_vector_size; i++) { + gather_index_index[dim_numbers.index_vector_dim()] = + ir_builder_->getInt64(i); + TF_ASSIGN_OR_RETURN(llvm::Value * gather_dim_component, + indices_generator(gather_index_index)); + add_to_unsafe_operand_index(gather_dim_component, i); + } + } + + IrArray::Index safe_operand_index; + for (int64 i = 0, e = unsafe_operand_index.size(); i < e; i++) { + safe_operand_index.push_back(ir_builder_->CreateURem( + unsafe_operand_index[i], + ir_builder_->getInt64(operand_shape.dimensions(i)))); + } + + return operand_generator(safe_operand_index); + }; case HloOpcode::kDynamicUpdateSlice: return [this, hlo, &operand_to_generator]( const IrArray::Index& index) -> StatusOr { diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h index 06cfb2a36c56c5..4c3195c29c859c 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h @@ -97,6 +97,10 @@ class IrArray { llvm::Value*& operator[](size_t i) { return multidim()[i]; } void push_back(llvm::Value* value) { multidim().push_back(value); } + void InsertAt(int64 index, llvm::Value* value) { + CHECK_LE(index, size()); + multidim().insert(multidim().begin() + index, value); + } using iterator = std::vector::iterator; using const_iterator = std::vector::const_iterator; diff --git a/tensorflow/compiler/xla/tests/gather_operation_test.cc b/tensorflow/compiler/xla/tests/gather_operation_test.cc index 4dd3acd9af1621..130456e61ca8a2 100644 --- a/tensorflow/compiler/xla/tests/gather_operation_test.cc +++ b/tensorflow/compiler/xla/tests/gather_operation_test.cc @@ -399,6 +399,184 @@ ENTRY main { RunTest(hlo_text, operand.get(), gather_indices.get()); } +XLA_TEST_F(GatherOperationTest, FusedTensorFlowGatherV2) { + const string hlo_text = R"( +HloModule FusedTensorFlowGatherV2 + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + gather = s32[3,2] gather(operand, indices), + output_window_dims={0}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=1, + window_bounds={3, 1} + one = s32[] constant(1) + one_broadcasted = s32[3,2] broadcast(one), dimensions={} + ROOT result = s32[3,2]{1,0} add(gather, one_broadcasted) +} +)"; + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, FusedTensorFlowGatherMultipleBatchDims) { + const string hlo_text = R"( +HloModule FusedTensorFlowGatherMultipleBatchDims + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2] parameter(1) + gather = s32[2,3,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=2, + window_bounds={3, 1} + one = s32[] constant(1) + one_broadcasted = s32[2,3,2] broadcast(one), dimensions={} + ROOT result = s32[2,3,2]{2,1,0} add(gather, one_broadcasted) +} +)"; + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{0, 2}, {2, 1}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, FusedTensorFlowGatherNdMultipleBatchDims) { + const string hlo_text = R"( +HloModule FusedTensorFlowGatherNdMultipleBatchDims + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2,2] parameter(1) + gather = s32[2,2] gather(operand, indices), + output_window_dims={}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=2, + window_bounds={1, 1} + one = s32[] constant(1) + one_broadcasted = s32[2,2] broadcast(one), dimensions={} + ROOT result = s32[2,2]{1,0} add(gather, one_broadcasted) +} +)"; + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + Literal::CreateR3({{{0, 2}, {2, 1}}, {{1, 2}, {2, 0}}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, FusedTensorFlowGatherNd) { + const string hlo_text = R"( +HloModule FusedTensorFlowGatherNd + +ENTRY main { + operand = s32[3,3,2] parameter(0) + indices = s32[2,2] parameter(1) + gather = s32[2,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=1, + window_bounds={1,1,2} + one = s32[] constant(1) + one_broadcasted = s32[2,2] broadcast(one), dimensions={} + ROOT result = s32[2,2]{1,0} add(gather, one_broadcasted) +} +)"; + std::unique_ptr operand = + Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{0, 0}, {1, 0}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, + FusedTensorFlowGatherNdNonDefaultIndexVectorDim) { + const string hlo_text = R"( +HloModule FusedTensorFlowGatherNd + +ENTRY main { + operand = s32[3,3,2] parameter(0) + indices = s32[2,2] parameter(1) + gather = s32[2,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1,2} + one = s32[] constant(1) + one_broadcasted = s32[2,2] broadcast(one), dimensions={} + ROOT result = s32[2,2]{1,0} add(gather, one_broadcasted) +} +)"; + std::unique_ptr operand = + Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{0, 0}, {1, 0}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, FusedDynamicSlice) { + const char* hlo_text = R"( +HloModule FusedDynamicSlice + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + gather = s32[1,1] gather(operand, indices), + output_window_dims={0,1}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1} + one = s32[] constant(1) + one_broadcasted = s32[1,1] broadcast(one), dimensions={} + ROOT result = s32[1,1]{1,0} add(gather, one_broadcasted) +} +)"; + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = Literal::CreateR1({1, 1}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, FusedBatchDynamicSlice) { + const string hlo_text = R"( +HloModule FusedBatchDynamicSlice + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2] parameter(1) + gather = s32[2,1,1] gather(operand, indices), + output_window_dims={1,2}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1} + one = s32[] constant(1) + one_broadcasted = s32[2,1,1] broadcast(one), dimensions={} + ROOT result = s32[2,1,1]{2,1,0} add(gather, one_broadcasted) +} +)"; + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{2, 1}, {1, 1}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + class GatherClientLibraryTest : public ClientLibraryTestBase {}; XLA_TEST_F(GatherClientLibraryTest, DISABLED_ON_GPU(Basic)) { From d84f9820a24214ce246092f0b1482cdaa1734a36 Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Fri, 27 Apr 2018 14:47:12 -0700 Subject: [PATCH 0118/1691] Minor eager service proto clarification. PiperOrigin-RevId: 194596337 --- tensorflow/core/protobuf/eager_service.proto | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/protobuf/eager_service.proto b/tensorflow/core/protobuf/eager_service.proto index c2325cc8039e4d..9a7d0edb35ecfd 100644 --- a/tensorflow/core/protobuf/eager_service.proto +++ b/tensorflow/core/protobuf/eager_service.proto @@ -121,10 +121,17 @@ message RegisterFunctionResponse { // Eager Service defines a TensorFlow service that executes operations eagerly // on a set of local devices, on behalf of a remote Eager executor. // -// The service impl will keep track of the various peers and devices it has +// The service impl will keep track of the various clients and devices it has // access to and allows the client to enqueue ops on any devices that it is able // to access and schedule data transfers from/to any of the peers. // +// A client can generate multiple contexts to be able to independently execute +// operations, but cannot share data between the two contexts. +// +// NOTE: Even though contexts generated by clients should be independent, the +// lower level tensorflow execution engine is not, so they might share some data +// (e.g. a Device's ResourceMgr). +// //////////////////////////////////////////////////////////////////////////////// service EagerService { // This initializes the worker, informing it about the other workers in the From fbd9ecd7361ff384bc05e30d2b44fc2a1f1da72b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Apr 2018 15:28:01 -0700 Subject: [PATCH 0119/1691] Fix broken ElementWiseFusionTest. PiperOrigin-RevId: 194602336 --- tensorflow/compiler/tests/BUILD | 5 ----- tensorflow/compiler/tests/jit_test.py | 3 ++- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index 991e65c8f528ce..6a7b8faac38b14 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -821,11 +821,6 @@ cuda_py_test( "//tensorflow/python:math_ops", "//tensorflow/python:nn_ops", ], - # TODO(b/62961789): Test fails with SIGABRT - tags = [ - "manual", - "notap", - ], ) cc_library( diff --git a/tensorflow/compiler/tests/jit_test.py b/tensorflow/compiler/tests/jit_test.py index 1f7da659e5590b..0310cdde660c91 100644 --- a/tensorflow/compiler/tests/jit_test.py +++ b/tensorflow/compiler/tests/jit_test.py @@ -489,7 +489,8 @@ def simpleTest(self, arg0, arg1, global_jit_level): def testElementWiseClustering(self): arg0 = np.random.rand(2, 2).astype(np.float32) arg1 = np.random.rand(2, 2).astype(np.float32) - os.environ["TF_XLA_FLAGS"] = "--tf_xla_fusion_only=true" + os.environ["TF_XLA_FLAGS"] = ("--tf_xla_fusion_only=true " + "--tf_xla_cpu_global_jit") tf_op, tf_count = self.simpleTest(arg0, arg1, config_pb2.OptimizerOptions.OFF) self.assertEqual(0, tf_count) From a52f64de874a0c2624ccdbab4f7b67eea9893e4c Mon Sep 17 00:00:00 2001 From: Nick Desaulniers Date: Fri, 27 Apr 2018 16:14:49 -0700 Subject: [PATCH 0120/1691] [TF:XLA:INTERPRETER] implement bfloat16 comparisons PiperOrigin-RevId: 194608854 --- tensorflow/compiler/xla/service/hlo_evaluator.cc | 5 +++++ .../compiler/xla/service/hlo_evaluator_test.cc | 16 ++++++++++++++++ 2 files changed, 21 insertions(+) diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index c5e30148345fec..f1dcef1dfcd470 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -2536,6 +2536,11 @@ Status HloEvaluator::HandleCompare(HloInstruction* compare) { } break; case F16: return Unimplemented("unhandled primitive type: F16."); + case BF16: { + TF_ASSIGN_OR_RETURN(evaluated_[compare], + Compare(compare->shape(), opcode, + lhs_literal, rhs_literal)); + } break; case F32: { TF_ASSIGN_OR_RETURN( evaluated_[compare], diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc index dd14dd38537a83..230147abfec10d 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc @@ -2005,6 +2005,22 @@ ENTRY main { *Evaluate({operand.get(), gather_indices.get()})); } +// Verifies that HloEvaluator evaluates a HLO instruction that performs +// element-wise comparison with 2 bfloat16 operands. +TEST_P(HloEvaluatorTest, DoesCompareBF16) { + // lhs >= rhs + auto lhs = Literal::CreateR2( + {{bfloat16(0.25), bfloat16(0.35), bfloat16(0.125)}, + {bfloat16(-0.25), bfloat16(-0.35), bfloat16(-0.125)}}); + auto rhs = Literal::CreateR2( + {{bfloat16(0.5), bfloat16(0.125), bfloat16(0.125)}, + {bfloat16(0.25), bfloat16(-0.375), bfloat16(-0.127)}}); + auto expected = + Literal::CreateR2({{false, true, true}, {false, true, true}}); + TestBinaryOp(HloOpcode::kGe, std::move(expected), std::move(lhs), + std::move(rhs)); +} + INSTANTIATE_TEST_CASE_P(HloEvaluatorTest_Instantiation, HloEvaluatorTest, ::testing::ValuesIn(use_bf16_params)); From 95e297c170d508444573c61c21d03971454626c0 Mon Sep 17 00:00:00 2001 From: Petros Mol Date: Fri, 27 Apr 2018 16:22:43 -0700 Subject: [PATCH 0121/1691] Minor fix to SDCAOptimizer documentation. PiperOrigin-RevId: 194609850 --- .../linear_optimizer/python/sdca_optimizer.py | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py b/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py index 5d4572bf6c761e..213c2eced5c7f9 100644 --- a/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py +++ b/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py @@ -37,18 +37,18 @@ class SDCAOptimizer(object): Example usage: ```python - real_feature_column = real_valued_column(...) - sparse_feature_column = sparse_column_with_hash_bucket(...) - sdca_optimizer = linear.SDCAOptimizer(example_id_column='example_id', - num_loss_partitions=1, - num_table_shards=1, - symmetric_l2_regularization=2.0) - classifier = tf.contrib.learn.LinearClassifier( - feature_columns=[real_feature_column, sparse_feature_column], - weight_column_name=..., - optimizer=sdca_optimizer) - classifier.fit(input_fn_train, steps=50) - classifier.evaluate(input_fn=input_fn_eval) + real_feature_column = real_valued_column(...) + sparse_feature_column = sparse_column_with_hash_bucket(...) + sdca_optimizer = linear.SDCAOptimizer(example_id_column='example_id', + num_loss_partitions=1, + num_table_shards=1, + symmetric_l2_regularization=2.0) + classifier = tf.contrib.learn.LinearClassifier( + feature_columns=[real_feature_column, sparse_feature_column], + weight_column_name=..., + optimizer=sdca_optimizer) + classifier.fit(input_fn_train, steps=50) + classifier.evaluate(input_fn=input_fn_eval) ``` Here the expectation is that the `input_fn_*` functions passed to train and From 4daebd253fe5d99a976a960d306d539d1c20743f Mon Sep 17 00:00:00 2001 From: Sandeep N Gupta <32845615+sandeepngupta@users.noreply.github.com> Date: Fri, 27 Apr 2018 16:38:24 -0700 Subject: [PATCH 0122/1691] Revised roadmap (#18939) Revised roadmap --- tensorflow/docs_src/community/roadmap.md | 74 ++++++++++++++++++------ 1 file changed, 55 insertions(+), 19 deletions(-) diff --git a/tensorflow/docs_src/community/roadmap.md b/tensorflow/docs_src/community/roadmap.md index a3170a10f2d12e..0463ca05fe5353 100644 --- a/tensorflow/docs_src/community/roadmap.md +++ b/tensorflow/docs_src/community/roadmap.md @@ -1,5 +1,5 @@ # Roadmap -**Last updated: Feb 15, 2018** +**Last updated: Apr 27, 2018** TensorFlow is a rapidly moving, community supported project. This document is intended to provide guidance about priorities and focus areas of the core set of TensorFlow @@ -14,12 +14,12 @@ expected in the next one to two releases. ### APIs #### High Level APIs: -* Easy multi-GPU utilization with Estimators +* Easy multi-GPU and TPU utilization with Estimators * Easy-to-use high-level pre-made estimators for Gradient Boosted Trees, Time Series, and other models #### Eager Execution: * Efficient utilization of multiple GPUs -* Distributed training (multi-machine) +* Distributed training support (multi-machine) * Performance improvements * Simpler export to a GraphDef/SavedModel @@ -31,14 +31,14 @@ to create Keras models Eager- style via Model subclassing) #### Official Models: * A set of -[reference models](https://github.com/tensorflow/models/tree/master/official) +[models](https://github.com/tensorflow/models/tree/master/official) across image recognition, speech, object detection, and translation that demonstrate best practices and serve as a starting point for high-performance model development. #### Contrib: -* Deprecation notices added to parts of tf.contrib where preferred implementations exist outside of tf.contrib. -* As much as possible, large projects inside tf.contrib moved to separate repositories. +* Deprecate parts of tf.contrib where preferred implementations exist outside of tf.contrib. +* As much as possible, move large projects inside tf.contrib to separate repositories. * The tf.contrib module will eventually be discontinued in its current form, experimental development will in future happen in other repositories. @@ -50,36 +50,72 @@ across image recognition, speech, object detection, and ### Platforms #### TensorFlow Lite: -* Increased coverage of supported ops in TensorFlow Lite +* Increase coverage of supported ops in TensorFlow Lite * Easier conversion of a trained TensorFlow graph for use on TensorFlow Lite * Support for GPU acceleration in TensorFlow Lite (iOS and Android) * Support for hardware accelerators via Android NeuralNets API -* Improved CPU performance by quantization and other network optimizations (eg. pruning, distillation) -* Increased support for devices beyond Android and iOS (eg. RPi, Cortex-M) +* Improve CPU performance by quantization and other network optimizations (eg. pruning, distillation) +* Increase support for devices beyond Android and iOS (eg. RPi, Cortex-M) + +#### TensorFlow.js: +* Release package for Node.js bindings to the TensorFlow C API through the TensorFlow.js backend interface +* Expand support for importing TensorFlow SavedModels and Keras models into browser with unified APIs supporting retraining in browser +* Improve Layers API and allow model exporting/saving +* Release tfjs-data API for efficient data input pipelines + +#### TensorFlow with Swift: +* Establish open source project including documentation, open design, and code availability. +* Continue implementing and refining implementation and design through 2018. +* Aim for implementation to be solid enough for general use later in 2018. ### Performance #### Distributed TensorFlow: -* Multi-GPU support optimized for a variety of GPU topologies -* Improved mechanisms for distributing computations on several machines +* Optimize Multi-GPU support for a variety of GPU topologies +* Improve mechanisms for distributing computations on several machines + +#### GPU Optimizations: +* Simplify mixed precision API with initial example model and guide. +* Finalize TensorRT API and move to core. +* CUDA 9.2 and NCCL 2.x default in TensorFlow builds. +* Optimizations for DGX-2. +* Remove support for CUDA less than 8.x and cuDNN less than 6.x. -#### Optimizations: -* Mixed precision training support with initial example model and guide -* Native TensorRT support + +#### CPU Optimizations * Int8 support for SkyLake via MKL * Dynamic loading of SIMD-optimized kernels +* MKL for Linux and Windows + +### End-to-end ML systems: +#### TensorFlow Hub: +* Expand support for module-types in TF Hub with TF Eager integration, Keras layers integration, and TensorFlow.js integration +* Accept variable-sized image input +* Improve multi-GPU estimator support +* Document and improve TPU integration + +#### TensorFlow Extended: +* Open source more of the TensorFlow Extended platform to facilitate adoption of TensorFlow in production settings. +* Release TFX libraries for Data Validation + +### Documentation and Resources: +* Update documentation, tutorials and Getting Started guides on all features and APIs +* Update [Youtube Tensorflow channel](https://youtube.com/tensorflow) weekly with new content: +Coding TensorFlow - where we teach folks coding with tensorflow +TensorFlow Meets - where we highlight community contributions +Ask TensorFlow - where we answer community questions +Guest and Showcase videos +* Update [Official TensorFlow blog](https://blog.tensorflow.org) with regular articles from Google team and the Community -### Documentation and Usability: -* Updated documentation, tutorials and Getting Started guides -* Process to enable external contributions to tutorials, documentation, and blogs showcasing best practice use-cases of TensorFlow and high-impact applications ### Community and Partner Engagement #### Special Interest Groups: -* Mobilizing the community to work together in focused domains +* Mobilize the community to work together in focused domains * [tf-distribute](https://groups.google.com/a/tensorflow.org/forum/#!forum/tf-distribute): build and packaging of TensorFlow -* More to be identified and launched +* SIG TensorBoard, SIG Rust, and more to be identified and launched #### Community: * Incorporate public feedback on significant design decisions via a Request-for-Comment (RFC) process * Formalize process for external contributions to land in TensorFlow and associated projects * Grow global TensorFlow communities and user groups * Collaborate with partners to co-develop and publish research papers +* Process to enable external contributions to tutorials, documentation, and blogs showcasing best practice use-cases of TensorFlow and high-impact applications From 8753e2ebde6c58b56675cc19ab7ff83072824a62 Mon Sep 17 00:00:00 2001 From: Yifei Feng <1192265+yifeif@users.noreply.github.com> Date: Fri, 27 Apr 2018 17:05:02 -0700 Subject: [PATCH 0123/1691] Fixing the mock import error for devel docker. (#18940) * Fixing the mock import error for devel docker. Same as #18843 --- tensorflow/tools/docker/Dockerfile.devel | 1 + tensorflow/tools/docker/Dockerfile.devel-gpu | 1 + 2 files changed, 2 insertions(+) diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index 390d7442c37b1d..5c49ac1d8d299a 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -31,6 +31,7 @@ RUN pip --no-cache-dir install \ ipykernel \ jupyter \ matplotlib \ + mock \ numpy \ scipy \ sklearn \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index 293028d229adba..196227861b2f73 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -40,6 +40,7 @@ RUN pip --no-cache-dir install \ ipykernel \ jupyter \ matplotlib \ + mock \ numpy \ scipy \ sklearn \ From e276bf65e2f3ec452eb28d0a9d34849d65663788 Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Fri, 27 Apr 2018 17:11:30 -0700 Subject: [PATCH 0124/1691] Fixes for review --- .../contrib/tensorrt/convert/convert_graph.cc | 10 +- .../contrib/tensorrt/convert/convert_nodes.cc | 4 +- .../contrib/tensorrt/convert/convert_nodes.h | 6 +- .../tensorrt/convert/trt_optimization_pass.cc | 1 - .../tensorrt/convert/trt_optimization_pass.h | 4 +- .../tensorrt/resources/trt_allocator.cc | 4 +- .../tensorrt/resources/trt_allocator.h | 7 +- .../contrib/tensorrt/segment/segment.cc | 92 ++++++++++--------- tensorflow/contrib/tensorrt/segment/segment.h | 82 +++++++++-------- .../contrib/tensorrt/segment/segment_test.cc | 10 +- .../contrib/tensorrt/test/test_tftrt.py | 8 +- 11 files changed, 116 insertions(+), 112 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 44b1a8f94cc9d7..632908f0783e74 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -150,7 +150,7 @@ struct ConvertGraphParams { const tensorflow::grappler::GraphProperties& current_graph_properties, std::unordered_map>* output_edges, int engine_precision_mode, const string& device_name, - std::shared_ptr allocator, int cuda_device_id) + std::shared_ptr allocator, int cuda_gpu_id) : graph(inp_graph), output_names(output_node_names), subgraph_node_ids(subgraph_node_id_numbers), @@ -161,7 +161,7 @@ struct ConvertGraphParams { precision_mode(engine_precision_mode), device_name_(device_name), allocator_(allocator), - cuda_device_id_(cuda_device_id) {} + cuda_gpu_id_(cuda_gpu_id) {} tensorflow::Graph& graph; const std::vector& output_names; const std::set& subgraph_node_ids; @@ -172,7 +172,7 @@ struct ConvertGraphParams { int precision_mode; string device_name_; std::shared_ptr allocator_; - int cuda_device_id_; + int cuda_gpu_id_; std::vector> subgraph_inputs; std::vector> subgraph_outputs; tensorflow::EdgeSet subgraph_incoming_edges; @@ -216,7 +216,7 @@ tensorflow::Status GetCalibNode(ConvertGraphParams* params) { params->max_batch_size, params->max_workspace_size_bytes, params->graph_properties, params->output_edge_map, &trt_node_def, params->precision_mode, params->device_name_, - params->allocator_, params->cuda_device_id_); + params->allocator_, params->cuda_gpu_id_); TF_RETURN_IF_ERROR(InjectCalibrationNode(s)); tensorflow::Status status; tensorflow::Node* trt_node = params->graph.AddNode(trt_node_def, &status); @@ -247,7 +247,7 @@ tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { params->max_batch_size, params->max_workspace_size_bytes, params->graph_properties, params->output_edge_map, &trt_node_def, params->precision_mode, params->device_name_, - params->allocator_, params->cuda_device_id_); + params->allocator_, params->cuda_gpu_id_); TF_RETURN_IF_ERROR(ConvertSubGraphToTensorRTNodeDef(s)); tensorflow::Status status; tensorflow::Node* trt_node = params->graph.AddNode(trt_node_def, &status); diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 8ed0ed7b7eb07f..ae0e861be54999 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2247,7 +2247,7 @@ tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) { auto op_res = new tensorflow::tensorrt::TRTCalibrationResource(); TF_CHECK_OK(op_rmgr->Create(calib_op_name, calib_op_name, op_res)); op_res->logger_ = new tensorflow::tensorrt::Logger(); - cudaSetDevice(s.cuda_device_id_); + cudaSetDevice(s.cuda_gpu_id_); op_res->builder_ = nvinfer1::createInferBuilder(*(op_res->logger_)); op_res->allocator_ = s.allocator_; #if NV_TENSORRT_MAJOR > 3 @@ -2481,7 +2481,7 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef( // Topological order is needed to build TRT network tensorflow::tensorrt::Logger trt_logger; - cudaSetDevice(s.cuda_device_id_); + cudaSetDevice(s.cuda_gpu_id_); auto trt_builder = infer_object(nvinfer1::createInferBuilder(trt_logger)); if (!trt_builder) { return tensorflow::errors::Internal( diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 8e1d7c99b6db15..50b0c37094a892 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -50,7 +50,7 @@ struct SubGraphParams { tensorflow::NodeDef* constructed_trt_node, int engine_precision_mode = FP32MODE, const string& device_name = "", std::shared_ptr allocator = 0, - int cuda_device_id = 0) + int cuda_gpu_id = 0) : graph(inp_graph), subgraph_node_ids(subgraph_node_id_numbers), input_inds(input_indices), @@ -63,7 +63,7 @@ struct SubGraphParams { precision_mode(engine_precision_mode), device_name_(device_name), allocator_(allocator), - cuda_device_id_(cuda_device_id) {} + cuda_gpu_id_(cuda_gpu_id) {} tensorflow::Graph& graph; const std::set& subgraph_node_ids; @@ -77,7 +77,7 @@ struct SubGraphParams { const int precision_mode; const string device_name_; std::shared_ptr allocator_; - const int cuda_device_id_; + const int cuda_gpu_id_; }; // TODO(sami): Replace references with const reference or pointers diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc index 999ad1274c3b33..743750998c052b 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc @@ -1,5 +1,4 @@ /* Copyright 2018 The TensorFlow Authors. All Rights Reserved. -1;4804;0c Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h index 81e3462a617145..aa9f2895504fd1 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h @@ -34,8 +34,8 @@ namespace tensorrt { namespace convert { class TRTOptimizationPass : public tensorflow::grappler::CustomGraphOptimizer { public: - TRTOptimizationPass(string optName = "TRTOptimizationPass") - : m_name_(optName), + TRTOptimizationPass(const string& name = "TRTOptimizationPass") + : m_name_(name), minimum_segment_size_(3), precision_mode_(0), maximum_batch_size_(-1), diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc index 9d40fea06b19b0..b94f8a2da7a9f1 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc @@ -30,6 +30,7 @@ void* TRTCudaAllocator::allocate(uint64_t size, uint64_t alignment, cudaMalloc(&memory, size); return memory; } + void TRTCudaAllocator::free(void* memory) { cudaFree(memory); } void* TRTDeviceAllocator::allocate(uint64_t size, uint64_t alignment, @@ -44,7 +45,8 @@ void* TRTDeviceAllocator::allocate(uint64_t size, uint64_t alignment, TRTDeviceAllocator::TRTDeviceAllocator(tensorflow::Allocator* allocator) : allocator_(allocator) { VLOG(1) << "Using " << allocator->Name() << " allocator from TensorFlow"; -}; +} + void TRTDeviceAllocator::free(void* memory) { VLOG(2) << "Deallocating " << memory; allocator_->DeallocateRaw(memory); diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.h b/tensorflow/contrib/tensorrt/resources/trt_allocator.h index 3001224b8d4a0d..05dcb7cde6b038 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.h +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.h @@ -21,6 +21,7 @@ limitations under the License. #include #include #include + #include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorflow/core/framework/allocator.h" #include "tensorflow/core/framework/resource_mgr.h" @@ -41,21 +42,21 @@ namespace tensorrt { class TRTCudaAllocator : public nvinfer1::IGpuAllocator { public: TRTCudaAllocator() {} - virtual ~TRTCudaAllocator(){}; + virtual ~TRTCudaAllocator() {}; void* allocate(uint64_t size, uint64_t alignment, uint32_t flags) override; void free(void* memory) override; }; + class TRTDeviceAllocator : public nvinfer1::IGpuAllocator { public: TRTDeviceAllocator(tensorflow::Allocator* allocator); - virtual ~TRTDeviceAllocator(){}; + virtual ~TRTDeviceAllocator() {}; void* allocate(uint64_t size, uint64_t alignment, uint32_t flags) override; void free(void* memory) override; private: tensorflow::Allocator* allocator_; }; -class AllocatorFactory {}; } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/segment/segment.cc b/tensorflow/contrib/tensorrt/segment/segment.cc index ac0d782a2b9be9..a76d17023663d7 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.cc +++ b/tensorflow/contrib/tensorrt/segment/segment.cc @@ -34,10 +34,11 @@ namespace segment { using ::tensorflow::strings::StrAppend; namespace { -bool check_cycles(const Graph* g, const Node* src, - const std::vector& start) { +bool CheckCycles(const SimpleGraph* g, const SimpleNode* src, + const std::vector& start) { + // copied from TF ReverseDFS struct Work { - Node* node; + SimpleNode* node; bool leave; // Are we entering or leaving n? }; @@ -74,7 +75,7 @@ bool check_cycles(const Graph* g, const Node* src, return false; } -bool CanContractEdge(const Edge* edge, const Graph* graph) { +bool CanContractEdge(const SimpleEdge* edge, const SimpleGraph* graph) { const auto src = edge->src(); const auto dst = edge->dst(); @@ -88,35 +89,36 @@ bool CanContractEdge(const Edge* edge, const Graph* graph) { // 1. Get all nodes incoming to 'dst', excluding 'src' // 2. Reverse DFS from those nodes // 3. If reverse DFS reaches 'src' then we have a cycle - std::vector dfs_start_nodes; - for (Node* node : dst->in_nodes()) { + std::vector dfs_start_nodes; + for (SimpleNode* node : dst->in_nodes()) { if (node != src) { dfs_start_nodes.push_back(node); } } - bool is_cycle = check_cycles(graph, src, dfs_start_nodes); + bool is_cycle = CheckCycles(graph, src, dfs_start_nodes); return !is_cycle; } } // namespace -Node::Node(const tensorflow::Node* node, const int id) : node_(node), id_(id) { +SimpleNode::SimpleNode(const tensorflow::Node* node, const int id) + : node_(node), id_(id) { if (node_) { in_edges_.reserve(node_->in_edges().size()); out_edges_.reserve(node_->out_edges().size()); } } -Graph::Graph(const tensorflow::Graph* g) : g_(g) { +SimpleGraph::SimpleGraph(const tensorflow::Graph* g) : g_(g) { int n_nodes = g_->num_node_ids(); nodes_.resize(n_nodes, nullptr); - nodes_[g->kSourceId] = new Node(g->source_node(), g->kSourceId); - nodes_[g->kSinkId] = new Node(g->sink_node(), g->kSinkId); + nodes_[g->kSourceId] = new SimpleNode(g->source_node(), g->kSourceId); + nodes_[g->kSinkId] = new SimpleNode(g->sink_node(), g->kSinkId); int n_edges = g->num_edge_ids(); edges_.resize(n_edges, nullptr); for (int i = 2; i < n_nodes; i++) { const auto n = g->FindNodeId(i); if (n) { - nodes_[i] = new Node(n, i); + nodes_[i] = new SimpleNode(n, i); } else { node_ids_.insert(i); } @@ -129,8 +131,8 @@ Graph::Graph(const tensorflow::Graph* g) : g_(g) { bool is_control = e->IsControlEdge(); auto src = nodes_[tfsrc->id()]; auto dst = nodes_[tfdst->id()]; - auto edge = - new Edge(i, src, e->src_output(), dst, e->dst_input(), is_control); + auto edge = new SimpleEdge(i, src, e->src_output(), dst, e->dst_input(), + is_control); edges_[i] = edge; src->out_edges_.push_back(edge); dst->in_edges_.push_back(edge); @@ -140,7 +142,8 @@ Graph::Graph(const tensorflow::Graph* g) : g_(g) { } } -void Graph::AddEdge(Node* src, int out_port, Node* dst, int in_port) { +void SimpleGraph::AddEdge(SimpleNode* src, int out_port, SimpleNode* dst, + int in_port) { int i = edges_.size(); if (edge_ids_.size()) { auto it = edge_ids_.begin(); @@ -151,18 +154,18 @@ void Graph::AddEdge(Node* src, int out_port, Node* dst, int in_port) { } bool is_control = (out_port == tensorflow::Graph::kControlSlot); is_control |= (in_port == tensorflow::Graph::kControlSlot); - auto edge = new Edge(i, src, out_port, dst, in_port, is_control); + auto edge = new SimpleEdge(i, src, out_port, dst, in_port, is_control); edges_[i] = edge; src->out_edges_.push_back(edge); dst->in_edges_.push_back(edge); } -void Graph::AddControlEdge(Node* src, Node* dst) { +void SimpleGraph::AddControlEdge(SimpleNode* src, SimpleNode* dst) { AddEdge(src, tensorflow::Graph::kControlSlot, dst, tensorflow::Graph::kControlSlot); } -void Graph::RemoveEdge(const Edge* edge) { +void SimpleGraph::RemoveEdge(const SimpleEdge* edge) { auto src = edge->src(); auto dst = edge->dst(); for (auto it = src->out_edges_.begin(); it != src->out_edges_.end(); ++it) { @@ -179,13 +182,13 @@ void Graph::RemoveEdge(const Edge* edge) { } } -Graph::~Graph() { +SimpleGraph::~SimpleGraph() { for (auto x : nodes_) delete x; for (auto x : edges_) delete x; } -void ContractEdge(Edge* edge, Graph* graph, - std::vector* remove_edges) { +void ContractEdge(SimpleEdge* edge, SimpleGraph* graph, + std::vector* remove_edges) { // Transfer all inputs and outputs of 'dst' to 'src' except edges // connecting the two. auto src = edge->src(); @@ -193,17 +196,17 @@ void ContractEdge(Edge* edge, Graph* graph, // We can use '0' for input/output index because we don't need them // to be accurate for the way we are using the graph. - std::vector in_edges(dst->in_edges().begin(), - dst->in_edges().end()); - for (const Edge* in_edge : in_edges) { + std::vector in_edges(dst->in_edges().begin(), + dst->in_edges().end()); + for (const SimpleEdge* in_edge : in_edges) { if (in_edge->IsControlEdge()) { if (in_edge->src() != src) { - Edge* e = const_cast(in_edge); + SimpleEdge* e = const_cast(in_edge); graph->AddControlEdge(e->src(), src); } } else { if (in_edge->src() != src) { - Edge* e = const_cast(in_edge); + SimpleEdge* e = const_cast(in_edge); if (e->src() == graph->source_node()) { graph->AddEdge(e->src(), e->src_output(), src, tensorflow::Graph::kControlSlot); @@ -214,14 +217,14 @@ void ContractEdge(Edge* edge, Graph* graph, } } - std::vector out_edges(dst->out_edges().begin(), - dst->out_edges().end()); - for (const Edge* out_edge : out_edges) { + std::vector out_edges(dst->out_edges().begin(), + dst->out_edges().end()); + for (const SimpleEdge* out_edge : out_edges) { if (out_edge->IsControlEdge()) { - Edge* e = const_cast(out_edge); + SimpleEdge* e = const_cast(out_edge); graph->AddControlEdge(src, e->dst()); } else { - Edge* e = const_cast(out_edge); + SimpleEdge* e = const_cast(out_edge); if (e->dst() == graph->sink_node()) { VLOG(1) << " edge to sink node " << src->name() << " -> " << e->dst()->name(); @@ -262,13 +265,13 @@ tensorflow::Status SegmentGraph( const std::function& candidate_fn, const SegmentOptions& options, SegmentNodesVector* segments) { // tensorflow::DumpGraph("Pre-Segment", &graph); - Graph* graph = new Graph(tf_graph); + SimpleGraph* graph = new SimpleGraph(tf_graph); // Use a union-find to collect the nodes that belong to the same // segment. A node value of nullptr indicates that the node is not a candidate // for TRT. - std::vector> node_segments; + std::vector> node_segments; for (int i = 0; i < graph->num_node_ids(); ++i) { - Node* node = graph->FindNodeId(i); + SimpleNode* node = graph->FindNodeId(i); if (options.exclude_node_list.count(node->name()) != 0 || !candidate_fn(node->tf_node())) { node = nullptr; @@ -288,12 +291,12 @@ tensorflow::Status SegmentGraph( tensorflow::GetPostOrder(*tf_graph, &tforder); // use postorder implementation from tensorflow and construct mirror in // internal format - std::vector order; + std::vector order; order.reserve(tforder.size()); for (const auto tfnode : tforder) { order.push_back(graph->FindNodeId(tfnode->id())); } - for (const Node* node : order) { + for (const SimpleNode* node : order) { // All output nodes of 'node' have been visited... VLOG(2) << "Trying node " << node->name() << " id=" << node->id(); @@ -307,8 +310,8 @@ tensorflow::Status SegmentGraph( // nodes. Iterate since combining two nodes may unblock other // combining. while (true) { - std::set contract_edges; - for (const Edge* out_edge : node->out_edges()) { + std::set contract_edges; + for (const SimpleEdge* out_edge : node->out_edges()) { VLOG(2) << "... out node " << out_edge->dst()->name() << " ( " << out_edge->dst()->id() << " <- " << node->id() << " )"; if (out_edge->IsControlEdge()) { @@ -336,9 +339,9 @@ tensorflow::Status SegmentGraph( // Contract edges and collect the adjacent nodes into the same // segment/subgraph. while (!contract_edges.empty()) { - const Edge* contract_edge = *contract_edges.begin(); - const Node* src = contract_edge->src(); - const Node* dst = contract_edge->dst(); + const SimpleEdge* contract_edge = *contract_edges.begin(); + const SimpleNode* src = contract_edge->src(); + const SimpleNode* dst = contract_edge->dst(); VLOG(2) << "Merge " << src->name() << " <- " << dst->name() << " (" << src->id() << " <- " << dst->id(); @@ -347,11 +350,11 @@ tensorflow::Status SegmentGraph( // Contracting the edge leaves disconnected graph edges. // Remove these from the graph and from 'contract_edges' so we // don't visit them again. - Edge* e = const_cast(contract_edge); - std::vector remove_edges; + SimpleEdge* e = const_cast(contract_edge); + std::vector remove_edges; ContractEdge(e, graph, &remove_edges); - for (const Edge* r : remove_edges) { + for (const SimpleEdge* r : remove_edges) { contract_edges.erase(r); graph->RemoveEdge(r); } @@ -399,6 +402,7 @@ tensorflow::Status SegmentGraph( << segment_node_names.size() << " nodes, dropping"; continue; } + // TODO(sami): Make segmenter placement aware once trtscopes are in place const auto& dev_itr = device_maps.find(itr.first); if (dev_itr == device_maps.end() || dev_itr->second.size() == 0) { VLOG(1) << "No device assigned to segment " << segments->size(); diff --git a/tensorflow/contrib/tensorrt/segment/segment.h b/tensorflow/contrib/tensorrt/segment/segment.h index 659fea1859009a..44a84cbd38c8b8 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.h +++ b/tensorflow/contrib/tensorrt/segment/segment.h @@ -30,43 +30,41 @@ namespace tensorrt { namespace segment { using SegmentNodesVector = std::vector, string>>; -class Node; -class Graph; -class Edge { +class SimpleNode; +class SimpleGraph; +class SimpleEdge { public: - Edge(int id, Node* src, int src_port, Node* dst, int dst_port, - bool is_control = false) + SimpleEdge(int id, SimpleNode* src, int src_port, SimpleNode* dst, + int dst_port, bool is_control = false) : id_(id), src_(src), src_port_(src_port), dst_(dst), dst_port_(dst_port), control_(is_control){}; - Node* src() const { return src_; } - Node* dst() const { return dst_; } + SimpleNode* src() const { return src_; } + SimpleNode* dst() const { return dst_; } int src_output() const { return src_port_; } int dst_input() const { return dst_port_; } int id() const { return id_; } bool IsControlEdge() const { return control_; } - ~Edge() {} + ~SimpleEdge() {} private: int id_; - Node* src_; + SimpleNode* src_; int src_port_; - Node* dst_; + SimpleNode* dst_; int dst_port_; bool control_; }; -class Node { - friend class Graph; - +class SimpleNode { public: - Node(const tensorflow::Node* node, const int id); - const std::vector& in_edges() const { return in_edges_; }; - const std::vector& out_edges() const { return out_edges_; }; - std::vector in_nodes() const { - std::vector res; + SimpleNode(const tensorflow::Node* node, const int id); + const std::vector& in_edges() const { return in_edges_; }; + const std::vector& out_edges() const { return out_edges_; }; + std::vector in_nodes() const { + std::vector res; res.reserve(in_edges_.size()); for (const auto e : in_edges_) { if (e) res.push_back(e->src()); @@ -79,32 +77,36 @@ class Node { private: const tensorflow::Node* node_; - std::vector in_edges_; - std::vector out_edges_; + std::vector in_edges_; + std::vector out_edges_; int id_; + + friend class SimpleGraph; }; -class Graph { +class SimpleGraph { public: - Graph(const tensorflow::Graph* g); - void AddControlEdge(Node* src, Node* dst); - void AddEdge(Node* src, int out_port, Node* dst, int in_port); - void RemoveEdge(const Edge*); - Node* FindNodeId(int node_id) { + SimpleGraph(const tensorflow::Graph* g); + void AddControlEdge(SimpleNode* src, SimpleNode* dst); + void AddEdge(SimpleNode* src, int out_port, SimpleNode* dst, int in_port); + void RemoveEdge(const SimpleEdge*); + SimpleNode* FindNodeId(int node_id) { if (node_id < 0 || node_id > (int)nodes_.size()) return nullptr; return nodes_[node_id]; } - ~Graph(); + ~SimpleGraph(); int num_node_ids() const { return nodes_.size(); } - const Node* source_node() const { + const SimpleNode* source_node() const { return nodes_[tensorflow::Graph::kSourceId]; } - const Node* sink_node() const { return nodes_[tensorflow::Graph::kSinkId]; } + const SimpleNode* sink_node() const { + return nodes_[tensorflow::Graph::kSinkId]; + } private: const tensorflow::Graph* g_; - std::vector nodes_; - std::vector edges_; + std::vector nodes_; + std::vector edges_; std::set edge_ids_; std::set node_ids_; }; @@ -114,15 +116,15 @@ struct SegmentOptions { std::set exclude_node_list; }; -// // Get the subgraphs of a graph that can be handled by TensorRT. -// // -// // @param gdef The GraphDef describing the network -// // @param candidate_fn A function that returns true for a NodeDef if -// // that node can be handled by TensorRT. -// // @param segments Returns the TensorRT segments/subgraphs. Each entry -// // in the vector describes a subgraph by giving a set of the names of -// // all the NodeDefs in that subgraph. -// // @return the status. +// Get the subgraphs of a graph that can be handled by TensorRT. +// +// @param gdef The GraphDef describing the network +// @param candidate_fn A function that returns true for a NodeDef if +// that node can be handled by TensorRT. +// @param segments Returns the TensorRT segments/subgraphs. Each entry +// in the vector describes a subgraph by giving a set of the names of +// all the NodeDefs in that subgraph. +// @return the status. tensorflow::Status SegmentGraph( const tensorflow::GraphDef& gdef, const std::function& candidate_fn, diff --git a/tensorflow/contrib/tensorrt/segment/segment_test.cc b/tensorflow/contrib/tensorrt/segment/segment_test.cc index 7fe824b12f1f92..8038085a060dc9 100644 --- a/tensorflow/contrib/tensorrt/segment/segment_test.cc +++ b/tensorflow/contrib/tensorrt/segment/segment_test.cc @@ -165,7 +165,7 @@ TEST_F(SegmentTest, Simple) { ASSERT_EQ(segments.size(), 1); std::vector expected{"add0", "add1", "add2", "add3", "add4"}; for (const auto& ex : expected) { - EXPECT_TRUE(segments[0].find(ex) != segments[0].end()) + EXPECT_TRUE(segments[0].first.find(ex) != segments[0].first.end()) << "Missing expected node " << ex; } TF_DeleteGraph(graph); @@ -278,13 +278,13 @@ TEST_F(SegmentTest, Multiple) { std::vector expected0{"add0", "add1", "add2", "add3"}; for (const auto& ex : expected0) { - EXPECT_TRUE(segments[0].find(ex) != segments[0].end()) + EXPECT_TRUE(segments[0].first.find(ex) != segments[0].first.end()) << "Missing expected node " << ex; } std::vector expected1{"add6", "add8"}; for (const auto& ex : expected1) { - EXPECT_TRUE(segments[1].find(ex) != segments[1].end()) + EXPECT_TRUE(segments[1].first.find(ex) != segments[1].first.end()) << "Missing expected node " << ex; } TF_DeleteGraph(graph); @@ -348,13 +348,13 @@ TEST_F(SegmentTest, BigIfElse) { std::vector expected0{"add3", "add4", "add5", "add6", "add7"}; for (const auto& ex : expected0) { - EXPECT_TRUE(segments[0].find(ex) != segments[0].end()) + EXPECT_TRUE(segments[0].first.find(ex) != segments[0].first.end()) << "Missing expected node " << ex; } std::vector expected1{"add0", "add1"}; for (const auto& ex : expected1) { - EXPECT_TRUE(segments[1].find(ex) != segments[1].end()) + EXPECT_TRUE(segments[1].first.find(ex) != segments[1].first.end()) << "Missing expected node " << ex; } TF_DeleteGraph(graph); diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py index 229532011734a8..175ccd80068625 100644 --- a/tensorflow/contrib/tensorrt/test/test_tftrt.py +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -66,7 +66,6 @@ def execute_graph(gdef, dumm_inp): """Run given graphdef once.""" print("executing") gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) - #graph_options = cpb2.GraphOptions(rewrite_options=opt_config) sessconfig = cpb2.ConfigProto(gpu_options=gpu_options) ops.reset_default_graph() g = ops.Graph() @@ -75,9 +74,6 @@ def execute_graph(gdef, dumm_inp): graph_def=gdef, return_elements=["input", "output"]) inp = inp.outputs[0] out = out.outputs[0] - # with csess.Session( - # config=cpb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess: - # val = sess.run(out, {inp: dumm_inp}) with csess.Session(config=sessconfig, graph=g) as sess: val = sess.run(out, {inp: dumm_inp}) return val @@ -105,7 +101,7 @@ def execute_calibration(gdef, dumm_inp): def user(run_graph=execute_graph, run_calibration=execute_calibration): - """ Example function that converts a graph to TFTRT graph """ + """Example function that converts a graph to TFTRT graph.""" inp_dims = (100, 24, 24, 2) dummy_input = np.random.random_sample(inp_dims) @@ -150,7 +146,7 @@ def user(run_graph=execute_graph, run_calibration=execute_calibration): def auto(): - """ Run the conversion as an optimization pass""" + """Run the conversion as an optimization pass.""" inp_dims = (100, 24, 24, 2) dummy_input = np.random.random_sample(inp_dims) orig_graph = get_simple_graph_def() From 864e0566bd0da15b5f93bcb1873c1e19b90f83cc Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Fri, 27 Apr 2018 17:08:57 -0700 Subject: [PATCH 0125/1691] Make RetryingFileSystem a template. PiperOrigin-RevId: 194614877 --- tensorflow/core/platform/cloud/BUILD | 3 - .../core/platform/cloud/gcs_file_system.h | 14 +- .../platform/cloud/retrying_file_system.cc | 207 ------------------ .../platform/cloud/retrying_file_system.h | 204 +++++++++++++++-- .../cloud/retrying_file_system_test.cc | 68 +++--- 5 files changed, 223 insertions(+), 273 deletions(-) delete mode 100644 tensorflow/core/platform/cloud/retrying_file_system.cc diff --git a/tensorflow/core/platform/cloud/BUILD b/tensorflow/core/platform/cloud/BUILD index be84316c482aa5..0fc1e4ae45c416 100644 --- a/tensorflow/core/platform/cloud/BUILD +++ b/tensorflow/core/platform/cloud/BUILD @@ -201,9 +201,6 @@ cc_library( cc_library( name = "retrying_file_system", - srcs = [ - "retrying_file_system.cc", - ], hdrs = [ "retrying_file_system.h", ], diff --git a/tensorflow/core/platform/cloud/gcs_file_system.h b/tensorflow/core/platform/cloud/gcs_file_system.h index 99c94c17515034..6250aa75948d22 100644 --- a/tensorflow/core/platform/cloud/gcs_file_system.h +++ b/tensorflow/core/platform/cloud/gcs_file_system.h @@ -256,18 +256,10 @@ class GcsFileSystem : public FileSystem { }; /// Google Cloud Storage implementation of a file system with retry on failures. -class RetryingGcsFileSystem : public RetryingFileSystem { +class RetryingGcsFileSystem : public RetryingFileSystem { public: - RetryingGcsFileSystem() : RetryingGcsFileSystem(new GcsFileSystem) {} - - void SetStats(GcsStatsInterface* stats) { underlying_->SetStats(stats); } - - private: - explicit RetryingGcsFileSystem(GcsFileSystem* fs) - : RetryingFileSystem(std::unique_ptr(fs)), underlying_(fs) {} - - // TODO(b/74259157): Refactor RetryingFileSystem to avoid holding this ptr. - GcsFileSystem* underlying_; + RetryingGcsFileSystem() + : RetryingFileSystem(std::unique_ptr(new GcsFileSystem)) {} }; } // namespace tensorflow diff --git a/tensorflow/core/platform/cloud/retrying_file_system.cc b/tensorflow/core/platform/cloud/retrying_file_system.cc deleted file mode 100644 index be9ebe67b18e7b..00000000000000 --- a/tensorflow/core/platform/cloud/retrying_file_system.cc +++ /dev/null @@ -1,207 +0,0 @@ -/* Copyright 2016 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/platform/cloud/retrying_file_system.h" -#include -#include "tensorflow/core/lib/core/errors.h" -#include "tensorflow/core/lib/random/random.h" -#include "tensorflow/core/platform/cloud/retrying_utils.h" -#include "tensorflow/core/platform/env.h" -#include "tensorflow/core/platform/file_system.h" - -namespace tensorflow { - -namespace { - -class RetryingRandomAccessFile : public RandomAccessFile { - public: - RetryingRandomAccessFile(std::unique_ptr base_file, - int64 delay_microseconds) - : base_file_(std::move(base_file)), - initial_delay_microseconds_(delay_microseconds) {} - - Status Read(uint64 offset, size_t n, StringPiece* result, - char* scratch) const override { - return RetryingUtils::CallWithRetries( - std::bind(&RandomAccessFile::Read, base_file_.get(), offset, n, result, - scratch), - initial_delay_microseconds_); - } - - private: - std::unique_ptr base_file_; - const int64 initial_delay_microseconds_; -}; - -class RetryingWritableFile : public WritableFile { - public: - RetryingWritableFile(std::unique_ptr base_file, - int64 delay_microseconds) - : base_file_(std::move(base_file)), - initial_delay_microseconds_(delay_microseconds) {} - - ~RetryingWritableFile() override { - // Makes sure the retrying version of Close() is called in the destructor. - Close().IgnoreError(); - } - - Status Append(const StringPiece& data) override { - return RetryingUtils::CallWithRetries( - std::bind(&WritableFile::Append, base_file_.get(), data), - initial_delay_microseconds_); - } - Status Close() override { - return RetryingUtils::CallWithRetries( - std::bind(&WritableFile::Close, base_file_.get()), - initial_delay_microseconds_); - } - Status Flush() override { - return RetryingUtils::CallWithRetries( - std::bind(&WritableFile::Flush, base_file_.get()), - initial_delay_microseconds_); - } - Status Sync() override { - return RetryingUtils::CallWithRetries( - std::bind(&WritableFile::Sync, base_file_.get()), - initial_delay_microseconds_); - } - - private: - std::unique_ptr base_file_; - const int64 initial_delay_microseconds_; -}; - -} // namespace - -Status RetryingFileSystem::NewRandomAccessFile( - const string& filename, std::unique_ptr* result) { - std::unique_ptr base_file; - TF_RETURN_IF_ERROR(RetryingUtils::CallWithRetries( - std::bind(&FileSystem::NewRandomAccessFile, base_file_system_.get(), - filename, &base_file), - initial_delay_microseconds_)); - result->reset(new RetryingRandomAccessFile(std::move(base_file), - initial_delay_microseconds_)); - return Status::OK(); -} - -Status RetryingFileSystem::NewWritableFile( - const string& filename, std::unique_ptr* result) { - std::unique_ptr base_file; - TF_RETURN_IF_ERROR(RetryingUtils::CallWithRetries( - std::bind(&FileSystem::NewWritableFile, base_file_system_.get(), filename, - &base_file), - initial_delay_microseconds_)); - result->reset(new RetryingWritableFile(std::move(base_file), - initial_delay_microseconds_)); - return Status::OK(); -} - -Status RetryingFileSystem::NewAppendableFile( - const string& filename, std::unique_ptr* result) { - std::unique_ptr base_file; - TF_RETURN_IF_ERROR(RetryingUtils::CallWithRetries( - std::bind(&FileSystem::NewAppendableFile, base_file_system_.get(), - filename, &base_file), - initial_delay_microseconds_)); - result->reset(new RetryingWritableFile(std::move(base_file), - initial_delay_microseconds_)); - return Status::OK(); -} - -Status RetryingFileSystem::NewReadOnlyMemoryRegionFromFile( - const string& filename, std::unique_ptr* result) { - return RetryingUtils::CallWithRetries( - std::bind(&FileSystem::NewReadOnlyMemoryRegionFromFile, - base_file_system_.get(), filename, result), - initial_delay_microseconds_); -} - -Status RetryingFileSystem::FileExists(const string& fname) { - return RetryingUtils::CallWithRetries( - std::bind(&FileSystem::FileExists, base_file_system_.get(), fname), - initial_delay_microseconds_); -} - -Status RetryingFileSystem::Stat(const string& fname, FileStatistics* stat) { - return RetryingUtils::CallWithRetries( - std::bind(&FileSystem::Stat, base_file_system_.get(), fname, stat), - initial_delay_microseconds_); -} - -Status RetryingFileSystem::GetChildren(const string& dir, - std::vector* result) { - return RetryingUtils::CallWithRetries( - std::bind(&FileSystem::GetChildren, base_file_system_.get(), dir, result), - initial_delay_microseconds_); -} - -Status RetryingFileSystem::GetMatchingPaths(const string& pattern, - std::vector* result) { - return RetryingUtils::CallWithRetries( - std::bind(&FileSystem::GetMatchingPaths, base_file_system_.get(), pattern, - result), - initial_delay_microseconds_); -} - -Status RetryingFileSystem::DeleteFile(const string& fname) { - return RetryingUtils::DeleteWithRetries( - std::bind(&FileSystem::DeleteFile, base_file_system_.get(), fname), - initial_delay_microseconds_); -} - -Status RetryingFileSystem::CreateDir(const string& dirname) { - return RetryingUtils::CallWithRetries( - std::bind(&FileSystem::CreateDir, base_file_system_.get(), dirname), - initial_delay_microseconds_); -} - -Status RetryingFileSystem::DeleteDir(const string& dirname) { - return RetryingUtils::DeleteWithRetries( - std::bind(&FileSystem::DeleteDir, base_file_system_.get(), dirname), - initial_delay_microseconds_); -} - -Status RetryingFileSystem::GetFileSize(const string& fname, uint64* file_size) { - return RetryingUtils::CallWithRetries( - std::bind(&FileSystem::GetFileSize, base_file_system_.get(), fname, - file_size), - initial_delay_microseconds_); -} - -Status RetryingFileSystem::RenameFile(const string& src, const string& target) { - return RetryingUtils::CallWithRetries( - std::bind(&FileSystem::RenameFile, base_file_system_.get(), src, target), - initial_delay_microseconds_); -} - -Status RetryingFileSystem::IsDirectory(const string& dirname) { - return RetryingUtils::CallWithRetries( - std::bind(&FileSystem::IsDirectory, base_file_system_.get(), dirname), - initial_delay_microseconds_); -} - -Status RetryingFileSystem::DeleteRecursively(const string& dirname, - int64* undeleted_files, - int64* undeleted_dirs) { - return RetryingUtils::DeleteWithRetries( - std::bind(&FileSystem::DeleteRecursively, base_file_system_.get(), - dirname, undeleted_files, undeleted_dirs), - initial_delay_microseconds_); -} - -void RetryingFileSystem::FlushCaches() { base_file_system_->FlushCaches(); } - -} // namespace tensorflow diff --git a/tensorflow/core/platform/cloud/retrying_file_system.h b/tensorflow/core/platform/cloud/retrying_file_system.h index a262a5fd940f9b..399a21617eedf2 100644 --- a/tensorflow/core/platform/cloud/retrying_file_system.h +++ b/tensorflow/core/platform/cloud/retrying_file_system.h @@ -16,17 +16,24 @@ limitations under the License. #ifndef TENSORFLOW_CORE_PLATFORM_RETRYING_FILE_SYSTEM_H_ #define TENSORFLOW_CORE_PLATFORM_RETRYING_FILE_SYSTEM_H_ +#include #include #include + +#include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/random/random.h" +#include "tensorflow/core/platform/cloud/retrying_utils.h" +#include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/file_system.h" namespace tensorflow { /// A wrapper to add retry logic to another file system. +template class RetryingFileSystem : public FileSystem { public: - RetryingFileSystem(std::unique_ptr base_file_system, + RetryingFileSystem(std::unique_ptr base_file_system, int64 delay_microseconds = 1000000) : base_file_system_(std::move(base_file_system)), initial_delay_microseconds_(delay_microseconds) {} @@ -45,39 +52,200 @@ class RetryingFileSystem : public FileSystem { const string& filename, std::unique_ptr* result) override; - Status FileExists(const string& fname) override; + Status FileExists(const string& fname) override { + return RetryingUtils::CallWithRetries( + std::bind(&FileSystem::FileExists, base_file_system_.get(), fname), + initial_delay_microseconds_); + } + + Status GetChildren(const string& dir, std::vector* result) override { + return RetryingUtils::CallWithRetries( + std::bind(&FileSystem::GetChildren, base_file_system_.get(), dir, + result), + initial_delay_microseconds_); + } + + Status GetMatchingPaths(const string& pattern, + std::vector* result) override { + return RetryingUtils::CallWithRetries( + std::bind(&FileSystem::GetMatchingPaths, base_file_system_.get(), + pattern, result), + initial_delay_microseconds_); + } + + Status Stat(const string& fname, FileStatistics* stat) override { + return RetryingUtils::CallWithRetries( + std::bind(&FileSystem::Stat, base_file_system_.get(), fname, stat), + initial_delay_microseconds_); + } + + Status DeleteFile(const string& fname) override { + return RetryingUtils::DeleteWithRetries( + std::bind(&FileSystem::DeleteFile, base_file_system_.get(), fname), + initial_delay_microseconds_); + } + + Status CreateDir(const string& dirname) override { + return RetryingUtils::CallWithRetries( + std::bind(&FileSystem::CreateDir, base_file_system_.get(), dirname), + initial_delay_microseconds_); + } + + Status DeleteDir(const string& dirname) override { + return RetryingUtils::DeleteWithRetries( + std::bind(&FileSystem::DeleteDir, base_file_system_.get(), dirname), + initial_delay_microseconds_); + } + + Status GetFileSize(const string& fname, uint64* file_size) override { + return RetryingUtils::CallWithRetries( + std::bind(&FileSystem::GetFileSize, base_file_system_.get(), fname, + file_size), + initial_delay_microseconds_); + } + + Status RenameFile(const string& src, const string& target) override { + return RetryingUtils::CallWithRetries( + std::bind(&FileSystem::RenameFile, base_file_system_.get(), src, + target), + initial_delay_microseconds_); + } + + Status IsDirectory(const string& dirname) override { + return RetryingUtils::CallWithRetries( + std::bind(&FileSystem::IsDirectory, base_file_system_.get(), dirname), + initial_delay_microseconds_); + } - Status GetChildren(const string& dir, std::vector* result) override; + Status DeleteRecursively(const string& dirname, int64* undeleted_files, + int64* undeleted_dirs) override { + return RetryingUtils::DeleteWithRetries( + std::bind(&FileSystem::DeleteRecursively, base_file_system_.get(), + dirname, undeleted_files, undeleted_dirs), + initial_delay_microseconds_); + } - Status GetMatchingPaths(const string& dir, - std::vector* result) override; + void FlushCaches() override { base_file_system_->FlushCaches(); } - Status Stat(const string& fname, FileStatistics* stat) override; + Underlying* underlying() const { return base_file_system_.get(); } - Status DeleteFile(const string& fname) override; + private: + std::unique_ptr base_file_system_; + const int64 initial_delay_microseconds_; - Status CreateDir(const string& dirname) override; + TF_DISALLOW_COPY_AND_ASSIGN(RetryingFileSystem); +}; - Status DeleteDir(const string& dirname) override; +namespace retrying_internals { - Status GetFileSize(const string& fname, uint64* file_size) override; +class RetryingRandomAccessFile : public RandomAccessFile { + public: + RetryingRandomAccessFile(std::unique_ptr base_file, + int64 delay_microseconds) + : base_file_(std::move(base_file)), + initial_delay_microseconds_(delay_microseconds) {} - Status RenameFile(const string& src, const string& target) override; + Status Read(uint64 offset, size_t n, StringPiece* result, + char* scratch) const override { + return RetryingUtils::CallWithRetries( + std::bind(&RandomAccessFile::Read, base_file_.get(), offset, n, result, + scratch), + initial_delay_microseconds_); + } - Status IsDirectory(const string& dir) override; + private: + std::unique_ptr base_file_; + const int64 initial_delay_microseconds_; +}; - Status DeleteRecursively(const string& dirname, int64* undeleted_files, - int64* undeleted_dirs) override; +class RetryingWritableFile : public WritableFile { + public: + RetryingWritableFile(std::unique_ptr base_file, + int64 delay_microseconds) + : base_file_(std::move(base_file)), + initial_delay_microseconds_(delay_microseconds) {} - void FlushCaches() override; + ~RetryingWritableFile() override { + // Makes sure the retrying version of Close() is called in the destructor. + Close().IgnoreError(); + } + + Status Append(const StringPiece& data) override { + return RetryingUtils::CallWithRetries( + std::bind(&WritableFile::Append, base_file_.get(), data), + initial_delay_microseconds_); + } + Status Close() override { + return RetryingUtils::CallWithRetries( + std::bind(&WritableFile::Close, base_file_.get()), + initial_delay_microseconds_); + } + Status Flush() override { + return RetryingUtils::CallWithRetries( + std::bind(&WritableFile::Flush, base_file_.get()), + initial_delay_microseconds_); + } + Status Sync() override { + return RetryingUtils::CallWithRetries( + std::bind(&WritableFile::Sync, base_file_.get()), + initial_delay_microseconds_); + } private: - std::unique_ptr base_file_system_; + std::unique_ptr base_file_; const int64 initial_delay_microseconds_; - - TF_DISALLOW_COPY_AND_ASSIGN(RetryingFileSystem); }; +} // namespace retrying_internals + +template +Status RetryingFileSystem::NewRandomAccessFile( + const string& filename, std::unique_ptr* result) { + std::unique_ptr base_file; + TF_RETURN_IF_ERROR(RetryingUtils::CallWithRetries( + std::bind(&FileSystem::NewRandomAccessFile, base_file_system_.get(), + filename, &base_file), + initial_delay_microseconds_)); + result->reset(new retrying_internals::RetryingRandomAccessFile( + std::move(base_file), initial_delay_microseconds_)); + return Status::OK(); +} + +template +Status RetryingFileSystem::NewWritableFile( + const string& filename, std::unique_ptr* result) { + std::unique_ptr base_file; + TF_RETURN_IF_ERROR(RetryingUtils::CallWithRetries( + std::bind(&FileSystem::NewWritableFile, base_file_system_.get(), filename, + &base_file), + initial_delay_microseconds_)); + result->reset(new retrying_internals::RetryingWritableFile( + std::move(base_file), initial_delay_microseconds_)); + return Status::OK(); +} + +template +Status RetryingFileSystem::NewAppendableFile( + const string& filename, std::unique_ptr* result) { + std::unique_ptr base_file; + TF_RETURN_IF_ERROR(RetryingUtils::CallWithRetries( + std::bind(&FileSystem::NewAppendableFile, base_file_system_.get(), + filename, &base_file), + initial_delay_microseconds_)); + result->reset(new retrying_internals::RetryingWritableFile( + std::move(base_file), initial_delay_microseconds_)); + return Status::OK(); +} + +template +Status RetryingFileSystem::NewReadOnlyMemoryRegionFromFile( + const string& filename, std::unique_ptr* result) { + return RetryingUtils::CallWithRetries( + std::bind(&FileSystem::NewReadOnlyMemoryRegionFromFile, + base_file_system_.get(), filename, result), + initial_delay_microseconds_); +} + } // namespace tensorflow #endif // TENSORFLOW_CORE_PLATFORM_RETRYING_FILE_SYSTEM_H_ diff --git a/tensorflow/core/platform/cloud/retrying_file_system_test.cc b/tensorflow/core/platform/cloud/retrying_file_system_test.cc index ee6886fef70328..ec2c470db797a1 100644 --- a/tensorflow/core/platform/cloud/retrying_file_system_test.cc +++ b/tensorflow/core/platform/cloud/retrying_file_system_test.cc @@ -184,7 +184,7 @@ TEST(RetryingFileSystemTest, NewRandomAccessFile_ImmediateSuccess) { std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); base_fs->random_access_file_to_return = std::move(base_file); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); // Retrieve the wrapped random access file. std::unique_ptr random_access_file; @@ -211,7 +211,7 @@ TEST(RetryingFileSystemTest, NewRandomAccessFile_SuccessWith3rdTry) { std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); base_fs->random_access_file_to_return = std::move(base_file); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); // Retrieve the wrapped random access file. std::unique_ptr random_access_file; @@ -235,7 +235,7 @@ TEST(RetryingFileSystemTest, NewRandomAccessFile_AllRetriesFailed) { std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); base_fs->random_access_file_to_return = std::move(base_file); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); // Retrieve the wrapped random access file. std::unique_ptr random_access_file; @@ -265,7 +265,7 @@ TEST(RetryingFileSystemTest, NewRandomAccessFile_NoRetriesForSomeErrors) { std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); base_fs->random_access_file_to_return = std::move(base_file); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); // Retrieve the wrapped random access file. std::unique_ptr random_access_file; @@ -291,7 +291,7 @@ TEST(RetryingFileSystemTest, NewWritableFile_ImmediateSuccess) { std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); base_fs->writable_file_to_return = std::move(base_file); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); // Retrieve the wrapped writable file. std::unique_ptr writable_file; @@ -317,7 +317,7 @@ TEST(RetryingFileSystemTest, NewWritableFile_SuccessWith3rdTry) { std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); base_fs->writable_file_to_return = std::move(base_file); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); // Retrieve the wrapped writable file. std::unique_ptr writable_file; @@ -343,7 +343,7 @@ TEST(RetryingFileSystemTest, NewWritableFile_SuccessWith3rdTry_ViaDestructor) { std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); base_fs->writable_file_to_return = std::move(base_file); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); // Retrieve the wrapped writable file. std::unique_ptr writable_file; @@ -368,7 +368,7 @@ TEST(RetryingFileSystemTest, NewAppendableFile_SuccessWith3rdTry) { std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); base_fs->writable_file_to_return = std::move(base_file); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); // Retrieve the wrapped appendable file. std::unique_ptr writable_file; @@ -391,7 +391,7 @@ TEST(RetryingFileSystemTest, NewWritableFile_AllRetriesFailed) { std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); base_fs->writable_file_to_return = std::move(base_file); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); // Retrieve the wrapped writable file. std::unique_ptr writable_file; @@ -412,7 +412,7 @@ TEST(RetryingFileSystemTest, std::make_tuple("NewReadOnlyMemoryRegionFromFile", Status::OK())}); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); std::unique_ptr result; TF_EXPECT_OK(fs.NewReadOnlyMemoryRegionFromFile("filename.txt", &result)); @@ -423,7 +423,7 @@ TEST(RetryingFileSystemTest, NewReadOnlyMemoryRegionFromFile_AllRetriesFailed) { CreateRetriableErrors("NewReadOnlyMemoryRegionFromFile", 11); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); std::unique_ptr result; const auto& status = @@ -440,7 +440,7 @@ TEST(RetryingFileSystemTest, GetChildren_SuccessWith2ndTry) { std::make_tuple("GetChildren", Status::OK())}); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); std::vector result; TF_EXPECT_OK(fs.GetChildren("gs://path", &result)); @@ -450,7 +450,7 @@ TEST(RetryingFileSystemTest, GetChildren_AllRetriesFailed) { ExpectedCalls expected_fs_calls = CreateRetriableErrors("GetChildren", 11); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); std::vector result; const auto& status = fs.GetChildren("gs://path", &result); @@ -466,7 +466,7 @@ TEST(RetryingFileSystemTest, GetMatchingPaths_SuccessWith2ndTry) { std::make_tuple("GetMatchingPaths", Status::OK())}); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); std::vector result; TF_EXPECT_OK(fs.GetMatchingPaths("gs://path/dir", &result)); @@ -477,7 +477,7 @@ TEST(RetryingFileSystemTest, GetMatchingPaths_AllRetriesFailed) { CreateRetriableErrors("GetMatchingPaths", 11); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); std::vector result; const auto& status = fs.GetMatchingPaths("gs://path/dir", &result); @@ -492,7 +492,7 @@ TEST(RetryingFileSystemTest, DeleteFile_SuccessWith2ndTry) { std::make_tuple("DeleteFile", Status::OK())}); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); std::vector result; TF_EXPECT_OK(fs.DeleteFile("gs://path/file.txt")); @@ -502,7 +502,7 @@ TEST(RetryingFileSystemTest, DeleteFile_AllRetriesFailed) { ExpectedCalls expected_fs_calls = CreateRetriableErrors("DeleteFile", 11); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); std::vector result; const auto& status = fs.DeleteFile("gs://path/file.txt"); @@ -517,7 +517,7 @@ TEST(RetryingFileSystemTest, CreateDir_SuccessWith2ndTry) { std::make_tuple("CreateDir", Status::OK())}); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); std::vector result; TF_EXPECT_OK(fs.CreateDir("gs://path/newdir")); @@ -527,7 +527,7 @@ TEST(RetryingFileSystemTest, CreateDir_AllRetriesFailed) { ExpectedCalls expected_fs_calls = CreateRetriableErrors("CreateDir", 11); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); std::vector result; const auto& status = fs.CreateDir("gs://path/newdir"); @@ -542,7 +542,7 @@ TEST(RetryingFileSystemTest, DeleteDir_SuccessWith2ndTry) { std::make_tuple("DeleteDir", Status::OK())}); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); std::vector result; TF_EXPECT_OK(fs.DeleteDir("gs://path/dir")); @@ -552,7 +552,7 @@ TEST(RetryingFileSystemTest, DeleteDir_AllRetriesFailed) { ExpectedCalls expected_fs_calls = CreateRetriableErrors("DeleteDir", 11); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); std::vector result; const auto& status = fs.DeleteDir("gs://path/dir"); @@ -568,7 +568,7 @@ TEST(RetryingFileSystemTest, GetFileSize_SuccessWith2ndTry) { std::make_tuple("GetFileSize", Status::OK())}); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); uint64 size; TF_EXPECT_OK(fs.GetFileSize("gs://path/file.txt", &size)); @@ -578,7 +578,7 @@ TEST(RetryingFileSystemTest, GetFileSize_AllRetriesFailed) { ExpectedCalls expected_fs_calls = CreateRetriableErrors("GetFileSize", 11); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); uint64 size; const auto& status = fs.GetFileSize("gs://path/file.txt", &size); @@ -593,7 +593,7 @@ TEST(RetryingFileSystemTest, RenameFile_SuccessWith2ndTry) { std::make_tuple("RenameFile", Status::OK())}); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); TF_EXPECT_OK(fs.RenameFile("old_name", "new_name")); } @@ -602,7 +602,7 @@ TEST(RetryingFileSystemTest, RenameFile_AllRetriesFailed) { ExpectedCalls expected_fs_calls = CreateRetriableErrors("RenameFile", 11); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); const auto& status = fs.RenameFile("old_name", "new_name"); EXPECT_TRUE( @@ -616,7 +616,7 @@ TEST(RetryingFileSystemTest, Stat_SuccessWith2ndTry) { std::make_tuple("Stat", Status::OK())}); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); FileStatistics stat; TF_EXPECT_OK(fs.Stat("file_name", &stat)); @@ -626,7 +626,7 @@ TEST(RetryingFileSystemTest, Stat_AllRetriesFailed) { ExpectedCalls expected_fs_calls = CreateRetriableErrors("Stat", 11); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); FileStatistics stat; const auto& status = fs.Stat("file_name", &stat); @@ -639,7 +639,7 @@ TEST(RetryingFileSystemTest, FileExists_AllRetriesFailed) { ExpectedCalls expected_fs_calls = CreateRetriableErrors("FileExists", 11); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); const auto& status = fs.FileExists("file_name"); EXPECT_TRUE( @@ -653,7 +653,7 @@ TEST(RetryingFileSystemTest, FileExists_SuccessWith2ndTry) { std::make_tuple("FileExists", Status::OK())}); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); TF_EXPECT_OK(fs.FileExists("gs://path/dir")); } @@ -665,7 +665,7 @@ TEST(RetryingFileSystemTest, IsDirectory_SuccessWith2ndTry) { std::make_tuple("IsDirectory", Status::OK())}); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); TF_EXPECT_OK(fs.IsDirectory("gs://path/dir")); } @@ -674,7 +674,7 @@ TEST(RetryingFileSystemTest, IsDirectory_AllRetriesFailed) { ExpectedCalls expected_fs_calls = CreateRetriableErrors("IsDirectory", 11); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); const auto& status = fs.IsDirectory("gs://path/dir"); EXPECT_TRUE( @@ -689,7 +689,7 @@ TEST(RetryingFileSystemTest, DeleteRecursively_SuccessWith2ndTry) { std::make_tuple("DeleteRecursively", Status::OK())}); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); int64 undeleted_files, undeleted_dirs; TF_EXPECT_OK( @@ -701,7 +701,7 @@ TEST(RetryingFileSystemTest, DeleteRecursively_AllRetriesFailed) { CreateRetriableErrors("DeleteRecursively", 11); std::unique_ptr base_fs( new MockFileSystem(expected_fs_calls)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); int64 undeleted_files, undeleted_dirs; const auto& status = @@ -715,7 +715,7 @@ TEST(RetryingFileSystemTest, FlushCaches) { ExpectedCalls none; bool flushed = false; std::unique_ptr base_fs(new MockFileSystem(none, &flushed)); - RetryingFileSystem fs(std::move(base_fs), 0); + RetryingFileSystem fs(std::move(base_fs), 0); fs.FlushCaches(); EXPECT_TRUE(flushed); } From b2b8dca5833344a0dfe4233ad57c907f3c553f0d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Apr 2018 18:24:57 -0700 Subject: [PATCH 0126/1691] [XLA] Fix bug in ShapeUtil::StripDegenerateDimensions PiperOrigin-RevId: 194621163 --- tensorflow/compiler/xla/shape_util.cc | 15 +++++++++++---- tensorflow/compiler/xla/shape_util_test.cc | 10 ++++++++++ 2 files changed, 21 insertions(+), 4 deletions(-) diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index ac7e201bfdceab..d58baa3220a73f 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -905,10 +905,17 @@ bool ShapeUtil::IsLeafIndex(const Shape& shape, const ShapeIndex& index) { std::is_permutation(minor_to_major.begin(), minor_to_major.end(), dims.begin())); } - Shape stripped_shape = - shape.has_layout() ? MakeShapeWithLayout(shape.element_type(), - dimension_sizes, minor_to_major) - : MakeShape(shape.element_type(), dimension_sizes); + Shape stripped_shape; + if (LayoutUtil::IsDenseArray(shape)) { + stripped_shape = MakeShapeWithLayout(shape.element_type(), dimension_sizes, + minor_to_major); + } else if (LayoutUtil::IsSparseArray(shape)) { + stripped_shape = + MakeShapeWithSparseLayout(shape.element_type(), dimension_sizes, + shape.layout().max_sparse_elements()); + } else { + stripped_shape = MakeShape(shape.element_type(), dimension_sizes); + } VLOG(10) << "Original_shape: " << HumanStringWithLayout(shape); VLOG(10) << "Stripped_shape: " << HumanStringWithLayout(stripped_shape); diff --git a/tensorflow/compiler/xla/shape_util_test.cc b/tensorflow/compiler/xla/shape_util_test.cc index 13582a2a267854..f7675e97da7b06 100644 --- a/tensorflow/compiler/xla/shape_util_test.cc +++ b/tensorflow/compiler/xla/shape_util_test.cc @@ -713,6 +713,16 @@ TEST(ShapeUtilTest, ReshapeIsBitcast_3x2x2_6x2_Dim1IsMostMinor) { ShapeUtil::MakeShapeWithLayout(F32, {6, 2}, {0, 1}))); } +TEST(ShapeUtilTest, StripDegenerateDimensions) { + EXPECT_TRUE(ShapeUtil::Equal(ShapeUtil::StripDegenerateDimensions( + ShapeUtil::MakeShape(F32, {3, 1, 2})), + ShapeUtil::MakeShape(F32, {3, 2}))); + EXPECT_TRUE(ShapeUtil::Equal( + ShapeUtil::StripDegenerateDimensions( + ShapeUtil::MakeShapeWithSparseLayout(F32, {3, 1, 2}, 10)), + ShapeUtil::MakeShapeWithSparseLayout(F32, {3, 2}, 10))); +} + TEST(AlgebraicSimplifierTest, ReshapeIsBitcast_3x2x2_6x2_Dim0IsMostMinor) { EXPECT_FALSE(ShapeUtil::ReshapeIsBitcast( ShapeUtil::MakeShapeWithLayout(F32, {3, 2, 2}, {0, 1, 2}), From 68efa500c0f8ec9c42072b25a5d1b5bf4f0afb21 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Fri, 27 Apr 2018 18:41:27 -0700 Subject: [PATCH 0127/1691] Split up ElementaIrEmitter::MakeElementGenerator into smaller functions; NFC PiperOrigin-RevId: 194622198 --- .../xla/service/elemental_ir_emitter.cc | 1028 +++++++++-------- .../xla/service/elemental_ir_emitter.h | 40 + 2 files changed, 572 insertions(+), 496 deletions(-) diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index 4b01c878fbc077..ae32d33766093c 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -1344,6 +1344,525 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeRngElementGenerator( }; } +StatusOr ElementalIrEmitter::EmitElementalSelect( + const HloInstruction* hlo, + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index) const { + TF_ASSIGN_OR_RETURN(llvm::Value * pred_value, + operand_to_generator.at(hlo->operand(0))( + ElementwiseSourceIndex(index, *hlo, 0))); + TF_ASSIGN_OR_RETURN(llvm::Value * on_true_value, + operand_to_generator.at(hlo->operand(1))( + ElementwiseSourceIndex(index, *hlo, 1))); + TF_ASSIGN_OR_RETURN(llvm::Value * on_false_value, + operand_to_generator.at(hlo->operand(2))( + ElementwiseSourceIndex(index, *hlo, 2))); + return ir_builder_->CreateSelect( + ir_builder_->CreateTrunc(pred_value, ir_builder_->getInt1Ty()), + on_true_value, on_false_value); +} + +StatusOr ElementalIrEmitter::EmitElementalClamp( + const HloInstruction* hlo, + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index) const { + TF_ASSIGN_OR_RETURN(llvm::Value * min_value, + operand_to_generator.at(hlo->operand(0))( + ElementwiseSourceIndex(index, *hlo, 0))); + TF_ASSIGN_OR_RETURN(llvm::Value * arg_value, + operand_to_generator.at(hlo->operand(1))( + ElementwiseSourceIndex(index, *hlo, 1))); + TF_ASSIGN_OR_RETURN(llvm::Value * max_value, + operand_to_generator.at(hlo->operand(2))( + ElementwiseSourceIndex(index, *hlo, 2))); + PrimitiveType prim_type = hlo->shape().element_type(); + if (primitive_util::IsFloatingPointType(prim_type)) { + return EmitFloatMin(max_value, EmitFloatMax(min_value, arg_value)); + } else if (primitive_util::IsIntegralType(prim_type)) { + bool is_signed = primitive_util::IsSignedIntegralType(prim_type); + return EmitIntegralMin( + max_value, EmitIntegralMax(min_value, arg_value, is_signed), is_signed); + } else { + return Unimplemented("Clamp unimplemented for %s", + PrimitiveType_Name(prim_type).c_str()); + } +} + +StatusOr ElementalIrEmitter::EmitElementalConcatenate( + const HloInstruction* hlo, + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& target_index) const { + const int64 concat_dim = hlo->dimensions(0); + auto source_index = target_index; + + llvm::BasicBlock* init_block = ir_builder_->GetInsertBlock(); + + // A terminator should be present iff we're emitting code + // into the middle (as opposed to the end) of a basic block. + CHECK_EQ(ir_builder_->GetInsertPoint() == init_block->end(), + init_block->getTerminator() == nullptr); + + llvm::BasicBlock* exit_block; + if (ir_builder_->GetInsertPoint() == init_block->end()) { + exit_block = llvm_ir::CreateBasicBlock( + /*insert_before=*/nullptr, IrName(hlo, "merge"), ir_builder_); + } else { + exit_block = init_block->splitBasicBlock(ir_builder_->GetInsertPoint(), + AsStringRef(IrName(hlo, "merge"))); + init_block->getTerminator()->eraseFromParent(); + } + + llvm_ir::SetToFirstInsertPoint(exit_block, ir_builder_); + llvm::PHINode* output = ir_builder_->CreatePHI( + llvm_ir::PrimitiveTypeToIrType(hlo->shape().element_type(), module_), + hlo->operands().size()); + auto prior_insert_point = ir_builder_->GetInsertPoint(); + + ir_builder_->SetInsertPoint(init_block); + + for (int64 operand_idx = 0; operand_idx < hlo->operand_count(); + ++operand_idx) { + const HloInstruction* operand = hlo->operand(operand_idx); + auto true_block = llvm_ir::CreateBasicBlock( + exit_block, StrCat("concat_index_from_operand", operand_idx), + ir_builder_); + auto false_block = llvm_ir::CreateBasicBlock( + exit_block, StrCat("concat_index_not_from_operand", operand_idx), + ir_builder_); + auto concat_dim_size = + llvm::ConstantInt::get(source_index[concat_dim]->getType(), + operand->shape().dimensions(concat_dim)); + ir_builder_->CreateCondBr( + ir_builder_->CreateICmpULT(source_index[concat_dim], concat_dim_size), + true_block, false_block); + + // Create the terminator of the true block before calling operand + // generators, because they require non-degenerate basic blocks. + ir_builder_->SetInsertPoint( + llvm::BranchInst::Create(exit_block, /*InsertAtEnd=*/true_block)); + TF_ASSIGN_OR_RETURN(llvm::Value * value, + operand_to_generator.at(operand)(source_index)); + output->addIncoming(value, ir_builder_->GetInsertBlock()); + + // Subtract the size of the concat dimension of the current operand + // from the source index. + ir_builder_->SetInsertPoint(false_block); + source_index[concat_dim] = + ir_builder_->CreateSub(source_index[concat_dim], concat_dim_size); + } + + ir_builder_->CreateUnreachable(); + ir_builder_->SetInsertPoint(exit_block, prior_insert_point); + return output; +} + +StatusOr ElementalIrEmitter::EmitElementalDynamicSlice( + const HloInstruction* hlo, + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index) const { + // Emit IR to read dynamic start indices from hlo->operand(1). + const HloInstruction* input_hlo = hlo->operand(0); + const int64 rank = ShapeUtil::Rank(input_hlo->shape()); + llvm_ir::IrArray::Index slice_start_index(rank); + for (int64 i = 0; i < rank; ++i) { + llvm_ir::IrArray::Index dim_index(1, ir_builder_->getInt64(i)); + TF_ASSIGN_OR_RETURN(llvm::Value * start_index_value, + operand_to_generator.at(hlo->operand(1))(dim_index)); + start_index_value->setName( + AsStringRef(IrName(hlo, StrCat("start_idx", i)))); + slice_start_index[i] = start_index_value; + } + + llvm_ir::IrArray::Index input_index(rank); + for (int64 i = 0; i < rank; ++i) { + // Emit IR which computes: + // input_index = (start_index + offset_index) % dim_size + // Security note: this is the code that keeps the indices in-bounds. + llvm::Value* dim_size = llvm::ConstantInt::get( + index[i]->getType(), input_hlo->shape().dimensions(i)); + llvm::Value* start_index = ir_builder_->CreateZExtOrBitCast( + slice_start_index[i], index[i]->getType()); + input_index[i] = ir_builder_->CreateURem( + ir_builder_->CreateAdd(start_index, index[i]), dim_size); + } + return operand_to_generator.at(input_hlo)(input_index); +} + +StatusOr ElementalIrEmitter::EmitElementalGather( + const HloInstruction* hlo, + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index) const { + const Shape& operand_shape = hlo->operand(0)->shape(); + const Shape& indices_shape = hlo->operand(1)->shape(); + const Shape& output_shape = hlo->shape(); + + const GatherDimensionNumbers& dim_numbers = hlo->gather_dimension_numbers(); + + const llvm_ir::ElementGenerator& operand_generator = + operand_to_generator.at(hlo->operand(0)); + const llvm_ir::ElementGenerator& indices_generator = + operand_to_generator.at(hlo->operand(1)); + + // This is the index into `operand` that holds the element we want to + // generate. This index "unsafe" as in the components in here may be + // out of bounds. + IrArray::Index unsafe_operand_index; + + // First copy in the window indices to unsafe_operand_index. + for (int64 i = 0, e = operand_shape.dimensions_size(), + unsafe_operand_index_dim = 0; + i < e; i++) { + if (c_binary_search(dim_numbers.elided_window_dims(), i)) { + unsafe_operand_index.push_back(ir_builder_->getInt64(0)); + } else { + unsafe_operand_index.push_back( + index[dim_numbers.output_window_dims(unsafe_operand_index_dim++)]); + } + } + + // This is the index of the index vector in the gather_indices tensor. + IrArray::Index gather_index_index; + { + std::vector gather_index_index_components; + for (int64 i = 0, e = output_shape.dimensions_size(); i < e; i++) { + if (!c_binary_search(dim_numbers.output_window_dims(), i)) { + gather_index_index.push_back(index[i]); + } + } + + if (gather_index_index.size() != indices_shape.dimensions_size()) { + gather_index_index.InsertAt(dim_numbers.index_vector_dim(), nullptr); + } + } + + auto add_to_unsafe_operand_index = [&](llvm::Value* index_component, + int64 dim) { + llvm::Value* gather_dim_component_extended = ir_builder_->CreateSExtOrTrunc( + index_component, ir_builder_->getInt64Ty()); + unsafe_operand_index[dim_numbers.gather_dims_to_operand_dims(dim)] = + ir_builder_->CreateAdd( + unsafe_operand_index[dim_numbers.gather_dims_to_operand_dims(dim)], + gather_dim_component_extended); + }; + + if (indices_shape.dimensions_size() == dim_numbers.index_vector_dim()) { + TF_ASSIGN_OR_RETURN(llvm::Value * gather_dim_component, + indices_generator(gather_index_index)); + add_to_unsafe_operand_index(gather_dim_component, 0); + } else { + int64 index_vector_size = + indices_shape.dimensions(dim_numbers.index_vector_dim()); + for (int64 i = 0; i < index_vector_size; i++) { + gather_index_index[dim_numbers.index_vector_dim()] = + ir_builder_->getInt64(i); + TF_ASSIGN_OR_RETURN(llvm::Value * gather_dim_component, + indices_generator(gather_index_index)); + add_to_unsafe_operand_index(gather_dim_component, i); + } + } + + IrArray::Index safe_operand_index; + for (int64 i = 0, e = unsafe_operand_index.size(); i < e; i++) { + safe_operand_index.push_back(ir_builder_->CreateURem( + unsafe_operand_index[i], + ir_builder_->getInt64(operand_shape.dimensions(i)))); + } + + return operand_generator(safe_operand_index); +} + +StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( + const HloInstruction* hlo, + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index) const { + const HloInstruction* input_hlo = hlo->operand(0); + const HloInstruction* update_hlo = hlo->operand(1); + const HloInstruction* start_hlo = hlo->operand(2); + // Calculate slice start/end indices. + const int64 rank = ShapeUtil::Rank(input_hlo->shape()); + llvm_ir::IrArray::Index slice_start_index(rank); + llvm_ir::IrArray::Index slice_limit_index(rank); + // Slice starts at update[index - slice_start_index_adjusted], + // where adjusted value = slice_start_index when in bounds, and + // adjusted value = slice_start_index - input_dim, when wrapping. + llvm_ir::IrArray::Index slice_start_index_adjusted(rank); + + // Slice intersection gathers (ANDs) conditions on all ranks for which + // 'input' is set to 'update' + llvm::Value* slice_intersection = ir_builder_->getTrue(); + + for (int64 i = 0; i < rank; ++i) { + // Emit IR to read dynamic start indices from 'start_hlo'. + llvm_ir::IrArray::Index dim_index(1, ir_builder_->getInt64(i)); + TF_ASSIGN_OR_RETURN(llvm::Value * start_index_value, + operand_to_generator.at(start_hlo)(dim_index)); + start_index_value->setName( + AsStringRef(IrName(hlo, StrCat("start_idx", i)))); + slice_start_index[i] = ir_builder_->CreateZExtOrBitCast( + start_index_value, index[i]->getType()); + + llvm::Value* input_dim_size = llvm::ConstantInt::get( + index[i]->getType(), input_hlo->shape().dimensions(i)); + llvm::Value* update_dim_size = llvm::ConstantInt::get( + index[i]->getType(), update_hlo->shape().dimensions(i)); + + // Generate code to handle wrapping semantics: + // slice_start_index[i] = slice_start_index[i] % input_dim_size; + // slice_limit_index[i] = slice_start_index[i] + update_dim_size. + // slice_start_index[i] is updated in place and it will now be in + // range. slice_limit_index[i] may be out of range, and it's being + // URem-ed below if so. + slice_start_index[i] = + ir_builder_->CreateURem(slice_start_index[i], input_dim_size); + slice_limit_index[i] = + ir_builder_->CreateAdd(slice_start_index[i], update_dim_size); + + // Test if slice_limit_index[i] is in bounds + llvm::Value* in_bounds = + ir_builder_->CreateICmpULE(slice_limit_index[i], input_dim_size); + llvm_ir::LlvmIfData if_in_bounds = + llvm_ir::EmitIfThenElse(in_bounds, "in_bounds", ir_builder_); + + // Handle true BB (slice_limit_index[i] <= input_dim_size). + SetToFirstInsertPoint(if_in_bounds.true_block, ir_builder_); + // Check that index[i] >= slice_start_index[i] && + // index[i] < slice_limit_index[i] + llvm::Value* slice_intersection_in_bounds = ir_builder_->CreateAnd( + slice_intersection, + ir_builder_->CreateICmpSGE(index[i], slice_start_index[i]), + "slice_intersection_in"); + slice_intersection_in_bounds = ir_builder_->CreateAnd( + slice_intersection_in_bounds, + ir_builder_->CreateICmpSLT(index[i], slice_limit_index[i]), + "slice_intersection_in"); + + // Handle false BB (slice_limit_index[i] > input_dim_size). + SetToFirstInsertPoint(if_in_bounds.false_block, ir_builder_); + // Check that index[i] >= slice_start_index[i] || + // index[i] < slice_limit_index[i]%input_dim_size. + llvm::Value* index_wraps = ir_builder_->CreateICmpSLT( + index[i], + ir_builder_->CreateURem(slice_limit_index[i], input_dim_size)); + llvm::Value* slice_intersection_or = ir_builder_->CreateOr( + ir_builder_->CreateICmpSGE(index[i], slice_start_index[i]), index_wraps, + "slice_intersection_out"); + llvm::Value* slice_intersection_out_of_bounds = ir_builder_->CreateAnd( + slice_intersection, slice_intersection_or, "slice_intersection_out"); + // Create value for slice_start_index_adjusted[i] when out of bounds. + // If within out-of-bounds if. + llvm_ir::LlvmIfData if_start_needs_adjustment = + llvm_ir::EmitIfThenElse(index_wraps, "adjust_start", ir_builder_); + SetToFirstInsertPoint(if_start_needs_adjustment.true_block, ir_builder_); + llvm::Value* slice_start_index_adjusted_oob = + ir_builder_->CreateSub(slice_start_index[i], input_dim_size); + SetToFirstInsertPoint(if_start_needs_adjustment.after_block, ir_builder_); + llvm::PHINode* slice_start_index_adjusted_phi = + ir_builder_->CreatePHI(slice_start_index_adjusted_oob->getType(), 2); + slice_start_index_adjusted_phi->addIncoming( + slice_start_index_adjusted_oob, if_start_needs_adjustment.true_block); + slice_start_index_adjusted_phi->addIncoming( + slice_start_index[i], if_start_needs_adjustment.false_block); + // End of if within if. + + // After checking in/out of bounds. + SetToFirstInsertPoint(if_in_bounds.after_block, ir_builder_); + llvm::PHINode* phi_slice_intersection = + ir_builder_->CreatePHI(slice_intersection->getType(), 2); + phi_slice_intersection->addIncoming(slice_intersection_in_bounds, + if_in_bounds.true_block); + phi_slice_intersection->addIncoming(slice_intersection_out_of_bounds, + if_start_needs_adjustment.after_block); + slice_intersection = phi_slice_intersection; + + llvm::PHINode* phi_index = + ir_builder_->CreatePHI(slice_start_index[i]->getType(), 2); + phi_index->addIncoming(slice_start_index[i], if_in_bounds.true_block); + phi_index->addIncoming(slice_start_index_adjusted_phi, + if_start_needs_adjustment.after_block); + slice_start_index_adjusted[i] = phi_index; + } + + // Emit: + // if (slice_intersection) -> return data from 'update'. + // else -> return data from 'input'. + llvm::Value* ret_value_addr = llvm_ir::EmitAllocaAtFunctionEntry( + llvm_ir::PrimitiveTypeToIrType(hlo->shape().element_type(), module_), + "ret_value_addr", ir_builder_); + llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( + slice_intersection, "slice_intersection", ir_builder_); + + // Handle true BB (return data from 'update') + SetToFirstInsertPoint(if_data.true_block, ir_builder_); + // Compute update index for intersection case. + llvm_ir::IrArray::Index update_index(rank); + for (int64 i = 0; i < rank; ++i) { + llvm::Value* update_dim_size = llvm::ConstantInt::get( + index[i]->getType(), update_hlo->shape().dimensions(i)); + // NOTE: Subtraction will be positive due to bounds checking above. + update_index[i] = ir_builder_->CreateURem( + ir_builder_->CreateSub(index[i], slice_start_index_adjusted[i]), + update_dim_size); + } + TF_ASSIGN_OR_RETURN(llvm::Value * true_value, + operand_to_generator.at(update_hlo)(update_index)); + ir_builder_->CreateStore(true_value, ret_value_addr); + + // Handle false BB (return data from 'input') + SetToFirstInsertPoint(if_data.false_block, ir_builder_); + TF_ASSIGN_OR_RETURN(llvm::Value * false_value, + operand_to_generator.at(input_hlo)(index)); + ir_builder_->CreateStore(false_value, ret_value_addr); + + SetToFirstInsertPoint(if_data.after_block, ir_builder_); + return ir_builder_->CreateLoad(ret_value_addr); +} + +StatusOr ElementalIrEmitter::EmitElementalPad( + const HloInstruction* hlo, + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& padded_index) const { + auto index = padded_index; + llvm::Value* in_bounds = ir_builder_->getTrue(); + for (size_t i = 0; i < index.size(); ++i) { + auto index_typed_const = [=](int64 n) { + return llvm::ConstantInt::get(index[i]->getType(), n); + }; + const auto& pad_dim = hlo->padding_config().dimensions(i); + index[i] = ir_builder_->CreateSub( + index[i], index_typed_const(pad_dim.edge_padding_low())); + in_bounds = ir_builder_->CreateAnd( + in_bounds, ir_builder_->CreateICmpSGE(index[i], index_typed_const(0)), + "in_bounds"); + in_bounds = ir_builder_->CreateAnd( + in_bounds, + ir_builder_->CreateICmpEQ( + index_typed_const(0), + ir_builder_->CreateURem( + index[i], index_typed_const(pad_dim.interior_padding() + 1))), + "in_bounds"); + index[i] = ir_builder_->CreateSDiv( + index[i], index_typed_const(pad_dim.interior_padding() + 1)); + in_bounds = ir_builder_->CreateAnd( + in_bounds, + ir_builder_->CreateICmpSLT( + index[i], + index_typed_const(hlo->operand(0)->shape().dimensions(i))), + "in_bounds"); + } + + // if (in_bounds) { + // ret_value = operand0[index]; // source + // } else { + // ret_value = *operand1; // padding + // } + llvm::Value* ret_value_addr = llvm_ir::EmitAllocaAtFunctionEntry( + llvm_ir::PrimitiveTypeToIrType(hlo->shape().element_type(), module_), + "pad_result_addr", ir_builder_); + llvm_ir::LlvmIfData if_data = + llvm_ir::EmitIfThenElse(in_bounds, "in_bounds", ir_builder_); + SetToFirstInsertPoint(if_data.true_block, ir_builder_); + TF_ASSIGN_OR_RETURN(llvm::Value * operand_value, + operand_to_generator.at(hlo->operand(0))(index)); + ir_builder_->CreateStore(operand_value, ret_value_addr); + + SetToFirstInsertPoint(if_data.false_block, ir_builder_); + TF_ASSIGN_OR_RETURN(llvm::Value * padding_value, + operand_to_generator.at(hlo->operand(1))({})); + ir_builder_->CreateStore(padding_value, ret_value_addr); + + SetToFirstInsertPoint(if_data.after_block, ir_builder_); + // Don't create phi(operand_value, padding_value) here, because invoking + // operand_to_generator may create new basic blocks, making the parent + // of operand_value or padding_value no longer a predecessor of + // if_data.after_block. + return ir_builder_->CreateLoad(ret_value_addr); +} + +StatusOr ElementalIrEmitter::EmitElementalDot( + const HloInstruction* hlo, + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& dot_result_index) const { + auto lhs_generator = operand_to_generator.at(hlo->operand(0)); + auto rhs_generator = operand_to_generator.at(hlo->operand(1)); + int64 contracted_dim_size = hlo->operand(0)->shape().dimensions( + hlo->operand(0)->shape().dimensions_size() - 1); + int64 lhs_dims = hlo->operand(0)->shape().dimensions_size(); + int64 rhs_dims = hlo->operand(1)->shape().dimensions_size(); + + std::unique_ptr inner_loop = llvm_ir::ForLoop::EmitForLoop( + IrName(hlo, "inner"), ir_builder_->getInt64(0), + ir_builder_->getInt64(contracted_dim_size), ir_builder_->getInt64(1), + ir_builder_); + + SetToFirstInsertPoint(inner_loop->GetPreheaderBasicBlock(), ir_builder_); + PrimitiveType primitive_type = hlo->shape().element_type(); + llvm::Type* primitive_type_llvm = + llvm_ir::PrimitiveTypeToIrType(primitive_type, module_); + llvm::Value* accumulator_alloca = llvm_ir::EmitAllocaAtFunctionEntry( + primitive_type_llvm, "dot_acc", ir_builder_); + ir_builder_->CreateStore(llvm::Constant::getNullValue(primitive_type_llvm), + accumulator_alloca); + + SetToFirstInsertPoint(inner_loop->GetBodyBasicBlock(), ir_builder_); + + // This is the inner reduction loop for a dot operation that produces + // one element in the output. If the operands to the dot operation have + // shapes [A,B,C,T] and [D,T,E], the result has a shape [A,B,C,D,E]. + // Given an output index [a,b,c,d,e] in the result, we compute: + // sum(lhs[a,b,c,t]*rhs[d,t,e] for t in [0, T)) + + IrArray::Index lhs_index, rhs_index; + + for (int64 i = 0; i < lhs_dims - 1; i++) { + lhs_index.push_back(dot_result_index[i]); + } + lhs_index.push_back(inner_loop->GetIndVarValue()); + + for (int64 i = 0; i < rhs_dims - 2; i++) { + rhs_index.push_back(dot_result_index[lhs_dims - 1 + i]); + } + rhs_index.push_back(inner_loop->GetIndVarValue()); + rhs_index.push_back(dot_result_index.back()); + + llvm::Value* current_accumulator = + ir_builder_->CreateLoad(accumulator_alloca); + TF_ASSIGN_OR_RETURN(llvm::Value * lhs_value, lhs_generator(lhs_index)); + TF_ASSIGN_OR_RETURN(llvm::Value * rhs_value, rhs_generator(rhs_index)); + llvm::Value* next_accumulator; + if (primitive_util::IsComplexType(primitive_type)) { + llvm::Value* product_real = ir_builder_->CreateFSub( + ir_builder_->CreateFMul(EmitExtractReal(lhs_value), + EmitExtractReal(rhs_value)), + ir_builder_->CreateFMul(EmitExtractImag(lhs_value), + EmitExtractImag(rhs_value))); + llvm::Value* product_imag = ir_builder_->CreateFAdd( + ir_builder_->CreateFMul(EmitExtractReal(lhs_value), + EmitExtractImag(rhs_value)), + ir_builder_->CreateFMul(EmitExtractImag(lhs_value), + EmitExtractReal(rhs_value))); + next_accumulator = ir_builder_->CreateInsertValue( + current_accumulator, + ir_builder_->CreateFAdd(EmitExtractReal(current_accumulator), + product_real), + {0}); + next_accumulator = ir_builder_->CreateInsertValue( + next_accumulator, + ir_builder_->CreateFAdd(EmitExtractImag(current_accumulator), + product_imag), + {1}); + } else if (primitive_util::IsFloatingPointType(primitive_type)) { + next_accumulator = ir_builder_->CreateFAdd( + current_accumulator, ir_builder_->CreateFMul(lhs_value, rhs_value)); + } else { + next_accumulator = ir_builder_->CreateAdd( + current_accumulator, ir_builder_->CreateMul(lhs_value, rhs_value)); + } + ir_builder_->CreateStore(next_accumulator, accumulator_alloca); + + SetToFirstInsertPoint(inner_loop->GetExitBasicBlock(), ir_builder_); + return ir_builder_->CreateLoad(accumulator_alloca); +} + llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( const HloInstruction* hlo, const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator) @@ -1411,43 +1930,12 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( case HloOpcode::kSelect: return [this, hlo, &operand_to_generator]( const IrArray::Index& index) -> StatusOr { - TF_ASSIGN_OR_RETURN(llvm::Value * pred_value, - operand_to_generator.at(hlo->operand(0))( - ElementwiseSourceIndex(index, *hlo, 0))); - TF_ASSIGN_OR_RETURN(llvm::Value * on_true_value, - operand_to_generator.at(hlo->operand(1))( - ElementwiseSourceIndex(index, *hlo, 1))); - TF_ASSIGN_OR_RETURN(llvm::Value * on_false_value, - operand_to_generator.at(hlo->operand(2))( - ElementwiseSourceIndex(index, *hlo, 2))); - return ir_builder_->CreateSelect( - ir_builder_->CreateTrunc(pred_value, ir_builder_->getInt1Ty()), - on_true_value, on_false_value); + return EmitElementalSelect(hlo, operand_to_generator, index); }; case HloOpcode::kClamp: return [this, hlo, &operand_to_generator]( const IrArray::Index& index) -> StatusOr { - TF_ASSIGN_OR_RETURN(llvm::Value * min_value, - operand_to_generator.at(hlo->operand(0))( - ElementwiseSourceIndex(index, *hlo, 0))); - TF_ASSIGN_OR_RETURN(llvm::Value * arg_value, - operand_to_generator.at(hlo->operand(1))( - ElementwiseSourceIndex(index, *hlo, 1))); - TF_ASSIGN_OR_RETURN(llvm::Value * max_value, - operand_to_generator.at(hlo->operand(2))( - ElementwiseSourceIndex(index, *hlo, 2))); - PrimitiveType prim_type = hlo->shape().element_type(); - if (primitive_util::IsFloatingPointType(prim_type)) { - return EmitFloatMin(max_value, EmitFloatMax(min_value, arg_value)); - } else if (primitive_util::IsIntegralType(prim_type)) { - bool is_signed = primitive_util::IsSignedIntegralType(prim_type); - return EmitIntegralMin( - max_value, EmitIntegralMax(min_value, arg_value, is_signed), - is_signed); - } else { - return Unimplemented("Clamp unimplemented for %s", - PrimitiveType_Name(prim_type).c_str()); - } + return EmitElementalClamp(hlo, operand_to_generator, index); }; case HloOpcode::kReducePrecision: return [this, hlo, &operand_to_generator]( @@ -1460,70 +1948,8 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( case HloOpcode::kConcatenate: return [this, hlo, &operand_to_generator]( const IrArray::Index target_index) -> StatusOr { - const int64 concat_dim = hlo->dimensions(0); - auto source_index = target_index; - - llvm::BasicBlock* init_block = ir_builder_->GetInsertBlock(); - - // A terminator should be present iff we're emitting code - // into the middle (as opposed to the end) of a basic block. - CHECK_EQ(ir_builder_->GetInsertPoint() == init_block->end(), - init_block->getTerminator() == nullptr); - - llvm::BasicBlock* exit_block; - if (ir_builder_->GetInsertPoint() == init_block->end()) { - exit_block = llvm_ir::CreateBasicBlock( - /*insert_before=*/nullptr, IrName(hlo, "merge"), ir_builder_); - } else { - exit_block = init_block->splitBasicBlock( - ir_builder_->GetInsertPoint(), AsStringRef(IrName(hlo, "merge"))); - init_block->getTerminator()->eraseFromParent(); - } - - llvm_ir::SetToFirstInsertPoint(exit_block, ir_builder_); - llvm::PHINode* output = - ir_builder_->CreatePHI(llvm_ir::PrimitiveTypeToIrType( - hlo->shape().element_type(), module_), - hlo->operands().size()); - auto prior_insert_point = ir_builder_->GetInsertPoint(); - - ir_builder_->SetInsertPoint(init_block); - - for (int64 operand_idx = 0; operand_idx < hlo->operand_count(); - ++operand_idx) { - const HloInstruction* operand = hlo->operand(operand_idx); - auto true_block = llvm_ir::CreateBasicBlock( - exit_block, StrCat("concat_index_from_operand", operand_idx), - ir_builder_); - auto false_block = llvm_ir::CreateBasicBlock( - exit_block, StrCat("concat_index_not_from_operand", operand_idx), - ir_builder_); - auto concat_dim_size = - llvm::ConstantInt::get(source_index[concat_dim]->getType(), - operand->shape().dimensions(concat_dim)); - ir_builder_->CreateCondBr( - ir_builder_->CreateICmpULT(source_index[concat_dim], - concat_dim_size), - true_block, false_block); - - // Create the terminator of the true block before calling operand - // generators, because they require non-degenerate basic blocks. - ir_builder_->SetInsertPoint( - llvm::BranchInst::Create(exit_block, /*InsertAtEnd=*/true_block)); - TF_ASSIGN_OR_RETURN(llvm::Value * value, - operand_to_generator.at(operand)(source_index)); - output->addIncoming(value, ir_builder_->GetInsertBlock()); - - // Subtract the size of the concat dimension of the current operand - // from the source index. - ir_builder_->SetInsertPoint(false_block); - source_index[concat_dim] = - ir_builder_->CreateSub(source_index[concat_dim], concat_dim_size); - } - - ir_builder_->CreateUnreachable(); - ir_builder_->SetInsertPoint(exit_block, prior_insert_point); - return output; + return EmitElementalConcatenate(hlo, operand_to_generator, + target_index); }; case HloOpcode::kReverse: return [this, hlo, &operand_to_generator]( @@ -1559,270 +1985,19 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( case HloOpcode::kDynamicSlice: return [this, hlo, &operand_to_generator]( const IrArray::Index& index) -> StatusOr { - // Emit IR to read dynamic start indices from hlo->operand(1). - const HloInstruction* input_hlo = hlo->operand(0); - const int64 rank = ShapeUtil::Rank(input_hlo->shape()); - llvm_ir::IrArray::Index slice_start_index(rank); - for (int64 i = 0; i < rank; ++i) { - llvm_ir::IrArray::Index dim_index(1, ir_builder_->getInt64(i)); - TF_ASSIGN_OR_RETURN( - llvm::Value * start_index_value, - operand_to_generator.at(hlo->operand(1))(dim_index)); - start_index_value->setName( - AsStringRef(IrName(hlo, StrCat("start_idx", i)))); - slice_start_index[i] = start_index_value; - } - - llvm_ir::IrArray::Index input_index(rank); - for (int64 i = 0; i < rank; ++i) { - // Emit IR which computes: - // input_index = (start_index + offset_index) % dim_size - // Security note: this is the code that keeps the indices in-bounds. - llvm::Value* dim_size = llvm::ConstantInt::get( - index[i]->getType(), input_hlo->shape().dimensions(i)); - llvm::Value* start_index = ir_builder_->CreateZExtOrBitCast( - slice_start_index[i], index[i]->getType()); - input_index[i] = ir_builder_->CreateURem( - ir_builder_->CreateAdd(start_index, index[i]), dim_size); - } - return operand_to_generator.at(input_hlo)(input_index); + return EmitElementalDynamicSlice(hlo, operand_to_generator, index); }; case HloOpcode::kGather: return [this, hlo, &operand_to_generator]( const IrArray::Index& index) -> StatusOr { - const Shape& operand_shape = hlo->operand(0)->shape(); - const Shape& indices_shape = hlo->operand(1)->shape(); - const Shape& output_shape = hlo->shape(); - - const GatherDimensionNumbers& dim_numbers = - hlo->gather_dimension_numbers(); - - const llvm_ir::ElementGenerator& operand_generator = - operand_to_generator.at(hlo->operand(0)); - const llvm_ir::ElementGenerator& indices_generator = - operand_to_generator.at(hlo->operand(1)); - - // This is the index into `operand` that holds the element we want to - // generate. This index "unsafe" as in the components in here may be - // out of bounds. - IrArray::Index unsafe_operand_index; - - // First copy in the window indices to unsafe_operand_index. - for (int64 i = 0, e = operand_shape.dimensions_size(), - unsafe_operand_index_dim = 0; - i < e; i++) { - if (c_binary_search(dim_numbers.elided_window_dims(), i)) { - unsafe_operand_index.push_back(ir_builder_->getInt64(0)); - } else { - unsafe_operand_index.push_back(index[dim_numbers.output_window_dims( - unsafe_operand_index_dim++)]); - } - } - - // This is the index of the index vector in the gather_indices tensor. - IrArray::Index gather_index_index; - { - std::vector gather_index_index_components; - for (int64 i = 0, e = output_shape.dimensions_size(); i < e; i++) { - if (!c_binary_search(dim_numbers.output_window_dims(), i)) { - gather_index_index.push_back(index[i]); - } - } - - if (gather_index_index.size() != indices_shape.dimensions_size()) { - gather_index_index.InsertAt(dim_numbers.index_vector_dim(), - nullptr); - } - } - - auto add_to_unsafe_operand_index = [&](llvm::Value* index_component, - int64 dim) { - llvm::Value* gather_dim_component_extended = - ir_builder_->CreateSExtOrTrunc(index_component, - ir_builder_->getInt64Ty()); - unsafe_operand_index[dim_numbers.gather_dims_to_operand_dims(dim)] = - ir_builder_->CreateAdd( - unsafe_operand_index[dim_numbers.gather_dims_to_operand_dims( - dim)], - gather_dim_component_extended); - }; - - if (indices_shape.dimensions_size() == dim_numbers.index_vector_dim()) { - TF_ASSIGN_OR_RETURN(llvm::Value * gather_dim_component, - indices_generator(gather_index_index)); - add_to_unsafe_operand_index(gather_dim_component, 0); - } else { - int64 index_vector_size = - indices_shape.dimensions(dim_numbers.index_vector_dim()); - for (int64 i = 0; i < index_vector_size; i++) { - gather_index_index[dim_numbers.index_vector_dim()] = - ir_builder_->getInt64(i); - TF_ASSIGN_OR_RETURN(llvm::Value * gather_dim_component, - indices_generator(gather_index_index)); - add_to_unsafe_operand_index(gather_dim_component, i); - } - } - - IrArray::Index safe_operand_index; - for (int64 i = 0, e = unsafe_operand_index.size(); i < e; i++) { - safe_operand_index.push_back(ir_builder_->CreateURem( - unsafe_operand_index[i], - ir_builder_->getInt64(operand_shape.dimensions(i)))); - } - - return operand_generator(safe_operand_index); + return EmitElementalGather(hlo, operand_to_generator, index); }; case HloOpcode::kDynamicUpdateSlice: return [this, hlo, &operand_to_generator]( const IrArray::Index& index) -> StatusOr { - const HloInstruction* input_hlo = hlo->operand(0); - const HloInstruction* update_hlo = hlo->operand(1); - const HloInstruction* start_hlo = hlo->operand(2); - // Calculate slice start/end indices. - const int64 rank = ShapeUtil::Rank(input_hlo->shape()); - llvm_ir::IrArray::Index slice_start_index(rank); - llvm_ir::IrArray::Index slice_limit_index(rank); - // Slice starts at update[index - slice_start_index_adjusted], - // where adjusted value = slice_start_index when in bounds, and - // adjusted value = slice_start_index - input_dim, when wrapping. - llvm_ir::IrArray::Index slice_start_index_adjusted(rank); - - // Slice intersection gathers (ANDs) conditions on all ranks for which - // 'input' is set to 'update' - llvm::Value* slice_intersection = ir_builder_->getTrue(); - - for (int64 i = 0; i < rank; ++i) { - // Emit IR to read dynamic start indices from 'start_hlo'. - llvm_ir::IrArray::Index dim_index(1, ir_builder_->getInt64(i)); - TF_ASSIGN_OR_RETURN(llvm::Value * start_index_value, - operand_to_generator.at(start_hlo)(dim_index)); - start_index_value->setName( - AsStringRef(IrName(hlo, StrCat("start_idx", i)))); - slice_start_index[i] = ir_builder_->CreateZExtOrBitCast( - start_index_value, index[i]->getType()); - - llvm::Value* input_dim_size = llvm::ConstantInt::get( - index[i]->getType(), input_hlo->shape().dimensions(i)); - llvm::Value* update_dim_size = llvm::ConstantInt::get( - index[i]->getType(), update_hlo->shape().dimensions(i)); - - // Generate code to handle wrapping semantics: - // slice_start_index[i] = slice_start_index[i] % input_dim_size; - // slice_limit_index[i] = slice_start_index[i] + update_dim_size. - // slice_start_index[i] is updated in place and it will now be in - // range. slice_limit_index[i] may be out of range, and it's being - // URem-ed below if so. - slice_start_index[i] = - ir_builder_->CreateURem(slice_start_index[i], input_dim_size); - slice_limit_index[i] = - ir_builder_->CreateAdd(slice_start_index[i], update_dim_size); - - // Test if slice_limit_index[i] is in bounds - llvm::Value* in_bounds = - ir_builder_->CreateICmpULE(slice_limit_index[i], input_dim_size); - llvm_ir::LlvmIfData if_in_bounds = - llvm_ir::EmitIfThenElse(in_bounds, "in_bounds", ir_builder_); - - // Handle true BB (slice_limit_index[i] <= input_dim_size). - SetToFirstInsertPoint(if_in_bounds.true_block, ir_builder_); - // Check that index[i] >= slice_start_index[i] && - // index[i] < slice_limit_index[i] - llvm::Value* slice_intersection_in_bounds = ir_builder_->CreateAnd( - slice_intersection, - ir_builder_->CreateICmpSGE(index[i], slice_start_index[i]), - "slice_intersection_in"); - slice_intersection_in_bounds = ir_builder_->CreateAnd( - slice_intersection_in_bounds, - ir_builder_->CreateICmpSLT(index[i], slice_limit_index[i]), - "slice_intersection_in"); - - // Handle false BB (slice_limit_index[i] > input_dim_size). - SetToFirstInsertPoint(if_in_bounds.false_block, ir_builder_); - // Check that index[i] >= slice_start_index[i] || - // index[i] < slice_limit_index[i]%input_dim_size. - llvm::Value* index_wraps = ir_builder_->CreateICmpSLT( - index[i], - ir_builder_->CreateURem(slice_limit_index[i], input_dim_size)); - llvm::Value* slice_intersection_or = ir_builder_->CreateOr( - ir_builder_->CreateICmpSGE(index[i], slice_start_index[i]), - index_wraps, "slice_intersection_out"); - llvm::Value* slice_intersection_out_of_bounds = - ir_builder_->CreateAnd(slice_intersection, slice_intersection_or, - "slice_intersection_out"); - // Create value for slice_start_index_adjusted[i] when out of bounds. - // If within out-of-bounds if. - llvm_ir::LlvmIfData if_start_needs_adjustment = - llvm_ir::EmitIfThenElse(index_wraps, "adjust_start", ir_builder_); - SetToFirstInsertPoint(if_start_needs_adjustment.true_block, - ir_builder_); - llvm::Value* slice_start_index_adjusted_oob = - ir_builder_->CreateSub(slice_start_index[i], input_dim_size); - SetToFirstInsertPoint(if_start_needs_adjustment.after_block, - ir_builder_); - llvm::PHINode* slice_start_index_adjusted_phi = - ir_builder_->CreatePHI(slice_start_index_adjusted_oob->getType(), - 2); - slice_start_index_adjusted_phi->addIncoming( - slice_start_index_adjusted_oob, - if_start_needs_adjustment.true_block); - slice_start_index_adjusted_phi->addIncoming( - slice_start_index[i], if_start_needs_adjustment.false_block); - // End of if within if. - - // After checking in/out of bounds. - SetToFirstInsertPoint(if_in_bounds.after_block, ir_builder_); - llvm::PHINode* phi_slice_intersection = - ir_builder_->CreatePHI(slice_intersection->getType(), 2); - phi_slice_intersection->addIncoming(slice_intersection_in_bounds, - if_in_bounds.true_block); - phi_slice_intersection->addIncoming( - slice_intersection_out_of_bounds, - if_start_needs_adjustment.after_block); - slice_intersection = phi_slice_intersection; - - llvm::PHINode* phi_index = - ir_builder_->CreatePHI(slice_start_index[i]->getType(), 2); - phi_index->addIncoming(slice_start_index[i], if_in_bounds.true_block); - phi_index->addIncoming(slice_start_index_adjusted_phi, - if_start_needs_adjustment.after_block); - slice_start_index_adjusted[i] = phi_index; - } - - // Emit: - // if (slice_intersection) -> return data from 'update'. - // else -> return data from 'input'. - llvm::Value* ret_value_addr = llvm_ir::EmitAllocaAtFunctionEntry( - llvm_ir::PrimitiveTypeToIrType(hlo->shape().element_type(), - module_), - "ret_value_addr", ir_builder_); - llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - slice_intersection, "slice_intersection", ir_builder_); - - // Handle true BB (return data from 'update') - SetToFirstInsertPoint(if_data.true_block, ir_builder_); - // Compute update index for intersection case. - llvm_ir::IrArray::Index update_index(rank); - for (int64 i = 0; i < rank; ++i) { - llvm::Value* update_dim_size = llvm::ConstantInt::get( - index[i]->getType(), update_hlo->shape().dimensions(i)); - // NOTE: Subtraction will be positive due to bounds checking above. - update_index[i] = ir_builder_->CreateURem( - ir_builder_->CreateSub(index[i], slice_start_index_adjusted[i]), - update_dim_size); - } - TF_ASSIGN_OR_RETURN(llvm::Value * true_value, - operand_to_generator.at(update_hlo)(update_index)); - ir_builder_->CreateStore(true_value, ret_value_addr); - - // Handle false BB (return data from 'input') - SetToFirstInsertPoint(if_data.false_block, ir_builder_); - TF_ASSIGN_OR_RETURN(llvm::Value * false_value, - operand_to_generator.at(input_hlo)(index)); - ir_builder_->CreateStore(false_value, ret_value_addr); - - SetToFirstInsertPoint(if_data.after_block, ir_builder_); - return ir_builder_->CreateLoad(ret_value_addr); + return EmitElementalDynamicUpdateSlice(hlo, operand_to_generator, + index); }; case HloOpcode::kBitcast: CHECK_EQ(ShapeUtil::ElementsIn(hlo->shape()), @@ -1851,155 +2026,16 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( case HloOpcode::kRng: return MakeRngElementGenerator(hlo, operand_to_generator); case HloOpcode::kPad: - return [=, &operand_to_generator]( + return [this, hlo, &operand_to_generator]( const IrArray::Index& padded_index) -> StatusOr { - auto index = padded_index; - llvm::Value* in_bounds = ir_builder_->getTrue(); - for (size_t i = 0; i < index.size(); ++i) { - auto index_typed_const = [=](int64 n) { - return llvm::ConstantInt::get(index[i]->getType(), n); - }; - const auto& pad_dim = hlo->padding_config().dimensions(i); - index[i] = ir_builder_->CreateSub( - index[i], index_typed_const(pad_dim.edge_padding_low())); - in_bounds = ir_builder_->CreateAnd( - in_bounds, - ir_builder_->CreateICmpSGE(index[i], index_typed_const(0)), - "in_bounds"); - in_bounds = ir_builder_->CreateAnd( - in_bounds, - ir_builder_->CreateICmpEQ( - index_typed_const(0), - ir_builder_->CreateURem( - index[i], - index_typed_const(pad_dim.interior_padding() + 1))), - "in_bounds"); - index[i] = ir_builder_->CreateSDiv( - index[i], index_typed_const(pad_dim.interior_padding() + 1)); - in_bounds = ir_builder_->CreateAnd( - in_bounds, - ir_builder_->CreateICmpSLT( - index[i], - index_typed_const(hlo->operand(0)->shape().dimensions(i))), - "in_bounds"); - } - - // if (in_bounds) { - // ret_value = operand0[index]; // source - // } else { - // ret_value = *operand1; // padding - // } - llvm::Value* ret_value_addr = llvm_ir::EmitAllocaAtFunctionEntry( - llvm_ir::PrimitiveTypeToIrType(hlo->shape().element_type(), - module_), - "pad_result_addr", ir_builder_); - llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(in_bounds, "in_bounds", ir_builder_); - SetToFirstInsertPoint(if_data.true_block, ir_builder_); - TF_ASSIGN_OR_RETURN(llvm::Value * operand_value, - operand_to_generator.at(hlo->operand(0))(index)); - ir_builder_->CreateStore(operand_value, ret_value_addr); - - SetToFirstInsertPoint(if_data.false_block, ir_builder_); - TF_ASSIGN_OR_RETURN(llvm::Value * padding_value, - operand_to_generator.at(hlo->operand(1))({})); - ir_builder_->CreateStore(padding_value, ret_value_addr); - - SetToFirstInsertPoint(if_data.after_block, ir_builder_); - // Don't create phi(operand_value, padding_value) here, because invoking - // operand_to_generator may create new basic blocks, making the parent - // of operand_value or padding_value no longer a predecessor of - // if_data.after_block. - return ir_builder_->CreateLoad(ret_value_addr); + return EmitElementalPad(hlo, operand_to_generator, padded_index); }; case HloOpcode::kDot: - return [=, &operand_to_generator](const IrArray::Index& dot_result_index) + return [this, hlo, + &operand_to_generator](const IrArray::Index& dot_result_index) -> StatusOr { - auto lhs_generator = operand_to_generator.at(hlo->operand(0)); - auto rhs_generator = operand_to_generator.at(hlo->operand(1)); - int64 contracted_dim_size = hlo->operand(0)->shape().dimensions( - hlo->operand(0)->shape().dimensions_size() - 1); - int64 lhs_dims = hlo->operand(0)->shape().dimensions_size(); - int64 rhs_dims = hlo->operand(1)->shape().dimensions_size(); - - std::unique_ptr inner_loop = - llvm_ir::ForLoop::EmitForLoop( - IrName(hlo, "inner"), ir_builder_->getInt64(0), - ir_builder_->getInt64(contracted_dim_size), - ir_builder_->getInt64(1), ir_builder_); - - SetToFirstInsertPoint(inner_loop->GetPreheaderBasicBlock(), - ir_builder_); - PrimitiveType primitive_type = hlo->shape().element_type(); - llvm::Type* primitive_type_llvm = - llvm_ir::PrimitiveTypeToIrType(primitive_type, module_); - llvm::Value* accumulator_alloca = llvm_ir::EmitAllocaAtFunctionEntry( - primitive_type_llvm, "dot_acc", ir_builder_); - ir_builder_->CreateStore( - llvm::Constant::getNullValue(primitive_type_llvm), - accumulator_alloca); - - SetToFirstInsertPoint(inner_loop->GetBodyBasicBlock(), ir_builder_); - - // This is the inner reduction loop for a dot operation that produces - // one element in the output. If the operands to the dot operation have - // shapes [A,B,C,T] and [D,T,E], the result has a shape [A,B,C,D,E]. - // Given an output index [a,b,c,d,e] in the result, we compute: - // sum(lhs[a,b,c,t]*rhs[d,t,e] for t in [0, T)) - - IrArray::Index lhs_index, rhs_index; - - for (int64 i = 0; i < lhs_dims - 1; i++) { - lhs_index.push_back(dot_result_index[i]); - } - lhs_index.push_back(inner_loop->GetIndVarValue()); - - for (int64 i = 0; i < rhs_dims - 2; i++) { - rhs_index.push_back(dot_result_index[lhs_dims - 1 + i]); - } - rhs_index.push_back(inner_loop->GetIndVarValue()); - rhs_index.push_back(dot_result_index.back()); - - llvm::Value* current_accumulator = - ir_builder_->CreateLoad(accumulator_alloca); - TF_ASSIGN_OR_RETURN(llvm::Value * lhs_value, lhs_generator(lhs_index)); - TF_ASSIGN_OR_RETURN(llvm::Value * rhs_value, rhs_generator(rhs_index)); - llvm::Value* next_accumulator; - if (primitive_util::IsComplexType(primitive_type)) { - llvm::Value* product_real = ir_builder_->CreateFSub( - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value))); - llvm::Value* product_imag = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractImag(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractReal(rhs_value))); - next_accumulator = ir_builder_->CreateInsertValue( - current_accumulator, - ir_builder_->CreateFAdd(EmitExtractReal(current_accumulator), - product_real), - {0}); - next_accumulator = ir_builder_->CreateInsertValue( - next_accumulator, - ir_builder_->CreateFAdd(EmitExtractImag(current_accumulator), - product_imag), - {1}); - } else if (primitive_util::IsFloatingPointType(primitive_type)) { - next_accumulator = ir_builder_->CreateFAdd( - current_accumulator, - ir_builder_->CreateFMul(lhs_value, rhs_value)); - } else { - next_accumulator = ir_builder_->CreateAdd( - current_accumulator, - ir_builder_->CreateMul(lhs_value, rhs_value)); - } - ir_builder_->CreateStore(next_accumulator, accumulator_alloca); - - SetToFirstInsertPoint(inner_loop->GetExitBasicBlock(), ir_builder_); - return ir_builder_->CreateLoad(accumulator_alloca); + return EmitElementalDot(hlo, operand_to_generator, dot_result_index); }; default: return [this, hlo, &operand_to_generator](const IrArray::Index& index) { diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/elemental_ir_emitter.h index c516a826d9e382..26dff0d96f1d0f 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.h +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.h @@ -142,6 +142,46 @@ class ElementalIrEmitter { return ir_builder_->getIntN(128, 0); } + StatusOr EmitElementalSelect( + const HloInstruction* hlo, + const HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index) const; + + StatusOr EmitElementalClamp( + const HloInstruction* hlo, + const HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index) const; + + StatusOr EmitElementalConcatenate( + const HloInstruction* hlo, + const HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& target_index) const; + + StatusOr EmitElementalDynamicSlice( + const HloInstruction* hlo, + const HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index) const; + + StatusOr EmitElementalGather( + const HloInstruction* hlo, + const HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index) const; + + StatusOr EmitElementalDynamicUpdateSlice( + const HloInstruction* hlo, + const HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index) const; + + StatusOr EmitElementalPad( + const HloInstruction* hlo, + const HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& padded_index) const; + + StatusOr EmitElementalDot( + const HloInstruction* hlo, + const HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& dot_result_index) const; + llvm::IRBuilder<>* const ir_builder_; llvm::Module* module_; From ce8e19a756f71fa66f60a28515c64c106ca7f6a1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Apr 2018 19:23:16 -0700 Subject: [PATCH 0128/1691] Add internal uint b stats to TfOpStats. PiperOrigin-RevId: 194625155 --- tensorflow/contrib/tpu/profiler/tf_op_stats.proto | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/tpu/profiler/tf_op_stats.proto b/tensorflow/contrib/tpu/profiler/tf_op_stats.proto index 63955d18068fc9..b9ac1a550c87e0 100644 --- a/tensorflow/contrib/tpu/profiler/tf_op_stats.proto +++ b/tensorflow/contrib/tpu/profiler/tf_op_stats.proto @@ -245,4 +245,6 @@ message TfOpStats { optional HostOpsResult host_ops = 8; // A map from core ID to name. map core_id_to_name_map = 9; + // The result for hw unit b stats. + optional bytes unit_b_stats = 10; } From 74747435c2442084e8de53bc73311152f270ae88 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Fri, 27 Apr 2018 20:06:35 -0700 Subject: [PATCH 0129/1691] HLO profiling for tfcompile. This CL extends the --xla_hlo_profile knob to tfcompile. tf_library rules can now set enable_xla_hlo_profiling to True to: - Have the generated code update per-HLO profile counters as it executes. - Have tfcompile generate and serialize an instance HloProfilePrinterData with a compiled model that can be used to pretty-print the collected profile counters. PiperOrigin-RevId: 194627272 --- tensorflow/compiler/aot/codegen.cc | 71 ++++++++++++--- tensorflow/compiler/aot/codegen.h | 10 +++ tensorflow/compiler/aot/codegen_test.cc | 2 +- tensorflow/compiler/aot/codegen_test_h.golden | 11 +++ tensorflow/compiler/aot/compile.cc | 1 + .../compiler/aot/embedded_protocol_buffers.cc | 74 ++++++++------- .../compiler/aot/embedded_protocol_buffers.h | 83 ++++++++++------- tensorflow/compiler/aot/tests/BUILD | 13 +++ .../compiler/aot/tests/tfcompile_test.cc | 60 +++++++++++++ tensorflow/compiler/aot/tfcompile.bzl | 13 ++- tensorflow/compiler/aot/tfcompile_main.cc | 2 + .../compiler/xla/service/cpu/cpu_compiler.cc | 90 ++++++++++++------- .../compiler/xla/service/cpu/cpu_compiler.h | 14 ++- 13 files changed, 322 insertions(+), 122 deletions(-) diff --git a/tensorflow/compiler/aot/codegen.cc b/tensorflow/compiler/aot/codegen.cc index 2cae85e8965216..0025842aead539 100644 --- a/tensorflow/compiler/aot/codegen.cc +++ b/tensorflow/compiler/aot/codegen.cc @@ -333,6 +333,20 @@ Status GenerateHeader(const CodegenOpts& opts, const tf2xla::Config& config, R"(#include "tensorflow/compiler/xla/xla_data.pb.h")" : ""; + const string include_hlo_profile_printer_data_proto = + opts.gen_hlo_profile_printer_data + ? R"(#include "tensorflow/compiler/xla/service/hlo_profile_printer_data.pb.h")" + : ""; + + // When HLO profiling is disabled we only forward declare the + // HloProfilePrinter protobuf. So we can only conditionally emit this code + // calling HloProfilePrinter::profile_counters_size. + const string assign_profile_counters_size = + opts.gen_hlo_profile_printer_data + ? "data->profile_counters_size = " + "data->hlo_profile_printer_data->profile_counters_size();" + : ""; + // Use a poor-man's text templating mechanism; first populate the full header // with placeholder tokens, and then rewrite the tokens with real values. *header = @@ -348,6 +362,7 @@ Status GenerateHeader(const CodegenOpts& opts, const tf2xla::Config& config, #define TFCOMPILE_GENERATED_{{ENTRY}}_H_ // NOLINT(build/header_guard) {{INCLUDE_XLA_DATA_PROTO}} +{{INCLUDE_HLO_PROFILE_PRINTER_DATA_PROTO}} #include "tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h" #include "tensorflow/core/platform/types.h" @@ -418,6 +433,8 @@ class {{CLASS}} : public tensorflow::XlaCompiledCpuFunction { data->arg_names = StaticArgNames(); data->result_names = StaticResultNames(); data->program_shape = StaticProgramShape(); + data->hlo_profile_printer_data = StaticHloProfilePrinterData(); + {{ASSIGN_PROFILE_COUNTERS_SIZE}} return data; }(); return *kStaticData; @@ -487,6 +504,13 @@ class {{CLASS}} : public tensorflow::XlaCompiledCpuFunction { static const xla::ProgramShape* kShape = {{PROGRAM_SHAPE_SHIM_EXPRESSION}}; return kShape; } + + // Metadata that can be used to pretty-print profile counters. + static const xla::HloProfilePrinterData* StaticHloProfilePrinterData() { + static const xla::HloProfilePrinterData* kHloProfilePrinterData = + {{HLO_PROFILE_PRINTER_DATA_SHIM_EXPRESSION}}; + return kHloProfilePrinterData; + } }; {{NS_END}} @@ -501,35 +525,41 @@ class {{CLASS}} : public tensorflow::XlaCompiledCpuFunction { {"{{ARG_NAMES_CODE}}", arg_names_code}, {"{{ARG_NUM}}", strings::StrCat(arg_sizes.size())}, {"{{ARG_SIZES}}", str_util::Join(arg_sizes, ", ")}, + {"{{ASSIGN_PROFILE_COUNTERS_SIZE}}", assign_profile_counters_size}, {"{{CLASS}}", opts.class_name}, + {"{{DECLS_FROM_OBJ_FILE}}", + str_util::Join(metadata_result.header_variable_decls, "\n")}, {"{{ENTRY}}", compile_result.entry_point}, + {"{{HLO_PROFILE_PRINTER_DATA_SHIM_EXPRESSION}}", + metadata_result.hlo_profile_printer_data_access_shim}, {"{{INCLUDE_XLA_DATA_PROTO}}", include_xla_data_proto}, + {"{{INCLUDE_HLO_PROFILE_PRINTER_DATA_PROTO}}", + include_hlo_profile_printer_data_proto}, {"{{METHODS_ARG}}\n", methods_arg}, {"{{METHODS_RESULT}}\n", methods_result}, {"{{NS_END}}\n", ns_end}, {"{{NS_START}}\n", ns_start}, {"{{PROGRAM_SHAPE}}", xla::ShapeUtil::HumanString(ps)}, + {"{{PROGRAM_SHAPE_SHIM_EXPRESSION}}", + metadata_result.program_shape_access_shim}, {"{{RESULT_INDEX}}", strings::StrCat(result_index)}, {"{{RESULT_NAMES_CODE}}", result_names_code}, {"{{TEMP_BYTES_ALIGNED}}", strings::StrCat(temp_bytes_aligned)}, {"{{TEMP_BYTES_TOTAL}}", strings::StrCat(temp_bytes_total)}, {"{{TEMP_NUM}}", strings::StrCat(temp_sizes.size())}, - {"{{TEMP_SIZES}}", str_util::Join(temp_sizes, ", ")}, - {"{{DECLS_FROM_OBJ_FILE}}", - str_util::Join(metadata_result.header_variable_decls, "\n")}, - {"{{PROGRAM_SHAPE_SHIM_EXPRESSION}}", - metadata_result.program_shape_access_shim}}; + {"{{TEMP_SIZES}}", str_util::Join(temp_sizes, ", ")}}; str_util::ReplaceAllPairs(header, rewrites); return Status::OK(); } -static string CreateUniqueIdentifierForProgramShape(const CodegenOpts& opts) { +static string CreateUniqueIdentifier(const CodegenOpts& opts, + StringPiece suffix) { string result = "__tfcompile"; for (const string& n : opts.namespaces) { strings::StrAppend(&result, "_", n); } - strings::StrAppend(&result, "_", opts.class_name, "_ProgramShape"); + strings::StrAppend(&result, "_", opts.class_name, "_", suffix); return result; } @@ -550,18 +580,31 @@ Status GenerateMetadata(const CodegenOpts& opts, // When asked to serialize a null protobuf, CreateEmbeddedProtocolBuffer gives // a shim that evaluates to nullptr, which is what we want. + ProtobufToEmbed program_shape_protobuf{ + CreateUniqueIdentifier(opts, "ProgramShape"), "xla::ProgramShape", + program_shape.get()}; + + ProtobufToEmbed hlo_profile_printer_data_protobuf{ + CreateUniqueIdentifier(opts, "HloProfilePrinterData"), + "xla::HloProfilePrinterData", + compile_result.aot->hlo_profile_printer_data()}; + TF_ASSIGN_OR_RETURN( - EmbeddedProtocolBuffer embedded_program_shape, - CreateEmbeddedProtocolBuffer(opts.target_triple, - CreateUniqueIdentifierForProgramShape(opts), - "xla::ProgramShape", program_shape.get())); + EmbeddedProtocolBuffers embedded_protobufs, + CreateEmbeddedProtocolBuffers( + opts.target_triple, + {program_shape_protobuf, hlo_profile_printer_data_protobuf})); metadata_result->program_shape_access_shim = - std::move(embedded_program_shape.cpp_shim_expression); + std::move(embedded_protobufs.cpp_shims[0].expression); + metadata_result->hlo_profile_printer_data_access_shim = + std::move(embedded_protobufs.cpp_shims[1].expression); + metadata_result->header_variable_decls.emplace_back( + std::move(embedded_protobufs.cpp_shims[0].variable_decl)); metadata_result->header_variable_decls.emplace_back( - std::move(embedded_program_shape.cpp_variable_decl)); + std::move(embedded_protobufs.cpp_shims[1].variable_decl)); metadata_result->object_file_data = - std::move(embedded_program_shape.object_file_data); + std::move(embedded_protobufs.object_file_data); return Status::OK(); } diff --git a/tensorflow/compiler/aot/codegen.h b/tensorflow/compiler/aot/codegen.h index 3430b1f96cf4d3..83f2d3ee11d09d 100644 --- a/tensorflow/compiler/aot/codegen.h +++ b/tensorflow/compiler/aot/codegen.h @@ -44,6 +44,10 @@ struct CodegenOpts { // If true, generate program shape data for the ProgramShape method. bool gen_program_shape = false; + + // If true, emit a serialized HloProfilePrinterData protobuf that can be used + // to pretty print HLO profile counters. + bool gen_hlo_profile_printer_data = false; }; // Describes a generated metadata object file. @@ -57,6 +61,12 @@ struct MetadataResult { // GenerateMetadata. string program_shape_access_shim; + // hlo_profile_printer_data_access_shim is a C++ expression that constructs + // the xla::HloProfilePrinterData instance for the CompileResult passed to + // GenerateMetadata. If the xla::HloProfilePrinterData is null then this is a + // C++ expression that evaluates to nullptr at runtime. + string hlo_profile_printer_data_access_shim; + // The contents of the object (".o") file. string object_file_data; }; diff --git a/tensorflow/compiler/aot/codegen_test.cc b/tensorflow/compiler/aot/codegen_test.cc index 2642536c4f67eb..29bc9c13b889c8 100644 --- a/tensorflow/compiler/aot/codegen_test.cc +++ b/tensorflow/compiler/aot/codegen_test.cc @@ -172,7 +172,7 @@ TEST(CodegenTest, Golden) { fetch->set_name("myfetch"); CompileResult compile_result; compile_result.aot.reset( - new xla::cpu::CpuAotCompilationResult({}, {1, -1, 2, -1, 3, 120}, 5)); + new xla::cpu::CpuAotCompilationResult({}, {1, -1, 2, -1, 3, 120}, 5, {})); compile_result.program_shape = xla::ShapeUtil::MakeProgramShape( { xla::ShapeUtil::MakeShape(xla::F32, {1, 2}), diff --git a/tensorflow/compiler/aot/codegen_test_h.golden b/tensorflow/compiler/aot/codegen_test_h.golden index ac3b5873318873..6e050cf56494e6 100644 --- a/tensorflow/compiler/aot/codegen_test_h.golden +++ b/tensorflow/compiler/aot/codegen_test_h.golden @@ -10,6 +10,7 @@ #define TFCOMPILE_GENERATED_entry_point_H_ // NOLINT(build/header_guard) #include "tensorflow/compiler/xla/xla_data.pb.h" + #include "tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h" #include "tensorflow/core/platform/types.h" @@ -23,6 +24,7 @@ extern "C" void entry_point( extern "C" char __tfcompile_foo_bar_MyClass_ProgramShape_protobuf_array_contents[]; + namespace foo { namespace bar { @@ -82,6 +84,8 @@ class MyClass : public tensorflow::XlaCompiledCpuFunction { data->arg_names = StaticArgNames(); data->result_names = StaticResultNames(); data->program_shape = StaticProgramShape(); + data->hlo_profile_printer_data = StaticHloProfilePrinterData(); + return data; }(); return *kStaticData; @@ -243,6 +247,13 @@ class MyClass : public tensorflow::XlaCompiledCpuFunction { }(); return kShape; } + + // Metadata that can be used to pretty-print profile counters. + static const xla::HloProfilePrinterData* StaticHloProfilePrinterData() { + static const xla::HloProfilePrinterData* kHloProfilePrinterData = + nullptr; + return kHloProfilePrinterData; + } }; } // end namespace bar diff --git a/tensorflow/compiler/aot/compile.cc b/tensorflow/compiler/aot/compile.cc index e17a7c4bf67321..31044ff85d6f0d 100644 --- a/tensorflow/compiler/aot/compile.cc +++ b/tensorflow/compiler/aot/compile.cc @@ -110,6 +110,7 @@ Status CompileGraph(const GraphDef& graph_def, const tf2xla::Config& config, flags.target_triple, flags.target_cpu, flags.target_features, flags.entry_point, xla::cpu::CpuAotCompilationOptions::RelocationModel::BigPic); + return CompileXla(client, computation, aot_opts, compile_result); } diff --git a/tensorflow/compiler/aot/embedded_protocol_buffers.cc b/tensorflow/compiler/aot/embedded_protocol_buffers.cc index 0048eec93bbe10..63d22de1ca4aa0 100644 --- a/tensorflow/compiler/aot/embedded_protocol_buffers.cc +++ b/tensorflow/compiler/aot/embedded_protocol_buffers.cc @@ -36,9 +36,8 @@ namespace tfcompile { using xla::llvm_ir::AsStringRef; -static std::unique_ptr CreateModuleWithEmbeddedProtocolBuffer( - llvm::LLVMContext* llvm_context, llvm::TargetMachine* target_machine, - const ::tensorflow::protobuf::MessageLite& proto, +static void AddEmbeddedProtocolBufferToLlvmModule( + llvm::Module* module, const ::tensorflow::protobuf::MessageLite& proto, StringPiece unique_identifier, string* protobuf_array_symbol_name, int64* protobuf_array_size) { string protobuf_array_contents = proto.SerializeAsString(); @@ -46,19 +45,14 @@ static std::unique_ptr CreateModuleWithEmbeddedProtocolBuffer( strings::StrCat(unique_identifier, "_protobuf_array_contents"); *protobuf_array_size = protobuf_array_contents.size(); - std::unique_ptr module = - MakeUnique("embedded_data_module", *llvm_context); - llvm::Constant* protobuf_array_initializer = - llvm::ConstantDataArray::getString(*llvm_context, + llvm::ConstantDataArray::getString(module->getContext(), AsStringRef(protobuf_array_contents), /*AddNull=*/false); new llvm::GlobalVariable( *module, protobuf_array_initializer->getType(), /*isConstant=*/true, llvm::GlobalValue::ExternalLinkage, protobuf_array_initializer, AsStringRef(*protobuf_array_symbol_name)); - - return module; } static string CreateCPPShimExpression(StringPiece qualified_cpp_protobuf_name, @@ -115,42 +109,44 @@ GetTargetMachineFromTriple(StringPiece target_triple) { /*Features=*/"", llvm::TargetOptions(), llvm::None)); } -StatusOr CreateEmbeddedProtocolBuffer( - StringPiece target_triple, StringPiece symbol_prefix, - StringPiece qualified_cpp_protobuf_name, - const ::tensorflow::protobuf::MessageLite* proto) { +StatusOr CreateEmbeddedProtocolBuffers( + StringPiece target_triple, + gtl::ArraySlice protobufs_to_embed) { TF_ASSIGN_OR_RETURN(std::unique_ptr target_machine, GetTargetMachineFromTriple(target_triple)); llvm::LLVMContext llvm_context; - string object_file, cpp_shim, cpp_variable_decl; - - if (proto) { - string protobuf_array_symbol_name; - int64 protobuf_array_size; - - std::unique_ptr module_with_serialized_proto = - CreateModuleWithEmbeddedProtocolBuffer( - &llvm_context, target_machine.get(), *proto, symbol_prefix, - &protobuf_array_symbol_name, &protobuf_array_size); - TF_ASSIGN_OR_RETURN(object_file, - CodegenModule(target_machine.get(), - std::move(module_with_serialized_proto))); - cpp_shim = CreateCPPShimExpression(qualified_cpp_protobuf_name, - protobuf_array_symbol_name, - protobuf_array_size); - - cpp_variable_decl = strings::StrCat("extern \"C\" char ", - protobuf_array_symbol_name, "[];"); - } else { - TF_ASSIGN_OR_RETURN( - object_file, - CodegenModule(target_machine.get(), - MakeUnique("empty_module", llvm_context))); - cpp_shim = "nullptr"; + std::unique_ptr module_with_serialized_proto = + MakeUnique("embedded_data_module", llvm_context); + + EmbeddedProtocolBuffers result; + + for (const ProtobufToEmbed& protobuf_to_embed : protobufs_to_embed) { + string cpp_shim, cpp_variable_decl; + if (protobuf_to_embed.message) { + string protobuf_array_symbol_name; + int64 protobuf_array_size; + + AddEmbeddedProtocolBufferToLlvmModule( + module_with_serialized_proto.get(), *protobuf_to_embed.message, + protobuf_to_embed.symbol_prefix, &protobuf_array_symbol_name, + &protobuf_array_size); + cpp_shim = CreateCPPShimExpression( + protobuf_to_embed.qualified_cpp_protobuf_name, + protobuf_array_symbol_name, protobuf_array_size); + + cpp_variable_decl = strings::StrCat("extern \"C\" char ", + protobuf_array_symbol_name, "[];"); + } else { + cpp_shim = "nullptr"; + } + result.cpp_shims.push_back({cpp_shim, cpp_variable_decl}); } - return {{cpp_shim, cpp_variable_decl, object_file}}; + TF_ASSIGN_OR_RETURN(result.object_file_data, + CodegenModule(target_machine.get(), + std::move(module_with_serialized_proto))); + return result; } } // namespace tfcompile diff --git a/tensorflow/compiler/aot/embedded_protocol_buffers.h b/tensorflow/compiler/aot/embedded_protocol_buffers.h index 8436e0ff67f352..ebfe4806c203e9 100644 --- a/tensorflow/compiler/aot/embedded_protocol_buffers.h +++ b/tensorflow/compiler/aot/embedded_protocol_buffers.h @@ -21,51 +21,70 @@ limitations under the License. #define TENSORFLOW_COMPILER_AOT_EMBEDDED_PROTOCOL_BUFFERS_H_ #include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/platform/protobuf.h" namespace tensorflow { namespace tfcompile { using xla::StatusOr; -// Represents a protocol buffer embedded into an object file and describes a way -// to access it at runtime. -struct EmbeddedProtocolBuffer { - // cpp_shim_expression is a C++ expression that creates an instance of said - // protocol buffer when executed. - string cpp_shim_expression; - - // cpp_variable_decl is an "extern C" array declaration that is used in - // cpp_shim_expression. It must be visible wherever cpp_shim_expression is - // emitted. - string cpp_variable_decl; - - // The contents of the object (".o") file the protocol buffer is embbed in. - // This needs to be linked in to any program that wants to execute - // cpp_variable_decl . +// Represents a set of protocol buffers embedded into an object file and +// describes how to access them at runtime. +struct EmbeddedProtocolBuffers { + // Each instance CPPShim describes how to generate C++ code to instantiate a + // protobuf instance from the corresponding static data emitted into the + // object file. + struct CPPShim { + // `expression` is a C++ expression that creates an instance of said + // protocol buffer when executed. + string expression; + + // `variable_decl` is an "extern C" array declaration that is used in + // `expression`. It must be visible wherever `expression` is emitted. + string variable_decl; + }; + + // Each cpp_shim corresponds to one embedded protocol buffer. + std::vector cpp_shims; + + // The contents of the object (".o") file the protocol buffers are embbed in. + // This needs to be linked in to any program that wants to execute any of the + // expressions in `cpp_shims`. string object_file_data; }; -// Creates an object file that contains `proto`. -// -// `proto` is allowed to be nullptr, in which case the generated C++ shim -// expression is just `nullptr`, and the generated object file does not define -// any symbols. +// Describes a protocol buffer to embed into an object file. +struct ProtobufToEmbed { + // `symbol_prefix` is prefix that is guaranteed to be unique across the binary + // or DSO the generated object file will be linked into. + string symbol_prefix; + + // `qualified_cpp_protobuf_name` is a qualified ("qualified" as in C++ + // namespace qualified) protocol buffer name. This is only used in + // CPPShim::expression so relatively qualified names are fine as long as + // they're valid wherever CPPShim::expression is emitted. + string qualified_cpp_protobuf_name; + + // `message` is the protocol buffer to be embedded. It is allowed to be + // nullptr, in which case the generated C++ shim expression is just `nullptr`, + // and the generated object file does not define any symbols. + const ::tensorflow::protobuf::MessageLite* message; +}; + +// Embeds a a sequence of protocol buffers into an object file. // // `target_triple` is the target triple for the target architecture for the // generated object file. // -// `symbol_prefix` is prefix that is guaranteed to be unique across the binary -// or DSO the generated object file will be linked into. -// -// `qualified_cpp_protobuf_name` is a qualified ("qualified" as in C++ -// namespace qualified) protocol buffer name. This needs is only used in -// EmbeddedProtocolBuffer::cpp_shim_expression so relatively qualified -// names are fine as long as they're valid wherever cpp_shim_expression -// is emitted. -StatusOr CreateEmbeddedProtocolBuffer( - StringPiece target_triple, StringPiece symbol_prefix, - StringPiece qualified_cpp_protobuf_name, - const ::tensorflow::protobuf::MessageLite* proto); +// `protobufs_to_embed` describes the protocol buffers to embed into the +// resulting object file. The C++ shim for protobufs_to_embed[i] is +// cpp_shims[i] in the returned EmbeddedProtocolBuffers instance. The contents +// of all the protocol buffers are embedded into a single .o file whose content +// is stored in the object_file_data field in the returned +// EmbeddedProtocolBuffers instance. +StatusOr CreateEmbeddedProtocolBuffers( + StringPiece target_triple, + gtl::ArraySlice protobufs_to_embed); } // namespace tfcompile } // namespace tensorflow diff --git a/tensorflow/compiler/aot/tests/BUILD b/tensorflow/compiler/aot/tests/BUILD index bb73cb19c57a65..222e26810ac115 100644 --- a/tensorflow/compiler/aot/tests/BUILD +++ b/tensorflow/compiler/aot/tests/BUILD @@ -163,6 +163,15 @@ tf_library( tfcompile_flags = "--gen_name_to_index --gen_program_shape", ) +tf_library( + name = "test_graph_tfmatmulandadd_with_profiling", + testonly = 1, + config = "test_graph_tfmatmulandadd.config.pbtxt", + cpp_class = "MatMulAndAddCompWithProfiling", + enable_xla_hlo_profiling = True, + graph = "test_graph_tfmatmulandadd.pb", +) + tf_library( name = "test_graph_tfsplits", testonly = 1, @@ -189,9 +198,13 @@ tf_cc_test( ":test_graph_tfgather", ":test_graph_tfmatmul", ":test_graph_tfmatmulandadd", + ":test_graph_tfmatmulandadd_with_profiling", ":test_graph_tfsplits", "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo_profile_printer", + "//tensorflow/core:lib", "//tensorflow/core:test", "//tensorflow/core:test_main", "//third_party/eigen3", diff --git a/tensorflow/compiler/aot/tests/tfcompile_test.cc b/tensorflow/compiler/aot/tests/tfcompile_test.cc index 67dbd643bfc7bf..aa9d968265b461 100644 --- a/tensorflow/compiler/aot/tests/tfcompile_test.cc +++ b/tensorflow/compiler/aot/tests/tfcompile_test.cc @@ -25,15 +25,22 @@ limitations under the License. #include "tensorflow/compiler/aot/tests/test_graph_tfgather.h" #include "tensorflow/compiler/aot/tests/test_graph_tfmatmul.h" #include "tensorflow/compiler/aot/tests/test_graph_tfmatmulandadd.h" +#include "tensorflow/compiler/aot/tests/test_graph_tfmatmulandadd_with_profiling.h" #include "tensorflow/compiler/aot/tests/test_graph_tfsplits.h" +#include "tensorflow/compiler/xla/service/hlo_profile_printer.h" #include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/platform/test.h" namespace tensorflow { namespace tfcompile { namespace { +using ::testing::HasSubstr; +using ::testing::UnorderedElementsAre; + TEST(TFCompileTest, Add) { AddComp add; EXPECT_EQ(add.arg0_data(), add.args()[0]); @@ -484,6 +491,59 @@ TEST(TFCompileTest, ProgramShape) { EXPECT_TRUE(ShapeUtil::Compatible(muladd_result1, f32_2x2)); } +TEST(TFCompileTest, HloProfiling) { + Eigen::ThreadPool tp(1); + Eigen::ThreadPoolDevice device(&tp, tp.NumThreads()); + + MatMulAndAddCompWithProfiling fn; + ASSERT_TRUE(fn.hlo_profiling_enabled()); + + fn.set_thread_pool(&device); + + // x = [[1, 2], [3, 4]] + fn.arg0(0, 0) = 1; + fn.arg0(0, 1) = 2; + fn.arg0(1, 0) = 3; + fn.arg0(1, 1) = 4; + + // y = [[10, 20], [30, 40]] + fn.arg1(0, 0) = 10; + fn.arg1(0, 1) = 20; + fn.arg1(1, 0) = 30; + fn.arg1(1, 1) = 40; + + EXPECT_TRUE(fn.Run()); + + string hlo_profile_as_string = + xla::PrintHloProfile(fn.hlo_profile_printer_data(), fn.profile_counters(), + /*clock_rate_ghz=*/1.0); + VLOG(1) << "HLO profile string:\n" << hlo_profile_as_string; + + std::vector hlo_profile_lines = + tensorflow::str_util::Split(hlo_profile_as_string, '\n'); + + auto header = HasSubstr("Execution profile for"); + auto total_cycles_profile_line = HasSubstr("[total]"); + auto dot_profile_line = HasSubstr( + "%dot = f32[2,2]{1,0} dot(f32[2,2]{1,0} %arg0, f32[2,2]{1,0} %arg1)"); + auto add_profile_line = HasSubstr( + "%add = f32[2,2]{1,0} add(f32[2,2]{1,0} %arg0, f32[2,2]{1,0} %arg1)"); + auto tuple_profile_line = HasSubstr( + "%tuple.2 = (f32[2,2]{1,0}, f32[2,2]{1,0}) tuple(f32[2,2]{1,0} %dot, " + "f32[2,2]{1,0} %add)"); + auto arg0_profile_line = HasSubstr("%arg0 = f32[2,2]{1,0} parameter(0)"); + auto arg1_profile_line = HasSubstr("%arg1 = f32[2,2]{1,0} parameter(1)"); + + hlo_profile_lines.erase(hlo_profile_lines.begin() + 7, + hlo_profile_lines.end()); + + EXPECT_THAT( + hlo_profile_lines, + UnorderedElementsAre(header, total_cycles_profile_line, dot_profile_line, + add_profile_line, tuple_profile_line, + arg0_profile_line, arg1_profile_line)); +} + } // namespace } // namespace tfcompile } // namespace tensorflow diff --git a/tensorflow/compiler/aot/tfcompile.bzl b/tensorflow/compiler/aot/tfcompile.bzl index 3a877c5337ff76..5c57fee326ca74 100644 --- a/tensorflow/compiler/aot/tfcompile.bzl +++ b/tensorflow/compiler/aot/tfcompile.bzl @@ -25,7 +25,8 @@ def tf_library(name, graph, config, visibility=None, testonly=None, tfcompile_flags=None, tfcompile_tool="//tensorflow/compiler/aot:tfcompile", - include_standard_runtime_deps=True, deps=None, tags=None): + include_standard_runtime_deps=True, + enable_xla_hlo_profiling=False, deps=None, tags=None): """Runs tfcompile to compile a TensorFlow graph into executable code. Given an invocation of tf_library(name="foo", ...), generates the following @@ -68,6 +69,8 @@ def tf_library(name, graph, config, include_standard_runtime_deps: If True, the standard list of kernel/runtime deps is added to deps. If False, deps must contain the full set of deps needed by the generated library. + enable_xla_hlo_profiling: Enable XLA HLO profiling in the generated program, + and emit metadata that lets us pretty-print the gathered profile counters. deps: a list of deps to include on the build rules for the generated library, added to the standard deps if standard_runtime_deps is True. tags: tags to apply to subsidiary build rules. @@ -137,6 +140,10 @@ def tf_library(name, graph, config, flags = tfcompile_flags else: flags = " ".join(["'" + arg.replace("'", "'\\''") + "'" for arg in (tfcompile_flags or [])]) + if enable_xla_hlo_profiling: + profiling_flag = "--xla_hlo_profile" + else: + profiling_flag = "" native.genrule( name=("gen_" + name), srcs=[ @@ -157,7 +164,7 @@ def tf_library(name, graph, config, " --out_header=$(@D)/" + header_file + " --out_metadata_object=$(@D)/" + metadata_object_file + " --out_function_object=$(@D)/" + function_object_file + - " " + flags), + " " + flags + " " + profiling_flag), tools=[tfcompile_tool], visibility=visibility, testonly=testonly, @@ -220,6 +227,8 @@ def tf_library(name, graph, config, ] + (need_xla_data_proto and [ # If we're generating the program shape, we must depend on the proto. "//tensorflow/compiler/xla:xla_data_proto", + ] or []) + (enable_xla_hlo_profiling and [ + "//tensorflow/compiler/xla/service:hlo_profile_printer_data" ] or []) + (include_standard_runtime_deps and [ # TODO(cwhipkey): only depend on kernel code that the model actually needed. "//tensorflow/compiler/tf2xla/kernels:index_ops_kernel_argmax_float_1d", diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc index 8ea014c2eede2c..839e1588b7be6c 100644 --- a/tensorflow/compiler/aot/tfcompile_main.cc +++ b/tensorflow/compiler/aot/tfcompile_main.cc @@ -100,6 +100,8 @@ Status Main(const MainFlags& flags) { if (flags.cpp_class.empty()) { return errors::InvalidArgument("Must specify --cpp_class"); } + codegen_opts.gen_hlo_profile_printer_data = + xla::legacy_flags::GetDebugOptionsFromFlags().xla_hlo_profile(); TF_RETURN_IF_ERROR(ParseCppClass(flags.cpp_class, &codegen_opts.class_name, &codegen_opts.namespaces)); diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index 150c12eeace5b7..ec2bb6c762d0bb 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -118,10 +118,12 @@ se::Platform::Id CpuAotCompilationOptions::PlatformId() const { CpuAotCompilationResult::CpuAotCompilationResult( ObjectFileData object_file_data, BufferSizes buffer_sizes, - int64 result_buffer_index) + int64 result_buffer_index, + std::unique_ptr hlo_profile_printer_data) : object_file_data_(std::move(object_file_data)), buffer_sizes_(std::move(buffer_sizes)), - result_buffer_index_(result_buffer_index) {} + result_buffer_index_(result_buffer_index), + hlo_profile_printer_data_(std::move(hlo_profile_printer_data)) {} CpuAotCompilationResult::~CpuAotCompilationResult() = default; @@ -171,14 +173,13 @@ class CollectProfileCandidates : public DfsHloVisitorWithDefault { public: static StatusOr> GetCandidatesForComputation( - HloComputation* computation, + const HloComputation& computation, const std::unordered_map& assigned_indices) { std::unordered_map hlo_to_profile_idx; CollectProfileCandidates profile_candidates_for_computation( &hlo_to_profile_idx, assigned_indices); - TF_RETURN_IF_ERROR( - computation->Accept(&profile_candidates_for_computation)); + TF_RETURN_IF_ERROR(computation.Accept(&profile_candidates_for_computation)); return hlo_to_profile_idx; } @@ -424,6 +425,41 @@ Status VerifyLlvmModule(const llvm::Module& llvm_module) { return Status::OK(); } +Status CreateHloProfilingArtifacts( + const HloModule& module, + std::unordered_map* + instruction_to_profile_idx, + std::unordered_map* + computation_to_profile_idx, + std::unique_ptr* hlo_profile_index_map, + std::unique_ptr* hlo_profile_printer_data) { + *hlo_profile_index_map = MakeUnique(module); + const HloComputation& entry_computation = *module.entry_computation(); + + TF_ASSIGN_OR_RETURN( + *instruction_to_profile_idx, + CollectProfileCandidates::GetCandidatesForComputation( + entry_computation, + (*hlo_profile_index_map)->instruction_to_profile_idx())); + + auto shape_size_bytes = [](const Shape& shape) { + // On the cpu, opaques are pointers. + if (ShapeUtil::IsOpaque(shape)) { + return static_cast(sizeof(void*)); + } + return ShapeUtil::ByteSizeOf(shape, sizeof(void*)); + }; + + HloCostAnalysis cost_analysis(shape_size_bytes); + TF_RETURN_IF_ERROR(entry_computation.Accept(&cost_analysis)); + *hlo_profile_printer_data = + CreateHloProfilePrinterData(**hlo_profile_index_map, cost_analysis); + *computation_to_profile_idx = + (*hlo_profile_index_map)->computation_to_profile_idx(); + + return Status::OK(); +} + } // namespace StatusOr> CpuCompiler::RunHloPasses( @@ -478,28 +514,9 @@ StatusOr> CpuCompiler::RunBackend( std::unique_ptr hlo_profile_index_map; std::unique_ptr hlo_profile_printer_data; if (module->config().hlo_profiling_enabled()) { - hlo_profile_index_map = MakeUnique(*module); - - TF_ASSIGN_OR_RETURN( - instruction_to_profile_idx, - CollectProfileCandidates::GetCandidatesForComputation( - entry_computation, - hlo_profile_index_map->instruction_to_profile_idx())); - - auto shape_size_bytes = [](const Shape& shape) { - // On the cpu, opaques are pointers. - if (ShapeUtil::IsOpaque(shape)) { - return static_cast(sizeof(void*)); - } - return ShapeUtil::ByteSizeOf(shape, sizeof(void*)); - }; - - HloCostAnalysis cost_analysis(shape_size_bytes); - TF_RETURN_IF_ERROR(entry_computation->Accept(&cost_analysis)); - hlo_profile_printer_data = - CreateHloProfilePrinterData(*hlo_profile_index_map, cost_analysis); - computation_to_profile_idx = - hlo_profile_index_map->computation_to_profile_idx(); + TF_RETURN_IF_ERROR(CreateHloProfilingArtifacts( + *module, &instruction_to_profile_idx, &computation_to_profile_idx, + &hlo_profile_index_map, &hlo_profile_printer_data)); } std::unique_ptr cpu_executable; @@ -715,11 +732,20 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, proto, xla_dump_optimized_hlo_proto_to, module->name())); } + std::unordered_map instruction_to_profile_idx; + std::unordered_map computation_to_profile_idx; + std::unique_ptr hlo_profile_index_map; + std::unique_ptr hlo_profile_printer_data; + + if (module->config().hlo_profiling_enabled()) { + TF_RETURN_IF_ERROR(CreateHloProfilingArtifacts( + *module, &instruction_to_profile_idx, &computation_to_profile_idx, + &hlo_profile_index_map, &hlo_profile_printer_data)); + } + IrEmitter ir_emitter(*module, *assignment, &llvm_module, - /*instruction_to_profile_idx=*/ - std::unordered_map{}, - /*computation_to_profile_idx=*/ - std::unordered_map{}, + std::move(instruction_to_profile_idx), + std::move(computation_to_profile_idx), target_machine.get(), /*external_constant_pool=*/nullptr); HloComputation* computation = module->entry_computation(); @@ -794,7 +820,7 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, results.emplace_back(MakeUnique( std::move(object_file_data), std::move(buffer_sizes), - result_slice.index())); + result_slice.index(), std::move(hlo_profile_printer_data))); } VLOG(1) << "Compilation finished"; diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h index 151af38438a980..65b05f04fa8d9c 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h @@ -76,10 +76,16 @@ class CpuAotCompilationOptions : public AotCompilationOptions { class CpuAotCompilationResult : public AotCompilationResult { public: - CpuAotCompilationResult(ObjectFileData object_file_data, - BufferSizes buffer_sizes, int64 result_buffer_index); + CpuAotCompilationResult( + ObjectFileData object_file_data, BufferSizes buffer_sizes, + int64 result_buffer_index, + std::unique_ptr hlo_profile_printer_data); ~CpuAotCompilationResult(); + HloProfilePrinterData* hlo_profile_printer_data() const { + return hlo_profile_printer_data_.get(); + } + const ObjectFileData& object_file_data() const { return object_file_data_; } const BufferSizes& buffer_sizes() const { return buffer_sizes_; } int64 result_buffer_index() const { return result_buffer_index_; } @@ -97,6 +103,10 @@ class CpuAotCompilationResult : public AotCompilationResult { // result of the computation. This buffer should be passed into the output // parameter when calling the compiled computation. const int64 result_buffer_index_; + + // Contains an instance of HloProfilePrinterData if HLO profiling is enabled, + // otherwise is nullptr. + std::unique_ptr hlo_profile_printer_data_; }; // CPU-targeting implementation of the XLA Compiler interface. From 1aef48eef86d3f6248afe3253a66f8f13800fb68 Mon Sep 17 00:00:00 2001 From: Patrick Nguyen Date: Fri, 27 Apr 2018 21:58:17 -0700 Subject: [PATCH 0130/1691] Properly export recurrent in contrib. The following symbols are available: - tf.contrib.recurrent.bidirectional_functional_rnn - tf.contrib.recurrent.functional_rnn - tf.contrib.recurrent.Recurrent PiperOrigin-RevId: 194632138 --- tensorflow/contrib/__init__.py | 2 +- tensorflow/contrib/recurrent/python/recurrent_api.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py index 7f33d460dce077..9f5459f41da3e5 100644 --- a/tensorflow/contrib/__init__.py +++ b/tensorflow/contrib/__init__.py @@ -69,7 +69,6 @@ from tensorflow.contrib import proto from tensorflow.contrib import quantization from tensorflow.contrib import quantize -from tensorflow.contrib import recurrent from tensorflow.contrib import reduce_slice_ops from tensorflow.contrib import resampler from tensorflow.contrib import rnn @@ -96,6 +95,7 @@ from tensorflow.contrib.lite.python import lite from tensorflow.contrib.optimizer_v2 import optimizer_v2_symbols as optimizer_v2 from tensorflow.contrib.receptive_field import receptive_field_api as receptive_field +from tensorflow.contrib.recurrent.python import recurrent_api as recurrent from tensorflow.contrib.remote_fused_graph import pylib as remote_fused_graph from tensorflow.contrib.specs import python as specs from tensorflow.contrib.summary import summary diff --git a/tensorflow/contrib/recurrent/python/recurrent_api.py b/tensorflow/contrib/recurrent/python/recurrent_api.py index ffe1dcf7dc4955..f1c97927dfe4c2 100644 --- a/tensorflow/contrib/recurrent/python/recurrent_api.py +++ b/tensorflow/contrib/recurrent/python/recurrent_api.py @@ -19,9 +19,9 @@ from __future__ import print_function # pylint: disable=unused-import -from tensorflow.contrib.recurrent.python.ops import functional_bidirectional_rnn -from tensorflow.contrib.recurrent.python.ops import functional_rnn -from tensorflow.contrib.recurrent.python.ops import Recurrent +from tensorflow.contrib.recurrent.python.ops.functional_rnn import bidirectional_functional_rnn +from tensorflow.contrib.recurrent.python.ops.functional_rnn import functional_rnn +from tensorflow.contrib.recurrent.python.ops.recurrent import Recurrent # pylint: enable=unused-import del absolute_import From d047a36a9d6d9cc7c0e15a01c4640a4177374827 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Apr 2018 22:57:36 -0700 Subject: [PATCH 0131/1691] Add test case on compiling dense layer node with XLA. PiperOrigin-RevId: 194634563 --- tensorflow/compiler/tests/BUILD | 2 ++ tensorflow/compiler/tests/jit_test.py | 19 +++++++++++++++++++ 2 files changed, 21 insertions(+) diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index 6a7b8faac38b14..a94b298f878320 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -818,8 +818,10 @@ cuda_py_test( "//tensorflow/python:framework", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:gradients", + "//tensorflow/python:layers", "//tensorflow/python:math_ops", "//tensorflow/python:nn_ops", + "//tensorflow/python:variables", ], ) diff --git a/tensorflow/compiler/tests/jit_test.py b/tensorflow/compiler/tests/jit_test.py index 0310cdde660c91..1ad83d80409734 100644 --- a/tensorflow/compiler/tests/jit_test.py +++ b/tensorflow/compiler/tests/jit_test.py @@ -29,11 +29,13 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops +from tensorflow.python.layers import layers from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import variables from tensorflow.python.platform import test jit_scope = jit.experimental_jit_scope @@ -450,6 +452,23 @@ def Forward(x): self.assertFalse(InLabels(labels, "Mul")) self.assertTrue(InLabels(labels, "_XlaLaunch")) + def testDenseLayer(self): + """Tests that the dense layer node is properly compiled.""" + + with self.test_session(config=NoRewriteSessionConfig()) as sess: + x = array_ops.placeholder(shape=[2, 3], dtype=np.float32) + with jit_scope(): + y = layers.dense(x, 3) + + sess.run(variables.initialize_all_variables()) + run_metadata = config_pb2.RunMetadata() + sess.run(y, {x: np.array([[1, 2, 3], [4, 5, 6]])}, + run_metadata=run_metadata, + options=config_pb2.RunOptions( + trace_level=config_pb2.RunOptions.FULL_TRACE)) + + self.assert_(MetadataHasXlaLaunch(run_metadata)) + class ElementWiseFusionTest(test.TestCase): From dd9b56f047dd615e367187e794364d5da24cee42 Mon Sep 17 00:00:00 2001 From: Martin Wicke <577277+martinwicke@users.noreply.github.com> Date: Fri, 27 Apr 2018 23:04:58 -0700 Subject: [PATCH 0132/1691] Fix docs rendering in placeholder docs page. --- tensorflow/python/ops/array_ops.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index c6ff02018236d4..e235047aff39f6 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -1718,8 +1718,10 @@ def placeholder(dtype, shape=None, name=None): print(sess.run(y, feed_dict={x: rand_array})) # Will succeed. ``` - @compatibility{eager} Placeholders are not compatible with eager execution. - + @compatibility(eager) + Placeholders are not compatible with eager execution. + @end_compatibility + Args: dtype: The type of elements in the tensor to be fed. shape: The shape of the tensor to be fed (optional). If the shape is not From 04e17da7ccd40c739d3a24daa2ad4d94bdd77dfe Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Apr 2018 23:35:42 -0700 Subject: [PATCH 0133/1691] Fix kernel creation bug, due to constant folding always use CPU. PiperOrigin-RevId: 194636076 --- .../grappler/optimizers/layout_optimizer_test.cc | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer_test.cc b/tensorflow/core/grappler/optimizers/layout_optimizer_test.cc index fc87f69b8c31d7..dad49cd74f8d26 100644 --- a/tensorflow/core/grappler/optimizers/layout_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/layout_optimizer_test.cc @@ -108,10 +108,8 @@ class LayoutOptimizerTest : public GrapplerTest { TensorShape filter_shape( {filter_size, filter_size, input_depth, filter_count}); - Tensor filter_data(DT_FLOAT, filter_shape); - test::FillIota(&filter_data, 1.0f); Output filter = - ops::Const(s->WithOpName("Filter"), Input::Initializer(filter_data)); + ops::Variable(s->WithOpName("Filter"), filter_shape, DT_FLOAT); int output_height = input_height; int output_width = input_width; @@ -143,6 +141,10 @@ class LayoutOptimizerTest : public GrapplerTest { return tensor; } + TensorShape GetAttrShape(const NodeDef& node) { + return TensorShape(node.attr().at({"shape"}).shape()); + } + Output SimpleFusedBatchNormGrad(tensorflow::Scope* s, bool is_training) { int batch_size = 16; int input_height = 8; @@ -200,9 +202,12 @@ TEST_F(LayoutOptimizerTest, Conv2DBackpropInput) { test::ExpectTensorEqual(input_sizes_expected, input_sizes); if (gpu_available_) { + TensorShape filter_shape = GetAttrShape(*node_map.GetNode("Filter")); + Tensor filter_data = GenerateRandomTensor(filter_shape); std::vector fetch = {"Fetch"}; - auto tensors_expected = EvaluateNodes(item.graph, fetch); - auto tensors = EvaluateNodes(output, fetch); + auto tensors_expected = + EvaluateNodes(item.graph, fetch, {{"Filter", filter_data}}); + auto tensors = EvaluateNodes(output, fetch, {{"Filter", filter_data}}); EXPECT_EQ(1, tensors_expected.size()); EXPECT_EQ(1, tensors.size()); test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6); From 102b0c87a024f95d619860b0ba492c93e4bd96c9 Mon Sep 17 00:00:00 2001 From: Anna R Date: Sat, 28 Apr 2018 00:13:09 -0700 Subject: [PATCH 0134/1691] Removing hidden_ops.txt file. PiperOrigin-RevId: 194637892 --- tensorflow/python/ops/hidden_ops.txt | 395 --------------------------- 1 file changed, 395 deletions(-) delete mode 100644 tensorflow/python/ops/hidden_ops.txt diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt deleted file mode 100644 index e1217e984c8f67..00000000000000 --- a/tensorflow/python/ops/hidden_ops.txt +++ /dev/null @@ -1,395 +0,0 @@ -# array_ops -BatchToSpace -BroadcastArgs -BroadcastGradientArgs -ConcatOffset -Concat -ConcatV2 -ConjugateTranspose -Const -DebugGradientIdentity -DebugGradientRefIdentity -EditDistance -ExpandDims -ListDiff -MirrorPad -MirrorPadGrad -OneHot -Pack -Pad -PadV2 -ParallelConcat -Placeholder -RefIdentity -Reverse -Snapshot -SpaceToBatch -Split -SplitV -Squeeze -Slice -TileGrad # Exported through array_grad instead of array_ops. -ZerosLike # TODO(josh11b): Use this instead of the Python version. -Unique -UniqueV2 -UniqueWithCounts -UniqueWithCountsV2 -Unpack - -# candidate_sampling_ops -AllCandidateSampler -ComputeAccidentalHits -FixedUnigramCandidateSampler -LearnedUnigramCandidateSampler -LogUniformCandidateSampler -ThreadUnsafeUnigramCandidateSampler -UniformCandidateSampler - -# checkpoint_ops -GenerateVocabRemapping -LoadAndRemapMatrix - - -# control_flow_ops -Switch -Merge -RefMerge -Exit -RefExit - -# ctc_ops -CTCLoss -CTCGreedyDecoder -CTCBeamSearchDecoder - -# data_flow_ops -Barrier -BarrierClose -BarrierIncompleteSize -BarrierInsertMany -BarrierReadySize -BarrierTakeMany -DeleteSessionTensor -FakeQueue -FIFOQueue -FIFOQueueV2 -GetSessionHandle -GetSessionHandleV2 -GetSessionTensor -HashTable -HashTableV2 -InitializeTable -InitializeTableV2 -InitializeTableFromTextFile -InitializeTableFromTextFileV2 -LookupTableExport -LookupTableExportV2 -LookupTableFind -LookupTableFindV2 -LookupTableImport -LookupTableImportV2 -LookupTableInsert -LookupTableInsertV2 -LookupTableSize -LookupTableSizeV2 -MutableDenseHashTable -MutableDenseHashTableV2 -MutableHashTable -MutableHashTableV2 -MutableHashTableOfTensors -MutableHashTableOfTensorsV2 -Mutex -MutexAcquire -MutexRelease -PaddingFIFOQueue -PaddingFIFOQueueV2 -PriorityQueue -PriorityQueueV2 -QueueClose -QueueCloseV2 -QueueDequeue -QueueDequeueV2 -QueueDequeueMany -QueueDequeueManyV2 -QueueDequeueUpTo -QueueDequeueUpToV2 -QueueEnqueue -QueueEnqueueV2 -QueueEnqueueMany -QueueEnqueueManyV2 -QueueSize -QueueSizeV2 -RandomShuffleQueue -RandomShuffleQueueV2 -Stack -StackClose -StackPop -StackPush -StackV2 -StackCloseV2 -StackPopV2 -StackPushV2 -TensorArray -TensorArrayClose -TensorArrayCloseV2 -TensorArrayConcat -TensorArrayConcatV2 -TensorArrayGather -TensorArrayGatherV2 -TensorArrayGrad -TensorArrayGradV2 -TensorArrayPack -TensorArrayPackV2 -TensorArrayRead -TensorArrayReadV2 -TensorArrayScatter -TensorArrayScatterV2 -TensorArraySize -TensorArraySizeV2 -TensorArraySplit -TensorArraySplitV2 -TensorArrayUnpack -TensorArrayUnpackV2 -TensorArrayV2 -TensorArrayWrite -TensorArrayWriteV2 -TensorArrayV3 -TensorArrayCloseV3 -TensorArrayConcatV3 -TensorArrayGatherV3 -TensorArrayGradV3 -TensorArrayReadV3 -TensorArrayPackV3 -TensorArrayScatterV3 -TensorArraySizeV3 -TensorArraySplitV3 -TensorArrayUnpackV3 -TensorArrayWriteV3 - -# functional_ops -SymbolicGradient - -# image_ops -AdjustContrastv2 -NonMaxSuppression -NonMaxSuppressionV2 -RandomCrop -ResizeBilinearGrad -ResizeBicubicGrad -ResizeNearestNeighborGrad -SampleDistortedBoundingBox -SampleDistortedBoundingBoxV2 -ScaleImageGrad - -# io_ops -FixedLengthRecordReader -IdentityReader -ReaderNumRecordsProduced -ReaderNumWorkUnitsCompleted -ReaderRead -ReaderReadUpTo -ReaderReset -ReaderRestoreState -ReaderSerializeState -ReaderWorkQueueLength -FixedLengthRecordReaderV2 -IdentityReaderV2 -ReaderNumRecordsProducedV2 -ReaderNumWorkUnitsCompletedV2 -ReaderReadV2 -ReaderReadUpToV2 -ReaderResetV2 -ReaderRestoreStateV2 -ReaderSerializeStateV2 -ReaderWorkQueueLengthV2 -Restore -RestoreSlice -Save -SaveSlices -ShardedFilename -ShardedFilespec -TextLineReader -TFRecordReader -WholeFileReader -TextLineReaderV2 -TFRecordReaderV2 -WholeFileReaderV2 -LMDBReader -DecodeCSV - -# linalg_ops -BatchCholesky -BatchCholeskyGrad -BatchMatrixDeterminant -BatchMatrixInverse -BatchMatrixSolve -BatchMatrixSolveLs -BatchMatrixTriangularSolve -BatchSelfAdjointEig -BatchSelfAdjointEigV2 -BatchSvd -LogMatrixDeterminant -MatrixExponential -MatrixLogarithm -MatrixSolveLs -SelfAdjointEig -SelfAdjointEigV2 -Svd - -# logging_ops -Assert -AudioSummary -AudioSummaryV2 -HistogramSummary -ImageSummary -MergeSummary -Print -ScalarSummary -TensorSummary -TensorSummaryV2 - -# math_ops -Abs -AccumulateNV2 -AddN -AddV2 -All -Any -BatchMatMul -BatchFFT -BatchFFT2D -BatchFFT3D -BatchIFFT -BatchIFFT2D -BatchIFFT3D -Bucketize -ClipByValue -Complex -ComplexAbs -Conj -FloorDiv -FloorMod -HistogramFixedWidth -Max -Mean -Min -Mul -Neg -Pow -Prod -Range -RealDiv -Select -SparseMatMul -Sub -Sum -MatMul -Sigmoid -Tanh -SigmoidGrad -TanhGrad -InvGrad -ReciprocalGrad -SqrtGrad -RsqrtGrad -TruncateDiv -TruncateMod - -# nn_ops -AvgPoolGrad # "*Grad" accessible through nn_grad instead of nn_ops. -AvgPool3DGrad -BatchNormWithGlobalNormalization -BatchNormWithGlobalNormalizationGrad -FusedBatchNorm -FusedBatchNormV2 -SoftmaxCrossEntropyWithLogits -SparseSoftmaxCrossEntropyWithLogits -LRNGrad -MaxPoolGrad -MaxPoolGradWithArgmax -MaxPoolGradGrad -MaxPoolGradGradWithArgmax -MaxPool3DGrad -MaxPool3DGradGrad -ReluGrad -Relu6Grad -EluGrad -SeluGrad -SoftplusGrad -SoftsignGrad -TopK -TopKV2 -BiasAdd -BiasAddV1 -Relu6 -AvgPool -MaxPool -MaxPoolV2 -Softmax -LogSoftmax -FractionalAvgPoolGrad -FractionalMaxPoolGrad -InTopK -InTopKV2 - -# parsing_ops -ParseExample -ParseSingleSequenceExample - -# random_ops -RandomGamma -RandomPoisson -RandomUniform -RandomUniformInt -RandomShuffle -RandomStandardNormal -ParameterizedTruncatedNormal -TruncatedNormal - -# script_ops -PyFunc -PyFuncStateless -EagerPyFunc - -# sdca_ops - -# state_ops -Variable -VariableV2 -TemporaryVariable -DestroyTemporaryVariable - -# sparse_ops -AddSparseToTensorsMap -AddManySparseToTensorsMap -TakeManySparseFromTensorsMap -DeserializeManySparse -DeserializeSparse -SerializeManySparse -SerializeSparse -SparseAdd -SparseAddGrad -SparseConcat -SparseCross -SparseFillEmptyRows -SparseFillEmptyRowsGrad -SparseSplit -SparseSelectLastK -SparseReorder -SparseReshape -SparseToDense -SparseTensorDenseAdd -SparseTensorDenseMatMul - -# string_ops -StringSplit - -# user_ops -Fact - -# training_ops -# (None) - -# word2vec deprecated ops -NegTrain -Skipgram From 4c5699582aa368edfbe058d770407a558729f305 Mon Sep 17 00:00:00 2001 From: Mingsheng Hong Date: Sat, 28 Apr 2018 08:55:08 -0700 Subject: [PATCH 0135/1691] This is Part 1 of Swift<->TF sends/recvs: support sending tensors from TF to Swift via direct session. The changes are: 1. Added an experimental TF C API TF_DequeueNamedTensor() to consume the queued tensors from a dequeue op. One use case is for the Swift host program to consume tensors sent by TF, where the queue is a Fifo queue managed by TF. Enqueuing tensors are done by running an enqueue op in a graph. The queued tensors are not persisted, and will be lost if the process/machine dies. The queue has a bounded capacity, to prevent producer from being unboundedly ahead of consumer. while caller of TF_DequeueNamedTensor() could have run the Fifo dequeue op directly, the extra level of indirection provided by this API allows us to more easily switch the queuing impl to another mechanism. If and once we stabilize on the Fifo queue based impl, we can remove this API. 2. Added a new S4TF runtime API _TFCReceiveTensorHandle() that receives a tensor via TF_DequeueNamedTensor(). 3. To support tensor receives in host program, taught PartitionCloner in TFPartition to insert SIL code to call _TFCReceiveTensorHandle(). 4. To support tensor sends in accelerator program, taught TFGraphLowering in generate QueueEnqueueV2 nodes in the TF graphs, with appropriate control dependence to make sure these nodes get executed. a) The enqueue produces no output tensor, and is executed only for its side effect. To ensure it is executed properly, control dependence is wired up. The general design is: before a TF_Function (can be a top level function or the body function of a while op) produces an output tensor OT, make OT control dependent on the enqueue op, so that enqueue gets run before the function returns. b) If tensor send occurs in a while loop body, the body logic currently gets lowered in 3 places: the while op cond function, the while op body function, and the ops at the same level as the while op itself (for running the last loop iteration). In this case, the correct TFGraph lowering is to run the enqueue in the last 2 out of the 3 places above. After this CL, the dual versions of the above (dequeuing via an op, and enqueuing via C API) will be added. PiperOrigin-RevId: 194658511 --- tensorflow/c/c_api_experimental.cc | 39 ++++++++++++++++++++++++++++++ tensorflow/c/c_api_experimental.h | 10 ++++++++ 2 files changed, 49 insertions(+) diff --git a/tensorflow/c/c_api_experimental.cc b/tensorflow/c/c_api_experimental.cc index d3916bc16778a9..82dbd3cdbc6e8f 100644 --- a/tensorflow/c/c_api_experimental.cc +++ b/tensorflow/c/c_api_experimental.cc @@ -8368,3 +8368,42 @@ TF_Operation* TF_MakeFileBasedIteratorGetNextWithDatasets( return getnext_node; #endif } + +TF_Tensor* TF_DequeueNamedTensor(TF_Session* session, int tensor_id, + TF_Status* status) { + assert(session); + { + tensorflow::mutex_lock c(session->graph->mu); + VLOG(1) << "Dequeuing named tensor with id " << tensor_id + << ", with input graph: " + << session->graph->graph.ToGraphDefDebug().DebugString(); + } + + TF_Operation* dequeue_op = TF_GraphOperationByName( + session->graph, + tensorflow::strings::StrCat("fifo_queue_dequeue_", tensor_id).c_str()); + if (dequeue_op == nullptr) { + status->status = tensorflow::errors::Internal( + "Unable to find the dequeue node in the TF graph."); + return nullptr; + } + + VLOG(1) << "Running the dequeue op"; + TF_Output output{dequeue_op, 0}; + TF_Tensor* ret; + TF_SessionRun(session, /*run_options*/ nullptr, + // input related parameters + /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0, + // output related parameters + /*outputs*/ &output, /*output_values*/ &ret, + /*noutputs*/ 1, + /*targets*/ nullptr, /*ntargets*/ 0, + /*run_metadata*/ nullptr, status); + if (VLOG_IS_ON(1) && status->status.ok()) { + tensorflow::Tensor tensor; + if (tensorflow::TF_TensorToTensor(ret, &tensor).ok()) { + VLOG(1) << "Dequeued tensor content: " << tensor.DebugString(); + } + } + return ret; +} diff --git a/tensorflow/c/c_api_experimental.h b/tensorflow/c/c_api_experimental.h index 88cb173cd25f42..e6757c065fc540 100644 --- a/tensorflow/c/c_api_experimental.h +++ b/tensorflow/c/c_api_experimental.h @@ -86,6 +86,16 @@ TF_CAPI_EXPORT extern TF_Operation* TF_MakeFileBasedIteratorGetNextWithDatasets( TF_Graph* graph, const char* file_path, int batch_size, unsigned char is_mnist, TF_Status* status); +// On success, dequeues a tensor from a TF-managed FifoQueue given by +// `tensor_id`, associated with `session`. Caller must call TF_DeleteTensor() +// over the returned tensor. If the queue is empty, this call is blocked. +// +// Tensors are enqueued via the corresponding TF enqueue op. +// TODO(hongm): Add support for `timeout_ms`. +TF_CAPI_EXPORT extern TF_Tensor* TF_DequeueNamedTensor(TF_Session* session, + int tensor_id, + TF_Status* status); + #ifdef __cplusplus } /* end extern "C" */ #endif From c01858350a1fc0f0fbf9a38fcd5c71e565343316 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sat, 28 Apr 2018 10:40:49 -0700 Subject: [PATCH 0136/1691] Allow not specifying eval_spec when evaluation is not necessarily run. PiperOrigin-RevId: 194661814 --- tensorflow/python/estimator/training.py | 32 +++++-- tensorflow/python/estimator/training_test.py | 94 +++++++++++++++++++- 2 files changed, 119 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py index 9d271758f63586..534c357067770b 100644 --- a/tensorflow/python/estimator/training.py +++ b/tensorflow/python/estimator/training.py @@ -201,7 +201,7 @@ def __new__(cls, * A tuple (features, labels): Where features is a `Tensor` or a dictionary of string feature name to `Tensor` and labels is a `Tensor` or a dictionary of string label name to `Tensor`. - + steps: Int. Positive number of steps for which to evaluate model. If `None`, evaluates until `input_fn` raises an end-of-input exception. See `Estimator.evaluate` for details. @@ -427,6 +427,8 @@ def eval_input_fn_eval: # returns x, y Raises: ValueError: if environment variable `TF_CONFIG` is incorrectly set. """ + _assert_eval_spec(eval_spec) # fail fast if eval_spec is invalid. + executor = _TrainingExecutor( estimator=estimator, train_spec=train_spec, eval_spec=eval_spec) @@ -481,10 +483,10 @@ def __init__(self, 'Got: {}'.format(type(train_spec))) self._train_spec = train_spec - if not isinstance(eval_spec, EvalSpec): - raise TypeError( - '`eval_spec` must have type `tf.estimator.EvalSpec`. ' - 'Got: {}'.format(type(eval_spec))) + if eval_spec and not isinstance(eval_spec, EvalSpec): + raise TypeError('`eval_spec` must be either `None` or have type ' + '`tf.estimator.EvalSpec`. Got: {}'.format( + type(eval_spec))) self._eval_spec = eval_spec self._train_hooks = _validate_hooks(train_hooks) @@ -580,6 +582,8 @@ def after_save(self, session, global_step_value): logging.info('Skip the current checkpoint eval due to throttle secs ' '({} secs).'.format(self._eval_throttle_secs)) + _assert_eval_spec(self._eval_spec) + # Final export signal: For any eval result with global_step >= train # max_steps, the evaluator will send the final export signal. There is a # small chance that the Estimator.train stopping logic sees a different @@ -628,6 +632,8 @@ def _should_stop_local_train(global_step): return True return False + _assert_eval_spec(self._eval_spec) + if self._eval_spec.throttle_secs <= 0: raise ValueError('eval_spec.throttle_secs should be positive, given: {}.' 'It is used do determine how long each training ' @@ -741,6 +747,9 @@ def _start_distributed_training(self, saving_listeners=None): def _start_continuous_evaluation(self): """Repeatedly calls `Estimator` evaluate and export until training ends.""" + + _assert_eval_spec(self._eval_spec) + start_delay_secs = self._eval_spec.start_delay_secs if start_delay_secs: logging.info('Waiting %f secs before starting eval.', start_delay_secs) @@ -769,6 +778,9 @@ def _start_continuous_evaluation(self): def _execute_evaluator_once(self, evaluator, continuous_eval_listener, throttle_secs): """Executes the `evaluator`.""" + + _assert_eval_spec(self._eval_spec) + start = time.time() eval_result = None @@ -807,7 +819,10 @@ class _Evaluator(object): def __init__(self, estimator, eval_spec, max_training_steps): self._estimator = estimator + + _assert_eval_spec(eval_spec) self._eval_spec = eval_spec + self._is_final_export_triggered = False self._previous_ckpt_path = None self._last_warning_time = 0 @@ -996,3 +1011,10 @@ def after_eval(self, eval_result): """ del eval_result return True + + +def _assert_eval_spec(eval_spec): + """Raise error if `eval_spec` is not of the right type.""" + if not isinstance(eval_spec, EvalSpec): + raise TypeError('`eval_spec` must have type `tf.estimator.EvalSpec`. ' + 'Got: {}'.format(type(eval_spec))) diff --git a/tensorflow/python/estimator/training_test.py b/tensorflow/python/estimator/training_test.py index 4f7da848086514..c04905ae65d0ef 100644 --- a/tensorflow/python/estimator/training_test.py +++ b/tensorflow/python/estimator/training_test.py @@ -72,6 +72,8 @@ 'An Exporter cannot have a name that is `None` or empty.') _INVALID_TRAIN_SPEC_MSG = '`train_spec` must have type `tf.estimator.TrainSpec`' _INVALID_EVAL_SPEC_MSG = '`eval_spec` must have type `tf.estimator.EvalSpec`' +_EVAL_SPEC_OR_NONE_MSG = ( + '`eval_spec` must be either `None` or have type `tf.estimator.EvalSpec`') _INVALID_EVAL_LISTENER_MSG = 'must have type `_ContinuousEvalListener`' _INVALID_CONFIG_FOR_STD_SERVER_MSG = 'Could not start server; .*TF_CONFIG' _INVALID_LOCAL_TASK_WITH_CLUSTER = '`task.type` in TF_CONFIG cannot be `local`' @@ -356,11 +358,23 @@ def test_invalid_estimator(self): training.train_and_evaluate(invalid_estimator, mock_train_spec, mock_eval_spec) + def test_fail_fast_if_invalid_eval_spec(self): + mock_est = test.mock.Mock(spec=estimator_lib.Estimator) + mock_train_spec = test.mock.Mock(spec=training.TrainSpec) + invalid_eval_spec = object() + + with test.mock.patch.object(training, '_TrainingExecutor') as mock_executor: + with self.assertRaisesRegexp(TypeError, _INVALID_EVAL_SPEC_MSG): + training.train_and_evaluate(mock_est, mock_train_spec, + invalid_eval_spec) + + mock_executor.assert_not_called() + class TrainingExecutorConstructorTest(test.TestCase): """Tests constructor of _TrainingExecutor.""" - def testRequiredArgumentsSet(self): + def test_required_arguments_set(self): estimator = estimator_lib.Estimator(model_fn=lambda features: features) train_spec = training.TrainSpec(input_fn=lambda: 1) eval_spec = training.EvalSpec(input_fn=lambda: 1) @@ -389,9 +403,17 @@ def test_invalid_eval_spec(self): train_spec = training.TrainSpec(input_fn=lambda: 1) invalid_eval_spec = object() - with self.assertRaisesRegexp(TypeError, _INVALID_EVAL_SPEC_MSG): + with self.assertRaisesRegexp(TypeError, _EVAL_SPEC_OR_NONE_MSG): training._TrainingExecutor(estimator, train_spec, invalid_eval_spec) + def test_eval_spec_none(self): + estimator = estimator_lib.Estimator(model_fn=lambda features: features) + train_spec = training.TrainSpec(input_fn=lambda: 1) + eval_spec = None + + # Tests that no error is raised. + training._TrainingExecutor(estimator, train_spec, eval_spec) + def test_invalid_train_hooks(self): estimator = estimator_lib.Estimator(model_fn=lambda features: features) train_spec = training.TrainSpec(input_fn=lambda: 1) @@ -457,6 +479,36 @@ def test_train_with_train_spec(self, mock_server, unused_mock_sleep): mock_est.evaluate.assert_not_called() mock_est.export_savedmodel.assert_not_called() + @test.mock.patch.object(time, 'sleep') + @test.mock.patch.object(server_lib, 'Server') + def test_train_with_no_eval_spec(self, mock_server, unused_mock_sleep): + mock_est = test.mock.Mock(spec=estimator_lib.Estimator) + mock_est.config = self._run_config + train_spec = training.TrainSpec( + input_fn=lambda: 1, max_steps=2, hooks=[_FakeHook()]) + eval_spec = None + mock_server_instance = mock_server.return_value + + executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) + self._run_task(executor) + + mock_server.assert_called_with( + mock_est.config.cluster_spec, + job_name=mock_est.config.task_type, + task_index=mock_est.config.task_id, + config=test.mock.ANY, + start=False) + + self.assertTrue(mock_server_instance.start.called) + + mock_est.train.assert_called_with( + input_fn=train_spec.input_fn, + max_steps=train_spec.max_steps, + hooks=list(train_spec.hooks), + saving_listeners=test.mock.ANY) + mock_est.evaluate.assert_not_called() + mock_est.export_savedmodel.assert_not_called() + @test.mock.patch.object(time, 'sleep') @test.mock.patch.object(server_lib, 'Server') def test_train_with_train_hooks(self, unused_mock_server, unused_mock_sleep): @@ -683,6 +735,20 @@ def test_train_with_train_spec(self, mock_server, unused_mock_sleep): saving_listeners=test.mock.ANY) mock_est.export_savedmodel.assert_not_called() + @test.mock.patch.object(time, 'sleep') + @test.mock.patch.object(server_lib, 'Server') + def test_train_with_no_eval_spec_fails(self, mock_server, unused_mock_sleep): + mock_est = test.mock.Mock(spec=estimator_lib.Estimator) + mock_est.evaluate = lambda *args, **kw: {ops.GraphKeys.GLOBAL_STEP: 123} + mock_est.config = self._run_config + train_spec = training.TrainSpec( + input_fn=lambda: 1, max_steps=2, hooks=[_FakeHook()]) + eval_spec = None + + executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) + with self.assertRaisesRegexp(TypeError, _INVALID_EVAL_SPEC_MSG): + executor.run_master() + @test.mock.patch.object(time, 'sleep') @test.mock.patch.object(server_lib, 'Server') def test_train_with_train_hooks(self, mock_server, unused_mock_sleep): @@ -980,6 +1046,19 @@ def test_evaluate_with_evaluate_spec(self): hooks=eval_spec.hooks) self.assertFalse(mock_est.train.called) + def test_evaluate_with_no_eval_spec_fails(self): + mock_est = test.mock.Mock(spec=estimator_lib.Estimator) + mock_est.latest_checkpoint.return_value = 'latest_it_is' + mock_train_spec = test.mock.Mock(spec=training.TrainSpec) + self._set_up_mock_est_to_train_and_evaluate_once(mock_est, mock_train_spec) + + eval_spec = None + + executor = training._TrainingExecutor(mock_est, mock_train_spec, eval_spec) + + with self.assertRaisesRegexp(TypeError, _INVALID_EVAL_SPEC_MSG): + executor.run_evaluator() + def test_evaluate_with_train_hooks(self): mock_est = test.mock.Mock(spec=estimator_lib.Estimator) mock_est.latest_checkpoint.return_value = 'latest_it_is' @@ -1635,6 +1714,17 @@ def test_train_and_evaluate_args(self): self.assertEqual(train_spec.input_fn, train_args['input_fn']) self.assertEqual(train_spec.max_steps, train_args['max_steps']) + def test_train_with_no_eval_spec_fails(self): + mock_est = test.mock.Mock(spec=estimator_lib.Estimator) + train_spec = training.TrainSpec( + input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()]) + eval_spec = None + + executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) + + with self.assertRaisesRegexp(TypeError, _INVALID_EVAL_SPEC_MSG): + executor.run_local() + def test_train_hooks(self): mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') mock_est.latest_checkpoint.return_value = 'checkpoint_path/' From fb1069781ffcbac222392a68c01a45fae264888e Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Sat, 28 Apr 2018 10:51:32 -0700 Subject: [PATCH 0137/1691] [tf.data] Use core::ScopedUnref to avoid resource leakage. If for whatever reason iterator_resource->set_iterator did not return Status::OK(), we would leak a reference on the iterator_resource. With this change, we won't leak the resource. PiperOrigin-RevId: 194662412 --- tensorflow/core/kernels/data/iterator_ops.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/data/iterator_ops.cc b/tensorflow/core/kernels/data/iterator_ops.cc index f5db97fd59ea88..a2f6c5fe2c3a4b 100644 --- a/tensorflow/core/kernels/data/iterator_ops.cc +++ b/tensorflow/core/kernels/data/iterator_ops.cc @@ -584,9 +584,9 @@ class MakeIteratorOp : public OpKernel { IteratorResource* iterator_resource; OP_REQUIRES_OK( ctx, LookupResource(ctx, HandleFromInput(ctx, 1), &iterator_resource)); + core::ScopedUnref unref(iterator_resource); OP_REQUIRES_OK(ctx, iterator_resource->set_iterator( dataset->MakeIterator("Iterator"))); - iterator_resource->Unref(); } }; From d07a8d4071b20d10226ea81758c9306ffce21317 Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Sat, 28 Apr 2018 11:31:12 -0700 Subject: [PATCH 0138/1691] Java: Release 1.8.0 PiperOrigin-RevId: 194663800 --- tensorflow/java/maven/libtensorflow/pom.xml | 2 +- tensorflow/java/maven/libtensorflow_jni/pom.xml | 2 +- tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml | 2 +- tensorflow/java/maven/pom.xml | 2 +- tensorflow/java/maven/proto/pom.xml | 2 +- tensorflow/java/maven/tensorflow/pom.xml | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml index 66985e3b18cd3f..08cc860f5795a4 100644 --- a/tensorflow/java/maven/libtensorflow/pom.xml +++ b/tensorflow/java/maven/libtensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.8.0-rc1 + 1.8.0 ../ libtensorflow diff --git a/tensorflow/java/maven/libtensorflow_jni/pom.xml b/tensorflow/java/maven/libtensorflow_jni/pom.xml index 34d4ba0b083d23..fcc7eacc33b7ba 100644 --- a/tensorflow/java/maven/libtensorflow_jni/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.8.0-rc1 + 1.8.0 ../ libtensorflow_jni diff --git a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml index 1909d08e41daa7..3d22d86a4970de 100644 --- a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.8.0-rc1 + 1.8.0 ../ libtensorflow_jni_gpu diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml index ba98732f5add32..0a09a5ea7cb967 100644 --- a/tensorflow/java/maven/pom.xml +++ b/tensorflow/java/maven/pom.xml @@ -6,7 +6,7 @@ 4.0.0 org.tensorflow parentpom - 1.8.0-rc1 + 1.8.0 pom https://www.tensorflow.org diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml index dee8c343598d17..77ec6a0ddbab27 100644 --- a/tensorflow/java/maven/proto/pom.xml +++ b/tensorflow/java/maven/proto/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.8.0-rc1 + 1.8.0 ../ proto diff --git a/tensorflow/java/maven/tensorflow/pom.xml b/tensorflow/java/maven/tensorflow/pom.xml index 95e024ace9762d..0df1f2814906e5 100644 --- a/tensorflow/java/maven/tensorflow/pom.xml +++ b/tensorflow/java/maven/tensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.8.0-rc1 + 1.8.0 ../ tensorflow From 5e0db783cec417b921352537a7b296473522a636 Mon Sep 17 00:00:00 2001 From: Stefan Schweter Date: Sat, 28 Apr 2018 21:03:15 +0200 Subject: [PATCH 0139/1691] Fix link to original LSTM paper (#18876) --- tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h index ace4827d8ce215..4a648e42837fbf 100644 --- a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h +++ b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h @@ -609,7 +609,7 @@ enum { * Long short-term memory unit (LSTM) recurrent network layer. * * The default non-peephole implementation is based on: - * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf + * http://www.bioinf.jku.at/publications/older/2604.pdf * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural * Computation, 9(8):1735-1780, 1997. * From 9f9b51165991e455a91f697c12981595441e123a Mon Sep 17 00:00:00 2001 From: Nehal J Wani Date: Sat, 28 Apr 2018 14:03:42 -0500 Subject: [PATCH 0140/1691] Fix typo in CMakeLists.txt (#18833) --- tensorflow/contrib/cmake/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt index d75b1b12a62e31..44e39f7f7b5da8 100644 --- a/tensorflow/contrib/cmake/CMakeLists.txt +++ b/tensorflow/contrib/cmake/CMakeLists.txt @@ -84,7 +84,7 @@ if (NOT WIN32) option(systemlib_ALL "Turn on every possible systemlib_* options" OFF) if (systemlib_ALL) - set (systmelib_ZLIB ON) + set (systemlib_ZLIB ON) endif (systemlib_ALL) endif() From c6aa3a0624ef7e1ff95cc07dde20c74105c4a584 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 28 Apr 2018 12:04:45 -0700 Subject: [PATCH 0141/1691] Add uint32 and uint64 support with tf.train.batch (#18805) * Add uint32 and uint64 support with tf.train.batch This fix tries to address the issue raised in 18586 to have uint32 and uint64 support with tf.train.batch. This fix add uint32 and uint64 to `CopyElementToSlice` for the support. This fix fixes 18586. Signed-off-by: Yong Tang * Add test case for uint32 with tf.train.batch Signed-off-by: Yong Tang * Add uint64 test case Signed-off-by: Yong Tang --- tensorflow/core/kernels/batch_util.cc | 2 ++ tensorflow/python/training/input_test.py | 22 ++++++++++++++++++++++ 2 files changed, 24 insertions(+) diff --git a/tensorflow/core/kernels/batch_util.cc b/tensorflow/core/kernels/batch_util.cc index 52be1ab8d0f23b..1182ed42e7a9ad 100644 --- a/tensorflow/core/kernels/batch_util.cc +++ b/tensorflow/core/kernels/batch_util.cc @@ -134,6 +134,8 @@ Status CopyElementToSlice(Tensor element, Tensor* parent, int64 index) { switch (element.dtype()) { TF_CALL_ALL_TYPES(HANDLE_TYPE); TF_CALL_QUANTIZED_TYPES(HANDLE_TYPE); + TF_CALL_uint32(HANDLE_TYPE); + TF_CALL_uint64(HANDLE_TYPE); #undef HANDLE_TYPE default: return errors::Unimplemented("CopyElementToSlice Unhandled data type: ", diff --git a/tensorflow/python/training/input_test.py b/tensorflow/python/training/input_test.py index 3a25bfe3432238..1b1e89cb26d336 100644 --- a/tensorflow/python/training/input_test.py +++ b/tensorflow/python/training/input_test.py @@ -497,6 +497,28 @@ def testOneThread(self): def testOneThreadDict(self): self._testOneThreadHelper(use_dict=True) + def testUint32DataTypes(self): + values = constant_op.constant([0, 1, 2, 3, 4, 5], dtype=dtypes.uint32) + batched = inp.batch([values], batch_size=2) + with self.test_session() as sess: + coord = coordinator.Coordinator() + threads = queue_runner_impl.start_queue_runners(sess=sess, coord=coord) + sess.run(batched) + coord.request_stop() + for thread in threads: + thread.join() + + def testUint64DataTypes(self): + values = constant_op.constant([0, 1, 2, 3, 4, 5], dtype=dtypes.uint64) + batched = inp.batch([values], batch_size=2) + with self.test_session() as sess: + coord = coordinator.Coordinator() + threads = queue_runner_impl.start_queue_runners(sess=sess, coord=coord) + sess.run(batched) + coord.request_stop() + for thread in threads: + thread.join() + def testOneThreadDynamicPad(self): with self.test_session() as sess: batch_size = 10 From c65ad957b8c7dc0946c50af8f263ec08b9367e19 Mon Sep 17 00:00:00 2001 From: Rholais Lii Date: Sun, 29 Apr 2018 03:05:00 +0800 Subject: [PATCH 0142/1691] Emphasis any `Estimator` (#18793) --- tensorflow/docs_src/get_started/checkpoints.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tensorflow/docs_src/get_started/checkpoints.md b/tensorflow/docs_src/get_started/checkpoints.md index 4aa07c7f2a0b56..8dfd91e3c8368f 100644 --- a/tensorflow/docs_src/get_started/checkpoints.md +++ b/tensorflow/docs_src/get_started/checkpoints.md @@ -38,8 +38,10 @@ Estimators automatically write the following to disk: uses to create visualizations. To specify the top-level directory in which the Estimator stores its -information, assign a value to the optional `model_dir` argument of any -Estimator's constructor. For example, the following code sets the `model_dir` +information, assign a value to the optional `model_dir` argument of *any* +`Estimator`'s constructor. +Taking `DNNClassifier` as an example, +the following code sets the `model_dir` argument to the `models/iris` directory: ```python From 17cb3cdd300cb8a16a91cd141dc5aa21a9b85ed9 Mon Sep 17 00:00:00 2001 From: QingYing Chen Date: Sun, 29 Apr 2018 03:05:34 +0800 Subject: [PATCH 0143/1691] Fix functions in CRF when sequence_lengths contains zero (#18487) * Fix computation of crf_log_norm when sequence length is zero * fix _single_seq_fn in crf when sequence_lengths contain zero --- .../crf/python/kernel_tests/crf_test.py | 24 +++++++++++++++---- tensorflow/contrib/crf/python/ops/crf.py | 23 ++++++++++++++---- 2 files changed, 39 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/crf/python/kernel_tests/crf_test.py b/tensorflow/contrib/crf/python/kernel_tests/crf_test.py index a5e065b93a23c3..74f2ec22ffaab1 100644 --- a/tensorflow/contrib/crf/python/kernel_tests/crf_test.py +++ b/tensorflow/contrib/crf/python/kernel_tests/crf_test.py @@ -152,6 +152,22 @@ def testCrfLogNorm(self): self.assertAllClose(tf_log_norm, tf_brute_force_log_norm) + def testCrfLogNormZeroSeqLength(self): + """ + Test `crf_log_norm` when `sequence_lengths` contains one or more zeros. + """ + with self.test_session() as sess: + inputs = constant_op.constant(np.ones([2, 10, 5], + dtype=np.float32)) + transition_params = constant_op.constant(np.ones([5, 5], + dtype=np.float32)) + sequence_lengths = constant_op.constant(np.zeros([2], + dtype=np.int32)) + expected_log_norm = np.zeros([2], dtype=np.float32) + log_norm = crf.crf_log_norm(inputs, sequence_lengths, transition_params) + tf_log_norm = sess.run(log_norm) + self.assertAllClose(tf_log_norm, expected_log_norm) + def testCrfLogLikelihood(self): inputs = np.array( [[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], dtype=np.float32) @@ -292,10 +308,10 @@ def testCrfDecodeZeroSeqLength(self): dtype=np.float32)) sequence_lengths = constant_op.constant(np.zeros([2], dtype=np.int32)) - values = crf.crf_decode(inputs, transition_params, sequence_lengths) - tags, scores = sess.run(values) - self.assertEqual(len(tags.shape), 2) - self.assertEqual(len(scores.shape), 1) + tags, scores = crf.crf_decode(inputs, transition_params, sequence_lengths) + tf_tags, tf_scores = sess.run([tags, scores]) + self.assertEqual(len(tf_tags.shape), 2) + self.assertEqual(len(tf_scores.shape), 1) if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/crf/python/ops/crf.py b/tensorflow/contrib/crf/python/ops/crf.py index e37c029cebf30e..d2beff849eb8d1 100644 --- a/tensorflow/contrib/crf/python/ops/crf.py +++ b/tensorflow/contrib/crf/python/ops/crf.py @@ -90,9 +90,13 @@ def _single_seq_fn(): batch_size = array_ops.shape(inputs, out_type=tag_indices.dtype)[0] example_inds = array_ops.reshape( math_ops.range(batch_size, dtype=tag_indices.dtype), [-1, 1]) - return array_ops.gather_nd( + sequence_scores = array_ops.gather_nd( array_ops.squeeze(inputs, [1]), array_ops.concat([example_inds, tag_indices], axis=1)) + sequence_scores = array_ops.where(math_ops.less_equal(sequence_lengths, 0), + array_ops.zeros_like(sequence_scores), + sequence_scores) + return sequence_scores def _multi_seq_fn(): # Compute the scores of the given tag sequence. @@ -128,7 +132,12 @@ def crf_log_norm(inputs, sequence_lengths, transition_params): # If max_seq_len is 1, we skip the algorithm and simply reduce_logsumexp over # the "initial state" (the unary potentials). def _single_seq_fn(): - return math_ops.reduce_logsumexp(first_input, [1]) + log_norm = math_ops.reduce_logsumexp(first_input, [1]) + # Mask `log_norm` of the sequences with length <= zero. + log_norm = array_ops.where(math_ops.less_equal(sequence_lengths, 0), + array_ops.zeros_like(log_norm), + log_norm) + return log_norm def _multi_seq_fn(): """Forward computation of alpha values.""" @@ -137,13 +146,19 @@ def _multi_seq_fn(): # Compute the alpha values in the forward algorithm in order to get the # partition function. forward_cell = CrfForwardRnnCell(transition_params) + # Sequence length is not allowed to be less than zero. + sequence_lengths_less_one = math_ops.maximum(0, sequence_lengths - 1) _, alphas = rnn.dynamic_rnn( cell=forward_cell, inputs=rest_of_input, - sequence_length=sequence_lengths - 1, + sequence_length=sequence_lengths_less_one, initial_state=first_input, dtype=dtypes.float32) log_norm = math_ops.reduce_logsumexp(alphas, [1]) + # Mask `log_norm` of the sequences with length <= zero. + log_norm = array_ops.where(math_ops.less_equal(sequence_lengths, 0), + array_ops.zeros_like(log_norm), + log_norm) return log_norm max_seq_len = array_ops.shape(inputs)[1] @@ -479,7 +494,7 @@ def _multi_seq_fn(): initial_state = array_ops.slice(potentials, [0, 0, 0], [-1, 1, -1]) initial_state = array_ops.squeeze(initial_state, axis=[1]) # [B, O] inputs = array_ops.slice(potentials, [0, 1, 0], [-1, -1, -1]) # [B, T-1, O] - # sequence length is not allowed to be less than zero + # Sequence length is not allowed to be less than zero. sequence_length_less_one = math_ops.maximum(0, sequence_length - 1) backpointers, last_score = rnn.dynamic_rnn( # [B, T - 1, O], [B, O] crf_fwd_cell, From 6f3cc9d368a17646f5838e36be3b1c25bf4534fe Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 28 Apr 2018 12:06:15 -0700 Subject: [PATCH 0144/1691] Pass dtype to constructor in LSTMCell (#18178) * Use float32 in case the dtype is not set in the constructor Signed-off-by: Yong Tang * Add test case for 16228. Signed-off-by: Yong Tang * Add test case where dype is passed explicitly. Signed-off-by: Yong Tang * Fix pylint issue Signed-off-by: Yong Tang * Replace strings to objects to address review feedback. Signed-off-by: Yong Tang --- .../rnn/python/kernel_tests/core_rnn_test.py | 15 +++++++++++++++ tensorflow/python/ops/rnn_cell_impl.py | 6 +++++- 2 files changed, 20 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py index de5df912921932..ba4933ddf793c5 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py @@ -307,6 +307,21 @@ def setUp(self): self._seed = 23489 np.random.seed(self._seed) + def testDType(self): + # Test case for GitHub issue 16228 + # Not passing dtype in constructor results in default float32 + lstm = rnn_cell.LSTMCell(10) + input_tensor = array_ops.ones([10, 50]) + lstm.build(input_tensor.get_shape()) + self.assertEqual(lstm._bias.dtype, dtypes.float32_ref) + + # Explicitly pass dtype in constructor + for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: + lstm = rnn_cell.LSTMCell(10, dtype=dtype) + input_tensor = array_ops.ones([10, 50]) + lstm.build(input_tensor.get_shape()) + self.assertEqual(lstm._bias.dtype, dtype._as_ref) + def testNoProjNoSharding(self): num_units = 3 input_size = 5 diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py index 86dc053c0fb8d0..67f753485b8c30 100644 --- a/tensorflow/python/ops/rnn_cell_impl.py +++ b/tensorflow/python/ops/rnn_cell_impl.py @@ -785,10 +785,14 @@ def build(self, inputs_shape): shape=[input_depth + h_depth, 4 * self._num_units], initializer=self._initializer, partitioner=maybe_partitioner) + if self.dtype is None: + initializer = init_ops.zeros_initializer + else: + initializer = init_ops.zeros_initializer(dtype=self.dtype) self._bias = self.add_variable( _BIAS_VARIABLE_NAME, shape=[4 * self._num_units], - initializer=init_ops.zeros_initializer(dtype=self.dtype)) + initializer=initializer) if self._use_peepholes: self._w_f_diag = self.add_variable("w_f_diag", shape=[self._num_units], initializer=self._initializer) From c45b05197623b375a056dd9577a778c5d5cc7d03 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Sat, 28 Apr 2018 23:30:22 +0300 Subject: [PATCH 0145/1691] [tf.data] A change to use Jenkins to test the Winsows build. don't submit with this change! --- tensorflow/contrib/data/python/kernel_tests/resample_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index b556525ce444b7..c08283a04163d1 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -60,7 +60,7 @@ class ResampleTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( ("InitialDistributionKnown", True), - ("InitialDistributionUnknown", False)) + ("InitialDistributionUnknown", True)) # THIS IS TO TEST THE WINDOWS BUILD DONT SUBMIT def testDistribution(self, initial_known): classes = np.random.randint(5, size=(20000,)) # Uniformly sampled target_dist = [0.9, 0.05, 0.05, 0.0, 0.0] From b384c339ee7d8440b6d4e39c09202c19f900aebe Mon Sep 17 00:00:00 2001 From: joel-shor Date: Sun, 29 Apr 2018 01:16:16 +0300 Subject: [PATCH 0146/1691] [tf.data] Possible bug fix to fix Winsows build. --- tensorflow/contrib/data/python/kernel_tests/resample_test.py | 4 ++-- tensorflow/contrib/data/python/ops/resampling.py | 1 - 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index c08283a04163d1..bbb8ca22f63a0c 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -60,9 +60,9 @@ class ResampleTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( ("InitialDistributionKnown", True), - ("InitialDistributionUnknown", True)) # THIS IS TO TEST THE WINDOWS BUILD DONT SUBMIT + ("InitialDistributionUnknown", False)) def testDistribution(self, initial_known): - classes = np.random.randint(5, size=(20000,)) # Uniformly sampled + classes = np.random.randint(5, size=(20000,), dtype=np.int64) target_dist = [0.9, 0.05, 0.05, 0.0, 0.0] initial_dist = [0.2] * 5 if initial_known else None dataset = dataset_ops.Dataset.from_tensor_slices(classes).shuffle( diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index 1194b8447a568d..bad6edd5147d83 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -79,7 +79,6 @@ def _apply_fn(dataset): lambda accept_prob, _: accept_prob) prob_of_original_ds = acceptance_and_original_prob_ds.map( lambda _, prob_original: prob_original) - prob_of_original = None filtered_ds = _filter_ds(dataset, acceptance_dist_ds, initial_dist_ds, class_values_ds, seed) # Prefetch filtered dataset for speed. From 9033bb2a175e344448772f5641020023badeacd8 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Sun, 29 Apr 2018 03:01:12 +0300 Subject: [PATCH 0147/1691] [tf.data] Undo previously unsuccessful bugfix, and try another one to fix the Windows build. don't submit with this change, because it includes some debugging! --- tensorflow/contrib/data/python/kernel_tests/resample_test.py | 2 +- tensorflow/contrib/data/python/ops/resampling.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index bbb8ca22f63a0c..b556525ce444b7 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -62,7 +62,7 @@ class ResampleTest(test.TestCase, parameterized.TestCase): ("InitialDistributionKnown", True), ("InitialDistributionUnknown", False)) def testDistribution(self, initial_known): - classes = np.random.randint(5, size=(20000,), dtype=np.int64) + classes = np.random.randint(5, size=(20000,)) # Uniformly sampled target_dist = [0.9, 0.05, 0.05, 0.0, 0.0] initial_dist = [0.2] * 5 if initial_known else None dataset = dataset_ops.Dataset.from_tensor_slices(classes).shuffle( diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index bad6edd5147d83..e65207f6750531 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -59,7 +59,7 @@ def _apply_fn(dataset): # Get initial distribution. if initial_dist is not None: - initial_dist_t = ops.convert_to_tensor(initial_dist, name="initial_dist") + initial_dist_t = math_ops.to_float(ops.convert_to_tensor(initial_dist, name="initial_dist")) acceptance_dist, prob_of_original = ( _calculate_acceptance_probs_with_mixing(initial_dist_t, target_dist_t)) @@ -291,4 +291,4 @@ def _calculate_acceptance_probs_with_mixing(initial_probs, target_probs): # TODO(joelshor): Simplify fraction, if possible. a_i = (ratio_l - m) / (max_ratio - m) - return a_i, m \ No newline at end of file + return math_ops.to_float(a_i), math_ops.to_float(m) \ No newline at end of file From 3a9c513c3f4303e5194474d804367c1f4831e3ee Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sat, 28 Apr 2018 19:47:42 -0700 Subject: [PATCH 0148/1691] Internally rewrite RevBlock to use @custom_gradient PiperOrigin-RevId: 194679657 --- .../layers/python/layers/rev_block_lib.py | 297 ++++++------------ .../python/layers/rev_block_lib_test.py | 96 +----- 2 files changed, 105 insertions(+), 288 deletions(-) diff --git a/tensorflow/contrib/layers/python/layers/rev_block_lib.py b/tensorflow/contrib/layers/python/layers/rev_block_lib.py index 1a439f0a4deb32..8ed9f446bcd5f2 100644 --- a/tensorflow/contrib/layers/python/layers/rev_block_lib.py +++ b/tensorflow/contrib/layers/python/layers/rev_block_lib.py @@ -35,7 +35,6 @@ from tensorflow.contrib.framework.python import ops as contrib_framework_ops from tensorflow.python.eager import backprop from tensorflow.python.framework import dtypes -from tensorflow.python.framework import function from tensorflow.python.framework import ops as framework_ops from tensorflow.python.layers import base from tensorflow.python.ops import array_ops @@ -155,7 +154,7 @@ def _scope_wrap(fn, scope): @functools.wraps(fn) def wrap(*args, **kwargs): - with variable_scope.variable_scope(scope): + with variable_scope.variable_scope(scope, use_resource=True): return fn(*args, **kwargs) return wrap @@ -230,95 +229,95 @@ def build(self, _): "build.") self.built = True - def _efficient_grad_fn(self, inputs, variables, ys, grad_ys): - """Custom gradient fn for a block of reversible residual layers.""" - # Inputs have passed through an Identity. Recover the original Tensors to - # be able to match up side inputs. - assert [u"Identity"] == list(set([x.op.type for x in inputs])) - inputs = [x.op.inputs[0] for x in inputs] - side_inputs = inputs[2:] - del inputs - - f_side_idxs = [None] * len(self.f_side_input) - g_side_idxs = [None] * len(self.g_side_input) - assert len(side_inputs) == len(self.f_side_input) + len(self.g_side_input) - - for i, t in enumerate(side_inputs): - if t in self.f_side_input: - f_side_idxs[self.f_side_input.index(t)] = i - elif t in self.g_side_input: - g_side_idxs[self.g_side_input.index(t)] = i - else: - assert False - - f_vars = [[] for _ in range(self.num_layers)] - g_vars = [[] for _ in range(self.num_layers)] - f_vars_idxs = [[] for _ in range(self.num_layers)] - g_vars_idxs = [[] for _ in range(self.num_layers)] - - for i, ref in enumerate(variables): - # Use the name to identify the layer number and function (f or g) - regex = LAYER_RE.match(ref.name) - layer_no = int(regex.group(1)) - fn_name = regex.group(2) - if fn_name == "f": - f_vars[layer_no].append(ref) - f_vars_idxs[layer_no].append(i) - else: - assert fn_name == "g" - g_vars[layer_no].append(ref) - g_vars_idxs[layer_no].append(i) - - f_var_grads = [] - g_var_grads = [] - f_side_grads = [] - g_side_grads = [] - - # Reverse variable containers to go backward - f_vars.reverse() - g_vars.reverse() - f = list(self.f) - g = list(self.g) - f.reverse() - g.reverse() - - with variable_scope.variable_scope(self.scope_name, reuse=True): - for i in xrange(self.num_layers): - ys, grad_ys, f_ret, g_ret = _rev_layer_backward( - ys, grad_ys, f[i], g[i], f_vars[i], self.f_side_input, g_vars[i], - self.g_side_input) - - grad_f_vars, grad_f_side = f_ret - grad_g_vars, grad_g_side = g_ret - f_var_grads.append(grad_f_vars) - g_var_grads.append(grad_g_vars) - f_side_grads.append(grad_f_side) - g_side_grads.append(grad_g_side) - - # Accumulate layer gradients for f_side_input and g_side_input - acc_f_side_grads = _acc_grads(*f_side_grads) - acc_g_side_grads = _acc_grads(*g_side_grads) - - # Use the stored idxs to put gradients in the passed-in order. - side_input_grads = [None] * len(side_inputs) - variable_grads = [None] * len(variables) - - # Variable gradients were collected in reverse layer order. Reverse to match - # idxs. - f_var_grads.reverse() - g_var_grads.reverse() - for idxs, grads in list(zip(f_vars_idxs, f_var_grads)) + list( - zip(g_vars_idxs, g_var_grads)): - for i, grad in zip(idxs, grads): - variable_grads[i] = grad - - for i, grad in zip(f_side_idxs, acc_f_side_grads): - side_input_grads[i] = grad - for i, grad in zip(g_side_idxs, acc_g_side_grads): - side_input_grads[i] = grad - - grad_x1, grad_x2 = grad_ys - return [grad_x1, grad_x2] + side_input_grads, variable_grads + def _make_efficient_grad_fn(self, inputs_, ys_): + def _efficient_grad_fn(*grad_ys, **kwargs): + """Custom gradient fn for a block of reversible residual layers.""" + inputs = inputs_ + ys = ys_ + variables = kwargs["variables"] + side_inputs = inputs[2:] + + f_side_idxs = [None] * len(self.f_side_input) + g_side_idxs = [None] * len(self.g_side_input) + assert len(side_inputs) == len(self.f_side_input) + len(self.g_side_input) + + for i, t in enumerate(side_inputs): + if t in self.f_side_input: + f_side_idxs[self.f_side_input.index(t)] = i + elif t in self.g_side_input: + g_side_idxs[self.g_side_input.index(t)] = i + else: + assert False + + f_vars = [[] for _ in range(self.num_layers)] + g_vars = [[] for _ in range(self.num_layers)] + f_vars_idxs = [[] for _ in range(self.num_layers)] + g_vars_idxs = [[] for _ in range(self.num_layers)] + + for i, ref in enumerate(variables): + # Use the name to identify the layer number and function (f or g) + regex = LAYER_RE.match(ref.name) + layer_no = int(regex.group(1)) + fn_name = regex.group(2) + if fn_name == "f": + f_vars[layer_no].append(ref) + f_vars_idxs[layer_no].append(i) + else: + assert fn_name == "g" + g_vars[layer_no].append(ref) + g_vars_idxs[layer_no].append(i) + + f_var_grads = [] + g_var_grads = [] + f_side_grads = [] + g_side_grads = [] + + # Reverse variable containers to go backward + f_vars.reverse() + g_vars.reverse() + f = list(self.f) + g = list(self.g) + f.reverse() + g.reverse() + + with variable_scope.variable_scope(self.scope_name, reuse=True): + for i in xrange(self.num_layers): + ys, grad_ys, f_ret, g_ret = _rev_layer_backward( + ys, grad_ys, f[i], g[i], f_vars[i], self.f_side_input, g_vars[i], + self.g_side_input) + + grad_f_vars, grad_f_side = f_ret + grad_g_vars, grad_g_side = g_ret + f_var_grads.append(grad_f_vars) + g_var_grads.append(grad_g_vars) + f_side_grads.append(grad_f_side) + g_side_grads.append(grad_g_side) + + # Accumulate layer gradients for f_side_input and g_side_input + acc_f_side_grads = _acc_grads(*f_side_grads) + acc_g_side_grads = _acc_grads(*g_side_grads) + + # Use the stored idxs to put gradients in the passed-in order. + side_input_grads = [None] * len(side_inputs) + variable_grads = [None] * len(variables) + + # Variable gradients were collected in reverse layer order. Reverse to + # match idxs. + f_var_grads.reverse() + g_var_grads.reverse() + for idxs, grads in list(zip(f_vars_idxs, f_var_grads)) + list( + zip(g_vars_idxs, g_var_grads)): + for i, grad in zip(idxs, grads): + variable_grads[i] = grad + + for i, grad in zip(f_side_idxs, acc_f_side_grads): + side_input_grads[i] = grad + for i, grad in zip(g_side_idxs, acc_g_side_grads): + side_input_grads[i] = grad + + grad_x1, grad_x2 = grad_ys + return [grad_x1, grad_x2] + side_input_grads, variable_grads + return _efficient_grad_fn def _forward(self, x1, x2): """Run forward through the reversible layers.""" @@ -326,10 +325,6 @@ def _forward(self, x1, x2): side_inputs = [self.f_side_input, self.g_side_input] flat_side_inputs = nest.flatten(side_inputs) - custom_grad_fn = ( - self._efficient_grad_fn if self._use_efficient_backprop else None) - - @_fn_with_custom_grad(custom_grad_fn) def _forward_wrap(x1_, x2_, *flat_side_inputs): f_side, g_side = nest.pack_sequence_as(side_inputs, flat_side_inputs) return _rev_block_forward( @@ -342,7 +337,16 @@ def _forward_wrap(x1_, x2_, *flat_side_inputs): g_side_input=g_side, gate_outputs=self._use_efficient_backprop) - return _forward_wrap(x1, x2, *flat_side_inputs) + @custom_gradient.custom_gradient + def _forward_with_custom_grad(*args): + out = _forward_wrap(*args) # pylint: disable=no-value-for-parameter + grad_fn = self._make_efficient_grad_fn(args, out) + return out, grad_fn + + if self._use_efficient_backprop: + return _forward_with_custom_grad(x1, x2, *flat_side_inputs) + else: + return _forward_wrap(x1, x2, *flat_side_inputs) def _backward(self, y1, y2): """Run backward through the reversible layers.""" @@ -560,107 +564,6 @@ def _underlying_variable_ref(t): return None -def _fn_with_custom_grad(grad_fn, use_global_vars=False): - """Decorator to create a subgraph with a custom gradient function. - - The subgraph created by the decorated function is NOT put in a Defun and so - does not suffer from the limitations of the Defun (all subgraph ops on the - same device, no summaries). - - Args: - grad_fn: function with signature - (inputs, variables, outputs, output_grads) -> (grad_inputs, grad_vars), - all of which are lists of Tensors. - use_global_vars: if True, variables will be the global variables created. - If False, will be the trainable variables. - - Returns: - Decorator for function such that the gradient is defined by grad_fn. - """ - - def dec(fn): - - @functools.wraps(fn) - def wrapped(*args): - return _fn_with_custom_grad_internal( - fn, args, grad_fn, use_global_vars=use_global_vars) - - return wrapped - - return dec - - -def _fn_with_custom_grad_internal(fn, inputs, grad_fn, use_global_vars=False): - """Create a subgraph with a custom gradient. - - Args: - fn: function that takes inputs as arguments and produces 1 or more Tensors. - inputs: list, will be passed as fn(*inputs). - grad_fn: function with signature - (inputs, vars, outputs, output_grads) -> (grad_inputs, grad_vars), - all of which are lists of Tensors. - use_global_vars: if True, variables will be the global variables created. - If False, will be the trainable variables. - - Returns: - fn(*inputs) - """ - vs = variable_scope.get_variable_scope() - get_vars_fn = ( - vs.global_variables if use_global_vars else vs.trainable_variables) - len_before_vars = len(get_vars_fn()) - inputs = [array_ops.identity(x) for x in inputs] - outputs = fn(*inputs) - train_vars = get_vars_fn()[len_before_vars:] - - if grad_fn is None: - return outputs - - if not (isinstance(outputs, tuple) or isinstance(outputs, list)): - outputs = [outputs] - outputs = list(outputs) - - defun_inputs = [inputs, train_vars, outputs] - - def custom_grad_fn(op, *dys): - """Custom grad fn applying grad_fn for identity Defun.""" - fn_inputs, fn_vars, fn_outputs = nest.pack_sequence_as( - defun_inputs, list(op.inputs)) - fn_vars = [_underlying_variable_ref(v) for v in fn_vars] - dys = list(dys) - assert len(fn_outputs) == len(outputs) - assert len(fn_outputs) == len(dys) - - grad_inputs, grad_vars = grad_fn(fn_inputs, fn_vars, fn_outputs, dys) - grad_outputs = [None] * len(fn_outputs) - return tuple(grad_inputs + grad_vars + grad_outputs) - - # The Defun takes as input the original inputs, the trainable variables - # created in fn, and the outputs. In the forward it passes through the - # outputs. In the backwards, it produces gradients for the original inputs - # and the trainable variables. - in_types = [t.dtype for t in inputs] - out_types = [t.dtype for t in outputs] - var_types = [t.dtype for t in train_vars] - - # Get a unique name for the Defun - with framework_ops.name_scope("identity_custom_grad") as ns: - defun_name = ns - - @function.Defun( - *(in_types + var_types + out_types), - func_name=defun_name, - python_grad_func=custom_grad_fn, - shape_func=lambda _: [t.get_shape() for t in outputs]) - def identity(*args): - _, _, outs = nest.pack_sequence_as(defun_inputs, args) - return tuple([array_ops.identity(t) for t in outs]) - - flat_inputs = nest.flatten(defun_inputs) - id_out = identity(*flat_inputs) - return id_out - - def _force_data_dependency(first_compute, then_compute): """Force all of `then_compute` to depend on all of `first_compute`. diff --git a/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py b/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py index 8107486d7d9a12..997f53b9e1bbf9 100644 --- a/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py +++ b/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py @@ -83,8 +83,8 @@ def g(x): sess.run(variables.global_variables_initializer()) y1, y2, y1_inv, y2_inv = sess.run([y1, y2, y1_inv, y2_inv]) - self.assertAllClose(y1, y1_inv) - self.assertAllClose(y2, y2_inv) + self.assertAllClose(y1, y1_inv, rtol=1e-5) + self.assertAllClose(y2, y2_inv, rtol=1e-5) def _testRevBlock(self, x=None, @@ -179,18 +179,16 @@ def f2(x): self._testRevBlock(f=[f1, f2, f1, f2]) - # TODO(rsepassi): Recent change to conv seems to have broken this test. Find - # out why. - def _testConvAndBatchNorm(self): + def testConvAndBatchNorm(self): x = random_ops.random_uniform( [self.BATCH_SIZE, 10, self.CHANNELS], dtype=dtypes.float32) def f(x): x = convolutional.conv1d(x, self.CHANNELS // 2, 3, padding="same") - x = layers.batch_norm(x, is_training=True) + x = layers.batch_norm(x, is_training=False) x = convolutional.conv1d(x, self.CHANNELS // 2, 3, padding="same") - x = layers.batch_norm(x, is_training=True) + x = layers.batch_norm(x, is_training=False) return x self._testRevBlock(x=x, f=f) @@ -345,89 +343,5 @@ def layer_with_recompute(inputs): self.assertTrue(grad is not None) -class FnWithCustomGradTest(test.TestCase): - - def testCorrectness(self): - - w = random_ops.random_uniform([6, 10]) - - def fn(a, b, c): - return core_layers.dense( - a, - 10, - use_bias=False, - kernel_initializer=lambda shape, dtype, partition_info: w - ) + math_ops.matmul(b, c) - - def grad_fn(inputs, trainable_variables, outputs, grad_outputs): - outputs = outputs[0] - grad_outputs = grad_outputs[0] - grad_inputs = gradients_impl.gradients( - outputs, inputs, grad_ys=grad_outputs) - grad_vars = gradients_impl.gradients( - outputs, trainable_variables, grad_ys=grad_outputs) - return grad_inputs, grad_vars - - custom_fn = rev_block_lib._fn_with_custom_grad(grad_fn)(fn) - - a = random_ops.random_uniform([11, 6]) - b = random_ops.random_uniform([11, 7]) - c = random_ops.random_uniform([7, 10]) - - out = fn(a, b, c) - custom_out = custom_fn(a, b, c) - self.assertEqual(out.get_shape().as_list(), - custom_out.get_shape().as_list()) - - loss = math_ops.reduce_mean(out) - custom_loss = math_ops.reduce_mean(custom_out) - - grads = gradients_impl.gradients( - loss, [a, b, c] + [variables.trainable_variables()[0]]) - custom_grads = gradients_impl.gradients( - custom_loss, [a, b, c] + [variables.trainable_variables()[1]]) - - with self.test_session() as sess: - sess.run(variables.global_variables_initializer()) - out_val, custom_out_val, grads_val, custom_grads_val = sess.run( - [out, custom_out, grads, custom_grads]) - self.assertAllClose(out_val, custom_out_val) - for g1, g2 in zip(grads_val, custom_grads_val): - self.assertAllClose(g1, g2) - - def testCustomGrad(self): - - def fn(a, b, c): - return core_layers.dense(a, 10, use_bias=False) + math_ops.matmul(b, c) - - def grad_fn(inputs, trainable_variables, unused_outputs, - unused_grad_outputs): - grad_inputs = [ - array_ops.ones_like(t) * (i + 1.) for i, t in enumerate(inputs) - ] - grad_vars = [ - array_ops.ones_like(t) * (i + len(inputs) + 1.) - for i, t in enumerate(trainable_variables) - ] - return grad_inputs, grad_vars - - a = random_ops.random_uniform([11, 6]) - b = random_ops.random_uniform([11, 7]) - c = random_ops.random_uniform([7, 10]) - w = random_ops.random_uniform([6, 10]) - out = rev_block_lib._fn_with_custom_grad(grad_fn)(fn)(a, b, c) - loss = math_ops.reduce_mean(out) - grads = gradients_impl.gradients( - loss, [a, b, c, variables.trainable_variables()[0]]) - expected_grads = [ - array_ops.ones_like(t) * (i + 1.) for i, t in enumerate([a, b, c, w]) - ] - with self.test_session() as sess: - sess.run(variables.global_variables_initializer()) - g_val, eg_val = sess.run([grads, expected_grads]) - for g1, g2 in zip(g_val, eg_val): - self.assertAllClose(g1, g2) - - if __name__ == "__main__": test.main() From d02745e20c02ba7506a920cc4c8b00415f82ee79 Mon Sep 17 00:00:00 2001 From: Dimitris Vardoulakis Date: Sat, 28 Apr 2018 22:19:22 -0700 Subject: [PATCH 0149/1691] [TF:XLA] - Require a module config when creating an HloModule. - All tests using HloTestBase create a module using CreateNewModule. PiperOrigin-RevId: 194684585 --- tensorflow/compiler/xla/reference_util.cc | 3 +- tensorflow/compiler/xla/service/BUILD | 2 + .../xla/service/algebraic_simplifier_test.cc | 101 ++++++++------- .../xla/service/buffer_assignment_test.cc | 12 +- .../compiler/xla/service/graphviz_example.cc | 3 +- .../xla/service/heap_simulator_test.cc | 6 +- .../xla/service/hlo_cost_analysis_test.cc | 8 +- .../xla/service/hlo_creation_utils_test.cc | 51 ++++---- .../compiler/xla/service/hlo_evaluator.cc | 3 +- .../xla/service/hlo_graph_dumper_test.cc | 18 +-- .../xla/service/hlo_instruction_test.cc | 122 +++++++++--------- tensorflow/compiler/xla/service/hlo_module.cc | 6 +- tensorflow/compiler/xla/service/hlo_module.h | 1 - .../xla/service/transpose_folding_test.cc | 50 +++---- .../zero_sized_hlo_elimination_test.cc | 6 +- .../compiler/xla/tests/hlo_test_base.cc | 5 +- tensorflow/compiler/xla/tests/hlo_test_base.h | 3 +- 17 files changed, 205 insertions(+), 195 deletions(-) diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc index df9dbc58308f04..c289c84cff7438 100644 --- a/tensorflow/compiler/xla/reference_util.cc +++ b/tensorflow/compiler/xla/reference_util.cc @@ -572,7 +572,8 @@ ReferenceUtil::ConvArray4DGeneralDimensionsDilated( b.AddInstruction(HloInstruction::CreateConvolve( shape, lhs_instruction, rhs_instruction, window, dnums)); - HloModule module("ReferenceUtil"); + HloModuleConfig config; + HloModule module("ReferenceUtil", config); auto computation = module.AddEntryComputation(b.Build()); HloEvaluator evaluator; diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index f39bfb8012d701..ed0da47681c7ef 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -1330,6 +1330,7 @@ tf_cc_test( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:window_util", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:hlo_verified_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", # fixdeps: keep "//tensorflow/core:lib", @@ -2420,6 +2421,7 @@ tf_cc_test( ":hlo_graph_dumper", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:xla_proto", + "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:test_utils", "//tensorflow/compiler/xla/tests:xla_internal_test_main", # fixdeps: keep "//tensorflow/core:lib", diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc index 20c549562d5153..d0c99bf818cd54 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc @@ -28,6 +28,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_pass_fix.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" #include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/window_util.h" @@ -1699,14 +1700,14 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopPad) { builder.AddInstruction(HloInstruction::CreatePad( ShapeUtil::MakeShape(F32, {2, 2}), param, zero, no_padding)); - HloModule module(TestName()); - HloComputation* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + HloComputation* computation = module->AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Pad(param, zero)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(&module).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), param); } @@ -1732,8 +1733,8 @@ TEST_F(AlgebraicSimplifierTest, NegativePadding) { HloInstruction* pad = builder.AddInstruction(HloInstruction::CreatePad( ShapeUtil::MakeShape(F32, {11, 5}), param, zero, padding)); - HloModule module(TestName()); - HloComputation* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + HloComputation* computation = module->AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); @@ -1751,7 +1752,7 @@ TEST_F(AlgebraicSimplifierTest, NegativePadding) { EXPECT_THAT(computation->root_instruction(), op::Pad(param, zero)); EXPECT_TRUE(has_negative_padding(pad)); - ASSERT_TRUE(simplifier.Run(&module).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Slice(op::Pad(param, zero))); EXPECT_FALSE( @@ -1766,14 +1767,14 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopReshape) { builder.AddInstruction( HloInstruction::CreateReshape(ShapeUtil::MakeShape(F32, {2, 3}), param)); - HloModule module(TestName()); - HloComputation* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + HloComputation* computation = module->AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Reshape(param)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(&module).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), param); } @@ -1789,14 +1790,14 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopSlice) { ShapeUtil::MakeShape(F32, {dim0, dim1}), param, /*start_indices=*/{0, 0}, /*limit_indices=*/{dim0, dim1}, /*strides=*/{1, 1})); - HloModule module(TestName()); - HloComputation* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + HloComputation* computation = module->AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Slice(param)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(&module).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), param); } @@ -1924,12 +1925,12 @@ TEST_F(AlgebraicSimplifierTest, ConvertConvToMatmul) { b.AddInstruction(HloInstruction::CreateConvolve(out_shape, input, filter, window, dnums)); - HloModule module(TestName()); - auto* computation = module.AddEntryComputation(b.Build()); + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(b.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/true, bitcasting_callback()); - if (!simplifier.Run(&module).ValueOrDie()) { + if (!simplifier.Run(module.get()).ValueOrDie()) { return "NO_CHANGE"; } auto* root = computation->root_instruction(); @@ -2044,15 +2045,15 @@ TEST_F(AlgebraicSimplifierTest, MaxMinToClamp) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kMaximum, min, max_value)); - HloModule module(TestName()); - auto computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Maximum(op::Minimum(param0, min_value), max_value)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(&module).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Clamp(max_value, param0, min_value)); @@ -2074,15 +2075,15 @@ TEST_F(AlgebraicSimplifierTest, MinMaxToClamp) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kMinimum, max, min_value)); - HloModule module(TestName()); - auto computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Minimum(op::Maximum(param0, max_value), min_value)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(&module).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Clamp(max_value, param0, min_value)); @@ -2105,15 +2106,15 @@ TEST_F(AlgebraicSimplifierTest, MinMaxWithBroadcastToClamp) { builder.AddInstruction( HloInstruction::CreateBinary(r1f32, HloOpcode::kMinimum, max, min_value)); - HloModule module(TestName()); - auto computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Minimum(op::Maximum(param0, max_value), min_value)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(&module).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Clamp(max_value, param0, min_value)); @@ -2135,15 +2136,15 @@ TEST_F(AlgebraicSimplifierTest, MinMaxNotToClamp) { builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kMinimum, max, min_value)); - HloModule module(TestName()); - auto computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Minimum(op::Maximum(param0, max_value), min_value)); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - EXPECT_FALSE(simplifier.Run(&module).ValueOrDie()); + EXPECT_FALSE(simplifier.Run(module.get()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Minimum(op::Maximum(param0, max_value), min_value)); @@ -2167,8 +2168,8 @@ TEST_F(AlgebraicSimplifierTest, MinEquationWithMaxNotToClamp) { builder.AddInstruction(HloInstruction::CreateBinary( r0f32, HloOpcode::kMinimum, fmax, min_value)); - HloModule module(TestName()); - auto computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); EXPECT_THAT(computation->root_instruction(), op::Minimum(op::Add(op::Maximum(param0, max_value), max_value), @@ -2176,7 +2177,7 @@ TEST_F(AlgebraicSimplifierTest, MinEquationWithMaxNotToClamp) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - EXPECT_FALSE(simplifier.Run(&module).ValueOrDie()); + EXPECT_FALSE(simplifier.Run(module.get()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Minimum(op::Add(op::Maximum(param0, max_value), max_value), @@ -2201,8 +2202,8 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToSlice) { HloInstruction* slice = builder.AddInstruction(HloInstruction::CreateSlice( slice_shape, broadcast, {0, 1, 2, 3}, {2, 3, 5, 6}, {1, 1, 1, 1})); - HloModule module(TestName()); - auto computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root, slice); @@ -2211,10 +2212,10 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToSlice) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(&module).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); // Running simplification again should not result in any further changes. - ASSERT_FALSE(simplifier.Run(&module).ValueOrDie()); + ASSERT_FALSE(simplifier.Run(module.get()).ValueOrDie()); root = computation->root_instruction(); EXPECT_THAT(root, op::Broadcast(scalar_param)); @@ -2242,8 +2243,8 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToTransposeReshape) { HloInstruction* reshape = builder.AddInstruction( HloInstruction::CreateReshape(reshape_shape, transpose)); - HloModule module(TestName()); - auto computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root, reshape); @@ -2251,7 +2252,7 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToTransposeReshape) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(&module).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); root = computation->root_instruction(); EXPECT_THAT(root, op::Broadcast(forty_two)); @@ -2260,7 +2261,7 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToTransposeReshape) { // Test that ReduceWindow(Pad(op, x), y) can simplify to ReduceWindow(op, x). TEST_F(AlgebraicSimplifierTest, FoldPadIntoReduceWindow) { - HloModule module(TestName()); + auto module = CreateNewModule(); HloComputation::Builder builder(TestName()); // Create operand to the pad. @@ -2289,7 +2290,7 @@ TEST_F(AlgebraicSimplifierTest, FoldPadIntoReduceWindow) { HloInstruction::CreateParameter(1, scalar_shape, "p1")); builder.AddInstruction( HloInstruction::CreateBinary(scalar_shape, HloOpcode::kAdd, p0, p1)); - add_computation = module.AddEmbeddedComputation(builder.Build()); + add_computation = module->AddEmbeddedComputation(builder.Build()); } // Create the reduce-window. @@ -2312,15 +2313,15 @@ TEST_F(AlgebraicSimplifierTest, FoldPadIntoReduceWindow) { add_computation)); // Build the computation and run the simplifier. - auto computation = module.AddEntryComputation(builder.Build()); + auto computation = module->AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root, reduce_window); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(&module).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); // Running simplification again should not result in any further changes. - ASSERT_FALSE(simplifier.Run(&module).ValueOrDie()); + ASSERT_FALSE(simplifier.Run(module.get()).ValueOrDie()); // Verify the result root = computation->root_instruction(); @@ -2341,7 +2342,7 @@ TEST_F(AlgebraicSimplifierTest, FoldPadIntoReduceWindow) { // Test that ReduceWindow(Convert(Pad(op, x)), y) can simplify to // ReduceWindow(Convert(op), x). TEST_F(AlgebraicSimplifierTest, FoldConvertedPadIntoReduceWindow) { - HloModule module(TestName()); + auto module = CreateNewModule(); HloComputation::Builder builder(TestName()); // Create operand to the pad. @@ -2374,7 +2375,7 @@ TEST_F(AlgebraicSimplifierTest, FoldConvertedPadIntoReduceWindow) { HloInstruction::CreateParameter(1, scalar_shape, "p1")); builder.AddInstruction( HloInstruction::CreateBinary(scalar_shape, HloOpcode::kAdd, p0, p1)); - add_computation = module.AddEmbeddedComputation(builder.Build()); + add_computation = module->AddEmbeddedComputation(builder.Build()); } // Create the reduce-window. @@ -2397,15 +2398,15 @@ TEST_F(AlgebraicSimplifierTest, FoldConvertedPadIntoReduceWindow) { add_computation)); // Build the computation and run the simplifier. - auto computation = module.AddEntryComputation(builder.Build()); + auto computation = module->AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(root, reduce_window); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(&module).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); // Running simplification again should not result in any further changes. - ASSERT_FALSE(simplifier.Run(&module).ValueOrDie()); + ASSERT_FALSE(simplifier.Run(module.get()).ValueOrDie()); // Verify the result root = computation->root_instruction(); @@ -2431,12 +2432,12 @@ TEST_F(AlgebraicSimplifierTest, ReversalOfTrivialDimensionsToBitcast) { builder.AddInstruction( HloInstruction::CreateReverse(shape, a, /*dimensions=*/{2, 3})); - HloModule module(TestName()); - auto computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(&module).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(a, root); diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc index 513a8785bbd52b..3ec9795a655041 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc @@ -1641,7 +1641,7 @@ static void RunCopyInsertion(HloModule* module) { } TEST_F(WhileBufferAssignmentTest, TwoForwardWhileLoops) { - auto module = xla::MakeUnique(TestName()); + auto module = CreateNewModule(); auto builder = HloComputation::Builder("entry"); auto input0 = builder.AddInstruction( @@ -1816,7 +1816,7 @@ TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) { }; // Build the entry computation as described in the comment above. - auto module = xla::MakeUnique(TestName()); + auto module = CreateNewModule(); auto builder = HloComputation::Builder("entry"); auto infeed = builder.AddInstruction(HloInstruction::CreateInfeed(r0s32, "")); @@ -1884,7 +1884,7 @@ TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) { } TEST_F(WhileBufferAssignmentTest, OneForwardBackwardWhileLoopSet) { - auto module = xla::MakeUnique(TestName()); + auto module = CreateNewModule(); auto builder = HloComputation::Builder("entry"); auto input0 = builder.AddInstruction( @@ -1929,7 +1929,7 @@ TEST_F(WhileBufferAssignmentTest, OneForwardBackwardWhileLoopSet) { } TEST_F(BufferAssignmentTest, TwoCalls) { - auto module = xla::MakeUnique(TestName()); + auto module = CreateNewModule(); Shape r0f32 = ShapeUtil::MakeShape(xla::F32, {}); HloComputation* sub_computation; { @@ -1994,7 +1994,7 @@ static bool IsPostOrderTraversal( } TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) { - auto module = xla::MakeUnique(TestName()); + auto module = CreateNewModule(); auto builder = HloComputation::Builder(TestName()); auto zero = builder.AddInstruction( @@ -2073,7 +2073,7 @@ TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) { } TEST_F(WhileBufferAssignmentTest, WhilesDontShareEntryParamIfLiveOut) { - auto module = xla::MakeUnique(TestName()); + auto module = CreateNewModule(); auto builder = HloComputation::Builder("entry"); auto input0 = builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/graphviz_example.cc b/tensorflow/compiler/xla/service/graphviz_example.cc index 05017008e2ddbe..acf661148699da 100644 --- a/tensorflow/compiler/xla/service/graphviz_example.cc +++ b/tensorflow/compiler/xla/service/graphviz_example.cc @@ -82,7 +82,8 @@ HloComputation* CallForwardingComputation(HloComputation* computation, // instructions. Sets the computation as the entry to an HLO module and returns // the module. std::unique_ptr MakeBigGraph() { - auto module = MakeUnique("BigGraph"); + HloModuleConfig config; + auto module = MakeUnique("BigGraph", config); auto builder = HloComputation::Builder("TestBigGraphvizGraph"); diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc index 688a271712ac24..e983fd11d4eefc 100644 --- a/tensorflow/compiler/xla/service/heap_simulator_test.cc +++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc @@ -76,7 +76,8 @@ class HeapSimulatorTracker { HeapSimulatorTracker( const string& name, std::unique_ptr computation, const std::vector& instruction_sequence) { - module_ = MakeUnique(name); + HloModuleConfig config; + module_ = MakeUnique(name, config); module_->AddEntryComputation(std::move(computation)); points_to_analysis_ = TuplePointsToAnalysis::Run(module_.get()).ConsumeValueOrDie(); @@ -94,7 +95,8 @@ class HeapSimulatorTracker { } explicit HeapSimulatorTracker(const string& name) { - module_ = MakeUnique(name); + HloModuleConfig config; + module_ = MakeUnique(name, config); } // Similar to the single entry computation constructor above, but runs the diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc index 3d055b327ee920..81cc7c4bdc1e00 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc @@ -370,8 +370,8 @@ TEST_F(FusionCostAnalysis, LoopFusion) { HloInstruction::CreateBinary(r2f32, HloOpcode::kSubtract, mul, clamp)); auto tuple = HloInstruction::CreateTuple({sub, sub, mul, c1}); - HloModule module(TestName()); - auto* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(builder.Build()); auto* fusion = computation->CreateFusionInstruction( {sub, mul, exp, clamp, add}, HloInstruction::FusionKind::kLoop); @@ -412,8 +412,8 @@ TEST_F(FusionCostAnalysis, NoLayout) { auto add = builder.AddInstruction(HloInstruction::CreateBinary( shape_with_layout, HloOpcode::kAdd, c1, broadcast)); - HloModule module(TestName()); - auto* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(builder.Build()); auto* fusion = computation->CreateFusionInstruction( {add, broadcast}, HloInstruction::FusionKind::kLoop); diff --git a/tensorflow/compiler/xla/service/hlo_creation_utils_test.cc b/tensorflow/compiler/xla/service/hlo_creation_utils_test.cc index 6b681a5bf6f34b..7e7c4f95fed737 100644 --- a/tensorflow/compiler/xla/service/hlo_creation_utils_test.cc +++ b/tensorflow/compiler/xla/service/hlo_creation_utils_test.cc @@ -19,27 +19,32 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" #include "tensorflow/core/platform/test.h" namespace xla { namespace { using tensorflow::gtl::ArraySlice; -std::unique_ptr CreateModuleWithProgramShape( - PrimitiveType primitive_type, ArraySlice input_shape_dims, - ArraySlice output_shape_dims, HloInstruction** param, - HloComputation** entry_computation) { - Shape input_shape = ShapeUtil::MakeShape(primitive_type, input_shape_dims); - Shape output_shape = ShapeUtil::MakeShape(primitive_type, output_shape_dims); - std::unique_ptr module = MakeUnique("test"); - *entry_computation = module->AddEntryComputation( - CreateComputationWithSignature({&input_shape}, output_shape, "entry") - .ValueOrDie()); - *param = (*entry_computation)->parameter_instruction(0); - return module; -} - -TEST(HloCreationUtilsTest, CollapseFirst1Dim) { +class HloCreationUtilsTest : public HloTestBase { + protected: + static std::unique_ptr CreateModuleWithProgramShape( + PrimitiveType primitive_type, ArraySlice input_shape_dims, + ArraySlice output_shape_dims, HloInstruction** param, + HloComputation** entry_computation) { + Shape input_shape = ShapeUtil::MakeShape(primitive_type, input_shape_dims); + Shape output_shape = + ShapeUtil::MakeShape(primitive_type, output_shape_dims); + auto module = CreateNewModule("test"); + *entry_computation = module->AddEntryComputation( + CreateComputationWithSignature({&input_shape}, output_shape, "entry") + .ValueOrDie()); + *param = (*entry_computation)->parameter_instruction(0); + return module; + } +}; + +TEST_F(HloCreationUtilsTest, CollapseFirst1Dim) { HloInstruction* param; HloComputation* entry_computation; @@ -59,7 +64,7 @@ TEST(HloCreationUtilsTest, CollapseFirst1Dim) { CHECK_EQ(*result_literal, *Literal::CreateR1({3, 4})); } -TEST(HloCreationUtilsTest, CollapseFirst2Dims) { +TEST_F(HloCreationUtilsTest, CollapseFirst2Dims) { HloInstruction* param; HloComputation* entry_computation; @@ -84,7 +89,7 @@ TEST(HloCreationUtilsTest, CollapseFirst2Dims) { {{1, 2}, {3, 4}, {5, 6}, {-1, -2}, {-3, -4}, {-5, -6}})); } -TEST(HloCreationUtilsTest, Prepend1DegenerateDim) { +TEST_F(HloCreationUtilsTest, Prepend1DegenerateDim) { HloInstruction* param; HloComputation* entry_computation; @@ -104,7 +109,7 @@ TEST(HloCreationUtilsTest, Prepend1DegenerateDim) { CHECK_EQ(*result_literal, *Literal::CreateR2({{9, 10}})); } -TEST(HloCreationUtilsTest, Prepend2DegenerateDims) { +TEST_F(HloCreationUtilsTest, Prepend2DegenerateDims) { HloInstruction* param; HloComputation* entry_computation; @@ -124,7 +129,7 @@ TEST(HloCreationUtilsTest, Prepend2DegenerateDims) { CHECK_EQ(*result_literal, *Literal::CreateR3({{{9, 10}}})); } -TEST(HloCreationUtilsTest, Prepend2DegenerateDimsToScalar) { +TEST_F(HloCreationUtilsTest, Prepend2DegenerateDimsToScalar) { HloInstruction* param; HloComputation* entry_computation; @@ -144,7 +149,7 @@ TEST(HloCreationUtilsTest, Prepend2DegenerateDimsToScalar) { CHECK_EQ(*result_literal, *Literal::CreateR2({{9}})); } -TEST(HloCreationUtilsTest, ExpandFirstDimInto3Dims) { +TEST_F(HloCreationUtilsTest, ExpandFirstDimInto3Dims) { HloInstruction* param; HloComputation* entry_computation; @@ -166,7 +171,7 @@ TEST(HloCreationUtilsTest, ExpandFirstDimInto3Dims) { *Literal::CreateR3({{{1, 2}}, {{3, 4}}, {{5, 6}}})); } -TEST(HloCreationUtilsTest, PadVectorWithZeros) { +TEST_F(HloCreationUtilsTest, PadVectorWithZeros) { HloInstruction* param; HloComputation* entry_computation; @@ -187,7 +192,7 @@ TEST(HloCreationUtilsTest, PadVectorWithZeros) { CHECK_EQ(*result_literal, *Literal::CreateR1({0, 0, 0, 3, 4, 0})); } -TEST(HloCreationUtilsTest, BroadcastZeros_S32) { +TEST_F(HloCreationUtilsTest, BroadcastZeros_S32) { HloInstruction* param; HloComputation* entry_computation; @@ -208,7 +213,7 @@ TEST(HloCreationUtilsTest, BroadcastZeros_S32) { CHECK_EQ(*result_literal, *Literal::CreateR2({{0, 0}, {0, 0}})); } -TEST(HloCreationUtilsTest, BroadcastZeros_F32) { +TEST_F(HloCreationUtilsTest, BroadcastZeros_F32) { HloInstruction* param; HloComputation* entry_computation; diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index f1dcef1dfcd470..8cf94123b71403 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -2968,9 +2968,10 @@ Status HloEvaluator::HandleCall(HloInstruction* call) { } Status HloEvaluator::HandleFusion(HloInstruction* fusion) { + HloModuleConfig config; // Attach cloned computation to an empty HLO module so the existing ones are // not modified. - HloModule empty_hlo_module("EmptyModuleForFusion"); + HloModule empty_hlo_module("EmptyModuleForFusion", config); auto cloned_fused_computation = fusion->fused_instructions_computation()->Clone( /*suffix=*/"clone_with_layout", &empty_hlo_module); diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc index 1f00aa41dc783f..b589cd573d8293 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" #include "tensorflow/compiler/xla/tests/test_utils.h" #include "tensorflow/compiler/xla/xla.pb.h" #include "tensorflow/core/lib/strings/strcat.h" @@ -47,7 +48,9 @@ class DotRenderer : public hlo_graph_dumper::GraphRendererInterface { XLA_REGISTER_GRAPH_RENDERER(DotRenderer); -TEST(HloGraphDumperTest, NestedFusion) { +class HloGraphDumperTest : public HloTestBase {}; + +TEST_F(HloGraphDumperTest, NestedFusion) { HloComputation::Builder b("b"); // Build param0 + param1 + param2 + param3 + param4. @@ -64,10 +67,9 @@ TEST(HloGraphDumperTest, NestedFusion) { sums.push_back(b.AddInstruction(HloInstruction::CreateBinary( shape, HloOpcode::kAdd, sums[i], params[i + 2]))); } - - HloModule m(TestName()); - m.AddEntryComputation(b.Build()); - HloComputation* root_computation = m.entry_computation(); + auto m = CreateNewModule(); + m->AddEntryComputation(b.Build()); + HloComputation* root_computation = m->entry_computation(); // Fuse into fusion(param0 + param1 + param2 + param3 + param4). auto* outer_fusion = root_computation->CreateFusionInstruction( @@ -117,13 +119,13 @@ TEST(HloGraphDumperTest, NestedFusion) { HasSubstr(inner_sum->name())); } -TEST(HloGraphDumperTest, Constant) { +TEST_F(HloGraphDumperTest, Constant) { HloComputation::Builder b("b"); auto instruction = b.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(-42))); instruction->set_name("i_am_a_constant_root_instruction"); - HloModule m(TestName()); - HloComputation* root_computation = m.AddEntryComputation(b.Build()); + auto m = CreateNewModule(); + HloComputation* root_computation = m->AddEntryComputation(b.Build()); string graph = hlo_graph_dumper::DumpGraph( *root_computation, /*label=*/"an_empty_graph", DebugOptions()); EXPECT_THAT(graph, HasSubstr("an_empty_graph")); diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc index f2980d309d01fd..5b65b1152c8298 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc @@ -149,8 +149,8 @@ TEST_F(HloInstructionTest, UserWithTwoOperands) { builder.AddInstruction(HloInstruction::CreateParameter(1, r0f32_, "bar")); auto add = builder.AddInstruction( HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, foo, bar)); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); EXPECT_THAT(add->operands(), UnorderedElementsAre(foo, bar)); EXPECT_THAT(foo->users(), UnorderedElementsAre(add)); @@ -186,8 +186,8 @@ TEST_F(HloInstructionTest, MultipleUsers) { HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, foo)); auto add = builder.AddInstruction( HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, foo, bar)); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); EXPECT_EQ(3, foo->user_count()); EXPECT_EQ(1, bar->user_count()); @@ -219,8 +219,8 @@ TEST_F(HloInstructionTest, RepeatedUser) { builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "foo")); auto add = builder.AddInstruction( HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, foo, foo)); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); EXPECT_EQ(1, foo->user_count()); @@ -254,8 +254,8 @@ TEST_F(HloInstructionTest, MultipleUsersAndOperands) { HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, c0, param1)); auto addtotal = builder.AddInstruction( HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, addleft, addright)); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); OpAndUserCollectingVisitor visitor; ASSERT_IS_OK(addtotal->Accept(&visitor)); @@ -303,8 +303,8 @@ TEST_F(HloInstructionTest, MultipleUsersAndOperandsWithUnaryOps) { HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, addleft, addright)); auto neg2 = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, addtotal)); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); OpAndUserCollectingVisitor visitor; ASSERT_IS_OK(neg2->Accept(&visitor)); @@ -325,7 +325,7 @@ TEST_F(HloInstructionTest, TrivialMap) { // Shape r0f32 = ShapeUtil::MakeShape(F32, {}); Shape f32a100x10 = ShapeUtil::MakeShape(F32, {100, 10}); - HloModule module(TestName()); + auto module = CreateNewModule(); // Builds an x+1.0 computation to use in a Map. auto embedded_builder = HloComputation::Builder("f32+1"); @@ -335,7 +335,7 @@ TEST_F(HloInstructionTest, TrivialMap) { HloInstruction::CreateConstant(Literal::CreateR0(1.0))); embedded_builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, param, value)); - auto add_f32 = module.AddEmbeddedComputation(embedded_builder.Build()); + auto add_f32 = module->AddEmbeddedComputation(embedded_builder.Build()); // Builds a parameter and feeds it to the map. HloComputation::Builder builder(TestName()); @@ -343,7 +343,7 @@ TEST_F(HloInstructionTest, TrivialMap) { HloInstruction::CreateParameter(0, f32a100x10, "")); auto map = builder.AddInstruction( HloInstruction::CreateMap(f32a100x10, {param0}, add_f32)); - module.AddEntryComputation(builder.Build()); + module->AddEntryComputation(builder.Build()); OpAndUserCollectingVisitor visitor; ASSERT_IS_OK(map->Accept(&visitor)); @@ -373,8 +373,8 @@ TEST_F(HloInstructionTest, TrivialReduce) { HloInstruction::CreateParameter(1, r0f32, "y")); embedded_builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, paramx, paramy)); - HloModule module(TestName()); - auto add_f32 = module.AddEmbeddedComputation(embedded_builder.Build()); + auto module = CreateNewModule(); + auto add_f32 = module->AddEmbeddedComputation(embedded_builder.Build()); // Builds a parameter and an initial value and feeds them to the reduce. HloComputation::Builder builder(TestName()); @@ -387,7 +387,7 @@ TEST_F(HloInstructionTest, TrivialReduce) { auto reduce = builder.AddInstruction( HloInstruction::CreateReduce(f32v100, param0, const0, /*dimensions_to_reduce=*/{1}, add_f32)); - module.AddEntryComputation(builder.Build()); + module->AddEntryComputation(builder.Build()); OpAndUserCollectingVisitor visitor; ASSERT_IS_OK(reduce->Accept(&visitor)); @@ -414,8 +414,8 @@ TEST_F(HloInstructionTest, ReplaceUseInBinaryOps) { HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, foo, foo)); builder.AddInstruction(HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, add_foobar, add_foofoo)); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); EXPECT_EQ(2, foo->user_count()); EXPECT_EQ(1, bar->user_count()); @@ -449,8 +449,8 @@ TEST_F(HloInstructionTest, ReplaceUseInVariadicOp) { builder.AddInstruction(HloInstruction::CreateTuple({foo, bar, baz, foo})); auto add_foobar = builder.AddInstruction( HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, foo, bar)); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); EXPECT_EQ(2, foo->user_count()); EXPECT_THAT(foo->users(), UnorderedElementsAre(tuple, add_foobar)); @@ -477,8 +477,8 @@ TEST_F(HloInstructionTest, ReplaceUseInUnaryOp) { HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, foo)); auto log = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kLog, foo)); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); EXPECT_EQ(2, foo->user_count()); EXPECT_THAT(foo->users(), UnorderedElementsAre(exp, log)); @@ -514,8 +514,8 @@ TEST_F(HloInstructionTest, ReplaceAllUsesWithInBinaryOps) { HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, foo, foo)); builder.AddInstruction(HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, add_foobar, add_foofoo)); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); EXPECT_EQ(2, foo->user_count()); EXPECT_EQ(1, bar->user_count()); @@ -544,8 +544,8 @@ TEST_F(HloInstructionTest, ReplaceAllUsesInMultipleOps) { auto exp = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, foo)); auto tuple = builder.AddInstruction(HloInstruction::CreateTuple({foo, bar})); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); EXPECT_EQ(3, foo->user_count()); EXPECT_EQ(2, bar->user_count()); @@ -609,8 +609,8 @@ TEST_F(HloInstructionTest, PostProcessAllVisitedNodes) { HloInstruction::CreateUnary(r0f32_, HloOpcode::kLog, foo)); auto add = builder.AddInstruction( HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, exp, log)); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); NodeCollectorAndPostProcessor visitor; ASSERT_IS_OK(add->Accept(&visitor)); @@ -627,8 +627,8 @@ TEST_F(HloInstructionTest, SingletonFusionOp) { HloInstruction::CreateConstant(Literal::CreateR0(1.1f))); auto exp = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant)); - HloModule module(TestName()); - auto* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(builder.Build()); auto* fusion = computation->CreateFusionInstruction( {exp}, HloInstruction::FusionKind::kLoop); @@ -645,8 +645,8 @@ TEST_F(HloInstructionTest, BinaryFusionOp) { HloInstruction::CreateConstant(Literal::CreateR0(42.1f))); auto add = builder.AddInstruction(HloInstruction::CreateBinary( r0f32_, HloOpcode::kAdd, constant1, constant2)); - HloModule module(TestName()); - auto* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(builder.Build()); auto* fusion = computation->CreateFusionInstruction( {add}, HloInstruction::FusionKind::kLoop); @@ -667,8 +667,8 @@ TEST_F(HloInstructionTest, ChainFusionOp) { auto exp3 = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, exp2)); - HloModule module(TestName()); - auto* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(builder.Build()); auto* fusion = computation->CreateFusionInstruction( {exp3, exp2, exp1}, HloInstruction::FusionKind::kLoop); @@ -690,8 +690,8 @@ TEST_F(HloInstructionTest, PreserveMetadataInFusionAndClone) { exp1->set_metadata(metadata); exp2->set_metadata(metadata); - HloModule module(TestName()); - auto* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(builder.Build()); auto* fusion = computation->CreateFusionInstruction( {exp2, exp1}, HloInstruction::FusionKind::kLoop); @@ -746,13 +746,13 @@ TEST_F(HloInstructionTest, PreserveTupleShapeThroughClone) { TEST_F(HloInstructionTest, FusionOpWithCalledComputations) { // Create a fusion instruction containing a single unary operation. const Shape scalar_shape = ShapeUtil::MakeShape(F32, {}); - HloModule module(TestName()); + auto module = CreateNewModule(); auto make_map_computation = [&]() { auto builder = HloComputation::Builder("FusionMap"); builder.AddInstruction( HloInstruction::CreateParameter(0, scalar_shape, "param")); - return module.AddEmbeddedComputation(builder.Build()); + return module->AddEmbeddedComputation(builder.Build()); }; HloComputation* computation_x = make_map_computation(); @@ -767,7 +767,7 @@ TEST_F(HloInstructionTest, FusionOpWithCalledComputations) { scalar_shape, {map_1_x}, computation_x, /*static_operands=*/{})); auto map_3_y = builder.AddInstruction(HloInstruction::CreateMap( scalar_shape, {map_2_x}, computation_y, /*static_operands=*/{})); - auto* computation = module.AddEntryComputation(builder.Build()); + auto* computation = module->AddEntryComputation(builder.Build()); auto* fusion = computation->CreateFusionInstruction( {map_3_y}, HloInstruction::FusionKind::kLoop); @@ -814,8 +814,8 @@ TEST_F(HloInstructionTest, ComplexFusionOp) { auto tuple = builder.AddInstruction(HloInstruction::CreateTuple({sub, sub, mul, c1})); - HloModule module(TestName()); - auto* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(builder.Build()); auto* fusion = computation->CreateFusionInstruction( {tuple, sub, mul, exp, clamp, add}, HloInstruction::FusionKind::kLoop); @@ -940,8 +940,8 @@ TEST_F(HloInstructionTest, FunctionVisitor) { HloInstruction::CreateUnary(f32, HloOpcode::kExp, param)); auto add = builder.AddInstruction( HloInstruction::CreateBinary(f32, HloOpcode::kAdd, negate, exp)); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); int visit_num = 0; std::unordered_map visit_order; @@ -969,8 +969,8 @@ TEST_F(HloInstructionTest, FullyElementwise) { builder.AddInstruction(HloInstruction::CreateParameter(1, r1f32, "y")); auto add = builder.AddInstruction( HloInstruction::CreateBinary(r1f32, HloOpcode::kAdd, x, y)); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); EXPECT_TRUE(add->IsElementwise()); for (int i = 0; i < add->operand_count(); ++i) { @@ -1013,8 +1013,8 @@ TEST_F(HloInstructionTest, PartiallyElementwise) { HloInstruction* max = builder.AddInstruction( HloInstruction::CreateBinary(r2f32, HloOpcode::kMaximum, div, broadcast)); - HloModule module(TestName()); - auto* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(builder.Build()); HloInstruction* fusion = computation->CreateFusionInstruction( {max, broadcast, div, mul}, HloInstruction::FusionKind::kLoop); EXPECT_FALSE(fusion->IsElementwise()); @@ -1056,8 +1056,8 @@ TEST_F(HloInstructionTest, PartiallyElementwiseWithReuse) { HloInstruction* sub = builder.AddInstruction(HloInstruction::CreateBinary( r1f32, HloOpcode::kSubtract, min, broadcast)); - HloModule module(TestName()); - auto* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(builder.Build()); HloInstruction* fusion = computation->CreateFusionInstruction( {sub, broadcast, min}, HloInstruction::FusionKind::kLoop); EXPECT_FALSE(fusion->IsElementwise()); @@ -1099,8 +1099,8 @@ TEST_F(HloInstructionTest, CloneOfFusionPreservesShape) { HloInstruction* dot = builder.AddInstruction( HloInstruction::CreateDot(sout, x, reshape, dot_dnums)); - HloModule module(TestName()); - auto* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(builder.Build()); HloInstruction* fusion = computation->CreateFusionInstruction( {dot, reshape}, HloInstruction::FusionKind::kTransposeDot); @@ -1118,7 +1118,7 @@ TEST_F(HloInstructionTest, CloneOfFusionPreservesShape) { } TEST_F(HloInstructionTest, FusionEquality) { - HloModule module(TestName()); + auto module = CreateNewModule(); HloComputation::Builder builder(TestName()); // Create two fusion instructions containing a single unary operation. @@ -1128,7 +1128,7 @@ TEST_F(HloInstructionTest, FusionEquality) { HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, parameter)); auto neg = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, parameter)); - auto* computation = module.AddEntryComputation(builder.Build()); + auto* computation = module->AddEntryComputation(builder.Build()); auto* fusion = computation->CreateFusionInstruction( {exp}, HloInstruction::FusionKind::kLoop); auto* fusion2 = computation->CreateFusionInstruction( @@ -1140,7 +1140,7 @@ TEST_F(HloInstructionTest, FusionEquality) { } TEST_F(HloInstructionTest, NestedFusionEquality) { - HloModule module(TestName()); + auto module = CreateNewModule(); HloComputation::Builder builder(TestName()); // Build a nested fusion computation. @@ -1166,7 +1166,7 @@ TEST_F(HloInstructionTest, NestedFusionEquality) { data_shape, HloOpcode::kSubtract, dot, add_operand)); builder.AddInstruction( HloInstruction::CreateBinary(data_shape, HloOpcode::kMultiply, add, sub)); - auto computation = module.AddEntryComputation(builder.Build()); + auto computation = module->AddEntryComputation(builder.Build()); auto nested_fusion = computation->CreateFusionInstruction( {dot, b_t}, HloInstruction::FusionKind::kTransposeDot); @@ -1244,8 +1244,8 @@ TEST_F(HloInstructionTest, Stringification) { "%dot = f32[5,20]{1,0} dot(f32[5,10]{1,0} %x, f32[10,20]{1,0} " "%transpose), lhs_contracting_dims={1}, rhs_contracting_dims={0}"); - HloModule module(TestName()); - auto* computation = module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(builder.Build()); HloInstruction* fusion = computation->CreateFusionInstruction( {dot, reshape}, HloInstruction::FusionKind::kTransposeDot); @@ -1295,8 +1295,8 @@ TEST_F(HloInstructionTest, StringifyGather_0) { /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 27, 26})); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); EXPECT_EQ(gather_instruction->ToString(), "%gather = f32[10,9,8,7,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} " @@ -1331,8 +1331,8 @@ TEST_F(HloInstructionTest, StringifyGather_1) { /*index_vector_dim=*/2), /*window_bounds=*/{30, 29, 28, 27, 26})); - HloModule module(TestName()); - module.AddEntryComputation(builder.Build()); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); EXPECT_EQ(gather_instruction->ToString(), "%gather = f32[10,9,7,6,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} " diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc index 08b9a29aeda2ee..d4bad16f7976fc 100644 --- a/tensorflow/compiler/xla/service/hlo_module.cc +++ b/tensorflow/compiler/xla/service/hlo_module.cc @@ -41,9 +41,6 @@ HloModule::HloModule(const string& name, entry_computation_handle_(entry_computation_handle), unique_id_(next_unique_module_id_++) {} -HloModule::HloModule(const string& name) - : name_(NameUniquer::GetSanitizedName(name)), - unique_id_(next_unique_module_id_++) {} HloModule::HloModule(const string& name, const HloModuleConfig& config) : name_(NameUniquer::GetSanitizedName(name)), config_(config), @@ -479,8 +476,7 @@ std::vector HloModule::MakeNonfusionComputations() const { std::unique_ptr HloModule::Clone(const string& suffix) const { VLOG(1) << "Cloning module :" << name_ << " --> " << suffix << "\n"; - auto module = MakeUnique(name_ + "-" + suffix); - module->config_ = config_; + auto module = MakeUnique(name_ + "-" + suffix, config_); module->entry_computation_handle_ = entry_computation_handle_; module->has_entry_computation_handle_ = has_entry_computation_handle_; diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h index 9f7f25202ba42b..aa843ead517479 100644 --- a/tensorflow/compiler/xla/service/hlo_module.h +++ b/tensorflow/compiler/xla/service/hlo_module.h @@ -55,7 +55,6 @@ class HloModule { // only be used for HloModules used outside of the XLA service (eg // tests). The versioned handle is used by the service in the compilation // cache. A default configuration is created for this module. - explicit HloModule(const string& name); explicit HloModule(const string& name, const HloModuleConfig& config); // Adds an entry computation to the module. A module can only have one entry diff --git a/tensorflow/compiler/xla/service/transpose_folding_test.cc b/tensorflow/compiler/xla/service/transpose_folding_test.cc index caa1a111ad880b..c7c41603459189 100644 --- a/tensorflow/compiler/xla/service/transpose_folding_test.cc +++ b/tensorflow/compiler/xla/service/transpose_folding_test.cc @@ -71,10 +71,10 @@ TEST_F(TransposeFoldingTest, FoldDotTranspose) { HloInstruction::CreateDot(ShapeUtil::MakeShape(F32, {2, 2}), /*lhs=*/x, /*rhs=*/transpose_y, dot_dnums)); - HloModule module("test_module"); + auto module = CreateNewModule("test_module"); HloComputation* entry_computation = - module.AddEntryComputation(builder.Build(dot)); - FoldTranspose(&module); + module->AddEntryComputation(builder.Build(dot)); + FoldTranspose(module.get()); // Instructions after folding: x, y, and the fusion. std::unordered_set instruction_set( @@ -114,10 +114,10 @@ TEST_F(TransposeFoldingTest, FoldDotTransposeConstant) { ShapeUtil::MakeShape(F32, {1, 3}), /*lhs=*/transpose0, /*rhs=*/transpose1, dot_dnums)); - HloModule module("test_module"); + auto module = CreateNewModule("test_module"); HloComputation* entry_computation = - module.AddEntryComputation(builder.Build(dot)); - FoldTranspose(&module); + module->AddEntryComputation(builder.Build(dot)); + FoldTranspose(module.get()); for (auto* instruction : entry_computation->instructions()) { if (instruction->opcode() == HloOpcode::kFusion) { @@ -149,10 +149,10 @@ TEST_F(TransposeFoldingTest, FuseDotWithConstantOperands) { HloInstruction* mul = builder.AddInstruction(HloInstruction::CreateBinary( add->shape(), HloOpcode::kMultiply, add, sub)); - HloModule module("fuse_with_constant_operands"); + auto module = CreateNewModule("fuse_with_constant_operands"); HloComputation* entry_computation = - module.AddEntryComputation(builder.Build(mul)); - HloInstruction* call = module.OutlineExpressionFromComputation( + module->AddEntryComputation(builder.Build(mul)); + HloInstruction* call = module->OutlineExpressionFromComputation( {add, sub, mul}, "", entry_computation); EXPECT_EQ(call, entry_computation->root_instruction()); HloComputation* callee_computation = call->to_apply(); @@ -182,14 +182,14 @@ TEST_F(TransposeFoldingTest, FoldDotTransposeInWhile) { HloInstruction::CreateDot(ShapeUtil::MakeShape(F32, {2, 2}), /*lhs=*/x, /*rhs=*/transpose_y, dot_dnums)); - HloModule module("test_module"); + auto module = CreateNewModule("test_module"); HloComputation* entry_computation = - module.AddEntryComputation(builder.Build(dot)); + module->AddEntryComputation(builder.Build(dot)); - HloInstruction* call = module.OutlineExpressionFromComputation( + HloInstruction* call = module->OutlineExpressionFromComputation( {transpose_y, dot}, "outlined", entry_computation); - FoldTranspose(&module); + FoldTranspose(module.get()); // Instructions after folding: x, y, and the fusion. std::unordered_set instruction_set( @@ -240,10 +240,10 @@ TEST_F(TransposeFoldingTest, FoldConvDimSwapTransposeRhs) { HloInstruction* conv = builder.AddInstruction(HloInstruction::CreateConvolve( conv_shape.ValueOrDie(), x, transpose_y, window, dnums)); - HloModule module("test_module"); + auto module = CreateNewModule("test_module"); HloComputation* entry_computation = - module.AddEntryComputation(builder.Build(conv)); - FoldTranspose(&module); + module->AddEntryComputation(builder.Build(conv)); + FoldTranspose(module.get()); // Instructions after folding: x, y, and the convolution. std::unordered_set instruction_set( @@ -293,10 +293,10 @@ TEST_F(TransposeFoldingTest, FoldConvComplexTransposeRhs) { HloInstruction* conv = builder.AddInstruction(HloInstruction::CreateConvolve( conv_shape.ValueOrDie(), x, transpose_y, window, dnums)); - HloModule module("test_module"); + auto module = CreateNewModule("test_module"); HloComputation* entry_computation = - module.AddEntryComputation(builder.Build(conv)); - FoldTranspose(&module); + module->AddEntryComputation(builder.Build(conv)); + FoldTranspose(module.get()); // Instructions after folding: x, y, and the convolution. std::unordered_set instruction_set( @@ -351,10 +351,10 @@ TEST_F(TransposeFoldingTest, FoldConvTransposeLhs) { HloInstruction* conv = builder.AddInstruction(HloInstruction::CreateConvolve( conv_shape.ValueOrDie(), transpose_x, y, window, dnums)); - HloModule module("test_module"); + auto module = CreateNewModule("test_module"); HloComputation* entry_computation = - module.AddEntryComputation(builder.Build(conv)); - FoldTranspose(&module); + module->AddEntryComputation(builder.Build(conv)); + FoldTranspose(module.get()); // Instructions after folding: x, y, and the convolution. std::unordered_set instruction_set( @@ -415,10 +415,10 @@ TEST_F(TransposeFoldingTest, FoldConvComplexTransposeLhs) { HloInstruction* conv = builder.AddInstruction(HloInstruction::CreateConvolve( conv_shape.ValueOrDie(), transpose_x, y, window, dnums)); - HloModule module("test_module"); + auto module = CreateNewModule("test_module"); HloComputation* entry_computation = - module.AddEntryComputation(builder.Build(conv)); - FoldTranspose(&module); + module->AddEntryComputation(builder.Build(conv)); + FoldTranspose(module.get()); // Instructions after folding: x, y, and the convolution. std::unordered_set instruction_set( diff --git a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc index 4f8cdc1e0e73cd..a4e67cc9d9b8ee 100644 --- a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc +++ b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc @@ -46,9 +46,9 @@ class ZeroSizedHloEliminationTest : public HloTestBase { 0, ShapeUtil::MakeShape(F32, {3, 0}), "zero sized param"))) {} StatusOr RunZeroSizedElimination() { - HloModule module("zero_sized_elimination_test_module"); - module.AddEntryComputation(builder_.Build()); - return ZeroSizedHloElimination{}.Run(&module); + auto module = CreateNewModule("zero_sized_elimination_test_module"); + module->AddEntryComputation(builder_.Build()); + return ZeroSizedHloElimination{}.Run(module.get()); } HloComputation::Builder builder_; diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc index 9984aba089be89..8b64f2e6315bc4 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.cc +++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc @@ -93,11 +93,10 @@ HloTestBase::HloTestBase(se::Platform* test_platform, } /* static */ -std::unique_ptr HloTestBase::CreateNewModule() { +std::unique_ptr HloTestBase::CreateNewModule(const string& name) { HloModuleConfig config; config.set_debug_options(GetDebugOptionsForTest()); - return MakeUnique(TestName(), VersionedComputationHandle(), - config); + return MakeUnique(name, VersionedComputationHandle(), config); } /*static*/ DebugOptions HloTestBase::GetDebugOptionsForTest() { diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h index 79fcea9403e6e2..6491208895f9ec 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.h +++ b/tensorflow/compiler/xla/tests/hlo_test_base.h @@ -85,7 +85,8 @@ class HloTestBase : public ::testing::Test { // options from command-line flags. If you want a fresh HloModule object and // then add HloComputations to it, it's recommended to use this method in your // tests. - static std::unique_ptr CreateNewModule(); + static std::unique_ptr CreateNewModule( + const string& name = TestName()); // Populates debug options from command-line flags and adjusts the options for // testing. It is recommended to use this when you need to pass in From 2e1f3efcb34380df1441660d9759b44bb07cf1cd Mon Sep 17 00:00:00 2001 From: Richard Wei Date: Sat, 28 Apr 2018 23:51:28 -0700 Subject: [PATCH 0150/1691] Update the Swift for TensorFlow community page. PiperOrigin-RevId: 194687897 --- tensorflow/docs_src/community/swift.md | 50 +++++++++++++++++++------- 1 file changed, 38 insertions(+), 12 deletions(-) diff --git a/tensorflow/docs_src/community/swift.md b/tensorflow/docs_src/community/swift.md index f065b207c61001..a7da189a5c2f97 100644 --- a/tensorflow/docs_src/community/swift.md +++ b/tensorflow/docs_src/community/swift.md @@ -1,18 +1,44 @@ -# Swift Community +

+ +

+ +# Swift for TensorFlow Welcome to the Swift for TensorFlow development community! -Swift for TensorFlow is the result of first-principles thinking applied to -machine learning frameworks and aims to take TensorFlow usability to new -heights. Swift for TensorFlow is based on the belief that machine learning is -important enough for first-class language and compiler support, and thus works -very differently from normal language bindings. - -First-class language and compiler support allow us to innovate in areas that -traditionally were out of bounds for machine learning libraries. Our -programming model combines the performance of TensorFlow graphs with the -flexibility and expressivity of Eager execution, while keeping a strong focus -on improved usability at every level of the stack. +Swift for TensorFlow is a new way to develop machine learning models. It +gives you the power of +[TensorFlow](https://www.tensorflow.org/programmers_guide/eager) directly +integrated into the [Swift programming language](https://swift.org/about). +With Swift, you can write the following imperative code, and Swift +automatically turns it into **a single TensorFlow Graph** and runs it +with the full performance of TensorFlow Sessions on CPU, GPU and +[TPU](https://cloud.google.com/tpu/docs/tpus). + +```swift +import TensorFlow + +var x = Tensor([[1, 2], [3, 4]]) + +for i in 1...5 { + x += x ⊗ x +} + +print(x) +``` + +Swift combines the flexibility of +[Eager Execution](https://www.tensorflow.org/programmers_guide/eager) with the +high performance of [Graphs and Sessions](https://www.tensorflow.org/programmers_guide/graphs). +Behind the scenes, Swift analyzes your Tensor code and automatically builds +graphs for you. Swift also catches type errors and shape mismatches before +running your code, and has [Automatic Differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation) +built right in. We believe that machine learning tools are so important that +they deserve **a first-class language and a compiler**. + +**Note:** Swift for TensorFlow is an early stage research project. It has been +released to enable open source development and is not yet ready for general use +by machine learning developers. ## Open Source From 87f7d4b894c08031ba5942c1c391199de793eb88 Mon Sep 17 00:00:00 2001 From: ManHyuk Date: Sun, 29 Apr 2018 16:07:33 +0900 Subject: [PATCH 0151/1691] fix typo --- .../tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh | 2 +- .../tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh b/tensorflow/tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh index 748a961e44c542..dc9af221ecf53b 100644 --- a/tensorflow/tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh @@ -44,7 +44,7 @@ source "tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh" \ run_configure_for_cpu_build -# Compliling the following test is extremely slow with -c opt +# Compiling the following test is extremely slow with -c opt slow_compiling_test="//tensorflow/core/kernels:eigen_backward_spatial_convolutions_test" # Find all the passing cc_tests on Windows and store them in a variable diff --git a/tensorflow/tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh b/tensorflow/tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh index f26f8727e51bf0..f1114f4ffa40dd 100644 --- a/tensorflow/tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh +++ b/tensorflow/tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh @@ -46,7 +46,7 @@ clean_output_base run_configure_for_gpu_build -# Compliling the following test is extremely slow with -c opt +# Compiling the following test is extremely slow with -c opt slow_compiling_test="//tensorflow/core/kernels:eigen_backward_spatial_convolutions_test" # Find all the passing cc_tests on Windows and store them in a variable From 45529aaac3f5c1d290c285a4e86c434600ec2d92 Mon Sep 17 00:00:00 2001 From: Sherry Moore Date: Sun, 29 Apr 2018 09:56:16 -0700 Subject: [PATCH 0152/1691] Added del_hparam(), the counter part of add_hparam. PiperOrigin-RevId: 194711291 --- .../contrib/training/python/training/hparam.py | 10 ++++++++++ .../training/python/training/hparam_test.py | 16 ++++++++++++++++ 2 files changed, 26 insertions(+) diff --git a/tensorflow/contrib/training/python/training/hparam.py b/tensorflow/contrib/training/python/training/hparam.py index 6c59b68053cfc6..f0418f04ba2c5c 100644 --- a/tensorflow/contrib/training/python/training/hparam.py +++ b/tensorflow/contrib/training/python/training/hparam.py @@ -502,6 +502,16 @@ def set_hparam(self, name, value): 'Must pass a list for multi-valued parameter: %s.' % name) setattr(self, name, _cast_to_type_if_compatible(name, param_type, value)) + def del_hparam(self, name): + """Removes the hyperparameter with key 'name'. + + Args: + name: Name of the hyperparameter. + """ + if hasattr(self, name): + delattr(self, name) + del self._hparam_types[name] + def parse(self, values): """Override hyperparameter values, parsing new values from a string. diff --git a/tensorflow/contrib/training/python/training/hparam_test.py b/tensorflow/contrib/training/python/training/hparam_test.py index 96eff86d8d48bb..11fd15b5275a3c 100644 --- a/tensorflow/contrib/training/python/training/hparam_test.py +++ b/tensorflow/contrib/training/python/training/hparam_test.py @@ -439,6 +439,22 @@ def testGet(self): self.assertEqual(123, hparams.get('unknown', 123)) self.assertEqual([1, 2, 3], hparams.get('unknown', [1, 2, 3])) + def testDel(self): + hparams = hparam.HParams(aaa=1, b=2.0) + + with self.assertRaises(ValueError): + hparams.set_hparam('aaa', 'will fail') + + with self.assertRaises(ValueError): + hparams.add_hparam('aaa', 'will fail') + + hparams.del_hparam('aaa') + hparams.add_hparam('aaa', 'will work') + self.assertEqual('will work', hparams.get('aaa')) + + hparams.set_hparam('aaa', 'still works') + self.assertEqual('still works', hparams.get('aaa')) + if __name__ == '__main__': test.main() From 70f592bbe4c31d35d99303c6334d15b790c1e191 Mon Sep 17 00:00:00 2001 From: rmanyari Date: Sun, 29 Apr 2018 17:23:08 -0400 Subject: [PATCH 0153/1691] add missing equality --- tensorflow/docs_src/get_started/feature_columns.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/get_started/feature_columns.md b/tensorflow/docs_src/get_started/feature_columns.md index 9c777a0077a768..79c26679793f2f 100644 --- a/tensorflow/docs_src/get_started/feature_columns.md +++ b/tensorflow/docs_src/get_started/feature_columns.md @@ -138,7 +138,7 @@ The model will represent the buckets as follows: |< 1960 | [1, 0, 0, 0] | |>= 1960 but < 1980 | [0, 1, 0, 0] | |>= 1980 but < 2000 | [0, 0, 1, 0] | -|> 2000 | [0, 0, 0, 1] | +|>= 2000 | [0, 0, 0, 1] | Why would you want to split a number—a perfectly valid input to your model—into a categorical value? Well, notice that the categorization splits a From 9310de4af4816e5820d1907a9550ed427321eb33 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Mon, 30 Apr 2018 00:52:37 +0300 Subject: [PATCH 0154/1691] [tf.data] Add a bunch of debugging for Jenkins to run on the Windows build. --- tensorflow/contrib/data/python/ops/resampling.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index e65207f6750531..4caa25197e3831 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -91,9 +91,18 @@ def _apply_fn(dataset): elif prob_original_static == 0: return filtered_ds else: + print('class_values_ds.output_shapes: %s', class_values_ds.output_shapes) + print('class_values_ds.output_types: %s', class_values_ds.output_types) + print('dataset.output_shapes: %s', dataset.output_shapes) + print('dataset.output_types: %s', dataset.output_types) + print('filtered_ds.output_shapes: %s', filtered_ds.output_shapes) + print('filtered_ds.output_types: %s', filtered_ds.output_types) + weights = prob_of_original_ds.map(lambda prob: [(prob, 1.0 - prob)]) + print('weights.output_shapes: %s', weights.output_shapes) + print('weights.output_types: %s', weights.output_types) return interleave_ops.sample_from_datasets( [dataset_ops.Dataset.zip((class_values_ds, dataset)), filtered_ds], - weights=prob_of_original_ds.map(lambda prob: [(prob, 1.0 - prob)]), + weights=weights, seed=seed) return _apply_fn From c41b546e4c193d61a79acf4cf4be621233d68ec0 Mon Sep 17 00:00:00 2001 From: Russell Power Date: Sun, 29 Apr 2018 15:30:22 -0700 Subject: [PATCH 0155/1691] Add support for a clean checkpoint and shutdown in response to a termination notice. PiperOrigin-RevId: 194722985 --- tensorflow/contrib/tpu/BUILD | 5 + tensorflow/contrib/tpu/ops/heartbeat_ops.cc | 37 +++ .../contrib/tpu/ops/tpu_configuration_ops.cc | 16 - .../contrib/tpu/python/tpu/session_support.py | 311 ++++++++++++++++++ .../contrib/tpu/python/tpu/tpu_estimator.py | 16 +- .../tpu/python/tpu/tpu_system_metadata.py | 6 +- tensorflow/core/BUILD | 12 - tensorflow/core/util/event.proto | 34 +- tensorflow/core/util/session_message.cc | 71 ---- tensorflow/core/util/session_message.h | 55 ---- 10 files changed, 399 insertions(+), 164 deletions(-) create mode 100644 tensorflow/contrib/tpu/ops/heartbeat_ops.cc create mode 100644 tensorflow/contrib/tpu/python/tpu/session_support.py delete mode 100644 tensorflow/core/util/session_message.cc delete mode 100644 tensorflow/core/util/session_message.h diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD index eac210418b57ea..0bdf6f64c9eeef 100644 --- a/tensorflow/contrib/tpu/BUILD +++ b/tensorflow/contrib/tpu/BUILD @@ -24,6 +24,7 @@ cc_library( name = "all_ops", deps = [ ":cross_replica_ops_op_lib", + ":heartbeat_ops_op_lib", ":host_compute_ops_op_lib", ":infeed_ops_op_lib", ":outfeed_ops_op_lib", @@ -71,6 +72,7 @@ py_library( tf_gen_op_libs( op_lib_names = [ "cross_replica_ops", + "heartbeat_ops", "host_compute_ops", "infeed_ops", "outfeed_ops", @@ -89,6 +91,7 @@ tf_custom_op_library( name = "python/ops/_tpu_ops.so", srcs = [ "ops/cross_replica_ops.cc", + "ops/heartbeat_ops.cc", "ops/host_compute_ops.cc", "ops/infeed_ops.cc", "ops/outfeed_ops.cc", @@ -106,6 +109,7 @@ tf_gen_op_wrapper_py( name = "tpu_ops", deps = [ ":cross_replica_ops_op_lib", + ":heartbeat_ops_op_lib", ":host_compute_ops_op_lib", ":infeed_ops_op_lib", ":outfeed_ops_op_lib", @@ -163,6 +167,7 @@ py_library( "python/tpu/bfloat16.py", "python/tpu/device_assignment.py", "python/tpu/keras_support.py", + "python/tpu/session_support.py", "python/tpu/topology.py", "python/tpu/tpu.py", "python/tpu/tpu_feed.py", diff --git a/tensorflow/contrib/tpu/ops/heartbeat_ops.cc b/tensorflow/contrib/tpu/ops/heartbeat_ops.cc new file mode 100644 index 00000000000000..ca0f5bc0e562cd --- /dev/null +++ b/tensorflow/contrib/tpu/ops/heartbeat_ops.cc @@ -0,0 +1,37 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/lib/core/status.h" + +namespace tensorflow { + +REGISTER_OP("WorkerHeartbeat") + .Input("request: string") + .Output("response: string") + .SetIsStateful() + .SetShapeFn(shape_inference::ScalarShape) + .Doc(R"doc( +Worker heartbeat op. + +Heartbeats may be sent periodically to indicate the coordinator is still active, +to retrieve the current worker status and to expedite shutdown when necessary. + +request: A string tensor containing a serialized WorkerHeartbeatRequest +response: A string tensor containing a serialized WorkerHeartbeatResponse +)doc"); + +} // namespace tensorflow diff --git a/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc b/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc index 7bf5c21d0b526e..d5600eef4a9dc6 100644 --- a/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc +++ b/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc @@ -214,20 +214,4 @@ An op that shuts down a running distributed TPU system. The Op returns an error if no system is running. )doc"); -REGISTER_OP("SessionStatus") - .Input("fetch_start_timestamp: double") - .Output("status: string") - .SetShapeFn(shape_inference::ScalarShape) - .Doc(R"doc( -Not for public usage. - -Returns messages from the current session as a serialized SessionStatusProto. - -This includes the current state of the compiler, along with any critical -logging or warning messages. - -fetch_start_timestamp: any messages earlier than this will be excluded from the -returned proto. -)doc"); - } // end namespace tensorflow diff --git a/tensorflow/contrib/tpu/python/tpu/session_support.py b/tensorflow/contrib/tpu/python/tpu/session_support.py new file mode 100644 index 00000000000000..7c25f6693cd27d --- /dev/null +++ b/tensorflow/contrib/tpu/python/tpu/session_support.py @@ -0,0 +1,311 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the 'License'); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an 'AS IS' BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ====================================== +"""Operations for handling session logging and shutdown notifications.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import threading + +import time +from google.protobuf import text_format + +from tensorflow.contrib.tpu.python.ops import tpu_ops +from tensorflow.core.protobuf import config_pb2 +from tensorflow.core.util import event_pb2 +from tensorflow.python.client import session as session_lib +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import session_run_hook +from tensorflow.python.training import training_util + + +class CoordinatorShutdownException(Exception): + """Raised when the coordinator needs to shutdown.""" + pass + + +class WorkerHeartbeatManager(object): + """Manages the status/heartbeat monitor for a set of workers.""" + + def __init__(self, session, devices, heartbeat_ops, request_placeholder): + """Construct a new WorkerHeartbeatManager. + + (Prefer using `WorkerHeartbeatManager.from_devices` when possible.) + + Args: + session: `tf.Session`, session to use for heartbeat operations. + devices: `list[string]` Set of devices to connect to. + heartbeat_ops: `list[tf.Operation]` Heartbeat operations. + request_placeholder: `tf.Placeholder[String]` Placeholder used to specify + the WorkerHeartbeatRequest protocol buffer. + """ + self._session = session + self._devices = devices + self._ops = heartbeat_ops + self._request_placeholder = request_placeholder + + @staticmethod + def from_devices(session, devices): + """Construct a heartbeat manager for the given devices.""" + if not devices: + logging.error('Trying to create heartbeat manager with no devices?') + + logging.info('Creating heartbeat manager for %s', devices) + request_placeholder = array_ops.placeholder( + name='worker_heartbeat_request', dtype=dtypes.string) + + heartbeat_ops = [] + for device in devices: + with ops.device(device): + heartbeat_ops.append(tpu_ops.worker_heartbeat(request_placeholder)) + + return WorkerHeartbeatManager(session, devices, heartbeat_ops, + request_placeholder) + + def configure(self, message): + """Configure heartbeat manager for all devices. + + Args: + message: `event_pb2.WorkerHeartbeatRequest` + + Returns: `None` + + """ + logging.info('Configuring worker heartbeat: %s', + text_format.MessageToString(message)) + self._session.run(self._ops, + {self._request_placeholder: message.SerializeToString()}) + + def ping(self, request=None, timeout_in_ms=5000): + """Ping all workers, returning the parsed status results.""" + if request is None: + request = event_pb2.WorkerHeartbeatRequest() + + options = config_pb2.RunOptions(timeout_in_ms=timeout_in_ms) + results = self._session.run( + self._ops, + feed_dict={self._request_placeholder: request.SerializeToString()}, + options=options) + parsed_results = [ + event_pb2.WorkerHeartbeatResponse.FromString(res_pb) + for res_pb in results + ] + logging.info('Results: %s', parsed_results) + return parsed_results + + def lame_workers(self): + """Ping all workers, returning manager containing lame workers (or None).""" + ping_results = self.ping() + lame_workers = [] + + for ping_response, device, op in zip(ping_results, self._devices, + self._ops): + if ping_response.health_status != event_pb2.OK: + lame_workers.append((device, op)) + + if not lame_workers: + return None + + bad_devices, bad_ops = zip(*lame_workers) + return WorkerHeartbeatManager(self._session, bad_devices, bad_ops, + self._request_placeholder) + + def shutdown(self, timeout_ms=10000): + """Shutdown all workers after `shutdown_timeout_secs`.""" + req = event_pb2.WorkerHeartbeatRequest( + watchdog_config=event_pb2.WatchdogConfig(timeout_ms=timeout_ms)) + self.configure(req) + + +def all_worker_devices(session): + """Return a list of devices for each worker in the system.""" + devices = session.list_devices() + return [device.name for device in devices if 'CPU' in device.name] + + +class WatchdogManager(threading.Thread): + """Configures worker watchdog timer and handles periodic pings. + + Usage: + # Ping workers every minute, shutting down workers if they haven't received + # a ping after 1 hour. + watchdog_manager = WatchdogManager( + ping_interval=60, shutdown_timeout=3600 + ) + + # Use as a context manager, resetting watchdog on context exit: + with watchdog_manager: + session.run(...) + + # Or setup globally; watchdog will remain active until program exit. + watchdog_manager.configure_and_run() + """ + + def __init__(self, + session, + devices=None, + ping_interval=60, + shutdown_timeout=3600): + """Initialize a watchdog manager. + + Args: + + session: Session connected to worker devices. A cloned session and graph + will be created for managing worker pings. + devices: Set of devices to monitor. If none, all workers will be + monitored. + ping_interval: Time, in seconds, between watchdog pings. + shutdown_timeout: Time, in seconds, before watchdog timeout. + """ + threading.Thread.__init__(self) + self.ping_interval = ping_interval + self.shutdown_timeout = shutdown_timeout + self.daemon = True + self._running = False + self._graph = ops.Graph() + self._session = session_lib.Session( + target=session.sess_str, graph=self._graph) + + with self._graph.as_default(): + if devices is None: + devices = all_worker_devices(self._session) + self._worker_manager = WorkerHeartbeatManager.from_devices( + self._session, devices) + + def configure_and_run(self): + logging.info('Enabling worker watchdog.') + self._running = True + self._worker_manager.configure( + event_pb2.WorkerHeartbeatRequest( + watchdog_config=event_pb2.WatchdogConfig( + timeout_ms=self.shutdown_timeout * 1000,))) + + self.start() + + def __enter__(self): + self.configure_and_run() + + def __exit__(self, exc_type, exc_val, exc_tb): + logging.info('Disabling worker watchdog.') + self._worker_manager.configure( + event_pb2.WorkerHeartbeatRequest( + watchdog_config=event_pb2.WatchdogConfig(timeout_ms=-1,))) + self._running = False + self.join() + + def run(self): + # Don't fetch logs or adjust timing: just ping the watchdog. + while self._running: + self._worker_manager.ping(request=None) + time.sleep(self.ping_interval) + + +class GracefulShutdownHook(session_run_hook.SessionRunHook): + """Session hook that watches for shutdown events. + + If a shutdown is indicated, `saver.save(checkpoint_prefix)` is executed, and a + SystemShutdown exception is raised to terminate the main session. If `saver` + is None the `SAVERS` collection will be read to find a saver. + + `on_shutdown_hooks` is an optional list of functions that should be called + after checkpointing. The function is called with (`run_context`, + `all_workers`, `lame_workers`). + + If `heartbeat_group` is not specified, it will default to all CPU workers + in the system. + """ + + def __init__(self, checkpoint_prefix, saver=None, on_shutdown_hooks=None): + self._saver = saver + self._checkpoint_prefix = checkpoint_prefix + self._on_shutdown_hooks = on_shutdown_hooks if on_shutdown_hooks else [] + + # Worker heartbeats are managed independently of the main training graph. + self._graph = ops.Graph() + self._workers = None + self._session = None + + def after_create_session(self, training_session, coord): # pylint: disable=unused-argument + # N.B. We have to pull the global step here to avoid it being unavailable + # at checkpoint time; the graph has been frozen at that point. + if training_util.get_global_step() is None and self.saver() is not None: + raise ValueError( + 'Saver defined but no global step. Run `get_or_create_global_step()`' + ' in your model definition to allow checkpointing.') + + with self._graph.as_default(): + self._session = session_lib.Session( + target=training_session.sess_str, graph=self._graph) + self._workers = WorkerHeartbeatManager.from_devices( + self._session, all_worker_devices(self._session)) + + self._workers.configure( + event_pb2.WorkerHeartbeatRequest( + shutdown_mode=event_pb2.WAIT_FOR_COORDINATOR)) + + def saver(self): + if self._saver: + return self._saver + + savers = ops.get_collection(ops.GraphKeys.SAVERS)[0] + if not savers: + return None + + if not isinstance(savers, list): + return savers + + assert len(savers) == 1, 'Only one saver supported.' + return savers[0] + + def after_run(self, run_context, run_values): + del run_values + + lame_workers = self._workers.lame_workers() + if lame_workers: + logging.info('ShutdownHook: lame workers found: %s', lame_workers) + + if self.saver(): + logging.info('ShutdownHook: saving checkpoint to %s', + self._checkpoint_prefix) + self.saver().save( + run_context.session, + self._checkpoint_prefix, + global_step=training_util.get_global_step(), + write_state=True, + ) + else: + logging.info('ShutdownHook: no Saver defined.') + + for fn in self._on_shutdown_hooks: + fn(run_context, self._workers, lame_workers) + + +def restart_computation(run_context, all_workers, lame_workers): + del run_context, lame_workers + logging.info('Shutting down all workers.') + all_workers.shutdown() + + logging.info('Terminating coordinator.') + raise CoordinatorShutdownException() + + +def shutdown_lame_workers(run_context, all_workers, lame_workers): + del run_context, all_workers + logging.info('Shutting down %s', lame_workers) + lame_workers.shutdown() diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 98eb0e240f0666..eb537b7b6ad2c7 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -20,6 +20,7 @@ import collections import copy +import os import signal import threading import time @@ -31,6 +32,7 @@ from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib.tpu.python.ops import tpu_ops +from tensorflow.contrib.tpu.python.tpu import session_support from tensorflow.contrib.tpu.python.tpu import tpu from tensorflow.contrib.tpu.python.tpu import tpu_config from tensorflow.contrib.tpu.python.tpu import tpu_context @@ -1551,7 +1553,7 @@ def end(self, session): class ExamplesPerSecondHook(basic_session_run_hooks.StepCounterHook): - """Count examples during runtime.""" + """"Calculate and report the number of examples/sec during training.""" def __init__(self, batch_size, @@ -2037,6 +2039,11 @@ def _model_fn(features, labels, mode, config, params): host_ops = host_call.create_tpu_hostcall() if host_ops is None: host_ops = [] + + shutdown_hooks = [] + if os.environ.get('TF_TPU_GRACEFUL_SHUTDOWN', '0') != '0': + shutdown_hooks.append(session_support.GracefulShutdownHook()) + hooks = [ TPUInfeedOutfeedSessionHook( ctx, @@ -2044,8 +2051,8 @@ def _model_fn(features, labels, mode, config, params): host_ops, run_infeed_loop_on_coordinator=( run_infeed_loop_on_coordinator)), - ExamplesPerSecondHook(ctx.global_batch_size, - output_dir=self.model_dir), + ExamplesPerSecondHook( + ctx.global_batch_size, output_dir=self.model_dir), InstallSignalHandlerHook(), training.LoggingTensorHook( { @@ -2053,7 +2060,8 @@ def _model_fn(features, labels, mode, config, params): 'step': training.get_global_step() }, every_n_secs=30) - ] + input_hooks + ] + input_hooks + shutdown_hooks + chief_hooks = [] if (self._config.save_checkpoints_secs or self._config.save_checkpoints_steps): diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py b/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py index 3ae350c7bb345c..894f21d0635ca4 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py @@ -60,7 +60,7 @@ def _query_tpu_system_metadata(master_address, run_config, with ops.Graph().as_default(): with session_lib.Session( master_address, - config=_get_session_config_with_timeout( + config=get_session_config_with_timeout( _PINGING_MASTER_TIMEOUT_IN_MS, run_config)) as sess: devices = sess.list_devices() for device in devices: @@ -133,7 +133,7 @@ def _obtain_topology(master_address, run_config): 'for model parallelism. This might take a while.', master_address) with ops.Graph().as_default(): - session_config = _get_session_config_with_timeout( + session_config = get_session_config_with_timeout( _INITIAL_TPU_SYSTEM_TIMEOUT_IN_MS, run_config) with session_lib.Session( master_address, config=session_config) as sess: @@ -146,7 +146,7 @@ def _obtain_topology(master_address, run_config): master_address)) -def _get_session_config_with_timeout(timeout_in_secs, run_config): +def get_session_config_with_timeout(timeout_in_secs, run_config): cluster_def = None if run_config.session_config and run_config.session_config.cluster_def.job: cluster_def = run_config.session_config.cluster_def diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 32ef0a9b1895cf..2a849a30193234 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -457,17 +457,6 @@ cc_library( ], ) -cc_library( - name = "session_message", - srcs = ["util/session_message.cc"], - hdrs = ["util/session_message.h"], - deps = [ - ":framework", - ":lib", - ":protos_all_cc", - ], -) - # Libraries that will eventually be moved into lib/core # Note that stringpiece_test can't be place here yet, because we are # required to use tf_cc_test, and that rule will change / into _ @@ -2149,7 +2138,6 @@ tf_cuda_library( "framework/resource_handle.cc", "util/memmapped_file_system.*", "util/memmapped_file_system_writer.*", - "util/session_message.cc", "util/version_info.cc", ], ) + select({ diff --git a/tensorflow/core/util/event.proto b/tensorflow/core/util/event.proto index 65d2c5a09c5c98..9ce85be551191d 100644 --- a/tensorflow/core/util/event.proto +++ b/tensorflow/core/util/event.proto @@ -81,7 +81,35 @@ message TaggedRunMetadata { bytes run_metadata = 2; } -// For communicating live events back to a coordinator -message SessionStatus { - repeated Event event = 1; +// Worker heartbeat messages. Support for these operations is currently +// internal and expected to change. + +// Current health status of a worker. +enum WorkerHealth { + OK = 0; // By default a worker is healthy. + RECEIVED_SHUTDOWN_SIGNAL = 1; + INTERNAL_ERROR = 2; +} + +// Indicates the behavior of the worker when an internal error or shutdown +// signal is received. +enum WorkerShutdownMode { + DEFAULT = 0; + SHUTDOWN_IMMEDIATELY = 1; + WAIT_FOR_COORDINATOR = 2; +} + +message WatchdogConfig { + int64 timeout_ms = 1; +} + +message WorkerHeartbeatRequest { + WorkerShutdownMode shutdown_mode = 1; + WatchdogConfig watchdog_config = 2; +} + +message WorkerHeartbeatResponse { + WorkerHealth health_status = 1; + repeated Event worker_log = 2; + string hostname = 3; } diff --git a/tensorflow/core/util/session_message.cc b/tensorflow/core/util/session_message.cc deleted file mode 100644 index 28a6517a1a3c58..00000000000000 --- a/tensorflow/core/util/session_message.cc +++ /dev/null @@ -1,71 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/util/session_message.h" -#include "tensorflow/core/framework/op_kernel.h" -#include "tensorflow/core/framework/resource_mgr.h" -#include "tensorflow/core/lib/strings/stringprintf.h" -#include "tensorflow/core/util/event.pb.h" - -static const int kMaxLogEvents = 1000; - -namespace tensorflow { - -SessionLogger::SessionLogger() : status_(new SessionStatus) {} - -SessionLogger::~SessionLogger() {} - -string SessionLogger::DebugString() { return "SessionLogger"; } - -void SessionLogger::Log(StringPiece message) { - mutex_lock lock(mu_); - - Event* event = status_->add_event(); - event->set_wall_time(Env::Default()->NowMicros()); - event->set_step(0); - LogMessage* log = event->mutable_log_message(); - log->set_message(message.ToString()); - log->set_level(LogMessage::INFO); - - // Clip log events by 10% if we overflow - if (status_->event_size() > kMaxLogEvents) { - auto events = status_->mutable_event(); - events->DeleteSubrange(0, kMaxLogEvents / 10); - } -} - -SessionLogger* GetSessionLogger(ResourceMgr* rm) { - SessionLogger* logger; - - std::function status_creator = - [](SessionLogger** result) { - *result = new SessionLogger(); - return Status::OK(); - }; - - if (!rm->LookupOrCreate("session", "status", &logger, - status_creator) - .ok()) { - return nullptr; - } - - return logger; -} - -void LogSessionMessage(ResourceMgr* rm, StringPiece message) { - return GetSessionLogger(rm)->Log(message); -} - -} // namespace tensorflow diff --git a/tensorflow/core/util/session_message.h b/tensorflow/core/util/session_message.h deleted file mode 100644 index c0f3d78b46a503..00000000000000 --- a/tensorflow/core/util/session_message.h +++ /dev/null @@ -1,55 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_CORE_UTIL_SESSION_MESSAGE_H_ -#define TENSORFLOW_CORE_UTIL_SESSION_MESSAGE_H_ - -#include "tensorflow/core/framework/resource_mgr.h" -#include "tensorflow/core/lib/core/stringpiece.h" -#include "tensorflow/core/platform/mutex.h" - -namespace tensorflow { - -class ResourceMgr; -class SessionStatus; - -class SessionLogger : public ResourceBase { - public: - SessionLogger(); - ~SessionLogger(); - - void Log(StringPiece message); - string DebugString() override; - - const SessionStatus& status() { return *status_; } - - private: - std::unique_ptr status_; - mutex mu_; -}; - -// Return a SessionLogger instance for the current session. If the logger -// will be used across multiple computations, you must explicitly acquire -// and release references using Ref()/Unref(). -// -// Returns nullptr if a logger cannot be created. -SessionLogger* GetSessionLogger(ResourceMgr* rm); - -// Attach `message` to the logger for the current session. -void LogSessionMessage(ResourceMgr* rm, StringPiece message); - -} // namespace tensorflow - -#endif // TENSORFLOW_CORE_UTIL_SESSION_MESSAGE_H From c2186af6c28f8817122b27f0cd29e16daeae68f1 Mon Sep 17 00:00:00 2001 From: Russell Power Date: Sun, 29 Apr 2018 15:37:12 -0700 Subject: [PATCH 0156/1691] Keras: Supply `maximum_iterations` to the TF backend when possible. PiperOrigin-RevId: 194723199 --- .../contrib/tpu/python/tpu/keras_support.py | 61 +++++++++++++++---- .../python/keras/_impl/keras/backend.py | 4 +- .../keras/_impl/keras/layers/wrappers.py | 1 + 3 files changed, 51 insertions(+), 15 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/keras_support.py b/tensorflow/contrib/tpu/python/tpu/keras_support.py index e86ca0a1d8f15e..b1d8d38a9a0e68 100644 --- a/tensorflow/contrib/tpu/python/tpu/keras_support.py +++ b/tensorflow/contrib/tpu/python/tpu/keras_support.py @@ -66,7 +66,6 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.training import training_util class TPUEmbedding(embeddings.Embedding): @@ -126,7 +125,9 @@ def _specialize_model(self, input_specs): """Specialize `self.model` (a Keras model) for the given input shapes.""" # Re-create our input and output layers inside our subgraph. They will be # attached to the true computation when we clone our model in `tpu_fn`. - K.set_learning_phase(self.execution_mode == model_fn_lib.ModeKeys.TRAIN) + K.set_learning_phase( + self.execution_mode == model_fn_lib.ModeKeys.TRAIN + ) # functools.partial and callable objects are not supported by tpu.rewrite def _model_fn(): @@ -161,9 +162,6 @@ def _model_fn(): if layer in self.model._output_layers: tpu_targets.append(tensor) - optimizer = self.model.optimizer - optimizer.iterations = training_util.get_or_create_global_step() - # Call our model with our infeed inputs (re-using the weights). model_outputs = self.model(tpu_inputs) child_model = models.Model(inputs=tpu_inputs, outputs=model_outputs) @@ -219,8 +217,6 @@ def _model_fn(): tpu_execute_op = tpu.rewrite(_model_fn) - K._initialize_variables(K.get_session()) # pylint-disable: protected-access - # Generate CPU side operations to enqueue features/labels and dequeue # outputs from the model call. with ops.device('/device:TPU:0'): @@ -296,7 +292,6 @@ def setup_tpu_session(master): target=master, config=config_pb2.ConfigProto(isolate_session_state=True)) K.set_session(session) K.get_session().run(tpu.initialize_system()) - K.manual_variable_initialization(True) return session @@ -357,10 +352,6 @@ def compile(self, raise ValueError( 'Optimizer must be a TFOptimizer, got: %s' % self.optimizer) - def train_on_batch(self, x, y, sample_weight=None, class_weight=None): - return super(KerasTPUModel, self).train_on_batch(x, y, sample_weight, - class_weight) - def _make_train_function(self): if not self.train_function: self.train_function = TPUFunction(self, model_fn_lib.ModeKeys.TRAIN) @@ -378,14 +369,58 @@ def _make_predict_function(self): return self.predict_function def cpu_model(self): - return models.Model( + cpu_model = models.Model( inputs=self.inputs, outputs=self.outputs, name=self.name, ) + if self.optimizer: + cpu_model.compile( + optimizer=self.optimizer, + loss=self.loss, + metrics=self.metrics, + loss_weights=self.loss_weights, + ) + + return cpu_model + + +def _validate_shapes(model): + """Validate that all layers in `model` have constant shape.""" + for layer in model.layers: + if isinstance(layer.input_shape, tuple): + input_shapes = [layer.input_shape] + else: + input_shapes = layer.input_shape + + if isinstance(layer.output_shape, tuple): + output_shapes = [layer.output_shape] + else: + output_shapes = layer.output_shape + + for shape in input_shapes + output_shapes: + for dim in shape[1:]: + if dim is None: + raise ValueError( + """ +Layer %(layer)s has a variable shape in a non-batch dimension. TPU models must +have constant shapes for all operations. + +You may have to specify `input_length` for RNN/TimeDistributed layers. + +Layer: %(layer)s +Input shape: %(input_shape)s +Output shape: %(output_shape)s + """ % { + 'layer': layer, + 'input_shape': layer.input_shape, + 'output_shape': layer.output_shape + }) + @experimental def tpu_model(model): + _validate_shapes(model) return KerasTPUModel( inputs=model.inputs, outputs=model.outputs, name=model.name) diff --git a/tensorflow/python/keras/_impl/keras/backend.py b/tensorflow/python/keras/_impl/keras/backend.py index 449410fe082421..b1f1270623ddcc 100644 --- a/tensorflow/python/keras/_impl/keras/backend.py +++ b/tensorflow/python/keras/_impl/keras/backend.py @@ -2998,7 +2998,7 @@ def rnn(step_function, constants: a list of constant values passed at each step. unroll: whether to unroll the RNN or to use a symbolic loop (`while_loop` or `scan` depending on backend). - input_length: Unused; exists for API compatibility. + input_length: If specified, assume time dimension is of this length. Returns: A tuple, `(last_output, outputs, new_states)`. @@ -3016,7 +3016,6 @@ def rnn(step_function, ValueError: if `mask` is provided (not `None`) but states is not provided (`len(states)` == 0). """ - del input_length ndim = len(inputs.get_shape()) if ndim < 3: raise ValueError('Input should be at least 3D.') @@ -3194,6 +3193,7 @@ def _step(time, output_ta_t, *states): cond=lambda time, *_: time < time_steps, body=_step, loop_vars=(time, output_ta) + states, + maximum_iterations=input_length, parallel_iterations=32, swap_memory=True) last_time = final_outputs[0] diff --git a/tensorflow/python/keras/_impl/keras/layers/wrappers.py b/tensorflow/python/keras/_impl/keras/layers/wrappers.py index 34a8eeeb5b5c4c..91b8c1148bec56 100644 --- a/tensorflow/python/keras/_impl/keras/layers/wrappers.py +++ b/tensorflow/python/keras/_impl/keras/layers/wrappers.py @@ -201,6 +201,7 @@ def step(x, _): step, inputs, initial_states=[], + input_length=input_shape[0], unroll=False) y = outputs else: From d5aaa2de393b7a4aebd6f4bdfafe08edfbb3c1b0 Mon Sep 17 00:00:00 2001 From: Ben Date: Mon, 30 Apr 2018 01:38:48 -0400 Subject: [PATCH 0157/1691] Use MKLROOT --- tensorflow/contrib/cmake/CMakeLists.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt index 44e39f7f7b5da8..d81f6a0ae8a445 100644 --- a/tensorflow/contrib/cmake/CMakeLists.txt +++ b/tensorflow/contrib/cmake/CMakeLists.txt @@ -327,6 +327,7 @@ if (tensorflow_ENABLE_MKL_SUPPORT) if (WIN32) find_path(MKL_HOME_PLATFORM mkl PATHS ${MKL_HOME} ${MKL_HOME}/../ ${MKL_HOME}/../../ + $ENV{MKLROOT} $ENV{MKLROOT}/../ $ENV{MKLROOT}/../../ PATH_SUFFIXES windows) set(MKL_INCLUDE_DIRS ${MKL_HOME_PLATFORM}/mkl/include) set(MKL_LINK_DIRS @@ -345,6 +346,7 @@ if (tensorflow_ENABLE_MKL_SUPPORT) # Fix me: complete the path on linux find_path(MKL_HOME_PLATFORM mkl HINTS ${MKL_HOME} ${MKL_HOME}/../ ${MKL_HOME}/../../ + $ENV{MKLROOT} $ENV{MKLROOT}/../ $ENV{MKLROOT}/../../ PATH_SUFFIXES linux) set(MKL_INCLUDE_DIRS ${MKL_HOME_PLATFORM}/mkl/include) set(MKL_LINK_DIRS) # incompleted From fc23d94b4c9c48c5abef87641cb6586fb9124d21 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Mon, 30 Apr 2018 11:45:27 +0300 Subject: [PATCH 0158/1691] [tf.data] Add a bunch of debugging for Jenkins to run on the Windows build. --- tensorflow/contrib/data/python/ops/BUILD | 1 + .../contrib/data/python/ops/resampling.py | 19 ++++++++++--------- 2 files changed, 11 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 7a3e42cc72755c..299062212d648f 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -204,6 +204,7 @@ py_library( "//tensorflow/python:random_ops", "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", + "//third_party/tensorflow/python:platform", ], ) diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index 4caa25197e3831..6b9ae772dcf55b 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import logging_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops +from google3.third_party.tensorflow.python.platform import tf_logging as logging def rejection_resample(class_func, target_dist, initial_dist=None, seed=None): @@ -91,15 +92,15 @@ def _apply_fn(dataset): elif prob_original_static == 0: return filtered_ds else: - print('class_values_ds.output_shapes: %s', class_values_ds.output_shapes) - print('class_values_ds.output_types: %s', class_values_ds.output_types) - print('dataset.output_shapes: %s', dataset.output_shapes) - print('dataset.output_types: %s', dataset.output_types) - print('filtered_ds.output_shapes: %s', filtered_ds.output_shapes) - print('filtered_ds.output_types: %s', filtered_ds.output_types) + logging.warn('class_values_ds.output_shapes: %s'% class_values_ds.output_shapes) + logging.warn('class_values_ds.output_types: %s'% class_values_ds.output_types) + logging.warn('dataset.output_shapes: %s'% dataset.output_shapes) + logging.warn('dataset.output_types: %s'% dataset.output_types) + logging.warn('filtered_ds.output_shapes: %s'% filtered_ds.output_shapes) + logging.warn('filtered_ds.output_types: %s'% filtered_ds.output_types) weights = prob_of_original_ds.map(lambda prob: [(prob, 1.0 - prob)]) - print('weights.output_shapes: %s', weights.output_shapes) - print('weights.output_types: %s', weights.output_types) + logging.warn('weights.output_shapes: %s'% weights.output_shapes) + logging.warn('weights.output_types: %s'% weights.output_types) return interleave_ops.sample_from_datasets( [dataset_ops.Dataset.zip((class_values_ds, dataset)), filtered_ds], weights=weights, @@ -151,7 +152,7 @@ def maybe_warn_on_large_rejection(accept_dist, initial_dist): return control_flow_ops.cond( math_ops.less(proportion_rejected, .5), lambda: accept_dist, - lambda: logging_ops.Print( # pylint: disable=g-long-lambda + lambda: logging_ops.logging.warn( # pylint: disable=g-long-lambda accept_dist, [proportion_rejected, initial_dist, accept_dist], message="Proportion of examples rejected by sampler is high: ", summarize=100, From 44ecd94792574be012d0a803c0b57ffec637c3e2 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Mon, 30 Apr 2018 11:54:56 +0300 Subject: [PATCH 0159/1691] [tf.data] Add a bunch of debugging for Jenkins to run on the Windows build. --- tensorflow/contrib/data/python/ops/BUILD | 2 +- tensorflow/contrib/data/python/ops/resampling.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 299062212d648f..6d94a2bd82a617 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -204,7 +204,7 @@ py_library( "//tensorflow/python:random_ops", "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", - "//third_party/tensorflow/python:platform", + "//tensorflow/python:platform", ], ) diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index 6b9ae772dcf55b..47bf6ecb583d48 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -31,7 +31,7 @@ from tensorflow.python.ops import logging_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops -from google3.third_party.tensorflow.python.platform import tf_logging as logging +from tensorflow.python.platform import tf_logging as logging def rejection_resample(class_func, target_dist, initial_dist=None, seed=None): From d4aa90c5eeb00bd46a2c7a5ee99d8eff04407e38 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Mon, 30 Apr 2018 12:20:20 +0300 Subject: [PATCH 0160/1691] [tf.data] Fix logging ops debug statement. --- tensorflow/contrib/data/python/ops/resampling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index 47bf6ecb583d48..6be41985bbf456 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -152,7 +152,7 @@ def maybe_warn_on_large_rejection(accept_dist, initial_dist): return control_flow_ops.cond( math_ops.less(proportion_rejected, .5), lambda: accept_dist, - lambda: logging_ops.logging.warn( # pylint: disable=g-long-lambda + lambda: logging_ops.Print( # pylint: disable=g-long-lambda accept_dist, [proportion_rejected, initial_dist, accept_dist], message="Proportion of examples rejected by sampler is high: ", summarize=100, From 19e7b123408fe6085294fe62479ddf0b31060ab2 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Mon, 30 Apr 2018 12:36:32 +0300 Subject: [PATCH 0161/1691] [tf.data] Properly format debug statements. --- tensorflow/contrib/data/python/ops/resampling.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index 6be41985bbf456..f041b7bcbf8236 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -92,15 +92,15 @@ def _apply_fn(dataset): elif prob_original_static == 0: return filtered_ds else: - logging.warn('class_values_ds.output_shapes: %s'% class_values_ds.output_shapes) - logging.warn('class_values_ds.output_types: %s'% class_values_ds.output_types) - logging.warn('dataset.output_shapes: %s'% dataset.output_shapes) - logging.warn('dataset.output_types: %s'% dataset.output_types) - logging.warn('filtered_ds.output_shapes: %s'% filtered_ds.output_shapes) - logging.warn('filtered_ds.output_types: %s'% filtered_ds.output_types) + logging.warn('class_values_ds.output_shapes: %s'% str(class_values_ds.output_shapes)) + logging.warn('class_values_ds.output_types: %s'% str(class_values_ds.output_types)) + logging.warn('dataset.output_shapes: %s'% str(dataset.output_shapes)) + logging.warn('dataset.output_types: %s'% str(dataset.output_types)) + logging.warn('filtered_ds.output_shapes: %s'% str(filtered_ds.output_shapes)) + logging.warn('filtered_ds.output_types: %s'% str(filtered_ds.output_types)) weights = prob_of_original_ds.map(lambda prob: [(prob, 1.0 - prob)]) - logging.warn('weights.output_shapes: %s'% weights.output_shapes) - logging.warn('weights.output_types: %s'% weights.output_types) + logging.warn('weights.output_shapes: %s'% str(weights.output_shapes)) + logging.warn('weights.output_types: %s'% str(weights.output_types)) return interleave_ops.sample_from_datasets( [dataset_ops.Dataset.zip((class_values_ds, dataset)), filtered_ds], weights=weights, From 914796d5e9bc7b0c619b53c7eb24cfe7d6c7fb9b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 04:21:09 -0700 Subject: [PATCH 0162/1691] Cleaning up tracing code. PiperOrigin-RevId: 194768567 --- tensorflow/compiler/jit/xla_device.cc | 13 +- .../compiler/xla/service/gpu/gpu_compiler.cc | 10 +- .../gpu/llvm_gpu_backend/gpu_backend_lib.cc | 2 +- tensorflow/core/common_runtime/copy_tensor.cc | 2 +- .../core/common_runtime/gpu/gpu_device.cc | 25 +- .../core/common_runtime/gpu/gpu_util.cc | 2 +- .../core/common_runtime/process_util.cc | 25 +- .../core/common_runtime/sycl/sycl_device.cc | 11 +- .../core/common_runtime/threadpool_device.cc | 22 +- .../rpc/grpc_master_service.cc | 4 +- .../rpc/grpc_remote_master.cc | 8 +- tensorflow/core/framework/dataset.h | 2 +- .../kernels/data/map_and_batch_dataset_op.cc | 4 +- tensorflow/core/kernels/function_ops.cc | 8 +- tensorflow/core/lib/core/threadpool.cc | 17 +- .../core/platform/default/device_tracer.cc | 31 +- tensorflow/core/platform/default/tracing.cc | 40 +- .../core/platform/default/tracing_impl.h | 22 +- tensorflow/core/platform/posix/tracing.cc | 40 -- tensorflow/core/platform/tracing.cc | 98 ++--- tensorflow/core/platform/tracing.h | 351 ++++++++---------- 21 files changed, 303 insertions(+), 434 deletions(-) delete mode 100644 tensorflow/core/platform/posix/tracing.cc diff --git a/tensorflow/compiler/jit/xla_device.cc b/tensorflow/compiler/jit/xla_device.cc index c814b7eb029054..70263b1ff93675 100644 --- a/tensorflow/compiler/jit/xla_device.cc +++ b/tensorflow/compiler/jit/xla_device.cc @@ -260,11 +260,10 @@ Status XlaDevice::FillContextMap(const Graph* graph, void XlaDevice::Compute(OpKernel* op_kernel, OpKernelContext* context) { VLOG(1) << "XlaDevice::Compute " << op_kernel->name() << ":" << op_kernel->type_string(); - // When TraceMe profiling is off (which is the default), the - // following TraceMe constructor is simply a conditional test of - // false value. Measurements show that its overhead is negligible. - port::Tracing::TraceMe trace_me(op_kernel->name(), op_kernel->type_string(), - op_kernel->IsExpensive()); + // When Xprof profiling is off (which is the default), constructing the + // activity is simple enough that its overhead is negligible. + tracing::ScopedActivity activity(op_kernel->name(), op_kernel->type_string(), + op_kernel->IsExpensive()); op_kernel->Compute(context); } @@ -272,8 +271,8 @@ void XlaDevice::ComputeAsync(AsyncOpKernel* op_kernel, OpKernelContext* context, AsyncOpKernel::DoneCallback done) { VLOG(1) << "XlaDevice::ComputeAsync " << op_kernel->name() << ":" << op_kernel->type_string(); - port::Tracing::TraceMe trace_me(op_kernel->name(), op_kernel->type_string(), - op_kernel->IsExpensive()); + tracing::ScopedActivity activity(op_kernel->name(), op_kernel->type_string(), + op_kernel->IsExpensive()); op_kernel->ComputeAsync(context, done); } diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc index 30bfc9351a5273..796c3070f22edd 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc @@ -100,7 +100,7 @@ namespace gpu { namespace { -using tensorflow::port::Tracing; +namespace tracing = tensorflow::tracing; // Returns the directory containing nvvm libdevice files. config_cuda_data_dir // should be equal to config().debug_options().xla_gpu_cuda_data_dir() of the @@ -410,7 +410,7 @@ void WarnIfBadDriverJITVersion() { // code (i.e. a cubin) as a byte array. StatusOr> CompilePtx(const string& ptx, int cc_major, int cc_minor) { - Tracing::TraceMe annotation("Compile PTX", /*is_expensive=*/true); + tracing::ScopedActivity activity("Compile PTX", /*is_expensive=*/true); const string ptxas_path = tensorflow::io::JoinPath(tensorflow::CudaRoot(), "bin", "ptxas"); VLOG(2) << "Using ptxas at " << ptxas_path; @@ -481,8 +481,8 @@ StatusOr> GpuCompiler::RunHloPasses( std::unique_ptr module, se::StreamExecutor* stream_exec, DeviceMemoryAllocator* device_allocator) { XLA_SCOPED_LOGGING_TIMER("GpuCompiler::RunHloPasses"); - Tracing::TraceMe annotation("HLO Transforms", module->name(), - /*is_expensive=*/true); + tracing::ScopedActivity activity("HLO Transforms", module->name(), + /*is_expensive=*/true); TF_RETURN_IF_ERROR( OptimizeHloModule(module.get(), stream_exec, device_allocator)); return std::move(module); @@ -692,7 +692,7 @@ std::vector GpuCompiler::CompilePtxOrGetCachedResult(const string& ptx, int cc_major, int cc_minor) { XLA_SCOPED_LOGGING_TIMER("GpuCompiler::CompilePtxOrGetCachedResult"); - Tracing::TraceMe annotation("PTX->CUBIN", /*is_expensive=*/true); + tracing::ScopedActivity activity("PTX->CUBIN", /*is_expensive=*/true); bool inserted; decltype(compilation_cache_.begin()) iter; // Pointers into compilation_cache_ where the ptx and (optional) cubin are diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc index df9d9be889ce83..d70cb07c57d48c 100644 --- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc +++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc @@ -491,7 +491,7 @@ StatusOr CompileToPtx(llvm::Module* module, string ptx; { - tensorflow::port::Tracing::TraceMe annotation( + tensorflow::tracing::ScopedActivity activity( "Compiling IR", llvm_ir::AsString(module->getName()), /*is_expensive=*/true); XLA_SCOPED_LOGGING_TIMER("Compile module " + diff --git a/tensorflow/core/common_runtime/copy_tensor.cc b/tensorflow/core/common_runtime/copy_tensor.cc index e35548729b993c..08d120c7a5bed6 100644 --- a/tensorflow/core/common_runtime/copy_tensor.cc +++ b/tensorflow/core/common_runtime/copy_tensor.cc @@ -237,7 +237,7 @@ void CopyTensor::ViaDMA(StringPiece edge_name, DeviceContext* send_dev_context, const AllocatorAttributes dst_alloc_attr, const Tensor* input, Tensor* output, StatusCallback done) { - port::Tracing::ScopedAnnotation annotation(edge_name); + tracing::ScopedAnnotation annotation(edge_name); VLOG(1) << "Copy " << edge_name; const DeviceType src_device_type( diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc index 944f0c82e706ca..9b434e5e2fdb94 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_device.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc @@ -406,12 +406,8 @@ Status BaseGPUDevice::FillContextMap(const Graph* graph, } void BaseGPUDevice::Compute(OpKernel* op_kernel, OpKernelContext* context) { - // ScopedActivity is cheap when tracing is not active, but we - // can avoid computing the Hash64. - // TODO(pbar) This would no longer be needed if Ops have a unique id. - const uint64 id = port::Tracing::IsActive() ? Hash64(op_kernel->name()) : 0; - port::Tracing::ScopedActivity region(port::Tracing::EventCategory::kCompute, - id); + tracing::ScopedRegion region(tracing::EventCategory::kCompute, + op_kernel->name()); // NOTE(tucker): We need to discriminate between Eigen GPU // operations and all others. If an operation is Eigen @@ -425,11 +421,9 @@ void BaseGPUDevice::Compute(OpKernel* op_kernel, OpKernelContext* context) { if (op_kernel->is_internal() && op_kernel->type_string() == "_Recv") { context->SetStatus(errors::Internal( "Invalid synchronous 'Compute' on GPU for '_Recv' op")); - } else if (port::Tracing::ScopedAnnotation::Enabled()) { - port::Tracing::ScopedAnnotation annotation(op_kernel->name(), - op_kernel->type_string()); - ComputeHelper(op_kernel, context); } else { + tracing::ScopedAnnotation annotation(op_kernel->name(), + op_kernel->type_string()); ComputeHelper(op_kernel, context); } } @@ -527,11 +521,10 @@ void BaseGPUDevice::ComputeAsync(AsyncOpKernel* op_kernel, << op_kernel->type_string() << " on GPU" << tf_gpu_id_ << " stream[" << stream_id << "]"; - // When TraceMe profiling is off (which is the default), the - // following TraceMe constructor is simply a conditional test of - // false value. Measurements show that its overhead is negligible. - port::Tracing::TraceMe activity(op_kernel->name(), op_kernel->type_string(), - op_kernel->IsExpensive()); + // When Xprof profiling is off (which is the default), constructing the + // activity is simple enough that its overhead is negligible. + tracing::ScopedActivity activity(op_kernel->name(), op_kernel->type_string(), + op_kernel->IsExpensive()); se::cuda::ScopedActivateExecutorContext scoped_activation{stream->parent()}; op_kernel->ComputeAsync(context, done); } @@ -573,7 +566,7 @@ Status BaseGPUDevice::MaybeCopyTensorToGPU( }, std::move(done), std::placeholders::_1); - port::Tracing::ScopedAnnotation annotation("MakeTensorFromProto"); + tracing::ScopedAnnotation annotation("MakeTensorFromProto"); device_contexts_[0]->CopyCPUTensorToDevice(&from, this, copy, std::move(wrapped_done)); return Status::OK(); diff --git a/tensorflow/core/common_runtime/gpu/gpu_util.cc b/tensorflow/core/common_runtime/gpu/gpu_util.cc index 7ba853fa51bd66..d38413d79c9cf9 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_util.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_util.cc @@ -149,7 +149,7 @@ void GPUUtil::SetProtoFromGPU(const Tensor& tensor, Device* dev, char* buf = nullptr; const int64 total_bytes = is_dead ? 0 : tensor.TotalBytes(); if (total_bytes > 0) { - port::Tracing::ScopedAnnotation annotation("SetProtoFromGPU"); + tracing::ScopedAnnotation annotation("SetProtoFromGPU"); alloc = ProcessState::singleton()->GetCUDAHostAllocator(0); buf = alloc->Allocate(total_bytes); if (LogMemory::IsEnabled()) { diff --git a/tensorflow/core/common_runtime/process_util.cc b/tensorflow/core/common_runtime/process_util.cc index f8f3a1ecd73d88..21912236d079bd 100644 --- a/tensorflow/core/common_runtime/process_util.cc +++ b/tensorflow/core/common_runtime/process_util.cc @@ -79,21 +79,18 @@ thread::ThreadPool* NewThreadPoolFromSessionOptions( } void SchedClosure(std::function closure) { - if (port::Tracing::IsActive()) { - const uint64 id = port::Tracing::UniqueId(); - port::Tracing::RecordEvent(port::Tracing::EventCategory::kScheduleClosure, - id); - std::function wrapper = std::bind( - [id](std::function closure) { - port::Tracing::ScopedActivity region( - port::Tracing::EventCategory::kRunClosure, id); - closure(); - }, - std::move(closure)); - Env::Default()->SchedClosure(std::move(wrapper)); - } else { - Env::Default()->SchedClosure(std::move(closure)); + if (!tracing::EventCollector::IsEnabled()) { + return Env::Default()->SchedClosure(std::move(closure)); } + uint64 id = tracing::GetUniqueArg(); + tracing::RecordEvent(tracing::EventCategory::kScheduleClosure, id); + + Env::Default()->SchedClosure(std::bind( + [id](std::function closure) { + tracing::ScopedRegion region(tracing::EventCategory::kRunClosure, id); + closure(); + }, + std::move(closure))); } void SchedNonBlockingClosureAfter(int64 micros, std::function closure) { diff --git a/tensorflow/core/common_runtime/sycl/sycl_device.cc b/tensorflow/core/common_runtime/sycl/sycl_device.cc index 6e1a45b3efa8b5..f3bd72f697cde1 100644 --- a/tensorflow/core/common_runtime/sycl/sycl_device.cc +++ b/tensorflow/core/common_runtime/sycl/sycl_device.cc @@ -27,12 +27,11 @@ SYCLDevice::~SYCLDevice() {} void SYCLDevice::Compute(OpKernel* op_kernel, OpKernelContext* context) { assert(context); - if (port::Tracing::IsActive()) { - // TODO(pbar) We really need a useful identifier of the graph node. - const uint64 id = Hash64(op_kernel->name()); - port::Tracing::ScopedActivity region(port::Tracing::EventCategory::kCompute, - id); - } + // When ThreadScape profiling is off (which is the default), constructing the + // following code is simple enough that its overhead is negligible. + tracing::ScopedRegion region(tracing::EventCategory::kCompute, + op_kernel->name()); + op_kernel->Compute(context); } diff --git a/tensorflow/core/common_runtime/threadpool_device.cc b/tensorflow/core/common_runtime/threadpool_device.cc index 6d8de6a3c06a84..f7a07fe503f26b 100644 --- a/tensorflow/core/common_runtime/threadpool_device.cc +++ b/tensorflow/core/common_runtime/threadpool_device.cc @@ -48,20 +48,14 @@ ThreadPoolDevice::ThreadPoolDevice(const SessionOptions& options, ThreadPoolDevice::~ThreadPoolDevice() {} void ThreadPoolDevice::Compute(OpKernel* op_kernel, OpKernelContext* context) { - // When TraceMe profiling is off (which is the default), the - // following TraceMe constructor is simply a conditional test of - // false value. Measurements show that its overhead is negligible. - port::Tracing::TraceMe trace_me(op_kernel->name(), op_kernel->type_string(), - op_kernel->IsExpensive()); - if (port::Tracing::IsActive()) { - // TODO(pbar) We really need a useful identifier of the graph node. - const uint64 id = Hash64(op_kernel->name()); - port::Tracing::ScopedActivity region(port::Tracing::EventCategory::kCompute, - id); - op_kernel->Compute(context); - } else { - op_kernel->Compute(context); - } + // When Xprof/ThreadScape profiling is off (which is the default), the + // following code is simple enough that its overhead is negligible. + tracing::ScopedActivity activity(op_kernel->name(), op_kernel->type_string(), + op_kernel->IsExpensive()); + tracing::ScopedRegion region(tracing::EventCategory::kCompute, + op_kernel->name()); + + op_kernel->Compute(context); } Allocator* ThreadPoolDevice::GetAllocator(AllocatorAttributes attr) { diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc b/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc index 23968e24c87ee1..e025e555dd0656 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc @@ -285,7 +285,7 @@ class GrpcMasterService : public AsyncServiceInterface { #undef ENQUEUE_REQUEST // Start tracing, including the ID attached to the RPC. - port::Tracing::TraceMe* TraceRpc( + tracing::ScopedActivity* TraceRpc( StringPiece name, const std::multimap<::grpc::string_ref, ::grpc::string_ref>& metadata) { StringPiece id; @@ -293,7 +293,7 @@ class GrpcMasterService : public AsyncServiceInterface { if (it != metadata.end()) { id = StringPiece(it->second.data(), it->second.size()); } - return new port::Tracing::TraceMe(name, id); + return new tracing::ScopedActivity(name, id); } TF_DISALLOW_COPY_AND_ASSIGN(GrpcMasterService); diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc b/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc index 1b92a79a67eae2..b832a2115cb809 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc @@ -119,11 +119,11 @@ class GrpcRemoteMaster : public MasterInterface { private: // Start tracing, attaching a unique ID to both the trace and the RPC. - port::Tracing::TraceMe TraceRpc(StringPiece name, - ::grpc::ClientContext* ctx) { - string trace_id = strings::StrCat(port::Tracing::UniqueId()); + tracing::ScopedActivity TraceRpc(StringPiece name, + ::grpc::ClientContext* ctx) { + string trace_id = strings::StrCat(tracing::GetUniqueArg()); ctx->AddMetadata(GrpcIdKey(), trace_id); - return port::Tracing::TraceMe(name, trace_id); + return tracing::ScopedActivity(name, trace_id); } void SetDeadline(::grpc::ClientContext* ctx, int64 time_in_ms) { diff --git a/tensorflow/core/framework/dataset.h b/tensorflow/core/framework/dataset.h index 8d127baac44401..775d9f6eb6a4e2 100644 --- a/tensorflow/core/framework/dataset.h +++ b/tensorflow/core/framework/dataset.h @@ -521,7 +521,7 @@ class DatasetIterator : public IteratorBase { Status GetNext(IteratorContext* ctx, std::vector* out_tensors, bool* end_of_sequence) final { - port::Tracing::TraceMe activity(params_.prefix); + tracing::ScopedActivity activity(params_.prefix); Status s = GetNextInternal(ctx, out_tensors, end_of_sequence); if (TF_PREDICT_FALSE(errors::IsOutOfRange(s) && !*end_of_sequence)) { s = errors::Internal( diff --git a/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc b/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc index 605ef3c0b79cb0..7bc43e20725461 100644 --- a/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc @@ -468,7 +468,7 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { void StartInvocationBatch(IteratorContext* ctx, int64 batch_index) EXCLUSIVE_LOCKS_REQUIRED(mu_) { - port::Tracing::TraceMe activity(strings::StrCat(prefix(), "::Start")); + tracing::ScopedActivity activity(strings::StrCat(prefix(), "::Start")); // Initialize batch result. { mutex_lock l(batch_results_[batch_index].mu); @@ -493,7 +493,7 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { Status WaitForBatch(int64 batch_index, int64* num_elements) EXCLUSIVE_LOCKS_REQUIRED(mu_) { - port::Tracing::TraceMe activity(strings::StrCat(prefix(), "::Wait")); + tracing::ScopedActivity activity(strings::StrCat(prefix(), "::Wait")); batch_results_[batch_index].counter->Wait(); Status status = Status::OK(); for (size_t i = 0; i < dataset()->batch_size_; ++i, ++*num_elements) { diff --git a/tensorflow/core/kernels/function_ops.cc b/tensorflow/core/kernels/function_ops.cc index f8e0267578054b..8f66f0a7b970f2 100644 --- a/tensorflow/core/kernels/function_ops.cc +++ b/tensorflow/core/kernels/function_ops.cc @@ -324,7 +324,7 @@ class RemoteCallOp : public AsyncOpKernel { handle = cached_entry->second; } else { VLOG(1) << "Instantiating " << func_.name() << " on " << target_device; - port::Tracing::TraceMe activity(strings::StrCat( + tracing::ScopedActivity activity(strings::StrCat( "RemoteCall: Instantiate: ", func_.name(), " on ", target_device)); OP_REQUIRES_OK_ASYNC( ctx, @@ -355,12 +355,12 @@ class RemoteCallOp : public AsyncOpKernel { args.push_back(argument); } auto* rets = new std::vector; - auto* trace = new port::Tracing::TraceMe(strings::StrCat( + auto* activity = new tracing::ScopedActivity(strings::StrCat( "RemoteCall: Run: ", func_.name(), " on ", target_device)); VLOG(1) << "Running " << func_.name() << " on " << target_device << " with handle: " << handle; lib->Run(opts, handle, args, rets, - [rets, trace, done, ctx](const Status& status) { + [rets, activity, done, ctx](const Status& status) { if (!status.ok()) { ctx->SetStatus(status); } else { @@ -369,7 +369,7 @@ class RemoteCallOp : public AsyncOpKernel { } } delete rets; - delete trace; + delete activity; done(); }); } diff --git a/tensorflow/core/lib/core/threadpool.cc b/tensorflow/core/lib/core/threadpool.cc index e55ed79d36cd2d..99684ae47b547d 100644 --- a/tensorflow/core/lib/core/threadpool.cc +++ b/tensorflow/core/lib/core/threadpool.cc @@ -59,10 +59,9 @@ struct EigenEnvironment { Task CreateTask(std::function f) { uint64 id = 0; - if (port::Tracing::IsActive()) { - id = port::Tracing::UniqueId(); - port::Tracing::RecordEvent(port::Tracing::EventCategory::kScheduleClosure, - id); + if (tracing::EventCollector::IsEnabled()) { + id = tracing::GetUniqueArg(); + tracing::RecordEvent(tracing::EventCategory::kScheduleClosure, id); } return Task{ std::unique_ptr(new TaskImpl{ @@ -75,13 +74,9 @@ struct EigenEnvironment { void ExecuteTask(const Task& t) { WithContext wc(t.f->context); - if (t.f->trace_id != 0) { - port::Tracing::ScopedActivity region( - port::Tracing::EventCategory::kRunClosure, t.f->trace_id); - t.f->f(); - } else { - t.f->f(); - } + tracing::ScopedRegion region(tracing::EventCategory::kRunClosure, + t.f->trace_id); + t.f->f(); } }; diff --git a/tensorflow/core/platform/default/device_tracer.cc b/tensorflow/core/platform/default/device_tracer.cc index 8e60a7f0910ff9..ccddf1eafc0c92 100644 --- a/tensorflow/core/platform/default/device_tracer.cc +++ b/tensorflow/core/platform/default/device_tracer.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/step_stats_collector.h" #include "tensorflow/core/framework/step_stats.pb.h" #include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/cupti_wrapper.h" #include "tensorflow/core/platform/env.h" @@ -288,7 +289,7 @@ TF_STATIC_THREAD_LOCAL_POD(const char *, tls_current_annotation); class DeviceTracerImpl : public DeviceTracer, public CUPTIClient, - public port::Tracing::Engine { + public tracing::TraceCollector { public: DeviceTracerImpl(); ~DeviceTracerImpl() override; @@ -298,25 +299,25 @@ class DeviceTracerImpl : public DeviceTracer, Status Stop() override; Status Collect(StepStatsCollector *collector) override; - // port::Tracing::Engine interface: - bool IsEnabled() const override { - // We only register the Engine while tracing is enabled. - return true; - } - Annotation *PushAnnotation(StringPiece name) override { - VLOG(2) << "PushAnnotation " << name; - struct Impl : public port::Tracing::Engine::Annotation { + // tracing::TraceCollector interface: + virtual std::unique_ptr CreateAnnotationHandle( + StringPiece name_part1, StringPiece name_part2) const { + struct Impl : public tracing::TraceCollector::Handle { string annotation; - explicit Impl(StringPiece n) : annotation(n.ToString()) { + explicit Impl(string &&name_scope) : annotation(name_scope) { + VLOG(2) << "CreateAnnotationHandle " << annotation; // Remember the most recent ScopedAnnotation for each thread. tls_current_annotation.get() = annotation.c_str(); } ~Impl() override { tls_current_annotation.get() = nullptr; } }; - return new Impl(name); + return std::unique_ptr( + new Impl{ConcatenateNames(name_part1, name_part2)}); } - Tracer *StartTracing(StringPiece label, bool is_expensive) override { - // We don't do anything with 'TraceMe' regions yet. + + virtual std::unique_ptr CreateActivityHandle(StringPiece, StringPiece, + bool) const { + // We don't do anything with 'Activities' yet. return nullptr; } @@ -410,7 +411,7 @@ Status DeviceTracerImpl::Start() { } // Register as a TraceEngine to receive ScopedAnnotations. - port::Tracing::RegisterEngine(this); + tracing::SetTraceCollector(this); // Intercept launch and memcpy calls to capture the Op name annotation. // TODO(pbar) Add callbacks for memcpy variants. @@ -458,7 +459,7 @@ Status DeviceTracerImpl::Stop() { return Status::OK(); } CUPTI_CALL(Unsubscribe(subscriber_)); - port::Tracing::RegisterEngine(nullptr); + tracing::SetTraceCollector(nullptr); TF_RETURN_IF_ERROR(cupti_manager_->DisableTrace()); end_walltime_us_ = NowInUsec(); CUPTI_CALL(GetTimestamp(&end_timestamp_)); diff --git a/tensorflow/core/platform/default/tracing.cc b/tensorflow/core/platform/default/tracing.cc index 422564fb3e4df8..3efcef09b8da27 100644 --- a/tensorflow/core/platform/default/tracing.cc +++ b/tensorflow/core/platform/default/tracing.cc @@ -15,21 +15,33 @@ limitations under the License. #include "tensorflow/core/platform/tracing.h" -namespace tensorflow { -namespace port { - -void Tracing::RegisterEvent(EventCategory id, const char* name) { - // TODO(opensource): implement -} +#include -void Tracing::Initialize() {} +#ifndef PLATFORM_WINDOWS +#include +#endif -static bool DoInit() { - Tracing::Initialize(); - return true; +namespace tensorflow { +namespace tracing { +namespace { +bool TryGetEnv(const char* name, const char** value) { + *value = getenv(name); + return *value != nullptr && (*value)[0] != '\0'; } - -static const bool dummy = DoInit(); - -} // namespace port +} // namespace + +void EventCollector::SetCurrentThreadName(const char*) {} + +const char* GetLogDir() { + const char* dir; + if (TryGetEnv("TEST_TMPDIR", &dir)) return dir; + if (TryGetEnv("TMP", &dir)) return dir; + if (TryGetEnv("TMPDIR", &dir)) return dir; +#ifndef PLATFORM_WINDOWS + dir = "/tmp"; + if (access(dir, R_OK | W_OK | X_OK) == 0) return dir; +#endif + return "."; // Default to current directory. +} +} // namespace tracing } // namespace tensorflow diff --git a/tensorflow/core/platform/default/tracing_impl.h b/tensorflow/core/platform/default/tracing_impl.h index 78345488969ee3..b1613784053ba2 100644 --- a/tensorflow/core/platform/default/tracing_impl.h +++ b/tensorflow/core/platform/default/tracing_impl.h @@ -21,13 +21,8 @@ limitations under the License. // IWYU pragma: private, include "third_party/tensorflow/core/platform/tracing.h" // IWYU pragma: friend third_party/tensorflow/core/platform/tracing.h -#include "tensorflow/core/lib/core/status.h" -#include "tensorflow/core/lib/random/random.h" #include "tensorflow/core/platform/tracing.h" -namespace tensorflow { -namespace port { - // Definitions that do nothing for platforms that don't have underlying thread // tracing support. #define TRACELITERAL(a) \ @@ -40,21 +35,12 @@ namespace port { do { \ } while (0) -inline uint64 Tracing::UniqueId() { return random::New64(); } -inline bool Tracing::IsActive() { return false; } -inline void Tracing::RegisterCurrentThread(const char* name) {} - -// Posts an atomic threadscape event with the supplied category and arg. -inline void Tracing::RecordEvent(EventCategory category, uint64 arg) { - // TODO(opensource): Implement -} - -inline Tracing::ScopedActivity::ScopedActivity(EventCategory category, - uint64 arg) {} +namespace tensorflow { +namespace tracing { -inline Tracing::ScopedActivity::~ScopedActivity() {} +inline bool EventCollector::IsEnabled() { return false; } -} // namespace port +} // namespace tracing } // namespace tensorflow #endif // TENSORFLOW_PLATFORM_DEFAULT_TRACING_IMPL_H_ diff --git a/tensorflow/core/platform/posix/tracing.cc b/tensorflow/core/platform/posix/tracing.cc deleted file mode 100644 index 1d1aa53f2ca328..00000000000000 --- a/tensorflow/core/platform/posix/tracing.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* Copyright 2016 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/platform/tracing.h" - -#include -#include - -namespace tensorflow { -namespace port { - -static bool TryGetEnv(const char* name, const char** value) { - *value = getenv(name); - return *value != nullptr && (*value)[0] != '\0'; -} - -const char* Tracing::LogDir() { - const char* dir; - if (TryGetEnv("TEST_TMPDIR", &dir)) return dir; - if (TryGetEnv("TMP", &dir)) return dir; - if (TryGetEnv("TMPDIR", &dir)) return dir; - dir = "/tmp"; - if (access(dir, R_OK | W_OK | X_OK) == 0) return dir; - return "."; // Default to current directory. -} - -} // namespace port -} // namespace tensorflow diff --git a/tensorflow/core/platform/tracing.cc b/tensorflow/core/platform/tracing.cc index f7d2a8e282de51..c0386c0a3fc052 100644 --- a/tensorflow/core/platform/tracing.cc +++ b/tensorflow/core/platform/tracing.cc @@ -15,24 +15,24 @@ limitations under the License. #include "tensorflow/core/platform/tracing.h" +#include #include #include #include #include +#include "tensorflow/core/lib/hash/hash.h" #include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" namespace tensorflow { +namespace tracing { +namespace { +std::atomic unique_arg{1}; +std::atomic trace_collector; +} // namespace -namespace port { - -int32 Tracing::category_id_[kEventCategoryMax]; -uint64 Tracing::event_mask_ = 0; -std::map* Tracing::name_map_ = new std::map; - -// This needs to be kept in sync with the EventCategory enumeration. -const char* Tracing::EventCategoryString(EventCategory category) { +const char* GetEventCategoryName(EventCategory category) { switch (category) { case EventCategory::kScheduleClosure: return "ScheduleClosure"; @@ -40,63 +40,45 @@ const char* Tracing::EventCategoryString(EventCategory category) { return "RunClosure"; case EventCategory::kCompute: return "Compute"; - case EventCategory::kEventCategoryMax: - return "EventCategoryMax"; + default: + return "Unknown"; } - return "Unknown"; } -// This function allows the user to specify arbitrary subsets of the -// supported Threadscape events and activities. -bool Tracing::ParseEventMask(const char* flagname, const string& value) { - VLOG(1) << flagname << " set to " << value; - int64 new_mask = 0; - std::vector events = - str_util::Split(value, ',', str_util::SkipEmpty()); - for (string name : events) { - bool clear = false; - int64 mask = 0; - if (name[0] == '!') { - // invert the sense of the flag - clear = true; - name = name.substr(1); - } - if (name == "ALL") { - mask = ~0; - } else { - auto it = name_map_->find(name); - int32 id; - if (it == name_map_->end()) { - id = -1; - } else { - id = it->second; - } - if (id < 0) { - LOG(ERROR) << "Can't parse event mask name " << name; - return false; - } - mask = 1 << id; - } - if (clear) { - new_mask &= ~mask; - } else { - new_mask |= mask; - } - } - // parsing was successful; set the permanent event mask - event_mask_ = new_mask; - return true; +std::array + EventCollector::instances_; + +void SetEventCollector(EventCategory category, + const EventCollector* collector) { + EventCollector::instances_[static_cast(category)] = collector; +} + +uint64 GetUniqueArg() { + return unique_arg.fetch_add(1, std::memory_order_relaxed); } -/*static*/ std::atomic Tracing::tracing_engine_; +uint64 GetArgForName(StringPiece name) { + return Hash64(name.data(), name.size()); +} -void Tracing::RegisterEngine(Engine* e) { - tracing_engine_.store(e, std::memory_order_release); +string TraceCollector::ConcatenateNames(StringPiece first, StringPiece second) { + std::string result; + bool has_two_parts = !first.empty() && !second.empty(); + result.reserve(first.size() + second.size() + + static_cast(has_two_parts)); + result.append(first.data(), first.size()); + if (has_two_parts) result.append({':'}); + result.append(second.data(), second.size()); + return result; } -Tracing::Engine::~Engine() {} -Tracing::Engine::Annotation::~Annotation() {} -Tracing::Engine::Tracer::~Tracer() {} +void SetTraceCollector(const TraceCollector* collector) { + return trace_collector.store(collector, std::memory_order_release); +} + +const TraceCollector* GetTraceCollector() { + return trace_collector.load(std::memory_order_acquire); +} -} // namespace port +} // namespace tracing } // namespace tensorflow diff --git a/tensorflow/core/platform/tracing.h b/tensorflow/core/platform/tracing.h index 3c6e7b0db59951..c322777705a7fc 100644 --- a/tensorflow/core/platform/tracing.h +++ b/tensorflow/core/platform/tracing.h @@ -18,6 +18,7 @@ limitations under the License. // Tracing interface +#include #include #include #include @@ -30,255 +31,205 @@ limitations under the License. #include "tensorflow/core/platform/types.h" namespace tensorflow { +namespace tracing { + +// This enumeration contains the identifiers of all TensorFlow CPU profiler +// events. It must be kept in sync with the code in GetEventCategoryName(). +enum struct EventCategory : unsigned { + kScheduleClosure = 0, + kRunClosure = 1, + kCompute = 2, + kNumCategories = 3 // sentinel - keep last +}; +constexpr unsigned GetNumEventCategories() { + return static_cast(EventCategory::kNumCategories); +} +const char* GetEventCategoryName(EventCategory); -namespace port { - -class Tracing { +// Interface for CPU profiler events. +class EventCollector { public: - // This enumeration contains the identifiers of all TensorFlow - // threadscape events and code regions. Threadscape assigns its - // own identifiers at runtime when we register our events and we - // cannot know in advance what IDs it will choose. The "RecordEvent" - // method and "ScopedActivity" use these event IDs for consistency - // and remap them to threadscape IDs at runtime. This enum is limited - // to 64 values since we use a bitmask to configure which events are - // enabled. It must also be kept in step with the code in - // "Tracing::EventCategoryString". - enum EventCategory { - kScheduleClosure = 0, - kRunClosure = 1, - kCompute = 2, - kEventCategoryMax = 3 // sentinel - keep last - }; - // Note: We currently only support up to 64 categories. - static_assert(kEventCategoryMax <= 64, "only support up to 64 events"); + virtual ~EventCollector() {} + virtual void RecordEvent(uint64 arg) const = 0; + virtual void StartRegion(uint64 arg) const = 0; + virtual void StopRegion() const = 0; - // Called by main programs to initialize tracing facilities - static void Initialize(); + // Annotates the current thread with a name. + static void SetCurrentThreadName(const char* name); + // Returns whether event collection is enabled. + static bool IsEnabled(); - // Return the pathname of the directory where we are writing log files. - static const char* LogDir(); + private: + friend void SetEventCollector(EventCategory, const EventCollector*); + friend const EventCollector* GetEventCollector(EventCategory); - // Returns a non-zero identifier which can be used to correlate - // related events. - static inline uint64 UniqueId(); + static std::array instances_; +}; +// Set the callback for RecordEvent and ScopedRegion of category. +// Not thread safe. Only call while EventCollector::IsEnabled returns false. +void SetEventCollector(EventCategory category, const EventCollector* collector); + +// Returns the callback for RecordEvent and ScopedRegion of category if +// EventCollector::IsEnabled(), otherwise returns null. +inline const EventCollector* GetEventCollector(EventCategory category) { + if (EventCollector::IsEnabled()) { + return EventCollector::instances_[static_cast(category)]; + } + return nullptr; +} - // Returns true if a trace is in progress. Can be used to reduce tracing - // overheads in fast-path code. - static inline bool IsActive(); +// Returns a unique id to pass to RecordEvent/ScopedRegion. Never returns zero. +uint64 GetUniqueArg(); - // Associate name with the current thread. - static void RegisterCurrentThread(const char* name); +// Returns an id for name to pass to RecordEvent/ScopedRegion. +uint64 GetArgForName(StringPiece name); - // Posts an event with the supplied category and arg. - static void RecordEvent(EventCategory category, uint64 arg); +// Records an atomic event through the currently registered EventCollector. +inline void RecordEvent(EventCategory category, uint64 arg) { + if (auto collector = GetEventCollector(category)) { + collector->RecordEvent(arg); + } +} - // Traces a region of code. Posts a tracing "EnterCodeRegion" event - // when created and an "ExitCodeRegion" event when destroyed. - class ScopedActivity { - public: - explicit ScopedActivity(EventCategory category, uint64 arg); - ~ScopedActivity(); +// Records an event for the duration of the instance lifetime through the +// currently registered EventCollector. +class ScopedRegion { + ScopedRegion(ScopedRegion&) = delete; // Not copy-constructible. + ScopedRegion& operator=(ScopedRegion&) = delete; // Not assignable. - private: -#if defined(PLATFORM_GOOGLE) - const bool enabled_; - const int32 region_id_; -#endif + public: + ScopedRegion(ScopedRegion&& other) noexcept // Move-constructible. + : collector_(other.collector_) { + other.collector_ = nullptr; + } - TF_DISALLOW_COPY_AND_ASSIGN(ScopedActivity); - }; + ScopedRegion(EventCategory category, uint64 arg) + : collector_(GetEventCollector(category)) { + if (collector_) { + collector_->StartRegion(arg); + } + } - // Trace collection engine can be registered with this module. - // If no engine is registered, ScopedAnnotation and TraceMe are no-ops. - class Engine; - static void RegisterEngine(Engine*); + // Same as ScopedRegion(category, GetUniqueArg()), but faster if + // EventCollector::IsEnaled() returns false. + ScopedRegion(EventCategory category) + : collector_(GetEventCollector(category)) { + if (collector_) { + collector_->StartRegion(GetUniqueArg()); + } + } - // Forward declaration of the GPU utility classes. - class ScopedAnnotation; - class TraceMe; + // Same as ScopedRegion(category, GetArgForName(name)), but faster if + // EventCollector::IsEnaled() returns false. + ScopedRegion(EventCategory category, StringPiece name) + : collector_(GetEventCollector(category)) { + if (collector_) { + collector_->StartRegion(GetArgForName(name)); + } + } - private: - friend class TracingTest; - friend class ScopedAnnotation; - friend class TraceMe; - - // TODO: TF_EXPORT is for building //tensorflow/contrib/data:_dataset_ops.so - // on Windows. Figure out a way to remove TF_EXPORT here. - TF_EXPORT static std::atomic tracing_engine_; - static Tracing::Engine* engine() { - return tracing_engine_.load(std::memory_order_acquire); + ~ScopedRegion() { + if (collector_) { + collector_->StopRegion(); + } } - static void RegisterEvent(EventCategory id, const char* name); - static const char* EventCategoryString(EventCategory category); - - // - // Parses event mask expressions in 'value' of the form: - // expr ::= (,)* - // term ::= | "!" - // event ::= "ALL" | | - // wait_event ::= "ENewSession" | "ECloseSession" | ... - // other_event ::= "Send" | "Wait" | ... - // ALL denotes all events, turns on tracing for this event, and - // ! turns off tracing for this event. - // If the expression can be parsed correctly it returns true and sets - // the event_mask_. Otherwise it returns false and the event_mask_ is left - // unchanged. - static bool ParseEventMask(const char* flagname, const string& value); - - // Bit mask of enabled trace categories. - static uint64 event_mask_; - - // Records the mappings between Threadscape IDs and the "EventCategory" enum. - static int32 category_id_[kEventCategoryMax]; - static std::map* name_map_; + bool IsEnabled() const { return collector_ != nullptr; } + + private: + const EventCollector* collector_; }; -// Trace collection engine that actually implements collection. -class Tracing::Engine { +// Interface for accelerator profiler annotations. +class TraceCollector { public: - Engine() {} - virtual ~Engine(); - - // Returns true if Tracing is currently enabled. - virtual bool IsEnabled() const = 0; - - // Represents an active annotation. - class Annotation { + class Handle { public: - Annotation() {} - virtual ~Annotation(); + virtual ~Handle() {} }; - // Represents an active trace. - class Tracer { - public: - Tracer() {} - virtual ~Tracer(); - }; + virtual ~TraceCollector() {} + virtual std::unique_ptr CreateAnnotationHandle( + StringPiece name_part1, StringPiece name_part2) const = 0; + virtual std::unique_ptr CreateActivityHandle( + StringPiece name_part1, StringPiece name_part2, + bool is_expensive) const = 0; - private: - friend class ScopedAnnotation; - friend class TraceMe; - - // Register the specified name as an annotation on the current thread. - // Caller should delete the result to remove the annotation. - // Annotations from the same thread are destroyed in a LIFO manner. - // May return nullptr if annotations are not supported. - virtual Annotation* PushAnnotation(StringPiece name) = 0; - - // Start tracing under the specified label. Caller should delete the result - // to stop tracing. - // May return nullptr if tracing is not supported. - virtual Tracer* StartTracing(StringPiece label, bool is_expensive) = 0; - // Same as above, but implementations can avoid copying the string. - virtual Tracer* StartTracing(string&& label, bool is_expensive) { - return StartTracing(StringPiece(label), is_expensive); - } + protected: + static string ConcatenateNames(StringPiece first, StringPiece second); - // Backwards compatibility one arg variants (assume is_expensive=true). - Tracer* StartTracing(StringPiece label) { - return StartTracing(label, /*is_expensive=*/true); - } - Tracer* StartTracing(string&& label) { - return StartTracing(StringPiece(label), /*is_expensive=*/true); - } + private: + friend void SetTraceCollector(const TraceCollector*); + friend const TraceCollector* GetTraceCollector(); }; +// Set the callback for ScopedAnnotation and ScopedActivity. +void SetTraceCollector(const TraceCollector* collector); +// Returns the callback for ScopedAnnotation and ScopedActivity. +const TraceCollector* GetTraceCollector(); -// This class permits a user to apply annotation on kernels and memcpys -// when launching them. While an annotation is in scope, all activities -// within that scope get their names replaced by the annotation. The kernel -// name replacement is done when constructing the protobuf for sending out to -// a client (e.g., the stubby requestor) for both API and Activity records. -// -// Ownership: The creator of ScopedAnnotation assumes ownership of the object. +// Adds an annotation to all activities for the duration of the instance +// lifetime through the currently registered TraceCollector. // // Usage: { -// ScopedAnnotation annotation("first set of kernels"); +// ScopedAnnotation annotation("my kernels"); // Kernel1<<>>; -// LaunchKernel2(); // Which eventually launches a cuda kernel. +// LaunchKernel2(); // Launches a CUDA kernel. // } -// In the above scenario, the GPUProf UI would show 2 kernels with the name -// "first set of kernels" executing -- they will appear as the same kernel. -class Tracing::ScopedAnnotation { +// This will add 'my kernels' to both kernels in the profiler UI +class ScopedAnnotation { public: - explicit ScopedAnnotation(StringPiece name); + explicit ScopedAnnotation(StringPiece name) + : ScopedAnnotation(name, StringPiece()) {} - // If tracing is enabled, set up an annotation with a label of - // ":". Can be cheaper than the + // If tracing is enabled, add a name scope of + // ":". This can be cheaper than the // single-argument constructor because the concatenation of the // label string is only done if tracing is enabled. - ScopedAnnotation(StringPiece name_part1, StringPiece name_part2); + ScopedAnnotation(StringPiece name_part1, StringPiece name_part2) + : handle_([&] { + auto trace_collector = GetTraceCollector(); + return trace_collector ? trace_collector->CreateAnnotationHandle( + name_part1, name_part2) + : nullptr; + }()) {} - // Returns true iff scoped annotations are active. - static bool Enabled() { - auto e = Tracing::engine(); - return e && e->IsEnabled(); - } + bool IsEnabled() const { return static_cast(handle_); } private: - std::unique_ptr annotation_; + std::unique_ptr handle_; }; -// TODO(opensource): clean up the scoped classes for GPU tracing. -// This class permits user-specified (CPU) tracing activities. A trace -// activity is started when an object of this class is created and stopped -// when the object is destroyed. -class Tracing::TraceMe { +// Adds an activity through the currently registered TraceCollector. +// The activity starts when an object of this class is created and stops when +// the object is destroyed. +class ScopedActivity { public: - explicit TraceMe(StringPiece name); - TraceMe(StringPiece name, bool is_expensive); + explicit ScopedActivity(StringPiece name, bool is_expensive = true) + : ScopedActivity(name, StringPiece(), is_expensive) {} - // If tracing is enabled, set up a traceMe with a label of + // If tracing is enabled, set up an activity with a label of // ":". This can be cheaper than the // single-argument constructor because the concatenation of the // label string is only done if tracing is enabled. - TraceMe(StringPiece name_part1, StringPiece name_part2); - TraceMe(StringPiece name_part1, StringPiece name_part2, bool is_expensive); + ScopedActivity(StringPiece name_part1, StringPiece name_part2, + bool is_expensive = true) + : handle_([&] { + auto trace_collector = GetTraceCollector(); + return trace_collector ? trace_collector->CreateActivityHandle( + name_part1, name_part2, is_expensive) + : nullptr; + }()) {} + + bool IsEnabled() const { return static_cast(handle_); } private: - std::unique_ptr tracer_; + std::unique_ptr handle_; }; -inline Tracing::ScopedAnnotation::ScopedAnnotation(StringPiece name) { - auto e = Tracing::engine(); - if (e && e->IsEnabled()) { - annotation_.reset(e->PushAnnotation(name)); - } -} - -inline Tracing::ScopedAnnotation::ScopedAnnotation(StringPiece name_part1, - StringPiece name_part2) { - auto e = Tracing::engine(); - if (e && e->IsEnabled()) { - annotation_.reset( - e->PushAnnotation(strings::StrCat(name_part1, ":", name_part2))); - } -} - -inline Tracing::TraceMe::TraceMe(StringPiece name) : TraceMe(name, true) {} - -inline Tracing::TraceMe::TraceMe(StringPiece name, bool is_expensive) { - auto e = Tracing::engine(); - if (e && e->IsEnabled()) { - tracer_.reset(e->StartTracing(name, is_expensive)); - } -} - -inline Tracing::TraceMe::TraceMe(StringPiece name_part1, StringPiece name_part2) - : TraceMe(name_part1, name_part2, true) {} - -inline Tracing::TraceMe::TraceMe(StringPiece name_part1, StringPiece name_part2, - bool is_expensive) { - auto e = Tracing::engine(); - if (e && e->IsEnabled()) { - tracer_.reset(e->StartTracing(strings::StrCat(name_part1, ":", name_part2), - is_expensive)); - } -} +// Return the pathname of the directory where we are writing log files. +const char* GetLogDir(); -} // namespace port +} // namespace tracing } // namespace tensorflow #if defined(PLATFORM_GOOGLE) From a5a51ad3a1200e2e5ef46c140bab717422e41ca2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 06:59:23 -0700 Subject: [PATCH 0163/1691] Adding a depthwise convolution kernel op (with label 'cudnn_grouped_convolution') which forwards to cuDNN grouped convolutions. PiperOrigin-RevId: 194780352 --- tensorflow/core/kernels/BUILD | 10 +- .../core/kernels/conv_grad_filter_ops.cc | 71 +++-- .../core/kernels/conv_grad_input_ops.cc | 74 +++-- tensorflow/core/kernels/conv_grad_ops.cc | 7 +- tensorflow/core/kernels/conv_ops.cc | 85 +++--- .../core/kernels/depthwise_conv_grad_op.cc | 263 +++++++++++++++--- tensorflow/core/kernels/depthwise_conv_op.cc | 118 +++++--- .../kernel_tests/depthwise_conv_op_test.py | 222 +++++++++------ tensorflow/stream_executor/cuda/cuda_dnn.cc | 18 +- tensorflow/stream_executor/dnn.cc | 1 + tensorflow/stream_executor/dnn.h | 6 + 11 files changed, 637 insertions(+), 238 deletions(-) diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 6355f136545ed2..3fb03cd5bd3a8d 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -3299,7 +3299,10 @@ tf_kernel_library( "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:nn_ops_op_lib", - ] + if_cuda(["@cub_archive//:cub"]), + ] + if_cuda([ + "@cub_archive//:cub", + "@local_config_cuda//cuda:cudnn", + ]), ) tf_kernel_library( @@ -3310,12 +3313,15 @@ tf_kernel_library( prefix = "depthwise_conv_grad_op", deps = [ ":bounds_check", + ":conv_ops", ":ops_util", "//tensorflow/core:core_cpu", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:nn_ops_op_lib", - ], + ] + if_cuda([ + "@local_config_cuda//cuda:cudnn", + ]), ) cc_library( diff --git a/tensorflow/core/kernels/conv_grad_filter_ops.cc b/tensorflow/core/kernels/conv_grad_filter_ops.cc index ef1e73e5ab1cbc..aca75176a565dd 100644 --- a/tensorflow/core/kernels/conv_grad_filter_ops.cc +++ b/tensorflow/core/kernels/conv_grad_filter_ops.cc @@ -96,7 +96,8 @@ template struct LaunchConv2DBackpropFilterOp { void operator()(OpKernelContext* ctx, bool use_cudnn, bool cudnn_use_autotune, const Tensor& out_backprop, const Tensor& input, - int row_stride, int col_stride, const Padding& padding, + int row_dilation, int col_dilation, int row_stride, + int col_stride, const Padding& padding, Tensor* filter_backprop, TensorFormat data_format) { const CPUDevice& d = ctx->eigen_device(); functor::SpatialConvolutionBackwardFilter()( @@ -275,7 +276,8 @@ class Conv2DFastBackpropFilterOp : public OpKernel { #endif LaunchConv2DBackpropFilterOp()( - context, false, false, out_backprop, input, dims.spatial_dims[0].stride, + context, false, false, out_backprop, input, + /*row_dilation=*/1, /*col_dilation=*/1, dims.spatial_dims[0].stride, dims.spatial_dims[1].stride, padding_, filter_backprop, data_format_); } @@ -523,6 +525,11 @@ TF_CALL_float(REGISTER_CPU_KERNELS); TF_CALL_double(REGISTER_CPU_KERNELS); #undef REGISTER_CPU_KERNELS +// To be used inside depthwise_conv_grad_op.cc. +template struct LaunchConv2DBackpropFilterOp; +template struct LaunchConv2DBackpropFilterOp; +template struct LaunchConv2DBackpropFilterOp; + // GPU definitions. #if GOOGLE_CUDA // The slow version (but compiles for GPU) @@ -690,10 +697,15 @@ void LaunchConv2DBackpropFilterOp::operator()( return; } + // If the filter in-depth (filter_shape.dim_size(2)) is 1 and smaller than the + // input depth, it's a depthwise convolution. More generally, if the filter + // in-depth divides but is smaller than the input depth, it is a grouped + // convolution. + bool is_grouped_convolution = filter_shape.dim_size(2) != dims.in_depth; bool cudnn_disable_conv_1x1_optimization_ = CudnnDisableConv1x1Optimization(); if (!cudnn_disable_conv_1x1_optimization_ && dims.spatial_dims[0].filter_size == 1 && - dims.spatial_dims[1].filter_size == 1 && + dims.spatial_dims[1].filter_size == 1 && !is_grouped_convolution && dims.spatial_dims[0].stride == 1 && dims.spatial_dims[1].stride == 1 && data_format == FORMAT_NHWC) { const uint64 m = dims.in_depth; @@ -734,9 +746,10 @@ void LaunchConv2DBackpropFilterOp::operator()( dims.spatial_dims[0].input_size && dims.spatial_dims[1].filter_size == dims.spatial_dims[1].input_size && - padding == VALID && data_format == FORMAT_NHWC) { - // The input data and filter have the same height/width, so call cublas - // directly. + !is_grouped_convolution && padding == VALID && + data_format == FORMAT_NHWC) { + // The input data and filter have the same height/width, and we are not + // using grouped convolution, so call cublas directly. const uint64 m = dims.spatial_dims[0].input_size * dims.spatial_dims[1].input_size * dims.in_depth; const uint64 k = dims.batch_size; @@ -802,15 +815,16 @@ void LaunchConv2DBackpropFilterOp::operator()( se::dnn::FilterDescriptor filter_desc; filter_desc.set_input_filter_height(dims.spatial_dims[0].filter_size) .set_input_filter_width(dims.spatial_dims[1].filter_size) - .set_input_feature_map_count(dims.in_depth) - .set_output_feature_map_count(dims.out_depth); + .set_input_feature_map_count(filter_shape.dim_size(2)) + .set_output_feature_map_count(filter_shape.dim_size(3)); se::dnn::ConvolutionDescriptor conv_desc; conv_desc.set_vertical_dilation_rate(dims.spatial_dims[0].dilation) .set_horizontal_dilation_rate(dims.spatial_dims[1].dilation) .set_vertical_filter_stride(dims.spatial_dims[0].stride) .set_horizontal_filter_stride(dims.spatial_dims[1].stride) .set_zero_padding_height(padding_rows / 2) - .set_zero_padding_width(padding_cols / 2); + .set_zero_padding_width(padding_cols / 2) + .set_group_count(dims.in_depth / filter_shape.dim_size(2)); // NOTE(zhengxq): // cuDNN only supports the following layouts : @@ -891,21 +905,22 @@ void LaunchConv2DBackpropFilterOp::operator()( int device_id = stream->parent()->device_ordinal(); DataType dtype = input.dtype(); ConvParameters conv_parameters = { - dims.batch_size, // batch - dims.in_depth, // in_depths - {{input_desc.height(), // in_rows - input_desc.width()}}, // in_cols - dims.out_depth, // out_depths - {{dims.spatial_dims[0].filter_size, // filter_rows - dims.spatial_dims[1].filter_size}}, // filter_cols - {{dims.spatial_dims[0].dilation, // dilation_rows - dims.spatial_dims[1].dilation}}, // dilation_cols - {{dims.spatial_dims[0].stride, // stride_rows - dims.spatial_dims[1].stride}}, // stride_cols - {{padding_rows, // padding_rows - padding_cols}}, // padding_cols - dtype, // tensor datatype - device_id, // device_id + dims.batch_size, // batch + dims.in_depth, // in_depths + {{input_desc.height(), // in_rows + input_desc.width()}}, // in_cols + dims.out_depth, // out_depths + {{dims.spatial_dims[0].filter_size, // filter_rows + dims.spatial_dims[1].filter_size, // filter_cols + filter_shape.dim_size(2)}}, // filter_depth + {{dims.spatial_dims[0].dilation, // dilation_rows + dims.spatial_dims[1].dilation}}, // dilation_cols + {{dims.spatial_dims[0].stride, // stride_rows + dims.spatial_dims[1].stride}}, // stride_cols + {{padding_rows, // padding_rows + padding_cols}}, // padding_cols + dtype, // tensor datatype + device_id, // device_id }; AlgorithmConfig algorithm_config; if (cudnn_use_autotune && !AutoTuneConvBwdFilter::GetInstance()->Find( @@ -1019,9 +1034,9 @@ namespace functor { typename TTypes::Tensor out, TensorFormat data_format); \ extern template struct PadInput; -DECLARE_GPU_SPEC(double); DECLARE_GPU_SPEC(float); DECLARE_GPU_SPEC(Eigen::half); +DECLARE_GPU_SPEC(double); #undef DECLARE_GPU_SPEC } // namespace functor @@ -1040,6 +1055,12 @@ REGISTER_KERNEL_BUILDER(Name("Conv2DBackpropFilter") .TypeConstraint("T") .HostMemory("filter_sizes"), Conv2DSlowBackpropFilterOp); + +// To be used inside depthwise_conv_grad_op.cc. +template struct LaunchConv2DBackpropFilterOp; +template struct LaunchConv2DBackpropFilterOp; +template struct LaunchConv2DBackpropFilterOp; + #endif // GOOGLE_CUDA } // namespace tensorflow diff --git a/tensorflow/core/kernels/conv_grad_input_ops.cc b/tensorflow/core/kernels/conv_grad_input_ops.cc index 35f2676023afb7..63a775afa8bd69 100644 --- a/tensorflow/core/kernels/conv_grad_input_ops.cc +++ b/tensorflow/core/kernels/conv_grad_input_ops.cc @@ -101,8 +101,9 @@ template struct LaunchConv2DBackpropInputOp { void operator()(OpKernelContext* ctx, bool use_cudnn, bool cudnn_use_autotune, const Tensor& out_backprop, const Tensor& filter, - int row_stride, int col_stride, const Padding& padding, - Tensor* in_backprop, TensorFormat data_format) { + int row_dilation, int col_dilation, int row_stride, + int col_stride, const Padding& padding, Tensor* in_backprop, + TensorFormat data_format) { const CPUDevice& d = ctx->eigen_device(); functor::SpatialConvolutionBackwardInput()( d, in_backprop->tensor(), filter.tensor(), @@ -280,8 +281,8 @@ class Conv2DFastBackpropInputOp : public OpKernel { LaunchConv2DBackpropInputOp()( context, false, false, out_backprop, filter, - dims.spatial_dims[0].stride, dims.spatial_dims[1].stride, padding_, - in_backprop, data_format_); + /*row_dilation=*/1, /*col_dilation=*/1, dims.spatial_dims[0].stride, + dims.spatial_dims[1].stride, padding_, in_backprop, data_format_); } private: @@ -595,6 +596,11 @@ TF_CALL_float(REGISTER_CPU_KERNELS); TF_CALL_double(REGISTER_CPU_KERNELS); #undef REGISTER_CPU_KERNELS +// To be used inside depthwise_conv_grad_op.cc. +template struct LaunchConv2DBackpropInputOp; +template struct LaunchConv2DBackpropInputOp; +template struct LaunchConv2DBackpropInputOp; + // GPU definitions. #if GOOGLE_CUDA // The slow version (but compiles for GPU) @@ -761,8 +767,13 @@ void LaunchConv2DBackpropInputOp::operator()( return; } + // If the filter in-depth (filter_shape.dim_size(2)) is 1 and smaller than the + // input depth, it's a depthwise convolution. More generally, if the filter + // in-depth divides but is smaller than the input depth, it is a grouped + // convolution. + bool is_grouped_convolution = filter_shape.dim_size(2) != dims.in_depth; if (dims.spatial_dims[0].filter_size == 1 && - dims.spatial_dims[1].filter_size == 1 && + dims.spatial_dims[1].filter_size == 1 && !is_grouped_convolution && dims.spatial_dims[0].stride == 1 && dims.spatial_dims[1].stride == 1 && data_format == FORMAT_NHWC) { // 1x1 filter, so call cublas directly. @@ -795,9 +806,10 @@ void LaunchConv2DBackpropInputOp::operator()( dims.spatial_dims[0].input_size && dims.spatial_dims[1].filter_size == dims.spatial_dims[1].input_size && - padding == VALID && data_format == FORMAT_NHWC) { - // The input data and filter have the same height/width, so call cublas - // directly. + !is_grouped_convolution && padding == VALID && + data_format == FORMAT_NHWC) { + // The input data and filter have the same height/width, and we are not + // using grouped convolution, so call cublas directly. const uint64 m = dims.batch_size; const uint64 k = dims.out_depth; const uint64 n = dims.spatial_dims[0].input_size * @@ -856,15 +868,16 @@ void LaunchConv2DBackpropInputOp::operator()( se::dnn::FilterDescriptor filter_desc; filter_desc.set_input_filter_height(dims.spatial_dims[0].filter_size) .set_input_filter_width(dims.spatial_dims[1].filter_size) - .set_input_feature_map_count(dims.in_depth) - .set_output_feature_map_count(dims.out_depth); + .set_input_feature_map_count(filter_shape.dim_size(2)) + .set_output_feature_map_count(filter_shape.dim_size(3)); se::dnn::ConvolutionDescriptor conv_desc; conv_desc.set_vertical_dilation_rate(dims.spatial_dims[0].dilation) .set_horizontal_dilation_rate(dims.spatial_dims[1].dilation) .set_vertical_filter_stride(dims.spatial_dims[0].stride) .set_horizontal_filter_stride(dims.spatial_dims[1].stride) .set_zero_padding_height(padding_rows / 2) - .set_zero_padding_width(padding_cols / 2); + .set_zero_padding_width(padding_cols / 2) + .set_group_count(dims.in_depth / filter_shape.dim_size(2)); // NOTE(keveman): // cuDNN only supports the following layouts : @@ -940,21 +953,22 @@ void LaunchConv2DBackpropInputOp::operator()( int device_id = stream->parent()->device_ordinal(); DataType dtype = out_backprop.dtype(); ConvParameters conv_parameters = { - dims.batch_size, // batch - dims.in_depth, // in_depths - {{input_desc.height(), // in_rows - input_desc.width()}}, // in_cols - dims.out_depth, // out_depths - {{dims.spatial_dims[0].filter_size, // filter_rows - dims.spatial_dims[1].filter_size}}, // filter_cols - {{dims.spatial_dims[0].dilation, // dilation_rows - dims.spatial_dims[1].dilation}}, // dilation_cols - {{dims.spatial_dims[0].stride, // stride_rows - dims.spatial_dims[1].stride}}, // stride_cols - {{padding_rows, // padding_rows - padding_cols}}, // padding_cols - dtype, // tensor data type - device_id, // device_id + dims.batch_size, // batch + dims.in_depth, // in_depths + {{input_desc.height(), // in_rows + input_desc.width()}}, // in_cols + dims.out_depth, // out_depths + {{dims.spatial_dims[0].filter_size, // filter_rows + dims.spatial_dims[1].filter_size, // filter_cols + filter_shape.dim_size(2)}}, // filter_depths + {{dims.spatial_dims[0].dilation, // dilation_rows + dims.spatial_dims[1].dilation}}, // dilation_cols + {{dims.spatial_dims[0].stride, // stride_rows + dims.spatial_dims[1].stride}}, // stride_cols + {{padding_rows, // padding_rows + padding_cols}}, // padding_cols + dtype, // tensor data type + device_id, // device_id }; AlgorithmConfig algorithm_config; if (cudnn_use_autotune && !AutoTuneConvBwdData::GetInstance()->Find( @@ -1092,9 +1106,9 @@ namespace functor { typename TTypes::Tensor out, TensorFormat data_format); \ extern template struct PadInput; -DECLARE_GPU_SPEC(double); DECLARE_GPU_SPEC(float); DECLARE_GPU_SPEC(Eigen::half); +DECLARE_GPU_SPEC(double); #undef DECLARE_GPU_SPEC } // namespace functor @@ -1113,6 +1127,12 @@ REGISTER_KERNEL_BUILDER(Name("Conv2DBackpropInput") .TypeConstraint("T") .HostMemory("input_sizes"), Conv2DSlowBackpropInputOp); + +// To be used inside depthwise_conv_grad_op.cc. +template struct LaunchConv2DBackpropInputOp; +template struct LaunchConv2DBackpropInputOp; +template struct LaunchConv2DBackpropInputOp; + #endif // GOOGLE_CUDA } // namespace tensorflow diff --git a/tensorflow/core/kernels/conv_grad_ops.cc b/tensorflow/core/kernels/conv_grad_ops.cc index 170ce31d1711a8..5bf709af08af41 100644 --- a/tensorflow/core/kernels/conv_grad_ops.cc +++ b/tensorflow/core/kernels/conv_grad_ops.cc @@ -127,16 +127,17 @@ Status ConvBackpropComputeDimensionsV2( dims->in_depth = input_shape.dim_size(feature_dim); // The input and output feature dimensions are the second last and last // dimensions of the filter Tensor. - if (dims->in_depth != filter_shape.dim_size(num_dims - 2)) { + VLOG(2) << "input vs filter_in depth " << dims->in_depth << " " + << filter_shape.dim_size(num_dims - 2); + if (dims->in_depth % filter_shape.dim_size(num_dims - 2)) { return errors::InvalidArgument( - label, ": input and filter must have the same depth"); + label, ": input depth must be evenly divisible by filter depth"); } dims->out_depth = filter_shape.dim_size(num_dims - 1); if (dims->out_depth != out_backprop_shape.dim_size(feature_dim)) { return errors::InvalidArgument( label, ": filter and out_backprop must have the same out_depth"); } - dims->spatial_dims.resize(num_spatial_dims); for (int i = 0; i < num_spatial_dims; ++i) { int image_dim = GetTensorSpatialDimIndex(num_dims, data_format, i); diff --git a/tensorflow/core/kernels/conv_ops.cc b/tensorflow/core/kernels/conv_ops.cc index c6d36b40fe7129..3b9886eece9ec7 100644 --- a/tensorflow/core/kernels/conv_ops.cc +++ b/tensorflow/core/kernels/conv_ops.cc @@ -18,10 +18,16 @@ limitations under the License. #define USE_EIGEN_TENSOR #define EIGEN_USE_THREADS +#if GOOGLE_CUDA +#define EIGEN_USE_GPU +#endif // GOOGLE_CUDA + #include "tensorflow/core/kernels/conv_ops.h" + #include #include #include + #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" @@ -32,9 +38,6 @@ limitations under the License. #include "tensorflow/core/kernels/conv_2d.h" #include "tensorflow/core/kernels/deep_conv2d.h" #include "tensorflow/core/kernels/ops_util.h" -#ifdef TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS -#include "tensorflow/core/kernels/xsmm_conv2d.h" -#endif #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/lib/strings/numbers.h" @@ -45,6 +48,10 @@ limitations under the License. #include "tensorflow/core/util/tensor_format.h" #include "tensorflow/core/util/use_cudnn.h" +#ifdef TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS +#include "tensorflow/core/kernels/xsmm_conv2d.h" +#endif + #if GOOGLE_CUDA #include "tensorflow/core/kernels/conv_ops_gpu.h" #include "tensorflow/core/platform/stream_executor.h" @@ -123,6 +130,10 @@ struct LaunchConv2DOp { "NHWC tensor format for now.")); return; } + const int64 in_depth = GetTensorDim(input, data_format, 'C'); + OP_REQUIRES(ctx, in_depth == filter.dim_size(2), + errors::Unimplemented("Generic conv implementation does not " + "support grouped convolutions for now.")); LaunchGeneric()(ctx, input, filter, row_stride, col_stride, row_dilation, col_dilation, padding, output, data_format); @@ -324,12 +335,13 @@ class Conv2DOp : public BinaryOp { } // The last dimension for input is in_depth. It must be the same as the - // filter's in_depth. + // filter's in_depth or be evenly divisible by filter's in_depth. const int64 in_depth = GetTensorDim(input, data_format_, 'C'); - OP_REQUIRES(context, in_depth == filter.dim_size(2), + const int64 patch_depth = filter.dim_size(2); + OP_REQUIRES(context, in_depth % patch_depth == 0, errors::InvalidArgument( - "input and filter must have the same depth: ", in_depth, - " vs ", filter.dim_size(2))); + "input depth must be evenly divisible by filter depth: ", + in_depth, " vs ", patch_depth)); // The last dimension for filter is out_depth. const int out_depth = static_cast(filter.dim_size(3)); @@ -386,6 +398,7 @@ class Conv2DOp : public BinaryOp { OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output)); VLOG(2) << "Conv2D: in_depth = " << in_depth + << ", patch_depth = " << patch_depth << ", input_cols = " << input_cols << ", filter_cols = " << filter_cols << ", input_rows = " << input_rows @@ -450,7 +463,9 @@ TF_CALL_double(REGISTER_CPU); #endif // USE_GEMM_FOR_CONV // To be used inside depthwise_conv_op.cc. +template struct LaunchConv2DOp; template struct LaunchConv2DOp; +template struct LaunchConv2DOp; #if GOOGLE_CUDA int64 GetCudnnWorkspaceLimit(const string& envvar_in_mb, @@ -498,13 +513,24 @@ void LaunchConv2DOp::operator()( } Tensor input = input_param; - - if (filter.dim_size(0) == 1 && filter.dim_size(1) == 1 && row_dilation == 1 && - col_dilation == 1 && row_stride == 1 && col_stride == 1 && - data_format == FORMAT_NHWC) { + const int64 in_batch = GetTensorDim(input, data_format, 'N'); + int64 in_rows = GetTensorDim(input, data_format, 'H'); + int64 in_cols = GetTensorDim(input, data_format, 'W'); + const int64 in_depths = GetTensorDim(input, data_format, 'C'); + const int64 patch_rows = filter.dim_size(0); + const int64 patch_cols = filter.dim_size(1); + const int64 patch_depths = filter.dim_size(2); + + // If the filter in-depth (patch_depths) is 1 and smaller than the input + // depth, it's a depthwise convolution. More generally, if the filter in-depth + // divides but is smaller than the input depth, it is a grouped convolution. + bool is_grouped_convolution = patch_depths != in_depths; + if (patch_rows == 1 && patch_cols == 1 && !is_grouped_convolution && + row_dilation == 1 && col_dilation == 1 && row_stride == 1 && + col_stride == 1 && data_format == FORMAT_NHWC) { // 1x1 filter, so call cublas directly. - const uint64 m = input.dim_size(0) * input.dim_size(1) * input.dim_size(2); - const uint64 k = filter.dim_size(2); + const uint64 m = in_batch * in_rows * in_cols; + const uint64 k = patch_depths; const uint64 n = filter.dim_size(3); auto a_ptr = AsDeviceMemory(input.template flat().data(), @@ -525,15 +551,14 @@ void LaunchConv2DOp::operator()( ", n=", n, ", k=", k)); } return; - } else if (filter.dim_size(0) == input.dim_size(1) && - filter.dim_size(1) == input.dim_size(2) && row_dilation == 1 && + } else if (patch_rows == in_rows && patch_cols == in_cols && + !is_grouped_convolution && row_dilation == 1 && col_dilation == 1 && padding == VALID && data_format == FORMAT_NHWC) { // The input data and filter have the same height/width, so call cublas // directly. - const uint64 m = input.dim_size(0); - const uint64 k = - filter.dim_size(0) * filter.dim_size(1) * filter.dim_size(2); + const uint64 m = in_batch; + const uint64 k = patch_rows * patch_cols * patch_depths; const uint64 n = filter.dim_size(3); auto a_ptr = AsDeviceMemory(input.template flat().data(), @@ -558,16 +583,10 @@ void LaunchConv2DOp::operator()( int padding_rows = 0; int padding_cols = 0; - const int64 in_batch = GetTensorDim(input, data_format, 'N'); - int64 in_rows = GetTensorDim(input, data_format, 'H'); - int64 in_cols = GetTensorDim(input, data_format, 'W'); - const int64 in_depths = GetTensorDim(input, data_format, 'C'); const int64 out_batch = GetTensorDim(*output, data_format, 'N'); const int64 out_rows = GetTensorDim(*output, data_format, 'H'); const int64 out_cols = GetTensorDim(*output, data_format, 'W'); const int64 out_depths = GetTensorDim(*output, data_format, 'C'); - const int64 patch_rows = filter.dim_size(0); - const int64 patch_cols = filter.dim_size(1); if (padding == SAME) { // Total padding on rows and cols is // Pr = (R' - 1) * S + (Kr - 1) * Dr + 1 - R @@ -642,9 +661,9 @@ void LaunchConv2DOp::operator()( .set_feature_map_count(out_depths) .set_layout(se::dnn::DataLayout::kBatchDepthYX); se::dnn::FilterDescriptor filter_desc; - filter_desc.set_input_filter_height(filter.dim_size(0)) - .set_input_filter_width(filter.dim_size(1)) - .set_input_feature_map_count(filter.dim_size(2)) + filter_desc.set_input_filter_height(patch_rows) + .set_input_filter_width(patch_cols) + .set_input_feature_map_count(patch_depths) .set_output_feature_map_count(filter.dim_size(3)); se::dnn::ConvolutionDescriptor conv_desc; conv_desc.set_vertical_dilation_rate(row_dilation) @@ -652,7 +671,8 @@ void LaunchConv2DOp::operator()( .set_vertical_filter_stride(row_stride) .set_horizontal_filter_stride(col_stride) .set_zero_padding_height(padding_rows / 2) - .set_zero_padding_width(padding_cols / 2); + .set_zero_padding_width(padding_cols / 2) + .set_group_count(in_depths / patch_depths); Tensor transformed_filter; OP_REQUIRES_OK(ctx, ctx->allocate_temp( @@ -695,7 +715,8 @@ void LaunchConv2DOp::operator()( in_cols}}, // in_cols out_depths, // out_depths {{patch_rows, // filter_rows - patch_cols}}, // filter_cols + patch_cols, // filter_cols + patch_depths}}, // filter_depths {{row_dilation, // dilation_rows col_dilation}}, // dilation_cols {{row_stride, // stride_rows @@ -812,9 +833,9 @@ namespace functor { typename TTypes::Tensor out, TensorFormat data_format); \ extern template struct PadInput -DECLARE_GPU_SPEC(double); DECLARE_GPU_SPEC(float); DECLARE_GPU_SPEC(Eigen::half); +DECLARE_GPU_SPEC(double); #undef DECLARE_GPU_SPEC } // namespace functor @@ -830,7 +851,9 @@ REGISTER_KERNEL_BUILDER( Conv2DOp); // To be used inside depthwise_conv_op.cc. -template class LaunchConv2DOp; +template struct LaunchConv2DOp; +template struct LaunchConv2DOp; +template struct LaunchConv2DOp; #endif // GOOGLE_CUDA diff --git a/tensorflow/core/kernels/depthwise_conv_grad_op.cc b/tensorflow/core/kernels/depthwise_conv_grad_op.cc index 91a9587174be4c..7afa21acb919e1 100644 --- a/tensorflow/core/kernels/depthwise_conv_grad_op.cc +++ b/tensorflow/core/kernels/depthwise_conv_grad_op.cc @@ -26,6 +26,7 @@ limitations under the License. #include "tensorflow/core/framework/tensor_types.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/kernels/bounds_check.h" +#include "tensorflow/core/kernels/conv_grad_ops.h" #include "tensorflow/core/kernels/depthwise_conv_op.h" #include "tensorflow/core/kernels/ops_util.h" #include "tensorflow/core/lib/core/status.h" @@ -33,9 +34,11 @@ limitations under the License. #include "tensorflow/core/platform/types.h" #include "tensorflow/core/util/padding.h" #include "tensorflow/core/util/tensor_format.h" +#include "tensorflow/core/util/use_cudnn.h" #include "tensorflow/core/util/work_sharder.h" #if GOOGLE_CUDA +#include "cuda/include/cudnn.h" #include "tensorflow/core/platform/stream_executor.h" #endif // GOOGLE_CUDA @@ -509,8 +512,19 @@ static void DepthwiseConvBackpropInputReference(const DepthwiseArgs& args, } } +// Extern template instantiated in conv_grad_input_ops.cc. +extern template struct LaunchConv2DBackpropInputOp; +extern template struct LaunchConv2DBackpropInputOp; +extern template struct LaunchConv2DBackpropInputOp; + #if GOOGLE_CUDA +// Extern template instantiated in conv_grad_input_ops.cc. +extern template struct LaunchConv2DBackpropInputOp; +extern template struct LaunchConv2DBackpropInputOp; +extern template struct LaunchConv2DBackpropInputOp; + +// Extern template instantiated in depthwise_conv_op_gpu.cu.cc. extern template struct LaunchDepthwiseConvBackpropInputOp; extern template struct LaunchDepthwiseConvBackpropInputOp; @@ -548,6 +562,12 @@ class DepthwiseConv2dNativeBackpropInputOp : public OpKernel { errors::InvalidArgument("Current implementation does not yet support " "strides in the batch and depth dimensions.")); OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_)); + + // For in_depth == 1 and grouped convolutions. + use_cudnn_ = CanUseCudnn(); + cudnn_use_autotune_ = CudnnUseAutotune(); + use_cudnn_grouped_conv_ = false; + dtype_ = DataTypeToEnum::value; } void Compute(OpKernelContext* context) override { @@ -560,6 +580,7 @@ class DepthwiseConv2dNativeBackpropInputOp : public OpKernel { input_sizes.dims())); TensorShape input_shape; const int32* in_sizes_data = input_sizes.template flat().data(); + for (int i = 0; i < input_sizes.NumElements(); ++i) { OP_REQUIRES(context, in_sizes_data[i] >= 0, errors::InvalidArgument("Dimension ", i, @@ -568,27 +589,77 @@ class DepthwiseConv2dNativeBackpropInputOp : public OpKernel { } const TensorShape& filter_shape = filter.shape(); EXTRACT_AND_VERIFY_DIMENSIONS("DepthwiseConv2DBackpropInput"); + Tensor* in_backprop = nullptr; OP_REQUIRES_OK(context, context->forward_input_or_allocate_output( {0}, 0, input_shape, &in_backprop)); - auto out_backprop_ptr = out_backprop.template flat().data(); - auto filter_ptr = filter.template flat().data(); - auto in_backprop_ptr = in_backprop->template flat().data(); + // If there is nothing to compute, return. if (input_shape.num_elements() == 0) { return; } + + // If in_depth==1, this operation is just a standard convolution. + // Depthwise convolution is a special case of cuDNN's grouped convolution. + bool use_cudnn = use_cudnn_ && (in_depth == 1 || use_cudnn_grouped_conv_); + + VLOG(2) << "DepthwiseConv2dNativeBackpropInput: " + << " Input: [" << batch << ", " << input_rows << ", " << input_cols + << ", " << in_depth << "]; Filter: [" << filter_rows << ", " + << filter_cols << ", " << in_depth << ", " << depth_multiplier + << "]; Output: [" << batch << ", " << out_rows << ", " << out_cols + << ", " << out_depth << "], stride = " << stride_ + << ", pad_rows = " << pad_rows << ", pad_cols = " << pad_cols + << ", Use cuDNN: " << use_cudnn; + + if (use_cudnn) { + // Reshape from TF depthwise filter to cuDNN grouped convolution filter: + // + // | TensorFlow | cuDNN + // -------------------------------------------------------------------- + // filter_out_depth | depth_multiplier | depth_multiplier * group_count + // filter_in_depth | in_depth | in_depth / group_count + // + // For depthwise convolution, we have group_count == in_depth. + int32 filter_in_depth = 1; + TensorShape shape = + TensorShape{filter_rows, filter_cols, filter_in_depth, out_depth}; + Tensor reshaped_filter(/*type=*/dtype_); + OP_REQUIRES( + context, reshaped_filter.CopyFrom(filter, shape), + errors::Internal( + "Failed to reshape filter tensor for grouped convolution.")); + // TODO(yangzihao): Send in arbitrary dilation rates after the dilated + // conv is supported. + launcher_(context, use_cudnn_, cudnn_use_autotune_, out_backprop, + reshaped_filter, /*row_dilation=*/1, /*col_dilation=*/1, + stride_, stride_, padding_, in_backprop, data_format_); + return; + } + + auto out_backprop_ptr = out_backprop.template flat().data(); + auto filter_ptr = filter.template flat().data(); + auto in_backprop_ptr = in_backprop->template flat().data(); LaunchDepthwiseConvBackpropInputOp()( context, args, out_backprop_ptr, filter_ptr, in_backprop_ptr, data_format_); } + protected: + bool use_cudnn_grouped_conv_; + private: std::vector strides_; Padding padding_; TensorFormat data_format_; int64 stride_; + // For in_depth == 1 and grouped convolutions. + LaunchConv2DBackpropInputOp launcher_; + bool use_cudnn_; + bool cudnn_use_autotune_; + DataType dtype_; + TF_DISALLOW_COPY_AND_ASSIGN(DepthwiseConv2dNativeBackpropInputOp); }; @@ -597,23 +668,52 @@ class DepthwiseConv2dNativeBackpropInputOp : public OpKernel { .Device(DEVICE_CPU) \ .TypeConstraint("T"), \ DepthwiseConv2dNativeBackpropInputOp); + +TF_CALL_half(REGISTER_CPU_KERNEL); TF_CALL_float(REGISTER_CPU_KERNEL); +#if !defined(PLATFORM_WINDOWS) || !defined(_DEBUG) TF_CALL_double(REGISTER_CPU_KERNEL); +#endif #undef REGISTER_CPU_KERNEL #if GOOGLE_CUDA -REGISTER_KERNEL_BUILDER(Name("DepthwiseConv2dNativeBackpropInput") - .Device(DEVICE_GPU) - .TypeConstraint("T") - .HostMemory("input_sizes"), - DepthwiseConv2dNativeBackpropInputOp); - -REGISTER_KERNEL_BUILDER( - Name("DepthwiseConv2dNativeBackpropInput") - .Device(DEVICE_GPU) - .TypeConstraint("T") - .HostMemory("input_sizes"), - DepthwiseConv2dNativeBackpropInputOp); + +#define REGISTER_GPU_KERNEL(T) \ + REGISTER_KERNEL_BUILDER(Name("DepthwiseConv2dNativeBackpropInput") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("input_sizes"), \ + DepthwiseConv2dNativeBackpropInputOp) + +TF_CALL_half(REGISTER_GPU_KERNEL); +TF_CALL_float(REGISTER_GPU_KERNEL); +TF_CALL_double(REGISTER_GPU_KERNEL); +#undef REGISTER_GPU_KERNEL + +#if CUDNN_VERSION >= 7000 +template +class DepthwiseConv2dGroupedConvBackpropInputOp + : public DepthwiseConv2dNativeBackpropInputOp { + public: + DepthwiseConv2dGroupedConvBackpropInputOp(OpKernelConstruction* context) + : DepthwiseConv2dNativeBackpropInputOp(context) { + this->use_cudnn_grouped_conv_ = true; + } +}; + +#define REGISTER_GROUPED_CONV_KERNEL(T) \ + REGISTER_KERNEL_BUILDER(Name("DepthwiseConv2dNativeBackpropInput") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("input_sizes") \ + .Label("cudnn_grouped_convolution"), \ + DepthwiseConv2dGroupedConvBackpropInputOp) + +TF_CALL_half(REGISTER_GROUPED_CONV_KERNEL); +TF_CALL_float(REGISTER_GROUPED_CONV_KERNEL); +TF_CALL_double(REGISTER_GROUPED_CONV_KERNEL); +#undef REGISTER_GROUPED_CONV_KERNEL +#endif // CUDNN_VERSION #endif // GOOGLE_CUDA // Kernels to compute the gradients of the filters for depthwise convolution. @@ -885,8 +985,19 @@ static void DepthwiseConvBackpropFilterReference(const DepthwiseArgs& args, } } +// Extern template instantiated in conv_grad_filter_ops.cc. +extern template struct LaunchConv2DBackpropFilterOp; +extern template struct LaunchConv2DBackpropFilterOp; +extern template struct LaunchConv2DBackpropFilterOp; + #if GOOGLE_CUDA +// Extern template instantiated in conv_grad_filter_ops.cc. +extern template struct LaunchConv2DBackpropFilterOp; +extern template struct LaunchConv2DBackpropFilterOp; +extern template struct LaunchConv2DBackpropFilterOp; + +// Extern template instantiated in depthwise_conv_op_gpu.cu.cc. extern template struct LaunchDepthwiseConvBackpropFilterOp; extern template struct LaunchDepthwiseConvBackpropFilterOp; @@ -924,6 +1035,21 @@ class DepthwiseConv2dNativeBackpropFilterOp : public OpKernel { errors::InvalidArgument("Current implementation does not yet support " "strides in the batch and depth dimensions.")); OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_)); + + // For in_depth == 1 and grouped convolutions. + use_cudnn_ = CanUseCudnn(); + cudnn_use_autotune_ = CudnnUseAutotune(); + use_cudnn_grouped_conv_ = false; + + if (std::is_same::value) { + dtype_ = DT_HALF; + } else if (std::is_same::value) { + dtype_ = DT_FLOAT; + } else if (std::is_same::value) { + dtype_ = DT_DOUBLE; + } else { + LOG(ERROR) << "Only half, float, and double are supported."; + } } void Compute(OpKernelContext* context) override { @@ -949,24 +1075,73 @@ class DepthwiseConv2dNativeBackpropFilterOp : public OpKernel { OP_REQUIRES_OK(context, context->forward_input_or_allocate_output( {1}, 0, filter_shape, &filter_backprop)); - auto out_backprop_ptr = out_backprop.template flat().data(); - auto input_ptr = input.template flat().data(); - auto filter_backprop_ptr = filter_backprop->template flat().data(); // If there is nothing to compute, return. if (filter_shape.num_elements() == 0) { return; } + + // If in_depth==1, this operation is just a standard convolution. + // Depthwise convolution is a special case of cuDNN's grouped convolution. + bool use_cudnn = use_cudnn_ && (in_depth == 1 || use_cudnn_grouped_conv_); + + VLOG(2) << "DepthwiseConv2dNativeBackpropFilter: " + << " Input: [" << batch << ", " << input_rows << ", " << input_cols + << ", " << in_depth << "]; Filter: [" << filter_rows << ", " + << filter_cols << ", " << in_depth << ", " << depth_multiplier + << "]; Output: [" << batch << ", " << out_rows << ", " << out_cols + << ", " << out_depth << "], stride = " << stride_ + << ", pad_rows = " << pad_rows << ", pad_cols = " << pad_cols + << ", Use cuDNN: " << use_cudnn; + + if (use_cudnn) { + // Reshape from TF depthwise filter to cuDNN grouped convolution filter: + // + // | TensorFlow | cuDNN + // -------------------------------------------------------------------- + // filter_out_depth | depth_multiplier | depth_multiplier * group_count + // filter_in_depth | in_depth | in_depth / group_count + // + // For depthwise convolution, we have group_count == in_depth. + int32 filter_in_depth = 1; + TensorShape shape = + TensorShape{filter_rows, filter_cols, filter_in_depth, out_depth}; + Tensor reshaped_filter(/*type=*/dtype_); + OP_REQUIRES( + context, reshaped_filter.CopyFrom(*filter_backprop, shape), + errors::Internal( + "Failed to reshape filter tensor for grouped convolution.")); + + // TODO(yangzihao): Send in arbitrary dilation rates after the dilated + // conv is supported. + launcher_(context, use_cudnn_, cudnn_use_autotune_, out_backprop, input, + /*row_dilation=*/1, /*col_dilation=*/1, stride_, stride_, + padding_, &reshaped_filter, data_format_); + return; + } + + auto out_backprop_ptr = out_backprop.template flat().data(); + auto input_ptr = input.template flat().data(); + auto filter_backprop_ptr = filter_backprop->template flat().data(); LaunchDepthwiseConvBackpropFilterOp()( context, args, out_backprop_ptr, input_ptr, filter_backprop_ptr, data_format_); } + protected: + bool use_cudnn_grouped_conv_; + private: std::vector strides_; Padding padding_; TensorFormat data_format_; int64 stride_; + // For in_depth == 1 and grouped convolutions. + LaunchConv2DBackpropFilterOp launcher_; + bool use_cudnn_; + bool cudnn_use_autotune_; + DataType dtype_; + TF_DISALLOW_COPY_AND_ASSIGN(DepthwiseConv2dNativeBackpropFilterOp); }; @@ -976,24 +1151,50 @@ class DepthwiseConv2dNativeBackpropFilterOp : public OpKernel { .Device(DEVICE_CPU) \ .TypeConstraint("T"), \ DepthwiseConv2dNativeBackpropFilterOp); +TF_CALL_half(REGISTER_CPU_KERNEL); TF_CALL_float(REGISTER_CPU_KERNEL); +#if !defined(PLATFORM_WINDOWS) || !defined(_DEBUG) TF_CALL_double(REGISTER_CPU_KERNEL); +#endif #undef REGISTER_CPU_KERNEL #if GOOGLE_CUDA -REGISTER_KERNEL_BUILDER( - Name("DepthwiseConv2dNativeBackpropFilter") - .Device(DEVICE_GPU) - .TypeConstraint("T") - .HostMemory("filter_sizes"), - DepthwiseConv2dNativeBackpropFilterOp); - -REGISTER_KERNEL_BUILDER( - Name("DepthwiseConv2dNativeBackpropFilter") - .Device(DEVICE_GPU) - .TypeConstraint("T") - .HostMemory("filter_sizes"), - DepthwiseConv2dNativeBackpropFilterOp); +#define REGISTER_GPU_KERNEL(T) \ + REGISTER_KERNEL_BUILDER(Name("DepthwiseConv2dNativeBackpropFilter") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("filter_sizes"), \ + DepthwiseConv2dNativeBackpropFilterOp) + +TF_CALL_half(REGISTER_GPU_KERNEL); +TF_CALL_float(REGISTER_GPU_KERNEL); +TF_CALL_double(REGISTER_GPU_KERNEL); +#undef REGISTER_GPU_KERNEL + +#if CUDNN_VERSION >= 7000 +template +class DepthwiseConv2dGroupedConvBackpropFilterOp + : public DepthwiseConv2dNativeBackpropFilterOp { + public: + DepthwiseConv2dGroupedConvBackpropFilterOp(OpKernelConstruction* context) + : DepthwiseConv2dNativeBackpropFilterOp(context) { + this->use_cudnn_grouped_conv_ = true; + } +}; + +#define REGISTER_GROUPED_CONV_KERNEL(T) \ + REGISTER_KERNEL_BUILDER(Name("DepthwiseConv2dNativeBackpropFilter") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("filter_sizes") \ + .Label("cudnn_grouped_convolution"), \ + DepthwiseConv2dGroupedConvBackpropFilterOp) + +TF_CALL_half(REGISTER_GROUPED_CONV_KERNEL); +TF_CALL_float(REGISTER_GROUPED_CONV_KERNEL); +TF_CALL_double(REGISTER_GROUPED_CONV_KERNEL); +#undef REGISTER_GROUPED_CONV_KERNEL +#endif // CUDNN_VERSION #endif // GOOGLE_CUDA } // namespace tensorflow diff --git a/tensorflow/core/kernels/depthwise_conv_op.cc b/tensorflow/core/kernels/depthwise_conv_op.cc index 6dedb1a61ef47c..d5f4a68120aabb 100644 --- a/tensorflow/core/kernels/depthwise_conv_op.cc +++ b/tensorflow/core/kernels/depthwise_conv_op.cc @@ -39,6 +39,7 @@ limitations under the License. #include "tensorflow/core/util/work_sharder.h" #if GOOGLE_CUDA +#include "cuda/include/cudnn.h" #include "tensorflow/core/platform/stream_executor.h" #endif // GOOGLE_CUDA @@ -241,18 +242,22 @@ struct LaunchDepthwiseConvOp { }; // Extern template instantiated in conv_ops.cc. +extern template struct LaunchConv2DOp; extern template struct LaunchConv2DOp; +extern template struct LaunchConv2DOp; #if GOOGLE_CUDA +// Extern template instantiated in conv_ops.cc. +extern template struct LaunchConv2DOp; +extern template struct LaunchConv2DOp; +extern template struct LaunchConv2DOp; + // Extern template instantiated in depthwise_conv_op_gpu.cc. extern template struct LaunchDepthwiseConvOp; extern template struct LaunchDepthwiseConvOp; extern template struct LaunchDepthwiseConvOp; -// Extern template instantiated in conv_ops.cc. -extern template struct LaunchConv2DOp; - #endif template @@ -284,9 +289,11 @@ class DepthwiseConv2dNativeOp : public BinaryOp { "strides in the batch and depth dimensions.")); OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_)); - // For special case when in_depth == 1. + // For in_depth == 1 and grouped convolutions. use_cudnn_ = CanUseCudnn(); cudnn_use_autotune_ = CudnnUseAutotune(); + use_cudnn_grouped_conv_ = false; + dtype_ = DataTypeToEnum::value; } void Compute(OpKernelContext* context) override { @@ -357,27 +364,47 @@ class DepthwiseConv2dNativeOp : public BinaryOp { Tensor* output = nullptr; OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output)); - VLOG(2) << "DepthwiseConv2dNative: " - << " Input: [" << batch << ", " << input_rows << ", " << input_cols - << ", " << in_depth << "]; Filter: [" << filter_rows << ", " - << filter_cols << ", " << in_depth << ", " << depth_multiplier - << "]; stride = " << stride_ << ", pad_rows = " << pad_rows - << ", pad_cols = " << pad_cols << ", output: [" << batch << ", " - << out_rows << ", " << out_cols << ", " << out_depth << "]"; - // If there is nothing to compute, return. if (out_shape.num_elements() == 0) { return; } - // If in_depth==1, this operation is just a standard convolution, so - // invoke that op. - if (std::is_same::value && in_depth == 1) { + // TODO(csigg): Have autotune decide if native is faster than cuDNN. + // If in_depth==1, this operation is just a standard convolution. + // Depthwise convolution is a special case of cuDNN's grouped convolution. + bool use_cudnn = use_cudnn_ && (in_depth == 1 || use_cudnn_grouped_conv_); + + VLOG(2) << "DepthwiseConv2dNative: " + << " Input: [" << batch << ", " << input_rows << ", " << input_cols + << ", " << in_depth << "]; Filter: [" << filter_rows << ", " + << filter_cols << ", " << in_depth << ", " << depth_multiplier + << "]; Output: [" << batch << ", " << out_rows << ", " << out_cols + << ", " << out_depth << "], stride = " << stride_ + << ", pad_rows = " << pad_rows << ", pad_cols = " << pad_cols + << ", Use cuDNN: " << use_cudnn; + + if (use_cudnn) { + // Reshape from TF depthwise filter to cuDNN grouped convolution filter: + // + // | TensorFlow | cuDNN + // -------------------------------------------------------------------- + // filter_out_depth | depth_multiplier | depth_multiplier * group_count + // filter_in_depth | in_depth | in_depth / group_count + // + // For depthwise convolution, we have group_count == in_depth. + int32 filter_in_depth = 1; + TensorShape shape = + TensorShape{filter_rows, filter_cols, filter_in_depth, out_depth}; + Tensor reshaped_filter(/*type=*/dtype_); + OP_REQUIRES( + context, reshaped_filter.CopyFrom(filter, shape), + errors::Internal( + "Failed to reshape filter tensor for grouped convolution.")); // TODO(yangzihao): Send in arbitrary dilation rates after the dilated // conv is supported. - launcher_(context, use_cudnn_, cudnn_use_autotune_, input, filter, - /*row_dilation=*/1, /*col_dilation=*/1, stride_, stride_, - padding_, output, data_format_); + launcher_(context, use_cudnn_, cudnn_use_autotune_, input, + reshaped_filter, /*row_dilation=*/1, /*col_dilation=*/1, + stride_, stride_, padding_, output, data_format_); return; } @@ -403,6 +430,9 @@ class DepthwiseConv2dNativeOp : public BinaryOp { output_ptr, data_format_); } + protected: + bool use_cudnn_grouped_conv_; + private: std::vector strides_; Padding padding_; @@ -410,10 +440,11 @@ class DepthwiseConv2dNativeOp : public BinaryOp { int64 stride_; // in height/width dimension. - // For the case in_depth == 1. + // For in_depth == 1 and grouped convolutions. LaunchConv2DOp launcher_; bool use_cudnn_; bool cudnn_use_autotune_; + DataType dtype_; TF_DISALLOW_COPY_AND_ASSIGN(DepthwiseConv2dNativeOp); }; @@ -421,7 +452,7 @@ class DepthwiseConv2dNativeOp : public BinaryOp { #define REGISTER_CPU_KERNEL(T) \ REGISTER_KERNEL_BUILDER( \ Name("DepthwiseConv2dNative").Device(DEVICE_CPU).TypeConstraint("T"), \ - DepthwiseConv2dNativeOp); + DepthwiseConv2dNativeOp) TF_CALL_half(REGISTER_CPU_KERNEL); TF_CALL_float(REGISTER_CPU_KERNEL); @@ -430,19 +461,38 @@ TF_CALL_double(REGISTER_CPU_KERNEL); #endif #if GOOGLE_CUDA -REGISTER_KERNEL_BUILDER(Name("DepthwiseConv2dNative") - .Device(DEVICE_GPU) - .TypeConstraint("T"), - DepthwiseConv2dNativeOp); - -REGISTER_KERNEL_BUILDER( - Name("DepthwiseConv2dNative").Device(DEVICE_GPU).TypeConstraint("T"), - DepthwiseConv2dNativeOp); - -REGISTER_KERNEL_BUILDER(Name("DepthwiseConv2dNative") - .Device(DEVICE_GPU) - .TypeConstraint("T"), - DepthwiseConv2dNativeOp); -#endif + +#define REGISTER_GPU_KERNEL(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("DepthwiseConv2dNative").Device(DEVICE_GPU).TypeConstraint("T"), \ + DepthwiseConv2dNativeOp) + +TF_CALL_half(REGISTER_GPU_KERNEL); +TF_CALL_float(REGISTER_GPU_KERNEL); +TF_CALL_double(REGISTER_GPU_KERNEL); + +#if CUDNN_VERSION >= 7000 +template +class DepthwiseConv2dGroupedConvOp + : public DepthwiseConv2dNativeOp { + public: + DepthwiseConv2dGroupedConvOp(OpKernelConstruction* context) + : DepthwiseConv2dNativeOp(context) { + this->use_cudnn_grouped_conv_ = true; + } +}; + +#define REGISTER_GROUPED_CONV_KERNEL(T) \ + REGISTER_KERNEL_BUILDER(Name("DepthwiseConv2dNative") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .Label("cudnn_grouped_convolution"), \ + DepthwiseConv2dGroupedConvOp) + +TF_CALL_half(REGISTER_GROUPED_CONV_KERNEL); +TF_CALL_float(REGISTER_GROUPED_CONV_KERNEL); +TF_CALL_double(REGISTER_GROUPED_CONV_KERNEL); +#endif // CUDNN_VERSION +#endif // GOOGLE_CUDA } // namespace tensorflow diff --git a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py index f7ae1a0f37ebf8..659dc0419a061b 100644 --- a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py +++ b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py @@ -22,12 +22,15 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import nn_impl from tensorflow.python.ops import nn_ops import tensorflow.python.ops.nn_grad # pylint: disable=unused-import from tensorflow.python.platform import test +from tensorflow.python.platform import tf_logging def ConfigsToTest(): @@ -98,6 +101,7 @@ def _VerifyValues(self, padding, data_type, use_gpu, + grouped_conv=False, data_format="NHWC"): """Verifies the output values of the convolution function. @@ -110,25 +114,26 @@ def _VerifyValues(self, padding: Padding type. data_type: The data type to use. use_gpu: Whether to use GPU. + grouped_conv: Whether to use cuDNN 7's grouped convolution. data_format: The data_format of the input. "NHWC" or "NCHW". """ - total_size_1 = 1 - total_size_2 = 1 + input_size = 1 + filter_size = 1 for s in tensor_in_sizes: - total_size_1 *= s + input_size *= s for s in filter_in_sizes: - total_size_2 *= s + filter_size *= s # Initializes the input and filter tensor with numbers incrementing from 1. - x1 = [f * 1.0 for f in range(1, total_size_1 + 1)] - x2 = [f * 1.0 for f in range(1, total_size_2 + 1)] - with self.test_session(use_gpu=use_gpu) as sess: - if data_type == dtypes.float16: - tolerance = 1e-5 - elif data_type == dtypes.float32: - tolerance = 1e-5 - else: - self.assertEqual(data_type, dtypes.float64) - tolerance = 1e-8 + x1 = [f * 1.0 / input_size for f in range(1, input_size + 1)] + x2 = [f * 1.0 / filter_size for f in range(1, filter_size + 1)] + ops.reset_default_graph() + graph = ops.get_default_graph() + with self.test_session(graph=graph, use_gpu=use_gpu) as sess: + tolerance = { + dtypes.float16: 4e-2, + dtypes.float32: 1e-8, + dtypes.float64: 1e-13, + }[data_type] t1 = constant_op.constant(x1, shape=tensor_in_sizes, dtype=data_type) t1.set_shape(tensor_in_sizes) @@ -142,25 +147,39 @@ def _VerifyValues(self, native_t1 = array_ops.transpose(t1, [0, 3, 1, 2]) strides = [1, 1, stride, stride] - conv_native = nn_ops.depthwise_conv2d_native( - native_t1, - t2, - strides=strides, - data_format=data_format, - padding=padding) + with sess.graph._kernel_label_map({ + "DepthwiseConv2dNative": "cudnn_grouped_convolution" + } if grouped_conv else {}): + conv_native = nn_ops.depthwise_conv2d_native( + native_t1, + t2, + strides=strides, + data_format=data_format, + padding=padding) if data_format == "NCHW": # Transpose back from NCHW to NHWC conv_native = array_ops.transpose(conv_native, [0, 2, 3, 1]) + try: + native_result = sess.run(conv_native) + except errors.InvalidArgumentError as e: + # Grouped convolution kernel is only registered for cuDNN 7. Silently + # return when we are running on an earlier version or without GPU. + if e.message.startswith( + "No OpKernel was registered to support Op 'DepthwiseConv2dNative'"): + tf_logging.warn("Skipping grouped convolution test") + return + raise e + conv_interface = nn_impl.depthwise_conv2d( t1, t2, strides=[1, stride, stride, 1], padding=padding) - - native_result = sess.run(conv_native) interface_result = sess.run(conv_interface) - print("data_type:", data_type, "use_gpu:", use_gpu, "max diff = ", - np.amax(np.absolute(native_result - interface_result))) + tf_logging.info( + "data_type: %r, use_gpu: %r, grouped_conv: %r, max diff = %f", + data_type, use_gpu, grouped_conv, + np.amax(np.absolute(native_result - interface_result))) self.assertArrayNear( np.ravel(native_result), np.ravel(interface_result), tolerance) self.assertShapeEqual(native_result, conv_native) @@ -169,11 +188,22 @@ def _VerifyValues(self, def testDepthwiseConv2D(self): for index, (input_size, filter_size, _, stride, padding) in enumerate(ConfigsToTest()): - print("Testing DepthwiseConv2D,", index, "th config:", input_size, "*", - filter_size, "stride:", stride, "padding:", padding) + tf_logging.info( + "Testing DepthwiseConv2D, %dth config: %r * %r, stride: %d, padding: " + "%s", index, input_size, filter_size, stride, padding) for data_type in [dtypes.float16, dtypes.float32, dtypes.float64]: + tf_logging.info("Testing without grouped_conv") self._VerifyValues( input_size, filter_size, stride, padding, data_type, use_gpu=True) + tf_logging.info("Testing with grouped_conv") + self._VerifyValues( + input_size, + filter_size, + stride, + padding, + data_type, + use_gpu=True, + grouped_conv=True) def testDepthwiseConv2DFormat(self): if not test.is_gpu_available(): @@ -181,8 +211,9 @@ def testDepthwiseConv2DFormat(self): for index, (input_size, filter_size, _, stride, padding) in enumerate(ConfigsToTest()): - print("Testing DepthwiseConv2DFormat,", index, "th config:", input_size, - "*", filter_size, "stride:", stride, "padding:", padding) + tf_logging.info( + "Testing DepthwiseConv2DFormat, %dth config: %r * %r, stride: %d, " + "padding: %s", index, input_size, filter_size, stride, padding) for data_type in [dtypes.float16, dtypes.float32, dtypes.float64]: self._VerifyValues( input_size, @@ -226,7 +257,7 @@ def _VerifyHandValues(self, tensor_in_sizes, filter_in_sizes, stride, padding, conv = nn_ops.depthwise_conv2d_native( t1, t2, strides=[1, stride, stride, 1], padding=padding) value = sess.run(conv) - print("value = ", value) + tf_logging.info("value = %r", value) self.assertArrayNear(expected, np.ravel(value), 1e-5) self.assertShapeEqual(value, conv) @@ -296,7 +327,7 @@ def testConv2D2x2Filter(self): expected=expected_output, use_gpu=True) - # Gradient checkers.This tests depthwise gradient computations for both + # Gradient checkers. This tests depthwise gradient computations for both # BackpropFilter and BackpropInput by comparing gradients computed by the # depthwise gradient ops with the gradients computed numerically (details can # be found in the compute_gradient_error(). @@ -310,6 +341,7 @@ def _ConstructAndTestGradient(self, data_type, test_input, use_gpu, + grouped_conv=False, data_format="NHWC"): input_size = 1 for x in input_shape: @@ -319,14 +351,14 @@ def _ConstructAndTestGradient(self, filter_size *= x input_data = [x * 1.0 / input_size for x in range(0, input_size)] filter_data = [x * 1.0 / filter_size for x in range(0, filter_size)] - with self.test_session(use_gpu=use_gpu): - if data_type == dtypes.float16: - tolerance = 0.002 - elif data_type == dtypes.float32: - tolerance = 0.002 - else: - self.assertEqual(data_type, dtypes.float64) - tolerance = 1e-8 + ops.reset_default_graph() + graph = ops.get_default_graph() + with self.test_session(graph=graph, use_gpu=use_gpu) as sess: + tolerance = { + dtypes.float16: 2e-0, + dtypes.float32: 5e-4, + dtypes.float64: 1e-12, + }[data_type] input_tensor = constant_op.constant( input_data, shape=input_shape, dtype=data_type, name="input") @@ -347,35 +379,49 @@ def _ConstructAndTestGradient(self, ] strides = [1, 1, stride, stride] - depthwise_conv2d = nn_ops.depthwise_conv2d_native( - native_input, - filter_tensor, - strides, - padding, - data_format=data_format, - name="depthwise_conv2d") + with sess.graph._kernel_label_map({ + "DepthwiseConv2dNative": "cudnn_grouped_convolution", + "DepthwiseConv2dNativeBackpropInput": "cudnn_grouped_convolution", + "DepthwiseConv2dNativeBackpropFilter": "cudnn_grouped_convolution", + } if grouped_conv else {}): + depthwise_conv2d = nn_ops.depthwise_conv2d_native( + native_input, + filter_tensor, + strides, + padding, + data_format=data_format, + name="depthwise_conv2d") self.assertEqual(output_shape, depthwise_conv2d.get_shape()) - if test_input: - err = gradient_checker.compute_gradient_error( - native_input, input_shape, depthwise_conv2d, output_shape) - else: - err = gradient_checker.compute_gradient_error(filter_tensor, - filter_shape, - depthwise_conv2d, - output_shape) - print("data_type:", data_type, "use_gpu:", use_gpu, ", error = ", err) + + try: + if test_input: + err = gradient_checker.compute_gradient_error( + native_input, input_shape, depthwise_conv2d, output_shape) + else: + err = gradient_checker.compute_gradient_error( + filter_tensor, filter_shape, depthwise_conv2d, output_shape) + except errors.InvalidArgumentError as e: + # Grouped convolution kernel is only registered for cuDNN 7. Silently + # return when we are running on an earlier version or without GPU. + if grouped_conv and e.message.startswith( + "No OpKernel was registered to support Op 'DepthwiseConv2dNative'"): + tf_logging.warn("Skipping grouped convolution test") + return + raise e + + tf_logging.info( + "data_type: %r, use_gpu: %r, grouped_conv: %r, error = %f", data_type, + use_gpu, grouped_conv, err) self.assertLess(err, tolerance) def testDepthwiseConv2DInputGrad(self): for index, (input_size, filter_size, output_size, stride, padding) in enumerate(CheckGradConfigsToTest()): - print("Testing DepthwiseConv2DInputGrad,", index, "th config:", - input_size, "*", filter_size, "stride:", stride, "padding:", - padding) - # Note: float16 test for DepthwiseConv2DInputGrad is not enabled, - # calculations are not very precise. - for data_type in [dtypes.float32, dtypes.float64]: + tf_logging.info( + "Testing DepthwiseConv2DInputGrad, %dth config: %r * %r, stride: %d, " + "padding: %s", index, input_size, filter_size, stride, padding) + for data_type in [dtypes.float16, dtypes.float32, dtypes.float64]: self._ConstructAndTestGradient( input_size, filter_size, @@ -385,6 +431,16 @@ def testDepthwiseConv2DInputGrad(self): data_type, test_input=True, use_gpu=True) + self._ConstructAndTestGradient( + input_size, + filter_size, + output_size, + stride, + padding, + data_type, + test_input=True, + use_gpu=True, + grouped_conv=True) def testDepthwiseConv2DInputGradFormat(self): if not test.is_gpu_available(): @@ -392,12 +448,11 @@ def testDepthwiseConv2DInputGradFormat(self): for index, (input_size, filter_size, output_size, stride, padding) in enumerate(CheckGradConfigsToTest()): - print("Testing DepthwiseConv2DInputGradFormat,", index, "th config:", - input_size, "*", filter_size, "stride:", stride, "padding:", - padding) - # Note: float16 test for DepthwiseConv2DInputGradFormat is not enabled, - # calculations are not very precise. - for data_type in [dtypes.float32, dtypes.float64]: + tf_logging.info( + "Testing DepthwiseConv2DInputGradFormat, %dth config: %r * %r, " + "stride: %d, padding: %s", index, input_size, filter_size, stride, + padding) + for data_type in [dtypes.float16, dtypes.float32, dtypes.float64]: self._ConstructAndTestGradient( input_size, filter_size, @@ -412,12 +467,10 @@ def testDepthwiseConv2DInputGradFormat(self): def testDepthwiseConv2DFilterGrad(self): for index, (input_size, filter_size, output_size, stride, padding) in enumerate(CheckGradConfigsToTest()): - print("Testing DepthwiseConv2DFilterGrad,", index, "th config:", - input_size, "*", filter_size, "stride:", stride, "padding:", - padding) - # Note: float16 test for DepthwiseConv2DFilterGrad is not enabled, - # calculations are not very precise. - for data_type in [dtypes.float32, dtypes.float64]: + tf_logging.info( + "Testing DepthwiseConv2DFilterGrad, %dth config: %r * %r, stride: " + "%d, padding: %s", index, input_size, filter_size, stride, padding) + for data_type in [dtypes.float16, dtypes.float32, dtypes.float64]: self._ConstructAndTestGradient( input_size, filter_size, @@ -434,12 +487,11 @@ def testDepthwiseConv2DFilterGradFormat(self): for index, (input_size, filter_size, output_size, stride, padding) in enumerate(CheckGradConfigsToTest()): - print("Testing DepthwiseConv2DFilterGradFormat,", index, "th config:", - input_size, "*", filter_size, "stride:", stride, "padding:", - padding) - # Note: float16 test for DepthwiseConv2DFilterGradFormat is not enabled, - # calculations are not very precise. - for data_type in [dtypes.float32, dtypes.float64]: + tf_logging.info( + "Testing DepthwiseConv2DFilterGradFormat, %dth config: %r * %r, " + "stride: %d, padding: %s", index, input_size, filter_size, stride, + padding) + for data_type in [dtypes.float16, dtypes.float32, dtypes.float64]: self._ConstructAndTestGradient( input_size, filter_size, @@ -494,9 +546,10 @@ def _GetVal(use_gpu): def testDepthwiseConv2DInputGradCompare(self): for index, (input_size, filter_size, output_size, stride, padding) in enumerate(ConfigsToTest()): - print("Testing DepthwiseConv2DInputGradCompare,", index, "th config:", - input_size, "*", filter_size, "stride:", stride, "padding:", - padding) + tf_logging.info( + "Testing DepthwiseConv2DInputGradCompare, %dth config: %r * %r, " + "stride: %d, padding: %s", index, input_size, filter_size, stride, + padding) self._CompareBackpropInputFloat(input_size, filter_size, output_size, stride, padding) self._CompareBackpropInputDouble(input_size, filter_size, output_size, @@ -545,9 +598,10 @@ def _GetVal(use_gpu): def testDepthwiseConv2DFilterGradCompare(self): for index, (input_size, filter_size, output_size, stride, padding) in enumerate(ConfigsToTest()): - print("Testing DepthwiseConv2DFilterGradCompare,", index, "th config:", - input_size, "*", filter_size, "stride:", stride, "padding:", - padding) + tf_logging.info( + "Testing DepthwiseConv2DFilterGradCompare, %dth config: %r * %r, " + "stride: %d, padding: %s", index, input_size, filter_size, stride, + padding) self._CompareBackpropFilterFloat(input_size, filter_size, output_size, stride, padding) self._CompareBackpropFilterDouble(input_size, filter_size, output_size, diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc index 42a77aa3f8e949..773cac2c40c991 100644 --- a/tensorflow/stream_executor/cuda/cuda_dnn.cc +++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc @@ -337,7 +337,9 @@ CUDNN_DNN_ROUTINE_EACH_R6_WITH_STREAM( #if CUDNN_VERSION >= 7000 #define CUDNN_DNN_ROUTINE_EACH_R7(__macro) \ __macro(cudnnSetConvolutionMathType) \ - __macro(cudnnSetRNNMatrixMathType) + __macro(cudnnSetRNNMatrixMathType) \ + __macro(cudnnSetConvolutionGroupCount) \ + __macro(cudnnGetConvolutionGroupCount) // clang-format on CUDNN_DNN_ROUTINE_EACH_R7(STREAM_EXECUTOR_CUDNN_WRAP) @@ -779,6 +781,20 @@ class ScopedConvolutionDescriptor { // NOTE(benbarsdell): This only applies if tensor op math is enabled // and algo selection is set to Default. this->set_use_tensor_op_math(true); + +#if CUDNN_MAJOR >= 7 + VLOG(2) << "Requesting grouped convolution: " + << convolution_descriptor.group_count(); + status = wrap::cudnnSetConvolutionGroupCount( + parent_, handle_, convolution_descriptor.group_count()); + if (status != CUDNN_STATUS_SUCCESS) { + LOG(FATAL) << "could not set cudnn convolution group count: " + << ToString(status); + } +#else + CHECK_EQ(convolution_descriptor.group_count(), 1) + << "Requested grouped convolution for cuDNN version < 7"; +#endif } void set_use_tensor_op_math(bool use_tensor_op_math) { diff --git a/tensorflow/stream_executor/dnn.cc b/tensorflow/stream_executor/dnn.cc index 031c82d3f4bfcb..eed93efc8d6552 100644 --- a/tensorflow/stream_executor/dnn.cc +++ b/tensorflow/stream_executor/dnn.cc @@ -434,6 +434,7 @@ ConvolutionDescriptor::ConvolutionDescriptor(int ndims) filter_strides_(ndims, 1), dilation_rates_(ndims, 1), pad_alignment_(PadAlignment::kDefault), + group_count_(1), ndims_(ndims) {} ConvolutionDescriptor::ConvolutionDescriptor() diff --git a/tensorflow/stream_executor/dnn.h b/tensorflow/stream_executor/dnn.h index 0c2e083b39d589..18606eb7179485 100644 --- a/tensorflow/stream_executor/dnn.h +++ b/tensorflow/stream_executor/dnn.h @@ -543,6 +543,10 @@ class ConvolutionDescriptor { pad_alignment_ = pad_alignment; return *this; } + ConvolutionDescriptor& set_group_count(int group_count) { + group_count_ = group_count; + return *this; + } int64 zero_padding_height() const { return GetDim(zero_padding_, DimIndex::Y); } @@ -566,6 +570,7 @@ class ConvolutionDescriptor { int filter_stride(DimIndex dim) const { return GetDim(filter_strides_, dim); } int dilation_rate(DimIndex dim) const { return GetDim(dilation_rates_, dim); } PadAlignment pad_alignment() const { return pad_alignment_; } + int group_count() const { return group_count_; } int ndims() const { return ndims_; } std::vector strides() const { return filter_strides_; } @@ -578,6 +583,7 @@ class ConvolutionDescriptor { std::vector filter_strides_; std::vector dilation_rates_; PadAlignment pad_alignment_; + int group_count_; int ndims_; // TODO(leary) cudnn provides these fields, but need to characterize what // their effect is -- they may be boolean rather than integral. From 2362e5d0ca38da1a8d3f3d26e2da77807d989e02 Mon Sep 17 00:00:00 2001 From: ManHyuk Date: Tue, 1 May 2018 00:01:30 +0900 Subject: [PATCH 0164/1691] fix typo (#18957) --- .../tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh | 2 +- .../tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh b/tensorflow/tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh index 748a961e44c542..dc9af221ecf53b 100644 --- a/tensorflow/tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh @@ -44,7 +44,7 @@ source "tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh" \ run_configure_for_cpu_build -# Compliling the following test is extremely slow with -c opt +# Compiling the following test is extremely slow with -c opt slow_compiling_test="//tensorflow/core/kernels:eigen_backward_spatial_convolutions_test" # Find all the passing cc_tests on Windows and store them in a variable diff --git a/tensorflow/tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh b/tensorflow/tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh index f26f8727e51bf0..f1114f4ffa40dd 100644 --- a/tensorflow/tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh +++ b/tensorflow/tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh @@ -46,7 +46,7 @@ clean_output_base run_configure_for_gpu_build -# Compliling the following test is extremely slow with -c opt +# Compiling the following test is extremely slow with -c opt slow_compiling_test="//tensorflow/core/kernels:eigen_backward_spatial_convolutions_test" # Find all the passing cc_tests on Windows and store them in a variable From 7df8b6409100de8364721420958f424ff7a3e0ec Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Mon, 30 Apr 2018 11:04:23 -0400 Subject: [PATCH 0165/1691] autograph: Update README (#18981) * Update README.md --- tensorflow/contrib/autograph/README.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/autograph/README.md b/tensorflow/contrib/autograph/README.md index 0fcbf5dd59cece..0ba99c396fc1c8 100644 --- a/tensorflow/contrib/autograph/README.md +++ b/tensorflow/contrib/autograph/README.md @@ -56,8 +56,6 @@ Use AutoGraph in one of the following ways, described below: 1. Annotations (simpler) 2. Functional API (more flexible) -NOTE: You can find more examples in this [interactive notebook](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb). - To get started, install the latest nightly TensorFlow build: ```shell @@ -70,6 +68,13 @@ Then import the `autograph` module from `tf.contrib`: from tensorflow.contrib import autograph as ag ``` +### Interactive demo notebooks + +For more extensive examples, check out these interactive notebooks: + + * [RNN trained using Keras and Estimators](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/rnn_keras_estimator.ipynb) + * [Demo from the TF Dev Summit 2018](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb) + ## Using with annotations Annotating a function or class with `@convert` converts it in place: From 541bd480c43ca48fcb1f4353d92687019b4cb765 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Mon, 30 Apr 2018 18:06:21 +0300 Subject: [PATCH 0166/1691] [tf.data] Explicitly make test's dataset int64. --- tensorflow/contrib/data/python/kernel_tests/resample_test.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index b556525ce444b7..bdc003a8a5bd64 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -65,6 +65,7 @@ def testDistribution(self, initial_known): classes = np.random.randint(5, size=(20000,)) # Uniformly sampled target_dist = [0.9, 0.05, 0.05, 0.0, 0.0] initial_dist = [0.2] * 5 if initial_known else None + classes = math_ops.to_int64(classes) # needed for Windows build. dataset = dataset_ops.Dataset.from_tensor_slices(classes).shuffle( 200, seed=21).map(lambda c: (c, string_ops.as_string(c))).repeat() From 5da0d0022e08e60a30b88e4ef28c7f864e50fd1e Mon Sep 17 00:00:00 2001 From: joel-shor Date: Mon, 30 Apr 2018 18:26:08 +0300 Subject: [PATCH 0167/1691] [tf.data] Removed debug code. --- tensorflow/contrib/data/python/ops/BUILD | 1 - tensorflow/contrib/data/python/ops/resampling.py | 16 +++------------- 2 files changed, 3 insertions(+), 14 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 6d94a2bd82a617..7a3e42cc72755c 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -204,7 +204,6 @@ py_library( "//tensorflow/python:random_ops", "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", - "//tensorflow/python:platform", ], ) diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index f041b7bcbf8236..bad6edd5147d83 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -31,7 +31,6 @@ from tensorflow.python.ops import logging_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops -from tensorflow.python.platform import tf_logging as logging def rejection_resample(class_func, target_dist, initial_dist=None, seed=None): @@ -60,7 +59,7 @@ def _apply_fn(dataset): # Get initial distribution. if initial_dist is not None: - initial_dist_t = math_ops.to_float(ops.convert_to_tensor(initial_dist, name="initial_dist")) + initial_dist_t = ops.convert_to_tensor(initial_dist, name="initial_dist") acceptance_dist, prob_of_original = ( _calculate_acceptance_probs_with_mixing(initial_dist_t, target_dist_t)) @@ -92,18 +91,9 @@ def _apply_fn(dataset): elif prob_original_static == 0: return filtered_ds else: - logging.warn('class_values_ds.output_shapes: %s'% str(class_values_ds.output_shapes)) - logging.warn('class_values_ds.output_types: %s'% str(class_values_ds.output_types)) - logging.warn('dataset.output_shapes: %s'% str(dataset.output_shapes)) - logging.warn('dataset.output_types: %s'% str(dataset.output_types)) - logging.warn('filtered_ds.output_shapes: %s'% str(filtered_ds.output_shapes)) - logging.warn('filtered_ds.output_types: %s'% str(filtered_ds.output_types)) - weights = prob_of_original_ds.map(lambda prob: [(prob, 1.0 - prob)]) - logging.warn('weights.output_shapes: %s'% str(weights.output_shapes)) - logging.warn('weights.output_types: %s'% str(weights.output_types)) return interleave_ops.sample_from_datasets( [dataset_ops.Dataset.zip((class_values_ds, dataset)), filtered_ds], - weights=weights, + weights=prob_of_original_ds.map(lambda prob: [(prob, 1.0 - prob)]), seed=seed) return _apply_fn @@ -301,4 +291,4 @@ def _calculate_acceptance_probs_with_mixing(initial_probs, target_probs): # TODO(joelshor): Simplify fraction, if possible. a_i = (ratio_l - m) / (max_ratio - m) - return math_ops.to_float(a_i), math_ops.to_float(m) \ No newline at end of file + return a_i, m \ No newline at end of file From aa2405ee79dbcfabb8862ef3e1f8ca60e52159a0 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Mon, 30 Apr 2018 09:29:31 -0700 Subject: [PATCH 0168/1691] Fixes to tape gradient for providing outputs and having multiple targets. PiperOrigin-RevId: 194796304 --- tensorflow/c/eager/tape.h | 65 ++++++++++-------------- tensorflow/python/eager/backprop.py | 8 ++- tensorflow/python/eager/backprop_test.py | 20 ++++++++ 3 files changed, 53 insertions(+), 40 deletions(-) diff --git a/tensorflow/c/eager/tape.h b/tensorflow/c/eager/tape.h index 97c323b8722803..8026076b9ef3bf 100644 --- a/tensorflow/c/eager/tape.h +++ b/tensorflow/c/eager/tape.h @@ -380,49 +380,39 @@ Status InitialGradients(const VSpace& vspace, gtl::ArraySlice output_gradients, const TensorTape& tensor_tape, const OpTape& op_tape, - const gtl::FlatMap& tensor_usage_counts, gtl::FlatMap>* result) { for (int i = 0; i < target_tensor_ids.size(); ++i) { const int64 id = target_tensor_ids[i]; - if (tensor_usage_counts.find(id) != tensor_usage_counts.end()) { - if (!output_gradients.empty() && output_gradients[i] != nullptr) { - // TODO(apassos) figure out how to print debugging information here. - return errors::InvalidArgument( - "A gradient was provided for a tensor which is used as part of the " - "computation."); - } - } else { - if (output_gradients.empty() || output_gradients[i] == nullptr) { - auto tensor_it = tensor_tape.find(id); - if (tensor_it != tensor_tape.end() && tensor_it->second != -1) { - auto op_it = op_tape.find(tensor_it->second); - if (op_it == op_tape.end()) { - return errors::Internal( - "Internal state of the gradient tape is invalid: " - "failed to find operation producing a tensor"); - } - bool found = false; - for (int j = 0; j < op_it->second.output_tensor_info.size(); ++j) { - if (op_it->second.output_tensor_info[j].id == id) { - found = true; - (*result)[id].push_back( - vspace.Ones(op_it->second.output_tensor_info[j].shape, - op_it->second.output_tensor_info[j].dtype)); - break; - } - } - if (!found) { - return errors::Internal( - "Internal state of the gradient tape is invalid: " - "none of operations outputs match expected tensor"); + if (output_gradients.empty() || output_gradients[i] == nullptr) { + auto tensor_it = tensor_tape.find(id); + if (tensor_it != tensor_tape.end() && tensor_it->second != -1) { + auto op_it = op_tape.find(tensor_it->second); + if (op_it == op_tape.end()) { + return errors::Internal( + "Internal state of the gradient tape is invalid: " + "failed to find operation producing a tensor"); + } + bool found = false; + for (int j = 0; j < op_it->second.output_tensor_info.size(); ++j) { + if (op_it->second.output_tensor_info[j].id == id) { + found = true; + (*result)[id].push_back( + vspace.Ones(op_it->second.output_tensor_info[j].shape, + op_it->second.output_tensor_info[j].dtype)); + break; } - } else { - // No record of the target tensor found on the tape, so no gradient - // needs to be computed from it. Do nothing. + } + if (!found) { + return errors::Internal( + "Internal state of the gradient tape is invalid: " + "none of operations outputs match expected tensor"); } } else { - (*result)[id].push_back(output_gradients[i]); + // No record of the target tensor found on the tape, so no gradient + // needs to be computed from it. Do nothing. } + } else { + (*result)[id].push_back(output_gradients[i]); } } return Status::OK(); @@ -451,8 +441,7 @@ Status GradientTape::ComputeGradient( InitialStack(state.op_tape, state.op_missing_tensor); gtl::FlatMap> gradients; Status s = InitialGradients(vspace, target_tensor_ids, output_gradients, - tensor_tape_, state.op_tape, - state.tensor_usage_counts, &gradients); + tensor_tape_, state.op_tape, &gradients); auto cleanup = [this, &state]() { if (!persistent_) { // Release all backprop functions diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index 92774d4d50e00c..07aec59cc82801 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -740,7 +740,7 @@ def gradient(self, target, sources, output_gradients=None): """Computes the gradient using operations recorded in context of this tape. Args: - target: Tensor to be differentiated. + target: Tensor (or list of tensors) to be differentiated. sources: a list or nested structure of Tensors or Variables. `target` will be differentiated against elements in `sources`. output_gradients: a list of gradients, one for each element of @@ -762,8 +762,12 @@ def gradient(self, target, sources, output_gradients=None): flat_sources = nest.flatten(sources) flat_sources = [_handle_or_self(x) for x in flat_sources] + if output_gradients is not None: + output_gradients = [None if x is None else ops.convert_to_tensor(x) + for x in nest.flatten(output_gradients)] + flat_grad = imperative_grad.imperative_grad( - _default_vspace, self._tape, [target], flat_sources, + _default_vspace, self._tape, nest.flatten(target), flat_sources, output_gradients=output_gradients) if not self._persistent: diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py index 991b4dbe7a688c..8d9959fe20768c 100644 --- a/tensorflow/python/eager/backprop_test.py +++ b/tensorflow/python/eager/backprop_test.py @@ -96,6 +96,26 @@ def fn(): self.assertAllEqual(grads_and_vars[0][0], 1.0) self.assertAllEqual(id(grads_and_vars[0][1]), id(x)) + def testTwoTargets(self): + with backprop.GradientTape() as t: + x = constant_op.constant(3.0) + y = constant_op.constant(2.0) + t.watch([x, y]) + xx = 2 * x + yy = 3 * y + dx, dy = t.gradient([xx, yy], [x, y]) + self.assertAllEqual(dx, 2.0) + self.assertAllEqual(dy, 3.0) + + def testOutputGradUsedInComputation(self): + with backprop.GradientTape() as t: + x = constant_op.constant(3.0) + y = constant_op.constant(2.0) + t.watch([x, y]) + loss = x * y + dx, = t.gradient([loss, x], [x], output_gradients=[1.0, 2.0]) + self.assertAllEqual(dx, 4.0) + def testDy(self): def f(x): From 1872f29b52d4bc4e32502715f461c4150e8c66a9 Mon Sep 17 00:00:00 2001 From: Tom Hennigan Date: Mon, 30 Apr 2018 09:31:54 -0700 Subject: [PATCH 0169/1691] Clarify return type for defun as zero or more `tf.Tensor`s. PiperOrigin-RevId: 194796621 --- tensorflow/python/eager/function.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index 426ee4c215a899..741bd2ac9c911f 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -716,8 +716,7 @@ def defun(func): objects. Non-Tensor python objects are treated as constants, and new function definitions are created internally based on their values. - func must return a tf.Tensor (NOT a Tensor) or a list of tf.Tensor (NOT a - Tensor). + func must return zero or more `tf.Tensor`. Control flow constructs (e.g., `if`, `while`) are not yet compatible with `defun`. @@ -748,7 +747,7 @@ def g(x, y): Returns: A callable that will execute the compiled function (and return zero - or more Tensor objects). + or more `tf.Tensor` objects). """ # TODO(apassos): deal with captured global state. Deal with control flow. try: From a3ae05d256a9fe82d9a2e50d3f72c3361c1162e4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 09:46:59 -0700 Subject: [PATCH 0170/1691] Remove manifest_merger that is being removed from Bazel 0.13.0. PiperOrigin-RevId: 194798790 --- tensorflow/contrib/lite/examples/android/BUILD | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/contrib/lite/examples/android/BUILD b/tensorflow/contrib/lite/examples/android/BUILD index 49280129971e38..57000072561303 100644 --- a/tensorflow/contrib/lite/examples/android/BUILD +++ b/tensorflow/contrib/lite/examples/android/BUILD @@ -42,7 +42,6 @@ android_binary( custom_package = "org.tensorflow.lite.demo", inline_constants = 1, manifest = "AndroidManifest.xml", - manifest_merger = "android", nocompress_extensions = [ ".tflite", ], From 09e529ff5adb916e40481563698dee72e8a15162 Mon Sep 17 00:00:00 2001 From: Ayush Dubey Date: Mon, 30 Apr 2018 10:36:00 -0700 Subject: [PATCH 0171/1691] Prepare nodes that will be allocated using ScopedAllocator. This includes changes to Executor that (1) set scope_id on nodes that are decorated with _scoped_allocator attribute, (2) mark such nodes to never forward input. PiperOrigin-RevId: 194807086 --- tensorflow/core/common_runtime/executor.cc | 114 +++++++++++++++++++-- tensorflow/core/graph/graph.cc | 1 + tensorflow/core/graph/graph.h | 10 +- 3 files changed, 115 insertions(+), 10 deletions(-) diff --git a/tensorflow/core/common_runtime/executor.cc b/tensorflow/core/common_runtime/executor.cc index 0c461a9ee98ca6..e389eb9b2a8b5c 100644 --- a/tensorflow/core/common_runtime/executor.cc +++ b/tensorflow/core/common_runtime/executor.cc @@ -322,6 +322,7 @@ class GraphView { void Initialize(const Graph* g); Status SetAllocAttrs(const Graph* g, const Device* device); + void SetScopedAllocatorAttrs(const std::vector& sa_nodes); NodeItem* node(size_t id) const { DCHECK_GE(id, 0); @@ -566,11 +567,46 @@ char* GraphView::InitializeNode(char* ptr, const Node* n) { DCHECK_EQ(item->input_type(i), n->input_type(i)); } - uint8* output_types = item->output_type_base(); - for (int i = 0; i < num_outputs; i++) { - output_types[i] = static_cast(n->output_type(i)); - DCHECK_EQ(item->output_type(i), n->output_type(i)); + // Check ScopedAllocatorAttrs and forward_from. Also assign output_types. + { + std::vector forward_input; + Status fwd_status = + GetNodeAttr(n->attrs(), "_forward_input", &forward_input); + std::vector scoped_allocator_attrs; + Status sa_status = + GetNodeAttr(n->attrs(), "_scoped_allocator", &scoped_allocator_attrs); + + int* forward_from = item->forward_from_base(); + uint8* output_types = item->output_type_base(); + for (int i = 0; i < num_outputs; ++i) { + output_types[i] = static_cast(n->output_type(i)); + DCHECK_EQ(item->output_type(i), n->output_type(i)); + + forward_from[i] = OpKernelContext::Params::kNoReservation; + if (sa_status.ok()) { + for (int j = 0; j < scoped_allocator_attrs.size(); j += 2) { + if (scoped_allocator_attrs[j] == i) { + // This output slot must be explicitly allocated from a + // ScopedAllocator. + forward_from[i] = OpKernelContext::Params::kNeverForward; + DCHECK_EQ(output_attrs[i].scope_id, 0); + output_attrs[i].scope_id = scoped_allocator_attrs[j + 1]; + } + } + } + if (fwd_status.ok() && forward_from[i] == -1) { + DCHECK_EQ(forward_input.size() % 2, 0); + for (int j = 0; j < forward_input.size(); j += 2) { + if (forward_input[j + 1] == i) { + DCHECK_EQ(forward_from[i], OpKernelContext::Params::kNoReservation); + forward_from[i] = forward_input[j]; + break; + } + } + } + } } + return ptr; } @@ -696,22 +732,85 @@ Status ExecutorImpl::Initialize() { return gview_.SetAllocAttrs(graph_.get(), params_.device); } +// If a Node has been marked to use a ScopedAllocator x for output i, then +// sc_attr will contain the subsequence (i, x) at an even offset. This function +// extracts and transfers that ScopedAllocator id to alloc_attr. For now, we +// only allow one ScopedAllocator use per Node. +bool ExtractScopedAllocatorAttr(const std::vector& sc_attr, + int output_index, + AllocatorAttributes* alloc_attr) { + DCHECK_LE(2, sc_attr.size()); + for (int i = 0; i < sc_attr.size(); i += 2) { + if (sc_attr[i] == output_index) { + CHECK_EQ(alloc_attr->scope_id, 0); + alloc_attr->scope_id = sc_attr[i + 1]; + return true; + } + } + return false; +} + +void GraphView::SetScopedAllocatorAttrs( + const std::vector& sa_nodes) { + for (const Node* sa : sa_nodes) { + NodeItem* sa_item = node(sa->id()); + AllocatorAttributes* sa_attrs = sa_item->output_attr_base(); + // Control edges out of the ScopedAllocator should be use instances, but may + // include a few other nodes. + for (const auto& e : sa->out_edges()) { + if (!e->IsControlEdge()) { + continue; + } + Node* use_node = e->dst(); + NodeItem* item = node(use_node->id()); + AllocatorAttributes* use_attrs = item->output_attr_base(); + std::vector scoped_allocator_attrs; + Status s = GetNodeAttr(use_node->attrs(), "_scoped_allocator", + &scoped_allocator_attrs); + if (!s.ok()) { + VLOG(2) << "Failed to find expected ScopedAllocator attr on " + << use_node->name(); + continue; + } + // There should be exactly one output using ScopedAllocation. + for (const auto& e : use_node->out_edges()) { + if (!e->IsControlEdge()) { + AllocatorAttributes attr; + if (ExtractScopedAllocatorAttr(scoped_allocator_attrs, + e->src_output(), &attr)) { + // Set the scope_id on this use instance node. + (use_attrs + e->src_output())->Merge(attr); + // Propagate the other attributes of this node back to the SA node. + attr = *(use_attrs + e->src_output()); + attr.scope_id = 0; + sa_attrs->Merge(attr); + } + } + } + } + } +} + Status GraphView::SetAllocAttrs(const Graph* g, const Device* device) { Status s; DeviceNameUtils::ParsedName local_dev_name = device->parsed_name(); + std::vector scoped_allocator_instances; for (const Node* n : g->nodes()) { NodeItem* item = node(n->id()); AllocatorAttributes* attrs = item->output_attr_base(); + if (IsScopedAllocator(n)) { + scoped_allocator_instances.push_back(n); + } // Examine the out edges of each node looking for special use // cases that may affect memory allocation attributes. - for (auto e : n->out_edges()) { + for (const auto& e : n->out_edges()) { if (!e->IsControlEdge()) { AllocatorAttributes attr; s = InferAllocAttr(n, e->dst(), local_dev_name, &attr); if (!s.ok()) return s; - if (attr.value != 0) { + if (attr.value != 0 || attr.scope_id != 0) { attrs[e->src_output()].Merge(attr); } } @@ -728,6 +827,7 @@ Status GraphView::SetAllocAttrs(const Graph* g, const Device* device) { } } } + SetScopedAllocatorAttrs(scoped_allocator_instances); return s; } @@ -1614,7 +1714,7 @@ void ExecutorState::Process(TaggedNode tagged_node, int64 scheduled_usec) { params.frame_iter = FrameAndIter(input_frame->frame_id, input_iter); params.is_input_dead = is_input_dead; params.output_attr_array = item.output_attrs(); - params.forward_from_array = nullptr; // later: item.forward_from(); + params.forward_from_array = item.forward_from(); if (item.kernel_is_async) { // Asynchronous computes. diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc index fb8a6c39e6786c..eeb6c60f717523 100644 --- a/tensorflow/core/graph/graph.cc +++ b/tensorflow/core/graph/graph.cc @@ -79,6 +79,7 @@ const std::unordered_map& Node::kNodeClassTable = {"Size", NC_METADATA}, {"Shape", NC_METADATA}, {"Rank", NC_METADATA}, + {"_ScopedAllocator", NC_SCOPED_ALLOCATOR}, }); #undef REF_CLASS diff --git a/tensorflow/core/graph/graph.h b/tensorflow/core/graph/graph.h index f7ca7d0620f4d4..83a69e6b2d8331 100644 --- a/tensorflow/core/graph/graph.h +++ b/tensorflow/core/graph/graph.h @@ -34,8 +34,8 @@ limitations under the License. // between output O of layer A and input I of layer B using // "input index" and "output index" labels per edge. -#ifndef TENSORFLOW_GRAPH_GRAPH_H_ -#define TENSORFLOW_GRAPH_GRAPH_H_ +#ifndef TENSORFLOW_CORE_GRAPH_GRAPH_H_ +#define TENSORFLOW_CORE_GRAPH_GRAPH_H_ #include #include @@ -162,6 +162,7 @@ class Node { } bool IsHostSend() const { return class_ == NC_HOST_SEND; } bool IsHostRecv() const { return class_ == NC_HOST_RECV; } + bool IsScopedAllocator() const { return class_ == NC_SCOPED_ALLOCATOR; } bool IsMetadata() const { return class_ == NC_METADATA; } @@ -233,6 +234,7 @@ class Node { NC_GET_SESSION_TENSOR, NC_DELETE_SESSION_TENSOR, NC_METADATA, + NC_SCOPED_ALLOCATOR, NC_OTHER // Not a special kind of node }; @@ -696,6 +698,8 @@ inline bool IsControlFlow(const Node* n) { return n->IsControlFlow(); } // (shape). Specifically, returns true for "Size", "Shape" and "Rank" ops. inline bool IsMetadata(const Node* n) { return n->IsMetadata(); } +inline bool IsScopedAllocator(const Node* n) { return n->IsScopedAllocator(); } + inline bool IsHostMemoryPreserving(const Node* node) { return IsIdentity(node) || IsControlFlow(node); } @@ -827,4 +831,4 @@ inline const string& Node::assigned_device_name() const { } // namespace tensorflow -#endif // TENSORFLOW_GRAPH_GRAPH_H_ +#endif // TENSORFLOW_CORE_GRAPH_GRAPH_H_ From 83e3c466b41f0235a19d5a511822b376a19cd982 Mon Sep 17 00:00:00 2001 From: ctiijima Date: Mon, 30 Apr 2018 10:55:26 -0700 Subject: [PATCH 0172/1691] Fixed some grammar errors. --- tensorflow/docs_src/community/benchmarks.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/tensorflow/docs_src/community/benchmarks.md b/tensorflow/docs_src/community/benchmarks.md index 67856ce8698aec..153ef4a015d475 100644 --- a/tensorflow/docs_src/community/benchmarks.md +++ b/tensorflow/docs_src/community/benchmarks.md @@ -1,14 +1,14 @@ # Defining and Running Benchmarks -This guide contains instructions for defining and running a TensorFlow benchmark. These benchmarks store output in [TestResults](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/test_log.proto) format. If these benchmarks are added to TensorFlow github repo, then we will run them daily with our continuous build and display a graph on our dashboard: https://benchmarks-dot-tensorflow-testing.appspot.com/. +This guide contains instructions for defining and running a TensorFlow benchmark. These benchmarks store output in [TestResults](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/test_log.proto) format. If these benchmarks are added to the TensorFlow github repo, we will run them daily with our continuous build and display a graph on our dashboard: https://benchmarks-dot-tensorflow-testing.appspot.com/. [TOC] ## Defining a Benchmark -Defining a TensorFlow benchmark requires extending from `tf.test.Benchmark` -class and calling `self.report_benchmark` method. For example, take a look at the sample benchmark code below: +Defining a TensorFlow benchmark requires extending the `tf.test.Benchmark` +class and calling the `self.report_benchmark` method. Below, you'll find an example of benchmark code: ```python import time @@ -54,20 +54,20 @@ Key points to note in the example above: ## Running with Python -Use the `--benchmarks` flag to run the benchmark with python. A [BenchmarkEntries](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/util/test_log.proto) proto will be printed. +Use the `--benchmarks` flag to run the benchmark with Python. A [BenchmarkEntries](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/util/test_log.proto) proto will be printed. ``` python sample_benchmark.py --benchmarks=SampleBenchmark ``` -Setting the flag as `--benchmarks=.` or `--benchmarks=all` would work as well. +Setting the flag as `--benchmarks=.` or `--benchmarks=all` works as well. -(Please ensure that Tensorflow is installed to successfully import the package in the line `import tensorflow as tf`. For installation instructions, see [Installing TensorFlow](https://www.tensorflow.org/install/). This step is not necessary when running with bazel.) +(Please ensure that Tensorflow is installed to successfully import the package in the line `import tensorflow as tf`. For installation instructions, see [Installing TensorFlow](https://www.tensorflow.org/install/). This step is not necessary when running with Bazel.) ## Adding a `bazel` Target -We have a special target called `tf_py_logged_benchmark` for benchmarks defined under TensorFlow github repo. `tf_py_logged_benchmark` should wrap around a regular `py_test` target. Running a `tf_py_logged_benchmark` would print a [TestResults](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/test_log.proto) proto. Defining a `tf_py_logged_benchmark` also lets us run it with TensorFlow continuous build. +We have a special target called `tf_py_logged_benchmark` for benchmarks defined under the TensorFlow github repo. `tf_py_logged_benchmark` should wrap around a regular `py_test` target. Running a `tf_py_logged_benchmark` would print a [TestResults](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/test_log.proto) proto. Defining a `tf_py_logged_benchmark` also lets us run it with TensorFlow continuous build. First, define a regular `py_test` target. See example below: @@ -82,7 +82,7 @@ py_test( ) ``` -You can run benchmarks in a `py_test` target by passing `--benchmarks` flag. The benchmark should just print out a [BenchmarkEntries](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/util/test_log.proto) proto. +You can run benchmarks in a `py_test` target by passing the `--benchmarks` flag. The benchmark should just print out a [BenchmarkEntries](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/util/test_log.proto) proto. ```shell bazel test :sample_benchmark --test_arg=--benchmarks=all @@ -90,7 +90,7 @@ bazel test :sample_benchmark --test_arg=--benchmarks=all Now, add the `tf_py_logged_benchmark` target (if available). This target would -pass in `--benchmarks=all` to the wrapped `py_test` target and provide a way to store output for our TensorFlow continuous build. `tf_py_logged_benchmark` target should be available in TensorFlow repository. +pass in `--benchmarks=all` to the wrapped `py_test` target and provide a way to store output for our TensorFlow continuous build. The target `tf_py_logged_benchmark` should be available in TensorFlow repository. ```build load("//tensorflow/tools/test:performance.bzl", "tf_py_logged_benchmark") From 9f2728bf9b5439fd5a286a1088d7543600974d4a Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Mon, 30 Apr 2018 11:01:54 -0700 Subject: [PATCH 0173/1691] Switch install get_started link PiperOrigin-RevId: 194811871 --- tensorflow/docs_src/install/install_linux.md | 2 +- tensorflow/docs_src/install/install_mac.md | 2 +- tensorflow/docs_src/install/install_sources.md | 2 +- tensorflow/docs_src/install/install_windows.md | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 1a349f54120cf3..e087b0c2218802 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -566,7 +566,7 @@ If you are new to machine learning, we recommend the following: * @{$get_started/get_started_for_beginners$Getting Started for ML Beginners} If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/premade_estimators$Getting Started with TensorFlow}. +@{$get_started/eager}. ## Common installation problems diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index a237d1af5408c4..af24aaaca84af3 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -409,7 +409,7 @@ If you are new to machine learning, we recommend the following: * @{$get_started/get_started_for_beginners$Getting Started for ML Beginners} If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/premade_estimators$Getting Started with TensorFlow}. +@{$get_started/eager}. ## Common installation problems diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index 71f066e4cb29ef..649c5b47511040 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -388,7 +388,7 @@ TensorFlow programs: 
Hello, TensorFlow!
-If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with TensorFlow}. +If you are new to TensorFlow, see @{$get_started/eager}. If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md index 86add74da15005..a139a49661ee4e 100644 --- a/tensorflow/docs_src/install/install_windows.md +++ b/tensorflow/docs_src/install/install_windows.md @@ -163,7 +163,7 @@ If you are new to machine learning, we recommend the following: * @{$get_started/get_started_for_beginners$Getting Started for ML Beginners} If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/premade_estimators$Getting Started with TensorFlow}. +@{$get_started/eager}. ## Common installation problems From aff407aa7c2650fd0437861a51e6b132e9440a51 Mon Sep 17 00:00:00 2001 From: Eli Bendersky Date: Mon, 30 Apr 2018 11:16:48 -0700 Subject: [PATCH 0174/1691] Add XLA logo and beef up the README --- tensorflow/compiler/xla/README.md | 8 +++++++- tensorflow/compiler/xla/xlalogo.png | Bin 0 -> 46785 bytes 2 files changed, 7 insertions(+), 1 deletion(-) create mode 100644 tensorflow/compiler/xla/xlalogo.png diff --git a/tensorflow/compiler/xla/README.md b/tensorflow/compiler/xla/README.md index c93c39e180655e..514b0c925dddb3 100644 --- a/tensorflow/compiler/xla/README.md +++ b/tensorflow/compiler/xla/README.md @@ -1 +1,7 @@ -This is the home of XLA. +XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear +algebra that optimizes TensorFlow computations. + +![XLA logo](xlalogo.png) + +See the [documentation](https://www.tensorflow.org/performance/xla/) for more +details. diff --git a/tensorflow/compiler/xla/xlalogo.png b/tensorflow/compiler/xla/xlalogo.png new file mode 100644 index 0000000000000000000000000000000000000000..7a0a295953d0c47b23718197dcbab1677b337455 GIT binary patch literal 46785 zcmY&<1yEaE^k?wk?hd7RvEuGf+$j|I;_hxm1I68?xVux_t+>0p+lFudyE{8fhRMrJ zUf#X;ob!`&!W88tkr4d-)m6d&pv{EkgaLqR5aNpg 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zpVTKSlFfK*J#46lm*fMD@A~~6D3$occ}{)|5t>*H0AOalKGeI^+1g5m*2B+bI^xpR`btuZ5R4j#shPVIDm~14mtIZrt))8cydM^iVUDv z_wxs>YSiJ@Ly7#UTjc1GX)1bq=#I%lNWML7HYJ)yBxf7rJMZ859;_4WUik+sfj}TC zX9-PbQzK_HUK2+%-~+_U%)-sc%*x2hqRPz8%gn>e&d&Jn3p1F~V)p-ez}C*x%G~|` z|A4#uh9&R-{=a`vu`_peGjcQoiJ00Mn~}=c8d;brn;Dt9JB*m|13v=ENGOO`i5h Date: Mon, 30 Apr 2018 11:14:51 -0700 Subject: [PATCH 0175/1691] Add snippet illustrating discretized logistic mixture for WaveNet. Currently, the example manually centers the bins in order to capture ?rounding? intervals and not ?ceiling? intervals. In the future, we may simplify the example by expanding QuantizedDistribution with a binning argument. PiperOrigin-RevId: 194814662 --- .../python/ops/quantized_distribution.py | 64 +++++++++++++++++-- 1 file changed, 57 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/distributions/python/ops/quantized_distribution.py b/tensorflow/contrib/distributions/python/ops/quantized_distribution.py index 1ef7651d03a338..eb94760ad71f5b 100644 --- a/tensorflow/contrib/distributions/python/ops/quantized_distribution.py +++ b/tensorflow/contrib/distributions/python/ops/quantized_distribution.py @@ -128,7 +128,7 @@ def _logsum_expbig_minus_expsmall(big, small): class QuantizedDistribution(distributions.Distribution): """Distribution representing the quantization `Y = ceiling(X)`. - #### Definition in terms of sampling. + #### Definition in Terms of Sampling ``` 1. Draw X @@ -138,7 +138,7 @@ class QuantizedDistribution(distributions.Distribution): 5. Return Y ``` - #### Definition in terms of the probability mass function. + #### Definition in Terms of the Probability Mass Function Given scalar random variable `X`, we define a discrete random variable `Y` supported on the integers as follows: @@ -170,12 +170,62 @@ class QuantizedDistribution(distributions.Distribution): `P[Y = j]` is still the mass of `X` within the `jth` interval. - #### Caveats + #### Examples + + We illustrate a mixture of discretized logistic distributions + [(Salimans et al., 2017)][1]. This is used, for example, for capturing 16-bit + audio in WaveNet [(van den Oord et al., 2017)][2]. The values range in + a 1-D integer domain of `[0, 2**16-1]`, and the discretization captures + `P(x - 0.5 < X <= x + 0.5)` for all `x` in the domain excluding the endpoints. + The lowest value has probability `P(X <= 0.5)` and the highest value has + probability `P(2**16 - 1.5 < X)`. + + Below we assume a `wavenet` function. It takes as `input` right-shifted audio + samples of shape `[..., sequence_length]`. It returns a real-valued tensor of + shape `[..., num_mixtures * 3]`, i.e., each mixture component has a `loc` and + `scale` parameter belonging to the logistic distribution, and a `logits` + parameter determining the unnormalized probability of that component. + + ```python + tfd = tf.contrib.distributions + tfb = tfd.bijectors + + net = wavenet(inputs) + loc, unconstrained_scale, logits = tf.split(net, + num_or_size_splits=3, + axis=-1) + scale = tf.nn.softplus(unconstrained_scale) + + # Form mixture of discretized logistic distributions. Note we shift the + # logistic distribution by -0.5. This lets the quantization capture "rounding" + # intervals, `(x-0.5, x+0.5]`, and not "ceiling" intervals, `(x-1, x]`. + discretized_logistic_dist = tfd.QuantizedDistribution( + distribution=tfd.TransformedDistribution( + distribution=tfd.Logistic(loc=loc, scale=scale), + bijector=tfb.AffineScalar(shift=-0.5)), + low=0., + high=2**16 - 1.) + mixture_dist = tfd.MixtureSameFamily( + mixture_distribution=tfd.Categorical(logits=logits), + components_distribution=discretized_logistic_dist) + + neg_log_likelihood = -tf.reduce_sum(mixture_dist.log_prob(targets)) + train_op = tf.train.AdamOptimizer().minimize(neg_log_likelihood) + ``` + + After instantiating `mixture_dist`, we illustrate maximum likelihood by + calculating its log-probability of audio samples as `target` and optimizing. + + #### References - Since evaluation of each `P[Y = j]` involves a cdf evaluation (rather than - a closed form function such as for a Poisson), computations such as mean and - entropy are better done with samples or approximations, and are not - implemented by this class. + [1]: Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P. Kingma. + PixelCNN++: Improving the PixelCNN with discretized logistic mixture + likelihood and other modifications. + _International Conference on Learning Representations_, 2017. + https://arxiv.org/abs/1701.05517 + [2]: Aaron van den Oord et al. Parallel WaveNet: Fast High-Fidelity Speech + Synthesis. _arXiv preprint arXiv:1711.10433_, 2017. + https://arxiv.org/abs/1711.10433 """ def __init__(self, From a616e7297c904ebab2bcd6ccd7a4fa4ba20ff5cc Mon Sep 17 00:00:00 2001 From: Eli Bendersky Date: Mon, 30 Apr 2018 11:19:25 -0700 Subject: [PATCH 0176/1691] Center-align the logo image and set size --- tensorflow/compiler/xla/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/README.md b/tensorflow/compiler/xla/README.md index 514b0c925dddb3..179e2e76b2ff87 100644 --- a/tensorflow/compiler/xla/README.md +++ b/tensorflow/compiler/xla/README.md @@ -1,7 +1,9 @@ XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that optimizes TensorFlow computations. -![XLA logo](xlalogo.png) +

+ +

See the [documentation](https://www.tensorflow.org/performance/xla/) for more details. From ac86a7a691a6d027f96bd04a0c009d09fbf5d4a1 Mon Sep 17 00:00:00 2001 From: Eli Bendersky Date: Mon, 30 Apr 2018 11:20:02 -0700 Subject: [PATCH 0177/1691] Reorder --- tensorflow/compiler/xla/README.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/tensorflow/compiler/xla/README.md b/tensorflow/compiler/xla/README.md index 179e2e76b2ff87..39f8caaa961dc7 100644 --- a/tensorflow/compiler/xla/README.md +++ b/tensorflow/compiler/xla/README.md @@ -1,9 +1,7 @@ -XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear -algebra that optimizes TensorFlow computations. -

-See the [documentation](https://www.tensorflow.org/performance/xla/) for more -details. +XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear +algebra that optimizes TensorFlow computations. See the +[documentation](https://www.tensorflow.org/performance/xla/) for more details. From 9388d7d276cea68f678d88e6f63beb4500906d16 Mon Sep 17 00:00:00 2001 From: Eli Bendersky Date: Mon, 30 Apr 2018 11:16:48 -0700 Subject: [PATCH 0178/1691] Add XLA logo and beef up the README --- tensorflow/compiler/xla/README.md | 8 +++++++- tensorflow/compiler/xla/xlalogo.png | Bin 0 -> 46785 bytes 2 files changed, 7 insertions(+), 1 deletion(-) create mode 100644 tensorflow/compiler/xla/xlalogo.png diff --git a/tensorflow/compiler/xla/README.md b/tensorflow/compiler/xla/README.md index c93c39e180655e..39f8caaa961dc7 100644 --- a/tensorflow/compiler/xla/README.md +++ b/tensorflow/compiler/xla/README.md @@ -1 +1,7 @@ -This is the home of XLA. +

+ +

+ +XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear +algebra that optimizes TensorFlow computations. 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z!Pe^=$hcXUpIaGvg$eaSrdCDIh8x%<&9n8IG*oHsV4=}BeA+3K1PjYkM1sj)>@9=M{2>B4s6ZN6vv|(-TBQPq-GOzrQ#2V7ic(5}GWtIwE z8rqeTKw$4X^!A2tMV}lJF;BFB=n`fnoy`r%RAD8SFG?Tut>t|#p!94O%f$r>7f5JZ zB;i;D9?WFYe9IW#M;osVOIbN8pz^5vT!#`x>&-ZFc;(kS9WSO4{fi<!A$<4jR9@Qq}XlIPhvY z9eyoS+y3%>xmTHd)lmm6kXqlPFFDUj1YS=o^T#DCFQq462gI{>XTgtP;W)#Zq<@P1 z+uWoMSAf-D@_%+>gFzB_)_xyG@UF@x!9F(S>k5(ZAm&(5OIfV35+(_iVwXIg#t%(~ zA62`aV9-uMNix=Bvv1hxB)43bv|Bzil7{VpbkoT$0X|KU^UG5K8@!6m!Gu36wCAo= zpVTKSlFfK*J#46lm*fMD@A~~6D3$occ}{)|5t>*H0AOalKGeI^+1g5m*2B+bI^xpR`btuZ5R4j#shPVIDm~14mtIZrt))8cydM^iVUDv z_wxs>YSiJ@Ly7#UTjc1GX)1bq=#I%lNWML7HYJ)yBxf7rJMZ859;_4WUik+sfj}TC zX9-PbQzK_HUK2+%-~+_U%)-sc%*x2hqRPz8%gn>e&d&Jn3p1F~V)p-ez}C*x%G~|` z|A4#uh9&R-{=a`vu`_peGjcQoiJ00Mn~}=c8d;brn;Dt9JB*m|13v=ENGOO`i5h Date: Mon, 30 Apr 2018 11:26:52 -0700 Subject: [PATCH 0179/1691] Add --keep_going flag to bazel query in pip_smoke_test to bypass bazel query cannot handle select statement. PiperOrigin-RevId: 194816816 --- tensorflow/tools/pip_package/pip_smoke_test.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/tensorflow/tools/pip_package/pip_smoke_test.py b/tensorflow/tools/pip_package/pip_smoke_test.py index e2518f6cbf0beb..1b692104f1cd35 100644 --- a/tensorflow/tools/pip_package/pip_smoke_test.py +++ b/tensorflow/tools/pip_package/pip_smoke_test.py @@ -79,6 +79,16 @@ ] +def bazel_query(query_target): + """Run bazel query on target.""" + try: + output = subprocess.check_output( + ["bazel", "query", "--keep_going", query_target]) + except subprocess.CalledProcessError as e: + output = e.output + return output + + def main(): """This script runs the pip smoke test. @@ -93,15 +103,13 @@ def main(): """ # pip_package_dependencies_list is the list of included files in pip packages - pip_package_dependencies = subprocess.check_output( - ["bazel", "query", PIP_PACKAGE_QUERY_EXPRESSION]) + pip_package_dependencies = bazel_query(PIP_PACKAGE_QUERY_EXPRESSION) pip_package_dependencies_list = pip_package_dependencies.strip().split("\n") print("Pip package superset size: %d" % len(pip_package_dependencies_list)) # tf_py_test_dependencies is the list of dependencies for all python # tests in tensorflow - tf_py_test_dependencies = subprocess.check_output( - ["bazel", "query", PY_TEST_QUERY_EXPRESSION]) + tf_py_test_dependencies = bazel_query(PY_TEST_QUERY_EXPRESSION) tf_py_test_dependencies_list = tf_py_test_dependencies.strip().split("\n") print("Pytest dependency subset size: %d" % len(tf_py_test_dependencies_list)) @@ -135,7 +143,7 @@ def main(): print("Affected Tests:") rdep_query = ("rdeps(kind(py_test, //tensorflow/python/...), %s)" % missing_dependency) - affected_tests = subprocess.check_output(["bazel", "query", rdep_query]) + affected_tests = bazel_query(rdep_query) affected_tests_list = affected_tests.split("\n")[:-2] print("\n".join(affected_tests_list)) From bdaa70c9e4b4215d68fd50ff120c8945ce53c18c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 11:51:03 -0700 Subject: [PATCH 0180/1691] -Miscellaneous code clean-up PiperOrigin-RevId: 194821201 --- .../graph_transformations/identify_relu1.cc | 5 ++-- .../graph_transformations/remove_unused_op.cc | 27 +++++-------------- .../resolve_constant_stack.cc | 12 ++++++--- .../contrib/lite/toco/import_tensorflow.cc | 14 ++++++++-- 4 files changed, 29 insertions(+), 29 deletions(-) diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc index de6d8889fb4ccd..bddb563206f763 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc @@ -79,8 +79,9 @@ bool IdentifyRelu1::Run(Model* model, std::size_t op_index) { const auto* max_op = op_0->type == OperatorType::kTensorFlowMaximum ? op_0 : op_1; - CHECK_EQ(min_op->inputs.size(), 2); - CHECK_EQ(max_op->inputs.size(), 2); + if (min_op->inputs.size() != 2 || max_op->inputs.size() != 2) { + return false; + } if (min_op->outputs.size() != 1 || max_op->outputs.size() != 1) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_unused_op.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_unused_op.cc index 8e6aaf544aa531..1956ab2d2021cd 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_unused_op.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_unused_op.cc @@ -88,13 +88,11 @@ bool RemoveUnusedOp::Run(Model* model, std::size_t op_index) { // At that point we know that none of the outputs is used, so we will // definitely remove the node and all its outputs. - // Remove any input array that is not used by anything else, - // and that is not the output of some other operator. + // Remove any input array that not the output of another op, and only used by + // this op. for (const auto& input : op->inputs) { - if (IsDiscardableArray(*model, input) && - CountOpsWithInput(*model, input) == 1 && - !GetOpWithOutput(*model, input)) { - model->EraseArray(input); + if (!GetOpWithOutput(*model, input)) { + DeleteArrayIfUsedOnce(input, model); } } @@ -102,22 +100,9 @@ bool RemoveUnusedOp::Run(Model* model, std::size_t op_index) { for (const auto& output : op->outputs) { // If the output array is the model's input array, don't remove that. // That's the case when cropping a model at a given --input_array. - if (!IsDiscardableArray(*model, output)) { - continue; - } - // Likewise, if the output array is a RNN state array, don't remove that. - bool found_output_as_rnn_state_array = false; - for (const auto& rnn_state : model->flags.rnn_states()) { - if (output == rnn_state.state_array()) { - found_output_as_rnn_state_array = true; - break; - } - } - if (found_output_as_rnn_state_array) { - continue; + if (IsDiscardableArray(*model, output)) { + model->EraseArray(output); } - // Generic case: do delete this output array. - model->EraseArray(output); } model->operators.erase(it); return true; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc index ea0d6dc8200897..69db1942cd52af 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc @@ -77,6 +77,13 @@ bool ResolveConstantStack::Run(Model* model, std::size_t op_index) { } } + int axis = op->axis; + if (axis < 0) { + // Handle negative axis + axis += model->GetArray(op->inputs[0]).shape().dims().size(); + } + CHECK_EQ(axis, 0) << "Stacking only supported along 0th axis"; + CHECK(!output_array.buffer); switch (output_array.data_type) { case ArrayDataType::kFloat: @@ -99,10 +106,7 @@ bool ResolveConstantStack::Run(Model* model, std::size_t op_index) { // Erase input arrays if no longer used for (const auto& input : op->inputs) { - if (IsDiscardableArray(*model, input) && - CountOpsWithInput(*model, input) == 1) { - model->EraseArray(input); - } + toco::DeleteArrayIfUsedOnce(input, model); } // Erase the operator diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index 2ed05cb3720662..61e4c9d542b339 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -451,8 +451,18 @@ void ConvertConvOperator(const NodeDef& node, if (HasAttr(node, "dilations")) { const auto& dilations = GetListAttr(node, "dilations"); CHECK_EQ(dilations.i_size(), 4); - CHECK_EQ(dilations.i(0), 1); - CHECK_EQ(dilations.i(3), 1); + CHECK_EQ(dilations.i(0), 1) + << "Can only import Conv ops with dilation along the height (1st) or " + "width (2nd) axis. TensorFlow op \"" + << node.name() << "\" had dilations:[ " << dilations.i(0) << ", " + << dilations.i(1) << ", " << dilations.i(2) << ", " << dilations.i(3) + << "]."; + CHECK_EQ(dilations.i(3), 1) + << "Can only import Conv ops with dilation along the height (1st) or " + "width (2nd) axis. TensorFlow op \"" + << node.name() << "\" had dilations:[ " << dilations.i(0) << ", " + << dilations.i(1) << ", " << dilations.i(2) << ", " << dilations.i(3) + << "]."; conv->dilation_height_factor = dilations.i(1); conv->dilation_width_factor = dilations.i(2); } else { From c3e9ca763cbacee961e247df02ec91b52cc59326 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 12:01:35 -0700 Subject: [PATCH 0181/1691] Fix bugs in AssignOp: 1. Releasing the unique_ptr would "leak" a TensorBuffer refcount. 2. The output shape is defined by rhs, not lhs. PiperOrigin-RevId: 194822802 --- tensorflow/core/kernels/assign_op.h | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow/core/kernels/assign_op.h b/tensorflow/core/kernels/assign_op.h index 2ed1628bf1a84b..19b38f9e68d3d7 100644 --- a/tensorflow/core/kernels/assign_op.h +++ b/tensorflow/core/kernels/assign_op.h @@ -78,11 +78,10 @@ class AssignOp : public OpKernel { // 1. Try to reuse the rhs. std::unique_ptr input_alias = context->forward_input( 1, OpKernelContext::Params::kNoReservation /*output_index*/, - old_lhs.dtype(), old_lhs.shape(), DEVICE_MEMORY, attr); + rhs.dtype(), rhs.shape(), DEVICE_MEMORY, attr); if (input_alias != nullptr) { // Transfer ownership to the ref. - context->replace_ref_input(0, *input_alias.release(), - /* lock_held */ true); + context->replace_ref_input(0, *input_alias, /* lock_held */ true); return; } From 8d5e87b157772c6ee131be7748245557e0df2c38 Mon Sep 17 00:00:00 2001 From: Benoit Steiner Date: Mon, 30 Apr 2018 12:13:00 -0700 Subject: [PATCH 0182/1691] Use the default rewriter config instead of a custom one PiperOrigin-RevId: 194824761 --- tensorflow/python/grappler/graph_placer.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/tensorflow/python/grappler/graph_placer.py b/tensorflow/python/grappler/graph_placer.py index 1cd51df4d96258..654013b23c5811 100644 --- a/tensorflow/python/grappler/graph_placer.py +++ b/tensorflow/python/grappler/graph_placer.py @@ -55,11 +55,6 @@ def PlaceGraph(metagraph, # Optimize the metagraph to speedup the placement rewriter_config = rewriter_config_pb2.RewriterConfig() - rewriter_config.optimizers.append("pruning") - rewriter_config.optimizers.append("constfold") - rewriter_config.optimizers.append("arithmetic") - rewriter_config.optimizers.append("dependency") - rewriter_config.optimizers.append("pruning") optimized_graph = tf_optimizer.OptimizeGraph( rewriter_config, metagraph, verbose=verbose, cluster=cluster) optimized_metagraph = meta_graph_pb2.MetaGraphDef() From 9d79acc6aae306e0444c193e945f0c87fe5bb509 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Mon, 30 Apr 2018 12:33:21 -0700 Subject: [PATCH 0183/1691] [TF:XLA] Bump open source llvm revision to r331173 PiperOrigin-RevId: 194827639 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 5f57485d74630d..152da547c1249a 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -452,11 +452,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/3b2f0b2c7e66d226a9342be5163da4240e2951a8.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/3b2f0b2c7e66d226a9342be5163da4240e2951a8.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/068c967842b83d22007eee4515b57e8d9aaccb82.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/068c967842b83d22007eee4515b57e8d9aaccb82.tar.gz", ], - sha256 = "49bb3cbb7c8e9af091c5a743fa7ae749656994408438f38c9b6ac6a052fdce56", - strip_prefix = "llvm-3b2f0b2c7e66d226a9342be5163da4240e2951a8", + sha256 = "4950432fb5cc68e5bf1f87a30b17dfdc69a5b93dac1e89d5274242d3ce7dae7c", + strip_prefix = "llvm-068c967842b83d22007eee4515b57e8d9aaccb82", build_file = clean_dep("//third_party/llvm:llvm.BUILD"), ) From 8609ef4db1a2af0da0c2c20b26756031637de3ff Mon Sep 17 00:00:00 2001 From: Igor Saprykin Date: Mon, 30 Apr 2018 12:41:12 -0700 Subject: [PATCH 0184/1691] When a mirrored variable is fetched in cross-tower mode, fetch its primary variable. This prevents errors like ValueError: Fetch argument MirroredVariable({'/job:localhost/replica:0/task:0/device:GPU:0': , '/job:localhost/replica:0/task:0/device:GPU:1': }) cannot be interpreted as a Tensor. (Device /job:localhost/replica:0/task:0/device:CPU:0 not found in ['/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1'] (current device )) I ran distribute/examples/resnet with and without the change and it fixed the problem. PiperOrigin-RevId: 194828672 --- tensorflow/contrib/distribute/python/values.py | 6 ++++++ .../contrib/distribute/python/values_test.py | 16 ++++++++++++++++ 2 files changed, 22 insertions(+) diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py index 8cb5276579f48f..466678ef2e09d4 100644 --- a/tensorflow/contrib/distribute/python/values.py +++ b/tensorflow/contrib/distribute/python/values.py @@ -229,6 +229,12 @@ def op(self): self._primary_var.op.type) return self.get().op + def _as_graph_element(self): + # pylint: disable=protected-access + if distribute_lib.get_cross_tower_context(): + return self._primary_var._as_graph_element() + return self.get()._as_graph_element() + def _should_act_as_resource_variable(self): """Pass resource_variable_ops.is_resource_variable check.""" pass diff --git a/tensorflow/contrib/distribute/python/values_test.py b/tensorflow/contrib/distribute/python/values_test.py index e96ce547415fcb..1d4e801cd84039 100644 --- a/tensorflow/contrib/distribute/python/values_test.py +++ b/tensorflow/contrib/distribute/python/values_test.py @@ -34,6 +34,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables as variables_lib from tensorflow.python.training import device_util from tensorflow.python.training import saver as saver_lib @@ -582,6 +583,21 @@ def testSaveNormalRestoreMirrored(self): save_path = self._save_normal() self._restore_mirrored(save_path) + @test_util.run_in_graph_and_eager_modes(config=config) + def testFetchAMirroredVariable(self): + if context.num_gpus() < 1 or context.executing_eagerly(): + self.skipTest("A GPU is not available for this test or it's eager mode.") + + with self.test_session( + graph=ops.Graph()) as sess, mirrored_strategy.MirroredStrategy( + ["/device:GPU:0"]).scope(): + with ops.device("/device:GPU:0"): + v = variable_scope.get_variable( + name="v", initializer=1., use_resource=True) + mirrored = values.MirroredVariable({"/device:GPU:0": v}, v) + sess.run(variables_lib.global_variables_initializer()) + sess.run({"complicated": mirrored}) + _devices = ["/device:GPU:0", "/device:CPU:0"] From 6e9d8abcdc44552a53475405f6cf0fdbffb40613 Mon Sep 17 00:00:00 2001 From: Tom Hennigan Date: Mon, 30 Apr 2018 12:47:35 -0700 Subject: [PATCH 0185/1691] Fix typos in tf.GradientTape documentation. PiperOrigin-RevId: 194829506 --- tensorflow/python/eager/backprop.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index 07aec59cc82801..d04b004451223a 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -681,8 +681,8 @@ class GradientTape(object): with tfe.GradientTape() as gg: gg.watch(x) y = x * x - dy_dx = gg.gradient(y, [x])[0] # Will compute to 6.0 - d2y_dx2 = g.gradient(dy_dx, [x])[0] # Will compute to 2.0 + dy_dx = gg.gradient(y, x) # Will compute to 6.0 + d2y_dx2 = g.gradient(dy_dx, x) # Will compute to 2.0 ``` By default, the resources held by a GradientTape are released as soon as @@ -697,8 +697,8 @@ class GradientTape(object): g.watch(x) y = x * x z = y * y - dy_dx = g.gradient(z, [x])[0] # 6.0 - dz_dx = g.gradient(y, [x])[0] # 108.0 (4*x^3 at x = 3) + dz_dx = g.gradient(z, x) # 108.0 (4*x^3 at x = 3) + dy_dx = g.gradient(y, x) # 6.0 del g # Drop the reference to the tape """ From 5cdcb47361e9923c418c16fee6510a472a928427 Mon Sep 17 00:00:00 2001 From: HyoukJoong Lee Date: Mon, 30 Apr 2018 12:49:33 -0700 Subject: [PATCH 0186/1691] Fix device assignment in xla/service/service.cc to build the assignment based on the provided device handles rather than using the default assignment. PiperOrigin-RevId: 194829761 --- .../xla/service/hlo_module_group_metadata.cc | 7 +++++++ .../xla/service/hlo_module_group_metadata.h | 3 +++ tensorflow/compiler/xla/service/service.cc | 13 ++++++++++--- 3 files changed, 20 insertions(+), 3 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc index 54c34ce1166516..3367d76ded68a7 100644 --- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc +++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc @@ -194,6 +194,13 @@ int64 HloModuleGroupMetadata::GetModuleId(const HloModule* module) const { LOG(FATAL) << "unknown module"; } +int64 HloModuleGroupMetadata::GetDeviceModulesCount() const { + return std::count_if(modules_.begin(), modules_.end(), + [](const HloModule* module) { + return !module->config().is_host_module(); + }); +} + Status HloModuleGroupMetadata::RecordInstructions() { const auto visitor = [this](HloInstruction* hlo) -> Status { if (hlo->opcode() == HloOpcode::kWhile) { diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h index c48a7ab0b59269..d6190826166683 100644 --- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h +++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h @@ -147,6 +147,9 @@ class HloModuleGroupMetadata { // the module in the module vector. int64 GetModuleId(const HloModule* module) const; + // Returns the number of modules for devices (excluding the host module). + int64 GetDeviceModulesCount() const; + // Returns the companion instructions for the given instruction. // // Precondition: IsCompanionWhile(instruction) is true. diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 6e0d07a12f906b..849488f4f99fe2 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -542,9 +542,16 @@ Service::ExecuteParallelAndRegisterResult( // profiled. std::map index_to_profiled_streams; - TF_ASSIGN_OR_RETURN(DeviceAssignment device_assignment, - backend->computation_placer()->AssignDevices( - options_.number_of_replicas(), executables.size())); + // Build DeviceAssignment for all cores based on the provided device handles. + DeviceAssignment device_assignment(options_.number_of_replicas(), + executables.size()); + for (int64 i = 0; i < executables.size(); i++) { + TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*backend, device_handles[i])); + CHECK_EQ(replicas.size(), arguments[i].size()); + for (int64 replica = 0; replica < replicas.size(); ++replica) { + device_assignment(replica, i) = replicas[replica]->device_ordinal(); + } + } for (int64 i = 0; i < executables.size(); i++) { // Stream executors for the replicas of the current computation. From 1986f009218a5aa1653f91ed1f40e6321a91c922 Mon Sep 17 00:00:00 2001 From: "Joshua V. Dillon" Date: Mon, 30 Apr 2018 13:34:46 -0700 Subject: [PATCH 0187/1691] Cleanup handling of non-Tensor valued event_ndims in Bijector. PiperOrigin-RevId: 194836408 --- .../distributions/bijector_test.py | 12 + .../python/ops/distributions/bijector_impl.py | 222 ++++++++++-------- 2 files changed, 137 insertions(+), 97 deletions(-) diff --git a/tensorflow/python/kernel_tests/distributions/bijector_test.py b/tensorflow/python/kernel_tests/distributions/bijector_test.py index 18582241e2fb69..33db014279de26 100644 --- a/tensorflow/python/kernel_tests/distributions/bijector_test.py +++ b/tensorflow/python/kernel_tests/distributions/bijector_test.py @@ -24,6 +24,7 @@ import six from tensorflow.python.framework import constant_op +from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector from tensorflow.python.platform import test @@ -275,6 +276,17 @@ def testReduceEventNdimsInverseConstJacobian(self): 8., self.evaluate(bij.inverse_log_det_jacobian(x, event_ndims=2))) + def testHandlesNonStaticEventNdims(self): + x_ = [[[1., 2.], [3., 4.]]] + x = array_ops.placeholder_with_default(x_, shape=None) + event_ndims = array_ops.placeholder(dtype=np.int32, shape=[]) + bij = ExpOnlyJacobian(forward_min_event_ndims=1) + bij.inverse_log_det_jacobian(x, event_ndims=event_ndims) + with self.test_session() as sess: + ildj = sess.run(bij.inverse_log_det_jacobian(x, event_ndims=event_ndims), + feed_dict={event_ndims: 1}) + self.assertAllClose(-np.log(x_), ildj) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/ops/distributions/bijector_impl.py b/tensorflow/python/ops/distributions/bijector_impl.py index 4ebc600d034603..36eee5ce78f010 100644 --- a/tensorflow/python/ops/distributions/bijector_impl.py +++ b/tensorflow/python/ops/distributions/bijector_impl.py @@ -23,6 +23,7 @@ import contextlib import re +import numpy as np import six from tensorflow.python.framework import dtypes @@ -146,15 +147,21 @@ class Bijector(object): for transforming a `Distribution` generated `Tensor`. A `Bijector` is characterized by three operations: - 1. Forward\ + 1. Forward + Useful for turning one random outcome into another random outcome from a different distribution. - 2. Inverse\ + + 2. Inverse + Useful for "reversing" a transformation to compute one probability in terms of another. - 3. `log_det_jacobian(x)`\ + + 3. `log_det_jacobian(x)` + "The log of the determinant of the matrix of all first-order partial - derivatives of the inverse function."\ + derivatives of the inverse function." + Useful for inverting a transformation to compute one probability in terms of another. Geometrically, the Jacobian determinant is the volume of the transformation and is used to scale the probability. @@ -520,6 +527,8 @@ def __init__(self, ValueError: If a member of `graph_parents` is not a `Tensor`. """ self._graph_parents = graph_parents or [] + forward_min_event_ndims = get_static_value(forward_min_event_ndims) + inverse_min_event_ndims = get_static_value(inverse_min_event_ndims) if forward_min_event_ndims is None and inverse_min_event_ndims is None: raise ValueError("Must specify at least one of `forward_min_event_ndims` " @@ -795,33 +804,37 @@ def _call_inverse_log_det_jacobian(self, y, event_ndims, name, **kwargs): return self._constant_ildj_map[event_ndims] y = ops.convert_to_tensor(y, name="y") self._maybe_assert_dtype(y) - if not self._is_injective: # No caching for non-injective - ildjs = self._inverse_log_det_jacobian(y, **kwargs) - return tuple(self._reduce_jacobian_det_over_event( - y, ildj, self.inverse_min_event_ndims, event_ndims) - for ildj in ildjs) - mapping = self._lookup(y=y, kwargs=kwargs) - if mapping.ildj_map is not None and event_ndims in mapping.ildj_map: - return mapping.ildj_map[event_ndims] - try: - x = None # Not needed; leave cache as is. - ildj = self._inverse_log_det_jacobian(y, **kwargs) - ildj = self._reduce_jacobian_det_over_event( - y, ildj, self.inverse_min_event_ndims, event_ndims) - except NotImplementedError as original_exception: + with ops.control_dependencies(self._check_valid_event_ndims( + min_event_ndims=self.inverse_min_event_ndims, + event_ndims=event_ndims)): + if not self._is_injective: # No caching for non-injective + ildjs = self._inverse_log_det_jacobian(y, **kwargs) + return tuple(self._reduce_jacobian_det_over_event( + y, ildj, self.inverse_min_event_ndims, event_ndims) + for ildj in ildjs) + mapping = self._lookup(y=y, kwargs=kwargs) + if mapping.ildj_map is not None and event_ndims in mapping.ildj_map: + return mapping.ildj_map[event_ndims] try: - x = mapping.x if mapping.x is not None else self._inverse(y, **kwargs) - ildj = -self._forward_log_det_jacobian(x, **kwargs) + x = None # Not needed; leave cache as is. + ildj = self._inverse_log_det_jacobian(y, **kwargs) ildj = self._reduce_jacobian_det_over_event( - x, ildj, self.forward_min_event_ndims, event_ndims) - except NotImplementedError: - raise original_exception - - mapping = mapping.merge(x=x, ildj_map={event_ndims: ildj}) - self._cache(mapping) - if self.is_constant_jacobian: - self._constant_ildj_map[event_ndims] = ildj - return ildj + y, ildj, self.inverse_min_event_ndims, event_ndims) + except NotImplementedError as original_exception: + try: + x = (mapping.x if mapping.x is not None + else self._inverse(y, **kwargs)) + ildj = -self._forward_log_det_jacobian(x, **kwargs) + ildj = self._reduce_jacobian_det_over_event( + x, ildj, self.forward_min_event_ndims, event_ndims) + except NotImplementedError: + raise original_exception + + mapping = mapping.merge(x=x, ildj_map={event_ndims: ildj}) + self._cache(mapping) + if self.is_constant_jacobian: + self._constant_ildj_map[event_ndims] = ildj + return ildj def inverse_log_det_jacobian( self, y, event_ndims, name="inverse_log_det_jacobian"): @@ -852,9 +865,7 @@ def inverse_log_det_jacobian( `self.dtype`. NotImplementedError: if `_inverse_log_det_jacobian` is not implemented. """ - with ops.control_dependencies(self._check_valid_event_ndims( - min_event_ndims=self.inverse_min_event_ndims, event_ndims=event_ndims)): - return self._call_inverse_log_det_jacobian(y, event_ndims, name) + return self._call_inverse_log_det_jacobian(y, event_ndims, name) def _forward_log_det_jacobian(self, x): """Subclass implementation of `forward_log_det_jacobian` public function. @@ -876,38 +887,46 @@ def _forward_log_det_jacobian(self, x): "forward_log_det_jacobian not implemented.") def _call_forward_log_det_jacobian(self, x, event_ndims, name, **kwargs): + if not self._is_injective: + raise NotImplementedError( + "forward_log_det_jacobian cannot be implemented for non-injective " + "transforms.") with self._name_scope(name, [x]): - if event_ndims in self._constant_ildj_map: - # Need "-1. *" to avoid invalid-unary-operand-type linter warning. - return -1. * self._constant_ildj_map[event_ndims] - x = ops.convert_to_tensor(x, name="x") - self._maybe_assert_dtype(x) - if not self._is_injective: - fldjs = self._forward_log_det_jacobian(x, **kwargs) # No caching. - return tuple(self._reduce_jacobian_det_over_event( - x, fldj, self.forward_min_event_ndims, event_ndims) - for fldj in fldjs) - mapping = self._lookup(x=x, kwargs=kwargs) - if mapping.ildj_map is not None and event_ndims in mapping.ildj_map: - return -mapping.ildj_map[event_ndims] - try: - y = None # Not needed; leave cache as is. - ildj = -self._forward_log_det_jacobian(x, **kwargs) - ildj = self._reduce_jacobian_det_over_event( - x, ildj, self.forward_min_event_ndims, event_ndims) - except NotImplementedError as original_exception: + with ops.control_dependencies(self._check_valid_event_ndims( + min_event_ndims=self.forward_min_event_ndims, + event_ndims=event_ndims)): + if event_ndims in self._constant_ildj_map: + # Need "-1. *" to avoid invalid-unary-operand-type linter warning. + return -1. * self._constant_ildj_map[event_ndims] + x = ops.convert_to_tensor(x, name="x") + self._maybe_assert_dtype(x) + if not self._is_injective: + fldjs = self._forward_log_det_jacobian(x, **kwargs) # No caching. + return tuple(self._reduce_jacobian_det_over_event( + x, fldj, self.forward_min_event_ndims, event_ndims) + for fldj in fldjs) + mapping = self._lookup(x=x, kwargs=kwargs) + if mapping.ildj_map is not None and event_ndims in mapping.ildj_map: + return -mapping.ildj_map[event_ndims] try: - y = mapping.y if mapping.y is not None else self._forward(x, **kwargs) - ildj = self._inverse_log_det_jacobian(y, **kwargs) + y = None # Not needed; leave cache as is. + ildj = -self._forward_log_det_jacobian(x, **kwargs) ildj = self._reduce_jacobian_det_over_event( - y, ildj, self.inverse_min_event_ndims, event_ndims) - except NotImplementedError: - raise original_exception - mapping = mapping.merge(y=y, ildj_map={event_ndims: ildj}) - self._cache(mapping) - if self.is_constant_jacobian: - self._constant_ildj_map[event_ndims] = ildj - return -ildj + x, ildj, self.forward_min_event_ndims, event_ndims) + except NotImplementedError as original_exception: + try: + y = (mapping.y if mapping.y is not None + else self._forward(x, **kwargs)) + ildj = self._inverse_log_det_jacobian(y, **kwargs) + ildj = self._reduce_jacobian_det_over_event( + y, ildj, self.inverse_min_event_ndims, event_ndims) + except NotImplementedError: + raise original_exception + mapping = mapping.merge(y=y, ildj_map={event_ndims: ildj}) + self._cache(mapping) + if self.is_constant_jacobian: + self._constant_ildj_map[event_ndims] = ildj + return -ildj def forward_log_det_jacobian( self, x, event_ndims, name="forward_log_det_jacobian"): @@ -933,13 +952,7 @@ def forward_log_det_jacobian( nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented, or this is a non-injective bijector. """ - if not self._is_injective: - raise NotImplementedError( - "forward_log_det_jacobian cannot be implemented for non-injective " - "transforms.") - with ops.control_dependencies(self._check_valid_event_ndims( - min_event_ndims=self.forward_min_event_ndims, event_ndims=event_ndims)): - return self._call_forward_log_det_jacobian(x, event_ndims, name) + return self._call_forward_log_det_jacobian(x, event_ndims, name) @contextlib.contextmanager def _name_scope(self, name=None, values=None): @@ -981,12 +994,14 @@ def _lookup(self, x=None, y=None, kwargs=None): def _reduce_jacobian_det_over_event( self, y, ildj, min_event_ndims, event_ndims): """Reduce jacobian over event_ndims - min_event_ndims.""" + assert_static(min_event_ndims) + if not self.is_constant_jacobian: return math_ops.reduce_sum( ildj, self._get_event_reduce_dims(min_event_ndims, event_ndims)) - # In this case, we need to tile the jacobian over the event and reduce. + # In this case, we need to tile the Jacobian over the event and reduce. y_rank = array_ops.rank(y) y_shape = array_ops.shape(y)[ y_rank - event_ndims : y_rank - min_event_ndims] @@ -997,47 +1012,60 @@ def _reduce_jacobian_det_over_event( axis=self._get_event_reduce_dims(min_event_ndims, event_ndims)) # The multiplication by ones can change the inferred static shape so we try # to recover as much as possible. - if (isinstance(event_ndims, int) and - y.get_shape().ndims and ildj.get_shape().ndims): - y_shape = y.get_shape() - y_shape = y_shape[y_shape.ndims - event_ndims : - y_shape.ndims - min_event_ndims] - ildj_shape = ildj.get_shape() - broadcast_shape = array_ops.broadcast_static_shape( - ildj_shape, y_shape) + event_ndims_ = get_static_value(event_ndims) + if (event_ndims_ is not None and + y.shape.ndims is not None and + ildj.shape.ndims is not None): + y_shape = y.shape[y.shape.ndims - event_ndims_ : + y.shape.ndims - min_event_ndims] + broadcast_shape = array_ops.broadcast_static_shape(ildj.shape, y_shape) reduced_ildj.set_shape( broadcast_shape[: broadcast_shape.ndims - ( - event_ndims - min_event_ndims)]) + event_ndims_ - min_event_ndims)]) return reduced_ildj def _get_event_reduce_dims(self, min_event_ndims, event_ndims): """Compute the reduction dimensions given event_ndims.""" - min_event_ndims_ = (min_event_ndims if isinstance(min_event_ndims, int) - else tensor_util.constant_value(min_event_ndims)) - event_ndims_ = (event_ndims if isinstance(event_ndims, int) - else tensor_util.constant_value(event_ndims)) + assert_static(min_event_ndims) + event_ndims_ = get_static_value(event_ndims, np.int32) - if min_event_ndims_ is not None and event_ndims_ is not None: - return [-index for index in range(1, event_ndims_ - min_event_ndims_ + 1)] + if event_ndims_ is not None: + return [-index for index in range(1, event_ndims_ - min_event_ndims + 1)] else: reduce_ndims = event_ndims - min_event_ndims return math_ops.range(-reduce_ndims, 0) def _check_valid_event_ndims(self, min_event_ndims, event_ndims): """Check whether event_ndims is atleast min_event_ndims.""" - min_event_ndims_ = (min_event_ndims if isinstance(min_event_ndims, int) - else tensor_util.constant_value(min_event_ndims)) - event_ndims_ = (event_ndims if isinstance(event_ndims, int) - else tensor_util.constant_value(event_ndims)) - - if min_event_ndims_ is not None and event_ndims_ is not None: - if min_event_ndims_ > event_ndims_: + assert_static(min_event_ndims) + event_ndims_ = get_static_value(event_ndims, np.int32) + assertions = [] + if event_ndims_ is not None: + if min_event_ndims > event_ndims_: raise ValueError("event_ndims ({}) must be larger than " "min_event_ndims ({})".format( - event_ndims_, min_event_ndims_)) - return [] - - if self.validate_args: - return [check_ops.assert_greater_equal(event_ndims, min_event_ndims)] - return [] + event_ndims_, min_event_ndims)) + elif self.validate_args: + assertions += [ + check_ops.assert_greater_equal(event_ndims, min_event_ndims)] + return assertions + + +def get_static_value(x, dtype=None): + """Helper which returns static value; casting when dtype is preferred.""" + if x is None: + return x + try: + x_ = tensor_util.constant_value(x) + except TypeError: + x_ = x + if x_ is None or dtype is None: + return x_ + return np.array(x_, dtype) + + +def assert_static(x): + """Helper which asserts that input arg is known statically.""" + if x is None or type(x) != type(get_static_value(x)): # pylint: disable=unidiomatic-typecheck + raise TypeError("Input must be known statically.") From 57b7c7befa52ee4a205536c0552422a750cbcd21 Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Mon, 30 Apr 2018 13:50:17 -0700 Subject: [PATCH 0188/1691] Fix a bug in profiler. PiperOrigin-RevId: 194838948 --- tensorflow/contrib/lite/interpreter.h | 4 +--- tensorflow/contrib/lite/profiling/profiler.h | 17 +++++++++++------ .../contrib/lite/profiling/profiler_test.cc | 14 ++++++++++++++ 3 files changed, 26 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index 6f3433abcf71b6..1074f64263b5d7 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -325,9 +325,7 @@ class Interpreter { void SetProfiler(profiling::Profiler* profiler) { profiler_ = profiler; } - profiling::Profiler* GetProfiler(profiling::Profiler* profiler) { - return profiler_; - } + profiling::Profiler* GetProfiler() { return profiler_; } // The default capacity of `tensors_` vector. static constexpr int kTensorsReservedCapacity = 128; diff --git a/tensorflow/contrib/lite/profiling/profiler.h b/tensorflow/contrib/lite/profiling/profiler.h index dfa98a6708edc8..8c3e4dc76d8061 100644 --- a/tensorflow/contrib/lite/profiling/profiler.h +++ b/tensorflow/contrib/lite/profiling/profiler.h @@ -85,7 +85,7 @@ class Profiler { std::vector GetProfileEvents() { std::vector profile_events; profile_events.reserve(buffer_.Size()); - for (int i = 0; i < buffer_.Size(); i++) { + for (size_t i = 0; i < buffer_.Size(); i++) { profile_events.push_back(buffer_.At(i)); } return profile_events; @@ -103,7 +103,9 @@ class ScopedProfile { // Adds a profile event to profile that begins with the construction // of object and ends when the object goes out of scope. // The lifetime of tag should be at least the lifetime of profiler. - ScopedProfile(Profiler* profiler, const char* tag) { + + ScopedProfile(Profiler* profiler, const char* tag) + : buffer_(nullptr), event_handle_(0) { if (profiler) { buffer_ = profiler->GetProfileBuffer(); event_handle_ = @@ -126,7 +128,8 @@ class ScopedOperatorProfile { // Adds a profile event to profile that begins with the construction // of object and ends when the object goes out of scope. // The lifetime of tag should be at least the lifetime of profiler. - ScopedOperatorProfile(Profiler* profiler, const char* tag, int node_index) { + ScopedOperatorProfile(Profiler* profiler, const char* tag, int node_index) + : buffer_(nullptr), event_handle_(0) { if (profiler) { buffer_ = profiler->GetProfileBuffer(); event_handle_ = buffer_->BeginEvent( @@ -148,9 +151,11 @@ class ScopedOperatorProfile { } // namespace profiling } // namespace tflite -#define SCOPED_OPERATOR_PROFILE(profiler, node_index) \ - tflite::profiling::ScopedOperatorProfile _profile((profiler), "OpInvoke", \ - (node_index)) +#define VARNAME_UNIQ(name, ctr) name##ctr + +#define SCOPED_OPERATOR_PROFILE(profiler, node_index) \ + tflite::profiling::ScopedOperatorProfile VARNAME_UNIQ( \ + _profile_, __COUNTER__)((profiler), "OpInvoke", (node_index)) #else namespace tflite { diff --git a/tensorflow/contrib/lite/profiling/profiler_test.cc b/tensorflow/contrib/lite/profiling/profiler_test.cc index 7ea1d8f7d341b6..0fba0450a0359e 100644 --- a/tensorflow/contrib/lite/profiling/profiler_test.cc +++ b/tensorflow/contrib/lite/profiling/profiler_test.cc @@ -93,6 +93,20 @@ TEST(ProfilingTest, ProfilesAreCollected) { #endif } +TEST(ProfilingTest, NullProfiler) { + Profiler* profiler = nullptr; + { SCOPED_OPERATOR_PROFILE(profiler, 1); } +} + +TEST(ProfilingTest, ScopedProfile) { + Profiler profiler; + profiler.StartProfiling(); + { SCOPED_OPERATOR_PROFILE(&profiler, 1); } + profiler.StopProfiling(); + auto profile_events = profiler.GetProfileEvents(); + EXPECT_EQ(1, profile_events.size()); +} + } // namespace } // namespace profiling } // namespace tflite From b7cb5fa0059b2ef6a40aa15a4d97a01ba2e57d85 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 13:51:34 -0700 Subject: [PATCH 0189/1691] Extend SDCAOptimizer functionality to prune negative indices (the default value for OOV with tf.feature_column.FeatureColumn, sparse / categorical). PiperOrigin-RevId: 194839178 --- .../python/learn/estimators/linear_test.py | 32 +++++++++++++++++++ .../linear_optimizer/python/sdca_optimizer.py | 8 +++++ 2 files changed, 40 insertions(+) diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py index d3bb0fda5765d8..0a863f0e20c05d 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py @@ -863,6 +863,38 @@ def input_fn(): scores = classifier.evaluate(input_fn=input_fn, steps=1) self.assertGreater(scores['accuracy'], 0.9) + def testSdcaOptimizerWeightedSparseFeaturesOOVWithNoOOVBuckets(self): + """LinearClassifier with SDCAOptimizer with OOV features (-1 IDs).""" + + def input_fn(): + return { + 'example_id': + constant_op.constant(['1', '2', '3']), + 'price': + sparse_tensor.SparseTensor( + values=[2., 3., 1.], + indices=[[0, 0], [1, 0], [2, 0]], + dense_shape=[3, 5]), + 'country': + sparse_tensor.SparseTensor( + # 'GB' is out of the vocabulary. + values=['IT', 'US', 'GB'], + indices=[[0, 0], [1, 0], [2, 0]], + dense_shape=[3, 5]) + }, constant_op.constant([[1], [0], [1]]) + + country = feature_column_lib.sparse_column_with_keys( + 'country', keys=['US', 'CA', 'MK', 'IT', 'CN']) + country_weighted_by_price = feature_column_lib.weighted_sparse_column( + country, 'price') + sdca_optimizer = sdca_optimizer_lib.SDCAOptimizer( + example_id_column='example_id') + classifier = linear.LinearClassifier( + feature_columns=[country_weighted_by_price], optimizer=sdca_optimizer) + classifier.fit(input_fn=input_fn, steps=50) + scores = classifier.evaluate(input_fn=input_fn, steps=1) + self.assertGreater(scores['accuracy'], 0.9) + def testSdcaOptimizerCrossedFeatures(self): """Tests LinearClassifier with SDCAOptimizer and crossed features.""" diff --git a/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py b/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py index 213c2eced5c7f9..12039ecc6f357a 100644 --- a/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py +++ b/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py @@ -198,6 +198,14 @@ def _training_examples_and_variables(): example_ids = array_ops.reshape(id_tensor.indices[:, 0], [-1]) flat_ids = array_ops.reshape(id_tensor.values, [-1]) + # Prune invalid IDs (< 0) from the flat_ids, example_ids, and + # weight_tensor. These can come from looking up an OOV entry in the + # vocabulary (default value being -1). + is_id_valid = math_ops.greater_equal(flat_ids, 0) + flat_ids = array_ops.boolean_mask(flat_ids, is_id_valid) + example_ids = array_ops.boolean_mask(example_ids, is_id_valid) + weight_tensor = array_ops.boolean_mask(weight_tensor, is_id_valid) + projection_length = math_ops.reduce_max(flat_ids) + 1 # project ids based on example ids so that we can dedup ids that # occur multiple times for a single example. From 0ed712e87742a455b56ece6fd828945f42765c52 Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Mon, 30 Apr 2018 14:08:29 -0700 Subject: [PATCH 0190/1691] [tf.data] Adding support for `tf.SparseTensor` into `tf.contrib.data.scan()` PiperOrigin-RevId: 194842266 --- .../kernel_tests/scan_dataset_op_test.py | 44 ++++++- .../contrib/data/python/ops/scan_ops.py | 122 ++++++++++++------ 2 files changed, 121 insertions(+), 45 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py index 1a97a84b2cba13..f544b1caa676b0 100644 --- a/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py @@ -28,6 +28,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors +from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.platform import test @@ -35,15 +36,19 @@ class ScanDatasetTest(test.TestCase): - def _count(self, start, step): - return dataset_ops.Dataset.from_tensors(0).repeat(None).apply( - scan_ops.scan(start, lambda state, _: (state + step, state))) + def _counting_dataset(self, start, scan_fn): + return dataset_ops.Dataset.from_tensors(0).repeat().apply( + scan_ops.scan(start, scan_fn)) def testCount(self): + def make_scan_fn(step): + return lambda state, _: (state + step, state) + start = array_ops.placeholder(dtypes.int32, shape=[]) step = array_ops.placeholder(dtypes.int32, shape=[]) take = array_ops.placeholder(dtypes.int64, shape=[]) - iterator = self._count(start, step).take(take).make_initializable_iterator() + iterator = self._counting_dataset( + start, make_scan_fn(step)).take(take).make_initializable_iterator() next_element = iterator.get_next() with self.test_session() as sess: @@ -78,6 +83,37 @@ def testFibonacci(self): self.assertEqual(5, self.evaluate(next_element())) self.assertEqual(8, self.evaluate(next_element())) + def testSparseCount(self): + def _sparse(i): + return sparse_tensor.SparseTensorValue( + indices=np.array([[0, 0]]), + values=(i * np.array([1])), + dense_shape=np.array([1, 1])) + + def make_scan_fn(step): + return lambda state, _: (_sparse(state.values[0] + step), state) + + start = array_ops.placeholder(dtypes.int32, shape=[]) + step = array_ops.placeholder(dtypes.int32, shape=[]) + take = array_ops.placeholder(dtypes.int64, shape=[]) + iterator = self._counting_dataset( + _sparse(start), + make_scan_fn(step)).take(take).make_initializable_iterator() + next_element = iterator.get_next() + + with self.test_session() as sess: + + for start_val, step_val, take_val in [(0, 1, 10), (0, 1, 0), (10, 1, 10), + (10, 2, 10), (10, -1, 10), + (10, -2, 10)]: + sess.run(iterator.initializer, + feed_dict={start: start_val, step: step_val, take: take_val}) + for expected, _ in zip( + itertools.count(start_val, step_val), range(take_val)): + self.assertEqual(expected, sess.run(next_element).values[0]) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + def testChangingStateShape(self): # Test the fixed-point shape invariant calculations: start with # initial values with known shapes, and use a scan function that diff --git a/tensorflow/contrib/data/python/ops/scan_ops.py b/tensorflow/contrib/data/python/ops/scan_ops.py index 60ef7efba4bb2b..e911ad0fa0541f 100644 --- a/tensorflow/contrib/data/python/ops/scan_ops.py +++ b/tensorflow/contrib/data/python/ops/scan_ops.py @@ -24,6 +24,7 @@ from tensorflow.python.data.util import sparse from tensorflow.python.framework import function from tensorflow.python.framework import ops +from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import gen_dataset_ops @@ -36,18 +37,22 @@ def __init__(self, input_dataset, initial_state, scan_func): self._input_dataset = input_dataset with ops.name_scope("initial_state"): + # Convert any `SparseTensorValue`s to `SparseTensor`s and all other + # values to tensors. self._initial_state = nest.pack_sequence_as(initial_state, [ - ops.convert_to_tensor(t, name="component_%d" % i) + sparse_tensor.SparseTensor.from_value(t) + if sparse_tensor.is_sparse(t) else ops.convert_to_tensor( + t, name="component_%d" % i) for i, t in enumerate(nest.flatten(initial_state)) ]) - # Compute initial values for the state shapes and types based on - # the initial state. These will be refined by running - # `tf_scan_func` one or more times below. - # TODO(b/68937811): Allow the initial state to be a tf.SparseTensor. + # Compute initial values for the state classes, shapes and types based on + # the initial state. The shapes may be refined by running `tf_scan_func` one + # or more times below. + self._state_classes = sparse.get_classes(self._initial_state) self._state_shapes = nest.pack_sequence_as( self._initial_state, - [t.shape for t in nest.flatten(self._initial_state)]) + [t.get_shape() for t in nest.flatten(self._initial_state)]) self._state_types = nest.pack_sequence_as( self._initial_state, [t.dtype for t in nest.flatten(self._initial_state)]) @@ -62,67 +67,102 @@ def __init__(self, input_dataset, initial_state, scan_func): need_to_rerun = True while need_to_rerun: - flat_state_shapes = nest.flatten(self._state_shapes) - flat_state_types = nest.flatten(self._state_types) - - # Create a list in which `tf_scan_func` will store the s + # Create a list in which `tf_scan_func` will store the new shapes. flat_new_state_shapes = [] - @function.Defun(*(flat_state_types + nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes)))) + @function.Defun(*(nest.flatten( + sparse.as_dense_types( + self._state_types, self._state_classes)) + nest.flatten( + sparse.as_dense_types(input_dataset.output_types, + input_dataset.output_classes)))) def tf_scan_func(*args): """A wrapper for Defun that facilitates shape inference.""" # Pass in shape information from the state and input_dataset. - # TODO(b/69424092): Check that neither inputs nor outputs are sparse. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, - flat_state_shapes + nest.flatten(dense_shapes)): + for arg, shape in zip( + args, + nest.flatten( + sparse.as_dense_shapes(self._state_shapes, self._state_classes)) + + nest.flatten( + sparse.as_dense_shapes(input_dataset.output_shapes, + input_dataset.output_classes))): arg.set_shape(shape) - pivot = len(flat_state_shapes) - old_state = nest.pack_sequence_as(self._initial_state, args[:pivot]) - input_value = nest.pack_sequence_as(input_dataset.output_types, - args[pivot:]) - - ret = scan_func(old_state, input_value) + pivot = len(nest.flatten(self._state_shapes)) + print(self._state_classes) + nested_state_args = nest.pack_sequence_as(self._state_types, + args[:pivot]) + nested_state_args = sparse.deserialize_sparse_tensors( + nested_state_args, self._state_types, self._state_shapes, + self._state_classes) + print(input_dataset.output_classes) + nested_input_args = nest.pack_sequence_as(input_dataset.output_types, + args[pivot:]) + nested_input_args = sparse.deserialize_sparse_tensors( + nested_input_args, input_dataset.output_types, + input_dataset.output_shapes, input_dataset.output_classes) + + ret = scan_func(nested_state_args, nested_input_args) if not isinstance(ret, collections.Sequence) or len(ret) != 2: raise TypeError("The scan function must return a pair comprising the " "new state and the output value.") + + # Convert any `SparseTensorValue`s to `SparseTensor`s and all other + # values to tensors. + ret = nest.pack_sequence_as(ret, [ + sparse_tensor.SparseTensor.from_value(t) + if sparse_tensor.is_sparse(t) else ops.convert_to_tensor(t) + for t in nest.flatten(ret) + ]) new_state, output_value = ret - flat_new_state = [ - ops.convert_to_tensor(t) for t in nest.flatten(new_state) - ] - flat_output_value = [ - ops.convert_to_tensor(t) for t in nest.flatten(output_value) - ] + # Extract and validate class information from the returned values. + for t, clazz in zip( + nest.flatten(new_state), nest.flatten(self._state_classes)): + if not isinstance(t, clazz): + raise TypeError( + "The element classes for the new state must match the initial " + "state. Expected %s; got %s." % + (self._state_classes, + nest.pack_sequence_as( + self._state_types, + [type(t) for t in nest.flatten(new_state)]))) + self._output_classes = sparse.get_classes(output_value) # Extract shape information from the returned values. - flat_new_state_shapes.extend([t.shape for t in flat_new_state]) + flat_new_state_shapes.extend( + [t.get_shape() for t in nest.flatten(new_state)]) self._output_shapes = nest.pack_sequence_as( - output_value, [t.shape for t in flat_output_value]) + output_value, [t.get_shape() for t in nest.flatten(output_value)]) # Extract and validate type information from the returned values. - for t, dtype in zip(flat_new_state, flat_state_types): + for t, dtype in zip( + nest.flatten(new_state), nest.flatten(self._state_types)): if t.dtype != dtype: raise TypeError( "The element types for the new state must match the initial " "state. Expected %s; got %s." % - (self._state_types, nest.pack_sequence_as( - self._state_types, [t.dtype for t in flat_new_state]))) - self._output_classes = nest.pack_sequence_as( - output_value, [ops.Tensor for _ in flat_output_value]) + (self._state_types, + nest.pack_sequence_as( + self._state_types, + [t.dtype for t in nest.flatten(new_state)]))) self._output_types = nest.pack_sequence_as( - output_value, [t.dtype for t in flat_output_value]) - - return flat_new_state + flat_output_value + output_value, [t.dtype for t in nest.flatten(output_value)]) + + # Serialize any sparse tensors. + new_state = nest.pack_sequence_as(new_state, [ + t for t in nest.flatten(sparse.serialize_sparse_tensors(new_state)) + ]) + output_value = nest.pack_sequence_as(output_value, [ + t for t in nest.flatten( + sparse.serialize_sparse_tensors(output_value)) + ]) + return nest.flatten(new_state) + nest.flatten(output_value) # Use the private method that will execute `tf_scan_func` but delay # adding it to the graph in case we need to rerun the function. tf_scan_func._create_definition_if_needed() # pylint: disable=protected-access + flat_state_shapes = nest.flatten(self._state_shapes) weakened_state_shapes = [ original.most_specific_compatible_shape(new) for original, new in zip(flat_state_shapes, flat_new_state_shapes) @@ -150,7 +190,7 @@ def _as_variant_tensor(self): input_t = self._input_dataset._as_variant_tensor() # pylint: disable=protected-access return gen_dataset_ops.scan_dataset( input_t, - nest.flatten(self._initial_state), + nest.flatten(sparse.serialize_sparse_tensors(self._initial_state)), self._scan_func.captured_inputs, f=self._scan_func, output_types=nest.flatten( From 4041ae0fac83060e6d17d26fa3a46ee7b69f9919 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 14:14:43 -0700 Subject: [PATCH 0191/1691] Push down const inputs into the function of specialized functions. PiperOrigin-RevId: 194843380 --- .../grappler/optimizers/function_optimizer.cc | 132 ++++++++++++++++-- .../optimizers/function_optimizer_test.cc | 61 ++++++++ 2 files changed, 183 insertions(+), 10 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc index 3a6de9e3b29e5d..1bec9086f7151f 100644 --- a/tensorflow/core/grappler/optimizers/function_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc @@ -79,6 +79,7 @@ class FunctionOptimizerContext { explicit FunctionOptimizerContext(RewriterConfig::Toggle opt_level, const GrapplerItem& item) : function_library_(OpRegistry::Global(), item.graph.library()) { + InitializeTrulyConstNodes(item); InitializeInlinedFunctions(opt_level, item); } @@ -86,20 +87,41 @@ class FunctionOptimizerContext { return function_library_; } - FunctionLibraryDefinition& mutable_function_library() { - return function_library_; + FunctionLibraryDefinition* mutable_function_library() { + return &function_library_; } bool IsInlinedFunction(const string& name) const { return inlined_functions_.count(name) > 0; } + bool IsTrulyConst(const string& name) const { + return TrulyConstNode(name) != nullptr; + } + + const NodeDef* TrulyConstNode(const string& name) const { + return gtl::FindWithDefault(truly_const_nodes_, name, nullptr); + } + // Find inlining candidate by name. Return nullptr if not found. const FunctionDef* FindInlinedFunction(const string& name) const { return gtl::FindWithDefault(inlined_functions_, name, nullptr); } private: + void InitializeTrulyConstNodes(const GrapplerItem& item) { + std::unordered_set feed_nodes; + for (const auto& feed : item.feed) { + feed_nodes.insert(NodeName(feed.first)); + } + + for (const NodeDef& node : item.graph.node()) { + if (IsConstant(node) && feed_nodes.count(node.name()) == 0) { + truly_const_nodes_[node.name()] = &node; + } + } + } + void InitializeInlinedFunctions(RewriterConfig::Toggle opt_level, const GrapplerItem& item) { bool aggressive = opt_level == RewriterConfig::AGGRESSIVE; @@ -123,10 +145,20 @@ class FunctionOptimizerContext { FunctionLibraryDefinition function_library_; // Functions that can be inlined into optimized graph. std::unordered_map inlined_functions_; + // Nodes that are Const and not in feed. + std::unordered_map truly_const_nodes_; TF_DISALLOW_COPY_AND_ASSIGN(FunctionOptimizerContext); }; +bool HasTrulyConstInputs(const NodeDef& node, + const FunctionOptimizerContext& ctx) { + const auto is_truly_const = [&ctx](const string& input) { + return ctx.IsTrulyConst(NodeName(input)); + }; + return std::any_of(node.input().begin(), node.input().end(), is_truly_const); +} + // Return trimmed FunctionDefLibrary with functions that are reachable from // the optimized graph. FunctionDefLibrary TrimFunctionLibrary(const FunctionLibraryDefinition& flib, @@ -208,6 +240,77 @@ FunctionDefLibrary TrimFunctionLibrary(const FunctionLibraryDefinition& flib, return lib; } +// Push all constant inputs of an instantiating node into the function body. +Status PushDownConstInputs(const NodeDef& func_node, + const FunctionOptimizerContext& ctx, + GrapplerFunctionItem* item, + std::unordered_set* const_inputs, + std::unordered_set* control_deps) { + // Record node control dependencies in the control_deps set. + const auto record_control_deps = [&](const NodeDef* const_input) { + for (int i = const_input->input_size() - 1; i >= 0; --i) { + const string& input = const_input->input(i); + if (IsControlInput(input)) + control_deps->insert(input); + else + break; + } + }; + + for (int i = func_node.input_size() - 1; i >= 0; --i) { + const string& input = func_node.input(i); + if (IsControlInput(input)) continue; + + const string node_name = NodeName(input); + if (ctx.IsTrulyConst(node_name)) { + VLOG(3) << "Push const into function body: input=" << input; + const auto* const_input = CHECK_NOTNULL(ctx.TrulyConstNode(node_name)); + const_inputs->insert(input); + record_control_deps(const_input); + TF_RETURN_IF_ERROR(ReplaceInputWithConst(*const_input, i, item)); + } + } + + return Status::OK(); +} + +// Remove inputs that were pushed into the function body, and attach their +// control dependencies to the function caller node. +void RemovePushedDownConstInputs(const std::unordered_set& const_inputs, + const std::unordered_set& control_deps, + NodeDef* specialized_func_node) { + // Nothing to do if it was no const inputs to the function node. + if (const_inputs.empty()) return; + + // Keep only non-const inputs. + std::vector keep_inputs; + const auto& inputs = specialized_func_node->input(); + std::copy_if(inputs.begin(), inputs.end(), std::back_inserter(keep_inputs), + [&](const string& input) { + return const_inputs.find(input) == const_inputs.end(); + }); + + specialized_func_node->clear_input(); + for (const auto& keep : keep_inputs) specialized_func_node->add_input(keep); + + // Attach control dependencies of pushed down const input to the caller node. + if (!control_deps.empty()) { + std::unordered_set existing_control_deps; + + for (const string& input : keep_inputs) { + existing_control_deps.insert(AsControlDependency(NodeName(input))); + } + + for (const string& ctrl : control_deps) { + if (existing_control_deps.find(ctrl) == existing_control_deps.end()) { + VLOG(3) << "Forward control dependency to function caller node: input=" + << ctrl; + specialized_func_node->add_input(ctrl); + } + } + } +} + Status SpecializeFunction(const NodeDef& func_node, const FunctionDef& func, FunctionOptimizerContext* ctx, GraphDef* optimized_graph) { @@ -219,11 +322,19 @@ Status SpecializeFunction(const NodeDef& func_node, const FunctionDef& func, const auto& flib = ctx->function_library(); - // Make a GrapplerFunctionItem and immediately convert it back to FunctionDef. + // Make a GrapplerFunctionItem and convert it back to FunctionDef after + // pushing all constant inputs into the function body. GrapplerFunctionItem item; TF_RETURN_IF_ERROR(MakeGrapplerFunctionItem(func, func_attr, flib, &item)); - // TODO(ezhulenev): Push down const inputs and known input shapes. + // Push const inputs into the function body, and keep track of their control + // dependencies. + std::unordered_set const_inputs; + std::unordered_set control_deps; + TF_RETURN_IF_ERROR(PushDownConstInputs(func_node, *ctx, &item, &const_inputs, + &control_deps)); + + // TODO(ezhulenev): Push down known input shapes. FunctionDef specialized_func; TF_RETURN_IF_ERROR(MakeFunctionDef(item, flib, &specialized_func)); @@ -237,13 +348,16 @@ Status SpecializeFunction(const NodeDef& func_node, const FunctionDef& func, // Add specialized function to the library. TF_RETURN_IF_ERROR( - ctx->mutable_function_library().AddFunctionDef(specialized_func)); + ctx->mutable_function_library()->AddFunctionDef(specialized_func)); // Add a function call node for the specialized function. NodeDef* specialized_func_node = optimized_graph->add_node(); *specialized_func_node = func_node; specialized_func_node->set_op(specialized_func_name); + // Update specialized node to remove inputs for pushed down consts. + RemovePushedDownConstInputs(const_inputs, control_deps, + specialized_func_node); return Status::OK(); } @@ -582,11 +696,9 @@ Status FunctionOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item, // Do not specialize if function has custom gradient. const string grad_func = ctx.function_library().FindGradient(func_name); - if (specialize_func && grad_func.empty() && IsParametrized(*func)) { - // TODO(ezhulenev): Specialize function call if input is a Const or has - // a known shape. Const input tensors can be pushed into the function - // body and removed from function inputs. - + if (specialize_func && grad_func.empty() && + (IsParametrized(*func) || HasTrulyConstInputs(node, ctx))) { + // TODO(ezhulenev): Specialize function call if input has a known shape. // Specialize function body for its instantiation attributes and inputs. TF_RETURN_IF_ERROR( SpecializeFunction(node, *func, &ctx, optimized_graph)); diff --git a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc index 6147e8a27c0e18..147a2644212e23 100644 --- a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc @@ -657,5 +657,66 @@ TEST_F(FunctionOptimizerTest, SpecializeFunction_XTimesTwo) { test::ExpectTensorEqual(tensors_expected[0], tensors[0]); } +TEST_F(FunctionOptimizerTest, SpecializeFunction_PushDownConstInput) { + using test::function::NDef; + + FunctionOptimizer optimizer(RewriterConfig::DEFAULT); + + FunctionDef mul_func = FunctionDefHelper::Create( + "MyMul", {"x:T", "y:T"}, {"z:T"}, {"T: {float, double}"}, + {{{"output"}, "Mul", {"x", "y"}, {{"T", "$T"}}}}, + /* Mapping between function returns and function node outputs. */ + {{"z", "output:z:0"}}); + + // Mark MyMul as noinline. + (*mul_func.mutable_attr())["_noinline"].set_b(true); + std::vector function_library = {mul_func}; + + // Build a graph to compute y = MyMul(x, 2.0). + const Tensor kTwo = test::AsScalar(2.0); + + GrapplerItem item; + item.graph = test::function::GDef( + {NDef("x", "Placeholder", {}, {{"dtype", DT_FLOAT}}, kDevice), + NDef("init", "NoOp", {}, {}, kDevice), + NDef("two", "Const", {"^init", "^x"}, + {{"dtype", DT_FLOAT}, {"value", kTwo}}, kDevice), + NDef("y", "MyMul", {"x", "two"}, {{"T", DT_FLOAT}}, kDevice), + NDef("z", "Identity", {"y"}, {{"T", DT_FLOAT}}, kDevice)}, + function_library); + + GraphDef output; + TF_EXPECT_OK(optimizer.Optimize(nullptr, item, &output)); + + // Make sure that specialized function was added to the library and original + // function was removed. + ASSERT_EQ(1, output.library().function_size()); + + const FunctionDef& specialized = output.library().function(0); + EXPECT_EQ("MyMul_specialized_for_y", specialized.signature().name()); + EXPECT_EQ(1, specialized.signature().input_arg_size()); + + // And 'y' node has control dependencies of a pushed down const node. + int count = 0; + for (const NodeDef& node : output.node()) { + if (node.name() == "y" && count++) { + ASSERT_EQ(2, node.input_size()); + EXPECT_EQ("x", node.input(0)); + EXPECT_EQ("^init", node.input(1)); + } + } + EXPECT_EQ(1, count); + + // And that graph evaluation yields the same result. + Tensor pi = test::AsScalar(3.14f); + item.fetch = {"z"}; + item.feed.emplace_back("x", pi); + + auto tensors_expected = EvaluateFetchNodes(item); + GrapplerItem optimized(item, std::move(output)); + auto tensors = EvaluateFetchNodes(optimized); + test::ExpectTensorEqual(tensors_expected[0], tensors[0]); +} + } // namespace grappler } // namespace tensorflow From fac11a7fbeed495938f2d1eafb75f77c88ebd068 Mon Sep 17 00:00:00 2001 From: Petros Mol Date: Mon, 30 Apr 2018 14:26:08 -0700 Subject: [PATCH 0192/1691] Removing an obsolete TODO PiperOrigin-RevId: 194845376 --- .../python/mappers/random_fourier_features_test.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/tensorflow/contrib/kernel_methods/python/mappers/random_fourier_features_test.py b/tensorflow/contrib/kernel_methods/python/mappers/random_fourier_features_test.py index 91929184a2e6f3..2ff4d41d75fe59 100644 --- a/tensorflow/contrib/kernel_methods/python/mappers/random_fourier_features_test.py +++ b/tensorflow/contrib/kernel_methods/python/mappers/random_fourier_features_test.py @@ -31,7 +31,7 @@ def _inner_product(x, y): - """Inner product between tensors x and y. + r"""Inner product between tensors x and y. The input tensors are assumed to be in ROW representation, that is, the method returns \\(x * y^T\\). @@ -131,10 +131,6 @@ def testGoodKernelApproximationAmortized(self): mapped_dim = 5000 stddev = 5.0 - # TODO(sibyl-vie3Poto): Reduce test's running time before moving to third_party. One - # possible way to speed the test up is to compute both the approximate and - # the exact kernel matrix directly using matrix operations instead of - # computing the values for each pair of points separately. points_shape = [1, input_dim] points = [ random_ops.random_uniform(shape=points_shape, maxval=1.0) From c0f0720445226375ac8a176d9d3de9c5e647fa4a Mon Sep 17 00:00:00 2001 From: Dimitris Vardoulakis Date: Mon, 30 Apr 2018 14:28:46 -0700 Subject: [PATCH 0193/1691] [TF:XLA] Fix some unexpected memory leak in hlo_graph_dumper_test. PiperOrigin-RevId: 194845792 --- tensorflow/compiler/xla/service/BUILD | 1 - .../xla/service/hlo_graph_dumper_test.cc | 19 +++++++++---------- 2 files changed, 9 insertions(+), 11 deletions(-) diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index ed0da47681c7ef..6e2510aa1081ad 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -2421,7 +2421,6 @@ tf_cc_test( ":hlo_graph_dumper", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:xla_proto", - "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:test_utils", "//tensorflow/compiler/xla/tests:xla_internal_test_main", # fixdeps: keep "//tensorflow/core:lib", diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc index b589cd573d8293..4843963243000d 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc @@ -20,7 +20,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/test.h" -#include "tensorflow/compiler/xla/tests/hlo_test_base.h" #include "tensorflow/compiler/xla/tests/test_utils.h" #include "tensorflow/compiler/xla/xla.pb.h" #include "tensorflow/core/lib/strings/strcat.h" @@ -48,9 +47,7 @@ class DotRenderer : public hlo_graph_dumper::GraphRendererInterface { XLA_REGISTER_GRAPH_RENDERER(DotRenderer); -class HloGraphDumperTest : public HloTestBase {}; - -TEST_F(HloGraphDumperTest, NestedFusion) { +TEST(HloGraphDumperTest, NestedFusion) { HloComputation::Builder b("b"); // Build param0 + param1 + param2 + param3 + param4. @@ -67,9 +64,10 @@ TEST_F(HloGraphDumperTest, NestedFusion) { sums.push_back(b.AddInstruction(HloInstruction::CreateBinary( shape, HloOpcode::kAdd, sums[i], params[i + 2]))); } - auto m = CreateNewModule(); - m->AddEntryComputation(b.Build()); - HloComputation* root_computation = m->entry_computation(); + HloModuleConfig config; + HloModule m(TestName(), config); + m.AddEntryComputation(b.Build()); + HloComputation* root_computation = m.entry_computation(); // Fuse into fusion(param0 + param1 + param2 + param3 + param4). auto* outer_fusion = root_computation->CreateFusionInstruction( @@ -119,13 +117,14 @@ TEST_F(HloGraphDumperTest, NestedFusion) { HasSubstr(inner_sum->name())); } -TEST_F(HloGraphDumperTest, Constant) { +TEST(HloGraphDumperTest, Constant) { HloComputation::Builder b("b"); auto instruction = b.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(-42))); instruction->set_name("i_am_a_constant_root_instruction"); - auto m = CreateNewModule(); - HloComputation* root_computation = m->AddEntryComputation(b.Build()); + HloModuleConfig config; + HloModule m(TestName(), config); + HloComputation* root_computation = m.AddEntryComputation(b.Build()); string graph = hlo_graph_dumper::DumpGraph( *root_computation, /*label=*/"an_empty_graph", DebugOptions()); EXPECT_THAT(graph, HasSubstr("an_empty_graph")); From ab02bce13e49fbd001c6db241d213dc2886a5792 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Mon, 30 Apr 2018 14:34:01 -0700 Subject: [PATCH 0194/1691] Do not cast int64 to int32 in keras embedding lookups. Often when working on the GPU with tf int64s are more efficient as int32s will be copied back and forth to the host quite a bit. PiperOrigin-RevId: 194846629 --- tensorflow/python/keras/_impl/keras/layers/embeddings.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/keras/_impl/keras/layers/embeddings.py b/tensorflow/python/keras/_impl/keras/layers/embeddings.py index 2b353ac007a33d..f7398845d400b1 100644 --- a/tensorflow/python/keras/_impl/keras/layers/embeddings.py +++ b/tensorflow/python/keras/_impl/keras/layers/embeddings.py @@ -153,7 +153,8 @@ def compute_output_shape(self, input_shape): return (input_shape[0],) + tuple(in_lens) + (self.output_dim,) def call(self, inputs): - if K.dtype(inputs) != 'int32': + dtype = K.dtype(inputs) + if dtype != 'int32' and dtype != 'int64': inputs = math_ops.cast(inputs, 'int32') out = embedding_ops.embedding_lookup(self.embeddings, inputs) return out From d2a4227636955958f9acbc7c60c72eb8cd9f6480 Mon Sep 17 00:00:00 2001 From: Eli Bendersky Date: Mon, 30 Apr 2018 14:39:25 -0700 Subject: [PATCH 0195/1691] Add XLA logo to its documentation page PiperOrigin-RevId: 194847599 --- tensorflow/docs_src/performance/xla/index.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tensorflow/docs_src/performance/xla/index.md b/tensorflow/docs_src/performance/xla/index.md index a8847830740302..8f5de83ea62923 100644 --- a/tensorflow/docs_src/performance/xla/index.md +++ b/tensorflow/docs_src/performance/xla/index.md @@ -1,5 +1,9 @@ # XLA Overview +
+ +
+ > Note: XLA is experimental and considered alpha. Most use cases will not > see improvements in performance (speed or decreased memory usage). We have > released XLA early so the Open Source Community can contribute to its From b8197b2190c185a138b18716100621192ee02b79 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 14:56:13 -0700 Subject: [PATCH 0196/1691] Implement unary chain hoisting optimization for Concat, Split, and SplitV. For Concat, hoist prefix chains of unary ops before concatenation, e.g. // Rewrites // Concat({Cos(Exp(a)), Cos(Exp(b)), Cos(Exp(c))}) // into // Cos(Exp(Concat({a, b, c}))). For Split/SplitV hoist unary postfix chains before the split, e.g. // Rewrites // [Cos(Exp(y)) for y in Split(x)] // into // [y for y in Split(Cos(Exp(x)))]. The new optimization is off by default. PiperOrigin-RevId: 194850318 --- .../optimizers/arithmetic_optimizer.cc | 420 ++++++++++++------ .../optimizers/arithmetic_optimizer.h | 5 +- .../optimizers/arithmetic_optimizer_test.cc | 179 +++++++- 3 files changed, 459 insertions(+), 145 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index 18076eee96e33a..bf59b254490561 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -302,6 +302,11 @@ class ArithmeticOptimizerStage : public GraphOptimizerStage { } } + bool IsInPreserveSet(const NodeDef& node) const { + return ctx().nodes_to_preserve->find(node.name()) != + ctx().nodes_to_preserve->end(); + } + private: // Extended context required for ArithmeticOptimizer. const ArithmeticOptimizerContext ctx_ext_; @@ -474,11 +479,6 @@ class ArithmeticNodesGroupOptimizerStage : public ArithmeticOptimizerStage { return group.root_node->device() == node.device(); } - bool IsInPreserveSet(const NodeDef& node) const { - return ctx().nodes_to_preserve->find(node.name()) != - ctx().nodes_to_preserve->end(); - } - bool IsAlreadyOptimized(const NodeDef& node) const { return optimized_nodes_.find(node.name()) != optimized_nodes_.end(); } @@ -1340,65 +1340,143 @@ class RemoveNegationStage : public ArithmeticOptimizerStage { }; // This optimization hoists the common prefix of unary ops of the inputs to -// concat out of the concat. -// For example: Concat([Exp(Sin(x)), Exp(Sin(y)), Exp(Sin(z))]) -> -// Exp(Sin(Concat([x, y, z]))). +// concat out of the concat, for example: +// Concat([Exp(Sin(x)), Exp(Sin(y)), Exp(Sin(z))]) +// becomes +// Exp(Sin(Concat([x, y, z]))). +// Similarly, it will hoist the common postfix of unary ops into Split or +// SplitV nodes, for example: +// [Exp(Sin(y)) for y in Split(x)] +// becomes +// [y for y in Split(Exp(Sin(x))] +// // TODO(rmlarsen): Support casting. We would have to change the type attribute -// on the concat node. -class HoistCWiseUnaryFromConcatStage : public ArithmeticOptimizerStage { +// on the concat/split node. +// TODO(rmlarsen): Handle Enter/Exit. +class HoistCWiseUnaryChainsStage : public ArithmeticOptimizerStage { public: - explicit HoistCWiseUnaryFromConcatStage( - const GraphOptimizerContext& ctx, - const ArithmeticOptimizerContext& ctx_ext) + explicit HoistCWiseUnaryChainsStage(const GraphOptimizerContext& ctx, + const ArithmeticOptimizerContext& ctx_ext) : ArithmeticOptimizerStage("", ctx, ctx_ext) {} - ~HoistCWiseUnaryFromConcatStage() override = default; + ~HoistCWiseUnaryChainsStage() override = default; + + struct ChainLink { + ChainLink() = default; + ChainLink(NodeDef* _node, int _port_origin) + : node(_node), port_origin(_port_origin) {} + NodeDef* node; // Node in a chain. + int port_origin; // Port on concat/split node from which this chain + // originates. + + bool operator<(const ChainLink& other) const { + if (port_origin < other.port_origin) { + return true; + } else if (port_origin > other.port_origin) { + return false; + } else { + return node->name() < other.node->name(); + } + } + }; + + // We use an ordinary set sorted on port and node name, so the order, and + // hence the node name used for the hoisted chain, will be deterministic. + using ChainLinkSet = std::set; bool IsSupported(const NodeDef* node) const override { - if (!IsConcat(*node)) return false; - const int n = node->attr().at("N").i(); - return n > 1; + if (IsInPreserveSet(*node)) return false; + if (IsConcat(*node)) { + const int n = node->attr().at("N").i(); + return n > 1; + } else if (IsSplit(*node) || IsSplitV(*node)) { + const int num_split = node->attr().at("num_split").i(); + return num_split > 1 && !IsAlreadyOptimized(*node); + } + return false; } - Status TrySimplify(NodeDef* concat_node, - string* simplified_node_name) override { + Status TrySimplify(NodeDef* node, string* simplified_node_name) override { + node_is_concat_ = IsConcat(*node); int prefix_length; std::set ctrl_inputs; + ChainLinkSet tails; TF_RETURN_IF_ERROR( - FindCommonUnaryOpPrefix(*concat_node, &prefix_length, &ctrl_inputs)); - if (prefix_length > 0) { + FindCommonUnaryOpChain(*node, &prefix_length, &tails, &ctrl_inputs)); + if (prefix_length > 0 && !tails.empty()) { TF_RETURN_IF_ERROR( - HoistUnaryOpPrefix(prefix_length, &ctrl_inputs, concat_node)); - AddToOptimizationQueue(concat_node); + HoistUnaryOpChain(prefix_length, tails, &ctrl_inputs, node)); } return Status::OK(); } private: - void RemoveControlInputs(std::set* removed_ctrl_inputs, - NodeDef* node) const { - const int num_inputs = node->input_size(); - for (int idx = num_inputs - 1; idx >= 0; --idx) { - const string& input = node->input(idx); - if (IsControlInput(input)) { - removed_ctrl_inputs->insert(input); - ctx().node_map->RemoveOutput(NodeName(input), node->name()); - node->mutable_input()->RemoveLast(); - } else { - break; + // Returns the length of the common unary chain of ops that can be + // hoisted to the other side of concat or split. + Status FindCommonUnaryOpChain(const NodeDef& root_node, int* prefix_length, + ChainLinkSet* tails, + std::set* ctrl_inputs) const { + *prefix_length = 0; + // Follow the chains starting at each concat input or split output as long + // as all the following conditions hold: + // 1. The ops in all chains are the same. + // 2. The ops are unary elemenwise op. + // 3. The op output has only a single consumer (concat only). + ChainLinkSet cur_tails; + TF_RETURN_IF_ERROR(InitializeChains(root_node, &cur_tails)); + if (cur_tails.size() < 2) { + return Status::OK(); + } + ctrl_inputs->clear(); + bool stop = false; + while (!stop && !cur_tails.empty() && + OpsAreSafeToHoist(root_node, cur_tails)) { + // We found one more link that can be hoisted. + ++(*prefix_length); + tails->swap(cur_tails); + GatherControlInputs(ctrl_inputs, *tails); + + // Advance tail pointers to the next level. + TF_RETURN_IF_ERROR(AdvanceTails(*tails, &cur_tails, &stop)); + } + return Status::OK(); + } + + // Hoists the chains to the other side of concat or split and attaches the + // control inputs gathered from them to the concat or split node. + Status HoistUnaryOpChain(const int prefix_length, const ChainLinkSet& tails, + std::set* ctrl_inputs, NodeDef* root_node) { + if (tails.empty()) { + return Status::OK(); + } + AddControlInputs(ctrl_inputs, root_node); + AddToOptimizationQueue(root_node); + optimized_nodes_.insert(root_node->name()); + if (node_is_concat_) { + return HoistChainForConcat(prefix_length, tails, root_node); + } else { + return HoistChainForSplit(prefix_length, tails, root_node); + } + } + + void GatherControlInputs(std::set* ctrl_inputs, + const ChainLinkSet& ops) const { + for (const auto& link : ops) { + const NodeDef* node = link.node; + for (int i = node->input_size() - 1; i >= 0; --i) { + const string& input = node->input(i); + if (!IsControlInput(input)) break; + ctrl_inputs->insert(input); } } } void AddControlInputs(std::set* new_ctrl_inputs, NodeDef* node) const { - for (int idx = node->input_size() - 1; idx >= 0; --idx) { - const string& existing_input = node->input(idx); - if (IsControlInput(existing_input)) { - new_ctrl_inputs->erase(existing_input); - } else { - break; - } + for (int i = node->input_size() - 1; i >= 0; --i) { + const string& existing_input = node->input(i); + if (!IsControlInput(existing_input)) break; + new_ctrl_inputs->erase(existing_input); } for (const string& new_input : *new_ctrl_inputs) { ctx().node_map->AddOutput(NodeName(new_input), node->name()); @@ -1406,113 +1484,193 @@ class HoistCWiseUnaryFromConcatStage : public ArithmeticOptimizerStage { } } - // Returns the length of the common unary prefix chain of ops that can be - // hoisted out of concat. - Status FindCommonUnaryOpPrefix(const NodeDef& concat_node, int* prefix_length, - std::set* ctrl_inputs) const { - *prefix_length = 0; - const int n = concat_node.attr().at("N").i(); - // Follow the chains backwards from each concat input as long as all the - // following conditions hold: - // 1. The ops in all chains are the same. - // 2. The op is a unary elemenwise op. - // 3. The op output has only a single consumer. - std::vector tail(n, nullptr); - const int start = concat_node.op() == "Concat" ? 1 : 0; - const int end = start + n; - // Set up tail pointers to point to the immediate inputs to Concat. - for (int i = start; i < end; ++i) { - if (IsControlInput(concat_node.input(i))) { - return errors::FailedPrecondition("Got control input ", - concat_node.input(i), - " where normal input was expected."); - } - TF_RETURN_IF_ERROR(GetInputNode(concat_node.input(i), &tail[i - start])); - } - - bool stop = false; - ctrl_inputs->clear(); - while (!stop) { - const NodeDef* tail0 = tail[0]; - if (!IsUnaryElementWise(*tail0)) break; - for (int chain = 0; chain < n; ++chain) { - // TODO(rmlarsen): Allow and hoist outgoing control edges. - if (tail[chain]->op() != tail0->op() || - ctx().node_map->GetOutputs(tail[chain]->name()).size() > 1) { - stop = true; - break; + Status InitializeChains(const NodeDef& node, ChainLinkSet* tails) const { + if (node_is_concat_) { + // Handle concat nodes by looking backwards in the graph. + const int n = node.attr().at("N").i(); + const int start = node.op() == "Concat" ? 1 : 0; + const int end = start + n; + // Set up tail pointers to point to the immediate inputs to Concat. + for (int input_port = start; input_port < end; ++input_port) { + if (IsControlInput(node.input(input_port))) { + return errors::FailedPrecondition( + "Got control input ", node.input(input_port), + " where normal input was expected."); } + NodeDef* tail; + TF_RETURN_IF_ERROR(GetInputNode(node.input(input_port), &tail)); + tails->insert(ChainLink(tail, input_port)); } - if (stop) break; - // We found one more op that can be hoisted. - ++(*prefix_length); - for (int chain = 0; chain < n; ++chain) { - RemoveControlInputs(ctrl_inputs, tail[chain]); - } - // Advance tail pointers to the next level. - for (int chain = 0; chain < n; ++chain) { - if (tail[chain]->input_size() == 0 || - IsControlInput(tail[chain]->input(0))) { - stop = true; - break; + return Status::OK(); + } else { + // Handle split nodes by looking forwards in the graph. + const auto& outputs = ctx().node_map->GetOutputs(node.name()); + for (NodeDef* output : outputs) { + if (IsControlInput(output->input(0))) continue; + int port; + const string node_name = ParseNodeName(output->input(0), &port); + if (node_name == node.name()) { + tails->insert(ChainLink(output, port)); } else { - NodeDef* new_tail = nullptr; - TF_RETURN_IF_ERROR(GetInputNode(tail[chain]->input(0), &new_tail)); - tail[chain] = new_tail; + // This output node has a non-control input other than the split node, + // abort. + tails->clear(); + return Status::OK(); } } } return Status::OK(); } - Status HoistUnaryOpPrefix(const int prefix_length, - std::set* ctrl_inputs, - NodeDef* concat_node) { - const int n = concat_node->attr().at("N").i(); - const int start = concat_node->op() == "Concat" ? 1 : 0; - const int end = start + n; - const std::set consumers = - ctx().node_map->GetOutputs(concat_node->name()); - AddControlInputs(ctrl_inputs, concat_node); - for (int chain = 0; chain < (end - start); ++chain) { - NodeDef* tail = nullptr; - const string concat_input = concat_node->input(chain + start); - for (int distance = 0; distance < prefix_length; ++distance) { - if (distance == 0) { - TF_RETURN_IF_ERROR(GetInputNode(concat_input, &tail)); - } else { - TF_RETURN_IF_ERROR(GetInputNode(tail->input(0), &tail)); + bool OpsAreSafeToHoist(const NodeDef& root_node, + const ChainLinkSet& ops) const { + if (ops.empty()) return true; + const NodeDef* op0 = ops.begin()->node; + if (!IsUnaryElementWise(*op0)) return false; + for (const auto& link : ops) { + const NodeDef* op = link.node; + if (op->device() != root_node.device() || op->op() != op0->op() || + IsInPreserveSet(*op)) { + return false; + } + if (node_is_concat_ && + ctx().node_map->GetOutputs(op->name()).size() > 1) { + // TODO(rmlarsen): Allow and hoist outgoing control edges. + return false; + } + } + return true; + } + + Status AdvanceTails(const ChainLinkSet& tails, ChainLinkSet* new_tails, + bool* stop) const { + *stop = true; + new_tails->clear(); + for (const auto& link : tails) { + const NodeDef* tail = link.node; + if (node_is_concat_) { + if (tail->input_size() == 0 || IsControlInput(tail->input(0))) { + return Status::OK(); + } + NodeDef* new_tail; + TF_RETURN_IF_ERROR(GetInputNode(tail->input(0), &new_tail)); + // Remember original port. + new_tails->insert(ChainLink(new_tail, link.port_origin)); + } else { + for (NodeDef* new_tail : ctx().node_map->GetOutputs(tail->name())) { + int port; + const string node_name = ParseNodeName(new_tail->input(0), &port); + if (node_name != tail->name()) { + return Status::OK(); + } + // Skip control outputs. + if (port >= 0) { + // Remember original port. + new_tails->insert(ChainLink(new_tail, link.port_origin)); + } } } + } + *stop = false; + return Status::OK(); + } + Status HoistChainForConcat(const int prefix_length, const ChainLinkSet& tails, + NodeDef* concat_node) { + const string& concat_name = concat_node->name(); + const int first_input = concat_node->op() == "Concat" ? 1 : 0; + for (const auto& link : tails) { + NodeDef* tail = CHECK_NOTNULL(link.node); + const int concat_port = link.port_origin; + CHECK_GE(concat_port, 0); + CHECK_LT(concat_port, concat_node->input_size()); + const string concat_input = concat_node->input(concat_port); // Hook the node following tail directly into the concat node. const string tail_input = tail->input(0); - concat_node->set_input(chain + start, tail_input); - ctx().node_map->UpdateInput(concat_node->name(), concat_input, - tail_input); - - if (chain == 0) { - // Reuse nodes in the first chain to process output of concat. - tail->set_input(0, concat_node->name()); - ctx().node_map->UpdateInput(tail->name(), tail_input, - concat_node->name()); + concat_node->set_input(concat_port, tail_input); + ctx().node_map->UpdateInput(concat_name, concat_input, tail_input); + if (concat_port == first_input) { // Update the consumers of concat to consume the end of the chain // instead. - for (NodeDef* consumer : consumers) { - for (int idx = 0; idx < consumer->input_size(); ++idx) { - if (consumer->input(idx) == concat_node->name()) { - consumer->set_input(idx, concat_input); - ctx().node_map->UpdateInput(consumer->name(), concat_node->name(), - concat_input); - } - } - AddToOptimizationQueue(consumer); - } + UpdateConsumers(concat_node, concat_input); + // Reuse nodes in the first chain to process output of concat. + tail->set_input(0, concat_name); + ctx().node_map->UpdateInput(tail->name(), tail_input, concat_name); } } return Status::OK(); } + + Status HoistChainForSplit(const int prefix_length, const ChainLinkSet& tails, + NodeDef* split_node) { + // Create a new chain before the split node to process the input tensor. + const string& split_name = split_node->name(); + auto root_scope_and_name = ParseNodeScopeAndName(split_name); + + // We use the first tail node in the set as a template to get the list of + // ops to apply (starting from the end). + NodeDef* cur_tail = tails.begin()->node; + NodeDef* cur_copy = AddCopyNode( + OptimizedNodeName(root_scope_and_name, cur_tail->name()), cur_tail); + cur_copy->clear_input(); + + // Update the split to take its input from the tail of the new chain. + const int value_slot = split_node->op() == "SplitV" ? 0 : 1; + const string orig_input = split_node->input(value_slot); + split_node->set_input(value_slot, cur_copy->name()); + ctx().node_map->UpdateInput(split_node->name(), orig_input, + cur_copy->name()); + TF_RETURN_IF_ERROR(GetInputNode(cur_tail->input(0), &cur_tail)); + + // Now walk backwards creating the rest of the chain. + while (cur_tail != split_node) { + NodeDef* new_copy = AddCopyNode( + OptimizedNodeName(root_scope_and_name, cur_tail->name()), cur_tail); + new_copy->clear_input(); + cur_copy->add_input(new_copy->name()); + ctx().node_map->AddOutput(new_copy->name(), cur_copy->name()); + cur_copy = new_copy; + TF_RETURN_IF_ERROR(GetInputNode(cur_tail->input(0), &cur_tail)); + } + // Connect the original input to the head of the new chain. + cur_copy->add_input(orig_input); + ctx().node_map->UpdateOutput(NodeName(orig_input), split_name, + cur_copy->name()); + + // Connect all consumers of the tail nodes directly to the + // output port of Split from which the chain started. + for (const auto& link : tails) { + UpdateConsumers(link.node, + link.port_origin == 0 + ? split_name + : strings::StrCat(split_name, ":", link.port_origin)); + } + return Status::OK(); + } + + // Update consumers of node to take new_input as input instead. + void UpdateConsumers(NodeDef* node, const string& new_input) { + const string& node_name = node->name(); + const std::set consumers = ctx().node_map->GetOutputs(node_name); + for (NodeDef* consumer : consumers) { + for (int i = 0; i < consumer->input_size(); ++i) { + if (consumer->input(i) == node_name) { + consumer->set_input(i, new_input); + ctx().node_map->UpdateInput(consumer->name(), node_name, new_input); + } + } + AddToOptimizationQueue(consumer); + } + } + + bool IsAlreadyOptimized(const NodeDef& node) const { + return optimized_nodes_.find(node.name()) != optimized_nodes_.end(); + } + + private: + bool node_is_concat_; + std::unordered_set optimized_nodes_; }; // Performs the conversion: @@ -2200,8 +2358,8 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) { pipeline.AddStage(ctx, ctx_ext); if (options_.remove_negation) pipeline.AddStage(ctx, ctx_ext); - if (options_.hoist_unary_out_of_concat) - pipeline.AddStage(ctx, ctx_ext); + if (options_.hoist_cwise_unary_chains) + pipeline.AddStage(ctx, ctx_ext); if (options_.convert_sqrt_div_to_rsqrt_mul) pipeline.AddStage(ctx, ctx_ext); @@ -2304,5 +2462,5 @@ void ArithmeticOptimizer::Feedback(Cluster* /*cluster*/, // Nothing to do for ArithmeticOptimizer. } -} // end namespace grappler -} // end namespace tensorflow +} // namespace grappler +} // namespace tensorflow diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h index 24a2a50719531c..3b297ec0aabb25 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h @@ -65,7 +65,7 @@ class ArithmeticOptimizer : public GraphOptimizer { bool remove_redundant_bitcast = true; bool remove_redundant_cast = true; bool remove_negation = true; - bool hoist_unary_out_of_concat = false; + bool hoist_cwise_unary_chains = false; bool convert_sqrt_div_to_rsqrt_mul = false; // Choose which arithmetic optimizer stages will be enabled for a given @@ -73,9 +73,6 @@ class ArithmeticOptimizer : public GraphOptimizer { static ArithmeticOptimizerOptions Default( RewriterConfig::Toggle opt_level) { ArithmeticOptimizerOptions options; - if (opt_level == RewriterConfig::AGGRESSIVE) { - options.hoist_unary_out_of_concat = true; - } return options; } }; diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc index 7485d99c3bd7ac..f903f53a352738 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc @@ -94,6 +94,16 @@ class ArithmeticOptimizerTest : public GrapplerTest { TF_EXPECT_OK(optimizer->Optimize(nullptr, *item, output)); } + // Run ArithmeticOptimizer twice to make sure the rewrite is idempotent. + void OptimizeTwiceAndPrune(ArithmeticOptimizer* optimizer, GrapplerItem* item, + GraphDef* output) { + TF_EXPECT_OK(optimizer->Optimize(nullptr, *item, output)); + item->graph.Swap(output); + TF_EXPECT_OK(optimizer->Optimize(nullptr, *item, output)); + item->graph.Swap(output); + TF_EXPECT_OK(ModelPruner().Optimize(nullptr, *item, output)); + } + // TODO(ezhulenev): Make private. After migration to stages each test // should explicitly enable required optimization for tests isolation void DisableAllStages(ArithmeticOptimizer* optimizer) { @@ -149,9 +159,9 @@ class ArithmeticOptimizerTest : public GrapplerTest { optimizer->options_.remove_negation = true; } - void EnableOnlyHoistCWiseUnaryFromConcat(ArithmeticOptimizer* optimizer) { + void EnableOnlyHoistCWiseUnaryChains(ArithmeticOptimizer* optimizer) { DisableAllStages(optimizer); - optimizer->options_.hoist_unary_out_of_concat = true; + optimizer->options_.hoist_cwise_unary_chains = true; } void EnableOnlySqrtDivToRsqrtMul(ArithmeticOptimizer* optimizer) { @@ -2136,14 +2146,18 @@ TEST_F(ArithmeticOptimizerTest, MinimizeBroadcasts_BuildTreeUp) { TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryFromConcat) { tensorflow::Scope s = tensorflow::Scope::NewRootScope(); - Output a = ops::Variable(s.WithOpName("a"), {32}, DT_FLOAT); - Output b = ops::Variable(s.WithOpName("b"), {32}, DT_FLOAT); - Output c = ops::Variable(s.WithOpName("c"), {32}, DT_FLOAT); + Output a = ops::Const(s.WithOpName("a"), 3.14f, {32}); + Output b = ops::Const(s.WithOpName("b"), 1.0f, {32}); + Output c = ops::Const(s.WithOpName("c"), 42.0f, {32}); Output axis = ops::Const(s.WithOpName("axis"), 0, {}); Output ctrl1 = ops::Const(s.WithOpName("ctrl1"), 1, {}); Output ctrl2 = ops::Const(s.WithOpName("ctrl2"), 2, {}); Output ctrl3 = ops::Const(s.WithOpName("ctrl3"), 3, {}); // Test case with chains of length 1. + // Rewrites + // Concat({Exp(a), Exp(b), Exp(c)}) + // into + // Exp(Concat({a, b, c})). Output sin_a = ops::Sin(s.WithOpName("sin_a").WithControlDependencies(ctrl3), a); Output exp_a = @@ -2156,6 +2170,10 @@ TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryFromConcat) { Output id = ops::Identity(s.WithOpName("id"), concat); // Test case with chains of length 2. + // Rewrites + // Concat({Cos(Exp(a)), Cos(Exp(b)), Cos(Exp(c))}) + // into + // Cos(Exp(Concat({a, b, c}))). Output exp_a2 = ops::Exp(s.WithOpName("exp_a2").WithControlDependencies(ctrl1), sin_a); Output exp_b2 = ops::Exp(s.WithOpName("exp_b2"), b); @@ -2173,11 +2191,13 @@ TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryFromConcat) { item.fetch = {"id", "id2"}; TF_CHECK_OK(s.ToGraphDef(&item.graph)); + auto tensors_expected = EvaluateNodes(item.graph, item.fetch); + GraphDef output; ArithmeticOptimizer optimizer; - EnableOnlyHoistCWiseUnaryFromConcat(&optimizer); + EnableOnlyHoistCWiseUnaryChains(&optimizer); + OptimizeTwiceAndPrune(&optimizer, &item, &output); - OptimizeAndPrune(&optimizer, &item, &output); int found = 0; for (const NodeDef& node : output.node()) { if (node.name() == "concat") { @@ -2191,8 +2211,9 @@ TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryFromConcat) { found++; } if (node.name() == "exp_a") { - EXPECT_EQ(1, node.input_size()); + EXPECT_EQ(2, node.input_size()); EXPECT_EQ("concat", node.input(0)); + EXPECT_EQ("^ctrl1", node.input(1)); found++; } if (node.name() == "id") { @@ -2213,13 +2234,15 @@ TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryFromConcat) { found++; } if (node.name() == "exp_a2") { - EXPECT_EQ(1, node.input_size()); + EXPECT_EQ(2, node.input_size()); EXPECT_EQ("concat2", node.input(0)); + EXPECT_EQ("^ctrl1", node.input(1)); found++; } if (node.name() == "cos_exp_a2") { - EXPECT_EQ(1, node.input_size()); + EXPECT_EQ(2, node.input_size()); EXPECT_EQ("exp_a2", node.input(0)); + EXPECT_EQ("^ctrl1", node.input(1)); found++; } if (node.name() == "id2") { @@ -2229,6 +2252,142 @@ TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryFromConcat) { } } EXPECT_EQ(7, found); + + auto tensors = EvaluateNodes(output, item.fetch); + EXPECT_EQ(tensors.size(), tensors_expected.size()); + EXPECT_EQ(tensors.size(), item.fetch.size()); + for (int i = 0; i < item.fetch.size(); ++i) { + test::ExpectTensorNear(tensors_expected[i], tensors[i], 1e-6); + } +} + +TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryIntoSplit) { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output x = ops::Const(s.WithOpName("x"), 3.1415f, {32}); + Output axis = ops::Const(s.WithOpName("axis"), 0, {}); + Output ctrl1 = ops::Const(s.WithOpName("ctrl1"), 1, {}); + Output ctrl2 = ops::Const(s.WithOpName("ctrl2"), 2, {}); + Output ctrl3 = ops::Const(s.WithOpName("ctrl3"), 3, {}); + // Test case with chains of length 1. + // Rewrites + // [Sin(y) for y in Split(x)] + // into + // [y for y in Split(Sin(x))]. + ops::Split split1(s.WithOpName("split1"), axis, x, 2); + Output sin_a = + ops::Sin(s.WithOpName("sin_a").WithControlDependencies(ctrl1), split1[0]); + Output id_a = ops::Identity(s.WithOpName("id_a"), sin_a); + Output sin_b = ops::Sin(s.WithOpName("sin_b"), split1[1]); + Output exp_b = ops::Exp(s.WithOpName("exp_b"), sin_b); + Output id_b = ops::Identity(s.WithOpName("id_b"), exp_b); + + // Test case with SplitV and chains of length 2. + // Rewrites + // [Cos(Exp(y)) for y in Split(x)] + // into + // [y for y in Split(Cos(Exp(x)))]. + Output size_splits2 = ops::Const(s.WithOpName("size_splits2"), {20, 12}, {2}); + ops::SplitV split2(s.WithOpName("split2"), x, size_splits2, axis, 2); + Output exp_a2 = ops::Exp( + s.WithOpName("exp_a2").WithControlDependencies(ctrl1), split2[0]); + Output exp_b2 = ops::Exp(s.WithOpName("exp_b2"), split2[1]); + Output cos_exp_a2 = ops::Cos( + s.WithOpName("cos_exp_a2").WithControlDependencies(ctrl2), exp_a2); + Output cos_exp_b2 = ops::Cos( + s.WithOpName("cos_exp_b2").WithControlDependencies(ctrl3), exp_b2); + Output id_a2 = ops::Identity(s.WithOpName("id_a2"), cos_exp_a2); + Output id_b2 = ops::Identity(s.WithOpName("id_b2"), cos_exp_b2); + + GrapplerItem item; + item.fetch = {"id_a", "id_b", "id_a2", "id_b2"}; + TF_CHECK_OK(s.ToGraphDef(&item.graph)); + + auto tensors_expected = EvaluateNodes(item.graph, item.fetch); + + GraphDef output; + ArithmeticOptimizer optimizer; + EnableOnlyHoistCWiseUnaryChains(&optimizer); + OptimizeTwiceAndPrune(&optimizer, &item, &output); + + int found = 0; + for (const NodeDef& node : output.node()) { + // The following 6 nodes should be pruned. + EXPECT_NE(node.name(), "sin_a"); + EXPECT_NE(node.name(), "sin_b"); + EXPECT_NE(node.name(), "exp_a2"); + EXPECT_NE(node.name(), "exp_b2"); + EXPECT_NE(node.name(), "cos_exp_a2"); + EXPECT_NE(node.name(), "cos_exp_b2"); + + if (node.name() == "split1") { + EXPECT_EQ(3, node.input_size()); + EXPECT_EQ("axis", node.input(0)); + EXPECT_EQ("ArithmeticOptimizer/_sin_a_split1", node.input(1)); + EXPECT_EQ("^ctrl1", node.input(2)); + found++; + } + if (node.name() == "ArithmeticOptimizer/_sin_a_split1") { + EXPECT_EQ("Sin", node.op()); + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("x", node.input(0)); + found++; + } + if (node.name() == "id_a") { + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("split1", node.input(0)); + found++; + } + if (node.name() == "exp_b") { + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("split1:1", node.input(0)); + found++; + } + if (node.name() == "id_b") { + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("exp_b", node.input(0)); + found++; + } + if (node.name() == "ArithmeticOptimizer/_exp_a2_split2") { + EXPECT_EQ("Exp", node.op()); + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("x", node.input(0)); + found++; + } + if (node.name() == "ArithmeticOptimizer/_cos_exp_a2_split2") { + EXPECT_EQ("Cos", node.op()); + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("ArithmeticOptimizer/_exp_a2_split2", node.input(0)); + found++; + } + if (node.name() == "split2") { + EXPECT_EQ(6, node.input_size()); + EXPECT_EQ("ArithmeticOptimizer/_cos_exp_a2_split2", node.input(0)); + EXPECT_EQ("size_splits2", node.input(1)); + EXPECT_EQ("axis", node.input(2)); + EXPECT_EQ("^ctrl1", node.input(3)); + EXPECT_EQ("^ctrl2", node.input(4)); + EXPECT_EQ("^ctrl3", node.input(5)); + found++; + } + if (node.name() == "id_a2") { + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("split2", node.input(0)); + found++; + } + if (node.name() == "id_b2") { + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("split2:1", node.input(0)); + found++; + } + } + EXPECT_EQ(10, found); + + auto tensors = EvaluateNodes(output, item.fetch); + EXPECT_EQ(tensors.size(), tensors_expected.size()); + EXPECT_EQ(tensors.size(), item.fetch.size()); + for (int i = 0; i < item.fetch.size(); ++i) { + test::ExpectTensorNear(tensors_expected[i], tensors[i], 1e-6); + } } } // namespace grappler From 9c961e80a6be0136fc43821f1ad01ea00f83acb3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 15:19:00 -0700 Subject: [PATCH 0197/1691] Enhancements to GRAPHVIZ_DOT output: -edge weights added to encourage straighter main data-flow -line thickness proportional to log(data_size) -set global parameter "nslimit" to prevent excessive layout time for difficult graphs PiperOrigin-RevId: 194854051 --- tensorflow/contrib/lite/toco/dump_graphviz.cc | 45 +++++++++++++++++-- 1 file changed, 41 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/lite/toco/dump_graphviz.cc b/tensorflow/contrib/lite/toco/dump_graphviz.cc index 5bb0e3ba4d289c..166ead918471ee 100644 --- a/tensorflow/contrib/lite/toco/dump_graphviz.cc +++ b/tensorflow/contrib/lite/toco/dump_graphviz.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/toco/dump_graphviz.h" +#include #include #include #include @@ -63,6 +64,7 @@ struct NodeProperties { // color will be chosen for the 'fontcolor' for the inside text // label, see Color::TextColorString. Color color; + float log2_buffer_size; }; // All colors in this file are from: @@ -162,9 +164,12 @@ NodeProperties GetPropertiesForArray(const Model& model, } node_properties.label += "]"; + int buffer_size = RequiredBufferSizeForShape(array.shape()); + node_properties.log2_buffer_size = + std::log2(static_cast(buffer_size)); + if (array.buffer) { const auto& array = model.GetArray(array_name); - int buffer_size = RequiredBufferSizeForShape(array.shape()); if (buffer_size <= 4) { AppendF(&node_properties.label, " = "); if (array.shape().dimensions_count() > 0) { @@ -194,6 +199,8 @@ NodeProperties GetPropertiesForArray(const Model& model, AppendF(&node_properties.label, "}"); } } + } else { + node_properties.log2_buffer_size = 0.0f; } if (array.minmax) { @@ -325,12 +332,18 @@ std::vector OperatorsToDump(const Model& model) { void DumpGraphviz(const Model& model, string* output_file_contents) { AppendF(output_file_contents, "digraph Computegraph {\n"); + // 'nslimit' is a graphviz (dot) paramater that limits the iterations during + // the layout phase. Omitting it allows infinite iterations, causing some + // complex graphs to never finish. A value of 125 produces good graphs + // while allowing complex graphs to finish. + AppendF(output_file_contents, "\t nslimit=125;\n"); constexpr char kNodeFormat[] = "\t \"%s\" [label=\"%s\", shape=%s, style=filled, fillcolor=\"#%s\", " "fontcolor = \"#%sDD\"];\n"; - constexpr char kEdgeFormat[] = "\t \"%s\" -> \"%s\";\n"; + constexpr char kEdgeFormat[] = + "\t \"%s\" -> \"%s\" [penwidth=%f, weight=%f];\n"; constexpr char kRNNBackEdgeFormat[] = "\t \"%s\" -> \"%s\" [color=\"#0F9D58\"];\n"; @@ -358,7 +371,22 @@ void DumpGraphviz(const Model& model, string* output_file_contents) { array_properties.color.FillColorString().c_str(), array_properties.color.TextColorString().c_str()); } - AppendF(output_file_contents, kEdgeFormat, input, operator_id); + + // Draw lines that transport more data thicker (Otherwise, where would the + // data fit? right?). + float line_width = + std::max(0.5f, array_properties.log2_buffer_size / 3.0f); + // Keep edges that transport more data shorter than those with less. + float weight = std::max(1.0f, array_properties.log2_buffer_size); + if (!IsInputArray(model, input) && + GetOpWithOutput(model, input) == nullptr) { + // Give the main line of data flow a straighter path by penalizing edges + // to standalone buffers. Weights are generally very large buffers that + // otherwise skew the layout without this. + weight = 1.0f; + } + AppendF(output_file_contents, kEdgeFormat, input, operator_id, line_width, + weight); already_added_arrays.insert(input); } // Add nodes and edges for all outputs of the operator. @@ -374,7 +402,16 @@ void DumpGraphviz(const Model& model, string* output_file_contents) { array_properties.color.FillColorString().c_str(), array_properties.color.TextColorString().c_str()); } - AppendF(output_file_contents, kEdgeFormat, operator_id, output); + + // See comments above regarding weight and line_width calculations. + float line_width = + std::max(0.5f, array_properties.log2_buffer_size / 3.0f); + float weight = std::max(1.0f, array_properties.log2_buffer_size); + if (!IsArrayConsumed(model, output)) { + weight = 1.0f; + } + AppendF(output_file_contents, kEdgeFormat, operator_id, output, + line_width, weight); already_added_arrays.insert(output); } } From 286d61b246280b3a8dea39ac2f7d48b7cdbd48dc Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 15:41:28 -0700 Subject: [PATCH 0198/1691] Do not allocate memory for literal as it will be allocated later. PiperOrigin-RevId: 194857422 --- tensorflow/compiler/xla/literal_util.h | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h index 8aa19222dc4b91..956ff7d21cc954 100644 --- a/tensorflow/compiler/xla/literal_util.h +++ b/tensorflow/compiler/xla/literal_util.h @@ -74,6 +74,10 @@ class Literal { Literal(const Literal& other) = delete; Literal& operator=(const Literal& other) = delete; Literal(Literal&& other); + // 'allocate_arrays' indicates whether to allocate memory for the arrays in + // the shape. If false, buffer pointers inside of the Literal::Pieces are set + // to nullptr. + Literal(const Shape& shape, bool allocate_arrays); Literal& operator=(Literal&& other); // Literals are equal if they have compatible shapes and the same data @@ -659,11 +663,6 @@ class Literal { int64 sparse_element_count() const; protected: - // 'allocate_arrays' indicates whether to allocate memory for the arrays in - // the shape. If false, buffer pointers inside of the Literal::Pieces are set - // to nullptr. - Literal(const Shape& shape, bool allocate_arrays); - // Internal template helper for the Literal::CopySliceFrom(), matching its // arguments one by one. template From 30fcdecc05e6b25ab8d451997904e40b2a76acd4 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Mon, 30 Apr 2018 15:56:26 -0700 Subject: [PATCH 0199/1691] Improve error message for pip_smoke_test. PiperOrigin-RevId: 194859591 --- tensorflow/tools/pip_package/pip_smoke_test.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/tensorflow/tools/pip_package/pip_smoke_test.py b/tensorflow/tools/pip_package/pip_smoke_test.py index 1b692104f1cd35..b23dde20199a36 100644 --- a/tensorflow/tools/pip_package/pip_smoke_test.py +++ b/tensorflow/tools/pip_package/pip_smoke_test.py @@ -147,10 +147,11 @@ def main(): affected_tests_list = affected_tests.split("\n")[:-2] print("\n".join(affected_tests_list)) - raise RuntimeError("""One or more dependencies are not in the pip package. -Please either blacklist the dependencies in -//tensorflow/tools/pip_package/pip_smoke_test.py -or add them to //tensorflow/tools/pip_package/BUILD.""") + raise RuntimeError(""" + One or more added test dependencies are not in the pip package. +If these test dependencies need to be in TensorFlow pip package, please add them to //tensorflow/tools/pip_package/BUILD. +Else either blacklist the dependencies in //tensorflow/tools/pip_package/pip_smoke_test.py +or add no_pip tag to the test.""") else: print("TEST PASSED") From 18343616da47a9c3eab79b5028ac3d8bf786f2ff Mon Sep 17 00:00:00 2001 From: Bixia Zheng Date: Mon, 30 Apr 2018 16:11:38 -0700 Subject: [PATCH 0200/1691] [XLA] Change the TF2XLA bridge to perform F16 reduction using F32 data type. Add test cases to test that reduce sum for bfloat16 and float16 doesn't lose too much precision. PiperOrigin-RevId: 194862078 --- tensorflow/compiler/tests/reduce_ops_test.py | 64 ++++++++++++++++++++ tensorflow/compiler/tf2xla/xla_helpers.cc | 2 +- 2 files changed, 65 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/tests/reduce_ops_test.py b/tensorflow/compiler/tests/reduce_ops_test.py index 2c084b04fa2f67..7420724bdbeab6 100644 --- a/tensorflow/compiler/tests/reduce_ops_test.py +++ b/tensorflow/compiler/tests/reduce_ops_test.py @@ -19,6 +19,7 @@ from __future__ import print_function import functools +import itertools import numpy as np from tensorflow.compiler.tests.xla_test import XLATestCase @@ -155,5 +156,68 @@ def testReduceAny(self): self._testReduction(math_ops.reduce_any, np.any, np.bool, self.BOOL_DATA) +class ReduceOpPrecisionTest(XLATestCase): + + def _testReduceSum(self, + expected_result, + dtype, + test_inputs, + rtol=1e-3, + atol=1e-4): + """Tests reduce sum on a list of input arrays. + + For each array in test_inputs, check that performing reduce sum on the array + produces a value that is close to the expected result. + + Args: + expected_result: the expected result. + dtype: the data type of the reduce sum operation. + test_inputs: a list of input arrays for the reduce sum operation. + rtol: the relative error. + atol: the absolute error. + """ + + for test_input in test_inputs: + with self.test_session() as sess: + with self.test_scope(): + a = array_ops.placeholder(dtype) + index = array_ops.placeholder(dtypes.int32) + out = math_ops.reduce_sum(a, index) + result = sess.run(out, { + a: np.array(test_input, dtype=dtype), + index: [0] + }) + # Compare the results using float32 type. + self.assertAllClose( + np.float32(result), + np.float32(expected_result), + rtol=rtol, + atol=atol) + + def testReduceSumF16(self): + """Tests the reduce sum of float16 doesn't lose too much precision.""" + + if np.float16 not in self.all_types: + return + + f16_max = np.finfo(np.float16).max + self._testReduceSum( + f16_max, np.float16, + itertools.permutations([f16_max, f16_max, f16_max * (-1.0)], 3)) + + def testReduceSumBF16(self): + """Tests the reduce sum of bfloat16 doesn't lose too much precision.""" + + if dtypes.bfloat16.as_numpy_dtype not in self.all_types: + return + + bf16_max = np.float32(dtypes.bfloat16.max) + f32_max = dtypes.float32.max + value = min(bf16_max, f32_max - bf16_max) + self._testReduceSum( + dtypes.bfloat16.as_numpy_dtype(value), dtypes.bfloat16.as_numpy_dtype, + itertools.permutations([bf16_max, value, bf16_max * (-1.0)], 3)) + + if __name__ == '__main__': googletest.main() diff --git a/tensorflow/compiler/tf2xla/xla_helpers.cc b/tensorflow/compiler/tf2xla/xla_helpers.cc index 62a5114837e07f..a3deb02a1f9a1b 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.cc +++ b/tensorflow/compiler/tf2xla/xla_helpers.cc @@ -278,7 +278,7 @@ Status XlaHelpers::OneHot(xla::ComputationBuilder* builder, int64 depth, } DataType XlaHelpers::SumAccumulationType(const DataType& dtype) { - if (dtype == DT_BFLOAT16) { + if (dtype == DT_BFLOAT16 || dtype == DT_HALF) { return DT_FLOAT; } return dtype; From 7141ed55dd0f36f698143812b44aeffc6129257b Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Mon, 30 Apr 2018 16:12:33 -0700 Subject: [PATCH 0201/1691] Add MultiNodeDataset and MultiNodeIterator which are intended to work for multi-node distribution strategy. PiperOrigin-RevId: 194862215 --- tensorflow/contrib/distribute/python/BUILD | 22 +++ .../python/multi_worker_test_base.py | 90 +++++++++++++ .../contrib/distribute/python/values.py | 95 +++++++++++++ .../contrib/distribute/python/values_test.py | 127 ++++++++++++++++++ 4 files changed, 334 insertions(+) create mode 100644 tensorflow/contrib/distribute/python/multi_worker_test_base.py diff --git a/tensorflow/contrib/distribute/python/BUILD b/tensorflow/contrib/distribute/python/BUILD index c2834d822664b9..aa1a956a2da892 100644 --- a/tensorflow/contrib/distribute/python/BUILD +++ b/tensorflow/contrib/distribute/python/BUILD @@ -42,6 +42,7 @@ cuda_py_test( srcs = ["values_test.py"], additional_deps = [ ":mirrored_strategy", + ":multi_worker_test_base", ":values", "//tensorflow/core:protos_all_py", "//tensorflow/python/data/ops:dataset_ops", @@ -57,6 +58,9 @@ cuda_py_test( "//tensorflow/python/eager:test", "//tensorflow/python/estimator:model_fn", ], + tags = [ + "no_pip", + ], ) py_library( @@ -216,6 +220,24 @@ cuda_py_test( ], ) +py_library( + name = "multi_worker_test_base", + testonly = 1, + srcs = ["multi_worker_test_base.py"], + srcs_version = "PY2AND3", + tags = [ + "no_pip", + ], + deps = [ + "//tensorflow/core:protos_all_py", + "//tensorflow/python:distributed_framework_test_lib", + "//tensorflow/python:platform", + "//tensorflow/python:session", + "//tensorflow/python:training", + "//tensorflow/python/eager:test", + ], +) + py_library( name = "step_fn", srcs = ["step_fn.py"], diff --git a/tensorflow/contrib/distribute/python/multi_worker_test_base.py b/tensorflow/contrib/distribute/python/multi_worker_test_base.py new file mode 100644 index 00000000000000..f659be5f42594b --- /dev/null +++ b/tensorflow/contrib/distribute/python/multi_worker_test_base.py @@ -0,0 +1,90 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Base testing class for strategies that require multiple nodes.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import contextlib +import copy + +from tensorflow.core.protobuf import config_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 +from tensorflow.python.client import session +from tensorflow.python.eager import test +from tensorflow.python.framework import test_util + + +class MultiWorkerTestBase(test.TestCase): + """Base class for testing multi node strategy and dataset.""" + + @classmethod + def setUpClass(cls): + """Create a local cluster with 2 workers.""" + num_workers = 2 + # Leave some memory for cuda runtime. + gpu_mem_frac = 0.7 / num_workers + default_config = config_pb2.ConfigProto() + default_config.gpu_options.per_process_gpu_memory_fraction = gpu_mem_frac + + # The local cluster takes some portion of the local GPUs and there is no way + # for the cluster to terminate unless using multiple processes. Therefore, + # we have to only create only one cluster throughout a test process. + workers, _ = test_util.create_local_cluster( + num_workers, num_ps=0, worker_config=default_config) + cls._master_target = workers[0].target + + @contextlib.contextmanager + def test_session(self, graph=None, config=None): + """Create a test session with master target set to the testing cluster. + + This overrides the base class' method, removes arguments that are not needed + by the multi-node case and creates a test session that connects to the local + testing cluster. + + Args: + graph: Optional graph to use during the returned session. + config: An optional config_pb2.ConfigProto to use to configure the + session. + + Yields: + A Session object that should be used as a context manager to surround + the graph building and execution code in a test case. + """ + if self.id().endswith('.test_session'): + self.skipTest('Not a test.') + + if config is None: + config = config_pb2.ConfigProto(allow_soft_placement=True) + else: + config = copy.deepcopy(config) + # Don't perform optimizations for tests so we don't inadvertently run + # gpu ops on cpu + config.graph_options.optimizer_options.opt_level = -1 + config.graph_options.rewrite_options.constant_folding = ( + rewriter_config_pb2.RewriterConfig.OFF) + + if graph is None: + if self._cached_session is None: # pylint: disable=access-member-before-definition + self._cached_session = session.Session( + graph=None, config=config, target=self._master_target) + sess = self._cached_session + with sess.graph.as_default(), sess.as_default(): + yield sess + else: + with session.Session( + graph=graph, config=config, target=self._master_target) as sess: + yield sess diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py index 466678ef2e09d4..18afdaa7b0688b 100644 --- a/tensorflow/contrib/distribute/python/values.py +++ b/tensorflow/contrib/distribute/python/values.py @@ -29,6 +29,7 @@ from tensorflow.contrib.data.python.ops import batching from tensorflow.contrib.distribute.python import prefetching_ops_v2 from tensorflow.python.eager import context +from tensorflow.python.framework import device as tf_device from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops @@ -576,6 +577,100 @@ def make_initializable_iterator(self): dataset_iterator, self._devices, self._prefetch_on_device) +class MultiWorkerDataIterator(object): + """An iterator (like `tf.data.Iterator`) into a `MultiWorkerDataset`.""" + + def __init__(self, iterators, worker_device_map): + """Initialize the MultiWorkerDataIterator object. + + Args: + iterators: a dict mapping from each worker to an iterator for + that worker. + worker_device_map: a dict mapping from each worker's devices to a list of + devices that belong to this worker. + + Raises: + ValueError: if iterators and worker_device_map are not compatible. + """ + self._iterators = iterators + self._worker_device_map = worker_device_map + if set(self._iterators) != set(self._worker_device_map): + raise ValueError("iterators and worker_device_map are not compatible.") + + @property + def initializer(self): + return control_flow_ops.group( + [iterator.initializer for iterator in self._iterators.values()]) + + def get_next(self, name=None): + """Scatter the input across hosts and devices.""" + index = {} + for worker, iterator in six.iteritems(self._iterators): + if name is not None: + d = tf_device.DeviceSpec.from_string(worker) + new_name = "%s_%s_%d" % (name, d.job, d.task) + else: + new_name = None + with ops.device(worker): + data_per_worker = iterator.get_next(name=new_name) + + worker_devices = self._worker_device_map[worker] + # Ungroup these per-device value so as to get a flat map from devices to + # values. + for d in worker_devices: + v = select_device(d, data_per_worker) + if d in index: + raise ValueError("Duplicated devices in worker_device_map: %r" % v) + index[d] = v + + return regroup(index) + + +class MultiWorkerDataset(object): + """Like a `tf.data.Dataset` that distributes data to different workers. + + Each worker gets one shard of the input dataset. It is currently not working + in + eager mode. + """ + + def __init__(self, dataset_fn, worker_device_map, prefetch_on_device=None): + """Initialize the MultiWorkerDataset object. + + Args: + dataset_fn: a function that returns a `tf.data.Dataset`. + worker_device_map: a dict mapping from each worker to a list of devices + that belong to this worker. + prefetch_on_device: whether to prefetch to devices. + """ + self._worker_device_map = worker_device_map + self._datasets = {} + # TODO(yuefengz, priyag): support different set of jobs for input + # processing. + for i, (worker, worker_devices) in enumerate( + six.iteritems(worker_device_map)): + with ops.device(worker): + worker_input = dataset_fn() + # TODO(yuefengz, priyag): support efficient sharding. + worker_input = worker_input.shard(len(worker_device_map), i) + self._datasets[worker] = PerDeviceDataset( + worker_input, worker_devices, prefetch_on_device=prefetch_on_device) + + def make_one_shot_iterator(self): + iterators = {} + for worker, dataset in six.iteritems(self._datasets): + with ops.device(worker): + iterators[worker] = dataset.make_one_shot_iterator() + return MultiWorkerDataIterator(iterators, self._worker_device_map) + + def make_initializable_iterator(self): + iterators = {} + for worker, dataset in six.iteritems(self._datasets): + with ops.device(worker): + iterators[worker] = dataset.make_initializable_iterator() + return MultiWorkerDataIterator(iterators, self._worker_device_map) + + class PerIteration(object): """Holds input for multiple iterations at once.""" diff --git a/tensorflow/contrib/distribute/python/values_test.py b/tensorflow/contrib/distribute/python/values_test.py index 1d4e801cd84039..9aeef9fa3e86f2 100644 --- a/tensorflow/contrib/distribute/python/values_test.py +++ b/tensorflow/contrib/distribute/python/values_test.py @@ -18,9 +18,11 @@ from __future__ import division from __future__ import print_function +import collections import os from tensorflow.contrib.distribute.python import mirrored_strategy +from tensorflow.contrib.distribute.python import multi_worker_test_base from tensorflow.contrib.distribute.python import values from tensorflow.core.protobuf import config_pb2 from tensorflow.python.data.ops import dataset_ops @@ -37,6 +39,7 @@ from tensorflow.python.ops import variables as variables_lib from tensorflow.python.training import device_util from tensorflow.python.training import saver as saver_lib +from tensorflow.python.util import nest @test_util.with_c_api @@ -437,6 +440,130 @@ def testInitializableIterator(self): self.evaluate(next_element) +class MultiWorkerDatasetTest(multi_worker_test_base.MultiWorkerTestBase): + + def _test_iterator(self, iterator, devices, expected_values): + next_element = iterator.get_next() + for device in devices: + v = values.select_device(device, next_element) + # The `v` here can be a tuple. + for element in nest.flatten(v): + self.assertTrue(element.device in device) + + for expected_value in expected_values: + actual = self.evaluate( + [values.select_device(d, next_element) for d in devices]) + self.assertEqual(expected_value, actual) + + with self.assertRaises(errors.OutOfRangeError): + self.evaluate([values.select_device(d, next_element) for d in devices]) + + def _test_dataset(self, dataset_fn, worker_device_map, devices, + expected_values): + multi_worker_dataset = values.MultiWorkerDataset( + dataset_fn, worker_device_map, prefetch_on_device=False) + multi_worker_iterator = multi_worker_dataset.make_one_shot_iterator() + self._test_iterator(multi_worker_iterator, devices, expected_values) + + def _cpu_devices(self): + worker_device_map = collections.OrderedDict( + [("/job:worker/replica:0/task:0", + ["/job:worker/replica:0/task:0/device:CPU:0"]), + ("/job:worker/replica:0/task:1", + ["/job:worker/replica:0/task:1/device:CPU:0"])]) + devices = [ + "/job:worker/replica:0/task:0/device:CPU:0", + "/job:worker/replica:0/task:1/device:CPU:0" + ] + return worker_device_map, devices + + def _cpu_and_one_gpu_devices(self): + # The worker_device_map doesn't have to be a OrderDict object, this is just + # to simplify the testing so that we can pass expected values as a list + # instead of a dict. + worker_device_map = collections.OrderedDict( + [("/job:worker/replica:0/task:0", [ + "/job:worker/replica:0/task:0/device:GPU:0", + "/job:worker/replica:0/task:0/device:CPU:0" + ]), ("/job:worker/replica:0/task:1", [ + "/job:worker/replica:0/task:1/device:GPU:0", + "/job:worker/replica:0/task:1/device:CPU:0" + ])]) + devices = [ + "/job:worker/replica:0/task:0/device:GPU:0", + "/job:worker/replica:0/task:0/device:CPU:0", + "/job:worker/replica:0/task:1/device:GPU:0", + "/job:worker/replica:0/task:1/device:CPU:0" + ] + return worker_device_map, devices + + def testDataDistributionOneDevicePerWorker(self): + worker_device_map, devices = self._cpu_devices() + with context.graph_mode(): + dataset_fn = lambda: dataset_ops.Dataset.range(8) + self._test_dataset(dataset_fn, worker_device_map, devices, + [[0, 1], [2, 3], [4, 5], [6, 7]]) + + def testDataDistributionTwoDevicePerWorker(self): + if context.num_gpus() < 1: + self.skipTest("A GPU is not available for this test.") + worker_device_map, devices = self._cpu_and_one_gpu_devices() + with context.graph_mode(): + dataset_fn = lambda: dataset_ops.Dataset.range(8) + self._test_dataset(dataset_fn, worker_device_map, devices, + [[0, 2, 1, 3], [4, 6, 5, 7]]) + + def testTupleDataset(self): + worker_device_map, devices = self._cpu_devices() + + with context.graph_mode(): + + def dataset_fn(): + dataset1 = dataset_ops.Dataset.range(8) + dataset2 = dataset_ops.Dataset.range(8).map(lambda x: x**2) + return dataset_ops.Dataset.zip((dataset1, dataset2)) + + expected_values = [ + [(i, i**2), (i + 1, (i + 1)**2)] for i in range(0, 8, 2) + ] + self._test_dataset(dataset_fn, worker_device_map, devices, + expected_values) + + def testInitializableIterator(self): + worker_device_map, devices = self._cpu_devices() + with context.graph_mode(): + dataset_fn = lambda: dataset_ops.Dataset.range(8) + multi_worker_dataset = values.MultiWorkerDataset( + dataset_fn, worker_device_map, prefetch_on_device=False) + multi_worker_iterator = multi_worker_dataset.make_initializable_iterator() + + self.evaluate(multi_worker_iterator.initializer) + self._test_iterator(multi_worker_iterator, devices, + [[0, 1], [2, 3], [4, 5], [6, 7]]) + + # After re-initializing the iterator, should be able to iterate again. + self.evaluate(multi_worker_iterator.initializer) + self._test_iterator(multi_worker_iterator, devices, + [[0, 1], [2, 3], [4, 5], [6, 7]]) + + def testValueErrorForIterator(self): + # Incompatiable arguments. + with self.assertRaises(ValueError): + values.MultiWorkerDataIterator({"w1": None}, {"w1": "d1", "w2": "d2"}) + + # Test duplicated devices under same worker. + worker_device_map, _ = self._cpu_devices() + worker_device_map["/job:worker/replica:0/task:0"].append( + "/job:worker/replica:0/task:0/device:CPU:0") + with context.graph_mode(): + dataset_fn = lambda: dataset_ops.Dataset.range(8) + multi_worker_dataset = values.MultiWorkerDataset( + dataset_fn, worker_device_map, prefetch_on_device=False) + multi_worker_iterator = multi_worker_dataset.make_initializable_iterator() + with self.assertRaises(ValueError): + multi_worker_iterator.get_next() + + @test_util.with_c_api class MirroredVariableTest(test.TestCase): From 1ff23a314f355a9ebaaf207dbeae56ebc1634d63 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 16:43:14 -0700 Subject: [PATCH 0202/1691] Small fix to prevent a crash if the delegate has not implemented FreeBufferHandle. PiperOrigin-RevId: 194866595 --- tensorflow/contrib/lite/interpreter.cc | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc index 9d8ea55fd1edc0..ebb0aedc2001a8 100644 --- a/tensorflow/contrib/lite/interpreter.cc +++ b/tensorflow/contrib/lite/interpreter.cc @@ -125,7 +125,8 @@ Interpreter::~Interpreter() { for (int i = 0; i < context_.tensors_size; i++) { TfLiteTensor* tensor = &context_.tensors[i]; - if (tensor->buffer_handle != kTfLiteNullBufferHandle) { + if (tensor->buffer_handle != kTfLiteNullBufferHandle && + tensor->delegate->FreeBufferHandle != nullptr) { tensor->delegate->FreeBufferHandle(tensor->delegate, &tensor->buffer_handle); } From 64bb1de61377f12859a719448b65b452b03047a7 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Mon, 30 Apr 2018 17:11:40 -0700 Subject: [PATCH 0203/1691] Faster reduce_logsoftmax (specially in eager) and bugfixes in broadcast_to PiperOrigin-RevId: 194870645 --- tensorflow/core/kernels/broadcast_to_op.h | 34 +++++- tensorflow/core/ops/array_ops.cc | 2 +- tensorflow/python/kernel_tests/BUILD | 16 +++ .../kernel_tests/reduce_benchmark_test.py | 107 ++++++++++++++++++ tensorflow/python/ops/math_ops.py | 11 +- 5 files changed, 161 insertions(+), 9 deletions(-) create mode 100644 tensorflow/python/kernel_tests/reduce_benchmark_test.py diff --git a/tensorflow/core/kernels/broadcast_to_op.h b/tensorflow/core/kernels/broadcast_to_op.h index 608e9b6ac9c161..73fdd5d28ea8d2 100644 --- a/tensorflow/core/kernels/broadcast_to_op.h +++ b/tensorflow/core/kernels/broadcast_to_op.h @@ -34,14 +34,37 @@ struct BroadcastTo { const TensorShape &input_shape) { #define BROADCAST_SHAPE(broadcast, reshape, NDIMS, input_shape, output_shape) \ for (int i = 0; i < NDIMS; i++) { \ - OP_REQUIRES(ctx, (broadcast[i] % reshape[i] == 0), \ - errors::InvalidArgument("invalid shape to broadcast from ", \ - input_shape.DebugString(), " to ", \ - output_shape.DebugString())); \ - broadcast[i] = broadcast[i] / reshape[i]; \ + if (reshape[i] != broadcast[i]) { \ + OP_REQUIRES(ctx, \ + ((reshape[i] != 0) && (broadcast[i] % reshape[i] == 0)), \ + errors::InvalidArgument("invalid shape to broadcast from ", \ + input_shape.DebugString(), " to ", \ + output_shape.DebugString())); \ + broadcast[i] = broadcast[i] / reshape[i]; \ + } else { \ + broadcast[i] = 1; \ + } \ } + if (output_shape.num_elements() == 0) { + return; + } + if (output_shape == input_shape) { + output_tensor.flat().device(d) = input_tensor.flat(); + return; + } + switch (output_shape.dims()) { + case 0: { + if (input_shape.dims() > 0) { + ctx->CtxFailure(errors::InvalidArgument( + "invalid shape to broadcast from ", input_shape.DebugString(), + " to ", output_shape.DebugString())); + break; + } + output_tensor.scalar().device(d) = input_tensor.scalar(); + break; + } case 1: { auto reshape = AsEigenDSizesWithPrefix<1>(input_shape); auto broadcast = output_shape.AsEigenDSizes<1>(); @@ -125,7 +148,6 @@ struct BroadcastTo { auto broadcast = output_shape.AsEigenDSizes<4>(); BROADCAST_SHAPE(broadcast, reshape, 4, input_shape, output_shape); - auto output = output_tensor.tensor(); switch (input_shape.dims()) { case 0: { diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc index 88fc03826a8dcc..fce0b93cd71fe6 100644 --- a/tensorflow/core/ops/array_ops.cc +++ b/tensorflow/core/ops/array_ops.cc @@ -466,7 +466,7 @@ REGISTER_OP("BroadcastTo") // so no check needed. if (i >= in_offset) { DimensionHandle in_dim = c->Dim(in, i - in_offset); - if (c->ValueKnown(in_dim)) { + if (c->ValueKnown(in_dim) && c->Value(in_dim) != 0) { if (c->Value(dim) % c->Value(in_dim) != 0) { return errors::InvalidArgument( "Cannot broadcast a tensor with shape ", c->DebugString(in), diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index b4ff094cdfab48..c892b6ee9a0071 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -112,6 +112,22 @@ cuda_py_test( tags = ["no_windows"], ) +cuda_py_test( + name = "reduce_benchmark_test", + srcs = ["reduce_benchmark_test.py"], + additional_deps = [ + "//tensorflow/python/eager:backprop", + "//tensorflow/python:client_testlib", + "//tensorflow/python/eager:context", + "//tensorflow/python:framework", + "//tensorflow/python:array_ops", + "//tensorflow/python:gradients", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform", + "//tensorflow/python:platform_benchmark", + ], +) + tf_py_test( name = "bincount_op_test", size = "small", diff --git a/tensorflow/python/kernel_tests/reduce_benchmark_test.py b/tensorflow/python/kernel_tests/reduce_benchmark_test.py new file mode 100644 index 00000000000000..3a2fb81157d923 --- /dev/null +++ b/tensorflow/python/kernel_tests/reduce_benchmark_test.py @@ -0,0 +1,107 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Simple benchmarks for reductions and their gradients.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import time + +import numpy as np +from six.moves import range # pylint: disable=redefined-builtin + +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.client import session +from tensorflow.python.eager import backprop +from tensorflow.python.eager import context +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gradients_impl +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test + + +class ReduceBenchmarks(test.Benchmark): + """Benchmarks for reductions.""" + + def _run(self, func, num_iters): + # call func to maybe warm up the GPU + func() + start = time.time() + for _ in range(num_iters): + func() + end = time.time() + mean_us = (end - start) * 1e6 / num_iters + self.report_benchmark( + iters=num_iters, + wall_time=mean_us, + extras={"examples_per_sec": num_iters / (end - start)}) + + def benchmark_reduce_sum_grad_eager(self): + with context.eager_mode(): + tensor = array_ops.zeros([100, 1000]) + + def fn(): + backprop.gradients_function(math_ops.reduce_sum, [0])(tensor) + + self._run(fn, 10000) + + def benchmark_reduce_sum_grad_eager_cpu(self): + with context.eager_mode(), ops.device("/cpu:0"): + tensor = array_ops.zeros([100, 1000]) + + def fn(): + backprop.gradients_function(math_ops.reduce_sum, [0])(tensor) + + self._run(fn, 10000) + + def benchmark_reduce_sum_grad_graph(self): + config = config_pb2.ConfigProto( + graph_options=config_pb2.GraphOptions( + optimizer_options=config_pb2.OptimizerOptions( + opt_level=config_pb2.OptimizerOptions.L0))) + with ops.Graph().as_default(), session.Session(config=config) as sess: + + tensor = constant_op.constant(np.zeros([100, 1000], dtype=np.float32)) + reduction = math_ops.reduce_sum(tensor) + grad, = gradients_impl.gradients(reduction, tensor) + + def fn(): + sess.run(grad.op) + + self._run(fn, 10000) + + def benchmark_reduce_sum_grad_graph_cpu(self): + config = config_pb2.ConfigProto( + graph_options=config_pb2.GraphOptions( + optimizer_options=config_pb2.OptimizerOptions( + opt_level=config_pb2.OptimizerOptions.L0))) + with ops.Graph().as_default(), session.Session(config=config) as sess: + + with ops.device("/cpu:0"): + tensor = constant_op.constant(np.zeros([100, 1000], dtype=np.float32)) + reduction = math_ops.reduce_sum(tensor) + grad, = gradients_impl.gradients(reduction, tensor) + + def fn(): + sess.run(grad.op) + + self._run(fn, 10000) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index b93727313711e7..57660578aa08f1 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -1757,6 +1757,7 @@ def reduce_logsumexp(input_tensor, "keep_dims", keep_dims) if keepdims is None: keepdims = False + input_tensor = ops.convert_to_tensor(input_tensor) with ops.name_scope(name, "ReduceLogSumExp", [input_tensor]) as name: raw_max = reduce_max( input_tensor, @@ -1769,13 +1770,13 @@ def reduce_logsumexp(input_tensor, array_ops.zeros_like(raw_max))) result = gen_math_ops.log( reduce_sum( - gen_math_ops.exp(input_tensor - my_max), + gen_math_ops.exp(gen_math_ops.sub(input_tensor, my_max)), axis, keepdims=keepdims, reduction_indices=reduction_indices)) if not keepdims: my_max = array_ops.reshape(my_max, array_ops.shape(result)) - result += my_max + result = gen_math_ops.add(result, my_max) return _may_reduce_to_scalar(keepdims, axis, reduction_indices, result) @@ -2475,6 +2476,12 @@ def reduced_shape(input_shape, axes): """ # Example: # cast needed for SparseTensor reductions + if context.executing_eagerly(): + input_shape = input_shape.numpy() + axes = axes.numpy() + input_shape[axes] = 1 + return input_shape + input_shape = to_int32(input_shape) # [2, 3, 5, 7] axes = to_int32(axes) # [1, 2] From b7978d48f4588feb717157a9dbfd2e1df678628b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 17:14:50 -0700 Subject: [PATCH 0204/1691] Internal cleanup. PiperOrigin-RevId: 194871141 --- .../org/tensorflow/lite/NativeInterpreterWrapper.java | 8 ++++---- .../test/java/org/tensorflow/lite/InterpreterTest.java | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java index 2fc803715be5e5..a43251cad13a4e 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java @@ -173,8 +173,8 @@ int getInputIndex(String name) { } else { throw new IllegalArgumentException( String.format( - "Input error: %s is not a valid name for any input. " - + "The indexes of the inputs are %s", + "Input error: '%s' is not a valid name for any input. Names of inputs and their " + + "indexes are %s", name, inputsIndexes.toString())); } } @@ -195,8 +195,8 @@ int getOutputIndex(String name) { } else { throw new IllegalArgumentException( String.format( - "Input error: %s is not a valid name for any output. " - + "The indexes of the outputs are %s", + "Input error: '%s' is not a valid name for any output. Names of outputs and their " + + "indexes are %s", name, outputsIndexes.toString())); } } diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java index 61d6c35ec86bee..210d9437241f11 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java @@ -195,8 +195,8 @@ public void testGetInputIndex() { assertThat(e) .hasMessageThat() .contains( - "WrongInputName is not a valid name for any input. The indexes of the inputs" - + " are {input=0}"); + "'WrongInputName' is not a valid name for any input. Names of inputs and their " + + "indexes are {input=0}"); } int index = interpreter.getInputIndex("input"); assertThat(index).isEqualTo(0); @@ -212,8 +212,8 @@ public void testGetOutputIndex() { assertThat(e) .hasMessageThat() .contains( - "WrongOutputName is not a valid name for any output. The indexes of the outputs" - + " are {MobilenetV1/Predictions/Softmax=0}"); + "'WrongOutputName' is not a valid name for any output. Names of outputs and their" + + " indexes are {MobilenetV1/Predictions/Softmax=0}"); } int index = interpreter.getOutputIndex("MobilenetV1/Predictions/Softmax"); assertThat(index).isEqualTo(0); From c89a1d9605427d74079774af7da37933f9ca153c Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Mon, 30 Apr 2018 17:38:38 -0700 Subject: [PATCH 0205/1691] [tf.data] Adding an experimental `group_by_reducer` transformation which groups elements of an input pipeline by a key, applies a reduce function to elements of each group "on-the-fly", and outputs the results once all input elements have been processed. PiperOrigin-RevId: 194874087 --- .../python/kernel_tests/bucketing_test.py | 174 ++++++++ .../kernel_tests/scan_dataset_op_test.py | 2 +- .../contrib/data/python/ops/grouping.py | 301 +++++++++++++ .../api_def_GroupByReducerDataset.pbtxt | 69 +++ tensorflow/core/kernels/data/BUILD | 15 + .../core/kernels/data/captured_function.cc | 14 + .../core/kernels/data/captured_function.h | 11 + .../data/group_by_reducer_dataset_op.cc | 422 ++++++++++++++++++ .../data/group_by_window_dataset_op.cc | 2 +- tensorflow/core/ops/dataset_ops.cc | 20 + 10 files changed, 1028 insertions(+), 2 deletions(-) create mode 100644 tensorflow/core/api_def/base_api/api_def_GroupByReducerDataset.pbtxt create mode 100644 tensorflow/core/kernels/data/group_by_reducer_dataset_op.cc diff --git a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py index 55a56b83a8efba..bd3e034211c4aa 100644 --- a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py @@ -28,6 +28,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops +from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops @@ -35,6 +36,179 @@ from tensorflow.python.platform import test +class GroupByReducerTest(test.TestCase): + + def checkResults(self, dataset, shapes, values): + self.assertEqual(shapes, dataset.output_shapes) + get_next = dataset.make_one_shot_iterator().get_next() + with self.test_session() as sess: + for expected in values: + got = sess.run(get_next) + self.assertEqual(got, expected) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + def testSum(self): + reducer = grouping.Reducer( + init_func=lambda _: np.int64(0), + reduce_func=lambda x, y: x + y, + finalize_func=lambda x: x) + for i in range(1, 11): + dataset = dataset_ops.Dataset.range(2 * i).apply( + grouping.group_by_reducer(lambda x: x % 2, reducer)) + self.checkResults( + dataset, shapes=tensor_shape.scalar(), values=[(i - 1) * i, i * i]) + + def testAverage(self): + + def reduce_fn(x, y): + return (x[0] * x[1] + math_ops.cast(y, dtypes.float32)) / ( + x[1] + 1), x[1] + 1 + + reducer = grouping.Reducer( + init_func=lambda _: (0.0, 0.0), + reduce_func=reduce_fn, + finalize_func=lambda x: x[0]) + for i in range(1, 11): + dataset = dataset_ops.Dataset.range(2 * i).apply( + grouping.group_by_reducer( + lambda x: math_ops.cast(x, dtypes.int64) % 2, reducer)) + self.checkResults( + dataset, shapes=tensor_shape.scalar(), values=[i - 1, i]) + + def testConcat(self): + components = np.array(list("abcdefghijklmnopqrst")).view(np.chararray) + reducer = grouping.Reducer( + init_func=lambda x: "", + reduce_func=lambda x, y: x + y[0], + finalize_func=lambda x: x) + for i in range(1, 11): + dataset = dataset_ops.Dataset.zip( + (dataset_ops.Dataset.from_tensor_slices(components), + dataset_ops.Dataset.range(2 * i))).apply( + grouping.group_by_reducer(lambda x, y: y % 2, reducer)) + self.checkResults( + dataset, + shapes=tensor_shape.scalar(), + values=[b"acegikmoqs" [:i], b"bdfhjlnprt" [:i]]) + + def testSparseSum(self): + def _sparse(i): + return sparse_tensor.SparseTensorValue( + indices=np.array([[0, 0]]), + values=(i * np.array([1], dtype=np.int64)), + dense_shape=np.array([1, 1])) + + reducer = grouping.Reducer( + init_func=lambda _: _sparse(np.int64(0)), + reduce_func=lambda x, y: _sparse(x.values[0] + y.values[0]), + finalize_func=lambda x: x.values[0]) + for i in range(1, 11): + dataset = dataset_ops.Dataset.range(2 * i).map(_sparse).apply( + grouping.group_by_reducer(lambda x: x.values[0] % 2, reducer)) + self.checkResults( + dataset, shapes=tensor_shape.scalar(), values=[(i - 1) * i, i * i]) + + def testChangingStateShape(self): + + def reduce_fn(x, _): + # Statically known rank, but dynamic length. + larger_dim = array_ops.concat([x[0], x[0]], 0) + # Statically unknown rank. + larger_rank = array_ops.expand_dims(x[1], 0) + return larger_dim, larger_rank + + reducer = grouping.Reducer( + init_func=lambda x: ([0], 1), + reduce_func=reduce_fn, + finalize_func=lambda x: x) + + for i in range(1, 11): + dataset = dataset_ops.Dataset.from_tensors(np.int64(0)).repeat(i).apply( + grouping.group_by_reducer(lambda x: x, reducer)) + self.assertEqual([None], dataset.output_shapes[0].as_list()) + self.assertIs(None, dataset.output_shapes[1].ndims) + iterator = dataset.make_one_shot_iterator() + get_next = iterator.get_next() + with self.test_session() as sess: + x, y = sess.run(get_next) + self.assertAllEqual([0] * (2**i), x) + self.assertAllEqual(np.array(1, ndmin=i), y) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + def testTypeMismatch(self): + reducer = grouping.Reducer( + init_func=lambda x: constant_op.constant(1, dtype=dtypes.int32), + reduce_func=lambda x, y: constant_op.constant(1, dtype=dtypes.int64), + finalize_func=lambda x: x) + + dataset = dataset_ops.Dataset.range(10) + with self.assertRaisesRegexp( + TypeError, + "The element types for the new state must match the initial state."): + dataset.apply( + grouping.group_by_reducer(lambda _: np.int64(0), reducer)) + + # TODO(b/78665031): Remove once non-scalar keys are supported. + def testInvalidKeyShape(self): + reducer = grouping.Reducer( + init_func=lambda x: np.int64(0), + reduce_func=lambda x, y: x + y, + finalize_func=lambda x: x) + + dataset = dataset_ops.Dataset.range(10) + with self.assertRaisesRegexp( + ValueError, "`key_func` must return a single tf.int64 tensor."): + dataset.apply( + grouping.group_by_reducer(lambda _: np.int64((0, 0)), reducer)) + + # TODO(b/78665031): Remove once non-int64 keys are supported. + def testInvalidKeyType(self): + reducer = grouping.Reducer( + init_func=lambda x: np.int64(0), + reduce_func=lambda x, y: x + y, + finalize_func=lambda x: x) + + dataset = dataset_ops.Dataset.range(10) + with self.assertRaisesRegexp( + ValueError, "`key_func` must return a single tf.int64 tensor."): + dataset.apply( + grouping.group_by_reducer(lambda _: "wrong", reducer)) + + +class GroupByReducerSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset(self, components): + reducer = grouping.Reducer( + init_func=lambda _: np.int64(0), + reduce_func=lambda x, y: x + y, + finalize_func=lambda x: x) + + return dataset_ops.Dataset.from_tensor_slices(components).apply( + grouping.group_by_reducer(lambda x: x % 5, reducer)) + + def testCoreGroupByReducer(self): + components = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.int64) + self.verify_unused_iterator( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + self.verify_init_before_restore( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + self.verify_multiple_breaks( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + self.verify_reset_restored_iterator( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + self.verify_restore_in_empty_graph( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + diff_components = np.array([5, 4, 3, 2, 1, 0], dtype=np.int64) + self.verify_restore_in_modified_graph( + lambda: self._build_dataset(components), + lambda: self._build_dataset(diff_components), + 5, + verify_exhausted=True) + + class GroupByWindowTest(test.TestCase): def testSimple(self): diff --git a/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py index f544b1caa676b0..eb2ceff893543f 100644 --- a/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py @@ -168,7 +168,7 @@ def _scan_fn(unused_state, unused_input_value): scan_ops.scan(constant_op.constant(1, dtype=dtypes.int32), _scan_fn)) -class ScanDatasetSerialzationTest( +class ScanDatasetSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): def _build_dataset(self, num_elements): diff --git a/tensorflow/contrib/data/python/ops/grouping.py b/tensorflow/contrib/data/python/ops/grouping.py index 0531f9cbb9da6e..ea229b5b27b117 100644 --- a/tensorflow/contrib/data/python/ops/grouping.py +++ b/tensorflow/contrib/data/python/ops/grouping.py @@ -26,6 +26,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops +from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops @@ -33,6 +34,35 @@ from tensorflow.python.ops import math_ops +def group_by_reducer(key_func, reducer): + """A transformation that groups elements and performs a reduction. + + This transformation maps element of a dataset to a key using `key_func` and + groups the elements by key. The `reducer` is used to process each group; its + `init_func` is used to initialize state for each group when it is created, the + `reduce_func` is used to update the state every time an element is mapped to + the matching group, and the `finalize_func` is used to map the final state to + an output value. + + Args: + key_func: A function mapping a nested structure of tensors + (having shapes and types defined by `self.output_shapes` and + `self.output_types`) to a scalar `tf.int64` tensor. + reducer: An instance of `Reducer`, which captures the reduction logic using + the `init_func`, `reduce_func`, and `finalize_func` functions. + + Returns: + A `Dataset` transformation function, which can be passed to + @{tf.data.Dataset.apply}. + """ + + def _apply_fn(dataset): + """Function from `Dataset` to `Dataset` that applies the transformation.""" + return GroupByReducerDataset(dataset, key_func, reducer) + + return _apply_fn + + def group_by_window(key_func, reduce_func, window_size=None, @@ -227,6 +257,250 @@ def output_types(self): return self._output_types +class GroupByReducerDataset(dataset_ops.Dataset): + """A `Dataset` that groups its input and performs a reduction.""" + + def __init__(self, input_dataset, key_func, reducer): + """See `group_by_reducer()` for details.""" + super(GroupByReducerDataset, self).__init__() + + self._input_dataset = input_dataset + + self._make_key_func(key_func, input_dataset) + self._make_init_func(reducer.init_func) + self._make_reduce_func(reducer.reduce_func, input_dataset) + self._make_finalize_func(reducer.finalize_func) + + def _make_key_func(self, key_func, input_dataset): + """Make wrapping Defun for key_func.""" + + @function.Defun(*nest.flatten( + sparse.as_dense_types(input_dataset.output_types, + input_dataset.output_classes))) + def tf_key_func(*args): + """A wrapper for Defun that facilitates shape inference.""" + # Pass in shape information from the input_dataset. + dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, + input_dataset.output_classes) + for arg, shape in zip(args, nest.flatten(dense_shapes)): + arg.set_shape(shape) + + nested_args = nest.pack_sequence_as(input_dataset.output_types, args) + nested_args = sparse.deserialize_sparse_tensors( + nested_args, input_dataset.output_types, input_dataset.output_shapes, + input_dataset.output_classes) + # pylint: disable=protected-access + if dataset_ops._should_unpack_args(nested_args): + ret = key_func(*nested_args) + # pylint: enable=protected-access + else: + ret = key_func(nested_args) + ret = ops.convert_to_tensor(ret) + if ret.dtype != dtypes.int64 or ret.get_shape() != tensor_shape.scalar(): + raise ValueError( + "`key_func` must return a single tf.int64 tensor. " + "Got type=%s and shape=%s" % (ret.dtype, ret.get_shape())) + return ret + + self._key_func = tf_key_func + self._key_func.add_to_graph(ops.get_default_graph()) + + def _make_init_func(self, init_func): + """Make wrapping Defun for init_func.""" + + @function.Defun(dtypes.int64) + def tf_init_func(key): + """A wrapper for Defun that facilitates shape inference.""" + key.set_shape([]) + ret = init_func(key) + # Convert any `SparseTensorValue`s to `SparseTensor`s and all other + # values to tensors. + ret = nest.pack_sequence_as(ret, [ + sparse_tensor.SparseTensor.from_value(t) + if sparse_tensor.is_sparse(t) else ops.convert_to_tensor(t) + for t in nest.flatten(ret) + ]) + + self._state_classes = sparse.get_classes(ret) + self._state_shapes = nest.pack_sequence_as( + ret, [t.get_shape() for t in nest.flatten(ret)]) + self._state_types = nest.pack_sequence_as( + ret, [t.dtype for t in nest.flatten(ret)]) + + # Serialize any sparse tensors. + ret = nest.pack_sequence_as( + ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) + return nest.flatten(ret) + + self._init_func = tf_init_func + self._init_func.add_to_graph(ops.get_default_graph()) + + def _make_reduce_func(self, reduce_func, input_dataset): + """Make wrapping Defun for reduce_func.""" + + # Iteratively rerun the reduce function until reaching a fixed point on + # `self._state_shapes`. + need_to_rerun = True + while need_to_rerun: + + # Create a list in which `tf_reduce_func` will store the new shapes. + flat_new_state_shapes = [] + + @function.Defun(*(nest.flatten( + sparse.as_dense_types( + self._state_types, self._state_classes)) + nest.flatten( + sparse.as_dense_types(input_dataset.output_types, + input_dataset.output_classes)))) + def tf_reduce_func(*args): + """A wrapper for Defun that facilitates shape inference.""" + for arg, shape in zip( + args, + nest.flatten( + sparse.as_dense_shapes(self._state_shapes, self._state_classes)) + + nest.flatten( + sparse.as_dense_shapes(input_dataset.output_shapes, + input_dataset.output_classes))): + arg.set_shape(shape) + + pivot = len(nest.flatten(self._state_shapes)) + nested_state_args = nest.pack_sequence_as(self._state_types, + args[:pivot]) + nested_state_args = sparse.deserialize_sparse_tensors( + nested_state_args, self._state_types, self._state_shapes, + self._state_classes) + nested_input_args = nest.pack_sequence_as(input_dataset.output_types, + args[pivot:]) + nested_input_args = sparse.deserialize_sparse_tensors( + nested_input_args, input_dataset.output_types, + input_dataset.output_shapes, input_dataset.output_classes) + + ret = reduce_func(nested_state_args, nested_input_args) + + # Convert any `SparseTensorValue`s to `SparseTensor`s and all other + # values to tensors. + ret = nest.pack_sequence_as(ret, [ + sparse_tensor.SparseTensor.from_value(t) + if sparse_tensor.is_sparse(t) else ops.convert_to_tensor(t) + for t in nest.flatten(ret) + ]) + + # Extract shape information from the returned values. + flat_new_state = nest.flatten(ret) + flat_new_state_shapes.extend([t.get_shape() for t in flat_new_state]) + + # Extract and validate type information from the returned values. + for t, dtype in zip(flat_new_state, nest.flatten(self._state_types)): + if t.dtype != dtype: + raise TypeError( + "The element types for the new state must match the initial " + "state. Expected %s; got %s." % + (self._state_types, + nest.pack_sequence_as(self._state_types, + [t.dtype for t in flat_new_state]))) + + # Serialize any sparse tensors. + ret = nest.pack_sequence_as( + ret, + [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) + return nest.flatten(ret) + + # Use the private method that will execute `tf_reduce_func` but delay + # adding it to the graph in case we need to rerun the function. + tf_reduce_func._create_definition_if_needed() # pylint: disable=protected-access + + flat_state_shapes = nest.flatten(self._state_shapes) + weakened_state_shapes = [ + old.most_specific_compatible_shape(new) + for old, new in zip(flat_state_shapes, flat_new_state_shapes) + ] + + need_to_rerun = False + for old_shape, weakened_shape in zip(flat_state_shapes, + weakened_state_shapes): + if old_shape.ndims is not None and ( + weakened_shape.ndims is None or + old_shape.as_list() != weakened_shape.as_list()): + need_to_rerun = True + break + + if need_to_rerun: + self._state_shapes = nest.pack_sequence_as(self._state_shapes, + weakened_state_shapes) + + self._reduce_func = tf_reduce_func + self._reduce_func.add_to_graph(ops.get_default_graph()) + + def _make_finalize_func(self, finalize_func): + """Make wrapping Defun for finalize_func.""" + + @function.Defun(*(nest.flatten( + sparse.as_dense_types(self._state_types, self._state_classes)))) + def tf_finalize_func(*args): + """A wrapper for Defun that facilitates shape inference.""" + for arg, shape in zip( + args, + nest.flatten( + sparse.as_dense_shapes(self._state_shapes, self._state_classes))): + arg.set_shape(shape) + + nested_args = nest.pack_sequence_as(self._state_types, args) + nested_args = sparse.deserialize_sparse_tensors( + nested_args, self._state_types, self._state_shapes, + self._state_classes) + + ret = finalize_func(nested_args) + + # Convert any `SparseTensorValue`s to `SparseTensor`s and all other + # values to tensors. + ret = nest.pack_sequence_as(ret, [ + sparse_tensor.SparseTensor.from_value(t) + if sparse_tensor.is_sparse(t) else ops.convert_to_tensor(t) + for t in nest.flatten(ret) + ]) + + self._output_classes = sparse.get_classes(ret) + self._output_shapes = nest.pack_sequence_as( + ret, [t.get_shape() for t in nest.flatten(ret)]) + self._output_types = nest.pack_sequence_as( + ret, [t.dtype for t in nest.flatten(ret)]) + + # Serialize any sparse tensors. + ret = nest.pack_sequence_as( + ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) + return nest.flatten(ret) + + self._finalize_func = tf_finalize_func + self._finalize_func.add_to_graph(ops.get_default_graph()) + + @property + def output_classes(self): + return self._output_classes + + @property + def output_shapes(self): + return self._output_shapes + + @property + def output_types(self): + return self._output_types + + def _as_variant_tensor(self): + return gen_dataset_ops.group_by_reducer_dataset( + self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access + self._key_func.captured_inputs, + self._init_func.captured_inputs, + self._reduce_func.captured_inputs, + self._finalize_func.captured_inputs, + key_func=self._key_func, + init_func=self._init_func, + reduce_func=self._reduce_func, + finalize_func=self._finalize_func, + output_types=nest.flatten( + sparse.as_dense_types(self.output_types, self.output_classes)), + output_shapes=nest.flatten( + sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + + class GroupByWindowDataset(dataset_ops.Dataset): """A `Dataset` that groups its input and performs a windowed reduction.""" @@ -336,3 +610,30 @@ def _as_variant_tensor(self): sparse.as_dense_types(self.output_types, self.output_classes)), output_shapes=nest.flatten( sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + + +class Reducer(object): + """A reducer is used for reducing a set of elements. + + A reducer is represented as a tuple of the three functions: + 1) initialization function: key => initial state + 2) reduce function: (old state, input) => new state + 3) finalization function: state => result + """ + + def __init__(self, init_func, reduce_func, finalize_func): + self._init_func = init_func + self._reduce_func = reduce_func + self._finalize_func = finalize_func + + @property + def init_func(self): + return self._init_func + + @property + def reduce_func(self): + return self._reduce_func + + @property + def finalize_func(self): + return self._finalize_func diff --git a/tensorflow/core/api_def/base_api/api_def_GroupByReducerDataset.pbtxt b/tensorflow/core/api_def/base_api/api_def_GroupByReducerDataset.pbtxt new file mode 100644 index 00000000000000..067ad4018b09d4 --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_GroupByReducerDataset.pbtxt @@ -0,0 +1,69 @@ +op { + graph_op_name: "GroupByReducerDataset" + visibility: HIDDEN + in_arg { + name: "input_dataset" + description: <* out_function) { + OpInputList argument_inputs; + TF_RETURN_IF_ERROR(ctx->input_list(argument, &argument_inputs)); + std::vector arguments_t; + arguments_t.reserve(argument_inputs.size()); + for (const Tensor& t : argument_inputs) { + arguments_t.push_back(t); + } + return CapturedFunction::Create(func, std::move(arguments_t), out_function); +} + CapturedFunction::~CapturedFunction() { if (lib_ != nullptr && f_handle_ != kInvalidHandle) { lib_->ReleaseHandle(f_handle_).IgnoreError(); diff --git a/tensorflow/core/kernels/data/captured_function.h b/tensorflow/core/kernels/data/captured_function.h index 490f5cd1e3b667..e9ad3e381d4ea0 100644 --- a/tensorflow/core/kernels/data/captured_function.h +++ b/tensorflow/core/kernels/data/captured_function.h @@ -40,12 +40,20 @@ class ResourceMgr; // context. class CapturedFunction { public: + // Creates a new instance from a list of named attributes and captured inputs. + // // NOTE(mrry): The `captured_inputs` are passed by value. For // efficiency, you are recommended to move this argument into the call. static Status Create(const NameAttrList& func, std::vector captured_inputs, std::unique_ptr* out_function); + // Creates a new instance using a list of named attributes, fetching captured + // inputs from a context argument. + static Status Create(const NameAttrList& func, OpKernelContext* ctx, + const string& argument, + std::unique_ptr* out_function); + ~CapturedFunction(); // Runs the "Captured function" using the given FLR and caches the lib and @@ -87,6 +95,9 @@ class CapturedFunction { std::vector* rets, FunctionLibraryRuntime::DoneCallback done); + // Returns the named list of function arguments. + const NameAttrList& func() { return func_; } + // Returns that additional captured inputs that will be passed to the function // when `Run*()` is called. const std::vector& captured_inputs() { return captured_inputs_; } diff --git a/tensorflow/core/kernels/data/group_by_reducer_dataset_op.cc b/tensorflow/core/kernels/data/group_by_reducer_dataset_op.cc new file mode 100644 index 00000000000000..c8aeaab9cba5e8 --- /dev/null +++ b/tensorflow/core/kernels/data/group_by_reducer_dataset_op.cc @@ -0,0 +1,422 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include + +#include "tensorflow/core/common_runtime/function.h" +#include "tensorflow/core/framework/partial_tensor_shape.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/kernels/data/captured_function.h" +#include "tensorflow/core/kernels/data/dataset.h" +#include "tensorflow/core/lib/random/random.h" + +namespace tensorflow { +namespace { + +// See documentation in ../ops/dataset_ops.cc for a high-level +// description of the following op. +class GroupByReducerDatasetOp : public UnaryDatasetOpKernel { + public: + explicit GroupByReducerDatasetOp(OpKernelConstruction* ctx) + : UnaryDatasetOpKernel(ctx), + graph_def_version_(ctx->graph_def_version()) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("key_func", &key_func_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("init_func", &init_func_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("reduce_func", &reduce_func_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("finalize_func", &finalize_func_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_)); + } + + void MakeDataset(OpKernelContext* ctx, DatasetBase* input, + DatasetBase** output) override { + std::unique_ptr captured_key_func; + OP_REQUIRES_OK(ctx, CapturedFunction::Create(key_func_, ctx, + "key_func_other_arguments", + &captured_key_func)); + std::unique_ptr captured_init_func; + OP_REQUIRES_OK(ctx, CapturedFunction::Create(init_func_, ctx, + "init_func_other_arguments", + &captured_init_func)); + std::unique_ptr captured_reduce_func; + OP_REQUIRES_OK(ctx, CapturedFunction::Create(reduce_func_, ctx, + "reduce_func_other_arguments", + &captured_reduce_func)); + std::unique_ptr captured_finalize_func; + OP_REQUIRES_OK(ctx, + CapturedFunction::Create(finalize_func_, ctx, + "finalize_func_other_arguments", + &captured_finalize_func)); + + *output = new Dataset( + ctx, input, std::move(captured_key_func), std::move(captured_init_func), + std::move(captured_reduce_func), std::move(captured_finalize_func), + output_types_, output_shapes_); + } + + private: + class Dataset : public GraphDatasetBase { + public: + Dataset(OpKernelContext* ctx, const DatasetBase* input, + std::unique_ptr captured_key_func, + std::unique_ptr captured_init_func, + std::unique_ptr captured_reduce_func, + std::unique_ptr captured_finalize_func, + const DataTypeVector& output_types, + const std::vector& output_shapes) + : GraphDatasetBase(ctx), + input_(input), + captured_key_func_(std::move(captured_key_func)), + captured_init_func_(std::move(captured_init_func)), + captured_reduce_func_(std::move(captured_reduce_func)), + captured_finalize_func_(std::move(captured_finalize_func)), + output_types_(output_types), + output_shapes_(output_shapes) { + input_->Ref(); + } + + ~Dataset() override { input_->Unref(); } + + std::unique_ptr MakeIterator( + const string& prefix) const override { + return std::unique_ptr( + new Iterator({this, strings::StrCat(prefix, "::GroupByReducer")})); + } + + const DataTypeVector& output_dtypes() const override { + return output_types_; + } + const std::vector& output_shapes() const override { + return output_shapes_; + } + + string DebugString() override { return "GroupByReducerDatasetOp::Dataset"; } + + protected: + Status AsGraphDefInternal(OpKernelContext* ctx, DatasetGraphDefBuilder* b, + Node** output) const override { + TF_RETURN_IF_ERROR(b->AddFunction(ctx, key_func().name())); + TF_RETURN_IF_ERROR(b->AddFunction(ctx, init_func().name())); + TF_RETURN_IF_ERROR(b->AddFunction(ctx, reduce_func().name())); + TF_RETURN_IF_ERROR(b->AddFunction(ctx, finalize_func().name())); + Node* input_graph_node = nullptr; + TF_RETURN_IF_ERROR(b->AddParentDataset(ctx, input_, &input_graph_node)); + + std::vector key_func_other_arguments_node; + DataTypeVector key_func_other_arguments_types; + TF_RETURN_IF_ERROR(OtherArgumentsNodeAndType( + b, captured_key_func_, &key_func_other_arguments_node, + &key_func_other_arguments_types)); + + std::vector init_func_other_arguments_node; + DataTypeVector init_func_other_arguments_types; + TF_RETURN_IF_ERROR(OtherArgumentsNodeAndType( + b, captured_init_func_, &init_func_other_arguments_node, + &init_func_other_arguments_types)); + + std::vector reduce_func_other_arguments_node; + DataTypeVector reduce_func_other_arguments_types; + TF_RETURN_IF_ERROR(OtherArgumentsNodeAndType( + b, captured_reduce_func_, &reduce_func_other_arguments_node, + &reduce_func_other_arguments_types)); + + std::vector finalize_func_other_arguments_node; + DataTypeVector finalize_func_other_arguments_types; + TF_RETURN_IF_ERROR(OtherArgumentsNodeAndType( + b, captured_finalize_func_, &finalize_func_other_arguments_node, + &finalize_func_other_arguments_types)); + + AttrValue key_func; + b->BuildAttrValue(this->key_func(), &key_func); + AttrValue init_func; + b->BuildAttrValue(this->init_func(), &init_func); + AttrValue reduce_func; + b->BuildAttrValue(this->reduce_func(), &reduce_func); + AttrValue finalize_func; + b->BuildAttrValue(this->finalize_func(), &finalize_func); + + AttrValue key_func_other_arguments_types_attr; + b->BuildAttrValue(key_func_other_arguments_types, + &key_func_other_arguments_types_attr); + AttrValue init_func_other_arguments_types_attr; + b->BuildAttrValue(init_func_other_arguments_types, + &init_func_other_arguments_types_attr); + AttrValue reduce_func_other_arguments_types_attr; + b->BuildAttrValue(reduce_func_other_arguments_types, + &reduce_func_other_arguments_types_attr); + AttrValue finalize_func_other_arguments_types_attr; + b->BuildAttrValue(finalize_func_other_arguments_types, + &finalize_func_other_arguments_types_attr); + + TF_RETURN_IF_ERROR(b->AddDataset( + this, {{0, input_graph_node}}, + {{1, key_func_other_arguments_node}, + {2, init_func_other_arguments_node}, + {3, reduce_func_other_arguments_node}, + {4, finalize_func_other_arguments_node}}, + {{"key_func", key_func}, + {"init_func", init_func}, + {"reduce_func", reduce_func}, + {"finalize_func", finalize_func}, + {"Tkey_func_other_arguments", key_func_other_arguments_types_attr}, + {"Tinit_func_other_arguments", init_func_other_arguments_types_attr}, + {"Treduce_func_other_arguments", + reduce_func_other_arguments_types_attr}, + {"Tfinalize_func_other_arguments", + finalize_func_other_arguments_types_attr}}, + output)); + return Status::OK(); + } + + private: + class Iterator : public DatasetIterator { + public: + explicit Iterator(const Params& params) + : DatasetIterator(params), + input_impl_(params.dataset->input_->MakeIterator(params.prefix)) {} + + Status GetNextInternal(IteratorContext* ctx, + std::vector* out_tensors, + bool* end_of_sequence) override { + mutex_lock l(mu_); + + // Iterate through the input dataset, keying input elements to reducers. + while (!end_of_input_) { + std::vector next_input_element; + TF_RETURN_IF_ERROR( + input_impl_->GetNext(ctx, &next_input_element, &end_of_input_)); + + if (!end_of_input_) { + // Run the key function on the input element. + std::vector key_func_output; + TF_RETURN_IF_ERROR( + dataset()->captured_key_func_->RunWithBorrowedArgs( + ctx, next_input_element, &key_func_output)); + + if (key_func_output.size() != 1 || + key_func_output[0].dtype() != DT_INT64 || + key_func_output[0].NumElements() != 1) { + // TODO(b/78665031): Support non-int64 keys. + return errors::InvalidArgument( + "`key_func` must return a scalar int64."); + } + const int64 key = key_func_output[0].scalar()(); + + if (states_.find(key) == states_.end()) { + // Run the init function to create the initial state. + std::vector init_func_output; + TF_RETURN_IF_ERROR(dataset()->captured_init_func_->Run( + ctx, std::move(key_func_output), &init_func_output)); + states_[key] = init_func_output; + } + + // Run the reduce function to update the current state. + std::vector args; + args.reserve(states_[key].size() + next_input_element.size()); + std::copy(states_[key].begin(), states_[key].end(), + std::back_inserter(args)); + std::copy(next_input_element.begin(), next_input_element.end(), + std::back_inserter(args)); + + std::vector reduce_func_output; + TF_RETURN_IF_ERROR(dataset()->captured_reduce_func_->Run( + ctx, std::move(args), &reduce_func_output)); + states_[key] = reduce_func_output; + } else { + keys_.resize(states_.size()); + int idx = 0; + for (auto it = states_.begin(); it != states_.end(); ++idx, ++it) { + keys_[idx] = it->first; + } + } + } + + if (keys_index_ == keys_.size()) { + *end_of_sequence = true; + return Status::OK(); + } + TF_RETURN_IF_ERROR( + dataset()->captured_finalize_func_->RunWithBorrowedArgs( + ctx, states_[keys_[keys_index_++]], out_tensors)); + return Status::OK(); + } + + protected: + Status SaveInternal(IteratorStateWriter* writer) override { + mutex_lock l(mu_); + TF_RETURN_IF_ERROR(SaveParent(writer, input_impl_)); + + if (end_of_input_) { + TF_RETURN_IF_ERROR( + writer->WriteScalar(full_name("end_of_input"), "")); + } + + // Saving states_. + if (!states_.empty()) { + TF_RETURN_IF_ERROR( + writer->WriteScalar(full_name("states_size"), states_.size())); + int idx = 0; + for (auto it = states_.begin(); it != states_.end(); ++idx, ++it) { + int64 key = it->first; + TF_RETURN_IF_ERROR(writer->WriteScalar( + full_name(strings::StrCat("states[", idx, "]->key")), key)); + if (!it->second.empty()) { + TF_RETURN_IF_ERROR(writer->WriteScalar( + full_name(strings::StrCat("states[", idx, "]->state_size")), + it->second.size())); + for (int j = 0; j < it->second.size(); ++j) { + TF_RETURN_IF_ERROR(writer->WriteTensor( + full_name( + strings::StrCat("states[", idx, "]->state[", j, "]")), + it->second[j])); + } + } + } + } + + // Saving keys_index_ and keys_. + if (end_of_input_) { + TF_RETURN_IF_ERROR( + writer->WriteScalar(full_name("keys_index"), keys_index_)); + if (!keys_.empty()) { + TF_RETURN_IF_ERROR( + writer->WriteScalar(full_name("keys_size"), keys_.size())); + for (int idx = 0; idx < keys_.size(); ++idx) { + TF_RETURN_IF_ERROR(writer->WriteScalar( + full_name(strings::StrCat("keys[", idx, "]")), keys_[idx])); + } + } + } + + return Status::OK(); + } + + Status RestoreInternal(IteratorContext* ctx, + IteratorStateReader* reader) override { + mutex_lock l(mu_); + TF_RETURN_IF_ERROR(RestoreParent(ctx, reader, input_impl_)); + + if (reader->Contains(full_name("end_of_input"))) end_of_input_ = true; + + // Restoring states_. + if (reader->Contains(full_name("states_size"))) { + int64 size; + TF_RETURN_IF_ERROR( + reader->ReadScalar(full_name("states_size"), &size)); + for (int idx = 0; idx < size; ++idx) { + int64 key; + TF_RETURN_IF_ERROR(reader->ReadScalar( + full_name(strings::StrCat("states[", idx, "]->key")), &key)); + std::vector state; + if (reader->Contains(full_name( + strings::StrCat("states[", idx, "]->state_size")))) { + int64 state_size; + TF_RETURN_IF_ERROR(reader->ReadScalar( + full_name(strings::StrCat("states[", idx, "]->state_size")), + &state_size)); + state.resize(state_size); + for (int j = 0; j < state_size; ++j) { + TF_RETURN_IF_ERROR(reader->ReadTensor( + full_name( + strings::StrCat("states[", idx, "]->state[", j, "]")), + &state[j])); + } + } + states_[key] = state; + } + } + + // Restoring keys_index_ and keys_. + if (end_of_input_) { + TF_RETURN_IF_ERROR( + reader->ReadScalar(full_name("keys_index"), &keys_index_)); + if (reader->Contains(full_name("keys_size"))) { + int64 size; + TF_RETURN_IF_ERROR( + reader->ReadScalar(full_name("keys_size"), &size)); + keys_.resize(size); + for (int idx = 0; idx < size; ++idx) { + int64 key; + TF_RETURN_IF_ERROR(reader->ReadScalar( + full_name(strings::StrCat("keys[", idx, "]")), &key)); + keys_[idx] = key; + } + } + } + + return Status::OK(); + } + + private: + mutex mu_; + std::unique_ptr input_impl_ GUARDED_BY(mu_); + bool end_of_input_ GUARDED_BY(mu_) = false; + std::map> states_ GUARDED_BY(mu_); + std::vector keys_ GUARDED_BY(mu_); + int64 keys_index_ GUARDED_BY(mu_) = 0; + }; + + const NameAttrList& key_func() const { return captured_key_func_->func(); } + + const NameAttrList& init_func() const { + return captured_init_func_->func(); + } + + const NameAttrList& reduce_func() const { + return captured_reduce_func_->func(); + } + + const NameAttrList& finalize_func() const { + return captured_finalize_func_->func(); + } + + Status OtherArgumentsNodeAndType( + DatasetGraphDefBuilder* b, + const std::unique_ptr& captured_func, + std::vector* other_arguments_node, + DataTypeVector* other_arguments_types) const { + other_arguments_node->reserve(captured_func->captured_inputs().size()); + other_arguments_types->reserve(captured_func->captured_inputs().size()); + for (const Tensor& t : captured_func->captured_inputs()) { + Node* node; + TF_RETURN_IF_ERROR(b->AddTensor(t, &node)); + other_arguments_node->emplace_back(node); + other_arguments_types->emplace_back(t.dtype()); + } + return Status::OK(); + } + + const DatasetBase* const input_; + const std::unique_ptr captured_key_func_; + const std::unique_ptr captured_init_func_; + const std::unique_ptr captured_reduce_func_; + const std::unique_ptr captured_finalize_func_; + const DataTypeVector output_types_; + const std::vector output_shapes_; + }; + + const int graph_def_version_; + DataTypeVector output_types_; + std::vector output_shapes_; + NameAttrList key_func_; + NameAttrList init_func_; + NameAttrList reduce_func_; + NameAttrList finalize_func_; +}; + +REGISTER_KERNEL_BUILDER(Name("GroupByReducerDataset").Device(DEVICE_CPU), + GroupByReducerDatasetOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/core/kernels/data/group_by_window_dataset_op.cc b/tensorflow/core/kernels/data/group_by_window_dataset_op.cc index 46f43dd1b1dcd7..03f847ce9c6e03 100644 --- a/tensorflow/core/kernels/data/group_by_window_dataset_op.cc +++ b/tensorflow/core/kernels/data/group_by_window_dataset_op.cc @@ -241,7 +241,7 @@ class GroupByWindowDatasetOp : public UnaryDatasetOpKernel { if (key_func_output.size() != 1 || key_func_output[0].dtype() != DT_INT64 || key_func_output[0].NumElements() != 1) { - // TODO(mrry): Support non-int64 keys. + // TODO(b/78665031): Support non-int64 keys. return errors::InvalidArgument( "`key_func` must return a scalar int64."); } diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc index 4ba3f15ef03eca..5f10ad24b69a5d 100644 --- a/tensorflow/core/ops/dataset_ops.cc +++ b/tensorflow/core/ops/dataset_ops.cc @@ -270,6 +270,26 @@ REGISTER_OP("ParallelInterleaveDataset") .Attr("output_shapes: list(shape) >= 1") .SetShapeFn(shape_inference::ScalarShape); +REGISTER_OP("GroupByReducerDataset") + .Input("input_dataset: variant") + .Input("key_func_other_arguments: Tkey_func_other_arguments") + .Input("init_func_other_arguments: Tinit_func_other_arguments") + .Input("reduce_func_other_arguments: Treduce_func_other_arguments") + .Input("finalize_func_other_arguments: Tfinalize_func_other_arguments") + .Output("handle: variant") + .Attr("key_func: func") + .Attr("init_func: func") + .Attr("reduce_func: func") + .Attr("finalize_func: func") + .Attr("Tkey_func_other_arguments: list(type) >= 0") + .Attr("Tinit_func_other_arguments: list(type) >= 0") + .Attr("Treduce_func_other_arguments: list(type) >= 0") + .Attr("Tfinalize_func_other_arguments: list(type) >= 0") + .Attr("output_types: list(type) >= 1") + .Attr("output_shapes: list(shape) >= 1") + .SetIsStateful() + .SetShapeFn(shape_inference::ScalarShape); + REGISTER_OP("GroupByWindowDataset") .Input("input_dataset: variant") .Input("key_func_other_arguments: Tkey_func_other_arguments") From 45bafe9a3589fc735c22c3c703f8689ea9c1e71e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 17:41:33 -0700 Subject: [PATCH 0206/1691] [XLA] Redesign: migrate tensorflow/compiler/tf2xla, tensorflow/compiler/aot: - xla::ComputationBuilder -> xla::XlaBuilder - xla::ComputationDataHandle -> xla::XlaOp - xla::Computation -> xla::XlaComputation - xla::CompileOnlyClient::AotComputationInstance -> xla::CompileOnlyClient::AotXlaComputationInstance - xla::SessionModule -> xla::HloSnapshot PiperOrigin-RevId: 194874462 --- tensorflow/compiler/aot/compile.cc | 12 +- .../compiler/aot/tests/tfcompile_test.cc | 14 +- tensorflow/compiler/tf2xla/BUILD | 7 +- tensorflow/compiler/tf2xla/graph_compiler.cc | 7 +- tensorflow/compiler/tf2xla/kernels/BUILD | 8 +- .../compiler/tf2xla/kernels/aggregate_ops.cc | 2 +- .../compiler/tf2xla/kernels/batch_norm_op.cc | 18 +-- .../tf2xla/kernels/batchtospace_op.cc | 16 +-- .../compiler/tf2xla/kernels/bias_ops.cc | 4 +- .../compiler/tf2xla/kernels/binary_ops.cc | 32 ++--- tensorflow/compiler/tf2xla/kernels/cast_op.cc | 12 +- .../compiler/tf2xla/kernels/categorical_op.cc | 6 +- .../tf2xla/kernels/clip_by_value_op.cc | 2 +- .../compiler/tf2xla/kernels/concat_op.cc | 10 +- .../compiler/tf2xla/kernels/const_op.cc | 2 +- .../compiler/tf2xla/kernels/conv_ops.cc | 49 ++++--- .../compiler/tf2xla/kernels/cross_op.cc | 2 +- .../compiler/tf2xla/kernels/cwise_ops.cc | 12 +- .../compiler/tf2xla/kernels/cwise_ops.h | 19 ++- .../tf2xla/kernels/depthtospace_op.cc | 12 +- tensorflow/compiler/tf2xla/kernels/diag_op.cc | 36 +++-- .../tf2xla/kernels/dynamic_slice_ops.cc | 4 +- .../tf2xla/kernels/dynamic_stitch_op.cc | 6 +- tensorflow/compiler/tf2xla/kernels/elu_op.cc | 11 +- .../kernels/extract_image_patches_op.cc | 6 +- .../tf2xla/kernels/fake_quantize_ops.cc | 134 ++++++++---------- tensorflow/compiler/tf2xla/kernels/fft_ops.cc | 5 +- tensorflow/compiler/tf2xla/kernels/fill_op.cc | 4 +- .../compiler/tf2xla/kernels/gather_op.cc | 20 ++- .../tf2xla/kernels/gather_op_helpers.h | 14 +- tensorflow/compiler/tf2xla/kernels/if_op.cc | 10 +- .../compiler/tf2xla/kernels/image_ops.cc | 69 +++++---- .../tf2xla/kernels/image_resize_ops.cc | 48 ++++--- .../compiler/tf2xla/kernels/index_ops.cc | 6 +- .../compiler/tf2xla/kernels/index_ops_cpu.cc | 6 +- .../compiler/tf2xla/kernels/l2loss_op.cc | 4 +- tensorflow/compiler/tf2xla/kernels/lrn_ops.cc | 14 +- .../compiler/tf2xla/kernels/matmul_op.cc | 4 +- .../tf2xla/kernels/matrix_band_part_op.cc | 12 +- .../tf2xla/kernels/matrix_set_diag_op.cc | 10 +- .../compiler/tf2xla/kernels/mirror_pad_op.cc | 17 +-- .../compiler/tf2xla/kernels/one_hot_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/pack_op.cc | 4 +- tensorflow/compiler/tf2xla/kernels/pad_op.cc | 2 +- .../compiler/tf2xla/kernels/pooling_ops.cc | 45 +++--- .../kernels/quantize_and_dequantize_op.cc | 16 +-- .../compiler/tf2xla/kernels/random_ops.cc | 58 ++++---- .../tf2xla/kernels/reduce_window_op.cc | 11 +- .../compiler/tf2xla/kernels/reduction_ops.cc | 63 ++++---- .../compiler/tf2xla/kernels/reduction_ops.h | 22 ++- .../tf2xla/kernels/reduction_ops_common.cc | 13 +- tensorflow/compiler/tf2xla/kernels/relu_op.cc | 10 +- .../compiler/tf2xla/kernels/retval_op.cc | 4 +- .../compiler/tf2xla/kernels/reverse_op.cc | 4 +- .../tf2xla/kernels/reverse_sequence_op.cc | 4 +- .../compiler/tf2xla/kernels/scan_ops.cc | 6 +- .../compiler/tf2xla/kernels/scatter_nd_op.cc | 2 +- .../tf2xla/kernels/segment_reduction_ops.cc | 10 +- .../compiler/tf2xla/kernels/select_op.cc | 2 +- .../compiler/tf2xla/kernels/sendrecv_ops.cc | 2 +- .../compiler/tf2xla/kernels/softmax_op.cc | 29 ++-- .../tf2xla/kernels/spacetobatch_op.cc | 17 +-- .../tf2xla/kernels/spacetodepth_op.cc | 12 +- .../compiler/tf2xla/kernels/split_op.cc | 2 +- .../compiler/tf2xla/kernels/stack_ops.cc | 32 ++--- .../tf2xla/kernels/stateless_random_ops.cc | 36 +++-- .../tf2xla/kernels/strided_slice_op.cc | 8 +- .../tf2xla/kernels/tensor_array_ops.cc | 82 +++++------ .../compiler/tf2xla/kernels/tile_ops.cc | 2 +- .../compiler/tf2xla/kernels/training_ops.cc | 103 +++++++------- .../compiler/tf2xla/kernels/unary_ops.cc | 36 +++-- .../compiler/tf2xla/kernels/variable_ops.cc | 14 +- .../compiler/tf2xla/kernels/while_op.cc | 16 +-- tensorflow/compiler/tf2xla/lib/BUILD | 26 ++-- tensorflow/compiler/tf2xla/lib/batch_dot.cc | 50 ++++--- tensorflow/compiler/tf2xla/lib/batch_dot.h | 12 +- tensorflow/compiler/tf2xla/lib/cholesky.cc | 50 +++---- tensorflow/compiler/tf2xla/lib/cholesky.h | 9 +- tensorflow/compiler/tf2xla/lib/scatter.cc | 58 ++++---- tensorflow/compiler/tf2xla/lib/scatter.h | 18 ++- .../compiler/tf2xla/lib/triangular_solve.cc | 131 +++++++++-------- .../compiler/tf2xla/lib/triangular_solve.h | 21 +-- .../tf2xla/lib/triangular_solve_test.cc | 50 +++---- tensorflow/compiler/tf2xla/lib/util.cc | 92 ++++++------ tensorflow/compiler/tf2xla/lib/util.h | 67 +++++---- tensorflow/compiler/tf2xla/lib/util_test.cc | 17 ++- tensorflow/compiler/tf2xla/lib/while_loop.cc | 52 +++---- tensorflow/compiler/tf2xla/lib/while_loop.h | 29 ++-- tensorflow/compiler/tf2xla/tf2xla.cc | 6 +- tensorflow/compiler/tf2xla/tf2xla.h | 12 +- tensorflow/compiler/tf2xla/tf2xla_test.cc | 2 +- .../compiler/tf2xla/xla_compilation_device.cc | 7 +- .../compiler/tf2xla/xla_compilation_device.h | 10 +- tensorflow/compiler/tf2xla/xla_compiler.cc | 47 +++--- tensorflow/compiler/tf2xla/xla_compiler.h | 18 ++- .../compiler/tf2xla/xla_compiler_test.cc | 36 ++--- tensorflow/compiler/tf2xla/xla_context.cc | 33 +++-- tensorflow/compiler/tf2xla/xla_context.h | 36 +++-- tensorflow/compiler/tf2xla/xla_helpers.cc | 95 ++++++------- tensorflow/compiler/tf2xla/xla_helpers.h | 66 ++++----- .../tf2xla/xla_jit_compiled_cpu_function.cc | 4 +- tensorflow/compiler/tf2xla/xla_op_kernel.cc | 71 +++++----- tensorflow/compiler/tf2xla/xla_op_kernel.h | 30 ++-- tensorflow/compiler/tf2xla/xla_resource.cc | 33 ++--- tensorflow/compiler/tf2xla/xla_resource.h | 29 ++-- tensorflow/compiler/xla/client/BUILD | 1 + tensorflow/compiler/xla/client/local_client.h | 1 + 107 files changed, 1218 insertions(+), 1356 deletions(-) diff --git a/tensorflow/compiler/aot/compile.cc b/tensorflow/compiler/aot/compile.cc index 31044ff85d6f0d..bbc35da2ef6d14 100644 --- a/tensorflow/compiler/aot/compile.cc +++ b/tensorflow/compiler/aot/compile.cc @@ -44,7 +44,7 @@ namespace { // Compiles the XLA computation into executable code. Status CompileXla(xla::CompileOnlyClient* client, - const xla::Computation& computation, + const xla::XlaComputation& computation, const xla::cpu::CpuAotCompilationOptions& aot_opts, CompileResult* compile_result) { // Retrieves arg and result layouts from the computation. @@ -62,7 +62,7 @@ Status CompileXla(xla::CompileOnlyClient* client, for (int i = 0; i < pshape->parameters_size(); ++i) { arg_layouts.push_back(pshape->mutable_parameters(i)); } - xla::CompileOnlyClient::AotComputationInstance instance; + xla::CompileOnlyClient::AotXlaComputationInstance instance; instance.computation = &computation; instance.argument_layouts = std::move(arg_layouts); instance.result_layout = &pshape->result(); @@ -93,14 +93,14 @@ Status CompileGraph(const GraphDef& graph_def, const tf2xla::Config& config, xla::CompileOnlyClient* client = xla::ClientLibrary::GetOrCreateCompileOnlyClient(cpu_platform) .ValueOrDie(); - xla::Computation computation; + xla::XlaComputation computation; TF_RETURN_IF_ERROR( ConvertGraphDefToXla(graph_def, config, client, &computation)); if (!flags.out_session_module.empty()) { - TF_ASSIGN_OR_RETURN(std::unique_ptr module, + TF_ASSIGN_OR_RETURN(std::unique_ptr module, computation.Snapshot()); - // Serialize the SessionModule deterministically so that all the outputs of - // a tf_library genrule are deterministic. + // Serialize the HloSnapshot deterministically so that all the outputs of a + // tf_library genrule are deterministic. string proto; TF_RET_CHECK(SerializeToStringDeterministic(*module, &proto)); TF_RETURN_IF_ERROR( diff --git a/tensorflow/compiler/aot/tests/tfcompile_test.cc b/tensorflow/compiler/aot/tests/tfcompile_test.cc index aa9d968265b461..27ba42b31fc250 100644 --- a/tensorflow/compiler/aot/tests/tfcompile_test.cc +++ b/tensorflow/compiler/aot/tests/tfcompile_test.cc @@ -525,14 +525,16 @@ TEST(TFCompileTest, HloProfiling) { auto header = HasSubstr("Execution profile for"); auto total_cycles_profile_line = HasSubstr("[total]"); auto dot_profile_line = HasSubstr( - "%dot = f32[2,2]{1,0} dot(f32[2,2]{1,0} %arg0, f32[2,2]{1,0} %arg1)"); + "%dot.0.2 = f32[2,2]{1,0} dot(f32[2,2]{1,0} %arg0.0.0, f32[2,2]{1,0} " + "%arg1.0.1)"); auto add_profile_line = HasSubstr( - "%add = f32[2,2]{1,0} add(f32[2,2]{1,0} %arg0, f32[2,2]{1,0} %arg1)"); + "%add.0.5 = f32[2,2]{1,0} add(f32[2,2]{1,0} %arg0.0.0, f32[2,2]{1,0} " + "%arg1.0.1)"); auto tuple_profile_line = HasSubstr( - "%tuple.2 = (f32[2,2]{1,0}, f32[2,2]{1,0}) tuple(f32[2,2]{1,0} %dot, " - "f32[2,2]{1,0} %add)"); - auto arg0_profile_line = HasSubstr("%arg0 = f32[2,2]{1,0} parameter(0)"); - auto arg1_profile_line = HasSubstr("%arg1 = f32[2,2]{1,0} parameter(1)"); + "%tuple.0.8 = (f32[2,2]{1,0}, f32[2,2]{1,0}) tuple(f32[2,2]{1,0} " + "%dot.0.2, f32[2,2]{1,0} %add.0.5)"); + auto arg0_profile_line = HasSubstr("%arg0.0.0 = f32[2,2]{1,0} parameter(0)"); + auto arg1_profile_line = HasSubstr("%arg1.0.1 = f32[2,2]{1,0} parameter(1)"); hlo_profile_lines.erase(hlo_profile_lines.begin() + 7, hlo_profile_lines.end()); diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD index 942504e6bd4c9c..4fca51f54d320e 100644 --- a/tensorflow/compiler/tf2xla/BUILD +++ b/tensorflow/compiler/tf2xla/BUILD @@ -81,7 +81,7 @@ cc_library( "//tensorflow/compiler/tf2xla/kernels:xla_cpu_only_ops", "//tensorflow/compiler/tf2xla/kernels:xla_ops", "//tensorflow/compiler/xla/client", - "//tensorflow/compiler/xla/client:computation", + "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", @@ -168,9 +168,9 @@ cc_library( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", - "//tensorflow/compiler/xla/client:computation", - "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", @@ -215,7 +215,6 @@ cc_library( visibility = ["//visibility:public"], deps = [ "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client:sharding_builder", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", diff --git a/tensorflow/compiler/tf2xla/graph_compiler.cc b/tensorflow/compiler/tf2xla/graph_compiler.cc index b20c1ffc7d8956..8115a26210a8e9 100644 --- a/tensorflow/compiler/tf2xla/graph_compiler.cc +++ b/tensorflow/compiler/tf2xla/graph_compiler.cc @@ -51,6 +51,7 @@ Status PrepareArguments(XlaOpKernelContext* ctx, Graph* graph, const std::vector& expressions, std::vector* args) { auto builder = ctx->builder(); + auto client = ctx->compiler()->client(); std::vector compile_time_constant_flags(expressions.size()); TF_RETURN_IF_ERROR( @@ -72,8 +73,10 @@ Status PrepareArguments(XlaOpKernelContext* ctx, Graph* graph, arg.kind = XlaCompiler::Argument::kConstant; TF_RET_CHECK(expressions[i]->resource() == nullptr) << "Input with resource is not yet implemented."; + TF_ASSIGN_OR_RETURN(auto constant_graph, builder->BuildConstantSubGraph( + expressions[i]->handle())); TF_ASSIGN_OR_RETURN(auto literal, - builder->ComputeConstant(expressions[i]->handle())); + client->ComputeConstant(constant_graph)); TF_RETURN_IF_ERROR( LiteralToHostTensor(*literal, arg.type, &arg.constant_value)); } else { @@ -212,7 +215,7 @@ Status GraphCompiler::CompileFunctionalNode(Node* n, TF_RET_CHECK(arguments.size() == expressions.size()); - std::vector handles; + std::vector handles; for (int64 i = 0; i < expressions.size(); ++i) { if (arguments[i].kind == XlaCompiler::Argument::kConstant) { continue; diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index 00fd08b1a07507..85ab4c41bf6a75 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -114,8 +114,8 @@ tf_kernel_library( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", - "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client/lib:arithmetic", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:framework", "//tensorflow/core:image_ops_op_lib", "//tensorflow/core:lib", @@ -151,7 +151,7 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/ops:xla_ops", "//tensorflow/compiler/xla:literal_util", - "//tensorflow/compiler/xla/client:computation_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", @@ -167,7 +167,7 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/ops:xla_ops", "//tensorflow/compiler/xla:literal_util", - "//tensorflow/compiler/xla/client:computation_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", @@ -203,8 +203,8 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla/client:client_library", - "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client/lib:arithmetic", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core/kernels:argmax_op", diff --git a/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc b/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc index 5c9f66df101bfb..1e59868621475c 100644 --- a/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc @@ -29,7 +29,7 @@ class AddNOp : public XlaOpKernel { OP_REQUIRES(ctx, ctx->num_inputs() >= 1, errors::InvalidArgument("AddN requires at least one argument")); - xla::ComputationDataHandle sum = ctx->Input(0); + xla::XlaOp sum = ctx->Input(0); for (int i = 1; i < ctx->num_inputs(); ++i) { sum = ctx->builder()->Add(sum, ctx->Input(i)); } diff --git a/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc b/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc index 931175be1111ed..15e1815a4cf07f 100644 --- a/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc @@ -48,9 +48,9 @@ class FusedBatchNormOp : public XlaOpKernel { OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(ctx->input_type(1), &scale_type)); - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); - xla::ComputationDataHandle input = ctx->Input(0); + xla::XlaOp input = ctx->Input(0); TensorShape input_shape = ctx->InputShape(0); int feature_index = @@ -62,7 +62,7 @@ class FusedBatchNormOp : public XlaOpKernel { input = builder->ConvertElementType(input, scale_type); if (is_training_) { - xla::ComputationDataHandle output = builder->BatchNormTraining( + xla::XlaOp output = builder->BatchNormTraining( input, ctx->Input(1), ctx->Input(2), epsilon_, feature_index); // In training mode, outputs the normalized value as well as the @@ -79,7 +79,7 @@ class FusedBatchNormOp : public XlaOpKernel { ctx->SetOutput(3, builder->GetTupleElement(output, 1)); ctx->SetOutput(4, builder->GetTupleElement(output, 2)); } else { - xla::ComputationDataHandle output = builder->BatchNormInference( + xla::XlaOp output = builder->BatchNormInference( input, ctx->Input(1), ctx->Input(2), ctx->Input(3), ctx->Input(4), epsilon_, feature_index); ctx->SetOutput(0, builder->ConvertElementType(output, input_type)); @@ -118,7 +118,7 @@ class FusedBatchNormGradOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* const b = ctx->builder(); + xla::XlaBuilder* const b = ctx->builder(); DataType input_dtype = ctx->input_type(0); DataType scale_dtype = ctx->input_type(2); @@ -137,11 +137,11 @@ class FusedBatchNormGradOp : public XlaOpKernel { const int feature_index = GetTensorFeatureDimIndex(input_dims, data_format_); - xla::ComputationDataHandle x_backprop; - xla::ComputationDataHandle scale_backprop; - xla::ComputationDataHandle offset_backprop; + xla::XlaOp x_backprop; + xla::XlaOp scale_backprop; + xla::XlaOp offset_backprop; if (is_training_) { - xla::ComputationDataHandle output = + xla::XlaOp output = b->BatchNormGrad(activations, scale, mean, var, grad_backprop, epsilon_, feature_index); diff --git a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc index 569950c2dfaeb6..642278ab994bf3 100644 --- a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc @@ -20,9 +20,8 @@ limitations under the License. namespace tensorflow { namespace { -void BatchToSpace(XlaOpKernelContext* ctx, - const xla::ComputationDataHandle& input, DataType input_dtype, - const TensorShape& input_tensor_shape, +void BatchToSpace(XlaOpKernelContext* ctx, const xla::XlaOp& input, + DataType input_dtype, const TensorShape& input_tensor_shape, gtl::ArraySlice block_shape, const xla::Literal& crops) { const int input_rank = input_tensor_shape.dims(); @@ -46,7 +45,7 @@ void BatchToSpace(XlaOpKernelContext* ctx, ", 2] instead of ", xla::ShapeUtil::HumanString(crops.shape()))); - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); const int64 batch_size = input_shape[0]; // Compute the product of the block_shape values. @@ -73,7 +72,7 @@ void BatchToSpace(XlaOpKernelContext* ctx, reshaped_shape[block_rank] = batch_size / block_num_elems; std::copy(input_shape.begin() + 1, input_shape.end(), reshaped_shape.begin() + block_rank + 1); - xla::ComputationDataHandle reshaped = b->Reshape(input, reshaped_shape); + xla::XlaOp reshaped = b->Reshape(input, reshaped_shape); // 2. Permute dimensions of `reshaped` to produce `permuted` of shape // [batch / prod(block_shape), @@ -91,7 +90,7 @@ void BatchToSpace(XlaOpKernelContext* ctx, } std::iota(permutation.begin() + 1 + block_rank * 2, permutation.end(), 1 + block_rank * 2); - xla::ComputationDataHandle permuted = b->Transpose(reshaped, permutation); + xla::XlaOp permuted = b->Transpose(reshaped, permutation); // 3. Reshape `permuted` to produce `reshaped_permuted` of shape // [batch / prod(block_shape), @@ -111,8 +110,7 @@ void BatchToSpace(XlaOpKernelContext* ctx, std::copy(remainder_shape.begin(), remainder_shape.end(), reshaped_permuted_shape.begin() + 1 + block_rank); - xla::ComputationDataHandle reshaped_permuted = - b->Reshape(permuted, reshaped_permuted_shape); + xla::XlaOp reshaped_permuted = b->Reshape(permuted, reshaped_permuted_shape); // 4. Crop the start and end of dimensions `[1, ..., M]` of // `reshaped_permuted` according to `crops` to produce the output of shape: @@ -139,7 +137,7 @@ void BatchToSpace(XlaOpKernelContext* ctx, "Cropped size must be non-negative: start: ", crop_start, " end: ", crop_end, " size ", reshaped_permuted_shape[1 + i])); } - xla::ComputationDataHandle output = + xla::XlaOp output = b->Slice(reshaped_permuted, start_indices, end_indices, strides); ctx->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/bias_ops.cc b/tensorflow/compiler/tf2xla/kernels/bias_ops.cc index ed33b8ed2e823f..9d677f426650ea 100644 --- a/tensorflow/compiler/tf2xla/kernels/bias_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/bias_ops.cc @@ -60,7 +60,7 @@ class BiasOp : public XlaOpKernel { "of the input tensor: ", bias_shape.DebugString(), " vs. ", input_shape.DebugString())); - xla::ComputationDataHandle result = + xla::XlaOp result = ctx->builder()->Add(ctx->Input(0), ctx->Input(1), {feature_dim}); ctx->SetOutput(0, result); } @@ -103,7 +103,7 @@ class BiasAddGradOp : public XlaOpKernel { std::iota(reduce_dims.begin(), reduce_dims.begin() + feature_dim, 0); std::iota(reduce_dims.begin() + feature_dim, reduce_dims.end(), feature_dim + 1); - xla::ComputationBuilder* const b = ctx->builder(); + xla::XlaBuilder* const b = ctx->builder(); const DataType accumulation_type = XlaHelpers::SumAccumulationType(input_type(0)); auto converted = diff --git a/tensorflow/compiler/tf2xla/kernels/binary_ops.cc b/tensorflow/compiler/tf2xla/kernels/binary_ops.cc index 2436a6074a11ad..f04cde878e9800 100644 --- a/tensorflow/compiler/tf2xla/kernels/binary_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/binary_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/types.h" @@ -34,14 +34,13 @@ namespace { class NAME##Op : public XlaBinaryOp { \ public: \ explicit NAME##Op(OpKernelConstruction* ctx) : XlaBinaryOp(ctx) {} \ - xla::ComputationDataHandle Computation( \ - XlaOpKernelContext* ctx, const xla::ComputationDataHandle& lhs, \ - const gtl::ArraySlice& lhs_shape, \ - const xla::ComputationDataHandle& rhs, \ + xla::XlaOp Computation( \ + XlaOpKernelContext* ctx, const xla::XlaOp& lhs, \ + const gtl::ArraySlice& lhs_shape, const xla::XlaOp& rhs, \ const gtl::ArraySlice& rhs_shape, \ const BCast& broadcast_helper, \ const std::vector& extend_dimensions) override { \ - xla::ComputationBuilder* b = ctx->builder(); \ + xla::XlaBuilder* b = ctx->builder(); \ return HLO; \ } \ }; \ @@ -63,11 +62,8 @@ XLA_MAKE_BINARY(Complex, b->Complex(lhs, rhs, extend_dimensions)); // } else { // return x / y; // } -static xla::ComputationDataHandle FloorDivImpl(xla::ComputationBuilder* b, - DataType dtype, - xla::ComputationDataHandle x, - xla::ComputationDataHandle y, - const BCast& broadcast_helper) { +static xla::XlaOp FloorDivImpl(xla::XlaBuilder* b, DataType dtype, xla::XlaOp x, + xla::XlaOp y, const BCast& broadcast_helper) { std::tie(x, y) = XlaBinaryOp::Broadcast(b, x, y, broadcast_helper); auto zero = XlaHelpers::Zero(b, dtype); auto one = XlaHelpers::One(b, dtype); @@ -87,11 +83,8 @@ XLA_MAKE_BINARY(FloorDiv, // Implementation of FloorMod. Pseudo-code: // T trunc_mod = std::fmod(x, y); // return (x < T(0)) == (y < T(0)) ? trunc_mod : std::fmod(trunc_mod + y, y); -static xla::ComputationDataHandle FloorModImpl(xla::ComputationBuilder* b, - DataType dtype, - xla::ComputationDataHandle x, - xla::ComputationDataHandle y, - const BCast& broadcast_helper) { +static xla::XlaOp FloorModImpl(xla::XlaBuilder* b, DataType dtype, xla::XlaOp x, + xla::XlaOp y, const BCast& broadcast_helper) { std::tie(x, y) = XlaBinaryOp::Broadcast(b, x, y, broadcast_helper); auto zero = XlaHelpers::Zero(b, dtype); auto same_sign = b->Eq(b->Lt(x, zero), b->Lt(y, zero)); @@ -127,8 +120,7 @@ XLA_MAKE_BINARY(SqrtGrad, XlaHelpers::FloatLiteral(b, input_type(0), 0.5)), lhs, extend_dimensions)); -static xla::ComputationDataHandle Square(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& x) { +static xla::XlaOp Square(xla::XlaBuilder* builder, const xla::XlaOp& x) { return builder->Mul(x, x); } @@ -175,11 +167,11 @@ class ApproximateEqualOp : public XlaOpKernel { // Computes the max of the scalar input x and 0. void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); auto abs = b->Abs(b->Sub(ctx->Input(0), ctx->Input(1))); auto abs_shape = b->GetShape(abs); OP_REQUIRES_OK(ctx, abs_shape.status()); - auto abs_type = abs_shape.ValueOrDie()->element_type(); + auto abs_type = abs_shape.ValueOrDie().element_type(); auto result = b->Lt( abs, b->ConvertElementType(b->ConstantR0(tolerance_), abs_type)); ctx->SetOutput(0, result); diff --git a/tensorflow/compiler/tf2xla/kernels/cast_op.cc b/tensorflow/compiler/tf2xla/kernels/cast_op.cc index c52b2dcb7e9ef8..e9d98c768572c5 100644 --- a/tensorflow/compiler/tf2xla/kernels/cast_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/cast_op.cc @@ -33,9 +33,9 @@ class CastOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* builder = ctx->builder(); - xla::ComputationDataHandle input = ctx->Input(0); - xla::ComputationDataHandle output; + xla::XlaBuilder* builder = ctx->builder(); + xla::XlaOp input = ctx->Input(0); + xla::XlaOp output; if (src_dtype_ == dst_dtype_) { output = input; @@ -72,9 +72,9 @@ class BitcastOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* builder = ctx->builder(); - xla::ComputationDataHandle input = ctx->Input(0); - xla::ComputationDataHandle output; + xla::XlaBuilder* builder = ctx->builder(); + xla::XlaOp input = ctx->Input(0); + xla::XlaOp output; if (src_dtype_ == dst_dtype_) { output = input; diff --git a/tensorflow/compiler/tf2xla/kernels/categorical_op.cc b/tensorflow/compiler/tf2xla/kernels/categorical_op.cc index 545aa364f937b2..835a7f568945f0 100644 --- a/tensorflow/compiler/tf2xla/kernels/categorical_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/categorical_op.cc @@ -34,7 +34,7 @@ class CategoricalOp : public XlaOpKernel { void Compile(XlaOpKernelContext* ctx) override { // Get the logits - const xla::ComputationDataHandle& logits = ctx->Input(0); + const xla::XlaOp& logits = ctx->Input(0); TensorShape logits_shape = ctx->InputShape(0); int64 num_samples; OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar(1, &num_samples)); @@ -56,7 +56,7 @@ class CategoricalOp : public XlaOpKernel { const int64 batch_size = logits_shape.dim_size(0); const int64 num_classes = logits_shape.dim_size(1); - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); std::array uniform_shape_array = { {batch_size, num_samples, num_classes}}; @@ -78,7 +78,7 @@ class CategoricalOp : public XlaOpKernel { /*broadcast_dimensions=*/{0, 2}); TensorShape softmax_shape(uniform_shape_array); - xla::ComputationDataHandle argmax; + xla::XlaOp argmax; OP_REQUIRES_OK( ctx, XlaHelpers::ArgMax(builder, ctx, softmax_entries, softmax_shape, diff --git a/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc b/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc index fdf75be7b11565..a00bc912f9f400 100644 --- a/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc @@ -29,7 +29,7 @@ class ClipByValueOp : public XlaOpKernel { const TensorShape min_shape = ctx->InputShape(1); const TensorShape max_shape = ctx->InputShape(2); - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); auto input = ctx->Input(0); auto min = ctx->Input(1); auto max = ctx->Input(2); diff --git a/tensorflow/compiler/tf2xla/kernels/concat_op.cc b/tensorflow/compiler/tf2xla/kernels/concat_op.cc index 1a246e8df9b2cd..78285affa1c399 100644 --- a/tensorflow/compiler/tf2xla/kernels/concat_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/concat_op.cc @@ -54,7 +54,7 @@ class ConcatBaseOp : public XlaOpKernel { // TODO(annarev): add a helper to support int64 input. const int32 concat_dim = literal.Get({}); - std::vector values; + std::vector values; std::vector shapes; OP_REQUIRES_OK(ctx, ctx->InputList("values", &values, &shapes)); const int N = values.size(); @@ -70,13 +70,13 @@ class ConcatBaseOp : public XlaOpKernel { "[", -input_dims, ", ", input_dims, "), but got ", concat_dim)); - // Make a vector holding the ComputationDataHandles for each of - // the inputs that has non-zero elements. - std::vector input_data; + // Make a vector holding the XlaOp for each of the inputs that has non-zero + // elements. + std::vector input_data; int output_concat_dim = 0; const bool input_is_scalar = IsLegacyScalar(input_shape); for (int i = 0; i < N; ++i) { - xla::ComputationDataHandle handle = values[i]; + xla::XlaOp handle = values[i]; const TensorShape& in_shape = shapes[i]; const bool in_is_scalar = IsLegacyScalar(in_shape); OP_REQUIRES( diff --git a/tensorflow/compiler/tf2xla/kernels/const_op.cc b/tensorflow/compiler/tf2xla/kernels/const_op.cc index 8f78b4c8f90cf0..59d06c654de18c 100644 --- a/tensorflow/compiler/tf2xla/kernels/const_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/const_op.cc @@ -45,7 +45,7 @@ class ConstOp : public XlaOpKernel { ctx->SetInvalidOutput(0); return; } - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); // To avoid blowups for large constants filled with the same value, // recognize that case and emit a scalar broadcast instead. diff --git a/tensorflow/compiler/tf2xla/kernels/conv_ops.cc b/tensorflow/compiler/tf2xla/kernels/conv_ops.cc index c0ee0c9c2ea849..627bad12f33c82 100644 --- a/tensorflow/compiler/tf2xla/kernels/conv_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/conv_ops.cc @@ -47,9 +47,8 @@ TensorShape ExpandedFilterShapeForDepthwiseConvolution( } // Broadcast zeros to ExpandedFilterShapeForDepthwiseConvolution. -xla::ComputationDataHandle CreateExpandedZero( - const TensorShape& filter_shape, DataType dtype, - xla::ComputationBuilder* builder) { +xla::XlaOp CreateExpandedZero(const TensorShape& filter_shape, DataType dtype, + xla::XlaBuilder* builder) { TensorShape expanded_filter_shape = ExpandedFilterShapeForDepthwiseConvolution(filter_shape); return builder->Broadcast(XlaHelpers::Zero(builder, dtype), @@ -87,8 +86,8 @@ xla::ComputationDataHandle CreateExpandedZero( // // Finally compare A and broadcasted B in dimension 2 amd return the result at // the beginning of the comment. -xla::ComputationDataHandle CreateExpandedFilterMask( - const TensorShape& filter_shape, xla::ComputationBuilder* builder) { +xla::XlaOp CreateExpandedFilterMask(const TensorShape& filter_shape, + xla::XlaBuilder* builder) { TensorShape expanded_filter_shape = ExpandedFilterShapeForDepthwiseConvolution(filter_shape); int64 depthwise_multiplier = filter_shape.dim_size(filter_shape.dims() - 1); @@ -96,11 +95,11 @@ xla::ComputationDataHandle CreateExpandedFilterMask( // Create a M sized linspace and an M*N sized linspace that will be // broadcasted into perpendicular dimensions and compared. - xla::ComputationDataHandle input_feature_iota; + xla::XlaOp input_feature_iota; // DT_INT32 Iota will always return status::OK(). TF_CHECK_OK(XlaHelpers::Iota(builder, DataType::DT_INT32, input_feature, &input_feature_iota)); - xla::ComputationDataHandle expanded_feature_iota; + xla::XlaOp expanded_feature_iota; TF_CHECK_OK(XlaHelpers::Iota(builder, DataType::DT_INT32, input_feature * depthwise_multiplier, &expanded_feature_iota)); @@ -126,10 +125,10 @@ xla::ComputationDataHandle CreateExpandedFilterMask( // Expands a filter of shape [H, W, ..., M, N] to [H, W, ..., M, M*N] by adding // zeros for the cross-depth filters. Used to build a depthwise convolution. -xla::ComputationDataHandle ExpandFilterForDepthwiseConvolution( - const TensorShape& filter_shape, DataType dtype, - const xla::ComputationDataHandle& filter, - xla::ComputationBuilder* builder) { +xla::XlaOp ExpandFilterForDepthwiseConvolution(const TensorShape& filter_shape, + DataType dtype, + const xla::XlaOp& filter, + xla::XlaBuilder* builder) { int64 depthwise_multiplier = filter_shape.dim_size(filter_shape.dims() - 1); int64 input_feature = filter_shape.dim_size(filter_shape.dims() - 2); TensorShape expanded_filter_shape = @@ -156,10 +155,11 @@ xla::ComputationDataHandle ExpandFilterForDepthwiseConvolution( } // Inverse of ExpandFilterForDepthwiseConvolution. -xla::ComputationDataHandle ContractFilterForDepthwiseBackprop( - XlaOpKernelContext* ctx, const TensorShape& filter_shape, DataType dtype, - const xla::ComputationDataHandle& filter_backprop, - xla::ComputationBuilder* builder) { +xla::XlaOp ContractFilterForDepthwiseBackprop(XlaOpKernelContext* ctx, + const TensorShape& filter_shape, + DataType dtype, + const xla::XlaOp& filter_backprop, + xla::XlaBuilder* builder) { TensorShape expanded_filter_shape = ExpandedFilterShapeForDepthwiseConvolution(filter_shape); auto masked_expanded_filter = builder->Select( @@ -248,9 +248,9 @@ class ConvOp : public XlaOpKernel { "input and filter must have the same depth: ", in_depth, " vs ", input_shape.dim_size(feature_dim))); - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); - xla::ComputationDataHandle filter = ctx->Input(1); + xla::XlaOp filter = ctx->Input(1); TensorShape expanded_filter_shape = filter_shape; if (depthwise_) { filter = ExpandFilterForDepthwiseConvolution( @@ -288,7 +288,7 @@ class ConvOp : public XlaOpKernel { &unused_output_size, &padding[i].first, &padding[i].second)); } - xla::ComputationDataHandle conv = + xla::XlaOp conv = b->ConvGeneralDilated(ctx->Input(0), filter, window_strides, padding, lhs_dilation, rhs_dilation, dims); ctx->SetOutput(0, conv); @@ -391,7 +391,7 @@ class ConvBackpropInputOp : public XlaOpKernel { expanded_filter_shape, out_backprop_shape, dilations_, strides_, padding_, data_format_, &dims)); - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); auto filter = ctx->Input(1); auto out_backprop = ctx->Input(2); @@ -435,12 +435,11 @@ class ConvBackpropInputOp : public XlaOpKernel { } // Mirror the filter in the spatial dimensions. - xla::ComputationDataHandle mirrored_weights = - b->Rev(filter, kernel_spatial_dims); + xla::XlaOp mirrored_weights = b->Rev(filter, kernel_spatial_dims); // activation gradients // = gradients (with padding and dilation) mirrored_weights - xla::ComputationDataHandle in_backprop = b->ConvGeneralDilated( + xla::XlaOp in_backprop = b->ConvGeneralDilated( out_backprop, mirrored_weights, /*window_strides=*/ones, padding, lhs_dilation, rhs_dilation, dnums); @@ -546,9 +545,9 @@ class ConvBackpropFilterOp : public XlaOpKernel { expanded_filter_shape, out_backprop_shape, dilations_, strides_, padding_, data_format_, &dims)); - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle activations = ctx->Input(0); - xla::ComputationDataHandle gradients = ctx->Input(2); + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp activations = ctx->Input(0); + xla::XlaOp gradients = ctx->Input(2); // The filter gradients are computed by a convolution of the input // activations and the output gradients, with some appropriate padding. diff --git a/tensorflow/compiler/tf2xla/kernels/cross_op.cc b/tensorflow/compiler/tf2xla/kernels/cross_op.cc index 3df8c00f1b8355..7fcd4170fb79a5 100644 --- a/tensorflow/compiler/tf2xla/kernels/cross_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/cross_op.cc @@ -53,7 +53,7 @@ class CrossOp : public XlaOpKernel { } std::vector strides(in0_shape.dims(), 1); - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); auto in0 = ctx->Input(0); auto in1 = ctx->Input(1); starts.back() = 0; diff --git a/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc b/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc index 0cf03ceb948a51..01aa1a83e79679 100644 --- a/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/util/bcast.h" @@ -75,7 +75,7 @@ void XlaBinaryOp::Compile(XlaOpKernelContext* ctx) { } // Call virtual method to emit the computation. - xla::ComputationDataHandle output = + xla::XlaOp output = Computation(ctx, lhs_handle, lhs_shape.dim_sizes(), rhs_handle, rhs_shape.dim_sizes(), bcast, extend_dimension); @@ -85,11 +85,9 @@ void XlaBinaryOp::Compile(XlaOpKernelContext* ctx) { ctx->SetOutput(0, output); } -/* static */ std::pair -XlaBinaryOp::Broadcast(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& lhs, - const xla::ComputationDataHandle& rhs, - const BCast& broadcast_helper) { +/* static */ std::pair XlaBinaryOp::Broadcast( + xla::XlaBuilder* builder, const xla::XlaOp& lhs, const xla::XlaOp& rhs, + const BCast& broadcast_helper) { // Manually construct the broadcasting since MapN does not do // automatic broadcasting. The bcast helper ensures that // lhs.reshape(bcast.x_reshape()).broadcast(bcast.x_bcast()) and diff --git a/tensorflow/compiler/tf2xla/kernels/cwise_ops.h b/tensorflow/compiler/tf2xla/kernels/cwise_ops.h index 5bc1d5fb1f08fb..4f92dbc8740b69 100644 --- a/tensorflow/compiler/tf2xla/kernels/cwise_ops.h +++ b/tensorflow/compiler/tf2xla/kernels/cwise_ops.h @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/util/bcast.h" @@ -30,7 +30,7 @@ namespace tensorflow { // inputs that can be broadcast to the same shape. The base class // contains pure virtual methods to override: description is a textual // description of the operation; and Computation adds the -// implementation of the operation to a xla::ComputationBuilder. For most +// implementation of the operation to a xla::XlaBuilder. For most // arithmetic Ops XLA handles the broadcasting automatically given the input // tensors. class XlaBinaryOp : public XlaOpKernel { @@ -55,10 +55,9 @@ class XlaBinaryOp : public XlaOpKernel { // higher-rank input should be matched when broadcasting the // lower-rank input. See comment below and the documentation on broadcasting // in the XLA documentation. - virtual xla::ComputationDataHandle Computation( - XlaOpKernelContext* ctx, const xla::ComputationDataHandle& lhs, - const gtl::ArraySlice& lhs_shape, - const xla::ComputationDataHandle& rhs, + virtual xla::XlaOp Computation( + XlaOpKernelContext* ctx, const xla::XlaOp& lhs, + const gtl::ArraySlice& lhs_shape, const xla::XlaOp& rhs, const gtl::ArraySlice& rhs_shape, const BCast& broadcast_helper, const std::vector& extend_dimensions) = 0; @@ -67,11 +66,9 @@ class XlaBinaryOp : public XlaOpKernel { // Helper function that performs the broadcasting described by // 'broadcast_helper', yielding arguments 'lhs' and 'rhs' that have the same // shape. - static std::pair - Broadcast(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& lhs, - const xla::ComputationDataHandle& rhs, - const BCast& broadcast_helper); + static std::pair Broadcast( + xla::XlaBuilder* builder, const xla::XlaOp& lhs, const xla::XlaOp& rhs, + const BCast& broadcast_helper); }; } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc index 96d7809f799563..23243f62462c63 100644 --- a/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc @@ -50,8 +50,8 @@ class DepthToSpaceOp : public XlaOpKernel { const gtl::InlinedVector input_shape = input_tensor_shape.dim_sizes(); - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle input = ctx->Input(0); + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp input = ctx->Input(0); int feature_dim = GetTensorFeatureDimIndex(input_rank, data_format_); int num_spatial_dims = GetTensorSpatialDims(input_rank, data_format_); @@ -130,7 +130,7 @@ class DepthToSpaceOp : public XlaOpKernel { ") is not divisible by square of the block size (", block_size_, ")")); - xla::ComputationDataHandle reshaped = b->Reshape(input, reshaped_shape); + xla::XlaOp reshaped = b->Reshape(input, reshaped_shape); // 2. Permute dimensions of `reshaped` to produce // `permuted_reshaped` of shape: @@ -141,8 +141,7 @@ class DepthToSpaceOp : public XlaOpKernel { // input_shape[2], // block_size_, // depth / (block_size_ * block_size_)] - xla::ComputationDataHandle permuted_reshaped = - b->Transpose(reshaped, transpose_order); + xla::XlaOp permuted_reshaped = b->Transpose(reshaped, transpose_order); // 3. Reshape `permuted_reshaped` to flatten `block_shape` into the // batch dimension, producing an output tensor of shape: @@ -152,8 +151,7 @@ class DepthToSpaceOp : public XlaOpKernel { // input_shape[2] * block_size_, // depth / (block_size_ * block_size_)] // - xla::ComputationDataHandle output = - b->Reshape(permuted_reshaped, output_shape); + xla::XlaOp output = b->Reshape(permuted_reshaped, output_shape); ctx->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/diag_op.cc b/tensorflow/compiler/tf2xla/kernels/diag_op.cc index 765ea922a532a0..931705ba837153 100644 --- a/tensorflow/compiler/tf2xla/kernels/diag_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/diag_op.cc @@ -25,10 +25,10 @@ namespace tensorflow { namespace { // Create a diagonal / batch diagonal matrix with 'input' on the diagonal. -xla::StatusOr CreateDiagonal( - const xla::ComputationDataHandle& input, int64 last_dim_size, +xla::StatusOr CreateDiagonal( + const xla::XlaOp& input, int64 last_dim_size, tensorflow::gtl::ArraySlice other_dims, XlaOpKernelContext* ctx, - xla::ComputationBuilder* builder) { + xla::XlaBuilder* builder) { // Create two matrices that have the following forms, and compare them: // // [[0, 0, 0, 0] [[0, 1, 2, 3] @@ -38,12 +38,11 @@ xla::StatusOr CreateDiagonal( // // This produces a predicate matrix of the right size, with "true" on the // diagonal. - xla::ComputationDataHandle iota; + xla::XlaOp iota; TF_RETURN_IF_ERROR( XlaHelpers::Iota(builder, DataType::DT_INT32, last_dim_size, &iota)); - xla::ComputationDataHandle iota_broadcast = - builder->Broadcast(iota, {last_dim_size}); - xla::ComputationDataHandle mask = builder->Eq(iota_broadcast, iota, {0}); + xla::XlaOp iota_broadcast = builder->Broadcast(iota, {last_dim_size}); + xla::XlaOp mask = builder->Eq(iota_broadcast, iota, {0}); // If this is a batched diagonal, broadcast the mask across the other // dimensions. @@ -65,8 +64,7 @@ xla::StatusOr CreateDiagonal( std::vector broadcast_dims(other_dims.begin(), other_dims.end()); broadcast_dims.push_back(1LL); broadcast_dims.push_back(last_dim_size); - xla::ComputationDataHandle input_broadcast = - builder->Reshape(input, broadcast_dims); + xla::XlaOp input_broadcast = builder->Reshape(input, broadcast_dims); broadcast_dims[broadcast_dims.size() - 2] = last_dim_size; xla::PrimitiveType element_type; @@ -74,7 +72,7 @@ xla::StatusOr CreateDiagonal( DataTypeToPrimitiveType(ctx->input_type(0), &element_type)); auto broadcast_shape = xla::ShapeUtil::MakeShape(element_type, broadcast_dims); - xla::ComputationDataHandle zeros = Zeros(builder, broadcast_shape); + xla::XlaOp zeros = Zeros(builder, broadcast_shape); input_broadcast = builder->Add(input_broadcast, zeros); return builder->Select(mask, input_broadcast, zeros); @@ -85,7 +83,7 @@ class DiagOp : public XlaOpKernel { explicit DiagOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); OP_REQUIRES(ctx, ctx->num_inputs() >= 1, errors::InvalidArgument("Diag op must have at an input")); @@ -96,7 +94,7 @@ class DiagOp : public XlaOpKernel { errors::InvalidArgument("Expected 1 <= dims, got shape ", input_shape.DebugString())); - xla::ComputationDataHandle input = ctx->Input(0); + xla::XlaOp input = ctx->Input(0); // Picture: // tf.diag([1, 2, 3, 4]) ==> [[1, 0, 0, 0] @@ -112,7 +110,7 @@ class DiagOp : public XlaOpKernel { auto diag_or_status = CreateDiagonal(input, size, /*other_dims=*/{}, ctx, builder); OP_REQUIRES_OK(ctx, diag_or_status.status()); - xla::ComputationDataHandle diag = diag_or_status.ValueOrDie(); + xla::XlaOp diag = diag_or_status.ValueOrDie(); // Reshapes to the final shape. std::vector new_dims(dims.size() * 2); @@ -131,7 +129,7 @@ class DiagPartOp : public XlaOpKernel { explicit DiagPartOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); const TensorShape input_shape = ctx->InputShape(0); auto dims = input_shape.dim_sizes(); @@ -158,7 +156,7 @@ class DiagPartOp : public XlaOpKernel { new_dims.push_back(dims[i]); } - xla::ComputationDataHandle diag = ctx->Input(0); + xla::XlaOp diag = ctx->Input(0); // TODO(b/30878775): use Slice with strides when supported, in place of // the Pad -> Reshape -> Slice. @@ -199,7 +197,7 @@ class MatrixDiagOp : public XlaOpKernel { explicit MatrixDiagOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); OP_REQUIRES(ctx, ctx->num_inputs() >= 1, errors::InvalidArgument("MatrixDiag op must have at an input")); @@ -210,7 +208,7 @@ class MatrixDiagOp : public XlaOpKernel { errors::InvalidArgument("Expected 1 <= dims, got shape ", input_shape.DebugString())); - xla::ComputationDataHandle diag = ctx->Input(0); + xla::XlaOp diag = ctx->Input(0); int last_dim = dims.size() - 1; int64 last_dim_size = input_shape.dim_size(last_dim); @@ -232,7 +230,7 @@ class MatrixDiagPartOp : public XlaOpKernel { explicit MatrixDiagPartOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); const TensorShape input_shape = ctx->InputShape(0); auto dims = input_shape.dim_sizes(); @@ -241,7 +239,7 @@ class MatrixDiagPartOp : public XlaOpKernel { errors::InvalidArgument("Expected 2 <= dims, got shape ", input_shape.DebugString())); - xla::ComputationDataHandle diag = ctx->Input(0); + xla::XlaOp diag = ctx->Input(0); int last_dim = dims.size() - 1; int64 last_dim_size = dims[last_dim]; diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc index 800ef5ab98d70a..0419de78b2ee83 100644 --- a/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/compiler/tf2xla/type_util.h" @@ -57,7 +57,7 @@ class DynamicUpdateSliceOp : public XlaOpKernel { input_shape.DebugString(), "; update shape is ", update_shape.DebugString())); - xla::ComputationDataHandle result = ctx->builder()->DynamicUpdateSlice( + xla::XlaOp result = ctx->builder()->DynamicUpdateSlice( ctx->Input(0), ctx->Input(1), ctx->Input(2)); ctx->SetOutput(0, result); } diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc index f2cd21ffb9ce88..dd4a1690877950 100644 --- a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc @@ -56,7 +56,7 @@ class DynamicStitchOp : public XlaOpKernel { std::vector indices_input; OP_REQUIRES_OK(ctx, ctx->ConstantInputList("indices", &indices_input)); - std::vector data; + std::vector data; std::vector data_shapes; OP_REQUIRES_OK(ctx, ctx->InputList("data", &data, &data_shapes)); @@ -136,7 +136,7 @@ class DynamicStitchOp : public XlaOpKernel { // Look up all the children expressions that represent the data // inputs. - std::vector input(indices.size()); + std::vector input(indices.size()); for (int input_num = 0; input_num < indices.size(); input_num++) { TensorShape new_shape; // first reshaped dimension is the number of indices for this input. @@ -166,7 +166,7 @@ class DynamicStitchOp : public XlaOpKernel { for (int d = indices0_shape.dims(); d < data0_shape.dims(); d++) { slice_limit[1 + d - indices0_shape.dims()] = data0_shape.dim_size(d); } - std::vector to_concat(number_of_indices); + std::vector to_concat(number_of_indices); for (int index_num = 0; index_num < number_of_indices; index_num++) { const auto& expression = input[src_input_vector[index_num]]; // Take the appropriate slice of data. diff --git a/tensorflow/compiler/tf2xla/kernels/elu_op.cc b/tensorflow/compiler/tf2xla/kernels/elu_op.cc index 2fd27c5ca7e87c..ed7462c16615f7 100644 --- a/tensorflow/compiler/tf2xla/kernels/elu_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/elu_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" @@ -32,7 +32,7 @@ class EluOp : public XlaOpKernel { explicit EluOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} // Computes the max of the scalar input x and 0. void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); const auto zero = XlaHelpers::Zero(b, input_type(0)); const auto one = XlaHelpers::One(b, input_type(0)); const auto pred = b->Gt(ctx->Input(0), zero); @@ -47,7 +47,7 @@ class EluGradOp : public XlaOpKernel { // Return the lhs (incoming gradient) if the rhs (input feature) > 0, // otherwise return lhs * (1 + rhs). void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); const auto zero = XlaHelpers::Zero(b, input_type(0)); const auto one = XlaHelpers::One(b, input_type(0)); const auto grad = ctx->Input(0); @@ -66,7 +66,7 @@ class SeluOp : public XlaOpKernel { explicit SeluOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} // Computes the max of the scalar input x and 0. void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); const auto zero = XlaHelpers::Zero(b, input_type(0)); const auto one = XlaHelpers::One(b, input_type(0)); const auto scale = XlaHelpers::FloatLiteral(b, input_type(0), @@ -86,9 +86,8 @@ class SeluGradOp : public XlaOpKernel { // Return the lhs (incoming gradient) if the rhs (input feature) > 0, // otherwise return lhs * (1 + rhs). void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); const auto zero = XlaHelpers::Zero(b, input_type(0)); - const auto one = XlaHelpers::One(b, input_type(0)); const auto scale = XlaHelpers::FloatLiteral(b, input_type(0), 1.0507009873554804934193349852946); const auto scale_alpha = XlaHelpers::FloatLiteral(b, input_type(0), diff --git a/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc b/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc index b2970eae20a3fb..6df01cabbf1d98 100644 --- a/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc @@ -93,7 +93,7 @@ class ExtractImagePatchesOp : public XlaOpKernel { input_shape.DebugString())); const int64 depth = input_shape.dim_size(feature_dim); - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); // The following code is equivalent to: // eye = np.eye(kH * kW * D).reshape([kH, kW, D, kH * kW * kD]) @@ -110,7 +110,7 @@ class ExtractImagePatchesOp : public XlaOpKernel { // Builds an identity matrix as a broadcast equality of iotas. // iota = np.arange(np.prod(ksize), depth) // filter = np.equal(np.reshape(iota, [-1, 1]), iota).astype(np.float32) - xla::ComputationDataHandle iota; + xla::XlaOp iota; TF_CHECK_OK(XlaHelpers::Iota(builder, DataType::DT_INT32, kernel_size * depth, &iota)); @@ -147,7 +147,7 @@ class ExtractImagePatchesOp : public XlaOpKernel { &padding[i].first, &padding[i].second)); } - xla::ComputationDataHandle conv = + xla::XlaOp conv = builder->ConvGeneralDilated(ctx->Input(0), filter, window_strides, padding, lhs_dilation, rhs_dilation, dims); ctx->SetOutput(0, conv); diff --git a/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc b/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc index 99470d70e709dd..8f0de0a524c908 100644 --- a/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc @@ -44,23 +44,20 @@ void CpuNudge(const float min, const float max, const float quant_min, } // An XLA version of CpuNudge(). -void XlaNudge(xla::ComputationBuilder* b, const DataType data_type, - const xla::ComputationDataHandle& min, - const xla::ComputationDataHandle& max, +void XlaNudge(xla::XlaBuilder* b, const DataType data_type, + const xla::XlaOp& min, const xla::XlaOp& max, const float quant_min_value, const float quant_max_value, - xla::ComputationDataHandle* nudged_min, - xla::ComputationDataHandle* nudged_max, - xla::ComputationDataHandle* scale) { + xla::XlaOp* nudged_min, xla::XlaOp* nudged_max, + xla::XlaOp* scale) { *scale = b->Div(b->Sub(max, min), XlaHelpers::FloatLiteral(b, data_type, quant_max_value - quant_min_value)); - xla::ComputationDataHandle quant_min = + xla::XlaOp quant_min = XlaHelpers::FloatLiteral(b, data_type, quant_min_value); - xla::ComputationDataHandle zero_point_from_min = - b->Sub(quant_min, b->Div(min, *scale)); - xla::ComputationDataHandle quant_max = + xla::XlaOp zero_point_from_min = b->Sub(quant_min, b->Div(min, *scale)); + xla::XlaOp quant_max = XlaHelpers::FloatLiteral(b, data_type, quant_max_value); - xla::ComputationDataHandle nudged_zero_point = + xla::XlaOp nudged_zero_point = b->Select(b->Le(zero_point_from_min, quant_min), quant_min, b->Select(b->Ge(zero_point_from_min, quant_max), quant_max, b->Round(zero_point_from_min))); @@ -68,22 +65,18 @@ void XlaNudge(xla::ComputationBuilder* b, const DataType data_type, *nudged_max = b->Mul(b->Sub(quant_max, nudged_zero_point), *scale); } -xla::ComputationDataHandle Quantize( - xla::ComputationBuilder* b, const xla::ComputationDataHandle& input, - const DataType data_type, - const xla::ComputationDataHandle& nudged_input_min, - const xla::ComputationDataHandle& nudged_input_max, - const xla::ComputationDataHandle& input_scale) { - xla::ComputationDataHandle one = XlaHelpers::FloatLiteral(b, data_type, 1.0f); - xla::ComputationDataHandle inv_scale = b->Div(one, input_scale); - xla::ComputationDataHandle half = - XlaHelpers::FloatLiteral(b, data_type, 0.5f); - - xla::ComputationDataHandle clamped = - b->Clamp(nudged_input_min, input, nudged_input_max); - xla::ComputationDataHandle clamped_shifted = - b->Sub(clamped, nudged_input_min); - xla::ComputationDataHandle rounded = +xla::XlaOp Quantize(xla::XlaBuilder* b, const xla::XlaOp& input, + const DataType data_type, + const xla::XlaOp& nudged_input_min, + const xla::XlaOp& nudged_input_max, + const xla::XlaOp& input_scale) { + xla::XlaOp one = XlaHelpers::FloatLiteral(b, data_type, 1.0f); + xla::XlaOp inv_scale = b->Div(one, input_scale); + xla::XlaOp half = XlaHelpers::FloatLiteral(b, data_type, 0.5f); + + xla::XlaOp clamped = b->Clamp(nudged_input_min, input, nudged_input_max); + xla::XlaOp clamped_shifted = b->Sub(clamped, nudged_input_min); + xla::XlaOp rounded = b->Floor(b->Add(b->Mul(clamped_shifted, inv_scale), half)); return b->Add(b->Mul(rounded, input_scale), nudged_input_min); } @@ -111,18 +104,18 @@ class FakeQuantWithMinMaxArgsOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationDataHandle input = ctx->Input(0); + xla::XlaOp input = ctx->Input(0); const DataType data_type = ctx->input_type(0); - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle nudged_input_min = + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp nudged_input_min = XlaHelpers::FloatLiteral(b, data_type, nudged_input_min_); - xla::ComputationDataHandle nudged_input_max = + xla::XlaOp nudged_input_max = XlaHelpers::FloatLiteral(b, data_type, nudged_input_max_); - xla::ComputationDataHandle input_scale = + xla::XlaOp input_scale = XlaHelpers::FloatLiteral(b, data_type, input_scale_); - xla::ComputationDataHandle output = Quantize( - b, input, data_type, nudged_input_min, nudged_input_max, input_scale); + xla::XlaOp output = Quantize(b, input, data_type, nudged_input_min, + nudged_input_max, input_scale); ctx->SetOutput(0, output); } @@ -159,23 +152,22 @@ class FakeQuantWithMinMaxArgsGradOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationDataHandle gradient = ctx->Input(0); + xla::XlaOp gradient = ctx->Input(0); const TensorShape gradient_shape = ctx->InputShape(0); - xla::ComputationDataHandle input = ctx->Input(1); + xla::XlaOp input = ctx->Input(1); const DataType data_type = ctx->input_type(1); - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle nudged_input_min = + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp nudged_input_min = XlaHelpers::FloatLiteral(b, data_type, nudged_input_min_); - xla::ComputationDataHandle nudged_input_max = + xla::XlaOp nudged_input_max = XlaHelpers::FloatLiteral(b, data_type, nudged_input_max_); - xla::ComputationDataHandle between_nudged_min_max = + xla::XlaOp between_nudged_min_max = b->And(b->Le(nudged_input_min, input), b->Le(input, nudged_input_max)); - xla::ComputationDataHandle zeroes = b->Broadcast( - XlaHelpers::Zero(b, data_type), gradient_shape.dim_sizes()); - xla::ComputationDataHandle output = - b->Select(between_nudged_min_max, gradient, zeroes); + xla::XlaOp zeroes = b->Broadcast(XlaHelpers::Zero(b, data_type), + gradient_shape.dim_sizes()); + xla::XlaOp output = b->Select(between_nudged_min_max, gradient, zeroes); ctx->SetOutput(0, output); } @@ -204,18 +196,18 @@ class FakeQuantWithMinMaxVarsOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationDataHandle input = ctx->Input(0); + xla::XlaOp input = ctx->Input(0); const DataType data_type = ctx->input_type(0); - xla::ComputationDataHandle input_min = ctx->Input(1); - xla::ComputationDataHandle input_max = ctx->Input(2); + xla::XlaOp input_min = ctx->Input(1); + xla::XlaOp input_max = ctx->Input(2); - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle nudged_input_min, nudged_input_max, input_scale; + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp nudged_input_min, nudged_input_max, input_scale; XlaNudge(b, data_type, input_min, input_max, quant_min_, quant_max_, &nudged_input_min, &nudged_input_max, &input_scale); - xla::ComputationDataHandle output = Quantize( - b, input, data_type, nudged_input_min, nudged_input_max, input_scale); + xla::XlaOp output = Quantize(b, input, data_type, nudged_input_min, + nudged_input_max, input_scale); ctx->SetOutput(0, output); } @@ -243,47 +235,43 @@ class FakeQuantWithMinMaxVarsGradOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationDataHandle gradient = ctx->Input(0); + xla::XlaOp gradient = ctx->Input(0); const TensorShape gradient_shape = ctx->InputShape(0); - xla::ComputationDataHandle input = ctx->Input(1); + xla::XlaOp input = ctx->Input(1); const DataType data_type = ctx->input_type(1); const DataType accumulation_type = XlaHelpers::SumAccumulationType(data_type); - xla::ComputationDataHandle input_min = ctx->Input(2); - xla::ComputationDataHandle input_max = ctx->Input(3); + xla::XlaOp input_min = ctx->Input(2); + xla::XlaOp input_max = ctx->Input(3); - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle nudged_input_min, nudged_input_max, input_scale; + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp nudged_input_min, nudged_input_max, input_scale; XlaNudge(b, data_type, input_min, input_max, quant_min_, quant_max_, &nudged_input_min, &nudged_input_max, &input_scale); - xla::ComputationDataHandle between_nudged_min_max = + xla::XlaOp between_nudged_min_max = b->And(b->Le(nudged_input_min, input), b->Le(input, nudged_input_max)); - xla::ComputationDataHandle zero = XlaHelpers::Zero(b, data_type); - xla::ComputationDataHandle zeroes = - b->Broadcast(zero, gradient_shape.dim_sizes()); - xla::ComputationDataHandle output0 = - b->Select(between_nudged_min_max, gradient, zeroes); + xla::XlaOp zero = XlaHelpers::Zero(b, data_type); + xla::XlaOp zeroes = b->Broadcast(zero, gradient_shape.dim_sizes()); + xla::XlaOp output0 = b->Select(between_nudged_min_max, gradient, zeroes); ctx->SetOutput(0, output0); - xla::ComputationDataHandle below_min = b->Lt(input, nudged_input_min); - xla::ComputationDataHandle select1 = b->Select(below_min, gradient, zeroes); - xla::ComputationDataHandle reduce1 = b->ReduceAll( + xla::XlaOp below_min = b->Lt(input, nudged_input_min); + xla::XlaOp select1 = b->Select(below_min, gradient, zeroes); + xla::XlaOp reduce1 = b->ReduceAll( XlaHelpers::ConvertElementType(b, select1, accumulation_type), XlaHelpers::Zero(b, accumulation_type), *ctx->GetOrCreateAdd(accumulation_type)); - xla::ComputationDataHandle output1 = - XlaHelpers::ConvertElementType(b, reduce1, data_type); + xla::XlaOp output1 = XlaHelpers::ConvertElementType(b, reduce1, data_type); ctx->SetOutput(1, output1); - xla::ComputationDataHandle above_max = b->Gt(input, nudged_input_max); - xla::ComputationDataHandle select2 = b->Select(above_max, gradient, zeroes); - xla::ComputationDataHandle reduce2 = b->ReduceAll( + xla::XlaOp above_max = b->Gt(input, nudged_input_max); + xla::XlaOp select2 = b->Select(above_max, gradient, zeroes); + xla::XlaOp reduce2 = b->ReduceAll( XlaHelpers::ConvertElementType(b, select2, accumulation_type), XlaHelpers::Zero(b, accumulation_type), *ctx->GetOrCreateAdd(accumulation_type)); - xla::ComputationDataHandle output2 = - XlaHelpers::ConvertElementType(b, reduce2, data_type); + xla::XlaOp output2 = XlaHelpers::ConvertElementType(b, reduce2, data_type); ctx->SetOutput(2, output2); } diff --git a/tensorflow/compiler/tf2xla/kernels/fft_ops.cc b/tensorflow/compiler/tf2xla/kernels/fft_ops.cc index a4f3c1c3ad9a92..fcb927dab0f5db 100644 --- a/tensorflow/compiler/tf2xla/kernels/fft_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/fft_ops.cc @@ -62,9 +62,8 @@ class GenericFftOp : public XlaOpKernel { } } - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle fft = - b->Fft(ctx->Input(0), fft_type_, fft_length); + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp fft = b->Fft(ctx->Input(0), fft_type_, fft_length); ctx->SetOutput(0, fft); } diff --git a/tensorflow/compiler/tf2xla/kernels/fill_op.cc b/tensorflow/compiler/tf2xla/kernels/fill_op.cc index eaa13b8dfacce9..e4467a0fb138ed 100644 --- a/tensorflow/compiler/tf2xla/kernels/fill_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/fill_op.cc @@ -48,7 +48,7 @@ class FillOp : public XlaOpKernel { 0, {dims_shape.num_elements()}, &dims_literal)); // Convert the dims literal into a vector that we can pass to - // ComputationBuilder. + // XlaBuilder. std::vector broadcast; broadcast.reserve(dims_literal.shape().dimensions(0)); for (int i = 0; i < dims_literal.shape().dimensions(0); ++i) { @@ -56,7 +56,7 @@ class FillOp : public XlaOpKernel { } // Look up the value input, reshaping to a scalar if it was a // 'legacy' scalar (secretly a vector). - xla::ComputationDataHandle data = ctx->Input(1); + xla::XlaOp data = ctx->Input(1); if (value_shape.dims() > 0) { CHECK_EQ(value_shape.dims(), 1); data = ctx->builder()->Reshape(data, {}); diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op.cc b/tensorflow/compiler/tf2xla/kernels/gather_op.cc index 0b79cb0916ee8a..d13e25bcddae16 100644 --- a/tensorflow/compiler/tf2xla/kernels/gather_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/gather_op.cc @@ -26,13 +26,11 @@ limitations under the License. namespace tensorflow { -Status XlaGather(const xla::ComputationDataHandle& input, - const TensorShape& input_shape, - const xla::ComputationDataHandle& indices, - const TensorShape& indices_shape, int64 axis, - bool indices_are_nd, DataType dtype, DataType index_type, - xla::ComputationBuilder* builder, - xla::ComputationDataHandle* gather_output) { +Status XlaGather(const xla::XlaOp& input, const TensorShape& input_shape, + const xla::XlaOp& indices, const TensorShape& indices_shape, + int64 axis, bool indices_are_nd, DataType dtype, + DataType index_type, xla::XlaBuilder* builder, + xla::XlaOp* gather_output) { // There is no deep reason why we need this precondition, but this is the only // combination that is used and tested today. CHECK(!indices_are_nd || axis == 0); @@ -153,7 +151,7 @@ class GatherOp : public XlaOpKernel { explicit GatherOp(OpKernelConstruction* context) : XlaOpKernel(context) {} void Compile(XlaOpKernelContext* context) override { - xla::ComputationBuilder* builder = context->builder(); + xla::XlaBuilder* builder = context->builder(); auto input = context->Input(0); auto input_shape = context->InputShape(0); auto indices = context->Input(1); @@ -182,7 +180,7 @@ class GatherOp : public XlaOpKernel { OP_REQUIRES(context, index_type == DT_INT32 || index_type == DT_INT64, errors::InvalidArgument("indices must be int32 or int64")); - xla::ComputationDataHandle gather; + xla::XlaOp gather; OP_REQUIRES_OK( context, XlaGather(input, input_shape, indices, indices_shape, axis, /*indices_are_nd=*/false, input_type(0), index_type, @@ -220,10 +218,10 @@ class GatherNdOp : public XlaOpKernel { indices_shape.dim_size(indices_shape.dims() - 1), " vs. ", params_shape.dims())); - xla::ComputationBuilder* builder = context->builder(); + xla::XlaBuilder* builder = context->builder(); auto params = context->Input(0); auto indices = context->Input(1); - xla::ComputationDataHandle gather; + xla::XlaOp gather; OP_REQUIRES_OK(context, XlaGather(params, params_shape, indices, indices_shape, /*axis=*/0, /*indices_are_nd=*/true, params_type, diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h b/tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h index f9376f0eabdc0f..d898e43b858bac 100644 --- a/tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h +++ b/tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/util/bcast.h" @@ -33,13 +33,11 @@ namespace tensorflow { // If `indices_are_nd` is true, the last dimension of `indices` are treated as // a multidimensional index values. Otherwise, `indices` is treated as a tensor // of scalar indices. -Status XlaGather(const xla::ComputationDataHandle& input, - const TensorShape& input_shape, - const xla::ComputationDataHandle& indices, - const TensorShape& indices_shape, int64 axis, - bool indices_are_nd, DataType dtype, DataType index_type, - xla::ComputationBuilder* builder, - xla::ComputationDataHandle* gather_output); +Status XlaGather(const xla::XlaOp& input, const TensorShape& input_shape, + const xla::XlaOp& indices, const TensorShape& indices_shape, + int64 axis, bool indices_are_nd, DataType dtype, + DataType index_type, xla::XlaBuilder* builder, + xla::XlaOp* gather_output); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/if_op.cc b/tensorflow/compiler/tf2xla/kernels/if_op.cc index eefbe55c815d80..8b9b026643cf35 100644 --- a/tensorflow/compiler/tf2xla/kernels/if_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/if_op.cc @@ -37,7 +37,7 @@ XlaIfOp::XlaIfOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { // TODO(b/35949885): There is duplication here with the handling of the // while_op. Refactor the common code out/rework. void XlaIfOp::Compile(XlaOpKernelContext* ctx) { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); OP_REQUIRES(ctx, cond_type_ == DT_BOOL, errors::InvalidArgument( @@ -48,7 +48,7 @@ void XlaIfOp::Compile(XlaOpKernelContext* ctx) { VLOG(1) << "Building If: " << input_types_.size() << " inputs"; - std::vector inputs(input_types_.size()); + std::vector inputs(input_types_.size()); std::vector arguments(input_types_.size()); for (int i = 0; i < input_types_.size(); ++i) { XlaCompiler::Argument& arg = arguments[i]; @@ -175,19 +175,19 @@ void XlaIfOp::Compile(XlaOpKernelContext* ctx) { "Mismatch in resource of then and else branch for resource ", i)); } - xla::ComputationDataHandle outputs = + xla::XlaOp outputs = b->Conditional(ctx->Input(0), b->Tuple(inputs), *then_result.computation, b->Tuple(inputs), *else_result.computation); // Sets non-variable outputs. for (int i = 0; i < output_types_.size(); ++i) { if (ctx->input_type(i) != DT_RESOURCE) { - xla::ComputationDataHandle output_handle = b->GetTupleElement(outputs, i); + xla::XlaOp output_handle = b->GetTupleElement(outputs, i); if (VLOG_IS_ON(2)) { LOG(INFO) << "Setting output " << i; auto shape_or = b->GetShape(output_handle); if (shape_or.ok()) { LOG(INFO) << "Shape for output " << i << ": " - << xla::ShapeUtil::HumanString(*shape_or.ValueOrDie()); + << xla::ShapeUtil::HumanString(shape_or.ValueOrDie()); } else { LOG(INFO) << "Shape unknown for output " << i; } diff --git a/tensorflow/compiler/tf2xla/kernels/image_ops.cc b/tensorflow/compiler/tf2xla/kernels/image_ops.cc index 5eeda79a935e81..1568b33679963c 100644 --- a/tensorflow/compiler/tf2xla/kernels/image_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/image_ops.cc @@ -23,10 +23,9 @@ namespace { // Converts 'input' from RGB format to HSV format. // 'shape' is the shape of the red/green/blue tensors. -std::array RGBToHSV( - XlaOpKernelContext* ctx, xla::ComputationBuilder* b, - const std::array& rgb, DataType dtype, - const TensorShape& shape) { +std::array RGBToHSV(XlaOpKernelContext* ctx, xla::XlaBuilder* b, + const std::array& rgb, + DataType dtype, const TensorShape& shape) { auto zero = XlaHelpers::Zero(b, dtype); auto one = XlaHelpers::One(b, dtype); @@ -54,12 +53,12 @@ std::array RGBToHSV( } // Converts 'input' from HSV format to RGB format. -std::array HSVToRGB( - xla::ComputationBuilder* b, - const std::array& hsv, DataType dtype) { - xla::ComputationDataHandle hue = hsv[0]; - xla::ComputationDataHandle saturation = hsv[1]; - xla::ComputationDataHandle value = hsv[2]; +std::array HSVToRGB(xla::XlaBuilder* b, + const std::array& hsv, + DataType dtype) { + xla::XlaOp hue = hsv[0]; + xla::XlaOp saturation = hsv[1]; + xla::XlaOp value = hsv[2]; auto zero = XlaHelpers::Zero(b, dtype); auto one = XlaHelpers::FloatLiteral(b, dtype, 1.0); auto two = XlaHelpers::FloatLiteral(b, dtype, 2.0); @@ -95,16 +94,16 @@ class RGBToHSVOp : public XlaOpKernel { errors::FailedPrecondition("input must have 3 channels but input has ", channels, " channels.")); - xla::ComputationBuilder* b = context->builder(); - xla::ComputationDataHandle input = context->Input(0); + xla::XlaBuilder* b = context->builder(); + xla::XlaOp input = context->Input(0); - xla::ComputationDataHandle red = + xla::XlaOp red = b->SliceInDim(input, /*start_index=*/0, /*limit_index=*/1, /*stride=*/1, /*dimno=*/channel_dim); - xla::ComputationDataHandle green = + xla::XlaOp green = b->SliceInDim(input, /*start_index=*/1, /*limit_index=*/2, /*stride=*/1, /*dimno=*/channel_dim); - xla::ComputationDataHandle blue = + xla::XlaOp blue = b->SliceInDim(input, /*start_index=*/2, /*limit_index=*/3, /*stride=*/1, /*dimno=*/channel_dim); TensorShape channel_shape = input_shape; @@ -133,15 +132,15 @@ class HSVToRGBOp : public XlaOpKernel { errors::FailedPrecondition("input must have 3 channels but input has ", channels, " channels.")); - xla::ComputationBuilder* b = context->builder(); - xla::ComputationDataHandle input = context->Input(0); - xla::ComputationDataHandle hue = + xla::XlaBuilder* b = context->builder(); + xla::XlaOp input = context->Input(0); + xla::XlaOp hue = b->SliceInDim(input, /*start_index=*/0, /*limit_index=*/1, /*stride=*/1, /*dimno=*/channel_dim); - xla::ComputationDataHandle saturation = + xla::XlaOp saturation = b->SliceInDim(input, /*start_index=*/1, /*limit_index=*/2, /*stride=*/1, /*dimno=*/channel_dim); - xla::ComputationDataHandle value = + xla::XlaOp value = b->SliceInDim(input, /*start_index=*/2, /*limit_index=*/3, /*stride=*/1, /*dimno=*/channel_dim); @@ -174,9 +173,9 @@ class AdjustContrastOpV2 : public XlaOpKernel { errors::InvalidArgument("contrast_factor must be scalar: ", factor_shape.DebugString())); - xla::ComputationBuilder* b = context->builder(); - xla::ComputationDataHandle input = context->Input(0); - xla::ComputationDataHandle factor = context->Input(1); + xla::XlaBuilder* b = context->builder(); + xla::XlaOp input = context->Input(0); + xla::XlaOp factor = context->Input(1); DataType type = context->input_type(0); @@ -221,19 +220,19 @@ class AdjustSaturationOp : public XlaOpKernel { errors::InvalidArgument("input must have 3 channels but instead has ", channels, " channels.")); - xla::ComputationBuilder* b = context->builder(); - xla::ComputationDataHandle input = context->Input(0); - xla::ComputationDataHandle scale = context->Input(1); + xla::XlaBuilder* b = context->builder(); + xla::XlaOp input = context->Input(0); + xla::XlaOp scale = context->Input(1); DataType type = context->input_type(0); - xla::ComputationDataHandle red = + xla::XlaOp red = b->SliceInDim(input, /*start_index=*/0, /*limit_index=*/1, /*stride=*/1, /*dimno=*/channel_dim); - xla::ComputationDataHandle green = + xla::XlaOp green = b->SliceInDim(input, /*start_index=*/1, /*limit_index=*/2, /*stride=*/1, /*dimno=*/channel_dim); - xla::ComputationDataHandle blue = + xla::XlaOp blue = b->SliceInDim(input, /*start_index=*/2, /*limit_index=*/3, /*stride=*/1, /*dimno=*/channel_dim); TensorShape channel_shape = input_shape; @@ -271,19 +270,19 @@ class AdjustHueOp : public XlaOpKernel { errors::InvalidArgument("input must have 3 channels but instead has ", channels, " channels.")); - xla::ComputationBuilder* b = context->builder(); - xla::ComputationDataHandle input = context->Input(0); - xla::ComputationDataHandle delta = context->Input(1); + xla::XlaBuilder* b = context->builder(); + xla::XlaOp input = context->Input(0); + xla::XlaOp delta = context->Input(1); DataType type = context->input_type(0); - xla::ComputationDataHandle red = + xla::XlaOp red = b->SliceInDim(input, /*start_index=*/0, /*limit_index=*/1, /*stride=*/1, /*dimno=*/channel_dim); - xla::ComputationDataHandle green = + xla::XlaOp green = b->SliceInDim(input, /*start_index=*/1, /*limit_index=*/2, /*stride=*/1, /*dimno=*/channel_dim); - xla::ComputationDataHandle blue = + xla::XlaOp blue = b->SliceInDim(input, /*start_index=*/2, /*limit_index=*/3, /*stride=*/1, /*dimno=*/channel_dim); TensorShape channel_shape = input_shape; diff --git a/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc b/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc index f36b3f594826c2..9058cbc7476257 100644 --- a/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc @@ -99,9 +99,9 @@ ResizeConvolutionDims ComputeResizeConvolutionParameters( return dims; } -xla::ComputationDataHandle MakeBilinearResizeKernel( - xla::ComputationBuilder* builder, gtl::ArraySlice kernel_size, - int64 channels) { +xla::XlaOp MakeBilinearResizeKernel(xla::XlaBuilder* builder, + gtl::ArraySlice kernel_size, + int64 channels) { // Form a 2D convolution kernel like: // 1 2 3 2 1 // 2 4 6 4 2 @@ -120,7 +120,7 @@ xla::ComputationDataHandle MakeBilinearResizeKernel( return kernel; }; - xla::ComputationDataHandle channels_iota; + xla::XlaOp channels_iota; // DT_INT32 Iota will always return status::OK(). TF_CHECK_OK( XlaHelpers::Iota(builder, DataType::DT_INT32, channels, &channels_iota)); @@ -139,10 +139,12 @@ xla::ComputationDataHandle MakeBilinearResizeKernel( /*broadcast_dimensions=*/{0}); } -xla::ComputationDataHandle ResizeUsingDilationAndConvolution( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& input, - const int num_spatial_dims, std::vector in_size, - std::vector out_size, const int64 channels) { +xla::XlaOp ResizeUsingDilationAndConvolution(xla::XlaBuilder* builder, + const xla::XlaOp& input, + const int num_spatial_dims, + std::vector in_size, + std::vector out_size, + const int64 channels) { // Picture for a 1x3 to 1x4 resize: // stride = 2, kernel size = 3 // Input: @@ -168,9 +170,9 @@ xla::ComputationDataHandle ResizeUsingDilationAndConvolution( ResizeConvolutionDims dims = ComputeResizeConvolutionParameters(in_size, out_size); - xla::ComputationDataHandle kernel = + xla::XlaOp kernel = MakeBilinearResizeKernel(builder, dims.kernel_size, channels); - xla::ComputationDataHandle output = builder->ConvGeneralDilated( + xla::XlaOp output = builder->ConvGeneralDilated( input, kernel, dims.stride, /*padding=*/ {{dims.kernel_size[0] - 1, dims.kernel_size[0] - 1}, @@ -189,10 +191,12 @@ xla::ComputationDataHandle ResizeUsingDilationAndConvolution( return output; } -xla::ComputationDataHandle ResizeUsingDilationAndConvolutionGradOp( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& grad, - const int num_spatial_dims, std::vector in_size, - std::vector grad_size, const int64 channels) { +xla::XlaOp ResizeUsingDilationAndConvolutionGradOp(xla::XlaBuilder* builder, + const xla::XlaOp& grad, + const int num_spatial_dims, + std::vector in_size, + std::vector grad_size, + const int64 channels) { ResizeConvolutionDims dims = ComputeResizeConvolutionParameters(in_size, grad_size); @@ -210,7 +214,7 @@ xla::ComputationDataHandle ResizeUsingDilationAndConvolutionGradOp( } dimension_numbers.set_kernel_input_feature_dimension(num_spatial_dims); dimension_numbers.set_kernel_output_feature_dimension(num_spatial_dims + 1); - xla::ComputationDataHandle kernel = + xla::XlaOp kernel = MakeBilinearResizeKernel(builder, dims.kernel_size, channels); // Broadcast the input kernel where the forward op expanded from a size == 1 @@ -223,7 +227,7 @@ xla::ComputationDataHandle ResizeUsingDilationAndConvolutionGradOp( } } - xla::ComputationDataHandle output = builder->ConvGeneralDilated( + xla::XlaOp output = builder->ConvGeneralDilated( grad, kernel, /*window_strides=*/dims.kernel_size, /*padding=*/ {{dims.kernel_size[0] - 1, dims.kernel_size[0] - 1}, @@ -258,7 +262,7 @@ class ResizeBilinearOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); TensorShape input_shape = ctx->InputShape(0); OP_REQUIRES(ctx, input_shape.dims() == 4, @@ -283,7 +287,7 @@ class ResizeBilinearOp : public XlaOpKernel { const int num_spatial_dims = 2; - xla::ComputationDataHandle input = ctx->Input(0); + xla::XlaOp input = ctx->Input(0); // If in_size[i] > 1 and out_size[i] == 1, slice out the first input in // dimension i. @@ -318,7 +322,7 @@ class ResizeBilinearOp : public XlaOpKernel { // from image of size axb -> cxd is same as resizing axb -> exf -> cxd. // // This makes the convolutions kernels smaller and the operation faster. - xla::ComputationDataHandle output = input; + xla::XlaOp output = input; while (in_size != out_size) { if (in_size[0] != 1 && in_size[1] != 1) { std::vector k = { @@ -369,7 +373,7 @@ class ResizeBilinearGradOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); TensorShape input_shape = ctx->InputShape(1); OP_REQUIRES(ctx, input_shape.dims() == 4, @@ -406,9 +410,9 @@ class ResizeBilinearGradOp : public XlaOpKernel { const int num_spatial_dims = 2; - xla::ComputationDataHandle grad = ctx->Input(0); + xla::XlaOp grad = ctx->Input(0); - xla::ComputationDataHandle output = grad; + xla::XlaOp output = grad; while (in_size != grad_size) { if (in_size[0] != 1 && in_size[1] != 1) { std::vector k = { diff --git a/tensorflow/compiler/tf2xla/kernels/index_ops.cc b/tensorflow/compiler/tf2xla/kernels/index_ops.cc index 7bf4b435f526af..36eb4c75454ed8 100644 --- a/tensorflow/compiler/tf2xla/kernels/index_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/index_ops.cc @@ -61,10 +61,10 @@ void XlaArgMinMaxOp::Compile(XlaOpKernelContext* ctx) { DataType index_type = output_type(0); - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle input = ctx->Input(0); + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp input = ctx->Input(0); - xla::ComputationDataHandle output; + xla::XlaOp output; if (is_min_) { OP_REQUIRES_OK(ctx, XlaHelpers::ArgMin(b, ctx, input, input_shape, input_type(0), diff --git a/tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc b/tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc index b1f3c3c298ce0c..2c2d88486fda99 100644 --- a/tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc +++ b/tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc @@ -71,10 +71,10 @@ class ArgMaxCustomCallOp : public XlaOpKernel { OP_REQUIRES(ctx, XlaContext::Get(ctx).allow_cpu_custom_calls(), errors::InvalidArgument( "ArgMax implementation requires a CustomCall on CPU")); - xla::ComputationBuilder& b = *ctx->builder(); + xla::XlaBuilder& b = *ctx->builder(); // XLA passes to the function, so it is not included here. - std::vector args; + std::vector args; args.push_back(ctx->Input(0)); args.push_back(b.ConstantLiteral( *xla::Literal::CreateR1(input_shape.dim_sizes()))); @@ -91,7 +91,7 @@ class ArgMaxCustomCallOp : public XlaOpKernel { // Tell XLA to call the custom code, defined in // index_ops_kernel_argmax_float_1d.cc. - xla::ComputationDataHandle output; + xla::XlaOp output; switch (input_shape.dims()) { case 1: output = b.CustomCall("argmax_float_1d_xla_impl", args, xla_shape); diff --git a/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc b/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc index c177f08d9c4687..1decf7d72d72bb 100644 --- a/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/kernels/no_op.h" @@ -33,7 +33,7 @@ class L2LossOp : public XlaOpKernel { std::iota(dims.begin(), dims.end(), 0); DataType dtype = ctx->input_type(0); - xla::ComputationBuilder* const b = ctx->builder(); + xla::XlaBuilder* const b = ctx->builder(); // output = sum(t ** 2) / 2 const DataType accumulation_type = XlaHelpers::SumAccumulationType(dtype); diff --git a/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc b/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc index 1cfee3070f384a..39fbf98a627491 100644 --- a/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc @@ -38,8 +38,8 @@ class LRNOp : public XlaOpKernel { OP_REQUIRES(ctx, in_shape.dims() == 4, errors::InvalidArgument("in must be 4-dimensional")); - xla::ComputationBuilder* builder = ctx->builder(); - xla::ComputationDataHandle input = ctx->Input(0); + xla::XlaBuilder* builder = ctx->builder(); + xla::XlaOp input = ctx->Input(0); // sqr_sum[a, b, c, d] = // sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2) @@ -111,10 +111,10 @@ class LRNGradOp : public XlaOpKernel { "input_grads, input_image, and out_image should have the same " "shape")); - xla::ComputationBuilder* builder = ctx->builder(); - xla::ComputationDataHandle in_grads = ctx->Input(0); - xla::ComputationDataHandle in_image = ctx->Input(1); - xla::ComputationDataHandle out_image = ctx->Input(2); + xla::XlaBuilder* builder = ctx->builder(); + xla::XlaOp in_grads = ctx->Input(0); + xla::XlaOp in_image = ctx->Input(1); + xla::XlaOp out_image = ctx->Input(2); // This code is ported from tensorflow/core/kernels/lrn_op.cc. In Python // pseudo-code, the Eigen code does this for each spatial position: @@ -166,7 +166,7 @@ class LRNGradOp : public XlaOpKernel { auto dy_reduced = XlaHelpers::ConvertElementType(builder, dy_reduce, input_type(0)); - xla::ComputationDataHandle gradients = builder->Add( + xla::XlaOp gradients = builder->Add( builder->Mul(in_image, dy_reduced), builder->Mul(in_grads, builder->Pow(norm, builder->ConstantR0(-beta_)))); diff --git a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc index 886baf8115243a..6949b296f4b9af 100644 --- a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc @@ -66,8 +66,8 @@ class MatMulOp : public XlaOpKernel { a_shape.DebugString(), ", In[1]: ", b_shape.DebugString())); - xla::ComputationDataHandle a = ctx->Input(0); - xla::ComputationDataHandle b = ctx->Input(1); + xla::XlaOp a = ctx->Input(0); + xla::XlaOp b = ctx->Input(1); if (is_sparse_) { if (a_type_ == DT_BFLOAT16) { a = ctx->builder()->ConvertElementType(a, xla::F32); diff --git a/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc b/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc index faa415a97b053b..fbd5dc0fdad448 100644 --- a/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc @@ -44,10 +44,10 @@ class MatrixBandPartOp : public XlaOpKernel { errors::InvalidArgument("num_upper must be scalar, got shape ", num_upper_in_shape.DebugString())); - xla::ComputationBuilder* builder = context->builder(); - xla::ComputationDataHandle input = context->Input(0); - xla::ComputationDataHandle num_lower = context->Input(1); - xla::ComputationDataHandle num_upper = context->Input(2); + xla::XlaBuilder* builder = context->builder(); + xla::XlaOp input = context->Input(0); + xla::XlaOp num_lower = context->Input(1); + xla::XlaOp num_upper = context->Input(2); DataType input_type = context->input_type(0); DataType index_type = context->input_type(1); @@ -58,10 +58,10 @@ class MatrixBandPartOp : public XlaOpKernel { // Compute 'offset', which is how many diagonals we are above/below the // diagonal. - xla::ComputationDataHandle iota_m; + xla::XlaOp iota_m; OP_REQUIRES_OK(context, XlaHelpers::Iota(builder, index_type, m, &iota_m)); - xla::ComputationDataHandle iota_n; + xla::XlaOp iota_n; OP_REQUIRES_OK(context, XlaHelpers::Iota(builder, index_type, n, &iota_n)); auto offset = builder->Sub(builder->Broadcast(iota_n, {m}), iota_m, diff --git a/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc b/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc index b2940bdcff75a0..db53f6fef8d6bf 100644 --- a/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc @@ -54,16 +54,16 @@ class MatrixSetDiagOp : public XlaOpKernel { input_shape.DebugString(), " and diagonal shape: ", diag_shape.DebugString())); - xla::ComputationBuilder* builder = context->builder(); - xla::ComputationDataHandle input = context->Input(0); - xla::ComputationDataHandle diag = context->Input(1); + xla::XlaBuilder* builder = context->builder(); + xla::XlaOp input = context->Input(0); + xla::XlaOp diag = context->Input(1); auto zero = XlaHelpers::Zero(builder, context->input_type(0)); // Create an indicator tensor that is true only on the diagonal. - xla::ComputationDataHandle iota_m; + xla::XlaOp iota_m; OP_REQUIRES_OK(context, XlaHelpers::Iota(builder, DT_INT32, m, &iota_m)); - xla::ComputationDataHandle iota_n; + xla::XlaOp iota_n; OP_REQUIRES_OK(context, XlaHelpers::Iota(builder, DT_INT32, n, &iota_n)); auto indicator = builder->Eq(iota_m, builder->Broadcast(iota_n, {m}), diff --git a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc index 05a36a031ad73b..7e9de3ef9b245c 100644 --- a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc @@ -25,10 +25,11 @@ class MirrorPadOp : public XlaOpKernel { public: explicit MirrorPadOp(OpKernelConstruction* context) : XlaOpKernel(context) {} - xla::StatusOr DoMirrorPad( - const xla::ComputationDataHandle& t, const xla::Shape& original_shape, - const xla::Literal& pad_literal, xla::ComputationBuilder* b) { - xla::ComputationDataHandle accum = t; + xla::StatusOr DoMirrorPad(const xla::XlaOp& t, + const xla::Shape& original_shape, + const xla::Literal& pad_literal, + xla::XlaBuilder* b) { + xla::XlaOp accum = t; for (int64 dimno = xla::ShapeUtil::Rank(original_shape) - 1; dimno >= 0; --dimno) { auto t_rev = b->Rev(accum, {dimno}); @@ -76,12 +77,12 @@ class MirrorPadOp : public XlaOpKernel { OP_REQUIRES_OK( ctx, ctx->ConstantInputReshaped(1, {fixed_dims, 2}, &pad_literal)); - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); auto in0 = ctx->Input(0); - xla::StatusOr> in0_shape = b->GetShape(in0); + xla::StatusOr in0_shape = b->GetShape(in0); OP_REQUIRES(ctx, in0_shape.ok(), in0_shape.status()); - xla::StatusOr accum_status = - DoMirrorPad(in0, *in0_shape.ValueOrDie(), pad_literal, b); + xla::StatusOr accum_status = + DoMirrorPad(in0, in0_shape.ValueOrDie(), pad_literal, b); OP_REQUIRES_OK(ctx, accum_status.status()); diff --git a/tensorflow/compiler/tf2xla/kernels/one_hot_op.cc b/tensorflow/compiler/tf2xla/kernels/one_hot_op.cc index 9f7c9913802d31..cac2eea96eeed7 100644 --- a/tensorflow/compiler/tf2xla/kernels/one_hot_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/one_hot_op.cc @@ -62,7 +62,7 @@ class OneHotOp : public XlaOpKernel { ctx, depth >= 0, errors::InvalidArgument("depth must be non-negative, got: ", depth)); - xla::ComputationDataHandle one_hot; + xla::XlaOp one_hot; OP_REQUIRES_OK( ctx, XlaHelpers::OneHot(ctx->builder(), depth, axis, input_type(0), indices_shape, ctx->Input(0), ctx->Input(2), diff --git a/tensorflow/compiler/tf2xla/kernels/pack_op.cc b/tensorflow/compiler/tf2xla/kernels/pack_op.cc index a4318e29d2532f..aecaabb6dcf46b 100644 --- a/tensorflow/compiler/tf2xla/kernels/pack_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/pack_op.cc @@ -43,7 +43,7 @@ class PackOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - std::vector values; + std::vector values; std::vector shapes; OP_REQUIRES_OK(ctx, ctx->InputList("values", &values, &shapes)); const int num = values.size(); @@ -69,7 +69,7 @@ class PackOp : public XlaOpKernel { -expanded_num_dims, ", ", expanded_num_dims, ")")); - std::vector reshaped_inputs(num); + std::vector reshaped_inputs(num); TensorShape child_shape(shapes[0]); child_shape.InsertDim(axis, 1); diff --git a/tensorflow/compiler/tf2xla/kernels/pad_op.cc b/tensorflow/compiler/tf2xla/kernels/pad_op.cc index 791351637aee61..7c95475e7b1f02 100644 --- a/tensorflow/compiler/tf2xla/kernels/pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/pad_op.cc @@ -70,7 +70,7 @@ class PadOp : public XlaOpKernel { } // PadV2 added a "constant_values" input that indicates the pad value. - xla::ComputationDataHandle constant_values; + xla::XlaOp constant_values; if (ctx->num_inputs() == 3) { OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(ctx->InputShape(2)), errors::InvalidArgument("constant_values must be a scalar.")); diff --git a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc index 5f635dd1bc6122..f8e7b48a0fd948 100644 --- a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc @@ -66,15 +66,15 @@ class PoolingOp : public XlaOpKernel { int num_dims() const { return num_spatial_dims_ + 2; } // Method that builds an initial value to use in reductions. - virtual xla::ComputationDataHandle InitValue(xla::ComputationBuilder* b) = 0; + virtual xla::XlaOp InitValue(xla::XlaBuilder* b) = 0; // The reduction operation to apply to each window. - virtual const xla::Computation* Reduction(XlaOpKernelContext* ctx) = 0; + virtual const xla::XlaComputation* Reduction(XlaOpKernelContext* ctx) = 0; // A post-processing operation to apply on the outputs of the ReduceWindow. - virtual xla::ComputationDataHandle PostProcessOutput( - XlaOpKernelContext* ctx, const xla::ComputationDataHandle& output, - DataType dtype, const TensorShape& input_shape) = 0; + virtual xla::XlaOp PostProcessOutput(XlaOpKernelContext* ctx, + const xla::XlaOp& output, DataType dtype, + const TensorShape& input_shape) = 0; void Compile(XlaOpKernelContext* ctx) override { std::vector ksize = ksize_; @@ -110,7 +110,7 @@ class PoolingOp : public XlaOpKernel { " operator must have ", num_dims(), " dimensions")); - xla::ComputationBuilder* const b = ctx->builder(); + xla::XlaBuilder* const b = ctx->builder(); auto input = XlaHelpers::ConvertElementType(b, ctx->Input(0), reduction_type_); auto reduce = ctx->builder()->ReduceWindow( @@ -135,17 +135,17 @@ class MaxPoolOp : public PoolingOp { : PoolingOp(ctx, /*num_spatial_dims=*/num_spatial_dims, /*reduction_type=*/ctx->input_type(0)) {} - xla::ComputationDataHandle InitValue(xla::ComputationBuilder* b) override { + xla::XlaOp InitValue(xla::XlaBuilder* b) override { return XlaHelpers::MinValue(b, reduction_type_); } - const xla::Computation* Reduction(XlaOpKernelContext* ctx) override { + const xla::XlaComputation* Reduction(XlaOpKernelContext* ctx) override { return ctx->GetOrCreateMax(reduction_type_); } - xla::ComputationDataHandle PostProcessOutput( - XlaOpKernelContext* ctx, const xla::ComputationDataHandle& output, - DataType dtype, const TensorShape& input_shape) override { + xla::XlaOp PostProcessOutput(XlaOpKernelContext* ctx, + const xla::XlaOp& output, DataType dtype, + const TensorShape& input_shape) override { return output; } }; @@ -176,9 +176,9 @@ REGISTER_XLA_OP(Name("MaxPool3D"), MaxPool3DOp); // Common computation shared between AvgPool and AvgPoolGrad. Divide each // element of an image by the count of elements that contributed to that // element during pooling. -static xla::ComputationDataHandle AvgPoolDivideByCount( - XlaOpKernelContext* ctx, const xla::ComputationDataHandle& output, - DataType dtype, const TensorShape& input_shape, xla::Padding padding, +static xla::XlaOp AvgPoolDivideByCount( + XlaOpKernelContext* ctx, const xla::XlaOp& output, DataType dtype, + const TensorShape& input_shape, xla::Padding padding, const std::vector& ksize, const std::vector& stride, int num_spatial_dims, TensorFormat data_format) { if (padding == xla::Padding::kValid) { @@ -234,17 +234,17 @@ class AvgPoolOp : public PoolingOp { /*reduction_type=*/ XlaHelpers::SumAccumulationType(ctx->input_type(0))) {} - xla::ComputationDataHandle InitValue(xla::ComputationBuilder* b) override { + xla::XlaOp InitValue(xla::XlaBuilder* b) override { return XlaHelpers::Zero(b, reduction_type_); } - const xla::Computation* Reduction(XlaOpKernelContext* ctx) override { + const xla::XlaComputation* Reduction(XlaOpKernelContext* ctx) override { return ctx->GetOrCreateAdd(reduction_type_); } - xla::ComputationDataHandle PostProcessOutput( - XlaOpKernelContext* ctx, const xla::ComputationDataHandle& output, - DataType dtype, const TensorShape& input_shape) override { + xla::XlaOp PostProcessOutput(XlaOpKernelContext* ctx, + const xla::XlaOp& output, DataType dtype, + const TensorShape& input_shape) override { return AvgPoolDivideByCount(ctx, output, dtype, input_shape, padding_, ksize_, stride_, num_spatial_dims_, data_format_); @@ -344,11 +344,10 @@ class MaxPoolGradOp : public XlaOpKernel { xla::PrimitiveType element_type; OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(input_type(2), &element_type)); - xla::ComputationDataHandle init_value = - XlaHelpers::Zero(ctx->builder(), input_type(2)); + xla::XlaOp init_value = XlaHelpers::Zero(ctx->builder(), input_type(2)); auto select = CreateScalarGeComputation(element_type, ctx->builder()); auto scatter = CreateScalarAddComputation(element_type, ctx->builder()); - xla::ComputationDataHandle gradients = ctx->builder()->SelectAndScatter( + xla::XlaOp gradients = ctx->builder()->SelectAndScatter( input, select, ksize_, stride_, xla_padding, out_backprop, init_value, scatter); @@ -462,7 +461,7 @@ class AvgPoolGradOp : public XlaOpKernel { // The input gradients are computed by a convolution of the output gradients // and the filter, with some appropriate padding. See the comment at the top // of conv_grad_ops.h for details. - xla::ComputationBuilder* const b = ctx->builder(); + xla::XlaBuilder* const b = ctx->builder(); auto out_backprop = ctx->Input(1); auto dtype = input_type(1); xla::Padding xla_padding = diff --git a/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc b/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc index 4171e076ff6d9d..661cd5923e1023 100644 --- a/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc @@ -35,7 +35,7 @@ class QuantizeAndDequantizeOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationDataHandle input = ctx->Input(0); + xla::XlaOp input = ctx->Input(0); const DataType data_type = ctx->input_type(0); // Comments taken from semantics description at @@ -46,8 +46,8 @@ class QuantizeAndDequantizeOp : public XlaOpKernel { // m = max(abs(input_min), abs(input_max)) if range_given is true, // m = max(abs(min_elem(input)), // abs(max_elem(input))) otherwise. - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle input_min, input_max; + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp input_min, input_max; if (range_given_) { double input_min_value, input_max_value; OP_REQUIRES_OK(ctx, ctx->ConstantInputAsFloatScalar(1, &input_min_value)); @@ -55,14 +55,14 @@ class QuantizeAndDequantizeOp : public XlaOpKernel { input_min = XlaHelpers::FloatLiteral(b, data_type, input_min_value); input_max = XlaHelpers::FloatLiteral(b, data_type, input_max_value); } else { - const xla::Computation* fmax = ctx->GetOrCreateMax(data_type); - const xla::Computation* fmin = ctx->GetOrCreateMin(data_type); + const xla::XlaComputation* fmax = ctx->GetOrCreateMax(data_type); + const xla::XlaComputation* fmin = ctx->GetOrCreateMin(data_type); input_min = b->ReduceAll(input, XlaHelpers::MaxValue(b, data_type), *fmin); input_max = b->ReduceAll(input, XlaHelpers::MinValue(b, data_type), *fmax); } - xla::ComputationDataHandle m = b->Max(b->Abs(input_min), b->Abs(input_max)); + xla::XlaOp m = b->Max(b->Abs(input_min), b->Abs(input_max)); // Next, we choose our fixed-point quantization buckets, [min_fixed, // max_fixed]. If signed_input is true, this is @@ -85,7 +85,7 @@ class QuantizeAndDequantizeOp : public XlaOpKernel { // From this we compute our scaling factor, s: // // s = (max_fixed - min_fixed) / (2 * m). - xla::ComputationDataHandle s = + xla::XlaOp s = b->Div(XlaHelpers::FloatLiteral(b, data_type, max_fixed - min_fixed), b->Mul(XlaHelpers::FloatLiteral(b, data_type, 2.0), m)); @@ -93,7 +93,7 @@ class QuantizeAndDequantizeOp : public XlaOpKernel { // e is transformed into e': // // e' = (e * s).round_to_nearest() / s. - xla::ComputationDataHandle result = b->Div(b->Round(b->Mul(input, s)), s); + xla::XlaOp result = b->Div(b->Round(b->Mul(input, s)), s); ctx->SetOutput(0, result); } diff --git a/tensorflow/compiler/tf2xla/kernels/random_ops.cc b/tensorflow/compiler/tf2xla/kernels/random_ops.cc index c0994c434bca51..5f5bd586376ab3 100644 --- a/tensorflow/compiler/tf2xla/kernels/random_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/random_ops.cc @@ -41,9 +41,9 @@ class RandomUniformOp : public XlaOpKernel { xla::Shape xla_shape; OP_REQUIRES_OK(ctx, TensorShapeToXLAShape(dtype, shape, &xla_shape)); - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle result = b->RngUniform( - XlaHelpers::Zero(b, dtype), XlaHelpers::One(b, dtype), xla_shape); + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp result = b->RngUniform(XlaHelpers::Zero(b, dtype), + XlaHelpers::One(b, dtype), xla_shape); ctx->SetOutput(0, result); } @@ -100,11 +100,11 @@ class RandomStandardNormalOp : public XlaOpKernel { xla::Shape xla_shape; OP_REQUIRES_OK(ctx, TensorShapeToXLAShape(dtype, shape, &xla_shape)); - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); // Normal distribution with a mean of 0 and a standard deviation of 1: - xla::ComputationDataHandle result = b->RngNormal( - XlaHelpers::Zero(b, dtype), XlaHelpers::One(b, dtype), xla_shape); + xla::XlaOp result = b->RngNormal(XlaHelpers::Zero(b, dtype), + XlaHelpers::One(b, dtype), xla_shape); ctx->SetOutput(0, result); } @@ -130,19 +130,18 @@ class TruncatedNormalOp : public XlaOpKernel { xla::Shape xla_element_shape = xla::ShapeUtil::MakeShape(xla_shape.element_type(), {}); - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle mean = XlaHelpers::Zero(b, dtype); - xla::ComputationDataHandle stddev = XlaHelpers::One(b, dtype); - xla::ComputationDataHandle candidate = - b->RngNormal(mean, stddev, xla_shape); + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp mean = XlaHelpers::Zero(b, dtype); + xla::XlaOp stddev = XlaHelpers::One(b, dtype); + xla::XlaOp candidate = b->RngNormal(mean, stddev, xla_shape); - auto two_sd = [dtype](bool negate, xla::ComputationBuilder* b) { + auto two_sd = [dtype](bool negate, xla::XlaBuilder* b) { return XlaHelpers::FloatLiteral(b, dtype, negate ? -2.0 : 2.0); }; - auto out_of_range_mask = [two_sd](xla::ComputationDataHandle candidate, - xla::ComputationBuilder* b) { - xla::ComputationDataHandle too_large = b->Gt(candidate, two_sd(false, b)); - xla::ComputationDataHandle too_small = b->Lt(candidate, two_sd(true, b)); + auto out_of_range_mask = [two_sd](xla::XlaOp candidate, + xla::XlaBuilder* b) { + xla::XlaOp too_large = b->Gt(candidate, two_sd(false, b)); + xla::XlaOp too_small = b->Lt(candidate, two_sd(true, b)); return b->Or(too_large, too_small); }; @@ -152,35 +151,32 @@ class TruncatedNormalOp : public XlaOpKernel { // out_of_range_mask := candidate < mean-2*sd || candidate > mean+2*sd // candidate = select(out_of_range_mask, rng_normal(), candidate) // } - std::unique_ptr test_builder = + std::unique_ptr test_builder = b->CreateSubBuilder("truncated_normal_test"); { auto* b = test_builder.get(); - xla::ComputationDataHandle candidate = - b->Parameter(0, xla_shape, "candidate"); - xla::ComputationDataHandle oor_mask = out_of_range_mask(candidate, b); + xla::XlaOp candidate = b->Parameter(0, xla_shape, "candidate"); + out_of_range_mask(candidate, b); OP_REQUIRES_OK(ctx, Any(out_of_range_mask(candidate, b), b).status()); } - std::unique_ptr body_builder = + std::unique_ptr body_builder = b->CreateSubBuilder("truncated_normal_body"); { auto* b = body_builder.get(); - xla::ComputationDataHandle candidate = - b->Parameter(0, xla_shape, "candidate"); - xla::ComputationDataHandle to_resample = out_of_range_mask(candidate, b); - xla::ComputationDataHandle mean = XlaHelpers::Zero(b, dtype); - xla::ComputationDataHandle stddev = XlaHelpers::One(b, dtype); + xla::XlaOp candidate = b->Parameter(0, xla_shape, "candidate"); + xla::XlaOp to_resample = out_of_range_mask(candidate, b); + xla::XlaOp mean = XlaHelpers::Zero(b, dtype); + xla::XlaOp stddev = XlaHelpers::One(b, dtype); b->Select(to_resample, b->RngNormal(mean, stddev, xla_shape), candidate); } - xla::StatusOr test_computation = test_builder->Build(); + xla::StatusOr test_computation = test_builder->Build(); OP_REQUIRES_OK(ctx, test_computation.status()); - xla::StatusOr body_computation = body_builder->Build(); + xla::StatusOr body_computation = body_builder->Build(); OP_REQUIRES_OK(ctx, body_computation.status()); - xla::ComputationDataHandle result = - b->While(test_computation.ValueOrDie(), body_computation.ValueOrDie(), - candidate); + xla::XlaOp result = b->While(test_computation.ValueOrDie(), + body_computation.ValueOrDie(), candidate); ctx->SetOutput(0, result); } diff --git a/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc b/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc index cb144bea9e429b..08894489ac77bb 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc @@ -19,7 +19,6 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/op_kernel.h" @@ -65,7 +64,7 @@ class ReduceWindowOp : public XlaOpKernel { "rank (", padding_high_.size(), " vs. ", rank, ")")); - xla::ComputationBuilder* builder = context->builder(); + xla::XlaBuilder* builder = context->builder(); // Build the reducer function. XlaCompiler::Argument reducer_arg; @@ -95,15 +94,15 @@ class ReduceWindowOp : public XlaOpKernel { xla::ShapeUtil::HumanString(reducer.xla_output_shape))); // Wraps the reducer in a computation that unpacks the output tuple. - xla::Computation wrapper; + xla::XlaComputation wrapper; { - std::unique_ptr cb = + std::unique_ptr cb = builder->CreateSubBuilder("wrapper"); auto x = cb->Parameter(0, scalar_shape, "x"); auto y = cb->Parameter(1, scalar_shape, "y"); auto outputs = cb->Call(*reducer.computation, {x, y}); cb->GetTupleElement(outputs, 0); - xla::StatusOr result = cb->Build(); + xla::StatusOr result = cb->Build(); OP_REQUIRES_OK(context, result.status()); wrapper = std::move(result.ValueOrDie()); } @@ -113,7 +112,7 @@ class ReduceWindowOp : public XlaOpKernel { padding[i] = {padding_low_[i], padding_high_[i]}; } - xla::ComputationDataHandle output = builder->ReduceWindowWithGeneralPadding( + xla::XlaOp output = builder->ReduceWindowWithGeneralPadding( context->Input(0), context->Input(1), wrapper, window_dimensions_, window_strides_, padding); context->SetOutput(0, output); diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc index 812d258cd1677e..0f425637795e96 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc @@ -30,13 +30,11 @@ class SumOp : public XlaReductionOp { explicit SumOp(OpKernelConstruction* ctx) : XlaReductionOp(ctx, XlaHelpers::SumAccumulationType(ctx->input_type(0))) {} - xla::ComputationDataHandle InitialValue( - xla::ComputationBuilder* builder) override { + xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { return XlaHelpers::Zero(builder, reduction_type_); } - void BuildReducer(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& scalar_lhs, - const xla::ComputationDataHandle& scalar_rhs) override { + void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, + const xla::XlaOp& scalar_rhs) override { builder->Add(scalar_lhs, scalar_rhs); } }; @@ -49,14 +47,12 @@ class ProdOp : public XlaReductionOp { : XlaReductionOp(ctx, XlaHelpers::SumAccumulationType(ctx->input_type(0))) {} - xla::ComputationDataHandle InitialValue( - xla::ComputationBuilder* builder) override { + xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { return XlaHelpers::One(builder, reduction_type_); } - void BuildReducer(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& scalar_lhs, - const xla::ComputationDataHandle& scalar_rhs) override { + void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, + const xla::XlaOp& scalar_rhs) override { builder->Mul(scalar_lhs, scalar_rhs); } }; @@ -69,14 +65,12 @@ class MinOp : public XlaReductionOp { explicit MinOp(OpKernelConstruction* ctx) : XlaReductionOp(ctx, ctx->input_type(0)) {} - xla::ComputationDataHandle InitialValue( - xla::ComputationBuilder* builder) override { + xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { return XlaHelpers::MaxValue(builder, reduction_type_); } - void BuildReducer(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& scalar_lhs, - const xla::ComputationDataHandle& scalar_rhs) override { + void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, + const xla::XlaOp& scalar_rhs) override { builder->Min(scalar_lhs, scalar_rhs); } }; @@ -88,14 +82,12 @@ class MaxOp : public XlaReductionOp { explicit MaxOp(OpKernelConstruction* ctx) : XlaReductionOp(ctx, ctx->input_type(0)) {} - xla::ComputationDataHandle InitialValue( - xla::ComputationBuilder* builder) override { + xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { return XlaHelpers::MinValue(builder, reduction_type_); } - void BuildReducer(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& scalar_lhs, - const xla::ComputationDataHandle& scalar_rhs) override { + void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, + const xla::XlaOp& scalar_rhs) override { builder->Max(scalar_lhs, scalar_rhs); } }; @@ -108,20 +100,17 @@ class MeanOp : public XlaReductionOp { : XlaReductionOp(ctx, XlaHelpers::SumAccumulationType(ctx->input_type(0))) {} - xla::ComputationDataHandle InitialValue( - xla::ComputationBuilder* builder) override { + xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { return XlaHelpers::Zero(builder, reduction_type_); } - void BuildReducer(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& scalar_lhs, - const xla::ComputationDataHandle& scalar_rhs) override { + void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, + const xla::XlaOp& scalar_rhs) override { builder->Add(scalar_lhs, scalar_rhs); } - xla::ComputationDataHandle BuildFinalizer( - xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& reduce_output, - int64 num_elements_reduced) override { + xla::XlaOp BuildFinalizer(xla::XlaBuilder* builder, + const xla::XlaOp& reduce_output, + int64 num_elements_reduced) override { auto divisor = XlaHelpers::IntegerLiteral(builder, input_type(0), num_elements_reduced); return builder->Div(reduce_output, divisor); @@ -136,14 +125,12 @@ class AllOp : public XlaReductionOp { explicit AllOp(OpKernelConstruction* ctx) : XlaReductionOp(ctx, ctx->input_type(0)) {} - xla::ComputationDataHandle InitialValue( - xla::ComputationBuilder* builder) override { + xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { return builder->ConstantR0(true); } - void BuildReducer(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& scalar_lhs, - const xla::ComputationDataHandle& scalar_rhs) override { + void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, + const xla::XlaOp& scalar_rhs) override { builder->And(scalar_lhs, scalar_rhs); } }; @@ -155,14 +142,12 @@ class AnyOp : public XlaReductionOp { explicit AnyOp(OpKernelConstruction* ctx) : XlaReductionOp(ctx, ctx->input_type(0)) {} - xla::ComputationDataHandle InitialValue( - xla::ComputationBuilder* builder) override { + xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { return builder->ConstantR0(false); } - void BuildReducer(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& scalar_lhs, - const xla::ComputationDataHandle& scalar_rhs) override { + void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, + const xla::XlaOp& scalar_rhs) override { builder->Or(scalar_lhs, scalar_rhs); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops.h b/tensorflow/compiler/tf2xla/kernels/reduction_ops.h index f3181f0dadc2d3..2ecfb854a1c862 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops.h +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops.h @@ -19,7 +19,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_KERNELS_REDUCTION_OPS_H_ #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { @@ -28,35 +28,33 @@ namespace tensorflow { // to override: description is a textual description of the mapped // function; InitialValue constructs the base case for the reduction; // BuildReducer adds the implementation of the reduction lambda to a -// xla::ComputationBuilder and BuildFinalizer adds the +// xla::XlaBuilder and BuildFinalizer adds the // implementation of the finalizer lambda (if there is one) to a -// xla::ComputationBuilder. +// xla::XlaBuilder. class XlaReductionOp : public XlaOpKernel { public: XlaReductionOp(OpKernelConstruction* ctx, DataType reduction_type); ~XlaReductionOp() override {} // Return the base case for the reduction. - virtual xla::ComputationDataHandle InitialValue( - xla::ComputationBuilder* builder) = 0; + virtual xla::XlaOp InitialValue(xla::XlaBuilder* builder) = 0; // Implement the (scalar,scalar)->scalar lambda that should be // applied to each pair of elements to be reduced. The desired // computation should be added to 'builder' and // '(scalar_lhs,scalar_rhs)' are the function's inputs. - virtual void BuildReducer(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& scalar_lhs, - const xla::ComputationDataHandle& scalar_rhs) = 0; + virtual void BuildReducer(xla::XlaBuilder* builder, + const xla::XlaOp& scalar_lhs, + const xla::XlaOp& scalar_rhs) = 0; // Applies a transformation to the output of the reduction. The desired // computation should be added to 'builder'. Argument 'reduce_output' is the // output of the reduction. 'num_elements_reduced' is the number of elements // that contributed to the reduction. Returns the transformed reduction // output, Defaults to returning 'reduce_output' unchanged. - virtual xla::ComputationDataHandle BuildFinalizer( - xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& reduce_output, - int64 num_elements_reduced); + virtual xla::XlaOp BuildFinalizer(xla::XlaBuilder* builder, + const xla::XlaOp& reduce_output, + int64 num_elements_reduced); void Compile(XlaOpKernelContext* ctx) override; diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc index 64fe765ae9a945..4fd5bfd03999a7 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc @@ -35,10 +35,9 @@ XlaReductionOp::XlaReductionOp(OpKernelConstruction* ctx, // Unless BuildFinalizer is overridden the reduction has no // finalizer. -xla::ComputationDataHandle XlaReductionOp::BuildFinalizer( - xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& reduce_output, - int64 num_elements_reduced) { +xla::XlaOp XlaReductionOp::BuildFinalizer(xla::XlaBuilder* builder, + const xla::XlaOp& reduce_output, + int64 num_elements_reduced) { return reduce_output; } @@ -96,9 +95,9 @@ void XlaReductionOp::Compile(XlaOpKernelContext* ctx) { string desc = ctx->op_kernel().name(); - xla::ComputationBuilder* const b = ctx->builder(); + xla::XlaBuilder* const b = ctx->builder(); // Construct the builder for the reduction lambda. - xla::ComputationBuilder r(b->client(), strings::StrCat(desc, "-reduction")); + xla::XlaBuilder r(strings::StrCat(desc, "-reduction")); xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(reduction_type_, &type)); @@ -110,7 +109,7 @@ void XlaReductionOp::Compile(XlaOpKernelContext* ctx) { auto ry = r.Parameter(1, xla::ShapeUtil::MakeShape(type, {}), "y"); // Call virtual method to build the reduction lambda. BuildReducer(&r, rx, ry); - xla::Computation reduction_computation = r.Build().ConsumeValueOrDie(); + xla::XlaComputation reduction_computation = r.Build().ConsumeValueOrDie(); auto reduce = b->Reduce(data, initial, reduction_computation, xla_axes); auto deconverted = XlaHelpers::ConvertElementType(b, reduce, input_type(0)); diff --git a/tensorflow/compiler/tf2xla/kernels/relu_op.cc b/tensorflow/compiler/tf2xla/kernels/relu_op.cc index 12a35529992e61..ba7d484d53d725 100644 --- a/tensorflow/compiler/tf2xla/kernels/relu_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/relu_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" @@ -32,7 +32,7 @@ class ReluOp : public XlaOpKernel { explicit ReluOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} // Computes the max of the scalar input x and 0. void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); auto zero = XlaHelpers::Zero(builder, input_type(0)); ctx->SetOutput(0, builder->Max(zero, ctx->Input(0))); } @@ -43,7 +43,7 @@ class Relu6Op : public XlaOpKernel { explicit Relu6Op(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} // Clamp the scalar input between 0 and 6. void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); auto zero = XlaHelpers::Zero(builder, input_type(0)); auto six = XlaHelpers::IntegerLiteral(builder, input_type(0), 6); ctx->SetOutput(0, builder->Clamp(zero, ctx->Input(0), six)); @@ -56,7 +56,7 @@ class ReluGradOp : public XlaOpKernel { // Return the lhs (incoming gradient) if the rhs (input feature) > 0, // otherwise return 0. void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); const TensorShape shape = ctx->InputShape(0); const auto zero = b->Broadcast(XlaHelpers::Zero(b, input_type(0)), shape.dim_sizes()); @@ -71,7 +71,7 @@ class Relu6GradOp : public XlaOpKernel { // Return the lhs (incoming gradient) if the rhs (input feature) > 0, // otherwise return 0. void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); const TensorShape shape = ctx->InputShape(0); const auto zero = b->Broadcast(XlaHelpers::Zero(b, input_type(0)), shape.dim_sizes()); diff --git a/tensorflow/compiler/tf2xla/kernels/retval_op.cc b/tensorflow/compiler/tf2xla/kernels/retval_op.cc index c283e3b02c2676..70547290eaed16 100644 --- a/tensorflow/compiler/tf2xla/kernels/retval_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/retval_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" @@ -45,7 +45,7 @@ class RetvalOp : public XlaOpKernel { // compilation. OP_REQUIRES_OK(ctx, frame->SetRetval(index_, input)); } else { - xla::ComputationDataHandle input = ctx->Input(0); + xla::XlaOp input = ctx->Input(0); const TensorShape input_shape = ctx->InputShape(0); auto is_constant = ctx->builder()->IsConstant(input); diff --git a/tensorflow/compiler/tf2xla/kernels/reverse_op.cc b/tensorflow/compiler/tf2xla/kernels/reverse_op.cc index e51d386926763e..2872a3c4d49d0d 100644 --- a/tensorflow/compiler/tf2xla/kernels/reverse_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reverse_op.cc @@ -48,7 +48,7 @@ class ReverseOp : public XlaOpKernel { ctx->SetOutput(0, ctx->Input(0)); return; } - // ComputationBuilder::Rev() requires concrete values for dimensions arg. + // XlaBuilder::Rev() requires concrete values for dimensions arg. xla::Literal lax; OP_REQUIRES_OK(ctx, ctx->ConstantInputReshaped(1, {x_shape.dims()}, &lax)); std::vector revdims(x_shape.dims()); @@ -90,7 +90,7 @@ class ReverseV2Op : public XlaOpKernel { ctx->SetOutput(0, ctx->Input(0)); return; } - // ComputationBuilder::Rev() requires concrete values for dimensions arg. + // XlaBuilder::Rev() requires concrete values for dimensions arg. std::vector axes; OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(1, &axes)); diff --git a/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc b/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc index 6bc5d3adb091cd..0ed4c4707df71c 100644 --- a/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc @@ -54,7 +54,7 @@ class ReverseSequenceOp : public XlaOpKernel { "), ", "(", seq_lens_shape.num_elements(), " vs. ", input_shape.dim_size(batch_dim_))); - xla::ComputationBuilder* builder = context->builder(); + xla::XlaBuilder* builder = context->builder(); const auto input = context->Input(0); const auto seq_lens = context->Input(1); @@ -155,7 +155,7 @@ class ReverseSequenceOp : public XlaOpKernel { auto output = builder->GetTupleElement(loop_output, 2); // Mask out elements after the sequence length. - xla::ComputationDataHandle iota; + xla::XlaOp iota; OP_REQUIRES_OK( context, XlaHelpers::Iota(builder, seq_lens_type, max_seq_len, &iota)); std::vector dims(input_shape.dims(), 1); diff --git a/tensorflow/compiler/tf2xla/kernels/scan_ops.cc b/tensorflow/compiler/tf2xla/kernels/scan_ops.cc index 4cfa28a0ce3d7d..1819fb543317ee 100644 --- a/tensorflow/compiler/tf2xla/kernels/scan_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/scan_ops.cc @@ -74,7 +74,7 @@ class ScanOp : public XlaOpKernel { return; } - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); std::vector window_strides(input_shape.dims(), 1); std::vector window_dims(input_shape.dims(), 1); @@ -91,8 +91,8 @@ class ScanOp : public XlaOpKernel { std::swap(padding[axis].first, padding[axis].second); } - xla::ComputationDataHandle init; - const xla::Computation* reducer; + xla::XlaOp init; + const xla::XlaComputation* reducer; if (sum_) { init = XlaHelpers::Zero(builder, dtype); reducer = ctx->GetOrCreateAdd(dtype); diff --git a/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc b/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc index 8433a29c4e203c..f2c63b4f9083ad 100644 --- a/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc @@ -102,7 +102,7 @@ class ScatterNdOp : public XlaOpKernel { OP_REQUIRES_OK(context, ValidateUpdateShape(buffer_shape, indices_shape, updates_shape)); - xla::ComputationBuilder* builder = context->builder(); + xla::XlaBuilder* builder = context->builder(); auto buffer = builder->Broadcast(XlaHelpers::Zero(builder, dtype), buffer_shape.dim_sizes()); auto indices = context->Input(0); diff --git a/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc index 498342a98881df..664078ca16c6d5 100644 --- a/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" namespace tensorflow { namespace { @@ -62,16 +62,16 @@ class UnsortedSegmentSum : public XlaOpKernel { d, " differs ", data_shape.dim_size(d), " vs. ", indices_shape.dim_size(d))); } - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); TensorShape buffer_shape = data_shape; buffer_shape.RemoveDimRange(0, indices_shape.dims()); buffer_shape.InsertDim(0, num_segments); auto buffer = builder->Broadcast(XlaHelpers::Zero(builder, dtype_), buffer_shape.dim_sizes()); - auto combiner = - [](xla::ComputationDataHandle a, xla::ComputationDataHandle b, - xla::ComputationBuilder* builder) { return builder->Add(a, b); }; + auto combiner = [](xla::XlaOp a, xla::XlaOp b, xla::XlaBuilder* builder) { + return builder->Add(a, b); + }; auto result = XlaScatter(buffer, /*updates=*/data, indices, /*indices_are_vectors=*/false, combiner, builder); diff --git a/tensorflow/compiler/tf2xla/kernels/select_op.cc b/tensorflow/compiler/tf2xla/kernels/select_op.cc index 8081d3c41c4363..f9f48164d63492 100644 --- a/tensorflow/compiler/tf2xla/kernels/select_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/select_op.cc @@ -40,7 +40,7 @@ class SelectOp : public XlaOpKernel { "'then' and 'else' must have the same size. but received: ", then_shape.DebugString(), " vs. ", else_shape.DebugString())); - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); auto cond_handle = ctx->Input(0); auto then_handle = ctx->Input(1); diff --git a/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc b/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc index d079b89861817a..9ce01d0d44509b 100644 --- a/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/softmax_op.cc b/tensorflow/compiler/tf2xla/kernels/softmax_op.cc index 463788b8b461c3..bbf5ee8b12186a 100644 --- a/tensorflow/compiler/tf2xla/kernels/softmax_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/softmax_op.cc @@ -43,8 +43,8 @@ class SoftmaxOp : public XlaOpKernel { const DataType type = input_type(0); auto logits = ctx->Input(0); - xla::ComputationBuilder* const b = ctx->builder(); - const xla::Computation& max_func = *ctx->GetOrCreateMax(type); + xla::XlaBuilder* const b = ctx->builder(); + const xla::XlaComputation& max_func = *ctx->GetOrCreateMax(type); // Find the max in each batch, resulting in a tensor of shape [batch] auto logits_max = @@ -76,16 +76,15 @@ class SoftmaxOp : public XlaOpKernel { REGISTER_XLA_OP(Name("Softmax"), SoftmaxOp); REGISTER_XLA_OP(Name("LogSoftmax"), SoftmaxOp); -std::pair -CrossEntropyWithLogits(XlaOpKernelContext* ctx, DataType type, - const xla::ComputationDataHandle& logits, - const xla::ComputationDataHandle& labels) { - const xla::Computation& max_func = *ctx->GetOrCreateMax(type); +std::pair CrossEntropyWithLogits( + XlaOpKernelContext* ctx, DataType type, const xla::XlaOp& logits, + const xla::XlaOp& labels) { + const xla::XlaComputation& max_func = *ctx->GetOrCreateMax(type); const int kBatchDim = 0; const int kClassDim = 1; - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); // Find the max in each batch, resulting in a tensor of shape [batch] auto logits_max = b->Reduce(logits, XlaHelpers::MinValue(b, type), max_func, {kClassDim}); @@ -123,7 +122,7 @@ CrossEntropyWithLogits(XlaOpKernelContext* ctx, DataType type, // backprop: prob - labels, where // prob = exp(logits - max_logits) / sum(exp(logits - max_logits)) // (where the division broadcasts along the batch dimension) - xla::ComputationDataHandle backprop = + xla::XlaOp backprop = b->Sub(b->Div(exp_shifted_logits, sum_exp, {kBatchDim}), labels); return {loss, backprop}; } @@ -150,7 +149,7 @@ class SoftmaxXentWithLogitsOp : public XlaOpKernel { auto logits = ctx->Input(0); auto labels = ctx->Input(1); - xla::ComputationDataHandle loss, backprop; + xla::XlaOp loss, backprop; std::tie(loss, backprop) = CrossEntropyWithLogits(ctx, type, logits, labels); ctx->SetOutput(0, loss); @@ -191,10 +190,10 @@ class SparseSoftmaxXentWithLogitsOp : public XlaOpKernel { DataType logits_type = input_type(0); DataType indices_type = input_type(1); - xla::ComputationDataHandle indices = ctx->Input(1); + xla::XlaOp indices = ctx->Input(1); - xla::ComputationBuilder* builder = ctx->builder(); - xla::ComputationDataHandle labels; + xla::XlaBuilder* builder = ctx->builder(); + xla::XlaOp labels; OP_REQUIRES_OK(ctx, XlaHelpers::OneHot( builder, depth, /*axis=*/1, input_type(1), labels_shape, @@ -207,7 +206,7 @@ class SparseSoftmaxXentWithLogitsOp : public XlaOpKernel { // Builds a vector of {batch_size} that is 0 if the index is in range, or // NaN otherwise; then add that vector to the labels to force out-of-range // values to NaNs. - xla::ComputationDataHandle nan_or_zero = builder->Select( + xla::XlaOp nan_or_zero = builder->Select( builder->And( builder->Le(XlaHelpers::Zero(builder, indices_type), indices), builder->Lt(indices, XlaHelpers::IntegerLiteral( @@ -218,7 +217,7 @@ class SparseSoftmaxXentWithLogitsOp : public XlaOpKernel { {batch_size})); labels = builder->Add(labels, nan_or_zero, {0}); - xla::ComputationDataHandle loss, backprop; + xla::XlaOp loss, backprop; std::tie(loss, backprop) = CrossEntropyWithLogits(ctx, logits_type, ctx->Input(0), labels); ctx->SetOutput(0, loss); diff --git a/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc b/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc index 01b46e160d1f1f..ec077924b5b5af 100644 --- a/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc @@ -20,9 +20,8 @@ limitations under the License. namespace tensorflow { namespace { -void SpaceToBatch(XlaOpKernelContext* ctx, - const xla::ComputationDataHandle& input, DataType input_dtype, - const TensorShape& input_tensor_shape, +void SpaceToBatch(XlaOpKernelContext* ctx, const xla::XlaOp& input, + DataType input_dtype, const TensorShape& input_tensor_shape, gtl::ArraySlice block_shape, const xla::Literal& paddings) { const int input_rank = input_tensor_shape.dims(); @@ -46,7 +45,7 @@ void SpaceToBatch(XlaOpKernelContext* ctx, ", 2] instead of ", xla::ShapeUtil::HumanString(paddings.shape()))); - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); // 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the // input according to `paddings` to produce `padded` of shape `padded_shape`. @@ -73,7 +72,7 @@ void SpaceToBatch(XlaOpKernelContext* ctx, errors::InvalidArgument( "The product of the block dimensions must be positive")); - xla::ComputationDataHandle padded = + xla::XlaOp padded = b->Pad(input, XlaHelpers::Zero(b, input_dtype), padding_config); // 2. Reshape `padded` to `reshaped_padded` of shape: @@ -101,8 +100,7 @@ void SpaceToBatch(XlaOpKernelContext* ctx, std::copy(remainder_shape.begin(), remainder_shape.end(), reshaped_padded_shape.begin() + 1 + 2 * block_rank); - xla::ComputationDataHandle reshaped_padded = - b->Reshape(padded, reshaped_padded_shape); + xla::XlaOp reshaped_padded = b->Reshape(padded, reshaped_padded_shape); // 3. Permute dimensions of `reshaped_padded` to produce // `permuted_reshaped_padded` of shape: @@ -121,7 +119,7 @@ void SpaceToBatch(XlaOpKernelContext* ctx, permutation[block_rank] = 0; std::iota(permutation.begin() + 1 + block_rank * 2, permutation.end(), 1 + block_rank * 2); - xla::ComputationDataHandle permuted_reshaped_padded = + xla::XlaOp permuted_reshaped_padded = b->Transpose(reshaped_padded, permutation); // 4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the @@ -142,8 +140,7 @@ void SpaceToBatch(XlaOpKernelContext* ctx, std::copy(remainder_shape.begin(), remainder_shape.end(), output_shape.begin() + 1 + block_rank); - xla::ComputationDataHandle output = - b->Reshape(permuted_reshaped_padded, output_shape); + xla::XlaOp output = b->Reshape(permuted_reshaped_padded, output_shape); ctx->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc b/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc index 806fda632cde64..4c5886ee2a0f63 100644 --- a/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc @@ -50,8 +50,8 @@ class SpaceToDepthOp : public XlaOpKernel { const gtl::InlinedVector input_shape = input_tensor_shape.dim_sizes(); - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle input = ctx->Input(0); + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp input = ctx->Input(0); int feature_dim = GetTensorFeatureDimIndex(input_rank, data_format_); int num_spatial_dims = GetTensorSpatialDims(input_rank, data_format_); @@ -135,7 +135,7 @@ class SpaceToDepthOp : public XlaOpKernel { // input_shape[1] / block_size_, block_size_, // input_shape[2] / block_size_, block_size_, // depth] - xla::ComputationDataHandle reshaped = b->Reshape(input, reshaped_shape); + xla::XlaOp reshaped = b->Reshape(input, reshaped_shape); // 2. Permute dimensions of `reshaped` to produce // `permuted_reshaped` of shape: @@ -145,8 +145,7 @@ class SpaceToDepthOp : public XlaOpKernel { // input_shape[2] / block_size_, // block_size_, block_size_, // depth] - xla::ComputationDataHandle permuted_reshaped = - b->Transpose(reshaped, transpose_order); + xla::XlaOp permuted_reshaped = b->Transpose(reshaped, transpose_order); // 3. Reshape `permuted_reshaped` to flatten `block_shape` into the // batch dimension, producing an output tensor of shape: @@ -156,8 +155,7 @@ class SpaceToDepthOp : public XlaOpKernel { // input_shape[2] / block_size_, // block_size_ * block_size_ * depth] // - xla::ComputationDataHandle output = - b->Reshape(permuted_reshaped, output_shape); + xla::XlaOp output = b->Reshape(permuted_reshaped, output_shape); ctx->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc index 43c15e75380535..8958b2e7701e62 100644 --- a/tensorflow/compiler/tf2xla/kernels/split_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc @@ -124,7 +124,7 @@ class SplitVOp : public XlaOpKernel { input_shape.dims(), "), but got ", split_dim_orig)); - xla::ComputationDataHandle input = ctx->Input(0); + xla::XlaOp input = ctx->Input(0); OP_REQUIRES(ctx, input_shape.dims() > 0, errors::InvalidArgument("Can't split a 0 dimensional input")); diff --git a/tensorflow/compiler/tf2xla/kernels/stack_ops.cc b/tensorflow/compiler/tf2xla/kernels/stack_ops.cc index 1a78c7ab9be701..0fb05a2be7b103 100644 --- a/tensorflow/compiler/tf2xla/kernels/stack_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/stack_ops.cc @@ -38,13 +38,13 @@ limitations under the License. namespace tensorflow { namespace { -Status GetStackShape(xla::ComputationBuilder* builder, XlaResource* resource, +Status GetStackShape(xla::XlaBuilder* builder, XlaResource* resource, TensorShape* stack_shape) { auto shape_or_status = builder->GetShape(resource->value()); if (!shape_or_status.ok()) { return shape_or_status.status(); } - xla::Shape shape = *shape_or_status.ValueOrDie(); + xla::Shape shape = shape_or_status.ValueOrDie(); TF_RET_CHECK(xla::ShapeUtil::IsTuple(shape)); return XLAShapeToTensorShape(xla::ShapeUtil::GetTupleElementShape(shape, 0), stack_shape); @@ -60,9 +60,8 @@ Status GetStackShape(xla::ComputationBuilder* builder, XlaResource* resource, // // TODO(phawkins): consider changing the API of the stack operators to // allow an optional element shape at stack construction time. -Status MaybeInitializeStack(xla::ComputationBuilder* builder, - XlaResource* resource, DataType dtype, - const TensorShape& elem_shape) { +Status MaybeInitializeStack(xla::XlaBuilder* builder, XlaResource* resource, + DataType dtype, const TensorShape& elem_shape) { if (resource->type() != dtype) { return errors::InvalidArgument( "Stack dtype is ", DataTypeString(resource->type()), @@ -75,8 +74,6 @@ Status MaybeInitializeStack(xla::ComputationBuilder* builder, if (!resource->initialized()) { // Stack has not been initialized. - xla::ComputationDataHandle zero = - XlaHelpers::Zero(builder, resource->type()); TF_RETURN_IF_ERROR(resource->SetTypeAndShape(dtype, elem_shape)); TF_RETURN_IF_ERROR(resource->SetZeroValue(builder)); } else { @@ -111,7 +108,7 @@ class StackOp : public XlaOpKernel { // We defer initializing the Stack resource until we see the first push. // Otherwise we do not know the shape of the stack elements. - xla::ComputationDataHandle value; + xla::XlaOp value; XlaContext& xc = XlaContext::Get(ctx); XlaResource* resource; string name = strings::StrCat("Stack: ", stack_name_); @@ -138,7 +135,7 @@ class StackPushOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); TensorShape elem_shape = ctx->InputShape(1); XlaResource* resource; @@ -147,9 +144,9 @@ class StackPushOp : public XlaOpKernel { // Initializes the Stack, if the element shape was not already known. OP_REQUIRES_OK(ctx, MaybeInitializeStack(b, resource, dtype_, elem_shape)); - xla::ComputationDataHandle ta = b->GetTupleElement(resource->value(), 0); - xla::ComputationDataHandle index = b->GetTupleElement(resource->value(), 1); - xla::ComputationDataHandle value = ctx->Input(1); + xla::XlaOp ta = b->GetTupleElement(resource->value(), 0); + xla::XlaOp index = b->GetTupleElement(resource->value(), 1); + xla::XlaOp value = ctx->Input(1); // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0]. auto start_indices = @@ -184,7 +181,7 @@ class StackPopOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); XlaResource* resource; OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource)); @@ -199,9 +196,9 @@ class StackPopOp : public XlaOpKernel { TensorShape stack_shape; OP_REQUIRES_OK(ctx, GetStackShape(b, resource, &stack_shape)); - xla::ComputationDataHandle state = resource->value(); - xla::ComputationDataHandle ta = b->GetTupleElement(state, 0); - xla::ComputationDataHandle index = b->GetTupleElement(state, 1); + xla::XlaOp state = resource->value(); + xla::XlaOp ta = b->GetTupleElement(state, 0); + xla::XlaOp index = b->GetTupleElement(state, 1); index = b->Sub(index, b->ConstantR0(1)); OP_REQUIRES_OK(ctx, resource->SetValue(b->Tuple({ta, index}))); @@ -216,8 +213,7 @@ class StackPopOp : public XlaOpKernel { // TODO(phawkins): We don't check the index is in bounds --- there is no // error mechanism in XLA. - xla::ComputationDataHandle read = - b->DynamicSlice(ta, start_indices, slice_shape); + xla::XlaOp read = b->DynamicSlice(ta, start_indices, slice_shape); // Remove the leading '1' dimension. std::vector value_shape(slice_shape.begin() + 1, slice_shape.end()); diff --git a/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc b/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc index 5bb773d97fc5ce..6340c225185e68 100644 --- a/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc @@ -30,9 +30,8 @@ namespace tensorflow { namespace { // Rotates a 32-bit integer 'v' left by 'distance' bits. -xla::ComputationDataHandle RotateLeftS32(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& v, - int distance) { +xla::XlaOp RotateLeftS32(xla::XlaBuilder* builder, const xla::XlaOp& v, + int distance) { return builder->Or( builder->ShiftLeft(v, builder->ConstantR0(distance)), builder->ShiftRightLogical(v, builder->ConstantR0(32 - distance))); @@ -40,25 +39,24 @@ xla::ComputationDataHandle RotateLeftS32(xla::ComputationBuilder* builder, // TODO(b/65209188): add a primitive XOR to XLA and call it here, rather than // building XOR out of other bitwise operators. -xla::ComputationDataHandle BitwiseXor(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& x, - const xla::ComputationDataHandle& y) { +xla::XlaOp BitwiseXor(xla::XlaBuilder* builder, const xla::XlaOp& x, + const xla::XlaOp& y) { return builder->Or(builder->And(x, builder->Not(y)), builder->And(builder->Not(x), y)); } -using ThreeFry2x32State = std::array; +using ThreeFry2x32State = std::array; // Implements the ThreeFry counter-based PRNG algorithm. // Salmon et al. SC 2011. Parallel random numbers: as easy as 1, 2, 3. // http://www.thesalmons.org/john/random123/papers/random123sc11.pdf -ThreeFry2x32State ThreeFry2x32(xla::ComputationBuilder* builder, +ThreeFry2x32State ThreeFry2x32(xla::XlaBuilder* builder, ThreeFry2x32State input, ThreeFry2x32State key) { // Rotation distances specified by the Threefry2x32 algorithm. constexpr std::array rotations = {13, 15, 26, 6, 17, 29, 16, 24}; ThreeFry2x32State x; - std::array ks; + std::array ks; // 0x1BD11BDA is a parity constant specified by the ThreeFry2x32 algorithm. ks[2] = builder->ConstantR0(0x1BD11BDA); for (int i = 0; i < 2; ++i) { @@ -121,10 +119,9 @@ ThreeFry2x32State ThreeFry2x32(xla::ComputationBuilder* builder, // Returns a tensor of 'shape' random values uniformly distributed in the range // [minval, maxval) -xla::ComputationDataHandle RandomUniform(xla::ComputationBuilder* builder, - const xla::ComputationDataHandle& seed, - const TensorShape& shape, - double minval, double maxval) { +xla::XlaOp RandomUniform(xla::XlaBuilder* builder, const xla::XlaOp& seed, + const TensorShape& shape, double minval, + double maxval) { // Split the seed into two 32-bit scalars to form a key. auto seed0 = builder->Reshape(builder->Slice(seed, {0}, {1}, {1}), {}); auto seed1 = builder->Reshape(builder->Slice(seed, {1}, {2}, {1}), {}); @@ -178,9 +175,8 @@ xla::ComputationDataHandle RandomUniform(xla::ComputationBuilder* builder, // p = sum_{i=1}^n gq[i]*w^i // } // return p*x -xla::ComputationDataHandle ErfInvF32(xla::ComputationBuilder* b, - const xla::ComputationDataHandle& x, - const TensorShape& shape) { +xla::XlaOp ErfInvF32(xla::XlaBuilder* b, const xla::XlaOp& x, + const TensorShape& shape) { constexpr int kDegree = 9; constexpr std::array w_less_than_5_constants = { 2.81022636e-08f, 3.43273939e-07f, -3.5233877e-06f, @@ -220,7 +216,7 @@ class StatelessRandomUniformOp : public XlaOpKernel { : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); TensorShape shape; OP_REQUIRES_OK(ctx, ctx->ConstantInputAsShape(0, &shape)); @@ -229,7 +225,7 @@ class StatelessRandomUniformOp : public XlaOpKernel { OP_REQUIRES(ctx, seed_shape.dims() == 1 && seed_shape.dim_size(0) == 2, errors::InvalidArgument("seed must have shape [2], not ", seed_shape.DebugString())); - xla::ComputationDataHandle seed = ctx->Input(1); + xla::XlaOp seed = ctx->Input(1); ctx->SetOutput(0, RandomUniform(builder, seed, shape, 0.0, 1.0)); } @@ -257,8 +253,8 @@ class StatelessRandomNormalOp : public XlaOpKernel { OP_REQUIRES(ctx, seed_shape == TensorShape({2}), errors::InvalidArgument("seed must have shape [2], not ", seed_shape.DebugString())); - xla::ComputationDataHandle seed = ctx->Input(1); - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaOp seed = ctx->Input(1); + xla::XlaBuilder* builder = ctx->builder(); auto uniform = RandomUniform(builder, seed, shape, -1.0, 1.0); // Convert uniform distribution to normal distribution by computing // sqrt(2) * erfinv(x) diff --git a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc index 6204aa4e27000f..55254c746e5eba 100644 --- a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc @@ -90,7 +90,7 @@ class StridedSliceOp : public XlaOpKernel { } } - xla::ComputationDataHandle slice = ctx->Input(0); + xla::XlaOp slice = ctx->Input(0); if (!dimensions_to_reverse.empty()) { slice = ctx->builder()->Rev(slice, dimensions_to_reverse); } @@ -168,7 +168,7 @@ class StridedSliceGradOp : public XlaOpKernel { auto zero = XlaHelpers::Zero(ctx->builder(), ctx->expected_output_dtype(0)); - xla::ComputationDataHandle grad = ctx->Input(4); + xla::XlaOp grad = ctx->Input(4); // Undo any new/shrink axes. grad = ctx->builder()->Reshape(grad, processing_shape.dim_sizes()); @@ -255,7 +255,7 @@ class StridedSliceAssignOp : public XlaOpKernel { &strides_tensor)); TensorShape lhs_shape; - xla::ComputationDataHandle lhs; + xla::XlaOp lhs; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, dtype_, &lhs_shape, &lhs)); const TensorShape rhs_shape = ctx->InputShape(4); @@ -284,7 +284,7 @@ class StridedSliceAssignOp : public XlaOpKernel { " does not match r-value shape ", rhs_shape.DebugString(), ". Automatic broadcasting not yet implemented.")); - xla::ComputationDataHandle rhs = ctx->Input(4); + xla::XlaOp rhs = ctx->Input(4); gtl::InlinedVector dimensions_to_reverse; gtl::InlinedVector slice_begin, slice_dims; diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc index 000b50af6bd86b..9adee78a1fd1fb 100644 --- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc @@ -47,7 +47,7 @@ namespace { // the TensorArray with elements of `elem_shape`. For both initialized and // uninitialized TensorArrays, checks that the tensor has a type compatible with // 'dtype' and shape compatible with 'elem_shape'. -Status MaybeInitializeTensorArray(xla::ComputationBuilder* builder, +Status MaybeInitializeTensorArray(xla::XlaBuilder* builder, XlaResource* resource, DataType dtype, const TensorShape& elem_shape) { if (resource->kind() != XlaResource::kTensorArray) { @@ -64,9 +64,6 @@ Status MaybeInitializeTensorArray(xla::ComputationBuilder* builder, << resource->name() << " size " << resource->tensor_array_size(); if (!resource->initialized()) { - xla::ComputationDataHandle zero = - XlaHelpers::Zero(builder, resource->type()); - TF_RETURN_IF_ERROR(resource->SetTypeAndShape(dtype, elem_shape)); TF_RETURN_IF_ERROR(resource->SetZeroValue(builder)); } else { @@ -77,7 +74,7 @@ Status MaybeInitializeTensorArray(xla::ComputationBuilder* builder, } TensorShape shape; TF_RETURN_IF_ERROR( - XLAShapeToTensorShape(*shape_or_status.ValueOrDie(), &shape)); + XLAShapeToTensorShape(shape_or_status.ValueOrDie(), &shape)); TensorShape ta_shape; ta_shape.AddDim(resource->tensor_array_size()); @@ -114,23 +111,21 @@ Status CheckTensorArrayIsInitialized(const string& op_name, } Status GetTensorArrayShape(const XlaResource* resource, - xla::ComputationBuilder* builder, - TensorShape* shape) { + xla::XlaBuilder* builder, TensorShape* shape) { *shape = resource->shape(); shape->InsertDim(0, resource->tensor_array_size()); return Status::OK(); } -// Like ComputationBuilder::DynamicUpdateSlice, but adds 'update' to the +// Like XlaBuilder::DynamicUpdateSlice, but adds 'update' to the // relevant slice of 'operand'. -xla::ComputationDataHandle DynamicAddSlice( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& operand, - const xla::ComputationDataHandle& update, - const gtl::ArraySlice& update_dims, - const xla::ComputationDataHandle& start_indices) { - xla::ComputationDataHandle current = +xla::XlaOp DynamicAddSlice(xla::XlaBuilder* builder, const xla::XlaOp& operand, + const xla::XlaOp& update, + const gtl::ArraySlice& update_dims, + const xla::XlaOp& start_indices) { + xla::XlaOp current = builder->DynamicSlice(operand, start_indices, update_dims); - xla::ComputationDataHandle sum = builder->Add(current, update); + xla::XlaOp sum = builder->Add(current, update); return builder->DynamicUpdateSlice(operand, sum, start_indices); } @@ -155,18 +150,18 @@ class TensorArrayOp : public XlaOpKernel { OP_REQUIRES(ctx, size >= 0, errors::InvalidArgument("TensorArray size must be >= 0")); - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); // Initializes the TensorArray value if we know the element shape. // Otherwise, defer initialization to the first write. - xla::ComputationDataHandle value; + xla::XlaOp value; TensorShape shape; if (element_shape_.IsFullyDefined()) { CHECK(element_shape_.AsTensorShape(&shape)); TensorShape ta_shape; ta_shape.AddDim(size); ta_shape.AppendShape(shape); - xla::ComputationDataHandle zero = XlaHelpers::Zero(b, dtype_); + xla::XlaOp zero = XlaHelpers::Zero(b, dtype_); value = b->Broadcast(zero, ta_shape.dim_sizes()); } @@ -202,7 +197,7 @@ class TensorArrayWriteOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); TensorShape elem_shape = ctx->InputShape(2); @@ -213,10 +208,10 @@ class TensorArrayWriteOp : public XlaOpKernel { OP_REQUIRES_OK(ctx, MaybeInitializeTensorArray(b, resource, dtype_, elem_shape)); - xla::ComputationDataHandle ta = resource->value(); - xla::ComputationDataHandle index = ctx->Input(1); - xla::ComputationDataHandle value = ctx->Input(2); - xla::ComputationDataHandle flow = ctx->Input(3); + xla::XlaOp ta = resource->value(); + xla::XlaOp index = ctx->Input(1); + xla::XlaOp value = ctx->Input(2); + xla::XlaOp flow = ctx->Input(3); // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0]. auto start_indices = @@ -227,7 +222,7 @@ class TensorArrayWriteOp : public XlaOpKernel { slice_shape.InsertDim(0, 1LL); auto update = b->Reshape(value, slice_shape.dim_sizes()); - xla::ComputationDataHandle written = + xla::XlaOp written = DynamicAddSlice(b, ta, update, slice_shape.dim_sizes(), start_indices); OP_REQUIRES_OK(ctx, resource->SetValue(written)); @@ -249,7 +244,7 @@ class TensorArrayReadOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); XlaResource* resource; OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource)); @@ -259,8 +254,8 @@ class TensorArrayReadOp : public XlaOpKernel { TensorShape ta_shape; OP_REQUIRES_OK(ctx, GetTensorArrayShape(resource, b, &ta_shape)); - xla::ComputationDataHandle ta = resource->value(); - xla::ComputationDataHandle index = ctx->Input(1); + xla::XlaOp ta = resource->value(); + xla::XlaOp index = ctx->Input(1); // start_indices of the DynamicSlice are [index, 0, 0, ..., 0]. auto start_indices = @@ -270,8 +265,7 @@ class TensorArrayReadOp : public XlaOpKernel { auto slice_shape = ta_shape.dim_sizes(); slice_shape[0] = 1LL; - xla::ComputationDataHandle read = - b->DynamicSlice(ta, start_indices, slice_shape); + xla::XlaOp read = b->DynamicSlice(ta, start_indices, slice_shape); // Remove the leading '1' dimension. std::vector value_shape(slice_shape.begin() + 1, slice_shape.end()); @@ -293,7 +287,7 @@ class TensorArrayGatherOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); XlaResource* resource; OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource)); @@ -309,7 +303,7 @@ class TensorArrayGatherOp : public XlaOpKernel { auto indices = ctx->Input(1); DataType index_type = ctx->input_type(1); - xla::ComputationDataHandle ta = resource->value(); + xla::XlaOp ta = resource->value(); // Look for the case where the gather takes a simple slice from the // tensor array (0, 1, 2, 3, 4, ..., N) @@ -337,7 +331,7 @@ class TensorArrayGatherOp : public XlaOpKernel { } } - xla::ComputationDataHandle gather; + xla::XlaOp gather; OP_REQUIRES_OK( ctx, XlaGather(ta, ta_shape, indices, indices_shape, /*axis=*/0, @@ -360,7 +354,7 @@ class TensorArrayScatterOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); const TensorShape value_shape = ctx->InputShape(2); @@ -375,11 +369,11 @@ class TensorArrayScatterOp : public XlaOpKernel { OP_REQUIRES(ctx, indices_shape.dims() >= 1, errors::InvalidArgument("indices must be rank 1")); const int num_indices = indices_shape.dim_size(0); - const xla::ComputationDataHandle indices = ctx->Input(1); + const xla::XlaOp indices = ctx->Input(1); - xla::ComputationDataHandle ta = resource->value(); - const xla::ComputationDataHandle value = ctx->Input(2); - const xla::ComputationDataHandle flow = ctx->Input(3); + xla::XlaOp ta = resource->value(); + const xla::XlaOp value = ctx->Input(2); + const xla::XlaOp flow = ctx->Input(3); // Look for the case where the scatter is for each sub-tensor in order. The // tensor array implementation allows for this to be a straight addition. @@ -443,7 +437,7 @@ class TensorArrayConcatOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); XlaResource* resource; OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource)); @@ -453,7 +447,7 @@ class TensorArrayConcatOp : public XlaOpKernel { TensorShape ta_shape; OP_REQUIRES_OK(ctx, GetTensorArrayShape(resource, b, &ta_shape)); - xla::ComputationDataHandle ta = resource->value(); + xla::XlaOp ta = resource->value(); auto ta_dims = ta_shape.dim_sizes(); std::vector shape(ta_dims.begin() + 1, ta_dims.end()); @@ -503,12 +497,12 @@ class TensorArraySplitOp : public XlaOpKernel { TensorShape elem_shape = value_shape; elem_shape.set_dim(0, length); - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); XlaResource* resource; OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource)); OP_REQUIRES_OK(ctx, MaybeInitializeTensorArray(b, resource, dtype_, elem_shape)); - xla::ComputationDataHandle ta = resource->value(); + xla::XlaOp ta = resource->value(); TensorShape ta_shape; ta_shape.AddDim(resource->tensor_array_size()); @@ -520,8 +514,8 @@ class TensorArraySplitOp : public XlaOpKernel { "TensorArray's size is not equal to the size of lengths (", lengths.size(), " vs. ", resource->tensor_array_size(), ")")); - const xla::ComputationDataHandle value = ctx->Input(1); - const xla::ComputationDataHandle flow = ctx->Input(3); + const xla::XlaOp value = ctx->Input(1); + const xla::XlaOp flow = ctx->Input(3); OP_REQUIRES(ctx, value_shape.num_elements() == ta_shape.num_elements(), errors::InvalidArgument("mismatched element count ", @@ -569,7 +563,7 @@ class TensorArrayGradOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); XlaResource* resource; OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &resource)); diff --git a/tensorflow/compiler/tf2xla/kernels/tile_ops.cc b/tensorflow/compiler/tf2xla/kernels/tile_ops.cc index 9aefcd4fc7f94a..e91075196bd841 100644 --- a/tensorflow/compiler/tf2xla/kernels/tile_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/tile_ops.cc @@ -112,7 +112,7 @@ class TileOp : public XlaOpKernel { flattened.push_back(i); flattened.push_back(i + output_shape.size()); } - xla::ComputationDataHandle output = + xla::XlaOp output = ctx->builder()->Reshape(broadcasted, flattened, output_shape); ctx->SetOutput(0, output); diff --git a/tensorflow/compiler/tf2xla/kernels/training_ops.cc b/tensorflow/compiler/tf2xla/kernels/training_ops.cc index f750f7003be288..34caefa050c0d5 100644 --- a/tensorflow/compiler/tf2xla/kernels/training_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/training_ops.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" @@ -30,8 +30,8 @@ class ResourceApplyGradientDescent : public XlaOpKernel { explicit ResourceApplyGradientDescent(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationDataHandle handle; - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaOp handle; + xla::XlaBuilder* b = ctx->builder(); DataType type = ctx->input_type(1); TensorShape var_shape; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, type, &var_shape, &handle)); @@ -63,12 +63,12 @@ class ResourceApplyMomentum : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); DataType type = ctx->input_type(2); TensorShape var_shape, accum_shape; - xla::ComputationDataHandle var, accum; + xla::XlaOp var, accum; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, type, &var_shape, &var)); OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(1, type, &accum_shape, &accum)); @@ -93,9 +93,9 @@ class ResourceApplyMomentum : public XlaOpKernel { errors::InvalidArgument("momentum is not a scalar: ", momentum_shape.DebugString())); - xla::ComputationDataHandle lr = ctx->Input(2); - xla::ComputationDataHandle grad = ctx->Input(3); - xla::ComputationDataHandle momentum = ctx->Input(4); + xla::XlaOp lr = ctx->Input(2); + xla::XlaOp grad = ctx->Input(3); + xla::XlaOp momentum = ctx->Input(4); accum = b->Add(b->Mul(accum, momentum), grad); if (use_nesterov_) { @@ -121,12 +121,12 @@ class ResourceApplyAdagrad : public XlaOpKernel { explicit ResourceApplyAdagrad(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); DataType type = ctx->input_type(2); TensorShape var_shape, accum_shape; - xla::ComputationDataHandle var, accum; + xla::XlaOp var, accum; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, type, &var_shape, &var)); OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(1, type, &accum_shape, &accum)); @@ -146,8 +146,8 @@ class ResourceApplyAdagrad : public XlaOpKernel { "var and grad do not have the same shape", var_shape.DebugString(), " ", grad_shape.DebugString())); - xla::ComputationDataHandle lr = ctx->Input(2); - xla::ComputationDataHandle grad = ctx->Input(3); + xla::XlaOp lr = ctx->Input(2); + xla::XlaOp grad = ctx->Input(3); accum = b->Add(accum, b->Pow(grad, XlaHelpers::FloatLiteral(b, type, 2.0))); var = b->Sub( @@ -168,7 +168,7 @@ class ResourceApplyAdam : public XlaOpKernel { void Compile(XlaOpKernelContext* ctx) override { TensorShape var_shape, m_shape, v_shape; - xla::ComputationDataHandle var, m, v; + xla::XlaOp var, m, v; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, dtype_, &var_shape, &var)); OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(1, dtype_, &m_shape, &m)); OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(2, dtype_, &v_shape, &v)); @@ -213,25 +213,25 @@ class ResourceApplyAdam : public XlaOpKernel { "var and grad do not have the same shape", var_shape.DebugString(), " ", grad_shape.DebugString())); - xla::ComputationDataHandle beta1_power = ctx->Input(3); - xla::ComputationDataHandle beta2_power = ctx->Input(4); - xla::ComputationDataHandle lr = ctx->Input(5); - xla::ComputationDataHandle beta1 = ctx->Input(6); - xla::ComputationDataHandle beta2 = ctx->Input(7); - xla::ComputationDataHandle epsilon = ctx->Input(8); - xla::ComputationDataHandle grad = ctx->Input(9); + xla::XlaOp beta1_power = ctx->Input(3); + xla::XlaOp beta2_power = ctx->Input(4); + xla::XlaOp lr = ctx->Input(5); + xla::XlaOp beta1 = ctx->Input(6); + xla::XlaOp beta2 = ctx->Input(7); + xla::XlaOp epsilon = ctx->Input(8); + xla::XlaOp grad = ctx->Input(9); // alpha <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t) // m_t <- beta1 * m_{t-1} + (1 - beta1) * g_t // v_t <- beta2 * v_{t-1} + (1 - beta2) * g_t * g_t // variable <- variable - alpha * m_t / (sqrt(v_t) + epsilon) - xla::ComputationBuilder* b = ctx->builder(); - xla::ComputationDataHandle half = XlaHelpers::FloatLiteral(b, dtype_, 0.5); - xla::ComputationDataHandle one = XlaHelpers::FloatLiteral(b, dtype_, 1.0); - xla::ComputationDataHandle two = XlaHelpers::FloatLiteral(b, dtype_, 2.0); + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp half = XlaHelpers::FloatLiteral(b, dtype_, 0.5); + xla::XlaOp one = XlaHelpers::FloatLiteral(b, dtype_, 1.0); + xla::XlaOp two = XlaHelpers::FloatLiteral(b, dtype_, 2.0); - xla::ComputationDataHandle alpha = + xla::XlaOp alpha = b->Div(b->Mul(lr, b->Pow(b->Sub(one, beta2_power), half)), b->Sub(one, beta1_power)); m = b->Add(m, b->Mul(b->Sub(grad, m), b->Sub(one, beta1))); @@ -255,12 +255,12 @@ class ResourceApplyRMSProp : public XlaOpKernel { explicit ResourceApplyRMSProp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); DataType type = ctx->input_type(3); TensorShape var_shape, ms_shape, mom_shape; - xla::ComputationDataHandle var, ms, mom; + xla::XlaOp var, ms, mom; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, type, &var_shape, &var)); OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(1, type, &ms_shape, &ms)); OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(2, type, &mom_shape, &mom)); @@ -297,11 +297,11 @@ class ResourceApplyRMSProp : public XlaOpKernel { "var and grad do not have the same shape", var_shape.DebugString(), " ", grad_shape.DebugString())); - xla::ComputationDataHandle lr = ctx->Input(3); - xla::ComputationDataHandle rho = ctx->Input(4); - xla::ComputationDataHandle momentum = ctx->Input(5); - xla::ComputationDataHandle epsilon = ctx->Input(6); - xla::ComputationDataHandle grad = ctx->Input(7); + xla::XlaOp lr = ctx->Input(3); + xla::XlaOp rho = ctx->Input(4); + xla::XlaOp momentum = ctx->Input(5); + xla::XlaOp epsilon = ctx->Input(6); + xla::XlaOp grad = ctx->Input(7); // ms <- rho * ms_{t-1} + (1-rho) * grad * grad // mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) @@ -320,16 +320,16 @@ class ResourceApplyRMSProp : public XlaOpKernel { // ms <- grad**2 (1 - rho) + ms * rho // // Which is the equation listed above. - xla::ComputationDataHandle new_ms = b->Add( + xla::XlaOp new_ms = b->Add( ms, b->Mul(b->Sub(b->Pow(grad, XlaHelpers::FloatLiteral(b, type, 2.0)), ms), b->Sub(XlaHelpers::FloatLiteral(b, type, 1.0), rho))); - xla::ComputationDataHandle new_mom = + xla::XlaOp new_mom = b->Add(b->Mul(mom, momentum), b->Mul(b->Mul(grad, lr), b->Pow(b->Add(new_ms, epsilon), XlaHelpers::FloatLiteral(b, type, -0.5)))); - xla::ComputationDataHandle new_var = b->Sub(var, new_mom); + xla::XlaOp new_var = b->Sub(var, new_mom); OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, type, new_var)); OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, type, new_ms)); @@ -341,10 +341,10 @@ REGISTER_XLA_OP(Name("ResourceApplyRMSProp").TypeConstraint("T", kFloatTypes), void CompileFtrl(XlaOpKernelContext* ctx, DataType dtype, bool has_l2_shrinkage) { - xla::ComputationBuilder* b = ctx->builder(); + xla::XlaBuilder* b = ctx->builder(); TensorShape var_shape, accum_shape, linear_shape; - xla::ComputationDataHandle var, accum, linear; + xla::XlaOp var, accum, linear; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, dtype, &var_shape, &var)); OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(1, dtype, &accum_shape, &accum)); OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(2, dtype, &linear_shape, &linear)); @@ -399,12 +399,12 @@ void CompileFtrl(XlaOpKernelContext* ctx, DataType dtype, errors::InvalidArgument("lr_power is not a scalar: ", lr_power_shape.DebugString())); - xla::ComputationDataHandle grad = ctx->Input(3); - xla::ComputationDataHandle lr = ctx->Input(4); - xla::ComputationDataHandle l1 = ctx->Input(5); - xla::ComputationDataHandle l2 = ctx->Input(6); - xla::ComputationDataHandle l2_shrinkage; - xla::ComputationDataHandle lr_power; + xla::XlaOp grad = ctx->Input(3); + xla::XlaOp lr = ctx->Input(4); + xla::XlaOp l1 = ctx->Input(5); + xla::XlaOp l2 = ctx->Input(6); + xla::XlaOp l2_shrinkage; + xla::XlaOp lr_power; if (has_l2_shrinkage) { l2_shrinkage = ctx->Input(7); lr_power = ctx->Input(8); @@ -421,26 +421,23 @@ void CompileFtrl(XlaOpKernelContext* ctx, DataType dtype, // var = (linear_clipped - linear) / quadratic // accum = new_accum - xla::ComputationDataHandle two = XlaHelpers::FloatLiteral(b, dtype, 2.0); - xla::ComputationDataHandle grad_to_use; + xla::XlaOp two = XlaHelpers::FloatLiteral(b, dtype, 2.0); + xla::XlaOp grad_to_use; if (has_l2_shrinkage) { grad_to_use = b->Add(grad, b->Mul(two, b->Mul(l2_shrinkage, var))); } else { grad_to_use = grad; } - xla::ComputationDataHandle new_accum = - b->Add(accum, b->Pow(grad_to_use, two)); - xla::ComputationDataHandle new_accum_lr_pow = - b->Pow(new_accum, b->Neg(lr_power)); - xla::ComputationDataHandle accum_lr_pow = b->Pow(accum, b->Neg(lr_power)); + xla::XlaOp new_accum = b->Add(accum, b->Pow(grad_to_use, two)); + xla::XlaOp new_accum_lr_pow = b->Pow(new_accum, b->Neg(lr_power)); + xla::XlaOp accum_lr_pow = b->Pow(accum, b->Neg(lr_power)); linear = b->Add( linear, b->Sub(grad_to_use, b->Mul(b->Div(b->Sub(new_accum_lr_pow, accum_lr_pow), lr), var))); - xla::ComputationDataHandle linear_clipped = b->Clamp(b->Neg(l1), linear, l1); - xla::ComputationDataHandle quadratic = - b->Add(b->Div(new_accum_lr_pow, lr), b->Mul(two, l2)); + xla::XlaOp linear_clipped = b->Clamp(b->Neg(l1), linear, l1); + xla::XlaOp quadratic = b->Add(b->Div(new_accum_lr_pow, lr), b->Mul(two, l2)); var = b->Div(b->Sub(linear_clipped, linear), quadratic); accum = new_accum; diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc index 7cb47f908d4ff4..a4f50f52ebe8b1 100644 --- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" namespace tensorflow { @@ -33,9 +33,9 @@ namespace { public: \ explicit NAME##Op(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} \ void Compile(XlaOpKernelContext* ctx) { \ - xla::ComputationBuilder* b = ctx->builder(); \ - xla::ComputationDataHandle x = ctx->Input(0); \ - xla::ComputationDataHandle y = COMPUTATION; \ + xla::XlaBuilder* b = ctx->builder(); \ + xla::XlaOp x = ctx->Input(0); \ + xla::XlaOp y = COMPUTATION; \ ctx->SetOutput(0, y); \ } \ }; \ @@ -124,9 +124,8 @@ XLAJIT_MAKE_UNARY(Neg, b->Neg(x)); // Implements Banker's rounding: numbers that are equidistant between two // integers are rounded towards even. -static xla::ComputationDataHandle Round(xla::ComputationBuilder* b, - DataType dtype, - const xla::ComputationDataHandle& x) { +static xla::XlaOp Round(xla::XlaBuilder* b, DataType dtype, + const xla::XlaOp& x) { auto half = XlaHelpers::FloatLiteral(b, dtype, 0.5); auto one = XlaHelpers::FloatLiteral(b, dtype, 1.0); auto two = XlaHelpers::FloatLiteral(b, dtype, 2.0); @@ -148,9 +147,8 @@ XLAJIT_MAKE_UNARY(Rsqrt, b->Pow(x, XlaHelpers::FloatLiteral(b, input_type(0), -0.5))); // Expresses sigmoid as a rescaled tanh: sigmoid(x) == (tanh(x/2) + 1) / 2. -static xla::ComputationDataHandle Sigmoid(xla::ComputationBuilder* b, - DataType dtype, - const xla::ComputationDataHandle& x) { +static xla::XlaOp Sigmoid(xla::XlaBuilder* b, DataType dtype, + const xla::XlaOp& x) { auto half = XlaHelpers::FloatLiteral(b, dtype, 0.5); return b->Add(half, b->Mul(half, b->Tanh(b->Mul(half, x)))); } @@ -162,20 +160,18 @@ XLAJIT_MAKE_UNARY(Sinh, b->Mul(b->Sub(b->Exp(x), b->Exp(b->Neg(x))), XlaHelpers::FloatLiteral(b, input_type(0), 0.5))); -static xla::ComputationDataHandle Softplus( - xla::ComputationBuilder* b, DataType dtype, - const xla::ComputationDataHandle& features) { - xla::ComputationDataHandle threshold = - b->Add(b->Log(XlaHelpers::Epsilon(b, dtype)), - XlaHelpers::FloatLiteral(b, dtype, 2.0)); +static xla::XlaOp Softplus(xla::XlaBuilder* b, DataType dtype, + const xla::XlaOp& features) { + xla::XlaOp threshold = b->Add(b->Log(XlaHelpers::Epsilon(b, dtype)), + XlaHelpers::FloatLiteral(b, dtype, 2.0)); // Value above which exp(x) may overflow, but softplus(x) == x // is within machine epsilon. - xla::ComputationDataHandle too_large = b->Gt(features, b->Neg(threshold)); + xla::XlaOp too_large = b->Gt(features, b->Neg(threshold)); // Value below which exp(x) may underflow, but softplus(x) == exp(x) // is within machine epsilon. - xla::ComputationDataHandle too_small = b->Lt(features, threshold); - xla::ComputationDataHandle features_exp = b->Exp(features); - xla::ComputationDataHandle output = b->Select( + xla::XlaOp too_small = b->Lt(features, threshold); + xla::XlaOp features_exp = b->Exp(features); + xla::XlaOp output = b->Select( too_large, features, b->Select(too_small, features_exp, b->Log(b->Add(features_exp, XlaHelpers::One(b, dtype))))); diff --git a/tensorflow/compiler/tf2xla/kernels/variable_ops.cc b/tensorflow/compiler/tf2xla/kernels/variable_ops.cc index 71173f5aead477..6109db8e89e5ee 100644 --- a/tensorflow/compiler/tf2xla/kernels/variable_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/variable_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" @@ -48,7 +48,7 @@ class ReadVariableOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationDataHandle handle; + xla::XlaOp handle; OP_REQUIRES_OK( ctx, ctx->ReadVariableInput(0, dtype_, /*shape=*/nullptr, &handle)); ctx->SetOutput(0, handle); @@ -74,7 +74,7 @@ class AssignAddVariableOp : public XlaOpKernel { explicit AssignAddVariableOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { DataType type = ctx->input_type(1); - xla::ComputationDataHandle handle; + xla::XlaOp handle; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, type, /*shape=*/nullptr, &handle)); handle = ctx->builder()->Add(handle, ctx->Input(1)); @@ -90,7 +90,7 @@ class AssignSubVariableOp : public XlaOpKernel { explicit AssignSubVariableOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { DataType type = ctx->input_type(1); - xla::ComputationDataHandle handle; + xla::XlaOp handle; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, type, /*shape=*/nullptr, &handle)); handle = ctx->builder()->Sub(handle, ctx->Input(1)); @@ -105,19 +105,19 @@ class ResourceGatherOp : public XlaOpKernel { public: explicit ResourceGatherOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); DataType type = ctx->expected_output_dtype(0); TensorShape resource_shape; - xla::ComputationDataHandle resource_handle; + xla::XlaOp resource_handle; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, type, &resource_shape, &resource_handle)); auto indices = ctx->Input(1); auto indices_shape = ctx->InputShape(1); DataType index_type = ctx->input_type(1); - xla::ComputationDataHandle gather; + xla::XlaOp gather; OP_REQUIRES_OK( ctx, XlaGather(resource_handle, resource_shape, indices, indices_shape, /*axis=*/0, /*indices_are_nd=*/false, type, index_type, diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc index 0ff1b65ae9179d..5467c5d9946846 100644 --- a/tensorflow/compiler/tf2xla/kernels/while_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/op_kernel.h" @@ -101,7 +101,7 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) { ctx, MakeXlaCompilerArgumentsFromInputs( ctx, &arguments, &has_uninitialized_vars, &has_tensor_arrays)); - xla::ComputationBuilder* builder = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); XlaCompiler* compiler = ctx->compiler(); VLOG(1) << "Compiling body"; @@ -234,7 +234,7 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) { xla::ShapeUtil::HumanString(cond.xla_output_shape))); int num_inputs = body.input_mapping.size(); - std::vector inputs(num_inputs); + std::vector inputs(num_inputs); for (int i = 0; i < num_inputs; ++i) { int input_num = body.input_mapping[i]; if (ctx->input_type(input_num) == DT_RESOURCE) { @@ -246,24 +246,24 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) { } } - xla::ComputationDataHandle init = builder->Tuple(inputs); + xla::XlaOp init = builder->Tuple(inputs); VLOG(1) << "Building while loop"; // Wraps the condition in a computation that unpacks the output tuple. - xla::Computation cond_wrapper; + xla::XlaComputation cond_wrapper; { - std::unique_ptr cb = + std::unique_ptr cb = builder->CreateSubBuilder("cond_wrapper"); auto inputs = cb->Parameter(0, cond_input_shape, "inputs"); auto outputs = cb->Call(*cond.computation, {inputs}); cb->GetTupleElement(outputs, 0); - xla::StatusOr result = cb->Build(); + xla::StatusOr result = cb->Build(); OP_REQUIRES_OK(ctx, result.status()); cond_wrapper = std::move(result.ValueOrDie()); } - xla::ComputationDataHandle while_result = + xla::XlaOp while_result = builder->While(cond_wrapper, *body.computation, init); // Sets non-variable outputs. diff --git a/tensorflow/compiler/tf2xla/lib/BUILD b/tensorflow/compiler/tf2xla/lib/BUILD index 12fdfb605d667b..04ad3694a0c0df 100644 --- a/tensorflow/compiler/tf2xla/lib/BUILD +++ b/tensorflow/compiler/tf2xla/lib/BUILD @@ -25,8 +25,8 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", - "//tensorflow/compiler/xla/client:computation", - "//tensorflow/compiler/xla/client:computation_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -44,8 +44,8 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", - "//tensorflow/compiler/xla/client:computation", - "//tensorflow/compiler/xla/client:computation_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -62,9 +62,9 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", - "//tensorflow/compiler/xla/client:computation", - "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client/lib:arithmetic", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -82,8 +82,8 @@ cc_library( "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", - "//tensorflow/compiler/xla/client:computation", - "//tensorflow/compiler/xla/client:computation_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -101,9 +101,9 @@ xla_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -122,8 +122,8 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", - "//tensorflow/compiler/xla/client:computation", - "//tensorflow/compiler/xla/client:computation_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -161,8 +161,8 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", - "//tensorflow/compiler/xla/client:computation", - "//tensorflow/compiler/xla/client:computation_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.cc b/tensorflow/compiler/tf2xla/lib/batch_dot.cc index 798f0fa78055e8..526694d5a0c712 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.cc +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.cc @@ -25,24 +25,22 @@ limitations under the License. namespace tensorflow { -xla::StatusOr BatchDot( - xla::ComputationBuilder* builder, xla::ComputationDataHandle x, - xla::ComputationDataHandle y, bool transpose_x, bool transpose_y, - bool conjugate_x, bool conjugate_y) { - TF_ASSIGN_OR_RETURN(std::unique_ptr x_shape, - builder->GetShape(x)); - TF_ASSIGN_OR_RETURN(std::unique_ptr y_shape, - builder->GetShape(y)); +xla::StatusOr BatchDot(xla::XlaBuilder* builder, xla::XlaOp x, + xla::XlaOp y, bool transpose_x, + bool transpose_y, bool conjugate_x, + bool conjugate_y) { + TF_ASSIGN_OR_RETURN(xla::Shape x_shape, builder->GetShape(x)); + TF_ASSIGN_OR_RETURN(xla::Shape y_shape, builder->GetShape(y)); // Check that both tensors have the same number of dimensions. There must be // at least two (the batch dimensions can be empty). - if (xla::ShapeUtil::Rank(*x_shape) != xla::ShapeUtil::Rank(*y_shape)) { + if (xla::ShapeUtil::Rank(x_shape) != xla::ShapeUtil::Rank(y_shape)) { return errors::InvalidArgument( "Arguments to BatchedDot have different ranks: ", - xla::ShapeUtil::HumanString(*x_shape), " vs. ", - xla::ShapeUtil::HumanString(*y_shape)); + xla::ShapeUtil::HumanString(x_shape), " vs. ", + xla::ShapeUtil::HumanString(y_shape)); } - const int ndims = xla::ShapeUtil::Rank(*x_shape); + const int ndims = xla::ShapeUtil::Rank(x_shape); if (ndims < 2) { return errors::InvalidArgument( "Arguments to BatchedDot must have rank >= 2: ", ndims); @@ -52,46 +50,46 @@ xla::StatusOr BatchDot( // valid. std::vector batch_dimension_numbers; for (int i = 0; i < ndims - 2; ++i) { - if (x_shape->dimensions(i) != y_shape->dimensions(i)) { + if (x_shape.dimensions(i) != y_shape.dimensions(i)) { return errors::InvalidArgument( "Dimension ", i, " of inputs to BatchedDot must be equal: ", - xla::ShapeUtil::HumanString(*x_shape), " vs ", - xla::ShapeUtil::HumanString(*y_shape)); + xla::ShapeUtil::HumanString(x_shape), " vs ", + xla::ShapeUtil::HumanString(y_shape)); } batch_dimension_numbers.push_back(i); } int x_inner_dim = transpose_x ? (ndims - 2) : (ndims - 1); int y_inner_dim = transpose_y ? (ndims - 1) : (ndims - 2); - if (x_shape->dimensions(x_inner_dim) != y_shape->dimensions(y_inner_dim)) { + if (x_shape.dimensions(x_inner_dim) != y_shape.dimensions(y_inner_dim)) { return errors::InvalidArgument( "Dimensions ", x_inner_dim, " and ", y_inner_dim, " of arguments to BatchedDot must be equal: ", - xla::ShapeUtil::HumanString(*x_shape), " transpose: ", transpose_x, - " vs. ", xla::ShapeUtil::HumanString(*y_shape), + xla::ShapeUtil::HumanString(x_shape), " transpose: ", transpose_x, + " vs. ", xla::ShapeUtil::HumanString(y_shape), " transpose: ", transpose_y); } // Check for zero lhs/rhs dim size. - if (xla::ShapeUtil::HasZeroElements(*x_shape) || - xla::ShapeUtil::HasZeroElements(*y_shape)) { + if (xla::ShapeUtil::HasZeroElements(x_shape) || + xla::ShapeUtil::HasZeroElements(y_shape)) { std::vector dimensions(batch_dimension_numbers.size()); for (int i = 0; i < batch_dimension_numbers.size(); ++i) { - dimensions[i] = x_shape->dimensions(batch_dimension_numbers[i]); + dimensions[i] = x_shape.dimensions(batch_dimension_numbers[i]); } int x_outer_dim = transpose_x ? (ndims - 1) : (ndims - 2); int y_outer_dim = transpose_y ? (ndims - 2) : (ndims - 1); - dimensions.push_back(x_shape->dimensions(x_outer_dim)); - dimensions.push_back(y_shape->dimensions(y_outer_dim)); + dimensions.push_back(x_shape.dimensions(x_outer_dim)); + dimensions.push_back(y_shape.dimensions(y_outer_dim)); return builder->Broadcast( - builder->ConstantLiteral(xla::Literal::Zero(x_shape->element_type())), + builder->ConstantLiteral(xla::Literal::Zero(x_shape.element_type())), dimensions); } - if (x_shape->element_type() == xla::C64 && conjugate_x) { + if (x_shape.element_type() == xla::C64 && conjugate_x) { x = builder->Conj(x); } - if (y_shape->element_type() == xla::C64 && conjugate_y) { + if (y_shape.element_type() == xla::C64 && conjugate_y) { y = builder->Conj(y); } diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.h b/tensorflow/compiler/tf2xla/lib/batch_dot.h index b230e885f10f45..1acc72033b05e7 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.h +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.h @@ -16,8 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_BATCH_DOT_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_BATCH_DOT_H_ -#include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" namespace tensorflow { @@ -43,10 +43,10 @@ namespace tensorflow { // It is computed as: // // output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :]) -xla::StatusOr BatchDot( - xla::ComputationBuilder* builder, xla::ComputationDataHandle x, - xla::ComputationDataHandle y, bool transpose_x, bool transpose_y, - bool conjugate_x = false, bool conjugate_y = false); +xla::StatusOr BatchDot(xla::XlaBuilder* builder, xla::XlaOp x, + xla::XlaOp y, bool transpose_x, + bool transpose_y, bool conjugate_x = false, + bool conjugate_y = false); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.cc b/tensorflow/compiler/tf2xla/lib/cholesky.cc index 203365e2ab07e0..83e73827862ca2 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.cc +++ b/tensorflow/compiler/tf2xla/lib/cholesky.cc @@ -47,23 +47,21 @@ namespace { // l[..., j+1:, j] = (a[..., j+1:, j] - np.dot(l[..., j+1:, :j], row_t)) / // l[..., j, j] // return l -xla::StatusOr CholeskyUnblocked( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& a) { - TF_ASSIGN_OR_RETURN(std::unique_ptr a_shape, - builder->GetShape(a)); - const int n_dims = xla::ShapeUtil::Rank(*a_shape); - const int64 n = xla::ShapeUtil::GetDimension(*a_shape, -1); - gtl::ArraySlice major_dims(xla::AsInt64Slice(a_shape->dimensions()), +xla::StatusOr CholeskyUnblocked(xla::XlaBuilder* builder, + const xla::XlaOp& a) { + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + const int n_dims = xla::ShapeUtil::Rank(a_shape); + const int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); + gtl::ArraySlice major_dims(xla::AsInt64Slice(a_shape.dimensions()), /*pos=*/0, /*len=*/n_dims - 2); - xla::ComputationDataHandle l = Zeros(builder, *a_shape); + xla::XlaOp l = Zeros(builder, a_shape); // Construct the for loop body to iterate over rows. - auto body_fn = [&](xla::ComputationDataHandle i, - gtl::ArraySlice loop_vars, - xla::ComputationBuilder* body_builder) - -> xla::StatusOr> { + auto body_fn = [&](xla::XlaOp i, gtl::ArraySlice loop_vars, + xla::XlaBuilder* body_builder) + -> xla::StatusOr> { xla::Shape col_shape; xla::Shape row_shape; for (int64 d : major_dims) { @@ -72,12 +70,12 @@ xla::StatusOr CholeskyUnblocked( } row_shape.add_dimensions(1); row_shape.add_dimensions(n); - row_shape.set_element_type(a_shape->element_type()); + row_shape.set_element_type(a_shape.element_type()); auto mask_zeros_row = Zeros(body_builder, row_shape); col_shape.add_dimensions(n); col_shape.add_dimensions(1); - col_shape.set_element_type(a_shape->element_type()); + col_shape.set_element_type(a_shape.element_type()); auto mask_zeros_col = Zeros(body_builder, col_shape); std::vector mask_vector(n); @@ -101,7 +99,7 @@ xla::StatusOr CholeskyUnblocked( TF_ASSIGN_OR_RETURN(auto a_ii, DynamicSliceInMinorDims(body_builder, body_a, {i, i}, {1, 1})); // np.dot(row, np.swapaxes(row, -1, -2)) - xla::ComputationDataHandle diag_dot; + xla::XlaOp diag_dot; TF_ASSIGN_OR_RETURN(diag_dot, BatchDot(body_builder, row, row, /*transpose_x=*/false, /*transpose_y=*/true)); @@ -109,7 +107,7 @@ xla::StatusOr CholeskyUnblocked( // np.swapaxes(row, -1, -2))) auto l_ii = body_builder->Pow( body_builder->Sub(a_ii, diag_dot), - FloatLiteral(body_builder, a_shape->element_type(), 0.5)); + FloatLiteral(body_builder, a_shape.element_type(), 0.5)); // a[..., i+1:, i] auto ip1 = body_builder->Add(i, body_builder->ConstantR0(1)); @@ -140,7 +138,7 @@ xla::StatusOr CholeskyUnblocked( TF_ASSIGN_OR_RETURN(body_l, DynamicUpdateSliceInMinorDims( body_builder, body_l, l_ii, {i, i})); - return std::vector{body_a, body_l}; + return std::vector{body_a, body_l}; }; TF_ASSIGN_OR_RETURN( @@ -152,22 +150,20 @@ xla::StatusOr CholeskyUnblocked( } // namespace -xla::StatusOr Cholesky( - xla::ComputationBuilder* builder, xla::ComputationDataHandle a, - int64 block_size) { - TF_ASSIGN_OR_RETURN(std::unique_ptr a_shape, - builder->GetShape(a)); - const int ndims = xla::ShapeUtil::Rank(*a_shape); +xla::StatusOr Cholesky(xla::XlaBuilder* builder, xla::XlaOp a, + int64 block_size) { + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + const int ndims = xla::ShapeUtil::Rank(a_shape); if (ndims < 2) { return errors::InvalidArgument( "Arguments to Cholesky must have rank >= 2: ", ndims); } - const int64 n = xla::ShapeUtil::GetDimension(*a_shape, -1); - if (n != xla::ShapeUtil::GetDimension(*a_shape, -2)) { + const int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); + if (n != xla::ShapeUtil::GetDimension(a_shape, -2)) { return errors::InvalidArgument( "Arguments to Cholesky must be square matrices: ", - xla::ShapeUtil::HumanString(*a_shape)); + xla::ShapeUtil::HumanString(a_shape)); } if (block_size < 1) { @@ -179,7 +175,7 @@ xla::StatusOr Cholesky( // Algorithm 1 from // Haidar, Azzam, et al. "High-performance Cholesky factorization for GPU-only // execution." Proceedings of General Purpose GPUs. ACM, 2017. - xla::ComputationDataHandle l = Zeros(builder, *a_shape); + xla::XlaOp l = Zeros(builder, a_shape); for (int64 i = 0; i < n; i += block_size) { int64 k = std::min(block_size, n - i); if (i > 0) { diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.h b/tensorflow/compiler/tf2xla/lib/cholesky.h index 17da8d8b22d107..20fca7969ece27 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.h +++ b/tensorflow/compiler/tf2xla/lib/cholesky.h @@ -16,8 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_CHOLESKY_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_CHOLESKY_H_ -#include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" namespace tensorflow { @@ -30,9 +30,8 @@ namespace tensorflow { // TODO(phawkins): check for negative values on the diagonal and return an // error, instead of silently yielding NaNs. // TODO(znado): handle the complex Hermitian case -xla::StatusOr Cholesky( - xla::ComputationBuilder* builder, xla::ComputationDataHandle a, - int64 block_size = 256); +xla::StatusOr Cholesky(xla::XlaBuilder* builder, xla::XlaOp a, + int64 block_size = 256); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/scatter.cc b/tensorflow/compiler/tf2xla/lib/scatter.cc index 45699233ea8b2a..d5a27abb2585f6 100644 --- a/tensorflow/compiler/tf2xla/lib/scatter.cc +++ b/tensorflow/compiler/tf2xla/lib/scatter.cc @@ -30,24 +30,19 @@ limitations under the License. namespace tensorflow { -xla::StatusOr XlaScatter( - const xla::ComputationDataHandle& buffer, - const xla::ComputationDataHandle& updates, - const xla::ComputationDataHandle& indices, bool indices_are_vectors, - const std::function& combiner, - xla::ComputationBuilder* builder) { - TF_ASSIGN_OR_RETURN(std::unique_ptr buffer_shape, - builder->GetShape(buffer)); - TF_ASSIGN_OR_RETURN(std::unique_ptr updates_shape, - builder->GetShape(updates)); - TF_ASSIGN_OR_RETURN(std::unique_ptr indices_shape, - builder->GetShape(indices)); +xla::StatusOr XlaScatter( + const xla::XlaOp& buffer, const xla::XlaOp& updates, + const xla::XlaOp& indices, bool indices_are_vectors, + const std::function& + combiner, + xla::XlaBuilder* builder) { + TF_ASSIGN_OR_RETURN(xla::Shape buffer_shape, builder->GetShape(buffer)); + TF_RETURN_IF_ERROR(builder->GetShape(updates).status()); + TF_ASSIGN_OR_RETURN(xla::Shape indices_shape, builder->GetShape(indices)); gtl::ArraySlice indices_dims = - xla::AsInt64Slice(indices_shape->dimensions()); + xla::AsInt64Slice(indices_shape.dimensions()); gtl::ArraySlice buffer_dims = - xla::AsInt64Slice(buffer_shape->dimensions()); + xla::AsInt64Slice(buffer_shape.dimensions()); // If the indices are N-dimensional, the minor dimension of indices contains // the indices to update. Otherwise the indices are all scalars. @@ -55,12 +50,12 @@ xla::StatusOr XlaScatter( if (indices_are_vectors) { TF_RET_CHECK(!indices_dims.empty()); num_index_dims = indices_dims.back(); - if (num_index_dims > xla::ShapeUtil::Rank(*buffer_shape)) { + if (num_index_dims > xla::ShapeUtil::Rank(buffer_shape)) { return errors::InvalidArgument( "The size of the minor dimension of the indices (shape: ", - xla::ShapeUtil::HumanString(*indices_shape), + xla::ShapeUtil::HumanString(indices_shape), ") must be <= the rank of the buffer (shape: ", - xla::ShapeUtil::HumanString(*buffer_shape), ")"); + xla::ShapeUtil::HumanString(buffer_shape), ")"); } indices_dims.pop_back(); } @@ -78,10 +73,10 @@ xla::StatusOr XlaScatter( // If any of the indexed dimensions are zero in the buffer, the update cannot // succeed since it updates a slice of size 1. for (int64 i = 0; i < num_index_dims; ++i) { - if (xla::ShapeUtil::GetDimension(*buffer_shape, i) == 0) { - return errors::InvalidArgument( - "Scatter dimension ", i, " is of size zero in tensor with shape ", - xla::ShapeUtil::HumanString(*buffer_shape)); + if (xla::ShapeUtil::GetDimension(buffer_shape, i) == 0) { + return errors::InvalidArgument("Scatter dimension ", i, + " is of size zero in tensor with shape ", + xla::ShapeUtil::HumanString(buffer_shape)); } } @@ -111,18 +106,17 @@ xla::StatusOr XlaScatter( // index = dynamic-slice(indices, i) // update = dynamic-slice(updates, i) // buffer = dynamic-update-slice(buffer, update, index) - auto body_fn = [&](xla::ComputationDataHandle i, - gtl::ArraySlice loop_vars, - xla::ComputationBuilder* body_builder) { + auto body_fn = [&](xla::XlaOp i, gtl::ArraySlice loop_vars, + xla::XlaBuilder* body_builder) { auto indices = loop_vars[0]; auto updates = loop_vars[1]; auto buffer = loop_vars[2]; auto zero_index = body_builder->ConstantLiteral( - xla::Literal::Zero(indices_shape->element_type())); + xla::Literal::Zero(indices_shape.element_type())); // Slice the i-th index from the indices array. - xla::ComputationDataHandle index; + xla::XlaOp index; auto indices_offset = body_builder->Reshape(i, {1}); if (indices_are_vectors) { indices_offset = body_builder->Pad(indices_offset, zero_index, @@ -180,12 +174,12 @@ xla::StatusOr XlaScatter( // Apply the update. buffer = body_builder->DynamicUpdateSlice(buffer, update, index); - return std::vector{indices, updates, buffer}; + return std::vector{indices, updates, buffer}; }; - TF_ASSIGN_OR_RETURN( - auto outputs, XlaForEachIndex(num_indices, indices_shape->element_type(), - body_fn, init, "scatter", builder)); + TF_ASSIGN_OR_RETURN(auto outputs, + XlaForEachIndex(num_indices, indices_shape.element_type(), + body_fn, init, "scatter", builder)); return outputs[2]; } diff --git a/tensorflow/compiler/tf2xla/lib/scatter.h b/tensorflow/compiler/tf2xla/lib/scatter.h index 41e6d3b195ebf9..87309e10ede320 100644 --- a/tensorflow/compiler/tf2xla/lib/scatter.h +++ b/tensorflow/compiler/tf2xla/lib/scatter.h @@ -18,8 +18,8 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" namespace tensorflow { @@ -39,14 +39,12 @@ namespace tensorflow { // If a `combiner` is provided, updates are combined with the existing values in // the buffer using the combiner function. Otherwise, the updates replace the // existing values. The order of updates is implementation-defined. -xla::StatusOr XlaScatter( - const xla::ComputationDataHandle& buffer, - const xla::ComputationDataHandle& updates, - const xla::ComputationDataHandle& indices, bool indices_are_vectors, - const std::function& combiner, - xla::ComputationBuilder* builder); +xla::StatusOr XlaScatter( + const xla::XlaOp& buffer, const xla::XlaOp& updates, + const xla::XlaOp& indices, bool indices_are_vectors, + const std::function& + combiner, + xla::XlaBuilder* builder); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc index 9bf5821b54abe3..d0279d4412bac6 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc @@ -29,21 +29,20 @@ limitations under the License. namespace tensorflow { -xla::StatusOr TriangularSolve( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& a, - xla::ComputationDataHandle b, bool left_side, bool lower, bool transpose_a, - bool conjugate_a, int64 block_size) { - TF_ASSIGN_OR_RETURN(std::unique_ptr a_shape, - builder->GetShape(a)); - TF_ASSIGN_OR_RETURN(std::unique_ptr b_shape, - builder->GetShape(b)); - if (xla::ShapeUtil::Rank(*a_shape) != xla::ShapeUtil::Rank(*b_shape)) { +xla::StatusOr TriangularSolve(xla::XlaBuilder* builder, + const xla::XlaOp& a, xla::XlaOp b, + bool left_side, bool lower, + bool transpose_a, bool conjugate_a, + int64 block_size) { + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); + if (xla::ShapeUtil::Rank(a_shape) != xla::ShapeUtil::Rank(b_shape)) { return errors::InvalidArgument( "Arguments to TriangularSolve have different ranks: ", - xla::ShapeUtil::HumanString(*a_shape), " vs. ", - xla::ShapeUtil::HumanString(*b_shape)); + xla::ShapeUtil::HumanString(a_shape), " vs. ", + xla::ShapeUtil::HumanString(b_shape)); } - const int ndims = xla::ShapeUtil::Rank(*a_shape); + const int ndims = xla::ShapeUtil::Rank(a_shape); if (ndims < 2) { return errors::InvalidArgument( "Arguments to TriangularSolve must have rank >= 2: ", ndims); @@ -51,30 +50,30 @@ xla::StatusOr TriangularSolve( // The batch dimensions must be equal. std::vector batch_dimensions; for (int i = 0; i < ndims - 2; ++i) { - int64 a_size = a_shape->dimensions(i); - int64 b_size = b_shape->dimensions(i); + int64 a_size = a_shape.dimensions(i); + int64 b_size = b_shape.dimensions(i); if (a_size != b_size) { return errors::InvalidArgument( "Batch dimensions of arguments to TriangularSolve must be equal: ", - xla::ShapeUtil::HumanString(*a_shape), " vs ", - xla::ShapeUtil::HumanString(*b_shape)); + xla::ShapeUtil::HumanString(a_shape), " vs ", + xla::ShapeUtil::HumanString(b_shape)); } batch_dimensions.push_back(a_size); } - if (xla::ShapeUtil::GetDimension(*a_shape, -1) != - xla::ShapeUtil::GetDimension(*a_shape, -2)) { + if (xla::ShapeUtil::GetDimension(a_shape, -1) != + xla::ShapeUtil::GetDimension(a_shape, -2)) { return errors::InvalidArgument( "The 'a' arguments to TriangularSolve must be square matrices: ", - xla::ShapeUtil::HumanString(*a_shape)); + xla::ShapeUtil::HumanString(a_shape)); } - const int64 m = xla::ShapeUtil::GetDimension(*b_shape, -2); - const int64 n = xla::ShapeUtil::GetDimension(*b_shape, -1); - if ((left_side ? m : n) != xla::ShapeUtil::GetDimension(*a_shape, -1)) { + const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2); + const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1); + if ((left_side ? m : n) != xla::ShapeUtil::GetDimension(a_shape, -1)) { return errors::InvalidArgument( "Arguments to TriangularSolve have incompatible matrix shapes: ", - xla::ShapeUtil::HumanString(*a_shape), " vs ", - xla::ShapeUtil::HumanString(*b_shape)); + xla::ShapeUtil::HumanString(a_shape), " vs ", + xla::ShapeUtil::HumanString(b_shape)); } if (block_size < 1) { @@ -85,24 +84,23 @@ xla::StatusOr TriangularSolve( // Applies a complex conjugation operation if `a` is complex and `conjugate_a` // is true, otherwise returns its argument. - auto maybe_conj = [&](xla::ComputationBuilder* builder, - xla::ComputationDataHandle x) { - auto perform_conj = a_shape->element_type() == xla::C64 && conjugate_a; + auto maybe_conj = [&](xla::XlaBuilder* builder, xla::XlaOp x) { + auto perform_conj = a_shape.element_type() == xla::C64 && conjugate_a; return perform_conj ? builder->Conj(x) : x; }; - std::map base_computations; + std::map base_computations; auto get_base_triangular_solve = - [&](int k) -> xla::StatusOr { - xla::Computation& computation = base_computations[k]; + [&](int k) -> xla::StatusOr { + xla::XlaComputation& computation = base_computations[k]; if (computation.IsNull()) { - std::unique_ptr sub = builder->CreateSubBuilder( + std::unique_ptr sub = builder->CreateSubBuilder( tensorflow::strings::StrCat("trsm_base_", k)); auto a_param = sub->Parameter( 0, xla::ShapeUtil::MakeShape( - b_shape->element_type(), + b_shape.element_type(), PrependMajorDims(sub.get(), batch_dimensions, {k, k})), "a"); @@ -115,7 +113,7 @@ xla::StatusOr TriangularSolve( auto b_param = sub->Parameter( 1, xla::ShapeUtil::MakeShape( - b_shape->element_type(), + b_shape.element_type(), PrependMajorDims(sub.get(), batch_dimensions, b_lastd)), "b"); @@ -142,7 +140,7 @@ xla::StatusOr TriangularSolve( return &computation; }; - xla::ComputationDataHandle output = Zeros(builder, *b_shape); + xla::XlaOp output = Zeros(builder, b_shape); // Right-looking blocked triangular solve. // For an explanation of the algorithm, see the TRSM discussion in: @@ -165,9 +163,9 @@ xla::StatusOr TriangularSolve( SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); TF_ASSIGN_OR_RETURN(auto b_slice, SliceInMinorDims(builder, b, {0, i}, {m, i + k})); - xla::ComputationDataHandle update; + xla::XlaOp update; if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::Computation * solve, + TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, get_base_triangular_solve(k)); update = builder->Call(*solve, {a_slice, b_slice}); } else { @@ -181,7 +179,7 @@ xla::StatusOr TriangularSolve( // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 // b[..., :, i+k:] -= np.matmul(output[..., :, i:i+k], a_slice_2) if (i + k < n) { - xla::ComputationDataHandle a_slice_2; + xla::XlaOp a_slice_2; if (lower) { TF_ASSIGN_OR_RETURN( a_slice_2, SliceInMinorDims(builder, a, {i + k, i}, {n, i + k})); @@ -215,9 +213,9 @@ xla::StatusOr TriangularSolve( SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); TF_ASSIGN_OR_RETURN(auto b_slice, SliceInMinorDims(builder, b, {i, 0}, {i + k, n})); - xla::ComputationDataHandle update; + xla::XlaOp update; if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::Computation * solve, + TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, get_base_triangular_solve(k)); update = builder->Call(*solve, {a_slice, b_slice}); } else { @@ -231,7 +229,7 @@ xla::StatusOr TriangularSolve( // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 // b[..., i+k:, :] -= np.matmul(a_slice_2, output[..., i:i+k, :]) if (i + k < m) { - xla::ComputationDataHandle a_slice_2; + xla::XlaOp a_slice_2; if (lower) { TF_ASSIGN_OR_RETURN( a_slice_2, SliceInMinorDims(builder, a, {i + k, i}, {m, i + k})); @@ -264,9 +262,9 @@ xla::StatusOr TriangularSolve( SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); TF_ASSIGN_OR_RETURN(auto b_slice, SliceInMinorDims(builder, b, {0, i}, {m, i + k})); - xla::ComputationDataHandle update; + xla::XlaOp update; if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::Computation * solve, + TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, get_base_triangular_solve(k)); update = builder->Call(*solve, {a_slice, b_slice}); } else { @@ -280,7 +278,7 @@ xla::StatusOr TriangularSolve( // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 // b[..., :, :i] -= np.matmul(out[..., :, i:i+k], a_slice_2) if (i - k >= 0) { - xla::ComputationDataHandle a_slice_2; + xla::XlaOp a_slice_2; if (lower) { TF_ASSIGN_OR_RETURN(a_slice_2, SliceInMinorDims(builder, a, {i, 0}, {i + k, i})); @@ -314,9 +312,9 @@ xla::StatusOr TriangularSolve( SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); TF_ASSIGN_OR_RETURN(auto b_slice, SliceInMinorDims(builder, b, {i, 0}, {i + k, n})); - xla::ComputationDataHandle update; + xla::XlaOp update; if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::Computation * solve, + TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, get_base_triangular_solve(k)); update = builder->Call(*solve, {a_slice, b_slice}); } else { @@ -330,7 +328,7 @@ xla::StatusOr TriangularSolve( // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 // b[..., :i, :] -= np.matmul(a_slice_2, out[..., i:i+k, :]) if (i - k >= 0) { - xla::ComputationDataHandle a_slice_2; + xla::XlaOp a_slice_2; if (lower) { TF_ASSIGN_OR_RETURN(a_slice_2, SliceInMinorDims(builder, a, {i, 0}, {i + k, i})); @@ -356,26 +354,25 @@ xla::StatusOr TriangularSolve( return output; } -xla::StatusOr TriangularSolveLeftLooking( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& a, - const xla::ComputationDataHandle& b, bool transpose_a, bool conjugate_a) { - TF_ASSIGN_OR_RETURN(std::unique_ptr a_shape, - builder->GetShape(a)); - TF_ASSIGN_OR_RETURN(std::unique_ptr b_shape, - builder->GetShape(b)); - const int64 m = xla::ShapeUtil::GetDimension(*b_shape, -2); - const int64 n = xla::ShapeUtil::GetDimension(*b_shape, -1); - const int64 ndims = xla::ShapeUtil::Rank(*a_shape); +xla::StatusOr TriangularSolveLeftLooking(xla::XlaBuilder* builder, + const xla::XlaOp& a, + const xla::XlaOp& b, + bool transpose_a, + bool conjugate_a) { + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); + const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2); + const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1); + const int64 ndims = xla::ShapeUtil::Rank(a_shape); std::vector batch_dimensions; for (int i = 0; i < ndims - 2; ++i) { - int64 a_size = a_shape->dimensions(i); + int64 a_size = a_shape.dimensions(i); batch_dimensions.push_back(a_size); } - auto maybe_conj = [&](xla::ComputationBuilder* builder, - xla::ComputationDataHandle x) { - auto perform_conj = a_shape->element_type() == xla::C64 && conjugate_a; + auto maybe_conj = [&](xla::XlaBuilder* builder, xla::XlaOp x) { + auto perform_conj = a_shape.element_type() == xla::C64 && conjugate_a; return perform_conj ? builder->Conj(x) : x; }; @@ -387,7 +384,7 @@ xla::StatusOr TriangularSolveLeftLooking( // output[..., m-1:, :] = b[..., m-1:, :] / a[..., m-1:, m-1:] // else: // output[..., :1, :] = b[..., :1, :] / a[..., :1, :1] - xla::ComputationDataHandle output = Zeros(builder, *b_shape); + xla::XlaOp output = Zeros(builder, b_shape); { auto i = transpose_a ? m - 1 : 0; TF_ASSIGN_OR_RETURN(auto a_slice, @@ -408,11 +405,11 @@ xla::StatusOr TriangularSolveLeftLooking( // The loop iteration counter is a scalar, incremented each iteration. xla::ShapeUtil::MakeShape(xla::S32, {}), // The output has the shape of b, with one row updated each iteration. - *b_shape, + b_shape, // The coefficient matrix a is a loop invariant. - *a_shape, + a_shape, // The right-hand-side matrix b is a loop invariant. - *b_shape}; + b_shape}; xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); auto init_i = builder->ConstantR0(transpose_a ? m - 2 : 1); auto init = builder->Tuple({init_i, output, a, b}); @@ -421,7 +418,7 @@ xla::StatusOr TriangularSolveLeftLooking( // def cond_fun(loop_carry): // i, output, a, b = loop_carry // return i >= 0 if transpose_a else i < m - std::unique_ptr condb = + std::unique_ptr condb = builder->CreateSubBuilder("TriangularSolveLeftLookingWhileCond"); { auto i = condb->GetTupleElement( @@ -451,7 +448,7 @@ xla::StatusOr TriangularSolveLeftLooking( // return (i + 1, output, a, b) // We have to do some extra FLOPs propagating zeros in the matrix multiply // because we can't have the size of its arguments depend on the loop counter. - std::unique_ptr bodyb = + std::unique_ptr bodyb = builder->CreateSubBuilder("TriangularSolveLeftLookingWhileBody"); { auto input_tuple = bodyb->Parameter(0, tuple_shape, @@ -475,7 +472,7 @@ xla::StatusOr TriangularSolveLeftLooking( // But since we can't have intermediate array sizes depend on the loop // counter, we instead exploit the fact that we initialized the output to // all zeros and use that as zero-padding (doing unnecessary FLOPs). - xla::ComputationDataHandle a_row; + xla::XlaOp a_row; if (transpose_a) { TF_ASSIGN_OR_RETURN(a_row, DynamicSliceInMinorDims(bodyb.get(), body_a, {zero, i}, {m, 1})); diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.h b/tensorflow/compiler/tf2xla/lib/triangular_solve.h index e32223bfdddda8..fd8f2489d18392 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.h +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.h @@ -16,8 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_TRIANGULAR_SOLVE_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_TRIANGULAR_SOLVE_H_ -#include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" namespace tensorflow { @@ -57,14 +57,17 @@ namespace tensorflow { // // Uses a blocked algorithm if `block_size` is > 1; if block_size == 1 then no // blocking is used. -xla::StatusOr TriangularSolve( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& a, - xla::ComputationDataHandle b, bool left_side, bool lower, bool transpose_a, - bool conjugate_a, int64 block_size = 256); +xla::StatusOr TriangularSolve(xla::XlaBuilder* builder, + const xla::XlaOp& a, xla::XlaOp b, + bool left_side, bool lower, + bool transpose_a, bool conjugate_a, + int64 block_size = 256); -xla::StatusOr TriangularSolveLeftLooking( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& a, - const xla::ComputationDataHandle& b, bool transpose_a, bool conjugate_a); +xla::StatusOr TriangularSolveLeftLooking(xla::XlaBuilder* builder, + const xla::XlaOp& a, + const xla::XlaOp& b, + bool transpose_a, + bool conjugate_a); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc index 66170706291626..87ea4763f7c235 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/array2d.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" @@ -80,9 +80,9 @@ xla::Array2D AValsFull() { } XLA_TEST_F(TriangularSolveTest, SimpleRightLowerTranspose) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b; + xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsRight(), 1, "b", &builder, &b); auto result = TriangularSolve(&builder, a, b, @@ -102,9 +102,9 @@ XLA_TEST_F(TriangularSolveTest, SimpleRightLowerTranspose) { } XLA_TEST_F(TriangularSolveTest, SimpleRightLowerNotranspose) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b; + xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsRight(), 1, "b", &builder, &b); auto result = TriangularSolve(&builder, a, b, @@ -124,9 +124,9 @@ XLA_TEST_F(TriangularSolveTest, SimpleRightLowerNotranspose) { } XLA_TEST_F(TriangularSolveTest, SimpleRightUpperTranspose) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b; + xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsUpper(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsRight(), 1, "b", &builder, &b); auto result = TriangularSolve(&builder, a, b, @@ -146,9 +146,9 @@ XLA_TEST_F(TriangularSolveTest, SimpleRightUpperTranspose) { } XLA_TEST_F(TriangularSolveTest, SimpleRightUpperNotranspose) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b; + xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsUpper(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsRight(), 1, "b", &builder, &b); auto result = TriangularSolve(&builder, a, b, @@ -168,9 +168,9 @@ XLA_TEST_F(TriangularSolveTest, SimpleRightUpperNotranspose) { } XLA_TEST_F(TriangularSolveTest, SimpleLeftLowerTranspose) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b; + xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); auto result = TriangularSolve(&builder, a, b, @@ -191,9 +191,9 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftLowerTranspose) { } XLA_TEST_F(TriangularSolveTest, SimpleLeftLowerNotranspose) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b; + xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); auto result = TriangularSolve(&builder, a, b, @@ -214,9 +214,9 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftLowerNotranspose) { } XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperTranspose) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b; + xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsUpper(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); auto result = TriangularSolve(&builder, a, b, @@ -237,9 +237,9 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperTranspose) { } XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperNotranspose) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b; + xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsUpper(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); auto result = TriangularSolve(&builder, a, b, @@ -260,9 +260,9 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperNotranspose) { } XLA_TEST_F(TriangularSolveTest, SimpleRightLowerTransposeConjugate) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b; + xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsLowerComplex(), 0, "a", &builder, &a); auto b_data = @@ -288,9 +288,9 @@ XLA_TEST_F(TriangularSolveTest, SimpleRightLowerTransposeConjugate) { } XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperTransposeNoconjugate) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b; + xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsUpperComplex(), 0, "a", &builder, &a); auto b_data = @@ -318,9 +318,9 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperTransposeNoconjugate) { } XLA_TEST_F(TriangularSolveLeftLookingTest, Simple) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b; + xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); auto result = TriangularSolveLeftLooking(&builder, a, b, @@ -340,9 +340,9 @@ XLA_TEST_F(TriangularSolveLeftLookingTest, Simple) { } XLA_TEST_F(TriangularSolveLeftLookingTest, NonzeroUpperTriangle) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b; + xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsFull(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); auto result = TriangularSolveLeftLooking(&builder, a, b, diff --git a/tensorflow/compiler/tf2xla/lib/util.cc b/tensorflow/compiler/tf2xla/lib/util.cc index 31d823ca336039..cc7b13571c3d06 100644 --- a/tensorflow/compiler/tf2xla/lib/util.cc +++ b/tensorflow/compiler/tf2xla/lib/util.cc @@ -27,15 +27,14 @@ limitations under the License. namespace tensorflow { -xla::ComputationDataHandle Zeros(xla::ComputationBuilder* builder, - const xla::Shape& shape) { +xla::XlaOp Zeros(xla::XlaBuilder* builder, const xla::Shape& shape) { return builder->Broadcast( builder->ConstantLiteral(xla::Literal::Zero(shape.element_type())), xla::AsInt64Slice(shape.dimensions())); } -xla::ComputationDataHandle FloatLiteral(xla::ComputationBuilder* builder, - xla::PrimitiveType type, double value) { +xla::XlaOp FloatLiteral(xla::XlaBuilder* builder, xla::PrimitiveType type, + double value) { switch (type) { case xla::F16: return builder->ConstantR0(static_cast(value)); @@ -57,9 +56,8 @@ xla::ComputationDataHandle FloatLiteral(xla::ComputationBuilder* builder, } } -xla::ComputationDataHandle IntegerLiteral(xla::ComputationBuilder* builder, - xla::PrimitiveType type, - int64 value) { +xla::XlaOp IntegerLiteral(xla::XlaBuilder* builder, xla::PrimitiveType type, + int64 value) { xla::Literal literal; switch (type) { case xla::U8: @@ -112,17 +110,18 @@ xla::ComputationDataHandle IntegerLiteral(xla::ComputationBuilder* builder, return builder->ConstantLiteral(literal); } -xla::StatusOr SliceInMinorDims( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, - gtl::ArraySlice start, gtl::ArraySlice end) { +xla::StatusOr SliceInMinorDims(xla::XlaBuilder* builder, + const xla::XlaOp& x, + gtl::ArraySlice start, + gtl::ArraySlice end) { TF_RET_CHECK(start.size() == end.size()); int64 n_minor_dims = start.size(); - TF_ASSIGN_OR_RETURN(std::unique_ptr shape, builder->GetShape(x)); + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); - const int64 n_dims = xla::ShapeUtil::Rank(*shape); + const int64 n_dims = xla::ShapeUtil::Rank(shape); TF_RET_CHECK(n_minor_dims <= n_dims); - gtl::ArraySlice major_dims(xla::AsInt64Slice(shape->dimensions()), + gtl::ArraySlice major_dims(xla::AsInt64Slice(shape.dimensions()), /*pos=*/0, /*len=*/n_dims - n_minor_dims); @@ -140,7 +139,7 @@ xla::StatusOr SliceInMinorDims( return builder->Slice(x, padded_start, padded_end, strides); } -std::vector PrependMajorDims(xla::ComputationBuilder* builder, +std::vector PrependMajorDims(xla::XlaBuilder* builder, const gtl::ArraySlice& major_dims, const gtl::ArraySlice& indices) { std::vector output(indices.size() + major_dims.size()); @@ -149,16 +148,16 @@ std::vector PrependMajorDims(xla::ComputationBuilder* builder, return output; } -xla::StatusOr DynamicSliceInMinorDims( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, - const std::vector& starts, +xla::StatusOr DynamicSliceInMinorDims( + xla::XlaBuilder* builder, const xla::XlaOp& x, + const std::vector& starts, const gtl::ArraySlice& sizes) { - TF_ASSIGN_OR_RETURN(std::unique_ptr shape, builder->GetShape(x)); - const int64 n_dims = xla::ShapeUtil::Rank(*shape); + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + const int64 n_dims = xla::ShapeUtil::Rank(shape); int64 n_minor_dims = starts.size(); TF_RET_CHECK(n_minor_dims == sizes.size()); TF_RET_CHECK(n_minor_dims <= n_dims); - gtl::ArraySlice major_dims(xla::AsInt64Slice(shape->dimensions()), + gtl::ArraySlice major_dims(xla::AsInt64Slice(shape.dimensions()), /*pos=*/0, /*len=*/n_dims - sizes.size()); TF_ASSIGN_OR_RETURN(auto padded_starts, @@ -167,27 +166,29 @@ xla::StatusOr DynamicSliceInMinorDims( return builder->DynamicSlice(x, padded_starts, padded_sizes); } -xla::StatusOr UpdateSlice( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, - const xla::ComputationDataHandle& update, gtl::ArraySlice start) { +xla::StatusOr UpdateSlice(xla::XlaBuilder* builder, + const xla::XlaOp& x, + const xla::XlaOp& update, + gtl::ArraySlice start) { // TODO(phawkins): make int64 work on all backends, remove the int32 cast. std::vector start_as_int32(start.begin(), start.end()); auto start_constant = builder->ConstantR1(start_as_int32); - TF_ASSIGN_OR_RETURN(std::unique_ptr shape, builder->GetShape(x)); - const int64 n_dims = xla::ShapeUtil::Rank(*shape); - TF_ASSIGN_OR_RETURN(std::unique_ptr start_constant_shape, + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + const int64 n_dims = xla::ShapeUtil::Rank(shape); + TF_ASSIGN_OR_RETURN(xla::Shape start_constant_shape, builder->GetShape(start_constant)); const int64 start_length = - xla::ShapeUtil::GetDimension(*start_constant_shape, -1); + xla::ShapeUtil::GetDimension(start_constant_shape, -1); TF_RET_CHECK(start_length == n_dims); return builder->DynamicUpdateSlice(x, update, start_constant); } -xla::StatusOr UpdateSliceInMinorDims( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, - const xla::ComputationDataHandle& update, gtl::ArraySlice start) { - TF_ASSIGN_OR_RETURN(std::unique_ptr shape, builder->GetShape(x)); - const int64 n_dims = xla::ShapeUtil::Rank(*shape); +xla::StatusOr UpdateSliceInMinorDims(xla::XlaBuilder* builder, + const xla::XlaOp& x, + const xla::XlaOp& update, + gtl::ArraySlice start) { + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + const int64 n_dims = xla::ShapeUtil::Rank(shape); const int64 n_minor_dims = start.size(); TF_RET_CHECK(n_minor_dims <= n_dims); std::vector padded_start(n_dims, 0); @@ -196,22 +197,21 @@ xla::StatusOr UpdateSliceInMinorDims( return UpdateSlice(builder, x, update, padded_start); } -xla::StatusOr DynamicUpdateSliceInMinorDims( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, - const xla::ComputationDataHandle& update, - const std::vector& starts) { +xla::StatusOr DynamicUpdateSliceInMinorDims( + xla::XlaBuilder* builder, const xla::XlaOp& x, const xla::XlaOp& update, + const std::vector& starts) { TF_ASSIGN_OR_RETURN(auto padded_starts, PrependZerosInMajorDims(builder, x, starts)); return builder->DynamicUpdateSlice(x, update, padded_starts); } -xla::StatusOr PrependZerosInMajorDims( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, - const std::vector& starts) { - TF_ASSIGN_OR_RETURN(std::unique_ptr shape, builder->GetShape(x)); - const int64 n_dims = xla::ShapeUtil::Rank(*shape); +xla::StatusOr PrependZerosInMajorDims( + xla::XlaBuilder* builder, const xla::XlaOp& x, + const std::vector& starts) { + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + const int64 n_dims = xla::ShapeUtil::Rank(shape); auto zero = builder->Reshape(builder->ConstantR0(0), {1}); - std::vector padded_starts(n_dims, zero); + std::vector padded_starts(n_dims, zero); for (int i = 0; i < starts.size(); ++i) { padded_starts[n_dims - starts.size() + i] = builder->Reshape(starts[i], {1}); @@ -219,10 +219,10 @@ xla::StatusOr PrependZerosInMajorDims( return builder->ConcatInDim(padded_starts, 0); } -xla::StatusOr TransposeInMinorDims( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x) { - TF_ASSIGN_OR_RETURN(std::unique_ptr shape, builder->GetShape(x)); - const int64 n_dims = xla::ShapeUtil::Rank(*shape); +xla::StatusOr TransposeInMinorDims(xla::XlaBuilder* builder, + const xla::XlaOp& x) { + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + const int64 n_dims = xla::ShapeUtil::Rank(shape); TF_RET_CHECK(n_dims >= 2); std::vector permutation(n_dims); std::iota(permutation.begin(), permutation.end(), 0); diff --git a/tensorflow/compiler/tf2xla/lib/util.h b/tensorflow/compiler/tf2xla/lib/util.h index b684123f1363cf..3df44ef0358c9e 100644 --- a/tensorflow/compiler/tf2xla/lib/util.h +++ b/tensorflow/compiler/tf2xla/lib/util.h @@ -16,75 +16,74 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_UTIL_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_UTIL_H_ -#include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/lib/gtl/array_slice.h" namespace tensorflow { // Returns a zero-filled tensor with shape `shape`. -xla::ComputationDataHandle Zeros(xla::ComputationBuilder* builder, - const xla::Shape& shape); +xla::XlaOp Zeros(xla::XlaBuilder* builder, const xla::Shape& shape); // Returns a floating point scalar constant of 'type' with 'value'. // If 'type' is complex, returns a real value with zero imaginary component. -xla::ComputationDataHandle FloatLiteral(xla::ComputationBuilder* builder, - xla::PrimitiveType type, double value); +xla::XlaOp FloatLiteral(xla::XlaBuilder* builder, xla::PrimitiveType type, + double value); // Makes a 1D tensor [0, ..., x, y] from two tensors x and y with zeros // prepended until the array is length n_dims. -xla::ComputationDataHandle PrependZerosInMajorDims( - xla::ComputationBuilder* builder, - gtl::ArraySlice starts); +xla::XlaOp PrependZerosInMajorDims(xla::XlaBuilder* builder, + gtl::ArraySlice starts); // Returns a integer scalar constant of 'type' with 'value'. // If 'type' is complex, returns a real value with zero imaginary component. -xla::ComputationDataHandle IntegerLiteral(xla::ComputationBuilder* builder, - xla::PrimitiveType type, int64 value); +xla::XlaOp IntegerLiteral(xla::XlaBuilder* builder, xla::PrimitiveType type, + int64 value); // Builds a vector of zeros of length rank(x) with the last two values being // those in `starts`. -xla::StatusOr PrependZerosInMajorDims( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, - const std::vector& starts); +xla::StatusOr PrependZerosInMajorDims( + xla::XlaBuilder* builder, const xla::XlaOp& x, + const std::vector& starts); // Performs a slice in the minor dimensions of a Tensor. -xla::StatusOr SliceInMinorDims( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, - gtl::ArraySlice start, gtl::ArraySlice end); +xla::StatusOr SliceInMinorDims(xla::XlaBuilder* builder, + const xla::XlaOp& x, + gtl::ArraySlice start, + gtl::ArraySlice end); // Builds a 1-d vector out of a concatenation of `major_dims` and `starts`. -std::vector PrependMajorDims(xla::ComputationBuilder* builder, +std::vector PrependMajorDims(xla::XlaBuilder* builder, const gtl::ArraySlice& major_dims, const gtl::ArraySlice& indices); // Performs a dynamic slice in the minor dimensions of a Tensor. -xla::StatusOr DynamicSliceInMinorDims( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, - const std::vector& starts, - const gtl::ArraySlice& sizes); +xla::StatusOr DynamicSliceInMinorDims( + xla::XlaBuilder* builder, const xla::XlaOp& x, + const std::vector& starts, const gtl::ArraySlice& sizes); // Updates a slice of 'x', i.e., // x[start[0], ..., start[n]] = update -xla::StatusOr UpdateSlice( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, - const xla::ComputationDataHandle& update, gtl::ArraySlice start); +xla::StatusOr UpdateSlice(xla::XlaBuilder* builder, + const xla::XlaOp& x, + const xla::XlaOp& update, + gtl::ArraySlice start); // Updates a slice of 'x', where 'start' contains a list of minor dimensions: // x[..., start[0], ..., start[n]] = update -xla::StatusOr UpdateSliceInMinorDims( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, - const xla::ComputationDataHandle& update, gtl::ArraySlice start); +xla::StatusOr UpdateSliceInMinorDims(xla::XlaBuilder* builder, + const xla::XlaOp& x, + const xla::XlaOp& update, + gtl::ArraySlice start); -xla::StatusOr DynamicUpdateSliceInMinorDims( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, - const xla::ComputationDataHandle& update, - const std::vector& starts); +xla::StatusOr DynamicUpdateSliceInMinorDims( + xla::XlaBuilder* builder, const xla::XlaOp& x, const xla::XlaOp& update, + const std::vector& starts); // Transposes a stack of matrices `x` by swapping the last two dimensions. -xla::StatusOr TransposeInMinorDims( - xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x); +xla::StatusOr TransposeInMinorDims(xla::XlaBuilder* builder, + const xla::XlaOp& x); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/util_test.cc b/tensorflow/compiler/tf2xla/lib/util_test.cc index b6bd33af2e42a4..265b39402c832f 100644 --- a/tensorflow/compiler/tf2xla/lib/util_test.cc +++ b/tensorflow/compiler/tf2xla/lib/util_test.cc @@ -21,7 +21,6 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/batch_dot.h" #include "tensorflow/compiler/xla/array2d.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" @@ -65,9 +64,9 @@ xla::Array3D BatchedAValsFull() { } XLA_TEST_F(UtilTest, Simple2dLookup) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, x, y; + xla::XlaOp a, x, y; auto a_data = CreateR2Parameter(BValsRight(), 0, "a", &builder, &a); auto x_data = CreateR0Parameter(2, 1, "x", &builder, &x); auto y_data = CreateR0Parameter(1, 2, "y", &builder, &y); @@ -80,9 +79,9 @@ XLA_TEST_F(UtilTest, Simple2dLookup) { } XLA_TEST_F(UtilTest, Simple3dLookup) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, index; + xla::XlaOp a, index; auto a_data = CreateR3Parameter(BatchedAValsFull(), 0, "a", &builder, &a); auto index_data = CreateR0Parameter(1, 1, "index", &builder, &index); @@ -97,9 +96,9 @@ XLA_TEST_F(UtilTest, Simple3dLookup) { } XLA_TEST_F(UtilTest, SimpleSliceUpdate) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); - xla::ComputationDataHandle a, b, x, y; + xla::XlaOp a, b, x, y; auto a_data = CreateR2Parameter(AValsFull(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter({{9, 1, -10}}, 1, "b", &builder, &b); auto x_data = CreateR0Parameter(2, 2, "x", &builder, &x); @@ -117,11 +116,11 @@ XLA_TEST_F(UtilTest, SimpleSliceUpdate) { } XLA_TEST_F(UtilTest, RowBatchDot) { - xla::ComputationBuilder builder(client_, TestName()); + xla::XlaBuilder builder(TestName()); int n = 4; - xla::ComputationDataHandle a, row, index; + xla::XlaOp a, row, index; auto a_data = CreateR3Parameter(BatchedAValsFull(), 0, "a", &builder, &a); auto row_data = CreateR3Parameter({{{9, 1, 0, 0}}, {{2, 4, 0, 0}}}, 1, diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.cc b/tensorflow/compiler/tf2xla/lib/while_loop.cc index 495d9c60780b0a..09ce594930efc0 100644 --- a/tensorflow/compiler/tf2xla/lib/while_loop.cc +++ b/tensorflow/compiler/tf2xla/lib/while_loop.cc @@ -20,24 +20,24 @@ limitations under the License. namespace tensorflow { -xla::StatusOr> XlaWhileLoop( +xla::StatusOr> XlaWhileLoop( const LoopConditionFunction& condition_function, const LoopBodyFunction& body_function, - gtl::ArraySlice initial_values, - StringPiece name, xla::ComputationBuilder* builder) { + gtl::ArraySlice initial_values, StringPiece name, + xla::XlaBuilder* builder) { int arity = initial_values.size(); std::vector var_shapes; var_shapes.reserve(arity); - for (const xla::ComputationDataHandle& input : initial_values) { + for (const xla::XlaOp& input : initial_values) { TF_ASSIGN_OR_RETURN(auto shape, builder->GetShape(input)); - var_shapes.push_back(std::move(*shape)); + var_shapes.push_back(std::move(shape)); } xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(var_shapes); // Unpacks a tuple into its component parts. - auto unpack_tuple = [](xla::ComputationDataHandle tuple, int arity, - xla::ComputationBuilder* builder) { - std::vector elements(arity); + auto unpack_tuple = [](xla::XlaOp tuple, int arity, + xla::XlaBuilder* builder) { + std::vector elements(arity); for (int i = 0; i < arity; ++i) { elements[i] = builder->GetTupleElement(tuple, i); } @@ -45,20 +45,20 @@ xla::StatusOr> XlaWhileLoop( }; // Build the condition. - std::unique_ptr cond_builder = + std::unique_ptr cond_builder = builder->CreateSubBuilder(strings::StrCat(name, "_condition")); { auto parameter = cond_builder->Parameter(0, tuple_shape, "parameter"); - TF_ASSIGN_OR_RETURN( - auto result, + TF_RETURN_IF_ERROR( condition_function(unpack_tuple(parameter, arity, cond_builder.get()), - cond_builder.get())); + cond_builder.get()) + .status()); } TF_ASSIGN_OR_RETURN(auto cond, cond_builder->Build()); // Build the body. - std::unique_ptr body_builder = + std::unique_ptr body_builder = builder->CreateSubBuilder(strings::StrCat(name, "_body")); { auto parameter = body_builder->Parameter(0, tuple_shape, "parameter"); @@ -78,38 +78,38 @@ xla::StatusOr> XlaWhileLoop( return unpack_tuple(outputs, arity, builder); } -xla::StatusOr> XlaForEachIndex( +xla::StatusOr> XlaForEachIndex( int64 num_iterations, xla::PrimitiveType num_iterations_type, const ForEachIndexBodyFunction& body_function, - gtl::ArraySlice initial_values, - StringPiece name, xla::ComputationBuilder* builder) { - auto while_cond_fn = [&](gtl::ArraySlice values, - xla::ComputationBuilder* cond_builder) - -> xla::StatusOr { + gtl::ArraySlice initial_values, StringPiece name, + xla::XlaBuilder* builder) { + auto while_cond_fn = + [&](gtl::ArraySlice values, + xla::XlaBuilder* cond_builder) -> xla::StatusOr { return cond_builder->Lt( values[0], IntegerLiteral(cond_builder, num_iterations_type, num_iterations)); }; - auto while_body_fn = [&](gtl::ArraySlice values, - xla::ComputationBuilder* body_builder) - -> xla::StatusOr> { - xla::ComputationDataHandle iteration = values[0]; + auto while_body_fn = [&](gtl::ArraySlice values, + xla::XlaBuilder* body_builder) + -> xla::StatusOr> { + xla::XlaOp iteration = values[0]; - std::vector updated_values; + std::vector updated_values; updated_values.reserve(values.size()); updated_values.push_back(body_builder->Add( iteration, body_builder->ConstantLiteral(xla::Literal::One(num_iterations_type)))); values.remove_prefix(1); - TF_ASSIGN_OR_RETURN(std::vector body_outputs, + TF_ASSIGN_OR_RETURN(std::vector body_outputs, body_function(iteration, values, body_builder)); updated_values.insert(updated_values.end(), body_outputs.begin(), body_outputs.end()); return updated_values; }; - std::vector values; + std::vector values; values.reserve(initial_values.size() + 1); values.push_back( builder->ConstantLiteral(xla::Literal::Zero(num_iterations_type))); diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.h b/tensorflow/compiler/tf2xla/lib/while_loop.h index 2e67a0c99b6deb..5b6684c995889e 100644 --- a/tensorflow/compiler/tf2xla/lib/while_loop.h +++ b/tensorflow/compiler/tf2xla/lib/while_loop.h @@ -19,8 +19,8 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/array_slice.h" @@ -29,14 +29,14 @@ namespace tensorflow { // Function that builds a loop condition. Takes as input a sequence of input // values, and returns a boolean value representing if the condition succeeds. -typedef std::function( - gtl::ArraySlice, xla::ComputationBuilder*)> +typedef std::function(gtl::ArraySlice, + xla::XlaBuilder*)> LoopConditionFunction; // Function that builds a loop body. Takes as input a sequence of input values // and returns a sequence of output values. -typedef std::function>( - gtl::ArraySlice, xla::ComputationBuilder*)> +typedef std::function>( + gtl::ArraySlice, xla::XlaBuilder*)> LoopBodyFunction; // Helper function for building an XLA while loop, where the values carried by @@ -47,27 +47,26 @@ typedef std::function>( // init: (a, b, c) // ) // 'name' is a descriptive name for the loop. -xla::StatusOr> XlaWhileLoop( +xla::StatusOr> XlaWhileLoop( const LoopConditionFunction& condition_function, const LoopBodyFunction& body_function, - gtl::ArraySlice initial_values, - StringPiece name, xla::ComputationBuilder* builder); + gtl::ArraySlice initial_values, StringPiece name, + xla::XlaBuilder* builder); // Builds an XLA loop that repeats a computation `num_iterations` times. // // The body function (ForEachIndexBodyFunction) takes as input a pair of // (current iteration number, loop-carried values), and returns an updated // vector of the loop-carried values. -typedef std::function>( - xla::ComputationDataHandle, gtl::ArraySlice, - xla::ComputationBuilder*)> +typedef std::function>( + xla::XlaOp, gtl::ArraySlice, xla::XlaBuilder*)> ForEachIndexBodyFunction; -xla::StatusOr> XlaForEachIndex( +xla::StatusOr> XlaForEachIndex( int64 num_iterations, xla::PrimitiveType num_iterations_type, const ForEachIndexBodyFunction& body_function, - gtl::ArraySlice initial_values, - StringPiece name, xla::ComputationBuilder* builder); + gtl::ArraySlice initial_values, StringPiece name, + xla::XlaBuilder* builder); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/tf2xla.cc b/tensorflow/compiler/tf2xla/tf2xla.cc index 6051d7dffd7493..3a08aa8cf4f5ce 100644 --- a/tensorflow/compiler/tf2xla/tf2xla.cc +++ b/tensorflow/compiler/tf2xla/tf2xla.cc @@ -251,7 +251,7 @@ Status CreateXlaArgs(const Graph& graph, // Converts the TensorFlow graph into an XLA computation, by executing the // graph symbolically, with each op building up the XLA HLO. Status ConvertGraphToXla(std::unique_ptr graph, xla::Client* client, - xla::Computation* computation) { + xla::XlaComputation* computation) { XlaOpRegistry::RegisterCompilationKernels(); for (Node* node : graph->nodes()) { node->set_assigned_device_name( @@ -303,7 +303,7 @@ Status ConvertGraphToXla(std::unique_ptr graph, xla::Client* client, } // InitGraph creates a graph based on the graph_def, that may then be converted -// to an xla::Computation via ConvertGraphToXla. +// to an xla::XlaComputation via ConvertGraphToXla. // // The graph is rewritten with _Arg and _Retval nodes, representing the inputs // and outputs of the function that will be compiled. Each feed id causes a new @@ -348,7 +348,7 @@ Status InitGraph(const GraphDef& graph_def, const tf2xla::Config& config, Status ConvertGraphDefToXla(const GraphDef& graph_def, const tf2xla::Config& config, xla::Client* client, - xla::Computation* computation) { + xla::XlaComputation* computation) { std::unique_ptr graph; TF_RETURN_IF_ERROR(InitGraph(graph_def, config, &graph)); TF_RETURN_IF_ERROR(ConvertGraphToXla(std::move(graph), client, computation)); diff --git a/tensorflow/compiler/tf2xla/tf2xla.h b/tensorflow/compiler/tf2xla/tf2xla.h index 473c431b12d441..d02fc56c5b8f58 100644 --- a/tensorflow/compiler/tf2xla/tf2xla.h +++ b/tensorflow/compiler/tf2xla/tf2xla.h @@ -18,21 +18,21 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/tf2xla.pb.h" #include "tensorflow/compiler/xla/client/client.h" -#include "tensorflow/compiler/xla/client/computation.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/core/framework/graph.pb.h" namespace tensorflow { -// Converts a tensorflow::GraphDef into an xla::Computation. The given `config` -// specifies the portion of the graph to convert, via feeds and fetches. Each -// feed is a positional input argument for the generated computation, while each -// fetch is a positional output argument. +// Converts a tensorflow::GraphDef into an xla::XlaComputation. The given +// `config` specifies the portion of the graph to convert, via feeds and +// fetches. Each feed is a positional input argument for the generated +// computation, while each fetch is a positional output argument. // // The computation is built in the context of the given `client`, which may // subsequently be used to compile or execute the computation. Status ConvertGraphDefToXla(const GraphDef& graph_def, const tf2xla::Config& config, xla::Client* client, - xla::Computation* computation); + xla::XlaComputation* computation); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/tf2xla_test.cc b/tensorflow/compiler/tf2xla/tf2xla_test.cc index b813668a9edd3a..84c133ffabe20d 100644 --- a/tensorflow/compiler/tf2xla/tf2xla_test.cc +++ b/tensorflow/compiler/tf2xla/tf2xla_test.cc @@ -69,7 +69,7 @@ TEST(ConvertGraphDefToXla, Sum) { tf2xla::Config config = SumConfig(); xla::LocalClient* client = xla::ClientLibrary::LocalClientOrDie(); - xla::Computation computation; + xla::XlaComputation computation; TF_EXPECT_OK(ConvertGraphDefToXla(graph_def, config, client, &computation)); // Set up arguments. diff --git a/tensorflow/compiler/tf2xla/xla_compilation_device.cc b/tensorflow/compiler/tf2xla/xla_compilation_device.cc index fcb0a4e63814b4..fe7ec633eca250 100644 --- a/tensorflow/compiler/tf2xla/xla_compilation_device.cc +++ b/tensorflow/compiler/tf2xla/xla_compilation_device.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/sharding_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/common_runtime/local_device.h" #include "tensorflow/core/framework/device_base.h" #include "tensorflow/core/platform/mem.h" @@ -108,7 +109,7 @@ void XlaCompilationDevice::Compute(OpKernel* op_kernel, // If no sharding metadata is found, XLA is free to use whatever device it // wants. In practice this usually has the effect of placing things on device // 0. - xla::ScopedShardingAssignment assign_sharding(b, op_sharding); + xla::XlaScopedShardingAssignment assign_sharding(b, op_sharding); op_kernel->Compute(context); b->ClearOpMetadata(); @@ -126,9 +127,7 @@ Status XlaCompilationDevice::MakeTensorFromProto( XlaExpression::XlaExpression() = default; -void XlaExpression::set_handle(const xla::ComputationDataHandle& h) { - handle_ = h; -} +void XlaExpression::set_handle(const xla::XlaOp& h) { handle_ = h; } void XlaExpression::set_constant_value(Tensor value) { has_constant_value_ = true; diff --git a/tensorflow/compiler/tf2xla/xla_compilation_device.h b/tensorflow/compiler/tf2xla/xla_compilation_device.h index 0243ee332fbdca..d0b9e34e162f34 100644 --- a/tensorflow/compiler/tf2xla/xla_compilation_device.h +++ b/tensorflow/compiler/tf2xla/xla_compilation_device.h @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/tf2xla/xla_resource.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/common_runtime/local_device.h" #include "tensorflow/core/framework/device_base.h" @@ -69,7 +69,7 @@ class XlaCompilationDevice : public LocalDevice { // A XlaExpression wraps an XLA computation. Each Tensor on an // XlaCompilationDevice contains an XlaExpression, and the shape of the Tensor -// matches the shape of the subcomputation in the ComputationDataHandle. Each +// matches the shape of the subcomputation in the XlaOp. Each // expression is either a constant, or a function of previously-compiled // expressions. class XlaExpression { @@ -78,8 +78,8 @@ class XlaExpression { // handle() stores the XLA handle of the computation that the // expression represents. - void set_handle(const xla::ComputationDataHandle& h); - const xla::ComputationDataHandle& handle() const { return handle_; } + void set_handle(const xla::XlaOp& h); + const xla::XlaOp& handle() const { return handle_; } void set_constant_value(Tensor value); bool has_constant_value() const { return has_constant_value_; } @@ -90,7 +90,7 @@ class XlaExpression { private: // The XLA handle of the expression's computation. - xla::ComputationDataHandle handle_; + xla::XlaOp handle_; // If this expression is a constant with a known value, 'constant_value' is a // host-memory Tensor containing the value. Used to avoid invoking XLA for diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc index c0e996768491a6..3d1946c332b0f9 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler.cc @@ -339,11 +339,11 @@ Status BuildComputation( const std::vector& arg_cores, const std::vector& retvals, const std::vector>& resources, - bool return_updated_values_for_all_resources, - xla::ComputationBuilder* builder, xla::Computation* computation, - int* num_computation_outputs, int* num_nonconst_outputs, + bool return_updated_values_for_all_resources, xla::XlaBuilder* builder, + xla::XlaComputation* computation, int* num_computation_outputs, + int* num_nonconst_outputs, std::vector* resource_updates) { - std::vector elems; + std::vector elems; elems.reserve(retvals.size()); for (const XlaExpression& retval : retvals) { if (!retval.has_constant_value()) { @@ -376,14 +376,12 @@ Status BuildComputation( const XlaCompiler::Argument& arg = args[resource->arg_num()]; const int core = arg_cores[resource->arg_num()]; DCHECK_LT(resource->arg_num(), arg_cores.size()); - bool modified = - resource->value().handle() != resource->initial_value().handle(); + bool modified = resource->value() != resource->initial_value(); // TensorArray gradients were modified if their values changed or there are // any newly created gradients. for (const auto& grad : resource->tensor_array_gradients()) { modified = modified || - grad.second->value().handle() != - grad.second->initial_value().handle() || + grad.second->value() != grad.second->initial_value() || arg.tensor_array_gradients.count(grad.first) == 0; } if (return_updated_values_for_all_resources || modified) { @@ -398,11 +396,11 @@ Status BuildComputation( } // Request that the value be returned on a specific core. - xla::ScopedShardingAssignment assign_sharding( + xla::XlaScopedShardingAssignment assign_sharding( builder, core == -1 ? tensorflow::gtl::optional() : xla::sharding_builder::AssignDevice(core)); - xla::ComputationDataHandle handle; + xla::XlaOp handle; TF_RETURN_IF_ERROR(resource->Pack(&handle, builder)); // Since we can't change the sharding metadata of as this point, @@ -421,7 +419,7 @@ Status BuildComputation( builder->Tuple(elems); builder->ClearOpMetadata(); - xla::StatusOr computation_status = builder->Build(); + xla::StatusOr computation_status = builder->Build(); if (!computation_status.ok()) { return computation_status.status(); } @@ -435,7 +433,7 @@ Status BuildComputation( // `args` are the arguments to the computation. Status XlaCompiler::BuildArguments( const Graph& graph, const std::vector& args, - bool use_tuple_arg, xla::ComputationBuilder* builder, XlaContext* context, + bool use_tuple_arg, xla::XlaBuilder* builder, XlaContext* context, std::vector* arg_cores, std::vector* arg_expressions, std::vector* input_mapping, std::vector* input_shapes, bool is_entry_computation) { @@ -461,8 +459,7 @@ Status XlaCompiler::BuildArguments( // alias. XlaResource* resource; TF_RETURN_IF_ERROR(context->CreateResource( - arg.resource_kind, i, arg.name, arg.type, arg.shape, - xla::ComputationDataHandle(), + arg.resource_kind, i, arg.name, arg.type, arg.shape, xla::XlaOp(), /*tensor_array_size=*/arg.tensor_array_size, /*tensor_array_gradients=*/arg.tensor_array_gradients, &resource)); arg_expression.set_resource(resource); @@ -531,9 +528,9 @@ Status XlaCompiler::BuildArguments( builder->SetOpMetadata(arg_metadata); // Build parameter handles for non-constant arguments. - std::vector arg_handles(input_mapping->size()); + std::vector arg_handles(input_mapping->size()); if (use_tuple_arg) { - xla::ComputationDataHandle tuple; + xla::XlaOp tuple; if (is_entry_computation) { xla::OpSharding tuple_sharding; tuple_sharding.set_type(xla::OpSharding::Type::OpSharding_Type_TUPLE); @@ -544,15 +541,15 @@ Status XlaCompiler::BuildArguments( core == -1 ? xla::sharding_builder::AssignDevice(root_device) : xla::sharding_builder::AssignDevice(core); } - xla::ScopedShardingAssignment assign_tuple_sharding(builder, - tuple_sharding); + xla::XlaScopedShardingAssignment assign_tuple_sharding(builder, + tuple_sharding); tuple = builder->Parameter(0, (*input_shapes)[0], "arg_tuple"); } else { tuple = builder->Parameter(0, (*input_shapes)[0], "arg_tuple"); } for (std::vector::size_type i = 0; i < input_mapping->size(); ++i) { const int core = (*arg_cores)[input_mapping->at(i)]; - xla::ScopedShardingAssignment assign_sharding( + xla::XlaScopedShardingAssignment assign_sharding( builder, core == -1 ? tensorflow::gtl::optional() : xla::sharding_builder::AssignDevice(core)); arg_handles[i] = builder->GetTupleElement(tuple, i); @@ -560,7 +557,7 @@ Status XlaCompiler::BuildArguments( } else { for (std::vector::size_type i = 0; i < input_mapping->size(); ++i) { const int core = (*arg_cores)[input_mapping->at(i)]; - xla::ScopedShardingAssignment assign_sharding( + xla::XlaScopedShardingAssignment assign_sharding( builder, core == -1 ? tensorflow::gtl::optional() : xla::sharding_builder::AssignDevice(core)); arg_handles[i] = @@ -647,7 +644,7 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options, std::unique_ptr graph, const std::vector& args, CompilationResult* result) { - VLOG(1) << "Executing graph symbolically to populate ComputationBuilder."; + VLOG(1) << "Executing graph symbolically to populate XlaBuilder."; if (VLOG_IS_ON(2)) { VLOG(2) << "XlaCompiler::CompileGraph: " @@ -663,7 +660,7 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options, TF_RETURN_IF_ERROR( FunctionalizeControlFlow(graph.get(), local_flib_def_.get())); - xla::ComputationBuilder builder(client(), name); + xla::XlaBuilder builder(name); XlaContext* context = new XlaContext(this, &builder, options_.allow_cpu_custom_calls, options.resolve_compile_time_constants, @@ -683,7 +680,7 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options, int num_nonconst_outputs; int num_computation_outputs; - result->computation = std::make_shared(); + result->computation = std::make_shared(); TF_RETURN_IF_ERROR(BuildComputation( args, arg_cores, context->retvals(), context->resources(), options.return_updated_values_for_all_resources, &builder, @@ -814,7 +811,7 @@ Status XlaCompiler::SetHostToDeviceMetadata( } Status XlaCompiler::GetHostComputeControlDependency( - const string& host_compute_name, xla::ComputationDataHandle* handle) { + const string& host_compute_name, xla::XlaOp* handle) { const auto iter = host_compute_control_output_.find(host_compute_name); if (iter == host_compute_control_output_.end()) { return errors::InvalidArgument( @@ -827,7 +824,7 @@ Status XlaCompiler::GetHostComputeControlDependency( } Status XlaCompiler::SetHostComputeControlDependency( - const string& host_compute_name, const xla::ComputationDataHandle& handle) { + const string& host_compute_name, const xla::XlaOp& handle) { if (host_compute_control_output_.find(host_compute_name) != host_compute_control_output_.end()) { return errors::InvalidArgument( diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h index 8f564f35ec8176..ca6cd822ef4eff 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.h +++ b/tensorflow/compiler/tf2xla/xla_compiler.h @@ -227,7 +227,7 @@ class XlaCompiler { std::vector resource_updates; // The XLA computation built from the tensorflow subgraph. - std::shared_ptr computation; + std::shared_ptr computation; }; struct Options { @@ -281,7 +281,7 @@ class XlaCompiler { const NameAttrList& fn_name_attrs, std::vector args, CompilationResult* result); - // Compiles a tensorflow::Graph into an xla::Computation. + // Compiles a tensorflow::Graph into an xla::XlaComputation. // Similar to CompileFunction, but takes a Graph as input rather than a // function. Status CompileGraph(const CompileOptions& options, string const& name, @@ -290,7 +290,7 @@ class XlaCompiler { CompilationResult* result); // Compiles a single Op, given by an OpKernelContext, into an - // xla::Computation. Similar to CompileFunction but takes a single Op as + // xla::XlaComputation. Similar to CompileFunction but takes a single Op as // input. Status CompileSingleOp(const CompileOptions& options, string const& name, OpKernelContext* ctx, @@ -337,10 +337,9 @@ class XlaCompiler { // a given HostCompute Op as long as the names are unique within the // compilation. Status GetHostComputeControlDependency(const string& host_compute_name, - xla::ComputationDataHandle* handle); - Status SetHostComputeControlDependency( - const string& host_compute_name, - const xla::ComputationDataHandle& handle); + xla::XlaOp* handle); + Status SetHostComputeControlDependency(const string& host_compute_name, + const xla::XlaOp& handle); const Options& options() const { return options_; } xla::Client* client() const { return options_.client; } @@ -358,7 +357,7 @@ class XlaCompiler { // `args` are the arguments to the computation. Status BuildArguments(const Graph& graph, const std::vector& args, - bool use_tuple_arg, xla::ComputationBuilder* builder, + bool use_tuple_arg, xla::XlaBuilder* builder, XlaContext* context, std::vector* arg_cores, std::vector* arg_expressions, std::vector* input_mapping, @@ -408,8 +407,7 @@ class XlaCompiler { std::unordered_map host_compute_sends_; std::unordered_map host_compute_recvs_; - std::unordered_map - host_compute_control_output_; + std::unordered_map host_compute_control_output_; TF_DISALLOW_COPY_AND_ASSIGN(XlaCompiler); }; diff --git a/tensorflow/compiler/tf2xla/xla_compiler_test.cc b/tensorflow/compiler/tf2xla/xla_compiler_test.cc index 096dc7160bfc0a..6b8918b2617973 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler_test.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler_test.cc @@ -164,7 +164,6 @@ REGISTER_XLA_OP(Name("DummyDuplicateOp").Device(DEVICE_CPU_XLA_JIT), REGISTER_XLA_OP(Name("DummyDuplicateOp").Device(DEVICE_GPU_XLA_JIT), DummyDuplicateOp); - // Tests compilation and execution of an empty graph. TEST_F(XlaCompilerTest, EmptyReturnValues) { XlaCompiler compiler(DefaultOptions()); @@ -433,21 +432,26 @@ TEST_F(XlaCompilerTest, DeterministicCompilation) { } for (int64 i = 1; i < test_count; ++i) { - auto m1 = - results[i - 1].computation->Snapshot().ValueOrDie()->entry().requests(); - auto m2 = - results[i].computation->Snapshot().ValueOrDie()->entry().requests(); - // Check if every entry is the same. - for (auto& entry1 : m1) { - int64 key = entry1.first; - auto value1 = entry1.second; - auto entry2 = m2.find(key); - auto value2 = entry2->second; - EXPECT_TRUE(entry2 != m2.end()); - string str1, str2; - value1.AppendToString(&str1); - value2.AppendToString(&str2); - EXPECT_EQ(str1, str2); + const auto& m1 = results[i - 1].computation->proto(); + const auto& m2 = results[i].computation->proto(); + ASSERT_EQ(m1.computations_size(), m2.computations_size()); + // Check if every hlo computation is the same. + for (int k = 0; k < m1.computations_size(); k++) { + const auto& c1 = m1.computations(k); + const auto& c2 = m2.computations(k); + ASSERT_EQ(c1.instructions_size(), c2.instructions_size()); + for (int j = 0; j < c1.instructions_size(); j++) { + auto instr1 = c1.instructions(j); + auto instr2 = c2.instructions(j); + instr1.clear_name(); + instr2.clear_name(); + // The names of instructions were uniquified by the XlaBuilder, the rest + // of the fields should be identical. + string str1, str2; + instr1.AppendPartialToString(&str1); + instr2.AppendPartialToString(&str2); + EXPECT_EQ(str1, str2); + } } } } diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc index 8423921086fec1..3dd2d183f3a538 100644 --- a/tensorflow/compiler/tf2xla/xla_context.cc +++ b/tensorflow/compiler/tf2xla/xla_context.cc @@ -25,7 +25,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/statusor.h" @@ -63,7 +63,7 @@ void XlaContext::set_args(std::vector args) { } XlaContext::XlaContext( - XlaCompiler* compiler, xla::ComputationBuilder* builder, + XlaCompiler* compiler, xla::XlaBuilder* builder, bool allow_cpu_custom_calls, bool resolve_compile_time_constants, const std::function* variable_representation_shape_fn) @@ -78,7 +78,7 @@ string XlaContext::DebugString() { return "TLA JIT context"; } // This is called by the Retval Op to associate a computed value // with a specific return value of the subgraph. void XlaContext::AddRetval(int retval_index, DataType type, - const xla::ComputationDataHandle& handle) { + const xla::XlaOp& handle) { VLOG(1) << "Added retval index " << retval_index << " to XLA computation"; // Add the return value to the list being built up. if (retvals_.size() <= retval_index) { @@ -104,13 +104,12 @@ Status XlaContext::AddConstRetval(int retval_index, DataType dtype, return Status::OK(); } -xla::ComputationBuilder* XlaContext::builder() { return builder_; } +xla::XlaBuilder* XlaContext::builder() { return builder_; } Status XlaContext::CreateResource( XlaResource::Kind kind, int arg_num, string name, DataType type, - TensorShape shape, const xla::ComputationDataHandle& handle, - int64 tensor_array_size, const std::set& tensor_array_gradients, - XlaResource** resource) { + TensorShape shape, const xla::XlaOp& handle, int64 tensor_array_size, + const std::set& tensor_array_gradients, XlaResource** resource) { resources_.emplace_back( new XlaResource(kind, arg_num, std::move(name), type, std::move(shape), handle, tensor_array_size, tensor_array_gradients)); @@ -123,11 +122,11 @@ TensorShape XlaContext::VariableRepresentationShape(const TensorShape& shape, return (*variable_representation_shape_fn_)(shape, type); } -const xla::Computation* XlaContext::GetOrCreateMax(const DataType type) { +const xla::XlaComputation* XlaContext::GetOrCreateMax(const DataType type) { return LookupOrCreate(type, &max_func_, [this, type] { const string type_string = DataTypeString(type); VLOG(1) << "Building Max() for " << type_string; - xla::ComputationBuilder b(builder()->client(), "max<" + type_string + ">"); + xla::XlaBuilder b("max<" + type_string + ">"); xla::PrimitiveType xla_type; TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type)); auto x = b.Parameter(0, xla::ShapeUtil::MakeShape(xla_type, {}), "x"); @@ -137,11 +136,11 @@ const xla::Computation* XlaContext::GetOrCreateMax(const DataType type) { }); } -const xla::Computation* XlaContext::GetOrCreateMin(const DataType type) { +const xla::XlaComputation* XlaContext::GetOrCreateMin(const DataType type) { return LookupOrCreate(type, &min_func_, [this, type] { const string type_string = DataTypeString(type); VLOG(1) << "Building Min() for " << type_string; - xla::ComputationBuilder b(builder()->client(), "min<" + type_string + ">"); + xla::XlaBuilder b("min<" + type_string + ">"); xla::PrimitiveType xla_type; TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type)); auto x = b.Parameter(0, xla::ShapeUtil::MakeShape(xla_type, {}), "x"); @@ -151,11 +150,11 @@ const xla::Computation* XlaContext::GetOrCreateMin(const DataType type) { }); } -const xla::Computation* XlaContext::GetOrCreateAdd(const DataType type) { +const xla::XlaComputation* XlaContext::GetOrCreateAdd(const DataType type) { return LookupOrCreate(type, &add_func_, [this, type] { const string type_string = DataTypeString(type); VLOG(1) << "Building Add() for " << type_string; - xla::ComputationBuilder b(builder()->client(), "add<" + type_string + ">"); + xla::XlaBuilder b("add<" + type_string + ">"); xla::PrimitiveType xla_type; TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type)); auto x = b.Parameter(0, xla::ShapeUtil::MakeShape(xla_type, {}), "x"); @@ -165,11 +164,11 @@ const xla::Computation* XlaContext::GetOrCreateAdd(const DataType type) { }); } -const xla::Computation* XlaContext::GetOrCreateMul(const DataType type) { +const xla::XlaComputation* XlaContext::GetOrCreateMul(const DataType type) { return LookupOrCreate(type, &mul_func_, [this, type] { const string type_string = DataTypeString(type); VLOG(1) << "Building Mul() for " << type_string; - xla::ComputationBuilder b(builder()->client(), "mul<" + type_string + ">"); + xla::XlaBuilder b("mul<" + type_string + ">"); xla::PrimitiveType xla_type; TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type)); auto x = b.Parameter(0, xla::ShapeUtil::MakeShape(xla_type, {}), "x"); @@ -179,9 +178,9 @@ const xla::Computation* XlaContext::GetOrCreateMul(const DataType type) { }); } -const xla::Computation* XlaContext::LookupOrCreate( +const xla::XlaComputation* XlaContext::LookupOrCreate( DataType type, ComputationMap* out, - const std::function& create) { + const std::function& create) { { const auto& entry = (*out)[type]; if (!entry.IsNull()) { diff --git a/tensorflow/compiler/tf2xla/xla_context.h b/tensorflow/compiler/tf2xla/xla_context.h index 00fbaba37c5429..1136ffe5073a8e 100644 --- a/tensorflow/compiler/tf2xla/xla_context.h +++ b/tensorflow/compiler/tf2xla/xla_context.h @@ -22,8 +22,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compilation_device.h" #include "tensorflow/compiler/tf2xla/xla_compiler.h" -#include "tensorflow/compiler/xla/client/computation.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/resource_mgr.h" @@ -43,7 +43,7 @@ class XlaContext : public ResourceBase { static XlaContext& Get(const XlaOpKernelContext* ctx); // Creates a new XlaContext. - XlaContext(XlaCompiler* compiler, xla::ComputationBuilder* builder, + XlaContext(XlaCompiler* compiler, xla::XlaBuilder* builder, bool allow_cpu_custom_calls, bool resolve_compile_time_constants, const std::function* variable_representation_shape_fn); @@ -53,9 +53,8 @@ class XlaContext : public ResourceBase { XlaCompiler* compiler() const { return compiler_; } - // Returns the ComputationBuilder that Ops use for compiling new - // expressions. - xla::ComputationBuilder* builder(); + // Returns the XlaBuilder that Ops use for compiling new expressions. + xla::XlaBuilder* builder(); bool allow_cpu_custom_calls() const { return allow_cpu_custom_calls_; } @@ -66,8 +65,7 @@ class XlaContext : public ResourceBase { // This is called by the Retval Op to associate a computed value // with a specific return value of the subgraph. - void AddRetval(int retval_index, DataType type, - const xla::ComputationDataHandle& handle); + void AddRetval(int retval_index, DataType type, const xla::XlaOp& handle); // As for Retval, but for return values that are compile-time constants. Status AddConstRetval(int retval_index, DataType dtype, @@ -79,8 +77,7 @@ class XlaContext : public ResourceBase { // Fails if the resource already exists. Status CreateResource(XlaResource::Kind kind, int arg_num, string name, DataType type, TensorShape shape, - const xla::ComputationDataHandle& handle, - int64 tensor_array_size, + const xla::XlaOp& handle, int64 tensor_array_size, const std::set& tensor_array_gradients, XlaResource** resource); @@ -96,22 +93,22 @@ class XlaContext : public ResourceBase { // Get an XLA lambda to compute Max. This is cached in the // XlaContext since it may be used by multiple Ops. There is a // separate specialization of the computation for each DataType. - const xla::Computation* GetOrCreateMax(const DataType type); + const xla::XlaComputation* GetOrCreateMax(const DataType type); // Get an XLA lambda to compute Min. This is cached in the // XlaContext since it may be used by multiple Ops. There is a // separate specialization of the computation for each DataType. - const xla::Computation* GetOrCreateMin(const DataType type); + const xla::XlaComputation* GetOrCreateMin(const DataType type); // Get an XLA lambda to compute Add. This is cached in the // XlaContext since it may be used by multiple Ops. There is a // separate specialization of the computation for each DataType. - const xla::Computation* GetOrCreateAdd(const DataType type); + const xla::XlaComputation* GetOrCreateAdd(const DataType type); // Get an XLA lambda to compute Mul. This is cached in the // XlaContext since it may be used by multiple Ops. There is a // separate specialization of the computation for each DataType. - const xla::Computation* GetOrCreateMul(const DataType type); + const xla::XlaComputation* GetOrCreateMul(const DataType type); // The name of the XlaContext resource during symbolic graph execution. static const char kXlaContextResourceName[]; @@ -119,9 +116,8 @@ class XlaContext : public ResourceBase { private: XlaCompiler* const compiler_; - // The ComputationBuilder used to construct the subgraph's compiled - // representation. - xla::ComputationBuilder* builder_; + // The XlaBuilder used to construct the subgraph's compiled representation. + xla::XlaBuilder* builder_; // Allow ops to emit CustomCall operations for CPU. const bool allow_cpu_custom_calls_; @@ -146,14 +142,14 @@ class XlaContext : public ResourceBase { variable_representation_shape_fn_; // Cache of prebuilt computations indexed by their type. - using ComputationMap = std::map; + using ComputationMap = std::map; // Finds the value for the given type in out map if it already // exists or makes a new value with create function and keeps it the // map. The returned value != nullptr and is owned by the map. - const xla::Computation* LookupOrCreate( + const xla::XlaComputation* LookupOrCreate( DataType type, ComputationMap* out, - const std::function& create); + const std::function& create); // Cached computation to compute Max of two elements, specialized by type. ComputationMap max_func_; diff --git a/tensorflow/compiler/tf2xla/xla_helpers.cc b/tensorflow/compiler/tf2xla/xla_helpers.cc index a3deb02a1f9a1b..f1594193af09c7 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.cc +++ b/tensorflow/compiler/tf2xla/xla_helpers.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/status.h" @@ -32,13 +32,12 @@ namespace tensorflow { namespace { -Status ArgMinMax(xla::ComputationBuilder* builder, XlaOpKernelContext* ctx, - const xla::ComputationDataHandle& input, - const TensorShape& input_shape, DataType input_type, - DataType output_type, int axis, bool is_min, - xla::ComputationDataHandle* argminmax) { - xla::ComputationDataHandle init_value; - const xla::Computation* reducer; +Status ArgMinMax(xla::XlaBuilder* builder, XlaOpKernelContext* ctx, + const xla::XlaOp& input, const TensorShape& input_shape, + DataType input_type, DataType output_type, int axis, + bool is_min, xla::XlaOp* argminmax) { + xla::XlaOp init_value; + const xla::XlaComputation* reducer; if (is_min) { init_value = XlaHelpers::MaxValue(builder, input_type); reducer = ctx->GetOrCreateMin(input_type); @@ -50,13 +49,13 @@ Status ArgMinMax(xla::ComputationBuilder* builder, XlaOpKernelContext* ctx, xla::PrimitiveType xla_output_type; TF_RETURN_IF_ERROR(DataTypeToPrimitiveType(output_type, &xla_output_type)); - xla::ComputationDataHandle input_max = builder->Reduce( - input, init_value, *reducer, /*dimensions_to_reduce=*/{axis}); + xla::XlaOp input_max = builder->Reduce(input, init_value, *reducer, + /*dimensions_to_reduce=*/{axis}); std::vector broadcast_dims(input_shape.dims() - 1); std::iota(broadcast_dims.begin(), broadcast_dims.begin() + axis, 0); std::iota(broadcast_dims.begin() + axis, broadcast_dims.end(), axis + 1); // Compute a mask that has 1s for elements equal to the maximum. - xla::ComputationDataHandle partial_mask = builder->ConvertElementType( + xla::XlaOp partial_mask = builder->ConvertElementType( builder->Eq(input, input_max, broadcast_dims), xla_output_type); // In order to make identity elements for a bitwise And, we: @@ -65,23 +64,23 @@ Status ArgMinMax(xla::ComputationBuilder* builder, XlaOpKernelContext* ctx, // 0xFF...F int32 bits_in_type = xla::ShapeUtil::ByteSizeOfPrimitiveType(xla_output_type) * 8 - 1; - xla::ComputationDataHandle shift_amount = + xla::XlaOp shift_amount = XlaHelpers::IntegerLiteral(builder, output_type, bits_in_type); - xla::ComputationDataHandle full_mask = builder->ShiftRightArithmetic( + xla::XlaOp full_mask = builder->ShiftRightArithmetic( builder->ShiftLeft(partial_mask, shift_amount), shift_amount); // And with the vector [0, 1, 2, ...] to convert each 0xFF...F into its // index. - xla::ComputationDataHandle iota; + xla::XlaOp iota; const int64 axis_size = input_shape.dim_size(axis); TF_RETURN_IF_ERROR(XlaHelpers::Iota(builder, output_type, axis_size, &iota)); - xla::ComputationDataHandle product = + xla::XlaOp product = builder->And(full_mask, iota, /*broadcast_dimensions=*/{axis}); // If there are multiple maximum elements, choose the one with the highest // index. - xla::ComputationDataHandle output = + xla::XlaOp output = builder->Reduce(product, XlaHelpers::MinValue(builder, output_type), *ctx->GetOrCreateMax(output_type), /*dimensions_to_reduce=*/{axis}); @@ -91,36 +90,31 @@ Status ArgMinMax(xla::ComputationBuilder* builder, XlaOpKernelContext* ctx, } // namespace -xla::ComputationDataHandle XlaHelpers::MinValue(xla::ComputationBuilder* b, - DataType data_type) { +xla::XlaOp XlaHelpers::MinValue(xla::XlaBuilder* b, DataType data_type) { xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); return b->ConstantLiteral(xla::Literal::MinValue(type)); } -xla::ComputationDataHandle XlaHelpers::MaxValue(xla::ComputationBuilder* b, - DataType data_type) { +xla::XlaOp XlaHelpers::MaxValue(xla::XlaBuilder* b, DataType data_type) { xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); return b->ConstantLiteral(xla::Literal::MaxValue(type)); } -xla::ComputationDataHandle XlaHelpers::Zero(xla::ComputationBuilder* b, - DataType data_type) { +xla::XlaOp XlaHelpers::Zero(xla::XlaBuilder* b, DataType data_type) { xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); return b->ConstantLiteral(xla::Literal::Zero(type)); } -xla::ComputationDataHandle XlaHelpers::One(xla::ComputationBuilder* b, - DataType data_type) { +xla::XlaOp XlaHelpers::One(xla::XlaBuilder* b, DataType data_type) { xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); return b->ConstantLiteral(xla::Literal::One(type)); } -xla::ComputationDataHandle XlaHelpers::Epsilon(xla::ComputationBuilder* b, - DataType data_type) { +xla::XlaOp XlaHelpers::Epsilon(xla::XlaBuilder* b, DataType data_type) { switch (data_type) { case DT_HALF: return b->ConstantR0( @@ -137,16 +131,15 @@ xla::ComputationDataHandle XlaHelpers::Epsilon(xla::ComputationBuilder* b, } } -xla::ComputationDataHandle XlaHelpers::IntegerLiteral( - xla::ComputationBuilder* b, DataType data_type, int64 value) { +xla::XlaOp XlaHelpers::IntegerLiteral(xla::XlaBuilder* b, DataType data_type, + int64 value) { xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); return ::tensorflow::IntegerLiteral(b, type, value); } -xla::ComputationDataHandle XlaHelpers::FloatLiteral(xla::ComputationBuilder* b, - DataType data_type, - double value) { +xla::XlaOp XlaHelpers::FloatLiteral(xla::XlaBuilder* b, DataType data_type, + double value) { xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); return ::tensorflow::FloatLiteral(b, type, value); @@ -183,28 +176,24 @@ static Tensor MakeLinspaceTensor(const TensorShape& shape, int64 depth) { return linspace; } -Status XlaHelpers::ArgMax(xla::ComputationBuilder* builder, - XlaOpKernelContext* ctx, - const xla::ComputationDataHandle& input, +Status XlaHelpers::ArgMax(xla::XlaBuilder* builder, XlaOpKernelContext* ctx, + const xla::XlaOp& input, const TensorShape& input_shape, DataType input_type, - DataType output_type, int axis, - xla::ComputationDataHandle* argmax) { + DataType output_type, int axis, xla::XlaOp* argmax) { return ArgMinMax(builder, ctx, input, input_shape, input_type, output_type, axis, /*is_min=*/false, argmax); } -Status XlaHelpers::ArgMin(xla::ComputationBuilder* builder, - XlaOpKernelContext* ctx, - const xla::ComputationDataHandle& input, +Status XlaHelpers::ArgMin(xla::XlaBuilder* builder, XlaOpKernelContext* ctx, + const xla::XlaOp& input, const TensorShape& input_shape, DataType input_type, - DataType output_type, int axis, - xla::ComputationDataHandle* argmin) { + DataType output_type, int axis, xla::XlaOp* argmin) { return ArgMinMax(builder, ctx, input, input_shape, input_type, output_type, axis, /*is_min=*/true, argmin); } -Status XlaHelpers::Iota(xla::ComputationBuilder* builder, DataType dtype, - int64 size, xla::ComputationDataHandle* iota) { +Status XlaHelpers::Iota(xla::XlaBuilder* builder, DataType dtype, int64 size, + xla::XlaOp* iota) { TensorShape linspace_shape({size}); Tensor linspace; switch (dtype) { @@ -227,13 +216,10 @@ Status XlaHelpers::Iota(xla::ComputationBuilder* builder, DataType dtype, return Status::OK(); } -Status XlaHelpers::OneHot(xla::ComputationBuilder* builder, int64 depth, - int axis, DataType index_type, - const TensorShape& indices_shape, - const xla::ComputationDataHandle& indices, - const xla::ComputationDataHandle& on_value, - const xla::ComputationDataHandle& off_value, - xla::ComputationDataHandle* one_hot) { +Status XlaHelpers::OneHot(xla::XlaBuilder* builder, int64 depth, int axis, + DataType index_type, const TensorShape& indices_shape, + const xla::XlaOp& indices, const xla::XlaOp& on_value, + const xla::XlaOp& off_value, xla::XlaOp* one_hot) { const int indices_dims = indices_shape.dims(); const int output_dims = indices_dims + 1; @@ -267,7 +253,7 @@ Status XlaHelpers::OneHot(xla::ComputationBuilder* builder, int64 depth, std::vector broadcast_dims(indices_shape.dims()); std::iota(broadcast_dims.begin(), broadcast_dims.begin() + axis, 0); std::iota(broadcast_dims.begin() + axis, broadcast_dims.end(), axis + 1); - xla::ComputationDataHandle one_hot_bool = builder->Eq( + xla::XlaOp one_hot_bool = builder->Eq( indices, builder->ConstantLiteral(linspace_literal), broadcast_dims); // Selects the user-provided off_value and on_value values. @@ -284,10 +270,9 @@ DataType XlaHelpers::SumAccumulationType(const DataType& dtype) { return dtype; } -xla::ComputationDataHandle XlaHelpers::ConvertElementType( - xla::ComputationBuilder* const builder, - const xla::ComputationDataHandle& operand, - const DataType new_element_type) { +xla::XlaOp XlaHelpers::ConvertElementType(xla::XlaBuilder* const builder, + const xla::XlaOp& operand, + const DataType new_element_type) { xla::PrimitiveType convert_to; TF_CHECK_OK(DataTypeToPrimitiveType(new_element_type, &convert_to)); return builder->ConvertElementType(operand, convert_to); diff --git a/tensorflow/compiler/tf2xla/xla_helpers.h b/tensorflow/compiler/tf2xla/xla_helpers.h index 68ab93b64a5fa8..c3fdc5252e7436 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.h +++ b/tensorflow/compiler/tf2xla/xla_helpers.h @@ -19,7 +19,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_XLA_HELPERS_H_ #include "tensorflow/compiler/tf2xla/xla_context.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/gtl/array_slice.h" @@ -30,41 +30,34 @@ class XlaHelpers { public: // Returns a handle representing the minimum value of a scalar // element of data_type. - static xla::ComputationDataHandle MinValue(xla::ComputationBuilder* b, - DataType data_type); + static xla::XlaOp MinValue(xla::XlaBuilder* b, DataType data_type); // Returns a handle representing the maximum value of a scalar // element of data_type. - static xla::ComputationDataHandle MaxValue(xla::ComputationBuilder* b, - DataType data_type); + static xla::XlaOp MaxValue(xla::XlaBuilder* b, DataType data_type); // Returns a handle representing the zero value of a scalar // element of data_type. - static xla::ComputationDataHandle Zero(xla::ComputationBuilder* b, - DataType data_type); + static xla::XlaOp Zero(xla::XlaBuilder* b, DataType data_type); // Returns a handle representing the one value of a scalar // element of data_type. - static xla::ComputationDataHandle One(xla::ComputationBuilder* b, - DataType data_type); + static xla::XlaOp One(xla::XlaBuilder* b, DataType data_type); // Returns the machine epsilon for floating-point type `data_type`, i.e., // the difference between 1.0 and the next representable value. - static xla::ComputationDataHandle Epsilon(xla::ComputationBuilder* b, - DataType data_type); + static xla::XlaOp Epsilon(xla::XlaBuilder* b, DataType data_type); // Returns a handle representing the given value of an integer scalar // element of data_type. // Note that unlike One and Zero, does not work on boolean types. - static xla::ComputationDataHandle IntegerLiteral(xla::ComputationBuilder* b, - DataType data_type, - int64 value); + static xla::XlaOp IntegerLiteral(xla::XlaBuilder* b, DataType data_type, + int64 value); // Returns a handle representing the given value of a floating-point scalar // element of data_type. - static xla::ComputationDataHandle FloatLiteral(xla::ComputationBuilder* b, - DataType data_type, - double value); + static xla::XlaOp FloatLiteral(xla::XlaBuilder* b, DataType data_type, + double value); // Reshapes literal 'input' to have 'shape'. Both the original shape and // 'shape' must contain the same number of elements. @@ -75,38 +68,32 @@ class XlaHelpers { // Sets `argmax` to the argmax of `input` along `axis`. `input_shape` and // `input_dtype` are the shape and dtype of `input` respectively, and // `output_type` is the dtype to use for `argmax`. - static Status ArgMax(xla::ComputationBuilder* builder, - XlaOpKernelContext* ctx, - const xla::ComputationDataHandle& input, - const TensorShape& input_shape, DataType input_type, - DataType output_type, int axis, - xla::ComputationDataHandle* argmax); + static Status ArgMax(xla::XlaBuilder* builder, XlaOpKernelContext* ctx, + const xla::XlaOp& input, const TensorShape& input_shape, + DataType input_type, DataType output_type, int axis, + xla::XlaOp* argmax); // Sets `argmin` to the argmin of `input` along `axis`. `input_shape` and // `input_dtype` are the shape and dtype of `input` respectively, and // `output_type` is the dtype to use for `argmin`. - static Status ArgMin(xla::ComputationBuilder* builder, - XlaOpKernelContext* ctx, - const xla::ComputationDataHandle& input, - const TensorShape& input_shape, DataType input_type, - DataType output_type, int axis, - xla::ComputationDataHandle* argmin); + static Status ArgMin(xla::XlaBuilder* builder, XlaOpKernelContext* ctx, + const xla::XlaOp& input, const TensorShape& input_shape, + DataType input_type, DataType output_type, int axis, + xla::XlaOp* argmin); // Sets *iota to a rank 1 tensor with values [0, 1, 2, ...] of `dtype`. - static Status Iota(xla::ComputationBuilder* builder, DataType dtype, - int64 size, xla::ComputationDataHandle* iota); + static Status Iota(xla::XlaBuilder* builder, DataType dtype, int64 size, + xla::XlaOp* iota); // Converts `indices` into a one-hot representation. `depth` is the size // of the new axis to add. `axis` is the position at which to add the new // axis. `indices_shape` is the shape of `indices`. `on_value` and // `off_value` represent the values to use for the on and off positions, // respectively. - static Status OneHot(xla::ComputationBuilder* builder, int64 depth, int axis, + static Status OneHot(xla::XlaBuilder* builder, int64 depth, int axis, DataType index_type, const TensorShape& indices_shape, - const xla::ComputationDataHandle& indices, - const xla::ComputationDataHandle& on_value, - const xla::ComputationDataHandle& off_value, - xla::ComputationDataHandle* one_hot); + const xla::XlaOp& indices, const xla::XlaOp& on_value, + const xla::XlaOp& off_value, xla::XlaOp* one_hot); // Certain DataTypes should use increased precision DataTypes when performing // reductions. This function remaps a given DataType to a higher precision @@ -115,10 +102,9 @@ class XlaHelpers { // A helper for creating a ConvertElementType xla op given a DataType rather // than the xla::PrimitiveType. - static xla::ComputationDataHandle ConvertElementType( - xla::ComputationBuilder* const builder, - const xla::ComputationDataHandle& operand, - const DataType new_element_type); + static xla::XlaOp ConvertElementType(xla::XlaBuilder* const builder, + const xla::XlaOp& operand, + const DataType new_element_type); }; } // end namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc b/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc index 1fe6e69ff2dc83..9e17756b27733e 100644 --- a/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc +++ b/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc @@ -112,10 +112,10 @@ void CollectNames(const T& entries, std::vector* nonempty_names, XlaJitCompiledCpuFunction::Compile( const GraphDef& graph_def, const tf2xla::Config& config, const xla::ExecutableBuildOptions& build_options) { - // Convert the graph_def into an xla::Computation. + // Convert the graph_def into an xla::XlaComputation. TF_ASSIGN_OR_RETURN(xla::LocalClient * client, xla::ClientLibrary::GetOrCreateLocalClient()); - xla::Computation computation; + xla::XlaComputation computation; TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToXla(graph_def, config, client, &computation)); diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc index c4bb90d58755f1..2b65f4d5d5936e 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc @@ -30,7 +30,7 @@ bool XlaOpKernelContext::ValidateInputsAreSameShape(OpKernel* op) { return context_->ValidateInputsAreSameShape(op); } -xla::ComputationBuilder* XlaOpKernelContext::builder() const { +xla::XlaBuilder* XlaOpKernelContext::builder() const { return XlaContext::Get(this).builder(); } @@ -38,9 +38,9 @@ xla::ComputationBuilder* XlaOpKernelContext::builder() const { static const XlaExpression* CastExpressionFromTensor(const Tensor& tensor) { const XlaExpression* expression = reinterpret_cast(tensor.tensor_data().data()); - CHECK(expression->handle().handle() != 0 || + CHECK(expression->handle().builder() != nullptr || expression->resource() != nullptr); - VLOG(1) << "Fetched T" << expression->handle().handle(); + VLOG(1) << "Fetched T" << expression->handle(); return expression; } @@ -48,20 +48,18 @@ static const XlaExpression* CastExpressionFromTensor(const Tensor& tensor) { static XlaExpression* CastExpressionFromUninitializedTensor(Tensor* tensor) { const XlaExpression* expression = reinterpret_cast(tensor->tensor_data().data()); - CHECK_EQ(expression->handle().handle(), 0); + CHECK_EQ(expression->handle().builder(), nullptr); return const_cast(expression); } -// Retrieves the ComputationDataHandle from an input Tensor to an Op. This -// computation was constructed by an Op that executed previously and -// created the output Tensor using CreateOutputTensorFromComputation -// or CreateConstantOutputTensor. -static const xla::ComputationDataHandle& GetComputationFromTensor( - const Tensor& tensor) { +// Retrieves the XlaOp from an input Tensor to an Op. This computation was +// constructed by an Op that executed previously and created the output Tensor +// using CreateOutputTensorFromComputation or CreateConstantOutputTensor. +static const xla::XlaOp& GetComputationFromTensor(const Tensor& tensor) { return CastExpressionFromTensor(tensor)->handle(); } -const xla::ComputationDataHandle& XlaOpKernelContext::Input(int index) { +const xla::XlaOp& XlaOpKernelContext::Input(int index) { return GetComputationFromTensor(context_->input(index)); } @@ -106,7 +104,7 @@ Status XlaOpKernelContext::ConstantInputReshaped( return HostTensorToLiteral(temp, constant_literal); } - xla::ComputationDataHandle handle = expression->handle(); + xla::XlaOp handle = expression->handle(); if (new_shape != tensor.shape()) { // Reshape the handle to the desired shape. handle = builder()->Reshape(handle, new_shape.dim_sizes()); @@ -141,8 +139,17 @@ Status XlaOpKernelContext::ConstantInputReshaped( } // Ask the XLA compiler to evaluate the data handle to a literal. + xla::StatusOr constant_graph = + builder()->BuildConstantSubGraph(handle); + if (!constant_graph.ok()) { + return errors::Internal( + "Error getting a compile-time constant graph for ", + context_->op_kernel().name(), " input ", index, + ".\nError: ", constant_graph.status().error_message()); + } xla::StatusOr> computed = - builder()->ComputeConstant(handle, &layout); + compiler()->client()->ComputeConstant(constant_graph.ValueOrDie(), + &layout); if (!computed.ok()) { return errors::Internal("Error evaluating ", context_->op_kernel().name(), " input ", index, @@ -260,9 +267,9 @@ Status XlaOpKernelContext::ConstantInputAsShape(int index, TensorShape* shape) { return Status::OK(); } -Status XlaOpKernelContext::InputList( - StringPiece name, std::vector* handles, - std::vector* shapes) { +Status XlaOpKernelContext::InputList(StringPiece name, + std::vector* handles, + std::vector* shapes) { OpInputList inputs; TF_RETURN_IF_ERROR(context_->input_list(name, &inputs)); handles->clear(); @@ -285,9 +292,9 @@ Status XlaOpKernelContext::ConstantInputList( return Status::OK(); } -Status XlaOpKernelContext::ReadVariableInput( - int index, DataType type, TensorShape* shape, - xla::ComputationDataHandle* value) { +Status XlaOpKernelContext::ReadVariableInput(int index, DataType type, + TensorShape* shape, + xla::XlaOp* value) { const Tensor& tensor = context_->input(index); const XlaExpression* expression = CastExpressionFromTensor(tensor); XlaResource* variable = expression->resource(); @@ -334,8 +341,7 @@ Status XlaOpKernelContext::GetVariableTypeAndShape(int index, DataType* type, return Status::OK(); } -void XlaOpKernelContext::SetOutput(int index, - const xla::ComputationDataHandle& handle) { +void XlaOpKernelContext::SetOutput(int index, const xla::XlaOp& handle) { // Makes the host Tensor that will refer to the expression. Tensor* output = nullptr; auto shape = builder()->GetShape(handle); @@ -349,7 +355,7 @@ void XlaOpKernelContext::SetOutput(int index, // corresponds. TensorShape tensor_shape; OP_REQUIRES_OK(context_, - XLAShapeToTensorShape(*shape.ValueOrDie(), &tensor_shape)); + XLAShapeToTensorShape(shape.ValueOrDie(), &tensor_shape)); OP_REQUIRES_OK(context_, context_->allocate_output(index, tensor_shape, &output)); @@ -364,8 +370,8 @@ void XlaOpKernelContext::SetConstantOutput(int index, const Tensor& constant) { xla::Literal literal; OP_REQUIRES_OK(context_, HostTensorToLiteral(constant, &literal)); - xla::ComputationDataHandle handle = builder()->ConstantLiteral(literal); - CHECK_NE(handle.handle(), 0); + xla::XlaOp handle = builder()->ConstantLiteral(literal); + CHECK_NE(handle.builder(), nullptr); // Make the Tensor that will refer to the expression. Tensor* output = nullptr; @@ -386,8 +392,7 @@ void XlaOpKernelContext::SetInvalidOutput(int index) { OP_REQUIRES_OK(context_, context_->allocate_output(index, TensorShape({}), &output)); XlaExpression* expression = CastExpressionFromUninitializedTensor(output); - xla::ComputationDataHandle handle; - handle.set_handle(0); + xla::XlaOp handle; expression->set_handle(handle); } @@ -410,8 +415,8 @@ Status XlaOpKernelContext::GetResourceInput(int index, XlaResource** resource) { } Status XlaOpKernelContext::AssignVariable(int input_index, DataType type, - xla::ComputationDataHandle handle) { - TF_RET_CHECK(handle.handle() != 0); + xla::XlaOp handle) { + TF_RET_CHECK(handle.builder() != nullptr); const XlaExpression* expression = CastExpressionFromTensor(context_->input(input_index)); @@ -425,7 +430,7 @@ Status XlaOpKernelContext::AssignVariable(int input_index, DataType type, } TensorShape shape; TF_RETURN_IF_ERROR( - XLAShapeToTensorShape(*shape_or_status.ValueOrDie(), &shape)); + XLAShapeToTensorShape(shape_or_status.ValueOrDie(), &shape)); TF_RETURN_IF_ERROR(variable->SetTypeAndShape(type, shape)); @@ -457,22 +462,22 @@ void XlaOpKernelContext::CtxFailureWithWarning(const char* file, int line, context_->CtxFailureWithWarning(file, line, s); } -const xla::Computation* XlaOpKernelContext::GetOrCreateMax( +const xla::XlaComputation* XlaOpKernelContext::GetOrCreateMax( const DataType type) { return XlaContext::Get(context_).GetOrCreateMax(type); } -const xla::Computation* XlaOpKernelContext::GetOrCreateMin( +const xla::XlaComputation* XlaOpKernelContext::GetOrCreateMin( const DataType type) { return XlaContext::Get(context_).GetOrCreateMin(type); } -const xla::Computation* XlaOpKernelContext::GetOrCreateAdd( +const xla::XlaComputation* XlaOpKernelContext::GetOrCreateAdd( const DataType type) { return XlaContext::Get(context_).GetOrCreateAdd(type); } -const xla::Computation* XlaOpKernelContext::GetOrCreateMul( +const xla::XlaComputation* XlaOpKernelContext::GetOrCreateMul( const DataType type) { return XlaContext::Get(context_).GetOrCreateMul(type); } diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.h b/tensorflow/compiler/tf2xla/xla_op_kernel.h index 4e4b97e0cec8d1..667dc262ca03ca 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.h +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.h @@ -17,7 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_XLA_OP_KERNEL_H_ #include "tensorflow/compiler/tf2xla/xla_compiler.h" -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/platform/macros.h" @@ -58,8 +58,8 @@ class XlaOpKernelContext { public: explicit XlaOpKernelContext(OpKernelContext* context); - // Returns the XLA ComputationBuilder containing the output of compilation. - xla::ComputationBuilder* builder() const; + // Returns the XLA XlaBuilder containing the output of compilation. + xla::XlaBuilder* builder() const; // Inputs @@ -72,10 +72,10 @@ class XlaOpKernelContext { // Returns the shape of input 'index'. TensorShape InputShape(int index); - // Returns input 'index' as a ComputationDataHandle. Unlike + // Returns input 'index' as a XlaOp. Unlike // OpKernelContext::Input returns a symbolic value rather than a concrete // Tensor. - const xla::ComputationDataHandle& Input(int index); + const xla::XlaOp& Input(int index); // Returns true if all inputs are the same shape, otherwise sets the // status to a non-OK value and returns false. @@ -85,8 +85,7 @@ class XlaOpKernelContext { // Returns the named list-valued immutable input in "list", as // defined in the OpDef. If the named output is not list-valued, // returns a one-element list. - Status InputList(StringPiece name, - std::vector* handles, + Status InputList(StringPiece name, std::vector* handles, std::vector* shapes); // Helper methods for constant inputs. @@ -132,10 +131,10 @@ class XlaOpKernelContext { return context_->expected_output_dtype(index); } - // Sets output 'index' to the ComputationDataHandle 'handle'. + // Sets output 'index' to the XlaOp 'handle'. // All outputs should be set using SetOutput and SetConstantOutput, not // via the underlying OpKernelContext. - void SetOutput(int index, const xla::ComputationDataHandle& handle); + void SetOutput(int index, const xla::XlaOp& handle); // Sets output 'index' to compile-time constant 'host_tensor', where // 'host_tensor' is a tensor in host memory. It is preferable to use @@ -168,14 +167,13 @@ class XlaOpKernelContext { // variable. Returns an error if the variable has not been initialized, or if // its type does not match `type`. Status ReadVariableInput(int index, DataType type, TensorShape* shape, - xla::ComputationDataHandle* value); + xla::XlaOp* value); // Assigns the value `handle` to the variable referenced by input // `input_index`. The variable must be of `type`. Returns an error if the // variable has been initialized with a different type or with a // different shape. - Status AssignVariable(int input_index, DataType type, - xla::ComputationDataHandle handle); + Status AssignVariable(int input_index, DataType type, xla::XlaOp handle); // Helper routines for the OP_REQUIRES macros void CtxFailure(const Status& s); @@ -205,22 +203,22 @@ class XlaOpKernelContext { // Gets an XLA lambda to compute Max. This is cached in the // XlaContext since it may be used by multiple Ops. There is a // separate specialization of the computation for each DataType. - const xla::Computation* GetOrCreateMax(const DataType type); + const xla::XlaComputation* GetOrCreateMax(const DataType type); // Gets an XLA lambda to compute Min. This is cached in the // XlaContext since it may be used by multiple Ops. There is a // separate specialization of the computation for each DataType. - const xla::Computation* GetOrCreateMin(const DataType type); + const xla::XlaComputation* GetOrCreateMin(const DataType type); // Gets an XLA lambda to compute Add. This is cached in the // XlaContext since it may be used by multiple Ops. There is a // separate specialization of the computation for each DataType. - const xla::Computation* GetOrCreateAdd(const DataType type); + const xla::XlaComputation* GetOrCreateAdd(const DataType type); // Gets an XLA lambda to compute Mul. This is cached in the // XlaContext since it may be used by multiple Ops. There is a // separate specialization of the computation for each DataType. - const xla::Computation* GetOrCreateMul(const DataType type); + const xla::XlaComputation* GetOrCreateMul(const DataType type); private: OpKernelContext* const context_; diff --git a/tensorflow/compiler/tf2xla/xla_resource.cc b/tensorflow/compiler/tf2xla/xla_resource.cc index c2075b44b82ba2..540c65c597f20d 100644 --- a/tensorflow/compiler/tf2xla/xla_resource.cc +++ b/tensorflow/compiler/tf2xla/xla_resource.cc @@ -26,8 +26,7 @@ limitations under the License. namespace tensorflow { XlaResource::XlaResource(Kind kind, int arg_num, string name, DataType type, - TensorShape shape, - const xla::ComputationDataHandle& initial_value, + TensorShape shape, const xla::XlaOp& initial_value, int64 tensor_array_size, const std::set& tensor_array_gradients) : kind_(kind), @@ -41,11 +40,10 @@ XlaResource::XlaResource(Kind kind, int arg_num, string name, DataType type, CHECK(kind_ != kInvalid); for (const string& gradient : tensor_array_gradients) { - tensor_array_gradients_[gradient].reset( - new XlaResource(/*kind=*/kTensorArray, /*arg_num=*/-1, - /*name=*/strings::StrCat("TensorArrayGrad: ", name_), - type_, shape_, xla::ComputationDataHandle(), - tensor_array_size_, /*tensor_array_gradients=*/{})); + tensor_array_gradients_[gradient].reset(new XlaResource( + /*kind=*/kTensorArray, /*arg_num=*/-1, + /*name=*/strings::StrCat("TensorArrayGrad: ", name_), type_, shape_, + xla::XlaOp(), tensor_array_size_, /*tensor_array_gradients=*/{})); } } @@ -73,7 +71,7 @@ Status XlaResource::SetTypeAndShape(DataType type, const TensorShape& shape) { return Status::OK(); } -Status XlaResource::SetValue(const xla::ComputationDataHandle& value) { +Status XlaResource::SetValue(const xla::XlaOp& value) { if (type_ == DT_INVALID) { return errors::InvalidArgument( "Resource '", name_, @@ -83,7 +81,7 @@ Status XlaResource::SetValue(const xla::ComputationDataHandle& value) { return Status::OK(); } -Status XlaResource::SetZeroValue(xla::ComputationBuilder* builder) { +Status XlaResource::SetZeroValue(xla::XlaBuilder* builder) { if (type_ == DT_INVALID) { return errors::InvalidArgument( "Resource '", name_, @@ -121,9 +119,9 @@ Status XlaResource::SetZeroValue(xla::ComputationBuilder* builder) { return Status::OK(); } -Status XlaResource::GetOrCreateTensorArrayGradient( - const string& source, xla::ComputationBuilder* builder, - XlaResource** gradient_out) { +Status XlaResource::GetOrCreateTensorArrayGradient(const string& source, + xla::XlaBuilder* builder, + XlaResource** gradient_out) { VLOG(2) << "Gradient lookup for resource: " << name_ << " gradient: " << source; TF_RET_CHECK(kind_ == kTensorArray); @@ -132,7 +130,7 @@ Status XlaResource::GetOrCreateTensorArrayGradient( TensorShape ta_shape; ta_shape.AddDim(tensor_array_size_); ta_shape.AppendShape(shape_); - xla::ComputationDataHandle gradient_value = builder->Broadcast( + xla::XlaOp gradient_value = builder->Broadcast( XlaHelpers::Zero(builder, type_), ta_shape.dim_sizes()); gradient.reset( new XlaResource(/*kind=*/kTensorArray, /*arg_num=*/-1, @@ -144,13 +142,12 @@ Status XlaResource::GetOrCreateTensorArrayGradient( return Status::OK(); } -Status XlaResource::Pack(xla::ComputationDataHandle* pack, - xla::ComputationBuilder* builder) const { +Status XlaResource::Pack(xla::XlaOp* pack, xla::XlaBuilder* builder) const { if (tensor_array_gradients_.empty()) { *pack = value_; } else { TF_RET_CHECK(kind_ == kTensorArray); - std::vector elems; + std::vector elems; elems.push_back(value_); for (const auto& gradient : tensor_array_gradients_) { elems.push_back(gradient.second->value_); @@ -161,8 +158,8 @@ Status XlaResource::Pack(xla::ComputationDataHandle* pack, } Status XlaResource::SetFromPack(const std::set& gradient_sources, - const xla::ComputationDataHandle& pack, - xla::ComputationBuilder* builder) { + const xla::XlaOp& pack, + xla::XlaBuilder* builder) { if (gradient_sources.empty()) { if (!initialized()) { initial_value_ = pack; diff --git a/tensorflow/compiler/tf2xla/xla_resource.h b/tensorflow/compiler/tf2xla/xla_resource.h index 1bb2c7274ecdf0..9ce36d1aa76223 100644 --- a/tensorflow/compiler/tf2xla/xla_resource.h +++ b/tensorflow/compiler/tf2xla/xla_resource.h @@ -18,7 +18,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/framework/types.pb.h" @@ -37,8 +37,7 @@ class XlaResource { }; XlaResource(Kind kind, int arg_num, string name, DataType type, - TensorShape shape, - const xla::ComputationDataHandle& initial_value, + TensorShape shape, const xla::XlaOp& initial_value, int64 tensor_array_size, const std::set& tensor_array_gradients); @@ -69,16 +68,14 @@ class XlaResource { // this is the shape of each entry in the TensorArray/Stack. const TensorShape& shape() const { return shape_; } - const xla::ComputationDataHandle& value() const { return value_; } + const xla::XlaOp& value() const { return value_; } // Value of the resource at computation entry. Used to detect which // variables have new values that need to be written back. - const xla::ComputationDataHandle& initial_value() const { - return initial_value_; - } + const xla::XlaOp& initial_value() const { return initial_value_; } // A variable is initialized if it has a value. - bool initialized() const { return value_.handle() > 0; } + bool initialized() const { return value_.builder() != nullptr; } // Sets the type and shape of the resource. The type and shape of a resource // must not change once the variable has been initialized. @@ -86,17 +83,17 @@ class XlaResource { // Sets the current value of the resource. Returns an error if the type is not // set to a valid value. - Status SetValue(const xla::ComputationDataHandle& value); + Status SetValue(const xla::XlaOp& value); // Sets the current value of the resource to an all-zero value. - Status SetZeroValue(xla::ComputationBuilder* builder); + Status SetZeroValue(xla::XlaBuilder* builder); // Looks up the gradient for `source`, or creates it if it does not already // exist. The call target must be an initialized TensorArray resource. A // TensorArray can have multiple named gradients; see the operator // documentation for TensorArrayGradV3 for details. Status GetOrCreateTensorArrayGradient(const string& source, - xla::ComputationBuilder* builder, + xla::XlaBuilder* builder, XlaResource** gradient_out); // Packs a resource into a single XLA value `pack`, suitable for use as @@ -104,8 +101,7 @@ class XlaResource { // gradients, sets `*pack` to `value`. // For TensorArrays with gradients, packs the value and its gradient values in // a tuple; the gradients values are packed in order by source name. - Status Pack(xla::ComputationDataHandle* pack, - xla::ComputationBuilder* builder) const; + Status Pack(xla::XlaOp* pack, xla::XlaBuilder* builder) const; // Updates the resource with values from `pack`. If `gradient_sources` is // non-empty, treats `pack` as a tuple that represents a TensorArray and @@ -114,8 +110,7 @@ class XlaResource { // values. // Opposite of Pack(). Status SetFromPack(const std::set& gradient_sources, - const xla::ComputationDataHandle& pack, - xla::ComputationBuilder* builder); + const xla::XlaOp& pack, xla::XlaBuilder* builder); // TensorArray and Stack specific fields @@ -144,8 +139,8 @@ class XlaResource { DataType type_; TensorShape shape_; - xla::ComputationDataHandle value_; - xla::ComputationDataHandle initial_value_; + xla::XlaOp value_; + xla::XlaOp initial_value_; int64 tensor_array_size_ = -1; diff --git a/tensorflow/compiler/xla/client/BUILD b/tensorflow/compiler/xla/client/BUILD index 286d06d12ffca7..aac3273d5fd144 100644 --- a/tensorflow/compiler/xla/client/BUILD +++ b/tensorflow/compiler/xla/client/BUILD @@ -106,6 +106,7 @@ cc_library( "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:backend", "//tensorflow/compiler/xla/service:compiler", "//tensorflow/compiler/xla/service:device_memory_allocator", diff --git a/tensorflow/compiler/xla/client/local_client.h b/tensorflow/compiler/xla/client/local_client.h index f306c520ede001..4ce7059f7e2fb3 100644 --- a/tensorflow/compiler/xla/client/local_client.h +++ b/tensorflow/compiler/xla/client/local_client.h @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/computation.h" #include "tensorflow/compiler/xla/client/executable_build_options.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/executable_run_options.h" #include "tensorflow/compiler/xla/service/compiler.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" From c8a0f6e92b3197c76c5aac1d2a7612e2f4b3fc56 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 17:46:35 -0700 Subject: [PATCH 0207/1691] Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 194874988 --- tensorflow/go/op/wrappers.go | 190 +++++++++++++++++------------------ 1 file changed, 95 insertions(+), 95 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 83de1c5a92edee..c12ea5156356f2 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -6655,6 +6655,101 @@ func FusedBatchNormV2(scope *Scope, x tf.Output, scale tf.Output, offset tf.Outp return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) } +// Reverses specific dimensions of a tensor. +// +// NOTE `tf.reverse` has now changed behavior in preparation for 1.0. +// `tf.reverse_v2` is currently an alias that will be deprecated before TF 1.0. +// +// Given a `tensor`, and a `int32` tensor `axis` representing the set of +// dimensions of `tensor` to reverse. This operation reverses each dimension +// `i` for which there exists `j` s.t. `axis[j] == i`. +// +// `tensor` can have up to 8 dimensions. The number of dimensions specified +// in `axis` may be 0 or more entries. If an index is specified more than +// once, a InvalidArgument error is raised. +// +// For example: +// +// ``` +// # tensor 't' is [[[[ 0, 1, 2, 3], +// # [ 4, 5, 6, 7], +// # [ 8, 9, 10, 11]], +// # [[12, 13, 14, 15], +// # [16, 17, 18, 19], +// # [20, 21, 22, 23]]]] +// # tensor 't' shape is [1, 2, 3, 4] +// +// # 'dims' is [3] or 'dims' is [-1] +// reverse(t, dims) ==> [[[[ 3, 2, 1, 0], +// [ 7, 6, 5, 4], +// [ 11, 10, 9, 8]], +// [[15, 14, 13, 12], +// [19, 18, 17, 16], +// [23, 22, 21, 20]]]] +// +// # 'dims' is '[1]' (or 'dims' is '[-3]') +// reverse(t, dims) ==> [[[[12, 13, 14, 15], +// [16, 17, 18, 19], +// [20, 21, 22, 23] +// [[ 0, 1, 2, 3], +// [ 4, 5, 6, 7], +// [ 8, 9, 10, 11]]]] +// +// # 'dims' is '[2]' (or 'dims' is '[-2]') +// reverse(t, dims) ==> [[[[8, 9, 10, 11], +// [4, 5, 6, 7], +// [0, 1, 2, 3]] +// [[20, 21, 22, 23], +// [16, 17, 18, 19], +// [12, 13, 14, 15]]]] +// ``` +// +// Arguments: +// tensor: Up to 8-D. +// axis: 1-D. The indices of the dimensions to reverse. Must be in the range +// `[-rank(tensor), rank(tensor))`. +// +// Returns The same shape as `tensor`. +func ReverseV2(scope *Scope, tensor tf.Output, axis tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReverseV2", + Input: []tf.Input{ + tensor, axis, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds `bias` to `value`. +// +// This is a deprecated version of BiasAdd and will be soon removed. +// +// This is a special case of `tf.add` where `bias` is restricted to be 1-D. +// Broadcasting is supported, so `value` may have any number of dimensions. +// +// Arguments: +// value: Any number of dimensions. +// bias: 1-D with size the last dimension of `value`. +// +// Returns Broadcasted sum of `value` and `bias`. +func BiasAddV1(scope *Scope, value tf.Output, bias tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BiasAddV1", + Input: []tf.Input{ + value, bias, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Transforms a Tensor into a serialized TensorProto proto. // // Arguments: @@ -13816,101 +13911,6 @@ func ResourceApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms return scope.AddOperation(opspec) } -// Adds `bias` to `value`. -// -// This is a deprecated version of BiasAdd and will be soon removed. -// -// This is a special case of `tf.add` where `bias` is restricted to be 1-D. -// Broadcasting is supported, so `value` may have any number of dimensions. -// -// Arguments: -// value: Any number of dimensions. -// bias: 1-D with size the last dimension of `value`. -// -// Returns Broadcasted sum of `value` and `bias`. -func BiasAddV1(scope *Scope, value tf.Output, bias tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BiasAddV1", - Input: []tf.Input{ - value, bias, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Reverses specific dimensions of a tensor. -// -// NOTE `tf.reverse` has now changed behavior in preparation for 1.0. -// `tf.reverse_v2` is currently an alias that will be deprecated before TF 1.0. -// -// Given a `tensor`, and a `int32` tensor `axis` representing the set of -// dimensions of `tensor` to reverse. This operation reverses each dimension -// `i` for which there exists `j` s.t. `axis[j] == i`. -// -// `tensor` can have up to 8 dimensions. The number of dimensions specified -// in `axis` may be 0 or more entries. If an index is specified more than -// once, a InvalidArgument error is raised. -// -// For example: -// -// ``` -// # tensor 't' is [[[[ 0, 1, 2, 3], -// # [ 4, 5, 6, 7], -// # [ 8, 9, 10, 11]], -// # [[12, 13, 14, 15], -// # [16, 17, 18, 19], -// # [20, 21, 22, 23]]]] -// # tensor 't' shape is [1, 2, 3, 4] -// -// # 'dims' is [3] or 'dims' is [-1] -// reverse(t, dims) ==> [[[[ 3, 2, 1, 0], -// [ 7, 6, 5, 4], -// [ 11, 10, 9, 8]], -// [[15, 14, 13, 12], -// [19, 18, 17, 16], -// [23, 22, 21, 20]]]] -// -// # 'dims' is '[1]' (or 'dims' is '[-3]') -// reverse(t, dims) ==> [[[[12, 13, 14, 15], -// [16, 17, 18, 19], -// [20, 21, 22, 23] -// [[ 0, 1, 2, 3], -// [ 4, 5, 6, 7], -// [ 8, 9, 10, 11]]]] -// -// # 'dims' is '[2]' (or 'dims' is '[-2]') -// reverse(t, dims) ==> [[[[8, 9, 10, 11], -// [4, 5, 6, 7], -// [0, 1, 2, 3]] -// [[20, 21, 22, 23], -// [16, 17, 18, 19], -// [12, 13, 14, 15]]]] -// ``` -// -// Arguments: -// tensor: Up to 8-D. -// axis: 1-D. The indices of the dimensions to reverse. Must be in the range -// `[-rank(tensor), rank(tensor))`. -// -// Returns The same shape as `tensor`. -func ReverseV2(scope *Scope, tensor tf.Output, axis tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ReverseV2", - Input: []tf.Input{ - tensor, axis, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // RealAttr is an optional argument to Real. type RealAttr func(optionalAttr) From 40721422bfc9cec546537799e16dd75f443d2db2 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Mon, 30 Apr 2018 18:01:23 -0700 Subject: [PATCH 0208/1691] Remove proto header import from core/framework/tracking_allocator.h The goal is to make kernels mostly independent of proto headers, which will let us lock down our .so import. PiperOrigin-RevId: 194876569 --- tensorflow/core/common_runtime/eager/kernel_and_device.cc | 1 + tensorflow/core/common_runtime/eager/kernel_and_device.h | 4 ++++ tensorflow/core/framework/tracking_allocator.h | 1 - 3 files changed, 5 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/common_runtime/eager/kernel_and_device.cc b/tensorflow/core/common_runtime/eager/kernel_and_device.cc index 0a4895a938a72a..a63b2b97112318 100644 --- a/tensorflow/core/common_runtime/eager/kernel_and_device.cc +++ b/tensorflow/core/common_runtime/eager/kernel_and_device.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/rendezvous_mgr.h" #include "tensorflow/core/framework/allocator.h" #include "tensorflow/core/framework/node_def.pb.h" +#include "tensorflow/core/framework/step_stats.pb.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/gtl/map_util.h" #include "tensorflow/core/lib/gtl/stl_util.h" diff --git a/tensorflow/core/common_runtime/eager/kernel_and_device.h b/tensorflow/core/common_runtime/eager/kernel_and_device.h index 46ec550c780aaa..f78d197fd55551 100644 --- a/tensorflow/core/common_runtime/eager/kernel_and_device.h +++ b/tensorflow/core/common_runtime/eager/kernel_and_device.h @@ -32,6 +32,10 @@ limitations under the License. namespace tensorflow { +// Forward declaration for proto class NodeExecStats so we do not need to +// include the proto header +class NodeExecStats; + // KernelAndDevice encapsulates an instantiated kernel and the device it is on. // // Also see: diff --git a/tensorflow/core/framework/tracking_allocator.h b/tensorflow/core/framework/tracking_allocator.h index f6c3c0b71b951c..661c28969e6143 100644 --- a/tensorflow/core/framework/tracking_allocator.h +++ b/tensorflow/core/framework/tracking_allocator.h @@ -18,7 +18,6 @@ limitations under the License. #include #include "tensorflow/core/framework/allocator.h" -#include "tensorflow/core/framework/step_stats.pb.h" #include "tensorflow/core/lib/core/refcount.h" #include "tensorflow/core/lib/gtl/inlined_vector.h" #include "tensorflow/core/platform/mutex.h" From 85d30bfcf412bd1ca06fa33548344bf40eedb4ac Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 18:05:37 -0700 Subject: [PATCH 0209/1691] Internal change. PiperOrigin-RevId: 194877173 --- .../kernels/bidirectional_sequence_lstm.cc | 70 ++++++++++++------- .../bidirectional_sequence_lstm_test.cc | 8 --- tensorflow/contrib/lite/kernels/lstm.cc | 49 ++++++++----- tensorflow/contrib/lite/kernels/lstm_test.cc | 4 -- .../lite/kernels/optional_tensor_test.cc | 4 -- .../kernels/unidirectional_sequence_lstm.cc | 47 +++++++++---- .../unidirectional_sequence_lstm_test.cc | 4 -- tensorflow/contrib/lite/models/speech_test.cc | 16 ++--- .../testdata/speech_asr_lm_model.test_spec | 20 +++--- .../identify_lstm_merge_inputs.cc | 9 ++- .../identify_lstm_split_inputs.cc | 7 +- .../toco/graph_transformations/lstm_utils.h | 8 +-- 12 files changed, 139 insertions(+), 107 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc index a64ac42bc43336..3ac0210f3645e6 100644 --- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc @@ -96,15 +96,23 @@ constexpr int kBwProjectionWeightsTensor = 33; // Optional constexpr int kBwProjectionBiasTensor = 34; // Optional // Output tensors. -constexpr int kFwScratchBufferTensor = 0; -constexpr int kFwOutputStateTensor = 1; -constexpr int kFwCellStateTensor = 2; -constexpr int kFwOutputTensor = 3; +constexpr int kFwOutputStateTensor = 0; +constexpr int kFwCellStateTensor = 1; +constexpr int kFwOutputTensor = 2; + +constexpr int kBwOutputStateTensor = 3; +constexpr int kBwCellStateTensor = 4; +constexpr int kBwOutputTensor = 5; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* scratch_tensor_index = new int; + context->AddTensors(context, 2, scratch_tensor_index); + return scratch_tensor_index; +} -constexpr int kBwScratchBufferTensor = 4; -constexpr int kBwOutputStateTensor = 5; -constexpr int kBwCellStateTensor = 6; -constexpr int kBwOutputTensor = 7; +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} // Check that input tensor dimensions matches with each other. TfLiteStatus CheckLstmTensorDimensions( @@ -296,9 +304,11 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, // Resize the output, state and scratch tensors based on the sizes of the input // tensors. Also check that the size of the input tensors match each other. TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + int* scratch_tensor_index = reinterpret_cast(node->user_data); + // Check we have all the inputs and outputs we need. TF_LITE_ENSURE_EQ(context, node->inputs->size, 35); - TF_LITE_ENSURE_EQ(context, node->outputs->size, 8); + TF_LITE_ENSURE_EQ(context, node->outputs->size, 6); // Inferring batch size, number of outputs and sequence length and // number of cells from the input tensors. @@ -330,12 +340,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* fw_output_state = GetOutput(context, node, kFwOutputStateTensor); TfLiteTensor* fw_cell_state = GetOutput(context, node, kFwCellStateTensor); - // TODO(ghodrat): Modify this as soon as we have a finalized method for - // scratch buffers. - TfLiteTensor* fw_scratch_buffer = - GetOutput(context, node, kFwScratchBufferTensor); - // Resize the output and output_state tensors. + // Resize the output, output_state and cell_state tensors. TfLiteIntArray* fw_output_size = TfLiteIntArrayCreate(3); fw_output_size->data[0] = max_time; fw_output_size->data[1] = n_batch; @@ -349,13 +355,21 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, fw_output_state, fw_output_state_size)); - // Resize the scratch buffer tensor. TfLiteIntArray* fw_cell_size = TfLiteIntArrayCreate(2); fw_cell_size->data[0] = n_batch; fw_cell_size->data[1] = n_fw_cell; TF_LITE_ENSURE_OK( context, context->ResizeTensor(context, fw_cell_state, fw_cell_size)); + // Create a scratch buffer tensor. + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(2); + node->temporaries->data[0] = *scratch_tensor_index; + TfLiteTensor* fw_scratch_buffer = + &context->tensors[node->temporaries->data[0]]; + fw_scratch_buffer->type = input->type; + fw_scratch_buffer->allocation_type = kTfLiteArenaRw; + // Mark state tensors as persistent tensors. fw_output_state->allocation_type = kTfLiteArenaRwPersistent; fw_cell_state->allocation_type = kTfLiteArenaRwPersistent; @@ -392,17 +406,13 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // Check that input tensor dimensions matches with each other. CheckInputTensorDimensions(context, node, n_input, n_bw_output, n_bw_cell); - // Get the pointer to output, state and scratch buffer tensors. + // Get the pointer to output, output_state and cell_state buffer tensors. TfLiteTensor* bw_output = GetOutput(context, node, kBwOutputTensor); TfLiteTensor* bw_output_state = GetOutput(context, node, kBwOutputStateTensor); TfLiteTensor* bw_cell_state = GetOutput(context, node, kBwCellStateTensor); - // TODO(ghodrat): Modify this as soon as we have a finalized method for - // scratch buffers. - TfLiteTensor* bw_scratch_buffer = - GetOutput(context, node, kBwScratchBufferTensor); - // Resize the output and output_state tensors. + // Resize the output, output_state and cell_state tensors. TfLiteIntArray* bw_output_size = TfLiteIntArrayCreate(3); bw_output_size->data[0] = max_time; bw_output_size->data[1] = n_batch; @@ -416,13 +426,19 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, bw_output_state, bw_output_state_size)); - // Resize the scratch buffer tensor. TfLiteIntArray* bw_cell_size = TfLiteIntArrayCreate(2); bw_cell_size->data[0] = n_batch; bw_cell_size->data[1] = n_bw_cell; TF_LITE_ENSURE_OK( context, context->ResizeTensor(context, bw_cell_state, bw_cell_size)); + // Create a scratch buffer tensor. + node->temporaries->data[1] = *(scratch_tensor_index) + 1; + TfLiteTensor* bw_scratch_buffer = + &context->tensors[node->temporaries->data[1]]; + bw_scratch_buffer->type = input->type; + bw_scratch_buffer->allocation_type = kTfLiteArenaRw; + // Mark state tensors as persistent tensors. bw_output_state->allocation_type = kTfLiteArenaRwPersistent; bw_cell_state->allocation_type = kTfLiteArenaRwPersistent; @@ -553,7 +569,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // Index the scratch buffers pointers to the global scratch buffer. TfLiteTensor* fw_scratch_buffer = - GetOutput(context, node, kFwScratchBufferTensor); + &context->tensors[node->temporaries->data[0]]; float* fw_input_gate_scratch = nullptr; float* fw_cell_scratch = nullptr; float* fw_forget_gate_scratch = nullptr; @@ -624,7 +640,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // Index the scratch buffers pointers to the global scratch buffer. TfLiteTensor* bw_scratch_buffer = - GetOutput(context, node, kBwScratchBufferTensor); + &context->tensors[node->temporaries->data[1]]; float* bw_input_gate_scratch = nullptr; float* bw_cell_scratch = nullptr; float* bw_forget_gate_scratch = nullptr; @@ -691,9 +707,9 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace bidirectional_sequence_lstm TfLiteRegistration* Register_BIDIRECTIONAL_SEQUENCE_LSTM() { - static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr, - bidirectional_sequence_lstm::Prepare, - bidirectional_sequence_lstm::Eval}; + static TfLiteRegistration r = { + bidirectional_sequence_lstm::Init, bidirectional_sequence_lstm::Free, + bidirectional_sequence_lstm::Prepare, bidirectional_sequence_lstm::Eval}; return &r; } diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc index cca857bac0633d..a18e1bce34ca03 100644 --- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc @@ -102,9 +102,6 @@ class BidirectionalLSTMOpModel : public SingleOpModel { fw_projection_bias_ = AddNullInput(); } - fw_scratch_buffer_ = AddOutput(TensorType_FLOAT32); - // TODO(ghodrat): Modify these states when we have a permanent solution for - // persistent buffer. fw_output_state_ = AddOutput(TensorType_FLOAT32); fw_cell_state_ = AddOutput(TensorType_FLOAT32); fw_output_ = AddOutput(TensorType_FLOAT32); @@ -164,9 +161,6 @@ class BidirectionalLSTMOpModel : public SingleOpModel { bw_projection_bias_ = AddNullInput(); } - bw_scratch_buffer_ = AddOutput(TensorType_FLOAT32); - // TODO(ghodrat): Modify these states when we have a permanent solution for - // persistent buffer. bw_output_state_ = AddOutput(TensorType_FLOAT32); bw_cell_state_ = AddOutput(TensorType_FLOAT32); bw_output_ = AddOutput(TensorType_FLOAT32); @@ -349,12 +343,10 @@ class BidirectionalLSTMOpModel : public SingleOpModel { int fw_output_; int fw_output_state_; int fw_cell_state_; - int fw_scratch_buffer_; int bw_output_; int bw_output_state_; int bw_cell_state_; - int bw_scratch_buffer_; int n_batch_; int n_input_; diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc index 8cf1165135bdb0..668226e6747009 100644 --- a/tensorflow/contrib/lite/kernels/lstm.cc +++ b/tensorflow/contrib/lite/kernels/lstm.cc @@ -66,10 +66,19 @@ constexpr int kProjectionWeightsTensor = 16; // Optional constexpr int kProjectionBiasTensor = 17; // Optional // Output tensors. -constexpr int kScratchBufferTensor = 0; -constexpr int kOutputStateTensor = 1; -constexpr int kCellStateTensor = 2; -constexpr int kOutputTensor = 3; +constexpr int kOutputStateTensor = 0; +constexpr int kCellStateTensor = 1; +constexpr int kOutputTensor = 2; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* scratch_tensor_index = new int; + context->AddTensors(context, 1, scratch_tensor_index); + return scratch_tensor_index; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} // Check that input tensor dimensions matches with each other. TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, @@ -220,12 +229,15 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, return kTfLiteOk; } -// Resize the output, state and scratch tensors based on the sizes of the input -// tensors. Also check that the size of the input tensors match each other. +// Resize the output, state tensors based on the sizes of the input tensors. +// Allocate a temporary scratch tensor. Also check that the sizes of the input +// tensors match each other. TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + int* scratch_tensor_index = reinterpret_cast(node->user_data); + // Check we have all the inputs and outputs we need. TF_LITE_ENSURE_EQ(context, node->inputs->size, 18); - TF_LITE_ENSURE_EQ(context, node->outputs->size, 4); + TF_LITE_ENSURE_EQ(context, node->outputs->size, 3); // Inferring batch size, number of outputs and number of cells from the // input tensors. @@ -250,15 +262,12 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // Check that input tensor dimensions matches with each other. CheckInputTensorDimensions(context, node, n_input, n_output, n_cell); - // Get the pointer to output, state and scratch buffer tensors. + // Get the pointer to output, output_state and cell_state tensors. TfLiteTensor* output = GetOutput(context, node, kOutputTensor); TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); TfLiteTensor* cell_state = GetOutput(context, node, kCellStateTensor); - // TODO(ghodrat): Modify this as soon as we have a finalized method for - // scratch buffers. - TfLiteTensor* scratch_buffer = GetOutput(context, node, kScratchBufferTensor); - // Resize the output and output_state tensors. + // Resize the output, output_state and cell_state tensors. TfLiteIntArray* output_size = TfLiteIntArrayCreate(2); output_size->data[0] = n_batch; output_size->data[1] = n_output; @@ -271,13 +280,20 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK( context, context->ResizeTensor(context, output_state, output_state_size)); - // Resize the output, state and scratch buffer tensors. TfLiteIntArray* cell_size = TfLiteIntArrayCreate(2); cell_size->data[0] = n_batch; cell_size->data[1] = n_cell; TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, cell_state, cell_size)); + // Create a scratch buffer tensor. + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(1); + node->temporaries->data[0] = *scratch_tensor_index; + TfLiteTensor* scratch_buffer = &context->tensors[node->temporaries->data[0]]; + scratch_buffer->type = input->type; + scratch_buffer->allocation_type = kTfLiteArenaRw; + // Mark state tensors as persistent tensors. output_state->allocation_type = kTfLiteArenaRwPersistent; cell_state->allocation_type = kTfLiteArenaRwPersistent; @@ -362,7 +378,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const bool use_peephole = (cell_to_output_weights != nullptr); // Index the scratch buffers pointers to the global scratch buffer. - TfLiteTensor* scratch_buffer = GetOutput(context, node, kScratchBufferTensor); + TfLiteTensor* scratch_buffer = &context->tensors[node->temporaries->data[0]]; + float* input_gate_scratch = nullptr; float* cell_scratch = nullptr; float* forget_gate_scratch = nullptr; @@ -433,8 +450,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace lstm TfLiteRegistration* Register_LSTM() { - static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr, - lstm::Prepare, lstm::Eval}; + static TfLiteRegistration r = {lstm::Init, lstm::Free, lstm::Prepare, + lstm::Eval}; return &r; } diff --git a/tensorflow/contrib/lite/kernels/lstm_test.cc b/tensorflow/contrib/lite/kernels/lstm_test.cc index c068286b0d84bc..d81220d8d30793 100644 --- a/tensorflow/contrib/lite/kernels/lstm_test.cc +++ b/tensorflow/contrib/lite/kernels/lstm_test.cc @@ -97,9 +97,6 @@ class LSTMOpModel : public SingleOpModel { projection_bias_ = AddNullInput(); } - scratch_buffer_ = AddOutput(TensorType_FLOAT32); - // TODO(ghodrat): Modify these states when we have a permanent solution for - // persistent buffer. output_state_ = AddOutput(TensorType_FLOAT32); cell_state_ = AddOutput(TensorType_FLOAT32); output_ = AddOutput(TensorType_FLOAT32); @@ -233,7 +230,6 @@ class LSTMOpModel : public SingleOpModel { int output_; int output_state_; int cell_state_; - int scratch_buffer_; int n_batch_; int n_input_; diff --git a/tensorflow/contrib/lite/kernels/optional_tensor_test.cc b/tensorflow/contrib/lite/kernels/optional_tensor_test.cc index cee3ec6197c698..bcad58406af1cd 100644 --- a/tensorflow/contrib/lite/kernels/optional_tensor_test.cc +++ b/tensorflow/contrib/lite/kernels/optional_tensor_test.cc @@ -95,9 +95,6 @@ class LSTMOpModel : public SingleOpModel { projection_bias_ = AddNullInput(); } - scratch_buffer_ = AddOutput(TensorType_FLOAT32); - // TODO(ghodrat): Modify these states when we have a permanent solution for - // persistent buffer. output_state_ = AddOutput(TensorType_FLOAT32); cell_state_ = AddOutput(TensorType_FLOAT32); output_ = AddOutput(TensorType_FLOAT32); @@ -235,7 +232,6 @@ class LSTMOpModel : public SingleOpModel { int output_; int output_state_; int cell_state_; - int scratch_buffer_; int n_batch_; int n_input_; diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc index 42941a97db70ad..3c1256d3a651a8 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc @@ -66,10 +66,19 @@ constexpr int kProjectionWeightsTensor = 16; // Optional constexpr int kProjectionBiasTensor = 17; // Optional // Output tensors. -constexpr int kScratchBufferTensor = 0; -constexpr int kOutputStateTensor = 1; -constexpr int kCellStateTensor = 2; -constexpr int kOutputTensor = 3; +constexpr int kOutputStateTensor = 0; +constexpr int kCellStateTensor = 1; +constexpr int kOutputTensor = 2; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* scratch_tensor_index = new int; + context->AddTensors(context, 1, scratch_tensor_index); + return scratch_tensor_index; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} // Check that input tensor dimensions matches with each other. TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, @@ -220,12 +229,15 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, return kTfLiteOk; } -// Resize the output, state and scratch tensors based on the sizes of the input -// tensors. Also check that the size of the input tensors match each other. +// Resize the output and state tensors based on the sizes of the input tensors. +// Allocate a temprory scratch tensor. Also check that the sizes of the input +// tensors match each other. TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + int* scratch_tensor_index = reinterpret_cast(node->user_data); + // Check we have all the inputs and outputs we need. TF_LITE_ENSURE_EQ(context, node->inputs->size, 18); - TF_LITE_ENSURE_EQ(context, node->outputs->size, 4); + TF_LITE_ENSURE_EQ(context, node->outputs->size, 3); // Inferring batch size, number of outputs and sequence length and // number of cells from the input tensors. @@ -251,15 +263,12 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // Check that input tensor dimensions matches with each other. CheckInputTensorDimensions(context, node, n_input, n_output, n_cell); - // Get the pointer to output, state and scratch buffer tensors. + // Get the pointer to output, output_state and cell_state buffer tensors. TfLiteTensor* output = GetOutput(context, node, kOutputTensor); TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); TfLiteTensor* cell_state = GetOutput(context, node, kCellStateTensor); - // TODO(ghodrat): Modify this as soon as we have a finalized method for - // scratch buffers. - TfLiteTensor* scratch_buffer = GetOutput(context, node, kScratchBufferTensor); - // Resize the output and output_state tensors. + // Resize the output, output_state and cell_state tensors. TfLiteIntArray* output_size = TfLiteIntArrayCreate(3); output_size->data[0] = max_time; output_size->data[1] = n_batch; @@ -273,13 +282,20 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK( context, context->ResizeTensor(context, output_state, output_state_size)); - // Resize the scratch buffer tensor. TfLiteIntArray* cell_size = TfLiteIntArrayCreate(2); cell_size->data[0] = n_batch; cell_size->data[1] = n_cell; TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, cell_state, cell_size)); + // Create a scratch buffer tensor. + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(1); + node->temporaries->data[0] = *scratch_tensor_index; + TfLiteTensor* scratch_buffer = &context->tensors[node->temporaries->data[0]]; + scratch_buffer->type = input->type; + scratch_buffer->allocation_type = kTfLiteArenaRw; + // Mark state tensors as persistent tensors. output_state->allocation_type = kTfLiteArenaRwPersistent; cell_state->allocation_type = kTfLiteArenaRwPersistent; @@ -365,7 +381,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const bool use_peephole = (cell_to_output_weights != nullptr); // Index the scratch buffers pointers to the global scratch buffer. - TfLiteTensor* scratch_buffer = GetOutput(context, node, kScratchBufferTensor); + TfLiteTensor* scratch_buffer = &context->tensors[node->temporaries->data[0]]; float* input_gate_scratch = nullptr; float* cell_scratch = nullptr; float* forget_gate_scratch = nullptr; @@ -439,7 +455,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace unidirectional_sequence_lstm TfLiteRegistration* Register_UNIDIRECTIONAL_SEQUENCE_LSTM() { - static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr, + static TfLiteRegistration r = {unidirectional_sequence_lstm::Init, + unidirectional_sequence_lstm::Free, unidirectional_sequence_lstm::Prepare, unidirectional_sequence_lstm::Eval}; return &r; diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm_test.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm_test.cc index 93b635ae576e99..5881ced7c7a616 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm_test.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm_test.cc @@ -100,9 +100,6 @@ class UnidirectionalLSTMOpModel : public SingleOpModel { projection_bias_ = AddNullInput(); } - scratch_buffer_ = AddOutput(TensorType_FLOAT32); - // TODO(ghodrat): Modify these states when we have a permanent solution for - // persistent buffer. output_state_ = AddOutput(TensorType_FLOAT32); cell_state_ = AddOutput(TensorType_FLOAT32); output_ = AddOutput(TensorType_FLOAT32); @@ -238,7 +235,6 @@ class UnidirectionalLSTMOpModel : public SingleOpModel { int output_; int output_state_; int cell_state_; - int scratch_buffer_; int n_batch_; int n_input_; diff --git a/tensorflow/contrib/lite/models/speech_test.cc b/tensorflow/contrib/lite/models/speech_test.cc index a354179a9480c1..206de1962d1964 100644 --- a/tensorflow/contrib/lite/models/speech_test.cc +++ b/tensorflow/contrib/lite/models/speech_test.cc @@ -131,8 +131,8 @@ TEST_P(SpeechTest, SpeakerIdOkGoogleTest) { ASSERT_TRUE(ConvertCsvData( "speech_speakerid_model.tflite", "speech_speakerid_model_in.csv", "speech_speakerid_model_out.csv", /*input_tensor=*/"0", - /*output_tensor=*/"66", - /*persistent_tensors=*/"19,20,40,41,61,62", + /*output_tensor=*/"63", + /*persistent_tensors=*/"18,19,38,39,58,59", /*sequence_size=*/80, &os)); testing::TfLiteDriver test_driver(/*use_nnapi=*/false); ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations())) @@ -144,8 +144,8 @@ TEST_P(SpeechTest, AsrAmTest) { ASSERT_TRUE( ConvertCsvData("speech_asr_am_model.tflite", "speech_asr_am_model_in.csv", "speech_asr_am_model_out.csv", /*input_tensor=*/"0", - /*output_tensor=*/"109", - /*persistent_tensors=*/"19,20,40,41,61,62,82,83,103,104", + /*output_tensor=*/"104", + /*persistent_tensors=*/"18,19,38,39,58,59,78,79,98,99", /*sequence_size=*/320, &os)); testing::TfLiteDriver test_driver(/*use_nnapi=*/false); ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations())) @@ -170,8 +170,8 @@ TEST_P(SpeechTest, EndpointerTest) { ASSERT_TRUE(ConvertCsvData( "speech_endpointer_model.tflite", "speech_endpointer_model_in.csv", "speech_endpointer_model_out.csv", /*input_tensor=*/"0", - /*output_tensor=*/"58", - /*persistent_tensors=*/"28,29,49,50", + /*output_tensor=*/"56", + /*persistent_tensors=*/"27,28,47,48", /*sequence_size=*/320, &os)); testing::TfLiteDriver test_driver(/*use_nnapi=*/false); ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations())) @@ -183,8 +183,8 @@ TEST_P(SpeechTest, TtsTest) { ASSERT_TRUE(ConvertCsvData("speech_tts_model.tflite", "speech_tts_model_in.csv", "speech_tts_model_out.csv", /*input_tensor=*/"0", - /*output_tensor=*/"74", - /*persistent_tensors=*/"25,26,46,47,67,68,73", + /*output_tensor=*/"71", + /*persistent_tensors=*/"24,25,44,45,64,65,70", /*sequence_size=*/334, &os)); testing::TfLiteDriver test_driver(/*use_nnapi=*/false); ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations())) diff --git a/tensorflow/contrib/lite/models/testdata/speech_asr_lm_model.test_spec b/tensorflow/contrib/lite/models/testdata/speech_asr_lm_model.test_spec index 5812de4b30382f..f7f518b75f5652 100644 --- a/tensorflow/contrib/lite/models/testdata/speech_asr_lm_model.test_spec +++ b/tensorflow/contrib/lite/models/testdata/speech_asr_lm_model.test_spec @@ -1,5 +1,5 @@ load_model: "speech_asr_lm_model.tflite" -init_state: "21,22,42,43,63,64" +init_state: "20,21,40,41,60,61" invoke { id: 3 input: "63982" @@ -18,7 +18,7 @@ invoke { input: "63981" output: "-0.314846" } -init_state: "21,22,42,43,63,64" +init_state: "20,21,40,41,60,61" invoke { id: 6 input: "63982" @@ -31,7 +31,7 @@ invoke { input: "3082" output: "-3.63721" } -init_state: "21,22,42,43,63,64" +init_state: "20,21,40,41,60,61" invoke { id: 8 input: "63982" @@ -44,7 +44,7 @@ invoke { input: "18965" output: "-6.93985" } -init_state: "21,22,42,43,63,64" +init_state: "20,21,40,41,60,61" invoke { id: 13 input: "63982" @@ -63,7 +63,7 @@ invoke { input: "63981" output: "-3.82091" } -init_state: "21,22,42,43,63,64" +init_state: "20,21,40,41,60,61" invoke { id: 19 input: "63982" @@ -88,7 +88,7 @@ invoke { input: "63981" output: "-0.677399" } -init_state: "21,22,42,43,63,64" +init_state: "20,21,40,41,60,61" invoke { id: 26 input: "63982" @@ -113,7 +113,7 @@ invoke { input: "63981" output: "0.415889" } -init_state: "21,22,42,43,63,64" +init_state: "20,21,40,41,60,61" invoke { id: 30 input: "63982" @@ -131,7 +131,7 @@ invoke { input: "51923" output: "-14.1147" } -init_state: "21,22,42,43,63,64" +init_state: "20,21,40,41,60,61" invoke { id: 34 input: "63982" @@ -144,7 +144,7 @@ invoke { input: "16318" output: "-1.54815" } -init_state: "21,22,42,43,63,64" +init_state: "20,21,40,41,60,61" invoke { id: 36 input: "63982" @@ -157,7 +157,7 @@ invoke { input: "28303" output: "-14.0947" } -init_state: "21,22,42,43,63,64" +init_state: "20,21,40,41,60,61" invoke { id: 38 input: "63982" diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_merge_inputs.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_merge_inputs.cc index 45335fd78c99a5..3f768bfee12ebe 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_merge_inputs.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_merge_inputs.cc @@ -146,16 +146,19 @@ bool MergeLstmCellInputs::Run(Model* model, std::size_t op_index) { lstm_cell_op->inputs[LstmCellOperator::PREV_ACTIV_INPUT] = prev_activ_input; lstm_cell_op->inputs[LstmCellOperator::PREV_STATE_INPUT] = prev_state_input; - // Reorder LstmCell's 4 outputs. + // Reorder LstmCell's 3 outputs. lstm_cell_op->outputs.resize(LstmCellOperator::NUM_OUTPUTS); lstm_cell_op->outputs[LstmCellOperator::ACTIV_OUTPUT] = src_op->outputs[kOutputTensor]; lstm_cell_op->outputs[LstmCellOperator::STATE_OUTPUT] = src_op->outputs[kCellStateTensor]; - lstm_cell_op->outputs[LstmCellOperator::CONCAT_TEMP] = - src_op->outputs[kScratchBufferTensor]; lstm_cell_op->outputs[LstmCellOperator::ACTIV_TEMP] = src_op->outputs[kOutputStateTensor]; + // Create a new temp array for the fourth output. + const string& concat_temp_array_name = + AvailableArrayName(*model, base_name + "concat_temp"); + model->GetOrCreateArray(concat_temp_array_name); + lstm_cell_op->outputs[LstmCellOperator::CONCAT_TEMP] = concat_temp_array_name; // Add the op into model. model->operators.emplace(op_it, std::move(lstm_cell_op)); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc index eca717680af281..8e66323bd769ca 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc @@ -138,10 +138,9 @@ bool SplitLstmCellInputs::Run(Model* model, std::size_t op_index) { CreateOptionalArray(model, &(lstm_cell_op->inputs[kProjectionBiasTensor]), base_name + "proj_bias"); - // Reorder LstmCell's outputs. - lstm_cell_op->outputs.resize(LstmCellOperator::NUM_OUTPUTS); - lstm_cell_op->outputs[kScratchBufferTensor] = - curr_op->outputs[LstmCellOperator::CONCAT_TEMP]; + // Reorder and resize LstmCell's outputs. + lstm_cell_op->outputs.resize( + ExtendedLstmCellOutputs::kExtendedLstmOutputCount); lstm_cell_op->outputs[kOutputStateTensor] = curr_op->outputs[LstmCellOperator::ACTIV_TEMP]; lstm_cell_op->outputs[kCellStateTensor] = diff --git a/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h b/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h index 4a9974ed4e0ebe..1c32a781698ec7 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h +++ b/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h @@ -51,10 +51,10 @@ enum ExtendedLstmCellInputs { }; enum ExtendedLstmCellOutputs { - kScratchBufferTensor = 0, - kOutputStateTensor = 1, - kCellStateTensor = 2, - kOutputTensor = 3 + kOutputStateTensor = 0, + kCellStateTensor = 1, + kOutputTensor = 2, + kExtendedLstmOutputCount = 3 }; // Create optional array used for optional tensor in ExtendedLstmCell inputs. From 88821b0e41f59ae60d02a6880706aef8a1aba024 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 18:41:36 -0700 Subject: [PATCH 0210/1691] [XLA] Redesign: dump HloSnapshot at the point where it used to dump the SessionModule. PiperOrigin-RevId: 194880385 --- tensorflow/compiler/xla/service/executable.cc | 13 +++ tensorflow/compiler/xla/service/executable.h | 13 ++- tensorflow/compiler/xla/service/service.cc | 91 +++++++++++++++++++ 3 files changed, 116 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/service/executable.cc b/tensorflow/compiler/xla/service/executable.cc index 021f09d310b718..8119478ce934da 100644 --- a/tensorflow/compiler/xla/service/executable.cc +++ b/tensorflow/compiler/xla/service/executable.cc @@ -143,6 +143,19 @@ Status Executable::DumpSessionModule() { *session_module_); } +Status Executable::DumpHloSnapshot() { + TF_RET_CHECK(dumping_snapshot()); + TF_RET_CHECK(hlo_snapshot_->has_hlo() && + hlo_snapshot_->hlo().has_hlo_module()); + const string& directory_path = + module_config().debug_options().xla_dump_executions_to(); + const auto& module = hlo_snapshot_->hlo().hlo_module(); + string filename = tensorflow::strings::Printf( + "computation_%lld__%s__execution_%lld", module.id(), + module.entry_computation_name().c_str(), ++execution_count_); + return Executable::DumpToDirectory(directory_path, filename, *hlo_snapshot_); +} + /* static */ Status Executable::DumpToDirectory( const string& directory_path, string filename, const SessionModule& session_module) { diff --git a/tensorflow/compiler/xla/service/executable.h b/tensorflow/compiler/xla/service/executable.h index f7af1ca5749297..99762f45866c48 100644 --- a/tensorflow/compiler/xla/service/executable.h +++ b/tensorflow/compiler/xla/service/executable.h @@ -144,7 +144,7 @@ class Executable { return hlo_module_->config().entry_computation_layout().result_shape(); } - // Dumping helpers. + // TODO(b/74197823): Delete the session module dumping helpers. void set_session_module(std::unique_ptr session_module) { session_module_ = std::move(session_module); } @@ -152,6 +152,14 @@ class Executable { SessionModule* session_module() const { return session_module_.get(); } Status DumpSessionModule(); + // Dumping helpers. + void set_hlo_snapshot(std::unique_ptr hlo_snapshot) { + hlo_snapshot_ = std::move(hlo_snapshot); + } + bool dumping_snapshot() const { return hlo_snapshot_ != nullptr; } + HloSnapshot* hlo_snapshot() const { return hlo_snapshot_.get(); } + Status DumpHloSnapshot(); + // Dump session_module to directory_path/filename. static Status DumpToDirectory(const string& directory_path, string filename, const SessionModule& session_module); @@ -174,6 +182,9 @@ class Executable { // SessionModule this was compiled from. Null if not dumping executions. std::unique_ptr session_module_; + // HloSnapshot this was compiled from. Null if not dumping executions. + std::unique_ptr hlo_snapshot_; + // Execution count, used to generate a unique filename for each dumped // execution. int64 execution_count_ = 0; diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 849488f4f99fe2..175ee96bbc78e6 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -91,6 +91,34 @@ tensorflow::Status RecordResult(const ShapedBuffer& result, return tensorflow::Status::OK(); } +// Records the arguments used to invoke a computation in an HloSnapshot proto. +tensorflow::Status RecordArguments( + const tensorflow::gtl::ArraySlice arguments, + se::StreamExecutor* executor, TransferManager* transfer_manager, + HloSnapshot* module) { + module->clear_arguments(); + for (const ShapedBuffer* argument : arguments) { + TF_ASSIGN_OR_RETURN( + std::unique_ptr literal, + transfer_manager->TransferLiteralFromDevice(executor, *argument)); + *module->add_arguments() = literal->ToProto(); + } + return tensorflow::Status::OK(); +} + +// Records the result of a computation in a HloSnapshot proto. +tensorflow::Status RecordResult(const ShapedBuffer& result, + se::StreamExecutor* executor, + TransferManager* transfer_manager, + HloSnapshot* module) { + module->clear_result(); + TF_ASSIGN_OR_RETURN( + std::unique_ptr literal, + transfer_manager->TransferLiteralFromDevice(executor, result)); + *module->mutable_result() = literal->ToProto(); + return tensorflow::Status::OK(); +} + } // namespace ServiceOptions& ServiceOptions::set_platform(se::Platform* platform) { @@ -409,6 +437,28 @@ StatusOr>> Service::BuildExecutables( DeviceMemoryAllocator* device_allocator) { VLOG(1) << Printf("BuildExecutable on service %p", this); + // Dump computation proto state if flag is set. + std::vector> hlo_snapshots; + for (int64 i = 0; i < module_protos.size(); ++i) { + const string& directory_path = + module_configs[i]->debug_options().xla_dump_computations_to(); + const string& execution_directory_path = + module_configs[i]->debug_options().xla_dump_executions_to(); + if (directory_path.empty() && execution_directory_path.empty()) { + continue; + } + auto hlo_snapshot = MakeUnique(); + *hlo_snapshot->mutable_hlo()->mutable_hlo_module() = *module_protos[i]; + if (!directory_path.empty()) { + string filename = + Printf("computation_%lld__%s", module_protos[i]->id(), + module_protos[i]->entry_computation_name().c_str()); + TF_RETURN_IF_ERROR( + Executable::DumpToDirectory(directory_path, filename, *hlo_snapshot)); + hlo_snapshots.push_back(std::move(hlo_snapshot)); + } + } + VLOG(1) << "Computations:"; for (const HloModuleProto* proto : module_protos) { VLOG(1) << proto->name(); @@ -429,6 +479,12 @@ StatusOr>> Service::BuildExecutables( backend->compiler()->Compile(std::move(modules), std::move(executors), device_allocator)); + for (size_t i = 0; i < module_protos.size(); ++i) { + if (!module_configs[i]->debug_options().xla_dump_executions_to().empty()) { + executables[i]->set_hlo_snapshot(std::move(hlo_snapshots[i])); + } + } + return std::move(executables); } @@ -1132,6 +1188,22 @@ StatusOr> Service::BuildExecutable( "BuildExecutable on service %p with serialized module proto: %s", this, module_proto.name().c_str()); + // Dump computation proto state if flag is set. + auto hlo_snapshot = MakeUnique(); + const string& directory_path = + module_config->debug_options().xla_dump_computations_to(); + const string& execution_directory_path = + module_config->debug_options().xla_dump_executions_to(); + if (!directory_path.empty() || !execution_directory_path.empty()) { + *hlo_snapshot->mutable_hlo()->mutable_hlo_module() = module_proto; + if (!directory_path.empty()) { + string filename = Printf("computation_%lld__%s", module_proto.id(), + module_proto.entry_computation_name().c_str()); + TF_RETURN_IF_ERROR( + Executable::DumpToDirectory(directory_path, filename, *hlo_snapshot)); + } + } + TF_ASSIGN_OR_RETURN(std::unique_ptr module, HloModule::CreateFromProto(module_proto, *module_config)); @@ -1182,12 +1254,31 @@ tensorflow::Status Service::ExecuteGraph(const ExecuteGraphRequest* arg, execute_backend_->default_stream_executor(), /*device_allocator=*/nullptr)); + if (executable->dumping_snapshot()) { + executable->hlo_snapshot()->set_execution_platform( + execute_backend_->platform()->Name()); + TF_RETURN_IF_ERROR(RecordArguments( + replicated_arguments.front(), + execute_backend_->default_stream_executor(), + execute_backend_->transfer_manager(), executable->hlo_snapshot())); + } + TF_ASSIGN_OR_RETURN( *result->mutable_output(), ExecuteAndRegisterResult( executable.get(), replicated_arguments, execute_backend_.get(), "result of " + arg->computation().name(), result->mutable_profile())); + if (executable->dumping_snapshot()) { + TF_ASSIGN_OR_RETURN( + const ShapedBuffer* result_buffer, + allocation_tracker_.ResolveForReplica(result->output(), 0)); + TF_RETURN_IF_ERROR(RecordResult( + *result_buffer, execute_backend_->default_stream_executor(), + execute_backend_->transfer_manager(), executable->hlo_snapshot())); + TF_RETURN_IF_ERROR(executable->DumpHloSnapshot()); + } + VLOG(1) << "successfully completed 'execute-graph' request"; return tensorflow::Status::OK(); } From 79ccb99e9396a7b480615c9ee4b924e851f67163 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 18:51:48 -0700 Subject: [PATCH 0211/1691] Move LinearOperatorKronecker and LinearOperatorBlockDiag to core. PiperOrigin-RevId: 194881237 --- tensorflow/contrib/linalg/BUILD | 44 ------ tensorflow/contrib/linalg/__init__.py | 4 +- tensorflow/python/kernel_tests/linalg/BUILD | 44 ++++++ .../linear_operator_block_diag_test.py | 2 +- .../linalg}/linear_operator_kronecker_test.py | 2 +- tensorflow/python/ops/linalg/linalg.py | 2 + .../ops/linalg}/linear_operator_block_diag.py | 6 + .../ops/linalg}/linear_operator_kronecker.py | 6 + ...ar-operator-block-diag.__metaclass__.pbtxt | 14 ++ ...w.linalg.-linear-operator-block-diag.pbtxt | 134 ++++++++++++++++++ ...ear-operator-kronecker.__metaclass__.pbtxt | 14 ++ ...ow.linalg.-linear-operator-kronecker.pbtxt | 134 ++++++++++++++++++ .../tools/api/golden/tensorflow.linalg.pbtxt | 8 ++ 13 files changed, 366 insertions(+), 48 deletions(-) rename tensorflow/{contrib/linalg/python/kernel_tests => python/kernel_tests/linalg}/linear_operator_block_diag_test.py (98%) rename tensorflow/{contrib/linalg/python/kernel_tests => python/kernel_tests/linalg}/linear_operator_kronecker_test.py (98%) rename tensorflow/{contrib/linalg/python/ops => python/ops/linalg}/linear_operator_block_diag.py (98%) rename tensorflow/{contrib/linalg/python/ops => python/ops/linalg}/linear_operator_kronecker.py (99%) create mode 100644 tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-block-diag.__metaclass__.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-block-diag.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-kronecker.__metaclass__.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-kronecker.pbtxt diff --git a/tensorflow/contrib/linalg/BUILD b/tensorflow/contrib/linalg/BUILD index 2e92ad6eb39d8a..78b7970069fec2 100644 --- a/tensorflow/contrib/linalg/BUILD +++ b/tensorflow/contrib/linalg/BUILD @@ -42,47 +42,3 @@ cuda_py_test( "//tensorflow/python:platform_test", ], ) - -cuda_py_test( - name = "linear_operator_block_diag_test", - size = "medium", - srcs = ["python/kernel_tests/linear_operator_block_diag_test.py"], - additional_deps = [ - ":linalg_py", - "//third_party/py/numpy", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform_test", - ], - shard_count = 5, - tags = [ - "noasan", - "optonly", - ], -) - -cuda_py_test( - name = "linear_operator_kronecker_test", - size = "medium", - srcs = ["python/kernel_tests/linear_operator_kronecker_test.py"], - additional_deps = [ - ":linalg_py", - "//third_party/py/numpy", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform_test", - ], - shard_count = 8, - tags = [ - "noasan", - "optonly", - ], -) diff --git a/tensorflow/contrib/linalg/__init__.py b/tensorflow/contrib/linalg/__init__.py index 554854da84715e..a262a099cf8f84 100644 --- a/tensorflow/contrib/linalg/__init__.py +++ b/tensorflow/contrib/linalg/__init__.py @@ -39,14 +39,14 @@ # pylint: disable=unused-import,wildcard-import,line-too-long,g-importing-member from tensorflow.contrib.linalg.python.ops.linear_operator_addition import * -from tensorflow.contrib.linalg.python.ops.linear_operator_block_diag import * -from tensorflow.contrib.linalg.python.ops.linear_operator_kronecker import * from tensorflow.python.ops.linalg.linear_operator import * +from tensorflow.python.ops.linalg.linear_operator_block_diag import * from tensorflow.python.ops.linalg.linear_operator_circulant import * from tensorflow.python.ops.linalg.linear_operator_composition import * from tensorflow.python.ops.linalg.linear_operator_diag import * from tensorflow.python.ops.linalg.linear_operator_full_matrix import * from tensorflow.python.ops.linalg.linear_operator_identity import * +from tensorflow.python.ops.linalg.linear_operator_kronecker import * from tensorflow.python.ops.linalg.linear_operator_low_rank_update import * from tensorflow.python.ops.linalg.linear_operator_lower_triangular import * diff --git a/tensorflow/python/kernel_tests/linalg/BUILD b/tensorflow/python/kernel_tests/linalg/BUILD index faeccc8fba9cc9..6573cb9a1a4bdd 100644 --- a/tensorflow/python/kernel_tests/linalg/BUILD +++ b/tensorflow/python/kernel_tests/linalg/BUILD @@ -24,6 +24,28 @@ cuda_py_test( ], ) +cuda_py_test( + name = "linear_operator_block_diag_test", + size = "medium", + srcs = ["linear_operator_block_diag_test.py"], + additional_deps = [ + "//tensorflow/python/ops/linalg", + "//third_party/py/numpy", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + ], + shard_count = 6, + tags = [ + "noasan", + "optonly", + ], +) + cuda_py_test( name = "linear_operator_composition_test", size = "medium", @@ -114,6 +136,28 @@ cuda_py_test( shard_count = 5, ) +cuda_py_test( + name = "linear_operator_kronecker_test", + size = "medium", + srcs = ["linear_operator_kronecker_test.py"], + additional_deps = [ + "//tensorflow/python/ops/linalg", + "//third_party/py/numpy", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + ], + shard_count = 8, + tags = [ + "noasan", + "optonly", + ], +) + cuda_py_test( name = "linear_operator_lower_triangular_test", size = "medium", diff --git a/tensorflow/contrib/linalg/python/kernel_tests/linear_operator_block_diag_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_block_diag_test.py similarity index 98% rename from tensorflow/contrib/linalg/python/kernel_tests/linear_operator_block_diag_test.py rename to tensorflow/python/kernel_tests/linalg/linear_operator_block_diag_test.py index e7407ede11409a..2b80f01b734411 100644 --- a/tensorflow/contrib/linalg/python/kernel_tests/linear_operator_block_diag_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_block_diag_test.py @@ -19,11 +19,11 @@ import numpy as np -from tensorflow.contrib.linalg.python.ops import linear_operator_block_diag as block_diag from tensorflow.python.framework import dtypes from tensorflow.python.framework import random_seed from tensorflow.python.ops import array_ops from tensorflow.python.ops.linalg import linalg as linalg_lib +from tensorflow.python.ops.linalg import linear_operator_block_diag as block_diag from tensorflow.python.ops.linalg import linear_operator_test_util from tensorflow.python.ops.linalg import linear_operator_util from tensorflow.python.platform import test diff --git a/tensorflow/contrib/linalg/python/kernel_tests/linear_operator_kronecker_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_kronecker_test.py similarity index 98% rename from tensorflow/contrib/linalg/python/kernel_tests/linear_operator_kronecker_test.py rename to tensorflow/python/kernel_tests/linalg/linear_operator_kronecker_test.py index 6574da22a188c7..cce1ecd45e543e 100644 --- a/tensorflow/contrib/linalg/python/kernel_tests/linear_operator_kronecker_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_kronecker_test.py @@ -19,12 +19,12 @@ import numpy as np -from tensorflow.contrib.linalg.python.ops import linear_operator_kronecker as kronecker from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.ops import array_ops from tensorflow.python.ops.linalg import linalg as linalg_lib +from tensorflow.python.ops.linalg import linear_operator_kronecker as kronecker from tensorflow.python.ops.linalg import linear_operator_test_util from tensorflow.python.ops.linalg import linear_operator_util from tensorflow.python.platform import test diff --git a/tensorflow/python/ops/linalg/linalg.py b/tensorflow/python/ops/linalg/linalg.py index d73c21cdc0bc28..a7ba0bbe9cbc4b 100644 --- a/tensorflow/python/ops/linalg/linalg.py +++ b/tensorflow/python/ops/linalg/linalg.py @@ -22,11 +22,13 @@ # pylint: disable=wildcard-import,unused-import from tensorflow.python.ops.linalg.linalg_impl import * from tensorflow.python.ops.linalg.linear_operator import * +from tensorflow.python.ops.linalg.linear_operator_block_diag import * from tensorflow.python.ops.linalg.linear_operator_circulant import * from tensorflow.python.ops.linalg.linear_operator_composition import * from tensorflow.python.ops.linalg.linear_operator_diag import * from tensorflow.python.ops.linalg.linear_operator_full_matrix import * from tensorflow.python.ops.linalg.linear_operator_identity import * +from tensorflow.python.ops.linalg.linear_operator_kronecker import * from tensorflow.python.ops.linalg.linear_operator_low_rank_update import * from tensorflow.python.ops.linalg.linear_operator_lower_triangular import * # pylint: enable=wildcard-import diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_block_diag.py b/tensorflow/python/ops/linalg/linear_operator_block_diag.py similarity index 98% rename from tensorflow/contrib/linalg/python/ops/linear_operator_block_diag.py rename to tensorflow/python/ops/linalg/linear_operator_block_diag.py index 9d3af66c92b59d..438c3496bdf427 100644 --- a/tensorflow/contrib/linalg/python/ops/linear_operator_block_diag.py +++ b/tensorflow/python/ops/linalg/linear_operator_block_diag.py @@ -27,8 +27,14 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops.linalg import linear_operator from tensorflow.python.ops.linalg import linear_operator_util +from tensorflow.python.util.tf_export import tf_export +__all__ = [ + "LinearOperatorBlockDiag", +] + +@tf_export("linalg.LinearOperatorBlockDiag") class LinearOperatorBlockDiag(linear_operator.LinearOperator): """Combines one or more `LinearOperators` in to a Block Diagonal matrix. diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_kronecker.py b/tensorflow/python/ops/linalg/linear_operator_kronecker.py similarity index 99% rename from tensorflow/contrib/linalg/python/ops/linear_operator_kronecker.py rename to tensorflow/python/ops/linalg/linear_operator_kronecker.py index 79080d194f59b7..da959f9a1c6d0c 100644 --- a/tensorflow/contrib/linalg/python/ops/linear_operator_kronecker.py +++ b/tensorflow/python/ops/linalg/linear_operator_kronecker.py @@ -28,6 +28,11 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops.linalg import linalg_impl as linalg from tensorflow.python.ops.linalg import linear_operator +from tensorflow.python.util.tf_export import tf_export + +__all__ = [ + "LinearOperatorKronecker", +] def _vec(x): @@ -59,6 +64,7 @@ def _rotate_last_dim(x, rotate_right=False): return array_ops.transpose(x, transpose_perm) +@tf_export("linalg.LinearOperatorKronecker") class LinearOperatorKronecker(linear_operator.LinearOperator): """Kronecker product between two `LinearOperators`. diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-block-diag.__metaclass__.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-block-diag.__metaclass__.pbtxt new file mode 100644 index 00000000000000..b6dee631760436 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-block-diag.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorBlockDiag.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-block-diag.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-block-diag.pbtxt new file mode 100644 index 00000000000000..973705dae2fabb --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-block-diag.pbtxt @@ -0,0 +1,134 @@ +path: "tensorflow.linalg.LinearOperatorBlockDiag" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "operators" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'operators\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-kronecker.__metaclass__.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-kronecker.__metaclass__.pbtxt new file mode 100644 index 00000000000000..5c6784dd021041 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-kronecker.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorKronecker.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-kronecker.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-kronecker.pbtxt new file mode 100644 index 00000000000000..c11d39082939ed --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-kronecker.pbtxt @@ -0,0 +1,134 @@ +path: "tensorflow.linalg.LinearOperatorKronecker" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "operators" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'operators\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt index 7a5c533872949a..00b92385433675 100644 --- a/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt @@ -4,6 +4,10 @@ tf_module { name: "LinearOperator" mtype: "" } + member { + name: "LinearOperatorBlockDiag" + mtype: "" + } member { name: "LinearOperatorCirculant" mtype: "" @@ -32,6 +36,10 @@ tf_module { name: "LinearOperatorIdentity" mtype: "" } + member { + name: "LinearOperatorKronecker" + mtype: "" + } member { name: "LinearOperatorLowRankUpdate" mtype: "" From 37191e98117c959fe5599df8b6f0d49b005b5782 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 30 Apr 2018 19:18:40 -0700 Subject: [PATCH 0212/1691] Update ops-related pbtxt files. PiperOrigin-RevId: 194883351 --- .../core/ops/compat/ops_history.v1.pbtxt | 76 +++++++++++++++++++ tensorflow/core/ops/ops.pbtxt | 76 +++++++++++++++++++ 2 files changed, 152 insertions(+) diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt index 71ba5f016a76e2..cb466ef81796dc 100644 --- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt +++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt @@ -24166,6 +24166,82 @@ op { } } } +op { + name: "GroupByReducerDataset" + input_arg { + name: "input_dataset" + type: DT_VARIANT + } + input_arg { + name: "key_func_other_arguments" + type_list_attr: "Tkey_func_other_arguments" + } + input_arg { + name: "init_func_other_arguments" + type_list_attr: "Tinit_func_other_arguments" + } + input_arg { + name: "reduce_func_other_arguments" + type_list_attr: "Treduce_func_other_arguments" + } + input_arg { + name: "finalize_func_other_arguments" + type_list_attr: "Tfinalize_func_other_arguments" + } + output_arg { + name: "handle" + type: DT_VARIANT + } + attr { + name: "key_func" + type: "func" + } + attr { + name: "init_func" + type: "func" + } + attr { + name: "reduce_func" + type: "func" + } + attr { + name: "finalize_func" + type: "func" + } + attr { + name: "Tkey_func_other_arguments" + type: "list(type)" + has_minimum: true + } + attr { + name: "Tinit_func_other_arguments" + type: "list(type)" + has_minimum: true + } + attr { + name: "Treduce_func_other_arguments" + type: "list(type)" + has_minimum: true + } + attr { + name: "Tfinalize_func_other_arguments" + type: "list(type)" + has_minimum: true + } + attr { + name: "output_types" + type: "list(type)" + has_minimum: true + minimum: 1 + } + attr { + name: "output_shapes" + type: "list(shape)" + has_minimum: true + minimum: 1 + } + is_stateful: true +} op { name: "GroupByWindowDataset" input_arg { diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt index 90368fe614eb2d..207dd1c3d7ecb9 100644 --- a/tensorflow/core/ops/ops.pbtxt +++ b/tensorflow/core/ops/ops.pbtxt @@ -11536,6 +11536,82 @@ op { } } } +op { + name: "GroupByReducerDataset" + input_arg { + name: "input_dataset" + type: DT_VARIANT + } + input_arg { + name: "key_func_other_arguments" + type_list_attr: "Tkey_func_other_arguments" + } + input_arg { + name: "init_func_other_arguments" + type_list_attr: "Tinit_func_other_arguments" + } + input_arg { + name: "reduce_func_other_arguments" + type_list_attr: "Treduce_func_other_arguments" + } + input_arg { + name: "finalize_func_other_arguments" + type_list_attr: "Tfinalize_func_other_arguments" + } + output_arg { + name: "handle" + type: DT_VARIANT + } + attr { + name: "key_func" + type: "func" + } + attr { + name: "init_func" + type: "func" + } + attr { + name: "reduce_func" + type: "func" + } + attr { + name: "finalize_func" + type: "func" + } + attr { + name: "Tkey_func_other_arguments" + type: "list(type)" + has_minimum: true + } + attr { + name: "Tinit_func_other_arguments" + type: "list(type)" + has_minimum: true + } + attr { + name: "Treduce_func_other_arguments" + type: "list(type)" + has_minimum: true + } + attr { + name: "Tfinalize_func_other_arguments" + type: "list(type)" + has_minimum: true + } + attr { + name: "output_types" + type: "list(type)" + has_minimum: true + minimum: 1 + } + attr { + name: "output_shapes" + type: "list(shape)" + has_minimum: true + minimum: 1 + } + is_stateful: true +} op { name: "GroupByWindowDataset" input_arg { From 7525a48ebf6f8175cd2845f0fa7ae8ae2a10e1c1 Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Mon, 30 Apr 2018 20:22:51 -0700 Subject: [PATCH 0213/1691] Fixes for review comments --- .../contrib/tensorrt/convert/convert_graph.cc | 4 +- .../contrib/tensorrt/convert/convert_graph.h | 2 - .../contrib/tensorrt/convert/convert_nodes.h | 6 +- .../tensorrt/convert/trt_optimization_pass.cc | 113 ++++---- .../tensorrt/convert/trt_optimization_pass.h | 14 +- .../contrib/tensorrt/kernels/trt_engine_op.cc | 36 +-- .../contrib/tensorrt/kernels/trt_engine_op.h | 1 + .../tensorrt/resources/trt_allocator.h | 7 +- .../contrib/tensorrt/segment/segment.cc | 252 ++++++++++++------ tensorflow/contrib/tensorrt/segment/segment.h | 81 +----- 10 files changed, 265 insertions(+), 251 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 632908f0783e74..c1979afcf8283d 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -349,7 +349,6 @@ tensorflow::Status ConvertGraphDefToTensorRT( // Layout optimization item.graph = graph_def; - tensorflow::grappler::LayoutOptimizer optimizer; tensorflow::grappler::Cluster* cluster; // virtual cluster @@ -417,6 +416,7 @@ tensorflow::Status ConvertAfterShapes( for (auto s : segments) { total_num_nodes_in_segments += s.first.size(); } + // Cluster may not be available std::map name_to_device_map; if (cluster) { for (const auto dm : cluster->GetDeviceSet()->devices()) { @@ -454,6 +454,8 @@ tensorflow::Status ConvertAfterShapes( cuda_device_id = cuda_gpu_id.value(); } tensorflow::GPUOptions gpuoptions; + // we need to us PM here since in python path there is no way to get to + // allocators auto pm = tensorflow::ProcessState::singleton(); // this should be instantiated by now auto dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.h b/tensorflow/contrib/tensorrt/convert/convert_graph.h index 23a83b50943abe..65a67d7e73e32f 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.h +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.h @@ -17,9 +17,7 @@ limitations under the License. #include -#include "tensorflow/contrib/tensorrt/segment/segment.h" #include "tensorflow/core/framework/graph.pb.h" -#include "tensorflow/core/graph/graph.h" #include "tensorflow/core/grappler/clusters/cluster.h" #include "tensorflow/core/grappler/costs/graph_properties.h" #include "tensorflow/core/lib/core/status.h" diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 50b0c37094a892..3f6592cd25ff01 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -22,14 +22,14 @@ limitations under the License. #include #include +#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" #include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/grappler/costs/graph_properties.h" #include "tensorflow/core/lib/core/status.h" - #if GOOGLE_CUDA #if GOOGLE_TENSORRT -#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" + namespace tensorflow { namespace tensorrt { namespace convert { @@ -49,7 +49,7 @@ struct SubGraphParams { std::unordered_map>* output_edges, tensorflow::NodeDef* constructed_trt_node, int engine_precision_mode = FP32MODE, const string& device_name = "", - std::shared_ptr allocator = 0, + std::shared_ptr allocator = nullptr, int cuda_gpu_id = 0) : graph(inp_graph), subgraph_node_ids(subgraph_node_id_numbers), diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc index 743750998c052b..21013fbf9eb120 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc @@ -22,18 +22,19 @@ limitations under the License. #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/public/session_options.h" -using tensorflow::str_util::Uppercase; -using tensorflow::strings::StrAppend; -using tensorflow::strings::StrCat; #if GOOGLE_CUDA #if GOOGLE_TENSORRT namespace tensorflow { namespace tensorrt { namespace convert { // TODO(sami): Remove VLOG messages once the code matures +using tensorflow::str_util::Uppercase; +using tensorflow::strings::StrAppend; +using tensorflow::strings::StrCat; + tensorflow::Status TRTOptimizationPass::Init( const tensorflow::RewriterConfig_CustomGraphOptimizer* config) { - VLOG(1) << "Called INIT for " << m_name_ << " with config = " << config; + VLOG(1) << "Called INIT for " << name_ << " with config = " << config; if (config == nullptr) { maximum_workspace_size_ = 2 << 30; return tensorflow::Status::OK(); @@ -65,10 +66,9 @@ tensorflow::Status TRTOptimizationPass::Init( return tensorflow::Status::OK(); }; -tensorflow::Status TRTOptimizationPass::Optimize( +void TRTOptimizationPass::PrintDebugInfo( tensorflow::grappler::Cluster* cluster, - const tensorflow::grappler::GrapplerItem& item, GraphDef* optimized_graph) { - VLOG(1) << "Called TRTOptimization Pass " << m_name_; + const tensorflow::grappler::GrapplerItem& item) { VLOG(1) << "Cluster = " << cluster; string offset(" "); string offset2 = StrCat(offset, offset); @@ -77,10 +77,10 @@ tensorflow::Status TRTOptimizationPass::Optimize( if (cluster) { VLOG(1) << offset << "type = " << cluster->type(); VLOG(1) << offset << "num warmup steps = " << cluster->NumWarmupSteps(); - const auto devNames = cluster->GetDeviceNames(); - if (devNames.size()) { + const auto dev_names = cluster->GetDeviceNames(); + if (dev_names.size()) { VLOG(1) << offset << " Device names:"; - for (const auto s : devNames) { + for (const auto s : dev_names) { VLOG(1) << offset2 << s; } } @@ -122,38 +122,15 @@ tensorflow::Status TRTOptimizationPass::Optimize( } } VLOG(1) << "item: " << item.id; - int max_dim = -1; if (item.feed.size()) { VLOG(1) << offset << "Feeds :"; for (const auto& f : item.feed) { const auto& shape = f.second.shape(); - if (shape.dims() > 0) { - if (shape.dim_size(0) > max_dim) max_dim = shape.dim_size(0); - } - VLOG(1) << offset2 << f.first << " = shaped " - << f.second.shape().DebugString(); + VLOG(1) << offset2 << f.first << " = shaped " << shape.DebugString(); } } else { VLOG(1) << offset << "No Feeds"; } - if (maximum_batch_size_ < 0) { // automatic batch size from input - if (max_dim > 0) { - maximum_batch_size_ = max_dim; - VLOG(1) << "Setting maximum batch size to " << max_dim; - } else { - maximum_batch_size_ = 128; - LOG(WARNING) << "Maximum batch size is not set" - " and can't be deduced from inputs setting it to" - << maximum_batch_size_ - << ". Suggest configuring it from configuration parameters"; - } - } else { - if (max_dim > maximum_batch_size_) { - LOG(WARNING) << "Configured batch size " << maximum_batch_size_ - << " is less than input batch size " << max_dim - << " adjusting maximum batch size to match input batch size"; - } - } if (item.fetch.size()) { VLOG(1) << offset << "Fetches :"; for (const auto& f : item.fetch) { @@ -182,9 +159,7 @@ tensorflow::Status TRTOptimizationPass::Optimize( } else { VLOG(1) << offset << "No keep ops"; } - VLOG(1) << item.graph.DebugString(); - tensorflow::grappler::GraphProperties static_graph_properties(item); - TF_RETURN_IF_ERROR(static_graph_properties.InferStatically(true)); + VLOG(3) << item.graph.DebugString(); for (const auto dev : cluster->GetDeviceSet()->devices()) { const auto& pname = dev->parsed_name(); VLOG(1) << "Device name= " << dev->name() @@ -192,6 +167,44 @@ tensorflow::Status TRTOptimizationPass::Optimize( << " has_id: " << pname.has_id << " has_job: " << pname.has_job << "has_type: " << pname.has_type << " type =" << pname.type; } +} + +tensorflow::Status TRTOptimizationPass::Optimize( + tensorflow::grappler::Cluster* cluster, + const tensorflow::grappler::GrapplerItem& item, GraphDef* optimized_graph) { + VLOG(1) << "Called TRTOptimization Pass " << name_; + if (VLOG_IS_ON(1)) { + PrintDebugInfo(cluster, item); + } + int max_dim = -1; + if (item.feed.size()) { + for (const auto& f : item.feed) { + const auto& shape = f.second.shape(); + if (shape.dims() > 0) { + if (shape.dim_size(0) > max_dim) max_dim = shape.dim_size(0); + } + } + } + if (maximum_batch_size_ < 0) { // automatic batch size from input + if (max_dim > 0) { + maximum_batch_size_ = max_dim; + VLOG(1) << "Setting maximum batch size to " << max_dim; + } else { + maximum_batch_size_ = 128; + LOG(WARNING) << "Maximum batch size is not set" + " and can't be deduced from inputs setting it to" + << maximum_batch_size_ + << ". Suggest configuring it from configuration parameters"; + } + } else { + if (max_dim > maximum_batch_size_) { + LOG(WARNING) << "Configured batch size " << maximum_batch_size_ + << " is less than input batch size " << max_dim + << " adjusting maximum batch size to match input batch size"; + } + } + tensorflow::grappler::GraphProperties static_graph_properties(item); + TF_RETURN_IF_ERROR(static_graph_properties.InferStatically(true)); auto status = tensorflow::tensorrt::convert::ConvertAfterShapes( item.graph, item.fetch, maximum_batch_size_, maximum_workspace_size_, optimized_graph, precision_mode_, minimum_segment_size_, @@ -205,20 +218,25 @@ void TRTOptimizationPass::Feedback( const tensorflow::grappler::GrapplerItem& item, const GraphDef& optimized_graph, double result) {} -using tensorflow::grappler::CustomGraphOptimizerRegistrar; -namespace { -class samiReg : public CustomGraphOptimizerRegistrar { + +} // namespace convert +} // namespace tensorrt +} // namespace tensorflow + +class VerboseCustomGraphOptimizerRegistrar + : public tensorflow::grappler::CustomGraphOptimizerRegistrar { public: - samiReg(const tensorflow::grappler::CustomGraphOptimizerRegistry::Creator& cr, - const string& name) - : CustomGraphOptimizerRegistrar(cr, name) { + VerboseCustomGraphOptimizerRegistrar( + const tensorflow::grappler::CustomGraphOptimizerRegistry::Creator& cr, + const tensorflow::string& name) + : tensorflow::grappler::CustomGraphOptimizerRegistrar(cr, name) { VLOG(1) << "Constructing a CustomOptimizationPass registration object for " << name; } }; -// static CustomGraphOptimizerRegistrar TRTOptimizationPass_Registrar([]() { -static samiReg TRTOptimizationPass_Registrar( + +static VerboseCustomGraphOptimizerRegistrar TRTOptimizationPass_Registrar( []() { VLOG(1) << "Instantiating CustomOptimizationPass object TensorRTOptimizer"; @@ -226,11 +244,6 @@ static samiReg TRTOptimizationPass_Registrar( "TensorRTOptimizer"); }, ("TensorRTOptimizer")); -} // namespace - -} // namespace convert -} // namespace tensorrt -} // namespace tensorflow #endif #endif diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h index aa9f2895504fd1..c554a5d784000a 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h @@ -16,11 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_TENSORRT_CONVERT_TRT_OPTIMIZATION_PASS_H_ #define TENSORFLOW_CONTRIB_TENSORRT_CONVERT_TRT_OPTIMIZATION_PASS_H_ -#include #include -#include -#include -#include #include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/grappler/optimizers/custom_graph_optimizer.h" @@ -35,14 +31,14 @@ namespace convert { class TRTOptimizationPass : public tensorflow::grappler::CustomGraphOptimizer { public: TRTOptimizationPass(const string& name = "TRTOptimizationPass") - : m_name_(name), + : name_(name), minimum_segment_size_(3), precision_mode_(0), maximum_batch_size_(-1), maximum_workspace_size_(-1) { - VLOG(1) << "Constructing " << m_name_; + VLOG(1) << "Constructing " << name_; }; - string name() const override { return m_name_; }; + string name() const override { return name_; }; tensorflow::Status Init(const tensorflow::RewriterConfig_CustomGraphOptimizer* config = nullptr) override; @@ -52,9 +48,11 @@ class TRTOptimizationPass : public tensorflow::grappler::CustomGraphOptimizer { void Feedback(tensorflow::grappler::Cluster* cluster, const tensorflow::grappler::GrapplerItem& item, const GraphDef& optimized_graph, double result) override; + void PrintDebugInfo(tensorflow::grappler::Cluster* cluster, + const tensorflow::grappler::GrapplerItem& item); private: - string m_name_; + string name_; int minimum_segment_size_; int precision_mode_; int maximum_batch_size_; diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 15a3bbd0d222c0..f10b10edec6b13 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -15,9 +15,6 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/kernels/trt_engine_op.h" #include "tensorflow/contrib/tensorrt/log/trt_logger.h" -#include "tensorflow/core/common_runtime/gpu/gpu_id.h" -#include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h" -#include "tensorflow/core/common_runtime/gpu/process_state.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor.h" #include "tensorflow/core/platform/types.h" @@ -42,38 +39,23 @@ TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("input_nodes", &input_nodes_)); OP_REQUIRES_OK(context, context->GetAttr("output_nodes", &output_nodes_)); - // TODO(samikama) runtime should be taken from a resourcemanager as well. - // Only engine should be in the op and context and runtime should be taken - // from resourcemanager - // TODO(jie): cudaSetDevice make sure trt engine is allocated on the same - // gpu where the input/output is also located. - // int gpu_id = context->device()->tensorflow_gpu_device_info()->gpu_id; - // cudaSetDevice(gpu_id); - // int device; - // cudaGetDevice(&device); - // if (gpu_id != device) LOG(FATAL) << "set device failed!"; +} +void TRTEngineOp::Compute(OpKernelContext* context) { // TODO(samikama) runtime should be taken from a resourcemanager as well. // Only engine should be in the op and context and runtime should be taken // from resourcemanager - // IRuntime* infer = nvinfer1::createInferRuntime(logger); - // trt_engine_ptr_.reset(infer->deserializeCudaEngine( - // serialized_engine.c_str(), serialized_engine.size(), nullptr)); - // trt_execution_context_ptr_.reset(trt_engine_ptr_->createExecutionContext()); - // Runtime is safe to delete after engine creation - // infer->destroy(); -} - -void TRTEngineOp::Compute(OpKernelContext* context) { if (!trt_execution_context_ptr_) { IRuntime* infer = nvinfer1::createInferRuntime(logger); #if NV_TENSORRT_MAJOR > 3 - tensorflow::TfGpuId tf_gpu_id( - context->device()->tensorflow_gpu_device_info()->gpu_id); - tensorflow::GPUOptions gpuoptions; - auto pm = tensorflow::ProcessState::singleton(); - auto dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); + auto device=context->device(); + auto dev_allocator=device->getAllocator(tensorflow::AllocatorAttributes()) + // tensorflow::TfGpuId tf_gpu_id( + // context->device()->tensorflow_gpu_device_info()->gpu_id); + // tensorflow::GPUOptions gpuoptions; + // auto pm = tensorflow::ProcessState::singleton(); + // auto dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); if (!dev_allocator) { LOG(FATAL) << "Can't find device allocator for gpu device" << tf_gpu_id; } diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h index 38ceec4704295e..fec4bd728b6cc2 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h @@ -32,6 +32,7 @@ namespace tensorflow { namespace tensorrt { class Logger; +// TODO(Sami): Remove this file? class TRTEngineOp : public OpKernel { public: explicit TRTEngineOp(OpKernelConstruction* context); diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.h b/tensorflow/contrib/tensorrt/resources/trt_allocator.h index 05dcb7cde6b038..dd4f8c7943cdbe 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.h +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.h @@ -40,17 +40,20 @@ class IGpuAllocator { namespace tensorflow { namespace tensorrt { class TRTCudaAllocator : public nvinfer1::IGpuAllocator { + // Allocator implementation that is using cuda allocator instead of device + // allocator in case we can't get device allocator from TF. public: TRTCudaAllocator() {} - virtual ~TRTCudaAllocator() {}; + virtual ~TRTCudaAllocator(){}; void* allocate(uint64_t size, uint64_t alignment, uint32_t flags) override; void free(void* memory) override; }; class TRTDeviceAllocator : public nvinfer1::IGpuAllocator { + // Allocator implementation wrapping TF device allocators. public: TRTDeviceAllocator(tensorflow::Allocator* allocator); - virtual ~TRTDeviceAllocator() {}; + virtual ~TRTDeviceAllocator(){}; void* allocate(uint64_t size, uint64_t alignment, uint32_t flags) override; void free(void* memory) override; diff --git a/tensorflow/contrib/tensorrt/segment/segment.cc b/tensorflow/contrib/tensorrt/segment/segment.cc index a76d17023663d7..7e094f552d14cf 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.cc +++ b/tensorflow/contrib/tensorrt/segment/segment.cc @@ -32,74 +32,92 @@ namespace tensorflow { namespace tensorrt { namespace segment { using ::tensorflow::strings::StrAppend; -namespace { - -bool CheckCycles(const SimpleGraph* g, const SimpleNode* src, - const std::vector& start) { - // copied from TF ReverseDFS - struct Work { - SimpleNode* node; - bool leave; // Are we entering or leaving n? - }; - - std::vector stack(start.size()); - for (int i = 0; i < start.size(); ++i) { - stack[i] = Work{start[i], false}; - } - - std::vector visited(g->num_node_ids(), false); - while (!stack.empty()) { - Work w = stack.back(); - stack.pop_back(); - - auto n = w.node; - if (w.leave) { - if (n == src) { - return true; - } - continue; - } - - if (visited[n->id()]) continue; - visited[n->id()] = true; - // Arrange to call leave(n) when all done with descendants. - stack.push_back(Work{n, true}); - - auto nodes = n->in_nodes(); - for (const auto node : nodes) { - if (!visited[node->id()]) { - stack.push_back(Work{node, false}); - } +// A simple graph representation to mirror tensorflow::Graph. This structure +// helps saving memory since segmenter modifies the graph in place, preventing +// the need to create a copy of the graph. It is composed of edges and nodes. +// Nodes keep pointers to original TF nodes. +class SimpleNode; +class SimpleGraph; +class SimpleEdge { + public: + SimpleEdge(int id, SimpleNode* src, int src_port, SimpleNode* dst, + int dst_port, bool is_control = false) + : id_(id), + src_(src), + src_port_(src_port), + dst_(dst), + dst_port_(dst_port), + control_(is_control){}; + SimpleNode* src() const { return src_; } + SimpleNode* dst() const { return dst_; } + int src_output() const { return src_port_; } + int dst_input() const { return dst_port_; } + int id() const { return id_; } + bool IsControlEdge() const { return control_; } + ~SimpleEdge() {} + + private: + int id_; + SimpleNode* src_; + int src_port_; + SimpleNode* dst_; + int dst_port_; + bool control_; +}; +class SimpleNode { + public: + SimpleNode(const tensorflow::Node* node, const int id); + const std::vector& in_edges() const { return in_edges_; }; + const std::vector& out_edges() const { return out_edges_; }; + std::vector in_nodes() const { + std::vector res; + res.reserve(in_edges_.size()); + for (const auto e : in_edges_) { + if (e) res.push_back(e->src()); } + return res; } - return false; -} - -bool CanContractEdge(const SimpleEdge* edge, const SimpleGraph* graph) { - const auto src = edge->src(); - const auto dst = edge->dst(); - - // Can't contract edge if doing so would cause a cycle in the - // graph. So, if there is a directed path from 'src' to 'dst', other - // than 'edge' (or any other direct edge from 'src' to 'dst'), then - // combining 'src' and 'dst' will cause a cycle along that path. - // - // In practice, to avoid modifying the graph and to take advantage - // of existing graph functions, we perform an equivalent. - // 1. Get all nodes incoming to 'dst', excluding 'src' - // 2. Reverse DFS from those nodes - // 3. If reverse DFS reaches 'src' then we have a cycle - std::vector dfs_start_nodes; - for (SimpleNode* node : dst->in_nodes()) { - if (node != src) { - dfs_start_nodes.push_back(node); - } + const string& name() const { return node_->name(); } + const tensorflow::Node* tf_node() const { return node_; } + int id() const { return id_; } + + private: + const tensorflow::Node* node_; + std::vector in_edges_; + std::vector out_edges_; + int id_; + + friend class SimpleGraph; +}; + +class SimpleGraph { + public: + SimpleGraph(const tensorflow::Graph* g); + void AddControlEdge(SimpleNode* src, SimpleNode* dst); + void AddEdge(SimpleNode* src, int out_port, SimpleNode* dst, int in_port); + void RemoveEdge(const SimpleEdge*); + SimpleNode* FindNodeId(int node_id) { + if (node_id < 0 || node_id > (int)nodes_.size()) return nullptr; + return nodes_[node_id]; + } + ~SimpleGraph(); + int num_node_ids() const { return nodes_.size(); } + const SimpleNode* source_node() const { + return nodes_[tensorflow::Graph::kSourceId]; + } + const SimpleNode* sink_node() const { + return nodes_[tensorflow::Graph::kSinkId]; } - bool is_cycle = CheckCycles(graph, src, dfs_start_nodes); - return !is_cycle; -} -} // namespace + private: + const tensorflow::Graph* g_; + std::vector nodes_; + std::vector edges_; + // edge_ids_ and node_ids_ contain freed indices. + std::set free_edge_ids_; + std::set free_node_ids_; +}; + SimpleNode::SimpleNode(const tensorflow::Node* node, const int id) : node_(node), id_(id) { if (node_) { @@ -120,7 +138,7 @@ SimpleGraph::SimpleGraph(const tensorflow::Graph* g) : g_(g) { if (n) { nodes_[i] = new SimpleNode(n, i); } else { - node_ids_.insert(i); + free_node_ids_.insert(i); } } for (int i = 0; i < n_edges; i++) { @@ -137,7 +155,7 @@ SimpleGraph::SimpleGraph(const tensorflow::Graph* g) : g_(g) { src->out_edges_.push_back(edge); dst->in_edges_.push_back(edge); } else { - edge_ids_.insert(i); + free_edge_ids_.insert(i); } } } @@ -145,12 +163,12 @@ SimpleGraph::SimpleGraph(const tensorflow::Graph* g) : g_(g) { void SimpleGraph::AddEdge(SimpleNode* src, int out_port, SimpleNode* dst, int in_port) { int i = edges_.size(); - if (edge_ids_.size()) { - auto it = edge_ids_.begin(); + if (free_edge_ids_.size()) { + auto it = free_edge_ids_.begin(); i = *it; - edge_ids_.erase(it); + free_edge_ids_.erase(it); } else { - edges_.push_back(0); + edges_.push_back(nullptr); } bool is_control = (out_port == tensorflow::Graph::kControlSlot); is_control |= (in_port == tensorflow::Graph::kControlSlot); @@ -187,7 +205,77 @@ SimpleGraph::~SimpleGraph() { for (auto x : edges_) delete x; } -void ContractEdge(SimpleEdge* edge, SimpleGraph* graph, +namespace { + +bool CheckCycles(const std::unique_ptr& g, const SimpleNode* src, + const std::vector& start) { + // copied from TF ReverseDFS. + struct Work { + SimpleNode* node; + bool leave; // Are we entering or leaving n? + }; + + std::vector stack(start.size()); + for (int i = 0; i < start.size(); ++i) { + stack[i] = Work{start[i], false}; + } + + std::vector visited(g->num_node_ids(), false); + while (!stack.empty()) { + Work w = stack.back(); + stack.pop_back(); + + auto n = w.node; + if (w.leave) { + if (n == src) { + return true; + } + continue; + } + + if (visited[n->id()]) continue; + visited[n->id()] = true; + // Arrange to call leave(n) when all done with descendants. + stack.push_back(Work{n, true}); + + auto nodes = n->in_nodes(); + for (const auto node : nodes) { + if (!visited[node->id()]) { + stack.push_back(Work{node, false}); + } + } + } + return false; +} + +bool CanContractEdge(const SimpleEdge* edge, + const std::unique_ptr& graph) { + const auto src = edge->src(); + const auto dst = edge->dst(); + + // Can't contract edge if doing so would cause a cycle in the + // graph. So, if there is a directed path from 'src' to 'dst', other + // than 'edge' (or any other direct edge from 'src' to 'dst'), then + // combining 'src' and 'dst' will cause a cycle along that path. + // + // In practice, to avoid modifying the graph and to take advantage + // of existing graph functions, we perform an equivalent. + // 1. Get all nodes incoming to 'dst', excluding 'src' + // 2. Reverse DFS from those nodes + // 3. If reverse DFS reaches 'src' then we have a cycle + std::vector dfs_start_nodes; + for (SimpleNode* node : dst->in_nodes()) { + if (node != src) { + dfs_start_nodes.push_back(node); + } + } + + bool is_cycle = CheckCycles(graph, src, dfs_start_nodes); + return !is_cycle; +} +} // namespace + +void ContractEdge(SimpleEdge* edge, std::unique_ptr& graph, std::vector* remove_edges) { // Transfer all inputs and outputs of 'dst' to 'src' except edges // connecting the two. @@ -265,7 +353,7 @@ tensorflow::Status SegmentGraph( const std::function& candidate_fn, const SegmentOptions& options, SegmentNodesVector* segments) { // tensorflow::DumpGraph("Pre-Segment", &graph); - SimpleGraph* graph = new SimpleGraph(tf_graph); + auto graph = std::unique_ptr(new SimpleGraph(tf_graph)); // Use a union-find to collect the nodes that belong to the same // segment. A node value of nullptr indicates that the node is not a candidate // for TRT. @@ -370,6 +458,11 @@ tensorflow::Status SegmentGraph( if ((u.Value() != nullptr) && (u.ParentValue() != nullptr)) { sg_map[u.ParentValue()->name()].insert(u.Value()->name()); auto tf_node = u.Value()->tf_node(); + // has_assigned_device_name() is expected to return true + // when called from optimization pass. However, since graph + // is converted back and forth between graph and graphdef, + // assigned devices demoted to requested devices. If the graph + // is passed directly to this module, assigned devices will be set. if (tf_node->has_assigned_device_name()) { device_maps[u.ParentValue()->name()].insert( tf_node->assigned_device_name()); @@ -421,15 +514,16 @@ tensorflow::Status SegmentGraph( std::make_pair(segment_node_names, *(dev_itr->second.begin()))); } } - for (const auto& d : device_maps) { - string s("Segment "); - StrAppend(&s, ": '", d.first, "' "); - for (const auto& dd : d.second) { - StrAppend(&s, dd, ", "); + if (VLOG_IS_ON(1)) { + for (const auto& d : device_maps) { + string s("Segment "); + StrAppend(&s, ": '", d.first, "' "); + for (const auto& dd : d.second) { + StrAppend(&s, dd, ", "); + } + VLOG(1) << "Devices " << s; } - VLOG(1) << "Devices " << s; } - delete graph; return tensorflow::Status::OK(); } diff --git a/tensorflow/contrib/tensorrt/segment/segment.h b/tensorflow/contrib/tensorrt/segment/segment.h index 44a84cbd38c8b8..c5aca4bf048328 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.h +++ b/tensorflow/contrib/tensorrt/segment/segment.h @@ -29,87 +29,10 @@ namespace tensorflow { namespace tensorrt { namespace segment { +// vector of segments, each entry contains a device name and a set of nodes in +// segment using SegmentNodesVector = std::vector, string>>; -class SimpleNode; -class SimpleGraph; -class SimpleEdge { - public: - SimpleEdge(int id, SimpleNode* src, int src_port, SimpleNode* dst, - int dst_port, bool is_control = false) - : id_(id), - src_(src), - src_port_(src_port), - dst_(dst), - dst_port_(dst_port), - control_(is_control){}; - SimpleNode* src() const { return src_; } - SimpleNode* dst() const { return dst_; } - int src_output() const { return src_port_; } - int dst_input() const { return dst_port_; } - int id() const { return id_; } - bool IsControlEdge() const { return control_; } - ~SimpleEdge() {} - private: - int id_; - SimpleNode* src_; - int src_port_; - SimpleNode* dst_; - int dst_port_; - bool control_; -}; -class SimpleNode { - public: - SimpleNode(const tensorflow::Node* node, const int id); - const std::vector& in_edges() const { return in_edges_; }; - const std::vector& out_edges() const { return out_edges_; }; - std::vector in_nodes() const { - std::vector res; - res.reserve(in_edges_.size()); - for (const auto e : in_edges_) { - if (e) res.push_back(e->src()); - } - return res; - } - const string& name() const { return node_->name(); } - const tensorflow::Node* tf_node() const { return node_; } - int id() const { return id_; } - - private: - const tensorflow::Node* node_; - std::vector in_edges_; - std::vector out_edges_; - int id_; - - friend class SimpleGraph; -}; - -class SimpleGraph { - public: - SimpleGraph(const tensorflow::Graph* g); - void AddControlEdge(SimpleNode* src, SimpleNode* dst); - void AddEdge(SimpleNode* src, int out_port, SimpleNode* dst, int in_port); - void RemoveEdge(const SimpleEdge*); - SimpleNode* FindNodeId(int node_id) { - if (node_id < 0 || node_id > (int)nodes_.size()) return nullptr; - return nodes_[node_id]; - } - ~SimpleGraph(); - int num_node_ids() const { return nodes_.size(); } - const SimpleNode* source_node() const { - return nodes_[tensorflow::Graph::kSourceId]; - } - const SimpleNode* sink_node() const { - return nodes_[tensorflow::Graph::kSinkId]; - } - - private: - const tensorflow::Graph* g_; - std::vector nodes_; - std::vector edges_; - std::set edge_ids_; - std::set node_ids_; -}; struct SegmentOptions { // Segment must contain at least this many nodes. int minimum_segment_size = 2; From 9e197152c04ebb81f055067534bd93322d182f0e Mon Sep 17 00:00:00 2001 From: Ben Date: Mon, 30 Apr 2018 23:30:22 -0400 Subject: [PATCH 0214/1691] Fix MSVC openmp flag (#18973) * Fix MSVC openmp flag --- tensorflow/contrib/cmake/CMakeLists.txt | 27 +++++++++++++++---------- 1 file changed, 16 insertions(+), 11 deletions(-) diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt index d81f6a0ae8a445..0708d6b7b9f0ba 100644 --- a/tensorflow/contrib/cmake/CMakeLists.txt +++ b/tensorflow/contrib/cmake/CMakeLists.txt @@ -172,19 +172,20 @@ if (tensorflow_OPTIMIZE_FOR_NATIVE_ARCH) endif() endif() +include(CheckCXXCompilerFlag) + +# OpenMP Support +CHECK_CXX_COMPILER_FLAG("-fopenmp" GCC_OPENMP_SUPPORT) +if (GCC_OPENMP_SUPPORT) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp") +endif() +CHECK_CXX_COMPILER_FLAG("/openmp" MSVC_OPENMP_SUPPORT) +if (MSVC_OPENMP_SUPPORT) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /openmp") +endif() + # MSVC SIMD instructions if (tensorflow_WIN_CPU_SIMD_OPTIONS) - include(CheckCXXCompilerFlag) - if (tensorflow_ENABLE_MKL_SUPPORT) - add_definitions(-DINTEL_MKL -DEIGEN_USE_VML) - if (NOT tensorflow_ENABLE_MKLDNN_SUPPORT) - add_definitions(-DINTEL_MKL_ML) - endif() - endif() - CHECK_CXX_COMPILER_FLAG("-fopenmp" COMPILER_OPT_OPENMP_SUPPORT) - if (COMPILER_OPT_OPENMP_SUPPORT) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp") - endif() if (WIN32) CHECK_CXX_COMPILER_FLAG(${tensorflow_WIN_CPU_SIMD_OPTIONS} COMPILER_OPT_WIN_CPU_SIMD_SUPPORTED) if(COMPILER_OPT_WIN_CPU_SIMD_SUPPORTED) @@ -323,7 +324,9 @@ if(HAIKU) list(APPEND tensorflow_EXTERNAL_LIBRARIES network) endif() +# MKL Support if (tensorflow_ENABLE_MKL_SUPPORT) + add_definitions(-DINTEL_MKL -DEIGEN_USE_VML) if (WIN32) find_path(MKL_HOME_PLATFORM mkl PATHS ${MKL_HOME} ${MKL_HOME}/../ ${MKL_HOME}/../../ @@ -359,6 +362,8 @@ if (tensorflow_ENABLE_MKL_SUPPORT) list(APPEND tensorflow_EXTERNAL_LIBRARIES ${mkldnn_STATIC_LIBRARIES}) list(APPEND tensorflow_EXTERNAL_DEPENDENCIES mkldnn) include_directories(${mkldnn_INCLUDE_DIRS}) + else (tensorflow_ENABLE_MKLDNN_SUPPORT) + add_definitions(-DINTEL_MKL_ML) endif() endif (tensorflow_ENABLE_MKL_SUPPORT) From d0f5bc17560fc97bcc7de9164aa3b237a8d5221d Mon Sep 17 00:00:00 2001 From: Maciej Date: Mon, 30 Apr 2018 22:30:58 -0500 Subject: [PATCH 0215/1691] Remove whitespace characters from tf_cuda_compute_capabilities user string (#18986) Remove all whitespace characters from the user specified tf_cuda_compute_capabilities string as this can results in errors during the split operation, and is easy for users to do as it is natural to insert a space after a comma --- configure.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/configure.py b/configure.py index b745e374a2baaf..fe15bfc1a43bac 100644 --- a/configure.py +++ b/configure.py @@ -1226,6 +1226,9 @@ def set_tf_cuda_compute_capabilities(environ_cp): ask_cuda_compute_capabilities, default_cuda_compute_capabilities) # Check whether all capabilities from the input is valid all_valid = True + # Remove all whitespace characters before splitting the string + # that users may insert by accident, as this will result in error + tf_cuda_compute_capabilities = ''.join(tf_cuda_compute_capabilities.split()) for compute_capability in tf_cuda_compute_capabilities.split(','): m = re.match('[0-9]+.[0-9]+', compute_capability) if not m: From 95b36432d2c04a8355d2de2aeb4817fb3042d639 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Tue, 1 May 2018 00:42:56 -0700 Subject: [PATCH 0216/1691] [XLA:CPU] Open source some tests. PiperOrigin-RevId: 194903752 --- .../compiler/xla/service/cpu/tests/BUILD | 126 +++++++ .../service/cpu/tests/cpu_bytesizeof_test.cc | 37 ++ .../xla/service/cpu/tests/cpu_codegen_test.h | 30 ++ .../cpu/tests/cpu_eigen_dot_operation_test.cc | 113 ++++++ .../cpu/tests/cpu_external_constants_test.cc | 73 ++++ .../xla/service/cpu/tests/cpu_fusion_test.cc | 330 ++++++++++++++++++ .../service/cpu/tests/cpu_intrinsic_test.cc | 151 ++++++++ .../xla/service/cpu/tests/cpu_noalias_test.cc | 136 ++++++++ tensorflow/compiler/xla/tests/filecheck.cc | 7 +- 9 files changed, 1002 insertions(+), 1 deletion(-) create mode 100644 tensorflow/compiler/xla/service/cpu/tests/BUILD create mode 100644 tensorflow/compiler/xla/service/cpu/tests/cpu_bytesizeof_test.cc create mode 100644 tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h create mode 100644 tensorflow/compiler/xla/service/cpu/tests/cpu_eigen_dot_operation_test.cc create mode 100644 tensorflow/compiler/xla/service/cpu/tests/cpu_external_constants_test.cc create mode 100644 tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc create mode 100644 tensorflow/compiler/xla/service/cpu/tests/cpu_intrinsic_test.cc create mode 100644 tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc diff --git a/tensorflow/compiler/xla/service/cpu/tests/BUILD b/tensorflow/compiler/xla/service/cpu/tests/BUILD new file mode 100644 index 00000000000000..9425b948c166b8 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/tests/BUILD @@ -0,0 +1,126 @@ +# Description: +# Tests for LLVM-based CPU backend for XLA. + +licenses(["notice"]) # Apache 2.0 + +package( + default_visibility = [":friends"], +) + +package_group( + name = "friends", + includes = [ + "//tensorflow/compiler/xla:friends", + ], +) + +load("//tensorflow:tensorflow.bzl", "tf_cc_test") + +# Filegroup used to collect source files for dependency checking. +filegroup( + name = "c_srcs", + data = glob([ + "**/*.cc", + "**/*.h", + ]), +) + +cc_library( + name = "cpu_codegen_test", + testonly = True, + hdrs = ["cpu_codegen_test.h"], + deps = [ + "//tensorflow/compiler/xla/service:cpu_plugin", + "//tensorflow/compiler/xla/tests:llvm_irgen_test_base", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "cpu_fusion_test", + srcs = ["cpu_fusion_test.cc"], + deps = [ + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:cpu_plugin", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service/cpu:cpu_instruction_fusion", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:literal_test_util", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "cpu_bytesizeof_test", + srcs = ["cpu_bytesizeof_test.cc"], + deps = [ + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "cpu_external_constants_test", + srcs = ["cpu_external_constants_test.cc"], + deps = [ + "//tensorflow/compiler/xla:array2d", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service/cpu/tests:cpu_codegen_test", + "//tensorflow/compiler/xla/tests:filecheck", + "//tensorflow/core:test", + ], +) + +tf_cc_test( + name = "cpu_noalias_test", + srcs = ["cpu_noalias_test.cc"], + deps = [ + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:buffer_assignment", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service/cpu/tests:cpu_codegen_test", + "//tensorflow/compiler/xla/service/llvm_ir:alias_analysis", + "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", + "//tensorflow/compiler/xla/tests:filecheck", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "@llvm//:core", + ], +) + +tf_cc_test( + name = "cpu_intrinsic_test", + srcs = ["cpu_intrinsic_test.cc"], + deps = [ + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service/cpu:cpu_compiler", + "//tensorflow/compiler/xla/service/cpu/tests:cpu_codegen_test", + "//tensorflow/core:lib", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "cpu_eigen_dot_operation_test", + srcs = ["cpu_eigen_dot_operation_test.cc"], + deps = [ + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service/cpu:cpu_compiler", + "//tensorflow/compiler/xla/service/cpu/tests:cpu_codegen_test", + "//tensorflow/core:lib", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_bytesizeof_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_bytesizeof_test.cc new file mode 100644 index 00000000000000..d5bbe7677ace67 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_bytesizeof_test.cc @@ -0,0 +1,37 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/core/platform/test.h" + +class CpuByteSizeOfTest : public ::testing::Test {}; + +TEST_F(CpuByteSizeOfTest, ARM32) { + llvm::DataLayout data_layout( + "e-m:e-p:32:32-i64:64-v128:64:128-a:0:32-n32-S64"); + auto tuple_shape = + xla::ShapeUtil::MakeTupleShape({xla::ShapeUtil::MakeShape(xla::F32, {})}); + EXPECT_EQ(xla::llvm_ir::ByteSizeOf(tuple_shape, data_layout), + data_layout.getPointerSize(0 /* default address space */)); +} + +TEST_F(CpuByteSizeOfTest, ARM64) { + llvm::DataLayout data_layout("e-m:e-i64:64-i128:128-n32:64-S128"); + auto tuple_shape = + xla::ShapeUtil::MakeTupleShape({xla::ShapeUtil::MakeShape(xla::F32, {})}); + EXPECT_EQ(xla::llvm_ir::ByteSizeOf(tuple_shape, data_layout), + data_layout.getPointerSize(0 /* default address space */)); +} diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h b/tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h new file mode 100644 index 00000000000000..7c8d07a10baf55 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h @@ -0,0 +1,30 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_TESTS_CPU_CODEGEN_TEST_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_TESTS_CPU_CODEGEN_TEST_H_ + +#include "tensorflow/compiler/xla/tests/llvm_irgen_test_base.h" + +namespace xla { +namespace cpu { + +// Tests that verify IR emitted by the CPU backend is as expected. +class CpuCodegenTest : public LLVMIRGenTestBase {}; + +} // namespace cpu +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_TESTS_CPU_CODEGEN_TEST_H_ diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_eigen_dot_operation_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_eigen_dot_operation_test.cc new file mode 100644 index 00000000000000..6fcce42eaa4599 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_eigen_dot_operation_test.cc @@ -0,0 +1,113 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// Tests that we call into Eigen for dot operations as needed. + +#include +#include +#include + +#include "tensorflow/compiler/xla/service/cpu/cpu_compiler.h" +#include "tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace cpu { +namespace { + +struct DotTestSpec { + PrimitiveType primitive_type; + string filecheck_lines; +}; + +string DotTestSpecToString(const ::testing::TestParamInfo& info) { + return PrimitiveType_Name(info.param.primitive_type); +} + +class CpuEigenDotOperationTest + : public CpuCodegenTest, + public ::testing::WithParamInterface { + protected: + void CompileAndCheck(std::unique_ptr entry_computation, + const string& filecheck_lines) { + CpuAotCompilationOptions options{ + /*triple=*/"x86_64", /*cpu_name=*/"", /*features=*/"", + /*entry_point_name=*/"entry", + /*relocation_model=*/CpuAotCompilationOptions::RelocationModel::Static}; + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(std::move(entry_computation)); + + CompileAheadOfTimeAndVerifyIr(std::move(hlo_module), options, + filecheck_lines, + /*match_optimized_ir=*/true); + } +}; + +TEST_P(CpuEigenDotOperationTest, SimpleDotOp) { + HloComputation::Builder builder(TestName()); + DotTestSpec spec = GetParam(); + + auto param_shape = ShapeUtil::MakeShape(spec.primitive_type, {128, 128}); + + HloInstruction* lhs = builder.AddInstruction( + HloInstruction::CreateParameter(0, param_shape, "input")); + HloInstruction* rhs = builder.AddInstruction( + HloInstruction::CreateParameter(1, param_shape, "input")); + + builder.AddInstruction( + HloInstruction::CreateCanonicalDot(param_shape, lhs, rhs)); + CompileAndCheck(builder.Build(), spec.filecheck_lines); +} + +TEST_P(CpuEigenDotOperationTest, DotTransposeOp) { + HloComputation::Builder builder(TestName()); + DotTestSpec spec = GetParam(); + + auto param_shape = ShapeUtil::MakeShape(spec.primitive_type, {128, 128}); + + HloInstruction* lhs = builder.AddInstruction( + HloInstruction::CreateParameter(0, param_shape, "input")); + HloInstruction* rhs = builder.AddInstruction( + HloInstruction::CreateParameter(1, param_shape, "input")); + HloInstruction* lhs_transposed = builder.AddInstruction( + HloInstruction::CreateTranspose(param_shape, lhs, {1, 0})); + + builder.AddInstruction( + HloInstruction::CreateCanonicalDot(param_shape, lhs_transposed, rhs)); + CompileAndCheck(builder.Build(), spec.filecheck_lines); +} + +std::vector GetDotTestCases() { + std::vector result; + result.push_back( + {F16, R"(CHECK: call void @__xla_cpu_runtime_EigenMatMulF16)"}); + result.push_back( + {F32, R"(CHECK: call void @__xla_cpu_runtime_EigenMatMulF32)"}); + result.push_back( + {F64, R"(CHECK: call void @__xla_cpu_runtime_EigenMatMulF64)"}); + return result; +} + +INSTANTIATE_TEST_CASE_P(CpuEigenDotOperationTestInstantiation, + CpuEigenDotOperationTest, + ::testing::ValuesIn(GetDotTestCases()), + DotTestSpecToString); + +} // namespace +} // namespace cpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_external_constants_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_external_constants_test.cc new file mode 100644 index 00000000000000..ed8f375bd6186e --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_external_constants_test.cc @@ -0,0 +1,73 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +#include "tensorflow/compiler/xla/array2d.h" +#include "tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/tests/filecheck.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace cpu { +namespace { +class CpuExternalConstantsTest : public CpuCodegenTest { + public: + void TestWithArray(int64 rows, int64 cols, const char* filecheck_pattern) { + HloComputation::Builder builder(TestName()); + + Array2D backing_array(rows, cols); + backing_array.FillUnique(); + + auto shape = ShapeUtil::MakeShape(F32, {rows, cols}); + + HloInstruction* constant = + builder.AddInstruction(HloInstruction::CreateConstant( + Literal::CreateR2FromArray2D(backing_array))); + HloInstruction* param = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "x")); + builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, constant)); + + std::unique_ptr module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + CompileAndVerifyIr(std::move(module), filecheck_pattern, + /*match_optimized_ir=*/false); + } +}; + +TEST_F(CpuExternalConstantsTest, Basic) { + TestWithArray(/*rows=*/1024, /*cols=*/1024, R"( +CHECK: @constant_global_0 = external constant [1024 x [1024 x float]], align 16 +)"); +} + +TEST_F(CpuExternalConstantsTest, BasicNegative) { + // The constant array in this test case is small enough that there is no need + // to externalize it. + TestWithArray(/*rows=*/4, /*cols=*/4, R"( +CHECK-NOT: @constant_global_0 = external constant [4 x [4 x float]], align 8 +CHECK: @0 = private constant [4 x [4 x float]] {{.*}}, align 8 +)"); +} +} // namespace +} // namespace cpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc new file mode 100644 index 00000000000000..23e7a3de4d8188 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc @@ -0,0 +1,330 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include +#include + +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tests/literal_test_util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace cpu { +namespace { + +class CpuFusionTest : public HloTestBase { + protected: + CpuFusionTest() {} + + ErrorSpec error_spec_{0.0001, 1e-5}; +}; + +TEST_F(CpuFusionTest, FuseTwoElementwiseOps) { + auto builder = HloComputation::Builder(TestName()); + auto input_literal1 = Literal::CreateR1({1.0, 2.0, 3.0}); + auto input_literal2 = Literal::CreateR1({-2.0, -42.0, 2.0}); + Shape vshape = input_literal1->shape(); + + auto input1 = builder.AddInstruction( + HloInstruction::CreateConstant(std::move(input_literal1))); + auto input2 = builder.AddInstruction( + HloInstruction::CreateConstant(std::move(input_literal2))); + + auto add1 = builder.AddInstruction( + HloInstruction::CreateBinary(vshape, HloOpcode::kAdd, input1, input2)); + builder.AddInstruction( + HloInstruction::CreateUnary(vshape, HloOpcode::kNegate, add1)); + + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + CpuInstructionFusion fusion; + EXPECT_TRUE(fusion.Run(module.get()).ValueOrDie()); + + // The computation root instruction was fused. Verify the fusion instruction + // is now the root. + auto computation = module->entry_computation(); + auto fusion_instruction = computation->root_instruction(); + EXPECT_EQ(HloOpcode::kFusion, fusion_instruction->opcode()); + EXPECT_EQ(HloOpcode::kNegate, + fusion_instruction->fused_expression_root()->opcode()); + // There should be four fused instructions: 2 parameters, the add, and the + // negate. + EXPECT_EQ(4, fusion_instruction->fused_instruction_count()); + + // Compile and execute the computation. + auto result = ExecuteAndTransfer(std::move(module), {}); + + // Check the output correctness. + LiteralTestUtil::ExpectR1Near({1.0, 40.0, -5.0}, *result, error_spec_); +} + +TEST_F(CpuFusionTest, FuseElementwiseOpChain) { + auto builder = HloComputation::Builder(TestName()); + auto input_literal = Literal::CreateR1({-1.5, -2.5, -3.0}); + Shape vshape = input_literal->shape(); + + auto input = builder.AddInstruction( + HloInstruction::CreateConstant(std::move(input_literal))); + auto negate = builder.AddInstruction( + HloInstruction::CreateUnary(vshape, HloOpcode::kNegate, input)); + auto ceil = builder.AddInstruction( + HloInstruction::CreateUnary(vshape, HloOpcode::kCeil, negate)); + auto exp = builder.AddInstruction( + HloInstruction::CreateUnary(vshape, HloOpcode::kExp, ceil)); + auto floor = builder.AddInstruction( + HloInstruction::CreateUnary(vshape, HloOpcode::kFloor, exp)); + auto two = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + builder.AddInstruction( + HloInstruction::CreateBinary(vshape, HloOpcode::kMultiply, two, floor)); + + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + CpuInstructionFusion fusion; + EXPECT_TRUE(fusion.Run(module.get()).ValueOrDie()); + + // The computation root instruction was fused. Verify the fusion instruction + // is now the root. + auto computation = module->entry_computation(); + auto fusion_instruction = computation->root_instruction(); + EXPECT_EQ(HloOpcode::kFusion, fusion_instruction->opcode()); + EXPECT_EQ(HloOpcode::kMultiply, + fusion_instruction->fused_expression_root()->opcode()); + // There should be 7 fused instructions: 2 parameters and the fused + // operations. + EXPECT_EQ(7, fusion_instruction->fused_instruction_count()); + + // Compile and execute the computation. + auto result = ExecuteAndTransfer(std::move(module), {}); + + // Check the output correctness. + LiteralTestUtil::ExpectR1Near({14.0, 40.0, 40.0}, *result, + error_spec_); +} + +TEST_F(CpuFusionTest, ElementwiseOpChainWithNonfusableInstruction) { + // Test a chain of fusable ops with a non-fusable op (a reduce) thrown in the + // middle. + auto module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + auto input_literal = Literal::CreateR1({-1.5, -2.5, -3.0}); + Shape vshape = input_literal->shape(); + + auto input = builder.AddInstruction( + HloInstruction::CreateConstant(std::move(input_literal))); + auto negate = builder.AddInstruction( + HloInstruction::CreateUnary(vshape, HloOpcode::kNegate, input)); + auto ceil = builder.AddInstruction( + HloInstruction::CreateUnary(vshape, HloOpcode::kCeil, negate)); + + auto cshape = ShapeUtil::MakeShape(F32, {6}); + auto concatenate = builder.AddInstruction( + HloInstruction::CreateConcatenate(cshape, {ceil, ceil}, /*dimension=*/0)); + + // Build an x+y computation to use in a reduce. + Shape r0f32 = ShapeUtil::MakeShape(F32, {}); + auto embedded_builder = HloComputation::Builder("f32+f32"); + embedded_builder.AddInstruction(HloInstruction::CreateBinary( + r0f32, HloOpcode::kAdd, + embedded_builder.AddInstruction( + HloInstruction::CreateParameter(0, r0f32, "x")), + embedded_builder.AddInstruction( + HloInstruction::CreateParameter(1, r0f32, "y")))); + auto add_f32 = module->AddEmbeddedComputation(embedded_builder.Build()); + + // This is a nop reduction. + auto reduce = builder.AddInstruction(HloInstruction::CreateReduce( + cshape, + builder.AddInstruction(HloInstruction::CreateReshape( + ShapeUtil::MakeShape(F32, {6, 1}), concatenate)), + /*init_value=*/ + builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(0))), + /*dimensions_to_reduce=*/{1}, add_f32)); + + auto exp = builder.AddInstruction( + HloInstruction::CreateUnary(cshape, HloOpcode::kExp, reduce)); + auto floor = builder.AddInstruction( + HloInstruction::CreateUnary(cshape, HloOpcode::kFloor, exp)); + auto two = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + builder.AddInstruction( + HloInstruction::CreateBinary(cshape, HloOpcode::kMultiply, two, floor)); + + module->AddEntryComputation(builder.Build()); + + CpuInstructionFusion fusion; + EXPECT_TRUE(fusion.Run(module.get()).ValueOrDie()); + + // The computation root instruction was fused. Verify the fusion instruction + // is now the root. + auto computation = module->entry_computation(); + + auto fusion_instruction1 = computation->root_instruction(); + EXPECT_EQ(HloOpcode::kFusion, fusion_instruction1->opcode()); + EXPECT_EQ(HloOpcode::kMultiply, + fusion_instruction1->fused_expression_root()->opcode()); + // There should be 5 fused instructions in the root fusion instruction: 2 + // parameters, multiply, floor, and exp. + EXPECT_EQ(5, fusion_instruction1->fused_instruction_count()) + << fusion_instruction1->fused_instructions_computation()->ToString(); + + auto fusion_instruction2 = reduce->operand(0); + EXPECT_EQ(HloOpcode::kFusion, fusion_instruction1->opcode()); + EXPECT_EQ(HloOpcode::kReshape, + fusion_instruction2->fused_expression_root()->opcode()); + // There should be 5 fused instructions in the second fusion instruction: 1 + // parameter, negate, ceil, concat, and reshape. + EXPECT_EQ(5, fusion_instruction2->fused_instruction_count()) + << fusion_instruction2->fused_instructions_computation()->ToString(); + + // Compile and execute the computation. + auto result = ExecuteAndTransfer(std::move(module), {}); + + // Check the output correctness. + LiteralTestUtil::ExpectR1Near({14.0, 40.0, 40.0, 14.0, 40.0, 40.0}, + *result, error_spec_); +} + +TEST_F(CpuFusionTest, TestOperandOrderToAvoidDuplication) { + // Test that the operands of an instruction to be fused are considered in the + // proper order to avoid duplication. Test input: + // + // constant = {...} + // negate = neg(constant) + // ceil = ceil(negate) + // add1 = add(negate, ceil) + // add2 = add(ceil, negate) + // + // In this example, the operands of both add1 and add2 should be fused in the + // order {ceil, negate} even though they have different orders in their + // operand vectors. Test for this problem by counting the number of nodes in + // each fusion instruction to ensure that negate is not duplicated. + auto builder = HloComputation::Builder(TestName()); + auto input_literal = Literal::CreateR1({1.0, 2.0, 3.0}); + Shape vshape = input_literal->shape(); + + auto constant = builder.AddInstruction( + HloInstruction::CreateConstant(std::move(input_literal))); + auto negate = builder.AddInstruction( + HloInstruction::CreateUnary(vshape, HloOpcode::kNegate, constant)); + auto ceil = builder.AddInstruction( + HloInstruction::CreateUnary(vshape, HloOpcode::kCeil, negate)); + + auto add1 = builder.AddInstruction( + HloInstruction::CreateBinary(vshape, HloOpcode::kMultiply, negate, ceil)); + auto add2 = builder.AddInstruction( + HloInstruction::CreateBinary(vshape, HloOpcode::kMultiply, ceil, negate)); + + // Tie together the two adds with a tuple to create a single root. + auto result = + builder.AddInstruction(HloInstruction::CreateTuple({add1, add2})); + + // Create computation and module. + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + // Run fusion. + CpuInstructionFusion fusion; + EXPECT_TRUE(fusion.Run(module.get()).ValueOrDie()); + + auto fusion1 = result->operand(0); + auto fusion2 = result->operand(1); + EXPECT_EQ(HloOpcode::kFusion, fusion1->opcode()); + EXPECT_EQ(HloOpcode::kFusion, fusion2->opcode()); + + // Each fusion instruction should have 4 fused instruction inside: add, ceil, + // negate, and the fused parameter. + EXPECT_EQ(4, fusion1->fused_instruction_count()); + EXPECT_EQ(4, fusion2->fused_instruction_count()); + + // Each fusion instruction should have one parameter and the parameter should + // be the constant. + EXPECT_EQ(1, fusion1->operand_count()); + EXPECT_EQ(constant, fusion1->operand(0)); + EXPECT_EQ(1, fusion2->operand_count()); + EXPECT_EQ(constant, fusion2->operand(0)); +} + +TEST_F(CpuFusionTest, DoNotDuplicateExpensiveOps) { + // Verify that expensive operations will not be fused if the fusion results in + // duplication. Test code: + // + // constant = 42.0 + // exp1 = exp(constant) + // negate1 = negate(exp1) + // exp2 = exp(constant) + // negate2 = negate(exp2) + // tuple = tuple(negate1, negate2, exp2) + // + // exp1 should be fused down into negate1, but exp2 will not be fused into + // negate2 because this will result in duplication of the expensive exp + // computation. The duplication is caused by the other use of exp2 in the + // tuple. + auto builder = HloComputation::Builder(TestName()); + auto input_literal1 = Literal::CreateR1({1.0, 2.0, 3.0}); + auto input_literal2 = Literal::CreateR1({-2.0, -42.0, 2.0}); + auto constant = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + Shape shape = constant->shape(); + + auto exp1 = builder.AddInstruction( + HloInstruction::CreateUnary(shape, HloOpcode::kExp, constant)); + auto negate1 = builder.AddInstruction( + HloInstruction::CreateUnary(shape, HloOpcode::kNegate, exp1)); + + auto exp2 = builder.AddInstruction( + HloInstruction::CreateUnary(shape, HloOpcode::kExp, constant)); + auto negate2 = builder.AddInstruction( + HloInstruction::CreateUnary(shape, HloOpcode::kNegate, exp2)); + + auto tuple = builder.AddInstruction( + HloInstruction::CreateTuple({negate1, negate2, exp2})); + + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + CpuInstructionFusion fusion; + EXPECT_TRUE(fusion.Run(module.get()).ValueOrDie()); + + // The only fusion instruction should be operand 0 of the tuple (formerly + // negate1). + EXPECT_EQ(HloOpcode::kFusion, tuple->operand(0)->opcode()); + EXPECT_EQ(HloOpcode::kNegate, tuple->operand(1)->opcode()); + EXPECT_EQ(HloOpcode::kExp, tuple->operand(2)->opcode()); + + auto fusion_inst = tuple->operand(0); + // There should be three fused instructions: negate2, exp2, and the fused + // parameter. + EXPECT_EQ(3, fusion_inst->fused_instruction_count()); + EXPECT_EQ(1, fusion_inst->operand_count()); + EXPECT_EQ(constant, fusion_inst->operand(0)); +} + +} // namespace +} // namespace cpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_intrinsic_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_intrinsic_test.cc new file mode 100644 index 00000000000000..973aac8766f5aa --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_intrinsic_test.cc @@ -0,0 +1,151 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include +#include + +#include "tensorflow/compiler/xla/service/cpu/cpu_compiler.h" +#include "tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace cpu { +namespace { + +const char* const kTriple_x86_64 = "x86_64-pc-linux"; +const char* const kTriple_android_arm = "armv7-none-android"; + +struct IntrinsicTestSpec { + HloOpcode opcode; + tensorflow::StringPiece triple; + tensorflow::StringPiece features; + tensorflow::StringPiece check_lines; +}; + +// Tests that unary functions get lowered using intrinsic calls. +class CpuUnaryIntrinsicTest + : public CpuCodegenTest, + public ::testing::WithParamInterface { + public: + static string Name(const ::testing::TestParamInfo& info) { + auto spec = info.param; + + string opcode = HloOpcodeString(spec.opcode); + opcode[0] = toupper(opcode[0]); + + string triple{spec.triple.data(), spec.triple.size()}; + if (triple == kTriple_x86_64) { + triple = "x86_64"; + } else if (triple == kTriple_android_arm) { + triple = "android_arm"; + } else { + triple = "Unknown"; + } + + string features{spec.features.data(), spec.features.size()}; + if (!features.empty()) { + std::replace_if(features.begin(), features.end(), + [](char c) { return c != '_' && !isalnum(c); }, '_'); + } else { + features = ""; + } + + return tensorflow::strings::StrCat(opcode.c_str(), "_On_", triple.c_str(), + features.empty() ? "" : "_With", + features.c_str()); + } +}; + +// Creates a module with a call to the unary op, and tests if the +// compiler replaced it with a call to the intrinsic. +TEST_P(CpuUnaryIntrinsicTest, DoIt) { + HloComputation::Builder builder(TestName()); + IntrinsicTestSpec spec = GetParam(); + + auto param_shape = ShapeUtil::MakeShape(F32, {1024}); + HloInstruction* param = builder.AddInstruction( + HloInstruction::CreateParameter(0, param_shape, "input")); + builder.AddInstruction( + HloInstruction::CreateUnary(param_shape, spec.opcode, param)); + std::unique_ptr computation = builder.Build(); + + string triple{spec.triple.data(), spec.triple.size()}; + string features{spec.features.data(), spec.features.size()}; + + CpuAotCompilationOptions options{ + /*triple=*/triple, /*cpu_name=*/"", /*features=*/features, + /*entry_point_name=*/"entry", + /*relocation_model=*/CpuAotCompilationOptions::RelocationModel::Static}; + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(std::move(computation)); + + string check_lines{spec.check_lines.data(), spec.check_lines.size()}; + + CompileAheadOfTimeAndVerifyIr(std::move(hlo_module), options, check_lines, + /*match_optimized_ir=*/true); +} + +IntrinsicTestSpec CpuUnaryIntrinsicTestCases[] = { + // The intrinsics are always inlined, so we match a line from it instead of + // a function call. + + IntrinsicTestSpec{ + HloOpcode::kExp, kTriple_x86_64, "", + R"(CHECK: fmul fast <4 x float> )"}, + + IntrinsicTestSpec{ + HloOpcode::kExp, kTriple_x86_64, "+avx", + R"(CHECK: fmul fast <8 x float> )"}, + + IntrinsicTestSpec{ + HloOpcode::kExp, kTriple_android_arm, "+neon", + R"(CHECK: fmul fast <4 x float> )"}, + + IntrinsicTestSpec{ + HloOpcode::kTanh, kTriple_x86_64, "", + R"(CHECK: fcmp fast uge <4 x float> %wide.load, )"}, + + IntrinsicTestSpec{ + HloOpcode::kTanh, kTriple_x86_64, "+avx", + R"(CHECK: fcmp fast uge <8 x float> %wide.load, )"}, + + IntrinsicTestSpec{ + HloOpcode::kTanh, kTriple_android_arm, "", + R"(CHECK: fcmp fast uge <4 x float> %wide.load, )"}, + + IntrinsicTestSpec{ + HloOpcode::kLog, kTriple_x86_64, "", + R"(CHECK: fadd fast <4 x float> )"}, + + IntrinsicTestSpec{ + HloOpcode::kLog, kTriple_x86_64, "+avx", + R"(CHECK: fadd fast <8 x float> )"}, + + IntrinsicTestSpec{ + HloOpcode::kLog, kTriple_android_arm, "", + R"(CHECK: fadd fast <4 x float> )"}}; + +INSTANTIATE_TEST_CASE_P(CpuUnaryIntrinsicTestInstantiation, + CpuUnaryIntrinsicTest, + ::testing::ValuesIn(CpuUnaryIntrinsicTestCases), + CpuUnaryIntrinsicTest::Name); + +} // namespace +} // namespace cpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc new file mode 100644 index 00000000000000..3b6b0ed7406561 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc @@ -0,0 +1,136 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +#include "llvm/IR/Module.h" +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/buffer_assignment.h" +#include "tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h" +#include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/tests/filecheck.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace cpu { + +class CpuNoAliasTest : public CpuCodegenTest {}; + +// Creates a simple HLO ir_module (runs concat(concat(x, y), x)), and then +// inspects the aliasing information for loads to its buffers. +TEST_F(CpuNoAliasTest, Concat) { + HloComputation::Builder builder(TestName()); + + std::unique_ptr literal = + Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + auto param_shape = ShapeUtil::MakeShape(F32, {2, 2}); + HloInstruction* param_x = builder.AddInstruction( + HloInstruction::CreateParameter(0, param_shape, "x")); + HloInstruction* param_y = builder.AddInstruction( + HloInstruction::CreateParameter(1, param_shape, "y")); + HloInstruction* concat1 = + builder.AddInstruction(HloInstruction::CreateConcatenate( + ShapeUtil::MakeShape(F32, {2, 4}), {param_x, param_y}, 1)); + HloInstruction* concat2 = + builder.AddInstruction(HloInstruction::CreateConcatenate( + ShapeUtil::MakeShape(F32, {2, 6}), {concat1, param_x}, 1)); + + std::unique_ptr computation = builder.Build(); + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(std::move(computation)); + + // Now that we have an HLO module, build an llvm_ir::AliasAnalysis for it. + auto status_or_buffer_assn = BufferAssigner::Run( + hlo_module.get(), MakeUnique(hlo_module.get()), + backend().compiler()->BufferSizeBytesFunction(), + [](LogicalBuffer::Color) { return /*alignment=*/1; }); + ASSERT_EQ(status_or_buffer_assn.status(), Status::OK()); + + llvm::LLVMContext context; + llvm_ir::AliasAnalysis aa(*hlo_module, *status_or_buffer_assn.ValueOrDie(), + &context); + + // Construct an LLVM module containing loads that we annotate as being from + // the buffers in the HLO module. We'll inspect these loads to ensure that + // they have the expected alias information. + llvm::Module ir_module("test", context); + llvm::Function* func = llvm::cast( + ir_module.getOrInsertFunction("test_fn", llvm::Type::getVoidTy(context))); + llvm::BasicBlock* bb = llvm::BasicBlock::Create(context, "body", func); + llvm::IRBuilder<> ir_builder(bb); + auto* zero = llvm::ConstantInt::get(llvm::Type::getInt32Ty(context), 0); + llvm_ir::IrArray::Index zero2D({zero, zero}); + + llvm::ArrayType* array2d_type = llvm::ArrayType::get( + llvm::ArrayType::get(llvm::Type::getFloatTy(context), 100), 100); + + { + llvm::Value* param_x_val = + ir_module.getOrInsertGlobal("param_x", array2d_type); + llvm_ir::IrArray param_x_array(param_x_val, param_shape); + aa.AddAliasingInformationToIrArray(*param_x, ¶m_x_array); + param_x_array.EmitReadArrayElement(zero2D, &ir_builder) + ->setName("read_param_x_array"); + } + + { + llvm::Value* concat1_val = + ir_module.getOrInsertGlobal("concat1", array2d_type); + auto shape = ShapeUtil::MakeShape(F32, {2, 4}); + llvm_ir::IrArray concat1_array(concat1_val, shape); + aa.AddAliasingInformationToIrArray(*concat1, &concat1_array); + concat1_array.EmitReadArrayElement(zero2D, &ir_builder) + ->setName("read_concat1_array"); + } + + { + llvm::Value* concat2_val = + ir_module.getOrInsertGlobal("concat2", array2d_type); + auto shape = ShapeUtil::MakeShape(F32, {2, 6}); + llvm_ir::IrArray concat2_array(concat2_val, shape); + aa.AddAliasingInformationToIrArray(*concat2, &concat2_array); + concat2_array.EmitReadArrayElement(zero2D, &ir_builder) + ->setName("read_concat2_array"); + } + + // Check the AA info in the loads. + const char* filecheck_pattern = R"( + CHECK: %read_param_x_array = load {{.*}} !noalias [[param_x_noalias:![0-9]+]] + CHECK: %read_concat1_array = load {{.*}} !alias.scope [[concat1_scope:![0-9]+]], !noalias [[concat1_noalias:![0-9]+]] + CHECK: %read_concat2_array = load {{.*}} !alias.scope [[concat1_noalias]], !noalias [[concat1_scope]] + CHECK-DAG: [[buf_size32:![0-9]+]] = !{!"buffer:{{.*}} size:32 + CHECK-DAG: [[buf_size48:![0-9]+]] = !{!"buffer:{{.*}} size:48 + CHECK-DAG: [[param_x_noalias]] = !{[[buf_size32]], [[buf_size48]]} + CHECK-DAG: [[concat1_scope]] = !{[[buf_size32]]} + CHECK-DAG: [[concat1_noalias]] = !{[[buf_size48]]} + )"; + + TF_ASSERT_OK_AND_ASSIGN( + bool filecheck_match, + RunFileCheck(llvm_ir::DumpModuleToString(ir_module), filecheck_pattern)); + EXPECT_TRUE(filecheck_match); +} + +} // namespace cpu +} // namespace xla diff --git a/tensorflow/compiler/xla/tests/filecheck.cc b/tensorflow/compiler/xla/tests/filecheck.cc index a5f6872c46c780..93d1c921c4a138 100644 --- a/tensorflow/compiler/xla/tests/filecheck.cc +++ b/tensorflow/compiler/xla/tests/filecheck.cc @@ -38,7 +38,7 @@ StatusOr RunFileCheck(const string& input, const string& pattern) { TF_RETURN_IF_ERROR(tensorflow::WriteStringToFile(env, pattern_path, pattern)); // Invoke FileCheck to check whether input matches `pattern`. - const char* file_check_path_suffix = "external/llvm/FileCheck"; + const char* file_check_path_suffix = "org_tensorflow/external/llvm/FileCheck"; string file_check_path; if (const char* test_srcdir = getenv("TEST_SRCDIR")) { file_check_path = JoinPath(test_srcdir, file_check_path_suffix); @@ -66,6 +66,11 @@ StatusOr RunFileCheck(const string& input, const string& pattern) { // the error message generated by FileCheck and the inputs. bool succeeded = (exit_status == 0); if (!succeeded) { + LOG(WARNING) << "Tried to execute FileCheck at " << file_check_path; + if (!env->FileExists(file_check_path).ok()) { + LOG(WARNING) << "NOTE: FileCheck binary does not exist!"; + } + LOG(WARNING) << "FileCheck error: " << standard_error; LOG(WARNING) << "FileCheck input was:"; XLA_LOG_LINES(tensorflow::WARNING, input); From a4343eb6cd10fa6c0fdfaa18585706d78e8c9d26 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 1 May 2018 03:25:26 -0700 Subject: [PATCH 0217/1691] Protocol buffer classes now list their fields in dir(cls) PiperOrigin-RevId: 194917415 --- .../tensorflow.-attr-value.-list-value.pbtxt | 36 +++--- .../api/golden/tensorflow.-attr-value.pbtxt | 48 +++---- ...ow.-config-proto.-device-count-entry.pbtxt | 8 +- .../api/golden/tensorflow.-config-proto.pbtxt | 72 +++++------ .../tools/api/golden/tensorflow.-event.pbtxt | 36 +++--- .../golden/tensorflow.-g-p-u-options.pbtxt | 48 +++---- .../api/golden/tensorflow.-graph-def.pbtxt | 16 +-- .../golden/tensorflow.-graph-options.pbtxt | 44 +++---- .../golden/tensorflow.-histogram-proto.pbtxt | 36 +++--- .../api/golden/tensorflow.-log-message.pbtxt | 16 +-- ...meta-graph-def.-collection-def-entry.pbtxt | 8 +- ...rflow.-meta-graph-def.-meta-info-def.pbtxt | 32 ++--- ...-meta-graph-def.-signature-def-entry.pbtxt | 8 +- .../golden/tensorflow.-meta-graph-def.pbtxt | 40 +++--- ...nsorflow.-name-attr-list.-attr-entry.pbtxt | 8 +- .../golden/tensorflow.-name-attr-list.pbtxt | 12 +- .../tensorflow.-node-def.-attr-entry.pbtxt | 8 +- .../api/golden/tensorflow.-node-def.pbtxt | 28 ++-- .../tensorflow.-optimizer-options.pbtxt | 44 +++---- .../api/golden/tensorflow.-run-metadata.pbtxt | 16 +-- .../api/golden/tensorflow.-run-options.pbtxt | 36 +++--- .../api/golden/tensorflow.-session-log.pbtxt | 24 ++-- ...rflow.-summary-metadata.-plugin-data.pbtxt | 12 +- .../golden/tensorflow.-summary-metadata.pbtxt | 20 +-- .../golden/tensorflow.-summary.-audio.pbtxt | 28 ++-- .../golden/tensorflow.-summary.-image.pbtxt | 24 ++-- .../golden/tensorflow.-summary.-value.pbtxt | 40 +++--- .../api/golden/tensorflow.-summary.pbtxt | 8 +- .../tensorflow.-tensor-info.-coo-sparse.pbtxt | 16 +-- .../api/golden/tensorflow.-tensor-info.pbtxt | 24 ++-- ...flow.profiler.-advice-proto.-checker.pbtxt | 4 +- ...ofiler.-advice-proto.-checkers-entry.pbtxt | 8 +- .../tensorflow.profiler.-advice-proto.pbtxt | 8 +- ...graph-node-proto.-input-shapes-entry.pbtxt | 8 +- ...ensorflow.profiler.-graph-node-proto.pbtxt | 120 +++++++++--------- ...low.profiler.-multi-graph-node-proto.pbtxt | 92 +++++++------- ...er.-op-log-proto.-id-to-string-entry.pbtxt | 8 +- .../tensorflow.profiler.-op-log-proto.pbtxt | 12 +- .../golden/tensorflow.summary.-event.pbtxt | 36 +++--- .../tensorflow.summary.-session-log.pbtxt | 24 ++-- ...sorflow.summary.-summary-description.pbtxt | 4 +- .../tensorflow.summary.-summary.-audio.pbtxt | 28 ++-- .../tensorflow.summary.-summary.-image.pbtxt | 24 ++-- .../tensorflow.summary.-summary.-value.pbtxt | 40 +++--- .../golden/tensorflow.summary.-summary.pbtxt | 8 +- ...sorflow.summary.-tagged-run-metadata.pbtxt | 8 +- .../golden/tensorflow.train.-bytes-list.pbtxt | 4 +- .../tensorflow.train.-cluster-def.pbtxt | 4 +- .../golden/tensorflow.train.-example.pbtxt | 4 +- .../tensorflow.train.-feature-list.pbtxt | 4 +- ...n.-feature-lists.-feature-list-entry.pbtxt | 8 +- .../tensorflow.train.-feature-lists.pbtxt | 8 +- .../golden/tensorflow.train.-feature.pbtxt | 16 +-- ...rflow.train.-features.-feature-entry.pbtxt | 8 +- .../golden/tensorflow.train.-features.pbtxt | 8 +- .../golden/tensorflow.train.-float-list.pbtxt | 4 +- .../golden/tensorflow.train.-int64-list.pbtxt | 4 +- ...nsorflow.train.-job-def.-tasks-entry.pbtxt | 8 +- .../golden/tensorflow.train.-job-def.pbtxt | 12 +- .../golden/tensorflow.train.-saver-def.pbtxt | 34 ++--- .../tensorflow.train.-sequence-example.pbtxt | 12 +- .../golden/tensorflow.train.-server-def.pbtxt | 28 ++-- 62 files changed, 697 insertions(+), 697 deletions(-) diff --git a/tensorflow/tools/api/golden/tensorflow.-attr-value.-list-value.pbtxt b/tensorflow/tools/api/golden/tensorflow.-attr-value.-list-value.pbtxt index 0fb1aaba2831e6..004d7169549395 100644 --- a/tensorflow/tools/api/golden/tensorflow.-attr-value.-list-value.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-attr-value.-list-value.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.AttrValue.ListValue" tf_class { is_instance: "" is_instance: "" - member { - name: "B_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -15,32 +11,36 @@ tf_class { mtype: "" } member { - name: "FUNC_FIELD_NUMBER" - mtype: "" + name: "b" + mtype: "" + } + member { + name: "f" + mtype: "" } member { - name: "F_FIELD_NUMBER" - mtype: "" + name: "func" + mtype: "" } member { - name: "I_FIELD_NUMBER" - mtype: "" + name: "i" + mtype: "" } member { - name: "SHAPE_FIELD_NUMBER" - mtype: "" + name: "s" + mtype: "" } member { - name: "S_FIELD_NUMBER" - mtype: "" + name: "shape" + mtype: "" } member { - name: "TENSOR_FIELD_NUMBER" - mtype: "" + name: "tensor" + mtype: "" } member { - name: "TYPE_FIELD_NUMBER" - mtype: "" + name: "type" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-attr-value.pbtxt b/tensorflow/tools/api/golden/tensorflow.-attr-value.pbtxt index e7a3a1f02faf10..2996e02483ebc0 100644 --- a/tensorflow/tools/api/golden/tensorflow.-attr-value.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-attr-value.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.AttrValue" tf_class { is_instance: "" is_instance: "" - member { - name: "B_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -15,44 +11,48 @@ tf_class { mtype: "" } member { - name: "FUNC_FIELD_NUMBER" - mtype: "" + name: "ListValue" + mtype: "" + } + member { + name: "b" + mtype: "" } member { - name: "F_FIELD_NUMBER" - mtype: "" + name: "f" + mtype: "" } member { - name: "I_FIELD_NUMBER" - mtype: "" + name: "func" + mtype: "" } member { - name: "LIST_FIELD_NUMBER" - mtype: "" + name: "i" + mtype: "" } member { - name: "ListValue" - mtype: "" + name: "list" + mtype: "" } member { - name: "PLACEHOLDER_FIELD_NUMBER" - mtype: "" + name: "placeholder" + mtype: "" } member { - name: "SHAPE_FIELD_NUMBER" - mtype: "" + name: "s" + mtype: "" } member { - name: "S_FIELD_NUMBER" - mtype: "" + name: "shape" + mtype: "" } member { - name: "TENSOR_FIELD_NUMBER" - mtype: "" + name: "tensor" + mtype: "" } member { - name: "TYPE_FIELD_NUMBER" - mtype: "" + name: "type" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-config-proto.-device-count-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.-config-proto.-device-count-entry.pbtxt index 29bb3be35cba5f..c7022e7593da36 100644 --- a/tensorflow/tools/api/golden/tensorflow.-config-proto.-device-count-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-config-proto.-device-count-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "KEY_FIELD_NUMBER" - mtype: "" + name: "key" + mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-config-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.-config-proto.pbtxt index 009d64aed09ddc..ca9530de85597a 100644 --- a/tensorflow/tools/api/golden/tensorflow.-config-proto.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-config-proto.pbtxt @@ -3,76 +3,76 @@ tf_class { is_instance: "" is_instance: "" member { - name: "ALLOW_SOFT_PLACEMENT_FIELD_NUMBER" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "CLUSTER_DEF_FIELD_NUMBER" - mtype: "" + name: "DeviceCountEntry" + mtype: "" } member { - name: "DESCRIPTOR" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "DEVICE_COUNT_FIELD_NUMBER" - mtype: "" + name: "allow_soft_placement" + mtype: "" } member { - name: "DEVICE_FILTERS_FIELD_NUMBER" - mtype: "" + name: "cluster_def" + mtype: "" } member { - name: "DeviceCountEntry" - mtype: "" + name: "device_count" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "device_filters" + mtype: "" } member { - name: "GPU_OPTIONS_FIELD_NUMBER" - mtype: "" + name: "gpu_options" + mtype: "" } member { - name: "GRAPH_OPTIONS_FIELD_NUMBER" - mtype: "" + name: "graph_options" + mtype: "" } member { - name: "INTER_OP_PARALLELISM_THREADS_FIELD_NUMBER" - mtype: "" + name: "inter_op_parallelism_threads" + mtype: "" } member { - name: "INTRA_OP_PARALLELISM_THREADS_FIELD_NUMBER" - mtype: "" + name: "intra_op_parallelism_threads" + mtype: "" } member { - name: "ISOLATE_SESSION_STATE_FIELD_NUMBER" - mtype: "" + name: "isolate_session_state" + mtype: "" } member { - name: "LOG_DEVICE_PLACEMENT_FIELD_NUMBER" - mtype: "" + name: "log_device_placement" + mtype: "" } member { - name: "OPERATION_TIMEOUT_IN_MS_FIELD_NUMBER" - mtype: "" + name: "operation_timeout_in_ms" + mtype: "" } member { - name: "PLACEMENT_PERIOD_FIELD_NUMBER" - mtype: "" + name: "placement_period" + mtype: "" } member { - name: "RPC_OPTIONS_FIELD_NUMBER" - mtype: "" + name: "rpc_options" + mtype: "" } member { - name: "SESSION_INTER_OP_THREAD_POOL_FIELD_NUMBER" - mtype: "" + name: "session_inter_op_thread_pool" + mtype: "" } member { - name: "USE_PER_SESSION_THREADS_FIELD_NUMBER" - mtype: "" + name: "use_per_session_threads" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-event.pbtxt b/tensorflow/tools/api/golden/tensorflow.-event.pbtxt index 9bf8c124288854..fa2f329a87da02 100644 --- a/tensorflow/tools/api/golden/tensorflow.-event.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-event.pbtxt @@ -11,40 +11,40 @@ tf_class { mtype: "" } member { - name: "FILE_VERSION_FIELD_NUMBER" - mtype: "" + name: "file_version" + mtype: "" } member { - name: "GRAPH_DEF_FIELD_NUMBER" - mtype: "" + name: "graph_def" + mtype: "" } member { - name: "LOG_MESSAGE_FIELD_NUMBER" - mtype: "" + name: "log_message" + mtype: "" } member { - name: "META_GRAPH_DEF_FIELD_NUMBER" - mtype: "" + name: "meta_graph_def" + mtype: "" } member { - name: "SESSION_LOG_FIELD_NUMBER" - mtype: "" + name: "session_log" + mtype: "" } member { - name: "STEP_FIELD_NUMBER" - mtype: "" + name: "step" + mtype: "" } member { - name: "SUMMARY_FIELD_NUMBER" - mtype: "" + name: "summary" + mtype: "" } member { - name: "TAGGED_RUN_METADATA_FIELD_NUMBER" - mtype: "" + name: "tagged_run_metadata" + mtype: "" } member { - name: "WALL_TIME_FIELD_NUMBER" - mtype: "" + name: "wall_time" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt b/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt index 875d802a9c458e..5119c7fa5b3dcd 100644 --- a/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt @@ -3,52 +3,52 @@ tf_class { is_instance: "" is_instance: "" member { - name: "ALLOCATOR_TYPE_FIELD_NUMBER" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "ALLOW_GROWTH_FIELD_NUMBER" - mtype: "" + name: "Experimental" + mtype: "" } member { - name: "DEFERRED_DELETION_BYTES_FIELD_NUMBER" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "DESCRIPTOR" - mtype: "" + name: "allocator_type" + mtype: "" } member { - name: "EXPERIMENTAL_FIELD_NUMBER" - mtype: "" + name: "allow_growth" + mtype: "" } member { - name: "Experimental" - mtype: "" + name: "deferred_deletion_bytes" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "experimental" + mtype: "" } member { - name: "FORCE_GPU_COMPATIBLE_FIELD_NUMBER" - mtype: "" + name: "force_gpu_compatible" + mtype: "" } member { - name: "PER_PROCESS_GPU_MEMORY_FRACTION_FIELD_NUMBER" - mtype: "" + name: "per_process_gpu_memory_fraction" + mtype: "" } member { - name: "POLLING_ACTIVE_DELAY_USECS_FIELD_NUMBER" - mtype: "" + name: "polling_active_delay_usecs" + mtype: "" } member { - name: "POLLING_INACTIVE_DELAY_MSECS_FIELD_NUMBER" - mtype: "" + name: "polling_inactive_delay_msecs" + mtype: "" } member { - name: "VISIBLE_DEVICE_LIST_FIELD_NUMBER" - mtype: "" + name: "visible_device_list" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-graph-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.-graph-def.pbtxt index 1495e847cb08ed..318a25a0923116 100644 --- a/tensorflow/tools/api/golden/tensorflow.-graph-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-graph-def.pbtxt @@ -11,20 +11,20 @@ tf_class { mtype: "" } member { - name: "LIBRARY_FIELD_NUMBER" - mtype: "" + name: "library" + mtype: "" } member { - name: "NODE_FIELD_NUMBER" - mtype: "" + name: "node" + mtype: "" } member { - name: "VERSIONS_FIELD_NUMBER" - mtype: "" + name: "version" + mtype: "" } member { - name: "VERSION_FIELD_NUMBER" - mtype: "" + name: "versions" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-graph-options.pbtxt b/tensorflow/tools/api/golden/tensorflow.-graph-options.pbtxt index 0844f891cad3d4..786d831c707ffa 100644 --- a/tensorflow/tools/api/golden/tensorflow.-graph-options.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-graph-options.pbtxt @@ -3,48 +3,48 @@ tf_class { is_instance: "" is_instance: "" member { - name: "BUILD_COST_MODEL_AFTER_FIELD_NUMBER" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "BUILD_COST_MODEL_FIELD_NUMBER" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "DESCRIPTOR" - mtype: "" + name: "build_cost_model" + mtype: "" } member { - name: "ENABLE_BFLOAT16_SENDRECV_FIELD_NUMBER" - mtype: "" + name: "build_cost_model_after" + mtype: "" } member { - name: "ENABLE_RECV_SCHEDULING_FIELD_NUMBER" - mtype: "" + name: "enable_bfloat16_sendrecv" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "enable_recv_scheduling" + mtype: "" } member { - name: "INFER_SHAPES_FIELD_NUMBER" - mtype: "" + name: "infer_shapes" + mtype: "" } member { - name: "OPTIMIZER_OPTIONS_FIELD_NUMBER" - mtype: "" + name: "optimizer_options" + mtype: "" } member { - name: "PLACE_PRUNED_GRAPH_FIELD_NUMBER" - mtype: "" + name: "place_pruned_graph" + mtype: "" } member { - name: "REWRITE_OPTIONS_FIELD_NUMBER" - mtype: "" + name: "rewrite_options" + mtype: "" } member { - name: "TIMELINE_STEP_FIELD_NUMBER" - mtype: "" + name: "timeline_step" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-histogram-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.-histogram-proto.pbtxt index 2567d2fe602938..3eb2d8873a4797 100644 --- a/tensorflow/tools/api/golden/tensorflow.-histogram-proto.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-histogram-proto.pbtxt @@ -2,14 +2,6 @@ path: "tensorflow.HistogramProto" tf_class { is_instance: "" is_instance: "" - member { - name: "BUCKET_FIELD_NUMBER" - mtype: "" - } - member { - name: "BUCKET_LIMIT_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -19,24 +11,32 @@ tf_class { mtype: "" } member { - name: "MAX_FIELD_NUMBER" - mtype: "" + name: "bucket" + mtype: "" + } + member { + name: "bucket_limit" + mtype: "" + } + member { + name: "max" + mtype: "" } member { - name: "MIN_FIELD_NUMBER" - mtype: "" + name: "min" + mtype: "" } member { - name: "NUM_FIELD_NUMBER" - mtype: "" + name: "num" + mtype: "" } member { - name: "SUM_FIELD_NUMBER" - mtype: "" + name: "sum" + mtype: "" } member { - name: "SUM_SQUARES_FIELD_NUMBER" - mtype: "" + name: "sum_squares" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-log-message.pbtxt b/tensorflow/tools/api/golden/tensorflow.-log-message.pbtxt index a43c5eb7e30c3c..760739f4f34cd3 100644 --- a/tensorflow/tools/api/golden/tensorflow.-log-message.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-log-message.pbtxt @@ -26,18 +26,10 @@ tf_class { name: "INFO" mtype: "" } - member { - name: "LEVEL_FIELD_NUMBER" - mtype: "" - } member { name: "Level" mtype: "" } - member { - name: "MESSAGE_FIELD_NUMBER" - mtype: "" - } member { name: "UNKNOWN" mtype: "" @@ -46,6 +38,14 @@ tf_class { name: "WARN" mtype: "" } + member { + name: "level" + mtype: "" + } + member { + name: "message" + mtype: "" + } member_method { name: "ByteSize" } diff --git a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt index 3572126fbfd77d..69bf5b31a1d9f9 100644 --- a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "KEY_FIELD_NUMBER" - mtype: "" + name: "key" + mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-meta-info-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-meta-info-def.pbtxt index b0e983115499c5..8a464f1cac1cac 100644 --- a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-meta-info-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-meta-info-def.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.MetaGraphDef.MetaInfoDef" tf_class { is_instance: "" is_instance: "" - member { - name: "ANY_INFO_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -15,28 +11,32 @@ tf_class { mtype: "" } member { - name: "META_GRAPH_VERSION_FIELD_NUMBER" - mtype: "" + name: "any_info" + mtype: "" + } + member { + name: "meta_graph_version" + mtype: "" } member { - name: "STRIPPED_DEFAULT_ATTRS_FIELD_NUMBER" - mtype: "" + name: "stripped_default_attrs" + mtype: "" } member { - name: "STRIPPED_OP_LIST_FIELD_NUMBER" - mtype: "" + name: "stripped_op_list" + mtype: "" } member { - name: "TAGS_FIELD_NUMBER" - mtype: "" + name: "tags" + mtype: "" } member { - name: "TENSORFLOW_GIT_VERSION_FIELD_NUMBER" - mtype: "" + name: "tensorflow_git_version" + mtype: "" } member { - name: "TENSORFLOW_VERSION_FIELD_NUMBER" - mtype: "" + name: "tensorflow_version" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt index 48fccac99d60b5..8c5949d067011f 100644 --- a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "KEY_FIELD_NUMBER" - mtype: "" + name: "key" + mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.pbtxt index 3e683a87159923..2be0432c008577 100644 --- a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.pbtxt @@ -2,14 +2,6 @@ path: "tensorflow.MetaGraphDef" tf_class { is_instance: "" is_instance: "" - member { - name: "ASSET_FILE_DEF_FIELD_NUMBER" - mtype: "" - } - member { - name: "COLLECTION_DEF_FIELD_NUMBER" - mtype: "" - } member { name: "CollectionDefEntry" mtype: "" @@ -23,28 +15,36 @@ tf_class { mtype: "" } member { - name: "GRAPH_DEF_FIELD_NUMBER" - mtype: "" + name: "MetaInfoDef" + mtype: "" + } + member { + name: "SignatureDefEntry" + mtype: "" } member { - name: "META_INFO_DEF_FIELD_NUMBER" - mtype: "" + name: "asset_file_def" + mtype: "" } member { - name: "MetaInfoDef" - mtype: "" + name: "collection_def" + mtype: "" } member { - name: "SAVER_DEF_FIELD_NUMBER" - mtype: "" + name: "graph_def" + mtype: "" } member { - name: "SIGNATURE_DEF_FIELD_NUMBER" - mtype: "" + name: "meta_info_def" + mtype: "" } member { - name: "SignatureDefEntry" - mtype: "" + name: "saver_def" + mtype: "" + } + member { + name: "signature_def" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-name-attr-list.-attr-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.-name-attr-list.-attr-entry.pbtxt index 2750bd780caa41..caf992f5a67ca1 100644 --- a/tensorflow/tools/api/golden/tensorflow.-name-attr-list.-attr-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-name-attr-list.-attr-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "KEY_FIELD_NUMBER" - mtype: "" + name: "key" + mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-name-attr-list.pbtxt b/tensorflow/tools/api/golden/tensorflow.-name-attr-list.pbtxt index d10faf67d027a4..45ddeece074c2f 100644 --- a/tensorflow/tools/api/golden/tensorflow.-name-attr-list.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-name-attr-list.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.NameAttrList" tf_class { is_instance: "" is_instance: "" - member { - name: "ATTR_FIELD_NUMBER" - mtype: "" - } member { name: "AttrEntry" mtype: "" @@ -19,8 +15,12 @@ tf_class { mtype: "" } member { - name: "NAME_FIELD_NUMBER" - mtype: "" + name: "attr" + mtype: "" + } + member { + name: "name" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-node-def.-attr-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.-node-def.-attr-entry.pbtxt index b1b62d60f1e8c9..30a9dc69f092d2 100644 --- a/tensorflow/tools/api/golden/tensorflow.-node-def.-attr-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-node-def.-attr-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "KEY_FIELD_NUMBER" - mtype: "" + name: "key" + mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-node-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.-node-def.pbtxt index b812b4df2b3c15..23319fdb2294ca 100644 --- a/tensorflow/tools/api/golden/tensorflow.-node-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-node-def.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.NodeDef" tf_class { is_instance: "" is_instance: "" - member { - name: "ATTR_FIELD_NUMBER" - mtype: "" - } member { name: "AttrEntry" mtype: "" @@ -14,25 +10,29 @@ tf_class { name: "DESCRIPTOR" mtype: "" } - member { - name: "DEVICE_FIELD_NUMBER" - mtype: "" - } member { name: "Extensions" mtype: "" } member { - name: "INPUT_FIELD_NUMBER" - mtype: "" + name: "attr" + mtype: "" + } + member { + name: "device" + mtype: "" + } + member { + name: "input" + mtype: "" } member { - name: "NAME_FIELD_NUMBER" - mtype: "" + name: "name" + mtype: "" } member { - name: "OP_FIELD_NUMBER" - mtype: "" + name: "op" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-optimizer-options.pbtxt b/tensorflow/tools/api/golden/tensorflow.-optimizer-options.pbtxt index 6cac5c4d99fd75..57da2e8b55181f 100644 --- a/tensorflow/tools/api/golden/tensorflow.-optimizer-options.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-optimizer-options.pbtxt @@ -10,26 +10,10 @@ tf_class { name: "DESCRIPTOR" mtype: "" } - member { - name: "DO_COMMON_SUBEXPRESSION_ELIMINATION_FIELD_NUMBER" - mtype: "" - } - member { - name: "DO_CONSTANT_FOLDING_FIELD_NUMBER" - mtype: "" - } - member { - name: "DO_FUNCTION_INLINING_FIELD_NUMBER" - mtype: "" - } member { name: "Extensions" mtype: "" } - member { - name: "GLOBAL_JIT_LEVEL_FIELD_NUMBER" - mtype: "" - } member { name: "GlobalJitLevel" mtype: "" @@ -46,10 +30,6 @@ tf_class { name: "Level" mtype: "" } - member { - name: "MAX_FOLDED_CONSTANT_IN_BYTES_FIELD_NUMBER" - mtype: "" - } member { name: "OFF" mtype: "" @@ -63,8 +43,28 @@ tf_class { mtype: "" } member { - name: "OPT_LEVEL_FIELD_NUMBER" - mtype: "" + name: "do_common_subexpression_elimination" + mtype: "" + } + member { + name: "do_constant_folding" + mtype: "" + } + member { + name: "do_function_inlining" + mtype: "" + } + member { + name: "global_jit_level" + mtype: "" + } + member { + name: "max_folded_constant_in_bytes" + mtype: "" + } + member { + name: "opt_level" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-run-metadata.pbtxt b/tensorflow/tools/api/golden/tensorflow.-run-metadata.pbtxt index 808fa0fa217a40..17b3d8816852ea 100644 --- a/tensorflow/tools/api/golden/tensorflow.-run-metadata.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-run-metadata.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.RunMetadata" tf_class { is_instance: "" is_instance: "" - member { - name: "COST_GRAPH_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -15,12 +11,16 @@ tf_class { mtype: "" } member { - name: "PARTITION_GRAPHS_FIELD_NUMBER" - mtype: "" + name: "cost_graph" + mtype: "" + } + member { + name: "partition_graphs" + mtype: "" } member { - name: "STEP_STATS_FIELD_NUMBER" - mtype: "" + name: "step_stats" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-run-options.pbtxt b/tensorflow/tools/api/golden/tensorflow.-run-options.pbtxt index 2f3e7f1a847dd3..7470e4b63d338e 100644 --- a/tensorflow/tools/api/golden/tensorflow.-run-options.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-run-options.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.RunOptions" tf_class { is_instance: "" is_instance: "" - member { - name: "DEBUG_OPTIONS_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -23,36 +19,40 @@ tf_class { mtype: "" } member { - name: "INTER_OP_THREAD_POOL_FIELD_NUMBER" + name: "NO_TRACE" mtype: "" } member { - name: "NO_TRACE" + name: "SOFTWARE_TRACE" mtype: "" } member { - name: "OUTPUT_PARTITION_GRAPHS_FIELD_NUMBER" - mtype: "" + name: "TraceLevel" + mtype: "" } member { - name: "REPORT_TENSOR_ALLOCATIONS_UPON_OOM_FIELD_NUMBER" - mtype: "" + name: "debug_options" + mtype: "" } member { - name: "SOFTWARE_TRACE" - mtype: "" + name: "inter_op_thread_pool" + mtype: "" } member { - name: "TIMEOUT_IN_MS_FIELD_NUMBER" - mtype: "" + name: "output_partition_graphs" + mtype: "" } member { - name: "TRACE_LEVEL_FIELD_NUMBER" - mtype: "" + name: "report_tensor_allocations_upon_oom" + mtype: "" } member { - name: "TraceLevel" - mtype: "" + name: "timeout_in_ms" + mtype: "" + } + member { + name: "trace_level" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-session-log.pbtxt b/tensorflow/tools/api/golden/tensorflow.-session-log.pbtxt index ec66d7f3354083..259a30546a748c 100644 --- a/tensorflow/tools/api/golden/tensorflow.-session-log.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-session-log.pbtxt @@ -6,10 +6,6 @@ tf_class { name: "CHECKPOINT" mtype: "" } - member { - name: "CHECKPOINT_PATH_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -18,18 +14,10 @@ tf_class { name: "Extensions" mtype: "" } - member { - name: "MSG_FIELD_NUMBER" - mtype: "" - } member { name: "START" mtype: "" } - member { - name: "STATUS_FIELD_NUMBER" - mtype: "" - } member { name: "STATUS_UNSPECIFIED" mtype: "" @@ -42,6 +30,18 @@ tf_class { name: "SessionStatus" mtype: "" } + member { + name: "checkpoint_path" + mtype: "" + } + member { + name: "msg" + mtype: "" + } + member { + name: "status" + mtype: "" + } member_method { name: "ByteSize" } diff --git a/tensorflow/tools/api/golden/tensorflow.-summary-metadata.-plugin-data.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.-plugin-data.pbtxt index 067f02ce8cbb1a..3d9ee9e0f28019 100644 --- a/tensorflow/tools/api/golden/tensorflow.-summary-metadata.-plugin-data.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.-plugin-data.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.SummaryMetadata.PluginData" tf_class { is_instance: "" is_instance: "" - member { - name: "CONTENT_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -15,8 +11,12 @@ tf_class { mtype: "" } member { - name: "PLUGIN_NAME_FIELD_NUMBER" - mtype: "" + name: "content" + mtype: "" + } + member { + name: "plugin_name" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt index b9156521ccbee2..9c69a2b96c2f06 100644 --- a/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt @@ -6,25 +6,25 @@ tf_class { name: "DESCRIPTOR" mtype: "" } - member { - name: "DISPLAY_NAME_FIELD_NUMBER" - mtype: "" - } member { name: "Extensions" mtype: "" } - member { - name: "PLUGIN_DATA_FIELD_NUMBER" - mtype: "" - } member { name: "PluginData" mtype: "" } member { - name: "SUMMARY_DESCRIPTION_FIELD_NUMBER" - mtype: "" + name: "display_name" + mtype: "" + } + member { + name: "plugin_data" + mtype: "" + } + member { + name: "summary_description" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-summary.-audio.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary.-audio.pbtxt index 781010d75e23c1..8e761b886163e3 100644 --- a/tensorflow/tools/api/golden/tensorflow.-summary.-audio.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-summary.-audio.pbtxt @@ -2,33 +2,33 @@ path: "tensorflow.Summary.Audio" tf_class { is_instance: "" is_instance: "" - member { - name: "CONTENT_TYPE_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" } - member { - name: "ENCODED_AUDIO_STRING_FIELD_NUMBER" - mtype: "" - } member { name: "Extensions" mtype: "" } member { - name: "LENGTH_FRAMES_FIELD_NUMBER" - mtype: "" + name: "content_type" + mtype: "" + } + member { + name: "encoded_audio_string" + mtype: "" + } + member { + name: "length_frames" + mtype: "" } member { - name: "NUM_CHANNELS_FIELD_NUMBER" - mtype: "" + name: "num_channels" + mtype: "" } member { - name: "SAMPLE_RATE_FIELD_NUMBER" - mtype: "" + name: "sample_rate" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-summary.-image.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary.-image.pbtxt index feb9c7ee9270a7..07b61d9e96796c 100644 --- a/tensorflow/tools/api/golden/tensorflow.-summary.-image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-summary.-image.pbtxt @@ -2,29 +2,29 @@ path: "tensorflow.Summary.Image" tf_class { is_instance: "" is_instance: "" - member { - name: "COLORSPACE_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" } - member { - name: "ENCODED_IMAGE_STRING_FIELD_NUMBER" - mtype: "" - } member { name: "Extensions" mtype: "" } member { - name: "HEIGHT_FIELD_NUMBER" - mtype: "" + name: "colorspace" + mtype: "" + } + member { + name: "encoded_image_string" + mtype: "" + } + member { + name: "height" + mtype: "" } member { - name: "WIDTH_FIELD_NUMBER" - mtype: "" + name: "width" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-summary.-value.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary.-value.pbtxt index ffb4f45fc5e2a9..77ba2e095eeba4 100644 --- a/tensorflow/tools/api/golden/tensorflow.-summary.-value.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-summary.-value.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.Summary.Value" tf_class { is_instance: "" is_instance: "" - member { - name: "AUDIO_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -15,36 +11,40 @@ tf_class { mtype: "" } member { - name: "HISTO_FIELD_NUMBER" - mtype: "" + name: "audio" + mtype: "" + } + member { + name: "histo" + mtype: "" } member { - name: "IMAGE_FIELD_NUMBER" - mtype: "" + name: "image" + mtype: "" } member { - name: "METADATA_FIELD_NUMBER" - mtype: "" + name: "metadata" + mtype: "" } member { - name: "NODE_NAME_FIELD_NUMBER" - mtype: "" + name: "node_name" + mtype: "" } member { - name: "OBSOLETE_OLD_STYLE_HISTOGRAM_FIELD_NUMBER" - mtype: "" + name: "obsolete_old_style_histogram" + mtype: "" } member { - name: "SIMPLE_VALUE_FIELD_NUMBER" - mtype: "" + name: "simple_value" + mtype: "" } member { - name: "TAG_FIELD_NUMBER" - mtype: "" + name: "tag" + mtype: "" } member { - name: "TENSOR_FIELD_NUMBER" - mtype: "" + name: "tensor" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-summary.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary.pbtxt index 38de17fa9e52b8..95263bdead6fcb 100644 --- a/tensorflow/tools/api/golden/tensorflow.-summary.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-summary.pbtxt @@ -18,14 +18,14 @@ tf_class { name: "Image" mtype: "" } - member { - name: "VALUE_FIELD_NUMBER" - mtype: "" - } member { name: "Value" mtype: "" } + member { + name: "value" + mtype: "" + } member_method { name: "ByteSize" } diff --git a/tensorflow/tools/api/golden/tensorflow.-tensor-info.-coo-sparse.pbtxt b/tensorflow/tools/api/golden/tensorflow.-tensor-info.-coo-sparse.pbtxt index 425c35e0674610..b1848311cfa408 100644 --- a/tensorflow/tools/api/golden/tensorflow.-tensor-info.-coo-sparse.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-tensor-info.-coo-sparse.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.TensorInfo.CooSparse" tf_class { is_instance: "" is_instance: "" - member { - name: "DENSE_SHAPE_TENSOR_NAME_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -15,12 +11,16 @@ tf_class { mtype: "" } member { - name: "INDICES_TENSOR_NAME_FIELD_NUMBER" - mtype: "" + name: "dense_shape_tensor_name" + mtype: "" + } + member { + name: "indices_tensor_name" + mtype: "" } member { - name: "VALUES_TENSOR_NAME_FIELD_NUMBER" - mtype: "" + name: "values_tensor_name" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-tensor-info.pbtxt b/tensorflow/tools/api/golden/tensorflow.-tensor-info.pbtxt index 41ea393be51bd7..9fd26d1b6c5005 100644 --- a/tensorflow/tools/api/golden/tensorflow.-tensor-info.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-tensor-info.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.TensorInfo" tf_class { is_instance: "" is_instance: "" - member { - name: "COO_SPARSE_FIELD_NUMBER" - mtype: "" - } member { name: "CooSparse" mtype: "" @@ -14,21 +10,25 @@ tf_class { name: "DESCRIPTOR" mtype: "" } - member { - name: "DTYPE_FIELD_NUMBER" - mtype: "" - } member { name: "Extensions" mtype: "" } member { - name: "NAME_FIELD_NUMBER" - mtype: "" + name: "coo_sparse" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "name" + mtype: "" } member { - name: "TENSOR_SHAPE_FIELD_NUMBER" - mtype: "" + name: "tensor_shape" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt index bd5c36f390add9..925ea6df9345bd 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "REPORTS_FIELD_NUMBER" - mtype: "" + name: "reports" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt index 7c8c68e155c99d..e7ca821951237f 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "KEY_FIELD_NUMBER" - mtype: "" + name: "key" + mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt index 1b789f4fc92ed6..330d6ee7befaab 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.profiler.AdviceProto" tf_class { is_instance: "" is_instance: "" - member { - name: "CHECKERS_FIELD_NUMBER" - mtype: "" - } member { name: "Checker" mtype: "" @@ -22,6 +18,10 @@ tf_class { name: "Extensions" mtype: "" } + member { + name: "checkers" + mtype: "" + } member_method { name: "ByteSize" } diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt index f0b9605bee1c7c..85aef3e8a40df5 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "KEY_FIELD_NUMBER" - mtype: "" + name: "key" + mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt index b80896a8a0f36a..2ecfb6a9715032 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt @@ -3,124 +3,124 @@ tf_class { is_instance: "" is_instance: "" member { - name: "ACCELERATOR_EXEC_MICROS_FIELD_NUMBER" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "CHILDREN_FIELD_NUMBER" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "CPU_EXEC_MICROS_FIELD_NUMBER" - mtype: "" + name: "InputShapesEntry" + mtype: "" } member { - name: "DESCRIPTOR" - mtype: "" + name: "accelerator_exec_micros" + mtype: "" } member { - name: "DEVICES_FIELD_NUMBER" - mtype: "" + name: "children" + mtype: "" } member { - name: "EXEC_MICROS_FIELD_NUMBER" - mtype: "" + name: "cpu_exec_micros" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "devices" + mtype: "" } member { - name: "FLOAT_OPS_FIELD_NUMBER" - mtype: "" + name: "exec_micros" + mtype: "" } member { - name: "INPUT_SHAPES_FIELD_NUMBER" - mtype: "" + name: "float_ops" + mtype: "" } member { - name: "InputShapesEntry" - mtype: "" + name: "input_shapes" + mtype: "" } member { - name: "NAME_FIELD_NUMBER" - mtype: "" + name: "name" + mtype: "" } member { - name: "OUTPUT_BYTES_FIELD_NUMBER" - mtype: "" + name: "output_bytes" + mtype: "" } member { - name: "PARAMETERS_FIELD_NUMBER" - mtype: "" + name: "parameters" + mtype: "" } member { - name: "PEAK_BYTES_FIELD_NUMBER" - mtype: "" + name: "peak_bytes" + mtype: "" } member { - name: "REQUESTED_BYTES_FIELD_NUMBER" - mtype: "" + name: "requested_bytes" + mtype: "" } member { - name: "RESIDUAL_BYTES_FIELD_NUMBER" - mtype: "" + name: "residual_bytes" + mtype: "" } member { - name: "RUN_COUNT_FIELD_NUMBER" - mtype: "" + name: "run_count" + mtype: "" } member { - name: "SHAPES_FIELD_NUMBER" - mtype: "" + name: "shapes" + mtype: "" } member { - name: "TENSOR_VALUE_FIELD_NUMBER" - mtype: "" + name: "tensor_value" + mtype: "" } member { - name: "TOTAL_ACCELERATOR_EXEC_MICROS_FIELD_NUMBER" - mtype: "" + name: "total_accelerator_exec_micros" + mtype: "" } member { - name: "TOTAL_CPU_EXEC_MICROS_FIELD_NUMBER" - mtype: "" + name: "total_cpu_exec_micros" + mtype: "" } member { - name: "TOTAL_DEFINITION_COUNT_FIELD_NUMBER" - mtype: "" + name: "total_definition_count" + mtype: "" } member { - name: "TOTAL_EXEC_MICROS_FIELD_NUMBER" - mtype: "" + name: "total_exec_micros" + mtype: "" } member { - name: "TOTAL_FLOAT_OPS_FIELD_NUMBER" - mtype: "" + name: "total_float_ops" + mtype: "" } member { - name: "TOTAL_OUTPUT_BYTES_FIELD_NUMBER" - mtype: "" + name: "total_output_bytes" + mtype: "" } member { - name: "TOTAL_PARAMETERS_FIELD_NUMBER" - mtype: "" + name: "total_parameters" + mtype: "" } member { - name: "TOTAL_PEAK_BYTES_FIELD_NUMBER" - mtype: "" + name: "total_peak_bytes" + mtype: "" } member { - name: "TOTAL_REQUESTED_BYTES_FIELD_NUMBER" - mtype: "" + name: "total_requested_bytes" + mtype: "" } member { - name: "TOTAL_RESIDUAL_BYTES_FIELD_NUMBER" - mtype: "" + name: "total_residual_bytes" + mtype: "" } member { - name: "TOTAL_RUN_COUNT_FIELD_NUMBER" - mtype: "" + name: "total_run_count" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt index 33deff64979132..b35d0d6e482fec 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt @@ -3,96 +3,96 @@ tf_class { is_instance: "" is_instance: "" member { - name: "ACCELERATOR_EXEC_MICROS_FIELD_NUMBER" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "CHILDREN_FIELD_NUMBER" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "CPU_EXEC_MICROS_FIELD_NUMBER" - mtype: "" + name: "accelerator_exec_micros" + mtype: "" } member { - name: "DESCRIPTOR" - mtype: "" + name: "children" + mtype: "" } member { - name: "EXEC_MICROS_FIELD_NUMBER" - mtype: "" + name: "cpu_exec_micros" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "exec_micros" + mtype: "" } member { - name: "FLOAT_OPS_FIELD_NUMBER" - mtype: "" + name: "float_ops" + mtype: "" } member { - name: "GRAPH_NODES_FIELD_NUMBER" - mtype: "" + name: "graph_nodes" + mtype: "" } member { - name: "NAME_FIELD_NUMBER" - mtype: "" + name: "name" + mtype: "" } member { - name: "OUTPUT_BYTES_FIELD_NUMBER" - mtype: "" + name: "output_bytes" + mtype: "" } member { - name: "PARAMETERS_FIELD_NUMBER" - mtype: "" + name: "parameters" + mtype: "" } member { - name: "PEAK_BYTES_FIELD_NUMBER" - mtype: "" + name: "peak_bytes" + mtype: "" } member { - name: "REQUESTED_BYTES_FIELD_NUMBER" - mtype: "" + name: "requested_bytes" + mtype: "" } member { - name: "RESIDUAL_BYTES_FIELD_NUMBER" - mtype: "" + name: "residual_bytes" + mtype: "" } member { - name: "TOTAL_ACCELERATOR_EXEC_MICROS_FIELD_NUMBER" - mtype: "" + name: "total_accelerator_exec_micros" + mtype: "" } member { - name: "TOTAL_CPU_EXEC_MICROS_FIELD_NUMBER" - mtype: "" + name: "total_cpu_exec_micros" + mtype: "" } member { - name: "TOTAL_EXEC_MICROS_FIELD_NUMBER" - mtype: "" + name: "total_exec_micros" + mtype: "" } member { - name: "TOTAL_FLOAT_OPS_FIELD_NUMBER" - mtype: "" + name: "total_float_ops" + mtype: "" } member { - name: "TOTAL_OUTPUT_BYTES_FIELD_NUMBER" - mtype: "" + name: "total_output_bytes" + mtype: "" } member { - name: "TOTAL_PARAMETERS_FIELD_NUMBER" - mtype: "" + name: "total_parameters" + mtype: "" } member { - name: "TOTAL_PEAK_BYTES_FIELD_NUMBER" - mtype: "" + name: "total_peak_bytes" + mtype: "" } member { - name: "TOTAL_REQUESTED_BYTES_FIELD_NUMBER" - mtype: "" + name: "total_requested_bytes" + mtype: "" } member { - name: "TOTAL_RESIDUAL_BYTES_FIELD_NUMBER" - mtype: "" + name: "total_residual_bytes" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt index 8c4727cf35bdfc..495a63cfebd1ae 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "KEY_FIELD_NUMBER" - mtype: "" + name: "key" + mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt index 1071a82b5ce139..b74d7f8a55f086 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt @@ -10,17 +10,17 @@ tf_class { name: "Extensions" mtype: "" } - member { - name: "ID_TO_STRING_FIELD_NUMBER" - mtype: "" - } member { name: "IdToStringEntry" mtype: "" } member { - name: "LOG_ENTRIES_FIELD_NUMBER" - mtype: "" + name: "id_to_string" + mtype: "" + } + member { + name: "log_entries" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-event.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-event.pbtxt index ab3449d80f6108..7ac8470a7aecbf 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-event.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-event.pbtxt @@ -11,40 +11,40 @@ tf_class { mtype: "" } member { - name: "FILE_VERSION_FIELD_NUMBER" - mtype: "" + name: "file_version" + mtype: "" } member { - name: "GRAPH_DEF_FIELD_NUMBER" - mtype: "" + name: "graph_def" + mtype: "" } member { - name: "LOG_MESSAGE_FIELD_NUMBER" - mtype: "" + name: "log_message" + mtype: "" } member { - name: "META_GRAPH_DEF_FIELD_NUMBER" - mtype: "" + name: "meta_graph_def" + mtype: "" } member { - name: "SESSION_LOG_FIELD_NUMBER" - mtype: "" + name: "session_log" + mtype: "" } member { - name: "STEP_FIELD_NUMBER" - mtype: "" + name: "step" + mtype: "" } member { - name: "SUMMARY_FIELD_NUMBER" - mtype: "" + name: "summary" + mtype: "" } member { - name: "TAGGED_RUN_METADATA_FIELD_NUMBER" - mtype: "" + name: "tagged_run_metadata" + mtype: "" } member { - name: "WALL_TIME_FIELD_NUMBER" - mtype: "" + name: "wall_time" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-session-log.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-session-log.pbtxt index 92ca4872caf1c1..d1e7e9eedb0e7a 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-session-log.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-session-log.pbtxt @@ -6,10 +6,6 @@ tf_class { name: "CHECKPOINT" mtype: "" } - member { - name: "CHECKPOINT_PATH_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -18,18 +14,10 @@ tf_class { name: "Extensions" mtype: "" } - member { - name: "MSG_FIELD_NUMBER" - mtype: "" - } member { name: "START" mtype: "" } - member { - name: "STATUS_FIELD_NUMBER" - mtype: "" - } member { name: "STATUS_UNSPECIFIED" mtype: "" @@ -42,6 +30,18 @@ tf_class { name: "SessionStatus" mtype: "" } + member { + name: "checkpoint_path" + mtype: "" + } + member { + name: "msg" + mtype: "" + } + member { + name: "status" + mtype: "" + } member_method { name: "ByteSize" } diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-summary-description.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-summary-description.pbtxt index f93da2196adbc2..6fe3c755c9fd92 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-summary-description.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-summary-description.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "TYPE_HINT_FIELD_NUMBER" - mtype: "" + name: "type_hint" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-summary.-audio.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-summary.-audio.pbtxt index 605e305e82cc3f..8cc842852439b4 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-summary.-audio.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-summary.-audio.pbtxt @@ -2,33 +2,33 @@ path: "tensorflow.summary.Summary.Audio" tf_class { is_instance: "" is_instance: "" - member { - name: "CONTENT_TYPE_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" } - member { - name: "ENCODED_AUDIO_STRING_FIELD_NUMBER" - mtype: "" - } member { name: "Extensions" mtype: "" } member { - name: "LENGTH_FRAMES_FIELD_NUMBER" - mtype: "" + name: "content_type" + mtype: "" + } + member { + name: "encoded_audio_string" + mtype: "" + } + member { + name: "length_frames" + mtype: "" } member { - name: "NUM_CHANNELS_FIELD_NUMBER" - mtype: "" + name: "num_channels" + mtype: "" } member { - name: "SAMPLE_RATE_FIELD_NUMBER" - mtype: "" + name: "sample_rate" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-summary.-image.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-summary.-image.pbtxt index 0646972196dc72..455452b550638a 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-summary.-image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-summary.-image.pbtxt @@ -2,29 +2,29 @@ path: "tensorflow.summary.Summary.Image" tf_class { is_instance: "" is_instance: "" - member { - name: "COLORSPACE_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" } - member { - name: "ENCODED_IMAGE_STRING_FIELD_NUMBER" - mtype: "" - } member { name: "Extensions" mtype: "" } member { - name: "HEIGHT_FIELD_NUMBER" - mtype: "" + name: "colorspace" + mtype: "" + } + member { + name: "encoded_image_string" + mtype: "" + } + member { + name: "height" + mtype: "" } member { - name: "WIDTH_FIELD_NUMBER" - mtype: "" + name: "width" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-summary.-value.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-summary.-value.pbtxt index b319cd03d9e867..bc9378c75edcf5 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-summary.-value.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-summary.-value.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.summary.Summary.Value" tf_class { is_instance: "" is_instance: "" - member { - name: "AUDIO_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -15,36 +11,40 @@ tf_class { mtype: "" } member { - name: "HISTO_FIELD_NUMBER" - mtype: "" + name: "audio" + mtype: "" + } + member { + name: "histo" + mtype: "" } member { - name: "IMAGE_FIELD_NUMBER" - mtype: "" + name: "image" + mtype: "" } member { - name: "METADATA_FIELD_NUMBER" - mtype: "" + name: "metadata" + mtype: "" } member { - name: "NODE_NAME_FIELD_NUMBER" - mtype: "" + name: "node_name" + mtype: "" } member { - name: "OBSOLETE_OLD_STYLE_HISTOGRAM_FIELD_NUMBER" - mtype: "" + name: "obsolete_old_style_histogram" + mtype: "" } member { - name: "SIMPLE_VALUE_FIELD_NUMBER" - mtype: "" + name: "simple_value" + mtype: "" } member { - name: "TAG_FIELD_NUMBER" - mtype: "" + name: "tag" + mtype: "" } member { - name: "TENSOR_FIELD_NUMBER" - mtype: "" + name: "tensor" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-summary.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-summary.pbtxt index 132ef1b7d2e933..c724074d8c1eaf 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-summary.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-summary.pbtxt @@ -18,14 +18,14 @@ tf_class { name: "Image" mtype: "" } - member { - name: "VALUE_FIELD_NUMBER" - mtype: "" - } member { name: "Value" mtype: "" } + member { + name: "value" + mtype: "" + } member_method { name: "ByteSize" } diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-tagged-run-metadata.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-tagged-run-metadata.pbtxt index 4dce20819de06f..5daec17b68963e 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-tagged-run-metadata.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-tagged-run-metadata.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "RUN_METADATA_FIELD_NUMBER" - mtype: "" + name: "run_metadata" + mtype: "" } member { - name: "TAG_FIELD_NUMBER" - mtype: "" + name: "tag" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-bytes-list.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-bytes-list.pbtxt index 8cf52b817f342a..5ca8b21ed03f64 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-bytes-list.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-bytes-list.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-cluster-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-cluster-def.pbtxt index 93ff856b09de15..76ed034e73d481 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-cluster-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-cluster-def.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "JOB_FIELD_NUMBER" - mtype: "" + name: "job" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-example.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-example.pbtxt index f7215a20372e98..f516cac1394949 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-example.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-example.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "FEATURES_FIELD_NUMBER" - mtype: "" + name: "features" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-feature-list.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-feature-list.pbtxt index 3ad98354d69453..b5b77fe3cd6a1f 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-feature-list.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-feature-list.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "FEATURE_FIELD_NUMBER" - mtype: "" + name: "feature" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.-feature-list-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.-feature-list-entry.pbtxt index cd171f4ca3ef1e..774cfc53af3f8b 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.-feature-list-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.-feature-list-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "KEY_FIELD_NUMBER" - mtype: "" + name: "key" + mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.pbtxt index 3d95017d584ad9..430f6b41b1d48b 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.pbtxt @@ -10,14 +10,14 @@ tf_class { name: "Extensions" mtype: "" } - member { - name: "FEATURE_LIST_FIELD_NUMBER" - mtype: "" - } member { name: "FeatureListEntry" mtype: "" } + member { + name: "feature_list" + mtype: "" + } member_method { name: "ByteSize" } diff --git a/tensorflow/tools/api/golden/tensorflow.train.-feature.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-feature.pbtxt index 9cca132bba91c4..48014a90babc23 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-feature.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-feature.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.train.Feature" tf_class { is_instance: "" is_instance: "" - member { - name: "BYTES_LIST_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -15,12 +11,16 @@ tf_class { mtype: "" } member { - name: "FLOAT_LIST_FIELD_NUMBER" - mtype: "" + name: "bytes_list" + mtype: "" + } + member { + name: "float_list" + mtype: "" } member { - name: "INT64_LIST_FIELD_NUMBER" - mtype: "" + name: "int64_list" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-features.-feature-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-features.-feature-entry.pbtxt index 858aee03415dea..8f68927d103162 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-features.-feature-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-features.-feature-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "KEY_FIELD_NUMBER" - mtype: "" + name: "key" + mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-features.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-features.pbtxt index 49cd12153bf307..94e24126f15cea 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-features.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-features.pbtxt @@ -10,14 +10,14 @@ tf_class { name: "Extensions" mtype: "" } - member { - name: "FEATURE_FIELD_NUMBER" - mtype: "" - } member { name: "FeatureEntry" mtype: "" } + member { + name: "feature" + mtype: "" + } member_method { name: "ByteSize" } diff --git a/tensorflow/tools/api/golden/tensorflow.train.-float-list.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-float-list.pbtxt index e3f01334b547fe..37413782a1020f 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-float-list.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-float-list.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-int64-list.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-int64-list.pbtxt index 8917dc122cfd0b..0c775cf46e3f9e 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-int64-list.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-int64-list.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-job-def.-tasks-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-job-def.-tasks-entry.pbtxt index ac6d81541a43e9..5f0fe5c8a0e90b 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-job-def.-tasks-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-job-def.-tasks-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "KEY_FIELD_NUMBER" - mtype: "" + name: "key" + mtype: "" } member { - name: "VALUE_FIELD_NUMBER" - mtype: "" + name: "value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-job-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-job-def.pbtxt index ce34537fa13b92..20a76e517f36d7 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-job-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-job-def.pbtxt @@ -11,16 +11,16 @@ tf_class { mtype: "" } member { - name: "NAME_FIELD_NUMBER" - mtype: "" + name: "TasksEntry" + mtype: "" } member { - name: "TASKS_FIELD_NUMBER" - mtype: "" + name: "name" + mtype: "" } member { - name: "TasksEntry" - mtype: "" + name: "tasks" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-saver-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-saver-def.pbtxt index 84498a64f5b045..24705d0558c20b 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-saver-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-saver-def.pbtxt @@ -15,44 +15,44 @@ tf_class { mtype: "" } member { - name: "FILENAME_TENSOR_NAME_FIELD_NUMBER" + name: "LEGACY" mtype: "" } member { - name: "KEEP_CHECKPOINT_EVERY_N_HOURS_FIELD_NUMBER" + name: "V1" mtype: "" } member { - name: "LEGACY" + name: "V2" mtype: "" } member { - name: "MAX_TO_KEEP_FIELD_NUMBER" - mtype: "" + name: "filename_tensor_name" + mtype: "" } member { - name: "RESTORE_OP_NAME_FIELD_NUMBER" - mtype: "" + name: "keep_checkpoint_every_n_hours" + mtype: "" } member { - name: "SAVE_TENSOR_NAME_FIELD_NUMBER" - mtype: "" + name: "max_to_keep" + mtype: "" } member { - name: "SHARDED_FIELD_NUMBER" - mtype: "" + name: "restore_op_name" + mtype: "" } member { - name: "V1" - mtype: "" + name: "save_tensor_name" + mtype: "" } member { - name: "V2" - mtype: "" + name: "sharded" + mtype: "" } member { - name: "VERSION_FIELD_NUMBER" - mtype: "" + name: "version" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-sequence-example.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-sequence-example.pbtxt index 9ab95537021167..4ad3ede3614469 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-sequence-example.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-sequence-example.pbtxt @@ -2,10 +2,6 @@ path: "tensorflow.train.SequenceExample" tf_class { is_instance: "" is_instance: "" - member { - name: "CONTEXT_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -15,8 +11,12 @@ tf_class { mtype: "" } member { - name: "FEATURE_LISTS_FIELD_NUMBER" - mtype: "" + name: "context" + mtype: "" + } + member { + name: "feature_lists" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-server-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-server-def.pbtxt index af0a3b73cc2ff3..d1358cc60d2ea7 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-server-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-server-def.pbtxt @@ -2,14 +2,6 @@ path: "tensorflow.train.ServerDef" tf_class { is_instance: "" is_instance: "" - member { - name: "CLUSTER_FIELD_NUMBER" - mtype: "" - } - member { - name: "DEFAULT_SESSION_CONFIG_FIELD_NUMBER" - mtype: "" - } member { name: "DESCRIPTOR" mtype: "" @@ -19,16 +11,24 @@ tf_class { mtype: "" } member { - name: "JOB_NAME_FIELD_NUMBER" - mtype: "" + name: "cluster" + mtype: "" + } + member { + name: "default_session_config" + mtype: "" + } + member { + name: "job_name" + mtype: "" } member { - name: "PROTOCOL_FIELD_NUMBER" - mtype: "" + name: "protocol" + mtype: "" } member { - name: "TASK_INDEX_FIELD_NUMBER" - mtype: "" + name: "task_index" + mtype: "" } member_method { name: "ByteSize" From 10337c91efe7e3975134a7b09ea598e85877c1b0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 1 May 2018 07:56:08 -0700 Subject: [PATCH 0218/1691] Preventing RemoveTrivialBinary from removing broadcasts. PiperOrigin-RevId: 194937001 --- .../toco/graph_transformations/quantize.cc | 5 +++++ .../remove_trivial_binary.cc | 19 +++++++++++++++++++ 2 files changed, 24 insertions(+) diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc index fa46e6bc3805d3..347302c7a50b81 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc @@ -96,6 +96,11 @@ const MinMax& GetOrComputeMinMax(Model* model, const string& array_name) { min = std::min(min, val); max = std::max(max, val); } + if (min == 0.f && max == 0.f) { + // Prevent downstream anger from quantized math that expects min and max + // to not be equal. + max = 1.f; + } auto& minmax = array.GetOrCreateMinMax(); minmax.min = min; minmax.max = max; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_binary.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_binary.cc index 95a50c61794092..0dfdc40e4c3410 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_binary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_binary.cc @@ -78,6 +78,25 @@ bool RemoveTrivialBinaryOperator::Run(Model* model, std::size_t op_index) { CHECK(is_input_constant[index_of_constant_input]); CHECK(!is_input_constant[index_of_variable_input]); + // If this was a broadcasting op we can't remove it as we need the broadcast. + // It's possible we could replace it with a cheaper op, though. + const auto& input_array_0 = model->GetArray(binary_op->inputs[0]); + const auto& input_array_1 = model->GetArray(binary_op->inputs[1]); + if (!input_array_0.has_shape() || !input_array_1.has_shape()) { + // Both input shapes must be known. + return false; + } + if (input_array_0.shape().dimensions_count() == + input_array_1.shape().dimensions_count() && + input_array_0.shape() != input_array_1.shape()) { + AddMessageF( + "Preserving %s even though it's trivial as we need to broadcast " + "(lhs %s, rhs %s)", + LogName(*binary_op), ShapeToString(input_array_0.shape()), + ShapeToString(input_array_1.shape())); + return false; + } + // Now check if the constant operand makes this binary // operator trivial. const auto& constant_input_array = From 449b9e56ed8974eefdb87d7cf08ef1c1841f9d6e Mon Sep 17 00:00:00 2001 From: joel-shor Date: Tue, 1 May 2018 18:33:18 +0300 Subject: [PATCH 0219/1691] [tf.data] More debug code, since the previous 'fix' wasn't a fix. --- tensorflow/contrib/data/python/ops/BUILD | 1 + tensorflow/contrib/data/python/ops/interleave_ops.py | 6 ++++++ 2 files changed, 7 insertions(+) diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 7a3e42cc72755c..091723e0c73ace 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -184,6 +184,7 @@ py_library( "//tensorflow/python/data/ops:readers", "//tensorflow/python/data/util:nest", "//tensorflow/python/data/util:sparse", + ""//tensorflow/python:platform", ], ) diff --git a/tensorflow/contrib/data/python/ops/interleave_ops.py b/tensorflow/contrib/data/python/ops/interleave_ops.py index 812a50ecbf1053..b3bf82ea3b7a50 100644 --- a/tensorflow/contrib/data/python/ops/interleave_ops.py +++ b/tensorflow/contrib/data/python/ops/interleave_ops.py @@ -30,6 +30,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.util import deprecation +from tensorflow.python.platform import tf_logging as logging def parallel_interleave(map_func, @@ -239,4 +240,9 @@ def select_dataset(logits, seed): selector_input = dataset_ops.Dataset.zip( (logits_ds, random_ops.RandomDataset(seed).batch(2))).map(select_dataset) + print('selector_input.output_types: ', selector_input.output_types) + print('selector_input.output_shapes: ', selector_input.output_shapes) + for i, dataset in enumerate(datasets): + print('dataset %i output_types: %s' % (i, dataset.output_types)) + print('dataset %i output_shapes: %s' % (i, dataset.output_shapes)) return DirectedInterleaveDataset(selector_input, datasets) From 2364000088aa95a913731c127c10f3bfffac9000 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Tue, 1 May 2018 18:35:33 +0300 Subject: [PATCH 0220/1691] [tf.data] More debug code, since the previous 'fix' wasn't a fix. --- tensorflow/contrib/data/python/ops/interleave_ops.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/interleave_ops.py b/tensorflow/contrib/data/python/ops/interleave_ops.py index b3bf82ea3b7a50..140abde21c1f5b 100644 --- a/tensorflow/contrib/data/python/ops/interleave_ops.py +++ b/tensorflow/contrib/data/python/ops/interleave_ops.py @@ -240,9 +240,9 @@ def select_dataset(logits, seed): selector_input = dataset_ops.Dataset.zip( (logits_ds, random_ops.RandomDataset(seed).batch(2))).map(select_dataset) - print('selector_input.output_types: ', selector_input.output_types) - print('selector_input.output_shapes: ', selector_input.output_shapes) + logging.warn('selector_input.output_types: ', selector_input.output_types) + logging.warn('selector_input.output_shapes: ', selector_input.output_shapes) for i, dataset in enumerate(datasets): - print('dataset %i output_types: %s' % (i, dataset.output_types)) - print('dataset %i output_shapes: %s' % (i, dataset.output_shapes)) + logging.warn('dataset %i output_types: %s' % (i, dataset.output_types)) + logging.warn('dataset %i output_shapes: %s' % (i, dataset.output_shapes)) return DirectedInterleaveDataset(selector_input, datasets) From 03cecc5eb3a0486bea54e496b000ce50d185c9dc Mon Sep 17 00:00:00 2001 From: joel-shor Date: Tue, 1 May 2018 18:58:55 +0300 Subject: [PATCH 0221/1691] [tf.data] Fix BUILD file. --- tensorflow/contrib/data/python/ops/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 091723e0c73ace..9959ccc0057cc4 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -184,7 +184,7 @@ py_library( "//tensorflow/python/data/ops:readers", "//tensorflow/python/data/util:nest", "//tensorflow/python/data/util:sparse", - ""//tensorflow/python:platform", + "//tensorflow/python:platform", ], ) From a82e0e7922d6dc657b42ef2b3a7a1a52194454c8 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 1 May 2018 09:07:57 -0700 Subject: [PATCH 0222/1691] Fix crash in HloGraphDumper where it crashes on tuple shaped constants The problem is that it tries to use a special logic for 0 element constants but the logic used to check the number of elements only supports array shapes. PiperOrigin-RevId: 194945246 --- .../compiler/xla/service/hlo_graph_dumper.cc | 2 +- .../xla/service/hlo_graph_dumper_test.cc | 18 ++++++++++++++++++ 2 files changed, 19 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc index 516e14b4642ae6..bb4db89f0a242c 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc @@ -804,7 +804,7 @@ string HloDotDumper::GetInstructionNodeInlinedOperands( // "{} (f32[42, 0, 10])". The alternative, calling Literal::ToString(), // enumerates all of its empty dimensions (e.g. "{ { {}, {} }, ..."), which // is just noise. - if (ShapeUtil::HasZeroElements(shape)) { + if (!ShapeUtil::IsTuple(shape) && ShapeUtil::HasZeroElements(shape)) { return Printf("{} (%s)", ShapeUtil::HumanString(constant->shape())); } diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc index 4843963243000d..8e52d926d85f1c 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc @@ -131,5 +131,23 @@ TEST(HloGraphDumperTest, Constant) { EXPECT_THAT(graph, Not(HasSubstr("i_am_a_constant_root_instruction"))); } +TEST(HloGraphDumperTest, TupleConstant) { + Shape tuple_shape = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShape(F32, {3, 2}), ShapeUtil::MakeShape(S32, {4, 5})}); + HloComputation::Builder b("b"); + auto constant = b.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateFromShape(tuple_shape))); + auto gte = b.AddInstruction(HloInstruction::CreateGetTupleElement( + ShapeUtil::MakeShape(F32, {3, 2}), constant, 0)); + + HloModuleConfig config; + HloModule m(TestName(), config); + HloComputation* root_computation = m.AddEntryComputation(b.Build(gte)); + string graph = hlo_graph_dumper::DumpGraph( + *root_computation, /*label=*/"tuple_constant", DebugOptions()); + EXPECT_THAT(graph, HasSubstr("tuple_constant")); + EXPECT_THAT(graph, HasSubstr("constant (f32[3,2], s32[4,5])")); +} + } // anonymous namespace } // namespace xla From da02e19813b6d03a6ea5ff7c910d3e71644fbb34 Mon Sep 17 00:00:00 2001 From: joel-shor Date: Tue, 1 May 2018 19:53:30 +0300 Subject: [PATCH 0223/1691] [tf.data] Fix debug output. --- tensorflow/contrib/data/python/ops/interleave_ops.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/interleave_ops.py b/tensorflow/contrib/data/python/ops/interleave_ops.py index 140abde21c1f5b..0852fc6be8240c 100644 --- a/tensorflow/contrib/data/python/ops/interleave_ops.py +++ b/tensorflow/contrib/data/python/ops/interleave_ops.py @@ -240,8 +240,8 @@ def select_dataset(logits, seed): selector_input = dataset_ops.Dataset.zip( (logits_ds, random_ops.RandomDataset(seed).batch(2))).map(select_dataset) - logging.warn('selector_input.output_types: ', selector_input.output_types) - logging.warn('selector_input.output_shapes: ', selector_input.output_shapes) + logging.warn('selector_input.output_types: %s', selector_input.output_types) + logging.warn('selector_input.output_shapes: %s', selector_input.output_shapes) for i, dataset in enumerate(datasets): logging.warn('dataset %i output_types: %s' % (i, dataset.output_types)) logging.warn('dataset %i output_shapes: %s' % (i, dataset.output_shapes)) From bb8220355eda0183a3c039bef1e72c5450f58c11 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Tue, 1 May 2018 11:04:26 -0700 Subject: [PATCH 0224/1691] Automated g4 rollback of changelist 194917415 PiperOrigin-RevId: 194962702 --- .../tensorflow.-attr-value.-list-value.pbtxt | 36 +++--- .../api/golden/tensorflow.-attr-value.pbtxt | 48 +++---- ...ow.-config-proto.-device-count-entry.pbtxt | 8 +- .../api/golden/tensorflow.-config-proto.pbtxt | 72 +++++------ .../tools/api/golden/tensorflow.-event.pbtxt | 36 +++--- .../golden/tensorflow.-g-p-u-options.pbtxt | 48 +++---- .../api/golden/tensorflow.-graph-def.pbtxt | 16 +-- .../golden/tensorflow.-graph-options.pbtxt | 44 +++---- .../golden/tensorflow.-histogram-proto.pbtxt | 36 +++--- .../api/golden/tensorflow.-log-message.pbtxt | 16 +-- ...meta-graph-def.-collection-def-entry.pbtxt | 8 +- ...rflow.-meta-graph-def.-meta-info-def.pbtxt | 32 ++--- ...-meta-graph-def.-signature-def-entry.pbtxt | 8 +- .../golden/tensorflow.-meta-graph-def.pbtxt | 40 +++--- ...nsorflow.-name-attr-list.-attr-entry.pbtxt | 8 +- .../golden/tensorflow.-name-attr-list.pbtxt | 12 +- .../tensorflow.-node-def.-attr-entry.pbtxt | 8 +- .../api/golden/tensorflow.-node-def.pbtxt | 28 ++-- .../tensorflow.-optimizer-options.pbtxt | 44 +++---- .../api/golden/tensorflow.-run-metadata.pbtxt | 16 +-- .../api/golden/tensorflow.-run-options.pbtxt | 36 +++--- .../api/golden/tensorflow.-session-log.pbtxt | 24 ++-- ...rflow.-summary-metadata.-plugin-data.pbtxt | 12 +- .../golden/tensorflow.-summary-metadata.pbtxt | 20 +-- .../golden/tensorflow.-summary.-audio.pbtxt | 28 ++-- .../golden/tensorflow.-summary.-image.pbtxt | 24 ++-- .../golden/tensorflow.-summary.-value.pbtxt | 40 +++--- .../api/golden/tensorflow.-summary.pbtxt | 8 +- .../tensorflow.-tensor-info.-coo-sparse.pbtxt | 16 +-- .../api/golden/tensorflow.-tensor-info.pbtxt | 24 ++-- ...flow.profiler.-advice-proto.-checker.pbtxt | 4 +- ...ofiler.-advice-proto.-checkers-entry.pbtxt | 8 +- .../tensorflow.profiler.-advice-proto.pbtxt | 8 +- ...graph-node-proto.-input-shapes-entry.pbtxt | 8 +- ...ensorflow.profiler.-graph-node-proto.pbtxt | 120 +++++++++--------- ...low.profiler.-multi-graph-node-proto.pbtxt | 92 +++++++------- ...er.-op-log-proto.-id-to-string-entry.pbtxt | 8 +- .../tensorflow.profiler.-op-log-proto.pbtxt | 12 +- .../golden/tensorflow.summary.-event.pbtxt | 36 +++--- .../tensorflow.summary.-session-log.pbtxt | 24 ++-- ...sorflow.summary.-summary-description.pbtxt | 4 +- .../tensorflow.summary.-summary.-audio.pbtxt | 28 ++-- .../tensorflow.summary.-summary.-image.pbtxt | 24 ++-- .../tensorflow.summary.-summary.-value.pbtxt | 40 +++--- .../golden/tensorflow.summary.-summary.pbtxt | 8 +- ...sorflow.summary.-tagged-run-metadata.pbtxt | 8 +- .../golden/tensorflow.train.-bytes-list.pbtxt | 4 +- .../tensorflow.train.-cluster-def.pbtxt | 4 +- .../golden/tensorflow.train.-example.pbtxt | 4 +- .../tensorflow.train.-feature-list.pbtxt | 4 +- ...n.-feature-lists.-feature-list-entry.pbtxt | 8 +- .../tensorflow.train.-feature-lists.pbtxt | 8 +- .../golden/tensorflow.train.-feature.pbtxt | 16 +-- ...rflow.train.-features.-feature-entry.pbtxt | 8 +- .../golden/tensorflow.train.-features.pbtxt | 8 +- .../golden/tensorflow.train.-float-list.pbtxt | 4 +- .../golden/tensorflow.train.-int64-list.pbtxt | 4 +- ...nsorflow.train.-job-def.-tasks-entry.pbtxt | 8 +- .../golden/tensorflow.train.-job-def.pbtxt | 12 +- .../golden/tensorflow.train.-saver-def.pbtxt | 34 ++--- .../tensorflow.train.-sequence-example.pbtxt | 12 +- .../golden/tensorflow.train.-server-def.pbtxt | 28 ++-- 62 files changed, 697 insertions(+), 697 deletions(-) diff --git a/tensorflow/tools/api/golden/tensorflow.-attr-value.-list-value.pbtxt b/tensorflow/tools/api/golden/tensorflow.-attr-value.-list-value.pbtxt index 004d7169549395..0fb1aaba2831e6 100644 --- a/tensorflow/tools/api/golden/tensorflow.-attr-value.-list-value.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-attr-value.-list-value.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.AttrValue.ListValue" tf_class { is_instance: "" is_instance: "" + member { + name: "B_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -11,36 +15,32 @@ tf_class { mtype: "" } member { - name: "b" - mtype: "" - } - member { - name: "f" - mtype: "" + name: "FUNC_FIELD_NUMBER" + mtype: "" } member { - name: "func" - mtype: "" + name: "F_FIELD_NUMBER" + mtype: "" } member { - name: "i" - mtype: "" + name: "I_FIELD_NUMBER" + mtype: "" } member { - name: "s" - mtype: "" + name: "SHAPE_FIELD_NUMBER" + mtype: "" } member { - name: "shape" - mtype: "" + name: "S_FIELD_NUMBER" + mtype: "" } member { - name: "tensor" - mtype: "" + name: "TENSOR_FIELD_NUMBER" + mtype: "" } member { - name: "type" - mtype: "" + name: "TYPE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-attr-value.pbtxt b/tensorflow/tools/api/golden/tensorflow.-attr-value.pbtxt index 2996e02483ebc0..e7a3a1f02faf10 100644 --- a/tensorflow/tools/api/golden/tensorflow.-attr-value.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-attr-value.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.AttrValue" tf_class { is_instance: "" is_instance: "" + member { + name: "B_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -11,48 +15,44 @@ tf_class { mtype: "" } member { - name: "ListValue" - mtype: "" - } - member { - name: "b" - mtype: "" + name: "FUNC_FIELD_NUMBER" + mtype: "" } member { - name: "f" - mtype: "" + name: "F_FIELD_NUMBER" + mtype: "" } member { - name: "func" - mtype: "" + name: "I_FIELD_NUMBER" + mtype: "" } member { - name: "i" - mtype: "" + name: "LIST_FIELD_NUMBER" + mtype: "" } member { - name: "list" - mtype: "" + name: "ListValue" + mtype: "" } member { - name: "placeholder" - mtype: "" + name: "PLACEHOLDER_FIELD_NUMBER" + mtype: "" } member { - name: "s" - mtype: "" + name: "SHAPE_FIELD_NUMBER" + mtype: "" } member { - name: "shape" - mtype: "" + name: "S_FIELD_NUMBER" + mtype: "" } member { - name: "tensor" - mtype: "" + name: "TENSOR_FIELD_NUMBER" + mtype: "" } member { - name: "type" - mtype: "" + name: "TYPE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-config-proto.-device-count-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.-config-proto.-device-count-entry.pbtxt index c7022e7593da36..29bb3be35cba5f 100644 --- a/tensorflow/tools/api/golden/tensorflow.-config-proto.-device-count-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-config-proto.-device-count-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "key" - mtype: "" + name: "KEY_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-config-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.-config-proto.pbtxt index ca9530de85597a..009d64aed09ddc 100644 --- a/tensorflow/tools/api/golden/tensorflow.-config-proto.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-config-proto.pbtxt @@ -3,76 +3,76 @@ tf_class { is_instance: "" is_instance: "" member { - name: "DESCRIPTOR" - mtype: "" + name: "ALLOW_SOFT_PLACEMENT_FIELD_NUMBER" + mtype: "" } member { - name: "DeviceCountEntry" - mtype: "" + name: "CLUSTER_DEF_FIELD_NUMBER" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "allow_soft_placement" - mtype: "" + name: "DEVICE_COUNT_FIELD_NUMBER" + mtype: "" } member { - name: "cluster_def" - mtype: "" + name: "DEVICE_FILTERS_FIELD_NUMBER" + mtype: "" } member { - name: "device_count" - mtype: "" + name: "DeviceCountEntry" + mtype: "" } member { - name: "device_filters" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "gpu_options" - mtype: "" + name: "GPU_OPTIONS_FIELD_NUMBER" + mtype: "" } member { - name: "graph_options" - mtype: "" + name: "GRAPH_OPTIONS_FIELD_NUMBER" + mtype: "" } member { - name: "inter_op_parallelism_threads" - mtype: "" + name: "INTER_OP_PARALLELISM_THREADS_FIELD_NUMBER" + mtype: "" } member { - name: "intra_op_parallelism_threads" - mtype: "" + name: "INTRA_OP_PARALLELISM_THREADS_FIELD_NUMBER" + mtype: "" } member { - name: "isolate_session_state" - mtype: "" + name: "ISOLATE_SESSION_STATE_FIELD_NUMBER" + mtype: "" } member { - name: "log_device_placement" - mtype: "" + name: "LOG_DEVICE_PLACEMENT_FIELD_NUMBER" + mtype: "" } member { - name: "operation_timeout_in_ms" - mtype: "" + name: "OPERATION_TIMEOUT_IN_MS_FIELD_NUMBER" + mtype: "" } member { - name: "placement_period" - mtype: "" + name: "PLACEMENT_PERIOD_FIELD_NUMBER" + mtype: "" } member { - name: "rpc_options" - mtype: "" + name: "RPC_OPTIONS_FIELD_NUMBER" + mtype: "" } member { - name: "session_inter_op_thread_pool" - mtype: "" + name: "SESSION_INTER_OP_THREAD_POOL_FIELD_NUMBER" + mtype: "" } member { - name: "use_per_session_threads" - mtype: "" + name: "USE_PER_SESSION_THREADS_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-event.pbtxt b/tensorflow/tools/api/golden/tensorflow.-event.pbtxt index fa2f329a87da02..9bf8c124288854 100644 --- a/tensorflow/tools/api/golden/tensorflow.-event.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-event.pbtxt @@ -11,40 +11,40 @@ tf_class { mtype: "" } member { - name: "file_version" - mtype: "" + name: "FILE_VERSION_FIELD_NUMBER" + mtype: "" } member { - name: "graph_def" - mtype: "" + name: "GRAPH_DEF_FIELD_NUMBER" + mtype: "" } member { - name: "log_message" - mtype: "" + name: "LOG_MESSAGE_FIELD_NUMBER" + mtype: "" } member { - name: "meta_graph_def" - mtype: "" + name: "META_GRAPH_DEF_FIELD_NUMBER" + mtype: "" } member { - name: "session_log" - mtype: "" + name: "SESSION_LOG_FIELD_NUMBER" + mtype: "" } member { - name: "step" - mtype: "" + name: "STEP_FIELD_NUMBER" + mtype: "" } member { - name: "summary" - mtype: "" + name: "SUMMARY_FIELD_NUMBER" + mtype: "" } member { - name: "tagged_run_metadata" - mtype: "" + name: "TAGGED_RUN_METADATA_FIELD_NUMBER" + mtype: "" } member { - name: "wall_time" - mtype: "" + name: "WALL_TIME_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt b/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt index 5119c7fa5b3dcd..875d802a9c458e 100644 --- a/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt @@ -3,52 +3,52 @@ tf_class { is_instance: "" is_instance: "" member { - name: "DESCRIPTOR" - mtype: "" + name: "ALLOCATOR_TYPE_FIELD_NUMBER" + mtype: "" } member { - name: "Experimental" - mtype: "" + name: "ALLOW_GROWTH_FIELD_NUMBER" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "DEFERRED_DELETION_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "allocator_type" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "allow_growth" - mtype: "" + name: "EXPERIMENTAL_FIELD_NUMBER" + mtype: "" } member { - name: "deferred_deletion_bytes" - mtype: "" + name: "Experimental" + mtype: "" } member { - name: "experimental" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "force_gpu_compatible" - mtype: "" + name: "FORCE_GPU_COMPATIBLE_FIELD_NUMBER" + mtype: "" } member { - name: "per_process_gpu_memory_fraction" - mtype: "" + name: "PER_PROCESS_GPU_MEMORY_FRACTION_FIELD_NUMBER" + mtype: "" } member { - name: "polling_active_delay_usecs" - mtype: "" + name: "POLLING_ACTIVE_DELAY_USECS_FIELD_NUMBER" + mtype: "" } member { - name: "polling_inactive_delay_msecs" - mtype: "" + name: "POLLING_INACTIVE_DELAY_MSECS_FIELD_NUMBER" + mtype: "" } member { - name: "visible_device_list" - mtype: "" + name: "VISIBLE_DEVICE_LIST_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-graph-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.-graph-def.pbtxt index 318a25a0923116..1495e847cb08ed 100644 --- a/tensorflow/tools/api/golden/tensorflow.-graph-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-graph-def.pbtxt @@ -11,20 +11,20 @@ tf_class { mtype: "" } member { - name: "library" - mtype: "" + name: "LIBRARY_FIELD_NUMBER" + mtype: "" } member { - name: "node" - mtype: "" + name: "NODE_FIELD_NUMBER" + mtype: "" } member { - name: "version" - mtype: "" + name: "VERSIONS_FIELD_NUMBER" + mtype: "" } member { - name: "versions" - mtype: "" + name: "VERSION_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-graph-options.pbtxt b/tensorflow/tools/api/golden/tensorflow.-graph-options.pbtxt index 786d831c707ffa..0844f891cad3d4 100644 --- a/tensorflow/tools/api/golden/tensorflow.-graph-options.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-graph-options.pbtxt @@ -3,48 +3,48 @@ tf_class { is_instance: "" is_instance: "" member { - name: "DESCRIPTOR" - mtype: "" + name: "BUILD_COST_MODEL_AFTER_FIELD_NUMBER" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "BUILD_COST_MODEL_FIELD_NUMBER" + mtype: "" } member { - name: "build_cost_model" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "build_cost_model_after" - mtype: "" + name: "ENABLE_BFLOAT16_SENDRECV_FIELD_NUMBER" + mtype: "" } member { - name: "enable_bfloat16_sendrecv" - mtype: "" + name: "ENABLE_RECV_SCHEDULING_FIELD_NUMBER" + mtype: "" } member { - name: "enable_recv_scheduling" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "infer_shapes" - mtype: "" + name: "INFER_SHAPES_FIELD_NUMBER" + mtype: "" } member { - name: "optimizer_options" - mtype: "" + name: "OPTIMIZER_OPTIONS_FIELD_NUMBER" + mtype: "" } member { - name: "place_pruned_graph" - mtype: "" + name: "PLACE_PRUNED_GRAPH_FIELD_NUMBER" + mtype: "" } member { - name: "rewrite_options" - mtype: "" + name: "REWRITE_OPTIONS_FIELD_NUMBER" + mtype: "" } member { - name: "timeline_step" - mtype: "" + name: "TIMELINE_STEP_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-histogram-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.-histogram-proto.pbtxt index 3eb2d8873a4797..2567d2fe602938 100644 --- a/tensorflow/tools/api/golden/tensorflow.-histogram-proto.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-histogram-proto.pbtxt @@ -3,40 +3,40 @@ tf_class { is_instance: "" is_instance: "" member { - name: "DESCRIPTOR" - mtype: "" + name: "BUCKET_FIELD_NUMBER" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "BUCKET_LIMIT_FIELD_NUMBER" + mtype: "" } member { - name: "bucket" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "bucket_limit" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "max" - mtype: "" + name: "MAX_FIELD_NUMBER" + mtype: "" } member { - name: "min" - mtype: "" + name: "MIN_FIELD_NUMBER" + mtype: "" } member { - name: "num" - mtype: "" + name: "NUM_FIELD_NUMBER" + mtype: "" } member { - name: "sum" - mtype: "" + name: "SUM_FIELD_NUMBER" + mtype: "" } member { - name: "sum_squares" - mtype: "" + name: "SUM_SQUARES_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-log-message.pbtxt b/tensorflow/tools/api/golden/tensorflow.-log-message.pbtxt index 760739f4f34cd3..a43c5eb7e30c3c 100644 --- a/tensorflow/tools/api/golden/tensorflow.-log-message.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-log-message.pbtxt @@ -26,25 +26,25 @@ tf_class { name: "INFO" mtype: "" } + member { + name: "LEVEL_FIELD_NUMBER" + mtype: "" + } member { name: "Level" mtype: "" } member { - name: "UNKNOWN" + name: "MESSAGE_FIELD_NUMBER" mtype: "" } member { - name: "WARN" + name: "UNKNOWN" mtype: "" } member { - name: "level" - mtype: "" - } - member { - name: "message" - mtype: "" + name: "WARN" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt index 69bf5b31a1d9f9..3572126fbfd77d 100644 --- a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "key" - mtype: "" + name: "KEY_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-meta-info-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-meta-info-def.pbtxt index 8a464f1cac1cac..b0e983115499c5 100644 --- a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-meta-info-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-meta-info-def.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.MetaGraphDef.MetaInfoDef" tf_class { is_instance: "" is_instance: "" + member { + name: "ANY_INFO_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -11,32 +15,28 @@ tf_class { mtype: "" } member { - name: "any_info" - mtype: "" - } - member { - name: "meta_graph_version" - mtype: "" + name: "META_GRAPH_VERSION_FIELD_NUMBER" + mtype: "" } member { - name: "stripped_default_attrs" - mtype: "" + name: "STRIPPED_DEFAULT_ATTRS_FIELD_NUMBER" + mtype: "" } member { - name: "stripped_op_list" - mtype: "" + name: "STRIPPED_OP_LIST_FIELD_NUMBER" + mtype: "" } member { - name: "tags" - mtype: "" + name: "TAGS_FIELD_NUMBER" + mtype: "" } member { - name: "tensorflow_git_version" - mtype: "" + name: "TENSORFLOW_GIT_VERSION_FIELD_NUMBER" + mtype: "" } member { - name: "tensorflow_version" - mtype: "" + name: "TENSORFLOW_VERSION_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt index 8c5949d067011f..48fccac99d60b5 100644 --- a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "key" - mtype: "" + name: "KEY_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.pbtxt index 2be0432c008577..3e683a87159923 100644 --- a/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-meta-graph-def.pbtxt @@ -2,6 +2,14 @@ path: "tensorflow.MetaGraphDef" tf_class { is_instance: "" is_instance: "" + member { + name: "ASSET_FILE_DEF_FIELD_NUMBER" + mtype: "" + } + member { + name: "COLLECTION_DEF_FIELD_NUMBER" + mtype: "" + } member { name: "CollectionDefEntry" mtype: "" @@ -15,36 +23,28 @@ tf_class { mtype: "" } member { - name: "MetaInfoDef" - mtype: "" - } - member { - name: "SignatureDefEntry" - mtype: "" - } - member { - name: "asset_file_def" - mtype: "" + name: "GRAPH_DEF_FIELD_NUMBER" + mtype: "" } member { - name: "collection_def" - mtype: "" + name: "META_INFO_DEF_FIELD_NUMBER" + mtype: "" } member { - name: "graph_def" - mtype: "" + name: "MetaInfoDef" + mtype: "" } member { - name: "meta_info_def" - mtype: "" + name: "SAVER_DEF_FIELD_NUMBER" + mtype: "" } member { - name: "saver_def" - mtype: "" + name: "SIGNATURE_DEF_FIELD_NUMBER" + mtype: "" } member { - name: "signature_def" - mtype: "" + name: "SignatureDefEntry" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-name-attr-list.-attr-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.-name-attr-list.-attr-entry.pbtxt index caf992f5a67ca1..2750bd780caa41 100644 --- a/tensorflow/tools/api/golden/tensorflow.-name-attr-list.-attr-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-name-attr-list.-attr-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "key" - mtype: "" + name: "KEY_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-name-attr-list.pbtxt b/tensorflow/tools/api/golden/tensorflow.-name-attr-list.pbtxt index 45ddeece074c2f..d10faf67d027a4 100644 --- a/tensorflow/tools/api/golden/tensorflow.-name-attr-list.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-name-attr-list.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.NameAttrList" tf_class { is_instance: "" is_instance: "" + member { + name: "ATTR_FIELD_NUMBER" + mtype: "" + } member { name: "AttrEntry" mtype: "" @@ -15,12 +19,8 @@ tf_class { mtype: "" } member { - name: "attr" - mtype: "" - } - member { - name: "name" - mtype: "" + name: "NAME_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-node-def.-attr-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.-node-def.-attr-entry.pbtxt index 30a9dc69f092d2..b1b62d60f1e8c9 100644 --- a/tensorflow/tools/api/golden/tensorflow.-node-def.-attr-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-node-def.-attr-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "key" - mtype: "" + name: "KEY_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-node-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.-node-def.pbtxt index 23319fdb2294ca..b812b4df2b3c15 100644 --- a/tensorflow/tools/api/golden/tensorflow.-node-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-node-def.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.NodeDef" tf_class { is_instance: "" is_instance: "" + member { + name: "ATTR_FIELD_NUMBER" + mtype: "" + } member { name: "AttrEntry" mtype: "" @@ -11,28 +15,24 @@ tf_class { mtype: "" } member { - name: "Extensions" - mtype: "" - } - member { - name: "attr" - mtype: "" + name: "DEVICE_FIELD_NUMBER" + mtype: "" } member { - name: "device" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "input" - mtype: "" + name: "INPUT_FIELD_NUMBER" + mtype: "" } member { - name: "name" - mtype: "" + name: "NAME_FIELD_NUMBER" + mtype: "" } member { - name: "op" - mtype: "" + name: "OP_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-optimizer-options.pbtxt b/tensorflow/tools/api/golden/tensorflow.-optimizer-options.pbtxt index 57da2e8b55181f..6cac5c4d99fd75 100644 --- a/tensorflow/tools/api/golden/tensorflow.-optimizer-options.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-optimizer-options.pbtxt @@ -10,10 +10,26 @@ tf_class { name: "DESCRIPTOR" mtype: "" } + member { + name: "DO_COMMON_SUBEXPRESSION_ELIMINATION_FIELD_NUMBER" + mtype: "" + } + member { + name: "DO_CONSTANT_FOLDING_FIELD_NUMBER" + mtype: "" + } + member { + name: "DO_FUNCTION_INLINING_FIELD_NUMBER" + mtype: "" + } member { name: "Extensions" mtype: "" } + member { + name: "GLOBAL_JIT_LEVEL_FIELD_NUMBER" + mtype: "" + } member { name: "GlobalJitLevel" mtype: "" @@ -30,6 +46,10 @@ tf_class { name: "Level" mtype: "" } + member { + name: "MAX_FOLDED_CONSTANT_IN_BYTES_FIELD_NUMBER" + mtype: "" + } member { name: "OFF" mtype: "" @@ -43,28 +63,8 @@ tf_class { mtype: "" } member { - name: "do_common_subexpression_elimination" - mtype: "" - } - member { - name: "do_constant_folding" - mtype: "" - } - member { - name: "do_function_inlining" - mtype: "" - } - member { - name: "global_jit_level" - mtype: "" - } - member { - name: "max_folded_constant_in_bytes" - mtype: "" - } - member { - name: "opt_level" - mtype: "" + name: "OPT_LEVEL_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-run-metadata.pbtxt b/tensorflow/tools/api/golden/tensorflow.-run-metadata.pbtxt index 17b3d8816852ea..808fa0fa217a40 100644 --- a/tensorflow/tools/api/golden/tensorflow.-run-metadata.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-run-metadata.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.RunMetadata" tf_class { is_instance: "" is_instance: "" + member { + name: "COST_GRAPH_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -11,16 +15,12 @@ tf_class { mtype: "" } member { - name: "cost_graph" - mtype: "" - } - member { - name: "partition_graphs" - mtype: "" + name: "PARTITION_GRAPHS_FIELD_NUMBER" + mtype: "" } member { - name: "step_stats" - mtype: "" + name: "STEP_STATS_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-run-options.pbtxt b/tensorflow/tools/api/golden/tensorflow.-run-options.pbtxt index 7470e4b63d338e..2f3e7f1a847dd3 100644 --- a/tensorflow/tools/api/golden/tensorflow.-run-options.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-run-options.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.RunOptions" tf_class { is_instance: "" is_instance: "" + member { + name: "DEBUG_OPTIONS_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -19,40 +23,36 @@ tf_class { mtype: "" } member { - name: "NO_TRACE" + name: "INTER_OP_THREAD_POOL_FIELD_NUMBER" mtype: "" } member { - name: "SOFTWARE_TRACE" + name: "NO_TRACE" mtype: "" } member { - name: "TraceLevel" - mtype: "" - } - member { - name: "debug_options" - mtype: "" + name: "OUTPUT_PARTITION_GRAPHS_FIELD_NUMBER" + mtype: "" } member { - name: "inter_op_thread_pool" - mtype: "" + name: "REPORT_TENSOR_ALLOCATIONS_UPON_OOM_FIELD_NUMBER" + mtype: "" } member { - name: "output_partition_graphs" - mtype: "" + name: "SOFTWARE_TRACE" + mtype: "" } member { - name: "report_tensor_allocations_upon_oom" - mtype: "" + name: "TIMEOUT_IN_MS_FIELD_NUMBER" + mtype: "" } member { - name: "timeout_in_ms" - mtype: "" + name: "TRACE_LEVEL_FIELD_NUMBER" + mtype: "" } member { - name: "trace_level" - mtype: "" + name: "TraceLevel" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-session-log.pbtxt b/tensorflow/tools/api/golden/tensorflow.-session-log.pbtxt index 259a30546a748c..ec66d7f3354083 100644 --- a/tensorflow/tools/api/golden/tensorflow.-session-log.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-session-log.pbtxt @@ -6,6 +6,10 @@ tf_class { name: "CHECKPOINT" mtype: "" } + member { + name: "CHECKPOINT_PATH_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -14,10 +18,18 @@ tf_class { name: "Extensions" mtype: "" } + member { + name: "MSG_FIELD_NUMBER" + mtype: "" + } member { name: "START" mtype: "" } + member { + name: "STATUS_FIELD_NUMBER" + mtype: "" + } member { name: "STATUS_UNSPECIFIED" mtype: "" @@ -30,18 +42,6 @@ tf_class { name: "SessionStatus" mtype: "" } - member { - name: "checkpoint_path" - mtype: "" - } - member { - name: "msg" - mtype: "" - } - member { - name: "status" - mtype: "" - } member_method { name: "ByteSize" } diff --git a/tensorflow/tools/api/golden/tensorflow.-summary-metadata.-plugin-data.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.-plugin-data.pbtxt index 3d9ee9e0f28019..067f02ce8cbb1a 100644 --- a/tensorflow/tools/api/golden/tensorflow.-summary-metadata.-plugin-data.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.-plugin-data.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.SummaryMetadata.PluginData" tf_class { is_instance: "" is_instance: "" + member { + name: "CONTENT_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -11,12 +15,8 @@ tf_class { mtype: "" } member { - name: "content" - mtype: "" - } - member { - name: "plugin_name" - mtype: "" + name: "PLUGIN_NAME_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt index 9c69a2b96c2f06..b9156521ccbee2 100644 --- a/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-summary-metadata.pbtxt @@ -7,24 +7,24 @@ tf_class { mtype: "" } member { - name: "Extensions" - mtype: "" + name: "DISPLAY_NAME_FIELD_NUMBER" + mtype: "" } member { - name: "PluginData" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "display_name" - mtype: "" + name: "PLUGIN_DATA_FIELD_NUMBER" + mtype: "" } member { - name: "plugin_data" - mtype: "" + name: "PluginData" + mtype: "" } member { - name: "summary_description" - mtype: "" + name: "SUMMARY_DESCRIPTION_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-summary.-audio.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary.-audio.pbtxt index 8e761b886163e3..781010d75e23c1 100644 --- a/tensorflow/tools/api/golden/tensorflow.-summary.-audio.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-summary.-audio.pbtxt @@ -3,32 +3,32 @@ tf_class { is_instance: "" is_instance: "" member { - name: "DESCRIPTOR" - mtype: "" + name: "CONTENT_TYPE_FIELD_NUMBER" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "content_type" - mtype: "" + name: "ENCODED_AUDIO_STRING_FIELD_NUMBER" + mtype: "" } member { - name: "encoded_audio_string" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "length_frames" - mtype: "" + name: "LENGTH_FRAMES_FIELD_NUMBER" + mtype: "" } member { - name: "num_channels" - mtype: "" + name: "NUM_CHANNELS_FIELD_NUMBER" + mtype: "" } member { - name: "sample_rate" - mtype: "" + name: "SAMPLE_RATE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-summary.-image.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary.-image.pbtxt index 07b61d9e96796c..feb9c7ee9270a7 100644 --- a/tensorflow/tools/api/golden/tensorflow.-summary.-image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-summary.-image.pbtxt @@ -3,28 +3,28 @@ tf_class { is_instance: "" is_instance: "" member { - name: "DESCRIPTOR" - mtype: "" + name: "COLORSPACE_FIELD_NUMBER" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "colorspace" - mtype: "" + name: "ENCODED_IMAGE_STRING_FIELD_NUMBER" + mtype: "" } member { - name: "encoded_image_string" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "height" - mtype: "" + name: "HEIGHT_FIELD_NUMBER" + mtype: "" } member { - name: "width" - mtype: "" + name: "WIDTH_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-summary.-value.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary.-value.pbtxt index 77ba2e095eeba4..ffb4f45fc5e2a9 100644 --- a/tensorflow/tools/api/golden/tensorflow.-summary.-value.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-summary.-value.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.Summary.Value" tf_class { is_instance: "" is_instance: "" + member { + name: "AUDIO_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -11,40 +15,36 @@ tf_class { mtype: "" } member { - name: "audio" - mtype: "" - } - member { - name: "histo" - mtype: "" + name: "HISTO_FIELD_NUMBER" + mtype: "" } member { - name: "image" - mtype: "" + name: "IMAGE_FIELD_NUMBER" + mtype: "" } member { - name: "metadata" - mtype: "" + name: "METADATA_FIELD_NUMBER" + mtype: "" } member { - name: "node_name" - mtype: "" + name: "NODE_NAME_FIELD_NUMBER" + mtype: "" } member { - name: "obsolete_old_style_histogram" - mtype: "" + name: "OBSOLETE_OLD_STYLE_HISTOGRAM_FIELD_NUMBER" + mtype: "" } member { - name: "simple_value" - mtype: "" + name: "SIMPLE_VALUE_FIELD_NUMBER" + mtype: "" } member { - name: "tag" - mtype: "" + name: "TAG_FIELD_NUMBER" + mtype: "" } member { - name: "tensor" - mtype: "" + name: "TENSOR_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-summary.pbtxt b/tensorflow/tools/api/golden/tensorflow.-summary.pbtxt index 95263bdead6fcb..38de17fa9e52b8 100644 --- a/tensorflow/tools/api/golden/tensorflow.-summary.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-summary.pbtxt @@ -19,12 +19,12 @@ tf_class { mtype: "" } member { - name: "Value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "Value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-tensor-info.-coo-sparse.pbtxt b/tensorflow/tools/api/golden/tensorflow.-tensor-info.-coo-sparse.pbtxt index b1848311cfa408..425c35e0674610 100644 --- a/tensorflow/tools/api/golden/tensorflow.-tensor-info.-coo-sparse.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-tensor-info.-coo-sparse.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.TensorInfo.CooSparse" tf_class { is_instance: "" is_instance: "" + member { + name: "DENSE_SHAPE_TENSOR_NAME_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -11,16 +15,12 @@ tf_class { mtype: "" } member { - name: "dense_shape_tensor_name" - mtype: "" - } - member { - name: "indices_tensor_name" - mtype: "" + name: "INDICES_TENSOR_NAME_FIELD_NUMBER" + mtype: "" } member { - name: "values_tensor_name" - mtype: "" + name: "VALUES_TENSOR_NAME_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.-tensor-info.pbtxt b/tensorflow/tools/api/golden/tensorflow.-tensor-info.pbtxt index 9fd26d1b6c5005..41ea393be51bd7 100644 --- a/tensorflow/tools/api/golden/tensorflow.-tensor-info.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-tensor-info.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.TensorInfo" tf_class { is_instance: "" is_instance: "" + member { + name: "COO_SPARSE_FIELD_NUMBER" + mtype: "" + } member { name: "CooSparse" mtype: "" @@ -11,24 +15,20 @@ tf_class { mtype: "" } member { - name: "Extensions" - mtype: "" - } - member { - name: "coo_sparse" - mtype: "" + name: "DTYPE_FIELD_NUMBER" + mtype: "" } member { - name: "dtype" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "name" - mtype: "" + name: "NAME_FIELD_NUMBER" + mtype: "" } member { - name: "tensor_shape" - mtype: "" + name: "TENSOR_SHAPE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt index 925ea6df9345bd..bd5c36f390add9 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "reports" - mtype: "" + name: "REPORTS_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt index e7ca821951237f..7c8c68e155c99d 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "key" - mtype: "" + name: "KEY_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt index 330d6ee7befaab..1b789f4fc92ed6 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.profiler.AdviceProto" tf_class { is_instance: "" is_instance: "" + member { + name: "CHECKERS_FIELD_NUMBER" + mtype: "" + } member { name: "Checker" mtype: "" @@ -18,10 +22,6 @@ tf_class { name: "Extensions" mtype: "" } - member { - name: "checkers" - mtype: "" - } member_method { name: "ByteSize" } diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt index 85aef3e8a40df5..f0b9605bee1c7c 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "key" - mtype: "" + name: "KEY_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt index 2ecfb6a9715032..b80896a8a0f36a 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt @@ -3,124 +3,124 @@ tf_class { is_instance: "" is_instance: "" member { - name: "DESCRIPTOR" - mtype: "" + name: "ACCELERATOR_EXEC_MICROS_FIELD_NUMBER" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "CHILDREN_FIELD_NUMBER" + mtype: "" } member { - name: "InputShapesEntry" - mtype: "" + name: "CPU_EXEC_MICROS_FIELD_NUMBER" + mtype: "" } member { - name: "accelerator_exec_micros" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "children" - mtype: "" + name: "DEVICES_FIELD_NUMBER" + mtype: "" } member { - name: "cpu_exec_micros" - mtype: "" + name: "EXEC_MICROS_FIELD_NUMBER" + mtype: "" } member { - name: "devices" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "exec_micros" - mtype: "" + name: "FLOAT_OPS_FIELD_NUMBER" + mtype: "" } member { - name: "float_ops" - mtype: "" + name: "INPUT_SHAPES_FIELD_NUMBER" + mtype: "" } member { - name: "input_shapes" - mtype: "" + name: "InputShapesEntry" + mtype: "" } member { - name: "name" - mtype: "" + name: "NAME_FIELD_NUMBER" + mtype: "" } member { - name: "output_bytes" - mtype: "" + name: "OUTPUT_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "parameters" - mtype: "" + name: "PARAMETERS_FIELD_NUMBER" + mtype: "" } member { - name: "peak_bytes" - mtype: "" + name: "PEAK_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "requested_bytes" - mtype: "" + name: "REQUESTED_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "residual_bytes" - mtype: "" + name: "RESIDUAL_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "run_count" - mtype: "" + name: "RUN_COUNT_FIELD_NUMBER" + mtype: "" } member { - name: "shapes" - mtype: "" + name: "SHAPES_FIELD_NUMBER" + mtype: "" } member { - name: "tensor_value" - mtype: "" + name: "TENSOR_VALUE_FIELD_NUMBER" + mtype: "" } member { - name: "total_accelerator_exec_micros" - mtype: "" + name: "TOTAL_ACCELERATOR_EXEC_MICROS_FIELD_NUMBER" + mtype: "" } member { - name: "total_cpu_exec_micros" - mtype: "" + name: "TOTAL_CPU_EXEC_MICROS_FIELD_NUMBER" + mtype: "" } member { - name: "total_definition_count" - mtype: "" + name: "TOTAL_DEFINITION_COUNT_FIELD_NUMBER" + mtype: "" } member { - name: "total_exec_micros" - mtype: "" + name: "TOTAL_EXEC_MICROS_FIELD_NUMBER" + mtype: "" } member { - name: "total_float_ops" - mtype: "" + name: "TOTAL_FLOAT_OPS_FIELD_NUMBER" + mtype: "" } member { - name: "total_output_bytes" - mtype: "" + name: "TOTAL_OUTPUT_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "total_parameters" - mtype: "" + name: "TOTAL_PARAMETERS_FIELD_NUMBER" + mtype: "" } member { - name: "total_peak_bytes" - mtype: "" + name: "TOTAL_PEAK_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "total_requested_bytes" - mtype: "" + name: "TOTAL_REQUESTED_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "total_residual_bytes" - mtype: "" + name: "TOTAL_RESIDUAL_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "total_run_count" - mtype: "" + name: "TOTAL_RUN_COUNT_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt index b35d0d6e482fec..33deff64979132 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt @@ -3,96 +3,96 @@ tf_class { is_instance: "" is_instance: "" member { - name: "DESCRIPTOR" - mtype: "" + name: "ACCELERATOR_EXEC_MICROS_FIELD_NUMBER" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "CHILDREN_FIELD_NUMBER" + mtype: "" } member { - name: "accelerator_exec_micros" - mtype: "" + name: "CPU_EXEC_MICROS_FIELD_NUMBER" + mtype: "" } member { - name: "children" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "cpu_exec_micros" - mtype: "" + name: "EXEC_MICROS_FIELD_NUMBER" + mtype: "" } member { - name: "exec_micros" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "float_ops" - mtype: "" + name: "FLOAT_OPS_FIELD_NUMBER" + mtype: "" } member { - name: "graph_nodes" - mtype: "" + name: "GRAPH_NODES_FIELD_NUMBER" + mtype: "" } member { - name: "name" - mtype: "" + name: "NAME_FIELD_NUMBER" + mtype: "" } member { - name: "output_bytes" - mtype: "" + name: "OUTPUT_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "parameters" - mtype: "" + name: "PARAMETERS_FIELD_NUMBER" + mtype: "" } member { - name: "peak_bytes" - mtype: "" + name: "PEAK_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "requested_bytes" - mtype: "" + name: "REQUESTED_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "residual_bytes" - mtype: "" + name: "RESIDUAL_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "total_accelerator_exec_micros" - mtype: "" + name: "TOTAL_ACCELERATOR_EXEC_MICROS_FIELD_NUMBER" + mtype: "" } member { - name: "total_cpu_exec_micros" - mtype: "" + name: "TOTAL_CPU_EXEC_MICROS_FIELD_NUMBER" + mtype: "" } member { - name: "total_exec_micros" - mtype: "" + name: "TOTAL_EXEC_MICROS_FIELD_NUMBER" + mtype: "" } member { - name: "total_float_ops" - mtype: "" + name: "TOTAL_FLOAT_OPS_FIELD_NUMBER" + mtype: "" } member { - name: "total_output_bytes" - mtype: "" + name: "TOTAL_OUTPUT_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "total_parameters" - mtype: "" + name: "TOTAL_PARAMETERS_FIELD_NUMBER" + mtype: "" } member { - name: "total_peak_bytes" - mtype: "" + name: "TOTAL_PEAK_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "total_requested_bytes" - mtype: "" + name: "TOTAL_REQUESTED_BYTES_FIELD_NUMBER" + mtype: "" } member { - name: "total_residual_bytes" - mtype: "" + name: "TOTAL_RESIDUAL_BYTES_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt index 495a63cfebd1ae..8c4727cf35bdfc 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "key" - mtype: "" + name: "KEY_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt index b74d7f8a55f086..1071a82b5ce139 100644 --- a/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt @@ -11,16 +11,16 @@ tf_class { mtype: "" } member { - name: "IdToStringEntry" - mtype: "" + name: "ID_TO_STRING_FIELD_NUMBER" + mtype: "" } member { - name: "id_to_string" - mtype: "" + name: "IdToStringEntry" + mtype: "" } member { - name: "log_entries" - mtype: "" + name: "LOG_ENTRIES_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-event.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-event.pbtxt index 7ac8470a7aecbf..ab3449d80f6108 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-event.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-event.pbtxt @@ -11,40 +11,40 @@ tf_class { mtype: "" } member { - name: "file_version" - mtype: "" + name: "FILE_VERSION_FIELD_NUMBER" + mtype: "" } member { - name: "graph_def" - mtype: "" + name: "GRAPH_DEF_FIELD_NUMBER" + mtype: "" } member { - name: "log_message" - mtype: "" + name: "LOG_MESSAGE_FIELD_NUMBER" + mtype: "" } member { - name: "meta_graph_def" - mtype: "" + name: "META_GRAPH_DEF_FIELD_NUMBER" + mtype: "" } member { - name: "session_log" - mtype: "" + name: "SESSION_LOG_FIELD_NUMBER" + mtype: "" } member { - name: "step" - mtype: "" + name: "STEP_FIELD_NUMBER" + mtype: "" } member { - name: "summary" - mtype: "" + name: "SUMMARY_FIELD_NUMBER" + mtype: "" } member { - name: "tagged_run_metadata" - mtype: "" + name: "TAGGED_RUN_METADATA_FIELD_NUMBER" + mtype: "" } member { - name: "wall_time" - mtype: "" + name: "WALL_TIME_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-session-log.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-session-log.pbtxt index d1e7e9eedb0e7a..92ca4872caf1c1 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-session-log.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-session-log.pbtxt @@ -6,6 +6,10 @@ tf_class { name: "CHECKPOINT" mtype: "" } + member { + name: "CHECKPOINT_PATH_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -14,10 +18,18 @@ tf_class { name: "Extensions" mtype: "" } + member { + name: "MSG_FIELD_NUMBER" + mtype: "" + } member { name: "START" mtype: "" } + member { + name: "STATUS_FIELD_NUMBER" + mtype: "" + } member { name: "STATUS_UNSPECIFIED" mtype: "" @@ -30,18 +42,6 @@ tf_class { name: "SessionStatus" mtype: "" } - member { - name: "checkpoint_path" - mtype: "" - } - member { - name: "msg" - mtype: "" - } - member { - name: "status" - mtype: "" - } member_method { name: "ByteSize" } diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-summary-description.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-summary-description.pbtxt index 6fe3c755c9fd92..f93da2196adbc2 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-summary-description.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-summary-description.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "type_hint" - mtype: "" + name: "TYPE_HINT_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-summary.-audio.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-summary.-audio.pbtxt index 8cc842852439b4..605e305e82cc3f 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-summary.-audio.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-summary.-audio.pbtxt @@ -3,32 +3,32 @@ tf_class { is_instance: "" is_instance: "" member { - name: "DESCRIPTOR" - mtype: "" + name: "CONTENT_TYPE_FIELD_NUMBER" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "content_type" - mtype: "" + name: "ENCODED_AUDIO_STRING_FIELD_NUMBER" + mtype: "" } member { - name: "encoded_audio_string" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "length_frames" - mtype: "" + name: "LENGTH_FRAMES_FIELD_NUMBER" + mtype: "" } member { - name: "num_channels" - mtype: "" + name: "NUM_CHANNELS_FIELD_NUMBER" + mtype: "" } member { - name: "sample_rate" - mtype: "" + name: "SAMPLE_RATE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-summary.-image.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-summary.-image.pbtxt index 455452b550638a..0646972196dc72 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-summary.-image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-summary.-image.pbtxt @@ -3,28 +3,28 @@ tf_class { is_instance: "" is_instance: "" member { - name: "DESCRIPTOR" - mtype: "" + name: "COLORSPACE_FIELD_NUMBER" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "colorspace" - mtype: "" + name: "ENCODED_IMAGE_STRING_FIELD_NUMBER" + mtype: "" } member { - name: "encoded_image_string" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "height" - mtype: "" + name: "HEIGHT_FIELD_NUMBER" + mtype: "" } member { - name: "width" - mtype: "" + name: "WIDTH_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-summary.-value.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-summary.-value.pbtxt index bc9378c75edcf5..b319cd03d9e867 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-summary.-value.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-summary.-value.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.summary.Summary.Value" tf_class { is_instance: "" is_instance: "" + member { + name: "AUDIO_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -11,40 +15,36 @@ tf_class { mtype: "" } member { - name: "audio" - mtype: "" - } - member { - name: "histo" - mtype: "" + name: "HISTO_FIELD_NUMBER" + mtype: "" } member { - name: "image" - mtype: "" + name: "IMAGE_FIELD_NUMBER" + mtype: "" } member { - name: "metadata" - mtype: "" + name: "METADATA_FIELD_NUMBER" + mtype: "" } member { - name: "node_name" - mtype: "" + name: "NODE_NAME_FIELD_NUMBER" + mtype: "" } member { - name: "obsolete_old_style_histogram" - mtype: "" + name: "OBSOLETE_OLD_STYLE_HISTOGRAM_FIELD_NUMBER" + mtype: "" } member { - name: "simple_value" - mtype: "" + name: "SIMPLE_VALUE_FIELD_NUMBER" + mtype: "" } member { - name: "tag" - mtype: "" + name: "TAG_FIELD_NUMBER" + mtype: "" } member { - name: "tensor" - mtype: "" + name: "TENSOR_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-summary.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-summary.pbtxt index c724074d8c1eaf..132ef1b7d2e933 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-summary.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-summary.pbtxt @@ -19,12 +19,12 @@ tf_class { mtype: "" } member { - name: "Value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "Value" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-tagged-run-metadata.pbtxt b/tensorflow/tools/api/golden/tensorflow.summary.-tagged-run-metadata.pbtxt index 5daec17b68963e..4dce20819de06f 100644 --- a/tensorflow/tools/api/golden/tensorflow.summary.-tagged-run-metadata.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.summary.-tagged-run-metadata.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "run_metadata" - mtype: "" + name: "RUN_METADATA_FIELD_NUMBER" + mtype: "" } member { - name: "tag" - mtype: "" + name: "TAG_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-bytes-list.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-bytes-list.pbtxt index 5ca8b21ed03f64..8cf52b817f342a 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-bytes-list.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-bytes-list.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-cluster-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-cluster-def.pbtxt index 76ed034e73d481..93ff856b09de15 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-cluster-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-cluster-def.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "job" - mtype: "" + name: "JOB_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-example.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-example.pbtxt index f516cac1394949..f7215a20372e98 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-example.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-example.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "features" - mtype: "" + name: "FEATURES_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-feature-list.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-feature-list.pbtxt index b5b77fe3cd6a1f..3ad98354d69453 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-feature-list.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-feature-list.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "feature" - mtype: "" + name: "FEATURE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.-feature-list-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.-feature-list-entry.pbtxt index 774cfc53af3f8b..cd171f4ca3ef1e 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.-feature-list-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.-feature-list-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "key" - mtype: "" + name: "KEY_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.pbtxt index 430f6b41b1d48b..3d95017d584ad9 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-feature-lists.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "FeatureListEntry" - mtype: "" + name: "FEATURE_LIST_FIELD_NUMBER" + mtype: "" } member { - name: "feature_list" - mtype: "" + name: "FeatureListEntry" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-feature.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-feature.pbtxt index 48014a90babc23..9cca132bba91c4 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-feature.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-feature.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.train.Feature" tf_class { is_instance: "" is_instance: "" + member { + name: "BYTES_LIST_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -11,16 +15,12 @@ tf_class { mtype: "" } member { - name: "bytes_list" - mtype: "" - } - member { - name: "float_list" - mtype: "" + name: "FLOAT_LIST_FIELD_NUMBER" + mtype: "" } member { - name: "int64_list" - mtype: "" + name: "INT64_LIST_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-features.-feature-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-features.-feature-entry.pbtxt index 8f68927d103162..858aee03415dea 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-features.-feature-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-features.-feature-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "key" - mtype: "" + name: "KEY_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-features.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-features.pbtxt index 94e24126f15cea..49cd12153bf307 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-features.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-features.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "FeatureEntry" - mtype: "" + name: "FEATURE_FIELD_NUMBER" + mtype: "" } member { - name: "feature" - mtype: "" + name: "FeatureEntry" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-float-list.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-float-list.pbtxt index 37413782a1020f..e3f01334b547fe 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-float-list.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-float-list.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-int64-list.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-int64-list.pbtxt index 0c775cf46e3f9e..8917dc122cfd0b 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-int64-list.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-int64-list.pbtxt @@ -11,8 +11,8 @@ tf_class { mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-job-def.-tasks-entry.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-job-def.-tasks-entry.pbtxt index 5f0fe5c8a0e90b..ac6d81541a43e9 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-job-def.-tasks-entry.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-job-def.-tasks-entry.pbtxt @@ -11,12 +11,12 @@ tf_class { mtype: "" } member { - name: "key" - mtype: "" + name: "KEY_FIELD_NUMBER" + mtype: "" } member { - name: "value" - mtype: "" + name: "VALUE_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-job-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-job-def.pbtxt index 20a76e517f36d7..ce34537fa13b92 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-job-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-job-def.pbtxt @@ -11,16 +11,16 @@ tf_class { mtype: "" } member { - name: "TasksEntry" - mtype: "" + name: "NAME_FIELD_NUMBER" + mtype: "" } member { - name: "name" - mtype: "" + name: "TASKS_FIELD_NUMBER" + mtype: "" } member { - name: "tasks" - mtype: "" + name: "TasksEntry" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-saver-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-saver-def.pbtxt index 24705d0558c20b..84498a64f5b045 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-saver-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-saver-def.pbtxt @@ -15,44 +15,44 @@ tf_class { mtype: "" } member { - name: "LEGACY" + name: "FILENAME_TENSOR_NAME_FIELD_NUMBER" mtype: "" } member { - name: "V1" + name: "KEEP_CHECKPOINT_EVERY_N_HOURS_FIELD_NUMBER" mtype: "" } member { - name: "V2" + name: "LEGACY" mtype: "" } member { - name: "filename_tensor_name" - mtype: "" + name: "MAX_TO_KEEP_FIELD_NUMBER" + mtype: "" } member { - name: "keep_checkpoint_every_n_hours" - mtype: "" + name: "RESTORE_OP_NAME_FIELD_NUMBER" + mtype: "" } member { - name: "max_to_keep" - mtype: "" + name: "SAVE_TENSOR_NAME_FIELD_NUMBER" + mtype: "" } member { - name: "restore_op_name" - mtype: "" + name: "SHARDED_FIELD_NUMBER" + mtype: "" } member { - name: "save_tensor_name" - mtype: "" + name: "V1" + mtype: "" } member { - name: "sharded" - mtype: "" + name: "V2" + mtype: "" } member { - name: "version" - mtype: "" + name: "VERSION_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-sequence-example.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-sequence-example.pbtxt index 4ad3ede3614469..9ab95537021167 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-sequence-example.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-sequence-example.pbtxt @@ -2,6 +2,10 @@ path: "tensorflow.train.SequenceExample" tf_class { is_instance: "" is_instance: "" + member { + name: "CONTEXT_FIELD_NUMBER" + mtype: "" + } member { name: "DESCRIPTOR" mtype: "" @@ -11,12 +15,8 @@ tf_class { mtype: "" } member { - name: "context" - mtype: "" - } - member { - name: "feature_lists" - mtype: "" + name: "FEATURE_LISTS_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" diff --git a/tensorflow/tools/api/golden/tensorflow.train.-server-def.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-server-def.pbtxt index d1358cc60d2ea7..af0a3b73cc2ff3 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-server-def.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-server-def.pbtxt @@ -3,32 +3,32 @@ tf_class { is_instance: "" is_instance: "" member { - name: "DESCRIPTOR" - mtype: "" + name: "CLUSTER_FIELD_NUMBER" + mtype: "" } member { - name: "Extensions" - mtype: "" + name: "DEFAULT_SESSION_CONFIG_FIELD_NUMBER" + mtype: "" } member { - name: "cluster" - mtype: "" + name: "DESCRIPTOR" + mtype: "" } member { - name: "default_session_config" - mtype: "" + name: "Extensions" + mtype: "" } member { - name: "job_name" - mtype: "" + name: "JOB_NAME_FIELD_NUMBER" + mtype: "" } member { - name: "protocol" - mtype: "" + name: "PROTOCOL_FIELD_NUMBER" + mtype: "" } member { - name: "task_index" - mtype: "" + name: "TASK_INDEX_FIELD_NUMBER" + mtype: "" } member_method { name: "ByteSize" From 9477a96f88d9921020450427636db281122703fe Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Tue, 1 May 2018 11:52:04 -0700 Subject: [PATCH 0225/1691] eager: Update sample notebooks with API changes in the last few releases. Most notably: - Avoid using tf.contrib.eager since equivalent functionality if available outside tf.contrib - Datasets can be directly iterated on. - Use tf.GradientTape instead of tf.contrib.eager.implicit_gradients PiperOrigin-RevId: 194971115 --- tensorflow/contrib/eager/README.md | 11 +- .../python/examples/notebooks/1_basics.ipynb | 364 ++++++-------- .../examples/notebooks/2_gradients.ipynb | 473 ++++-------------- .../examples/notebooks/3_datasets.ipynb | 43 +- 4 files changed, 278 insertions(+), 613 deletions(-) diff --git a/tensorflow/contrib/eager/README.md b/tensorflow/contrib/eager/README.md index 9a3b780af888a5..762685db14b968 100644 --- a/tensorflow/contrib/eager/README.md +++ b/tensorflow/contrib/eager/README.md @@ -37,7 +37,7 @@ support for distributed and multi-GPU training and performance. ## Installation -Eager execution is included in TensorFlow versions 1.7 and above. +For eager execution, we recommend using TensorFlow version 1.8 or newer. Installation instructions at https://www.tensorflow.org/install/ ## Documentation @@ -48,12 +48,3 @@ For an introduction to eager execution in TensorFlow, see: - Notebook: [Basic Usage](python/examples/notebooks/1_basics.ipynb) - Notebook: [Gradients](python/examples/notebooks/2_gradients.ipynb) - Notebook: [Importing Data](python/examples/notebooks/3_datasets.ipynb) - -## Changelog - -- 2017/10/31: Initial preview release (in TensorFlow 1.5) -- 2017/12/01: Example of dynamic neural network: - [SPINN: Stack-augmented Parser-Interpreter Neural Network](https://arxiv.org/abs/1603.06021). - See [README.md](python/examples/spinn/README.md) for details. -- 2017/03: Core functionality moved out of the experimental tf.contrib namespace - in TensorFlow 1.7. diff --git a/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb index 459f2f4a7d2afa..0279db80fa3cb3 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb @@ -1,11 +1,27 @@ { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Eager Execution Tutorial: Basics", + "version": "0.3.2", + "views": {}, + "default_view": {}, + "provenance": [ + { + "file_id": "0B0kLcpwLFwKEVm9XNkFueGk4bTg", + "timestamp": 1504118841551 + } + ] + } + }, "cells": [ { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "U9i2Dsh-ziXr" + "id": "U9i2Dsh-ziXr", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "# Eager Execution Tutorial: Basics\n", "\n", @@ -21,11 +37,11 @@ ] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "z1JcS5iBXMRO" + "id": "z1JcS5iBXMRO", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "# Step 1: Import Eager\n", "\n", @@ -33,34 +49,34 @@ ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { - "cellView": "code", + "id": "RlIWhyeLoYnG", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, - "colab_type": "code", - "id": "RlIWhyeLoYnG" + "cellView": "code" }, - "outputs": [], + "cell_type": "code", "source": [ "# Import TensorFlow.\n", "import tensorflow as tf\n", "\n", "# Import TensorFlow eager execution support (subject to future changes).\n", - "import tensorflow.contrib.eager as tfe" - ] + "tfe = tf.contrib.eager" + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "H9UySOPLXdaw" + "id": "H9UySOPLXdaw", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "# Step 2: Enable eager execution\n", "\n", @@ -69,30 +85,30 @@ ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { - "cellView": "code", + "id": "WPTUfGq6kJ5w", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, - "colab_type": "code", - "id": "WPTUfGq6kJ5w" + "cellView": "code" }, - "outputs": [], + "cell_type": "code", "source": [ - "tfe.enable_eager_execution()" - ] + "tf.enable_eager_execution()" + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "twBfWd5xyu_d" + "id": "twBfWd5xyu_d", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "# Step 3: Interactively Use TensorFlow!\n", "\n", @@ -102,20 +118,18 @@ ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { - "cellView": "code", + "id": "ngUe237Wt48W", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, - "colab_type": "code", - "id": "ngUe237Wt48W" + "cellView": "code" }, - "outputs": [], + "cell_type": "code", "source": [ "print(tf.add(1, 2))\n", "print(tf.add([1, 2], [3, 4]))\n", @@ -131,32 +145,32 @@ "# Most TensorFlow ops are directly usable with eager execution, giving\n", "# results immediately.\n", "print(tf.contrib.signal.hamming_window(x * y + 1))" - ] + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "IDY4WsYRhP81" + "id": "IDY4WsYRhP81", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "Numpy arrays are supported, too:" ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { + "id": "lCUWzso6mbqR", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - }, - "colab_type": "code", - "id": "lCUWzso6mbqR" + } }, - "outputs": [], + "cell_type": "code", "source": [ "import numpy as np\n", "\n", @@ -168,14 +182,16 @@ "\n", "print(\"Multiplied by 42:\")\n", "print(tf.multiply(ones, 42))" - ] + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "PBNP8yTRfu_X" + "id": "PBNP8yTRfu_X", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "# Step 4: Define and Print TensorFlow Variables\n", "\n", @@ -183,73 +199,66 @@ ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { - "cellView": "code", + "id": "3Twf_Rw-gQFM", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, - "colab_type": "code", - "id": "3Twf_Rw-gQFM" + "cellView": "code" }, - "outputs": [], + "cell_type": "code", "source": [ - "x = tf.get_variable(name=\"x\", shape=[], dtype=tf.float32, initializer=tf.zeros_initializer)" - ] + "x = tfe.Variable(0.)" + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "45G7094TxsMb" + "id": "45G7094TxsMb", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "## Printing TensorFlow Variables" ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { - "cellView": "code", + "id": "UJBJeZ5XxuwA", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, - "colab_type": "code", - "id": "UJBJeZ5XxuwA" + "cellView": "code" }, - "outputs": [], + "cell_type": "code", "source": [ "# This does NOT print the Variable's actual value:\n", "print(\"Printing a TensorFlow Variable:\")\n", "print(x)\n", "print(\"\")\n", "\n", - "# A TensorFlow variable represents a reference to a tensor.\n", - "# The `read_value()` method provides access to the current value of the\n", - "# variable. Tensorflow Variables are automatically initialized according to the\n", - "# semantics defined in tf.get_variable().\n", - "print(\"Printing a TensorFlow Variable's value using .read_value():\")\n", - "print(x.read_value())\n", - "print(\"\")\n", "\n", - "print(\"Printing a TensorFlow Variable's value using .read_value().numpy():\")\n", - "print(x.read_value().numpy())" - ] + "print(\"Printing a TensorFlow Variable's value as a numpy array:\")\n", + "print(x.numpy())" + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "2njjWHcTpBEn" + "id": "2njjWHcTpBEn", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "## Changing a TensorFlow Variable's value\n", "\n", @@ -257,64 +266,64 @@ ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { + "id": "v3wr6Erbo_hB", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - }, - "colab_type": "code", - "id": "v3wr6Erbo_hB" + } }, - "outputs": [], + "cell_type": "code", "source": [ "x.assign(42)\n", - "print(x.read_value())\n", + "print(x)\n", "\n", "x.assign_add(3)\n", - "print(x.read_value())" - ] + "print(x)" + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "uhtynjHVpTB5" + "id": "uhtynjHVpTB5", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "## Use a Variable just like any other Tensor" ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { + "id": "7PbktdnHoehR", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - }, - "colab_type": "code", - "id": "7PbktdnHoehR" + } }, - "outputs": [], + "cell_type": "code", "source": [ "print(x + 3)\n", "\n", "# This code will broadcast the value across the list of numbers:\n", "print(x * [1, 2, 4])" - ] + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "GVChqwlwy1SI" + "id": "GVChqwlwy1SI", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "# Step 5: Debug Errors with Instant Feedback\n", "\n", @@ -326,60 +335,58 @@ ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { - "cellView": "code", + "id": "23ap04N0v4k0", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, - "colab_type": "code", - "id": "23ap04N0v4k0" + "cellView": "code" }, - "outputs": [], + "cell_type": "code", "source": [ "vector = tf.constant([10.0, 20.0, 30.0, 40.0])" - ] + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { - "cellView": "code", + "id": "FCUMsIYxxRRa", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, - "colab_type": "code", - "id": "FCUMsIYxxRRa" + "cellView": "code" }, - "outputs": [], + "cell_type": "code", "source": [ "# Works, because the values of `begin` and `size` (the 2nd and 3rd input\n", "# arguments) are within the bound of `vector`.\n", "print(tf.slice(vector, [1], [3]))" - ] + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { - "cellView": "code", + "id": "T8me2oCNxpFp", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, - "colab_type": "code", - "id": "T8me2oCNxpFp" + "cellView": "code" }, - "outputs": [], + "cell_type": "code", "source": [ "# The following does NOT work, because the value of `size` (the 3rd\n", "# argument) causes the indices to go out of the bounds of `vector`. The\n", @@ -388,87 +395,86 @@ " print(tf.slice(vector, [1], [4]))\n", "except tf.OpError as e:\n", " print(\"Caught error: %s\" % e)" - ] + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "irxJhAgar84v" + "id": "irxJhAgar84v", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "# Step 6: Using the GPU\n", "\n", - "You can place Tensors on the GPU by calling a Tensor's `.gpu()` method.\n", + "You can explicitly place Tensors on the GPU by calling a Tensor's `.gpu()` method. The `.device` property tells you whether the Tensor is backed by CPU or GPU memory.\n", "\n", "The first operation executing on the GPU may be slow as TensorFlow initializes. Subsequent uses will be much faster." ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { + "id": "7J4N9baqaKCL", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - }, - "colab_type": "code", - "id": "7J4N9baqaKCL" + } }, - "outputs": [], + "cell_type": "code", "source": [ - "# The example code from here on will work only if your notebook\n", - "# is running on a machine with a functional CUDA GPU. The following\n", - "# line checks that.\n", - "is_gpu_available = tfe.num_gpus() \u003e 0\n", - "\n", "# Create some Tensors\n", "SIZE = 1000\n", - "cpu_tensor = tf.random_normal([SIZE, SIZE])\n", + "tensor = tf.random_normal([SIZE, SIZE])\n", + "print(tensor.device)\n", "\n", - "if is_gpu_available:\n", - " gpu_tensor = cpu_tensor.gpu()\n", + "\n", + "if tf.test.is_gpu_available():\n", + " gpu_tensor = tensor.gpu()\n", + " cpu_tensor = tensor.cpu()\n", "else:\n", - " print(\"GPU not available.\")" - ] + " print(\"GPU not available.\")\n", + " cpu_tensor = tensor" + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { + "id": "4E-2n7VbzY1n", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - }, - "colab_type": "code", - "id": "4E-2n7VbzY1n" + } }, - "outputs": [], + "cell_type": "code", "source": [ "# Time a CPU-based matrix multiplication\n", "\n", "print(\"Time to conduct matmul on CPU:\")\n", "%time tf.matmul(cpu_tensor, cpu_tensor)" - ] + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { + "id": "vbSFW-T5zhZF", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - }, - "colab_type": "code", - "id": "vbSFW-T5zhZF" + } }, - "outputs": [], + "cell_type": "code", "source": [ "# Time GPU-based matrix multiplications.\n", "\n", @@ -481,51 +487,9 @@ " # Subsequent uses are much faster:\n", " print(\"Time to conduct second matmul on GPU:\")\n", " %time tf.matmul(gpu_tensor, gpu_tensor)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "E5pIOe3Rz7iW" - }, - "outputs": [], - "source": [ - "# Second timing demo for GPUs, after it has been used once:\n", - "\n", - "cpu_tensor = tf.random_normal([SIZE, SIZE])\n", - "print(\"Time to conduct CPU matmul:\")\n", - "%time tf.matmul(cpu_tensor, cpu_tensor)\n", - "print()\n", - "\n", - "if is_gpu_available:\n", - " gpu_tensor = cpu_tensor.gpu()\n", - " print(\"Time to conduct GPU matmul:\")\n", - " %time tf.matmul(gpu_tensor, gpu_tensor)" - ] - } - ], - "metadata": { - "colab": { - "default_view": {}, - "name": "Eager Execution Tutorial: Basics", - "provenance": [ - { - "file_id": "0B0kLcpwLFwKEVm9XNkFueGk4bTg", - "timestamp": 1504118841551 - } ], - "version": "0.3.2", - "views": {} + "execution_count": 0, + "outputs": [] } - }, - "nbformat": 4, - "nbformat_minor": 0 -} + ] +} \ No newline at end of file diff --git a/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb index e6c7c117333e1e..1e65b27bc8be8b 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb @@ -43,11 +43,9 @@ "# Import TensorFlow.\n", "import tensorflow as tf\n", "\n", - "# Import TensorFlow eager execution support (subject to future changes).\n", - "import tensorflow.contrib.eager as tfe\n", "\n", "# Enable eager execution.\n", - "tfe.enable_eager_execution()" + "tf.enable_eager_execution()" ] }, { @@ -106,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 0, "metadata": { "cellView": "code", "colab": { @@ -114,34 +112,30 @@ "startup": false, "wait_interval": 0 }, - "height": 360, - "output_extras": [ - { - "item_id": 1 - } - ] + "base_uri": "https://localhost:8080/", + "height": 347 }, "colab_type": "code", "executionInfo": { - "elapsed": 127, + "elapsed": 374, "status": "ok", - "timestamp": 1505502830690, + "timestamp": 1525154227149, "user": { "displayName": "", "photoUrl": "", "userId": "" }, - "user_tz": 240 + "user_tz": 420 }, "id": "O4lsC4ckAcar", - "outputId": "2f760690-cafb-4777-b970-91d839f99faf" + "outputId": "f8becb3f-498b-4cb7-9ef3-608a68cb65d0" }, "outputs": [ { "data": { - "image/png": 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++y7Hbvewb18ql1/+BCUlz7FtWwuqMxiAhUDAjlH8X3stgaSkROLjm4GvEmnr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\nPyQ+uNKCpR6MAAAAAElFTkSuQmCC\n", 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vvPMOfD4fnnnmGfz+97+HyWTCmjVr8Mwzz+Cxxx4DANx+++24/vrrM3bRRJS+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\n9yZJehp0+eDxBbD17VNpN6nIFqmRfnQ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"text/plain": [ - "\u003cmatplotlib.figure.Figure at 0xa813090\u003e" + "\u003cmatplotlib.figure.Figure at 0x7f7a18dfb8d0\u003e" ] }, "metadata": { @@ -155,7 +149,7 @@ "\n", "import matplotlib.pyplot as plt\n", "\n", - "plt.scatter(inputs.numpy(), labels.numpy())\n", + "plt.scatter(inputs, labels)\n", "plt.show()" ] }, @@ -168,14 +162,12 @@ "source": [ "## Step 2: Define our TensorFlow variables\n", "\n", - "We'll use Keras's object-oriented [`Dense`](https://www.tensorflow.org/api_docs/python/tf/contrib/keras/layers/Dense) layer to create our variables. In this case, we'll create a `Dense` layer with a single weight and bias.\n", - "\n", - "(**Note**: We're using the implementation of `Dense` found in `tf.layers.Dense` though the documentation link is for `tf.contrib.keras.layers.Dense`. When TensorFlow 1.4 is released, the documentation will also be in `tf.layers.Dense`) " + "We'll use Keras's object-oriented [`Dense`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense) layer to create our variables. In this case, we'll create a `Dense` layer with a single weight and bias." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 0, "metadata": { "cellView": "code", "colab": { @@ -183,27 +175,23 @@ "startup": false, "wait_interval": 0 }, - "height": 34, - "output_extras": [ - { - "item_id": 1 - } - ] + "base_uri": "https://localhost:8080/", + "height": 34 }, "colab_type": "code", "executionInfo": { - "elapsed": 22, + "elapsed": 332, "status": "ok", - "timestamp": 1505502830753, + "timestamp": 1525154229931, "user": { "displayName": "", "photoUrl": "", "userId": "" }, - "user_tz": 240 + "user_tz": 420 }, "id": "z9r-ZeyrXu3A", - "outputId": "6230a7a3-29fe-4d08-f101-da80425bad82" + "outputId": "e19a698e-5892-4fcd-80d3-1394605ee72c" }, "outputs": [ { @@ -212,7 +200,7 @@ "[]" ] }, - "execution_count": 4, + "execution_count": 48, "metadata": { "tags": [] }, @@ -222,7 +210,7 @@ "source": [ "# Create TensorFlow Variables using Keras's Dense layer.\n", "\n", - "wb = tf.layers.Dense(units=1, use_bias=True)\n", + "wb = tf.keras.layers.Dense(units=1, use_bias=True)\n", "\n", "# We can access the underlying TensorFlow variables using wb.variables.\n", "# However, the variables won't exist until the dimensions of the input\n", @@ -240,7 +228,7 @@ "id": "docKLUaonYG_" }, "source": [ - "## Step 3: Define our loss function\n", + "## Step 3: *Define the loss function*\n", "\n", "Our loss function is the standard L2 loss (where we reduce the loss to its mean across its inputs)." ] @@ -261,15 +249,14 @@ }, "outputs": [], "source": [ - "def loss_fn(inputs, labels, wb):\n", + "def loss_fn(predictions, labels):\n", " \"\"\"Calculates the mean L2 loss for our linear model.\"\"\"\n", - " predictions = wb(inputs)\n", " return tf.reduce_mean(tf.square(predictions - labels))" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 0, "metadata": { "cellView": "code", "colab": { @@ -277,36 +264,32 @@ "startup": false, "wait_interval": 0 }, - "height": 34, - "output_extras": [ - { - "item_id": 1 - } - ] + "base_uri": "https://localhost:8080/", + "height": 34 }, "colab_type": "code", "executionInfo": { - "elapsed": 24, + "elapsed": 348, "status": "ok", - "timestamp": 1505502830875, + "timestamp": 1525154234538, "user": { "displayName": "", "photoUrl": "", "userId": "" }, - "user_tz": 240 + "user_tz": 420 }, "id": "RkNbXoXkpjVH", - "outputId": "c36fc98d-3a57-4074-901d-c10ae017ae3f" + "outputId": "e4688f3c-e29f-416d-f541-6d81953b5660" }, "outputs": [ { "data": { "text/plain": [ - "\u003ctf.Tensor: id=40, shape=(), dtype=float32, numpy=7.3549819\u003e" + "\u003ctf.Tensor: id=1252, shape=(), dtype=float32, numpy=16.979801\u003e" ] }, - "execution_count": 6, + "execution_count": 50, "metadata": { "tags": [] }, @@ -316,47 +299,43 @@ "source": [ "# Test loss function (optional).\n", "\n", - "loss_fn(inputs, labels, wb)" + "loss_fn(wb(inputs), labels)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 0, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, - "height": 51, - "output_extras": [ - { - "item_id": 1 - } - ] + "base_uri": "https://localhost:8080/", + "height": 51 }, "colab_type": "code", "executionInfo": { - "elapsed": 57, + "elapsed": 418, "status": "ok", - "timestamp": 1505502830981, + "timestamp": 1525154260083, "user": { "displayName": "", "photoUrl": "", "userId": "" }, - "user_tz": 240 + "user_tz": 420 }, "id": "K_7beXoHOU7t", - "outputId": "1ad0856a-02ec-4117-a6c0-b41030981d87" + "outputId": "8f55c028-fe2b-4edb-ad68-a849afc60623" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "w: tf.Tensor([[ 1.56891453]], shape=(1, 1), dtype=float32)\n", - "b: tf.Tensor([ 0.], shape=(1,), dtype=float32)\n" + "w: -0.311619\n", + "b: 0.000000\n" ] } ], @@ -364,31 +343,20 @@ "# At this point, the variables exist, and can now be queried:\n", "\n", "w, b = wb.variables\n", - "print(\"w: \" + str(w.read_value()))\n", - "print(\"b: \" + str(b.read_value()))" + "print(\"w: %f\" % w.numpy())\n", + "print(\"b: %f\" % b.numpy())" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "YIlebeb_qYtC" + "id": "JVDWpL9VYWdP" }, "source": [ - "## Step 4: Create our gradients function using `implicit_value_and_gradients()`\n", - "\n", - "With a loss function defined, we can calculate gradients and apply them to our variables to update them.\n", + "## Step 4: Create an optimizer\n", "\n", - "To calculate the gradients, we wrap our loss function using the `implicit_value_and_gradients()` function.\n", - "\n", - "`implicit_value_and_gradients()` returns a function that accepts the same inputs as the function passed in, and returns a tuple consisting of:\n", - "\n", - "1. the value returned by the function passed in (in this case, the loss calculated by `loss_fn()`), and\n", - "1. a list of tuples consisting of:\n", - " 1. The value of the gradient (a `tf.Tensor`) with respect to a given variable\n", - " 1. The corresponding variable (`tf.Variable`)\n", - "\n", - "Test it out below to get a feel for what it does. Notice how the first value of the returned tuple (the loss) is the same as the value returned in the cell above that tests our loss function." + "We'll use a `GradientDescentOptimizer` to fit our model." ] }, { @@ -403,87 +371,29 @@ } }, "colab_type": "code", - "id": "v1spZQ4NwW1U" + "id": "DudNEebMKDWN" }, "outputs": [], "source": [ - "# Produce our gradients function. See description above for details about\n", - "# the returned function's signature.\n", - "\n", - "value_and_gradients_fn = tfe.implicit_value_and_gradients(loss_fn)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "cellView": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "height": 153, - "output_extras": [ - { - "item_id": 1 - } - ] - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 46, - "status": "ok", - "timestamp": 1505502831114, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 240 - }, - "id": "21WMcpsmFFLd", - "outputId": "f51b3171-33f5-4f87-8bf7-0be2dc8edc8a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Outputs of value_and_gradients_fn:\n", - "Loss: tf.Tensor(7.35498, shape=(), dtype=float32)\n", - "\n", - "Gradient: tf.Tensor([[-3.00773573]], shape=(1, 1), dtype=float32)\n", - "Variable: \u003ctf.Variable 'dense/kernel:0' shape=(1, 1) dtype=float32\u003e\n", - "\n", - "Gradient: tf.Tensor([-4.06519032], shape=(1,), dtype=float32)\n", - "Variable: \u003ctf.Variable 'dense/bias:0' shape=(1,) dtype=float32\u003e\n" - ] - } - ], - "source": [ - "# Show outputs of value_and_gradients_fn.\n", - "\n", - "print(\"Outputs of value_and_gradients_fn:\")\n", - "\n", - "value, grads_and_vars = value_and_gradients_fn(inputs, labels, wb)\n", - "\n", - "print('Loss: {}'.format(value))\n", - "for (grad, var) in grads_and_vars:\n", - " print(\"\")\n", - " print('Gradient: {}\\nVariable: {}'.format(grad, var))" + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "JVDWpL9VYWdP" + "id": "YBeJYxY8YaiO" }, "source": [ - "## Step 5: Create an optimizer\n", + "### Step 5: Define a training step\n", "\n", - "We'll use a `GradientDescentOptimizer` to fit our model." + "To fit model variables to the data we'll need to:\n", + "\n", + "1. Calculate the gradients of the loss with respect to the model variables.\n", + "2. Use `optimizer` to compute updates to the variable values based on those gradients.\n", + "\n", + "To calculate the gradients, we use the [`tf.GradientTape`](https://www.tensorflow.org/api_docs/python/tf/GradientTape) context manager\n", + "and its `gradient` function to compute gradients through computation conducted within its context:\n" ] }, { @@ -498,94 +408,72 @@ } }, "colab_type": "code", - "id": "DudNEebMKDWN" + "id": "diDZfrMJM3OC" }, "outputs": [], "source": [ - "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)" + "def run_step(inputs, labels):\n", + " with tf.GradientTape() as g:\n", + " loss = loss_fn(wb(inputs), labels)\n", + " # Compute the partial derivatives of loss with respect to the variables\n", + " grads = g.gradient(loss, wb.variables)\n", + " optimizer.apply_gradients(zip(grads, wb.variables))\n", + " return loss" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "YBeJYxY8YaiO" + "id": "1WWepgmJQOzc" }, "source": [ - "### Step 5a: Test Our Optimizer\n", - "\n", - "Now we have everything needed to start fitting our variables to the data!\n", - "\n", - "In the next cell, we'll demo these capabilities. We'll:\n", - "\n", - "1. Print the current values of `w` and `b`\n", - "1. Calculate the loss and gradients\n", - "1. Apply the gradients\n", - "1. Print out the new values of `w` and `b`\n", - "\n", - "You can run the cell multiple times. Each time, you should see the values of `w` and `b` get closer to their true values of 3 and 2." + "Repeatedly running the training step will nudge the variables towards the values that best fit the data (i.e., \"w\" will move closer to 3.0, while \"b\" will tend to 2.0):\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 0, "metadata": { - "cellView": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, - "height": 102, - "output_extras": [ - { - "item_id": 1 - } - ] + "base_uri": "https://localhost:8080/", + "height": 51 }, "colab_type": "code", "executionInfo": { - "elapsed": 103, + "elapsed": 380, "status": "ok", - "timestamp": 1505502831285, + "timestamp": 1525154412590, "user": { "displayName": "", "photoUrl": "", "userId": "" }, - "user_tz": 240 + "user_tz": 420 }, - "id": "diDZfrMJM3OC", - "outputId": "d585fff0-ecb3-4e98-9b33-bbae07a95d8c" + "id": "ya5Qxz5XQlhU", + "outputId": "8dd47155-a6c1-44c5-c279-617c803f1723" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Values of w, b, BEFORE applying gradients:\n", - "(array([[ 1.56891453]], dtype=float32), array([ 0.], dtype=float32))\n", - "()\n", - "Values of w, b, AFTER applying gradients:\n", - "(array([[ 1.86968815]], dtype=float32), array([ 0.40651903], dtype=float32))\n" + "Values of w, b BEFORE applying gradients: 2.725763, 1.894334\n", + "Values of w, b AFTER applying gradients: 2.774932, 1.922555\n" ] } ], "source": [ - "# Test the optimizer.\n", - "\n", - "print(\"Values of w, b, BEFORE applying gradients:\")\n", "w, b = wb.variables\n", - "print(w.read_value().numpy(), b.read_value().numpy())\n", - "print()\n", - "\n", - "# Calculate the gradients:\n", - "empirical_loss, gradients_and_variables = value_and_gradients_fn(\n", - " inputs, labels, wb)\n", - "optimizer.apply_gradients(gradients_and_variables)\n", - "\n", - "print(\"Values of w, b, AFTER applying gradients:\")\n", - "print(w.read_value().numpy(), b.read_value().numpy())" + "print(\"Values of w, b BEFORE applying gradients: %f, %f\" % (w.numpy(), b.numpy()))\n", + "run_step(inputs, labels)\n", + "print(\"Values of w, b AFTER applying gradients: %f, %f\" % (w.numpy(), b.numpy()))\n" ] }, { @@ -602,51 +490,44 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 0, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, - "height": 397, - "output_extras": [ - { - "item_id": 1 - }, - { - "item_id": 2 - } - ] + "base_uri": "https://localhost:8080/", + "height": 364 }, "colab_type": "code", "executionInfo": { - "elapsed": 225, + "elapsed": 580, "status": "ok", - "timestamp": 1505502831550, + "timestamp": 1525154278709, "user": { "displayName": "", "photoUrl": "", "userId": "" }, - "user_tz": 240 + "user_tz": 420 }, "id": "VukGe-huNaJ4", - "outputId": "f0a8d665-1910-477c-d8ab-c94ccdc4afcd" + "outputId": "c79c8e63-c781-451e-f74f-20815d8da49f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2.111051321029663, 2.3047544956207275, 2.4602210521698, 2.5850086212158203, 2.6851789951324463, 2.7655951976776123, 2.830157995223999, 2.8819968700408936, 2.9236228466033936, 2.9570505619049072]\n" + "[0.9409681558609009, 1.3733772039413452, 1.7128530740737915, 1.9793939590454102, 2.188689708709717, 2.3530514240264893, 2.4821391105651855, 2.583533763885498, 2.6631851196289062, 2.7257626056671143]\n" ] }, { "data": { - "image/png": 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8lHDsTLHDvZo3KPZ1WcerhCI9VGY9XwFwGDkan3++Cr16PY2tW3ejevUaFd9+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\nAAAAAElFTkSuQmCC\n", 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Dq1atkLscIqJqjeFbTeQfflSjRg3MnDkVp0+fkrskIqJqi+FbjQQF1cKnny5G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\nviIiIln2BzQKNGAGnBgwAAAAAElFTkSuQmCC\n", "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0xc1dc310\u003e" + "\u003cmatplotlib.figure.Figure at 0x7f7a18df6b50\u003e" ] }, "metadata": { @@ -668,13 +549,10 @@ " w_at_step = []\n", " b_at_step = []\n", " for step_num in range(num_training_steps):\n", - " loss, gradients_and_variables = value_and_gradients_fn(inputs, labels, wb)\n", - " loss_at_step.append(np.asscalar(loss.numpy()))\n", - " \n", - " optimizer.apply_gradients(gradients_and_variables)\n", + " loss_at_step.append(run_step(inputs, labels))\n", " w, b = wb.variables\n", - " w_at_step.append(np.asscalar(w.read_value().numpy()))\n", - " b_at_step.append(np.asscalar(b.read_value().numpy()))\n", + " w_at_step.append(np.asscalar(w.numpy()))\n", + " b_at_step.append(np.asscalar(b.numpy()))\n", "\n", " print(w_at_step)\n", " t = range(0, num_training_steps)\n", @@ -688,171 +566,12 @@ "\n", "train_model(inputs, labels, wb, optimizer, num_training_steps)" ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "UNurY9VJ-hpH" - }, - "source": [ - "## Other Ways to Compute Gradients\n", - "\n", - "Using our loss function as an example (`loss_fn()`), there are several other ways we could compute gradients:\n", - "\n", - "1. `tfe.implicit_gradients()`\n", - "1. `tfe.gradients_function()`\n", - "1. `tfe.implicit_value_and_gradients()`\n", - "1. `tfe.value_and_gradients_function()`\n", - "\n", - "Each of these functions does the following:\n", - "* Wraps a function.\n", - "* Returns a function with the same input signature as the wrapped function.\n", - "\n", - "They differ only in what information they return.\n", - "\n", - "### Gradients-only functions\n", - "\n", - "The following two functions return a function that returns only the variables' gradients:\n", - "\n", - "1. `tfe.gradients_function()`: Returns the partial derivatives of the function `f()` with respect to the parameters of `f()`.\n", - "1. `tfe.implicit_gradients()`: Returns the partial derivatives of the function `f()` with respect to the trainable parameters (`tf.Variable`) used by `f()`.\n", - "\n", - "In our example above, the `tf.layers.Dense` object encapsulates the trainable parameters.\n", - "\n", - "### Value and gradients functions\n", - "\n", - "The following two functions are identical to their counterparts above, except that they also return the value of the wrapped function.\n", - "\n", - "1. `tfe.implicit_value_and_gradients()`\n", - "1. `tfe.value_and_gradients_function()`\n", - "\n", - "### Gradient demos\n", - "\n", - "In the demos below, we show examples for the `implicit_*` functions, since our existing loss function works seamlessly with these versions. (The other versions require that your parameters are tensors and tensors only; in our example, we're using a `Dense` layer.)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "height": 85, - "output_extras": [ - { - "item_id": 1 - } - ] - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 100, - "status": "ok", - "timestamp": 1505502831671, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 240 - }, - "id": "aEoCftnfAIH5", - "outputId": "72f1c1dc-a574-463f-f860-c4e5f48fcdaa" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[(\u003ctf.Tensor: id=673, shape=(1, 1), dtype=float32, numpy=array([[-0.26846504]], dtype=float32)\u003e,\n", - " \u003ctf.Variable 'dense/kernel:0' shape=(1, 1) dtype=float32\u003e),\n", - " (\u003ctf.Tensor: id=671, shape=(1,), dtype=float32, numpy=array([-0.32890949], dtype=float32)\u003e,\n", - " \u003ctf.Variable 'dense/bias:0' shape=(1,) dtype=float32\u003e)]" - ] - }, - "execution_count": 13, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "# tfe.implicit_gradients() demo\n", - "gradients_fn = tfe.implicit_gradients(loss_fn)\n", - "\n", - "# Returns only gradients and variables:\n", - "gradients_fn(inputs, labels, wb)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "height": 102, - "output_extras": [ - { - "item_id": 1 - } - ] - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 88, - "status": "ok", - "timestamp": 1505502831785, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 240 - }, - "id": "bbgCUdCzAVhH", - "outputId": "152aa9b6-9e42-4b7e-848a-9423c0b1929c" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(\u003ctf.Tensor: id=688, shape=(), dtype=float32, numpy=1.0623235\u003e,\n", - " [(\u003ctf.Tensor: id=720, shape=(1, 1), dtype=float32, numpy=array([[-0.26846504]], dtype=float32)\u003e,\n", - " \u003ctf.Variable 'dense/kernel:0' shape=(1, 1) dtype=float32\u003e),\n", - " (\u003ctf.Tensor: id=718, shape=(1,), dtype=float32, numpy=array([-0.32890949], dtype=float32)\u003e,\n", - " \u003ctf.Variable 'dense/bias:0' shape=(1,) dtype=float32\u003e)])" - ] - }, - "execution_count": 14, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "# tfe.implicit_value_and_gradients() demo\n", - "value_gradients_fn = tfe.implicit_value_and_gradients(loss_fn)\n", - "\n", - "# Returns the value returned by the function passed in, gradients, and variables:\n", - "value_gradients_fn(inputs, labels, wb)" - ] } ], "metadata": { "colab": { + "collapsed_sections": [], "default_view": {}, - "last_runtime": { - "build_target": "", - "kind": "local" - }, "name": "Eager Execution Tutorial: Working with Gradients", "provenance": [], "version": "0.3.2", diff --git a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb index 0088da5c4b583d..bfcc7feb075c40 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb @@ -16,7 +16,9 @@ "\n", "We recommend using the `Dataset`s API for building performant, complex input pipelines from simple, re-usable pieces that will feed your model's training or evaluation loops.\n", "\n", - "If you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly different. You will use a Pythonic `Iterator()` class instead of using `make_one_shot_iterator()` and `get_next()`. As a result, the discussion on iterators in the [Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets) is not relevant when eager execution is enabled." + "If you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly simpler.\n", + "You can use Python iteration over the `tf.data.Dataset` object and do not need to explicitly create an `tf.data.Iterator` object.\n", + "As a result, the discussion on iterators in the [Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets) is not relevant when eager execution is enabled." ] }, { @@ -48,11 +50,8 @@ "# Import TensorFlow.\n", "import tensorflow as tf\n", "\n", - "# Import TensorFlow eager execution support (subject to future changes).\n", - "import tensorflow.contrib.eager as tfe\n", - "\n", "# Enable eager execution\n", - "tfe.enable_eager_execution()" + "tf.enable_eager_execution()" ] }, { @@ -137,32 +136,27 @@ "source": [ "# Step 3: Iterate\n", "\n", - "Use `tfe.Iterator` on the `Dataset` object to get a Python iterator over the contents of the dataset.\n", - "\n", - "If you're familiar with the use of `Dataset`s in TensorFlow graphs, note that this process of iteration is different. Here there are no calls to `Dataset.make_one_shot_iterator()` and no `get_next()` calls." + "When eager execution is enabled `Dataset` objects support iteration.\n", + "If you're familiar with the use of `Dataset`s in TensorFlow graphs, note that there is no need for calls to `Dataset.make_one_shot_iterator()` or `get_next()` calls." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 0, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, - "height": 153, - "output_extras": [ - { - "item_id": 1 - } - ] + "base_uri": "https://localhost:8080/", + "height": 153 }, "colab_type": "code", "executionInfo": { - "elapsed": 201, + "elapsed": 388, "status": "ok", - "timestamp": 1505952405928, + "timestamp": 1525154629129, "user": { "displayName": "", "photoUrl": "", @@ -171,7 +165,7 @@ "user_tz": 420 }, "id": "lCUWzso6mbqR", - "outputId": "ec027d30-96c6-4ea4-9ee1-ef74ec1ae29a" + "outputId": "8e4b0298-d27d-4ac7-e26a-ef94af0594ec" }, "outputs": [ { @@ -179,9 +173,9 @@ "output_type": "stream", "text": [ "Elements of ds_tensors:\n", - "tf.Tensor([4 9], shape=(2,), dtype=int32)\n", + "tf.Tensor([1 9], shape=(2,), dtype=int32)\n", "tf.Tensor([16 25], shape=(2,), dtype=int32)\n", - "tf.Tensor([36 1], shape=(2,), dtype=int32)\n", + "tf.Tensor([ 4 36], shape=(2,), dtype=int32)\n", "\n", "Elements in ds_file:\n", "tf.Tensor(['Line 1' 'Line 2'], shape=(2,), dtype=string)\n", @@ -191,22 +185,19 @@ ], "source": [ "print('Elements of ds_tensors:')\n", - "for x in tfe.Iterator(ds_tensors):\n", + "for x in ds_tensors:\n", " print(x)\n", "\n", "print('\\nElements in ds_file:')\n", - "for x in tfe.Iterator(ds_file):\n", + "for x in ds_file:\n", " print(x)" ] } ], "metadata": { "colab": { + "collapsed_sections": [], "default_view": {}, - "last_runtime": { - "build_target": "", - "kind": "local" - }, "name": "Eager Execution Tutorial: Importing Data", "provenance": [], "version": "0.3.2", From 07c58859c2ec62757f110dc56da9946d415b72ee Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 1 May 2018 11:52:43 -0700 Subject: [PATCH 0226/1691] Boosted trees: support indicator column. PiperOrigin-RevId: 194971229 --- .../python/estimator/canned/boosted_trees.py | 292 ++++++++++++------ .../estimator/canned/boosted_trees_test.py | 58 ++++ .../boosted_trees/stats_ops_test.py | 55 +++- 3 files changed, 315 insertions(+), 90 deletions(-) diff --git a/tensorflow/python/estimator/canned/boosted_trees.py b/tensorflow/python/estimator/canned/boosted_trees.py index 085dace1b3eb1b..d281fd90ea74b2 100644 --- a/tensorflow/python/estimator/canned/boosted_trees.py +++ b/tensorflow/python/estimator/canned/boosted_trees.py @@ -49,35 +49,10 @@ _HOLD_FOR_MULTI_CLASS_SUPPORT = object() _HOLD_FOR_MULTI_DIM_SUPPORT = object() +_DUMMY_NUM_BUCKETS = -1 -def _get_max_buckets(feature_columns): - """Gets the maximum number of buckets from feature_columns. - - Args: - feature_columns: a list/set of tf.feature_column. - - Returns: - max_buckets: the maximum number of buckets among bucketized_columns. - - Raises: - ValueError: when unsupported feature_columns are given. - """ - if not feature_columns: - raise ValueError('feature_columns must be a non-empty list/set of ' - 'tf.feature_column.') - max_buckets = 1 - for fc in feature_columns: - if isinstance(fc, feature_column_lib._BucketizedColumn): # pylint:disable=protected-access - # N boundaries creates (N+1) buckets. - max_buckets = max(max_buckets, len(fc.boundaries) + 1) - else: - raise ValueError('For now, only bucketized_column is supported but ' - 'got: {}'.format(fc)) - return max_buckets - - -def _get_transformed_features(features, feature_columns): +def _get_transformed_features(features, sorted_feature_columns): """Gets the transformed features from features/feature_columns pair. Args: @@ -91,22 +66,33 @@ def _get_transformed_features(features, feature_columns): ValueError: when unsupported features/columns are tried. """ # pylint:disable=protected-access - for fc in feature_columns: - if not isinstance(fc, feature_column_lib._BucketizedColumn): - raise ValueError('For now, only bucketized_column is supported but ' - 'got: {}'.format(fc)) transformed_features = feature_column_lib._transform_features( - features, feature_columns) - # pylint:enable=protected-access + features, sorted_feature_columns) result_features = [] - for column in sorted(transformed_features, key=lambda tc: tc.name): - source_name = column.source_column.name - squeezed_tensor = array_ops.squeeze(transformed_features[column], axis=1) - if len(squeezed_tensor.shape) > 1: - raise ValueError('For now, only supports features equivalent to rank 1 ' - 'but column `{}` got: {}'.format( - source_name, features[source_name].shape)) - result_features.append(squeezed_tensor) + for column in sorted_feature_columns: + if isinstance(column, feature_column_lib._BucketizedColumn): + source_name = column.source_column.name + squeezed_tensor = array_ops.squeeze(transformed_features[column], axis=1) + if len(squeezed_tensor.shape) > 1: + raise ValueError('For now, only supports features equivalent to rank 1 ' + 'but column `{}` got: {}'.format( + source_name, features[source_name].shape)) + result_features.append(squeezed_tensor) + elif isinstance(column, feature_column_lib._IndicatorColumn): + source_name = column.categorical_column.name + tensor = math_ops.to_int32(transformed_features[column]) + if len(tensor.shape) > 2: + raise ValueError('Rank of indicator column must be no more than 2, ' + 'but column `{}` got: {}'.format( + source_name, features[source_name].shape)) + unstacked = array_ops.unstack(tensor, axis=1) + result_features.extend(unstacked) + else: + raise ValueError( + 'For now, only bucketized_column and indicator_column is supported ' + 'but got: {}'.format(column)) + # pylint:enable=protected-access + return result_features @@ -120,9 +106,87 @@ def _local_variable(tensor, name=None): name=name) -def _cache_transformed_features(features, feature_columns, batch_size): +def _group_features_by_num_buckets(sorted_feature_columns): + """Groups feature ids by the number of buckets. + + Derives the feature ids based on iterating through ordered feature columns + and groups them by the number of buckets each feature require. Returns a + sorted list of buckets and a list of lists of feature ids for each of those + buckets. + + Args: + sorted_feature_columns: a list/set of tf.feature_column sorted by name. + + Returns: + bucket_size_list: a list of required bucket sizes. + feature_ids_list: a list of lists of feature ids for each bucket size. + + Raises: + ValueError: when unsupported features columns are provided. + """ + bucket_size_to_feature_ids_dict = collections.OrderedDict() + + # TODO(nponomareva) for now we preserve the previous functionality and bucket + # all numeric into the same num of buckets. Can be easily changed to using + # each numeric's real buckets num, but we need to test that it does not cause + # a performance hit. + + # We will replace this dummy key with the real max after we calculate it. + bucket_size_to_feature_ids_dict[_DUMMY_NUM_BUCKETS] = [] + + max_buckets_for_bucketized = 2 + max_buckets_for_indicator = 2 + + feature_idx = 0 + # pylint:disable=protected-access + + for column in sorted_feature_columns: + if isinstance(column, feature_column_lib._IndicatorColumn): + num_categorical_features = column.categorical_column._num_buckets + if max_buckets_for_indicator not in bucket_size_to_feature_ids_dict: + bucket_size_to_feature_ids_dict[max_buckets_for_indicator] = [] + + for _ in range(num_categorical_features): + # We use bucket size of 2 for categorical. + bucket_size_to_feature_ids_dict[max_buckets_for_indicator].append( + feature_idx) + feature_idx += 1 + elif isinstance(column, feature_column_lib._BucketizedColumn): + max_buckets_for_bucketized = max(max_buckets_for_bucketized, + len(column.boundaries) + 1) + bucket_size_to_feature_ids_dict[_DUMMY_NUM_BUCKETS].append(feature_idx) + feature_idx += 1 + elif not isinstance(column, feature_column_lib._IndicatorColumn): # pylint:disable=protected-access + raise ValueError( + 'For now, only bucketized_column and indicator column are supported ' + 'but got: {}'.format(column)) + + # pylint:enable=protected-access + # Replace the dummy key with the real max num of buckets for all bucketized + # columns. + bucket_size_to_feature_ids_dict[ + max_buckets_for_bucketized] = bucket_size_to_feature_ids_dict[ + _DUMMY_NUM_BUCKETS] + del bucket_size_to_feature_ids_dict[_DUMMY_NUM_BUCKETS] + + feature_ids_list = list(bucket_size_to_feature_ids_dict.values()) + bucket_size_list = list(bucket_size_to_feature_ids_dict.keys()) + return bucket_size_list, feature_ids_list + + +def _calculate_num_features(sorted_feature_columns): + num_features = 0 + for column in sorted_feature_columns: + if isinstance(column, feature_column_lib._IndicatorColumn): # pylint:disable=protected-access + num_features += column.categorical_column._num_buckets # pylint:disable=protected-access + else: + num_features += 1 + return num_features + + +def _cache_transformed_features(features, sorted_feature_columns, batch_size): """Transform features and cache, then returns (cached_features, cache_op).""" - num_features = len(feature_columns) + num_features = _calculate_num_features(sorted_feature_columns) cached_features = [ _local_variable( array_ops.zeros([batch_size], dtype=dtypes.int32), @@ -132,7 +196,7 @@ def _cache_transformed_features(features, feature_columns, batch_size): are_features_cached = _local_variable(False, name='are_features_cached') def cache_features_and_return(): - """Caches transoformed features. + """Caches transformed features. The intention is to hide get_transformed_features() from the graph by caching the result except the first step, since bucketize operation @@ -144,7 +208,8 @@ def cache_features_and_return(): the graph. """ - transformed_features = _get_transformed_features(features, feature_columns) + transformed_features = _get_transformed_features(features, + sorted_feature_columns) cached = [ state_ops.assign(cached_features[i], transformed_features[i]) for i in range(num_features) @@ -349,6 +414,8 @@ def _bt_model_fn( ValueError: mode or params are invalid, or features has the wrong type. """ is_single_machine = (config.num_worker_replicas <= 1) + + sorted_feature_columns = sorted(feature_columns, key=lambda tc: tc.name) if train_in_memory: assert n_batches_per_layer == 1, ( 'When train_in_memory is enabled, input_fn should return the entire ' @@ -364,24 +431,26 @@ def _bt_model_fn( # the dimension max_splits_per_layer, instead of max_splits (for the entire # tree). max_splits = (1 << tree_hparams.max_depth) - 1 - max_buckets = _get_max_buckets(feature_columns) train_op = [] with ops.name_scope(name) as name: # Prepare. global_step = training_util.get_or_create_global_step() - num_features = len(feature_columns) + bucket_size_list, feature_ids_list = _group_features_by_num_buckets( + sorted_feature_columns) # Extract input features and set up cache for training. training_state_cache = None if mode == model_fn.ModeKeys.TRAIN and train_in_memory: # cache transformed features as well for in-memory training. batch_size = array_ops.shape(labels)[0] - input_feature_list, input_cache_op = _cache_transformed_features( - features, feature_columns, batch_size) + input_feature_list, input_cache_op = ( + _cache_transformed_features(features, sorted_feature_columns, + batch_size)) train_op.append(input_cache_op) training_state_cache = _CacheTrainingStatesUsingVariables( batch_size, head.logits_dimension) else: - input_feature_list = _get_transformed_features(features, feature_columns) + input_feature_list = _get_transformed_features(features, + sorted_feature_columns) if mode == model_fn.ModeKeys.TRAIN and example_id_column_name: example_ids = features[example_id_column_name] training_state_cache = _CacheTrainingStatesUsingHashTable( @@ -446,34 +515,61 @@ def _train_op_fn(loss): gradients = gradients_impl.gradients(loss, logits, name='Gradients')[0] hessians = gradients_impl.gradients( gradients, logits, name='Hessians')[0] - stats_summary_list = [ - array_ops.squeeze( - boosted_trees_ops.make_stats_summary( - node_ids=node_ids, - gradients=gradients, - hessians=hessians, - bucketized_features_list=[input_feature_list[f]], - max_splits=max_splits, - num_buckets=max_buckets), - axis=0) for f in range(num_features) - ] - - def grow_tree_from_stats_summaries(stats_summary_list): + + stats_summaries_list = [] + for i, feature_ids in enumerate(feature_ids_list): + num_buckets = bucket_size_list[i] + summaries = [ + array_ops.squeeze( + boosted_trees_ops.make_stats_summary( + node_ids=node_ids, + gradients=gradients, + hessians=hessians, + bucketized_features_list=[input_feature_list[f]], + max_splits=max_splits, + num_buckets=num_buckets), + axis=0) for f in feature_ids + ] + stats_summaries_list.append(summaries) + + accumulators = [] + + def grow_tree_from_stats_summaries(stats_summaries_list, + feature_ids_list): """Updates ensemble based on the best gains from stats summaries.""" - (node_ids_per_feature, gains_list, thresholds_list, - left_node_contribs_list, right_node_contribs_list) = ( - boosted_trees_ops.calculate_best_gains_per_feature( - node_id_range=last_layer_nodes_range, - stats_summary_list=stats_summary_list, - l1=tree_hparams.l1, - l2=tree_hparams.l2, - tree_complexity=tree_hparams.tree_complexity, - min_node_weight=tree_hparams.min_node_weight, - max_splits=max_splits)) + node_ids_per_feature = [] + gains_list = [] + thresholds_list = [] + left_node_contribs_list = [] + right_node_contribs_list = [] + all_feature_ids = [] + + assert len(stats_summaries_list) == len(feature_ids_list) + + for i, feature_ids in enumerate(feature_ids_list): + (numeric_node_ids_per_feature, numeric_gains_list, + numeric_thresholds_list, numeric_left_node_contribs_list, + numeric_right_node_contribs_list) = ( + boosted_trees_ops.calculate_best_gains_per_feature( + node_id_range=last_layer_nodes_range, + stats_summary_list=stats_summaries_list[i], + l1=tree_hparams.l1, + l2=tree_hparams.l2, + tree_complexity=tree_hparams.tree_complexity, + min_node_weight=tree_hparams.min_node_weight, + max_splits=max_splits)) + + all_feature_ids += feature_ids + node_ids_per_feature += numeric_node_ids_per_feature + gains_list += numeric_gains_list + thresholds_list += numeric_thresholds_list + left_node_contribs_list += numeric_left_node_contribs_list + right_node_contribs_list += numeric_right_node_contribs_list + grow_op = boosted_trees_ops.update_ensemble( # Confirm if local_tree_ensemble or tree_ensemble should be used. tree_ensemble.resource_handle, - feature_ids=math_ops.range(0, num_features, dtype=dtypes.int32), + feature_ids=all_feature_ids, node_ids=node_ids_per_feature, gains=gains_list, thresholds=thresholds_list, @@ -486,32 +582,50 @@ def grow_tree_from_stats_summaries(stats_summary_list): if train_in_memory and is_single_machine: train_op.append(distribute_lib.increment_var(global_step)) - train_op.append(grow_tree_from_stats_summaries(stats_summary_list)) + train_op.append( + grow_tree_from_stats_summaries(stats_summaries_list, + feature_ids_list)) else: - summary_accumulator = data_flow_ops.ConditionalAccumulator( - dtype=dtypes.float32, - # The stats consist of gradients and hessians (the last dimension). - shape=[num_features, max_splits, max_buckets, 2], - shared_name='stats_summary_accumulator') - apply_grad = summary_accumulator.apply_grad( - array_ops.stack(stats_summary_list, axis=0), stamp_token) + dependencies = [] + + for i, feature_ids in enumerate(feature_ids_list): + stats_summaries = stats_summaries_list[i] + accumulator = data_flow_ops.ConditionalAccumulator( + dtype=dtypes.float32, + # The stats consist of grads and hessians (the last dimension). + shape=[len(feature_ids), max_splits, bucket_size_list[i], 2], + shared_name='numeric_stats_summary_accumulator_' + str(i)) + accumulators.append(accumulator) + + apply_grad = accumulator.apply_grad( + array_ops.stack(stats_summaries, axis=0), stamp_token) + dependencies.append(apply_grad) def grow_tree_from_accumulated_summaries_fn(): """Updates the tree with the best layer from accumulated summaries.""" # Take out the accumulated summaries from the accumulator and grow. - stats_summary_list = array_ops.unstack( - summary_accumulator.take_grad(1), axis=0) - grow_op = grow_tree_from_stats_summaries(stats_summary_list) + stats_summaries_list = [] + + stats_summaries_list = [ + array_ops.unstack(accumulator.take_grad(1), axis=0) + for accumulator in accumulators + ] + + grow_op = grow_tree_from_stats_summaries(stats_summaries_list, + feature_ids_list) return grow_op - with ops.control_dependencies([apply_grad]): + with ops.control_dependencies(dependencies): train_op.append(distribute_lib.increment_var(global_step)) if config.is_chief: + min_accumulated = math_ops.reduce_min( + array_ops.stack( + [acc.num_accumulated() for acc in accumulators])) + train_op.append( control_flow_ops.cond( - math_ops.greater_equal( - summary_accumulator.num_accumulated(), - n_batches_per_layer), + math_ops.greater_equal(min_accumulated, + n_batches_per_layer), grow_tree_from_accumulated_summaries_fn, control_flow_ops.no_op, name='wait_until_n_batches_accumulated')) diff --git a/tensorflow/python/estimator/canned/boosted_trees_test.py b/tensorflow/python/estimator/canned/boosted_trees_test.py index c8c52d3bc649c9..95bb9b5a3b5c0b 100644 --- a/tensorflow/python/estimator/canned/boosted_trees_test.py +++ b/tensorflow/python/estimator/canned/boosted_trees_test.py @@ -46,6 +46,7 @@ [3.0, 20.0, 50.0, -100.0, 102.75], # feature_2 quantized:[2,3,3,0,3] ], dtype=np.float32) + CLASSIFICATION_LABELS = [[0.], [1.], [1.], [0.], [0.]] REGRESSION_LABELS = [[1.5], [0.3], [0.2], [2.], [5.]] FEATURES_DICT = {'f_%d' % i: INPUT_FEATURES[i] for i in range(NUM_FEATURES)} @@ -101,17 +102,25 @@ def setUp(self): def _assert_checkpoint(self, model_dir, global_step, finalized_trees, attempted_layers): + self._assert_checkpoint_and_return_model(model_dir, global_step, + finalized_trees, attempted_layers) + + def _assert_checkpoint_and_return_model(self, model_dir, global_step, + finalized_trees, attempted_layers): reader = checkpoint_utils.load_checkpoint(model_dir) self.assertEqual(global_step, reader.get_tensor(ops.GraphKeys.GLOBAL_STEP)) serialized = reader.get_tensor('boosted_trees:0_serialized') ensemble_proto = boosted_trees_pb2.TreeEnsemble() ensemble_proto.ParseFromString(serialized) + self.assertEqual( finalized_trees, sum([1 for t in ensemble_proto.tree_metadata if t.is_finalized])) self.assertEqual(attempted_layers, ensemble_proto.growing_metadata.num_layers_attempted) + return ensemble_proto + def testTrainAndEvaluateBinaryClassifier(self): input_fn = _make_train_input_fn(is_classification=True) @@ -325,6 +334,55 @@ def testTrainRegressorWithDatasetWhenInputIsOverEarlier(self): [[0.353850], [0.254100], [0.106850], [0.712100], [1.012100]], [pred['predictions'] for pred in predictions]) + def testTrainEvaluateAndPredictWithIndicatorColumn(self): + categorical = feature_column.categorical_column_with_vocabulary_list( + key='categorical', vocabulary_list=('bad', 'good', 'ok')) + feature_indicator = feature_column.indicator_column(categorical) + bucketized_col = feature_column.bucketized_column( + feature_column.numeric_column( + 'an_uninformative_feature', dtype=dtypes.float32), + BUCKET_BOUNDARIES) + + labels = np.array([[0.], [5.7], [5.7], [0.], [0.]], dtype=np.float32) + # Our categorical feature defines the labels perfectly + input_fn = numpy_io.numpy_input_fn( + x={ + 'an_uninformative_feature': np.array([1, 1, 1, 1, 1]), + 'categorical': np.array(['bad', 'good', 'good', 'ok', 'bad']), + }, + y=labels, + batch_size=5, + shuffle=False) + + # Train depth 1 tree. + est = boosted_trees.BoostedTreesRegressor( + feature_columns=[bucketized_col, feature_indicator], + n_batches_per_layer=1, + n_trees=1, + learning_rate=1.0, + max_depth=1) + + num_steps = 1 + est.train(input_fn, steps=num_steps) + ensemble = self._assert_checkpoint_and_return_model( + est.model_dir, global_step=1, finalized_trees=1, attempted_layers=1) + + # We learnt perfectly. + eval_res = est.evaluate(input_fn=input_fn, steps=1) + self.assertAllClose(eval_res['loss'], 0) + + predictions = list(est.predict(input_fn)) + self.assertAllClose( + labels, + [pred['predictions'] for pred in predictions]) + + self.assertEqual(3, len(ensemble.trees[0].nodes)) + + # Check that the split happened on 'good' value, which will be encoded as + # feature with index 2 (0-numeric, 1 - 'bad') + self.assertEqual(2, ensemble.trees[0].nodes[0].bucketized_split.feature_id) + self.assertEqual(0, ensemble.trees[0].nodes[0].bucketized_split.threshold) + class ModelFnTests(test_util.TensorFlowTestCase): """Tests bt_model_fn including unexposed internal functionalities.""" diff --git a/tensorflow/python/kernel_tests/boosted_trees/stats_ops_test.py b/tensorflow/python/kernel_tests/boosted_trees/stats_ops_test.py index f0bb84e69a5ae2..5cceb98cff26ec 100644 --- a/tensorflow/python/kernel_tests/boosted_trees/stats_ops_test.py +++ b/tensorflow/python/kernel_tests/boosted_trees/stats_ops_test.py @@ -224,7 +224,7 @@ def testCalculateBestGainsWithTreeComplexity(self): self.assertAllClose([[[-.424658], [-.6]], [[-.043478], [.485294]]], sess.run(right_node_contribs_list)) - def testCalculateBestGainsWithMinNodeWEight(self): + def testCalculateBestGainsWithMinNodeWeight(self): """Testing Gain calculation without any regularization.""" with self.test_session() as sess: max_splits = 7 @@ -271,6 +271,59 @@ def testCalculateBestGainsWithMinNodeWEight(self): self.assertAllClose([[[-0.75]], [[-0.014925]]], sess.run(right_node_contribs_list)) + def testCalculateBestGainsWithMinNodeWeightNoSplitOnFeturePossible(self): + """Testing Gain calculation without any regularization.""" + with self.test_session() as sess: + max_splits = 7 + node_id_range = [1, 3] # node 1 through 2 will be processed. + stats_summary_list = [ + [ + [[0., 0.], [.08, .09], [0., 0.], [0., 0.]], # node 0; ignored + [[0., 0.], [.15, .0036], [.06, .007], [.1, .2]], # node 1 + [[0., 0.], [-.33, .068], [0., 0.], [.3, .04]], # node 2 + [[0., 0.], [0., 0.], [0., 0.], [0., 0.]], # node 3; ignored + [[0., 0.], [0., 0.], [0., 0.], [0., 0.]], # node 4; ignored + [[0., 0.], [0., 0.], [0., 0.], [0., 0.]], # node 5; ignored + [[0., 0.], [0., 0.], [0., 0.], [0., 0.]], # node 6; ignored + ], # feature 0 + [ + [[0., 0.], [0., 0.], [.08, .09], [0., 0.]], # node 0; ignored + [[0., 0.], [.3, .5], [-.05, .6], [.06, .07]], # node 1 + [[.1, .1], [.2, .03], [-.4, .05], [.07, .08]], # node 2 + [[0., 0.], [0., 0.], [0., 0.], [0., 0.]], # node 3; ignored + [[0., 0.], [0., 0.], [0., 0.], [0., 0.]], # node 4; ignored + [[0., 0.], [0., 0.], [0., 0.], [0., 0.]], # node 5; ignored + [[0., 0.], [0., 0.], [0., 0.], [0., 0.]], # node 6; ignored + ], # feature 1 + ] # num_features * shape=[max_splits, num_buckets, 2] + + (node_ids_list, _, _, _, + _) = boosted_trees_ops.calculate_best_gains_per_feature( + node_id_range, + stats_summary_list, + l1=0.0, + l2=0.0, + tree_complexity=0.0, + min_node_weight=1, + max_splits=max_splits) + + # We can't split either of the nodes on the first feature + self.assertEqual(2, len(sess.run(node_ids_list))) + self.assertAllEqual([], sess.run(node_ids_list)[0]) + self.assertAllEqual([1], sess.run(node_ids_list)[1]) + + # Now check when we can't split on any feature + (node_ids_list, _, _, _, + _) = boosted_trees_ops.calculate_best_gains_per_feature( + node_id_range, + stats_summary_list, + l1=0.0, + l2=0.0, + tree_complexity=0.0, + min_node_weight=10, + max_splits=max_splits) + self.assertAllEqual([[], []], sess.run(node_ids_list)) + def testMakeStatsSummarySimple(self): """Simple test for MakeStatsSummary.""" with self.test_session(): From 8e918c3d202bb0eed6b423eb78a6ef45629f952e Mon Sep 17 00:00:00 2001 From: RJ Ryan Date: Tue, 1 May 2018 12:02:59 -0700 Subject: [PATCH 0227/1691] Improve shape inference for tf.contrib.signal.frame. PiperOrigin-RevId: 194972934 --- .../signal/python/kernel_tests/shape_ops_test.py | 2 +- tensorflow/contrib/signal/python/ops/shape_ops.py | 14 +++++++------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/signal/python/kernel_tests/shape_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/shape_ops_test.py index 64cc8c7ea54673..f1320501535f87 100644 --- a/tensorflow/contrib/signal/python/kernel_tests/shape_ops_test.py +++ b/tensorflow/contrib/signal/python/kernel_tests/shape_ops_test.py @@ -119,7 +119,7 @@ def test_shape_inference(self): frame_step = 1 result = shape_ops.frame(signal, frame_length, frame_step, pad_end=True, pad_value=99, axis=1) - self.assertEqual([1, None, None, 3, 4], result.shape.as_list()) + self.assertEqual([1, 2, None, 3, 4], result.shape.as_list()) result = shape_ops.frame(signal, frame_length, frame_step, pad_end=False, axis=1) diff --git a/tensorflow/contrib/signal/python/ops/shape_ops.py b/tensorflow/contrib/signal/python/ops/shape_ops.py index 1ddc2941ec4029..91862f0cc0ba53 100644 --- a/tensorflow/contrib/signal/python/ops/shape_ops.py +++ b/tensorflow/contrib/signal/python/ops/shape_ops.py @@ -43,13 +43,13 @@ def _infer_frame_shape(signal, frame_length, frame_step, pad_end, axis): outer_dimensions = signal_shape[:axis] inner_dimensions = signal_shape[axis:][1:] if signal_shape and frame_axis is not None: - if frame_step and frame_length is not None: - if pad_end: - # Double negative is so that we round up. - num_frames = -(-frame_axis // frame_step) - else: - num_frames = (frame_axis - frame_length + frame_step) // frame_step - num_frames = max(0, num_frames) + if frame_step is not None and pad_end: + # Double negative is so that we round up. + num_frames = max(0, -(-frame_axis // frame_step)) + elif frame_step is not None and frame_length is not None: + assert not pad_end + num_frames = max( + 0, (frame_axis - frame_length + frame_step) // frame_step) return outer_dimensions + [num_frames, frame_length] + inner_dimensions From 5c18dc63d752af4a810ed70c6aa18d4f7dd2601a Mon Sep 17 00:00:00 2001 From: Benoit Steiner Date: Tue, 1 May 2018 12:17:48 -0700 Subject: [PATCH 0228/1691] Simplified shape inference. PiperOrigin-RevId: 194975603 --- .../core/grappler/costs/graph_properties.cc | 354 ++++++++---------- .../core/grappler/costs/graph_properties.h | 34 +- .../grappler/costs/graph_properties_test.cc | 15 +- tensorflow/core/grappler/op_types.cc | 4 + tensorflow/core/grappler/op_types.h | 2 +- tensorflow/core/grappler/utils.cc | 24 +- tensorflow/core/grappler/utils.h | 19 +- .../core/grappler/utils/topological_sort.cc | 18 +- .../core/grappler/utils/topological_sort.h | 4 +- .../grappler/utils/topological_sort_test.cc | 34 +- 10 files changed, 251 insertions(+), 257 deletions(-) diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc index 313f63149d5432..a12d9b932bef54 100644 --- a/tensorflow/core/grappler/costs/graph_properties.cc +++ b/tensorflow/core/grappler/costs/graph_properties.cc @@ -256,18 +256,14 @@ typename DisjointSet::Rep* DisjointSet::Find(Handle value) { return root; } -bool IsQueue(const NodeDef& node) { - return str_util::EndsWith(node.op(), "QueueV2"); +bool IsEnqueue(const NodeDef& n) { + return (n.op().find("Enqueue") != std::string::npos && + n.op().find("EnqueueMany") == std::string::npos); } -// Returns true if the node is an Enter op AND its input is a Queue. -bool IsEnterWithQueue(const NodeDef& node, const GraphView& graph) { - if (IsEnter(node)) { - GraphView::InputPort input(&node, 0); - GraphView::OutputPort fanin = graph.GetRegularFanin(input); - return IsQueue(*fanin.node); - } - return false; +bool IsDequeue(const NodeDef& n) { + return (n.op().find("Dequeue") != std::string::npos && + n.op().find("DequeueMany") == std::string::npos); } bool HasAnyUnknownDimensions(const TensorShapeProto& proto) { @@ -428,7 +424,8 @@ class SymbolicShapeRefiner { } return it->second.inference_context.get(); } - Status UpdateNode(const NodeDef* node, bool relax, bool* refined) { + + Status UpdateNode(const NodeDef* node, bool* refined) { NodeContext* node_context = GetNodeContext(node); if (node_context == nullptr) { TF_RETURN_IF_ERROR(AddNode(node)); @@ -519,8 +516,12 @@ class SymbolicShapeRefiner { } } + // Make sure we schedule the fanout of resources (which have no input) + // whenever the resources are updated. + *refined |= inference_context->num_inputs() == 0; + if (!*refined) { - // No input shape has changed, we're done + // No input shape has changed, we're done. return Status::OK(); } @@ -573,51 +574,6 @@ class SymbolicShapeRefiner { } }; - // Compute the shape of the tensors outputed by node 'node' at output port - // 'port_index' as the intersection of shape1 and shape2. - ShapeHandle OutputAsIntersection(const NodeDef* node, int port_index, - ShapeHandle shape1, ShapeHandle shape2) { - if (shape1.SameHandle(shape2)) { - return shape1; - } - InferenceContext* ctx = GetContext(node); - ShapeHandle merged = shape1; - if (!ctx->RankKnown(shape2) && !ctx->RankKnown(shape1)) { - // Return either one since they're expected to represent the same value. - return shape1; - } else if (!ctx->RankKnown(shape2) && ctx->RankKnown(shape1)) { - return shape1; - } else if (ctx->RankKnown(shape2) && !ctx->RankKnown(shape1)) { - return shape2; - } else { - const int rank = ctx->Rank(shape1); - if (ctx->Rank(shape2) != rank) { - // We detected an inconsistency, return an unknown shape. This can - // happen in the fanout of a merge node since during the initial - // propagation we optimistically assume that all the inputs to the merge - // node have the same shape. - return GetUnknownOutputShape(node, port_index); - } - for (int d = 0; d < rank; ++d) { - if (!ctx->Dim(shape1, d).SameHandle(ctx->Dim(shape2, d))) { - if (ctx->Value(ctx->Dim(shape1, d)) != - ctx->Value(ctx->Dim(shape2, d))) { - DimensionHandle new_dim; - if (ctx->Value(ctx->Dim(shape1, d)) < 0) { - new_dim = ctx->Dim(shape2, d); - } else if (ctx->Value(ctx->Dim(shape2, d)) < 0) { - new_dim = ctx->Dim(shape1, d); - } else { - new_dim = GetUnknownOutputDim(node, port_index, d); - } - TF_CHECK_OK(ctx->ReplaceDim(merged, d, new_dim, &merged)); - } - } - } - } - return merged; - } - // Compute the shape of the tensors outputed by node 'node' at output port // 'port_index' as the union of shape1 and shape2. ShapeHandle OutputAsUnion(const NodeDef* node, int port_index, @@ -822,6 +778,7 @@ class SymbolicShapeRefiner { status.Update(SetUnknownShape(&node, output_port)); } } + return status; } @@ -884,29 +841,6 @@ class SymbolicShapeManager { DisjointSet dims_; }; -Status GraphProperties::MergeEnqueueShapesAndTypes( - SymbolicShapeRefiner* shape_refiner, const NodeDef* qnode, - const std::vector& shapes_and_types, - std::vector* queue_shapes_and_types) { - if (shapes_and_types.size() != queue_shapes_and_types->size()) { - return errors::InvalidArgument( - "Enqueue nodes mixed number of tensors: ", shapes_and_types.size(), - " vs ", queue_shapes_and_types->size()); - } - for (size_t i = 0; i < shapes_and_types.size(); ++i) { - const ShapeAndType& a = shapes_and_types[i]; - ShapeAndType& b = (*queue_shapes_and_types)[i]; - if (a.dtype != b.dtype) { - return errors::InvalidArgument("Enqueue nodes mixed dtypes for tensor ", - i, ": ", DataTypeString(a.dtype), " vs ", - DataTypeString(b.dtype)); - } - - b.shape = shape_refiner->OutputAsIntersection(qnode, i, a.shape, b.shape); - } - return Status::OK(); -} - Status GraphProperties::RelaxEnqueueShapesAndMergeTypes( SymbolicShapeRefiner* shape_refiner, const NodeDef* qnode, const std::vector& shapes_and_types, @@ -936,7 +870,7 @@ Status GraphProperties::RelaxEnqueueShapesAndMergeTypes( // inputs are UnknownShapes. So we need to ignore the input from NextIteration // nodes to propagate any known shape from the Merge node. Status GraphProperties::UpdateMergeNode(SymbolicShapeRefiner* shape_refiner, - const NodeDef* node, bool relax, + const NodeDef* node, bool* new_shapes) const { InferenceContext* c = shape_refiner->GetContext(node); if (!c) { @@ -955,15 +889,8 @@ Status GraphProperties::UpdateMergeNode(SymbolicShapeRefiner* shape_refiner, bool out_initialized = false; for (const GraphView::Edge fanin : shape_refiner->graph().GetFaninEdges(*node, false)) { - // Skip back edges during the initial propagation phase. This is equivalent - // to assuming that all the inputs to the merge nodes are fed by the same - // shape, and will be corrected as needed in the relaxation phase. - if (!relax && IsNextIteration(*fanin.src.node)) { - continue; - } - InferenceContext* in = shape_refiner->GetContext(fanin.src.node); - if (!relax && !in) { + if (!in) { // Handling a loop for the first time, the back edge won't have any shape // info. continue; @@ -976,11 +903,7 @@ Status GraphProperties::UpdateMergeNode(SymbolicShapeRefiner* shape_refiner, out = input; continue; } - if (relax) { - out = shape_refiner->OutputAsUnion(node, 0, input, out); - } else { - out = shape_refiner->OutputAsIntersection(node, 0, input, out); - } + out = shape_refiner->OutputAsUnion(node, 0, input, out); } if (*new_shapes || !shape_refiner->EquivalentShapes(out, c->output(0))) { @@ -994,11 +917,10 @@ Status GraphProperties::UpdateMergeNode(SymbolicShapeRefiner* shape_refiner, // Manually propagate the input shape for Enter nodes and update any Merge node // outputs. Status GraphProperties::UpdateEnter(SymbolicShapeRefiner* shape_refiner, - const NodeDef* node, bool relax, - bool* new_shapes) { + const NodeDef* node, bool* new_shapes) { auto enter_ctx = shape_refiner->GetContext(node); if (!enter_ctx) { - TF_RETURN_IF_ERROR(shape_refiner->UpdateNode(node, relax, new_shapes)); + TF_RETURN_IF_ERROR(shape_refiner->UpdateNode(node, new_shapes)); enter_ctx = shape_refiner->GetContext(node); } @@ -1012,53 +934,54 @@ Status GraphProperties::UpdateEnter(SymbolicShapeRefiner* shape_refiner, enter_ctx->set_output(0, input); *new_shapes = true; } + auto* outputs = in->output_handle_shapes_and_types(fanin.port_id); + if (outputs) { + enter_ctx->set_input_handle_shapes_and_types(0, *outputs); + enter_ctx->set_output_handle_shapes_and_types(0, *outputs); + *new_shapes = true; + } return Status::OK(); } -Status GraphProperties::UpdateShapes(SymbolicShapeRefiner* shape_refiner, - bool relax, const NodeDef* n, - bool* new_shapes) const { +Status GraphProperties::UpdateShapes( + SymbolicShapeRefiner* shape_refiner, + const std::unordered_map& resource_handles, + const NodeDef* n, bool* new_shapes) const { if (IsEnter(*n)) { // The Enter shape function always forwards an UnknownShape, so do the right // thing here. - TF_RETURN_IF_ERROR(UpdateEnter(shape_refiner, n, relax, new_shapes)); + TF_RETURN_IF_ERROR(UpdateEnter(shape_refiner, n, new_shapes)); } else if (IsMerge(*n)) { // Properly handle merge nodes. - TF_RETURN_IF_ERROR(UpdateMergeNode(shape_refiner, n, relax, new_shapes)); + TF_RETURN_IF_ERROR(UpdateMergeNode(shape_refiner, n, new_shapes)); + } else if (IsEnqueue(*n)) { + TF_RETURN_IF_ERROR( + UpdateEnqueue(n, resource_handles, shape_refiner, new_shapes)); } else { // Rely on regular TF shape refinement for all the other nodes. - bool updated = false; - TF_RETURN_IF_ERROR(shape_refiner->UpdateNode(n, relax, &updated)); - if (updated) { - // We want to avoid propagating through loops on the merge pass because - // the shapes are not guaranteed to converge. - if (relax || !IsNextIteration(*n)) { - *new_shapes = true; - } - } + TF_RETURN_IF_ERROR(shape_refiner->UpdateNode(n, new_shapes)); } return Status::OK(); } // Propagates the shapes in the transitive fan-out of . Status GraphProperties::PropagateShapes( - SymbolicShapeRefiner* shape_refiner, bool relax, TopoQueue* new_shapes, - const std::unordered_map>& resources, + SymbolicShapeRefiner* shape_refiner, TopoQueue* new_shapes, + const std::unordered_map& resource_handles, int num_loops) const { // Limit the number of iterations to prevent infinite loops in the presence of // incorrect shape functions. The algoritm should converge in at most // num_nested_loops^2 * max_rank. We approximate max_rank with the constant 4. // The same applies to resources. - VLOG(1) << "Propagating (relax=" << relax << ") " << new_shapes->size() - << " new shapes through " << num_loops << " loops and " - << resources.size() << " resources" << std::endl; + VLOG(1) << "Propagating " << new_shapes->size() << " new shapes through " + << num_loops << " loops and " << resource_handles.size() + << " resources" << std::endl; const int64 max_loop_length = item_.graph.node_size(); const int64 max_rank = 4; const int64 max_loop_iterations = max_rank * max_loop_length * std::max(1, num_loops * num_loops); - const int64 num_queues = resources.size(); + const int64 num_queues = resource_handles.size(); const int64 max_resource_iterations = num_queues * num_queues * max_rank; int64 num_resource_iterations = 0; @@ -1068,22 +991,22 @@ Status GraphProperties::PropagateShapes( num_loop_iterations++ < max_loop_iterations) { const NodeDef* n = new_shapes->pop(); bool updated = false; - TF_RETURN_IF_ERROR(UpdateShapes(shape_refiner, relax, n, &updated)); + TF_RETURN_IF_ERROR( + UpdateShapes(shape_refiner, resource_handles, n, &updated)); if (updated) { - for (const GraphView::InputPort fanout : + for (const GraphView::InputPort& fanout : shape_refiner->graph().GetFanouts(*n, false)) { new_shapes->push(fanout.node); } + // Make sure the corresponding queue nodes are (re)processed. + if (IsEnqueue(*n)) { + auto it = resource_handles.find(n); + if (it != resource_handles.end()) { + new_shapes->push(it->second); + } + } } } - - for (const auto& resource : resources) { - // Resources need special handling: since the enqueue nodes are in the - // fanout of the queues, we need to manually propagate the shapes from - // enqueue node to the corresponding queue. - TF_RETURN_IF_ERROR(UpdateResource(resource.first, resource.second, - shape_refiner, new_shapes)); - } } while (!new_shapes->empty() && num_resource_iterations++ < max_resource_iterations); @@ -1094,54 +1017,48 @@ Status GraphProperties::PropagateShapes( return Status::OK(); } -Status GraphProperties::UpdateResource( - const NodeDef* qnode, - const std::unordered_set& queue_inputs, - SymbolicShapeRefiner* shape_refiner, TopoQueue* new_shapes) { - // Proceed only if qnode is a queue or an Enter with queue input. - if (!IsQueue(*qnode) && !IsEnterWithQueue(*qnode, shape_refiner->graph())) { +Status GraphProperties::UpdateEnqueue( + const NodeDef* enqueue_node, + const std::unordered_map& resource_handles, + SymbolicShapeRefiner* shape_refiner, bool* new_shapes) { + auto ctx = shape_refiner->GetNodeContext(enqueue_node); + if (!ctx) { + TF_RETURN_IF_ERROR(shape_refiner->AddNode(enqueue_node)); + ctx = CHECK_NOTNULL(shape_refiner->GetNodeContext(enqueue_node)); + } + + auto it = resource_handles.find(enqueue_node); + if (it == resource_handles.end()) { + // The corresponding queue was not found, there isn't much we can do. return Status::OK(); } + const NodeDef* qnode = it->second; auto qctx = shape_refiner->GetContext(qnode); if (!qctx) { return Status::OK(); } auto* queue_handle_data = qctx->output_handle_shapes_and_types(0); - // Merge all inputs into the enqueue node, regardless of which phase we - // are in. - std::vector queue_shapes_and_types; - for (const auto& node : queue_inputs) { - auto ctx = shape_refiner->GetNodeContext(node); - if (!ctx) { - continue; - } - // TODO(bsteiner): handle EnqueueMany as well. - if (node->op().find("Enqueue") != std::string::npos && - node->op().find("EnqueueMany") == std::string::npos) { - std::vector shapes_and_types; - for (int i = 1; i < ctx->input_types.size(); ++i) { - shapes_and_types.push_back( - {ctx->inference_context->input(i), ctx->input_types[i]}); - } - if (queue_shapes_and_types.empty()) { - queue_shapes_and_types = shapes_and_types; - } else { - TF_RETURN_IF_ERROR(RelaxEnqueueShapesAndMergeTypes( - shape_refiner, qnode, shapes_and_types, &queue_shapes_and_types)); - } - } + // TODO(bsteiner): handle EnqueueMany as well. + std::vector shapes_and_types; + for (int i = 1; i < ctx->input_types.size(); ++i) { + GraphView::InputPort inp(enqueue_node, i); + GraphView::OutputPort fanin = shape_refiner->graph().GetRegularFanin(inp); + InferenceContext* in = shape_refiner->GetContext(fanin.node); + ShapeHandle input = in->output(fanin.port_id); + ctx->inference_context->SetInput(i, input); + shapes_and_types.push_back({input, ctx->input_types[i]}); } - if (queue_handle_data == nullptr || - !shape_refiner->EquivalentShapesAndTypes(*queue_handle_data, - queue_shapes_and_types)) { - qctx->set_output_handle_shapes_and_types(0, queue_shapes_and_types); - - for (const GraphView::InputPort fanout : - shape_refiner->graph().GetFanouts(*qnode, false)) { - new_shapes->push(fanout.node); - } + if (queue_handle_data == nullptr) { + qctx->set_output_handle_shapes_and_types(0, shapes_and_types); + *new_shapes = true; + } else { + TF_RETURN_IF_ERROR(RelaxEnqueueShapesAndMergeTypes( + shape_refiner, qnode, *queue_handle_data, &shapes_and_types)); + *new_shapes |= !shape_refiner->EquivalentShapesAndTypes(*queue_handle_data, + shapes_and_types); + qctx->set_output_handle_shapes_and_types(0, shapes_and_types); } return Status::OK(); @@ -1159,75 +1076,96 @@ Status GraphProperties::InferStatically(bool assume_valid_feeds) { } } - std::unordered_map topo_order; - TF_RETURN_IF_ERROR(ComputeTopologicalOrder(item_.graph, &topo_order)); - GraphView graph_view(&item_.graph); // List the resources and the nodes using them. Also collect the Merge nodes, // fed nodes, and primary inputs. - std::unordered_map> + std::unordered_map, + std::unordered_set>> resources; std::unordered_set merge_nodes; std::unordered_set fed_nodes; std::unordered_set primary_inputs; int num_loops = 0; for (const NodeDef& node : item_.graph.node()) { + if (IsQueue(node)) { + for (const GraphView::InputPort& fanout : + graph_view.GetFanouts(node, false)) { + if (IsEnter(*fanout.node)) { + const NodeDef& enter = *fanout.node; + for (const GraphView::InputPort& fanout : + graph_view.GetFanouts(enter, false)) { + if (IsEnqueue(*fanout.node)) { + resources[&node].first.insert(fanout.node); + } else if (IsDequeue(*fanout.node)) { + resources[&node].second.insert(fanout.node); + } + } + } else { + if (IsEnqueue(*fanout.node)) { + resources[&node].first.insert(fanout.node); + } else if (IsDequeue(*fanout.node)) { + resources[&node].second.insert(fanout.node); + } + } + } + } if (NumNonControlInputs(node) == 0) { primary_inputs.insert(&node); } else if (IsMerge(node)) { merge_nodes.insert(&node); } else if (IsNextIteration(node)) { ++num_loops; - } else { - const OpRegistrationData* op_data; - TF_RETURN_IF_ERROR(function_library.LookUp(node.op(), &op_data)); - DataTypeVector input_types; - DataTypeVector output_types; - TF_RETURN_IF_ERROR(InOutTypesForNode(node, op_data->op_def, &input_types, - &output_types)); - for (int i = 0; i < input_types.size(); ++i) { - if (input_types[i] == DataType::DT_RESOURCE) { - GraphView::InputPort input(&node, i); - const GraphView::OutputPort resource = - graph_view.GetRegularFanin(input); - resources[resource.node].insert(&node); - } - } } if (fed_ports.find(node.name()) != fed_ports.end()) { fed_nodes.insert(&node); } } - SymbolicShapeRefiner refiner(graph_view, fed_ports); - - // We propagate shapes through the graph in two phases. In the first phase, we - // exclusively merge shapes but we do not propagate shapes through the - // backedge of loops (i.e. the NextIteration node). Then on the second phase, - // we exclusively relax shapes and propagate shapes through loops until - // reaching fixed point. - for (int relax = 0; relax < 2; relax++) { - TopoQueue new_shapes(topo_order); - // Seed the propagation of shapes through merge nodes. - if (relax) { - for (const NodeDef* node : merge_nodes) { - new_shapes.push(node); + std::unordered_map resource_handles; + std::vector> extra_deps; + for (const auto& resource : resources) { + for (const NodeDef* src : resource.second.first) { + resource_handles[src] = resource.first; + for (const NodeDef* tgt : resource.second.second) { + // Add control edges from enqueue to dequeue nodes to ensure they are + // processed in their logical order. + extra_deps.emplace_back(src, tgt); } } - // Also seed the propagation of shapes in the fanout of primary inputs. - for (const NodeDef* node : primary_inputs) { - new_shapes.push(node); - } - // Also seed the propagation of shapes in the fanout of fed nodes. - for (const NodeDef* node : fed_nodes) { - new_shapes.push(node); + } + + std::unordered_map topo_order; + Status s = ComputeTopologicalOrder(item_.graph, &topo_order, &extra_deps); + if (!s.ok()) { + if (extra_deps.empty()) { + return s; + } else { + // There is a loop between queues: we'll just use the graph topological + // order. This will make the shape inference less precise but since this + // isn't common it's not worth to figure out where to break the loop and + // do a proper relaxation. + TF_RETURN_IF_ERROR( + ComputeTopologicalOrder(item_.graph, &topo_order, nullptr)); } - // Propagate shapes normally. - TF_RETURN_IF_ERROR( - PropagateShapes(&refiner, relax, &new_shapes, resources, num_loops)); } + SymbolicShapeRefiner refiner(graph_view, fed_ports); + + TopoQueue new_shapes(topo_order); + // Also seed the propagation of shapes in the fanout of primary inputs. + for (const NodeDef* node : primary_inputs) { + new_shapes.push(node); + } + // Also seed the propagation of shapes in the fanout of fed nodes. + for (const NodeDef* node : fed_nodes) { + new_shapes.push(node); + } + // Propagate shapes normally. + TF_RETURN_IF_ERROR( + PropagateShapes(&refiner, &new_shapes, resource_handles, num_loops)); + // Track shapes globally across the graph. SymbolicShapeManager shape_manager; bool found_error = false; diff --git a/tensorflow/core/grappler/costs/graph_properties.h b/tensorflow/core/grappler/costs/graph_properties.h index 7d685b58337213..ecc10fddb8a306 100644 --- a/tensorflow/core/grappler/costs/graph_properties.h +++ b/tensorflow/core/grappler/costs/graph_properties.h @@ -75,12 +75,6 @@ class GraphProperties { void ClearOutputProperties(const string& node_name); private: - // Merges shapes , determined from an EnqueueV2 node, into - // <*queue_shapes_and_types>. - static Status MergeEnqueueShapesAndTypes( - SymbolicShapeRefiner* shape_refiner, const NodeDef* qnode, - const std::vector& shapes_and_types, - std::vector* queue_shapes_and_types); // Relaxes shapes , determined from an EnqueueV2 node, into // <*queue_shapes_and_types>. static Status RelaxEnqueueShapesAndMergeTypes( @@ -88,31 +82,33 @@ class GraphProperties { const std::vector& shapes_and_types, std::vector* queue_shapes_and_types); - // Update the shapes for qnode. If output shapes of qnode have changed, - // enqueue its fanout in 'new_shapes'. - static Status UpdateResource( - const NodeDef* qnode, - const std::unordered_set& queue_inputs, - SymbolicShapeRefiner* shape_refiner, TopoQueue* new_shapes); + // Update the shapes of the enqueue node, port them over to the corresponding + // queue, and schedule the reprocessing of the queue if needed. + static Status UpdateEnqueue( + const NodeDef* enqueue_node, + const std::unordered_map& + resource_handles, + SymbolicShapeRefiner* shape_refiner, bool* new_shapes); // Update the output shapes of a Merge node, and enqueue its fanout in // new_shapes if needed. Status UpdateMergeNode(SymbolicShapeRefiner* shape_refiner, - const NodeDef* node, bool relax, - bool* new_shapes) const; + const NodeDef* node, bool* new_shapes) const; // Process the Enter node, and enqueue its fanout in new_shapes if needed. static Status UpdateEnter(SymbolicShapeRefiner* shape_refiner, - const NodeDef* node, bool relax, bool* new_shapes); + const NodeDef* node, bool* new_shapes); // Update the shapes for node 'n'. If output shapes for n have changed, // enqueue its fanout in 'new_shapes'. - Status UpdateShapes(SymbolicShapeRefiner* shape_refiner, bool relax, + Status UpdateShapes(SymbolicShapeRefiner* shape_refiner, + const std::unordered_map& + resource_handles, const NodeDef* n, bool* new_shapes) const; // Propagate the shapes for the nodes enqueued in new_shapes and their // transitive fanout until a fixed point is reached. Status PropagateShapes( - SymbolicShapeRefiner* shape_refiner, bool relax, TopoQueue* new_shapes, - const std::unordered_map>& resources, + SymbolicShapeRefiner* shape_refiner, TopoQueue* new_shapes, + const std::unordered_map& + resource_handles, int num_loops) const; // Data members diff --git a/tensorflow/core/grappler/costs/graph_properties_test.cc b/tensorflow/core/grappler/costs/graph_properties_test.cc index afe334dfa2fafd..a53f6414c307e0 100644 --- a/tensorflow/core/grappler/costs/graph_properties_test.cc +++ b/tensorflow/core/grappler/costs/graph_properties_test.cc @@ -282,20 +282,11 @@ TEST_F(GraphPropertiesTest, Queues) { auto dequeue2 = ops::QueueDequeue(root.WithOpName("Dequeue2"), q2, {DataType::DT_FLOAT}); - // Create a queue that feeds itself. - auto q3 = - ops::RandomShuffleQueue(root.WithOpName("Queue3"), {DataType::DT_FLOAT}); - auto dequeue3 = - ops::QueueDequeue(root.WithOpName("Dequeue3"), q3, {DataType::DT_FLOAT}); - auto merge3 = ops::Merge(root.WithOpName("Merge3"), {dequeue3[0], square2}); - auto enqueue3 = - ops::QueueEnqueue(root.WithOpName("Enqueue3"), q3, {merge3.output}); - auto q4 = ops::RandomShuffleQueue(root.WithOpName("Queue4"), {DataType::DT_FLOAT}); auto enqueue4 = ops::QueueEnqueue(root.WithOpName("Enqueue4"), q4, {square2}); auto enqueue4_2 = - ops::QueueEnqueue(root.WithOpName("Enqueue4_2"), q4, {dequeue3[0]}); + ops::QueueEnqueue(root.WithOpName("Enqueue4_2"), q4, {dequeue2[0]}); auto dequeue4 = ops::QueueDequeue(root.WithOpName("Dequeue4"), q4, {DataType::DT_FLOAT}); @@ -327,10 +318,6 @@ TEST_F(GraphPropertiesTest, Queues) { ASSERT_EQ(1, props2.size()); EXPECT_EQ("float: [3,7]", PropToString(props2[0])); - const auto props3 = properties.GetOutputProperties("Dequeue3"); - ASSERT_EQ(1, props3.size()); - EXPECT_EQ("float: [3,7]", PropToString(props3[0])); - // The dequeue3 op shape is unknown. The square2 op shape is known. Verify // that we merge the 2 properly to determine the shape of the data coming out // of the queue. diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc index 7a89c263744d60..839b0bbfc984d6 100644 --- a/tensorflow/core/grappler/op_types.cc +++ b/tensorflow/core/grappler/op_types.cc @@ -250,6 +250,10 @@ bool IsPrint(const NodeDef& node) { return node.op() == "Print"; } bool IsProd(const NodeDef& node) { return node.op() == "Prod"; } +bool IsQueue(const NodeDef& node) { + return str_util::EndsWith(node.op(), "QueueV2"); +} + bool IsRandomShuffle(const NodeDef& node) { return node.op() == "RandomShuffle"; } diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h index 976d23e52795ba..bd8d3a44e4901c 100644 --- a/tensorflow/core/grappler/op_types.h +++ b/tensorflow/core/grappler/op_types.h @@ -21,7 +21,6 @@ limitations under the License. namespace tensorflow { namespace grappler { - bool IsAdd(const NodeDef& node); bool IsAddN(const NodeDef& node); bool IsAll(const NodeDef& node); @@ -98,6 +97,7 @@ bool IsPolygamma(const NodeDef& node); bool IsPrint(const NodeDef& node); bool IsProd(const NodeDef& node); bool IsPow(const NodeDef& node); +bool IsQueue(const NodeDef& node); bool IsRandomShuffle(const NodeDef& node); bool IsReal(const NodeDef& node); bool IsRealDiv(const NodeDef& node); diff --git a/tensorflow/core/grappler/utils.cc b/tensorflow/core/grappler/utils.cc index 7398d2c896dc01..6db6d71447a646 100644 --- a/tensorflow/core/grappler/utils.cc +++ b/tensorflow/core/grappler/utils.cc @@ -361,8 +361,11 @@ inline void STLSortAndRemoveDuplicates(T* v) { } } // namespace -Status SimpleGraphView::Initialize(const GraphDef& graph, bool dedup_inputs, - bool dedup_outputs) { +Status SimpleGraphView::Initialize( + const GraphDef& graph, + const std::vector>* + extra_dependencies, + bool dedup_inputs, bool dedup_outputs) { graph_ = &graph; const int num_nodes = graph.node_size(); inputs_.clear(); @@ -381,6 +384,23 @@ Status SimpleGraphView::Initialize(const GraphDef& graph, bool dedup_inputs, index_to_name_.push_back(node.name()); } + if (extra_dependencies) { + for (const auto& dep : *extra_dependencies) { + auto itr_src = name_to_index_.find(dep.first->name()); + if (itr_src == name_to_index_.end()) { + return errors::InvalidArgument("Non-existent src ", dep.first->name()); + } + auto itr_tgt = name_to_index_.find(dep.second->name()); + if (itr_tgt == name_to_index_.end()) { + return errors::InvalidArgument("Non-existent tgt ", dep.second->name()); + } + const int src_idx = itr_src->second; + const int tgt_idx = itr_tgt->second; + inputs_[tgt_idx].push_back(src_idx); + outputs_[src_idx].push_back(tgt_idx); + } + } + // Build forward and reverse adjacency lists. for (int node_idx = 0; node_idx < num_nodes; ++node_idx) { const NodeDef& node = graph.node(node_idx); diff --git a/tensorflow/core/grappler/utils.h b/tensorflow/core/grappler/utils.h index 54cb26bafa9c4a..15f6b367b0178a 100644 --- a/tensorflow/core/grappler/utils.h +++ b/tensorflow/core/grappler/utils.h @@ -211,11 +211,24 @@ Status SetTensorValue(DataType dtype, int value, Tensor* tensor); class SimpleGraphView { public: + // Build a graph view for the specified graphdef. Status Initialize(const GraphDef& graph) { - return Initialize(graph, true, true); + return Initialize(graph, nullptr, true, true); } - Status Initialize(const GraphDef& graph, bool dedup_inputs, - bool dedup_outputs); + // Build a graph view for the specified graphdef augmented with the additional + // edges specified in 'extra_dependencies' if any. Note that + // extra_dependencies can be null. + Status Initialize( + const GraphDef& graph, + const std::vector>* + extra_dependencies) { + return Initialize(graph, extra_dependencies, true, true); + } + Status Initialize( + const GraphDef& graph, + const std::vector>* + extra_dependencies, + bool dedup_inputs, bool dedup_outputs); const GraphDef* graph() const { return graph_; } inline int num_nodes() const { return index_to_name_.size(); } diff --git a/tensorflow/core/grappler/utils/topological_sort.cc b/tensorflow/core/grappler/utils/topological_sort.cc index a8e464d09d6c2e..ff89035902270c 100644 --- a/tensorflow/core/grappler/utils/topological_sort.cc +++ b/tensorflow/core/grappler/utils/topological_sort.cc @@ -26,10 +26,12 @@ namespace grappler { // Kahn's algorithm is implemented. // For details, see https://en.wikipedia.org/wiki/Topological_sorting -Status ComputeTopologicalOrder(const GraphDef& graph, - std::vector* ready_nodes) { +Status ComputeTopologicalOrder( + const GraphDef& graph, std::vector* ready_nodes, + const std::vector>* + extra_dependencies) { SimpleGraphView graph_view; - TF_RETURN_IF_ERROR(graph_view.Initialize(graph)); + TF_RETURN_IF_ERROR(graph_view.Initialize(graph, extra_dependencies)); ready_nodes->reserve(graph_view.num_nodes()); @@ -70,10 +72,12 @@ Status ComputeTopologicalOrder(const GraphDef& graph, } Status ComputeTopologicalOrder( - const GraphDef& graph, - std::unordered_map* topo_order) { + const GraphDef& graph, std::unordered_map* topo_order, + const std::vector>* + extra_dependencies) { std::vector ready_nodes; - TF_RETURN_IF_ERROR(ComputeTopologicalOrder(graph, &ready_nodes)); + TF_RETURN_IF_ERROR( + ComputeTopologicalOrder(graph, &ready_nodes, extra_dependencies)); topo_order->reserve(graph.node_size()); for (int i = 0; i < ready_nodes.size(); ++i) { (*topo_order)[&graph.node(ready_nodes[i])] = i; @@ -83,7 +87,7 @@ Status ComputeTopologicalOrder( Status TopologicalSort(GraphDef* graph) { std::vector ready_nodes; - TF_RETURN_IF_ERROR(ComputeTopologicalOrder(*graph, &ready_nodes)); + TF_RETURN_IF_ERROR(ComputeTopologicalOrder(*graph, &ready_nodes, nullptr)); PermuteNodesInPlace(graph, &ready_nodes, /*invert_permutation=*/true); return Status::OK(); } diff --git a/tensorflow/core/grappler/utils/topological_sort.h b/tensorflow/core/grappler/utils/topological_sort.h index 668c88dc751c87..bc0299a7b8c908 100644 --- a/tensorflow/core/grappler/utils/topological_sort.h +++ b/tensorflow/core/grappler/utils/topological_sort.h @@ -24,7 +24,9 @@ namespace grappler { // Compute a topological ordering for the graph nodes. Status ComputeTopologicalOrder( - const GraphDef& graph, std::unordered_map* topo_order); + const GraphDef& graph, std::unordered_map* topo_order, + const std::vector>* + extra_dependencies); // Sort a graph in topological order. Status TopologicalSort(GraphDef* graph); diff --git a/tensorflow/core/grappler/utils/topological_sort_test.cc b/tensorflow/core/grappler/utils/topological_sort_test.cc index f5c95009d240f3..48b7eb50bd9f2a 100644 --- a/tensorflow/core/grappler/utils/topological_sort_test.cc +++ b/tensorflow/core/grappler/utils/topological_sort_test.cc @@ -53,7 +53,7 @@ TEST_F(TopologicalSortTest, NoLoop) { *graph.add_node() = CreateNode("4", {}); std::unordered_map topo_order; - TF_EXPECT_OK(ComputeTopologicalOrder(graph, &topo_order)); + TF_EXPECT_OK(ComputeTopologicalOrder(graph, &topo_order, nullptr)); const std::vector order = {"5", "4", "2", "0", "3", "1"}; for (const auto& topo : topo_order) { @@ -80,7 +80,7 @@ TEST_F(TopologicalSortTest, WithLoop) { *graph.add_node() = CreateNode("1", {}); std::unordered_map topo_order; - TF_EXPECT_OK(ComputeTopologicalOrder(graph, &topo_order)); + TF_EXPECT_OK(ComputeTopologicalOrder(graph, &topo_order, nullptr)); const std::vector order = {"1", "2", "3", "4", "5"}; for (const auto& topo : topo_order) { @@ -143,6 +143,36 @@ TEST_F(TopologicalSortTest, Idempotent) { } } +TEST_F(TopologicalSortTest, ExtraDependencies) { + GraphDef graph; + *graph.add_node() = CreateNode("2", {"5"}); + *graph.add_node() = CreateNode("0", {"5", "4"}); + *graph.add_node() = CreateNode("1", {"4", "3"}); + *graph.add_node() = CreateNode("3", {"2"}); + *graph.add_node() = CreateNode("5", {}); + *graph.add_node() = CreateNode("4", {}); + + // Add an edge from 4 to 5. + std::vector> extra_dependencies; + extra_dependencies.emplace_back(&graph.node(5), &graph.node(4)); + + std::unordered_map topo_order; + TF_EXPECT_OK( + ComputeTopologicalOrder(graph, &topo_order, &extra_dependencies)); + + const std::vector order = {"4", "5", "2", "0", "3", "1"}; + for (const auto& topo : topo_order) { + const string& node_name = topo.first->name(); + const int topo_order = topo.second; + EXPECT_EQ(node_name, order[topo_order]); + } + + // Add an edge from 0 to 4. This will create a loop + extra_dependencies.emplace_back(&graph.node(1), &graph.node(5)); + EXPECT_FALSE( + ComputeTopologicalOrder(graph, &topo_order, &extra_dependencies).ok()); +} + } // namespace } // namespace grappler } // namespace tensorflow From 59677dc14f5ed28e5d858abc318b1a492f37425f Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Tue, 1 May 2018 12:24:38 -0700 Subject: [PATCH 0229/1691] Add device_util.resolve method which merges with current device as well. PiperOrigin-RevId: 194976633 --- .../distribute/python/cross_tower_ops.py | 7 +- .../distribute/python/mirrored_strategy.py | 1 - tensorflow/python/BUILD | 1 + tensorflow/python/training/device_util.py | 27 +++++- .../python/training/device_util_test.py | 89 +++++++++++++++++++ tensorflow/python/training/distribute.py | 3 + 6 files changed, 122 insertions(+), 6 deletions(-) create mode 100644 tensorflow/python/training/device_util_test.py diff --git a/tensorflow/contrib/distribute/python/cross_tower_ops.py b/tensorflow/contrib/distribute/python/cross_tower_ops.py index cff717db80f0bd..c6a1bf6a9f6582 100644 --- a/tensorflow/contrib/distribute/python/cross_tower_ops.py +++ b/tensorflow/contrib/distribute/python/cross_tower_ops.py @@ -53,15 +53,14 @@ def _validate_value_destination_pairs(value_destination_pairs): return True +# TODO(yuefengz): consider calling this function in the caller of CrossTowerOps. def _get_devices_from(destinations): if isinstance(destinations, value_lib.DistributedValues): return list(destinations.devices) elif isinstance(destinations, six.string_types): - return [device_util.canonicalize(destinations)] + return [device_util.resolve(destinations)] else: - return [ - device_util.canonicalize(destination) for destination in destinations - ] + return [device_util.resolve(destination) for destination in destinations] def _devices_match(left, right): diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py index 6efd578a775da7..2e57b025837e38 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py @@ -321,7 +321,6 @@ def _update_non_slot(self, colocate_with, fn, *args, **kwargs): def _fetch(self, val, destination, fn): """Return a copy of `val` or `fn(val)` on `destination`.""" - assert isinstance(destination, six.string_types) if isinstance(val, values.TowerLocalVariable): val = self.reduce(val.reduce_method, val, destinations=destination) with ops.device(destination): diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 44d9147bb63598..087b89b1250376 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -4032,6 +4032,7 @@ cuda_py_tests( "training/basic_loops_test.py", "training/coordinator_test.py", "training/device_setter_test.py", + "training/device_util_test.py", "training/ftrl_test.py", "training/gradient_descent_test.py", "training/learning_rate_decay_test.py", diff --git a/tensorflow/python/training/device_util.py b/tensorflow/python/training/device_util.py index f1137e80ab4394..e31fa02d60679d 100644 --- a/tensorflow/python/training/device_util.py +++ b/tensorflow/python/training/device_util.py @@ -23,17 +23,42 @@ from tensorflow.python.framework import ops -def canonicalize(d): +def canonicalize(d, default=None): + """Canonicalize device string. + + If d has missing components, the rest would be deduced from the `default` + argument or from '/job:localhost/replica:0/task:0/device:CPU:0'. For example: + If d = '/cpu:0', default='/job:worker/task:1', it returns + '/job:worker/replica:0/task:1/device:CPU:0'. + If d = '/cpu:0', default='/job:worker', it returns + '/job:worker/replica:0/task:0/device:CPU:0'. + If d = '/gpu:0', default=None, it returns + '/job:localhost/replica:0/task:0/device:GPU:0'. + + Args: + d: a device string. + default: a string for default device if d doesn't have all components. + + Returns: + a canonicalized device string. + """ d = tf_device.DeviceSpec.from_string(d) assert d.device_type is None or d.device_type == d.device_type.upper(), ( "Device type '%s' must be all-caps." % (d.device_type,)) # Fill in missing device fields using defaults. result = tf_device.DeviceSpec( job="localhost", replica=0, task=0, device_type="CPU", device_index=0) + if default: + result.merge_from(tf_device.DeviceSpec.from_string(default)) result.merge_from(d) return result.to_string() +def resolve(d): + """Canonicalize `d` with current device as default.""" + return canonicalize(d, default=current()) + + class _FakeNodeDef(object): """A fake NodeDef for _FakeOperation.""" diff --git a/tensorflow/python/training/device_util_test.py b/tensorflow/python/training/device_util_test.py new file mode 100644 index 00000000000000..61525e21f508bc --- /dev/null +++ b/tensorflow/python/training/device_util_test.py @@ -0,0 +1,89 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for device utilities.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.eager import context +from tensorflow.python.framework import ops +from tensorflow.python.platform import test +from tensorflow.python.training import device_util + + +class DeviceUtilTest(test.TestCase): + + def testCurrentDeviceWithGlobalGraph(self): + with ops.device("/cpu:0"): + self.assertEqual(device_util.current(), "/device:CPU:0") + + with ops.device("/job:worker"): + with ops.device("/cpu:0"): + self.assertEqual(device_util.current(), "/job:worker/device:CPU:0") + + with ops.device("/cpu:0"): + with ops.device("/gpu:0"): + self.assertEqual(device_util.current(), "/device:GPU:0") + + def testCurrentDeviceWithNonGlobalGraph(self): + with ops.Graph().as_default(): + with ops.device("/cpu:0"): + self.assertEqual(device_util.current(), "/device:CPU:0") + + def testCurrentDeviceWithEager(self): + with context.eager_mode(): + with ops.device("/cpu:0"): + self.assertEqual(device_util.current(), + "/job:localhost/replica:0/task:0/device:CPU:0") + + def testCanonicalizeWithoutDefaultDevice(self): + self.assertEqual( + device_util.canonicalize("/cpu:0"), + "/job:localhost/replica:0/task:0/device:CPU:0") + self.assertEqual( + device_util.canonicalize("/job:worker/cpu:0"), + "/job:worker/replica:0/task:0/device:CPU:0") + self.assertEqual( + device_util.canonicalize("/job:worker/task:1/cpu:0"), + "/job:worker/replica:0/task:1/device:CPU:0") + + def testCanonicalizeWithDefaultDevice(self): + self.assertEqual( + device_util.canonicalize("/job:worker/task:1/cpu:0", default="/gpu:0"), + "/job:worker/replica:0/task:1/device:CPU:0") + self.assertEqual( + device_util.canonicalize("/job:worker/task:1", default="/gpu:0"), + "/job:worker/replica:0/task:1/device:GPU:0") + self.assertEqual( + device_util.canonicalize("/cpu:0", default="/job:worker"), + "/job:worker/replica:0/task:0/device:CPU:0") + + def testResolveWithDeviceScope(self): + with ops.device("/gpu:0"): + self.assertEqual( + device_util.resolve("/job:worker/task:1/cpu:0"), + "/job:worker/replica:0/task:1/device:CPU:0") + self.assertEqual( + device_util.resolve("/job:worker/task:1"), + "/job:worker/replica:0/task:1/device:GPU:0") + with ops.device("/job:worker"): + self.assertEqual( + device_util.resolve("/cpu:0"), + "/job:worker/replica:0/task:0/device:CPU:0") + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/training/distribute.py b/tensorflow/python/training/distribute.py index 21ec5292adb5bb..6aeecb31dd9b16 100644 --- a/tensorflow/python/training/distribute.py +++ b/tensorflow/python/training/distribute.py @@ -19,6 +19,7 @@ from __future__ import print_function import threading +import six from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import ops @@ -896,6 +897,8 @@ def fetch(self, val, destination="/device:CPU:0", fn=lambda x: x): A `Tensor` on `destination`. """ _require_cross_tower_context(self) + assert isinstance(destination, six.string_types) + destination = device_util.resolve(destination) return self._fetch(val, destination, fn) def _fetch(self, val, destination, fn): From 87ebe118d0c3767d4a3caaef4ba5538f37311ad1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 1 May 2018 12:39:52 -0700 Subject: [PATCH 0230/1691] Implements matrix multiply-accumulate for linear no-offset (aka symmetric) quantizer. PiperOrigin-RevId: 194978865 --- .../contrib/lite/kernels/internal/BUILD | 1 + .../internal/optimized/neon_tensor_utils.cc | 125 +++++++ .../internal/optimized/neon_tensor_utils.h | 8 + .../internal/optimized/tensor_utils_impl.h | 10 + .../reference/portable_tensor_utils.cc | 24 ++ .../reference/portable_tensor_utils.h | 14 + .../lite/kernels/internal/tensor_utils.h | 30 +- .../kernels/internal/tensor_utils_test.cc | 323 ++++++++++++++++++ 8 files changed, 529 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD index c5539afb9c84d0..df29172f83a4b1 100644 --- a/tensorflow/contrib/lite/kernels/internal/BUILD +++ b/tensorflow/contrib/lite/kernels/internal/BUILD @@ -303,6 +303,7 @@ cc_library( ], hdrs = [ "common.h", + "compatibility.h", "optimized/cpu_check.h", "optimized/neon_tensor_utils.h", "optimized/tensor_utils_impl.h", diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc index 47dfcbeb01a046..65f25168e3a207 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" #include "tensorflow/contrib/lite/kernels/internal/common.h" +#include "tensorflow/contrib/lite/kernels/internal/compatibility.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h" #include "tensorflow/contrib/lite/kernels/internal/round.h" @@ -27,6 +28,22 @@ limitations under the License. namespace tflite { namespace tensor_utils { +namespace { + +// Allocates, at least, size bytes of uninitialized storage whose alignment is +// specified by alignment. The size parameter must be an integral multiple of +// alignment. +// Caller is responsible by freeing the allocated memory by calling free on +// the passed freeing_buffer pointer. +void* aligned_alloc(size_t alignment, size_t size, void** freeing_buffer) { + *freeing_buffer = malloc(size + alignment); + const size_t offset = ((uintptr_t)*freeing_buffer) % alignment; // NOLINT + return offset == 0 + ? *freeing_buffer + : ((char*)*freeing_buffer + (alignment - offset)); // NOLINT +} + +} // namespace void NeonMatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, int m_cols, const float* vector, @@ -114,6 +131,114 @@ void NeonMatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, delete[] vector_cache_float32x4; } +void NeonMatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, const float* scaling_factors, + int n_batch, float* __restrict__ result, int result_stride) { + const int kWeightsPerUint32 = 4; + const int kWeightsPerNeonLane = 16; + // If the number of rows is not divisible by kWeightsPerUint32, we set a + // flag and allocate an aligned memory block. The flag is used to use the + // aligned memory block later in the kernel loop. + bool unaligned = false; + int8* aligned_row = nullptr; + void* aligned_row_free = nullptr; + if ((m_cols & (kWeightsPerUint32 - 1)) != 0) { + unaligned = true; + aligned_row = (int8*)aligned_alloc(kWeightsPerUint32, m_cols, // NOLINT + &aligned_row_free); + } + void* aligned_vec_free = nullptr; + int8* aligned_vec = (int8*)aligned_alloc(kWeightsPerUint32, m_cols, // NOLINT + &aligned_vec_free); + + // If m_cols is not at least kWeightsPerNeonLane, we cannot use the main + // vectorized loop, and we need to process sequentially. postamble_start shows + // the start index where this should happen. + const int postamble_start = m_cols - (m_cols & (kWeightsPerNeonLane - 1)); + + int batch, row, col; + for (batch = 0; batch < n_batch; ++batch) { + const float batch_scaling_factor_inv = 1.0 / scaling_factors[batch]; + // Copy the vector data to an aligned vector. + memcpy(aligned_vec, vectors + batch * m_cols, sizeof(int8) * m_cols); + // Compute dot-product for every column. + for (row = 0; row < m_rows; ++row, result += result_stride) { + // Get the address of the first element of the row. + int8* row_ptr = (int8*)matrix + row * m_cols; // NOLINT + if (unaligned) { + memcpy(aligned_row, row_ptr, sizeof(int8) * m_cols); + row_ptr = aligned_row; + } + + // Initialize the dot product sum for the row to 0. + int32x4_t dotprod = vmovq_n_s32(0); + + // Prefetch the row to cache. + __builtin_prefetch(row_ptr, 0 /* prefetch for read */, + 3 /* temporal locality */); + + // For every block of 16 8-bit elements. + col = 0; + for (; col < postamble_start; col += kWeightsPerNeonLane) { + // Load 16 8-bit values from the row and vector, each, to operate on. + // Here the assumption is that each buffer is 4-byte aligned. + TFLITE_CHECK_EQ((uintptr_t)(&row_ptr[col]) & (kWeightsPerUint32 - 1), + 0); + const int8x16_t s1_8x16 = vld1q_s8((const int8_t*)(aligned_vec + col)); + const int8x16_t s2_8x16 = vld1q_s8((const int8_t*)(row_ptr + col)); + // Multiply the low bits (i.e. the lower 8 8bit numbers in the + // registers). + int16x8_t prod_16x8 = + vmull_s8(vget_low_s8(s1_8x16), vget_low_s8(s2_8x16)); + // Multiply the high bits (i.e. the lower 8 8bit numbers in the + // registers), and accumulate with the result of the low bits product. + // The assumption here is that overflow will not happen as we quantize + // our values to be in the range [-127, 127]. As such the sum of the 2 + // products is always strictly smaller than 15-bits (32767 in absolute + // value). + prod_16x8 = + vmlal_s8(prod_16x8, vget_high_s8(s1_8x16), vget_high_s8(s2_8x16)); + + dotprod = vpadalq_s16(dotprod, prod_16x8); + } // for col + + int32 postable_sum = 0; + // Postamble loop. + // TODO(raziel): if (ABSL_PREDICT_FALSE(postamble_start < m_rows)) + if (postamble_start < m_cols) { + col = postamble_start; + if ((m_cols - postamble_start) >= (kWeightsPerNeonLane >> 1)) { + // Load 8 8-bit values from the row and column each to operate on. + // Here the assumption is that each buffer is 4-bytes aligned. + TFLITE_CHECK_EQ((uintptr_t)(&row_ptr[col]) & (kWeightsPerUint32 - 1), + 0); + const int8x8_t s1_8x8 = vld1_s8((const int8_t*)(aligned_vec + col)); + const int8x8_t s2_8x8 = vld1_s8((const int8_t*)(row_ptr + col)); + const int16x8_t prod_16x8 = vmull_s8(s1_8x8, s2_8x8); + dotprod = vpadalq_s16(dotprod, prod_16x8); + col += (kWeightsPerNeonLane >> 1); + } + for (; col < m_cols; ++col) { + postable_sum += row_ptr[col] * aligned_vec[col]; + } // for col + } + // Add the 4 intermediate sum values to get the final dot-prod value for + // this row. + int64x2_t pairwiseAdded = vpaddlq_s32(dotprod); + int32 neon_sum = + vgetq_lane_s64(pairwiseAdded, 0) + vgetq_lane_s64(pairwiseAdded, 1); + + *result += ((neon_sum + postable_sum) * batch_scaling_factor_inv); + } // for row + } // for batch + + if (unaligned) { + free(aligned_row_free); + } + free(aligned_vec_free); +} + void NeonVectorVectorCwiseProduct(const float* vector1, const float* vector2, int v_size, float* result) { // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h index 3b6f4bd583a85d..9e60d0657b49ed 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h @@ -32,6 +32,14 @@ void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, vector, n_batch, result, result_stride); } +void MatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, const float* scaling_factors, + int n_batch, float* __restrict__ result, int result_stride) { + NEON_OR_PORTABLE(MatrixBatchVectorMultiplyAccumulate, matrix, m_rows, m_cols, + vectors, scaling_factors, n_batch, result, result_stride); +} + void VectorVectorCwiseProduct(const float* vector1, const float* vector2, int v_size, float* result) { NEON_OR_PORTABLE(VectorVectorCwiseProduct, vector1, vector2, v_size, result); diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h b/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h index 19220470f4ef73..d570dadd86b4dc 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h @@ -40,6 +40,16 @@ void NeonMatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, int n_batch, float* result, int result_stride); +// Matrix multiplication for quantized values using symmetric quantization. +void PortableMatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, const float* scaling_factors, + int n_batch, float* __restrict__ result, int result_stride); +void NeonMatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, const float* scaling_factors, + int n_batch, float* __restrict__ result, int result_stride); + // Cwise product of two vectors. void PortableVectorVectorCwiseProduct(const float* vector1, const float* vector2, int v_size, diff --git a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc index 5e7586eeda7f21..2607adc0c18aea 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc @@ -69,6 +69,30 @@ void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix, } } +void PortableMatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, const float* scaling_factors, + int n_batch, float* __restrict__ result, int result_stride) { + int batch, row, col; + for (batch = 0; batch < n_batch; ++batch, vectors += m_cols) { + const float batch_scaling_factor_inv = 1.0 / scaling_factors[batch]; + // Get the address of the first row. + int8_t* row_ptr = (int8_t*)matrix; // NOLINT + for (row = 0; row < m_rows; ++row, result += result_stride) { + // Initialize the dot product sum for the row to 0. + int32_t dotprod = 0; + // Prefetch the row to cache. + __builtin_prefetch(row_ptr, 0 /* prefetch for read */, + 3 /* temporal locality */); + // For every block of 16 8-bit elements (128-bit register) from each row. + for (col = 0; col < m_cols; ++col, ++row_ptr) { + dotprod += (*row_ptr) * (vectors[col]); + } // for col + *result += (dotprod * batch_scaling_factor_inv); + } // for row + } // for batch +} + void PortableVectorVectorCwiseProduct(const float* vector1, const float* vector2, int v_size, float* result) { diff --git a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h index 478cda8e193971..1757a9f5e52994 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h @@ -37,6 +37,11 @@ void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix, int n_batch, float* result, int result_stride); +void PortableMatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, const float* scaling_factors, + int n_batch, float* __restrict__ result, int result_stride); + // Cwise product of two vectors. void PortableVectorVectorCwiseProduct(const float* vector1, const float* vector2, int v_size, @@ -122,6 +127,15 @@ void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, n_batch, result, result_stride); } +void MatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vector, const float* scaling_factors, + int n_batch, float* __restrict__ result, int result_stride) { + PortableMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vector, + scaling_factors, n_batch, result, + result_stride); +} + void VectorVectorCwiseProduct(const float* vector1, const float* vector2, int v_size, float* result) { PortableVectorVectorCwiseProduct(vector1, vector2, v_size, result); diff --git a/tensorflow/contrib/lite/kernels/internal/tensor_utils.h b/tensorflow/contrib/lite/kernels/internal/tensor_utils.h index 997dc4425d31e8..e1c9ccd84b09fd 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/tensor_utils.h @@ -31,17 +31,35 @@ void SymmetricQuantizeFloats(const float* values, const int size, int8_t* quantized_values, float* min, float* max, float* scaling_factor); -// Multiply a matrix by a batch vector, and store results in a batch-size -// vector using a stride value provided in result_stride. 'result_stride' shows -// how the number of elements between consecutive result values. For example -// result_stride = 1, will cause the output to look like this: -// [O_1, 0_2, ... O_rows] in memory, but result_stride = 3, will cause it to be -// arranged like this in memory: [O_1, x, x, 0_2, x, x, ..., O_rows] +// Multiplies a matrix by a "batched" vector (i.e. a matrix with a batch +// dimension composed by input vectors independent from each other). The result +// of the multiplication is accumulated to the passed result buffer. +// More specifically, for a matrix M of shape [n, i] and a batched-vector +// of shape [i, batch] it will first compute the product of shape [n, batch]. +// This product will be accumulated to the result buffer, using a stride value +// provided in result_stride (the number of elements between consecutive result +// values). For example result_stride = 1, will cause the output to look like +// this: +// [O_1, 0_2, ... O_rows] +// but result_stride = 3, will cause it to be arranged like this in memory: +// [O_1, x, x, 0_2, x, x, ..., O_rows] void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, int m_cols, const float* vector, int n_batch, float* result, int result_stride); +// Same as the function above, but for values quantized using symmetric +// quantization (e.g. by calling SymmetricQuantizeFloats). +// The passed scaling factors is a buffer of the quantization scaling factors +// that will be used to dequentize the products into the final result buffer. +// These scaling factors are the multiplication of the matrix scaling factor +// by the vector's scaling factor, one per batch (i.e. this allows quantizing +// each batch in the batch-vector matrix independently). +void MatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, const float* scaling_factors, + int n_batch, float* __restrict__ result, int result_stride); + // Cwise product of two vectors. void VectorVectorCwiseProduct(const float* vector1, const float* vector2, int v_size, float* result); diff --git a/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc b/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc index 22b016746fe0fb..3d8a2eada0c301 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc @@ -107,6 +107,329 @@ TEST(uKernels, MatrixBatchVectorMultiplyAccumulateTest) { -1., 3., 7., 3., 23., 3.}))); } +TEST(uKernels, MatrixBatchVectorMultiplyAccumulateSymmetricQuantizedTest) { + // Note we use 29 columns as this exercises all the neon kernel: the + // 16-block SIMD code, the 8-block postamble, and the leftover postamble. + const int a_rows = 4, a_cols = 29; + const int kWeightsPerUint32 = 4; + const float a_float_data[] = { + /* 1st row */ + 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 10.1, 11.11, 12.12, 13.13, + 14.14, 15.15, 16.16, 17.17, 18.18, 19.19, 20.2, 21.21, 22.22, 23.23, + 24.24, 25.25, 26.26, 27.27, 28.28, 0, + /* 2nd row */ + -1.1, -2.2, -3.3, -4.4, -5.5, -6.6, -7.7, -8.8, -9.9, -10.1, -11.11, + -12.12, -13.13, -14.14, -15.15, -16.16, -17.17, -18.18, -19.19, -20.2, + -21.21, -22.22, -23.23, -24.24, -25.25, -26.26, -27.27, -28.28, 0, + /* 3rd row */ + 1.1, -2.2, 3.3, -4.4, 5.5, -6.6, 7.7, -8.8, 9.9, -10.1, 11.11, -12.12, + 13.13, -14.14, 15.15, -16.16, 17.17, -18.18, 19.19, -20.2, 21.21, -22.22, + 23.23, -24.24, 25.25, -26.26, 27.27, -28.28, 0, + /* 4th row */ + -1.1, 2.2, -3.3, 4.4, -5.5, 6.6, -7.7, 8.8, -9.9, 10.1, -11.11, 12.12, + -13.13, 14.14, -15.15, 16.16, -17.17, 18.18, -19.19, 20.2, -21.21, 22.22, + -23.23, 24.24, -25.25, 26.26, -27.27, 28.28, 0}; + + int8* a_int8_data = reinterpret_cast( + aligned_malloc(a_rows * a_cols, kWeightsPerUint32)); + float a_min, a_max; + float scaling_factor_a; + SymmetricQuantizeFloats(a_float_data, a_rows * a_cols, a_int8_data, &a_min, + &a_max, &scaling_factor_a); + const int8 expected_a_int8_data[] = { + /* 1st row */ + 5, + 10, + 15, + 20, + 25, + 30, + 35, + 40, + 44, + 45, + 50, + 54, + 59, + 64, + 68, + 73, + 77, + 82, + 86, + 91, + 95, + 100, + 104, + 109, + 113, + 118, + 122, + 127, + 0, + /* 2nd row */ + -5, + -10, + -15, + -20, + -25, + -30, + -35, + -40, + -44, + -45, + -50, + -54, + -59, + -64, + -68, + -73, + -77, + -82, + -86, + -91, + -95, + -100, + -104, + -109, + -113, + -118, + -122, + -127, + 0, + /* 3rd row */ + 5, + -10, + 15, + -20, + 25, + -30, + 35, + -40, + 44, + -45, + 50, + -54, + 59, + -64, + 68, + -73, + 77, + -82, + 86, + -91, + 95, + -100, + 104, + -109, + 113, + -118, + 122, + -127, + 0, + /* 4th row */ + -5, + 10, + -15, + 20, + -25, + 30, + -35, + 40, + -44, + 45, + -50, + 54, + -59, + 64, + -68, + 73, + -77, + 82, + -86, + 91, + -95, + 100, + -104, + 109, + -113, + 118, + -122, + 127, + 0, + }; + for (int i = 0; i < a_rows * a_cols; ++i) { + EXPECT_EQ(expected_a_int8_data[i], a_int8_data[i]); + } + + const int b_rows = 29, b_cols = 1, batches = 2; + const float b_float_data[] = { + /* batch 1 */ + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + -1.0, + 1.0, + /* batch 2 */ + 2.5, + -2.1, + 3.0, + -1.3, + 1.3, + -1.1, + 2.0, + -1.7, + 1.9, + -1.5, + 0.5, + -0.7, + 0.8, + -0.3, + 2.8, + -2.8, + 1.1, + -2.3, + 1.9, + -1.9, + 2.1, + -0.5, + 2.4, + -0.1, + 1.0, + -2.5, + 0.7, + -1.9, + 0.2, + }; + + // Quantized values of B: + int8 b_int8_data[b_rows * b_cols * batches]; + float b_min, b_max; + float scaling_factor_b[batches]; + SymmetricQuantizeFloats(b_float_data, b_rows * b_cols, b_int8_data, &b_min, + &b_max, &scaling_factor_b[0]); + SymmetricQuantizeFloats(&b_float_data[b_rows * b_cols], b_rows * b_cols, + &b_int8_data[b_rows * b_cols], &b_min, &b_max, + &scaling_factor_b[1]); + + const int8 expected_b_int8_data[] = { + /* batch 1 */ + 127, + -127, + 127, + -127, + 127, + -127, + 127, + -127, + 127, + -127, + 127, + -127, + 127, + -127, + 127, + -127, + 127, + -127, + 127, + -127, + 127, + -127, + 127, + -127, + 127, + -127, + 127, + -127, + 127, + /* batch 2 */ + 106, + -89, + 127, + -55, + 55, + -47, + 85, + -72, + 80, + -64, + 21, + -30, + 34, + -13, + 119, + -119, + 47, + -97, + 80, + -80, + 89, + -21, + 102, + -4, + 42, + -106, + 30, + -80, + 8, + }; + for (int i = 0; i < b_rows * b_cols * batches; ++i) { + EXPECT_EQ(expected_b_int8_data[i], b_int8_data[i]); + } + + // Full float operation results in: + // -13.69, 13.69, 414.11, -414.11 + // -6.325, 6.325, 631.263, -631.263 + float c_float_data[a_rows * b_cols * batches]; + for (int i = 0; i < a_rows * b_cols * batches; ++i) { + c_float_data[i] = 0.0; + } + + // Testing product. + const float scaling_factor_c[2] = { + scaling_factor_a * scaling_factor_b[0], + scaling_factor_a * scaling_factor_b[1], + }; + MatrixBatchVectorMultiplyAccumulate(a_int8_data, a_rows, a_cols, b_int8_data, + scaling_factor_c, batches, c_float_data, + /*result_stride=*/1); + + // Assert we obtain the expected recovered float values. + const float expected_c_float_data[] = { + -14.474, 14.474, 414.402, -414.402, -6.92228, 6.92228, 632.042, -632.042, + }; + for (int i = 0; i < a_rows * b_cols * batches; ++i) { + EXPECT_NEAR(expected_c_float_data[i], c_float_data[i], 0.001); + } + + aligned_free(a_int8_data); +} + TEST(uKernels, VectorVectorCwiseProductTest) { constexpr int kVectorSize = 10; static float input1[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0, From af2d983bcdabc5291ffa919a2c20654e4c0a8c07 Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Tue, 1 May 2018 12:54:04 -0700 Subject: [PATCH 0231/1691] Review updates --- tensorflow/contrib/tensorrt/convert/convert_graph.cc | 2 +- tensorflow/contrib/tensorrt/segment/segment.cc | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index c1979afcf8283d..8459ad4a619eee 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -416,7 +416,7 @@ tensorflow::Status ConvertAfterShapes( for (auto s : segments) { total_num_nodes_in_segments += s.first.size(); } - // Cluster may not be available + // We are creating the map here since cluster may not be available in all cases std::map name_to_device_map; if (cluster) { for (const auto dm : cluster->GetDeviceSet()->devices()) { diff --git a/tensorflow/contrib/tensorrt/segment/segment.cc b/tensorflow/contrib/tensorrt/segment/segment.cc index 7e094f552d14cf..4901e30a875585 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.cc +++ b/tensorflow/contrib/tensorrt/segment/segment.cc @@ -113,7 +113,7 @@ class SimpleGraph { const tensorflow::Graph* g_; std::vector nodes_; std::vector edges_; - // edge_ids_ and node_ids_ contain freed indices. + // free_edge_ids_ and free_node_ids_ contain freed indices. std::set free_edge_ids_; std::set free_node_ids_; }; @@ -352,7 +352,7 @@ tensorflow::Status SegmentGraph( tensorflow::Graph* tf_graph, const std::function& candidate_fn, const SegmentOptions& options, SegmentNodesVector* segments) { - // tensorflow::DumpGraph("Pre-Segment", &graph); + auto graph = std::unique_ptr(new SimpleGraph(tf_graph)); // Use a union-find to collect the nodes that belong to the same // segment. A node value of nullptr indicates that the node is not a candidate From ee236bd4c4251d6a2a87409b4d47470534c975b0 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Tue, 1 May 2018 12:56:29 -0700 Subject: [PATCH 0232/1691] Add a pointer from Device to its owning DeviceMgr. Allow remote function execution on TPU devices. PiperOrigin-RevId: 194981511 --- tensorflow/core/common_runtime/device.h | 11 +++++++++++ tensorflow/core/common_runtime/device_mgr.cc | 3 +++ .../process_function_library_runtime.cc | 3 ++- 3 files changed, 16 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/common_runtime/device.h b/tensorflow/core/common_runtime/device.h index 5918cd9bbf35a7..b537666492ce29 100644 --- a/tensorflow/core/common_runtime/device.h +++ b/tensorflow/core/common_runtime/device.h @@ -51,6 +51,8 @@ limitations under the License. namespace tensorflow { +class DeviceMgr; + class Device : public DeviceBase { public: Device(Env* env, const DeviceAttributes& device_attributes); @@ -133,6 +135,10 @@ class Device : public DeviceBase { // Returns the resource manager associated w/ this device. virtual ResourceMgr* resource_manager() { return rmgr_; } + // Returns the device manager that owns this device, or nullptr if this Device + // is not owned by a device manager. + DeviceMgr* device_mgr() const { return device_mgr_; } + // Summarizes the status of this Device, for debugging. string DebugString() const { return ProtoDebugString(device_attributes_); } @@ -158,6 +164,11 @@ class Device : public DeviceBase { } private: + friend class DeviceMgr; + + // Pointer to the device manager that owns this device. Not owned. + DeviceMgr* device_mgr_ = nullptr; + const DeviceAttributes device_attributes_; DeviceNameUtils::ParsedName parsed_name_; diff --git a/tensorflow/core/common_runtime/device_mgr.cc b/tensorflow/core/common_runtime/device_mgr.cc index a77601ba79bf29..470abc14312928 100644 --- a/tensorflow/core/common_runtime/device_mgr.cc +++ b/tensorflow/core/common_runtime/device_mgr.cc @@ -27,6 +27,9 @@ namespace tensorflow { DeviceMgr::DeviceMgr(const std::vector& devices) : name_backing_store_(128) { for (Device* d : devices) { + CHECK(d->device_mgr_ == nullptr); + d->device_mgr_ = this; + devices_.push_back(d); // Register under the (1) full name and (2) canonical name. diff --git a/tensorflow/core/common_runtime/process_function_library_runtime.cc b/tensorflow/core/common_runtime/process_function_library_runtime.cc index e61ed8c4794883..668ce877493a06 100644 --- a/tensorflow/core/common_runtime/process_function_library_runtime.cc +++ b/tensorflow/core/common_runtime/process_function_library_runtime.cc @@ -144,7 +144,8 @@ Status ProcessFunctionLibraryRuntime::GetDeviceContext( } Device* device = flr->device(); string device_type = device->parsed_name().type; - if (device_type == "CPU" || device_type == "TPU_SYSTEM") { + if (device_type == "CPU" || device_type == "TPU_SYSTEM" || + device_type == "TPU") { // "TPU_SYSTEM" indicates that `device` is a CPU. return Status::OK(); } From 57207f2b9d5bf9edffb72a9fe377492454abd9ec Mon Sep 17 00:00:00 2001 From: Priya Gupta Date: Tue, 1 May 2018 13:01:41 -0700 Subject: [PATCH 0233/1691] Add utility to auto shard a dataset pipeline in the appropriate place by locating the file readers and sharding their input files. PiperOrigin-RevId: 194982311 --- .../contrib/data/python/ops/batching.py | 4 +- tensorflow/contrib/distribute/python/BUILD | 31 ++ .../contrib/distribute/python/input_ops.py | 141 ++++++++++ .../distribute/python/input_ops_test.py | 265 ++++++++++++++++++ tensorflow/python/data/ops/readers.py | 15 + 5 files changed, 454 insertions(+), 2 deletions(-) create mode 100644 tensorflow/contrib/distribute/python/input_ops.py create mode 100644 tensorflow/contrib/distribute/python/input_ops_test.py diff --git a/tensorflow/contrib/data/python/ops/batching.py b/tensorflow/contrib/data/python/ops/batching.py index 2152bcde84aae6..42ec2b0b017973 100644 --- a/tensorflow/contrib/data/python/ops/batching.py +++ b/tensorflow/contrib/data/python/ops/batching.py @@ -364,7 +364,7 @@ def __init__(self, with the structure of `dataset`. """ super(_RestructuredDataset, self).__init__() - self._dataset = dataset + self._input_dataset = dataset if not allow_unsafe_cast: # Validate that the types are compatible. @@ -408,7 +408,7 @@ def __init__(self, self._output_classes = output_classes def _as_variant_tensor(self): - return self._dataset._as_variant_tensor() # pylint: disable=protected-access + return self._input_dataset._as_variant_tensor() # pylint: disable=protected-access @property def output_classes(self): diff --git a/tensorflow/contrib/distribute/python/BUILD b/tensorflow/contrib/distribute/python/BUILD index aa1a956a2da892..cdb3a8d65eab8b 100644 --- a/tensorflow/contrib/distribute/python/BUILD +++ b/tensorflow/contrib/distribute/python/BUILD @@ -501,3 +501,34 @@ cuda_py_test( "//tensorflow/python/data/ops:iterator_ops", ], ) + +py_library( + name = "input_ops", + srcs = ["input_ops.py"], + visibility = ["//tensorflow:internal"], + deps = [ + "//tensorflow/python:framework_ops", + "//tensorflow/python/data/util:nest", + ], +) + +cuda_py_test( + name = "input_ops_test", + srcs = ["input_ops_test.py"], + additional_deps = [ + ":input_ops", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/contrib/data/python/ops:batching", + "//tensorflow/contrib/data/python/ops:interleave_ops", + "//tensorflow/python:errors", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_ops", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:io_ops", + "//tensorflow/python/data/ops:readers", + "//tensorflow/python:util", + ], + tags = [ + "no_pip", + ], +) diff --git a/tensorflow/contrib/distribute/python/input_ops.py b/tensorflow/contrib/distribute/python/input_ops.py new file mode 100644 index 00000000000000..1f24f629479b6a --- /dev/null +++ b/tensorflow/contrib/distribute/python/input_ops.py @@ -0,0 +1,141 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Input-pipeline utilities for Distribution strategies.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.data.ops import readers +from tensorflow.python.data.util import nest +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import tf_logging + +# TODO(priyag): Any other reader datasets to consider here? +_READER_DATASET_OPS = [ + "TextLineDataset", + "TFRecordDataset", + "FixedLengthRecordDataset" +] + + +# pylint: disable=protected-access +def auto_shard_dataset(dataset, num_shards, index): + """Shard the input pipeline by sharding the underlying list of files. + + Args: + dataset: A `tf.data.Dataset` instance, typically the result of a bunch of + dataset transformations. + num_shards: A `tf.int64` scalar `tf.Tensor`, representing the number of + shards operating in parallel. Same usage as in `Dataset.shard`. + index: A `tf.int64` scalar `tf.Tensor`, representing the worker index. + Same usage as in `Dataset.shard`. + + Returns: + A modified `Dataset` obtained by updating the pipeline sharded by the + files. + + Raises: + NotImplementedError: If we cannot automatically determine a good way to + shard the input dataset. + """ + + # TODO(priyag): Clone datasets instead of updating in place, similar to the + # clone method for TFRecordDataset. + def _auto_shard_impl(dataset, found_reader_op): + """Recursive implementation of auto sharding.""" + + if not found_reader_op: + # TODO(priyag): Make this check more robust by enforcing some common + # property on reader datasets. + if (isinstance(dataset, readers.TextLineDataset) or + isinstance(dataset, readers.FixedLengthRecordDataset)): + filenames_tensor = dataset._filenames + num_files = array_ops.size(filenames_tensor) + sharded_filenames_tensor = array_ops.gather( + filenames_tensor, math_ops.range(index, num_files, num_shards)) + dataset._filenames = sharded_filenames_tensor + return dataset + elif isinstance(dataset, readers.TFRecordDataset): + # `TFRecordDataset` needs to be handled separately than other readers + # because it converts filenames to a dataset first. Also, we clone it + # instead of updating in place because it has special logic in the + # constructor. Eventually we will change all cases to clone datasets + # instead of updating in-place. + return dataset._clone( + filenames=dataset._filenames.shard(num_shards, index)) + elif hasattr(dataset, "_map_func"): + # TODO(priyag): Make this check more robust by enforcing some common + # property on all map/flatmap/interleave datasets. + map_func_def = dataset._map_func.definition + for node in map_func_def.node_def: + if node.op in _READER_DATASET_OPS: + found_reader_op = True + break + elif node.op == "FlatMapDataset": + # TODO(priyag): Should this check for other map datasets? Should it + # be recursive? It is too specific to implementation of + # TFRecordDataset right now. + nested_func_name = node.attr["f"].func.name + nested_func = ops.get_default_graph()._functions[nested_func_name] + for nested_node in nested_func.definition.node_def: + if nested_node.op in _READER_DATASET_OPS: + found_reader_op = True + break + if found_reader_op: + break + if found_reader_op: + dataset._input_dataset = _auto_shard_impl( + dataset._input_dataset, found_reader_op) + return dataset + + # TODO(priyag): Make _input_dataset(s) a common property of all datasets to + # make this check more robust. + if hasattr(dataset, "_input_dataset"): + dataset._input_dataset = _auto_shard_impl( + dataset._input_dataset, found_reader_op) + if hasattr(dataset, "_dataset_to_concatenate"): + # Special case for `ConcatentateDataset`. We want to shard all input + # datasets. + dataset._dataset_to_concatenate = _auto_shard_impl( + dataset._dataset_to_concatenate, found_reader_op) + return dataset + + if hasattr(dataset, "_datasets"): + # Special case for `ZipDataset`. + dataset._datasets = nest.pack_sequence_as(dataset._datasets, [ + _auto_shard_impl(ds, found_reader_op) + for ds in nest.flatten(dataset._datasets) + ]) + return dataset + + if not found_reader_op: + tf_logging.warn( + "Could not find a standard reader in the input pipeline" + "(one of TextLineDataset, TFRecordDataset, FixedLengthRecordDataset)." + "Falling back to sharding the dataset anyway. Please verify" + "correctness of auto-sharding for your input.") + + # TODO(priyag): What do we want to do if the number of filenames is + # uneven in the number of shards? By default, this will just return as + # many items it can before throwing OutOfRangeError. + # TODO(priyag): This will shard the filenames before any shuffling of the + # filename dataset. It might be desirable to shard after shuffling + # filenames? If so, how do we achieve that? + return dataset.shard(num_shards, index) + + return _auto_shard_impl(dataset=dataset, found_reader_op=False) diff --git a/tensorflow/contrib/distribute/python/input_ops_test.py b/tensorflow/contrib/distribute/python/input_ops_test.py new file mode 100644 index 00000000000000..16179c3a4903c8 --- /dev/null +++ b/tensorflow/contrib/distribute/python/input_ops_test.py @@ -0,0 +1,265 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for input pipeline modifications for distribution strategies.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.contrib.data.python.ops import batching +from tensorflow.contrib.data.python.ops import interleave_ops +from tensorflow.contrib.distribute.python import input_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.ops import readers +from tensorflow.python.framework import errors +from tensorflow.python.lib.io import python_io +from tensorflow.python.platform import test +from tensorflow.python.util import compat + + +class AutoShardDatasetTest(test.TestCase): + + def setUp(self): + super(AutoShardDatasetTest, self).setUp() + self._num_files = 10 + self._num_records = 4 + self._num_shards = 2 + self._shard_index = 0 + self._record_bytes = 10 + + def _record(self, r, f): + return compat.as_bytes("Record %d of file %d" % (r, f)) + + def _text_line(self, r, f): + return compat.as_bytes("Text line %d of file %d" % (r, f)) + + def _fixed_length_record(self, r, f): + return compat.as_bytes(str((r * f) % 10) * self._record_bytes) + + def _createTFRecordFiles(self): + filenames = [] + for i in range(self._num_files): + fn = os.path.join(self.get_temp_dir(), "tf_record.%d.txt" % i) + filenames.append(fn) + writer = python_io.TFRecordWriter(fn) + for j in range(self._num_records): + record = self._record(j, i) + writer.write(record) + writer.close() + return filenames + + def _createTextFiles(self): + filenames = [] + for i in range(self._num_files): + fn = os.path.join(self.get_temp_dir(), "text_line.%d.txt" % i) + filenames.append(fn) + contents = [] + for j in range(self._num_records): + contents.append(self._text_line(j, i)) + if j + 1 != self._num_records or i == 0: + contents.append(b"\r\n") + contents = b"".join(contents) + + with open(fn, "wb") as f: + f.write(contents) + return filenames + + def _createFixedLengthRecordFiles(self): + filenames = [] + for i in range(self._num_files): + fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i) + filenames.append(fn) + with open(fn, "wb") as f: + for j in range(self._num_records): + f.write(self._fixed_length_record(j, i)) + return filenames + + def _verifySimpleShardingOutput(self, dataset, record_fn): + iterator = dataset.make_one_shot_iterator() + next_element = iterator.get_next() + with self.test_session() as sess: + for f in range(self._shard_index, self._num_files, self._num_shards): + for r in range(self._num_records): + self.assertAllEqual(record_fn(r, f), sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testTFRecordDataset(self): + dataset = readers.TFRecordDataset(self._createTFRecordFiles()) + dataset = input_ops.auto_shard_dataset( + dataset, self._num_shards, self._shard_index) + + self._verifySimpleShardingOutput(dataset, self._record) + + def testFlatMap(self): + dataset = dataset_ops.Dataset.from_tensor_slices( + self._createTFRecordFiles()) + dataset = dataset.flat_map(readers.TFRecordDataset) + dataset = input_ops.auto_shard_dataset( + dataset, self._num_shards, self._shard_index) + + self._verifySimpleShardingOutput(dataset, self._record) + + def testInterleave(self): + dataset = dataset_ops.Dataset.from_tensor_slices( + self._createTFRecordFiles()) + dataset = dataset.interleave( + readers.TFRecordDataset, cycle_length=4, block_length=self._num_records) + dataset = input_ops.auto_shard_dataset( + dataset, self._num_shards, self._shard_index) + + # Since block_length == num records in each file, the output will still + # contain records in order of files. + self._verifySimpleShardingOutput(dataset, self._record) + + def testParallelInterleave(self): + dataset = dataset_ops.Dataset.from_tensor_slices( + self._createTFRecordFiles()) + dataset = dataset.apply(interleave_ops.parallel_interleave( + readers.TFRecordDataset, + cycle_length=4, + block_length=self._num_records)) + dataset = input_ops.auto_shard_dataset( + dataset, self._num_shards, self._shard_index) + + # Since block_length == num records in each file, the output will still + # contain records in order of files. + self._verifySimpleShardingOutput(dataset, self._record) + + def testListfiles(self): + filenames = self._createTFRecordFiles() + file_pattern = filenames[0].rsplit("/", 1)[0] + "/tf_record.*.txt" + dataset = dataset_ops.Dataset.list_files(file_pattern, shuffle=False) + dataset = dataset.flat_map(readers.TFRecordDataset) + dataset = input_ops.auto_shard_dataset( + dataset, self._num_shards, self._shard_index) + + iterator = dataset.make_one_shot_iterator() + next_element = iterator.get_next() + with self.test_session() as sess: + actual, expected = [], [] + for f in range(self._shard_index, self._num_files, self._num_shards): + for r in range(self._num_records): + actual.append(sess.run(next_element)) + expected.append(self._record(r, f)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + self.assertAllEqual(expected, actual) + + def testComplexPipeline(self): + # Setup a complex input pipeline. + batch_size = 2 + num_epochs = 5 + dataset = dataset_ops.Dataset.from_tensor_slices( + self._createTFRecordFiles()) + dataset = dataset.shuffle(buffer_size=self._num_files) + dataset = dataset.flat_map(readers.TFRecordDataset) + dataset = dataset.prefetch(buffer_size=batch_size) + dataset = dataset.shuffle(2 * self._num_files * self._num_records) + dataset = dataset.repeat(num_epochs) + dataset = dataset.apply(batching.map_and_batch( + lambda x: x, batch_size=batch_size)) + dataset = dataset.prefetch(buffer_size=None) + + # Auto shard. + dataset = input_ops.auto_shard_dataset( + dataset, self._num_shards, self._shard_index) + + # Verify output. + iterator = dataset.make_one_shot_iterator() + next_element = iterator.get_next() + with self.test_session() as sess: + actual = [] + num_iterations = (self._num_files * self._num_records * num_epochs) // ( + self._num_shards * batch_size) + for _ in range(num_iterations): + actual.extend(sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + expected = [] + for f in range(0, self._num_files, self._num_shards): + for r in range(self._num_records): + expected.append(self._record(r, f)) + expected *= num_epochs + + self.assertAllEqual(sorted(expected), sorted(actual)) + + def testZip(self): + dataset1 = readers.TFRecordDataset(self._createTFRecordFiles()) + dataset2 = readers.TextLineDataset(self._createTextFiles()) + dataset = dataset_ops.Dataset.zip((dataset1, dataset2)) + dataset = input_ops.auto_shard_dataset( + dataset, self._num_shards, self._shard_index) + + record_fn = lambda r, f: (self._record(r, f), self._text_line(r, f)) + self._verifySimpleShardingOutput(dataset, record_fn) + + def testConcat(self): + dataset1 = readers.TFRecordDataset(self._createTFRecordFiles()) + dataset2 = readers.TextLineDataset(self._createTextFiles()) + dataset = dataset1.concatenate(dataset2) + dataset = input_ops.auto_shard_dataset( + dataset, self._num_shards, self._shard_index) + + iterator = dataset.make_one_shot_iterator() + next_element = iterator.get_next() + with self.test_session() as sess: + for f in range(self._shard_index, self._num_files, self._num_shards): + for r in range(self._num_records): + self.assertAllEqual(self._record(r, f), sess.run(next_element)) + for f in range(self._shard_index, self._num_files, self._num_shards): + for r in range(self._num_records): + self.assertAllEqual(self._text_line(r, f), sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testTextLineReader(self): + dataset = readers.TextLineDataset(self._createTextFiles()) + dataset = input_ops.auto_shard_dataset( + dataset, self._num_shards, self._shard_index) + + self._verifySimpleShardingOutput(dataset, self._text_line) + + def testTextLineReaderWithFlatMap(self): + dataset = dataset_ops.Dataset.from_tensor_slices(self._createTextFiles()) + dataset = dataset.flat_map(readers.TextLineDataset) + dataset = input_ops.auto_shard_dataset( + dataset, self._num_shards, self._shard_index) + + self._verifySimpleShardingOutput(dataset, self._text_line) + + def testFixedLengthReader(self): + dataset = readers.FixedLengthRecordDataset( + self._createFixedLengthRecordFiles(), self._record_bytes) + dataset = input_ops.auto_shard_dataset( + dataset, self._num_shards, self._shard_index) + + self._verifySimpleShardingOutput(dataset, self._fixed_length_record) + + def testFixedLengthReaderWithFlatMap(self): + dataset = dataset_ops.Dataset.from_tensor_slices( + self._createFixedLengthRecordFiles()) + dataset = dataset.flat_map( + lambda f: readers.FixedLengthRecordDataset(f, self._record_bytes)) + dataset = input_ops.auto_shard_dataset( + dataset, self._num_shards, self._shard_index) + + self._verifySimpleShardingOutput(dataset, self._fixed_length_record) + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/data/ops/readers.py b/tensorflow/python/data/ops/readers.py index fe033f5546498d..a73a8b5cdc494d 100644 --- a/tensorflow/python/data/ops/readers.py +++ b/tensorflow/python/data/ops/readers.py @@ -197,6 +197,11 @@ def __init__(self, filenames, compression_type=None, buffer_size=None, filenames = array_ops.reshape(filenames, [-1], name="flat_filenames") filenames = dataset_ops.Dataset.from_tensor_slices(filenames) + self._filenames = filenames + self._compression_type = compression_type + self._buffer_size = buffer_size + self._num_parallel_reads = num_parallel_reads + def read_one_file(filename): return _TFRecordDataset(filename, compression_type, buffer_size) @@ -208,6 +213,16 @@ def read_one_file(filename): block_length=1, sloppy=False, buffer_output_elements=None, prefetch_input_elements=None) + def _clone(self, + filenames=None, + compression_type=None, + buffer_size=None, + num_parallel_reads=None): + return TFRecordDataset(filenames or self._filenames, + compression_type or self._compression_type, + buffer_size or self._buffer_size, + num_parallel_reads or self._num_parallel_reads) + def _as_variant_tensor(self): return self._impl._as_variant_tensor() # pylint: disable=protected-access From 1a50cd4ca8c4fe1c1a9ea14f219fd98be8704a7d Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Tue, 1 May 2018 13:07:03 -0700 Subject: [PATCH 0234/1691] Open source infeed test PiperOrigin-RevId: 194983270 --- .../compiler/xla/service/cpu/tests/BUILD | 23 ++ .../xla/service/cpu/tests/cpu_infeed_test.cc | 294 ++++++++++++++++++ 2 files changed, 317 insertions(+) create mode 100644 tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc diff --git a/tensorflow/compiler/xla/service/cpu/tests/BUILD b/tensorflow/compiler/xla/service/cpu/tests/BUILD index 9425b948c166b8..bfd95c3fe06a6c 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/BUILD +++ b/tensorflow/compiler/xla/service/cpu/tests/BUILD @@ -124,3 +124,26 @@ tf_cc_test( "//tensorflow/core:test_main", ], ) + +tf_cc_test( + name = "cpu_infeed_test", + srcs = ["cpu_infeed_test.cc"], + deps = [ + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:global_data", + "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client/lib:arithmetic", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/service:cpu_plugin", + "//tensorflow/compiler/xla/tests:client_library_test_base", + "//tensorflow/compiler/xla/tests:literal_test_util", + "//tensorflow/core:lib", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc new file mode 100644 index 00000000000000..dd63b998e9b6d0 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc @@ -0,0 +1,294 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +#include "tensorflow/compiler/xla/client/global_data.h" +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/compiler/xla/test_helpers.h" +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/compiler/xla/tests/literal_test_util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/math/math_util.h" +#include "tensorflow/core/platform/env.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { +namespace { + +class InfeedTest : public ClientLibraryTestBase { + protected: + // Transfers the given literal to the infeed interface of the device, and + // check if the returned data from Infeed HLO is same as the literal. + void TestInfeedRoundTrip(const Literal& literal) { + // TODO(b/31037751) Explicitly reset the Infeed state so that the + // test is not affected by the state from the previous tests by + // adding ClearInfeed if necessary when it is implemented. For now + // don't use ResetDevice since it is not implemented on CPU. + ASSERT_IS_OK(client_->TransferToInfeed(literal)); + XlaBuilder builder(TestName()); + builder.Infeed(literal.shape()); + if (ShapeUtil::IsTuple(literal.shape())) { + // TODO(b/30609564): Use ComputeAndCompareLiteral instead. + ComputeAndCompareTuple(&builder, literal, {}); + } else { + ComputeAndCompareLiteral(&builder, literal, {}); + } + } +}; + +TEST_F(InfeedTest, SingleInfeedR0Bool) { + TestInfeedRoundTrip(*Literal::CreateR0(true)); +} + +TEST_F(InfeedTest, SingleInfeedR1U32) { + TestInfeedRoundTrip(*Literal::CreateR1({1, 2, 3})); +} + +TEST_F(InfeedTest, SingleInfeedR2F32) { + TestInfeedRoundTrip(*Literal::CreateR2F32Linspace(0.0, 1.0, 128, 64)); +} + +TEST_F(InfeedTest, SingleInfeedR3F32) { + TestInfeedRoundTrip( + *Literal::CreateR3({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}})); +} + +TEST_F(InfeedTest, SingleInfeedR3F32DifferentLayout) { + const Layout r3_dim0minor = LayoutUtil::MakeLayout({0, 1, 2}); + const Layout r3_dim0major = LayoutUtil::MakeLayout({2, 1, 0}); + + TestInfeedRoundTrip( + *Literal::CreateR3WithLayout({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}}, + r3_dim0minor)); + + TestInfeedRoundTrip( + *Literal::CreateR3WithLayout({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}}, + r3_dim0major)); +} + +TEST_F(InfeedTest, SingleInfeedR4S32) { + TestInfeedRoundTrip(*Literal::CreateR4( + {{{{1, -2}, {-4, 5}, {6, 7}}, {{8, 9}, {10, 11}, {12, 13}}}, + {{{10, 3}, {7, -2}, {3, 6}}, {{2, 5}, {-11, 5}, {-2, -5}}}})); +} + +TEST_F(InfeedTest, SingleInfeedTuple) { + TestInfeedRoundTrip( + *Literal::MakeTuple({Literal::CreateR1({1, 2, 3}).get(), + Literal::CreateR0(false).get()})); +} + +TEST_F(InfeedTest, SingleInfeedEmptyTuple) { + TestInfeedRoundTrip(*Literal::MakeTuple({})); +} + +// Tests Infeed operation used in a while loop, as in the code below. The +// computation is launched asynchronously, and then infeed data is transferred. +// +// float acc = 0.0f; +// while (acc < 40.0f) { +// acc += reduce_add(Infeed()); +// } +// return acc; +// TODO(b/30671675) enable this test once asynchronous execution is +// implemented for CPU. +TEST_F(InfeedTest, DISABLED_SingleInfeedInWhile) { + XlaBuilder builder(TestName()); + const auto infeed_shape = ShapeUtil::MakeShape(F32, {3}); + const auto result_shape = ShapeUtil::MakeShape(F32, {}); + + // Create a computation for the condition: repeat until (prev < 40.0f) holds. + XlaComputation condition; + { + XlaBuilder builder("condition"); + auto prev = builder.Parameter(0, result_shape, "prev"); + builder.Gt(builder.ConstantR0(40.0f), prev); + condition = builder.Build().ConsumeValueOrDie(); + } + // Create a computation for the body: add the reduced value of the Infeed + // data to the result variable. + XlaComputation body; + { + XlaBuilder builder("body"); + auto prev = builder.Parameter(0, result_shape, "prev"); + auto infeed = builder.Infeed(infeed_shape); + auto addend = + builder.Reduce(infeed, builder.ConstantR0(0.0f), + CreateScalarAddComputation(F32, &builder), {0}); + builder.Add(prev, addend); + body = builder.Build().ConsumeValueOrDie(); + } + // Create a While node with computations for the condition and the body. + auto init = builder.ConstantR0(0.0f); + builder.While(condition, body, init); + + // Build and asynchronously launch the computation. + auto computation = builder.Build().ConsumeValueOrDie(); + std::unique_ptr result; + tensorflow::Thread* computation_thread = + tensorflow::Env::Default()->StartThread( + tensorflow::ThreadOptions{}, "computation_thread", [&] { + result = client_->Execute(computation, {}, &execution_options_) + .ValueOrDie(); + }); + + // Send 5 Infeed data of shape F32[3]. + ASSERT_IS_OK(client_->TransferToInfeed(*Literal::CreateR1({1, 2, 3}))); + ASSERT_IS_OK(client_->TransferToInfeed(*Literal::CreateR1({4, 5, 6}))); + ASSERT_IS_OK(client_->TransferToInfeed(*Literal::CreateR1({7, 8, 9}))); + ASSERT_IS_OK( + client_->TransferToInfeed(*Literal::CreateR1({10, 11, 12}))); + ASSERT_IS_OK( + client_->TransferToInfeed(*Literal::CreateR1({13, 14, 15}))); + + delete computation_thread; // Joins the thread. + auto result_literal = client_->Transfer(*result).ConsumeValueOrDie(); + + // Only the first 3 infeed data should be added. + LiteralTestUtil::ExpectR0Near(45.0f, *result_literal, ErrorSpec{1e-7}); +} + +// Tests two Infeed operations with a total order. The order is enforced by +// using the result of the first while loop as the initial value of the second +// while loop. The shapes of both Infeeds are Tuples, where the first tuple +// element (R1F32) is for the data to reduce and accumulate, and the second +// tuple element (PRED) to indicate whether the loop should continue. The +// computation is launched asynchronously, and then infeed data is transferred. +// +// float acc = 0.0f; +// continue = true; +// while (!continue) { +// (data, continue) = Infeed(shape1); +// acc += reduce_add(data) +// } +// continue = true; +// while(!continue) { +// (data, continue) = Infeed(shape2); +// acc += reduce_add(data) +// } +// return acc; +// TODO(b/30671675) enable this test once asynchronous execution is +// implemented for CPU. +TEST_F(InfeedTest, DISABLED_TwoInfeedsInTotalOrder) { + XlaBuilder builder(TestName()); + const auto infeed1_shape = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShape(F32, {2}), ShapeUtil::MakeShape(PRED, {})}); + const auto infeed2_shape = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShape(F32, {3}), ShapeUtil::MakeShape(PRED, {})}); + const auto result_shape = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShape(F32, {}), ShapeUtil::MakeShape(PRED, {})}); + + // Create a computation for the condition: repeat until the second tuple + // element is false. + XlaComputation condition; + { + XlaBuilder builder("condition"); + auto prev = builder.Parameter(0, result_shape, "prev"); + builder.GetTupleElement(prev, 1); + condition = builder.Build().ConsumeValueOrDie(); + } + + // A lambda that builds the body computation of a while loop with the given + // infeed shape, and returns the computation with the ownership. + // + // The body adds the reduced value of the Infeed data (first tuple element) + // to the previous accumulator, and returns the accumulator and the continue + // flag (second tuple element) as a tuple. + const auto build_body = [this, &result_shape](const Shape& infeed_shape) { + XlaComputation body; + XlaBuilder builder("body"); + auto prev = builder.Parameter(0, result_shape, "prev"); + auto infeed = builder.Infeed(infeed_shape); + auto addend = builder.Reduce( + builder.GetTupleElement(infeed, 0), builder.ConstantR0(0.0f), + CreateScalarAddComputation(F32, &builder), {0}); + auto result = builder.Add(builder.GetTupleElement(prev, 0), addend); + builder.Tuple({result, builder.GetTupleElement(infeed, 1)}); + return builder.Build().ConsumeValueOrDie(); + }; + + // Create the first while loop with infeed1_shape. + auto init = builder.Tuple( + {builder.ConstantR0(0.0f), builder.ConstantR0(true)}); + auto while1 = builder.While(condition, build_body(infeed1_shape), init); + auto result1 = builder.Tuple( + {builder.GetTupleElement(while1, 0), builder.ConstantR0(true)}); + + // Create the second while loop with infeed2_shape. Note that the result from + // the first while loop is used as the initial value. + auto while2 = builder.While(condition, build_body(infeed2_shape), result1); + builder.GetTupleElement(while2, 0); + + // Build the computation. + auto computation = builder.Build().ConsumeValueOrDie(); + + // Send the first 4 Infeed data of shape Tuple(F32[2], PRED). + ASSERT_IS_OK(client_->TransferToInfeed( + *Literal::MakeTuple({Literal::CreateR1({1, 2}).get(), + Literal::CreateR0(true).get()}))); + ASSERT_IS_OK(client_->TransferToInfeed( + *Literal::MakeTuple({Literal::CreateR1({3, 4}).get(), + Literal::CreateR0(true).get()}))); + ASSERT_IS_OK(client_->TransferToInfeed( + *Literal::MakeTuple({Literal::CreateR1({5, 6}).get(), + Literal::CreateR0(true).get()}))); + ASSERT_IS_OK(client_->TransferToInfeed( + *Literal::MakeTuple({Literal::CreateR1({7, 8}).get(), + Literal::CreateR0(false).get()}))); + + // Asynchronously launch the execution on the device. + std::unique_ptr result; + tensorflow::Thread* computation_thread = + tensorflow::Env::Default()->StartThread( + tensorflow::ThreadOptions{}, "computation_thread", [&] { + result = client_->Execute(computation, {}, &execution_options_) + .ValueOrDie(); + }); + + // Wait for a second to ensure testing that the execution is waiting on the + // Infeed data, and send the rest Infeed data of shape Tuple(F32[3], PRED). + sleep(1); + ASSERT_IS_OK(client_->TransferToInfeed( + *Literal::MakeTuple({Literal::CreateR1({1, 2, 3}).get(), + Literal::CreateR0(true).get()}))); + ASSERT_IS_OK(client_->TransferToInfeed( + *Literal::MakeTuple({Literal::CreateR1({7, 8, 9}).get(), + Literal::CreateR0(false).get()}))); + ASSERT_IS_OK(client_->TransferToInfeed( + *Literal::MakeTuple({Literal::CreateR1({4, 5, 6}).get(), + Literal::CreateR0(true).get()}))); + + // Wait for the execution to be done, and transfer the result. + delete computation_thread; // Joins the thread. + auto result_literal = client_->Transfer(*result).ConsumeValueOrDie(); + + // Only the first 6 infeed data should be added. + LiteralTestUtil::ExpectR0Near(66.0f, *result_literal, ErrorSpec{1e-7}); +} + +} // namespace +} // namespace xla From 9149558a639efe82baf1b5201feccf2411343a8a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 1 May 2018 13:15:53 -0700 Subject: [PATCH 0235/1691] Collective Ops Part 5 Distributed-mode implementations of DeviceResolverInterface and ParamResolverInterface. Extend Worker interface with new methods in support of these interfaces. This change is part of a series of changes introducing infrastructure for collective ops and initial implementations of reduction and broadcast. PiperOrigin-RevId: 194984585 --- tensorflow/core/distributed_runtime/BUILD | 75 ++++ .../collective_param_resolver_distributed.cc | 404 ++++++++++++++++++ .../collective_param_resolver_distributed.h | 90 ++++ ...lective_param_resolver_distributed_test.cc | 324 ++++++++++++++ .../device_resolver_distributed.cc | 133 ++++++ .../device_resolver_distributed.h | 67 +++ .../device_resolver_distributed_test.cc | 217 ++++++++++ .../rpc/grpc_remote_worker.cc | 27 ++ .../rpc/grpc_worker_service.cc | 47 ++ .../rpc/grpc_worker_service_impl.cc | 6 + .../rpc/grpc_worker_service_impl.h | 5 +- .../core/distributed_runtime/test_utils.h | 173 ++++++++ tensorflow/core/distributed_runtime/worker.cc | 87 ++-- tensorflow/core/distributed_runtime/worker.h | 17 +- .../core/distributed_runtime/worker_env.h | 5 + .../distributed_runtime/worker_interface.h | 19 + tensorflow/core/protobuf/worker.proto | 70 +++ tensorflow/core/protobuf/worker_service.proto | 10 + 18 files changed, 1744 insertions(+), 32 deletions(-) create mode 100644 tensorflow/core/distributed_runtime/collective_param_resolver_distributed.cc create mode 100644 tensorflow/core/distributed_runtime/collective_param_resolver_distributed.h create mode 100644 tensorflow/core/distributed_runtime/collective_param_resolver_distributed_test.cc create mode 100644 tensorflow/core/distributed_runtime/device_resolver_distributed.cc create mode 100644 tensorflow/core/distributed_runtime/device_resolver_distributed.h create mode 100644 tensorflow/core/distributed_runtime/device_resolver_distributed_test.cc create mode 100644 tensorflow/core/distributed_runtime/test_utils.h diff --git a/tensorflow/core/distributed_runtime/BUILD b/tensorflow/core/distributed_runtime/BUILD index 343dd5d4560adb..256ce527a423f3 100644 --- a/tensorflow/core/distributed_runtime/BUILD +++ b/tensorflow/core/distributed_runtime/BUILD @@ -452,6 +452,81 @@ cc_library( ], ) +cc_library( + name = "collective_param_resolver_distributed", + srcs = ["collective_param_resolver_distributed.cc"], + hdrs = ["collective_param_resolver_distributed.h"], + deps = [ + ":call_options", + ":device_resolver_distributed", + ":worker_cache", + "//tensorflow/core:core_cpu_internal", + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:worker_proto_cc", + ], +) + +cc_library( + name = "test_utils", + srcs = [], + hdrs = ["test_utils.h"], + deps = [ + ":worker_cache", + ":worker_interface", + ], +) + +tf_cc_test( + name = "collective_param_resolver_distributed_test", + size = "small", + srcs = ["collective_param_resolver_distributed_test.cc"], + deps = [ + ":collective_param_resolver_distributed", + ":device_resolver_distributed", + ":test_utils", + "//tensorflow/core:core_cpu_lib", + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "//tensorflow/core:testlib", + ], +) + +cc_library( + name = "device_resolver_distributed", + srcs = ["device_resolver_distributed.cc"], + hdrs = ["device_resolver_distributed.h"], + deps = [ + ":worker_cache", + "//tensorflow/core:core_cpu_internal", + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:worker_proto_cc", + ], +) + +tf_cc_test( + name = "device_resolver_distributed_test", + size = "small", + srcs = ["device_resolver_distributed_test.cc"], + deps = [ + ":device_resolver_distributed", + ":test_utils", + "//tensorflow/core:core_cpu_lib", + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "//tensorflow/core:testlib", + ], +) + # TODO(mrry): Move executor_test.cc to ../common_runtime when once it no longer depends # on grpc_testlib. tf_cuda_cc_tests( diff --git a/tensorflow/core/distributed_runtime/collective_param_resolver_distributed.cc b/tensorflow/core/distributed_runtime/collective_param_resolver_distributed.cc new file mode 100644 index 00000000000000..ecf5db811073f2 --- /dev/null +++ b/tensorflow/core/distributed_runtime/collective_param_resolver_distributed.cc @@ -0,0 +1,404 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/core/distributed_runtime/collective_param_resolver_distributed.h" + +#include "tensorflow/core/distributed_runtime/call_options.h" +#include "tensorflow/core/distributed_runtime/device_resolver_distributed.h" +#include "tensorflow/core/distributed_runtime/worker_cache.h" +#include "tensorflow/core/protobuf/config.pb.h" + +// TODO(tucker): When we're ready to enable collectives this const will +// transition to a settable config member. +static const char FLAGS_collective_group_leader[] = + "/job:worker/replica:0/task:0"; + +namespace tensorflow { +namespace { +// Supports client side cancellation of WorkerInterface calls via +// registration with a CancellationManager. Note that ParamResolverInterface +// calls are done on behalf of an Op execution which needs to abort if the +// step in which it executes is cancelled. +class CancellableCall { + public: + CancellableCall(CancellationManager* cancel_mgr, const string& remote_worker, + WorkerCacheInterface* wc) + : cancel_mgr_(cancel_mgr), remote_worker_(remote_worker), wc_(wc) { + wi_ = wc_->CreateWorker(remote_worker_); + } + virtual ~CancellableCall() { wc_->ReleaseWorker(remote_worker_, wi_); } + + virtual void IssueCall(const StatusCallback& done) = 0; + + void Start(const StatusCallback& done) { + CancellationToken token = cancel_mgr_->get_cancellation_token(); + const bool not_yet_cancelled = cancel_mgr_->RegisterCallback( + token, [this, token]() { opts_.StartCancel(); }); + if (not_yet_cancelled) { + IssueCall([this, token, done](const Status& s) { + cancel_mgr_->DeregisterCallback(token); + done(s); + }); + } else { + done(errors::Cancelled("RPC Request was cancelled")); + } + } + + protected: + mutable mutex mu_; + CancellationManager* cancel_mgr_; // Not owned + const string remote_worker_; + WorkerCacheInterface* wc_; // Not owned + WorkerInterface* wi_; // Owned by wc_, must be released. + CallOptions opts_; +}; + +class CompleteGroupCall : public CancellableCall { + public: + CompleteGroupCall(const CollGroupParams& group, const string& device_name, + CancellationManager* cancel_mgr, + const string& remote_worker, WorkerCacheInterface* wc) + : CancellableCall(cancel_mgr, remote_worker, wc) { + req_.set_group_key(group.group_key); + req_.set_group_size(group.group_size); + req_.set_device_type(group.device_type.type_string()); + req_.add_device_name(device_name); + } + ~CompleteGroupCall() override {} + + void IssueCall(const StatusCallback& done) override { + wi_->CompleteGroupAsync(&opts_, &req_, &resp_, done); + } + + CompleteGroupRequest req_; + CompleteGroupResponse resp_; +}; + +class CompleteInstanceCall : public CancellableCall { + public: + CompleteInstanceCall(const CollGroupParams& group, + const CollInstanceParams& instance, + const string& node_name, const string& device_name, + bool is_source, CancellationManager* cancel_mgr, + const string& remote_worker, WorkerCacheInterface* wc) + : CancellableCall(cancel_mgr, remote_worker, wc) { + req_.set_name(node_name); + req_.set_type(instance.type); + req_.set_data_type(instance.data_type); + instance.shape.AsProto(req_.mutable_shape()); + req_.set_group_key(group.group_key); + req_.set_group_size(group.group_size); + req_.set_instance_key(instance.instance_key); + req_.set_device_type(group.device_type.type_string()); + for (int32 offset : instance.impl_details.subdiv_offsets) { + req_.add_subdiv_offset(offset); + } + req_.set_device(device_name); + req_.set_is_source(is_source); + } + + ~CompleteInstanceCall() override {} + + void IssueCall(const StatusCallback& done) override { + wi_->CompleteInstanceAsync(&opts_, &req_, &resp_, done); + } + + CompleteInstanceRequest req_; + CompleteInstanceResponse resp_; +}; + +} // namespace + +CollectiveParamResolverDistributed::CollectiveParamResolverDistributed( + const ConfigProto& config, const DeviceMgr* dev_mgr, + DeviceResolverDistributed* dev_resolver, WorkerCacheInterface* worker_cache, + const string& task_name) + : CollectiveParamResolverLocal(dev_mgr, dev_resolver, task_name), + worker_cache_(worker_cache), + group_leader_(task_name == FLAGS_collective_group_leader + ? "" + : FLAGS_collective_group_leader) {} + +void CollectiveParamResolverDistributed::CompleteParamsAsync( + const string& device, CollectiveParams* cp, CancellationManager* cancel_mgr, + const StatusCallback& done) { + CompleteGroupDistributed(device, cp, cancel_mgr, + [this, device, cp, cancel_mgr, done]( + const Status& s, const GroupRec* gr) { + if (s.ok()) { + CompleteInstanceDistributed(device, gr, cp, + cancel_mgr, done); + } else { + done(s); + } + }); +} + +void CollectiveParamResolverDistributed::CompleteGroupAsync( + const CompleteGroupRequest* request, CompleteGroupResponse* response, + CancellationManager* cancel_mgr, const StatusCallback& done) { + CollectiveParams cp; + cp.group.group_key = request->group_key(); + cp.group.group_size = request->group_size(); + cp.group.device_type = DeviceType(request->device_type()); + for (const string& dn : request->device_name()) { + cp.instance.device_names.push_back(dn); + } + CompleteGroupDistributed( + cp.instance.device_names[0], &cp, cancel_mgr, + [this, response, done](const Status& s, const GroupRec* gr) { + if (s.ok()) { + mutex_lock l(gr->mu); + response->set_group_key(gr->group.group_key); + response->set_group_size(gr->group.group_size); + response->set_device_type(gr->group.device_type.type_string()); + response->set_num_tasks(gr->task_set.size()); + for (const string& dn : gr->device_list) { + response->add_device_name(dn); + } + for (const string& tn : gr->task_list) { + response->add_task_name(tn); + } + } else { + LOG(ERROR) << "Bad status from CompleteGroupDistributed: " << s; + } + done(s); + }); +} + +void CollectiveParamResolverDistributed::CompleteInstanceAsync( + const CompleteInstanceRequest* request, CompleteInstanceResponse* response, + CancellationManager* cancel_mgr, const StatusCallback& done) { + CollectiveParams* cp = new CollectiveParams; + cp->name = request->name(); + cp->group.group_key = request->group_key(); + cp->group.group_size = request->group_size(); + cp->group.device_type = DeviceType(request->device_type()); + cp->instance.type = CollectiveType(request->type()); + cp->instance.instance_key = request->instance_key(); + cp->instance.data_type = request->data_type(); + cp->instance.shape = TensorShape(request->shape()); + for (int32 offset : request->subdiv_offset()) { + cp->instance.impl_details.subdiv_offsets.push_back(offset); + } + VLOG(1) << "New cp " << cp << " for device " << request->device() << " : " + << cp->ToString(); + StatusCallback done_and_cleanup = [this, cp, done](const Status& s) { + done(s); + delete cp; + }; + // Start by completing the group. + CompleteGroupDistributed( + request->device(), cp, cancel_mgr, + [this, cp, request, response, cancel_mgr, done_and_cleanup]( + const Status& cg_status, const GroupRec* gr) { + if (cg_status.ok()) { + // Then complete the instance. + CompleteInstanceDistributed( + request->device(), gr, cp, cancel_mgr, + [this, gr, cp, response, + done_and_cleanup](const Status& ci_status) { + if (ci_status.ok()) { + // Now source_rank should be known, so + // retrieve it. + FindInstanceRec( + gr, cp, + [this, gr, cp, response, done_and_cleanup]( + const Status& fi_status, InstanceRec* ir) { + if (fi_status.ok()) { + mutex_lock l(ir->out_mu); + response->set_instance_key(cp->instance.instance_key); + response->set_source_rank(ir->source_rank); + done_and_cleanup(fi_status); + } else { + done_and_cleanup(fi_status); + } + }); + } else { + done_and_cleanup(ci_status); + } + }); + } else { + done_and_cleanup(cg_status); + } + }); +} + +bool CollectiveParamResolverDistributed::GroupIsCached(int32 group_key) { + mutex_lock l(group_mu_); + const auto& it = group_table_.find(group_key); + return it != group_table_.end(); +} + +Status CollectiveParamResolverDistributed::UpdateGroupCache( + const CompleteGroupResponse& resp) { + // Build a new record from resp. + std::unique_ptr gr(new GroupRec); + mutex_lock grl(gr->mu); + gr->group.device_type = DeviceType(resp.device_type()); + gr->group.group_key = resp.group_key(); + gr->group.group_size = resp.group_size(); + gr->group.num_tasks = resp.num_tasks(); + if (resp.device_name_size() != gr->group.group_size) { + return errors::Internal( + "CompleteGroupResponse group_size doesn't match device_name list"); + } + for (const string& dn : resp.device_name()) { + gr->device_set.insert(dn); + gr->device_list.push_back(dn); + } + if (resp.task_name_size() != gr->group.group_size) { + return errors::Internal( + "CompleteGroupResponse group_size doesn't match task_name list"); + } + for (const string& tn : resp.task_name()) { + gr->task_list.push_back(tn); + gr->task_set.insert(tn); + } + CHECK_EQ(gr->task_set.size(), gr->group.num_tasks); + { + // Group membership should never change. Once a record is in group_table_ + // it never gets removed. + mutex_lock l(group_mu_); + auto it = group_table_.find(gr->group.group_key); + if (it == group_table_.end()) { + group_table_[gr->group.group_key] = std::move(gr); + } + } + return Status::OK(); +} + +void CollectiveParamResolverDistributed::CompleteGroupDistributed( + const string& device, CollectiveParams* cp, CancellationManager* cancel_mgr, + const GroupRecCallback& done) { + VLOG(1) << "CompleteGroupDistributed group_key=" << cp->group.group_key + << " dev: " << device << " is_leader=" << (group_leader_.empty()); + VLOG(0) << "cp: " << cp->ToString(); + if (group_leader_.empty()) { + // This is the group leader, so resolution is local. + return CompleteGroupLocal(device, cp, done); + } else if (!GroupIsCached(cp->group.group_key)) { + // Need to update Group cache from the leader. + CompleteGroupCall* call = new CompleteGroupCall( + cp->group, device, cancel_mgr, group_leader_, worker_cache_); + call->Start([this, device, cp, call, done](const Status& s) { + if (s.ok()) { + Status status = UpdateGroupCache(call->resp_); + if (status.ok()) { + CompleteGroupLocal(device, cp, done); + } else { + done(status, nullptr); + } + } else { + done(s, nullptr); + } + delete call; + }); + return; + } else { + return CompleteGroupLocal(device, cp, done); + } +} + +bool CollectiveParamResolverDistributed::InstanceIsCached(int32 instance_key) { + mutex_lock l(instance_mu_); + const auto& it = instance_table_.find(instance_key); + return it != instance_table_.end(); +} + +void CollectiveParamResolverDistributed::UpdateInstanceCache( + const GroupRec* gr, CollectiveParams* cp, + const CompleteInstanceResponse& resp, const StatusCallback& done) { + Notification note; + InstanceRec* ir = nullptr; + int32 source_rank = resp.source_rank(); + + auto continue_with_ir = [this, cp, &ir, source_rank, done](const Status& s) { + if (!s.ok()) { + done(s); + return; + } + Status status; + do { + mutex_lock l(ir->out_mu); + if (ir->source_rank != source_rank) { + if (ir->source_rank >= 0) { + ir->status = errors::Internal( + "UpdateInstanceCache: CompleteInstanceResponse for instance ", + cp->instance.instance_key, " gives source_rank=", source_rank, + " but cache already holds value=", ir->source_rank); + status = ir->status; + break; + } + ir->source_rank = source_rank; + } + if (ir->known_count < cp->group.group_size) { + ir->known_count = cp->group.group_size; + if (ir->known.size() != cp->group.group_size) { + ir->status = errors::Internal( + "UpdateInstanceCache:: CompleteInstanceResponse for instance ", + cp->instance.instance_key, " has known.size()=", ir->known.size(), + " < group_size=", cp->group.group_size); + status = ir->status; + break; + } + for (int i = 0; i < ir->known.size(); ++i) { + ir->known[i] = true; + } + } + status = ir->status; + } while (false); + // Callback outside of lock. + done(status); + }; + + FindInstanceRec( + gr, cp, [this, &ir, continue_with_ir](const Status s, InstanceRec* irec) { + ir = irec; + continue_with_ir(s); + }); +} + +void CollectiveParamResolverDistributed::CompleteInstanceDistributed( + const string& device, const GroupRec* gr, CollectiveParams* cp, + CancellationManager* cancel_mgr, const StatusCallback& done) { + if (group_leader_.empty()) { + // This is the group leader so resolution is local. + return CompleteInstanceLocal(device, gr, cp, cp->is_source, done); + } else if (InstanceIsCached(cp->instance.instance_key)) { + return CompleteInstanceLocal(device, gr, cp, cp->is_source, done); + } else { + CompleteInstanceCall* call = new CompleteInstanceCall( + cp->group, cp->instance, cp->name, device, cp->is_source, cancel_mgr, + group_leader_, worker_cache_); + call->Start([this, device, gr, cp, call, done](const Status& s) { + if (s.ok()) { + UpdateInstanceCache( + gr, cp, call->resp_, [this, device, gr, cp, done](const Status& s) { + if (!s.ok()) { + done(s); + } else { + CompleteInstanceLocal(device, gr, cp, cp->is_source, done); + } + }); + } else { + done(s); + } + delete call; + }); + return; + } +} + +} // namespace tensorflow diff --git a/tensorflow/core/distributed_runtime/collective_param_resolver_distributed.h b/tensorflow/core/distributed_runtime/collective_param_resolver_distributed.h new file mode 100644 index 00000000000000..a35131d8350c15 --- /dev/null +++ b/tensorflow/core/distributed_runtime/collective_param_resolver_distributed.h @@ -0,0 +1,90 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_COLLECTIVE_PARAM_RESOLVER_DISTRIBUTED_H_ +#define TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_COLLECTIVE_PARAM_RESOLVER_DISTRIBUTED_H_ + +#include "tensorflow/core/common_runtime/collective_param_resolver_local.h" + +namespace tensorflow { +class ConfigProto; +class WorkerCacheInterface; +class DeviceResolverDistributed; +class DeviceMgr; + +class CollectiveParamResolverDistributed : public CollectiveParamResolverLocal { + public: + CollectiveParamResolverDistributed(const ConfigProto& config, + const DeviceMgr* dev_mgr, + DeviceResolverDistributed* dev_resolver, + WorkerCacheInterface* worker_cache, + const string& task_name); + + void CompleteParamsAsync(const string& device, CollectiveParams* cp, + CancellationManager* cancel_mgr, + const StatusCallback& done) override; + + void CompleteGroupAsync(const CompleteGroupRequest* request, + CompleteGroupResponse* response, + CancellationManager* cancel_mgr, + const StatusCallback& done) override; + + void CompleteInstanceAsync(const CompleteInstanceRequest* request, + CompleteInstanceResponse* response, + CancellationManager* cancel_mgr, + const StatusCallback& done) override; + + protected: + // Returns true iff there's an entry for this group_key in the + // local group_table_. + bool GroupIsCached(int32 group_key) LOCKS_EXCLUDED(group_mu_); + + // Updates group_table_ with contents of resp. + Status UpdateGroupCache(const CompleteGroupResponse& resp) + LOCKS_EXCLUDED(group_mu_); + + // Finds the GroupRec that corresponds to cp->group_key and also + // populates cp->group from that GroupRec. + // + // Semantics are like those of CompleteGroupLocal but will make a + // remote call to the group leader if necessary. + void CompleteGroupDistributed(const string& device, CollectiveParams* cp, + CancellationManager* cancel_mgr, + const GroupRecCallback& done); + + // Returns true iff there's an entry for this instance_key in the + // local instance_table_. + bool InstanceIsCached(int32 instance_key) LOCKS_EXCLUDED(instance_mu_); + + // Updates instance_table_ with contents of resp. + void UpdateInstanceCache(const GroupRec* gr, CollectiveParams* cp, + const CompleteInstanceResponse& resp, + const StatusCallback& done) + LOCKS_EXCLUDED(instance_mu_, gr->mu, group_mu_); + + // Finish populating *cp. Semantics are like those of + // CompleteInstanceLocal but will make a remote call to the group + // leader if necessary. + void CompleteInstanceDistributed(const string& device, const GroupRec* gr, + CollectiveParams* cp, + CancellationManager* cancel_mgr, + const StatusCallback& done) + LOCKS_EXCLUDED(instance_mu_, gr->mu, group_mu_); + + WorkerCacheInterface* worker_cache_; // Not owned + const string group_leader_; +}; + +} // namespace tensorflow +#endif // TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_COLLECTIVE_PARAM_RESOLVER_DISTRIBUTED_H_ diff --git a/tensorflow/core/distributed_runtime/collective_param_resolver_distributed_test.cc b/tensorflow/core/distributed_runtime/collective_param_resolver_distributed_test.cc new file mode 100644 index 00000000000000..95a010286d6ce2 --- /dev/null +++ b/tensorflow/core/distributed_runtime/collective_param_resolver_distributed_test.cc @@ -0,0 +1,324 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/distributed_runtime/collective_param_resolver_distributed.h" + +#include "tensorflow/core/common_runtime/device_mgr.h" +#include "tensorflow/core/distributed_runtime/device_resolver_distributed.h" +#include "tensorflow/core/distributed_runtime/test_utils.h" +#include "tensorflow/core/framework/cancellation.h" +#include "tensorflow/core/lib/core/notification.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/util/device_name_utils.h" + +namespace tensorflow { +namespace { + +static Device* NewDevice(const string& type, const string& name) { + class FakeDevice : public Device { + public: + explicit FakeDevice(const DeviceAttributes& attr) : Device(nullptr, attr) {} + Status Sync() override { return Status::OK(); } + Allocator* GetAllocator(AllocatorAttributes) override { return nullptr; } + }; + DeviceAttributes attr; + attr.set_name(name); + attr.set_device_type(type); + attr.mutable_locality()->set_numa_node(3); // a non-default value + return new FakeDevice(attr); +} + +class FakeWorker : public TestWorkerInterface { + public: + FakeWorker(const string& name, DeviceMgr* dev_mgr, + CollectiveParamResolverDistributed* cpres) + : name_(name), device_mgr_(dev_mgr), param_resolver_(cpres) {} + + void GetStatusAsync(const GetStatusRequest* request, + GetStatusResponse* response, + StatusCallback done) override { + std::vector dev_attr; + device_mgr_->ListDeviceAttributes(&dev_attr); + for (const auto& da : dev_attr) { + *response->add_device_attributes() = da; + } + done(Status::OK()); + } + + void CompleteGroupAsync(CallOptions* opts, + const CompleteGroupRequest* request, + CompleteGroupResponse* response, + StatusCallback done) override { + param_resolver_->CompleteGroupAsync(request, response, &cm_, done); + } + + void CompleteInstanceAsync(CallOptions* ops, + const CompleteInstanceRequest* request, + CompleteInstanceResponse* response, + StatusCallback done) override { + param_resolver_->CompleteInstanceAsync(request, response, &cm_, done); + } + + private: + string name_; + DeviceMgr* device_mgr_; + CancellationManager cm_; + CollectiveParamResolverDistributed* param_resolver_; +}; + +class FakeCache : public TestWorkerCache { + public: + // Override the Locality methods to actually pass through to the + // worker. + bool GetDeviceLocalityNonBlocking(const string& device, + DeviceLocality* locality) override { + return false; + } + + void GetDeviceLocalityAsync(const string& device, DeviceLocality* locality, + StatusCallback done) override { + string task_name; + string dev_part; + if (!DeviceNameUtils::SplitDeviceName(device, &task_name, &dev_part)) { + done(errors::Internal("failed to parse device name")); + return; + } + auto it = workers_.find(task_name); + if (it == workers_.end()) { + done(errors::Internal("failed to find worker ", task_name)); + return; + } + WorkerInterface* wi = it->second; + GetStatusRequest req; + GetStatusResponse resp; + Notification note; + Status status = wi->GetStatus(&req, &resp); + if (!status.ok()) { + done(status); + return; + } + for (const auto& it : resp.device_attributes()) { + if (it.name() == device) { + *locality = it.locality(); + done(Status::OK()); + return; + } + } + done(errors::Internal("device not found: ", device)); + } +}; + +class DeviceResDistTest : public ::testing::Test { + protected: + DeviceResDistTest() {} + + ~DeviceResDistTest() override { + for (DeviceMgr* dm : device_mgrs_) { + delete dm; + } + for (auto it : dev_resolvers_) { + delete it.second; + } + for (auto it : cp_resolvers_) { + delete it.second; + } + for (FakeWorker* w : workers_) { + delete w; + } + } + + void DefineWorkers(int num_workers, int num_devices, + const string& device_type) { + ConfigProto config; + for (int w = 0; w < num_workers; ++w) { + string name = strings::StrCat("/job:worker/replica:0/task:", w); + // TODO(tucker): When config option becomes available, set here. + // if (w == 0) { + // config.set_collective_group_leader(name); + // } + DefineWorker(config, name, device_type, num_devices); + } + } + + void DefineWorker(const ConfigProto& config, const string& worker_name, + const string& device_type, int num_devices) { + std::vector devices; + for (int i = 0; i < num_devices; ++i) { + devices.push_back(NewDevice( + device_type, + strings::StrCat(worker_name, "/device:", device_type, ":", i))); + } + DeviceMgr* dev_mgr = new DeviceMgr(devices); + device_mgrs_.push_back(dev_mgr); + std::vector* dv = &dev_by_task_[worker_name]; + for (auto d : devices) { + dv->push_back(d->name()); + } + DeviceResolverDistributed* dev_res = + new DeviceResolverDistributed(dev_mgr, &wc_, worker_name); + dev_resolvers_[worker_name] = dev_res; + CollectiveParamResolverDistributed* cp_res = + new CollectiveParamResolverDistributed(config, dev_mgr, dev_res, &wc_, + worker_name); + cp_resolvers_[worker_name] = cp_res; + FakeWorker* fw = new FakeWorker(worker_name, dev_mgr, cp_res); + workers_.push_back(fw); + wc_.AddWorker(worker_name, fw); + } + + void DefineCollectiveParams(int num_workers, int num_devices) { + const int kGroupKey = 5; + const int kInstanceKey = 3; + for (int wi = 0; wi < num_workers; ++wi) { + string task_name = strings::StrCat("/job:worker/replica:0/task:", wi); + for (int di = 0; di < num_devices; ++di) { + string device_name = strings::StrCat(task_name, "/device:CPU:", di); + cp_.push_back(CollectiveParams()); + CollectiveParams& cp = cp_.back(); + cp.group.group_key = kGroupKey; + cp.group.group_size = num_workers * num_devices; + cp.group.device_type = DEVICE_CPU; + cp.group.num_tasks = num_workers; + cp.instance.instance_key = kInstanceKey; + cp.instance.type = REDUCTION_COLLECTIVE; + cp.instance.data_type = DT_FLOAT; + cp.instance.shape = TensorShape({64}); + cp.instance.impl_details.subdiv_offsets.push_back(0); + } + } + } + + void IssueRequests(int num_workers, int num_devices) { + const int device_count = num_workers * num_devices; + { + mutex_lock l(mu_); + num_done_ = 0; + } + cp_.resize(device_count); + status_.resize(device_count); + int idx = 0; + for (int wi = 0; wi < num_workers; ++wi) { + for (int di = 0; di < num_devices; ++di) { + IssueRequest(num_workers, num_devices, idx); + ++idx; + } + } + } + + void IssueRequest(int num_workers, int num_devices, int idx) { + int device_count = num_workers * num_devices; + int wi = idx / num_devices; + int di = idx % num_devices; + string task_name = strings::StrCat("/job:worker/replica:0/task:", wi); + string device_name = strings::StrCat(task_name, "/device:CPU:", di); + while (idx >= cp_.size()) { + status_.resize(idx + 1); + cp_.resize(idx + 1); + } + CollectiveParams* cp = &cp_[idx]; + CollectiveParamResolverDistributed* cp_res = cp_resolvers_[task_name]; + CHECK(cp_res); + cp_res->CompleteParamsAsync(device_name, cp, &cm_, + [this, idx, device_count](const Status& s) { + status_[idx] = s; + { + mutex_lock l(mu_); + ++num_done_; + if (num_done_ == device_count) { + done_.notify_all(); + } + } + }); + } + + void ValidateCollectiveParams(int num_workers, int num_devices) { + int device_count = num_workers * num_devices; + { + mutex_lock l(mu_); + if (num_done_ < device_count) { + done_.wait(l); + } + } + // Verify that all cp_ values get the same set of task and device + // names, with unique default_rank in the expected order. + const int dev_count = num_workers * num_devices; + for (int wi = 0; wi < num_workers; ++wi) { + string task_name = strings::StrCat("/job:worker/replica:0/task:", wi); + for (int di = 0; di < num_devices; ++di) { + string device_name = strings::StrCat(task_name, "/device:CPU:", di); + int idx = wi * num_devices + di; + TF_ASSERT_OK(status_[idx]); + EXPECT_EQ(cp_[idx].default_rank, idx); + EXPECT_EQ(cp_[idx].instance.device_names.size(), dev_count); + EXPECT_EQ(cp_[idx].instance.device_names[idx], device_name); + EXPECT_EQ(cp_[idx].instance.task_names[idx], task_name); + if (idx > 0) { + for (int i = 0; i < dev_count; ++i) { + EXPECT_EQ(cp_[0].instance.device_names[i], + cp_[idx].instance.device_names[i]); + EXPECT_EQ(cp_[0].instance.task_names[i], + cp_[idx].instance.task_names[i]); + } + } + } + } + } + + FakeCache wc_; + CancellationManager cm_; + std::vector device_mgrs_; + std::unordered_map dev_resolvers_; + std::unordered_map cp_resolvers_; + std::unordered_map> dev_by_task_; + std::vector workers_; + std::vector cp_; + std::vector status_; + mutex mu_; + int num_done_ GUARDED_BY(mu_); + condition_variable done_; +}; + +TEST_F(DeviceResDistTest, Workers1Devices1) { + const int num_workers = 1; + const int num_devices = 1; + DefineWorkers(num_workers, num_devices, "CPU"); + DefineCollectiveParams(num_workers, num_devices); + IssueRequests(num_workers, num_devices); + ValidateCollectiveParams(num_workers, num_devices); +} + +TEST_F(DeviceResDistTest, Workers2Devices2) { + const int num_workers = 2; + const int num_devices = 2; + DefineWorkers(num_workers, num_devices, "CPU"); + DefineCollectiveParams(num_workers, num_devices); + IssueRequests(num_workers, num_devices); + ValidateCollectiveParams(num_workers, num_devices); +} + +TEST_F(DeviceResDistTest, Workers4Devices3) { + const int num_workers = 4; + const int num_devices = 3; + DefineWorkers(num_workers, num_devices, "CPU"); + DefineCollectiveParams(num_workers, num_devices); + IssueRequests(num_workers, num_devices); + ValidateCollectiveParams(num_workers, num_devices); +} + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/core/distributed_runtime/device_resolver_distributed.cc b/tensorflow/core/distributed_runtime/device_resolver_distributed.cc new file mode 100644 index 00000000000000..038974cb3903f4 --- /dev/null +++ b/tensorflow/core/distributed_runtime/device_resolver_distributed.cc @@ -0,0 +1,133 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/core/distributed_runtime/device_resolver_distributed.h" + +#include "tensorflow/core/common_runtime/device_mgr.h" +#include "tensorflow/core/distributed_runtime/worker_cache.h" + +namespace tensorflow { +DeviceResolverDistributed::DeviceResolverDistributed( + const DeviceMgr* dev_mgr, WorkerCacheInterface* worker_cache, + const string& task_name) + : dev_mgr_(dev_mgr), worker_cache_(worker_cache), task_name_(task_name) {} + +void DeviceResolverDistributed::GetLocalityAsync(const string& device, + const string& task, + DeviceLocality* locality, + const StatusCallback& done) { + if (task.empty() || task == task_name_) { + // Device is local to this task. + Device* dev; + Status s = dev_mgr_->LookupDevice(device, &dev); + if (s.ok()) { + *locality = dev->attributes().locality(); + } + done(s); + return; + } else { + // Lookup of a remote device: first try the local cache. + bool found = false; + { + mutex_lock l(mu_); + auto it = attr_table_.find(device); + if (it != attr_table_.end()) { + *locality = it->second.locality(); + found = true; + } + } + if (found) { + done(Status::OK()); + return; + } + } + // Device is remote and no cache entry was found. Refresh the cache + // then retry the lookup. + RefreshRemoteAttributes( + device, task, [this, device, task, locality, done](const Status& s) { + if (!s.ok()) { + done(s); + } else { + GetLocalityAsync(device, task, locality, done); + } + }); +} + +void DeviceResolverDistributed::GetDeviceLocalitiesAsync( + const CollInstanceParams& inst_params, + std::vector* localities, const StatusCallback& done) { + localities->clear(); + GetDeviceLocalitiesRecursive(inst_params, localities, done); +} + +void DeviceResolverDistributed::GetDeviceLocalitiesRecursive( + const CollInstanceParams& inst_params, + std::vector* localities, const StatusCallback& done) { + size_t i = localities->size(); + if (i < inst_params.device_names.size()) { + localities->push_back(DeviceLocality()); + GetLocalityAsync(inst_params.device_names[i], inst_params.task_names[i], + &localities->back(), + [this, &inst_params, localities, done](const Status& s) { + if (!s.ok()) { + done(s); + return; + } else { + GetDeviceLocalitiesRecursive(inst_params, localities, + done); + } + }); + } else { + done(Status::OK()); + } +} + +void DeviceResolverDistributed::RefreshRemoteAttributes( + const string& device, const string& task, const StatusCallback& done) { + GetStatusRequest* req = new GetStatusRequest; + GetStatusResponse* resp = new GetStatusResponse; + WorkerInterface* worker = worker_cache_->CreateWorker(task); + CHECK(worker) << "Failed to get worker for " << task; + worker->GetStatusAsync( + req, resp, [this, device, task, req, resp, worker, done](Status s) { + if (s.ok()) { + mutex_lock l(mu_); + for (const DeviceAttributes& da : resp->device_attributes()) { + attr_table_[da.name()] = da; + } + } + done(s); + delete req; + delete resp; + worker_cache_->ReleaseWorker(task, worker); + }); +} + +void DeviceResolverDistributed::ClearTask(const string& task) { + mutex_lock l(mu_); + // First find all the keys belonging to the task. + std::unordered_set task_keys; + for (const auto& it : attr_table_) { + const string& device_name = it.first; + if (DeviceNameUtils::IsSameAddressSpace(task, device_name)) { + task_keys.insert(device_name); + } + } + // Then delete them. + for (const string& key : task_keys) { + attr_table_.erase(key); + } +} + +} // namespace tensorflow diff --git a/tensorflow/core/distributed_runtime/device_resolver_distributed.h b/tensorflow/core/distributed_runtime/device_resolver_distributed.h new file mode 100644 index 00000000000000..ac68ec68731d5b --- /dev/null +++ b/tensorflow/core/distributed_runtime/device_resolver_distributed.h @@ -0,0 +1,67 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_DEVICE_RESOLVER_DISTRIBUTED_H_ +#define TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_DEVICE_RESOLVER_DISTRIBUTED_H_ + +#include +#include + +#include "tensorflow/core/framework/collective.h" +#include "tensorflow/core/framework/device_attributes.pb.h" +#include "tensorflow/core/lib/gtl/flatmap.h" + +namespace tensorflow { +class DeviceMgr; +class WorkerCacheInterface; + +class DeviceResolverDistributed : public DeviceResolverInterface { + public: + DeviceResolverDistributed(const DeviceMgr* dev_mgr, + WorkerCacheInterface* worker_cache, + const string& task_name); + + virtual ~DeviceResolverDistributed() {} + + void GetDeviceLocalitiesAsync(const CollInstanceParams& inst_params, + std::vector* localities, + const StatusCallback& done) override; + + void GetLocalityAsync(const string& device, const string& task, + DeviceLocality* locality, + const StatusCallback& done) override; + + void ClearTask(const string& task) override; + + protected: + // Loads attr_table_ with device attributes retrieved from remote task. + void RefreshRemoteAttributes(const string& device, const string& task, + const StatusCallback& done) LOCKS_EXCLUDED(mu_); + + // Subroutine used by GetDeviceLocalitiesAsync. Recursively extends + // *localities with DeviceLocality of the corresponding device named + // by inst_params.instance.device_names. + void GetDeviceLocalitiesRecursive(const CollInstanceParams& inst_params, + std::vector* localities, + const StatusCallback& done); + + const DeviceMgr* dev_mgr_; // Not owned + WorkerCacheInterface* worker_cache_; // Not owned + const string task_name_; + mutex mu_; + gtl::FlatMap attr_table_ GUARDED_BY(mu_); +}; + +} // namespace tensorflow +#endif // TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_DEVICE_RESOLVER_DISTRIBUTED_H_ diff --git a/tensorflow/core/distributed_runtime/device_resolver_distributed_test.cc b/tensorflow/core/distributed_runtime/device_resolver_distributed_test.cc new file mode 100644 index 00000000000000..ae44b98bd52d6d --- /dev/null +++ b/tensorflow/core/distributed_runtime/device_resolver_distributed_test.cc @@ -0,0 +1,217 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/distributed_runtime/device_resolver_distributed.h" + +#include "tensorflow/core/common_runtime/device_mgr.h" +#include "tensorflow/core/distributed_runtime/test_utils.h" +#include "tensorflow/core/lib/core/notification.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/util/device_name_utils.h" + +namespace tensorflow { +namespace { + +// Subclass of DeviceResolverDistributed which behaves identically but +// allows access to the attr_table_. +class TestableDeviceResolverDistributed : public DeviceResolverDistributed { + public: + TestableDeviceResolverDistributed(const DeviceMgr* dev_mgr, + WorkerCacheInterface* worker_cache, + const string& task) + : DeviceResolverDistributed(dev_mgr, worker_cache, task) {} + + gtl::FlatMap& attr_table() { return attr_table_; } +}; + +// Create a fake 'Device' whose only interesting attribute is a non-default +// DeviceLocality. +static Device* NewDevice(const string& type, const string& name, + int numa_node) { + class FakeDevice : public Device { + public: + explicit FakeDevice(const DeviceAttributes& attr) : Device(nullptr, attr) {} + Status Sync() override { return Status::OK(); } + Allocator* GetAllocator(AllocatorAttributes) override { return nullptr; } + }; + DeviceAttributes attr; + attr.set_name(name); + attr.set_device_type(type); + attr.mutable_locality()->set_numa_node(numa_node); + return new FakeDevice(attr); +} + +// Create a fake WorkerInterface that responds to requests without RPCs, +// in this case returning the DeviceAttributes of a fake remote worker. +class FakeWorker : public TestWorkerInterface { + public: + FakeWorker(const string& name, DeviceMgr* dev_mgr, + DeviceResolverDistributed* dres) + : name_(name), device_mgr_(dev_mgr), device_resolver_(dres) {} + + void GetStatusAsync(const GetStatusRequest* request, + GetStatusResponse* response, + StatusCallback done) override { + std::vector dev_attr; + device_mgr_->ListDeviceAttributes(&dev_attr); + for (const auto& da : dev_attr) { + *response->add_device_attributes() = da; + } + done(Status::OK()); + } + + private: + string name_; + DeviceMgr* device_mgr_; + DeviceResolverDistributed* device_resolver_; +}; + +// An implementation of WorkerCacheInterface that routes all requests +// to local FakeWorkers, implementing only the methods needed for tests. +class FakeCache : public TestWorkerCache { + public: + // Override the Locality methods to actually pass through to the + // worker. + bool GetDeviceLocalityNonBlocking(const string& device, + DeviceLocality* locality) override { + return false; + } + + void GetDeviceLocalityAsync(const string& device, DeviceLocality* locality, + StatusCallback done) override { + string task_name; + string dev_part; + if (!DeviceNameUtils::SplitDeviceName(device, &task_name, &dev_part)) { + done(errors::Internal("failed to parse device name")); + return; + } + auto it = workers_.find(task_name); + if (it == workers_.end()) { + done(errors::Internal("failed to find worker ", task_name)); + return; + } + WorkerInterface* wi = it->second; + GetStatusRequest req; + GetStatusResponse resp; + Notification note; + Status status = wi->GetStatus(&req, &resp); + if (!status.ok()) { + done(status); + return; + } + for (const auto& it : resp.device_attributes()) { + if (it.name() == device) { + *locality = it.locality(); + done(Status::OK()); + return; + } + } + done(errors::Internal("device not found: ", device)); + } +}; + +class DeviceResDistTest : public ::testing::Test { + protected: + DeviceResDistTest() {} + + ~DeviceResDistTest() override { + for (DeviceMgr* dm : device_mgrs_) { + delete dm; + } + for (auto it : resolvers_) { + delete it.second; + } + for (FakeWorker* w : workers_) { + delete w; + } + } + + void DefineWorkers(int num_workers, int num_devices, + const string& device_type) { + for (int w = 0; w < num_workers; ++w) { + string name = strings::StrCat("/job:worker/replica:0/task:", w); + DefineWorker(name, device_type, num_devices); + } + } + + void DefineWorker(const string& worker_name, const string& device_type, + int num_devices) { + std::vector devices; + for (int i = 0; i < num_devices; ++i) { + devices.push_back(NewDevice( + device_type, + strings::StrCat(worker_name, "/device:", device_type, ":", i), i)); + } + DeviceMgr* dev_mgr = new DeviceMgr(devices); + TestableDeviceResolverDistributed* dev_res = + new TestableDeviceResolverDistributed(dev_mgr, &wc_, worker_name); + resolvers_[worker_name] = dev_res; + device_mgrs_.push_back(dev_mgr); + std::vector* dv = &dev_by_task_[worker_name]; + for (auto d : devices) { + dv->push_back(d->name()); + } + FakeWorker* fw = new FakeWorker(worker_name, dev_mgr, dev_res); + workers_.push_back(fw); + wc_.AddWorker(worker_name, fw); + } + + FakeCache wc_; + std::vector device_mgrs_; + std::unordered_map resolvers_; + std::unordered_map> dev_by_task_; + std::vector workers_; +}; + +TEST_F(DeviceResDistTest, Workers3Devices4) { + DefineWorkers(3, 4, "CPU"); + // Check that every device is available from every task. + for (auto it : resolvers_) { + DeviceResolverDistributed* dres = it.second; + for (auto it2 : dev_by_task_) { + const string& task_name = it2.first; + for (const auto& dev_name : it2.second) { + DeviceNameUtils::ParsedName parsed; + ASSERT_TRUE(DeviceNameUtils::ParseFullName(dev_name, &parsed)); + Notification note; + Status status; + DeviceLocality locality; + dres->GetLocalityAsync(dev_name, task_name, &locality, + [this, ¬e, &status](const Status& s) { + status = s; + note.Notify(); + }); + note.WaitForNotification(); + TF_EXPECT_OK(status); + EXPECT_EQ(parsed.id, locality.numa_node()); + } + } + } + // Clear just task 0 from all. + const string w0_name = "/job:worker/replica:0/task:0"; + for (auto it : resolvers_) { + if (it.first == w0_name) continue; + TestableDeviceResolverDistributed* dres = it.second; + EXPECT_EQ(8, it.second->attr_table().size()); + dres->ClearTask("/job:worker/replica:0/task:0"); + EXPECT_EQ(4, it.second->attr_table().size()); + } +} + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc b/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc index 895bbd97b76921..5b7b74ce636dcb 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc @@ -56,6 +56,9 @@ class GrpcRemoteWorker : public WorkerInterface { recvtensor_(Method(GrpcWorkerMethod::kRecvTensor)), logging_(Method(GrpcWorkerMethod::kLogging)), tracing_(Method(GrpcWorkerMethod::kTracing)), + completegroup_(Method(GrpcWorkerMethod::kCompleteGroup)), + instancesource_(Method(GrpcWorkerMethod::kCompleteInstance)), + getstepsequence_(Method(GrpcWorkerMethod::kGetStepSequence)), logger_(logger) {} ~GrpcRemoteWorker() override {} @@ -115,6 +118,27 @@ class GrpcRemoteWorker : public WorkerInterface { IssueRequest(request, response, cleanupall_, std::move(done)); } + void CompleteGroupAsync(CallOptions* call_opts, + const CompleteGroupRequest* request, + CompleteGroupResponse* response, + StatusCallback done) override { + IssueRequest(request, response, completegroup_, std::move(done), call_opts); + } + + void CompleteInstanceAsync(CallOptions* call_opts, + const CompleteInstanceRequest* request, + CompleteInstanceResponse* response, + StatusCallback done) override { + IssueRequest(request, response, instancesource_, std::move(done), + call_opts); + } + + void GetStepSequenceAsync(const GetStepSequenceRequest* request, + GetStepSequenceResponse* response, + StatusCallback done) override { + IssueRequest(request, response, getstepsequence_, std::move(done)); + } + void RecvTensorAsync(CallOptions* call_opts, const RecvTensorRequest* request, TensorResponse* response, StatusCallback done) override { VLOG(1) << "RecvTensorAsync req: " << request->DebugString(); @@ -217,6 +241,9 @@ class GrpcRemoteWorker : public WorkerInterface { const ::grpc::string recvtensor_; const ::grpc::string logging_; const ::grpc::string tracing_; + const ::grpc::string completegroup_; + const ::grpc::string instancesource_; + const ::grpc::string getstepsequence_; // Support for logging. WorkerCacheLogger* logger_; diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc index b20e744a97160a..bbf7391377903b 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc @@ -172,6 +172,12 @@ class GrpcWorkerService : public AsyncServiceInterface { ENQUEUE_REQUEST(Logging, false); ENQUEUE_REQUEST(Tracing, false); + for (int i = 0; i < 10; ++i) { + ENQUEUE_REQUEST(CompleteGroup, false); + ENQUEUE_REQUEST(CompleteInstance, false); + ENQUEUE_REQUEST(GetStepSequence, false); + } + void* tag; bool ok; @@ -318,6 +324,47 @@ class GrpcWorkerService : public AsyncServiceInterface { }); ENQUEUE_REQUEST(Tracing, false); } + + void CompleteGroupHandler( + WorkerCall* call) { + Schedule([this, call]() { + CallOptions* call_opts = new CallOptions; + call->SetCancelCallback([call_opts]() { call_opts->StartCancel(); }); + worker_->CompleteGroupAsync(call_opts, &call->request, &call->response, + [call, call_opts](const Status& s) { + call->ClearCancelCallback(); + delete call_opts; + call->SendResponse(ToGrpcStatus(s)); + }); + }); + ENQUEUE_REQUEST(CompleteGroup, false); + } + + void CompleteInstanceHandler( + WorkerCall* call) { + Schedule([this, call]() { + CallOptions* call_opts = new CallOptions; + call->SetCancelCallback([call_opts]() { call_opts->StartCancel(); }); + worker_->CompleteInstanceAsync(call_opts, &call->request, + &call->response, + [call, call_opts](const Status& s) { + call->ClearCancelCallback(); + delete call_opts; + call->SendResponse(ToGrpcStatus(s)); + }); + }); + ENQUEUE_REQUEST(CompleteInstance, false); + } + + void GetStepSequenceHandler( + WorkerCall* call) { + Schedule([this, call]() { + worker_->GetStepSequenceAsync( + &call->request, &call->response, + [call](const Status& s) { call->SendResponse(ToGrpcStatus(s)); }); + }); + ENQUEUE_REQUEST(GetStepSequence, false); + } #undef ENQUEUE_REQUEST void EnqueueRecvTensorRequestRaw() { diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc index 05a9db10d3c379..a91cc0692af71b 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc @@ -50,6 +50,12 @@ const char* GrpcWorkerMethodName(GrpcWorkerMethod id) { return "/tensorflow.WorkerService/Logging"; case GrpcWorkerMethod::kTracing: return "/tensorflow.WorkerService/Tracing"; + case GrpcWorkerMethod::kCompleteGroup: + return "/tensorflow.WorkerService/CompleteGroup"; + case GrpcWorkerMethod::kCompleteInstance: + return "/tensorflow.WorkerService/CompleteInstance"; + case GrpcWorkerMethod::kGetStepSequence: + return "/tensorflow.WorkerService/GetStepSequence"; } // Shouldn't be reached. LOG(FATAL) << "Invalid id: this line shouldn't be reached."; diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h index a54ea9379628ed..c5104c6a50182a 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h +++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h @@ -83,9 +83,12 @@ enum class GrpcWorkerMethod { kRecvTensor, kLogging, kTracing, + kCompleteGroup, + kCompleteInstance, + kGetStepSequence, }; static const int kGrpcNumWorkerMethods = - static_cast(GrpcWorkerMethod::kTracing) + 1; + static_cast(GrpcWorkerMethod::kGetStepSequence) + 1; const char* GrpcWorkerMethodName(GrpcWorkerMethod id); diff --git a/tensorflow/core/distributed_runtime/test_utils.h b/tensorflow/core/distributed_runtime/test_utils.h new file mode 100644 index 00000000000000..0ed078241f3a58 --- /dev/null +++ b/tensorflow/core/distributed_runtime/test_utils.h @@ -0,0 +1,173 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_TEST_UTILS_H_ +#define TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_TEST_UTILS_H_ + +#include +#include "tensorflow/core/distributed_runtime/worker_cache.h" +#include "tensorflow/core/distributed_runtime/worker_interface.h" + +namespace tensorflow { + +// Some utilities for testing distributed-mode components in a single process +// without RPCs. + +// Implements the worker interface with methods that just respond with +// "unimplemented" status. Override just the methods needed for +// testing. +class TestWorkerInterface : public WorkerInterface { + public: + void GetStatusAsync(const GetStatusRequest* request, + GetStatusResponse* response, + StatusCallback done) override { + done(errors::Unimplemented("GetStatusAsync")); + } + + void CreateWorkerSessionAsync(const CreateWorkerSessionRequest* request, + CreateWorkerSessionResponse* response, + StatusCallback done) override { + done(errors::Unimplemented("CreateWorkerSessionAsync")); + } + + void DeleteWorkerSessionAsync(CallOptions* opts, + const DeleteWorkerSessionRequest* request, + DeleteWorkerSessionResponse* response, + StatusCallback done) override { + done(errors::Unimplemented("DeleteWorkerSessionAsync")); + } + + void RegisterGraphAsync(const RegisterGraphRequest* request, + RegisterGraphResponse* response, + StatusCallback done) override { + done(errors::Unimplemented("RegisterGraphAsync")); + } + + void DeregisterGraphAsync(const DeregisterGraphRequest* request, + DeregisterGraphResponse* response, + StatusCallback done) override { + done(errors::Unimplemented("DeregisterGraphAsync")); + } + + void RunGraphAsync(CallOptions* opts, RunGraphRequestWrapper* request, + MutableRunGraphResponseWrapper* repsonse, + StatusCallback done) override { + done(errors::Unimplemented("RunGraphAsync")); + } + + void CleanupGraphAsync(const CleanupGraphRequest* request, + CleanupGraphResponse* response, + StatusCallback done) override { + done(errors::Unimplemented("RunGraphAsync")); + } + + void CleanupAllAsync(const CleanupAllRequest* request, + CleanupAllResponse* response, + StatusCallback done) override { + done(errors::Unimplemented("RunGraphAsync")); + } + + void RecvTensorAsync(CallOptions* opts, const RecvTensorRequest* request, + TensorResponse* response, StatusCallback done) override { + done(errors::Unimplemented("RunGraphAsync")); + } + + void LoggingAsync(const LoggingRequest* request, LoggingResponse* response, + StatusCallback done) override { + done(errors::Unimplemented("RunGraphAsync")); + } + + void TracingAsync(const TracingRequest* request, TracingResponse* response, + StatusCallback done) override { + done(errors::Unimplemented("RunGraphAsync")); + } + + void CompleteGroupAsync(CallOptions* opts, + const CompleteGroupRequest* request, + CompleteGroupResponse* response, + StatusCallback done) override { + done(errors::Unimplemented("RunGraphAsync")); + } + + void CompleteInstanceAsync(CallOptions* ops, + const CompleteInstanceRequest* request, + CompleteInstanceResponse* response, + StatusCallback done) override { + done(errors::Unimplemented("RunGraphAsync")); + } + + void GetStepSequenceAsync(const GetStepSequenceRequest* request, + GetStepSequenceResponse* response, + StatusCallback done) override { + done(errors::Unimplemented("RunGraphAsync")); + } +}; + +class TestWorkerCache : public WorkerCacheInterface { + public: + virtual ~TestWorkerCache() {} + + void AddWorker(const string& target, WorkerInterface* wi) { + workers_[target] = wi; + } + + void AddDevice(const string& device_name, const DeviceLocality& dev_loc) { + localities_[device_name] = dev_loc; + } + + void ListWorkers(std::vector* workers) const override { + workers->clear(); + for (auto it : workers_) { + workers->push_back(it.first); + } + } + + WorkerInterface* CreateWorker(const string& target) override { + auto it = workers_.find(target); + if (it != workers_.end()) { + return it->second; + } + return nullptr; + } + + void ReleaseWorker(const string& target, WorkerInterface* worker) override {} + + bool GetDeviceLocalityNonBlocking(const string& device, + DeviceLocality* locality) override { + auto it = localities_.find(device); + if (it != localities_.end()) { + *locality = it->second; + return true; + } + return false; + } + + void GetDeviceLocalityAsync(const string& device, DeviceLocality* locality, + StatusCallback done) override { + auto it = localities_.find(device); + if (it != localities_.end()) { + *locality = it->second; + done(Status::OK()); + return; + } + done(errors::Internal("Device not found: ", device)); + } + + protected: + std::unordered_map workers_; + std::unordered_map localities_; +}; + +} // namespace tensorflow +#endif // TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_TEST_UTILS_H_ diff --git a/tensorflow/core/distributed_runtime/worker.cc b/tensorflow/core/distributed_runtime/worker.cc index e9073ef9f66d5e..d682ac8f34cd3e 100644 --- a/tensorflow/core/distributed_runtime/worker.cc +++ b/tensorflow/core/distributed_runtime/worker.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/core/distributed_runtime/worker.h" +#include "tensorflow/core/common_runtime/collective_executor_mgr.h" #include "tensorflow/core/common_runtime/device_mgr.h" #include "tensorflow/core/common_runtime/process_util.h" #include "tensorflow/core/common_runtime/step_stats_collector.h" @@ -25,8 +26,7 @@ limitations under the License. namespace tensorflow { -Worker::Worker(WorkerEnv* env) - : env_(env), cancellation_manager_(new CancellationManager) {} +Worker::Worker(WorkerEnv* env) : env_(env) {} void Worker::GetStatusAsync(const GetStatusRequest* request, GetStatusResponse* response, StatusCallback done) { @@ -185,19 +185,16 @@ void Worker::DoRunGraph(CallOptions* opts, RunGraphRequestWrapper* request, AbortStep(step_id); }); CancellationToken token; - { - mutex_lock l(mu_); - token = cancellation_manager_->get_cancellation_token(); - bool already_cancelled = !cancellation_manager_->RegisterCallback( - token, [cm]() { cm->StartCancel(); }); - if (already_cancelled) { - opts->ClearCancelCallback(); - delete cm; - delete collector; - delete out; - done(errors::Aborted("Call was aborted")); - return; - } + token = cancellation_manager_.get_cancellation_token(); + bool already_cancelled = !cancellation_manager_.RegisterCallback( + token, [cm]() { cm->StartCancel(); }); + if (already_cancelled) { + opts->ClearCancelCallback(); + delete cm; + delete collector; + delete out; + done(errors::Aborted("Call was aborted")); + return; } session->graph_mgr->ExecuteAsync( request->graph_handle(), step_id, session.get(), request->exec_opts(), @@ -208,10 +205,7 @@ void Worker::DoRunGraph(CallOptions* opts, RunGraphRequestWrapper* request, s = session->graph_mgr->RecvOutputs(step_id, out); } opts->ClearCancelCallback(); - { - mutex_lock l(mu_); - cancellation_manager_->DeregisterCallback(token); - } + cancellation_manager_.DeregisterCallback(token); delete cm; if (s.ok()) { @@ -276,20 +270,14 @@ void Worker::DoPartialRunGraph(CallOptions* opts, // executors. if (is_new_partial_run) { CancellationToken token; - { - mutex_lock l(mu_); - token = cancellation_manager_->get_cancellation_token(); - cancellation_manager_->RegisterCallback(token, - [cm]() { cm->StartCancel(); }); - } + token = cancellation_manager_.get_cancellation_token(); + cancellation_manager_.RegisterCallback(token, + [cm]() { cm->StartCancel(); }); session->graph_mgr->ExecuteAsync( graph_handle, step_id, session.get(), request->exec_opts(), nullptr /* collector */, nullptr /* response */, cm, in, [this, token, step_id, session](Status s) { - { - mutex_lock l(mu_); - cancellation_manager_->DeregisterCallback(token); - } + cancellation_manager_.DeregisterCallback(token); partial_run_mgr_.ExecutorDone(step_id, s); }); } else { @@ -324,6 +312,9 @@ void Worker::CleanupGraphAsync(const CleanupGraphRequest* request, StatusCallback done) { const int64 step_id = request->step_id(); env_->rendezvous_mgr->Cleanup(step_id); + if (env_->collective_executor_mgr) { + env_->collective_executor_mgr->Cleanup(step_id); + } done(Status::OK()); } @@ -346,6 +337,44 @@ void Worker::TracingAsync(const TracingRequest* request, done(errors::Unimplemented("Tracing")); } +void Worker::CompleteGroupAsync(CallOptions* opts, + const CompleteGroupRequest* request, + CompleteGroupResponse* response, + StatusCallback done) { + if (env_->collective_executor_mgr) { + env_->collective_executor_mgr->GetParamResolver()->CompleteGroupAsync( + request, response, &cancellation_manager_, done); + } else { + done( + errors::Internal("Runtime not initialized with CollectiveExecutorMgr")); + } +} + +void Worker::CompleteInstanceAsync(CallOptions* opts, + const CompleteInstanceRequest* request, + CompleteInstanceResponse* response, + StatusCallback done) { + if (env_->collective_executor_mgr) { + env_->collective_executor_mgr->GetParamResolver()->CompleteInstanceAsync( + request, response, &cancellation_manager_, done); + } else { + done( + errors::Internal("Runtime not initialized with CollectiveExecutorMgr")); + } +} + +void Worker::GetStepSequenceAsync(const GetStepSequenceRequest* request, + GetStepSequenceResponse* response, + StatusCallback done) { + if (env_->collective_executor_mgr) { + env_->collective_executor_mgr->GetStepSequenceAsync(request, response, + done); + } else { + done( + errors::Internal("Runtime not initialized with CollectiveExecutorMgr")); + } +} + // Helper for RecvTensor. Validates "key" and returns the source // device in "*src_dev". Status Worker::PrepareRecvTensor(const Rendezvous::ParsedKey& parsed, diff --git a/tensorflow/core/distributed_runtime/worker.h b/tensorflow/core/distributed_runtime/worker.h index 19aeeb752c43d7..b5a9ada502b201 100644 --- a/tensorflow/core/distributed_runtime/worker.h +++ b/tensorflow/core/distributed_runtime/worker.h @@ -90,6 +90,20 @@ class Worker : public WorkerInterface { void TracingAsync(const TracingRequest* request, TracingResponse* response, StatusCallback done) override; + void CompleteGroupAsync(CallOptions* opts, + const CompleteGroupRequest* request, + CompleteGroupResponse* response, + StatusCallback done) override; + + void CompleteInstanceAsync(CallOptions* opts, + const CompleteInstanceRequest* request, + CompleteInstanceResponse* response, + StatusCallback done) override; + + void GetStepSequenceAsync(const GetStepSequenceRequest* request, + GetStepSequenceResponse* response, + StatusCallback done) override; + protected: WorkerEnv* const env_; // Not owned. @@ -101,8 +115,7 @@ class Worker : public WorkerInterface { private: PartialRunMgr partial_run_mgr_; - mutex mu_; - CancellationManager* cancellation_manager_ GUARDED_BY(mu_); + CancellationManager cancellation_manager_; Status PrepareRunGraph(RunGraphRequestWrapper* req, GraphMgr::NamedTensors* in, diff --git a/tensorflow/core/distributed_runtime/worker_env.h b/tensorflow/core/distributed_runtime/worker_env.h index 793d58c8a1c6c5..93d933bfa60314 100644 --- a/tensorflow/core/distributed_runtime/worker_env.h +++ b/tensorflow/core/distributed_runtime/worker_env.h @@ -25,6 +25,7 @@ namespace thread { class ThreadPool; } // namespace thread +class CollectiveExecutorMgrInterface; class Device; class DeviceMgr; class Env; @@ -57,6 +58,10 @@ struct WorkerEnv { // A set of rendezvous keyed by step ids. RendezvousMgrInterface* rendezvous_mgr = nullptr; + // Generates per-step CollectiveExecutors and has access to utilities + // supporting collective operations. + CollectiveExecutorMgrInterface* collective_executor_mgr = nullptr; + // A pool of threads for scheduling compute work. thread::ThreadPool* compute_pool = nullptr; }; diff --git a/tensorflow/core/distributed_runtime/worker_interface.h b/tensorflow/core/distributed_runtime/worker_interface.h index a1597ee798ff27..bad31d27b231db 100644 --- a/tensorflow/core/distributed_runtime/worker_interface.h +++ b/tensorflow/core/distributed_runtime/worker_interface.h @@ -112,6 +112,20 @@ class WorkerInterface { virtual void TracingAsync(const TracingRequest* request, TracingResponse* response, StatusCallback done) = 0; + virtual void CompleteGroupAsync(CallOptions* opts, + const CompleteGroupRequest* request, + CompleteGroupResponse* response, + StatusCallback done) = 0; + + virtual void CompleteInstanceAsync(CallOptions* ops, + const CompleteInstanceRequest* request, + CompleteInstanceResponse* response, + StatusCallback done) = 0; + + virtual void GetStepSequenceAsync(const GetStepSequenceRequest* request, + GetStepSequenceResponse* response, + StatusCallback done) = 0; + Status GetStatus(const GetStatusRequest* request, GetStatusResponse* response) { return CallAndWait(&ME::GetStatusAsync, request, response); @@ -156,6 +170,11 @@ class WorkerInterface { return CallAndWait(&ME::TracingAsync, request, response); } + Status GetStepSequence(const GetStepSequenceRequest* request, + GetStepSequenceResponse* response) { + return CallAndWait(&ME::GetStepSequenceAsync, request, response); + } + protected: // Instances of WorkerInterface must be deleted by a call to // WorkerCacheInterface::ReleaseWorker(). diff --git a/tensorflow/core/protobuf/worker.proto b/tensorflow/core/protobuf/worker.proto index 1819a352481e3d..602f6a1ef143e2 100644 --- a/tensorflow/core/protobuf/worker.proto +++ b/tensorflow/core/protobuf/worker.proto @@ -27,6 +27,8 @@ import "tensorflow/core/framework/step_stats.proto"; import "tensorflow/core/framework/device_attributes.proto"; import "tensorflow/core/framework/graph.proto"; import "tensorflow/core/framework/tensor.proto"; +import "tensorflow/core/framework/tensor_shape.proto"; +import "tensorflow/core/framework/types.proto"; import "tensorflow/core/lib/core/error_codes.proto"; import "tensorflow/core/protobuf/config.proto"; import "tensorflow/core/protobuf/debug.proto"; @@ -413,3 +415,71 @@ message TracingRequest { message TracingResponse { } + +//////////////////////////////////////////////////////////////////////////////// +// +// Collective Op dynamic group resolution messages. +// +//////////////////////////////////////////////////////////////////////////////// + +// Supplies one or more device names as members of the group identified by +// group_key. Service will respond when all group_size devices become known. +// All devices in group must have same type. +message CompleteGroupRequest { + int32 group_key = 1; + int32 group_size = 2; + string device_type = 3; + repeated string device_name = 4; +} + +// Gives the complete membership of the group identified by group_key. +message CompleteGroupResponse { + int32 group_key = 1; + int32 group_size = 2; + string device_type = 3; + int32 num_tasks = 4; // number of distinct tasks hosting the devices + repeated string device_name = 5; + repeated string task_name = 6; // task name prefixes of device_names +} + +// Supplies data about one collective op belonging to the instance identified +// by instance_key. Service will respond when all group_size ops have +// become known. Most of the data being sent is for correctness checking, +// to ensure that all ops in the instance share common attributes. +message CompleteInstanceRequest { + string name = 1; + int32 type = 2; + DataType data_type = 3; + TensorShapeProto shape = 4; + int32 group_key = 5; + int32 group_size = 6; + int32 instance_key = 7; + string device_type = 8; + repeated int32 subdiv_offset = 9; + string device = 10; + bool is_source = 11; +} + +// Confirms that every op in the instance has consistently declared itself. +// Also gives the source_rank in case of broadcast. +message CompleteInstanceResponse { + int32 instance_key = 1; + int32 source_rank = 2; +} + +// Request for next agreed-upon step_id for the specified graph_keys. +// This is used to enable multiple graphs containing nodes from +// a common collective instance to coordinate using the same step_ids. +message GetStepSequenceRequest { + repeated int64 graph_key = 1; +} + +message StepSequence { + int64 graph_key = 1; + int64 next_step_id = 2; +} + +// Next valid step_ids for one or more graph_keys. +message GetStepSequenceResponse { + repeated StepSequence step_sequence = 1; +} diff --git a/tensorflow/core/protobuf/worker_service.proto b/tensorflow/core/protobuf/worker_service.proto index e1bfb04d7c53a5..01c76c01a9215d 100644 --- a/tensorflow/core/protobuf/worker_service.proto +++ b/tensorflow/core/protobuf/worker_service.proto @@ -72,4 +72,14 @@ service WorkerService { // See worker.proto for details. rpc Tracing(TracingRequest) returns (TracingResponse); + + // See worker.proto for details. + rpc GetStepSequence(GetStepSequenceRequest) returns (GetStepSequenceResponse); + + // See worker.proto for details. + rpc CompleteGroup(CompleteGroupRequest) returns (CompleteGroupResponse); + + // See worker.proto for details. + rpc CompleteInstance(CompleteInstanceRequest) + returns (CompleteInstanceResponse); } From 3b7f22f9180935919bab478adb45037b1f0d38c2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 1 May 2018 13:34:39 -0700 Subject: [PATCH 0236/1691] Relax the stringent memory allocator constraints in AssignOp if a Grappler graph analysis determines it to be safe. This will allow Assign to reuse the input buffer to initialize the variable in many cases. PiperOrigin-RevId: 194988134 --- tensorflow/core/grappler/op_types.cc | 4 + tensorflow/core/grappler/op_types.h | 1 + .../grappler/optimizers/memory_optimizer.cc | 76 ++++++++++ .../optimizers/memory_optimizer_test.cc | 134 ++++++++++++++++++ tensorflow/core/grappler/utils.cc | 6 +- tensorflow/core/grappler/utils.h | 1 + tensorflow/core/kernels/assign_op.h | 73 +++++----- .../core/kernels/resource_variable_ops.cc | 18 ++- 8 files changed, 274 insertions(+), 39 deletions(-) diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc index 839b0bbfc984d6..bf6d4c09212f30 100644 --- a/tensorflow/core/grappler/op_types.cc +++ b/tensorflow/core/grappler/op_types.cc @@ -54,6 +54,10 @@ bool IsApproximateEqual(const NodeDef& node) { bool IsAvgPoolGrad(const NodeDef& node) { return node.op() == "AvgPoolGrad"; } +bool IsAssign(const NodeDef& node) { + return node.op() == "Assign" || node.op() == "AssignVariableOp"; +} + bool IsAssert(const NodeDef& node) { return node.op() == "Assert"; } bool IsAtan2(const NodeDef& node) { return node.op() == "Atan2"; } diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h index bd8d3a44e4901c..3dddf3f1ea8bba 100644 --- a/tensorflow/core/grappler/op_types.h +++ b/tensorflow/core/grappler/op_types.h @@ -30,6 +30,7 @@ bool IsAnyDiv(const NodeDef& node); bool IsApproximateEqual(const NodeDef& node); bool IsAvgPoolGrad(const NodeDef& node); bool IsAssert(const NodeDef& node); +bool IsAssign(const NodeDef& node); bool IsAtan2(const NodeDef& node); bool IsBetainc(const NodeDef& node); bool IsBiasAdd(const NodeDef& node); diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc index c1fee0e993dd18..7c6468bfcbca51 100644 --- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc @@ -1219,6 +1219,80 @@ bool SwappingPass(RewriterConfig::MemOptType optimization_level, return updated_graph; } +// TODO(rmlarsen): Add distributed TF test. +Status RelaxAllocatorConstraints(GraphDef* optimized_graph) { + std::unordered_set devices; + std::vector assign_nodes; + bool found_send = false; + for (int i = 0; i < optimized_graph->node_size(); ++i) { + const NodeDef& node = optimized_graph->node(i); + devices.insert(node.device()); + if (IsAssign(node)) { + assign_nodes.push_back(i); + } + if (IsSend(node)) { + found_send = true; + break; + } + } + if (!found_send && devices.size() == 1) { + for (int assign_idx : assign_nodes) { + // Set an attribute telling AssignOp to ignore allocator constraints. + NodeDef* assign_node = optimized_graph->mutable_node(assign_idx); + (*assign_node->mutable_attr())["_grappler_relax_allocator_constraints"] + .set_b(true); + } + return Status::OK(); + } + + std::unordered_set optimized_nodes; + SimpleGraphView graph_view; + TF_RETURN_IF_ERROR(graph_view.Initialize(*optimized_graph)); + for (int i : assign_nodes) { + if (optimized_nodes.find(i) == optimized_nodes.end()) { + const NodeDef& node = optimized_graph->node(i); + optimized_nodes.insert(i); + std::vector assign_nodes_in_fanout; + assign_nodes_in_fanout.push_back(i); + std::set transitive_fanout; + graph_view.DepthFirstSearch(std::unordered_set{}, i, + &transitive_fanout); + const string& assign_device = node.device(); + bool relax_constraint = true; + // If all nodes in the transitive fanout are on the same device as the + // assign node, there is no need to allocate the output in pinned memory. + for (int fanout : transitive_fanout) { + const NodeDef& fanout_node = optimized_graph->node(fanout); + if (relax_constraint && + (fanout_node.device() != assign_device || IsSend(fanout_node))) { + relax_constraint = false; + } + if (optimized_nodes.find(fanout) == optimized_nodes.end() && + IsAssign(fanout_node)) { + assign_nodes_in_fanout.push_back(fanout); + } + } + + for (int assign_idx : assign_nodes_in_fanout) { + if (relax_constraint) { + // If all devices match in fanout of node(i) then, by transitivity, + // they must also match in the fanout of other assign nodes + // node(assign_idx) in the fanout, so we can process them here, + // and save computing their transitive fanout later. + optimized_nodes.insert(assign_idx); + + // Set an attribute telling AssignOp to ignore allocator constraints. + NodeDef* assign_node = optimized_graph->mutable_node(assign_idx); + (*assign_node + ->mutable_attr())["_grappler_relax_allocator_constraints"] + .set_b(true); + } + } + } + } + return Status::OK(); +} + Status MemoryOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item, GraphDef* optimized_graph) { *optimized_graph = item.graph; @@ -1251,6 +1325,8 @@ Status MemoryOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item, } } + TF_RETURN_IF_ERROR(RelaxAllocatorConstraints(&optimized_item.graph)); + optimized_graph->Swap(&optimized_item.graph); return Status::OK(); } diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc index a1f80802ddc2b3..a3f0e078616efe 100644 --- a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc @@ -440,6 +440,140 @@ TEST_F(MemoryOptimizerTest, AccumulationRewrites) { } } +class RelaxAllocatorConstraintsTest : public GrapplerTest {}; + +TEST_F(RelaxAllocatorConstraintsTest, SameDevice) { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output constant = ops::Const(s.WithOpName("constant").WithDevice("/cpu:0"), + -3.14f, {128, 128}); + Output variable = ops::Variable(s.WithOpName("variable").WithDevice("/cpu:0"), + {128, 128}, DT_FLOAT); + Output assign = ops::Assign(s.WithOpName("assign").WithDevice("/cpu:0"), + variable, constant); + Output exp = ops::Exp(s.WithOpName("exp").WithDevice("/cpu:0"), assign); + + GrapplerItem item; + TF_CHECK_OK(s.ToGraphDef(&item.graph)); + + MemoryOptimizer optimizer(RewriterConfig::MANUAL); + GraphDef output; + TF_EXPECT_OK(optimizer.Optimize(nullptr, item, &output)); + + auto node = output.node(2); + EXPECT_EQ("assign", node.name()); + EXPECT_EQ(1, node.attr().count("_grappler_relax_allocator_constraints")); + EXPECT_EQ(true, node.attr().at("_grappler_relax_allocator_constraints").b()); + + item.fetch = {"exp"}; + item.init_ops = {"variable"}; + auto tensors_expected = EvaluateFetchNodes(item); + GrapplerItem optimized(item, std::move(output)); + auto tensors = EvaluateFetchNodes(optimized); + test::ExpectTensorEqual(tensors_expected[0], tensors[0]); +} + +TEST_F(RelaxAllocatorConstraintsTest, DifferentDevice) { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output constant = ops::Const(s.WithOpName("constant").WithDevice("/cpu:0"), + -3.14f, {128, 128}); + Output variable = ops::Variable(s.WithOpName("variable").WithDevice("/cpu:0"), + {128, 128}, DT_FLOAT); + Output assign = ops::Assign(s.WithOpName("assign").WithDevice("/cpu:0"), + variable, constant); + // exp runs on a different device, so we cannot relax the allocation + // constraints on assign. + Output exp = ops::Exp(s.WithOpName("exp").WithDevice("/gpu:0"), assign); + + GrapplerItem item; + TF_CHECK_OK(s.ToGraphDef(&item.graph)); + + MemoryOptimizer optimizer(RewriterConfig::MANUAL); + GraphDef output; + TF_EXPECT_OK(optimizer.Optimize(nullptr, item, &output)); + + auto node = output.node(2); + EXPECT_EQ("assign", node.name()); + EXPECT_EQ(0, node.attr().count("_grappler_relax_allocator_constraints")); +#if GOOGLE_CUDA + item.fetch = {"exp"}; + item.init_ops = {"variable"}; + auto tensors_expected = EvaluateFetchNodes(item); + GrapplerItem optimized(item, std::move(output)); + auto tensors = EvaluateFetchNodes(optimized); + test::ExpectTensorEqual(tensors_expected[0], tensors[0]); +#endif +} + +TEST_F(RelaxAllocatorConstraintsTest, SendNode) { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output constant = ops::Const(s.WithOpName("constant").WithDevice("/cpu:0"), + -3.14f, {128, 128}); + Output variable = ops::Variable(s.WithOpName("variable").WithDevice("/cpu:0"), + {128, 128}, DT_FLOAT); + Output assign = ops::Assign(s.WithOpName("assign").WithDevice("/cpu:0"), + variable, constant); + + GrapplerItem item; + TF_CHECK_OK(s.ToGraphDef(&item.graph)); + NodeDef* send = item.graph.add_node(); + // Add a send node to the graph in the fanout of "assign". + send->set_name("send"); + send->set_op("_Send"); + send->add_input("assign"); + + MemoryOptimizer optimizer(RewriterConfig::MANUAL); + GraphDef output; + TF_EXPECT_OK(optimizer.Optimize(nullptr, item, &output)); + + auto node = output.node(2); + EXPECT_EQ("assign", node.name()); + EXPECT_EQ(0, node.attr().count("_grappler_relax_allocator_constraints")); +} + +TEST_F(RelaxAllocatorConstraintsTest, AssignNodeInFanout) { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output constant0 = ops::Const(s.WithOpName("constant0").WithDevice("/cpu:0"), + -42.0f, {128, 128}); + Output variable0 = ops::Variable( + s.WithOpName("variable0").WithDevice("/cpu:0"), {128, 128}, DT_FLOAT); + Output assign0 = ops::Assign(s.WithOpName("assign0").WithDevice("/cpu:0"), + variable0, constant0); + // The rest of the graph is on a second device, so we can relax the + // constraint for assign1, but not for assign0. + Output exp1 = ops::Exp(s.WithOpName("exp1").WithDevice("/gpu:0"), assign0); + Output variable1 = ops::Variable( + s.WithOpName("variable1").WithDevice("/gpu:0"), {128, 128}, DT_FLOAT); + Output assign1 = ops::Assign(s.WithOpName("assign1").WithDevice("/gpu:0"), + variable1, exp1); + + GrapplerItem item; + TF_CHECK_OK(s.ToGraphDef(&item.graph)); + + MemoryOptimizer optimizer(RewriterConfig::MANUAL); + GraphDef output; + TF_EXPECT_OK(optimizer.Optimize(nullptr, item, &output)); + + auto node = output.node(3); + EXPECT_EQ("assign0", node.name()); + EXPECT_EQ(0, node.attr().count("_grappler_relax_allocator_constraints")); + + node = output.node(5); + EXPECT_EQ("assign1", node.name()); + EXPECT_EQ(1, node.attr().count("_grappler_relax_allocator_constraints")); + EXPECT_EQ(true, node.attr().at("_grappler_relax_allocator_constraints").b()); + +#if GOOGLE_CUDA + item.fetch = {"assign0", "assign1"}; + item.init_ops = {"exp1", "variable1"}; + auto tensors_expected = EvaluateFetchNodes(item); + GrapplerItem optimized(item, std::move(output)); + auto tensors = EvaluateFetchNodes(optimized); + for (int i = 0; i < tensors_expected.size(); ++i) { + test::ExpectTensorEqual(tensors_expected[i], tensors[i]); + } +#endif +} + } // namespace } // namespace grappler } // namespace tensorflow diff --git a/tensorflow/core/grappler/utils.cc b/tensorflow/core/grappler/utils.cc index 6db6d71447a646..c8e63f95e1855f 100644 --- a/tensorflow/core/grappler/utils.cc +++ b/tensorflow/core/grappler/utils.cc @@ -435,7 +435,8 @@ void SimpleGraphView::DepthFirstSearch( std::set* nodes_found) const { nodes_found->clear(); const string& op_type = graph_->node(root_node).op(); - if (op_types_to_traverse.find(op_type) == op_types_to_traverse.end()) { + if (!op_types_to_traverse.empty() && + op_types_to_traverse.find(op_type) == op_types_to_traverse.end()) { return; } std::vector stack; @@ -446,7 +447,8 @@ void SimpleGraphView::DepthFirstSearch( stack.pop_back(); nodes_found->insert(node_idx); const string& op_type = graph_->node(node_idx).op(); - if (op_types_to_traverse.find(op_type) != op_types_to_traverse.end()) { + if (op_types_to_traverse.empty() || + op_types_to_traverse.find(op_type) != op_types_to_traverse.end()) { for (auto output_idx : this->outputs(node_idx)) { if (nodes_found->find(output_idx) == nodes_found->end()) { stack.push_back(output_idx); diff --git a/tensorflow/core/grappler/utils.h b/tensorflow/core/grappler/utils.h index 15f6b367b0178a..9776e99f207ebf 100644 --- a/tensorflow/core/grappler/utils.h +++ b/tensorflow/core/grappler/utils.h @@ -251,6 +251,7 @@ class SimpleGraphView { // visited in nodes_found. If a node has an op in `op_types_to_traverse`, the // walk continues to its children. It is assumed that *graph_ was not modified // after the call to Initialize(). + // If `op_types_to_traverse` is empty the DFS will traverse any node type. void DepthFirstSearch(const std::unordered_set& op_types_to_traverse, int node_idx, std::set* nodes_found) const; diff --git a/tensorflow/core/kernels/assign_op.h b/tensorflow/core/kernels/assign_op.h index 19b38f9e68d3d7..a450b1d1eeffd8 100644 --- a/tensorflow/core/kernels/assign_op.h +++ b/tensorflow/core/kernels/assign_op.h @@ -36,6 +36,12 @@ class AssignOp : public OpKernel { context->GetAttr("validate_shape", &validate_shape_)); OP_REQUIRES(context, IsRefType(context->input_type(0)), errors::InvalidArgument("lhs input needs to be a ref type")); + if (!context + ->GetAttr("_grappler_relax_allocator_constraints", + &relax_constraints_) + .ok()) { + relax_constraints_ = false; + } } void Compute(OpKernelContext* context) override { @@ -44,48 +50,37 @@ class AssignOp : public OpKernel { // We always return the input ref. context->forward_ref_input_to_ref_output(0, 0); - // We can't always know how this value will be used downstream, - // so make conservative assumptions in specifying constraints on - // the memory allocation attributes. - // TODO(rmlarsen): These conservative constraints make buffer - // forwarding unlikely to happen very often. Try to use graph analysis - // (possibly the InferAllocAttr pass in the executer) to improve the - // situation. + // We can't always know how this value will be used downstream, so make + // conservative assumptions in specifying constraints on the memory + // allocation attributes, unless the Grappler graph analysis determined that + // it was safe not to. AllocatorAttributes attr; - attr.set_gpu_compatible(true); - attr.set_nic_compatible(true); + if (!relax_constraints_) { + attr.set_gpu_compatible(true); + attr.set_nic_compatible(true); + } { mutex_lock l(*context->input_ref_mutex(0)); const Tensor& old_lhs = context->mutable_input(0, /* lock_held */ true); const bool same_shape = old_lhs.shape().IsSameSize(rhs.shape()); if (validate_shape_) { - OP_REQUIRES( - context, same_shape, - errors::InvalidArgument( - "Assign requires shapes of both tensors to match. lhs shape= ", - old_lhs.shape().DebugString(), - " rhs shape= ", rhs.shape().DebugString())); + OP_REQUIRES(context, same_shape, + errors::InvalidArgument( + "Assign requires shapes of both tensors to match. " + "lhs shape= ", + old_lhs.shape().DebugString(), + " rhs shape= ", rhs.shape().DebugString())); } // In the code below we try to minimize the amount of memory allocation // and copying by trying the following two shortcuts: - // 1. If we can reuse the rhs buffer we avoid both a memory allocation - // and copying. - // 2. If the lhs is initialized and has the same number of elements as the - // rhs we can avoid a memory allocation. - - // 1. Try to reuse the rhs. - std::unique_ptr input_alias = context->forward_input( - 1, OpKernelContext::Params::kNoReservation /*output_index*/, - rhs.dtype(), rhs.shape(), DEVICE_MEMORY, attr); - if (input_alias != nullptr) { - // Transfer ownership to the ref. - context->replace_ref_input(0, *input_alias, /* lock_held */ true); - return; - } + // 1. If the lhs is initialized and has the same number of elements as + // the rhs we can avoid a memory allocation. + // 2. If we can reuse the rhs buffer we avoid both a memory allocation + // and copying. - // 2. Try to copy into an existing buffer. + // 1. Try to copy into an existing buffer. if (old_lhs.IsInitialized() && old_lhs.shape().num_elements() == rhs.shape().num_elements()) { // The existing lhs tensor has already been initialized and the right @@ -95,15 +90,26 @@ class AssignOp : public OpKernel { reshaped_old_lhs = old_lhs; } else { CHECK(reshaped_old_lhs.CopyFrom(old_lhs, rhs.shape())); - context->replace_ref_input(0, reshaped_old_lhs, /* lock_held */ true); + context->replace_ref_input(0, reshaped_old_lhs, + /* lock_held */ true); } if (use_exclusive_lock_) { Copy(context, &reshaped_old_lhs, rhs); return; } } else { - // Create a new persistent tensor whose shape matches the right hand - // side, hand off to lhs and copy the rhs into it. + // 2. Try to reuse the rhs. + std::unique_ptr input_alias = context->forward_input( + 1, OpKernelContext::Params::kNoReservation /*output_index*/, + rhs.dtype(), rhs.shape(), DEVICE_MEMORY, attr); + if (input_alias != nullptr) { + // Update the ref to point to the new buffer. + context->replace_ref_input(0, *input_alias, /* lock_held */ true); + return; + } + + // Otherwise, create a new persistent tensor whose shape matches the + // right hand side, hand off to lhs and copy the rhs into it. PersistentTensor copy; Tensor* copyTensor = nullptr; OP_REQUIRES_OK( @@ -132,6 +138,7 @@ class AssignOp : public OpKernel { bool use_exclusive_lock_; bool validate_shape_; + bool relax_constraints_; }; } // end namespace tensorflow diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc index 916869fb566f11..a8bcc7f7dc2677 100644 --- a/tensorflow/core/kernels/resource_variable_ops.cc +++ b/tensorflow/core/kernels/resource_variable_ops.cc @@ -211,6 +211,11 @@ class AssignVariableOp : public OpKernel { public: explicit AssignVariableOp(OpKernelConstruction* c) : OpKernel(c) { OP_REQUIRES_OK(c, c->GetAttr("dtype", &dtype_)); + if (!c->GetAttr("_grappler_relax_allocator_constraints", + &relax_constraints_) + .ok()) { + relax_constraints_ = false; + } } void Compute(OpKernelContext* context) override { @@ -228,8 +233,10 @@ class AssignVariableOp : public OpKernel { PersistentTensor unused; Tensor* tmp; AllocatorAttributes attr; - attr.set_gpu_compatible(true); - attr.set_nic_compatible(true); + if (!relax_constraints_) { + attr.set_gpu_compatible(true); + attr.set_nic_compatible(true); + } TF_RETURN_IF_ERROR(context->allocate_persistent( dtype_, context->input(1).shape(), &unused, &tmp, attr)); *(*ptr)->tensor() = *tmp; @@ -245,8 +252,10 @@ class AssignVariableOp : public OpKernel { const Tensor& value = context->input(1); AllocatorAttributes attr; - attr.set_gpu_compatible(true); - attr.set_nic_compatible(true); + if (!relax_constraints_) { + attr.set_gpu_compatible(true); + attr.set_nic_compatible(true); + } // Copying is unnecessary if we are the last user of the value // tensor, we can just adopt the input tensor's buffer instead. @@ -277,6 +286,7 @@ class AssignVariableOp : public OpKernel { private: DataType dtype_; + bool relax_constraints_; }; template From 594f970f81089c91f713bbdda48d44ef99f80c9e Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Tue, 1 May 2018 13:44:58 -0700 Subject: [PATCH 0237/1691] Update schema. PiperOrigin-RevId: 194989704 --- .../contrib/lite/schema/schema_generated.h | 27 +++++++++++++++---- 1 file changed, 22 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index 25ed9abd9f8ded..57af973460561b 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -4711,6 +4711,7 @@ struct ModelT : public flatbuffers::NativeTable { std::vector> subgraphs; std::string description; std::vector> buffers; + std::vector metadata_buffer; ModelT() : version(0) { } @@ -4723,7 +4724,8 @@ struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VT_OPERATOR_CODES = 6, VT_SUBGRAPHS = 8, VT_DESCRIPTION = 10, - VT_BUFFERS = 12 + VT_BUFFERS = 12, + VT_METADATA_BUFFER = 14 }; uint32_t version() const { return GetField(VT_VERSION, 0); @@ -4740,6 +4742,9 @@ struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const flatbuffers::Vector> *buffers() const { return GetPointer> *>(VT_BUFFERS); } + const flatbuffers::Vector *metadata_buffer() const { + return GetPointer *>(VT_METADATA_BUFFER); + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_VERSION) && @@ -4754,6 +4759,8 @@ struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VerifyOffset(verifier, VT_BUFFERS) && verifier.Verify(buffers()) && verifier.VerifyVectorOfTables(buffers()) && + VerifyOffset(verifier, VT_METADATA_BUFFER) && + verifier.Verify(metadata_buffer()) && verifier.EndTable(); } ModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; @@ -4779,6 +4786,9 @@ struct ModelBuilder { void add_buffers(flatbuffers::Offset>> buffers) { fbb_.AddOffset(Model::VT_BUFFERS, buffers); } + void add_metadata_buffer(flatbuffers::Offset> metadata_buffer) { + fbb_.AddOffset(Model::VT_METADATA_BUFFER, metadata_buffer); + } explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -4797,8 +4807,10 @@ inline flatbuffers::Offset CreateModel( flatbuffers::Offset>> operator_codes = 0, flatbuffers::Offset>> subgraphs = 0, flatbuffers::Offset description = 0, - flatbuffers::Offset>> buffers = 0) { + flatbuffers::Offset>> buffers = 0, + flatbuffers::Offset> metadata_buffer = 0) { ModelBuilder builder_(_fbb); + builder_.add_metadata_buffer(metadata_buffer); builder_.add_buffers(buffers); builder_.add_description(description); builder_.add_subgraphs(subgraphs); @@ -4813,14 +4825,16 @@ inline flatbuffers::Offset CreateModelDirect( const std::vector> *operator_codes = nullptr, const std::vector> *subgraphs = nullptr, const char *description = nullptr, - const std::vector> *buffers = nullptr) { + const std::vector> *buffers = nullptr, + const std::vector *metadata_buffer = nullptr) { return tflite::CreateModel( _fbb, version, operator_codes ? _fbb.CreateVector>(*operator_codes) : 0, subgraphs ? _fbb.CreateVector>(*subgraphs) : 0, description ? _fbb.CreateString(description) : 0, - buffers ? _fbb.CreateVector>(*buffers) : 0); + buffers ? _fbb.CreateVector>(*buffers) : 0, + metadata_buffer ? _fbb.CreateVector(*metadata_buffer) : 0); } flatbuffers::Offset CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); @@ -6207,6 +6221,7 @@ inline void Model::UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t * { auto _e = subgraphs(); if (_e) { _o->subgraphs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->subgraphs[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } }; { auto _e = description(); if (_e) _o->description = _e->str(); }; { auto _e = buffers(); if (_e) { _o->buffers.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->buffers[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } }; + { auto _e = metadata_buffer(); if (_e) { _o->metadata_buffer.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->metadata_buffer[_i] = _e->Get(_i); } } }; } inline flatbuffers::Offset Model::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) { @@ -6222,13 +6237,15 @@ inline flatbuffers::Offset CreateModel(flatbuffers::FlatBufferBuilder &_f auto _subgraphs = _o->subgraphs.size() ? _fbb.CreateVector> (_o->subgraphs.size(), [](size_t i, _VectorArgs *__va) { return CreateSubGraph(*__va->__fbb, __va->__o->subgraphs[i].get(), __va->__rehasher); }, &_va ) : 0; auto _description = _o->description.empty() ? 0 : _fbb.CreateString(_o->description); auto _buffers = _o->buffers.size() ? _fbb.CreateVector> (_o->buffers.size(), [](size_t i, _VectorArgs *__va) { return CreateBuffer(*__va->__fbb, __va->__o->buffers[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _metadata_buffer = _o->metadata_buffer.size() ? _fbb.CreateVector(_o->metadata_buffer) : 0; return tflite::CreateModel( _fbb, _version, _operator_codes, _subgraphs, _description, - _buffers); + _buffers, + _metadata_buffer); } inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type) { From 75c1896fb26a91ae8d895e24bfc128084cba4e9e Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Tue, 1 May 2018 13:54:34 -0700 Subject: [PATCH 0238/1691] Update community/swift PiperOrigin-RevId: 194991305 --- tensorflow/docs_src/community/swift.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/docs_src/community/swift.md b/tensorflow/docs_src/community/swift.md index a7da189a5c2f97..e5a0f02a8c3633 100644 --- a/tensorflow/docs_src/community/swift.md +++ b/tensorflow/docs_src/community/swift.md @@ -8,7 +8,7 @@ Welcome to the Swift for TensorFlow development community! Swift for TensorFlow is a new way to develop machine learning models. It gives you the power of -[TensorFlow](https://www.tensorflow.org/programmers_guide/eager) directly +[TensorFlow](programmers_guide/eager) directly integrated into the [Swift programming language](https://swift.org/about). With Swift, you can write the following imperative code, and Swift automatically turns it into **a single TensorFlow Graph** and runs it @@ -28,15 +28,15 @@ print(x) ``` Swift combines the flexibility of -[Eager Execution](https://www.tensorflow.org/programmers_guide/eager) with the -high performance of [Graphs and Sessions](https://www.tensorflow.org/programmers_guide/graphs). +[Eager Execution](programmers_guide/eager) with the +high performance of [Graphs and Sessions](programmers_guide/graphs). Behind the scenes, Swift analyzes your Tensor code and automatically builds graphs for you. Swift also catches type errors and shape mismatches before running your code, and has [Automatic Differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation) built right in. We believe that machine learning tools are so important that they deserve **a first-class language and a compiler**. -**Note:** Swift for TensorFlow is an early stage research project. It has been +Note: Swift for TensorFlow is an early stage research project. It has been released to enable open source development and is not yet ready for general use by machine learning developers. From 7cbbd3525b4232f2dc8cd117852c26ec472aa9b2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 1 May 2018 14:04:59 -0700 Subject: [PATCH 0239/1691] Enable checkpointless eval and predict for tf.estimator. PiperOrigin-RevId: 194993191 --- tensorflow/python/estimator/estimator.py | 17 ++++++---- tensorflow/python/estimator/estimator_test.py | 32 +++++++++++++------ 2 files changed, 32 insertions(+), 17 deletions(-) diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 23638451103e05..63099b44bbf7ba 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -400,7 +400,9 @@ def evaluate(self, input_fn, steps=None, hooks=None, checkpoint_path=None, hooks: List of `SessionRunHook` subclass instances. Used for callbacks inside the evaluation call. checkpoint_path: Path of a specific checkpoint to evaluate. If `None`, the - latest checkpoint in `model_dir` is used. + latest checkpoint in `model_dir` is used. If there are no checkpoints + in `model_dir`, evaluation is run with newly initialized `Variables` + instead of restored from checkpoint. name: Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear @@ -464,7 +466,9 @@ def predict(self, hooks: List of `SessionRunHook` subclass instances. Used for callbacks inside the prediction call. checkpoint_path: Path of a specific checkpoint to predict. If `None`, the - latest checkpoint in `model_dir` is used. + latest checkpoint in `model_dir` is used. If there are no checkpoints + in `model_dir`, prediction is run with newly initialized `Variables` + instead of restored from checkpoint. yield_single_examples: If False, yield the whole batch as returned by the `model_fn` instead of decomposing the batch into individual elements. This is useful if `model_fn` returns some tensors whose first dimension @@ -487,9 +491,8 @@ def predict(self, if not checkpoint_path: checkpoint_path = saver.latest_checkpoint(self._model_dir) if not checkpoint_path: - raise ValueError( - 'Could not find trained model in model_dir: {}.'.format( - self._model_dir)) + logging.info('Could not find trained model in model_dir: {}, running ' + 'initialization to predict.'.format(self._model_dir)) with ops.Graph().as_default() as g: random_seed.set_random_seed(self._config.tf_random_seed) @@ -1068,8 +1071,8 @@ def _evaluate_model(self, if not checkpoint_path: latest_path = saver.latest_checkpoint(self._model_dir) if not latest_path: - raise ValueError('Could not find trained model in model_dir: {}.'. - format(self._model_dir)) + logging.info('Could not find trained model in model_dir: {}, running ' + 'initialization to evaluate.'.format(self._model_dir)) checkpoint_path = latest_path # Setup output directory. diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py index 0fea86124cc58a..74114fab3b7ae2 100644 --- a/tensorflow/python/estimator/estimator_test.py +++ b/tensorflow/python/estimator/estimator_test.py @@ -1067,11 +1067,19 @@ def _model_fn(features, labels, mode): ValueError, 'model_fn should return an EstimatorSpec'): est.evaluate(dummy_input_fn, steps=1) - def test_no_trained_model(self): - est = estimator.Estimator(model_fn=_model_fn_with_eval_metric_ops) - with self.assertRaisesRegexp( - ValueError, 'Could not find trained model in model_dir'): - est.evaluate(dummy_input_fn, steps=1) + def test_no_checkpoint_uses_init(self): + def _model_fn(features, labels, mode, params): + del features, labels, params + return model_fn_lib.EstimatorSpec( + mode, + loss=constant_op.constant(1.), + eval_metric_ops={'metric': metrics_lib.mean( + variables.Variable(2.) + 1)}) + est = estimator.Estimator(model_fn=_model_fn) + metrics = est.evaluate(dummy_input_fn, steps=1) + # Metric value here is set to 1 + the value of the Variable that is newly + # initialized (since there is no checkpoint). + self.assertEqual(3., metrics['metric']) def test_scores(self): est = estimator.Estimator( @@ -1331,11 +1339,15 @@ def _input_fn(mode, params, config): next(est.predict(_input_fn)) self.assertEqual(1, input_fn_call_count[0]) - def test_no_trained_model_in_model_dir(self): - est = estimator.Estimator(model_fn=model_fn_global_step_incrementer) - with self.assertRaisesRegexp(ValueError, - 'Could not find trained model in model_dir'): - next(est.predict(dummy_input_fn)) + def test_no_checkpoint_uses_init(self): + def _model_fn(features, labels, mode, params, config): + del features, labels, params, config + x = variables.Variable([[3.]], name='x') + return model_fn_lib.EstimatorSpec(mode, predictions=math_ops.add(x, 1.)) + est = estimator.Estimator(model_fn=_model_fn) + # Expected prediction value is 1 + the value of the Variable that is newly + # initialized (since there is no checkpoint). + self.assertEqual(4., next(est.predict(dummy_input_fn))) def test_no_trained_model_invalid_checkpoint_path(self): est = estimator.Estimator(model_fn=model_fn_global_step_incrementer) From 46bf1e8934b3bc8edeff3f218a50b0ee5806e96b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 1 May 2018 14:27:33 -0700 Subject: [PATCH 0240/1691] Make tower-local variables non-trainable even with the default DistributionStrategy. PiperOrigin-RevId: 194996819 --- tensorflow/python/training/distribute.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/training/distribute.py b/tensorflow/python/training/distribute.py index 6aeecb31dd9b16..c16b05102edd27 100644 --- a/tensorflow/python/training/distribute.py +++ b/tensorflow/python/training/distribute.py @@ -1127,8 +1127,7 @@ def scope(self): def creator(next_creator, *args, **kwargs): _require_distribution_strategy_scope(self) - if kwargs.pop("tower_local_reduce_method", None) is not None: - kwargs["trainable"] = False + kwargs.pop("tower_local_reduce_method", None) return next_creator(*args, **kwargs) return _CurrentDistributionContext( @@ -1138,7 +1137,7 @@ def tower_local_var_scope(self, reduce_method): """Does not set to resource variables.""" def create_tower_local_variable(next_creator, *args, **kwargs): _require_distribution_strategy_scope(self) - kwargs["tower_local_reduce_method"] = reduce_method + kwargs["trainable"] = False return next_creator(*args, **kwargs) _require_distribution_strategy_scope(self) From 325d0ef21a48bea1cc618a2bd24a9776de417ce5 Mon Sep 17 00:00:00 2001 From: Patrick Nguyen Date: Tue, 1 May 2018 14:28:36 -0700 Subject: [PATCH 0241/1691] Merge changes from github. PiperOrigin-RevId: 194997009 --- .gitignore | 1 + tensorflow/c/c_api_test.cc | 2 +- tensorflow/cc/gradients/array_grad.cc | 36 ++ tensorflow/cc/gradients/array_grad_test.cc | 24 + tensorflow/contrib/autograph/README.md | 9 +- tensorflow/contrib/cmake/CMakeLists.txt | 6 +- .../contrib/cmake/tf_core_kernels.cmake | 10 + .../contrib/cmake/tf_stream_executor.cmake | 2 + .../crf/python/kernel_tests/crf_test.py | 24 +- tensorflow/contrib/crf/python/ops/crf.py | 23 +- .../kernel_tests/bijectors/ordered_test.py | 109 ++++ .../python/ops/bijectors/__init__.py | 2 + .../ops/bijectors/cholesky_outer_product.py | 2 +- .../python/ops/bijectors/invert.py | 4 +- .../ops/bijectors/masked_autoregressive.py | 4 +- .../python/ops/bijectors/ordered.py | 125 ++++ .../python/ops/bijectors/permute.py | 4 +- .../python/ops/bijectors/real_nvp.py | 4 +- .../python/ops/bijectors/reshape.py | 4 +- .../python/ops/bijectors/weibull.py | 2 +- .../contrib/distributions/python/ops/shape.py | 2 +- .../factorization/python/ops/gmm_ops.py | 2 +- .../contrib/layers/python/layers/layers.py | 142 ++++- .../layers/python/layers/layers_test.py | 15 +- .../layers/python/layers/target_column.py | 4 +- .../learn/python/learn/estimators/head.py | 4 +- .../learn/python/learn/ops/losses_ops.py | 2 +- .../lite/examples/label_image/label_image.cc | 45 +- .../lite/examples/label_image/label_image.h | 1 + .../res/layout/fragment_camera2_basic.xml | 28 + tensorflow/contrib/lite/kernels/topk_v2.cc | 4 +- .../contrib/lite/kernels/topk_v2_test.cc | 2 +- .../contrib/lite/nnapi/NeuralNetworksShim.h | 2 +- .../contrib/lite/profiling/profile_buffer.h | 8 +- .../propagate_array_data_types.cc | 9 + .../propagate_fixed_sizes.cc | 4 +- .../contrib/lite/toco/import_tensorflow.cc | 2 +- tensorflow/contrib/lite/toco/tooling_util.cc | 9 +- tensorflow/contrib/mpi/mpi_utils.h | 1 + .../python/training/lazy_adam_optimizer.py | 6 +- tensorflow/contrib/optimizer_v2/adam.py | 20 +- .../rnn/python/kernel_tests/core_rnn_test.py | 15 + .../tensor_forest/client/eval_metrics.py | 4 +- .../hybrid/python/layers/fully_connected.py | 2 +- .../tensor_forest/python/tensor_forest.py | 2 +- .../contrib/tensorrt/convert/convert_graph.cc | 13 +- .../timeseries/state_management_test.py | 2 +- .../state_space_models/kalman_filter.py | 6 +- tensorflow/core/BUILD | 4 +- .../api_def/base_api/api_def_ApplyAdam.pbtxt | 8 +- .../core/api_def/base_api/api_def_Pad.pbtxt | 1 + .../api_def/base_api/api_def_QuantizeV2.pbtxt | 6 + .../base_api/api_def_ResourceApplyAdam.pbtxt | 8 +- .../api_def/base_api/api_def_ScatterNd.pbtxt | 10 +- .../common_runtime/graph_execution_state.cc | 2 +- tensorflow/core/graph/mkl_layout_pass.cc | 8 +- tensorflow/core/grappler/clusters/BUILD | 2 + tensorflow/core/grappler/clusters/cluster.h | 6 + .../core/grappler/clusters/virtual_cluster.cc | 8 + .../core/grappler/clusters/virtual_cluster.h | 4 + .../core/grappler/costs/virtual_scheduler.h | 4 +- .../custom_graph_optimizer_registry.h | 2 +- .../grappler/optimizers/meta_optimizer.cc | 17 +- tensorflow/core/kernels/batch_util.cc | 2 + tensorflow/core/kernels/cwise_op_floor_div.cc | 4 +- tensorflow/core/kernels/mkl_conv_ops.cc | 414 +++++++++---- tensorflow/core/kernels/scatter_nd_op.cc | 1 + .../core/kernels/scatter_nd_op_cpu_impl.h | 1 + .../core/kernels/segment_reduction_ops.h | 29 - .../core/platform/default/gpu/cupti_wrapper.h | 2 +- tensorflow/core/public/version.h | 2 +- tensorflow/core/util/mkl_util.h | 87 ++- tensorflow/docs_src/community/roadmap.md | 74 ++- .../docs_src/get_started/checkpoints.md | 6 +- .../docs_src/get_started/feature_columns.md | 2 +- tensorflow/docs_src/get_started/index.md | 2 +- tensorflow/docs_src/install/install_c.md | 2 +- tensorflow/docs_src/install/install_go.md | 2 +- tensorflow/docs_src/install/install_java.md | 22 +- tensorflow/docs_src/install/install_linux.md | 571 +++++++++--------- tensorflow/docs_src/install/install_mac.md | 10 +- .../docs_src/install/install_sources.md | 4 +- .../docs_src/performance/xla/tfcompile.md | 6 +- .../examples/tutorials/estimators/__init__.py | 0 .../examples/tutorials/input_fn/__init__.py | 0 .../examples/tutorials/layers/__init__.py | 0 .../examples/tutorials/monitors/__init__.py | 0 .../tutorials/monitors/iris_monitors.py | 6 +- tensorflow/go/README.md | 2 +- tensorflow/go/op/wrappers.go | 2 +- tensorflow/python/estimator/estimator.py | 4 +- .../python/keras/_impl/keras/estimator.py | 22 +- .../keras/_impl/keras/estimator_test.py | 24 +- .../python/kernel_tests/division_past_test.py | 3 +- .../kernel_tests/reduce_join_op_test.py | 2 +- .../python/kernel_tests/reduction_ops_test.py | 35 +- .../kernel_tests/scatter_nd_ops_test.py | 40 ++ tensorflow/python/ops/array_grad.py | 2 +- tensorflow/python/ops/array_ops.py | 10 +- tensorflow/python/ops/image_ops_impl.py | 10 +- tensorflow/python/ops/math_ops.py | 15 +- tensorflow/python/ops/nn_impl.py | 4 +- tensorflow/python/ops/rnn_cell_impl.py | 6 +- tensorflow/python/training/adam.py | 20 +- tensorflow/python/training/input_test.py | 22 + .../python/training/monitored_session.py | 14 +- .../golden/tensorflow.train.-scaffold.pbtxt | 4 + .../tools/ci_build/ci_parameterized_build.sh | 2 +- .../install/install_python3.5_pip_packages.sh | 3 + .../install/install_python3.6_pip_packages.sh | 4 + .../windows/cpu/bazel/run_cc_test_windows.sh | 2 +- .../windows/gpu/bazel/run_cc_test_windows.sh | 2 +- tensorflow/tools/docker/Dockerfile | 1 + tensorflow/tools/docker/Dockerfile.devel | 4 + tensorflow/tools/docker/Dockerfile.devel-gpu | 4 + tensorflow/tools/docker/Dockerfile.gpu | 1 + tensorflow/tools/docker/README.md | 12 +- .../graph_transforms/fold_old_batch_norms.cc | 3 + tensorflow/tools/pip_package/setup.py | 4 +- tensorflow/workspace.bzl | 32 +- .../BackwardSpatialConvolutions.h | 4 +- 121 files changed, 1748 insertions(+), 663 deletions(-) create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py create mode 100644 tensorflow/contrib/distributions/python/ops/bijectors/ordered.py create mode 100644 tensorflow/examples/tutorials/estimators/__init__.py create mode 100644 tensorflow/examples/tutorials/input_fn/__init__.py create mode 100644 tensorflow/examples/tutorials/layers/__init__.py create mode 100644 tensorflow/examples/tutorials/monitors/__init__.py diff --git a/.gitignore b/.gitignore index be75938ec401b1..828bbe9bd33638 100644 --- a/.gitignore +++ b/.gitignore @@ -27,6 +27,7 @@ Podfile.lock /tensorflow/contrib/lite/examples/ios/simple/data/*.txt /tensorflow/contrib/lite/examples/ios/simple/data/*.tflite xcuserdata/** +/api_init_files_list.txt # Android .gradle diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc index ca80db23ed3ccb..9b86425aa5fbc2 100644 --- a/tensorflow/c/c_api_test.cc +++ b/tensorflow/c/c_api_test.cc @@ -1700,7 +1700,7 @@ TEST_F(CApiGradientsTest, OpWithNoGradientRegistered_NoGradInputs) { TestGradientsError(false); } -// REGISTER_OP for CApiTestAttributesTest test cases. +// REGISTER_OP for CApiAttributesTest test cases. // Registers two ops, each with a single attribute called 'v'. // The attribute in one op will have a type 'type', the other // will have list(type). diff --git a/tensorflow/cc/gradients/array_grad.cc b/tensorflow/cc/gradients/array_grad.cc index 6545e4ee3eb406..ff348fadb24e29 100644 --- a/tensorflow/cc/gradients/array_grad.cc +++ b/tensorflow/cc/gradients/array_grad.cc @@ -385,6 +385,42 @@ Status MirrorPadGradGrad(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("MirrorPadGrad", MirrorPadGradGrad); +Status StridedSliceGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + Input x = Shape(scope, op.input(0)); + Input begin = op.input(1); + Input end = op.input(2); + Input strides = op.input(3); + int64 begin_mask; + int64 end_mask; + int64 ellipsis_mask; + int64 new_axis_mask; + int64 shrink_axis_mask; + TF_RETURN_IF_ERROR( + GetNodeAttr(op.node()->attrs(), "begin_mask", &begin_mask)); + TF_RETURN_IF_ERROR(GetNodeAttr(op.node()->attrs(), "end_mask", &end_mask)); + TF_RETURN_IF_ERROR( + GetNodeAttr(op.node()->attrs(), "ellipsis_mask", &ellipsis_mask)); + TF_RETURN_IF_ERROR( + GetNodeAttr(op.node()->attrs(), "new_axis_mask", &new_axis_mask)); + TF_RETURN_IF_ERROR( + GetNodeAttr(op.node()->attrs(), "shrink_axis_mask", &shrink_axis_mask)); + grad_outputs->push_back( + StridedSliceGrad(scope, x, begin, end, strides, grad_inputs[0], + StridedSliceGrad::BeginMask(begin_mask) + .EndMask(end_mask) + .EllipsisMask(ellipsis_mask) + .NewAxisMask(new_axis_mask) + .ShrinkAxisMask(shrink_axis_mask))); + // No gradients returned for begin, end and strides + grad_outputs->push_back(NoGradient()); + grad_outputs->push_back(NoGradient()); + grad_outputs->push_back(NoGradient()); + return scope.status(); +} +REGISTER_GRADIENT_OP("StridedSlice", StridedSliceGradHelper); + } // anonymous namespace } // namespace ops } // namespace tensorflow diff --git a/tensorflow/cc/gradients/array_grad_test.cc b/tensorflow/cc/gradients/array_grad_test.cc index 4a215fcc9299cf..de3bd0fc9e2493 100644 --- a/tensorflow/cc/gradients/array_grad_test.cc +++ b/tensorflow/cc/gradients/array_grad_test.cc @@ -354,5 +354,29 @@ TEST_F(ArrayGradTest, MirrorPadGradGrad_Symmetric) { RunTest(x, x_shape, y, y_shape); } +TEST_F(ArrayGradTest, StridedSliceGrad) { + TensorShape x_shape({6, 4, 4}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + + // y = x[2:6:2, 1:3, 1:3] + auto y = StridedSlice(scope_, x, {2, 1, 1}, {6, 3, 3}, {2, 1, 1}); + // y.shape = [2, 2, 2]; + RunTest(x, x_shape, y, {2, 2, 2}); + + // y = x[2:6:2, 1:3, 1:3] + // begin_mask = 1<<1 (ignore begin_index = 1) + // end_mask = 1<<2 (ignore end_index = 2) + y = StridedSlice(scope_, x, {2, 1, 1}, {6, 3, 3}, {2, 1, 1}, + StridedSlice::BeginMask(1 << 1).EndMask(1 << 2)); + // y.shape = [2, 3, 3]; + RunTest(x, x_shape, y, {2, 3, 3}); + + // y = [tf.newaxis, 2:6:2, 1:3, 1:3] + y = StridedSlice(scope_, x, {0, 2, 1, 1}, {0, 6, 3, 3}, {1, 2, 1, 1}, + StridedSlice::NewAxisMask(1 << 0)); + // y.shape = [1, 2, 2, 2]; + RunTest(x, x_shape, y, {1, 2, 2, 2}); +} + } // namespace } // namespace tensorflow diff --git a/tensorflow/contrib/autograph/README.md b/tensorflow/contrib/autograph/README.md index 0fcbf5dd59cece..0ba99c396fc1c8 100644 --- a/tensorflow/contrib/autograph/README.md +++ b/tensorflow/contrib/autograph/README.md @@ -56,8 +56,6 @@ Use AutoGraph in one of the following ways, described below: 1. Annotations (simpler) 2. Functional API (more flexible) -NOTE: You can find more examples in this [interactive notebook](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb). - To get started, install the latest nightly TensorFlow build: ```shell @@ -70,6 +68,13 @@ Then import the `autograph` module from `tf.contrib`: from tensorflow.contrib import autograph as ag ``` +### Interactive demo notebooks + +For more extensive examples, check out these interactive notebooks: + + * [RNN trained using Keras and Estimators](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/rnn_keras_estimator.ipynb) + * [Demo from the TF Dev Summit 2018](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb) + ## Using with annotations Annotating a function or class with `@convert` converts it in place: diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt index 5f38a8e5c755cd..44e39f7f7b5da8 100644 --- a/tensorflow/contrib/cmake/CMakeLists.txt +++ b/tensorflow/contrib/cmake/CMakeLists.txt @@ -84,7 +84,7 @@ if (NOT WIN32) option(systemlib_ALL "Turn on every possible systemlib_* options" OFF) if (systemlib_ALL) - set (systmelib_ZLIB ON) + set (systemlib_ZLIB ON) endif (systemlib_ALL) endif() @@ -471,6 +471,10 @@ if (tensorflow_ENABLE_GPU) include_directories(${tensorflow_source_dir}/third_party/gpus) # add cuda libraries to tensorflow_EXTERNAL_LIBRARIES list(APPEND tensorflow_EXTERNAL_LIBRARIES ${CUDA_LIBRARIES}) + if(NOT WIN32) + # add gomp to tensorflow_EXTERNAL_LIBRARIES, needed by libcusolver.so + list(APPEND tensorflow_EXTERNAL_LIBRARIES gomp) + endif() # NOTE(mrry): Update these flags when the version of CUDA or cuDNN used # in the default build is upgraded. diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake index 376496b33f404e..f38c9e05135f9f 100644 --- a/tensorflow/contrib/cmake/tf_core_kernels.cmake +++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake @@ -177,6 +177,16 @@ if(WIN32) "${tensorflow_source_dir}/tensorflow/contrib/nccl/ops/nccl_ops.cc" ) list(REMOVE_ITEM tf_core_kernels_srcs ${tf_core_kernels_windows_exclude_srcs}) +else(WIN32) + if(tensorflow_ENABLE_GPU) + file(GLOB_RECURSE tf_core_kernels_gpu_exclude_srcs + # temporarily disable nccl as it needs to be ported with gpu + "${tensorflow_source_dir}/tensorflow/contrib/nccl/kernels/nccl_manager.cc" + "${tensorflow_source_dir}/tensorflow/contrib/nccl/kernels/nccl_ops.cc" + "${tensorflow_source_dir}/tensorflow/contrib/nccl/ops/nccl_ops.cc" + ) + list(REMOVE_ITEM tf_core_kernels_srcs ${tf_core_kernels_gpu_exclude_srcs}) + endif(tensorflow_ENABLE_GPU) endif(WIN32) file(GLOB_RECURSE tf_core_gpu_kernels_srcs diff --git a/tensorflow/contrib/cmake/tf_stream_executor.cmake b/tensorflow/contrib/cmake/tf_stream_executor.cmake index af48ef1fd40456..9a37b681194d4e 100644 --- a/tensorflow/contrib/cmake/tf_stream_executor.cmake +++ b/tensorflow/contrib/cmake/tf_stream_executor.cmake @@ -64,6 +64,8 @@ file(GLOB tf_stream_executor_srcs if (tensorflow_ENABLE_GPU) file(GLOB tf_stream_executor_gpu_srcs "${tensorflow_source_dir}/tensorflow/stream_executor/cuda/*.cc" + "${tensorflow_source_dir}/tensorflow/compiler/xla/statusor.h" + "${tensorflow_source_dir}/tensorflow/compiler/xla/statusor.cc" ) if (NOT tensorflow_BUILD_CC_TESTS) file(GLOB tf_stream_executor_gpu_tests diff --git a/tensorflow/contrib/crf/python/kernel_tests/crf_test.py b/tensorflow/contrib/crf/python/kernel_tests/crf_test.py index a5e065b93a23c3..74f2ec22ffaab1 100644 --- a/tensorflow/contrib/crf/python/kernel_tests/crf_test.py +++ b/tensorflow/contrib/crf/python/kernel_tests/crf_test.py @@ -152,6 +152,22 @@ def testCrfLogNorm(self): self.assertAllClose(tf_log_norm, tf_brute_force_log_norm) + def testCrfLogNormZeroSeqLength(self): + """ + Test `crf_log_norm` when `sequence_lengths` contains one or more zeros. + """ + with self.test_session() as sess: + inputs = constant_op.constant(np.ones([2, 10, 5], + dtype=np.float32)) + transition_params = constant_op.constant(np.ones([5, 5], + dtype=np.float32)) + sequence_lengths = constant_op.constant(np.zeros([2], + dtype=np.int32)) + expected_log_norm = np.zeros([2], dtype=np.float32) + log_norm = crf.crf_log_norm(inputs, sequence_lengths, transition_params) + tf_log_norm = sess.run(log_norm) + self.assertAllClose(tf_log_norm, expected_log_norm) + def testCrfLogLikelihood(self): inputs = np.array( [[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], dtype=np.float32) @@ -292,10 +308,10 @@ def testCrfDecodeZeroSeqLength(self): dtype=np.float32)) sequence_lengths = constant_op.constant(np.zeros([2], dtype=np.int32)) - values = crf.crf_decode(inputs, transition_params, sequence_lengths) - tags, scores = sess.run(values) - self.assertEqual(len(tags.shape), 2) - self.assertEqual(len(scores.shape), 1) + tags, scores = crf.crf_decode(inputs, transition_params, sequence_lengths) + tf_tags, tf_scores = sess.run([tags, scores]) + self.assertEqual(len(tf_tags.shape), 2) + self.assertEqual(len(tf_scores.shape), 1) if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/crf/python/ops/crf.py b/tensorflow/contrib/crf/python/ops/crf.py index e37c029cebf30e..d2beff849eb8d1 100644 --- a/tensorflow/contrib/crf/python/ops/crf.py +++ b/tensorflow/contrib/crf/python/ops/crf.py @@ -90,9 +90,13 @@ def _single_seq_fn(): batch_size = array_ops.shape(inputs, out_type=tag_indices.dtype)[0] example_inds = array_ops.reshape( math_ops.range(batch_size, dtype=tag_indices.dtype), [-1, 1]) - return array_ops.gather_nd( + sequence_scores = array_ops.gather_nd( array_ops.squeeze(inputs, [1]), array_ops.concat([example_inds, tag_indices], axis=1)) + sequence_scores = array_ops.where(math_ops.less_equal(sequence_lengths, 0), + array_ops.zeros_like(sequence_scores), + sequence_scores) + return sequence_scores def _multi_seq_fn(): # Compute the scores of the given tag sequence. @@ -128,7 +132,12 @@ def crf_log_norm(inputs, sequence_lengths, transition_params): # If max_seq_len is 1, we skip the algorithm and simply reduce_logsumexp over # the "initial state" (the unary potentials). def _single_seq_fn(): - return math_ops.reduce_logsumexp(first_input, [1]) + log_norm = math_ops.reduce_logsumexp(first_input, [1]) + # Mask `log_norm` of the sequences with length <= zero. + log_norm = array_ops.where(math_ops.less_equal(sequence_lengths, 0), + array_ops.zeros_like(log_norm), + log_norm) + return log_norm def _multi_seq_fn(): """Forward computation of alpha values.""" @@ -137,13 +146,19 @@ def _multi_seq_fn(): # Compute the alpha values in the forward algorithm in order to get the # partition function. forward_cell = CrfForwardRnnCell(transition_params) + # Sequence length is not allowed to be less than zero. + sequence_lengths_less_one = math_ops.maximum(0, sequence_lengths - 1) _, alphas = rnn.dynamic_rnn( cell=forward_cell, inputs=rest_of_input, - sequence_length=sequence_lengths - 1, + sequence_length=sequence_lengths_less_one, initial_state=first_input, dtype=dtypes.float32) log_norm = math_ops.reduce_logsumexp(alphas, [1]) + # Mask `log_norm` of the sequences with length <= zero. + log_norm = array_ops.where(math_ops.less_equal(sequence_lengths, 0), + array_ops.zeros_like(log_norm), + log_norm) return log_norm max_seq_len = array_ops.shape(inputs)[1] @@ -479,7 +494,7 @@ def _multi_seq_fn(): initial_state = array_ops.slice(potentials, [0, 0, 0], [-1, 1, -1]) initial_state = array_ops.squeeze(initial_state, axis=[1]) # [B, O] inputs = array_ops.slice(potentials, [0, 1, 0], [-1, -1, -1]) # [B, T-1, O] - # sequence length is not allowed to be less than zero + # Sequence length is not allowed to be less than zero. sequence_length_less_one = math_ops.maximum(0, sequence_length - 1) backpointers, last_score = rnn.dynamic_rnn( # [B, T - 1, O], [B, O] crf_fwd_cell, diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py new file mode 100644 index 00000000000000..a5f5219588fb3b --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py @@ -0,0 +1,109 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for Bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.distributions.python.ops.bijectors.ordered import Ordered +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import test_util +from tensorflow.python.ops import array_ops +from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite +from tensorflow.python.platform import test + + + +class OrderedBijectorTest(test.TestCase): + """Tests correctness of the ordered transformation.""" + + def setUp(self): + self._rng = np.random.RandomState(42) + + @test_util.run_in_graph_and_eager_modes() + def testBijectorVector(self): + with self.test_session(): + ordered = Ordered() + self.assertEqual("ordered", ordered.name) + x = np.asarray([[2., 3, 4], [4., 8, 13]]) + y = [[2., 0, 0], [4., np.log(4.), np.log(5.)]] + self.assertAllClose(y, self.evaluate(ordered.forward(x))) + self.assertAllClose(x, self.evaluate(ordered.inverse(y))) + self.assertAllClose( + np.sum(np.asarray(y)[..., 1:], axis=-1), + self.evaluate(ordered.inverse_log_det_jacobian(y, event_ndims=1)), + atol=0., + rtol=1e-7) + self.assertAllClose( + self.evaluate(-ordered.inverse_log_det_jacobian(y, event_ndims=1)), + self.evaluate(ordered.forward_log_det_jacobian(x, event_ndims=1)), + atol=0., + rtol=1e-7) + + def testBijectorUnknownShape(self): + with self.test_session(): + ordered = Ordered() + self.assertEqual("ordered", ordered.name) + x = array_ops.placeholder(shape=[2, None], dtype=dtypes.float32) + real_x = np.asarray([[2., 3, 4], [4., 8, 13]]) + y = array_ops.placeholder(shape=[2, None], dtype=dtypes.float32) + real_y = [[2., 0, 0], [4., np.log(4.), np.log(5.)]] + self.assertAllClose(real_y, ordered.forward(x).eval( + feed_dict={x: real_x})) + self.assertAllClose(real_x, ordered.inverse(y).eval( + feed_dict={y: real_y})) + self.assertAllClose( + np.sum(np.asarray(real_y)[..., 1:], axis=-1), + ordered.inverse_log_det_jacobian(y, event_ndims=1).eval( + feed_dict={y: real_y}), + atol=0., + rtol=1e-7) + self.assertAllClose( + -ordered.inverse_log_det_jacobian(y, event_ndims=1).eval( + feed_dict={y: real_y}), + ordered.forward_log_det_jacobian(x, event_ndims=1).eval( + feed_dict={x: real_x}), + atol=0., + rtol=1e-7) + + @test_util.run_in_graph_and_eager_modes() + def testShapeGetters(self): + with self.test_session(): + x = tensor_shape.TensorShape([4]) + y = tensor_shape.TensorShape([4]) + bijector = Ordered(validate_args=True) + self.assertAllEqual(y, bijector.forward_event_shape(x)) + self.assertAllEqual(y.as_list(), + self.evaluate(bijector.forward_event_shape_tensor( + x.as_list()))) + self.assertAllEqual(x, bijector.inverse_event_shape(y)) + self.assertAllEqual(x.as_list(), + self.evaluate(bijector.inverse_event_shape_tensor( + y.as_list()))) + + def testBijectiveAndFinite(self): + with self.test_session(): + ordered = Ordered() + x = np.sort(self._rng.randn(3, 10), axis=-1).astype(np.float32) + y = (self._rng.randn(3, 10)).astype(np.float32) + assert_bijective_and_finite(ordered, x, y, event_ndims=1) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py index babce80396cfc4..51478dbeffaabc 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py @@ -30,6 +30,7 @@ @@Invert @@Kumaraswamy @@MaskedAutoregressiveFlow +@@Ordered @@Permute @@PowerTransform @@RealNVP @@ -67,6 +68,7 @@ from tensorflow.contrib.distributions.python.ops.bijectors.invert import * from tensorflow.contrib.distributions.python.ops.bijectors.kumaraswamy import * from tensorflow.contrib.distributions.python.ops.bijectors.masked_autoregressive import * +from tensorflow.contrib.distributions.python.ops.bijectors.ordered import * from tensorflow.contrib.distributions.python.ops.bijectors.permute import * from tensorflow.contrib.distributions.python.ops.bijectors.power_transform import * from tensorflow.contrib.distributions.python.ops.bijectors.real_nvp import * diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py b/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py index caae2adcfac764..ecdb8967f43e59 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py @@ -170,7 +170,7 @@ def _forward_log_det_jacobian(self, x): sum_weighted_log_diag = array_ops.squeeze( math_ops.matmul(math_ops.log(diag), exponents[..., array_ops.newaxis]), - squeeze_dims=-1) + axis=-1) fldj = p_float * np.log(2.) + sum_weighted_log_diag return fldj diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/invert.py b/tensorflow/contrib/distributions/python/ops/bijectors/invert.py index 1904239a0e7009..84a3289ba2160e 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/invert.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/invert.py @@ -18,14 +18,14 @@ from __future__ import division from __future__ import print_function -from tensorflow.python.ops.distributions import bijector as bijector_lib +from tensorflow.python.ops.distributions import bijector __all__ = [ "Invert", ] -class Invert(bijector_lib.Bijector): +class Invert(bijector.Bijector): """Bijector which inverts another Bijector. Example Use: [ExpGammaDistribution (see Background & Context)]( diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py b/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py index ef56cf6ddda4dc..83667b0e80cfcc 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py @@ -32,7 +32,7 @@ from tensorflow.python.ops import nn_ops from tensorflow.python.ops import template as template_ops from tensorflow.python.ops import variable_scope as variable_scope_lib -from tensorflow.python.ops.distributions import bijector as bijector_lib +from tensorflow.python.ops.distributions import bijector __all__ = [ @@ -42,7 +42,7 @@ ] -class MaskedAutoregressiveFlow(bijector_lib.Bijector): +class MaskedAutoregressiveFlow(bijector.Bijector): """Affine MaskedAutoregressiveFlow bijector for vector-valued events. The affine autoregressive flow [(Papamakarios et al., 2016)][3] provides a diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/ordered.py b/tensorflow/contrib/distributions/python/ops/bijectors/ordered.py new file mode 100644 index 00000000000000..3f03592f314cc1 --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/bijectors/ordered.py @@ -0,0 +1,125 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Ordered bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops.distributions import bijector + + +__all__ = [ + "Ordered", +] + + +class Ordered(bijector.Bijector): + """Bijector which maps a tensor x_k that has increasing elements in the last + dimension to an unconstrained tensor y_k. + + Both the domain and the codomain of the mapping is [-inf, inf], however, + the input of the forward mapping must be strictly increasing. + The inverse of the bijector applied to a normal random vector `y ~ N(0, 1)` + gives back a sorted random vector with the same distribution `x ~ N(0, 1)` + where `x = sort(y)` + + On the last dimension of the tensor, Ordered bijector performs: + `y[0] = x[0]` + `y[1:] = math_ops.log(x[1:] - x[:-1])` + + #### Example Use: + + ```python + bijector.Ordered().forward([2, 3, 4]) + # Result: [2., 0., 0.] + + bijector.Ordered().inverse([0.06428002, -1.07774478, -0.71530371]) + # Result: [0.06428002, 0.40464228, 0.8936858] + ``` + """ + + def __init__(self, validate_args=False, name="ordered"): + super(Ordered, self).__init__( + forward_min_event_ndims=1, + validate_args=validate_args, + name=name) + + def _forward_event_shape(self, input_shape): + if input_shape.ndims is None or input_shape[-1] is None: + return input_shape + return tensor_shape.TensorShape([input_shape[-1]]) + + def _forward_event_shape_tensor(self, input_shape): + return (input_shape[-1])[..., array_ops.newaxis] + + def _inverse_event_shape(self, output_shape): + if output_shape.ndims is None or output_shape[-1] is None: + return output_shape + if output_shape[-1] <= 1: + raise ValueError("output_shape[-1] = %d <= 1" % output_shape[-1]) + return tensor_shape.TensorShape([output_shape[-1]]) + + def _inverse_event_shape_tensor(self, output_shape): + if self.validate_args: + is_greater_one = check_ops.assert_greater( + output_shape[-1], 1, message="Need last dimension greater than 1.") + output_shape = control_flow_ops.with_dependencies( + [is_greater_one], output_shape) + return (output_shape[-1])[..., array_ops.newaxis] + + def _forward(self, x): + x = self._maybe_assert_valid_x(x) + y0 = x[..., 0, array_ops.newaxis] + yk = math_ops.log(x[..., 1:] - x[..., :-1]) + y = array_ops.concat([y0, yk], axis=-1) + return y + + def _inverse(self, y): + x0 = y[..., 0, array_ops.newaxis] + xk = math_ops.exp(y[..., 1:]) + x = array_ops.concat([x0, xk], axis=-1) + return math_ops.cumsum(x, axis=-1) + + def _inverse_log_det_jacobian(self, y): + # The Jacobian of the inverse mapping is lower + # triangular, with the diagonal elements being: + # J[i,i] = 1 if i=1, and + # exp(y_i) if 1 #include #include +#include #include #include #include @@ -70,6 +71,23 @@ TfLiteStatus ReadLabelsFile(const string& file_name, return kTfLiteOk; } +void PrintProfilingInfo(const profiling::ProfileEvent* e, uint32_t op_index, + TfLiteRegistration registration) { + // output something like + // time (ms) , Node xxx, OpCode xxx, symblic name + // 5.352, Node 5, OpCode 4, DEPTHWISE_CONV_2D + + + LOG(INFO) << std::fixed << std::setw(10) << std::setprecision(3) + << (e->end_timestamp_us - e->begin_timestamp_us) / 1000.0 + << ", Node " << std::setw(3) << std::setprecision(3) << op_index + << ", OpCode " << std::setw(3) << std::setprecision(3) + << registration.builtin_code << ", " + << EnumNameBuiltinOperator( + (BuiltinOperator)registration.builtin_code) + << "\n"; +} + void RunInference(Settings* s) { if (!s->model_name.c_str()) { LOG(ERROR) << "no model file name\n"; @@ -166,6 +184,11 @@ void RunInference(Settings* s) { exit(-1); } + profiling::Profiler* profiler = new profiling::Profiler(); + interpreter->SetProfiler(profiler); + + if (s->profiling) profiler->StartProfiling(); + struct timeval start_time, stop_time; gettimeofday(&start_time, NULL); for (int i = 0; i < s->loop_count; i++) { @@ -179,6 +202,18 @@ void RunInference(Settings* s) { << (get_us(stop_time) - get_us(start_time)) / (s->loop_count * 1000) << " ms \n"; + if (s->profiling) { + profiler->StopProfiling(); + auto profile_events = profiler->GetProfileEvents(); + for (int i = 0; i < profile_events.size(); i++) { + auto op_index = profile_events[i]->event_metadata; + const auto node_and_registration = + interpreter->node_and_registration(op_index); + const TfLiteRegistration registration = node_and_registration->second; + PrintProfilingInfo(profile_events[i], op_index, registration); + } + } + const int output_size = 1000; const size_t num_results = 5; const float threshold = 0.001f; @@ -217,13 +252,14 @@ void RunInference(Settings* s) { void display_usage() { LOG(INFO) << "label_image\n" - << "--accelerated, -a: [0|1], use Android NNAPI or note\n" + << "--accelerated, -a: [0|1], use Android NNAPI or not\n" << "--count, -c: loop interpreter->Invoke() for certain times\n" << "--input_mean, -b: input mean\n" << "--input_std, -s: input standard deviation\n" << "--image, -i: image_name.bmp\n" << "--labels, -l: labels for the model\n" << "--tflite_model, -m: model_name.tflite\n" + << "--profiling, -p: [0|1], profiling or not\n" << "--threads, -t: number of threads\n" << "--verbose, -v: [0|1] print more information\n" << "\n"; @@ -241,6 +277,7 @@ int Main(int argc, char** argv) { {"image", required_argument, 0, 'i'}, {"labels", required_argument, 0, 'l'}, {"tflite_model", required_argument, 0, 'm'}, + {"profiling", required_argument, 0, 'p'}, {"threads", required_argument, 0, 't'}, {"input_mean", required_argument, 0, 'b'}, {"input_std", required_argument, 0, 's'}, @@ -249,7 +286,7 @@ int Main(int argc, char** argv) { /* getopt_long stores the option index here. */ int option_index = 0; - c = getopt_long(argc, argv, "a:b:c:f:i:l:m:s:t:v:", long_options, + c = getopt_long(argc, argv, "a:b:c:f:i:l:m:p:s:t:v:", long_options, &option_index); /* Detect the end of the options. */ @@ -276,6 +313,10 @@ int Main(int argc, char** argv) { case 'm': s.model_name = optarg; break; + case 'p': + s.profiling = strtol( // NOLINT(runtime/deprecated_fn) + optarg, (char**)NULL, 10); + break; case 's': s.input_std = strtod(optarg, NULL); break; diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.h b/tensorflow/contrib/lite/examples/label_image/label_image.h index 4de32e33fb4ef2..4b48014e1c77ec 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.h +++ b/tensorflow/contrib/lite/examples/label_image/label_image.h @@ -25,6 +25,7 @@ struct Settings { bool verbose = false; bool accel = false; bool input_floating = false; + bool profiling = false; int loop_count = 1; float input_mean = 127.5f; float input_std = 127.5f; diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml b/tensorflow/contrib/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml index 2c4ce844733f12..d12435d5abda45 100644 --- a/tensorflow/contrib/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml +++ b/tensorflow/contrib/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml @@ -84,4 +84,32 @@ android:visibility="visible" /> + + + + + + + diff --git a/tensorflow/contrib/lite/kernels/topk_v2.cc b/tensorflow/contrib/lite/kernels/topk_v2.cc index 807e84609f8b23..ad9b744f1af271 100644 --- a/tensorflow/contrib/lite/kernels/topk_v2.cc +++ b/tensorflow/contrib/lite/kernels/topk_v2.cc @@ -25,8 +25,8 @@ namespace builtin { namespace topk_v2 { constexpr int kInputTensor = 0; constexpr int kInputTopK = 1; -constexpr int kOutputIndexes = 0; -constexpr int kOutputValues = 1; +constexpr int kOutputValues = 0; +constexpr int kOutputIndexes = 1; namespace { TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node) { diff --git a/tensorflow/contrib/lite/kernels/topk_v2_test.cc b/tensorflow/contrib/lite/kernels/topk_v2_test.cc index 29f2a057cd45e1..212f8acc76d4af 100644 --- a/tensorflow/contrib/lite/kernels/topk_v2_test.cc +++ b/tensorflow/contrib/lite/kernels/topk_v2_test.cc @@ -31,8 +31,8 @@ class TopKV2OpModel : public SingleOpModel { int top_k) { input_ = AddInput(input_type); top_k_ = AddInput(TensorType_INT32); - output_indexes_ = AddOutput(TensorType_INT32); output_values_ = AddOutput(input_type); + output_indexes_ = AddOutput(TensorType_INT32); SetBuiltinOp(BuiltinOperator_TOPK_V2, BuiltinOptions_TopKV2Options, 0); BuildInterpreter({input_shape, {1}}); PopulateTensor(top_k_, {top_k}); diff --git a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h index ace4827d8ce215..4a648e42837fbf 100644 --- a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h +++ b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h @@ -609,7 +609,7 @@ enum { * Long short-term memory unit (LSTM) recurrent network layer. * * The default non-peephole implementation is based on: - * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf + * http://www.bioinf.jku.at/publications/older/2604.pdf * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural * Computation, 9(8):1735-1780, 1997. * diff --git a/tensorflow/contrib/lite/profiling/profile_buffer.h b/tensorflow/contrib/lite/profiling/profile_buffer.h index b2f565376c3d1c..299b2a9cad161c 100644 --- a/tensorflow/contrib/lite/profiling/profile_buffer.h +++ b/tensorflow/contrib/lite/profiling/profile_buffer.h @@ -37,9 +37,9 @@ struct ProfileEvent { // Label of the event. This usually describes the event. const char* tag; // Timestamp in microseconds when the event began. - int64_t begin_timestamp_us; + uint64_t begin_timestamp_us; // Timestamp in microseconds when the event ended. - int64_t end_timestamp_us; + uint64_t end_timestamp_us; // The field containing the type of event. This must be one of the event types // in EventType. EventType event_type; @@ -74,7 +74,7 @@ class ProfileBuffer { if (!enabled_) { return kInvalidEventHandle; } - int64_t timestamp = NowMicros(); + uint64_t timestamp = NowMicros(); int index = current_index_ % event_buffer_.size(); event_buffer_[index].tag = tag; event_buffer_[index].event_type = event_type; @@ -134,7 +134,7 @@ class ProfileBuffer { } private: - static int64_t NowMicros() { + static uint64_t NowMicros() { // TODO(shashishekhar): Refactor this to a separate file. struct timeval tv; gettimeofday(&tv, nullptr); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc index 89ad58f887f364..c1cf79f62614c4 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc @@ -124,6 +124,15 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { SetDataTypeForAllOutputs(model, op, rand_op->dtype); break; } + case OperatorType::kTopK_V2: { + // topk(values: T, k: int32) -> values: T, indices: int32 + CHECK_EQ(op->inputs.size(), 2); + CHECK_EQ(op->outputs.size(), 2); + CHECK(model->GetArray(op->inputs[1]).data_type == ArrayDataType::kInt32); + model->GetArray(op->outputs[0]).data_type = model->GetArray(op->inputs[0]).data_type; + model->GetArray(op->outputs[1]).data_type = ArrayDataType ::kInt32; + break; + } case OperatorType::kTensorFlowUnsupported: { auto* unsupported_op = static_cast(op); // Some output tensors from the op could be eliminated by optimization. diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc index 19037bc50385b0..4923f83d91defb 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -1087,8 +1087,8 @@ void ProcessGatherOperator(Model* model, GatherOperator* op) { void ProcessTopkV2Operator(Model* model, TopKV2Operator* op) { const auto& input_values = model->GetArray(op->inputs[0]); const auto& input_k = model->GetArray(op->inputs[1]); - auto& output_indexes = model->GetArray(op->outputs[0]); - auto& output_values = model->GetArray(op->outputs[1]); + auto& output_values = model->GetArray(op->outputs[0]); + auto& output_indexes = model->GetArray(op->outputs[1]); // Bail if we already know the output shape. if (output_indexes.has_shape()) { diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index 61e4c9d542b339..fa8b26bce00d12 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -1991,7 +1991,7 @@ void ConvertTopKV2Operator(const NodeDef& node, op->inputs.push_back(node.input(1)); } // The op has two outputs. - op->outputs.push_back(node.name() + ":0"); + op->outputs.push_back(node.name()); op->outputs.push_back(node.name() + ":1"); model->operators.emplace_back(op.release()); } diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 5a341294db5e5c..f334c51bbb35b8 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -825,11 +825,6 @@ void FixNoOrphanedArray(Model* model) { void CheckEachArray(const Model& model) { for (const auto& array_entry : model.GetArrayMap()) { const auto& array = array_entry.second; - if (array->has_shape()) { - for (int d : array->shape().dims()) { - CHECK_GE(d, 1); - } - } // It's OK to have a buffer or an alloc, but not both. // (Since allocs are for transient arrays without a buffer). CHECK(!array->buffer || !array->alloc); @@ -839,6 +834,10 @@ void CheckEachArray(const Model& model) { // The presence of a fixed buffer should imply the presence of a fixed // shape. CHECK(array->has_shape()); + // Constant buffer should has a valid shape. + for (int d : array->shape().dims()) { + CHECK_GE(d, 1); + } // The shape flat-size should agree with the buffer length. CHECK_EQ(array->buffer->Length(), RequiredBufferSizeForShape(array->shape())); diff --git a/tensorflow/contrib/mpi/mpi_utils.h b/tensorflow/contrib/mpi/mpi_utils.h index df055ff5673114..4091925fc0d7ab 100644 --- a/tensorflow/contrib/mpi/mpi_utils.h +++ b/tensorflow/contrib/mpi/mpi_utils.h @@ -22,6 +22,7 @@ limitations under the License. #include #include +#include "tensorflow/core/platform/logging.h" #include "tensorflow/core/lib/strings/str_util.h" // Skip MPI C++ bindings support, this matches the usage in other places diff --git a/tensorflow/contrib/opt/python/training/lazy_adam_optimizer.py b/tensorflow/contrib/opt/python/training/lazy_adam_optimizer.py index aeca900bc8ff4c..72117c1e81a164 100644 --- a/tensorflow/contrib/opt/python/training/lazy_adam_optimizer.py +++ b/tensorflow/contrib/opt/python/training/lazy_adam_optimizer.py @@ -56,21 +56,21 @@ def _apply_sparse(self, grad, var): epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype) lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power)) - # m := beta1 * m + (1 - beta1) * g_t + # \\(m := beta1 * m + (1 - beta1) * g_t\\) m = self.get_slot(var, "m") m_t = state_ops.scatter_update(m, grad.indices, beta1_t * array_ops.gather(m, grad.indices) + (1 - beta1_t) * grad.values, use_locking=self._use_locking) - # v := beta2 * v + (1 - beta2) * (g_t * g_t) + # \\(v := beta2 * v + (1 - beta2) * (g_t * g_t)\\) v = self.get_slot(var, "v") v_t = state_ops.scatter_update(v, grad.indices, beta2_t * array_ops.gather(v, grad.indices) + (1 - beta2_t) * math_ops.square(grad.values), use_locking=self._use_locking) - # variable -= learning_rate * m_t / (epsilon_t + sqrt(v_t)) + # \\(variable -= learning_rate * m_t / (epsilon_t + sqrt(v_t))\\) m_t_slice = array_ops.gather(m_t, grad.indices) v_t_slice = array_ops.gather(v_t, grad.indices) denominator_slice = math_ops.sqrt(v_t_slice) + epsilon_t diff --git a/tensorflow/contrib/optimizer_v2/adam.py b/tensorflow/contrib/optimizer_v2/adam.py index 42b7f92a76c197..d538ad0fb02699 100644 --- a/tensorflow/contrib/optimizer_v2/adam.py +++ b/tensorflow/contrib/optimizer_v2/adam.py @@ -40,23 +40,19 @@ def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, Initialization: - ``` - m_0 <- 0 (Initialize initial 1st moment vector) - v_0 <- 0 (Initialize initial 2nd moment vector) - t <- 0 (Initialize timestep) - ``` + $$m_0 := 0 (Initialize initial 1st moment vector)$$ + $$v_0 := 0 (Initialize initial 2nd moment vector)$$ + $$t := 0 (Initialize timestep)$$ The update rule for `variable` with gradient `g` uses an optimization described at the end of section2 of the paper: - ``` - t <- t + 1 - lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t) + $$t := t + 1$$ + $$lr_t := \text{learning_rate} * \sqrt{(1 - beta_2^t) / (1 - beta_1^t)}$$ - m_t <- beta1 * m_{t-1} + (1 - beta1) * g - v_t <- beta2 * v_{t-1} + (1 - beta2) * g * g - variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon) - ``` + $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$ + $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$ + $$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$ The default value of 1e-8 for epsilon might not be a good default in general. For example, when training an Inception network on ImageNet a diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py index de5df912921932..ba4933ddf793c5 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py @@ -307,6 +307,21 @@ def setUp(self): self._seed = 23489 np.random.seed(self._seed) + def testDType(self): + # Test case for GitHub issue 16228 + # Not passing dtype in constructor results in default float32 + lstm = rnn_cell.LSTMCell(10) + input_tensor = array_ops.ones([10, 50]) + lstm.build(input_tensor.get_shape()) + self.assertEqual(lstm._bias.dtype, dtypes.float32_ref) + + # Explicitly pass dtype in constructor + for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]: + lstm = rnn_cell.LSTMCell(10, dtype=dtype) + input_tensor = array_ops.ones([10, 50]) + lstm.build(input_tensor.get_shape()) + self.assertEqual(lstm._bias.dtype, dtype._as_ref) + def testNoProjNoSharding(self): num_units = 3 input_size = 5 diff --git a/tensorflow/contrib/tensor_forest/client/eval_metrics.py b/tensorflow/contrib/tensor_forest/client/eval_metrics.py index 90033015ebc5e4..e893e1d1c836cc 100644 --- a/tensorflow/contrib/tensor_forest/client/eval_metrics.py +++ b/tensorflow/contrib/tensor_forest/client/eval_metrics.py @@ -37,7 +37,7 @@ def _top_k_generator(k): def _top_k(probabilities, targets): targets = math_ops.to_int32(targets) if targets.get_shape().ndims > 1: - targets = array_ops.squeeze(targets, squeeze_dims=[1]) + targets = array_ops.squeeze(targets, axis=[1]) return metric_ops.streaming_mean(nn.in_top_k(probabilities, targets, k)) return _top_k @@ -57,7 +57,7 @@ def _r2(probabilities, targets, weights=None): def _squeeze_and_onehot(targets, depth): - targets = array_ops.squeeze(targets, squeeze_dims=[1]) + targets = array_ops.squeeze(targets, axis=[1]) return array_ops.one_hot(math_ops.to_int32(targets), depth) diff --git a/tensorflow/contrib/tensor_forest/hybrid/python/layers/fully_connected.py b/tensorflow/contrib/tensor_forest/hybrid/python/layers/fully_connected.py index ff3ab21eaa9a4a..745a5b1caf2fe3 100644 --- a/tensorflow/contrib/tensor_forest/hybrid/python/layers/fully_connected.py +++ b/tensorflow/contrib/tensor_forest/hybrid/python/layers/fully_connected.py @@ -55,7 +55,7 @@ def inference_graph(self, data): # There is always one activation per instance by definition, so squeeze # away the extra dimension. - return array_ops.squeeze(nn_activations, squeeze_dims=[1]) + return array_ops.squeeze(nn_activations, axis=[1]) class FlattenedFullyConnectedLayer(hybrid_layer.HybridLayer): diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest.py b/tensorflow/contrib/tensor_forest/python/tensor_forest.py index b9bcbb170b04fe..7a35a70bbe3112 100644 --- a/tensorflow/contrib/tensor_forest/python/tensor_forest.py +++ b/tensorflow/contrib/tensor_forest/python/tensor_forest.py @@ -445,7 +445,7 @@ def training_graph(self, mask = math_ops.less( r, array_ops.ones_like(r) * self.params.bagging_fraction) gather_indices = array_ops.squeeze( - array_ops.where(mask), squeeze_dims=[1]) + array_ops.where(mask), axis=[1]) # TODO(thomaswc): Calculate out-of-bag data and labels, and store # them for use in calculating statistics later. tree_data = array_ops.gather(processed_dense_features, gather_indices) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index b412b296e02751..07740277115fe4 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -111,20 +111,22 @@ void GetSubGraphOutgoingEdges(const tensorflow::Graph& graph, } } -std::pair ParseTensorName(string name, int default_idx = 0) { +std::pair ParseTensorName(const string& name, + int default_idx = 0) { + string name_no_idx = name; int idx = default_idx; - size_t sep = name.find_last_of(':'); + const size_t sep = name_no_idx.find_last_of(':'); if (sep != string::npos) { - name = name.substr(0, sep); + name_no_idx = name_no_idx.substr(0, sep); idx = std::stoi(name.substr(sep + 1)); } - return std::make_pair(name, idx); + return std::make_pair(name_no_idx, idx); } std::unordered_map> BuildTensorNameMap( const std::vector& tensor_names) { std::unordered_map> result; - for (string const& tensor_name : tensor_names) { + for (const string& tensor_name : tensor_names) { string node_name; int index; std::tie(node_name, index) = ParseTensorName(tensor_name); @@ -132,6 +134,7 @@ std::unordered_map> BuildTensorNameMap( } return result; } + // TODO(sami): convert references to pointers struct ConvertGraphParams { ConvertGraphParams( diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_management_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_management_test.py index d5dce30fda0353..5f7e3da2db6da2 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/state_management_test.py +++ b/tensorflow/contrib/timeseries/python/timeseries/state_management_test.py @@ -78,7 +78,7 @@ def per_step_batch_loss(self, features, mode, state): batch_end_values = array_ops.squeeze( array_ops.slice(values, [0, array_ops.shape(times)[1] - 1, 0], [-1, 1, -1]), - squeeze_dims=[1, 2]) + axis=[1, 2]) # A pretty odd but easy to think about loss: L1 loss on the batch end # values. loss = math_ops.reduce_sum( diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter.py index 1fcd3e391b63c2..a614386121e000 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter.py +++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter.py @@ -170,7 +170,7 @@ def predict_state_mean(self, prior_state, transition_matrices): math_ops.matmul( transition_matrices, prior_state[..., None]), - squeeze_dims=[-1]) + axis=[-1]) return advanced_state def predict_state_var( @@ -254,7 +254,7 @@ def posterior_from_prior_state(self, prior_state, prior_state_var, kalman_gain_transposed, array_ops.expand_dims(residual, -1), adjoint_a=True), - squeeze_dims=[-1]) + axis=[-1]) gain_obs = math_ops.matmul( kalman_gain_transposed, observation_model, adjoint_a=True) identity_extradim = linalg_ops.eye( @@ -332,7 +332,7 @@ def observed_from_state(self, state_mean, state_var, observation_model, array_ops.expand_dims(state_mean, 1), observation_model, adjoint_b=True), - squeeze_dims=[1]) + axis=[1]) observed_var = math_ops.matmul( math_ops.matmul(observation_model, state_var), observation_model, diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 2a849a30193234..76ff372cd0099a 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -2292,7 +2292,9 @@ tf_cuda_library( CORE_CPU_BASE_HDRS = GRAPH_HDRS + [ "common_runtime/device.h", + "common_runtime/device_factory.h", "common_runtime/device_mgr.h", + "common_runtime/device_set.h", "common_runtime/eval_const_tensor.h", "common_runtime/graph_runner.h", "common_runtime/shape_refiner.h", @@ -2350,9 +2352,7 @@ CORE_CPU_LIB_HEADERS = CORE_CPU_BASE_HDRS + [ "common_runtime/copy_tensor.h", "common_runtime/costmodel_manager.h", "common_runtime/debugger_state_interface.h", - "common_runtime/device_factory.h", "common_runtime/device_resolver_local.h", - "common_runtime/device_set.h", "common_runtime/dma_helper.h", "common_runtime/eigen_thread_pool.h", "common_runtime/executor.h", diff --git a/tensorflow/core/api_def/base_api/api_def_ApplyAdam.pbtxt b/tensorflow/core/api_def/base_api/api_def_ApplyAdam.pbtxt index c2858a1bfbb5a3..b90f5473c89cbe 100644 --- a/tensorflow/core/api_def/base_api/api_def_ApplyAdam.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_ApplyAdam.pbtxt @@ -82,9 +82,9 @@ END } summary: "Update \'*var\' according to the Adam algorithm." description: < [[0, 0, 0, 0, 0, 0] [0, 0, 2, 2, 0, 0] [0, 0, 0, 0, 0, 0]] ``` + END } diff --git a/tensorflow/core/api_def/base_api/api_def_QuantizeV2.pbtxt b/tensorflow/core/api_def/base_api/api_def_QuantizeV2.pbtxt index b9e75caf02b3b5..37ac10dddb7fc5 100644 --- a/tensorflow/core/api_def/base_api/api_def_QuantizeV2.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_QuantizeV2.pbtxt @@ -44,6 +44,7 @@ In 'MIN_COMBINED' mode, each value of the tensor will undergo the following: out[i] = (in[i] - min_range) * range(T) / (max_range - min_range) if T == qint8, out[i] -= (range(T) + 1) / 2.0 ``` + here `range(T) = numeric_limits::max() - numeric_limits::min()` *MIN_COMBINED Mode Example* @@ -87,6 +88,7 @@ choosing to elide the lowest possible value for symmetry (e.g., output range is We first find the range of values in our tensor. The range we use is always centered on 0, so we find m such that + ```c++ m = max(abs(input_min), abs(input_max)) ``` @@ -95,6 +97,7 @@ Our input tensor range is then `[-m, m]`. Next, we choose our fixed-point quantization buckets, `[min_fixed, max_fixed]`. If T is signed, this is + ``` num_bits = sizeof(T) * 8 [min_fixed, max_fixed] = @@ -102,16 +105,19 @@ If T is signed, this is ``` Otherwise, if T is unsigned, the fixed-point range is + ``` [min_fixed, max_fixed] = [0, (1 << num_bits) - 1] ``` From this we compute our scaling factor, s: + ```c++ s = (max_fixed - min_fixed) / (2 * m) ``` Now we can quantize the elements of our tensor: + ```c++ result = round(input * s) ``` diff --git a/tensorflow/core/api_def/base_api/api_def_ResourceApplyAdam.pbtxt b/tensorflow/core/api_def/base_api/api_def_ResourceApplyAdam.pbtxt index bea1fd67627cc6..ad0aeac00426b5 100644 --- a/tensorflow/core/api_def/base_api/api_def_ResourceApplyAdam.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_ResourceApplyAdam.pbtxt @@ -76,9 +76,9 @@ END } summary: "Update \'*var\' according to the Adam algorithm." description: <assigned_device_name().empty() && - !StringPiece(n->assigned_device_name()).contains(kCPUDeviceSubStr)) { + !str_util::StrContains(n->assigned_device_name(),kCPUDeviceSubStr)) { result = false; reason = "Op has been assigned a runtime device that is not CPU."; } // If user has specifically assigned this op to a non-CPU device, then No. if (!n->def().device().empty() && - !StringPiece(n->def().device()).contains(kCPUDeviceSubStr)) { + !str_util::StrContains(n->def().device(),kCPUDeviceSubStr)) { result = false; reason = "User has assigned a device that is not CPU."; } @@ -2691,14 +2691,14 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // If Op has been specifically assigned to a non-CPU device, then No. if (!n->assigned_device_name().empty() && - !StringPiece(n->assigned_device_name()).contains(kCPUDeviceSubStr)) { + !str_util::StrContains(n->assigned_device_name(),kCPUDeviceSubStr)) { result = false; reason = "Op has been assigned a runtime device that is not CPU."; } // If user has specifically assigned this op to a non-CPU device, then No. if (!n->def().device().empty() && - !StringPiece(n->def().device()).contains(kCPUDeviceSubStr)) { + !str_util::StrContains(n->def().device(),kCPUDeviceSubStr)) { result = false; reason = "User has assigned a device that is not CPU."; } diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD index 9ecf5a6cf789fe..30c6126fbb58c1 100644 --- a/tensorflow/core/grappler/clusters/BUILD +++ b/tensorflow/core/grappler/clusters/BUILD @@ -56,6 +56,7 @@ cc_library( ], visibility = ["//visibility:public"], deps = [ + "//tensorflow/core:core_cpu_base", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", @@ -73,6 +74,7 @@ cc_library( visibility = ["//visibility:public"], deps = [ ":cluster", + "//tensorflow/core:core_cpu_base", "//tensorflow/core:framework", "//tensorflow/core:protos_all_cc", "//tensorflow/core/grappler/costs:op_level_cost_estimator", diff --git a/tensorflow/core/grappler/clusters/cluster.h b/tensorflow/core/grappler/clusters/cluster.h index 0796ba65ecc4a6..d33aaa7e4c16cd 100644 --- a/tensorflow/core/grappler/clusters/cluster.h +++ b/tensorflow/core/grappler/clusters/cluster.h @@ -21,6 +21,7 @@ limitations under the License. #include #include +#include "tensorflow/core/common_runtime/device_set.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/grappler/grappler_item.h" #include "tensorflow/core/lib/core/status.h" @@ -92,6 +93,10 @@ class Cluster { // sorted alphabetically. const std::vector GetDeviceNames() const; + // The DeviceSet is not always available, but when it is it contains a + // superset of the devices listed in GetDevices/GetDeviceNames(). + const DeviceSet* GetDeviceSet() const { return device_set_; } + // Enables collecting the allocator stats. Call with enable=true must be made // before Provision(). virtual Status EnablePeakMemoryStats(bool enable) { @@ -119,6 +124,7 @@ class Cluster { protected: std::unordered_map devices_; + const DeviceSet* device_set_ = nullptr; // Not owned const int timeout_s_; SessionOptions options_; RunOptions run_options_; diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.cc b/tensorflow/core/grappler/clusters/virtual_cluster.cc index abfa7bc48e6e24..5c9b2320b5bbf4 100644 --- a/tensorflow/core/grappler/clusters/virtual_cluster.cc +++ b/tensorflow/core/grappler/clusters/virtual_cluster.cc @@ -37,6 +37,14 @@ VirtualCluster::VirtualCluster( : Cluster(0), node_estimator_(node_estimator), node_manager_(node_manager) { devices_ = devices; } + +VirtualCluster::VirtualCluster( + const std::unordered_map& devices, + const DeviceSet* device_set) + : VirtualCluster(devices) { + device_set_ = device_set; +} + VirtualCluster::~VirtualCluster() {} Status VirtualCluster::Provision() { return Status::OK(); } diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.h b/tensorflow/core/grappler/clusters/virtual_cluster.h index e5967bac3dcc30..eebac68e1b5acf 100644 --- a/tensorflow/core/grappler/clusters/virtual_cluster.h +++ b/tensorflow/core/grappler/clusters/virtual_cluster.h @@ -17,6 +17,8 @@ limitations under the License. #define TENSORFLOW_CORE_GRAPPLER_CLUSTERS_VIRTUAL_CLUSTER_H_ #include + +#include "tensorflow/core/common_runtime/device_set.h" #include "tensorflow/core/grappler/clusters/cluster.h" #include "tensorflow/core/grappler/costs/op_level_cost_estimator.h" #include "tensorflow/core/grappler/costs/virtual_scheduler.h" @@ -34,6 +36,8 @@ class VirtualCluster : public Cluster { VirtualCluster(const std::unordered_map& devices, OpLevelCostEstimator* node_estimator, ReadyNodeManager* node_manager); + VirtualCluster(const std::unordered_map& devices, + const DeviceSet* device_set); ~VirtualCluster() override; diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h index 5116c8183cb4c5..67bf1e6980e550 100644 --- a/tensorflow/core/grappler/costs/virtual_scheduler.h +++ b/tensorflow/core/grappler/costs/virtual_scheduler.h @@ -199,7 +199,7 @@ class FirstReadyManager : public ReadyNodeManager { // current node. std::vector nodes_; // Newly added nodes are added to waiting_queue_. That way, GetCurrNode(), - // wihch returns the front of the nodes_, always returns the same node, + // which returns the front of the nodes_, always returns the same node, // even if any of new nodes has time_ready smaller than the current node's. std::vector waiting_queue_; // Comparator functor for heap; stl heap is max heap, so we use "greater than" @@ -212,7 +212,7 @@ class FirstReadyManager : public ReadyNodeManager { }; // CompositeNodeManager has a few other NodeManagers: per-device LIFO for normal -// ops (neither _Send nor _Recv) and FirstyReadyManagers for _Send ops and _Recv +// ops (neither _Send nor _Recv) and FirstReadyManagers for _Send ops and _Recv // ops, and then it chooses FirstReady among the ops chosen from each // internal NodeManagers. The objective is to maximize producer-consumer // locality within device, while processing nodes across devices, including diff --git a/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h b/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h index 796da913737b9d..3148a5f809f0df 100644 --- a/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h +++ b/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h @@ -33,7 +33,7 @@ class CustomGraphOptimizerRegistry { static std::vector GetRegisteredOptimizers(); typedef std::function Creator; - // Regsiter graph optimizer which can be called during program initialization. + // Register graph optimizer which can be called during program initialization. // This class is not thread-safe. static void RegisterOptimizerOrDie(const Creator& optimizer_creator, const string& name); diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc index 2edc4da9dcb91b..5230177dcab296 100644 --- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc @@ -160,13 +160,26 @@ Status MetaOptimizer::InitializeOptimizersByName( VLOG(2) << "Can't register an optimizer by name: " << optimizer_name; } } + for (const auto& optimizer_config : cfg_.custom_optimizers()) { + auto custom_optimizer = CustomGraphOptimizerRegistry::CreateByNameOrNull( + optimizer_config.name()); + if (custom_optimizer) { + VLOG(2) << "Registered custom configurable graph optimizer: " + << optimizer_config.name(); + TF_RETURN_IF_ERROR(custom_optimizer->Init(&optimizer_config)); + optimizers->push_back(std::move(custom_optimizer)); + } else { + VLOG(2) << "Can't register an optimizer by name: " + << optimizer_config.name(); + } + } return Status::OK(); } Status MetaOptimizer::OptimizeGraph(Cluster* cluster, const GrapplerItem& item, GraphDef* optimized_graph) { std::vector> optimizers; - if (cfg_.optimizers().empty()) { + if (cfg_.optimizers().empty() && cfg_.custom_optimizers().empty()) { TF_RETURN_IF_ERROR(InitializeOptimizers(&optimizers)); } else { TF_RETURN_IF_ERROR(InitializeOptimizersByName(&optimizers)); @@ -337,7 +350,7 @@ bool MetaOptimizerEnabled(const RewriterConfig& cfg) { cfg.auto_parallel().enable() || cfg.memory_optimization() != RewriterConfig::NO_MEM_OPT || cfg.debug_stripper() == RewriterConfig::ON || - !cfg.optimizers().empty(); + !cfg.optimizers().empty() || !cfg.custom_optimizers().empty(); } Status RunMetaOptimizer(const GrapplerItem& item, const RewriterConfig& cfg, diff --git a/tensorflow/core/kernels/batch_util.cc b/tensorflow/core/kernels/batch_util.cc index 52be1ab8d0f23b..1182ed42e7a9ad 100644 --- a/tensorflow/core/kernels/batch_util.cc +++ b/tensorflow/core/kernels/batch_util.cc @@ -134,6 +134,8 @@ Status CopyElementToSlice(Tensor element, Tensor* parent, int64 index) { switch (element.dtype()) { TF_CALL_ALL_TYPES(HANDLE_TYPE); TF_CALL_QUANTIZED_TYPES(HANDLE_TYPE); + TF_CALL_uint32(HANDLE_TYPE); + TF_CALL_uint64(HANDLE_TYPE); #undef HANDLE_TYPE default: return errors::Unimplemented("CopyElementToSlice Unhandled data type: ", diff --git a/tensorflow/core/kernels/cwise_op_floor_div.cc b/tensorflow/core/kernels/cwise_op_floor_div.cc index fecbf859897bd1..24da61fdf6ceed 100644 --- a/tensorflow/core/kernels/cwise_op_floor_div.cc +++ b/tensorflow/core/kernels/cwise_op_floor_div.cc @@ -16,8 +16,8 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_common.h" namespace tensorflow { -REGISTER5(BinaryOp, CPU, "FloorDiv", functor::safe_floor_div, uint8, uint16, - int16, int32, int64); +REGISTER6(BinaryOp, CPU, "FloorDiv", functor::safe_floor_div, uint8, uint16, + int8, int16, int32, int64); REGISTER3(BinaryOp, CPU, "FloorDiv", functor::floor_div_real, float, Eigen::half, double); diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc index f0818eb96daaab..f2b14f12789d0a 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_ops.cc @@ -20,6 +20,7 @@ limitations under the License. #include #include #include +#include #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" @@ -42,14 +43,13 @@ limitations under the License. #include "tensorflow/core/util/mkl_util.h" #ifndef INTEL_MKL_ML - #include "mkldnn.hpp" using mkldnn::prop_kind; using mkldnn::stream; - -using mkldnn::convolution_direct; using mkldnn::convolution_forward; +using mkldnn::convolution_direct; + #else #include "mkl_dnn.h" #include "mkl_dnn_types.h" @@ -57,11 +57,232 @@ using mkldnn::convolution_forward; namespace tensorflow { +#ifndef INTEL_MKL_ML + +struct ConvFwdDimensions { + memory::dims src_dims; + memory::dims filter_dims; + memory::dims bias_dims; + memory::dims dst_dims; + memory::dims strides; + memory::dims dilations; + memory::dims padding_left; + memory::dims padding_right; + + ConvFwdDimensions(memory::dims src_dims, + memory::dims filter_dims, memory::dims bias_dims, + memory::dims dst_dims, memory::dims strides, + memory::dims dilations, memory::dims padding_left, + memory::dims padding_right) : + src_dims(src_dims), filter_dims(filter_dims), + bias_dims(bias_dims), dst_dims(dst_dims), + strides(strides), dilations(dilations), + padding_left(padding_left), padding_right(padding_right) { + } +}; + +template +class Conv2DFwd : public DnnOp { + public: + explicit Conv2DFwd(const ConvFwdDimensions& convFwdDims) { + fwd_stream_.reset(new stream(stream::kind::eager)); + // create conv primitive + if (conv_fwd_ == nullptr) { + Setup(convFwdDims); + } + } + + ~Conv2DFwd() {} + + // Convolution forward execute with bias + // src_data: input data buffer of src + // filter_data: input data buffer of filter (weights) + // bias_data: input data buffer of bias + // dst_data: output data buffer of dst + void Execute(T* src_data, T* filter_data, T* bias_data, T* dst_data) { + src_mem_->set_data_handle(static_cast(src_data)); + filter_mem_->set_data_handle(static_cast(filter_data)); + bias_mem_->set_data_handle(static_cast(bias_data)); + dst_mem_->set_data_handle(static_cast(dst_data)); + fwd_stream_->submit(fwd_primitives_); + + // after exec, set data handle back + src_mem_->set_data_handle(DummyData); + filter_mem_->set_data_handle(DummyData); + bias_mem_->set_data_handle(DummyData); + dst_mem_->set_data_handle(DummyData); + + return; + } + + // Convolution forward execute without bias + // src_data: input data buffer of src + // filter_data: input data buffer of filter (weights) + // dst_data: output data buffer of dst + void Execute(T* src_data, T* filter_data, T* dst_data) { + src_mem_->set_data_handle(static_cast(src_data)); + filter_mem_->set_data_handle(static_cast(filter_data)); + dst_mem_->set_data_handle(static_cast(dst_data)); + fwd_stream_->submit(fwd_primitives_); + + // after exec, set data handle back + src_mem_->set_data_handle(DummyData); + filter_mem_->set_data_handle(DummyData); + dst_mem_->set_data_handle(DummyData); + + return; + } + + // expected memory format for this primitive instance + memory::format src_fmt_; + memory::format filter_fmt_; + + // convolution primitive + std::shared_ptr fwd_pd_; + std::shared_ptr conv_fwd_; + + private: + void Setup(const ConvFwdDimensions& convFwdDims) { + // create memory descriptors for convolution data w/ no specified format + src_md_.reset(new memory::desc({convFwdDims.src_dims}, + MklDnnType(), memory::format::any)); + + filter_md_.reset(new memory::desc({convFwdDims.filter_dims}, + MklDnnType(), memory::format::any)); + + dst_md_.reset(new memory::desc({convFwdDims.dst_dims}, + MklDnnType(), memory::format::any)); + + if (!convFwdDims.bias_dims.empty()) + bias_md_.reset(new memory::desc({convFwdDims.bias_dims}, + MklDnnType(), memory::format::any)); + + // create a convolution + if (!convFwdDims.bias_dims.empty()) { + fwd_desc_.reset(new convolution_forward::desc(prop_kind::forward, + convolution_direct, *src_md_, *filter_md_, *bias_md_, *dst_md_, + convFwdDims.strides, convFwdDims.dilations, convFwdDims.padding_left, + convFwdDims.padding_right, padding_kind::zero)); + } else { + fwd_desc_.reset(new convolution_forward::desc(prop_kind::forward, + convolution_direct, *src_md_, *filter_md_, *dst_md_, + convFwdDims.strides, convFwdDims.dilations, convFwdDims.padding_left, + convFwdDims.padding_right, padding_kind::zero)); + } + + fwd_pd_.reset(new convolution_forward::primitive_desc( + *fwd_desc_, cpu_engine_)); + + // store the expected memory format + src_fmt_ = static_cast( + fwd_pd_.get()->src_primitive_desc().desc().data.format); + + filter_fmt_ = static_cast( + fwd_pd_.get()->weights_primitive_desc().desc().data.format); + + // create memory primitive based on dummy data + src_mem_.reset(new memory(fwd_pd_.get()->src_primitive_desc(), DummyData)); + filter_mem_.reset(new memory(fwd_pd_.get()->weights_primitive_desc(), + DummyData)); + dst_mem_.reset(new memory(fwd_pd_.get()->dst_primitive_desc(), DummyData)); + + // create convolution primitive and add it to net + if (!convFwdDims.bias_dims.empty()) { + bias_mem_.reset(new memory({{{convFwdDims.bias_dims}, MklDnnType(), + memory::format::x}, cpu_engine_}, DummyData)); + conv_fwd_.reset(new convolution_forward(*fwd_pd_, *src_mem_, + *filter_mem_, *bias_mem_, *dst_mem_)); + } else { + conv_fwd_.reset(new convolution_forward(*fwd_pd_, *src_mem_, + *filter_mem_, *dst_mem_)); + } + + fwd_primitives_.push_back(*conv_fwd_); + return; + } + + // MKLDNN memory + std::shared_ptr src_mem_; + std::shared_ptr filter_mem_; + std::shared_ptr bias_mem_; + std::shared_ptr dst_mem_; + + std::shared_ptr fwd_stream_; + std::vector fwd_primitives_; + + // desc & prmitive desc + std::shared_ptr fwd_desc_; + + // memory desc + std::shared_ptr src_md_; + std::shared_ptr filter_md_; + std::shared_ptr bias_md_; + std::shared_ptr dst_md_; + + engine cpu_engine_ = engine(engine::cpu, 0); +}; + +template +class Conv2DFwdFactory : public DnnOpFactory { + public: + static Conv2DFwd* Get(const ConvFwdDimensions& convFwdDims) { + Conv2DFwd* conv2d_fwd = nullptr; + + // try to find a suitable one in pool + conv2d_fwd = dynamic_cast*> ( + Conv2DFwdFactory::GetInstance().GetConv2DFwd(convFwdDims)); + + if (conv2d_fwd == nullptr) { + conv2d_fwd = new Conv2DFwd(convFwdDims); + Conv2DFwdFactory::GetInstance().SetConv2DFwd( + convFwdDims, conv2d_fwd); + } + return conv2d_fwd; + } + + private: + Conv2DFwdFactory() {} + ~Conv2DFwdFactory() {} + + static const int kDilationH = 0, kDilationW = 1; + + static Conv2DFwdFactory& GetInstance() { + static Conv2DFwdFactory instance_; + return instance_; + } + + static std::string CreateKey(const ConvFwdDimensions& convFwdDims) { + std::string prefix = "conv2d_fwd_"; + FactoryKeyCreator key_creator; + key_creator.AddAsKey(prefix); + key_creator.AddAsKey(convFwdDims.src_dims); + key_creator.AddAsKey(convFwdDims.filter_dims); + key_creator.AddAsKey(convFwdDims.bias_dims); + key_creator.AddAsKey(convFwdDims.dst_dims); + key_creator.AddAsKey(convFwdDims.strides); + key_creator.AddAsKey(convFwdDims.dilations); + key_creator.AddAsKey(convFwdDims.padding_left); + key_creator.AddAsKey(convFwdDims.padding_right); + return key_creator.GetKey(); + } + + DnnOp* GetConv2DFwd(const ConvFwdDimensions& convFwdDims) { + std::string key = CreateKey(convFwdDims); + return this->GetOp(key); + } + + void SetConv2DFwd(const ConvFwdDimensions& convFwdDims, DnnOp *op) { + std::string key = CreateKey(convFwdDims); + this->SetOp(key, op); + } +}; + +#endif + typedef Eigen::ThreadPoolDevice CPUDevice; -// MKL-DNN is now default. MKL-ML must be specified explicitly. +// For now, MKL-ML is default. So making MKL-DNN not a default choice. #ifdef INTEL_MKL_ML - template class MklConv2DOp : public OpKernel { public: @@ -528,8 +749,6 @@ class MklConv2DOp : public OpKernel { void Compute(OpKernelContext* context) override { try { - auto cpu_engine = engine(engine::cpu, 0); - // Input tensors const Tensor& src_tensor = MklGetInput(context, kInputIndex_Src); const Tensor& filter_tensor = MklGetInput(context, kInputIndex_Filter); @@ -538,16 +757,16 @@ class MklConv2DOp : public OpKernel { GetMklShape(context, kInputIndex_Src, &src_mkl_shape); GetMklShape(context, kInputIndex_Filter, &filter_mkl_shape); OP_REQUIRES(context, filter_mkl_shape.IsMklTensor() == false, - errors::InvalidArgument("Filter should not be in " - "Mkl Layout")); + errors::InvalidArgument("Filter should not be in " + "Mkl Layout")); MklDnnData src(&cpu_engine); MklDnnData filter(&cpu_engine); - MklDnnData output(&cpu_engine); + MklDnnData dst(&cpu_engine); // output - memory::dims src_dims, filter_dims, padding_l, padding_r, + memory::dims src_dims, filter_dims, padding_left, padding_right, dilations, strides; - memory::dims output_dims_tf_order, output_dims_mkl_order; + memory::dims dst_dims_tf_order, dst_dims_mkl_order; // Get shapes of input tensors in MKL-DNN order MklDnnConvUtil conv_utl(context, strides_, padding_, data_format_, @@ -555,31 +774,29 @@ class MklConv2DOp : public OpKernel { auto src_tf_shape = GetTfShape(context, kInputIndex_Src); auto filter_tf_shape = GetTfShape(context, kInputIndex_Filter); conv_utl.GetConvFwdSizesInMklOrder( - src_tf_shape, filter_tf_shape, &src_dims, &filter_dims, &strides, - &dilations, &output_dims_tf_order, &output_dims_mkl_order, - &padding_l, &padding_r); + src_tf_shape, filter_tf_shape, &src_dims, &filter_dims, + &strides, &dilations, &dst_dims_tf_order, &dst_dims_mkl_order, + &padding_left, &padding_right); if (!context->status().ok()) return; // Check for corner case - if there is nothing to compute, return. - TensorShape output_tf_shape = MklDnnDimsToTFShape(output_dims_tf_order); + TensorShape dst_tf_shape = MklDnnDimsToTFShape(dst_dims_tf_order); // Corner cases: output with 0 elements and 0 batch size. - Tensor* output_tensor = nullptr; - if (output_tf_shape.num_elements() == 0 || output_dims_tf_order[0] == 0) { - // TODO(jbobba): Verify correctness here - // Need semantics for Null MKL tensor - MklDnnShape output_mkl_shape; - output_mkl_shape.SetMklTensor(false); - - AllocateOutputSetMklShape(context, kOutputIndex_Dst, &output_tensor, - src_tf_shape, output_mkl_shape); + Tensor* dst_tensor = nullptr; + if (dst_tf_shape.num_elements() == 0 || + dst_dims_tf_order[0] == 0) { + MklDnnShape dst_mkl_shape; + dst_mkl_shape.SetMklTensor(false); + AllocateOutputSetMklShape(context, kOutputIndex_Dst, + &dst_tensor, src_tf_shape, dst_mkl_shape); // MklConv2D also outputs converted filter as 2nd output of Conv2D. filter_mkl_shape.SetMklTensor(false); Tensor* output_filter_tensor = nullptr; AllocateOutputSetMklShape(context, kOutputIndex_Filter, - &output_filter_tensor, filter_tf_shape, - filter_mkl_shape); + &output_filter_tensor, + filter_tf_shape, filter_mkl_shape); return; } @@ -587,6 +804,7 @@ class MklConv2DOp : public OpKernel { // Describe how the inputs and outputs of Convolution look like. Also // specify buffers containing actual input and output data. auto tf_fmt = TFDataFormatToMklDnnDataFormat(data_format_); + // If input is in MKL layout, then simply grab input layout; otherwise, // construct input Tf layout. For TF layout, although input shape // (src_dims) required is in MKL-DNN order, the layout is Tensorflow's @@ -595,6 +813,7 @@ class MklConv2DOp : public OpKernel { ? src_mkl_shape.GetMklLayout() : memory::desc(src_dims, MklDnnType(), tf_fmt); src.SetUsrMem(src_md, &src_tensor); + // Although filter shape (filter_dims) required is in MKL-DNN order, // the layout is Tensorflow's layout (HWIO). auto filter_md = filter_mkl_shape.IsMklTensor() // Should NEVER be true @@ -603,98 +822,70 @@ class MklConv2DOp : public OpKernel { memory::format::hwio); filter.SetUsrMem(filter_md, &filter_tensor); - // Set output shape (output_dims) required in MKL-DNN order. - // Currently, we set output layout as Tensorflow's layout (NHWC or NCHW - // depending on data format). But later we propagate Mkl layout of the - // output to the next op directly. - output.SetUsrMem(output_dims_mkl_order, tf_fmt); - - // Create memory descriptors for convolution data w/ no specified format. - src.SetOpMemDesc(src_dims, memory::format::any); - filter.SetOpMemDesc(filter_dims, memory::format::any); - output.SetOpMemDesc(output_dims_mkl_order, memory::format::any); - // MKLDNN dilation starts from 0. dilations[kDilationH] -= 1; dilations[kDilationW] -= 1; + // get a conv2d fwd from primitive pool + Conv2DFwd *conv2d_fwd = nullptr; + if (biasEnabled) { + memory::dims bias_dims = {}; + conv_utl.GetBiasSizeInMklOrder(kInputIndex_Bias, &bias_dims); + ConvFwdDimensions convFwdDims(src_dims, filter_dims, bias_dims, + dst_dims_mkl_order, strides, dilations, padding_left, padding_right); + conv2d_fwd = Conv2DFwdFactory::Get(convFwdDims); + } else { + ConvFwdDimensions convFwdDims(src_dims, filter_dims, NONE_DIMS, + dst_dims_mkl_order, strides, dilations, padding_left, padding_right); + conv2d_fwd = Conv2DFwdFactory::Get(convFwdDims); + } + + // allocate output tensors output_tensor and filter_out_tensor + std::shared_ptr + conv_fwd_pd = conv2d_fwd->fwd_pd_; + AllocateOutputTensor(context, *conv_fwd_pd, + dst_dims_mkl_order, tf_fmt, &dst_tensor); + Tensor* filter_out_tensor = nullptr; + AllocateFilterOutputTensor(context, *conv_fwd_pd, + TFShapeToMklDnnDims(filter_tf_shape), + &filter_out_tensor); + + T* dst_data = static_cast(dst_tensor->flat().data()); + + // check whether src/filter need reorder + std::vector net; + if (src_md.data.format != conv2d_fwd->src_fmt_) + src.CheckReorderToOpMem( + conv_fwd_pd.get()->src_primitive_desc(), &net); + + if (filter_md.data.format != conv2d_fwd->filter_fmt_) + filter.CheckReorderToOpMem( + conv_fwd_pd.get()->weights_primitive_desc(), + filter.GetTensorBuffer(filter_out_tensor), &net); + stream(stream::kind::eager).submit(net).wait(); + + T* src_data = static_cast( + src.GetOpMem().get_data_handle()); + T* filter_data = static_cast( + filter.GetOpMem().get_data_handle()); + + // execute convolution if (biasEnabled) { - // Create convolution primitive with Bias. - MklDnnData bias(&cpu_engine); - memory::dims bias_size; - conv_utl.GetBiasSizeInMklOrder(kInputIndex_Bias, &bias_size); - const Tensor& bias_tensor = MklGetInput(context, kInputIndex_Bias); - bias.SetUsrMem(bias_size, memory::format::x, &bias_tensor); - bias.SetOpMemDesc(bias_size, memory::format::any); - - // Create convolution primitive with Bias. - // Use MKLDNN dilated convolution in case of dilated rate (>0). - auto conv_desc = (dilations[kDilationH] > 0 || - dilations[kDilationW] > 0) ? - convolution_forward::desc(prop_kind::forward, - convolution_direct, src.GetOpMemDesc(), - filter.GetOpMemDesc(), bias.GetOpMemDesc(), - output.GetOpMemDesc(), strides, dilations, - padding_l, padding_r, - TFPaddingToMklDnnPadding(padding_)): - convolution_forward::desc(prop_kind::forward, - convolution_direct, src.GetOpMemDesc(), - filter.GetOpMemDesc(), bias.GetOpMemDesc(), - output.GetOpMemDesc(), strides, - padding_l, padding_r, - TFPaddingToMklDnnPadding(padding_)); - - auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, - cpu_engine); - AllocateOutputTensor(context, conv_prim_desc, - output_dims_mkl_order, tf_fmt, &output_tensor); - // Set data handle for output. - output.SetUsrMemDataHandle(output_tensor); - - Tensor* filter_out_tensor = nullptr; - AllocateFilterOutputTensor(context, conv_prim_desc, - TFShapeToMklDnnDims(filter_tf_shape), - &filter_out_tensor); - - PrepareAndExecuteNet(conv_prim_desc, &src, &filter, &bias, &output, - filter_out_tensor); + const Tensor& bias_tensor = MklGetInput(context, kInputIndex_Bias); + T* bias_data = static_cast(const_cast( + bias_tensor.flat().data())); + + conv2d_fwd->Execute(src_data, filter_data, bias_data, dst_data); } else { - // Create convolution primitive without Bias. - // Use MKLDNN dilated convolution in case of dilated rate (>0). - auto conv_desc = (dilations[kDilationH] > 0 || - dilations[kDilationW] > 0) ? - convolution_forward::desc(prop_kind::forward, - convolution_direct, src.GetOpMemDesc(), - filter.GetOpMemDesc(), output.GetOpMemDesc(), - strides, dilations, padding_l, padding_r, - TFPaddingToMklDnnPadding(padding_)): - convolution_forward::desc(prop_kind::forward, - convolution_direct, src.GetOpMemDesc(), - filter.GetOpMemDesc(), output.GetOpMemDesc(), - strides, padding_l, padding_r, - TFPaddingToMklDnnPadding(padding_)); - - auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, - cpu_engine); - AllocateOutputTensor(context, conv_prim_desc, output_dims_mkl_order, - tf_fmt, &output_tensor); - // Set data handle for output. - output.SetUsrMemDataHandle(output_tensor); - - Tensor* filter_out_tensor = nullptr; - AllocateFilterOutputTensor(context, conv_prim_desc, - TFShapeToMklDnnDims(filter_tf_shape), - &filter_out_tensor); - PrepareAndExecuteNet(conv_prim_desc, &src, &filter, - nullptr, &output, filter_out_tensor); + conv2d_fwd->Execute(src_data, filter_data, dst_data); } - } catch (mkldnn::error& e) { + } catch (mkldnn::error &e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + std::string(e.message) + ", in file " + - std::string(__FILE__) + ":" + std::to_string(__LINE__); - OP_REQUIRES_OK( - context, - errors::Aborted("Operation received an exception:", error_msg)); + ", message: " + std::string(e.message) + + ", in file " + std::string(__FILE__) + ":" + + std::to_string(__LINE__); + OP_REQUIRES_OK(context, + errors::Aborted("Operation received an exception:", error_msg)); } } @@ -706,6 +897,7 @@ class MklConv2DOp : public OpKernel { const int kInputIndex_Src = 0, kInputIndex_Filter = 1, kInputIndex_Bias = 2; const int kOutputIndex_Dst = 0, kOutputIndex_Filter = 1; const int kDilationH = 0, kDilationW = 1; + engine cpu_engine = engine(engine::cpu, 0); // Allocate output tensor. void AllocateOutputTensor( diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc index 3a95dd17733985..0caa7bd3179a79 100644 --- a/tensorflow/core/kernels/scatter_nd_op.cc +++ b/tensorflow/core/kernels/scatter_nd_op.cc @@ -241,6 +241,7 @@ class ScatterNdUpdateOp : public OpKernel { TF_CALL_NUMBER_TYPES(REGISTER_SCATTER_ND_ADD_SUB_CPU); TF_CALL_NUMBER_TYPES(REGISTER_SCATTER_ND_UPDATE_CPU); TF_CALL_NUMBER_TYPES(REGISTER_SCATTER_ND_CPU); +TF_CALL_string(REGISTER_SCATTER_ND_CPU); // Registers GPU kernels. #if GOOGLE_CUDA diff --git a/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h b/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h index e82660dcc1dcf9..7cfffa20c5a491 100644 --- a/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h +++ b/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h @@ -160,6 +160,7 @@ struct ScatterNdFunctor { REGISTER_SCATTER_ND_INDEX(type, scatter_nd_op::UpdateOp::SUB); TF_CALL_ALL_TYPES(REGISTER_SCATTER_ND_UPDATE); +REGISTER_SCATTER_ND_INDEX(string, scatter_nd_op::UpdateOp::ADD); TF_CALL_NUMBER_TYPES(REGISTER_SCATTER_ND_MATH) #undef REGISTER_SCATTER_ND_MATH diff --git a/tensorflow/core/kernels/segment_reduction_ops.h b/tensorflow/core/kernels/segment_reduction_ops.h index bedd9659663df5..4abfbfb1a66c37 100644 --- a/tensorflow/core/kernels/segment_reduction_ops.h +++ b/tensorflow/core/kernels/segment_reduction_ops.h @@ -16,35 +16,6 @@ limitations under the License. #ifndef TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ #define TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ - -// This file requires the following include because it uses CudaAtomicMax: -// #include "tensorflow/core/util/cuda_kernel_helper.h" - -// Unfortunately we can't add the #include, since it breaks compilation for -// non-GPU targets. This only breaks in clang, because it's more strict for -// template code and CudaAtomicMax is used in template context. - -// This file requires the following include because it uses CudaAtomicMax: -// #include "tensorflow/core/util/cuda_kernel_helper.h" - -// Unfortunately we can't add the #include, since it breaks compilation for -// non-GPU targets. This only breaks in clang, because it's more strict for -// template code and CudaAtomicMax is used in template context. - -// This file requires the following include because it uses CudaAtomicMax: -// #include "tensorflow/core/util/cuda_kernel_helper.h" - -// Unfortunately we can't add the #include, since it breaks compilation for -// non-GPU targets. This only breaks in clang, because it's more strict for -// template code and CudaAtomicMax is used in template context. - -// This file requires the following include because it uses CudaAtomicMax: -// #include "tensorflow/core/util/cuda_kernel_helper.h" - -// Unfortunately we can't add the #include, since it breaks compilation for -// non-GPU targets. This only breaks in clang, because it's more strict for -// template code and CudaAtomicMax is used in template context. - // This file requires the following include because it uses CudaAtomicMax: // #include "tensorflow/core/util/cuda_kernel_helper.h" diff --git a/tensorflow/core/platform/default/gpu/cupti_wrapper.h b/tensorflow/core/platform/default/gpu/cupti_wrapper.h index acd889e47496f8..e3ebe6ca1d025b 100644 --- a/tensorflow/core/platform/default/gpu/cupti_wrapper.h +++ b/tensorflow/core/platform/default/gpu/cupti_wrapper.h @@ -23,7 +23,7 @@ limitations under the License. #if defined(WIN32) #include "extras/CUPTI/include/cupti.h" #else -#include "cuda/extras/CUPTI/include/cupti.h" +#include "cupti.h" #endif namespace perftools { namespace gputools { diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index 0ca7d8475fc629..ba69efb289a42a 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -24,7 +24,7 @@ limitations under the License. // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1", // "-beta", "-rc", "-rc.1") -#define TF_VERSION_SUFFIX "-rc0" +#define TF_VERSION_SUFFIX "-rc1" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h index bc6d2d77a4d121..50a8e305749eec 100644 --- a/tensorflow/core/util/mkl_util.h +++ b/tensorflow/core/util/mkl_util.h @@ -19,6 +19,8 @@ limitations under the License. #include #include +#include +#include #include "mkl_dnn.h" #include "mkl_dnn_types.h" @@ -1759,7 +1761,90 @@ class MklDnnData { } }; -#endif // INTEL_MKL_ML +/// Base class for operations with reuse of DNN primitives +/// +class DnnOp { + public: + virtual ~DnnOp() {} + + // Dummy data. Its size, hard-coded as 256 here, does + // not matter since MKL should never operate on this buffer. + unsigned char DummyData[256]; +}; + +const mkldnn::memory::dims NONE_DIMS = {}; +// This constant is used to declare dummy buffer (size), for MKL primitives +template +class DnnOpFactory { + public: + DnnOpFactory() {} + ~DnnOpFactory() {} + + DnnOp* GetOp(const std::string& key) { + auto stream_iter = DnnOpFactory::GetHashMap().find(key); + if (stream_iter == DnnOpFactory::GetHashMap().end()) { + return nullptr; + } else { + return stream_iter->second; + } + } + + void SetOp(const std::string& key, DnnOp* op) { + auto stream_iter = DnnOpFactory::GetHashMap().find(key); + + CHECK(stream_iter == DnnOpFactory::GetHashMap().end()); + + DnnOpFactory::GetHashMap()[key] = op; + } + + private: + static inline std::unordered_map &GetHashMap() { + static thread_local std::unordered_map map_; + return map_; + } +}; + +// utility class for creating keys of MKL primitive pool. +class FactoryKeyCreator { + public: + FactoryKeyCreator() { + key_.reserve(kMaxKeyLength); + } + + ~FactoryKeyCreator() {} + + void AddAsKey(const string &str) { + auto buffer = reinterpret_cast(str.c_str()); + Append(buffer, str.length()); + } + + void AddAsKey(const mkldnn::memory::dims &dims) { + for (unsigned int i = 0; i < dims.size(); i++) { + AddAsKey(dims[i]); + } + } + + template + void AddAsKey(const T data) { + auto buffer = reinterpret_cast(&data); + Append(buffer, sizeof(T)); + } + + std::string GetKey() { + return key_; + } + + private: + string key_; + const char delimiter = 'x'; + const int kMaxKeyLength = 256; + void Append(const char* data, int len) { + key_.append(data, len); + key_.append(1, delimiter); + } +}; + +#endif // INTEL_MKL_DNN } // namespace tensorflow #endif // INTEL_MKL diff --git a/tensorflow/docs_src/community/roadmap.md b/tensorflow/docs_src/community/roadmap.md index a3170a10f2d12e..0463ca05fe5353 100644 --- a/tensorflow/docs_src/community/roadmap.md +++ b/tensorflow/docs_src/community/roadmap.md @@ -1,5 +1,5 @@ # Roadmap -**Last updated: Feb 15, 2018** +**Last updated: Apr 27, 2018** TensorFlow is a rapidly moving, community supported project. This document is intended to provide guidance about priorities and focus areas of the core set of TensorFlow @@ -14,12 +14,12 @@ expected in the next one to two releases. ### APIs #### High Level APIs: -* Easy multi-GPU utilization with Estimators +* Easy multi-GPU and TPU utilization with Estimators * Easy-to-use high-level pre-made estimators for Gradient Boosted Trees, Time Series, and other models #### Eager Execution: * Efficient utilization of multiple GPUs -* Distributed training (multi-machine) +* Distributed training support (multi-machine) * Performance improvements * Simpler export to a GraphDef/SavedModel @@ -31,14 +31,14 @@ to create Keras models Eager- style via Model subclassing) #### Official Models: * A set of -[reference models](https://github.com/tensorflow/models/tree/master/official) +[models](https://github.com/tensorflow/models/tree/master/official) across image recognition, speech, object detection, and translation that demonstrate best practices and serve as a starting point for high-performance model development. #### Contrib: -* Deprecation notices added to parts of tf.contrib where preferred implementations exist outside of tf.contrib. -* As much as possible, large projects inside tf.contrib moved to separate repositories. +* Deprecate parts of tf.contrib where preferred implementations exist outside of tf.contrib. +* As much as possible, move large projects inside tf.contrib to separate repositories. * The tf.contrib module will eventually be discontinued in its current form, experimental development will in future happen in other repositories. @@ -50,36 +50,72 @@ across image recognition, speech, object detection, and ### Platforms #### TensorFlow Lite: -* Increased coverage of supported ops in TensorFlow Lite +* Increase coverage of supported ops in TensorFlow Lite * Easier conversion of a trained TensorFlow graph for use on TensorFlow Lite * Support for GPU acceleration in TensorFlow Lite (iOS and Android) * Support for hardware accelerators via Android NeuralNets API -* Improved CPU performance by quantization and other network optimizations (eg. pruning, distillation) -* Increased support for devices beyond Android and iOS (eg. RPi, Cortex-M) +* Improve CPU performance by quantization and other network optimizations (eg. pruning, distillation) +* Increase support for devices beyond Android and iOS (eg. RPi, Cortex-M) + +#### TensorFlow.js: +* Release package for Node.js bindings to the TensorFlow C API through the TensorFlow.js backend interface +* Expand support for importing TensorFlow SavedModels and Keras models into browser with unified APIs supporting retraining in browser +* Improve Layers API and allow model exporting/saving +* Release tfjs-data API for efficient data input pipelines + +#### TensorFlow with Swift: +* Establish open source project including documentation, open design, and code availability. +* Continue implementing and refining implementation and design through 2018. +* Aim for implementation to be solid enough for general use later in 2018. ### Performance #### Distributed TensorFlow: -* Multi-GPU support optimized for a variety of GPU topologies -* Improved mechanisms for distributing computations on several machines +* Optimize Multi-GPU support for a variety of GPU topologies +* Improve mechanisms for distributing computations on several machines + +#### GPU Optimizations: +* Simplify mixed precision API with initial example model and guide. +* Finalize TensorRT API and move to core. +* CUDA 9.2 and NCCL 2.x default in TensorFlow builds. +* Optimizations for DGX-2. +* Remove support for CUDA less than 8.x and cuDNN less than 6.x. -#### Optimizations: -* Mixed precision training support with initial example model and guide -* Native TensorRT support + +#### CPU Optimizations * Int8 support for SkyLake via MKL * Dynamic loading of SIMD-optimized kernels +* MKL for Linux and Windows + +### End-to-end ML systems: +#### TensorFlow Hub: +* Expand support for module-types in TF Hub with TF Eager integration, Keras layers integration, and TensorFlow.js integration +* Accept variable-sized image input +* Improve multi-GPU estimator support +* Document and improve TPU integration + +#### TensorFlow Extended: +* Open source more of the TensorFlow Extended platform to facilitate adoption of TensorFlow in production settings. +* Release TFX libraries for Data Validation + +### Documentation and Resources: +* Update documentation, tutorials and Getting Started guides on all features and APIs +* Update [Youtube Tensorflow channel](https://youtube.com/tensorflow) weekly with new content: +Coding TensorFlow - where we teach folks coding with tensorflow +TensorFlow Meets - where we highlight community contributions +Ask TensorFlow - where we answer community questions +Guest and Showcase videos +* Update [Official TensorFlow blog](https://blog.tensorflow.org) with regular articles from Google team and the Community -### Documentation and Usability: -* Updated documentation, tutorials and Getting Started guides -* Process to enable external contributions to tutorials, documentation, and blogs showcasing best practice use-cases of TensorFlow and high-impact applications ### Community and Partner Engagement #### Special Interest Groups: -* Mobilizing the community to work together in focused domains +* Mobilize the community to work together in focused domains * [tf-distribute](https://groups.google.com/a/tensorflow.org/forum/#!forum/tf-distribute): build and packaging of TensorFlow -* More to be identified and launched +* SIG TensorBoard, SIG Rust, and more to be identified and launched #### Community: * Incorporate public feedback on significant design decisions via a Request-for-Comment (RFC) process * Formalize process for external contributions to land in TensorFlow and associated projects * Grow global TensorFlow communities and user groups * Collaborate with partners to co-develop and publish research papers +* Process to enable external contributions to tutorials, documentation, and blogs showcasing best practice use-cases of TensorFlow and high-impact applications diff --git a/tensorflow/docs_src/get_started/checkpoints.md b/tensorflow/docs_src/get_started/checkpoints.md index 4aa07c7f2a0b56..8dfd91e3c8368f 100644 --- a/tensorflow/docs_src/get_started/checkpoints.md +++ b/tensorflow/docs_src/get_started/checkpoints.md @@ -38,8 +38,10 @@ Estimators automatically write the following to disk: uses to create visualizations. To specify the top-level directory in which the Estimator stores its -information, assign a value to the optional `model_dir` argument of any -Estimator's constructor. For example, the following code sets the `model_dir` +information, assign a value to the optional `model_dir` argument of *any* +`Estimator`'s constructor. +Taking `DNNClassifier` as an example, +the following code sets the `model_dir` argument to the `models/iris` directory: ```python diff --git a/tensorflow/docs_src/get_started/feature_columns.md b/tensorflow/docs_src/get_started/feature_columns.md index 9c777a0077a768..79c26679793f2f 100644 --- a/tensorflow/docs_src/get_started/feature_columns.md +++ b/tensorflow/docs_src/get_started/feature_columns.md @@ -138,7 +138,7 @@ The model will represent the buckets as follows: |< 1960 | [1, 0, 0, 0] | |>= 1960 but < 1980 | [0, 1, 0, 0] | |>= 1980 but < 2000 | [0, 0, 1, 0] | -|> 2000 | [0, 0, 0, 1] | +|>= 2000 | [0, 0, 0, 1] | Why would you want to split a number—a perfectly valid input to your model—into a categorical value? Well, notice that the categorization splits a diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md index b28cb9df75d94a..746126c7206905 100644 --- a/tensorflow/docs_src/get_started/index.md +++ b/tensorflow/docs_src/get_started/index.md @@ -10,7 +10,7 @@ course prior to diving into TensorFlow documentation: TensorFlow is a tool for machine learning. While it contains a wide range of functionality, TensorFlow is mainly designed for deep neural network models. -The easiest way to get started with tensorflow is using Eager Execution. +The easiest way to get started with TensorFlow is using Eager Execution. * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow. diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 995b8ae6663ed6..8c165aad52499a 100644 --- a/tensorflow/docs_src/install/install_c.md +++ b/tensorflow/docs_src/install/install_c.md @@ -38,7 +38,7 @@ enable TensorFlow for C: OS="linux" # Change to "darwin" for macOS TARGET_DIRECTORY="/usr/local" curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.8.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.8.0-rc1.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 2938a8f7eef8aa..26cbcc9a9b0a99 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.8.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.8.0-rc1.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 05604d95c5efbc..05b28787017487 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.8.0-rc0 + 1.8.0-rc1 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.8.0-rc0 + 1.8.0-rc1 @@ -124,12 +124,12 @@ instead: org.tensorflow libtensorflow - 1.8.0-rc0 + 1.8.0-rc1 org.tensorflow libtensorflow_jni_gpu - 1.8.0-rc0 + 1.8.0-rc1 ``` @@ -148,7 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0-rc0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0-rc1.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.8.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.8.0-rc1.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -175,10 +175,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: Take the following steps to install TensorFlow for Java on Windows: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0-rc0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0-rc1.jar), which is the TensorFlow Java Archive (JAR). 2. Download the following Java Native Interface (JNI) file appropriate for - [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.8.0-rc0.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.8.0-rc1.zip). 3. Extract this .zip file. @@ -227,7 +227,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -
javac -cp libtensorflow-1.8.0-rc0.jar HelloTF.java
+
javac -cp libtensorflow-1.8.0-rc1.jar HelloTF.java
### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.8.0-rc0.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.8.0-rc1.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.8.0-rc0.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.8.0-rc1.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, you've successfully installed TensorFlow for Java and are ready to use the API. If the program diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index e087b0c2218802..761555ca9a55ef 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -1,350 +1,273 @@ # Installing TensorFlow on Ubuntu -This guide explains how to install TensorFlow on Ubuntu. Although these -instructions might also work on other Linux variants, we have only -tested (and we only support) these instructions on machines meeting the -following requirements: +This guide explains how to install TensorFlow on Ubuntu Linux. While these +instructions may work on other Linux variants, they are tested and supported with +the following system requirements: - * 64-bit desktops or laptops - * Ubuntu 16.04 or higher +* 64-bit desktops or laptops +* Ubuntu 16.04 or higher -## Determine which TensorFlow to install +## Choose which TensorFlow to install -You must choose one of the following types of TensorFlow to install: +The following TensorFlow variants are available for installation: - * **TensorFlow with CPU support only**. If your system does not have a - NVIDIA® GPU, you must install this version. Note that this version of - TensorFlow is typically much easier to install (typically, - in 5 or 10 minutes), so even if you have an NVIDIA GPU, we recommend - installing this version first. - * **TensorFlow with GPU support**. TensorFlow programs typically run - significantly faster on a GPU than on a CPU. Therefore, if your - system has a NVIDIA® GPU meeting the prerequisites shown below and you - need to run performance-critical applications, you should ultimately - install this version. +* __TensorFlow with CPU support only__. If your system does not have a + NVIDIA® GPU, you must install this version. This version of TensorFlow is + usually easier to install, so even if you have an NVIDIA GPU, we recommend + installing this version first. +* __TensorFlow with GPU support__. TensorFlow programs usually run much faster on + a GPU instead of a CPU. If you run performance-critical applications and your + system has an NVIDIA® GPU that meets the prerequisites, you should install + this version. See [TensorFlow GPU support](#NVIDIARequirements) for details. - -### NVIDIA requirements to run TensorFlow with GPU support - -If you are installing TensorFlow with GPU support using one of the -mechanisms described in this guide, then the following NVIDIA software -must be installed on your system: - - * [CUDA Toolkit 9.0](http://nvidia.com/cuda). For details, see - [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/). - Ensure that you append the relevant CUDA pathnames to the - `LD_LIBRARY_PATH` environment variable as described in the - NVIDIA documentation. - * [cuDNN SDK v7](http://developer.nvidia.com/cudnn). For details, see - [NVIDIA's documentation](http://docs.nvidia.com/deeplearning/sdk/cudnn-install/). - Ensure that you create the `CUDA_HOME` environment variable as - described in the NVIDIA documentation. - * GPU card with CUDA Compute Capability 3.0 or higher for building - from source and 3.5 or higher for our binaries. See - [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for - a list of supported GPU cards. - * [GPU drivers](http://nvidia.com/driver) supporting your version of the CUDA - Toolkit. - * The libcupti-dev library, which is the NVIDIA CUDA Profile Tools Interface. - This library provides advanced profiling support. To install this library, - issue the following command for CUDA Toolkit >= 8.0: - - 
-    $ sudo apt-get install cuda-command-line-tools
-
- - and add its path to your `LD_LIBRARY_PATH` environment variable: - - 
-    $ export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}/usr/local/cuda/extras/CUPTI/lib64
-
- - For CUDA Toolkit <= 7.5 do: - - 
-    $ sudo apt-get install libcupti-dev
-
- - * **[OPTIONAL]** For optimized inferencing performance, you can also install - **NVIDIA TensorRT 3.0**. The minimal set of TensorRT runtime components needed - for use with the pre-built `tensorflow-gpu` package can be installed as follows: - - 
-    $ wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvinfer-runtime-trt-repo-ubuntu1404-3.0.4-ga-cuda9.0_1.0-1_amd64.deb
-    $ sudo dpkg -i nvinfer-runtime-trt-repo-ubuntu1404-3.0.4-ga-cuda9.0_1.0-1_amd64.deb
-    $ sudo apt-get update
-    $ sudo apt-get install -y --allow-downgrades libnvinfer-dev libcudnn7-dev=7.0.5.15-1+cuda9.0 libcudnn7=7.0.5.15-1+cuda9.0
-
- - **IMPORTANT:** For compatibility with the pre-built `tensorflow-gpu` - package, please use the Ubuntu **14.04** package of TensorRT as shown above, - even when installing onto an Ubuntu 16.04 system.
-
- To build the TensorFlow-TensorRT integration module from source rather than - using pre-built binaries, see the [module documentation](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/tensorrt#using-tensorrt-in-tensorflow). - For detailed TensorRT installation instructions, see [NVIDIA's TensorRT documentation](http://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html).
-
- To avoid cuDNN version conflicts during later system upgrades, you can hold - the cuDNN version at 7.0.5: - - 
-    $  sudo apt-mark hold libcudnn7 libcudnn7-dev
-
- - To later allow upgrades, you can remove the hold: - - 
-    $  sudo apt-mark unhold libcudnn7 libcudnn7-dev
-
- -If you have an earlier version of the preceding packages, please upgrade to -the specified versions. If upgrading is not possible, then you may still run -TensorFlow with GPU support, if you @{$install_sources$install TensorFlow from Sources}. - - -## Determine how to install TensorFlow - -You must pick the mechanism by which you install TensorFlow. The -supported choices are as follows: - - * [Virtualenv](#InstallingVirtualenv) - * ["native" pip](#InstallingNativePip) - * [Docker](#InstallingDocker) - * [Anaconda](#InstallingAnaconda) - * installing from sources, which is documented in - [a separate guide](https://www.tensorflow.org/install/install_sources). - -**We recommend the Virtualenv installation.** -[Virtualenv](https://virtualenv.pypa.io/en/stable/) -is a virtual Python environment isolated from other Python development, -incapable of interfering with or being affected by other Python programs -on the same machine. During the Virtualenv installation process, -you will install not only TensorFlow but also all the packages that -TensorFlow requires. (This is actually pretty easy.) -To start working with TensorFlow, you simply need to "activate" the -virtual environment. All in all, Virtualenv provides a safe and -reliable mechanism for installing and running TensorFlow. - -Native pip installs TensorFlow directly on your system without going -through any container system. **We recommend the native pip install for -system administrators aiming to make TensorFlow available to everyone on a -multi-user system.** Since a native pip installation is not walled-off in -a separate container, the pip installation might interfere with other -Python-based installations on your system. However, if you understand pip -and your Python environment, a native pip installation often entails only -a single command. - -Docker completely isolates the TensorFlow installation -from pre-existing packages on your machine. The Docker container contains -TensorFlow and all its dependencies. Note that the Docker image can be quite -large (hundreds of MBs). You might choose the Docker installation if you are -incorporating TensorFlow into a larger application architecture that already -uses Docker. -In Anaconda, you may use conda to create a virtual environment. -However, within Anaconda, we recommend installing TensorFlow with the -`pip install` command, not with the `conda install` command. +## How to install TensorFlow -**NOTE:** The conda package is community supported, not officially supported. -That is, the TensorFlow team neither tests nor maintains the conda package. -Use that package at your own risk. +There are a few options to install TensorFlow on your machine: +* [Use pip in a virtual environment](#InstallingVirtualenv) *(recommended)* +* [Use pip in your system environment](#InstallingNativePip) +* [Configure a Docker container](#InstallingDocker) +* [Use pip in Anaconda](#InstallingAnaconda) +* [Install TensorFlow from source](/install/install_sources) -## Installing with Virtualenv +### Use `pip` in a virtual environment -Take the following steps to install TensorFlow with Virtualenv: +Key Point: Using a virtual environment is the recommended install method. - 1. Install pip and Virtualenv by issuing one of the following commands: +The [Virtualenv](https://virtualenv.pypa.io/en/stable/) tool creates virtual +Python environments that are isolated from other Python development on the same +machine. In this scenario, you install TensorFlow and its dependencies within a +virtual environment that is available when *activated*. Virtualenv provides a +reliable way to install and run TensorFlow while avoiding conflicts with the rest +of the system. - 
$ sudo apt-get install python-pip python-dev python-virtualenv # for Python 2.7
-    $ sudo apt-get install python3-pip python3-dev python-virtualenv # for Python 3.n
+##### 1. Install Python, `pip`, and `virtualenv`. - 2. Create a Virtualenv environment by issuing one of the following commands: +On Ubuntu, Python is automatically installed and `pip` is *usually* installed. +Confirm the `python` and `pip` versions: - 
$ virtualenv --system-site-packages targetDirectory # for Python 2.7
-    $ virtualenv --system-site-packages -p python3 targetDirectory # for Python 3.n
+
+  python -V  # or: python3 -V
+  pip -V     # or: pip3 -V
+
+ +To install these packages on Ubuntu: + +
+  sudo apt-get install python-pip python-dev python-virtualenv   # for Python 2.7
+  sudo apt-get install python3-pip python3-dev python-virtualenv # for Python 3.n
+
- where targetDirectory specifies the top of the - Virtualenv tree. Our instructions assume that - targetDirectory is `~/tensorflow`, but you may - choose any directory. +We *recommend* using `pip` version 8.1 or higher. If using a release before +version 8.1, upgrade `pip`: - 3. Activate the Virtualenv environment by issuing one of the following - commands: +
+  sudo pip install -U pip
+
+ +If not using Ubuntu and [setuptools](https://pypi.org/project/setuptools/) is +installed, use `easy_install` to install `pip`: + +
+  easy_install -U pip
+
- 
$ source ~/tensorflow/bin/activate # bash, sh, ksh, or zsh
-    $ source ~/tensorflow/bin/activate.csh  # csh or tcsh
-    $ . ~/tensorflow/bin/activate.fish  # fish
+##### 2. Create a directory for the virtual environment and choose a Python interpreter. - The preceding source command should change your prompt - to the following: +
+  mkdir ~/tensorflow  # somewhere to work out of
+  cd ~/tensorflow
+  # Choose one of the following Python environments for the ./venv directory:
+  virtualenv --system-site-packages venv            # Use python default (Python 2.7)
+  virtualenv --system-site-packages -p python3 venv # Use Python 3.n
+
- 
(tensorflow)$
+##### 3. Activate the Virtualenv environment. - 4. Ensure pip ≥8.1 is installed: +Use one of these shell-specific commands to activate the virtual environment: - 
(tensorflow)$ easy_install -U pip
+
+  source ~/tensorflow/venv/bin/activate      # bash, sh, ksh, or zsh
+  source ~/tensorflow/venv/bin/activate.csh  # csh or tcsh
+  . ~/tensorflow/venv/bin/activate.fish      # fish
+
- 5. Issue one of the following commands to install TensorFlow in the active - Virtualenv environment: +When the Virtualenv is activated, the shell prompt displays as `(venv) $`. - 
(tensorflow)$ pip install --upgrade tensorflow      # for Python 2.7
-    (tensorflow)$ pip3 install --upgrade tensorflow     # for Python 3.n
-    (tensorflow)$ pip install --upgrade tensorflow-gpu  # for Python 2.7 and GPU
-    (tensorflow)$ pip3 install --upgrade tensorflow-gpu # for Python 3.n and GPU
+##### 4. Upgrade `pip` in the virtual environment. - If the above command succeeds, skip Step 6. If the preceding - command fails, perform Step 6. +Within the active virtual environment, upgrade `pip`: - 6. (Optional) If Step 5 failed (typically because you invoked a pip version - lower than 8.1), install TensorFlow in the active Virtualenv environment - by issuing a command of the following format: +
+(venv)$ pip install -U pip
+
- 
(tensorflow)$ pip install --upgrade tfBinaryURL   # Python 2.7
-    (tensorflow)$ pip3 install --upgrade tfBinaryURL  # Python 3.n
+You can install other Python packages within the virtual environment without +affecting packages outside the `virtualenv`. - where tfBinaryURL identifies the URL of the - TensorFlow Python package. The appropriate value of - tfBinaryURLdepends on the operating system, - Python version, and GPU support. Find the appropriate value for - tfBinaryURL for your system - [here](#the_url_of_the_tensorflow_python_package). For example, if you - are installing TensorFlow for Linux, Python 3.4, and CPU-only support, - issue the following command to install TensorFlow in the active - Virtualenv environment: +##### 5. Install TensorFlow in the virtual environment. - 
(tensorflow)$ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc0-cp34-cp34m-linux_x86_64.whl
+Choose one of the available TensorFlow packages for installation: -If you encounter installation problems, see -[Common Installation Problems](#common_installation_problems). +* `tensorflow` —Current release for CPU +* `tensorflow-gpu` —Current release with GPU support +* `tf-nightly` —Nightly build for CPU +* `tf-nightly-gpu` —Nightly build with GPU support +Within an active Virtualenv environment, use `pip` to install the package: -### Next Steps +
+  pip install -U tensorflow
+
-After installing TensorFlow, -[validate the installation](#ValidateYourInstallation). +Use `pip list` to show the packages installed in the virtual environment. +[Validate the install](#ValidateYourInstallation) and test the version: -Note that you must activate the Virtualenv environment each time you -use TensorFlow. If the Virtualenv environment is not currently active, -invoke one of the following commands: +
+(venv)$ python -c "import tensorflow as tf; print(tf.__version__)"
+
-
 $ source ~/tensorflow/bin/activate      # bash, sh, ksh, or zsh
-$ source ~/tensorflow/bin/activate.csh  # csh or tcsh
+Success: TensorFlow is now installed. -When the Virtualenv environment is active, you may run -TensorFlow programs from this shell. Your prompt will become -the following to indicate that your tensorflow environment is active: +Use the `deactivate` command to stop the Python virtual environment. -
(tensorflow)$
+#### Problems -When you are done using TensorFlow, you may deactivate the -environment by invoking the `deactivate` function as follows: +If the above steps failed, try installing the TensorFlow binary using the remote +URL of the `pip` package: -
(tensorflow)$ deactivate
+
+(venv)$ pip install --upgrade remote-pkg-URL   # Python 2.7
+(venv)$ pip3 install --upgrade remote-pkg-URL  # Python 3.n
+
-The prompt will revert back to your default prompt (as defined by the -`PS1` environment variable). +The remote-pkg-URL depends on the operating system, Python version, +and GPU support. See [here](#the_url_of_the_tensorflow_python_package) for the +URL naming scheme and location. +See [Common Installation Problems](#common_installation_problems) if you +encounter problems. -### Uninstalling TensorFlow +#### Uninstall TensorFlow -To uninstall TensorFlow, simply remove the tree you created. -For example: +To uninstall TensorFlow, remove the Virtualenv directory you created in step 2: -
$ rm -r targetDirectory
+
+  deactivate  # stop the virtualenv
+  rm -r ~/tensorflow/venv
+
-## Installing with native pip +### Use `pip` in your system environment + +Use `pip` to install the TensorFlow package directly on your system without +using a container or virtual environment for isolation. This method is +recommended for system administrators that want a TensorFlow installation that is +available to everyone on a multi-user system. -You may install TensorFlow through pip, choosing between a simple -installation procedure or a more complex one. +Since a system install is not isolated, it could interfere with other +Python-based installations. But if you understand `pip` and your Python +environment, a system `pip` install is straightforward. -**Note:** The +See the [REQUIRED_PACKAGES section of setup.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py) -lists the TensorFlow packages that pip will install or upgrade. +for a list of packages that TensorFlow installs. +##### 1. Install Python, `pip`, and `virtualenv`. -### Prerequisite: Python and Pip +On Ubuntu, Python is automatically installed and `pip` is *usually* installed. +Confirm the `python` and `pip` versions: -Python is automatically installed on Ubuntu. Take a moment to confirm -(by issuing a `python -V` command) that one of the following Python -versions is already installed on your system: +
+  python -V  # or: python3 -V
+  pip -V     # or: pip3 -V
+
+ +To install these packages on Ubuntu: - * Python 2.7 - * Python 3.4+ +
+  sudo apt-get install python-pip python-dev   # for Python 2.7
+  sudo apt-get install python3-pip python3-dev # for Python 3.n
+
-The pip or pip3 package manager is *usually* installed on Ubuntu. Take a -moment to confirm (by issuing a `pip -V` or `pip3 -V` command) -that pip or pip3 is installed. We strongly recommend version 8.1 or higher -of pip or pip3. If Version 8.1 or later is not installed, issue the -following command, which will either install or upgrade to the latest -pip version: +We *recommend* using `pip` version 8.1 or higher. If using a release before +version 8.1, upgrade `pip`: -
$ sudo apt-get install python-pip python-dev   # for Python 2.7
-$ sudo apt-get install python3-pip python3-dev # for Python 3.n
+
+  sudo pip install -U pip
 

 
+If not using Ubuntu and [setuptools](https://pypi.org/project/setuptools/) is
+installed, use `easy_install` to install `pip`:
 
-### Install TensorFlow
+
+  easy_install -U pip
+

 
-Assuming the prerequisite software is installed on your Linux host,
-take the following steps:
+##### 2. Install TensorFlow on system.
 
-  1. Install TensorFlow by invoking **one** of the following commands:
+Choose one of the available TensorFlow packages for installation:
 
-     
$ pip install tensorflow      # Python 2.7; CPU support (no GPU support)
-    $ pip3 install tensorflow     # Python 3.n; CPU support (no GPU support)
-    $ pip install tensorflow-gpu  # Python 2.7;  GPU support
-    $ pip3 install tensorflow-gpu # Python 3.n; GPU support 

+* `tensorflow` —Current release for CPU
+* `tensorflow-gpu` —Current release with GPU support
+* `tf-nightly` —Nightly build for CPU
+* `tf-nightly-gpu` —Nightly build with GPU support
 
-     If the preceding command runs to completion, you should now
-     [validate your installation](#ValidateYourInstallation).
+And use `pip` to install the package for Python 2 or 3:
 
-  2. (Optional.) If Step 1 failed, install the latest version of TensorFlow
-     by issuing a command of the following format:
+
+  sudo pip install -U tensorflow   # Python 2.7
+  sudo pip3 install -U tensorflow  # Python 3.n
+

 
-     
$ sudo pip  install --upgrade tfBinaryURL   # Python 2.7
-    $ sudo pip3 install --upgrade tfBinaryURL   # Python 3.n 

+Use `pip list` to show the packages installed on the system.
+[Validate the install](#ValidateYourInstallation) and test the version:
 
-     where tfBinaryURL identifies the URL of the
-     TensorFlow Python package. The appropriate value of
-     tfBinaryURL depends on the operating system,
-     Python version, and GPU support. Find the appropriate value for
-     tfBinaryURL
-     [here](#the_url_of_the_tensorflow_python_package).  For example, to
-     install TensorFlow for Linux, Python 3.4, and CPU-only support, issue
-     the following command:
+
+  python -c "import tensorflow as tf; print(tf.__version__)"
+

 
-     
-     $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc0-cp34-cp34m-linux_x86_64.whl
-     

+Success: TensorFlow is now installed.
 
-     If this step fails, see
-     [Common Installation Problems](#common_installation_problems).
+#### Problems
 
+If the above steps failed, try installing the TensorFlow binary using the remote
+URL of the `pip` package:
 
-### Next Steps
+
+  sudo pip install --upgrade remote-pkg-URL   # Python 2.7
+  sudo pip3 install --upgrade remote-pkg-URL  # Python 3.n
+

 
-After installing TensorFlow, [validate your installation](#ValidateYourInstallation).
+The remote-pkg-URL depends on the operating system, Python version,
+and GPU support. See [here](#the_url_of_the_tensorflow_python_package) for the
+URL naming scheme and location.
 
+See [Common Installation Problems](#common_installation_problems) if you
+encounter problems.
 
-### Uninstalling TensorFlow
+#### Uninstall TensorFlow
 
-To uninstall TensorFlow, issue one of following commands:
+To uninstall TensorFlow on your system, use one of following commands:
 
-
-$ sudo pip uninstall tensorflow  # for Python 2.7
-$ sudo pip3 uninstall tensorflow # for Python 3.n
+
+  sudo pip uninstall tensorflow   # for Python 2.7
+  sudo pip3 uninstall tensorflow  # for Python 3.n
 

 
-
 
-## Installing with Docker
+### Configure a Docker container
+
+Docker completely isolates the TensorFlow installation
+from pre-existing packages on your machine. The Docker container contains
+TensorFlow and all its dependencies. Note that the Docker image can be quite
+large (hundreds of MBs). You might choose the Docker installation if you are
+incorporating TensorFlow into a larger application architecture that already
+uses Docker.
 
 Take the following steps to install TensorFlow through Docker:
 
@@ -364,7 +287,7 @@ Take the following steps to install TensorFlow through Docker:
 The remainder of this section explains how to launch a Docker container.
 
 
-### CPU-only
+#### CPU-only
 
 To launch a Docker container with CPU-only support (that is, without
 GPU support), enter a command of the following format:
@@ -414,7 +337,7 @@ $ docker run -it -p 8888:8888 tensorflow/tensorflow
 Docker will download the TensorFlow binary image the first time you launch it.
 
 
-### GPU support
+#### GPU support
 
 Prior to installing TensorFlow with GPU support, ensure that your system meets all
 [NVIDIA software requirements](#NVIDIARequirements).  To launch a Docker container
@@ -470,14 +393,22 @@ For more details see the
 [TensorFlow docker readme](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/docker).
 
 
-### Next Steps
+#### Next Steps
 
 You should now
 [validate your installation](#ValidateYourInstallation).
 
 
 
-## Installing with Anaconda
+### Use `pip` in Anaconda
+
+Anaconda provides the `conda` utility to create a virtual environment. However,
+within Anaconda, we recommend installing TensorFlow using the `pip install`
+command and *not* with the `conda install` command.
+
+Caution: `conda` is a community supported package this is not officially
+maintained by the TensorFlow team. Use this package at your own risk since it is
+not tested on new TensorFlow releases.
 
 Take the following steps to install TensorFlow in an Anaconda environment:
 
@@ -507,7 +438,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc0-cp34-cp34m-linux_x86_64.whl

+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc1-cp34-cp34m-linux_x86_64.whl

 
 
 ## Validate your installation
@@ -563,11 +494,89 @@ installation problems](#common_installation_problems).
 If you are new to machine learning, we recommend the following:
 
 *  [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course)
-*  @{$get_started/get_started_for_beginners$Getting Started for ML Beginners}
+*  @{$get_started/eager}
 
 If you are experienced with machine learning but new to TensorFlow, see
 @{$get_started/eager}.
 
+
+## TensorFlow GPU support
+
+To install TensorFlow with GPU support, configure the following NVIDIA® software
+on your system:
+
+* [CUDA Toolkit 9.0](http://nvidia.com/cuda). For details, see
+  [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
+  Append the relevant CUDA pathnames to the `LD_LIBRARY_PATH` environmental
+  variable as described in the NVIDIA documentation.
+* [cuDNN SDK v7](http://developer.nvidia.com/cudnn). For details, see
+  [NVIDIA's documentation](http://docs.nvidia.com/deeplearning/sdk/cudnn-install/).
+  Create the `CUDA_HOME` environment variable as described in the NVIDIA
+  documentation.
+* A GPU card with CUDA Compute Capability 3.0 or higher for building TensorFlow
+  from source. To use the TensorFlow binaries, version 3.5 or higher is required.
+  See the [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a
+  list of supported GPU cards.
+* [GPU drivers](http://nvidia.com/driver) that support your version of the CUDA
+  Toolkit.
+* The `libcupti-dev` library is the NVIDIA CUDA Profile Tools Interface. This
+  library provides advanced profiling support. To install this library,
+  use the following command for CUDA Toolkit >= 8.0:
+
+
+  sudo apt-get install cuda-command-line-tools
+

+
+Add this path to the `LD_LIBRARY_PATH` environmental variable:
+
+
+  export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}/usr/local/cuda/extras/CUPTI/lib64
+

+
+For CUDA Toolkit <= 7.5 use:
+
+
+  sudo apt-get install libcupti-dev
+

+
+* *OPTIONAL*:  For optimized performance during inference, install
+  *NVIDIA TensorRT 3.0*. To install the minimal amount of TensorRT
+  runtime components required to use with the pre-built `tensorflow-gpu` package:
+
+
+  wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvinfer-runtime-trt-repo-ubuntu1404-3.0.4-ga-cuda9.0_1.0-1_amd64.deb
+  sudo dpkg -i nvinfer-runtime-trt-repo-ubuntu1404-3.0.4-ga-cuda9.0_1.0-1_amd64.deb
+  sudo apt-get update
+  sudo apt-get install -y --allow-downgrades libnvinfer-dev libcudnn7-dev=7.0.5.15-1+cuda9.0 libcudnn7=7.0.5.15-1+cuda9.0
+

+
+Note: For compatibility with the pre-built `tensorflow-gpu` package, use the
+Ubuntu *14.04* package of TensorRT (shown above). Use this even when installing
+on an Ubuntu 16.04 system.
+
+To build the TensorFlow-TensorRT integration module from source instead of using
+the pre-built binaries, see the
+[module documentation](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/tensorrt#using-tensorrt-in-tensorflow).
+For detailed TensorRT installation instructions, see
+[NVIDIA's TensorRT documentation](http://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html).
+
+To avoid cuDNN version conflicts during later system upgrades, hold the cuDNN
+version at 7.0.5:
+
+
+  sudo apt-mark hold libcudnn7 libcudnn7-dev
+

+
+To allow upgrades, remove the this hold:
+
+
+  sudo apt-mark unhold libcudnn7 libcudnn7-dev
+

+
+If you have an earlier version of the preceding packages, upgrade to the
+specified versions. If upgrading is not possible, you can still run TensorFlow
+with GPU support by @{$install_sources}.
+
 
 ## Common installation problems
 
@@ -581,7 +590,7 @@ ask a new question about it on Stack Overflow and specify
 the `tensorflow` tag.
 
 
-
+
@@ -681,14 +690,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc1-cp27-none-linux_x86_64.whl
 

 
 
 GPU support:
 
 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0rc1-cp27-none-linux_x86_64.whl
 

 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -700,14 +709,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc1-cp34-cp34m-linux_x86_64.whl
 

 
 
 GPU support:
 
 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0rc1-cp34-cp34m-linux_x86_64.whl
 

 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -719,14 +728,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc1-cp35-cp35m-linux_x86_64.whl
 

 
 
 GPU support:
 
 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0rc1-cp35-cp35m-linux_x86_64.whl
 

 
 
@@ -738,14 +747,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0rc1-cp36-cp36m-linux_x86_64.whl
 

 
 
 GPU support:
 
 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0rc1-cp36-cp36m-linux_x86_64.whl
 

 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index af24aaaca84af3..90d9ea028822ef 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -119,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      TensorFlow in the active Virtualenv is as follows:
 
      
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc0-py3-none-any.whl

+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc1-py3-none-any.whl
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -242,7 +242,7 @@ take the following steps:
      issue the following command:
 
      
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc0-py3-none-any.whl 

+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc1-py3-none-any.whl
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc0-py2-none-any.whl

+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc1-py2-none-any.whl
@@ -524,7 +524,7 @@ The value you specify depends on your Python version.
 
 
 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc1-py2-none-any.whl
 

 
 
@@ -532,5 +532,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc0-py2-none-a
 
 
 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0rc1-py3-none-any.whl
 

diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index 649c5b47511040..a4fec382f4add4 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -354,10 +354,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.8.0rc0 on Linux:
+for TensorFlow 1.8.0rc1 on Linux:
 
 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.8.0rc0-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.8.0rc1-py2-none-any.whl
 

 
 ## Validate your installation
diff --git a/tensorflow/docs_src/performance/xla/tfcompile.md b/tensorflow/docs_src/performance/xla/tfcompile.md
index f57ca3948dd52d..8521d7eacb4a7f 100644
--- a/tensorflow/docs_src/performance/xla/tfcompile.md
+++ b/tensorflow/docs_src/performance/xla/tfcompile.md
@@ -86,7 +86,7 @@ code. `tf_library` utilizes `tfcompile` to compile the TensorFlow graph into
 executable code.
 
 ```build
-load("//third_party/tensorflow/compiler/aot:tfcompile.bzl", "tf_library")
+load("//tensorflow/compiler/aot:tfcompile.bzl", "tf_library")
 
 # Use the tf_library macro to compile your graph into executable code.
 tf_library(
@@ -258,8 +258,8 @@ file.
 
 ```build
 # Example of linking your binary
-# Also see //third_party/tensorflow/compiler/aot/tests/BUILD
-load("//third_party/tensorflow/compiler/aot:tfcompile.bzl", "tf_library")
+# Also see //tensorflow/compiler/aot/tests/BUILD
+load("//tensorflow/compiler/aot:tfcompile.bzl", "tf_library")
 
 # The same tf_library call from step 2 above.
 tf_library(
diff --git a/tensorflow/examples/tutorials/estimators/__init__.py b/tensorflow/examples/tutorials/estimators/__init__.py
new file mode 100644
index 00000000000000..e69de29bb2d1d6
diff --git a/tensorflow/examples/tutorials/input_fn/__init__.py b/tensorflow/examples/tutorials/input_fn/__init__.py
new file mode 100644
index 00000000000000..e69de29bb2d1d6
diff --git a/tensorflow/examples/tutorials/layers/__init__.py b/tensorflow/examples/tutorials/layers/__init__.py
new file mode 100644
index 00000000000000..e69de29bb2d1d6
diff --git a/tensorflow/examples/tutorials/monitors/__init__.py b/tensorflow/examples/tutorials/monitors/__init__.py
new file mode 100644
index 00000000000000..e69de29bb2d1d6
diff --git a/tensorflow/examples/tutorials/monitors/iris_monitors.py b/tensorflow/examples/tutorials/monitors/iris_monitors.py
index 850d105f7b1b33..a2b7fe60237da0 100644
--- a/tensorflow/examples/tutorials/monitors/iris_monitors.py
+++ b/tensorflow/examples/tutorials/monitors/iris_monitors.py
@@ -32,9 +32,9 @@
 def main(unused_argv):
   # Load datasets.
   training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
-      filename=IRIS_TRAINING, target_dtype=np.int, features_dtype=np.float)
+      filename=IRIS_TRAINING, target_dtype=np.int, features_dtype=np.float32)
   test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
-      filename=IRIS_TEST, target_dtype=np.int, features_dtype=np.float)
+      filename=IRIS_TEST, target_dtype=np.int, features_dtype=np.float32)
 
   validation_metrics = {
       "accuracy":
@@ -83,7 +83,7 @@ def main(unused_argv):
 
   # Classify two new flower samples.
   new_samples = np.array(
-      [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)
+      [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=np.float32)
   y = list(classifier.predict(new_samples))
   print("Predictions: {}".format(str(y)))
 
diff --git a/tensorflow/go/README.md b/tensorflow/go/README.md
index b1bd87eb0c3b3a..e251356ec8e973 100644
--- a/tensorflow/go/README.md
+++ b/tensorflow/go/README.md
@@ -5,7 +5,7 @@ Construct and execute TensorFlow graphs in Go.
 [![GoDoc](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go?status.svg)](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go)
 
 > *WARNING*: The API defined in this package is not stable and can change
-> without notice. The same goes for the awkward package path
+> without notice. The same goes for the package path:
 > (`github.com/tensorflow/tensorflow/tensorflow/go`).
 
 ## Quickstart
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index c12ea5156356f2..2f1be51ada8665 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -21386,7 +21386,7 @@ func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr {
 //    generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
 //
 // The `bad_color` argument is the color to use in the generated images for
-// non-finite input values.  It is a `unit8` 1-D tensor of length `channels`.
+// non-finite input values.  It is a `uint8` 1-D tensor of length `channels`.
 // Each element must be in the range `[0, 255]` (It represents the value of a
 // pixel in the output image).  Non-finite values in the input tensor are
 // replaced by this tensor in the output image.  The default value is the color
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 63099b44bbf7ba..0970f001240c5b 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -644,11 +644,9 @@ def export_savedmodel(
               sharded=True)
           saver_for_restore.restore(session, checkpoint_path)
 
-          # pylint: disable=protected-access
           local_init_op = (
               estimator_spec.scaffold.local_init_op or
-              monitored_session.Scaffold._default_local_init_op())
-          # pylint: enable=protected-access
+              monitored_session.Scaffold.default_local_init_op())
 
           # Perform the export
           builder = saved_model_builder.SavedModelBuilder(temp_export_dir)
diff --git a/tensorflow/python/keras/_impl/keras/estimator.py b/tensorflow/python/keras/_impl/keras/estimator.py
index b922a6c68399e6..c3c3fceb454773 100644
--- a/tensorflow/python/keras/_impl/keras/estimator.py
+++ b/tensorflow/python/keras/_impl/keras/estimator.py
@@ -29,12 +29,14 @@
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import random_seed
 from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
+from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras import models
 from tensorflow.python.keras._impl.keras import optimizers
 from tensorflow.python.keras._impl.keras.engine.base_layer import Layer
 from tensorflow.python.keras._impl.keras.engine.network import Network
 from tensorflow.python.keras._impl.keras.utils.generic_utils import CustomObjectScope
+from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import metrics as metrics_module
 from tensorflow.python.ops import variables as variables_module
@@ -55,6 +57,17 @@ def _cast_tensor_to_floatx(x):
     return math_ops.cast(x, K.floatx())
 
 
+def _convert_tensor(x):
+  """Create or cast tensor if needed."""
+  if not tensor_util.is_tensor(x):
+    # x is a numpy array
+    x = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(x)
+  if check_ops.is_numeric_tensor(x):
+    # is_numeric_tensor returns False if provided with a numpy array
+    x = _cast_tensor_to_floatx(x)
+  return x
+
+
 def _any_variable_initalized():
   """Check if any variable has been initialized in the Keras model.
 
@@ -86,7 +99,7 @@ def _create_ordered_io(keras_model, estimator_io, is_input=True):
   if isinstance(estimator_io, (list, tuple)):
     # Case currently not supported by most built-in input_fn,
     # but it's good to have for sanity
-    return [_cast_tensor_to_floatx(x) for x in estimator_io]
+    return [_convert_tensor(x) for x in estimator_io]
   elif isinstance(estimator_io, dict):
     if is_input:
       if keras_model._is_graph_network:
@@ -108,12 +121,12 @@ def _create_ordered_io(keras_model, estimator_io, is_input=True):
             'It needs to match one '
             'of the following: %s' % ('input' if is_input else 'output', key,
                                       ', '.join(keras_io_names)))
-      tensors = [_cast_tensor_to_floatx(estimator_io[io_name])
+      tensors = [_convert_tensor(estimator_io[io_name])
                  for io_name in keras_io_names]
     return tensors
   else:
     # Plain array.
-    return _cast_tensor_to_floatx(estimator_io)
+    return _convert_tensor(estimator_io)
 
 
 def _in_place_subclassed_model_reset(model):
@@ -274,8 +287,7 @@ def _clone_and_build_model(mode,
                                         is_input=False)
   else:
     target_tensors = [
-        _cast_tensor_to_floatx(
-            sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(labels))
+        _convert_tensor(labels)
     ]
 
   if keras_model._is_graph_network:
diff --git a/tensorflow/python/keras/_impl/keras/estimator_test.py b/tensorflow/python/keras/_impl/keras/estimator_test.py
index 653cdc01e24566..80fa87d0410871 100644
--- a/tensorflow/python/keras/_impl/keras/estimator_test.py
+++ b/tensorflow/python/keras/_impl/keras/estimator_test.py
@@ -30,6 +30,7 @@
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
 from tensorflow.python.keras._impl import keras
+from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras import testing_utils
 from tensorflow.python.keras._impl.keras.applications import mobilenet
 from tensorflow.python.keras._impl.keras.optimizers import SGD
@@ -142,16 +143,20 @@ def randomize_io_type(array, name):
 
 
 def multi_inputs_multi_outputs_model():
-  # test multi-input layer
   a = keras.layers.Input(shape=(16,), name='input_a')
   b = keras.layers.Input(shape=(16,), name='input_b')
+  m = keras.layers.Input(shape=(8,), dtype='bool', name='input_m')
   dense = keras.layers.Dense(8, name='dense_1')
+
   a_2 = dense(a)
+  # Apply a mask
+  s_2 = keras.layers.Lambda(lambda k:
+                            K.switch(k[0], k[1], K.zeros_like(k[1])))([m, a_2])
   b_2 = dense(b)
-  merged = keras.layers.concatenate([a_2, b_2], name='merge')
+  merged = keras.layers.concatenate([s_2, b_2], name='merge')
   c = keras.layers.Dense(3, activation='softmax', name='dense_2')(merged)
   d = keras.layers.Dense(2, activation='softmax', name='dense_3')(merged)
-  model = keras.models.Model(inputs=[a, b], outputs=[c, d])
+  model = keras.models.Model(inputs=[a, b, m], outputs=[c, d])
   model.compile(
       loss='categorical_crossentropy',
       optimizer='rmsprop',
@@ -352,18 +357,27 @@ def test_multi_inputs_multi_outputs(self):
         test_samples=50,
         input_shape=(16,),
         num_classes=2)
+    np.random.seed(_RANDOM_SEED)
+    (input_m_train, _), (input_m_test, _) = testing_utils.get_test_data(
+        train_samples=_TRAIN_SIZE,
+        test_samples=50,
+        input_shape=(8,),
+        num_classes=2)
+
     c_train = keras.utils.to_categorical(c_train)
     c_test = keras.utils.to_categorical(c_test)
     d_train = keras.utils.to_categorical(d_train)
     d_test = keras.utils.to_categorical(d_test)
 
     def train_input_fn():
-      input_dict = {'input_a': a_train, 'input_b': b_train}
+      input_dict = {'input_a': a_train, 'input_b': b_train,
+                    'input_m': input_m_train > 0}
       output_dict = {'dense_2': c_train, 'dense_3': d_train}
       return input_dict, output_dict
 
     def eval_input_fn():
-      input_dict = {'input_a': a_test, 'input_b': b_test}
+      input_dict = {'input_a': a_test, 'input_b': b_test,
+                    'input_m': input_m_test > 0}
       output_dict = {'dense_2': c_test, 'dense_3': d_test}
       return input_dict, output_dict
 
diff --git a/tensorflow/python/kernel_tests/division_past_test.py b/tensorflow/python/kernel_tests/division_past_test.py
index 2ff2f894077ebd..9ddd62e63cc49d 100644
--- a/tensorflow/python/kernel_tests/division_past_test.py
+++ b/tensorflow/python/kernel_tests/division_past_test.py
@@ -35,8 +35,7 @@ def testDivision(self):
     """Test all the different ways to divide."""
     values = [1, 2, 7, 11]
     functions = (lambda x: x), constant_op.constant
-    # TODO(irving): Test int8, int16 once we support casts for those.
-    dtypes = np.int32, np.int64, np.float32, np.float64
+    dtypes = np.int8, np.int16, np.int32, np.int64, np.float32, np.float64
 
     tensors = []
     checks = []
diff --git a/tensorflow/python/kernel_tests/reduce_join_op_test.py b/tensorflow/python/kernel_tests/reduce_join_op_test.py
index 7f3049b9f841c5..fb9e5cc2a3727b 100644
--- a/tensorflow/python/kernel_tests/reduce_join_op_test.py
+++ b/tensorflow/python/kernel_tests/reduce_join_op_test.py
@@ -160,7 +160,7 @@ def _testMultipleReduceJoin(self,
             separator=separator)
       if not reduction_indices:
         truth = constant_op.constant(truth)
-      truth_squeezed = array_ops.squeeze(truth, squeeze_dims=reduction_indices)
+      truth_squeezed = array_ops.squeeze(truth, axis=reduction_indices)
       output_array = output.eval()
       output_keep_dims_array = output_keep_dims.eval()
       truth_array = truth.eval()
diff --git a/tensorflow/python/kernel_tests/reduction_ops_test.py b/tensorflow/python/kernel_tests/reduction_ops_test.py
index 589ea54973c109..ea78b58d88f7ff 100644
--- a/tensorflow/python/kernel_tests/reduction_ops_test.py
+++ b/tensorflow/python/kernel_tests/reduction_ops_test.py
@@ -889,9 +889,9 @@ def testEmpty(self):
 
 class CountNonzeroReductionTest(test.TestCase):
 
-  def _compare(self, x, reduction_axes, keepdims, use_gpu=False,
+  def _compare(self, x, reduction_axes, keepdims, use_gpu=False, zero=0,
                feed_dict=None):
-    np_ans = (x != 0).astype(np.int32)
+    np_ans = (x != zero).astype(np.int32)
     if reduction_axes is None:
       np_ans = np.sum(np_ans, keepdims=keepdims)
     else:
@@ -958,6 +958,37 @@ def testDegenerate(self):
           y = math_ops.count_nonzero(x, [0])
           self.assertAllEqual(y.eval(), np.zeros(9938))
 
+  def testStringReduce(self):
+    # Test case for GitHub issue 18712
+    with self.test_session() as sess:
+      v = math_ops.count_nonzero(constant_op.constant(["test"]))
+      self.assertAllClose(sess.run(v), 1)
+
+  def testStringReduce1D(self):
+    # Create a 1D array of strings
+    x = np.asarray(["", "", "a", "", "", "b"])
+    self._compare(x, None, keepdims=False, zero=np.str(""))
+    self._compare(x, [], keepdims=False, zero=np.str(""))
+    self._compare(x, [0], keepdims=False, zero=np.str(""))
+    self._compare(x, None, keepdims=True, zero=np.str(""))
+    self._compare(x, [], keepdims=True, zero=np.str(""))
+    self._compare(x, [0], keepdims=True, zero=np.str(""))
+
+  def testStringReduce2D(self):
+    # Create a 2D array of strings
+    x = np.asarray([["", "", "a", "", "", "b"],
+                    ["", "c", "", "d", "", ""],
+                    ["e", "", "f", "", "", ""]])
+    self._compare(x, None, keepdims=False, zero=np.str(""))
+    self._compare(x, [], keepdims=False, zero=np.str(""))
+    self._compare(x, [0], keepdims=False, zero=np.str(""))
+    self._compare(x, [1], keepdims=False, zero=np.str(""))
+    self._compare(x, [0, 1], keepdims=False, zero=np.str(""))
+    self._compare(x, None, keepdims=True, zero=np.str(""))
+    self._compare(x, [], keepdims=True, zero=np.str(""))
+    self._compare(x, [0], keepdims=True, zero=np.str(""))
+    self._compare(x, [0, 1], keepdims=True, zero=np.str(""))
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py
index 9f5794951524b2..b7477a768ab718 100644
--- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py
+++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py
@@ -364,6 +364,42 @@ def scatter_nd(self, indices, updates, shape, input_=None):
     del input_  # input_ is not used in scatter_nd
     return array_ops.scatter_nd(indices, updates, shape)
 
+  def testString(self):
+    indices = constant_op.constant([[4], [3], [1], [7]],
+                                   dtype=dtypes.int32)
+    updates = constant_op.constant(["four", "three", "one", "seven"],
+                                   dtype=dtypes.string)
+    expected = np.array([b"", b"one", b"", b"three", b"four",
+                         b"", b"", b"seven"])
+    scatter = self.scatter_nd(indices, updates, shape=(8,))
+    with self.test_session() as sess:
+      result = sess.run(scatter)
+      self.assertAllEqual(expected, result)
+
+    # Same indice is updated twice by same value.
+    indices = constant_op.constant([[4], [3], [3], [7]],
+                                   dtype=dtypes.int32)
+    updates = constant_op.constant(["a", "b", "b", "c"],
+                                   dtype=dtypes.string)
+    expected = np.array([b"", b"", b"", b"bb", b"a", b"", b"", b"c"])
+    scatter = self.scatter_nd(indices, updates, shape=(8,))
+    with self.test_session() as sess:
+      result = sess.run(scatter)
+      self.assertAllEqual(expected, result)
+
+    # Same indice is updated twice by different value.
+    indices = constant_op.constant([[4], [3], [3], [7]],
+                                   dtype=dtypes.int32)
+    updates = constant_op.constant(["a", "b", "c", "d"],
+                                   dtype=dtypes.string)
+    expected = [np.array([b"", b"", b"", b"bc", b"a", b"", b"", b"d"]),
+                np.array([b"", b"", b"", b"cb", b"a", b"", b"", b"d"])]
+    scatter = self.scatter_nd(indices, updates, shape=(8,))
+    with self.test_session() as sess:
+      result = sess.run(scatter)
+      self.assertTrue(np.array_equal(result, expected[0]) or
+                      np.array_equal(result, expected[1]))
+
   def testRank3ValidShape(self):
     indices = array_ops.zeros([2, 2, 2], dtypes.int32)
     updates = array_ops.zeros([2, 2, 2], dtypes.int32)
@@ -584,6 +620,10 @@ def scatter_nd(self, indices, updates, shape, input_=None):
         shape, dtype=updates.dtype))
     return array_ops.scatter_nd_non_aliasing_add(input_, indices, updates)
 
+  def testString(self):
+    # Not supported yet.
+    pass
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py
index 57d2657838760a..3678bd4c1f6a45 100644
--- a/tensorflow/python/ops/array_grad.py
+++ b/tensorflow/python/ops/array_grad.py
@@ -196,7 +196,7 @@ def _ExtractInputShapes(inputs):
             array_ops.where(
                 math_ops.logical_and(grad.indices >= start,
                                      grad.indices < end)),
-            squeeze_dims=[1])
+            axis=[1])
         new_indices = array_ops.gather(grad.indices, indices_to_select) - start
         new_values = array_ops.gather(grad.values, indices_to_select)
         out_grads.append(ops.IndexedSlices(new_values, new_indices, size))
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 3c2593066ad347..e235047aff39f6 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -994,9 +994,7 @@ def unstack(value, num=None, axis=0, name="unstack"):
     `value[:, i, :, :]` and each tensor in `output` will have shape `(A, C, D)`.
   Etc.
 
-  This is the opposite of stack.  The numpy equivalent is
-
-      tf.unstack(x, n) = np.unstack(x)
+  This is the opposite of stack.
 
   Args:
     value: A rank `R > 0` `Tensor` to be unstacked.
@@ -1720,8 +1718,10 @@ def placeholder(dtype, shape=None, name=None):
     print(sess.run(y, feed_dict={x: rand_array}))  # Will succeed.
   ```
 
-  @compatibility{eager} Placeholders are not compatible with eager execution.
-
+  @compatibility(eager)
+  Placeholders are not compatible with eager execution.
+  @end_compatibility
+  
   Args:
     dtype: The type of elements in the tensor to be fed.
     shape: The shape of the tensor to be fed (optional). If the shape is not
diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py
index 601010bce9efaf..bd5b2ae83b5dd1 100644
--- a/tensorflow/python/ops/image_ops_impl.py
+++ b/tensorflow/python/ops/image_ops_impl.py
@@ -652,7 +652,7 @@ def pad_to_bounding_box(image, offset_height, offset_width, target_height,
     padded.set_shape(padded_shape)
 
     if not is_batch:
-      padded = array_ops.squeeze(padded, squeeze_dims=[0])
+      padded = array_ops.squeeze(padded, axis=[0])
 
     return padded
 
@@ -732,7 +732,7 @@ def crop_to_bounding_box(image, offset_height, offset_width, target_height,
     cropped.set_shape(cropped_shape)
 
     if not is_batch:
-      cropped = array_ops.squeeze(cropped, squeeze_dims=[0])
+      cropped = array_ops.squeeze(cropped, axis=[0])
 
     return cropped
 
@@ -849,7 +849,7 @@ def equal_(x, y):
     resized = control_flow_ops.with_dependencies(assert_ops, resized)
 
     if not is_batch:
-      resized = array_ops.squeeze(resized, squeeze_dims=[0])
+      resized = array_ops.squeeze(resized, axis=[0])
 
     return resized
 
@@ -942,7 +942,7 @@ def resize_images(images,
            for x in [new_width_const, width, new_height_const, height]) and (
                width == new_width_const and height == new_height_const):
       if not is_batch:
-        images = array_ops.squeeze(images, squeeze_dims=[0])
+        images = array_ops.squeeze(images, axis=[0])
       return images
 
     if method == ResizeMethod.BILINEAR:
@@ -965,7 +965,7 @@ def resize_images(images,
     images.set_shape([None, new_height_const, new_width_const, None])
 
     if not is_batch:
-      images = array_ops.squeeze(images, squeeze_dims=[0])
+      images = array_ops.squeeze(images, axis=[0])
     return images
 
 
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 57660578aa08f1..7ac3bd8091f1cc 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -1338,8 +1338,18 @@ def count_nonzero(input_tensor,
   tf.count_nonzero(x, [0, 1])  # 3
   ```
 
+  **NOTE** Strings are compared against zero-length empty string `""`. Any
+  string with a size greater than zero is already considered as nonzero.
+
+  For example:
+  ```python
+  x = tf.constant(["", "a", "  ", "b", ""])
+  tf.count_nonzero(x) # 3, with "a", "  ", and "b" as nonzero strings.
+  ```
+
   Args:
-    input_tensor: The tensor to reduce. Should be of numeric type, or `bool`.
+    input_tensor: The tensor to reduce. Should be of numeric type, `bool`,
+      or `string`.
     axis: The dimensions to reduce. If `None` (the default),
       reduces all dimensions. Must be in the range
       `[-rank(input_tensor), rank(input_tensor))`.
@@ -1359,7 +1369,8 @@ def count_nonzero(input_tensor,
 
   with ops.name_scope(name, "count_nonzero", [input_tensor]):
     input_tensor = ops.convert_to_tensor(input_tensor, name="input_tensor")
-    zero = input_tensor.dtype.as_numpy_dtype()
+    # A scalar of 'zero' is enough as `not_equal` will broadcast.
+    zero = array_ops.zeros([], dtype=input_tensor.dtype)
     return cast(
         reduce_sum(
             # int64 reduction happens on GPU
diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py
index d0d5ed07ced362..576627e78ed10d 100644
--- a/tensorflow/python/ops/nn_impl.py
+++ b/tensorflow/python/ops/nn_impl.py
@@ -765,9 +765,9 @@ def weighted_moments(x, axes, frequency_weights, name=None, keep_dims=False):
     weighted_variance = math_ops.multiply(weighted_distsq, divisor)
 
     if not keep_dims:
-      weighted_mean = array_ops.squeeze(weighted_mean, squeeze_dims=axes)
+      weighted_mean = array_ops.squeeze(weighted_mean, axis=axes)
       weighted_variance = array_ops.squeeze(
-          weighted_variance, squeeze_dims=axes)
+          weighted_variance, axis=axes)
 
     if needs_cast:
       weighted_mean = math_ops.cast(weighted_mean, dtypes.float16)
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index 86dc053c0fb8d0..67f753485b8c30 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -785,10 +785,14 @@ def build(self, inputs_shape):
         shape=[input_depth + h_depth, 4 * self._num_units],
         initializer=self._initializer,
         partitioner=maybe_partitioner)
+    if self.dtype is None:
+      initializer = init_ops.zeros_initializer
+    else:
+      initializer = init_ops.zeros_initializer(dtype=self.dtype)
     self._bias = self.add_variable(
         _BIAS_VARIABLE_NAME,
         shape=[4 * self._num_units],
-        initializer=init_ops.zeros_initializer(dtype=self.dtype))
+        initializer=initializer)
     if self._use_peepholes:
       self._w_f_diag = self.add_variable("w_f_diag", shape=[self._num_units],
                                          initializer=self._initializer)
diff --git a/tensorflow/python/training/adam.py b/tensorflow/python/training/adam.py
index 006e360389b404..6fa3ff66583ce0 100644
--- a/tensorflow/python/training/adam.py
+++ b/tensorflow/python/training/adam.py
@@ -43,23 +43,19 @@ def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8,
 
     Initialization:
 
-    ```
-    m_0 <- 0 (Initialize initial 1st moment vector)
-    v_0 <- 0 (Initialize initial 2nd moment vector)
-    t <- 0 (Initialize timestep)
-    ```
+    $$m_0 := 0 (Initialize initial 1st moment vector)$$
+    $$v_0 := 0 (Initialize initial 2nd moment vector)$$
+    $$t := 0 (Initialize timestep)$$
 
     The update rule for `variable` with gradient `g` uses an optimization
     described at the end of section2 of the paper:
 
-    ```
-    t <- t + 1
-    lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
+    $$t := t + 1$$
+    $$lr_t := \text{learning_rate} * \sqrt{(1 - beta_2^t) / (1 - beta_1^t)}$$
 
-    m_t <- beta1 * m_{t-1} + (1 - beta1) * g
-    v_t <- beta2 * v_{t-1} + (1 - beta2) * g * g
-    variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
-    ```
+    $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$
+    $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$
+    $$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$
 
     The default value of 1e-8 for epsilon might not be a good default in
     general. For example, when training an Inception network on ImageNet a
diff --git a/tensorflow/python/training/input_test.py b/tensorflow/python/training/input_test.py
index 3a25bfe3432238..1b1e89cb26d336 100644
--- a/tensorflow/python/training/input_test.py
+++ b/tensorflow/python/training/input_test.py
@@ -497,6 +497,28 @@ def testOneThread(self):
   def testOneThreadDict(self):
     self._testOneThreadHelper(use_dict=True)
 
+  def testUint32DataTypes(self):
+    values = constant_op.constant([0, 1, 2, 3, 4, 5], dtype=dtypes.uint32)
+    batched = inp.batch([values], batch_size=2)
+    with self.test_session() as sess:
+      coord = coordinator.Coordinator()
+      threads = queue_runner_impl.start_queue_runners(sess=sess, coord=coord)
+      sess.run(batched)
+      coord.request_stop()
+      for thread in threads:
+        thread.join()
+
+  def testUint64DataTypes(self):
+    values = constant_op.constant([0, 1, 2, 3, 4, 5], dtype=dtypes.uint64)
+    batched = inp.batch([values], batch_size=2)
+    with self.test_session() as sess:
+      coord = coordinator.Coordinator()
+      threads = queue_runner_impl.start_queue_runners(sess=sess, coord=coord)
+      sess.run(batched)
+      coord.request_stop()
+      for thread in threads:
+        thread.join()
+
   def testOneThreadDynamicPad(self):
     with self.test_session() as sess:
       batch_size = 10
diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py
index 4ce6f6d0026741..f584a009d946a1 100644
--- a/tensorflow/python/training/monitored_session.py
+++ b/tensorflow/python/training/monitored_session.py
@@ -202,7 +202,7 @@ def default_ready_for_local_init_op():
     if self._local_init_op is None:
       self._local_init_op = Scaffold.get_or_default(
           'local_init_op', ops.GraphKeys.LOCAL_INIT_OP,
-          Scaffold._default_local_init_op)
+          Scaffold.default_local_init_op)
     if self._summary_op is None:
       self._summary_op = Scaffold.get_or_default('summary_op',
                                                  ops.GraphKeys.SUMMARY_OP,
@@ -267,7 +267,17 @@ def get_or_default(arg_name, collection_key, default_constructor):
     return op
 
   @staticmethod
-  def _default_local_init_op():
+  def default_local_init_op():
+    """Returns an op that groups the default local init ops.
+
+    This op is used during session initialization when a Scaffold is
+    initialized without specifying the local_init_op arg. It includes
+    `tf.local_variables_initializer`, `tf.tables_initializer`, and also
+    initializes local session resources.
+
+    Returns:
+      The default Scaffold local init op.
+    """
     return control_flow_ops.group(
         variables.local_variables_initializer(),
         lookup_ops.tables_initializer(),
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-scaffold.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-scaffold.pbtxt
index 62b956c5ef7dc5..38cc98b48e78aa 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-scaffold.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-scaffold.pbtxt
@@ -38,6 +38,10 @@ tf_class {
     name: "__init__"
     argspec: "args=[\'self\', \'init_op\', \'init_feed_dict\', \'init_fn\', \'ready_op\', \'ready_for_local_init_op\', \'local_init_op\', \'summary_op\', \'saver\', \'copy_from_scaffold\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
   }
+  member_method {
+    name: "default_local_init_op"
+    argspec: "args=[], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "finalize"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh
index 9d23b508aa1c1d..797e0a6db52aa6 100755
--- a/tensorflow/tools/ci_build/ci_parameterized_build.sh
+++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh
@@ -237,7 +237,7 @@ function get_cuda_capability_version() {
 CTYPE=${TF_BUILD_CONTAINER_TYPE}
 
 # Determine if the machine is a Mac
-OPT_FLAG=""
+OPT_FLAG="--test_output=errors"
 if [[ "$(uname -s)" == "Darwin" ]]; then
   DO_DOCKER=0
 
diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
index aefc49f6048214..204a82f647eed5 100755
--- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
@@ -39,6 +39,9 @@ if [[ -z $pip35_version ]]; then
 fi
 
 set -e
+pip3.5 install --upgrade setuptools
+pip3.5 install --upgrade pip
+
 pip3.5 install --upgrade virtualenv
 
 # Install six.
diff --git a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh
index bfaa044c82887b..275abeb6697924 100755
--- a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh
@@ -49,9 +49,13 @@ cd Python-3.6.1
 make altinstall
 ln -s /usr/local/bin/pip3.6 /usr/local/bin/pip3
 
+pip3 install --upgrade setuptools
+pip3 install --upgrade pip
+
 pip3 install --upgrade virtualenv
 
 set -e
+
 # Install six.
 pip3 install --upgrade absl-py
 pip3 install --upgrade six==1.10.0
diff --git a/tensorflow/tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh b/tensorflow/tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh
index 748a961e44c542..dc9af221ecf53b 100644
--- a/tensorflow/tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh
+++ b/tensorflow/tools/ci_build/windows/cpu/bazel/run_cc_test_windows.sh
@@ -44,7 +44,7 @@ source "tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh" \
 
 run_configure_for_cpu_build
 
-# Compliling the following test is extremely slow with -c opt
+# Compiling the following test is extremely slow with -c opt
 slow_compiling_test="//tensorflow/core/kernels:eigen_backward_spatial_convolutions_test"
 
 # Find all the passing cc_tests on Windows and store them in a variable
diff --git a/tensorflow/tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh b/tensorflow/tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh
index f26f8727e51bf0..f1114f4ffa40dd 100644
--- a/tensorflow/tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh
+++ b/tensorflow/tools/ci_build/windows/gpu/bazel/run_cc_test_windows.sh
@@ -46,7 +46,7 @@ clean_output_base
 
 run_configure_for_gpu_build
 
-# Compliling the following test is extremely slow with -c opt
+# Compiling the following test is extremely slow with -c opt
 slow_compiling_test="//tensorflow/core/kernels:eigen_backward_spatial_convolutions_test"
 
 # Find all the passing cc_tests on Windows and store them in a variable
diff --git a/tensorflow/tools/docker/Dockerfile b/tensorflow/tools/docker/Dockerfile
index 78cb4d250e84a4..a3ff8211e3e819 100644
--- a/tensorflow/tools/docker/Dockerfile
+++ b/tensorflow/tools/docker/Dockerfile
@@ -7,6 +7,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         build-essential \
         curl \
         libfreetype6-dev \
+        libhdf5-serial-dev \
         libpng12-dev \
         libzmq3-dev \
         pkg-config \
diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel
index 390d7442c37b1d..b9996395d02bfb 100644
--- a/tensorflow/tools/docker/Dockerfile.devel
+++ b/tensorflow/tools/docker/Dockerfile.devel
@@ -8,6 +8,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         git \
         libcurl3-dev \
         libfreetype6-dev \
+        libhdf5-serial-dev \
         libpng12-dev \
         libzmq3-dev \
         pkg-config \
@@ -28,9 +29,12 @@ RUN curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \
     rm get-pip.py
 
 RUN pip --no-cache-dir install \
+        Pillow \
+        h5py \
         ipykernel \
         jupyter \
         matplotlib \
+        mock \
         numpy \
         scipy \
         sklearn \
diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu
index 293028d229adba..7e5e6ef2d5b024 100644
--- a/tensorflow/tools/docker/Dockerfile.devel-gpu
+++ b/tensorflow/tools/docker/Dockerfile.devel-gpu
@@ -17,6 +17,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         libcudnn7-dev=7.0.5.15-1+cuda9.0 \
         libcurl3-dev \
         libfreetype6-dev \
+        libhdf5-serial-dev \
         libpng12-dev \
         libzmq3-dev \
         pkg-config \
@@ -37,9 +38,12 @@ RUN curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \
     rm get-pip.py
 
 RUN pip --no-cache-dir install \
+        Pillow \
+        h5py \
         ipykernel \
         jupyter \
         matplotlib \
+        mock \
         numpy \
         scipy \
         sklearn \
diff --git a/tensorflow/tools/docker/Dockerfile.gpu b/tensorflow/tools/docker/Dockerfile.gpu
index 9e1708662e7974..bff4a203920769 100644
--- a/tensorflow/tools/docker/Dockerfile.gpu
+++ b/tensorflow/tools/docker/Dockerfile.gpu
@@ -14,6 +14,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         curl \
         libcudnn7=7.0.5.15-1+cuda9.0 \
         libfreetype6-dev \
+        libhdf5-serial-dev \
         libpng12-dev \
         libzmq3-dev \
         pkg-config \
diff --git a/tensorflow/tools/docker/README.md b/tensorflow/tools/docker/README.md
index f46c56e11aa72c..525f2995ceecd4 100644
--- a/tensorflow/tools/docker/README.md
+++ b/tensorflow/tools/docker/README.md
@@ -16,12 +16,12 @@ quick links here:
 
 We currently maintain two Docker container images:
 
-* `gcr.io/tensorflow/tensorflow` - TensorFlow with all dependencies - CPU only!
+* `tensorflow/tensorflow` - TensorFlow with all dependencies - CPU only!
 
-* `gcr.io/tensorflow/tensorflow:latest-gpu` - TensorFlow with all dependencies
+* `tensorflow/tensorflow:latest-gpu` - TensorFlow with all dependencies
   and support for NVidia CUDA
 
-Note: We also publish the same containers into
+Note: We store all our containers on 
 [Docker Hub](https://hub.docker.com/r/tensorflow/tensorflow/tags/).
 
 
@@ -29,12 +29,12 @@ Note: We also publish the same containers into
 
 Run non-GPU container using
 
-    $ docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow
+    $ docker run -it -p 8888:8888 tensorflow/tensorflow
 
 For GPU support install NVidia drivers (ideally latest) and
 [nvidia-docker](https://github.com/NVIDIA/nvidia-docker). Run using
 
-    $ nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu
+    $ nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:latest-gpu
 
 
 Note: If you would have a problem running nvidia-docker you may try the old method
@@ -44,7 +44,7 @@ it there and try using nvidia-docker as described above.
     $ # The old, not recommended way to run docker with gpu support:
     $ export CUDA_SO=$(\ls /usr/lib/x86_64-linux-gnu/libcuda.* | xargs -I{} echo '-v {}:{}')
     $ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
-    $ docker run -it -p 8888:8888 $CUDA_SO $DEVICES gcr.io/tensorflow/tensorflow:latest-gpu
+    $ docker run -it -p 8888:8888 $CUDA_SO $DEVICES tensorflow/tensorflow:latest-gpu
 
 
 ## More containers
diff --git a/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc b/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc
index d86f65325be1c3..f1d361e07d8f00 100644
--- a/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc
+++ b/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc
@@ -159,6 +159,9 @@ Status FuseScaleOffsetToConvWeights(const std::vector& scale_values,
   NodeDef bias_add_node;
   bias_add_node.set_op("BiasAdd");
   bias_add_node.set_name(conv_output_name);
+  if (!conv_node.attr().count("data_format")) {
+    CopyNodeAttr(conv_node, "data_format", "data_format", &bias_add_node);
+  }
   CopyNodeAttr(conv_node, "T", "T", &bias_add_node);
   AddNodeInput(conv_node.name(), &bias_add_node);
   AddNodeInput(bias_offset_node.name(), &bias_add_node);
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index f84a91d009f534..937d41c36ca33a 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -31,7 +31,7 @@
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.8.0-rc0'
+_VERSION = '1.8.0-rc1'
 
 REQUIRED_PACKAGES = [
     'absl-py >= 0.1.6',
@@ -69,7 +69,7 @@
 if 'tf_nightly' in project_name:
   for i, pkg in enumerate(REQUIRED_PACKAGES):
     if 'tensorboard' in pkg:
-      REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.8.0a0, < 1.9.0a0'
+      REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.9.0a0, < 1.10.0a0'
       break
 
 # weakref.finalize and enum were introduced in Python 3.4
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 152da547c1249a..16da59c5cf0c1c 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -50,31 +50,31 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   mkl_repository(
       name = "mkl_linux",
       urls = [
-          "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.12/mklml_lnx_2018.0.1.20171227.tgz",
-          "https://github.com/intel/mkl-dnn/releases/download/v0.12/mklml_lnx_2018.0.1.20171227.tgz",
+          "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_lnx_2018.0.2.20180127.tgz",
+          "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_lnx_2018.0.2.20180127.tgz",
       ],
-      sha256 = "feacc3d82565c1231470359b42c696236fae873704e0b013436afba5fd4fd30f",
-      strip_prefix = "mklml_lnx_2018.0.1.20171227",
+      sha256 = "74844bd77294742bf2396ff040369d1aa4cdd9e826fcd38cf8398ae83564d146",
+      strip_prefix = "mklml_lnx_2018.0.2.20180127",
       build_file = clean_dep("//third_party/mkl:mkl.BUILD")
   )
   mkl_repository(
       name = "mkl_windows",
       urls = [
-          "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.12/mklml_win_2018.0.1.20171227.zip",
-          "https://github.com/intel/mkl-dnn/releases/download/v0.12/mklml_win_2018.0.1.20171227.zip"
+          "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_win_2018.0.2.20180127.zip",
+          "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_win_2018.0.2.20180127.zip"
       ],
-      sha256 = "24bae8d7b22b431a654acadea43f2243c46ae6b1e5a73a4a936825f31d284ee4",
-      strip_prefix = "mklml_win_2018.0.1.20171227",
+      sha256 = "d8fbf0faa0684bffa3548005d05fe5cfe56ff9dbc0e15e7612d7ac01055a6ded",
+      strip_prefix = "mklml_win_2018.0.2.20180127",
       build_file = clean_dep("//third_party/mkl:mkl.BUILD")
   )
   mkl_repository(
       name = "mkl_darwin",
       urls = [
-          "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.12/mklml_mac_2018.0.1.20171227.tgz",
-          "https://github.com/intel/mkl-dnn/releases/download/v0.12/mklml_mac_2018.0.1.20171227.tgz"
+          "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_mac_2018.0.2.20180127.tgz",
+          "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_mac_2018.0.2.20180127.tgz"
       ],
-      sha256 = "0e954ec6fd3dc5e37f64c4043f6b5613dd687558da3df1028b3b7c29ff5cf77f",
-      strip_prefix = "mklml_mac_2018.0.1.20171227",
+      sha256 = "aa740d71e14562bfea56e6829e6dc186e7487cbcf6748a88dec73826b7ec1943",
+      strip_prefix = "mklml_mac_2018.0.2.20180127",
       build_file = clean_dep("//third_party/mkl:mkl.BUILD")
   )
 
@@ -85,11 +85,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "mkl_dnn",
       urls = [
-          "https://mirror.bazel.build/github.com/intel/mkl-dnn/archive/v0.12.tar.gz",
-          "https://github.com/intel/mkl-dnn/archive/v0.12.tar.gz",
+          "https://mirror.bazel.build/github.com/intel/mkl-dnn/archive/v0.13.tar.gz",
+          "https://github.com/intel/mkl-dnn/archive/v0.13.tar.gz",
       ],
-      sha256 = "86fa2a8c12a56e3b725945acedeaa82492746be02545aba6d710f097e013e19e",
-      strip_prefix = "mkl-dnn-0.12",
+      sha256 = "d2cfd93a70cfe86ebe054477c530c9b5c1218b70f75856eb6d1956c68ee89e8f",
+      strip_prefix = "mkl-dnn-0.13",
       build_file = clean_dep("//third_party/mkl_dnn:mkldnn.BUILD"),
   )
 
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h
index 188dc75bf62c6e..0f4ada246c702a 100644
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h
+++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h
@@ -280,9 +280,9 @@ SpatialConvolutionBackwardKernel(const Input& input, const OutputBackward& outpu
     eigen_assert(input_dims[0] == pre_contract_dims[0]);
   }
 
-  // We will contract along dimensions (1, 2) in in and (1, 3) in out, if
+  // We will contract along dimensions (1, 2) in and (1, 3) in out, if
   // this is col-major.
-  // For row-major, it's dimensions (0, 1) in in and (0, 2) in out.
+  // For row-major, it's dimensions (0, 1) in and (0, 2) in out.
   array, 2> contract_dims;
   if (isColMajor) {
     // col-major: in.contract(output.patches)

From fdcdf752dca18d479932119a2445e0129fcd54a9 Mon Sep 17 00:00:00 2001
From: Mark Heffernan 
Date: Tue, 1 May 2018 14:52:40 -0700
Subject: [PATCH 0242/1691] Fix bug in peak buffer accounting in buffer
 assignment. Buffer assignment keeps track of the set of logical buffers which
 are live at the point of peak memory usage for each allocation. Previously
 colocated buffers were not properly accounted for. This CL addresses this
 problem.

PiperOrigin-RevId: 195001567
---
 .../compiler/xla/service/buffer_assignment.cc | 196 ++++++++----------
 .../compiler/xla/service/buffer_assignment.h  |  23 +-
 .../xla/service/buffer_assignment_test.cc     |  77 ++++++-
 3 files changed, 169 insertions(+), 127 deletions(-)

diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index dbe45e932cdeed..94ccfedf6289b4 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -292,112 +292,6 @@ BufferAllocationProto BufferAllocation::ToProto() const {
   return proto;
 }
 
-std::pair>
-BufferAllocation::ComputePeakMemoryLogicalBuffers() const {
-  if (HeapTraces().empty()) {
-    // Just return the largest LogicalBuffer in the allocation.
-    const LogicalBuffer* largest_buffer = nullptr;
-    int64 largest_size = 0;
-    for (const auto& pair : assigned_buffers()) {
-      const LogicalBuffer* buffer = pair.first;
-      int64 size = pair.second.size;
-      if (largest_buffer == nullptr) {
-        largest_buffer = buffer;
-        largest_size = size;
-        continue;
-      }
-      // Tie-break with LogicalBuffer::Id so the return value is stable relative
-      // to changing addresses.
-      if (size > largest_size ||
-          ((size == largest_size) && (largest_buffer->id() > buffer->id()))) {
-        largest_buffer = buffer;
-        largest_size = size;
-      }
-    }
-    CHECK(largest_buffer != nullptr)
-        << "No logical buffers in allocation: " << ToString();
-    return {largest_size, {largest_buffer}};
-  }
-
-  // Create a map from LogicalBuffer::Id to LogicalBuffer* for the logical
-  // buffers in this allocation.
-  tensorflow::gtl::FlatMap
-      id_to_buffer;
-  tensorflow::gtl::FlatMap buffer_sizes;
-  for (const auto& pair : assigned_buffers()) {
-    const LogicalBuffer* buffer = pair.first;
-    const OffsetSize& offset_size = pair.second;
-    id_to_buffer[buffer->id()] = buffer;
-    buffer_sizes[buffer] = offset_size.size;
-  }
-
-  // Returns how much the given event increases the total size of live
-  // buffers. Can be negative.
-  auto memory_delta = [this, &id_to_buffer, &buffer_sizes](
-                          const HeapSimulatorTrace::Event& event) -> int64 {
-    const LogicalBuffer* buffer = id_to_buffer.at(event.buffer_id());
-    const int64 buffer_size = buffer_sizes.at(buffer);
-    if (event.kind() == HeapSimulatorTrace::Event::ALLOC) {
-      return buffer_size;
-    } else if (event.kind() == HeapSimulatorTrace::Event::SHARE_WITH) {
-      // Sharing a buffer does not change the live set size for the purposes of
-      // the heap simulator. Even though the shared-with buffer may be smaller,
-      // the entire allocation remains live.
-      return 0;
-    } else if (event.kind() == HeapSimulatorTrace::Event::FREE) {
-      return -1 * buffer_size;
-    }
-    LOG(FATAL) << "Unknown event kind: " << event.kind();
-  };
-
-  int64 total_max_live_size = 0;
-  std::vector live_buffers_vector;
-  for (const HeapSimulatorTrace& heap_trace : HeapTraces()) {
-    // First compute the size of the maximal live set.
-    int64 max_live_size = 0;
-    int64 live_size = 0;
-    for (const auto& event : heap_trace.events()) {
-      live_size += memory_delta(event);
-      if (max_live_size < live_size) {
-        max_live_size = live_size;
-      }
-    }
-
-    // Next gather the set of logical buffers live at the earliest point of
-    // maximal live set size.
-    tensorflow::gtl::FlatSet live_buffers;
-    live_size = 0;
-    for (const auto& event : heap_trace.events()) {
-      const LogicalBuffer* buffer = id_to_buffer.at(event.buffer_id());
-      if (event.kind() == HeapSimulatorTrace::Event::ALLOC) {
-        InsertOrDie(&live_buffers, buffer);
-      } else if (event.kind() == HeapSimulatorTrace::Event::SHARE_WITH) {
-        // Nothing to do.
-      } else if (event.kind() == HeapSimulatorTrace::Event::FREE) {
-        CHECK(ContainsKey(live_buffers, buffer));
-        live_buffers.erase(buffer);
-      }
-
-      live_size += memory_delta(event);
-      if (live_size == max_live_size) {
-        break;
-      }
-    }
-    CHECK_EQ(live_size, max_live_size);
-    total_max_live_size += max_live_size;
-
-    live_buffers_vector.insert(live_buffers_vector.end(), live_buffers.begin(),
-                               live_buffers.end());
-  }
-
-  // Stabily sort the live buffers.
-  std::sort(live_buffers_vector.begin(), live_buffers_vector.end(),
-            [](const LogicalBuffer* a, const LogicalBuffer* b) {
-              return a->id() < b->id();
-            });
-  return {total_max_live_size, live_buffers_vector};
-}
-
 string BufferAllocation::ToString() const {
   string output;
   Appendf(&output, "allocation %lld: %p, size %lld", index_, this, size());
@@ -610,6 +504,7 @@ BufferAllocation* BufferAssignment::NewAllocation(const LogicalBuffer& buffer,
   BufferAllocation* allocation =
       NewEmptyAllocation(size, is_thread_local, is_reusable, buffer.color());
   AddAssignment(allocation, buffer, /*offset=*/0, size);
+  allocation->peak_buffers_.push_back(&buffer);
   return allocation;
 }
 
@@ -680,6 +575,10 @@ void BufferAssignment::CombineTempAllocations() {
         CHECK_EQ(temp_allocation.HeapTraces().size(), 1);
         combined_allocation->AddHeapTrace(temp_allocation.HeapTraces().front());
       }
+      combined_allocation->peak_buffers_.insert(
+          combined_allocation->peak_buffers_.end(),
+          temp_allocation.peak_buffers_.begin(),
+          temp_allocation.peak_buffers_.end());
     }
     // Replace all existing temporary allocations with the new combined
     // allocations.
@@ -1228,6 +1127,89 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering(
   return Status::OK();
 }
 
+namespace {
+
+// Computes and returns the set of logical buffers live at the point of maximal
+// liveness in the given heap trace. LogicalBuffers are (stabily) sorted by id.
+std::vector ComputePeakMemoryLogicalBuffers(
+    const BufferAllocation& allocation, const HeapSimulatorTrace& heap_trace) {
+  // Create a map from LogicalBuffer::Id to LogicalBuffer* for the logical
+  // buffers in this allocation.
+  tensorflow::gtl::FlatMap
+      id_to_buffer;
+  tensorflow::gtl::FlatMap buffer_sizes;
+  for (const auto& pair : allocation.assigned_buffers()) {
+    const LogicalBuffer* buffer = pair.first;
+    const BufferAllocation::OffsetSize& offset_size = pair.second;
+    id_to_buffer[buffer->id()] = buffer;
+    buffer_sizes[buffer] = offset_size.size;
+  }
+
+  // Returns how much the given event increases the total size of live
+  // buffers. Can be negative.
+  auto memory_delta = [&id_to_buffer, &buffer_sizes](
+                          const HeapSimulatorTrace::Event& event) -> int64 {
+    const LogicalBuffer* buffer = id_to_buffer.at(event.buffer_id());
+    const int64 buffer_size = buffer_sizes.at(buffer);
+    if (event.kind() == HeapSimulatorTrace::Event::ALLOC) {
+      return buffer_size;
+    } else if (event.kind() == HeapSimulatorTrace::Event::SHARE_WITH) {
+      // Sharing a buffer does not change the live set size for the purposes of
+      // the heap simulator. Even though the shared-with buffer may be smaller,
+      // the entire allocation remains live.
+      return 0;
+    } else if (event.kind() == HeapSimulatorTrace::Event::FREE) {
+      return -1 * buffer_size;
+    }
+    LOG(FATAL) << "Unknown event kind: " << event.kind();
+  };
+
+  // First compute the size of the maximal live set.
+  int64 max_live_size = 0;
+  int64 live_size = 0;
+  for (const auto& event : heap_trace.events()) {
+    live_size += memory_delta(event);
+    if (max_live_size < live_size) {
+      max_live_size = live_size;
+    }
+  }
+
+  // Next gather the set of logical buffers live at the earliest point of
+  // maximal live set size.
+  tensorflow::gtl::FlatSet live_buffers;
+  live_size = 0;
+  for (const auto& event : heap_trace.events()) {
+    const LogicalBuffer* buffer = id_to_buffer.at(event.buffer_id());
+    if (event.kind() == HeapSimulatorTrace::Event::ALLOC) {
+      InsertOrDie(&live_buffers, buffer);
+    } else if (event.kind() == HeapSimulatorTrace::Event::SHARE_WITH) {
+      // Nothing to do.
+    } else if (event.kind() == HeapSimulatorTrace::Event::FREE) {
+      CHECK(ContainsKey(live_buffers, buffer));
+      live_buffers.erase(buffer);
+    }
+
+    live_size += memory_delta(event);
+    if (live_size == max_live_size) {
+      break;
+    }
+  }
+  CHECK_EQ(live_size, max_live_size);
+
+  std::vector live_buffers_vector;
+  live_buffers_vector.insert(live_buffers_vector.end(), live_buffers.begin(),
+                             live_buffers.end());
+
+  // Stabily sort the live buffers.
+  std::sort(live_buffers_vector.begin(), live_buffers_vector.end(),
+            [](const LogicalBuffer* a, const LogicalBuffer* b) {
+              return a->id() < b->id();
+            });
+  return live_buffers_vector;
+}
+
+}  // namespace
+
 void BufferAssigner::AssignBuffersFromHeapSimulator(
     const HeapSimulator::Result& result, BufferAssignment* assignment,
     LogicalBuffer::Color color) {
@@ -1246,6 +1228,8 @@ void BufferAssigner::AssignBuffersFromHeapSimulator(
     const HeapSimulator::Chunk& chunk = buffer_chunk.second;
     assignment->AddAssignment(allocation, buffer, chunk.offset, chunk.size);
   }
+  allocation->peak_buffers_ =
+      ComputePeakMemoryLogicalBuffers(*allocation, result.debug_trace);
 
   VLOG(1) << "Ran heap simulation for allocation: " << allocation->ToString();
   allocation->AddHeapTrace(result.debug_trace);
diff --git a/tensorflow/compiler/xla/service/buffer_assignment.h b/tensorflow/compiler/xla/service/buffer_assignment.h
index 3086d0e2ca0026..15fd905e8d5939 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.h
+++ b/tensorflow/compiler/xla/service/buffer_assignment.h
@@ -206,17 +206,15 @@ class BufferAllocation {
     return heap_traces_;
   }
 
-  // Compute and return the LogicalBuffers which are live at the point of peak
-  // memory usage for the given allocation. The point of peak memory usage is
-  // the point at which the total size of all live logical buffers is
-  // maximal. If peak memory is reached at multiple points, the set of logical
-  // buffers live at the earliest maximal point is returned. The vector is
-  // stabily asserted by LogicalBuffer::Index.
-  //
-  // The return value is a pair of total size of the logical buffers at peak,
-  // and the buffers themselves.
-  std::pair>
-  ComputePeakMemoryLogicalBuffers() const;
+  // Returns the LogicalBuffers which are live at the point of peak memory usage
+  // for this allocation. The point of peak memory usage is the point at which
+  // the total size of all live logical buffers is maximal. If peak memory is
+  // reached at multiple points, the set of logical buffers live at the earliest
+  // maximal point is returned. The vector is stabily sorted by
+  // LogicalBuffer::Index.
+  const std::vector& PeakMemoryLogicalBuffers() const {
+    return peak_buffers_;
+  }
 
   // Get the number of bytes lost to fragmentation. This is equal to the
   // difference between the size of the allocation and the size of the maximal
@@ -291,6 +289,9 @@ class BufferAllocation {
 
   int64 fragmentation_bytes_ = 0;
   std::vector heap_traces_;
+
+  // Set of buffers live at the point of peak memory usage for this allocation.
+  std::vector peak_buffers_;
 };
 
 // Add stream operators for nicer output of CHECK/RET_CHECK failures.
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index 3ec9795a655041..40cf6483aae082 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -1519,12 +1519,8 @@ TEST_F(BufferAssignmentTest, TrivialPeakBuffers) {
   // single logical buffer should be exactly the logical buffer in that
   // allocation.
   const BufferAllocation& mul_buffer = GetTopLevelAllocation(*buffers, mul);
-  int64 peak_size;
-  std::vector peak_buffers;
-
-  std::tie(peak_size, peak_buffers) =
-      mul_buffer.ComputePeakMemoryLogicalBuffers();
-  EXPECT_EQ(peak_size, ShapeUtil::ByteSizeOf(f32vec100_));
+  const std::vector& peak_buffers =
+      mul_buffer.PeakMemoryLogicalBuffers();
   ASSERT_EQ(peak_buffers.size(), 1);
   EXPECT_EQ(peak_buffers[0]->instruction(), mul);
 }
@@ -1555,6 +1551,7 @@ TEST_F(BufferAssignmentTest, PeakBuffers) {
       HloInstruction::CreateConcatenate(concat_shape, {rev, neg}, 0));
   // Make the root tiny so no interior nodes can share its buffer.
   auto root = builder.AddInstruction(HloInstruction::CreateSlice(
+
       ShapeUtil::MakeShape(F32, {1}), concat, {0}, {1}, {1}));
 
   auto module = CreateNewModule();
@@ -1569,12 +1566,10 @@ TEST_F(BufferAssignmentTest, PeakBuffers) {
   EXPECT_TRUE(buffer.IsPreallocatedTempBuffer());
   ASSERT_EQ(buffer.assigned_buffers().size(), 4);
 
-  int64 peak_size;
-  std::vector peak_buffers;
-  std::tie(peak_size, peak_buffers) = buffer.ComputePeakMemoryLogicalBuffers();
+  const std::vector& peak_buffers =
+      buffer.PeakMemoryLogicalBuffers();
 
   // The peak live set should be concat and its inputs.
-  EXPECT_EQ(peak_size, ShapeUtil::ByteSizeOf(ShapeUtil::MakeShape(F32, {400})));
   ASSERT_EQ(peak_buffers.size(), 3);
   std::vector peak_instructions;
   for (const LogicalBuffer* logical_buffer : peak_buffers) {
@@ -1583,6 +1578,68 @@ TEST_F(BufferAssignmentTest, PeakBuffers) {
   EXPECT_THAT(peak_instructions, UnorderedElementsAre(rev, neg, concat));
 }
 
+TEST_F(BufferAssignmentTest, PeakBuffersWhile) {
+  auto module = CreateNewModule();
+  const Shape shape = ShapeUtil::MakeShape(F32, {123, 123});
+  HloComputation* condition;
+  {
+    auto b = HloComputation::Builder(TestName() + ".cond");
+    b.AddInstruction(HloInstruction::CreateParameter(0, shape, "x"));
+    b.AddInstruction(
+        HloInstruction::CreateConstant(Literal::CreateR0(true)));
+    condition = module->AddEmbeddedComputation(b.Build());
+  }
+  HloComputation* body;
+  {
+    auto b = HloComputation::Builder(TestName() + ".body");
+    auto param =
+        b.AddInstruction(HloInstruction::CreateParameter(0, shape, "x"));
+    b.AddInstruction(
+        HloInstruction::CreateUnary(shape, HloOpcode::kNegate, param));
+    body = module->AddEmbeddedComputation(b.Build());
+  }
+  auto builder = HloComputation::Builder(TestName());
+  auto param =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "p0"));
+  auto copy = builder.AddInstruction(
+      HloInstruction::CreateUnary(shape, HloOpcode::kCopy, param));
+  auto while_op = builder.AddInstruction(
+      HloInstruction::CreateWhile(shape, condition, body, copy));
+  // This broadcast should get a temporary allocation which is merged with the
+  // allocation for the while. Peak buffers should include the while and the
+  // broadcast.
+  auto bcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
+      ShapeUtil::MakeShape(F32, {123, 123, 123}), while_op, {0, 1}));
+  builder.AddInstruction(HloInstruction::CreateReverse(
+      ShapeUtil::MakeShape(F32, {123, 123, 123}), bcast, {0}));
+  module->AddEntryComputation(builder.Build());
+
+  auto buffers = RunBufferAssignment(module.get());
+  const BufferAllocation& buffer = GetTopLevelAllocation(*buffers, bcast);
+  const std::vector& peak_buffers =
+      buffer.PeakMemoryLogicalBuffers();
+  ASSERT_EQ(peak_buffers.size(), 2);
+
+  // The peak buffers should include the broadcast and one of the colocated
+  // buffers of the while (body param, condition param, body root, or the while
+  // itself).
+  const LogicalBuffer* bcast_buffer;
+  const LogicalBuffer* nonbcast_buffer;
+  if (peak_buffers[0]->instruction() == bcast) {
+    bcast_buffer = peak_buffers[0];
+    nonbcast_buffer = peak_buffers[1];
+  } else {
+    bcast_buffer = peak_buffers[1];
+    nonbcast_buffer = peak_buffers[0];
+  }
+  EXPECT_EQ(bcast_buffer->instruction(), bcast);
+  EXPECT_TRUE(
+      nonbcast_buffer->instruction() == while_op ||
+      nonbcast_buffer->instruction() == body->parameter_instruction(0) ||
+      nonbcast_buffer->instruction() == body->root_instruction() ||
+      nonbcast_buffer->instruction() == condition->parameter_instruction(0));
+}
+
 class WhileBufferAssignmentTest : public HloTestBase {
  protected:
   std::unique_ptr BuildWhileConditionComputation(

From 19ad98e8393547076706285b922bd801d763033f Mon Sep 17 00:00:00 2001
From: joel-shor 
Date: Wed, 2 May 2018 00:56:43 +0300
Subject: [PATCH 0243/1691] [tf.data] Fix debug output.

---
 tensorflow/contrib/data/python/ops/interleave_ops.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/data/python/ops/interleave_ops.py b/tensorflow/contrib/data/python/ops/interleave_ops.py
index 0852fc6be8240c..2a9c5b45f82615 100644
--- a/tensorflow/contrib/data/python/ops/interleave_ops.py
+++ b/tensorflow/contrib/data/python/ops/interleave_ops.py
@@ -240,9 +240,9 @@ def select_dataset(logits, seed):
   selector_input = dataset_ops.Dataset.zip(
       (logits_ds, random_ops.RandomDataset(seed).batch(2))).map(select_dataset)
 
-  logging.warn('selector_input.output_types: %s', selector_input.output_types)
-  logging.warn('selector_input.output_shapes: %s', selector_input.output_shapes)
+  logging.warn('selector_input.output_types: %s', str(selector_input.output_types))
+  logging.warn('selector_input.output_shapes: %s', str(selector_input.output_shapes))
   for i, dataset in enumerate(datasets):
-    logging.warn('dataset %i output_types: %s' % (i, dataset.output_types))
-    logging.warn('dataset %i output_shapes: %s' % (i, dataset.output_shapes))
+    logging.warn('dataset %i output_types: %s' % (i, str(dataset.output_types)))
+    logging.warn('dataset %i output_shapes: %s' % (i, str(dataset.output_shapes)))
   return DirectedInterleaveDataset(selector_input, datasets)

From 415ea7360d3f57249fc18e068852a8b8ce6d7f77 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 1 May 2018 15:00:20 -0700
Subject: [PATCH 0244/1691] Making ids unique in nn.embedding_lookup_sparse.
 This helps to reduce RPC calls for looking up the embeddings when there are
 repeated ids in the batch.

PiperOrigin-RevId: 195002785
---
 tensorflow/python/ops/embedding_ops.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/ops/embedding_ops.py b/tensorflow/python/ops/embedding_ops.py
index 6f2a34c731c42e..bcc717b043f226 100644
--- a/tensorflow/python/ops/embedding_ops.py
+++ b/tensorflow/python/ops/embedding_ops.py
@@ -385,7 +385,7 @@ def embedding_lookup_sparse(params,
       ```
 
   Raises:
-    TypeError: If `sp_ids` is not a `SparseTensor`, or if `sp_weights` is 
+    TypeError: If `sp_ids` is not a `SparseTensor`, or if `sp_weights` is
       neither `None` nor `SparseTensor`.
     ValueError: If `combiner` is not one of {"mean", "sqrtn", "sum"}.
   """
@@ -421,10 +421,7 @@ def embedding_lookup_sparse(params,
       segment_ids = math_ops.cast(segment_ids, dtypes.int32)
 
     ids = sp_ids.values
-    if ignore_weights:
-      ids, idx = array_ops.unique(ids)
-    else:
-      idx = None
+    ids, idx = array_ops.unique(ids)
 
     embeddings = embedding_lookup(
         params, ids, partition_strategy=partition_strategy, max_norm=max_norm)
@@ -433,6 +430,8 @@ def embedding_lookup_sparse(params,
       if weights.dtype != embeddings.dtype:
         weights = math_ops.cast(weights, embeddings.dtype)
 
+      embeddings = array_ops.gather(embeddings, idx)
+
       # Reshape weights to allow broadcast
       ones = array_ops.fill(
           array_ops.expand_dims(array_ops.rank(embeddings) - 1, 0), 1)

From 707b0c9cc4d5335d04fce4addb8ed2f158cbd1c0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 1 May 2018 15:01:22 -0700
Subject: [PATCH 0245/1691] Minor JNI performance improvement.

PiperOrigin-RevId: 195002949
---
 tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc b/tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc
index 17f4be09c63a9e..005dca0253d2c3 100644
--- a/tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc
+++ b/tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc
@@ -238,10 +238,6 @@ Java_org_tensorflow_lite_Tensor_shape(JNIEnv* env, jclass clazz, jlong handle) {
   if (tensor == nullptr) return nullptr;
   int num_dims = tensor->dims->size;
   jintArray result = env->NewIntArray(num_dims);
-  jint* dims = env->GetIntArrayElements(result, nullptr);
-  for (int i = 0; i < num_dims; ++i) {
-    dims[i] = static_cast(tensor->dims->data[i]);
-  }
-  env->ReleaseIntArrayElements(result, dims, 0);
+  env->SetIntArrayRegion(result, 0, num_dims, tensor->dims->data);
   return result;
 }

From 6f10fb5b583cb7b883a41a45a69b22fd84eeb10e Mon Sep 17 00:00:00 2001
From: Benoit Steiner 
Date: Tue, 1 May 2018 15:19:42 -0700
Subject: [PATCH 0246/1691] Fixed some outdated comments

PiperOrigin-RevId: 195006088
---
 .../core/grappler/costs/graph_properties.cc      | 16 ++++++----------
 1 file changed, 6 insertions(+), 10 deletions(-)

diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index a12d9b932bef54..431efb08cbb0ea 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -382,7 +382,7 @@ class TopoQueue {
   std::set queue_;
 };
 
-// Merge and relax symbolic shapes.
+// Processes symbolic shapes.
 // Each symbolic shape or dimension is represented by a handle. Unlike the TF
 // shape refiner which creates new handles every time it processes an unknown
 // shape/dimension, the symbolic shape refiner assigns a specific handle to each
@@ -864,11 +864,8 @@ Status GraphProperties::RelaxEnqueueShapesAndMergeTypes(
   return Status::OK();
 }
 
-// If a Merge node has a NextIteration node as an input then that input will
-// try to forward an UnknownShape at graph construction time. However, the
-// Merge shape function will always propagate an UnknownShape if any of its
-// inputs are UnknownShapes. So we need to ignore the input from NextIteration
-// nodes to propagate any known shape from the Merge node.
+// Compute the output shape of the merge node as the union of the available
+// input shapes.
 Status GraphProperties::UpdateMergeNode(SymbolicShapeRefiner* shape_refiner,
                                         const NodeDef* node,
                                         bool* new_shapes) const {
@@ -914,8 +911,7 @@ Status GraphProperties::UpdateMergeNode(SymbolicShapeRefiner* shape_refiner,
   return Status::OK();
 }
 
-// Manually propagate the input shape for Enter nodes and update any Merge node
-// outputs.
+// Manually propagate the input shape for Enter nodes.
 Status GraphProperties::UpdateEnter(SymbolicShapeRefiner* shape_refiner,
                                     const NodeDef* node, bool* new_shapes) {
   auto enter_ctx = shape_refiner->GetContext(node);
@@ -955,6 +951,8 @@ Status GraphProperties::UpdateShapes(
     // Properly handle merge nodes.
     TF_RETURN_IF_ERROR(UpdateMergeNode(shape_refiner, n, new_shapes));
   } else if (IsEnqueue(*n)) {
+    // Make sure the shapes of enqueued tensors are propagated to the queue
+    // itself.
     TF_RETURN_IF_ERROR(
         UpdateEnqueue(n, resource_handles, shape_refiner, new_shapes));
   } else {
@@ -1209,7 +1207,6 @@ Status GraphProperties::InferStatically(bool assume_valid_feeds) {
 
     // Fill input properties.
     {
-      // CHECK_EQ(ctx->num_inputs(), node.num_inputs());
       auto& input_properties = input_properties_[node.name()];
 
       // Should always be empty, node names in graph are supposed to be unique.
@@ -1233,7 +1230,6 @@ Status GraphProperties::InferStatically(bool assume_valid_feeds) {
 
     // Fill output properties.
     {
-      // CHECK_EQ(ctx->num_outputs(), node->num_outputs());
       auto& output_properties = output_properties_[node.name()];
 
       // Should always be empty, node names in graph are supposed to be unique.

From 33978e881c8b0aed71e26858641736313a486c12 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 1 May 2018 15:47:26 -0700
Subject: [PATCH 0247/1691] Go: Update generated wrapper functions for
 TensorFlow ops. PiperOrigin-RevId: 195010310

---
 tensorflow/go/op/wrappers.go | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 2f1be51ada8665..c12ea5156356f2 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -21386,7 +21386,7 @@ func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr {
 //    generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
 //
 // The `bad_color` argument is the color to use in the generated images for
-// non-finite input values.  It is a `uint8` 1-D tensor of length `channels`.
+// non-finite input values.  It is a `unit8` 1-D tensor of length `channels`.
 // Each element must be in the range `[0, 255]` (It represents the value of a
 // pixel in the output image).  Non-finite values in the input tensor are
 // replaced by this tensor in the output image.  The default value is the color

From b25e6fe32cccd29ec4cb4014bbb45d62b75835b4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 1 May 2018 15:47:27 -0700
Subject: [PATCH 0248/1691] Implementation of the fully-connected TFLite Op
 using the symmetric quantization.

PiperOrigin-RevId: 195010312
---
 tensorflow/contrib/lite/kernels/BUILD         |   2 +
 .../contrib/lite/kernels/fully_connected.cc   | 117 ++++++++++++++-
 .../lite/kernels/fully_connected_test.cc      | 141 ++++++++++++++++--
 tensorflow/contrib/lite/kernels/test_util.h   |  17 +++
 4 files changed, 255 insertions(+), 22 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD
index 689f9bfa7151eb..57b3136ccec646 100644
--- a/tensorflow/contrib/lite/kernels/BUILD
+++ b/tensorflow/contrib/lite/kernels/BUILD
@@ -31,6 +31,7 @@ cc_library(
         "//tensorflow/contrib/lite:framework",
         "//tensorflow/contrib/lite:schema_fbs_version",
         "//tensorflow/contrib/lite:string_util",
+        "//tensorflow/contrib/lite/kernels/internal:tensor_utils",
         "//tensorflow/contrib/lite/testing:util",
         "//tensorflow/core:tflite_portable_logging",
         "@com_google_googletest//:gtest",
@@ -672,6 +673,7 @@ tf_cc_test(
         ":builtin_ops",
         "//tensorflow/contrib/lite:framework",
         "//tensorflow/contrib/lite/kernels:test_util",
+        "//tensorflow/contrib/lite/kernels/internal:tensor_utils",
         "@com_google_absl//absl/memory",
         "@com_google_googletest//:gtest",
     ],
diff --git a/tensorflow/contrib/lite/kernels/fully_connected.cc b/tensorflow/contrib/lite/kernels/fully_connected.cc
index 888e67966c0a40..c5bf50da5f9c34 100644
--- a/tensorflow/contrib/lite/kernels/fully_connected.cc
+++ b/tensorflow/contrib/lite/kernels/fully_connected.cc
@@ -55,19 +55,24 @@ struct OpData {
   // uint8_t these would be 0 and 255.
   int32_t output_activation_min;
   int32_t output_activation_max;
+  // The index of the temporary tensor where the quantized inputs are cached.
+  int input_quantized_index;
 };
 
 constexpr int kInputTensor = 0;
 constexpr int kWeightsTensor = 1;
 constexpr int kBiasTensor = 2;
 constexpr int kOutputTensor = 0;
+constexpr int kScratchBufferTensor = 1;
 
 void* Init(TfLiteContext* context, const char* buffer, size_t length) {
   // This is a builtin op, so we don't use the contents in 'buffer', if any.
   // Instead, we allocate a new object to carry information from Prepare() to
   // Eval().
   gemm_support::IncrementUsageCounter(context);
-  return new OpData;
+  auto* op_data = new OpData;
+  context->AddTensors(context, 1, &op_data->input_quantized_index);
+  return op_data;
 }
 
 void Free(TfLiteContext* context, void* buffer) {
@@ -121,6 +126,27 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
                                   &data->output_activation_max);
   }
 
+  // If we have to perform on-the-fly quantization (with quantized weights and
+  // float inputs) first we need to quantize the inputs. Allocate a temporary
+  // buffer to store the intermediate quantized values.
+  if (input->type == kTfLiteFloat32 && filter->type == kTfLiteUInt8) {
+    TfLiteIntArrayFree(node->temporaries);
+    node->temporaries = TfLiteIntArrayCreate(1);
+    node->temporaries->data[0] = data->input_quantized_index;
+
+    TfLiteTensor* input_quantized =
+        &context->tensors[node->temporaries->data[0]];
+    input_quantized->type = kTfLiteUInt8;
+    input_quantized->allocation_type = kTfLiteArenaRw;
+
+    // TODO(raziel): add this logic to ResizeTensor.
+    if (!TfLiteIntArrayEqual(input_quantized->dims, input->dims)) {
+      TfLiteIntArray* input_quantized_size = TfLiteIntArrayCopy(input->dims);
+      TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_quantized,
+                                                       input_quantized_size));
+    }
+  }
+
   // Resize output.
   TfLiteIntArray* output_size_array = TfLiteIntArrayCreate(2);
   output_size_array->data[0] = batch_size;
@@ -163,6 +189,74 @@ TfLiteStatus EvalPie(TfLiteContext* context, TfLiteNode* node,
   return kTfLiteOk;
 }
 
+TfLiteStatus EvalPieQuantized(TfLiteContext* context, TfLiteNode* node,
+                              TfLiteFullyConnectedParams* params, OpData* data,
+                              TfLiteTensor* input, TfLiteTensor* filter,
+                              TfLiteTensor* bias, TfLiteTensor* input_quantized,
+                              TfLiteTensor* output) {
+  // Check the types for this hybrid Op.
+  TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32);
+  TF_LITE_ENSURE_EQ(context, filter->type, kTfLiteUInt8);
+  TF_LITE_ENSURE_EQ(context, bias->type, kTfLiteFloat32);
+  TF_LITE_ENSURE_EQ(context, output->type, kTfLiteFloat32);
+
+  int total_input_size = 1;
+  for (int i = 0; i < input->dims->size; i++) {
+    total_input_size *= input->dims->data[i];
+  }
+
+  const int input_size = filter->dims->data[1];
+  const int batch_size = total_input_size / filter->dims->data[1];
+  const int num_units = filter->dims->data[0];
+
+  // Output = bias if bias tensor exists.
+  if (bias) {
+    tensor_utils::VectorBatchVectorAssign(bias->data.f, num_units, batch_size,
+                                          output->data.f);
+  } else {
+    tensor_utils::ZeroVector(output->data.f, batch_size * num_units);
+  }
+
+  // TODO(mirkov): change std::minmax_element with a vectorized call.
+  auto minmax_element =
+      std::minmax_element(input->data.f, input->data.f + total_input_size);
+  // Save matrix multiplication computation for all zero input.
+  if (*minmax_element.first == 0.0 && *minmax_element.second == 0.0) {
+    tensor_utils::ApplyActivationToVector(output->data.f,
+                                          batch_size * num_units,
+                                          params->activation, output->data.f);
+    return kTfLiteOk;
+  }
+
+  // Quantize input from float to uint8 + quantization params (scaling factor).
+  float min, max;
+  float* scaling_factors = new float[batch_size];
+
+  // Quantize each batch independently.
+  for (int b = 0; b < batch_size; ++b) {
+    const int offset = b * input_size;
+    tensor_utils::SymmetricQuantizeFloats(
+        input->data.f + offset, input_size,
+        reinterpret_cast(input_quantized->data.uint8) + offset, &min,
+        &max, &scaling_factors[b]);
+    // Incorporate scaling of the filter.
+    scaling_factors[b] *= filter->params.scale;
+  }
+
+  // Compute output += weight * quantized_input
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      reinterpret_cast(filter->data.uint8), num_units, input_size,
+      reinterpret_cast(input_quantized->data.uint8), scaling_factors,
+      batch_size, output->data.f, /*result_stride=*/1);
+
+  // Apply activation function to floats.
+  tensor_utils::ApplyActivationToVector(output->data.f, batch_size * num_units,
+                                        params->activation, output->data.f);
+  delete[] scaling_factors;
+
+  return kTfLiteOk;
+}
+
 #define TF_LITE_MACRO_DISPATCH(macro_name, params, target_namespace) \
   if (params->activation == kTfLiteActNone) {                        \
     macro_name(target_namespace, kNone);                             \
@@ -178,7 +272,8 @@ template 
 TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
                            TfLiteFullyConnectedParams* params, OpData* data,
                            TfLiteTensor* input, TfLiteTensor* filter,
-                           TfLiteTensor* bias, TfLiteTensor* output) {
+                           TfLiteTensor* bias, TfLiteTensor* input_quantized,
+                           TfLiteTensor* output) {
   gemmlowp::GemmContext* gemm_context = gemm_support::GetFromContext(context);
 
   int32_t input_offset = -input->params.zero_point;
@@ -195,9 +290,15 @@ TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
   if (kernel_type == kReference) {
     TF_LITE_FULLY_CONNECTED(reference_ops);
   } else if (kernel_type == kPie) {
-    // TODO(ahentz): we don't have a quantized version of the PIE kernels, so
-    // we just defer to the MINI ones.
-    TF_LITE_FULLY_CONNECTED(optimized_ops);
+    if (input->type == kTfLiteFloat32) {
+      // Pie currently only supports quantized models and float inputs/outputs.
+      return EvalPieQuantized(context, node, params, data, input, filter, bias,
+                              input_quantized, output);
+    } else {
+      // TODO(ahentz): we don't have a quantized version of the PIE kernels, so
+      // we just defer to the MINI ones.
+      TF_LITE_FULLY_CONNECTED(optimized_ops);
+    }
   } else {
     TF_LITE_FULLY_CONNECTED(optimized_ops);
   }
@@ -245,13 +346,15 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
-  switch (input->type) {  // Already know in/out types are same.
+  TfLiteTensor* input_quantized = &context->tensors[node->temporaries->data[0]];
+
+  switch (filter->type) {  // Already know in/out types are same.
     case kTfLiteFloat32:
       return EvalFloat(context, node, params, data, input, filter,
                                     bias, output);
     case kTfLiteUInt8:
       return EvalQuantized(context, node, params, data, input,
-                                        filter, bias, output);
+                                        filter, bias, input_quantized, output);
     default:
       context->ReportError(context, "Type not currently supported.");
       return kTfLiteError;
diff --git a/tensorflow/contrib/lite/kernels/fully_connected_test.cc b/tensorflow/contrib/lite/kernels/fully_connected_test.cc
index 87413000a93a0a..05dd028b484c09 100644
--- a/tensorflow/contrib/lite/kernels/fully_connected_test.cc
+++ b/tensorflow/contrib/lite/kernels/fully_connected_test.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include 
 #include "absl/memory/memory.h"
 #include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h"
 #include "tensorflow/contrib/lite/kernels/register.h"
 #include "tensorflow/contrib/lite/kernels/test_util.h"
 #include "tensorflow/contrib/lite/model.h"
@@ -224,6 +225,60 @@ class QuantizedFullyConnectedOpModel : public BaseFullyConnectedOpModel {
   }
 };
 
+// In the hybrid model the weights are quantized (to uint8). But the bias,
+// input (and output) are expected to be in float precision.
+class HybridFullyConnectedOpModel : public SingleOpModel {
+ public:
+  HybridFullyConnectedOpModel(int units, int batches, const TensorData& input,
+                              const TensorData& weights,
+                              const TensorData& output = {TensorType_FLOAT32})
+      : batches_(batches), units_(units) {
+    int total_input_size = 1;
+    for (int i = 0; i < input.shape.size(); ++i) {
+      total_input_size *= input.shape[i];
+    }
+    input_size_ = total_input_size / batches_;
+
+    input_ = AddInput(input);
+    weights_ = AddInput(weights);
+
+    TensorData bias{TensorType_FLOAT32, {units_}};
+    bias_ = AddInput(bias);
+
+    output_ = AddOutput(output);
+
+    SetBuiltinOp(
+        BuiltinOperator_FULLY_CONNECTED, BuiltinOptions_FullyConnectedOptions,
+        CreateFullyConnectedOptions(builder_, ActivationFunctionType_RELU)
+            .Union());
+    resolver_ = absl::make_unique(
+        BuiltinOperator_FULLY_CONNECTED,
+        ops::builtin::Register_FULLY_CONNECTED_PIE());
+    BuildInterpreter({GetShape(input_), GetShape(weights_), GetShape(bias_)});
+  }
+  void SetBias(std::initializer_list f) { PopulateTensor(bias_, f); }
+  void SetWeights(std::initializer_list data) {
+    SymmetricQuantizeAndPopulate(weights_, data);
+  }
+
+  void SetInput(std::initializer_list f) { PopulateTensor(input_, f); }
+  std::vector GetOutput() { return ExtractVector(output_); }
+
+  int input_size() { return input_size_; }
+  int num_units() { return units_; }
+  int num_batches() { return batches_; }
+
+ protected:
+  int input_;
+  int weights_;
+  int bias_;
+  int output_;
+
+  int batches_;
+  int units_;
+  int input_size_;
+};
+
 const auto kKernelMap = new std::map({
     {"Reference", ops::builtin::Register_FULLY_CONNECTED_REF()},
     {"NeonOptimized", ops::builtin::Register_FULLY_CONNECTED_NEON_OPT()},
@@ -231,18 +286,43 @@ const auto kKernelMap = new std::map({
     {"Pie", ops::builtin::Register_FULLY_CONNECTED_PIE()},
 });
 
-class FullyConnectedOpTest : public SingleOpTest {
+class FloatFullyConnectedOpTest : public SingleOpTest {
  protected:
   const std::map& GetKernelMap() override {
     return *kKernelMap;
   }
 };
 
+const auto kKernelMapNoPie = new std::map({
+    {"Reference", ops::builtin::Register_FULLY_CONNECTED_REF()},
+    {"NeonOptimized", ops::builtin::Register_FULLY_CONNECTED_NEON_OPT()},
+    {"GenericOptimized", ops::builtin::Register_FULLY_CONNECTED_GENERIC_OPT()},
+});
+
+class QuantizedFullyConnectedOpTest : public SingleOpTest {
+ protected:
+  const std::map& GetKernelMap() override {
+    return *kKernelMapNoPie;
+  }
+};
+
+const auto kKernelMapPie = new std::map({
+    {"Pie", ops::builtin::Register_FULLY_CONNECTED_PIE()},
+});
+
+// Hybrid mode is used by the Pie quantized kernel.
+class HybridFullyConnectedOpTest : public SingleOpTest {
+ protected:
+  const std::map& GetKernelMap() override {
+    return *kKernelMapPie;
+  }
+};
+
 // TODO(ahentz): add more small tests like this one, focused on making sure the
 // calculations are correct.
-TEST_P(FullyConnectedOpTest, SimpleTest) {
-  FloatFullyConnectedOpModel m(GetRegistration(), 3, 2,
-                               {TensorType_FLOAT32, {2, 10}});
+TEST_P(FloatFullyConnectedOpTest, SimpleTest) {
+  FloatFullyConnectedOpModel m(GetRegistration(), /*units=*/3, /*batches=*/2,
+                               /*input=*/{TensorType_FLOAT32, {2, 10}});
   m.SetWeights({
       1, 2, 3, 4, 5, 6, 7, 8, 9, 10,  // u = 0
       1, 2, 3, 4, 5, 6, 7, 8, 9, 10,  // u = 1
@@ -260,9 +340,9 @@ TEST_P(FullyConnectedOpTest, SimpleTest) {
   EXPECT_THAT(m.GetOutput(), ElementsAre(24, 25, 26, 58, 59, 60));
 }
 
-TEST_P(FullyConnectedOpTest, SimpleTestQuantized) {
+TEST_P(QuantizedFullyConnectedOpTest, SimpleTestQuantized) {
   QuantizedFullyConnectedOpModel m(
-      GetRegistration(), 3, 2,
+      GetRegistration(), /*units=*/3, /*batches*/ 2,
       /*input=*/{TensorType_UINT8, {2, 10}, -63.5, 64},
       /*output=*/{TensorType_UINT8, {}, -127, 128});
 
@@ -288,13 +368,40 @@ TEST_P(FullyConnectedOpTest, SimpleTestQuantized) {
   EXPECT_THAT(m.GetOutput(), ElementsAre(151, 152, 153, 185, 186, 187));
 }
 
-TEST(FullyConnectedOpTest, SimpleTest4DInput) {
+TEST(HybridFullyConnectedOpTest, SimpleTestQuantized) {
+  HybridFullyConnectedOpModel m(
+      /*units=*/3, /*batches=*/2,
+      /*input=*/{TensorType_FLOAT32, {2, 10}},
+      /*weights=*/{TensorType_UINT8, {3, 10}, -63.5, 64});  // PIE
+
+  m.SetWeights({
+      1, 2, 3, 4, 5, 6, 7, 8, 9, 10,  // u = 0
+      1, 2, 3, 4, 5, 6, 7, 8, 9, 10,  // u = 1
+      1, 2, 3, 4, 5, 6, 7, 8, 9, 10,  // u = 1
+  });
+  m.SetBias({1, 2, 3});
+
+  m.SetInput({
+      1, 2, 3, 4, 5, 6, 7, 8,  -9, -10,  // b = 0
+      1, 2, 3, 4, 5, 6, 7, -8, 9,  -10,  // b = 1
+  });
+
+  m.Invoke();
+
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear(
+                                 {
+                                     24, 25, 26,  //
+                                     58, 59, 60,  //
+                                 },
+                                 /*max_abs_error=*/1.3f)));
+}
+
+TEST(FloatFullyConnectedOpTest, SimpleTest4DInput) {
   // Note that it is not required that the first dimension be the number of
   // batches. All we care is that the input can be evenly distributed in
   // batches. In this case, we need the input to have multiples of '2'.
   FloatFullyConnectedOpModel m(ops::builtin::Register_FULLY_CONNECTED_PIE(),
-                               /*units=*/3,
-                               /*batches=*/2,
+                               /*units=*/3, /*batches=*/2,
                                /*input=*/{TensorType_FLOAT32, {4, 1, 5, 1}});
   m.SetWeights({
       1, 2, 3, 4, 5, 6, 7, 8, 9, 10,  // u = 0
@@ -316,9 +423,9 @@ TEST(FullyConnectedOpTest, SimpleTest4DInput) {
                              }));
 }
 
-TEST_P(FullyConnectedOpTest, SimpleTest4dInputQuantized) {
+TEST_P(QuantizedFullyConnectedOpTest, SimpleTest4dInputQuantized) {
   QuantizedFullyConnectedOpModel m(
-      GetRegistration(), 3, 2,
+      GetRegistration(), /*units=*/3, /*batches=*/2,
       /*input=*/{TensorType_UINT8, {4, 1, 5, 1}, -63.5, 64},
       /*output=*/{TensorType_UINT8, {}, -127, 128});
 
@@ -345,14 +452,18 @@ TEST_P(FullyConnectedOpTest, SimpleTest4dInputQuantized) {
 }
 
 INSTANTIATE_TEST_CASE_P(
-    FullyConnectedOpTest, FullyConnectedOpTest,
+    FloatFullyConnectedOpTest, FloatFullyConnectedOpTest,
     ::testing::ValuesIn(SingleOpTest::GetKernelTags(*kKernelMap)));
 
+INSTANTIATE_TEST_CASE_P(
+    QuantizedFullyConnectedOpTest, QuantizedFullyConnectedOpTest,
+    ::testing::ValuesIn(SingleOpTest::GetKernelTags(*kKernelMapNoPie)));
+
 // TODO(ahentz): Reconsider this test. Having arbitrary weights makes it hard
 // to debug errors and doesn't necessarily test all the important details.
-TEST_P(FullyConnectedOpTest, BlackBoxTest) {
-  FloatFullyConnectedOpModel m(GetRegistration(), 16, 2,
-                               {TensorType_FLOAT32, {2, 8}});
+TEST_P(FloatFullyConnectedOpTest, BlackBoxTest) {
+  FloatFullyConnectedOpModel m(GetRegistration(), /*units=*/16, /*batches=*/2,
+                               /*input=*/{TensorType_FLOAT32, {2, 8}});
   m.SetWeights(
       {0.091327,  0.103366,  -0.316505, -0.083120, 0.149366,  -0.196636,
        -0.123672, 0.062800,  0.063031,  0.191670,  -0.062001, -0.061504,
diff --git a/tensorflow/contrib/lite/kernels/test_util.h b/tensorflow/contrib/lite/kernels/test_util.h
index a9064d54e7704d..6fb6fe27ebace8 100644
--- a/tensorflow/contrib/lite/kernels/test_util.h
+++ b/tensorflow/contrib/lite/kernels/test_util.h
@@ -21,6 +21,7 @@ limitations under the License.
 #include 
 
 #include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h"
 #include "tensorflow/contrib/lite/kernels/register.h"
 #include "tensorflow/contrib/lite/model.h"
 #include "tensorflow/contrib/lite/string_util.h"
@@ -133,6 +134,22 @@ class SingleOpModel {
     PopulateTensor(index, 0, q.data(), q.data() + q.size());
   }
 
+  void SymmetricQuantizeAndPopulate(int index,
+                                    std::initializer_list data) {
+    TfLiteTensor* t = interpreter_->tensor(index);
+    std::vector values(data);
+    const int length = values.size();
+    std::vector q(length);
+    float min, max, scaling_factor;
+    tensor_utils::SymmetricQuantizeFloats(values.data(), length, q.data(), &min,
+                                          &max, &scaling_factor);
+    // Update quantization params.
+    t->params.scale = scaling_factor;
+    t->params.zero_point = 0;
+    PopulateTensor(index, /*offset=*/0, reinterpret_cast(q.data()),
+                   reinterpret_cast(q.data() + q.size()));
+  }
+
   const std::vector& GetShape(int id) { return tensor_data_.at(id).shape; }
 
   float GetScale(int id) { return tensor_data_.at(id).scale; }

From b2aebe0721f630a2cbc4769d1d5b9eb5b1691824 Mon Sep 17 00:00:00 2001
From: joel-shor 
Date: Wed, 2 May 2018 02:01:14 +0300
Subject: [PATCH 0249/1691] [tf.data] Try fixing the Windows build by adding
 the directed interleave kernel to this cmake file.

---
 tensorflow/contrib/cmake/tf_core_kernels.cmake | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index f38c9e05135f9f..1505d3e2083b5a 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -68,6 +68,7 @@ if(tensorflow_BUILD_CONTRIB_KERNELS)
       "${tensorflow_source_dir}/tensorflow/contrib/coder/kernels/range_coder_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/coder/kernels/range_coder_ops_util.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/coder/ops/coder_ops.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/directed_interleave_dataset_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/prefetching_kernels.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc"

From 29cd3f96322f3d5326a2dbe6a9c502919159c9fc Mon Sep 17 00:00:00 2001
From: joel-shor 
Date: Wed, 2 May 2018 02:14:30 +0300
Subject: [PATCH 0250/1691] [tf.data] Remove debug code.

---
 tensorflow/contrib/data/python/ops/BUILD             | 1 -
 tensorflow/contrib/data/python/ops/interleave_ops.py | 6 ------
 2 files changed, 7 deletions(-)

diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD
index 9959ccc0057cc4..7a3e42cc72755c 100644
--- a/tensorflow/contrib/data/python/ops/BUILD
+++ b/tensorflow/contrib/data/python/ops/BUILD
@@ -184,7 +184,6 @@ py_library(
         "//tensorflow/python/data/ops:readers",
         "//tensorflow/python/data/util:nest",
         "//tensorflow/python/data/util:sparse",
-        "//tensorflow/python:platform",
     ],
 )
 
diff --git a/tensorflow/contrib/data/python/ops/interleave_ops.py b/tensorflow/contrib/data/python/ops/interleave_ops.py
index 2a9c5b45f82615..812a50ecbf1053 100644
--- a/tensorflow/contrib/data/python/ops/interleave_ops.py
+++ b/tensorflow/contrib/data/python/ops/interleave_ops.py
@@ -30,7 +30,6 @@
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.util import deprecation
-from tensorflow.python.platform import tf_logging as logging
 
 
 def parallel_interleave(map_func,
@@ -240,9 +239,4 @@ def select_dataset(logits, seed):
   selector_input = dataset_ops.Dataset.zip(
       (logits_ds, random_ops.RandomDataset(seed).batch(2))).map(select_dataset)
 
-  logging.warn('selector_input.output_types: %s', str(selector_input.output_types))
-  logging.warn('selector_input.output_shapes: %s', str(selector_input.output_shapes))
-  for i, dataset in enumerate(datasets):
-    logging.warn('dataset %i output_types: %s' % (i, str(dataset.output_types)))
-    logging.warn('dataset %i output_shapes: %s' % (i, str(dataset.output_shapes)))
   return DirectedInterleaveDataset(selector_input, datasets)

From 210abebd3febdd2c44ab5021bcebf8f1f5d451c4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 1 May 2018 16:17:21 -0700
Subject: [PATCH 0251/1691] [TF:XLA] Separate on-host and on-device shape and
 layout in HloModule.

Previously, only one layout was stored with an HLO module. This CL allows
HLO passes to modify the on-device layouts without affecting the on-host
layout (provided by the client)

PiperOrigin-RevId: 195014875
---
 .../compiler/xla/client/local_client.cc       | 36 ++++++++++---
 tensorflow/compiler/xla/client/local_client.h |  9 ----
 .../compiler/xla/service/cpu/cpu_compiler.cc  |  2 +-
 .../xla/service/cpu/cpu_executable.cc         |  5 +-
 .../xla/service/cpu/cpu_layout_assignment.h   |  3 +-
 .../service/cpu/cpu_layout_assignment_test.cc |  4 +-
 tensorflow/compiler/xla/service/executable.h  |  4 +-
 .../compiler/xla/service/gpu/gpu_compiler.cc  |  2 +-
 .../xla/service/gpu/gpu_layout_assignment.h   |  3 +-
 .../service/gpu/gpu_layout_assignment_test.cc |  8 +--
 tensorflow/compiler/xla/service/hlo_module.cc | 17 +++---
 tensorflow/compiler/xla/service/hlo_module.h  | 16 ++++--
 .../compiler/xla/service/hlo_module_config.cc | 17 ++++--
 .../compiler/xla/service/hlo_module_config.h  | 43 ++++++++++-----
 .../xla/service/interpreter/compiler.cc       |  2 +-
 .../compiler/xla/service/layout_assignment.cc | 15 +++---
 .../compiler/xla/service/layout_assignment.h  |  4 +-
 .../xla/service/layout_assignment_test.cc     |  8 +--
 tensorflow/compiler/xla/service/service.cc    | 52 +++++++++++++++----
 tensorflow/compiler/xla/service/service.h     |  3 ++
 tensorflow/compiler/xla/tests/BUILD           |  1 +
 tensorflow/compiler/xla/tests/hlo_test_base.h | 20 +++++--
 .../compiler/xla/tools/parser/hlo_parser.cc   | 10 +++-
 .../xla/tools/parser/hlo_parser_test.cc       |  2 +-
 24 files changed, 195 insertions(+), 91 deletions(-)

diff --git a/tensorflow/compiler/xla/client/local_client.cc b/tensorflow/compiler/xla/client/local_client.cc
index 1c1270590375ab..1acc6f86860e52 100644
--- a/tensorflow/compiler/xla/client/local_client.cc
+++ b/tensorflow/compiler/xla/client/local_client.cc
@@ -51,27 +51,49 @@ LocalExecutable::LocalExecutable(std::unique_ptr executable,
 tensorflow::Status LocalExecutable::ValidateExecutionOptions(
     const tensorflow::gtl::ArraySlice arguments,
     const ExecutableRunOptions& run_options, const Backend& backend) {
-  const ComputationLayout& computation_layout =
-      executable_->module_config().entry_computation_layout();
+  const ComputationLayout& host_computation_layout =
+      executable_->module_config().host_entry_computation_layout();
+  const ComputationLayout& device_computation_layout =
+      executable_->module_config().device_entry_computation_layout();
 
   // Check argument number, shapes, and layouts.
-  if (arguments.size() != computation_layout.parameter_count()) {
+  if (arguments.size() != host_computation_layout.parameter_count()) {
     return InvalidArgument(
         "invalid number of arguments for computation: expected %d, got %zu",
-        computation_layout.parameter_count(), arguments.size());
+        host_computation_layout.parameter_count(), arguments.size());
+  }
+  if (arguments.size() != device_computation_layout.parameter_count()) {
+    return InvalidArgument(
+        "invalid number of arguments for computation: expected %d, got %zu",
+        device_computation_layout.parameter_count(), arguments.size());
   }
   for (int i = 0; i < arguments.size(); ++i) {
-    if (!computation_layout.parameter_layout(i).MatchesLayoutInShape(
+    if (!host_computation_layout.parameter_layout(i).MatchesLayoutInShape(
             arguments[i]->on_host_shape())) {
       return InvalidParameterArgument(
           executable_.get(), i,
-          "Argument does not match shape or layout of computation parameter "
+          "Argument does not match host shape or layout of computation "
+          "parameter "
           "%d: want %s, got %s",
           i,
-          ShapeUtil::HumanString(computation_layout.parameter_layout(i).shape())
+          ShapeUtil::HumanString(
+              host_computation_layout.parameter_layout(i).shape())
               .c_str(),
           ShapeUtil::HumanString(arguments[i]->on_host_shape()).c_str());
     }
+    if (!device_computation_layout.parameter_layout(i).MatchesLayoutInShape(
+            arguments[i]->on_device_shape())) {
+      return InvalidParameterArgument(
+          executable_.get(), i,
+          "Argument does not match device shape or layout of computation "
+          "parameter "
+          "%d: want %s, got %s",
+          i,
+          ShapeUtil::HumanString(
+              device_computation_layout.parameter_layout(i).shape())
+              .c_str(),
+          ShapeUtil::HumanString(arguments[i]->on_device_shape()).c_str());
+    }
   }
 
   if (run_options.stream() != nullptr) {
diff --git a/tensorflow/compiler/xla/client/local_client.h b/tensorflow/compiler/xla/client/local_client.h
index 4ce7059f7e2fb3..d8fd7a5623d1fe 100644
--- a/tensorflow/compiler/xla/client/local_client.h
+++ b/tensorflow/compiler/xla/client/local_client.h
@@ -43,15 +43,6 @@ class LocalExecutable {
       const tensorflow::gtl::ArraySlice arguments,
       ExecutableRunOptions run_options);
 
-  // Return the layout (contained in a shape) of the result produced by the
-  // computation.
-  const Shape& result_layout() const {
-    return executable_->module_config()
-        .entry_computation_layout()
-        .result_layout()
-        .shape();
-  }
-
   // Return the options used to build the executable.
   const ExecutableBuildOptions& build_options() const { return build_options_; }
 
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index ec2bb6c762d0bb..d8ba289f296cdf 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -294,7 +294,7 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) {
       ReducePrecisionInsertion::PassTiming::AFTER_FUSION);
 
   pipeline.AddPass(
-      module->mutable_entry_computation_layout());
+      module->device_entry_computation_layout());
   // The LayoutAssignment pass may leave behind kCopy instructions which are
   // duplicate or NOPs, so remove them with algebraic simplification and CSE.
   pipeline.AddPass>(
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc
index aabf4d5161e3af..32613b86907830 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc
@@ -249,8 +249,9 @@ StatusOr CpuExecutable::CreateResultShapedBuffer(
     std::vector* buffers_in_result) {
   se::Stream* stream = run_options->stream();
   ScopedShapedBuffer result_buffer(
-      /*on_host_shape=*/result_shape(), /*on_device_shape=*/result_shape(),
-      run_options->allocator(), stream->parent()->device_ordinal());
+      /*on_host_shape=*/host_result_shape(),
+      /*on_device_shape=*/host_result_shape(), run_options->allocator(),
+      stream->parent()->device_ordinal());
 
   // Copy DeviceMemoryBase values which contain the array(s) of the result into
   // the respective location in ShapedBuffer which is returned to the caller.
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.h b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.h
index c8edbb9e15a5b6..09adb5cb02abba 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.h
@@ -27,7 +27,8 @@ namespace cpu {
 // layout constraints for operands and results of library calls.
 class CpuLayoutAssignment : public LayoutAssignment {
  public:
-  explicit CpuLayoutAssignment(ComputationLayout* entry_computation_layout)
+  explicit CpuLayoutAssignment(
+      const ComputationLayout& entry_computation_layout)
       : LayoutAssignment(entry_computation_layout) {}
   ~CpuLayoutAssignment() override {}
 
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment_test.cc
index 6ba030fff3bbc5..ba4c5a23d3e043 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment_test.cc
@@ -49,7 +49,7 @@ class CpuLayoutAssignmentTest : public HloTestBase {
  protected:
   void AssignLayouts(HloModule* module,
                      ComputationLayout* entry_computation_layout) {
-    cpu::CpuLayoutAssignment layout_assignment(entry_computation_layout);
+    cpu::CpuLayoutAssignment layout_assignment(*entry_computation_layout);
     EXPECT_IS_OK(layout_assignment.Run(module).status());
   }
 };
@@ -311,7 +311,7 @@ static StatusOr RunDotOutputFusion(
   result.addend_fusion_param = fusion_instruction->operand(
       fused_add->operand(1 - dot_operand_idx_in_add)->parameter_number());
 
-  cpu::CpuLayoutAssignment layout_assignment(&computation_layout);
+  cpu::CpuLayoutAssignment layout_assignment(computation_layout);
   TF_ASSIGN_OR_RETURN(result.layout_assignment_changed_something,
                       layout_assignment.Run(module));
 
diff --git a/tensorflow/compiler/xla/service/executable.h b/tensorflow/compiler/xla/service/executable.h
index 99762f45866c48..4f0466c544738f 100644
--- a/tensorflow/compiler/xla/service/executable.h
+++ b/tensorflow/compiler/xla/service/executable.h
@@ -140,8 +140,8 @@ class Executable {
 
   // The shape (including layout) that results from this execution. This is the
   // shape of the DeviceMemoryBase result value in ExecuteOnStream above.
-  const Shape& result_shape() const {
-    return hlo_module_->config().entry_computation_layout().result_shape();
+  const Shape& host_result_shape() const {
+    return hlo_module_->config().host_entry_computation_layout().result_shape();
   }
 
   // TODO(b/74197823): Delete the session module dumping helpers.
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
index 796c3070f22edd..4fdc4c89618bc0 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
@@ -248,7 +248,7 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
   {
     HloPassPipeline pipeline("layout_assignment");
     pipeline.AddPass(
-        hlo_module->mutable_entry_computation_layout());
+        hlo_module->device_entry_computation_layout());
 
     // The LayoutAssignment pass may leave behind kCopy instructions which are
     // duplicate or NOPs, so remove them with algebraic simplification and CSE.
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.h b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.h
index 86a3a7111fd794..51aae79c3d8d00 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.h
+++ b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.h
@@ -27,7 +27,8 @@ namespace gpu {
 // layout constraints for operands and results of library calls.
 class GpuLayoutAssignment : public LayoutAssignment {
  public:
-  explicit GpuLayoutAssignment(ComputationLayout* entry_computation_layout)
+  explicit GpuLayoutAssignment(
+      const ComputationLayout& entry_computation_layout)
       : LayoutAssignment(entry_computation_layout) {}
   ~GpuLayoutAssignment() override {}
 
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment_test.cc b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment_test.cc
index 4c45d2e94aebce..7c801955943021 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment_test.cc
@@ -69,7 +69,7 @@ TEST_F(LayoutAssignmentTest, Elementwise) {
         *computation_layout.mutable_result_layout() =
             ShapeLayout(result_shape_with_layout);
 
-        GpuLayoutAssignment layout_assignment(&computation_layout);
+        GpuLayoutAssignment layout_assignment(computation_layout);
         EXPECT_TRUE(layout_assignment.Run(module.get()).ValueOrDie());
 
         for (const HloInstruction* operand : add->operands()) {
@@ -156,7 +156,7 @@ TEST_F(LayoutAssignmentTest, BatchNormInference) {
         *computation_layout.mutable_result_layout() = ShapeLayout(result_shape);
       }
 
-      GpuLayoutAssignment layout_assignment(&computation_layout);
+      GpuLayoutAssignment layout_assignment(computation_layout);
       EXPECT_TRUE(layout_assignment.Run(module.get()).ValueOrDie());
 
       // The first operand to batchnorm should have the same layout as the
@@ -225,7 +225,7 @@ TEST_F(LayoutAssignmentTest, BatchNormTraining) {
                 {result_shape, offset_scale_shape, offset_scale_shape}));
       }
 
-      GpuLayoutAssignment layout_assignment(&computation_layout);
+      GpuLayoutAssignment layout_assignment(computation_layout);
       EXPECT_TRUE(layout_assignment.Run(module.get()).ValueOrDie());
 
       // The first operand to batchnorm should have the same layout as the
@@ -305,7 +305,7 @@ TEST_F(LayoutAssignmentTest, BatchNormGrad) {
                   {result_shape, scale_shape, scale_shape}));
         }
 
-        GpuLayoutAssignment layout_assignment(&computation_layout);
+        GpuLayoutAssignment layout_assignment(computation_layout);
         EXPECT_TRUE(layout_assignment.Run(module.get()).ValueOrDie());
 
         // The first and fourth operands to the batchnorm call should have the
diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc
index d4bad16f7976fc..987c4b27190f77 100644
--- a/tensorflow/compiler/xla/service/hlo_module.cc
+++ b/tensorflow/compiler/xla/service/hlo_module.cc
@@ -55,7 +55,7 @@ HloComputation* HloModule::AddComputationInternal(
 
     // If the module configuration has no entry layout computation set, create a
     // default one based on the program shape.
-    if (!config_.has_entry_computation_layout()) {
+    if (!config_.has_host_entry_computation_layout()) {
       config_.SetDefaultComputationLayout(
           entry_computation_->ComputeProgramShape());
     }
@@ -229,11 +229,14 @@ StatusOr> HloModule::CreateFromProto(
   TF_RET_CHECK(proto.has_program_shape())
       << "No program shape found in the proto";
   const auto& expected_program_shape = proto.program_shape();
-  TF_RET_CHECK(expected_program_shape.parameters_size() ==
-               module_config.entry_computation_layout().parameter_count());
+  TF_RET_CHECK(
+      expected_program_shape.parameters_size() ==
+      module_config.device_entry_computation_layout().parameter_count());
   for (int i = 0; i < expected_program_shape.parameters_size(); ++i) {
     const Shape& parameter_shape =
-        module_config.entry_computation_layout().parameter_layout(i).shape();
+        module_config.device_entry_computation_layout()
+            .parameter_layout(i)
+            .shape();
     TF_RET_CHECK(ShapeUtil::Compatible(expected_program_shape.parameters(i),
                                        parameter_shape))
         << "HloModuleConfig has different shape for parameter " << i
@@ -243,7 +246,7 @@ StatusOr> HloModule::CreateFromProto(
         << ", actual: " << ShapeUtil::HumanStringWithLayout(parameter_shape);
   }
   const Shape& result_shape =
-      module_config.entry_computation_layout().result_layout().shape();
+      module_config.device_entry_computation_layout().result_layout().shape();
   TF_RET_CHECK(
       ShapeUtil::Compatible(expected_program_shape.result(), result_shape))
       << "HloModuleConfig has different result shape than the HLO module. "
@@ -303,7 +306,7 @@ StatusOr HloModule::CreateModuleConfigFromProto(
   // The module config is constructed with default layouts regardless of what is
   // passed in via the ProgramShape. Set the layouts to the appropriate values.
   ComputationLayout* entry_layout =
-      module_config.mutable_entry_computation_layout();
+      module_config.mutable_host_entry_computation_layout();
   for (int64 i = 0; i < entry_layout->parameter_count(); ++i) {
     TF_RETURN_IF_ERROR(
         entry_layout->mutable_parameter_layout(i)->CopyLayoutFromShape(
@@ -311,6 +314,8 @@ StatusOr HloModule::CreateModuleConfigFromProto(
   }
   TF_RETURN_IF_ERROR(entry_layout->mutable_result_layout()->CopyLayoutFromShape(
       program_shape.result()));
+  *module_config.mutable_device_entry_computation_layout() =
+      module_config.host_entry_computation_layout();
 
   return module_config;
 }
diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h
index aa843ead517479..82d790ec3b405d 100644
--- a/tensorflow/compiler/xla/service/hlo_module.h
+++ b/tensorflow/compiler/xla/service/hlo_module.h
@@ -98,12 +98,20 @@ class HloModule {
     return entry_computation_;
   }
 
-  ComputationLayout* mutable_entry_computation_layout() {
-    return config_.mutable_entry_computation_layout();
+  ComputationLayout* mutable_host_entry_computation_layout() {
+    return config_.mutable_host_entry_computation_layout();
   }
 
-  const ComputationLayout& entry_computation_layout() const {
-    return config_.entry_computation_layout();
+  const ComputationLayout& host_entry_computation_layout() const {
+    return config_.host_entry_computation_layout();
+  }
+
+  ComputationLayout* mutable_device_entry_computation_layout() {
+    return config_.mutable_device_entry_computation_layout();
+  }
+
+  const ComputationLayout& device_entry_computation_layout() const {
+    return config_.device_entry_computation_layout();
   }
 
   const VersionedComputationHandle& entry_computation_handle() const {
diff --git a/tensorflow/compiler/xla/service/hlo_module_config.cc b/tensorflow/compiler/xla/service/hlo_module_config.cc
index 4205b0402cb8b2..dae5578a3158fe 100644
--- a/tensorflow/compiler/xla/service/hlo_module_config.cc
+++ b/tensorflow/compiler/xla/service/hlo_module_config.cc
@@ -31,11 +31,13 @@ using tensorflow::strings::StrAppend;
 HloModuleConfig::HloModuleConfig() {}
 
 HloModuleConfig::HloModuleConfig(const ProgramShape& program_shape)
-    : entry_computation_layout_(program_shape) {}
+    : host_entry_computation_layout_(program_shape),
+      device_entry_computation_layout_(program_shape) {}
 
 void HloModuleConfig::SetDefaultComputationLayout(
     const ProgramShape& program_shape) {
-  entry_computation_layout_ = ComputationLayout(program_shape);
+  host_entry_computation_layout_ = ComputationLayout(program_shape);
+  device_entry_computation_layout_ = ComputationLayout(program_shape);
 }
 
 string HloModuleConfig::compilation_cache_key() const {
@@ -44,11 +46,18 @@ string HloModuleConfig::compilation_cache_key() const {
   StrAppend(&key, "::(");
   std::vector params;
   for (const ShapeLayout& param_layout :
-       entry_computation_layout_->parameter_layouts()) {
+       host_entry_computation_layout_->parameter_layouts()) {
     params.push_back(param_layout.shape().DebugString());
   }
   StrAppend(&key, tensorflow::str_util::Join(params, ", "), ") => ",
-            entry_computation_layout_->result_shape().SerializeAsString());
+            host_entry_computation_layout_->result_shape().SerializeAsString());
+  for (const ShapeLayout& param_layout :
+       device_entry_computation_layout_->parameter_layouts()) {
+    params.push_back(param_layout.shape().DebugString());
+  }
+  StrAppend(
+      &key, tensorflow::str_util::Join(params, ", "), ") => ",
+      device_entry_computation_layout_->result_shape().SerializeAsString());
   if (seed() != 0) {
     // TODO(b/32083678): force recompilation to reset global state.
     static std::atomic counter{0};
diff --git a/tensorflow/compiler/xla/service/hlo_module_config.h b/tensorflow/compiler/xla/service/hlo_module_config.h
index 586a03d412681c..cdb0b29a2399b3 100644
--- a/tensorflow/compiler/xla/service/hlo_module_config.h
+++ b/tensorflow/compiler/xla/service/hlo_module_config.h
@@ -41,26 +41,44 @@ class HloModuleConfig {
   explicit HloModuleConfig(const ProgramShape& program_shape);
 
   // Checks if this config has an entry computation layout already.
-  bool has_entry_computation_layout() const {
-    return entry_computation_layout_.has_value();
+  bool has_host_entry_computation_layout() const {
+    return host_entry_computation_layout_.has_value();
+  }
+
+  bool has_device_entry_computation_layout() const {
+    return device_entry_computation_layout_.has_value();
   }
 
   // Sets the entry computation layout for this config. If the entry computation
   // layout already exists, it is silently replaced.
   void SetDefaultComputationLayout(const ProgramShape& program_shape);
 
-  // Returns a constant reference to the layout of the entry computation.
+  // Returns a constant reference to the on-host layout of the entry
+  // computation. Assumes the layout was set.
+  const ComputationLayout& host_entry_computation_layout() const {
+    CHECK(host_entry_computation_layout_.has_value());
+    return *host_entry_computation_layout_;
+  }
+
+  // Returns a mutable pointer to the layout of the on-host entry computation.
   // Assumes the layout was set.
-  const ComputationLayout& entry_computation_layout() const {
-    CHECK(entry_computation_layout_.has_value());
-    return *entry_computation_layout_;
+  ComputationLayout* mutable_host_entry_computation_layout() {
+    CHECK(host_entry_computation_layout_.has_value());
+    return &(*host_entry_computation_layout_);
   }
 
-  // Returns a mutable pointer to the layout of the entry computation. Assumes
-  // the layout was set.
-  ComputationLayout* mutable_entry_computation_layout() {
-    CHECK(entry_computation_layout_.has_value());
-    return &(*entry_computation_layout_);
+  // Returns a constant reference to the on-device layout of the entry
+  // computation. Assumes the layout was set.
+  const ComputationLayout& device_entry_computation_layout() const {
+    CHECK(device_entry_computation_layout_.has_value());
+    return *device_entry_computation_layout_;
+  }
+
+  // Returns a mutable pointer to the layout of the on-device entry computation.
+  // Assumes the layout was set.
+  ComputationLayout* mutable_device_entry_computation_layout() {
+    CHECK(device_entry_computation_layout_.has_value());
+    return &(*device_entry_computation_layout_);
   }
 
   // Returns whether to enable HLO-level profiling.
@@ -109,7 +127,8 @@ class HloModuleConfig {
  private:
   // If you add new members, be sure to update compilation_cache_key.
 
-  tensorflow::gtl::optional entry_computation_layout_;
+  tensorflow::gtl::optional host_entry_computation_layout_;
+  tensorflow::gtl::optional device_entry_computation_layout_;
 
   // Whether this is a 'host module'.
   bool is_host_module_ = false;
diff --git a/tensorflow/compiler/xla/service/interpreter/compiler.cc b/tensorflow/compiler/xla/service/interpreter/compiler.cc
index 76b3ecad26fe92..eecbbcb93df64b 100644
--- a/tensorflow/compiler/xla/service/interpreter/compiler.cc
+++ b/tensorflow/compiler/xla/service/interpreter/compiler.cc
@@ -45,7 +45,7 @@ Status InterpreterCompiler::RunHloOptimization(HloModule* hlo_module) {
   HloPassPipeline pipeline("Interpreter");
 
   pipeline.AddPass(
-      hlo_module->mutable_entry_computation_layout());
+      hlo_module->device_entry_computation_layout());
 
   return pipeline.Run(hlo_module).status();
 }
diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc
index 2494569db53f26..cfa7ba5e81ddd0 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment.cc
@@ -909,22 +909,19 @@ Status LayoutAssignment::CheckLayouts(HloModule* module) {
 }
 
 LayoutAssignment::LayoutAssignment(
-    ComputationLayout* entry_computation_layout,
+    const ComputationLayout& entry_computation_layout,
     ChannelLayoutConstraints* channel_constraints)
     : entry_computation_layout_(entry_computation_layout),
       channel_layout_constraints_(channel_constraints) {
   VLOG(1) << "entry computation layout given to layout assignment: "
-          << entry_computation_layout_->ToString();
+          << entry_computation_layout_.ToString();
   // Layouts of all parameter instructions must be set.
   for (const ShapeLayout& parameter_layout :
-       entry_computation_layout_->parameter_layouts()) {
+       entry_computation_layout_.parameter_layouts()) {
     CHECK(parameter_layout.LayoutIsSet());
   }
-  // If the result layout is not set, then choose the default.
-  // TODO(b/29118294): Choose a better layout in this case.
-  if (!entry_computation_layout_->result_layout().LayoutIsSet()) {
-    entry_computation_layout_->mutable_result_layout()->SetToDefaultLayout();
-  }
+  // TODO(b/29118294): Choose a better layout if the result layout is not set.
+  CHECK(entry_computation_layout_.result_layout().LayoutIsSet());
 }
 
 std::unique_ptr LayoutAssignment::ChooseOperandLayoutFromOutputLayout(
@@ -1597,7 +1594,7 @@ StatusOr LayoutAssignment::Run(HloModule* module) {
     }
     if (computation == module->entry_computation()) {
       TF_RETURN_IF_ERROR(RunOnComputation(
-          *entry_computation_layout_, *points_to_analysis,
+          entry_computation_layout_, *points_to_analysis,
           module->entry_computation(), channel_layout_constraints_));
     } else {
       ComputationLayout computation_layout(computation->ComputeProgramShape());
diff --git a/tensorflow/compiler/xla/service/layout_assignment.h b/tensorflow/compiler/xla/service/layout_assignment.h
index ae4986d6ad9bc3..c83ae0388b4250 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.h
+++ b/tensorflow/compiler/xla/service/layout_assignment.h
@@ -288,7 +288,7 @@ class LayoutAssignment : public HloPassInterface {
   // If channel_constraints is nullptr, no kSend or kRecvs must be contained
   // within any module passed to `Run`.
   explicit LayoutAssignment(
-      ComputationLayout* entry_computation_layout,
+      const ComputationLayout& entry_computation_layout,
       ChannelLayoutConstraints* channel_constraints = nullptr);
   ~LayoutAssignment() override {}
   tensorflow::StringPiece name() const override { return "layout-assignment"; }
@@ -402,7 +402,7 @@ class LayoutAssignment : public HloPassInterface {
   // necessary conditions.
   Status CheckLayouts(HloModule* module);
 
-  ComputationLayout* entry_computation_layout_;
+  const ComputationLayout& entry_computation_layout_;
 
  protected:
   // Sets up the copy instruction according to the characteristic (sharding,
diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc
index 4b1c9bad41de80..7e1bb11eaada0e 100644
--- a/tensorflow/compiler/xla/service/layout_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc
@@ -53,7 +53,7 @@ class LayoutAssignmentTest : public HloTestBase {
  protected:
   void AssignLayouts(HloModule* module,
                      ComputationLayout* entry_computation_layout) {
-    LayoutAssignment layout_assignment(entry_computation_layout);
+    LayoutAssignment layout_assignment(*entry_computation_layout);
     EXPECT_IS_OK(layout_assignment.Run(module).status());
   }
 };
@@ -285,7 +285,7 @@ TEST_F(LayoutAssignmentTest, ConflictingLayoutTuple) {
   TF_CHECK_OK(computation_layout.mutable_result_layout()->CopyLayoutFromShape(
       result_shape));
 
-  LayoutAssignment layout_assignment(&computation_layout);
+  LayoutAssignment layout_assignment(computation_layout);
   AssignLayouts(module.get(), &computation_layout);
 
   // Layout assignment should have deep copied the result of the computation to
@@ -488,7 +488,7 @@ class OperandsMustBeTheSameLayoutAssignment : public LayoutAssignment {
  public:
   explicit OperandsMustBeTheSameLayoutAssignment(
       ComputationLayout* entry_computation_layout)
-      : LayoutAssignment(entry_computation_layout) {}
+      : LayoutAssignment(*entry_computation_layout) {}
 
  protected:
   Status PropagateBufferConstraint(
@@ -808,7 +808,7 @@ TEST_F(LayoutAssignmentTest, InternalErrorOnBitcast) {
 
   ComputationLayout computation_layout(
       module->entry_computation()->ComputeProgramShape());
-  LayoutAssignment layout_assignment(&computation_layout);
+  LayoutAssignment layout_assignment(computation_layout);
   Status error_status = layout_assignment.Run(module.get()).status();
   EXPECT_FALSE(error_status.ok());
   EXPECT_THAT(
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 175ee96bbc78e6..6ce03ab39d4dc8 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -296,8 +296,10 @@ StatusOr> Service::CreateModuleConfig(
     const ExecutionOptions* execution_options,
     const UserComputation* user_computation) {
   auto config = MakeUnique(program_shape);
-  auto* computation_layout = config->mutable_entry_computation_layout();
-
+  ComputationLayout* host_computation_layout =
+      config->mutable_host_entry_computation_layout();
+  ComputationLayout* device_computation_layout =
+      config->mutable_device_entry_computation_layout();
   if (program_shape.parameters_size() != argument_shapes.size()) {
     return InvalidArgument("computation takes %d parameters, but %zu given",
                            program_shape.parameters_size(),
@@ -322,9 +324,10 @@ StatusOr> Service::CreateModuleConfig(
           i, ShapeUtil::HumanString(program_shape.parameters(i)).c_str(),
           ShapeUtil::HumanString(*argument_shapes[i]).c_str());
     }
-    TF_RETURN_IF_ERROR(
-        computation_layout->mutable_parameter_layout(i)->CopyLayoutFromShape(
-            *argument_shapes[i]));
+    TF_RETURN_IF_ERROR(host_computation_layout->mutable_parameter_layout(i)
+                           ->CopyLayoutFromShape(*argument_shapes[i]));
+    TF_RETURN_IF_ERROR(device_computation_layout->mutable_parameter_layout(i)
+                           ->CopyLayoutFromShape(*argument_shapes[i]));
   }
   if (execution_options != nullptr &&
       execution_options->has_shape_with_output_layout()) {
@@ -333,10 +336,17 @@ StatusOr> Service::CreateModuleConfig(
     TF_RETURN_IF_ERROR(ValidateResultShapeWithLayout(shape_with_output_layout,
                                                      program_shape.result()));
     TF_RETURN_IF_ERROR(
-        computation_layout->mutable_result_layout()->CopyLayoutFromShape(
+        host_computation_layout->mutable_result_layout()->CopyLayoutFromShape(
+            shape_with_output_layout));
+    TF_RETURN_IF_ERROR(
+        device_computation_layout->mutable_result_layout()->CopyLayoutFromShape(
             shape_with_output_layout));
   } else {
-    computation_layout->mutable_result_layout()->Clear();
+    // If the result layout is not set, then choose the default.
+    // TODO(b/29118294): Allow the compiler to choose a better layout in this
+    // case.
+    host_computation_layout->mutable_result_layout()->SetToDefaultLayout();
+    device_computation_layout->mutable_result_layout()->SetToDefaultLayout();
   }
 
   config->set_replica_count(options_.number_of_replicas());
@@ -488,6 +498,22 @@ StatusOr>> Service::BuildExecutables(
   return std::move(executables);
 }
 
+Status Service::ValidateEntryComputationLayout(HloModule* module) {
+  const ComputationLayout& on_device =
+      module->device_entry_computation_layout();
+  for (int64 i = 0; i < on_device.parameter_count(); ++i) {
+    TF_RET_CHECK(ShapeUtil::Equal(
+        on_device.parameter_shape(i),
+        execute_backend_->transfer_manager()->HostShapeToDeviceShape(
+            module->host_entry_computation_layout().parameter_shape(i))));
+  }
+  TF_RET_CHECK(ShapeUtil::Equal(
+      module->device_entry_computation_layout().result_shape(),
+      execute_backend_->transfer_manager()->HostShapeToDeviceShape(
+          module->host_entry_computation_layout().result_shape())));
+  return tensorflow::Status::OK();
+}
+
 StatusOr> Service::BuildExecutable(
     const VersionedComputationHandle& versioned_handle,
     std::unique_ptr module_config, Backend* backend,
@@ -526,6 +552,8 @@ StatusOr> Service::BuildExecutable(
   TF_ASSIGN_OR_RETURN(
       module, backend->compiler()->RunHloPasses(std::move(module), executor,
                                                 device_allocator));
+  // Check that on-host and on-device shapes are consistent.
+  TF_RETURN_IF_ERROR(ValidateEntryComputationLayout(module.get()));
 
   TF_ASSIGN_OR_RETURN(std::unique_ptr executable,
                       backend->compiler()->RunBackend(
@@ -889,7 +917,7 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg,
         CreateModuleConfig(*program_shape, replicated_arguments.front(),
                            request.execution_options(), user_computation));
     VLOG(3) << "ExecuteParallel created HloModuleConfig computation layout: "
-            << module_config->entry_computation_layout().ToString();
+            << module_config->host_entry_computation_layout().ToString();
 
     // Adds to the vectors to build and execute the computations after the loop.
     all_arguments.push_back(replicated_arguments);
@@ -992,7 +1020,7 @@ tensorflow::Status Service::ExecuteGraphParallel(
                            /*user_computation=*/nullptr));
     VLOG(3)
         << "ExecuteGraphParallel created HloModuleConfig computation layout: "
-        << module_config->entry_computation_layout().ToString();
+        << module_config->host_entry_computation_layout().ToString();
 
     // Adds to the vectors to build and execute the computations after the loop.
     all_arguments.push_back(replicated_arguments);
@@ -1142,7 +1170,7 @@ tensorflow::Status Service::Execute(const ExecuteRequest* arg,
                          arg->execution_options(), user_computation));
 
   VLOG(3) << "Execute created HloModuleConfig computation layout: "
-          << module_config->entry_computation_layout().ToString();
+          << module_config->host_entry_computation_layout().ToString();
 
   TF_ASSIGN_OR_RETURN(
       std::shared_ptr executable,
@@ -1212,6 +1240,8 @@ StatusOr> Service::BuildExecutable(
   TF_ASSIGN_OR_RETURN(
       module, backend->compiler()->RunHloPasses(std::move(module), executor,
                                                 device_allocator));
+  // Check that on-host and on-device shapes are consistent.
+  TF_RETURN_IF_ERROR(ValidateEntryComputationLayout(module.get()));
 
   TF_ASSIGN_OR_RETURN(std::unique_ptr executable,
                       backend->compiler()->RunBackend(
@@ -1313,7 +1343,7 @@ tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg,
                          arg->execution_options(), user_computation));
 
   VLOG(3) << "ExecuteAsync created HloModuleConfig computation layout: "
-          << module_config->entry_computation_layout().ToString();
+          << module_config->host_entry_computation_layout().ToString();
 
   ExecutionProfile profile;
 
diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h
index 476bd0597de735..f84fe407e05da3 100644
--- a/tensorflow/compiler/xla/service/service.h
+++ b/tensorflow/compiler/xla/service/service.h
@@ -295,6 +295,9 @@ class Service : public ServiceInterface {
       const ExecutionOptions& execution_options,
       tensorflow::gtl::ArraySlice arguments);
 
+  // Assert that host- and device-shapes are in a consistent state.
+  Status ValidateEntryComputationLayout(HloModule* module);
+
  protected:
   friend class LocalExecutable;
 
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 840292010d50fd..54cf0543b89773 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -632,6 +632,7 @@ xla_test(
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h
index 6491208895f9ec..9539ae06801628 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.h
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.h
@@ -177,9 +177,13 @@ class HloTestBase : public ::testing::Test {
   // 'layout'.
   void ForceParameterLayout(HloModule* module, int64 param_no,
                             const Layout& layout) {
-    ASSERT_LT(param_no,
-              module->mutable_entry_computation_layout()->parameter_count());
-    module->mutable_entry_computation_layout()
+    ASSERT_LT(
+        param_no,
+        module->mutable_host_entry_computation_layout()->parameter_count());
+    module->mutable_host_entry_computation_layout()
+        ->mutable_parameter_layout(param_no)
+        ->ResetLayout(layout);
+    module->mutable_device_entry_computation_layout()
         ->mutable_parameter_layout(param_no)
         ->ResetLayout(layout);
   }
@@ -187,7 +191,10 @@ class HloTestBase : public ::testing::Test {
   // Convenience method to force the layout of the computation result in a
   // module. The result layout of 'module' is set to 'layout'.
   void ForceResultLayout(HloModule* module, const Layout& layout) {
-    module->mutable_entry_computation_layout()
+    module->mutable_host_entry_computation_layout()
+        ->mutable_result_layout()
+        ->ResetLayout(layout);
+    module->mutable_device_entry_computation_layout()
         ->mutable_result_layout()
         ->ResetLayout(layout);
   }
@@ -195,7 +202,10 @@ class HloTestBase : public ::testing::Test {
   // Convenience method to clear the layout of the computation result in
   // 'module'.
   void ForceClearResultLayout(HloModule* module) {
-    module->mutable_entry_computation_layout()
+    module->mutable_host_entry_computation_layout()
+        ->mutable_result_layout()
+        ->Clear();
+    module->mutable_device_entry_computation_layout()
         ->mutable_result_layout()
         ->Clear();
   }
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
index fdbfc0210ea63a..1bb31ddb7b6fdf 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
@@ -303,12 +303,18 @@ bool HloParser::ParseComputations() {
     // set the layouts to what the hlo text says.
     for (int p = 0; p < computation->num_parameters(); p++) {
       const Shape& param_shape = computation->parameter_instruction(p)->shape();
-      TF_CHECK_OK(module_->mutable_entry_computation_layout()
+      TF_CHECK_OK(module_->mutable_host_entry_computation_layout()
+                      ->mutable_parameter_layout(p)
+                      ->CopyLayoutFromShape(param_shape));
+      TF_CHECK_OK(module_->mutable_device_entry_computation_layout()
                       ->mutable_parameter_layout(p)
                       ->CopyLayoutFromShape(param_shape));
     }
     const Shape& result_shape = computation->root_instruction()->shape();
-    TF_CHECK_OK(module_->mutable_entry_computation_layout()
+    TF_CHECK_OK(module_->mutable_host_entry_computation_layout()
+                    ->mutable_result_layout()
+                    ->CopyLayoutFromShape(result_shape));
+    TF_CHECK_OK(module_->mutable_device_entry_computation_layout()
                     ->mutable_result_layout()
                     ->CopyLayoutFromShape(result_shape));
   }
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
index adc8b1d620eb65..4e085bc89c6dc6 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
@@ -1239,7 +1239,7 @@ ENTRY %Reduce (input: f32[8,16,256]) -> f32[8,16] {
 
   auto module = Parse(original);
   TF_ASSERT_OK(module.status());
-  auto program_layout = module.ValueOrDie()->entry_computation_layout();
+  auto program_layout = module.ValueOrDie()->host_entry_computation_layout();
   ASSERT_EQ(program_layout.parameter_count(), 1);
   auto param_layout = program_layout.parameter_layout(0).layout();
   auto result_layout = program_layout.result_layout().layout();

From fb8f040f2a927c6df149238da7c4278cf781d081 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 1 May 2018 16:20:47 -0700
Subject: [PATCH 0252/1691] Allow `warm_start_from` argument to be a SavedModel
 path.

PiperOrigin-RevId: 195015356
---
 tensorflow/python/estimator/estimator.py      | 28 ++++++++++-----
 tensorflow/python/estimator/estimator_test.py | 35 +++++++++++++++++++
 2 files changed, 54 insertions(+), 9 deletions(-)

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 0970f001240c5b..3691c99ddac6b4 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -155,12 +155,12 @@ def __init__(self, model_fn, model_dir=None, config=None, params=None,
       config: Configuration object.
       params: `dict` of hyper parameters that will be passed into `model_fn`.
               Keys are names of parameters, values are basic python types.
-      warm_start_from: Optional string filepath to a checkpoint to warm-start
-                       from, or a `tf.estimator.WarmStartSettings` object to
-                       fully configure warm-starting.  If the string filepath is
-                       provided instead of a `WarmStartSettings`, then all
-                       variables are warm-started, and it is assumed that
-                       vocabularies and Tensor names are unchanged.
+      warm_start_from: Optional string filepath to a checkpoint or SavedModel to
+                       warm-start from, or a `tf.estimator.WarmStartSettings`
+                       object to fully configure warm-starting.  If the string
+                       filepath is provided instead of a `WarmStartSettings`,
+                       then all variables are warm-started, and it is assumed
+                       that vocabularies and Tensor names are unchanged.
 
     Raises:
       ValueError: parameters of `model_fn` don't match `params`.
@@ -1502,7 +1502,7 @@ def _get_default_warm_start_settings(warm_start_from):
 
   Args:
     warm_start_from: Either a string representing the filepath of a checkpoint
-      to initialize from, or an instance of WarmStartSettings.
+      or SavedModel to initialize from, or an instance of WarmStartSettings.
 
   Returns:
     Either None or an instance of WarmStartSettings.
@@ -1513,9 +1513,19 @@ def _get_default_warm_start_settings(warm_start_from):
   """
   if warm_start_from is None:
     return None
-  if isinstance(warm_start_from, six.string_types):
+  if isinstance(warm_start_from, (six.string_types, six.binary_type)):
+    # Infer that this is a SavedModel if export_path +
+    # 'variables/variables.index' exists, and if so, construct the
+    # WarmStartSettings pointing to export_path + 'variables/variables'.
+    if gfile.Exists(os.path.join(compat.as_bytes(warm_start_from),
+                                 compat.as_bytes('variables/variables.index'))):
+      logging.info('Warm-starting from a SavedModel')
+      return WarmStartSettings(ckpt_to_initialize_from=os.path.join(
+          compat.as_bytes(warm_start_from),
+          compat.as_bytes('variables/variables')))
     return WarmStartSettings(ckpt_to_initialize_from=warm_start_from)
   elif isinstance(warm_start_from, WarmStartSettings):
     return warm_start_from
   else:
-    raise ValueError('warm_start_from must be a string or a WarmStartSettings')
+    raise ValueError('warm_start_from must be a string or a WarmStartSettings, '
+                     'instead got {}'.format(type(warm_start_from)))
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 74114fab3b7ae2..4d958f8b43ff2c 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -658,6 +658,41 @@ def _variable_creating_model_fn(features, labels, mode):
         5, estimator._load_global_step_from_checkpoint_dir(
             warm_started_est.model_dir))
 
+  def test_warm_starts_from_savedmodel(self):
+    def _make_model_fn(x):
+      def _variable_creating_and_export_model_fn(features, labels, mode):
+        _, _ = features, labels
+        variable_scope.get_variable('x', initializer=x)
+        global_step = training.get_global_step()
+        return model_fn_lib.EstimatorSpec(
+            mode,
+            predictions={'y': constant_op.constant(1.0)},
+            loss=constant_op.constant(1.),
+            train_op=state_ops.assign_add(global_step, 1),
+            export_outputs={'test': export_output.ClassificationOutput(
+                constant_op.constant([4.2]), constant_op.constant(['label']))})
+      return _variable_creating_and_export_model_fn
+
+    est = estimator.Estimator(model_fn=_make_model_fn(42.))
+    est.train(dummy_input_fn, steps=10)
+    feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),
+                    'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}
+    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
+        feature_spec)
+    tmpdir = tempfile.mkdtemp()
+    export_dir_base = os.path.join(
+        compat.as_bytes(tmpdir), compat.as_bytes('export'))
+    export_dir = est.export_savedmodel(
+        export_dir_base, serving_input_receiver_fn)
+
+    warm_started_est = estimator.Estimator(
+        model_fn=_make_model_fn(36.),
+        warm_start_from=export_dir)
+    warm_started_est.train(dummy_input_fn, steps=5)
+    # warm_start is called after the model_fn, so x should have the value
+    # from the SavedModel.
+    self.assertEqual(42., warm_started_est.get_variable_value('x'))
+
   def test_max_step(self):
     est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)
     est.train(dummy_input_fn, max_steps=5)

From f5dbc1e16622f433f41f195bb33f56d674a004ce Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 1 May 2018 16:33:03 -0700
Subject: [PATCH 0253/1691] Check for overflow in shape calculation.

PiperOrigin-RevId: 195017114
---
 tensorflow/contrib/lite/toco/BUILD            |  12 +
 .../contrib/lite/toco/import_tensorflow.cc    | 505 ++++++++++--------
 .../lite/toco/import_tensorflow_test.cc       | 160 ++++++
 tensorflow/contrib/lite/toco/toco_port.h      |   5 +
 tensorflow/contrib/lite/toco/tooling_util.h   |  29 +
 .../contrib/lite/toco/tooling_util_test.cc    |  81 +++
 6 files changed, 562 insertions(+), 230 deletions(-)
 create mode 100644 tensorflow/contrib/lite/toco/import_tensorflow_test.cc

diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD
index f92e546ab8aa3c..f16225fd665277 100644
--- a/tensorflow/contrib/lite/toco/BUILD
+++ b/tensorflow/contrib/lite/toco/BUILD
@@ -364,6 +364,18 @@ cc_library(
     }),
 )
 
+tf_cc_test(
+    name = "import_tensorflow_test",
+    srcs = ["import_tensorflow_test.cc"],
+    deps = [
+        ":toco_tooling",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:graph",
+        "//tensorflow/core:protos_all_cc",
+        "@com_google_googletest//:gtest_main",
+    ],
+)
+
 cc_library(
     name = "tooling_util",
     srcs = [
diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc
index fa8b26bce00d12..453ff29b0d08ec 100644
--- a/tensorflow/contrib/lite/toco/import_tensorflow.cc
+++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc
@@ -62,6 +62,9 @@ using tensorflow::TensorProto;
 using tensorflow::TensorShapeProto;
 
 namespace toco {
+
+using port::Status;
+
 namespace {
 bool HasAttr(const NodeDef& node, const string& attr_name) {
   return node.attr().count(attr_name) > 0;
@@ -113,7 +116,7 @@ const TensorShapeProto& GetShapeAttr(const NodeDef& node,
 }
 
 const TensorProto& GetTensorAttr(const NodeDef& node, const string& attr_name) {
-  CHECK(HasAttr(node, attr_name));
+  CHECK(HasAttr(node, attr_name)) << "No attr named '" << attr_name << "'";
   const auto& attr = node.attr().at(attr_name);
   CHECK_EQ(attr.value_case(), AttrValue::kTensor);
   return attr.tensor();
@@ -145,9 +148,9 @@ ArrayDataType ConvertDataType(tensorflow::DataType dtype) {
   return ArrayDataType::kNone;
 }
 
-void ImportShape(const TFLITE_PROTO_NS::RepeatedPtrField<
-                     tensorflow::TensorShapeProto_Dim>& input_dims,
-                 Shape* shape) {
+Status ImportShape(const TFLITE_PROTO_NS::RepeatedPtrField<
+                       tensorflow::TensorShapeProto_Dim>& input_dims,
+                   int* input_flat_size, Shape* shape) {
   std::vector input_dims_only_sizes;
   for (auto& d : input_dims) {
     if (d.size() == 0) {
@@ -155,23 +158,33 @@ void ImportShape(const TFLITE_PROTO_NS::RepeatedPtrField<
       // them of flat size 0 even though they have other nonzero dims.
       // This breaks our invariant, that array dims can't be 0.
       // For now, tweaking this to record a 0-D shape instead.
-      input_dims_only_sizes.clear();
-      break;
+      shape->mutable_dims()->clear();
+      if (input_flat_size != nullptr) *input_flat_size = 0;
+      return Status::OK();
+    }
+    // TensorFlow's shapes use int64s, while TOCO uses ints.
+    if (d.size() > std::numeric_limits::max()) {
+      return Status(false, "Shape element overflows");
     }
+
     input_dims_only_sizes.push_back(d.size());
   }
   *shape->mutable_dims() = input_dims_only_sizes;
+
+  if (input_flat_size == nullptr) return Status::OK();
+
+  return NumElements(input_dims_only_sizes, input_flat_size);
 }
 
-void ImportFloatArray(const TensorProto& input_tensor, Array* output_array) {
+Status ImportFloatArray(const TensorProto& input_tensor, Array* output_array) {
   CHECK_EQ(input_tensor.dtype(), DT_FLOAT);
   const auto& input_shape = input_tensor.tensor_shape();
   CHECK_LE(input_shape.dim_size(), 4);
-  ImportShape(input_shape.dim(), output_array->mutable_shape());
-  int input_flat_size = 1;
-  for (int k = 0; k < input_shape.dim_size(); k++) {
-    input_flat_size *= input_shape.dim(k).size();
-  }
+  int input_flat_size;
+  auto status = ImportShape(input_shape.dim(), &input_flat_size,
+                            output_array->mutable_shape());
+  if (!status.ok()) return status;
+
   auto& output_float_data =
       output_array->GetMutableBuffer().data;
   output_float_data.resize(RequiredBufferSizeForShape(output_array->shape()),
@@ -189,20 +202,22 @@ void ImportFloatArray(const TensorProto& input_tensor, Array* output_array) {
     toco::port::CopyToBuffer(input_tensor.tensor_content(),
                              reinterpret_cast(output_float_data.data()));
   } else {
-    LOG(FATAL) << "Neither input_content nor float_val have the right "
-                  "dimensions for this float tensor.";
+    return Status(false,
+                  "Neither input_content nor float_val have the right "
+                  "dimensions for this float tensor");
   }
+  return Status::OK();
 }
 
-void ImportQuint8Array(const TensorProto& input_tensor, Array* output_array) {
+Status ImportQuint8Array(const TensorProto& input_tensor, Array* output_array) {
   CHECK_EQ(input_tensor.dtype(), DT_QUINT8);
   const auto& input_shape = input_tensor.tensor_shape();
   CHECK_LE(input_shape.dim_size(), 4);
-  ImportShape(input_shape.dim(), output_array->mutable_shape());
-  int input_flat_size = 1;
-  for (int k = 0; k < input_shape.dim_size(); k++) {
-    input_flat_size *= input_shape.dim(k).size();
-  }
+  int input_flat_size;
+  auto status = ImportShape(input_shape.dim(), &input_flat_size,
+                            output_array->mutable_shape());
+  if (!status.ok()) return status;
+
   auto& output_int_data =
       output_array->GetMutableBuffer().data;
   output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0);
@@ -215,20 +230,22 @@ void ImportQuint8Array(const TensorProto& input_tensor, Array* output_array) {
     toco::port::CopyToBuffer(input_tensor.tensor_content(),
                              reinterpret_cast(output_int_data.data()));
   } else {
-    LOG(FATAL) << "Neither input_content nor int_val have the right "
-                  "dimensions for this uint8 tensor.";
+    return Status(false,
+                  "Neither input_content nor int_val have the right dimensions "
+                  "for this uint8 tensor");
   }
+  return Status::OK();
 }
 
-void ImportInt32Array(const TensorProto& input_tensor, Array* output_array) {
+Status ImportInt32Array(const TensorProto& input_tensor, Array* output_array) {
   CHECK_EQ(input_tensor.dtype(), DT_INT32);
   const auto& input_shape = input_tensor.tensor_shape();
   CHECK_LE(input_shape.dim_size(), 4);
-  ImportShape(input_shape.dim(), output_array->mutable_shape());
-  int input_flat_size = 1;
-  for (int k = 0; k < input_shape.dim_size(); k++) {
-    input_flat_size *= input_shape.dim(k).size();
-  }
+  int input_flat_size;
+  auto status = ImportShape(input_shape.dim(), &input_flat_size,
+                            output_array->mutable_shape());
+  if (!status.ok()) return status;
+
   auto& output_int_data =
       output_array->GetMutableBuffer().data;
   output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0);
@@ -241,20 +258,22 @@ void ImportInt32Array(const TensorProto& input_tensor, Array* output_array) {
     toco::port::CopyToBuffer(input_tensor.tensor_content(),
                              reinterpret_cast(output_int_data.data()));
   } else {
-    LOG(FATAL) << "Neither input_content nor int_val have the right "
-                  "dimensions for this int32 tensor.";
+    return Status(false,
+                  "Neither input_content nor int_val have the right dimensions "
+                  "for this int32 tensor");
   }
+  return Status::OK();
 }
 
-void ImportInt64Array(const TensorProto& input_tensor, Array* output_array) {
+Status ImportInt64Array(const TensorProto& input_tensor, Array* output_array) {
   CHECK_EQ(input_tensor.dtype(), DT_INT64);
   const auto& input_shape = input_tensor.tensor_shape();
   CHECK_LE(input_shape.dim_size(), 4);
-  ImportShape(input_shape.dim(), output_array->mutable_shape());
-  int input_flat_size = 1;
-  for (int k = 0; k < input_shape.dim_size(); k++) {
-    input_flat_size *= input_shape.dim(k).size();
-  }
+  int input_flat_size;
+  auto status = ImportShape(input_shape.dim(), &input_flat_size,
+                            output_array->mutable_shape());
+  if (!status.ok()) return status;
+
   auto& output_int_data =
       output_array->GetMutableBuffer().data;
   output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0);
@@ -267,20 +286,22 @@ void ImportInt64Array(const TensorProto& input_tensor, Array* output_array) {
     toco::port::CopyToBuffer(input_tensor.tensor_content(),
                              reinterpret_cast(output_int_data.data()));
   } else {
-    LOG(FATAL) << "Neither input_content nor int64_val have the right "
-                  "dimensions for this int64 tensor.";
+    return Status(false,
+                  "Neither input_content nor int64_val have the right "
+                  "dimensions for this int64 tensor");
   }
+  return Status::OK();
 }
 
-void ImportBoolArray(const TensorProto& input_tensor, Array* output_array) {
+Status ImportBoolArray(const TensorProto& input_tensor, Array* output_array) {
   CHECK_EQ(input_tensor.dtype(), DT_BOOL);
   const auto& input_shape = input_tensor.tensor_shape();
   CHECK_LE(input_shape.dim_size(), 4);
-  ImportShape(input_shape.dim(), output_array->mutable_shape());
-  int input_flat_size = 1;
-  for (int k = 0; k < input_shape.dim_size(); k++) {
-    input_flat_size *= input_shape.dim(k).size();
-  }
+  int input_flat_size;
+  auto status = ImportShape(input_shape.dim(), &input_flat_size,
+                            output_array->mutable_shape());
+  if (!status.ok()) return status;
+
   auto& output_bool_data =
       output_array->GetMutableBuffer().data;
   output_bool_data.resize(RequiredBufferSizeForShape(output_array->shape()),
@@ -300,20 +321,25 @@ void ImportBoolArray(const TensorProto& input_tensor, Array* output_array) {
     // assuming that 'false' is implied.
     // So far only encountered that in an array with 1 entry, let's
     // require that until we encounter a graph where that's not the case.
-    CHECK_EQ(output_bool_data.size(), 1);
+    if (output_bool_data.size() != 1) {
+      return Status(false,
+                    "Neither input_content nor bool_val have the right "
+                    "dimensions for this bool tensor");
+    }
     output_bool_data[0] = false;
   }
+  return Status::OK();
 }
 
-void ImportStringArray(const TensorProto& input_tensor, Array* output_array) {
+Status ImportStringArray(const TensorProto& input_tensor, Array* output_array) {
   CHECK_EQ(input_tensor.dtype(), DT_STRING);
   const auto& input_shape = input_tensor.tensor_shape();
   CHECK_LE(input_shape.dim_size(), 4);
-  ImportShape(input_shape.dim(), output_array->mutable_shape());
-  int input_flat_size = 1;
-  for (int k = 0; k < input_shape.dim_size(); k++) {
-    input_flat_size *= input_shape.dim(k).size();
-  }
+  int input_flat_size;
+  auto status = ImportShape(input_shape.dim(), &input_flat_size,
+                            output_array->mutable_shape());
+  if (!status.ok()) return status;
+
   auto& output_string_data =
       output_array->GetMutableBuffer().data;
   output_string_data.resize(RequiredBufferSizeForShape(output_array->shape()));
@@ -324,6 +350,7 @@ void ImportStringArray(const TensorProto& input_tensor, Array* output_array) {
   for (int i = 0; i < input_flat_size; ++i) {
     output_string_data[i] = input_tensor.string_val(i);
   }
+  return Status::OK();
 }
 
 // Count the number of inputs of a given node. If
@@ -363,38 +390,40 @@ string CreateConstArray(Model* model, string const& name,
   return array_name;
 }
 
-void ConvertConstOperator(const NodeDef& node,
-                          const TensorFlowImportFlags& tf_import_flags,
-                          Model* model) {
+Status ConvertConstOperator(const NodeDef& node,
+                            const TensorFlowImportFlags& tf_import_flags,
+                            Model* model) {
   CHECK_EQ(node.op(), "Const");
   const auto& tensor = GetTensorAttr(node, "value");
   const auto dtype = GetDataTypeAttr(node, "dtype");
 
+  Status status = Status::OK();
+
   auto& array = model->GetOrCreateArray(node.name());
   switch (dtype) {
     case DT_FLOAT:
       array.data_type = ArrayDataType::kFloat;
-      ImportFloatArray(tensor, &array);
+      status = ImportFloatArray(tensor, &array);
       break;
     case DT_INT32:
       array.data_type = ArrayDataType::kInt32;
-      ImportInt32Array(tensor, &array);
+      status = ImportInt32Array(tensor, &array);
       break;
     case DT_QUINT8:
       array.data_type = ArrayDataType::kUint8;
-      ImportQuint8Array(tensor, &array);
+      status = ImportQuint8Array(tensor, &array);
       break;
     case DT_INT64:
       array.data_type = ArrayDataType::kInt64;
-      ImportInt64Array(tensor, &array);
+      status = ImportInt64Array(tensor, &array);
       break;
     case DT_STRING:
       array.data_type = ArrayDataType::kString;
-      ImportStringArray(tensor, &array);
+      status = ImportStringArray(tensor, &array);
       break;
     case DT_BOOL:
       array.data_type = ArrayDataType::kBool;
-      ImportBoolArray(tensor, &array);
+      status = ImportBoolArray(tensor, &array);
       break;
     default:
       array.data_type = ArrayDataType::kNone;
@@ -404,6 +433,10 @@ void ConvertConstOperator(const NodeDef& node,
       array.GetMutableBuffer();
       break;
   }
+  if (!status.ok()) {
+    status.AppendMessage(" (while processing node '" + node.name() + "')");
+  }
+  return status;
 }
 
 void ConvertConvOperator(const NodeDef& node,
@@ -2033,6 +2066,186 @@ void ConvertDynamicStitchOperator(const NodeDef& node,
 
 }  // namespace
 
+namespace internal {
+Status ImportTensorFlowNode(const tensorflow::NodeDef& node,
+                            const TensorFlowImportFlags& tf_import_flags,
+                            Model* model) {
+  // TODO(ahentz): Historically these functions all CHECK-fail on error. We've
+  // been slowly converting them to return Status.
+  if (node.op() == "Const") {
+    return ConvertConstOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Conv2D") {
+    ConvertConvOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Conv2DBackpropInput") {
+    ConvertTransposeConvOperator(node, tf_import_flags, model);
+  } else if (node.op() == "DepthwiseConv2dNative") {
+    ConvertDepthwiseConvOperator(node, tf_import_flags, model);
+  } else if (node.op() == "DepthToSpace") {
+    ConvertDepthToSpaceOperator(node, tf_import_flags, model);
+  } else if (node.op() == "SpaceToDepth") {
+    ConvertSpaceToDepthOperator(node, tf_import_flags, model);
+  } else if (node.op() == "BiasAdd") {
+    ConvertBiasAddOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Relu") {
+    ConvertReluOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Relu6") {
+    ConvertRelu6Operator(node, tf_import_flags, model);
+  } else if (node.op() == "Sigmoid") {
+    ConvertLogisticOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Tanh") {
+    ConvertTanhOperator(node, tf_import_flags, model);
+  } else if (node.op() == "MaxPool") {
+    ConvertMaxPoolOperator(node, tf_import_flags, model);
+  } else if (node.op() == "AvgPool") {
+    ConvertAvgPoolOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Reshape") {
+    ConvertReshapeOperator(node, tf_import_flags, model);
+  } else if (node.op() == "BatchMatMul") {
+    ConvertBatchMatMulOperator(node, tf_import_flags, model);
+  } else if (node.op() == "MatMul") {
+    ConvertMatMulOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Div" || node.op() == "RealDiv") {
+    ConvertDivOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Identity" || node.op() == "CheckNumerics" ||
+             node.op() == "StopGradient") {
+    ConvertIdentityOperator(node, tf_import_flags, model);
+  } else if (node.op() == "FakeQuantWithMinMaxVars") {
+    ConvertFakeQuantWithMinMaxVars(node, tf_import_flags, model);
+  } else if (node.op() == "FakeQuantWithMinMaxArgs") {
+    ConvertFakeQuantWithMinMaxArgs(node, tf_import_flags, model);
+  } else if (node.op() == "Neg") {
+    ConvertNegOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Rsqrt") {
+    ConvertRsqrtOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Squeeze") {
+    ConvertSqueezeOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Sqrt") {
+    ConvertSqrtOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Square") {
+    ConvertSquareOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Add") {
+    ConvertAddOperator(node, tf_import_flags, model);
+  } else if (node.op() == "AddN") {
+    ConvertAddNOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Mul") {
+    ConvertMulOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Sub") {
+    ConvertSubOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Sum") {
+    ConvertSumOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Tile") {
+    ConvertTileOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Concat" || node.op() == "ConcatV2") {
+    ConvertConcatOperator(node, tf_import_flags, model);
+  } else if (node.op() == "LRN") {
+    ConvertLRNOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Softmax") {
+    ConvertSoftmaxOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Log") {
+    ConvertLogOperator(node, tf_import_flags, model);
+  } else if (node.op() == "LogSoftmax") {
+    ConvertLogSoftmaxOperator(node, tf_import_flags, model);
+  } else if (node.op() == "All") {
+    ConvertAllOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Assert") {
+    ConvertAssertOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Less") {
+    ConvertLessOperator(node, tf_import_flags, model);
+  } else if (node.op() == "LessEqual") {
+    ConvertLessEqualOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Greater") {
+    ConvertGreaterOperator(node, tf_import_flags, model);
+  } else if (node.op() == "GreaterEqual") {
+    ConvertGreaterEqualOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Max") {
+    ConvertMaxOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Min") {
+    ConvertMinOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Maximum") {
+    ConvertMaximumOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Minimum") {
+    ConvertMinimumOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Merge") {
+    ConvertMergeOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Pad") {
+    ConvertPadOperator(node, tf_import_flags, model);
+  } else if (node.op() == "StridedSlice") {
+    ConvertStridedSliceOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Shape") {
+    ConvertShapeOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Slice") {
+    ConvertSliceOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Split") {
+    ConvertSplitOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Switch") {
+    ConvertSwitchOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Placeholder") {
+    ConvertPlaceholderOperator(node, tf_import_flags, model);
+  } else if (node.op() == "PlaceholderWithDefault") {
+    ConvertIdentityOperator(node, tf_import_flags, model);
+  } else if (node.op() == "LegacyFedInput") {
+    ConvertPlaceholderOperator(node, tf_import_flags, model);
+  } else if (node.op() == "NoOp") {
+    ConvertNoOpOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Cast") {
+    ConvertCastOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Floor") {
+    ConvertFloorOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Gather" || node.op() == "GatherV2") {
+    ConvertGatherOperator(node, tf_import_flags, model);
+  } else if (node.op() == "ResizeBilinear") {
+    ConvertResizeBilinearOperator(node, tf_import_flags, model);
+  } else if (node.op() == "BatchNormWithGlobalNormalization") {
+    ConvertBatchNormWithGlobalNormalizationOperator(node, tf_import_flags,
+                                                    model);
+  } else if (node.op() == "FusedBatchNorm") {
+    ConvertFusedBatchNormOperator(node, tf_import_flags, model);
+  } else if (node.op() == "SpaceToBatchND") {
+    ConvertSpaceToBatchNDOperator(node, tf_import_flags, model);
+  } else if (node.op() == "BatchToSpaceND") {
+    ConvertBatchToSpaceNDOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Mean") {
+    ConvertMeanOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Svdf") {
+    ConvertSvdfOperator(node, tf_import_flags, model);
+  } else if (node.op() == "NextIteration") {
+    ConvertOperatorSpecialCasedAsRNNBackEdge(node, tf_import_flags, model);
+  } else if (node.op() == "ExpandDims") {
+    ConvertExpandDimsOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Fill") {
+    ConvertFillOperator(node, tf_import_flags, model);
+  } else if (node.op() == "FloorDiv") {
+    ConvertFloorDivOperator(node, tf_import_flags, model);
+  } else if (node.op() == "FloorMod") {
+    ConvertFloorModOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Range") {
+    ConvertRangeOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Rank") {
+    ConvertRankOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Stack" || node.op() == "Pack") {
+    ConvertStackOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Transpose") {
+    ConvertTransposeOperator(node, tf_import_flags, model);
+  } else if (node.op() == "ArgMax") {
+    ConvertArgMaxOperator(node, tf_import_flags, model);
+  } else if (node.op() == "Exp") {
+    ConvertExpOperator(node, tf_import_flags, model);
+  } else if (node.op() == "TopK" || node.op() == "TopKV2") {
+    ConvertTopKV2Operator(node, tf_import_flags, model);
+  } else if (node.op() == "DynamicPartition") {
+    ConvertDynamicPartitionOperator(node, tf_import_flags, model);
+  } else if (node.op() == "DynamicStitch" ||
+             node.op() == "ParallelDynamicStitch") {
+    ConvertDynamicStitchOperator(node, tf_import_flags, model);
+  } else if (node.op() == "RandomUniform") {
+    ConvertRandomUniform(node, tf_import_flags, model);
+  } else {
+    ConvertUnsupportedOperator(node, tf_import_flags, model);
+  }
+  return Status::OK();
+}
+}  // namespace internal
+
 std::unique_ptr ImportTensorFlowGraphDef(
     const ModelFlags& model_flags, const TensorFlowImportFlags& tf_import_flags,
     const GraphDef& tf_graph) {
@@ -2058,176 +2271,8 @@ std::unique_ptr ImportTensorFlowGraphDef(
 
   for (auto node : inlined_graph.node()) {
     StripZeroOutputIndexFromInputs(&node);
-    if (node.op() == "Const") {
-      ConvertConstOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Conv2D") {
-      ConvertConvOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Conv2DBackpropInput") {
-      ConvertTransposeConvOperator(node, tf_import_flags, model);
-    } else if (node.op() == "DepthwiseConv2dNative") {
-      ConvertDepthwiseConvOperator(node, tf_import_flags, model);
-    } else if (node.op() == "DepthToSpace") {
-      ConvertDepthToSpaceOperator(node, tf_import_flags, model);
-    } else if (node.op() == "SpaceToDepth") {
-      ConvertSpaceToDepthOperator(node, tf_import_flags, model);
-    } else if (node.op() == "BiasAdd") {
-      ConvertBiasAddOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Relu") {
-      ConvertReluOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Relu6") {
-      ConvertRelu6Operator(node, tf_import_flags, model);
-    } else if (node.op() == "Sigmoid") {
-      ConvertLogisticOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Tanh") {
-      ConvertTanhOperator(node, tf_import_flags, model);
-    } else if (node.op() == "MaxPool") {
-      ConvertMaxPoolOperator(node, tf_import_flags, model);
-    } else if (node.op() == "AvgPool") {
-      ConvertAvgPoolOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Reshape") {
-      ConvertReshapeOperator(node, tf_import_flags, model);
-    } else if (node.op() == "BatchMatMul") {
-      ConvertBatchMatMulOperator(node, tf_import_flags, model);
-    } else if (node.op() == "MatMul") {
-      ConvertMatMulOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Div" || node.op() == "RealDiv") {
-      ConvertDivOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Identity" || node.op() == "CheckNumerics" ||
-               node.op() == "StopGradient") {
-      ConvertIdentityOperator(node, tf_import_flags, model);
-    } else if (node.op() == "FakeQuantWithMinMaxVars") {
-      ConvertFakeQuantWithMinMaxVars(node, tf_import_flags, model);
-    } else if (node.op() == "FakeQuantWithMinMaxArgs") {
-      ConvertFakeQuantWithMinMaxArgs(node, tf_import_flags, model);
-    } else if (node.op() == "Neg") {
-      ConvertNegOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Rsqrt") {
-      ConvertRsqrtOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Squeeze") {
-      ConvertSqueezeOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Sqrt") {
-      ConvertSqrtOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Square") {
-      ConvertSquareOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Add") {
-      ConvertAddOperator(node, tf_import_flags, model);
-    } else if (node.op() == "AddN") {
-      ConvertAddNOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Mul") {
-      ConvertMulOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Sub") {
-      ConvertSubOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Sum") {
-      ConvertSumOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Tile") {
-      ConvertTileOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Concat" || node.op() == "ConcatV2") {
-      ConvertConcatOperator(node, tf_import_flags, model);
-    } else if (node.op() == "LRN") {
-      ConvertLRNOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Softmax") {
-      ConvertSoftmaxOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Log") {
-      ConvertLogOperator(node, tf_import_flags, model);
-    } else if (node.op() == "LogSoftmax") {
-      ConvertLogSoftmaxOperator(node, tf_import_flags, model);
-    } else if (node.op() == "All") {
-      ConvertAllOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Assert") {
-      ConvertAssertOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Less") {
-      ConvertLessOperator(node, tf_import_flags, model);
-    } else if (node.op() == "LessEqual") {
-      ConvertLessEqualOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Greater") {
-      ConvertGreaterOperator(node, tf_import_flags, model);
-    } else if (node.op() == "GreaterEqual") {
-      ConvertGreaterEqualOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Max") {
-      ConvertMaxOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Min") {
-      ConvertMinOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Maximum") {
-      ConvertMaximumOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Minimum") {
-      ConvertMinimumOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Merge") {
-      ConvertMergeOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Pad") {
-      ConvertPadOperator(node, tf_import_flags, model);
-    } else if (node.op() == "StridedSlice") {
-      ConvertStridedSliceOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Shape") {
-      ConvertShapeOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Slice") {
-      ConvertSliceOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Split") {
-      ConvertSplitOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Switch") {
-      ConvertSwitchOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Placeholder") {
-      ConvertPlaceholderOperator(node, tf_import_flags, model);
-    } else if (node.op() == "PlaceholderWithDefault") {
-      ConvertIdentityOperator(node, tf_import_flags, model);
-    } else if (node.op() == "LegacyFedInput") {
-      ConvertPlaceholderOperator(node, tf_import_flags, model);
-    } else if (node.op() == "NoOp") {
-      ConvertNoOpOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Cast") {
-      ConvertCastOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Floor") {
-      ConvertFloorOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Gather" || node.op() == "GatherV2") {
-      ConvertGatherOperator(node, tf_import_flags, model);
-    } else if (node.op() == "ResizeBilinear") {
-      ConvertResizeBilinearOperator(node, tf_import_flags, model);
-    } else if (node.op() == "BatchNormWithGlobalNormalization") {
-      ConvertBatchNormWithGlobalNormalizationOperator(node, tf_import_flags,
-                                                      model);
-    } else if (node.op() == "FusedBatchNorm") {
-      ConvertFusedBatchNormOperator(node, tf_import_flags, model);
-    } else if (node.op() == "SpaceToBatchND") {
-      ConvertSpaceToBatchNDOperator(node, tf_import_flags, model);
-    } else if (node.op() == "BatchToSpaceND") {
-      ConvertBatchToSpaceNDOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Mean") {
-      ConvertMeanOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Svdf") {
-      ConvertSvdfOperator(node, tf_import_flags, model);
-    } else if (node.op() == "NextIteration") {
-      ConvertOperatorSpecialCasedAsRNNBackEdge(node, tf_import_flags, model);
-    } else if (node.op() == "ExpandDims") {
-      ConvertExpandDimsOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Fill") {
-      ConvertFillOperator(node, tf_import_flags, model);
-    } else if (node.op() == "FloorDiv") {
-      ConvertFloorDivOperator(node, tf_import_flags, model);
-    } else if (node.op() == "FloorMod") {
-      ConvertFloorModOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Range") {
-      ConvertRangeOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Rank") {
-      ConvertRankOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Stack" || node.op() == "Pack") {
-      ConvertStackOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Transpose") {
-      ConvertTransposeOperator(node, tf_import_flags, model);
-    } else if (node.op() == "ArgMax") {
-      ConvertArgMaxOperator(node, tf_import_flags, model);
-    } else if (node.op() == "Exp") {
-      ConvertExpOperator(node, tf_import_flags, model);
-    } else if (node.op() == "TopK" || node.op() == "TopKV2") {
-      ConvertTopKV2Operator(node, tf_import_flags, model);
-    } else if (node.op() == "DynamicPartition") {
-      ConvertDynamicPartitionOperator(node, tf_import_flags, model);
-    } else if (node.op() == "DynamicStitch" ||
-               node.op() == "ParallelDynamicStitch") {
-      ConvertDynamicStitchOperator(node, tf_import_flags, model);
-    } else if (node.op() == "RandomUniform") {
-      ConvertRandomUniform(node, tf_import_flags, model);
-    } else {
-      ConvertUnsupportedOperator(node, tf_import_flags, model);
-    }
+    auto status = internal::ImportTensorFlowNode(node, tf_import_flags, model);
+    CHECK(status.ok()) << status.error_message();
   }
 
   ResolveModelFlags(model_flags, model);
diff --git a/tensorflow/contrib/lite/toco/import_tensorflow_test.cc b/tensorflow/contrib/lite/toco/import_tensorflow_test.cc
new file mode 100644
index 00000000000000..5dc78f73ad2e2a
--- /dev/null
+++ b/tensorflow/contrib/lite/toco/import_tensorflow_test.cc
@@ -0,0 +1,160 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/contrib/lite/toco/import_tensorflow.h"
+
+#include 
+#include 
+#include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/attr_value_util.h"
+#include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
+
+namespace toco {
+
+using port::Status;
+using tensorflow::AttrValue;
+using tensorflow::DT_BOOL;
+using tensorflow::DT_FLOAT;
+using tensorflow::DT_INT32;
+using tensorflow::DT_INT64;
+using tensorflow::DT_QUINT8;
+using tensorflow::DT_STRING;
+using tensorflow::NodeDef;
+
+namespace internal {
+Status ImportTensorFlowNode(const NodeDef&, const TensorFlowImportFlags&,
+                            Model*);
+}  // namespace internal
+
+namespace {
+
+class ShapeImportTest : public ::testing::TestWithParam {
+ protected:
+  ShapeImportTest() {}
+
+  void BuildConstNode(std::initializer_list shape,
+                      tensorflow::DataType dtype, int64_t num_elements,
+                      NodeDef* node) {
+    node->set_op("Const");
+    node->set_name("Node1");
+
+    // An attribute describing the type of this const node.
+    AttrValue dtype_attr;
+    SetAttrValue(dtype, &dtype_attr);
+    (*node->mutable_attr())["dtype"] = dtype_attr;
+
+    // An attribute describing the content of this const node.
+    tensorflow::TensorProto t;
+    t.set_dtype(dtype);
+    auto* s = t.mutable_tensor_shape();
+    for (auto d : shape) {
+      s->add_dim()->set_size(d);
+    }
+
+    // TODO(ahentz): also need to test via tensor_content()
+    switch (dtype) {
+      case DT_FLOAT:
+        for (int64_t i = 0; i < num_elements; ++i) {
+          t.add_float_val(i / 10000.0);
+        }
+        break;
+      case DT_INT32:
+        for (int64_t i = 0; i < num_elements; ++i) {
+          t.add_int_val(i % std::numeric_limits::max());
+        }
+        break;
+      case DT_QUINT8:
+        for (int64_t i = 0; i < num_elements; ++i) {
+          t.add_int_val(i % std::numeric_limits::max());
+        }
+        break;
+      case DT_INT64:
+        for (int64_t i = 0; i < num_elements; ++i) {
+          t.add_int64_val(i);
+        }
+        break;
+      case DT_STRING:
+        break;
+      case DT_BOOL:
+        for (int64_t i = 0; i < num_elements; ++i) {
+          t.add_bool_val(i % 2);
+        }
+        break;
+      default:
+        break;
+    }
+
+    AttrValue value_attr;
+    SetAttrValue(t, &value_attr);
+    (*node->mutable_attr())["value"] = value_attr;
+  }
+
+  Status ImportNode(const NodeDef& node) {
+    Model model;
+    return internal::ImportTensorFlowNode(node, TensorFlowImportFlags(),
+                                          &model);
+  }
+};
+
+std::vector TestTypes() {
+  return {DT_FLOAT, DT_INT32, DT_INT64, DT_BOOL, DT_QUINT8};
+}
+
+TEST_P(ShapeImportTest, ShapeElementIsNegative) {
+  NodeDef node;
+  BuildConstNode({1, -2, 10}, GetParam(), 0, &node);
+  auto status = ImportNode(node);
+  EXPECT_EQ(status.error_message(),
+            "Tensor shape should not include negative values (while processing "
+            "node 'Node1')");
+}
+INSTANTIATE_TEST_CASE_P(ShapeElementIsNegative, ShapeImportTest,
+                        ::testing::ValuesIn(TestTypes()));
+
+TEST_P(ShapeImportTest, ShapeElementTooLarge) {
+  NodeDef node;
+  BuildConstNode({3000000000}, GetParam(), 0, &node);
+  auto status = ImportNode(node);
+  EXPECT_EQ(status.error_message(),
+            "Shape element overflows (while processing node 'Node1')");
+}
+INSTANTIATE_TEST_CASE_P(ShapeElementTooLarge, ShapeImportTest,
+                        ::testing::ValuesIn(TestTypes()));
+
+TEST_P(ShapeImportTest, ShapeTooLarge) {
+  NodeDef node;
+  BuildConstNode({1000000, 2000000, 2000000, 2000000}, GetParam(), 0, &node);
+  auto status = ImportNode(node);
+  EXPECT_EQ(status.error_message(),
+            "Tensor shape is too large (while processing node 'Node1')");
+}
+INSTANTIATE_TEST_CASE_P(ShapeTooLarge, ShapeImportTest,
+                        ::testing::ValuesIn(TestTypes()));
+
+TEST_P(ShapeImportTest, ValidShapeButZeroElements) {
+  NodeDef node;
+  BuildConstNode({1, 2, 2, 2}, GetParam(), 0, &node);
+  auto status = ImportNode(node);
+  EXPECT_THAT(status.error_message(),
+              ::testing::MatchesRegex(
+                  "Neither input_content nor .*_val have the right dimensions "
+                  "for this .* tensor .while processing node 'Node1'."));
+}
+INSTANTIATE_TEST_CASE_P(ValidShapeButZeroElements, ShapeImportTest,
+                        ::testing::ValuesIn(TestTypes()));
+
+}  // namespace
+}  // namespace toco
diff --git a/tensorflow/contrib/lite/toco/toco_port.h b/tensorflow/contrib/lite/toco/toco_port.h
index 2d5c231bef3508..906792ef569e5b 100644
--- a/tensorflow/contrib/lite/toco/toco_port.h
+++ b/tensorflow/contrib/lite/toco/toco_port.h
@@ -38,10 +38,15 @@ namespace port {
 
 class Status {
  public:
+  static Status OK() { return Status(true, ""); }
+
+  // Create a failed status with no message.
   Status() {}
 
   Status(bool ok, const string& message) : ok_(ok), message_(message) {}
 
+  void AppendMessage(const string& message) { message_ += message; }
+
   bool ok() const { return ok_; }
 
   const string error_message() const { return message_; }
diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h
index 5cc15fa57b3ea4..f5b596df0f346b 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.h
+++ b/tensorflow/contrib/lite/toco/tooling_util.h
@@ -294,6 +294,35 @@ void FinishBuildingRNNStates(Model* model);
 
 void UseArraysExtraInfo(Model* model, bool quantize_output);
 
+// Calculates the number of elements in tensor given a shape. Shape elements
+// are assumed to be of type T, while the result total is of type U. If U
+// doesn't have enough range to represent the sum of elements, an error is
+// returned.
+template 
+port::Status NumElements(const std::vector& shape, U* num_elements) {
+  static_assert(
+      std::numeric_limits::max() <= std::numeric_limits::max(),
+      "vector type exceed capabilities of NumElements");
+
+  *num_elements = 1;
+  for (const T& dim : shape) {
+    if (dim < 0) {
+      // TensorFlow's shapes sometimes include -1 to represent an "unknown"
+      // size but TOCO isn't able to create arrays of unknown sizes and will
+      // crash in RequiredBufferSizeForShape().
+      return port::Status(false,
+                          "Tensor shape should not include negative values");
+    }
+    if (static_cast(dim) >
+        std::numeric_limits::max() / *num_elements) {
+      *num_elements = 0;
+      return port::Status(false, "Tensor shape is too large");
+    }
+    *num_elements *= dim;
+  }
+  return port::Status::OK();
+}
+
 }  // namespace toco
 
 #endif  // TENSORFLOW_CONTRIB_LITE_TOCO_TOOLING_UTIL_H_
diff --git a/tensorflow/contrib/lite/toco/tooling_util_test.cc b/tensorflow/contrib/lite/toco/tooling_util_test.cc
index 22955ce95661a9..87fd30db2cf548 100644
--- a/tensorflow/contrib/lite/toco/tooling_util_test.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util_test.cc
@@ -93,4 +93,85 @@ TEST_P(ShapeTest, Agrees) {
 INSTANTIATE_TEST_CASE_P(AgreeBroadcast, ShapeTest,
                         ::testing::ValuesIn(CreateShapePairs()));
 
+static const char kNegativeValuesMessage[] =
+    "Tensor shape should not include negative values";
+static const char kLargeTensorMessage[] = "Tensor shape is too large";
+
+TEST(NumElementsTest, Int) {
+  int count;
+  port::Status status = port::Status::OK();
+
+  status = NumElements(std::vector{1024, 1024, 2047}, &count);
+  EXPECT_TRUE(status.ok());
+  EXPECT_EQ(count, 2146435072);
+
+  status = NumElements(std::vector{1, 2, -3}, &count);
+  EXPECT_EQ(status.error_message(), kNegativeValuesMessage);
+
+  status = NumElements(std::vector{1024, 1024, 2048}, &count);
+  EXPECT_EQ(status.error_message(), kLargeTensorMessage);
+}
+
+TEST(NumElementsTest, Int32) {
+  int32_t count;
+  port::Status status = port::Status::OK();
+
+  status = NumElements(std::vector{1024, 1024, 2047}, &count);
+  EXPECT_TRUE(status.ok());
+  EXPECT_EQ(count, 2146435072);
+
+  status = NumElements(std::vector{1, 2, -3}, &count);
+  EXPECT_EQ(status.error_message(), kNegativeValuesMessage);
+
+  status = NumElements(std::vector{1024, 1024, 2048}, &count);
+  EXPECT_EQ(status.error_message(), kLargeTensorMessage);
+}
+
+TEST(NumElementsTest, Int64) {
+  int64_t count;
+  port::Status status = port::Status::OK();
+
+  status = NumElements(std::vector{16777216, 16777216, 32767}, &count);
+  EXPECT_TRUE(status.ok());
+  EXPECT_EQ(count, 9223090561878065152LL);
+
+  status = NumElements(std::vector{1, 2, -3}, &count);
+  EXPECT_EQ(status.error_message(), kNegativeValuesMessage);
+
+  status = NumElements(std::vector{16777216, 16777216, 32768}, &count);
+  EXPECT_EQ(status.error_message(), kLargeTensorMessage);
+}
+
+TEST(NumElementsTest, UnsignedInt32) {
+  uint32_t count;
+  port::Status status = port::Status::OK();
+
+  status = NumElements(std::vector{1024, 2048, 2047}, &count);
+  EXPECT_TRUE(status.ok());
+  EXPECT_EQ(count, 4292870144);
+
+  status = NumElements(std::vector{1, 2, -3}, &count);
+  EXPECT_EQ(status.error_message(), kNegativeValuesMessage);
+
+  status = NumElements(std::vector{1024, 2048, 2048}, &count);
+  EXPECT_EQ(status.error_message(), kLargeTensorMessage);
+}
+
+TEST(NumElementsTest, UnsignedInt64) {
+  uint64_t count;
+  port::Status status = port::Status::OK();
+
+  status =
+      NumElements(std::vector{16777216, 16777216, 65535}, &count);
+  EXPECT_TRUE(status.ok());
+  EXPECT_EQ(count, 18446462598732840960ULL);
+
+  status = NumElements(std::vector{1, 2, -3}, &count);
+  EXPECT_EQ(status.error_message(), kNegativeValuesMessage);
+
+  status =
+      NumElements(std::vector{16777216, 16777216, 65536}, &count);
+  EXPECT_EQ(status.error_message(), kLargeTensorMessage);
+}
+
 }  // namespace toco

From 1aa7aaa731ad8b64345fbfec2f53b49a77a9b94d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 1 May 2018 16:38:19 -0700
Subject: [PATCH 0254/1691] Adds logistic_regression_head.

PiperOrigin-RevId: 195017830
---
 tensorflow/contrib/estimator/__init__.py      |   1 +
 .../estimator/python/estimator/head.py        |  69 +++++++++-
 .../estimator/python/estimator/head_test.py   | 119 ++++++++++++++++++
 3 files changed, 187 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/estimator/__init__.py b/tensorflow/contrib/estimator/__init__.py
index be20d1b7770d3f..f66d844660e55d 100644
--- a/tensorflow/contrib/estimator/__init__.py
+++ b/tensorflow/contrib/estimator/__init__.py
@@ -38,6 +38,7 @@
     'binary_classification_head',
     'clip_gradients_by_norm',
     'forward_features',
+    'logistic_regression_head',
     'multi_class_head',
     'multi_head',
     'multi_label_head',
diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py
index 3dcf0374c8a12b..2a6d17e81bdc37 100644
--- a/tensorflow/contrib/estimator/python/estimator/head.py
+++ b/tensorflow/contrib/estimator/python/estimator/head.py
@@ -205,8 +205,9 @@ def regression_head(weight_column=None,
   shape `[D0, D1, ... DN, label_dimension]`.
 
   Also supports custom `inverse_link_fn`, also known as 'mean function'.
-  `inverse_link_fn` takes `logits` as argument and returns predicted values.
-  This function is the inverse of the link function defined in
+  `inverse_link_fn` is only used in `PREDICT` mode. It takes `logits` as
+  argument and returns predicted values. This function is the inverse of the
+  link function defined in
   https://en.wikipedia.org/wiki/Generalized_linear_model#Link_function
   Namely, for poisson regression, set `inverse_link_fn=tf.exp`.
 
@@ -305,6 +306,70 @@ def _poisson_loss(labels, logits):
       name=name)
 
 
+def logistic_regression_head(
+    weight_column=None,
+    loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE,
+    name=None):
+  """Creates a `_Head` for logistic regression.
+
+  Uses `sigmoid_cross_entropy_with_logits` loss, which is the same as
+  `binary_classification_head`. The differences compared to
+  `binary_classification_head` are:
+
+  * Does not support `label_vocabulary`. Instead, labels must be float in the
+    range [0, 1].
+  * Does not calculate some metrics that do not make sense, such as AUC.
+  * In `PREDICT` mode, only returns logits and predictions
+    (`=tf.sigmoid(logits)`), whereas `binary_classification_head` also returns
+    probabilities, classes, and class_ids.
+  * Export output defaults to `RegressionOutput`, whereas
+    `binary_classification_head` defaults to `PredictOutput`.
+
+  The head expects `logits` with shape `[D0, D1, ... DN, 1]`.
+  In many applications, the shape is `[batch_size, 1]`.
+
+  The `labels` shape must match `logits`, namely
+  `[D0, D1, ... DN]` or `[D0, D1, ... DN, 1]`.
+
+  If `weight_column` is specified, weights must be of shape
+  `[D0, D1, ... DN]` or `[D0, D1, ... DN, 1]`.
+
+  This is implemented as a generalized linear model, see
+  https://en.wikipedia.org/wiki/Generalized_linear_model.
+
+  Args:
+    weight_column: A string or a `_NumericColumn` created by
+      `tf.feature_column.numeric_column` defining feature column representing
+      weights. It is used to down weight or boost examples during training. It
+      will be multiplied by the loss of the example.
+    loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to
+      reduce training loss over batch and label dimension. Defaults to
+      `SUM_OVER_BATCH_SIZE`, namely weighted sum of losses divided by
+      `batch size * label_dimension`. See `tf.losses.Reduction`.
+    name: name of the head. If provided, summary and metrics keys will be
+      suffixed by `"/" + name`. Also used as `name_scope` when creating ops.
+
+  Returns:
+    An instance of `_Head` for logistic regression.
+
+  Raises:
+    ValueError: If `loss_reduction` is invalid.
+  """
+  def _logistic_loss(labels, logits):
+    labels = head_lib._assert_range(  # pylint:disable=protected-access
+        labels, n_classes=2, message='Labels must be in range [0, 1]')
+    return nn.sigmoid_cross_entropy_with_logits(
+        labels=labels, logits=logits)
+  # TODO(roumposg): Rename to _regression_head, since it supports loss_fn arg.
+  return head_lib._regression_head_with_mean_squared_error_loss(  # pylint:disable=protected-access
+      weight_column=weight_column,
+      label_dimension=1,
+      loss_reduction=loss_reduction,
+      loss_fn=_logistic_loss,
+      inverse_link_fn=math_ops.sigmoid,
+      name=name)
+
+
 def multi_label_head(n_classes,
                      weight_column=None,
                      thresholds=None,
diff --git a/tensorflow/contrib/estimator/python/estimator/head_test.py b/tensorflow/contrib/estimator/python/estimator/head_test.py
index 98962ca4277a3e..19b86df5565a85 100644
--- a/tensorflow/contrib/estimator/python/estimator/head_test.py
+++ b/tensorflow/contrib/estimator/python/estimator/head_test.py
@@ -1211,5 +1211,124 @@ def test_predict(self):
       self.assertAllClose(logits, spec.predictions[keys.LOGITS].eval())
 
 
+class LogisticRegressionHead(test.TestCase):
+
+  def setUp(self):
+    ops.reset_default_graph()
+
+  def test_train(self):
+    head = head_lib.logistic_regression_head()
+
+    # Create estimator spec.
+    logits = np.array([[0], [-1], [1]], dtype=np.float32)
+    labels = np.array([[.4], [.6], [.8]], dtype=np.float32)
+    # Following the documentation in
+    # tf.nn.sigmoid_cross_entropy_with_logits:
+    # With x = logits, z = labels.
+    # loss  = max(x, 0) - x * z + log(1 + exp(-abs(x)))
+    # loss = [0 - 0 * 0.4 + ln(1 + exp(-0)),
+    #         0 + 1 * 0.6 + ln(1 + exp(-1)),
+    #         1 - 1 * 0.8 + ln(1 + exp(-1))]
+    #      = [0.6931, 0.9133, 0.5133]
+    # training_loss = (0.6931 + 0.9133 + 0.5133) / 3
+    expected_loss = 0.7066
+    atol = 0.001
+    expected_train_result = b'my_train_op'
+    def _train_op_fn(loss):
+      with ops.control_dependencies((check_ops.assert_near(
+          math_ops.to_float(expected_loss), math_ops.to_float(loss),
+          atol=atol, name='assert_loss'),)):
+        return constant_op.constant(expected_train_result)
+
+    spec = head.create_estimator_spec(
+        features={'x': np.array(((42.,),), dtype=np.int32)},
+        mode=model_fn.ModeKeys.TRAIN,
+        logits=logits,
+        labels=labels,
+        train_op_fn=_train_op_fn)
+
+    with self.test_session() as sess:
+      _initialize_variables(self, spec.scaffold)
+      loss, train_result = sess.run([spec.loss, spec.train_op])
+      self.assertAlmostEqual(expected_loss, loss, delta=atol)
+      self.assertEqual(expected_train_result, train_result)
+
+  def test_train_labels_too_large(self):
+    head = head_lib.logistic_regression_head()
+
+    # Create estimator spec.
+    logits = np.array([[0], [-1], [1]], dtype=np.float32)
+    labels = np.array([[.4], [1.2], [.8]], dtype=np.float32)
+    expected_train_result = b'my_train_op'
+    def _train_op_fn(loss):
+      del loss
+      return constant_op.constant(expected_train_result)
+
+    spec = head.create_estimator_spec(
+        features={'x': np.array(((42.,),), dtype=np.int32)},
+        mode=model_fn.ModeKeys.TRAIN,
+        logits=logits,
+        labels=labels,
+        train_op_fn=_train_op_fn)
+
+    with self.test_session() as sess:
+      _initialize_variables(self, spec.scaffold)
+      with self.assertRaisesRegexp(
+          errors.InvalidArgumentError,
+          r'\[Labels must be in range \[0, 1\]\] .* \[\[0.4\]\[1.2\]\[0.8\]\]'):
+        _ = sess.run(spec.loss)
+
+  def test_train_labels_negative(self):
+    head = head_lib.logistic_regression_head()
+
+    # Create estimator spec.
+    logits = np.array([[0], [-1], [1]], dtype=np.float32)
+    labels = np.array([[.4], [-0.2], [.8]], dtype=np.float32)
+    expected_train_result = b'my_train_op'
+    def _train_op_fn(loss):
+      del loss
+      return constant_op.constant(expected_train_result)
+
+    spec = head.create_estimator_spec(
+        features={'x': np.array(((42.,),), dtype=np.int32)},
+        mode=model_fn.ModeKeys.TRAIN,
+        logits=logits,
+        labels=labels,
+        train_op_fn=_train_op_fn)
+
+    with self.test_session() as sess:
+      _initialize_variables(self, spec.scaffold)
+      with self.assertRaisesRegexp(
+          errors.InvalidArgumentError,
+          r'\[Labels must be in range \[0, 1\]\] .* \[\[0.4\]\[-0.2\]\[0.8\]\]'
+      ):
+        _ = sess.run(spec.loss)
+
+  def test_predict(self):
+    head = head_lib.logistic_regression_head()
+
+    # Create estimator spec.
+    logits = np.array([[0], [-1], [1]], dtype=np.float32)
+    expected_predictions = 1. / (1. + np.exp(-logits))
+    spec = head.create_estimator_spec(
+        features={'x': np.array(((42.,),), dtype=np.int32)},
+        mode=model_fn.ModeKeys.PREDICT,
+        logits=logits)
+
+    # Assert spec contains expected tensors.
+    keys = prediction_keys.PredictionKeys
+    self.assertItemsEqual(
+        (keys.PREDICTIONS, keys.LOGITS), spec.predictions.keys())
+    self.assertEqual(dtypes.float32, spec.predictions[keys.PREDICTIONS].dtype)
+    self.assertEqual(dtypes.float32, spec.predictions[keys.LOGITS].dtype)
+
+    # Assert predictions.
+    with self.test_session():
+      _initialize_variables(self, spec.scaffold)
+      self.assertAllClose(
+          expected_predictions, spec.predictions[keys.PREDICTIONS].eval())
+      self.assertAllClose(logits, spec.predictions[keys.LOGITS].eval())
+
+
 if __name__ == '__main__':
   test.main()

From 2b547749bccd73bb95d05b6e73f26ea8ae9f3be6 Mon Sep 17 00:00:00 2001
From: Benoit Steiner 
Date: Tue, 1 May 2018 16:38:23 -0700
Subject: [PATCH 0255/1691] Avoid making a copy of the graph needlessly

PiperOrigin-RevId: 195017837
---
 tensorflow/core/grappler/costs/graph_properties.cc | 2 +-
 tensorflow/core/grappler/costs/graph_properties.h  | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index 431efb08cbb0ea..2c7b57971a6164 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -1074,7 +1074,7 @@ Status GraphProperties::InferStatically(bool assume_valid_feeds) {
     }
   }
 
-  GraphView graph_view(&item_.graph);
+  GraphView graph_view(const_cast(&item_.graph));
 
   // List the resources and the nodes using them. Also collect the Merge nodes,
   // fed nodes, and primary inputs.
diff --git a/tensorflow/core/grappler/costs/graph_properties.h b/tensorflow/core/grappler/costs/graph_properties.h
index ecc10fddb8a306..8703613a120590 100644
--- a/tensorflow/core/grappler/costs/graph_properties.h
+++ b/tensorflow/core/grappler/costs/graph_properties.h
@@ -38,6 +38,7 @@ class TopoQueue;
 // and data type properties.
 class GraphProperties {
  public:
+  // The item must outlive the properties
   explicit GraphProperties(const GrapplerItem& item) : item_(item) {}
 
   // Infer the shapes through abstract interpretation. Feed information can be
@@ -112,7 +113,7 @@ class GraphProperties {
       int num_loops) const;
 
   // Data members
-  GrapplerItem item_;
+  const GrapplerItem& item_;
   std::map> input_properties_;
   std::map> output_properties_;
   const std::vector missing_properties_;

From 833803d76093bfcd738136694e2d78db8856b5ae Mon Sep 17 00:00:00 2001
From: Rachel Lim 
Date: Tue, 1 May 2018 16:52:24 -0700
Subject: [PATCH 0256/1691] Fix wrongly ordered lines

PiperOrigin-RevId: 195019769
---
 tensorflow/core/ops/dataset_ops.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc
index 5f10ad24b69a5d..73174c184c631f 100644
--- a/tensorflow/core/ops/dataset_ops.cc
+++ b/tensorflow/core/ops/dataset_ops.cc
@@ -478,11 +478,11 @@ REGISTER_OP("TextLineDataset")
       shape_inference::ShapeHandle unused;
       // `filenames` must be a scalar or a vector.
       TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(0), 1, &unused));
-      return shape_inference::ScalarShape(c);
       // `compression_type` could only be a scalar.
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       // `buffer_size` could only be a scalar.
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
+      return shape_inference::ScalarShape(c);
     });
 
 REGISTER_OP("SqlDataset")

From 448b2ca80f91782f50c50f72bf7feafedcd744b6 Mon Sep 17 00:00:00 2001
From: Allen Lavoie 
Date: Tue, 1 May 2018 16:53:51 -0700
Subject: [PATCH 0257/1691] Sharding for
 tensorflow/contrib/timeseries/python/timeseries/state_space_models:structural_ensemble_test

PiperOrigin-RevId: 195019968
---
 .../timeseries/python/timeseries/state_space_models/BUILD      | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
index 5d33e23a427bd5..3c07a74ed8af9e 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD
@@ -176,8 +176,9 @@ py_library(
 
 py_test(
     name = "structural_ensemble_test",
-    timeout = "long",  # Moderate but for asan/tsan timeouts
+    timeout = "long",  # Moderate but for asan/tsan/msan timeouts
     srcs = ["structural_ensemble_test.py"],
+    shard_count = 4,
     srcs_version = "PY2AND3",
     deps = [
         ":state_space_model",

From f453b62176cf57659ca0485e3b37b8f08c05b966 Mon Sep 17 00:00:00 2001
From: Mustafa Ispir 
Date: Tue, 1 May 2018 17:21:24 -0700
Subject: [PATCH 0258/1691] test fix

PiperOrigin-RevId: 195023740
---
 .../contrib/layers/python/layers/embedding_ops_test.py      | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/embedding_ops_test.py b/tensorflow/contrib/layers/python/layers/embedding_ops_test.py
index bf2514498202e9..dd2395f8c9748d 100644
--- a/tensorflow/contrib/layers/python/layers/embedding_ops_test.py
+++ b/tensorflow/contrib/layers/python/layers/embedding_ops_test.py
@@ -31,6 +31,7 @@
 from tensorflow.python.framework import errors_impl
 from tensorflow.python.framework import random_seed
 from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
+from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import init_ops
@@ -691,11 +692,12 @@ def _GroupByBatchEntry(self, vals, vals_per_batch_entry):
       index += num_val
     return grouped_vals
 
+  @test_util.enable_c_shapes
   def testEmbeddingLookupSparse(self):
     vocab_size = 13
     batch_size = 10
     param_shape = [2, 5]
-    expected_lookup_result_shape = [None] + param_shape
+    expected_lookup_result_shape = param_shape
 
     sp_ids, sp_weights, ids, weights, vals_per_batch_entry = (
         self._RandomIdsAndWeights(batch_size, vocab_size))
@@ -719,7 +721,7 @@ def testEmbeddingLookupSparse(self):
                 None if ignore_weights else sp_weights,
                 combiner=combiner)
 
-        self.assertEqual(embedding_sum.get_shape().as_list(),
+        self.assertEqual(embedding_sum.get_shape().as_list()[1:],
                          expected_lookup_result_shape)
 
         tf_embedding_sum = embedding_sum.eval(feed_dict=feed_dict)

From 62356ad4fde3cab5eb3b565b802badb02b4ab835 Mon Sep 17 00:00:00 2001
From: Patrick Nguyen 
Date: Tue, 1 May 2018 17:48:36 -0700
Subject: [PATCH 0259/1691] Re-apply CL 194140820, which reverts #18251
 (convolution change).

PiperOrigin-RevId: 195027049
---
 .../contrib/layers/python/layers/layers.py    | 142 +-----------------
 .../layers/python/layers/layers_test.py       |  15 +-
 2 files changed, 7 insertions(+), 150 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index 2f3e57653c5d6d..25c3b1e7ea09b8 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -932,8 +932,7 @@ def convolution(inputs,
                 variables_collections=None,
                 outputs_collections=None,
                 trainable=True,
-                scope=None,
-                conv_dims=None):
+                scope=None):
   """Adds an N-D convolution followed by an optional batch_norm layer.
 
   It is required that 1 <= N <= 3.
@@ -994,10 +993,6 @@ def convolution(inputs,
     trainable: If `True` also add variables to the graph collection
       `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
     scope: Optional scope for `variable_scope`.
-    conv_dims: Optional convolution dimensionality, when set it would use the
-      corresponding convolution (e.g. 2 for Conv 2D, 3 for Conv 3D, ..). When
-      leaved to None it would select the convolution dimensionality based on
-      the input rank (i.e. Conv ND, with N = input_rank - 2).
 
   Returns:
     A tensor representing the output of the operation.
@@ -1020,9 +1015,6 @@ def convolution(inputs,
     inputs = ops.convert_to_tensor(inputs)
     input_rank = inputs.get_shape().ndims
 
-    if conv_dims is not None and conv_dims + 2 != input_rank:
-      raise ValueError('Convolution expects input with rank %d, got %d' %
-                       (conv_dims + 2, input_rank))
     if input_rank == 3:
       layer_class = convolutional_layers.Convolution1D
     elif input_rank == 4:
@@ -1069,134 +1061,10 @@ def convolution(inputs,
       outputs = activation_fn(outputs)
     return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
 
-@add_arg_scope
-def convolution1d(inputs,
-                  num_outputs,
-                  kernel_size,
-                  stride=1,
-                  padding='SAME',
-                  data_format=None,
-                  rate=1,
-                  activation_fn=nn.relu,
-                  normalizer_fn=None,
-                  normalizer_params=None,
-                  weights_initializer=initializers.xavier_initializer(),
-                  weights_regularizer=None,
-                  biases_initializer=init_ops.zeros_initializer(),
-                  biases_regularizer=None,
-                  reuse=None,
-                  variables_collections=None,
-                  outputs_collections=None,
-                  trainable=True,
-                  scope=None):
-  return convolution(inputs,
-                     num_outputs,
-                     kernel_size,
-                     stride,
-                     padding,
-                     data_format,
-                     rate,
-                     activation_fn,
-                     normalizer_fn,
-                     normalizer_params,
-                     weights_initializer,
-                     weights_regularizer,
-                     biases_initializer,
-                     biases_regularizer,
-                     reuse,
-                     variables_collections,
-                     outputs_collections,
-                     trainable,
-                     scope,
-                     conv_dims=1)
-
-convolution1d.__doc__ = convolution.__doc__
 
-@add_arg_scope
-def convolution2d(inputs,
-                  num_outputs,
-                  kernel_size,
-                  stride=1,
-                  padding='SAME',
-                  data_format=None,
-                  rate=1,
-                  activation_fn=nn.relu,
-                  normalizer_fn=None,
-                  normalizer_params=None,
-                  weights_initializer=initializers.xavier_initializer(),
-                  weights_regularizer=None,
-                  biases_initializer=init_ops.zeros_initializer(),
-                  biases_regularizer=None,
-                  reuse=None,
-                  variables_collections=None,
-                  outputs_collections=None,
-                  trainable=True,
-                  scope=None):
-  return convolution(inputs,
-                     num_outputs,
-                     kernel_size,
-                     stride,
-                     padding,
-                     data_format,
-                     rate,
-                     activation_fn,
-                     normalizer_fn,
-                     normalizer_params,
-                     weights_initializer,
-                     weights_regularizer,
-                     biases_initializer,
-                     biases_regularizer,
-                     reuse,
-                     variables_collections,
-                     outputs_collections,
-                     trainable,
-                     scope,
-                     conv_dims=2)
-
-convolution2d.__doc__ = convolution.__doc__
+convolution2d = convolution
+convolution3d = convolution
 
-@add_arg_scope
-def convolution3d(inputs,
-                  num_outputs,
-                  kernel_size,
-                  stride=1,
-                  padding='SAME',
-                  data_format=None,
-                  rate=1,
-                  activation_fn=nn.relu,
-                  normalizer_fn=None,
-                  normalizer_params=None,
-                  weights_initializer=initializers.xavier_initializer(),
-                  weights_regularizer=None,
-                  biases_initializer=init_ops.zeros_initializer(),
-                  biases_regularizer=None,
-                  reuse=None,
-                  variables_collections=None,
-                  outputs_collections=None,
-                  trainable=True,
-                  scope=None):
-  return convolution(inputs,
-                     num_outputs,
-                     kernel_size,
-                     stride,
-                     padding,
-                     data_format,
-                     rate,
-                     activation_fn,
-                     normalizer_fn,
-                     normalizer_params,
-                     weights_initializer,
-                     weights_regularizer,
-                     biases_initializer,
-                     biases_regularizer,
-                     reuse,
-                     variables_collections,
-                     outputs_collections,
-                     trainable,
-                     scope,
-                     conv_dims=3)
-
-convolution3d.__doc__ = convolution.__doc__
 
 @add_arg_scope
 def convolution2d_in_plane(
@@ -1543,7 +1411,7 @@ def dense_to_sparse(tensor, eos_token=0, outputs_collections=None, scope=None):
   Args:
      tensor: An `int` `Tensor` to be converted to a `Sparse`.
      eos_token: An integer.
-       It is part of the target label that signifies the end of a sentence.
+       It is part of the target label that signfies the end of a sentence.
      outputs_collections: Collection to add the outputs.
      scope: Optional scope for name_scope.
   """
@@ -1687,7 +1555,7 @@ def _inner_flatten(inputs, new_rank, output_collections=None, scope=None):
     output_collections: Collection to which the outputs will be added.
     scope: Optional scope for `name_scope`.
   Returns:
-    A `Tensor` or `SparseTensor` containing the same values as `inputs`, but
+    A `Tensor` or `SparseTensor` conataining the same values as `inputs`, but
     with innermost dimensions flattened to obtain rank `new_rank`.
 
   Raises:
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index b01fd5d5c95ac1..997f910a2a9756 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -310,17 +310,6 @@ def testCreateDimensions(self):
 
 class ConvolutionTest(test.TestCase):
 
-  def testInvalidShape(self):
-    with self.test_session():
-      images_2d = random_ops.random_uniform((5, 7, 9, 3), seed=1)
-      with self.assertRaisesRegexp(
-          ValueError, 'Convolution expects input with rank 5, got 4'):
-        layers_lib.convolution3d(images_2d, 32, 3)
-      images_3d = random_ops.random_uniform((5, 6, 7, 9, 3), seed=1)
-      with self.assertRaisesRegexp(
-          ValueError, 'Convolution expects input with rank 4, got 5'):
-        layers_lib.convolution2d(images_3d, 32, 3)
-
   def testInvalidDataFormat(self):
     height, width = 7, 9
     with self.test_session():
@@ -3166,7 +3155,7 @@ def testRepeat(self):
     with self.test_session():
       images = np.random.uniform(size=(5, height, width, 3)).astype(np.float32)
       output = _layers.repeat(images, 3, layers_lib.conv2d, 32, [3, 3])
-      self.assertEqual(output.op.name, 'Repeat/convolution2d_3/Relu')
+      self.assertEqual(output.op.name, 'Repeat/convolution_3/Relu')
       self.assertListEqual(output.get_shape().as_list(), [5, 3, 3, 32])
 
   def testRepeatWithScope(self):
@@ -3760,7 +3749,7 @@ def testStackConvolution2d(self):
           layers_lib.convolution2d, [10, 20, 30],
           kernel_size=[3, 3],
           padding='SAME')
-      self.assertEqual(output.op.name, 'Stack/convolution2d_3/Relu')
+      self.assertEqual(output.op.name, 'Stack/convolution_3/Relu')
       self.assertListEqual(output.get_shape().as_list(), [5, 3, 3, 30])
 
   def testStackWithScope(self):

From 92939c55e47b10c6d1ccd82bb31d877efca12235 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 1 May 2018 17:57:02 -0700
Subject: [PATCH 0260/1691] Internal change.

PiperOrigin-RevId: 195027918
---
 tensorflow/contrib/lite/kernels/fully_connected.cc | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/fully_connected.cc b/tensorflow/contrib/lite/kernels/fully_connected.cc
index c5bf50da5f9c34..470b52b7bc4e65 100644
--- a/tensorflow/contrib/lite/kernels/fully_connected.cc
+++ b/tensorflow/contrib/lite/kernels/fully_connected.cc
@@ -272,8 +272,7 @@ template 
 TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
                            TfLiteFullyConnectedParams* params, OpData* data,
                            TfLiteTensor* input, TfLiteTensor* filter,
-                           TfLiteTensor* bias, TfLiteTensor* input_quantized,
-                           TfLiteTensor* output) {
+                           TfLiteTensor* bias, TfLiteTensor* output) {
   gemmlowp::GemmContext* gemm_context = gemm_support::GetFromContext(context);
 
   int32_t input_offset = -input->params.zero_point;
@@ -292,6 +291,8 @@ TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
   } else if (kernel_type == kPie) {
     if (input->type == kTfLiteFloat32) {
       // Pie currently only supports quantized models and float inputs/outputs.
+      TfLiteTensor* input_quantized =
+          &context->tensors[node->temporaries->data[0]];
       return EvalPieQuantized(context, node, params, data, input, filter, bias,
                               input_quantized, output);
     } else {
@@ -346,15 +347,13 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
-  TfLiteTensor* input_quantized = &context->tensors[node->temporaries->data[0]];
-
   switch (filter->type) {  // Already know in/out types are same.
     case kTfLiteFloat32:
       return EvalFloat(context, node, params, data, input, filter,
                                     bias, output);
     case kTfLiteUInt8:
       return EvalQuantized(context, node, params, data, input,
-                                        filter, bias, input_quantized, output);
+                                        filter, bias, output);
     default:
       context->ReportError(context, "Type not currently supported.");
       return kTfLiteError;

From c8ae9e86f33053484b05e405dadd2c8a98b8b41b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 1 May 2018 17:59:59 -0700
Subject: [PATCH 0261/1691] Internal change

PiperOrigin-RevId: 195028221
---
 .../contrib/factorization/python/ops/factorization_ops.py     | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/factorization/python/ops/factorization_ops.py b/tensorflow/contrib/factorization/python/ops/factorization_ops.py
index 811fa89bc38c61..5cef4068ed119d 100644
--- a/tensorflow/contrib/factorization/python/ops/factorization_ops.py
+++ b/tensorflow/contrib/factorization/python/ops/factorization_ops.py
@@ -107,7 +107,7 @@ class WALSModel(object):
       # the prep_gramian_op for row(column) can be run.
       worker_init_op = model.worker_init
 
-      # To be run once per integration sweep before the row(column) update
+      # To be run once per iteration sweep before the row(column) update
       # initialize ops can be run. Note that in the distributed training
       # situations, this should only be run by the chief trainer. All other
       # trainers need to block until this is done.
@@ -436,7 +436,7 @@ def _prepare_gramian(self, factors, gramian):
       gramian: Variable storing the gramian calculated from the factors.
 
     Returns:
-      A op that updates the gramian with the calculated value from the factors.
+      An op that updates the gramian with the calculated value from the factors.
     """
     partial_gramians = []
     for f in factors:

From 69b2c639f55b065a5dbf829351034441bebc8437 Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Tue, 1 May 2018 18:46:31 -0700
Subject: [PATCH 0262/1691] [XLA:CPU] Re-use the same llvm::GlobalVariable for
 identical literals

This isn't necessary today, but it will be after an optimization change I'm
about to make.

LLVM has a constant merging pass too, but one of the motivations here is to
avoid the LLVM compile time overhead of having many large arrays in the IR.

PiperOrigin-RevId: 195032900
---
 tensorflow/compiler/xla/layout_util.cc        |  22 +++
 tensorflow/compiler/xla/layout_util.h         |   3 +
 tensorflow/compiler/xla/literal_util.cc       |  22 +++
 tensorflow/compiler/xla/literal_util.h        |   4 +
 .../compiler/xla/service/cpu/cpu_compiler.cc  |   2 +
 .../compiler/xla/service/cpu/ir_emitter.cc    |  29 ++--
 .../compiler/xla/service/cpu/ir_emitter.h     |  16 +++
 .../compiler/xla/service/cpu/tests/BUILD      |  14 ++
 .../cpu/tests/cpu_literal_caching_test.cc     | 125 ++++++++++++++++++
 tensorflow/compiler/xla/shape_util.cc         |  23 ++++
 tensorflow/compiler/xla/shape_util.h          |   3 +
 .../xla/tests/llvm_irgen_test_base.cc         |   4 +-
 tensorflow/compiler/xla/xla_data.proto        |  18 ++-
 tensorflow/core/lib/hash/hash.h               |  19 ++-
 14 files changed, 289 insertions(+), 15 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc

diff --git a/tensorflow/compiler/xla/layout_util.cc b/tensorflow/compiler/xla/layout_util.cc
index fdc4bbdd8b162b..c6f8f6766e9d01 100644
--- a/tensorflow/compiler/xla/layout_util.cc
+++ b/tensorflow/compiler/xla/layout_util.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/hash/hash.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
@@ -465,4 +466,25 @@ std::ostream& operator<<(std::ostream& out, const Layout& layout) {
   return out;
 }
 
+/*static*/ size_t LayoutUtil::Hash(const Layout& layout) {
+  using tensorflow::hash;
+  using tensorflow::Hash64Combine;
+
+  size_t hash_value = hash()(layout.format());
+
+  for (int64 minor_to_major : layout.minor_to_major()) {
+    hash_value = Hash64Combine(hash_value, hash()(minor_to_major));
+  }
+
+  for (int64 padded_dim : layout.padded_dimensions()) {
+    hash_value = Hash64Combine(hash_value, hash()(padded_dim));
+  }
+
+  hash_value =
+      Hash64Combine(hash_value, hash()(layout.padding_value()));
+  hash_value = Hash64Combine(hash_value, layout.max_sparse_elements());
+
+  return hash_value;
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/layout_util.h b/tensorflow/compiler/xla/layout_util.h
index 6c54eb2201b66a..6cec7501015e2d 100644
--- a/tensorflow/compiler/xla/layout_util.h
+++ b/tensorflow/compiler/xla/layout_util.h
@@ -195,6 +195,9 @@ class LayoutUtil {
   static bool AreDimensionsConsecutive(const Layout& layout,
                                        tensorflow::gtl::ArraySlice dims);
 
+  // Compute a hash for `layout`.
+  static size_t Hash(const Layout& layout);
+
  private:
   TF_DISALLOW_COPY_AND_ASSIGN(LayoutUtil);
 };
diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index bb6dd4f9098aef..b3b5e34ba220c7 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/core/casts.h"
 #include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/hash/hash.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
@@ -2148,6 +2149,27 @@ string Literal::GetR1U8AsString() const {
   return LiteralView(literal, view_root);
 }
 
+size_t Literal::Hash() const {
+  using tensorflow::Hash64;
+  using tensorflow::Hash64Combine;
+
+  size_t hash_value = ShapeUtil::Hash(shape());
+
+  ShapeUtil::ForEachSubshape(
+      shape(), [&](const Shape& subshape, const ShapeIndex& index) {
+        if (ShapeUtil::IsTuple(subshape)) {
+          return;
+        }
+
+        CHECK(LayoutUtil::IsDense(subshape.layout()));
+        hash_value = Hash64Combine(
+            hash_value, Hash64(static_cast(untyped_data(index)),
+                               size_bytes(index)));
+      });
+
+  return hash_value;
+}
+
 LiteralView::LiteralView(const Literal& literal, const ShapeIndex& view_root) {
   shape_ = ShapeUtil::GetSubshape(literal.shape(), view_root);
   pieces_ = ShapeTree(shape_);
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index 956ff7d21cc954..290f38807840f9 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -662,6 +662,10 @@ class Literal {
   // LayoutUtil::MaxSparseElements(SetSubshape(shape(), index).layout()).
   int64 sparse_element_count() const;
 
+  // Compute a hash for this literal.  This literal must not be a sparse tensor
+  // or a tuple containing a sparse tensor.
+  size_t Hash() const;
+
  protected:
   // Internal template helper for the Literal::CopySliceFrom(), matching its
   // arguments one by one.
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index d8ba289f296cdf..e298d67e0937ae 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -787,6 +787,8 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules,
       TF_RETURN_IF_ERROR(verify_status);
     }
 
+    XLA_VLOG_LINES(2, "LLVM IR:\n" + llvm_ir::DumpModuleToString(llvm_module));
+
     Disassembler disassembler(*target_machine);
     CompilerFunctor compiler_functor(
         target_machine.get(), &disassembler, opt_level,
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index d582b5aaae9379..e473389a297f54 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -160,10 +160,8 @@ Status IrEmitter::HandleBitcast(HloInstruction* bitcast) {
   return Status::OK();
 }
 
-Status IrEmitter::HandleConstant(HloInstruction* constant) {
-  VLOG(2) << "HandleConstant: " << constant->ToString();
-  const Literal& literal = constant->literal();
-  llvm::GlobalVariable* global_for_const;
+llvm::GlobalVariable* IrEmitter::EmitGlobalForLiteral(const Literal& literal) {
+  llvm::GlobalVariable* result;
 
   // We avoid creating large constants in the LLVM IR since LLVM is not
   // efficient for large constant arrays.  We still emit "small enough" constant
@@ -174,27 +172,42 @@ Status IrEmitter::HandleConstant(HloInstruction* constant) {
       ByteSizeOf(literal.shape()) >= kMaxInternalConstantSizeInBytes) {
     string global_name = tensorflow::strings::StrCat(
         "constant_global_", external_global_constant_counter_++);
-    global_for_const = new llvm::GlobalVariable(
+    result = new llvm::GlobalVariable(
         /*Module=*/*module_,
         /*Type=*/IrShapeType(literal.shape()),
         /*isConstant=*/true,
         /*Linkage=*/llvm::GlobalValue::ExternalLinkage,
         /*Initializer=*/nullptr,
         /*Name=*/AsStringRef(global_name));
-    global_for_const->setAlignment(MinimumAlignmentForShape(literal.shape()));
+    result->setAlignment(MinimumAlignmentForShape(literal.shape()));
     external_constant_pool_->Insert(global_name, literal,
                                     MinimumAlignmentForShape(literal.shape()));
   } else {
     llvm::Constant* initializer =
         llvm_ir::ConvertLiteralToIrConstant(literal, module_);
-    global_for_const = new llvm::GlobalVariable(
+    result = new llvm::GlobalVariable(
         /*Module=*/*module_,
         /*Type=*/initializer->getType(),
         /*isConstant=*/true,
         /*Linkage=*/llvm::GlobalValue::PrivateLinkage,
         /*Initializer=*/initializer,
         /*Name=*/"");
-    global_for_const->setAlignment(MinimumAlignmentForShape(literal.shape()));
+    result->setAlignment(MinimumAlignmentForShape(literal.shape()));
+  }
+  return result;
+}
+
+Status IrEmitter::HandleConstant(HloInstruction* constant) {
+  VLOG(2) << "HandleConstant: " << constant->ToString();
+  const Literal& literal = constant->literal();
+  llvm::GlobalVariable* global_for_const;
+
+  auto it = emitted_literals_.find(&literal);
+  if (it != emitted_literals_.end()) {
+    global_for_const = it->second;
+  } else {
+    global_for_const = EmitGlobalForLiteral(literal);
+    emitted_literals_[&literal] = global_for_const;
   }
   emitted_value_[constant] = global_for_const;
   VLOG(2) << "  emitted value: " << llvm_ir::DumpToString(*global_for_const);
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
index 0f2f3d1817d6e8..5a040760804fa5 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
@@ -530,6 +530,8 @@ class IrEmitter : public DfsHloVisitorWithDefault {
   Status EmitXfeedTransfer(XfeedKind kind, const Shape& shape,
                            llvm::Value* program_buffer_address);
 
+  llvm::GlobalVariable* EmitGlobalForLiteral(const Literal& literal);
+
   const HloModuleConfig& hlo_module_config_;
 
   bool is_top_level_computation_;
@@ -539,6 +541,20 @@ class IrEmitter : public DfsHloVisitorWithDefault {
   int64 external_global_constant_counter_ = 0;
   ExternalConstantPool* external_constant_pool_;
 
+  struct LiteralPtrHashFunctor {
+    size_t operator()(const Literal* literal) const { return literal->Hash(); }
+  };
+
+  struct LiteralPtrEqualityFunctor {
+    bool operator()(const Literal* lhs, const Literal* rhs) const {
+      return *lhs == *rhs;
+    }
+  };
+
+  tensorflow::gtl::FlatMap
+      emitted_literals_;
+
   TF_DISALLOW_COPY_AND_ASSIGN(IrEmitter);
 };
 
diff --git a/tensorflow/compiler/xla/service/cpu/tests/BUILD b/tensorflow/compiler/xla/service/cpu/tests/BUILD
index bfd95c3fe06a6c..4ddb7a85bc396a 100644
--- a/tensorflow/compiler/xla/service/cpu/tests/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/tests/BUILD
@@ -147,3 +147,17 @@ tf_cc_test(
         "//tensorflow/core:test_main",
     ],
 )
+
+tf_cc_test(
+    name = "cpu_literal_caching_test",
+    srcs = ["cpu_literal_caching_test.cc"],
+    deps = [
+        "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/compiler/xla/service/cpu:cpu_compiler",
+        "//tensorflow/compiler/xla/service/cpu/tests:cpu_codegen_test",
+        "//tensorflow/compiler/xla/tools/parser:hlo_parser",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc
new file mode 100644
index 00000000000000..f0404d07d9a5e5
--- /dev/null
+++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc
@@ -0,0 +1,125 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/cpu/cpu_compiler.h"
+#include "tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h"
+#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h"
+
+namespace xla {
+namespace cpu {
+namespace {
+class CpuExternalConstantsTest : public CpuCodegenTest {};
+
+TEST_F(CpuExternalConstantsTest, RepeatedArrayConstants) {
+  // We use a while loop here to force the two constant HloInstructions to be in
+  // different computations.  Otherwise the HLO optimizer itself CSEs them.
+  const string hlo_text = R"(
+HloModule RepeatedConstants
+
+while_body {
+  arg_body = f32[2,3,2] parameter(0)
+  ROOT const = f32[2,3,2] constant(
+  f32[2,3,2]
+    {{{1, 2}, {1001, 1002}, {2001, 2002}},
+     {{2, 1}, {2001, 3002}, {2001, 2002}}})
+}
+
+while_cond {
+  arg_cond = f32[2,3,2] parameter(0)
+  ROOT unknown = pred[] infeed()
+}
+
+ENTRY main {
+  param = f32[2,3,2] parameter(0)
+  const_a = f32[2,3,2] constant(
+  f32[2,3,2]
+    {{{1, 2}, {1001, 1002}, {2001, 2002}},
+     {{2, 1}, {2001, 3002}, {2001, 2002}}})
+  const_b = f32[2,3,2] while(f32[2,3,2] const_a), condition=while_cond, body=while_body
+
+  out0 = () outfeed(f32[2,3,2] const_a)
+  out1 = () outfeed(f32[2,3,2] const_b)
+
+  ROOT root = f32[] constant(1)
+}
+)";
+
+  string filecheck_pattern = R"(
+CHECK: private constant [2 x [3 x [2 x float]]]
+CHECK-NOT: private constant [2 x [3 x [2 x float]]]
+)";
+
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module,
+                          tools::Parse(hlo_text));
+
+  CpuAotCompilationOptions options{
+      /*triple=*/"x86_64-pc-linux", /*cpu_name=*/"", /*features=*/"",
+      /*entry_point_name=*/"entry",
+      /*relocation_model=*/CpuAotCompilationOptions::RelocationModel::Static};
+
+  CompileAheadOfTimeAndVerifyIr(std::move(module), options, filecheck_pattern,
+                                /*match_optimized_ir=*/false);
+}
+
+TEST_F(CpuExternalConstantsTest, RepeatedTupleConstants) {
+  // We use a while loop here to force the two constant HloInstructions to be in
+  // different computations.  Otherwise the HLO optimizer itself CSEs them.
+  const string hlo_text = R"(
+HloModule RepeatedConstants
+
+while_body {
+  arg_body = (f32[2,1]{1,0}, f32[2]{0}) parameter(0)
+  ROOT const = (f32[2,1]{1,0}, f32[2]{0}) constant((f32[2,1], f32[2]) ( f32[2,1] { { 1 }, { 2 } }, {2, 42} ))
+}
+
+while_cond {
+  arg_cond = (f32[2,1]{1,0}, f32[2]{0}) parameter(0)
+  ROOT unknown = pred[] infeed()
+}
+
+ENTRY main {
+  param = f32[2,3,2] parameter(0)
+  const_a = (f32[2,1]{1,0}, f32[2]{0}) constant((f32[2,1], f32[2]) ( f32[2,1] { { 1 }, { 2 } }, {2, 42} ))
+  const_b = (f32[2,1]{1,0}, f32[2]{0}) while((f32[2,1]{1,0}, f32[2]{0}) const_a), condition=while_cond, body=while_body
+
+  out0 = () outfeed((f32[2,1]{1,0}, f32[2]{0}) const_a)
+  out1 = () outfeed((f32[2,1]{1,0}, f32[2]{0}) const_b)
+
+  ROOT root = f32[] constant(1)
+}
+)";
+
+  string filecheck_pattern = R"(
+CHECK: private constant [2 x float]
+CHECK: private constant [2 x [1 x float]]
+CHECK-NOT: private constant [2 x float]
+CHECK-NOT: private constant [2 x [1 x float]]
+)";
+
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module,
+                          tools::Parse(hlo_text));
+
+  CpuAotCompilationOptions options{
+      /*triple=*/"x86_64-pc-linux", /*cpu_name=*/"", /*features=*/"",
+      /*entry_point_name=*/"entry",
+      /*relocation_model=*/CpuAotCompilationOptions::RelocationModel::Static};
+
+  CompileAheadOfTimeAndVerifyIr(std::move(module), options, filecheck_pattern,
+                                /*match_optimized_ir=*/false);
+}
+
+}  // namespace
+}  // namespace cpu
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc
index d58baa3220a73f..c330473cda990a 100644
--- a/tensorflow/compiler/xla/shape_util.cc
+++ b/tensorflow/compiler/xla/shape_util.cc
@@ -32,6 +32,7 @@ limitations under the License.
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/lib/gtl/iterator_range.h"
 #include "tensorflow/core/lib/gtl/optional.h"
+#include "tensorflow/core/lib/hash/hash.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
@@ -1472,4 +1473,26 @@ std::ostream& operator<<(std::ostream& out, const Shape& shape) {
   return out;
 }
 
+/*static*/ size_t ShapeUtil::Hash(const Shape& shape) {
+  using tensorflow::hash;
+  using tensorflow::Hash64Combine;
+
+  size_t hash_value = hash()(shape.element_type());
+
+  if (shape.tuple_shapes().empty()) {
+    for (int64 dim : shape.dimensions()) {
+      hash_value = Hash64Combine(hash_value, hash()(dim));
+    }
+
+    hash_value = Hash64Combine(hash_value, LayoutUtil::Hash(shape.layout()));
+  } else {
+    hash_value = 0;
+    for (const Shape& subshape : shape.tuple_shapes()) {
+      hash_value = Hash64Combine(hash_value, ShapeUtil::Hash(subshape));
+    }
+  }
+
+  return hash_value;
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h
index 5fa728e7c2fa5f..cb8bf5a2b9e5d0 100644
--- a/tensorflow/compiler/xla/shape_util.h
+++ b/tensorflow/compiler/xla/shape_util.h
@@ -650,6 +650,9 @@ class ShapeUtil {
               .ok());
   }
 
+  // Compute a hash for `shape`.
+  static size_t Hash(const Shape& shape);
+
  private:
   // Validates all of the non-layout properties of the shape -- this is a helper
   // used by both the layout-optional and layout-required public method.
diff --git a/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc b/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc
index 3023df47cda33f..2c45f19c090d26 100644
--- a/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc
+++ b/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc
@@ -62,8 +62,8 @@ void LLVMIRGenTestBase::CompileAheadOfTimeAndVerifyIr(
     std::unique_ptr hlo_module, const AotCompilationOptions& options,
     const string& pattern, bool match_optimized_ir) {
   SetIrHook(match_optimized_ir);
-  ASSERT_TRUE(
-      CompileToAotCompilationResult(std::move(hlo_module), options).ok());
+  TF_ASSERT_OK(
+      CompileToAotCompilationResult(std::move(hlo_module), options).status());
   ResetIrHook();
 
   StatusOr filecheck_result = RunFileCheck(ir_, pattern);
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index d23f9e5918f54c..750d72d797b4f8 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -134,6 +134,8 @@ enum Format {
 // example, Convert) are ignored.
 //
 // See the XLA documentation for more information on shapes and layouts.
+//
+// LINT.IfChange
 message Layout {
   // The method used to store the data in memory. The format determines which of
   // the other fields are used by the layout.
@@ -159,9 +161,12 @@ message Layout {
   // memory.  This field must be unset unless the format is SPARSE.
   int64 max_sparse_elements = 5;
 
-  // Important: if any field is added, be sure to modify ShapeUtil::Equal()
-  // appropriately to account for the new field.
+  // Important: if any field is added, be sure to modify ShapeUtil::Equal() and
+  // LayoutUtil::Hash appropriately to account for the new field.
 }
+// LINT.ThenChange( \
+//     https://www.tensorflow.org/code/tensorflow/compiler/xla/shape_util.cc,      \
+//     https://www.tensorflow.org/code/tensorflow/compiler/xla/layout_util.cc)
 
 // A shape describes the number of dimensions in the array, the size of each
 // dimension, and the primitive component type.
@@ -170,6 +175,8 @@ message Layout {
 // defined.
 //
 // See the XLA documentation for more information on shapes and layouts.
+//
+// LINT.IfChange
 message Shape {
   reserved 1;
   reserved "rank";
@@ -190,9 +197,12 @@ message Shape {
   // The layout used to back this shape.
   Layout layout = 5;
 
-  // Important: if any field is added, be sure to modify ShapeUtil::Equal() and
-  // ShapeUtil::Compatible() appropriately to account for the new field.
+  // Important: if any field is added, be sure to modify ShapeUtil::Equal(),
+  // ShapeUtil::Compatible() and ShapeUtil::Hash() appropriately to account for
+  // the new field.
 }
+// LINT.ThenChange( \
+//     https://www.tensorflow.org/code/tensorflow/compiler/xla/shape_util.cc)
 
 // Shape of the parameters and output of a computation (like a traditional
 // function signature).
diff --git a/tensorflow/core/lib/hash/hash.h b/tensorflow/core/lib/hash/hash.h
index ca05e6346e2045..3f85303c0f6573 100644
--- a/tensorflow/core/lib/hash/hash.h
+++ b/tensorflow/core/lib/hash/hash.h
@@ -21,6 +21,7 @@ limitations under the License.
 #include 
 #include 
 
+#include 
 #include 
 
 #include "tensorflow/core/lib/core/stringpiece.h"
@@ -49,11 +50,27 @@ inline uint64 Hash64Combine(uint64 a, uint64 b) {
 // In particular, tensorflow::hash is not the identity function for pointers.
 // This is important for power-of-two sized hashtables like FlatMap and FlatSet,
 // because otherwise they waste the majority of their hash buckets.
-template 
+//
+// The second type argument is only used for SFNIAE below.
+template 
 struct hash {
   size_t operator()(const T& t) const { return std::hash()(t); }
 };
 
+template 
+struct hash::value>::type> {
+  size_t operator()(T value) const {
+    // This works around a defect in the std::hash C++ spec that isn't fixed in
+    // (at least) gcc 4.8.4:
+    // http://www.open-std.org/jtc1/sc22/wg21/docs/lwg-defects.html#2148
+    //
+    // We should be able to remove this and use the default
+    // tensorflow::hash() once we stop building with GCC versions old
+    // enough to not have this defect fixed.
+    return std::hash()(static_cast(value));
+  }
+};
+
 template 
 struct hash {
   size_t operator()(const T* t) const {

From c0f1080188c5c6955cfa3b3c086ac262b1e5ec02 Mon Sep 17 00:00:00 2001
From: Patrick Nguyen 
Date: Tue, 1 May 2018 19:02:10 -0700
Subject: [PATCH 0263/1691] Make the CRF work when sequence_lengths are int32.

PiperOrigin-RevId: 195034218
---
 tensorflow/contrib/crf/python/ops/crf.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/crf/python/ops/crf.py b/tensorflow/contrib/crf/python/ops/crf.py
index d2beff849eb8d1..2d2cbdc1990ed9 100644
--- a/tensorflow/contrib/crf/python/ops/crf.py
+++ b/tensorflow/contrib/crf/python/ops/crf.py
@@ -52,6 +52,7 @@
 
 import numpy as np
 
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.layers import utils
 from tensorflow.python.ops import array_ops
@@ -147,7 +148,9 @@ def _multi_seq_fn():
     # partition function.
     forward_cell = CrfForwardRnnCell(transition_params)
     # Sequence length is not allowed to be less than zero.
-    sequence_lengths_less_one = math_ops.maximum(0, sequence_lengths - 1)
+    sequence_lengths_less_one = math_ops.maximum(
+        constant_op.constant(0, dtype=sequence_lengths.dtype),
+        sequence_lengths - 1)
     _, alphas = rnn.dynamic_rnn(
         cell=forward_cell,
         inputs=rest_of_input,

From b50f6325143486eb82b5654f8794f0771b54dd4d Mon Sep 17 00:00:00 2001
From: Dan Moldovan 
Date: Tue, 1 May 2018 19:05:39 -0700
Subject: [PATCH 0264/1691] Minor refactor: establish some operator naming
 conventions and apply them, so that the interface is a bit more consistent.

PiperOrigin-RevId: 195034691
---
 .../autograph/converters/break_statements.py  |   4 +-
 .../autograph/converters/control_flow.py      |  24 ++--
 .../contrib/autograph/operators/__init__.py   |  16 ++-
 .../autograph/operators/control_flow.py       | 105 ++++++++++--------
 .../autograph/operators/control_flow_test.py  |  30 ++---
 5 files changed, 99 insertions(+), 80 deletions(-)

diff --git a/tensorflow/contrib/autograph/converters/break_statements.py b/tensorflow/contrib/autograph/converters/break_statements.py
index 91de82f0a78cca..1be1c96dd31bf0 100644
--- a/tensorflow/contrib/autograph/converters/break_statements.py
+++ b/tensorflow/contrib/autograph/converters/break_statements.py
@@ -114,9 +114,9 @@ def visit_For(self, node):
           template,
           var_name=break_var,
           for_stmt=node)
-      extra_cond = templates.replace_as_expression(
+      extra_test = templates.replace_as_expression(
           'not var_name', var_name=break_var)
-      anno.setanno(node[1], 'extra_cond', extra_cond)
+      anno.setanno(node[1], 'extra_test', extra_test)
 
     return node
 
diff --git a/tensorflow/contrib/autograph/converters/control_flow.py b/tensorflow/contrib/autograph/converters/control_flow.py
index 2e26cdb3d9387d..935a2786db0289 100644
--- a/tensorflow/contrib/autograph/converters/control_flow.py
+++ b/tensorflow/contrib/autograph/converters/control_flow.py
@@ -207,7 +207,7 @@ def test_name(state_ssf):
       def body_name(state_ssf):
         body
         return state_ssf,
-      state_ast_tuple = ag__.while_loop(
+      state_ast_tuple = ag__.while_stmt(
           test_name, body_name, (state,), (extra_deps,))
     """
     node = templates.replace(
@@ -252,31 +252,31 @@ def visit_For(self, node):
       state_ast_tuple = gast.Tuple([n.ast() for n in state], None)
 
     node_body = ast_util.rename_symbols(node.body, ssf_map)
-    if anno.hasanno(node, 'extra_cond'):
-      extra_cond = anno.getanno(node, 'extra_cond')
-      extra_cond = ast_util.rename_symbols(extra_cond, ssf_map)
+    if anno.hasanno(node, 'extra_test'):
+      extra_test = anno.getanno(node, 'extra_test')
+      extra_test = ast_util.rename_symbols(extra_test, ssf_map)
     else:
-      extra_cond = parser.parse_expression('True')
+      extra_test = parser.parse_expression('True')
 
     template = """
-      def extra_cond_name(state_ssf):
-        return extra_cond_expr
+      def extra_test_name(state_ssf):
+        return extra_test_expr
       def body_name(iterate, state_ssf):
         body
         return state_ssf,
-      state_ast_tuple = ag__.for_loop(
-          iterated, extra_cond_name, body_name, (state,))
+      state_ast_tuple = ag__.for_stmt(
+          iter_, extra_test_name, body_name, (state,))
     """
     node = templates.replace(
         template,
         state=state,
         state_ssf=state_ssf,
         state_ast_tuple=state_ast_tuple,
-        iterated=node.iter,
+        iter_=node.iter,
         iterate=node.target,
-        extra_cond_name=self.context.namer.new_symbol('extra_cond',
+        extra_test_name=self.context.namer.new_symbol('extra_test',
                                                       all_referenced),
-        extra_cond_expr=extra_cond,
+        extra_test_expr=extra_test,
         body_name=self.context.namer.new_symbol('loop_body', all_referenced),
         body=node_body)
 
diff --git a/tensorflow/contrib/autograph/operators/__init__.py b/tensorflow/contrib/autograph/operators/__init__.py
index 04b4734551d322..38b761d97d54bd 100644
--- a/tensorflow/contrib/autograph/operators/__init__.py
+++ b/tensorflow/contrib/autograph/operators/__init__.py
@@ -19,11 +19,19 @@
 closures for the body.
 """
 
+# Naming conventions:
+#  * operator names match the name usually used for the respective Python
+#    idiom; examples: for_stmt, list_append
+#  * operator arguments match either of:
+#    - the corresponding Python AST attribute (e.g. the condition of an if
+#      statement is called test) if the operator represents an AST construct
+#    - the names used in the Python docs, if the operator is a function (e.g.
+#      list_ and x for append, see
+#      https://docs.python.org/3.7/tutorial/datastructures.html)
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-# TODO(mdan): Add a container for implementation-specific toggles (throughout).
-
-from tensorflow.contrib.autograph.operators.control_flow import for_loop
-from tensorflow.contrib.autograph.operators.control_flow import while_loop
+from tensorflow.contrib.autograph.operators.control_flow import for_stmt
+from tensorflow.contrib.autograph.operators.control_flow import while_stmt
diff --git a/tensorflow/contrib/autograph/operators/control_flow.py b/tensorflow/contrib/autograph/operators/control_flow.py
index d9d8b0d593e537..9f7202821f0d0b 100644
--- a/tensorflow/contrib/autograph/operators/control_flow.py
+++ b/tensorflow/contrib/autograph/operators/control_flow.py
@@ -25,44 +25,55 @@
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import gen_math_ops
 
-# TODO(mdan): Rename _loop to _stmt to follow Python nomenclature.
-# TODO(mdan): Rename arguments to match the AST names.
 
-
-def for_loop(iterated, extra_cond, loop_body, init_state):
+def for_stmt(iter_, extra_test, body, init_state):
   """Functional form of a for statement.
 
-  The loop operates on a so-called state, which includes all symbols that are
-  variant across loop iterations, excluding the iterate. In what follows we
-  refer to state as either a tuple of entities that represent an actual state,
-  or a list of arguments of the corresponding types.
+  The loop operates on a state, which includes all symbols that are
+  variant across loop iterations, excluding the iterate as well as the
+  variables local to the loop.
+
+  For example, given the loop below that calculates the geometric and
+  arithmetic means or some numbers:
+
+    geo_mean = 1
+    arith_mean = 0
+    for i in range(n):
+      a = numbers[i]
+      geo_mean *= a
+      arith_mean += a
+
+  The state is represented by the variables geo_mean and arith_mean. The
+  argument for initial_state may contain the tuple (1, 0), the body will
+  include the arguments geo_mean and arith_mean and will return a tuple
+  representing the new values for geo_mean and respectively arith_mean.
 
   Args:
-    iterated: The entity being iterated over.
-    extra_cond: Callable with the state as arguments, and boolean return type.
+    iter_: The entity being iterated over.
+    extra_test: Callable with the state as arguments, and boolean return type.
         An additionnal loop condition.
-    loop_body: Callable with the iterate and the state as arguments, and
+    body: Callable with the iterate and the state as arguments, and
         state as return type. The actual loop body.
     init_state: Tuple containing the initial state.
 
   Returns:
     Tuple containing the final state.
   """
-  if tensor_util.is_tensor(iterated):
-    return _known_len_for_loop(iterated, extra_cond, loop_body, init_state)
-  elif isinstance(iterated, dataset_ops.Dataset):
-    return _dataset_for_loop(iterated, extra_cond, loop_body, init_state)
+  if tensor_util.is_tensor(iter_):
+    return _known_len_for_stmt(iter_, extra_test, body, init_state)
+  elif isinstance(iter_, dataset_ops.Dataset):
+    return _dataset_for_stmt(iter_, extra_test, body, init_state)
   else:
-    return _py_for_loop(iterated, extra_cond, loop_body, init_state)
+    return _py_for_stmt(iter_, extra_test, body, init_state)
 
 
-def _py_for_loop(iterated, extra_cond, loop_body, init_state):
-  """Overload of for_loop that executes a Python for loop."""
+def _py_for_stmt(iter_, extra_test, body, init_state):
+  """Overload of for_stmt that executes a Python for loop."""
   state = init_state
-  for iterate in iterated:
-    if not extra_cond(*state):
+  for target in iter_:
+    if not extra_test(*state):
       break
-    state = loop_body(iterate, *state)
+    state = body(target, *state)
 
   # TODO(mdan): Remove this special case.
   if len(state) == 1:
@@ -70,23 +81,23 @@ def _py_for_loop(iterated, extra_cond, loop_body, init_state):
   return state
 
 
-def _known_len_for_loop(iterated, extra_cond, loop_body, init_state):
-  """Overload of for_loop that iterates over objects that define a length."""
-  n = builtins.dynamic_len(iterated)
+def _known_len_for_stmt(iter_, extra_test, body, init_state):
+  """Overload of for_stmt that iterates over objects that define a length."""
+  n = builtins.dynamic_len(iter_)
 
   def while_body(iterate_index, *state):
-    iterate = iterated[iterate_index]
-    new_state = loop_body(iterate, *state)
+    iterate = iter_[iterate_index]
+    new_state = body(iterate, *state)
     return (iterate_index + 1,) + new_state
 
   def while_cond(iterate_index, *state):
-    return gen_math_ops.logical_and(iterate_index < n, extra_cond(*state))
+    return gen_math_ops.logical_and(iterate_index < n, extra_test(*state))
 
-  results = while_loop(
+  results = while_stmt(
       while_cond,
       while_body,
       init_state=(0,) + init_state,
-      extra_deps=(iterated,),
+      extra_deps=(iter_,),
       opts=dict(maximum_iterations=n))
   # Dropping the iteration index because it's not syntactically visible.
   results = results[1:]
@@ -97,8 +108,8 @@ def while_cond(iterate_index, *state):
   return results
 
 
-def _dataset_for_loop(ds, extra_cond, loop_body, init_state):
-  """Overload of for_loop that iterates over TF Datasets."""
+def _dataset_for_stmt(ds, extra_test, body, init_state):
+  """Overload of for_stmt that iterates over TF Datasets."""
   # Because Datsets only expose get_next, in the style of Python iterators,
   # we are forced to unpack the loop as:
   #
@@ -117,15 +128,15 @@ def tag_with(ds, tag):
     epoch_number, iterate = iterator.get_next()
 
     def while_body(epoch_number, iterate, *state):
-      new_state = loop_body(iterate, *state)
+      new_state = body(iterate, *state)
       epoch_number, iterate = iterator.get_next()
       return (epoch_number, iterate) + new_state
 
     def while_cond(epoch_number, iterate, *state):
       del iterate
-      return gen_math_ops.logical_and(epoch_number < 1, extra_cond(*state))
+      return gen_math_ops.logical_and(epoch_number < 1, extra_test(*state))
 
-    results = while_loop(
+    results = while_stmt(
         while_cond,
         while_body,
         init_state=(epoch_number, iterate) + init_state,
@@ -140,7 +151,7 @@ def while_cond(epoch_number, iterate, *state):
   return results
 
 
-def while_loop(loop_cond, loop_body, init_state, extra_deps, opts=None):
+def while_stmt(test, body, init_state, extra_deps, opts=None):
   """Functional form of a while statement.
 
   The loop operates on a so-called state, which includes all symbols that are
@@ -149,13 +160,13 @@ def while_loop(loop_cond, loop_body, init_state, extra_deps, opts=None):
   of the corresponding types.
 
   Args:
-    loop_cond: Callable with the state as arguments, and boolean return type.
+    test: Callable with the state as arguments, and boolean return type.
         The loop condition.
-    loop_body: Callable with the state as arguments, and state as return type.
+    body: Callable with the state as arguments, and state as return type.
         The actual loop body.
     init_state: Tuple containing the initial state.
     extra_deps: Tuple containing additional entities on which the loop may
-        depend, such as loop invariants referenced by loop_cond. Used
+        depend, such as loop invariants referenced by test. Used
         exclusively for dispatch control.
     opts: Optional dict of extra loop parameters.
 
@@ -166,24 +177,24 @@ def while_loop(loop_cond, loop_body, init_state, extra_deps, opts=None):
   # That could be somethins as simple as a collection of dispatch rules, with
   # some prioritization.
   if any(tensor_util.is_tensor(v) for v in init_state + extra_deps):
-    return _tf_while_loop(loop_cond, loop_body, init_state, opts)
+    return _tf_while_stmt(test, body, init_state, opts)
   else:
-    return _py_while_loop(loop_cond, loop_body, init_state, opts)
+    return _py_while_stmt(test, body, init_state, opts)
 
 
-def _tf_while_loop(loop_cond, loop_body, init_state, opts):
-  """Overload of while_loop that stages a TF while_loop."""
+def _tf_while_stmt(test, body, init_state, opts):
+  """Overload of while_stmt that stages a TF while_stmt."""
   if opts is None:
     opts = {}
-  return control_flow_ops.while_loop(loop_cond, loop_body, init_state, **opts)
+  return control_flow_ops.while_loop(test, body, init_state, **opts)
 
 
-def _py_while_loop(loop_cond, loop_body, init_state, opts):
-  """Overload of while_loop that executes a Python while loop."""
+def _py_while_stmt(test, body, init_state, opts):
+  """Overload of while_stmt that executes a Python while loop."""
   del opts
   state = init_state
-  while loop_cond(*state):
-    state = loop_body(*state)
+  while test(*state):
+    state = body(*state)
   return state
 
 
diff --git a/tensorflow/contrib/autograph/operators/control_flow_test.py b/tensorflow/contrib/autograph/operators/control_flow_test.py
index a0cd0bfa82bb05..b14d7edba38461 100644
--- a/tensorflow/contrib/autograph/operators/control_flow_test.py
+++ b/tensorflow/contrib/autograph/operators/control_flow_test.py
@@ -29,28 +29,28 @@
 class ForLoopTest(test.TestCase):
 
   def test_tensor(self):
-    s = control_flow.for_loop(
+    s = control_flow.for_stmt(
         constant_op.constant([1, 2, 3, 4]),
-        extra_cond=lambda s: True,
-        loop_body=lambda i, s: (s + i,),
+        extra_test=lambda s: True,
+        body=lambda i, s: (s + i,),
         init_state=(0,))
     with self.test_session() as sess:
       self.assertEqual((10,), sess.run(s))
 
   def test_python(self):
-    s = control_flow.for_loop(
+    s = control_flow.for_stmt(
         range(5),
-        extra_cond=lambda s: True,
-        loop_body=lambda i, s: (s + i,),
+        extra_test=lambda s: True,
+        body=lambda i, s: (s + i,),
         init_state=(0,))
     self.assertEqual(10, s)
 
   def test_dataset(self):
     to_int32 = lambda i: math_ops.cast(i, dtypes.int32)
-    s = control_flow.for_loop(
+    s = control_flow.for_stmt(
         dataset_ops.Dataset.range(5).map(to_int32),
-        extra_cond=lambda s: True,
-        loop_body=lambda i, s: (s + i,),
+        extra_test=lambda s: True,
+        body=lambda i, s: (s + i,),
         init_state=(0,))
     with self.test_session() as sess:
       self.assertEqual((10,), sess.run(s))
@@ -60,9 +60,9 @@ class WhileLoopTest(test.TestCase):
 
   def test_tensor(self):
     n = constant_op.constant(5)
-    results = control_flow.while_loop(
-        loop_cond=lambda i, s: i < n,
-        loop_body=lambda i, s: (i + 1, s + i,),
+    results = control_flow.while_stmt(
+        test=lambda i, s: i < n,
+        body=lambda i, s: (i + 1, s + i,),
         init_state=(0, 0),
         extra_deps=(n,))
     with self.test_session() as sess:
@@ -70,9 +70,9 @@ def test_tensor(self):
 
   def test_python(self):
     n = 5
-    results = control_flow.while_loop(
-        loop_cond=lambda i, s: i < n,
-        loop_body=lambda i, s: (i + 1, s + i),
+    results = control_flow.while_stmt(
+        test=lambda i, s: i < n,
+        body=lambda i, s: (i + 1, s + i),
         init_state=(0, 0),
         extra_deps=(n,))
     self.assertEqual((5, 10), results)

From 7715b7b0650c2f20b47189a060580a45e510acd8 Mon Sep 17 00:00:00 2001
From: Mark Heffernan 
Date: Tue, 1 May 2018 20:14:29 -0700
Subject: [PATCH 0265/1691] Add missing colocated element in test in
 buffer_assignment_test. This was resulting in a flaky test because sometimes
 the live set would include this missing colocated element perhaps because the
 buffers in the allocation has some nondeterministic order (read from a map?).

PiperOrigin-RevId: 195039311
---
 tensorflow/compiler/xla/service/buffer_assignment_test.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index 40cf6483aae082..f6d6b5c36a478e 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -1634,6 +1634,7 @@ TEST_F(BufferAssignmentTest, PeakBuffersWhile) {
   }
   EXPECT_EQ(bcast_buffer->instruction(), bcast);
   EXPECT_TRUE(
+      nonbcast_buffer->instruction() == copy ||
       nonbcast_buffer->instruction() == while_op ||
       nonbcast_buffer->instruction() == body->parameter_instruction(0) ||
       nonbcast_buffer->instruction() == body->root_instruction() ||

From 5e1448f691afe6e9ba57bb67497311c45b855b82 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 01:36:18 -0700
Subject: [PATCH 0266/1691] BUGFIX: Convert inputs and list of gradients into
 tuple if they are not instance of tuple. Otherwise this causes "unhashable
 keys" error when we try to hash. Also fixed lint error.

PiperOrigin-RevId: 195061425
---
 tensorflow/contrib/kfac/python/ops/fisher_blocks.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/kfac/python/ops/fisher_blocks.py b/tensorflow/contrib/kfac/python/ops/fisher_blocks.py
index 32c776cb381f1b..3a5c8eb5f9630f 100644
--- a/tensorflow/contrib/kfac/python/ops/fisher_blocks.py
+++ b/tensorflow/contrib/kfac/python/ops/fisher_blocks.py
@@ -673,9 +673,6 @@ class KroneckerProductFB(FisherBlock):
   output factors.
   """
 
-  def __init__(self, layer_collection):
-    super(KroneckerProductFB, self).__init__(layer_collection)
-
   def _setup_damping(self, damping, normalization=None):
     """Makes functions that compute the damping values for both factors."""
     def compute_damping():
@@ -1309,6 +1306,8 @@ def _process_data(self, grads_list):
       else:
         raise ValueError("Global config variable TOWER_STRATEGY must be one of "
                          "'concat' or 'separate'.")
+    else:
+      inputs = tuple(inputs)
 
     # Now we perform the analogous processing for grads_list
     if isinstance(grads_list[0][0], (list, tuple)):
@@ -1351,6 +1350,8 @@ def _process_data(self, grads_list):
       else:
         raise ValueError("Global config variable TOWER_STRATEGY must be one of "
                          "'concat' or 'separate'.")
+    else:
+      grads_list = tuple(tuple(grads) for grads in grads_list)
 
     if self._num_uses is None:
       raise ValueError("You must supply a value for the num_uses argument if "

From af4c683798738000b067f60e5ab8abe0115b29c8 Mon Sep 17 00:00:00 2001
From: Sergii Khomenko 
Date: Wed, 2 May 2018 15:39:46 +0200
Subject: [PATCH 0267/1691] Fix string issue for temp_export_dir (#18951)

* Fix string issue for temp_export_dir
---
 .../learn/python/learn/utils/saved_model_export_utils.py     | 3 ++-
 tensorflow/python/saved_model/builder_impl.py                | 5 +++--
 tensorflow/python/training/saver.py                          | 2 +-
 3 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
index c7cdb4131215c3..f8106d1e4a7e79 100644
--- a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
+++ b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
@@ -343,7 +343,8 @@ def get_temp_export_dir(timestamped_export_dir):
   """
   (dirname, basename) = os.path.split(timestamped_export_dir)
   temp_export_dir = os.path.join(
-      compat.as_bytes(dirname), compat.as_bytes('temp-{}'.format(basename)))
+      compat.as_bytes(dirname),
+      compat.as_bytes('temp-{}'.format(compat.as_text(basename))))
   return temp_export_dir
 
 
diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py
index 3447d917e9bf2d..01903ae596b84b 100644
--- a/tensorflow/python/saved_model/builder_impl.py
+++ b/tensorflow/python/saved_model/builder_impl.py
@@ -132,7 +132,8 @@ def _save_and_write_assets(self, assets_collection_to_add=None):
       if not file_io.file_exists(asset_destination_filepath):
         file_io.copy(asset_source_filepath, asset_destination_filepath)
 
-    tf_logging.info("Assets written to: %s", assets_destination_dir)
+    tf_logging.info("Assets written to: %s",
+                    compat.as_text(assets_destination_dir))
 
   def _maybe_add_legacy_init_op(self, legacy_init_op=None):
     """Add legacy init op to the SavedModel.
@@ -441,7 +442,7 @@ def save(self, as_text=False):
           compat.as_bytes(self._export_dir),
           compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB))
       file_io.write_string_to_file(path, self._saved_model.SerializeToString())
-    tf_logging.info("SavedModel written to: %s", path)
+    tf_logging.info("SavedModel written to: %s", compat.as_text(path))
 
     return path
 
diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index 53e821c995900c..8134fd74aa7f4a 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -1725,7 +1725,7 @@ def restore(self, sess, save_path):
       return
     if save_path is None:
       raise ValueError("Can't load save_path when it is None.")
-    logging.info("Restoring parameters from %s", save_path)
+    logging.info("Restoring parameters from %s", compat.as_text(save_path))
     try:
       if context.executing_eagerly():
         self._build_eager(save_path, build_save=False, build_restore=True)

From 6a09fcdbf047f5ab3e154238ed142883dd89af44 Mon Sep 17 00:00:00 2001
From: Clayne Robison 
Date: Wed, 2 May 2018 06:47:58 -0700
Subject: [PATCH 0268/1691] Reverting changes from 495d511 that break
 install_pip_packages.sh in Ubuntu 16.04 containers, causing nightly mkl ci
 builds to fail. (#18888)

---
 .../tools/ci_build/install/install_pip_packages.sh    | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh
index 5aaf544afdcb88..982161cefeefdd 100755
--- a/tensorflow/tools/ci_build/install/install_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh
@@ -17,14 +17,9 @@
 set -e
 
 # We don't apt-get install so that we can install a newer version of pip.
-# Only needed for Ubuntu 14.04 ,and not needed for Ubuntu 16.04 / Debian 8,9
-if $(cat /etc/*-release | grep -q 14.04); then
-  easy_install -U pip==9.0.3
-  easy_install3 -U pip==9.0.3
-else
-  pip2 install --upgrade pip==9.0.3
-  pip3 install --upgrade pip==9.0.3
-fi
+# Only needed for Ubuntu 14.04 and 16.04; not needed for 18.04 and Debian 8,9?
+easy_install -U pip==9.0.3
+easy_install3 -U pip==9.0.3
 
 # Install pip packages from whl files to avoid the time-consuming process of
 # building from source.

From e02d08f6d0b1637babf34b022e9326b25d8471e1 Mon Sep 17 00:00:00 2001
From: Paul Van Eck 
Date: Wed, 2 May 2018 07:08:30 -0700
Subject: [PATCH 0269/1691] Allow tfdbg mouse down scroll in curses UI (#18942)

* Allow tfdbg mouse down scroll in curses UI

This commit allows users to continuously scroll the screen when the mouse
is held down on the scroll bar when using the curses UI.

* Only allow click-hold scrolling on scroll bar arrows
---
 tensorflow/python/debug/cli/curses_ui.py | 36 +++++++++++++++++++++---
 1 file changed, 32 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/debug/cli/curses_ui.py b/tensorflow/python/debug/cli/curses_ui.py
index f66cefb427c9cc..7b87972d694981 100644
--- a/tensorflow/python/debug/cli/curses_ui.py
+++ b/tensorflow/python/debug/cli/curses_ui.py
@@ -190,8 +190,6 @@ def layout(self):
     return layout
 
   def get_click_command(self, mouse_y):
-    # TODO(cais): Support continuous scrolling when the mouse button is held
-    # down.
     if self._output_num_rows <= 1:
       return None
     elif mouse_y == self._min_y:
@@ -271,6 +269,10 @@ class CursesUI(base_ui.BaseUI):
 
   _UI_WAIT_MESSAGE = "Processing..."
 
+  # The delay (in ms) between each update of the scroll bar when the mouse
+  # button is held down on the scroll bar. Controls how fast the screen scrolls.
+  _MOUSE_SCROLL_DELAY_MS = 100
+
   _single_instance_lock = threading.Lock()
 
   def __init__(self, on_ui_exit=None, config=None):
@@ -855,7 +857,30 @@ def _on_textbox_keypress(self, x):
       except curses.error:
         mouse_event_type = None
 
-      if mouse_event_type == curses.BUTTON1_RELEASED:
+      if mouse_event_type == curses.BUTTON1_PRESSED:
+        # Logic for held mouse-triggered scrolling.
+        if mouse_x >= self._max_x - 2:
+          # Disable blocking on checking for user input.
+          self._command_window.nodelay(True)
+
+          # Loop while mouse button is pressed.
+          while mouse_event_type == curses.BUTTON1_PRESSED:
+            # Sleep for a bit.
+            curses.napms(self._MOUSE_SCROLL_DELAY_MS)
+            scroll_command = self._scroll_bar.get_click_command(mouse_y)
+            if scroll_command in (_SCROLL_UP_A_LINE, _SCROLL_DOWN_A_LINE):
+              self._scroll_output(scroll_command)
+
+            # Check to see if different mouse event is in queue.
+            self._command_window.getch()
+            try:
+              _, _, _, _, mouse_event_type = self._screen_getmouse()
+            except curses.error:
+              pass
+
+          self._command_window.nodelay(False)
+          return x
+      elif mouse_event_type == curses.BUTTON1_RELEASED:
         # Logic for mouse-triggered scrolling.
         if mouse_x >= self._max_x - 2:
           scroll_command = self._scroll_bar.get_click_command(mouse_y)
@@ -1677,4 +1702,7 @@ def _set_mouse_enabled(self, enabled):
       self._redraw_output()
 
   def _screen_set_mousemask(self):
-    curses.mousemask(self._mouse_enabled)
+    if self._mouse_enabled:
+      curses.mousemask(curses.BUTTON1_RELEASED | curses.BUTTON1_PRESSED)
+    else:
+      curses.mousemask(0)

From ba1c33faeb6df1ae363888e2e7330e219f0679ea Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 07:51:53 -0700
Subject: [PATCH 0270/1691] ArraysExtraInfo: Add name_regexp field and regexp
 name matching. PiperOrigin-RevId: 195091587

---
 tensorflow/contrib/lite/toco/BUILD            |  1 +
 .../contrib/lite/toco/model_flags.proto       |  3 +-
 tensorflow/contrib/lite/toco/tooling_util.cc  | 79 ++++++++++++-------
 3 files changed, 53 insertions(+), 30 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD
index f16225fd665277..a3eff8ac701de3 100644
--- a/tensorflow/contrib/lite/toco/BUILD
+++ b/tensorflow/contrib/lite/toco/BUILD
@@ -396,6 +396,7 @@ cc_library(
         ":toco_port",
         ":types_proto_cc",
         "//tensorflow/core:lib",
+        "//third_party/re2",
         "@com_google_absl//absl/strings",
         "@protobuf_archive//:protobuf_headers",
     ],
diff --git a/tensorflow/contrib/lite/toco/model_flags.proto b/tensorflow/contrib/lite/toco/model_flags.proto
index d23e80c464c9fe..6c1c53658c0736 100644
--- a/tensorflow/contrib/lite/toco/model_flags.proto
+++ b/tensorflow/contrib/lite/toco/model_flags.proto
@@ -96,8 +96,9 @@ message RnnState {
 // model that does not already contain such MinMax information.
 message ArraysExtraInfo {
   message Entry {
-    // Next ID to use: 7.
+    // Next ID to use: 8.
     optional string name = 1;
+    optional string name_regexp = 7;
     optional double min = 2;
     optional double max = 3;
     optional IODataType data_type = 4;
diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index f334c51bbb35b8..36f38ba8b0aa3d 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -26,6 +26,7 @@ limitations under the License.
 #include "absl/strings/str_join.h"
 #include "absl/strings/str_replace.h"
 #include "absl/strings/str_split.h"
+#include "third_party/re2/re2.h"
 #include "tensorflow/contrib/lite/toco/dump_graphviz.h"
 #include "tensorflow/contrib/lite/toco/model_flags.pb.h"
 #include "tensorflow/contrib/lite/toco/toco_graphviz_dump_options.h"
@@ -1983,38 +1984,58 @@ void FinishBuildingRNNStates(Model* model) {
   }
 }
 
+// Returns the array names that match the ArraysExtraInfo's name and
+// name_regexp. The regexp match is for a full match.
+std::unordered_set ScanArrayNames(
+    const Model& model, const toco::ArraysExtraInfo_Entry& entry) {
+  std::unordered_set matches;
+  if (model.HasArray(entry.name())) {
+    matches.insert(entry.name());
+  }
+  if (!entry.name_regexp().empty()) {
+    const auto& arrays = model.GetArrayMap();
+    const RE2 name_regexp = {entry.name_regexp()};
+    for (auto it = arrays.begin(); it != arrays.end(); ++it) {
+      if (RE2::FullMatch(it->first, name_regexp)) {
+        matches.insert(it->first);
+      }
+    }
+  }
+  return matches;
+}
+
 void UseArraysExtraInfo(Model* model, bool quantize_output) {
   for (const auto& entry : model->flags.arrays_extra_info().entries()) {
-    if (!model->HasArray(entry.name())) {
-      continue;
-    }
-    auto& array = model->GetArray(entry.name());
-    if (entry.has_min() || entry.has_max()) {
-      CHECK_EQ(entry.has_min(), entry.has_max());
-      auto& minmax = array.GetOrCreateMinMax();
-      minmax.min = entry.min();
-      minmax.max = entry.max();
-    }
-    if (entry.has_data_type() && quantize_output) {
-      array.final_data_type =
-          ConvertIODataTypeToArrayDataType(entry.data_type());
-    }
-    if (entry.has_shape()) {
-      array.clear_shape();
-      // Make sure to create the shape even if there are no dims, to
-      // correctly record 0-D shapes.
-      array.mutable_shape();
-      for (int dim : entry.shape().dims()) {
-        array.mutable_shape()->mutable_dims()->push_back(dim);
+    const auto matches = ScanArrayNames(*model, entry);
+    for (const auto& matched_name : matches) {
+      auto& array = model->GetArray(matched_name);
+      if (entry.has_min() || entry.has_max()) {
+        CHECK_EQ(entry.has_min(), entry.has_max());
+        auto& minmax = array.GetOrCreateMinMax();
+        minmax.min = entry.min();
+        minmax.max = entry.max();
       }
-    }
-    if (entry.has_constant_float_value()) {
-      CHECK(array.has_shape());
-      if (array.data_type == ArrayDataType::kFloat) {
-        auto& data = array.GetMutableBuffer().data;
-        data.resize(RequiredBufferSizeForShape(array.shape()));
-        for (float& f : data) {
-          f = entry.constant_float_value();
+      if (entry.has_data_type() && quantize_output) {
+        array.final_data_type =
+            ConvertIODataTypeToArrayDataType(entry.data_type());
+      }
+      if (entry.has_shape()) {
+        array.clear_shape();
+        // Make sure to create the shape even if there are no dims, to
+        // correctly record 0-D shapes.
+        array.mutable_shape();
+        for (int dim : entry.shape().dims()) {
+          array.mutable_shape()->mutable_dims()->push_back(dim);
+        }
+      }
+      if (entry.has_constant_float_value()) {
+        CHECK(array.has_shape());
+        if (array.data_type == ArrayDataType::kFloat) {
+          auto& data = array.GetMutableBuffer().data;
+          data.resize(RequiredBufferSizeForShape(array.shape()));
+          for (float& f : data) {
+            f = entry.constant_float_value();
+          }
         }
       }
     }

From 72fd2b8e97f301039ac0eb60435cbbddf36212f6 Mon Sep 17 00:00:00 2001
From: Priya Gupta 
Date: Wed, 2 May 2018 08:04:09 -0700
Subject: [PATCH 0271/1691] Use experimental auto_sharding in multi worker
 dataset.

PiperOrigin-RevId: 195092992
---
 tensorflow/contrib/distribute/python/BUILD     | 1 +
 tensorflow/contrib/distribute/python/values.py | 5 +++--
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/distribute/python/BUILD b/tensorflow/contrib/distribute/python/BUILD
index cdb3a8d65eab8b..aaafc184bf3b15 100644
--- a/tensorflow/contrib/distribute/python/BUILD
+++ b/tensorflow/contrib/distribute/python/BUILD
@@ -21,6 +21,7 @@ py_library(
     srcs = ["values.py"],
     visibility = ["//tensorflow:internal"],
     deps = [
+        ":input_ops",
         ":prefetching_ops_v2",
         "//tensorflow/contrib/data/python/ops:batching",
         "//tensorflow/contrib/eager/python:datasets",
diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py
index 18afdaa7b0688b..aaf177d07ead69 100644
--- a/tensorflow/contrib/distribute/python/values.py
+++ b/tensorflow/contrib/distribute/python/values.py
@@ -27,6 +27,7 @@
 import six
 
 from tensorflow.contrib.data.python.ops import batching
+from tensorflow.contrib.distribute.python import input_ops
 from tensorflow.contrib.distribute.python import prefetching_ops_v2
 from tensorflow.python.eager import context
 from tensorflow.python.framework import device as tf_device
@@ -651,8 +652,8 @@ def __init__(self, dataset_fn, worker_device_map, prefetch_on_device=None):
         six.iteritems(worker_device_map)):
       with ops.device(worker):
         worker_input = dataset_fn()
-        # TODO(yuefengz, priyag): support efficient sharding.
-        worker_input = worker_input.shard(len(worker_device_map), i)
+        worker_input = input_ops.auto_shard_dataset(
+            worker_input, len(worker_device_map), i)
         self._datasets[worker] = PerDeviceDataset(
             worker_input, worker_devices, prefetch_on_device=prefetch_on_device)
 

From 22eed5405906de6df8846bd9ce4ee0a57917aa3c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 08:51:07 -0700
Subject: [PATCH 0272/1691] Automated g4 rollback of changelist 195091587

PiperOrigin-RevId: 195098224
---
 tensorflow/contrib/lite/toco/BUILD            |  1 -
 .../contrib/lite/toco/model_flags.proto       |  3 +-
 tensorflow/contrib/lite/toco/tooling_util.cc  | 79 +++++++------------
 3 files changed, 30 insertions(+), 53 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD
index a3eff8ac701de3..f16225fd665277 100644
--- a/tensorflow/contrib/lite/toco/BUILD
+++ b/tensorflow/contrib/lite/toco/BUILD
@@ -396,7 +396,6 @@ cc_library(
         ":toco_port",
         ":types_proto_cc",
         "//tensorflow/core:lib",
-        "//third_party/re2",
         "@com_google_absl//absl/strings",
         "@protobuf_archive//:protobuf_headers",
     ],
diff --git a/tensorflow/contrib/lite/toco/model_flags.proto b/tensorflow/contrib/lite/toco/model_flags.proto
index 6c1c53658c0736..d23e80c464c9fe 100644
--- a/tensorflow/contrib/lite/toco/model_flags.proto
+++ b/tensorflow/contrib/lite/toco/model_flags.proto
@@ -96,9 +96,8 @@ message RnnState {
 // model that does not already contain such MinMax information.
 message ArraysExtraInfo {
   message Entry {
-    // Next ID to use: 8.
+    // Next ID to use: 7.
     optional string name = 1;
-    optional string name_regexp = 7;
     optional double min = 2;
     optional double max = 3;
     optional IODataType data_type = 4;
diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index 36f38ba8b0aa3d..f334c51bbb35b8 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -26,7 +26,6 @@ limitations under the License.
 #include "absl/strings/str_join.h"
 #include "absl/strings/str_replace.h"
 #include "absl/strings/str_split.h"
-#include "third_party/re2/re2.h"
 #include "tensorflow/contrib/lite/toco/dump_graphviz.h"
 #include "tensorflow/contrib/lite/toco/model_flags.pb.h"
 #include "tensorflow/contrib/lite/toco/toco_graphviz_dump_options.h"
@@ -1984,58 +1983,38 @@ void FinishBuildingRNNStates(Model* model) {
   }
 }
 
-// Returns the array names that match the ArraysExtraInfo's name and
-// name_regexp. The regexp match is for a full match.
-std::unordered_set ScanArrayNames(
-    const Model& model, const toco::ArraysExtraInfo_Entry& entry) {
-  std::unordered_set matches;
-  if (model.HasArray(entry.name())) {
-    matches.insert(entry.name());
-  }
-  if (!entry.name_regexp().empty()) {
-    const auto& arrays = model.GetArrayMap();
-    const RE2 name_regexp = {entry.name_regexp()};
-    for (auto it = arrays.begin(); it != arrays.end(); ++it) {
-      if (RE2::FullMatch(it->first, name_regexp)) {
-        matches.insert(it->first);
-      }
-    }
-  }
-  return matches;
-}
-
 void UseArraysExtraInfo(Model* model, bool quantize_output) {
   for (const auto& entry : model->flags.arrays_extra_info().entries()) {
-    const auto matches = ScanArrayNames(*model, entry);
-    for (const auto& matched_name : matches) {
-      auto& array = model->GetArray(matched_name);
-      if (entry.has_min() || entry.has_max()) {
-        CHECK_EQ(entry.has_min(), entry.has_max());
-        auto& minmax = array.GetOrCreateMinMax();
-        minmax.min = entry.min();
-        minmax.max = entry.max();
-      }
-      if (entry.has_data_type() && quantize_output) {
-        array.final_data_type =
-            ConvertIODataTypeToArrayDataType(entry.data_type());
-      }
-      if (entry.has_shape()) {
-        array.clear_shape();
-        // Make sure to create the shape even if there are no dims, to
-        // correctly record 0-D shapes.
-        array.mutable_shape();
-        for (int dim : entry.shape().dims()) {
-          array.mutable_shape()->mutable_dims()->push_back(dim);
-        }
+    if (!model->HasArray(entry.name())) {
+      continue;
+    }
+    auto& array = model->GetArray(entry.name());
+    if (entry.has_min() || entry.has_max()) {
+      CHECK_EQ(entry.has_min(), entry.has_max());
+      auto& minmax = array.GetOrCreateMinMax();
+      minmax.min = entry.min();
+      minmax.max = entry.max();
+    }
+    if (entry.has_data_type() && quantize_output) {
+      array.final_data_type =
+          ConvertIODataTypeToArrayDataType(entry.data_type());
+    }
+    if (entry.has_shape()) {
+      array.clear_shape();
+      // Make sure to create the shape even if there are no dims, to
+      // correctly record 0-D shapes.
+      array.mutable_shape();
+      for (int dim : entry.shape().dims()) {
+        array.mutable_shape()->mutable_dims()->push_back(dim);
       }
-      if (entry.has_constant_float_value()) {
-        CHECK(array.has_shape());
-        if (array.data_type == ArrayDataType::kFloat) {
-          auto& data = array.GetMutableBuffer().data;
-          data.resize(RequiredBufferSizeForShape(array.shape()));
-          for (float& f : data) {
-            f = entry.constant_float_value();
-          }
+    }
+    if (entry.has_constant_float_value()) {
+      CHECK(array.has_shape());
+      if (array.data_type == ArrayDataType::kFloat) {
+        auto& data = array.GetMutableBuffer().data;
+        data.resize(RequiredBufferSizeForShape(array.shape()));
+        for (float& f : data) {
+          f = entry.constant_float_value();
         }
       }
     }

From d6d4355a39a56a1b0d0abc7ce74d8307a1925459 Mon Sep 17 00:00:00 2001
From: Tony Wang 
Date: Wed, 2 May 2018 09:52:10 -0700
Subject: [PATCH 0273/1691] Add Name String to GraphOptimizationPass and Log
 Registered Passes

Added a name string to GraphOptimization class and set to the class name through REGISTER_OPTIMIZATION macro.
Modified RunGrouping function to log the name and phase of optimization pass that's running.
Added two additional functions to log all registered optimization passes in the order of execution.

PiperOrigin-RevId: 195106355
---
 .../common_runtime/optimization_registry.cc   | 21 +++++++++++++++++++
 .../common_runtime/optimization_registry.h    | 18 ++++++++++++++--
 2 files changed, 37 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/common_runtime/optimization_registry.cc b/tensorflow/core/common_runtime/optimization_registry.cc
index 7f270b4d4e4277..bf49a758b2550a 100644
--- a/tensorflow/core/common_runtime/optimization_registry.cc
+++ b/tensorflow/core/common_runtime/optimization_registry.cc
@@ -36,6 +36,8 @@ Status OptimizationPassRegistry::RunGrouping(
     for (auto& phase : group->second) {
       VLOG(1) << "Running optimization phase " << phase.first;
       for (auto& pass : phase.second) {
+        VLOG(1) << "Running optimization pass: "
+                << pass->GetOptimizationPassName();
         Status s = pass->Run(options);
         if (!s.ok()) return s;
       }
@@ -44,4 +46,23 @@ Status OptimizationPassRegistry::RunGrouping(
   return Status::OK();
 }
 
+void OptimizationPassRegistry::LogGrouping(Grouping grouping, int vlog_level) {
+  auto group = groups_.find(grouping);
+  if (group != groups_.end()) {
+    for (auto& phase : group->second) {
+      for (auto& pass : phase.second) {
+        VLOG(vlog_level) << "Registered optimization pass grouping " << grouping
+                         << " phase " << phase.first << ": "
+                         << pass->GetOptimizationPassName();
+      }
+    }
+  }
+}
+
+void OptimizationPassRegistry::LogAllGroupings(int vlog_level) {
+  for (auto group = groups_.begin(); group != groups_.end(); ++group) {
+    LogGrouping(group->first, vlog_level);
+  }
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/common_runtime/optimization_registry.h b/tensorflow/core/common_runtime/optimization_registry.h
index a469c8aa4ea640..1b535faf196fd0 100644
--- a/tensorflow/core/common_runtime/optimization_registry.h
+++ b/tensorflow/core/common_runtime/optimization_registry.h
@@ -65,6 +65,13 @@ class GraphOptimizationPass {
  public:
   virtual ~GraphOptimizationPass() {}
   virtual Status Run(const GraphOptimizationPassOptions& options) = 0;
+  void SetOptimizationPassName(string name) { _optimization_pass_name = name; }
+  string GetOptimizationPassName() { return _optimization_pass_name; }
+
+ private:
+  // The name of the opitmization pass, which is the same as the inherited class
+  // name.
+  string _optimization_pass_name;
 };
 
 // The key is a 'phase' number. Phases are executed in increasing
@@ -95,6 +102,10 @@ class OptimizationPassRegistry {
   // Returns the global registry of optimization passes.
   static OptimizationPassRegistry* Global();
 
+  // Prints registered optimization passes for debugging.
+  void LogGrouping(Grouping grouping, int vlog_level);
+  void LogAllGroupings(int vlog_level);
+
  private:
   std::map groups_;
 };
@@ -105,7 +116,9 @@ class OptimizationPassRegistration {
  public:
   OptimizationPassRegistration(OptimizationPassRegistry::Grouping grouping,
                                int phase,
-                               std::unique_ptr pass) {
+                               std::unique_ptr pass,
+                               string optimization_pass_name) {
+    pass->SetOptimizationPassName(optimization_pass_name);
     OptimizationPassRegistry::Global()->Register(grouping, phase,
                                                  std::move(pass));
   }
@@ -123,7 +136,8 @@ class OptimizationPassRegistration {
   static optimization_registration::OptimizationPassRegistration       \
       register_optimization_##ctr(                                     \
           grouping, phase,                                             \
-          std::unique_ptr(new optimization))
+          std::unique_ptr(new optimization()),  \
+          #optimization)
 
 }  // namespace tensorflow
 

From c4394346027fa01f12261e6fea6a1b7f19ac21a9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 10:02:09 -0700
Subject: [PATCH 0274/1691] Instantiate SwapDimension1And2InTensor3 for
 Eigen::half

PiperOrigin-RevId: 195107839
---
 tensorflow/core/kernels/conv_ops_gpu_3.cu.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
index 2503b475dc10e6..180531b8c09d63 100644
--- a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
+++ b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
@@ -1027,6 +1027,7 @@ template struct functor::SwapDimension1And2InTensor3;
 template struct functor::SwapDimension1And2InTensor3;
+template struct functor::SwapDimension1And2InTensor3;
 
 template struct functor::SwapDimension0And2InTensor3;
 template struct functor::SwapDimension0And2InTensor3;

From 1f47bbd1e09a9ed4086d0484024e8989a65274a9 Mon Sep 17 00:00:00 2001
From: Benoit Steiner 
Date: Wed, 2 May 2018 10:07:13 -0700
Subject: [PATCH 0275/1691] Optimized the analysis of rank and size operations.

PiperOrigin-RevId: 195108832
---
 .../core/grappler/costs/graph_properties.cc   | 32 +++++++++++++++++++
 tensorflow/core/grappler/op_types.cc          |  4 +++
 tensorflow/core/grappler/op_types.h           |  2 ++
 3 files changed, 38 insertions(+)

diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index 2c7b57971a6164..69b22561b2b4aa 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -475,6 +475,38 @@ class SymbolicShapeRefiner {
               }
             }
           }
+        } else if (IsRank(*input)) {
+          if (c->inference_context->RankKnown(c->inference_context->input(0))) {
+            int32 rank =
+                c->inference_context->Rank(c->inference_context->input(0));
+            Tensor t(DT_INT32, {});
+            t.flat()(0) = rank;
+            const_values[dst_input] = t;
+            input_tensors[dst_input] = &const_values[dst_input];
+          }
+        } else if (IsSize(*input)) {
+          DimensionHandle size =
+              c->inference_context->NumElements(c->inference_context->input(0));
+          if (c->inference_context->ValueKnown(size)) {
+            int64 sz = c->inference_context->Value(size);
+            bool valid = false;
+            if (input->attr().at("T").type() == DT_INT32) {
+              if (sz < std::numeric_limits::max()) {
+                Tensor t(DT_INT32, {});
+                t.flat()(0) = sz;
+                const_values[dst_input] = t;
+                valid = true;
+              }
+            } else {
+              Tensor t(DT_INT64, {});
+              t.flat()(0) = sz;
+              const_values[dst_input] = t;
+              valid = true;
+            }
+            if (valid) {
+              input_tensors[dst_input] = &const_values[dst_input];
+            }
+          }
         }
 
         if (c->output_tensors_as_shapes.size() > src_output) {
diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index bf6d4c09212f30..7c936dfca19a5e 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -262,6 +262,8 @@ bool IsRandomShuffle(const NodeDef& node) {
   return node.op() == "RandomShuffle";
 }
 
+bool IsRank(const NodeDef& node) { return node.op() == "Rank"; }
+
 bool IsReal(const NodeDef& node) { return node.op() == "Real"; }
 
 bool IsRealDiv(const NodeDef& node) { return node.op() == "RealDiv"; }
@@ -317,6 +319,8 @@ bool IsShuffle(const NodeDef& node) { return node.op() == "Shuffle"; }
 
 bool IsSigmoidGrad(const NodeDef& node) { return node.op() == "SigmoidGrad"; }
 
+bool IsSize(const NodeDef& node) { return node.op() == "Size"; }
+
 bool IsSlice(const NodeDef& node) { return node.op() == "Slice"; }
 
 bool IsSoftplusGrad(const NodeDef& node) { return node.op() == "SoftplusGrad"; }
diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h
index 3dddf3f1ea8bba..7a1b438768659d 100644
--- a/tensorflow/core/grappler/op_types.h
+++ b/tensorflow/core/grappler/op_types.h
@@ -100,6 +100,7 @@ bool IsProd(const NodeDef& node);
 bool IsPow(const NodeDef& node);
 bool IsQueue(const NodeDef& node);
 bool IsRandomShuffle(const NodeDef& node);
+bool IsRank(const NodeDef& node);
 bool IsReal(const NodeDef& node);
 bool IsRealDiv(const NodeDef& node);
 bool IsRelu6Grad(const NodeDef& node);
@@ -116,6 +117,7 @@ bool IsRsqrtGrad(const NodeDef& node);
 bool IsSelect(const NodeDef& node);
 bool IsSeluGrad(const NodeDef& node);
 bool IsSend(const NodeDef& node);
+bool IsSize(const NodeDef& node);
 bool IsSlice(const NodeDef& node);
 bool IsShape(const NodeDef& node);
 bool IsShapeN(const NodeDef& node);

From bc86da090f2f2e850768bbdfd603c7217aecdb53 Mon Sep 17 00:00:00 2001
From: Andrew Selle 
Date: Wed, 2 May 2018 10:33:07 -0700
Subject: [PATCH 0276/1691] Fix Makefile to not use benchmark anymore (switch
 to minimal) (#19019)

Minimal uses nothing and does almost nothing, but it does nothing
requiring protos or rest of tensorflow runtime.

Benchmark_model originally was more like this, but it became
useful for actually benchmarking, making it less useful as a minimal
example.
---
 tensorflow/contrib/lite/Makefile              | 19 +++--
 .../contrib/lite/examples/minimal/minimal.cc  | 71 +++++++++++++++++++
 2 files changed, 80 insertions(+), 10 deletions(-)
 create mode 100644 tensorflow/contrib/lite/examples/minimal/minimal.cc

diff --git a/tensorflow/contrib/lite/Makefile b/tensorflow/contrib/lite/Makefile
index 65fba52d461461..e4f86e258afe3d 100644
--- a/tensorflow/contrib/lite/Makefile
+++ b/tensorflow/contrib/lite/Makefile
@@ -1,4 +1,3 @@
-
 # Find where we're running from, so we can store generated files here.
 ifeq ($(origin MAKEFILE_DIR), undefined)
 	MAKEFILE_DIR := $(shell dirname $(realpath $(lastword $(MAKEFILE_LIST))))
@@ -69,12 +68,12 @@ LIB_NAME := libtensorflow-lite.a
 LIB_PATH := $(LIBDIR)$(LIB_NAME)
 
 # A small example program that shows how to link against the library.
-BENCHMARK_PATH := $(BINDIR)benchmark_model
+MINIMAL_PATH := $(BINDIR)minimal
 
-BENCHMARK_SRCS := \
-tensorflow/contrib/lite/tools/benchmark_model.cc
-BENCHMARK_OBJS := $(addprefix $(OBJDIR), \
-$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(BENCHMARK_SRCS))))
+MINIMAL_SRCS := \
+tensorflow/contrib/lite/examples/minimal/minimal.cc
+MINIMAL_OBJS := $(addprefix $(OBJDIR), \
+$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(MINIMAL_SRCS))))
 
 # What sources we want to compile, must be kept in sync with the main Bazel
 # build files.
@@ -100,7 +99,7 @@ $(wildcard tensorflow/contrib/lite/*/*test.cc) \
 $(wildcard tensorflow/contrib/lite/*/*/*test.cc) \
 $(wildcard tensorflow/contrib/lite/*/*/*/*test.cc) \
 $(wildcard tensorflow/contrib/lite/kernels/test_util.cc) \
-$(BENCHMARK_SRCS)
+$(MINIMAL_SRCS)
 # Filter out all the excluded files.
 TF_LITE_CC_SRCS := $(filter-out $(CORE_CC_EXCLUDE_SRCS), $(CORE_CC_ALL_SRCS))
 # File names of the intermediate files target compilation generates.
@@ -119,17 +118,17 @@ $(OBJDIR)%.o: %.c
 	$(CC) $(CCFLAGS) $(INCLUDES) -c $< -o $@
 
 # The target that's compiled if there's no command-line arguments.
-all: $(LIB_PATH) $(BENCHMARK_PATH)
+all: $(LIB_PATH)  $(MINIMAL_PATH)
 
 # Gathers together all the objects we've compiled into a single '.a' archive.
 $(LIB_PATH): $(LIB_OBJS)
 	@mkdir -p $(dir $@)
 	$(AR) $(ARFLAGS) $(LIB_PATH) $(LIB_OBJS)
 
-$(BENCHMARK_PATH): $(BENCHMARK_OBJS) $(LIB_PATH)
+$(MINIMAL_PATH): $(MINIMAL_OBJS) $(LIB_PATH)
 	@mkdir -p $(dir $@)
 	$(CXX) $(CXXFLAGS) $(INCLUDES) \
-	-o $(BENCHMARK_PATH) $(BENCHMARK_OBJS) \
+	-o $(MINIMAL_PATH) $(MINIMAL_OBJS) \
 	$(LIBFLAGS) $(LIB_PATH) $(LDFLAGS) $(LIBS)
 
 # Gets rid of all generated files.
diff --git a/tensorflow/contrib/lite/examples/minimal/minimal.cc b/tensorflow/contrib/lite/examples/minimal/minimal.cc
new file mode 100644
index 00000000000000..106e3b027055b6
--- /dev/null
+++ b/tensorflow/contrib/lite/examples/minimal/minimal.cc
@@ -0,0 +1,71 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/contrib/lite/model.h"
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include 
+
+// This is an example that is minimal to read a model
+// from disk and perform inference. There is no data being loaded
+// that is up to you to add as a user.
+//
+// NOTE: Do not add any dependencies to this that cannot be built with
+// the minimal makefile. This example must remain trivial to build with
+// the minimal build tool.
+//
+// Usage: minimal 
+
+using namespace tflite;
+
+#define TFLITE_MINIMAL_CHECK(x) \
+  if(!(x)) {                                                    \
+    fprintf(stderr, "Error at %s:%d\n",  __FILE__, __LINE__); \
+    exit(1); \
+  }
+
+
+int main(int argc, char *argv[]) {
+  if(argc != 2) {
+    fprintf(stderr, "Usage: %s \n");
+    return 1;
+  }
+  const char* filename = argv[1];
+
+  // Load model
+  std::unique_ptr model
+      = tflite::FlatBufferModel::BuildFromFile(filename);
+  TFLITE_MINIMAL_CHECK(model != nullptr);
+
+  // Build the interpreter
+  tflite::ops::builtin::BuiltinOpResolver resolver;
+  InterpreterBuilder builder(*model.get(), resolver);
+  std::unique_ptr interpreter;
+  builder(&interpreter);
+  TFLITE_MINIMAL_CHECK(interpreter != nullptr);
+
+  // Allocate tensor buffers.
+  TFLITE_MINIMAL_CHECK(interpreter->AllocateTensors() == kTfLiteOk);
+
+  // Fill input buffers
+  // TODO(user): Insert code to fill input tensors
+
+  // Run inference
+  TFLITE_MINIMAL_CHECK(interpreter->Invoke() == kTfLiteOk);
+
+  // Read output buffers
+  // TODO(user): Insert getting data out code.
+
+  return 0;
+}

From e408e8171540386d3dfed0a7c4fa1d0e1cc812cd Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Wed, 2 May 2018 10:36:26 -0700
Subject: [PATCH 0277/1691] Internal-only change.

PiperOrigin-RevId: 195113702
---
 tensorflow/tensorflow.bzl | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index e5cc886b3251f9..b2cec7655faad6 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -1507,6 +1507,7 @@ def tf_py_wrap_cc(name,
 # 2. When --define=no_tensorflow_py_deps=false (by default), it's a normal py_test.
 def py_test(deps=[], data=[], **kwargs):
   native.py_test(
+      # TODO(jlebar): Ideally we'd use tcmalloc here.,
       deps=select({
           "//conditions:default": deps,
           clean_dep("//tensorflow:no_tensorflow_py_deps"): [],

From 489640a0d00ea7b5826937781cd1bf3a520b0b5d Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Wed, 2 May 2018 10:43:25 -0700
Subject: [PATCH 0278/1691] Fix some nits in cpu_literal_caching_test that I
 noticed after submission

PiperOrigin-RevId: 195114829
---
 .../service/cpu/tests/cpu_literal_caching_test.cc  | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc
index f0404d07d9a5e5..b10eb74635cd35 100644
--- a/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc
@@ -20,9 +20,9 @@ limitations under the License.
 namespace xla {
 namespace cpu {
 namespace {
-class CpuExternalConstantsTest : public CpuCodegenTest {};
+class CpuDuplicateConstantsTest : public CpuCodegenTest {};
 
-TEST_F(CpuExternalConstantsTest, RepeatedArrayConstants) {
+TEST_F(CpuDuplicateConstantsTest, RepeatedArrayConstants) {
   // We use a while loop here to force the two constant HloInstructions to be in
   // different computations.  Otherwise the HLO optimizer itself CSEs them.
   const string hlo_text = R"(
@@ -56,6 +56,10 @@ ENTRY main {
 }
 )";
 
+  // TODO(b/78879738): The fake "f32[] constant(1)" root is only needed to work
+  // around b/78879738.  Once b/78879738 is fixed, we can set one of the
+  // outfeeds as the root.
+
   string filecheck_pattern = R"(
 CHECK: private constant [2 x [3 x [2 x float]]]
 CHECK-NOT: private constant [2 x [3 x [2 x float]]]
@@ -73,7 +77,7 @@ CHECK-NOT: private constant [2 x [3 x [2 x float]]]
                                 /*match_optimized_ir=*/false);
 }
 
-TEST_F(CpuExternalConstantsTest, RepeatedTupleConstants) {
+TEST_F(CpuDuplicateConstantsTest, RepeatedTupleConstants) {
   // We use a while loop here to force the two constant HloInstructions to be in
   // different computations.  Otherwise the HLO optimizer itself CSEs them.
   const string hlo_text = R"(
@@ -101,6 +105,10 @@ ENTRY main {
 }
 )";
 
+  // TODO(b/78879738): The fake "f32[] constant(1)" root is only needed to work
+  // around b/78879738.  Once b/78879738 is fixed, we can set one of the
+  // outfeeds as the root.
+
   string filecheck_pattern = R"(
 CHECK: private constant [2 x float]
 CHECK: private constant [2 x [1 x float]]

From 55972370f3243ca061b882120383545d70642cf8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 10:57:13 -0700
Subject: [PATCH 0279/1691] Initialize all members of CollectiveParams at
 construction time to avoid warnings of uninit memory access by dynamic
 analysis tools.

PiperOrigin-RevId: 195117321
---
 tensorflow/core/framework/collective.h | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/framework/collective.h b/tensorflow/core/framework/collective.h
index f6fe12e7ef6da7..f8d27d38687a93 100644
--- a/tensorflow/core/framework/collective.h
+++ b/tensorflow/core/framework/collective.h
@@ -52,7 +52,8 @@ struct CollGroupParams {
   DeviceType device_type;
   int32 num_tasks;  // number of distinct tasks in group
   string ToString() const;
-  CollGroupParams() : device_type(DEVICE_CPU) {}
+  CollGroupParams()
+      : group_key(0), group_size(0), device_type(DEVICE_CPU), num_tasks(0) {}
 };
 
 // The best implementation of a collective op depends on many factors
@@ -71,10 +72,11 @@ struct CollImplDetails {
 
 // Data common to all members of a collective instance.
 struct CollInstanceParams {
-  int32 instance_key;  // Identifies all participating graph nodes.
-  CollectiveType type;
-  DataType data_type;
-  TensorShape shape;
+  // Identifies all participating graph nodes.
+  int32 instance_key = -1;
+  CollectiveType type = UNDEFINED_COLLECTIVE;
+  DataType data_type = DT_FLOAT;
+  TensorShape shape = {0};
   // Fully qualified name of device for each member, in default rank order.
   std::vector device_names;
   // Task name prefix of corresponding device name.
@@ -99,8 +101,8 @@ struct CollectiveParams {
   CollInstanceParams instance;
   CollTaskParams task;
 
-  string name;             // node name used only for log or error messages
-  int default_rank;        // index of this op within device_names
+  string name = "";        // node name used only for log or error messages
+  int default_rank = -1;   // index of this op within device_names
   bool is_source = false;  // broadcast only
   // Rank of this device in each subdivision permutation.
   std::vector subdiv_rank;

From c08bf79144b3acc731018147e92fd389bcb60b2d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 10:57:49 -0700
Subject: [PATCH 0280/1691] Renames
 _regression_head_with_mean_squared_error_loss to _regression_head.

PiperOrigin-RevId: 195117425
---
 .../estimator/python/estimator/head.py        |   7 +-
 .../python/estimator/canned/baseline.py       |   2 +-
 .../python/estimator/canned/boosted_trees.py  |   2 +-
 tensorflow/python/estimator/canned/dnn.py     |   3 +-
 .../estimator/canned/dnn_linear_combined.py   |   3 +-
 tensorflow/python/estimator/canned/head.py    |   2 +-
 .../python/estimator/canned/head_test.py      | 105 ++++++++----------
 tensorflow/python/estimator/canned/linear.py  |   2 +-
 8 files changed, 53 insertions(+), 73 deletions(-)

diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py
index 2a6d17e81bdc37..5d19bf4714ff6f 100644
--- a/tensorflow/contrib/estimator/python/estimator/head.py
+++ b/tensorflow/contrib/estimator/python/estimator/head.py
@@ -235,7 +235,7 @@ def regression_head(weight_column=None,
   Raises:
     ValueError: If `label_dimension` or `loss_reduction` is invalid.
   """
-  return head_lib._regression_head_with_mean_squared_error_loss(  # pylint:disable=protected-access
+  return head_lib._regression_head(  # pylint:disable=protected-access
       weight_column=weight_column,
       label_dimension=label_dimension,
       loss_reduction=loss_reduction,
@@ -297,7 +297,7 @@ def poisson_regression_head(
   def _poisson_loss(labels, logits):
     return nn.log_poisson_loss(
         targets=labels, log_input=logits, compute_full_loss=compute_full_loss)
-  return head_lib._regression_head_with_mean_squared_error_loss(  # pylint:disable=protected-access
+  return head_lib._regression_head(  # pylint:disable=protected-access
       weight_column=weight_column,
       label_dimension=label_dimension,
       loss_reduction=loss_reduction,
@@ -360,8 +360,7 @@ def _logistic_loss(labels, logits):
         labels, n_classes=2, message='Labels must be in range [0, 1]')
     return nn.sigmoid_cross_entropy_with_logits(
         labels=labels, logits=logits)
-  # TODO(roumposg): Rename to _regression_head, since it supports loss_fn arg.
-  return head_lib._regression_head_with_mean_squared_error_loss(  # pylint:disable=protected-access
+  return head_lib._regression_head(  # pylint:disable=protected-access
       weight_column=weight_column,
       label_dimension=1,
       loss_reduction=loss_reduction,
diff --git a/tensorflow/python/estimator/canned/baseline.py b/tensorflow/python/estimator/canned/baseline.py
index 3e92a77543e3d2..980c0573726945 100644
--- a/tensorflow/python/estimator/canned/baseline.py
+++ b/tensorflow/python/estimator/canned/baseline.py
@@ -344,7 +344,7 @@ def __init__(self,
       A `BaselineRegressor` estimator.
     """
 
-    head = head_lib._regression_head_with_mean_squared_error_loss(  # pylint: disable=protected-access
+    head = head_lib._regression_head(  # pylint: disable=protected-access
         label_dimension=label_dimension,
         weight_column=weight_column,
         loss_reduction=loss_reduction)
diff --git a/tensorflow/python/estimator/canned/boosted_trees.py b/tensorflow/python/estimator/canned/boosted_trees.py
index d281fd90ea74b2..6d7a3299f70c87 100644
--- a/tensorflow/python/estimator/canned/boosted_trees.py
+++ b/tensorflow/python/estimator/canned/boosted_trees.py
@@ -690,7 +690,7 @@ def _create_regression_head(label_dimension, weight_column=None):
     raise ValueError('For now only 1 dimension regression is supported.'
                      'label_dimension given as {}'.format(label_dimension))
   # pylint: disable=protected-access
-  return head_lib._regression_head_with_mean_squared_error_loss(
+  return head_lib._regression_head(
       label_dimension=label_dimension,
       weight_column=weight_column,
       loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE)
diff --git a/tensorflow/python/estimator/canned/dnn.py b/tensorflow/python/estimator/canned/dnn.py
index 6382622e0b5c72..973a6ec74777de 100644
--- a/tensorflow/python/estimator/canned/dnn.py
+++ b/tensorflow/python/estimator/canned/dnn.py
@@ -481,8 +481,7 @@ def _model_fn(features, labels, mode, config):
           features=features,
           labels=labels,
           mode=mode,
-          head=head_lib.  # pylint: disable=protected-access
-          _regression_head_with_mean_squared_error_loss(
+          head=head_lib._regression_head(  # pylint: disable=protected-access
               label_dimension=label_dimension, weight_column=weight_column,
               loss_reduction=loss_reduction),
           hidden_units=hidden_units,
diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined.py b/tensorflow/python/estimator/canned/dnn_linear_combined.py
index f47706db2fc5f9..95efc0a028bc90 100644
--- a/tensorflow/python/estimator/canned/dnn_linear_combined.py
+++ b/tensorflow/python/estimator/canned/dnn_linear_combined.py
@@ -553,8 +553,7 @@ def _model_fn(features, labels, mode, config):
           features=features,
           labels=labels,
           mode=mode,
-          head=head_lib.  # pylint: disable=protected-access
-          _regression_head_with_mean_squared_error_loss(
+          head=head_lib._regression_head(  # pylint: disable=protected-access
               label_dimension=label_dimension, weight_column=weight_column,
               loss_reduction=loss_reduction),
           linear_feature_columns=linear_feature_columns,
diff --git a/tensorflow/python/estimator/canned/head.py b/tensorflow/python/estimator/canned/head.py
index efa4bdf5980a34..48f448d7f5f917 100644
--- a/tensorflow/python/estimator/canned/head.py
+++ b/tensorflow/python/estimator/canned/head.py
@@ -1197,7 +1197,7 @@ def create_estimator_spec(
         train_op=train_op)
 
 
-def _regression_head_with_mean_squared_error_loss(
+def _regression_head(
     weight_column=None,
     label_dimension=1,
     loss_reduction=losses.Reduction.SUM,
diff --git a/tensorflow/python/estimator/canned/head_test.py b/tensorflow/python/estimator/canned/head_test.py
index 7da3df01dc48d9..32a63399362811 100644
--- a/tensorflow/python/estimator/canned/head_test.py
+++ b/tensorflow/python/estimator/canned/head_test.py
@@ -2607,26 +2607,24 @@ def test_multi_dim_weighted_eval(self):
           rtol=tol, atol=tol)
 
 
-class RegressionHeadWithMeanSquaredErrorLossTest(test.TestCase):
+class RegressionHead(test.TestCase):
 
   def setUp(self):
     ops.reset_default_graph()
 
   def test_invalid_label_dimension(self):
     with self.assertRaisesRegexp(ValueError, r'Invalid label_dimension'):
-      head_lib._regression_head_with_mean_squared_error_loss(label_dimension=-1)
+      head_lib._regression_head(label_dimension=-1)
     with self.assertRaisesRegexp(ValueError, r'Invalid label_dimension'):
-      head_lib._regression_head_with_mean_squared_error_loss(label_dimension=0)
+      head_lib._regression_head(label_dimension=0)
 
   def test_invalid_loss_reduction(self):
     with self.assertRaisesRegexp(
         ValueError, r'Invalid loss_reduction: invalid_loss_reduction'):
-      head_lib._regression_head_with_mean_squared_error_loss(
-          loss_reduction='invalid_loss_reduction')
+      head_lib._regression_head(loss_reduction='invalid_loss_reduction')
     with self.assertRaisesRegexp(
         ValueError, r'Invalid loss_reduction: none'):
-      head_lib._regression_head_with_mean_squared_error_loss(
-          loss_reduction=losses.Reduction.NONE)
+      head_lib._regression_head(loss_reduction=losses.Reduction.NONE)
 
   def test_loss_fn_arg_labels_missing(self):
     def _loss_fn(logits):
@@ -2635,7 +2633,7 @@ def _loss_fn(logits):
         ValueError,
         r'loss_fn must contain argument: labels\. '
         r'Given arguments: \(\'logits\',\)'):
-      head_lib._regression_head_with_mean_squared_error_loss(loss_fn=_loss_fn)
+      head_lib._regression_head(loss_fn=_loss_fn)
 
   def test_loss_fn_arg_logits_missing(self):
     def _loss_fn(labels):
@@ -2644,12 +2642,12 @@ def _loss_fn(labels):
         ValueError,
         r'loss_fn must contain argument: logits\. '
         r'Given arguments: \(\'labels\',\)'):
-      head_lib._regression_head_with_mean_squared_error_loss(loss_fn=_loss_fn)
+      head_lib._regression_head(loss_fn=_loss_fn)
 
   def test_loss_fn_arg_features_ok(self):
     def _loss_fn(labels, logits, features):
       del labels, logits, features  # Unused
-      head_lib._regression_head_with_mean_squared_error_loss(loss_fn=_loss_fn)
+      head_lib._regression_head(loss_fn=_loss_fn)
 
   def test_loss_fn_arg_invalid(self):
     def _loss_fn(labels, logits, name=None):
@@ -2657,11 +2655,10 @@ def _loss_fn(labels, logits, name=None):
     with self.assertRaisesRegexp(
         ValueError,
         r'loss_fn has unexpected args: \[\'name\'\]'):
-      head_lib._regression_head_with_mean_squared_error_loss(loss_fn=_loss_fn)
+      head_lib._regression_head(loss_fn=_loss_fn)
 
   def test_invalid_logits(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        label_dimension=3)
+    head = head_lib._regression_head(label_dimension=3)
     self.assertEqual(3, head.logits_dimension)
     logits_1d = np.array(((45.,), (41.,),))
 
@@ -2685,8 +2682,7 @@ def test_invalid_logits(self):
         })
 
   def test_incompatible_labels_eval(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        label_dimension=3)
+    head = head_lib._regression_head(label_dimension=3)
     self.assertEqual(3, head.logits_dimension)
     values_3d = np.array(((45., 46., 47.), (41., 42., 43.),))
     values_1d = np.array(((43.,), (44.,),))
@@ -2732,8 +2728,7 @@ def test_incompatible_labels_eval(self):
         })
 
   def test_incompatible_labels_train(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        label_dimension=3)
+    head = head_lib._regression_head(label_dimension=3)
     self.assertEqual(3, head.logits_dimension)
     values_3d = np.array(((45., 46., 47.), (41., 42., 43.),))
     values_1d = np.array(((43.,), (44.,),))
@@ -2784,12 +2779,11 @@ def test_incompatible_labels_train(self):
         })
 
   def test_name(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        name='foo')
+    head = head_lib._regression_head(name='foo')
     self.assertEqual('foo', head.name)
 
   def test_predict(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss()
+    head = head_lib._regression_head()
     self.assertEqual(1, head.logits_dimension)
 
     # Create estimator spec.
@@ -2826,8 +2820,7 @@ def test_predict(self):
   def test_predict_with_inverse_link_fn(self):
     def _inverse_link_fn(logits):
       return logits - 10.
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        inverse_link_fn=_inverse_link_fn)
+    head = head_lib._regression_head(inverse_link_fn=_inverse_link_fn)
 
     # Create estimator spec.
     logits = np.array(((45,), (41,),), dtype=np.int32)
@@ -2866,7 +2859,7 @@ def _inverse_link_fn(logits):
           logits, spec.export_outputs['predict'].outputs['logits'].eval())
 
   def test_eval_create_loss(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss()
+    head = head_lib._regression_head()
     logits = np.array(((45,), (41,),), dtype=np.float32)
     labels = np.array(((43,), (44,),), dtype=np.int32)
     features = {'x': np.array(((42,),), dtype=np.float32)}
@@ -2895,8 +2888,7 @@ def _loss_fn(labels, logits):
           data=[logits])
       with ops.control_dependencies([check_labels, check_logits]):
         return constant_op.constant(loss)
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        label_dimension=2, loss_fn=_loss_fn)
+    head = head_lib._regression_head(label_dimension=2, loss_fn=_loss_fn)
 
     actual_training_loss = head.create_loss(
         features={'x': np.array(((42,),), dtype=np.int32)},
@@ -2913,8 +2905,7 @@ def test_eval_create_loss_loss_fn_wrong_shape(self):
     def _loss_fn(labels, logits):
       del labels, logits  # Unused
       return constant_op.constant(loss)
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        label_dimension=2, loss_fn=_loss_fn)
+    head = head_lib._regression_head(label_dimension=2, loss_fn=_loss_fn)
 
     logits = np.array([[-1., 1.], [-2., 2.]], dtype=np.float32)
     labels = np.array([[1., 0.], [2., -1.]], dtype=np.float32)
@@ -2933,7 +2924,7 @@ def _loss_fn(labels, logits):
 
   def test_eval_labels_none(self):
     """Tests that error is raised when labels is None."""
-    head = head_lib._regression_head_with_mean_squared_error_loss()
+    head = head_lib._regression_head()
 
     with self.assertRaisesRegexp(
         ValueError, r'You must provide a labels Tensor\. Given: None\.'):
@@ -2944,7 +2935,7 @@ def test_eval_labels_none(self):
           labels=None)
 
   def test_eval(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss()
+    head = head_lib._regression_head()
     self.assertEqual(1, head.logits_dimension)
 
     logits = np.array(((45,), (41,),), dtype=np.float32)
@@ -2986,8 +2977,7 @@ def test_eval(self):
       self.assertAllClose(expected_loss_mean, loss_mean_value_op.eval())
 
   def test_eval_metric_ops_with_head_name_for_regression(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        name='some_regression_head')
+    head = head_lib._regression_head(name='some_regression_head')
     logits = np.array(((1,), (9,)), dtype=np.float32)
     labels = np.array(((1,), (1,)), dtype=np.int64)
     features = {'x': np.array(((42,),), dtype=np.int32)}
@@ -3004,7 +2994,7 @@ def test_eval_metric_ops_with_head_name_for_regression(self):
     self.assertItemsEqual(expected_metric_keys, spec.eval_metric_ops.keys())
 
   def test_eval_with_regularization_losses(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss(
+    head = head_lib._regression_head(
         loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE)
     self.assertEqual(1, head.logits_dimension)
 
@@ -3049,7 +3039,7 @@ def test_eval_with_regularization_losses(self):
           expected_metrics, {k: value_ops[k].eval() for k in value_ops})
 
   def test_train_create_loss(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss()
+    head = head_lib._regression_head()
     logits = np.array(((45,), (41,),), dtype=np.float32)
     labels = np.array(((43,), (44,),), dtype=np.int32)
     features = {'x': np.array(((42,),), dtype=np.float32)}
@@ -3073,7 +3063,7 @@ def test_train_create_loss(self):
 
   def test_train_create_loss_loss_reduction(self):
     """Tests create_loss with loss_reduction."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
+    head = head_lib._regression_head(
         loss_reduction=losses.Reduction.SUM_BY_NONZERO_WEIGHTS)
     logits = np.array(((45,), (41,),), dtype=np.float32)
     labels = np.array(((43,), (44,),), dtype=np.int32)
@@ -3098,7 +3088,7 @@ def test_train_create_loss_loss_reduction(self):
 
   def test_train_labels_none(self):
     """Tests that error is raised when labels is None."""
-    head = head_lib._regression_head_with_mean_squared_error_loss()
+    head = head_lib._regression_head()
     def _no_op_train_fn(loss):
       del loss
       return control_flow_ops.no_op()
@@ -3113,7 +3103,7 @@ def _no_op_train_fn(loss):
           train_op_fn=_no_op_train_fn)
 
   def test_train(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss()
+    head = head_lib._regression_head()
     self.assertEqual(1, head.logits_dimension)
 
     # Create estimator spec.
@@ -3163,7 +3153,7 @@ def _train_op_fn(loss):
       }, summary_str)
 
   def test_train_with_optimizer(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss()
+    head = head_lib._regression_head()
     self.assertEqual(1, head.logits_dimension)
 
     # Create estimator spec.
@@ -3197,8 +3187,7 @@ def minimize(self, loss, global_step):
       self.assertEqual(expected_train_result, train_result)
 
   def test_train_summaries_with_head_name(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        name='some_regression_head')
+    head = head_lib._regression_head(name='some_regression_head')
     self.assertEqual(1, head.logits_dimension)
 
     # Create estimator spec.
@@ -3237,7 +3226,7 @@ def _train_op_fn(loss):
           summary_str)
 
   def test_train_with_regularization_losses(self):
-    head = head_lib._regression_head_with_mean_squared_error_loss(
+    head = head_lib._regression_head(
         loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE)
     self.assertEqual(1, head.logits_dimension)
 
@@ -3285,8 +3274,7 @@ def _train_op_fn(loss):
 
   def test_weighted_multi_example_eval(self):
     """1d label, 3 examples, 1 batch."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        weight_column='label_weights')
+    head = head_lib._regression_head(weight_column='label_weights')
     self.assertEqual(1, head.logits_dimension)
 
     # Create estimator spec.
@@ -3330,7 +3318,7 @@ def test_weighted_multi_example_eval(self):
 
   def test_weight_with_numeric_column(self):
     """1d label, 3 examples, 1 batch."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
+    head = head_lib._regression_head(
         weight_column=feature_column_lib.numeric_column(
             'label_weights', normalizer_fn=lambda x: x + 1.))
 
@@ -3356,8 +3344,7 @@ def test_weight_with_numeric_column(self):
 
   def test_weighted_multi_example_train(self):
     """1d label, 3 examples, 1 batch."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        weight_column='label_weights')
+    head = head_lib._regression_head(weight_column='label_weights')
     self.assertEqual(1, head.logits_dimension)
 
     # Create estimator spec.
@@ -3408,8 +3395,7 @@ def _train_op_fn(loss):
 
   def test_train_one_dim_create_loss(self):
     """Tests create_loss with 1D labels and weights (shape [batch_size])."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        weight_column='label_weights')
+    head = head_lib._regression_head(weight_column='label_weights')
     logits = np.array(((45,), (41,), (44,)), dtype=np.float32)
     x_feature_rank_1 = np.array((42., 43., 44.,), dtype=np.float32)
     weight_rank_1 = np.array((1., .1, 1.5,), dtype=np.float64)
@@ -3435,8 +3421,7 @@ def test_train_one_dim_create_loss(self):
 
   def test_train_one_dim(self):
     """Tests train with 1D labels and weights (shape [batch_size])."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        weight_column='label_weights')
+    head = head_lib._regression_head(weight_column='label_weights')
     self.assertEqual(1, head.logits_dimension)
 
     # Create estimator spec.
@@ -3493,7 +3478,7 @@ def _train_op_fn(loss):
 
   def test_weighted_multi_value_eval_create_loss(self):
     """3d label, 1 example, 1 batch."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
+    head = head_lib._regression_head(
         weight_column='label_weights', label_dimension=3)
     logits = np.array(((45., 41., 44.),))
     labels = np.array(((35., 42., 45.),))
@@ -3515,7 +3500,7 @@ def test_weighted_multi_value_eval_create_loss(self):
 
   def test_weighted_multi_value_eval(self):
     """3d label, 1 example, 1 batch."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
+    head = head_lib._regression_head(
         weight_column='label_weights', label_dimension=3)
     self.assertEqual(3, head.logits_dimension)
 
@@ -3562,7 +3547,7 @@ def test_weighted_multi_value_eval(self):
 
   def test_weighted_multi_value_train_create_loss(self):
     """3d label, 1 example, 1 batch."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
+    head = head_lib._regression_head(
         weight_column='label_weights', label_dimension=3)
     logits = np.array(((45., 41., 44.),))
     labels = np.array(((35., 42., 45.),))
@@ -3584,7 +3569,7 @@ def test_weighted_multi_value_train_create_loss(self):
 
   def test_weighted_multi_value_train(self):
     """3d label, 1 example, 1 batch."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
+    head = head_lib._regression_head(
         weight_column='label_weights', label_dimension=3)
     self.assertEqual(3, head.logits_dimension)
 
@@ -3639,8 +3624,7 @@ def _train_op_fn(loss):
 
   def test_weighted_multi_batch_eval(self):
     """1d label, 1 example, 3 batches."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        weight_column='label_weights')
+    head = head_lib._regression_head(weight_column='label_weights')
     self.assertEqual(1, head.logits_dimension)
 
     # Create estimator spec.
@@ -3705,8 +3689,7 @@ def test_weighted_multi_batch_eval(self):
 
   def test_weighted_multi_batch_train(self):
     """1d label, 1 example, 3 batches."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
-        weight_column='label_weights')
+    head = head_lib._regression_head(weight_column='label_weights')
     self.assertEqual(1, head.logits_dimension)
 
     # Create estimator spec.
@@ -3755,7 +3738,7 @@ def test_weighted_multi_batch_train(self):
   def test_multi_dim_weighted_train_create_loss(self):
     """Logits, labels of shape [2, 2, 3], weight shape [2, 2]."""
     label_dimension = 3
-    head = head_lib._regression_head_with_mean_squared_error_loss(
+    head = head_lib._regression_head(
         weight_column='label_weights', label_dimension=label_dimension)
     logits = np.array([[[00., 01., 02.], [10., 11., 12.]],
                        [[20., 21., 22.], [30., 31., 32.]]])
@@ -3785,7 +3768,7 @@ def test_multi_dim_weighted_train_create_loss(self):
 
   def test_multi_dim_weighted_train(self):
     """Logits, labels of shape [2, 2, 3], weight shape [2, 2]."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
+    head = head_lib._regression_head(
         weight_column='label_weights', label_dimension=3)
     logits = np.array([[[00., 01., 02.], [10., 11., 12.]],
                        [[20., 21., 22.], [30., 31., 32.]]])
@@ -3816,7 +3799,7 @@ def _train_op_fn(loss):
 
   def test_multi_dim_train_weights_wrong_inner_dim(self):
     """Logits, labels of shape [2, 2, 3], weight shape [2, 1]."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
+    head = head_lib._regression_head(
         weight_column='label_weights', label_dimension=3)
     logits = np.array([[[00., 01., 02.], [10., 11., 12.]],
                        [[20., 21., 22.], [30., 31., 32.]]])
@@ -3844,7 +3827,7 @@ def _no_op_train_fn(loss):
 
   def test_multi_dim_train_weights_wrong_outer_dim(self):
     """Logits, labels of shape [2, 2, 3], weight shape [2, 2, 2]."""
-    head = head_lib._regression_head_with_mean_squared_error_loss(
+    head = head_lib._regression_head(
         weight_column='label_weights', label_dimension=3)
     logits = np.array([[[00., 01., 02.], [10., 11., 12.]],
                        [[20., 21., 22.], [30., 31., 32.]]])
diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py
index e7ec4179917a88..81657f0c016445 100644
--- a/tensorflow/python/estimator/canned/linear.py
+++ b/tensorflow/python/estimator/canned/linear.py
@@ -415,7 +415,7 @@ def __init__(self,
       loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how
         to reduce training loss over batch. Defaults to `SUM`.
     """
-    head = head_lib._regression_head_with_mean_squared_error_loss(  # pylint: disable=protected-access
+    head = head_lib._regression_head(  # pylint: disable=protected-access
         label_dimension=label_dimension, weight_column=weight_column,
         loss_reduction=loss_reduction)
 

From 1cc225858f9d7fb4d8772a7f0e962b71f780ad54 Mon Sep 17 00:00:00 2001
From: Peter Hawkins 
Date: Wed, 2 May 2018 11:12:58 -0700
Subject: [PATCH 0281/1691] Automated g4 rollback of changelist 194981511

PiperOrigin-RevId: 195120627
---
 tensorflow/core/common_runtime/device.h               | 11 -----------
 tensorflow/core/common_runtime/device_mgr.cc          |  3 ---
 .../process_function_library_runtime.cc               |  3 +--
 3 files changed, 1 insertion(+), 16 deletions(-)

diff --git a/tensorflow/core/common_runtime/device.h b/tensorflow/core/common_runtime/device.h
index b537666492ce29..5918cd9bbf35a7 100644
--- a/tensorflow/core/common_runtime/device.h
+++ b/tensorflow/core/common_runtime/device.h
@@ -51,8 +51,6 @@ limitations under the License.
 
 namespace tensorflow {
 
-class DeviceMgr;
-
 class Device : public DeviceBase {
  public:
   Device(Env* env, const DeviceAttributes& device_attributes);
@@ -135,10 +133,6 @@ class Device : public DeviceBase {
   // Returns the resource manager associated w/ this device.
   virtual ResourceMgr* resource_manager() { return rmgr_; }
 
-  // Returns the device manager that owns this device, or nullptr if this Device
-  // is not owned by a device manager.
-  DeviceMgr* device_mgr() const { return device_mgr_; }
-
   // Summarizes the status of this Device, for debugging.
   string DebugString() const { return ProtoDebugString(device_attributes_); }
 
@@ -164,11 +158,6 @@ class Device : public DeviceBase {
   }
 
  private:
-  friend class DeviceMgr;
-
-  // Pointer to the device manager that owns this device. Not owned.
-  DeviceMgr* device_mgr_ = nullptr;
-
   const DeviceAttributes device_attributes_;
   DeviceNameUtils::ParsedName parsed_name_;
 
diff --git a/tensorflow/core/common_runtime/device_mgr.cc b/tensorflow/core/common_runtime/device_mgr.cc
index 470abc14312928..a77601ba79bf29 100644
--- a/tensorflow/core/common_runtime/device_mgr.cc
+++ b/tensorflow/core/common_runtime/device_mgr.cc
@@ -27,9 +27,6 @@ namespace tensorflow {
 DeviceMgr::DeviceMgr(const std::vector& devices)
     : name_backing_store_(128) {
   for (Device* d : devices) {
-    CHECK(d->device_mgr_ == nullptr);
-    d->device_mgr_ = this;
-
     devices_.push_back(d);
 
     // Register under the (1) full name and (2) canonical name.
diff --git a/tensorflow/core/common_runtime/process_function_library_runtime.cc b/tensorflow/core/common_runtime/process_function_library_runtime.cc
index 668ce877493a06..e61ed8c4794883 100644
--- a/tensorflow/core/common_runtime/process_function_library_runtime.cc
+++ b/tensorflow/core/common_runtime/process_function_library_runtime.cc
@@ -144,8 +144,7 @@ Status ProcessFunctionLibraryRuntime::GetDeviceContext(
   }
   Device* device = flr->device();
   string device_type = device->parsed_name().type;
-  if (device_type == "CPU" || device_type == "TPU_SYSTEM" ||
-      device_type == "TPU") {
+  if (device_type == "CPU" || device_type == "TPU_SYSTEM") {
     // "TPU_SYSTEM" indicates that `device` is a CPU.
     return Status::OK();
   }

From 156483f1dc6b2e706482976f09f866d226a4dfee Mon Sep 17 00:00:00 2001
From: Benjamin Kramer 
Date: Wed, 2 May 2018 11:17:47 -0700
Subject: [PATCH 0282/1691] [XLA:GPU] Unroll unfused elementwise op kernels.

So far we only unrolled loop fusions, elementwise ops is a logical extension.
We don't spend a lot of time in unfused elementwise ops in benchmarks, so this
is only worth a small speedup on V100.

PiperOrigin-RevId: 195121530
---
 .../xla/service/gpu/ir_emitter_unnested.cc    | 46 +++++++++++++------
 .../compiler/xla/tests/hlo_test_base.cc       |  5 +-
 2 files changed, 35 insertions(+), 16 deletions(-)

diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
index 26e497762f2a6f..9f37235d322968 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
+++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
@@ -257,8 +257,36 @@ llvm::Function* IrEmitterUnnested::BuildKernelPrototype(
   return kernel;
 }
 
+namespace {
+// Computes the maximum valid unroll factor for a given instruction.
+int ComputeMaxUnrollFactor(const HloInstruction* hlo) {
+  int max_unroll_factor = hlo->GetModule()
+                              ->config()
+                              .debug_options()
+                              .xla_gpu_max_kernel_unroll_factor();
+
+  // Find the largest possible power of two to unroll by.
+  // TODO(kramerb): Make this smarter.
+  int64 num_elements = ShapeUtil::ElementsIn(hlo->shape());
+  for (int i = max_unroll_factor; i > 1; i /= 2) {
+    if (num_elements % i == 0) {
+      return i;
+    }
+  }
+
+  // Cannot unroll.
+  return 1;
+}
+}  // namespace
+
 Status IrEmitterUnnested::DefaultAction(HloInstruction* hlo) {
-  thunk_sequence_->emplace_back(BuildKernelThunk(hlo));
+  int unroll_factor = 1;
+  // Unfused elementwise operations are usually memory bound, unroll them.
+  if (hlo->IsElementwise()) {
+    unroll_factor = ComputeMaxUnrollFactor(hlo);
+  }
+
+  thunk_sequence_->emplace_back(BuildKernelThunk(hlo, unroll_factor));
   return IrEmitter::DefaultAction(hlo);
 }
 
@@ -537,23 +565,11 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) {
     return Status::OK();
   }
 
-  int max_unroll_factor = fusion->GetModule()
-                              ->config()
-                              .debug_options()
-                              .xla_gpu_max_kernel_unroll_factor();
-
-  // Find the largest possible power of two to unroll by.
-  // TODO(kramerb): Make this smarter.
   int unroll_factor = 1;
+  // TODO(kramerb): Unrolling multi-output loop fusions too.
   if (!fusion->IsMultiOutputFusion()) {
     CHECK(fusion->fusion_kind() == HloInstruction::FusionKind::kLoop);
-    int64 num_elements = ShapeUtil::ElementsIn(fusion->shape());
-    for (int i = max_unroll_factor; i > 1; i /= 2) {
-      if (num_elements % i == 0) {
-        unroll_factor = i;
-        break;
-      }
-    }
+    unroll_factor = ComputeMaxUnrollFactor(fusion);
   }
 
   thunk_sequence_->emplace_back(BuildKernelThunk(fusion, unroll_factor));
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc
index 8b64f2e6315bc4..12598579c7032e 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.cc
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc
@@ -95,7 +95,10 @@ HloTestBase::HloTestBase(se::Platform* test_platform,
 /* static */
 std::unique_ptr HloTestBase::CreateNewModule(const string& name) {
   HloModuleConfig config;
-  config.set_debug_options(GetDebugOptionsForTest());
+  auto debug_options = HloTestBase::GetDebugOptionsForTest();
+  debug_options.set_xla_gpu_max_kernel_unroll_factor(1);
+  config.set_debug_options(debug_options);
+
   return MakeUnique(name, VersionedComputationHandle(), config);
 }
 

From df9bb02c647e395dbb2da393f7de085320e7c5c9 Mon Sep 17 00:00:00 2001
From: gracehoney <31743510+aaroey@users.noreply.github.com>
Date: Wed, 2 May 2018 11:21:02 -0700
Subject: [PATCH 0283/1691] Fix formatting and linter issues

---
 tensorflow/contrib/tensorrt/BUILD             |  5 ++-
 .../contrib/tensorrt/convert/convert_graph.cc | 20 +++------
 .../contrib/tensorrt/convert/convert_nodes.cc | 20 ++++-----
 .../tensorrt/convert/trt_optimization_pass.cc |  5 +--
 .../tensorrt/convert/trt_optimization_pass.h  | 15 +++++--
 .../contrib/tensorrt/kernels/trt_engine_op.cc | 14 +++---
 .../contrib/tensorrt/kernels/trt_engine_op.h  |  8 ++--
 .../tensorrt/resources/trt_allocator.cc       |  3 ++
 .../tensorrt/resources/trt_allocator.h        | 23 +++++-----
 .../tensorrt/resources/trt_resources.h        | 45 ++++++++++---------
 .../contrib/tensorrt/segment/segment.cc       | 31 +++++++------
 tensorflow/contrib/tensorrt/segment/segment.h |  2 +-
 12 files changed, 98 insertions(+), 93 deletions(-)

diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD
index 1792fa310a6bf6..675f0b1fd6ede5 100644
--- a/tensorflow/contrib/tensorrt/BUILD
+++ b/tensorflow/contrib/tensorrt/BUILD
@@ -88,7 +88,6 @@ cc_library(
         ":trt_logging",
         ":trt_resources",
         "//tensorflow/core:gpu_headers_lib",
-        "//tensorflow/core:gpu_runtime",
         "//tensorflow/core:lib_proto_parsing",
         "//tensorflow/core:stream_executor_headers_lib",
     ] + if_tensorrt([
@@ -212,7 +211,6 @@ tf_cuda_library(
         ":trt_logging",
         "//tensorflow/core:framework_headers_lib",
         "//tensorflow/core:framework_lite",
-        "//tensorflow/core:core_cpu_lib",
         "//tensorflow/core:lib_proto_parsing",
     ] + if_tensorrt([
         "@local_config_tensorrt//:nv_infer",
@@ -236,6 +234,9 @@ tf_cuda_library(
         ":segment",
         ":trt_logging",
         ":trt_resources",
+        "//tensorflow/core/grappler/clusters:cluster",
+        "//tensorflow/core/grappler/optimizers:custom_graph_optimizer",
+        "//tensorflow/core/grappler/optimizers:custom_graph_optimizer_registry",
         "//tensorflow/core/grappler:grappler_item",
         "//tensorflow/core/grappler:utils",
         "//tensorflow/core:framework",
diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc
index 8459ad4a619eee..a8c07df4a00550 100644
--- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc
+++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc
@@ -30,13 +30,10 @@ limitations under the License.
 #include "tensorflow/core/graph/algorithm.h"
 #include "tensorflow/core/graph/graph.h"
 #include "tensorflow/core/graph/graph_constructor.h"
-#include "tensorflow/core/grappler/clusters/utils.h"
 #include "tensorflow/core/grappler/clusters/virtual_cluster.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/devices.h"
 #include "tensorflow/core/grappler/grappler_item.h"
-#include "tensorflow/core/grappler/optimizers/constant_folding.h"
-#include "tensorflow/core/grappler/optimizers/layout_optimizer.h"
 #include "tensorflow/core/grappler/optimizers/meta_optimizer.h"
 #include "tensorflow/core/grappler/utils.h"
 #include "tensorflow/core/lib/core/errors.h"
@@ -206,7 +203,7 @@ static tensorflow::Status FillSubGraphEdgeSets(ConvertGraphParams* p) {
                              subgraph_outputs_set.begin(),
                              subgraph_outputs_set.end());
   return tensorflow::Status::OK();
-};
+}
 
 tensorflow::Status GetCalibNode(ConvertGraphParams* params) {
   TF_RETURN_IF_ERROR(FillSubGraphEdgeSets(params));
@@ -345,18 +342,11 @@ tensorflow::Status ConvertGraphDefToTensorRT(
   // optimization pass
   tensorflow::grappler::GrapplerItem item;
   item.fetch = output_names;
-  tensorflow::GraphDef gdef;
 
-  // Layout optimization
-  item.graph = graph_def;
-  tensorflow::grappler::Cluster* cluster;
-
-  // virtual cluster
   tensorflow::DeviceProperties device_properties;
-
   device_properties.set_type("GPU");
   device_properties.mutable_environment()->insert({"architecture", "6"});
-  cluster =
+  tensorflow::grappler::Cluster* cluster =
       new tensorflow::grappler::VirtualCluster({{"/GPU:0", device_properties}});
 
   // single machine
@@ -366,6 +356,7 @@ tensorflow::Status ConvertGraphDefToTensorRT(
   VLOG(2) << "gpus: " << num_gpus;
   tensorflow::RewriterConfig rw_cfg;
   tensorflow::grappler::MetaOptimizer meta_opt(nullptr, rw_cfg);
+  tensorflow::GraphDef gdef;
   TF_RETURN_IF_ERROR(meta_opt.Optimize(cluster, item, &gdef));
   item.graph = gdef;
 
@@ -416,9 +407,12 @@ tensorflow::Status ConvertAfterShapes(
   for (auto s : segments) {
     total_num_nodes_in_segments += s.first.size();
   }
-  // We are creating the map here since cluster may not be available in all cases
+  // We create the map here since cluster may not be available in all cases.
   std::map name_to_device_map;
   if (cluster) {
+    // TODO(aaroey): consider using DeviceSet::FindDeviceByName(), as in a
+    // distributed environment, devices from different workers can have same
+    // short name.
     for (const auto dm : cluster->GetDeviceSet()->devices()) {
       name_to_device_map[dm->name()] = dm;
     }
diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
index ae0e861be54999..4d3710a51459e0 100644
--- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
+++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
@@ -482,7 +482,7 @@ class Converter {
     weights.SetValues(weight_store_->store_.back().data());
     return weights;
   }
-  bool isFP16() { return fp16_; };
+  bool isFP16() { return fp16_; }
   TRT_ShapedWeights get_temp_weights_like(const TRT_ShapedWeights& weights) {
     return this->get_temp_weights(weights.type_, weights.shape_);
   }
@@ -673,7 +673,7 @@ std::function LambdaFactory::unary() {
     case OP_CATEGORY::RSQRT: {
       VLOG(2) << "RSQRT GETS DONE";
       return [](Eigen::half t) -> Eigen::half {
-        return Eigen::half(1.0 / sqrt(float(t)));
+        return Eigen::half(1.0 / sqrt(static_cast(t)));
       };
     }
     case OP_CATEGORY::NEG:
@@ -2328,8 +2328,8 @@ tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) {
             << ", at node: " << node_name
             << "with output entry from shape_map: " << op_info_vec.size();
     // TODO(ben,jie): update TRT input format/dimension
-    nvinfer1::DimsCHW input_dim_psuedo_chw;
-    for (int i = 0; i < 3; i++) input_dim_psuedo_chw.d[i] = 1;
+    nvinfer1::DimsCHW input_dim_pseudo_chw;
+    for (int i = 0; i < 3; i++) input_dim_pseudo_chw.d[i] = 1;
 
     // TODO(jie): TRT 3.x only support 4 dimensional input tensor.
     //            update the code once TRT 4.0 comes out.
@@ -2343,7 +2343,7 @@ tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) {
     for (int i = 1; i < op_info.shape().dim_size(); i++) {
       VLOG(2) << "dimension: " << i
               << " , size: " << op_info.shape().dim(i).size();
-      input_dim_psuedo_chw.d[i - 1] = op_info.shape().dim(i).size();
+      input_dim_pseudo_chw.d[i - 1] = op_info.shape().dim(i).size();
     }
 
     // TODO(ben,jie): proper way to restore input tensor name?
@@ -2354,7 +2354,7 @@ tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) {
 
     input_names.push_back(input_tensor_name);
     nvinfer1::ITensor* input_tensor = converter.network()->addInput(
-        input_tensor_name.c_str(), dtype, input_dim_psuedo_chw);
+        input_tensor_name.c_str(), dtype, input_dim_pseudo_chw);
 
     if (!input_tensor)
       return tensorflow::errors::InvalidArgument(
@@ -2572,8 +2572,8 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef(
             << ", at node: " << node_name
             << " with output entry from shape_map: " << op_info_vec.size();
     // TODO(ben,jie): update TRT input format/dimension
-    nvinfer1::DimsCHW input_dim_psuedo_chw;
-    for (int i = 0; i < 3; i++) input_dim_psuedo_chw.d[i] = 1;
+    nvinfer1::DimsCHW input_dim_pseudo_chw;
+    for (int i = 0; i < 3; i++) input_dim_pseudo_chw.d[i] = 1;
 
     // TODO(jie): TRT 3.x only support 4 dimensional input tensor.
     //            update the code once TRT 4.0 comes out.
@@ -2587,7 +2587,7 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef(
     for (int i = 1; i < op_info.shape().dim_size(); i++) {
       VLOG(2) << "dimension: " << i
               << " , size: " << op_info.shape().dim(i).size();
-      input_dim_psuedo_chw.d[i - 1] = op_info.shape().dim(i).size();
+      input_dim_pseudo_chw.d[i - 1] = op_info.shape().dim(i).size();
     }
 
     // TODO(ben,jie): proper way to restore input tensor name?
@@ -2598,7 +2598,7 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef(
 
     input_names.push_back(input_tensor_name);
     nvinfer1::ITensor* input_tensor = converter.network()->addInput(
-        input_tensor_name.c_str(), dtype, input_dim_psuedo_chw);
+        input_tensor_name.c_str(), dtype, input_dim_pseudo_chw);
 
     if (!input_tensor)
       return tensorflow::errors::InvalidArgument(
diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc
index 21013fbf9eb120..8f634b1f747173 100644
--- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc
+++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc
@@ -20,7 +20,6 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/logging.h"
-#include "tensorflow/core/public/session_options.h"
 
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
@@ -64,7 +63,7 @@ tensorflow::Status TRTOptimizationPass::Init(
     }
   }
   return tensorflow::Status::OK();
-};
+}
 
 void TRTOptimizationPass::PrintDebugInfo(
     tensorflow::grappler::Cluster* cluster,
@@ -218,8 +217,6 @@ void TRTOptimizationPass::Feedback(
     const tensorflow::grappler::GrapplerItem& item,
     const GraphDef& optimized_graph, double result) {}
 
-
-
 }  // namespace convert
 }  // namespace tensorrt
 }  // namespace tensorflow
diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h
index c554a5d784000a..d8ecead23efaa5 100644
--- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h
+++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h
@@ -28,6 +28,7 @@ limitations under the License.
 namespace tensorflow {
 namespace tensorrt {
 namespace convert {
+
 class TRTOptimizationPass : public tensorflow::grappler::CustomGraphOptimizer {
  public:
   TRTOptimizationPass(const string& name = "TRTOptimizationPass")
@@ -37,17 +38,21 @@ class TRTOptimizationPass : public tensorflow::grappler::CustomGraphOptimizer {
         maximum_batch_size_(-1),
         maximum_workspace_size_(-1) {
     VLOG(1) << "Constructing " << name_;
-  };
+  }
+
   string name() const override { return name_; };
+
   tensorflow::Status Init(const tensorflow::RewriterConfig_CustomGraphOptimizer*
                               config = nullptr) override;
 
   tensorflow::Status Optimize(tensorflow::grappler::Cluster* cluster,
                               const tensorflow::grappler::GrapplerItem& item,
                               GraphDef* optimized_graph) override;
+
   void Feedback(tensorflow::grappler::Cluster* cluster,
                 const tensorflow::grappler::GrapplerItem& item,
                 const GraphDef& optimized_graph, double result) override;
+
   void PrintDebugInfo(tensorflow::grappler::Cluster* cluster,
                       const tensorflow::grappler::GrapplerItem& item);
 
@@ -58,9 +63,11 @@ class TRTOptimizationPass : public tensorflow::grappler::CustomGraphOptimizer {
   int maximum_batch_size_;
   int64_t maximum_workspace_size_;
 };
+
 }  // namespace convert
 }  // namespace tensorrt
 }  // namespace tensorflow
-#endif
-#endif
-#endif
+
+#endif  // GOOGLE_CUDA
+#endif  // GOOGLE_TENSORRT
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_CONVERT_TRT_OPTIMIZATION_PASS_H_
diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc
index f10b10edec6b13..5c5b2e3c073d5f 100644
--- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc
+++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc
@@ -38,7 +38,6 @@ TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) : OpKernel(context) {
   // register input output node name in trt_sub_graph
   OP_REQUIRES_OK(context, context->GetAttr("input_nodes", &input_nodes_));
   OP_REQUIRES_OK(context, context->GetAttr("output_nodes", &output_nodes_));
-
 }
 
 void TRTEngineOp::Compute(OpKernelContext* context) {
@@ -49,15 +48,12 @@ void TRTEngineOp::Compute(OpKernelContext* context) {
   if (!trt_execution_context_ptr_) {
     IRuntime* infer = nvinfer1::createInferRuntime(logger);
 #if NV_TENSORRT_MAJOR > 3
-    auto device=context->device();
-    auto dev_allocator=device->getAllocator(tensorflow::AllocatorAttributes())
-    // tensorflow::TfGpuId tf_gpu_id(
-    //     context->device()->tensorflow_gpu_device_info()->gpu_id);
-    // tensorflow::GPUOptions gpuoptions;
-    // auto pm = tensorflow::ProcessState::singleton();
-    // auto dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1);
+    auto device = context->device();
+    auto dev_allocator =
+        device->GetAllocator(tensorflow::AllocatorAttributes());
     if (!dev_allocator) {
-      LOG(FATAL) << "Can't find device allocator for gpu device" << tf_gpu_id;
+      LOG(FATAL) << "Can't find device allocator for gpu device "
+                 << device->name();
     }
     allocator_ = std::make_shared(dev_allocator);
     infer->setGpuAllocator(allocator_.get());
diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h
index fec4bd728b6cc2..e613a71422852e 100644
--- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h
+++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h
@@ -17,15 +17,15 @@ limitations under the License.
 #define TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_ENGINE_OP_H_
 
 #include 
-#include 
 #include 
 
-#if GOOGLE_CUDA
-#if GOOGLE_TENSORRT
-#include "cuda/include/cuda_runtime_api.h"
 #include "tensorflow/contrib/tensorrt/resources/trt_allocator.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "cuda/include/cuda_runtime_api.h"
 #include "tensorrt/include/NvInfer.h"
 
 namespace tensorflow {
diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc
index b94f8a2da7a9f1..0f0508331c1305 100644
--- a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc
+++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc
@@ -16,8 +16,10 @@ limitations under the License.
 #include "tensorflow/contrib/tensorrt/resources/trt_allocator.h"
 
 #include "tensorflow/core/platform/logging.h"
+
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
+
 #if NV_TENSORRT_MAJOR > 2
 #include "cuda/include/cuda_runtime_api.h"
 
@@ -54,6 +56,7 @@ void TRTDeviceAllocator::free(void* memory) {
 
 }  // namespace tensorrt
 }  // namespace tensorflow
+
 #endif
 #endif
 #endif
diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.h b/tensorflow/contrib/tensorrt/resources/trt_allocator.h
index dd4f8c7943cdbe..a0c2540a7698bc 100644
--- a/tensorflow/contrib/tensorrt/resources/trt_allocator.h
+++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.h
@@ -16,35 +16,34 @@ limitations under the License.
 #ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_
 #define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_
 
-#include 
-#include 
-#include 
-#include 
-#include 
 
 #include "tensorflow/contrib/tensorrt/log/trt_logger.h"
 #include "tensorflow/core/framework/allocator.h"
-#include "tensorflow/core/framework/resource_mgr.h"
+
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
 #include "tensorrt/include/NvInfer.h"
+
 #if NV_TENSORRT_MAJOR == 3
-// define interface here temporarily until TRT 4.0 is released
+// Define interface here temporarily until TRT 4.0 is released
 namespace nvinfer1 {
 class IGpuAllocator {
+ public:
   virtual void* allocate(uint64_t size, uint64_t alignment, uint32_t flags) = 0;
   virtual void free(void* memory) = 0;
 };
 }  // namespace nvinfer1
 #endif
+
 namespace tensorflow {
 namespace tensorrt {
+
 class TRTCudaAllocator : public nvinfer1::IGpuAllocator {
   // Allocator implementation that is using cuda allocator instead of device
   // allocator in case we can't get device allocator from TF.
  public:
   TRTCudaAllocator() {}
-  virtual ~TRTCudaAllocator(){};
+  virtual ~TRTCudaAllocator() {}
   void* allocate(uint64_t size, uint64_t alignment, uint32_t flags) override;
   void free(void* memory) override;
 };
@@ -53,7 +52,7 @@ class TRTDeviceAllocator : public nvinfer1::IGpuAllocator {
   // Allocator implementation wrapping TF device allocators.
  public:
   TRTDeviceAllocator(tensorflow::Allocator* allocator);
-  virtual ~TRTDeviceAllocator(){};
+  virtual ~TRTDeviceAllocator() {}
   void* allocate(uint64_t size, uint64_t alignment, uint32_t flags) override;
   void free(void* memory) override;
 
@@ -64,6 +63,6 @@ class TRTDeviceAllocator : public nvinfer1::IGpuAllocator {
 }  // namespace tensorrt
 }  // namespace tensorflow
 
-#endif
-#endif
-#endif
+#endif  // GOOGLE_TENSORRT
+#endif  // GOOGLE_CUDA
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_
diff --git a/tensorflow/contrib/tensorrt/resources/trt_resources.h b/tensorflow/contrib/tensorrt/resources/trt_resources.h
index 166ca9c3deb0cd..e3469124acd4b9 100644
--- a/tensorflow/contrib/tensorrt/resources/trt_resources.h
+++ b/tensorflow/contrib/tensorrt/resources/trt_resources.h
@@ -13,22 +13,24 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRTRESOURCES_H_
-#define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRTRESOURCES_H_
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_RESOURCES_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_RESOURCES_H_
 
 #include 
 #include 
 #include 
 #include 
 #include 
+
 #include "tensorflow/contrib/tensorrt/log/trt_logger.h"
+#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h"
+#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h"
 #include "tensorflow/core/framework/resource_mgr.h"
 
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
-#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h"
+
 #include "tensorrt/include/NvInfer.h"
-#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h"
 
 namespace tensorflow {
 namespace tensorrt {
@@ -41,6 +43,11 @@ class TRTCalibrationResource : public tensorflow::ResourceBase {
         engine_(nullptr),
         logger_(nullptr),
         thr_(nullptr) {}
+
+  ~TRTCalibrationResource() {
+    VLOG(0) << "Destroying Calibration Resource " << std::endl << DebugString();
+  }
+
   string DebugString() override {
     std::stringstream oss;
     oss << " Calibrator = " << std::hex << calibrator_ << std::dec << std::endl
@@ -48,13 +55,12 @@ class TRTCalibrationResource : public tensorflow::ResourceBase {
         << " Network    = " << std::hex << network_ << std::dec << std::endl
         << " Engine     = " << std::hex << engine_ << std::dec << std::endl
         << " Logger     = " << std::hex << logger_ << std::dec << std::endl
-        << " Allocator  = " << std::hex << allocator_.get()<< std::dec << std::endl
+        << " Allocator  = " << std::hex << allocator_.get() << std::dec
+        << std::endl
         << " Thread     = " << std::hex << thr_ << std::dec << std::endl;
     return oss.str();
   }
-  ~TRTCalibrationResource() {
-    VLOG(0) << "Destroying Calibration Resource " << std::endl << DebugString();
-  }
+
   TRTInt8Calibrator* calibrator_;
   nvinfer1::IBuilder* builder_;
   nvinfer1::INetworkDefinition* network_;
@@ -68,31 +74,28 @@ class TRTCalibrationResource : public tensorflow::ResourceBase {
 class TRTWeightStore : public tensorflow::ResourceBase {
  public:
   TRTWeightStore() {}
-  std::list> store_;
+
+  virtual ~TRTWeightStore() { VLOG(1) << "Destroying store" << DebugString(); }
+
   string DebugString() override {
     std::stringstream oss;
-    size_t lenBytes = 0;
+    size_t len_bytes = 0;
     for (const auto& v : store_) {
-      lenBytes += v.size() * sizeof(uint8_t);
+      len_bytes += v.size() * sizeof(uint8_t);
     }
     oss << " Number of entries     = " << store_.size() << std::endl
         << " Total number of bytes = "
-        << store_.size() * sizeof(std::vector) + lenBytes << std::endl;
+        << store_.size() * sizeof(std::vector) + len_bytes
+        << std::endl;
     return oss.str();
   }
-  virtual ~TRTWeightStore() { VLOG(1) << "Destroying store" << DebugString(); }
-};
 
-class TRTEngineResource : public tensorflow::ResourceBase {
- public:
-  TRTEngineResource() : runtime_(nullptr), ctx_(nullptr){};
-  string DebugString() override { return string(""); }
-  nvinfer1::IRuntime* runtime_;
-  nvinfer1::IExecutionContext* ctx_;
+  std::list> store_;
 };
 
 }  // namespace tensorrt
 }  // namespace tensorflow
-#endif  // TENSORFLOW_CONTRIB_TENSORRT_RESOURCEMGR_TRTRESOURCES_H_
+
 #endif
 #endif
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_RESOURCES_H_
diff --git a/tensorflow/contrib/tensorrt/segment/segment.cc b/tensorflow/contrib/tensorrt/segment/segment.cc
index 4901e30a875585..cc42913ecadc3e 100644
--- a/tensorflow/contrib/tensorrt/segment/segment.cc
+++ b/tensorflow/contrib/tensorrt/segment/segment.cc
@@ -47,14 +47,15 @@ class SimpleEdge {
         src_port_(src_port),
         dst_(dst),
         dst_port_(dst_port),
-        control_(is_control){};
+        control_(is_control) {}
+  ~SimpleEdge() {}
+
   SimpleNode* src() const { return src_; }
   SimpleNode* dst() const { return dst_; }
   int src_output() const { return src_port_; }
   int dst_input() const { return dst_port_; }
   int id() const { return id_; }
   bool IsControlEdge() const { return control_; }
-  ~SimpleEdge() {}
 
  private:
   int id_;
@@ -64,11 +65,13 @@ class SimpleEdge {
   int dst_port_;
   bool control_;
 };
+
 class SimpleNode {
  public:
   SimpleNode(const tensorflow::Node* node, const int id);
-  const std::vector& in_edges() const { return in_edges_; };
-  const std::vector& out_edges() const { return out_edges_; };
+
+  const std::vector& in_edges() const { return in_edges_; }
+  const std::vector& out_edges() const { return out_edges_; }
   std::vector in_nodes() const {
     std::vector res;
     res.reserve(in_edges_.size());
@@ -92,15 +95,18 @@ class SimpleNode {
 
 class SimpleGraph {
  public:
-  SimpleGraph(const tensorflow::Graph* g);
+  explicit SimpleGraph(const tensorflow::Graph* g);
+  ~SimpleGraph();
+
   void AddControlEdge(SimpleNode* src, SimpleNode* dst);
   void AddEdge(SimpleNode* src, int out_port, SimpleNode* dst, int in_port);
   void RemoveEdge(const SimpleEdge*);
   SimpleNode* FindNodeId(int node_id) {
-    if (node_id < 0 || node_id > (int)nodes_.size()) return nullptr;
+    if (node_id < 0 || node_id > static_cast(nodes_.size())) {
+      return nullptr;
+    }
     return nodes_[node_id];
   }
-  ~SimpleGraph();
   int num_node_ids() const { return nodes_.size(); }
   const SimpleNode* source_node() const {
     return nodes_[tensorflow::Graph::kSourceId];
@@ -163,7 +169,7 @@ SimpleGraph::SimpleGraph(const tensorflow::Graph* g) : g_(g) {
 void SimpleGraph::AddEdge(SimpleNode* src, int out_port, SimpleNode* dst,
                           int in_port) {
   int i = edges_.size();
-  if (free_edge_ids_.size()) {
+  if (!free_edge_ids_.empty()) {
     auto it = free_edge_ids_.begin();
     i = *it;
     free_edge_ids_.erase(it);
@@ -275,7 +281,7 @@ bool CanContractEdge(const SimpleEdge* edge,
 }
 }  // namespace
 
-void ContractEdge(SimpleEdge* edge, std::unique_ptr& graph,
+void ContractEdge(SimpleEdge* edge, SimpleGraph* graph,
                   std::vector* remove_edges) {
   // Transfer all inputs and outputs of 'dst' to 'src' except edges
   // connecting the two.
@@ -352,7 +358,6 @@ tensorflow::Status SegmentGraph(
     tensorflow::Graph* tf_graph,
     const std::function& candidate_fn,
     const SegmentOptions& options, SegmentNodesVector* segments) {
-
   auto graph = std::unique_ptr(new SimpleGraph(tf_graph));
   // Use a union-find to collect the nodes that belong to the same
   // segment. A node value of nullptr indicates that the node is not a candidate
@@ -440,7 +445,7 @@ tensorflow::Status SegmentGraph(
         // don't visit them again.
         SimpleEdge* e = const_cast(contract_edge);
         std::vector remove_edges;
-        ContractEdge(e, graph, &remove_edges);
+        ContractEdge(e, graph.get(), &remove_edges);
 
         for (const SimpleEdge* r : remove_edges) {
           contract_edges.erase(r);
@@ -466,7 +471,7 @@ tensorflow::Status SegmentGraph(
       if (tf_node->has_assigned_device_name()) {
         device_maps[u.ParentValue()->name()].insert(
             tf_node->assigned_device_name());
-      } else if (tf_node->requested_device().size() > 0) {
+      } else if (!tf_node->requested_device().empty()) {
         device_maps[u.ParentValue()->name()].insert(
             tf_node->requested_device());
       } else {
@@ -497,7 +502,7 @@ tensorflow::Status SegmentGraph(
     }
     // TODO(sami): Make segmenter placement aware once trtscopes are in place
     const auto& dev_itr = device_maps.find(itr.first);
-    if (dev_itr == device_maps.end() || dev_itr->second.size() == 0) {
+    if (dev_itr == device_maps.end() || dev_itr->second.empty()) {
       VLOG(1) << "No device assigned to segment " << segments->size();
       segments->emplace_back(std::make_pair(segment_node_names, string()));
     } else if (dev_itr->second.size() > 1) {
diff --git a/tensorflow/contrib/tensorrt/segment/segment.h b/tensorflow/contrib/tensorrt/segment/segment.h
index c5aca4bf048328..1568dd915344e6 100644
--- a/tensorflow/contrib/tensorrt/segment/segment.h
+++ b/tensorflow/contrib/tensorrt/segment/segment.h
@@ -63,7 +63,7 @@ tensorflow::Status SegmentGraph(
 // all the NodeDefs in that subgraph.
 // @return the status.
 tensorflow::Status SegmentGraph(
-    tensorflow::Graph* graph,
+    tensorflow::Graph* tf_graph,
     const std::function& candidate_fn,
     const SegmentOptions& options, SegmentNodesVector* segments);
 

From fb820662625d30e7e137580dae142a3eaf933335 Mon Sep 17 00:00:00 2001
From: Anna R 
Date: Wed, 2 May 2018 11:38:44 -0700
Subject: [PATCH 0284/1691] Copy module list before iterating over it. Also,
 import python module for clarity

---
 tensorflow/tools/api/generator/create_python_api.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py
index c06a39bfbdf06c..d1e7f23fbcacc4 100644
--- a/tensorflow/tools/api/generator/create_python_api.py
+++ b/tensorflow/tools/api/generator/create_python_api.py
@@ -23,6 +23,7 @@
 import os
 import sys
 
+from tensorflow import python  # pylint: disable=unused-import
 from tensorflow.python.util import tf_decorator
 
 
@@ -158,7 +159,8 @@ def get_api_init_text():
 
   # Traverse over everything imported above. Specifically,
   # we want to traverse over TensorFlow Python modules.
-  for module in sys.modules.values():
+  module_list = list(sys.modules.values())
+  for module in module_list:
     # Only look at tensorflow modules.
     if (not module or not hasattr(module, '__name__') or
         'tensorflow.' not in module.__name__):

From 9b6cba1d739e36ec2da59a593afb09bf17307650 Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Wed, 2 May 2018 11:40:09 -0700
Subject: [PATCH 0285/1691] Internal-only change.

PiperOrigin-RevId: 195125476
---
 tensorflow/python/kernel_tests/linalg/BUILD | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/kernel_tests/linalg/BUILD b/tensorflow/python/kernel_tests/linalg/BUILD
index 6573cb9a1a4bdd..052f11f92e90b0 100644
--- a/tensorflow/python/kernel_tests/linalg/BUILD
+++ b/tensorflow/python/kernel_tests/linalg/BUILD
@@ -62,7 +62,10 @@ cuda_py_test(
         "//tensorflow/python:platform_test",
     ],
     shard_count = 5,
-    tags = ["noasan"],  # times out b/63678675
+    tags = [
+        "noasan",  # times out, b/63678675
+        "optonly",  # times out
+    ],
 )
 
 cuda_py_test(

From 1ea4a77c6ccd2c783aedb2ccaf76f46b018c12c5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 11:45:15 -0700
Subject: [PATCH 0286/1691] Replaced calls to tensorflow::StringPiece::ToString
 with std::string conversions. That is, instances of sp.ToString() are
 replaced with std::string(sp).

This will allow tensorflow::StringPiece::ToString to be removed, which is necessary before it can be replaced with absl::string_view.

PiperOrigin-RevId: 195126422
---
 tensorflow/core/kernels/gpu_utils.h           |  3 +-
 .../kernels/merge_v2_checkpoints_op_test.cc   |  2 +-
 .../remote_fused_graph_execute_utils.cc       | 32 ++++----
 .../core/kernels/save_restore_v2_ops.cc       |  4 +-
 tensorflow/core/kernels/string_strip_op.cc    |  2 +-
 tensorflow/core/kernels/tensor_array_ops.cc   |  2 +-
 .../core/kernels/whole_file_read_ops.cc       |  2 +-
 tensorflow/core/lib/strings/numbers.h         |  4 +-
 tensorflow/core/lib/strings/scanner_test.cc   | 82 +++++++++----------
 tensorflow/core/lib/strings/str_util.cc       |  4 +-
 tensorflow/core/lib/strings/str_util.h        |  2 +-
 tensorflow/core/platform/env.cc               |  4 +-
 tensorflow/core/platform/env_test.cc          |  2 +-
 tensorflow/core/platform/file_system.cc       |  2 +-
 .../core/platform/file_system_helper.cc       |  2 +-
 tensorflow/core/platform/file_system_test.cc  |  2 +-
 tensorflow/core/util/command_line_flags.cc    |  2 +-
 tensorflow/core/util/env_var.cc               |  8 +-
 .../core/util/example_proto_fast_parsing.cc   |  2 +-
 19 files changed, 82 insertions(+), 81 deletions(-)

diff --git a/tensorflow/core/kernels/gpu_utils.h b/tensorflow/core/kernels/gpu_utils.h
index 2f64619afc1036..c7dbefa0b43353 100644
--- a/tensorflow/core/kernels/gpu_utils.h
+++ b/tensorflow/core/kernels/gpu_utils.h
@@ -123,7 +123,8 @@ class AutoTuneMap {
   string GetActionSummary(StringPiece action, const Parameters& params,
                           const Config& config) {
     return strings::Printf("autotune_map %s %s: %s -> (%s)", name_.c_str(),
-                           action.ToString().c_str(), params.ToString().c_str(),
+                           std::string(action).c_str(),
+                           params.ToString().c_str(),
                            config.ToString().c_str());
   }
 
diff --git a/tensorflow/core/kernels/merge_v2_checkpoints_op_test.cc b/tensorflow/core/kernels/merge_v2_checkpoints_op_test.cc
index 3b9e9e9b75aa23..10e468ce469d90 100644
--- a/tensorflow/core/kernels/merge_v2_checkpoints_op_test.cc
+++ b/tensorflow/core/kernels/merge_v2_checkpoints_op_test.cc
@@ -115,7 +115,7 @@ class MergeV2CheckpointsOpTest : public OpsTestBase {
     for (int i = 0; i < 2; ++i) {
       int directory_found =
           Env::Default()
-              ->IsDirectory(io::Dirname(prefixes[i]).ToString())
+              ->IsDirectory(std::string(io::Dirname(prefixes[i])))
               .code();
       if (delete_old_dirs) {
         EXPECT_EQ(error::NOT_FOUND, directory_found);
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
index cc4d9a49a00139..194a711d983288 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
@@ -47,7 +47,7 @@ std::unordered_set BuildNodeSetFromNodeNamesAndPorts(
   std::unordered_set retval;
   for (const string& node_name_and_port : node_names_and_ports) {
     const TensorId tid = ParseTensorName(node_name_and_port);
-    retval.emplace(tid.first.ToString());
+    retval.emplace(std::string(tid.first));
   }
   return retval;
 }
@@ -64,7 +64,7 @@ Node* FindMutableNodeByName(const string& name, Graph* graph) {
 const NodeDef* FindNodeDefByName(const string& input,
                                  const GraphDef& graph_def) {
   const TensorId tid = ParseTensorName(input);
-  const string name = tid.first.ToString();
+  const string name = std::string(tid.first);
   for (const NodeDef& node_def : graph_def.node()) {
     if (node_def.name() == name) {
       return &node_def;
@@ -77,7 +77,7 @@ bool IsSameNodeName(const NodeDef& node_def, const string& node_name_and_port,
                     TensorId* tid) {
   CHECK_NOTNULL(tid);
   *tid = ParseTensorName(node_name_and_port);
-  if (node_def.name() == tid->first.ToString()) {
+  if (node_def.name() == tid->first) {
     return true;
   }
   return false;
@@ -326,7 +326,7 @@ RemoteFusedGraphExecuteUtils::GetExecutorBuildRegistry() {
     const string& node_name) {
   for (const std::pair& pair : input_tensor_vector) {
     const TensorId tid = ParseTensorName(pair.first);
-    if (node_name == tid.first.ToString()) {
+    if (node_name == tid.first) {
       return true;
     }
   }
@@ -423,7 +423,7 @@ RemoteFusedGraphExecuteUtils::AddOutputTensorShapeTypeByTensorShapeMap(
   std::vector data_types;
   std::vector shapes;
   const TensorId tid = ParseTensorName(name_and_port);
-  const string node_name = tid.first.ToString();
+  const string node_name = std::string(tid.first);
   const int port = tid.second;
   const NodeDef* node_def = FindNodeDefByName(node_name, graph_def);
   CHECK_NOTNULL(node_def);
@@ -522,7 +522,7 @@ RemoteFusedGraphExecuteUtils::GetTensorShapeType(
     const TensorShapeMap& tensor_shape_map, const string& node_name) {
   if (node_name.find(':') != string::npos) {
     const TensorId tid = ParseTensorName(node_name);
-    return GetTensorShapeType(tensor_shape_map, tid.first.ToString(),
+    return GetTensorShapeType(tensor_shape_map, std::string(tid.first),
                               tid.second);
   } else {
     return GetTensorShapeType(tensor_shape_map, node_name, 0);
@@ -570,7 +570,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteGraphInputsAndOutputsFromProto(
   const TensorId tid = ParseTensorName(name);
   CHECK_EQ(tensor_shape_map->count(name), 0);
   tensor_shape_map->emplace(
-      tid.first.ToString(),
+      std::string(tid.first),
       std::make_pair(tid.second,
                      std::make_pair(tensor.dtype(), tensor.shape())));
 }
@@ -692,7 +692,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
   std::vector node_out_list;
   for (const string& input : inputs) {
     const TensorId tid = ParseTensorName(input);
-    Node* node = FindMutableNodeByName(tid.first.ToString(), graph);
+    Node* node = FindMutableNodeByName(std::string(tid.first), graph);
     CHECK_NOTNULL(node);
     node_out_list.emplace_back(node, tid.second);
   }
@@ -848,7 +848,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
 
   for (const string& subgraph_input : std::get<1>(cluster)) {
     const TensorId tid = ParseTensorName(subgraph_input);
-    const string subgraph_input_name = tid.first.ToString();
+    const string subgraph_input_name = std::string(tid.first);
     const int subgraph_input_port = tid.second;
     const NodeDef* node_def = FindNodeDefByName(subgraph_input_name, graph_def);
     CHECK_NOTNULL(node_def);
@@ -895,7 +895,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
   std::deque queue;
   for (const string& output : border_outputs) {
     const TensorId tid = ParseTensorName(output);
-    const string& output_node_name = tid.first.ToString();
+    const string& output_node_name = std::string(tid.first);
     for (const Node* node : graph.nodes()) {
       if (output_node_name == node->name()) {
         queue.push_back(node);
@@ -916,8 +916,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
       bool input_found = false;
       for (const string& input : border_inputs) {
         const TensorId tid = ParseTensorName(input);
-        if (tid.first.ToString() == src_node->name() &&
-            tid.second == src_port) {
+        if (tid.first == src_node->name() && tid.second == src_port) {
           input_found = true;
           border_input_nodes.insert(src_node);
         }
@@ -976,7 +975,7 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
       for (int j = 0; j < border_outputs.size(); ++j) {
         const string& output = border_outputs.at(j);
         const TensorId tid = ParseTensorName(output);
-        const string output_name = tid.first.ToString();
+        const string output_name = std::string(tid.first);
         Node* src_node = edge->src();
         if (src_node != nullptr && src_node->name() == output_name &&
             edge->src_output() == tid.second) {
@@ -996,11 +995,12 @@ RemoteFusedGraphExecuteUtils::BuildRemoteFusedGraphExecuteOpNode(
   // RemoteFusedGraphExecuteOpNode
   for (const string& output : outputs) {
     const TensorId output_tid = ParseTensorName(output);
-    const string output_name = output_tid.first.ToString();
+    const string output_name = std::string(output_tid.first);
     for (size_t i = 0; i < border_outputs.size(); ++i) {
       const TensorId subgraph_output_tid =
           ParseTensorName(border_outputs.at(i));
-      const string& subgraph_output_name = subgraph_output_tid.first.ToString();
+      const string& subgraph_output_name =
+          std::string(subgraph_output_tid.first);
       if (output_name == subgraph_output_name) {
         LOG(INFO) << "As graph output and subgraph output are same, "
                   << "the graph output node is replaced by identity node";
@@ -1435,7 +1435,7 @@ RemoteFusedGraphExecuteUtils::BuildNodeMapFromOpsDefinitions(
     GraphDef* graph_def) {
   const TensorId tid = ParseTensorName(input);
   CHECK_EQ(0, tid.second);
-  const string node_name = tid.first.ToString();
+  const string node_name = std::string(tid.first);
   for (NodeDef& node : *graph_def->mutable_node()) {
     if (node.name() != node_name) {
       continue;
diff --git a/tensorflow/core/kernels/save_restore_v2_ops.cc b/tensorflow/core/kernels/save_restore_v2_ops.cc
index 3acf290ea20992..ab4de6c815ceb0 100644
--- a/tensorflow/core/kernels/save_restore_v2_ops.cc
+++ b/tensorflow/core/kernels/save_restore_v2_ops.cc
@@ -220,9 +220,9 @@ class MergeV2Checkpoints : public OpKernel {
         context, tensorflow::MergeBundles(env, input_prefixes, merged_prefix));
 
     if (delete_old_dirs_) {
-      const string& merged_dir = io::Dirname(merged_prefix).ToString();
+      const string& merged_dir = std::string(io::Dirname(merged_prefix));
       for (const string& input_prefix : input_prefixes) {
-        const string& dirname = io::Dirname(input_prefix).ToString();
+        const string& dirname = std::string(io::Dirname(input_prefix));
         if (dirname == merged_dir) continue;
         Status status = env->DeleteDir(dirname);
         // For sharded save, only the first delete will go through and all
diff --git a/tensorflow/core/kernels/string_strip_op.cc b/tensorflow/core/kernels/string_strip_op.cc
index ae700f42942da0..2aeafa28c441fb 100644
--- a/tensorflow/core/kernels/string_strip_op.cc
+++ b/tensorflow/core/kernels/string_strip_op.cc
@@ -43,7 +43,7 @@ class StringStripOp : public OpKernel {
     for (int64 i = 0; i < input.size(); ++i) {
       StringPiece entry(input(i));
       str_util::RemoveWhitespaceContext(&entry);
-      output(i) = entry.ToString();
+      output(i) = std::string(entry);
     }
   }
 };
diff --git a/tensorflow/core/kernels/tensor_array_ops.cc b/tensorflow/core/kernels/tensor_array_ops.cc
index 7ec26d95e6886d..ef9748b1aad129 100644
--- a/tensorflow/core/kernels/tensor_array_ops.cc
+++ b/tensorflow/core/kernels/tensor_array_ops.cc
@@ -293,7 +293,7 @@ class TensorArrayGradOp : public TensorArrayCreationOp {
                                        resource.name());
       }
       tensor_array_name =
-          StringPiece(resource.name()).substr(container.size()).ToString();
+          std::string(StringPiece(resource.name()).substr(container.size()));
     }
 
     auto output_handle = tensor_array_output_handle->flat();
diff --git a/tensorflow/core/kernels/whole_file_read_ops.cc b/tensorflow/core/kernels/whole_file_read_ops.cc
index 17a39ce29b4ea8..ed2bf3e8e2fb0b 100644
--- a/tensorflow/core/kernels/whole_file_read_ops.cc
+++ b/tensorflow/core/kernels/whole_file_read_ops.cc
@@ -134,7 +134,7 @@ class WriteFileOp : public OpKernel {
                     "Contents tensor must be scalar, but had shape: ",
                     contents_input->shape().DebugString()));
     const string& filename = filename_input->scalar()();
-    const string dir = io::Dirname(filename).ToString();
+    const string dir = std::string(io::Dirname(filename));
     if (!context->env()->FileExists(dir).ok()) {
       OP_REQUIRES_OK(context, context->env()->RecursivelyCreateDir(dir));
     }
diff --git a/tensorflow/core/lib/strings/numbers.h b/tensorflow/core/lib/strings/numbers.h
index e9add428492f3e..9cb56415cb6044 100644
--- a/tensorflow/core/lib/strings/numbers.h
+++ b/tensorflow/core/lib/strings/numbers.h
@@ -140,11 +140,11 @@ inline bool ProtoParseNumeric(StringPiece s, uint64* value) {
 }
 
 inline bool ProtoParseNumeric(StringPiece s, float* value) {
-  return safe_strtof(s.ToString().c_str(), value);
+  return safe_strtof(std::string(s).c_str(), value);
 }
 
 inline bool ProtoParseNumeric(StringPiece s, double* value) {
-  return safe_strtod(s.ToString().c_str(), value);
+  return safe_strtod(std::string(s).c_str(), value);
 }
 
 // Convert strings to number of type T.
diff --git a/tensorflow/core/lib/strings/scanner_test.cc b/tensorflow/core/lib/strings/scanner_test.cc
index 55ff3405c35dda..b0f568a03e1739 100644
--- a/tensorflow/core/lib/strings/scanner_test.cc
+++ b/tensorflow/core/lib/strings/scanner_test.cc
@@ -42,24 +42,24 @@ TEST_F(ScannerTest, Any) {
                   .Any(Scanner::DIGIT)
                   .Any(Scanner::LETTER)
                   .GetResult(&remaining, &match));
-  EXPECT_EQ("   horse", match.ToString());
-  EXPECT_EQ("0123", remaining.ToString());
+  EXPECT_EQ("   horse", match);
+  EXPECT_EQ("0123", remaining);
 
   EXPECT_TRUE(Scanner("")
                   .Any(Scanner::SPACE)
                   .Any(Scanner::DIGIT)
                   .Any(Scanner::LETTER)
                   .GetResult(&remaining, &match));
-  EXPECT_EQ("", remaining.ToString());
-  EXPECT_EQ("", match.ToString());
+  EXPECT_EQ("", remaining);
+  EXPECT_EQ("", match);
 
   EXPECT_TRUE(Scanner("----")
                   .Any(Scanner::SPACE)
                   .Any(Scanner::DIGIT)
                   .Any(Scanner::LETTER)
                   .GetResult(&remaining, &match));
-  EXPECT_EQ("----", remaining.ToString());
-  EXPECT_EQ("", match.ToString());
+  EXPECT_EQ("----", remaining);
+  EXPECT_EQ("", match);
 }
 
 TEST_F(ScannerTest, AnySpace) {
@@ -69,8 +69,8 @@ TEST_F(ScannerTest, AnySpace) {
                   .One(Scanner::LETTER)
                   .AnySpace()
                   .GetResult(&remaining, &match));
-  EXPECT_EQ("  a ", match.ToString());
-  EXPECT_EQ("b ", remaining.ToString());
+  EXPECT_EQ("  a ", match);
+  EXPECT_EQ("b ", remaining);
 }
 
 TEST_F(ScannerTest, AnyEscapedNewline) {
@@ -143,8 +143,8 @@ TEST_F(ScannerTest, ScanUntil) {
                   .ScanUntil('\'')
                   .OneLiteral("'")
                   .GetResult(&remaining, &match));
-  EXPECT_EQ(R"( \\'rest)", remaining.ToString());
-  EXPECT_EQ(R"(' \1 \2 \3 \')", match.ToString());
+  EXPECT_EQ(R"( \\'rest)", remaining);
+  EXPECT_EQ(R"(' \1 \2 \3 \')", match);
 
   // The "scan until" character is not present.
   remaining = match = "unset";
@@ -152,15 +152,15 @@ TEST_F(ScannerTest, ScanUntil) {
                    .OneLiteral("'")
                    .ScanUntil('\'')
                    .GetResult(&remaining, &match));
-  EXPECT_EQ("unset", remaining.ToString());
-  EXPECT_EQ("unset", match.ToString());
+  EXPECT_EQ("unset", remaining);
+  EXPECT_EQ("unset", match);
 
   // Scan until an escape character.
   remaining = match = "";
   EXPECT_TRUE(
       Scanner(R"(123\456)").ScanUntil('\\').GetResult(&remaining, &match));
-  EXPECT_EQ(R"(\456)", remaining.ToString());
-  EXPECT_EQ("123", match.ToString());
+  EXPECT_EQ(R"(\456)", remaining);
+  EXPECT_EQ("123", match);
 }
 
 TEST_F(ScannerTest, ScanEscapedUntil) {
@@ -170,8 +170,8 @@ TEST_F(ScannerTest, ScanEscapedUntil) {
                   .ScanEscapedUntil('\'')
                   .OneLiteral("'")
                   .GetResult(&remaining, &match));
-  EXPECT_EQ("rest", remaining.ToString());
-  EXPECT_EQ(R"(' \1 \2 \3 \' \\')", match.ToString());
+  EXPECT_EQ("rest", remaining);
+  EXPECT_EQ(R"(' \1 \2 \3 \' \\')", match);
 
   // The "scan until" character is not present.
   remaining = match = "unset";
@@ -179,27 +179,27 @@ TEST_F(ScannerTest, ScanEscapedUntil) {
                    .OneLiteral("'")
                    .ScanEscapedUntil('\'')
                    .GetResult(&remaining, &match));
-  EXPECT_EQ("unset", remaining.ToString());
-  EXPECT_EQ("unset", match.ToString());
+  EXPECT_EQ("unset", remaining);
+  EXPECT_EQ("unset", match);
 }
 
 TEST_F(ScannerTest, ZeroOrOneLiteral) {
   StringPiece remaining, match;
   EXPECT_TRUE(
       Scanner("abc").ZeroOrOneLiteral("abC").GetResult(&remaining, &match));
-  EXPECT_EQ("abc", remaining.ToString());
-  EXPECT_EQ("", match.ToString());
+  EXPECT_EQ("abc", remaining);
+  EXPECT_EQ("", match);
 
   EXPECT_TRUE(
       Scanner("abcd").ZeroOrOneLiteral("ab").ZeroOrOneLiteral("c").GetResult(
           &remaining, &match));
-  EXPECT_EQ("d", remaining.ToString());
-  EXPECT_EQ("abc", match.ToString());
+  EXPECT_EQ("d", remaining);
+  EXPECT_EQ("abc", match);
 
   EXPECT_TRUE(
       Scanner("").ZeroOrOneLiteral("abc").GetResult(&remaining, &match));
-  EXPECT_EQ("", remaining.ToString());
-  EXPECT_EQ("", match.ToString());
+  EXPECT_EQ("", remaining);
+  EXPECT_EQ("", match);
 }
 
 // Test output of GetResult (including the forms with optional params),
@@ -215,24 +215,24 @@ TEST_F(ScannerTest, CaptureAndGetResult) {
                   .StopCapture()
                   .Any(Scanner::SPACE)
                   .GetResult(&remaining, &match));
-  EXPECT_EQ("second", remaining.ToString());
-  EXPECT_EQ("first", match.ToString());
+  EXPECT_EQ("second", remaining);
+  EXPECT_EQ("first", match);
   EXPECT_TRUE(scan.GetResult());
   remaining = "";
   EXPECT_TRUE(scan.GetResult(&remaining));
-  EXPECT_EQ("second", remaining.ToString());
+  EXPECT_EQ("second", remaining);
   remaining = "";
   match = "";
   EXPECT_TRUE(scan.GetResult(&remaining, &match));
-  EXPECT_EQ("second", remaining.ToString());
-  EXPECT_EQ("first", match.ToString());
+  EXPECT_EQ("second", remaining);
+  EXPECT_EQ("first", match);
 
   scan.RestartCapture().One(Scanner::LETTER).One(Scanner::LETTER);
   remaining = "";
   match = "";
   EXPECT_TRUE(scan.GetResult(&remaining, &match));
-  EXPECT_EQ("cond", remaining.ToString());
-  EXPECT_EQ("se", match.ToString());
+  EXPECT_EQ("cond", remaining);
+  EXPECT_EQ("se", match);
 }
 
 // Tests that if StopCapture is not called, then calling GetResult, then
@@ -242,14 +242,14 @@ TEST_F(ScannerTest, MultipleGetResultExtendsCapture) {
 
   Scanner scan("one2three");
   EXPECT_TRUE(scan.Many(Scanner::LETTER).GetResult(&remaining, &match));
-  EXPECT_EQ("2three", remaining.ToString());
-  EXPECT_EQ("one", match.ToString());
+  EXPECT_EQ("2three", remaining);
+  EXPECT_EQ("one", match);
   EXPECT_TRUE(scan.Many(Scanner::DIGIT).GetResult(&remaining, &match));
-  EXPECT_EQ("three", remaining.ToString());
-  EXPECT_EQ("one2", match.ToString());
+  EXPECT_EQ("three", remaining);
+  EXPECT_EQ("one2", match);
   EXPECT_TRUE(scan.Many(Scanner::LETTER).GetResult(&remaining, &match));
-  EXPECT_EQ("", remaining.ToString());
-  EXPECT_EQ("one2three", match.ToString());
+  EXPECT_EQ("", remaining);
+  EXPECT_EQ("one2three", match);
 }
 
 TEST_F(ScannerTest, FailedMatchDoesntChangeResult) {
@@ -258,8 +258,8 @@ TEST_F(ScannerTest, FailedMatchDoesntChangeResult) {
   StringPiece remaining = "rem";
   StringPiece match = "match";
   EXPECT_FALSE(scan.One(Scanner::SPACE).GetResult(&remaining, &match));
-  EXPECT_EQ("rem", remaining.ToString());
-  EXPECT_EQ("match", match.ToString());
+  EXPECT_EQ("rem", remaining);
+  EXPECT_EQ("match", match);
 }
 
 TEST_F(ScannerTest, DefaultCapturesAll) {
@@ -271,8 +271,8 @@ TEST_F(ScannerTest, DefaultCapturesAll) {
                   .AnySpace()
                   .Any(Scanner::LETTER)
                   .GetResult(&remaining, &match));
-  EXPECT_EQ("", remaining.ToString());
-  EXPECT_EQ("a b", match.ToString());
+  EXPECT_EQ("", remaining);
+  EXPECT_EQ("a b", match);
 }
 
 TEST_F(ScannerTest, AllCharClasses) {
diff --git a/tensorflow/core/lib/strings/str_util.cc b/tensorflow/core/lib/strings/str_util.cc
index 4598b8ccc79fcd..cab8f81585922e 100644
--- a/tensorflow/core/lib/strings/str_util.cc
+++ b/tensorflow/core/lib/strings/str_util.cc
@@ -332,7 +332,7 @@ string StringReplace(StringPiece s, StringPiece oldsub, StringPiece newsub,
                      bool replace_all) {
   // TODO(jlebar): We could avoid having to shift data around in the string if
   // we had a StringPiece::find() overload that searched for a StringPiece.
-  string res = s.ToString();
+  string res = std::string(s);
   size_t pos = 0;
   while ((pos = res.find(oldsub.data(), pos, oldsub.size())) != string::npos) {
     res.replace(pos, oldsub.size(), newsub.data(), newsub.size());
@@ -449,7 +449,7 @@ bool SplitAndParseAsFloats(StringPiece text, char delim,
   return SplitAndParseAsInts(text, delim,
                                     [](StringPiece str, float* value) {
                                       return strings::safe_strtof(
-                                          str.ToString().c_str(), value);
+                                          std::string(str).c_str(), value);
                                     },
                                     result);
 }
diff --git a/tensorflow/core/lib/strings/str_util.h b/tensorflow/core/lib/strings/str_util.h
index e97d00b975e2f6..c887db7eff21a5 100644
--- a/tensorflow/core/lib/strings/str_util.h
+++ b/tensorflow/core/lib/strings/str_util.h
@@ -205,7 +205,7 @@ std::vector Split(StringPiece text, StringPiece delims, Predicate p) {
       if ((i == text.size()) || (delims.find(text[i]) != StringPiece::npos)) {
         StringPiece token(text.data() + token_start, i - token_start);
         if (p(token)) {
-          result.push_back(token.ToString());
+          result.push_back(std::string(token));
         }
         token_start = i + 1;
       }
diff --git a/tensorflow/core/platform/env.cc b/tensorflow/core/platform/env.cc
index b9a9ef85eb16e6..fe7d0aa7d15919 100644
--- a/tensorflow/core/platform/env.cc
+++ b/tensorflow/core/platform/env.cc
@@ -92,7 +92,7 @@ Env::Env() : file_system_registry_(new FileSystemRegistryImpl) {}
 Status Env::GetFileSystemForFile(const string& fname, FileSystem** result) {
   StringPiece scheme, host, path;
   io::ParseURI(fname, &scheme, &host, &path);
-  FileSystem* file_system = file_system_registry_->Lookup(scheme.ToString());
+  FileSystem* file_system = file_system_registry_->Lookup(std::string(scheme));
   if (!file_system) {
     if (scheme.empty()) {
       scheme = "[local]";
@@ -166,7 +166,7 @@ bool Env::FilesExist(const std::vector& files,
   for (const auto& file : files) {
     StringPiece scheme, host, path;
     io::ParseURI(file, &scheme, &host, &path);
-    files_per_fs[scheme.ToString()].push_back(file);
+    files_per_fs[std::string(scheme)].push_back(file);
   }
 
   std::unordered_map per_file_status;
diff --git a/tensorflow/core/platform/env_test.cc b/tensorflow/core/platform/env_test.cc
index a70a417e6a2f3a..c461a40086360f 100644
--- a/tensorflow/core/platform/env_test.cc
+++ b/tensorflow/core/platform/env_test.cc
@@ -357,7 +357,7 @@ TEST_F(DefaultEnvTest, LocalTempFilename) {
   CHECK_EQ(error::OUT_OF_RANGE,
            file_to_read->Read(0 /* offset */, 1024 /* n */, &content, scratch)
                .code());
-  EXPECT_EQ("Null", content.ToString());
+  EXPECT_EQ("Null", content);
 
   // Delete the temporary file.
   TF_CHECK_OK(env->DeleteFile(filename));
diff --git a/tensorflow/core/platform/file_system.cc b/tensorflow/core/platform/file_system.cc
index b55e94d552ed3a..922773684b00bb 100644
--- a/tensorflow/core/platform/file_system.cc
+++ b/tensorflow/core/platform/file_system.cc
@@ -158,7 +158,7 @@ Status FileSystem::RecursivelyCreateDir(const string& dirname) {
   std::reverse(sub_dirs.begin(), sub_dirs.end());
 
   // Now create the directories.
-  string built_path = remaining_dir.ToString();
+  string built_path = std::string(remaining_dir);
   for (const StringPiece sub_dir : sub_dirs) {
     built_path = io::JoinPath(built_path, sub_dir);
     Status status = CreateDir(io::CreateURI(scheme, host, built_path));
diff --git a/tensorflow/core/platform/file_system_helper.cc b/tensorflow/core/platform/file_system_helper.cc
index 22c5057281959f..0ba0e6304f67c0 100644
--- a/tensorflow/core/platform/file_system_helper.cc
+++ b/tensorflow/core/platform/file_system_helper.cc
@@ -59,7 +59,7 @@ Status GetMatchingPaths(FileSystem* fs, Env* env, const string& pattern,
   string fixed_prefix = pattern.substr(0, pattern.find_first_of("*?[\\"));
   string eval_pattern = pattern;
   std::vector all_files;
-  string dir = io::Dirname(fixed_prefix).ToString();
+  string dir = std::string(io::Dirname(fixed_prefix));
   // If dir is empty then we need to fix up fixed_prefix and eval_pattern to
   // include . as the top level directory.
   if (dir.empty()) {
diff --git a/tensorflow/core/platform/file_system_test.cc b/tensorflow/core/platform/file_system_test.cc
index f261b8f5761506..c0a16c95f930e0 100644
--- a/tensorflow/core/platform/file_system_test.cc
+++ b/tensorflow/core/platform/file_system_test.cc
@@ -125,7 +125,7 @@ class InterPlanetaryFileSystem : public NullFileSystem {
     ASSERT_EQ(scheme, "ipfs");
     ASSERT_EQ(host, "solarsystem");
     str_util::ConsumePrefix(&path, "/");
-    *parsed_path = path.ToString();
+    *parsed_path = std::string(path);
   }
 
   std::map> celestial_bodies_ = {
diff --git a/tensorflow/core/util/command_line_flags.cc b/tensorflow/core/util/command_line_flags.cc
index 480ce94fcaeddd..8c27d01917ab80 100644
--- a/tensorflow/core/util/command_line_flags.cc
+++ b/tensorflow/core/util/command_line_flags.cc
@@ -32,7 +32,7 @@ bool ParseStringFlag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
   if (str_util::ConsumePrefix(&arg, "--") &&
       str_util::ConsumePrefix(&arg, flag) &&
       str_util::ConsumePrefix(&arg, "=")) {
-    *value_parsing_ok = hook(arg.ToString());
+    *value_parsing_ok = hook(std::string(arg));
     return true;
   }
 
diff --git a/tensorflow/core/util/env_var.cc b/tensorflow/core/util/env_var.cc
index c844850179235c..8d43bcc9270453 100644
--- a/tensorflow/core/util/env_var.cc
+++ b/tensorflow/core/util/env_var.cc
@@ -28,7 +28,7 @@ namespace tensorflow {
 Status ReadBoolFromEnvVar(StringPiece env_var_name, bool default_val,
                           bool* value) {
   *value = default_val;
-  const char* tf_env_var_val = getenv(env_var_name.ToString().c_str());
+  const char* tf_env_var_val = getenv(std::string(env_var_name).c_str());
   if (tf_env_var_val == nullptr) {
     return Status::OK();
   }
@@ -48,7 +48,7 @@ Status ReadBoolFromEnvVar(StringPiece env_var_name, bool default_val,
 Status ReadInt64FromEnvVar(StringPiece env_var_name, int64 default_val,
                            int64* value) {
   *value = default_val;
-  const char* tf_env_var_val = getenv(env_var_name.ToString().c_str());
+  const char* tf_env_var_val = getenv(std::string(env_var_name).c_str());
   if (tf_env_var_val == nullptr) {
     return Status::OK();
   }
@@ -62,11 +62,11 @@ Status ReadInt64FromEnvVar(StringPiece env_var_name, int64 default_val,
 
 Status ReadStringFromEnvVar(StringPiece env_var_name, StringPiece default_val,
                             string* value) {
-  const char* tf_env_var_val = getenv(env_var_name.ToString().c_str());
+  const char* tf_env_var_val = getenv(std::string(env_var_name).c_str());
   if (tf_env_var_val != nullptr) {
     *value = tf_env_var_val;
   } else {
-    *value = default_val.ToString();
+    *value = std::string(default_val);
   }
   return Status::OK();
 }
diff --git a/tensorflow/core/util/example_proto_fast_parsing.cc b/tensorflow/core/util/example_proto_fast_parsing.cc
index 7946fa1782ab3e..3ce7988057208e 100644
--- a/tensorflow/core/util/example_proto_fast_parsing.cc
+++ b/tensorflow/core/util/example_proto_fast_parsing.cc
@@ -353,7 +353,7 @@ bool TestFastParse(const string& serialized, Example* example) {
     // I.e. last entry in the map overwrites all the previous ones.
     parsed::FeatureMapEntry& name_and_feature =
         parsed_example[parsed_example_size - i - 1];
-    string name = name_and_feature.first.ToString();
+    string name = std::string(name_and_feature.first);
     if ((*features.mutable_feature()).count(name) > 0) continue;
 
     auto& value = (*features.mutable_feature())[name];

From 3c0afb1cf6679097c2316fda8803b3679b37871f Mon Sep 17 00:00:00 2001
From: Bixia Zheng 
Date: Wed, 2 May 2018 11:57:24 -0700
Subject: [PATCH 0287/1691] Turn on two half precision tests for GPU.

PiperOrigin-RevId: 195128326
---
 tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
index 7fa61eb33c2930..6cb470caf8fb57 100644
--- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
@@ -52,12 +52,7 @@ class MatOpsSimpleTest : public ClientLibraryTestBase {};
 template 
 class MatOpsSimpleTest_F16F32 : public MatOpsSimpleTest {};
 
-// TODO(bixia): This test for F16 failed on GPU 02-25-2018.
-#ifdef XLA_TEST_BACKEND_GPU
-TYPED_TEST_CASE(MatOpsSimpleTest_F16F32, ::testing::Types);
-#else
 TYPED_TEST_CASE(MatOpsSimpleTest_F16F32, TypesF16F32);
-#endif
 
 XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, ExpTwoByTwoValues) {
   using T = TypeParam;
@@ -171,11 +166,8 @@ string PrintTestLinspaceMaxParam(
 }
 
 #ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16
-// TODO(bixia): This test failed on GPU 02-25-2018
-#ifdef XLA_TEST_BACKEND_CPU
 XLA_TEST_P(TestLinspaceMaxParametric, TestF16) { TestImpl(); }
 #endif
-#endif
 XLA_TEST_P(TestLinspaceMaxParametric, TestF32) { TestImpl(); }
 
 INSTANTIATE_TEST_CASE_P(

From a08db2f231e303017efb1378bec191c87a0faed7 Mon Sep 17 00:00:00 2001
From: Smit Shilu 
Date: Wed, 2 May 2018 15:23:31 -0400
Subject: [PATCH 0288/1691] command Typo

---
 tensorflow/contrib/lite/g3doc/rpi.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/lite/g3doc/rpi.md b/tensorflow/contrib/lite/g3doc/rpi.md
index 7a3a231626d0e1..ab507893074142 100644
--- a/tensorflow/contrib/lite/g3doc/rpi.md
+++ b/tensorflow/contrib/lite/g3doc/rpi.md
@@ -32,7 +32,7 @@ This has been tested on Raspberry Pi 3b, Raspbian GNU/Linux 9.1 (stretch), gcc v
 
 Log in to you RPI, install the toolchain.
 ```bash
-sudo apt-get instal build-essential
+sudo apt-get install build-essential
 ```
 
 First, clone this TensorFlow repository. Run this at the root of the repository:

From ce0ef2275bda40a6edcd738ccede61ccd3dd824b Mon Sep 17 00:00:00 2001
From: Asim Shankar 
Date: Wed, 2 May 2018 12:32:28 -0700
Subject: [PATCH 0289/1691] docs: Link to the appropriately branched version of
 the live colab notebooks.

And update that link on release changes.

PiperOrigin-RevId: 195133689
---
 tensorflow/docs_src/get_started/eager.md    |  2 +-
 tensorflow/tools/ci_build/update_version.py | 10 ++++++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/get_started/eager.md b/tensorflow/docs_src/get_started/eager.md
index ad89f0154c06d9..f08ac74425b6dc 100644
--- a/tensorflow/docs_src/get_started/eager.md
+++ b/tensorflow/docs_src/get_started/eager.md
@@ -1,3 +1,3 @@
 # Get Started with Eager Execution
 
-[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/eager.ipynb)
+[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.8.0/samples/core/get_started/eager.ipynb)
diff --git a/tensorflow/tools/ci_build/update_version.py b/tensorflow/tools/ci_build/update_version.py
index 52a0da9a14847e..9ddb2190487c26 100755
--- a/tensorflow/tools/ci_build/update_version.py
+++ b/tensorflow/tools/ci_build/update_version.py
@@ -248,6 +248,16 @@ def update_md_files(old_version, new_version):
     replace_string_in_line(r"%s<\/version>" % old_version,
                            "%s" % new_version, filepath)
 
+  # Update any links to colab notebooks.
+  def colab_url(version):
+    version_string = "%d.%d.%d" % (version.major, version.minor, version.patch)
+    prefix = "https://colab.research.google.com/github/tensorflow/models/blob/r"
+    return prefix + version_string + "/"
+
+  replace_string_in_line(
+      colab_url(old_version), colab_url(new_version),
+      "%s/docs_src/get_started/eager.md" % TF_SRC_DIR)
+
 
 def major_minor_change(old_version, new_version):
   """Check if a major or minor change occurred."""

From 262b176e27a3bcd01d518bea6d57683625df42b6 Mon Sep 17 00:00:00 2001
From: Benoit Steiner 
Date: Wed, 2 May 2018 12:58:06 -0700
Subject: [PATCH 0290/1691] Added support for packing of symbolic shapes

PiperOrigin-RevId: 195137239
---
 .../core/grappler/costs/graph_properties.cc   | 23 +++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index 69b22561b2b4aa..23d25cba8d2961 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -770,6 +770,29 @@ class SymbolicShapeRefiner {
           c->output_tensors_as_shapes.resize(1);
           c->output_tensors_as_shapes[0] = result;
         }
+      } else if (IsPack(node)) {
+        // A Pack node concatenating scalars is often used to generate a shape.
+        std::vector dims;
+        bool valid = true;
+        for (int i = 0; i < ic->num_inputs(); ++i) {
+          const Tensor* t = ic->input_tensor(i);
+          if (t) {
+            if (t->dims() != 0 ||
+                (t->dtype() != DT_INT32 && t->dtype() != DT_INT64)) {
+              valid = false;
+              break;
+            }
+            int64 size = t->dtype() == DT_INT32 ? t->scalar()()
+                                                : t->scalar()();
+            dims.push_back(size < 0 ? ic->UnknownDim() : ic->MakeDim(size));
+          } else {
+            dims.push_back(ic->UnknownDim());
+          }
+        }
+        if (valid) {
+          c->output_tensors_as_shapes.resize(1);
+          c->output_tensors_as_shapes[0] = ic->MakeShape(dims);
+        }
       } else if (IsSlice(node)) {
         ShapeHandle input = ic->input_tensors_as_shapes()[0];
         bool valid = ic->RankKnown(input);

From ad491ad2c258fdb71cc0cea5bffe7931622e749f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 13:05:15 -0700
Subject: [PATCH 0291/1691] [XLA] Redesign: Dump HloSnapshot in local service
 as well. And support replaying HloSnapshot.

PiperOrigin-RevId: 195138472
---
 .../xla/service/compile_only_service.cc       | 16 +++++++
 tensorflow/compiler/xla/tools/BUILD           |  1 +
 .../compiler/xla/tools/replay_computation.cc  | 44 +++++++++++++++++--
 3 files changed, 58 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc
index c9f78a0f9f1c0e..d39fd7307ae1b5 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.cc
+++ b/tensorflow/compiler/xla/service/compile_only_service.cc
@@ -70,6 +70,22 @@ CompileOnlyService::CompileAheadOfTime(
     TF_RET_CHECK(instance.computation.has_program_shape());
 
     const DebugOptions& debug_options = options.debug_options();
+
+    // Dump computation proto if flag is set.
+    const string& directory_path = debug_options.xla_dump_computations_to();
+    if (!directory_path.empty()) {
+      HloSnapshot hlo_snapshot;
+      *hlo_snapshot.mutable_hlo()->mutable_hlo_module() = instance.computation;
+      string filename = tensorflow::strings::StrCat(
+          "computation_", instance.computation.id(), "__",
+          instance.computation.entry_computation_name());
+      const string& per_host_path = tensorflow::io::JoinPath(
+          directory_path, tensorflow::port::Hostname());
+
+      TF_RETURN_IF_ERROR(
+          Executable::DumpToDirectory(per_host_path, filename, hlo_snapshot));
+    }
+
     const auto& program_shape = instance.computation.program_shape();
     ExecutionOptions execution_options;
     *execution_options.mutable_debug_options() = debug_options;
diff --git a/tensorflow/compiler/xla/tools/BUILD b/tensorflow/compiler/xla/tools/BUILD
index 0bc4045a549031..78ab2dccafc37a 100644
--- a/tensorflow/compiler/xla/tools/BUILD
+++ b/tensorflow/compiler/xla/tools/BUILD
@@ -88,6 +88,7 @@ cc_library(
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:testing",
+        "//tensorflow/compiler/xla/service:hlo_proto",
         "//tensorflow/compiler/xla/service:session_proto",
         "//tensorflow/compiler/xla/tests:test_utils",
         "//tensorflow/core:framework_internal",
diff --git a/tensorflow/compiler/xla/tools/replay_computation.cc b/tensorflow/compiler/xla/tools/replay_computation.cc
index 62a353ad09af00..d8cedad65ea68e 100644
--- a/tensorflow/compiler/xla/tools/replay_computation.cc
+++ b/tensorflow/compiler/xla/tools/replay_computation.cc
@@ -42,6 +42,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/execution_options_util.h"
 #include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/service/hlo.pb.h"
 #include "tensorflow/compiler/xla/service/session.pb.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -75,9 +76,14 @@ struct Options {
 //
 // Similarly, infeeds fake data of shape fake_infeed_shape if it is provided;
 // otherwise, no infeed is performed.
-StatusOr> ReplayComputation(
-    const SessionModule& module, Client* client, const Options& opts) {
-  TF_ASSIGN_OR_RETURN(Computation computation, client->LoadSnapshot(module));
+template 
+StatusOr> ReplayComputation(const ModuleT& module,
+                                                     Client* client,
+                                                     const Options& opts) {
+  static_assert(std::is_same::value ||
+                    std::is_same::value,
+                "Proto must be in HloSnapshot or SessionModule format");
+  TF_ASSIGN_OR_RETURN(auto computation, client->LoadSnapshot(module));
 
   std::vector> arguments;
   if (opts.use_fake_data) {
@@ -153,6 +159,38 @@ int RealMain(tensorflow::gtl::ArraySlice args, const Options& opts) {
   tensorflow::Env* env = tensorflow::Env::Default();
   int exit_status = EXIT_SUCCESS;
   for (char* arg : args) {
+    HloSnapshot snapshot;
+    auto status = tensorflow::ReadBinaryProto(env, arg, &snapshot);
+    if (status.ok()) {
+      StatusOr> result_status =
+          ReplayComputation(snapshot, client, opts);
+      if (!result_status.ok()) {
+        fprintf(stderr, "%s: error: %s\n", arg,
+                result_status.status().ToString().c_str());
+        exit_status = EXIT_FAILURE;
+        continue;
+      }
+
+      std::unique_ptr result = result_status.ConsumeValueOrDie();
+      if (result != nullptr) {
+        fprintf(stdout, "%s: %s :: %s:%s\n", arg,
+                snapshot.hlo().hlo_module().name().c_str(),
+                ShapeUtil::HumanString(result->shape()).c_str(),
+                result->ToString().c_str());
+        if (snapshot.has_result()) {
+          std::unique_ptr literal =
+              Literal::CreateFromProto(snapshot.result()).ConsumeValueOrDie();
+          fprintf(stdout, "was %s:%s\n",
+                  ShapeUtil::HumanString(snapshot.result().shape()).c_str(),
+                  literal->ToString().c_str());
+        }
+      }
+
+      continue;
+    }
+    fprintf(stderr, "%s: is not HloSnapshot: %s. Trying as SessionModule...\n",
+            arg, status.ToString().c_str());
+
     SessionModule module;
     TF_CHECK_OK(tensorflow::ReadBinaryProto(env, arg, &module));
     StatusOr> result_status =

From b182fd88e10b1d36f30a349e312c3a7ae5f3cc95 Mon Sep 17 00:00:00 2001
From: Jiri Simsa 
Date: Wed, 2 May 2018 13:05:51 -0700
Subject: [PATCH 0292/1691] Increasing test size to reflect recent additions
 and prevent test timeouts.

PiperOrigin-RevId: 195138565
---
 tensorflow/contrib/data/python/kernel_tests/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD
index d59dd17aea4261..7643c2a9fc9ea9 100644
--- a/tensorflow/contrib/data/python/kernel_tests/BUILD
+++ b/tensorflow/contrib/data/python/kernel_tests/BUILD
@@ -32,7 +32,7 @@ py_test(
 
 py_test(
     name = "bucketing_test",
-    size = "small",
+    size = "medium",
     srcs = ["bucketing_test.py"],
     srcs_version = "PY2AND3",
     deps = [

From 79f6d50d784cf27c6e1fb5200ca5022a334198fe Mon Sep 17 00:00:00 2001
From: Saurabh Saxena 
Date: Wed, 2 May 2018 13:23:56 -0700
Subject: [PATCH 0293/1691] Fix tsan failure in batch_dataset_op_test. The
 error was being caused because we were trying to save invocation_results_`
 while the function call was in progress. Now we wait for all invocations to
 finish before saving both `invocation_results_` and `batch_results_`.

Did local A/B testing for tsan.
Before: 12/100 failed
After: All passed
PiperOrigin-RevId: 195141349
---
 .../kernels/data/map_and_batch_dataset_op.cc  | 22 +++++++++++--------
 1 file changed, 13 insertions(+), 9 deletions(-)

diff --git a/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc b/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc
index 7bc43e20725461..c9551fbf16a2be 100644
--- a/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc
+++ b/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc
@@ -272,6 +272,15 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel {
         TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("current_batch_index"),
                                                current_batch_index_));
         TF_RETURN_IF_ERROR(SaveParent(writer, input_impl_));
+        // Wait for the map_fn dispatches made in `InvokeFunctionLocked` to
+        // finish. This may delay saving a checkpoint by a bit but keeps the
+        // code clean and also saves us from checkpointing the state of the
+        // `BlockingCounter`.
+        std::vector num_elements(batch_results_.size());
+        for (size_t i = 0; i < batch_results_.size(); i++) {
+          WaitForBatch(i, &num_elements[i]).IgnoreError();
+        }
+
         TF_RETURN_IF_ERROR(writer->WriteScalar(
             full_name("invocation_results_size"), invocation_results_.size()));
         for (size_t i = 0; i < invocation_results_.size(); ++i) {
@@ -280,7 +289,8 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel {
         TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("batch_results_size"),
                                                batch_results_.size()));
         for (size_t i = 0; i < batch_results_.size(); ++i) {
-          TF_RETURN_IF_ERROR(WriteBatchResultLocked(writer, i));
+          TF_RETURN_IF_ERROR(
+              WriteBatchResultLocked(writer, i, num_elements[i]));
         }
         return Status::OK();
       }
@@ -567,15 +577,9 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel {
         return Status::OK();
       }
 
-      Status WriteBatchResultLocked(IteratorStateWriter* writer, size_t index)
+      Status WriteBatchResultLocked(IteratorStateWriter* writer, size_t index,
+                                    int64 num_elements)
           EXCLUSIVE_LOCKS_REQUIRED(mu_) {
-        // Wait for the map_fn dispatches made in `InvokeFunctionLocked` to
-        // finish. This may delay saving a checkpoint by a bit but keeps the
-        // code clean and also saves us from checkpointing the state of the
-        // `BlockingCounter`.
-        int64 num_elements = 0;
-        WaitForBatch(index, &num_elements).IgnoreError();
-
         const BatchResult& result = batch_results_[index];
         string prefix = strings::StrCat("batch_results_", index);
         {

From 2706eeb1fbd4a2cf0e1af8efa3c7f3539944079e Mon Sep 17 00:00:00 2001
From: Jiri Simsa 
Date: Wed, 2 May 2018 13:29:01 -0700
Subject: [PATCH 0294/1691] Re-enabling a test.

PiperOrigin-RevId: 195142105
---
 .../contrib/data/python/kernel_tests/batch_dataset_op_test.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py
index a4a0ce79b6013d..6588fd04acb027 100644
--- a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py
@@ -630,9 +630,7 @@ def _build_dataset_dense_to_sparse(self, components):
         lambda x: array_ops.fill([x], x)).apply(
             batching.dense_to_sparse_batch(4, [12]))
 
-  # TODO(b/70988345): Re-enable when sparse tensors are properly supported by
-  # the DatasetSerializationTestBase.
-  def _testDenseToSparseBatchDatasetCore(self):
+  def testDenseToSparseBatchDatasetCore(self):
     components = np.random.randint(5, size=(40,)).astype(np.int32)
     diff_comp = np.random.randint(2, size=(100,)).astype(np.int32)
 

From f9e8a75036154a73f256783eccf53bca6612d606 Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Wed, 2 May 2018 13:36:31 -0700
Subject: [PATCH 0295/1691] [XLA] Add new optimization that sinks constants
 into while loop bodies

Example transformation:

   state = (..., const, ...)
   while (pred(state)) {
     (..., v, ...) = state
     use(v)
     state = (..., v, ...)
   }

 =>

   state = (..., const, ...)
   while (pred(state)) {
     (..., v, ...) = state
     use(const)
     state = (..., v, ...)
   }

PiperOrigin-RevId: 195143323
---
 tensorflow/compiler/xla/service/BUILD         |  27 +++
 tensorflow/compiler/xla/service/cpu/BUILD     |   1 +
 .../compiler/xla/service/cpu/cpu_compiler.cc  |   2 +
 tensorflow/compiler/xla/service/hlo_module.cc |   8 +
 tensorflow/compiler/xla/service/hlo_module.h  |   5 +
 .../service/while_loop_constant_sinking.cc    | 128 +++++++++++
 .../xla/service/while_loop_constant_sinking.h |  68 ++++++
 .../while_loop_constant_sinking_test.cc       | 200 ++++++++++++++++++
 .../while_loop_invariant_code_motion.cc       |  27 +--
 tensorflow/compiler/xla/service/while_util.cc |  17 ++
 tensorflow/compiler/xla/service/while_util.h  |   6 +
 .../compiler/xla/service/while_util_test.cc   |  37 ++++
 12 files changed, 506 insertions(+), 20 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/while_loop_constant_sinking.cc
 create mode 100644 tensorflow/compiler/xla/service/while_loop_constant_sinking.h
 create mode 100644 tensorflow/compiler/xla/service/while_loop_constant_sinking_test.cc

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 6e2510aa1081ad..17964cdd59f827 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -2687,6 +2687,33 @@ tf_cc_test(
     ],
 )
 
+cc_library(
+    name = "while_loop_constant_sinking",
+    srcs = ["while_loop_constant_sinking.cc"],
+    hdrs = ["while_loop_constant_sinking.h"],
+    deps = [
+        ":hlo",
+        ":hlo_pass",
+        ":while_util",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/core:lib",
+    ],
+)
+
+tf_cc_test(
+    name = "while_loop_constant_sinking_test",
+    srcs = ["while_loop_constant_sinking_test.cc"],
+    deps = [
+        ":hlo_matchers",
+        ":while_loop_constant_sinking",
+        "//tensorflow/compiler/xla:test",
+        "//tensorflow/compiler/xla/tests:hlo_verified_test_base",
+        "//tensorflow/compiler/xla/tools/parser:hlo_parser",
+        "//tensorflow/core:test",
+    ],
+)
+
 cc_library(
     name = "despecializer",
     srcs = ["despecializer.cc"],
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 2fc6c6bd551575..cb81e413a363c6 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -131,6 +131,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:reshape_mover",
         "//tensorflow/compiler/xla/service:transpose_folding",
         "//tensorflow/compiler/xla/service:tuple_simplifier",
+        "//tensorflow/compiler/xla/service:while_loop_constant_sinking",
         "//tensorflow/compiler/xla/service:while_loop_invariant_code_motion",
         "//tensorflow/compiler/xla/service:while_loop_simplifier",
         "//tensorflow/compiler/xla/service:zero_sized_hlo_elimination",
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index e298d67e0937ae..91ed6e427ac7c2 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -87,6 +87,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/reshape_mover.h"
 #include "tensorflow/compiler/xla/service/transpose_folding.h"
 #include "tensorflow/compiler/xla/service/tuple_simplifier.h"
+#include "tensorflow/compiler/xla/service/while_loop_constant_sinking.h"
 #include "tensorflow/compiler/xla/service/while_loop_invariant_code_motion.h"
 #include "tensorflow/compiler/xla/service/while_loop_simplifier.h"
 #include "tensorflow/compiler/xla/service/zero_sized_hlo_elimination.h"
@@ -270,6 +271,7 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) {
 
     pass.AddPass();
     pass.AddPass();
+    pass.AddPass();
     pass.AddPass();
     pass.AddPass();
     pass.AddPass();
diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc
index 987c4b27190f77..c7a71928675391 100644
--- a/tensorflow/compiler/xla/service/hlo_module.cc
+++ b/tensorflow/compiler/xla/service/hlo_module.cc
@@ -540,6 +540,14 @@ uint64 HloModule::RandomNew64() const {
   return rng_();
 }
 
+HloComputation* HloModule::GetComputationWithName(
+    tensorflow::StringPiece name) {
+  auto it = c_find_if(computations(), [&](HloComputation* computation) {
+    return computation->name() == name;
+  });
+  return it == computations().end() ? nullptr : *it;
+}
+
 /* static */ std::atomic HloModule::next_unique_module_id_(0);
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h
index 82d790ec3b405d..f9674df812dbbc 100644
--- a/tensorflow/compiler/xla/service/hlo_module.h
+++ b/tensorflow/compiler/xla/service/hlo_module.h
@@ -32,6 +32,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/name_uniquer.h"
 #include "tensorflow/compiler/xla/service/versioned_computation_handle.h"
 #include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/lib/gtl/iterator_range.h"
 #include "tensorflow/core/platform/logging.h"
@@ -138,6 +139,10 @@ class HloModule {
             MakeUnwrappingIterator(computations_.end())};
   }
 
+  // Returns the computation in this module that has the name `name`.  Returns
+  // null if there is no such computation.
+  HloComputation* GetComputationWithName(tensorflow::StringPiece name);
+
   // Gets the number of computations in this module.
   int64 computation_count() const { return computations_.size(); }
 
diff --git a/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc b/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc
new file mode 100644
index 00000000000000..10fc4958fae064
--- /dev/null
+++ b/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc
@@ -0,0 +1,128 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/while_loop_constant_sinking.h"
+#include "tensorflow/compiler/xla/service/while_util.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/gtl/flatmap.h"
+#include "tensorflow/core/lib/gtl/inlined_vector.h"
+
+namespace xla {
+
+// Replaces all uses of old_instr with new_instr except the use at
+// `while_body_root` (which must be a tuple instruction) at index `tuple_index`.
+// This utility helps us replace an instruction in the while body with a
+// constant while still keeping it trivially loop invariant.
+static Status ReplaceUsesWhileKeepingLoopInvariance(
+    HloInstruction* old_instr, HloInstruction* new_instr,
+    HloInstruction* while_body_root, int64 tuple_index) {
+  CHECK_EQ(while_body_root->opcode(), HloOpcode::kTuple);
+
+  std::vector users;
+  users.reserve(old_instr->user_count());
+  c_copy(old_instr->users(), std::back_inserter(users));
+
+  for (auto* user : users) {
+    for (int64 i = 0, e = user->operand_count(); i < e; i++) {
+      if (user->operand(i) == old_instr &&
+          !(user == while_body_root && i == tuple_index)) {
+        TF_RETURN_IF_ERROR(user->ReplaceOperandWith(i, new_instr));
+      }
+    }
+  }
+
+  return Status::OK();
+}
+
+StatusOr WhileLoopConstantSinking::TrySinkingConstantsIntoWhileBody(
+    HloInstruction* while_instr) {
+  HloComputation* while_body = while_instr->while_body();
+
+  const HloInstruction& init_value = *while_instr->operand(0);
+  if (init_value.opcode() != HloOpcode::kTuple) {
+    return false;
+  }
+
+  bool changed = false;
+
+  for (HloInstruction* invariant_gte :
+       WhileUtil::GetInvariantGTEsForWhileBody(*while_body)) {
+    int64 index = invariant_gte->tuple_index();
+    const HloInstruction& invariant_value = *init_value.operand(index);
+    if (invariant_value.opcode() == HloOpcode::kConstant) {
+      auto* constant_instr =
+          while_body->AddInstruction(invariant_value.Clone(/*suffix=*/".sunk"));
+      TF_RETURN_IF_ERROR(ReplaceUsesWhileKeepingLoopInvariance(
+          invariant_gte, constant_instr, while_body->root_instruction(),
+          index));
+      changed = true;
+    }
+  }
+
+  return changed;
+}
+
+StatusOr WhileLoopConstantSinking::Run(HloModule* module) {
+  VLOG(2) << "HLO module before WhileLoopConstantSinking:";
+  XLA_VLOG_LINES(2, module->ToString());
+
+  bool changed = false;
+  std::vector while_instrs;
+  for (auto* comp : module->MakeNonfusionComputations()) {
+    // Right now we don't particulary care about optimizing while-of-while
+    // patterns.  If/When we do, we'll want to visit the outer while (while_0)
+    // before we visit the inner while (while_1):
+    //
+    // while_1_body(state) {
+    //   val = gte(state, 0) // Loop invariant
+    //   use(val)
+    // }
+    //
+    // while_0_body(state) {
+    //   val = gte(state, 0) // Loop invariant
+    //   while_1 = while(init=tuple(val, ...), body=while_1_body, ...)
+    //   ...
+    // }
+    //
+    // main {
+    //   while_0 = while(init=(constant, ...), body=while_0_body, ...)
+    // }
+    //
+    // This will let us sink the constant into the outer while first and then
+    // into the inner while in a single run of this pass.
+    c_copy_if(comp->instructions(), std::back_inserter(while_instrs),
+              [](const HloInstruction* instr) {
+                return instr->opcode() == HloOpcode::kWhile;
+              });
+  }
+
+  for (HloInstruction* while_instr : while_instrs) {
+    // We only sink into while loop bodies, but this can be extended to
+    // transform conditions as well.
+    TF_ASSIGN_OR_RETURN(bool result,
+                        TrySinkingConstantsIntoWhileBody(while_instr));
+    changed |= result;
+  }
+
+  if (changed) {
+    VLOG(2) << "HLO module after WhileLoopConstantSinking:";
+    XLA_VLOG_LINES(2, module->ToString());
+  } else {
+    VLOG(2) << "HLO module unchanged after WhileLoopConstantSinking";
+  }
+
+  return changed;
+}
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/while_loop_constant_sinking.h b/tensorflow/compiler/xla/service/while_loop_constant_sinking.h
new file mode 100644
index 00000000000000..21fb8568a84985
--- /dev/null
+++ b/tensorflow/compiler/xla/service/while_loop_constant_sinking.h
@@ -0,0 +1,68 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_WHILE_LOOP_CONSTANT_SINKING_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_WHILE_LOOP_CONSTANT_SINKING_H_
+
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
+#include "tensorflow/compiler/xla/statusor.h"
+
+namespace xla {
+
+// Sinks while loop invariant values that happen to be constants into the while
+// loop body.  This is probably not a win in isolation but may unlock further
+// optimizations like constant folding.
+//
+//   state = (..., const, ...)
+//   while (pred(state)) {
+//     (..., v, ...) = state
+//     use(v)
+//     state = (..., v, ...)
+//   }
+//
+// =>
+//
+//   state = (..., const, ...)
+//   while (pred(state)) {
+//     (..., v, ...) = state
+//     use(const)
+//     state = (..., v, ...)
+//   }
+//
+// Note that it leaves the `v` in place to keep that component of the state
+// tuple trivially loop invariant.  WhileLoopSimplifier will later get rid of
+// `v`.
+//
+// We only sink into while loop bodies, but this can be extended to transform
+// conditions as well.
+//
+// TODO(b/79121449):  We should also sink broadcasts of constants.
+class WhileLoopConstantSinking : public HloPassInterface {
+ public:
+  ~WhileLoopConstantSinking() override = default;
+
+  tensorflow::StringPiece name() const override {
+    return "while-loop-invariant-code-motion";
+  }
+
+  StatusOr Run(HloModule* module) override;
+
+ private:
+  StatusOr TrySinkingConstantsIntoWhileBody(HloInstruction* while_instr);
+};
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_WHILE_LOOP_CONSTANT_SINKING_H_
diff --git a/tensorflow/compiler/xla/service/while_loop_constant_sinking_test.cc b/tensorflow/compiler/xla/service/while_loop_constant_sinking_test.cc
new file mode 100644
index 00000000000000..0d2288d8ea6ebb
--- /dev/null
+++ b/tensorflow/compiler/xla/service/while_loop_constant_sinking_test.cc
@@ -0,0 +1,200 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/while_loop_constant_sinking.h"
+
+#include "tensorflow/compiler/xla/service/hlo_matchers.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+
+namespace xla {
+namespace {
+
+namespace op = xla::testing::opcode_matchers;
+using ::testing::_;
+
+class WhileLoopConstantSinkingTest : public ::testing::Test {};
+
+TEST_F(WhileLoopConstantSinkingTest, SinkOneConstant) {
+  const char* const hlo_string = R"(
+HloModule ModuleWithWhile
+
+body {
+  p_body = (f32[2],f32[2]) parameter(0)
+  p_body.0 = f32[2] get-tuple-element((f32[2],f32[2]) p_body), index=0
+  p_body.1 = f32[2] get-tuple-element((f32[2],f32[2]) p_body), index=1
+
+  add.0 = f32[2] add(p_body.0, p_body.1)
+  ROOT root = (f32[2],f32[2]) tuple(add.0, p_body.1)
+}
+
+condition {
+  p_cond = (f32[2],f32[2]) parameter(0)
+  ROOT result = pred[] constant(true)
+}
+
+ENTRY entry {
+  const_0 = f32[2] constant({1, 2})
+  const_1 = f32[2] constant({2, 1})
+  while_init = (f32[2],f32[2]) tuple(const_0, const_1)
+  ROOT while = (f32[2],f32[2]) while(while_init), condition=condition, body=body
+}
+)";
+
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module,
+                          tools::Parse(hlo_string));
+
+  TF_ASSERT_OK_AND_ASSIGN(bool changed,
+                          WhileLoopConstantSinking{}.Run(module.get()));
+  ASSERT_TRUE(changed);
+
+  auto* while_body = module->GetComputationWithName("body");
+  EXPECT_THAT(while_body->root_instruction(),
+              op::Tuple(op::Add(_, op::Constant()), _));
+}
+
+TEST_F(WhileLoopConstantSinkingTest, KeepConstantsLoopInvariant) {
+  const char* const hlo_string = R"(
+HloModule ModuleWithWhile
+
+body {
+  p_body = (f32[2],f32[2],f32[2]) parameter(0)
+  p_body.0 = f32[2] get-tuple-element((f32[2],f32[2],f32[2]) p_body), index=0
+  p_body.1 = f32[2] get-tuple-element((f32[2],f32[2],f32[2]) p_body), index=1
+  p_body.2 = f32[2] get-tuple-element((f32[2],f32[2],f32[2]) p_body), index=2
+
+  add.0 = f32[2] add(p_body.1, p_body.2)
+  ROOT root = (f32[2],f32[2],f32[2]) tuple(add.0, p_body.1, p_body.2)
+}
+
+condition {
+  p_cond = (f32[2],f32[2],f32[2]) parameter(0)
+  ROOT result = pred[] constant(true)
+}
+
+ENTRY entry {
+  const_0 = f32[2] constant({1, 2})
+  const_1 = f32[2] constant({2, 1})
+  const_2 = f32[2] constant({3, 1})
+  while_init = (f32[2],f32[2],f32[2]) tuple(const_0, const_1, const_2)
+  ROOT while = (f32[2],f32[2],f32[2]) while(while_init), condition=condition, body=body
+}
+)";
+
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module,
+                          tools::Parse(hlo_string));
+
+  TF_ASSERT_OK_AND_ASSIGN(bool changed,
+                          WhileLoopConstantSinking{}.Run(module.get()));
+  ASSERT_TRUE(changed);
+
+  auto* while_body = module->GetComputationWithName("body");
+  EXPECT_THAT(while_body->root_instruction(),
+              op::Tuple(op::Add(op::Constant(), op::Constant()),
+                        op::GetTupleElement(op::Parameter(0)),
+                        op::GetTupleElement(op::Parameter(0))));
+}
+
+TEST_F(WhileLoopConstantSinkingTest, TupleShapedConstants) {
+  const char* const hlo_string = R"(
+HloModule ModuleWithWhile
+
+body {
+  p_b = (f32[2],(f32[2],f32[2])) parameter(0)
+  p_b.0 = f32[2] get-tuple-element((f32[2],f32[2],f32[2]) p_b), index=0
+  p_b.1 = (f32[2],f32[2]) get-tuple-element((f32[2],(f32[2],f32[2])) p_b), index=1
+
+  p_b.1.1 = f32[2] get-tuple-element(p_b.1), index=0
+
+  ROOT root = (f32[2],f32[2],f32[2]) tuple(p_b.1.1, p_b.1)
+}
+
+condition {
+  p_cond = (f32[2],(f32[2],f32[2])) parameter(0)
+  ROOT result = pred[] constant(true)
+}
+
+ENTRY entry {
+  const_0 = f32[2] constant({1, 2})
+  const_1 = (f32[2], f32[2]) constant((f32[2], f32[2]) ({2, 1},{3,1}))
+  while_init = (f32[2],(f32[2],f32[2])) tuple(const_0, const_1)
+  ROOT while = (f32[2],(f32[2],f32[2])) while(while_init), condition=condition, body=body
+}
+)";
+
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module,
+                          tools::Parse(hlo_string));
+
+  TF_ASSERT_OK_AND_ASSIGN(bool changed,
+                          WhileLoopConstantSinking{}.Run(module.get()));
+  ASSERT_TRUE(changed);
+
+  auto* while_body = module->GetComputationWithName("body");
+  EXPECT_THAT(while_body->root_instruction(),
+              op::Tuple(op::GetTupleElement(op::Constant(), 0),
+                        op::GetTupleElement(op::Parameter(0))));
+}
+
+TEST_F(WhileLoopConstantSinkingTest, DuplicateGTEs) {
+  // This test shows that the pass fails to optimize non-canonical IR.
+  //
+  // Even though the input IR has a constant value for p_b.2.dup,
+  // WhileLoopConstantSinking doesn't try to detect this.  Instead, it relies on
+  // prior runs of HLO CSE to have commoned these identical GTE instructions.
+
+  const char* const hlo_string = R"(
+HloModule ModuleWithWhile
+
+body {
+  p_b = (f32[2],f32[2],f32[2]) parameter(0)
+
+  p_b.1     = f32[2] get-tuple-element((f32[2],f32[2],f32[2]) p_b), index=1
+  p_b.2     = f32[2] get-tuple-element((f32[2],f32[2],f32[2]) p_b), index=2
+  p_b.2.dup = f32[2] get-tuple-element((f32[2],f32[2],f32[2]) p_b), index=2
+
+  add.0 = f32[2] add(p_b.1, p_b.2.dup)
+  ROOT root = (f32[2],f32[2],f32[2]) tuple(add.0, p_b.1, p_b.2)
+}
+
+condition {
+  p_cond = (f32[2],f32[2],f32[2]) parameter(0)
+  ROOT result = pred[] constant(true)
+}
+
+ENTRY entry {
+  const_0 = f32[2] constant({1, 2})
+  const_1 = f32[2] constant({2, 1})
+  const_2 = f32[2] constant({3, 1})
+  while_init = (f32[2],f32[2],f32[2]) tuple(const_0, const_1, const_2)
+  ROOT while = (f32[2],f32[2],f32[2]) while(while_init), condition=condition, body=body
+}
+)";
+
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module,
+                          tools::Parse(hlo_string));
+
+  TF_ASSERT_OK_AND_ASSIGN(bool changed,
+                          WhileLoopConstantSinking{}.Run(module.get()));
+  ASSERT_TRUE(changed);
+
+  auto* while_body = module->GetComputationWithName("body");
+  EXPECT_THAT(while_body->root_instruction(),
+              op::Tuple(op::Add(op::Constant(), ::testing::Not(op::Constant())),
+                        op::GetTupleElement(op::Parameter(0)),
+                        op::GetTupleElement(op::Parameter(0))));
+}
+}  // namespace
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc
index 3ef0cdff675125..321fdeb1ea313d 100644
--- a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc
+++ b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc
@@ -115,25 +115,6 @@ static bool NotWorthHoistingIndividually(const HloInstruction& instruction) {
   }
 }
 
-// Populates `gte_set` with the GetTupleElement instructions in `while_body`
-// that access elements in the parameter tuple that don't change across
-// iterations.  Assumes `while_body` is the body computation of the while loop
-// in question.
-static void GatherInvariantGTEs(HloComputation* while_body,
-                                FlatSet* gte_set) {
-  const HloInstruction::InstructionVector root_operands =
-      while_body->root_instruction()->operands();
-  for (int i = 0; i < root_operands.size(); i++) {
-    HloInstruction* instr = root_operands[i];
-    if (instr->opcode() == HloOpcode::kGetTupleElement &&
-        instr->tuple_index() == i &&
-        instr->operand(0) == while_body->parameter_instruction(0) &&
-        ShapeUtil::IsArray(instr->shape())) {
-      InsertOrDie(gte_set, instr);
-    }
-  }
-}
-
 static StatusOr TryHoistingInvariantInstructionsFromWhileBody(
     HloInstruction* while_instr) {
   auto print_no_metadata = HloPrintOptions{}.set_print_metadata(false);
@@ -172,7 +153,13 @@ static StatusOr TryHoistingInvariantInstructionsFromWhileBody(
   // unhoisted_invariant_instructions -- they can be legally hoisted, but there
   // is no benefit to hoisting them unless something that uses it is also
   // hoisted.
-  GatherInvariantGTEs(while_body, &unhoisted_invariant_instructions);
+  for (auto* instr : WhileUtil::GetInvariantGTEsForWhileBody(*while_body)) {
+    if (ShapeUtil::IsArray(instr->shape())) {
+      // TODO(b/79147885): We should try to generalize this to tuples for
+      // uniformity's sake, if nothing else.
+      InsertOrDie(&unhoisted_invariant_instructions, instr);
+    }
+  }
 
   if (unhoisted_invariant_instructions.empty()) {
     // There are no obviously loop invariant elements in the state being
diff --git a/tensorflow/compiler/xla/service/while_util.cc b/tensorflow/compiler/xla/service/while_util.cc
index bd0794184328b7..ed20b36292a7f2 100644
--- a/tensorflow/compiler/xla/service/while_util.cc
+++ b/tensorflow/compiler/xla/service/while_util.cc
@@ -244,4 +244,21 @@ static Shape MakeLoopStateShape(const WhileUtil::LoopStateTy& init_values) {
   }
   return result;
 }
+
+/*static*/ std::vector WhileUtil::GetInvariantGTEsForWhileBody(
+    const HloComputation& while_body) {
+  std::vector result;
+  const HloInstruction::InstructionVector root_operands =
+      while_body.root_instruction()->operands();
+  for (int i = 0; i < root_operands.size(); i++) {
+    HloInstruction* instr = root_operands[i];
+    if (instr->opcode() == HloOpcode::kGetTupleElement &&
+        instr->tuple_index() == i &&
+        instr->operand(0) == while_body.parameter_instruction(0)) {
+      result.push_back(instr);
+    }
+  }
+  return result;
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/while_util.h b/tensorflow/compiler/xla/service/while_util.h
index 1688d4674269c3..322d27b88cae60 100644
--- a/tensorflow/compiler/xla/service/while_util.h
+++ b/tensorflow/compiler/xla/service/while_util.h
@@ -74,6 +74,12 @@ class WhileUtil {
       HloComputation* computation, int32 trip_count,
       const LoopStateTy& init_values,
       const LoopBodyGeneratorTy& loop_body_generator);
+
+  // Returns the GetTupleElement instructions in `while_body` that access
+  // elements in the parameter tuple that don't change across iterations.
+  // Assumes `while_body` is the body computation of the while loop in question.
+  static std::vector GetInvariantGTEsForWhileBody(
+      const HloComputation& while_body);
 };
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/service/while_util_test.cc b/tensorflow/compiler/xla/service/while_util_test.cc
index cf0d0db99bd92b..974bc542a34d0a 100644
--- a/tensorflow/compiler/xla/service/while_util_test.cc
+++ b/tensorflow/compiler/xla/service/while_util_test.cc
@@ -126,5 +126,42 @@ TEST(WhileUtilTest, MakeTwoInstructionsLive) {
                         op::GetTupleElement(op::Parameter(0), 3)));
 }
 
+TEST(WhileUtilTest, GetInvariantGTEsForWhileBody) {
+  const char* const hlo_string = R"(
+HloModule ModuleWithWhile
+
+body {
+  param.b = (s32[], s32[]) parameter(0)
+  gte.0 = s32[] get-tuple-element(param.b), index=0
+  gte.1 = s32[] get-tuple-element(param.b), index=1
+  add = s32[] add(gte.0, gte.1)
+  ROOT tuple = (s32[], s32[]) tuple(gte.0, add)
+}
+
+cond {
+  param.c = (s32[], s32[]) parameter(0)
+  ROOT constant = pred[] constant(true)
+}
+
+ENTRY main {
+  init = (s32[], s32[]) parameter(0)
+  ROOT while = (s32[], s32[]) while(init), condition=cond, body=body
+}
+)";
+
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module,
+                          tools::Parse(hlo_string));
+
+  HloComputation* while_body = module->GetComputationWithName("body");
+
+  ASSERT_NE(while_body, nullptr)
+      << "Expected exactly one while_body computation";
+
+  std::vector gte_list =
+      WhileUtil::GetInvariantGTEsForWhileBody(*while_body);
+
+  ASSERT_EQ(gte_list.size(), 1);
+  EXPECT_EQ((*gte_list.begin())->name(), "gte.0");
+}
 }  // namespace
 }  // namespace xla

From d750a1310d5312b934e5f1689d0abd467847b7d1 Mon Sep 17 00:00:00 2001
From: Anna R 
Date: Wed, 2 May 2018 13:44:32 -0700
Subject: [PATCH 0296/1691] Copy module list file in the while statement
 instead of creating a new variable

---
 tensorflow/tools/api/generator/create_python_api.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py
index d1e7f23fbcacc4..788f6d3573adda 100644
--- a/tensorflow/tools/api/generator/create_python_api.py
+++ b/tensorflow/tools/api/generator/create_python_api.py
@@ -159,8 +159,7 @@ def get_api_init_text():
 
   # Traverse over everything imported above. Specifically,
   # we want to traverse over TensorFlow Python modules.
-  module_list = list(sys.modules.values())
-  for module in module_list:
+  for module in list(sys.modules.values()):
     # Only look at tensorflow modules.
     if (not module or not hasattr(module, '__name__') or
         'tensorflow.' not in module.__name__):

From bd6c00aabe9a34715a5b2026eeccac4bc2a8d0de Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 13:42:40 -0700
Subject: [PATCH 0297/1691] Fix a bug in create_python_api.py

I got an error complaining about "RuntimeError: dictionary changed size during iteration", this change fixes it.

PiperOrigin-RevId: 195144333
---
 tensorflow/tools/api/generator/create_python_api.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py
index c06a39bfbdf06c..65baa6e4b45dc5 100644
--- a/tensorflow/tools/api/generator/create_python_api.py
+++ b/tensorflow/tools/api/generator/create_python_api.py
@@ -158,7 +158,7 @@ def get_api_init_text():
 
   # Traverse over everything imported above. Specifically,
   # we want to traverse over TensorFlow Python modules.
-  for module in sys.modules.values():
+  for module in list(sys.modules.values()):
     # Only look at tensorflow modules.
     if (not module or not hasattr(module, '__name__') or
         'tensorflow.' not in module.__name__):

From 8f610384b61f7b1b62302b9a861c1d4b19b36b33 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 13:44:30 -0700
Subject: [PATCH 0298/1691] Updated ABSL to latest version in workspace.bzl.

PiperOrigin-RevId: 195144612
---
 tensorflow/workspace.bzl | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 16da59c5cf0c1c..f4f7bc461560ab 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -96,11 +96,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "com_google_absl",
       urls = [
-          "https://mirror.bazel.build/github.com/abseil/abseil-cpp/archive/720c017e30339fd1786ce4aac68bc8559736e53f.tar.gz",
-          "https://github.com/abseil/abseil-cpp/archive/720c017e30339fd1786ce4aac68bc8559736e53f.tar.gz",
+          "https://mirror.bazel.build/github.com/abseil/abseil-cpp/archive/9613678332c976568272c8f4a78631a29159271d.tar.gz",
+          "https://github.com/abseil/abseil-cpp/archive/9613678332c976568272c8f4a78631a29159271d.tar.gz",
       ],
-     sha256 = "5996380e3e8b981f55d1c8d58e709c00dbb4806ba367be75d0925a68cc2f6478",
-     strip_prefix = "abseil-cpp-720c017e30339fd1786ce4aac68bc8559736e53f",
+     sha256 = "1273a1434ced93bc3e703a48c5dced058c95e995c8c009e9bdcb24a69e2180e9",
+     strip_prefix = "abseil-cpp-9613678332c976568272c8f4a78631a29159271d",
      build_file = clean_dep("//third_party:com_google_absl.BUILD"),
   )
 
@@ -299,11 +299,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "absl_py",
       urls = [
-          "https://mirror.bazel.build/github.com/abseil/abseil-py/archive/acec853355ef987eae48a8d87a79351c15dff593.tar.gz",
-          "https://github.com/abseil/abseil-py/archive/acec853355ef987eae48a8d87a79351c15dff593.tar.gz",
+          "https://mirror.bazel.build/github.com/abseil/abseil-py/archive/ea8c4d2ddbf3fba610c4d613260561699b776db8.tar.gz",
+          "https://github.com/abseil/abseil-py/archive/ea8c4d2ddbf3fba610c4d613260561699b776db8.tar.gz",
       ],
-      sha256 = "29e4584e778bee13aa4093824133d131d927cc160561892880118d9ff7b95a6a",
-      strip_prefix = "abseil-py-acec853355ef987eae48a8d87a79351c15dff593",
+      sha256 = "c30b48e0d2580ef1412e55c5c0e1dab8db2ee4ab56e2075eccff29c90c7c7059",
+      strip_prefix = "abseil-py-ea8c4d2ddbf3fba610c4d613260561699b776db8",
   )
 
   tf_http_archive(

From bf70368d36df3ee9a16f5285940d73fb54d911c0 Mon Sep 17 00:00:00 2001
From: gracehoney <31743510+aaroey@users.noreply.github.com>
Date: Wed, 2 May 2018 14:46:12 -0700
Subject: [PATCH 0299/1691] Fix breaking tests

---
 tensorflow/contrib/tensorrt/convert/convert_graph.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc
index a8c07df4a00550..4df54a749f5a2d 100644
--- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc
+++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc
@@ -342,6 +342,7 @@ tensorflow::Status ConvertGraphDefToTensorRT(
   // optimization pass
   tensorflow::grappler::GrapplerItem item;
   item.fetch = output_names;
+  item.graph = graph_def;
 
   tensorflow::DeviceProperties device_properties;
   device_properties.set_type("GPU");

From 08fec96547a673084589e1be45f4bde0246f6fdf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 14:58:57 -0700
Subject: [PATCH 0300/1691] Fix support for batch_normalization with mixed
 precision

When the type of the input tensor `x` is not the same as the type of
the parameters `mean`, `variance`, `offset`, and `scale`, a cast is
required.

This mixed precision case occurs when using the BatchNormalization
layer with a data type of float16 or bfloat16.

PiperOrigin-RevId: 195157279
---
 tensorflow/python/ops/nn_batchnorm_test.py | 20 ++++++++++++++------
 tensorflow/python/ops/nn_impl.py           |  6 ++++--
 2 files changed, 18 insertions(+), 8 deletions(-)

diff --git a/tensorflow/python/ops/nn_batchnorm_test.py b/tensorflow/python/ops/nn_batchnorm_test.py
index 3ac2c8eb17ef31..1508ff44ceaea4 100644
--- a/tensorflow/python/ops/nn_batchnorm_test.py
+++ b/tensorflow/python/ops/nn_batchnorm_test.py
@@ -292,12 +292,16 @@ def testBatchNormKeepDims(self):
             self.assertAllClose(
                 tf_batch_norm, keep_dims_tf_batch_norm, atol=0.000001)
 
-  def _testBatchNormArbitraryShapes(self, x_shape, param_shape, atol=0.0001):
-    x_val = np.random.random_sample(x_shape).astype(np.float32)
-    m_val = np.random.random_sample(param_shape).astype(np.float32)
-    v_val = np.random.random_sample(param_shape).astype(np.float32)
-    beta_val = np.random.random_sample(param_shape).astype(np.float32)
-    gamma_val = np.random.random_sample(param_shape).astype(np.float32)
+  def _testBatchNormArbitraryShapes(self, x_shape, param_shape, atol=0.0001,
+                                    dtype=dtypes.float32,
+                                    param_dtype=dtypes.float32):
+    numpy_dtype = dtype.as_numpy_dtype
+    numpy_param_dtype = param_dtype.as_numpy_dtype
+    x_val = np.random.random_sample(x_shape).astype(numpy_dtype)
+    m_val = np.random.random_sample(param_shape).astype(numpy_param_dtype)
+    v_val = np.random.random_sample(param_shape).astype(numpy_param_dtype)
+    beta_val = np.random.random_sample(param_shape).astype(numpy_param_dtype)
+    gamma_val = np.random.random_sample(param_shape).astype(numpy_param_dtype)
     for use_gpu in [True, False]:
       with self.test_session(use_gpu=use_gpu) as sess:
         x = constant_op.constant(x_val, name="x")
@@ -332,6 +336,10 @@ def testBatchNormArbitraryShapes(self):
     self._testBatchNormArbitraryShapes(
         (2, 3, 2, 4, 5), (1, 1, 1, 4, 5), atol=0.005)
 
+  def testBatchNormMixedPrecision(self):
+    self._testBatchNormArbitraryShapes((3, 3), (1, 3), dtype=dtypes.float16,
+                                       param_dtype=dtypes.float32, atol=0.001)
+
 
 @test_util.with_c_api
 class SufficientStatisticsTest(test.TestCase):
diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py
index 576627e78ed10d..783d4858925d3e 100644
--- a/tensorflow/python/ops/nn_impl.py
+++ b/tensorflow/python/ops/nn_impl.py
@@ -830,8 +830,10 @@ def batch_normalization(x,
     inv = math_ops.rsqrt(variance + variance_epsilon)
     if scale is not None:
       inv *= scale
-    return x * inv + (
-        offset - mean * inv if offset is not None else -mean * inv)
+    # Note: tensorflow/contrib/quantize/python/fold_batch_norms.py depends on
+    # the precise order of ops that are generated by the expression below.
+    return x * math_ops.cast(inv, x.dtype) + math_ops.cast(
+        offset - mean * inv if offset is not None else -mean * inv, x.dtype)
 
 
 @tf_export("nn.fused_batch_norm")

From d030ea951a477e2e141c13fed42681bcc5e97b4a Mon Sep 17 00:00:00 2001
From: Chris Ying 
Date: Wed, 2 May 2018 15:03:33 -0700
Subject: [PATCH 0301/1691] Add steps_per_run to LoggingTensorHook and
 StepCounterHook and other logging bug fixes.

PiperOrigin-RevId: 195158238
---
 .../contrib/tpu/python/tpu/tpu_estimator.py   | 61 ++++++++----
 tensorflow/python/estimator/estimator.py      | 21 +++--
 .../training/basic_session_run_hooks.py       | 16 ++--
 .../training/basic_session_run_hooks_test.py  | 93 +++++++++++++++++--
 ...sorflow.train.-checkpoint-saver-hook.pbtxt |  2 +-
 5 files changed, 151 insertions(+), 42 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index eb537b7b6ad2c7..534042b42c6ab9 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -459,11 +459,9 @@ def after_create_session(self, session, coord):
     session.run(self._init_ops,
                 options=config_pb2.RunOptions(timeout_in_ms=5 * 60 * 1000))
 
-    logging.info('Start infeed thread controller')
     self._infeed_controller = self._create_infeed_controller(
         name='InfeedController', target=self._run_infeed, args=(session,))
 
-    logging.info('Start outfeed thread controller')
     self._outfeed_controller = _OpQueueContext(
         name='OutfeedController', target=self._run_outfeed, args=(session,))
 
@@ -1553,7 +1551,7 @@ def end(self, session):
 
 
 class ExamplesPerSecondHook(basic_session_run_hooks.StepCounterHook):
-  """"Calculate and report the number of examples/sec during training."""
+  """Calculate and report global_step/sec and examples/sec during runtime."""
 
   def __init__(self,
                batch_size,
@@ -1569,12 +1567,18 @@ def __init__(self,
         summary_writer=summary_writer)
 
   def _log_and_record(self, elapsed_steps, elapsed_time, global_step):
-    examples_per_sec = self._batch_size * elapsed_steps / elapsed_time
+    global_step_per_sec = elapsed_steps / elapsed_time
+    examples_per_sec = self._batch_size * global_step_per_sec
     if self._summary_writer is not None:
+      global_step_summary = Summary(value=[
+          Summary.Value(tag='global_step/sec', simple_value=global_step_per_sec)
+      ])
       example_summary = Summary(value=[
-          Summary.Value(tag='examples_sec', simple_value=examples_per_sec)
+          Summary.Value(tag='examples/sec', simple_value=examples_per_sec)
       ])
+      self._summary_writer.add_summary(global_step_summary, global_step)
       self._summary_writer.add_summary(example_summary, global_step)
+    logging.info('global_step/sec: %g', global_step_per_sec)
     logging.info('examples/sec: %g', examples_per_sec)
 
 
@@ -1844,6 +1848,12 @@ def __init__(self,
     # config.model_dir.
     model_function = self._augment_model_fn(model_fn, batch_axis)
 
+    # Overwrite log_step_count_steps to disable TensorLoggingHook and
+    # StepCounterHook from being created in Estimator. TPUEstimator already
+    # added equivalent hooks in _augment_model_fn above.
+    self._log_every_n_steps = config.log_step_count_steps
+    config = config.replace(log_step_count_steps=None)
+
     # Passing non-None params as wrapped model_fn has it.
     params = params or {}
     super(TPUEstimator, self).__init__(
@@ -2039,39 +2049,50 @@ def _model_fn(features, labels, mode, config, params):
           host_ops = host_call.create_tpu_hostcall()
           if host_ops is None:
             host_ops = []
-
           shutdown_hooks = []
           if os.environ.get('TF_TPU_GRACEFUL_SHUTDOWN', '0') != '0':
             shutdown_hooks.append(session_support.GracefulShutdownHook())
-
-          hooks = [
+          with ops.control_dependencies([loss]):
+            global_step = array_ops.identity(training.get_global_step())
+          hooks = input_hooks + shutdown_hooks
+          logging_hook_frequency = (    # Divide and round up
+              (self._log_every_n_steps +
+               self._config.tpu_config.iterations_per_loop - 1) //
+              self._config.tpu_config.iterations_per_loop)
+          hooks.extend([
               TPUInfeedOutfeedSessionHook(
                   ctx,
                   enqueue_ops,
                   host_ops,
                   run_infeed_loop_on_coordinator=(
                       run_infeed_loop_on_coordinator)),
-              ExamplesPerSecondHook(
-                  ctx.global_batch_size, output_dir=self.model_dir),
               InstallSignalHandlerHook(),
               training.LoggingTensorHook(
                   {
                       'loss': array_ops.identity(loss),
-                      'step': training.get_global_step()
+                      'step': global_step,
                   },
-                  every_n_secs=30)
-          ] + input_hooks + shutdown_hooks
+                  every_n_iter=logging_hook_frequency)
+          ])
+          examples_hook = ExamplesPerSecondHook(
+              ctx.global_batch_size,
+              output_dir=self.model_dir,
+              every_n_steps=self._log_every_n_steps)
+          examples_hook._set_steps_per_run(   # pylint: disable=protected-access
+              self._config.tpu_config.iterations_per_loop)
+          hooks.append(examples_hook)
 
           chief_hooks = []
           if (self._config.save_checkpoints_secs or
               self._config.save_checkpoints_steps):
-            chief_hooks.append(
-                training.CheckpointSaverHook(
-                    self.model_dir,
-                    save_secs=self._config.save_checkpoints_secs,
-                    save_steps=self._config.save_checkpoints_steps,
-                    steps_per_run=self._config.tpu_config.iterations_per_loop,
-                    scaffold=scaffold))
+            checkpoint_hook = training.CheckpointSaverHook(
+                self.model_dir,
+                save_secs=self._config.save_checkpoints_secs,
+                save_steps=self._config.save_checkpoints_steps,
+                scaffold=scaffold)
+            checkpoint_hook._set_steps_per_run(   # pylint: disable=protected-access
+                self._config.tpu_config.iterations_per_loop)
+            chief_hooks.append(checkpoint_hook)
           summary.scalar(model_fn_lib.LOSS_METRIC_KEY, loss)
           with ops.control_dependencies([loss]):
             update_ops = _sync_variables_ops()
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 3691c99ddac6b4..946f093ba7aa95 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -994,15 +994,18 @@ def _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks,
       summary.scalar('loss', estimator_spec.loss)
     ops.add_to_collection(ops.GraphKeys.LOSSES, estimator_spec.loss)
     worker_hooks.extend(hooks)
-    worker_hooks.extend([
-        training.NanTensorHook(estimator_spec.loss),
-        training.LoggingTensorHook(
-            {
-                'loss': estimator_spec.loss,
-                'step': global_step_tensor
-            },
-            every_n_iter=self._config.log_step_count_steps)
-    ])
+    worker_hooks.append(
+        training.NanTensorHook(estimator_spec.loss)
+    )
+    if self._config.log_step_count_steps is not None:
+      worker_hooks.append(
+          training.LoggingTensorHook(
+              {
+                  'loss': estimator_spec.loss,
+                  'step': global_step_tensor
+              },
+              every_n_iter=self._config.log_step_count_steps)
+      )
     worker_hooks.extend(estimator_spec.training_hooks)
 
     if not (estimator_spec.scaffold.saver or
diff --git a/tensorflow/python/training/basic_session_run_hooks.py b/tensorflow/python/training/basic_session_run_hooks.py
index d1cc7d8ce33ac6..abcf76a2204f33 100644
--- a/tensorflow/python/training/basic_session_run_hooks.py
+++ b/tensorflow/python/training/basic_session_run_hooks.py
@@ -380,8 +380,7 @@ def __init__(self,
                saver=None,
                checkpoint_basename="model.ckpt",
                scaffold=None,
-               listeners=None,
-               steps_per_run=1):
+               listeners=None):
     """Initializes a `CheckpointSaverHook`.
 
     Args:
@@ -394,9 +393,6 @@ def __init__(self,
       listeners: List of `CheckpointSaverListener` subclass instances.
         Used for callbacks that run immediately before or after this hook saves
         the checkpoint.
-      steps_per_run: `int`, number of steps that occur between each invocation
-        of the hook. Primarily used for TPU workloads which run multiple steps
-        in a while loop in a single Session.run.
 
     Raises:
       ValueError: One of `save_steps` or `save_secs` should be set.
@@ -412,6 +408,9 @@ def __init__(self,
     self._timer = SecondOrStepTimer(every_secs=save_secs,
                                     every_steps=save_steps)
     self._listeners = listeners or []
+    self._steps_per_run = 1
+
+  def _set_steps_per_run(self, steps_per_run):
     self._steps_per_run = steps_per_run
 
   def begin(self):
@@ -522,6 +521,10 @@ def __init__(self,
     self._output_dir = output_dir
     self._last_global_step = None
     self._global_step_check_count = 0
+    self._steps_per_run = 1
+
+  def _set_steps_per_run(self, steps_per_run):
+    self._steps_per_run = steps_per_run
 
   def begin(self):
     if self._summary_writer is None and self._output_dir:
@@ -547,7 +550,8 @@ def after_run(self, run_context, run_values):
     _ = run_context
 
     stale_global_step = run_values.results
-    if self._timer.should_trigger_for_step(stale_global_step+1):
+    if self._timer.should_trigger_for_step(
+        stale_global_step + self._steps_per_run):
       # get the real value after train op.
       global_step = run_context.session.run(self._global_step_tensor)
       if self._timer.should_trigger_for_step(global_step):
diff --git a/tensorflow/python/training/basic_session_run_hooks_test.py b/tensorflow/python/training/basic_session_run_hooks_test.py
index 31898562f81fac..7344ce2758658e 100644
--- a/tensorflow/python/training/basic_session_run_hooks_test.py
+++ b/tensorflow/python/training/basic_session_run_hooks_test.py
@@ -764,8 +764,8 @@ def test_save_steps_saves_in_first_step(self):
       hook = basic_session_run_hooks.CheckpointSaverHook(
           self.model_dir,
           save_steps=2*self.steps_per_run,
-          scaffold=self.scaffold,
-          steps_per_run=self.steps_per_run)
+          scaffold=self.scaffold)
+      hook._set_steps_per_run(self.steps_per_run)
       hook.begin()
       self.scaffold.finalize()
       with session_lib.Session() as sess:
@@ -781,8 +781,8 @@ def test_save_steps_saves_periodically(self):
       hook = basic_session_run_hooks.CheckpointSaverHook(
           self.model_dir,
           save_steps=2*self.steps_per_run,
-          scaffold=self.scaffold,
-          steps_per_run=self.steps_per_run)
+          scaffold=self.scaffold)
+      hook._set_steps_per_run(self.steps_per_run)
       hook.begin()
       self.scaffold.finalize()
       with session_lib.Session() as sess:
@@ -823,8 +823,8 @@ def test_save_steps_saves_at_end(self):
       hook = basic_session_run_hooks.CheckpointSaverHook(
           self.model_dir,
           save_steps=2*self.steps_per_run,
-          scaffold=self.scaffold,
-          steps_per_run=self.steps_per_run)
+          scaffold=self.scaffold)
+      hook._set_steps_per_run(self.steps_per_run)
       hook.begin()
       self.scaffold.finalize()
       with session_lib.Session() as sess:
@@ -997,6 +997,87 @@ def test_log_warning_if_global_step_not_increased(self):
             'global step.*has not been increased')
       hook.end(sess)
 
+  def _setup_steps_per_run_test(self,
+                                every_n_steps,
+                                steps_per_run,
+                                graph,
+                                sess):
+    variables.get_or_create_global_step()
+    self.train_op = training_util._increment_global_step(steps_per_run)
+    self.summary_writer = fake_summary_writer.FakeSummaryWriter(
+        self.log_dir, graph)
+    self.hook = basic_session_run_hooks.StepCounterHook(
+        summary_writer=self.summary_writer, every_n_steps=every_n_steps)
+    self.hook._set_steps_per_run(steps_per_run)
+    self.hook.begin()
+    sess.run(variables_lib.global_variables_initializer())
+    self.mon_sess = monitored_session._HookedSession(sess, [self.hook])
+
+  def test_steps_per_run_less_than_every_n_steps(self):
+    with ops.Graph().as_default() as g, session_lib.Session() as sess:
+      self._setup_steps_per_run_test(10, 5, g, sess)
+
+      # Logs at 15, 25
+      for _ in range(5):
+        time.sleep(0.01)
+        self.mon_sess.run(self.train_op)
+
+      self.hook.end(sess)
+      self.summary_writer.assert_summaries(
+          test_case=self,
+          expected_logdir=self.log_dir,
+          expected_graph=g,
+          expected_summaries={})
+      self.assertItemsEqual([15, 25], self.summary_writer.summaries.keys())
+      for step in [15, 25]:
+        summary_value = self.summary_writer.summaries[step][0].value[0]
+        self.assertEqual('global_step/sec', summary_value.tag)
+        self.assertGreater(summary_value.simple_value, 0)
+
+  def test_steps_per_run_equal_every_n_steps(self):
+    with ops.Graph().as_default() as g, session_lib.Session() as sess:
+      self._setup_steps_per_run_test(5, 5, g, sess)
+
+      # Logs at 10, 15, 20, 25
+      for _ in range(5):
+        time.sleep(0.01)
+        self.mon_sess.run(self.train_op)
+
+      self.hook.end(sess)
+      self.summary_writer.assert_summaries(
+          test_case=self,
+          expected_logdir=self.log_dir,
+          expected_graph=g,
+          expected_summaries={})
+      self.assertItemsEqual([10, 15, 20, 25],
+                            self.summary_writer.summaries.keys())
+      for step in [10, 15, 20, 25]:
+        summary_value = self.summary_writer.summaries[step][0].value[0]
+        self.assertEqual('global_step/sec', summary_value.tag)
+        self.assertGreater(summary_value.simple_value, 0)
+
+  def test_steps_per_run_greater_than_every_n_steps(self):
+    with ops.Graph().as_default() as g, session_lib.Session() as sess:
+      self._setup_steps_per_run_test(5, 10, g, sess)
+
+      # Logs at 20, 30, 40, 50
+      for _ in range(5):
+        time.sleep(0.01)
+        self.mon_sess.run(self.train_op)
+
+      self.hook.end(sess)
+      self.summary_writer.assert_summaries(
+          test_case=self,
+          expected_logdir=self.log_dir,
+          expected_graph=g,
+          expected_summaries={})
+      self.assertItemsEqual([20, 30, 40, 50],
+                            self.summary_writer.summaries.keys())
+      for step in [20, 30, 40, 50]:
+        summary_value = self.summary_writer.summaries[step][0].value[0]
+        self.assertEqual('global_step/sec', summary_value.tag)
+        self.assertGreater(summary_value.simple_value, 0)
+
 
 class SummarySaverHookTest(test.TestCase):
 
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-checkpoint-saver-hook.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-checkpoint-saver-hook.pbtxt
index 327799729c9e7d..c3037baa8c951e 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-checkpoint-saver-hook.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-checkpoint-saver-hook.pbtxt
@@ -5,7 +5,7 @@ tf_class {
   is_instance: ""
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'checkpoint_dir\', \'save_secs\', \'save_steps\', \'saver\', \'checkpoint_basename\', \'scaffold\', \'listeners\', \'steps_per_run\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'model.ckpt\', \'None\', \'None\', \'1\'], "
+    argspec: "args=[\'self\', \'checkpoint_dir\', \'save_secs\', \'save_steps\', \'saver\', \'checkpoint_basename\', \'scaffold\', \'listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'model.ckpt\', \'None\', \'None\'], "
   }
   member_method {
     name: "after_create_session"

From 1d92d5037e1cec1a5099234a5568b68c7e675576 Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Wed, 2 May 2018 15:05:58 -0700
Subject: [PATCH 0302/1691] [TF:XLA] Bump open source llvm revision to r331338

PiperOrigin-RevId: 195158710
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index f4f7bc461560ab..94cac4f8fa957f 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -452,11 +452,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/068c967842b83d22007eee4515b57e8d9aaccb82.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/068c967842b83d22007eee4515b57e8d9aaccb82.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/a5108a08ceab35886a7df07c86f96aedd3d94bb7.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/a5108a08ceab35886a7df07c86f96aedd3d94bb7.tar.gz",
       ],
-      sha256 = "4950432fb5cc68e5bf1f87a30b17dfdc69a5b93dac1e89d5274242d3ce7dae7c",
-      strip_prefix = "llvm-068c967842b83d22007eee4515b57e8d9aaccb82",
+      sha256 = "79cae03ebbdfd812bb69c460e1325ca069b5c576f7c7071f8216cf2b0975e36f",
+      strip_prefix = "llvm-a5108a08ceab35886a7df07c86f96aedd3d94bb7",
       build_file = clean_dep("//third_party/llvm:llvm.BUILD"),
   )
 

From 9180cc254dff42368af126aa68eb82823ef67736 Mon Sep 17 00:00:00 2001
From: Yuanzhong Xu 
Date: Wed, 2 May 2018 15:14:08 -0700
Subject: [PATCH 0303/1691] [XLA] BF16 propagation: do not change if
 propagation is confined inside a fusion.

We now use a set to track all the potential changes, and do the actual changes
on the HLOs at the end. This also makes the boolean return value (whether
anything is changed) correct.

PiperOrigin-RevId: 195160025
---
 .../xla/service/bfloat16_propagation.cc       | 421 ++++++++++++------
 .../xla/service/bfloat16_propagation.h        |  93 +++-
 .../xla/service/bfloat16_propagation_test.cc  |  31 ++
 3 files changed, 387 insertions(+), 158 deletions(-)

diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.cc b/tensorflow/compiler/xla/service/bfloat16_propagation.cc
index 43ebe92c5ec1c9..ed0746980f87ac 100644
--- a/tensorflow/compiler/xla/service/bfloat16_propagation.cc
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation.cc
@@ -33,7 +33,7 @@ BFloat16Propagation::BFloat16Propagation(
     const BFloat16Support* bfloat16_support)
     : bfloat16_support_(bfloat16_support) {}
 
-void BFloat16Propagation::DetermineAndMutateFusionComputationPrecision(
+void BFloat16Propagation::DetermineFusionComputationPrecision(
     HloInstruction* fusion) {
   CHECK_EQ(fusion->opcode(), HloOpcode::kFusion);
   if (!bfloat16_support_->SupportsMixedPrecisions(*fusion)) {
@@ -48,15 +48,13 @@ void BFloat16Propagation::DetermineAndMutateFusionComputationPrecision(
   auto root = fusion->fused_instructions_computation()->root_instruction();
 
   // Adjust root's element types according to the fusion's output shape.
-  ShapeUtil::ForEachMutableSubshape(
-      root->mutable_shape(), [&](Shape* subshape, const ShapeIndex& index) {
-        if (subshape->element_type() != F32) {
+  ShapeUtil::ForEachSubshape(
+      root->shape(), [&](const Shape& subshape, const ShapeIndex& index) {
+        if (subshape.element_type() != F32) {
           return;
         }
-        if (ShapeUtil::GetSubshape(fusion->shape(), index).element_type() ==
-            BF16) {
-          subshape->set_element_type(BF16);
-          changed_ = true;
+        if (OutputTypeAfterChange(fusion, index) == BF16) {
+          AddToOrRemoveFromBF16ChangeSet(root, index, BF16);
           VLOG(2) << "Fused root " << root->ToString() << " at shape index "
                   << index << " changed to BF16 precision for fusion "
                   << fusion->ToString();
@@ -67,13 +65,101 @@ void BFloat16Propagation::DetermineAndMutateFusionComputationPrecision(
   auto insts =
       fusion->fused_instructions_computation()->MakeInstructionPostOrder();
   for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
-    DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false);
+    DetermineInstructionPrecision(*inst_it, /*skip_parameters=*/false);
   }
-  computations_visited_in_mutation_pass_.insert(
+  computations_visited_in_backward_pass_.insert(
       fusion->fused_instructions_computation());
+
+  RevertIfFusionInternalBF16Changes(fusion);
+}
+
+void BFloat16Propagation::RevertIfFusionInternalBF16Changes(
+    HloInstruction* fusion) {
+  auto has_changes = [this](HloInstruction* inst) {
+    auto it = changes_to_bf16_.find(inst);
+    return it != changes_to_bf16_.end() && !it->second.empty();
+  };
+
+  auto root = fusion->fused_instructions_computation()->root_instruction();
+  tensorflow::gtl::FlatSet changed_root_buffers;
+
+  auto root_changes_it = changes_to_bf16_.find(root);
+  if (root_changes_it != changes_to_bf16_.end()) {
+    for (const auto& index : root_changes_it->second) {
+      for (const HloValue* value :
+           dataflow_->GetValueSet(root, index).values()) {
+        changed_root_buffers.insert(value);
+      }
+    }
+  }
+
+  auto aliases_changed_root_buffer =
+      [this, &changed_root_buffers](const HloInstruction* inst) {
+        bool aliasing = false;
+        ShapeUtil::ForEachSubshape(
+            inst->shape(), [&](const Shape& subshape, const ShapeIndex& index) {
+              if (aliasing) {
+                // Skip if aliasing is already found.
+                return;
+              }
+              // Only F32 buffers are considered for changing to BF16 in this
+              // pass.
+              if (subshape.element_type() != F32) {
+                return;
+              }
+              for (const HloValue* value :
+                   dataflow_->GetValueSet(inst, index).values()) {
+                if (ContainsKey(changed_root_buffers, value)) {
+                  aliasing = true;
+                  break;
+                }
+              }
+            });
+        return aliasing;
+      };
+
+  for (auto inst :
+       fusion->fused_instructions_computation()->MakeInstructionPostOrder()) {
+    if (inst->opcode() == HloOpcode::kParameter) {
+      continue;
+    }
+    if (aliases_changed_root_buffer(inst)) {
+      continue;
+    }
+    if (inst->opcode() == HloOpcode::kFusion) {
+      bool parameter_reverted = false;
+      for (int64 i = 0; i < inst->operand_count(); ++i) {
+        if (has_changes(inst->mutable_operand(i))) {
+          // Changes on the operand have not been reverted.
+          continue;
+        }
+        auto* fused_parameter = inst->fused_parameter(i);
+        if (has_changes(fused_parameter)) {
+          changes_to_bf16_.erase(fused_parameter);
+          parameter_reverted = true;
+        }
+      }
+      if (parameter_reverted) {
+        RevertIfFusionInternalBF16Changes(inst);
+      }
+    }
+    if (!has_changes(inst)) {
+      continue;
+    }
+    bool revert_changes = true;
+    for (auto operand : inst->operands()) {
+      if (has_changes(operand)) {
+        revert_changes = false;
+        break;
+      }
+    }
+    if (revert_changes) {
+      changes_to_bf16_.erase(inst);
+    }
+  }
 }
 
-void BFloat16Propagation::DetermineAndMutateWhileComputationsPrecision(
+void BFloat16Propagation::DetermineWhileComputationsPrecision(
     HloInstruction* while_hlo) {
   CHECK_EQ(while_hlo->opcode(), HloOpcode::kWhile);
 
@@ -86,16 +172,14 @@ void BFloat16Propagation::DetermineAndMutateWhileComputationsPrecision(
   auto body_root = body->root_instruction();
   HloComputation* condition = while_hlo->while_condition();
 
-  ShapeUtil::ForEachMutableSubshape(
-      body_root->mutable_shape(),
-      [this, while_hlo, body_root](Shape* subshape, const ShapeIndex& index) {
-        if (subshape->element_type() != F32) {
+  ShapeUtil::ForEachSubshape(
+      body_root->shape(), [this, while_hlo, body_root](
+                              const Shape& subshape, const ShapeIndex& index) {
+        if (subshape.element_type() != F32) {
           return;
         }
-        if (ShapeUtil::GetSubshape(while_hlo->shape(), index).element_type() ==
-            BF16) {
-          subshape->set_element_type(BF16);
-          changed_ = true;
+        if (OutputTypeAfterChange(while_hlo, index) == BF16) {
+          AddToOrRemoveFromBF16ChangeSet(body_root, index, BF16);
           VLOG(2) << "While body root " << body_root->ToString()
                   << " at shape index " << index
                   << " changed to BF16 precision for while "
@@ -106,30 +190,30 @@ void BFloat16Propagation::DetermineAndMutateWhileComputationsPrecision(
   auto body_insts = body->MakeInstructionPostOrder();
   for (auto inst_it = body_insts.rbegin(); inst_it != body_insts.rend();
        ++inst_it) {
-    DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false);
+    DetermineInstructionPrecision(*inst_it, /*skip_parameters=*/false);
   }
-  computations_visited_in_mutation_pass_.insert(body);
+  computations_visited_in_backward_pass_.insert(body);
 
   auto condition_insts = condition->MakeInstructionPostOrder();
   for (auto inst_it = condition_insts.rbegin();
        inst_it != condition_insts.rend(); ++inst_it) {
-    DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false);
+    DetermineInstructionPrecision(*inst_it, /*skip_parameters=*/false);
   }
-  computations_visited_in_mutation_pass_.insert(condition);
+  computations_visited_in_backward_pass_.insert(condition);
 }
 
 bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo,
                                               const ShapeIndex& index) const {
-  auto value_set = dataflow_->GetValueSet(&hlo, index);
+  auto& value_set = dataflow_->GetValueSet(&hlo, index);
   for (const HloValue* value : value_set.values()) {
     if (ContainsKey(values_that_must_be_kept_as_f32_, value)) {
       return false;
     }
-    if (value->shape().element_type() == BF16) {
+    if (ValueTypeAfterChange(value) == BF16) {
       continue;
     }
     for (const HloUse& use : value->uses()) {
-      if (!ContainsKey(instructions_visited_in_mutation_pass_,
+      if (!ContainsKey(instructions_visited_in_backward_pass_,
                        use.instruction)) {
         // We don't know yet whether use.instruction will consume BF16 since it
         // hasn't been visited. Although we visit instructions in reverse
@@ -145,26 +229,23 @@ bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo,
       // precision, or a called computation's parameters have been changed to
       // BF16 for fusions or whiles.
       if (use.instruction->opcode() == HloOpcode::kFusion) {
-        const auto* fused_parameter =
+        auto* fused_parameter =
             use.instruction->fused_parameter(use.operand_number);
-        if (ShapeUtil::GetSubshape(fused_parameter->shape(), use.operand_index)
-                .element_type() != BF16) {
+        if (OutputTypeAfterChange(fused_parameter, use.operand_index) != BF16) {
           return false;
         }
         continue;
       } else if (use.instruction->opcode() == HloOpcode::kWhile) {
-        const auto* cond_parameter =
+        auto* cond_parameter =
             use.instruction->while_condition()->parameter_instruction(
                 use.operand_number);
-        if (ShapeUtil::GetSubshape(cond_parameter->shape(), use.operand_index)
-                .element_type() != BF16) {
+        if (OutputTypeAfterChange(cond_parameter, use.operand_index) != BF16) {
           return false;
         }
-        const auto* body_parameter =
+        auto* body_parameter =
             use.instruction->while_body()->parameter_instruction(
                 use.operand_number);
-        if (ShapeUtil::GetSubshape(body_parameter->shape(), use.operand_index)
-                .element_type() != BF16) {
+        if (OutputTypeAfterChange(body_parameter, use.operand_index) != BF16) {
           return false;
         }
         continue;
@@ -174,19 +255,20 @@ bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo,
         continue;
       }
       // If the op propagates precision and it outputs a BF16, then it's OK to
-      // supply BF16 also as the input. In the backward mutation pass, the users
-      // shapes should have already been processed.
+      // supply BF16 also as the input. In the backward pass, the users shapes
+      // should have already been processed.
       PrimitiveType user_output_type = PRIMITIVE_TYPE_INVALID;
       if (use.instruction->opcode() == HloOpcode::kTuple ||
           (use.instruction->opcode() == HloOpcode::kCrossReplicaSum &&
            ShapeUtil::IsTuple(use.instruction->shape()))) {
-        user_output_type = ShapeUtil::GetSubshape(
-                               ShapeUtil::GetSubshape(use.instruction->shape(),
-                                                      {use.operand_number}),
-                               use.operand_index)
-                               .element_type();
+        ShapeIndex use_output_index{use.operand_number};
+        for (int64 i : use.operand_index) {
+          use_output_index.push_back(i);
+        }
+        user_output_type =
+            OutputTypeAfterChange(use.instruction, use_output_index);
       } else {
-        user_output_type = use.instruction->shape().element_type();
+        user_output_type = OutputTypeAfterChange(use.instruction, {});
       }
       if (bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision(
               *use.instruction, use.operand_number) &&
@@ -199,8 +281,8 @@ bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo,
   return true;
 }
 
-void BFloat16Propagation::DetermineAndMutateInstructionPrecision(
-    HloInstruction* hlo, bool skip_parameters) {
+void BFloat16Propagation::DetermineInstructionPrecision(HloInstruction* hlo,
+                                                        bool skip_parameters) {
   // We handle any fusion computation or while body/condition after the
   // instruction is handled, because we need to know the output shape of a
   // fusion or while before propagating inside its  computations.
@@ -209,12 +291,12 @@ void BFloat16Propagation::DetermineAndMutateInstructionPrecision(
       [this, hlo, &postpone_processing_called_computations] {
         if (!postpone_processing_called_computations) {
           if (hlo->opcode() == HloOpcode::kFusion) {
-            DetermineAndMutateFusionComputationPrecision(hlo);
+            DetermineFusionComputationPrecision(hlo);
           } else if (hlo->opcode() == HloOpcode::kWhile) {
-            DetermineAndMutateWhileComputationsPrecision(hlo);
+            DetermineWhileComputationsPrecision(hlo);
           }
         }
-        instructions_visited_in_mutation_pass_.insert(hlo);
+        instructions_visited_in_backward_pass_.insert(hlo);
       });
 
   if (hlo->opcode() == HloOpcode::kWhile &&
@@ -245,9 +327,9 @@ void BFloat16Propagation::DetermineAndMutateInstructionPrecision(
   CHECK(hlo->parent() != nullptr);
   if (hlo == hlo->parent()->root_instruction()) {
     if (!hlo->parent()->IsFusionComputation()) {
-      ShapeUtil::ForEachSubshape(hlo->shape(), [&](const Shape& subshape,
+      ShapeUtil::ForEachSubshape(hlo->shape(), [&](const Shape& /* subshape */,
                                                    const ShapeIndex& index) {
-        if (subshape.element_type() != F32) {
+        if (OutputTypeAfterChange(hlo, index) != F32) {
           return;
         }
         for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) {
@@ -269,13 +351,12 @@ void BFloat16Propagation::DetermineAndMutateInstructionPrecision(
     return;
   }
 
-  ShapeUtil::ForEachMutableSubshape(
-      hlo->mutable_shape(),
-      [hlo, this](Shape* subshape, const ShapeIndex& index) {
-        if (subshape->element_type() == F32 &&
+  ShapeUtil::ForEachSubshape(
+      hlo->shape(),
+      [hlo, this](const Shape& /* subshape */, const ShapeIndex& index) {
+        if (OutputTypeAfterChange(hlo, index) == F32 &&
             AllUsersConsumeBF16(*hlo, index)) {
-          subshape->set_element_type(BF16);
-          changed_ = true;
+          AddToOrRemoveFromBF16ChangeSet(hlo, index, BF16);
           VLOG(2) << "HloInstruction output at shape index " << index
                   << " changed to BF16 precision: " << hlo->ToString();
         }
@@ -308,26 +389,24 @@ void BFloat16Propagation::AdjustCalledComputationParameters(
         CHECK_EQ(operands.size(), computation->num_parameters());
         for (int64 i = 0; i < operands.size(); ++i) {
           auto parameter = computation->parameter_instruction(i);
-          ShapeUtil::ForEachMutableSubshape(
-              parameter->mutable_shape(),
-              [this, i, hlo, &operands, parameter](Shape* subshape,
+          ShapeUtil::ForEachSubshape(
+              parameter->shape(),
+              [this, i, hlo, &operands, parameter](const Shape& /* subshape */,
                                                    const ShapeIndex& index) {
                 if (!ShapeUtil::IsLeafIndex(parameter->shape(), index)) {
                   return;
                 }
                 PrimitiveType operand_type =
-                    ShapeUtil::GetSubshape(operands[i]->shape(), index)
-                        .element_type();
-                if (subshape->element_type() == operand_type) {
+                    OutputTypeAfterChange(operands[i], index);
+                if (OutputTypeAfterChange(parameter, index) == operand_type) {
                   return;
                 }
-                CHECK(operand_type == F32 || operand_type == BF16);
-                subshape->set_element_type(operand_type);
-                changed_ = true;
+                AddToOrRemoveFromBF16ChangeSet(parameter, index, operand_type);
                 VLOG(2) << "Called computation parameter "
                         << parameter->ToString() << " at shape index " << index
-                        << " adjusted to match operand in HLO "
-                        << hlo->ToString();
+                        << " adjusted to "
+                        << (operand_type == BF16 ? "BF16" : "F32")
+                        << " to match operand in HLO " << hlo->ToString();
               });
         }
       };
@@ -348,51 +427,48 @@ void BFloat16Propagation::AdjustCalledComputationParameters(
 
 void BFloat16Propagation::AdjustCalledComputationRoot(HloInstruction* hlo) {
   auto adjust_computation = [this, hlo](HloComputation* computation,
-                                        const Shape& output_shape) {
+                                        HloInstruction* output) {
     // Adjust root.
     HloInstruction* root = computation->root_instruction();
-    ShapeUtil::ForEachMutableSubshape(
-        root->mutable_shape(), [this, hlo, root, &output_shape](
-                                   Shape* subshape, const ShapeIndex& index) {
-          if (!ShapeUtil::IsLeafIndex(hlo->shape(), index)) {
-            return;
-          }
-          const PrimitiveType output_type =
-              ShapeUtil::GetSubshape(output_shape, index).element_type();
-          if (subshape->element_type() == output_type) {
-            return;
-          }
-          CHECK(output_type == F32 || output_type == BF16);
-          subshape->set_element_type(output_type);
-          // It's possible that output_type is F32, but the root instruction's
-          // type is BF16; e.g., a fusion node's output was changed to BF16
-          // initially but then adjusted back to F32, and the fusion computation
-          // is now being adjusted after the fusion node.
-          if (output_type == F32) {
-            for (const auto* value :
-                 dataflow_->GetValueSet(root, index).values()) {
-              // We rely on the fact that this adjustment works in reverse
-              // topological order so that called computation will be
-              // processed later. Adding the value to
-              // values_that_must_be_kept_as_f32_ will ensure the
-              // correctness of the adjustment for HLOs that will be
-              // processed later.
-              values_that_must_be_kept_as_f32_.insert(value);
-            }
-          }
-          changed_ = true;
-          VLOG(2) << "Called computation root " << root->ToString()
-                  << " at shape index " << index
-                  << " adjusted to match output shape of " << hlo->ToString();
-        });
+    ShapeUtil::ForEachSubshape(root->shape(), [this, hlo, root, output](
+                                                  const Shape& /* subshape */,
+                                                  const ShapeIndex& index) {
+      if (!ShapeUtil::IsLeafIndex(hlo->shape(), index)) {
+        return;
+      }
+      const PrimitiveType output_type = OutputTypeAfterChange(output, index);
+      if (OutputTypeAfterChange(root, index) == output_type) {
+        return;
+      }
+      AddToOrRemoveFromBF16ChangeSet(root, index, output_type);
+      // It's possible that output_type is F32, but the root instruction's
+      // type is BF16; e.g., a fusion node's output was changed to BF16
+      // initially but then adjusted back to F32, and the fusion computation
+      // is now being adjusted after the fusion node.
+      if (output_type == F32) {
+        for (const auto* value : dataflow_->GetValueSet(root, index).values()) {
+          // We rely on the fact that this adjustment works in reverse
+          // topological order so that called computation will be
+          // processed later. Adding the value to
+          // values_that_must_be_kept_as_f32_ will ensure the
+          // correctness of the adjustment for HLOs that will be
+          // processed later.
+          values_that_must_be_kept_as_f32_.insert(value);
+        }
+      }
+      VLOG(2) << "Called computation root " << root->ToString()
+              << " at shape index " << index << " adjusted to "
+              << (output_type == BF16 ? "BF16" : "F32")
+              << " to match output shape of " << hlo->ToString();
+    });
   };
 
   switch (hlo->opcode()) {
     case HloOpcode::kFusion:
-      adjust_computation(hlo->fused_instructions_computation(), hlo->shape());
+      adjust_computation(hlo->fused_instructions_computation(), hlo);
       break;
     case HloOpcode::kWhile:
-      adjust_computation(hlo->while_body(), hlo->shape());
+      adjust_computation(hlo->while_body(), hlo);
       break;
     default:
       break;
@@ -409,16 +485,19 @@ bool BFloat16Propagation::ResolveInconsistencyOfAliasingBuffersHelper(
   for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
     auto hlo = *inst_it;
     auto adjust_hlo_output = [this, hlo, ¶meter_changed](
-                                 Shape* subshape, const ShapeIndex& index) {
-      if (subshape->element_type() != F32 && subshape->element_type() != BF16) {
+                                 const Shape& /* subshape */,
+                                 const ShapeIndex& index) {
+      auto output_type = OutputTypeAfterChange(hlo, index);
+      if (output_type != F32 && output_type != BF16) {
         return;
       }
       PrimitiveType type = BF16;
       for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) {
-        if (value->shape().element_type() == BF16) {
+        auto value_type = ValueTypeAfterChange(value);
+        if (value_type == BF16) {
           continue;
         }
-        CHECK_EQ(value->shape().element_type(), F32);
+        CHECK_EQ(value_type, F32);
         type = F32;
         break;
       }
@@ -437,16 +516,17 @@ bool BFloat16Propagation::ResolveInconsistencyOfAliasingBuffersHelper(
           values_that_must_be_kept_as_f32_.insert(value);
         }
       }
-      if (type != subshape->element_type()) {
-        subshape->set_element_type(type);
+      if (type != output_type) {
+        AddToOrRemoveFromBF16ChangeSet(hlo, index, type);
         VLOG(2) << "HloInstruction output at shape index " << index
-                << " adjusted to " << *subshape << ": " << hlo->ToString();
+                << " adjusted to " << (type == BF16 ? "BF16" : "F32") << ": "
+                << hlo->ToString();
         if (hlo->opcode() == HloOpcode::kParameter) {
           parameter_changed = true;
         }
       }
     };
-    ShapeUtil::ForEachMutableSubshape(hlo->mutable_shape(), adjust_hlo_output);
+    ShapeUtil::ForEachSubshape(hlo->shape(), adjust_hlo_output);
     AdjustCalledComputationRoot(hlo);
     if (hlo->opcode() == HloOpcode::kWhile) {
       // We need to run on the while body and condition repeatedly until a fixed
@@ -463,8 +543,7 @@ bool BFloat16Propagation::ResolveInconsistencyOfAliasingBuffersHelper(
              ResolveInconsistencyOfAliasingBuffersHelper(hlo->while_body(),
                                                          &visited_in_while)) {
         visited_in_while.clear();
-        ShapeUtil::ForEachMutableSubshape(hlo->mutable_shape(),
-                                          adjust_hlo_output);
+        ShapeUtil::ForEachSubshape(hlo->shape(), adjust_hlo_output);
         AdjustCalledComputationRoot(hlo);
       }
       visited_computations->insert(visited_in_while.begin(),
@@ -478,7 +557,7 @@ bool BFloat16Propagation::ResolveInconsistencyOfAliasingBuffersHelper(
   return parameter_changed;
 }
 
-Status BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers(
+void BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers(
     HloModule* module) {
   std::list computations_topological_order =
       module->MakeComputationPostOrder();
@@ -490,7 +569,9 @@ Status BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers(
     }
     ResolveInconsistencyOfAliasingBuffersHelper(*comp_it, &resolved);
   }
+}
 
+Status BFloat16Propagation::ResolveInconsistentFusions(HloModule* module) {
   // We could have changed a fusion computation's root shape to have a different
   // precision than the fusion node's output, if the fusion root does not
   // define a buffer (e.g., a tuple). Now we add conversions after such fusion
@@ -517,7 +598,7 @@ Status BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers(
   // (2) after adding conversion
   // (3) after tuple simplifier and DCE.
   bool needs_tuple_simplifier = false;
-  for (auto computation : computations_topological_order) {
+  for (auto computation : module->MakeComputationPostOrder()) {
     auto insts = computation->MakeInstructionPostOrder();
     for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
       auto hlo = *inst_it;
@@ -587,7 +668,14 @@ Status BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers(
       needs_tuple_simplifier |= ShapeUtil::IsTuple(hlo->shape());
     }
   }
+  if (needs_tuple_simplifier) {
+    TupleSimplifier tuple_simplifier;
+    TF_RETURN_IF_ERROR(tuple_simplifier.Run(module).status());
+  }
+  return Status::OK();
+}
 
+Status BFloat16Propagation::ResolveConvertedConstants(HloModule* module) {
   // We may have converted some constants from F32 to BF16, so adjust the
   // constant literals in such cases. We do this here instead of when the
   // constant node's is changed because 1) the HloInstruction interface does not
@@ -598,8 +686,7 @@ Status BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers(
   // can avoid repeated conversions.
   //
   // TODO(b/73833576): Consider resetting literal in HloInstruction.
-  bool needs_dce = needs_tuple_simplifier;
-  for (auto computation : computations_topological_order) {
+  for (auto computation : module->MakeComputationPostOrder()) {
     for (auto hlo : computation->MakeInstructionPostOrder()) {
       if (hlo->opcode() != HloOpcode::kConstant) {
         continue;
@@ -612,23 +699,13 @@ Status BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers(
         auto new_constant = computation->AddInstruction(
             HloInstruction::CreateConstant(std::move(converted_literal)));
         TF_RETURN_IF_ERROR(hlo->ReplaceAllUsesWith(new_constant));
-        needs_dce = true;
       }
     }
   }
-
-  if (needs_tuple_simplifier) {
-    TupleSimplifier tuple_simplifier;
-    TF_RETURN_IF_ERROR(tuple_simplifier.Run(module).status());
-  }
-  if (needs_dce) {
-    HloDCE dce;
-    TF_RETURN_IF_ERROR(dce.Run(module).status());
-  }
   return Status::OK();
 }
 
-Status BFloat16Propagation::RemoveNoopConversions(HloModule* module) {
+Status BFloat16Propagation::SkipNoopConversions(HloModule* module) {
   for (auto computation : module->computations()) {
     for (auto hlo : computation->MakeInstructionPostOrder()) {
       if (hlo->opcode() != HloOpcode::kConvert) {
@@ -643,7 +720,6 @@ Status BFloat16Propagation::RemoveNoopConversions(HloModule* module) {
       if (is_root) {
         computation->set_root_instruction(source);
       }
-      TF_RETURN_IF_ERROR(computation->RemoveInstructionAndUnusedOperands(hlo));
     }
   }
   return Status::OK();
@@ -652,8 +728,18 @@ Status BFloat16Propagation::RemoveNoopConversions(HloModule* module) {
 // The algorithm first does a forward pass (parameters to root) to determine a
 // set of instructions to consider using bfloat16, then does a backward pass to
 // determine the precisions of those instructions according to the need of
-// their users.
+// their users. During the backward pass, the potential changes are stored in
+// changes_to_bf16_ which are subject to further adjustments then applied to the
+// HLOs.
 StatusOr BFloat16Propagation::Run(HloModule* module) {
+  consider_using_bfloat16_.clear();
+  instructions_visited_in_backward_pass_.clear();
+  computations_visited_in_backward_pass_.clear();
+  values_that_must_be_kept_as_f32_.clear();
+  caller_counts_.clear();
+  changes_to_bf16_.clear();
+  changed_ = false;
+
   TF_ASSIGN_OR_RETURN(dataflow_, HloDataflowAnalysis::Run(*module));
 
   std::list computations_topological_order =
@@ -686,8 +772,24 @@ StatusOr BFloat16Propagation::Run(HloModule* module) {
     }
     auto insts = (*comp_it)->MakeInstructionPostOrder();
     for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
-      DetermineAndMutateInstructionPrecision(*inst_it,
-                                             /*skip_parameters=*/true);
+      DetermineInstructionPrecision(*inst_it,
+                                    /*skip_parameters=*/true);
+    }
+  }
+
+  // It's possible that an instruction does not define a buffer, but the
+  // defining instruction's shape has changed. So we need to adjust the output
+  // shapes of instructions according to the HLO values they refer to.
+  ResolveInconsistencyOfAliasingBuffers(module);
+
+  // Apply the changes in changes_to_bf16_.
+  for (auto& change : changes_to_bf16_) {
+    auto shape = change.first->mutable_shape();
+    for (const auto& index : change.second) {
+      auto subshape = ShapeUtil::GetMutableSubshape(shape, index);
+      CHECK_EQ(subshape->element_type(), F32);
+      subshape->set_element_type(BF16);
+      changed_ = true;
     }
   }
 
@@ -695,15 +797,56 @@ StatusOr BFloat16Propagation::Run(HloModule* module) {
     return false;
   }
 
-  // It's possible that an instruction does not define a buffer, but the
-  // defining instruction's shape has changed. So we need to adjust the output
-  // shapes of instructions according to the HLO values they refer to.
-  TF_RETURN_IF_ERROR(ResolveInconsistencyOfAliasingBuffers(module));
+  TF_RETURN_IF_ERROR(ResolveInconsistentFusions(module));
+  TF_RETURN_IF_ERROR(ResolveConvertedConstants(module));
 
   // This pass could have turned an F32 -> BF16 conversion to a no-op (BF16 ->
-  // BF16), so we remove them now.
-  TF_RETURN_IF_ERROR(RemoveNoopConversions(module));
+  // BF16), so we skip them now.
+  TF_RETURN_IF_ERROR(SkipNoopConversions(module));
+
+  {
+    // We may have dead HLOs after ResolveInconsistentFusions,
+    // ResolveConvertedConstants and SkipNoopConversions.
+    HloDCE dce;
+    TF_RETURN_IF_ERROR(dce.Run(module).status());
+  }
   return true;
 }
 
+PrimitiveType BFloat16Propagation::OutputTypeAfterChange(
+    HloInstruction* hlo, const ShapeIndex& index) const {
+  PrimitiveType type_on_hlo =
+      ShapeUtil::GetSubshape(hlo->shape(), index).element_type();
+  if (type_on_hlo != F32) {
+    return type_on_hlo;
+  }
+  auto it = changes_to_bf16_.find(hlo);
+  if (it == changes_to_bf16_.end()) {
+    return type_on_hlo;
+  }
+  return ContainsKey(it->second, index) ? BF16 : F32;
+}
+
+PrimitiveType BFloat16Propagation::ValueTypeAfterChange(
+    const HloValue* value) const {
+  auto hlo = value->defining_instruction();
+  const auto& position = value->defining_position();
+  return OutputTypeAfterChange(hlo, position.index);
+}
+
+void BFloat16Propagation::AddToOrRemoveFromBF16ChangeSet(
+    HloInstruction* hlo, const ShapeIndex& index, PrimitiveType target_type) {
+  if (target_type == BF16) {
+    auto& entry = changes_to_bf16_[hlo];
+    entry.insert(index);
+  } else {
+    CHECK_EQ(target_type, F32);
+    auto it = changes_to_bf16_.find(hlo);
+    if (it == changes_to_bf16_.end()) {
+      return;
+    }
+    it->second.erase(index);
+  }
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.h b/tensorflow/compiler/xla/service/bfloat16_propagation.h
index 1744e9db90aeff..de0355ddfca127 100644
--- a/tensorflow/compiler/xla/service/bfloat16_propagation.h
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation.h
@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
+#include "tensorflow/core/lib/hash/hash.h"
 
 namespace xla {
 
@@ -85,30 +86,39 @@ class BFloat16Propagation : public HloPassInterface {
   tensorflow::gtl::FlatSet consider_using_bfloat16_;
 
   // ***************************
-  // Functions called and state produced by the backward mutation pass (from
-  // root to parameters).
+  // Functions called and state produced by the backward pass (from root to
+  // parameters) that finds opportunities to use BF16.
 
-  // Determines the precision for the given instruction in the mutation pass.
-  void DetermineAndMutateInstructionPrecision(HloInstruction* hlo,
-                                              bool skip_parameters);
+  // Determines the precision for the given instruction in the
+  // opportunity-finding pass.
+  void DetermineInstructionPrecision(HloInstruction* hlo, bool skip_parameters);
 
-  // Special handling in the mutation pass for fusion computations.
+  // Special handling in the opportunity-finding pass for fusion computations.
   //
   // Precondition: hlo->opcode() == kFusion
-  void DetermineAndMutateFusionComputationPrecision(HloInstruction* fusion);
+  void DetermineFusionComputationPrecision(HloInstruction* fusion);
 
-  // Special handling in the mutation pass for while computations.
+  // Reverts changes to BF16 that will not propagate outside a fusion
+  // computation. This avoids BF16 casts overhead inside a fusion which won't
+  // save memory bandwidth.
+  //
+  // Precondition: hlo->opcode() == kFusion
+  void RevertIfFusionInternalBF16Changes(HloInstruction* fusion);
+
+  // Special handling in the opportunity-finding pass for while computations.
   //
   // Precondition: hlo->opcode() == kWhile
-  void DetermineAndMutateWhileComputationsPrecision(HloInstruction* while_hlo);
+  void DetermineWhileComputationsPrecision(HloInstruction* while_hlo);
 
-  // The set of HloInstructions that have been visited in the mutation pass.
+  // The set of HloInstructions that have been visited in the
+  // opportunity-finding pass.
   tensorflow::gtl::FlatSet
-      instructions_visited_in_mutation_pass_;
+      instructions_visited_in_backward_pass_;
 
-  // The set of HloComputations that have been visited in the mutation pass.
+  // The set of HloComputations that have been visited in the
+  // opportunity-finding pass.
   tensorflow::gtl::FlatSet
-      computations_visited_in_mutation_pass_;
+      computations_visited_in_backward_pass_;
 
   // ***************************
   // Functions called by the final inconsistency resolving pass.
@@ -116,7 +126,7 @@ class BFloat16Propagation : public HloPassInterface {
   // Adjusts the output shapes of HloInstructions such that if two
   // HloInstructions have aliasing buffers in their outputs, they must have the
   // same precision.
-  Status ResolveInconsistencyOfAliasingBuffers(HloModule* module);
+  void ResolveInconsistencyOfAliasingBuffers(HloModule* module);
 
   // Resolves inconsistency of aliasing buffers for the given computation, and
   // recursively runs on a while instruction's condition and body until a fixed
@@ -134,9 +144,19 @@ class BFloat16Propagation : public HloPassInterface {
   void AdjustCalledComputationRoot(HloInstruction* hlo);
 
   // ***************************
-  // Removes no-op conversions (same source and target shapes) that can be
-  // produced this pass.
-  Status RemoveNoopConversions(HloModule* module);
+  // Functions called after changes in changes_to_bf16_ are applied.
+
+  // Resolves inconsistencies introduced by this pass for fusions with
+  // tuple-type output.
+  Status ResolveInconsistentFusions(HloModule* module);
+
+  // Converts the literals in kConstant HLOs which have their types changed to
+  // BF16 by this pass.
+  Status ResolveConvertedConstants(HloModule* module);
+
+  // Skips no-op conversions (same source and target shapes) that can be
+  // produced this pass, i.e., replaces them in their uses with their operands.
+  Status SkipNoopConversions(HloModule* module);
 
   // ***************************
   // Functions called and state used by two or more passes.
@@ -146,6 +166,23 @@ class BFloat16Propagation : public HloPassInterface {
   bool AllUsersConsumeBF16(const HloInstruction& hlo,
                            const ShapeIndex& index) const;
 
+  // The output element type of the HLO at the given shape index after changes
+  // in changes_to_bf16_ are applied.
+  PrimitiveType OutputTypeAfterChange(HloInstruction* hlo,
+                                      const ShapeIndex& index) const;
+
+  // The element type of the HLO value after changes in changes_to_bf16_ are
+  // applied.
+  PrimitiveType ValueTypeAfterChange(const HloValue* value) const;
+
+  // If target_type == BF16, adds the HLO at the given index to
+  // changes_to_bf16_; otherwise, target_type must be F32 and this function
+  // removes the HLO at the given index from changes_to_bf16_ if it was earlier
+  // added.
+  void AddToOrRemoveFromBF16ChangeSet(HloInstruction* hlo,
+                                      const ShapeIndex& index,
+                                      PrimitiveType target_type);
+
   // The set of F32 HLO values that must be kept in F32.
   tensorflow::gtl::FlatSet values_that_must_be_kept_as_f32_;
 
@@ -153,10 +190,28 @@ class BFloat16Propagation : public HloPassInterface {
   // module. Populated at the beginning of this pass.
   tensorflow::gtl::FlatMap caller_counts_;
 
+  // We first store the potential F32-to-BF16 changes to changes_to_bf16_, which
+  // are subject to further adjustment, then finally applied to the HLOs. This
+  // avoids setting changed_ to true but all changes are reverted during
+  // adjustment.
+  struct IndexHasher {
+    int64 operator()(const ShapeIndex& index) const {
+      int64 hash = 0;
+      for (int64 i : index) {
+        hash = tensorflow::Hash64Combine(hash, std::hash()(i));
+      }
+      return hash;
+    }
+  };
+  tensorflow::gtl::FlatMap>
+      changes_to_bf16_;
+
+  // Whether the last processed HLO module has been changed by this pass.
+  bool changed_ = false;
+
   const BFloat16Support* bfloat16_support_;
   std::unique_ptr dataflow_;
-
-  bool changed_ = false;
 };
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
index 183db1652e498e..313910a861f7f4 100644
--- a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
@@ -323,6 +323,37 @@ TEST_F(BFloat16PropagationTest, PropagateThroughFusion) {
   EXPECT_TRUE(OutputsBF16(b_f1));
 }
 
+// Tests that changes to BF16 that cannot be propagated outside a fusion are
+// discarded.
+TEST_F(BFloat16PropagationTest, DiscardFusionInternalBF16Changes) {
+  auto module = CreateNewModule();
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {2, 4});
+
+  HloInstruction* param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, shape, "param"));
+  HloInstruction* add = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, param));
+
+  auto builder_f = HloComputation::Builder("fusion");
+  HloInstruction* a_f =
+      builder_f.AddInstruction(HloInstruction::CreateParameter(0, shape, "a"));
+  HloInstruction* b_f =
+      builder_f.AddInstruction(HloInstruction::CreateParameter(1, shape, "b"));
+  HloInstruction* add_f = builder_f.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a_f, b_f));
+  HloInstruction* dot_f = builder_f.AddInstruction(HloInstruction::CreateBinary(
+      ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, add_f, add_f));
+  auto comp_f = module->AddEmbeddedComputation(builder_f.Build());
+  auto fusion = builder.AddInstruction(HloInstruction::CreateFusion(
+      dot_f->shape(), HloInstruction::FusionKind::kCustom, {add, add}, comp_f));
+
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_FALSE(PropagatePrecision(module.get()));
+  EXPECT_EQ(computation->root_instruction(), fusion);
+}
+
 // Tests that if 1) the root instruction of a fusion is a tuple, 2) the fusion
 // outputs are only used by a dot, and 3) one element of the tuple is used by
 // an add in the fusion computation, then the propagation pass should create a

From 4704ae7af1918755d72f159f49d98d35da6eb6fa Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 15:21:17 -0700
Subject: [PATCH 0304/1691] Optimize LogicalOr and LogicalAnd with all true or
 false inputs:

LogicalOr(x, true) = true
LogicalOr(x, false) = x
LogicalAnd(x, true) = x
LogicalAnd(x, false) = false

and similar if the first argument is constant.

PiperOrigin-RevId: 195161140
---
 .../grappler/optimizers/constant_folding.cc   | 113 +++++++++++-------
 .../grappler/optimizers/constant_folding.h    |  13 +-
 .../optimizers/constant_folding_test.cc       |  50 ++++++--
 .../feature_column/feature_column_test.py     |  20 +++-
 4 files changed, 132 insertions(+), 64 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 4801f18619e672..47d882768634c4 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -866,6 +866,25 @@ Status CreateConstantTensorAttrValue(DataType type, double value,
 }
 
 #undef SET_TENSOR_CAL_CASE
+
+DataType GetDataTypeFromNodeOrProps(const NodeDef& node,
+                                    const GraphProperties& properties) {
+  DataType dtype = DT_INVALID;
+  if (node.attr().count("T") == 1) {
+    dtype = node.attr().at("T").type();
+  } else if (node.attr().count("dtype") == 1) {
+    dtype = node.attr().at("dtype").type();
+  } else if (IsLogicalOr(node) || IsLogicalAnd(node)) {
+    dtype = DT_BOOL;
+  } else {
+    auto output_props = properties.GetOutputProperties(node.name());
+    if (!output_props.empty()) {
+      dtype = output_props[0].dtype();
+    }
+  }
+  return dtype;
+}
+
 }  // namespace
 
 // static
@@ -1412,6 +1431,7 @@ bool ConstantFolding::IsOnes(const NodeDef& node) const {
   }
   const auto dtype = node.attr().at("dtype").type();
   switch (dtype) {
+    IS_ONES_CASE(DT_BOOL);
     IS_ONES_CASE(DT_HALF);
     IS_ONES_CASE(DT_BFLOAT16);
     IS_ONES_CASE(DT_FLOAT);
@@ -1447,6 +1467,7 @@ bool ConstantFolding::IsZeros(const NodeDef& node) const {
   }
   const auto dtype = node.attr().at("dtype").type();
   switch (dtype) {
+    IS_ZEROS_CASE(DT_BOOL);
     IS_ZEROS_CASE(DT_HALF);
     IS_ZEROS_CASE(DT_BFLOAT16);
     IS_ZEROS_CASE(DT_FLOAT);
@@ -1466,14 +1487,15 @@ bool ConstantFolding::IsZeros(const NodeDef& node) const {
   return false;
 }
 
-void ConstantFolding::ReplaceOperationWithIdentity(int input_to_forward,
-                                                   NodeDef* node,
-                                                   GraphDef* graph) {
+void ConstantFolding::ReplaceOperationWithIdentity(
+    int input_to_forward, const GraphProperties& properties, NodeDef* node,
+    GraphDef* graph) {
+  const DataType dtype = GetDataTypeFromNodeOrProps(*node, properties);
+  if (dtype == DT_INVALID) return;
+
   node->set_op("Identity");
-  DataType dtype = node->attr().at("T").type();
   node->clear_attr();
   (*node->mutable_attr())["T"].set_type(dtype);
-
   // Propagate the designated input through the identity.
   node->mutable_input()->SwapElements(0, input_to_forward);
   // Add all other inputs as control dependencies.
@@ -1489,14 +1511,15 @@ void ConstantFolding::ReplaceOperationWithIdentity(int input_to_forward,
   graph_modified_ = true;
 }
 
-void ConstantFolding::ReplaceOperationWithSnapshot(int input_to_forward,
-                                                   NodeDef* node,
-                                                   GraphDef* graph) {
+void ConstantFolding::ReplaceOperationWithSnapshot(
+    int input_to_forward, const GraphProperties& properties, NodeDef* node,
+    GraphDef* graph) {
+  const DataType dtype = GetDataTypeFromNodeOrProps(*node, properties);
+  if (dtype == DT_INVALID) return;
+
   node->set_op("Snapshot");
-  DataType dtype = node->attr().at("T").type();
   node->clear_attr();
   (*node->mutable_attr())["T"].set_type(dtype);
-
   // Propagate the designated input through the Snapshot.
   node->mutable_input()->SwapElements(0, input_to_forward);
   // Add all other inputs as control dependencies.
@@ -1535,15 +1558,18 @@ void ConstantFolding::ReplaceSubtractionFromZeroByNegation(NodeDef* node,
 }
 
 Status ConstantFolding::ReplaceOperationWithConstant(
-    double value, const AttrValue& dtype_attr, const TensorShapeProto& shape,
-    NodeDef* node, GraphDef* graph) {
+    double value, const GraphProperties& properties,
+    const TensorShapeProto& shape, NodeDef* node, GraphDef* graph) {
+  const DataType dtype = GetDataTypeFromNodeOrProps(*node, properties);
+  if (dtype == DT_INVALID) return Status::OK();
+
   AttrValue tensor_attr;
-  TF_RETURN_IF_ERROR(CreateConstantTensorAttrValue(dtype_attr.type(), value,
-                                                   shape, &tensor_attr));
+  TF_RETURN_IF_ERROR(
+      CreateConstantTensorAttrValue(dtype, value, shape, &tensor_attr));
+  node->set_op("Const");
   node->clear_attr();
-  node->mutable_attr()->insert({"dtype", dtype_attr});
+  (*node->mutable_attr())["dtype"].set_type(dtype);
   node->mutable_attr()->insert({"value", tensor_attr});
-  node->set_op("Const");
   // Convert all inputs to control dependencies.
   for (int i = 0; i < node->input_size(); ++i) {
     if (IsControlInput(node->input(i))) {
@@ -1566,12 +1592,12 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
     NodeDef* node = optimized_graph->mutable_node(i);
 
     if (IsSplit(*node) && node->attr().at("num_split").i() == 1) {
-      ReplaceOperationWithIdentity(1, node, optimized_graph);
+      ReplaceOperationWithIdentity(1, *properties, node, optimized_graph);
       continue;
     }
 
     if (IsSplitV(*node) && node->attr().at("num_split").i() == 1) {
-      ReplaceOperationWithIdentity(0, node, optimized_graph);
+      ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
       continue;
     }
 
@@ -1611,7 +1637,7 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
           replaceable &= shape.dim(j).size() == 1 || j == permutation[j];
         }
         if (replaceable) {
-          ReplaceOperationWithIdentity(0, node, optimized_graph);
+          ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
           continue;
         }
       }
@@ -1626,7 +1652,7 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
       // unknown_rank == false && (dim_size == 0 || first dim is of size 1)
       if (!shape.unknown_rank() &&
           (shape.dim_size() == 0 || shape.dim(0).size() == 1)) {
-        ReplaceOperationWithIdentity(0, node, optimized_graph);
+        ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
         continue;
       }
     }
@@ -1651,11 +1677,11 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
         for (int j = 0; j < axis.NumElements(); ++j) {
           // value of axis can be negative.
           if (axis.dtype() == DT_INT64) {
-            target_axes.insert(
-                (axis.vec()(j) + shape.dim_size()) % shape.dim_size());
+            target_axes.insert((axis.vec()(j) + shape.dim_size()) %
+                               shape.dim_size());
           } else {
-            target_axes.insert(
-                (axis.vec()(j) + shape.dim_size()) % shape.dim_size());
+            target_axes.insert((axis.vec()(j) + shape.dim_size()) %
+                               shape.dim_size());
           }
         }
 
@@ -1669,7 +1695,7 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
                          target_axes.find(j) == target_axes.end();
         }
         if (replaceable) {
-          ReplaceOperationWithIdentity(0, node, optimized_graph);
+          ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
           continue;
         }
       }
@@ -1711,7 +1737,7 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
           }
         }
         if (replaceable) {
-          ReplaceOperationWithIdentity(0, node, optimized_graph);
+          ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
           continue;
         }
       }
@@ -1740,7 +1766,7 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
           }
         }
         if (replaceable) {
-          ReplaceOperationWithIdentity(0, node, optimized_graph);
+          ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
           continue;
         }
       }
@@ -1764,7 +1790,7 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
           replaceable &= flatten(j) == 0;
         }
         if (replaceable) {
-          ReplaceOperationWithIdentity(0, node, optimized_graph);
+          ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
           continue;
         }
       }
@@ -1784,7 +1810,7 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
         replaceable &= shape.dim(j).size() > 1;
       }
       if (replaceable) {
-        ReplaceOperationWithIdentity(0, node, optimized_graph);
+        ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
         continue;
       }
     }
@@ -1996,9 +2022,9 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
       continue;
     }
 
-    const bool is_mul = IsMul(*node);
+    const bool is_mul = IsMul(*node) || IsLogicalAnd(*node);
     const bool is_matmul = IsMatMul(*node);
-    const bool is_add = IsAdd(*node) || IsBiasAdd(*node);
+    const bool is_add = IsAdd(*node) || IsBiasAdd(*node) || IsLogicalOr(*node);
     const bool is_sub = IsSub(*node);
     const bool is_any_div = IsAnyDiv(*node);
     // Simplify arithmetic operations with ones or zeros.
@@ -2025,7 +2051,7 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
       if (y_matches_output_shape &&
           ((is_mul && x_is_one) || (is_add && x_is_zero))) {
         // 1 * y = y or 0 + y = y.
-        ReplaceOperationWithSnapshot(1, node, optimized_graph);
+        ReplaceOperationWithSnapshot(1, *properties, node, optimized_graph);
         continue;
       }
 
@@ -2052,10 +2078,18 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
       if (x_matches_output_shape && (((is_mul || is_any_div) && y_is_one) ||
                                      ((is_add || is_sub) && y_is_zero))) {
         // x * 1 = x or x / 1 = x or x +/- 0 = x
-        ReplaceOperationWithSnapshot(0, node, optimized_graph);
+        ReplaceOperationWithSnapshot(0, *properties, node, optimized_graph);
         continue;
       }
 
+      // x OR true = true OR y = true.
+      const PartialTensorShape shp(output_shape);
+      if (shp.IsFullyDefined() && IsLogicalOr(*node) &&
+          (y_is_one || x_is_one)) {
+        TF_RETURN_IF_ERROR(ReplaceOperationWithConstant(
+            1, *properties, output_shape, node, optimized_graph));
+      }
+
       // Simplify multiplication and matmul by zeros.
       // Also optimize zeros divided by a tensor, but only if we are in
       // aggressive mode, since we might get rid of divisions by zero.
@@ -2063,26 +2097,19 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
           is_any_div && x_is_zero && is_aggressive;
       if ((x_is_zero || y_is_zero) &&
           (is_mul || is_matmul || optimize_zeros_divided_by_y)) {
-        const PartialTensorShape shp(output_shape);
         if (shp.IsFullyDefined()) {
-          AttrValue dtype_attr;
-          if (node->op() == "SparseMatMul") {
-            dtype_attr.set_type(DT_FLOAT);
-          } else {
-            dtype_attr = node->attr().at("T");
-          }
           TF_RETURN_IF_ERROR(ReplaceOperationWithConstant(
-              0, dtype_attr, output_shape, node, optimized_graph));
+              0, *properties, output_shape, node, optimized_graph));
           continue;
         }
         // Even if an input shape is only partially known, we may known that it
         // matches the output shape and thus forward the corresponding zero
         // input.
         if ((is_mul || is_any_div) && x_is_zero && x_matches_output_shape) {
-          ReplaceOperationWithIdentity(0, node, optimized_graph);
+          ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
           continue;
         } else if (is_mul && y_is_zero && y_matches_output_shape) {
-          ReplaceOperationWithIdentity(1, node, optimized_graph);
+          ReplaceOperationWithIdentity(1, *properties, node, optimized_graph);
           continue;
         }
       }
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index eb06cd081f7f3e..a694f1721ad416 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -78,12 +78,15 @@ class ConstantFolding : public GraphOptimizer {
 
   bool IsOnes(const NodeDef& node) const;
   bool IsZeros(const NodeDef& node) const;
-  void ReplaceOperationWithIdentity(int input_to_forward, NodeDef* node,
-                                    GraphDef* graph);
-  void ReplaceOperationWithSnapshot(int input_to_forward, NodeDef* node,
-                                    GraphDef* graph);
+  void ReplaceOperationWithIdentity(int input_to_forward,
+                                    const GraphProperties& properties,
+                                    NodeDef* node, GraphDef* graph);
+  void ReplaceOperationWithSnapshot(int input_to_forward,
+                                    const GraphProperties& properties,
+                                    NodeDef* node, GraphDef* graph);
   void ReplaceSubtractionFromZeroByNegation(NodeDef* node, GraphDef* graph);
-  Status ReplaceOperationWithConstant(double value, const AttrValue& dtype_attr,
+  Status ReplaceOperationWithConstant(double value,
+                                      const GraphProperties& properties,
                                       const TensorShapeProto& shape,
                                       NodeDef* node, GraphDef* graph);
   void ReplaceDivisionOfOnesByReciprocal(NodeDef* node, GraphDef* graph);
diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index 306ddd22d739d4..f018b217e66365 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -47,18 +47,30 @@ class ConstantFoldingTest : public GrapplerTest {
     }
     Output zeros = ops::Const(s.WithOpName("zeros"), zeros_t);
     Output ones = ops::Const(s.WithOpName("ones"), ones_t);
-    Output mul1 = ops::Mul(s.WithOpName("mul1"), x, zeros);
-    Output mul2 = ops::Mul(s.WithOpName("mul2"), x, ones);
-
+    Output mul1;
+    Output mul2;
+    Output add1;
+    Output add2;
+    if (DTYPE == DT_BOOL) {
+      mul1 = ops::LogicalAnd(s.WithOpName("mul1"), x, zeros);
+      mul2 = ops::LogicalAnd(s.WithOpName("mul2"), x, ones);
+      add1 = ops::LogicalOr(s.WithOpName("add1"), x, zeros);
+      add2 = ops::LogicalOr(s.WithOpName("add2"), x, ones);
+    } else {
+      mul1 = ops::Mul(s.WithOpName("mul1"), x, zeros);
+      mul2 = ops::Mul(s.WithOpName("mul2"), x, ones);
+      add1 = ops::Add(s.WithOpName("add1"), x, zeros);
+      add1 = ops::Add(s.WithOpName("add2"), x, ones);
+    }
     GrapplerItem item;
     TF_CHECK_OK(s.ToGraphDef(&item.graph));
-    item.fetch = {"mul1", "mul2"};
+    item.fetch = {"mul1", "mul2", "add1", "add2"};
     ConstantFolding optimizer(nullptr /* cpu_device */);
     GraphDef output;
     Status status = optimizer.Optimize(nullptr, item, &output);
     TF_EXPECT_OK(status);
-    LOG(INFO) << output.DebugString();
-    EXPECT_EQ(5, output.node_size());
+
+    EXPECT_EQ(7, output.node_size());
     for (int i = 0; i < output.node_size(); ++i) {
       const NodeDef& node = output.node(i);
       const string& name = node.name();
@@ -70,14 +82,27 @@ class ConstantFoldingTest : public GrapplerTest {
         EXPECT_EQ("Snapshot", node.op());
         EXPECT_EQ("x", node.input(0));
         EXPECT_EQ("^ones", node.input(1));
+      } else if (name == "add1") {
+        EXPECT_EQ("Snapshot", node.op());
+        EXPECT_EQ("x", node.input(0));
+        EXPECT_EQ("^zeros", node.input(1));
+      } else if (name == "add2") {
+        if (DTYPE == DT_BOOL) {
+          EXPECT_EQ("Const", node.op());
+          EXPECT_EQ("^x", node.input(0));
+          EXPECT_EQ("^ones", node.input(1));
+        } else {
+          EXPECT_EQ("Add", node.op());
+          EXPECT_EQ("x", node.input(0));
+          EXPECT_EQ("ones", node.input(1));
+        }
       }
     }
-    auto tensors_expected =
-        EvaluateNodes(item.graph, {"mul1", "mul2"}, {{"x", x_t}});
-    auto tensors = EvaluateNodes(output, {"mul1", "mul2"}, {{"x", x_t}});
-    EXPECT_EQ(2, tensors_expected.size());
-    EXPECT_EQ(2, tensors.size());
-    for (int i = 0; i < 2; ++i) {
+    auto tensors_expected = EvaluateNodes(item.graph, item.fetch, {{"x", x_t}});
+    auto tensors = EvaluateNodes(output, item.fetch, {{"x", x_t}});
+    EXPECT_EQ(4, tensors_expected.size());
+    EXPECT_EQ(4, tensors.size());
+    for (int i = 0; i < item.fetch.size(); ++i) {
       test::ExpectTensorEqual(tensors_expected[i], tensors[i]);
     }
   }
@@ -393,6 +418,7 @@ TEST_F(ConstantFoldingTest, NeutralElement) {
 }
 
 TEST_F(ConstantFoldingTest, NeutralElement_ShortFloats) {
+  SimpleNeutralElementTest();
   SimpleNeutralElementTest();
   SimpleNeutralElementTest();
 }
diff --git a/tensorflow/python/feature_column/feature_column_test.py b/tensorflow/python/feature_column/feature_column_test.py
index d963dd9b551c0e..b06540489ff842 100644
--- a/tensorflow/python/feature_column/feature_column_test.py
+++ b/tensorflow/python/feature_column/feature_column_test.py
@@ -25,6 +25,8 @@
 
 from tensorflow.core.example import example_pb2
 from tensorflow.core.example import feature_pb2
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.eager import backprop
 from tensorflow.python.eager import context
@@ -54,8 +56,8 @@
 from tensorflow.python.training import queue_runner_impl
 
 
-def _initialized_session():
-  sess = session.Session()
+def _initialized_session(config=None):
+  sess = session.Session(config=config)
   sess.run(variables_lib.global_variables_initializer())
   sess.run(lookup_ops.tables_initializer())
   return sess
@@ -6191,7 +6193,12 @@ def test_keras_linear_model_mismatched_dense_values(self):
               'values': ((.5,), (1.,))
           }, (column,),
           sparse_combiner='mean')
-      with _initialized_session():
+      # Disabling the constant folding optimizer here since it changes the
+      # error message differently on CPU and GPU.
+      config = config_pb2.ConfigProto()
+      config.graph_options.rewrite_options.constant_folding = (
+          rewriter_config_pb2.RewriterConfig.OFF)
+      with _initialized_session(config):
         with self.assertRaisesRegexp(errors.OpError, 'Incompatible shapes'):
           predictions.eval()
 
@@ -6284,7 +6291,12 @@ def test_linear_model_mismatched_dense_values(self):
               'values': ((.5,), (1.,))
           }, (column,),
           sparse_combiner='mean')
-      with _initialized_session():
+      # Disabling the constant folding optimizer here since it changes the
+      # error message differently on CPU and GPU.
+      config = config_pb2.ConfigProto()
+      config.graph_options.rewrite_options.constant_folding = (
+          rewriter_config_pb2.RewriterConfig.OFF)
+      with _initialized_session(config):
         with self.assertRaisesRegexp(errors.OpError, 'Incompatible shapes'):
           predictions.eval()
 

From 5e9e6967b47989aa9084fb328f5ef0c40fc146ef Mon Sep 17 00:00:00 2001
From: Peter Hawkins 
Date: Wed, 2 May 2018 16:22:41 -0700
Subject: [PATCH 0305/1691] Replaced calls to tensorflow::StringPiece::ToString
 with std::string conversions. That is, instances of sp.ToString() are
 replaced with std::string(sp).

This will allow tensorflow::StringPiece::ToString to be removed, which is necessary before it can be replaced with absl::string_view.

PiperOrigin-RevId: 195162126
---
 tensorflow/c/c_api.cc                            |  2 +-
 tensorflow/c/c_api_test.cc                       |  4 ++--
 tensorflow/c/checkpoint_reader.cc                |  6 +++---
 tensorflow/compiler/xla/shape_util.cc            |  8 ++++----
 tensorflow/compiler/xla/text_literal_reader.cc   | 10 +++++-----
 tensorflow/compiler/xla/text_literal_writer.cc   |  2 +-
 tensorflow/core/common_runtime/bfc_allocator.cc  |  2 +-
 tensorflow/core/common_runtime/graph_runner.cc   |  4 ++--
 tensorflow/core/common_runtime/session_state.cc  |  2 +-
 .../core/common_runtime/step_stats_collector.cc  |  6 +++---
 .../core/lib/monitoring/collection_registry.cc   |  8 ++++----
 .../core/lib/monitoring/collection_registry.h    |  4 ++--
 tensorflow/core/lib/monitoring/metric_def.h      |  4 ++--
 .../core/platform/cloud/curl_http_request.cc     |  4 ++--
 .../core/platform/cloud/gcs_file_system.cc       | 14 +++++++-------
 tensorflow/core/platform/cloud/oauth_client.cc   |  4 ++--
 .../core/platform/cloud/oauth_client_test.cc     |  8 ++++----
 tensorflow/stream_executor/lib/env.h             |  2 +-
 tensorflow/stream_executor/lib/path.cc           |  2 +-
 tensorflow/stream_executor/lib/str_util.h        |  2 +-
 .../freeze_requantization_ranges.cc              |  5 ++---
 .../graph_transforms/sparsify_gather_test.cc     |  4 ++--
 .../tools/graph_transforms/transform_graph.cc    | 16 ++++++++--------
 .../tools/graph_transforms/transform_utils.cc    |  2 +-
 24 files changed, 62 insertions(+), 63 deletions(-)

diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc
index 18eeb2816807ec..b86b277ac3200b 100644
--- a/tensorflow/c/c_api.cc
+++ b/tensorflow/c/c_api.cc
@@ -2097,7 +2097,7 @@ static void GraphImportGraphDefLocked(TF_Graph* graph, const GraphDef& def,
 
   for (int i = 0; i < size; ++i) {
     TensorId id = results.missing_unused_input_map_keys[i];
-    tf_results->missing_unused_key_names_data.push_back(id.first.ToString());
+    tf_results->missing_unused_key_names_data.push_back(std::string(id.first));
     tf_results->missing_unused_key_names[i] =
         tf_results->missing_unused_key_names_data.back().c_str();
     tf_results->missing_unused_key_indexes[i] = id.second;
diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc
index 9b86425aa5fbc2..577f10c5e69ea9 100644
--- a/tensorflow/c/c_api_test.cc
+++ b/tensorflow/c/c_api_test.cc
@@ -1368,7 +1368,7 @@ TEST(CAPI, SavedModel) {
   }
 
   const tensorflow::string input_op_name =
-      tensorflow::ParseTensorName(input_name).first.ToString();
+      std::string(tensorflow::ParseTensorName(input_name).first);
   TF_Operation* input_op =
       TF_GraphOperationByName(graph, input_op_name.c_str());
   ASSERT_TRUE(input_op != nullptr);
@@ -1376,7 +1376,7 @@ TEST(CAPI, SavedModel) {
   ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
 
   const tensorflow::string output_op_name =
-      tensorflow::ParseTensorName(output_name).first.ToString();
+      std::string(tensorflow::ParseTensorName(output_name).first);
   TF_Operation* output_op =
       TF_GraphOperationByName(graph, output_op_name.c_str());
   ASSERT_TRUE(output_op != nullptr);
diff --git a/tensorflow/c/checkpoint_reader.cc b/tensorflow/c/checkpoint_reader.cc
index b1f7bdaa5420a5..74bc25a491ac01 100644
--- a/tensorflow/c/checkpoint_reader.cc
+++ b/tensorflow/c/checkpoint_reader.cc
@@ -125,7 +125,7 @@ CheckpointReader::BuildV2VarMaps() {
       const auto& slice_proto = entry.slices(i);
       CHECK(filtered_keys
                 .insert(EncodeTensorNameSlice(
-                    v2_reader_->key().ToString() /* full var's name */,
+                    std::string(v2_reader_->key()) /* full var's name */,
                     TensorSlice(slice_proto)))
                 .second);
     }
@@ -138,11 +138,11 @@ CheckpointReader::BuildV2VarMaps() {
       new TensorSliceReader::VarToDataTypeMap);
   v2_reader_->Seek(kHeaderEntryKey);
   for (v2_reader_->Next(); v2_reader_->Valid(); v2_reader_->Next()) {
-    if (filtered_keys.count(v2_reader_->key().ToString()) > 0) continue;
+    if (filtered_keys.count(std::string(v2_reader_->key())) > 0) continue;
     CHECK(entry.ParseFromArray(v2_reader_->value().data(),
                                v2_reader_->value().size()))
         << entry.InitializationErrorString();
-    string key = v2_reader_->key().ToString();
+    string key = std::string(v2_reader_->key());
     (*var_to_shape_map)[key] = TensorShape(entry.shape());
     (*var_to_data_type_map)[key] = DataType(entry.dtype());
   }
diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc
index c330473cda990a..7a897f6f8f99e6 100644
--- a/tensorflow/compiler/xla/shape_util.cc
+++ b/tensorflow/compiler/xla/shape_util.cc
@@ -511,7 +511,7 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) {
         break;
       } else if (must_end) {
         return InvalidArgument("Expected end of tuple; got: \"%s\"",
-                               s->ToString().c_str());
+                               std::string(*s).c_str());
       }
       shapes.emplace_back();
       TF_ASSIGN_OR_RETURN(shapes.back(), ParseShapeStringInternal(s));
@@ -541,7 +541,7 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) {
       if (!tensorflow::strings::safe_strto64(input.c_str(), &element)) {
         return InvalidArgument(
             "Invalid s64 value in parsed shape string: \"%s\" in \"%s\"",
-            input.c_str(), s->ToString().c_str());
+            input.c_str(), std::string(*s).c_str());
       }
       return element;
     };
@@ -594,7 +594,7 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) {
   }
 
   return InvalidArgument("Invalid shape string to parse: \"%s\"",
-                         s->ToString().c_str());
+                         std::string(*s).c_str());
 }
 }  // namespace
 
@@ -603,7 +603,7 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) {
   TF_ASSIGN_OR_RETURN(Shape shape, ParseShapeStringInternal(&s));
   if (!s.empty()) {
     return InvalidArgument("Invalid shape string to parse: \"%s\"",
-                           s.ToString().c_str());
+                           std::string(s).c_str());
   }
   return shape;
 }
diff --git a/tensorflow/compiler/xla/text_literal_reader.cc b/tensorflow/compiler/xla/text_literal_reader.cc
index 44f874cd2ae8e6..56702feab9a4e8 100644
--- a/tensorflow/compiler/xla/text_literal_reader.cc
+++ b/tensorflow/compiler/xla/text_literal_reader.cc
@@ -42,7 +42,7 @@ StatusOr> TextLiteralReader::ReadPath(
       << "TextLiteralReader no longer supports reading .gz files";
   std::unique_ptr file;
   Status s =
-      tensorflow::Env::Default()->NewRandomAccessFile(path.ToString(), &file);
+      tensorflow::Env::Default()->NewRandomAccessFile(std::string(path), &file);
   if (!s.ok()) {
     return s;
   }
@@ -92,7 +92,7 @@ StatusOr> TextLiteralReader::ReadAllLines() {
 
   tensorflow::StringPiece sp(shape_string);
   if (tensorflow::str_util::RemoveWhitespaceContext(&sp) > 0) {
-    string tmp = sp.ToString();
+    string tmp = std::string(sp);
     shape_string = tmp;
   }
   TF_ASSIGN_OR_RETURN(Shape shape, ShapeUtil::ParseShapeString(shape_string));
@@ -124,10 +124,10 @@ StatusOr> TextLiteralReader::ReadAllLines() {
                              line.c_str());
     }
     float value;
-    if (!tensorflow::strings::safe_strtof(value_string.ToString().c_str(),
+    if (!tensorflow::strings::safe_strtof(std::string(value_string).c_str(),
                                           &value)) {
       return InvalidArgument("could not parse value as float: \"%s\"",
-                             value_string.ToString().c_str());
+                             std::string(value_string).c_str());
     }
     SplitByDelimToStringPieces(coordinates_string, ',', &coordinates);
     coordinate_values.clear();
@@ -136,7 +136,7 @@ StatusOr> TextLiteralReader::ReadAllLines() {
       if (!tensorflow::strings::safe_strto64(piece, &coordinate_value)) {
         return InvalidArgument(
             "could not parse coordinate member as int64: \"%s\"",
-            piece.ToString().c_str());
+            std::string(piece).c_str());
       }
       coordinate_values.push_back(coordinate_value);
     }
diff --git a/tensorflow/compiler/xla/text_literal_writer.cc b/tensorflow/compiler/xla/text_literal_writer.cc
index 3fee467594d842..6e3061b78a554f 100644
--- a/tensorflow/compiler/xla/text_literal_writer.cc
+++ b/tensorflow/compiler/xla/text_literal_writer.cc
@@ -33,7 +33,7 @@ namespace xla {
 /* static */ tensorflow::Status TextLiteralWriter::WriteToPath(
     const Literal& literal, tensorflow::StringPiece path) {
   std::unique_ptr f;
-  auto s = tensorflow::Env::Default()->NewWritableFile(path.ToString(), &f);
+  auto s = tensorflow::Env::Default()->NewWritableFile(std::string(path), &f);
   if (!s.ok()) {
     return s;
   }
diff --git a/tensorflow/core/common_runtime/bfc_allocator.cc b/tensorflow/core/common_runtime/bfc_allocator.cc
index e9f839289af482..8f2a4197563af5 100644
--- a/tensorflow/core/common_runtime/bfc_allocator.cc
+++ b/tensorflow/core/common_runtime/bfc_allocator.cc
@@ -616,7 +616,7 @@ string BFCAllocator::RenderOccupancy() {
     region_offset += region.memory_size();
   }
 
-  return StringPiece(rendered, resolution).ToString();
+  return std::string(rendered, resolution);
 }
 
 void BFCAllocator::DumpMemoryLog(size_t num_bytes) {
diff --git a/tensorflow/core/common_runtime/graph_runner.cc b/tensorflow/core/common_runtime/graph_runner.cc
index 790f2eaa1e9de9..adf2ef6f4468dd 100644
--- a/tensorflow/core/common_runtime/graph_runner.cc
+++ b/tensorflow/core/common_runtime/graph_runner.cc
@@ -56,7 +56,7 @@ class SimpleRendezvous : public Rendezvous {
     }
 
     mutex_lock l(mu_);
-    string edge_name = parsed.edge_name.ToString();
+    string edge_name = std::string(parsed.edge_name);
     if (table_.count(edge_name) > 0) {
       return errors::Internal("Send of an already sent tensor");
     }
@@ -69,7 +69,7 @@ class SimpleRendezvous : public Rendezvous {
     Tensor tensor;
     Status status = Status::OK();
     {
-      string key = parsed.edge_name.ToString();
+      string key = std::string(parsed.edge_name);
       mutex_lock l(mu_);
       if (table_.count(key) <= 0) {
         status = errors::Internal("Did not find key ", key);
diff --git a/tensorflow/core/common_runtime/session_state.cc b/tensorflow/core/common_runtime/session_state.cc
index 6befa53dff0227..65ff356e73af0c 100644
--- a/tensorflow/core/common_runtime/session_state.cc
+++ b/tensorflow/core/common_runtime/session_state.cc
@@ -70,7 +70,7 @@ Status TensorStore::SaveTensors(const std::vector& output_names,
     // Save only the tensors in output_names in the session.
     for (const string& name : output_names) {
       TensorId id(ParseTensorName(name));
-      const string& op_name = id.first.ToString();
+      const string& op_name = std::string(id.first);
       auto it = tensors_.find(op_name);
       if (it != tensors_.end()) {
         // Save the tensor to the session state.
diff --git a/tensorflow/core/common_runtime/step_stats_collector.cc b/tensorflow/core/common_runtime/step_stats_collector.cc
index f21536d586edcc..af6880c6b3a110 100644
--- a/tensorflow/core/common_runtime/step_stats_collector.cc
+++ b/tensorflow/core/common_runtime/step_stats_collector.cc
@@ -94,7 +94,7 @@ static int ExtractGpuWithStreamAll(string device_name) {
   } else {
     // Convert the captured string into an integer. But first we need to put
     // the digits back in order
-    string ordered_capture = capture.ToString();
+    string ordered_capture = std::string(capture);
     std::reverse(ordered_capture.begin(), ordered_capture.end());
     int gpu_id;
     CHECK(strings::safe_strto32(ordered_capture, &gpu_id));
@@ -123,7 +123,7 @@ static int ExtractGpuWithoutStream(string device_name) {
   } else {
     // Convert the captured string into an integer. But first we need to put
     // the digits back in order
-    string ordered_capture = capture.ToString();
+    string ordered_capture = std::string(capture);
     std::reverse(ordered_capture.begin(), ordered_capture.end());
     int gpu_id;
     CHECK(strings::safe_strto32(ordered_capture, &gpu_id));
@@ -170,7 +170,7 @@ void StepStatsCollector::BuildCostModel(
 
   for (auto& itr : per_device_stats) {
     const StringPiece device_name = itr.first;
-    const int gpu_id = ExtractGpuWithoutStream(device_name.ToString());
+    const int gpu_id = ExtractGpuWithoutStream(std::string(device_name));
     if (gpu_id >= 0) {
       // Reference the gpu hardware stats in addition to the regular stats
       // for this gpu device if they're available.
diff --git a/tensorflow/core/lib/monitoring/collection_registry.cc b/tensorflow/core/lib/monitoring/collection_registry.cc
index d3fd7132de5f0e..8c28620ff9c7fd 100644
--- a/tensorflow/core/lib/monitoring/collection_registry.cc
+++ b/tensorflow/core/lib/monitoring/collection_registry.cc
@@ -38,15 +38,15 @@ void Collector::CollectMetricDescriptor(
     mutex_lock l(mu_);
     return collected_metrics_->metric_descriptor_map
         .insert(std::make_pair(
-            metric_def->name().ToString(),
+            std::string(metric_def->name()),
             std::unique_ptr(new MetricDescriptor())))
         .first->second.get();
   }();
-  metric_descriptor->name = metric_def->name().ToString();
-  metric_descriptor->description = metric_def->description().ToString();
+  metric_descriptor->name = std::string(metric_def->name());
+  metric_descriptor->description = std::string(metric_def->description());
 
   for (const StringPiece label_name : metric_def->label_descriptions()) {
-    metric_descriptor->label_names.push_back(label_name.ToString());
+    metric_descriptor->label_names.push_back(std::string(label_name));
   }
 
   metric_descriptor->metric_kind = metric_def->kind();
diff --git a/tensorflow/core/lib/monitoring/collection_registry.h b/tensorflow/core/lib/monitoring/collection_registry.h
index 63cc0f550df79c..20f0444f8b656b 100644
--- a/tensorflow/core/lib/monitoring/collection_registry.h
+++ b/tensorflow/core/lib/monitoring/collection_registry.h
@@ -72,7 +72,7 @@ class MetricCollector {
         registration_time_millis_(registration_time_millis),
         collector_(collector),
         point_set_(point_set) {
-    point_set_->metric_name = metric_def->name().ToString();
+    point_set_->metric_name = std::string(metric_def->name());
   }
 
   const MetricDef* const metric_def_;
@@ -261,7 +261,7 @@ class Collector {
     auto* const point_set = [&]() {
       mutex_lock l(mu_);
       return collected_metrics_->point_set_map
-          .insert(std::make_pair(metric_def->name().ToString(),
+          .insert(std::make_pair(std::string(metric_def->name()),
                                  std::unique_ptr(new PointSet())))
           .first->second.get();
     }();
diff --git a/tensorflow/core/lib/monitoring/metric_def.h b/tensorflow/core/lib/monitoring/metric_def.h
index 5ecadcc4272581..6f9468566570f2 100644
--- a/tensorflow/core/lib/monitoring/metric_def.h
+++ b/tensorflow/core/lib/monitoring/metric_def.h
@@ -98,8 +98,8 @@ class AbstractMetricDef {
                     const std::vector& label_descriptions)
       : kind_(kind),
         value_type_(value_type),
-        name_(name.ToString()),
-        description_(description.ToString()),
+        name_(std::string(name)),
+        description_(std::string(description)),
         label_descriptions_(std::vector(label_descriptions.begin(),
                                                 label_descriptions.end())) {}
 
diff --git a/tensorflow/core/platform/cloud/curl_http_request.cc b/tensorflow/core/platform/cloud/curl_http_request.cc
index 1ac6a7531b0c0b..081d4cf043aabd 100644
--- a/tensorflow/core/platform/cloud/curl_http_request.cc
+++ b/tensorflow/core/platform/cloud/curl_http_request.cc
@@ -407,9 +407,9 @@ size_t CurlHttpRequest::HeaderCallback(const void* ptr, size_t size,
           .StopCapture()
           .OneLiteral(": ")
           .GetResult(&value, &name)) {
-    string str_value = value.ToString();
+    string str_value = std::string(value);
     str_util::StripTrailingWhitespace(&str_value);
-    that->response_headers_[name.ToString()] = str_value;
+    that->response_headers_[std::string(name)] = str_value;
   }
   return size * nmemb;
 }
diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc
index 2d9c99c124a3f2..f1e18403ec83dc 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system.cc
@@ -167,13 +167,13 @@ Status ParseGcsPath(StringPiece fname, bool empty_object_ok, string* bucket,
     return errors::InvalidArgument("GCS path doesn't start with 'gs://': ",
                                    fname);
   }
-  *bucket = bucketp.ToString();
+  *bucket = std::string(bucketp);
   if (bucket->empty() || *bucket == ".") {
     return errors::InvalidArgument("GCS path doesn't contain a bucket name: ",
                                    fname);
   }
   str_util::ConsumePrefix(&objectp, "/");
-  *object = objectp.ToString();
+  *object = std::string(objectp);
   if (!empty_object_ok && object->empty()) {
     return errors::InvalidArgument("GCS path doesn't contain an object name: ",
                                    fname);
@@ -212,7 +212,7 @@ std::set AddAllSubpaths(const std::vector& paths) {
   for (const string& path : paths) {
     StringPiece subpath = io::Dirname(path);
     while (!subpath.empty()) {
-      result.emplace(subpath.ToString());
+      result.emplace(std::string(subpath));
       subpath = io::Dirname(subpath);
     }
   }
@@ -704,7 +704,7 @@ GcsFileSystem::GcsFileSystem()
 
       if (!header_name.empty() && !header_value.empty()) {
         additional_header_.reset(new std::pair(
-            header_name.ToString(), header_value.ToString()));
+            std::string(header_name), std::string(header_value)));
 
         VLOG(1) << "GCS additional header ENABLED. "
                 << "Name: " << additional_header_->first << ", "
@@ -1095,7 +1095,7 @@ Status GcsFileSystem::GetMatchingPaths(const string& pattern,
         // Find the fixed prefix by looking for the first wildcard.
         const string& fixed_prefix =
             pattern.substr(0, pattern.find_first_of("*?[\\"));
-        const string& dir = io::Dirname(fixed_prefix).ToString();
+        const string& dir = std::string(io::Dirname(fixed_prefix));
         if (dir.empty()) {
           return errors::InvalidArgument(
               "A GCS pattern doesn't have a bucket name: ", pattern);
@@ -1192,7 +1192,7 @@ Status GcsFileSystem::GetChildrenBounded(const string& dirname,
               " doesn't match the prefix ", object_prefix));
         }
         if (!relative_path.empty() || include_self_directory_marker) {
-          result->emplace_back(relative_path.ToString());
+          result->emplace_back(std::string(relative_path));
         }
         if (++retrieved_results >= max_results) {
           return Status::OK();
@@ -1220,7 +1220,7 @@ Status GcsFileSystem::GetChildrenBounded(const string& dirname,
               "Unexpected response: the returned folder name ", prefix_str,
               " doesn't match the prefix ", object_prefix);
         }
-        result->emplace_back(relative_path.ToString());
+        result->emplace_back(std::string(relative_path));
         if (++retrieved_results >= max_results) {
           return Status::OK();
         }
diff --git a/tensorflow/core/platform/cloud/oauth_client.cc b/tensorflow/core/platform/cloud/oauth_client.cc
index 06849f9093099b..59ad3cbcc2031f 100644
--- a/tensorflow/core/platform/cloud/oauth_client.cc
+++ b/tensorflow/core/platform/cloud/oauth_client.cc
@@ -216,7 +216,7 @@ Status OAuthClient::GetTokenFromServiceAccountJson(
   // Send the request to the Google OAuth 2.0 server to get the token.
   std::unique_ptr request(http_request_factory_->Create());
   std::vector response_buffer;
-  request->SetUri(oauth_server_uri.ToString());
+  request->SetUri(std::string(oauth_server_uri));
   request->SetPostFromBuffer(request_body.c_str(), request_body.size());
   request->SetResultBuffer(&response_buffer);
   TF_RETURN_IF_ERROR(request->Send());
@@ -248,7 +248,7 @@ Status OAuthClient::GetTokenFromRefreshTokenJson(
 
   std::unique_ptr request(http_request_factory_->Create());
   std::vector response_buffer;
-  request->SetUri(oauth_server_uri.ToString());
+  request->SetUri(std::string(oauth_server_uri));
   request->SetPostFromBuffer(request_body.c_str(), request_body.size());
   request->SetResultBuffer(&response_buffer);
   TF_RETURN_IF_ERROR(request->Send());
diff --git a/tensorflow/core/platform/cloud/oauth_client_test.cc b/tensorflow/core/platform/cloud/oauth_client_test.cc
index ad569758cc6ec1..4ffa72288bb5ea 100644
--- a/tensorflow/core/platform/cloud/oauth_client_test.cc
+++ b/tensorflow/core/platform/cloud/oauth_client_test.cc
@@ -124,11 +124,11 @@ TEST(OAuthClientTest, GetTokenFromServiceAccountJson) {
                   .OneLiteral("&assertion=")
                   .GetResult(&assertion, &grant_type));
   EXPECT_EQ("urn%3Aietf%3Aparams%3Aoauth%3Agrant-type%3Ajwt-bearer",
-            grant_type.ToString());
+            grant_type);
 
-  int last_dot = assertion.ToString().find_last_of(".");
-  string header_dot_claim = assertion.ToString().substr(0, last_dot);
-  string signature_encoded = assertion.ToString().substr(last_dot + 1);
+  int last_dot = std::string(assertion).find_last_of(".");
+  string header_dot_claim = std::string(assertion.substr(0, last_dot));
+  string signature_encoded = std::string(assertion.substr(last_dot + 1));
 
   // Check that 'signature' signs 'header_dot_claim'.
 
diff --git a/tensorflow/stream_executor/lib/env.h b/tensorflow/stream_executor/lib/env.h
index 776eba04080e9e..3ef8deb72e8ffa 100644
--- a/tensorflow/stream_executor/lib/env.h
+++ b/tensorflow/stream_executor/lib/env.h
@@ -32,7 +32,7 @@ inline Status FileExists(const string& filename) {
 }
 
 inline Status FileExists(const port::StringPiece& filename) {
-  return Env::Default()->FileExists(filename.ToString());
+  return Env::Default()->FileExists(std::string(filename));
 }
 
 }  // namespace port
diff --git a/tensorflow/stream_executor/lib/path.cc b/tensorflow/stream_executor/lib/path.cc
index 56e08c316f9575..58a862206c7855 100644
--- a/tensorflow/stream_executor/lib/path.cc
+++ b/tensorflow/stream_executor/lib/path.cc
@@ -33,7 +33,7 @@ string JoinPathImpl(std::initializer_list paths) {
     if (path.empty()) continue;
 
     if (result.empty()) {
-      result = path.ToString();
+      result = std::string(path);
       continue;
     }
 
diff --git a/tensorflow/stream_executor/lib/str_util.h b/tensorflow/stream_executor/lib/str_util.h
index a81c6668184c15..b02fe4f56f24be 100644
--- a/tensorflow/stream_executor/lib/str_util.h
+++ b/tensorflow/stream_executor/lib/str_util.h
@@ -31,7 +31,7 @@ inline string StripSuffixString(port::StringPiece str, port::StringPiece suffix)
   if (tensorflow::str_util::EndsWith(str, suffix)) {
     str.remove_suffix(suffix.size());
   }
-  return str.ToString();
+  return std::string(str);
 }
 
 using tensorflow::str_util::Lowercase;
diff --git a/tensorflow/tools/graph_transforms/freeze_requantization_ranges.cc b/tensorflow/tools/graph_transforms/freeze_requantization_ranges.cc
index f401723808c086..c8dc2a7c4df243 100644
--- a/tensorflow/tools/graph_transforms/freeze_requantization_ranges.cc
+++ b/tensorflow/tools/graph_transforms/freeze_requantization_ranges.cc
@@ -92,9 +92,8 @@ Status ExtractMinMaxRecords(const string& log_file_name,
     if (!str_util::EndsWith(name_string, print_suffix)) {
       continue;
     }
-    string name =
-        name_string.substr(0, name_string.size() - print_suffix.size())
-            .ToString();
+    string name = std::string(
+        name_string.substr(0, name_string.size() - print_suffix.size()));
     records->push_back({name, min, max});
   }
   return Status::OK();
diff --git a/tensorflow/tools/graph_transforms/sparsify_gather_test.cc b/tensorflow/tools/graph_transforms/sparsify_gather_test.cc
index d41321c9a6df75..dd95779a1fb717 100644
--- a/tensorflow/tools/graph_transforms/sparsify_gather_test.cc
+++ b/tensorflow/tools/graph_transforms/sparsify_gather_test.cc
@@ -42,8 +42,8 @@ class SparsifyGatherTest : public ::testing::Test {
                       const std::vector& inputs, GraphDef* graph_def,
                       bool control_dep = false) {
     NodeDef* node_def = graph_def->add_node();
-    node_def->set_name(name.ToString());
-    node_def->set_op(op.ToString());
+    node_def->set_name(std::string(name));
+    node_def->set_op(std::string(op));
     if (!control_dep) {
       std::for_each(inputs.begin(), inputs.end(), [&node_def](NodeDef* input) {
         node_def->add_input(input->name());
diff --git a/tensorflow/tools/graph_transforms/transform_graph.cc b/tensorflow/tools/graph_transforms/transform_graph.cc
index 8ce8f5e24b9f00..3b9dd3dd2d4e24 100644
--- a/tensorflow/tools/graph_transforms/transform_graph.cc
+++ b/tensorflow/tools/graph_transforms/transform_graph.cc
@@ -65,19 +65,19 @@ Status ParseTransformParameters(const string& transforms_string,
               .GetResult(&remaining, &transform_name);
       if (!found_transform_name) {
         return errors::InvalidArgument("Looking for transform name, but found ",
-                                       remaining.ToString().c_str());
+                                       std::string(remaining).c_str());
       }
       if (Scanner(remaining).OneLiteral("(").GetResult(&remaining, &match)) {
         state = TRANSFORM_PARAM_NAME;
       } else {
         // Add a transform with no parameters.
-        params_list->push_back({transform_name.ToString(), func_parameters});
+        params_list->push_back({std::string(transform_name), func_parameters});
         transform_name = "";
         state = TRANSFORM_NAME;
       }
     } else if (state == TRANSFORM_PARAM_NAME) {
       if (Scanner(remaining).OneLiteral(")").GetResult(&remaining, &match)) {
-        params_list->push_back({transform_name.ToString(), func_parameters});
+        params_list->push_back({std::string(transform_name), func_parameters});
         transform_name = "";
         state = TRANSFORM_NAME;
       } else {
@@ -92,13 +92,13 @@ Status ParseTransformParameters(const string& transforms_string,
         if (!found_parameter_name) {
           return errors::InvalidArgument(
               "Looking for parameter name, but found ",
-              remaining.ToString().c_str());
+              std::string(remaining).c_str());
         }
         if (Scanner(remaining).OneLiteral("=").GetResult(&remaining, &match)) {
           state = TRANSFORM_PARAM_VALUE;
         } else {
           return errors::InvalidArgument("Looking for =, but found ",
-                                         remaining.ToString().c_str());
+                                         std::string(remaining).c_str());
         }
       }
     } else if (state == TRANSFORM_PARAM_VALUE) {
@@ -120,10 +120,10 @@ Status ParseTransformParameters(const string& transforms_string,
       }
       if (!found_parameter_value) {
         return errors::InvalidArgument("Looking for parameter name, but found ",
-                                       remaining.ToString().c_str());
+                                       std::string(remaining).c_str());
       }
-      func_parameters[parameter_name.ToString()].push_back(
-          parameter_value.ToString());
+      func_parameters[std::string(parameter_name)].push_back(
+          std::string(parameter_value));
       // Eat up any trailing quotes.
       Scanner(remaining).ZeroOrOneLiteral("\"").GetResult(&remaining, &match);
       Scanner(remaining).ZeroOrOneLiteral("'").GetResult(&remaining, &match);
diff --git a/tensorflow/tools/graph_transforms/transform_utils.cc b/tensorflow/tools/graph_transforms/transform_utils.cc
index 367048965d146d..af17fd75bc1cca 100644
--- a/tensorflow/tools/graph_transforms/transform_utils.cc
+++ b/tensorflow/tools/graph_transforms/transform_utils.cc
@@ -93,7 +93,7 @@ void NodeNamePartsFromInput(const string& input_name, string* prefix,
   } else {
     *prefix = "";
   }
-  *node_name = node_name_piece.ToString();
+  *node_name = std::string(node_name_piece);
 }
 
 string NodeNameFromInput(const string& input_name) {

From 4c256cda4f29ce5f634be44628e2c4c639974dc3 Mon Sep 17 00:00:00 2001
From: Priya Gupta 
Date: Wed, 2 May 2018 15:30:30 -0700
Subject: [PATCH 0306/1691] Add prefetching to one device distribution
 strategy.

PiperOrigin-RevId: 195162570
---
 .../contrib/distribute/python/one_device_strategy.py       | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/distribute/python/one_device_strategy.py b/tensorflow/contrib/distribute/python/one_device_strategy.py
index 646d2a5c3b3b0b..64aa3692010f73 100644
--- a/tensorflow/contrib/distribute/python/one_device_strategy.py
+++ b/tensorflow/contrib/distribute/python/one_device_strategy.py
@@ -36,9 +36,10 @@ class OneDeviceStrategy(distribute_lib.DistributionStrategy):
   # doing something that won't work with other DistributionStrategy
   # implementations?
 
-  def __init__(self, device):
+  def __init__(self, device, prefetch_on_device=None):
     super(OneDeviceStrategy, self).__init__()
     self._device = device
+    self._prefetch_on_device = prefetch_on_device
 
   def _create_variable(self, next_creator, *args, **kwargs):
     # No need to distinguish tower-local variables when not mirroring,
@@ -61,7 +62,9 @@ def _create_variable(self, next_creator, *args, **kwargs):
       return next_creator(*args, **kwargs)
 
   def distribute_dataset(self, dataset_fn):
-    return self._call_dataset_fn(dataset_fn)
+    return values.PerDeviceDataset(
+        self._call_dataset_fn(dataset_fn), [self._device],
+        self._prefetch_on_device)
 
   def _broadcast(self, tensor, destinations):
     return tensor

From 85566b2420833a4ba59241330eeceedea4f98e3c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 15:35:11 -0700
Subject: [PATCH 0307/1691] Adding a version of rolled triangular solver code
 for the right-multiply case, which is used in Cholesky decomposition. 
 Replacing the unrolled version with a While loop drastically reduces XLA
 compilation times which allows much larger models to be run on TPU.

PiperOrigin-RevId: 195163298
---
 .../compiler/tf2xla/lib/triangular_solve.cc   | 179 +++++++++++++++---
 .../compiler/tf2xla/lib/triangular_solve.h    |   6 +
 tensorflow/compiler/tf2xla/lib/util.cc        |   7 +
 tensorflow/compiler/tf2xla/lib/util.h         |   5 +
 4 files changed, 173 insertions(+), 24 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc
index d0279d4412bac6..b4503601f94baa 100644
--- a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc
+++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc
@@ -1,4 +1,4 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -82,13 +82,6 @@ xla::StatusOr TriangularSolve(xla::XlaBuilder* builder,
         block_size);
   }
 
-  // Applies a complex conjugation operation if `a` is complex and `conjugate_a`
-  // is true, otherwise returns its argument.
-  auto maybe_conj = [&](xla::XlaBuilder* builder, xla::XlaOp x) {
-    auto perform_conj = a_shape.element_type() == xla::C64 && conjugate_a;
-    return perform_conj ? builder->Conj(x) : x;
-  };
-
   std::map base_computations;
   auto get_base_triangular_solve =
       [&](int k) -> xla::StatusOr {
@@ -117,16 +110,21 @@ xla::StatusOr TriangularSolve(xla::XlaBuilder* builder,
               PrependMajorDims(sub.get(), batch_dimensions, b_lastd)),
           "b");
 
-      // We use a left-looking subroutine on the block diagonal in some common
-      // cases, while falling back to a recursive call in unsupported cases. The
-      // left-looking subroutine is written with a While loop and so yields much
-      // faster compile times. Moreover, the left-looking variant can give
-      // higher performance on smaller (sub)problems.
+      // We use a left-looking or right-looking subroutine on the block diagonal
+      // in the lower=true cases, while falling back to a recursive call in
+      // others. The left-looking and right-looking subroutines are written with
+      // a While loop and so yields much faster compile times. Moreover, they
+      // can give higher performance on smaller (sub)problems.
       if (left_side && lower) {
         TF_RETURN_IF_ERROR(TriangularSolveLeftLooking(sub.get(), a_param,
                                                       b_param, transpose_a,
                                                       conjugate_a)
                                .status());
+      } else if (!left_side && lower) {
+        TF_RETURN_IF_ERROR(TriangularSolveRightLooking(sub.get(), a_param,
+                                                       b_param, transpose_a,
+                                                       conjugate_a)
+                               .status());
       } else {
         TF_RETURN_IF_ERROR(TriangularSolve(sub.get(), a_param, b_param,
                                            left_side, lower, transpose_a,
@@ -169,7 +167,9 @@ xla::StatusOr TriangularSolve(xla::XlaBuilder* builder,
                             get_base_triangular_solve(k));
         update = builder->Call(*solve, {a_slice, b_slice});
       } else {
-        update = builder->Div(b_slice, maybe_conj(builder, a_slice));
+        TF_ASSIGN_OR_RETURN(auto a_slice_conj,
+                            MaybeConjugate(builder, a_slice, conjugate_a));
+        update = builder->Div(b_slice, a_slice_conj);
       }
       TF_ASSIGN_OR_RETURN(
           output, UpdateSliceInMinorDims(builder, output, update, {0, i}));
@@ -219,7 +219,9 @@ xla::StatusOr TriangularSolve(xla::XlaBuilder* builder,
                             get_base_triangular_solve(k));
         update = builder->Call(*solve, {a_slice, b_slice});
       } else {
-        update = builder->Div(b_slice, maybe_conj(builder, a_slice));
+        TF_ASSIGN_OR_RETURN(auto a_slice_conj,
+                            MaybeConjugate(builder, a_slice, conjugate_a));
+        update = builder->Div(b_slice, a_slice_conj);
       }
       TF_ASSIGN_OR_RETURN(
           output, UpdateSliceInMinorDims(builder, output, update, {i, 0}));
@@ -268,7 +270,9 @@ xla::StatusOr TriangularSolve(xla::XlaBuilder* builder,
                             get_base_triangular_solve(k));
         update = builder->Call(*solve, {a_slice, b_slice});
       } else {
-        update = builder->Div(b_slice, maybe_conj(builder, a_slice));
+        TF_ASSIGN_OR_RETURN(auto a_slice_conj,
+                            MaybeConjugate(builder, a_slice, conjugate_a));
+        update = builder->Div(b_slice, a_slice_conj);
       }
       TF_ASSIGN_OR_RETURN(
           output, UpdateSliceInMinorDims(builder, output, update, {0, i}));
@@ -318,7 +322,9 @@ xla::StatusOr TriangularSolve(xla::XlaBuilder* builder,
                             get_base_triangular_solve(k));
         update = builder->Call(*solve, {a_slice, b_slice});
       } else {
-        update = builder->Div(b_slice, maybe_conj(builder, a_slice));
+        TF_ASSIGN_OR_RETURN(auto a_slice_conj,
+                            MaybeConjugate(builder, a_slice, conjugate_a));
+        update = builder->Div(b_slice, a_slice_conj);
       }
       TF_ASSIGN_OR_RETURN(
           output, UpdateSliceInMinorDims(builder, output, update, {i, 0}));
@@ -371,11 +377,6 @@ xla::StatusOr TriangularSolveLeftLooking(xla::XlaBuilder* builder,
     batch_dimensions.push_back(a_size);
   }
 
-  auto maybe_conj = [&](xla::XlaBuilder* builder, xla::XlaOp x) {
-    auto perform_conj = a_shape.element_type() == xla::C64 && conjugate_a;
-    return perform_conj ? builder->Conj(x) : x;
-  };
-
   // The main computation is performed in a While loop.
 
   // Allocate the output and set its first or last row,
@@ -391,7 +392,9 @@ xla::StatusOr TriangularSolveLeftLooking(xla::XlaBuilder* builder,
                         SliceInMinorDims(builder, a, {i, i}, {i + 1, i + 1}));
     TF_ASSIGN_OR_RETURN(auto b_slice,
                         SliceInMinorDims(builder, b, {i, 0}, {i + 1, n}));
-    auto update = builder->Div(b_slice, maybe_conj(builder, a_slice));
+    TF_ASSIGN_OR_RETURN(auto a_slice_conj,
+                        MaybeConjugate(builder, a_slice, conjugate_a));
+    auto update = builder->Div(b_slice, a_slice_conj);
     TF_ASSIGN_OR_RETURN(
         output, UpdateSliceInMinorDims(builder, output, update, {i, 0}));
   }
@@ -493,7 +496,9 @@ xla::StatusOr TriangularSolveLeftLooking(xla::XlaBuilder* builder,
     // body_out[..., i:i+1, :] = result_row / a[..., i:i+1, i:i+1]
     TF_ASSIGN_OR_RETURN(auto a_elt, DynamicSliceInMinorDims(bodyb.get(), body_a,
                                                             {i, i}, {1, 1}));
-    auto div_result = bodyb->Div(result_row, maybe_conj(bodyb.get(), a_elt));
+    TF_ASSIGN_OR_RETURN(auto a_elt_conj,
+                        MaybeConjugate(bodyb.get(), a_elt, conjugate_a));
+    auto div_result = bodyb->Div(result_row, a_elt_conj);
     TF_ASSIGN_OR_RETURN(body_out,
                         DynamicUpdateSliceInMinorDims(bodyb.get(), body_out,
                                                       div_result, {i, zero}));
@@ -513,4 +518,130 @@ xla::StatusOr TriangularSolveLeftLooking(xla::XlaBuilder* builder,
   return builder->GetTupleElement(triangular_solve_left_looking_while, 1);
 }
 
+xla::StatusOr TriangularSolveRightLooking(xla::XlaBuilder* builder,
+                                                      const xla::XlaOp& a,
+                                                      const xla::XlaOp& b,
+                                                      bool transpose_a,
+                                                      bool conjugate_a) {
+  TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a));
+  TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b));
+  const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2);
+  const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1);
+  const int64 ndims = xla::ShapeUtil::Rank(a_shape);
+
+  std::vector batch_dimensions;
+  for (int i = 0; i < ndims - 2; ++i) {
+    int64 a_size = a_shape.dimensions(i);
+    batch_dimensions.push_back(a_size);
+  }
+
+  // The main computation is performed in a While loop.
+  xla::XlaOp output = Zeros(builder, b_shape);
+
+  // Construct the initial loop carry tuple,
+  // if transpose_a:
+  //   init = (0, output, a, b)
+  // else:
+  //   init = (n-1, output, a, b)
+  std::vector tuple_shapes = {
+      // The loop iteration counter is a scalar, incremented each iteration.
+      xla::ShapeUtil::MakeShape(xla::S32, {}),
+      // The output has the shape of b, with one row updated each iteration.
+      b_shape,
+      // The coefficient matrix a is a loop invariant.
+      a_shape,
+      // The right-hand-side matrix b is a loop invariant.
+      b_shape};
+  xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes);
+  auto init_i = builder->ConstantR0(transpose_a ? 0 : n - 1);
+  auto init = builder->Tuple({init_i, output, a, b});
+
+  // Construct the loop condition function,
+  // def cond_fun(loop_carry):
+  //   i, output, a, b = loop_carry
+  //   return i < n if transpose_a else i >= 0
+  std::unique_ptr condb =
+      builder->CreateSubBuilder("TriangularSolveRightLookingWhileCond");
+  {
+    auto i = condb->GetTupleElement(
+        condb->Parameter(0, tuple_shape,
+                         "TriangularSolveRightLookingWhileTuple"),
+        0);
+    if (transpose_a) {
+      condb->Lt(i, condb->ConstantR0(n));
+    } else {
+      condb->Ge(i, condb->ConstantR0(0));
+    }
+  }
+  TF_ASSIGN_OR_RETURN(auto cond, condb->Build());
+
+  // Construct the loop body function,
+  // def body_fun(loop_carry):
+  //   i, output, a, b = loop_carry
+  //   if transpose_a:
+  //     a_row = np.swapaxes(a[..., :, i:i+1], -1 -2)
+  //   else:
+  //     a_row = a[..., :, i:i+1]
+  //   result_row = b[..., :, i:i+1] - np.matmul(output, a_row)
+  //   output[..., :, i:i+1] = result_row / a[..., i:i+1, i:i+1]
+  //   if transpose_a:
+  //     return (i - 1, output, a, b)
+  //   else:
+  //     return (i + 1, output, a, b)
+  // We have to do some extra FLOPs propagating zeros in the matrix multiply
+  // because we can't have the size of its arguments depend on the loop counter.
+  std::unique_ptr bodyb =
+      builder->CreateSubBuilder("TriangularSolveRightLookingWhileBody");
+  {
+    auto input_tuple = bodyb->Parameter(
+        0, tuple_shape, "TriangularSolveRightLookingWhileTuple");
+
+    // i, output, a, b = loop_carry
+    auto i = bodyb->GetTupleElement(input_tuple, 0);
+    auto body_out = bodyb->GetTupleElement(input_tuple, 1);
+    auto body_a = bodyb->GetTupleElement(input_tuple, 2);
+    auto body_b = bodyb->GetTupleElement(input_tuple, 3);
+    auto zero = bodyb->ConstantR0(0);
+
+    // We'd like to implement b[..., :, i:i+1] - np.matmul(output, a[..., :,
+    // i:i+1]) But since we can't have intermediate array sizes depend on the
+    // loop counter, we instead exploit the fact that we initialized the output
+    // to all zeros and use that as zero-padding (doing unnecessary FLOPs).
+    TF_ASSIGN_OR_RETURN(auto b_update, BatchDot(bodyb.get(), body_out, body_a,
+                                                /*transpose_x=*/false,
+                                                /*transpose_y=*/transpose_a,
+                                                /*conjugate_x=*/false,
+                                                /*conjugate_y=*/conjugate_a));
+    // result = b - np.matmul(output, a)
+    auto result = bodyb->Sub(body_b, b_update);
+    // result_row = result[..., :, i:i+1]
+    TF_ASSIGN_OR_RETURN(
+        auto result_row,
+        DynamicSliceInMinorDims(bodyb.get(), result, {zero, i}, {m, 1}));
+
+    // body_out[..., :, i:i+1] = result_row / a[..., i:i+1, i:i+1]
+    TF_ASSIGN_OR_RETURN(auto a_ii, DynamicSliceInMinorDims(bodyb.get(), body_a,
+                                                           {i, i}, {1, 1}));
+    TF_ASSIGN_OR_RETURN(auto a_ii_conj,
+                        MaybeConjugate(bodyb.get(), a_ii, conjugate_a));
+    auto div_result = bodyb->Div(result_row, a_ii_conj);
+    TF_ASSIGN_OR_RETURN(body_out,
+                        DynamicUpdateSliceInMinorDims(bodyb.get(), body_out,
+                                                      div_result, {zero, i}));
+
+    // if transpose_a:
+    //   return (i + 1, body_out, a, b)
+    // else:
+    //   return (i - 1, body_out, a, b)
+    auto next_i = bodyb->Add(i, bodyb->ConstantR0(transpose_a ? 1 : -1));
+    bodyb->Tuple({next_i, body_out, body_a, body_b});
+  }
+  TF_ASSIGN_OR_RETURN(auto body, bodyb->Build());
+
+  // Construct the While loop and return the result,
+  // return while_loop(cond_fun, body_fun, init)[1]
+  auto triangular_solve_left_looking_while = builder->While(cond, body, init);
+  return builder->GetTupleElement(triangular_solve_left_looking_while, 1);
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.h b/tensorflow/compiler/tf2xla/lib/triangular_solve.h
index fd8f2489d18392..540c26b2473df9 100644
--- a/tensorflow/compiler/tf2xla/lib/triangular_solve.h
+++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.h
@@ -69,6 +69,12 @@ xla::StatusOr TriangularSolveLeftLooking(xla::XlaBuilder* builder,
                                                      bool transpose_a,
                                                      bool conjugate_a);
 
+xla::StatusOr TriangularSolveRightLooking(xla::XlaBuilder* builder,
+                                                      const xla::XlaOp& a,
+                                                      const xla::XlaOp& b,
+                                                      bool transpose_a,
+                                                      bool conjugate_a);
+
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_COMPILER_TF2XLA_LIB_TRIANGULAR_SOLVE_H_
diff --git a/tensorflow/compiler/tf2xla/lib/util.cc b/tensorflow/compiler/tf2xla/lib/util.cc
index cc7b13571c3d06..d9ff7e6259f3fb 100644
--- a/tensorflow/compiler/tf2xla/lib/util.cc
+++ b/tensorflow/compiler/tf2xla/lib/util.cc
@@ -230,4 +230,11 @@ xla::StatusOr TransposeInMinorDims(xla::XlaBuilder* builder,
   return builder->Transpose(x, permutation);
 }
 
+xla::StatusOr MaybeConjugate(xla::XlaBuilder* builder,
+                                         const xla::XlaOp& x, bool conjugate) {
+  TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x));
+  auto perform_conj = shape.element_type() == xla::C64 && conjugate;
+  return perform_conj ? builder->Conj(x) : x;
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/tf2xla/lib/util.h b/tensorflow/compiler/tf2xla/lib/util.h
index 3df44ef0358c9e..3c120a2548576d 100644
--- a/tensorflow/compiler/tf2xla/lib/util.h
+++ b/tensorflow/compiler/tf2xla/lib/util.h
@@ -85,6 +85,11 @@ xla::StatusOr DynamicUpdateSliceInMinorDims(
 xla::StatusOr TransposeInMinorDims(xla::XlaBuilder* builder,
                                                const xla::XlaOp& x);
 
+// Applies a complex conjugation operation if `a` is complex and `conjugate_a`
+// is true, otherwise returns its argument.
+xla::StatusOr MaybeConjugate(xla::XlaBuilder* builder,
+                                         const xla::XlaOp& x, bool conjugate);
+
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_COMPILER_TF2XLA_LIB_UTIL_H_

From 0237e86297087ba3e700ac9218f846e6e662c60f Mon Sep 17 00:00:00 2001
From: Jianwei Xie 
Date: Wed, 2 May 2018 15:36:54 -0700
Subject: [PATCH 0308/1691] Adds the EvalListener support for run_local.

PiperOrigin-RevId: 195163507
---
 tensorflow/python/estimator/training.py      | 10 +++
 tensorflow/python/estimator/training_test.py | 67 ++++++++++++++++++++
 2 files changed, 77 insertions(+)

diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py
index 534c357067770b..41ffa371aae61d 100644
--- a/tensorflow/python/estimator/training.py
+++ b/tensorflow/python/estimator/training.py
@@ -656,6 +656,11 @@ def _should_stop_local_train(global_step):
           max_steps=self._train_spec.max_steps,
           hooks=train_hooks)
 
+      if not self._continuous_eval_listener.before_eval():
+        logging.info('Exiting training and evaluation lopp, as requested by '
+                     '_ContinuousEvalListener.before_eval.')
+        break
+
       # Final export signal: For any eval result with global_step >= train
       # max_steps, the evaluator will send the final export signal. The
       # _should_stop_local_train will then end the while True as the stopping
@@ -669,6 +674,11 @@ def _should_stop_local_train(global_step):
         raise RuntimeError('There was no new checkpoint after the training. '
                            'Eval status: {}'.format(eval_result.status))
 
+      if not self._continuous_eval_listener.after_eval(eval_result):
+        logging.info('Exiting evaluation, as requested by '
+                     '_ContinuousEvalListener.after_eval.')
+        break
+
       if _should_stop_local_train(
           eval_result.metrics[ops.GraphKeys.GLOBAL_STEP]):
         break
diff --git a/tensorflow/python/estimator/training_test.py b/tensorflow/python/estimator/training_test.py
index c04905ae65d0ef..3b6f5e18cb50d8 100644
--- a/tensorflow/python/estimator/training_test.py
+++ b/tensorflow/python/estimator/training_test.py
@@ -1628,6 +1628,73 @@ def export(estimator, export_path, checkpoint_path, eval_result,
     self.assertEqual(3, mock_est.times_export_was_called)
     self.assertEqual(1, mock_est.times_final_export_was_called)
 
+  def test_runs_with_eval_listener_before_eval(self):
+    mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/')
+    mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn
+
+    train_spec = training.TrainSpec(input_fn=lambda: 1, max_steps=300)
+    eval_spec = training.EvalSpec(input_fn=lambda: 1, throttle_secs=100)
+    # should be called 2 times without the evallistener
+    mock_est.evaluate.side_effect = [{
+        _GLOBAL_STEP_KEY: train_spec.max_steps - 50
+    }, {
+        _GLOBAL_STEP_KEY: train_spec.max_steps
+    }]
+
+    class _Listener(training._ContinuousEvalListener):
+
+      def __init__(self):
+        self.call_count = 0
+
+      def before_eval(self):
+        self.call_count += 1
+        return False  # Will stop the run_local before first eval.
+
+    listener = _Listener()
+
+    executor = training._TrainingExecutor(
+        mock_est, train_spec, eval_spec, continuous_eval_listener=listener)
+    executor.run_local()
+
+    self.assertEqual(1, mock_est.train.call_count)
+    self.assertEqual(0, mock_est.evaluate.call_count)
+    self.assertEqual(1, listener.call_count)
+
+  def test_runs_with_eval_listener_after_eval(self):
+    mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/')
+    mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn
+
+    train_spec = training.TrainSpec(input_fn=lambda: 1, max_steps=300)
+    eval_spec = training.EvalSpec(input_fn=lambda: 1, throttle_secs=100)
+    # should be called 2 times without the evallistener
+    mock_est.evaluate.side_effect = [{
+        _GLOBAL_STEP_KEY: train_spec.max_steps - 50
+    }, {
+        _GLOBAL_STEP_KEY: train_spec.max_steps
+    }]
+
+    class _Listener(training._ContinuousEvalListener):
+
+      def __init__(self, test_case):
+        self.call_count = 0
+        self._test_case = test_case
+
+      def after_eval(self, eval_result):
+        self.call_count += 1
+        self._test_case.assertEqual(
+            train_spec.max_steps - 50, eval_result.metrics[_GLOBAL_STEP_KEY])
+        return False  # Will stop the run_local after first eval.
+
+    listener = _Listener(test_case=self)
+
+    executor = training._TrainingExecutor(
+        mock_est, train_spec, eval_spec, continuous_eval_listener=listener)
+    executor.run_local()
+
+    self.assertEqual(1, mock_est.train.call_count)
+    self.assertEqual(1, mock_est.evaluate.call_count)
+    self.assertEqual(1, listener.call_count)
+
   def test_handles_no_new_checkpoint_found(self):
     mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/')
     mock_est.latest_checkpoint.return_value = (

From 49f2afe21e3cada8951205d00e877c873a33754c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 15:51:16 -0700
Subject: [PATCH 0309/1691] Allow evaluation and prediction through
 warm-starting (no current checkpoint / model_dir).

PiperOrigin-RevId: 195165732
---
 tensorflow/python/estimator/BUILD             |  1 +
 tensorflow/python/estimator/estimator.py      | 20 ++++-
 tensorflow/python/estimator/estimator_test.py | 89 +++++++++++++++++++
 3 files changed, 106 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD
index c6bb9b9be7cb80..56dec1eaa1f608 100644
--- a/tensorflow/python/estimator/BUILD
+++ b/tensorflow/python/estimator/BUILD
@@ -478,6 +478,7 @@ py_library(
         "//tensorflow/python:util",
         "//tensorflow/python/data",
         "//tensorflow/python/saved_model:builder",
+        "//tensorflow/python/saved_model:constants",
         "//tensorflow/python/saved_model:tag_constants",
         "//third_party/py/numpy",
         "@six_archive//:six",
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 946f093ba7aa95..530a4a24efc54f 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -50,6 +50,7 @@
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.saved_model import builder as saved_model_builder
+from tensorflow.python.saved_model import constants
 from tensorflow.python.saved_model import tag_constants
 from tensorflow.python.summary import summary
 from tensorflow.python.summary.writer import writer_cache
@@ -493,7 +494,6 @@ def predict(self,
       if not checkpoint_path:
         logging.info('Could not find trained model in model_dir: {}, running '
                      'initialization to predict.'.format(self._model_dir))
-
       with ops.Graph().as_default() as g:
         random_seed.set_random_seed(self._config.tf_random_seed)
         self._create_and_assert_global_step(g)
@@ -501,6 +501,10 @@ def predict(self,
             input_fn, model_fn_lib.ModeKeys.PREDICT)
         estimator_spec = self._call_model_fn(
             features, None, model_fn_lib.ModeKeys.PREDICT, self.config)
+
+        # Call to warm_start has to be after model_fn is called.
+        self._maybe_warm_start(checkpoint_path)
+
         predictions = self._extract_keys(
             estimator_spec.predictions, predict_keys)
         all_hooks = list(input_hooks)
@@ -982,9 +986,7 @@ def _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks,
     if self._warm_start_settings:
       logging.info('Warm-starting with WarmStartSettings: %s' %
                    (self._warm_start_settings,))
-      # pylint: disable=protected-access
       warm_starting_util.warm_start(*self._warm_start_settings)
-      # pylint: enable=protected-access
     # Check if the user created a loss summary, and add one if they didn't.
     # We assume here that the summary is called 'loss'. If it is not, we will
     # make another one with the name 'loss' to ensure it shows up in the right
@@ -1089,6 +1091,9 @@ def _evaluate_model(self,
       estimator_spec = self._call_model_fn(
           features, labels, model_fn_lib.ModeKeys.EVAL, self.config)
 
+      # Call to warm_start has to be after model_fn is called.
+      self._maybe_warm_start(checkpoint_path)
+
       if model_fn_lib.LOSS_METRIC_KEY in estimator_spec.eval_metric_ops:
         raise ValueError(
             'Metric with name "%s" is not allowed, because Estimator ' % (
@@ -1126,6 +1131,12 @@ def _evaluate_model(self,
 
     return eval_results
 
+  def _maybe_warm_start(self, checkpoint_path):
+    if not checkpoint_path and self._warm_start_settings:
+      logging.info('Warm-starting with WarmStartSettings: %s' %
+                   (self._warm_start_settings,))
+      warm_starting_util.warm_start(*self._warm_start_settings)
+
 
 def create_per_tower_ready_op(scaffold):
   """Create a Scaffold.ready_op inside a tower."""
@@ -1525,7 +1536,8 @@ def _get_default_warm_start_settings(warm_start_from):
       logging.info('Warm-starting from a SavedModel')
       return WarmStartSettings(ckpt_to_initialize_from=os.path.join(
           compat.as_bytes(warm_start_from),
-          compat.as_bytes('variables/variables')))
+          compat.as_bytes('{}/{}'.format(constants.VARIABLES_DIRECTORY,
+                                         constants.VARIABLES_FILENAME))))
     return WarmStartSettings(ckpt_to_initialize_from=warm_start_from)
   elif isinstance(warm_start_from, WarmStartSettings):
     return warm_start_from
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 4d958f8b43ff2c..76b45b7f57633b 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -1116,6 +1116,52 @@ def _model_fn(features, labels, mode, params):
     # initialized (since there is no checkpoint).
     self.assertEqual(3., metrics['metric'])
 
+  def test_no_checkpoint_uses_init_with_warm_starting(self):
+    def _make_model_fn(x):
+      def _variable_creating_and_export_model_fn(features, labels, mode):
+        _, _ = features, labels
+        x_var = variable_scope.get_variable('x', initializer=x)
+        global_step = training.get_global_step()
+        return model_fn_lib.EstimatorSpec(
+            mode,
+            predictions={'y': constant_op.constant(1.0)},
+            loss=constant_op.constant(1.),
+            eval_metric_ops={'metric': metrics_lib.mean(x_var + 1)},
+            train_op=state_ops.assign_add(global_step, 1),
+            export_outputs={'test': export_output.ClassificationOutput(
+                constant_op.constant([4.2]), constant_op.constant(['label']))})
+      return _variable_creating_and_export_model_fn
+
+    first_est = estimator.Estimator(model_fn=_make_model_fn(42.))
+    first_est.train(dummy_input_fn, steps=10)
+    feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),
+                    'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}
+    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
+        feature_spec)
+    tmpdir = tempfile.mkdtemp()
+    export_dir_base = os.path.join(
+        compat.as_bytes(tmpdir), compat.as_bytes('export'))
+    exported_path = first_est.export_savedmodel(export_dir_base,
+                                                serving_input_receiver_fn)
+
+    # Test that we can pass either warm_start_from as an external checkpoint
+    # or an exported SavedModel.
+    est = estimator.Estimator(model_fn=_make_model_fn(52.),
+                              warm_start_from=exported_path)
+    metrics = est.evaluate(dummy_input_fn, steps=1)
+    # Metric value here is set to 1 + the value of the Variable that is
+    # warm-started from the SavedModel of the first model (42.), as opposed to
+    # the initialization in the new model_fn (52.).
+    self.assertEqual(43., metrics['metric'])
+
+    est = estimator.Estimator(model_fn=_make_model_fn(62.),
+                              warm_start_from=first_est.model_dir)
+    metrics = est.evaluate(dummy_input_fn, steps=1)
+    # Metric value here is set to 1 + the value of the Variable that is
+    # warm-started from a checkpoint of the first model (42.), as opposed to
+    # the initialization in the new model_fn (52.).
+    self.assertEqual(43., metrics['metric'])
+
   def test_scores(self):
     est = estimator.Estimator(
         model_fn=_model_fn_with_eval_metric_ops,
@@ -1384,6 +1430,49 @@ def _model_fn(features, labels, mode, params, config):
     # initialized (since there is no checkpoint).
     self.assertEqual(4., next(est.predict(dummy_input_fn)))
 
+  def test_no_checkpoint_uses_init_with_warm_starting(self):
+    def _make_model_fn(x):
+      def _variable_creating_and_export_model_fn(features, labels, mode):
+        _, _ = features, labels
+        x_var = variables.Variable([[x]], name='x')
+        return model_fn_lib.EstimatorSpec(
+            mode,
+            predictions=math_ops.add(x_var, 1.),
+            loss=constant_op.constant(1.),
+            train_op=state_ops.assign_add(training.get_global_step(), 1),
+            export_outputs={'test': export_output.ClassificationOutput(
+                constant_op.constant([4.2]),
+                constant_op.constant(['label']))})
+      return _variable_creating_and_export_model_fn
+
+    first_est = estimator.Estimator(model_fn=_make_model_fn(3.))
+    first_est.train(dummy_input_fn, steps=10)
+    feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),
+                    'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}
+    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
+        feature_spec)
+    tmpdir = tempfile.mkdtemp()
+    export_dir_base = os.path.join(
+        compat.as_bytes(tmpdir), compat.as_bytes('export'))
+    exported_path = first_est.export_savedmodel(export_dir_base,
+                                                serving_input_receiver_fn)
+
+    # Test that we can pass either warm_start_from as an external checkpoint
+    # or an exported SavedModel.
+    est = estimator.Estimator(model_fn=_make_model_fn(30.),
+                              warm_start_from=exported_path)
+    # Prediction here is set to 1 + the value of the Variable that is
+    # warm-started from the SavedModel of the first model (3.), as opposed to
+    # the initialization in the new model_fn (30.).
+    self.assertEqual(4., next(est.predict(dummy_input_fn)))
+
+    est = estimator.Estimator(model_fn=_make_model_fn(40.),
+                              warm_start_from=first_est.model_dir)
+    # Prediction here is set to 1 + the value of the Variable that is
+    # warm-started from a checkpoint of the first model (3.), as opposed to
+    # the initialization in the new model_fn (40.).
+    self.assertEqual(4., next(est.predict(dummy_input_fn)))
+
   def test_no_trained_model_invalid_checkpoint_path(self):
     est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)
     with self.assertRaises(ValueError):

From 30927ec6b625121bae1b89b07f9faeaebaed321f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 16:04:09 -0700
Subject: [PATCH 0310/1691] Mark all nodes processed by AddOpsRewrite/MinBCast
 stages with a tag.

PiperOrigin-RevId: 195167597
---
 ...direct_session_with_tracking_alloc_test.cc |  4 +-
 .../optimizers/arithmetic_optimizer.cc        | 77 +++++++++++--------
 .../grappler/optimizers/meta_optimizer.cc     |  2 +-
 .../python/grappler/layout_optimizer_test.py  |  8 +-
 4 files changed, 53 insertions(+), 38 deletions(-)

diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
index b4dd521bbc80c8..695423b2cb1993 100644
--- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
@@ -102,9 +102,9 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) {
         EXPECT_EQ(2, shape.dim(0).size());
         EXPECT_EQ(1, shape.dim(1).size());
         if (node->name() == y->name()) {
-          EXPECT_EQ(7, cm->AllocationId(node, 0));
+          EXPECT_EQ(9, cm->AllocationId(node, 0));
         } else {
-          EXPECT_EQ(8, cm->AllocationId(node, 0));
+          EXPECT_EQ(10, cm->AllocationId(node, 0));
         }
       }
       EXPECT_LE(0, cm->MaxExecutionTime(node));
diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
index bf59b254490561..d6510ba681aa2b 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -24,6 +24,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.h"
@@ -49,6 +50,12 @@ namespace tensorflow {
 namespace grappler {
 namespace {
 
+// Mark nodes created or optimized by a stage with a tag.
+constexpr char kAddOpsRewriteTag[] =
+    "_grappler:ArithmeticOptimizer:AddOpsRewriteStage";
+constexpr char kMinimizeBroadcastsTag[] =
+    "_grappler:ArithmeticOptimizer:MinimizeBroadcasts";
+
 // Extract values from a Const op to `values`. Returns true if succeeds.
 template 
 bool ValuesFromConstNode(const NodeDef& node, std::vector* values) {
@@ -142,18 +149,6 @@ bool MaybeAddControlInput(const string& new_input, NodeDef* node,
   return !already_exists;
 }
 
-int CopyControlInputs(const NodeDef& from, NodeDef* to, GraphDef* graph,
-                      NodeMap* node_map) {
-  int num_copied = 0;
-  for (const string& input : from.input()) {
-    if (IsControlInput(input) &&
-        MaybeAddControlInput(input, to, graph, node_map)) {
-      ++num_copied;
-    }
-  }
-  return num_copied;
-}
-
 void SetDataTypeToAttr(DataType dtype, const string& attr_name, NodeDef* node) {
   (*node->mutable_attr())[attr_name].set_type(dtype);
 }
@@ -326,7 +321,7 @@ class ArithmeticNodesGroupOptimizerStage : public ArithmeticOptimizerStage {
   explicit ArithmeticNodesGroupOptimizerStage(
       const string& name, const GraphOptimizerContext& ctx,
       const ArithmeticOptimizerContext ctx_ext)
-      : ArithmeticOptimizerStage(name, ctx, ctx_ext), optimized_nodes_{} {}
+      : ArithmeticOptimizerStage(name, ctx, ctx_ext) {}
   ~ArithmeticNodesGroupOptimizerStage() override = default;
 
   // Input name with a statically inferred shape from GraphProperties
@@ -465,13 +460,16 @@ class ArithmeticNodesGroupOptimizerStage : public ArithmeticOptimizerStage {
     return signature;
   }
 
-  void AddToOptimizedNodes(const NodeDef* node) {
-    optimized_nodes_.insert(node->name());
+  void MarkWithTag(const StringPiece tag, NodeDef* node) {
+    AddNodeAttr(tag, true, node);
   }
 
-  void AddAllMembersToOptimizedNodes(const OptimizedNodesGroup& group) {
-    AddToOptimizedNodes(group.root_node);
-    for (const NodeDef* opt : group.optimized_nodes) AddToOptimizedNodes(opt);
+  void MarkAllMembersWithTag(const OptimizedNodesGroup& group,
+                             const StringPiece tag) const {
+    AddNodeAttr(tag, true, group.root_node);
+    for (NodeDef* optimized_node : group.optimized_nodes) {
+      AddNodeAttr(tag, true, optimized_node);
+    }
   }
 
   bool IsOnTheSameDevice(const OptimizedNodesGroup& group,
@@ -479,13 +477,19 @@ class ArithmeticNodesGroupOptimizerStage : public ArithmeticOptimizerStage {
     return group.root_node->device() == node.device();
   }
 
-  bool IsAlreadyOptimized(const NodeDef& node) const {
-    return optimized_nodes_.find(node.name()) != optimized_nodes_.end();
+  bool IsInPreserveSet(const NodeDef& node) const {
+    return ctx().nodes_to_preserve->find(node.name()) !=
+           ctx().nodes_to_preserve->end();
   }
 
- private:
-  // set of nodes already processed by this optimizer stage
-  std::unordered_set optimized_nodes_;
+  bool IsMarkedWithTag(const NodeDef& node, const StringPiece tag) const {
+    return HasNodeAttr(node, tag);
+  }
+
+  bool IsMarkedWithAnyTag(const NodeDef& node, const StringPiece tag1,
+                          const StringPiece tag2) const {
+    return IsMarkedWithTag(node, tag1) || IsMarkedWithTag(node, tag2);
+  }
 };
 
 // Rewrite a tree of Add/AddN with a single AddN operation, consuming all the
@@ -561,7 +565,7 @@ class AddOpsRewriteStage : public ArithmeticNodesGroupOptimizerStage {
     if (!IsAdd(node) && !IsAddN(node)) {
       return false;
     }
-    if (IsInPreserveSet(node) || IsAlreadyOptimized(node)) {
+    if (IsInPreserveSet(node) || IsMarkedWithTag(node, kAddOpsRewriteTag)) {
       return false;
     }
     // TODO(ezhulenev): relax this condition for root node
@@ -579,7 +583,7 @@ class AddOpsRewriteStage : public ArithmeticNodesGroupOptimizerStage {
             << " num_inputs=" << group.inputs.size();
 
     // Do not optimize any of the nodes that are part of this group.
-    AddAllMembersToOptimizedNodes(group);
+    MarkAllMembersWithTag(group, kAddOpsRewriteTag);
 
     // All new nodes will be placed under the scope of a root node.
     auto root_scope_and_name = ParseNodeScopeAndName(group.root_node->name());
@@ -688,7 +692,7 @@ class AddOpsRewriteStage : public ArithmeticNodesGroupOptimizerStage {
       node->add_input(inputAndShape.input);
     }
 
-    AddToOptimizedNodes(node);
+    MarkWithTag(kAddOpsRewriteTag, node);
     return InputAndShape(node_name, shape);
   }
 
@@ -705,14 +709,13 @@ class AddOpsRewriteStage : public ArithmeticNodesGroupOptimizerStage {
     node->set_op("Add");
     node->set_device(root_node.device());
     (*node->mutable_attr())["T"].set_type(dtype);
+    node->add_input(left.input);
+    node->add_input(right.input);
 
     ctx().node_map->AddOutput(left.input, node_name);
     ctx().node_map->AddOutput(right.input, node_name);
 
-    node->add_input(left.input);
-    node->add_input(right.input);
-
-    AddToOptimizedNodes(node);
+    MarkWithTag(kAddOpsRewriteTag, node);
     return InputAndShape(
         node_name, TensorShapeProto());  // shape is not important at this point
   }
@@ -960,7 +963,9 @@ class MinimizeBroadcasts : public ArithmeticNodesGroupOptimizerStage {
 
   bool IsSupported(const NodeDef* node) const override {
     if (!IsBinaryAssociative(*node)) return false;
-    if (IsAlreadyOptimized(*node)) return false;
+
+    if (IsMarkedWithAnyTag(*node, kMinimizeBroadcastsTag, kAddOpsRewriteTag))
+      return false;
 
     // has a symbolically defined shape with broadcastable inputs
     OpInfo::TensorProperties properties;
@@ -984,7 +989,11 @@ class MinimizeBroadcasts : public ArithmeticNodesGroupOptimizerStage {
     if (!IsSameOp(group, node)) {
       return false;
     }
-    if (IsInPreserveSet(node) || IsAlreadyOptimized(node)) {
+    if (IsInPreserveSet(node)) {
+      return false;
+    }
+    // Nodes optimized by AddOpsRewrite already have optimal broadcasts.
+    if (IsMarkedWithAnyTag(node, kMinimizeBroadcastsTag, kAddOpsRewriteTag)) {
       return false;
     }
     if (IsDrivenByControlDependency(node) || DrivesControlDependency(node)) {
@@ -1019,7 +1028,7 @@ class MinimizeBroadcasts : public ArithmeticNodesGroupOptimizerStage {
             << " num_optimized_nodes=" << group.optimized_nodes.size();
 
     // Do not optimize any of the nodes that are part of this group.
-    AddAllMembersToOptimizedNodes(group);
+    MarkAllMembersWithTag(group, kMinimizeBroadcastsTag);
 
     if (CountUniqueShapes(group.inputs) <= 1) {
       VLOG(3) << "Skip min-bcast group with single unique shape";
@@ -1905,6 +1914,8 @@ void ArithmeticOptimizer::DedupComputations() {
           FeedsInPlaceOp(graph_view, *node)) {
         continue;
       }
+      VLOG(3) << "Remove duplicated node: node=" << node->name()
+              << " representative=" << rep->name();
       const std::set& fanouts = node_map_->GetOutputs(node->name());
       for (NodeDef* fanout : fanouts) {
         for (int i = 0; i < fanout->input_size(); ++i) {
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index 5230177dcab296..0c8e18d7ab18b6 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -65,7 +65,7 @@ int NumIterations(const RewriterConfig& cfg) {
 // Check if optimizer is allowed to run only once.
 bool IsRunOnceOptimizer(const string& name) {
   return name == "layout" || name == "memory_optimizer" ||
-         name == "arithmetic_optimizer" || name == "loop_optimizer";
+         name == "loop_optimizer";
 }
 
 }  // namespace
diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py
index e3dd4b0bdfbb28..2d6925d1a82580 100644
--- a/tensorflow/python/grappler/layout_optimizer_test.py
+++ b/tensorflow/python/grappler/layout_optimizer_test.py
@@ -150,10 +150,14 @@ def _loop_with_vec_and_4d():
 def _get_config(layout_optimizer=True):
   if layout_optimizer:
     rewrite_options = rewriter_config_pb2.RewriterConfig(
-        layout_optimizer=rewriter_config_pb2.RewriterConfig.ON)
+        layout_optimizer=rewriter_config_pb2.RewriterConfig.ON,
+        # do not remove duplicated nodes
+        arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF)
   else:
     rewrite_options = rewriter_config_pb2.RewriterConfig(
-        layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF)
+        layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF,
+        # do not remove duplicated nodes
+        arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF)
   graph_options = config_pb2.GraphOptions(
       rewrite_options=rewrite_options, build_cost_model=1)
   config = config_pb2.ConfigProto(graph_options=graph_options)

From 1f4efb78320e1406c0cc9ce4b8753f3d2511048e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 16:05:43 -0700
Subject: [PATCH 0311/1691] Add RNNEstimator which takes in arbitrary heads.

PiperOrigin-RevId: 195167853
---
 tensorflow/contrib/estimator/BUILD            |   4 +
 tensorflow/contrib/estimator/__init__.py      |   1 +
 .../contrib/estimator/python/estimator/rnn.py | 164 ++++++++++++++++--
 .../estimator/python/estimator/rnn_test.py    | 119 ++++++++-----
 4 files changed, 237 insertions(+), 51 deletions(-)

diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD
index b473de86ee8be9..41a817673d8801 100644
--- a/tensorflow/contrib/estimator/BUILD
+++ b/tensorflow/contrib/estimator/BUILD
@@ -452,18 +452,22 @@ py_test(
         "notsan",
     ],
     deps = [
+        ":head",
         ":rnn",
+        "//tensorflow/contrib/data",
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:check_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
+        "//tensorflow/python:lib",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
         "//tensorflow/python/estimator:numpy_io",
+        "//tensorflow/python/estimator:parsing_utils",
         "//tensorflow/python/feature_column",
         "//third_party/py/numpy",
         "@six_archive//:six",
diff --git a/tensorflow/contrib/estimator/__init__.py b/tensorflow/contrib/estimator/__init__.py
index f66d844660e55d..d43b3ea6bf2718 100644
--- a/tensorflow/contrib/estimator/__init__.py
+++ b/tensorflow/contrib/estimator/__init__.py
@@ -55,6 +55,7 @@
     'replicate_model_fn',
     'TowerOptimizer',
     'RNNClassifier',
+    'RNNEstimator',
 ]
 
 remove_undocumented(__name__, allowed_exception_list=_allowed_symbols)
diff --git a/tensorflow/contrib/estimator/python/estimator/rnn.py b/tensorflow/contrib/estimator/python/estimator/rnn.py
index b475c12f5af3ae..7f385fd76e88ab 100644
--- a/tensorflow/contrib/estimator/python/estimator/rnn.py
+++ b/tensorflow/contrib/estimator/python/estimator/rnn.py
@@ -328,6 +328,19 @@ def _train_op_fn(loss):
         logits=logits)
 
 
+def _assert_rnn_cell_fn(rnn_cell_fn, num_units, cell_type):
+  """Assert arguments are valid and return rnn_cell_fn."""
+  if rnn_cell_fn and (num_units or cell_type != USE_DEFAULT):
+    raise ValueError(
+        'num_units and cell_type must not be specified when using rnn_cell_fn'
+    )
+  if not rnn_cell_fn:
+    if cell_type == USE_DEFAULT:
+      cell_type = 'basic_rnn'
+    rnn_cell_fn = _make_rnn_cell_fn(num_units, cell_type)
+  return rnn_cell_fn
+
+
 class RNNClassifier(estimator.Estimator):
   """A classifier for TensorFlow RNN models.
 
@@ -341,8 +354,8 @@ class RNNClassifier(estimator.Estimator):
   token_emb = embedding_column(categorical_column=token_sequence, ...)
 
   estimator = RNNClassifier(
-      num_units=[32, 16], cell_type='lstm',
-      sequence_feature_columns=[token_emb])
+      sequence_feature_columns=[token_emb],
+      num_units=[32, 16], cell_type='lstm')
 
   # Input builders
   def input_fn_train: # returns x, y
@@ -438,8 +451,8 @@ def __init__(self,
         encoded as integer values in {0, 1,..., n_classes-1} for `n_classes`>2 .
         Also there will be errors if vocabulary is not provided and labels are
         string.
-      optimizer: An instance of `tf.Optimizer` used to train the model. Defaults
-        to Adagrad optimizer.
+      optimizer: An instance of `tf.Optimizer` or string specifying optimizer
+        type. Defaults to Adagrad optimizer.
       input_layer_partitioner: Optional. Partitioner for input layer. Defaults
         to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
       config: `RunConfig` object to configure the runtime settings.
@@ -448,14 +461,7 @@ def __init__(self,
       ValueError: If `num_units`, `cell_type`, and `rnn_cell_fn` are not
         compatible.
     """
-    if rnn_cell_fn and (num_units or cell_type != USE_DEFAULT):
-      raise ValueError(
-          'num_units and cell_type must not be specified when using rnn_cell_fn'
-      )
-    if not rnn_cell_fn:
-      if cell_type == USE_DEFAULT:
-        cell_type = 'basic_rnn'
-      rnn_cell_fn = _make_rnn_cell_fn(num_units, cell_type)
+    rnn_cell_fn = _assert_rnn_cell_fn(rnn_cell_fn, num_units, cell_type)
 
     if n_classes == 2:
       head = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss(  # pylint: disable=protected-access
@@ -479,3 +485,137 @@ def _model_fn(features, labels, mode, config):
           config=config)
     super(RNNClassifier, self).__init__(
         model_fn=_model_fn, model_dir=model_dir, config=config)
+
+
+class RNNEstimator(estimator.Estimator):
+  """An Estimator for TensorFlow RNN models with user-specified head.
+
+  Example:
+
+  ```python
+  token_sequence = sequence_categorical_column_with_hash_bucket(...)
+  token_emb = embedding_column(categorical_column=token_sequence, ...)
+
+  estimator = RNNEstimator(
+      head=tf.contrib.estimator.regression_head(),
+      sequence_feature_columns=[token_emb],
+      num_units=[32, 16], cell_type='lstm')
+
+  # Or with custom RNN cell:
+  def rnn_cell_fn(mode):
+    cells = [ tf.contrib.rnn.LSTMCell(size) for size in [32, 16] ]
+    if mode == tf.estimator.ModeKeys.TRAIN:
+      cells = [ tf.contrib.rnn.DropoutWrapper(cell, input_keep_prob=0.5)
+                    for cell in cells ]
+    return tf.contrib.rnn.MultiRNNCell(cells)
+
+  estimator = RNNEstimator(
+      head=tf.contrib.estimator.regression_head(),
+      sequence_feature_columns=[token_emb],
+      rnn_cell_fn=rnn_cell_fn)
+
+  # Input builders
+  def input_fn_train: # returns x, y
+    pass
+  estimator.train(input_fn=input_fn_train, steps=100)
+
+  def input_fn_eval: # returns x, y
+    pass
+  metrics = estimator.evaluate(input_fn=input_fn_eval, steps=10)
+  def input_fn_predict: # returns x, None
+    pass
+  predictions = estimator.predict(input_fn=input_fn_predict)
+  ```
+
+  Input of `train` and `evaluate` should have following features,
+  otherwise there will be a `KeyError`:
+
+  * if the head's `weight_column` is not `None`, a feature with
+    `key=weight_column` whose value is a `Tensor`.
+  * for each `column` in `sequence_feature_columns`:
+    - a feature with `key=column.name` whose `value` is a `SparseTensor`.
+  * for each `column` in `context_feature_columns`:
+    - if `column` is a `_CategoricalColumn`, a feature with `key=column.name`
+      whose `value` is a `SparseTensor`.
+    - if `column` is a `_WeightedCategoricalColumn`, two features: the first
+      with `key` the id column name, the second with `key` the weight column
+      name. Both features' `value` must be a `SparseTensor`.
+    - if `column` is a `_DenseColumn`, a feature with `key=column.name`
+      whose `value` is a `Tensor`.
+
+  Loss and predicted output are determined by the specified head.
+
+  @compatibility(eager)
+  Estimators are not compatible with eager execution.
+  @end_compatibility
+  """
+
+  def __init__(self,
+               head,
+               sequence_feature_columns,
+               context_feature_columns=None,
+               num_units=None,
+               cell_type=USE_DEFAULT,
+               rnn_cell_fn=None,
+               model_dir=None,
+               optimizer='Adagrad',
+               input_layer_partitioner=None,
+               config=None):
+    """Initializes a `RNNClassifier` instance.
+
+    Args:
+      head: A `_Head` instance constructed with a method such as
+        `tf.contrib.estimator.multi_label_head`. This specifies the model's
+        output and loss function to be optimized.
+      sequence_feature_columns: An iterable containing the `FeatureColumn`s
+        that represent sequential input. All items in the set should either be
+        sequence columns (e.g. `sequence_numeric_column`) or constructed from
+        one (e.g. `embedding_column` with `sequence_categorical_column_*` as
+        input).
+      context_feature_columns: An iterable containing the `FeatureColumn`s
+        for contextual input. The data represented by these columns will be
+        replicated and given to the RNN at each timestep. These columns must be
+        instances of classes derived from `_DenseColumn` such as
+        `numeric_column`, not the sequential variants.
+      num_units: Iterable of integer number of hidden units per RNN layer. If
+        set, `cell_type` must also be specified and `rnn_cell_fn` must be
+        `None`.
+      cell_type: A subclass of `tf.nn.rnn_cell.RNNCell` or a string specifying
+        the cell type. Supported strings are: `'basic_rnn'`, `'lstm'`, and
+        `'gru'`. If set, `num_units` must also be specified and `rnn_cell_fn`
+        must be `None`.
+      rnn_cell_fn: A function with one argument, a `tf.estimator.ModeKeys`, and
+        returns an object of type `tf.nn.rnn_cell.RNNCell` that will be used to
+        construct the RNN. If set, `num_units` and `cell_type` cannot be set.
+        This is for advanced users who need additional customization beyond
+        `num_units` and `cell_type`. Note that `tf.nn.rnn_cell.MultiRNNCell` is
+        needed for stacked RNNs.
+      model_dir: Directory to save model parameters, graph and etc. This can
+        also be used to load checkpoints from the directory into a estimator to
+        continue training a previously saved model.
+      optimizer: An instance of `tf.Optimizer` or string specifying optimizer
+        type. Defaults to Adagrad optimizer.
+      input_layer_partitioner: Optional. Partitioner for input layer. Defaults
+        to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
+      config: `RunConfig` object to configure the runtime settings.
+
+    Raises:
+      ValueError: If `num_units`, `cell_type`, and `rnn_cell_fn` are not
+        compatible.
+    """
+    rnn_cell_fn = _assert_rnn_cell_fn(rnn_cell_fn, num_units, cell_type)
+
+    def _model_fn(features, labels, mode, config):
+      return _rnn_model_fn(
+          features=features,
+          labels=labels,
+          mode=mode,
+          head=head,
+          rnn_cell_fn=rnn_cell_fn,
+          sequence_feature_columns=tuple(sequence_feature_columns or []),
+          context_feature_columns=tuple(context_feature_columns or []),
+          optimizer=optimizer,
+          input_layer_partitioner=input_layer_partitioner,
+          config=config)
+    super(RNNEstimator, self).__init__(
+        model_fn=_model_fn, model_dir=model_dir, config=config)
diff --git a/tensorflow/contrib/estimator/python/estimator/rnn_test.py b/tensorflow/contrib/estimator/python/estimator/rnn_test.py
index 393f94f5c7de02..959b40371aa5fa 100644
--- a/tensorflow/contrib/estimator/python/estimator/rnn_test.py
+++ b/tensorflow/contrib/estimator/python/estimator/rnn_test.py
@@ -25,12 +25,15 @@
 import numpy as np
 import six
 
+from tensorflow.contrib.data.python.ops import readers
+from tensorflow.contrib.estimator.python.estimator import head as head_lib
 from tensorflow.contrib.estimator.python.estimator import rnn
 from tensorflow.contrib.feature_column.python.feature_column import sequence_feature_column as seq_fc
 from tensorflow.core.example import example_pb2
 from tensorflow.core.example import feature_pb2
 from tensorflow.python.estimator import model_fn
 from tensorflow.python.estimator.canned import metric_keys
+from tensorflow.python.estimator.canned import parsing_utils
 from tensorflow.python.estimator.canned import prediction_keys
 from tensorflow.python.estimator.export import export
 from tensorflow.python.estimator.inputs import numpy_io
@@ -38,9 +41,9 @@
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.lib.io import python_io
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import parsing_ops
 from tensorflow.python.ops import partitioned_variables
 from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import state_ops
@@ -50,7 +53,6 @@
 from tensorflow.python.platform import test
 from tensorflow.python.summary.writer import writer_cache
 from tensorflow.python.training import checkpoint_utils
-from tensorflow.python.training import input as input_lib
 from tensorflow.python.training import monitored_session
 from tensorflow.python.training import optimizer
 from tensorflow.python.training import training_util
@@ -984,7 +986,10 @@ def predict_input_fn():
                      predictions[prediction_keys.PredictionKeys.CLASSES])
 
 
-class RNNClassifierIntegrationTest(test.TestCase):
+class BaseRNNClassificationIntegrationTest(object):
+
+  def __init__(self, _create_estimator_fn):
+    self._create_estimator_fn = _create_estimator_fn
 
   def setUp(self):
     self._model_dir = tempfile.mkdtemp()
@@ -994,20 +999,11 @@ def tearDown(self):
       writer_cache.FileWriterCache.clear()
       shutil.rmtree(self._model_dir)
 
-  def _test_complete_flow(
-      self, train_input_fn, eval_input_fn, predict_input_fn, n_classes,
-      batch_size):
-    col = seq_fc.sequence_categorical_column_with_hash_bucket(
-        'tokens', hash_bucket_size=10)
-    embed = fc.embedding_column(col, dimension=2)
-    feature_columns = [embed]
-
+  def _test_complete_flow(self, feature_columns, train_input_fn, eval_input_fn,
+                          predict_input_fn, n_classes, batch_size):
     cell_units = [4, 2]
-    est = rnn.RNNClassifier(
-        num_units=cell_units,
-        sequence_feature_columns=feature_columns,
-        n_classes=n_classes,
-        model_dir=self._model_dir)
+    est = self._create_estimator_fn(feature_columns, n_classes, cell_units,
+                                    self._model_dir)
 
     # TRAIN
     num_steps = 10
@@ -1026,10 +1022,10 @@ def _test_complete_flow(
     self.assertAllEqual((batch_size, n_classes), predicted_proba.shape)
 
     # EXPORT
-    feature_spec = {
-        'tokens': parsing_ops.VarLenFeature(dtypes.string),
-        'label': parsing_ops.FixedLenFeature([1], dtypes.int64),
-    }
+    feature_spec = parsing_utils.classifier_parse_example_spec(
+        feature_columns,
+        label_key='label',
+        label_dtype=dtypes.int64)
     serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
         feature_spec)
     export_dir = est.export_savedmodel(tempfile.mkdtemp(),
@@ -1069,7 +1065,13 @@ def testNumpyInputFn(self):
         batch_size=batch_size,
         shuffle=False)
 
+    col = seq_fc.sequence_categorical_column_with_hash_bucket(
+        'tokens', hash_bucket_size=10)
+    embed = fc.embedding_column(col, dimension=2)
+    feature_columns = [embed]
+
     self._test_complete_flow(
+        feature_columns=feature_columns,
         train_input_fn=train_input_fn,
         eval_input_fn=eval_input_fn,
         predict_input_fn=predict_input_fn,
@@ -1082,7 +1084,8 @@ def testParseExampleInputFn(self):
     batch_size = 10
     words = [b'dog', b'cat', b'bird', b'the', b'a', b'sat', b'flew', b'slept']
 
-    serialized_examples = []
+    _, examples_file = tempfile.mkstemp()
+    writer = python_io.TFRecordWriter(examples_file)
     for _ in range(batch_size):
       sequence_length = random.randint(1, len(words))
       sentence = random.sample(words, sequence_length)
@@ -1096,30 +1099,36 @@ def testParseExampleInputFn(self):
                   feature_pb2.Feature(int64_list=feature_pb2.Int64List(
                       value=[label])),
           }))
-      serialized_examples.append(example.SerializeToString())
+      writer.write(example.SerializeToString())
+    writer.close()
+
+    col = seq_fc.sequence_categorical_column_with_hash_bucket(
+        'tokens', hash_bucket_size=10)
+    embed = fc.embedding_column(col, dimension=2)
+    feature_columns = [embed]
+    feature_spec = parsing_utils.classifier_parse_example_spec(
+        feature_columns,
+        label_key='label',
+        label_dtype=dtypes.int64)
 
-    feature_spec = {
-        'tokens': parsing_ops.VarLenFeature(dtypes.string),
-        'label': parsing_ops.FixedLenFeature([1], dtypes.int64),
-    }
     def _train_input_fn():
-      features = parsing_ops.parse_example(serialized_examples, feature_spec)
-      labels = features.pop('label')
-      return features, labels
+      dataset = readers.make_batched_features_dataset(
+          examples_file, batch_size, feature_spec)
+      return dataset.map(lambda features: (features, features.pop('label')))
     def _eval_input_fn():
-      features = parsing_ops.parse_example(
-          input_lib.limit_epochs(serialized_examples, num_epochs=1),
-          feature_spec)
-      labels = features.pop('label')
-      return features, labels
+      dataset = readers.make_batched_features_dataset(
+          examples_file, batch_size, feature_spec, num_epochs=1)
+      return dataset.map(lambda features: (features, features.pop('label')))
     def _predict_input_fn():
-      features = parsing_ops.parse_example(
-          input_lib.limit_epochs(serialized_examples, num_epochs=1),
-          feature_spec)
-      features.pop('label')
-      return features, None
+      dataset = readers.make_batched_features_dataset(
+          examples_file, batch_size, feature_spec, num_epochs=1)
+      def features_fn(features):
+        features.pop('label')
+        return features
+      return dataset.map(features_fn)
 
     self._test_complete_flow(
+        feature_columns=feature_columns,
         train_input_fn=_train_input_fn,
         eval_input_fn=_eval_input_fn,
         predict_input_fn=_predict_input_fn,
@@ -1127,5 +1136,37 @@ def _predict_input_fn():
         batch_size=batch_size)
 
 
+def _rnn_classifier_fn(feature_columns, n_classes, cell_units, model_dir):
+  return rnn.RNNClassifier(
+      num_units=cell_units,
+      sequence_feature_columns=feature_columns,
+      n_classes=n_classes,
+      model_dir=model_dir)
+
+
+class RNNClassifierIntegrationTest(BaseRNNClassificationIntegrationTest,
+                                   test.TestCase):
+
+  def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
+    test.TestCase.__init__(self, methodName)
+    BaseRNNClassificationIntegrationTest.__init__(self, _rnn_classifier_fn)
+
+
+def _rnn_estimator_fn(feature_columns, n_classes, cell_units, model_dir):
+  return rnn.RNNEstimator(
+      head=head_lib.multi_class_head(n_classes=n_classes),
+      num_units=cell_units,
+      sequence_feature_columns=feature_columns,
+      model_dir=model_dir)
+
+
+class RNNEstimatorIntegrationTest(BaseRNNClassificationIntegrationTest,
+                                  test.TestCase):
+
+  def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
+    test.TestCase.__init__(self, methodName)
+    BaseRNNClassificationIntegrationTest.__init__(self, _rnn_estimator_fn)
+
+
 if __name__ == '__main__':
   test.main()

From c7a5787fef8daf3e44313cbd48591464f9643f56 Mon Sep 17 00:00:00 2001
From: Ayush Dubey 
Date: Wed, 2 May 2018 16:13:06 -0700
Subject: [PATCH 0312/1691] Enable reshape of _ScopedAllocatorConcat output.

The _ScopedAllocatorConcat kernel outputs the backing tensor after performing
runtime bounds checks.  However, the shape of the backing tensor may not match
the desired output shape of the concat operation.

This change adds a "reshape" boolean attribute to _ScopedAllocatorConcat kernel.
When this attribute is set to true, the kernel outputs a reshaped backing tensor
according to the "shape" attribute.

PiperOrigin-RevId: 195169105
---
 .../core/kernels/scoped_allocator_ops.cc      | 39 +++++++----
 .../core/kernels/scoped_allocator_ops_test.cc | 64 ++++++++++++++++---
 tensorflow/core/ops/scoped_allocator_ops.cc   | 11 ++--
 3 files changed, 89 insertions(+), 25 deletions(-)

diff --git a/tensorflow/core/kernels/scoped_allocator_ops.cc b/tensorflow/core/kernels/scoped_allocator_ops.cc
index d7b25ffad04083..1800ee8c1f975b 100644
--- a/tensorflow/core/kernels/scoped_allocator_ops.cc
+++ b/tensorflow/core/kernels/scoped_allocator_ops.cc
@@ -94,7 +94,8 @@ class ScopedAllocatorConcatOp : public OpKernel {
       : OpKernel(context) {
     OP_REQUIRES_OK(context, context->GetAttr("shape", &shape_));
     OP_REQUIRES_OK(context, context->GetAttr("T", &dtype_));
-    // This stuff is just for debugging
+    OP_REQUIRES_OK(context, context->GetAttr("reshape", &reshape_));
+    // These attributes are just for debugging.
     OP_REQUIRES_OK(context, context->GetAttr("sa_name", &name_));
     OP_REQUIRES_OK(context, context->GetAttr("id", &id_));
     device_ = context->device();
@@ -114,11 +115,14 @@ class ScopedAllocatorConcatOp : public OpKernel {
                                         backing_tensor.NumElements(),
                                         " is not equal to expected ",
                                         shape_.num_elements()));
-    VLOG(1) << "_ScopedAllocatorConcatOp outputting backing tensor at "
-            << DMAHelper::base(&backing_tensor);
-    Tensor backing_copy(backing_tensor);
-    context->set_output(0, backing_copy);
-    const TensorBuffer* backing_buf = DMAHelper::buffer(&backing_copy);
+    Tensor output(dtype_);
+    if (reshape_) {
+      CHECK(output.CopyFrom(backing_tensor, shape_));
+    } else {
+      CHECK(output.CopyFrom(backing_tensor, backing_tensor.shape()));
+    }
+    context->set_output(0, output);
+    const TensorBuffer* backing_buf = DMAHelper::buffer(&output);
     const void* backing_tensor_lb = backing_buf->data();
     const void* backing_tensor_ub = static_cast(
         static_cast(backing_tensor_lb) + backing_buf->size());
@@ -126,17 +130,27 @@ class ScopedAllocatorConcatOp : public OpKernel {
     for (int i = 1; i < context->num_inputs(); ++i) {
       const TensorBuffer* input_buf = DMAHelper::buffer(&context->input(i));
       const void* input_lb = input_buf->data();
-      OP_REQUIRES(
-          context, input_lb >= backing_tensor_lb,
-          errors::InvalidArgument("Lower bound check fail for input ", i,
-                                  " to node ", context->op_kernel().name()));
       const void* input_ub = static_cast(
           static_cast(input_lb) + input_buf->size());
+      OP_REQUIRES(
+          context, input_lb >= backing_tensor_lb,
+          errors::InvalidArgument(
+              "Lower bound check fail for input ", i, " from node ",
+              context->op_kernel().requested_input(i), " to node ",
+              context->op_kernel().name(), " input bounds = [", input_lb, ", ",
+              input_ub, "]", " backing_tensor bounds = [", backing_tensor_lb,
+              ", ", backing_tensor_ub, "]"));
       OP_REQUIRES(
           context, input_ub <= backing_tensor_ub,
-          errors::InvalidArgument("Upper bound check fail for input ", i,
-                                  " to node ", context->op_kernel().name()));
+          errors::InvalidArgument(
+              "Upper bound check fail for input ", i, " from node ",
+              context->op_kernel().requested_input(i), " to node ",
+              context->op_kernel().name(), " input bounds = [", input_lb, ", ",
+              input_ub, "]", " backing_tensor bounds = [", backing_tensor_lb,
+              ", ", backing_tensor_ub, "]"));
     }
+    VLOG(1) << "_ScopedAllocatorConcatOp outputting backing tensor at "
+            << backing_buf;
   }
 
  private:
@@ -144,6 +158,7 @@ class ScopedAllocatorConcatOp : public OpKernel {
   DataType dtype_;
   string name_;
   int32 id_;
+  bool reshape_;
   DeviceBase* device_;
 };
 
diff --git a/tensorflow/core/kernels/scoped_allocator_ops_test.cc b/tensorflow/core/kernels/scoped_allocator_ops_test.cc
index 3d36c8b7d43748..019c6619ee1e7f 100644
--- a/tensorflow/core/kernels/scoped_allocator_ops_test.cc
+++ b/tensorflow/core/kernels/scoped_allocator_ops_test.cc
@@ -120,18 +120,43 @@ void PrepOp(DataType dtype, int32 id,
 
 class ScopedAllocatorConcatOpTest : public OpsTestBase {
  protected:
-  void MakeOp(const TensorShape& shape, DataType dtype, const string& name,
-              int32 id, int32 num_tensors) {
+  void BuildNodeDef(const TensorShape& shape, DataType dtype,
+                    const string& name, int32 id, int32 num_tensors) {
+    TF_EXPECT_OK(
+        NodeDefBuilder("scoped_allocator_concat_op", "_ScopedAllocatorConcat")
+            .Attr("shape", shape)
+            .Attr("T", dtype)
+            .Attr("N", num_tensors)
+            .Attr("sa_name", name)
+            .Attr("id", id)
+            .Input(FakeInput(dtype))               // backing tensor
+            .Input(FakeInput(num_tensors, dtype))  // list of tensors
+            .Finalize(node_def()));
+    shape_ = shape;
+    reshape_ = false;
+  }
+
+  void BuildNodeDefWithReshape(const TensorShape& shape, DataType dtype,
+                               bool reshape, const string& name, int32 id,
+                               int32 num_tensors) {
     TF_EXPECT_OK(
         NodeDefBuilder("scoped_allocator_concat_op", "_ScopedAllocatorConcat")
             .Attr("shape", shape)
             .Attr("T", dtype)
+            .Attr("reshape", reshape)
             .Attr("N", num_tensors)
             .Attr("sa_name", name)
             .Attr("id", id)
             .Input(FakeInput(dtype))               // backing tensor
             .Input(FakeInput(num_tensors, dtype))  // list of tensors
             .Finalize(node_def()));
+    shape_ = shape;
+    reshape_ = reshape;
+  }
+
+  void MakeOp(const TensorShape& shape, DataType dtype, bool reshape,
+              const string& name, int32 id, int32 num_tensors) {
+    BuildNodeDefWithReshape(shape, dtype, reshape, name, id, num_tensors);
     TF_EXPECT_OK(InitOp());
   }
 
@@ -141,7 +166,7 @@ class ScopedAllocatorConcatOpTest : public OpsTestBase {
     std::vector tensors;
     std::vector fields;
     PrepOp(dtype, id, fields_shapes, &fields, &backing_tensor, allocator(),
-           device_->GetScopedAllocatorMgr(), "split", &tensors, &inputs_,
+           device_->GetScopedAllocatorMgr(), "concat", &tensors, &inputs_,
            input_types_);
 
     TF_ASSERT_OK(RunOpKernel());
@@ -155,34 +180,55 @@ class ScopedAllocatorConcatOpTest : public OpsTestBase {
     CHECK_EQ(DMAHelper::base(&input), DMAHelper::base(&output));
     CHECK_EQ(input.dtype(), output.dtype());
     CHECK_EQ(input.NumElements(), output.NumElements());
+    if (reshape_) {
+      CHECK_EQ(shape_, output.shape());
+    } else {
+      TensorShape expected_shape({input.NumElements()});
+      CHECK_EQ(expected_shape, output.shape());
+    }
 
     // Free the backing tensor which was allocated in PrepOp.
     delete backing_tensor;
   }
+
+ private:
+  TensorShape shape_;
+  bool reshape_;
 };
 
 TEST_F(ScopedAllocatorConcatOpTest, Success1) {
-  MakeOp({32}, DT_FLOAT, "test", 120, 2);
+  MakeOp({32}, DT_FLOAT, false, "test", 120, 2);
   ExecOp(DT_FLOAT, 120, {{16}, {16}});
 }
 
 TEST_F(ScopedAllocatorConcatOpTest, Success2) {
-  MakeOp({2, 2, 2}, DT_DOUBLE, "test", 120, 2);
+  MakeOp({2, 2, 2}, DT_DOUBLE, false, "test", 120, 2);
   ExecOp(DT_DOUBLE, 120, {{2, 2}, {2, 2}});
 }
 
 TEST_F(ScopedAllocatorConcatOpTest, Success3) {
-  MakeOp({3, 3, 3}, DT_HALF, "test", 120, 3);
+  MakeOp({3, 3, 3}, DT_HALF, false, "test", 120, 3);
   ExecOp(DT_HALF, 120, {{3, 3}, {3, 3}, {3, 3}});
 }
 
+TEST_F(ScopedAllocatorConcatOpTest, Reshape) {
+  MakeOp({2, 2, 2}, DT_DOUBLE, true, "test", 120, 2);
+  ExecOp(DT_DOUBLE, 120, {{2, 2}, {2, 2}});
+}
+
+TEST_F(ScopedAllocatorConcatOpTest, NoReshapeAttr) {
+  BuildNodeDef({3, 4, 4}, DT_HALF, "test", 120, 3);
+  TF_EXPECT_OK(InitOp());
+  ExecOp(DT_HALF, 120, {{4, 4}, {4, 4}, {4, 4}});
+}
+
 TEST_F(ScopedAllocatorConcatOpTest, FailDtypeCheck) {
-  MakeOp({8}, DT_FLOAT, "test", 120, 2);
+  MakeOp({8}, DT_FLOAT, false, "test", 120, 2);
   EXPECT_DEATH(ExecOp(DT_DOUBLE, 120, {{4}, {4}}), "");
 }
 
 TEST_F(ScopedAllocatorConcatOpTest, FailNumElementsCheck) {
-  MakeOp({32}, DT_FLOAT, "test", 120, 2);
+  MakeOp({32}, DT_FLOAT, false, "test", 120, 2);
   AddInputFromArray({8}, {0, 1, 2, 3, 4, 5, 6, 7});
   AddInputFromArray({4}, {0, 1, 2, 3});
   AddInputFromArray({4}, {4, 5, 6, 7});
@@ -193,7 +239,7 @@ TEST_F(ScopedAllocatorConcatOpTest, FailNumElementsCheck) {
 // This test should fail because the backing tensor and the input tensors are
 // unrelated, i.e. the inputs are not slices of the backing tensor.
 TEST_F(ScopedAllocatorConcatOpTest, FailBounds) {
-  MakeOp({8}, DT_DOUBLE, "test", 120, 2);
+  MakeOp({8}, DT_DOUBLE, false, "test", 120, 2);
   AddInputFromArray({8}, {0, 1, 2, 3, 4, 5, 6, 7});
   AddInputFromArray({4}, {0, 1, 2, 3});
   AddInputFromArray({4}, {4, 5, 6, 7});
diff --git a/tensorflow/core/ops/scoped_allocator_ops.cc b/tensorflow/core/ops/scoped_allocator_ops.cc
index f053a53f4cf02e..1e0dcdac96cba1 100644
--- a/tensorflow/core/ops/scoped_allocator_ops.cc
+++ b/tensorflow/core/ops/scoped_allocator_ops.cc
@@ -43,6 +43,7 @@ REGISTER_OP("_ScopedAllocatorConcat")
     .Input("inputs: N * T")
     .Attr("shape: shape")
     .Attr("T: type")
+    .Attr("reshape: bool = false")
     .Attr("sa_name: string")
     .Attr("id: int")
     .Attr("N: int >= 2")
@@ -69,10 +70,12 @@ REGISTER_OP("_ScopedAllocatorSplit")
     .SetIsStateful()
     .SetShapeFn(shape_inference::ExplicitShape)
     .Doc(R"doc(
-Acts like a Concat Op that merges multple tensors into one, however it must
-only be used in conjunction with a ScopedAllocator which is backing the memory
-of all of its input tensors so that actually it just outputs a read-only
-reference to that ScopedAllocator's backing tensor.
+Acts roughly like a SplitV Op that splits one tensor into multiple tensors
+but must only be used in conjunction with corresponding ScopedAllocator
+and ScopedAllocatorConcat instances.  In practice it is provided as inputs
+the backing tensor as first input, which contains the concatenated values,
+and a list of alias tensors as its other input and it simply outputs that
+second list.
 
 This is an experimental op for internal use only.  It is possible to use this
 op in unsafe ways.

From 8a022d3d0e1bc521ccfee74174e75821bdc1bfa9 Mon Sep 17 00:00:00 2001
From: Francois Chollet 
Date: Wed, 2 May 2018 16:58:35 -0700
Subject: [PATCH 0313/1691] Allow `Layer.add_loss` to receive non-tensor; fixes
 error triggered when using a weight regularizer of factor 0.

PiperOrigin-RevId: 195175909
---
 tensorflow/python/keras/_impl/keras/engine/base_layer.py | 3 +++
 tensorflow/python/keras/_impl/keras/regularizers_test.py | 5 +++++
 2 files changed, 8 insertions(+)

diff --git a/tensorflow/python/keras/_impl/keras/engine/base_layer.py b/tensorflow/python/keras/_impl/keras/engine/base_layer.py
index a3e78c95dc9957..3af4eaabe90217 100644
--- a/tensorflow/python/keras/_impl/keras/engine/base_layer.py
+++ b/tensorflow/python/keras/_impl/keras/engine/base_layer.py
@@ -29,6 +29,7 @@
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras._impl.keras import backend
 from tensorflow.python.keras._impl.keras import constraints
 from tensorflow.python.keras._impl.keras import initializers
@@ -390,6 +391,8 @@ def add_loss(self, losses, inputs=None):
       raise RuntimeError('Layer.add_loss not supported in Eager mode.')
 
     losses = generic_utils.to_list(losses)
+    losses = [ops.convert_to_tensor(loss, dtype=backend.floatx())
+              if not tensor_util.is_tensor(loss) else loss for loss in losses]
     self._losses += losses
     if inputs is None:
       for loss in losses:
diff --git a/tensorflow/python/keras/_impl/keras/regularizers_test.py b/tensorflow/python/keras/_impl/keras/regularizers_test.py
index 9a1612b7779d1e..c4f04833ba51d8 100644
--- a/tensorflow/python/keras/_impl/keras/regularizers_test.py
+++ b/tensorflow/python/keras/_impl/keras/regularizers_test.py
@@ -71,6 +71,11 @@ def test_activity_regularization(self):
         model.fit(x_train, y_train, batch_size=10,
                   epochs=1, verbose=0)
 
+  def test_zero_regularization(self):
+    inputs = keras.backend.ones(shape=(10, 10))
+    layer = keras.layers.Dense(3, kernel_regularizer=keras.regularizers.l2(0))
+    layer(inputs)
+
 
 if __name__ == '__main__':
   test.main()

From dde83d4bee2c524cb5bd0adc4f702c9fc5ac6f3f Mon Sep 17 00:00:00 2001
From: Benoit Steiner 
Date: Wed, 2 May 2018 17:00:16 -0700
Subject: [PATCH 0314/1691] Handle negative values when slicing symbolic shapes

PiperOrigin-RevId: 195176133
---
 tensorflow/core/grappler/costs/graph_properties.cc | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index 23d25cba8d2961..eaf7634daa31d1 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -804,11 +804,16 @@ class SymbolicShapeRefiner {
           int64 start = slice_offset->dtype() == DT_INT32
                             ? slice_offset->flat()(0)
                             : slice_offset->flat()(0);
-          int64 end = start + (slice_size->dtype() == DT_INT32
-                                   ? slice_size->flat()(0)
-                                   : slice_size->flat()(0));
+          int64 size =
+              (slice_size->dtype() == DT_INT32 ? slice_size->flat()(0)
+                                               : slice_size->flat()(0));
           ShapeHandle result;
-          TF_RETURN_IF_ERROR(ic->Subshape(input, start, end, &result));
+          if (size == -1) {
+            TF_RETURN_IF_ERROR(ic->Subshape(input, start, &result));
+          } else {
+            int64 end = start + size;
+            TF_RETURN_IF_ERROR(ic->Subshape(input, start, end, &result));
+          }
           c->output_tensors_as_shapes.resize(1);
           c->output_tensors_as_shapes[0] = result;
         }

From a1ef905926d12b0362c0dcf6d669e1c3d2ffcf70 Mon Sep 17 00:00:00 2001
From: Jeremy Lau 
Date: Wed, 2 May 2018 17:25:10 -0700
Subject: [PATCH 0315/1691] BufferValue is a new base class for LogicalBuffer
 and HloValue. This makes it easier to migrate from
 TuplePointsToAnalysis/LogicalBuffer to HloDataflowAnalysis/HloValue. No
 functional changes.

PiperOrigin-RevId: 195179676
---
 tensorflow/compiler/xla/service/BUILD         |  22 +++
 .../xla/service/buffer_assignment_test.cc     |   3 +-
 .../compiler/xla/service/buffer_value.cc      |  66 +++++++
 .../compiler/xla/service/buffer_value.h       | 177 ++++++++++++++++++
 tensorflow/compiler/xla/service/compiler.h    |   5 +-
 tensorflow/compiler/xla/service/gpu/BUILD     |   1 +
 .../compiler/xla/service/gpu/hlo_schedule.cc  |   3 +-
 .../xla/service/heap_simulator_test.cc        |   5 +-
 .../xla/service/hlo_rematerialization.cc      |   3 +-
 .../xla/service/hlo_scheduling_test.cc        |   6 +-
 tensorflow/compiler/xla/service/hlo_value.cc  |   8 +-
 tensorflow/compiler/xla/service/hlo_value.h   |  51 ++---
 .../compiler/xla/service/logical_buffer.cc    |  42 +----
 .../compiler/xla/service/logical_buffer.h     | 123 +-----------
 14 files changed, 318 insertions(+), 197 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/buffer_value.cc
 create mode 100644 tensorflow/compiler/xla/service/buffer_value.h

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 17964cdd59f827..0b8b22b44ca7d8 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -780,6 +780,7 @@ cc_library(
     srcs = ["compiler.cc"],
     hdrs = ["compiler.h"],
     deps = [
+        ":buffer_value",
         ":executable",
         ":hlo",
         ":hlo_module_config",
@@ -1014,6 +1015,7 @@ tf_cc_test(
     srcs = ["buffer_assignment_test.cc"],
     deps = [
         ":buffer_assignment",
+        ":buffer_value",
         ":call_graph",
         ":computation_tracker",
         ":copy_insertion",
@@ -1095,6 +1097,7 @@ tf_cc_test(
     name = "heap_simulator_test",
     srcs = ["heap_simulator_test.cc"],
     deps = [
+        ":buffer_value",
         ":heap_simulator",
         ":hlo",
         ":hlo_ordering",
@@ -1163,6 +1166,7 @@ tf_cc_test(
     name = "hlo_scheduling_test",
     srcs = ["hlo_scheduling_test.cc"],
     deps = [
+        ":buffer_value",
         ":hlo",
         ":hlo_ordering",
         ":hlo_scheduling",
@@ -1749,11 +1753,27 @@ tf_cc_test(
     ],
 )
 
+cc_library(
+    name = "buffer_value",
+    srcs = ["buffer_value.cc"],
+    hdrs = ["buffer_value.h"],
+    deps = [
+        ":hlo",
+        ":hlo_proto",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+)
+
 cc_library(
     name = "logical_buffer",
     srcs = ["logical_buffer.cc"],
     hdrs = ["logical_buffer.h"],
     deps = [
+        ":buffer_value",
         ":hlo",
         ":hlo_proto",
         "//tensorflow/compiler/xla:shape_util",
@@ -1769,6 +1789,7 @@ cc_library(
     srcs = ["hlo_value.cc"],
     hdrs = ["hlo_value.h"],
     deps = [
+        ":buffer_value",
         ":hlo",
         "//tensorflow/compiler/xla:shape_tree",
         "//tensorflow/compiler/xla:shape_util",
@@ -2066,6 +2087,7 @@ cc_library(
     hdrs = ["hlo_rematerialization.h"],
     deps = [
         ":buffer_liveness",
+        ":buffer_value",
         ":call_graph",
         ":flatten_call_graph",
         ":hlo",
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index f6d6b5c36a478e..a4fb0eefaca094 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -23,6 +23,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/buffer_value.h"
 #include "tensorflow/compiler/xla/service/call_graph.h"
 #include "tensorflow/compiler/xla/service/computation_tracker.h"
 #include "tensorflow/compiler/xla/service/copy_insertion.h"
@@ -1684,7 +1685,7 @@ class WhileBufferAssignmentTest : public HloTestBase {
         .ConsumeValueOrDie();
   }
 
-  static int64 ByteSizeOf(const LogicalBuffer& buffer) {
+  static int64 ByteSizeOf(const BufferValue& buffer) {
     return ShapeUtil::ByteSizeOf(buffer.shape(), sizeof(void*));
   }
 
diff --git a/tensorflow/compiler/xla/service/buffer_value.cc b/tensorflow/compiler/xla/service/buffer_value.cc
new file mode 100644
index 00000000000000..df1a5ca435d0f3
--- /dev/null
+++ b/tensorflow/compiler/xla/service/buffer_value.cc
@@ -0,0 +1,66 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/buffer_value.h"
+
+#include 
+
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace xla {
+
+BufferValue::BufferValue(HloInstruction* instruction, const ShapeIndex& index,
+                         Id id)
+    : id_(id) {
+  const Shape& shape = ShapeUtil::GetSubshape(instruction->shape(), index);
+  is_array_ = ShapeUtil::IsArray(shape);
+  is_tuple_ = ShapeUtil::IsTuple(shape);
+}
+
+BufferValue::~BufferValue() {}
+
+std::ostream& operator<<(std::ostream& out, const BufferValue& buffer) {
+  out << buffer.ToString();
+  return out;
+}
+
+/*static*/ LogicalBufferProto::Location BufferValue::ToLocationProto(
+    const HloInstruction& instruction, const ShapeIndex& index) {
+  LogicalBufferProto::Location proto;
+  proto.set_computation_name(instruction.parent()->name());
+  proto.set_instruction_name(instruction.name());
+  for (const int64 index_entry : index) {
+    proto.add_shape_index(index_entry);
+  }
+  return proto;
+}
+
+LogicalBufferProto BufferValue::ToProto(const SizeFunction& size_fn) const {
+  LogicalBufferProto proto;
+  proto.set_id(id());
+  proto.set_size(size_fn(*this));
+  LogicalBufferProto::Location proto_location =
+      ToLocationProto(*instruction(), index());
+  proto.mutable_defined_at()->Swap(&proto_location);
+  proto.set_color(color().value());
+  return proto;
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/buffer_value.h b/tensorflow/compiler/xla/service/buffer_value.h
new file mode 100644
index 00000000000000..f4be16e0843f64
--- /dev/null
+++ b/tensorflow/compiler/xla/service/buffer_value.h
@@ -0,0 +1,177 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_BUFFER_VALUE_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_BUFFER_VALUE_H_
+
+#include 
+#include 
+
+#include "tensorflow/compiler/xla/service/hlo.pb.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/gtl/array_slice.h"
+#include "tensorflow/core/lib/gtl/int_type.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace xla {
+
+// Abstract class describing a value used by one of the dataflow analyses -
+// TuplePointsToAnalysis or HloDataflowAnalysis.
+// TODO(b/78906445) Delete this class when TuplePointsToAnalysis is unused.
+//
+// XLA arrays are trivially a single BufferValue. Tuples are made up of more
+// than one BufferValue: an BufferValue for the pointer vector, and an
+// BufferValue for each child element.
+//
+// Every BufferValue is defined by a particular instruction and most
+// instructions define only a single BufferValue. Instructions which define a
+// single BufferValue include array-shaped instructions such as Add but also
+// includes Tuple-shaped instructions such as Tuple. The Tuple instruction
+// defines a single BufferValue which is a vector of pointers to the values
+// containing the Tuple instruction's operands. Though the result of the Tuple
+// instruction includes multiple values only the top-level BufferValue (the
+// vector of pointers) is defined by the Tuple instruction. The values
+// containing the tuple elements are defined by earlier instructions, usually
+// the operands of the Tuple instruction.
+//
+// Instructions which construct both the tuple *and* the tuple elements define
+// more than one BufferValue. This includes (at least) tuple-shaped Constant,
+// Parameter, Infeed and While instructions. These tuple-shaped instructions do
+// not assemble a tuple from existing BufferValues like the Tuple instruction
+// does, but rather define all the BufferValues in the tuple.
+//
+// Some instructions, such as Bitcast, define no buffers. These instructions
+// simply forward buffers from their operands.
+//
+// The BufferValue object describes which HLO instruction defines a buffer and
+// where within that instruction's output shape the buffer is defined. The
+// location within the output shape is indicated by BufferValue::index() which
+// is defined identically to the index used in ShapeUtil::GetSubshape().
+// Examples:
+//
+// %add = Add(%foo, %bar)
+// %tuple_constant = Constant({1, {42, 43}})
+//
+// %add defines a single array-shaped buffer BufferValue(%add, {}) which holds
+// the array result of the add operation. The nested-tuple-shaped
+// %tuple_constant defines 5 buffers described by the following BufferValue
+// objects:
+//
+//   BufferValue(%tuple_constant, {})      // "Top-level" buffer: vector of
+//                                         //  pointers to BufferValues at
+//                                         //  indices {0} and {1}
+//   BufferValue(%tuple_constant, {0})     // Holds value "1"
+//   BufferValue(%tuple_constant, {1})     // Holds nested tuple: vector of
+//                                         //  pointers to BufferValues at
+//                                         //  indices {1, 0} and {1, 1}
+//   BufferValue(%tuple_constant, {1, 0})  // Holds value "42"
+//   BufferValue(%tuple_constant, {1, 1})  // Holds value "43"
+
+class BufferValue {
+ public:
+  TF_LIB_GTL_DEFINE_INT_TYPE(Color, int64);
+
+  // Id is a unique identifier for the BufferValue to facilitate efficient
+  // collections of BufferValues with stable iteration order.
+  using Id = int64;
+
+  // Functions which return the size and alignment of a logical buffer in bytes.
+  using SizeFunction = std::function;
+  using AlignmentFunction = std::function;
+
+  virtual ~BufferValue();
+
+  Id id() const { return id_; }
+
+  // Return the instruction that defines the buffer.
+  virtual HloInstruction* instruction() const = 0;
+
+  // Return the index within the output of the instruction where the buffer is
+  // defined. Index used defined as in ShapeUtil::GetSubshape()
+  virtual const ShapeIndex& index() const = 0;
+
+  // Return the color of the BufferValue. Differently colored buffers can not be
+  // parts of the same allocation.
+  Color color() const {
+    CHECK_NE(color_, kInvalidColor)
+        << "Should not query the color of a buffer that was never colored";
+    return color_;
+  }
+
+  void set_color(Color color) {
+    CHECK_NE(color, kInvalidColor)
+        << "Should not set the color of a buffer to the invalid color";
+    color_ = color;
+  }
+
+  bool has_color() const { return color_ != kInvalidColor; }
+
+  // Return the shape of the buffer. This reference points into the shape field
+  // of the instruction defining the buffer.  Therefore, the returned shape will
+  // contain the layout of instruction, if any.
+  virtual const Shape& shape() const = 0;
+
+  // Returns true if this buffer is the top-level output buffer of the defining
+  // HLO instruction. This is equivalent to index == {}.
+  bool IsTopLevel() const { return index().empty(); }
+
+  // Whether this buffer contains a tuple.
+  bool IsTuple() const { return is_tuple_; }
+
+  // Whether this buffer contains an array.
+  bool IsArray() const { return is_array_; }
+
+  // operator< is required for std::set.
+  bool operator<(const BufferValue& other) const { return id_ < other.id_; }
+
+  virtual string ToString() const = 0;
+
+  // TODO(lauj) rename LogicalBufferProto to BufferValueProto.
+  LogicalBufferProto ToProto(const SizeFunction& size_fn) const;
+
+  // Returns the LogicalBufferProto::Location that serializes the given
+  // instruction and index.
+  static LogicalBufferProto::Location ToLocationProto(
+      const HloInstruction& instruction, const ShapeIndex& index);
+
+  const Color kInvalidColor = Color(-1);
+
+ protected:
+  BufferValue(HloInstruction* instruction, const ShapeIndex& index, Id id);
+
+ private:
+  // The definining instruction and index are not stored here; they can be found
+  // in the LogicalBuffer and HloValue subclasses. This class exists only to
+  // support migrations from TuplePointsToAnalysis to HloDataflowAnalysis, by
+  // allowing abstract use of LogicalBuffer or HloValue. After those migrations
+  // are complete, this class should be deleted (b/78906445). Because we plan to
+  // delete LogicalBuffer and this class, we don't refactor all the shared
+  // features from LogicalBuffer and HloValue into this class.
+  Id id_ : 62;
+  bool is_array_ : 1;
+  bool is_tuple_ : 1;
+  Color color_ = kInvalidColor;
+};
+
+std::ostream& operator<<(std::ostream& out, const BufferValue& buffer);
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_BUFFER_VALUE_H_
diff --git a/tensorflow/compiler/xla/service/compiler.h b/tensorflow/compiler/xla/service/compiler.h
index 5c14591d93cc99..a4b59d1ba9b24e 100644
--- a/tensorflow/compiler/xla/service/compiler.h
+++ b/tensorflow/compiler/xla/service/compiler.h
@@ -25,6 +25,7 @@ limitations under the License.
 #include 
 #include 
 
+#include "tensorflow/compiler/xla/service/buffer_value.h"
 #include "tensorflow/compiler/xla/service/executable.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_module_config.h"
@@ -181,9 +182,9 @@ class Compiler {
 
   // Returns a function that computes the size in bytes of a given
   // logical buffer.
-  std::function BufferSizeBytesFunction() {
+  std::function BufferSizeBytesFunction() {
     HloCostAnalysis::ShapeSizeFunction shape_size = ShapeSizeBytesFunction();
-    return [shape_size](const LogicalBuffer& buffer) {
+    return [shape_size](const BufferValue& buffer) {
       return shape_size(buffer.shape());
     };
   }
diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD
index f1707442fe3354..7cb7f550730eeb 100644
--- a/tensorflow/compiler/xla/service/gpu/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/BUILD
@@ -620,6 +620,7 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla/service:buffer_value",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_ordering",
         "//tensorflow/compiler/xla/service:hlo_reachability",
diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
index 42c1539e86c2ab..f766f968826d96 100644
--- a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
+++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/gpu/hlo_schedule.h"
 
 #include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/buffer_value.h"
 #include "tensorflow/compiler/xla/service/hlo_reachability.h"
 #include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -199,7 +200,7 @@ StatusOr> HloSchedule::Build(
     TF_ASSIGN_OR_RETURN(
         schedule->thunk_launch_order_,
         CreateMemoryMinimizingSequence(
-            *entry_computation, [pointer_size](const LogicalBuffer& buffer) {
+            *entry_computation, [pointer_size](const BufferValue& buffer) {
               return ShapeUtil::ByteSizeOf(buffer.shape(), pointer_size);
             }));
   } else {
diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc
index e983fd11d4eefc..fd56a603bb6f84 100644
--- a/tensorflow/compiler/xla/service/heap_simulator_test.cc
+++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include 
 
 #include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/service/buffer_value.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
@@ -85,7 +86,7 @@ class HeapSimulatorTracker {
     // size of the buffers doesn't matter, so we always return 0.  We rely on
     // the secondary sorting criteria of DecreasingSizeRunsHeap to sort calls by
     // buffer id, for determinism in the tests.
-    auto zero_size = [](const LogicalBuffer& buffer) { return 0; };
+    auto zero_size = [](const BufferValue& buffer) { return 0; };
     auto algorithm = MakeUnique(
         MakeUnique(&actual_calls_));
     result_ = HeapSimulator::Run(
@@ -119,7 +120,7 @@ class HeapSimulatorTracker {
     // the sequence. This lets us ensure the Alloc calls are in the sequence
     // order. The Free calls are sorted by LogicalBuffer.id, which is at least
     // deterministic.
-    auto size_fn = [&reverse_position](const LogicalBuffer& buffer) {
+    auto size_fn = [&reverse_position](const BufferValue& buffer) {
       return reverse_position[buffer.instruction()];
     };
     auto algorithm = MakeUnique(
diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc
index b0632448933df4..b171d41a31ed23 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc
@@ -22,6 +22,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
+#include "tensorflow/compiler/xla/service/buffer_value.h"
 #include "tensorflow/compiler/xla/service/flatten_call_graph.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_dce.h"
@@ -1216,7 +1217,7 @@ StatusOr HloRematerialization::Run(
   // Create initial sequence of HLO instructions.
   TF_ASSIGN_OR_RETURN(*sequence, CreateMemoryMinimizingSequence(
                                      *module,
-                                     [this](const LogicalBuffer& buffer) {
+                                     [this](const BufferValue& buffer) {
                                        return size_function_(buffer.shape());
                                      },
                                      scheduler_algorithm_));
diff --git a/tensorflow/compiler/xla/service/hlo_scheduling_test.cc b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc
index 74544c4a67a819..92df7c1427f282 100644
--- a/tensorflow/compiler/xla/service/hlo_scheduling_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc
@@ -77,7 +77,7 @@ TEST_F(MinimumMemoryForSequenceTest, MultiComputation) {
   HloComputation* entry_computation =
       module->AddEntryComputation(builder.Build());
 
-  auto size_fn = [](const LogicalBuffer& buffer) {
+  auto size_fn = [](const BufferValue& buffer) {
     return ShapeUtil::ByteSizeOf(buffer.shape(), /*pointer_size=*/8);
   };
 
@@ -124,7 +124,7 @@ TEST_F(HloSchedulingTest, LastUseScheduledFirst) {
 
   TF_ASSERT_OK_AND_ASSIGN(
       SequentialHloOrdering::HloModuleSequence sequence,
-      CreateMemoryMinimizingSequence(*module, [](const LogicalBuffer& buffer) {
+      CreateMemoryMinimizingSequence(*module, [](const BufferValue& buffer) {
         return ShapeUtil::ByteSizeOf(buffer.shape());
       }));
   // Verify that all instructions are in the sequence.
@@ -160,7 +160,7 @@ ENTRY root {
   TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module,
                           tools::Parse(module_str));
 
-  auto size_fn = [](const LogicalBuffer& buffer) {
+  auto size_fn = [](const BufferValue& buffer) {
     return ShapeUtil::ByteSizeOf(buffer.shape(), /*pointer_size=*/8);
   };
   TF_ASSERT_OK_AND_ASSIGN(
diff --git a/tensorflow/compiler/xla/service/hlo_value.cc b/tensorflow/compiler/xla/service/hlo_value.cc
index 05b7dce3d1ecf9..7b27dbfec376b8 100644
--- a/tensorflow/compiler/xla/service/hlo_value.cc
+++ b/tensorflow/compiler/xla/service/hlo_value.cc
@@ -29,9 +29,11 @@ limitations under the License.
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/types.h"
 
 namespace xla {
 
@@ -69,7 +71,7 @@ std::ostream& operator<<(std::ostream& out, const HloUse& use) {
 
 HloValue::HloValue(HloValue::Id id, HloInstruction* instruction,
                    const ShapeIndex& index, bool is_phi)
-    : id_(id), is_phi_(is_phi) {
+    : BufferValue(instruction, index, id), is_phi_(is_phi) {
   // The defining position is always the first element in the positions_ vector.
   positions_.push_back(HloPosition{instruction, index});
 }
@@ -90,8 +92,8 @@ string HloValue::ToShortString() const {
   string index_str = ShapeUtil::IsTuple(defining_instruction()->shape())
                          ? defining_index().ToString()
                          : "";
-  return StrCat(id_, " ", is_phi_ ? "PHI " : "", defining_instruction()->name(),
-                index_str);
+  return StrCat(id(), " ", is_phi_ ? "PHI " : "",
+                defining_instruction()->name(), index_str);
 }
 
 string HloValue::ToString(int indent) const {
diff --git a/tensorflow/compiler/xla/service/hlo_value.h b/tensorflow/compiler/xla/service/hlo_value.h
index 2a711e8b42590c..a1151f65e07dff 100644
--- a/tensorflow/compiler/xla/service/hlo_value.h
+++ b/tensorflow/compiler/xla/service/hlo_value.h
@@ -16,16 +16,20 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_VALUE_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_VALUE_H_
 
-#include 
+#include 
 #include 
 #include 
 
+#include "tensorflow/compiler/xla/service/buffer_value.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/shape_tree.h"
+#include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
+#include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/types.h"
 
 namespace xla {
 
@@ -80,30 +84,9 @@ struct HloUse {
 
 std::ostream& operator<<(std::ostream& out, const HloUse& use);
 
-// Class describing a value used by the dataflow analysis. XLA arrays are
-// trivially a single HloValue. Tuples are made up of more than one HloValue: an
-// HloValue for the pointer vector, and an HloValue for each child element.
-//
-// Every HloValue is defined by a particular instruction and most instructions
-// define only a single HloValue. Instructions which define a single HloValue
-// include array-shaped instructions such as Add but also includes Tuple-shaped
-// instructions such as Tuple. The Tuple instruction defines a single HloValue
-// which is a vector of pointers to the values containing the Tuple
-// instruction's operands. Though the result of the Tuple instruction includes
-// multiple values only the top-level HloValue (the vector of pointers) is
-// defined by the Tuple instruction. The values containing the tuple elements
-// are defined by earlier instructions, usually the operands of the Tuple
-// instruction.
-//
-// Instructions which construct both the tuple *and* the tuple elements define
-// more than one HloValue. This includes (at least) tuple-shaped Constant,
-// Parameter, Infeed and While instructions. These tuple-shaped instructions do
-// not assemble a tuple from existing HloValues like the Tuple instruction does,
-// but rather define all the HloValues in the tuple.
-class HloValue {
+// HloDataflowAnalysis uses this subclass of BufferValue.
+class HloValue : public BufferValue {
  public:
-  using Id = int64;
-
   // Predicate comparing HloValues by increasing id, useful for std::sort.
   static bool IdLessThan(const HloValue* a, const HloValue* b) {
     return a->id() < b->id();
@@ -120,6 +103,7 @@ class HloValue {
   // dataflow analysis (HloDataflowAnalysis::ssa_form_ is true).
   HloValue(Id id, HloInstruction* instruction, const ShapeIndex& index,
            bool is_phi = false);
+  ~HloValue() override {}
 
   // Sets the positions in the module at which the HloValue appears. Updates
   // uses. Should be called once and only once. The defining position should not
@@ -127,10 +111,6 @@ class HloValue {
   void SetPositionsAndComputeUses(
       tensorflow::gtl::ArraySlice positions);
 
-  // Return a unique identifier for this HloValue. This value is used for stable
-  // sorting and iteration
-  Id id() const { return id_; }
-
   // Returns whether this value is a phi value.
   bool is_phi() const { return is_phi_; }
 
@@ -142,12 +122,18 @@ class HloValue {
     return defining_position().instruction;
   }
 
+  HloInstruction* instruction() const override {
+    return defining_instruction();
+  }
+
   // Return the shape index at which this HloValue is defined in the output of
   // its defining instruction.
   const ShapeIndex& defining_index() const { return defining_position().index; }
 
+  const ShapeIndex& index() const override { return defining_index(); }
+
   // Return the shape of this HloValue.
-  const Shape& shape() const { return defining_position().shape(); }
+  const Shape& shape() const override { return defining_position().shape(); }
 
   // Return all positions of the HloValue in the module.
   const std::vector& positions() const { return positions_; }
@@ -164,12 +150,11 @@ class HloValue {
   // Return a single-line string representation of the value.
   string ToShortString() const;
 
-  string ToString(int indent = 0) const;
+  string ToString(int indent) const;
 
- private:
-  // Unique identifier for this HloValue. Used for stable sorting and iteration.
-  const Id id_;
+  string ToString() const override { return ToString(0); }
 
+ private:
   // Whether this instruction is a phi value.
   const bool is_phi_;
 
diff --git a/tensorflow/compiler/xla/service/logical_buffer.cc b/tensorflow/compiler/xla/service/logical_buffer.cc
index 68553bed121917..1b3de8ad173d16 100644
--- a/tensorflow/compiler/xla/service/logical_buffer.cc
+++ b/tensorflow/compiler/xla/service/logical_buffer.cc
@@ -15,9 +15,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/logical_buffer.h"
 
-#include 
-#include 
-
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -28,43 +25,16 @@ namespace xla {
 
 LogicalBuffer::LogicalBuffer(HloInstruction* instruction,
                              const ShapeIndex& index, Id id)
-    : instruction_(instruction), id_(id), color_(kInvalidColor), index_(index) {
-  const auto& s = shape();
-  is_array_ = ShapeUtil::IsArray(s);
-  is_tuple_ = ShapeUtil::IsTuple(s);
-}
+    : BufferValue(instruction, index, id),
+      instruction_(instruction),
+      index_(index) {}
+
+LogicalBuffer::~LogicalBuffer() {}
 
 string LogicalBuffer::ToString() const {
   return tensorflow::strings::StrCat(instruction_->name(), "[",
                                      tensorflow::str_util::Join(index_, ","),
-                                     "](#", id_, " @", color_.value(), ")");
-}
-
-std::ostream& operator<<(std::ostream& out, const LogicalBuffer& buffer) {
-  out << buffer.ToString();
-  return out;
-}
-
-/*static*/ LogicalBufferProto::Location LogicalBuffer::ToLocationProto(
-    const HloInstruction& instruction, const ShapeIndex& index) {
-  LogicalBufferProto::Location proto;
-  proto.set_computation_name(instruction.parent()->name());
-  proto.set_instruction_name(instruction.name());
-  for (const int64 index_entry : index) {
-    proto.add_shape_index(index_entry);
-  }
-  return proto;
-}
-
-LogicalBufferProto LogicalBuffer::ToProto(const SizeFunction& size_fn) const {
-  LogicalBufferProto proto;
-  proto.set_id(id_);
-  proto.set_size(size_fn(*this));
-  LogicalBufferProto::Location proto_location =
-      ToLocationProto(*instruction_, index_);
-  proto.mutable_defined_at()->Swap(&proto_location);
-  proto.set_color(color_.value());
-  return proto;
+                                     "](#", id(), " @", color().value(), ")");
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/logical_buffer.h b/tensorflow/compiler/xla/service/logical_buffer.h
index 67b205e289e626..f9ba5a554740c9 100644
--- a/tensorflow/compiler/xla/service/logical_buffer.h
+++ b/tensorflow/compiler/xla/service/logical_buffer.h
@@ -16,11 +16,9 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_LOGICAL_BUFFER_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_LOGICAL_BUFFER_H_
 
-#include 
-#include 
 #include 
-#include 
 
+#include "tensorflow/compiler/xla/service/buffer_value.h"
 #include "tensorflow/compiler/xla/service/hlo.pb.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -33,133 +31,30 @@ limitations under the License.
 
 namespace xla {
 
-// Class describing a contiguous sequence of elements (ie, C array) which form
-// the components of Shaped values in XLA. XLA arrays are trivially a
-// single LogicalBuffer. Tuple values are made up of more than one
-// LogicalBuffer: a LogicalBuffer for the pointers to elements, and a
-// LogicalBuffer for each child element.
-//
-// Every buffer is defined by a particular instruction and most instructions
-// define only a single buffer. Instructions which define a single buffer
-// include array-shaped instructions such as Add but also includes Tuple-shaped
-// instructions such as Tuple. The Tuple instruction defines a single buffer
-// which is a vector of pointers to the buffers containing the Tuple
-// instruction's operands. Though the result of the Tuple instruction includes
-// multiple buffers only the top-level buffer (the vector of pointers) is
-// defined by the Tuple instruction. The buffers containing the tuple elements
-// are defined by earlier instructions, usually the operands of the Tuple
-// instruction.
-//
-// Instructions which construct both the tuple *and* the tuple elements define
-// more than one buffer. This includes (at least) tuple-shaped Constant,
-// Parameter, Infeed and While instructions. The tuple-shaped instructions do
-// not assemble a tuple from existing buffers like the Tuple instruction does,
-// but rather define the entire tuple.
-//
-// Some instructions, such as Bitcast, define no buffers. These instructions
-// simply forward buffers from their operands.
-//
-// The LogicalBuffer object describes which HLO instruction defines a buffer and
-// where within that instruction's output shape the buffer is defined. The
-// location within the output shape is indicated by LogicalBuffer::index() which
-// is defined identically to the index used in
-// ShapeUtil::GetSubshape(). Examples:
-//
-// %add = Add(%foo, %bar)
-// %tuple_constant = Constant({1, {42, 43}})
-//
-// %add defines a single array-shaped buffer LogicalBuffer(%add, {}) which holds
-// the array result of the add operation. The nested-tuple-shaped
-// %tuple_constant defines 5 buffers described by the following LogicalBuffer
-// objects:
-//
-//   LogicalBuffer(%tuple_constant, {})      // "Top-level" buffer: vector of
-//                                           //  pointers to LogicalBuffers at
-//                                           //  indices {0} and {1}
-//   LogicalBuffer(%tuple_constant, {0})     // Holds value "1"
-//   LogicalBuffer(%tuple_constant, {1})     // Holds nested tuple: vector of
-//                                           //  pointers to LogicalBuffers at
-//                                           //  indices {1, 0} and {1, 1}
-//   LogicalBuffer(%tuple_constant, {1, 0})  // Holds value "42"
-//   LogicalBuffer(%tuple_constant, {1, 1})  // Holds value "43"
-class LogicalBuffer {
+// TuplePointsToAnalysis uses this subclass of BufferValue.
+class LogicalBuffer : public BufferValue {
  public:
-  TF_LIB_GTL_DEFINE_INT_TYPE(Color, int64);
-
-  // Id is a unique identifier for the LogicalBuffer to facilitate efficient
-  // collections of LogicalBuffers with stable iteration order.
-  // LogicalBuffers are typically created and accessed through
-  // TuplePointsToAnalysis, and points-to analysis assigns each LogicalBuffer a
-  // unique value.
-  using Id = int64;
-
-  // Functions which return the size and alignment of a logical buffer in bytes.
-  using SizeFunction = std::function;
-  using AlignmentFunction = std::function;
-
   LogicalBuffer(HloInstruction* instruction, const ShapeIndex& index, Id id);
-
-  Id id() const { return id_; }
+  ~LogicalBuffer() override;
 
   // Return the instruction that defines the buffer.
-  HloInstruction* instruction() const { return instruction_; }
+  HloInstruction* instruction() const override { return instruction_; }
 
   // Return the index within the output of the instruction where the buffer is
   // defined. Index used defined as in ShapeUtil::GetSubshape()
-  const ShapeIndex& index() const { return index_; }
-
-  // Return the color of the logical buffer. Differently colored buffers can
-  // not be parts of the same allocation.
-  Color color() const {
-    CHECK_NE(color_, kInvalidColor)
-        << "Should not query the color of a buffer that was never colored";
-    return color_;
-  }
-
-  void set_color(Color color) {
-    CHECK_NE(color, kInvalidColor)
-        << "Should not set the color of a buffer to the invalid color";
-    color_ = color;
-  }
-
-  bool has_color() const { return color_ != kInvalidColor; }
+  const ShapeIndex& index() const override { return index_; }
 
   // Return the shape of the buffer. This reference points into the shape field
   // of the instruction defining the buffer.  Therefore, the returned shape will
   // contain the layout of instruction, if any.
-  const Shape& shape() const {
+  const Shape& shape() const override {
     return ShapeUtil::GetSubshape(instruction_->shape(), index_);
   }
 
-  // Returns true if this buffer is the top-level output buffer of the defining
-  // HLO instruction. This is equivalent to index == {}.
-  bool IsTopLevel() const { return index_.empty(); }
-
-  // Whether this buffer contains a tuple.
-  bool IsTuple() const { return is_tuple_; }
-
-  // Whether this buffer contains an array.
-  bool IsArray() const { return is_array_; }
-
-  // operator< is required for std::set.
-  bool operator<(const LogicalBuffer& other) const { return id_ < other.id_; }
-
-  string ToString() const;
-  LogicalBufferProto ToProto(const SizeFunction& size_fn) const;
-
-  // Returns the LogicalBufferProto::Location that serializes the given
-  // instruction and index.
-  static LogicalBufferProto::Location ToLocationProto(
-      const HloInstruction& instruction, const ShapeIndex& index);
-
-  const Color kInvalidColor = Color(-1);
+  string ToString() const override;
 
  private:
   HloInstruction* instruction_;
-  Id id_ : 62;
-  bool is_array_ : 1;
-  bool is_tuple_ : 1;
-  Color color_;
   ShapeIndex index_;
 
   // Similar to HLO constructs (HloInstruction, etc), pointers are used for
@@ -167,8 +62,6 @@ class LogicalBuffer {
   TF_DISALLOW_COPY_AND_ASSIGN(LogicalBuffer);
 };
 
-std::ostream& operator<<(std::ostream& out, const LogicalBuffer& buffer);
-
 }  // namespace xla
 
 #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_LOGICAL_BUFFER_H_

From 7833890a0da5226e4c409b1020155f1718c0edb2 Mon Sep 17 00:00:00 2001
From: Anna R 
Date: Wed, 2 May 2018 17:41:26 -0700
Subject: [PATCH 0316/1691] Add a collect_trace option to run_op_benchmark for
 cases when callers just want to pass RunOptions.FULL_TRACE but don't want to
 store trace in extras.

PiperOrigin-RevId: 195181533
---
 tensorflow/python/kernel_tests/benchmark_test.py | 12 ++++++++----
 tensorflow/python/platform/benchmark.py          | 14 ++++++++++----
 2 files changed, 18 insertions(+), 8 deletions(-)

diff --git a/tensorflow/python/kernel_tests/benchmark_test.py b/tensorflow/python/kernel_tests/benchmark_test.py
index 623343602dffa8..78b6e38d949b28 100644
--- a/tensorflow/python/kernel_tests/benchmark_test.py
+++ b/tensorflow/python/kernel_tests/benchmark_test.py
@@ -67,7 +67,7 @@ def benchmark_times_an_op(self):
     with session.Session() as sess:
       a = constant_op.constant(0.0)
       a_plus_a = a + a
-      self.run_op_benchmark(
+      return self.run_op_benchmark(
           sess, a_plus_a, min_iters=1000, store_trace=True, name="op_benchmark")
 
 
@@ -148,7 +148,7 @@ def testReportingBenchmark(self):
       reporting = TestReportingBenchmark()
       reporting.benchmarkReport1()  # This should write
       reporting.benchmarkReport2()  # This should write
-      reporting.benchmark_times_an_op()  # This should write
+      benchmark_values3 = reporting.benchmark_times_an_op()  # This should write
 
       # Check the files were written
       self.assertTrue(gfile.Exists(expected_output_file))
@@ -186,8 +186,12 @@ def read_benchmark_entry(f):
       self.assertEquals(expected_3.name, read_benchmark_3.name)
       self.assertEquals(expected_3.iters, read_benchmark_3.iters)
       self.assertGreater(read_benchmark_3.wall_time, 0)
-      full_trace = read_benchmark_3.extras["full_trace_chrome_format"]
-      json_trace = json.loads(full_trace.string_value)
+
+      # Trace is not stored in benchmark entry. Instead we get it from
+      # return value of `run_op_benchmark` call.
+      full_trace = benchmark_values3["extras"]["full_trace_chrome_format"]
+      json_trace = json.loads(full_trace)
+
       self.assertTrue(isinstance(json_trace, dict))
       self.assertTrue("traceEvents" in json_trace.keys())
       allocator_keys = [k for k in read_benchmark_3.extras.keys()
diff --git a/tensorflow/python/platform/benchmark.py b/tensorflow/python/platform/benchmark.py
index 12dae94a6404e5..eba2baaf6f836c 100644
--- a/tensorflow/python/platform/benchmark.py
+++ b/tensorflow/python/platform/benchmark.py
@@ -213,9 +213,10 @@ def run_op_benchmark(self,
       burn_iters: Number of burn-in iterations to run.
       min_iters: Minimum number of iterations to use for timing.
       store_trace: Boolean, whether to run an extra untimed iteration and
-        store the trace of iteration in the benchmark report.
+        store the trace of iteration in returned extras.
         The trace will be stored as a string in Google Chrome trace format
-        in the extras field "full_trace_chrome_format".
+        in the extras field "full_trace_chrome_format". Note that trace
+        will not be stored in test_log_pb2.TestResults proto.
       store_memory_usage: Boolean, whether to run an extra untimed iteration,
         calculate memory usage, and store that in extras fields.
       name: (optional) Override the BenchmarkEntry name with `name`.
@@ -227,7 +228,9 @@ def run_op_benchmark(self,
 
     Returns:
       A `dict` containing the key-value pairs that were passed to
-      `report_benchmark`.
+      `report_benchmark`. If `store_trace` option is used, then
+      `full_chrome_trace_format` will be included in return dictionary even
+      though it is not passed to `report_benchmark` with `extras`.
     """
     for _ in range(burn_iters):
       sess.run(op_or_tensor, feed_dict=feed_dict)
@@ -242,6 +245,7 @@ def run_op_benchmark(self,
       deltas[i] = delta
 
     extras = extras if extras is not None else {}
+    unreported_extras = {}
     if store_trace or store_memory_usage:
       run_options = config_pb2.RunOptions(
           trace_level=config_pb2.RunOptions.FULL_TRACE)
@@ -251,7 +255,8 @@ def run_op_benchmark(self,
       tl = timeline.Timeline(run_metadata.step_stats)
 
       if store_trace:
-        extras["full_trace_chrome_format"] = tl.generate_chrome_trace_format()
+        unreported_extras["full_trace_chrome_format"] = (
+            tl.generate_chrome_trace_format())
 
       if store_memory_usage:
         step_stats_analysis = tl.analyze_step_stats(show_memory=True)
@@ -277,6 +282,7 @@ def _median(x):
         "throughput": mbs / median_delta
     }
     self.report_benchmark(**benchmark_values)
+    benchmark_values["extras"].update(unreported_extras)
     return benchmark_values
 
 

From a44996a84b24c43cca40c685a009fd59275755ab Mon Sep 17 00:00:00 2001
From: Yong Tang 
Date: Thu, 3 May 2018 02:45:29 +0200
Subject: [PATCH 0317/1691] Add go_package to proto definition files (#17262)

* Add go_package to proto definition files

This fix tries to address the issue raised in 16282 by
add go_package to proto files, so that generated go files
have correct path.

This fix fixes 16282.

Signed-off-by: Yong Tang 

* Add go_package to proto definition in tensorflow/core/framework

Signed-off-by: Yong Tang 

* Add go_package to proto definition in tensorflow/core/example

Signed-off-by: Yong Tang 

* Add go_package to proto definition in tensorflow/core/example

Signed-off-by: Yong Tang 
---
 tensorflow/core/example/example.proto                           | 2 +-
 tensorflow/core/example/example_parser_configuration.proto      | 1 +
 tensorflow/core/example/feature.proto                           | 2 +-
 tensorflow/core/framework/allocation_description.proto          | 1 +
 tensorflow/core/framework/api_def.proto                         | 1 +
 tensorflow/core/framework/attr_value.proto                      | 2 +-
 tensorflow/core/framework/cost_graph.proto                      | 2 +-
 tensorflow/core/framework/device_attributes.proto               | 1 +
 tensorflow/core/framework/function.proto                        | 2 +-
 tensorflow/core/framework/graph.proto                           | 2 +-
 tensorflow/core/framework/graph_transfer_info.proto             | 2 +-
 tensorflow/core/framework/iterator.proto                        | 1 +
 tensorflow/core/framework/kernel_def.proto                      | 2 +-
 tensorflow/core/framework/log_memory.proto                      | 2 +-
 tensorflow/core/framework/node_def.proto                        | 2 +-
 tensorflow/core/framework/op_def.proto                          | 2 +-
 tensorflow/core/framework/reader_base.proto                     | 1 +
 tensorflow/core/framework/remote_fused_graph_execute_info.proto | 2 +-
 tensorflow/core/framework/resource_handle.proto                 | 1 +
 tensorflow/core/framework/step_stats.proto                      | 2 +-
 tensorflow/core/framework/summary.proto                         | 2 +-
 tensorflow/core/framework/tensor.proto                          | 2 +-
 tensorflow/core/framework/tensor_description.proto              | 2 +-
 tensorflow/core/framework/tensor_shape.proto                    | 1 +
 tensorflow/core/framework/tensor_slice.proto                    | 1 +
 tensorflow/core/framework/types.proto                           | 1 +
 tensorflow/core/framework/variable.proto                        | 1 +
 tensorflow/core/framework/versions.proto                        | 1 +
 tensorflow/core/lib/core/error_codes.proto                      | 1 +
 tensorflow/core/protobuf/cluster.proto                          | 1 +
 tensorflow/core/protobuf/config.proto                           | 2 +-
 tensorflow/core/protobuf/control_flow.proto                     | 1 +
 tensorflow/core/protobuf/critical_section.proto                 | 1 +
 tensorflow/core/protobuf/debug.proto                            | 1 +
 tensorflow/core/protobuf/device_properties.proto                | 1 +
 tensorflow/core/protobuf/master.proto                           | 2 +-
 tensorflow/core/protobuf/master_service.proto                   | 2 +-
 tensorflow/core/protobuf/meta_graph.proto                       | 2 +-
 tensorflow/core/protobuf/named_tensor.proto                     | 2 +-
 tensorflow/core/protobuf/queue_runner.proto                     | 2 +-
 tensorflow/core/protobuf/rewriter_config.proto                  | 1 +
 tensorflow/core/protobuf/saved_model.proto                      | 2 +-
 tensorflow/core/protobuf/saver.proto                            | 1 +
 tensorflow/core/protobuf/tensor_bundle.proto                    | 2 +-
 tensorflow/core/protobuf/tensorflow_server.proto                | 2 +-
 tensorflow/core/protobuf/worker.proto                           | 2 +-
 tensorflow/core/protobuf/worker_service.proto                   | 2 +-
 47 files changed, 47 insertions(+), 27 deletions(-)

diff --git a/tensorflow/core/example/example.proto b/tensorflow/core/example/example.proto
index b2b723278b046b..e7142a4ef97132 100644
--- a/tensorflow/core/example/example.proto
+++ b/tensorflow/core/example/example.proto
@@ -7,7 +7,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "ExampleProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.example";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/example";
 package tensorflow;
 
 // An Example is a mostly-normalized data format for storing data for
diff --git a/tensorflow/core/example/example_parser_configuration.proto b/tensorflow/core/example/example_parser_configuration.proto
index 15846c0e302960..b2c115d80e35ec 100644
--- a/tensorflow/core/example/example_parser_configuration.proto
+++ b/tensorflow/core/example/example_parser_configuration.proto
@@ -6,6 +6,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "ExampleParserConfigurationProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.example";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/example";
 package tensorflow;
 
 import "tensorflow/core/framework/tensor_shape.proto";
diff --git a/tensorflow/core/example/feature.proto b/tensorflow/core/example/feature.proto
index da3dc59a120409..6d81974aac33b7 100644
--- a/tensorflow/core/example/feature.proto
+++ b/tensorflow/core/example/feature.proto
@@ -58,7 +58,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "FeatureProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.example";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/example";
 package tensorflow;
 
 // Containers to hold repeated fundamental values.
diff --git a/tensorflow/core/framework/allocation_description.proto b/tensorflow/core/framework/allocation_description.proto
index bb1037c2dfe46a..64133b05e18f90 100644
--- a/tensorflow/core/framework/allocation_description.proto
+++ b/tensorflow/core/framework/allocation_description.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "AllocationDescriptionProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 
 message AllocationDescription {
   // Total number of bytes requested
diff --git a/tensorflow/core/framework/api_def.proto b/tensorflow/core/framework/api_def.proto
index 98c38efc0e9a8e..cce02d84b215d0 100644
--- a/tensorflow/core/framework/api_def.proto
+++ b/tensorflow/core/framework/api_def.proto
@@ -8,6 +8,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "ApiDefProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/attr_value.proto";
 
 // Used to specify and override the default API & behavior in the
diff --git a/tensorflow/core/framework/attr_value.proto b/tensorflow/core/framework/attr_value.proto
index 62f0a9050fb82c..054e3ec97cc144 100644
--- a/tensorflow/core/framework/attr_value.proto
+++ b/tensorflow/core/framework/attr_value.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "AttrValueProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/tensor.proto";
 import "tensorflow/core/framework/tensor_shape.proto";
 import "tensorflow/core/framework/types.proto";
diff --git a/tensorflow/core/framework/cost_graph.proto b/tensorflow/core/framework/cost_graph.proto
index 7885b0171a55a4..19d765cd32e05a 100644
--- a/tensorflow/core/framework/cost_graph.proto
+++ b/tensorflow/core/framework/cost_graph.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "CostGraphProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/tensor_shape.proto";
 import "tensorflow/core/framework/types.proto";
 
diff --git a/tensorflow/core/framework/device_attributes.proto b/tensorflow/core/framework/device_attributes.proto
index 0b3c0d5bdf9f3d..44236ca9798abc 100644
--- a/tensorflow/core/framework/device_attributes.proto
+++ b/tensorflow/core/framework/device_attributes.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "DeviceAttributesProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 
 message InterconnectLink {
   int32 device_id = 1;
diff --git a/tensorflow/core/framework/function.proto b/tensorflow/core/framework/function.proto
index 72e3c438314bd8..e69d3938d93d10 100644
--- a/tensorflow/core/framework/function.proto
+++ b/tensorflow/core/framework/function.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "FunctionProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/attr_value.proto";
 import "tensorflow/core/framework/node_def.proto";
 import "tensorflow/core/framework/op_def.proto";
diff --git a/tensorflow/core/framework/graph.proto b/tensorflow/core/framework/graph.proto
index 7d6e16d5c129a0..76d358971d7d33 100644
--- a/tensorflow/core/framework/graph.proto
+++ b/tensorflow/core/framework/graph.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "GraphProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/node_def.proto";
 import "tensorflow/core/framework/function.proto";
 import "tensorflow/core/framework/versions.proto";
diff --git a/tensorflow/core/framework/graph_transfer_info.proto b/tensorflow/core/framework/graph_transfer_info.proto
index 41dd54d78c0395..232297d460dbe8 100644
--- a/tensorflow/core/framework/graph_transfer_info.proto
+++ b/tensorflow/core/framework/graph_transfer_info.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "GraphTransferInfoProto";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/types.proto";
 
 message GraphTransferNodeInput {
diff --git a/tensorflow/core/framework/iterator.proto b/tensorflow/core/framework/iterator.proto
index 7e5f5ea2e0c2f9..f015342e13313e 100644
--- a/tensorflow/core/framework/iterator.proto
+++ b/tensorflow/core/framework/iterator.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "IteratorProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.util";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 
 // Protocol buffer representing the metadata for an iterator's state stored
 // as a Variant tensor.
diff --git a/tensorflow/core/framework/kernel_def.proto b/tensorflow/core/framework/kernel_def.proto
index 65e9ef04a06651..a17b9c8492b68c 100644
--- a/tensorflow/core/framework/kernel_def.proto
+++ b/tensorflow/core/framework/kernel_def.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "KernelDefProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/attr_value.proto";
 
 message KernelDef {
diff --git a/tensorflow/core/framework/log_memory.proto b/tensorflow/core/framework/log_memory.proto
index d1e126330d20b6..7f37eadc3bed0a 100644
--- a/tensorflow/core/framework/log_memory.proto
+++ b/tensorflow/core/framework/log_memory.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "LogMemoryProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/tensor_description.proto";
 
 message MemoryLogStep {
diff --git a/tensorflow/core/framework/node_def.proto b/tensorflow/core/framework/node_def.proto
index 8fcee32e298661..0a095f903f9f6b 100644
--- a/tensorflow/core/framework/node_def.proto
+++ b/tensorflow/core/framework/node_def.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "NodeProto";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/attr_value.proto";
 
 message NodeDef {
diff --git a/tensorflow/core/framework/op_def.proto b/tensorflow/core/framework/op_def.proto
index ca0e5e7133af61..aea2d2bb09a2c2 100644
--- a/tensorflow/core/framework/op_def.proto
+++ b/tensorflow/core/framework/op_def.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "OpDefProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/attr_value.proto";
 import "tensorflow/core/framework/types.proto";
 
diff --git a/tensorflow/core/framework/reader_base.proto b/tensorflow/core/framework/reader_base.proto
index 1b8b965ee105fb..9e187cfa791f69 100644
--- a/tensorflow/core/framework/reader_base.proto
+++ b/tensorflow/core/framework/reader_base.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "ReaderBaseProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 
 // For serializing and restoring the state of ReaderBase, see
 // reader_base.h for details.
diff --git a/tensorflow/core/framework/remote_fused_graph_execute_info.proto b/tensorflow/core/framework/remote_fused_graph_execute_info.proto
index 946da40d0e315a..10072724d2f554 100644
--- a/tensorflow/core/framework/remote_fused_graph_execute_info.proto
+++ b/tensorflow/core/framework/remote_fused_graph_execute_info.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "RemoteFusedGraphExecuteInfoProto";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/graph.proto";
 import "tensorflow/core/framework/tensor_shape.proto";
 import "tensorflow/core/framework/types.proto";
diff --git a/tensorflow/core/framework/resource_handle.proto b/tensorflow/core/framework/resource_handle.proto
index b1921337f5fd0b..a54d3d906ca985 100644
--- a/tensorflow/core/framework/resource_handle.proto
+++ b/tensorflow/core/framework/resource_handle.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "ResourceHandle";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 
 // Protocol buffer representing a handle to a tensorflow resource. Handles are
 // not valid across executions, but can be serialized back and forth from within
diff --git a/tensorflow/core/framework/step_stats.proto b/tensorflow/core/framework/step_stats.proto
index 65c8089d51141b..d98999cb54bd84 100644
--- a/tensorflow/core/framework/step_stats.proto
+++ b/tensorflow/core/framework/step_stats.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "StepStatsProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/allocation_description.proto";
 import "tensorflow/core/framework/tensor_description.proto";
 
diff --git a/tensorflow/core/framework/summary.proto b/tensorflow/core/framework/summary.proto
index 55879f87831eb9..532e4fcd87b78e 100644
--- a/tensorflow/core/framework/summary.proto
+++ b/tensorflow/core/framework/summary.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "SummaryProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/tensor.proto";
 
 // Metadata associated with a series of Summary data
diff --git a/tensorflow/core/framework/tensor.proto b/tensorflow/core/framework/tensor.proto
index abbf16e8103326..55921af1d0f7ab 100644
--- a/tensorflow/core/framework/tensor.proto
+++ b/tensorflow/core/framework/tensor.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "TensorProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/resource_handle.proto";
 import "tensorflow/core/framework/tensor_shape.proto";
 import "tensorflow/core/framework/types.proto";
diff --git a/tensorflow/core/framework/tensor_description.proto b/tensorflow/core/framework/tensor_description.proto
index 6ac3c1b8810878..4c23c7e6205ada 100644
--- a/tensorflow/core/framework/tensor_description.proto
+++ b/tensorflow/core/framework/tensor_description.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "TensorDescriptionProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 import "tensorflow/core/framework/types.proto";
 import "tensorflow/core/framework/tensor_shape.proto";
 import "tensorflow/core/framework/allocation_description.proto";
diff --git a/tensorflow/core/framework/tensor_shape.proto b/tensorflow/core/framework/tensor_shape.proto
index 1ec3c5323c2c73..286156a0123303 100644
--- a/tensorflow/core/framework/tensor_shape.proto
+++ b/tensorflow/core/framework/tensor_shape.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "TensorShapeProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 
 package tensorflow;
 
diff --git a/tensorflow/core/framework/tensor_slice.proto b/tensorflow/core/framework/tensor_slice.proto
index 24b01661dc4691..a5c366ed6061f3 100644
--- a/tensorflow/core/framework/tensor_slice.proto
+++ b/tensorflow/core/framework/tensor_slice.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "TensorSliceProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 
 package tensorflow;
 
diff --git a/tensorflow/core/framework/types.proto b/tensorflow/core/framework/types.proto
index e003fd00106fba..03835d1b923d4f 100644
--- a/tensorflow/core/framework/types.proto
+++ b/tensorflow/core/framework/types.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "TypesProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 
 // LINT.IfChange
 enum DataType {
diff --git a/tensorflow/core/framework/variable.proto b/tensorflow/core/framework/variable.proto
index e0df01cc9b7758..93ae423babb937 100644
--- a/tensorflow/core/framework/variable.proto
+++ b/tensorflow/core/framework/variable.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "VariableProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 
 // Protocol buffer representing a Variable.
 message VariableDef {
diff --git a/tensorflow/core/framework/versions.proto b/tensorflow/core/framework/versions.proto
index 7d5e58ae7d4230..dd2ec55238728f 100644
--- a/tensorflow/core/framework/versions.proto
+++ b/tensorflow/core/framework/versions.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "VersionsProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
 
 // Version information for a piece of serialized data
 //
diff --git a/tensorflow/core/lib/core/error_codes.proto b/tensorflow/core/lib/core/error_codes.proto
index b82d3891460cb4..5ced65a97331cd 100644
--- a/tensorflow/core/lib/core/error_codes.proto
+++ b/tensorflow/core/lib/core/error_codes.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "ErrorCodesProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/lib/core";
 
 // The canonical error codes for TensorFlow APIs.
 //
diff --git a/tensorflow/core/protobuf/cluster.proto b/tensorflow/core/protobuf/cluster.proto
index 33c87eefe022ee..c696d345e0cfb9 100644
--- a/tensorflow/core/protobuf/cluster.proto
+++ b/tensorflow/core/protobuf/cluster.proto
@@ -20,6 +20,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "ClusterProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.distruntime";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 
 // This file contains protos to be used when defining a TensorFlow
 // cluster.
diff --git a/tensorflow/core/protobuf/config.proto b/tensorflow/core/protobuf/config.proto
index c1a0075b6468cd..078e76e7dcb74e 100644
--- a/tensorflow/core/protobuf/config.proto
+++ b/tensorflow/core/protobuf/config.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "ConfigProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 import "tensorflow/core/framework/cost_graph.proto";
 import "tensorflow/core/framework/graph.proto";
 import "tensorflow/core/framework/step_stats.proto";
diff --git a/tensorflow/core/protobuf/control_flow.proto b/tensorflow/core/protobuf/control_flow.proto
index 3c05b4f0e22e5c..5f44878c44c90b 100644
--- a/tensorflow/core/protobuf/control_flow.proto
+++ b/tensorflow/core/protobuf/control_flow.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "ControlFlowProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 
 // Control flow context related protocol buffers.
 
diff --git a/tensorflow/core/protobuf/critical_section.proto b/tensorflow/core/protobuf/critical_section.proto
index 0b3f531e6d9f59..7954e7ba87c1b9 100644
--- a/tensorflow/core/protobuf/critical_section.proto
+++ b/tensorflow/core/protobuf/critical_section.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "CriticalSectionProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 
 // Protocol buffer representing a CriticalSection.
 message CriticalSectionDef {
diff --git a/tensorflow/core/protobuf/debug.proto b/tensorflow/core/protobuf/debug.proto
index 56983f3b7d464f..499900f965ac2b 100644
--- a/tensorflow/core/protobuf/debug.proto
+++ b/tensorflow/core/protobuf/debug.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "DebugProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 
 // EXPERIMENTAL. Option for watching a node.
 message DebugTensorWatch {
diff --git a/tensorflow/core/protobuf/device_properties.proto b/tensorflow/core/protobuf/device_properties.proto
index 3bd30159003484..11e1258e75e6bb 100644
--- a/tensorflow/core/protobuf/device_properties.proto
+++ b/tensorflow/core/protobuf/device_properties.proto
@@ -18,6 +18,7 @@ syntax = "proto3";
 package tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "DevicePropertiesProtos";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 
 message DeviceProperties {
   // Device type (CPU, GPU, ...)
diff --git a/tensorflow/core/protobuf/master.proto b/tensorflow/core/protobuf/master.proto
index 96c91536f73865..03022875e64ace 100644
--- a/tensorflow/core/protobuf/master.proto
+++ b/tensorflow/core/protobuf/master.proto
@@ -20,7 +20,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "DistributedRuntimeProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.distruntime";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 import "tensorflow/core/framework/device_attributes.proto";
 import "tensorflow/core/framework/graph.proto";
 import "tensorflow/core/framework/tensor.proto";
diff --git a/tensorflow/core/protobuf/master_service.proto b/tensorflow/core/protobuf/master_service.proto
index 1170611f372327..ce0e4f643544ee 100644
--- a/tensorflow/core/protobuf/master_service.proto
+++ b/tensorflow/core/protobuf/master_service.proto
@@ -19,7 +19,7 @@ package tensorflow.grpc;
 option java_outer_classname = "MasterServiceProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.distruntime";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 import "tensorflow/core/protobuf/master.proto";
 
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/tensorflow/core/protobuf/meta_graph.proto b/tensorflow/core/protobuf/meta_graph.proto
index fd86c0da12b26c..75a2a88ed72cd9 100644
--- a/tensorflow/core/protobuf/meta_graph.proto
+++ b/tensorflow/core/protobuf/meta_graph.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "MetaGraphProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 import "google/protobuf/any.proto";
 
 import "tensorflow/core/framework/graph.proto";
diff --git a/tensorflow/core/protobuf/named_tensor.proto b/tensorflow/core/protobuf/named_tensor.proto
index dd4976e3546268..6e2f7feee29f2f 100644
--- a/tensorflow/core/protobuf/named_tensor.proto
+++ b/tensorflow/core/protobuf/named_tensor.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "NamedTensorProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 import "tensorflow/core/framework/tensor.proto";
 
 // A pair of tensor name and tensor values.
diff --git a/tensorflow/core/protobuf/queue_runner.proto b/tensorflow/core/protobuf/queue_runner.proto
index 05a48d0acf7581..f4df649f7d6548 100644
--- a/tensorflow/core/protobuf/queue_runner.proto
+++ b/tensorflow/core/protobuf/queue_runner.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "QueueRunnerProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 import "tensorflow/core/lib/core/error_codes.proto";
 
 // Protocol buffer representing a QueueRunner.
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index 029b27cd043705..a15ccdfd87b1c7 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "RewriterConfigProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 
 import "tensorflow/core/framework/attr_value.proto";
 
diff --git a/tensorflow/core/protobuf/saved_model.proto b/tensorflow/core/protobuf/saved_model.proto
index c2595ddf884b08..03789d3df72f2a 100644
--- a/tensorflow/core/protobuf/saved_model.proto
+++ b/tensorflow/core/protobuf/saved_model.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "SavedModelProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 import "tensorflow/core/protobuf/meta_graph.proto";
 
 // SavedModel is the high level serialization format for TensorFlow Models.
diff --git a/tensorflow/core/protobuf/saver.proto b/tensorflow/core/protobuf/saver.proto
index a757d3f756ab73..4245386145907f 100644
--- a/tensorflow/core/protobuf/saver.proto
+++ b/tensorflow/core/protobuf/saver.proto
@@ -5,6 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "SaverProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.util";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 
 // Protocol buffer representing the configuration of a Saver.
 message SaverDef {
diff --git a/tensorflow/core/protobuf/tensor_bundle.proto b/tensorflow/core/protobuf/tensor_bundle.proto
index 80e87f14f941b9..681c01bbbd40fd 100644
--- a/tensorflow/core/protobuf/tensor_bundle.proto
+++ b/tensorflow/core/protobuf/tensor_bundle.proto
@@ -5,7 +5,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "TensorBundleProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.util";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 import "tensorflow/core/framework/tensor_shape.proto";
 import "tensorflow/core/framework/tensor_slice.proto";
 import "tensorflow/core/framework/types.proto";
diff --git a/tensorflow/core/protobuf/tensorflow_server.proto b/tensorflow/core/protobuf/tensorflow_server.proto
index 6199e707e5ad03..be25804a1b4a94 100644
--- a/tensorflow/core/protobuf/tensorflow_server.proto
+++ b/tensorflow/core/protobuf/tensorflow_server.proto
@@ -23,7 +23,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "ServerProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.distruntime";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 // Defines the configuration of a single TensorFlow server.
 message ServerDef {
   // The cluster of which this server is a member.
diff --git a/tensorflow/core/protobuf/worker.proto b/tensorflow/core/protobuf/worker.proto
index 602f6a1ef143e2..d714d85ce68ce3 100644
--- a/tensorflow/core/protobuf/worker.proto
+++ b/tensorflow/core/protobuf/worker.proto
@@ -20,7 +20,7 @@ option cc_enable_arenas = true;
 option java_outer_classname = "WorkerProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.distruntime";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 import "google/protobuf/any.proto";
 import "tensorflow/core/framework/cost_graph.proto";
 import "tensorflow/core/framework/step_stats.proto";
diff --git a/tensorflow/core/protobuf/worker_service.proto b/tensorflow/core/protobuf/worker_service.proto
index 01c76c01a9215d..025fa7ca59452f 100644
--- a/tensorflow/core/protobuf/worker_service.proto
+++ b/tensorflow/core/protobuf/worker_service.proto
@@ -19,7 +19,7 @@ package tensorflow.grpc;
 option java_outer_classname = "WorkerServiceProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.distruntime";
-
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf";
 import "tensorflow/core/protobuf/worker.proto";
 
 ////////////////////////////////////////////////////////////////////////////////

From df5ae5ac2a58131737a11e417ac34a663efb3574 Mon Sep 17 00:00:00 2001
From: Sunitha Kambhampati 
Date: Wed, 2 May 2018 17:52:38 -0700
Subject: [PATCH 0318/1691] Add some todo's

---
 tensorflow/contrib/tensorboard/db/summary_db_writer.cc      | 1 +
 tensorflow/contrib/tensorboard/db/summary_db_writer_test.cc | 1 +
 2 files changed, 2 insertions(+)

diff --git a/tensorflow/contrib/tensorboard/db/summary_db_writer.cc b/tensorflow/contrib/tensorboard/db/summary_db_writer.cc
index 046a2d38849676..630c0607ae21d0 100644
--- a/tensorflow/contrib/tensorboard/db/summary_db_writer.cc
+++ b/tensorflow/contrib/tensorboard/db/summary_db_writer.cc
@@ -1183,6 +1183,7 @@ class SummaryDbWriter : public SummaryWriterInterface {
     Tensor t{DT_DOUBLE, {k, 3}};
     auto data = t.flat();
     for (int i = 0, j = 0; i < k; ++i) {
+      // TODO(nickfelt): reconcile with TensorBoard's data_compat.py
       // From summary.proto
       // Parallel arrays encoding the bucket boundaries and the bucket values.
       // bucket(i) is the count for the bucket i.  The range for
diff --git a/tensorflow/contrib/tensorboard/db/summary_db_writer_test.cc b/tensorflow/contrib/tensorboard/db/summary_db_writer_test.cc
index cb51325d15ff91..2044692b6e746b 100644
--- a/tensorflow/contrib/tensorboard/db/summary_db_writer_test.cc
+++ b/tensorflow/contrib/tensorboard/db/summary_db_writer_test.cc
@@ -131,6 +131,7 @@ TEST_F(SummaryDbWriterTest, WriteHistogram_VerifyTensorValues) {
   writer_->Unref();
   writer_ = nullptr;
 
+  // TODO(nickfelt): implement QueryTensor() to encapsulate this
   // Verify the data
   string result = QueryString("SELECT data FROM Tensors");
   const double* val = reinterpret_cast(result.data());

From 8f0a90b711480c12716d1a3b1094cc8b34939f2d Mon Sep 17 00:00:00 2001
From: RJ Ryan 
Date: Wed, 2 May 2018 17:57:27 -0700
Subject: [PATCH 0319/1691] Add complex128 support to FFT, FFT2D, FFT3D, IFFT,
 IFFT2D, and IFFT3D.

NumPy automatically upcasts to complex128 when computing FFTs, leading to issues like:
#10749

This change allows users to choose between 32-bit and 64-bit precision FFTs on CPU and GPU.

PiperOrigin-RevId: 195183206
---
 tensorflow/compiler/tf2xla/kernels/fft_ops.cc |  17 +-
 tensorflow/core/kernels/fft_ops.cc            |  78 +++++++---
 tensorflow/core/ops/spectral_ops.cc           |  30 ++--
 .../python/kernel_tests/fft_ops_test.py       | 145 +++++++++++-------
 .../linalg/linear_operator_circulant_test.py  |   6 +-
 tensorflow/python/ops/spectral_grad.py        |  30 ++--
 6 files changed, 196 insertions(+), 110 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/kernels/fft_ops.cc b/tensorflow/compiler/tf2xla/kernels/fft_ops.cc
index fcb927dab0f5db..933924cad1c7ca 100644
--- a/tensorflow/compiler/tf2xla/kernels/fft_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/fft_ops.cc
@@ -81,9 +81,11 @@ class FFTOp : public GenericFftOp {
   explicit FFTOp(OpKernelConstruction* ctx)
       : GenericFftOp(ctx, /*fft_type=*/FftType::FFT, /*fft_rank=*/FFTRank) {}
 };
-REGISTER_XLA_OP(Name("FFT"), FFTOp<1>);
-REGISTER_XLA_OP(Name("FFT2D"), FFTOp<2>);
-REGISTER_XLA_OP(Name("FFT3D"), FFTOp<3>);
+REGISTER_XLA_OP(Name("FFT").TypeConstraint("Tcomplex", DT_COMPLEX64), FFTOp<1>);
+REGISTER_XLA_OP(Name("FFT2D").TypeConstraint("Tcomplex", DT_COMPLEX64),
+                FFTOp<2>);
+REGISTER_XLA_OP(Name("FFT3D").TypeConstraint("Tcomplex", DT_COMPLEX64),
+                FFTOp<3>);
 
 template 
 class IFFTOp : public GenericFftOp {
@@ -91,9 +93,12 @@ class IFFTOp : public GenericFftOp {
   explicit IFFTOp(OpKernelConstruction* ctx)
       : GenericFftOp(ctx, /*fft_type=*/FftType::IFFT, /*fft_rank=*/FFTRank) {}
 };
-REGISTER_XLA_OP(Name("IFFT"), IFFTOp<1>);
-REGISTER_XLA_OP(Name("IFFT2D"), IFFTOp<2>);
-REGISTER_XLA_OP(Name("IFFT3D"), IFFTOp<3>);
+REGISTER_XLA_OP(Name("IFFT").TypeConstraint("Tcomplex", DT_COMPLEX64),
+                IFFTOp<1>);
+REGISTER_XLA_OP(Name("IFFT2D").TypeConstraint("Tcomplex", DT_COMPLEX64),
+                IFFTOp<2>);
+REGISTER_XLA_OP(Name("IFFT3D").TypeConstraint("Tcomplex", DT_COMPLEX64),
+                IFFTOp<3>);
 
 template 
 class RFFTOp : public GenericFftOp {
diff --git a/tensorflow/core/kernels/fft_ops.cc b/tensorflow/core/kernels/fft_ops.cc
index 661bf5fc5fb43e..d7105a71bb8419 100644
--- a/tensorflow/core/kernels/fft_ops.cc
+++ b/tensorflow/core/kernels/fft_ops.cc
@@ -129,13 +129,23 @@ class FFTCPU : public FFTBase {
     auto device = ctx->eigen_device();
 
     if (!IsReal()) {
-      auto input = Tensor(in).flat_inner_dims();
-      // Compute the FFT using eigen.
-      auto output = out->flat_inner_dims();
+      // Compute the FFT using Eigen.
       constexpr auto direction =
           Forward ? Eigen::FFT_FORWARD : Eigen::FFT_REVERSE;
-      output.device(device) =
-          input.template fft(axes);
+      if (in.dtype() == DT_COMPLEX64) {
+        DCHECK_EQ(out->dtype(), DT_COMPLEX64);
+        auto input = Tensor(in).flat_inner_dims();
+        auto output = out->flat_inner_dims();
+        output.device(device) =
+            input.template fft(axes);
+      } else {
+        DCHECK_EQ(DT_COMPLEX128, in.dtype());
+        DCHECK_EQ(DT_COMPLEX128, out->dtype());
+        auto input = Tensor(in).flat_inner_dims();
+        auto output = out->flat_inner_dims();
+        output.device(device) =
+            input.template fft(axes);
+      }
     } else {
       if (IsForward()) {
         auto input = Tensor(in).flat_inner_dims();
@@ -392,10 +402,16 @@ class FFTGPUBase : public FFTBase {
     }
 
     constexpr bool kInPlaceFft = false;
+    const bool is_complex128 = in.dtype() == DT_COMPLEX128;
+    // complex128 real FFT is not supported yet.
+    DCHECK(!IsReal() || !is_complex128);
+
     const auto kFftType =
         IsReal() ? (IsForward() ? se::fft::Type::kR2C : se::fft::Type::kC2R)
-                 : (IsForward() ? se::fft::Type::kC2CForward
-                                : se::fft::Type::kC2CInverse);
+                 : (IsForward() ? (is_complex128 ? se::fft::Type::kZ2ZForward
+                                                 : se::fft::Type::kC2CForward)
+                                : (is_complex128 ? se::fft::Type::kZ2ZInverse
+                                                 : se::fft::Type::kC2CInverse));
 
     CufftScratchAllocator scratch_allocator(CufftScratchSize, ctx);
     auto plan =
@@ -428,20 +444,42 @@ class FFTGPUBase : public FFTBase {
                              input_shape.DebugString()));
       }
     } else {
-      auto src = AsDeviceMemory(in.flat().data());
-      auto dst = AsDeviceMemory(out->flat().data());
-      OP_REQUIRES(
-          ctx, stream->ThenFft(plan.get(), src, &dst).ok(),
-          errors::Internal("fft failed : type=", static_cast(kFftType),
-                           " in.shape=", input_shape.DebugString()));
-      if (!IsForward()) {
-        auto alpha = complex64(1.f / output_distance);
+      if (!is_complex128) {
+        DCHECK_EQ(in.dtype(), DT_COMPLEX64);
+        DCHECK_EQ(out->dtype(), DT_COMPLEX64);
+        auto src = AsDeviceMemory(in.flat().data());
+        auto dst = AsDeviceMemory(out->flat().data());
         OP_REQUIRES(
-            ctx,
-            stream->ThenBlasScal(output_shape.num_elements(), alpha, &dst, 1)
-                .ok(),
-            errors::Internal("BlasScal failed : in.shape=",
-                             input_shape.DebugString()));
+            ctx, stream->ThenFft(plan.get(), src, &dst).ok(),
+            errors::Internal("fft failed : type=", static_cast(kFftType),
+                             " in.shape=", input_shape.DebugString()));
+        if (!IsForward()) {
+          float alpha = 1.f / output_distance;
+          OP_REQUIRES(
+              ctx,
+              stream->ThenBlasScal(output_shape.num_elements(), alpha, &dst, 1)
+                  .ok(),
+              errors::Internal("BlasScal failed : in.shape=",
+                               input_shape.DebugString()));
+        }
+      } else {
+        DCHECK_EQ(in.dtype(), DT_COMPLEX128);
+        DCHECK_EQ(out->dtype(), DT_COMPLEX128);
+        auto src = AsDeviceMemory(in.flat().data());
+        auto dst = AsDeviceMemory(out->flat().data());
+        OP_REQUIRES(
+            ctx, stream->ThenFft(plan.get(), src, &dst).ok(),
+            errors::Internal("fft failed : type=", static_cast(kFftType),
+                             " in.shape=", input_shape.DebugString()));
+        if (!IsForward()) {
+          double alpha = 1.0 / output_distance;
+          OP_REQUIRES(
+              ctx,
+              stream->ThenBlasScal(output_shape.num_elements(), alpha, &dst, 1)
+                  .ok(),
+              errors::Internal("BlasScal failed : in.shape=",
+                               input_shape.DebugString()));
+        }
       }
     }
   }
diff --git a/tensorflow/core/ops/spectral_ops.cc b/tensorflow/core/ops/spectral_ops.cc
index 2790aee37e93d3..b1ae7040f02dee 100644
--- a/tensorflow/core/ops/spectral_ops.cc
+++ b/tensorflow/core/ops/spectral_ops.cc
@@ -25,43 +25,49 @@ using shape_inference::InferenceContext;
 using shape_inference::ShapeHandle;
 
 REGISTER_OP("FFT")
-    .Input("input: complex64")
-    .Output("output: complex64")
+    .Input("input: Tcomplex")
+    .Output("output: Tcomplex")
+    .Attr("Tcomplex: {complex64, complex128} = DT_COMPLEX64")
     .SetShapeFn([](InferenceContext* c) {
       return shape_inference::UnchangedShapeWithRankAtLeast(c, 1);
     });
 
 REGISTER_OP("IFFT")
-    .Input("input: complex64")
-    .Output("output: complex64")
+    .Input("input: Tcomplex")
+    .Output("output: Tcomplex")
+    .Attr("Tcomplex: {complex64, complex128} = DT_COMPLEX64")
     .SetShapeFn([](InferenceContext* c) {
       return shape_inference::UnchangedShapeWithRankAtLeast(c, 1);
     });
 
 REGISTER_OP("FFT2D")
-    .Input("input: complex64")
-    .Output("output: complex64")
+    .Input("input: Tcomplex")
+    .Output("output: Tcomplex")
+    .Attr("Tcomplex: {complex64, complex128} = DT_COMPLEX64")
     .SetShapeFn([](InferenceContext* c) {
       return shape_inference::UnchangedShapeWithRankAtLeast(c, 2);
     });
 
 REGISTER_OP("IFFT2D")
-    .Input("input: complex64")
-    .Output("output: complex64")
+    .Input("input: Tcomplex")
+    .Output("output: Tcomplex")
+    .Attr("Tcomplex: {complex64, complex128} = DT_COMPLEX64")
     .SetShapeFn([](InferenceContext* c) {
       return shape_inference::UnchangedShapeWithRankAtLeast(c, 2);
     });
 
 REGISTER_OP("FFT3D")
-    .Input("input: complex64")
-    .Output("output: complex64")
+    .Input("input: Tcomplex")
+    .Output("output: Tcomplex")
+    .Attr("Tcomplex: {complex64, complex128} = DT_COMPLEX64")
     .SetShapeFn([](InferenceContext* c) {
       return shape_inference::UnchangedShapeWithRankAtLeast(c, 3);
     });
 
 REGISTER_OP("IFFT3D")
-    .Input("input: complex64")
-    .Output("output: complex64")
+    .Input("input: Tcomplex")
+    .Output("output: Tcomplex")
+    .Attr("Tcomplex: {complex64, complex128} = DT_COMPLEX64")
     .SetShapeFn([](InferenceContext* c) {
       return shape_inference::UnchangedShapeWithRankAtLeast(c, 3);
     });
diff --git a/tensorflow/python/kernel_tests/fft_ops_test.py b/tensorflow/python/kernel_tests/fft_ops_test.py
index b9e2aa1f3a4ebb..629acedda5c5f0 100644
--- a/tensorflow/python/kernel_tests/fft_ops_test.py
+++ b/tensorflow/python/kernel_tests/fft_ops_test.py
@@ -38,11 +38,13 @@
 
 class BaseFFTOpsTest(test.TestCase):
 
-  def _compare(self, x, rank, fft_length=None, use_placeholder=False):
-    self._compareForward(x, rank, fft_length, use_placeholder)
-    self._compareBackward(x, rank, fft_length, use_placeholder)
+  def _compare(self, x, rank, fft_length=None, use_placeholder=False,
+               rtol=1e-4, atol=1e-4):
+    self._compareForward(x, rank, fft_length, use_placeholder, rtol, atol)
+    self._compareBackward(x, rank, fft_length, use_placeholder, rtol, atol)
 
-  def _compareForward(self, x, rank, fft_length=None, use_placeholder=False):
+  def _compareForward(self, x, rank, fft_length=None, use_placeholder=False,
+                      rtol=1e-4, atol=1e-4):
     x_np = self._npFFT(x, rank, fft_length)
     if use_placeholder:
       x_ph = array_ops.placeholder(dtype=dtypes.as_dtype(x.dtype))
@@ -50,9 +52,10 @@ def _compareForward(self, x, rank, fft_length=None, use_placeholder=False):
     else:
       x_tf = self._tfFFT(x, rank, fft_length)
 
-    self.assertAllClose(x_np, x_tf, rtol=1e-4, atol=1e-4)
+    self.assertAllClose(x_np, x_tf, rtol=rtol, atol=atol)
 
-  def _compareBackward(self, x, rank, fft_length=None, use_placeholder=False):
+  def _compareBackward(self, x, rank, fft_length=None, use_placeholder=False,
+                       rtol=1e-4, atol=1e-4):
     x_np = self._npIFFT(x, rank, fft_length)
     if use_placeholder:
       x_ph = array_ops.placeholder(dtype=dtypes.as_dtype(x.dtype))
@@ -60,7 +63,7 @@ def _compareBackward(self, x, rank, fft_length=None, use_placeholder=False):
     else:
       x_tf = self._tfIFFT(x, rank, fft_length)
 
-    self.assertAllClose(x_np, x_tf, rtol=1e-4, atol=1e-4)
+    self.assertAllClose(x_np, x_tf, rtol=rtol, atol=atol)
 
   def _checkMemoryFail(self, x, rank):
     config = config_pb2.ConfigProto()
@@ -68,7 +71,8 @@ def _checkMemoryFail(self, x, rank):
     with self.test_session(config=config, force_gpu=True):
       self._tfFFT(x, rank, fft_length=None)
 
-  def _checkGradComplex(self, func, x, y, result_is_complex=True):
+  def _checkGradComplex(self, func, x, y, result_is_complex=True,
+                        rtol=1e-2, atol=1e-2):
     with self.test_session(use_gpu=True):
       inx = ops.convert_to_tensor(x)
       iny = ops.convert_to_tensor(y)
@@ -85,10 +89,10 @@ def _checkGradComplex(self, func, x, y, result_is_complex=True):
            x_init_value=[x, y],
            delta=1e-2)
 
-    self.assertAllClose(x_jacob_t, x_jacob_n, rtol=1e-2, atol=1e-2)
-    self.assertAllClose(y_jacob_t, y_jacob_n, rtol=1e-2, atol=1e-2)
+    self.assertAllClose(x_jacob_t, x_jacob_n, rtol=rtol, atol=atol)
+    self.assertAllClose(y_jacob_t, y_jacob_n, rtol=rtol, atol=atol)
 
-  def _checkGradReal(self, func, x):
+  def _checkGradReal(self, func, x, rtol=1e-2, atol=1e-2):
     with self.test_session(use_gpu=True):
       inx = ops.convert_to_tensor(x)
       # func is a forward RFFT function (batched or unbatched).
@@ -98,7 +102,7 @@ def _checkGradReal(self, func, x):
       x_jacob_t, x_jacob_n = test.compute_gradient(
           inx, list(x.shape), loss, [1], x_init_value=x, delta=1e-2)
 
-    self.assertAllClose(x_jacob_t, x_jacob_n, rtol=1e-2, atol=1e-2)
+    self.assertAllClose(x_jacob_t, x_jacob_n, rtol=rtol, atol=atol)
 
 
 class FFTOpsTest(BaseFFTOpsTest):
@@ -155,27 +159,30 @@ def _tfIFFTForRank(self, rank):
 
   def testEmpty(self):
     with spectral_ops_test_util.fft_kernel_label_map():
-      for rank in VALID_FFT_RANKS:
-        for dims in xrange(rank, rank + 3):
-          x = np.zeros((0,) * dims).astype(np.complex64)
-          self.assertEqual(x.shape, self._tfFFT(x, rank).shape)
-          self.assertEqual(x.shape, self._tfIFFT(x, rank).shape)
+      for np_type in (np.complex64, np.complex128):
+        for rank in VALID_FFT_RANKS:
+          for dims in xrange(rank, rank + 3):
+            x = np.zeros((0,) * dims).astype(np_type)
+            self.assertEqual(x.shape, self._tfFFT(x, rank).shape)
+            self.assertEqual(x.shape, self._tfIFFT(x, rank).shape)
 
   def testBasic(self):
     with spectral_ops_test_util.fft_kernel_label_map():
-      for rank in VALID_FFT_RANKS:
-        for dims in xrange(rank, rank + 3):
-          self._compare(
-              np.mod(np.arange(np.power(4, dims)), 10).reshape(
-                  (4,) * dims).astype(np.complex64), rank)
+      for np_type, tol in ((np.complex64, 1e-4), (np.complex128, 1e-8)):
+        for rank in VALID_FFT_RANKS:
+          for dims in xrange(rank, rank + 3):
+            self._compare(
+                np.mod(np.arange(np.power(4, dims)), 10).reshape(
+                    (4,) * dims).astype(np_type), rank, rtol=tol, atol=tol)
 
   def testLargeBatch(self):
     if test.is_gpu_available(cuda_only=True):
       rank = 1
       for dims in xrange(rank, rank + 3):
-        self._compare(
-            np.mod(np.arange(np.power(128, dims)), 10).reshape(
-                (128,) * dims).astype(np.complex64), rank)
+        for np_type, tol in ((np.complex64, 1e-4), (np.complex128, 1e-5)):
+          self._compare(
+              np.mod(np.arange(np.power(128, dims)), 10).reshape(
+                  (128,) * dims).astype(np_type), rank, rtol=tol, atol=tol)
 
   # TODO(yangzihao): Disable before we can figure out a way to
   # properly test memory fail for large batch fft.
@@ -189,27 +196,49 @@ def testLargeBatch(self):
 
   def testBasicPlaceholder(self):
     with spectral_ops_test_util.fft_kernel_label_map():
-      for rank in VALID_FFT_RANKS:
-        for dims in xrange(rank, rank + 3):
-          self._compare(
-              np.mod(np.arange(np.power(4, dims)), 10).reshape(
-                  (4,) * dims).astype(np.complex64),
-              rank,
-              use_placeholder=True)
+      for np_type, tol in ((np.complex64, 1e-4), (np.complex128, 1e-8)):
+        for rank in VALID_FFT_RANKS:
+          for dims in xrange(rank, rank + 3):
+            self._compare(
+                np.mod(np.arange(np.power(4, dims)), 10).reshape(
+                    (4,) * dims).astype(np_type),
+                rank, use_placeholder=True, rtol=tol, atol=tol)
 
   def testRandom(self):
     with spectral_ops_test_util.fft_kernel_label_map():
-      np.random.seed(12345)
+      for np_type, tol in ((np.complex64, 1e-4), (np.complex128, 5e-6)):
+        def gen(shape):
+          n = np.prod(shape)
+          re = np.random.uniform(size=n)
+          im = np.random.uniform(size=n)
+          return (re + im * 1j).reshape(shape)
 
-      def gen(shape):
-        n = np.prod(shape)
-        re = np.random.uniform(size=n)
-        im = np.random.uniform(size=n)
-        return (re + im * 1j).reshape(shape)
+        for rank in VALID_FFT_RANKS:
+          for dims in xrange(rank, rank + 3):
+            self._compare(gen((4,) * dims).astype(np_type), rank,
+                          rtol=tol, atol=tol)
 
-      for rank in VALID_FFT_RANKS:
-        for dims in xrange(rank, rank + 3):
-          self._compare(gen((4,) * dims), rank)
+  def testRandom1D(self):
+    with spectral_ops_test_util.fft_kernel_label_map():
+      for np_type in (np.complex64, np.complex128):
+        has_gpu = test.is_gpu_available(cuda_only=True)
+        tol = {(np.complex64, True): 1e-4,
+               (np.complex64, False): 1e-2,
+               (np.complex128, True): 1e-4,
+               (np.complex128, False): 1e-2}[(np_type, has_gpu)]
+        def gen(shape):
+          n = np.prod(shape)
+          re = np.random.uniform(size=n)
+          im = np.random.uniform(size=n)
+          return (re + im * 1j).reshape(shape)
+
+        # Check a variety of power-of-2 FFT sizes.
+        for dim in (128, 256, 512, 1024):
+          self._compare(gen((dim,)).astype(np_type), 1, rtol=tol, atol=tol)
+
+        # Check a variety of non-power-of-2 FFT sizes.
+        for dim in (127, 255, 511, 1023):
+          self._compare(gen((dim,)).astype(np_type), 1, rtol=tol, atol=tol)
 
   def testError(self):
     for rank in VALID_FFT_RANKS:
@@ -224,22 +253,27 @@ def testError(self):
 
   def testGrad_Simple(self):
     with spectral_ops_test_util.fft_kernel_label_map():
-      for rank in VALID_FFT_RANKS:
-        for dims in xrange(rank, rank + 2):
-          re = np.ones(shape=(4,) * dims, dtype=np.float32) / 10.0
-          im = np.zeros(shape=(4,) * dims, dtype=np.float32)
-          self._checkGradComplex(self._tfFFTForRank(rank), re, im)
-          self._checkGradComplex(self._tfIFFTForRank(rank), re, im)
+      for np_type, tol in ((np.float32, 1e-4), (np.float64, 1e-10)):
+        for rank in VALID_FFT_RANKS:
+          for dims in xrange(rank, rank + 2):
+            re = np.ones(shape=(4,) * dims, dtype=np_type) / 10.0
+            im = np.zeros(shape=(4,) * dims, dtype=np_type)
+            self._checkGradComplex(self._tfFFTForRank(rank), re, im,
+                                   rtol=tol, atol=tol)
+            self._checkGradComplex(self._tfIFFTForRank(rank), re, im,
+                                   rtol=tol, atol=tol)
 
   def testGrad_Random(self):
     with spectral_ops_test_util.fft_kernel_label_map():
-      np.random.seed(54321)
-      for rank in VALID_FFT_RANKS:
-        for dims in xrange(rank, rank + 2):
-          re = np.random.rand(*((3,) * dims)).astype(np.float32) * 2 - 1
-          im = np.random.rand(*((3,) * dims)).astype(np.float32) * 2 - 1
-          self._checkGradComplex(self._tfFFTForRank(rank), re, im)
-          self._checkGradComplex(self._tfIFFTForRank(rank), re, im)
+      for np_type, tol in ((np.float32, 1e-2), (np.float64, 1e-10)):
+        for rank in VALID_FFT_RANKS:
+          for dims in xrange(rank, rank + 2):
+            re = np.random.rand(*((3,) * dims)).astype(np_type) * 2 - 1
+            im = np.random.rand(*((3,) * dims)).astype(np_type) * 2 - 1
+            self._checkGradComplex(self._tfFFTForRank(rank), re, im,
+                                   rtol=tol, atol=tol)
+            self._checkGradComplex(self._tfIFFTForRank(rank), re, im,
+                                   rtol=tol, atol=tol)
 
 
 class RFFTOpsTest(BaseFFTOpsTest):
@@ -395,8 +429,6 @@ def testFftLength(self):
 
   def testRandom(self):
     with spectral_ops_test_util.fft_kernel_label_map():
-      np.random.seed(12345)
-
       def gen_real(shape):
         n = np.prod(shape)
         re = np.random.uniform(size=n)
@@ -491,7 +523,6 @@ def testGrad_Simple(self):
 
   def testGrad_Random(self):
     with spectral_ops_test_util.fft_kernel_label_map():
-      np.random.seed(54321)
       for rank in VALID_FFT_RANKS:
         # rfft3d/irfft3d do not have gradients yet.
         if rank == 3:
diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_circulant_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_circulant_test.py
index e7f2f1c12bf46b..5713d169696c78 100644
--- a/tensorflow/python/kernel_tests/linalg/linear_operator_circulant_test.py
+++ b/tensorflow/python/kernel_tests/linalg/linear_operator_circulant_test.py
@@ -73,7 +73,7 @@ def _spectrum_to_circulant_1d(self, spectrum, shape, dtype):
       x = np.zeros([domain_dimension])
       # x is a basis vector.
       x[m] = 1.0
-      fft_x = math_ops.fft(x)
+      fft_x = math_ops.fft(x.astype(np.complex64))
       h_convolve_x = math_ops.ifft(spectrum * fft_x)
       matrix_rows.append(h_convolve_x)
     matrix = array_ops.stack(matrix_rows, axis=-1)
@@ -91,7 +91,7 @@ class LinearOperatorCirculantTestSelfAdjointOperator(
 
   @property
   def _dtypes_to_test(self):
-    # This operator will always be complex because, although the specturm is
+    # This operator will always be complex because, although the spectrum is
     # real, the matrix will not be real.
     return [dtypes.complex64]
 
@@ -408,7 +408,7 @@ def _spectrum_to_circulant_2d(self, spectrum, shape, dtype):
         x = np.zeros(block_shape)
         # x is a basis vector.
         x[n0, n1] = 1.0
-        fft_x = math_ops.fft2d(x)
+        fft_x = math_ops.fft2d(x.astype(np.complex64))
         h_convolve_x = math_ops.ifft2d(spectrum * fft_x)
         # We want the flat version of the action of the operator on a basis
         # vector, not the block version.
diff --git a/tensorflow/python/ops/spectral_grad.py b/tensorflow/python/ops/spectral_grad.py
index deb0a571780373..0af24114acbe5f 100644
--- a/tensorflow/python/ops/spectral_grad.py
+++ b/tensorflow/python/ops/spectral_grad.py
@@ -32,38 +32,44 @@ def _FFTSizeForGrad(grad, rank):
 
 @ops.RegisterGradient("FFT")
 def _FFTGrad(_, grad):
-  size = math_ops.cast(_FFTSizeForGrad(grad, 1), dtypes.float32)
-  return spectral_ops.ifft(grad) * math_ops.complex(size, 0.)
+  size = math_ops.cast(_FFTSizeForGrad(grad, 1), grad.dtype)
+  return spectral_ops.ifft(grad) * size
 
 
 @ops.RegisterGradient("IFFT")
 def _IFFTGrad(_, grad):
-  rsize = 1. / math_ops.cast(_FFTSizeForGrad(grad, 1), dtypes.float32)
-  return spectral_ops.fft(grad) * math_ops.complex(rsize, 0.)
+  rsize = math_ops.cast(
+      1. / math_ops.cast(_FFTSizeForGrad(grad, 1), grad.dtype.real_dtype),
+      grad.dtype)
+  return spectral_ops.fft(grad) * rsize
 
 
 @ops.RegisterGradient("FFT2D")
 def _FFT2DGrad(_, grad):
-  size = math_ops.cast(_FFTSizeForGrad(grad, 2), dtypes.float32)
-  return spectral_ops.ifft2d(grad) * math_ops.complex(size, 0.)
+  size = math_ops.cast(_FFTSizeForGrad(grad, 2), grad.dtype)
+  return spectral_ops.ifft2d(grad) * size
 
 
 @ops.RegisterGradient("IFFT2D")
 def _IFFT2DGrad(_, grad):
-  rsize = 1. / math_ops.cast(_FFTSizeForGrad(grad, 2), dtypes.float32)
-  return spectral_ops.fft2d(grad) * math_ops.complex(rsize, 0.)
+  rsize = math_ops.cast(
+      1. / math_ops.cast(_FFTSizeForGrad(grad, 2), grad.dtype.real_dtype),
+      grad.dtype)
+  return spectral_ops.fft2d(grad) * rsize
 
 
 @ops.RegisterGradient("FFT3D")
 def _FFT3DGrad(_, grad):
-  size = math_ops.cast(_FFTSizeForGrad(grad, 3), dtypes.float32)
-  return spectral_ops.ifft3d(grad) * math_ops.complex(size, 0.)
+  size = math_ops.cast(_FFTSizeForGrad(grad, 3), grad.dtype)
+  return spectral_ops.ifft3d(grad) * size
 
 
 @ops.RegisterGradient("IFFT3D")
 def _IFFT3DGrad(_, grad):
-  rsize = 1. / math_ops.cast(_FFTSizeForGrad(grad, 3), dtypes.float32)
-  return spectral_ops.fft3d(grad) * math_ops.complex(rsize, 0.)
+  rsize = math_ops.cast(
+      1. / math_ops.cast(_FFTSizeForGrad(grad, 3), grad.dtype.real_dtype),
+      grad.dtype)
+  return spectral_ops.fft3d(grad) * rsize
 
 
 def _RFFTGradHelper(rank, irfft_fn):

From db329cfe2dee382033ad3b3f5e1d906ff489a24d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 18:11:25 -0700
Subject: [PATCH 0320/1691] Automated g4 rollback of changelist 195091587

PiperOrigin-RevId: 195184798
---
 tensorflow/contrib/lite/toco/BUILD            |  1 +
 .../contrib/lite/toco/model_flags.proto       |  3 +-
 tensorflow/contrib/lite/toco/tooling_util.cc  | 79 ++++++++++++-------
 3 files changed, 53 insertions(+), 30 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD
index f16225fd665277..ce0a74724a4008 100644
--- a/tensorflow/contrib/lite/toco/BUILD
+++ b/tensorflow/contrib/lite/toco/BUILD
@@ -397,6 +397,7 @@ cc_library(
         ":types_proto_cc",
         "//tensorflow/core:lib",
         "@com_google_absl//absl/strings",
+        "@com_googlesource_code_re2//:re2",
         "@protobuf_archive//:protobuf_headers",
     ],
 )
diff --git a/tensorflow/contrib/lite/toco/model_flags.proto b/tensorflow/contrib/lite/toco/model_flags.proto
index d23e80c464c9fe..6c1c53658c0736 100644
--- a/tensorflow/contrib/lite/toco/model_flags.proto
+++ b/tensorflow/contrib/lite/toco/model_flags.proto
@@ -96,8 +96,9 @@ message RnnState {
 // model that does not already contain such MinMax information.
 message ArraysExtraInfo {
   message Entry {
-    // Next ID to use: 7.
+    // Next ID to use: 8.
     optional string name = 1;
+    optional string name_regexp = 7;
     optional double min = 2;
     optional double max = 3;
     optional IODataType data_type = 4;
diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index f334c51bbb35b8..11293a5fe508ec 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -26,6 +26,7 @@ limitations under the License.
 #include "absl/strings/str_join.h"
 #include "absl/strings/str_replace.h"
 #include "absl/strings/str_split.h"
+#include "re2/re2.h"
 #include "tensorflow/contrib/lite/toco/dump_graphviz.h"
 #include "tensorflow/contrib/lite/toco/model_flags.pb.h"
 #include "tensorflow/contrib/lite/toco/toco_graphviz_dump_options.h"
@@ -1983,38 +1984,58 @@ void FinishBuildingRNNStates(Model* model) {
   }
 }
 
+// Returns the array names that match the ArraysExtraInfo's name and
+// name_regexp. The regexp match is for a full match.
+std::unordered_set ScanArrayNames(
+    const Model& model, const toco::ArraysExtraInfo_Entry& entry) {
+  std::unordered_set matches;
+  if (model.HasArray(entry.name())) {
+    matches.insert(entry.name());
+  }
+  if (!entry.name_regexp().empty()) {
+    const auto& arrays = model.GetArrayMap();
+    const RE2 name_regexp = {entry.name_regexp()};
+    for (auto it = arrays.begin(); it != arrays.end(); ++it) {
+      if (RE2::FullMatch(it->first, name_regexp)) {
+        matches.insert(it->first);
+      }
+    }
+  }
+  return matches;
+}
+
 void UseArraysExtraInfo(Model* model, bool quantize_output) {
   for (const auto& entry : model->flags.arrays_extra_info().entries()) {
-    if (!model->HasArray(entry.name())) {
-      continue;
-    }
-    auto& array = model->GetArray(entry.name());
-    if (entry.has_min() || entry.has_max()) {
-      CHECK_EQ(entry.has_min(), entry.has_max());
-      auto& minmax = array.GetOrCreateMinMax();
-      minmax.min = entry.min();
-      minmax.max = entry.max();
-    }
-    if (entry.has_data_type() && quantize_output) {
-      array.final_data_type =
-          ConvertIODataTypeToArrayDataType(entry.data_type());
-    }
-    if (entry.has_shape()) {
-      array.clear_shape();
-      // Make sure to create the shape even if there are no dims, to
-      // correctly record 0-D shapes.
-      array.mutable_shape();
-      for (int dim : entry.shape().dims()) {
-        array.mutable_shape()->mutable_dims()->push_back(dim);
+    const auto matches = ScanArrayNames(*model, entry);
+    for (const auto& matched_name : matches) {
+      auto& array = model->GetArray(matched_name);
+      if (entry.has_min() || entry.has_max()) {
+        CHECK_EQ(entry.has_min(), entry.has_max());
+        auto& minmax = array.GetOrCreateMinMax();
+        minmax.min = entry.min();
+        minmax.max = entry.max();
       }
-    }
-    if (entry.has_constant_float_value()) {
-      CHECK(array.has_shape());
-      if (array.data_type == ArrayDataType::kFloat) {
-        auto& data = array.GetMutableBuffer().data;
-        data.resize(RequiredBufferSizeForShape(array.shape()));
-        for (float& f : data) {
-          f = entry.constant_float_value();
+      if (entry.has_data_type() && quantize_output) {
+        array.final_data_type =
+            ConvertIODataTypeToArrayDataType(entry.data_type());
+      }
+      if (entry.has_shape()) {
+        array.clear_shape();
+        // Make sure to create the shape even if there are no dims, to
+        // correctly record 0-D shapes.
+        array.mutable_shape();
+        for (int dim : entry.shape().dims()) {
+          array.mutable_shape()->mutable_dims()->push_back(dim);
+        }
+      }
+      if (entry.has_constant_float_value()) {
+        CHECK(array.has_shape());
+        if (array.data_type == ArrayDataType::kFloat) {
+          auto& data = array.GetMutableBuffer().data;
+          data.resize(RequiredBufferSizeForShape(array.shape()));
+          for (float& f : data) {
+            f = entry.constant_float_value();
+          }
         }
       }
     }

From 1a4f746ffd82376f6e9ad420d96943ff89e7013a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 18:19:16 -0700
Subject: [PATCH 0321/1691] Remove duplicated emplace_back floor operator.

PiperOrigin-RevId: 195185567
---
 tensorflow/contrib/lite/toco/tflite/operator.cc | 2 --
 1 file changed, 2 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc
index fce3bad3266e85..d2e14ac5e0d7b0 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator.cc
+++ b/tensorflow/contrib/lite/toco/tflite/operator.cc
@@ -901,8 +901,6 @@ std::vector> BuildOperatorList() {
       "MINIMUM", OperatorType::kTensorFlowMinimum));
   ops.emplace_back(new SimpleOperator(
       "LESS", OperatorType::kTensorFlowLess));
-  ops.emplace_back(
-      new SimpleOperator("FLOOR", OperatorType::kFloor));
 
   return ops;
 }

From 2b1a03c2ad502329a1f2b1368a40913ef21e97a0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 18:35:55 -0700
Subject: [PATCH 0322/1691] Compute shape of segment_ids dynamically in
 _unsorted_segment_N

PiperOrigin-RevId: 195186950
---
 tensorflow/python/ops/math_ops.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 7ac3bd8091f1cc..ab5997e85c6030 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -2515,7 +2515,8 @@ def _unsorted_segment_N(data, segment_ids, num_segments):
       of segment entries with 0-entries set to 1 to allow division by N.
   """
   # bincount doesn't support negative indices so we use unsorted_segment_sum
-  ones_tensor = array_ops.ones(segment_ids.shape, dtype=data.dtype)
+  segment_ids_shape = array_ops.shape_internal(segment_ids)
+  ones_tensor = array_ops.ones(segment_ids_shape, dtype=data.dtype)
   N = gen_math_ops.unsorted_segment_sum(ones_tensor, segment_ids, num_segments)
   # add dimensions for all non-reduced axes
   ndims_output = data.shape.ndims - segment_ids.shape.ndims

From 223be4abe74592a781735a6b66e12cb0146f0830 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 18:52:02 -0700
Subject: [PATCH 0323/1691] Replaced calls to tensorflow::StringPiece::ToString
 with std::string conversions. That is, instances of sp.ToString() are
 replaced with std::string(sp).

This will allow tensorflow::StringPiece::ToString to be removed, which is necessary before it can be replaced with absl::string_view.

PiperOrigin-RevId: 195188185
---
 tensorflow/cc/framework/cc_op_gen.cc             |  2 +-
 tensorflow/cc/framework/scope.cc                 |  2 +-
 tensorflow/compiler/tf2xla/tf2xla_util.cc        |  2 +-
 tensorflow/compiler/tf2xla/xla_op_registry.cc    | 14 +++++++-------
 .../compiler/xla/service/llvm_ir/llvm_loop.cc    |  4 ++--
 .../compiler/xla/service/llvm_ir/llvm_loop.h     |  2 +-
 .../compiler/xla/tools/parser/hlo_lexer.cc       |  2 +-
 .../compiler/xla/tools/parser/hlo_parser.cc      |  2 +-
 tensorflow/core/debug/debug_graph_utils.cc       |  7 +++----
 tensorflow/core/debug/debug_io_utils.cc          | 10 +++++-----
 .../core/distributed_runtime/master_session.cc   |  6 +++---
 .../core/distributed_runtime/remote_device.cc    |  2 +-
 tensorflow/core/grappler/utils.h                 |  2 +-
 .../core/kernels/hexagon/graph_transferer.cc     |  2 +-
 .../kernels/hexagon/hexagon_control_wrapper.cc   |  2 +-
 tensorflow/core/lib/io/path.cc                   |  6 +++---
 tensorflow/core/lib/io/table_test.cc             |  6 +++---
 .../core/util/tensor_bundle/tensor_bundle.cc     | 16 ++++++++--------
 .../util/tensor_bundle/tensor_bundle_test.cc     |  2 +-
 tensorflow/stream_executor/kernel.cc             |  2 +-
 tensorflow/stream_executor/kernel_spec.cc        |  6 +++---
 21 files changed, 49 insertions(+), 50 deletions(-)

diff --git a/tensorflow/cc/framework/cc_op_gen.cc b/tensorflow/cc/framework/cc_op_gen.cc
index d73121c7b701ec..d6a4f141b6bb8c 100644
--- a/tensorflow/cc/framework/cc_op_gen.cc
+++ b/tensorflow/cc/framework/cc_op_gen.cc
@@ -440,7 +440,7 @@ string AvoidCPPKeywords(StringPiece name) {
   if (IsCPPKeyword(name)) {
     return strings::StrCat(name, "_");
   }
-  return name.ToString();
+  return std::string(name);
 }
 
 void InferArgAttributes(const OpDef::ArgDef& arg,
diff --git a/tensorflow/cc/framework/scope.cc b/tensorflow/cc/framework/scope.cc
index c143b978338815..62a889181e787f 100644
--- a/tensorflow/cc/framework/scope.cc
+++ b/tensorflow/cc/framework/scope.cc
@@ -220,7 +220,7 @@ std::unordered_set Scope::Impl::GetColocationConstraints(
     for (const string& entry : node_constraints) {
       StringPiece s(entry);
       if (str_util::ConsumePrefix(&s, kColocationGroupPrefix)) {
-        current_constraints.insert(s.ToString());
+        current_constraints.insert(std::string(s));
       }
     }
   } else {
diff --git a/tensorflow/compiler/tf2xla/tf2xla_util.cc b/tensorflow/compiler/tf2xla/tf2xla_util.cc
index 7ec85aa3cdec62..9203e8d9e607e9 100644
--- a/tensorflow/compiler/tf2xla/tf2xla_util.cc
+++ b/tensorflow/compiler/tf2xla/tf2xla_util.cc
@@ -232,7 +232,7 @@ Status PruneGraphDefInto(const tf2xla::Config& config, const GraphDef& in,
     // Push input nodes of the currently visited node to name_queue.
     for (const string& in_edge : map_entry.second->input()) {
       auto id = ParseTensorName(in_edge);
-      const string node_name = id.first.ToString();
+      const string node_name = std::string(id.first);
       if (feed_tensors.find(std::make_pair(node_name, id.second)) ==
           feed_tensors.end()) {
         name_queue.push(node_name);
diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.cc b/tensorflow/compiler/tf2xla/xla_op_registry.cc
index bbe808595d9583..e309cb1e34db7f 100644
--- a/tensorflow/compiler/tf2xla/xla_op_registry.cc
+++ b/tensorflow/compiler/tf2xla/xla_op_registry.cc
@@ -311,7 +311,7 @@ XlaOpRegistry& XlaOpRegistry::Instance() {
 
 XlaOpRegistrationBuilder::XlaOpRegistrationBuilder(StringPiece name) {
   registration_.reset(new XlaOpRegistry::OpRegistration);
-  registration_->name = name.ToString();
+  registration_->name = std::string(name);
 }
 
 XlaOpRegistrationBuilder XlaOpRegistrationBuilder::Name(StringPiece name) {
@@ -323,14 +323,14 @@ XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::Device(
     gtl::ArraySlice devices) {
   registration_->has_device_whitelist = true;
   for (StringPiece device : devices) {
-    registration_->device_whitelist.insert(device.ToString());
+    registration_->device_whitelist.insert(std::string(device));
   }
   return *this;
 }
 
 XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::Device(StringPiece device) {
   registration_->has_device_whitelist = true;
-  registration_->device_whitelist.insert(device.ToString());
+  registration_->device_whitelist.insert(std::string(device));
   return *this;
 }
 
@@ -347,7 +347,7 @@ XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::AllowResourceTypes() {
 XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::TypeConstraint(
     StringPiece attr_name, DataType allowed) {
   std::set& types =
-      registration_->type_constraints[attr_name.ToString()];
+      registration_->type_constraints[std::string(attr_name)];
   types.insert(allowed);
   return *this;
 }
@@ -355,7 +355,7 @@ XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::TypeConstraint(
 XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::TypeConstraint(
     StringPiece attr_name, gtl::ArraySlice allowed) {
   std::set& types =
-      registration_->type_constraints[attr_name.ToString()];
+      registration_->type_constraints[std::string(attr_name)];
   for (DataType t : allowed) {
     types.insert(t);
   }
@@ -364,7 +364,7 @@ XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::TypeConstraint(
 
 XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::CompileTimeConstInput(
     StringPiece input_name) {
-  registration_->compile_time_constant_inputs.insert(input_name.ToString());
+  registration_->compile_time_constant_inputs.insert(std::string(input_name));
   return *this;
 }
 
@@ -394,7 +394,7 @@ XlaBackendRegistrar::XlaBackendRegistrar(
     StringPiece name, gtl::ArraySlice types,
     XlaOpRegistry::BackendOpFilter op_filter) {
   XlaOpRegistry& registry = XlaOpRegistry::Instance();
-  registry.RegisterBackend(name.ToString(), types, op_filter);
+  registry.RegisterBackend(std::string(name), types, op_filter);
 }
 
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc
index 7b227ce294176c..497b48ff227d7d 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc
@@ -36,8 +36,8 @@ ForLoop::ForLoop(tensorflow::StringPiece prefix, tensorflow::StringPiece suffix,
                  llvm::Value* start_index, llvm::Value* end_index,
                  llvm::Value* step, bool prevent_unrolling,
                  bool prevent_vectorization)
-    : prefix_(prefix.ToString()),
-      suffix_(suffix.ToString()),
+    : prefix_(std::string(prefix)),
+      suffix_(std::string(suffix)),
       start_index_(start_index),
       end_index_(end_index),
       step_(step),
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h
index 20069ce5a28184..d915f95db13491 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h
@@ -174,7 +174,7 @@ class ForLoopNest {
       : ForLoopNest(/*name=*/"", ir_builder) {}
 
   ForLoopNest(tensorflow::StringPiece name, llvm::IRBuilder<>* ir_builder)
-      : name_(name.ToString()),
+      : name_(std::string(name)),
         outer_loop_preheader_bb_(nullptr),
         outer_loop_exit_bb_(nullptr),
         inner_loop_body_bb_(nullptr),
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_lexer.cc b/tensorflow/compiler/xla/tools/parser/hlo_lexer.cc
index fc0e4444521247..350db126535e41 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_lexer.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_lexer.cc
@@ -230,7 +230,7 @@ TokKind HloLexer::LexIdentifier() {
     }
   }
 
-  str_val_ = identifier.ToString();
+  str_val_ = std::string(identifier);
   return TokKind::kIdent;
 }
 
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
index 1bb31ddb7b6fdf..3a945fb3b1b54e 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
@@ -242,7 +242,7 @@ bool HloParser::Error(LocTy loc, StringPiece msg) {
   std::vector error_lines;
   error_lines.push_back(
       StrCat("was parsing ", line, ":", col, ": error: ", msg));
-  error_lines.push_back(lexer_.GetLine(loc).ToString());
+  error_lines.push_back(std::string(lexer_.GetLine(loc)));
   error_lines.push_back(col == 0 ? "" : StrCat(string(col - 1, ' '), "^"));
 
   error_.push_back(tensorflow::str_util::Join(error_lines, "\n"));
diff --git a/tensorflow/core/debug/debug_graph_utils.cc b/tensorflow/core/debug/debug_graph_utils.cc
index 4539ea5c0cb6a1..7641edea523679 100644
--- a/tensorflow/core/debug/debug_graph_utils.cc
+++ b/tensorflow/core/debug/debug_graph_utils.cc
@@ -356,10 +356,9 @@ Status DebugNodeInserter::ParseDebugOpName(
             "Malformed attributes in debug op name \"", debug_op_name, "\"");
       }
 
-      const string key = seg.substr(0, eq_index).ToString();
-      const string value =
-          seg.substr(eq_index + 1, attribute_seg.size() - eq_index - 1)
-              .ToString();
+      const string key = std::string(seg.substr(0, eq_index));
+      const string value = std::string(
+          seg.substr(eq_index + 1, attribute_seg.size() - eq_index - 1));
       if (key.empty() || value.empty()) {
         return errors::InvalidArgument(
             "Malformed attributes in debug op name \"", debug_op_name, "\"");
diff --git a/tensorflow/core/debug/debug_io_utils.cc b/tensorflow/core/debug/debug_io_utils.cc
index baa8c08fdf1508..4998a7acfe23b4 100644
--- a/tensorflow/core/debug/debug_io_utils.cc
+++ b/tensorflow/core/debug/debug_io_utils.cc
@@ -399,8 +399,8 @@ Status DebugIO::PublishDebugMetadata(
                               strings::Printf("%.14lld", session_run_index))),
           Env::Default()->NowMicros());
       status.Update(DebugFileIO::DumpEventProtoToFile(
-          event, io::Dirname(core_metadata_path).ToString(),
-          io::Basename(core_metadata_path).ToString()));
+          event, std::string(io::Dirname(core_metadata_path)),
+          std::string(io::Basename(core_metadata_path))));
     }
   }
 
@@ -632,8 +632,8 @@ Status DebugFileIO::DumpTensorToEventFile(const DebugNodeKey& debug_node_key,
   std::vector events;
   TF_RETURN_IF_ERROR(
       WrapTensorAsEvents(debug_node_key, tensor, wall_time_us, 0, &events));
-  return DumpEventProtoToFile(events[0], io::Dirname(file_path).ToString(),
-                              io::Basename(file_path).ToString());
+  return DumpEventProtoToFile(events[0], std::string(io::Dirname(file_path)),
+                              std::string(io::Basename(file_path)));
 }
 
 Status DebugFileIO::RecursiveCreateDir(Env* env, const string& dir) {
@@ -642,7 +642,7 @@ Status DebugFileIO::RecursiveCreateDir(Env* env, const string& dir) {
     return Status::OK();
   }
 
-  string parent_dir = io::Dirname(dir).ToString();
+  string parent_dir = std::string(io::Dirname(dir));
   if (!env->FileExists(parent_dir).ok()) {
     // The parent path does not exist yet, create it first.
     Status s = RecursiveCreateDir(env, parent_dir);  // Recursive call
diff --git a/tensorflow/core/distributed_runtime/master_session.cc b/tensorflow/core/distributed_runtime/master_session.cc
index 83afc5b1a4676b..08fbe8b144f76d 100644
--- a/tensorflow/core/distributed_runtime/master_session.cc
+++ b/tensorflow/core/distributed_runtime/master_session.cc
@@ -606,7 +606,7 @@ Status MasterSession::ReffedClientGraph::RunPartitionsHelper(
     // inadvertently slowing down the normal run path.
     if (is_partial_) {
       for (const auto& name_index : feeds) {
-        const auto iter = part.feed_key.find(name_index.first.ToString());
+        const auto iter = part.feed_key.find(std::string(name_index.first));
         if (iter == part.feed_key.end()) {
           // The provided feed must be for a different partition.
           continue;
@@ -950,7 +950,7 @@ Status MasterSession::ReffedClientGraph::CheckFetches(
     // Skip if already fed.
     if (input.second) continue;
     TensorId id(ParseTensorName(input.first));
-    const Node* n = execution_state->get_node_by_name(id.first.ToString());
+    const Node* n = execution_state->get_node_by_name(std::string(id.first));
     if (n == nullptr) {
       return errors::NotFound("Feed ", input.first, ": not found");
     }
@@ -966,7 +966,7 @@ Status MasterSession::ReffedClientGraph::CheckFetches(
   for (size_t i = 0; i < req.num_fetches(); ++i) {
     const string& fetch = req.fetch_name(i);
     const TensorId id(ParseTensorName(fetch));
-    const Node* n = execution_state->get_node_by_name(id.first.ToString());
+    const Node* n = execution_state->get_node_by_name(std::string(id.first));
     if (n == nullptr) {
       return errors::NotFound("Fetch ", fetch, ": not found");
     }
diff --git a/tensorflow/core/distributed_runtime/remote_device.cc b/tensorflow/core/distributed_runtime/remote_device.cc
index ec26ac44b5f424..15e5919c54a539 100644
--- a/tensorflow/core/distributed_runtime/remote_device.cc
+++ b/tensorflow/core/distributed_runtime/remote_device.cc
@@ -37,7 +37,7 @@ string GetLocalDeviceName(StringPiece fullname) {
   auto pos = fullname.rfind('/');
   CHECK_NE(pos, StringPiece::npos);
   fullname.remove_prefix(pos + 1);
-  return fullname.ToString();
+  return std::string(fullname);
 }
 
 class RemoteDevice : public Device {
diff --git a/tensorflow/core/grappler/utils.h b/tensorflow/core/grappler/utils.h
index 9776e99f207ebf..b87ae055469b67 100644
--- a/tensorflow/core/grappler/utils.h
+++ b/tensorflow/core/grappler/utils.h
@@ -139,7 +139,7 @@ inline StringPiece ParseNodeNameAsStringPiece(const string& name,
 
 // Returns the node name and position in a single call.
 inline string ParseNodeName(const string& name, int* position) {
-  return ParseNodeNameAsStringPiece(name, position).ToString();
+  return std::string(ParseNodeNameAsStringPiece(name, position));
 }
 
 // Add a prefix to a node name with a custom delimiter.
diff --git a/tensorflow/core/kernels/hexagon/graph_transferer.cc b/tensorflow/core/kernels/hexagon/graph_transferer.cc
index 7960cb4b0552de..e05de3fe8e0eca 100644
--- a/tensorflow/core/kernels/hexagon/graph_transferer.cc
+++ b/tensorflow/core/kernels/hexagon/graph_transferer.cc
@@ -161,7 +161,7 @@ Status GraphTransferer::LoadGraphFromProto(
 
   for (const string& output_node_name : output_node_names) {
     const TensorId tid = ParseTensorName(output_node_name);
-    const string node_name = tid.first.ToString();
+    const string node_name = std::string(tid.first);
     const int port = tid.second;
     const int node_id = node_name_to_id_cache_map_.at(node_name);
     const Node* node = node_name_cache_list_.at(node_id);
diff --git a/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc b/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc
index 3810cbe5b55a44..1580b72605256a 100644
--- a/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc
+++ b/tensorflow/core/kernels/hexagon/hexagon_control_wrapper.cc
@@ -168,7 +168,7 @@ bool HexagonControlWrapper::SetupGraph() {
     new_output_node_info.set_output_count(0);
 
     const TensorId tid = ParseTensorName(graph_output.name());
-    const string node_name = tid.first.ToString();
+    const string node_name = std::string(tid.first);
     const int port = tid.second;
     // Register node input for the new output node
     const GraphTransferNodeInfo* node_info =
diff --git a/tensorflow/core/lib/io/path.cc b/tensorflow/core/lib/io/path.cc
index 996fbf62e5c173..b62206012cc93b 100644
--- a/tensorflow/core/lib/io/path.cc
+++ b/tensorflow/core/lib/io/path.cc
@@ -42,7 +42,7 @@ string JoinPathImpl(std::initializer_list paths) {
     if (path.empty()) continue;
 
     if (result.empty()) {
-      result = path.ToString();
+      result = std::string(path);
       continue;
     }
 
@@ -124,7 +124,7 @@ StringPiece Extension(StringPiece path) {
 }
 
 string CleanPath(StringPiece unclean_path) {
-  string path = unclean_path.ToString();
+  string path = std::string(unclean_path);
   const char* src = path.c_str();
   string::iterator dst = path.begin();
 
@@ -237,7 +237,7 @@ void ParseURI(StringPiece remaining, StringPiece* scheme, StringPiece* host,
 
 string CreateURI(StringPiece scheme, StringPiece host, StringPiece path) {
   if (scheme.empty()) {
-    return path.ToString();
+    return std::string(path);
   }
   return strings::StrCat(scheme, "://", host, path);
 }
diff --git a/tensorflow/core/lib/io/table_test.cc b/tensorflow/core/lib/io/table_test.cc
index 78a3fa501c6836..9e3309f0a7b21d 100644
--- a/tensorflow/core/lib/io/table_test.cc
+++ b/tensorflow/core/lib/io/table_test.cc
@@ -147,7 +147,7 @@ class Constructor {
   virtual ~Constructor() {}
 
   void Add(const string& key, const StringPiece& value) {
-    data_[key] = value.ToString();
+    data_[key] = std::string(value);
   }
 
   // Finish constructing the data structure with all the keys that have
@@ -188,7 +188,7 @@ class BlockConstructor : public Constructor {
       builder.Add(it->first, it->second);
     }
     // Open the block
-    data_ = builder.Finish().ToString();
+    data_ = std::string(builder.Finish());
     BlockContents contents;
     contents.data = data_;
     contents.cachable = false;
@@ -515,7 +515,7 @@ TEST_F(Harness, Randomized) {
       for (int e = 0; e < num_entries; e++) {
         string v;
         Add(test::RandomKey(&rnd, rnd.Skewed(4)),
-            test::RandomString(&rnd, rnd.Skewed(5), &v).ToString());
+            std::string(test::RandomString(&rnd, rnd.Skewed(5), &v)));
       }
       Test(&rnd);
     }
diff --git a/tensorflow/core/util/tensor_bundle/tensor_bundle.cc b/tensorflow/core/util/tensor_bundle/tensor_bundle.cc
index 0426fee0e26797..71906147069074 100644
--- a/tensorflow/core/util/tensor_bundle/tensor_bundle.cc
+++ b/tensorflow/core/util/tensor_bundle/tensor_bundle.cc
@@ -370,14 +370,14 @@ Status PadAlignment(FileOutputBuffer* out, int alignment, int64* size) {
 BundleWriter::BundleWriter(Env* env, StringPiece prefix, const Options& options)
     : env_(env),
       options_(options),
-      prefix_(prefix.ToString()),
+      prefix_(std::string(prefix)),
       tmp_metadata_path_(strings::StrCat(MetaFilename(prefix_), ".tempstate",
                                          random::New64())),
       tmp_data_path_(strings::StrCat(DataFilename(prefix_, 0, 1), ".tempstate",
                                      random::New64())),
       out_(nullptr),
       size_(0) {
-  status_ = env_->CreateDir(io::Dirname(prefix_).ToString());
+  status_ = env_->CreateDir(std::string(io::Dirname(prefix_)));
   if (!status_.ok() && !errors::IsAlreadyExists(status_)) {
     return;
   }
@@ -394,7 +394,7 @@ BundleWriter::BundleWriter(Env* env, StringPiece prefix, const Options& options)
 Status BundleWriter::Add(StringPiece key, const Tensor& val) {
   if (!status_.ok()) return status_;
   CHECK_NE(key, kHeaderEntryKey);
-  const string key_string = key.ToString();
+  const string key_string = std::string(key);
   if (entries_.find(key_string) != entries_.end()) {
     status_ = errors::InvalidArgument("Adding duplicate key: ", key);
     return status_;
@@ -445,7 +445,7 @@ Status BundleWriter::AddSlice(StringPiece full_tensor_key,
   // In the case of a sharded save, MergeBundles() is responsible for merging
   // the "slices" field of multiple metadata entries corresponding to the same
   // full tensor.
-  const string full_tensor_key_string = full_tensor_key.ToString();
+  const string full_tensor_key_string = std::string(full_tensor_key);
   BundleEntryProto* full_entry = &entries_[full_tensor_key_string];
   if (full_entry->dtype() != DT_INVALID) {
     CHECK_EQ(full_entry->dtype(), slice_tensor.dtype());
@@ -600,7 +600,7 @@ static Status MergeOneBundle(Env* env, StringPiece prefix,
   // Loops through the non-header to-merge entries.
   BundleEntryProto to_merge_entry;
   for (; iter->Valid(); iter->Next()) {
-    const string key = iter->key().ToString();
+    const string key = std::string(iter->key());
     const auto entry_iter = merge_state->entries.find(key);
 
     // Illegal: the duplicated entry is a non-slice tensor.
@@ -649,7 +649,7 @@ Status MergeBundles(Env* env, gtl::ArraySlice prefixes,
   // Merges all metadata tables.
   // TODO(zhifengc): KeyValue sorter if it becomes too big.
   MergeState merge;
-  Status status = env->CreateDir(io::Dirname(merged_prefix).ToString());
+  Status status = env->CreateDir(std::string(io::Dirname(merged_prefix)));
   if (!status.ok() && !errors::IsAlreadyExists(status)) return status;
   for (int i = 0; i < prefixes.size(); ++i) {
     TF_RETURN_IF_ERROR(MergeOneBundle(env, prefixes[i], &merge));
@@ -697,7 +697,7 @@ Status MergeBundles(Env* env, gtl::ArraySlice prefixes,
 
 BundleReader::BundleReader(Env* env, StringPiece prefix)
     : env_(env),
-      prefix_(prefix.ToString()),
+      prefix_(std::string(prefix)),
       metadata_(nullptr),
       table_(nullptr),
       iter_(nullptr) {
@@ -919,7 +919,7 @@ Status BundleReader::GetSliceValue(StringPiece full_tensor_key,
 
   const TensorShape full_shape(TensorShape(full_tensor_entry.shape()));
   std::vector> details;
-  const string full_tensor_key_string = full_tensor_key.ToString();
+  const string full_tensor_key_string = std::string(full_tensor_key);
   const TensorSliceSet* tss =
       gtl::FindPtrOrNull(tensor_slices_, full_tensor_key_string);
 
diff --git a/tensorflow/core/util/tensor_bundle/tensor_bundle_test.cc b/tensorflow/core/util/tensor_bundle/tensor_bundle_test.cc
index 7f166f0ec0aeee..92ce8ae00eaf7c 100644
--- a/tensorflow/core/util/tensor_bundle/tensor_bundle_test.cc
+++ b/tensorflow/core/util/tensor_bundle/tensor_bundle_test.cc
@@ -107,7 +107,7 @@ std::vector AllTensorKeys(BundleReader* reader) {
   reader->Seek(kHeaderEntryKey);
   reader->Next();
   for (; reader->Valid(); reader->Next()) {
-    ret.push_back(reader->key().ToString());
+    ret.push_back(std::string(reader->key()));
   }
   return ret;
 }
diff --git a/tensorflow/stream_executor/kernel.cc b/tensorflow/stream_executor/kernel.cc
index d1aa596b73da3d..7c1923da51fae7 100644
--- a/tensorflow/stream_executor/kernel.cc
+++ b/tensorflow/stream_executor/kernel.cc
@@ -94,7 +94,7 @@ KernelCacheConfig KernelBase::GetPreferredCacheConfig() const {
 static const char *kStubPrefix = "__device_stub_";
 
 void KernelBase::set_name(port::StringPiece name) {
-  name_ = name.ToString();
+  name_ = std::string(name);
   port::StringPiece stubless_name = name;
   if (tensorflow::str_util::StartsWith(name, kStubPrefix)) {
     stubless_name.remove_prefix(strlen(kStubPrefix));
diff --git a/tensorflow/stream_executor/kernel_spec.cc b/tensorflow/stream_executor/kernel_spec.cc
index 6a1f0a591ff087..f0a5785b72f53a 100644
--- a/tensorflow/stream_executor/kernel_spec.cc
+++ b/tensorflow/stream_executor/kernel_spec.cc
@@ -18,11 +18,11 @@ limitations under the License.
 namespace stream_executor {
 
 KernelLoaderSpec::KernelLoaderSpec(port::StringPiece kernelname)
-    : kernelname_(kernelname.ToString()) {}
+    : kernelname_(std::string(kernelname)) {}
 
 OnDiskKernelLoaderSpec::OnDiskKernelLoaderSpec(port::StringPiece filename,
                                                port::StringPiece kernelname)
-    : KernelLoaderSpec(kernelname), filename_(filename.ToString()) {}
+    : KernelLoaderSpec(kernelname), filename_(std::string(filename)) {}
 
 CudaPtxOnDisk::CudaPtxOnDisk(port::StringPiece filename,
                              port::StringPiece kernelname)
@@ -161,7 +161,7 @@ OpenCLTextOnDisk::OpenCLTextOnDisk(port::StringPiece filename,
 
 OpenCLTextInMemory::OpenCLTextInMemory(port::StringPiece text,
                                        port::StringPiece kernelname)
-    : KernelLoaderSpec(kernelname), text_(text.ToString()) {}
+    : KernelLoaderSpec(kernelname), text_(std::string(text)) {}
 
 OpenCLBinaryOnDisk::OpenCLBinaryOnDisk(port::StringPiece filename,
                                        port::StringPiece kernelname)

From ebad5d624c6c08a3fdb4ffac6051b4888fc36790 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 2 May 2018 19:19:12 -0700
Subject: [PATCH 0324/1691] Update ops-related pbtxt files.

PiperOrigin-RevId: 195190335
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 144 ++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 102 +++++++++++--
 2 files changed, 234 insertions(+), 12 deletions(-)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index cb466ef81796dc..3db00d8180c242 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -20998,6 +20998,30 @@ op {
     type: DT_COMPLEX64
   }
 }
+op {
+  name: "FFT"
+  input_arg {
+    name: "input"
+    type_attr: "Tcomplex"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "Tcomplex"
+  }
+  attr {
+    name: "Tcomplex"
+    type: "type"
+    default_value {
+      type: DT_COMPLEX64
+    }
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "FFT2D"
   input_arg {
@@ -21009,6 +21033,30 @@ op {
     type: DT_COMPLEX64
   }
 }
+op {
+  name: "FFT2D"
+  input_arg {
+    name: "input"
+    type_attr: "Tcomplex"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "Tcomplex"
+  }
+  attr {
+    name: "Tcomplex"
+    type: "type"
+    default_value {
+      type: DT_COMPLEX64
+    }
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "FFT3D"
   input_arg {
@@ -21020,6 +21068,30 @@ op {
     type: DT_COMPLEX64
   }
 }
+op {
+  name: "FFT3D"
+  input_arg {
+    name: "input"
+    type_attr: "Tcomplex"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "Tcomplex"
+  }
+  attr {
+    name: "Tcomplex"
+    type: "type"
+    default_value {
+      type: DT_COMPLEX64
+    }
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "FIFOQueue"
   output_arg {
@@ -24711,6 +24783,30 @@ op {
     type: DT_COMPLEX64
   }
 }
+op {
+  name: "IFFT"
+  input_arg {
+    name: "input"
+    type_attr: "Tcomplex"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "Tcomplex"
+  }
+  attr {
+    name: "Tcomplex"
+    type: "type"
+    default_value {
+      type: DT_COMPLEX64
+    }
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "IFFT2D"
   input_arg {
@@ -24722,6 +24818,30 @@ op {
     type: DT_COMPLEX64
   }
 }
+op {
+  name: "IFFT2D"
+  input_arg {
+    name: "input"
+    type_attr: "Tcomplex"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "Tcomplex"
+  }
+  attr {
+    name: "Tcomplex"
+    type: "type"
+    default_value {
+      type: DT_COMPLEX64
+    }
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "IFFT3D"
   input_arg {
@@ -24733,6 +24853,30 @@ op {
     type: DT_COMPLEX64
   }
 }
+op {
+  name: "IFFT3D"
+  input_arg {
+    name: "input"
+    type_attr: "Tcomplex"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "Tcomplex"
+  }
+  attr {
+    name: "Tcomplex"
+    type: "type"
+    default_value {
+      type: DT_COMPLEX64
+    }
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
 op {
   name: "IRFFT"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 207dd1c3d7ecb9..7156440b46ea08 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -9709,33 +9709,72 @@ op {
   name: "FFT"
   input_arg {
     name: "input"
-    type: DT_COMPLEX64
+    type_attr: "Tcomplex"
   }
   output_arg {
     name: "output"
-    type: DT_COMPLEX64
+    type_attr: "Tcomplex"
+  }
+  attr {
+    name: "Tcomplex"
+    type: "type"
+    default_value {
+      type: DT_COMPLEX64
+    }
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
   }
 }
 op {
   name: "FFT2D"
   input_arg {
     name: "input"
-    type: DT_COMPLEX64
+    type_attr: "Tcomplex"
   }
   output_arg {
     name: "output"
-    type: DT_COMPLEX64
+    type_attr: "Tcomplex"
+  }
+  attr {
+    name: "Tcomplex"
+    type: "type"
+    default_value {
+      type: DT_COMPLEX64
+    }
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
   }
 }
 op {
   name: "FFT3D"
   input_arg {
     name: "input"
-    type: DT_COMPLEX64
+    type_attr: "Tcomplex"
   }
   output_arg {
     name: "output"
-    type: DT_COMPLEX64
+    type_attr: "Tcomplex"
+  }
+  attr {
+    name: "Tcomplex"
+    type: "type"
+    default_value {
+      type: DT_COMPLEX64
+    }
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
   }
 }
 op {
@@ -11877,33 +11916,72 @@ op {
   name: "IFFT"
   input_arg {
     name: "input"
-    type: DT_COMPLEX64
+    type_attr: "Tcomplex"
   }
   output_arg {
     name: "output"
-    type: DT_COMPLEX64
+    type_attr: "Tcomplex"
+  }
+  attr {
+    name: "Tcomplex"
+    type: "type"
+    default_value {
+      type: DT_COMPLEX64
+    }
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
   }
 }
 op {
   name: "IFFT2D"
   input_arg {
     name: "input"
-    type: DT_COMPLEX64
+    type_attr: "Tcomplex"
   }
   output_arg {
     name: "output"
-    type: DT_COMPLEX64
+    type_attr: "Tcomplex"
+  }
+  attr {
+    name: "Tcomplex"
+    type: "type"
+    default_value {
+      type: DT_COMPLEX64
+    }
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
   }
 }
 op {
   name: "IFFT3D"
   input_arg {
     name: "input"
-    type: DT_COMPLEX64
+    type_attr: "Tcomplex"
   }
   output_arg {
     name: "output"
-    type: DT_COMPLEX64
+    type_attr: "Tcomplex"
+  }
+  attr {
+    name: "Tcomplex"
+    type: "type"
+    default_value {
+      type: DT_COMPLEX64
+    }
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
   }
 }
 op {

From 71f97c8cd9304a8e1cf8e309e15000d5831b212a Mon Sep 17 00:00:00 2001
From: Mostafa Alaa 
Date: Wed, 2 May 2018 19:53:18 -0700
Subject: [PATCH 0325/1691] Fix tf.variable_scope unique name after entering
 root scope Closes #18702.

PiperOrigin-RevId: 195192460
---
 tensorflow/python/ops/variable_scope.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index ba213ef884165f..adb0f59948a9be 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -1175,7 +1175,7 @@ def open_variable_scope(self, scope_name):
 
   def close_variable_subscopes(self, scope_name):
     for k in list(self.variable_scopes_count.keys()):
-      if not scope_name or k.startswith(scope_name + "/"):
+      if scope_name is None or k.startswith(scope_name + "/"):
         self.variable_scopes_count[k] = 0
 
   def variable_scope_count(self, scope_name):

From 0e141b75a557a646750e4af06530892af5a8da20 Mon Sep 17 00:00:00 2001
From: Taehoon Lee 
Date: Thu, 3 May 2018 12:01:04 +0900
Subject: [PATCH 0326/1691] Fix typos (#18475)

---
 tensorflow/compiler/xla/service/copy_insertion.cc               | 2 +-
 tensorflow/compiler/xla/service/hlo_evaluator.cc                | 2 +-
 tensorflow/contrib/kfac/python/ops/optimizer.py                 | 2 +-
 .../contrib/lite/kernels/internal/optimized/optimized_ops.h     | 2 +-
 .../contrib/lite/kernels/internal/reference/reference_ops.h     | 2 +-
 tensorflow/contrib/lite/testing/generate_examples.py            | 2 +-
 tensorflow/contrib/lite/toco/toco_flags.proto                   | 2 +-
 .../contrib/opt/python/training/model_average_optimizer_test.py | 2 +-
 tensorflow/python/keras/_impl/keras/engine/training_eager.py    | 2 +-
 9 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc
index 40519ecc799c8f..cbe2ba2e50ab21 100644
--- a/tensorflow/compiler/xla/service/copy_insertion.cc
+++ b/tensorflow/compiler/xla/service/copy_insertion.cc
@@ -65,7 +65,7 @@ struct SpecialCaseCopyPolicy {
   // output tuple.
   bool copy_root_replicated_buffers = false;
   // If true, insert a copy if a buffer coming from a constant or a parameter
-  // is found wihtin the output tuple.
+  // is found within the output tuple.
   bool copy_parameters_and_constants = false;
 };
 
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 8cf94123b71403..1071f5b184bd77 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -1193,7 +1193,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
           // specifically:
           // - For lhs, the non-contracting dimensions, including the batch
           // dimension have the same index as the `result_index`.
-          // - For rhs, the batch dimension is set seperately from other
+          // - For rhs, the batch dimension is set separately from other
           // non-contracting dimensions, since these other non-contracting
           // dimensions in rhs follow the non-contracting dimensions of lhs in
           // the resulting index.
diff --git a/tensorflow/contrib/kfac/python/ops/optimizer.py b/tensorflow/contrib/kfac/python/ops/optimizer.py
index 45a760c9f1013d..7203804af36cb4 100644
--- a/tensorflow/contrib/kfac/python/ops/optimizer.py
+++ b/tensorflow/contrib/kfac/python/ops/optimizer.py
@@ -114,7 +114,7 @@ def __init__(self,
     self._estimation_mode = estimation_mode
     self._colocate_gradients_with_ops = colocate_gradients_with_ops
 
-    # The below paramaters are required only if damping needs to be adapated.
+    # The below parameters are required only if damping needs to be adapated.
     # These parameters can be set by calling
     # set_damping_adaptation_params() explicitly.
     self._damping_adaptation_decay = 0.95
diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index 3d6042c31fef4c..2a70c36c954914 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -3355,7 +3355,7 @@ inline void Concatenation(int concat_dim, const uint8* const* input_data,
                           const int32 output_zeropoint,
                           const float output_scale) {
   // The arguments input_zeropoint and input_scale are expected to be an array
-  // that have the quantization paramaters for all the inputs to the concat
+  // that have the quantization parameters for all the inputs to the concat
   // operator.
   gemmlowp::ScopedProfilingLabel label("Concatenation");
   TFLITE_DCHECK_GT(inputs_count, 1);
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index d41ade4c9d9ec2..445687cd15b3bf 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -1628,7 +1628,7 @@ inline void Concatenation(int concat_dim, const uint8* const* input_data,
                           const int32 output_zeropoint,
                           const float output_scale) {
   // The arguments input_zeropoint and input_scale are expected to be an array
-  // that have the quantization paramaters for all the inputs to the concat
+  // that have the quantization parameters for all the inputs to the concat
   // operator.
   TFLITE_DCHECK_GT(inputs_count, 1);
   int64_t concat_size = 0;
diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index 2f8f7a1a795629..e4851d60771f26 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -109,7 +109,7 @@
 
 
 class ExtraTocoOptions(object):
-  """Additonal toco options besides input, output, shape."""
+  """Additional toco options besides input, output, shape."""
 
   def __init__(self):
     # Whether to ignore control dependency nodes.
diff --git a/tensorflow/contrib/lite/toco/toco_flags.proto b/tensorflow/contrib/lite/toco/toco_flags.proto
index a04017a6bf05fb..802cf3e2e4caeb 100644
--- a/tensorflow/contrib/lite/toco/toco_flags.proto
+++ b/tensorflow/contrib/lite/toco/toco_flags.proto
@@ -127,7 +127,7 @@ message TocoFlags {
   // transformations that are necessary in order to generate inference
   // code for these graphs. Such graphs should be fixed, but as a
   // temporary work-around, setting this reorder_across_fake_quant flag
-  // allows toco to perform necessary graph transformaitons on them,
+  // allows toco to perform necessary graph transformations on them,
   // at the cost of no longer faithfully matching inference and training
   // arithmetic.
   optional bool reorder_across_fake_quant = 8;
diff --git a/tensorflow/contrib/opt/python/training/model_average_optimizer_test.py b/tensorflow/contrib/opt/python/training/model_average_optimizer_test.py
index 6cca0a8a009456..bfb3350b59ef4b 100644
--- a/tensorflow/contrib/opt/python/training/model_average_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/model_average_optimizer_test.py
@@ -146,7 +146,7 @@ def test1Workers2Period(self):
     self.assertAllEqual(1.0, sessions[0].run(global_var_1))
     self.assertAllEqual(0, sessions[0].run(global_step))
 
-    # iteration 2, global varibale update
+    # iteration 2, global variable update
     thread_0 = self.checkedThread(
         target=self._run, args=(train_ops[0], sessions[0]))
     thread_1 = self.checkedThread(
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
index 34adeb7599d657..b9c99b22224480 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
@@ -181,7 +181,7 @@ def slice_arrays(arrays, indices, contiguous=True):
   """Slices batches out of provided arrays (workaround for eager tensors).
 
   Unfortunately eager tensors don't have the same slicing behavior as
-  Numpy arrays (they folow  the same slicing behavior as symbolic TF tensors),
+  Numpy arrays (they follow the same slicing behavior as symbolic TF tensors),
   hence we cannot use `generic_utils.slice_arrays` directly
   and we have to implement this workaround based on `concat`. This has a
   performance cost.

From f6000468263c5db7befbf5c320e8b3af7d90b819 Mon Sep 17 00:00:00 2001
From: Andrew Selle 
Date: Wed, 2 May 2018 21:15:01 -0700
Subject: [PATCH 0327/1691] Expose Interpreter to tensorflow.contrib.lite

PiperOrigin-RevId: 195198645
---
 tensorflow/contrib/lite/BUILD                 |  2 +
 tensorflow/contrib/lite/python/BUILD          |  1 +
 tensorflow/contrib/lite/python/interpreter.py | 18 ++++-
 .../interpreter_wrapper.cc                    |  2 +-
 tensorflow/contrib/lite/python/lite.py        |  2 +
 .../tools/pip_package/pip_smoke_test.py       | 73 +++++++++++++------
 6 files changed, 70 insertions(+), 28 deletions(-)

diff --git a/tensorflow/contrib/lite/BUILD b/tensorflow/contrib/lite/BUILD
index 1534f97d760015..10065e894c48d4 100644
--- a/tensorflow/contrib/lite/BUILD
+++ b/tensorflow/contrib/lite/BUILD
@@ -92,6 +92,8 @@ cc_library(
     deps = [":context"],
 )
 
+exports_files(["builtin_ops.h"])
+
 cc_library(
     name = "string",
     hdrs = [
diff --git a/tensorflow/contrib/lite/python/BUILD b/tensorflow/contrib/lite/python/BUILD
index e6dcc7aa099ccd..4920e83970d1cb 100644
--- a/tensorflow/contrib/lite/python/BUILD
+++ b/tensorflow/contrib/lite/python/BUILD
@@ -44,6 +44,7 @@ py_library(
     deps = [
         ":convert",
         ":convert_saved_model",
+        ":interpreter",
         ":op_hint",
     ],
 )
diff --git a/tensorflow/contrib/lite/python/interpreter.py b/tensorflow/contrib/lite/python/interpreter.py
index cb9c0d31218955..5fbc55145217dd 100644
--- a/tensorflow/contrib/lite/python/interpreter.py
+++ b/tensorflow/contrib/lite/python/interpreter.py
@@ -17,7 +17,19 @@
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.lite.python.interpreter_wrapper import tensorflow_wrap_interpreter_wrapper as interpreter_wrapper
+from tensorflow.python.util.lazy_loader import LazyLoader
+
+# Lazy load since some of the performance benchmark skylark rules
+# break dependencies. Must use double quotes to match code internal rewrite
+# rule.
+# pylint: disable=g-inconsistent-quotes
+_interpreter_wrapper = LazyLoader(
+    "_interpreter_wrapper", globals(),
+    "tensorflow.contrib.lite.python.interpreter_wrapper."
+    "tensorflow_wrap_interpreter_wrapper")
+# pylint: enable=g-inconsistent-quotes
+
+del LazyLoader
 
 
 class Interpreter(object):
@@ -35,13 +47,13 @@ def __init__(self, model_path=None, model_content=None):
     """
     if model_path and not model_content:
       self._interpreter = (
-          interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromFile(
+          _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromFile(
               model_path))
       if not self._interpreter:
         raise ValueError('Failed to open {}'.format(model_path))
     elif model_content and not model_path:
       self._interpreter = (
-          interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromBuffer(
+          _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromBuffer(
               model_content, len(model_content)))
       if not self._interpreter:
         raise ValueError(
diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc
index 04fc098129854e..16f4f30b943134 100644
--- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc
+++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc
@@ -116,7 +116,7 @@ PyObject* PyArrayFromIntVector(const int* data, npy_intp size) {
 PyObject* PyTupleFromQuantizationParam(const TfLiteQuantizationParams& param) {
   PyObject* result = PyTuple_New(2);
   PyTuple_SET_ITEM(result, 0, PyFloat_FromDouble(param.scale));
-  PyTuple_SET_ITEM(result, 1, PyInt_FromLong(param.zero_point));
+  PyTuple_SET_ITEM(result, 1, PyLong_FromLong(param.zero_point));
   return result;
 }
 
diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py
index 4ea40201f73bb0..86b25e68acaf5d 100644
--- a/tensorflow/contrib/lite/python/lite.py
+++ b/tensorflow/contrib/lite/python/lite.py
@@ -19,6 +19,7 @@
 @@toco_convert
 @@toco_convert_protos
 @@tflite_from_saved_model
+@@Interpreter
 @@OpHint
 @@convert_op_hints_to_stubs
 
@@ -31,6 +32,7 @@
 from tensorflow.contrib.lite.python.convert import toco_convert
 from tensorflow.contrib.lite.python.convert import toco_convert_protos
 from tensorflow.contrib.lite.python.convert_saved_model import tflite_from_saved_model
+from tensorflow.contrib.lite.python.interpreter import Interpreter
 from tensorflow.contrib.lite.python.op_hint import convert_op_hints_to_stubs
 from tensorflow.contrib.lite.python.op_hint import OpHint
 # pylint: enable=unused-import
diff --git a/tensorflow/tools/pip_package/pip_smoke_test.py b/tensorflow/tools/pip_package/pip_smoke_test.py
index b23dde20199a36..401f833dbd6ae4 100644
--- a/tensorflow/tools/pip_package/pip_smoke_test.py
+++ b/tensorflow/tools/pip_package/pip_smoke_test.py
@@ -30,15 +30,42 @@
 PIP_PACKAGE_QUERY_EXPRESSION = (
     "deps(//tensorflow/tools/pip_package:build_pip_package)")
 
-# pylint: disable=g-backslash-continuation
-PY_TEST_QUERY_EXPRESSION = 'deps(\
-  filter("^((?!benchmark).)*$",\
-  kind(py_test,\
-  //tensorflow/python/... \
-  + //tensorflow/contrib/... \
-  - //tensorflow/contrib/tensorboard/... \
-  - attr(tags, "manual|no_pip", //tensorflow/...))), 1)'
-# pylint: enable=g-backslash-continuation
+
+def GetBuild(dir_base):
+  """Get the list of BUILD file all targets recursively startind at dir_base."""
+  items = []
+  for root, _, files in os.walk(dir_base):
+    for name in files:
+      if (name == "BUILD" and
+          root.find("tensorflow/contrib/lite/examples/android") == -1):
+        items.append("//" + root + ":all")
+  return items
+
+
+def BuildPyTestDependencies():
+  python_targets = GetBuild("tensorflow/python")
+  contrib_targets = GetBuild("tensorflow/contrib")
+  tensorboard_targets = GetBuild("tensorflow/contrib/tensorboard")
+  tensorflow_targets = GetBuild("tensorflow")
+  # Build list of test targets,
+  # python + contrib - tensorboard - attr(manual|pno_pip)
+  targets = " + ".join(python_targets)
+  for t in contrib_targets:
+    targets += " + " + t
+  for t in tensorboard_targets:
+    targets += " - " + t
+  targets += ' - attr(tags, "manual|no_pip", %s)' % " + ".join(
+      tensorflow_targets)
+  query_kind = "kind(py_test, %s)" % targets
+  # Skip benchmarks etc.
+  query_filter = 'filter("^((?!benchmark).)*$", %s)' % query_kind
+  # Get the dependencies
+  query_deps = "deps(%s, 1)" % query_filter
+
+  return python_targets, query_deps
+
+
+PYTHON_TARGETS, PY_TEST_QUERY_EXPRESSION = BuildPyTestDependencies()
 
 # Hard-coded blacklist of files if not included in pip package
 # TODO(amitpatankar): Clean up blacklist.
@@ -79,16 +106,6 @@
 ]
 
 
-def bazel_query(query_target):
-  """Run bazel query on target."""
-  try:
-    output = subprocess.check_output(
-        ["bazel", "query", "--keep_going", query_target])
-  except subprocess.CalledProcessError as e:
-    output = e.output
-  return output
-
-
 def main():
   """This script runs the pip smoke test.
 
@@ -103,14 +120,22 @@ def main():
   """
 
   # pip_package_dependencies_list is the list of included files in pip packages
-  pip_package_dependencies = bazel_query(PIP_PACKAGE_QUERY_EXPRESSION)
+  pip_package_dependencies = subprocess.check_output(
+      ["bazel", "cquery", PIP_PACKAGE_QUERY_EXPRESSION])
   pip_package_dependencies_list = pip_package_dependencies.strip().split("\n")
+  pip_package_dependencies_list = [
+      x.split()[0] for x in pip_package_dependencies_list
+  ]
   print("Pip package superset size: %d" % len(pip_package_dependencies_list))
 
   # tf_py_test_dependencies is the list of dependencies for all python
   # tests in tensorflow
-  tf_py_test_dependencies = bazel_query(PY_TEST_QUERY_EXPRESSION)
+  tf_py_test_dependencies = subprocess.check_output(
+      ["bazel", "cquery", PY_TEST_QUERY_EXPRESSION])
   tf_py_test_dependencies_list = tf_py_test_dependencies.strip().split("\n")
+  tf_py_test_dependencies_list = [
+      x.split()[0] for x in tf_py_test_dependencies.strip().split("\n")
+  ]
   print("Pytest dependency subset size: %d" % len(tf_py_test_dependencies_list))
 
   missing_dependencies = []
@@ -141,9 +166,9 @@ def main():
     for missing_dependency in missing_dependencies:
       print("\nMissing dependency: %s " % missing_dependency)
       print("Affected Tests:")
-      rdep_query = ("rdeps(kind(py_test, //tensorflow/python/...), %s)" %
-                    missing_dependency)
-      affected_tests = bazel_query(rdep_query)
+      rdep_query = ("rdeps(kind(py_test, %s), %s)" %
+                    (" + ".join(PYTHON_TARGETS), missing_dependency))
+      affected_tests = subprocess.check_output(["bazel", "cquery", rdep_query])
       affected_tests_list = affected_tests.split("\n")[:-2]
       print("\n".join(affected_tests_list))
 

From 985351dc1ab33cedbfd7790dd9cccc36d2d4b150 Mon Sep 17 00:00:00 2001
From: Tony Wang 
Date: Wed, 2 May 2018 21:37:04 -0700
Subject: [PATCH 0328/1691] Simplify getter and setter method for
 GraphOptimizationPass::name_

PiperOrigin-RevId: 195199912
---
 .../core/common_runtime/optimization_registry.cc     |  6 ++----
 .../core/common_runtime/optimization_registry.h      | 12 ++++++------
 2 files changed, 8 insertions(+), 10 deletions(-)

diff --git a/tensorflow/core/common_runtime/optimization_registry.cc b/tensorflow/core/common_runtime/optimization_registry.cc
index bf49a758b2550a..6ac047295dce8f 100644
--- a/tensorflow/core/common_runtime/optimization_registry.cc
+++ b/tensorflow/core/common_runtime/optimization_registry.cc
@@ -36,8 +36,7 @@ Status OptimizationPassRegistry::RunGrouping(
     for (auto& phase : group->second) {
       VLOG(1) << "Running optimization phase " << phase.first;
       for (auto& pass : phase.second) {
-        VLOG(1) << "Running optimization pass: "
-                << pass->GetOptimizationPassName();
+        VLOG(1) << "Running optimization pass: " << pass->name();
         Status s = pass->Run(options);
         if (!s.ok()) return s;
       }
@@ -52,8 +51,7 @@ void OptimizationPassRegistry::LogGrouping(Grouping grouping, int vlog_level) {
     for (auto& phase : group->second) {
       for (auto& pass : phase.second) {
         VLOG(vlog_level) << "Registered optimization pass grouping " << grouping
-                         << " phase " << phase.first << ": "
-                         << pass->GetOptimizationPassName();
+                         << " phase " << phase.first << ": " << pass->name();
       }
     }
   }
diff --git a/tensorflow/core/common_runtime/optimization_registry.h b/tensorflow/core/common_runtime/optimization_registry.h
index 1b535faf196fd0..f5d265aa24bfc1 100644
--- a/tensorflow/core/common_runtime/optimization_registry.h
+++ b/tensorflow/core/common_runtime/optimization_registry.h
@@ -65,13 +65,13 @@ class GraphOptimizationPass {
  public:
   virtual ~GraphOptimizationPass() {}
   virtual Status Run(const GraphOptimizationPassOptions& options) = 0;
-  void SetOptimizationPassName(string name) { _optimization_pass_name = name; }
-  string GetOptimizationPassName() { return _optimization_pass_name; }
+  void set_name(const string& name) { name_ = name; }
+  string name() const { return name_; }
 
  private:
-  // The name of the opitmization pass, which is the same as the inherited class
-  // name.
-  string _optimization_pass_name;
+  // The name of the opitimization pass, which is the same as the inherited
+  // class name.
+  string name_;
 };
 
 // The key is a 'phase' number. Phases are executed in increasing
@@ -118,7 +118,7 @@ class OptimizationPassRegistration {
                                int phase,
                                std::unique_ptr pass,
                                string optimization_pass_name) {
-    pass->SetOptimizationPassName(optimization_pass_name);
+    pass->set_name(optimization_pass_name);
     OptimizationPassRegistry::Global()->Register(grouping, phase,
                                                  std::move(pass));
   }

From 17d877b21af1a3b99fd20b8ede8196040ac37486 Mon Sep 17 00:00:00 2001
From: Elson Rodriguez 
Date: Wed, 2 May 2018 21:58:44 -0700
Subject: [PATCH 0329/1691] Enabling support for S3 and Google Storage for the
 MKL Docker image. (#19039)

---
 tensorflow/tools/docker/Dockerfile.devel-cpu-mkl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl
index c65e0b72bc582d..a6cd44ced1d546 100644
--- a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl
+++ b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl
@@ -35,10 +35,10 @@ ENV CI_BUILD_PYTHON=python \
     PYTHON_LIB_PATH=/usr/local/lib/python2.7/dist-packages \
     CC_OPT_FLAGS='-march=native' \
     TF_NEED_JEMALLOC=0 \
-    TF_NEED_GCP=0 \
+    TF_NEED_GCP=1 \
     TF_NEED_CUDA=0 \
     TF_NEED_HDFS=0 \
-    TF_NEED_S3=0 \
+    TF_NEED_S3=1 \
     TF_NEED_OPENCL=0 \
     TF_NEED_GDR=0 \
     TF_ENABLE_XLA=0 \

From 283e8fe7e191f8e0e2ca6ea62b8b4553c30a6286 Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar 
Date: Thu, 3 May 2018 00:16:09 -0700
Subject: [PATCH 0330/1691] Use tensorflow size to determine number of elements
 instead of the static shape, which can sometimes be missing.

PiperOrigin-RevId: 195209826
---
 tensorflow/contrib/quantize/python/fold_batch_norms.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms.py b/tensorflow/contrib/quantize/python/fold_batch_norms.py
index 1f286bc39a21d4..76f695dce0d1c4 100644
--- a/tensorflow/contrib/quantize/python/fold_batch_norms.py
+++ b/tensorflow/contrib/quantize/python/fold_batch_norms.py
@@ -414,7 +414,8 @@ def _CloneWithNewOperands(layer_op, input_tensor, weight_tensor):
 def _FoldFusedBatchNormGrad(op, unused_grad_y, grad_mean, grad_var, unused_1,
                             unused_2):
   x = op.inputs[0]
-  n = x.get_shape().num_elements() / grad_mean.get_shape().num_elements()
+  n = math_ops.cast(
+      array_ops.size(x) / array_ops.size(grad_mean), dtypes.float32)
   dmean_dx = grad_mean / n
   dvar_dx = 2 * grad_var * (x - op.outputs[1]) / (n - 1)
   return (dmean_dx + dvar_dx), None, None, None, None

From 090d21c18f16303e740136e8a4e0f62c63df4e10 Mon Sep 17 00:00:00 2001
From: wangsiyu 
Date: Thu, 3 May 2018 18:31:29 +0800
Subject: [PATCH 0331/1691] fix bug of declaring regularization loss mutiple
 times when reusing partitioned variables in tf.layers

---
 tensorflow/python/layers/base.py      | 13 ++++++++++++-
 tensorflow/python/layers/base_test.py | 15 +++++++++++++++
 2 files changed, 27 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index 64db49c900c21d..c050e6be040d8d 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -233,7 +233,8 @@ def add_weight(self, name, shape, dtype=None,
             getter=vs.get_variable)
 
         if regularizer:
-          if context.executing_eagerly() or variable not in existing_variables:
+          if context.executing_eagerly() or _should_add_regularizer(
+              variable, existing_variables):
             self._handle_weight_regularization(name, variable, regularizer)
 
         if init_graph is not None:
@@ -354,3 +355,13 @@ def _add_elements_to_collection(elements, collection_list):
       if element not in collection_set:
         collection.append(element)
 
+def _should_add_regularizer(variable, existing_variable_set):
+  result = True
+  if isinstance(variable, tf_variables.PartitionedVariable):
+    for var in variable._get_variable_list():
+      if var in existing_variable_set:
+        result = False
+        break
+  else:
+    result = variable not in existing_variable_set
+  return result
diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py
index f08b552840f5ff..361e3de7aa5afc 100644
--- a/tensorflow/python/layers/base_test.py
+++ b/tensorflow/python/layers/base_test.py
@@ -30,6 +30,7 @@
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import partitioned_variables
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope
@@ -95,6 +96,20 @@ def testAddWeight(self):
           regularizer=regularizer)
       self.assertEqual(len(layer.losses), 1)
 
+  def testReusePartitionedVaraiblesAndRegularizers(self):
+    regularizer = lambda x: math_ops.reduce_sum(x) * 1e-3
+    partitioner = partitioned_variables.fixed_size_partitioner(3)
+    for i in xrange(2):
+      with variable_scope.variable_scope(variable_scope.get_variable_scope(),
+                                         partitioner=partitioner,
+                                         reuse=False if i == 0 else True):
+        layer = base_layers.Layer(name='my_layer')
+        variable = layer.add_variable(
+            'reg_part_var', [4, 4],
+            initializer=init_ops.zeros_initializer(),
+            regularizer=regularizer)
+    self.assertEqual(len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 3)
+
   def testNoEagerActivityRegularizer(self):
     with context.eager_mode():
       with self.assertRaisesRegexp(ValueError, 'activity_regularizer'):

From 3e68ec6cd2ce5c9f69c83122d854dccc8ee7ff6a Mon Sep 17 00:00:00 2001
From: Letian Feng 
Date: Thu, 3 May 2018 15:46:16 +0200
Subject: [PATCH 0332/1691] correct code snippets to python3 style (#19052)

---
 tensorflow/docs_src/programmers_guide/tensors.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/tensors.md b/tensorflow/docs_src/programmers_guide/tensors.md
index 58a80d533927e4..1248c3cabe23c8 100644
--- a/tensorflow/docs_src/programmers_guide/tensors.md
+++ b/tensorflow/docs_src/programmers_guide/tensors.md
@@ -265,7 +265,7 @@ example:
 ```python
 constant = tf.constant([1, 2, 3])
 tensor = constant * constant
-print tensor.eval()
+print(tensor.eval())
 ```
 
 The `eval` method only works when a default `tf.Session` is active (see
@@ -306,8 +306,8 @@ Note that you rarely want to use the following pattern when printing a
 
 ``` python
 t = <>
-print t  # This will print the symbolic tensor when the graph is being built.
-         # This tensor does not have a value in this context.
+print(t)  # This will print the symbolic tensor when the graph is being built.
+          # This tensor does not have a value in this context.
 ```
 
 This code prints the `tf.Tensor` object (which represents deferred computation)

From a2d35bddc7a1ab58b859ef396501472d7986ff0f Mon Sep 17 00:00:00 2001
From: gracehoney <31743510+aaroey@users.noreply.github.com>
Date: Thu, 3 May 2018 07:50:13 -0700
Subject: [PATCH 0333/1691] Fix build dependency; add missing OpKernel; fix
 some formatting issues

---
 .../tensorrt/custom_plugin_examples/BUILD     | 105 ++++++++----------
 .../tensorrt/custom_plugin_examples/inc_op.py |   5 +-
 .../inc_op_kernel.cu.cc                       |  42 +++++++
 .../custom_plugin_examples/inc_op_kernel.h    |   2 +-
 .../{inc_op_plugin.cu.cc => inc_op_plugin.cc} |  13 ++-
 .../custom_plugin_examples/inc_op_plugin.h    |  18 +--
 .../custom_plugin_examples/plugin_test.py     |  10 +-
 .../contrib/tensorrt/kernels/trt_engine_op.cc |   2 +-
 tensorflow/contrib/tensorrt/log/trt_logger.h  |   2 +-
 .../tensorrt/plugin/trt_plugin_factory.cc     |   6 +
 .../tensorrt/plugin/trt_plugin_factory.h      |  12 +-
 .../tensorrt/plugin/trt_plugins_test.cc       |  47 +++++---
 12 files changed, 162 insertions(+), 102 deletions(-)
 rename tensorflow/contrib/tensorrt/custom_plugin_examples/{inc_op_plugin.cu.cc => inc_op_plugin.cc} (90%)

diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
index 3b1a7fb6f33a1c..a45d4423bbc0f7 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
@@ -8,74 +8,39 @@ package(default_visibility = ["//tensorflow:__subpackages__"])
 
 load(
     "//tensorflow:tensorflow.bzl",
+    "tf_copts",
     "tf_custom_op_library",
-    "tf_cuda_library",
+    "tf_custom_op_library_additional_deps",
     "tf_gen_op_libs",
     "tf_gen_op_wrapper_py",
-    "tf_py_wrap_cc",
-    "tf_copts",
-    "tf_py_test",
+    "tf_kernel_library",
 )
+load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library")
+load("//tensorflow:tensorflow.bzl", "tf_py_test")
+load("//tensorflow:tensorflow.bzl", "tf_py_wrap_cc")
 load(
     "@local_config_tensorrt//:build_defs.bzl",
     "if_tensorrt",
 )
-load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library")
-load("//tensorflow:tensorflow.bzl", "tf_kernel_library")
-
-tf_kernel_library(
-    name = "_inc_op_plugin_kernel",
-    gpu_srcs = [
-        "inc_op_kernel.cu.cc",
-        "inc_op_kernel.h",
-        "inc_op_plugin.cu.cc",
-        "inc_op_plugin.h",
-    ],
-    deps = [
-        "//tensorflow/contrib/tensorrt:trt_plugins",
-    ] + if_tensorrt([
-        "@local_config_tensorrt//:nv_infer",
-    ]),
-)
 
 tf_gen_op_libs(
     op_lib_names = [
         "inc_op",
     ],
-    deps = [
-        "//tensorflow/contrib/tensorrt:trt_plugins",
-    ] + if_tensorrt([
-        "@local_config_tensorrt//:nv_infer",
-    ]),
 )
 
 tf_gen_op_wrapper_py(
     name = "inc_op",
-    gen_locally = True,
     deps = [
         ":inc_op_op_lib",
     ],
 )
 
-tf_py_wrap_cc(
-    name = "plugin_wrap",
-    srcs = [
-        "plugin_wrap.i",
-    ],
-    copts = tf_copts(),
-    deps = [
-        ":_inc_op_plugin_kernel",
-        "//tensorflow/core:framework_lite",
-        "//util/python:python_headers",
-    ],
-)
-
 tf_custom_op_library(
     name = "_inc_op.so",
     srcs = ["ops/inc_op.cc"],
     deps = [
         "//tensorflow/core:lib_proto_parsing",
-        "//tensorflow/contrib/tensorrt:trt_plugins",
     ],
 )
 
@@ -85,6 +50,10 @@ tf_custom_op_py_library(
     dso = [
         ":_inc_op.so",
     ],
+    kernels = [
+        ":inc_op_op_lib",
+        ":inc_op_plugin_kernel",
+    ],
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:framework_for_generated_wrappers",
@@ -101,30 +70,54 @@ py_library(
     ],
 )
 
-tf_py_test(
-    name = "plugin_test",
-    size = "small",
-    srcs = [
-        "plugin_test.py",
+tf_kernel_library(
+    name = "inc_op_plugin_kernel",
+    srcs = ["inc_op_plugin.cc"],
+    hdrs = [
+        "inc_op_plugin.h",
     ],
-    additional_deps = [
-        ":init_py",
-        "//tensorflow/contrib/util:util_py",
-        "//tensorflow/contrib/tensorrt:init_py",
-        "//tensorflow/python:platform",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:tf_optimizer",
+    gpu_srcs = [
+        "inc_op_kernel.h",
+        "inc_op_kernel.cu.cc"
+    ],
+    deps = [
+        "//tensorflow/contrib/tensorrt:trt_plugins",
+    ] + if_tensorrt([
+        "@local_config_tensorrt//:nv_infer",
+    ]) + tf_custom_op_library_additional_deps(),
+)
+
+tf_py_wrap_cc(
+    name = "plugin_wrap",
+    srcs = ["plugin_wrap.i"],
+    copts = tf_copts(),
+    deps = [
+        ":inc_op_plugin_kernel",
+        "//tensorflow/core:framework_lite",
+        "//util/python:python_headers",
     ],
 )
 
 py_library(
     name = "init_py",
-    srcs = [
-        "__init__.py",
-    ],
+    srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":inc_op_py",
         ":plugin_wrap",
     ],
 )
+
+tf_py_test(
+    name = "plugin_test",
+    size = "small",
+    srcs = ["plugin_test.py"],
+    additional_deps = [
+        ":init_py",
+        "//tensorflow/contrib/util:util_py",
+        "//tensorflow/contrib/tensorrt:init_py",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:tf_optimizer",
+    ],
+)
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op.py
index ef8e26fbdedfa7..47fd55e2f6753a 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op.py
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op.py
@@ -18,13 +18,14 @@
 from __future__ import print_function
 
 import platform
-import os
 
 if platform.system() != "Windows":
+  # pylint: disable=g-import-not-at-top
   from tensorflow.contrib.util import loader
   from tensorflow.python.platform import resource_loader
+  # pylint: enable=g-import-not-at-top
 
   _inc_op = loader.load_op_library(
-      os.path.join(os.path.dirname(os.path.realpath(__file__)),"_inc_op.so"))
+      resource_loader.get_path_to_datafile("_inc_op.so"))
 else:
   raise RuntimeError("Windows not supported")
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc
index 38e1e01d9546e3..ee9fbe0ea119fb 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc
@@ -15,8 +15,14 @@ limitations under the License.
 
 #include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h"
 
+#include 
+
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/platform/stream_executor.h"
+
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
+#include "cuda/include/cuda_runtime_api.h"
 
 namespace tensorflow {
 namespace tensorrt {
@@ -35,6 +41,42 @@ void IncrementKernel(const float* d_input, float inc, float* d_output,
                                                             d_output, count);
 }
 
+// Note: this kernel definition is not needed in the plugin_test rule, but it is
+// required for correctness of the TF program, i.e. if not using plugin or when
+// run with trt optimization pass, the test should work.
+class IncPluginTRT : public OpKernel {
+ public:
+  explicit IncPluginTRT(OpKernelConstruction* context) : OpKernel(context) {
+    std::vector inc_list;
+    OP_REQUIRES_OK(context, context->GetAttr("inc", &inc_list));
+    OP_REQUIRES(context, inc_list.size() == 1,
+                errors::InvalidArgument(
+                    "The increment list should contain single element."));
+    inc_ = inc_list[0];
+  }
+
+  void Compute(OpKernelContext* context) override {
+    const Tensor& input_tensor = context->input(0);
+    const TensorShape& input_shape = input_tensor.shape();
+    Tensor* output_tensor = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, input_shape, &output_tensor));
+    const cudaStream_t* stream = CHECK_NOTNULL(
+        reinterpret_cast(context->op_device_context()
+                                                  ->stream()
+                                                  ->implementation()
+                                                  ->CudaStreamMemberHack()));
+    IncrementKernel(input_tensor.flat().data(), inc_,
+                    output_tensor->flat().data(),
+                    input_shape.num_elements(), *stream);
+  }
+
+ private:
+  float inc_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("IncPluginTRT").Device(DEVICE_GPU), IncPluginTRT);
+
 }  // namespace tensorrt
 }  // namespace tensorflow
 
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h
index 13156dad8fd574..1d0ec0b6b083ad 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h
@@ -18,11 +18,11 @@ limitations under the License.
 
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
+#include "cuda/include/cuda_runtime_api.h"
 
 namespace tensorflow {
 namespace tensorrt {
 
-__global__ void VecInc(float* vec, float inc, float* dest, int n);
 void IncrementKernel(const float* d_input, float inc, float* d_output,
                      int count, cudaStream_t stream);
 
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cu.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc
similarity index 90%
rename from tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cu.cc
rename to tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc
index 508ced587bd566..489bc15def5156 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cu.cc
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc
@@ -13,9 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h"
-#include 
 #include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h"
+#include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h"
 #include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h"
 
 #if GOOGLE_CUDA
@@ -24,7 +23,7 @@ limitations under the License.
 namespace tensorflow {
 namespace tensorrt {
 
-const string IncOpPlugin::plugin_name_ = "IncPluginTRT";
+const char* kPluginName = "IncPluginTRT";
 
 IncOpPlugin* CreateIncPlugin() { return new IncOpPlugin(); }
 
@@ -33,14 +32,16 @@ IncOpPlugin* CreateIncPluginDeserialize(const void* buffer, size_t length) {
 }
 
 bool RegisterIncOpPlugin() {
-  if (PluginFactoryTensorRT::GetInstance()->IsPlugin(IncOpPlugin::plugin_name_))
+  if (PluginFactoryTensorRT::GetInstance()->IsPlugin(kPluginName))
     return false;
   return PluginFactoryTensorRT::GetInstance()->RegisterPlugin(
-      IncOpPlugin::plugin_name_, CreateIncPluginDeserialize, CreateIncPlugin);
+      kPluginName, CreateIncPluginDeserialize, CreateIncPlugin);
 }
 
+IncOpPlugin::IncOpPlugin() : plugin_name_(kPluginName) {}
+
 IncOpPlugin::IncOpPlugin(const void* serialized_data, size_t length)
-    : PluginTensorRT(serialized_data, length) {
+    : PluginTensorRT(serialized_data, length), plugin_name_(kPluginName) {
   // account for the consumed pointer.
   size_t consumed_data = PluginTensorRT::getSerializationSize();
   assert(length - consumed_data >= sizeof(float));
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h
index 87404a755c24de..0676abe7687a49 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h
@@ -29,13 +29,17 @@ namespace tensorrt {
 
 class IncOpPlugin : public PluginTensorRT {
  public:
-  static const string plugin_name_;
-  IncOpPlugin() {};
+  IncOpPlugin();
+
   IncOpPlugin(const void* serialized_data, size_t length);
+
   const string& GetPluginName() const override { return plugin_name_; };
+
   bool Finalize() override { return true; };
+
   bool SetAttribute(const string& key, const void* ptr,
                     const size_t size) override;
+
   bool GetAttribute(const string& key, const void** ptr,
                     size_t* size) const override;
 
@@ -71,14 +75,11 @@ class IncOpPlugin : public PluginTensorRT {
   }
 
   void serialize(void* buffer) override {
-    // serializa parent stuff
-    //   OpName
+    // Serialize parent data.
     PluginTensorRT::serialize(buffer);
-
-    // incremented buffer after parent serialization;
+    // Incremented buffer after parent serialization.
     buffer =
         static_cast(buffer) + PluginTensorRT::getSerializationSize();
-
     std::memcpy(buffer, &inc_, sizeof(float));
     buffer = static_cast(buffer) + sizeof(float);
   }
@@ -86,6 +87,9 @@ class IncOpPlugin : public PluginTensorRT {
  protected:
   float inc_;
   nvinfer1::Dims dim_;
+
+ private:
+  const string plugin_name_;
 };
 
 IncOpPlugin* CreateIncPlugin();
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py
index 9f773c66a99075..d1815fdf33acb3 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py
@@ -39,6 +39,7 @@
 # the python api handles registration to the plugin factory
 from tensorflow.contrib.tensorrt import custom_plugin_examples
 
+
 def get_plugin_graph_def():
   """Create a simple graph and return its graph_def."""
   g = ops.Graph()
@@ -49,15 +50,16 @@ def get_plugin_graph_def():
     v = nn_ops.max_pool(
         relu, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool")
 
-    # insert custom_op in the graph 
+    # insert custom_op in the graph
     v = custom_plugin_examples.inc_op(v, inc=[16.5], name="plugin_test")
 
-    v = v*2.0
+    v = v * 2.0
     v = nn.relu(v)
     v = nn.relu(v)
     array_ops.squeeze(v, name="output")
   return g.as_graph_def()
 
+
 def run_graph(gdef, dumm_inp):
   """Run given graphdef once."""
   gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50)
@@ -74,6 +76,7 @@ def run_graph(gdef, dumm_inp):
     val = sess.run(out, {inp: dumm_inp})
   return val
 
+
 if "__main__" in __name__:
   inp_dims = (5, 24, 24, 2)
   dummy_input = numpy.ones(inp_dims).astype(numpy.float32)
@@ -88,8 +91,7 @@ def run_graph(gdef, dumm_inp):
       max_batch_size=inp_dims[0],
       max_workspace_size_bytes=1 << 25,
       precision_mode="FP32",
-      minimum_segment_size=2
-  )
+      minimum_segment_size=2)
   o2 = run_graph(trt_graph, dummy_input)
   if o2.reshape([-1])[0] == 35:
     print("pass")
diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc
index c39bc12f73d74f..71453631e2a480 100644
--- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc
+++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc
@@ -13,9 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 #include "tensorflow/contrib/tensorrt/kernels/trt_engine_op.h"
-#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h"
 
 #include "tensorflow/contrib/tensorrt/log/trt_logger.h"
+#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/stream_executor.h"
 #include "tensorflow/core/platform/types.h"
diff --git a/tensorflow/contrib/tensorrt/log/trt_logger.h b/tensorflow/contrib/tensorrt/log/trt_logger.h
index 3495dc63185027..96ccacb791e401 100644
--- a/tensorflow/contrib/tensorrt/log/trt_logger.h
+++ b/tensorflow/contrib/tensorrt/log/trt_logger.h
@@ -28,7 +28,7 @@ namespace tensorrt {
 // Logger for GIE info/warning/errors
 class Logger : public nvinfer1::ILogger {
  public:
-  Logger(string name = "DefaultLogger") : name_(name) {};
+  Logger(string name = "DefaultLogger") : name_(name) {}
   void log(nvinfer1::ILogger::Severity severity, const char* msg) override;
 
  private:
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc
index 736a1321fe7215..b608e602a7b37f 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc
@@ -21,6 +21,12 @@ limitations under the License.
 namespace tensorflow {
 namespace tensorrt {
 
+PluginFactoryTensorRT* PluginFactoryTensorRT::GetInstance() {
+  static PluginFactoryTensorRT* factory_instance =
+      new PluginFactoryTensorRT();
+  return factory_instance;
+}
+
 PluginTensorRT* PluginFactoryTensorRT::createPlugin(const char* layer_name,
                                                     const void* serial_data,
                                                     size_t serial_length) {
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
index 4e4a3af4cab5f7..a088ffb8425470 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
@@ -31,19 +31,15 @@ namespace tensorrt {
 
 class PluginFactoryTensorRT : public nvinfer1::IPluginFactory {
  public:
-  // deserialization method
+  static PluginFactoryTensorRT* GetInstance();
+
+  // Deserialization method
   PluginTensorRT* createPlugin(const char* layer_name, const void* serial_data,
                                size_t serial_length) override;
 
-  // plugin construction, PluginFactoryTensorRT owns the plugin;
+  // Plugin construction, PluginFactoryTensorRT owns the plugin.
   PluginTensorRT* CreatePlugin(const string& op_name);
 
-  static PluginFactoryTensorRT* GetInstance() {
-    static PluginFactoryTensorRT* factory_instance =
-        new PluginFactoryTensorRT();
-    return factory_instance;
-  }
-
   bool RegisterPlugin(const string& op_name,
                       PluginDeserializeFunc deserialize_func,
                       PluginConstructFunc construct_func);
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc
index b834c5511f9956..ae5a3e874212ae 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc
@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h"
+#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/test.h"
@@ -20,8 +22,6 @@ limitations under the License.
 
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
-#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h"
-#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h"
 #include "tensorrt/include/NvInfer.h"
 
 namespace tensorflow {
@@ -30,34 +30,49 @@ namespace test {
 
 class StubPlugin : public PluginTensorRT {
  public:
-  static const string plugin_name_;
-  StubPlugin() {};
+  static const char* kPluginName;
+
+  StubPlugin() : plugin_name_(kPluginName) {}
+
   StubPlugin(const void* serialized_data, size_t length)
-      : PluginTensorRT(serialized_data, length) {};
-  const string& GetPluginName() override { return plugin_name_; };
-  virtual bool Finalize() { return true; };
+      : PluginTensorRT(serialized_data, length) {}
+
+  const string& GetPluginName() override { return plugin_name_; }
+
+  virtual bool Finalize() { return true; }
+
   virtual bool SetAttribute(const string& key, const void* ptr,
                             const size_t size) {
     return true;
-  };
+  }
+
   virtual bool GetAttribute(const string& key, const void* ptr, size_t& size) {
     return true;
-  };
+  }
+
   int getNbOutputs() const override { return 1; }
+
   nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs,
                                      int nbInputDims) override {
     return inputs[0];
   }
+
   int initialize() override { return 0; }
+
   void terminate() override {}
+
   size_t getWorkspaceSize(int maxBatchSize) const override { return 0; }
+
   int enqueue(int batch_size, const void* const* inputs, void** outputs,
               void* workspace, cudaStream_t stream) override {
     return 0;
   }
+
+ private:
+  const string plugin_name_;
 };
 
-const string StubPlugin::plugin_name_ = "StubPlugin";
+const char* StubPlugin::kPluginName = "StubPlugin";
 
 StubPlugin* CreateStubPlugin() { return new StubPlugin(); }
 
@@ -70,32 +85,32 @@ class PluginTest : public ::testing::Test {
  public:
   bool RegisterStubPlugin() {
     if (PluginFactoryTensorRT::GetInstance()->IsPlugin(
-            StubPlugin::plugin_name_)) {
+            StubPlugin::kPluginName)) {
       return true;
     }
     return PluginFactoryTensorRT::GetInstance()->RegisterPlugin(
-        StubPlugin::plugin_name_, CreateStubPluginDeserialize,
+        StubPlugin::kPluginName, CreateStubPluginDeserialize,
         CreateStubPlugin);
   }
 };
 
 TEST_F(PluginTest, Registration) {
   EXPECT_FALSE(
-      PluginFactoryTensorRT::GetInstance()->IsPlugin(StubPlugin::plugin_name_));
+      PluginFactoryTensorRT::GetInstance()->IsPlugin(StubPlugin::kPluginName));
   EXPECT_TRUE(RegisterStubPlugin());
 
   ASSERT_TRUE(
-      PluginFactoryTensorRT::GetInstance()->IsPlugin(StubPlugin::plugin_name_));
+      PluginFactoryTensorRT::GetInstance()->IsPlugin(StubPlugin::kPluginName));
 }
 
 TEST_F(PluginTest, CreationDeletion) {
   EXPECT_TRUE(RegisterStubPlugin());
   ASSERT_TRUE(
-      PluginFactoryTensorRT::GetInstance()->IsPlugin(StubPlugin::plugin_name_));
+      PluginFactoryTensorRT::GetInstance()->IsPlugin(StubPlugin::kPluginName));
 
   PluginFactoryTensorRT::GetInstance()->DestroyPlugins();
   ASSERT_TRUE(PluginFactoryTensorRT::GetInstance()->CreatePlugin(
-      StubPlugin::plugin_name_));
+      StubPlugin::kPluginName));
   ASSERT_EQ(1, PluginFactoryTensorRT::GetInstance()->CountOwnedPlugins());
   PluginFactoryTensorRT::GetInstance()->DestroyPlugins();
   ASSERT_EQ(0, PluginFactoryTensorRT::GetInstance()->CountOwnedPlugins());

From 03de4a4a6cbfab49c2921d0cac5ccac31c0815f8 Mon Sep 17 00:00:00 2001
From: gracehoney <31743510+aaroey@users.noreply.github.com>
Date: Thu, 3 May 2018 08:20:51 -0700
Subject: [PATCH 0334/1691] Move/rename the plugin factory test file; delete
 duplicate test file; fix minor formatting issues.

---
 tensorflow/contrib/tensorrt/BUILD             | 10 ++-
 .../tensorrt/custom_plugin_examples/BUILD     |  6 +-
 .../custom_plugin_examples/plugin_test.py     |  5 --
 .../contrib/tensorrt/plugin/trt_plugin.h      |  6 +-
 .../tensorrt/plugin/trt_plugin_factory.h      |  9 +-
 ...ins_test.cc => trt_plugin_factory_test.cc} |  6 +-
 .../tensorrt/plugin/trt_plugin_utils.h        |  1 +
 tensorflow/contrib/tensorrt/plugin_test.py    | 88 -------------------
 8 files changed, 26 insertions(+), 105 deletions(-)
 rename tensorflow/contrib/tensorrt/plugin/{trt_plugins_test.cc => trt_plugin_factory_test.cc} (96%)
 delete mode 100644 tensorflow/contrib/tensorrt/plugin_test.py

diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD
index 5fda11eccb2300..79e525edae8722 100644
--- a/tensorflow/contrib/tensorrt/BUILD
+++ b/tensorflow/contrib/tensorrt/BUILD
@@ -282,7 +282,7 @@ tf_cc_test(
     ],
 )
 
-# Library for the plugin factory 
+# Library for the plugin factory
 tf_cuda_library(
     name = "trt_plugins",
     srcs = [
@@ -304,9 +304,13 @@ tf_cuda_library(
 )
 
 tf_cuda_cc_test(
-    name = "trt_plugins_test",
+    name = "trt_plugin_factory_test",
     size = "small",
-    srcs = ["plugin/trt_plugins_test.cc"],
+    srcs = ["plugin/trt_plugin_factory_test.cc"],
+    tags = [
+        "manual",
+        "notap",
+    ],
     deps = [
         ":trt_plugins",
         "//tensorflow/core:test",
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
index a45d4423bbc0f7..c68e69457da27a 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
@@ -78,7 +78,7 @@ tf_kernel_library(
     ],
     gpu_srcs = [
         "inc_op_kernel.h",
-        "inc_op_kernel.cu.cc"
+        "inc_op_kernel.cu.cc",
     ],
     deps = [
         "//tensorflow/contrib/tensorrt:trt_plugins",
@@ -120,4 +120,8 @@ tf_py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:tf_optimizer",
     ],
+    tags = [
+        "manual",
+        "notap",
+    ],
 )
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py
index d1815fdf33acb3..cb40e084935367 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py
@@ -18,11 +18,6 @@
 from __future__ import division
 from __future__ import print_function
 
-# normally we should do import tensorflow as tf and then
-# tf.placeholder, tf.constant, tf.nn.conv2d etc but
-# it looks like internal builds don't like it so
-# importing every module individually
-
 from tensorflow.contrib import tensorrt
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.client import session
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin.h
index dca377c2d2b836..d80ec44372af54 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin.h
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin.h
@@ -19,6 +19,7 @@ limitations under the License.
 #include 
 #include 
 #include 
+
 #include "tensorflow/core/platform/types.h"
 
 #if GOOGLE_CUDA
@@ -35,9 +36,11 @@ namespace tensorrt {
 // PluginDeserializeFunc & PluginConstructFunc through PluginFactoryTensorRT
 class PluginTensorRT : public nvinfer1::IPlugin {
  public:
-  PluginTensorRT() {};
+  PluginTensorRT() {}
   PluginTensorRT(const void* serialized_data, size_t length);
+
   virtual const string& GetPluginName() const = 0;
+
   virtual bool Finalize() = 0;
 
   virtual bool SetAttribute(const string& key, const void* ptr,
@@ -53,6 +56,7 @@ class PluginTensorRT : public nvinfer1::IPlugin {
                               const size_t size);
 
   virtual size_t getSerializationSize() override;
+
   virtual void serialize(void* buffer) override;
 
  protected:
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
index a088ffb8425470..6d2992bbbbc14d 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
@@ -19,8 +19,9 @@ limitations under the License.
 #include 
 #include 
 #include 
-#include "trt_plugin.h"
-#include "trt_plugin_utils.h"
+
+#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h"
+#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h"
 
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
@@ -54,12 +55,12 @@ class PluginFactoryTensorRT : public nvinfer1::IPluginFactory {
 
  protected:
   std::unordered_map >
+                     std::pair>
       plugin_registry_;
 
   // TODO(jie): Owned plugin should be associated with different sessions;
   //            should really hand ownership of plugins to resource management;
-  std::vector > owned_plugins_;
+  std::vector> owned_plugins_;
   std::mutex instance_m_;
 };
 
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory_test.cc
similarity index 96%
rename from tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc
rename to tensorflow/contrib/tensorrt/plugin/trt_plugin_factory_test.cc
index ae5a3e874212ae..c5b0e75eb1d146 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugins_test.cc
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory_test.cc
@@ -81,7 +81,7 @@ StubPlugin* CreateStubPluginDeserialize(const void* serialized_data,
   return new StubPlugin(serialized_data, length);
 }
 
-class PluginTest : public ::testing::Test {
+class TrtPluginFactoryTest : public ::testing::Test {
  public:
   bool RegisterStubPlugin() {
     if (PluginFactoryTensorRT::GetInstance()->IsPlugin(
@@ -94,7 +94,7 @@ class PluginTest : public ::testing::Test {
   }
 };
 
-TEST_F(PluginTest, Registration) {
+TEST_F(TrtPluginFactoryTest, Registration) {
   EXPECT_FALSE(
       PluginFactoryTensorRT::GetInstance()->IsPlugin(StubPlugin::kPluginName));
   EXPECT_TRUE(RegisterStubPlugin());
@@ -103,7 +103,7 @@ TEST_F(PluginTest, Registration) {
       PluginFactoryTensorRT::GetInstance()->IsPlugin(StubPlugin::kPluginName));
 }
 
-TEST_F(PluginTest, CreationDeletion) {
+TEST_F(TrtPluginFactoryTest, CreationDeletion) {
   EXPECT_TRUE(RegisterStubPlugin());
   ASSERT_TRUE(
       PluginFactoryTensorRT::GetInstance()->IsPlugin(StubPlugin::kPluginName));
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h
index a94c67bba025a1..4ff6fbedb4e6e8 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h
@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_UTILS
 
 #include 
+
 #include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h"
 #include "tensorflow/core/platform/types.h"
 
diff --git a/tensorflow/contrib/tensorrt/plugin_test.py b/tensorflow/contrib/tensorrt/plugin_test.py
deleted file mode 100644
index 7c3e765bff4a40..00000000000000
--- a/tensorflow/contrib/tensorrt/plugin_test.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Script to show usage of TensorRT custom op & plugin."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib import tensorrt
-from tensorflow.core.protobuf import config_pb2
-from tensorflow.python.client import session
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import importer
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import nn
-from tensorflow.python.ops import nn_ops
-import numpy as np
-
-# import custom_op as plugin op
-# the python api handles registration to the plugin factory
-from tensorflow.contrib.tensorrt import custom_plugin_examples
-
-def get_plugin_graph_def():
-  """Create a simple graph and return its graph_def."""
-  g = ops.Graph()
-  with g.as_default():
-    a = array_ops.placeholder(
-        dtype=dtypes.float32, shape=(None, 24, 24, 2), name="input")
-    relu = nn.relu(a, "relu")
-    v = nn_ops.max_pool(
-        relu, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool")
-
-    # insert custom_op in the graph 
-    v = custom_plugin_examples.inc_op(v, inc=[16.5], name="plugin_test")
-
-    v = v*2.0
-    v = nn.relu(v)
-    v = nn.relu(v)
-    array_ops.squeeze(v, name="output")
-  return g.as_graph_def()
-
-def run_graph(gdef, dumm_inp):
-  """Run given graphdef once."""
-  gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50)
-  ops.reset_default_graph()
-  g = ops.Graph()
-  with g.as_default():
-    inp, out = importer.import_graph_def(
-        graph_def=gdef, return_elements=["input", "output"])
-    inp = inp.outputs[0]
-    out = out.outputs[0]
-
-  with session.Session(
-      config=config_pb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess:
-    val = sess.run(out, {inp: dumm_inp})
-  return val
-
-if "__main__" in __name__:
-  inp_dims = (5, 24, 24, 2)
-  dummy_input = np.ones(inp_dims).astype(np.float32)
-  orig_graph = get_plugin_graph_def()  # graph with plugin node
-
-  # trigger conversion.
-  # plugin nodes have been registered during import, converter will be able to
-  # create corresponding plugin layer during conversion.
-  trt_graph = tensorrt.create_inference_graph(
-      input_graph_def=orig_graph,
-      outputs=["output"],
-      max_batch_size=inp_dims[0],
-      max_workspace_size_bytes=1 << 25,
-      precision_mode="FP32",
-      minimum_segment_size=2
-  )
-  o2 = run_graph(trt_graph, dummy_input)
-  print (o2)

From 4984a60e7147edef532ca1b15050471e81e45841 Mon Sep 17 00:00:00 2001
From: ctiijima 
Date: Thu, 3 May 2018 08:34:07 -0700
Subject: [PATCH 0335/1691] Grammar fixes on architecture.md (#19035)

---
 tensorflow/docs_src/extend/architecture.md | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/docs_src/extend/architecture.md b/tensorflow/docs_src/extend/architecture.md
index c0fc714a4405d6..c8f522a03ab0c1 100644
--- a/tensorflow/docs_src/extend/architecture.md
+++ b/tensorflow/docs_src/extend/architecture.md
@@ -4,8 +4,8 @@ We designed TensorFlow for large-scale distributed training and inference, but
 it is also flexible enough to support experimentation with new machine
 learning models and system-level optimizations.
 
-This document describes the system architecture that makes possible this
-combination of scale and flexibility. It assumes that you have basic familiarity
+This document describes the system architecture that makes this
+combination of scale and flexibility possible. It assumes that you have basic familiarity
 with TensorFlow programming concepts such as the computation graph, operations,
 and sessions. See @{$programmers_guide/low_level_intro$this document}
 for an introduction to these topics. Some familiarity
@@ -15,8 +15,8 @@ will also be helpful.
 This document is for developers who want to extend TensorFlow in some way not
 supported by current APIs, hardware engineers who want to optimize for
 TensorFlow, implementers of machine learning systems working on scaling and
-distribution, or anyone who wants to look under Tensorflow's hood. After
-reading it you should understand TensorFlow architecture well enough to read
+distribution, or anyone who wants to look under Tensorflow's hood. By the end of this document 
+you should understand the TensorFlow architecture well enough to read
 and modify the core TensorFlow code.
 
 ## Overview
@@ -35,7 +35,7 @@ This document focuses on the following layers:
 *  **Client**:
    *  Defines the computation as a dataflow graph.
    *  Initiates graph execution using a [**session**](
-      https://www.tensorflow.org/code/tensorflow/python/client/session.py)
+      https://www.tensorflow.org/code/tensorflow/python/client/session.py).
 *  **Distributed Master**
    *  Prunes a specific subgraph from the graph, as defined by the arguments
       to Session.run().
@@ -55,7 +55,7 @@ Figure 2 illustrates the interaction of these components. "/job:worker/task:0" a
 server": a task responsible for storing and updating the model's parameters.
 Other tasks send updates to these parameters as they work on optimizing the
 parameters. This particular division of labor between tasks is not required, but
-it is common for distributed training.
+ is common for distributed training.
 
 ![TensorFlow Architecture Diagram](https://www.tensorflow.org/images/diag1.svg){: width="500"}
 
@@ -193,7 +193,7 @@ https://www.tensorflow.org/code/tensorflow/contrib/nccl/python/ops/nccl_ops.py))
 
 ## Kernel Implementations
 
-The runtime contains over 200 standard operations, including mathematical, array
+The runtime contains over 200 standard operations including mathematical, array
 manipulation, control flow, and state management operations. Each of these
 operations can have kernel implementations optimized for a variety of devices.
 Many of the operation kernels are implemented using Eigen::Tensor, which uses

From a88a7e312581ba0c2173188019a420c888df9a10 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 09:10:06 -0700
Subject: [PATCH 0336/1691] Post-transform pass to dedupe large constant
 arrays.

PiperOrigin-RevId: 195260578
---
 tensorflow/contrib/lite/toco/args.h           |   1 +
 .../contrib/lite/toco/toco_cmdline_flags.cc   |   6 +
 tensorflow/contrib/lite/toco/toco_flags.proto |   6 +-
 tensorflow/contrib/lite/toco/toco_tooling.cc  |   5 +
 tensorflow/contrib/lite/toco/tooling_util.cc  | 130 ++++++++++++++++++
 tensorflow/contrib/lite/toco/tooling_util.h   |  16 +++
 6 files changed, 163 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/lite/toco/args.h b/tensorflow/contrib/lite/toco/args.h
index fe30b88344c534..6c0311af0a9267 100644
--- a/tensorflow/contrib/lite/toco/args.h
+++ b/tensorflow/contrib/lite/toco/args.h
@@ -241,6 +241,7 @@ struct ParsedTocoFlags {
   Arg drop_control_dependency = Arg(false);
   Arg propagate_fake_quant_num_bits = Arg(false);
   Arg allow_nudging_weights_to_use_fast_gemm_kernel = Arg(false);
+  Arg dedupe_array_min_size_bytes = Arg(64);
 };
 
 }  // namespace toco
diff --git a/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc b/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc
index 1611c4d0c0b148..7786a4ada335ab 100644
--- a/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc
+++ b/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc
@@ -148,6 +148,11 @@ bool ParseTocoFlagsFromCommandLineFlags(
            "Some fast uint8 GEMM kernels require uint8 weights to avoid the "
            "value 0. This flag allows nudging them to 1 to allow proceeding, "
            "with moderate inaccuracy."),
+      Flag("dedupe_array_min_size_bytes",
+           parsed_flags.dedupe_array_min_size_bytes.bind(),
+           parsed_flags.dedupe_array_min_size_bytes.default_value(),
+           "Minimum size of constant arrays to deduplicate; arrays smaller "
+           "will not be deduplicated."),
   };
   bool asked_for_help =
       *argc == 2 && (!strcmp(argv[1], "--help") || !strcmp(argv[1], "-help"));
@@ -239,6 +244,7 @@ void ReadTocoFlagsFromCommandLineFlags(const ParsedTocoFlags& parsed_toco_flags,
   READ_TOCO_FLAG(propagate_fake_quant_num_bits, FlagRequirement::kNone);
   READ_TOCO_FLAG(allow_nudging_weights_to_use_fast_gemm_kernel,
                  FlagRequirement::kNone);
+  READ_TOCO_FLAG(dedupe_array_min_size_bytes, FlagRequirement::kNone);
 
   // Deprecated flag handling.
   if (parsed_toco_flags.input_type.specified()) {
diff --git a/tensorflow/contrib/lite/toco/toco_flags.proto b/tensorflow/contrib/lite/toco/toco_flags.proto
index a04017a6bf05fb..253f022e6b3ade 100644
--- a/tensorflow/contrib/lite/toco/toco_flags.proto
+++ b/tensorflow/contrib/lite/toco/toco_flags.proto
@@ -37,7 +37,7 @@ enum FileFormat {
 // of as properties of models, instead describing how models are to be
 // processed in the context of the present tooling job.
 //
-// Next ID to use: 18.
+// Next ID to use: 19.
 message TocoFlags {
   // Input file format
   optional FileFormat input_format = 1;
@@ -161,4 +161,8 @@ message TocoFlags {
   // This flag allows nudging them to 1 to allow proceeding, with moderate
   // inaccuracy.
   optional bool allow_nudging_weights_to_use_fast_gemm_kernel = 17;
+
+  // Minimum size of constant arrays to deduplicate; arrays smaller will not be
+  // deduplicated.
+  optional int64 dedupe_array_min_size_bytes = 18 [default = 64];
 }
diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc
index 7252ec2ea4d886..6973b22c5a817b 100644
--- a/tensorflow/contrib/lite/toco/toco_tooling.cc
+++ b/tensorflow/contrib/lite/toco/toco_tooling.cc
@@ -345,6 +345,11 @@ void Transform(const TocoFlags& toco_flags, Model* model) {
     EncodeConstantArraysMinMaxByWrappingThemInFakeQuantNodes(model);
   }
 
+  // Deduplicate large constant arrays.
+  if (toco_flags.has_dedupe_array_min_size_bytes()) {
+    DedupeConstantArrays(model, toco_flags.dedupe_array_min_size_bytes());
+  }
+
   LogDump(kLogLevelModelChanged, "AFTER TRANSFORMATIONS", *model);
 
   if (output_format != GRAPHVIZ_DOT && output_format != TFLITE) {
diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index 11293a5fe508ec..86ee1f3761330d 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -260,6 +260,23 @@ Operator* GetFirstOpWithInput(const Model& model, const string& array_name) {
   return it == model.operators.end() ? nullptr : it->get();
 }
 
+void ReplaceArrayUsage(Model* model, const string& old_array_name,
+                       const string& new_array_name) {
+  for (auto& op_it : model->operators) {
+    Operator* op = op_it.get();
+    for (size_t i = 0; i < op->inputs.size(); ++i) {
+      if (op->inputs[i] == old_array_name) {
+        op->inputs[i] = new_array_name;
+      }
+    }
+    for (size_t i = 0; i < op->outputs.size(); ++i) {
+      if (op->outputs[i] == old_array_name) {
+        op->outputs[i] = new_array_name;
+      }
+    }
+  }
+}
+
 string FormatArraysList(const Model& model, const std::vector& list) {
   if (list.empty()) {
     return "[]";
@@ -648,6 +665,65 @@ bool IsConstantParameterArray(const Model& model, const string& name) {
   return !!model.GetArray(name).buffer;
 }
 
+namespace {
+template 
+bool CompareArrayBuffers(const Array& lhs_array, const Array& rhs_array) {
+  CHECK(lhs_array.data_type == rhs_array.data_type) << "Data types must match";
+  CHECK(lhs_array.buffer) << "LHS must be constant";
+  CHECK(rhs_array.buffer) << "RHS must be constant";
+  const auto& lhs_data = lhs_array.GetBuffer().data;
+  const auto& rhs_data = rhs_array.GetBuffer().data;
+  CHECK_EQ(lhs_data.size(), rhs_data.size())
+      << "Buffer sizes must match in element count";
+  for (int i = 0; i < lhs_data.size(); ++i) {
+    if (lhs_data[i] != rhs_data[i]) {
+      return false;
+    }
+  }
+  return true;
+}
+}  // namespace
+
+bool CompareConstantArrays(const Array& lhs_array, const Array& rhs_array) {
+  bool attrs_equal =
+      lhs_array.shape() == rhs_array.shape() &&
+      lhs_array.data_type == rhs_array.data_type &&
+      lhs_array.final_data_type == rhs_array.final_data_type &&
+      lhs_array.minmax == rhs_array.minmax &&
+      lhs_array.quantization_params == rhs_array.quantization_params;
+  if (!attrs_equal) {
+    return false;
+  }
+  switch (lhs_array.data_type) {
+    case ArrayDataType::kBool:
+      return CompareArrayBuffers(lhs_array, rhs_array);
+    case ArrayDataType::kFloat:
+      return CompareArrayBuffers(lhs_array, rhs_array);
+    case ArrayDataType::kInt8:
+      return CompareArrayBuffers(lhs_array, rhs_array);
+    case ArrayDataType::kUint8:
+      return CompareArrayBuffers(lhs_array, rhs_array);
+    case ArrayDataType::kInt16:
+      return CompareArrayBuffers(lhs_array, rhs_array);
+    case ArrayDataType::kUint16:
+      return CompareArrayBuffers(lhs_array, rhs_array);
+    case ArrayDataType::kInt32:
+      return CompareArrayBuffers(lhs_array, rhs_array);
+    case ArrayDataType::kUint32:
+      return CompareArrayBuffers(lhs_array, rhs_array);
+    case ArrayDataType::kInt64:
+      return CompareArrayBuffers(lhs_array, rhs_array);
+    case ArrayDataType::kUint64:
+      return CompareArrayBuffers(lhs_array, rhs_array);
+    case ArrayDataType::kString:
+      return CompareArrayBuffers(lhs_array, rhs_array);
+    default:
+      LOG(FATAL) << "Unsupported data type: "
+                 << ArrayDataTypeName(lhs_array.data_type);
+      return false;
+  }
+}
+
 namespace {
 // Take an array name, which may be something like "name:3_5" and make it
 // acceptable as a TF node name, say "name_3_5";
@@ -1072,6 +1148,60 @@ void FixEdgeArrays(Model* model) {
   }
 }
 
+void DedupeConstantArrays(Model* model, size_t min_size) {
+  // Walk all 0..N and compare with the remaining n+1..N.
+  // This lets us avoid N^2 comparisions and erase duplicate arrays while
+  // iterating.
+  const auto& array_map = model->GetArrayMap();
+  for (auto lhs_array_it = array_map.begin(); lhs_array_it != array_map.end();
+       ++lhs_array_it) {
+    const auto& lhs_array_name = lhs_array_it->first;
+    const auto& lhs_array = *lhs_array_it->second;
+    if (!IsConstantParameterArray(*model, lhs_array_name)) {
+      // Not a constant array; skip.
+      continue;
+    }
+    ArrayDataType final_data_type =
+        lhs_array.final_data_type != ArrayDataType::kNone
+            ? lhs_array.final_data_type
+            : lhs_array.data_type;
+    size_t array_byte_size =
+        lhs_array.buffer->Length() * ElementSize(final_data_type);
+    if (array_byte_size < min_size) {
+      // Too small; skip.
+      continue;
+    }
+
+    auto next_lhs_array_it = lhs_array_it;
+    ++next_lhs_array_it;
+    for (auto rhs_array_it = next_lhs_array_it;
+         rhs_array_it != array_map.end();) {
+      const auto& rhs_array_name = rhs_array_it->first;
+      const auto& rhs_array = *rhs_array_it->second;
+      ++rhs_array_it;
+      if (!IsConstantParameterArray(*model, rhs_array_name)) {
+        // Not a constant array; skip.
+        continue;
+      }
+      if (!IsDiscardableArray(*model, rhs_array_name)) {
+        // Can't remove the array as it's not discardable (such as an IO edge).
+        continue;
+      }
+      if (!CompareConstantArrays(lhs_array, rhs_array)) {
+        // Arrays aren't equal; skip.
+        continue;
+      }
+
+      // Arrays can be deduped!
+      VLOG(1) << "Deduplicating arrays; using " << lhs_array_name
+              << " in place of " << rhs_array_name;
+      ReplaceArrayUsage(model, rhs_array_name, lhs_array_name);
+      // Note: rhs_array_it above is already incremented so this is safe.
+      model->EraseArray(rhs_array_name);
+    }
+  }
+}
+
 namespace {
 void CopyArrayAttribs(const Array& source_array, Array* target_array) {
   target_array->data_type = source_array.data_type;
diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h
index f5b596df0f346b..1f596ca8e5a28f 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.h
+++ b/tensorflow/contrib/lite/toco/tooling_util.h
@@ -88,6 +88,10 @@ std::vector>::iterator FindOpWithInput(
 Operator* GetOpWithInput(const Model& model, const string& array_name);
 Operator* GetFirstOpWithInput(const Model& model, const string& array_name);
 
+// Replaces all uses of the |old_array_name| with the |new_array_name|.
+void ReplaceArrayUsage(Model* model, const string& old_array_name,
+                       const string& new_array_name);
+
 std::vector>::const_iterator FindOp(
     const Model& model, const Operator* op);
 std::vector>::iterator FindOp(Model& model,
@@ -138,6 +142,9 @@ int RequiredBufferSizeForShape(const Shape& shape);
 
 bool IsConstantParameterArray(const Model& model, const string& name);
 
+// Compares two constant parameter arrays for exact equality.
+bool CompareConstantArrays(const Array& lhs_array, const Array& rhs_array);
+
 void CheckNoMissingArray(const Model& model);
 void CheckInvariants(const Model& model);
 
@@ -150,6 +157,15 @@ void FixNoOrphanedArray(Model* model);
 // Fixes input/output arrays that may have issues during export or inference.
 void FixEdgeArrays(Model* model);
 
+// Finds and deduplicates large constant arrays in the model.
+// After constant propagation runs it's possible to end up with several of the
+// same large array (whether they be zeros or otherwise).
+//
+// |min_size| is used to adjust the minimum size in bytes of an array before
+// it's considered for deduping. As deduping can make the graphs more difficult
+// to read this helps prevent small arrays from spidering out.
+void DedupeConstantArrays(Model* model, size_t min_size);
+
 // Copies the contents of an array into another.
 // Expects that the shape and data type match.
 template 

From 487fa7b1a48c151362ab1b16cdda6bbc78f5d6dc Mon Sep 17 00:00:00 2001
From: "Nicholas Nadeau, P.Eng., AVS" 
Date: Thu, 3 May 2018 13:47:06 -0400
Subject: [PATCH 0337/1691] Fixed Typos (#18806)

* fixed typos
---
 RELEASE.md                                                    | 2 +-
 tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc         | 2 +-
 tensorflow/compiler/xla/literal_util.h                        | 2 +-
 tensorflow/compiler/xla/service/conditional_simplifier.cc     | 2 +-
 tensorflow/compiler/xla/service/cpu/ir_function.h             | 4 ++--
 tensorflow/compiler/xla/service/cpu/shape_partition.h         | 2 +-
 tensorflow/compiler/xla/service/despecializer.h               | 2 +-
 tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h     | 2 +-
 tensorflow/compiler/xla/service/interpreter/README.md         | 2 +-
 tensorflow/compiler/xla/service/layout_assignment.h           | 4 ++--
 tensorflow/compiler/xla/service/reduce_precision_insertion.cc | 2 +-
 tensorflow/compiler/xla/service/source_map_util.h             | 2 +-
 tensorflow/contrib/autograph/impl/config.py                   | 2 +-
 tensorflow/contrib/autograph/operators/control_flow.py        | 2 +-
 .../boosted_trees/python/training/functions/gbdt_batch.py     | 2 +-
 .../python/ops/bijectors/cholesky_outer_product.py            | 2 +-
 tensorflow/contrib/eager/README.md                            | 2 +-
 tensorflow/contrib/ffmpeg/ffmpeg_lib.h                        | 2 +-
 .../contrib/framework/python/ops/critical_section_ops.py      | 2 +-
 .../contrib/gan/python/features/python/conditioning_utils.py  | 2 +-
 tensorflow/contrib/graph_editor/transform.py                  | 2 +-
 tensorflow/contrib/image/__init__.py                          | 2 +-
 tensorflow/contrib/kfac/examples/convnet.py                   | 2 +-
 tensorflow/contrib/kfac/python/ops/optimizer.py               | 4 ++--
 tensorflow/contrib/kfac/python/ops/placement.py               | 2 +-
 .../contrib/lite/kernels/internal/reference/reference_ops.h   | 2 +-
 tensorflow/contrib/lite/schema/schema.fbs                     | 2 +-
 tensorflow/contrib/lite/schema/schema_v0.fbs                  | 2 +-
 tensorflow/contrib/lite/schema/schema_v1.fbs                  | 2 +-
 tensorflow/contrib/lite/schema/schema_v2.fbs                  | 2 +-
 tensorflow/contrib/lite/schema/schema_v3.fbs                  | 4 ++--
 tensorflow/contrib/lite/testing/generate_examples.py          | 4 ++--
 tensorflow/contrib/lite/testing/tflite_driver.cc              | 4 ++--
 tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md        | 4 ++--
 tensorflow/contrib/lite/toco/import_tensorflow.cc             | 2 +-
 tensorflow/contrib/lite/toco/tflite/operator.h                | 4 ++--
 tensorflow/contrib/lite/toco/tflite/types_test.cc             | 2 +-
 .../opt/python/training/elastic_average_optimizer_test.py     | 2 +-
 .../opt/python/training/model_average_optimizer_test.py       | 2 +-
 tensorflow/contrib/tensorrt/convert/convert_nodes.cc          | 2 +-
 tensorflow/contrib/verbs/README.md                            | 2 +-
 tensorflow/core/common_runtime/broadcaster.cc                 | 4 ++--
 tensorflow/core/common_runtime/buf_rendezvous.h               | 2 +-
 tensorflow/core/common_runtime/ring_reducer.cc                | 2 +-
 tensorflow/core/common_runtime/scoped_allocator_mgr.cc        | 2 +-
 tensorflow/core/debug/debug_io_utils.cc                       | 2 +-
 tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc  | 2 +-
 tensorflow/core/framework/op_gen_lib.h                        | 4 ++--
 tensorflow/core/framework/op_kernel.h                         | 2 +-
 tensorflow/core/graph/while_context.h                         | 2 +-
 tensorflow/core/grappler/costs/graph_properties.cc            | 3 +--
 tensorflow/core/grappler/costs/virtual_scheduler.h            | 2 +-
 tensorflow/core/grappler/optimizers/layout_optimizer.cc       | 2 +-
 .../kernels/batching_util/adaptive_shared_batch_scheduler.h   | 2 +-
 tensorflow/core/kernels/conv_ops_gpu_3.cu.cc                  | 2 +-
 tensorflow/core/kernels/nth_element_op.cc                     | 2 +-
 tensorflow/core/kernels/roll_op.cc                            | 2 +-
 tensorflow/core/platform/cloud/gcs_file_system.cc             | 2 +-
 tensorflow/core/platform/cloud/gcs_throttle.h                 | 2 +-
 tensorflow/core/profiler/g3doc/command_line.md                | 2 +-
 tensorflow/core/protobuf/rewriter_config.proto                | 2 +-
 tensorflow/core/util/cuda_device_functions.h                  | 2 +-
 tensorflow/core/util/mkl_util.h                               | 2 +-
 tensorflow/core/util/tensor_format.h                          | 2 +-
 tensorflow/docs_src/api_guides/python/reading_data.md         | 2 +-
 tensorflow/docs_src/deploy/s3.md                              | 2 +-
 tensorflow/docs_src/mobile/mobile_intro.md                    | 2 +-
 tensorflow/python/data/util/nest.py                           | 2 +-
 tensorflow/python/estimator/estimator.py                      | 2 +-
 .../python/estimator/inputs/queues/feeding_functions.py       | 2 +-
 tensorflow/python/feature_column/feature_column.py            | 2 +-
 tensorflow/python/framework/ops.py                            | 2 +-
 tensorflow/python/framework/test_util.py                      | 2 +-
 tensorflow/python/keras/_impl/keras/engine/network.py         | 2 +-
 tensorflow/python/keras/_impl/keras/engine/saving_test.py     | 4 ++--
 tensorflow/python/keras/_impl/keras/estimator.py              | 2 +-
 tensorflow/python/kernel_tests/distributions/util_test.py     | 2 +-
 tensorflow/python/kernel_tests/manip_ops_test.py              | 2 +-
 tensorflow/python/ops/math_ops.py                             | 2 +-
 tensorflow/python/training/distribute.py                      | 2 +-
 tensorflow/python/util/util.cc                                | 2 +-
 tensorflow/python/util/util.h                                 | 2 +-
 tensorflow/stream_executor/cuda/cuda_dnn.h                    | 2 +-
 tensorflow/tensorflow.bzl                                     | 2 +-
 tensorflow/tools/graph_transforms/README.md                   | 2 +-
 third_party/examples/eager/spinn/README.md                    | 2 +-
 86 files changed, 97 insertions(+), 98 deletions(-)

diff --git a/RELEASE.md b/RELEASE.md
index 55923a2c9b27ce..84d9d52868ecd5 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -236,7 +236,7 @@ Yoni Tsafir, yordun, Yuan (Terry) Tang, Yuxin Wu, zhengdi, Zhengsheng Wei, 田
   * Add `complex64` support to XLA compiler.
   * `bfloat` support is now added to XLA infrastructure.
   * Make `ClusterSpec` propagation work with XLA devices.
-  * Use a determinisitic executor to generate XLA graph.
+  * Use a deterministic executor to generate XLA graph.
 * `tf.contrib`:
   * `tf.contrib.distributions`:
     * Add `tf.contrib.distributions.Autoregressive`.
diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc
index f06debaf316c01..6d1e3325ebd35b 100644
--- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc
+++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc
@@ -240,7 +240,7 @@ class Encapsulator {
   // Once edges between compiled and outside_compilation clusters have been
   // replaced by send/recv ops, some dependencies may no longer be apparent.
   // A clustering pass finds all the dependencies between HC nodes that are only
-  // present as a result of edges between nodes in outside_compilaton clusters.
+  // present as a result of edges between nodes in outside_compilation clusters.
   // Suppose there is a path from outside_compilation cluster C in subgraph S
   // to outside_compilation cluster D in subgraph T. If S != T then a control
   // edge is added from the call node for S to the call node for T, which
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index 290f38807840f9..c6bd03bf21ac8d 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -286,7 +286,7 @@ class Literal {
 
   // Creates a new value that has the equivalent value as this literal, but
   // conforms to new_layout; e.g. a literal matrix that was in {0, 1}
-  // minor-to-major dimension layout can be re-layed-out as {1, 0}
+  // minor-to-major dimension layout can be re-laid-out as {1, 0}
   // minor-to-major dimension layout and the value in the cell at any given
   // logical index (i0, i1) will be the same.
   //
diff --git a/tensorflow/compiler/xla/service/conditional_simplifier.cc b/tensorflow/compiler/xla/service/conditional_simplifier.cc
index e560abc87f8456..e9ec796121fff2 100644
--- a/tensorflow/compiler/xla/service/conditional_simplifier.cc
+++ b/tensorflow/compiler/xla/service/conditional_simplifier.cc
@@ -35,7 +35,7 @@ namespace xla {
 
 // Tries to replace a conditional with a call operation of the corresponding
 // computation. If the given conditional has a constant predicate, tries to
-// replace it with a call to its true/false computation as appropirate and then
+// replace it with a call to its true/false computation as appropriate and then
 // inline that computation.
 //
 // Returns true if it made a change to the graph.
diff --git a/tensorflow/compiler/xla/service/cpu/ir_function.h b/tensorflow/compiler/xla/service/cpu/ir_function.h
index 557aa4a6bfc2ef..2e55181eed867a 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_function.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_function.h
@@ -33,8 +33,8 @@ namespace cpu {
 // emitters for function and function argument access.
 // The llvm::Function is created with the standard function signature
 // used in the XLA CPU backend (see ir_function.cc for argument details).
-// In addtion IrFunction saves the callers IR insert point during contruction,
-// and restores it after desctruction.
+// In addition IrFunction saves the callers IR insert point during construction,
+// and restores it after destruction.
 //
 // Example usage:
 //
diff --git a/tensorflow/compiler/xla/service/cpu/shape_partition.h b/tensorflow/compiler/xla/service/cpu/shape_partition.h
index 33d02b70e61e33..db2cda2936c834 100644
--- a/tensorflow/compiler/xla/service/cpu/shape_partition.h
+++ b/tensorflow/compiler/xla/service/cpu/shape_partition.h
@@ -38,7 +38,7 @@ namespace cpu {
 //
 //     [0, 1), [1, 2), [2, 3), [3, 4), [4, 5) [5, 8)
 //
-//   Note that the last partition has residule because the dimension size is
+//   Note that the last partition has residual because the dimension size is
 //   not a multiple of the partition count.
 //
 //
diff --git a/tensorflow/compiler/xla/service/despecializer.h b/tensorflow/compiler/xla/service/despecializer.h
index af48f4ab6e506d..cc1695b7f86380 100644
--- a/tensorflow/compiler/xla/service/despecializer.h
+++ b/tensorflow/compiler/xla/service/despecializer.h
@@ -25,7 +25,7 @@ namespace xla {
 
 // Creates an HloPassPipeline containing multiple HloPasses that can
 // despecialize an optimized HloModule. This is useful to run an HloModule
-// optimized for one specfic platform on a different platform (undoing platform
+// optimized for one specific platform on a different platform (undoing platform
 // specific passes) with matching numerics for comparison.
 //
 // Current despecialization passes are Defuser, ImplicitBroadcastRemover,
diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h
index b842f480c6257c..b41ab2162ab81f 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h
+++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h
@@ -38,7 +38,7 @@ namespace gpu {
 //
 // Examples of things that are not unnested computations:
 //
-//  - The reducer of a kReduce HLO.  This is emited using IrEmitterNested.
+//  - The reducer of a kReduce HLO.  This is emitted using IrEmitterNested.
 //  - The body of a fusion node.  IrEmitterUnenested emits the relevant code
 //    within a kernel function using FusedIrEmitter.  (FusedIrEmitter is not
 //    really an IrEmitter, but is more an "IR generator generator".)
diff --git a/tensorflow/compiler/xla/service/interpreter/README.md b/tensorflow/compiler/xla/service/interpreter/README.md
index 4c19a1b916d421..0b21b251c3f663 100644
--- a/tensorflow/compiler/xla/service/interpreter/README.md
+++ b/tensorflow/compiler/xla/service/interpreter/README.md
@@ -5,7 +5,7 @@ evaluating the result of the HLO graph directly with HloEvaluator, without
 lowering it further (to LLVM IR for example) before execution as other backends
 (CPU and GPU for example) do.
 
-Its key componenets are:
+Its key components are:
 
 *   [`InterpreterCompiler`] despite the inherited naming of "compiler", all
     `InterpreterCompiler` really does is the following:
diff --git a/tensorflow/compiler/xla/service/layout_assignment.h b/tensorflow/compiler/xla/service/layout_assignment.h
index c83ae0388b4250..9663a793fdd7d4 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.h
+++ b/tensorflow/compiler/xla/service/layout_assignment.h
@@ -281,8 +281,8 @@ class LayoutAssignment : public HloPassInterface {
   // the case that no particular layout is requested.
   //
   // channel_constraints is both an input and output. Any sends or recvs that
-  // are present in channel_constraints will be layed out as constrained. Any
-  // unconstrained sends or recvs will be layed out as locally optimal and their
+  // are present in channel_constraints will be laid out as constrained. Any
+  // unconstrained sends or recvs will be laid out as locally optimal and their
   // layout will be added as a constraint to channel_constraints.
   //
   // If channel_constraints is nullptr, no kSend or kRecvs must be contained
diff --git a/tensorflow/compiler/xla/service/reduce_precision_insertion.cc b/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
index e2c07e38271df8..688cceff0cd10d 100644
--- a/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
+++ b/tensorflow/compiler/xla/service/reduce_precision_insertion.cc
@@ -75,7 +75,7 @@ StatusOr ReducePrecisionInsertion::insert_after(
     return false;
   }
 
-  // Check that we haven't already inserted an equivalant reduce-precision
+  // Check that we haven't already inserted an equivalent reduce-precision
   // operation after this instruction.  (The zero-user case occurs when this is
   // the root instruction.)
   if (instruction->user_count() > 0) {
diff --git a/tensorflow/compiler/xla/service/source_map_util.h b/tensorflow/compiler/xla/service/source_map_util.h
index a776d745f4e56c..18e2651abb1600 100644
--- a/tensorflow/compiler/xla/service/source_map_util.h
+++ b/tensorflow/compiler/xla/service/source_map_util.h
@@ -23,7 +23,7 @@ limitations under the License.
 namespace xla {
 namespace source_map_util {
 
-// Creates an INVALID_ARUGMENT status with the given format string.
+// Creates an INVALID_ARGUMENT status with the given format string.
 //
 // Also, attempts to extract the OpMetadata for parameter_number on executable
 // and append it to the status message for source mapping to user code.
diff --git a/tensorflow/contrib/autograph/impl/config.py b/tensorflow/contrib/autograph/impl/config.py
index 2600088595a127..878bb7e12f2b39 100644
--- a/tensorflow/contrib/autograph/impl/config.py
+++ b/tensorflow/contrib/autograph/impl/config.py
@@ -33,7 +33,7 @@
     (utils.__name__,),
 
     # All of tensorflow's subpackages. Unlike the root tf module, they don't
-    # have well-known names. Not refering to the module directly to avoid
+    # have well-known names. Not referring to the module directly to avoid
     # circular imports.
     (
         utils.__name__[:-len('.contrib.autograph.utils')],),
diff --git a/tensorflow/contrib/autograph/operators/control_flow.py b/tensorflow/contrib/autograph/operators/control_flow.py
index 9f7202821f0d0b..671c9ccc13eaa8 100644
--- a/tensorflow/contrib/autograph/operators/control_flow.py
+++ b/tensorflow/contrib/autograph/operators/control_flow.py
@@ -174,7 +174,7 @@ def while_stmt(test, body, init_state, extra_deps, opts=None):
     Tuple containing the final state.
   """
   # TODO(mdan): Consider adding a generic mechanism for dynamic dispatch.
-  # That could be somethins as simple as a collection of dispatch rules, with
+  # That could be something as simple as a collection of dispatch rules, with
   # some prioritization.
   if any(tensor_util.is_tensor(v) for v in init_state + extra_deps):
     return _tf_while_stmt(test, body, init_state, opts)
diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py
index 08c1dcdd028829..e53d86ec612f29 100644
--- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py
+++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py
@@ -369,7 +369,7 @@ def _predict_and_return_dict(self, ensemble_handle, ensemble_stamp, mode):
     Returns:
       a dictionary of prediction results -
         ENSEMBLE_STAMP, PREDICTION, PARTITION_IDS,
-        NUM_LAYER_ATTEMPTED, NUM_TREES_ATTEMPED.
+        NUM_LAYER_ATTEMPTED, NUM_TREES_ATTEMPTED.
     """
     ensemble_stats = training_ops.tree_ensemble_stats(ensemble_handle,
                                                       ensemble_stamp)
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py b/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py
index ecdb8967f43e59..268c8d03426d43 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py
@@ -53,7 +53,7 @@ class CholeskyOuterProduct(bijector.Bijector):
   its spectrum), and that the product of two positive-diagonal lower-triangular
   matrices is another positive-diagonal lower-triangular matrix.
 
-  A simple inductive argument (proceding one column of L_3 at a time) shows
+  A simple inductive argument (proceeding one column of L_3 at a time) shows
   that, if `I = L_3 @ L_3.T`, with L_3 being lower-triangular with positive-
   diagonal, then `L_3 = I`. Thus, `L_1 = L_2`, proving injectivity of g.
 
diff --git a/tensorflow/contrib/eager/README.md b/tensorflow/contrib/eager/README.md
index 762685db14b968..4384431e7b9c3e 100644
--- a/tensorflow/contrib/eager/README.md
+++ b/tensorflow/contrib/eager/README.md
@@ -1,6 +1,6 @@
 # Eager Execution
 
-Eager execution provides an imperative interface to TensorFlow (similiar to
+Eager execution provides an imperative interface to TensorFlow (similar to
 [NumPy](http://www.numpy.org)). When you enable eager execution, TensorFlow
 operations execute immediately; you do not execute a pre-constructed graph with
 [`Session.run()`](https://www.tensorflow.org/api_docs/python/tf/Session).
diff --git a/tensorflow/contrib/ffmpeg/ffmpeg_lib.h b/tensorflow/contrib/ffmpeg/ffmpeg_lib.h
index a8d5a0dd83fb50..bf2aa75545813f 100644
--- a/tensorflow/contrib/ffmpeg/ffmpeg_lib.h
+++ b/tensorflow/contrib/ffmpeg/ffmpeg_lib.h
@@ -53,7 +53,7 @@ Status CreateAudioFile(const string& audio_format_id, int32 bits_per_second,
                        int32 samples_per_second, int32 channel_count,
                        const std::vector& samples, string* output_data);
 
-// Reads an video file using ffmpeg adn converts it into a RGB24 in uint8
+// Reads an video file using ffmpeg and converts it into a RGB24 in uint8
 // [frames, height, width, 3]. The w, h, and frames are obtained from ffmpeg.
 Status ReadVideoFile(const string& filename, std::vector* output_data,
                      uint32* width, uint32* height, uint32* frames);
diff --git a/tensorflow/contrib/framework/python/ops/critical_section_ops.py b/tensorflow/contrib/framework/python/ops/critical_section_ops.py
index bd764ed57a6da0..72835c3ad86e63 100644
--- a/tensorflow/contrib/framework/python/ops/critical_section_ops.py
+++ b/tensorflow/contrib/framework/python/ops/critical_section_ops.py
@@ -202,7 +202,7 @@ def execute(self, fn, *args, **kwargs):
         or lazy way that may cause a deadlock.
       ValueError: If `exclusive_resource_access` is not provided (is `True`) and
         another `CriticalSection` has an execution requesting the same
-        resources as in `*args`, `**kwargs`, and any additionaly captured
+        resources as in `*args`, `**kwargs`, and any additionally captured
         inputs in `fn`.  Note, even if `exclusive_resource_access` is `True`,
         if another execution in another `CriticalSection` was created without
         `exclusive_resource_access=True`, a `ValueError` will be raised.
diff --git a/tensorflow/contrib/gan/python/features/python/conditioning_utils.py b/tensorflow/contrib/gan/python/features/python/conditioning_utils.py
index df71187fbd98c8..a9b8faa7126253 100644
--- a/tensorflow/contrib/gan/python/features/python/conditioning_utils.py
+++ b/tensorflow/contrib/gan/python/features/python/conditioning_utils.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Miscellanous utilities for TFGAN code and examples."""
+"""Miscellaneous utilities for TFGAN code and examples."""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/graph_editor/transform.py b/tensorflow/contrib/graph_editor/transform.py
index a320a3f232fc1d..592d37b432ee60 100644
--- a/tensorflow/contrib/graph_editor/transform.py
+++ b/tensorflow/contrib/graph_editor/transform.py
@@ -677,7 +677,7 @@ def replace_t_with_replacement_handler(info, t):
 
 
 def _add_control_flow_ops(ops, control_ios):
-  """Complete `ops` so that the tranformed graph is valid.
+  """Complete `ops` so that the transformed graph is valid.
 
   Partially copying a graph can lead to a malformed graph. For instance,
   copying half of a while construct is likely to result in an invalid graph.
diff --git a/tensorflow/contrib/image/__init__.py b/tensorflow/contrib/image/__init__.py
index 8f406ace1d5dcc..f230d93da4a9c0 100755
--- a/tensorflow/contrib/image/__init__.py
+++ b/tensorflow/contrib/image/__init__.py
@@ -17,7 +17,7 @@
 ### API
 
 This module provides functions for image manipulation; currently, chrominance
-transformas (including changing saturation and hue) in YIQ space and
+transforms (including changing saturation and hue) in YIQ space and
 projective transforms (including rotation) are supported.
 
 ## Image Transformation `Ops`
diff --git a/tensorflow/contrib/kfac/examples/convnet.py b/tensorflow/contrib/kfac/examples/convnet.py
index b261f41bf97db1..d6b1a61b716ab7 100644
--- a/tensorflow/contrib/kfac/examples/convnet.py
+++ b/tensorflow/contrib/kfac/examples/convnet.py
@@ -325,7 +325,7 @@ def distributed_grads_only_and_ops_chief_worker(
 
   All workers perform gradient computation. Chief worker applies gradient after
   averaging the gradients obtained from all the workers. All workers block
-  execution untill the update is applied. Chief worker runs covariance and
+  execution until the update is applied. Chief worker runs covariance and
   inverse update ops. Covariance and inverse matrices are placed on parameter
   servers in a round robin manner. For further details on synchronous
   distributed optimization check `tf.train.SyncReplicasOptimizer`.
diff --git a/tensorflow/contrib/kfac/python/ops/optimizer.py b/tensorflow/contrib/kfac/python/ops/optimizer.py
index 7203804af36cb4..b7f63d8d94a7a4 100644
--- a/tensorflow/contrib/kfac/python/ops/optimizer.py
+++ b/tensorflow/contrib/kfac/python/ops/optimizer.py
@@ -66,7 +66,7 @@ def __init__(self,
           the local approximation with the Fisher information matrix, and to
           regularize the update direction by making it closer to the gradient.
           If damping is adapted during training then this value is used for
-          initializing damping varaible.
+          initializing damping variable.
           (Higher damping means the update looks more like a standard gradient
           update - see Tikhonov regularization.)
       layer_collection: The layer collection object, which holds the fisher
@@ -195,7 +195,7 @@ def set_damping_adaptation_params(self,
       min_damping: `float`(Optional), Minimum value the damping parameter
         can take. Default value 1e-5.
       damping_adaptation_decay: `float`(Optional), The `damping` parameter is
-        multipled by the `damping_adaptation_decay` every
+        multiplied by the `damping_adaptation_decay` every
         `damping_adaptation_interval` number of iterations. Default value 0.99.
       damping_adaptation_interval: `int`(Optional), Number of steps in between
         updating the `damping` parameter. Default value 5.
diff --git a/tensorflow/contrib/kfac/python/ops/placement.py b/tensorflow/contrib/kfac/python/ops/placement.py
index 8a20ebe19844e6..c4454325aebe13 100644
--- a/tensorflow/contrib/kfac/python/ops/placement.py
+++ b/tensorflow/contrib/kfac/python/ops/placement.py
@@ -51,7 +51,7 @@ def __init__(self, cov_devices=None, inv_devices=None, **kwargs):
     self._inv_devices = inv_devices
 
   def make_vars_and_create_op_thunks(self, scope=None):
-    """Make vars and create op thunks w/ a round-robin device placement strat.
+    """Make vars and create op thunks w/ a round-robin device placement start.
 
     For each factor, all of that factor's cov variables and their associated
     update ops will be placed on a particular device.  A new device is chosen
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index 445687cd15b3bf..e2e1cf4478f4b5 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -1814,7 +1814,7 @@ inline void LstmCell(const float* input_data, const Dims<4>& input_dims,
 // requiring a power-of-two representation interval. Thus, we should right
 // away quantize this array to a power-of-two interval; otherwise,
 // implementation will need to rescale that, losing any benefit that a tighter
-// representation interval might otherwise yield, while introducting some
+// representation interval might otherwise yield, while introducing some
 // numerical error and computational overhead.
 //
 // Now, Logistic and Tanh
diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs
index b16baf02dcfa12..ff56c31720fee4 100644
--- a/tensorflow/contrib/lite/schema/schema.fbs
+++ b/tensorflow/contrib/lite/schema/schema.fbs
@@ -65,7 +65,7 @@ table Tensor {
   quantization:QuantizationParameters;  // Optional.
 }
 
-// A list of builtin operators. Builtin operators a slighlty faster than custom
+// A list of builtin operators. Builtin operators are slightly faster than custom
 // ones, but not by much. Moreover, while custom operators accept an opaque
 // object containing configuration parameters, builtins have a predetermined
 // set of acceptable options.
diff --git a/tensorflow/contrib/lite/schema/schema_v0.fbs b/tensorflow/contrib/lite/schema/schema_v0.fbs
index 852ea988f3ddc7..891d8366ccae35 100644
--- a/tensorflow/contrib/lite/schema/schema_v0.fbs
+++ b/tensorflow/contrib/lite/schema/schema_v0.fbs
@@ -48,7 +48,7 @@ table Tensor {
   quantization:QuantizationParameters;  // Optional.
 }
 
-// A list of builtin operators. Builtin operators a slighlty faster than custom
+// A list of builtin operators. Builtin operators are slightly faster than custom
 // ones, but not by much. Moreover, while custom operators accept an opaque
 // object containing configuration parameters, builtins have a predetermined
 // set of acceptable options.
diff --git a/tensorflow/contrib/lite/schema/schema_v1.fbs b/tensorflow/contrib/lite/schema/schema_v1.fbs
index 06cd9408edb710..b438b569e67ac5 100644
--- a/tensorflow/contrib/lite/schema/schema_v1.fbs
+++ b/tensorflow/contrib/lite/schema/schema_v1.fbs
@@ -53,7 +53,7 @@ table Tensor {
   quantization:QuantizationParameters;  // Optional.
 }
 
-// A list of builtin operators. Builtin operators a slighlty faster than custom
+// A list of builtin operators. Builtin operators are slightly faster than custom
 // ones, but not by much. Moreover, while custom operators accept an opaque
 // object containing configuration parameters, builtins have a predetermined
 // set of acceptable options.
diff --git a/tensorflow/contrib/lite/schema/schema_v2.fbs b/tensorflow/contrib/lite/schema/schema_v2.fbs
index 96731c8aaebf69..b90408ff6d09fd 100644
--- a/tensorflow/contrib/lite/schema/schema_v2.fbs
+++ b/tensorflow/contrib/lite/schema/schema_v2.fbs
@@ -54,7 +54,7 @@ table Tensor {
   quantization:QuantizationParameters;  // Optional.
 }
 
-// A list of builtin operators. Builtin operators a slighlty faster than custom
+// A list of builtin operators. Builtin operators are slightly faster than custom
 // ones, but not by much. Moreover, while custom operators accept an opaque
 // object containing configuration parameters, builtins have a predetermined
 // set of acceptable options.
diff --git a/tensorflow/contrib/lite/schema/schema_v3.fbs b/tensorflow/contrib/lite/schema/schema_v3.fbs
index cedefe08f35cbb..020da38493980d 100644
--- a/tensorflow/contrib/lite/schema/schema_v3.fbs
+++ b/tensorflow/contrib/lite/schema/schema_v3.fbs
@@ -53,7 +53,7 @@ table Tensor {
   type:TensorType;
   // An index that refers to the buffers table at the root of the model. Or,
   // if there is no data buffer associated (i.e. intermediate results), then
-  // this is 0 (which refers to an always existant empty buffer).
+  // this is 0 (which refers to an always existent empty buffer).
   //
   // The data_buffer itself is an opaque container, with the assumption that the
   // target device is little-endian. In addition, all builtin operators assume
@@ -64,7 +64,7 @@ table Tensor {
   quantization:QuantizationParameters;  // Optional.
 }
 
-// A list of builtin operators. Builtin operators a slighlty faster than custom
+// A list of builtin operators. Builtin operators are slightly faster than custom
 // ones, but not by much. Moreover, while custom operators accept an opaque
 // object containing configuration parameters, builtins have a predetermined
 // set of acceptable options.
diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index e4851d60771f26..fd093321653bb0 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -1758,7 +1758,7 @@ def make_strided_slice_tests(zip_path):
           "shrink_axis_mask": [None, 1, 8, 11, 15, -1],
           "constant_indices": [False, True],
       },
-      # TODO(b/73170889) Restore test paramaters removed in cl/191608113.
+      # TODO(b/73170889) Restore test parameters removed in cl/191608113.
       # 2-D
       {
           "dtype": [tf.float32, tf.int32, tf.int64],
@@ -1899,7 +1899,7 @@ def build_graph(parameters):
     return inputs_after_split, [out]
 
   def build_inputs(parameters, sess, inputs, outputs):
-    """Feed inputs, assign vairables, and freeze graph."""
+    """Feed inputs, assign variables, and freeze graph."""
 
     with tf.variable_scope("", reuse=True):
       kernel = tf.get_variable("rnn/basic_lstm_cell/kernel")
diff --git a/tensorflow/contrib/lite/testing/tflite_driver.cc b/tensorflow/contrib/lite/testing/tflite_driver.cc
index 58fe5bd6e40b3d..75ac24719aa8fa 100644
--- a/tensorflow/contrib/lite/testing/tflite_driver.cc
+++ b/tensorflow/contrib/lite/testing/tflite_driver.cc
@@ -226,8 +226,8 @@ void TfLiteDriver::SetExpectation(int id, const string& csv_values) {
   if (!IsValid()) return;
   auto* tensor = interpreter_->tensor(id);
   if (expected_output_.count(id) != 0) {
-    fprintf(stderr, "Overriden expectation for tensor %d\n", id);
-    Invalidate("Overriden expectation");
+    fprintf(stderr, "Overridden expectation for tensor %d\n", id);
+    Invalidate("Overridden expectation");
   }
   expected_output_[id].reset(new Expectation);
   switch (tensor->type) {
diff --git a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md
index 495014c6fc67ab..7680cdd344814b 100644
--- a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md
+++ b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md
@@ -115,7 +115,7 @@ bazel run --config=opt \
 
 In order to evaluate the possible benefit of generating a quantized graph, TOCO
 allows "dummy-quantization" on float graphs. The flags `--default_ranges_min`
-and `--default_ranges_max` accept plausable values for the min-max ranges of the
+and `--default_ranges_max` accept plausible values for the min-max ranges of the
 values in all arrays that do not have min-max information. "Dummy-quantization"
 will produce lower accuracy but will emulate the performance of a correctly
 quantized model.
@@ -338,7 +338,7 @@ below outline the use cases for each.
 ### Using `--output_format=GRAPHVIZ_DOT`
 
 The first way to get a graphviz rendering is to pass `GRAPHVIZ_DOT` into
-`--output_format`. This results in a plausable visualization of the graph. This
+`--output_format`. This results in a plausible visualization of the graph. This
 reduces the requirements that normally exist during conversion between other
 input and output formats. For example, this may be useful if conversion from
 TENSORFLOW_GRAPHDEF to TFLITE is failing.
diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc
index 453ff29b0d08ec..8efe6ab7b9c608 100644
--- a/tensorflow/contrib/lite/toco/import_tensorflow.cc
+++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc
@@ -144,7 +144,7 @@ ArrayDataType ConvertDataType(tensorflow::DataType dtype) {
   else if (dtype == DT_STRING)
     return ArrayDataType::kString;
   else
-    LOG(INFO) << "Unsupported data type in placehoder op: " << dtype;
+    LOG(INFO) << "Unsupported data type in placeholder op: " << dtype;
   return ArrayDataType::kNone;
 }
 
diff --git a/tensorflow/contrib/lite/toco/tflite/operator.h b/tensorflow/contrib/lite/toco/tflite/operator.h
index 88af3d6ab6c6af..85f7bdafe04979 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator.h
+++ b/tensorflow/contrib/lite/toco/tflite/operator.h
@@ -25,10 +25,10 @@ namespace tflite {
 
 class BaseOperator;
 
-// Return a map contained all knwo TF Lite Operators, keyed by their names.
+// Return a map contained all know TF Lite Operators, keyed by their names.
 std::map> BuildOperatorByNameMap();
 
-// Return a map contained all knwo TF Lite Operators, keyed by the type of
+// Return a map contained all know TF Lite Operators, keyed by the type of
 // their tf.mini counterparts.
 std::map> BuildOperatorByTypeMap();
 
diff --git a/tensorflow/contrib/lite/toco/tflite/types_test.cc b/tensorflow/contrib/lite/toco/tflite/types_test.cc
index 29fb0b2af22ef1..efb849f42283de 100644
--- a/tensorflow/contrib/lite/toco/tflite/types_test.cc
+++ b/tensorflow/contrib/lite/toco/tflite/types_test.cc
@@ -44,7 +44,7 @@ template 
 Array ToFlatBufferAndBack(std::initializer_list<::toco::DataType> items) {
   // NOTE: This test does not construct the full buffers list. Since
   // Deserialize normally takes a buffer, we need to synthesize one and provide
-  // an index that is non-zero so the buffer is not assumed to be emtpy.
+  // an index that is non-zero so the buffer is not assumed to be empty.
   Array src;
   src.data_type = T;
   src.GetMutableBuffer().data = items;
diff --git a/tensorflow/contrib/opt/python/training/elastic_average_optimizer_test.py b/tensorflow/contrib/opt/python/training/elastic_average_optimizer_test.py
index 37539b959959b5..5ed8057b865cf4 100644
--- a/tensorflow/contrib/opt/python/training/elastic_average_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/elastic_average_optimizer_test.py
@@ -58,7 +58,7 @@ def create_local_cluster(num_workers, num_ps, protocol="grpc"):
 
 
 # Creates the workers and return their sessions, graphs, train_ops.
-# Cheif worker will update at last
+# Chief worker will update at last
 def _get_workers(num_workers, period, workers, moving_rate):
   sessions = []
   graphs = []
diff --git a/tensorflow/contrib/opt/python/training/model_average_optimizer_test.py b/tensorflow/contrib/opt/python/training/model_average_optimizer_test.py
index bfb3350b59ef4b..3acd9402684fa2 100644
--- a/tensorflow/contrib/opt/python/training/model_average_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/model_average_optimizer_test.py
@@ -57,7 +57,7 @@ def create_local_cluster(num_workers, num_ps, protocol="grpc"):
 
 
 # Creates the workers and return their sessions, graphs, train_ops.
-# Cheif worker will update at last
+# Chief worker will update at last
 def _get_workers(num_workers, steps, workers):
   sessions = []
   graphs = []
diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
index 4d3710a51459e0..3767596f8c20a3 100644
--- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
+++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
@@ -2145,7 +2145,7 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode(
   if (!status.ok() || !calib_res->calibrator_) {
     return tensorflow::errors::FailedPrecondition(
         "You must run calibration"
-        " and inference conversion in the same proces");
+        " and inference conversion in the same process");
   }
 
   calib_res->calibrator_->setDone();
diff --git a/tensorflow/contrib/verbs/README.md b/tensorflow/contrib/verbs/README.md
index 4b6104a8b4d542..3137bfd03e3faa 100644
--- a/tensorflow/contrib/verbs/README.md
+++ b/tensorflow/contrib/verbs/README.md
@@ -159,7 +159,7 @@ When the receiver receives the RDMA write, it will locate the relevant **RdmaTen
 	* step_id - Step ID.
 	* request_index - Request index.
 	* remote_addr/rkey - Address/rkey of the reallocated result/proxy tensor.
-* **RDMA_MESSAGE_ERROR_STATUS** - (sender ==> receiver) Notify the receiver that an error had occured on the sender side, so it can propagate it to the upper levels.
+* **RDMA_MESSAGE_ERROR_STATUS** - (sender ==> receiver) Notify the receiver that an error had occurred on the sender side, so it can propagate it to the upper levels.
 	* type - The message type.
 	* name (name_size) - Name of the requested tensor.
 	* step_id - Step ID.
diff --git a/tensorflow/core/common_runtime/broadcaster.cc b/tensorflow/core/common_runtime/broadcaster.cc
index 5e8af8653dc011..e42d3f6b92b98b 100644
--- a/tensorflow/core/common_runtime/broadcaster.cc
+++ b/tensorflow/core/common_runtime/broadcaster.cc
@@ -80,7 +80,7 @@ void Broadcaster::Run(StatusCallback done) {
 // continuing to occupy its current position.  Hence we calculate as
 // though each device's rank is actually r+1, then subtract 1 again to
 // get the descendent ranks.  If the source is not rank 0 then its
-// decendents include both {0,1} and the descendents of its current
+// descendants include both {0,1} and the descendents of its current
 // position.  Where a non-0-rank source is a descendent of another
 // device, no send to it is necessary.
 
@@ -115,7 +115,7 @@ void Broadcaster::TreeSendTo(const CollectiveParams& cp,
   DCHECK_NE(successor_rank, my_rank);
   if (cp.is_source && source_rank != 0) {
     // The source sends to rank 0,1 in addition to its positional
-    // decendents.
+    // descendants.
     if (cp.group.group_size > 1) {
       targets->push_back(0);
     }
diff --git a/tensorflow/core/common_runtime/buf_rendezvous.h b/tensorflow/core/common_runtime/buf_rendezvous.h
index e94e88b323ec74..9eb9f060f6bac2 100644
--- a/tensorflow/core/common_runtime/buf_rendezvous.h
+++ b/tensorflow/core/common_runtime/buf_rendezvous.h
@@ -79,7 +79,7 @@ class BufRendezvous {
                   const ProducerCallback& done);
 
   // Called to request access to a Tensor value corresponding to key.
-  // Consumer is provide with a Hook as soon as availble.
+  // Consumer is provide with a Hook as soon as available.
   void ConsumeBuf(const string& key, const ConsumerCallback& done);
 
   // Consumer must call this function when it's done reading the Hook provided
diff --git a/tensorflow/core/common_runtime/ring_reducer.cc b/tensorflow/core/common_runtime/ring_reducer.cc
index a17281835ea5f5..a74c502a9265a5 100644
--- a/tensorflow/core/common_runtime/ring_reducer.cc
+++ b/tensorflow/core/common_runtime/ring_reducer.cc
@@ -275,7 +275,7 @@ void RingReducer::InitRingField(RingField* rf, int chunk_idx, int subdiv_idx,
   // Note on field indexing: There are group_size_ devices in the
   // instance, implying the same number of chunks per tensor, where a
   // chunk is the unit of data transferred in a time step.  However, if
-  // a device can simultaenously send data by 2 or more independent
+  // a device can simultaneously send data by 2 or more independent
   // channels we can speed up the transfer by subdividing chunks and
   // processing multiple subdivisions at once.  So the actual number
   // of RingFields is group_size_ * num_subdivs_.
diff --git a/tensorflow/core/common_runtime/scoped_allocator_mgr.cc b/tensorflow/core/common_runtime/scoped_allocator_mgr.cc
index be79cc4507124f..c045596a69b60d 100644
--- a/tensorflow/core/common_runtime/scoped_allocator_mgr.cc
+++ b/tensorflow/core/common_runtime/scoped_allocator_mgr.cc
@@ -104,7 +104,7 @@ ScopedAllocatorContainer::~ScopedAllocatorContainer() {
   // contents deleted via Drop.  When when a step ends early
   // (e.g. through abnormal termination) we need to clean up
   // explicitly.  So long as graph execution of the associated step has
-  // completey terminated this should be safe.
+  // completely terminated this should be safe.
   for (auto& it : allocators_) {
     if (it.second.field_index == ScopedAllocator::kBackingIndex) {
       delete it.second.scoped_allocator;
diff --git a/tensorflow/core/debug/debug_io_utils.cc b/tensorflow/core/debug/debug_io_utils.cc
index baa8c08fdf1508..63be4ef53afca1 100644
--- a/tensorflow/core/debug/debug_io_utils.cc
+++ b/tensorflow/core/debug/debug_io_utils.cc
@@ -52,7 +52,7 @@ namespace {
 
 // Creates an Event proto representing a chunk of a Tensor. This method only
 // populates the field of the Event proto that represent the envelope
-// informaion (e.g., timestmap, device_name, num_chunks, chunk_index, dtype,
+// information (e.g., timestamp, device_name, num_chunks, chunk_index, dtype,
 // shape). It does not set the value.tensor field, which should be set by the
 // caller separately.
 Event PrepareChunkEventProto(const DebugNodeKey& debug_node_key,
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc
index 18998bbccbb44d..b9f21ea211bdbd 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc
@@ -115,7 +115,7 @@ class GrpcWorkerCache : public WorkerCachePartial {
 
   size_t AssignWorkerToThread(const string& target) {
     // Round-robin target assignment, but keeps the same target on the same
-    // polling thread always, as this is important for gRPC performace
+    // polling thread always, as this is important for gRPC performance
     mutex_lock lock(assignment_mu_);
     auto it = target_assignments_.find(target);
     if (it == target_assignments_.end()) {
diff --git a/tensorflow/core/framework/op_gen_lib.h b/tensorflow/core/framework/op_gen_lib.h
index ff38e4b22141a7..533dd64805c679 100644
--- a/tensorflow/core/framework/op_gen_lib.h
+++ b/tensorflow/core/framework/op_gen_lib.h
@@ -59,14 +59,14 @@ class ApiDefMap {
   // You can call this method multiple times to load multiple
   // sets of files. Api definitions are merged if the same
   // op definition is loaded multiple times. Later-loaded
-  // definitions take precedense.
+  // definitions take precedence.
   // ApiDefs loaded from files must contain a subset of ops defined
   // in the OpList passed to the constructor.
   Status LoadFileList(Env* env, const std::vector& filenames);
 
   // Load a single file. Api definitions are merged if the same
   // op definition is loaded multiple times. Later-loaded
-  // definitions take precedense.
+  // definitions take precedence.
   // ApiDefs loaded from file must contain a subset of ops defined
   // in the OpList passed to the constructor.
   Status LoadFile(Env* env, const string& filename);
diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h
index 67943377b9f5cd..f577664709c064 100644
--- a/tensorflow/core/framework/op_kernel.h
+++ b/tensorflow/core/framework/op_kernel.h
@@ -534,7 +534,7 @@ class OpKernelContext {
     Rendezvous* rendezvous = nullptr;
 
     // Mechanism for executing a collective op that needs to coordinate
-    // with parallel instances runing on other devices.
+    // with parallel instances running on other devices.
     CollectiveExecutor* collective_executor = nullptr;
 
     // The session state for this op.
diff --git a/tensorflow/core/graph/while_context.h b/tensorflow/core/graph/while_context.h
index 5944e368979ce0..2a83eb7bd8eb94 100644
--- a/tensorflow/core/graph/while_context.h
+++ b/tensorflow/core/graph/while_context.h
@@ -31,7 +31,7 @@ namespace tensorflow {
 // future to support these features.
 //
 // TODO(skyewm): de/serialize in MetaGraphDef so imported while loops will be
-// differentiable. Figure out backwards compatability story.
+// differentiable. Figure out backwards compatibility story.
 class WhileContext {
  public:
   WhileContext(StringPiece frame_name, std::vector enter_nodes,
diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index 2c7b57971a6164..fd0547cf86894e 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -574,7 +574,6 @@ class SymbolicShapeRefiner {
     }
   };
 
-  // Compute the shape of the tensors outputed by node 'node' at output port
   // 'port_index' as the union of shape1 and shape2.
   ShapeHandle OutputAsUnion(const NodeDef* node, int port_index,
                             ShapeHandle shape1, ShapeHandle shape2) {
@@ -968,7 +967,7 @@ Status GraphProperties::PropagateShapes(
     const std::unordered_map& resource_handles,
     int num_loops) const {
   // Limit the number of iterations to prevent infinite loops in the presence of
-  // incorrect shape functions. The algoritm should converge in at most
+  // incorrect shape functions. The algorithm should converge in at most
   // num_nested_loops^2 * max_rank. We approximate max_rank with the constant 4.
   // The same applies to resources.
   VLOG(1) << "Propagating " << new_shapes->size() << " new shapes through "
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h
index 67bf1e6980e550..34d48819ac25ed 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.h
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.h
@@ -328,7 +328,7 @@ class VirtualScheduler {
   Costs graph_costs_;                   // Graph cost.
   std::map op_to_cost_;  // Per-op cost.
 
-  // Auxilliary data structures for constructing NodeState and DeviceState.
+  // Auxiliary data structures for constructing NodeState and DeviceState.
   GraphProperties graph_properties_;
   Cluster* cluster_;  // Not owned.
 
diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
index 87ab4608627e7b..e08ab1eb673e12 100644
--- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc
@@ -2183,7 +2183,7 @@ Status LayoutOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
 
   TuningConfig config;
   config.no_gemm = true;
-  // TODO(yaozhang): Enable tuning with various TuningConfig choices wtih
+  // TODO(yaozhang): Enable tuning with various TuningConfig choices with
   // the measurement-based estimator.
   status = Tune(item, graph_properties, config, output);
   if (!status.ok()) {
diff --git a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h
index f5ced95febfdfa..ae652961db3e81 100644
--- a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h
+++ b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h
@@ -76,7 +76,7 @@ class AdaptiveSharedBatchScheduler
           AdaptiveSharedBatchScheduler> {
  public:
   ~AdaptiveSharedBatchScheduler() {
-    // Finish processing batches before destorying other class members.
+    // Finish processing batches before destroying other class members.
     batch_thread_pool_.reset();
   }
 
diff --git a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
index 2503b475dc10e6..8e426ddf2b7b3d 100644
--- a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
+++ b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc
@@ -595,7 +595,7 @@ constexpr bool TileSizeOnNonLongSideFrontier(int TileLongSide,
   // For a tile size combination (longside, shortside), lying on the frontier
   // implies that (longside, shortside) is on or within the frontier but
   // (longside*2, shortside) or (longside, shortside+1) is not. With the above
-  // critereon, we simply need to use !TileSizeOnLongSideFrontier to ensure that
+  // criterion, we simply need to use !TileSizeOnLongSideFrontier to ensure that
   // it is not on the long side frontier.
   return !TileSizeOutsideFrontier(TileLongSide, TileShortSide, size_of_t) &&
          (TileSizeOutsideFrontier(TileLongSide * 2, TileShortSide, size_of_t) ||
diff --git a/tensorflow/core/kernels/nth_element_op.cc b/tensorflow/core/kernels/nth_element_op.cc
index 7f12eb953a31ec..0e43cc19aae513 100644
--- a/tensorflow/core/kernels/nth_element_op.cc
+++ b/tensorflow/core/kernels/nth_element_op.cc
@@ -114,7 +114,7 @@ struct NthElementFunctor {
 
     auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads());
     // The average time complexity of partition-based nth_element (BFPRT) is
-    // O(n), althought the worst time complexity could be O(n^2). Here, 20 is a
+    // O(n), although the worst time complexity could be O(n^2). Here, 20 is a
     // empirical factor of cost_per_unit.
     Shard(worker_threads.num_threads, worker_threads.workers, num_rows,
           20 * last_dim, SubNthElement);
diff --git a/tensorflow/core/kernels/roll_op.cc b/tensorflow/core/kernels/roll_op.cc
index 4b630809c5a854..f5ebf0ea2e29ea 100644
--- a/tensorflow/core/kernels/roll_op.cc
+++ b/tensorflow/core/kernels/roll_op.cc
@@ -84,7 +84,7 @@ void DoRoll(OpKernelContext* context, const int64 num_elements,
   // Shard
   auto worker_threads = context->device()->tensorflow_cpu_worker_threads();
   // 15 - expiramentally determined with float and bool types
-  const int cost_per_element = 15 * sizeof(T);  // rough esitmate
+  const int cost_per_element = 15 * sizeof(T);  // rough estimate
   Shard(worker_threads->num_threads, worker_threads->workers, num_elements,
         cost_per_element, std::move(work));
 }
diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc
index 2d9c99c124a3f2..4da7510c01bf83 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system.cc
@@ -103,7 +103,7 @@ constexpr char kResolveCacheSecs[] = "GCS_RESOLVE_REFRESH_SECS";
 // The environment variable to configure the http request's connection timeout.
 constexpr char kRequestConnectionTimeout[] =
     "GCS_REQUEST_CONNECTION_TIMEOUT_SECS";
-// The environment varaible to configure the http request's idle timeout.
+// The environment variable to configure the http request's idle timeout.
 constexpr char kRequestIdleTimeout[] = "GCS_REQUEST_IDLE_TIMEOUT_SECS";
 // The environment variable to configure the overall request timeout for
 // metadata requests.
diff --git a/tensorflow/core/platform/cloud/gcs_throttle.h b/tensorflow/core/platform/cloud/gcs_throttle.h
index 97a858e3fecfbb..8c9e2e074cbc20 100644
--- a/tensorflow/core/platform/cloud/gcs_throttle.h
+++ b/tensorflow/core/platform/cloud/gcs_throttle.h
@@ -132,7 +132,7 @@ class GcsThrottle {
    * UpdateState updates the available_tokens_ and last_updated_secs_ variables.
    *
    * UpdateState should be called in order to mark the passage of time, and
-   * therefore add tokens to the availble_tokens_ pool.
+   * therefore add tokens to the available_tokens_ pool.
    */
   void UpdateState() EXCLUSIVE_LOCKS_REQUIRED(mu_);
 
diff --git a/tensorflow/core/profiler/g3doc/command_line.md b/tensorflow/core/profiler/g3doc/command_line.md
index bbaf55e613f6f3..cc6d9def4724aa 100644
--- a/tensorflow/core/profiler/g3doc/command_line.md
+++ b/tensorflow/core/profiler/g3doc/command_line.md
@@ -82,7 +82,7 @@ bazel-bin/tensorflow/core/profiler/profiler \
 #
 # Alternatively, user can pass separate files.
 #
-# --graph_path contains the model architecutre and tensor shapes.
+# --graph_path contains the model architecture and tensor shapes.
 # --run_meta_path contains the memory and time information.
 # --op_log_path contains float operation and code traces.
 # --checkpoint_path contains the model checkpoint data.
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index a15ccdfd87b1c7..5372ef24b88658 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -32,7 +32,7 @@ message RewriterConfig {
     AGGRESSIVE = 3;
   }
 
-  // Enum controling the number of times to run optimizers. The default is to
+  // Enum controlling the number of times to run optimizers. The default is to
   // run them once.
   enum NumIterationsType {
     DEFAULT_NUM_ITERS = 0;
diff --git a/tensorflow/core/util/cuda_device_functions.h b/tensorflow/core/util/cuda_device_functions.h
index f2d4e470c82d9a..b91f8bb8ef0c36 100644
--- a/tensorflow/core/util/cuda_device_functions.h
+++ b/tensorflow/core/util/cuda_device_functions.h
@@ -537,7 +537,7 @@ __device__ detail::ToTypeIfConvertible CudaAtomicSub(T* ptr, U value) {
   return atomicSub(ptr, value);
 }
 
-// Specializations of substraction which add the negative value.
+// Specializations of subtraction which add the negative value.
 __device__ inline float CudaAtomicSub(float* ptr, float value) {
   return CudaAtomicAdd(ptr, -value);
 }
diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h
index 50a8e305749eec..8105121e7ce809 100644
--- a/tensorflow/core/util/mkl_util.h
+++ b/tensorflow/core/util/mkl_util.h
@@ -1359,7 +1359,7 @@ inline memory::dims MklDnnDimsInNCHW(const memory::dims& in_dims,
 /// Map MklDnn memory::dims object into TensorShape object.
 ///
 /// This function will simply map input shape in MKL-DNN memory::dims format
-/// in Tensorflow's TensorShape object by perserving dimension order.
+/// in Tensorflow's TensorShape object by preserving dimension order.
 ///
 /// @input MKL-DNN memory::dims object
 /// @output TensorShape corresponding to memory::dims
diff --git a/tensorflow/core/util/tensor_format.h b/tensorflow/core/util/tensor_format.h
index 646673512cf18f..517b85a5ba8c7c 100644
--- a/tensorflow/core/util/tensor_format.h
+++ b/tensorflow/core/util/tensor_format.h
@@ -61,7 +61,7 @@ enum FilterTensorFormat {
   FORMAT_OIHW = 1,
 
   // OIHW_VECT_I is the most performant tensor format for cudnn6's quantized
-  // int8 convolution and fused convolution. It is analagous to the NCHW_VECT_C
+  // int8 convolution and fused convolution. It is analogous to the NCHW_VECT_C
   // data format. It is laid out in the same order as OIHW, except that the size
   // of the Input Channels dimension is divided by 4, and a new dimension of
   // size 4 is appended, which packs 4 adjacent input channel weights into an
diff --git a/tensorflow/docs_src/api_guides/python/reading_data.md b/tensorflow/docs_src/api_guides/python/reading_data.md
index b3ca9583704eb3..5bbbfd32160f71 100644
--- a/tensorflow/docs_src/api_guides/python/reading_data.md
+++ b/tensorflow/docs_src/api_guides/python/reading_data.md
@@ -184,7 +184,7 @@ The recommended way to read a TFRecord file is with a @{tf.data.TFRecordDataset}
     dataset = dataset.map(decode)
 ```
 
-To acomplish the same task with a queue based input pipeline requires the following code 
+To accomplish the same task with a queue based input pipeline requires the following code
 (using the same `decode` function from the above example): 
 
 ``` python
diff --git a/tensorflow/docs_src/deploy/s3.md b/tensorflow/docs_src/deploy/s3.md
index ef3b030e3277c1..9ef9674338a905 100644
--- a/tensorflow/docs_src/deploy/s3.md
+++ b/tensorflow/docs_src/deploy/s3.md
@@ -1,6 +1,6 @@
 # How to run TensorFlow on S3
 
-Tensorflow supports reading and writing data to S3. S3 is an object storage API which is nearly ubiquitious, and can help in situations where data must accessed by multiple actors, such as in distributed training.
+Tensorflow supports reading and writing data to S3. S3 is an object storage API which is nearly ubiquitous, and can help in situations where data must accessed by multiple actors, such as in distributed training.
 
 This document guides you through the required setup, and provides examples on usage.
 
diff --git a/tensorflow/docs_src/mobile/mobile_intro.md b/tensorflow/docs_src/mobile/mobile_intro.md
index 69b63ae7d22ced..39dda0b45fa2ff 100644
--- a/tensorflow/docs_src/mobile/mobile_intro.md
+++ b/tensorflow/docs_src/mobile/mobile_intro.md
@@ -212,7 +212,7 @@ handle the task then it will be difficult to train a computer to do better.
 
 After you’ve solved any fundamental issues with your use case, you need to
 create a labeled dataset to define what problem you’re trying to solve. This
-step is extremely important, moreso than picking which model to use. You want it
+step is extremely important, more than picking which model to use. You want it
 to be as representative as possible of your actual use case, since the model
 will only be effective at the task you teach it. It’s also worth investing in
 tools to make labeling the data as efficient and accurate as possible. For
diff --git a/tensorflow/python/data/util/nest.py b/tensorflow/python/data/util/nest.py
index eff6e02c1484b2..7ee3d92cadd5d7 100644
--- a/tensorflow/python/data/util/nest.py
+++ b/tensorflow/python/data/util/nest.py
@@ -114,7 +114,7 @@ def is_sequence(seq):
   NOTE(mrry): This differs from `tensorflow.python.util.nest.is_sequence()`,
   which *does* treat a Python list as a sequence. For ergonomic
   reasons, `tf.data` users would prefer to treat lists as
-  implict `tf.Tensor` objects, and dicts as (nested) sequences.
+  implicit `tf.Tensor` objects, and dicts as (nested) sequences.
 
   Args:
     seq: an input sequence.
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 3691c99ddac6b4..bd16e33262741a 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -883,7 +883,7 @@ def _train_model_distributed(self, input_fn, hooks, saving_listeners):
             model_fn_lib.ModeKeys.TRAIN,
             self.config)
 
-        # TODO(anjalisridhar): Figure out how to resolve the folowing scaffold
+        # TODO(anjalisridhar): Figure out how to resolve the following scaffold
         # parameters: init_feed_dict, init_fn.
         scaffold_list = self._distribution.unwrap(
             grouped_estimator_spec.scaffold)
diff --git a/tensorflow/python/estimator/inputs/queues/feeding_functions.py b/tensorflow/python/estimator/inputs/queues/feeding_functions.py
index 8e5d8141a1a15d..8e2ec83020abc5 100644
--- a/tensorflow/python/estimator/inputs/queues/feeding_functions.py
+++ b/tensorflow/python/estimator/inputs/queues/feeding_functions.py
@@ -52,7 +52,7 @@ def _fill_array(arr, seq, fillvalue=0):
   If length of seq is less than arr padded length, fillvalue used.
   Args:
     arr: Padded tensor of shape [batch_size, ..., max_padded_dim_len].
-    seq: Non-padded list of data sampels of shape
+    seq: Non-padded list of data samples of shape
       [batch_size, ..., padded_dim(None)]
     fillvalue: Default fillvalue to use.
   """
diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py
index c16c3cda4892b8..9e6429e59ea9bd 100644
--- a/tensorflow/python/feature_column/feature_column.py
+++ b/tensorflow/python/feature_column/feature_column.py
@@ -48,7 +48,7 @@
 
       embedded_dept_column = embedding_column(
           categorical_column_with_vocabulary_list(
-              "department", ["math", "philosphy", ...]), dimension=10)
+              "department", ["math", "philosophy", ...]), dimension=10)
 
   * Wide (aka linear) models (`LinearClassifier`, `LinearRegressor`).
 
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index dd9acdd9ebb817..908d0da35e89cb 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -2573,7 +2573,7 @@ def set_shape_and_handle_data_for_outputs(op):
 
   When _USE_C_API = True, this is lazily called when a tensor's shape is first
   requested. Usually this should work automatically, but some edge cases may
-  require manaully calling this first to make sure Tensor._shape_val and
+  require manually calling this first to make sure Tensor._shape_val and
   Tensor._handle_data are set (e.g. manually overriding _handle_data, copying a
   Tensor).
   """
diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index dc56d88066cbe6..5e02e7e3ec605e 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -674,7 +674,7 @@ def test_foo(self):
 
 
   Args:
-    __unused__: Prevents sliently skipping tests.
+    __unused__: Prevents silently skipping tests.
     config: An optional config_pb2.ConfigProto to use to configure the
       session when executing graphs.
     use_gpu: If True, attempt to run as many operations as possible on GPU.
diff --git a/tensorflow/python/keras/_impl/keras/engine/network.py b/tensorflow/python/keras/_impl/keras/engine/network.py
index a0229be346fc69..3197d49fcee4da 100644
--- a/tensorflow/python/keras/_impl/keras/engine/network.py
+++ b/tensorflow/python/keras/_impl/keras/engine/network.py
@@ -115,7 +115,7 @@ def _base_init(self, name=None):
     # Entries are unique. Includes input and output layers.
     self._layers = []
 
-    # Used in symbolic mode only, only in conjonction with graph-networks
+    # Used in symbolic mode only, only in conjunction with graph-networks
     self._outbound_nodes = []
     self._inbound_nodes = []
 
diff --git a/tensorflow/python/keras/_impl/keras/engine/saving_test.py b/tensorflow/python/keras/_impl/keras/engine/saving_test.py
index 709a8e9fb1e1ba..c0b16b6bf5a050 100644
--- a/tensorflow/python/keras/_impl/keras/engine/saving_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/saving_test.py
@@ -457,7 +457,7 @@ def test_saving_model_with_long_layer_names(self):
       with h5py.File(fname, 'r') as h5file:
         num_names_arrays = len([attr for attr in h5file['model_weights'].attrs
                                 if attr.startswith('layer_names')])
-      # The chunking of layer names array should have happend.
+      # The chunking of layer names array should have happened.
       self.assertGreater(num_names_arrays, 0)
       out2 = model.predict(x)
       self.assertAllClose(out, out2, atol=1e-05)
@@ -502,7 +502,7 @@ def test_saving_model_with_long_weights_names(self):
         num_weight_arrays = len(
             [attr for attr in h5file['model_weights']['nested_model'].attrs
              if attr.startswith('weight_names')])
-      # The chunking of layer names array should have happend.
+      # The chunking of layer names array should have happened.
       self.assertGreater(num_weight_arrays, 0)
       out2 = model.predict(x)
       self.assertAllClose(out, out2, atol=1e-05)
diff --git a/tensorflow/python/keras/_impl/keras/estimator.py b/tensorflow/python/keras/_impl/keras/estimator.py
index c3c3fceb454773..5c79c964c8171c 100644
--- a/tensorflow/python/keras/_impl/keras/estimator.py
+++ b/tensorflow/python/keras/_impl/keras/estimator.py
@@ -72,7 +72,7 @@ def _any_variable_initalized():
   """Check if any variable has been initialized in the Keras model.
 
   Returns:
-    boolean, True if at least one variable has been initalized, else False.
+    boolean, True if at least one variable has been initialized, else False.
   """
   variables = variables_module.global_variables()
   for v in variables:
diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py
index f54f146e0ac102..5ec80b95eefabe 100644
--- a/tensorflow/python/kernel_tests/distributions/util_test.py
+++ b/tensorflow/python/kernel_tests/distributions/util_test.py
@@ -703,7 +703,7 @@ def _fill_triangular(self, x, upper=False):
       raise ValueError("Invalid shape.")
     n = np.int32(n)
     # We can't do: `x[..., -(n**2-m):]` because this doesn't correctly handle
-    # `m == n == 1`. Hence, we do absoulte indexing.
+    # `m == n == 1`. Hence, we do absolute indexing.
     x_tail = x[..., (m - (n * n - m)):]
     y = np.concatenate(
         [x, x_tail[..., ::-1]] if upper else [x_tail, x[..., ::-1]],
diff --git a/tensorflow/python/kernel_tests/manip_ops_test.py b/tensorflow/python/kernel_tests/manip_ops_test.py
index f31426713c49ba..dc3ea386714c98 100644
--- a/tensorflow/python/kernel_tests/manip_ops_test.py
+++ b/tensorflow/python/kernel_tests/manip_ops_test.py
@@ -93,7 +93,7 @@ def testComplexTypes(self):
   def testNegativeAxis(self):
     self._testAll(np.random.randint(-100, 100, (5)).astype(np.int32), 3, -1)
     self._testAll(np.random.randint(-100, 100, (4, 4)).astype(np.int32), 3, -2)
-    # Make sure negative axis shoudl be 0 <= axis + dims < dims
+    # Make sure negative axis should be 0 <= axis + dims < dims
     with self.test_session():
       with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
                                    "is out of range"):
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 7ac3bd8091f1cc..477f870060492c 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -1285,7 +1285,7 @@ def reduce_sum(input_tensor,
     The reduced tensor, of the same dtype as the input_tensor.
 
   @compatibility(numpy)
-  Equivalent to np.sum appart the fact that numpy upcast uint8 and int32 to
+  Equivalent to np.sum apart the fact that numpy upcast uint8 and int32 to
   int64 while tensorflow returns the same dtype as the input.
   @end_compatibility
   """
diff --git a/tensorflow/python/training/distribute.py b/tensorflow/python/training/distribute.py
index c16b05102edd27..30c857fd18db2f 100644
--- a/tensorflow/python/training/distribute.py
+++ b/tensorflow/python/training/distribute.py
@@ -734,7 +734,7 @@ def call_for_each_tower(self, fn, *args, **kwargs):
     `fn` may call `tf.get_tower_context()` to access methods such as
     `tower_id()` and `merge_call()`.
 
-    `merge_call()` is used to communicate betwen the towers and
+    `merge_call()` is used to communicate between the towers and
     re-enter the cross-tower context. All towers pause their execution
     having encountered a `merge_call()` call. After that the
     `merge_fn`-function is executed. Its results are then unwrapped and
diff --git a/tensorflow/python/util/util.cc b/tensorflow/python/util/util.cc
index 70aee4a3f663c8..9c8d50da7351d9 100644
--- a/tensorflow/python/util/util.cc
+++ b/tensorflow/python/util/util.cc
@@ -234,7 +234,7 @@ void SetDifferentKeysError(PyObject* dict1, PyObject* dict2, string* error_msg,
 
 // Returns true iff there were no "internal" errors. In other words,
 // errors that has nothing to do with structure checking.
-// If an "internal" error occured, the appropriate Python error will be
+// If an "internal" error occurred, the appropriate Python error will be
 // set and the caller can propage it directly to the user.
 //
 // Both `error_msg` and `is_type_error` must be non-null. `error_msg` must
diff --git a/tensorflow/python/util/util.h b/tensorflow/python/util/util.h
index c325baa5f86820..4bb80d8289e958 100644
--- a/tensorflow/python/util/util.h
+++ b/tensorflow/python/util/util.h
@@ -97,7 +97,7 @@ PyObject* AssertSameStructure(PyObject* o1, PyObject* o2, bool check_types);
 // used instead. The same convention is followed in `pack_sequence_as`. This
 // correctly repacks dicts and `OrderedDict`s after they have been flattened,
 // and also allows flattening an `OrderedDict` and then repacking it back using
-// a correponding plain dict, or vice-versa.
+// a corresponding plain dict, or vice-versa.
 // Dictionaries with non-sortable keys cannot be flattened.
 //
 // Args:
diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.h b/tensorflow/stream_executor/cuda/cuda_dnn.h
index 7d53dbe4a5c50c..dfe2779949239f 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.h
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.h
@@ -639,7 +639,7 @@ class CudnnSupport : public dnn::DnnSupport {
   // Guards the enqueueing of DNN operations via the dnn_handle_ below, and
   // access to current_dnn_stream_.
   //
-  // This is a public member because we need to add thread safty annotations in
+  // This is a public member because we need to add thread safety annotations in
   // the cudnn wrapper functions in the cc file, which need to access this
   // mutex (the annotations require C++ permission checks).
   mutex dnn_handle_mutex_;
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index e5cc886b3251f9..f8f83add6a07f4 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -1492,7 +1492,7 @@ def tf_py_wrap_cc(name,
 # This macro is for running python tests against system installed pip package
 # on Windows.
 #
-# py_test is built as an exectuable python zip file on Windows, which contains all
+# py_test is built as an executable python zip file on Windows, which contains all
 # dependencies of the target. Because of the C++ extensions, it would be very
 # inefficient if the py_test zips all runfiles, plus we don't need them when running
 # tests against system installed pip package. So we'd like to get rid of the deps
diff --git a/tensorflow/tools/graph_transforms/README.md b/tensorflow/tools/graph_transforms/README.md
index 67badb4869029b..9f6f553ba1e4c6 100644
--- a/tensorflow/tools/graph_transforms/README.md
+++ b/tensorflow/tools/graph_transforms/README.md
@@ -388,7 +388,7 @@ input is collapsed down into a simple constant.
 Args:
 
 *   clear_output_shapes: Clears tensor shape information saved as attributes.
-    Some older graphs containes out-of-date information and may cause import
+    Some older graphs contains out-of-date information and may cause import
     errors. Defaults to true.
 
 Prerequisites: None
diff --git a/third_party/examples/eager/spinn/README.md b/third_party/examples/eager/spinn/README.md
index 7f477d19208257..fbb1fde837b92b 100644
--- a/third_party/examples/eager/spinn/README.md
+++ b/third_party/examples/eager/spinn/README.md
@@ -70,7 +70,7 @@ Other eager execution examples can be found under [tensorflow/contrib/eager/pyth
 - After training, you may use the model to perform inference on input data in
   the SNLI data format. The premise and hypotheses sentences are specified with
   the command-line flags `--inference_premise` and `--inference_hypothesis`,
-  respecitvely. Each sentence should include the words, as well as parentheses
+  respectively. Each sentence should include the words, as well as parentheses
   representing a binary parsing of the sentence. The words and parentheses
   should all be separated by spaces. For instance,
 

From ebcde41d721ec554a7840cb18e4e8a7a489e424a Mon Sep 17 00:00:00 2001
From: Aditya Yogi 
Date: Thu, 3 May 2018 23:43:40 +0530
Subject: [PATCH 0338/1691] Update learning.py (#19064)

---
 tensorflow/contrib/slim/python/slim/learning.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/slim/python/slim/learning.py b/tensorflow/contrib/slim/python/slim/learning.py
index 8a2c74742a8ebb..6e55b9407bce5c 100644
--- a/tensorflow/contrib/slim/python/slim/learning.py
+++ b/tensorflow/contrib/slim/python/slim/learning.py
@@ -571,7 +571,7 @@ def train(train_op,
       default, two `Boolean`, scalar ops called "should_stop" and "should_log"
       are provided.
     log_every_n_steps: The frequency, in terms of global steps, that the loss
-      and global step and logged.
+      and global step are logged.
     graph: The graph to pass to the supervisor. If no graph is supplied the
       default graph is used.
     master: The address of the tensorflow master.

From eb88fd1ef5505e3f8617cc7105052fbce0e4af9e Mon Sep 17 00:00:00 2001
From: gracehoney <31743510+aaroey@users.noreply.github.com>
Date: Thu, 3 May 2018 11:17:10 -0700
Subject: [PATCH 0339/1691] Add a macro for registering the plugin so we don't
 need to depend on swig; remove the swig file; fix build dependencies; fix
 tf_custom_op_library by adding GOOGLE_TENSORRT macro when gpu_srcs is not
 empty.

---
 .../tensorrt/custom_plugin_examples/BUILD     | 69 +++++++++----------
 .../custom_plugin_examples/__init__.py        |  2 -
 .../inc_op_kernel.cu.cc                       |  1 +
 .../custom_plugin_examples/inc_op_plugin.cc   |  7 +-
 .../custom_plugin_examples/inc_op_plugin.h    |  5 +-
 .../custom_plugin_examples/plugin_wrap.i      | 31 ---------
 .../tensorrt/plugin/trt_plugin_factory.h      | 25 +++++++
 tensorflow/tensorflow.bzl                     |  2 +-
 8 files changed, 61 insertions(+), 81 deletions(-)
 delete mode 100644 tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_wrap.i

diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
index c68e69457da27a..e623b547811a76 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
@@ -24,24 +24,48 @@ load(
 )
 
 tf_gen_op_libs(
-    op_lib_names = [
-        "inc_op",
-    ],
+    op_lib_names = ["inc_op"],
 )
 
 tf_gen_op_wrapper_py(
     name = "inc_op",
-    deps = [
-        ":inc_op_op_lib",
-    ],
+    deps = [":inc_op_op_lib"],
 )
 
 tf_custom_op_library(
     name = "_inc_op.so",
-    srcs = ["ops/inc_op.cc"],
+    srcs = [
+        "inc_op_kernel.h",
+        "inc_op_plugin.cc",
+        "inc_op_plugin.h",
+        "ops/inc_op.cc",
+    ],
+    gpu_srcs = [
+        "inc_op_kernel.h",
+        "inc_op_kernel.cu.cc",
+    ],
     deps = [
-        "//tensorflow/core:lib_proto_parsing",
+        "//tensorflow/contrib/tensorrt:trt_plugins",
+    ] + if_tensorrt([
+        "@local_config_tensorrt//:nv_infer",
+    ]),
+)
+
+tf_kernel_library(
+    name = "inc_op_plugin_kernel",
+    srcs = ["inc_op_plugin.cc"],
+    hdrs = [
+        "inc_op_plugin.h",
+    ],
+    gpu_srcs = [
+        "inc_op_kernel.h",
+        "inc_op_kernel.cu.cc",
     ],
+    deps = [
+        "//tensorflow/contrib/tensorrt:trt_plugins",
+    ] + if_tensorrt([
+        "@local_config_tensorrt//:nv_infer",
+    ]) + tf_custom_op_library_additional_deps(),
 )
 
 tf_custom_op_py_library(
@@ -70,41 +94,12 @@ py_library(
     ],
 )
 
-tf_kernel_library(
-    name = "inc_op_plugin_kernel",
-    srcs = ["inc_op_plugin.cc"],
-    hdrs = [
-        "inc_op_plugin.h",
-    ],
-    gpu_srcs = [
-        "inc_op_kernel.h",
-        "inc_op_kernel.cu.cc",
-    ],
-    deps = [
-        "//tensorflow/contrib/tensorrt:trt_plugins",
-    ] + if_tensorrt([
-        "@local_config_tensorrt//:nv_infer",
-    ]) + tf_custom_op_library_additional_deps(),
-)
-
-tf_py_wrap_cc(
-    name = "plugin_wrap",
-    srcs = ["plugin_wrap.i"],
-    copts = tf_copts(),
-    deps = [
-        ":inc_op_plugin_kernel",
-        "//tensorflow/core:framework_lite",
-        "//util/python:python_headers",
-    ],
-)
-
 py_library(
     name = "init_py",
     srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
     deps = [
         ":inc_op_py",
-        ":plugin_wrap",
     ],
 )
 
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py
index e4cd0ae8a055df..e06904ab564d90 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py
@@ -19,8 +19,6 @@
 from __future__ import print_function
 
 from tensorflow.contrib.tensorrt.custom_plugin_examples.ops import gen_inc_op
-from tensorflow.contrib.tensorrt.custom_plugin_examples.plugin_wrap import inc_op_register
 from tensorflow.contrib.tensorrt.custom_plugin_examples import inc_op as import_inc_op_so
 
 inc_op = gen_inc_op.inc_plugin_trt
-inc_op_register()
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc
index ee9fbe0ea119fb..abbc0c5680a85d 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #if GOOGLE_TENSORRT
 #include "cuda/include/cuda_runtime_api.h"
 
+
 namespace tensorflow {
 namespace tensorrt {
 
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc
index 489bc15def5156..d56aedc6d40d7e 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc
@@ -31,12 +31,7 @@ IncOpPlugin* CreateIncPluginDeserialize(const void* buffer, size_t length) {
   return new IncOpPlugin(buffer, length);
 }
 
-bool RegisterIncOpPlugin() {
-  if (PluginFactoryTensorRT::GetInstance()->IsPlugin(kPluginName))
-    return false;
-  return PluginFactoryTensorRT::GetInstance()->RegisterPlugin(
-      kPluginName, CreateIncPluginDeserialize, CreateIncPlugin);
-}
+REGISTER_TRT_PLUGIN(kPluginName, CreateIncPluginDeserialize, CreateIncPlugin);
 
 IncOpPlugin::IncOpPlugin() : plugin_name_(kPluginName) {}
 
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h
index 0676abe7687a49..60153546d2e303 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h
@@ -18,6 +18,7 @@ limitations under the License.
 
 #include 
 #include 
+
 #include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h"
 
 #if GOOGLE_CUDA
@@ -92,10 +93,6 @@ class IncOpPlugin : public PluginTensorRT {
   const string plugin_name_;
 };
 
-IncOpPlugin* CreateIncPlugin();
-IncOpPlugin* CreateIncPluginDeserialize(const void*, size_t);
-bool RegisterIncOpPlugin();
-
 }  // namespace tensorrt
 }  // namespace tensorflow
 
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_wrap.i b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_wrap.i
deleted file mode 100644
index 9882daa8426d8b..00000000000000
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_wrap.i
+++ /dev/null
@@ -1,31 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/* Wrap inc_op_plugin */
-%module inc_op_plugin
-%{
-#include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h"
-extern bool tensorflow::tensorrt::RegisterIncOpPlugin();
-%}
-
-%{
-bool inc_op_register() {
-  return tensorflow::tensorrt::RegisterIncOpPlugin();
-}
-%}
-
-extern bool tensorflow::tensorrt::RegisterIncOpPlugin();
-
-bool inc_op_register();
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
index 6d2992bbbbc14d..54fbca593014f7 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
@@ -22,6 +22,8 @@ limitations under the License.
 
 #include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h"
 #include "tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/macros.h"
 
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
@@ -64,6 +66,29 @@ class PluginFactoryTensorRT : public nvinfer1::IPluginFactory {
   std::mutex instance_m_;
 };
 
+class TrtPluginRegistrar {
+ public:
+  TrtPluginRegistrar(const string& name,
+                     PluginDeserializeFunc deserialize_func,
+                     PluginConstructFunc construct_func) {
+    auto factory = PluginFactoryTensorRT::GetInstance();
+    QCHECK(factory->RegisterPlugin(name, deserialize_func, construct_func))
+        << "Failed to register plugin: " << name;
+  }
+};
+
+#define REGISTER_TRT_PLUGIN(name, deserialize_func, construct_func) \
+    REGISTER_TRT_PLUGIN_UNIQ_HELPER(                                \
+        __COUNTER__, name, deserialize_func, construct_func)
+#define REGISTER_TRT_PLUGIN_UNIQ_HELPER(         \
+    ctr, name, deserialize_func, construct_func) \
+    REGISTER_TRT_PLUGIN_UNIQ(ctr, name, deserialize_func, construct_func)
+#define REGISTER_TRT_PLUGIN_UNIQ(ctr, name, deserialize_func, construct_func) \
+    static ::tensorflow::tensorrt::TrtPluginRegistrar                         \
+        trt_plugin_registrar##ctr TF_ATTRIBUTE_UNUSED =                       \
+            ::tensorflow::tensorrt::TrtPluginRegistrar(                       \
+                name, deserialize_func, construct_func)
+
 }  // namespace tensorrt
 }  // namespace tensorflow
 
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index e5cc886b3251f9..c27f89436548c8 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -1309,7 +1309,7 @@ def tf_custom_op_library(name, srcs=[], gpu_srcs=[], deps=[], linkopts=[]):
     native.cc_library(
         name=basename + "_gpu",
         srcs=gpu_srcs,
-        copts=_cuda_copts(),
+        copts=_cuda_copts() + if_tensorrt(["-DGOOGLE_TENSORRT=1"]),
         deps=deps + if_cuda(cuda_deps))
     cuda_deps.extend([":" + basename + "_gpu"])
 

From 85a47596caf89705aae8ffcb57fcdaecb22fe356 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 11:16:06 -0700
Subject: [PATCH 0340/1691] [XLA] Redesign: add ExecuteGraph to grpc service.

PiperOrigin-RevId: 195281004
---
 tensorflow/compiler/xla/rpc/BUILD               | 2 +-
 tensorflow/compiler/xla/rpc/grpc_client_test.cc | 4 ++--
 tensorflow/compiler/xla/rpc/grpc_service.cc     | 7 +++++++
 tensorflow/compiler/xla/rpc/grpc_service.h      | 4 ++++
 third_party/libxsmm.BUILD                       | 2 +-
 5 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/tensorflow/compiler/xla/rpc/BUILD b/tensorflow/compiler/xla/rpc/BUILD
index 977f8637873a4b..0d56a9a477b159 100644
--- a/tensorflow/compiler/xla/rpc/BUILD
+++ b/tensorflow/compiler/xla/rpc/BUILD
@@ -55,7 +55,7 @@ tf_cc_test(
     deps = [
         ":grpc_stub",
         "//tensorflow/compiler/xla/client",
-        "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/rpc/grpc_client_test.cc b/tensorflow/compiler/xla/rpc/grpc_client_test.cc
index b559ee4b5a345d..10997c0719dfb8 100644
--- a/tensorflow/compiler/xla/rpc/grpc_client_test.cc
+++ b/tensorflow/compiler/xla/rpc/grpc_client_test.cc
@@ -24,7 +24,7 @@ limitations under the License.
 #include "grpc++/security/credentials.h"
 
 #include "tensorflow/compiler/xla/client/client.h"
-#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
 #include "tensorflow/compiler/xla/rpc/grpc_stub.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
 #include "tensorflow/core/lib/io/path.h"
@@ -84,7 +84,7 @@ TEST_F(GRPCClientTestBase, ItsAlive) {
 }
 
 TEST_F(GRPCClientTestBase, AxpyTenValues) {
-  ComputationBuilder builder(client_.get(), "axpy_10");
+  XlaBuilder builder("axpy_10");
   auto alpha = builder.ConstantR0(3.1415926535);
   auto x = builder.ConstantR1(
       {-1.0, 1.0, 2.0, -2.0, -3.0, 3.0, 4.0, -4.0, -5.0, 5.0});
diff --git a/tensorflow/compiler/xla/rpc/grpc_service.cc b/tensorflow/compiler/xla/rpc/grpc_service.cc
index 0b100bd108e239..ffb72fc73c5bc1 100644
--- a/tensorflow/compiler/xla/rpc/grpc_service.cc
+++ b/tensorflow/compiler/xla/rpc/grpc_service.cc
@@ -75,6 +75,13 @@ ::grpc::Status GRPCService::Execute(::grpc::ServerContext* context,
       [this, arg, result]() { return service_->Execute(arg, result); });
 }
 
+::grpc::Status GRPCService::ExecuteGraph(::grpc::ServerContext* /*context*/,
+                                         const ExecuteGraphRequest* arg,
+                                         ExecuteResponse* result) {
+  return DelegateRPC(
+      [this, arg, result]() { return service_->ExecuteGraph(arg, result); });
+}
+
 ::grpc::Status GRPCService::ExecuteAsync(::grpc::ServerContext* context,
                                          const ExecuteAsyncRequest* arg,
                                          ExecuteAsyncResponse* result) {
diff --git a/tensorflow/compiler/xla/rpc/grpc_service.h b/tensorflow/compiler/xla/rpc/grpc_service.h
index fad74375bd59f7..50f02796f2d45b 100644
--- a/tensorflow/compiler/xla/rpc/grpc_service.h
+++ b/tensorflow/compiler/xla/rpc/grpc_service.h
@@ -54,6 +54,10 @@ class GRPCService : public grpc::XlaService::Service {
                          const ExecuteRequest* arg,
                          ExecuteResponse* result) override;
 
+  ::grpc::Status ExecuteGraph(::grpc::ServerContext* context,
+                              const ExecuteGraphRequest* arg,
+                              ExecuteResponse* result) override;
+
   ::grpc::Status ExecuteAsync(::grpc::ServerContext* context,
                               const ExecuteAsyncRequest* arg,
                               ExecuteAsyncResponse* result) override;
diff --git a/third_party/libxsmm.BUILD b/third_party/libxsmm.BUILD
index 4124f2db637689..78ed1f4e168891 100644
--- a/third_party/libxsmm.BUILD
+++ b/third_party/libxsmm.BUILD
@@ -38,8 +38,8 @@ genrule(
         ":libxsmm_interface",
     ],
     visibility = [
-        "//tensorflow/core/kernels:__pkg__",
         "//third_party/eigen3:__pkg__",
+        "//tensorflow/core/kernels:__pkg__",
     ],
 )
 

From a16ba4fc0d3faec077c689f3f361264978a2d3cb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 12:00:57 -0700
Subject: [PATCH 0341/1691] Do not delegate temporary tensors to NNAPI.   -
 also added delegation for MUL, and set the default scale to be 0.0f.

PiperOrigin-RevId: 195288948
---
 tensorflow/contrib/lite/nnapi_delegate.cc | 39 +++++++++++++++++++----
 1 file changed, 33 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc
index 6a78f30fd1dba5..e1895dd38e9329 100644
--- a/tensorflow/contrib/lite/nnapi_delegate.cc
+++ b/tensorflow/contrib/lite/nnapi_delegate.cc
@@ -72,11 +72,23 @@ NNAPIDelegate::~NNAPIDelegate() {
 // Adds the tensors of the interpreter to the NN API model.
 // Returns the number of operands added.
 uint32_t addTensorOperands(tflite::Interpreter* interpreter,
-                           ANeuralNetworksModel* nn_model) {
+                           ANeuralNetworksModel* nn_model,
+                           const std::vector& skip_list) {
   uint32_t next_id = 0;
   for (size_t i = 0; i < interpreter->tensors_size(); i++) {
+    // skip temporaries tensors.
+    bool shouldSkip = false;
+    for (auto skip_idx : skip_list) {
+      if (i == skip_idx) {
+        shouldSkip = true;
+        break;
+      }
+    }
+    if (shouldSkip) continue;
+
     int32_t nn_type = 0;
-    float scale = 1.0f;
+    // NNAPI requires 32-bit float scale to be zero, tflite doesn't care
+    float scale = 0.0f;
     int32_t zeroPoint = 0;
     TfLiteTensor* tensor = interpreter->tensor(i);
     switch (tensor->type) {
@@ -116,11 +128,11 @@ uint32_t addTensorOperands(tflite::Interpreter* interpreter,
       if (const NNAPIAllocation* alloc = dynamic_cast(
               static_cast(tensor->allocation))) {
         CHECK_NN(ANeuralNetworksModel_setOperandValueFromMemory(
-            nn_model, i, alloc->memory(), alloc->offset(tensor->data.raw),
+            nn_model, next_id, alloc->memory(), alloc->offset(tensor->data.raw),
             tensor->bytes));
       } else {
         CHECK_NN(ANeuralNetworksModel_setOperandValue(
-            nn_model, i, tensor->data.raw, tensor->bytes));
+            nn_model, next_id, tensor->data.raw, tensor->bytes));
       }
     }
     ++next_id;
@@ -253,6 +265,10 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
         nn_op_type = ANEURALNETWORKS_ADD;
         add_add_params();
         break;
+      case tflite::BuiltinOperator_MUL:
+        nn_op_type = ANEURALNETWORKS_MUL;
+        add_add_params();
+        break;
       case tflite::BuiltinOperator_AVERAGE_POOL_2D:
         add_pooling_params(node.builtin_data);
         nn_op_type = ANEURALNETWORKS_AVERAGE_POOL_2D;
@@ -330,7 +346,6 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM:
       case tflite::BuiltinOperator_L2_NORMALIZATION:
       case tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION:
-      case tflite::BuiltinOperator_MUL:
       case tflite::BuiltinOperator_PAD:
       case tflite::BuiltinOperator_RESIZE_BILINEAR:
       case tflite::BuiltinOperator_CALL:
@@ -381,7 +396,19 @@ TfLiteStatus NNAPIDelegate::BuildGraph(Interpreter* interpreter) {
   if (!nn_model_) {
     CHECK_NN(ANeuralNetworksModel_create(&nn_model_));
 
-    uint32_t next_id = addTensorOperands(interpreter, nn_model_);
+    // Find all the temporary tensors and put them in a skip_list.
+    std::vector skip_list;
+    for (size_t i = 0; i < interpreter->nodes_size(); i++) {
+      const auto* node_and_registration = interpreter->node_and_registration(i);
+      const TfLiteNode& node = node_and_registration->first;
+      if (node.temporaries != nullptr) {
+        for (int j = 0; j < node.temporaries->size; j++) {
+          skip_list.push_back(static_cast(node.temporaries->data[j]));
+        }
+      }
+    }
+
+    uint32_t next_id = addTensorOperands(interpreter, nn_model_, skip_list);
     AddOpsAndParams(interpreter, nn_model_, next_id);
     CHECK_NN(ANeuralNetworksModel_identifyInputsAndOutputs(
         nn_model_, static_cast(interpreter->inputs().size()),

From e5854637cc3f8099586f18ed144fd6d4f90a6fc7 Mon Sep 17 00:00:00 2001
From: Yao Zhang 
Date: Thu, 3 May 2018 12:19:01 -0700
Subject: [PATCH 0342/1691] Simplify file reading and support SavedModel.

PiperOrigin-RevId: 195291836
---
 .../python/grappler/cost_analyzer_tool.py     | 75 ++++++++++---------
 1 file changed, 41 insertions(+), 34 deletions(-)

diff --git a/tensorflow/python/grappler/cost_analyzer_tool.py b/tensorflow/python/grappler/cost_analyzer_tool.py
index 0853db25240696..e6229e18566d7b 100644
--- a/tensorflow/python/grappler/cost_analyzer_tool.py
+++ b/tensorflow/python/grappler/cost_analyzer_tool.py
@@ -21,11 +21,13 @@
 import argparse
 import sys
 
+from google.protobuf import message
 from google.protobuf import text_format
 from tensorflow.contrib.fused_conv.ops import gen_fused_conv2d_bias_activation_op  # pylint: disable=unused-import
 from tensorflow.core.framework import graph_pb2
 from tensorflow.core.protobuf import meta_graph_pb2
 from tensorflow.core.protobuf import rewriter_config_pb2
+from tensorflow.core.protobuf import saved_model_pb2
 from tensorflow.python.framework import importer
 from tensorflow.python.framework import ops
 from tensorflow.python.grappler import cost_analyzer
@@ -37,33 +39,42 @@
 
 def get_metagraph():
   """Constructs and returns a MetaGraphDef from the input file."""
-  if FLAGS.metagraphdef:
-    with gfile.GFile(FLAGS.metagraphdef) as meta_file:
-      metagraph = meta_graph_pb2.MetaGraphDef()
-      if FLAGS.metagraphdef.endswith(".pbtxt"):
-        text_format.Merge(meta_file.read(), metagraph)
-      else:
-        metagraph.ParseFromString(meta_file.read())
-    if FLAGS.fetch is not None:
-      fetch_collection = meta_graph_pb2.CollectionDef()
-      for fetch in FLAGS.fetch.split(","):
-        fetch_collection.node_list.value.append(fetch)
-      metagraph.collection_def["train_op"].CopyFrom(fetch_collection)
-  else:
-    with gfile.GFile(FLAGS.graphdef) as graph_file:
-      graph_def = graph_pb2.GraphDef()
-      if FLAGS.graphdef.endswith(".pbtxt"):
-        text_format.Merge(graph_file.read(), graph_def)
-      else:
-        graph_def.ParseFromString(graph_file.read())
-      importer.import_graph_def(graph_def, name="")
-      graph = ops.get_default_graph()
-      for fetch in FLAGS.fetch.split(","):
-        fetch_op = graph.get_operation_by_name(fetch)
-        graph.add_to_collection("train_op", fetch_op)
-      metagraph = saver.export_meta_graph(
-          graph_def=graph.as_graph_def(), graph=graph)
-  return metagraph
+  with gfile.GFile(FLAGS.input) as input_file:
+    input_data = input_file.read()
+    try:
+      saved_model = saved_model_pb2.SavedModel()
+      text_format.Merge(input_data, saved_model)
+      meta_graph = saved_model.meta_graphs[0]
+    except text_format.ParseError:
+      try:
+        saved_model.ParseFromString(input_data)
+        meta_graph = saved_model.meta_graphs[0]
+      except message.DecodeError:
+        try:
+          meta_graph = meta_graph_pb2.MetaGraphDef()
+          text_format.Merge(input_data, meta_graph)
+        except text_format.ParseError:
+          try:
+            meta_graph.ParseFromString(input_data)
+          except message.DecodeError:
+            try:
+              graph_def = graph_pb2.GraphDef()
+              text_format.Merge(input_data, graph_def)
+            except text_format.ParseError:
+              try:
+                graph_def.ParseFromString(input_data)
+              except message.DecodeError:
+                raise ValueError("Invalid input file.")
+            importer.import_graph_def(graph_def, name="")
+            graph = ops.get_default_graph()
+            meta_graph = saver.export_meta_graph(
+                graph_def=graph.as_graph_def(), graph=graph)
+  if FLAGS.fetch is not None:
+    fetch_collection = meta_graph_pb2.CollectionDef()
+    for fetch in FLAGS.fetch.split(","):
+      fetch_collection.node_list.value.append(fetch)
+    meta_graph.collection_def["train_op"].CopyFrom(fetch_collection)
+  return meta_graph
 
 
 def main(_):
@@ -85,15 +96,11 @@ def main(_):
 if __name__ == "__main__":
   parser = argparse.ArgumentParser()
   parser.add_argument(
-      "--metagraphdef",
+      "--input",
       type=str,
       default=None,
-      help="Input .meta MetaGraphDef file path.")
-  parser.add_argument(
-      "--graphdef",
-      type=str,
-      default=None,
-      help="Input .pb GraphDef file path.")
+      help="Input file path. Accept SavedModel, MetaGraphDef, and GraphDef in "
+      "either binary or text format.")
   parser.add_argument(
       "--fetch",
       type=str,

From 4b767a835b61061ef4d167dc1ee935f2f85a3e87 Mon Sep 17 00:00:00 2001
From: Allen Lavoie 
Date: Thu, 3 May 2018 12:53:47 -0700
Subject: [PATCH 0343/1691] Small fix for an eager colab notebook.

PiperOrigin-RevId: 195296384
---
 .../contrib/eager/python/examples/notebooks/1_basics.ipynb      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb
index 0279db80fa3cb3..9fd2d8d1254e32 100644
--- a/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb
+++ b/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb
@@ -478,7 +478,7 @@
       "source": [
         "# Time GPU-based matrix multiplications.\n",
         "\n",
-        "if is_gpu_available:\n",
+        "if tf.test.is_gpu_available():\n",
         "  # First use of the GPU will be slow:\n",
         "  print(\"Time to conduct first matmul on GPU:\")\n",
         "  %time tf.matmul(gpu_tensor, gpu_tensor)\n",

From 775d1c03c1772c0c2e10e5884af8d9363cfdf314 Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Thu, 3 May 2018 12:59:33 -0700
Subject: [PATCH 0344/1691] [TF:XLA] Bump open source llvm revision to r331442

PiperOrigin-RevId: 195297133
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 94cac4f8fa957f..8b6ad0a138928c 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -452,11 +452,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/a5108a08ceab35886a7df07c86f96aedd3d94bb7.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/a5108a08ceab35886a7df07c86f96aedd3d94bb7.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/b3f6a6a61625296bb532a65c0bf51b91b05b3361.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/b3f6a6a61625296bb532a65c0bf51b91b05b3361.tar.gz",
       ],
-      sha256 = "79cae03ebbdfd812bb69c460e1325ca069b5c576f7c7071f8216cf2b0975e36f",
-      strip_prefix = "llvm-a5108a08ceab35886a7df07c86f96aedd3d94bb7",
+      sha256 = "93895b289a78a47a1e75652e12a1b9a6c119f086a509b00e0084cf2bb944b709",
+      strip_prefix = "llvm-b3f6a6a61625296bb532a65c0bf51b91b05b3361",
       build_file = clean_dep("//third_party/llvm:llvm.BUILD"),
   )
 

From ceda30408f66a7eea86dc359164deb662d5a32d0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 13:00:56 -0700
Subject: [PATCH 0345/1691] Enable unary chain hoisting optimization for
 concat/split/splitv by default.

PiperOrigin-RevId: 195297330
---
 tensorflow/core/grappler/op_types.cc          | 38 ++++++++++++-------
 tensorflow/core/grappler/op_types.h           |  4 ++
 .../optimizers/arithmetic_optimizer.cc        | 18 ++++++---
 .../optimizers/arithmetic_optimizer.h         |  2 +-
 .../optimizers/arithmetic_optimizer_test.cc   | 16 ++++----
 5 files changed, 51 insertions(+), 27 deletions(-)

diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index 7c936dfca19a5e..c48dc00941c8ef 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -476,28 +476,40 @@ bool IsInvolution(const NodeDef& node) {
   return involution_ops->count(node.op()) > 0;
 }
 
+bool IsValueAndOrderAndShapePreserving(const NodeDef& node) {
+  if (NumNonControlInputs(node) == 1 && IsAggregate(node)) {
+    return true;
+  }
+  static const std::unordered_set*
+      value_and_order_and_shape_preserving_ops =
+          CHECK_NOTNULL((new const std::unordered_set{
+              "CheckNumerics",
+              "DebugGradientIdentity",
+              "DeepCopy"
+              "Enter",
+              "Exit",
+              "Identity",
+              "IdentityN",
+              "PreventGradient",
+              "Print",
+              "Snapshot",
+              "StopGradient",
+          }));
+  return value_and_order_and_shape_preserving_ops->count(node.op()) > 0;
+}
+
 bool IsValueAndOrderPreserving(const NodeDef& node) {
   if (NumNonControlInputs(node) == 1 && IsAggregate(node)) {
     return true;
   }
   static const std::unordered_set* value_and_order_preserving_ops =
       CHECK_NOTNULL((new const std::unordered_set{
-          "CheckNumerics",
-          "DebugGradientIdentity",
-          "DeepCopy"
-          "Enter",
-          "Exit",
           "ExpandDims",
-          "Identity",
-          "IdentityN",
-          "PreventGradient",
-          "Print",
-          "Reshape",
           "Snapshot",
           "Squeeze",
-          "StopGradient",
       }));
-  return value_and_order_preserving_ops->count(node.op()) > 0;
+  return value_and_order_preserving_ops->count(node.op()) > 0 ||
+         IsValueAndOrderAndShapePreserving(node);
 }
 
 bool IsValuePreserving(const NodeDef& node) {
@@ -564,7 +576,7 @@ bool IsUnaryElementWise(const NodeDef& node) {
           "Tanh",
       }));
   return element_wise_ops->count(node.op()) > 0 ||
-         (!IsIdentityN(node) && IsValueAndOrderPreserving(node));
+         (!IsIdentityN(node) && IsValueAndOrderAndShapePreserving(node));
 }
 
 bool HasOpDef(const NodeDef& node) {
diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h
index 7a1b438768659d..e33dd215388700 100644
--- a/tensorflow/core/grappler/op_types.h
+++ b/tensorflow/core/grappler/op_types.h
@@ -174,6 +174,10 @@ bool ModifiesInputsInPlace(const NodeDef& node);
 // own inverse such that f(f(x)) == x.
 bool IsInvolution(const NodeDef& node);
 
+// Returns true if the op preserves the order and value of elements
+// and shape of its first input tensor.
+bool IsValueAndOrderAndShapePreserving(const NodeDef& node);
+
 // Returns true if the op preserves the order and value of elements in its
 // first input tensor and possible changes its shape.
 bool IsValueAndOrderPreserving(const NodeDef& node);
diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
index d6510ba681aa2b..2a5654f75224f1 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -1400,6 +1400,11 @@ class HoistCWiseUnaryChainsStage : public ArithmeticOptimizerStage {
       return n > 1;
     } else if (IsSplit(*node) || IsSplitV(*node)) {
       const int num_split = node->attr().at("num_split").i();
+      if (NumNonControlOutputs(*node, *ctx().node_map) > num_split) {
+        // TODO(rmlarsen): Remove this constraint when we have optimizations
+        // in place for merging slices into splits.
+        return false;
+      }
       return num_split > 1 && !IsAlreadyOptimized(*node);
     }
     return false;
@@ -1458,13 +1463,13 @@ class HoistCWiseUnaryChainsStage : public ArithmeticOptimizerStage {
     if (tails.empty()) {
       return Status::OK();
     }
-    AddControlInputs(ctrl_inputs, root_node);
     AddToOptimizationQueue(root_node);
     optimized_nodes_.insert(root_node->name());
     if (node_is_concat_) {
+      AddControlInputs(ctrl_inputs, root_node);
       return HoistChainForConcat(prefix_length, tails, root_node);
     } else {
-      return HoistChainForSplit(prefix_length, tails, root_node);
+      return HoistChainForSplit(prefix_length, tails, ctrl_inputs, root_node);
     }
   }
 
@@ -1542,9 +1547,8 @@ class HoistCWiseUnaryChainsStage : public ArithmeticOptimizerStage {
           IsInPreserveSet(*op)) {
         return false;
       }
-      if (node_is_concat_ &&
-          ctx().node_map->GetOutputs(op->name()).size() > 1) {
-        // TODO(rmlarsen): Allow and hoist outgoing control edges.
+      if (ctx().node_map->GetOutputs(op->name()).size() > 1) {
+        // TODO(rmlarsen): Allow outgoing control edges.
         return false;
       }
     }
@@ -1612,6 +1616,7 @@ class HoistCWiseUnaryChainsStage : public ArithmeticOptimizerStage {
   }
 
   Status HoistChainForSplit(const int prefix_length, const ChainLinkSet& tails,
+                            std::set* ctrl_inputs,
                             NodeDef* split_node) {
     // Create a new chain before the split node to process the input tensor.
     const string& split_name = split_node->name();
@@ -1646,6 +1651,9 @@ class HoistCWiseUnaryChainsStage : public ArithmeticOptimizerStage {
     cur_copy->add_input(orig_input);
     ctx().node_map->UpdateOutput(NodeName(orig_input), split_name,
                                  cur_copy->name());
+    // Make sure all the control inputs are satisfied before running the first
+    // node in the new chain.
+    AddControlInputs(ctrl_inputs, cur_copy);
 
     // Connect all consumers of the tail nodes directly to the
     // output port of Split from which the chain started.
diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h
index 3b297ec0aabb25..6309dc1a33d146 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h
@@ -65,7 +65,7 @@ class ArithmeticOptimizer : public GraphOptimizer {
     bool remove_redundant_bitcast = true;
     bool remove_redundant_cast = true;
     bool remove_negation = true;
-    bool hoist_cwise_unary_chains = false;
+    bool hoist_cwise_unary_chains = true;
     bool convert_sqrt_div_to_rsqrt_mul = false;
 
     // Choose which arithmetic optimizer stages will be enabled for a given
diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
index f903f53a352738..d32743f3f25015 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
@@ -2320,16 +2320,16 @@ TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryIntoSplit) {
     EXPECT_NE(node.name(), "cos_exp_b2");
 
     if (node.name() == "split1") {
-      EXPECT_EQ(3, node.input_size());
+      EXPECT_EQ(2, node.input_size());
       EXPECT_EQ("axis", node.input(0));
       EXPECT_EQ("ArithmeticOptimizer/_sin_a_split1", node.input(1));
-      EXPECT_EQ("^ctrl1", node.input(2));
       found++;
     }
     if (node.name() == "ArithmeticOptimizer/_sin_a_split1") {
       EXPECT_EQ("Sin", node.op());
-      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ(2, node.input_size());
       EXPECT_EQ("x", node.input(0));
+      EXPECT_EQ("^ctrl1", node.input(1));
       found++;
     }
     if (node.name() == "id_a") {
@@ -2349,8 +2349,11 @@ TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryIntoSplit) {
     }
     if (node.name() == "ArithmeticOptimizer/_exp_a2_split2") {
       EXPECT_EQ("Exp", node.op());
-      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ(4, node.input_size());
       EXPECT_EQ("x", node.input(0));
+      EXPECT_EQ("^ctrl1", node.input(1));
+      EXPECT_EQ("^ctrl2", node.input(2));
+      EXPECT_EQ("^ctrl3", node.input(3));
       found++;
     }
     if (node.name() == "ArithmeticOptimizer/_cos_exp_a2_split2") {
@@ -2360,13 +2363,10 @@ TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryIntoSplit) {
       found++;
     }
     if (node.name() == "split2") {
-      EXPECT_EQ(6, node.input_size());
+      EXPECT_EQ(3, node.input_size());
       EXPECT_EQ("ArithmeticOptimizer/_cos_exp_a2_split2", node.input(0));
       EXPECT_EQ("size_splits2", node.input(1));
       EXPECT_EQ("axis", node.input(2));
-      EXPECT_EQ("^ctrl1", node.input(3));
-      EXPECT_EQ("^ctrl2", node.input(4));
-      EXPECT_EQ("^ctrl3", node.input(5));
       found++;
     }
     if (node.name() == "id_a2") {

From fded0f901c99087b100191273e28692f9b4569ee Mon Sep 17 00:00:00 2001
From: Ruoxin Sang 
Date: Thu, 3 May 2018 13:03:48 -0700
Subject: [PATCH 0346/1691] Change all std::bind usages in GCS to lambdas. Fix
 the wrong #define Guard name in retrying_file_system.h.

PiperOrigin-RevId: 195297877
---
 .../core/platform/cloud/gcs_dns_cache.cc      |  4 +-
 .../core/platform/cloud/gcs_file_system.cc    |  5 +-
 .../platform/cloud/retrying_file_system.h     | 81 ++++++++++---------
 .../core/platform/cloud/retrying_utils.cc     |  6 +-
 4 files changed, 52 insertions(+), 44 deletions(-)

diff --git a/tensorflow/core/platform/cloud/gcs_dns_cache.cc b/tensorflow/core/platform/cloud/gcs_dns_cache.cc
index 4d9aff4d24f06c..f2e64662a92309 100644
--- a/tensorflow/core/platform/cloud/gcs_dns_cache.cc
+++ b/tensorflow/core/platform/cloud/gcs_dns_cache.cc
@@ -71,8 +71,8 @@ void GcsDnsCache::AnnotateRequest(HttpRequest* request) {
     addresses_ = ResolveNames(kCachedDomainNames);
 
     // Note: we opt to use a thread instead of a delayed closure.
-    worker_.reset(env_->StartThread(
-        {}, "gcs_dns_worker", std::bind(&GcsDnsCache::WorkerThread, this)));
+    worker_.reset(env_->StartThread({}, "gcs_dns_worker",
+                                    [this]() { return WorkerThread(); }));
     started_ = true;
   }
 
diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc
index f1e18403ec83dc..488f9cc75d4584 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system.cc
@@ -1397,8 +1397,7 @@ Status GcsFileSystem::RenameObject(const string& src, const string& target) {
   // on the server side, we can't just retry the whole RenameFile operation
   // because the source object is already gone.
   return RetryingUtils::DeleteWithRetries(
-      std::bind(&GcsFileSystem::DeleteFile, this, src),
-      initial_retry_delay_usec_);
+      [this, &src]() { return DeleteFile(src); }, initial_retry_delay_usec_);
 }
 
 Status GcsFileSystem::IsDirectory(const string& fname) {
@@ -1454,7 +1453,7 @@ Status GcsFileSystem::DeleteRecursively(const string& dirname,
     // and therefore RetryingFileSystem won't pay attention to the failures,
     // we need to make sure these failures are properly retried.
     const auto& delete_file_status = RetryingUtils::DeleteWithRetries(
-        std::bind(&GcsFileSystem::DeleteFile, this, full_path),
+        [this, &full_path]() { return DeleteFile(full_path); },
         initial_retry_delay_usec_);
     if (!delete_file_status.ok()) {
       if (IsDirectory(full_path).ok()) {
diff --git a/tensorflow/core/platform/cloud/retrying_file_system.h b/tensorflow/core/platform/cloud/retrying_file_system.h
index 399a21617eedf2..92aa72be89e4b3 100644
--- a/tensorflow/core/platform/cloud/retrying_file_system.h
+++ b/tensorflow/core/platform/cloud/retrying_file_system.h
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_CORE_PLATFORM_RETRYING_FILE_SYSTEM_H_
-#define TENSORFLOW_CORE_PLATFORM_RETRYING_FILE_SYSTEM_H_
+#ifndef TENSORFLOW_CORE_PLATFORM_CLOUD_RETRYING_FILE_SYSTEM_H_
+#define TENSORFLOW_CORE_PLATFORM_CLOUD_RETRYING_FILE_SYSTEM_H_
 
 #include 
 #include 
@@ -54,74 +54,80 @@ class RetryingFileSystem : public FileSystem {
 
   Status FileExists(const string& fname) override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&FileSystem::FileExists, base_file_system_.get(), fname),
+        [this, &fname]() { return base_file_system_->FileExists(fname); },
         initial_delay_microseconds_);
   }
 
   Status GetChildren(const string& dir, std::vector* result) override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&FileSystem::GetChildren, base_file_system_.get(), dir,
-                  result),
+        [this, &dir, result]() {
+          return base_file_system_->GetChildren(dir, result);
+        },
         initial_delay_microseconds_);
   }
 
   Status GetMatchingPaths(const string& pattern,
                           std::vector* result) override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&FileSystem::GetMatchingPaths, base_file_system_.get(),
-                  pattern, result),
+        [this, &pattern, result]() {
+          return base_file_system_->GetMatchingPaths(pattern, result);
+        },
         initial_delay_microseconds_);
   }
 
   Status Stat(const string& fname, FileStatistics* stat) override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&FileSystem::Stat, base_file_system_.get(), fname, stat),
+        [this, &fname, stat]() { return base_file_system_->Stat(fname, stat); },
         initial_delay_microseconds_);
   }
 
   Status DeleteFile(const string& fname) override {
     return RetryingUtils::DeleteWithRetries(
-        std::bind(&FileSystem::DeleteFile, base_file_system_.get(), fname),
+        [this, &fname]() { return base_file_system_->DeleteFile(fname); },
         initial_delay_microseconds_);
   }
 
   Status CreateDir(const string& dirname) override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&FileSystem::CreateDir, base_file_system_.get(), dirname),
+        [this, &dirname]() { return base_file_system_->CreateDir(dirname); },
         initial_delay_microseconds_);
   }
 
   Status DeleteDir(const string& dirname) override {
     return RetryingUtils::DeleteWithRetries(
-        std::bind(&FileSystem::DeleteDir, base_file_system_.get(), dirname),
+        [this, &dirname]() { return base_file_system_->DeleteDir(dirname); },
         initial_delay_microseconds_);
   }
 
   Status GetFileSize(const string& fname, uint64* file_size) override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&FileSystem::GetFileSize, base_file_system_.get(), fname,
-                  file_size),
+        [this, &fname, file_size]() {
+          return base_file_system_->GetFileSize(fname, file_size);
+        },
         initial_delay_microseconds_);
   }
 
   Status RenameFile(const string& src, const string& target) override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&FileSystem::RenameFile, base_file_system_.get(), src,
-                  target),
+        [this, &src, &target]() {
+          return base_file_system_->RenameFile(src, target);
+        },
         initial_delay_microseconds_);
   }
 
   Status IsDirectory(const string& dirname) override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&FileSystem::IsDirectory, base_file_system_.get(), dirname),
+        [this, &dirname]() { return base_file_system_->IsDirectory(dirname); },
         initial_delay_microseconds_);
   }
 
   Status DeleteRecursively(const string& dirname, int64* undeleted_files,
                            int64* undeleted_dirs) override {
     return RetryingUtils::DeleteWithRetries(
-        std::bind(&FileSystem::DeleteRecursively, base_file_system_.get(),
-                  dirname, undeleted_files, undeleted_dirs),
+        [this, &dirname, undeleted_files, undeleted_dirs]() {
+          return base_file_system_->DeleteRecursively(dirname, undeleted_files,
+                                                      undeleted_dirs);
+        },
         initial_delay_microseconds_);
   }
 
@@ -148,8 +154,9 @@ class RetryingRandomAccessFile : public RandomAccessFile {
   Status Read(uint64 offset, size_t n, StringPiece* result,
               char* scratch) const override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&RandomAccessFile::Read, base_file_.get(), offset, n, result,
-                  scratch),
+        [this, offset, n, result, scratch]() {
+          return base_file_->Read(offset, n, result, scratch);
+        },
         initial_delay_microseconds_);
   }
 
@@ -172,23 +179,20 @@ class RetryingWritableFile : public WritableFile {
 
   Status Append(const StringPiece& data) override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&WritableFile::Append, base_file_.get(), data),
+        [this, &data]() { return base_file_->Append(data); },
         initial_delay_microseconds_);
   }
   Status Close() override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&WritableFile::Close, base_file_.get()),
-        initial_delay_microseconds_);
+        [this]() { return base_file_->Close(); }, initial_delay_microseconds_);
   }
   Status Flush() override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&WritableFile::Flush, base_file_.get()),
-        initial_delay_microseconds_);
+        [this]() { return base_file_->Flush(); }, initial_delay_microseconds_);
   }
   Status Sync() override {
     return RetryingUtils::CallWithRetries(
-        std::bind(&WritableFile::Sync, base_file_.get()),
-        initial_delay_microseconds_);
+        [this]() { return base_file_->Sync(); }, initial_delay_microseconds_);
   }
 
  private:
@@ -203,8 +207,9 @@ Status RetryingFileSystem::NewRandomAccessFile(
     const string& filename, std::unique_ptr* result) {
   std::unique_ptr base_file;
   TF_RETURN_IF_ERROR(RetryingUtils::CallWithRetries(
-      std::bind(&FileSystem::NewRandomAccessFile, base_file_system_.get(),
-                filename, &base_file),
+      [this, &filename, &base_file]() {
+        return base_file_system_->NewRandomAccessFile(filename, &base_file);
+      },
       initial_delay_microseconds_));
   result->reset(new retrying_internals::RetryingRandomAccessFile(
       std::move(base_file), initial_delay_microseconds_));
@@ -216,8 +221,9 @@ Status RetryingFileSystem::NewWritableFile(
     const string& filename, std::unique_ptr* result) {
   std::unique_ptr base_file;
   TF_RETURN_IF_ERROR(RetryingUtils::CallWithRetries(
-      std::bind(&FileSystem::NewWritableFile, base_file_system_.get(), filename,
-                &base_file),
+      [this, &filename, &base_file]() {
+        return base_file_system_->NewWritableFile(filename, &base_file);
+      },
       initial_delay_microseconds_));
   result->reset(new retrying_internals::RetryingWritableFile(
       std::move(base_file), initial_delay_microseconds_));
@@ -229,8 +235,9 @@ Status RetryingFileSystem::NewAppendableFile(
     const string& filename, std::unique_ptr* result) {
   std::unique_ptr base_file;
   TF_RETURN_IF_ERROR(RetryingUtils::CallWithRetries(
-      std::bind(&FileSystem::NewAppendableFile, base_file_system_.get(),
-                filename, &base_file),
+      [this, &filename, &base_file]() {
+        return base_file_system_->NewAppendableFile(filename, &base_file);
+      },
       initial_delay_microseconds_));
   result->reset(new retrying_internals::RetryingWritableFile(
       std::move(base_file), initial_delay_microseconds_));
@@ -241,11 +248,13 @@ template 
 Status RetryingFileSystem::NewReadOnlyMemoryRegionFromFile(
     const string& filename, std::unique_ptr* result) {
   return RetryingUtils::CallWithRetries(
-      std::bind(&FileSystem::NewReadOnlyMemoryRegionFromFile,
-                base_file_system_.get(), filename, result),
+      [this, &filename, result]() {
+        return base_file_system_->NewReadOnlyMemoryRegionFromFile(filename,
+                                                                  result);
+      },
       initial_delay_microseconds_);
 }
 
 }  // namespace tensorflow
 
-#endif  // TENSORFLOW_CORE_PLATFORM_RETRYING_FILE_SYSTEM_H_
+#endif  // TENSORFLOW_CORE_PLATFORM_CLOUD_RETRYING_FILE_SYSTEM_H_
diff --git a/tensorflow/core/platform/cloud/retrying_utils.cc b/tensorflow/core/platform/cloud/retrying_utils.cc
index 99691ecfb9d5a5..d2df42202487f1 100644
--- a/tensorflow/core/platform/cloud/retrying_utils.cc
+++ b/tensorflow/core/platform/cloud/retrying_utils.cc
@@ -44,9 +44,9 @@ bool IsRetriable(error::Code code) {
 
 Status RetryingUtils::CallWithRetries(const std::function& f,
                                       const int64 initial_delay_microseconds) {
-  return CallWithRetries(f, initial_delay_microseconds,
-                         std::bind(&Env::SleepForMicroseconds, Env::Default(),
-                                   std::placeholders::_1));
+  return CallWithRetries(f, initial_delay_microseconds, [](int64 micros) {
+    return Env::Default()->SleepForMicroseconds(micros);
+  });
 }
 
 Status RetryingUtils::CallWithRetries(

From 278e68cedbb80c6f3342856bfccf688a808e461a Mon Sep 17 00:00:00 2001
From: Benoit Steiner 
Date: Thu, 3 May 2018 13:09:28 -0700
Subject: [PATCH 0347/1691] Simplified the implementation of shape_n since the
 optimized code path isn't needed anymore and can be incorrect in some rare
 cases.

PiperOrigin-RevId: 195298813
---
 tensorflow/python/ops/array_ops.py                | 10 +---------
 tensorflow/python/profiler/model_analyzer_test.py |  2 +-
 2 files changed, 2 insertions(+), 10 deletions(-)

diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index e235047aff39f6..96df15684b8571 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -263,15 +263,7 @@ def shape_n(input, out_type=dtypes.int32, name=None):
       type `out_type`.
   """
 
-  output = gen_array_ops.shape_n(input, out_type=out_type, name=name)
-  if not context.executing_eagerly():
-    for i, input_tensor in enumerate(input):
-      input_tensor = ops.convert_to_tensor(input_tensor)
-      input_shape = input_tensor.get_shape()
-      if input_shape.is_fully_defined():
-        output[i] = constant(
-            input_shape.as_list(), dtype=out_type, name=name)
-  return output
+  return gen_array_ops.shape_n(input, out_type=out_type, name=name)
 
 
 @tf_export("size")
diff --git a/tensorflow/python/profiler/model_analyzer_test.py b/tensorflow/python/profiler/model_analyzer_test.py
index 04ba28c219e276..75580fc6308345 100644
--- a/tensorflow/python/profiler/model_analyzer_test.py
+++ b/tensorflow/python/profiler/model_analyzer_test.py
@@ -232,7 +232,7 @@ def testComplexCodeView(self):
 
         self.assertLess(0, tfprof_node.total_exec_micros)
         self.assertEqual(2844, tfprof_node.total_parameters)
-        self.assertLess(168800, tfprof_node.total_float_ops)
+        self.assertLess(145660, tfprof_node.total_float_ops)
         self.assertEqual(8, len(tfprof_node.children))
         self.assertEqual('_TFProfRoot', tfprof_node.name)
         self.assertEqual(

From 41dcb67efd272e9ce0e5071433f42a9d540ec6dc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 13:09:30 -0700
Subject: [PATCH 0348/1691] Fix bugs in model pruner.

PiperOrigin-RevId: 195298816
---
 tensorflow/core/grappler/optimizers/model_pruner.cc | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/core/grappler/optimizers/model_pruner.cc b/tensorflow/core/grappler/optimizers/model_pruner.cc
index 3311e970108d94..36eab4999d0ff3 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner.cc
@@ -70,6 +70,7 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
     }
     // Try to keep the nodes ordered somewhat topologically since this helps
     // further optimizations perform better.
+    runnable_item.graph.mutable_node()->Reserve(keep.size());
     for (int i = keep.size() - 1; i >= 0; --i) {
       *runnable_item.graph.add_node() = *keep[i];
     }
@@ -113,6 +114,7 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
     }
   }
 
+  pruned_graph->Clear();
   *pruned_graph->mutable_library() = item.graph.library();
   *pruned_graph->mutable_versions() = item.graph.versions();
 
@@ -122,6 +124,7 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
   }
 
   const bool fetches_are_known = !item.fetch.empty();
+  pruned_graph->mutable_node()->Reserve(runnable_item.graph.node_size());
   for (auto& node : runnable_item.graph.node()) {
     if (!fetches_are_known ||
         nodes_to_delete.find(&node) == nodes_to_delete.end()) {
@@ -134,6 +137,7 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
   VLOG(1) << "Pruned " << nodes_to_delete.size()
           << " nodes from the graph. The graph now contains "
           << pruned_graph->node_size() << " nodes.";
+  CHECK_LE(pruned_graph->node_size(), item.graph.node_size());
 
   return Status::OK();
 }

From 5a64e609d0eb94244067f5d7514605863c9f37c3 Mon Sep 17 00:00:00 2001
From: Allen Lavoie 
Date: Thu, 3 May 2018 13:22:33 -0700
Subject: [PATCH 0349/1691] Checkpointable: Utilities to read object metadata

Useful for inspecting checkpoints programatically (e.g. in unit tests).

PiperOrigin-RevId: 195300780
---
 tensorflow/contrib/checkpoint/__init__.py     |  4 ++
 .../contrib/checkpoint/python/visualize.py    | 16 +-------
 .../eager/python/examples/spinn/spinn_test.py | 10 ++---
 .../python/training/checkpointable_utils.py   | 38 +++++++++++++++++++
 .../training/checkpointable_utils_test.py     | 16 ++++++++
 5 files changed, 64 insertions(+), 20 deletions(-)

diff --git a/tensorflow/contrib/checkpoint/__init__.py b/tensorflow/contrib/checkpoint/__init__.py
index 1192cc44a17823..d2c30f121539f8 100644
--- a/tensorflow/contrib/checkpoint/__init__.py
+++ b/tensorflow/contrib/checkpoint/__init__.py
@@ -16,7 +16,9 @@
 
 
 For creating and managing dependencies:
+@@CheckpointableObjectGraph
 @@dot_graph_from_checkpoint
+@@object_metadata
 @@split_dependency
 """
 
@@ -26,6 +28,8 @@
 
 from tensorflow.contrib.checkpoint.python.split_dependency import split_dependency
 from tensorflow.contrib.checkpoint.python.visualize import dot_graph_from_checkpoint
+from tensorflow.core.protobuf.checkpointable_object_graph_pb2 import CheckpointableObjectGraph
+from tensorflow.python.training.checkpointable_utils import object_metadata
 
 from tensorflow.python.util.all_util import remove_undocumented
 
diff --git a/tensorflow/contrib/checkpoint/python/visualize.py b/tensorflow/contrib/checkpoint/python/visualize.py
index 86fbdb41d2c378..9a3b23bb2c30ee 100644
--- a/tensorflow/contrib/checkpoint/python/visualize.py
+++ b/tensorflow/contrib/checkpoint/python/visualize.py
@@ -17,10 +17,9 @@
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.core.protobuf import checkpointable_object_graph_pb2
 from tensorflow.python import pywrap_tensorflow
-from tensorflow.python.framework import errors_impl
 from tensorflow.python.training import checkpointable
+from tensorflow.python.training import checkpointable_utils
 
 
 def dot_graph_from_checkpoint(save_path):
@@ -52,20 +51,9 @@ def dot_graph_from_checkpoint(save_path):
     A graph in DOT format as a string.
   """
   reader = pywrap_tensorflow.NewCheckpointReader(save_path)
-  try:
-    object_graph_string = reader.get_tensor(
-        checkpointable.OBJECT_GRAPH_PROTO_KEY)
-  except errors_impl.NotFoundError:
-    raise ValueError(
-        ('The specified checkpoint "%s" does not appear to be object-based (it '
-         'is missing the key "%s"). Likely it was created with a name-based '
-         'saver and does not contain an object dependency graph.') % (
-             save_path, checkpointable.OBJECT_GRAPH_PROTO_KEY))
+  object_graph = checkpointable_utils.object_metadata(save_path)
   shape_map = reader.get_variable_to_shape_map()
   dtype_map = reader.get_variable_to_dtype_map()
-  object_graph = (
-      checkpointable_object_graph_pb2.CheckpointableObjectGraph())
-  object_graph.ParseFromString(object_graph_string)
   graph = 'digraph {\n'
   def _escape(name):
     return name.replace('"', '\\"')
diff --git a/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py b/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py
index f825a2a7363fbe..1e4746d01ca1a8 100644
--- a/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py
+++ b/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py
@@ -34,10 +34,10 @@
 from tensorflow.contrib.eager.python.examples.spinn import data
 from third_party.examples.eager.spinn import spinn
 from tensorflow.contrib.summary import summary_test_util
-from tensorflow.core.protobuf import checkpointable_object_graph_pb2
 from tensorflow.python.eager import test
 from tensorflow.python.framework import test_util
-from tensorflow.python.training import checkpoint_utils
+from tensorflow.python.training import checkpointable_utils
+from tensorflow.python.training import saver
 # pylint: enable=g-bad-import-order
 
 
@@ -421,10 +421,8 @@ def testTrainSpinn(self):
 
     # 5. Verify that checkpoints exist and contains all the expected variables.
     self.assertTrue(glob.glob(os.path.join(config.logdir, "ckpt*")))
-    object_graph_string = checkpoint_utils.load_variable(
-        config.logdir, name="_CHECKPOINTABLE_OBJECT_GRAPH")
-    object_graph = checkpointable_object_graph_pb2.CheckpointableObjectGraph()
-    object_graph.ParseFromString(object_graph_string)
+    object_graph = checkpointable_utils.object_metadata(
+        saver.latest_checkpoint(config.logdir))
     ckpt_variable_names = set()
     for node in object_graph.nodes:
       for attribute in node.attributes:
diff --git a/tensorflow/python/training/checkpointable_utils.py b/tensorflow/python/training/checkpointable_utils.py
index 9cdd53cbf9629b..cf4112ff99b3d6 100644
--- a/tensorflow/python/training/checkpointable_utils.py
+++ b/tensorflow/python/training/checkpointable_utils.py
@@ -159,6 +159,44 @@ def add_variable(checkpointable, name, shape=None, dtype=dtypes.float32,
       initializer=initializer, getter=_default_getter)
 
 
+def object_metadata(save_path):
+  """Retrieves information about the objects in a checkpoint.
+
+  Example usage:
+
+  ```python
+  object_graph = tf.contrib.checkpoint.object_metadata(
+      tf.train.latest_checkpoint(checkpoint_directory))
+  ckpt_variable_names = set()
+  for node in object_graph.nodes:
+    for attribute in node.attributes:
+      ckpt_variable_names.add(attribute.full_name)
+  ```
+
+  Args:
+    save_path: The path to the checkpoint, as returned by `save` or
+      `tf.train.latest_checkpoint`.
+  Returns:
+    A parsed `tf.contrib.checkpoint.CheckpointableObjectGraph` protocol buffer.
+  Raises:
+    ValueError: If an object graph was not found in the checkpoint.
+  """
+  reader = pywrap_tensorflow.NewCheckpointReader(save_path)
+  try:
+    object_graph_string = reader.get_tensor(
+        checkpointable_lib.OBJECT_GRAPH_PROTO_KEY)
+  except errors_impl.NotFoundError:
+    raise ValueError(
+        ('The specified checkpoint "%s" does not appear to be object-based (it '
+         'is missing the key "%s"). Likely it was created with a name-based '
+         'saver and does not contain an object dependency graph.') % (
+             save_path, checkpointable_lib.OBJECT_GRAPH_PROTO_KEY))
+  object_graph_proto = (
+      checkpointable_object_graph_pb2.CheckpointableObjectGraph())
+  object_graph_proto.ParseFromString(object_graph_string)
+  return object_graph_proto
+
+
 def _breadth_first_checkpointable_traversal(root_checkpointable):
   """Find shortest paths to all variables owned by dependencies of root."""
   bfs_sorted = []
diff --git a/tensorflow/python/training/checkpointable_utils_test.py b/tensorflow/python/training/checkpointable_utils_test.py
index 40dfeb28d50a2b..3b8166bf37a6a8 100644
--- a/tensorflow/python/training/checkpointable_utils_test.py
+++ b/tensorflow/python/training/checkpointable_utils_test.py
@@ -155,6 +155,22 @@ def testShapeDtype(self):
     self.assertEqual(dtypes.float64, v2.dtype)
     self.assertAllEqual([1., 1., 1.], self.evaluate(v2))
 
+  def testObjectMetadata(self):
+    with context.eager_mode():
+      checkpoint_directory = self.get_temp_dir()
+      checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+      dense = core.Dense(1)
+      checkpoint = checkpointable_utils.Checkpoint(dense=dense)
+      dense(constant_op.constant([[1.]]))
+      save_path = checkpoint.save(checkpoint_prefix)
+
+    objects = checkpointable_utils.object_metadata(save_path)
+    all_variable_names = []
+    for obj in objects.nodes:
+      for attribute in obj.attributes:
+        all_variable_names.append(attribute.full_name)
+    self.assertIn("dense/kernel", all_variable_names)
+
 
 class _MirroringSaveable(saver_lib.BaseSaverBuilder.SaveableObject):
 

From 7529268d692c1c888f93924e6ca5e10fd3183b80 Mon Sep 17 00:00:00 2001
From: Shanqing Cai 
Date: Thu, 3 May 2018 13:30:12 -0700
Subject: [PATCH 0350/1691] tfdbg + tflearn: replace deprecated classes and
 methods in example & docs

* `tf.contrib.learn.Experiment` is deprecated. Remove it from debug_tflearn_iris.py.
* Use `tf.estimator.DNNClassifier`, instead of the older one from `tf.contrib.learn`.
* Use `train()`, instead of `fit()` of Estimators.
* `Estimator.predict()` supports hooks. Add example lines for that.

PiperOrigin-RevId: 195301913
---
 .../docs_src/programmers_guide/debugger.md    | 89 ++++++-------------
 tensorflow/python/debug/BUILD                 |  1 -
 .../debug/examples/debug_tflearn_iris.py      | 83 ++++++++---------
 3 files changed, 65 insertions(+), 108 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index f7817b06d4c8bd..6bd941886d7fe8 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -34,7 +34,7 @@ type of bug in TensorFlow model development.
 The following example is for users who use the low-level
 [`Session`](https://www.tensorflow.org/api_docs/python/tf/Session) API of
 TensorFlow. A later section of this document describes how to use **tfdbg**
-with a higher-level API, namely tf-learn `Estimator`s and `Experiment`s.
+with a higher-level API, namely `Estimator`s.
 To *observe* such an issue, run the following command without the debugger (the
 source code can be found
 [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/debug/examples/debug_mnist.py)):
@@ -418,21 +418,20 @@ run -f has_inf_or_nan`
 Confirm that no tensors are flagged as containing `nan` or `inf` values, and
 accuracy now continues to rise rather than getting stuck. Success!
 
-## Debugging tf-learn Estimators and Experiments
+## Debugging TensorFlow Estimators
 
 This section explains how to debug TensorFlow programs that use the `Estimator`
-and `Experiment` APIs. Part of the convenience provided by these APIs is that
+APIs. Part of the convenience provided by these APIs is that
 they manage `Session`s internally. This makes the `LocalCLIDebugWrapperSession`
 described in the preceding sections inapplicable. Fortunately, you can still
 debug them by using special `hook`s provided by `tfdbg`.
 
-### Debugging tf.contrib.learn Estimators
-
-Currently, `tfdbg` can debug the
-@{tf.contrib.learn.BaseEstimator.fit$`fit()`}
-@{tf.contrib.learn.BaseEstimator.evaluate$`evaluate()`}
-methods of tf-learn `Estimator`s. To debug `Estimator.fit()`,
-create a `LocalCLIDebugHook` and supply it in the `monitors` argument. For example:
+`tfdbg` can debug the
+@{tf.estimator.Estimator.train$`train()`},
+@{tf.estimator.Estimator.evaluate$`evaluate()`} and
+@{tf.estimator.Estimator.predict$`predict()`}
+methods of tf-learn `Estimator`s. To debug `Estimator.train()`,
+create a `LocalCLIDebugHook` and supply it in the `hooks` argument. For example:
 
 ```python
 # First, let your BUILD target depend on "//tensorflow/python/debug:debug_py"
@@ -443,67 +442,33 @@ from tensorflow.python import debug as tf_debug
 # Create a LocalCLIDebugHook and use it as a monitor when calling fit().
 hooks = [tf_debug.LocalCLIDebugHook()]
 
-classifier.fit(x=training_set.data,
-               y=training_set.target,
-               steps=1000,
-               monitors=hooks)
+# To debug `train`:
+classifier.train(input_fn,
+                 steps=1000,
+                 hooks=hooks)
 ```
 
-To debug `Estimator.evaluate()`, assign hooks to the `hooks` parameter, as in
-the following example:
+Similarly, to debug `Estimator.evaluate()` and `Estimator.predict()`, assign
+hooks to the `hooks` parameter, as in the following example:
 
 ```python
-accuracy_score = classifier.evaluate(x=test_set.data,
-                                     y=test_set.target,
+# To debug `evaluate`:
+accuracy_score = classifier.evaluate(eval_input_fn,
                                      hooks=hooks)["accuracy"]
-```
 
+# To debug `predict`:
+predict_results = classifier.predict(predict_input_fn, hooks=hooks)
+```
 
 [debug_tflearn_iris.py](https://www.tensorflow.org/code/tensorflow/python/debug/examples/debug_tflearn_iris.py),
-based on [tf-learn's iris tutorial](https://www.tensorflow.org/versions/r1.2/get_started/tflearn), contains a full example of how to
-use the tfdbg with `Estimator`s. To run this example, do:
+based on [tf-learn's iris tutorial](https://www.tensorflow.org/versions/r1.8/get_started/tflearn),
+contains a full example of how to use the tfdbg with `Estimator`s.
+To run this example, do:
 
 ```none
 python -m tensorflow.python.debug.examples.debug_tflearn_iris --debug
 ```
 
-### Debugging tf.contrib.learn Experiments
-
-`Experiment` is a construct in `tf.contrib.learn` at a higher level than
-`Estimator`.
-It provides a single interface for training and evaluating a model. To debug
-the `train()` and `evaluate()` calls to an `Experiment` object, you can
-use the keyword arguments `train_monitors` and `eval_hooks`, respectively, when
-calling its constructor. For example:
-
-```python
-# First, let your BUILD target depend on "//tensorflow/python/debug:debug_py"
-# (You don't need to worry about the BUILD dependency if you are using a pip
-#  install of open-source TensorFlow.)
-from tensorflow.python import debug as tf_debug
-
-hooks = [tf_debug.LocalCLIDebugHook()]
-
-ex = experiment.Experiment(classifier,
-                           train_input_fn=iris_input_fn,
-                           eval_input_fn=iris_input_fn,
-                           train_steps=FLAGS.train_steps,
-                           eval_delay_secs=0,
-                           eval_steps=1,
-                           train_monitors=hooks,
-                           eval_hooks=hooks)
-
-ex.train()
-accuracy_score = ex.evaluate()["accuracy"]
-```
-
-To build and run the `debug_tflearn_iris` example in the `Experiment` mode, do:
-
-```none
-python -m tensorflow.python.debug.examples.debug_tflearn_iris \
-    --use_experiment --debug
-```
-
 The `LocalCLIDebugHook` also allows you to configure a `watch_fn` that can be
 used to flexibly specify what `Tensor`s to watch on different `Session.run()`
 calls, as a function of the `fetches` and `feed_dict` and other states. See
@@ -573,7 +538,7 @@ Often, your model is running on a remote machine or a process that you don't
 have terminal access to. To perform model debugging in such cases, you can use
 the `offline_analyzer` binary of `tfdbg` (described below). It operates on
 dumped data directories. This can be done to both the lower-level `Session` API
-and the higher-level `Estimator` and `Experiment` APIs.
+and the higher-level `Estimator` API.
 
 ### Debugging Remote tf.Sessions
 
@@ -636,7 +601,7 @@ can be inspected offline. See
 [the proto definition](https://www.tensorflow.org/code/tensorflow/core/protobuf/debug.proto)
 for more details.
 
-### Debugging Remotely-Running tf-learn Estimators and Experiments
+### Debugging Remotely-Running Estimators
 
 If your remote TensorFlow server runs `Estimator`s,
 you can use the non-interactive `DumpingDebugHook`. For example:
@@ -652,8 +617,8 @@ hooks = [tf_debug.DumpingDebugHook("/shared/storage/location/tfdbg_dumps_1")]
 
 Then this `hook` can be used in the same way as the `LocalCLIDebugHook` examples
 described earlier in this document.
-As the training and/or evalution of `Estimator` or `Experiment`
-happens, tfdbg creates directories having the following name pattern:
+As the training, evalution or prediction happens with `Estimator`,
+tfdbg creates directories having the following name pattern:
 `/shared/storage/location/tfdbg_dumps_1/run__`.
 Each directory corresponds to a `Session.run()` call that underlies
 the `fit()` or `evaluate()` call. You can load these directories and inspect
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index b5760df1ed47be..183994ddaa72b5 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -449,7 +449,6 @@ py_binary(
     deps = [
         ":debug_py",
         "//tensorflow:tensorflow_py",
-        "//third_party/py/numpy",
         "@six_archive//:six",
     ],
 )
diff --git a/tensorflow/python/debug/examples/debug_tflearn_iris.py b/tensorflow/python/debug/examples/debug_tflearn_iris.py
index 4f4666ee4fa51e..00090b21fe35ac 100644
--- a/tensorflow/python/debug/examples/debug_tflearn_iris.py
+++ b/tensorflow/python/debug/examples/debug_tflearn_iris.py
@@ -22,11 +22,9 @@
 import sys
 import tempfile
 
-import numpy as np
 from six.moves import urllib
 import tensorflow as tf
 
-from tensorflow.contrib.learn.python.learn import experiment
 from tensorflow.contrib.learn.python.learn.datasets import base
 from tensorflow.python import debug as tf_debug
 
@@ -82,28 +80,34 @@ def iris_input_fn():
 def main(_):
   # Load datasets.
   if FLAGS.fake_data:
-    training_set = tf.contrib.learn.datasets.base.Dataset(
-        np.random.random([120, 4]),
-        np.random.random_integers(3, size=[120]) - 1)
-    test_set = tf.contrib.learn.datasets.base.Dataset(
-        np.random.random([30, 4]),
-        np.random.random_integers(3, size=[30]) - 1)
+    def training_input_fn():
+      return ({"features": tf.random_normal([128, 4])},
+              tf.random_uniform([128], minval=0, maxval=3, dtype=tf.int32))
+    def test_input_fn():
+      return ({"features": tf.random_normal([32, 4])},
+              tf.random_uniform([32], minval=0, maxval=3, dtype=tf.int32))
+    feature_columns = [
+        tf.feature_column.numeric_column("features", shape=(4,))]
   else:
     training_data_path, test_data_path = maybe_download_data(FLAGS.data_dir)
-    training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
-        filename=training_data_path,
-        target_dtype=np.int,
-        features_dtype=np.float32)
-    test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
-        filename=test_data_path, target_dtype=np.int, features_dtype=np.float32)
-
-  # Specify that all features have real-value data
-  feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
+    column_names = [
+        "sepal_length", "sepal_width", "petal_length", "petal_width", "label"]
+    batch_size = 32
+    def training_input_fn():
+      return tf.contrib.data.make_csv_dataset(
+          [training_data_path], batch_size,
+          column_names=column_names, label_name="label")
+    def test_input_fn():
+      return tf.contrib.data.make_csv_dataset(
+          [test_data_path], batch_size,
+          column_names=column_names, label_name="label")
+    feature_columns = [tf.feature_column.numeric_column(feature)
+                       for feature in column_names[:-1]]
 
   # Build 3 layer DNN with 10, 20, 10 units respectively.
   model_dir = FLAGS.model_dir or tempfile.mkdtemp(prefix="debug_tflearn_iris_")
 
-  classifier = tf.contrib.learn.DNNClassifier(
+  classifier = tf.estimator.DNNClassifier(
       feature_columns=feature_columns,
       hidden_units=[10, 20, 10],
       n_classes=3,
@@ -121,32 +125,23 @@ def main(_):
     debug_hook = tf_debug.TensorBoardDebugHook(FLAGS.tensorboard_debug_address)
   hooks = [debug_hook]
 
-  if not FLAGS.use_experiment:
-    # Fit model.
-    classifier.fit(x=training_set.data,
-                   y=training_set.target,
+  # Train model, using tfdbg hook.
+  classifier.train(training_input_fn,
                    steps=FLAGS.train_steps,
-                   monitors=hooks)
+                   hooks=hooks)
 
-    # Evaluate accuracy.
-    accuracy_score = classifier.evaluate(x=test_set.data,
-                                         y=test_set.target,
-                                         hooks=hooks)["accuracy"]
-  else:
-    ex = experiment.Experiment(classifier,
-                               train_input_fn=iris_input_fn,
-                               eval_input_fn=iris_input_fn,
-                               train_steps=FLAGS.train_steps,
-                               eval_delay_secs=0,
-                               eval_steps=1,
-                               train_monitors=hooks,
-                               eval_hooks=hooks)
-    ex.train()
-    accuracy_score = ex.evaluate()["accuracy"]
+  # Evaluate accuracy, using tfdbg hook.
+  accuracy_score = classifier.evaluate(test_input_fn,
+                                       steps=FLAGS.eval_steps,
+                                       hooks=hooks)["accuracy"]
 
   print("After training %d steps, Accuracy = %f" %
         (FLAGS.train_steps, accuracy_score))
 
+  # Make predictions, using tfdbg hook.
+  predict_results = classifier.predict(test_input_fn, hooks=hooks)
+  print("A prediction result: %s" % predict_results.next())
+
 
 if __name__ == "__main__":
   parser = argparse.ArgumentParser()
@@ -165,14 +160,12 @@ def main(_):
       "--train_steps",
       type=int,
       default=10,
-      help="Number of steps to run trainer.")
+      help="Number of steps to run training for.")
   parser.add_argument(
-      "--use_experiment",
-      type="bool",
-      nargs="?",
-      const=True,
-      default=False,
-      help="Use tf.contrib.learn Experiment to run training and evaluation")
+      "--eval_steps",
+      type=int,
+      default=1,
+      help="Number of steps to run evaluation foir.")
   parser.add_argument(
       "--ui_type",
       type=str,

From e629595e8f629f2de7db225463136b0e331bd71c Mon Sep 17 00:00:00 2001
From: gracehoney <31743510+aaroey@users.noreply.github.com>
Date: Thu, 3 May 2018 15:00:57 -0700
Subject: [PATCH 0351/1691] Simplify build dependencies; fix python import
 order; fix multiple singleton issues by inlining the singleton method.

---
 tensorflow/contrib/tensorrt/BUILD                    |  2 --
 .../contrib/tensorrt/custom_plugin_examples/BUILD    | 12 ++----------
 .../tensorrt/custom_plugin_examples/plugin_test.py   |  9 +++------
 .../contrib/tensorrt/plugin/trt_plugin_factory.cc    |  6 ------
 .../contrib/tensorrt/plugin/trt_plugin_factory.h     |  8 +++++++-
 5 files changed, 12 insertions(+), 25 deletions(-)

diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD
index 79e525edae8722..5b56feed0fcadd 100644
--- a/tensorflow/contrib/tensorrt/BUILD
+++ b/tensorflow/contrib/tensorrt/BUILD
@@ -259,7 +259,6 @@ cc_library(
         "segment/segment.h",
         "segment/union_find.h",
     ],
-    linkstatic = 1,
     deps = [
         "//tensorflow/core:graph",
         "//tensorflow/core:lib_proto_parsing",
@@ -295,7 +294,6 @@ tf_cuda_library(
         "plugin/trt_plugin_factory.h",
         "plugin/trt_plugin_utils.h",
     ],
-    linkstatic = 1,
     deps = [
         "//tensorflow/core:platform_base",
     ] + if_tensorrt([
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
index e623b547811a76..6f81ac2b444501 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
@@ -85,21 +85,13 @@ tf_custom_op_py_library(
     ],
 )
 
-py_library(
-    name = "inc_op_py",
-    srcs_version = "PY2AND3",
-    deps = [
-        ":inc_op",
-        ":inc_op_loader",
-    ],
-)
-
 py_library(
     name = "init_py",
     srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":inc_op_py",
+        ":inc_op",
+        ":inc_op_loader",
     ],
 )
 
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py
index cb40e084935367..aedfb162113d38 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py
@@ -18,7 +18,10 @@
 from __future__ import division
 from __future__ import print_function
 
+import numpy
+
 from tensorflow.contrib import tensorrt
+from tensorflow.contrib.tensorrt import custom_plugin_examples
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.framework import dtypes
@@ -27,12 +30,6 @@
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import nn_ops
-from tensorflow.python.framework import errors
-import numpy
-
-# import custom_op as plugin op
-# the python api handles registration to the plugin factory
-from tensorflow.contrib.tensorrt import custom_plugin_examples
 
 
 def get_plugin_graph_def():
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc
index b608e602a7b37f..736a1321fe7215 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc
@@ -21,12 +21,6 @@ limitations under the License.
 namespace tensorflow {
 namespace tensorrt {
 
-PluginFactoryTensorRT* PluginFactoryTensorRT::GetInstance() {
-  static PluginFactoryTensorRT* factory_instance =
-      new PluginFactoryTensorRT();
-  return factory_instance;
-}
-
 PluginTensorRT* PluginFactoryTensorRT::createPlugin(const char* layer_name,
                                                     const void* serial_data,
                                                     size_t serial_length) {
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
index 54fbca593014f7..0eee705fb985eb 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
@@ -34,7 +34,13 @@ namespace tensorrt {
 
 class PluginFactoryTensorRT : public nvinfer1::IPluginFactory {
  public:
-  static PluginFactoryTensorRT* GetInstance();
+  // TODO(aaroey): this static method has to be inlined to make the singleton a
+  // unique global symbol. Find a way to fix it.
+  static PluginFactoryTensorRT* GetInstance() {
+    static PluginFactoryTensorRT* factory_instance =
+        new PluginFactoryTensorRT();
+    return factory_instance;
+  }
 
   // Deserialization method
   PluginTensorRT* createPlugin(const char* layer_name, const void* serial_data,

From 2dc7575123ffa0e6413fc3d2700968ef25f049de Mon Sep 17 00:00:00 2001
From: Sergii Khomenko 
Date: Fri, 4 May 2018 04:22:09 +0200
Subject: [PATCH 0352/1691] Fix minor typos (#19070)

---
 tensorflow/python/estimator/training.py            | 2 +-
 tensorflow/python/feature_column/feature_column.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py
index 534c357067770b..95366132d9c470 100644
--- a/tensorflow/python/estimator/training.py
+++ b/tensorflow/python/estimator/training.py
@@ -588,7 +588,7 @@ def after_save(self, session, global_step_value):
     # max_steps, the evaluator will send the final export signal. There is a
     # small chance that the Estimator.train stopping logic sees a different
     # global_step value (due to global step race condition and the fact the
-    # saver sees a larger value for checkpoing saving), which does not end
+    # saver sees a larger value for checkpoint saving), which does not end
     # the training. When the training ends, a new checkpoint is generated, which
     # triggers the listener again. So, it could be the case the final export is
     # triggered twice.
diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py
index 9e6429e59ea9bd..40386ae7aa6cea 100644
--- a/tensorflow/python/feature_column/feature_column.py
+++ b/tensorflow/python/feature_column/feature_column.py
@@ -280,7 +280,7 @@ def input_layer(features,
 # TODO(akshayka): InputLayer should be a subclass of Layer, and it
 # should implement the logic in input_layer using Layer's build-and-call
 # paradigm; input_layer should create an instance of InputLayer and
-# return the result of inovking its apply method, just as functional layers do.
+# return the result of invoking its apply method, just as functional layers do.
 class InputLayer(object):
   """An object-oriented version of `input_layer` that reuses variables."""
 
@@ -834,7 +834,7 @@ def shared_embedding_columns(
     tensor_name_in_ckpt=None, max_norm=None, trainable=True):
   """List of dense columns that convert from sparse, categorical input.
 
-  This is similar to `embedding_column`, except that that it produces a list of
+  This is similar to `embedding_column`, except that it produces a list of
   embedding columns that share the same embedding weights.
 
   Use this when your inputs are sparse and of the same type (e.g. watched and

From fe9b2637cfe39cf11eb3d0494948a733b7fc1d7d Mon Sep 17 00:00:00 2001
From: Karl Lessard 
Date: Thu, 29 Mar 2018 05:28:16 +0800
Subject: [PATCH 0353/1691] Parse op definition and generate a Java Op class.

---
 tensorflow/java/BUILD                         |   4 +
 tensorflow/java/src/gen/cc/java_defs.h        |  76 ++--
 tensorflow/java/src/gen/cc/op_gen_main.cc     |  22 +-
 tensorflow/java/src/gen/cc/op_generator.cc    | 406 +++++++++++++++--
 tensorflow/java/src/gen/cc/op_generator.h     |  42 +-
 tensorflow/java/src/gen/cc/op_parser.cc       | 417 ++++++++++++++++++
 tensorflow/java/src/gen/cc/op_parser.h        | 137 ++++++
 tensorflow/java/src/gen/cc/source_writer.cc   | 127 +++---
 tensorflow/java/src/gen/cc/source_writer.h    |  55 ++-
 .../java/src/gen/cc/source_writer_test.cc     |  82 ++--
 tensorflow/java/src/gen/gen_ops.bzl           |  29 +-
 .../src/gen/resources/license.snippet.java    |  14 +
 12 files changed, 1201 insertions(+), 210 deletions(-)
 create mode 100644 tensorflow/java/src/gen/cc/op_parser.cc
 create mode 100644 tensorflow/java/src/gen/cc/op_parser.h
 create mode 100644 tensorflow/java/src/gen/resources/license.snippet.java

diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD
index ab7d698a45b7fc..635a4e807d8012 100644
--- a/tensorflow/java/BUILD
+++ b/tensorflow/java/BUILD
@@ -70,6 +70,7 @@ filegroup(
 
 tf_java_op_gen_srcjar(
     name = "java_op_gen_sources",
+    api_def_srcs = ["//tensorflow/core/api_def:base_api_def"],
     gen_base_package = "org.tensorflow.op",
     gen_tool = "java_op_gen_tool",
     ops_libs = [
@@ -111,11 +112,13 @@ cc_library(
     name = "java_op_gen_lib",
     srcs = [
         "src/gen/cc/op_generator.cc",
+        "src/gen/cc/op_parser.cc",
         "src/gen/cc/source_writer.cc",
     ],
     hdrs = [
         "src/gen/cc/java_defs.h",
         "src/gen/cc/op_generator.h",
+        "src/gen/cc/op_parser.h",
         "src/gen/cc/source_writer.h",
     ],
     copts = tf_copts(),
@@ -124,6 +127,7 @@ cc_library(
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
+        "//tensorflow/core:op_gen_lib",
     ],
 )
 
diff --git a/tensorflow/java/src/gen/cc/java_defs.h b/tensorflow/java/src/gen/cc/java_defs.h
index 59f8beaee78a2f..2065477f580137 100644
--- a/tensorflow/java/src/gen/cc/java_defs.h
+++ b/tensorflow/java/src/gen/cc/java_defs.h
@@ -18,12 +18,15 @@ limitations under the License.
 
 #include 
 #include 
+#include 
+#include 
 
 namespace tensorflow {
 namespace java {
 
 // An enumeration of different modifiers commonly used in Java
 enum Modifier {
+  PACKAGE   = 0,
   PUBLIC    = (1 << 0),
   PROTECTED = (1 << 1),
   PRIVATE   = (1 << 2),
@@ -72,6 +75,12 @@ class Type {
     // Reflection API does
     return Type(Type::PRIMITIVE, "void");
   }
+  static Type Generic(const string& name) {
+    return Type(Type::GENERIC, name);
+  }
+  static Type Wildcard() {
+    return Type(Type::GENERIC, "");
+  }
   static Type Class(const string& name, const string& package = "") {
     return Type(Type::CLASS, name, package);
   }
@@ -81,9 +90,6 @@ class Type {
   static Type Enum(const string& name, const string& package = "") {
     return Type(Type::ENUM, name, package);
   }
-  static Type Generic(const string& name = "") {
-    return Type(Type::GENERIC, name);
-  }
   static Type ClassOf(const Type& type) {
     return Class("Class").add_parameter(type);
   }
@@ -96,11 +102,10 @@ class Type {
   const Kind& kind() const { return kind_; }
   const string& name() const { return name_; }
   const string& package() const { return package_; }
-  const string& description() const { return description_; }
-  Type& description(const string& description) {
-    description_ = description;
-    return *this;
+  const string full_name() const {
+    return package_.empty() ? name_ : package_ + "." + name_;
   }
+  bool unknown() const { return name_.empty(); }  // only wildcards has no name
   const std::list& parameters() const { return parameters_; }
   Type& add_parameter(const Type& parameter) {
     parameters_.push_back(parameter);
@@ -120,14 +125,6 @@ class Type {
     }
     return *this;
   }
-  // Returns true if "type" is of a known collection type (only a few for now)
-  bool IsCollection() const {
-    return name_ == "List" || name_ == "Iterable";
-  }
-  // Returns true if this instance is a wildcard ()
-  bool IsWildcard() const {
-    return kind_ == GENERIC && name_.empty();
-  }
 
  protected:
   Type(Kind kind, const string& name, const string& package = "")
@@ -137,7 +134,6 @@ class Type {
   Kind kind_;
   string name_;
   string package_;
-  string description_;
   std::list parameters_;
   std::list annotations_;
   std::list supertypes_;
@@ -180,16 +176,11 @@ class Variable {
   const string& name() const { return name_; }
   const Type& type() const { return type_; }
   bool variadic() const { return variadic_; }
-  const string& description() const { return description_; }
-  Variable& description(const string& description) {
-    description_ = description;
-    return *this;
-  }
+
  private:
   string name_;
   Type type_;
   bool variadic_;
-  string description_;
 
   Variable(const string& name, const Type& type, bool variadic)
     : name_(name), type_(type), variadic_(variadic) {}
@@ -210,16 +201,6 @@ class Method {
   bool constructor() const { return constructor_; }
   const string& name() const { return name_; }
   const Type& return_type() const { return return_type_; }
-  const string& description() const { return description_; }
-  Method& description(const string& description) {
-    description_ = description;
-    return *this;
-  }
-  const string& return_description() const { return return_description_; }
-  Method& return_description(const string& description) {
-    return_description_ = description;
-    return *this;
-  }
   const std::list& arguments() const { return arguments_; }
   Method& add_argument(const Variable& var) {
     arguments_.push_back(var);
@@ -235,8 +216,6 @@ class Method {
   string name_;
   Type return_type_;
   bool constructor_;
-  string description_;
-  string return_description_;
   std::list arguments_;
   std::list annotations_;
 
@@ -244,6 +223,35 @@ class Method {
     : name_(name), return_type_(return_type), constructor_(constructor) {}
 };
 
+// A definition of a documentation bloc for a Java element (JavaDoc)
+class Javadoc {
+ public:
+  static Javadoc Create(const string& brief = "") {
+    return Javadoc(brief);
+  }
+  const string& brief() const { return brief_; }
+  const string& details() const { return description_; }
+  Javadoc& details(const string description) {
+    description_ = description;
+    return *this;
+  }
+  const std::list> tags() const { return tags_; }
+  Javadoc& add_tag(const string& tag, const string& text) {
+    tags_.push_back(std::make_pair(tag, text));
+    return *this;
+  }
+  Javadoc& add_param_tag(const string& name, const string& text) {
+    return add_tag("param", name + " " + text);
+  }
+
+ private:
+  string brief_;
+  string description_;
+  std::list> tags_;
+
+  explicit Javadoc(const string& brief) : brief_(brief) {}
+};
+
 }  // namespace java
 }  // namespace tensorflow
 
diff --git a/tensorflow/java/src/gen/cc/op_gen_main.cc b/tensorflow/java/src/gen/cc/op_gen_main.cc
index bea99f3d7f6bea..015200023f97c2 100644
--- a/tensorflow/java/src/gen/cc/op_gen_main.cc
+++ b/tensorflow/java/src/gen/cc/op_gen_main.cc
@@ -48,8 +48,11 @@ const char kUsageHeader[] =
     "through\n"
     "the 'org.tensorflow.op.Ops' API as a group until the generated classes "
     "are compiled using an appropriate annotation processor.\n\n"
-    "Finally, the '--base_package' overrides the default parent package "
-    "under which the generated subpackage and classes are to be located.\n\n";
+    "The '--base_package' overrides the default parent package under which "
+    "the generated subpackage and classes are to be located.\n\n"
+    "Finally, a list of directories of API proto definitions can be provided "
+    "to override default values found in the ops definitions, ordered by\n"
+    "priority (the last having precedence over the first).\n\n";
 
 }  // namespace java
 }  // namespace tensorflow
@@ -60,7 +63,7 @@ int main(int argc, char* argv[]) {
   tensorflow::string base_package = "org.tensorflow.op";
   std::vector flag_list = {
       tensorflow::Flag("output_dir", &output_dir,
-                       "Root directory into which output files are generated"),
+          "Root directory into which output files are generated"),
       tensorflow::Flag(
           "lib_name", &lib_name,
           "A name, in snake_case, used to classify this set of operations"),
@@ -72,12 +75,15 @@ int main(int argc, char* argv[]) {
   bool parsed_flags_ok = tensorflow::Flags::Parse(&argc, argv, flag_list);
   tensorflow::port::InitMain(usage.c_str(), &argc, &argv);
   QCHECK(parsed_flags_ok && !lib_name.empty() && !output_dir.empty()) << usage;
-
-  tensorflow::java::OpGenerator generator;
+  std::vector api_dirs;
+  if (argc > 1) {
+    api_dirs = tensorflow::str_util::Split(argv[1], ",",
+        tensorflow::str_util::SkipEmpty());
+  }
+  tensorflow::java::OpGenerator generator(base_package, output_dir, api_dirs);
   tensorflow::OpList ops;
-  tensorflow::OpRegistry::Global()->Export(true, &ops);
-  tensorflow::Status status =
-      generator.Run(ops, lib_name, base_package, output_dir);
+  tensorflow::OpRegistry::Global()->Export(false, &ops);
+  tensorflow::Status status = generator.Run(ops, lib_name);
   TF_QCHECK_OK(status);
 
   return 0;
diff --git a/tensorflow/java/src/gen/cc/op_generator.cc b/tensorflow/java/src/gen/cc/op_generator.cc
index def06baf2db43e..c9b57f570616f4 100644
--- a/tensorflow/java/src/gen/cc/op_generator.cc
+++ b/tensorflow/java/src/gen/cc/op_generator.cc
@@ -14,53 +14,409 @@ limitations under the License.
 ==============================================================================*/
 
 #include 
+#include 
+#include 
+#include 
+#include 
 
+#include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/framework/op_gen_lib.h"
+#include "tensorflow/java/src/gen/cc/java_defs.h"
+#include "tensorflow/java/src/gen/cc/source_writer.h"
+#include "tensorflow/java/src/gen/cc/op_parser.h"
 #include "tensorflow/java/src/gen/cc/op_generator.h"
 
 namespace tensorflow {
 namespace java {
 namespace {
 
-string CamelCase(const string& str, char delimiter, bool upper) {
-  string result;
-  bool cap = upper;
-  for (string::const_iterator it = str.begin(); it != str.end(); ++it) {
-    const char c = *it;
-    if (c == delimiter) {
-      cap = true;
-    } else if (cap) {
-      result += toupper(c);
-      cap = false;
+const char* kLicenseSnippet =
+    "tensorflow/java/src/gen/resources/license.snippet.java";
+
+const std::map kPrimitiveAttrTypes = {
+  { "Boolean", Type::Boolean() },
+  { "Byte", Type::Byte() },
+  { "Character", Type::Byte() },
+  { "Float", Type::Float() },
+  { "Integer", Type::Long() },
+  { "Long", Type::Long() },
+  { "Short", Type::Long() },
+  { "Double", Type::Float() },
+};
+
+enum RenderMode {
+  DEFAULT,
+  SINGLE_OUTPUT,
+  SINGLE_LIST_OUTPUT
+};
+
+void CollectOpDependencies(const OpSpec& op, RenderMode mode,
+    std::list* out) {
+  out->push_back(Type::Class("Operation", "org.tensorflow"));
+  out->push_back(Type::Class("OperationBuilder", "org.tensorflow"));
+  out->push_back(Type::Class("Scope", "org.tensorflow.op"));
+  if (mode == SINGLE_OUTPUT) {
+    out->push_back(Type::Class("Output", "org.tensorflow"));
+  } else if (mode == SINGLE_LIST_OUTPUT) {
+    out->push_back(Type::Interface("Iterator", "java.util"));
+  }
+  // Don't pay attention to duplicate types in the dependency list, they will
+  // be filtered out by the SourceWriter.
+  for (const OpSpec::Operand& input : op.inputs()) {
+    out->push_back(input.var().type());
+    if (input.iterable()) {
+      out->push_back(Type::Class("Operands", "org.tensorflow.op"));
+    }
+  }
+  for (const OpSpec::Operand& output : op.outputs()) {
+    out->push_back(output.var().type());
+    if (output.iterable()) {
+      out->push_back(Type::Class("Arrays", "java.util"));
+    }
+  }
+  for (const OpSpec::Operand& attribute : op.attributes()) {
+    out->push_back(attribute.var().type());
+    if (attribute.var().type().name() == "Class") {
+      out->push_back(Type::Enum("DataType", "org.tensorflow"));
+    }
+  }
+  for (const OpSpec::Operand& option : op.options()) {
+    out->push_back(option.var().type());
+  }
+}
+
+void WriteSetAttrDirective(const OpSpec::Operand& attr, bool optional,
+    SourceWriter* writer) {
+  string var = optional ? "opts." + attr.var().name() : attr.var().name();
+  if (attr.iterable()) {
+    const Type& type = attr.data_type();
+    std::map::const_iterator it =
+      kPrimitiveAttrTypes.find(type.name());
+    if (it != kPrimitiveAttrTypes.end()) {
+      string array = attr.var().name() + "Array";
+      writer->AppendType(it->second)
+          .Append("[] " + array + " = new ")
+          .AppendType(it->second)
+          .Append("[" + var + ".size()];")
+          .EndLine();
+      writer->BeginBlock("for (int i = 0; i < " + array + ".length; ++i)")
+          .Append(array + "[i] = " + var + ".get(i);")
+          .EndLine()
+          .EndBlock()
+          .Append("opBuilder.setAttr(\"" + attr.graph_name() + "\", " + array)
+          .Append(");")
+          .EndLine();
     } else {
-      result += c;
+      writer->Append("opBuilder.setAttr(\"" + attr.graph_name() + "\", " + var)
+          .Append(".toArray(new ")
+          .AppendType(type)
+          .Append("[" + var + ".size()]));")
+          .EndLine();
     }
+  } else {
+    Type type = attr.var().type();
+    writer->Append("opBuilder.setAttr(\"" + attr.graph_name() + "\", ");
+    if (type.name() == "Class") {
+      writer->Append("DataType.fromClass(" + attr.var().name() + "));");
+    } else {
+      writer->Append(var + ");");
+    }
+    writer->EndLine();
   }
-  return result;
 }
 
-}  // namespace
+void RenderFactoryMethod(const OpSpec& op, const Type& op_class,
+    SourceWriter* writer) {
+  Method factory = Method::Create("create", op_class);
+  Javadoc factory_doc = Javadoc::Create(
+      "Factory method to create a class to wrap a new " + op_class.name()
+      + " operation to the graph.");
+  Variable scope =
+      Variable::Create("scope", Type::Class("Scope", "org.tensorflow.op"));
+  factory.add_argument(scope);
+  factory_doc.add_param_tag(scope.name(), "Current graph scope");
+  for (const OpSpec::Operand& input : op.inputs()) {
+    factory.add_argument(input.var());
+    factory_doc.add_param_tag(input.var().name(), input.description());
+  }
+  for (const OpSpec::Operand& attribute : op.attributes()) {
+    factory.add_argument(attribute.var());
+    factory_doc.add_param_tag(attribute.var().name(), attribute.description());
+  }
+  if (!op.options().empty()) {
+    factory.add_argument(Variable::Varargs("options", Type::Class("Options")));
+    factory_doc.add_param_tag("options", "carries optional attributes values");
+  }
+  factory_doc.add_tag("return", "a new instance of " + op_class.name());
+  writer->BeginMethod(factory, PUBLIC|STATIC, &factory_doc);
+  writer->Append("OperationBuilder opBuilder = scope.graph().opBuilder(\""
+      + op.graph_name() + "\", scope.makeOpName(\""
+      + op_class.name() + "\"));");
+  writer->EndLine();
 
-OpGenerator::OpGenerator() : env(Env::Default()) {}
+  for (const OpSpec::Operand& input : op.inputs()) {
+    if (input.iterable()) {
+      writer->Append("opBuilder.addInputList(Operands.asOutputs("
+          + input.var().name() + "));");
+      writer->EndLine();
+    } else {
+      writer->Append("opBuilder.addInput(" + input.var().name()
+          + ".asOutput());");
+      writer->EndLine();
+    }
+  }
+  for (const OpSpec::Operand& attribute : op.attributes()) {
+    WriteSetAttrDirective(attribute, false, writer);
+  }
+  if (!op.options().empty()) {
+    writer->BeginBlock("if (options != null)")
+        .BeginBlock("for (Options opts : options)");
+    for (const OpSpec::Operand& option : op.options()) {
+      writer->BeginBlock("if (opts." + option.var().name() + " != null)");
+      WriteSetAttrDirective(option, true, writer);
+      writer->EndBlock();
+    }
+    writer->EndBlock().EndBlock();
+  }
+  writer->Append("return new ")
+      .AppendType(op_class)
+      .Append("(opBuilder.build());")
+      .EndLine();
+  writer->EndMethod();
+}
 
-OpGenerator::~OpGenerator() {}
+void RenderConstructor(const OpSpec& op, const Type& op_class,
+    SourceWriter* writer) {
+  Method constructor = Method::ConstructorFor(op_class)
+    .add_argument(
+        Variable::Create("operation",
+            Type::Class("Operation", "org.tensorflow")));
+  for (const OpSpec::Operand& output : op.outputs()) {
+    if (output.iterable() && !output.data_type().unknown()) {
+      constructor.add_annotation(
+          Annotation::Create("SuppressWarnings").attributes("\"unchecked\""));
+      break;
+    }
+  }
+  writer->BeginMethod(constructor, PRIVATE)
+      .Append("super(operation);")
+      .EndLine();
+  if (op.outputs().size() > 0) {
+    writer->Append("int outputIdx = 0;")
+        .EndLine();
+    for (const OpSpec::Operand& output : op.outputs()) {
+      if (output.iterable()) {
+        string var_length = output.var().name() + "Length";
+        writer->Append("int " + var_length)
+            .Append(" = operation.outputListLength(\"" + output.graph_name()
+                + "\");")
+            .EndLine()
+            .Append(output.var().name() + " = Arrays.asList(");
+        if (!output.data_type().unknown()) {
+          writer->Append("(")
+              .AppendType(output.var().type().parameters().front())
+              .Append("[])");
+        }
+        writer->Append("operation.outputList(outputIdx, " + var_length + "));")
+            .EndLine()
+            .Append("outputIdx += " + var_length + ";")
+            .EndLine();
+      } else {
+        writer->Append(output.var().name()
+                + " = operation.output(outputIdx++);")
+            .EndLine();
+      }
+    }
+  }
+  writer->EndMethod();
+}
 
-Status OpGenerator::Run(const OpList& ops, const string& lib_name,
-                        const string& base_package, const string& output_dir) {
-  const string package =
-      base_package + '.' + str_util::StringReplace(lib_name, "_", "", true);
-  const string package_path =
-      output_dir + '/' + str_util::StringReplace(package, ".", "/", true);
-  const string group = CamelCase(lib_name, '_', false);
+void RenderGettersAndSetters(const OpSpec& op, SourceWriter* writer) {
+  for (const OpSpec::Operand& option : op.options()) {
+    Method setter = Method::Create(option.var().name(), Type::Class("Options"))
+        .add_argument(option.var());
+    Javadoc setter_doc = Javadoc::Create()
+        .add_param_tag(option.var().name(), option.description());
+    writer->BeginMethod(setter, PUBLIC|STATIC, &setter_doc)
+        .Append("return new Options()." + option.var().name() + "("
+            + option.var().name() + ");")
+        .EndLine()
+        .EndMethod();
+  }
+  for (const OpSpec::Operand& output : op.outputs()) {
+    Method getter = Method::Create(output.var().name(), output.var().type());
+    Javadoc getter_doc = Javadoc::Create(output.description());
+    writer->BeginMethod(getter, PUBLIC, &getter_doc)
+        .Append("return " + output.var().name() + ";")
+        .EndLine()
+        .EndMethod();
+  }
+}
+
+void RenderInterfaceImpl(const OpSpec& op, RenderMode mode,
+    SourceWriter* writer) {
+  OpSpec::Operand output = op.outputs().front();
+
+  if (mode == SINGLE_OUTPUT) {
+    bool cast2obj = output.data_type().unknown();
+    Type return_type = Type::Class("Output", "org.tensorflow")
+        .add_parameter(cast2obj ? Type::Class("Object") : output.data_type());
+    Method as_output = Method::Create("asOutput", return_type)
+        .add_annotation(Annotation::Create("Override"));
+    if (cast2obj) {
+      as_output.add_annotation(
+          Annotation::Create("SuppressWarnings").attributes("\"unchecked\""));
+    }
+    writer->BeginMethod(as_output, PUBLIC);
+    if (cast2obj) {
+      writer->Append("return (").AppendType(return_type).Append(") ");
+    } else {
+      writer->Append("return ");
+    }
+    writer->Append(output.var().name() + ";")
+        .EndLine()
+        .EndMethod();
+
+  } else if (mode == SINGLE_LIST_OUTPUT) {
+    Type operand = Type::Interface("Operand", "org.tensorflow");
+    if (output.data_type().unknown()) {
+      operand.add_parameter(Type::Class("Object"));
+    } else {
+      operand.add_parameter(output.data_type());
+    }
+    Type return_type = Type::Interface("Iterator", "java.util")
+        .add_parameter(operand);
+    Method iterator = Method::Create("iterator", return_type)
+        .add_annotation(Annotation::Create("Override"))
+        .add_annotation(Annotation::Create("SuppressWarnings")
+            .attributes("{\"rawtypes\", \"unchecked\"}"));
+    // cast the output list using a raw List
+    writer->BeginMethod(iterator, PUBLIC)
+        .Append("return (" + return_type.name() + ") ")
+        .Append(output.var().name() + ".iterator();")
+        .EndLine()
+        .EndMethod();
+  }
+}
+
+void RenderOptionsClass(const OpSpec& op, SourceWriter* writer) {
+  Type options_class = Type::Class("Options");
+  Javadoc options_doc = Javadoc::Create(
+      "Class holding optional attributes of this operation");
+  writer->BeginInnerType(options_class, PUBLIC | STATIC, &options_doc);
+  for (const OpSpec::Operand& option : op.options()) {
+    Method setter = Method::Create(option.var().name(), options_class)
+        .add_argument(option.var());
+    Javadoc setter_doc = Javadoc::Create()
+        .add_param_tag(option.var().name(), option.description());
+    writer->BeginMethod(setter, PUBLIC, &setter_doc)
+        .Append("this." + option.var().name() + " = " + option.var().name()
+            + ";")
+        .EndLine()
+        .Append("return this;")
+        .EndLine()
+        .EndMethod();
+  }
+  writer->EndLine();
+  for (const OpSpec::Operand& option : op.options()) {
+    writer->WriteField(option.var(), PRIVATE);
+  }
+  Method constructor = Method::ConstructorFor(options_class);
+  writer->BeginMethod(constructor, PRIVATE).EndMethod();
+  writer->EndType();
+}
 
-  if (!env->FileExists(package_path).ok()) {
-    TF_CHECK_OK(env->RecursivelyCreateDir(package_path));
+void RenderEndpoint(const OpSpec& op, const OpSpec::Endpoint& endpoint,
+    SourceWriter* writer) {
+  RenderMode mode = DEFAULT;
+  if (op.outputs().size() == 1) {
+    mode = op.outputs().front().iterable() ? SINGLE_LIST_OUTPUT : SINGLE_OUTPUT;
+  }
+  std::list dependencies;
+  CollectOpDependencies(op, mode, &dependencies);
+  const Type& op_class = endpoint.type();
+  writer->WriteFromFile(kLicenseSnippet)
+      .EndLine()
+      .Append("// This file is machine generated, DO NOT EDIT!")
+      .EndLine()
+      .EndLine()
+      .BeginType(op_class, PUBLIC|FINAL, &dependencies, &endpoint.javadoc());
+  if (!op.options().empty()) {
+    RenderOptionsClass(op, writer);
   }
+  RenderFactoryMethod(op, op_class, writer);
+  RenderGettersAndSetters(op, writer);
+  if (mode != DEFAULT) {
+    RenderInterfaceImpl(op, mode, writer);
+  }
+  writer->EndLine();
+  for (const OpSpec::Operand& output : op.outputs()) {
+    writer->WriteField(output.var(), PRIVATE);
+  }
+  RenderConstructor(op, op_class, writer);
+  writer->EndType();
+}
+
+}  // namespace
+
+OpGenerator::OpGenerator(const string& base_package, const string& output_dir,
+    const std::vector& api_dirs, Env* env)
+  : base_package_(base_package), output_dir_(output_dir), api_dirs_(api_dirs),
+    env_(env) {
+}
 
+Status OpGenerator::Run(const OpList& op_list, const string& lib_name) {
   LOG(INFO) << "Generating Java wrappers for '" << lib_name << "' operations";
-  // TODO(karllessard) generate wrappers from list of ops
+  ApiDefMap api_map(op_list);
+  if (!api_dirs_.empty()) {
+    // Only load api files that correspond to the requested "op_list"
+    for (const auto& op : op_list.op()) {
+      for (const auto& api_def_dir : api_dirs_) {
+        const std::string api_def_file_pattern =
+            io::JoinPath(api_def_dir, "api_def_" + op.name() + ".pbtxt");
+        if (env_->FileExists(api_def_file_pattern).ok()) {
+          TF_CHECK_OK(api_map.LoadFile(env_, api_def_file_pattern));
+        }
+      }
+    }
+  }
+  api_map.UpdateDocs();
+  for (const auto& op_def : op_list.op()) {
+    const ApiDef* api_def = api_map.GetApiDef(op_def.name());
+    if (api_def->visibility() != ApiDef::SKIP) {
+      Status status = GenerateOp(op_def, *api_def, lib_name);
+      if (status != Status::OK()) {
+        LOG(ERROR) << "Fail to generate Java wrapper for operation \""
+            << op_def.name() << "\"";
+      }
+    }
+  }
+  return Status::OK();
+}
+
+Status OpGenerator::GenerateOp(const OpDef& op_def, const ApiDef& api_def,
+    const string& lib_name) {
+  std::unique_ptr op;
+  OpParser op_parser(op_def, api_def, lib_name, base_package_);
+  op_parser.Parse(&op);
+  for (const OpSpec::Endpoint& endpoint : op->endpoints()) {
+    string package_path = io::JoinPath(output_dir_,
+        str_util::StringReplace(endpoint.type().package(), ".", "/", true));
+    if (!env_->FileExists(package_path).ok()) {
+      TF_CHECK_OK(Env::Default()->RecursivelyCreateDir(package_path));
+    }
+    string file_path =
+        io::JoinPath(package_path, endpoint.type().name() + ".java");
+    std::unique_ptr file;
+    TF_CHECK_OK(env_->NewWritableFile(file_path, &file));
 
+    SourceFileWriter writer(file.get());
+    RenderEndpoint(*op, endpoint, &writer);
+  }
   return Status::OK();
 }
 
diff --git a/tensorflow/java/src/gen/cc/op_generator.h b/tensorflow/java/src/gen/cc/op_generator.h
index 4b55ed3ed94f11..19d8db95fbb11b 100644
--- a/tensorflow/java/src/gen/cc/op_generator.h
+++ b/tensorflow/java/src/gen/cc/op_generator.h
@@ -17,34 +17,42 @@ limitations under the License.
 #define TENSORFLOW_JAVA_SRC_GEN_CC_OP_GENERATOR_H_
 
 #include 
+#include 
 
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/framework/op_def.pb.h"
+#include "tensorflow/core/framework/api_def.pb.h"
 #include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
 namespace java {
 
-/// \brief A generator of Java operation wrappers.
-///
-/// Such generator is normally ran only once per executable, outputting
-/// wrappers for the all registered operations it has been compiled with.
-/// Nonetheless, it is designed to support multiple runs, giving a different
-/// list of operations on each cycle.
+// A generator of Java operation wrappers.
+//
+// Such generator is normally ran only once per executable, outputting
+// wrappers for the all registered operations it has been compiled with.
+// Nonetheless, it is designed to support multiple runs, giving a different
+// list of operations on each cycle.
 class OpGenerator {
  public:
-  OpGenerator();
-  virtual ~OpGenerator();
+  OpGenerator(const string& base_package, const string& output_dir,
+      const std::vector& api_dirs, Env* env = Env::Default());
+  virtual ~OpGenerator() = default;
 
-  /// \brief Generates wrappers for the given list of 'ops'.
-  ///
-  /// Output files are generated in //,
-  /// where 'lib_package' is derived from 'lib_name'.
-  Status Run(const OpList& ops, const string& lib_name,
-             const string& base_package, const string& output_dir);
+  // Generates wrappers for the given list of 'ops'.
+  //
+  // Output files are generated in //,
+  // where 'lib_package' is derived from 'lib_name'.
+  Status Run(const OpList& op_list, const string& lib_name);
 
  private:
-  Env* env;
+  string base_package_;
+  string output_dir_;
+  std::vector api_dirs_;
+  Env* env_;
+
+  Status GenerateOp(const OpDef& op_def, const ApiDef& api_def,
+    const string& lib_name);
 };
 
 }  // namespace java
diff --git a/tensorflow/java/src/gen/cc/op_parser.cc b/tensorflow/java/src/gen/cc/op_parser.cc
new file mode 100644
index 00000000000000..0541e343d80c2d
--- /dev/null
+++ b/tensorflow/java/src/gen/cc/op_parser.cc
@@ -0,0 +1,417 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include 
+#include 
+#include 
+#include 
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/java/src/gen/cc/op_parser.h"
+
+namespace tensorflow {
+namespace java {
+namespace {
+
+string SnakeToCamelCase(const string& str, bool upper = false) {
+  string result;
+  bool cap = upper;
+  for (string::const_iterator it = str.begin(); it != str.end(); ++it) {
+    const char c = *it;
+    if (c == '_') {
+      cap = true;
+    } else if (cap) {
+      result += toupper(c);
+      cap = false;
+    } else {
+      result += c;
+    }
+  }
+  return result;
+}
+
+bool IsRealNumber(DataType type) {
+  for (DataType dt : RealNumberTypes()) {
+    if (type == dt) {
+      return true;
+    }
+  }
+  return false;
+}
+
+bool IsRealNumbers(const AttrValue& values) {
+  if (values.has_list()) {
+    for (int i = 0; i < values.list().type_size(); ++i) {
+      if (!IsRealNumber(values.list().type(i))) {
+        return false;
+      }
+    }
+    return true;
+  }
+  return IsRealNumber(values.type());
+}
+
+string ParseDocumentation(const string& text) {
+  std::stringstream javadoc_text;
+  string::const_iterator c_iter = text.cbegin();
+  bool code = false;
+  bool emphasis = false;
+  bool list = false;
+  while (c_iter != text.cend()) {
+    char c = *c_iter++;
+    int count = 1;
+    switch (c) {
+    case '\n':
+      if (!code) {
+        // consumes all subsequent newlines, if there are more than one,
+        // then there are two choices:
+        // - if the next line starts with an asterisk, we are enumerating
+        //   a list of items
+        // - otherwise, we are starting a new paragraph
+        for (; c_iter != text.cend() && *c_iter == '\n'; ++count, ++c_iter) {}
+        if (c_iter != text.cend()) {
+          if (count > 1) {
+            if (*c_iter != '*' && list) {
+              javadoc_text << "\n\n";
+              list = false;
+            } else if (*c_iter == '*' && !list) {
+              javadoc_text << "\n
    \n
  • ";
+              list = true;
+              c_iter++;
+            } else {
+              javadoc_text << "\n
    \n";
+            }
+          } else if (list && *c_iter == '*') {
+            javadoc_text << "
    • \n
  • ";
+            c_iter++;
+          } else {
+            javadoc_text << '\n';
+          }
+        }
+      }
+      break;
+    case '`':
+      // consumes all subsequent backquotes, those are use enclose code.
+      // if there are more than 3, we are dealing with a pre-formatted block,
+      // otherwise it is a single-line code snippet
+      for (; c_iter != text.cend() && *c_iter == '`'; ++count, ++c_iter) {}
+      if (count >= 3) {
+        javadoc_text << (code ? "\n}" : "
    {@code\n");
+      } else {
+        javadoc_text << (code ? "}" : "{@code ");
+      }
+      code = !code;
+      break;
+    case '*':
+      if (!code) {
+        // consumes all subsequent asterisks, if there are more than one, then
+        // we put the text in bold, otherwise in italic
+        for (; c_iter != text.cend() && *c_iter == '*'; ++count, ++c_iter) {}
+        if (count > 1) {
+          javadoc_text << (emphasis ? "" : "");
+        } else {
+          javadoc_text << (emphasis ? "" : "");
+        }
+        emphasis = !emphasis;
+      } else {
+        javadoc_text << '*';
+      }
+      break;
+    default:
+      javadoc_text << c;
+      break;
+    }
+  }
+  return javadoc_text.str();
+}
+
+}  // namespace
+
+OpParser::OpParser(const OpDef& op_def, const ApiDef& api_def,
+    const string& lib_name, const string& base_package)
+  : op_def_(op_def), op_api_(api_def), lib_name_(lib_name),
+    base_package_(base_package) {
+}
+
+void OpParser::Parse(std::unique_ptr* op_ptr) {
+  visited_attrs_.clear();
+  next_generic_ = 'T';
+  op_ptr->reset(new OpSpec(op_api_.graph_op_name()));
+  for (const string& next_input_name : op_api_.arg_order()) {
+    for (int i = 0; i < op_def_.input_arg().size(); ++i) {
+      if (op_def_.input_arg(i).name() == next_input_name) {
+        ParseInput(op_def_.input_arg(i), op_api_.in_arg(i), op_ptr->get());
+        break;
+      }
+    }
+  }
+  for (int i = 0; i < op_def_.attr().size(); ++i) {
+    ParseAttribute(op_def_.attr(i), op_api_.attr(i), op_ptr->get());
+  }
+  for (int i = 0; i < op_def_.output_arg().size(); ++i) {
+    ParseOutput(op_def_.output_arg(i), op_api_.out_arg(i), op_ptr->get());
+  }
+  BuildEndpoints(op_ptr->get());
+}
+
+void OpParser::BuildEndpoints(OpSpec* op) {
+  Javadoc op_doc = Javadoc::Create(ParseDocumentation(op_api_.summary()))
+    .details(ParseDocumentation(op_api_.description()));
+  std::vector op_supertypes;
+  op_supertypes.push_back(Type::Class("PrimitiveOp", "org.tensorflow.op"));
+  std::map op_generics;
+  for (const OpSpec::Operand& output : op->outputs()) {
+    // declare generic output parameters at the Op class level
+    const Type& data_type = output.data_type();
+    if (data_type.kind() == Type::GENERIC && !data_type.unknown()
+        && op_generics.find(data_type.name()) == op_generics.end()) {
+      op_generics.insert(std::make_pair(data_type.name(), &data_type));
+      op_doc.add_param_tag("<" + data_type.name() + ">",
+          "data type of output '" + output.var().name() + "'");
+    }
+    // implement the Op as an (iteration of) Operand if it has only one output
+    if (op->outputs().size() == 1) {
+      Type operand_inf(Type::Interface("Operand", "org.tensorflow"));
+      operand_inf.add_parameter(data_type.unknown() ?
+          Type::Class("Object") : data_type);
+      op_supertypes.push_back(output.iterable() ?
+          Type::IterableOf(operand_inf) : operand_inf);
+    }
+  }
+  for (const auto& endpoint_def : op_api_.endpoint()) {
+    std::vector name_tokens = str_util::Split(endpoint_def.name(), ".");
+    // if the endpoint specifies a package, use it, otherwise derive it from the
+    // op library name.
+    string name;
+    string package;
+    if (name_tokens.size() > 1) {
+      package = str_util::Lowercase(name_tokens.at(0));
+      name = name_tokens.at(1);
+    } else {
+      package = str_util::StringReplace(lib_name_, "_", "", true);
+      name = name_tokens.at(0);
+    }
+    Type endpoint(Type::Class(name, base_package_ + "." + package));
+    Javadoc endpoint_doc(op_doc);
+    for (const auto& parameter : op_generics) {
+      endpoint.add_parameter(*parameter.second);
+    }
+    for (const Type& supertype : op_supertypes) {
+      endpoint.add_supertype(supertype);
+    }
+    if (endpoint_def.deprecation_version() > 0) {
+      string explanation;
+      if (op_api_.endpoint(0).deprecation_version() == 0) {
+        explanation = ", use {@link "
+            + op->endpoints().at(0).type().full_name()
+            + "} instead";
+      } else {
+        explanation = op_def_.deprecation().explanation();
+      }
+      endpoint_doc.add_tag("deprecated", explanation);
+      endpoint.add_annotation(Annotation::Create("Deprecated"));
+    }
+    // only visible ops should be annotated for exposure in the Ops Graph API
+    if (op_api_.visibility() != ApiDef::HIDDEN) {
+      string group_name = SnakeToCamelCase(lib_name_);
+      endpoint.add_annotation(
+          Annotation::Create("Operator", "org.tensorflow.op.annotation")
+            .attributes("group = \"" + group_name + "\""));
+    }
+    op->add_endpoint(endpoint, endpoint_doc);
+  }
+}
+
+void OpParser::ParseInput(const OpDef_ArgDef& input_def,
+    const ApiDef::Arg& input_api, OpSpec* op) {
+  bool iterable = false;
+  Type data_type = DataTypeOf(input_def, &iterable);
+  Type type = Type::Interface("Operand", "org.tensorflow")
+    .add_parameter(data_type);
+  if (iterable) {
+    type = Type::IterableOf(type);
+  }
+  op->add_input(OpSpec::Operand(input_api.name(),
+      Variable::Create(SnakeToCamelCase(input_api.rename_to()), type),
+      data_type,
+      ParseDocumentation(input_api.description()),
+      iterable));
+}
+
+void OpParser::ParseOutput(const OpDef_ArgDef& output_def,
+    const ApiDef::Arg& output_api, OpSpec* op) {
+  bool iterable = false;
+  Type data_type = DataTypeOf(output_def, &iterable);
+  Type type = Type::Class("Output", "org.tensorflow")
+    .add_parameter(data_type);
+  if (iterable) {
+    type = Type::ListOf(type);
+  }
+  op->add_output(OpSpec::Operand(output_api.name(),
+      Variable::Create(SnakeToCamelCase(output_api.rename_to()), type),
+      data_type,
+      ParseDocumentation(output_api.description()),
+      iterable));
+}
+
+void OpParser::ParseAttribute(const OpDef_AttrDef& attr_def,
+    const ApiDef::Attr& attr_api, OpSpec* op) {
+  // do not parse attributes already visited, they have probably been inferred
+  // before as an input argument type
+  if (visited_attrs_.find(attr_def.name()) != visited_attrs_.cend()) {
+    return;
+  }
+  bool iterable = false;
+  Type data_type = DataTypeOf(attr_def, &iterable);
+  // generic attributes should be passed as an explicit type
+  bool explicit_type = data_type.kind() == Type::GENERIC && !iterable;
+  Type type = explicit_type ?
+      Type::Class("Class").add_parameter(data_type) : data_type;
+  if (iterable) {
+    type = Type::ListOf(data_type);
+  }
+  OpSpec::Operand attr(attr_api.name(),
+      Variable::Create(SnakeToCamelCase(attr_api.rename_to()), type),
+      data_type,
+      ParseDocumentation(attr_api.description()),
+      iterable);
+  // attributes with a default value are optional
+  if (attr_api.has_default_value() && !explicit_type) {
+    op->add_option(attr);
+  } else {
+    op->add_attribute(attr);
+  }
+  visited_attrs_.insert(std::make_pair(attr_api.name(), data_type));
+}
+
+Type OpParser::DataTypeOf(const OpDef_ArgDef& arg, bool* iterable_out) {
+  if (!arg.number_attr().empty()) {
+    visited_attrs_.insert(std::make_pair(arg.number_attr(), Type::Int()));
+    *iterable_out = true;
+  }
+  if (arg.type() != DataType::DT_INVALID) {
+    // resolve type from DataType
+    switch (arg.type()) {
+      case DataType::DT_BOOL:
+        return Type::Class("Boolean");
+
+      case DataType::DT_STRING:
+        return Type::Class("String");
+
+      case DataType::DT_FLOAT:
+        return Type::Class("Float");
+
+      case DataType::DT_DOUBLE:
+        return Type::Class("Double");
+
+      case DataType::DT_UINT8:
+        return Type::Class("UInt8", "org.tensorflow.types");
+
+      case DataType::DT_INT32:
+        return Type::Class("Integer");
+
+      case DataType::DT_INT64:
+        return Type::Class("Long");
+
+      case DataType::DT_RESOURCE:
+        // TODO(karllessard) create a Resource utility class that could be
+        // used to store a resource and its type (passed in a second argument).
+        // For now, we need to force a wildcard and we will unfortunately lose
+        // track of the resource type.
+        return Type::Wildcard();
+
+      default:
+        break;
+    }
+  } else {
+    // resolve type from type attribute
+    string attr_name = arg.type_attr();
+    if (attr_name.empty()) {
+      attr_name = arg.type_list_attr();
+      if (!attr_name.empty()) {
+        *iterable_out = true;
+        Type type = Type::Wildcard();
+        visited_attrs_.insert(std::make_pair(attr_name, type));
+        return type;
+      }
+    }
+    for (const auto& attr : op_def_.attr()) {
+      if (attr.name() == attr_name) {
+        Type type = DataTypeOf(attr, iterable_out);
+        visited_attrs_.insert(std::make_pair(attr_name, type));
+        return type;
+      }
+    }
+  }
+  LOG(WARNING) << "Data type for arg \"" << arg.name() << "\" is unknown";
+  return Type::Wildcard();
+}
+
+Type OpParser::DataTypeOf(const OpDef_AttrDef& attr, bool* iterable_out) {
+  std::map::const_iterator it = visited_attrs_.find(attr.name());
+  if (it != visited_attrs_.cend()) {
+    return it->second;
+  }
+  string attr_type = attr.type();
+  if (attr.type().compare(0, 5, "list(") == 0) {
+    attr_type = attr_type.substr(5, attr.type().find_last_of(')') - 5);
+    *iterable_out = true;
+  }
+  if (attr_type == "type") {
+    if (*iterable_out) {
+      return Type::Enum("DataType", "org.tensorflow");
+    }
+    return GetNextGenericTensorType(attr.allowed_values());
+  }
+  if (attr_type == "string") {
+    return Type::Class("String");
+  }
+  if (attr_type == "int") {
+    return Type::Class("Integer");
+  }
+  if (attr_type == "float") {
+    return Type::Class("Float");
+  }
+  if (attr_type == "bool") {
+    return Type::Class("Boolean");
+  }
+  if (attr_type == "shape") {
+    return Type::Class("Shape", "org.tensorflow");
+  }
+  if (attr_type == "tensor") {
+    return Type::Class("Tensor", "org.tensorflow")
+      .add_parameter(Type::Wildcard());
+  }
+  LOG(WARNING) << "Data type for attribute \"" << attr_type << "\" is unknown";
+  return *iterable_out ? Type::Wildcard() : Type::Class("Object");
+}
+
+Type OpParser::GetNextGenericTensorType(const AttrValue& allowed_values)  {
+  Type generic = Type::Generic(string(1, next_generic_));
+  next_generic_ = (next_generic_ == 'Z') ? 'A' : next_generic_ + 1;
+
+  // when only real numbers are allowed, enforce that restriction in the Java by
+  // extending the generic from java.lang.Number
+  if (IsRealNumbers(allowed_values)) {
+    generic.add_supertype(Type::Class("Number"));
+  }
+  return generic;
+}
+
+}  // namespace java
+}  // namespace tensorflow
diff --git a/tensorflow/java/src/gen/cc/op_parser.h b/tensorflow/java/src/gen/cc/op_parser.h
new file mode 100644
index 00000000000000..42855127ccdaa6
--- /dev/null
+++ b/tensorflow/java/src/gen/cc/op_parser.h
@@ -0,0 +1,137 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_JAVA_SRC_GEN_CC_OP_PARSER_H_
+#define TENSORFLOW_JAVA_SRC_GEN_CC_OP_PARSER_H_
+
+#include 
+#include 
+#include 
+#include 
+
+#include "tensorflow/core/framework/op_def.pb.h"
+#include "tensorflow/core/framework/api_def.pb.h"
+#include "tensorflow/java/src/gen/cc/java_defs.h"
+
+namespace tensorflow {
+namespace java {
+
+// Specification of a TensorFlow operation to generate.
+//
+// This is the result of an operation definition parsing, see OpParser::Parse().
+class OpSpec {
+ public:
+  class Endpoint {
+   public:
+    Endpoint(const Type& type, const Javadoc& javadoc)
+      : type_(type), javadoc_(javadoc) {}
+    const Type& type() const { return type_; }
+    const Javadoc& javadoc() const { return javadoc_; }
+
+   private:
+    Type type_;
+    Javadoc javadoc_;
+  };
+
+  class Operand {
+   public:
+    Operand(const string& graph_name, const Variable& var,
+        const Type& data_type, const string& description, bool iterable)
+     : graph_name_(graph_name), var_(var), data_type_(data_type),
+       description_(description), iterable_(iterable) {}
+    const string& graph_name() const { return graph_name_; }
+    const Variable& var() const { return var_; }
+    Variable* var_ptr() { return &var_; }
+    const Type& data_type() const { return data_type_; }
+    const string& description() const { return description_; }
+    bool iterable() const { return iterable_; }
+
+   private:
+    string graph_name_;
+    Variable var_;
+    Type data_type_;
+    string description_;
+    bool iterable_;
+  };
+
+  explicit OpSpec(const string& graph_name) : graph_name_(graph_name) {}
+  const string& graph_name() const { return graph_name_; }
+  const std::vector endpoints() const { return endpoints_; }
+  void add_endpoint(const Type& type, const Javadoc& javadoc) {
+    endpoints_.push_back(Endpoint(type, javadoc));
+  }
+  const std::vector& inputs() const { return inputs_; }
+  void add_input(const Operand& input) {
+    inputs_.push_back(input);
+  }
+  const std::vector& outputs() const { return outputs_; }
+  void add_output(const Operand& output) {
+    outputs_.push_back(output);
+  }
+  const std::vector& attributes() const { return attributes_; }
+  void add_attribute(const Operand& attribute) {
+    attributes_.push_back(attribute);
+  }
+  const std::vector& options() const { return options_; }
+  void add_option(const Operand& option) {
+    options_.push_back(option);
+  }
+
+ private:
+  string graph_name_;
+  std::vector endpoints_;
+  std::vector inputs_;
+  std::vector outputs_;
+  std::vector attributes_;
+  std::vector options_;
+};
+
+// A parser of ops proto definitions.
+//
+// This object parses the definition and the api of an TensorFlow operation to
+// produce a specification that can be used for Java source code rendering.
+class OpParser {
+ public:
+  OpParser(const OpDef& op_def, const ApiDef& api_def, const string& lib_name,
+      const string& base_package);
+  virtual ~OpParser() = default;
+
+  // Produces an operation specification from its proto definitions.
+  void Parse(std::unique_ptr* op_ptr);
+
+ private:
+  OpDef op_def_;
+  ApiDef op_api_;
+  string lib_name_;
+  string base_package_;
+  std::map visited_attrs_;
+  char next_generic_ = 0;
+
+  void BuildEndpoints(OpSpec* op);
+  void ParseInput(const OpDef_ArgDef& input_def,
+      const ApiDef::Arg& input_api, OpSpec* op);
+  void ParseOutput(const OpDef_ArgDef& output_def,
+      const ApiDef::Arg& output_api, OpSpec* op);
+  void ParseAttribute(const OpDef_AttrDef& attr_def,
+      const ApiDef::Attr& attr_api, OpSpec* op);
+  Type DataTypeOf(const OpDef_ArgDef& arg_def, bool *iterable_out);
+  Type DataTypeOf(const OpDef_AttrDef& attr_def, bool *iterable_out);
+  Type GetNextGenericTensorType(const AttrValue& allowed_values);
+};
+
+}  // namespace java
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_JAVA_SRC_GEN_CC_OP_PARSER_H_
diff --git a/tensorflow/java/src/gen/cc/source_writer.cc b/tensorflow/java/src/gen/cc/source_writer.cc
index a02f75ad6e7f5f..b1de5af6ba1697 100644
--- a/tensorflow/java/src/gen/cc/source_writer.cc
+++ b/tensorflow/java/src/gen/cc/source_writer.cc
@@ -15,7 +15,7 @@ limitations under the License.
 
 #include 
 #include 
-#include 
+#include 
 
 #include "tensorflow/java/src/gen/cc/source_writer.h"
 
@@ -83,20 +83,20 @@ SourceWriter& SourceWriter::Append(const StringPiece& str) {
 }
 
 SourceWriter& SourceWriter::AppendType(const Type& type) {
-  if (type.kind() == Type::Kind::GENERIC && type.name().empty()) {
+  if (type.unknown()) {
     Append("?");
   } else {
     Append(type.name());
-  }
-  if (!type.parameters().empty()) {
-    Append("<");
-    for (const Type& t : type.parameters()) {
-      if (&t != &type.parameters().front()) {
-        Append(", ");
+    if (!type.parameters().empty()) {
+      Append("<");
+      for (const Type& t : type.parameters()) {
+        if (&t != &type.parameters().front()) {
+          Append(", ");
+        }
+        AppendType(t);
       }
-      AppendType(t);
+      Append(">");
     }
-    Append(">");
   }
   return *this;
 }
@@ -107,7 +107,21 @@ SourceWriter& SourceWriter::EndLine() {
   return *this;
 }
 
-SourceWriter& SourceWriter::BeginMethod(const Method& method, int modifiers) {
+SourceWriter& SourceWriter::BeginBlock(const string& expression) {
+  if (!expression.empty()) {
+    Append(expression + " {");
+  } else {
+    Append(newline_ ? "{" : " {");
+  }
+  return EndLine().Indent(2);
+}
+
+SourceWriter& SourceWriter::EndBlock() {
+  return Indent(-2).Append("}").EndLine();
+}
+
+SourceWriter& SourceWriter::BeginMethod(const Method& method, int modifiers,
+    const Javadoc* javadoc) {
   GenericNamespace* generic_namespace = PushGenericNamespace(modifiers);
   if (!method.constructor()) {
     generic_namespace->Visit(method.return_type());
@@ -116,8 +130,9 @@ SourceWriter& SourceWriter::BeginMethod(const Method& method, int modifiers) {
     generic_namespace->Visit(v.type());
   }
   EndLine();
-  WriteDoc(method.description(), method.return_description(),
-      &method.arguments());
+  if (javadoc != nullptr) {
+    WriteJavadoc(*javadoc);
+  }
   if (!method.annotations().empty()) {
     WriteAnnotations(method.annotations());
   }
@@ -145,29 +160,35 @@ SourceWriter& SourceWriter::EndMethod() {
   return *this;
 }
 
-SourceWriter& SourceWriter::BeginType(const Type& type,
-    const std::list* dependencies, int modifiers) {
+SourceWriter& SourceWriter::BeginType(const Type& type, int modifiers,
+    const std::list* extra_dependencies, const Javadoc* javadoc) {
   if (!type.package().empty()) {
     Append("package ").Append(type.package()).Append(";").EndLine();
   }
-  if (dependencies != nullptr && !dependencies->empty()) {
-    TypeImporter type_importer(type.package());
-    for (const Type& t : *dependencies) {
+  TypeImporter type_importer(type.package());
+  type_importer.Visit(type);
+  if (extra_dependencies != nullptr) {
+    for (const Type& t : *extra_dependencies) {
       type_importer.Visit(t);
     }
+  }
+  if (!type_importer.imports().empty()) {
     EndLine();
     for (const string& s : type_importer.imports()) {
       Append("import ").Append(s).Append(";").EndLine();
     }
   }
-  return BeginInnerType(type, modifiers);
+  return BeginInnerType(type, modifiers, javadoc);
 }
 
-SourceWriter& SourceWriter::BeginInnerType(const Type& type, int modifiers) {
+SourceWriter& SourceWriter::BeginInnerType(const Type& type, int modifiers,
+    const Javadoc* javadoc) {
   GenericNamespace* generic_namespace = PushGenericNamespace(modifiers);
   generic_namespace->Visit(type);
   EndLine();
-  WriteDoc(type.description());
+  if (javadoc != nullptr) {
+    WriteJavadoc(*javadoc);
+  }
   if (!type.annotations().empty()) {
     WriteAnnotations(type.annotations());
   }
@@ -200,14 +221,15 @@ SourceWriter& SourceWriter::EndType() {
   return *this;
 }
 
-SourceWriter& SourceWriter::WriteFields(const std::list& fields,
-    int modifiers) {
-  EndLine();
-  for (const Variable& v : fields) {
-    WriteModifiers(modifiers);
-    AppendType(v.type()).Append(" ").Append(v.name()).Append(";");
-    EndLine();
+SourceWriter& SourceWriter::WriteField(const Variable& field, int modifiers,
+    const Javadoc* javadoc) {
+  // If present, write field javadoc only as one brief line
+  if (javadoc != nullptr && !javadoc->brief().empty()) {
+    Append("/** ").Append(javadoc->brief()).Append(" */").EndLine();
   }
+  WriteModifiers(modifiers);
+  AppendType(field.type()).Append(" ").Append(field.name()).Append(";");
+  EndLine();
   return *this;
 }
 
@@ -228,39 +250,33 @@ SourceWriter& SourceWriter::WriteModifiers(int modifiers) {
   return *this;
 }
 
-SourceWriter& SourceWriter::WriteDoc(const string& description,
-    const string& return_description, const std::list* parameters) {
-  if (description.empty() && return_description.empty()
-      && (parameters == nullptr || parameters->empty())) {
-    return *this;  // no doc to write
-  }
+SourceWriter& SourceWriter::WriteJavadoc(const Javadoc& javadoc) {
+  Append("/**").Prefix(" * ").EndLine();
   bool do_line_break = false;
-  Append("/**").EndLine().Prefix(" * ");
-  if (!description.empty()) {
-    Write(description).EndLine();
+  if (!javadoc.brief().empty()) {
+    Write(javadoc.brief()).EndLine();
     do_line_break = true;
   }
-  if (parameters != nullptr && !parameters->empty()) {
+  if (!javadoc.details().empty()) {
     if (do_line_break) {
-      EndLine();
-      do_line_break = false;
-    }
-    for (const Variable& v : *parameters) {
-      Append("@param ").Append(v.name());
-      if (!v.description().empty()) {
-        Append(" ").Write(v.description());
-      }
-      EndLine();
+      Append("
    ").EndLine();
     }
+    Write(javadoc.details()).EndLine();
+    do_line_break = true;
   }
-  if (!return_description.empty()) {
+  if (!javadoc.tags().empty()) {
     if (do_line_break) {
       EndLine();
-      do_line_break = false;
     }
-    Append("@return ").Write(return_description).EndLine();
+    for (const auto& p : javadoc.tags()) {
+      Append("@" + p.first);
+      if (!p.second.empty()) {
+        Append(" ").Write(p.second);
+      }
+      EndLine();
+    }
   }
-  return Prefix("").Append(" **/").EndLine();
+  return Prefix("").Append(" */").EndLine();
 }
 
 SourceWriter& SourceWriter::WriteAnnotations(
@@ -311,20 +327,19 @@ void SourceWriter::PopGenericNamespace() {
 void SourceWriter::TypeVisitor::Visit(const Type& type) {
   DoVisit(type);
   for (const Type& t : type.parameters()) {
-    DoVisit(t);
+    Visit(t);
   }
   for (const Annotation& t : type.annotations()) {
     DoVisit(t);
   }
   for (const Type& t : type.supertypes()) {
-    DoVisit(t);
+    Visit(t);
   }
 }
 
 void SourceWriter::GenericNamespace::DoVisit(const Type& type) {
   // ignore non-generic parameters, wildcards and generics already declared
-  if (type.kind() == Type::GENERIC
-      && !type.IsWildcard()
+  if (type.kind() == Type::GENERIC && !type.unknown()
       && generic_names_.find(type.name()) == generic_names_.end()) {
     declared_types_.push_back(&type);
     generic_names_.insert(type.name());
@@ -333,7 +348,7 @@ void SourceWriter::GenericNamespace::DoVisit(const Type& type) {
 
 void SourceWriter::TypeImporter::DoVisit(const Type& type) {
   if (!type.package().empty() && type.package() != current_package_) {
-    imports_.insert(type.package() + '.' + type.name());
+    imports_.insert(type.full_name());
   }
 }
 
diff --git a/tensorflow/java/src/gen/cc/source_writer.h b/tensorflow/java/src/gen/cc/source_writer.h
index f011acd30aae39..1f0febe9a3135a 100644
--- a/tensorflow/java/src/gen/cc/source_writer.h
+++ b/tensorflow/java/src/gen/cc/source_writer.h
@@ -93,25 +93,22 @@ class SourceWriter {
   // This method appends a new opening brace to the current data and indent the
   // next lines according to Google Java Style Guide. The block can optionally
   // be preceded by an expression (e.g. Append("if(true)").BeginBlock();)
-  SourceWriter& BeginBlock() {
-    return Append(newline_ ? "{" : " {").EndLine().Indent(2);
-  }
+  SourceWriter& BeginBlock(const string& expr = "");
 
   // Ends the current block of source code.
   //
   // This method appends a new closing brace to the current data and outdent the
   // next lines back to the margin used before BeginBlock() was invoked.
-  SourceWriter& EndBlock() {
-    return Indent(-2).Append("}").EndLine();
-  }
+  SourceWriter& EndBlock();
 
   // Begins to write a method.
   //
   // This method outputs the signature of the Java method from the data passed
-  // in the 'method' parameter and starts a new block. Additionnal modifiers can
-  // also be passed in parameter to define the accesses and the scope of this
-  // method.
-  SourceWriter& BeginMethod(const Method& method, int modifiers = 0);
+  // in the 'method' parameter and starts a new block. Modifiers are also passed
+  // in parameter to define the access scope of this method and, optionally,
+  // a Javadoc.
+  SourceWriter& BeginMethod(const Method& method, int modifiers,
+      const Javadoc* javadoc = nullptr);
 
   // Ends the current method.
   //
@@ -122,22 +119,24 @@ class SourceWriter {
   // Begins to write the main type of a source file.
   //
   // This method outputs the declaration of the Java type from the data passed
-  // in the 'type' parameter and starts a new block. Additionnal modifiers can
-  // also be passed in parameter to define the accesses and the scope of this
-  // type.
+  // in the 'type' parameter and starts a new block. Modifiers are also passed
+  // in parameter to define the access scope of this type and, optionally,
+  // a Javadoc.
   //
-  // If not null, all types found in the 'dependencies' list will be imported
-  // before declaring the new type.
-  SourceWriter& BeginType(const Type& clazz,
-      const std::list* dependencies, int modifiers = 0);
+  // If not null, all types found in the 'extra_dependencies' list will be
+  // imported before declaring the new type.
+  SourceWriter& BeginType(const Type& clazz, int modifiers,
+      const std::list* extra_dependencies = nullptr,
+      const Javadoc* javadoc = nullptr);
 
   // Begins to write a new inner type.
   //
   // This method outputs the declaration of the Java type from the data passed
-  // in the 'type' parameter and starts a new block. Additionnal modifiers can
-  // also be passed in parameter to define the accesses and the scope of this
-  // type.
-  SourceWriter& BeginInnerType(const Type& type, int modifiers = 0);
+  // in the 'type' parameter and starts a new block. Modifiers are also passed
+  // in parameter to define the accesses and the scope of this type and,
+  // optionally, a Javadoc.
+  SourceWriter& BeginInnerType(const Type& type, int modifiers,
+      const Javadoc* javadoc = nullptr);
 
   // Ends the current type.
   //
@@ -145,13 +144,13 @@ class SourceWriter {
   // BeginType() or BeginInnerType() prior to this.
   SourceWriter& EndType();
 
-  // Writes a list of variables as fields of a type.
+  // Writes a variable as fields of a type.
   //
   // This method must be called within the definition of a type (see BeginType()
-  // or BeginInnerType()). Additional modifiers can also be passed in parameter
-  // to define the accesses and the scope of those fields.
-  SourceWriter& WriteFields(const std::list& fields,
-      int modifiers = 0);
+  // or BeginInnerType()). Modifiers are also be passed in parameter to define
+  // the accesses and the scope of this field and, optionally, a Javadoc.
+  SourceWriter& WriteField(const Variable& field, int modifiers,
+      const Javadoc* javadoc = nullptr);
 
  protected:
   virtual void DoAppend(const StringPiece& str) = 0;
@@ -207,9 +206,7 @@ class SourceWriter {
   std::stack generic_namespaces_;
 
   SourceWriter& WriteModifiers(int modifiers);
-  SourceWriter& WriteDoc(const string& description,
-    const string& return_description = "",
-    const std::list* parameters = nullptr);
+  SourceWriter& WriteJavadoc(const Javadoc& javadoc);
   SourceWriter& WriteAnnotations(const std::list& annotations);
   SourceWriter& WriteGenerics(const std::list& generics);
   GenericNamespace* PushGenericNamespace(int modifiers);
diff --git a/tensorflow/java/src/gen/cc/source_writer_test.cc b/tensorflow/java/src/gen/cc/source_writer_test.cc
index 4bce2fea7040a0..8bd42d9d0e890d 100644
--- a/tensorflow/java/src/gen/cc/source_writer_test.cc
+++ b/tensorflow/java/src/gen/cc/source_writer_test.cc
@@ -250,7 +250,7 @@ TEST(StreamTest, Types) {
         .AppendType(generic).Append(", ")
         .AppendType(Type::ListOf(generic)).Append(", ")
         .AppendType(Type::ListOf(Type::IterableOf(generic))).Append(", ")
-        .AppendType(Type::ListOf(Type::Generic()));
+        .AppendType(Type::ListOf(Type::Wildcard()));
 
   const char* expected =
       "int, String, T, List, List>, List";
@@ -282,7 +282,7 @@ TEST(WriteType, SimpleClass) {
   SourceBufferWriter writer;
   Type clazz = Type::Class("Test", "org.tensorflow");
 
-  writer.BeginType(clazz, nullptr, PUBLIC).EndType();
+  writer.BeginType(clazz, PUBLIC).EndType();
 
   const char* expected =
       "package org.tensorflow;\n\n"
@@ -300,7 +300,7 @@ TEST(WriteType, SimpleClassWithDependencies) {
   deps.push_back(Type::Class("SamePackageType", "org.tensorflow"));
   deps.push_back(Type::Class("NoPackageType"));
 
-  writer.BeginType(clazz, &deps, PUBLIC).EndType();
+  writer.BeginType(clazz, PUBLIC, &deps).EndType();
 
   const char* expected =
       "package org.tensorflow;\n\n"
@@ -313,18 +313,21 @@ TEST(WriteType, SimpleClassWithDependencies) {
 TEST(WriteType, AnnotatedAndDocumentedClass) {
   SourceBufferWriter writer;
   Type clazz = Type::Class("Test", "org.tensorflow");
-  clazz.description("This class has a\n
    \nmultiline description.");
+  Javadoc clazz_doc;
+  clazz_doc.brief("Javadoc test")
+      .details("This is a\nmultiline description.");
   clazz.add_annotation(Annotation::Create("Bean"));
   clazz.add_annotation(Annotation::Create("SuppressWarnings")
       .attributes("\"rawtypes\""));
 
-  writer.BeginType(clazz, nullptr, PUBLIC).EndType();
+  writer.BeginType(clazz, PUBLIC, nullptr, &clazz_doc).EndType();
 
   const char* expected =
       "package org.tensorflow;\n\n"
       "/**\n"
-      " * This class has a\n"
+      " * Javadoc test\n"
       " * 
    \n"
+      " * This is a\n"
       " * multiline description.\n"
       " **/\n"
       "@Bean\n"
@@ -339,7 +342,7 @@ TEST(WriteType, ParameterizedClass) {
   clazz.add_parameter(Type::Generic("T"));
   clazz.add_parameter(Type::Generic("U").add_supertype(Type::Class("Number")));
 
-  writer.BeginType(clazz, nullptr, PUBLIC).EndType();
+  writer.BeginType(clazz, PUBLIC).EndType();
 
   const char* expected =
       "package org.tensorflow;\n\n"
@@ -358,7 +361,7 @@ TEST(WriteType, ParameterizedClassAndSupertypes) {
   clazz.add_supertype(Type::Interface("Runnable"));
   clazz.add_supertype(Type::Class("SuperTest").add_parameter(type_t));
 
-  writer.BeginType(clazz, nullptr, PUBLIC).EndType();
+  writer.BeginType(clazz, PUBLIC).EndType();
 
   const char* expected =
       "package org.tensorflow;\n\n"
@@ -372,24 +375,24 @@ TEST(WriteType, ParameterizedClassFields) {
   Type clazz = Type::Class("Test", "org.tensorflow");
   Type type_t = Type::Generic("T").add_supertype(Type::Class("Number"));
   clazz.add_parameter(type_t);
-  std::list static_fields;
-  static_fields.push_back(Variable::Create("field1", Type::Class("String")));
-  std::list member_fields;
-  member_fields.push_back(Variable::Create("field2", Type::Class("String")));
-  member_fields.push_back(Variable::Create("field3", type_t));
-
-  writer.BeginType(clazz, nullptr, PUBLIC)
-          .WriteFields(static_fields, STATIC | PUBLIC | FINAL)
-          .WriteFields(member_fields, PRIVATE)
+  Variable field1 = Variable::Create("field1", Type::Class("String"));
+  Variable field2 = Variable::Create("field2", Type::Class("String"));
+  Variable field3 = Variable::Create("field3", type_t);
+  Javadoc field3_doc;
+  field3_doc.brief("This variable is documented");
+
+  writer.BeginType(clazz, PUBLIC)
+          .WriteField(field1, STATIC | PUBLIC | FINAL)
+          .WriteField(field2, PRIVATE)
+          .WriteField(field3, PRIVATE, &field3_doc)
         .EndType();
 
   const char* expected =
       "package org.tensorflow;\n\n"
       "public class Test {\n"
-      "  \n"
       "  public static final String field1;\n"
-      "  \n"
       "  private String field2;\n"
+      "  /** This variable is documented */\n"
       "  private T field3;\n"
       "}\n";
   ASSERT_STREQ(expected, writer.str().data());
@@ -400,7 +403,7 @@ TEST(WriteType, SimpleInnerClass) {
   Type clazz = Type::Class("Test", "org.tensorflow");
   Type inner_class = Type::Class("InnerTest");
 
-  writer.BeginType(clazz, nullptr, PUBLIC)
+  writer.BeginType(clazz, PUBLIC)
           .BeginInnerType(inner_class, PUBLIC)
           .EndType()
         .EndType();
@@ -423,7 +426,7 @@ TEST(WriteType, StaticParameterizedInnerClass) {
   Type inner_class = Type::Class("InnerTest");
   inner_class.add_parameter(type_t);
 
-  writer.BeginType(clazz, nullptr, PUBLIC)
+  writer.BeginType(clazz, PUBLIC)
           .BeginInnerType(inner_class, PUBLIC | STATIC)
           .EndType()
         .EndType();
@@ -443,7 +446,7 @@ TEST(WriteMethod, SimpleMethod) {
   Type clazz = Type::Class("Test", "org.tensorflow");
   Method method = Method::Create("doNothing", Type::Void());
 
-  writer.BeginType(clazz, nullptr, PUBLIC)
+  writer.BeginType(clazz, PUBLIC)
           .BeginMethod(method, PUBLIC).EndMethod()
         .EndType();
 
@@ -461,13 +464,15 @@ TEST(WriteMethod, AnnotatedAndDocumentedMethod) {
   SourceBufferWriter writer;
   Type clazz = Type::Class("Test", "org.tensorflow");
   Method method = Method::Create("doNothing", Type::Void());
-  method.description("This method has a\n
    \nmultiline description.");
+  Javadoc method_doc;
+  method_doc.brief("Javadoc test")
+      .details("This method has a\nmultiline description.");
   method.add_annotation(Annotation::Create("Override"));
   method.add_annotation(Annotation::Create("SuppressWarnings")
       .attributes("\"rawtypes\""));
 
-  writer.BeginType(clazz, nullptr, PUBLIC)
-          .BeginMethod(method, PUBLIC).EndMethod()
+  writer.BeginType(clazz, PUBLIC)
+          .BeginMethod(method, PUBLIC, &method_doc).EndMethod()
         .EndType();
 
   const char* expected =
@@ -475,8 +480,9 @@ TEST(WriteMethod, AnnotatedAndDocumentedMethod) {
       "public class Test {\n"
       "  \n"
       "  /**\n"
-      "   * This method has a\n"
+      "   * Javadoc test\n"
       "   * 
    \n"
+      "   * This method has a\n"
       "   * multiline description.\n"
       "   **/\n"
       "  @Override\n"
@@ -490,16 +496,18 @@ TEST(WriteMethod, AnnotatedAndDocumentedMethod) {
 TEST(WriteMethod, DocumentedMethodWithArguments) {
   SourceBufferWriter writer;
   Type clazz = Type::Class("Test", "org.tensorflow");
+  Variable reverse = Variable::Create("reverse", Type::Boolean());
   Method method = Method::Create("boolToInt", Type::Int());
-  method.description("Converts a boolean to an int");
-  method.return_description("int value for this boolean");
   method.add_argument(Variable::Create("b", Type::Boolean()));
-  Variable reverse = Variable::Create("reverse", Type::Boolean());
-  reverse.description("if true, value is reversed");
   method.add_argument(reverse);
-
-  writer.BeginType(clazz, nullptr, PUBLIC)
-          .BeginMethod(method, PUBLIC)
+  Javadoc method_doc;
+  method_doc.brief("Converts a boolean to an int")
+      .details("This method will convert\na boolean to an int")
+      .add_param_tag(reverse.name(), "if true, value is reversed")
+      .add_tag("return", "int value for this boolean");
+
+  writer.BeginType(clazz, PUBLIC)
+          .BeginMethod(method, PUBLIC, &method_doc)
             .Append("if (b && !reverse)")
             .BeginBlock()
               .Append("return 1;").EndLine()
@@ -514,8 +522,10 @@ TEST(WriteMethod, DocumentedMethodWithArguments) {
       "  \n"
       "  /**\n"
       "   * Converts a boolean to an int\n"
+      "   * 
    \n"
+      "   * This method will convert\n"
+      "   * a boolean to an int\n"
       "   * \n"
-      "   * @param b\n"
       "   * @param reverse if true, value is reversed\n"
       "   * @return int value for this boolean\n"
       "   **/\n"
@@ -536,7 +546,7 @@ TEST(WriteMethod, ParameterizedMethod) {
   clazz.add_parameter(type_t);
   Method method = Method::Create("doNothing", type_t);
 
-  writer.BeginType(clazz, nullptr, PUBLIC)
+  writer.BeginType(clazz, PUBLIC)
           .BeginMethod(method, PUBLIC)
             .Append("return null;").EndLine()
           .EndMethod()
@@ -560,7 +570,7 @@ TEST(WriteMethod, StaticParameterizedMethod) {
   clazz.add_parameter(type_t);
   Method method = Method::Create("doNothing", type_t);
 
-  writer.BeginType(clazz, nullptr, PUBLIC)
+  writer.BeginType(clazz, PUBLIC)
           .BeginMethod(method, PUBLIC | STATIC)
             .Append("return null;").EndLine()
           .EndMethod()
diff --git a/tensorflow/java/src/gen/gen_ops.bzl b/tensorflow/java/src/gen/gen_ops.bzl
index a6650fc4ea0b67..1e7899cf7af333 100644
--- a/tensorflow/java/src/gen/gen_ops.bzl
+++ b/tensorflow/java/src/gen/gen_ops.bzl
@@ -1,9 +1,11 @@
 # -*- Python -*-
 
-load("//tensorflow:tensorflow.bzl",
-     "tf_binary_additional_srcs",
-     "tf_cc_binary",
-     "tf_copts")
+load(
+    "//tensorflow:tensorflow.bzl",
+    "tf_binary_additional_srcs",
+    "tf_cc_binary",
+    "tf_copts",
+)
 
 # Given a list of "ops_libs" (a list of files in the core/ops directory
 # without their .cc extensions), generate Java wrapper code for all operations
@@ -27,16 +29,31 @@ def tf_java_op_gen_srcjar(name,
                           ops_libs_pkg="//tensorflow/core",
                           out_dir="ops/",
                           out_src_dir="src/main/java/",
+                          api_def_srcs=[],
                           visibility=["//tensorflow/java:__pkg__"]):
 
   gen_tools = []
   gen_cmds = ["rm -rf $(@D)"]  # Always start from fresh when generating source files
+  srcs = api_def_srcs[:]
 
   # Construct an op generator binary for each ops library.
   for ops_lib in ops_libs:
     gen_lib = ops_lib[:ops_lib.rfind("_")]
     out_gen_tool = out_dir + ops_lib + "_gen_tool"
 
+    if not api_def_srcs:
+      api_def_args_str = ","
+    else:
+      api_def_args = []
+      for api_def_src in api_def_srcs:
+        # Add directory of the first ApiDef source to args.
+        # We are assuming all ApiDefs in a single api_def_src are in the
+        # same directory.
+        api_def_args.append(
+            " $$(dirname $$(echo $(locations " + api_def_src +
+            ") | cut -d\" \" -f1))")
+      api_def_args_str = ",".join(api_def_args)
+
     tf_cc_binary(
         name=out_gen_tool,
         copts=tf_copts(),
@@ -48,7 +65,8 @@ def tf_java_op_gen_srcjar(name,
     gen_cmds += ["$(location :" + out_gen_tool + ")" +
                  " --output_dir=$(@D)/" + out_src_dir +
                  " --lib_name=" + gen_lib +
-                 " --base_package=" + gen_base_package]
+                 " --base_package=" + gen_base_package +
+                 " " + api_def_args_str]
 
   # Generate a source archive containing generated code for these ops.
   gen_srcjar = out_dir + name + ".srcjar"
@@ -57,6 +75,7 @@ def tf_java_op_gen_srcjar(name,
   gen_tools += tf_binary_additional_srcs()
   native.genrule(
       name=name,
+      srcs=srcs,
       outs=[gen_srcjar],
       tools=gen_tools,
       cmd="&&".join(gen_cmds))
diff --git a/tensorflow/java/src/gen/resources/license.snippet.java b/tensorflow/java/src/gen/resources/license.snippet.java
new file mode 100644
index 00000000000000..90285ec669f34a
--- /dev/null
+++ b/tensorflow/java/src/gen/resources/license.snippet.java
@@ -0,0 +1,14 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/

From 7e80197f020895fea41eda36b08135b747a9a4f1 Mon Sep 17 00:00:00 2001
From: "karl@kubx.ca" 
Date: Fri, 6 Apr 2018 08:56:54 -0400
Subject: [PATCH 0354/1691] Improve Javadoc and include first code review

---
 tensorflow/java/BUILD                         |  23 +-
 tensorflow/java/src/gen/cc/java_defs.h        |  12 +-
 tensorflow/java/src/gen/cc/op_gen_main.cc     |  48 +-
 tensorflow/java/src/gen/cc/op_generator.cc    | 224 ++++++----
 tensorflow/java/src/gen/cc/op_generator.h     |  25 +-
 tensorflow/java/src/gen/cc/op_parser.cc       | 417 ------------------
 tensorflow/java/src/gen/cc/op_parser.h        | 137 ------
 tensorflow/java/src/gen/cc/op_specs.cc        | 390 ++++++++++++++++
 tensorflow/java/src/gen/cc/op_specs.h         | 152 +++++++
 tensorflow/java/src/gen/cc/source_writer.cc   |   2 +-
 tensorflow/java/src/gen/cc/source_writer.h    |   2 +-
 .../java/src/gen/cc/source_writer_test.cc     |  20 +-
 tensorflow/java/src/gen/gen_ops.bzl           |  68 +--
 ...ense.snippet.java => license.java.snippet} |   0
 14 files changed, 760 insertions(+), 760 deletions(-)
 delete mode 100644 tensorflow/java/src/gen/cc/op_parser.cc
 delete mode 100644 tensorflow/java/src/gen/cc/op_parser.h
 create mode 100644 tensorflow/java/src/gen/cc/op_specs.cc
 create mode 100644 tensorflow/java/src/gen/cc/op_specs.h
 rename tensorflow/java/src/gen/resources/{license.snippet.java => license.java.snippet} (100%)

diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD
index 635a4e807d8012..17566e1a9c671c 100644
--- a/tensorflow/java/BUILD
+++ b/tensorflow/java/BUILD
@@ -68,9 +68,13 @@ filegroup(
     ],
 )
 
+# Build the gen tool as a library, as it will be linked to a core/ops binary
+# files before making it an executable.
 tf_java_op_gen_srcjar(
     name = "java_op_gen_sources",
-    api_def_srcs = ["//tensorflow/core/api_def:base_api_def"],
+    api_def_srcs = [
+        "//tensorflow/core/api_def:base_api_def",
+    ],
     gen_base_package = "org.tensorflow.op",
     gen_tool = "java_op_gen_tool",
     ops_libs = [
@@ -95,30 +99,17 @@ tf_java_op_gen_srcjar(
     ],
 )
 
-# Build the gen tool as a library, as it will be linked to a core/ops binary
-# file before making it an executable. See tf_java_op_gen_srcjar().
-cc_library(
-    name = "java_op_gen_tool",
-    srcs = [
-        "src/gen/cc/op_gen_main.cc",
-    ],
-    copts = tf_copts(),
-    deps = [
-        ":java_op_gen_lib",
-    ],
-)
-
 cc_library(
     name = "java_op_gen_lib",
     srcs = [
         "src/gen/cc/op_generator.cc",
-        "src/gen/cc/op_parser.cc",
+        "src/gen/cc/op_specs.cc",
         "src/gen/cc/source_writer.cc",
     ],
     hdrs = [
         "src/gen/cc/java_defs.h",
         "src/gen/cc/op_generator.h",
-        "src/gen/cc/op_parser.h",
+        "src/gen/cc/op_specs.h",
         "src/gen/cc/source_writer.h",
     ],
     copts = tf_copts(),
diff --git a/tensorflow/java/src/gen/cc/java_defs.h b/tensorflow/java/src/gen/cc/java_defs.h
index 2065477f580137..81ac67eb2f2b2f 100644
--- a/tensorflow/java/src/gen/cc/java_defs.h
+++ b/tensorflow/java/src/gen/cc/java_defs.h
@@ -1,4 +1,4 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -230,12 +230,12 @@ class Javadoc {
     return Javadoc(brief);
   }
   const string& brief() const { return brief_; }
-  const string& details() const { return description_; }
-  Javadoc& details(const string description) {
-    description_ = description;
+  const string& details() const { return details_; }
+  Javadoc& details(const string& details) {
+    details_ = details;
     return *this;
   }
-  const std::list> tags() const { return tags_; }
+  const std::list>& tags() const { return tags_; }
   Javadoc& add_tag(const string& tag, const string& text) {
     tags_.push_back(std::make_pair(tag, text));
     return *this;
@@ -246,7 +246,7 @@ class Javadoc {
 
  private:
   string brief_;
-  string description_;
+  string details_;
   std::list> tags_;
 
   explicit Javadoc(const string& brief) : brief_(brief) {}
diff --git a/tensorflow/java/src/gen/cc/op_gen_main.cc b/tensorflow/java/src/gen/cc/op_gen_main.cc
index 015200023f97c2..458141b877fa96 100644
--- a/tensorflow/java/src/gen/cc/op_gen_main.cc
+++ b/tensorflow/java/src/gen/cc/op_gen_main.cc
@@ -1,4 +1,4 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
@@ -36,55 +36,41 @@ const char kUsageHeader[] =
     "Operation wrappers are generated under the path specified by the "
     "'--output_dir' argument. This path can be absolute or relative to the\n"
     "current working directory and will be created if it does not exists.\n\n"
-    "The '--lib_name' argument is used to classify the set of operations. If "
-    "the chosen name contains more than one word, it must be provided in \n"
-    "snake_case. This value is declined into other meaningful names, such as "
-    "the group and package of the generated operations. For example,\n"
-    "'--lib_name=my_lib' generates the operations under the "
-    "'org.tensorflow.op.mylib' package and add them to the 'myLib()' operator\n"
-    "group.\n\n"
-    "Note that the operator group assigned to the generated wrappers is just "
-    "an annotation tag at this stage. Operations will not be available "
-    "through\n"
-    "the 'org.tensorflow.op.Ops' API as a group until the generated classes "
-    "are compiled using an appropriate annotation processor.\n\n"
+    "Note that the operations will not be available through the "
+    "'org.tensorflow.op.Ops' API until the generated classes are compiled\n"
+    "using an appropriate annotation processor.\n\n"
     "The '--base_package' overrides the default parent package under which "
     "the generated subpackage and classes are to be located.\n\n"
-    "Finally, a list of directories of API proto definitions can be provided "
-    "to override default values found in the ops definitions, ordered by\n"
-    "priority (the last having precedence over the first).\n\n";
+    "Finally, the `--api_dirs` argument takes a list of comma-seperated "
+    "directories of API definitions can be provided to override default\n"
+    "values found in the ops definitions. Directories are ordered by priority "
+    "(the last having precedence over the first).\n\n";
 
 }  // namespace java
 }  // namespace tensorflow
 
 int main(int argc, char* argv[]) {
-  tensorflow::string lib_name;
   tensorflow::string output_dir;
   tensorflow::string base_package = "org.tensorflow.op";
+  tensorflow::string api_dirs_str;
   std::vector flag_list = {
       tensorflow::Flag("output_dir", &output_dir,
           "Root directory into which output files are generated"),
-      tensorflow::Flag(
-          "lib_name", &lib_name,
-          "A name, in snake_case, used to classify this set of operations"),
-      tensorflow::Flag(
-          "base_package", &base_package,
-          "Package parent to the generated subpackage and classes")};
+      tensorflow::Flag("base_package", &base_package,
+          "Package parent to the generated subpackage and classes"),
+      tensorflow::Flag("api_dirs", &api_dirs_str,
+          "List of directories that contains the ops api definitions")};
   tensorflow::string usage = tensorflow::java::kUsageHeader;
   usage += tensorflow::Flags::Usage(argv[0], flag_list);
   bool parsed_flags_ok = tensorflow::Flags::Parse(&argc, argv, flag_list);
   tensorflow::port::InitMain(usage.c_str(), &argc, &argv);
-  QCHECK(parsed_flags_ok && !lib_name.empty() && !output_dir.empty()) << usage;
-  std::vector api_dirs;
-  if (argc > 1) {
-    api_dirs = tensorflow::str_util::Split(argv[1], ",",
-        tensorflow::str_util::SkipEmpty());
-  }
+  QCHECK(parsed_flags_ok && !output_dir.empty()) << usage;
+  std::vector api_dirs = tensorflow::str_util::Split(
+      api_dirs_str, ",", tensorflow::str_util::SkipEmpty());
   tensorflow::java::OpGenerator generator(base_package, output_dir, api_dirs);
   tensorflow::OpList ops;
   tensorflow::OpRegistry::Global()->Export(false, &ops);
-  tensorflow::Status status = generator.Run(ops, lib_name);
-  TF_QCHECK_OK(status);
+  TF_CHECK_OK(generator.Run(ops));
 
   return 0;
 }
diff --git a/tensorflow/java/src/gen/cc/op_generator.cc b/tensorflow/java/src/gen/cc/op_generator.cc
index c9b57f570616f4..c32ad3b1099524 100644
--- a/tensorflow/java/src/gen/cc/op_generator.cc
+++ b/tensorflow/java/src/gen/cc/op_generator.cc
@@ -1,4 +1,4 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -18,6 +18,7 @@ limitations under the License.
 #include 
 #include 
 #include 
+#include 
 
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -27,15 +28,15 @@ limitations under the License.
 #include "tensorflow/core/framework/op_gen_lib.h"
 #include "tensorflow/java/src/gen/cc/java_defs.h"
 #include "tensorflow/java/src/gen/cc/source_writer.h"
-#include "tensorflow/java/src/gen/cc/op_parser.h"
 #include "tensorflow/java/src/gen/cc/op_generator.h"
+#include "tensorflow/java/src/gen/cc/op_specs.h"
 
 namespace tensorflow {
 namespace java {
 namespace {
 
 const char* kLicenseSnippet =
-    "tensorflow/java/src/gen/resources/license.snippet.java";
+    "tensorflow/java/src/gen/resources/license.java.snippet";
 
 const std::map kPrimitiveAttrTypes = {
   { "Boolean", Type::Boolean() },
@@ -66,34 +67,34 @@ void CollectOpDependencies(const OpSpec& op, RenderMode mode,
   }
   // Don't pay attention to duplicate types in the dependency list, they will
   // be filtered out by the SourceWriter.
-  for (const OpSpec::Operand& input : op.inputs()) {
+  for (const ArgumentSpec& input : op.inputs()) {
     out->push_back(input.var().type());
     if (input.iterable()) {
       out->push_back(Type::Class("Operands", "org.tensorflow.op"));
     }
   }
-  for (const OpSpec::Operand& output : op.outputs()) {
+  for (const ArgumentSpec& output : op.outputs()) {
     out->push_back(output.var().type());
     if (output.iterable()) {
       out->push_back(Type::Class("Arrays", "java.util"));
     }
   }
-  for (const OpSpec::Operand& attribute : op.attributes()) {
+  for (const AttributeSpec& attribute : op.attributes()) {
     out->push_back(attribute.var().type());
     if (attribute.var().type().name() == "Class") {
       out->push_back(Type::Enum("DataType", "org.tensorflow"));
     }
   }
-  for (const OpSpec::Operand& option : op.options()) {
-    out->push_back(option.var().type());
+  for (const AttributeSpec& optional_attribute : op.optional_attributes()) {
+    out->push_back(optional_attribute.var().type());
   }
 }
 
-void WriteSetAttrDirective(const OpSpec::Operand& attr, bool optional,
+void WriteSetAttrDirective(const AttributeSpec& attr, bool optional,
     SourceWriter* writer) {
   string var = optional ? "opts." + attr.var().name() : attr.var().name();
   if (attr.iterable()) {
-    const Type& type = attr.data_type();
+    const Type& type = attr.type();
     std::map::const_iterator it =
       kPrimitiveAttrTypes.find(type.name());
     if (it != kPrimitiveAttrTypes.end()) {
@@ -107,11 +108,11 @@ void WriteSetAttrDirective(const OpSpec::Operand& attr, bool optional,
           .Append(array + "[i] = " + var + ".get(i);")
           .EndLine()
           .EndBlock()
-          .Append("opBuilder.setAttr(\"" + attr.graph_name() + "\", " + array)
+          .Append("opBuilder.setAttr(\"" + attr.op_def_name() + "\", " + array)
           .Append(");")
           .EndLine();
     } else {
-      writer->Append("opBuilder.setAttr(\"" + attr.graph_name() + "\", " + var)
+      writer->Append("opBuilder.setAttr(\"" + attr.op_def_name() + "\", " + var)
           .Append(".toArray(new ")
           .AppendType(type)
           .Append("[" + var + ".size()]));")
@@ -119,7 +120,7 @@ void WriteSetAttrDirective(const OpSpec::Operand& attr, bool optional,
     }
   } else {
     Type type = attr.var().type();
-    writer->Append("opBuilder.setAttr(\"" + attr.graph_name() + "\", ");
+    writer->Append("opBuilder.setAttr(\"" + attr.op_def_name() + "\", ");
     if (type.name() == "Class") {
       writer->Append("DataType.fromClass(" + attr.var().name() + "));");
     } else {
@@ -139,26 +140,26 @@ void RenderFactoryMethod(const OpSpec& op, const Type& op_class,
       Variable::Create("scope", Type::Class("Scope", "org.tensorflow.op"));
   factory.add_argument(scope);
   factory_doc.add_param_tag(scope.name(), "Current graph scope");
-  for (const OpSpec::Operand& input : op.inputs()) {
+  for (const ArgumentSpec& input : op.inputs()) {
     factory.add_argument(input.var());
     factory_doc.add_param_tag(input.var().name(), input.description());
   }
-  for (const OpSpec::Operand& attribute : op.attributes()) {
+  for (const AttributeSpec& attribute : op.attributes()) {
     factory.add_argument(attribute.var());
     factory_doc.add_param_tag(attribute.var().name(), attribute.description());
   }
-  if (!op.options().empty()) {
+  if (!op.optional_attributes().empty()) {
     factory.add_argument(Variable::Varargs("options", Type::Class("Options")));
     factory_doc.add_param_tag("options", "carries optional attributes values");
   }
   factory_doc.add_tag("return", "a new instance of " + op_class.name());
   writer->BeginMethod(factory, PUBLIC|STATIC, &factory_doc);
   writer->Append("OperationBuilder opBuilder = scope.graph().opBuilder(\""
-      + op.graph_name() + "\", scope.makeOpName(\""
+      + op.graph_op_name() + "\", scope.makeOpName(\""
       + op_class.name() + "\"));");
   writer->EndLine();
 
-  for (const OpSpec::Operand& input : op.inputs()) {
+  for (const ArgumentSpec& input : op.inputs()) {
     if (input.iterable()) {
       writer->Append("opBuilder.addInputList(Operands.asOutputs("
           + input.var().name() + "));");
@@ -169,15 +170,15 @@ void RenderFactoryMethod(const OpSpec& op, const Type& op_class,
       writer->EndLine();
     }
   }
-  for (const OpSpec::Operand& attribute : op.attributes()) {
+  for (const AttributeSpec& attribute : op.attributes()) {
     WriteSetAttrDirective(attribute, false, writer);
   }
-  if (!op.options().empty()) {
+  if (!op.optional_attributes().empty()) {
     writer->BeginBlock("if (options != null)")
         .BeginBlock("for (Options opts : options)");
-    for (const OpSpec::Operand& option : op.options()) {
-      writer->BeginBlock("if (opts." + option.var().name() + " != null)");
-      WriteSetAttrDirective(option, true, writer);
+    for (const AttributeSpec& attribute : op.optional_attributes()) {
+      writer->BeginBlock("if (opts." + attribute.var().name() + " != null)");
+      WriteSetAttrDirective(attribute, true, writer);
       writer->EndBlock();
     }
     writer->EndBlock().EndBlock();
@@ -195,8 +196,8 @@ void RenderConstructor(const OpSpec& op, const Type& op_class,
     .add_argument(
         Variable::Create("operation",
             Type::Class("Operation", "org.tensorflow")));
-  for (const OpSpec::Operand& output : op.outputs()) {
-    if (output.iterable() && !output.data_type().unknown()) {
+  for (const ArgumentSpec& output : op.outputs()) {
+    if (output.iterable() && !output.type().unknown()) {
       constructor.add_annotation(
           Annotation::Create("SuppressWarnings").attributes("\"unchecked\""));
       break;
@@ -208,15 +209,15 @@ void RenderConstructor(const OpSpec& op, const Type& op_class,
   if (op.outputs().size() > 0) {
     writer->Append("int outputIdx = 0;")
         .EndLine();
-    for (const OpSpec::Operand& output : op.outputs()) {
+    for (const ArgumentSpec& output : op.outputs()) {
       if (output.iterable()) {
         string var_length = output.var().name() + "Length";
         writer->Append("int " + var_length)
-            .Append(" = operation.outputListLength(\"" + output.graph_name()
+            .Append(" = operation.outputListLength(\"" + output.op_def_name()
                 + "\");")
             .EndLine()
             .Append(output.var().name() + " = Arrays.asList(");
-        if (!output.data_type().unknown()) {
+        if (!output.type().unknown()) {
           writer->Append("(")
               .AppendType(output.var().type().parameters().front())
               .Append("[])");
@@ -236,18 +237,19 @@ void RenderConstructor(const OpSpec& op, const Type& op_class,
 }
 
 void RenderGettersAndSetters(const OpSpec& op, SourceWriter* writer) {
-  for (const OpSpec::Operand& option : op.options()) {
-    Method setter = Method::Create(option.var().name(), Type::Class("Options"))
-        .add_argument(option.var());
+  for (const AttributeSpec& attribute : op.optional_attributes()) {
+    Method setter =
+        Method::Create(attribute.var().name(), Type::Class("Options"))
+            .add_argument(attribute.var());
     Javadoc setter_doc = Javadoc::Create()
-        .add_param_tag(option.var().name(), option.description());
+        .add_param_tag(attribute.var().name(), attribute.description());
     writer->BeginMethod(setter, PUBLIC|STATIC, &setter_doc)
-        .Append("return new Options()." + option.var().name() + "("
-            + option.var().name() + ");")
+        .Append("return new Options()." + attribute.var().name() + "("
+            + attribute.var().name() + ");")
         .EndLine()
         .EndMethod();
   }
-  for (const OpSpec::Operand& output : op.outputs()) {
+  for (const ArgumentSpec& output : op.outputs()) {
     Method getter = Method::Create(output.var().name(), output.var().type());
     Javadoc getter_doc = Javadoc::Create(output.description());
     writer->BeginMethod(getter, PUBLIC, &getter_doc)
@@ -259,12 +261,12 @@ void RenderGettersAndSetters(const OpSpec& op, SourceWriter* writer) {
 
 void RenderInterfaceImpl(const OpSpec& op, RenderMode mode,
     SourceWriter* writer) {
-  OpSpec::Operand output = op.outputs().front();
+  ArgumentSpec output = op.outputs().front();
 
   if (mode == SINGLE_OUTPUT) {
-    bool cast2obj = output.data_type().unknown();
+    bool cast2obj = output.type().unknown();
     Type return_type = Type::Class("Output", "org.tensorflow")
-        .add_parameter(cast2obj ? Type::Class("Object") : output.data_type());
+        .add_parameter(cast2obj ? Type::Class("Object") : output.type());
     Method as_output = Method::Create("asOutput", return_type)
         .add_annotation(Annotation::Create("Override"));
     if (cast2obj) {
@@ -283,10 +285,10 @@ void RenderInterfaceImpl(const OpSpec& op, RenderMode mode,
 
   } else if (mode == SINGLE_LIST_OUTPUT) {
     Type operand = Type::Interface("Operand", "org.tensorflow");
-    if (output.data_type().unknown()) {
+    if (output.type().unknown()) {
       operand.add_parameter(Type::Class("Object"));
     } else {
-      operand.add_parameter(output.data_type());
+      operand.add_parameter(output.type());
     }
     Type return_type = Type::Interface("Iterator", "java.util")
         .add_parameter(operand);
@@ -308,57 +310,119 @@ void RenderOptionsClass(const OpSpec& op, SourceWriter* writer) {
   Javadoc options_doc = Javadoc::Create(
       "Class holding optional attributes of this operation");
   writer->BeginInnerType(options_class, PUBLIC | STATIC, &options_doc);
-  for (const OpSpec::Operand& option : op.options()) {
-    Method setter = Method::Create(option.var().name(), options_class)
-        .add_argument(option.var());
+  for (const AttributeSpec& attribute : op.optional_attributes()) {
+    Method setter = Method::Create(attribute.var().name(), options_class)
+        .add_argument(attribute.var());
     Javadoc setter_doc = Javadoc::Create()
-        .add_param_tag(option.var().name(), option.description());
+        .add_param_tag(attribute.var().name(), attribute.description());
     writer->BeginMethod(setter, PUBLIC, &setter_doc)
-        .Append("this." + option.var().name() + " = " + option.var().name()
-            + ";")
+        .Append("this." + attribute.var().name() + " = "
+            + attribute.var().name() + ";")
         .EndLine()
         .Append("return this;")
         .EndLine()
         .EndMethod();
   }
   writer->EndLine();
-  for (const OpSpec::Operand& option : op.options()) {
-    writer->WriteField(option.var(), PRIVATE);
+  for (const AttributeSpec& optional_attribute : op.optional_attributes()) {
+    writer->WriteField(optional_attribute.var(), PRIVATE);
   }
   Method constructor = Method::ConstructorFor(options_class);
   writer->BeginMethod(constructor, PRIVATE).EndMethod();
   writer->EndType();
 }
 
-void RenderEndpoint(const OpSpec& op, const OpSpec::Endpoint& endpoint,
-    SourceWriter* writer) {
+inline Type ClassOf(const EndpointSpec& endpoint, const string& base_package) {
+  return Type::Class(endpoint.name(),
+      base_package + "." + str_util::Lowercase(endpoint.package()));
+}
+
+void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint,
+    const string& base_package, const string& output_dir, Env* env) {
+  Type op_class(ClassOf(endpoint, base_package)
+      .add_supertype(Type::Class("PrimitiveOp", "org.tensorflow.op")));
+  Javadoc op_javadoc(endpoint.javadoc());
+
+  // implement Operand (or Iterable) if the op has only one output
   RenderMode mode = DEFAULT;
   if (op.outputs().size() == 1) {
-    mode = op.outputs().front().iterable() ? SINGLE_LIST_OUTPUT : SINGLE_OUTPUT;
+    const ArgumentSpec& output = op.outputs().front();
+    Type operand_type(output.type().unknown() ?
+        Type::Class("Object") : output.type());
+    Type operand_inf(Type::Interface("Operand", "org.tensorflow")
+        .add_parameter(operand_type));
+    if (output.iterable()) {
+      mode = SINGLE_LIST_OUTPUT;
+      op_class.add_supertype(Type::IterableOf(operand_inf));
+    } else {
+      mode = SINGLE_OUTPUT;
+      op_class.add_supertype(operand_inf);
+    }
+  }
+  // declare all outputs generics at the op class level
+  std::set generics;
+  for (const ArgumentSpec& output : op.outputs()) {
+    if (output.type().kind() == Type::GENERIC && !output.type().unknown()
+        && generics.find(output.type().name()) == generics.end()) {
+      op_class.add_parameter(output.type());
+      op_javadoc.add_param_tag("<" + output.type().name() + ">",
+          "data type of output {@code " + output.var().name() + "}");
+      generics.insert(output.type().name());
+    }
+  }
+  // handle endpoint deprecation
+  if (endpoint.deprecated()) {
+    op_class.add_annotation(Annotation::Create("Deprecated"));
+    string explanation;
+    if (!op.endpoints().front().deprecated()) {
+      explanation = "use {@link " +
+          ClassOf(op.endpoints().front(), base_package).full_name()
+          + "} instead";
+    } else {
+      explanation = op.deprecation_explanation();
+    }
+    op_javadoc.add_tag("deprecated", explanation);
   }
+  // expose the op in the Ops Graph API only if it is visible
+  if (!op.hidden()) {
+    op_class.add_annotation(
+        Annotation::Create("Operator", "org.tensorflow.op.annotation")
+          .attributes("group = \"" + endpoint.package() + "\""));
+  }
+  // create op class file
+  string op_dir = io::JoinPath(output_dir,
+      str_util::StringReplace(op_class.package(), ".", "/", true));
+  if (!env->FileExists(op_dir).ok()) {
+    TF_CHECK_OK(Env::Default()->RecursivelyCreateDir(op_dir));
+  }
+  std::unique_ptr op_file;
+  TF_CHECK_OK(env->NewWritableFile(
+      io::JoinPath(op_dir, op_class.name() + ".java"), &op_file));
+
+  // render endpoint source code
+  SourceFileWriter writer(op_file.get());
   std::list dependencies;
   CollectOpDependencies(op, mode, &dependencies);
-  const Type& op_class = endpoint.type();
-  writer->WriteFromFile(kLicenseSnippet)
+  writer.WriteFromFile(kLicenseSnippet)
       .EndLine()
       .Append("// This file is machine generated, DO NOT EDIT!")
       .EndLine()
       .EndLine()
-      .BeginType(op_class, PUBLIC|FINAL, &dependencies, &endpoint.javadoc());
-  if (!op.options().empty()) {
-    RenderOptionsClass(op, writer);
+      .BeginType(op_class, PUBLIC|FINAL, &dependencies, &op_javadoc);
+  if (!op.optional_attributes().empty()) {
+    RenderOptionsClass(op, &writer);
   }
-  RenderFactoryMethod(op, op_class, writer);
-  RenderGettersAndSetters(op, writer);
+  RenderFactoryMethod(op, op_class, &writer);
+  RenderGettersAndSetters(op, &writer);
   if (mode != DEFAULT) {
-    RenderInterfaceImpl(op, mode, writer);
+    RenderInterfaceImpl(op, mode, &writer);
   }
-  writer->EndLine();
-  for (const OpSpec::Operand& output : op.outputs()) {
-    writer->WriteField(output.var(), PRIVATE);
+  writer.EndLine();
+  for (const ArgumentSpec& output : op.outputs()) {
+    writer.WriteField(output.var(), PRIVATE);
   }
-  RenderConstructor(op, op_class, writer);
-  writer->EndType();
+  RenderConstructor(op, op_class, &writer);
+  writer.EndType();
 }
 
 }  // namespace
@@ -369,8 +433,7 @@ OpGenerator::OpGenerator(const string& base_package, const string& output_dir,
     env_(env) {
 }
 
-Status OpGenerator::Run(const OpList& op_list, const string& lib_name) {
-  LOG(INFO) << "Generating Java wrappers for '" << lib_name << "' operations";
+Status OpGenerator::Run(const OpList& op_list) {
   ApiDefMap api_map(op_list);
   if (!api_dirs_.empty()) {
     // Only load api files that correspond to the requested "op_list"
@@ -388,37 +451,14 @@ Status OpGenerator::Run(const OpList& op_list, const string& lib_name) {
   for (const auto& op_def : op_list.op()) {
     const ApiDef* api_def = api_map.GetApiDef(op_def.name());
     if (api_def->visibility() != ApiDef::SKIP) {
-      Status status = GenerateOp(op_def, *api_def, lib_name);
-      if (status != Status::OK()) {
-        LOG(ERROR) << "Fail to generate Java wrapper for operation \""
-            << op_def.name() << "\"";
+      OpSpec op(OpSpec::Create(op_def, *api_def));
+      for (const EndpointSpec& endpoint : op.endpoints()) {
+        GenerateOp(op, endpoint, base_package_, output_dir_, env_);
       }
     }
   }
   return Status::OK();
 }
 
-Status OpGenerator::GenerateOp(const OpDef& op_def, const ApiDef& api_def,
-    const string& lib_name) {
-  std::unique_ptr op;
-  OpParser op_parser(op_def, api_def, lib_name, base_package_);
-  op_parser.Parse(&op);
-  for (const OpSpec::Endpoint& endpoint : op->endpoints()) {
-    string package_path = io::JoinPath(output_dir_,
-        str_util::StringReplace(endpoint.type().package(), ".", "/", true));
-    if (!env_->FileExists(package_path).ok()) {
-      TF_CHECK_OK(Env::Default()->RecursivelyCreateDir(package_path));
-    }
-    string file_path =
-        io::JoinPath(package_path, endpoint.type().name() + ".java");
-    std::unique_ptr file;
-    TF_CHECK_OK(env_->NewWritableFile(file_path, &file));
-
-    SourceFileWriter writer(file.get());
-    RenderEndpoint(*op, endpoint, &writer);
-  }
-  return Status::OK();
-}
-
 }  // namespace java
 }  // namespace tensorflow
diff --git a/tensorflow/java/src/gen/cc/op_generator.h b/tensorflow/java/src/gen/cc/op_generator.h
index 19d8db95fbb11b..06b08e852a5531 100644
--- a/tensorflow/java/src/gen/cc/op_generator.h
+++ b/tensorflow/java/src/gen/cc/op_generator.h
@@ -1,4 +1,4 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -23,36 +23,33 @@ limitations under the License.
 #include "tensorflow/core/framework/api_def.pb.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/java/src/gen/cc/op_specs.h"
 
 namespace tensorflow {
 namespace java {
 
 // A generator of Java operation wrappers.
 //
-// Such generator is normally ran only once per executable, outputting
-// wrappers for the all registered operations it has been compiled with.
-// Nonetheless, it is designed to support multiple runs, giving a different
-// list of operations on each cycle.
+// This generator takes a list of ops definitions in input and outputs
+// a Java Op wrapper for each of them in the provided directory. The same
+// generator instance can be invoked multiple times with a different list of
+// ops definitions.
 class OpGenerator {
  public:
   OpGenerator(const string& base_package, const string& output_dir,
       const std::vector& api_dirs, Env* env = Env::Default());
-  virtual ~OpGenerator() = default;
 
   // Generates wrappers for the given list of 'ops'.
   //
   // Output files are generated in //,
-  // where 'lib_package' is derived from 'lib_name'.
-  Status Run(const OpList& op_list, const string& lib_name);
+  // where 'lib_package' is derived from ops endpoints.
+  Status Run(const OpList& op_list);
 
  private:
-  string base_package_;
-  string output_dir_;
-  std::vector api_dirs_;
+  const string base_package_;
+  const string output_dir_;
+  const std::vector api_dirs_;
   Env* env_;
-
-  Status GenerateOp(const OpDef& op_def, const ApiDef& api_def,
-    const string& lib_name);
 };
 
 }  // namespace java
diff --git a/tensorflow/java/src/gen/cc/op_parser.cc b/tensorflow/java/src/gen/cc/op_parser.cc
deleted file mode 100644
index 0541e343d80c2d..00000000000000
--- a/tensorflow/java/src/gen/cc/op_parser.cc
+++ /dev/null
@@ -1,417 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include 
-#include 
-#include 
-#include 
-
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/types.h"
-#include "tensorflow/core/lib/strings/str_util.h"
-#include "tensorflow/core/platform/logging.h"
-#include "tensorflow/java/src/gen/cc/op_parser.h"
-
-namespace tensorflow {
-namespace java {
-namespace {
-
-string SnakeToCamelCase(const string& str, bool upper = false) {
-  string result;
-  bool cap = upper;
-  for (string::const_iterator it = str.begin(); it != str.end(); ++it) {
-    const char c = *it;
-    if (c == '_') {
-      cap = true;
-    } else if (cap) {
-      result += toupper(c);
-      cap = false;
-    } else {
-      result += c;
-    }
-  }
-  return result;
-}
-
-bool IsRealNumber(DataType type) {
-  for (DataType dt : RealNumberTypes()) {
-    if (type == dt) {
-      return true;
-    }
-  }
-  return false;
-}
-
-bool IsRealNumbers(const AttrValue& values) {
-  if (values.has_list()) {
-    for (int i = 0; i < values.list().type_size(); ++i) {
-      if (!IsRealNumber(values.list().type(i))) {
-        return false;
-      }
-    }
-    return true;
-  }
-  return IsRealNumber(values.type());
-}
-
-string ParseDocumentation(const string& text) {
-  std::stringstream javadoc_text;
-  string::const_iterator c_iter = text.cbegin();
-  bool code = false;
-  bool emphasis = false;
-  bool list = false;
-  while (c_iter != text.cend()) {
-    char c = *c_iter++;
-    int count = 1;
-    switch (c) {
-    case '\n':
-      if (!code) {
-        // consumes all subsequent newlines, if there are more than one,
-        // then there are two choices:
-        // - if the next line starts with an asterisk, we are enumerating
-        //   a list of items
-        // - otherwise, we are starting a new paragraph
-        for (; c_iter != text.cend() && *c_iter == '\n'; ++count, ++c_iter) {}
-        if (c_iter != text.cend()) {
-          if (count > 1) {
-            if (*c_iter != '*' && list) {
-              javadoc_text << "
    • \n
\n";
-              list = false;
-            } else if (*c_iter == '*' && !list) {
-              javadoc_text << "\n\n";
+        in_list = false;
+      } else if (!input.starts_with("```")) {
+        // new paragraph (not required if a 
 block follows)
+        javadoc_text << "
\n";
       }
-    } else if (markup.starts_with("```") && text.empty()) {
-      // create a multiline code block
-      re2::StringPiece language;
-      RE2::Consume(&input, "[\\w\\+]+", &language);
-      if (FindAndCut(&input, markup.ToString() + "\n*", &text)) {
-        javadoc_text << "
\n{@code" << text << "}\n
\n";
+    } else if (markup.starts_with("```")) {
+      // code blocks
+      if (FindAndCut(&input, "```\\s*\n*", &text)) {
+        javadoc_text << "
{@code\n" << text << "}
\n";
       } else {
-        javadoc_text << markup << language;
+        javadoc_text << markup;
       }
     } else if (markup.starts_with("`")) {
-      // write inlined code
+      // inlined code
       if (FindAndCut(&input, markup, &text)) {
         javadoc_text << "{@code " << text << "}";
       } else {
         javadoc_text << markup;
       }
     } else if (markup == "**") {
-      // emphase text (strong)
+      // text emphasis (strong)
       if (FindAndCut(&input, "\\b\\*{2}", &text)) {
         javadoc_text << "" << ParseDocumentation(text) << "";
       } else {
         javadoc_text << markup;
       }
     } else if (markup == "*") {
-      // emphase text (light)
+      // text emphasis (normal)
       if (FindAndCut(&input, "\\b\\*{1}", &text)) {
         javadoc_text << "" << ParseDocumentation(text) << "";
       } else {
         javadoc_text << markup;
       }
-    } else if (markup == "[") {
-      // add an external link
+    } else if (markup.starts_with("[")) {
+      // hyperlinks
       string label;
       string link;
       if (RE2::Consume(&input, "([^\\[]+)\\]\\((http.+)\\)", &label, &link)) {
@@ -277,6 +281,7 @@ string ParseDocumentation(re2::StringPiece input) {
         javadoc_text << markup;
       }
     } else {
+      // safe fallback
       javadoc_text << markup;
     }
   }

From eac1479f04181fb107c85af29a709eb369831972 Mon Sep 17 00:00:00 2001
From: "karl@kubx.ca" 
Date: Mon, 30 Apr 2018 07:38:48 -0400
Subject: [PATCH 0357/1691] Simplify and improve generics handling in generator

---
 tensorflow/java/build_defs.bzl             |   1 +
 tensorflow/java/src/gen/cc/op_gen_main.cc  |   4 +-
 tensorflow/java/src/gen/cc/op_generator.cc | 155 +++++++++------------
 tensorflow/java/src/gen/cc/op_generator.h  |  13 +-
 tensorflow/java/src/gen/cc/op_specs.cc     |  81 ++++++-----
 tensorflow/java/src/gen/cc/op_specs.h      |  16 ++-
 6 files changed, 132 insertions(+), 138 deletions(-)

diff --git a/tensorflow/java/build_defs.bzl b/tensorflow/java/build_defs.bzl
index ab7f60d03dfd04..e1916ca4d9d6aa 100644
--- a/tensorflow/java/build_defs.bzl
+++ b/tensorflow/java/build_defs.bzl
@@ -15,6 +15,7 @@ JAVA_VERSION_OPTS = [
 XLINT_OPTS = [
     "-Werror",
     "-Xlint:all",
+    "-Xlint:-processing",
     "-Xlint:-serial",
     "-Xlint:-try",
     "-Xlint:-classfile", # see b/32750402, go/javac-warnings#classfile
diff --git a/tensorflow/java/src/gen/cc/op_gen_main.cc b/tensorflow/java/src/gen/cc/op_gen_main.cc
index 458141b877fa96..a508c965163145 100644
--- a/tensorflow/java/src/gen/cc/op_gen_main.cc
+++ b/tensorflow/java/src/gen/cc/op_gen_main.cc
@@ -67,10 +67,10 @@ int main(int argc, char* argv[]) {
   QCHECK(parsed_flags_ok && !output_dir.empty()) << usage;
   std::vector api_dirs = tensorflow::str_util::Split(
       api_dirs_str, ",", tensorflow::str_util::SkipEmpty());
-  tensorflow::java::OpGenerator generator(base_package, output_dir, api_dirs);
+  tensorflow::java::OpGenerator generator(api_dirs);
   tensorflow::OpList ops;
   tensorflow::OpRegistry::Global()->Export(false, &ops);
-  TF_CHECK_OK(generator.Run(ops));
+  TF_CHECK_OK(generator.Run(ops, base_package, output_dir));
 
   return 0;
 }
diff --git a/tensorflow/java/src/gen/cc/op_generator.cc b/tensorflow/java/src/gen/cc/op_generator.cc
index 00f84bc9cdcfbf..2327a4daf1627d 100644
--- a/tensorflow/java/src/gen/cc/op_generator.cc
+++ b/tensorflow/java/src/gen/cc/op_generator.cc
@@ -19,6 +19,7 @@ limitations under the License.
 #include 
 #include 
 #include 
+#include 
 
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -38,23 +39,18 @@ namespace {
 const char* kLicenseSnippet =
     "tensorflow/java/src/gen/resources/license.java.snippet";
 
-const std::map kPrimitiveAttrTypes = {
-  { "Boolean", Type::Boolean() },
-  { "Byte", Type::Byte() },
-  { "Character", Type::Byte() },
-  { "Float", Type::Float() },
-  { "Integer", Type::Long() },
-  { "Long", Type::Long() },
-  { "Short", Type::Long() },
-  { "Double", Type::Float() },
-};
-
 enum RenderMode {
   DEFAULT,
   SINGLE_OUTPUT,
   SINGLE_LIST_OUTPUT
 };
 
+inline void AddArgument(const Variable& var, const string& description,
+    Method* method_out, Javadoc* javadoc_out) {
+  method_out->add_argument(var);
+  javadoc_out->add_param_tag(var.name(), description);
+}
+
 void CollectOpDependencies(const OpSpec& op, RenderMode mode,
     std::list* out) {
   out->push_back(Type::Class("Operation", "org.tensorflow"));
@@ -81,9 +77,7 @@ void CollectOpDependencies(const OpSpec& op, RenderMode mode,
   }
   for (const AttributeSpec& attribute : op.attributes()) {
     out->push_back(attribute.var().type());
-    if (attribute.var().type().name() == "Class") {
-      out->push_back(Type::Enum("DataType", "org.tensorflow"));
-    }
+    out->push_back(attribute.jni_type());
   }
   for (const AttributeSpec& optional_attribute : op.optional_attributes()) {
     out->push_back(optional_attribute.var().type());
@@ -92,45 +86,38 @@ void CollectOpDependencies(const OpSpec& op, RenderMode mode,
 
 void WriteSetAttrDirective(const AttributeSpec& attr, bool optional,
     SourceWriter* writer) {
-  string var = optional ? "opts." + attr.var().name() : attr.var().name();
+  string var_name = optional ? "opts." + attr.var().name() : attr.var().name();
   if (attr.iterable()) {
-    const Type& type = attr.type();
-    std::map::const_iterator it =
-      kPrimitiveAttrTypes.find(type.name());
-    if (it != kPrimitiveAttrTypes.end()) {
-      string array = attr.var().name() + "Array";
-      writer->AppendType(it->second)
-          .Append("[] " + array + " = new ")
-          .AppendType(it->second)
-          .Append("[" + var + ".size()];")
-          .EndLine();
-      writer->BeginBlock("for (int i = 0; i < " + array + ".length; ++i)")
-          .Append(array + "[i] = " + var + ".get(i);")
-          .EndLine()
-          .EndBlock()
-          .Append("opBuilder.setAttr(\"" + attr.op_def_name() + "\", " + array)
-          .Append(");")
-          .EndLine();
+    string array_name = attr.var().name() + "Array";
+    writer->AppendType(attr.jni_type())
+        .Append("[] " + array_name + " = new ")
+        .AppendType(attr.jni_type())
+        .Append("[" + var_name + ".size()];")
+        .EndLine()
+        .BeginBlock("for (int i = 0; i < " + array_name + ".length; ++i)")
+        .Append(array_name + "[i] = ");
+    if (attr.type().kind() == Type::GENERIC) {
+      writer->Append("DataType.fromClass(" + var_name + ".get(i));");
     } else {
-      writer->Append("opBuilder.setAttr(\"" + attr.op_def_name() + "\", " + var)
-          .Append(".toArray(new ")
-          .AppendType(type)
-          .Append("[" + var + ".size()]));")
-          .EndLine();
+      writer->Append(var_name + ".get(i);");
     }
+    writer->EndLine()
+        .EndBlock()
+        .Append("opBuilder.setAttr(\"" + attr.op_def_name() + "\", ")
+        .Append(array_name + ");")
+        .EndLine();
   } else {
-    Type type = attr.var().type();
     writer->Append("opBuilder.setAttr(\"" + attr.op_def_name() + "\", ");
-    if (type.name() == "Class") {
-      writer->Append("DataType.fromClass(" + attr.var().name() + "));");
+    if (attr.var().type().name() == "Class") {
+      writer->Append("DataType.fromClass(" + var_name + "));");
     } else {
-      writer->Append(var + ");");
+      writer->Append(var_name + ");");
     }
     writer->EndLine();
   }
 }
 
-void RenderFactoryMethod(const OpSpec& op, const Type& op_class,
+void RenderFactoryMethods(const OpSpec& op, const Type& op_class,
     SourceWriter* writer) {
   Method factory = Method::Create("create", op_class);
   Javadoc factory_doc = Javadoc::Create(
@@ -138,27 +125,24 @@ void RenderFactoryMethod(const OpSpec& op, const Type& op_class,
       + " operation to the graph.");
   Variable scope =
       Variable::Create("scope", Type::Class("Scope", "org.tensorflow.op"));
-  factory.add_argument(scope);
-  factory_doc.add_param_tag(scope.name(), "Current graph scope");
+  AddArgument(scope, "current graph scope", &factory, &factory_doc);
   for (const ArgumentSpec& input : op.inputs()) {
-    factory.add_argument(input.var());
-    factory_doc.add_param_tag(input.var().name(), input.description());
+    AddArgument(input.var(), input.description(), &factory, &factory_doc);
   }
-  for (const AttributeSpec& attribute : op.attributes()) {
-    factory.add_argument(attribute.var());
-    factory_doc.add_param_tag(attribute.var().name(), attribute.description());
+  for (const AttributeSpec& attr : op.attributes()) {
+    AddArgument(attr.var(), attr.description(), &factory, &factory_doc);
   }
   if (!op.optional_attributes().empty()) {
-    factory.add_argument(Variable::Varargs("options", Type::Class("Options")));
-    factory_doc.add_param_tag("options", "carries optional attributes values");
+    AddArgument(Variable::Varargs("options", Type::Class("Options")),
+        "carries optional attributes values", &factory, &factory_doc);
   }
   factory_doc.add_tag("return", "a new instance of " + op_class.name());
+
   writer->BeginMethod(factory, PUBLIC|STATIC, &factory_doc);
   writer->Append("OperationBuilder opBuilder = scope.graph().opBuilder(\""
       + op.graph_op_name() + "\", scope.makeOpName(\""
       + op_class.name() + "\"));");
   writer->EndLine();
-
   for (const ArgumentSpec& input : op.inputs()) {
     if (input.iterable()) {
       writer->Append("opBuilder.addInputList(Operands.asOutputs("
@@ -192,10 +176,9 @@ void RenderFactoryMethod(const OpSpec& op, const Type& op_class,
 
 void RenderConstructor(const OpSpec& op, const Type& op_class,
     SourceWriter* writer) {
-  Method constructor = Method::ConstructorFor(op_class)
-    .add_argument(
-        Variable::Create("operation",
-            Type::Class("Operation", "org.tensorflow")));
+  Variable operation =
+      Variable::Create("operation", Type::Class("Operation", "org.tensorflow"));
+  Method constructor = Method::ConstructorFor(op_class).add_argument(operation);
   for (const ArgumentSpec& output : op.outputs()) {
     if (output.iterable() && !output.type().unknown()) {
       constructor.add_annotation(
@@ -237,15 +220,14 @@ void RenderConstructor(const OpSpec& op, const Type& op_class,
 }
 
 void RenderGettersAndSetters(const OpSpec& op, SourceWriter* writer) {
-  for (const AttributeSpec& attribute : op.optional_attributes()) {
+  for (const AttributeSpec& attr : op.optional_attributes()) {
     Method setter =
-        Method::Create(attribute.var().name(), Type::Class("Options"))
-            .add_argument(attribute.var());
-    Javadoc setter_doc = Javadoc::Create()
-        .add_param_tag(attribute.var().name(), attribute.description());
+        Method::Create(attr.var().name(), Type::Class("Options"));
+    Javadoc setter_doc = Javadoc::Create();
+    AddArgument(attr.var(), attr.description(), &setter, &setter_doc);
     writer->BeginMethod(setter, PUBLIC|STATIC, &setter_doc)
-        .Append("return new Options()." + attribute.var().name() + "("
-            + attribute.var().name() + ");")
+        .Append("return new Options()." + attr.var().name() + "("
+            + attr.var().name() + ");")
         .EndLine()
         .EndMethod();
   }
@@ -311,14 +293,12 @@ void RenderOptionsClass(const OpSpec& op, const Type& op_class,
   Javadoc options_doc = Javadoc::Create(
       "Optional attributes for {@link " + op_class.full_name() + "}");
   writer->BeginInnerType(options_class, PUBLIC | STATIC, &options_doc);
-  for (const AttributeSpec& attribute : op.optional_attributes()) {
-    Method setter = Method::Create(attribute.var().name(), options_class)
-        .add_argument(attribute.var());
-    Javadoc setter_doc = Javadoc::Create()
-        .add_param_tag(attribute.var().name(), attribute.description());
+  for (const AttributeSpec& attr : op.optional_attributes()) {
+    Method setter = Method::Create(attr.var().name(), options_class);
+    Javadoc setter_doc = Javadoc::Create();
+    AddArgument(attr.var(), attr.description(), &setter, &setter_doc);
     writer->BeginMethod(setter, PUBLIC, &setter_doc)
-        .Append("this." + attribute.var().name() + " = "
-            + attribute.var().name() + ";")
+        .Append("this." + attr.var().name() + " = " + attr.var().name() + ";")
         .EndLine()
         .Append("return this;")
         .EndLine()
@@ -339,12 +319,13 @@ inline Type ClassOf(const EndpointSpec& endpoint, const string& base_package) {
 }
 
 void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint,
-    const string& base_package, const string& output_dir, Env* env) {
+    const string& base_package, const string& output_dir, Env* env,
+    const std::tm* timestamp) {
   Type op_class(ClassOf(endpoint, base_package)
       .add_supertype(Type::Class("PrimitiveOp", "org.tensorflow.op")));
   Javadoc op_javadoc(endpoint.javadoc());
 
-  // implement Operand (or Iterable) if the op has only one output
+  // op interfaces
   RenderMode mode = DEFAULT;
   if (op.outputs().size() == 1) {
     const ArgumentSpec& output = op.outputs().front();
@@ -360,18 +341,22 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint,
       op_class.add_supertype(operand_inf);
     }
   }
-  // declare all outputs generics at the op class level
+  // op generic parameters
   std::set generics;
   for (const ArgumentSpec& output : op.outputs()) {
     if (output.type().kind() == Type::GENERIC && !output.type().unknown()
         && generics.find(output.type().name()) == generics.end()) {
       op_class.add_parameter(output.type());
       op_javadoc.add_param_tag("<" + output.type().name() + ">",
-          "data type of output {@code " + output.var().name() + "}");
+          "data type for {@code " + output.var().name() + "()} output");
       generics.insert(output.type().name());
     }
   }
-  // handle endpoint deprecation
+  // op annotations
+  char date[20];
+  strftime(date, sizeof date, "%FT%TZ", timestamp);
+  op_class.add_annotation(Annotation::Create("Generated", "javax.annotation")
+      .attributes(string("value = \"op_generator\", date = \"") + date + "\""));
   if (endpoint.deprecated()) {
     op_class.add_annotation(Annotation::Create("Deprecated"));
     string explanation;
@@ -384,8 +369,8 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint,
     }
     op_javadoc.add_tag("deprecated", explanation);
   }
-  // expose the op in the Ops Graph API only if it is visible
   if (!op.hidden()) {
+    // expose the op in the Ops Graph API only if it is visible
     op_class.add_annotation(
         Annotation::Create("Operator", "org.tensorflow.op.annotation")
           .attributes("group = \"" + endpoint.package() + "\""));
@@ -405,15 +390,12 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint,
   std::list dependencies;
   CollectOpDependencies(op, mode, &dependencies);
   writer.WriteFromFile(kLicenseSnippet)
-      .EndLine()
-      .Append("// This file is machine generated, DO NOT EDIT!")
-      .EndLine()
       .EndLine()
       .BeginType(op_class, PUBLIC|FINAL, &dependencies, &op_javadoc);
   if (!op.optional_attributes().empty()) {
     RenderOptionsClass(op, op_class, &writer);
   }
-  RenderFactoryMethod(op, op_class, &writer);
+  RenderFactoryMethods(op, op_class, &writer);
   RenderGettersAndSetters(op, &writer);
   if (mode != DEFAULT) {
     RenderInterfaceImpl(op, mode, &writer);
@@ -428,13 +410,8 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint,
 
 }  // namespace
 
-OpGenerator::OpGenerator(const string& base_package, const string& output_dir,
-    const std::vector& api_dirs, Env* env)
-  : base_package_(base_package), output_dir_(output_dir), api_dirs_(api_dirs),
-    env_(env) {
-}
-
-Status OpGenerator::Run(const OpList& op_list) {
+Status OpGenerator::Run(const OpList& op_list, const string& base_package,
+    const string& output_dir) {
   ApiDefMap api_map(op_list);
   if (!api_dirs_.empty()) {
     // Only load api files that correspond to the requested "op_list"
@@ -449,12 +426,14 @@ Status OpGenerator::Run(const OpList& op_list) {
     }
   }
   api_map.UpdateDocs();
+  time_t now;
+  time(&now);
   for (const auto& op_def : op_list.op()) {
     const ApiDef* api_def = api_map.GetApiDef(op_def.name());
     if (api_def->visibility() != ApiDef::SKIP) {
       OpSpec op(OpSpec::Create(op_def, *api_def));
       for (const EndpointSpec& endpoint : op.endpoints()) {
-        GenerateOp(op, endpoint, base_package_, output_dir_, env_);
+        GenerateOp(op, endpoint, base_package, output_dir, env_, gmtime(&now));
       }
     }
   }
diff --git a/tensorflow/java/src/gen/cc/op_generator.h b/tensorflow/java/src/gen/cc/op_generator.h
index 06b08e852a5531..b789e11fa95908 100644
--- a/tensorflow/java/src/gen/cc/op_generator.h
+++ b/tensorflow/java/src/gen/cc/op_generator.h
@@ -36,18 +36,17 @@ namespace java {
 // ops definitions.
 class OpGenerator {
  public:
-  OpGenerator(const string& base_package, const string& output_dir,
-      const std::vector& api_dirs, Env* env = Env::Default());
+  explicit OpGenerator(const std::vector& api_dirs,
+      Env* env = Env::Default()) : api_dirs_(api_dirs), env_(env) {}
 
   // Generates wrappers for the given list of 'ops'.
   //
-  // Output files are generated in //,
-  // where 'lib_package' is derived from ops endpoints.
-  Status Run(const OpList& op_list);
+  // Output files are generated in //,
+  // where 'op_package' is derived from ops endpoints.
+  Status Run(const OpList& op_list, const string& base_package,
+      const string& output_dir);
 
  private:
-  const string base_package_;
-  const string output_dir_;
   const std::vector api_dirs_;
   Env* env_;
 };
diff --git a/tensorflow/java/src/gen/cc/op_specs.cc b/tensorflow/java/src/gen/cc/op_specs.cc
index a0e7a180f2ad3a..dcc6388614f804 100644
--- a/tensorflow/java/src/gen/cc/op_specs.cc
+++ b/tensorflow/java/src/gen/cc/op_specs.cc
@@ -46,14 +46,30 @@ class TypeResolver {
   explicit TypeResolver(const OpDef& op_def) : op_def_(op_def) {}
 
   Type TypeOf(const OpDef_ArgDef& arg_def, bool *iterable_out);
-  Type TypeOf(const OpDef_AttrDef& attr_def, bool *iterable_out);
+  std::pair TypeOf(const OpDef_AttrDef& attr_def,
+      bool *iterable_out);
   bool IsAttributeVisited(const string& attr_name) {
     return visited_attrs_.find(attr_name) != visited_attrs_.cend();
   }
+
  private:
   const OpDef op_def_;
   std::map visited_attrs_;
-  char next_generic_ = 'T';
+  char next_generic_letter_ = 'T';
+
+  std::pair MakeTypePair(const Type& type, const Type& jni_type) {
+    return std::make_pair(type, jni_type);
+  }
+  std::pair MakeTypePair(const Type& type) {
+    return std::make_pair(type, type);
+  }
+  Type NextGeneric() {
+    char generic_letter = next_generic_letter_++;
+    if (next_generic_letter_ > 'Z') {
+      next_generic_letter_ = 'A';
+    }
+    return Type::Generic(string(1, generic_letter));
+  }
 };
 
 Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def,
@@ -107,7 +123,7 @@ Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def,
     } else {
       for (const auto& attr_def : op_def_.attr()) {
         if (attr_def.name() == arg_def.type_attr()) {
-          type = TypeOf(attr_def, iterable_out);
+          type = TypeOf(attr_def, iterable_out).first;
           break;
         }
       }
@@ -125,51 +141,47 @@ Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def,
   return type;
 }
 
-Type TypeResolver::TypeOf(const OpDef_AttrDef& attr_def,
+std::pair TypeResolver::TypeOf(const OpDef_AttrDef& attr_def,
     bool* iterable_out) {
+  std::pair types = MakeTypePair(Type::Wildcard());
   *iterable_out = false;
   StringPiece attr_type = attr_def.type();
   if (str_util::ConsumePrefix(&attr_type, "list(")) {
     attr_type.remove_suffix(1);  // remove closing brace
     *iterable_out = true;
   }
-  Type type = *iterable_out ? Type::Wildcard() : Type::Class("Object");
-  if (attr_type == "type") {
-    if (*iterable_out) {
-      type = Type::Enum("DataType", "org.tensorflow");
-    } else {
-      type = Type::Generic(string(1, next_generic_));
-      next_generic_ = (next_generic_ == 'Z') ? 'A' : next_generic_ + 1;
-      if (IsRealNumbers(attr_def.allowed_values())) {
-        // enforce real numbers datasets by extending java.lang.Number
-        type.add_supertype(Type::Class("Number"));
-      }
-    }
-  } else if (attr_type == "string") {
-    type = Type::Class("String");
+  if (attr_type == "string") {
+    types = MakeTypePair(Type::Class("String"));
 
   } else if (attr_type == "int") {
-    type = Type::Class("Integer");
+    types = MakeTypePair(Type::Class("Long"), Type::Long());
 
   } else if (attr_type == "float") {
-    type = Type::Class("Float");
+    types = MakeTypePair(Type::Class("Float"), Type::Float());
 
   } else if (attr_type == "bool") {
-    type = Type::Class("Boolean");
+    types = MakeTypePair(Type::Class("Boolean"), Type::Boolean());
 
   } else if (attr_type == "shape") {
-    type = Type::Class("Shape", "org.tensorflow");
+    types = MakeTypePair(Type::Class("Shape", "org.tensorflow"));
 
   } else if (attr_type == "tensor") {
-    type = Type::Class("Tensor", "org.tensorflow")
-        .add_parameter(Type::Wildcard());
+    types = MakeTypePair(Type::Class("Tensor", "org.tensorflow")
+        .add_parameter(Type::Wildcard()));
+
+  } else if (attr_type == "type") {
+    Type type = *iterable_out ? Type::Wildcard() : NextGeneric();
+    if (IsRealNumbers(attr_def.allowed_values())) {
+      type.add_supertype(Type::Class("Number"));
+    }
+    types = MakeTypePair(type, Type::Enum("DataType", "org.tensorflow"));
 
   } else {
     LOG(FATAL) << "Cannot resolve data type for attribute \"" << attr_type
         << "\" in operation \"" << op_def_.name() << "\"";
   }
-  visited_attrs_.insert(std::make_pair(attr_def.name(), type));
-  return type;
+  visited_attrs_.insert(std::make_pair(attr_def.name(), types.first));
+  return types;
 }
 
 string SnakeToCamelCase(const string& str, bool upper = false) {
@@ -307,19 +319,19 @@ ArgumentSpec CreateInput(const OpDef_ArgDef& input_def,
 AttributeSpec CreateAttribute(const OpDef_AttrDef& attr_def,
     const ApiDef::Attr& attr_api_def, TypeResolver* type_resolver) {
   bool iterable = false;
-  Type type = type_resolver->TypeOf(attr_def, &iterable);
-  // type attributes must be passed explicitly in methods as a Class<> parameter
-  bool is_explicit = type.kind() == Type::GENERIC && !iterable;
-  Type var_type = is_explicit ? Type::Class("Class").add_parameter(type) : type;
+  std::pair types = type_resolver->TypeOf(attr_def, &iterable);
+  Type var_type = types.first.kind() == Type::GENERIC ?
+      Type::Class("Class").add_parameter(types.first) : types.first;
   if (iterable) {
-    var_type = Type::ListOf(type);
+    var_type = Type::ListOf(var_type);
   }
   return AttributeSpec(attr_api_def.name(),
       Variable::Create(SnakeToCamelCase(attr_api_def.rename_to()), var_type),
-      type,
+      types.first,
+      types.second,
       ParseDocumentation(attr_api_def.description()),
       iterable,
-      attr_api_def.has_default_value() && !is_explicit);
+      attr_api_def.has_default_value());
 }
 
 ArgumentSpec CreateOutput(const OpDef_ArgDef& output_def,
@@ -340,7 +352,6 @@ ArgumentSpec CreateOutput(const OpDef_ArgDef& output_def,
 
 EndpointSpec CreateEndpoint(const OpDef& op_def, const ApiDef& api_def,
     const ApiDef_Endpoint& endpoint_def) {
-
   std::vector name_tokens = str_util::Split(endpoint_def.name(), ".");
   string package;
   string name;
@@ -381,7 +392,7 @@ OpSpec OpSpec::Create(const OpDef& op_def, const ApiDef& api_def) {
       AttributeSpec attr = CreateAttribute(op_def.attr(i), api_def.attr(i),
           &type_resolver);
       // attributes with a default value are optional
-      if (attr.optional()) {
+      if (attr.has_default_value() && attr.type().kind() != Type::GENERIC) {
         op.optional_attributes_.push_back(attr);
       } else {
         op.attributes_.push_back(attr);
diff --git a/tensorflow/java/src/gen/cc/op_specs.h b/tensorflow/java/src/gen/cc/op_specs.h
index 55c2c3f3079a8b..7d64391446ee89 100644
--- a/tensorflow/java/src/gen/cc/op_specs.h
+++ b/tensorflow/java/src/gen/cc/op_specs.h
@@ -21,6 +21,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/core/framework/api_def.pb.h"
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/java/src/gen/cc/java_defs.h"
 
 namespace tensorflow {
@@ -87,20 +88,23 @@ class AttributeSpec : public ArgumentSpec {
   // op_def_name: attribute name, as known by TensorFlow core
   // var: a variable to represent this attribute in Java
   // type: the type of this attribute
+  // jni_type: the type of this attribute in JNI layer (see OperationBuilder)
   // description: a description of this attribute, in javadoc
   // iterable: true if this attribute is a list
-  // optional: true if this attribute does not require to be set explicitly
+  // has_default_value: true if this attribute has a default value if not set
   AttributeSpec(const string& op_def_name, const Variable& var,
-      const Type& type, const string& description, bool iterable,
-      bool optional)
+      const Type& type, const Type& jni_type, const string& description,
+      bool iterable, bool has_default_value)
     : ArgumentSpec(op_def_name, var, type, description, iterable),
-      optional_(optional) {}
+      jni_type_(jni_type), has_default_value_(has_default_value) {}
   virtual ~AttributeSpec() = default;
 
-  bool optional() const { return optional_; }
+  const Type& jni_type() const { return jni_type_; }
+  bool has_default_value() const { return has_default_value_; }
 
  private:
-  const bool optional_;
+  const Type jni_type_;
+  const bool has_default_value_;
 };
 
 class OpSpec {

From dd1ef8fa8f6861e53e8a7953c171b3e9253043ed Mon Sep 17 00:00:00 2001
From: "karl@kubx.ca" 
Date: Thu, 3 May 2018 22:39:35 -0400
Subject: [PATCH 0358/1691] Second code review

---
 tensorflow/core/api_def/BUILD                 |  7 ++
 .../java_api/api_def_FilterDataset.pbtxt      |  4 +
 .../java_api/api_def_FlatMapDataset.pbtxt     |  4 +
 .../core/api_def/java_api/api_def_For.pbtxt   |  4 +
 .../java_api/api_def_GeneratorDataset.pbtxt   |  4 +
 .../api_def_GroupByWindowDataset.pbtxt        |  4 +
 .../core/api_def/java_api/api_def_If.pbtxt    |  4 +
 .../java_api/api_def_InterleaveDataset.pbtxt  |  4 +
 .../java_api/api_def_MapAndBatchDataset.pbtxt |  4 +
 .../api_def/java_api/api_def_MapDataset.pbtxt |  4 +
 .../java_api/api_def_OneShotIterator.pbtxt    |  4 +
 .../api_def_ParallelInterleaveDataset.pbtxt   |  4 +
 .../java_api/api_def_ParallelMapDataset.pbtxt |  4 +
 .../api_def/java_api/api_def_RemoteCall.pbtxt |  4 +
 .../java_api/api_def_ScanDataset.pbtxt        |  4 +
 .../java_api/api_def_SymbolicGradient.pbtxt   |  4 +
 .../core/api_def/java_api/api_def_While.pbtxt |  4 +
 tensorflow/java/BUILD                         | 39 ++++------
 tensorflow/java/src/gen/cc/java_defs.h        |  6 +-
 tensorflow/java/src/gen/cc/op_gen_main.cc     |  2 +-
 tensorflow/java/src/gen/cc/op_generator.cc    | 77 +++++++++++--------
 tensorflow/java/src/gen/cc/op_generator.h     |  2 +-
 tensorflow/java/src/gen/cc/op_specs.cc        | 25 +++++-
 tensorflow/java/src/gen/cc/op_specs.h         | 17 +++-
 tensorflow/java/src/gen/cc/source_writer.cc   | 20 +++--
 tensorflow/java/src/gen/cc/source_writer.h    |  2 +-
 .../java/src/gen/cc/source_writer_test.cc     |  2 +-
 tensorflow/java/src/gen/gen_ops.bzl           | 41 +++-------
 28 files changed, 195 insertions(+), 109 deletions(-)
 create mode 100644 tensorflow/core/api_def/java_api/api_def_FilterDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_FlatMapDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_For.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_GeneratorDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_GroupByWindowDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_If.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_InterleaveDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_MapDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_OneShotIterator.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_ParallelMapDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_RemoteCall.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_ScanDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_SymbolicGradient.pbtxt
 create mode 100644 tensorflow/core/api_def/java_api/api_def_While.pbtxt

diff --git a/tensorflow/core/api_def/BUILD b/tensorflow/core/api_def/BUILD
index 19d643880966f7..06b797e32edc04 100644
--- a/tensorflow/core/api_def/BUILD
+++ b/tensorflow/core/api_def/BUILD
@@ -4,6 +4,7 @@
 # The following targets can be used to access ApiDefs:
 #   :base_api_def
 #   :python_api_def
+#   :java_api_def
 
 package(
     default_visibility = ["//visibility:private"],
@@ -29,6 +30,12 @@ filegroup(
     visibility = ["//tensorflow:internal"],
 )
 
+filegroup(
+    name = "java_api_def",
+    srcs = glob(["java_api/*"]),
+    visibility = ["//tensorflow:internal"],
+)
+
 cc_library(
     name = "excluded_ops_lib",
     srcs = ["excluded_ops.cc"],
diff --git a/tensorflow/core/api_def/java_api/api_def_FilterDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_FilterDataset.pbtxt
new file mode 100644
index 00000000000000..debd7e570972c1
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_FilterDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "FilterDataset"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_FlatMapDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_FlatMapDataset.pbtxt
new file mode 100644
index 00000000000000..329ab15ef53ae7
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_FlatMapDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "FlatMapDataset"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_For.pbtxt b/tensorflow/core/api_def/java_api/api_def_For.pbtxt
new file mode 100644
index 00000000000000..caabc947bb2461
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_For.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "For"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_GeneratorDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_GeneratorDataset.pbtxt
new file mode 100644
index 00000000000000..a6e5167c305130
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_GeneratorDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "GeneratorDataset"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_GroupByWindowDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_GroupByWindowDataset.pbtxt
new file mode 100644
index 00000000000000..4c0b2084a8a450
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_GroupByWindowDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "GroupByWindowDataset"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_If.pbtxt b/tensorflow/core/api_def/java_api/api_def_If.pbtxt
new file mode 100644
index 00000000000000..13b8635ca79d11
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_If.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "If"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_InterleaveDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_InterleaveDataset.pbtxt
new file mode 100644
index 00000000000000..ed748d4d2a408f
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_InterleaveDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "InterleaveDataset"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt
new file mode 100644
index 00000000000000..cb96bf63d8f0d1
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MapAndBatchDataset"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_MapDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapDataset.pbtxt
new file mode 100644
index 00000000000000..e0ab8dd9db62eb
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_MapDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MapDataset"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_OneShotIterator.pbtxt b/tensorflow/core/api_def/java_api/api_def_OneShotIterator.pbtxt
new file mode 100644
index 00000000000000..13130e68822adf
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_OneShotIterator.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "OneShotIterator"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt
new file mode 100644
index 00000000000000..6a985d24fa7406
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ParallelInterleaveDataset"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_ParallelMapDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParallelMapDataset.pbtxt
new file mode 100644
index 00000000000000..64f25b9e5e9f73
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_ParallelMapDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ParallelMapDataset"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_RemoteCall.pbtxt b/tensorflow/core/api_def/java_api/api_def_RemoteCall.pbtxt
new file mode 100644
index 00000000000000..2ccb5c8cf339e8
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_RemoteCall.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RemoteCall"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_ScanDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScanDataset.pbtxt
new file mode 100644
index 00000000000000..3463e60049c602
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_ScanDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ScanDataset"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_SymbolicGradient.pbtxt b/tensorflow/core/api_def/java_api/api_def_SymbolicGradient.pbtxt
new file mode 100644
index 00000000000000..88c3acea74010d
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_SymbolicGradient.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SymbolicGradient"
+  visibility: SKIP
+}
diff --git a/tensorflow/core/api_def/java_api/api_def_While.pbtxt b/tensorflow/core/api_def/java_api/api_def_While.pbtxt
new file mode 100644
index 00000000000000..33756682c3aa60
--- /dev/null
+++ b/tensorflow/core/api_def/java_api/api_def_While.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "While"
+  visibility: SKIP
+}
\ No newline at end of file
diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD
index 17566e1a9c671c..7cd0208dbf29c2 100644
--- a/tensorflow/java/BUILD
+++ b/tensorflow/java/BUILD
@@ -68,34 +68,27 @@ filegroup(
     ],
 )
 
-# Build the gen tool as a library, as it will be linked to a core/ops binary
-# files before making it an executable.
 tf_java_op_gen_srcjar(
     name = "java_op_gen_sources",
     api_def_srcs = [
         "//tensorflow/core/api_def:base_api_def",
+        "//tensorflow/core/api_def:java_api_def",
     ],
-    gen_base_package = "org.tensorflow.op",
-    gen_tool = "java_op_gen_tool",
-    ops_libs = [
-        "array_ops",
-        "candidate_sampling_ops",
-        "control_flow_ops",
-        "data_flow_ops",
-        "image_ops",
-        "io_ops",
-        "linalg_ops",
-        "logging_ops",
-        "math_ops",
-        "nn_ops",
-        "no_op",
-        "parsing_ops",
-        "random_ops",
-        "sparse_ops",
-        "state_ops",
-        "string_ops",
-        "training_ops",
-        "user_ops",
+    base_package = "org.tensorflow.op",
+    gen_tool = ":java_op_gen_tool",
+)
+
+tf_cc_binary(
+    name = "java_op_gen_tool",
+    srcs = [
+        "src/gen/cc/op_gen_main.cc",
+    ],
+    copts = tf_copts(),
+    linkopts = ["-lm"],
+    linkstatic = 1,
+    deps = [
+        ":java_op_gen_lib",
+        "//tensorflow/core:ops",
     ],
 )
 
diff --git a/tensorflow/java/src/gen/cc/java_defs.h b/tensorflow/java/src/gen/cc/java_defs.h
index 81ac67eb2f2b2f..62575f6683089b 100644
--- a/tensorflow/java/src/gen/cc/java_defs.h
+++ b/tensorflow/java/src/gen/cc/java_defs.h
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -102,10 +102,10 @@ class Type {
   const Kind& kind() const { return kind_; }
   const string& name() const { return name_; }
   const string& package() const { return package_; }
-  const string full_name() const {
+  const string canonical_name() const {
     return package_.empty() ? name_ : package_ + "." + name_;
   }
-  bool unknown() const { return name_.empty(); }  // only wildcards has no name
+  bool wildcard() const { return name_.empty(); }  // only wildcards has no name
   const std::list& parameters() const { return parameters_; }
   Type& add_parameter(const Type& parameter) {
     parameters_.push_back(parameter);
diff --git a/tensorflow/java/src/gen/cc/op_gen_main.cc b/tensorflow/java/src/gen/cc/op_gen_main.cc
index a508c965163145..6c35cd9595a0aa 100644
--- a/tensorflow/java/src/gen/cc/op_gen_main.cc
+++ b/tensorflow/java/src/gen/cc/op_gen_main.cc
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
diff --git a/tensorflow/java/src/gen/cc/op_generator.cc b/tensorflow/java/src/gen/cc/op_generator.cc
index 2327a4daf1627d..7355b3a395ec6a 100644
--- a/tensorflow/java/src/gen/cc/op_generator.cc
+++ b/tensorflow/java/src/gen/cc/op_generator.cc
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -19,7 +19,6 @@ limitations under the License.
 #include 
 #include 
 #include 
-#include 
 
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -39,13 +38,26 @@ namespace {
 const char* kLicenseSnippet =
     "tensorflow/java/src/gen/resources/license.java.snippet";
 
+// There is three different modes to render an op class, depending on the
+// number and type of outputs it has:
+//
+// DEFAULT: This mode does not provide any specialization for the op class, it
+//          is applied when the operation does not comply with any other mode
+//
+// OPERAND: The op class implements the Operand interface, allowing an
+//          instance to be passed directly in input to another operation
+//
+// LIST_OPERAND: The op class implements the Iterable> interface,
+//          allowing an instance to be passed directly as a list input to
+//          another operation
+//
 enum RenderMode {
   DEFAULT,
-  SINGLE_OUTPUT,
-  SINGLE_LIST_OUTPUT
+  OPERAND,
+  LIST_OPERAND
 };
 
-inline void AddArgument(const Variable& var, const string& description,
+void AddArgument(const Variable& var, const string& description,
     Method* method_out, Javadoc* javadoc_out) {
   method_out->add_argument(var);
   javadoc_out->add_param_tag(var.name(), description);
@@ -56,9 +68,9 @@ void CollectOpDependencies(const OpSpec& op, RenderMode mode,
   out->push_back(Type::Class("Operation", "org.tensorflow"));
   out->push_back(Type::Class("OperationBuilder", "org.tensorflow"));
   out->push_back(Type::Class("Scope", "org.tensorflow.op"));
-  if (mode == SINGLE_OUTPUT) {
+  if (mode == OPERAND) {
     out->push_back(Type::Class("Output", "org.tensorflow"));
-  } else if (mode == SINGLE_LIST_OUTPUT) {
+  } else if (mode == LIST_OPERAND) {
     out->push_back(Type::Interface("Iterator", "java.util"));
   }
   // Don't pay attention to duplicate types in the dependency list, they will
@@ -180,7 +192,7 @@ void RenderConstructor(const OpSpec& op, const Type& op_class,
       Variable::Create("operation", Type::Class("Operation", "org.tensorflow"));
   Method constructor = Method::ConstructorFor(op_class).add_argument(operation);
   for (const ArgumentSpec& output : op.outputs()) {
-    if (output.iterable() && !output.type().unknown()) {
+    if (output.iterable() && !output.type().wildcard()) {
       constructor.add_annotation(
           Annotation::Create("SuppressWarnings").attributes("\"unchecked\""));
       break;
@@ -200,7 +212,7 @@ void RenderConstructor(const OpSpec& op, const Type& op_class,
                 + "\");")
             .EndLine()
             .Append(output.var().name() + " = Arrays.asList(");
-        if (!output.type().unknown()) {
+        if (!output.type().wildcard()) {
           writer->Append("(")
               .AppendType(output.var().type().parameters().front())
               .Append("[])");
@@ -245,8 +257,8 @@ void RenderInterfaceImpl(const OpSpec& op, RenderMode mode,
     SourceWriter* writer) {
   ArgumentSpec output = op.outputs().front();
 
-  if (mode == SINGLE_OUTPUT) {
-    bool cast2obj = output.type().unknown();
+  if (mode == OPERAND) {
+    bool cast2obj = output.type().wildcard();
     Type return_type = Type::Class("Output", "org.tensorflow")
         .add_parameter(cast2obj ? Type::Class("Object") : output.type());
     Method as_output = Method::Create("asOutput", return_type)
@@ -265,9 +277,9 @@ void RenderInterfaceImpl(const OpSpec& op, RenderMode mode,
         .EndLine()
         .EndMethod();
 
-  } else if (mode == SINGLE_LIST_OUTPUT) {
+  } else if (mode == LIST_OPERAND) {
     Type operand = Type::Interface("Operand", "org.tensorflow");
-    if (output.type().unknown()) {
+    if (output.type().wildcard()) {
       operand.add_parameter(Type::Class("Object"));
     } else {
       operand.add_parameter(output.type());
@@ -291,7 +303,7 @@ void RenderOptionsClass(const OpSpec& op, const Type& op_class,
     SourceWriter* writer) {
   Type options_class = Type::Class("Options");
   Javadoc options_doc = Javadoc::Create(
-      "Optional attributes for {@link " + op_class.full_name() + "}");
+      "Optional attributes for {@link " + op_class.canonical_name() + "}");
   writer->BeginInnerType(options_class, PUBLIC | STATIC, &options_doc);
   for (const AttributeSpec& attr : op.optional_attributes()) {
     Method setter = Method::Create(attr.var().name(), options_class);
@@ -319,8 +331,7 @@ inline Type ClassOf(const EndpointSpec& endpoint, const string& base_package) {
 }
 
 void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint,
-    const string& base_package, const string& output_dir, Env* env,
-    const std::tm* timestamp) {
+    const string& base_package, const string& output_dir, Env* env) {
   Type op_class(ClassOf(endpoint, base_package)
       .add_supertype(Type::Class("PrimitiveOp", "org.tensorflow.op")));
   Javadoc op_javadoc(endpoint.javadoc());
@@ -329,22 +340,22 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint,
   RenderMode mode = DEFAULT;
   if (op.outputs().size() == 1) {
     const ArgumentSpec& output = op.outputs().front();
-    Type operand_type(output.type().unknown() ?
+    Type operand_type(output.type().wildcard() ?
         Type::Class("Object") : output.type());
     Type operand_inf(Type::Interface("Operand", "org.tensorflow")
         .add_parameter(operand_type));
     if (output.iterable()) {
-      mode = SINGLE_LIST_OUTPUT;
+      mode = LIST_OPERAND;
       op_class.add_supertype(Type::IterableOf(operand_inf));
     } else {
-      mode = SINGLE_OUTPUT;
+      mode = OPERAND;
       op_class.add_supertype(operand_inf);
     }
   }
   // op generic parameters
   std::set generics;
   for (const ArgumentSpec& output : op.outputs()) {
-    if (output.type().kind() == Type::GENERIC && !output.type().unknown()
+    if (output.type().kind() == Type::GENERIC && !output.type().wildcard()
         && generics.find(output.type().name()) == generics.end()) {
       op_class.add_parameter(output.type());
       op_javadoc.add_param_tag("<" + output.type().name() + ">",
@@ -353,16 +364,15 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint,
     }
   }
   // op annotations
-  char date[20];
-  strftime(date, sizeof date, "%FT%TZ", timestamp);
-  op_class.add_annotation(Annotation::Create("Generated", "javax.annotation")
-      .attributes(string("value = \"op_generator\", date = \"") + date + "\""));
+  op_class.add_annotation(
+      Annotation::Create("Generated", "javax.annotation")
+          .attributes("value = \"TensorFlow Java Op Generator\""));
   if (endpoint.deprecated()) {
     op_class.add_annotation(Annotation::Create("Deprecated"));
     string explanation;
     if (!op.endpoints().front().deprecated()) {
       explanation = "use {@link " +
-          ClassOf(op.endpoints().front(), base_package).full_name()
+          ClassOf(op.endpoints().front(), base_package).canonical_name()
           + "} instead";
     } else {
       explanation = op.deprecation_explanation();
@@ -376,14 +386,16 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint,
           .attributes("group = \"" + endpoint.package() + "\""));
   }
   // create op class file
-  string op_dir = io::JoinPath(output_dir,
+  const string op_dir_name = io::JoinPath(output_dir,
       str_util::StringReplace(op_class.package(), ".", "/", true));
-  if (!env->FileExists(op_dir).ok()) {
-    TF_CHECK_OK(Env::Default()->RecursivelyCreateDir(op_dir));
+  if (!env->FileExists(op_dir_name).ok()) {
+    TF_CHECK_OK(Env::Default()->RecursivelyCreateDir(op_dir_name))
+        << op_dir_name;
   }
+  const string op_file_name = op_class.name() + ".java";
   std::unique_ptr op_file;
   TF_CHECK_OK(env->NewWritableFile(
-      io::JoinPath(op_dir, op_class.name() + ".java"), &op_file));
+      io::JoinPath(op_dir_name, op_file_name), &op_file)) << op_file_name;
 
   // render endpoint source code
   SourceFileWriter writer(op_file.get());
@@ -420,20 +432,19 @@ Status OpGenerator::Run(const OpList& op_list, const string& base_package,
         const std::string api_def_file_pattern =
             io::JoinPath(api_def_dir, "api_def_" + op.name() + ".pbtxt");
         if (env_->FileExists(api_def_file_pattern).ok()) {
-          TF_CHECK_OK(api_map.LoadFile(env_, api_def_file_pattern));
+          TF_CHECK_OK(api_map.LoadFile(env_, api_def_file_pattern))
+              << api_def_file_pattern;
         }
       }
     }
   }
   api_map.UpdateDocs();
-  time_t now;
-  time(&now);
   for (const auto& op_def : op_list.op()) {
     const ApiDef* api_def = api_map.GetApiDef(op_def.name());
     if (api_def->visibility() != ApiDef::SKIP) {
       OpSpec op(OpSpec::Create(op_def, *api_def));
       for (const EndpointSpec& endpoint : op.endpoints()) {
-        GenerateOp(op, endpoint, base_package, output_dir, env_, gmtime(&now));
+        GenerateOp(op, endpoint, base_package, output_dir, env_);
       }
     }
   }
diff --git a/tensorflow/java/src/gen/cc/op_generator.h b/tensorflow/java/src/gen/cc/op_generator.h
index b789e11fa95908..cfe842070a7794 100644
--- a/tensorflow/java/src/gen/cc/op_generator.h
+++ b/tensorflow/java/src/gen/cc/op_generator.h
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
diff --git a/tensorflow/java/src/gen/cc/op_specs.cc b/tensorflow/java/src/gen/cc/op_specs.cc
index dcc6388614f804..081062ceaf2d0b 100644
--- a/tensorflow/java/src/gen/cc/op_specs.cc
+++ b/tensorflow/java/src/gen/cc/op_specs.cc
@@ -45,9 +45,26 @@ class TypeResolver {
  public:
   explicit TypeResolver(const OpDef& op_def) : op_def_(op_def) {}
 
+  // Returns the class type of an input/output argument
+  //
+  // For example, if the argument's datatype is DT_STRING, this method will
+  // return "java.lang.String", so the argument can become "Operand"
+  // in the Ops API
   Type TypeOf(const OpDef_ArgDef& arg_def, bool *iterable_out);
-  std::pair TypeOf(const OpDef_AttrDef& attr_def,
+
+  // Returns types of an input attribute
+  //
+  // The first element of the pair is the class type of this attribute while
+  // the second is its JNI/primitive type equivalent, required for explicit
+  // unboxing.
+  //
+  // For example, if the attribute is of type "float", this method will return
+  // , so the attribute can be used as a "Float" object
+  // in the Ops API and casted to a "float" when passing through the JNI layer.
+  std::pair TypesOf(const OpDef_AttrDef& attr_def,
       bool *iterable_out);
+
+  // Returns true if the type of this attribute has already been resolved
   bool IsAttributeVisited(const string& attr_name) {
     return visited_attrs_.find(attr_name) != visited_attrs_.cend();
   }
@@ -123,7 +140,7 @@ Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def,
     } else {
       for (const auto& attr_def : op_def_.attr()) {
         if (attr_def.name() == arg_def.type_attr()) {
-          type = TypeOf(attr_def, iterable_out).first;
+          type = TypesOf(attr_def, iterable_out).first;
           break;
         }
       }
@@ -141,7 +158,7 @@ Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def,
   return type;
 }
 
-std::pair TypeResolver::TypeOf(const OpDef_AttrDef& attr_def,
+std::pair TypeResolver::TypesOf(const OpDef_AttrDef& attr_def,
     bool* iterable_out) {
   std::pair types = MakeTypePair(Type::Wildcard());
   *iterable_out = false;
@@ -319,7 +336,7 @@ ArgumentSpec CreateInput(const OpDef_ArgDef& input_def,
 AttributeSpec CreateAttribute(const OpDef_AttrDef& attr_def,
     const ApiDef::Attr& attr_api_def, TypeResolver* type_resolver) {
   bool iterable = false;
-  std::pair types = type_resolver->TypeOf(attr_def, &iterable);
+  std::pair types = type_resolver->TypesOf(attr_def, &iterable);
   Type var_type = types.first.kind() == Type::GENERIC ?
       Type::Class("Class").add_parameter(types.first) : types.first;
   if (iterable) {
diff --git a/tensorflow/java/src/gen/cc/op_specs.h b/tensorflow/java/src/gen/cc/op_specs.h
index 7d64391446ee89..81582ea207fef9 100644
--- a/tensorflow/java/src/gen/cc/op_specs.h
+++ b/tensorflow/java/src/gen/cc/op_specs.h
@@ -65,7 +65,6 @@ class ArgumentSpec {
       const Type& type, const string& description, bool iterable)
     : op_def_name_(op_def_name), var_(var), type_(type),
       description_(description), iterable_(iterable) {}
-  virtual ~ArgumentSpec() = default;
 
   const string& op_def_name() const { return op_def_name_; }
   const Variable& var() const { return var_; }
@@ -81,7 +80,7 @@ class ArgumentSpec {
   const bool iterable_;
 };
 
-class AttributeSpec : public ArgumentSpec {
+class AttributeSpec {
  public:
   // A specification for an operation attribute
   //
@@ -95,14 +94,24 @@ class AttributeSpec : public ArgumentSpec {
   AttributeSpec(const string& op_def_name, const Variable& var,
       const Type& type, const Type& jni_type, const string& description,
       bool iterable, bool has_default_value)
-    : ArgumentSpec(op_def_name, var, type, description, iterable),
+    : op_def_name_(op_def_name), var_(var), type_(type),
+      description_(description), iterable_(iterable),
       jni_type_(jni_type), has_default_value_(has_default_value) {}
-  virtual ~AttributeSpec() = default;
 
+  const string& op_def_name() const { return op_def_name_; }
+  const Variable& var() const { return var_; }
+  const Type& type() const { return type_; }
+  const string& description() const { return description_; }
+  bool iterable() const { return iterable_; }
   const Type& jni_type() const { return jni_type_; }
   bool has_default_value() const { return has_default_value_; }
 
  private:
+  const string op_def_name_;
+  const Variable var_;
+  const Type type_;
+  const string description_;
+  const bool iterable_;
   const Type jni_type_;
   const bool has_default_value_;
 };
diff --git a/tensorflow/java/src/gen/cc/source_writer.cc b/tensorflow/java/src/gen/cc/source_writer.cc
index 7e427787f90dd5..56806cbb6dc5da 100644
--- a/tensorflow/java/src/gen/cc/source_writer.cc
+++ b/tensorflow/java/src/gen/cc/source_writer.cc
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -83,17 +83,19 @@ SourceWriter& SourceWriter::Append(const StringPiece& str) {
 }
 
 SourceWriter& SourceWriter::AppendType(const Type& type) {
-  if (type.unknown()) {
+  if (type.wildcard()) {
     Append("?");
   } else {
     Append(type.name());
     if (!type.parameters().empty()) {
       Append("<");
+      bool first = true;
       for (const Type& t : type.parameters()) {
-        if (&t != &type.parameters().front()) {
+        if (!first) {
           Append(", ");
         }
         AppendType(t);
+        first = false;
       }
       Append(">");
     }
@@ -145,11 +147,13 @@ SourceWriter& SourceWriter::BeginMethod(const Method& method, int modifiers,
     AppendType(method.return_type()).Append(" ");
   }
   Append(method.name()).Append("(");
+  bool first = true;
   for (const Variable& v : method.arguments()) {
-    if (&v != &method.arguments().front()) {
+    if (!first) {
       Append(", ");
     }
     AppendType(v.type()).Append(v.variadic() ? "... " : " ").Append(v.name());
+    first = false;
   }
   return Append(")").BeginBlock();
 }
@@ -294,14 +298,16 @@ SourceWriter& SourceWriter::WriteAnnotations(
 SourceWriter& SourceWriter::WriteGenerics(
     const std::list& generics) {
   Append("<");
+  bool first = true;
   for (const Type* pt : generics) {
-    if (pt != generics.front()) {
+    if (!first) {
       Append(", ");
     }
     Append(pt->name());
     if (!pt->supertypes().empty()) {
       Append(" extends ").AppendType(pt->supertypes().front());
     }
+    first = false;
   }
   return Append(">");
 }
@@ -339,7 +345,7 @@ void SourceWriter::TypeVisitor::Visit(const Type& type) {
 
 void SourceWriter::GenericNamespace::DoVisit(const Type& type) {
   // ignore non-generic parameters, wildcards and generics already declared
-  if (type.kind() == Type::GENERIC && !type.unknown()
+  if (type.kind() == Type::GENERIC && !type.wildcard()
       && generic_names_.find(type.name()) == generic_names_.end()) {
     declared_types_.push_back(&type);
     generic_names_.insert(type.name());
@@ -348,7 +354,7 @@ void SourceWriter::GenericNamespace::DoVisit(const Type& type) {
 
 void SourceWriter::TypeImporter::DoVisit(const Type& type) {
   if (!type.package().empty() && type.package() != current_package_) {
-    imports_.insert(type.full_name());
+    imports_.insert(type.canonical_name());
   }
 }
 
diff --git a/tensorflow/java/src/gen/cc/source_writer.h b/tensorflow/java/src/gen/cc/source_writer.h
index bcae33cccedf0f..1f0febe9a3135a 100644
--- a/tensorflow/java/src/gen/cc/source_writer.h
+++ b/tensorflow/java/src/gen/cc/source_writer.h
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
diff --git a/tensorflow/java/src/gen/cc/source_writer_test.cc b/tensorflow/java/src/gen/cc/source_writer_test.cc
index 875ad99ae24801..b9a5fee9bea166 100644
--- a/tensorflow/java/src/gen/cc/source_writer_test.cc
+++ b/tensorflow/java/src/gen/cc/source_writer_test.cc
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
diff --git a/tensorflow/java/src/gen/gen_ops.bzl b/tensorflow/java/src/gen/gen_ops.bzl
index 7017b526494e1b..f4ff34ea0361fb 100644
--- a/tensorflow/java/src/gen/gen_ops.bzl
+++ b/tensorflow/java/src/gen/gen_ops.bzl
@@ -3,33 +3,26 @@
 load(
     "//tensorflow:tensorflow.bzl",
     "tf_binary_additional_srcs",
-    "tf_cc_binary",
-    "tf_copts",
 )
 
-# Given a list of "ops_libs" (a list of files in the core/ops directory
-# without their .cc extensions), generate Java wrapper code for all operations
-# found in the ops files.
-# Then, combine all those source files into a single archive (.srcjar).
+# Generate Java wrapper classes for all registered core operations and package
+# them into a single source archive (.srcjar).
 #
 # For example:
-#  tf_java_op_gen_srcjar("gen_sources", "gen_tool", "my.package", [ "array_ops", "math_ops" ])
+#  tf_java_op_gen_srcjar("gen_sources", ":gen_tool", "my.package")
 #
-# will create a genrule named "gen_sources" that first generate source files:
-#     ops/src/main/java/my/package/array/*.java
-#     ops/src/main/java/my/package/math/*.java
+# will create a genrule named "gen_sources" that generates source files under
+#     ops/src/main/java/my/package/**/*.java
 #
-# and then archive those source files in:
+# and then archive those source files into
 #     ops/gen_sources.srcjar
 #
 def tf_java_op_gen_srcjar(name,
                           gen_tool,
-                          gen_base_package,
-                          ops_libs=[],
-                          ops_libs_pkg="//tensorflow/core",
+                          base_package,
+                          api_def_srcs=[],
                           out_dir="ops/",
                           out_src_dir="src/main/java/",
-                          api_def_srcs=[],
                           visibility=["//tensorflow/java:__pkg__"]):
 
   gen_cmds = ["rm -rf $(@D)"]  # Always start from fresh when generating source files
@@ -48,23 +41,9 @@ def tf_java_op_gen_srcjar(name,
           ") | cut -d\" \" -f1))")
     api_def_args_str = ",".join(api_def_args)
 
-  gen_tool_deps = [":java_op_gen_lib"]
-  for ops_lib in ops_libs:
-    gen_tool_deps.append(ops_libs_pkg + ":" + ops_lib + "_op_lib")
-
-  tf_cc_binary(
-      name=gen_tool,
-      srcs=[
-          "src/gen/cc/op_gen_main.cc",
-      ],
-      copts=tf_copts(),
-      linkopts=["-lm"],
-      linkstatic=1,  # Faster to link this one-time-use binary dynamically
-      deps = gen_tool_deps)
-
-  gen_cmds += ["$(location :" + gen_tool + ")" +
+  gen_cmds += ["$(location " + gen_tool + ")" +
                " --output_dir=$(@D)/" + out_src_dir +
-               " --base_package=" + gen_base_package +
+               " --base_package=" + base_package +
                " --api_dirs=" + api_def_args_str]
 
   # Generate a source archive containing generated code for these ops.

From aaa345f5a662aab524bbee3912c605919239bef6 Mon Sep 17 00:00:00 2001
From: wangsiyu 
Date: Fri, 4 May 2018 10:52:26 +0800
Subject: [PATCH 0359/1691] refine by using iterator of partitioned variable

---
 tensorflow/python/layers/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index c050e6be040d8d..f7b2e471b27f72 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -358,7 +358,7 @@ def _add_elements_to_collection(elements, collection_list):
 def _should_add_regularizer(variable, existing_variable_set):
   result = True
   if isinstance(variable, tf_variables.PartitionedVariable):
-    for var in variable._get_variable_list():
+    for var in variable:
       if var in existing_variable_set:
         result = False
         break

From de9256f61a9d71a30b175e46116fc5d87063ceaa Mon Sep 17 00:00:00 2001
From: "William D. Irons" 
Date: Fri, 4 May 2018 08:19:03 -0500
Subject: [PATCH 0360/1691] Add conditions:default to mkl build (#19008)

If building on a system that is not darwin, linux_x86_64, or
windows, the select statement in third_party/mkl/BUILD fails to
find a match and fails. Need to use no mkl libraries for non-x86
systems

Fixes #18084
---
 third_party/mkl/BUILD | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/third_party/mkl/BUILD b/third_party/mkl/BUILD
index c2adf578c703f5..017613abb0246f 100644
--- a/third_party/mkl/BUILD
+++ b/third_party/mkl/BUILD
@@ -34,6 +34,7 @@ filegroup(
         "@org_tensorflow//tensorflow:windows": [
             "@mkl_windows//:LICENSE",
         ],
+        "//conditions:default": []
     }),
     visibility = ["//visibility:public"],
 )
@@ -54,5 +55,6 @@ cc_library(
             "@mkl_windows//:mkl_headers",
             "@mkl_windows//:mkl_libs_windows",
         ],
+        "//conditions:default": []
     }),
 )

From 7f0d43c1b7462645767712cd5942d754a5f7adb7 Mon Sep 17 00:00:00 2001
From: manhyuk 
Date: Sat, 5 May 2018 01:53:35 +0900
Subject: [PATCH 0361/1691] fix typo

---
 tensorflow/contrib/tpu/python/tpu/tpu_context.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_context.py b/tensorflow/contrib/tpu/python/tpu/tpu_context.py
index fbc1173e49fd6e..4d7bc6a5a65eaa 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_context.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_context.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ===================================================================
-"""TPU system metdata and associated tooling."""
+"""TPU system metadata and associated tooling."""
 
 from __future__ import absolute_import
 from __future__ import division

From 9b43bd6459e410fc8d3dd1beba9f9a6a254096ba Mon Sep 17 00:00:00 2001
From: Akshay Agrawal 
Date: Thu, 3 May 2018 13:40:20 -0700
Subject: [PATCH 0362/1691] Documentation for tf.contrib.eager.py_func

PiperOrigin-RevId: 195303454
---
 tensorflow/python/ops/script_ops.py | 66 ++++++++++++++++++++++++++---
 1 file changed, 60 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py
index 9f1dd2c4fdb823..f87c5dc5e39b7b 100644
--- a/tensorflow/python/ops/script_ops.py
+++ b/tensorflow/python/ops/script_ops.py
@@ -243,14 +243,68 @@ def _internal_py_func(func, inp, Tout, stateful=None, eager=False, name=None):
 
 
 def eager_py_func(func, inp, Tout, name=None):
-  """Wraps a python function into a TensorFlow op.
+  """Wraps a python function into a TensorFlow op that executes it eagerly.
 
-  When the returned op is executed, `func` is invoked with eager execution
-  enabled. Inputs are Tensor objects and func must return None or objects
-  that may be converted to Tensor objects.
+  This function allows expressing computations in a TensorFlow graph as
+  Python functions. In particular, it wraps a Python function `func`
+  in a TensorFlow operation that executes it with eager exeuction enabled. As a
+  consequence, `tf.contrib.eager.py_func` makes it possible to express control
+  flow using Python constructs (`if`, `while`, `for`, etc.), instead of
+  TensorFlow control flow constructs (@{tf.cond}, @{tf.while_loop}). For
+  example, you might use `tf.contrib.eager.py_func` to implement the log huber
+  function:
+
+  ```python
+  def log_huber(x, m):
+    if tf.abs(x) <= m:
+      return x ** 2
+    else:
+      return m ** 2 * (1 - 2 * tf.log(m) + tf.log(x ** 2))
+
+  x = tf.placeholder(tf.float32)
+  m = tf.placeholder(tf.float32)
+
+  y = tf.contrib.eager.py_func(func=log_huber, inp=[x, m], Tout=tf.float32)
+
+  with tf.Session() as sess:
+    # The session executes `log_huber` eagerly. Given the feed values below,
+    # it will take the second branch, so `output` evaluates to 7.24372.
+    output = sess.run(y, feed_dict={x: 3.0, m: 2.0})
+  ```
+
+  You can also use `tf.contrib.eager.py_func` to debug your models at runtime
+  using Python tools, i.e., you can isolate portions of your code that
+  you want to debug, wrap them in Python functions and insert `pdb` tracepoints
+  or print statements as desired, and wrap those functions in
+  `tf.contrib.eager.py_func`.
+
+  For more information on eager execution, see @{$programmers_guide/eager}.
+
+  `tf.contrib.eager.py_func` is similar in spirit to @{tf.py_func}, but unlike
+  the latter, the former lets you use TensorFlow operations in the wrapped
+  Python function. In particular, while @{tf.py_func} only runs on CPUs and
+  wraps functions that take NumPy arrays as inputs and return NumPy arrays as
+  outputs, `tf.contrib.eager.py_func` can be placed on GPUs and wraps functions
+  that take Tensors as inputs, execute TensorFlow operations in their bodies,
+  and return Tensors as outputs.
+
+  `tf.contrib.eager.py_func` is not differentiable, though a gradient may be
+  implemented in the future; if you would like to differentiate through it,
+  please file an issue on Github.
+
+  Like @{tf.py_func}, `tf.contrib.eager.py_func` has the following limitations
+  with respect to serialization and distribution:
+
+  * The body of the function (i.e. `func`) will not be serialized in a
+    `GraphDef`. Therefore, you should not use this function if you need to
+    serialize your model and restore it in a different environment.
+
+  * The operation must run in the same address space as the Python program
+    that calls `tf.contrib.eager.py_func()`. If you are using distributed
+    TensorFlow, you must run a `tf.train.Server` in the same process as the
+    program that calls `tf.contrib.eager.py_func()` and you must pin the created
+    operation to a device in that server (e.g. using `with tf.device():`).
 
-  This function has the same limitations as `py_func` with respect to
-  serialization and distribution.
 
   Args:
     func: A Python function which accepts a list of `Tensor` objects

From 86d3435503e20e44ab37c87613481f7a35d0c14e Mon Sep 17 00:00:00 2001
From: Jianwei Xie 
Date: Thu, 3 May 2018 13:54:29 -0700
Subject: [PATCH 0363/1691] Fix a typo.

PiperOrigin-RevId: 195305770
---
 tensorflow/python/estimator/training.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py
index 41ffa371aae61d..2f14a6f5605b51 100644
--- a/tensorflow/python/estimator/training.py
+++ b/tensorflow/python/estimator/training.py
@@ -657,7 +657,7 @@ def _should_stop_local_train(global_step):
           hooks=train_hooks)
 
       if not self._continuous_eval_listener.before_eval():
-        logging.info('Exiting training and evaluation lopp, as requested by '
+        logging.info('Exiting training and evaluation loop, as requested by '
                      '_ContinuousEvalListener.before_eval.')
         break
 

From 518dfea0d6d45448a360a49635fe815a28730c46 Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Thu, 3 May 2018 14:00:56 -0700
Subject: [PATCH 0364/1691] [XLA:CPU] Remove dead function + DCHECK, NFC

There isn't a lot of benefit to fixing the function to do that it says it does
since I'm adding support for lowering batch matmul which will break this
precondition anyway.

PiperOrigin-RevId: 195306803
---
 tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc | 4 ----
 tensorflow/compiler/xla/service/cpu/dot_op_emitter.h  | 4 ----
 2 files changed, 8 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
index 495fecc4aa8b3c..801c5239081d17 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
@@ -557,8 +557,6 @@ DotOpEmitter::DotOpEmitter(
   return dot_emitter.Emit();
 }
 
-bool DotOpEmitter::ShapesAreLegalForRuntimeDot() const { return true; }
-
 bool DotOpEmitter::EmitLlvmIrDotIfProfitable() {
   if (dot_.shape().dimensions_size() != 2) {
     return false;
@@ -908,8 +906,6 @@ tensorflow::Status DotOpEmitter::EmitScalarDot() {
 }
 
 tensorflow::Status DotOpEmitter::EmitCallToRuntime() {
-  DCHECK(ShapesAreLegalForRuntimeDot());
-
   // The signature of the Eigen runtime matmul function is:
   //
   //   (void)(void* run_options, float* out, float* lhs, float* rhs,
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
index 9d748eb81f7850..47e09243340840 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
@@ -99,10 +99,6 @@ class DotOpEmitter {
       llvm_ir::ForLoopNest* loop_nest, const llvm_ir::IrArray& operand_array,
       int64 reduction_dimension, tensorflow::StringPiece name_suffix);
 
-  // Our runtime operation requires that all arrays have the same layout,
-  // no padding, and a rank of two.
-  bool ShapesAreLegalForRuntimeDot() const;
-
   // Represents the dimensions of a matrix-matrix multiply operation.
   struct MatMultDims {
     // The number of rows in the LHS.

From a4a9e372f6af694e91ef7aaae9f23867d0ec0fc2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 14:11:13 -0700
Subject: [PATCH 0365/1691] Optimize idempotent ops, e.g.,
 Snapshot(Snapshot(x)) => Snapshot(x)

PiperOrigin-RevId: 195308675
---
 tensorflow/core/grappler/op_types.cc          | 15 ++--
 tensorflow/core/grappler/op_types.h           |  5 ++
 .../optimizers/arithmetic_optimizer.cc        | 30 ++++++++
 .../optimizers/arithmetic_optimizer.h         |  1 +
 .../optimizers/arithmetic_optimizer_test.cc   | 68 +++++++++++++++++++
 tensorflow/python/grappler/cluster_test.py    |  4 +-
 .../profiler/internal/run_metadata_test.py    |  6 +-
 7 files changed, 121 insertions(+), 8 deletions(-)

diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index c48dc00941c8ef..e633ecf78989f7 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -323,6 +323,8 @@ bool IsSize(const NodeDef& node) { return node.op() == "Size"; }
 
 bool IsSlice(const NodeDef& node) { return node.op() == "Slice"; }
 
+bool IsSnapshot(const NodeDef& node) { return node.op() == "Snapshot"; }
+
 bool IsSoftplusGrad(const NodeDef& node) { return node.op() == "SoftplusGrad"; }
 
 bool IsSoftsignGrad(const NodeDef& node) { return node.op() == "SoftsignGrad"; }
@@ -488,14 +490,13 @@ bool IsValueAndOrderAndShapePreserving(const NodeDef& node) {
               "DeepCopy"
               "Enter",
               "Exit",
-              "Identity",
-              "IdentityN",
               "PreventGradient",
               "Print",
               "Snapshot",
               "StopGradient",
           }));
-  return value_and_order_and_shape_preserving_ops->count(node.op()) > 0;
+  return value_and_order_and_shape_preserving_ops->count(node.op()) > 0 ||
+         IsIdentity(node);
 }
 
 bool IsValueAndOrderPreserving(const NodeDef& node) {
@@ -505,7 +506,7 @@ bool IsValueAndOrderPreserving(const NodeDef& node) {
   static const std::unordered_set* value_and_order_preserving_ops =
       CHECK_NOTNULL((new const std::unordered_set{
           "ExpandDims",
-          "Snapshot",
+          "Reshape",
           "Squeeze",
       }));
   return value_and_order_preserving_ops->count(node.op()) > 0 ||
@@ -576,7 +577,7 @@ bool IsUnaryElementWise(const NodeDef& node) {
           "Tanh",
       }));
   return element_wise_ops->count(node.op()) > 0 ||
-         (!IsIdentityN(node) && IsValueAndOrderAndShapePreserving(node));
+         IsValueAndOrderAndShapePreserving(node);
 }
 
 bool HasOpDef(const NodeDef& node) {
@@ -584,5 +585,9 @@ bool HasOpDef(const NodeDef& node) {
   return OpRegistry::Global()->LookUpOpDef(node.op(), &op_def).ok();
 }
 
+bool IsIdempotent(const NodeDef& node) {
+  return IsValueAndOrderAndShapePreserving(node) && IsFreeOfSideEffect(node);
+}
+
 }  // namespace grappler
 }  // end namespace tensorflow
diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h
index e33dd215388700..f6105d710e41c0 100644
--- a/tensorflow/core/grappler/op_types.h
+++ b/tensorflow/core/grappler/op_types.h
@@ -123,6 +123,7 @@ bool IsShape(const NodeDef& node);
 bool IsShapeN(const NodeDef& node);
 bool IsShuffle(const NodeDef& node);
 bool IsSigmoidGrad(const NodeDef& node);
+bool IsSnapshot(const NodeDef& node);
 bool IsSoftplusGrad(const NodeDef& node);
 bool IsSoftsignGrad(const NodeDef& node);
 bool IsSplit(const NodeDef& node);
@@ -187,6 +188,10 @@ bool IsValueAndOrderPreserving(const NodeDef& node);
 // function returns true if the op commutes with all element-wise operations.
 bool IsValuePreserving(const NodeDef& node);
 
+// Returns true if node is idempotent w.r.t. its first input, i.e. if
+// Op(Op(x, y, z), y, z) = Op(x, y, z).
+bool IsIdempotent(const NodeDef& node);
+
 bool IsUnaryElementWise(const NodeDef& node);
 
 // Returns true if we can find an opdef corresponding to the op of the node.
diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
index 2a5654f75224f1..29f49079c4eecc 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -295,6 +295,7 @@ class ArithmeticOptimizerStage : public GraphOptimizerStage {
         }
       }
     }
+    DedupControlInputs(target_node);
   }
 
   bool IsInPreserveSet(const NodeDef& node) const {
@@ -1690,6 +1691,32 @@ class HoistCWiseUnaryChainsStage : public ArithmeticOptimizerStage {
   std::unordered_set optimized_nodes_;
 };
 
+class RemoveIdempotentStage : public ArithmeticOptimizerStage {
+ public:
+  explicit RemoveIdempotentStage(const GraphOptimizerContext& ctx,
+                                 const ArithmeticOptimizerContext& ctx_ext)
+      : ArithmeticOptimizerStage("RemoveIdempotent", ctx, ctx_ext) {}
+  ~RemoveIdempotentStage() override = default;
+
+  bool IsSupported(const NodeDef* node) const override {
+    return IsIdempotent(*node) && !IsInPreserveSet(*node);
+  }
+
+  Status TrySimplify(NodeDef* node, string* simplified_node_name) override {
+    NodeDef* input;
+    TF_RETURN_IF_ERROR(GetInputNode(node->input(0), &input));
+    auto root_scope_and_name = ParseNodeScopeAndName(node->name());
+    const string new_name = OptimizedNodeName(root_scope_and_name);
+    if (input->op() == node->op() && input->device() == node->device() &&
+        IsIdempotent(*input) && !ctx().node_map->NodeExists(new_name)) {
+      NodeDef* new_input_node = AddCopyNode(new_name, input);
+      ForwardControlDependencies(new_input_node, {node});
+      *simplified_node_name = new_input_node->name();
+    }
+    return Status::OK();
+  }
+};
+
 // Performs the conversion:
 // Div(x, Sqrt(y)) => Mul(x, Rsqrt(y))
 // TODO(srjoglekar): Generalize to optimize cases like (x / pow(y, z)).
@@ -1975,6 +2002,7 @@ void ArithmeticOptimizer::ForwardControlDependencies(
       }
     }
   }
+  DedupControlInputs(target_node);
 }
 
 // TODO(ezhulenev): extract each individual simplify rewrite into separate
@@ -2381,6 +2409,8 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) {
     pipeline.AddStage(ctx, ctx_ext);
   if (options_.convert_sqrt_div_to_rsqrt_mul)
     pipeline.AddStage(ctx, ctx_ext);
+  if (options_.remove_idempotent)
+    pipeline.AddStage(ctx, ctx_ext);
 
   VLOG(1) << "Run " << pipeline.NumStages() << " arithmetic optimizer stages: "
           << str_util::Join(pipeline.StageNames(), ", ");
diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h
index 6309dc1a33d146..3f9feac55f62f0 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h
@@ -67,6 +67,7 @@ class ArithmeticOptimizer : public GraphOptimizer {
     bool remove_negation = true;
     bool hoist_cwise_unary_chains = true;
     bool convert_sqrt_div_to_rsqrt_mul = false;
+    bool remove_idempotent = true;
 
     // Choose which arithmetic optimizer stages will be enabled for a given
     // optimization level by default.
diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
index d32743f3f25015..e109e666331675 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
@@ -83,6 +83,7 @@ class ArithmeticOptimizerTest : public GrapplerTest {
                         GraphDef* output) {
     TF_EXPECT_OK(optimizer->Optimize(nullptr, *item, output));
     item->graph.Swap(output);
+    output->Clear();
     TF_EXPECT_OK(ModelPruner().Optimize(nullptr, *item, output));
   }
 
@@ -91,6 +92,7 @@ class ArithmeticOptimizerTest : public GrapplerTest {
                      GraphDef* output) {
     TF_EXPECT_OK(optimizer->Optimize(nullptr, *item, output));
     item->graph.Swap(output);
+    output->Clear();
     TF_EXPECT_OK(optimizer->Optimize(nullptr, *item, output));
   }
 
@@ -99,8 +101,10 @@ class ArithmeticOptimizerTest : public GrapplerTest {
                              GraphDef* output) {
     TF_EXPECT_OK(optimizer->Optimize(nullptr, *item, output));
     item->graph.Swap(output);
+    output->Clear();
     TF_EXPECT_OK(optimizer->Optimize(nullptr, *item, output));
     item->graph.Swap(output);
+    output->Clear();
     TF_EXPECT_OK(ModelPruner().Optimize(nullptr, *item, output));
   }
 
@@ -168,6 +172,11 @@ class ArithmeticOptimizerTest : public GrapplerTest {
     DisableAllStages(optimizer);
     optimizer->options_.convert_sqrt_div_to_rsqrt_mul = true;
   }
+
+  void EnableOnlyRemoveIdempotent(ArithmeticOptimizer* optimizer) {
+    DisableAllStages(optimizer);
+    optimizer->options_.remove_idempotent = true;
+  }
 };
 
 TEST_F(ArithmeticOptimizerTest, NoOp) {
@@ -2390,5 +2399,64 @@ TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryIntoSplit) {
   }
 }
 
+TEST_F(ArithmeticOptimizerTest, RemoveIdempotent) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+  Output a = ops::Const(s.WithOpName("a"), 3.14f, {32});
+  Output ctrl1 = ops::Const(s.WithOpName("ctrl1"), 1, {});
+  Output ctrl2 = ops::Const(s.WithOpName("ctrl2"), 2, {});
+  Output sn1 =
+      ops::Snapshot(s.WithOpName("sn1").WithControlDependencies(ctrl1), a);
+  Output sn2 =
+      ops::Snapshot(s.WithOpName("sn2").WithControlDependencies(ctrl2), sn1);
+  Output out1 = ops::Identity(s.WithOpName("out1"), sn2);
+  Output id1 = ops::Identity(s.WithOpName("id1"), a);
+  Output id2 = ops::Identity(s.WithOpName("id2"), id1);
+  Output out2 = ops::Identity(s.WithOpName("out2"), id2);
+  GrapplerItem item;
+  item.fetch = {"out1", "out2"};
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch);
+
+  GraphDef output;
+  ArithmeticOptimizer optimizer;
+  EnableOnlyRemoveIdempotent(&optimizer);
+  OptimizeTwice(&optimizer, &item, &output);
+
+  EXPECT_EQ(11, output.node_size());
+  int found = 0;
+  for (const NodeDef& node : output.node()) {
+    if (node.name() == "out1") {
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("ArithmeticOptimizer/RemoveIdempotent_sn2", node.input(0));
+      found++;
+    } else if (node.name() == "ArithmeticOptimizer/RemoveIdempotent_sn2") {
+      EXPECT_EQ(3, node.input_size());
+      EXPECT_EQ("Snapshot", node.op());
+      EXPECT_EQ("a", node.input(0));
+      EXPECT_EQ("^ctrl1", node.input(1));
+      EXPECT_EQ("^ctrl2", node.input(2));
+      found++;
+    } else if (node.name() == "out2") {
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("ArithmeticOptimizer/RemoveIdempotent_id2", node.input(0));
+      found++;
+    } else if (node.name() == "ArithmeticOptimizer/RemoveIdempotent_id2") {
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("a", node.input(0));
+      found++;
+    }
+  }
+  EXPECT_EQ(4, found);
+
+  auto tensors = EvaluateNodes(output, item.fetch);
+  EXPECT_EQ(tensors.size(), tensors_expected.size());
+  EXPECT_EQ(tensors.size(), item.fetch.size());
+  for (int i = 0; i < item.fetch.size(); ++i) {
+    test::ExpectTensorNear(tensors_expected[i], tensors[i], 1e-6);
+  }
+}
+
 }  // namespace grappler
 }  // namespace tensorflow
diff --git a/tensorflow/python/grappler/cluster_test.py b/tensorflow/python/grappler/cluster_test.py
index 26c6f22d34b27c..541747867fa81b 100644
--- a/tensorflow/python/grappler/cluster_test.py
+++ b/tensorflow/python/grappler/cluster_test.py
@@ -45,7 +45,7 @@ def testBasic(self):
       op_perfs, run_time, step_stats = grappler_cluster.MeasureCosts(
           grappler_item)
       self.assertTrue(run_time > 0)
-      self.assertEqual(len(op_perfs), 8)
+      self.assertEqual(len(op_perfs), 4)
       self.assertTrue(step_stats.dev_stats)
 
   def testNoDetailedStats(self):
@@ -129,7 +129,7 @@ def testContext(self):
         disable_detailed_stats=False, disable_timeline=False) as gcluster:
       op_perfs, run_time, step_stats = gcluster.MeasureCosts(grappler_item)
       self.assertTrue(run_time > 0)
-      self.assertEqual(len(op_perfs), 8)
+      self.assertEqual(len(op_perfs), 4)
       self.assertTrue(step_stats.dev_stats)
 
   def testAvailableOps(self):
diff --git a/tensorflow/python/profiler/internal/run_metadata_test.py b/tensorflow/python/profiler/internal/run_metadata_test.py
index fd893d6cde66e5..216cc3dd54b785 100644
--- a/tensorflow/python/profiler/internal/run_metadata_test.py
+++ b/tensorflow/python/profiler/internal/run_metadata_test.py
@@ -23,6 +23,7 @@
 import six
 
 from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import math_ops
@@ -65,7 +66,10 @@ def _run_model():
   w = random_ops.random_normal(shape=[SIZE, 2 * SIZE])
   y = math_ops.matmul(x, w)
 
-  with session.Session() as sess:
+  config = config_pb2.ConfigProto()
+  config.graph_options.rewrite_options.arithmetic_optimization = (
+      rewriter_config_pb2.RewriterConfig.OFF)
+  with session.Session(config=config) as sess:
     run_metadata = config_pb2.RunMetadata()
     opts = builder.time_and_memory()
     opts['min_micros'] = 0

From 05425f25ee1f8b83624127cf0f403b6751e7d70a Mon Sep 17 00:00:00 2001
From: Nick Desaulniers 
Date: Thu, 3 May 2018 14:16:27 -0700
Subject: [PATCH 0366/1691] [TF:XLA] clean up interface to xla::VerifyHloModule

It seems that the first argument, platform, is unused.

PiperOrigin-RevId: 195309504
---
 tensorflow/compiler/xla/tests/test_utils.cc | 3 +--
 tensorflow/compiler/xla/tests/test_utils.h  | 2 +-
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/test_utils.cc b/tensorflow/compiler/xla/tests/test_utils.cc
index 997a1d8273736a..810cc25f1b5b11 100644
--- a/tensorflow/compiler/xla/tests/test_utils.cc
+++ b/tensorflow/compiler/xla/tests/test_utils.cc
@@ -339,8 +339,7 @@ StatusOr>> MakeFakeArguments(
   return std::move(arguments);
 }
 
-Status VerifyHloModule(const se::Platform& platform, HloModule* const module,
-                       bool allow_mixed_precision) {
+Status VerifyHloModule(HloModule* const module, bool allow_mixed_precision) {
   return HloVerifier(allow_mixed_precision).Run(module).status();
 }
 
diff --git a/tensorflow/compiler/xla/tests/test_utils.h b/tensorflow/compiler/xla/tests/test_utils.h
index 30c147910cae85..f483cdebea5c7c 100644
--- a/tensorflow/compiler/xla/tests/test_utils.h
+++ b/tensorflow/compiler/xla/tests/test_utils.h
@@ -68,7 +68,7 @@ StatusOr>> MakeFakeArguments(
 
 // Check that a given module satisfies various constraints before trying to
 // execute it.
-Status VerifyHloModule(const se::Platform& platform, HloModule* const module,
+Status VerifyHloModule(HloModule* const module,
                        bool allow_mixed_precision = false);
 
 }  // namespace xla

From 316e0bab900d2a513e4e9622940181414e0d0596 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 14:18:07 -0700
Subject: [PATCH 0367/1691] Add separate get_read and get_updated helpers that
 work on code exceprts. Handle corner case for AugAssign. Fix bug in
 _node_sets_self_attribute.

PiperOrigin-RevId: 195309809
---
 .../pyct/static_analysis/activity.py          |  79 ++++++++--
 .../pyct/static_analysis/activity_test.py     | 136 +++++++++++++++---
 2 files changed, 187 insertions(+), 28 deletions(-)

diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/activity.py b/tensorflow/contrib/autograph/pyct/static_analysis/activity.py
index 2c14c2c8c23810..4d7b0cbb7b8f6e 100644
--- a/tensorflow/contrib/autograph/pyct/static_analysis/activity.py
+++ b/tensorflow/contrib/autograph/pyct/static_analysis/activity.py
@@ -23,11 +23,12 @@
 import gast
 
 from tensorflow.contrib.autograph.pyct import anno
+from tensorflow.contrib.autograph.pyct import qual_names
 from tensorflow.contrib.autograph.pyct import transformer
-from tensorflow.contrib.autograph.pyct.qual_names import QN
 from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno
 
 # TODO(mdan): Add support for PY3 (e.g. Param vs arg).
+# TODO(alexbw): Ignore named literals (e.g. None)
 
 
 class Scope(object):
@@ -43,16 +44,20 @@ class Scope(object):
     used: identifiers referenced in this scope
   """
 
-  def __init__(self, parent, isolated=True):
+  def __init__(self, parent, isolated=True, add_unknown_symbols=False):
     """Create a new scope.
 
     Args:
       parent: A Scope or None.
       isolated: Whether the scope is isolated, that is, whether variables
           created in this scope should be visible to the parent scope.
+      add_unknown_symbols: Whether to handle attributed and subscripts
+          without having first seen the base name.
+          E.g., analyzing the statement 'x.y = z' without first having seen 'x'.
     """
     self.isolated = isolated
     self.parent = parent
+    self.add_unknown_symbols = add_unknown_symbols
     self.modified = set()
     self.created = set()
     self.used = set()
@@ -134,13 +139,17 @@ def mark_param(self, name):
     self.params.add(name)
 
   def mark_creation(self, name, writes_create_symbol=False):
+    """Mark a qualified name as created."""
     if name.is_composite():
       parent = name.parent
-      if self.has(parent):
-        if not writes_create_symbol:
-          return
+      if not writes_create_symbol:
+        return
       else:
-        raise ValueError('Unknown symbol "%s".' % parent)
+        if not self.has(parent):
+          if self.add_unknown_symbols:
+            self.mark_read(parent)
+          else:
+            raise ValueError('Unknown symbol "%s".' % parent)
     self.created.add(name)
 
   def mark_write(self, name):
@@ -163,17 +172,25 @@ def mark_returned(self, name):
 
 
 class ActivityAnalyzer(transformer.Base):
-  """Annotates nodes with local scope information. See Scope."""
+  """Annotates nodes with local scope information.
 
-  def __init__(self, context, parent_scope):
+  See Scope.
+
+  The use of this class requires that qual_names.resolve() has been called on
+  the node. This class will ignore nodes have not been
+  annotated with their qualified names.
+  """
+
+  def __init__(self, context, parent_scope=None, add_unknown_symbols=False):
     super(ActivityAnalyzer, self).__init__(context)
-    self.scope = Scope(parent_scope)
+    self.scope = Scope(parent_scope, None, add_unknown_symbols)
     self._in_return_statement = False
+    self._in_aug_assign = False
 
   @property
   def _in_constructor(self):
-    innermost = self.enclosing_entities[-1]
     if len(self.enclosing_entities) > 1:
+      innermost = self.enclosing_entities[-1]
       parent = self.enclosing_entities[-2]
       return isinstance(parent, gast.ClassDef) and innermost.name == '__init__'
     return False
@@ -184,6 +201,7 @@ def _node_sets_self_attribute(self, node):
       # TODO(mdan): The 'self' argument is not guaranteed to be called 'self'.
       if qn.has_attr and qn.parent.qn == ('self',):
         return True
+    return False
 
   def _track_symbol(self,
                     node,
@@ -201,12 +219,14 @@ def _track_symbol(self,
         self.scope.mark_write(qn.parent)
       if writes_create_symbol:
         self.scope.mark_creation(qn, writes_create_symbol=True)
+      if self._in_aug_assign:
+        self.scope.mark_read(qn)
     elif isinstance(node.ctx, gast.Load):
       self.scope.mark_read(qn)
     elif isinstance(node.ctx, gast.Param):
       # Param contexts appear in function defs, so they have the meaning of
       # defining a variable.
-      # TODO(mdan): This bay be incorrect with nested functions.
+      # TODO(mdan): This may be incorrect with nested functions.
       # For nested functions, we'll have to add the notion of hiding args from
       # the parent scope, not writing to them.
       self.scope.mark_creation(qn)
@@ -222,6 +242,14 @@ def _track_symbol(self,
     if self._in_return_statement:
       self.scope.mark_returned(qn)
 
+  def visit_AugAssign(self, node):
+    # Special rules for AugAssign. In Assign, the target is only written,
+    # but in AugAssig (e.g. a += b), the target is both read and written.
+    self._in_aug_assign = True
+    self.generic_visit(node)
+    self._in_aug_assign = False
+    return node
+
   def visit_Name(self, node):
     self.generic_visit(node)
     self._track_symbol(node)
@@ -295,7 +323,7 @@ def _process_parallel_blocks(self, parent, children):
 
   def visit_FunctionDef(self, node):
     if self.scope:
-      qn = QN(node.name)
+      qn = qual_names.QN(node.name)
       self.scope.mark_write(qn)
     current_scope = self.scope
     body_scope = Scope(current_scope, isolated=True)
@@ -355,5 +383,32 @@ def visit_Return(self, node):
     return node
 
 
+def get_read(node, context):
+  """Return the variable names as QNs (qual_names.py) read by this statement."""
+  analyzer = ActivityAnalyzer(context, None, True)
+  analyzer.visit(node)
+  return analyzer.scope.used
+
+
+def get_updated(node, context):
+  """Return the variable names created or mutated by this statement.
+
+  This function considers assign statements, augmented assign statements, and
+  the targets of for loops, as well as function arguments.
+  For example, `x[0] = 2` will return `x`, `x, y = 3, 4` will return `x` and
+  `y`, `for i in range(x)` will return `i`, etc.
+  Args:
+    node: An AST node
+    context: An EntityContext instance
+
+  Returns:
+    A set of variable names (QNs, see qual_names.py) of all the variables
+    created or mutated.
+  """
+  analyzer = ActivityAnalyzer(context, None, True)
+  analyzer.visit(node)
+  return analyzer.scope.created | analyzer.scope.modified
+
+
 def resolve(node, context, parent_scope=None):
   return ActivityAnalyzer(context, parent_scope).visit(node)
diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py
index ef79a295bfa394..fdbd349af9d332 100644
--- a/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py
+++ b/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py
@@ -123,7 +123,7 @@ def _parse_and_analyze(self, test_fn):
         recursive=True)
     node = qual_names.resolve(node)
     node = activity.resolve(node, ctx)
-    return node
+    return node, ctx
 
   def test_local_markers(self):
 
@@ -133,7 +133,7 @@ def test_fn(a):  # pylint:disable=unused-argument
         b -= 1
       return b
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     self.assertFalse(
         anno.getanno(node.body[0].body[0].value,
                      NodeAnno.IS_LOCAL))  # c in b = c
@@ -156,6 +156,7 @@ def assertSymbolSetsAre(self, expected, actual, name):
                               expected - actual, actual - expected))
 
   def assertScopeIsRmc(self, scope, used, modified, created):
+    """Assert the scope contains specific used, modified & created variables."""
     self.assertSymbolSetsAre(used, scope.used, 'read')
     self.assertSymbolSetsAre(modified, scope.modified, 'modified')
     self.assertSymbolSetsAre(created, scope.created, 'created')
@@ -168,7 +169,7 @@ def test_fn(a):
       print(a, b)
       return c
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     print_node = node.body[0].body[2]
     if isinstance(print_node, gast.Print):
       # Python 2
@@ -191,7 +192,7 @@ def test_fn(a):
       foo(a, b)  # pylint:disable=undefined-variable
       return c
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     call_node = node.body[0].body[2].value
     # We basically need to detect which variables are captured by the call
     # arguments.
@@ -208,7 +209,7 @@ def test_fn(a):
       foo(a.b, a.c)
       return a.d
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     call_node = node.body[0].body[1].value
     self.assertScopeIsRmc(
         anno.getanno(call_node, NodeAnno.ARGS_SCOPE),
@@ -234,7 +235,7 @@ def test_fn(a):
       foo(a[0], a[b])
       return a[c]
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     call_node = node.body[0].body[2].value
     self.assertScopeIsRmc(
         anno.getanno(call_node, NodeAnno.ARGS_SCOPE),
@@ -258,7 +259,7 @@ def test_fn(a):
         b -= 1
       return b, c
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     while_node = node.body[0].body[1]
     self.assertScopeIsRmc(
         anno.getanno(while_node, NodeAnno.BODY_SCOPE), ('b',), ('b', 'c'),
@@ -278,7 +279,7 @@ def test_fn(a):
         b -= 1
       return b, c
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     for_node = node.body[0].body[1]
     self.assertScopeIsRmc(
         anno.getanno(for_node, NodeAnno.BODY_SCOPE), ('b',), ('b', 'c'), ('c',))
@@ -299,7 +300,7 @@ def test_fn(x):
         u = -y
       return z, u
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     if_node = node.body[0].body[0]
     self.assertScopeIsRmc(
         anno.getanno(if_node, NodeAnno.BODY_SCOPE), ('x', 'y'), ('x', 'y', 'z'),
@@ -326,7 +327,7 @@ def test_fn(a):
         d = 1
       return d
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     if_node = node.body[0].body[0]
     self.assertScopeIsRmc(
         anno.getanno(if_node, NodeAnno.BODY_SCOPE),
@@ -358,7 +359,7 @@ def test_fn(a, b, c, e):
         d = 1
       return d
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     if_node = node.body[0].body[0]
     self.assertScopeIsRmc(
         anno.getanno(if_node, NodeAnno.BODY_SCOPE),
@@ -390,7 +391,7 @@ def test_fn(b):
           a = b * b
       return a
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     inner_if_node = node.body[0].body[0].body[0]
     self.assertScopeIsRmc(
         anno.getanno(inner_if_node, NodeAnno.BODY_SCOPE), ('b',), ('a',),
@@ -413,7 +414,7 @@ def f(x):
         b -= f(i)
       return b, c
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     fn_def_node = node.body[0].body[0]
 
     self.assertScopeIsRmc(
@@ -434,7 +435,7 @@ def __init__(self, a):
         self.b = a
         self.b.c = 1
 
-    node = self._parse_and_analyze(TestClass)
+    node, _ = self._parse_and_analyze(TestClass)
     init_node = node.body[0].body[0]
     self.assertScopeIsRmc(
         anno.getanno(init_node, NodeAnno.BODY_SCOPE),
@@ -448,15 +449,118 @@ def test_aug_assign_subscripts(self):
     def test_fn(a):
       a[0] += 1
 
-    node = self._parse_and_analyze(test_fn)
+    node, _ = self._parse_and_analyze(test_fn)
     fn_node = node.body[0]
     self.assertScopeIsRmc(
         anno.getanno(fn_node, NodeAnno.BODY_SCOPE),
-        ('a',),
+        ('a', 'a[0]'),
         ('a', 'a[0]'),
         ('a',),
     )
 
+  def test_return_vars_are_read(self):
+
+    def test_fn(a, b, c):  # pylint: disable=unused-argument
+      return c
+
+    node, _ = self._parse_and_analyze(test_fn)
+    fn_node = node.body[0]
+    self.assertScopeIsRmc(
+        anno.getanno(fn_node, NodeAnno.BODY_SCOPE),
+        ('c',),
+        (),
+        (
+            'a',
+            'b',
+            'c',
+        ),
+    )
+
+  def test_aug_assign(self):
+
+    def test_fn(a, b):
+      a += b
+
+    node, _ = self._parse_and_analyze(test_fn)
+    fn_node = node.body[0]
+    self.assertScopeIsRmc(
+        anno.getanno(fn_node, NodeAnno.BODY_SCOPE),
+        ('a', 'b'),
+        ('a'),
+        ('a', 'b'),
+    )
+
+  def test_aug_assign_rvalues(self):
+
+    a = dict(bar=3)
+
+    def foo():
+      return a
+
+    def test_fn(x):
+      foo()['bar'] += x
+
+    node, _ = self._parse_and_analyze(test_fn)
+    fn_node = node.body[0]
+    self.assertScopeIsRmc(
+        anno.getanno(fn_node, NodeAnno.BODY_SCOPE),
+        ('foo', 'x'),
+        (),
+        ('x',),
+    )
+
+  def test_params_created(self):
+
+    def test_fn(a, b):  # pylint: disable=unused-argument
+      return b
+
+    node, _ = self._parse_and_analyze(test_fn)
+    fn_node = node.body[0]
+    self.assertScopeIsRmc(
+        anno.getanno(fn_node, NodeAnno.BODY_SCOPE), ('b',), (('')),
+        (('a', 'b')))
+
+  def test_get_read(self):
+
+    def test_fn(x, y):
+      z = test_fn(x, y)
+      return z
+
+    node, ctx = self._parse_and_analyze(test_fn)
+    node = node.body[0].body[0]
+    read_vars = activity.get_read(node, ctx)
+    self.assertEqual(read_vars, set(map(qual_names.QN, ('test_fn', 'x', 'y'))))
+
+    def test_fn2(x, y, z):
+      z += test_fn2(x, y, z)
+      return z
+
+    node, ctx = self._parse_and_analyze(test_fn2)
+    node = node.body[0].body[0]
+    read_vars = activity.get_read(node, ctx)
+    self.assertEqual(read_vars,
+                     set(map(qual_names.QN, ('test_fn2', 'x', 'y', 'z'))))
+
+  def test_get_updated(self):
+
+    def test_fn(x, y):
+      z = test_fn(x, y)
+      return z
+
+    node, ctx = self._parse_and_analyze(test_fn)
+    node = node.body[0].body[0]
+    updated_vars = activity.get_updated(node, ctx)
+    self.assertEqual(updated_vars, set(map(qual_names.QN, ('z'))))
+
+    def test_fn2(x, y, z):
+      z += test_fn2(x, y, z)
+      return z
+
+    node, ctx = self._parse_and_analyze(test_fn2)
+    node = node.body[0].body[0]
+    updated_vars = activity.get_updated(node, ctx)
+    self.assertEqual(updated_vars, set(map(qual_names.QN, ('z'))))
+
 
 if __name__ == '__main__':
   test.main()

From 28d43e5ada3c1e16b81c64b08cbbc273407a0347 Mon Sep 17 00:00:00 2001
From: Zhixian Yan 
Date: Thu, 3 May 2018 14:34:27 -0700
Subject: [PATCH 0368/1691] Add tflite listed models with accuracy and
 performance numbers.

PiperOrigin-RevId: 195312636
---
 tensorflow/contrib/lite/g3doc/models.md | 87 +++++++++++++++++--------
 1 file changed, 61 insertions(+), 26 deletions(-)

diff --git a/tensorflow/contrib/lite/g3doc/models.md b/tensorflow/contrib/lite/g3doc/models.md
index d8134d5a00097b..c1c8ef049f693d 100644
--- a/tensorflow/contrib/lite/g3doc/models.md
+++ b/tensorflow/contrib/lite/g3doc/models.md
@@ -1,28 +1,63 @@
 # List of Hosted Models
 
-*   [NASNet large](https://storage.googleapis.com/download.tensorflow.org/models/tflite/nasnet_large_2018_03_27.zip)
-*   [NASNet mobile](https://storage.googleapis.com/download.tensorflow.org/models/tflite/nasnet_mobile_2018_03_27.zip)
-*   [ResNet v2 101](https://storage.googleapis.com/download.tensorflow.org/models/tflite/resnet_v2_101_2018_03_27.zip)
-*   [ResNet v2 50](https://storage.googleapis.com/download.tensorflow.org/models/tflite/resnet_v2_50_2018_03_27.zip)
-*   [Inception ResNet v2](https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_resnet_v2_2018_03_27.zip)
-*   [Inception v4](https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v4_2018_03_27.zip)
-*   [Inception v3 2015](https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_2015_2017_11_10.zip)
-*   [Inception v3 Slim 2016](https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip)
-*   [Mobilenet 0.25 128 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_0.25_128_float_2017_11_08.zip)
-*   [Mobilenet 0.25 160 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_0.25_160_float_2017_11_08.zip)
-*   [Mobilenet 0.25 192 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_0.25_192_float_2017_11_08.zip)
-*   [Mobilenet 0.25 224 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_0.25_224_float_2017_11_08.zip)
-*   [Mobilenet 0.50 128 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_0.50_128_float_2017_11_08.zip)
-*   [Mobilenet 0.50 160 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_0.50_160_float_2017_11_08.zip)
-*   [Mobilenet 0.50 192 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_0.50_192_float_2017_11_08.zip)
-*   [Mobilenet 0.50 224 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_0.50_224_float_2017_11_08.zip)
-*   [Mobilenet 0.75 128 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_0.75_128_float_2017_11_08.zip)
-*   [Mobilenet 0.75 160 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_0.75_160_float_2017_11_08.zip)
-*   [Mobilenet 0.75 192 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_0.75_192_float_2017_11_08.zip)
-*   [Mobilenet 0.75 224 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_0.75_224_float_2017_11_08.zip)
-*   [Mobilenet 1.0 128 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_128_float_2017_11_08.zip)
-*   [Mobilenet 1.0 160 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_160_float_2017_11_08.zip)
-*   [Mobilenet 1.0 192 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_192_float_2017_11_08.zip)
-*   [Mobilenet 1.0 224 Float](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_float_2017_11_08.zip)
-*   [Mobilenet 1.0 224 Quant](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip)
-*   [Smart Reply 1.0 Android ](https://storage.googleapis.com/download.tensorflow.org/models/tflite/smartreply_1.0_2017_11_01.zip)
+## Image classification (Float Models)
+
+Model Name          | Paper_Model_Files^                                                                                                                                                                        | Model_Size | Top-1 Accuracy | Top-5 Accuracy | TF Lite Performance^^ | Tensorflow Performance
+------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | ---------: | -------------: | -------------: | --------------------: | ---------------------:
+DenseNet            | [paper](https://arxiv.org/abs/1608.06993), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/densenet_2018_04_27.tgz)            | 43.6 Mb    | 64.2%          | 85.6%          | 894 ms                | 1262 ms
+SqueezeNet          | [paper](https://arxiv.org/abs/1602.07360), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz)          | 5.0 Mb     | 49.0%          | 72.9%          | 224 ms                | 255 ms
+NASNet mobile       | [paper](https://arxiv.org/abs/1707.07012), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_mobile_2018_04_27.tgz)       | 21.4 Mb    | 72.2%          | 90.6%          | 261 ms                | 389 ms
+NASNet large        | [paper](https://arxiv.org/abs/1707.07012), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_large_2018_04_27.tgz)        | 355.3 Mb   | 82.1%          | 95.8%          | 6697 ms               | 7940 ms
+ResNet_V2_50        | [paper](https://arxiv.org/abs/1603.05027), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/resnet_v2_50_2018_04_27.tgz)        | 102.3 Mb   | 68.1%          | 88.4%          | 942 ms                | 1008 ms
+ResNet_V2_101       | [paper](https://arxiv.org/abs/1603.05027), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/resnet_v2_101_2018_04_27.tgz)       | 178.3 Mb   | 70.4%          | 89.6%          | 1880 ms               | 1970 ms
+Inception_V3        | [paper](http://arxiv.org/abs/1512.00567), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz)         | 95.3 Mb    | 76.9%          | 93.5%          | 1433 ms               | 1522 ms
+Inception_V4        | [paper](http://arxiv.org/abs/1602.07261), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz)         | 170.7 Mb   | 79.6%          | 94.6%          | 2986 ms               | 3139 ms
+Inception_ResNet_V2 | [paper](https://arxiv.org/abs/1602.07261), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_resnet_v2_2018_04_27.tgz) | 121.0 Mb   | 76.8%          | 93.5%          | 2731 ms               | 2926 ms
+Mobilenet_0.25_128  | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128.tgz)                                       | 1.9 Mb     | 41.5%          | 66.3%          | 6.2 ms                | 13.0 ms
+Mobilenet_0.25_160  | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_160.tgz)                                       | 1.9 Mb     | 45.5%          | 70.3%          | 8.6 ms                | 19.5 ms
+Mobilenet_0.25_192  | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_192.tgz)                                       | 1.9 Mb     | 47.7%          | 72.3%          | 12.1 ms               | 27.8 ms
+Mobilenet_0.25_224  | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_224.tgz)                                       | 1.9 Mb     | 49.8%          | 74.2%          | 16.2 ms               | 37.3 ms
+Mobilenet_0.50_128  | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_128.tgz)                                        | 5.3 Mb     | 56.3%          | 79.4%          | 18.1 ms               | 29.9 ms
+Mobilenet_0.50_160  | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_160.tgz)                                        | 5.3 Mb     | 59.1%          | 81.9%          | 26.8 ms               | 45.9 ms
+Mobilenet_0.50_192  | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_192.tgz)                                        | 5.3 Mb     | 61.7%          | 83.6%          | 35.6 ms               | 65.3 ms
+Mobilenet_0.50_224  | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_224.tgz)                                        | 5.3 Mb     | 63.3%          | 84.9%          | 47.6 ms               | 164.2 ms
+Mobilenet_0.75_128  | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_128.tgz)                                       | 10.3 Mb    | 62.1%          | 83.9%          | 34.6 ms               | 48.7 ms
+Mobilenet_0.75_160  | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_160.tgz)                                       | 10.3 Mb    | 65.3%          | 86.0%          | 51.3 ms               | 75.2 ms
+Mobilenet_0.75_192  | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_192.tgz)                                       | 10.3 Mb    | 67.2%          | 87.3%          | 71.7 ms               | 107.0 ms
+Mobilenet_0.75_224  | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_224.tgz)                                       | 10.3 Mb    | 68.4%          | 88.2%          | 95.7 ms               | 143.4 ms
+Mobilenet_1.0_128   | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_128.tgz)                                        | 16.9 Mb    | 65.2%          | 85.8%          | 57.4 ms               | 76.8 ms
+Mobilenet_1.0_160   | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_160.tgz)                                        | 16.9 Mb    | 68.0%          | 87.7%          | 86.0 ms               | 117.7 ms
+Mobilenet_1.0_192   | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_192.tgz)                                        | 16.9 Mb    | 70.0%          | 89.2%          | 118.6 ms              | 167.3 ms
+Mobilenet_1.0_224   | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz)                                        | 16.9 Mb    | 70.9%          | 89.9%          | 160.1 ms              | 224.3 ms
+
+^ The model files include both TF Lite FlatBuffer and Tensorflow frozen Graph.
+
+^^ The performance numbers are generated in the benchmark on Pixel-2 using
+single thread large core.
+
+## Image classification (Quantized Models)
+
+Model Name               | Paper_Model_Files                                                                                                                                         | Model_Size | Top-1 Accuracy | Top-5 Accuracy | TF Lite Performance
+------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | ---------: | -------------: | -------------: | ------------------:
+Mobilenet_0.25_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128_quant.tgz) | 0.5 Mb     | 39.9%          | 65.8%          | 3.7 ms
+Mobilenet_0.25_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_160_quant.tgz) | 0.5 Mb     | 43.5%          | 69.1%          | 5.5 ms
+Mobilenet_0.25_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_192_quant.tgz) | 0.5 Mb     | 45.8%          | 71.9%          | 7.9 ms
+Mobilenet_0.25_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_224_quant.tgz) | 0.5 Mb     | 48.2%          | 73.8%          | 10.4 ms
+Mobilenet_0.50_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_128_quant.tgz)  | 1.4 Mb     | 54.9%          | 78.9%          | 8.8 ms
+Mobilenet_0.50_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_160_quant.tgz)  | 1.4 Mb     | 57.7%          | 81.3%          | 13.0 ms
+Mobilenet_0.50_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_192_quant.tgz)  | 1.4 Mb     | 60.4%          | 83.2%          | 18.3 ms
+Mobilenet_0.50_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_224_quant.tgz)  | 1.4 Mb     | 62.2%          | 84.5%          | 24.7 ms
+Mobilenet_0.75_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_128_quant.tgz) | 2.6 Mb     | 59.8%          | 82.8%          | 16.2 ms
+Mobilenet_0.75_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_160_quant.tgz) | 2.6 Mb     | 63.9%          | 85.5%          | 24.3 ms
+Mobilenet_0.75_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_192_quant.tgz) | 2.6 Mb     | 66.2%          | 87.1%          | 33.8 ms
+Mobilenet_0.75_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_224_quant.tgz) | 2.6 Mb     | 67.9%          | 88.1%          | 45.4 ms
+Mobilenet_1.0_128_quant  | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_128_quant.tgz)  | 4.3 Mb     | 64.0%          | 85.5%          | 24.9 ms
+Mobilenet_1.0_160_quant  | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_160_quant.tgz)  | 4.3 Mb     | 67.3%          | 87.7%          | 37.4 ms
+Mobilenet_1.0_192_quant  | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_192_quant.tgz)  | 4.3 Mb     | 69.0%          | 88.9%          | 51.9 ms
+Mobilenet_1.0_224_quant  | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz)  | 4.3 Mb     | 69.7%          | 89.5%          | 70.2 ms
+
+## Other models
+
+Model                   | TF Lite FlatBuffer
+----------------------- | :----------------:
+Smart Reply 1.0 Android | [reference](https://research.googleblog.com/2017/11/on-device-conversational-modeling-with.html), [tflite](https://storage.googleapis.com/download.tensorflow.org/models/smartreply_1.0_2017_11_01.zip)

From 4f4b15cece96c6cfa749c3fcf3288f1f47986210 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 15:20:05 -0700
Subject: [PATCH 0369/1691] Fix bug that disabled loop invariant node motion
 optimizer. Disable it options, since it is broken in the presence of gradient
 stacks.

Get rid of an unnecessary copy of the graph.

PiperOrigin-RevId: 195319766
---
 .../core/grappler/optimizers/loop_optimizer.cc     | 14 ++++++--------
 .../core/grappler/optimizers/loop_optimizer.h      |  2 +-
 2 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer.cc b/tensorflow/core/grappler/optimizers/loop_optimizer.cc
index f7994221bb30cc..5adc5b9227ff94 100644
--- a/tensorflow/core/grappler/optimizers/loop_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/loop_optimizer.cc
@@ -474,15 +474,13 @@ std::vector GetStackPushNodesToConvert(
   return nodes_to_convert;
 }
 
-Status RemoveStackOps(const GrapplerItem& item, GraphDef* optimized_graph) {
-  const std::unordered_set nodes_to_preserve = item.NodesToPreserve();
-  const GraphDef& graph = item.graph;
-  *optimized_graph = graph;
+Status RemoveStackOps(const std::unordered_set& nodes_to_preserve,
+                      GraphDef* optimized_graph) {
   NodeMap node_map(optimized_graph);
   SimpleGraphView graph_view;
-  TF_RETURN_IF_ERROR(graph_view.Initialize(graph));
-  for (int node_idx = 0; node_idx < graph.node_size(); ++node_idx) {
-    if (IsStackOp(graph.node(node_idx))) {
+  TF_RETURN_IF_ERROR(graph_view.Initialize(*optimized_graph));
+  for (int node_idx = 0; node_idx < optimized_graph->node_size(); ++node_idx) {
+    if (IsStackOp(optimized_graph->node(node_idx))) {
       for (int push_node_idx : GetStackPushNodesToConvert(
                graph_view, nodes_to_preserve, node_idx)) {
         // We found push nodes without corresponding pops. Convert them to
@@ -517,7 +515,7 @@ Status LoopOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
     TF_RETURN_IF_ERROR(linm_optimizer.Optimize());
   }
   if (options_.enable_stack_push_removal) {
-    TF_RETURN_IF_ERROR(RemoveStackOps(item, optimized_graph));
+    TF_RETURN_IF_ERROR(RemoveStackOps(item.NodesToPreserve(), optimized_graph));
   }
 
   return Status::OK();
diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer.h b/tensorflow/core/grappler/optimizers/loop_optimizer.h
index a422505d23c197..764506f7c1a4f3 100644
--- a/tensorflow/core/grappler/optimizers/loop_optimizer.h
+++ b/tensorflow/core/grappler/optimizers/loop_optimizer.h
@@ -52,7 +52,7 @@ class LoopOptimizer : public GraphOptimizer {
 
   // Granular control for loop optimizer stages.
   struct LoopOptimizerOptions {
-    bool enable_loop_invariant_node_motion = true;
+    bool enable_loop_invariant_node_motion = false;
     bool enable_stack_push_removal = true;
 
     static LoopOptimizerOptions Default(RewriterConfig::Toggle opt_level) {

From f25dd60858bc9ebe7b618aa966c2ddc1eef1f775 Mon Sep 17 00:00:00 2001
From: Dan Moldovan 
Date: Thu, 3 May 2018 15:39:46 -0700
Subject: [PATCH 0370/1691] Use tuple instead of list to reduce the chance of
 it being picked by the list conversions.

PiperOrigin-RevId: 195322522
---
 tensorflow/contrib/autograph/converters/asserts.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/autograph/converters/asserts.py b/tensorflow/contrib/autograph/converters/asserts.py
index 2d9e2c58e3afce..3b0db677ce5e41 100644
--- a/tensorflow/contrib/autograph/converters/asserts.py
+++ b/tensorflow/contrib/autograph/converters/asserts.py
@@ -33,7 +33,7 @@ def visit_Assert(self, node):
     # Note: The lone tf.Assert call will be wrapped with control_dependencies
     # by side_effect_guards.
     template = """
-      tf.Assert(test, [msg])
+      tf.Assert(test, (msg,))
     """
 
     if node.msg is None:

From 549d63acd35872061ae42c36c94df6dbef18ee2b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 15:42:23 -0700
Subject: [PATCH 0371/1691] Do not hoist nodes that modify frame info.

PiperOrigin-RevId: 195322927
---
 tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
index 29f49079c4eecc..adfae2e1a34eb8 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -1541,7 +1541,7 @@ class HoistCWiseUnaryChainsStage : public ArithmeticOptimizerStage {
                          const ChainLinkSet& ops) const {
     if (ops.empty()) return true;
     const NodeDef* op0 = ops.begin()->node;
-    if (!IsUnaryElementWise(*op0)) return false;
+    if (ModifiesFrameInfo(*op0) || !IsUnaryElementWise(*op0)) return false;
     for (const auto& link : ops) {
       const NodeDef* op = link.node;
       if (op->device() != root_node.device() || op->op() != op0->op() ||

From 200f4a2089cd4bef7832679cd121a2dbe85d6180 Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Thu, 3 May 2018 15:58:43 -0700
Subject: [PATCH 0372/1691] Fix oom_test so that it doesn't try to allocate a
 giant host buffer when run without --config=cuda.  Sadly the best way I could
 come up with is pretty hacky.

PiperOrigin-RevId: 195325149
---
 tensorflow/compiler/tests/oom_test.py | 29 ++++++++++++++++++++-------
 1 file changed, 22 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/tests/oom_test.py b/tensorflow/compiler/tests/oom_test.py
index 1434e965e3d7ea..d68d32057a3677 100644
--- a/tensorflow/compiler/tests/oom_test.py
+++ b/tensorflow/compiler/tests/oom_test.py
@@ -22,6 +22,8 @@
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
 from tensorflow.python.platform import googletest
 
 
@@ -42,20 +44,33 @@ def testOutputOutOfMemory(self):
     """
 
     def test_loop():
-      size = 2e8
+      size = int(2e8)
       while True:
         with self.test_session():
-          # Force the compiled code to not be constant by feeding in an addend.
-          p = array_ops.placeholder(dtypes.float32, shape=[])
+          # Force the compiled code to not be constant by feeding in a
+          # parameter.
+          p = array_ops.placeholder(dtypes.float32, shape=[2, 1, 1])
           with self.test_scope():
-            # Create a large R1 tensor.
-            c = array_ops.zeros([size, 1]) + p
+            # Create a computation that produces a large R1 tensor as an
+            # intermediate result.  Reduce it down so that if this file was
+            # compiled without --config=cuda, we don't force a D2H copy of a
+            # large tensor and potentially OOM the host.
+            #
+            # This is a bit tricky because XLA:GPU doesn't currently support RNG
+            # ops.  Here we rely on the fact that XLA doesn't do algebraic
+            # simplifications on conv(, ).
+            c = math_ops.reduce_sum(
+                nn_ops.convolution(
+                    array_ops.ones([1, size, 1]),
+                    p,
+                    padding='SAME',
+                    data_format='NWC'))
 
-            c.eval(feed_dict={p: 1.0})
+            c.eval(feed_dict={p: [[[1.0]], [[2.0]]]})
             size *= 2
 
     self.assertRaises(errors.ResourceExhaustedError, test_loop)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
   googletest.main()

From 04d5adbf848eba94e5c352cd0843a094b9fa0a4a Mon Sep 17 00:00:00 2001
From: Jeremy Lau 
Date: Thu, 3 May 2018 16:08:48 -0700
Subject: [PATCH 0373/1691] Fix bugs in LogicalBuffer::ToString and
 BufferValue::ToProto: these functions may be called before set_color(), but
 color() check fails when no color is set.

PiperOrigin-RevId: 195327063
---
 tensorflow/compiler/xla/service/buffer_value.cc   | 4 +++-
 tensorflow/compiler/xla/service/logical_buffer.cc | 6 +++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/xla/service/buffer_value.cc b/tensorflow/compiler/xla/service/buffer_value.cc
index df1a5ca435d0f3..2bc556a9e27013 100644
--- a/tensorflow/compiler/xla/service/buffer_value.cc
+++ b/tensorflow/compiler/xla/service/buffer_value.cc
@@ -59,7 +59,9 @@ LogicalBufferProto BufferValue::ToProto(const SizeFunction& size_fn) const {
   LogicalBufferProto::Location proto_location =
       ToLocationProto(*instruction(), index());
   proto.mutable_defined_at()->Swap(&proto_location);
-  proto.set_color(color().value());
+  if (has_color()) {
+    proto.set_color(color().value());
+  }
   return proto;
 }
 
diff --git a/tensorflow/compiler/xla/service/logical_buffer.cc b/tensorflow/compiler/xla/service/logical_buffer.cc
index 1b3de8ad173d16..c742d35a7bcafa 100644
--- a/tensorflow/compiler/xla/service/logical_buffer.cc
+++ b/tensorflow/compiler/xla/service/logical_buffer.cc
@@ -32,9 +32,13 @@ LogicalBuffer::LogicalBuffer(HloInstruction* instruction,
 LogicalBuffer::~LogicalBuffer() {}
 
 string LogicalBuffer::ToString() const {
+  string color_string;
+  if (has_color()) {
+    color_string = tensorflow::strings::StrCat(" @", color().value());
+  }
   return tensorflow::strings::StrCat(instruction_->name(), "[",
                                      tensorflow::str_util::Join(index_, ","),
-                                     "](#", id(), " @", color().value(), ")");
+                                     "](#", id(), color_string, ")");
 }
 
 }  // namespace xla

From c9a92808ab8c1e19fd0a6bba5b9814c8c2c42511 Mon Sep 17 00:00:00 2001
From: Russell Power 
Date: Thu, 3 May 2018 16:16:05 -0700
Subject: [PATCH 0374/1691] Adjust worker shutdown hooks for TPUs

PiperOrigin-RevId: 195328247
---
 .../contrib/tpu/python/tpu/session_support.py | 47 +++++++++++++++----
 .../contrib/tpu/python/tpu/tpu_estimator.py   | 23 ++++++++-
 2 files changed, 58 insertions(+), 12 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/session_support.py b/tensorflow/contrib/tpu/python/tpu/session_support.py
index 7c25f6693cd27d..3455e0b4a67406 100644
--- a/tensorflow/contrib/tpu/python/tpu/session_support.py
+++ b/tensorflow/contrib/tpu/python/tpu/session_support.py
@@ -126,12 +126,21 @@ def lame_workers(self):
     return WorkerHeartbeatManager(self._session, bad_devices, bad_ops,
                                   self._request_placeholder)
 
+  def __repr__(self):
+    return 'HeartbeatManager(%s)' % ','.join(self._devices)
+
   def shutdown(self, timeout_ms=10000):
     """Shutdown all workers after `shutdown_timeout_secs`."""
+    logging.info('Shutting down %s.', self)
     req = event_pb2.WorkerHeartbeatRequest(
         watchdog_config=event_pb2.WatchdogConfig(timeout_ms=timeout_ms))
     self.configure(req)
 
+    # Wait for workers to shutdown.  This isn't strictly required
+    # but it avoids triggering multiple checkpoints with the same lame worker.
+    logging.info('Waiting %dms for worker shutdown.', timeout_ms)
+    time.sleep(timeout_ms / 1000)
+
 
 def all_worker_devices(session):
   """Return a list of devices for each worker in the system."""
@@ -250,6 +259,7 @@ def after_create_session(self, training_session, coord):  # pylint: disable=unus
           ' in your model definition to allow checkpointing.')
 
     with self._graph.as_default():
+      logging.info('Installing graceful shutdown hook.')
       self._session = session_lib.Session(
           target=training_session.sess_str, graph=self._graph)
       self._workers = WorkerHeartbeatManager.from_devices(
@@ -296,16 +306,33 @@ def after_run(self, run_context, run_values):
         fn(run_context, self._workers, lame_workers)
 
 
-def restart_computation(run_context, all_workers, lame_workers):
-  del run_context, lame_workers
-  logging.info('Shutting down all workers.')
-  all_workers.shutdown()
+class RestartComputation(object):
+  """Restart the entire computation.
+
+  This hook shuts down all workers and returns control to the top-level by
+  throwing a CoordinatorShutdownException.
+  """
+
+  def __init__(self, timeout_ms=10000):
+    self.timeout_ms = timeout_ms
+
+  def __call__(self, run_context, all_workers, lame_workers):
+    del run_context, lame_workers
+    all_workers.shutdown(timeout_ms=self.timeout_ms)
+
+    logging.info('Terminating coordinator.')
+    raise CoordinatorShutdownException()
+
 
-  logging.info('Terminating coordinator.')
-  raise CoordinatorShutdownException()
+class ShutdownLameWorkers(object):
+  """Shutdown lamed workers.
+
+  Processing will continue normally (typically by waiting for the down
+  workers to be restarted).
+  """
 
+  def __init__(self, timeout_ms=10000):
+    self.timeout_in_ms = timeout_ms
 
-def shutdown_lame_workers(run_context, all_workers, lame_workers):
-  del run_context, all_workers
-  logging.info('Shutting down %s', lame_workers)
-  lame_workers.shutdown()
+  def __call__(self, run_context, all_workers, lame_workers):
+    lame_workers.shutdown(timeout_ms=self.timeout_in_ms)
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 534042b42c6ab9..a69bfa9a20bed7 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -2049,9 +2049,28 @@ def _model_fn(features, labels, mode, config, params):
           host_ops = host_call.create_tpu_hostcall()
           if host_ops is None:
             host_ops = []
+
           shutdown_hooks = []
-          if os.environ.get('TF_TPU_GRACEFUL_SHUTDOWN', '0') != '0':
-            shutdown_hooks.append(session_support.GracefulShutdownHook())
+          shutdown_mode = os.environ.get('TF_TPU_GRACEFUL_SHUTDOWN_MODE',
+                                         'shutdown_worker')
+          if shutdown_mode:
+            if shutdown_mode == 'shutdown_worker':
+              finalizer_hooks = [
+                  session_support.ShutdownLameWorkers(timeout_ms=1000),
+              ]
+            elif shutdown_mode == 'shutdown_computation':
+              finalizer_hooks = [
+                  session_support.RestartComputation(timeout_ms=1000),
+              ]
+            else:
+              raise ValueError('Unknown TF_TPU_GRACEFUL_SHUTDOWN_MODE "%s"' %
+                               shutdown_mode)
+
+            shutdown_hooks.append(session_support.GracefulShutdownHook(
+                checkpoint_prefix=self.model_dir + '/model.ckpt',
+                on_shutdown_hooks=finalizer_hooks
+            ))
+
           with ops.control_dependencies([loss]):
             global_step = array_ops.identity(training.get_global_step())
           hooks = input_hooks + shutdown_hooks

From 4a74a5058f7cb3ac096fa582941d9ab801ba6d65 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 16:34:11 -0700
Subject: [PATCH 0375/1691] Fix flaky test time-outs for dnn_test and rnn_test.

PiperOrigin-RevId: 195331183
---
 tensorflow/contrib/estimator/BUILD | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD
index 41a817673d8801..571e2e3a5df08e 100644
--- a/tensorflow/contrib/estimator/BUILD
+++ b/tensorflow/contrib/estimator/BUILD
@@ -77,6 +77,7 @@ py_test(
     tags = [
         "no_pip",
         "notsan",
+        "optonly",  # times out http://b/79220679
     ],
     deps = [
         ":dnn",
@@ -450,6 +451,7 @@ py_test(
         "no_pip",
         "noasan",  # times out
         "notsan",
+        "optonly",  # times out http://b/79220679
     ],
     deps = [
         ":head",

From 213a98d893105945540e0169faa124ac7e1200ba Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 17:03:03 -0700
Subject: [PATCH 0376/1691] [XLA] Redesign: deprecate ComputationBuilder.

PiperOrigin-RevId: 195335330
---
 tensorflow/compiler/xla/client/computation.h  |  2 +
 .../compiler/xla/client/computation_builder.h |  2 +
 tensorflow/compiler/xla/client/lib/BUILD      |  5 +-
 .../compiler/xla/client/lib/arithmetic.cc     | 90 +----------------
 .../compiler/xla/client/lib/arithmetic.h      | 55 +----------
 tensorflow/compiler/xla/client/lib/testing.cc | 16 ++-
 tensorflow/compiler/xla/client/lib/testing.h  |  1 -
 tensorflow/compiler/xla/service/BUILD         | 10 +-
 tensorflow/compiler/xla/service/cpu/BUILD     |  4 +-
 .../xla/service/cpu/sample_harness.cc         | 10 +-
 .../xla/service/hlo_cost_analysis_test.cc     | 73 ++++++--------
 .../xla/service/hlo_evaluator_test.cc         | 10 +-
 .../xla/service/hlo_tfgraph_builder_test.cc   |  1 -
 .../xla/service/transpose_folding_test.cc     | 10 +-
 .../zero_sized_hlo_elimination_test.cc        |  1 -
 tensorflow/compiler/xla/tests/BUILD           | 18 ----
 .../xla/tests/local_client_aot_test_helper.cc | 19 ++--
 .../xla/tests/set_return_value_test.cc        | 98 -------------------
 .../xla/tests/vector_ops_simple_test.cc       |  3 +-
 19 files changed, 85 insertions(+), 343 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/tests/set_return_value_test.cc

diff --git a/tensorflow/compiler/xla/client/computation.h b/tensorflow/compiler/xla/client/computation.h
index a53fc9e9cf3470..9a1bcde7638729 100644
--- a/tensorflow/compiler/xla/client/computation.h
+++ b/tensorflow/compiler/xla/client/computation.h
@@ -30,6 +30,8 @@ namespace xla {
 // Wraps a ComputationHandle protobuf with a lifetime. Computation is
 // movable and not copyable to capture the same kind of unique
 // ownership that std::unique_ptr represents.
+//
+// TODO(b/74197823): Deprecated. Use XlaComputation instead.
 class Computation {
  public:
   // Creates a null Computation.
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index 9431c2c459a564..ac1eb915cc52df 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -48,6 +48,8 @@ namespace xla {
 // deferred from being handled until Build() is called.
 //
 // Thread-compatible.
+//
+// TODO(b/74197823): Deprecated. Use XlaBuilder instead.
 class ComputationBuilder {
  public:
   // client: client in which to build the computation.
diff --git a/tensorflow/compiler/xla/client/lib/BUILD b/tensorflow/compiler/xla/client/lib/BUILD
index 59c4a53c05a454..d49d959a6c8112 100644
--- a/tensorflow/compiler/xla/client/lib/BUILD
+++ b/tensorflow/compiler/xla/client/lib/BUILD
@@ -22,8 +22,6 @@ cc_library(
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
         "//tensorflow/core:lib",
@@ -43,9 +41,8 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
+        "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
         "//tensorflow/compiler/xla/tests:test_utils",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.cc b/tensorflow/compiler/xla/client/lib/arithmetic.cc
index 63df449e0b3bdd..a1d34796ccfd86 100644
--- a/tensorflow/compiler/xla/client/lib/arithmetic.cc
+++ b/tensorflow/compiler/xla/client/lib/arithmetic.cc
@@ -17,7 +17,8 @@ limitations under the License.
 
 #include 
 
-#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -27,28 +28,6 @@ limitations under the License.
 namespace xla {
 namespace {
 
-using InstructionGenerator =
-    ComputationDataHandle (*)(ComputationBuilder*, const ComputationDataHandle&,
-                              const ComputationDataHandle&);
-
-Computation CreateScalarComputation(const string& name, PrimitiveType type,
-                                    ComputationBuilder* builder,
-                                    InstructionGenerator generator) {
-  std::unique_ptr b;
-  if (type == PRED) {
-    b = builder->CreateSubBuilder(name);
-  } else {
-    b = builder->CreateSubBuilder(
-        tensorflow::strings::StrCat(name, "_", PrimitiveType_Name(type)));
-  }
-
-  const Shape scalar = ShapeUtil::MakeShape(type, {});
-  auto lhs = b->Parameter(0, scalar, "lhs");
-  auto rhs = b->Parameter(1, scalar, "rhs");
-  generator(b.get(), lhs, rhs);
-  return b->BuildAndNoteError();
-}
-
 using XlaOpGenerator = XlaOp (*)(XlaBuilder*, const XlaOp&, const XlaOp&);
 
 XlaComputation CreateScalarComputation(const string& name, PrimitiveType type,
@@ -71,71 +50,6 @@ XlaComputation CreateScalarComputation(const string& name, PrimitiveType type,
 
 }  // namespace
 
-Computation CreateScalarAddComputation(PrimitiveType type,
-                                       ComputationBuilder* builder) {
-  return CreateScalarComputation(
-      "add", type, builder,
-      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
-         const ComputationDataHandle& rhs) { return b->Add(lhs, rhs); });
-}
-
-Computation CreateScalarMultiplyComputation(PrimitiveType type,
-                                            ComputationBuilder* builder) {
-  return CreateScalarComputation(
-      "mul", type, builder,
-      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
-         const ComputationDataHandle& rhs) { return b->Mul(lhs, rhs); });
-}
-
-Computation CreateScalarGeComputation(PrimitiveType type,
-                                      ComputationBuilder* builder) {
-  return CreateScalarComputation(
-      "ge", type, builder,
-      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
-         const ComputationDataHandle& rhs) { return b->Ge(lhs, rhs); });
-}
-
-Computation CreateScalarMaxComputation(PrimitiveType type,
-                                       ComputationBuilder* builder) {
-  return CreateScalarComputation(
-      "max", type, builder,
-      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
-         const ComputationDataHandle& rhs) { return b->Max(lhs, rhs); });
-}
-
-Computation CreateScalarMinComputation(PrimitiveType type,
-                                       ComputationBuilder* builder) {
-  return CreateScalarComputation(
-      "min", type, builder,
-      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
-         const ComputationDataHandle& rhs) { return b->Min(lhs, rhs); });
-}
-
-Computation CreateScalarAndComputation(ComputationBuilder* builder) {
-  return CreateScalarComputation(
-      "and", PRED, builder,
-      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
-         const ComputationDataHandle& rhs) { return b->And(lhs, rhs); });
-}
-
-Computation CreateScalarOrComputation(ComputationBuilder* builder) {
-  return CreateScalarComputation(
-      "or", PRED, builder,
-      [](ComputationBuilder* b, const ComputationDataHandle& lhs,
-         const ComputationDataHandle& rhs) { return b->Or(lhs, rhs); });
-}
-
-StatusOr Any(const ComputationDataHandle& predicates,
-                                    ComputationBuilder* builder) {
-  auto f = builder->ConstantR0(false);
-  Computation logical_or = CreateScalarOrComputation(builder);
-  TF_ASSIGN_OR_RETURN(std::unique_ptr predicates_shape,
-                      builder->GetShape(predicates));
-  std::vector all_dimensions(ShapeUtil::Rank(*predicates_shape));
-  std::iota(all_dimensions.begin(), all_dimensions.end(), 0);
-  return builder->Reduce(predicates, f, logical_or, all_dimensions);
-}
-
 XlaComputation CreateScalarAddComputation(PrimitiveType type,
                                           XlaBuilder* builder) {
   return CreateScalarComputation(
diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.h b/tensorflow/compiler/xla/client/lib/arithmetic.h
index f4d3fc801590fe..64b6b7d6335316 100644
--- a/tensorflow/compiler/xla/client/lib/arithmetic.h
+++ b/tensorflow/compiler/xla/client/lib/arithmetic.h
@@ -18,83 +18,38 @@ limitations under the License.
 
 #include 
 
-#include "tensorflow/compiler/xla/client/computation.h"
-#include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_computation.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 
 namespace xla {
 
-// Creates a scalar add computation and returns it.
-Computation CreateScalarAddComputation(PrimitiveType type,
-                                       ComputationBuilder* builder);
-
-// Creates a scalar multiply computation and returns it.
-Computation CreateScalarMultiplyComputation(PrimitiveType type,
-                                            ComputationBuilder* builder);
-
-// Creates a scalar ge computation and returns it.
-Computation CreateScalarGeComputation(PrimitiveType type,
-                                      ComputationBuilder* builder);
-
-// Creates a scalar max computation and returns it.
-Computation CreateScalarMaxComputation(PrimitiveType type,
-                                       ComputationBuilder* builder);
-
-// Creates a scalar min computation and returns it.
-Computation CreateScalarMinComputation(PrimitiveType type,
-                                       ComputationBuilder* builder);
-
-// Creates a scalar logical AND computation and returns it.
-Computation CreateScalarAndComputation(ComputationBuilder* builder);
-
-// Creates a scalar logical OR computation and returns it.
-Computation CreateScalarOrComputation(ComputationBuilder* builder);
-
-// Returns whether any predicate in "predicates" is set.
-//
-// Note: if predicates is zero-sized, Any() vacuously returns false.
-StatusOr Any(const ComputationDataHandle& predicates,
-                                    ComputationBuilder* builder);
-
-// TODO(b/74197823): This is a part of a NOT YET ready refactor.
-//
 // Creates a scalar add computation and returns it.
 XlaComputation CreateScalarAddComputation(PrimitiveType type,
                                           XlaBuilder* builder);
-// TODO(b/74197823): This is a part of a NOT YET ready refactor.
-//
+
 // Creates a scalar multiply computation and returns it.
 XlaComputation CreateScalarMultiplyComputation(PrimitiveType type,
                                                XlaBuilder* builder);
-// TODO(b/74197823): This is a part of a NOT YET ready refactor.
-//
+
 // Creates a scalar ge computation and returns it.
 XlaComputation CreateScalarGeComputation(PrimitiveType type,
                                          XlaBuilder* builder);
-// TODO(b/74197823): This is a part of a NOT YET ready refactor.
-//
+
 // Creates a scalar max computation and returns it.
 XlaComputation CreateScalarMaxComputation(PrimitiveType type,
                                           XlaBuilder* builder);
-// TODO(b/74197823): This is a part of a NOT YET ready refactor.
-//
+
 // Creates a scalar min computation and returns it.
 XlaComputation CreateScalarMinComputation(PrimitiveType type,
                                           XlaBuilder* builder);
-// TODO(b/74197823): This is a part of a NOT YET ready refactor.
-//
+
 // Creates a scalar logical AND computation and returns it.
 XlaComputation CreateScalarAndComputation(XlaBuilder* builder);
 
-// TODO(b/74197823): This is a part of a NOT YET ready refactor.
-//
 // Creates a scalar logical OR computation and returns it.
 XlaComputation CreateScalarOrComputation(XlaBuilder* builder);
 
-// TODO(b/74197823): This is a part of a NOT YET ready refactor.
-//
 // Returns whether any predicate in "predicates" is set.
 //
 // Note: if predicates is zero-sized, Any() vacuously returns false.
diff --git a/tensorflow/compiler/xla/client/lib/testing.cc b/tensorflow/compiler/xla/client/lib/testing.cc
index 311dc4bdd72cfd..9cd87f74735ff5 100644
--- a/tensorflow/compiler/xla/client/lib/testing.cc
+++ b/tensorflow/compiler/xla/client/lib/testing.cc
@@ -15,8 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/lib/testing.h"
 
-#include "tensorflow/compiler/xla/client/computation.h"
-#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
 #include "tensorflow/compiler/xla/execution_options_util.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -46,16 +45,14 @@ int64 DataSizeOfShape(const Shape& shape) {
   return total_size;
 }
 
-// Create a ComputationDataHandle for an op what generates fake data with the
-// given shape.
-ComputationDataHandle BuildFakeDataOpOnDevice(const Shape& shape,
-                                              ComputationBuilder* builder) {
+// Creates a XlaOp for an op what generates fake data with the given shape.
+XlaOp BuildFakeDataOpOnDevice(const Shape& shape, XlaBuilder* builder) {
   if (ShapeUtil::IsArray(shape)) {
     return builder->Broadcast(
         builder->ConstantLiteral(Literal::One(shape.element_type())),
         AsInt64Slice(shape.dimensions()));
   }
-  std::vector parts;
+  std::vector parts;
   for (const Shape& s : shape.tuple_shapes()) {
     parts.push_back(BuildFakeDataOpOnDevice(s, builder));
   }
@@ -64,11 +61,10 @@ ComputationDataHandle BuildFakeDataOpOnDevice(const Shape& shape,
 
 std::unique_ptr MakeFakeDataViaDeviceOrDie(const Shape& shape,
                                                        Client* client) {
-  ComputationBuilder b(
-      client,
+  XlaBuilder b(
       tensorflow::strings::StrCat("make_fake_", ShapeUtil::HumanString(shape)));
   BuildFakeDataOpOnDevice(shape, &b);
-  Computation computation = b.Build().ConsumeValueOrDie();
+  XlaComputation computation = b.Build().ConsumeValueOrDie();
 
   auto execution_options = CreateDefaultExecutionOptions();
   *execution_options.mutable_shape_with_output_layout() = shape;
diff --git a/tensorflow/compiler/xla/client/lib/testing.h b/tensorflow/compiler/xla/client/lib/testing.h
index 1dc2622972d5fd..9e06141b1f13d2 100644
--- a/tensorflow/compiler/xla/client/lib/testing.h
+++ b/tensorflow/compiler/xla/client/lib/testing.h
@@ -20,7 +20,6 @@ limitations under the License.
 #include 
 
 #include "tensorflow/compiler/xla/client/client.h"
-#include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_computation.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 0b8b22b44ca7d8..9c362d8cad4642 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -233,7 +233,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/service:hlo_element_type_converter",
         "//tensorflow/compiler/xla/tests:hlo_verified_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
@@ -1669,10 +1669,10 @@ tf_cc_test(
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla/client",
         "//tensorflow/compiler/xla/client:client_library",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
+        "//tensorflow/compiler/xla/client/xla_client:xla_builder",
+        "//tensorflow/compiler/xla/client/xla_client:xla_computation",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:lib",
@@ -2406,7 +2406,6 @@ tf_cc_test(
     srcs = ["hlo_tfgraph_builder_test.cc"],
     deps = [
         ":hlo_tfgraph_builder",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:protos_all_cc",
@@ -2475,7 +2474,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/service/gpu:ir_emission_utils",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
@@ -2512,6 +2511,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
+        "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index cb81e413a363c6..7e6d58c7fa5cca 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -365,10 +365,10 @@ tf_cc_binary(
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client",
         "//tensorflow/compiler/xla/client:client_library",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/client/xla_client:xla_builder",
+        "//tensorflow/compiler/xla/client/xla_client:xla_computation",
         "//tensorflow/core:lib",
     ],
 )
diff --git a/tensorflow/compiler/xla/service/cpu/sample_harness.cc b/tensorflow/compiler/xla/service/cpu/sample_harness.cc
index b3f4609d465efb..167aa4adda995a 100644
--- a/tensorflow/compiler/xla/service/cpu/sample_harness.cc
+++ b/tensorflow/compiler/xla/service/cpu/sample_harness.cc
@@ -19,10 +19,10 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array4d.h"
 #include "tensorflow/compiler/xla/client/client.h"
 #include "tensorflow/compiler/xla/client/client_library.h"
-#include "tensorflow/compiler/xla/client/computation.h"
-#include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -48,13 +48,13 @@ int main(int argc, char** argv) {
       client->TransferToServer(*param1_literal).ConsumeValueOrDie();
 
   // Build computation.
-  xla::ComputationBuilder builder(client, "");
+  xla::XlaBuilder builder("");
   auto p0 = builder.Parameter(0, param0_literal->shape(), "param0");
   auto p1 = builder.Parameter(1, param1_literal->shape(), "param1");
   auto add = builder.Add(p1, p0, {0});
 
-  xla::StatusOr computation_status = builder.Build();
-  xla::Computation computation = computation_status.ConsumeValueOrDie();
+  xla::StatusOr computation_status = builder.Build();
+  xla::XlaComputation computation = computation_status.ConsumeValueOrDie();
 
   // Execute and transfer result of computation.
   xla::ExecutionProfile profile;
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
index 81cc7c4bdc1e00..16fdda8a8b9ade 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc
@@ -20,16 +20,13 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/client/client.h"
 #include "tensorflow/compiler/xla/client/client_library.h"
-#include "tensorflow/compiler/xla/client/computation.h"
-#include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/padding.h"
-#include "tensorflow/compiler/xla/service/computation_tracker.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/local_service.h"
 #include "tensorflow/compiler/xla/service/service.h"
-#include "tensorflow/compiler/xla/service/user_computation.h"
-#include "tensorflow/compiler/xla/service/versioned_computation_handle.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/tests/hlo_test_base.h"
 #include "tensorflow/core/platform/logging.h"
@@ -58,11 +55,10 @@ class HloCostAnalysisTest : public ::testing::Test {
         // whitebox accesses to the user computation built from the client,
         // as shown in the BuildHloGraph functions below.
         service_(static_cast(ClientLibrary::GetXlaService(
-            static_cast(client_)->platform()))),
-        computation_tracker_(service_->computation_tracker()) {
+            static_cast(client_)->platform()))) {
     // Create a computation for a unary user function: x => exp(x + 0.5)
     {
-      ComputationBuilder builder(client_, "add_and_exp");
+      XlaBuilder builder("add_and_exp");
       auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x");
       auto half = builder.ConstantR0(0.5);
       builder.Exp(builder.Add(x, half));
@@ -73,7 +69,7 @@ class HloCostAnalysisTest : public ::testing::Test {
 
     // Create a computation for a binary user function: (x, y) => x + y
     {
-      ComputationBuilder builder(client_, "add");
+      XlaBuilder builder("add");
       auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x");
       auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y");
       builder.Add(x, y);
@@ -84,7 +80,7 @@ class HloCostAnalysisTest : public ::testing::Test {
 
     // Create a computation for a sigmoid function: x => 1 / (1 + exp(-x))
     {
-      ComputationBuilder builder(client_, "sigmoid");
+      XlaBuilder builder("sigmoid");
       auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x");
       auto one = builder.ConstantR0(1.0);
       builder.Div(one, builder.Add(one, builder.Exp(builder.Neg(x))));
@@ -95,7 +91,7 @@ class HloCostAnalysisTest : public ::testing::Test {
 
     // Create a computation for a binary max function: (x, y) => max (x, y)
     {
-      ComputationBuilder builder(client_, "max");
+      XlaBuilder builder("max");
       auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x");
       auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y");
       builder.Max(x, y);
@@ -106,7 +102,7 @@ class HloCostAnalysisTest : public ::testing::Test {
 
     // Create a computation for a binary GT function: (x, y) => x > y
     {
-      ComputationBuilder builder(client_, "gt");
+      XlaBuilder builder("gt");
       auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x");
       auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y");
       builder.Gt(x, y);
@@ -117,35 +113,30 @@ class HloCostAnalysisTest : public ::testing::Test {
   }
 
   // Build HLO graph from the given builder and return the HLO module.
-  std::unique_ptr BuildHloGraph(ComputationBuilder* builder) {
+  std::unique_ptr BuildHloGraph(XlaBuilder* builder) {
     auto computation_status = builder->Build();
     TF_CHECK_OK(computation_status.status());
     auto computation = computation_status.ConsumeValueOrDie();
-    auto user_computation_status =
-        computation_tracker_.Resolve(computation.handle());
-    TF_CHECK_OK(user_computation_status.status());
-    auto user_computation = user_computation_status.ConsumeValueOrDie();
-    VersionedComputationHandle versioned_handle =
-        user_computation->GetVersionedHandle();
-    return std::move(
-        computation_tracker_.BuildHloModule(versioned_handle, HloModuleConfig())
-            .ValueOrDie());
+    auto config = HloModule::CreateModuleConfigFromProto(computation.proto(),
+                                                         DebugOptions())
+                      .ConsumeValueOrDie();
+    return HloModule::CreateFromProto(computation.proto(), config)
+        .ConsumeValueOrDie();
   }
 
   Client* client_;
   Service* service_;
-  const ComputationTracker& computation_tracker_;
 
   // User computations used for higher order operations (e.g., Map, Reduce).
-  Computation add_;
-  Computation add_and_exp_;
-  Computation sigmoid_;
-  Computation max_;
-  Computation gt_;
+  XlaComputation add_;
+  XlaComputation add_and_exp_;
+  XlaComputation sigmoid_;
+  XlaComputation max_;
+  XlaComputation gt_;
 };
 
 TEST_F(HloCostAnalysisTest, MatrixMultiply) {
-  ComputationBuilder builder(client_, "matrix_multiply");
+  XlaBuilder builder("matrix_multiply");
   auto lhs = builder.Parameter(0, ShapeUtil::MakeShape(F32, {10, 5}), "lhs");
   auto rhs = builder.Parameter(1, ShapeUtil::MakeShape(F32, {5, 30}), "rhs");
   auto result = builder.Dot(lhs, rhs);
@@ -167,7 +158,7 @@ TEST_F(HloCostAnalysisTest, MatrixMultiply) {
 }
 
 TEST_F(HloCostAnalysisTest, Map) {
-  ComputationBuilder builder(client_, "map");
+  XlaBuilder builder("map");
   auto input = builder.Parameter(0, ShapeUtil::MakeShape(F32, {10}), "in");
   auto result = builder.Map({input}, add_and_exp_, {0});
 
@@ -184,7 +175,7 @@ TEST_F(HloCostAnalysisTest, Map) {
 }
 
 TEST_F(HloCostAnalysisTest, Convolution) {
-  ComputationBuilder builder(client_, "convolution");
+  XlaBuilder builder("convolution");
   auto input = builder.Parameter(
       0,
       ShapeUtil::MakeShape(F32, {/*p_dim=*/1, /*z_dim=*/1, /*y_dim=*/10,
@@ -213,7 +204,7 @@ TEST_F(HloCostAnalysisTest, Convolution) {
 }
 
 TEST_F(HloCostAnalysisTest, Reduce) {
-  ComputationBuilder builder(client_, "reduce");
+  XlaBuilder builder("reduce");
   auto input =
       builder.Parameter(0, ShapeUtil::MakeShape(F32, {10, 20}), "input");
   auto result =
@@ -231,7 +222,7 @@ TEST_F(HloCostAnalysisTest, Reduce) {
 }
 
 TEST_F(HloCostAnalysisTest, ReduceWindow) {
-  ComputationBuilder builder(client_, "reduce_window");
+  XlaBuilder builder("reduce_window");
   auto input =
       builder.Parameter(0, ShapeUtil::MakeShape(F32, {10, 20}), "input");
   auto result = builder.ReduceWindow(input, builder.ConstantR0(0), add_,
@@ -248,7 +239,7 @@ TEST_F(HloCostAnalysisTest, ReduceWindow) {
 }
 
 TEST_F(HloCostAnalysisTest, SelectAndScatter) {
-  ComputationBuilder builder(client_, "select_and_scatter");
+  XlaBuilder builder("select_and_scatter");
   auto operand =
       builder.Parameter(0, ShapeUtil::MakeShape(F32, {10, 20}), "input");
   auto source =
@@ -269,7 +260,7 @@ TEST_F(HloCostAnalysisTest, SelectAndScatter) {
 }
 
 TEST_F(HloCostAnalysisTest, Broadcast) {
-  ComputationBuilder b(client_, "broadcast");
+  XlaBuilder b("broadcast");
   b.Broadcast(b.ConstantR0(42), {10, 7});
   auto hlo_module = BuildHloGraph(&b);
   HloCostAnalysis analysis(ShapeSize);
@@ -280,7 +271,7 @@ TEST_F(HloCostAnalysisTest, Broadcast) {
 
 // Calculates the computation cost of a graph with more than one HLO node.
 TEST_F(HloCostAnalysisTest, FullyConnectedForward) {
-  ComputationBuilder builder(client_, "fully_connected_forward");
+  XlaBuilder builder("fully_connected_forward");
   auto input =
       builder.Parameter(0, ShapeUtil::MakeShape(F32, {10, 5}), "input");
   auto weight =
@@ -305,7 +296,7 @@ TEST_F(HloCostAnalysisTest, FullyConnectedForward) {
 TEST_F(HloCostAnalysisTest, MatmulAndConvolutionCanBeTheSameComputation) {
   HloCostAnalysis conv_analysis(ShapeSize);
   {
-    ComputationBuilder builder(client_, "conv_looking_matmul");
+    XlaBuilder builder("conv_looking_matmul");
     auto lhs = builder.Parameter(0, ShapeUtil::MakeShape(F32, {64, 64, 1, 1}),
                                  "input");
     auto rhs = builder.Parameter(1, ShapeUtil::MakeShape(F32, {64, 64, 1, 1}),
@@ -318,7 +309,7 @@ TEST_F(HloCostAnalysisTest, MatmulAndConvolutionCanBeTheSameComputation) {
 
   HloCostAnalysis matmul_analysis(ShapeSize);
   {
-    ComputationBuilder builder(client_, "matmul");
+    XlaBuilder builder("matmul");
     auto lhs =
         builder.Parameter(0, ShapeUtil::MakeShape(F32, {64, 64}), "input");
     auto rhs =
@@ -427,7 +418,7 @@ TEST_F(FusionCostAnalysis, NoLayout) {
 TEST_F(HloCostAnalysisTest, TupleCost) {
   HloCostAnalysis analysis(ShapeSize);
   {
-    ComputationBuilder builder(client_, "matmul");
+    XlaBuilder builder("matmul");
     auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {123}), "x");
     auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {42}), "y");
     auto tuple = builder.Tuple({x, y});
@@ -443,7 +434,7 @@ TEST_F(HloCostAnalysisTest, TupleCost) {
 }
 
 TEST_F(HloCostAnalysisTest, BaseDilatedConvolution) {
-  ComputationBuilder builder(client_, "BaseDilatedConvolution");
+  XlaBuilder builder("BaseDilatedConvolution");
   auto input = builder.Parameter(
       0,
       ShapeUtil::MakeShape(F32, {/*p_dim=*/1, /*z_dim=*/1, /*y_dim=*/10,
@@ -458,7 +449,7 @@ TEST_F(HloCostAnalysisTest, BaseDilatedConvolution) {
   auto result = builder.ConvGeneralDilated(
       input, kernel, /*window_strides=*/{1, 1}, /*padding=*/{{1, 1}, {1, 1}},
       /*lhs_dilation=*/{3, 5}, /*rhs_dilation=*/{7, 11},
-      ComputationBuilder::CreateDefaultConvDimensionNumbers(2));
+      XlaBuilder::CreateDefaultConvDimensionNumbers(2));
 
   // Run HLO cost analysis.
   auto hlo_module = BuildHloGraph(&builder);
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
index 230147abfec10d..cc16446778cbea 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
@@ -21,7 +21,7 @@ limitations under the License.
 #include 
 #include 
 
-#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/reference_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
@@ -827,7 +827,7 @@ TEST_P(HloEvaluatorTest, Simple4x4Conv2DWith2x2Kernel) {
   *window.add_dimensions() = dim;
 
   ConvolutionDimensionNumbers dnums =
-      ComputationBuilder::CreateDefaultConvDimensionNumbers(2);
+      XlaBuilder::CreateDefaultConvDimensionNumbers(2);
 
   const Shape& shape = ShapeUtil::MakeShape(F32, {1, 1, 4, 4});
   b.AddInstruction(HloInstruction::CreateConvolve(
@@ -1046,7 +1046,7 @@ TEST_P(HloEvaluatorTest, DilatedBaseConv2DWithHighPadding) {
   *window.add_dimensions() = dim;
 
   ConvolutionDimensionNumbers dnums =
-      ComputationBuilder::CreateDefaultConvDimensionNumbers(2);
+      XlaBuilder::CreateDefaultConvDimensionNumbers(2);
 
   const Shape& shape = ShapeUtil::MakeShape(F32, {1, 1, 7, 7});
   b.AddInstruction(HloInstruction::CreateConvolve(
@@ -1109,7 +1109,7 @@ TEST_P(HloEvaluatorTest, DilatedBaseConv2DWithLowAndHighPadding) {
   *window.add_dimensions() = dim;
 
   ConvolutionDimensionNumbers dnums =
-      ComputationBuilder::CreateDefaultConvDimensionNumbers(2);
+      XlaBuilder::CreateDefaultConvDimensionNumbers(2);
 
   const Shape& shape = ShapeUtil::MakeShape(F32, {1, 1, 8, 8});
   b.AddInstruction(HloInstruction::CreateConvolve(
@@ -1180,7 +1180,7 @@ TEST_P(HloEvaluatorTest,
   *window.add_dimensions() = dim;
 
   ConvolutionDimensionNumbers dnums =
-      ComputationBuilder::CreateDefaultConvDimensionNumbers(2);
+      XlaBuilder::CreateDefaultConvDimensionNumbers(2);
 
   const Shape& shape = ShapeUtil::MakeShape(F32, {1, 1, 9, 3});
   b.AddInstruction(HloInstruction::CreateConvolve(
diff --git a/tensorflow/compiler/xla/service/hlo_tfgraph_builder_test.cc b/tensorflow/compiler/xla/service/hlo_tfgraph_builder_test.cc
index f8d98f06785967..be156d765dc10d 100644
--- a/tensorflow/compiler/xla/service/hlo_tfgraph_builder_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_tfgraph_builder_test.cc
@@ -14,7 +14,6 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/hlo_tfgraph_builder.h"
-#include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/tests/hlo_test_base.h"
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
diff --git a/tensorflow/compiler/xla/service/transpose_folding_test.cc b/tensorflow/compiler/xla/service/transpose_folding_test.cc
index c7c41603459189..0319109f7fc54c 100644
--- a/tensorflow/compiler/xla/service/transpose_folding_test.cc
+++ b/tensorflow/compiler/xla/service/transpose_folding_test.cc
@@ -19,7 +19,7 @@ limitations under the License.
 #include 
 #include 
 
-#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
@@ -222,7 +222,7 @@ TEST_F(TransposeFoldingTest, FoldConvDimSwapTransposeRhs) {
   HloInstruction* transpose_y =
       builder.AddInstruction(HloInstruction::CreateTranspose(
           ShapeUtil::MakeShape(F32, {2, 3, 1, 1}), y, {1, 0, 2, 3}));
-  auto dnums = ComputationBuilder::CreateDefaultConvDimensionNumbers();
+  auto dnums = XlaBuilder::CreateDefaultConvDimensionNumbers();
   Window window;
   for (int i = 0; i < 2; ++i) {
     WindowDimension* dim = window.add_dimensions();
@@ -275,7 +275,7 @@ TEST_F(TransposeFoldingTest, FoldConvComplexTransposeRhs) {
   HloInstruction* transpose_y =
       builder.AddInstruction(HloInstruction::CreateTranspose(
           ShapeUtil::MakeShape(F32, {2, 3, 1, 1}), y, {1, 3, 0, 2}));
-  auto dnums = ComputationBuilder::CreateDefaultConvDimensionNumbers();
+  auto dnums = XlaBuilder::CreateDefaultConvDimensionNumbers();
   Window window;
   for (int i = 0; i < 2; ++i) {
     WindowDimension* dim = window.add_dimensions();
@@ -334,7 +334,7 @@ TEST_F(TransposeFoldingTest, FoldConvTransposeLhs) {
   HloInstruction* transpose_x =
       builder.AddInstruction(HloInstruction::CreateTranspose(
           ShapeUtil::MakeShape(F32, {2, 3, 1, 1}), x, {1, 0, 2, 3}));
-  auto dnums = ComputationBuilder::CreateDefaultConvDimensionNumbers();
+  auto dnums = XlaBuilder::CreateDefaultConvDimensionNumbers();
   Window window;
   for (int i = 0; i < 2; ++i) {
     WindowDimension* dim = window.add_dimensions();
@@ -398,7 +398,7 @@ TEST_F(TransposeFoldingTest, FoldConvComplexTransposeLhs) {
   HloInstruction* transpose_x =
       builder.AddInstruction(HloInstruction::CreateTranspose(
           ShapeUtil::MakeShape(F32, {2, 3, 1, 1}), x, {1, 0, 3, 2}));
-  auto dnums = ComputationBuilder::CreateDefaultConvDimensionNumbers();
+  auto dnums = XlaBuilder::CreateDefaultConvDimensionNumbers();
   Window window;
   for (int i = 0; i < 2; ++i) {
     WindowDimension* dim = window.add_dimensions();
diff --git a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc
index a4e67cc9d9b8ee..f5331280ee9f25 100644
--- a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc
+++ b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc
@@ -19,7 +19,6 @@ limitations under the License.
 #include 
 #include 
 
-#include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 54cf0543b89773..0571ff50554c5d 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -1933,24 +1933,6 @@ xla_test(
     ],
 )
 
-xla_test(
-    name = "set_return_value_test",
-    srcs = ["set_return_value_test.cc"],
-    deps = [
-        "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla/client:computation_builder",
-        "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/client/xla_client:xla_builder",
-        "//tensorflow/compiler/xla/client/xla_client:xla_computation",
-        "//tensorflow/compiler/xla/tests:client_library_test_base",
-        "//tensorflow/compiler/xla/tests:hlo_test_base",
-        "//tensorflow/compiler/xla/tests:literal_test_util",
-        "//tensorflow/compiler/xla/tests:xla_internal_test_main",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:test",
-    ],
-)
-
 xla_test(
     name = "reshape_motion_test",
     srcs = ["reshape_motion_test.cc"],
diff --git a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc
index 3704ddd8010bf7..a366afe8262e1f 100644
--- a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc
+++ b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc
@@ -21,7 +21,8 @@ limitations under the License.
 
 #include "llvm/ADT/Triple.h"
 #include "tensorflow/compiler/xla/client/client_library.h"
-#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_compiler.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
 #include "tensorflow/compiler/xla/types.h"
@@ -29,27 +30,31 @@ limitations under the License.
 #include "tensorflow/core/platform/init_main.h"
 #include "tensorflow/core/platform/logging.h"
 
+namespace {
+
 using xla::string;
 
-xla::Computation Doubler(xla::Client* client) {
-  xla::ComputationBuilder builder(client, "doubler");
+xla::XlaComputation Doubler() {
+  xla::XlaBuilder builder("doubler");
   auto r0f32 = xla::ShapeUtil::MakeShape(xla::F32, {});
   auto x = builder.Parameter(0, r0f32, "x");
   builder.Mul(x, builder.ConstantR0(2.0));
   return std::move(builder.Build().ValueOrDie());
 }
 
+}  // namespace
+
 int main(int argc, char** argv) {
   tensorflow::port::InitMain(argv[0], &argc, &argv);
 
   auto client = xla::ClientLibrary::GetOrCreateCompileOnlyClient().ValueOrDie();
 
-  xla::ComputationBuilder builder(client, "aot_test_helper");
+  xla::XlaBuilder builder("aot_test_helper");
   auto opaque_shape = xla::ShapeUtil::MakeOpaqueShape();
   auto opaque_param = builder.Parameter(0, opaque_shape, "x");
   auto r0f32 = xla::ShapeUtil::MakeShape(xla::F32, {});
   auto sum = builder.CustomCall("SumStructElements", {opaque_param}, r0f32);
-  builder.Call(Doubler(client), {sum});
+  builder.Call(Doubler(), {sum});
 
   if (argc != 2) {
     LOG(FATAL) << "local_client_aot_test_helper TARGET_CPU";
@@ -71,8 +76,8 @@ int main(int argc, char** argv) {
 
   llvm::Triple triple(xla::llvm_ir::AsStringRef(triple_string));
 
-  xla::Computation computation = builder.Build().ConsumeValueOrDie();
-  xla::CompileOnlyClient::AotComputationInstance instance{
+  xla::XlaComputation computation = builder.Build().ConsumeValueOrDie();
+  xla::CompileOnlyClient::AotXlaComputationInstance instance{
       &computation, /*argument_layouts=*/{&opaque_shape}, &r0f32};
 
   xla::cpu::CpuAotCompilationOptions options(
diff --git a/tensorflow/compiler/xla/tests/set_return_value_test.cc b/tensorflow/compiler/xla/tests/set_return_value_test.cc
deleted file mode 100644
index 29f79ec28a1ae6..00000000000000
--- a/tensorflow/compiler/xla/tests/set_return_value_test.cc
+++ /dev/null
@@ -1,98 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include 
-
-#include "tensorflow/compiler/xla/client/computation_builder.h"
-#include "tensorflow/compiler/xla/client/local_client.h"
-#include "tensorflow/compiler/xla/tests/client_library_test_base.h"
-#include "tensorflow/compiler/xla/tests/literal_test_util.h"
-#include "tensorflow/core/lib/core/status.h"
-#include "tensorflow/core/platform/test.h"
-
-namespace xla {
-namespace {
-
-class SetReturnValueTest : public ClientLibraryTestBase {};
-
-TEST_F(SetReturnValueTest, NoSetValue) {
-  ComputationBuilder builder(client_, "no_set_value");
-  auto alpha = builder.ConstantR0(1.0);
-  auto x = builder.ConstantR1(
-      {-1.0, 1.0, 2.0, -2.0, -3.0, 3.0, 4.0, -4.0, -5.0, 5.0});
-  auto ax = builder.Add(alpha, x);
-  auto aax = builder.Add(alpha, ax);
-
-  std::vector expected = {1.0, 3.0, 4.0,  0.0,  -1.0,
-                                 5.0, 6.0, -2.0, -3.0, 7.0};
-
-  ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(SetReturnValueTest, SetValue) {
-  ComputationBuilder builder(client_, "set_value");
-  auto alpha = builder.ConstantR0(1.0);
-  auto x = builder.ConstantR1(
-      {-1.0, 1.0, 2.0, -2.0, -3.0, 3.0, 4.0, -4.0, -5.0, 5.0});
-  auto ax = builder.Add(alpha, x);
-  auto aax = builder.Add(alpha, ax);
-  auto builder_status = builder.SetReturnValue(ax);
-  EXPECT_TRUE(builder_status.ok());
-
-  std::vector expected = {0.0, 2.0, 3.0,  -1.0, -2.0,
-                                 4.0, 5.0, -3.0, -4.0, 6.0};
-
-  ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(SetReturnValueTest, SetValueAndModify) {
-  ComputationBuilder builder(client_, "set_value_and_modify");
-  auto alpha = builder.ConstantR0(1.0);
-  auto x = builder.ConstantR1(
-      {-1.0, 1.0, 2.0, -2.0, -3.0, 3.0, 4.0, -4.0, -5.0, 5.0});
-  auto ax = builder.Add(alpha, x);
-  auto aax = builder.Add(alpha, ax);
-  auto builder_status = builder.SetReturnValue(ax);
-  EXPECT_TRUE(builder_status.ok());
-  auto aaax = builder.Add(alpha, aax);
-
-  std::vector expected = {0.0, 2.0, 3.0,  -1.0, -2.0,
-                                 4.0, 5.0, -3.0, -4.0, 6.0};
-
-  ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001));
-}
-
-TEST_F(SetReturnValueTest, SetValueMultipleTimesAndModify) {
-  ComputationBuilder builder(client_, "set_value_multiple_times_and_modify");
-  auto alpha = builder.ConstantR0(1.0);
-  auto x = builder.ConstantR1(
-      {-1.0, 1.0, 2.0, -2.0, -3.0, 3.0, 4.0, -4.0, -5.0, 5.0});
-  auto ax = builder.Add(alpha, x);
-  auto aax = builder.Add(alpha, ax);
-  auto builder_status = builder.SetReturnValue(aax);
-  EXPECT_TRUE(builder_status.ok());
-  auto aaax = builder.Add(alpha, aax);
-  builder_status = builder.SetReturnValue(ax);
-  EXPECT_TRUE(builder_status.ok());
-  auto aaaax = builder.Add(alpha, aaax);
-
-  std::vector expected = {0.0, 2.0, 3.0,  -1.0, -2.0,
-                                 4.0, 5.0, -3.0, -4.0, 6.0};
-
-  ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001));
-}
-
-}  // namespace
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc b/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc
index 3dded3f7157195..5cce7a2bf82c1a 100644
--- a/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc
@@ -18,7 +18,6 @@ limitations under the License.
 #include 
 
 #include "tensorflow/compiler/xla/array4d.h"
-#include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/lib/arithmetic.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
@@ -350,7 +349,7 @@ XLA_TEST_F(VecOpsSimpleTest, ClampTenValuesConstantNonzeroLower) {
 }
 
 XLA_TEST_F(VecOpsSimpleTest, ClampValuesConstantS64) {
-  ComputationBuilder builder(client_, TestName());
+  XlaBuilder builder(TestName());
   auto zero = builder.ConstantR0(0);
   auto one = builder.ConstantR0(10);
   auto x = builder.ConstantR1({-3, 3, 9, 13});

From fc7b593cda65f4a3a3de0cc733270f0864f820e2 Mon Sep 17 00:00:00 2001
From: Ruoxin Sang 
Date: Thu, 3 May 2018 17:21:26 -0700
Subject: [PATCH 0377/1691] Clear the stat cache of the target when renaming
 the file.

PiperOrigin-RevId: 195337886
---
 .../core/platform/cloud/gcs_file_system.cc    |  4 +-
 .../platform/cloud/gcs_file_system_test.cc    | 72 +++++++++++++++++++
 2 files changed, 74 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc
index 488f9cc75d4584..e44e89743485c6 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system.cc
@@ -1375,9 +1375,9 @@ Status GcsFileSystem::RenameObject(const string& src, const string& target) {
   request->SetResultBuffer(&output_buffer);
   TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(), " when renaming ", src,
                                   " to ", target);
-  // Flush the target from the block cache.  The source will be flushed in the
+  // Flush the target from the caches.  The source will be flushed in the
   // DeleteFile call below.
-  file_block_cache_->RemoveFile(target);
+  ClearFileCaches(target);
   Json::Value root;
   TF_RETURN_IF_ERROR(ParseJson(output_buffer, &root));
   bool done;
diff --git a/tensorflow/core/platform/cloud/gcs_file_system_test.cc b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
index c6392999543960..28be13869b6947 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system_test.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
@@ -1902,6 +1902,78 @@ TEST(GcsFileSystemTest, RenameFile_Object) {
   EXPECT_EQ("fedcba98", result);
 }
 
+TEST(GcsFileSystemTest, RenameFile_Object_FlushTargetStatCache) {
+  std::vector requests(
+      {// Stat the target file.
+       new FakeHttpRequest(
+           "Uri: https://www.googleapis.com/storage/v1/b/bucket/o/"
+           "path%2Fdst.txt?fields=size%2Cupdated\n"
+           "Auth Token: fake_token\n"
+           "Timeouts: 5 1 10\n",
+           strings::StrCat("{\"size\": \"1000\","
+                           "\"updated\": \"2016-04-29T23:15:24.896Z\"}")),
+       // IsDirectory is checking whether there are children objects.
+       new FakeHttpRequest(
+           "Uri: https://www.googleapis.com/storage/v1/b/bucket/o?"
+           "fields=items%2Fname%2CnextPageToken&prefix=path%2Fsrc.txt%2F"
+           "&maxResults=1\n"
+           "Auth Token: fake_token\n"
+           "Timeouts: 5 1 10\n",
+           "{}"),
+       // IsDirectory is checking if the path exists as an object.
+       new FakeHttpRequest(
+           "Uri: https://www.googleapis.com/storage/v1/b/bucket/o/"
+           "path%2Fsrc.txt?fields=size%2Cupdated\n"
+           "Auth Token: fake_token\n"
+           "Timeouts: 5 1 10\n",
+           strings::StrCat("{\"size\": \"1010\","
+                           "\"updated\": \"2016-04-29T23:15:24.896Z\"}")),
+       // Copying to the new location.
+       new FakeHttpRequest(
+           "Uri: https://www.googleapis.com/storage/v1/b/bucket/o/"
+           "path%2Fsrc.txt/rewriteTo/b/bucket/o/path%2Fdst.txt\n"
+           "Auth Token: fake_token\n"
+           "Post: yes\n"
+           "Timeouts: 5 1 10\n",
+           "{\"done\": true}"),
+       // Deleting the original file.
+       new FakeHttpRequest(
+           "Uri: https://www.googleapis.com/storage/v1/b/bucket/o/"
+           "path%2Fsrc.txt\n"
+           "Auth Token: fake_token\n"
+           "Timeouts: 5 1 10\n"
+           "Delete: yes\n",
+           ""),
+       new FakeHttpRequest(
+           "Uri: https://www.googleapis.com/storage/v1/b/bucket/o/"
+           "path%2Fdst.txt?fields=size%2Cupdated\n"
+           "Auth Token: fake_token\n"
+           "Timeouts: 5 1 10\n",
+           strings::StrCat("{\"size\": \"1010\","
+                           "\"updated\": \"2016-04-29T23:15:24.896Z\"}"))});
+  GcsFileSystem fs(
+      std::unique_ptr(new FakeAuthProvider),
+      std::unique_ptr(
+          new FakeHttpRequestFactory(&requests)),
+      0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+      3600 /* stat cache max age */, 0 /* stat cache max entries */,
+      0 /* matching paths cache max age */,
+      0 /* matching paths cache max entries */, 0 /* initial retry delay*/,
+      kTestTimeoutConfig, nullptr /* gcs additional header */);
+  // Do an initial stat of the destination file to load their contents into the
+  // stat cache.
+  FileStatistics stat_before_renaming;
+  TF_EXPECT_OK(fs.Stat("gs://bucket/path/dst.txt", &stat_before_renaming));
+  EXPECT_EQ(1000, stat_before_renaming.length);
+
+  TF_EXPECT_OK(
+      fs.RenameFile("gs://bucket/path/src.txt", "gs://bucket/path/dst.txt"));
+
+  FileStatistics stat_after_renaming;
+  TF_EXPECT_OK(fs.Stat("gs://bucket/path/dst.txt", &stat_after_renaming));
+  EXPECT_EQ(1010, stat_after_renaming.length);
+}
+
 /// Tests the scenario when deletion returns a failure, but actually succeeds.
 TEST(GcsFileSystemTest, RenameFile_Object_DeletionRetried) {
   std::vector requests(

From fa7b5a9d1ab654bbd466487e39a8b3f83c17f3f0 Mon Sep 17 00:00:00 2001
From: Chris Leary 
Date: Thu, 3 May 2018 18:08:34 -0700
Subject: [PATCH 0378/1691] [XLA] Make LocalShapedBuffer::FromLiteral fallible
 by passing StatusOr wrapper.

PiperOrigin-RevId: 195345724
---
 .../xla/python/local_computation_builder.cc      | 16 ++++++++--------
 .../xla/python/local_computation_builder.h       |  6 ++++--
 .../xla/python/local_computation_builder.i       | 13 +++++++++++++
 3 files changed, 25 insertions(+), 10 deletions(-)

diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc
index 7102f467373edc..044458164ff89c 100644
--- a/tensorflow/compiler/xla/python/local_computation_builder.cc
+++ b/tensorflow/compiler/xla/python/local_computation_builder.cc
@@ -104,25 +104,25 @@ static StatusOr ToBuffer(LocalClient* client,
 }
 
 /* static */
-LocalShapedBuffer* LocalShapedBuffer::FromLiteral(
+StatusOr LocalShapedBuffer::FromLiteral(
     const Literal& argument,
     const tensorflow::gtl::optional& shape_with_layout) {
   LocalClient* client = GetOrCreateLocalClient();
-  ScopedShapedBuffer buf = [&] {
+  StatusOr buf = [&] {
     if (shape_with_layout) {
       std::unique_ptr relaid =
           argument.Relayout(shape_with_layout.value());
-      return ToBuffer(client, /*device_ordinal=*/0, *relaid)
-          .ConsumeValueOrDie();
+      return ToBuffer(client, /*device_ordinal=*/0, *relaid);
     }
-    return ToBuffer(client, /*device_ordinal=*/0, argument).ConsumeValueOrDie();
+    return ToBuffer(client, /*device_ordinal=*/0, argument);
   }();
-  return new LocalShapedBuffer(std::move(buf));
+  TF_RETURN_IF_ERROR(buf.status());
+  return new LocalShapedBuffer(std::move(buf).ValueOrDie());
 }
 
-std::unique_ptr LocalShapedBuffer::ToLiteral() const {
+StatusOr> LocalShapedBuffer::ToLiteral() const {
   LocalClient* client = GetOrCreateLocalClient();
-  return client->ShapedBufferToLiteral(*shaped_buffer()).ConsumeValueOrDie();
+  return client->ShapedBufferToLiteral(*shaped_buffer());
 }
 
 CompiledLocalComputation::CompiledLocalComputation(
diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h
index e1048909ab29c2..5ec097846a59fd 100644
--- a/tensorflow/compiler/xla/python/local_computation_builder.h
+++ b/tensorflow/compiler/xla/python/local_computation_builder.h
@@ -59,12 +59,14 @@ StatusOr > TransferFromOutfeedLocalReplica(
 // client.
 class LocalShapedBuffer {
  public:
-  static LocalShapedBuffer* FromLiteral(
+  static StatusOr FromLiteral(
       const Literal& argument,
       const tensorflow::gtl::optional& shape_with_layout);
+
   LocalShapedBuffer(ScopedShapedBuffer shaped_buffer);
   const ScopedShapedBuffer* shaped_buffer() const;
-  std::unique_ptr ToLiteral() const;
+
+  StatusOr > ToLiteral() const;
 
  private:
   ScopedShapedBuffer shaped_buffer_;
diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i
index ac792e8189bda9..b8cce5a5f7105e 100644
--- a/tensorflow/compiler/xla/python/local_computation_builder.i
+++ b/tensorflow/compiler/xla/python/local_computation_builder.i
@@ -205,6 +205,19 @@ tensorflow::ImportNumpy();
   }
 }
 
+%typemap(out) StatusOr {
+  if ($1.ok()) {
+    auto* value = $1.ValueOrDie();
+    {
+      auto* $1 = value;
+      $typemap(out, xla::swig::LocalShapedBuffer*)
+    }
+  } else {
+    PyErr_SetString(PyExc_RuntimeError, $1.status().ToString().c_str());
+    SWIG_fail;
+  }
+}
+
 %typemap(out) StatusOr< std::unique_ptr > {
   if ($1.ok()) {
     std::unique_ptr value = $1.ConsumeValueOrDie();

From 0abbff6c0bdf0ee4690def786513298afc8b772a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 19:45:59 -0700
Subject: [PATCH 0379/1691] [XLA] Redesign: cleanup client_library_test_base.

PiperOrigin-RevId: 195357555
---
 .../xla/tests/client_library_test_base.cc     | 221 +---------------
 .../xla/tests/client_library_test_base.h      | 243 ++++++------------
 2 files changed, 92 insertions(+), 372 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc
index 22660c35dcaa0e..c09e7eaf2bb94d 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.cc
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc
@@ -94,27 +94,13 @@ string ClientLibraryTestBase::TestName() const {
   return ::testing::UnitTest::GetInstance()->current_test_info()->name();
 }
 
-template 
 StatusOr> ClientLibraryTestBase::Execute(
-    BuilderT* builder, tensorflow::gtl::ArraySlice arguments) {
+    XlaBuilder* builder, tensorflow::gtl::ArraySlice arguments) {
   // Build the computation, as a convenience.
   TF_ASSIGN_OR_RETURN(auto computation, builder->Build());
   return client_->Execute(computation, arguments, &execution_options_);
 }
 
-StatusOr> ClientLibraryTestBase::ExecuteAndTransfer(
-    const Computation& computation,
-    tensorflow::gtl::ArraySlice arguments,
-    const Shape* shape_with_output_layout) {
-  ExecutionOptions execution_options = execution_options_;
-  if (shape_with_output_layout != nullptr) {
-    *execution_options.mutable_shape_with_output_layout() =
-        *shape_with_output_layout;
-  }
-  return client_->ExecuteAndTransfer(computation, arguments,
-                                     &execution_options);
-}
-
 StatusOr> ClientLibraryTestBase::ExecuteAndTransfer(
     const XlaComputation& computation,
     tensorflow::gtl::ArraySlice arguments,
@@ -128,17 +114,6 @@ StatusOr> ClientLibraryTestBase::ExecuteAndTransfer(
                                      &execution_options);
 }
 
-template <>
-StatusOr> ClientLibraryTestBase::ExecuteAndTransfer(
-    ComputationBuilder* builder,
-    tensorflow::gtl::ArraySlice arguments,
-    const Shape* shape_with_output_layout) {
-  // Build the computation, as a convenience.
-  TF_ASSIGN_OR_RETURN(auto computation, builder->Build());
-  return ExecuteAndTransfer(computation, arguments, shape_with_output_layout);
-}
-
-template <>
 StatusOr> ClientLibraryTestBase::ExecuteAndTransfer(
     XlaBuilder* builder, tensorflow::gtl::ArraySlice arguments,
     const Shape* shape_with_output_layout) {
@@ -162,18 +137,6 @@ ClientLibraryTestBase::ExecuteAndTransferReference(
                                          &execution_options);
 }
 
-std::unique_ptr ClientLibraryTestBase::ExecuteOrDie(
-    ComputationBuilder* builder,
-    tensorflow::gtl::ArraySlice arguments) {
-  return Execute(builder, arguments).ConsumeValueOrDie();
-}
-
-std::unique_ptr ClientLibraryTestBase::ExecuteAndTransferOrDie(
-    ComputationBuilder* builder,
-    tensorflow::gtl::ArraySlice arguments) {
-  return ExecuteAndTransfer(builder, arguments).ConsumeValueOrDie();
-}
-
 string ClientLibraryTestBase::ExecuteToString(
     XlaBuilder* builder, tensorflow::gtl::ArraySlice arguments) {
   auto computation_status = builder->Build();
@@ -191,32 +154,6 @@ string ClientLibraryTestBase::ExecuteToString(
   }
 }
 
-string ClientLibraryTestBase::ExecuteToString(
-    ComputationBuilder* builder,
-    tensorflow::gtl::ArraySlice arguments) {
-  auto computation_status = builder->Build();
-  if (!computation_status.ok()) {
-    return computation_status.status().ToString();
-  }
-  auto computation = computation_status.ConsumeValueOrDie();
-
-  auto result =
-      client_->ExecuteAndTransfer(computation, arguments, &execution_options_);
-  if (!result.ok()) {
-    return result.status().ToString();
-  } else {
-    return result.ValueOrDie()->ToString();
-  }
-}
-
-void ClientLibraryTestBase::ComputeAndCompareR1(
-    ComputationBuilder* builder, const tensorflow::core::Bitmap& expected,
-    tensorflow::gtl::ArraySlice arguments) {
-  std::unique_ptr expected_literal = Literal::CreateR1(expected);
-  ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal,
-                                                  arguments);
-}
-
 void ClientLibraryTestBase::ComputeAndCompareR1(
     XlaBuilder* builder, const tensorflow::core::Bitmap& expected,
     tensorflow::gtl::ArraySlice arguments) {
@@ -225,18 +162,16 @@ void ClientLibraryTestBase::ComputeAndCompareR1(
                                                   arguments);
 }
 
-template 
 void ClientLibraryTestBase::ComputeAndCompareLiteral(
-    BuilderT* builder, const Literal& expected,
+    XlaBuilder* builder, const Literal& expected,
     tensorflow::gtl::ArraySlice arguments,
     const Shape* shape_with_layout) {
   EXPECT_IS_OK(ComputeAndCompareLiteralWithStatus(builder, expected, arguments,
                                                   shape_with_layout));
 }
 
-template 
 void ClientLibraryTestBase::ComputeAndCompareLiteral(
-    BuilderT* builder, const Literal& expected,
+    XlaBuilder* builder, const Literal& expected,
     tensorflow::gtl::ArraySlice arguments, ErrorSpec error,
     const Shape* shape_with_layout) {
   EXPECT_IS_OK(ComputeAndCompareLiteralWithStatus(builder, expected, arguments,
@@ -245,7 +180,7 @@ void ClientLibraryTestBase::ComputeAndCompareLiteral(
 
 tensorflow::Status
 ClientLibraryTestBase::ComputeAndCompareLiteralWithAllOutputLayouts(
-    const xla::Computation& computation, const Literal& expected,
+    const xla::XlaComputation& computation, const Literal& expected,
     tensorflow::gtl::ArraySlice arguments,
     const std::function& verify_output) {
@@ -271,7 +206,7 @@ ClientLibraryTestBase::ComputeAndCompareLiteralWithAllOutputLayouts(
 
 tensorflow::Status
 ClientLibraryTestBase::ComputeAndCompareLiteralWithAllInputLayouts(
-    const xla::Computation& computation, const Literal& expected,
+    const xla::XlaComputation& computation, const Literal& /*expected*/,
     tensorflow::gtl::ArraySlice arguments,
     const std::function& verify_output,
@@ -334,28 +269,8 @@ ClientLibraryTestBase::ComputeAndCompareLiteralWithAllInputLayouts(
   return choose(0);
 }
 
-tensorflow::Status
-ClientLibraryTestBase::ComputeAndCompareLiteralWithAllOutputLayouts(
-    const xla::XlaComputation& /*computation*/, const Literal& /*expected*/,
-    tensorflow::gtl::ArraySlice /*arguments*/,
-    const std::function& /*verify_output*/) {
-  return Unimplemented("not yet implemented for XlaComputation");
-}
-
-tensorflow::Status
-ClientLibraryTestBase::ComputeAndCompareLiteralWithAllInputLayouts(
-    const xla::XlaComputation& /*computation*/, const Literal& /*expected*/,
-    tensorflow::gtl::ArraySlice /*arguments*/,
-    const std::function& /*verify_output*/,
-    const Shape* /*output_with_layout*/) {
-  return Unimplemented("not yet implemented for XlaComputation");
-}
-
-template 
 tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
-    BuilderT* builder, const Literal& expected,
+    XlaBuilder* builder, const Literal& expected,
     tensorflow::gtl::ArraySlice arguments_passed_in,
     const Shape* shape_with_layout) {
   std::vector arguments(arguments_passed_in.begin(),
@@ -412,9 +327,8 @@ tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
   return tensorflow::Status::OK();
 }
 
-template 
 tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
-    BuilderT* builder, const Literal& expected,
+    XlaBuilder* builder, const Literal& expected,
     tensorflow::gtl::ArraySlice arguments_passed_in,
     ErrorSpec error, const Shape* shape_with_layout) {
   std::vector arguments(arguments_passed_in.begin(),
@@ -484,9 +398,8 @@ void ClientLibraryTestBase::ComputeAndCompareR1U8(
   EXPECT_EQ(expected, actual->GetR1U8AsString());
 }
 
-template 
 void ClientLibraryTestBase::ComputeAndCompareTuple(
-    BuilderT* builder, const Literal& expected,
+    XlaBuilder* builder, const Literal& expected,
     tensorflow::gtl::ArraySlice arguments) {
   auto actual_status = ExecuteAndTransfer(builder, arguments);
   EXPECT_IS_OK(actual_status.status());
@@ -497,9 +410,8 @@ void ClientLibraryTestBase::ComputeAndCompareTuple(
   LiteralTestUtil::ExpectEqual(expected, *actual);
 }
 
-template 
 void ClientLibraryTestBase::ComputeAndCompareTuple(
-    BuilderT* builder, const Literal& expected,
+    XlaBuilder* builder, const Literal& expected,
     tensorflow::gtl::ArraySlice arguments, ErrorSpec error) {
   auto actual_status = ExecuteAndTransfer(builder, arguments);
   EXPECT_IS_OK(actual_status.status());
@@ -510,60 +422,6 @@ void ClientLibraryTestBase::ComputeAndCompareTuple(
   LiteralTestUtil::ExpectNear(expected, *actual, error);
 }
 
-void ClientLibraryTestBase::ComputeAndCompare(
-    ComputationBuilder* builder, const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice arguments) {
-  auto status_or_data = ComputeValueAndReference(builder, operand, arguments);
-  EXPECT_IS_OK(status_or_data);
-  if (!status_or_data.ok()) {
-    return;
-  }
-  std::unique_ptr reference, result;
-  std::tie(reference, result) = status_or_data.ConsumeValueOrDie();
-  LiteralTestUtil::ExpectEqual(*reference, *result);
-}
-
-void ClientLibraryTestBase::ComputeAndCompare(
-    ComputationBuilder* builder, const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice arguments, ErrorSpec error) {
-  auto status_or_data = ComputeValueAndReference(builder, operand, arguments);
-  EXPECT_IS_OK(status_or_data);
-  if (!status_or_data.ok()) {
-    return;
-  }
-  std::unique_ptr reference, result;
-  std::tie(reference, result) = status_or_data.ConsumeValueOrDie();
-  LiteralTestUtil::ExpectNear(*reference, *result, error);
-}
-
-StatusOr, std::unique_ptr>>
-ClientLibraryTestBase::ComputeValueAndReference(
-    ComputationBuilder* builder, const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice arguments) {
-  // Transfer the arguments to the executor service. We put the unique_ptr's
-  // into a vector to keep the data alive on the service until the end of this
-  // function.
-  std::vector> argument_data;
-  for (const auto& arg : arguments) {
-    TF_ASSIGN_OR_RETURN(auto data, client_->TransferToServer(arg));
-    argument_data.push_back(std::move(data));
-  }
-
-  // Create raw pointers to the GlobalData for the rest of the call stack.
-  std::vector argument_data_ptr;
-  std::transform(
-      argument_data.begin(), argument_data.end(),
-      std::back_inserter(argument_data_ptr),
-      [](const std::unique_ptr& data) { return data.get(); });
-
-  TF_ASSIGN_OR_RETURN(
-      auto reference,
-      builder->ComputeConstant(operand, /*output_layout=*/nullptr, arguments));
-  TF_ASSIGN_OR_RETURN(auto result,
-                      ExecuteAndTransfer(builder, argument_data_ptr));
-  return std::make_pair(std::move(reference), std::move(result));
-}
-
 void ClientLibraryTestBase::ComputeAndCompare(
     XlaBuilder* builder, tensorflow::gtl::ArraySlice arguments) {
   auto status_or_data = ComputeValueAndReference(builder, arguments);
@@ -651,8 +509,8 @@ XlaComputation ClientLibraryTestBase::CreateScalarMax() {
   return computation_status.ConsumeValueOrDie();
 }
 
-Computation ClientLibraryTestBase::CreateScalarReluSensitivity() {
-  ComputationBuilder builder(client_, "relu_sensitivity");
+XlaComputation ClientLibraryTestBase::CreateScalarReluSensitivity() {
+  XlaBuilder builder("relu_sensitivity");
   auto shape = ShapeUtil::MakeShape(use_bfloat16_ ? BF16 : F32, {});
   auto activation = builder.Parameter(0, shape, "activation");
   auto backprop = builder.Parameter(1, shape, "backprop");
@@ -693,14 +551,6 @@ ClientLibraryTestBase::CreatePatternedMatrixWithZeroPadding(int rows, int cols,
   return array;
 }
 
-ComputationDataHandle ClientLibraryTestBase::AddParam(
-    const Literal& argument, ComputationBuilder* builder) {
-  ComputationDataHandle data_handle;
-  arguments_.push_back(CreateParameterAndTransferLiteral(
-      arguments_.size(), argument, "", builder, &data_handle));
-  return data_handle;
-}
-
 XlaOp ClientLibraryTestBase::AddParam(const Literal& argument,
                                       XlaBuilder* builder) {
   XlaOp data_handle;
@@ -709,59 +559,10 @@ XlaOp ClientLibraryTestBase::AddParam(const Literal& argument,
   return data_handle;
 }
 
-ComputationDataHandle ClientLibraryTestBase::CreateConstantFromLiteral(
-    const Literal& literal, ComputationBuilder* builder) {
-  return builder->ConstantLiteral(
-      use_bfloat16_ ? *LiteralTestUtil::ConvertF32ToBF16(literal) : literal);
-}
-
 XlaOp ClientLibraryTestBase::CreateConstantFromLiteral(const Literal& literal,
                                                        XlaBuilder* builder) {
   return builder->ConstantLiteral(
       use_bfloat16_ ? *LiteralTestUtil::ConvertF32ToBF16(literal) : literal);
 }
 
-template void ClientLibraryTestBase::ComputeAndCompareLiteral(
-    ComputationBuilder* builder, const Literal& expected,
-    tensorflow::gtl::ArraySlice arguments,
-    const Shape* shape_with_layout);
-
-template void ClientLibraryTestBase::ComputeAndCompareLiteral(
-    XlaBuilder* builder, const Literal& expected,
-    tensorflow::gtl::ArraySlice arguments,
-    const Shape* shape_with_layout);
-
-template void ClientLibraryTestBase::ComputeAndCompareLiteral(
-    ComputationBuilder* builder, const Literal& expected,
-    tensorflow::gtl::ArraySlice arguments, ErrorSpec error,
-    const Shape* shape_with_layout);
-
-template void ClientLibraryTestBase::ComputeAndCompareLiteral(
-    XlaBuilder* builder, const Literal& expected,
-    tensorflow::gtl::ArraySlice arguments, ErrorSpec error,
-    const Shape* shape_with_layout);
-
-template void ClientLibraryTestBase::ComputeAndCompareTuple(
-    ComputationBuilder* builder, const Literal& expected,
-    tensorflow::gtl::ArraySlice arguments);
-
-template void ClientLibraryTestBase::ComputeAndCompareTuple(
-    XlaBuilder* builder, const Literal& expected,
-    tensorflow::gtl::ArraySlice arguments);
-
-template void ClientLibraryTestBase::ComputeAndCompareTuple(
-    ComputationBuilder* builder, const Literal& expected,
-    tensorflow::gtl::ArraySlice arguments, ErrorSpec error);
-
-template void ClientLibraryTestBase::ComputeAndCompareTuple(
-    XlaBuilder* builder, const Literal& expected,
-    tensorflow::gtl::ArraySlice arguments, ErrorSpec error);
-
-template StatusOr> ClientLibraryTestBase::Execute(
-    ComputationBuilder* builder,
-    tensorflow::gtl::ArraySlice arguments);
-
-template StatusOr> ClientLibraryTestBase::Execute(
-    XlaBuilder* builder, tensorflow::gtl::ArraySlice arguments);
-
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h
index 32eea7c2f3a65d..e58979a3035dd5 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.h
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.h
@@ -25,10 +25,9 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/array4d.h"
 #include "tensorflow/compiler/xla/client/client_library.h"
-#include "tensorflow/compiler/xla/client/computation.h"
-#include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -91,21 +90,11 @@ class ClientLibraryTestBase : public ::testing::Test {
   // Convenience methods for building and running a computation with the member
   // execution options. Modify execution_options_ in your test if you want to
   // customize the options.
-  template 
   StatusOr> Execute(
-      BuilderT* builder, tensorflow::gtl::ArraySlice arguments);
+      XlaBuilder* builder, tensorflow::gtl::ArraySlice arguments);
 
-  // TODO(b/74197823): Remove the template type 'BuilderT' in all methods once
-  // the migration to XlaBuilder is complete.
-
-  template 
   StatusOr> ExecuteAndTransfer(
-      BuilderT* builder, tensorflow::gtl::ArraySlice arguments,
-      const Shape* shape_with_output_layout = nullptr);
-
-  StatusOr> ExecuteAndTransfer(
-      const Computation& computation,
-      tensorflow::gtl::ArraySlice arguments,
+      XlaBuilder* builder, tensorflow::gtl::ArraySlice arguments,
       const Shape* shape_with_output_layout = nullptr);
 
   StatusOr> ExecuteAndTransfer(
@@ -121,101 +110,90 @@ class ClientLibraryTestBase : public ::testing::Test {
       tensorflow::gtl::ArraySlice arguments,
       const Shape* shape_with_output_layout = nullptr);
 
-  // Convenience OrDie variants of above methods.
-  std::unique_ptr ExecuteOrDie(
-      ComputationBuilder* builder,
-      tensorflow::gtl::ArraySlice arguments);
-  std::unique_ptr ExecuteAndTransferOrDie(
-      ComputationBuilder* builder,
-      tensorflow::gtl::ArraySlice arguments);
-
   // Run a computation and return its value as a string. If an error
   // occurs, then instead return the error as a string.
   string ExecuteToString(XlaBuilder* builder,
                          tensorflow::gtl::ArraySlice arguments);
-  string ExecuteToString(ComputationBuilder* builder,
-                         tensorflow::gtl::ArraySlice arguments);
 
   // Convenience methods for building and running a computation, transferring
   // the result, and comparing it to the expected value(s). Methods are
   // templated on the native host type which maps to specific XLA types (See
-  // ComputationBuilder/XlaBuilder for details). For each rank, two forms are
+  // XlaBuilder for details). For each rank, two forms are
   // provided: one for floating point types with an ErrorSpec parameter, and one
   // for integral types without the ErrorSpec parameter.
-  template 
-  void ComputeAndCompareR0(BuilderT* builder, NativeT expected,
+  template 
+  void ComputeAndCompareR0(XlaBuilder* builder, NativeT expected,
                            tensorflow::gtl::ArraySlice arguments);
-  template 
-  void ComputeAndCompareR0(BuilderT* builder, NativeT expected,
+  template 
+  void ComputeAndCompareR0(XlaBuilder* builder, NativeT expected,
                            tensorflow::gtl::ArraySlice arguments,
                            ErrorSpec error);
 
-  template 
-  void ComputeAndCompareR1(BuilderT* builder,
+  template 
+  void ComputeAndCompareR1(XlaBuilder* builder,
                            tensorflow::gtl::ArraySlice expected,
                            tensorflow::gtl::ArraySlice arguments);
-  template 
-  void ComputeAndCompareR1(BuilderT* builder,
+  template 
+  void ComputeAndCompareR1(XlaBuilder* builder,
                            tensorflow::gtl::ArraySlice expected,
                            tensorflow::gtl::ArraySlice arguments,
                            ErrorSpec error);
 
   // As above, but uses a bitmap to hold the predicate vector to avoid
   // deficiencies of vector.
-  void ComputeAndCompareR1(ComputationBuilder* builder,
-                           const tensorflow::core::Bitmap& expected,
-                           tensorflow::gtl::ArraySlice arguments);
   void ComputeAndCompareR1(XlaBuilder* builder,
                            const tensorflow::core::Bitmap& expected,
                            tensorflow::gtl::ArraySlice arguments);
 
-  template 
-  void ComputeAndCompareR2(BuilderT* builder, const Array2D& expected,
+  template 
+  void ComputeAndCompareR2(XlaBuilder* builder,
+                           const Array2D& expected,
                            tensorflow::gtl::ArraySlice arguments);
-  template 
-  void ComputeAndCompareR2(BuilderT* builder, const Array2D& expected,
+  template 
+  void ComputeAndCompareR2(XlaBuilder* builder,
+                           const Array2D& expected,
                            tensorflow::gtl::ArraySlice arguments,
                            ErrorSpec error);
 
-  template 
-  void ComputeAndCompareR3(BuilderT* builder, const Array3D& expected,
+  template 
+  void ComputeAndCompareR3(XlaBuilder* builder,
+                           const Array3D& expected,
                            tensorflow::gtl::ArraySlice arguments);
-  template 
-  void ComputeAndCompareR3(BuilderT* builder, const Array3D& expected,
+  template 
+  void ComputeAndCompareR3(XlaBuilder* builder,
+                           const Array3D& expected,
                            tensorflow::gtl::ArraySlice arguments,
                            ErrorSpec error);
 
-  template 
-  void ComputeAndCompareR4(BuilderT* builder, const Array4D& expected,
+  template 
+  void ComputeAndCompareR4(XlaBuilder* builder,
+                           const Array4D& expected,
                            tensorflow::gtl::ArraySlice arguments);
-  template 
-  void ComputeAndCompareR4(BuilderT* builder, const Array4D& expected,
+  template 
+  void ComputeAndCompareR4(XlaBuilder* builder,
+                           const Array4D& expected,
                            tensorflow::gtl::ArraySlice arguments,
                            ErrorSpec error);
 
   // Build and run the computation and compare the result with the given
   // literal. shape_with_layout indicates the result layout to request when
   // calling Execute.
-  template 
   void ComputeAndCompareLiteral(
-      BuilderT* builder, const Literal& expected,
+      XlaBuilder* builder, const Literal& expected,
       tensorflow::gtl::ArraySlice arguments,
       const Shape* shape_with_layout = nullptr);
-  template 
   void ComputeAndCompareLiteral(
-      BuilderT* builder, const Literal& expected,
+      XlaBuilder* builder, const Literal& expected,
       tensorflow::gtl::ArraySlice arguments, ErrorSpec error,
       const Shape* shape_with_layout = nullptr);
 
   // ComputeAndCompare variant which returns an error status.
-  template 
   tensorflow::Status ComputeAndCompareLiteralWithStatus(
-      BuilderT* builder, const Literal& expected,
+      XlaBuilder* builder, const Literal& expected,
       tensorflow::gtl::ArraySlice arguments,
       const Shape* shape_with_layout = nullptr);
-  template 
   tensorflow::Status ComputeAndCompareLiteralWithStatus(
-      BuilderT* builder, const Literal& expected,
+      XlaBuilder* builder, const Literal& expected,
       tensorflow::gtl::ArraySlice arguments, ErrorSpec error,
       const Shape* shape_with_layout = nullptr);
 
@@ -227,25 +205,13 @@ class ClientLibraryTestBase : public ::testing::Test {
 
   // Convenience method for running a built computation, transferring the
   // result, and comparing it to the expected tuple literal.
-  template 
   void ComputeAndCompareTuple(
-      BuilderT* builder, const Literal& expected,
+      XlaBuilder* builder, const Literal& expected,
       tensorflow::gtl::ArraySlice arguments);
-  template 
   void ComputeAndCompareTuple(
-      BuilderT* builder, const Literal& expected,
+      XlaBuilder* builder, const Literal& expected,
       tensorflow::gtl::ArraySlice arguments, ErrorSpec error);
 
-  // Convenience method for running a built computation and comparing the result
-  // with the HloEvaluator.
-  void ComputeAndCompare(ComputationBuilder* builder,
-                         const ComputationDataHandle& operand,
-                         tensorflow::gtl::ArraySlice arguments);
-  void ComputeAndCompare(ComputationBuilder* builder,
-                         const ComputationDataHandle& operand,
-                         tensorflow::gtl::ArraySlice arguments,
-                         ErrorSpec error);
-
   // Convenience method for running a built computation and comparing the result
   // with the reference result.
   void ComputeAndCompare(XlaBuilder* builder,
@@ -257,7 +223,7 @@ class ClientLibraryTestBase : public ::testing::Test {
   // Create scalar operations for use in reductions.
   XlaComputation CreateScalarRelu();
   XlaComputation CreateScalarMax();
-  Computation CreateScalarReluSensitivity();
+  XlaComputation CreateScalarReluSensitivity();
 
   // Special case convenience functions for creating filled arrays.
 
@@ -297,34 +263,25 @@ class ClientLibraryTestBase : public ::testing::Test {
   // server, then stores into "data_handle" the global handle for that
   // parameter. When the use_bfloat16 flag is set but the literal has F32
   // elements, the literal will be converted to BF16 before being transferred.
-  template 
   std::unique_ptr CreateParameterAndTransferLiteral(
       int64 parameter_number, const Literal& literal, const string& name,
-      BuilderT* builder, HandleT* data_handle);
+      XlaBuilder* builder, XlaOp* data_handle);
 
   // As above, but the caller can specify the device that the literal is
   // transferred to. If device_handle is nullptr, the literal will be
   // transferred to the default device.
-  template 
   std::unique_ptr CreateParameterAndTransferLiteral(
       int64 parameter_number, const Literal& literal, const string& name,
-      const DeviceHandle* device_handle, BuilderT* builder,
-      HandleT* data_handle);
+      const DeviceHandle* device_handle, XlaBuilder* builder,
+      XlaOp* data_handle);
 
   // Creates a parameter instruction and sets the value that will be passed to
   // the computation as specified. This function must be used for all parameters
   // or none and no parameters must be passed when invoking the computation if
   // using this mechanism. If using this mechanism, then each parameter must be
   // set exactly once. The first added parameter gets index 0, then 1 and so on.
-  ComputationDataHandle AddParam(const Literal& argument,
-                                 ComputationBuilder* builder);
   XlaOp AddParam(const Literal& argument, XlaBuilder* builder);
 
-  template 
-  ComputationDataHandle AddParam(const Array& argument,
-                                 ComputationBuilder* builder) {
-    return AddParam(*Literal::CreateFromArray(argument), builder);
-  }
   template 
   XlaOp AddParam(const Array& argument, XlaBuilder* builder) {
     return AddParam(*Literal::CreateFromArray(argument), builder);
@@ -333,18 +290,11 @@ class ClientLibraryTestBase : public ::testing::Test {
   // Creates a constant instruction with the given literal. When the
   // use_bfloat16 flag is set but the literal has F32 elements, the elements
   // will be converted to BF16s.
-  ComputationDataHandle CreateConstantFromLiteral(const Literal& literal,
-                                                  ComputationBuilder* builder);
   XlaOp CreateConstantFromLiteral(const Literal& literal, XlaBuilder* builder);
 
   // Creates a constant instruction with the given array. When the use_bfloat16
   // flag is set but the array has float elements, the elements will be
   // converted to bfloat16s.
-  template 
-  ComputationDataHandle CreateConstantFromArray(const Array& array,
-                                                ComputationBuilder* builder) {
-    return CreateConstantFromLiteral(*Literal::CreateFromArray(array), builder);
-  }
 
   template 
   XlaOp CreateConstantFromArray(const Array& array,
@@ -353,13 +303,6 @@ class ClientLibraryTestBase : public ::testing::Test {
   }
 
   // Same as CreateConstantFromArray, but for scalars.
-  template 
-  ComputationDataHandle CreateConstantFromScalar(NativeT value,
-                                                 ComputationBuilder* builder) {
-    return CreateConstantFromLiteral(*Literal::CreateR0(value),
-                                     builder);
-  }
-
   template 
   XlaOp CreateConstantFromScalar(NativeT value, XlaBuilder* builder) {
     return CreateConstantFromLiteral(*Literal::CreateR0(value),
@@ -374,12 +317,12 @@ class ClientLibraryTestBase : public ::testing::Test {
   //
   // When the use_bfloat16 flag is set but NativeT is float, the data will be
   // converted to bfloat16.
-  template 
+  template 
   std::unique_ptr CreateR0Parameter(NativeT value,
                                                 int64 parameter_number,
                                                 const string& name,
-                                                BuilderT* builder,
-                                                HandleT* data_handle);
+                                                XlaBuilder* builder,
+                                                XlaOp* data_handle);
 
   // Creates a parameter instruction that wraps the given values and then stores
   // into "data_handle" the global handle for that parameter.
@@ -389,10 +332,10 @@ class ClientLibraryTestBase : public ::testing::Test {
   //
   // When the use_bfloat16 flag is set but NativeT is float, the data will be
   // converted to bfloat16.
-  template 
+  template 
   std::unique_ptr CreateR1Parameter(
       tensorflow::gtl::ArraySlice values, int64 parameter_number,
-      const string& name, BuilderT* builder, HandleT* data_handle);
+      const string& name, XlaBuilder* builder, XlaOp* data_handle);
 
   // Creates a parameter instruction that wraps the given constant array
   // "array_2d" and then stores to "data_handle" the global handle for that
@@ -403,10 +346,10 @@ class ClientLibraryTestBase : public ::testing::Test {
   //
   // When the use_bfloat16 flag is set but NativeT is float, the data will be
   // converted to bfloat16.
-  template 
+  template 
   std::unique_ptr CreateR2Parameter(
       const Array2D& array_2d, int64 parameter_number,
-      const string& name, BuilderT* builder, HandleT* data_handle);
+      const string& name, XlaBuilder* builder, XlaOp* data_handle);
 
   // Creates a parameter instruction that wraps the given constant array
   // "array_3d" and then stores to "data_handle" the global handle for that
@@ -417,10 +360,10 @@ class ClientLibraryTestBase : public ::testing::Test {
   //
   // When the use_bfloat16 flag is set but NativeT is float, the data will be
   // converted to bfloat16.
-  template 
+  template 
   std::unique_ptr CreateR3Parameter(
       const Array3D& array_3d, int64 parameter_number,
-      const string& name, BuilderT* builder, HandleT* data_handle);
+      const string& name, XlaBuilder* builder, XlaOp* data_handle);
 
   // Getter and setter for the use_bfloat16 flag, which indicates whether to run
   // tests with all float-type input/output converted to bfloat16.
@@ -435,21 +378,6 @@ class ClientLibraryTestBase : public ::testing::Test {
   ExecutionOptions execution_options_;
 
  private:
-  // Build and run the computation with all permutations of output layouts.
-  tensorflow::Status ComputeAndCompareLiteralWithAllOutputLayouts(
-      const xla::Computation& computation, const Literal& expected,
-      tensorflow::gtl::ArraySlice arguments,
-      const std::function& verify_output);
-  // Build and run the computation with all permutations of layouts of all input
-  // arguments.
-  tensorflow::Status ComputeAndCompareLiteralWithAllInputLayouts(
-      const xla::Computation& computation, const Literal& expected,
-      tensorflow::gtl::ArraySlice arguments,
-      const std::function& verify_output,
-      const Shape* output_with_layout = nullptr);
-
   tensorflow::Status ComputeAndCompareLiteralWithAllOutputLayouts(
       const xla::XlaComputation& computation, const Literal& expected,
       tensorflow::gtl::ArraySlice arguments,
@@ -462,13 +390,6 @@ class ClientLibraryTestBase : public ::testing::Test {
                                const string& error_message)>& verify_output,
       const Shape* output_with_layout = nullptr);
 
-  // Executes the computation and calculates the expected reference value using
-  // the HloEvaluator. Returns two literals in the order of (expected, actual).
-  StatusOr, std::unique_ptr>>
-  ComputeValueAndReference(ComputationBuilder* builder,
-                           const ComputationDataHandle& operand,
-                           tensorflow::gtl::ArraySlice arguments);
-
   // Executes the computation and calculates the expected reference value using
   // the reference client. Returns two literals in the order of (expected,
   // actual).
@@ -484,9 +405,9 @@ class ClientLibraryTestBase : public ::testing::Test {
   std::vector> arguments_;
 };
 
-template 
+template 
 void ClientLibraryTestBase::ComputeAndCompareR0(
-    BuilderT* builder, NativeT expected,
+    XlaBuilder* builder, NativeT expected,
     tensorflow::gtl::ArraySlice arguments) {
   std::unique_ptr expected_literal =
       Literal::CreateR0(expected);
@@ -494,9 +415,9 @@ void ClientLibraryTestBase::ComputeAndCompareR0(
                                                   arguments);
 }
 
-template 
+template 
 void ClientLibraryTestBase::ComputeAndCompareR0(
-    BuilderT* builder, NativeT expected,
+    XlaBuilder* builder, NativeT expected,
     tensorflow::gtl::ArraySlice arguments, ErrorSpec error) {
   static_assert(std::is_same::value ||
                     std::is_same::value ||
@@ -510,9 +431,9 @@ void ClientLibraryTestBase::ComputeAndCompareR0(
                                                   arguments, error);
 }
 
-template 
+template 
 void ClientLibraryTestBase::ComputeAndCompareR1(
-    BuilderT* builder, tensorflow::gtl::ArraySlice expected,
+    XlaBuilder* builder, tensorflow::gtl::ArraySlice expected,
     tensorflow::gtl::ArraySlice arguments) {
   std::unique_ptr expected_literal =
       Literal::CreateR1(expected);
@@ -520,9 +441,9 @@ void ClientLibraryTestBase::ComputeAndCompareR1(
                                                   arguments);
 }
 
-template 
+template 
 void ClientLibraryTestBase::ComputeAndCompareR1(
-    BuilderT* builder, tensorflow::gtl::ArraySlice expected,
+    XlaBuilder* builder, tensorflow::gtl::ArraySlice expected,
     tensorflow::gtl::ArraySlice arguments, ErrorSpec error) {
   static_assert(std::is_same::value ||
                     std::is_same::value ||
@@ -536,9 +457,9 @@ void ClientLibraryTestBase::ComputeAndCompareR1(
                                                   arguments, error);
 }
 
-template 
+template 
 void ClientLibraryTestBase::ComputeAndCompareR2(
-    BuilderT* builder, const Array2D& expected,
+    XlaBuilder* builder, const Array2D& expected,
     tensorflow::gtl::ArraySlice arguments) {
   std::unique_ptr expected_literal =
       Literal::CreateR2FromArray2D(expected);
@@ -546,9 +467,9 @@ void ClientLibraryTestBase::ComputeAndCompareR2(
                                                   arguments);
 }
 
-template 
+template 
 void ClientLibraryTestBase::ComputeAndCompareR2(
-    BuilderT* builder, const Array2D& expected,
+    XlaBuilder* builder, const Array2D& expected,
     tensorflow::gtl::ArraySlice arguments, ErrorSpec error) {
   static_assert(std::is_same::value ||
                     std::is_same::value ||
@@ -562,9 +483,9 @@ void ClientLibraryTestBase::ComputeAndCompareR2(
                                                   arguments, error);
 }
 
-template 
+template 
 void ClientLibraryTestBase::ComputeAndCompareR3(
-    BuilderT* builder, const Array3D& expected,
+    XlaBuilder* builder, const Array3D& expected,
     tensorflow::gtl::ArraySlice arguments) {
   std::unique_ptr expected_literal =
       Literal::CreateR3FromArray3D(expected);
@@ -572,9 +493,9 @@ void ClientLibraryTestBase::ComputeAndCompareR3(
                                                   arguments);
 }
 
-template 
+template 
 void ClientLibraryTestBase::ComputeAndCompareR3(
-    BuilderT* builder, const Array3D& expected,
+    XlaBuilder* builder, const Array3D& expected,
     tensorflow::gtl::ArraySlice arguments, ErrorSpec error) {
   static_assert(std::is_same::value ||
                     std::is_same::value ||
@@ -588,9 +509,9 @@ void ClientLibraryTestBase::ComputeAndCompareR3(
                                                   arguments, error);
 }
 
-template 
+template 
 void ClientLibraryTestBase::ComputeAndCompareR4(
-    BuilderT* builder, const Array4D& expected,
+    XlaBuilder* builder, const Array4D& expected,
     tensorflow::gtl::ArraySlice arguments) {
   std::unique_ptr expected_literal =
       Literal::CreateR4FromArray4D(expected);
@@ -598,9 +519,9 @@ void ClientLibraryTestBase::ComputeAndCompareR4(
                                                   arguments);
 }
 
-template 
+template 
 void ClientLibraryTestBase::ComputeAndCompareR4(
-    BuilderT* builder, const Array4D& expected,
+    XlaBuilder* builder, const Array4D& expected,
     tensorflow::gtl::ArraySlice arguments, ErrorSpec error) {
   static_assert(std::is_same::value ||
                     std::is_same::value ||
@@ -614,10 +535,10 @@ void ClientLibraryTestBase::ComputeAndCompareR4(
                                                   arguments, error);
 }
 
-template 
+template 
 std::unique_ptr ClientLibraryTestBase::CreateR0Parameter(
     NativeT value, int64 parameter_number, const string& name,
-    BuilderT* builder, HandleT* data_handle) {
+    XlaBuilder* builder, XlaOp* data_handle) {
   std::unique_ptr literal = Literal::CreateR0(value);
   if (use_bfloat16_ && literal->shape().element_type() == F32) {
     literal = LiteralTestUtil::ConvertF32ToBF16(*literal);
@@ -628,10 +549,10 @@ std::unique_ptr ClientLibraryTestBase::CreateR0Parameter(
   return data;
 }
 
-template 
+template 
 std::unique_ptr ClientLibraryTestBase::CreateR1Parameter(
     tensorflow::gtl::ArraySlice values, int64 parameter_number,
-    const string& name, BuilderT* builder, HandleT* data_handle) {
+    const string& name, XlaBuilder* builder, XlaOp* data_handle) {
   std::unique_ptr literal = Literal::CreateR1(values);
   if (use_bfloat16_ && literal->shape().element_type() == F32) {
     literal = LiteralTestUtil::ConvertF32ToBF16(*literal);
@@ -642,10 +563,10 @@ std::unique_ptr ClientLibraryTestBase::CreateR1Parameter(
   return data;
 }
 
-template 
+template 
 std::unique_ptr ClientLibraryTestBase::CreateR2Parameter(
     const Array2D& array_2d, int64 parameter_number,
-    const string& name, BuilderT* builder, HandleT* data_handle) {
+    const string& name, XlaBuilder* builder, XlaOp* data_handle) {
   std::unique_ptr literal = Literal::CreateR2FromArray2D(array_2d);
   if (use_bfloat16_ && literal->shape().element_type() == F32) {
     literal = LiteralTestUtil::ConvertF32ToBF16(*literal);
@@ -656,10 +577,10 @@ std::unique_ptr ClientLibraryTestBase::CreateR2Parameter(
   return data;
 }
 
-template 
+template 
 std::unique_ptr ClientLibraryTestBase::CreateR3Parameter(
     const Array3D& array_3d, int64 parameter_number,
-    const string& name, BuilderT* builder, HandleT* data_handle) {
+    const string& name, XlaBuilder* builder, XlaOp* data_handle) {
   std::unique_ptr literal = Literal::CreateR3FromArray3D(array_3d);
   if (use_bfloat16_ && literal->shape().element_type() == F32) {
     literal = LiteralTestUtil::ConvertF32ToBF16(*literal);
@@ -695,23 +616,21 @@ std::unique_ptr> ClientLibraryTestBase::CreatePseudorandomR2(
   return result;
 }
 
-template 
 std::unique_ptr
 ClientLibraryTestBase::CreateParameterAndTransferLiteral(int64 parameter_number,
                                                          const Literal& literal,
                                                          const string& name,
-                                                         BuilderT* builder,
-                                                         HandleT* data_handle) {
+                                                         XlaBuilder* builder,
+                                                         XlaOp* data_handle) {
   return CreateParameterAndTransferLiteral(parameter_number, literal, name,
                                            nullptr, builder, data_handle);
 }
 
-template 
 std::unique_ptr
 ClientLibraryTestBase::CreateParameterAndTransferLiteral(
     int64 parameter_number, const Literal& literal, const string& name,
-    const DeviceHandle* device_handle, BuilderT* builder,
-    HandleT* data_handle) {
+    const DeviceHandle* device_handle, XlaBuilder* builder,
+    XlaOp* data_handle) {
   const Literal* param_literal = &literal;
   std::unique_ptr converted_literal;
   if (use_bfloat16_) {

From 8ec11ae8eb7b97caced73ed3971209236e2aef5c Mon Sep 17 00:00:00 2001
From: Yuefeng Zhou 
Date: Thu, 3 May 2018 22:01:39 -0700
Subject: [PATCH 0380/1691] Add the MultiWorkerMirroredStrategy

PiperOrigin-RevId: 195368876
---
 tensorflow/contrib/distribute/python/BUILD    |  13 ++
 .../distribute/python/mirrored_strategy.py    |   1 +
 .../python/multi_worker_strategy.py           | 141 ++++++++++++++++++
 .../python/multi_worker_strategy_test.py      |  64 ++++++++
 .../distribute/python/one_device_strategy.py  |   1 +
 tensorflow/python/training/distribute.py      |  20 ++-
 6 files changed, 238 insertions(+), 2 deletions(-)
 create mode 100644 tensorflow/contrib/distribute/python/multi_worker_strategy.py
 create mode 100644 tensorflow/contrib/distribute/python/multi_worker_strategy_test.py

diff --git a/tensorflow/contrib/distribute/python/BUILD b/tensorflow/contrib/distribute/python/BUILD
index aaafc184bf3b15..8dfcaf6032e160 100644
--- a/tensorflow/contrib/distribute/python/BUILD
+++ b/tensorflow/contrib/distribute/python/BUILD
@@ -86,6 +86,19 @@ py_library(
     ],
 )
 
+py_library(
+    name = "multi_worker_strategy",
+    srcs = ["multi_worker_strategy.py"],
+    visibility = ["//tensorflow:internal"],
+    deps = [
+        ":mirrored_strategy",
+        ":values",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:training",
+        "//tensorflow/python:util",
+    ],
+)
+
 py_library(
     name = "one_device_strategy",
     srcs = ["one_device_strategy.py"],
diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py
index 2e57b025837e38..8237b23dbbdb10 100644
--- a/tensorflow/contrib/distribute/python/mirrored_strategy.py
+++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py
@@ -80,6 +80,7 @@ def __init__(self,
         dict((d, i) for i, d in enumerate(devices)))
     self._cross_tower_ops = cross_tower_ops
     self._prefetch_on_device = prefetch_on_device
+    # TODO(yuefengz): consider setting the default device.
 
   def _create_variable(self, next_creator, *args, **kwargs):
     """Create a mirrored variable. See `DistributionStrategy.scope`."""
diff --git a/tensorflow/contrib/distribute/python/multi_worker_strategy.py b/tensorflow/contrib/distribute/python/multi_worker_strategy.py
new file mode 100644
index 00000000000000..a552b370ebf359
--- /dev/null
+++ b/tensorflow/contrib/distribute/python/multi_worker_strategy.py
@@ -0,0 +1,141 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Classes implementing a mirrored DistributionStrategy for multiple workers."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from functools import partial
+
+from tensorflow.contrib.distribute.python import values
+from tensorflow.contrib.distribute.python.mirrored_strategy import MirroredStrategy
+from tensorflow.core.protobuf import cluster_pb2
+from tensorflow.python.training import device_util
+from tensorflow.python.training import server_lib
+from tensorflow.python.util import nest
+
+
+# TODO(yuefengz): support between-graph replication.
+# TODO(yuefengz): merge this class into its base class.
+# TODO(yuefengz): in some cases, we probably want to use configure method to
+# configure this class.
+# TODO(yuefengz): MirroredStrategy.worker_devices may be confusing after the
+# class is introduced.
+class MultiWorkerMirroredStrategy(MirroredStrategy):
+  """Mirrored strategy that works on multiple workers with in-graph replication.
+
+  There are several important concepts for distributed TensorFlow, e.g.
+  `client`, `job`, 'task', `cluster`, `in-graph replication` and
+  'synchronous training' and they have already been defined in the
+  [TensorFlow's documentation](https://www.tensorflow.org/deploy/distributed).
+  The distribution strategy inherits these concepts as well and in addition to
+  that we also clarify several more concepts:
+    * **In-graph replication**: the `client` creates a single `tf.Graph` that
+    specifies tasks for devices on all workers. The `client` then creates a
+    client session which will talk to the `master` service of a `worker`. Then
+    the `master` will parition the graph and distribute the work to all
+    participating workers.
+    * **Worker**: A `worker` is a TensorFlow `task` that usually maps to one
+    physical machine. We will have multiple `worker`s with different `task`
+    index. They all do similar things except for one worker checkpointing model
+    variables, writing summaries, etc. in addition to its ordinary work.
+
+  This class maps one tower to one device on a worker. It mirrors all model
+  variables on all towers. For example, if you have two `worker`s and each
+  `worker` has 4 GPUs, it will create 8 copies of the model variables on these 8
+  GPUs. Then like in MirroredStrategy, each tower performs their computation
+  with their own copy of variables unless in cross-tower model where variable or
+  tensor reduction happens.
+  """
+
+  def __init__(self,
+               num_gpus_per_worker=1,
+               worker_job_name=None,
+               num_workers=None,
+               cluster=None,
+               cross_tower_ops=None,
+               prefetch_on_device=None):
+    """Initialize the strategy object.
+
+    Args:
+      num_gpus_per_worker: number of GPUs per work. If it is zero, the local
+        CPU will be used.
+      worker_job_name: the job name for `worker`, typically just 'worker'.
+      num_workers: the number of workers. If it is 0, it regenerates to
+        single-worker MirroredStrategy.
+      cluster: a `tf.train.ClusterSpec` object or a dict that can be used to
+        construct a `tf.train.ClusterSpec` object or a `tf.train.ClusterDef`
+        proto buffer. It is an alternative way to initialize this object.
+      cross_tower_ops: the cross tower ops to use. If None, a default one will
+        be used. If configure method is called, a best one for the configuration
+        will be chosen.
+      prefetch_on_device: a boolean to specify whether to prefetech input to
+        each worker's devices.
+
+    Raises:
+      ValueError: if got an unexpected `cluster`.
+    """
+    if cluster is None:
+      self._workers = [
+          '/job:%s/task:%d' % (worker_job_name, task_index)
+          for task_index in range(num_workers)
+      ]
+    else:
+      if isinstance(cluster, (dict, cluster_pb2.ClusterDef)):
+        cluster_spec = server_lib.ClusterSpec(cluster)
+      elif isinstance(cluster, server_lib.ClusterSpec):
+        cluster_spec = cluster
+      else:
+        raise ValueError(
+            "`cluster_spec' should be dict or a `tf.train.ClusterSpec` or a "
+            '`tf.train.ClusterDef` object')
+
+      self._workers = []
+      for job in sorted(cluster_spec.jobs):
+        for task in range(cluster_spec.num_tasks(job)):
+          self._workers.append('/job:%s/task:%d' % (job, task))
+
+    self._num_gpus_per_worker = num_gpus_per_worker
+    if num_gpus_per_worker > 0:
+      self._worker_device_map = {
+          worker: [
+              device_util.canonicalize(worker + '/device:GPU:%d' % gpu)
+              for gpu in range(num_gpus_per_worker)
+          ] for worker in self._workers
+      }
+    else:
+      self._worker_device_map = {
+          worker: [device_util.canonicalize(worker, '/device:CPU:0')]
+          for worker in self._workers
+      }
+    self._devices = nest.flatten(self._worker_device_map.values())
+
+    super(MultiWorkerMirroredStrategy, self).__init__(
+        devices=self._devices, prefetch_on_device=prefetch_on_device)
+
+    # Setting `_default_device` will add a device scope in the
+    # distribution.scope. We set the default device to the first worker. When
+    # users specify device under distribution.scope by
+    #   with tf.device("/cpu:0"):
+    #     ...
+    # their ops will end up on the cpu device of its first worker, e.g.
+    # "/job:worker/task:0/device:CPU:0". Note this is not used in tower mode.
+    self._default_device = self._workers[0]
+
+  def distribute_dataset(self, dataset_fn):
+    return values.MultiWorkerDataset(
+        partial(self._call_dataset_fn, dataset_fn), self._worker_device_map,
+        self._prefetch_on_device)
diff --git a/tensorflow/contrib/distribute/python/multi_worker_strategy_test.py b/tensorflow/contrib/distribute/python/multi_worker_strategy_test.py
new file mode 100644
index 00000000000000..ee7588163e42ee
--- /dev/null
+++ b/tensorflow/contrib/distribute/python/multi_worker_strategy_test.py
@@ -0,0 +1,64 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for MultiWorkerMirroredStrategy."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.distribute.python import multi_worker_strategy
+from tensorflow.contrib.distribute.python import multi_worker_test_base
+from tensorflow.contrib.distribute.python import strategy_test_lib
+from tensorflow.python.eager import context
+from tensorflow.python.eager import test
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.training import server_lib
+
+
+@test_util.with_c_api
+class MultiWorkerStrategyTest(multi_worker_test_base.MultiWorkerTestBase,
+                              strategy_test_lib.DistributionTestBase):
+
+  def _get_distribution_strategy(self):
+    return multi_worker_strategy.MultiWorkerMirroredStrategy(
+        cluster=server_lib.ClusterSpec({
+            'worker': ['/job:worker/task:0', '/job:worker/task:1']
+        }),
+        num_gpus_per_worker=context.num_gpus())
+
+  def testMinimizeLossGraph(self):
+    self._test_minimize_loss_graph(self._get_distribution_strategy())
+
+
+class DeviceScopeTest(test.TestCase):
+  """Test the device scope of MultiWorkerMirroredStrategy."""
+
+  def testDeviceScope(self):
+    with context.graph_mode():
+      strategy = multi_worker_strategy.MultiWorkerMirroredStrategy(
+          cluster={'worker': ['/job:worker/task:0', '/job:worker/task:1']},
+          num_gpus_per_worker=context.num_gpus())
+      with strategy.scope():
+        a = constant_op.constant(1.)
+        with ops.device('/cpu:0'):
+          b = constant_op.constant(1.)
+        self.assertEqual(a.device, '/job:worker/task:0')
+        self.assertEqual(b.device, '/job:worker/task:0/device:CPU:0')
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/distribute/python/one_device_strategy.py b/tensorflow/contrib/distribute/python/one_device_strategy.py
index 64aa3692010f73..09b6d4a515ab46 100644
--- a/tensorflow/contrib/distribute/python/one_device_strategy.py
+++ b/tensorflow/contrib/distribute/python/one_device_strategy.py
@@ -40,6 +40,7 @@ def __init__(self, device, prefetch_on_device=None):
     super(OneDeviceStrategy, self).__init__()
     self._device = device
     self._prefetch_on_device = prefetch_on_device
+    self._default_device = device
 
   def _create_variable(self, next_creator, *args, **kwargs):
     # No need to distinguish tower-local variables when not mirroring,
diff --git a/tensorflow/python/training/distribute.py b/tensorflow/python/training/distribute.py
index c16b05102edd27..21f81ee1878254 100644
--- a/tensorflow/python/training/distribute.py
+++ b/tensorflow/python/training/distribute.py
@@ -290,19 +290,31 @@ def _require_distribution_strategy_scope(distribution_strategy):
 class _CurrentDistributionContext(object):
   """Context manager for setting the `DistributionStrategy` and var creator."""
 
-  def __init__(self, distribution_strategy, var_creator_scope, var_scope=None):
+  def __init__(self,
+               distribution_strategy,
+               var_creator_scope,
+               var_scope=None,
+               default_device=None):
     self._context = _CrossTowerThreadMode(distribution_strategy)
     self._var_creator_scope = var_creator_scope
     self._var_scope = var_scope
+    if default_device:
+      self._device_scope = ops.device(default_device)
+    else:
+      self._device_scope = None
 
   def __enter__(self):
     _push_per_thread_mode(self._context)
     if self._var_scope:
       self._var_scope.__enter__()
     self._var_creator_scope.__enter__()
+    if self._device_scope:
+      self._device_scope.__enter__()
     return self._context.distribution_strategy
 
   def __exit__(self, exception_type, exception_value, traceback):
+    if self._device_scope:
+      self._device_scope.__exit__(exception_type, exception_value, traceback)
     self._var_creator_scope.__exit__(exception_type, exception_value, traceback)
     if self._var_scope:
       self._var_scope.__exit__(exception_type, exception_value, traceback)
@@ -557,6 +569,9 @@ class DistributionStrategy(object):
   # TODO(josh11b): List of towers with their worker and parameter devices
   #   (where the parameter devices may overlap in the ps case).
 
+  def __init__(self):
+    self._default_device = None
+
   def scope(self):
     """Returns a context manager selecting this DistributionStrategy as current.
 
@@ -587,7 +602,8 @@ def disable_partitioned_variables(getter, *args, **kwargs):
         self, variable_scope.variable_creator_scope(creator_with_resource_vars),
         variable_scope.variable_scope(
             variable_scope.get_variable_scope(),
-            custom_getter=disable_partitioned_variables))
+            custom_getter=disable_partitioned_variables),
+        self._default_device)
 
   def _create_variable(self, next_creator, *args, **kwargs):
     # Note: should support "colocate_with" argument.

From da0dcb21501b765932e392ae710ebbecefeb309c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 3 May 2018 23:36:02 -0700
Subject: [PATCH 0381/1691] Internal change.

PiperOrigin-RevId: 195374319
---
 .../kernels/bidirectional_sequence_lstm.cc    |  6 ++---
 tensorflow/contrib/lite/kernels/kernel_util.h |  4 +++
 tensorflow/contrib/lite/kernels/lstm.cc       |  4 +--
 tensorflow/contrib/lite/kernels/mean.cc       | 16 ++++++------
 tensorflow/contrib/lite/kernels/svdf.cc       | 26 +++++++++----------
 .../kernels/unidirectional_sequence_lstm.cc   |  4 +--
 6 files changed, 30 insertions(+), 30 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc
index 3ac0210f3645e6..a35ba23cedec43 100644
--- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc
+++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc
@@ -365,8 +365,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TfLiteIntArrayFree(node->temporaries);
   node->temporaries = TfLiteIntArrayCreate(2);
   node->temporaries->data[0] = *scratch_tensor_index;
-  TfLiteTensor* fw_scratch_buffer =
-      &context->tensors[node->temporaries->data[0]];
+  TfLiteTensor* fw_scratch_buffer = GetTemporary(context, node, /*index=*/0);
   fw_scratch_buffer->type = input->type;
   fw_scratch_buffer->allocation_type = kTfLiteArenaRw;
 
@@ -434,8 +433,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   // Create a scratch buffer tensor.
   node->temporaries->data[1] = *(scratch_tensor_index) + 1;
-  TfLiteTensor* bw_scratch_buffer =
-      &context->tensors[node->temporaries->data[1]];
+  TfLiteTensor* bw_scratch_buffer = GetTemporary(context, node, /*index=*/1);
   bw_scratch_buffer->type = input->type;
   bw_scratch_buffer->allocation_type = kTfLiteArenaRw;
 
diff --git a/tensorflow/contrib/lite/kernels/kernel_util.h b/tensorflow/contrib/lite/kernels/kernel_util.h
index 2f407b5da31594..e225443a67b2ac 100644
--- a/tensorflow/contrib/lite/kernels/kernel_util.h
+++ b/tensorflow/contrib/lite/kernels/kernel_util.h
@@ -32,6 +32,10 @@ inline TfLiteTensor* GetOutput(TfLiteContext* context, TfLiteNode* node,
                                int index) {
   return &context->tensors[node->outputs->data[index]];
 }
+inline TfLiteTensor* GetTemporary(TfLiteContext* context, TfLiteNode* node,
+                                  int index) {
+  return &context->tensors[node->temporaries->data[index]];
+}
 inline int NumInputs(const TfLiteNode* node) { return node->inputs->size; }
 inline int NumOutputs(const TfLiteNode* node) { return node->outputs->size; }
 
diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc
index 668226e6747009..a1521efbb4e2df 100644
--- a/tensorflow/contrib/lite/kernels/lstm.cc
+++ b/tensorflow/contrib/lite/kernels/lstm.cc
@@ -290,7 +290,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TfLiteIntArrayFree(node->temporaries);
   node->temporaries = TfLiteIntArrayCreate(1);
   node->temporaries->data[0] = *scratch_tensor_index;
-  TfLiteTensor* scratch_buffer = &context->tensors[node->temporaries->data[0]];
+  TfLiteTensor* scratch_buffer = GetTemporary(context, node, /*index=*/0);
   scratch_buffer->type = input->type;
   scratch_buffer->allocation_type = kTfLiteArenaRw;
 
@@ -378,7 +378,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   const bool use_peephole = (cell_to_output_weights != nullptr);
 
   // Index the scratch buffers pointers to the global scratch buffer.
-  TfLiteTensor* scratch_buffer = &context->tensors[node->temporaries->data[0]];
+  TfLiteTensor* scratch_buffer = GetTemporary(context, node, /*index=*/0);
 
   float* input_gate_scratch = nullptr;
   float* cell_scratch = nullptr;
diff --git a/tensorflow/contrib/lite/kernels/mean.cc b/tensorflow/contrib/lite/kernels/mean.cc
index 047bdd1039b993..98f80e32d95b47 100644
--- a/tensorflow/contrib/lite/kernels/mean.cc
+++ b/tensorflow/contrib/lite/kernels/mean.cc
@@ -146,7 +146,7 @@ TfLiteStatus InitializeTemporaries(TfLiteContext* context, TfLiteNode* node,
   TfLiteIntArrayFree(node->temporaries);
   node->temporaries = TfLiteIntArrayCreate(3);
   node->temporaries->data[0] = *scratch_tensor_index;
-  TfLiteTensor* scratch_tensor = &context->tensors[node->temporaries->data[0]];
+  TfLiteTensor* scratch_tensor = GetTemporary(context, node, /*index=*/0);
   scratch_tensor->type = kTfLiteInt32;
   scratch_tensor->allocation_type = kTfLiteArenaRw;
   TfLiteIntArray* index_size = TfLiteIntArrayCreate(1);
@@ -156,11 +156,11 @@ TfLiteStatus InitializeTemporaries(TfLiteContext* context, TfLiteNode* node,
 
   // Creates a temp tensor to store resolved axis given input data.
   node->temporaries->data[1] = *scratch_tensor_index + 1;
-  TfLiteTensor* resolved_axis = &context->tensors[node->temporaries->data[1]];
+  TfLiteTensor* resolved_axis = GetTemporary(context, node, /*index=*/1);
   resolved_axis->type = kTfLiteInt32;
   // Creates a temp tensor to store temp sums when calculating mean.
   node->temporaries->data[2] = *scratch_tensor_index + 2;
-  TfLiteTensor* temp_sum = &context->tensors[node->temporaries->data[2]];
+  TfLiteTensor* temp_sum = GetTemporary(context, node, /*index=*/2);
   switch (op_context->input->type) {
     case kTfLiteFloat32:
       temp_sum->type = kTfLiteFloat32;
@@ -187,8 +187,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   MeanContext op_context(context, node);
   TF_LITE_ENSURE_OK(context, InitializeTemporaries(context, node, &op_context));
 
-  TfLiteTensor* resolved_axis = &context->tensors[node->temporaries->data[1]];
-  TfLiteTensor* temp_sum = &context->tensors[node->temporaries->data[2]];
+  TfLiteTensor* resolved_axis = GetTemporary(context, node, /*index=*/1);
+  TfLiteTensor* temp_sum = GetTemporary(context, node, /*index=*/2);
   // Leaves work to Eval if axis is not constant; else resizes output.
   if (!IsConstantTensor(op_context.axis)) {
     SetTensorToDynamic(op_context.output);
@@ -208,9 +208,9 @@ template 
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   MeanContext op_context(context, node);
   int num_axis = static_cast(NumElements(op_context.axis));
-  TfLiteTensor* temp_index = &context->tensors[node->temporaries->data[0]];
-  TfLiteTensor* resolved_axis = &context->tensors[node->temporaries->data[1]];
-  TfLiteTensor* temp_sum = &context->tensors[node->temporaries->data[2]];
+  TfLiteTensor* temp_index = GetTemporary(context, node, /*index=*/0);
+  TfLiteTensor* resolved_axis = GetTemporary(context, node, /*index=*/1);
+  TfLiteTensor* temp_sum = GetTemporary(context, node, /*index=*/2);
   // Resize the output tensor if the output tensor is dynamic.
   if (IsDynamicTensor(op_context.output)) {
     TF_LITE_ENSURE_OK(context,
diff --git a/tensorflow/contrib/lite/kernels/svdf.cc b/tensorflow/contrib/lite/kernels/svdf.cc
index c69755447d5093..13da51c7a78c36 100644
--- a/tensorflow/contrib/lite/kernels/svdf.cc
+++ b/tensorflow/contrib/lite/kernels/svdf.cc
@@ -37,7 +37,7 @@ constexpr int kWeightsFeatureTensor = 1;
 constexpr int kWeightsTimeTensor = 2;
 constexpr int kBiasTensor = 3;
 constexpr int kStateTensor = 0;
-constexpr int KOutputTensor = 1;
+constexpr int kOutputTensor = 1;
 
 void* Init(TfLiteContext* context, const char* buffer, size_t length) {
   auto* scratch_tensor_index = new int;
@@ -59,9 +59,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   TfLiteTensor* input = &context->tensors[node->inputs->data[kInputTensor]];
   TfLiteTensor* weights_feature =
-      &context->tensors[node->inputs->data[kWeightsFeatureTensor]];
-  TfLiteTensor* weights_time =
-      &context->tensors[node->inputs->data[kWeightsTimeTensor]];
+      GetInput(context, node, kWeightsFeatureTensor);
+  TfLiteTensor* weights_time = GetInput(context, node, kWeightsTimeTensor);
 
   // Check all the parameters of tensor match within themselves and match the
   // input configuration.
@@ -79,8 +78,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
     TF_LITE_ASSERT_EQ(bias->dims->data[0], num_units);
   }
 
-  TfLiteTensor* state = &context->tensors[node->outputs->data[kStateTensor]];
-  TfLiteTensor* output = &context->tensors[node->outputs->data[KOutputTensor]];
+  TfLiteTensor* state = GetOutput(context, node, kStateTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   // Resize state.
   // For each batch, the state is a 2-D tensor: memory_size * num_filters
@@ -112,7 +111,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   scratch_size_array->data[0] = batch_size;
   scratch_size_array->data[1] = num_filters;
 
-  TfLiteTensor* scratch_tensor = &context->tensors[node->temporaries->data[0]];
+  TfLiteTensor* scratch_tensor = GetTemporary(context, node, /*index=*/0);
   scratch_tensor->type = input->type;
   scratch_tensor->allocation_type = kTfLiteArenaRw;
   TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_tensor,
@@ -124,15 +123,14 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->builtin_data);
 
-  TfLiteTensor* input = &context->tensors[node->inputs->data[kInputTensor]];
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* weights_feature =
-      &context->tensors[node->inputs->data[kWeightsFeatureTensor]];
-  TfLiteTensor* weights_time =
-      &context->tensors[node->inputs->data[kWeightsTimeTensor]];
+      GetInput(context, node, kWeightsFeatureTensor);
+  TfLiteTensor* weights_time = GetInput(context, node, kWeightsTimeTensor);
 
-  TfLiteTensor* state = &context->tensors[node->outputs->data[kStateTensor]];
-  TfLiteTensor* output = &context->tensors[node->outputs->data[KOutputTensor]];
-  TfLiteTensor* scratch = &context->tensors[node->temporaries->data[0]];
+  TfLiteTensor* state = GetOutput(context, node, kStateTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+  TfLiteTensor* scratch = GetTemporary(context, node, /*index=*/0);
 
   TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor);
 
diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc
index 3c1256d3a651a8..5987bf68b5a73e 100644
--- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc
+++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc
@@ -292,7 +292,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TfLiteIntArrayFree(node->temporaries);
   node->temporaries = TfLiteIntArrayCreate(1);
   node->temporaries->data[0] = *scratch_tensor_index;
-  TfLiteTensor* scratch_buffer = &context->tensors[node->temporaries->data[0]];
+  TfLiteTensor* scratch_buffer = GetTemporary(context, node, /*index=*/0);
   scratch_buffer->type = input->type;
   scratch_buffer->allocation_type = kTfLiteArenaRw;
 
@@ -381,7 +381,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   const bool use_peephole = (cell_to_output_weights != nullptr);
 
   // Index the scratch buffers pointers to the global scratch buffer.
-  TfLiteTensor* scratch_buffer = &context->tensors[node->temporaries->data[0]];
+  TfLiteTensor* scratch_buffer = GetTemporary(context, node, /*index=*/0);
   float* input_gate_scratch = nullptr;
   float* cell_scratch = nullptr;
   float* forget_gate_scratch = nullptr;

From 0bb55f02022e88affefc111cf9a8cf70a046d1da Mon Sep 17 00:00:00 2001
From: HyoukJoong Lee 
Date: Fri, 4 May 2018 00:51:58 -0700
Subject: [PATCH 0382/1691] Automated g4 rollback of changelist 194829761

PiperOrigin-RevId: 195379693
---
 .../xla/service/hlo_module_group_metadata.cc        |  7 -------
 .../xla/service/hlo_module_group_metadata.h         |  3 ---
 tensorflow/compiler/xla/service/service.cc          | 13 +++----------
 3 files changed, 3 insertions(+), 20 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc
index 3367d76ded68a7..54c34ce1166516 100644
--- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc
+++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc
@@ -194,13 +194,6 @@ int64 HloModuleGroupMetadata::GetModuleId(const HloModule* module) const {
   LOG(FATAL) << "unknown module";
 }
 
-int64 HloModuleGroupMetadata::GetDeviceModulesCount() const {
-  return std::count_if(modules_.begin(), modules_.end(),
-                       [](const HloModule* module) {
-                         return !module->config().is_host_module();
-                       });
-}
-
 Status HloModuleGroupMetadata::RecordInstructions() {
   const auto visitor = [this](HloInstruction* hlo) -> Status {
     if (hlo->opcode() == HloOpcode::kWhile) {
diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h
index d6190826166683..c48a7ab0b59269 100644
--- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h
+++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h
@@ -147,9 +147,6 @@ class HloModuleGroupMetadata {
   // the module in the module vector.
   int64 GetModuleId(const HloModule* module) const;
 
-  // Returns the number of modules for devices (excluding the host module).
-  int64 GetDeviceModulesCount() const;
-
   // Returns the companion instructions for the given instruction.
   //
   // Precondition: IsCompanionWhile(instruction) is true.
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 6ce03ab39d4dc8..495f8801ba82ec 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -626,16 +626,9 @@ Service::ExecuteParallelAndRegisterResult(
   // profiled.
   std::map index_to_profiled_streams;
 
-  // Build DeviceAssignment for all cores based on the provided device handles.
-  DeviceAssignment device_assignment(options_.number_of_replicas(),
-                                     executables.size());
-  for (int64 i = 0; i < executables.size(); i++) {
-    TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*backend, device_handles[i]));
-    CHECK_EQ(replicas.size(), arguments[i].size());
-    for (int64 replica = 0; replica < replicas.size(); ++replica) {
-      device_assignment(replica, i) = replicas[replica]->device_ordinal();
-    }
-  }
+  TF_ASSIGN_OR_RETURN(DeviceAssignment device_assignment,
+                      backend->computation_placer()->AssignDevices(
+                          options_.number_of_replicas(), executables.size()));
 
   for (int64 i = 0; i < executables.size(); i++) {
     // Stream executors for the replicas of the current computation.

From 1284047dca0dd58745a31cd2fd68da3173c7e120 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 4 May 2018 01:47:12 -0700
Subject: [PATCH 0383/1691]   * Don't copy on-host and on-device shapes
 locally.   * Use ForEachMutableElement rather than the iterators, as it is
 much quicker.

There is still room for improvement; ForEachMutableElement is linear in the number of nodes in the shape tree but we want to be linear in the number of nodes in the sub shape tree. But I feel this is a good enough improvement.

PiperOrigin-RevId: 195384423
---
 tensorflow/compiler/jit/BUILD                 | 25 ++++++++
 tensorflow/compiler/jit/xla_launch_util.cc    | 22 ++++---
 tensorflow/compiler/jit/xla_launch_util.h     | 11 ++++
 .../compiler/jit/xla_launch_util_test.cc      | 64 +++++++++++++++++++
 4 files changed, 113 insertions(+), 9 deletions(-)
 create mode 100644 tensorflow/compiler/jit/xla_launch_util_test.cc

diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index af2965bba5b91a..07136d6a746604 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -360,6 +360,31 @@ tf_cc_test(
     ],
 )
 
+tf_cc_test(
+    name = "xla_launch_util_test",
+    size = "small",
+    srcs = ["xla_launch_util_test.cc"],
+    deps = [
+        ":common",
+        ":xla_compilation_cache",
+        ":xla_launch_util",
+        ":xla_tensor",
+        "//tensorflow/compiler/tf2xla:common",
+        "//tensorflow/compiler/tf2xla:xla_compiler",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla/client:client_library",
+        "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/core:core_cpu_internal",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:gpu_runtime",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:test",
+        "//tensorflow/core/kernels:variable_ops",
+    ],
+)
+
 # This target can be used by XLA device plugins to prevent circular dependencies, and provides access to all of the required headers for building a device library.
 cc_header_only_library(
     name = "xla_jit_headers_lib",
diff --git a/tensorflow/compiler/jit/xla_launch_util.cc b/tensorflow/compiler/jit/xla_launch_util.cc
index 2a7f04271d4b7e..33e53612b91315 100644
--- a/tensorflow/compiler/jit/xla_launch_util.cc
+++ b/tensorflow/compiler/jit/xla_launch_util.cc
@@ -77,16 +77,16 @@ Status XlaAllocator::Deallocate(int device_ordinal, se::DeviceMemoryBase* mem) {
   return Status::OK();
 }
 
-namespace {
+namespace internal {
 // Return the 'index''th subtree of the given ShapedBuffer as a
 // ScopedShapedBuffer. The returned ScopedShapedBuffer takes ownership of the
 // subtree, and sets the input's buffer pointers to nullptr for the subtree.
 ScopedShapedBuffer ExtractSubShapedBuffer(
     ShapedBuffer* shaped_buffer, int index,
     xla::DeviceMemoryAllocator* allocator) {
-  xla::Shape on_host_shape = xla::ShapeUtil::GetTupleElementShape(
+  const xla::Shape& on_host_shape = xla::ShapeUtil::GetTupleElementShape(
       shaped_buffer->on_host_shape(), index);
-  xla::Shape on_device_shape = xla::ShapeUtil::GetTupleElementShape(
+  const xla::Shape& on_device_shape = xla::ShapeUtil::GetTupleElementShape(
       shaped_buffer->on_device_shape(), index);
 
   ShapedBuffer sub_shaped_buffer(on_host_shape, on_device_shape,
@@ -98,14 +98,18 @@ ScopedShapedBuffer ExtractSubShapedBuffer(
   sub_shape_tree.CopySubtreeFrom(shape_tree,
                                  /*source_base_index=*/{index},
                                  /*target_base_index=*/{});
-  for (auto& index_to_buffer : shape_tree) {
-    if (!index_to_buffer.first.empty() && index_to_buffer.first[0] == index) {
-      index_to_buffer.second = se::DeviceMemoryBase(nullptr, 0);
-    }
-  }
+  shape_tree.ForEachMutableElement(
+      [index](const xla::ShapeIndex& shape_index,
+              tensorflow::se::DeviceMemoryBase* data) {
+        // shape_index is empty for the root node. Ignore that.
+        if (!shape_index.empty() && shape_index[0] == index) {
+          *data = tensorflow::se::DeviceMemoryBase(nullptr, 0);
+        }
+      });
   return ScopedShapedBuffer(std::move(sub_shaped_buffer), allocator);
 }
-}  // namespace
+}  // namespace internal
+using internal::ExtractSubShapedBuffer;
 
 XlaComputationLaunchContext::XlaComputationLaunchContext(
     int64 num_resource_args, xla::LocalClient* client,
diff --git a/tensorflow/compiler/jit/xla_launch_util.h b/tensorflow/compiler/jit/xla_launch_util.h
index 8a6ff3b0c75120..38291b0bd429b2 100644
--- a/tensorflow/compiler/jit/xla_launch_util.h
+++ b/tensorflow/compiler/jit/xla_launch_util.h
@@ -140,6 +140,17 @@ class XlaTensorBuffer : public TensorBuffer {
   Allocator* allocator_;
 };
 
+// Exposed in this header file for microbenchmarking purposes, but this is an
+// internal implementation detail.
+namespace internal {
+// Return the 'index''th subtree of the given ShapedBuffer as a
+// ScopedShapedBuffer. The returned ScopedShapedBuffer takes ownership of the
+// subtree, and sets the input's buffer pointers to nullptr for the subtree.
+xla::ScopedShapedBuffer ExtractSubShapedBuffer(
+    xla::ShapedBuffer* shaped_buffer, int index,
+    xla::DeviceMemoryAllocator* allocator);
+}  // namespace internal
+
 }  // namespace tensorflow
 
 #endif
diff --git a/tensorflow/compiler/jit/xla_launch_util_test.cc b/tensorflow/compiler/jit/xla_launch_util_test.cc
new file mode 100644
index 00000000000000..27813efc0bc0ae
--- /dev/null
+++ b/tensorflow/compiler/jit/xla_launch_util_test.cc
@@ -0,0 +1,64 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Contains microbenchmarks for performance critical functions in
+// xla_launch_util.cc.
+
+#include "tensorflow/compiler/jit/xla_launch_util.h"
+#include "tensorflow/compiler/tf2xla/shape_util.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/platform/test_benchmark.h"
+
+// Test ExtractSubBuffer with different depths (depth of ShapeTree) and fan-outs
+// (cardinality of each non-leaf node's children).
+void BM_ExtractSubBuffer(int iters, int depth, int fan_out) {
+  tensorflow::testing::StopTiming();
+  xla::Shape shape = xla::ShapeUtil::MakeShape(xla::F32, {32, 64, 128});
+  for (int i = 0; i < depth; ++i) {
+    std::vector shapes(fan_out, shape);
+    shape = xla::ShapeUtil::MakeTupleShape(shapes);
+  }
+  xla::ShapedBuffer shaped_buffer(shape, shape, /*platform=*/nullptr,
+                                  /*device_ordinal=*/0);
+  tensorflow::testing::StartTiming();
+  for (int i = 0; i < iters; ++i) {
+    // Extract a buffer from approximately the middle of the first level of the
+    // tree.
+    tensorflow::internal::ExtractSubShapedBuffer(&shaped_buffer,
+                                                 /*index=*/fan_out / 2,
+                                                 /*allocator=*/nullptr)
+        .release();
+  }
+}
+
+BENCHMARK(BM_ExtractSubBuffer)
+    ->ArgPair(1, 4)
+    ->ArgPair(1, 8)
+    ->ArgPair(1, 32)
+    ->ArgPair(1, 64)
+    ->ArgPair(1, 128)
+    ->ArgPair(1, 256)
+    ->ArgPair(1, 512)
+    ->ArgPair(2, 4)
+    ->ArgPair(2, 8)
+    ->ArgPair(2, 32)
+    ->ArgPair(2, 64)
+    ->ArgPair(2, 128);
+
+int main(int argc, char** argv) {
+  testing::InitGoogleTest(&argc, argv);
+  tensorflow::testing::RunBenchmarks();
+  return RUN_ALL_TESTS();
+}

From 73a1908b3c50d2f665a3a9af491e217d814edb40 Mon Sep 17 00:00:00 2001
From: Tom Hennigan 
Date: Fri, 4 May 2018 01:57:02 -0700
Subject: [PATCH 0384/1691] Prefer non-nested GradientTape.gradient call when
 only one source is passed.

PiperOrigin-RevId: 195385406
---
 tensorflow/docs_src/programmers_guide/eager.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/eager.md b/tensorflow/docs_src/programmers_guide/eager.md
index 595e6be4af78d7..5926e9f7f4cef9 100644
--- a/tensorflow/docs_src/programmers_guide/eager.md
+++ b/tensorflow/docs_src/programmers_guide/eager.md
@@ -227,8 +227,8 @@ w = tfe.Variable([[1.0]])
 with tf.GradientTape() as tape:
   loss = w * w
 
-grad = tape.gradient(loss, [w])
-print(grad)  # => [tf.Tensor([[ 2.]], shape=(1, 1), dtype=float32)]
+grad = tape.gradient(loss, w)
+print(grad)  # => tf.Tensor([[ 2.]], shape=(1, 1), dtype=float32)
 ```
 
 Here's an example of `tf.GradientTape` that records forward-pass operations
@@ -596,7 +596,7 @@ def line_search_step(fn, init_x, rate=1.0):
     # Variables are automatically recorded, but manually watch a tensor
     tape.watch(init_x)
     value = fn(init_x)
-  grad, = tape.gradient(value, [init_x])
+  grad = tape.gradient(value, init_x)
   grad_norm = tf.reduce_sum(grad * grad)
   init_value = value
   while value > init_value - rate * grad_norm:

From c183c5600b1393767c8c85aad34a436feb3bbe75 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 4 May 2018 02:04:33 -0700
Subject: [PATCH 0385/1691] Fixing some linter errors in TF documentation
 (Github > GitHub, the the > the).

PiperOrigin-RevId: 195386172
---
 tensorflow/docs_src/deploy/index.md                          | 2 +-
 tensorflow/docs_src/get_started/get_started_for_beginners.md | 2 +-
 tensorflow/docs_src/mobile/android_build.md                  | 4 ++--
 tensorflow/docs_src/mobile/linking_libs.md                   | 2 +-
 tensorflow/docs_src/mobile/mobile_intro.md                   | 4 ++--
 tensorflow/docs_src/mobile/tflite/index.md                   | 2 +-
 6 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/tensorflow/docs_src/deploy/index.md b/tensorflow/docs_src/deploy/index.md
index 07b1bc9257ff7b..61edba04b46b7a 100644
--- a/tensorflow/docs_src/deploy/index.md
+++ b/tensorflow/docs_src/deploy/index.md
@@ -14,4 +14,4 @@ the following documents:
     designed for production environments. TensorFlow Serving provides
     out-of-the-box integration with TensorFlow models.
     [Source code for TensorFlow Serving](https://github.com/tensorflow/serving)
-    is available on Github.
+    is available on GitHub.
diff --git a/tensorflow/docs_src/get_started/get_started_for_beginners.md b/tensorflow/docs_src/get_started/get_started_for_beginners.md
index fbe0ed74f82bb3..d5a80e22c5dd19 100644
--- a/tensorflow/docs_src/get_started/get_started_for_beginners.md
+++ b/tensorflow/docs_src/get_started/get_started_for_beginners.md
@@ -233,7 +233,7 @@ The Iris program requires the data from the following two .csv files:
 *   `http://download.tensorflow.org/data/iris_training.csv`, which contains
     the training set.
 *   `http://download.tensorflow.org/data/iris_test.csv`, which contains the
-    the test set.
+    test set.
 
 The **training set** contains the examples that we'll use to train the model;
 the **test set** contains the examples that we'll use to evaluate the trained
diff --git a/tensorflow/docs_src/mobile/android_build.md b/tensorflow/docs_src/mobile/android_build.md
index c35530061dcaf2..f4b07db4591ddd 100644
--- a/tensorflow/docs_src/mobile/android_build.md
+++ b/tensorflow/docs_src/mobile/android_build.md
@@ -26,7 +26,7 @@ If you haven't already, do the following two things:
 - Install [Android Studio](https://developer.android.com/studio/index.html),
   following the instructions on their website.
 
-- Clone the TensorFlow repository from Github:
+- Clone the TensorFlow repository from GitHub:
 
         git clone https://github.com/tensorflow/tensorflow
 
@@ -37,7 +37,7 @@ If you haven't already, do the following two things:
 
 2. From the **Open File or Project** window that appears, navigate to and select
     the `tensorflow/examples/android` directory from wherever you cloned the
-    TensorFlow Github repo.  Click OK.
+    TensorFlow GitHub repo.  Click OK.
 
     If it asks you to do a Gradle Sync, click OK.
 
diff --git a/tensorflow/docs_src/mobile/linking_libs.md b/tensorflow/docs_src/mobile/linking_libs.md
index 2a0a77c92d309e..cf0db590210593 100644
--- a/tensorflow/docs_src/mobile/linking_libs.md
+++ b/tensorflow/docs_src/mobile/linking_libs.md
@@ -32,7 +32,7 @@ include this functionality in your program:
 2. Download the nightly precompiled version from
 [ci.tensorflow.org](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/).
 
-3. Build the JAR file yourself using the instructions [in our Android Github repo](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/android)
+3. Build the JAR file yourself using the instructions [in our Android GitHub repo](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/android)
 
 ### iOS
 
diff --git a/tensorflow/docs_src/mobile/mobile_intro.md b/tensorflow/docs_src/mobile/mobile_intro.md
index 69b63ae7d22ced..1b0b9b44b469af 100644
--- a/tensorflow/docs_src/mobile/mobile_intro.md
+++ b/tensorflow/docs_src/mobile/mobile_intro.md
@@ -80,7 +80,7 @@ tracking is especially important for applications where you’re trying to count
 how many objects are present over time, since it gives you a good idea when a
 new object enters or leaves the scene. We have some sample code for this
 available for Android [on
-Github](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android),
+GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android),
 and also a [more general object detection
 model](https://github.com/tensorflow/models/tree/master/research/object_detection/README.md)
 available as well.
@@ -231,7 +231,7 @@ process.
 The next step is to pick an effective model to use. You might be able to avoid
 training a model from scratch if someone else has already implemented a model
 similar to what you need; we have a repository of models implemented in
-TensorFlow [on Github](https://github.com/tensorflow/models) that you can look
+TensorFlow [on GitHub](https://github.com/tensorflow/models) that you can look
 through. Lean towards the simplest model you can find, and try to get started as
 soon as you have even a small amount of labelled data, since you’ll get the best
 results when you’re able to iterate quickly. The shorter the time it takes to
diff --git a/tensorflow/docs_src/mobile/tflite/index.md b/tensorflow/docs_src/mobile/tflite/index.md
index 11f11ea4dc54b9..01881ccf3bb15b 100644
--- a/tensorflow/docs_src/mobile/tflite/index.md
+++ b/tensorflow/docs_src/mobile/tflite/index.md
@@ -11,7 +11,7 @@ optimizing the kernels for mobile apps, pre-fused activations, and quantized
 kernels that allow smaller and faster (fixed-point math) models.
 
 Most of our TensorFlow Lite documentation is [on
-Github](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite)
+GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite)
 for the time being.
 
 ## What does TensorFlow Lite contain?

From 7a7bbc303c451fea5b3dd93109028531a89a18ab Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 4 May 2018 02:22:14 -0700
Subject: [PATCH 0386/1691] Do not crash on ROOT outfeed operations.

PiperOrigin-RevId: 195388075
---
 .../compiler/xla/service/cpu/ir_emitter.cc    |  8 ++-
 .../compiler/xla/service/cpu/tests/BUILD      | 14 +++++
 .../cpu/tests/cpu_literal_caching_test.cc     | 16 +-----
 .../xla/service/cpu/tests/cpu_outfeed_test.cc | 57 +++++++++++++++++++
 4 files changed, 79 insertions(+), 16 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/cpu/tests/cpu_outfeed_test.cc

diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index e473389a297f54..6347ee2a2a1750 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -2563,8 +2563,12 @@ Status IrEmitter::FinishVisit(HloInstruction* root) {
   // nothing to do since the result was already written directly into the output
   // buffer.
   VLOG(2) << "FinishVisit root: " << root->ToString();
-  llvm::Value* root_value = GetEmittedValueFor(root);
-  VLOG(2) << "  value: " << llvm_ir::DumpToString(*root_value);
+  if (root->opcode() == HloOpcode::kOutfeed) {
+    VLOG(2) << "  outfeed with value: "
+            << llvm_ir::DumpToString(*GetEmittedValueFor(root->operand(0)));
+  } else {
+    VLOG(2) << "  value: " << llvm_ir::DumpToString(*GetEmittedValueFor(root));
+  }
 
   auto record_complete_computation = [&](llvm::Value* prof_counter) {
     if (prof_counter) {
diff --git a/tensorflow/compiler/xla/service/cpu/tests/BUILD b/tensorflow/compiler/xla/service/cpu/tests/BUILD
index 4ddb7a85bc396a..18a915e5339623 100644
--- a/tensorflow/compiler/xla/service/cpu/tests/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/tests/BUILD
@@ -161,3 +161,17 @@ tf_cc_test(
         "//tensorflow/core:test_main",
     ],
 )
+
+tf_cc_test(
+    name = "cpu_outfeed_test",
+    srcs = ["cpu_outfeed_test.cc"],
+    deps = [
+        "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/compiler/xla/service/cpu:cpu_compiler",
+        "//tensorflow/compiler/xla/service/cpu/tests:cpu_codegen_test",
+        "//tensorflow/compiler/xla/tools/parser:hlo_parser",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc
index b10eb74635cd35..d6e0425c5542be 100644
--- a/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc
@@ -50,16 +50,10 @@ ENTRY main {
   const_b = f32[2,3,2] while(f32[2,3,2] const_a), condition=while_cond, body=while_body
 
   out0 = () outfeed(f32[2,3,2] const_a)
-  out1 = () outfeed(f32[2,3,2] const_b)
-
-  ROOT root = f32[] constant(1)
+  ROOT out1 = () outfeed(f32[2,3,2] const_b)
 }
 )";
 
-  // TODO(b/78879738): The fake "f32[] constant(1)" root is only needed to work
-  // around b/78879738.  Once b/78879738 is fixed, we can set one of the
-  // outfeeds as the root.
-
   string filecheck_pattern = R"(
 CHECK: private constant [2 x [3 x [2 x float]]]
 CHECK-NOT: private constant [2 x [3 x [2 x float]]]
@@ -99,16 +93,10 @@ ENTRY main {
   const_b = (f32[2,1]{1,0}, f32[2]{0}) while((f32[2,1]{1,0}, f32[2]{0}) const_a), condition=while_cond, body=while_body
 
   out0 = () outfeed((f32[2,1]{1,0}, f32[2]{0}) const_a)
-  out1 = () outfeed((f32[2,1]{1,0}, f32[2]{0}) const_b)
-
-  ROOT root = f32[] constant(1)
+  ROOT out1 = () outfeed((f32[2,1]{1,0}, f32[2]{0}) const_b)
 }
 )";
 
-  // TODO(b/78879738): The fake "f32[] constant(1)" root is only needed to work
-  // around b/78879738.  Once b/78879738 is fixed, we can set one of the
-  // outfeeds as the root.
-
   string filecheck_pattern = R"(
 CHECK: private constant [2 x float]
 CHECK: private constant [2 x [1 x float]]
diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_outfeed_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_outfeed_test.cc
new file mode 100644
index 00000000000000..879372eb13884c
--- /dev/null
+++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_outfeed_test.cc
@@ -0,0 +1,57 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/cpu/cpu_compiler.h"
+#include "tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h"
+#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h"
+
+namespace xla {
+namespace cpu {
+namespace {
+class CpuOutfeedTest : public CpuCodegenTest {};
+
+TEST_F(CpuOutfeedTest, OutfeedRoot) {
+  const string hlo_text = R"(
+HloModule Outfeed
+
+ENTRY main {
+  const_a = f32[2,3,2] constant(
+  f32[2,3,2]
+    {{{1, 2}, {1001, 1002}, {2001, 2002}},
+     {{2, 1}, {2001, 3002}, {2001, 2002}}})
+
+  ROOT out = () outfeed(f32[2,3,2] const_a)
+}
+)";
+
+  string filecheck_pattern = R"(
+CHECK: private constant [2 x [3 x [2 x float]]]
+)";
+
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module,
+                          tools::Parse(hlo_text));
+
+  CpuAotCompilationOptions options{
+      /*triple=*/"x86_64-pc-linux", /*cpu_name=*/"", /*features=*/"",
+      /*entry_point_name=*/"entry",
+      /*relocation_model=*/CpuAotCompilationOptions::RelocationModel::Static};
+
+  CompileAheadOfTimeAndVerifyIr(std::move(module), options, filecheck_pattern,
+                                /*match_optimized_ir=*/false);
+}
+
+}  // namespace
+}  // namespace cpu
+}  // namespace xla

From 34bb6643654b9a207b93d046d5fde807eb7ee499 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 4 May 2018 03:27:18 -0700
Subject: [PATCH 0387/1691] Fix HloSharding::GetSubSharding to return correct
 array shardings

Previously it always returned a tuple sharding even if the specified
index was referenceing a non-tuple element.

PiperOrigin-RevId: 195393313
---
 tensorflow/compiler/xla/service/hlo_sharding.cc | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_sharding.cc b/tensorflow/compiler/xla/service/hlo_sharding.cc
index 994de441237493..7f7e3f7dab03ce 100644
--- a/tensorflow/compiler/xla/service/hlo_sharding.cc
+++ b/tensorflow/compiler/xla/service/hlo_sharding.cc
@@ -367,10 +367,14 @@ HloSharding HloSharding::GetSubSharding(const Shape& shape,
                                         const ShapeIndex& index) const {
   CHECK(IsTuple());
 
-  ShapeTree sub_shape_tree(ShapeUtil::GetSubshape(shape, index),
-                                        Replicate());
+  Shape sub_shape = ShapeUtil::GetSubshape(shape, index);
+  ShapeTree sub_shape_tree(sub_shape, Replicate());
   sub_shape_tree.CopySubtreeFrom(GetAsShapeTree(shape), index, {});
-  return Tuple(sub_shape_tree);
+  if (ShapeUtil::IsTuple(sub_shape)) {
+    return Tuple(sub_shape_tree);
+  } else {
+    return sub_shape_tree.element({});
+  }
 }
 
 std::ostream& operator<<(std::ostream& out, const HloSharding& sharding) {

From 2d6170fc0afee7269cab7f84647f2a65b86e7020 Mon Sep 17 00:00:00 2001
From: Benjamin Kramer 
Date: Fri, 4 May 2018 03:43:00 -0700
Subject: [PATCH 0388/1691] [XLA] Remove template keyword on non-template
 methods.

This is an error with clang trunk.

PiperOrigin-RevId: 195394277
---
 tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc | 9 +++------
 third_party/libxsmm.BUILD                               | 2 +-
 2 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
index 6cb470caf8fb57..464cc012140d48 100644
--- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
@@ -67,8 +67,7 @@ XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, ExpTwoByTwoValues) {
       Literal::CreateR2FromArray2D({{2.71828f, 1.00000f},    // row 0
                                        {0.36788f, 1.64872f}});  // row 1
 
-  this->template ComputeAndCompareLiteral(&builder, *expected, {},
-                                          ErrorSpec(1e-5));
+  this->ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5));
 }
 
 XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MapTwoByTwo) {
@@ -96,8 +95,7 @@ XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MapTwoByTwo) {
   std::unique_ptr expected =
       Literal::CreateR2FromArray2D({{1.5f, 0.5f},     // row 0
                                        {-0.5f, 1.0f}});  // row 1
-  this->template ComputeAndCompareLiteral(&builder, *expected, {},
-                                          ErrorSpec(1e-5));
+  this->ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5));
 }
 
 XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MaxTwoByTwoValues) {
@@ -116,8 +114,7 @@ XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MaxTwoByTwoValues) {
   std::unique_ptr expected =
       Literal::CreateR2FromArray2D({{7.0f, 6.0f},     // row 0
                                        {3.0f, -4.0f}});  // row 1
-  this->template ComputeAndCompareLiteral(&builder, *expected, {},
-                                          ErrorSpec(1e-6));
+  this->ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-6));
 }
 
 struct TestLinspaceMaxParam {
diff --git a/third_party/libxsmm.BUILD b/third_party/libxsmm.BUILD
index 78ed1f4e168891..4124f2db637689 100644
--- a/third_party/libxsmm.BUILD
+++ b/third_party/libxsmm.BUILD
@@ -38,8 +38,8 @@ genrule(
         ":libxsmm_interface",
     ],
     visibility = [
-        "//third_party/eigen3:__pkg__",
         "//tensorflow/core/kernels:__pkg__",
+        "//third_party/eigen3:__pkg__",
     ],
 )
 

From 3db0e545d2460be0392dfcaa304231cd2105648e Mon Sep 17 00:00:00 2001
From: Peter Hawkins 
Date: Fri, 4 May 2018 10:18:46 -0700
Subject: [PATCH 0389/1691] Change RecvTensor RPC implementation to use
 DeviceContext::CopyDeviceTensorToCPU rather than calling
 GPUUtil::CopyGPUTensorToCPU. The direct call into the GPU code is problematic
 for non-GPU devices.

PiperOrigin-RevId: 195433287
---
 tensorflow/core/distributed_runtime/rpc/BUILD |  1 -
 .../rpc/grpc_worker_service.cc                | 24 +++++++------------
 2 files changed, 9 insertions(+), 16 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/rpc/BUILD b/tensorflow/core/distributed_runtime/rpc/BUILD
index e973a22f45e908..c2719f54622b25 100644
--- a/tensorflow/core/distributed_runtime/rpc/BUILD
+++ b/tensorflow/core/distributed_runtime/rpc/BUILD
@@ -169,7 +169,6 @@ tf_cuda_library(
         ":grpc_worker_service_impl",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
-        "//tensorflow/core:gpu_runtime",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:worker_proto_cc",
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
index bbf7391377903b..26fad1fc3c92d5 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
@@ -23,9 +23,6 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device.h"
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
-#if GOOGLE_CUDA
-#include "tensorflow/core/common_runtime/gpu/gpu_util.h"
-#endif  // GOOGLE_CUDA
 #include "tensorflow/core/common_runtime/local_device.h"
 #include "tensorflow/core/common_runtime/process_util.h"
 #include "tensorflow/core/common_runtime/step_stats_collector.h"
@@ -439,10 +436,10 @@ void GrpcWorker::GrpcRecvTensorAsync(CallOptions* opts,
   opts->SetCancelCallback([this, step_id]() { AbortStep(step_id); });
   env_->rendezvous_mgr->RecvLocalAsync(
       step_id, parsed,
-      [opts, response, done, src_dev](const Status& status,
-                                      const Rendezvous::Args& send_args,
-                                      const Rendezvous::Args& recv_args,
-                                      const Tensor& val, const bool is_dead) {
+      [opts, response, done, src_dev, request](
+          const Status& status, const Rendezvous::Args& send_args,
+          const Rendezvous::Args& recv_args, const Tensor& val,
+          const bool is_dead) {
         opts->ClearCancelCallback();
         if (status.ok()) {
           // DMA can only be used for Tensors that do not fall into
@@ -455,8 +452,7 @@ void GrpcWorker::GrpcRecvTensorAsync(CallOptions* opts,
           {
             // Non-DMA cases.
             if (src_dev->tensorflow_gpu_device_info() && (!on_host)) {
-#if GOOGLE_CUDA
-              const DeviceContext* send_dev_context = send_args.device_context;
+              DeviceContext* send_dev_context = send_args.device_context;
               AllocatorAttributes alloc_attrs;
               alloc_attrs.set_gpu_compatible(true);
               alloc_attrs.set_on_host(true);
@@ -465,7 +461,8 @@ void GrpcWorker::GrpcRecvTensorAsync(CallOptions* opts,
               CHECK(send_dev_context)
                   << "send dev name: " << src_dev->name()
                   << " gpu_info: " << src_dev->tensorflow_gpu_device_info();
-              // "val" is on a GPU. Uses GPUUtil to fill the copy on host.
+              // "val" is on an accelerator device. Uses the device_context to
+              // fill the copy on host.
               StatusCallback copy_ready = [response, done, copy,
                                            is_dead](const Status& s) {
                 // The value is now ready to be returned on the wire.
@@ -474,11 +471,8 @@ void GrpcWorker::GrpcRecvTensorAsync(CallOptions* opts,
                 delete copy;
               };
 
-              GPUUtil::CopyGPUTensorToCPU(src_dev, send_dev_context, &val, copy,
-                                          copy_ready);
-#else
-              done(errors::Internal("No GPU device in process"));
-#endif  // GOOGLE_CUDA
+              send_dev_context->CopyDeviceTensorToCPU(
+                  &val, request->rendezvous_key(), src_dev, copy, copy_ready);
             } else {
               grpc::EncodeTensorToByteBuffer(is_dead, val, response);
               done(Status::OK());

From a5f44b3519627859fb476a9cad1acc354bfa649f Mon Sep 17 00:00:00 2001
From: Alan Chiao 
Date: Fri, 4 May 2018 10:31:01 -0700
Subject: [PATCH 0390/1691] Implement neg op

PiperOrigin-RevId: 195435079
---
 tensorflow/contrib/lite/builtin_ops.h         |   1 +
 .../lite/g3doc/tf_ops_compatibility.md        |  11 ++
 tensorflow/contrib/lite/kernels/BUILD         |  14 ++
 tensorflow/contrib/lite/kernels/neg.cc        |  79 +++++++++++
 tensorflow/contrib/lite/kernels/neg_test.cc   |  80 +++++++++++
 tensorflow/contrib/lite/kernels/register.cc   |   2 +
 tensorflow/contrib/lite/model.cc              |   1 +
 tensorflow/contrib/lite/nnapi_delegate.cc     |   1 +
 tensorflow/contrib/lite/schema/schema.fbs     |   5 +
 .../contrib/lite/schema/schema_generated.h    | 124 +++++++++++++++++-
 tensorflow/contrib/lite/testing/BUILD         |   1 +
 .../contrib/lite/testing/generate_examples.py |  25 ++++
 .../testing/generated_examples_zip_test.cc    |   1 +
 .../contrib/lite/toco/tflite/operator.cc      |   3 +-
 .../contrib/lite/toco/tflite/operator_test.cc |   1 +
 15 files changed, 341 insertions(+), 8 deletions(-)
 create mode 100644 tensorflow/contrib/lite/kernels/neg.cc
 create mode 100644 tensorflow/contrib/lite/kernels/neg_test.cc

diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h
index 21e0e04ef6bc5b..962a7a89707032 100644
--- a/tensorflow/contrib/lite/builtin_ops.h
+++ b/tensorflow/contrib/lite/builtin_ops.h
@@ -84,6 +84,7 @@ typedef enum {
   kTfLiteBuiltinArgMax = 56,
   kTfLiteBuiltinMinimum = 57,
   kTfLiteBuiltinLess = 58,
+  kTfLiteBuiltinNeg = 59,
 } TfLiteBuiltinOperator;
 
 #ifdef __cplusplus
diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
index aa28f8d050944e..0051ee84ec38f8 100644
--- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
+++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
@@ -397,6 +397,17 @@ Options {
 }
 ```
 
+**NEG**
+
+```
+Inputs {
+  0: a tensor
+}
+Outputs {
+  0: elementwise negation of the input tensor
+}
+```
+
 **PAD**
 
 ```
diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD
index 57b3136ccec646..feab18b5c23b43 100644
--- a/tensorflow/contrib/lite/kernels/BUILD
+++ b/tensorflow/contrib/lite/kernels/BUILD
@@ -158,6 +158,7 @@ cc_library(
         "mean.cc",
         "mfcc.cc",
         "mul.cc",
+        "neg.cc",
         "pad.cc",
         "pooling.cc",
         "register.cc",
@@ -856,6 +857,19 @@ tf_cc_test(
     ],
 )
 
+tf_cc_test(
+    name = "neg_test",
+    size = "small",
+    srcs = ["neg_test.cc"],
+    tags = ["tflite_not_portable_ios"],
+    deps = [
+        ":builtin_ops",
+        "//tensorflow/contrib/lite:framework",
+        "//tensorflow/contrib/lite/kernels:test_util",
+        "@com_google_googletest//:gtest",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/lite/kernels/neg.cc b/tensorflow/contrib/lite/kernels/neg.cc
new file mode 100644
index 00000000000000..692da817272958
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/neg.cc
@@ -0,0 +1,79 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/lite/context.h"
+#include "tensorflow/contrib/lite/kernels/kernel_util.h"
+
+namespace tflite {
+namespace ops {
+namespace builtin {
+namespace neg {
+
+constexpr int kInputTensor = 0;
+constexpr int kOutputTensor = 0;
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  output->type = input->type;
+  return context->ResizeTensor(context, output,
+                               TfLiteIntArrayCopy(input->dims));
+}
+
+template 
+void Negate(const T* in_data, int num_elements, T* out_data) {
+  // TODO(alanchiao): add vectorized version.
+  for (int i = 0; i < num_elements; ++i) {
+    out_data[i] = -in_data[i];
+  }
+}
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+  const int num_elements = NumElements(input);
+  switch (input->type) {
+    case kTfLiteInt64:
+      Negate(input->data.i64, num_elements, output->data.i64);
+      break;
+    case kTfLiteInt32:
+      Negate(input->data.i32, num_elements, output->data.i32);
+      break;
+    case kTfLiteFloat32:
+      Negate(input->data.f, num_elements, output->data.f);
+      break;
+    default:
+      context->ReportError(
+          context, "Neg only currently supports int64, int32, and float32.",
+          input->type);
+      return kTfLiteError;
+  }
+  return kTfLiteOk;
+}
+
+}  // namespace neg
+
+TfLiteRegistration* Register_NEG() {
+  static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr,
+                                 neg::Prepare, neg::Eval};
+  return &r;
+}
+
+}  // namespace builtin
+}  // namespace ops
+}  // namespace tflite
diff --git a/tensorflow/contrib/lite/kernels/neg_test.cc b/tensorflow/contrib/lite/kernels/neg_test.cc
new file mode 100644
index 00000000000000..3c95ac8cc2727f
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/neg_test.cc
@@ -0,0 +1,80 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include 
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/kernels/test_util.h"
+#include "tensorflow/contrib/lite/model.h"
+
+namespace tflite {
+namespace {
+
+using ::testing::ElementsAreArray;
+
+class NegOpModel : public SingleOpModel {
+ public:
+  NegOpModel(const TensorData& input, const TensorData& output) {
+    input_ = AddInput(input);
+    output_ = AddOutput(output);
+    SetBuiltinOp(BuiltinOperator_NEG, BuiltinOptions_NegOptions,
+                 CreateNegOptions(builder_).Union());
+    BuildInterpreter({GetShape(input_)});
+  }
+
+  template 
+  void SetInput(std::initializer_list data) {
+    PopulateTensor(input_, data);
+  }
+
+  template 
+  std::vector GetOutput() {
+    return ExtractVector(output_);
+  }
+
+ protected:
+  int input_;
+  int output_;
+};
+
+TEST(NegOpModel, NegFloat) {
+  NegOpModel m({TensorType_FLOAT32, {2, 3}}, {TensorType_FLOAT32, {2, 3}});
+  m.SetInput({-2.0f, -1.0f, 0.f, 1.0f, 2.0f, 3.0f});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutput(),
+              ElementsAreArray({2.0f, 1.0f, 0.f, -1.0f, -2.0f, -3.0f}));
+}
+
+TEST(NegOpModel, NegInt32) {
+  NegOpModel m({TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 3}});
+  m.SetInput({-2, -1, 0, 1, 2, 3});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray({2, 1, 0, -1, -2, -3}));
+}
+
+TEST(NegOpModel, NegInt64) {
+  NegOpModel m({TensorType_INT64, {2, 3}}, {TensorType_INT64, {2, 3}});
+  m.SetInput({-2, -1, 0, 1, 2, 3});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray({2, 1, 0, -1, -2, -3}));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}
diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc
index f91d188ffa45fc..29ea718a96a9d9 100644
--- a/tensorflow/contrib/lite/kernels/register.cc
+++ b/tensorflow/contrib/lite/kernels/register.cc
@@ -81,6 +81,7 @@ TfLiteRegistration* Register_MINIMUM();
 TfLiteRegistration* Register_ARG_MAX();
 TfLiteRegistration* Register_LESS();
 TfLiteRegistration* Register_FLOOR();
+TfLiteRegistration* Register_NEG();
 
 BuiltinOpResolver::BuiltinOpResolver() {
   AddBuiltin(BuiltinOperator_RELU, Register_RELU());
@@ -143,6 +144,7 @@ BuiltinOpResolver::BuiltinOpResolver() {
   AddBuiltin(BuiltinOperator_ARG_MAX, Register_ARG_MAX());
   AddBuiltin(BuiltinOperator_LESS, Register_LESS());
   AddBuiltin(BuiltinOperator_FLOOR, Register_FLOOR());
+  AddBuiltin(BuiltinOperator_NEG, Register_NEG());
 
   // TODO(andrewharp, ahentz): Move these somewhere more appropriate so that
   // custom ops aren't always included by default.
diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc
index e15f1be7d38802..590f042e216709 100644
--- a/tensorflow/contrib/lite/model.cc
+++ b/tensorflow/contrib/lite/model.cc
@@ -351,6 +351,7 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type,
     case BuiltinOperator_DEQUANTIZE:
     case BuiltinOperator_PRELU:
     case BuiltinOperator_FLOOR:
+    case BuiltinOperator_NEG:
       break;
     case BuiltinOperator_CAST: {
       TfLiteCastParams* params = MallocPOD();
diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc
index e1895dd38e9329..6eac18c4f5e0f4 100644
--- a/tensorflow/contrib/lite/nnapi_delegate.cc
+++ b/tensorflow/contrib/lite/nnapi_delegate.cc
@@ -372,6 +372,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       case tflite::BuiltinOperator_MINIMUM:
       case tflite::BuiltinOperator_ARG_MAX:
       case tflite::BuiltinOperator_LESS:
+      case tflite::BuiltinOperator_NEG:
         FATAL("Op code %d is currently not delegated to NNAPI", builtin);
         nn_op_type = -1;  // set to invalid
         break;
diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs
index b16baf02dcfa12..265b1dd3fe8960 100644
--- a/tensorflow/contrib/lite/schema/schema.fbs
+++ b/tensorflow/contrib/lite/schema/schema.fbs
@@ -136,6 +136,7 @@ enum BuiltinOperator : byte {
   ARG_MAX = 56,
   MINIMUM = 57,
   LESS = 58,
+  NEG = 59,
 }
 
 // Options for the builtin operators.
@@ -181,6 +182,7 @@ union BuiltinOptions {
   MaximumMinimumOptions,
   ArgMaxOptions,
   LessOptions,
+  NegOptions,
 }
 
 enum Padding : byte { SAME, VALID }
@@ -406,6 +408,9 @@ table ArgMaxOptions {
 table LessOptions {
 }
 
+table NegOptions {
+}
+
 // An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
 // builtin, or a string if the operator is custom.
 table OperatorCode {
diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h
index 57af973460561b..c172f77aa99327 100755
--- a/tensorflow/contrib/lite/schema/schema_generated.h
+++ b/tensorflow/contrib/lite/schema/schema_generated.h
@@ -154,6 +154,9 @@ struct ArgMaxOptionsT;
 struct LessOptions;
 struct LessOptionsT;
 
+struct NegOptions;
+struct NegOptionsT;
+
 struct OperatorCode;
 struct OperatorCodeT;
 
@@ -272,11 +275,12 @@ enum BuiltinOperator {
   BuiltinOperator_ARG_MAX = 56,
   BuiltinOperator_MINIMUM = 57,
   BuiltinOperator_LESS = 58,
+  BuiltinOperator_NEG = 59,
   BuiltinOperator_MIN = BuiltinOperator_ADD,
-  BuiltinOperator_MAX = BuiltinOperator_LESS
+  BuiltinOperator_MAX = BuiltinOperator_NEG
 };
 
-inline BuiltinOperator (&EnumValuesBuiltinOperator())[58] {
+inline BuiltinOperator (&EnumValuesBuiltinOperator())[59] {
   static BuiltinOperator values[] = {
     BuiltinOperator_ADD,
     BuiltinOperator_AVERAGE_POOL_2D,
@@ -335,7 +339,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[58] {
     BuiltinOperator_MAXIMUM,
     BuiltinOperator_ARG_MAX,
     BuiltinOperator_MINIMUM,
-    BuiltinOperator_LESS
+    BuiltinOperator_LESS,
+    BuiltinOperator_NEG
   };
   return values;
 }
@@ -401,6 +406,7 @@ inline const char **EnumNamesBuiltinOperator() {
     "ARG_MAX",
     "MINIMUM",
     "LESS",
+    "NEG",
     nullptr
   };
   return names;
@@ -454,11 +460,12 @@ enum BuiltinOptions {
   BuiltinOptions_MaximumMinimumOptions = 39,
   BuiltinOptions_ArgMaxOptions = 40,
   BuiltinOptions_LessOptions = 41,
+  BuiltinOptions_NegOptions = 42,
   BuiltinOptions_MIN = BuiltinOptions_NONE,
-  BuiltinOptions_MAX = BuiltinOptions_LessOptions
+  BuiltinOptions_MAX = BuiltinOptions_NegOptions
 };
 
-inline BuiltinOptions (&EnumValuesBuiltinOptions())[42] {
+inline BuiltinOptions (&EnumValuesBuiltinOptions())[43] {
   static BuiltinOptions values[] = {
     BuiltinOptions_NONE,
     BuiltinOptions_Conv2DOptions,
@@ -501,7 +508,8 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[42] {
     BuiltinOptions_DequantizeOptions,
     BuiltinOptions_MaximumMinimumOptions,
     BuiltinOptions_ArgMaxOptions,
-    BuiltinOptions_LessOptions
+    BuiltinOptions_LessOptions,
+    BuiltinOptions_NegOptions
   };
   return values;
 }
@@ -550,6 +558,7 @@ inline const char **EnumNamesBuiltinOptions() {
     "MaximumMinimumOptions",
     "ArgMaxOptions",
     "LessOptions",
+    "NegOptions",
     nullptr
   };
   return names;
@@ -728,6 +737,10 @@ template<> struct BuiltinOptionsTraits {
   static const BuiltinOptions enum_value = BuiltinOptions_LessOptions;
 };
 
+template<> struct BuiltinOptionsTraits {
+  static const BuiltinOptions enum_value = BuiltinOptions_NegOptions;
+};
+
 struct BuiltinOptionsUnion {
   BuiltinOptions type;
   void *value;
@@ -1087,6 +1100,14 @@ struct BuiltinOptionsUnion {
     return type == BuiltinOptions_LessOptions ?
       reinterpret_cast(value) : nullptr;
   }
+  NegOptionsT *AsNegOptions() {
+    return type == BuiltinOptions_NegOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
+  const NegOptionsT *AsNegOptions() const {
+    return type == BuiltinOptions_NegOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
 };
 
 bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type);
@@ -4014,6 +4035,46 @@ inline flatbuffers::Offset CreateLessOptions(
 
 flatbuffers::Offset CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
 
+struct NegOptionsT : public flatbuffers::NativeTable {
+  typedef NegOptions TableType;
+  NegOptionsT() {
+  }
+};
+
+struct NegOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef NegOptionsT NativeTableType;
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           verifier.EndTable();
+  }
+  NegOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct NegOptionsBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  explicit NegOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  NegOptionsBuilder &operator=(const NegOptionsBuilder &);
+  flatbuffers::Offset Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset CreateNegOptions(
+    flatbuffers::FlatBufferBuilder &_fbb) {
+  NegOptionsBuilder builder_(_fbb);
+  return builder_.Finish();
+}
+
+flatbuffers::Offset CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
 struct OperatorCodeT : public flatbuffers::NativeTable {
   typedef OperatorCode TableType;
   BuiltinOperator builtin_code;
@@ -4254,6 +4315,9 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
   const LessOptions *builtin_options_as_LessOptions() const {
     return builtin_options_type() == BuiltinOptions_LessOptions ? static_cast(builtin_options()) : nullptr;
   }
+  const NegOptions *builtin_options_as_NegOptions() const {
+    return builtin_options_type() == BuiltinOptions_NegOptions ? static_cast(builtin_options()) : nullptr;
+  }
   const flatbuffers::Vector *custom_options() const {
     return GetPointer *>(VT_CUSTOM_OPTIONS);
   }
@@ -4444,6 +4508,10 @@ template<> inline const LessOptions *Operator::builtin_options_as()
   return builtin_options_as_LessOptions();
 }
 
+template<> inline const NegOptions *Operator::builtin_options_as() const {
+  return builtin_options_as_NegOptions();
+}
+
 struct OperatorBuilder {
   flatbuffers::FlatBufferBuilder &fbb_;
   flatbuffers::uoffset_t start_;
@@ -6070,6 +6138,29 @@ inline flatbuffers::Offset CreateLessOptions(flatbuffers::FlatBuffe
       _fbb);
 }
 
+inline NegOptionsT *NegOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new NegOptionsT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void NegOptions::UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+}
+
+inline flatbuffers::Offset NegOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateNegOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NegOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  return tflite::CreateNegOptions(
+      _fbb);
+}
+
 inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
   auto _o = new OperatorCodeT();
   UnPackTo(_o, _resolver);
@@ -6417,6 +6508,10 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob
       auto ptr = reinterpret_cast(obj);
       return verifier.VerifyTable(ptr);
     }
+    case BuiltinOptions_NegOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return verifier.VerifyTable(ptr);
+    }
     default: return false;
   }
 }
@@ -6599,6 +6694,10 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c
       auto ptr = reinterpret_cast(obj);
       return ptr->UnPack(resolver);
     }
+    case BuiltinOptions_NegOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return ptr->UnPack(resolver);
+    }
     default: return nullptr;
   }
 }
@@ -6769,6 +6868,10 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff
       auto ptr = reinterpret_cast(value);
       return CreateLessOptions(_fbb, ptr, _rehasher).Union();
     }
+    case BuiltinOptions_NegOptions: {
+      auto ptr = reinterpret_cast(value);
+      return CreateNegOptions(_fbb, ptr, _rehasher).Union();
+    }
     default: return 0;
   }
 }
@@ -6939,6 +7042,10 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL
       value = new LessOptionsT(*reinterpret_cast(u.value));
       break;
     }
+    case BuiltinOptions_NegOptions: {
+      value = new NegOptionsT(*reinterpret_cast(u.value));
+      break;
+    }
     default:
       break;
   }
@@ -7151,6 +7258,11 @@ inline void BuiltinOptionsUnion::Reset() {
       delete ptr;
       break;
     }
+    case BuiltinOptions_NegOptions: {
+      auto ptr = reinterpret_cast(value);
+      delete ptr;
+      break;
+    }
     default: break;
   }
   value = nullptr;
diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD
index a1162cef38693e..211de63d58d093 100644
--- a/tensorflow/contrib/lite/testing/BUILD
+++ b/tensorflow/contrib/lite/testing/BUILD
@@ -43,6 +43,7 @@ gen_zipped_test_files(
         "mean.zip",
         "minimum.zip",
         "mul.zip",
+        "neg.zip",
         "pad.zip",
         "relu.zip",
         "relu1.zip",
diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index 2f8f7a1a795629..7e892769bfa4c9 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -2061,6 +2061,31 @@ def build_inputs(parameters, sess, inputs, outputs):
   make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
 
 
+def make_neg_tests(zip_path):
+  """Make a set of tests to do neg."""
+
+  test_parameters = [{
+      "input_dtype": [tf.float32, tf.int32],
+      "input_shape": [[1, 3, 4, 3], [5]],
+  }]
+
+  def build_graph(parameters):
+    """Build the neg op testing graph."""
+    input_tensor = tf.placeholder(
+        dtype=parameters["input_dtype"],
+        name="input",
+        shape=parameters["input_shape"])
+    out = tf.negative(input_tensor)
+    return [input_tensor], [out]
+
+  def build_inputs(parameters, sess, inputs, outputs):
+    values = create_tensor_data(parameters["input_dtype"],
+                                parameters["input_shape"])
+    return [values], sess.run(outputs, feed_dict=dict(zip(inputs, [values])))
+
+  make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
+
+
 # Toco binary path provided by the generate rule.
 bin_path = None
 
diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
index 34abb213c937cc..0673a3bb462d7c 100644
--- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
+++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
@@ -266,6 +266,7 @@ INSTANTIATE_TESTS(maximum)
 INSTANTIATE_TESTS(mean)
 INSTANTIATE_TESTS(minimum)
 INSTANTIATE_TESTS(mul)
+INSTANTIATE_TESTS(neg)
 INSTANTIATE_TESTS(pad)
 // INSTANTIATE_TESTS(prelu)
 INSTANTIATE_TESTS(relu)
diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc
index d2e14ac5e0d7b0..e18ae805c044b9 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator.cc
+++ b/tensorflow/contrib/lite/toco/tflite/operator.cc
@@ -872,7 +872,6 @@ std::vector> BuildOperatorList() {
   // attributes.
   ops.emplace_back(
       new SimpleOperator("ADDN", OperatorType::kAddN));
-  ops.emplace_back(new SimpleOperator("NEG", OperatorType::kNeg));
   ops.emplace_back(new SimpleOperator(
       "RSQRT", OperatorType::kTensorFlowRsqrt));
   // Simple Operators.
@@ -901,7 +900,7 @@ std::vector> BuildOperatorList() {
       "MINIMUM", OperatorType::kTensorFlowMinimum));
   ops.emplace_back(new SimpleOperator(
       "LESS", OperatorType::kTensorFlowLess));
-
+  ops.emplace_back(new SimpleOperator("NEG", OperatorType::kNeg));
   return ops;
 }
 }  // namespace
diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc
index 36ed741541eadb..2b6c32b07c4a2e 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc
+++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc
@@ -115,6 +115,7 @@ TEST_F(OperatorTest, SimpleOperators) {
       "MINIMUM", OperatorType::kTensorFlowMinimum);
   CheckSimpleOperator("LESS",
                                               OperatorType::kTensorFlowLess);
+  CheckSimpleOperator("NEG", OperatorType::kNeg);
 }
 
 TEST_F(OperatorTest, BuiltinAdd) {

From 47f1bd90658dd6858fb4bbefd4ef8acbef4ca931 Mon Sep 17 00:00:00 2001
From: Allen Lavoie 
Date: Fri, 4 May 2018 10:37:42 -0700
Subject: [PATCH 0391/1691] TFTS: Make it easier to swap in different
 autoregressive models.

Adds a very simple LSTM encoder/decoder option as an example.

ARModel's new constructor argument is a bit awkward, since Estimator's new graphs mean we need a Model factory rather than a Model (or to un-build the model?). It's still a much more pleasant way to write autoregressive models than fiddling with ARModel directly, since ARModel handles collecting all the features (and the prediction loop, etc.). Happy to hear other ideas for an API.

PiperOrigin-RevId: 195436186
---
 .../timeseries/python/timeseries/ar_model.py  | 284 +++++++++++++++---
 .../python/timeseries/ar_model_test.py        |  86 ++++--
 .../python/timeseries/estimators.py           |  17 +-
 .../python/timeseries/estimators_test.py      |  17 +-
 4 files changed, 319 insertions(+), 85 deletions(-)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/ar_model.py b/tensorflow/contrib/timeseries/python/timeseries/ar_model.py
index 558d9480b495ca..ce96180c9271b9 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/ar_model.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/ar_model.py
@@ -20,6 +20,7 @@
 
 from tensorflow.contrib import distributions
 
+from tensorflow.contrib.rnn.python.ops import lstm_ops
 from tensorflow.contrib.timeseries.python.timeseries import model
 from tensorflow.contrib.timeseries.python.timeseries import model_utils
 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import PredictionFeatures
@@ -29,6 +30,9 @@
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.keras._impl.keras.engine import sequential
+from tensorflow.python.keras._impl.keras.engine import training
+from tensorflow.python.keras._impl.keras.layers import core
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
@@ -40,12 +44,150 @@
 from tensorflow.python.ops import variable_scope
 
 
+class FlatPredictionModel(training.Model):
+  """Flattens input and output windows and puts them through dense layers.
+
+  This model does not operate on its own, but rather is a plugin to
+  `ARModel`. See `ARModel`'s constructor documentation
+  (`prediction_model_factory`) for a usage example.
+  """
+
+  def __init__(self,
+               num_features,
+               input_window_size,
+               output_window_size,
+               hidden_layer_sizes=None):
+    """Construct the flat prediction model.
+
+    Args:
+      num_features: number of input features per time step.
+      input_window_size: Number of past time steps of data to look at when doing
+        the regression.
+      output_window_size: Number of future time steps to predict. Note that
+        setting it to > 1 empirically seems to give a better fit.
+      hidden_layer_sizes: list of sizes of hidden layers.
+    """
+    super(FlatPredictionModel, self).__init__()
+    self._input_flatten = core.Flatten()
+    self._output_flatten = core.Flatten()
+    if hidden_layer_sizes:
+      self._hidden_layers = sequential.Sequential([
+          core.Dense(layer_size, activation=nn_ops.relu)
+          for layer_size in hidden_layer_sizes])
+    else:
+      self._hidden_layers = None
+    self._mean_transform = core.Dense(num_features * output_window_size,
+                                      name="predicted_mean")
+    self._covariance_transform = core.Dense(num_features * output_window_size,
+                                            name="log_sigma_square")
+    self._prediction_shape = [-1, output_window_size, num_features]
+
+  def call(self, input_window_features, output_window_features):
+    """Compute predictions from input and output windows.
+
+    Args:
+      input_window_features: A floating point Tensor with shape [batch size,
+        input window size, input features]. The batch dimension may not have
+        static shape information, but the window size and number of input
+        features are known at graph construction time and recorded in the static
+        shape information for the `input_window_features` `Tensor`. Note that
+        `input_window_size` may be zero.
+      output_window_features: A floating point Tensor with shape [batch size,
+        output window size, output features]. As with `input_window_features`,
+        the last two dimensions have static shape information. If there are no
+        output features, the size of the last dimension will be zero.
+    Returns:
+      A dictionary of predictions with keys "mean" and "covariance" (only
+      diagonal covariances are currently supported). Each has shape
+      [batch size, output window size, num_features], where num_features is the
+      same as the constructor argument.
+    """
+    if input_window_features.shape[1].value == 0:
+      # TODO(allenl): Make reshape()'s static shape information work on
+      # zero-size Tensors? Currently this special case is required because
+      # otherwise the Dense layers get unknown last dimensions.
+      activation = self._output_flatten(output_window_features)
+    elif output_window_features.shape[2].value == 0:
+      activation = self._input_flatten(input_window_features)
+    else:
+      activation = array_ops.concat(
+          [self._input_flatten(input_window_features),
+           self._output_flatten(output_window_features)],
+          axis=1)
+    if self._hidden_layers:
+      activation = self._hidden_layers(activation)
+    predicted_mean = array_ops.reshape(
+        self._mean_transform(activation),
+        self._prediction_shape)
+    predicted_covariance = array_ops.reshape(
+        gen_math_ops.exp(self._covariance_transform(activation)),
+        self._prediction_shape)
+    return {"mean": predicted_mean,
+            "covariance": predicted_covariance}
+
+
+class LSTMPredictionModel(training.Model):
+  """A simple encoder/decoder model using an LSTM.
+
+  This model does not operate on its own, but rather is a plugin to
+  `ARModel`. See `ARModel`'s constructor documentation
+  (`prediction_model_factory`) for a usage example.
+  """
+
+  def __init__(self,
+               num_features,
+               input_window_size,
+               output_window_size,
+               num_units=128):
+    """Construct the LSTM prediction model.
+
+    Args:
+      num_features: number of input features per time step.
+      input_window_size: Number of past time steps of data to look at when doing
+        the regression.
+      output_window_size: Number of future time steps to predict. Note that
+        setting it to > 1 empirically seems to give a better fit.
+      num_units: The number of units in the encoder and decoder LSTM cells.
+    """
+    super(LSTMPredictionModel, self).__init__()
+    self._encoder = lstm_ops.LSTMBlockFusedCell(
+        num_units=num_units, name="encoder")
+    self._decoder = lstm_ops.LSTMBlockFusedCell(
+        num_units=num_units, name="decoder")
+    self._mean_transform = core.Dense(num_features,
+                                      name="mean_transform")
+    self._covariance_transform = core.Dense(num_features,
+                                            name="covariance_transform")
+
+  def call(self, input_window_features, output_window_features):
+    """Compute predictions from input and output windows."""
+    # Convert to time major
+    input_window_features = array_ops.transpose(input_window_features,
+                                                [1, 0, 2])
+    output_window_features = array_ops.transpose(output_window_features,
+                                                 [1, 0, 2])
+    _, encoder_state = self._encoder(
+        input_window_features, dtype=self.dtype)
+    decoder_output, _ = self._decoder(
+        output_window_features, dtype=self.dtype,
+        initial_state=encoder_state)
+
+    # Switch back to batch major
+    decoder_output = array_ops.transpose(decoder_output, [1, 0, 2])
+    predicted_mean = self._mean_transform(decoder_output)
+    predicted_covariance = gen_math_ops.exp(
+        self._covariance_transform(decoder_output))
+    return {"mean": predicted_mean,
+            "covariance": predicted_covariance}
+
+
 class ARModel(model.TimeSeriesModel):
   """Auto-regressive model, both linear and non-linear.
 
   Features to the model include time and values of input_window_size timesteps,
-  and times for output_window_size timesteps. These are passed through zero or
-  more hidden layers, and then fed to a loss function (e.g. squared loss).
+  and times for output_window_size timesteps. These are passed through a
+  configurable prediction model, and then fed to a loss function (e.g. squared
+  loss).
 
   Note that this class can also be used to regress against time only by setting
   the input_window_size to zero.
@@ -58,9 +200,9 @@ def __init__(self,
                input_window_size,
                output_window_size,
                num_features,
+               prediction_model_factory=FlatPredictionModel,
                num_time_buckets=10,
                loss=NORMAL_LIKELIHOOD_LOSS,
-               hidden_layer_sizes=None,
                exogenous_feature_columns=None):
     """Constructs an auto-regressive model.
 
@@ -73,6 +215,22 @@ def __init__(self,
       output_window_size: Number of future time steps to predict. Note that
         setting it to > 1 empirically seems to give a better fit.
       num_features: number of input features per time step.
+      prediction_model_factory: A callable taking arguments `num_features`,
+        `input_window_size`, and `output_window_size` and returning a
+        `tf.keras.Model`. The `Model`'s `call()` takes two arguments: an input
+        window and an output window, and returns a dictionary of
+        predictions. See `FlatPredictionModel` for an example. Example usage:
+
+        ```python
+        model = ar_model.ARModel(
+          periodicities=2, num_features=3,
+          prediction_model_factory=functools.partial(
+              FlatPredictionModel,
+              hidden_layer_sizes=[10, 10]))
+        ```
+
+        The default model computes predictions as a linear function of flattened
+        input and output windows.
       num_time_buckets: Number of buckets into which to divide (time %
         periodicity) for generating time based features.
       loss: Loss function to use for training. Currently supported values are
@@ -81,18 +239,15 @@ def __init__(self,
         SQUARED_LOSS, the evaluation loss is reported based on un-scaled
         observations and predictions, while the training loss is computed on
         normalized data (if input statistics are available).
-      hidden_layer_sizes: list of sizes of hidden layers.
       exogenous_feature_columns: A list of `tf.feature_column`s (for example
           `tf.feature_column.embedding_column`) corresponding to exogenous
           features which provide extra information to the model but are not part
           of the series to be predicted. Passed to
           `tf.feature_column.input_layer`.
     """
+    self._model_factory = prediction_model_factory
     self.input_window_size = input_window_size
     self.output_window_size = output_window_size
-    if hidden_layer_sizes is None:
-      hidden_layer_sizes = []
-    self.hidden_layer_sizes = hidden_layer_sizes
     self.window_size = self.input_window_size + self.output_window_size
     self.loss = loss
     super(ARModel, self).__init__(
@@ -115,6 +270,19 @@ def __init__(self,
     assert len(self._periods) or self.input_window_size
     assert output_window_size > 0
 
+  def initialize_graph(self, input_statistics=None):
+    super(ARModel, self).initialize_graph(input_statistics=input_statistics)
+    self._model_scope = variable_scope.variable_scope(
+        # The trailing slash means we strip all enclosing variable_scopes, which
+        # unfortunately is necessary because the model gets called inside and
+        # outside a "while" scope (for prediction and training respectively),
+        # and the variables names need to match.
+        "model/", use_resource=True)
+    self._model_instance = self._model_factory(
+        num_features=self.num_features,
+        input_window_size=self.input_window_size,
+        output_window_size=self.output_window_size)
+
   def get_start_state(self):
     # State which matches the format we'll return later. Typically this will not
     # be used by the model directly, but the shapes and dtypes should match so
@@ -166,17 +334,6 @@ def _predicted_mean_op(self, activations):
     return array_ops.reshape(predicted_mean,
                              [-1, self.output_window_size, self.num_features])
 
-  def _create_hidden_stack(self, activation, activation_size):
-    activations = []
-    for layer_number, layer_size in enumerate(self.hidden_layer_sizes):
-      # TODO(agarwal): Migrate to fully_connected in tf slim
-      activation = model_utils.fully_connected(
-          activation, activation_size, layer_size,
-          name="layer_{}".format(layer_number))
-      activation_size = layer_size
-      activations.append((activation, activation_size))
-    return activations
-
   def prediction_ops(self, times, values, exogenous_regressors):
     """Compute model predictions given input data.
 
@@ -195,7 +352,7 @@ def prediction_ops(self, times, values, exogenous_regressors):
       self.num_features].
     """
     times.get_shape().assert_is_compatible_with([None, self.window_size])
-    activations = []
+    batch_size = array_ops.shape(times)[0]
     if self.input_window_size:
       values.get_shape().assert_is_compatible_with(
           [None, self.input_window_size, self.num_features])
@@ -203,39 +360,66 @@ def prediction_ops(self, times, values, exogenous_regressors):
       exogenous_regressors.get_shape().assert_is_compatible_with(
           [None, self.window_size, self.exogenous_size])
     # Create input features.
-    activation_components = []
+    input_window_features = []
+    input_feature_size = 0
+    output_window_features = []
+    output_feature_size = 0
     if self._periods:
       _, time_features = self._compute_time_features(times)
-      activation_size = self.window_size * self._buckets * len(self._periods)
-      activation_components.append(
-          array_ops.reshape(time_features, [-1, activation_size]))
-    else:
-      activation_size = 0
+      num_time_features = self._buckets * len(self._periods)
+      time_features = array_ops.reshape(
+          time_features,
+          [batch_size,
+           self.window_size,
+           num_time_features])
+      input_time_features, output_time_features = array_ops.split(
+          time_features, (self.input_window_size, self.output_window_size),
+          axis=1)
+      input_feature_size += num_time_features
+      output_feature_size += num_time_features
+      input_window_features.append(input_time_features)
+      output_window_features.append(output_time_features)
     if self.input_window_size:
       inp = array_ops.slice(values, [0, 0, 0], [-1, self.input_window_size, -1])
-      inp_size = self.input_window_size * self.num_features
-      inp = array_ops.reshape(inp, [-1, inp_size])
-      activation_components.append(inp)
-      activation_size += inp_size
+      input_window_features.append(
+          array_ops.reshape(
+              inp,
+              [batch_size, self.input_window_size, self.num_features]))
+      input_feature_size += self.num_features
     if self.exogenous_size:
-      exogenous_size = self.window_size * self.exogenous_size
-      activation_size += exogenous_size
-      exogenous_flattened = array_ops.reshape(
-          exogenous_regressors, [-1, exogenous_size])
-      activation_components.append(exogenous_flattened)
-    assert activation_size
-    assert activation_components
-    activation = array_ops.concat(activation_components, axis=1)
-    activations.append((activation, activation_size))
-    # Create hidden layers.
-    activations += self._create_hidden_stack(activation, activation_size)
-    # Create mean and convariance ops.
-    predicted_mean = self._predicted_mean_op(activations)
-    predicted_covariance = self._predicted_covariance_op(activations,
-                                                         self.num_features)
-    return {"activations": activations,
-            "mean": predicted_mean,
-            "covariance": predicted_covariance}
+      input_exogenous_features, output_exogenous_features = array_ops.split(
+          exogenous_regressors,
+          (self.input_window_size, self.output_window_size),
+          axis=1)
+      input_feature_size += self.exogenous_size
+      output_feature_size += self.exogenous_size
+      input_window_features.append(input_exogenous_features)
+      output_window_features.append(output_exogenous_features)
+    assert input_window_features
+    input_window_features = array_ops.concat(input_window_features, axis=2)
+    if output_window_features:
+      output_window_features = array_ops.concat(output_window_features, axis=2)
+    else:
+      output_window_features = array_ops.zeros(
+          [batch_size, self.output_window_size, 0],
+          dtype=self.dtype)
+    static_batch_size = times.get_shape()[0].value
+    input_window_features.set_shape(
+        [static_batch_size, self.input_window_size, input_feature_size])
+    output_window_features.set_shape(
+        [static_batch_size, self.output_window_size, output_feature_size])
+    return self._output_window_predictions(input_window_features,
+                                           output_window_features)
+
+  def _output_window_predictions(
+      self, input_window_features, output_window_features):
+    with self._model_scope:
+      predictions = self._model_instance(
+          input_window_features, output_window_features)
+      result_shape = [None, self.output_window_size, self.num_features]
+      for v in predictions.values():
+        v.set_shape(result_shape)
+      return predictions
 
   def loss_op(self, targets, prediction_ops):
     """Create loss_op."""
@@ -286,6 +470,8 @@ def predict(self, features):
       values are Tensors of shape [batch_size, predict window size,
       num_features] and correspond to the values passed in `TIMES`.
     """
+    if not self._graph_initialized:
+      self.initialize_graph()
     predict_times = math_ops.cast(
         ops.convert_to_tensor(features[PredictionFeatures.TIMES]), dtypes.int32)
     exogenous_regressors = self._process_exogenous_features(
@@ -701,9 +887,9 @@ def __init__(self,
                input_window_size,
                output_window_size,
                num_features,
+               prediction_model_factory=FlatPredictionModel,
                anomaly_distribution=GAUSSIAN_ANOMALY,
                num_time_buckets=10,
-               hidden_layer_sizes=None,
                exogenous_feature_columns=None):
     assert (anomaly_prior_probability < 1.0 and
             anomaly_prior_probability > 0.0)
@@ -719,7 +905,7 @@ def __init__(self,
         input_window_size=input_window_size,
         output_window_size=output_window_size,
         loss=ARModel.NORMAL_LIKELIHOOD_LOSS,
-        hidden_layer_sizes=hidden_layer_sizes,
+        prediction_model_factory=prediction_model_factory,
         exogenous_feature_columns=exogenous_feature_columns)
 
   def _create_anomaly_ops(self, times, values, prediction_ops_dict):
diff --git a/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py b/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py
index d078ac8d46397d..63f5d3568bc208 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py
@@ -18,12 +18,13 @@
 from __future__ import division
 from __future__ import print_function
 
+import functools
+
 import numpy as np
 
+from tensorflow.contrib.timeseries.python.timeseries import ar_model
 from tensorflow.contrib.timeseries.python.timeseries import input_pipeline
 from tensorflow.contrib.timeseries.python.timeseries import test_utils
-from tensorflow.contrib.timeseries.python.timeseries.ar_model import AnomalyMixtureARModel
-from tensorflow.contrib.timeseries.python.timeseries.ar_model import ARModel
 from tensorflow.contrib.timeseries.python.timeseries.estimators import ARRegressor
 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import PredictionFeatures
 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
@@ -91,7 +92,7 @@ def train_helper(self, input_window_size, loss,
     np.random.seed(3)
     data_noise_stddev = 0.2
     if max_loss is None:
-      if loss == ARModel.NORMAL_LIKELIHOOD_LOSS:
+      if loss == ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS:
         max_loss = 1.0
       else:
         max_loss = 0.05 / (data_noise_stddev ** 2)
@@ -137,7 +138,7 @@ def tf_random_seed(self):
     test_loss = test_evaluation["loss"]
     logging.info("Final test loss: %f", test_loss)
     self.assertLess(test_loss, max_loss)
-    if loss == ARModel.SQUARED_LOSS:
+    if loss == ar_model.ARModel.SQUARED_LOSS:
       # Test that the evaluation loss is reported without input scaling.
       self.assertAllClose(
           test_loss,
@@ -169,7 +170,7 @@ def prediction_input_fn():
     predicted_mean = predictions["mean"][:, 0]
     true_values = predict_true_values[0, :, 0]
 
-    if loss == ARModel.NORMAL_LIKELIHOOD_LOSS:
+    if loss == ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS:
       variances = predictions["covariance"][:, 0]
       standard_deviations = np.sqrt(variances)
       # Note that we may get tighter bounds with more training steps.
@@ -180,26 +181,26 @@ def prediction_input_fn():
   def test_time_regression_squared(self):
     self.train_helper(input_window_size=0,
                       train_steps=350,
-                      loss=ARModel.SQUARED_LOSS)
+                      loss=ar_model.ARModel.SQUARED_LOSS)
 
   def test_autoregression_squared(self):
     self.train_helper(input_window_size=15,
-                      loss=ARModel.SQUARED_LOSS)
+                      loss=ar_model.ARModel.SQUARED_LOSS)
 
   def test_autoregression_short_input_window(self):
     self.train_helper(input_window_size=8,
-                      loss=ARModel.SQUARED_LOSS)
+                      loss=ar_model.ARModel.SQUARED_LOSS)
 
   def test_autoregression_normal(self):
     self.train_helper(input_window_size=10,
-                      loss=ARModel.NORMAL_LIKELIHOOD_LOSS,
+                      loss=ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS,
                       train_steps=300,
                       max_loss=1.5,
                       anomaly_distribution=None)
 
   def test_autoregression_normal_multiple_periods(self):
     self.train_helper(input_window_size=10,
-                      loss=ARModel.NORMAL_LIKELIHOOD_LOSS,
+                      loss=ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS,
                       max_loss=2.0,
                       multiple_periods=True,
                       anomaly_distribution=None)
@@ -207,15 +208,15 @@ def test_autoregression_normal_multiple_periods(self):
   def test_autoregression_normal_anomalies_normal(self):
     self.train_helper(
         input_window_size=10,
-        loss=ARModel.NORMAL_LIKELIHOOD_LOSS,
-        anomaly_distribution=AnomalyMixtureARModel.GAUSSIAN_ANOMALY)
+        loss=ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS,
+        anomaly_distribution=ar_model.AnomalyMixtureARModel.GAUSSIAN_ANOMALY)
 
   def test_autoregression_normal_anomalies_cauchy(self):
     self.train_helper(
         input_window_size=10,
         max_loss=1.5,
-        loss=ARModel.NORMAL_LIKELIHOOD_LOSS,
-        anomaly_distribution=AnomalyMixtureARModel.CAUCHY_ANOMALY)
+        loss=ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS,
+        anomaly_distribution=ar_model.AnomalyMixtureARModel.CAUCHY_ANOMALY)
 
   def test_wrong_window_size(self):
     estimator = ARRegressor(
@@ -237,15 +238,38 @@ def _good_data():
     with self.assertRaisesRegexp(ValueError, "requires a window of at least"):
       estimator.evaluate(input_fn=_bad_window_size_input_fn, steps=1)
 
-  def test_predictions_direct(self):
+  def test_predictions_direct_flat(self):
+    g = ops.Graph()
+    with g.as_default():
+      model = ar_model.ARModel(periodicities=2,
+                               num_features=1,
+                               num_time_buckets=10,
+                               input_window_size=2,
+                               output_window_size=2,
+                               prediction_model_factory=functools.partial(
+                                   ar_model.FlatPredictionModel,
+                                   hidden_layer_sizes=[40, 10]))
+      with session.Session():
+        predicted_values = model.predict({
+            PredictionFeatures.TIMES: [[4, 6, 10]],
+            PredictionFeatures.STATE_TUPLE: (
+                [[1, 2]], [[[1.], [2.]]], [[[], []]])
+        })
+        variables.global_variables_initializer().run()
+        self.assertAllEqual(predicted_values["mean"].eval().shape,
+                            [1, 3, 1])
+
+  def test_predictions_direct_lstm(self):
     g = ops.Graph()
     with g.as_default():
-      model = ARModel(periodicities=2,
-                      num_features=1,
-                      num_time_buckets=10,
-                      input_window_size=2,
-                      output_window_size=2,
-                      hidden_layer_sizes=[40, 10])
+      model = ar_model.ARModel(periodicities=2,
+                               num_features=1,
+                               num_time_buckets=10,
+                               input_window_size=2,
+                               output_window_size=2,
+                               prediction_model_factory=functools.partial(
+                                   ar_model.LSTMPredictionModel,
+                                   num_units=16))
       with session.Session():
         predicted_values = model.predict({
             PredictionFeatures.TIMES: [[4, 6, 10]],
@@ -259,11 +283,11 @@ def test_predictions_direct(self):
   def test_long_eval(self):
     g = ops.Graph()
     with g.as_default():
-      model = ARModel(periodicities=2,
-                      num_features=1,
-                      num_time_buckets=10,
-                      input_window_size=2,
-                      output_window_size=1)
+      model = ar_model.ARModel(periodicities=2,
+                               num_features=1,
+                               num_time_buckets=10,
+                               input_window_size=2,
+                               output_window_size=1)
       raw_features = {
           TrainEvalFeatures.TIMES: [[1, 3, 5, 7, 11]],
           TrainEvalFeatures.VALUES: [[[1.], [2.], [3.], [4.], [5.]]]}
@@ -309,11 +333,11 @@ def test_long_eval(self):
   def test_long_eval_discard_indivisible(self):
     g = ops.Graph()
     with g.as_default():
-      model = ARModel(periodicities=2,
-                      num_features=1,
-                      num_time_buckets=10,
-                      input_window_size=2,
-                      output_window_size=2)
+      model = ar_model.ARModel(periodicities=2,
+                               num_features=1,
+                               num_time_buckets=10,
+                               input_window_size=2,
+                               output_window_size=2)
       raw_features = {
           TrainEvalFeatures.TIMES: [[1, 3, 5, 7, 11]],
           TrainEvalFeatures.VALUES: [[[1.], [2.], [3.], [4.], [5.]]]}
diff --git a/tensorflow/contrib/timeseries/python/timeseries/estimators.py b/tensorflow/contrib/timeseries/python/timeseries/estimators.py
index f4608ca2d1cc28..4ec8d26116159f 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/estimators.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/estimators.py
@@ -18,6 +18,8 @@
 from __future__ import division
 from __future__ import print_function
 
+import functools
+
 from tensorflow.contrib.timeseries.python.timeseries import ar_model
 from tensorflow.contrib.timeseries.python.timeseries import feature_keys
 from tensorflow.contrib.timeseries.python.timeseries import head as ts_head_lib
@@ -61,7 +63,10 @@ def __init__(self, model, state_manager=None, optimizer=None, model_dir=None,
     input_statistics_generator = math_utils.InputStatisticsFromMiniBatch(
         dtype=model.dtype, num_features=model.num_features)
     if state_manager is None:
-      state_manager = state_management.PassthroughStateManager()
+      if isinstance(model, ar_model.ARModel):
+        state_manager = state_management.FilteringOnlyStateManager()
+      else:
+        state_manager = state_management.PassthroughStateManager()
     if optimizer is None:
       optimizer = train.AdamOptimizer(0.02)
     self._model = model
@@ -246,11 +251,13 @@ def __init__(
         anomaly_distribution = ar_model.AnomalyMixtureARModel.GAUSSIAN_ANOMALY
       model = ar_model.ARModel(
           periodicities=periodicities, num_features=num_features,
+          prediction_model_factory=functools.partial(
+              ar_model.FlatPredictionModel,
+              hidden_layer_sizes=hidden_layer_sizes),
           exogenous_feature_columns=exogenous_feature_columns,
           num_time_buckets=num_time_buckets,
           input_window_size=input_window_size,
-          output_window_size=output_window_size, loss=loss,
-          hidden_layer_sizes=hidden_layer_sizes)
+          output_window_size=output_window_size, loss=loss)
     else:
       if loss != ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS:
         raise ValueError(
@@ -261,9 +268,11 @@ def __init__(
           input_window_size=input_window_size,
           output_window_size=output_window_size,
           num_features=num_features,
+          prediction_model_factory=functools.partial(
+              ar_model.FlatPredictionModel,
+              hidden_layer_sizes=hidden_layer_sizes),
           exogenous_feature_columns=exogenous_feature_columns,
           num_time_buckets=num_time_buckets,
-          hidden_layer_sizes=hidden_layer_sizes,
           anomaly_prior_probability=anomaly_prior_probability,
           anomaly_distribution=anomaly_distribution)
     state_manager = state_management.FilteringOnlyStateManager()
diff --git a/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py b/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py
index eebee053f8e600..706742ca287a7d 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py
@@ -16,6 +16,7 @@
 from __future__ import division
 from __future__ import print_function
 
+import functools
 import tempfile
 
 import numpy
@@ -178,7 +179,7 @@ def _fit_restore_fit_test_template(self, estimator_fn, dtype):
             session=sess)
         self.assertAllEqual([10, 15, 1], predictions["mean"].shape)
 
-  def test_fit_restore_fit_ar_regressor(self):
+  def test_fit_restore_fit_ar_flat(self):
     def _estimator_fn(model_dir, exogenous_feature_columns):
       return estimators.ARRegressor(
           periodicities=10, input_window_size=10, output_window_size=6,
@@ -189,6 +190,20 @@ def _estimator_fn(model_dir, exogenous_feature_columns):
           exogenous_feature_columns=exogenous_feature_columns)
     self._fit_restore_fit_test_template(_estimator_fn, dtype=dtypes.float32)
 
+  def test_fit_restore_fit_ar_lstm(self):
+    def _estimator_fn(model_dir, exogenous_feature_columns):
+      return estimators.TimeSeriesRegressor(
+          model=ar_model.ARModel(
+              periodicities=10, input_window_size=10, output_window_size=6,
+              num_features=1,
+              exogenous_feature_columns=exogenous_feature_columns,
+              prediction_model_factory=functools.partial(
+                  ar_model.LSTMPredictionModel,
+                  num_units=10)),
+          config=_SeedRunConfig(),
+          model_dir=model_dir)
+    self._fit_restore_fit_test_template(_estimator_fn, dtype=dtypes.float32)
+
   def test_fit_restore_fit_structural_ensemble_regressor(self):
     dtype = dtypes.float32
     def _estimator_fn(model_dir, exogenous_feature_columns):

From e32c42a6deed1f8ed1dcdeaaba0acf74685c18e3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 4 May 2018 10:47:38 -0700
Subject: [PATCH 0392/1691] Improve broadcast add implementation.

PiperOrigin-RevId: 195437679
---
 .../internal/optimized/optimized_ops.h        | 87 ++++++++++++++++++-
 .../internal/reference/reference_ops.h        | 81 +++++++++++++++++
 2 files changed, 165 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index 3d6042c31fef4c..47767269723a27 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -2593,7 +2593,7 @@ inline void Add(int left_shift, const uint8* input1_data,
   }
 #endif  // NEON
 
-  for (; i < size; i++) {
+  for (; i < size; ++i) {
     const int32 input1_val = input1_offset + input1_data[i];
     const int32 input2_val = input2_offset + input2_data[i];
     const int32 shifted_input1_val = input1_val * (1 << left_shift);
@@ -2750,7 +2750,7 @@ inline void BroadcastAdd(int left_shift, const uint8* input1_data,
                          int32 output_activation_min,
                          int32 output_activation_max, uint8* output_data,
                          const Dims<4>& output_dims) {
-  gemmlowp::ScopedProfilingLabel label("BroadcastAdd/8bit");
+  gemmlowp::ScopedProfilingLabel label("BroadcastAddGeneric/8bit");
 
   NdArrayDesc<4> desc1;
   NdArrayDesc<4> desc2;
@@ -2799,6 +2799,60 @@ inline void BroadcastAdd(int left_shift, const uint8* input1_data,
   }
 }
 
+inline void BroadcastAddFivefold(
+    int y0, int y1, int y2, int y3, int y4, int left_shift,
+    const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset,
+    int32 input1_multiplier, int input1_shift, const uint8* input2_data,
+    const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier,
+    int input2_shift, int32 output_offset, int32 output_multiplier,
+    int output_shift, int32 output_activation_min, int32 output_activation_max,
+    uint8* output_data, const Dims<4>& output_dims) {
+  gemmlowp::ScopedProfilingLabel label("BroadcastAddFivefold/8bit");
+
+  // Fivefold nested loops. The second input resets its position for each
+  // iteration of the second loop. The first input resets its position at the
+  // beginning of the fourth loop. The innermost loop is an elementwise add of
+  // sections of the arrays.
+  uint8* output_data_ptr = output_data;
+  const uint8* input1_data_ptr = input1_data;
+  const uint8* input2_data_reset = input2_data;
+  for (int i4 = 0; i4 < y4; ++i4) {
+    const uint8* input2_data_ptr;
+    for (int i3 = 0; i3 < y3; ++i3) {
+      input2_data_ptr = input2_data_reset;
+      for (int i2 = 0; i2 < y2; ++i2) {
+        for (int i1 = 0; i1 < y1; ++i1) {
+          for (int i0 = 0; i0 < y0; ++i0) {
+            const int32 input1_val = input1_offset + input1_data_ptr[i0];
+            const int32 input2_val = input2_offset + input2_data_ptr[i0];
+            const int32 shifted_input1_val = input1_val * (1 << left_shift);
+            const int32 shifted_input2_val = input2_val * (1 << left_shift);
+            const int32 scaled_input1_val =
+                MultiplyByQuantizedMultiplierSmallerThanOne(
+                    shifted_input1_val, input1_multiplier, input1_shift);
+            const int32 scaled_input2_val =
+                MultiplyByQuantizedMultiplierSmallerThanOne(
+                    shifted_input2_val, input2_multiplier, input2_shift);
+            const int32 raw_sum = scaled_input1_val + scaled_input2_val;
+            const int32 raw_output =
+                MultiplyByQuantizedMultiplierSmallerThanOne(
+                    raw_sum, output_multiplier, output_shift) +
+                output_offset;
+            const int32 clamped_output =
+                std::min(output_activation_max,
+                         std::max(output_activation_min, raw_output));
+            output_data_ptr[i0] = static_cast(clamped_output);
+          }
+          input2_data_ptr += y0;
+          output_data_ptr += y0;
+        }
+        input1_data_ptr += y0;
+      }
+    }
+    input2_data_reset = input2_data_ptr;
+  }
+}
+
 template 
 inline void BroadcastAdd(int left_shift, const uint8* input1_data,
                          const Dims<4>& input1_dims, int32 input1_offset,
@@ -2827,6 +2881,33 @@ inline void BroadcastAdd(int left_shift, const uint8* input1_data,
                output_activation_max, output_data, output_dims);
 }
 
+template 
+inline void BroadcastAddFivefold(
+    int y0, int y1, int y2, int y3, int y4, int left_shift,
+    const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset,
+    int32 input1_multiplier, int input1_shift, const uint8* input2_data,
+    const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier,
+    int input2_shift, int32 output_offset, int32 output_multiplier,
+    int output_shift, int32 output_activation_min, int32 output_activation_max,
+    uint8* output_data, const Dims<4>& output_dims) {
+  static_assert(Ac == FusedActivationFunctionType::kNone ||
+                    Ac == FusedActivationFunctionType::kRelu ||
+                    Ac == FusedActivationFunctionType::kRelu6 ||
+                    Ac == FusedActivationFunctionType::kRelu1,
+                "");
+  TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
+  if (Ac == FusedActivationFunctionType::kNone) {
+    TFLITE_DCHECK_EQ(output_activation_min, 0);
+    TFLITE_DCHECK_EQ(output_activation_max, 255);
+  }
+  BroadcastAddFivefold(y0, y1, y2, y3, y4, left_shift, input1_data, input1_dims,
+                       input1_offset, input1_multiplier, input1_shift,
+                       input2_data, input2_dims, input2_offset,
+                       input2_multiplier, input2_shift, output_offset,
+                       output_multiplier, output_shift, output_activation_min,
+                       output_activation_max, output_data, output_dims);
+}
+
 inline void Mul(const float* input1_data, const Dims<4>& input1_dims,
                 const float* input2_data, const Dims<4>& input2_dims,
                 float output_activation_min, float output_activation_max,
@@ -4375,7 +4456,7 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims,
   using FixedPointAccum = gemmlowp::FixedPoint;
   using FixedPoint0 = gemmlowp::FixedPoint;
 
-gemmlowp::ScopedProfilingLabel label("Softmax/8bit");
+  gemmlowp::ScopedProfilingLabel label("Softmax/8bit");
   const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
   const int height = MatchingArraySize(input_dims, 2, output_dims, 2);
   const int width = MatchingArraySize(input_dims, 1, output_dims, 1);
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index d41ade4c9d9ec2..c6ed614593dfb2 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -1189,6 +1189,60 @@ inline void BroadcastAdd(int left_shift, const uint8* input1_data,
   }
 }
 
+inline void BroadcastAddFivefold(
+    int y0, int y1, int y2, int y3, int y4, int left_shift,
+    const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset,
+    int32 input1_multiplier, int input1_shift, const uint8* input2_data,
+    const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier,
+    int input2_shift, int32 output_offset, int32 output_multiplier,
+    int output_shift, int32 output_activation_min, int32 output_activation_max,
+    uint8* output_data, const Dims<4>& output_dims) {
+  gemmlowp::ScopedProfilingLabel label("BroadcastAddFivefold/8bit");
+
+  int sb1 = y0;
+  int sa2 = y0;
+  int sb2 = y0 * y1;
+  int sa3 = y0 * y2;
+  int sa4 = y0 * y2 * y3;
+  int sb4 = y0 * y1 * y2;
+
+  uint8* output_data_ptr = output_data;
+  for (int i4 = 0; i4 < y4; ++i4) {
+    for (int i3 = 0; i3 < y3; ++i3) {
+      for (int i2 = 0; i2 < y2; ++i2) {
+        for (int i1 = 0; i1 < y1; ++i1) {
+          for (int i0 = 0; i0 < y0; ++i0) {
+            const int32 input1_val =
+                input1_offset +
+                input1_data[i4 * sa4 + i3 * sa3 + i2 * sa2 + i0];
+            const int32 input2_val =
+                input2_offset +
+                input2_data[i4 * sb4 + i2 * sb2 + i1 * sb1 + i0];
+            const int32 shifted_input1_val = input1_val * (1 << left_shift);
+            const int32 shifted_input2_val = input2_val * (1 << left_shift);
+            const int32 scaled_input1_val =
+                MultiplyByQuantizedMultiplierSmallerThanOne(
+                    shifted_input1_val, input1_multiplier, input1_shift);
+            const int32 scaled_input2_val =
+                MultiplyByQuantizedMultiplierSmallerThanOne(
+                    shifted_input2_val, input2_multiplier, input2_shift);
+            const int32 raw_sum = scaled_input1_val + scaled_input2_val;
+            const int32 raw_output =
+                MultiplyByQuantizedMultiplierSmallerThanOne(
+                    raw_sum, output_multiplier, output_shift) +
+                output_offset;
+            const int32 clamped_output =
+                std::min(output_activation_max,
+                         std::max(output_activation_min, raw_output));
+            *output_data_ptr = static_cast(clamped_output);
+            ++output_data_ptr;
+          }
+        }
+      }
+    }
+  }
+}
+
 template 
 inline void BroadcastAdd(int left_shift, const uint8* input1_data,
                          const Dims<4>& input1_dims, int32 input1_offset,
@@ -1217,6 +1271,33 @@ inline void BroadcastAdd(int left_shift, const uint8* input1_data,
                output_activation_max, output_data, output_dims);
 }
 
+template 
+inline void BroadcastAddFivefold(
+    int y0, int y1, int y2, int y3, int y4, int left_shift,
+    const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset,
+    int32 input1_multiplier, int input1_shift, const uint8* input2_data,
+    const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier,
+    int input2_shift, int32 output_offset, int32 output_multiplier,
+    int output_shift, int32 output_activation_min, int32 output_activation_max,
+    uint8* output_data, const Dims<4>& output_dims) {
+  static_assert(Ac == FusedActivationFunctionType::kNone ||
+                    Ac == FusedActivationFunctionType::kRelu ||
+                    Ac == FusedActivationFunctionType::kRelu6 ||
+                    Ac == FusedActivationFunctionType::kRelu1,
+                "");
+  TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
+  if (Ac == FusedActivationFunctionType::kNone) {
+    TFLITE_DCHECK_EQ(output_activation_min, 0);
+    TFLITE_DCHECK_EQ(output_activation_max, 255);
+  }
+  BroadcastAddFivefold(y0, y1, y2, y3, y4, left_shift, input1_data, input1_dims,
+                       input1_offset, input1_multiplier, input1_shift,
+                       input2_data, input2_dims, input2_offset,
+                       input2_multiplier, input2_shift, output_offset,
+                       output_multiplier, output_shift, output_activation_min,
+                       output_activation_max, output_data, output_dims);
+}
+
 inline void Mul(const float* input1_data, const Dims<4>& input1_dims,
                 const float* input2_data, const Dims<4>& input2_dims,
                 float output_activation_min, float output_activation_max,

From 09d0e300f9a136838102c94e4b5cf0d4d0876ace Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 4 May 2018 10:50:29 -0700
Subject: [PATCH 0393/1691] Internal clean up: change scanf to use int64_t
 instead of int64

PiperOrigin-RevId: 195438212
---
 tensorflow/core/lib/strings/numbers.cc     | 4 ++--
 tensorflow/core/util/command_line_flags.cc | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/lib/strings/numbers.cc b/tensorflow/core/lib/strings/numbers.cc
index e4b909296e8608..987e4fe7330c38 100644
--- a/tensorflow/core/lib/strings/numbers.cc
+++ b/tensorflow/core/lib/strings/numbers.cc
@@ -392,8 +392,8 @@ string FpToString(Fprint fp) {
 
 bool StringToFp(const string& s, Fprint* fp) {
   char junk;
-  uint64 result;
-  if (sscanf(s.c_str(), "%llx%c", &result, &junk) == 1) {
+  uint64_t result;
+  if (sscanf(s.c_str(), "%lx%c", &result, &junk) == 1) {
     *fp = result;
     return true;
   } else {
diff --git a/tensorflow/core/util/command_line_flags.cc b/tensorflow/core/util/command_line_flags.cc
index 8c27d01917ab80..b281acb2b0261f 100644
--- a/tensorflow/core/util/command_line_flags.cc
+++ b/tensorflow/core/util/command_line_flags.cc
@@ -69,8 +69,8 @@ bool ParseInt64Flag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
       str_util::ConsumePrefix(&arg, flag) &&
       str_util::ConsumePrefix(&arg, "=")) {
     char extra;
-    int64 parsed_int64;
-    if (sscanf(arg.data(), "%lld%c", &parsed_int64, &extra) != 1) {
+    int64_t parsed_int64;
+    if (sscanf(arg.data(), "%ld%c", &parsed_int64, &extra) != 1) {
       LOG(ERROR) << "Couldn't interpret value " << arg << " for flag " << flag
                  << ".";
       *value_parsing_ok = false;

From 01a70dc43d32eb5add5f1cb5de2d6c98ed88dd83 Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar 
Date: Fri, 4 May 2018 11:17:17 -0700
Subject: [PATCH 0394/1691] Add operations before Identity operations should be
 quantized.

Fixes #19014

PiperOrigin-RevId: 195443326
---
 .../contrib/quantize/python/quantize.py       |  6 ++++--
 .../contrib/quantize/python/quantize_test.py  | 20 +++++++++----------
 2 files changed, 14 insertions(+), 12 deletions(-)

diff --git a/tensorflow/contrib/quantize/python/quantize.py b/tensorflow/contrib/quantize/python/quantize.py
index efc1a94b3c6e34..60616ea749cd3f 100644
--- a/tensorflow/contrib/quantize/python/quantize.py
+++ b/tensorflow/contrib/quantize/python/quantize.py
@@ -33,7 +33,7 @@
 _QUANTIZABLE_TYPES = {'Conv2D', 'MatMul', 'DepthwiseConv2dNative'}
 
 # Activations that are supported by the quantization rewrite.
-_ACTIVATION_TYPES = {'Relu', 'Relu6', 'Identity'}
+_ACTIVATION_TYPES = {'Relu', 'Relu6'}
 
 
 def Quantize(graph,
@@ -267,8 +267,10 @@ def _FindLayersToQuantize(graph):
 
   # The input to the activation can come from bias add, fold bias add, the
   # bypasses.
+  # TODO(suharshs): We should ideally skip Identity operations instead of
+  # treating them as an activation.
   activation_pattern = graph_matcher.OpTypePattern(
-      '|'.join(_ACTIVATION_TYPES),
+      '|'.join(_ACTIVATION_TYPES) + '|Identity',
       inputs=[
           graph_matcher.OneofPattern([
               bias_add_pattern, folded_bias_add_pattern, bypass_pattern_a,
diff --git a/tensorflow/contrib/quantize/python/quantize_test.py b/tensorflow/contrib/quantize/python/quantize_test.py
index 5e479f39468042..e7360ae03ca535 100644
--- a/tensorflow/contrib/quantize/python/quantize_test.py
+++ b/tensorflow/contrib/quantize/python/quantize_test.py
@@ -74,7 +74,7 @@ def _TestInsertQuantOpForAddAfterConv2d(self, is_training):
                     weights_initializer=self._WeightInit(0.09),
                     activation_fn=None, scope='test/test')
       node = math_ops.add(conv, input2, name='test/add')
-      node = array_ops.identity(node, name='test/identity')
+      node = nn_ops.relu6(node, name='test/relu6')
       update_barrier = control_flow_ops.no_op(name='update_barrier')
       with ops.control_dependencies([update_barrier]):
         array_ops.identity(node, name='control_dependency')
@@ -97,7 +97,7 @@ def _TestInsertQuantOpForAddAfterConv2d(self, is_training):
         for output in quant_op.outputs:
           consumers.extend(output.consumers())
 
-        self.assertNotIn('test/identity', [c.name for c in consumers])
+        self.assertNotIn('test/relu6', [c.name for c in consumers])
 
   def testInsertQuantOpForAddAfterSeparableConv2d(self):
     self._RunTestOverParameters(
@@ -114,7 +114,7 @@ def _TestInsertQuantOpForAddAfterSeparableConv2d(self, is_training):
                               weights_initializer=self._WeightInit(0.09),
                               activation_fn=None, scope='test/test')
       node = math_ops.add(conv, input2, name='test/add')
-      node = array_ops.identity(node, name='test/identity')
+      node = nn_ops.relu6(node, name='test/relu6')
       update_barrier = control_flow_ops.no_op(name='update_barrier')
       with ops.control_dependencies([update_barrier]):
         array_ops.identity(node, name='control_dependency')
@@ -135,7 +135,7 @@ def _TestInsertQuantOpForAddAfterSeparableConv2d(self, is_training):
         for output in quant_op.outputs:
           consumers.extend(output.consumers())
 
-        self.assertNotIn('test/identity', [c.name for c in consumers])
+        self.assertNotIn('test/relu6', [c.name for c in consumers])
 
   def testFinalLayerQuantized(self):
     self._RunTestOverParameters(self._TestFinalLayerQuantized)
@@ -174,7 +174,7 @@ def _TestPostActivationBypassQuantized(self, is_training):
           stride=2,
           padding='SAME',
           weights_initializer=self._WeightInit(0.09),
-          activation_fn=array_ops.identity,
+          activation_fn=nn_ops.relu6,
           scope='test/test')
       bypass_tensor = math_ops.add(conv, input2, name='test/add')
       # The output of the post_activation bypass will be another layer.
@@ -184,7 +184,7 @@ def _TestPostActivationBypassQuantized(self, is_training):
           stride=2,
           padding='SAME',
           weights_initializer=self._WeightInit(0.09),
-          activation_fn=array_ops.identity,
+          activation_fn=nn_ops.relu6,
           scope='test/unused')
 
       quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8)
@@ -212,7 +212,7 @@ def _TestOverlappingPostActivationBypassQuantized(self, is_training):
           stride=2,
           padding='SAME',
           weights_initializer=self._WeightInit(0.09),
-          activation_fn=array_ops.identity,
+          activation_fn=nn_ops.relu6,
           scope='test/test1')
 
       # The bypass of this conv is the post activation bypass of the previous
@@ -227,7 +227,7 @@ def _TestOverlappingPostActivationBypassQuantized(self, is_training):
           scope='test/test2')
 
       bypass_tensor = math_ops.add(conv1, conv2, name='test/add')
-      _ = array_ops.identity(bypass_tensor, name='test/output')
+      _ = nn_ops.relu6(bypass_tensor, name='test/output')
 
       quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8)
 
@@ -248,11 +248,11 @@ def _TestOverlappingPostActivationBypassQuantized(self, is_training):
           'test/test1/act_quant/FakeQuantWithMinMaxVars' in op_names)
       self.assertTrue('test/act_quant/FakeQuantWithMinMaxVars' in op_names)
       self.assertEqual(
-          'Identity',
+          'Relu6',
           graph.get_operation_by_name(
               'test/test1/act_quant/FakeQuantWithMinMaxVars').inputs[0].op.type)
       self.assertEqual(
-          'Identity',
+          'Relu6',
           graph.get_operation_by_name(
               'test/act_quant/FakeQuantWithMinMaxVars').inputs[0].op.type)
 

From a2cba4a627f880cf8160de624fc1ad947c01e973 Mon Sep 17 00:00:00 2001
From: mbhuiyan 
Date: Fri, 4 May 2018 12:02:28 -0700
Subject: [PATCH 0395/1691]  if MKL is used allocation id is set to 9 and 10

---
 .../direct_session_with_tracking_alloc_test.cc  | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
index 0ff022a8bceff5..29c8c8daecfbbb 100644
--- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
@@ -101,18 +101,21 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) {
         EXPECT_EQ(2, shape.dim_size());
         EXPECT_EQ(2, shape.dim(0).size());
         EXPECT_EQ(1, shape.dim(1).size());
-#ifndef INTEL_MKL
+#ifdef INTEL_MKL
         // if MKL is used, it goes through various additional 
         // graph rewrite pass. In TF, everytime a graph pass 
         // happens, "constant" nodes are allocated
         // and deallocated. Each allocation calls the
-        // (FindChunkPtr of BFCAllocator)
-        // , which increments the value of AllocationId. 
+        // (FindChunkPtr of BFCAllocator),
+        // which increments the value of AllocationId. 
         // Thus AllocationId becomes more than 3 and 4 if 
-        // MKL is used, they can be 10 and 11 or 
-        // other numbers. If MKL is used
-        // following check will not hold. 
-        // Thus, skipping the check if MKL is used.
+        // MKL is used. Now they are 9 and 10 for MKL. 
+        if (node->name() == y->name()) {
+          EXPECT_EQ(9, cm->AllocationId(node, 0));
+        } else {
+          EXPECT_EQ(10, cm->AllocationId(node, 0));
+        }
+#else
         if (node->name() == y->name()) {
           EXPECT_EQ(3, cm->AllocationId(node, 0));
         } else {

From 2314acf98fb874317dd17ef3daf438d7af87f900 Mon Sep 17 00:00:00 2001
From: Anya Petrova 
Date: Fri, 4 May 2018 13:22:03 -0700
Subject: [PATCH 0396/1691] Fix a small typo.

---
 SECURITY.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/SECURITY.md b/SECURITY.md
index a5ce3a62ee202f..01886b613e5d93 100644
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -173,7 +173,7 @@ the progress being made towards a fix and announcement.
 In addition, please include the following information along with your report:
 
 * Your name and affiliation (if any).
-* A description the technical details of the vulnerabilities. It is very
+* A description of the technical details of the vulnerabilities. It is very
   important to let us know how we can reproduce your findings.
 * An explanation who can exploit this vulnerability, and what they gain when
   doing so -- write an attack scenario. This will help us evaluate your report

From f368558429f5ebdbc0a187c3801dccf1ca6963c7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 4 May 2018 11:40:01 -0700
Subject: [PATCH 0397/1691] [XLA] Cleanup client_library_test_base: move
 definition of CreateParameterAndTransferLiteral to .cc file

PiperOrigin-RevId: 195446864
---
 .../xla/tests/client_library_test_base.cc     | 29 +++++++++++++++++++
 .../xla/tests/client_library_test_base.h      | 29 -------------------
 2 files changed, 29 insertions(+), 29 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc
index c09e7eaf2bb94d..41f9a5f66649dd 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.cc
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc
@@ -565,4 +565,33 @@ XlaOp ClientLibraryTestBase::CreateConstantFromLiteral(const Literal& literal,
       use_bfloat16_ ? *LiteralTestUtil::ConvertF32ToBF16(literal) : literal);
 }
 
+std::unique_ptr
+ClientLibraryTestBase::CreateParameterAndTransferLiteral(int64 parameter_number,
+                                                         const Literal& literal,
+                                                         const string& name,
+                                                         XlaBuilder* builder,
+                                                         XlaOp* data_handle) {
+  return CreateParameterAndTransferLiteral(parameter_number, literal, name,
+                                           nullptr, builder, data_handle);
+}
+
+std::unique_ptr
+ClientLibraryTestBase::CreateParameterAndTransferLiteral(
+    int64 parameter_number, const Literal& literal, const string& name,
+    const DeviceHandle* device_handle, XlaBuilder* builder,
+    XlaOp* data_handle) {
+  const Literal* param_literal = &literal;
+  std::unique_ptr converted_literal;
+  if (use_bfloat16_) {
+    converted_literal = LiteralTestUtil::ConvertF32ToBF16(literal);
+    param_literal = converted_literal.get();
+  }
+  std::unique_ptr data =
+      client_->TransferToServer(*param_literal, device_handle)
+          .ConsumeValueOrDie();
+  *data_handle =
+      builder->Parameter(parameter_number, param_literal->shape(), name);
+  return data;
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h
index e58979a3035dd5..16e838e60ffbd7 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.h
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.h
@@ -616,35 +616,6 @@ std::unique_ptr> ClientLibraryTestBase::CreatePseudorandomR2(
   return result;
 }
 
-std::unique_ptr
-ClientLibraryTestBase::CreateParameterAndTransferLiteral(int64 parameter_number,
-                                                         const Literal& literal,
-                                                         const string& name,
-                                                         XlaBuilder* builder,
-                                                         XlaOp* data_handle) {
-  return CreateParameterAndTransferLiteral(parameter_number, literal, name,
-                                           nullptr, builder, data_handle);
-}
-
-std::unique_ptr
-ClientLibraryTestBase::CreateParameterAndTransferLiteral(
-    int64 parameter_number, const Literal& literal, const string& name,
-    const DeviceHandle* device_handle, XlaBuilder* builder,
-    XlaOp* data_handle) {
-  const Literal* param_literal = &literal;
-  std::unique_ptr converted_literal;
-  if (use_bfloat16_) {
-    converted_literal = LiteralTestUtil::ConvertF32ToBF16(literal);
-    param_literal = converted_literal.get();
-  }
-  std::unique_ptr data =
-      client_->TransferToServer(*param_literal, device_handle)
-          .ConsumeValueOrDie();
-  *data_handle =
-      builder->Parameter(parameter_number, param_literal->shape(), name);
-  return data;
-}
-
 }  // namespace xla
 
 #endif  // TENSORFLOW_COMPILER_XLA_TESTS_CLIENT_LIBRARY_TEST_BASE_H_

From be9b87375adecad9bd8bb12c81b2566c77a68ad7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 4 May 2018 11:40:20 -0700
Subject: [PATCH 0398/1691] [XLA] Redesign: migrate the SWIG wrapped xla
 client. Added LocalOp that wraps XlaOp, so that it's fully visible to swig.

PiperOrigin-RevId: 195446939
---
 tensorflow/compiler/xla/python/BUILD          |   3 +-
 .../xla/python/local_computation_builder.cc   | 315 ++++++++-------
 .../xla/python/local_computation_builder.h    | 206 +++++-----
 .../xla/python/local_computation_builder.i    |  53 +--
 tensorflow/compiler/xla/python/xla_client.py  | 362 +++++++-----------
 5 files changed, 415 insertions(+), 524 deletions(-)

diff --git a/tensorflow/compiler/xla/python/BUILD b/tensorflow/compiler/xla/python/BUILD
index ecb87bd8893276..932cce943f7c04 100644
--- a/tensorflow/compiler/xla/python/BUILD
+++ b/tensorflow/compiler/xla/python/BUILD
@@ -49,9 +49,10 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:client_library",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:executable_build_options",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/client/xla_client:xla_builder",
+        "//tensorflow/compiler/xla/client/xla_client:xla_computation",
         "//tensorflow/compiler/xla/service:shaped_buffer",
         "//tensorflow/core:framework_lite",
         "//tensorflow/core:lib",
diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc
index 044458164ff89c..df262c97bfcd91 100644
--- a/tensorflow/compiler/xla/python/local_computation_builder.cc
+++ b/tensorflow/compiler/xla/python/local_computation_builder.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/python/local_computation_builder.h"
 #include "tensorflow/compiler/xla/executable_run_options.h"
+#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/platform/default/thread_annotations.h"
 
@@ -248,7 +249,7 @@ LocalShapedBuffer* CompiledLocalComputation::ExecuteWithShapedBuffers(
   return new LocalShapedBuffer(std::move(result_buffer));
 }
 
-LocalComputation::LocalComputation(Computation computation)
+LocalComputation::LocalComputation(XlaComputation computation)
     : computation_(std::move(computation)) {}
 
 StatusOr LocalComputation::Compile(
@@ -271,7 +272,7 @@ StatusOr LocalComputation::Compile(
   return new CompiledLocalComputation(std::move(local_executable));
 }
 
-const Computation& LocalComputation::computation() const {
+const XlaComputation& LocalComputation::computation() const {
   return computation_;
 }
 
@@ -281,8 +282,12 @@ StatusOr LocalComputation::GetReturnValueShape() const {
   return std::move(*program_shape.mutable_result());
 }
 
+LocalOp::LocalOp(const XlaOp& op) : op_(op) {}
+
+const XlaOp& LocalOp::op() const { return op_; }
+
 LocalComputationBuilder::LocalComputationBuilder(const string& computation_name)
-    : builder_(GetOrCreateLocalClient(), computation_name) {}
+    : builder_(computation_name) {}
 
 void LocalComputationBuilder::SetOpMetadata(const OpMetadata& metadata) {
   builder_.SetOpMetadata(metadata);
@@ -291,19 +296,21 @@ void LocalComputationBuilder::SetOpMetadata(const OpMetadata& metadata) {
 void LocalComputationBuilder::ClearOpMetadata() { builder_.ClearOpMetadata(); }
 
 StatusOr LocalComputationBuilder::Build() {
-  TF_ASSIGN_OR_RETURN(Computation computation, builder_.Build());
+  TF_ASSIGN_OR_RETURN(XlaComputation computation, builder_.Build());
   return new LocalComputation(std::move(computation));
 }
 
-ComputationDataHandle LocalComputationBuilder::Parameter(int64 parameter_number,
-                                                         const Shape& shape,
-                                                         const string& name) {
+LocalOp LocalComputationBuilder::Parameter(int64 parameter_number,
+                                           const Shape& shape,
+                                           const string& name) {
   return builder_.Parameter(parameter_number, shape, name);
 }
 
 std::unique_ptr LocalComputationBuilder::GetShape(
-    const ComputationDataHandle& operand) {
-  return builder_.GetShape(operand).ConsumeValueOrDie();
+    const LocalOp& operand) {
+  auto result = MakeUnique();
+  *result = builder_.GetShape(operand.op()).ValueOrDie();
+  return result;
 }
 
 StatusOr LocalComputationBuilder::GetReturnValueShape() {
@@ -311,222 +318,236 @@ StatusOr LocalComputationBuilder::GetReturnValueShape() {
   return program_shape.result();
 }
 
-ComputationDataHandle LocalComputationBuilder::Infeed(const Shape& shape) {
+LocalOp LocalComputationBuilder::Infeed(const Shape& shape) {
   return builder_.Infeed(shape);
 }
 
-void LocalComputationBuilder::Outfeed(const ComputationDataHandle& operand,
+void LocalComputationBuilder::Outfeed(const LocalOp& operand,
                                       const Shape& shape,
                                       const string& outfeed_config) {
-  builder_.Outfeed(operand, shape, outfeed_config);
+  builder_.Outfeed(operand.op(), shape, outfeed_config);
 }
 
-ComputationDataHandle LocalComputationBuilder::ConstantLiteral(
-    const Literal& literal) {
+LocalOp LocalComputationBuilder::ConstantLiteral(const Literal& literal) {
   return builder_.ConstantLiteral(literal);
 }
 
-ComputationDataHandle LocalComputationBuilder::Broadcast(
-    const ComputationDataHandle& operand,
+LocalOp LocalComputationBuilder::Broadcast(
+    const LocalOp& operand,
     tensorflow::gtl::ArraySlice broadcast_sizes) {
-  return builder_.Broadcast(operand, broadcast_sizes);
+  return builder_.Broadcast(operand.op(), broadcast_sizes);
 }
 
-ComputationDataHandle LocalComputationBuilder::Pad(
-    const ComputationDataHandle& operand,
-    const ComputationDataHandle& padding_value,
-    const PaddingConfig& padding_config) {
-  return builder_.Pad(operand, padding_value, padding_config);
+LocalOp LocalComputationBuilder::Pad(const LocalOp& operand,
+                                     const LocalOp& padding_value,
+                                     const PaddingConfig& padding_config) {
+  return builder_.Pad(operand.op(), padding_value.op(), padding_config);
 }
 
-ComputationDataHandle LocalComputationBuilder::Reshape(
-    const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice dimensions,
+LocalOp LocalComputationBuilder::Reshape(
+    const LocalOp& operand, tensorflow::gtl::ArraySlice dimensions,
     tensorflow::gtl::ArraySlice new_sizes) {
-  return builder_.Reshape(operand, dimensions, new_sizes);
+  return builder_.Reshape(operand.op(), dimensions, new_sizes);
 }
 
-ComputationDataHandle LocalComputationBuilder::Collapse(
-    const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice dimensions) {
-  return builder_.Collapse(operand, dimensions);
+LocalOp LocalComputationBuilder::Collapse(
+    const LocalOp& operand, tensorflow::gtl::ArraySlice dimensions) {
+  return builder_.Collapse(operand.op(), dimensions);
 }
 
-ComputationDataHandle LocalComputationBuilder::CrossReplicaSum(
-    const ComputationDataHandle& operand) {
-  return builder_.CrossReplicaSum(operand);
+LocalOp LocalComputationBuilder::CrossReplicaSum(const LocalOp& operand) {
+  return builder_.CrossReplicaSum(operand.op());
 }
 
-ComputationDataHandle LocalComputationBuilder::Slice(
-    const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice start_indices,
+LocalOp LocalComputationBuilder::Slice(
+    const LocalOp& operand, tensorflow::gtl::ArraySlice start_indices,
     tensorflow::gtl::ArraySlice limit_indices,
     tensorflow::gtl::ArraySlice strides) {
-  return builder_.Slice(operand, start_indices, limit_indices, strides);
+  return builder_.Slice(operand.op(), start_indices, limit_indices, strides);
 }
 
-ComputationDataHandle LocalComputationBuilder::SliceInDim(
-    const ComputationDataHandle& operand, int64 start_index, int64 limit_index,
-    int64 stride, int64 dimno) {
-  return builder_.SliceInDim(operand, start_index, limit_index, stride, dimno);
+LocalOp LocalComputationBuilder::SliceInDim(const LocalOp& operand,
+                                            int64 start_index,
+                                            int64 limit_index, int64 stride,
+                                            int64 dimno) {
+  return builder_.SliceInDim(operand.op(), start_index, limit_index, stride,
+                             dimno);
 }
 
-ComputationDataHandle LocalComputationBuilder::DynamicSlice(
-    const ComputationDataHandle& operand,
-    const ComputationDataHandle& start_indices,
+LocalOp LocalComputationBuilder::DynamicSlice(
+    const LocalOp& operand, const LocalOp& start_indices,
     tensorflow::gtl::ArraySlice slice_sizes) {
-  return builder_.DynamicSlice(operand, start_indices, slice_sizes);
+  return builder_.DynamicSlice(operand.op(), start_indices.op(), slice_sizes);
 }
 
-ComputationDataHandle LocalComputationBuilder::DynamicUpdateSlice(
-    const ComputationDataHandle& operand, const ComputationDataHandle& update,
-    const ComputationDataHandle& start_indices) {
-  return builder_.DynamicUpdateSlice(operand, update, start_indices);
+LocalOp LocalComputationBuilder::DynamicUpdateSlice(
+    const LocalOp& operand, const LocalOp& update,
+    const LocalOp& start_indices) {
+  return builder_.DynamicUpdateSlice(operand.op(), update.op(),
+                                     start_indices.op());
 }
 
-ComputationDataHandle LocalComputationBuilder::ConcatInDim(
-    tensorflow::gtl::ArraySlice operands,
-    int64 dimension) {
-  return builder_.ConcatInDim(operands, dimension);
+LocalOp LocalComputationBuilder::ConcatInDim(
+    tensorflow::gtl::ArraySlice operands, int64 dimension) {
+  std::vector xla_ops;
+  xla_ops.reserve(operands.size());
+  for (const auto& op : operands) {
+    xla_ops.push_back(op.op());
+  }
+  return builder_.ConcatInDim(xla_ops, dimension);
 }
 
-ComputationDataHandle
-LocalComputationBuilder::SelectAndScatterWithGeneralPadding(
-    const ComputationDataHandle& operand, const LocalComputation& select,
+LocalOp LocalComputationBuilder::SelectAndScatterWithGeneralPadding(
+    const LocalOp& operand, const LocalComputation& select,
     tensorflow::gtl::ArraySlice window_dimensions,
     tensorflow::gtl::ArraySlice window_strides,
     tensorflow::gtl::ArraySlice> padding,
-    const ComputationDataHandle& source,
-    const ComputationDataHandle& init_value, const LocalComputation& scatter) {
+    const LocalOp& source, const LocalOp& init_value,
+    const LocalComputation& scatter) {
   return builder_.SelectAndScatterWithGeneralPadding(
-      operand, select.computation(), window_dimensions, window_strides, padding,
-      source, init_value, scatter.computation());
+      operand.op(), select.computation(), window_dimensions, window_strides,
+      padding, source.op(), init_value.op(), scatter.computation());
 }
 
-ComputationDataHandle LocalComputationBuilder::Tuple(
-    tensorflow::gtl::ArraySlice elements) {
-  return builder_.Tuple(elements);
+LocalOp LocalComputationBuilder::Tuple(
+    tensorflow::gtl::ArraySlice elements) {
+  std::vector xla_ops;
+  xla_ops.reserve(elements.size());
+  for (const auto& op : elements) {
+    xla_ops.push_back(op.op());
+  }
+
+  return builder_.Tuple(xla_ops);
 }
 
-ComputationDataHandle LocalComputationBuilder::GetTupleElement(
-    const ComputationDataHandle& tuple_data, int64 index) {
-  return builder_.GetTupleElement(tuple_data, index);
+LocalOp LocalComputationBuilder::GetTupleElement(const LocalOp& tuple_data,
+                                                 int64 index) {
+  return builder_.GetTupleElement(tuple_data.op(), index);
 }
 
-ComputationDataHandle LocalComputationBuilder::Dot(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs) {
-  return builder_.Dot(lhs, rhs);
+LocalOp LocalComputationBuilder::Dot(const LocalOp& lhs, const LocalOp& rhs) {
+  return builder_.Dot(lhs.op(), rhs.op());
 }
 
-ComputationDataHandle LocalComputationBuilder::DotGeneral(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
+LocalOp LocalComputationBuilder::DotGeneral(
+    const LocalOp& lhs, const LocalOp& rhs,
     const DotDimensionNumbers& dimension_numbers) {
-  return builder_.DotGeneral(lhs, rhs, dimension_numbers);
+  return builder_.DotGeneral(lhs.op(), rhs.op(), dimension_numbers);
 }
 
-ComputationDataHandle LocalComputationBuilder::ConvGeneralDilated(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
+LocalOp LocalComputationBuilder::ConvGeneralDilated(
+    const LocalOp& lhs, const LocalOp& rhs,
     tensorflow::gtl::ArraySlice window_strides,
     tensorflow::gtl::ArraySlice> padding,
     tensorflow::gtl::ArraySlice lhs_dilation,
     tensorflow::gtl::ArraySlice rhs_dilation,
     const ConvolutionDimensionNumbers& dimension_numbers) {
-  return builder_.ConvGeneralDilated(lhs, rhs, window_strides, padding,
-                                     lhs_dilation, rhs_dilation,
+  return builder_.ConvGeneralDilated(lhs.op(), rhs.op(), window_strides,
+                                     padding, lhs_dilation, rhs_dilation,
                                      dimension_numbers);
 }
 
-ComputationDataHandle LocalComputationBuilder::ConvertElementType(
-    const ComputationDataHandle& operand, PrimitiveType new_element_type) {
-  return builder_.ConvertElementType(operand, new_element_type);
+LocalOp LocalComputationBuilder::ConvertElementType(
+    const LocalOp& operand, PrimitiveType new_element_type) {
+  return builder_.ConvertElementType(operand.op(), new_element_type);
 }
 
-ComputationDataHandle LocalComputationBuilder::Call(
+LocalOp LocalComputationBuilder::Call(
     const LocalComputation& local_computation,
-    tensorflow::gtl::ArraySlice operands) {
-  return builder_.Call(local_computation.computation(), operands);
+    tensorflow::gtl::ArraySlice operands) {
+  std::vector xla_ops;
+  xla_ops.reserve(operands.size());
+  for (const auto& op : operands) {
+    xla_ops.push_back(op.op());
+  }
+  return builder_.Call(local_computation.computation(), xla_ops);
 }
 
-ComputationDataHandle LocalComputationBuilder::Transpose(
-    const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice permutation) {
-  return builder_.Transpose(operand, permutation);
+LocalOp LocalComputationBuilder::Transpose(
+    const LocalOp& operand, tensorflow::gtl::ArraySlice permutation) {
+  return builder_.Transpose(operand.op(), permutation);
 }
 
-ComputationDataHandle LocalComputationBuilder::Rev(
-    const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice dimensions) {
-  return builder_.Rev(operand, dimensions);
+LocalOp LocalComputationBuilder::Rev(
+    const LocalOp& operand, tensorflow::gtl::ArraySlice dimensions) {
+  return builder_.Rev(operand.op(), dimensions);
 }
 
-ComputationDataHandle LocalComputationBuilder::Map(
-    tensorflow::gtl::ArraySlice operands,
+LocalOp LocalComputationBuilder::Map(
+    tensorflow::gtl::ArraySlice operands,
     const LocalComputation& local_computation,
     tensorflow::gtl::ArraySlice dimensions,
-    tensorflow::gtl::ArraySlice static_operands) {
-  return builder_.Map(operands, local_computation.computation(), dimensions,
-                      static_operands);
+    tensorflow::gtl::ArraySlice static_operands) {
+  std::vector xla_ops;
+  xla_ops.reserve(operands.size());
+  for (const auto& op : operands) {
+    xla_ops.push_back(op.op());
+  }
+
+  std::vector static_xla_ops;
+  static_xla_ops.reserve(static_operands.size());
+  for (const auto& op : static_operands) {
+    static_xla_ops.push_back(op.op());
+  }
+
+  return builder_.Map(xla_ops, local_computation.computation(), dimensions,
+                      static_xla_ops);
 }
 
-ComputationDataHandle LocalComputationBuilder::Reduce(
-    const ComputationDataHandle& operand,
-    const ComputationDataHandle& init_value,
+LocalOp LocalComputationBuilder::Reduce(
+    const LocalOp& operand, const LocalOp& init_value,
     const LocalComputation& local_computation,
     tensorflow::gtl::ArraySlice dimensions_to_reduce) {
-  return builder_.Reduce(operand, init_value, local_computation.computation(),
-                         dimensions_to_reduce);
+  return builder_.Reduce(operand.op(), init_value.op(),
+                         local_computation.computation(), dimensions_to_reduce);
 }
 
-ComputationDataHandle LocalComputationBuilder::ReduceWindowWithGeneralPadding(
-    const ComputationDataHandle& operand,
-    const ComputationDataHandle& init_value,
+LocalOp LocalComputationBuilder::ReduceWindowWithGeneralPadding(
+    const LocalOp& operand, const LocalOp& init_value,
     const LocalComputation& local_computation,
     tensorflow::gtl::ArraySlice window_dimensions,
     tensorflow::gtl::ArraySlice window_strides,
     tensorflow::gtl::ArraySlice> padding) {
   return builder_.ReduceWindowWithGeneralPadding(
-      operand, init_value, local_computation.computation(), window_dimensions,
-      window_strides, padding);
+      operand.op(), init_value.op(), local_computation.computation(),
+      window_dimensions, window_strides, padding);
 }
 
-ComputationDataHandle LocalComputationBuilder::RngNormal(
-    const ComputationDataHandle& mu, const ComputationDataHandle& sigma,
-    const Shape& shape) {
-  return builder_.RngNormal(mu, sigma, shape);
+LocalOp LocalComputationBuilder::RngNormal(const LocalOp& mu,
+                                           const LocalOp& sigma,
+                                           const Shape& shape) {
+  return builder_.RngNormal(mu.op(), sigma.op(), shape);
 }
 
-ComputationDataHandle LocalComputationBuilder::RngUniform(
-    const ComputationDataHandle& a, const ComputationDataHandle& b,
-    const Shape& shape) {
-  return builder_.RngUniform(a, b, shape);
+LocalOp LocalComputationBuilder::RngUniform(const LocalOp& a, const LocalOp& b,
+                                            const Shape& shape) {
+  return builder_.RngUniform(a.op(), b.op(), shape);
 }
 
-ComputationDataHandle LocalComputationBuilder::While(
-    const LocalComputation& condition, const LocalComputation& body,
-    const ComputationDataHandle& init) {
-  return builder_.While(condition.computation(), body.computation(), init);
+LocalOp LocalComputationBuilder::While(const LocalComputation& condition,
+                                       const LocalComputation& body,
+                                       const LocalOp& init) {
+  return builder_.While(condition.computation(), body.computation(), init.op());
 }
 
-ComputationDataHandle LocalComputationBuilder::Conditional(
-    const ComputationDataHandle& predicate,
-    const ComputationDataHandle& true_operand,
-    const LocalComputation& true_computation,
-    const ComputationDataHandle& false_operand,
+LocalOp LocalComputationBuilder::Conditional(
+    const LocalOp& predicate, const LocalOp& true_operand,
+    const LocalComputation& true_computation, const LocalOp& false_operand,
     const LocalComputation& false_computation) {
-  return builder_.Conditional(predicate, true_operand,
-                              true_computation.computation(), false_operand,
-                              false_computation.computation());
+  return builder_.Conditional(
+      predicate.op(), true_operand.op(), true_computation.computation(),
+      false_operand.op(), false_computation.computation());
 }
 
-StatusOr LocalComputationBuilder::IsConstant(
-    const ComputationDataHandle& operand, int64 num_parameters) {
-  return builder_.IsConstant(operand, num_parameters);
+StatusOr LocalComputationBuilder::IsConstant(const LocalOp& operand) {
+  return builder_.IsConstant(operand.op());
 }
 
-StatusOr> LocalComputationBuilder::ComputeConstant(
-    const ComputationDataHandle& operand, const Layout* output_layout,
-    tensorflow::gtl::ArraySlice parameters) {
-  return builder_.ComputeConstant(operand, output_layout, parameters);
+StatusOr LocalComputationBuilder::BuildConstantSubGraph(
+    const LocalOp& operand) {
+  TF_ASSIGN_OR_RETURN(XlaComputation computation,
+                      builder_.BuildConstantSubGraph(operand.op()));
+  return new LocalComputation(std::move(computation));
 }
 
 #define _FORWARD(method_name, return_sig, args_sig, args)    \
@@ -534,23 +555,19 @@ StatusOr> LocalComputationBuilder::ComputeConstant(
     return builder_.method_name args;                        \
   }
 
-#define _FORWARD_UNOP(method_name)             \
-  _FORWARD(method_name, ComputationDataHandle, \
-           (const ComputationDataHandle& operand), (operand))
-
-#define _FORWARD_BINOP(method_name)                                        \
-  _FORWARD(                                                                \
-      method_name, ComputationDataHandle,                                  \
-      (const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, \
-       tensorflow::gtl::ArraySlice broadcast_dimensions),           \
-      (lhs, rhs, broadcast_dimensions))
-
-#define _FORWARD_TRIOP(method_name)                                        \
-  _FORWARD(                                                                \
-      method_name, ComputationDataHandle,                                  \
-      (const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, \
-       const ComputationDataHandle& ehs),                                  \
-      (lhs, rhs, ehs))
+#define _FORWARD_UNOP(method_name) \
+  _FORWARD(method_name, LocalOp, (const LocalOp& operand), (operand.op()))
+
+#define _FORWARD_BINOP(method_name)                                   \
+  _FORWARD(method_name, LocalOp,                                      \
+           (const LocalOp& lhs, const LocalOp& rhs,                   \
+            tensorflow::gtl::ArraySlice broadcast_dimensions), \
+           (lhs.op(), rhs.op(), broadcast_dimensions))
+
+#define _FORWARD_TRIOP(method_name)                                      \
+  _FORWARD(method_name, LocalOp,                                         \
+           (const LocalOp& lhs, const LocalOp& rhs, const LocalOp& ehs), \
+           (lhs.op(), rhs.op(), ehs.op()))
 
 _FORWARD_TRIOP(Select)
 _FORWARD_TRIOP(Clamp)
diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h
index 5ec097846a59fd..a06b85b4ea28c4 100644
--- a/tensorflow/compiler/xla/python/local_computation_builder.h
+++ b/tensorflow/compiler/xla/python/local_computation_builder.h
@@ -17,9 +17,10 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_PYTHON_LOCAL_COMPUTATION_BUILDER_H_
 
 #include "tensorflow/compiler/xla/client/client_library.h"
-#include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/executable_build_options.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
+#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h"
 #include "tensorflow/compiler/xla/service/shaped_buffer.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
@@ -97,25 +98,37 @@ class CompiledLocalComputation {
   std::unique_ptr executable_;
 };
 
-// Wraps a Computation produced by a LocalComputationBuilder. The
+// Wraps a XlaComputation produced by a LocalComputationBuilder. The
 // Compile method compiles the computation to a (local) executable via
 // the client library's local client. This class is intended to be
 // made available to Python via SWIG.
 class LocalComputation {
  public:
-  LocalComputation(Computation computation);
+  LocalComputation(XlaComputation computation);
 
   StatusOr Compile(
       const std::vector& argument_shapes,
       const ExecutableBuildOptions* build_options);
 
-  const Computation& computation() const;
+  const XlaComputation& computation() const;
 
   // Returns the return-value shape for this computation.
   StatusOr GetReturnValueShape() const;
 
  private:
-  Computation computation_;
+  XlaComputation computation_;
+};
+
+// Wraps a XlaOp produced by a LocalComputationBuilder. This class is intended
+// to be made available to Python via SWIG.
+class LocalOp {
+ public:
+  LocalOp(const XlaOp& op);
+
+  const XlaOp& op() const;
+
+ private:
+  XlaOp op_;
 };
 
 // Wraps the ComputationBuilder API in order to:
@@ -135,166 +148,137 @@ class LocalComputationBuilder {
   // Returns an owned LocalComputation to the caller on success.
   StatusOr Build();
 
-  ComputationDataHandle Parameter(int64 parameter_number, const Shape& shape,
-                                  const string& name);
+  LocalOp Parameter(int64 parameter_number, const Shape& shape,
+                    const string& name);
 
-  std::unique_ptr GetShape(const ComputationDataHandle& operand);
+  std::unique_ptr GetShape(const LocalOp& operand);
 
   // Returns the shape of the current return value for the computation.
   StatusOr GetReturnValueShape();
 
-  ComputationDataHandle Infeed(const Shape& shape);
+  LocalOp Infeed(const Shape& shape);
 
-  void Outfeed(const ComputationDataHandle& operand, const Shape& shape,
+  void Outfeed(const LocalOp& operand, const Shape& shape,
                const string& outfeed_config);
 
-  ComputationDataHandle ConstantLiteral(const Literal& literal);
+  LocalOp ConstantLiteral(const Literal& literal);
 
-  ComputationDataHandle Broadcast(
-      const ComputationDataHandle& operand,
-      tensorflow::gtl::ArraySlice broadcast_sizes);
+  LocalOp Broadcast(const LocalOp& operand,
+                    tensorflow::gtl::ArraySlice broadcast_sizes);
 
-  ComputationDataHandle Pad(const ComputationDataHandle& operand,
-                            const ComputationDataHandle& padding_value,
-                            const PaddingConfig& padding_config);
+  LocalOp Pad(const LocalOp& operand, const LocalOp& padding_value,
+              const PaddingConfig& padding_config);
 
-  ComputationDataHandle Reshape(const ComputationDataHandle& operand,
-                                tensorflow::gtl::ArraySlice dimensions,
-                                tensorflow::gtl::ArraySlice new_sizes);
+  LocalOp Reshape(const LocalOp& operand,
+                  tensorflow::gtl::ArraySlice dimensions,
+                  tensorflow::gtl::ArraySlice new_sizes);
 
-  ComputationDataHandle Collapse(const ComputationDataHandle& operand,
-                                 tensorflow::gtl::ArraySlice dimensions);
+  LocalOp Collapse(const LocalOp& operand,
+                   tensorflow::gtl::ArraySlice dimensions);
 
-  ComputationDataHandle CrossReplicaSum(const ComputationDataHandle& operand);
+  LocalOp CrossReplicaSum(const LocalOp& operand);
 
-  ComputationDataHandle Slice(const ComputationDataHandle& operand,
-                              tensorflow::gtl::ArraySlice start_indices,
-                              tensorflow::gtl::ArraySlice limit_indices,
-                              tensorflow::gtl::ArraySlice strides);
+  LocalOp Slice(const LocalOp& operand,
+                tensorflow::gtl::ArraySlice start_indices,
+                tensorflow::gtl::ArraySlice limit_indices,
+                tensorflow::gtl::ArraySlice strides);
 
-  ComputationDataHandle SliceInDim(const ComputationDataHandle& operand,
-                                   int64 start_index, int64 limit_index,
-                                   int64 stride, int64 dimno);
+  LocalOp SliceInDim(const LocalOp& operand, int64 start_index,
+                     int64 limit_index, int64 stride, int64 dimno);
 
-  ComputationDataHandle DynamicSlice(
-      const ComputationDataHandle& operand,
-      const ComputationDataHandle& start_indices,
-      tensorflow::gtl::ArraySlice slice_sizes);
+  LocalOp DynamicSlice(const LocalOp& operand, const LocalOp& start_indices,
+                       tensorflow::gtl::ArraySlice slice_sizes);
 
-  ComputationDataHandle DynamicUpdateSlice(
-      const ComputationDataHandle& operand, const ComputationDataHandle& update,
-      const ComputationDataHandle& start_indices);
+  LocalOp DynamicUpdateSlice(const LocalOp& operand, const LocalOp& update,
+                             const LocalOp& start_indices);
 
-  ComputationDataHandle ConcatInDim(
-      tensorflow::gtl::ArraySlice operands,
-      int64 dimension);
+  LocalOp ConcatInDim(tensorflow::gtl::ArraySlice operands,
+                      int64 dimension);
 
-  ComputationDataHandle SelectAndScatterWithGeneralPadding(
-      const ComputationDataHandle& operand, const LocalComputation& select,
+  LocalOp SelectAndScatterWithGeneralPadding(
+      const LocalOp& operand, const LocalComputation& select,
       tensorflow::gtl::ArraySlice window_dimensions,
       tensorflow::gtl::ArraySlice window_strides,
       tensorflow::gtl::ArraySlice > padding,
-      const ComputationDataHandle& source,
-      const ComputationDataHandle& init_value, const LocalComputation& scatter);
+      const LocalOp& source, const LocalOp& init_value,
+      const LocalComputation& scatter);
 
-  ComputationDataHandle Tuple(
-      tensorflow::gtl::ArraySlice elements);
+  LocalOp Tuple(tensorflow::gtl::ArraySlice elements);
 
-  ComputationDataHandle GetTupleElement(const ComputationDataHandle& tuple_data,
-                                        int64 index);
+  LocalOp GetTupleElement(const LocalOp& tuple_data, int64 index);
 
-  ComputationDataHandle Dot(const ComputationDataHandle& lhs,
-                            const ComputationDataHandle& rhs);
+  LocalOp Dot(const LocalOp& lhs, const LocalOp& rhs);
 
-  ComputationDataHandle DotGeneral(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      const DotDimensionNumbers& dimension_numbers);
+  LocalOp DotGeneral(const LocalOp& lhs, const LocalOp& rhs,
+                     const DotDimensionNumbers& dimension_numbers);
 
-  ComputationDataHandle ConvGeneralDilated(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
+  LocalOp ConvGeneralDilated(
+      const LocalOp& lhs, const LocalOp& rhs,
       tensorflow::gtl::ArraySlice window_strides,
       tensorflow::gtl::ArraySlice > padding,
       tensorflow::gtl::ArraySlice lhs_dilation,
       tensorflow::gtl::ArraySlice rhs_dilation,
       const ConvolutionDimensionNumbers& dimension_numbers);
 
-  ComputationDataHandle ConvertElementType(const ComputationDataHandle& operand,
-                                           PrimitiveType new_element_type);
+  LocalOp ConvertElementType(const LocalOp& operand,
+                             PrimitiveType new_element_type);
 
-  ComputationDataHandle Call(
-      const LocalComputation& local_computation,
-      tensorflow::gtl::ArraySlice operands);
+  LocalOp Call(const LocalComputation& local_computation,
+               tensorflow::gtl::ArraySlice operands);
 
-  ComputationDataHandle Transpose(
-      const ComputationDataHandle& operand,
-      tensorflow::gtl::ArraySlice permutation);
+  LocalOp Transpose(const LocalOp& operand,
+                    tensorflow::gtl::ArraySlice permutation);
 
-  ComputationDataHandle Rev(const ComputationDataHandle& operand,
-                            tensorflow::gtl::ArraySlice dimensions);
+  LocalOp Rev(const LocalOp& operand,
+              tensorflow::gtl::ArraySlice dimensions);
 
-  ComputationDataHandle Map(
-      tensorflow::gtl::ArraySlice operands,
-      const LocalComputation& local_computation,
-      tensorflow::gtl::ArraySlice dimensions,
-      tensorflow::gtl::ArraySlice static_operands);
+  LocalOp Map(tensorflow::gtl::ArraySlice operands,
+              const LocalComputation& local_computation,
+              tensorflow::gtl::ArraySlice dimensions,
+              tensorflow::gtl::ArraySlice static_operands);
 
-  ComputationDataHandle Reduce(
-      const ComputationDataHandle& operand,
-      const ComputationDataHandle& init_value,
-      const LocalComputation& local_computation,
-      tensorflow::gtl::ArraySlice dimensions_to_reduce);
+  LocalOp Reduce(const LocalOp& operand, const LocalOp& init_value,
+                 const LocalComputation& local_computation,
+                 tensorflow::gtl::ArraySlice dimensions_to_reduce);
 
-  ComputationDataHandle ReduceWindowWithGeneralPadding(
-      const ComputationDataHandle& operand,
-      const ComputationDataHandle& init_value,
+  LocalOp ReduceWindowWithGeneralPadding(
+      const LocalOp& operand, const LocalOp& init_value,
       const LocalComputation& local_computation,
       tensorflow::gtl::ArraySlice window_dimensions,
       tensorflow::gtl::ArraySlice window_strides,
       tensorflow::gtl::ArraySlice > padding);
 
-  ComputationDataHandle RngNormal(const ComputationDataHandle& mu,
-                                  const ComputationDataHandle& sigma,
-                                  const Shape& shape);
+  LocalOp RngNormal(const LocalOp& mu, const LocalOp& sigma,
+                    const Shape& shape);
 
-  ComputationDataHandle RngUniform(const ComputationDataHandle& a,
-                                   const ComputationDataHandle& b,
-                                   const Shape& shape);
+  LocalOp RngUniform(const LocalOp& a, const LocalOp& b, const Shape& shape);
 
-  ComputationDataHandle While(const LocalComputation& condition,
-                              const LocalComputation& body,
-                              const ComputationDataHandle& init);
+  LocalOp While(const LocalComputation& condition, const LocalComputation& body,
+                const LocalOp& init);
 
-  ComputationDataHandle Conditional(const ComputationDataHandle& predicate,
-                                    const ComputationDataHandle& true_operand,
-                                    const LocalComputation& true_computation,
-                                    const ComputationDataHandle& false_operand,
-                                    const LocalComputation& false_computation);
+  LocalOp Conditional(const LocalOp& predicate, const LocalOp& true_operand,
+                      const LocalComputation& true_computation,
+                      const LocalOp& false_operand,
+                      const LocalComputation& false_computation);
 
-  StatusOr IsConstant(const ComputationDataHandle& operand,
-                            int64 num_parameters);
+  StatusOr IsConstant(const LocalOp& operand);
 
-  StatusOr > ComputeConstant(
-      const ComputationDataHandle& operand, const Layout* output_layout,
-      tensorflow::gtl::ArraySlice parameters);
+  StatusOr BuildConstantSubGraph(const LocalOp& operand);
 
 #define _FORWARD(method_name, return_sig, args_sig) \
   return_sig method_name args_sig;
 
-#define _FORWARD_UNOP(method_name)             \
-  _FORWARD(method_name, ComputationDataHandle, \
-           (const ComputationDataHandle& operand))
+#define _FORWARD_UNOP(method_name) \
+  _FORWARD(method_name, LocalOp, (const LocalOp& operand))
 
-#define _FORWARD_BINOP(method_name)                                        \
-  _FORWARD(                                                                \
-      method_name, ComputationDataHandle,                                  \
-      (const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, \
-       tensorflow::gtl::ArraySlice broadcast_dimensions))
+#define _FORWARD_BINOP(method_name)                 \
+  _FORWARD(method_name, LocalOp,                    \
+           (const LocalOp& lhs, const LocalOp& rhs, \
+            tensorflow::gtl::ArraySlice broadcast_dimensions))
 
-#define _FORWARD_TRIOP(method_name)                                        \
-  _FORWARD(                                                                \
-      method_name, ComputationDataHandle,                                  \
-      (const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, \
-       const ComputationDataHandle& ehs))
+#define _FORWARD_TRIOP(method_name) \
+  _FORWARD(method_name, LocalOp,    \
+           (const LocalOp& lhs, const LocalOp& rhs, const LocalOp& ehs))
 
   _FORWARD_TRIOP(Select)
   _FORWARD_TRIOP(Clamp)
@@ -338,7 +322,7 @@ class LocalComputationBuilder {
 #undef _FORWARD_TRIOP
 
  private:
-  ComputationBuilder builder_;
+  XlaBuilder builder_;
 };
 
 // Functions for freeing resources from the Python side.
diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i
index b8cce5a5f7105e..04c56bbba95fbf 100644
--- a/tensorflow/compiler/xla/python/local_computation_builder.i
+++ b/tensorflow/compiler/xla/python/local_computation_builder.i
@@ -22,9 +22,8 @@ limitations under the License.
 //
 //    C++                                  Python
 // -------------------------------------+---------------------------------------
-//  ComputationDataHandle              <-> int
 //  ArraySlice                  <-  sequence of int
-//  ArraySlice  <-  sequence of int
+//  ArraySlice                <-  sequence of LocalOp
 //  Literal                            <-> (nested tuple of) numpy ndarray
 //  std::vector               <-  sequence of (nested tuple of) ndarray
 //  Shape                               -> pair holding (dtype, dimensions)
@@ -91,12 +90,9 @@ limitations under the License.
 // One central reason for the Python-side indirection is that the
 // Python-side objects produced by the typemaps in this file are
 // further packaged up by xla_client before being passed on. For
-// instance, xla_client wraps the long produced for a C++
-// ComputationDataHandle in a Python ComputationDataHandle proto,
-// rather than exposing a raw long outside of the client. Similarly,
-// the Python pair produced for a C++ Shape is further wrapped in a
-// Python class (xla_client.Shape) so as not to expose the raw pair
-// externally.
+// instance, the Python pair produced for a C++ Shape is further
+// wrapped in a Python class (xla_client.Shape) so as not to expose
+// the raw pair externally.
 //
 // Other SWIG object wrappers (e.g. of LocalComputation) are further
 // wrapped by xla_client in order to set up a custom destructor that
@@ -124,6 +120,7 @@ using namespace xla;
 using namespace xla::swig;
 
 namespace xla {
+
 namespace swig {
 
 bool GetIntAttr(PyObject* o, const char* field, int64* result) {
@@ -177,21 +174,6 @@ bool HandleStringAttribute(PyObject* o,
 tensorflow::ImportNumpy();
 %}
 
-// ComputationDataHandle
-
-%typemap(in) const ComputationDataHandle& (ComputationDataHandle temp) {
-  const int64 handle = numpy::PyIntOrPyLongToLong($input);
-  if (handle == -1 && PyErr_Occurred()) {
-    SWIG_fail;
-  }
-  temp.set_handle(handle);
-  $1 = &temp;
-}
-
-%typemap(out) ComputationDataHandle {
-  $result = numpy::LongToPyIntOrPyLong($1.handle());
-}
-
 %typemap(out) StatusOr {
   if ($1.ok()) {
     auto* value = $1.ValueOrDie();
@@ -301,33 +283,23 @@ tensorflow::ImportNumpy();
   $1 = temps;
 }
 
-// ComputationDataHandle
+// ArraySlice
 
-%typemap(in) tensorflow::gtl::ArraySlice
-    (std::vector temps) {
+%typemap(in) tensorflow::gtl::ArraySlice(
+      std::vector temps) {
   if (!PySequence_Check($input)) {
     PyErr_SetString(PyExc_TypeError, "Argument is not a sequence");
     SWIG_fail;
   }
   const int size = PySequence_Size($input);
-  temps.resize(size);
   for (int i = 0; i < size; ++i) {
     PyObject* o = PySequence_GetItem($input, i);
-    PyObject* py_int = numpy::PyNumberToPyInt(o);
-    if (!py_int) {
-      PyErr_SetString(
-          PyExc_TypeError,
-          "Argument sequence element cannot be converted to int");
-      SWIG_fail;
-    }
-    const int64 handle = numpy::PyIntOrPyLongToLong(py_int);
-    if (handle == -1 && PyErr_Occurred()) {
-      Py_DECREF(py_int);
-      Py_DECREF(o);
+    LocalOp* op;
+    if ((SWIG_ConvertPtr(o, (void**)&op, $descriptor(xla::swig::LocalOp*),
+                         SWIG_POINTER_EXCEPTION)) == -1) {
       SWIG_fail;
     }
-    temps[i].set_handle(handle);
-    Py_DECREF(py_int);
+    temps.push_back(*op);
     Py_DECREF(o);
   }
   $1 = temps;
@@ -934,6 +906,7 @@ tensorflow::ImportNumpy();
 %unignore xla::swig::LocalComputation;
 %unignore xla::swig::LocalComputation::Compile;
 %unignore xla::swig::LocalComputation::GetReturnValueShape;
+%unignore xla::swig::LocalOp;
 %unignore xla::swig::LocalComputationBuilder;
 %unignore xla::swig::LocalComputationBuilder::LocalComputationBuilder;
 %unignore xla::swig::LocalComputationBuilder::Build;
diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py
index f6809b6b871d7e..1d5b75d1bee2dc 100644
--- a/tensorflow/compiler/xla/python/xla_client.py
+++ b/tensorflow/compiler/xla/python/xla_client.py
@@ -335,20 +335,6 @@ def _wrap_shape(shape_info):
     return Shape.array_shape(dtype, dims)
 
 
-def _wrap_data_handle(handle):
-  cdh = xla_data_pb2.ComputationDataHandle()
-  cdh.handle = handle
-  return cdh
-
-
-def _unwrap_data_handle(handle_proto):
-  return handle_proto.handle
-
-
-def _unwrap_data_handles(handle_protos):
-  return [_unwrap_data_handle(cdh) for cdh in handle_protos]
-
-
 def require_numpy_array_layout(value):
   if isinstance(value, tuple):
     return tuple(require_numpy_array_layout(x) for x in value)
@@ -535,9 +521,9 @@ def Infeed(self, shape):
     queue for subsequent use in the computation.
 
     Returns:
-      A  ComputationDataHandle message.
+      A LocalOp.
     """
-    return _wrap_data_handle(self._client.Infeed(shape))
+    return self._client.Infeed(shape)
 
   def Outfeed(self, operand):
     """Enqueues an outfeed op onto the computation.
@@ -545,9 +531,7 @@ def Outfeed(self, operand):
     Outfeed operations enqueue data, using the given operand, onto the XLA
     outfeed queue for subsequent dequeue via the client API.
     """
-    self._client.Outfeed(
-        _unwrap_data_handle(operand), self.GetShape(operand),
-        ''.encode('utf-8'))
+    self._client.Outfeed(operand, self.GetShape(operand), ''.encode('utf-8'))
 
   def Constant(self, value):
     """Enqueues a constant op onto the computation.
@@ -557,10 +541,10 @@ def Constant(self, value):
              to one of the supported types.
 
     Returns:
-      A ComputationDataHandle message.
+      A LocalOp.
     """
     value = require_numpy_array_layout(value)
-    return _wrap_data_handle(self._client.ConstantLiteral(value))
+    return self._client.ConstantLiteral(value)
 
   def ConstantF32Scalar(self, value):
     """Convenience method to enqueue a scalar F32 constant op.
@@ -569,7 +553,7 @@ def ConstantF32Scalar(self, value):
       value: a floating-point number.
 
     Returns:
-      A ComputationDataHandle message.
+      A LocalOp.
     """
     return self.Constant(np.array(value, dtype=np.float32))
 
@@ -580,7 +564,7 @@ def ConstantF64Scalar(self, value):
       value: a floating-point number.
 
     Returns:
-      A ComputationDataHandle message.
+      A LocalOp.
     """
     return self.Constant(np.array(value, dtype=np.float64))
 
@@ -591,7 +575,7 @@ def ConstantS32Scalar(self, value):
       value: a floating-point number.
 
     Returns:
-      A ComputationDataHandle message.
+      A LocalOp.
     """
     return self.Constant(np.array(value, dtype=np.int32))
 
@@ -602,7 +586,7 @@ def ConstantS64Scalar(self, value):
       value: a floating-point number.
 
     Returns:
-      A ComputationDataHandle message.
+      A LocalOp.
     """
     return self.Constant(np.array(value, dtype=np.int64))
 
@@ -613,7 +597,7 @@ def ConstantPredScalar(self, value):
       value: a boolean value.
 
     Returns:
-      A ComputationDataHandle message.
+      A LocalOp.
     """
     return self.Constant(np.array(value, dtype=np.bool))
 
@@ -629,15 +613,14 @@ def ParameterWithShape(self, shape, name=None, parameter_num=None):
         parameters, use it for *all* parameters to avoid clashes.
 
     Returns:
-      A ComputationDataHandle message.
+      A LocalOp.
     """
     if name is None:
       name = ''
     if parameter_num is None:
       parameter_num = next(self._parameter_numbering)
 
-    return _wrap_data_handle(
-        self._client.Parameter(parameter_num, shape, name.encode('utf8')))
+    return self._client.Parameter(parameter_num, shape, name.encode('utf8'))
 
   def ParameterFromNumpy(self, value, name=None, parameter_num=None):
     """Enqueues a Parameter op onto the computation.
@@ -649,7 +632,7 @@ def ParameterFromNumpy(self, value, name=None, parameter_num=None):
       parameter_num: as in ParameterWithShape.
 
     Returns:
-      A ComputationDataHandle message.
+      A LocalOp.
     """
     return self.ParameterWithShape(
         Shape.from_pyval(value), name=name, parameter_num=parameter_num)
@@ -658,14 +641,13 @@ def Broadcast(self, operand, sizes):
     """Enqueues a broadcast operation onto the computation.
 
     Args:
-      operand: the operand ComputationDataHandle to broadcast.
+      operand: the operand LocalOp to broadcast.
       sizes: an iterable of broadcast sizes.
 
     Returns:
-      A ComputationDataHandle representing the added broadcast op.
+      A LocalOp representing the added broadcast op.
     """
-    return _wrap_data_handle(
-        self._client.Broadcast(_unwrap_data_handle(operand), sizes))
+    return self._client.Broadcast(operand, sizes)
 
   def Concatenate(self, operands, dimension):
     """Enqueues a concatenate operation onto the computation.
@@ -675,10 +657,9 @@ def Concatenate(self, operands, dimension):
       dimension: the dimension in which to perform the concatenation.
 
     Returns:
-      A ComputationDataHandle representing the added concatenate op.
+      A LocalOp representing the added concatenate op.
     """
-    return _wrap_data_handle(
-        self._client.ConcatInDim(_unwrap_data_handles(operands), dimension))
+    return self._client.ConcatInDim(operands, dimension)
 
   def ConvertElementType(self, operand, new_element_type):
     """Enqueues an element type conversion operation onto the computation.
@@ -688,14 +669,12 @@ def ConvertElementType(self, operand, new_element_type):
       new_element_type: the target primitive type.
 
     Returns:
-      A ComputationDataHandle representing the added conversion op.
+      A LocalOp representing the added conversion op.
     """
-    return _wrap_data_handle(
-        self._client.ConvertElementType(
-            _unwrap_data_handle(operand), new_element_type))
+    return self._client.ConvertElementType(operand, new_element_type)
 
   def GetShape(self, operand):
-    return _wrap_shape(self._client.GetShape(_unwrap_data_handle(operand)))
+    return _wrap_shape(self._client.GetShape(operand))
 
   def GetReturnValueShape(self):
     return _wrap_shape(self._client.GetReturnValueShape())
@@ -707,40 +686,35 @@ def Pad(self, operand, padding_value, padding_config):
     """Enqueues a Pad operation onto the computation.
 
     Args:
-      operand: ComputationDataHandle representing the array to pad.
-      padding_value: ComputationDataHandle representing the scalar pad value.
+      operand: LocalOp representing the array to pad.
+      padding_value: LocalOp representing the scalar pad value.
       padding_config: either an xla_data_pb2.PaddingConfig or a list of integer
         triples (edge_padding_low, edge_padding_high, interior_padding)
         representing the configuration of the padding operation.
 
     Returns:
-      A ComputationDataHandle representing the added Pad op.
+      A LocalOp representing the added Pad op.
     """
     if not isinstance(padding_config, xla_data_pb2.PaddingConfig):
       padding_config = GetPaddingConfigFromTriples(padding_config)
-    return _wrap_data_handle(
-        self._client.Pad(_unwrap_data_handle(operand),
-                         _unwrap_data_handle(padding_value),
-                         padding_config))
+    return self._client.Pad(operand, padding_value, padding_config)
 
   def Reshape(self, operand, dimensions, new_sizes):
     """Enqueues a reshape op onto the computation.
 
     Args:
-      operand: ComputationDataHandle representing the array to be reshaped.
+      operand: LocalOp representing the array to be reshaped.
       dimensions: sequence of integers encoding the order in which dimensions
         are collapsed or None, in which case dimensions are flattened in order.
       new_sizes: sequence of integers encoding the new dimension sizes (shape).
 
     Returns:
-      A ComputationDataHandle representing the added Reshape op.
+      A LocalOp representing the added Reshape op.
     """
     if dimensions is None:
       ndim = len(self.GetShape(operand).dimensions())
       dimensions = tuple(range(ndim))
-    return _wrap_data_handle(
-        self._client.Reshape(
-            _unwrap_data_handle(operand), dimensions, new_sizes))
+    return self._client.Reshape(operand, dimensions, new_sizes)
 
   def CrossReplicaSum(self, operand):
     """CrossReplicaSum op.
@@ -749,67 +723,56 @@ def CrossReplicaSum(self, operand):
       operand: the operand to sum across replica instances.
 
     Returns:
-      A ComputationDataHandle that has the sum of the value among all replicas.
+      A LocalOp that has the sum of the value among all replicas.
     """
-    return _wrap_data_handle(
-        self._client.CrossReplicaSum(_unwrap_data_handle(operand)))
+    return self._client.CrossReplicaSum(operand)
 
   def Collapse(self, operand, dimensions):
     """Collapse op."""
-    return _wrap_data_handle(
-        self._client.Collapse(_unwrap_data_handle(operand), dimensions))
+    return self._client.Collapse(operand, dimensions)
 
   def Trans(self, operand):
     """Specialized matrix transpose op."""
-    return _wrap_data_handle(
-        self._client.Transpose(_unwrap_data_handle(operand), [1, 0]))
+    return self._client.Transpose(operand, [1, 0])
 
   def Transpose(self, operand, permutation):
     """Transpose op."""
-    return _wrap_data_handle(
-        self._client.Transpose(_unwrap_data_handle(operand), permutation))
+    return self._client.Transpose(operand, permutation)
 
   def Rev(self, operand, dimensions):
     """Rev op."""
-    return _wrap_data_handle(
-        self._client.Rev(_unwrap_data_handle(operand), dimensions))
+    return self._client.Rev(operand, dimensions)
 
   def Clamp(self, min, operand, max):  # pylint: disable=redefined-builtin
     """Clamp op."""
-    return _wrap_data_handle(
-        self._client.Clamp(_unwrap_data_handle(min),
-                           _unwrap_data_handle(operand),
-                           _unwrap_data_handle(max)))
+    return self._client.Clamp(min, operand, max)
 
   def SelectAndScatter(self, operand, select, window_dimensions, window_strides,
                        padding, source, init_value, scatter):
     """Select and scatter op, used by the gradient of ReduceWindow.
 
     Args:
-      operand: ComputationDataHandle for array of dimension N and type T over
+      operand: LocalOp for array of dimension N and type T over
         which the windows slide.
       select: Computation of type (T, T) -> Pred to apply to the elements of
         each window to indicate which element is selected.
       window_dimensions: sequence of N integers for dimensions of the window.
       window_strides: sequence of N integers for the strides of the window.
       padding: PaddingType representing either 'SAME' or 'VALID ' padding.
-      source: ComputationDataHandle for array of type T with values to scatter.
-      init_value: ComputationDataHandle of scalar type T for initial out value.
+      source: LocalOp for array of type T with values to scatter.
+      init_value: LocalOp of scalar type T for initial out value.
       scatter: Computation of type (T, T) -> T to apply to each scatter source
         element with its destination element.
 
     Returns:
-      A ComputationDataHandle representing the added SelectAndScatter op.
+      A LocalOp representing the added SelectAndScatter op.
     """
     pads = _convert_padding_type_to_pad_values(
         padding, self.GetShape(operand).dimensions(),
         window_dimensions, window_strides)
-    return _wrap_data_handle(
-        self._client.SelectAndScatterWithGeneralPadding(
-            _unwrap_data_handle(operand), select.c_local_computation,
-            window_dimensions, window_strides, pads,
-            _unwrap_data_handle(source), _unwrap_data_handle(init_value),
-            scatter.c_local_computation))
+    return self._client.SelectAndScatterWithGeneralPadding(
+        operand, select.c_local_computation, window_dimensions, window_strides,
+        pads, source, init_value, scatter.c_local_computation)
 
   def Select(self, pred, on_true, on_false):
     """Element-wise selection op.
@@ -817,17 +780,13 @@ def Select(self, pred, on_true, on_false):
     Constructs an output array from elements of two input arrays, based on the
     values of a predicate array.
     """
-    return _wrap_data_handle(
-        self._client.Select(
-            _unwrap_data_handle(pred),
-            _unwrap_data_handle(on_true),
-            _unwrap_data_handle(on_false)))
+    return self._client.Select(pred, on_true, on_false)
 
   def Slice(self, operand, start_indices, limit_indices, strides=None):
     """Enqueues a slice operation onto the computation.
 
     Args:
-      operand: ComputationDataHandle for the N dimensional array to be sliced.
+      operand: LocalOp for the N dimensional array to be sliced.
       start_indices: iterable of N integers containing the starting indices of
         the slice for each dimension.
       limit_indices: iterable of N integers containing the ending indices
@@ -836,207 +795,177 @@ def Slice(self, operand, start_indices, limit_indices, strides=None):
         each dimension.
 
     Returns:
-      A ComputationDataHandle representing the added Slice op.
+      A LocalOp representing the added Slice op.
     """
     if strides is None:
       start_indices = list(start_indices)
       strides = [1] * len(start_indices)
-    return _wrap_data_handle(
-        self._client.Slice(
-            _unwrap_data_handle(operand), start_indices, limit_indices,
-            strides))
+    return self._client.Slice(operand, start_indices, limit_indices, strides)
 
   def SliceInDim(self, operand, start_index, limit_index, stride, dimno):
     """Enqueues a slice-in-dimension operation onto the computation.
 
     Args:
-      operand: ComputationDataHandle for the N dimensional array to be sliced.
+      operand: LocalOp for the N dimensional array to be sliced.
       start_index: an integer containing the start index of the slice.
       limit_index: an integer containing the end index of the slice.
       stride: an integer containing the stride size for the slice.
       dimno: an integer indicating the dimension along which to slice.
 
     Returns:
-      A ComputationDataHandle representing the added Slice op.
+      A LocalOp representing the added Slice op.
     """
-    return _wrap_data_handle(
-        self._client.SliceInDim(
-            _unwrap_data_handle(operand), start_index, limit_index, stride,
-            dimno))
+    return self._client.SliceInDim(operand, start_index, limit_index, stride,
+                                   dimno)
 
   def DynamicSlice(self, operand, start_indices, slice_sizes):
     """Enqueues a slice op with dynamic start indices onto the computation.
 
     Args:
-      operand: ComputationDataHandle for the N dimensional array to be sliced.
-      start_indices: ComputationDataHandle for the 1D array of N integers
+      operand: LocalOp for the N dimensional array to be sliced.
+      start_indices: LocalOp for the 1D array of N integers
         containing the starting indices of the slice.
       slice_sizes: iterable of N integers containing the slice sizes in each
         dimension.
 
     Returns:
-      A ComputationDataHandle representing the added DynamicSlice op.
+      A LocalOp representing the added DynamicSlice op.
     """
-    return _wrap_data_handle(
-        self._client.DynamicSlice(
-            _unwrap_data_handle(operand),
-            _unwrap_data_handle(start_indices),
-            slice_sizes))
+    return self._client.DynamicSlice(operand, start_indices, slice_sizes)
 
   def DynamicUpdateSlice(self, operand, update, start_indices):
     """Enqueues a dynamic update slice operation onto the computation.
 
     Args:
-      operand: ComputationDataHandle for the N dimensional array to be updated.
+      operand: LocalOp for the N dimensional array to be updated.
       update: N dimensional array comprising the slice update.
       start_indices: Rank-1 array of N integers comprising the starting indices
         of the slice along each dimension.
     Returns:
-      A ComputationDataHandle representing the added DynamicUpdateSlice op.
+      A LocalOp representing the added DynamicUpdateSlice op.
     """
-    return _wrap_data_handle(
-        self._client.DynamicUpdateSlice(
-            _unwrap_data_handle(operand),
-            _unwrap_data_handle(update),
-            _unwrap_data_handle(start_indices)))
+    return self._client.DynamicUpdateSlice(operand, update, start_indices)
 
   def Tuple(self, *ops):
     """Enqueues a tuple operation onto the computation.
 
     Args:
-      ops: a sequence of tuple operands (each a ComputationDataHandle).
+      ops: a sequence of tuple operands (each a LocalOp).
 
     Returns:
-      A ComputationDataHandle representing the added Tuple op.
+      A LocalOp representing the added Tuple op.
     """
-    return _wrap_data_handle(self._client.Tuple(_unwrap_data_handles(ops)))
+    return self._client.Tuple(ops)
 
   def GetTupleElement(self, tup, index):
     """Enqueues a 'get tuple element' operation onto the computation.
 
     Args:
-      tup: the tuple operand (a ComputationDataHandle).
+      tup: the tuple operand (a LocalOp).
       index: numeric index to select from the tuple.
 
     Returns:
-      A ComputationDataHandle representing the added GetTupleElement op.
+      A LocalOp representing the added GetTupleElement op.
     """
-    return _wrap_data_handle(
-        self._client.GetTupleElement(_unwrap_data_handle(tup), index))
+    return self._client.GetTupleElement(tup, index)
 
   def Call(self, computation_to_apply, operands):
     """Enqueues a call operation onto the computation.
 
     Args:
       computation_to_apply: a Computation object.
-      operands: an iterable of ComputationDataHandle. The number and types of
+      operands: an iterable of LocalOp. The number and types of
         operands must match the arity of computation_to_apply.
 
     Returns:
-      A ComputationDataHandle representing the added call op.
+      A LocalOp representing the added call op.
     """
-    return _wrap_data_handle(
-        self._client.Call(computation_to_apply.c_local_computation,
-                          _unwrap_data_handles(operands)))
+    return self._client.Call(computation_to_apply.c_local_computation, operands)
 
   def Map(self, operands, computation_to_apply, dimensions, static_operands=()):
     """Enqueues a map operation onto the computation.
 
     Args:
-      operands: an iterable of ComputationDataHandle.
+      operands: an iterable of LocalOp.
       computation_to_apply: a Computation object.
       dimensions: dimensions over which to apply map the function.
       static_operands: auxiliary arguments passed to the applied computation.
 
     Returns:
-      A ComputationDataHandle representing the added Map op.
+      A LocalOp representing the added Map op.
     """
-    return _wrap_data_handle(
-        self._client.Map(
-            _unwrap_data_handles(operands),
-            computation_to_apply.c_local_computation,
-            dimensions,
-            _unwrap_data_handles(static_operands)))
+    return self._client.Map(operands, computation_to_apply.c_local_computation,
+                            dimensions, static_operands)
 
   def Reduce(self, operand, init_value, computation_to_apply, dimensions):
     """Enqueues a reduction operation onto the computation.
 
     Args:
-      operand: reduction operand (ComputationDataHandle).
-      init_value: reduction initial value (ComputationDataHandle).
+      operand: reduction operand (LocalOp).
+      init_value: reduction initial value (LocalOp).
       computation_to_apply: a Computation object - binary reduction function.
       dimensions: sequence of dimensions (integers) to reduce on.
 
     Returns:
-      A ComputationDataHandle representing the added Reduce op.
+      A LocalOp representing the added Reduce op.
     """
-    return _wrap_data_handle(
-        self._client.Reduce(
-            _unwrap_data_handle(operand),
-            _unwrap_data_handle(init_value),
-            computation_to_apply.c_local_computation,
-            dimensions))
+    return self._client.Reduce(operand, init_value,
+                               computation_to_apply.c_local_computation,
+                               dimensions)
 
   def ReduceWindow(self, operand, init_value, computation_to_apply,
                    window_dimensions, window_strides, padding):
     """Enqueues a windowed reduction operation onto the computation.
 
     Args:
-      operand: reduction operand (ComputationDataHandle).
-      init_value: reduction initial value (ComputationDataHandle).
+      operand: reduction operand (LocalOp).
+      init_value: reduction initial value (LocalOp).
       computation_to_apply: a binary reduction function (Computation).
       window_dimensions: dimensions of window (sequence of integers).
       window_strides: strides for window (sequence of integers).
       padding: PaddingType representing either 'SAME' or 'VALID' padding.
 
     Returns:
-      A ComputationDataHandle representing the added ReduceWindow op.
+      A LocalOp representing the added ReduceWindow op.
     """
     pads = _convert_padding_type_to_pad_values(
         padding, self.GetShape(operand).dimensions(), window_dimensions,
         window_strides)
-    return _wrap_data_handle(
-        self._client.ReduceWindowWithGeneralPadding(
-            _unwrap_data_handle(operand),
-            _unwrap_data_handle(init_value),
-            computation_to_apply.c_local_computation,
-            window_dimensions, window_strides, pads))
+    return self._client.ReduceWindowWithGeneralPadding(
+        operand, init_value, computation_to_apply.c_local_computation,
+        window_dimensions, window_strides, pads)
 
   def RngNormal(self, mu, sigma, dims):
     """Enqueues an RngNormal operation onto the computation.
 
     Args:
-      mu: A ComputationDataHandle to an F32 scalar specifying the mean.
-      sigma: A ComputationDataHandle to an F32 scalar specifying the standard
+      mu: A LocalOp to an F32 scalar specifying the mean.
+      sigma: A LocalOp to an F32 scalar specifying the standard
         deviation.
       dims: A 1D array-like of nonnegative integers specifying the dimensions.
 
-    Returns: a ComputationDataHandle to the generated array of F32 values.
+    Returns: a LocalOp to the generated array of F32 values.
     """
     shape = Shape.array_shape(self.GetShape(mu).element_type(), dims)
-    return _wrap_data_handle(
-        self._client.RngNormal(
-            _unwrap_data_handle(mu), _unwrap_data_handle(sigma), shape))
+    return self._client.RngNormal(mu, sigma, shape)
 
   def RngUniform(self, a, b, dims):
     """Enqueues an RngUniform operation onto the computation.
 
     Args:
-      a: a ComputationDataHandle to an F32, S32, or U32 scalar (consistent with
+      a: a LocalOp to an F32, S32, or U32 scalar (consistent with
         the type of b) specifying the low end of the interval [a, b) over which
         values are generated.
-      b: a ComputationDataHandle to an F32, S32, or U32 scalar (consistent with
+      b: a LocalOp to an F32, S32, or U32 scalar (consistent with
         the type of a) specifying the high end of the interval [a, b) over which
         values are generated.
       dims: A 1D array-like of nonnegative integers specifying the dimensions.
 
-    Returns: a ComputationDataHandle to the generated array of values with the
+    Returns: a LocalOp to the generated array of values with the
       same numeric type (F32, S32, or U32) as the arguments a and b.
     """
     shape = Shape.array_shape(self.GetShape(a).element_type(), dims)
-    return _wrap_data_handle(
-        self._client.RngUniform(
-            _unwrap_data_handle(a), _unwrap_data_handle(b), shape))
+    return self._client.RngUniform(a, b, shape)
 
   def While(self, cond, body, init):
     """Enqueues a While operation onto the computation.
@@ -1044,112 +973,105 @@ def While(self, cond, body, init):
     Args:
       cond: a Computation for the loop condition, which has type T -> PRED
       body: a Computation for the loop body, which has type T -> T
-      init: a ComputationDataHandle for the initial parameter, which has type T
+      init: a LocalOp for the initial parameter, which has type T
 
-    Returns: a ComputationDataHandle representing the While operation.
+    Returns: a LocalOp representing the While operation.
     """
-    return _wrap_data_handle(
-        self._client.While(cond.c_local_computation,
-                           body.c_local_computation,
-                           _unwrap_data_handle(init)))
+    return self._client.While(cond.c_local_computation,
+                              body.c_local_computation, init)
 
   def Conditional(self, pred, true_operand, true_computation, false_operand,
                   false_computation):
     """Enqueues a Conditional operation onto the computation.
 
     Args:
-      predicate: a ComputationDataHandle to test, which has scalar type PRED
-      true_operand: a ComputationDataHandle of type T_0
+      predicate: a LocalOp to test, which has scalar type PRED
+      true_operand: a LocalOp of type T_0
       true_computation: a Computation to apply to true_operand, type T_0 -> S
       false_operand: a ComputationDatahandle of type T_1
       false_computation: a Computation to apply to false_operand, type T_1 -> S
 
-    Returns: a ComputationDataHandle representing the Conditional operation.
+    Returns: a LocalOp representing the Conditional operation.
     """
-    return _wrap_data_handle(
-        self._client.Conditional(
-            _unwrap_data_handle(pred), _unwrap_data_handle(true_operand),
-            true_computation.c_local_computation,
-            _unwrap_data_handle(false_operand),
-            false_computation.c_local_computation))
+    return self._client.Conditional(
+        pred, true_operand, true_computation.c_local_computation, false_operand,
+        false_computation.c_local_computation)
 
-  def IsConstant(self, operand, num_parameters=0):
-    """Enqueues an IsConstant operation onto the computation.
+  def IsConstant(self, operand):
+    """Checks whether the given operand is a compile-time constant.
 
     Args:
       operand: a ComputationDataHandle to test.
-      num_parameters: optional int, number of computation parameters to treat as
-        constant (default 0).
 
     Returns: bool indicating whether `operand` is a compile-time constant,
-      meaning its value does not depend on parameters with index greater than or
-      equal to `num_parameters`.
+      meaning its value does not depend on any parametersor, or on stateful
+      operators such as `RngNormal` or `Infeed`.
+    """
+    return self._client.IsConstant(operand)
+
+  def BuildConstantSubGraph(self, operand):
+    """Builds a constant sub graph.
+
+    Args:
+      operand: a LocalOp to test.
+    Returns: a LocalComputation that is rooted on the given `operand` which is a
+      compile-time constant.
     """
-    return self._client.IsConstant(_unwrap_data_handle(operand), num_parameters)
+    return self._client.BuildConstantSubGraph(operand)
 
   def Dot(self, lhs, rhs):
     """Enqueues a dot operation onto the computation.
 
     Args:
-      lhs: ComputationDataHandle for the rank 1 or rank 2 left-hand-side array.
-      rhs: ComputationDataHandle for the rank 1 or rank 2 right-hand-side array.
+      lhs: LocalOp for the rank 1 or rank 2 left-hand-side array.
+      rhs: LocalOp for the rank 1 or rank 2 right-hand-side array.
 
-    Returns: a ComputationDataHandle representing the Dot operation.
+    Returns: a LocalOp representing the Dot operation.
     """
-    return _wrap_data_handle(
-        self._client.Dot(_unwrap_data_handle(lhs), _unwrap_data_handle(rhs)))
+    return self._client.Dot(lhs, rhs)
 
   def DotGeneral(self, lhs, rhs, dimension_numbers):
     """Enqueues a general dot operation onto the computation.
 
     Args:
-      lhs: ComputationDataHandle for the left-hand-side array.
-      rhs: ComputationDataHandle for the right-hand-side array.
+      lhs: LocalOp for the left-hand-side array.
+      rhs: LocalOp for the right-hand-side array.
       dimension_numbers: either an xla_data_pb2.DotDimensionNumbers or a nested
         tuple ((lhs_contract, rhs_contract), (lhs_batch, rhs_batch)) of lists of
         integers representing the dimensions to treat as contracting dimensions
         and batch dimensions on each input operand.
 
-    Returns: a ComputationDataHandle representing the DotGeneral operation.
+    Returns: a LocalOp representing the DotGeneral operation.
     """
     if not isinstance(dimension_numbers, xla_data_pb2.DotDimensionNumbers):
       dimension_numbers = GetDotDimensionsFromLists(dimension_numbers)
-    return _wrap_data_handle(
-        self._client.DotGeneral(
-            _unwrap_data_handle(lhs), _unwrap_data_handle(rhs),
-            dimension_numbers))
+    return self._client.DotGeneral(lhs, rhs, dimension_numbers)
 
   def Conv(self, lhs, rhs, window_strides, padding):
     """Enqueues a Conv operation onto the computation.
 
     Args:
-      lhs: ComputationDataHandle for the rank N+2 array of inputs.
-      rhs: ComputationDataHandle for the rank N+2 array of kernel weights.
+      lhs: LocalOp for the rank N+2 array of inputs.
+      rhs: LocalOp for the rank N+2 array of kernel weights.
       window_strides: length-N array-like of integer kernel strides.
       padding: PaddingType representing either 'SAME' or 'VALID' padding.
 
-    Returns: a ComputationDataHandle representing the Conv operation.
+    Returns: a LocalOp representing the Conv operation.
     """
     pads = _convert_padding_type_to_pad_values(
         padding, self.GetShape(lhs).dimensions()[2:],
         self.GetShape(rhs).dimensions()[2:], window_strides)
     dimension_numbers = self._GetConvDimensionNumbers(len(window_strides))
-    return _wrap_data_handle(
-        self._client.ConvGeneralDilated(_unwrap_data_handle(lhs),
-                                        _unwrap_data_handle(rhs),
-                                        window_strides,
-                                        pads,
-                                        (),
-                                        (),
-                                        dimension_numbers))
+    return self._client.ConvGeneralDilated(lhs, rhs, window_strides, pads, (),
+                                           (), dimension_numbers)
 
   def ConvWithGeneralPadding(self, lhs, rhs, window_strides, padding,
                              lhs_dilation, rhs_dilation):
     """Enqueues a ConvWithGeneralPadding operation onto the computation.
 
     Args:
-      lhs: ComputationDataHandle for the rank N+2 array of inputs.
-      rhs: ComputationDataHandle for the rank N+2 array of kernel weights.
+      lhs: LocalOp for the rank N+2 array of inputs.
+      rhs: LocalOp for the rank N+2 array of kernel weights.
       window_strides: length-N array-like of kernel strides.
       padding: length-N array-like of pairs of integers of (low, high) padding.
       lhs_dilation: length-N array-like of dilation factors.
@@ -1159,14 +1081,9 @@ def ConvWithGeneralPadding(self, lhs, rhs, window_strides, padding,
       A ComputationdataHandle representing the added ConvWithGeneralPadding op.
     """
     dimension_numbers = self._GetConvDimensionNumbers(len(window_strides))
-    return _wrap_data_handle(
-        self._client.ConvGeneralDilated(_unwrap_data_handle(lhs),
-                                        _unwrap_data_handle(rhs),
-                                        window_strides,
-                                        padding,
-                                        lhs_dilation,
-                                        rhs_dilation,
-                                        dimension_numbers))
+    return self._client.ConvGeneralDilated(lhs, rhs, window_strides, padding,
+                                           lhs_dilation, rhs_dilation,
+                                           dimension_numbers)
 
   def _GetConvDimensionNumbers(self, num_spatial_dims):
     """Create ConvolutionDimensionNumbers proto for convolutions."""
@@ -1196,15 +1113,14 @@ def forward_to_local_builder_with_handles(target_method, is_binop=False):
     """Generate a forwarding method that wraps/unwraps data handles."""
 
     def forward(self, *args, **kwargs):
-      unwrapped_args = [_unwrap_data_handle(arg) for arg in args]
+      arg_list = list(args)
 
-      if is_binop and len(unwrapped_args) < 3:
-        unwrapped_args.append(kwargs.get('broadcast_dimensions', ()))
+      if is_binop and len(arg_list) < 3:
+        arg_list.append(kwargs.get('broadcast_dimensions', ()))
 
-      return _wrap_data_handle(
-          target_method(
-              self._client,  # pylint: disable=protected-access
-              *unwrapped_args))
+      return target_method(
+          self._client,  # pylint: disable=protected-access
+          *arg_list)
 
     return forward
 

From 5ca373b4b64167f8b0fcab96d7d2e7886ea31b6a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 4 May 2018 12:28:42 -0700
Subject: [PATCH 0399/1691] Some fixes to support another TF graph: 1. Fix
 ResolveBatchNormalization to avoid deleting arrays that may still be used. 2.
 Correctly count the number of ops using a given array, even when some ops use
 the same array as more than one of their inputs. 3. In PropagateFixedSizes
 for Concatenation ops, when resolving a -1 wildcard to a fixed value, we were
 doing so in a local 'axis' variable without actually updating op->axis! The
 resulting -1 value still in op->axis tripped runtime code, causing the
 concatenation to misbehave during inference.

PiperOrigin-RevId: 195454037
---
 .../graph_transformations/propagate_fixed_sizes.cc    | 11 +++++------
 .../resolve_batch_normalization.cc                    |  6 +++---
 tensorflow/contrib/lite/toco/tooling_util.cc          |  4 ++++
 3 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
index 4923f83d91defb..b02b02c5bec1fd 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
@@ -670,8 +670,7 @@ void ProcessConcatenationOperator(Model* model, ConcatenationOperator* op) {
   const auto& first_input_array = model->GetArray(op->inputs[0]);
   output_array.copy_shape(first_input_array.shape());
   // Negative axis means the count starts at the back of the dims().
-  int axis = op->axis;
-  if (axis < 0) axis += first_input_array.shape().dims().size();
+  if (op->axis < 0) op->axis += first_input_array.shape().dims().size();
   // Determine the concat size, and enfore that all inputs have
   // the same dimensions count.
   int concat_size = 0;
@@ -684,14 +683,14 @@ void ProcessConcatenationOperator(Model* model, ConcatenationOperator* op) {
     CHECK_EQ(input_array.shape().dimensions_count(),
              output_array.shape().dimensions_count());
     const std::vector& input_dims = input_array.shape().dims();
-    CHECK_LT(axis, input_dims.size());
-    concat_size += input_dims[axis];
+    CHECK_LT(op->axis, input_dims.size());
+    concat_size += input_dims[op->axis];
   }
   // Write out the concat_size on the output array shape.
   auto& output_shape = *output_array.mutable_shape();
   auto& output_dims = *output_shape.mutable_dims();
-  CHECK_LT(axis, output_shape.dimensions_count());
-  output_dims[axis] = concat_size;
+  CHECK_LT(op->axis, output_shape.dimensions_count());
+  output_dims[op->axis] = concat_size;
 }
 
 void ProcessRangeOperator(Model* model, RangeOperator* op) {
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_batch_normalization.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_batch_normalization.cc
index 2b3ee36ad10e24..8f2c1f81628398 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_batch_normalization.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_batch_normalization.cc
@@ -134,9 +134,9 @@ bool ResolveBatchNormalization::Run(Model* model, std::size_t op_index) {
   }
 
   // Remove the old param arrays
-  model->EraseArray(bn_op->inputs[1]);
-  model->EraseArray(bn_op->inputs[2]);
-  model->EraseArray(bn_op->inputs[3]);
+  DeleteArrayIfUsedOnce(bn_op->inputs[1], model);
+  DeleteArrayIfUsedOnce(bn_op->inputs[2], model);
+  DeleteArrayIfUsedOnce(bn_op->inputs[3], model);
 
   // Remove the old operator
   DCHECK_EQ(bn_it->get(), bn_op);
diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index 86ee1f3761330d..341d45e7537c5e 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -143,6 +143,10 @@ int CountOpsWithInput(const Model& model, const string& array_name) {
     for (auto& input : op->inputs) {
       if (input == array_name) {
         count++;
+        // Breaking here is important: some graphs have ops that use the
+        // same array as more than one of their inputs, and in that case
+        // we want it counted only once.
+        break;
       }
     }
   }

From 67b5e724121c5874425936fe01318642508d9975 Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Fri, 4 May 2018 14:40:02 -0700
Subject: [PATCH 0400/1691] [XLA:GPU] Mark floating-point division as an
 inexpensive op.

"Expensive" really means "so expensive you'd choose not to fuse in order
to avoid doing it twice".  FP division definitely isn't that expensive.

PiperOrigin-RevId: 195473524
---
 .../xla/service/gpu/instruction_fusion.cc     | 13 +++++
 .../xla/service/gpu/instruction_fusion.h      |  2 +
 .../service/gpu/instruction_fusion_test.cc    | 56 +++++++++++++++++++
 3 files changed, 71 insertions(+)

diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc
index 85ecbe8fdb3470..c5eb7211859c8f 100644
--- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc
@@ -48,6 +48,19 @@ bool IsFusile(const HloInstruction& hlo) {
 
 }  // namespace
 
+/*static*/ bool GpuInstructionFusion::IsExpensive(
+    const HloInstruction& instruction) {
+  switch (instruction.opcode()) {
+    // We say that floating-point division is cheap on the GPU.
+    case HloOpcode::kDivide:
+      return !ShapeUtil::ElementIsFloating(instruction.shape()) &&
+             InstructionFusion::IsExpensive(instruction);
+
+    default:
+      return InstructionFusion::IsExpensive(instruction);
+  }
+}
+
 bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer,
                                       int64 operand_index) {
   HloInstruction* producer = consumer->mutable_operand(operand_index);
diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.h b/tensorflow/compiler/xla/service/gpu/instruction_fusion.h
index bb2990e6dfc9de..9fb06b0a244186 100644
--- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.h
+++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.h
@@ -27,6 +27,8 @@ class GpuInstructionFusion : public InstructionFusion {
   explicit GpuInstructionFusion(bool may_duplicate)
       : InstructionFusion(GpuInstructionFusion::IsExpensive, may_duplicate) {}
 
+  static bool IsExpensive(const HloInstruction& instruction);
+
   bool ShouldFuse(HloInstruction* consumer, int64 operand_index) override;
 
   HloInstruction::FusionKind ChooseKind(
diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc
index 4b231c449f8f10..6c9a805ad637ce 100644
--- a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc
+++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc
@@ -253,5 +253,61 @@ TEST_F(InstructionFusionTest, DotOutputFusion) {
                    op::Dot(op::Parameter(), op::Transpose(op::Parameter()))));
 }
 
+// Compute sum(1/p0), where p0 has type f32, twice.  Check that the division is
+// duplicated and fused into both reduces.
+TEST_F(InstructionFusionTest, FloatingPointDivIsCheap) {
+  auto module = tools::Parse(R"(
+  HloModule test_module
+  Add {
+    lhs = f32[] parameter(0)
+    rhs = f32[] parameter(1)
+    ROOT add = f32[] add(lhs, rhs)
+  }
+  ENTRY TestComputation {
+    zero = f32[] constant(0)
+    one = f32[] constant(1)
+    p0 = f32[100] parameter(0)
+    recip = f32[100] divide(one, p0)
+    sum1 = f32[] reduce(recip, zero), dimensions={0}, to_apply=Add
+    sum2 = f32[] reduce(recip, zero), dimensions={0}, to_apply=Add
+    ROOT root = (f32[], f32[]) tuple(sum1, sum2)
+  })")
+                    .ValueOrDie();
+
+  EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/true)
+                  .Run(module.get())
+                  .ValueOrDie());
+
+  HloInstruction* root = module->entry_computation()->root_instruction();
+  EXPECT_THAT(root, op::Tuple(op::Fusion(), op::Fusion()));
+}
+
+// Compute sum(100/p0), where p0 has type s32, twice.  Check that the division
+// is *not* duplicated and fused into both reduces, because we say that integer
+// division is not cheap.
+TEST_F(InstructionFusionTest, IntegerDivIsNotCheap) {
+  auto module = tools::Parse(R"(
+  HloModule test_module
+  Add {
+    lhs = s32[] parameter(0)
+    rhs = s32[] parameter(1)
+    ROOT add = s32[] add(lhs, rhs)
+  }
+  ENTRY TestComputation {
+    zero = s32[] constant(0)
+    one_hundred = s32[] constant(100)
+    p0 = s32[100] parameter(0)
+    recip = s32[100] divide(one_hundred, p0)
+    sum1 = s32[] reduce(recip, zero), dimensions={0}, to_apply=Add
+    sum2 = s32[] reduce(recip, zero), dimensions={0}, to_apply=Add
+    ROOT mul = (s32[], s32[]) tuple(sum1, sum2)
+  })")
+                    .ValueOrDie();
+
+  EXPECT_FALSE(GpuInstructionFusion(/*may_duplicate=*/true)
+                   .Run(module.get())
+                   .ValueOrDie());
+}
+
 }  // namespace gpu
 }  // namespace xla

From 4d0388d22060a61f40965127c153c681b2412c50 Mon Sep 17 00:00:00 2001
From: James Qin 
Date: Fri, 4 May 2018 14:53:58 -0700
Subject: [PATCH 0401/1691] Fix build failure for macos py3

PiperOrigin-RevId: 195475780
---
 tensorflow/python/debug/examples/debug_tflearn_iris.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/debug/examples/debug_tflearn_iris.py b/tensorflow/python/debug/examples/debug_tflearn_iris.py
index 00090b21fe35ac..7cbaae46b4f60f 100644
--- a/tensorflow/python/debug/examples/debug_tflearn_iris.py
+++ b/tensorflow/python/debug/examples/debug_tflearn_iris.py
@@ -140,7 +140,7 @@ def test_input_fn():
 
   # Make predictions, using tfdbg hook.
   predict_results = classifier.predict(test_input_fn, hooks=hooks)
-  print("A prediction result: %s" % predict_results.next())
+  print("A prediction result: %s" % next(predict_results))
 
 
 if __name__ == "__main__":

From cb1775e9525ae621d23708a3d64a6cad897be95e Mon Sep 17 00:00:00 2001
From: Benoit Steiner 
Date: Fri, 4 May 2018 15:14:00 -0700
Subject: [PATCH 0402/1691] Identify and prune nodes that can never be executed

PiperOrigin-RevId: 195478951
---
 tensorflow/core/grappler/optimizers/BUILD     |   1 +
 .../grappler/optimizers/loop_optimizer.cc     | 140 ++++++++++++++++++
 .../core/grappler/optimizers/loop_optimizer.h |   1 +
 .../optimizers/loop_optimizer_test.cc         | 107 +++++++++++++
 tensorflow/core/grappler/utils.h              |   4 +-
 5 files changed, 251 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 5b5e1e024e8cfa..900dfa95c59ec3 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -604,6 +604,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core/grappler:graph_view",
         "//tensorflow/core/grappler:grappler_item",
         "//tensorflow/core/grappler:op_types",
         "//tensorflow/core/grappler:utils",
diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer.cc b/tensorflow/core/grappler/optimizers/loop_optimizer.cc
index 5adc5b9227ff94..7d3520febc44a0 100644
--- a/tensorflow/core/grappler/optimizers/loop_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/loop_optimizer.cc
@@ -27,6 +27,7 @@ limitations under the License.
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/grappler/graph_view.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/grappler/op_types.h"
 #include "tensorflow/core/grappler/optimizers/constant_folding.h"
@@ -504,6 +505,140 @@ Status RemoveStackOps(const std::unordered_set& nodes_to_preserve,
   return Status::OK();
 }
 
+Status RemoveDeadBranches(const std::unordered_set& nodes_to_preserve,
+                          GraphDef* optimized_graph) {
+  std::unordered_set dead_nodes;
+  std::unordered_map> dead_merge_inputs;
+  // TODO(bsteiner): also rewrite switches as identity. For now we just record
+  // them
+  std::unordered_set
+      identity_switches;
+
+  GraphView view(optimized_graph);
+  for (const NodeDef& node : optimized_graph->node()) {
+    if (!IsSwitch(node)) {
+      continue;
+    }
+    if (nodes_to_preserve.find(node.name()) != nodes_to_preserve.end()) {
+      continue;
+    }
+    GraphView::InputPort ctrl_port(&node, 1);
+    GraphView::OutputPort ctrl_node = view.GetRegularFanin(ctrl_port);
+    if (!IsConstant(*ctrl_node.node)) {
+      continue;
+    }
+    Tensor selector;
+    CHECK(selector.FromProto(ctrl_node.node->attr().at("value").tensor()));
+    const int dead_fanout = selector.scalar()() ? 0 : 1;
+    GraphView::OutputPort dead(const_cast(&node), dead_fanout);
+    identity_switches.insert(dead);
+
+    SetVector zombie_inputs;
+    for (const GraphView::InputPort& port : view.GetFanout(dead)) {
+      if (dead_nodes.find(port.node) == dead_nodes.end()) {
+        zombie_inputs.PushBack(port);
+      }
+    }
+    // If we encounter a single node that must be preserved in the fanout of the
+    // switch node we need to preserve the entire switch fanout: we therefore
+    // work on a local copy that only gets committed to the master copy once the
+    // whole fanout has been explored.
+    std::unordered_set local_dead_nodes = dead_nodes;
+    std::unordered_map> local_dead_merge_inputs =
+        dead_merge_inputs;
+    bool found_node_to_preserve = false;
+    while (!found_node_to_preserve && !zombie_inputs.Empty()) {
+      GraphView::InputPort dead = zombie_inputs.PopBack();
+      if (nodes_to_preserve.find(dead.node->name()) !=
+          nodes_to_preserve.end()) {
+        found_node_to_preserve = true;
+        break;
+      }
+
+      if (local_dead_nodes.find(dead.node) != local_dead_nodes.end()) {
+        continue;
+      }
+
+      if (IsMerge(*dead.node)) {
+        const int fanout = dead.node->attr().at("N").i();
+        if (fanout > 2) {
+          // This never happens in practice, so we'll just skip these to
+          // simplify the code for now.
+          found_node_to_preserve = true;
+          break;
+        }
+        GraphView::OutputPort value_index(dead.node, 1);
+        const std::unordered_set&
+            index_fanout = view.GetFanout(value_index);
+        if (!index_fanout.empty()) {
+          // The 2nd output (that indicates which input is propagated) is
+          // connected. This never happens in practice, so we'll just skip this
+          // case to simplify the code for now.
+          found_node_to_preserve = true;
+          break;
+        }
+
+        bool fully_dead = false;
+        if (dead.port_id < 0) {
+          // If the control dependency never gets triggered the merge will also
+          // never get triggered.
+          local_dead_nodes.insert(dead.node);
+          fully_dead = true;
+        } else {
+          local_dead_merge_inputs[dead.node].insert(dead.port_id);
+          if (local_dead_merge_inputs[dead.node].size() ==
+              dead.node->attr().at("N").i()) {
+            fully_dead = true;
+          }
+          if (fully_dead) {
+            local_dead_nodes.insert(dead.node);
+            for (const GraphView::InputPort& port :
+                 view.GetFanouts(*dead.node, true)) {
+              zombie_inputs.PushBack(port);
+            }
+          }
+        }
+      } else {
+        if (local_dead_nodes.insert(dead.node).second) {
+          for (const GraphView::InputPort& dead_fanout :
+               view.GetFanouts(*dead.node, true)) {
+            zombie_inputs.PushBack(dead_fanout);
+          }
+        }
+      }
+    }
+    if (!found_node_to_preserve) {
+      std::swap(dead_nodes, local_dead_nodes);
+      std::swap(dead_merge_inputs, local_dead_merge_inputs);
+    }
+  }
+
+  int last = optimized_graph->node_size() - 1;
+  for (int i = optimized_graph->node_size() - 1; i >= 0; --i) {
+    NodeDef* node = optimized_graph->mutable_node(i);
+    if (dead_nodes.find(node) != dead_nodes.end()) {
+      optimized_graph->mutable_node()->SwapElements(i, last);
+      last--;
+    }
+  }
+  optimized_graph->mutable_node()->DeleteSubrange(last + 1, dead_nodes.size());
+
+  for (const auto& itr : dead_merge_inputs) {
+    NodeDef* dead_node = itr.first;
+    if (dead_nodes.find(dead_node) != dead_nodes.end()) {
+      // The node has been pruned since all its inputs are dead.
+      continue;
+    }
+    const std::set& dead_inputs = itr.second;
+    for (int index : dead_inputs) {
+      dead_node->mutable_input()->DeleteSubrange(index, 1);
+    }
+    dead_node->set_op("Identity");
+    dead_node->mutable_attr()->erase("N");
+  }
+  return Status::OK();
+}
+
 }  // namespace
 
 Status LoopOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
@@ -517,6 +652,11 @@ Status LoopOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
   if (options_.enable_stack_push_removal) {
     TF_RETURN_IF_ERROR(RemoveStackOps(item.NodesToPreserve(), optimized_graph));
   }
+  if (opt_level_ == RewriterConfig::AGGRESSIVE &&
+      options_.enable_dead_branch_removal) {
+    TF_RETURN_IF_ERROR(
+        RemoveDeadBranches(item.NodesToPreserve(), optimized_graph));
+  }
 
   return Status::OK();
 }
diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer.h b/tensorflow/core/grappler/optimizers/loop_optimizer.h
index 764506f7c1a4f3..85b8e655439b28 100644
--- a/tensorflow/core/grappler/optimizers/loop_optimizer.h
+++ b/tensorflow/core/grappler/optimizers/loop_optimizer.h
@@ -54,6 +54,7 @@ class LoopOptimizer : public GraphOptimizer {
   struct LoopOptimizerOptions {
     bool enable_loop_invariant_node_motion = false;
     bool enable_stack_push_removal = true;
+    bool enable_dead_branch_removal = true;
 
     static LoopOptimizerOptions Default(RewriterConfig::Toggle opt_level) {
       LoopOptimizerOptions options;
diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc b/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc
index 10ec544424e651..6fd177b7103eac 100644
--- a/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc
@@ -589,5 +589,112 @@ TEST_F(LoopOptimizerTest, RemovePushWithoutMatchingPop) {
   }
 }
 
+TEST_F(LoopOptimizerTest, RemoveDeadBranches) {
+  Scope scope = Scope::NewRootScope();
+  Output v_in = ops::Variable(scope.WithOpName("v_in"), {3}, DT_FLOAT);
+
+  Output ctrl1 = ops::Const(scope.WithOpName("ctrl1"), false, TensorShape({}));
+  ops::Switch s1(scope.WithOpName("switch1"), v_in, ctrl1);
+  Output square1 = ops::Square(scope.WithOpName("square1"), s1.output_false);
+  Output sqrt1 = ops::Sqrt(scope.WithOpName("sqrt1"), s1.output_true);
+
+  Output ctrl2 = ops::Const(scope.WithOpName("ctrl2"), true, TensorShape({}));
+  ops::Switch s2(scope.WithOpName("switch2"), v_in, ctrl2);
+  Output square2 = ops::Square(scope.WithOpName("square2"), s2.output_false);
+  Output sqrt2 = ops::Sqrt(scope.WithOpName("sqrt2"), s2.output_true);
+
+  Output ctrl3 = ops::Const(scope.WithOpName("ctrl3"), false, TensorShape({}));
+  ops::Switch s3(scope.WithOpName("switch3"), v_in, ctrl3);
+  Output square3 = ops::Square(scope.WithOpName("square3"), s3.output_false);
+  Output sqrt3 = ops::Sqrt(scope.WithOpName("sqrt3"), s3.output_true);
+
+  Output ctrl4 = ops::Const(scope.WithOpName("ctrl4"), false, TensorShape({}));
+  ops::Switch s4(scope.WithOpName("switch4"), v_in, ctrl4);
+  Output square4 = ops::Square(scope.WithOpName("square4"), s4.output_false);
+  Output sqrt4 = ops::Sqrt(scope.WithOpName("sqrt4"), s4.output_true);
+
+  ops::Merge m1(scope.WithOpName("m1"), {square1, sqrt1});
+  ops::Merge m2(scope.WithOpName("m2"), {v_in, square1});
+  ops::Merge m3(scope.WithOpName("m3"), {v_in, sqrt1});
+  ops::Merge m4(scope.WithOpName("m4"), {square1, sqrt2});
+  ops::Merge m5(scope.WithOpName("m5"), {square2, sqrt1});
+  ops::Merge m6(scope.WithOpName("m6").WithControlDependencies(sqrt2),
+                {v_in, square1});
+  ops::Merge m7(scope.WithOpName("m7").WithControlDependencies(sqrt1),
+                {v_in, square1});
+
+  ops::Switch s5(scope.WithOpName("switch5"), v_in, ctrl1);
+  Output id1 = ops::Identity(scope.WithOpName("id1"), s5.output_false);
+  Output id2 = ops::Identity(scope.WithOpName("id2"), s5.output_true);
+  ops::Merge m8(scope.WithOpName("m8"), {id1, id2});
+
+  ops::Switch s6(scope.WithOpName("switch6"), v_in, ctrl1);
+  Output id3 = ops::Identity(scope.WithOpName("id3"), s6.output_false);
+  Output id4 = ops::Identity(scope.WithOpName("id4"), s6.output_true);
+  ops::Merge m9(scope.WithOpName("m9"), {id3, id4});
+
+  GrapplerItem item;
+  item.fetch.push_back("m8");
+  item.fetch.push_back("id4");
+
+  TF_CHECK_OK(scope.ToGraphDef(&item.graph));
+
+  LoopOptimizer optimizer(RewriterConfig::AGGRESSIVE);
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_CHECK_OK(status);
+
+  for (const NodeDef& node : output.node()) {
+    // These nodes should have been pruned
+    EXPECT_NE("Square1", node.name());
+    EXPECT_NE("Sqrt2", node.name());
+    EXPECT_NE("m5", node.name());
+    EXPECT_NE("m7", node.name());
+
+    if (node.name() == "m1") {
+      // sqrt1 is dead
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("square1", node.input(0));
+    } else if (node.name() == "m2") {
+      // both inputs are alive
+      EXPECT_EQ("Merge", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("v_in", node.input(0));
+      EXPECT_EQ("square1", node.input(1));
+    } else if (node.name() == "m3") {
+      // sqrt1 is dead
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("v_in", node.input(0));
+    } else if (node.name() == "m4") {
+      // both inputs are alive
+      EXPECT_EQ("Merge", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("square1", node.input(0));
+      EXPECT_EQ("sqrt2", node.input(1));
+    } else if (node.name() == "m6") {
+      // both inputs are alive and the control dependency can get triggered
+      EXPECT_EQ("Merge", node.op());
+      EXPECT_EQ(3, node.input_size());
+      EXPECT_EQ("v_in", node.input(0));
+      EXPECT_EQ("square1", node.input(1));
+      EXPECT_EQ("^sqrt2", node.input(2));
+    } else if (node.name() == "m8") {
+      // The node is to be preserved because of a fetch
+      EXPECT_EQ("Merge", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("id1", node.input(0));
+      EXPECT_EQ("id2", node.input(1));
+    } else if (node.name() == "m9") {
+      // The node is to be preserved because of a fetch
+      EXPECT_EQ("Merge", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("id3", node.input(0));
+      EXPECT_EQ("id4", node.input(1));
+    }
+  }
+}
+
 }  // namespace grappler
 }  // namespace tensorflow
diff --git a/tensorflow/core/grappler/utils.h b/tensorflow/core/grappler/utils.h
index b87ae055469b67..1c6fef59eaec8e 100644
--- a/tensorflow/core/grappler/utils.h
+++ b/tensorflow/core/grappler/utils.h
@@ -65,7 +65,7 @@ class NodeMap {
 // A vector with a set. The set stores the same elements as the vector, and
 // quickly answers whether a value is in the vector. Duplicated elements are not
 // allowed for now.
-template 
+template >
 class SetVector {
  public:
   // Returns false if value already existed in the set, true otherwise.
@@ -91,7 +91,7 @@ class SetVector {
   void Reserve(int64 size) { vector_.reserve(size); }
 
  private:
-  std::unordered_set set_;
+  std::unordered_set set_;
   std::vector vector_;
 };
 

From 77a866ced3ca76c96b74af2759e432bfe250566f Mon Sep 17 00:00:00 2001
From: manhyuk 
Date: Sat, 5 May 2018 21:01:01 +0900
Subject: [PATCH 0403/1691] fix typo

---
 .../hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc b/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
index 60281951dda940..66939fbb0f0d3b 100644
--- a/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
+++ b/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
@@ -115,7 +115,7 @@ static void CheckOpsSupport(const GraphDef& graph_def,
       HexagonOpsDefinitions::getInstance();
   LOG(INFO) << "Checking " << graph_def.node_size() << " nodes";
   LOG(INFO) << "dump_all_nodes = " << dump_all_nodes
-            << ", dump_shape_and_tpye = " << dump_shape_and_type;
+            << ", dump_shape_and_type = " << dump_shape_and_type;
 
   std::unordered_set unsupported_ops;
   bool all_supported = true;

From cad5d6694aced77ab3c9141be2eea121bc6c9cb7 Mon Sep 17 00:00:00 2001
From: manhyuk 
Date: Sat, 5 May 2018 21:02:58 +0900
Subject: [PATCH 0404/1691] fix typo

---
 tensorflow/compiler/xla/shape_util.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h
index cb8bf5a2b9e5d0..82c75f85d838f9 100644
--- a/tensorflow/compiler/xla/shape_util.h
+++ b/tensorflow/compiler/xla/shape_util.h
@@ -231,7 +231,7 @@ class ShapeUtil {
   }
 
   // Returns the higher-precision element type if a and b are both floating
-  // point types; otherwise, checks that that they have the same element type
+  // point types; otherwise, checks that they have the same element type
   // and returns it.
   static PrimitiveType HigherPrecisionElementType(const Shape& a,
                                                   const Shape& b) {

From c92de2f3fc81c701ab29408a8a84cd6e41e96fe5 Mon Sep 17 00:00:00 2001
From: "karl@kubx.ca" 
Date: Sat, 5 May 2018 10:44:20 -0400
Subject: [PATCH 0405/1691] Skip all ops with function attribute by default

---
 tensorflow/core/api_def/BUILD                      |  6 ------
 .../api_def/java_api/api_def_FilterDataset.pbtxt   |  4 ----
 .../api_def/java_api/api_def_FlatMapDataset.pbtxt  |  4 ----
 tensorflow/core/api_def/java_api/api_def_For.pbtxt |  4 ----
 .../java_api/api_def_GeneratorDataset.pbtxt        |  4 ----
 .../java_api/api_def_GroupByWindowDataset.pbtxt    |  4 ----
 tensorflow/core/api_def/java_api/api_def_If.pbtxt  |  4 ----
 .../java_api/api_def_InterleaveDataset.pbtxt       |  4 ----
 .../java_api/api_def_MapAndBatchDataset.pbtxt      |  4 ----
 .../core/api_def/java_api/api_def_MapDataset.pbtxt |  4 ----
 .../api_def/java_api/api_def_OneShotIterator.pbtxt |  4 ----
 .../api_def_ParallelInterleaveDataset.pbtxt        |  4 ----
 .../java_api/api_def_ParallelMapDataset.pbtxt      |  4 ----
 .../core/api_def/java_api/api_def_RemoteCall.pbtxt |  4 ----
 .../api_def/java_api/api_def_ScanDataset.pbtxt     |  4 ----
 .../java_api/api_def_SymbolicGradient.pbtxt        |  4 ----
 .../core/api_def/java_api/api_def_While.pbtxt      |  4 ----
 tensorflow/java/BUILD                              |  1 -
 tensorflow/java/src/gen/cc/op_generator.cc         | 14 +++++++++++++-
 19 files changed, 13 insertions(+), 72 deletions(-)
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_FilterDataset.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_FlatMapDataset.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_For.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_GeneratorDataset.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_GroupByWindowDataset.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_If.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_InterleaveDataset.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_MapDataset.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_OneShotIterator.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_ParallelMapDataset.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_RemoteCall.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_ScanDataset.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_SymbolicGradient.pbtxt
 delete mode 100644 tensorflow/core/api_def/java_api/api_def_While.pbtxt

diff --git a/tensorflow/core/api_def/BUILD b/tensorflow/core/api_def/BUILD
index 06b797e32edc04..1454a1d9b2f8d1 100644
--- a/tensorflow/core/api_def/BUILD
+++ b/tensorflow/core/api_def/BUILD
@@ -30,12 +30,6 @@ filegroup(
     visibility = ["//tensorflow:internal"],
 )
 
-filegroup(
-    name = "java_api_def",
-    srcs = glob(["java_api/*"]),
-    visibility = ["//tensorflow:internal"],
-)
-
 cc_library(
     name = "excluded_ops_lib",
     srcs = ["excluded_ops.cc"],
diff --git a/tensorflow/core/api_def/java_api/api_def_FilterDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_FilterDataset.pbtxt
deleted file mode 100644
index debd7e570972c1..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_FilterDataset.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "FilterDataset"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_FlatMapDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_FlatMapDataset.pbtxt
deleted file mode 100644
index 329ab15ef53ae7..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_FlatMapDataset.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "FlatMapDataset"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_For.pbtxt b/tensorflow/core/api_def/java_api/api_def_For.pbtxt
deleted file mode 100644
index caabc947bb2461..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_For.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "For"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_GeneratorDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_GeneratorDataset.pbtxt
deleted file mode 100644
index a6e5167c305130..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_GeneratorDataset.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "GeneratorDataset"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_GroupByWindowDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_GroupByWindowDataset.pbtxt
deleted file mode 100644
index 4c0b2084a8a450..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_GroupByWindowDataset.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "GroupByWindowDataset"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_If.pbtxt b/tensorflow/core/api_def/java_api/api_def_If.pbtxt
deleted file mode 100644
index 13b8635ca79d11..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_If.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "If"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_InterleaveDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_InterleaveDataset.pbtxt
deleted file mode 100644
index ed748d4d2a408f..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_InterleaveDataset.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "InterleaveDataset"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt
deleted file mode 100644
index cb96bf63d8f0d1..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "MapAndBatchDataset"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_MapDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapDataset.pbtxt
deleted file mode 100644
index e0ab8dd9db62eb..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_MapDataset.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "MapDataset"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_OneShotIterator.pbtxt b/tensorflow/core/api_def/java_api/api_def_OneShotIterator.pbtxt
deleted file mode 100644
index 13130e68822adf..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_OneShotIterator.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "OneShotIterator"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt
deleted file mode 100644
index 6a985d24fa7406..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "ParallelInterleaveDataset"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_ParallelMapDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParallelMapDataset.pbtxt
deleted file mode 100644
index 64f25b9e5e9f73..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_ParallelMapDataset.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "ParallelMapDataset"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_RemoteCall.pbtxt b/tensorflow/core/api_def/java_api/api_def_RemoteCall.pbtxt
deleted file mode 100644
index 2ccb5c8cf339e8..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_RemoteCall.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "RemoteCall"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_ScanDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScanDataset.pbtxt
deleted file mode 100644
index 3463e60049c602..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_ScanDataset.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "ScanDataset"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_SymbolicGradient.pbtxt b/tensorflow/core/api_def/java_api/api_def_SymbolicGradient.pbtxt
deleted file mode 100644
index 88c3acea74010d..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_SymbolicGradient.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "SymbolicGradient"
-  visibility: SKIP
-}
diff --git a/tensorflow/core/api_def/java_api/api_def_While.pbtxt b/tensorflow/core/api_def/java_api/api_def_While.pbtxt
deleted file mode 100644
index 33756682c3aa60..00000000000000
--- a/tensorflow/core/api_def/java_api/api_def_While.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "While"
-  visibility: SKIP
-}
\ No newline at end of file
diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD
index 7cd0208dbf29c2..0cc8e7c3e2c166 100644
--- a/tensorflow/java/BUILD
+++ b/tensorflow/java/BUILD
@@ -72,7 +72,6 @@ tf_java_op_gen_srcjar(
     name = "java_op_gen_sources",
     api_def_srcs = [
         "//tensorflow/core/api_def:base_api_def",
-        "//tensorflow/core/api_def:java_api_def",
     ],
     base_package = "org.tensorflow.op",
     gen_tool = ":java_op_gen_tool",
diff --git a/tensorflow/java/src/gen/cc/op_generator.cc b/tensorflow/java/src/gen/cc/op_generator.cc
index 7355b3a395ec6a..f4cefbe9333da3 100644
--- a/tensorflow/java/src/gen/cc/op_generator.cc
+++ b/tensorflow/java/src/gen/cc/op_generator.cc
@@ -420,6 +420,18 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint,
   writer.EndType();
 }
 
+bool CanGenerateOp(const OpDef& op_def, const ApiDef& api_def) {
+  if (api_def.visibility() == ApiDef::SKIP) {
+    return false;
+  }
+  for (const auto& attr : op_def.attr()) {
+    if (attr.type() == "func") {
+      return false;  // TODO(karllessard) add support for function attributes
+    }
+  }
+  return true;
+}
+
 }  // namespace
 
 Status OpGenerator::Run(const OpList& op_list, const string& base_package,
@@ -441,7 +453,7 @@ Status OpGenerator::Run(const OpList& op_list, const string& base_package,
   api_map.UpdateDocs();
   for (const auto& op_def : op_list.op()) {
     const ApiDef* api_def = api_map.GetApiDef(op_def.name());
-    if (api_def->visibility() != ApiDef::SKIP) {
+    if (CanGenerateOp(op_def, *api_def)) {
       OpSpec op(OpSpec::Create(op_def, *api_def));
       for (const EndpointSpec& endpoint : op.endpoints()) {
         GenerateOp(op, endpoint, base_package, output_dir, env_);

From 90bbbdcc42a67c93ba8dcbc66f9c1d06909c48cb Mon Sep 17 00:00:00 2001
From: Karl Lessard 
Date: Sat, 5 May 2018 10:48:22 -0400
Subject: [PATCH 0406/1691] Remove comment left-over

---
 tensorflow/core/api_def/BUILD | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tensorflow/core/api_def/BUILD b/tensorflow/core/api_def/BUILD
index 1454a1d9b2f8d1..19d643880966f7 100644
--- a/tensorflow/core/api_def/BUILD
+++ b/tensorflow/core/api_def/BUILD
@@ -4,7 +4,6 @@
 # The following targets can be used to access ApiDefs:
 #   :base_api_def
 #   :python_api_def
-#   :java_api_def
 
 package(
     default_visibility = ["//visibility:private"],

From ab48fb528221152299fb08da8116d2eca54b8423 Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Fri, 4 May 2018 15:40:07 -0700
Subject: [PATCH 0407/1691] [XLA] Print allowed attributes when the user
 specifies an invalid attr.

PiperOrigin-RevId: 195482974
---
 .../compiler/xla/tools/parser/hlo_parser.cc   | 30 +++++++++++++------
 .../xla/tools/parser/hlo_parser_test.cc       |  2 +-
 2 files changed, 22 insertions(+), 10 deletions(-)

diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
index 3a945fb3b1b54e..40dc0730ce25ea 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
@@ -30,6 +30,7 @@ namespace {
 
 using tensorflow::StringPiece;
 using tensorflow::gtl::optional;
+using tensorflow::str_util::Join;
 using tensorflow::str_util::Split;
 using tensorflow::str_util::SplitAndParseAsInts;
 using tensorflow::strings::Printf;
@@ -53,7 +54,7 @@ class HloParser {
   std::unique_ptr ConsumeHloModule() { return std::move(module_); }
 
   // Returns the error information.
-  string GetError() const { return tensorflow::str_util::Join(error_, "\n"); }
+  string GetError() const { return Join(error_, "\n"); }
 
  private:
   // ParseXXX returns false if an error occurred.
@@ -245,7 +246,7 @@ bool HloParser::Error(LocTy loc, StringPiece msg) {
   error_lines.push_back(std::string(lexer_.GetLine(loc)));
   error_lines.push_back(col == 0 ? "" : StrCat(string(col - 1, ' '), "^"));
 
-  error_.push_back(tensorflow::str_util::Join(error_lines, "\n"));
+  error_.push_back(Join(error_lines, "\n"));
   VLOG(1) << "Error: " << error_.back();
   return false;
 }
@@ -1488,11 +1489,10 @@ bool HloParser::ParseDenseLiteral(std::unique_ptr* literal,
     std::vector elems_seen_until_dim(elems_seen_per_dim.begin(),
                                             elems_seen_per_dim.begin() + dim);
     return StrCat("[",
-                  tensorflow::str_util::Join(
-                      elems_seen_until_dim, ",",
-                      [](string* out, const int64& num_elems) {
-                        tensorflow::strings::StrAppend(out, num_elems - 1);
-                      }),
+                  Join(elems_seen_until_dim, ",",
+                       [](string* out, const int64& num_elems) {
+                         tensorflow::strings::StrAppend(out, num_elems - 1);
+                       }),
                   "]");
   };
   do {
@@ -1680,7 +1680,7 @@ bool HloParser::ParseSparseLiteralHelper(std::unique_ptr* literal,
         return Error(
             index_loc,
             StrCat("invalid multi-dimension index for shape with rank ", rank,
-                   ": [", tensorflow::str_util::Join(index, ", "), "]"));
+                   ": [", Join(index, ", "), "]"));
       }
     }
     if (!ParseToken(TokKind::kColon,
@@ -1848,7 +1848,19 @@ bool HloParser::ParseAttributeHelper(
   }
   auto attr_it = attrs.find(name);
   if (attr_it == attrs.end()) {
-    return Error(loc, Printf("unexpected attribute %s", name.c_str()));
+    string allowed_attrs;
+    if (attrs.empty()) {
+      allowed_attrs = "No attributes are allowed here.";
+    } else {
+      allowed_attrs = StrCat(
+          "Allowed attributes: ",
+          Join(attrs, ", ",
+               [&](string* out, const std::pair& kv) {
+                 StrAppend(out, kv.first);
+               }));
+    }
+    return Error(loc, Printf("unexpected attribute \"%s\".  %s", name.c_str(),
+                             allowed_attrs.c_str()));
   }
   AttrTy attr_type = attr_it->second.attr_type;
   void* attr_out_ptr = attr_it->second.result;
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
index 4e085bc89c6dc6..d38d8907a60538 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
@@ -1138,7 +1138,7 @@ ENTRY %TwoSendRecvBothWayRecvFist.v3 () -> f32[] {
 
 )";
   ExpectHasSubstr(Parse(original).status().error_message(),
-                  "unexpected attribute calls");
+                  "unexpected attribute \"calls\"");
 }
 
 TEST_F(HloParserTest, MissingAttribute) {

From 008a3b69a601dc68fd940eb8a03b0c445714a339 Mon Sep 17 00:00:00 2001
From: Karmel Allison 
Date: Fri, 4 May 2018 16:01:02 -0700
Subject: [PATCH 0408/1691] Add the ability to export separate SavedModels for
 train and eval mode to Estimator with two new methods, available in
 tf.contrib: export_all_saved_models and export_saved_model_for_mode.

PiperOrigin-RevId: 195485922
---
 tensorflow/contrib/estimator/BUILD            |  38 ++
 tensorflow/contrib/estimator/__init__.py      |   3 +
 .../estimator/python/estimator/export.py      | 216 ++++++++++
 .../estimator/python/estimator/export_test.py | 391 ++++++++++++++++++
 tensorflow/python/estimator/BUILD             |   1 +
 tensorflow/python/estimator/estimator.py      | 346 +++++++++++++---
 tensorflow/python/estimator/estimator_test.py | 336 ++++++++++++++-
 tensorflow/python/estimator/export/export.py  | 325 +++++++++++----
 .../python/estimator/export/export_output.py  | 223 +++++++++-
 .../estimator/export/export_output_test.py    | 110 +++++
 .../python/estimator/export/export_test.py    | 253 +++++++++++-
 tensorflow/python/estimator/model_fn.py       |   8 +
 tensorflow/python/saved_model/builder_impl.py |  54 ++-
 tensorflow/python/saved_model/constants.py    |   6 +
 .../python/saved_model/saved_model_test.py    |  90 ++++
 .../python/saved_model/signature_constants.py |   6 +
 .../python/saved_model/signature_def_utils.py |   2 +
 .../saved_model/signature_def_utils_impl.py   |  56 +++
 .../saved_model/signature_def_utils_test.py   |  95 +++++
 .../python/saved_model/tag_constants.py       |   5 +
 20 files changed, 2373 insertions(+), 191 deletions(-)
 create mode 100644 tensorflow/contrib/estimator/python/estimator/export.py
 create mode 100644 tensorflow/contrib/estimator/python/estimator/export_test.py

diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD
index 571e2e3a5df08e..e9a68801efccc1 100644
--- a/tensorflow/contrib/estimator/BUILD
+++ b/tensorflow/contrib/estimator/BUILD
@@ -17,6 +17,7 @@ py_library(
         ":boosted_trees",
         ":dnn",
         ":dnn_linear_combined",
+        ":export",
         ":extenders",
         ":head",
         ":linear",
@@ -180,6 +181,43 @@ py_test(
     ],
 )
 
+py_library(
+    name = "export",
+    srcs = [
+        "python/estimator/export.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python/estimator:model_fn",
+    ],
+)
+
+py_test(
+    name = "export_test",
+    size = "medium",
+    srcs = ["python/estimator/export_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["notsan"],  # b/62863147
+    deps = [
+        ":export",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:metrics",
+        "//tensorflow/python:parsing_ops",
+        "//tensorflow/python:session",
+        "//tensorflow/python:state_ops",
+        "//tensorflow/python:training",
+        "//tensorflow/python:util",
+        "//tensorflow/python:variables",
+        "//tensorflow/python/estimator",
+        "//tensorflow/python/estimator:export_export",
+        "//tensorflow/python/estimator:export_output",
+        "//tensorflow/python/estimator:model_fn",
+        "//tensorflow/python/saved_model:loader",
+        "//tensorflow/python/saved_model:tag_constants",
+    ],
+)
+
 py_library(
     name = "head",
     srcs = [
diff --git a/tensorflow/contrib/estimator/__init__.py b/tensorflow/contrib/estimator/__init__.py
index d43b3ea6bf2718..ec502f86ddb724 100644
--- a/tensorflow/contrib/estimator/__init__.py
+++ b/tensorflow/contrib/estimator/__init__.py
@@ -22,6 +22,7 @@
 from tensorflow.contrib.estimator.python.estimator.boosted_trees import *
 from tensorflow.contrib.estimator.python.estimator.dnn import *
 from tensorflow.contrib.estimator.python.estimator.dnn_linear_combined import *
+from tensorflow.contrib.estimator.python.estimator.export import *
 from tensorflow.contrib.estimator.python.estimator.extenders import *
 from tensorflow.contrib.estimator.python.estimator.head import *
 from tensorflow.contrib.estimator.python.estimator.linear import *
@@ -56,6 +57,8 @@
     'TowerOptimizer',
     'RNNClassifier',
     'RNNEstimator',
+    'export_saved_model_for_mode',
+    'export_all_saved_models',
 ]
 
 remove_undocumented(__name__, allowed_exception_list=_allowed_symbols)
diff --git a/tensorflow/contrib/estimator/python/estimator/export.py b/tensorflow/contrib/estimator/python/estimator/export.py
new file mode 100644
index 00000000000000..e7e366a3f26fa6
--- /dev/null
+++ b/tensorflow/contrib/estimator/python/estimator/export.py
@@ -0,0 +1,216 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Wrapper for methods to export train/eval graphs from Estimator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.estimator import model_fn as model_fn_lib
+
+
+def export_saved_model_for_mode(
+    estimator, export_dir_base, input_receiver_fn,
+    assets_extra=None,
+    as_text=False,
+    checkpoint_path=None,
+    strip_default_attrs=False,
+    mode=model_fn_lib.ModeKeys.PREDICT):
+  # pylint: disable=line-too-long
+  """Exports a single train/eval/predict graph as a SavedModel.
+
+  For a detailed guide, see
+  @{$saved_model#using_savedmodel_with_estimators$Using SavedModel with Estimators}.
+
+  Sample usage:
+  ```python
+  classifier = tf.estimator.LinearClassifier(
+      feature_columns=[age, language])
+  classifier.train(input_fn=input_fn, steps=1000)
+
+  feature_spec = {
+      'age': tf.placeholder(dtype=tf.int64),
+      'language': array_ops.placeholder(dtype=tf.string)
+  }
+  label_spec = tf.placeholder(dtype=dtypes.int64)
+
+  train_rcvr_fn = tf.contrib.estimator.build_raw_supervised_input_receiver_fn(
+      feature_spec, label_spec)
+
+  export_dir = tf.contrib.estimator.export_saved_model_for_mode(
+      classifier,
+      export_dir_base='my_model/',
+      input_receiver_fn=train_rcvr_fn,
+      mode=model_fn_lib.ModeKeys.TRAIN)
+
+  # export_dir is a timestamped directory with the SavedModel, which
+  # can be used for serving, analysis with TFMA, or directly loaded in.
+  with ops.Graph().as_default() as graph:
+    with session.Session(graph=graph) as sess:
+      loader.load(sess, [tag_constants.TRAINING], export_dir)
+      ...
+  ```
+
+  This method takes an input_receiver_fn and mode. For the mode passed in,
+  this method builds a new graph by calling the input_receiver_fn to obtain
+  feature and label `Tensor`s. Next, this method calls the `Estimator`'s
+  model_fn in the passed mode to generate the model graph based on
+  those features and labels, and restores the given checkpoint
+  (or, lacking that, the most recent checkpoint) into the graph.
+  Finally, it creates a timestamped export directory below the
+  export_dir_base, and writes a `SavedModel` into it containing
+  the `MetaGraphDef` for the given mode and its associated signatures.
+
+  For prediction, the exported `MetaGraphDef` will provide one `SignatureDef`
+  for each element of the export_outputs dict returned from the model_fn,
+  named using the same keys.  One of these keys is always
+  signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which
+  signature will be served when a serving request does not specify one.
+  For each signature, the outputs are provided by the corresponding
+  `ExportOutput`s, and the inputs are always the input receivers provided by
+  the serving_input_receiver_fn.
+
+  For training and evaluation, the train_op is stored in an extra collection,
+  and loss, metrics, and predictions are included in a SignatureDef for the
+  mode in question.
+
+  Extra assets may be written into the SavedModel via the assets_extra
+  argument.  This should be a dict, where each key gives a destination path
+  (including the filename) relative to the assets.extra directory.  The
+  corresponding value gives the full path of the source file to be copied.
+  For example, the simple case of copying a single file without renaming it
+  is specified as `{'my_asset_file.txt': '/path/to/my_asset_file.txt'}`.
+
+  Args:
+    estimator: an instance of tf.estimator.Estimator
+    export_dir_base: A string containing a directory in which to create
+      timestamped subdirectories containing exported SavedModels.
+    input_receiver_fn: a function that takes no argument and
+      returns the appropriate subclass of `InputReceiver`.
+    assets_extra: A dict specifying how to populate the assets.extra directory
+      within the exported SavedModel, or `None` if no extra assets are needed.
+    as_text: whether to write the SavedModel proto in text format.
+    checkpoint_path: The checkpoint path to export.  If `None` (the default),
+      the most recent checkpoint found within the model directory is chosen.
+    strip_default_attrs: Boolean. If `True`, default-valued attributes will be
+      removed from the NodeDefs. For a detailed guide, see
+      [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
+    mode: tf.estimator.ModeKeys value indicating with mode will be exported.
+
+  Returns:
+    The string path to the exported directory.
+
+  Raises:
+    ValueError: if input_receiver_fn is None, no export_outputs
+      are provided, or no checkpoint can be found.
+  """
+  # pylint: enable=line-too-long
+
+  # pylint: disable=protected-access
+  return estimator._export_saved_model_for_mode(
+      export_dir_base, input_receiver_fn,
+      assets_extra=assets_extra,
+      as_text=as_text,
+      checkpoint_path=checkpoint_path,
+      strip_default_attrs=strip_default_attrs,
+      mode=mode)
+  # pylint: enable=protected-access
+
+
+def export_all_saved_models(
+    estimator, export_dir_base, input_receiver_fn_map,
+    assets_extra=None,
+    as_text=False,
+    checkpoint_path=None,
+    strip_default_attrs=False):
+  # pylint: disable=line-too-long
+  """Exports requested train/eval/predict graphs as separate SavedModels.
+
+  This is a wrapper around export_saved_model_for_mode that accepts
+  multiple modes simultaneously and creates directories for each under
+  export_dir_base. See `Estimator.export_saved_model_for_mode` for
+  further details as to how the export works for each mode.
+
+  Sample usage:
+  ```python
+  classifier = tf.estimator.LinearClassifier(
+      feature_columns=[age, language])
+  classifier.train(input_fn=input_fn)
+
+  feature_spec = {
+      'age': tf.placeholder(dtype=tf.int64),
+      'language': array_ops.placeholder(dtype=tf.string)
+  }
+  label_spec = tf.placeholder(dtype=dtypes.int64)
+
+  train_rcvr_fn = tf.contrib.estimator.build_raw_supervised_input_receiver_fn(
+      feature_spec, label_spec)
+
+  serve_rcvr_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
+      feature_spec)
+
+  rcvr_fn_map = {
+      model_fn_lib.ModeKeys.TRAIN: train_rcvr_fn,
+      model_fn_lib.ModeKeys.PREDICT: serve_rcvr_fn,
+  }
+
+  export_dirs = tf.contrib.estimator.export_all_saved_models(
+      classifier,
+      export_dir_base='my_model/',
+      input_receiver_fn_map=rcvr_fn_map)
+
+  # export_dirs is a dict of directories with SavedModels, which
+  # can be used for serving, analysis with TFMA, or directly loaded in.
+  with ops.Graph().as_default() as graph:
+    with session.Session(graph=graph) as sess:
+      loader.load(sess, [tag_constants.TRAINING],
+                  export_dirs[tf.estimator.ModeKeys.TRAIN])
+      ...
+  ```
+
+  Args:
+    estimator: an instance of tf.estimator.Estimator
+    export_dir_base: A string containing a directory in which to create
+      timestamped subdirectories containing exported SavedModels.
+    input_receiver_fn_map: dict of tf.estimator.ModeKeys to input_receiver_fn
+      mappings, where the input_receiver_fn is a function that takes no
+      argument and returns the appropriate subclass of `InputReceiver`.
+    assets_extra: A dict specifying how to populate the assets.extra directory
+      within the exported SavedModel, or `None` if no extra assets are needed.
+    as_text: whether to write the SavedModel proto in text format.
+    checkpoint_path: The checkpoint path to export.  If `None` (the default),
+      the most recent checkpoint found within the model directory is chosen.
+    strip_default_attrs: Boolean. If `True`, default-valued attributes will be
+      removed from the NodeDefs. For a detailed guide, see
+      [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
+
+  Returns:
+    A dict of tf.estimator.ModeKeys value to string path for each exported
+    directory.
+
+  Raises:
+    ValueError: if any input_receiver_fn is None, no export_outputs
+      are provided, or no checkpoint can be found.
+  """
+  # pylint: enable=line-too-long
+
+  # pylint: disable=protected-access
+  return estimator._export_all_saved_models(
+      export_dir_base, input_receiver_fn_map,
+      assets_extra=assets_extra,
+      as_text=as_text,
+      checkpoint_path=checkpoint_path,
+      strip_default_attrs=strip_default_attrs)
+  # pylint: enable=protected-access
diff --git a/tensorflow/contrib/estimator/python/estimator/export_test.py b/tensorflow/contrib/estimator/python/estimator/export_test.py
new file mode 100644
index 00000000000000..89d02582e18e39
--- /dev/null
+++ b/tensorflow/contrib/estimator/python/estimator/export_test.py
@@ -0,0 +1,391 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for contrib wrapping of export_saved_model_for_mode functionality.
+
+These are direct copies of the tests included in core, with import locations
+changed. These should be removed when the functionality in core is part of the
+public API.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import tempfile
+
+from tensorflow.contrib.estimator.python.estimator import export as contrib_export
+from tensorflow.python.client import session
+from tensorflow.python.estimator import estimator
+from tensorflow.python.estimator import model_fn as model_fn_lib
+from tensorflow.python.estimator.export import export
+from tensorflow.python.estimator.export import export_output
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import metrics as metrics_lib
+from tensorflow.python.ops import parsing_ops
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import gfile
+from tensorflow.python.platform import test
+from tensorflow.python.saved_model import loader
+from tensorflow.python.saved_model import tag_constants
+from tensorflow.python.training import training
+from tensorflow.python.util import compat
+
+
+def _model_fn_for_export_tests(features, labels, mode):
+  _, _ = features, labels
+  variables.Variable(1., name='weight')
+  scores = constant_op.constant([3.])
+  classes = constant_op.constant(['wumpus'])
+  update_global_step = state_ops.assign_add(training.get_global_step(), 1)
+  with ops.control_dependencies([update_global_step]):
+    train_op = constant_op.constant(2.)
+  return model_fn_lib.EstimatorSpec(
+      mode,
+      predictions=constant_op.constant(10.),
+      loss=constant_op.constant(1.),
+      train_op=train_op,
+      export_outputs={
+          'test': export_output.ClassificationOutput(scores, classes)})
+
+
+def _x_y_input_fn():
+  return ({'x': constant_op.constant([[1], [1]]),
+           'y': constant_op.constant([[2], [2]])},
+          constant_op.constant([[1], [1]]))
+
+
+def _model_fn_with_x_y(features, labels, mode):
+  _ = labels
+  variables.Variable(1., name='weight')
+  scores = constant_op.constant([3.])
+  classes = constant_op.constant(['wumpus'])
+  if mode == model_fn_lib.ModeKeys.PREDICT:
+    variables.Variable(36., name='name_collision')
+    return model_fn_lib.EstimatorSpec(
+        mode,
+        predictions=constant_op.constant(10.),
+        export_outputs={
+            'test': export_output.ClassificationOutput(scores, classes)})
+  else:
+    prefix = 'eval_' if mode == model_fn_lib.ModeKeys.EVAL else ''
+
+    multiplied = math_ops.multiply(
+        features['x'], features['y'], name='{}multiplied'.format(prefix))
+    metrics = {'mean': metrics_lib.mean(features['x'] - features['y'],
+                                        name='{}mean'.format(prefix))}
+    variables.Variable(1., name='later_var')
+    variables.Variable(3., name='name_collision')
+    return model_fn_lib.EstimatorSpec(
+        mode,
+        predictions=multiplied,
+        loss=constant_op.constant(1.),
+        train_op=state_ops.assign_add(training.get_global_step(), 1),
+        eval_metric_ops=metrics)
+
+
+def _get_serving_input_receiver_fn():
+  feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),
+                  'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}
+  return export.build_parsing_serving_input_receiver_fn(feature_spec)
+
+
+def _get_supervised_input_receiver_fn():
+  feature_spec = {
+      'x': array_ops.placeholder(
+          dtype=dtypes.int64, shape=(2, 1), name='feature_x'),
+      'y': array_ops.placeholder(
+          dtype=dtypes.int64, shape=(2, 1), name='feature_y')
+      }
+  label_spec = array_ops.placeholder(
+      dtype=dtypes.float32, shape=[1], name='truth')
+
+  return export.build_raw_supervised_input_receiver_fn(
+      feature_spec, label_spec)
+
+
+class EstimatorExportTest(test.TestCase):
+
+  def test_export_saved_model_train(self):
+    self._test_export_saved_model_for_mode(
+        _get_supervised_input_receiver_fn(), model_fn_lib.ModeKeys.TRAIN)
+
+  def test_export_saved_model_eval(self):
+    self._test_export_saved_model_for_mode(
+        _get_supervised_input_receiver_fn(), model_fn_lib.ModeKeys.EVAL)
+
+  def test_export_saved_model_predict(self):
+    self._test_export_saved_model_for_mode(
+        _get_serving_input_receiver_fn(), model_fn_lib.ModeKeys.PREDICT)
+
+  def _test_export_saved_model_for_mode(self, input_receiver_fn, mode):
+    tmpdir = tempfile.mkdtemp()
+    est = estimator.Estimator(model_fn=_model_fn_for_export_tests)
+    est.train(input_fn=_x_y_input_fn, steps=1)
+
+    # Perform the export.
+    export_dir_base = os.path.join(
+        compat.as_bytes(tmpdir), compat.as_bytes('export'))
+    export_dir = contrib_export.export_saved_model_for_mode(
+        est, export_dir_base, input_receiver_fn, mode=mode)
+
+    # Check that all the files are in the right places.
+    self.assertTrue(gfile.Exists(export_dir_base))
+    self._validate_exported_files(export_dir)
+
+    # Restore, to validate that the export was well-formed.
+    tag_set = model_fn_lib.EXPORT_TAG_MAP[mode]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, tag_set, export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertFalse('name_collision_1' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_proto_roundtrip_receiver_map(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    self.assertEqual(len(export_dirs), 1)
+    # Restore, to validate that the export was well-formed.
+    export_dir = export_dirs[model_fn_lib.ModeKeys.PREDICT]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.SERVING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('input_example_tensor' in graph_ops)
+        self.assertTrue('ParseExample/ParseExample' in graph_ops)
+        self.assertFalse('feature_x' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_proto_roundtrip_train_only(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    self.assertEqual(len(export_dirs), 1)
+    # Restore, to validate that the export was well-formed.
+    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.TRAINING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('multiplied' in graph_ops)
+        self.assertTrue('mean/update_op' in graph_ops)
+        self.assertFalse('eval_multiplied' in graph_ops)
+        self.assertTrue('feature_x' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_proto_roundtrip_eval_only(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn()
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    self.assertEqual(len(export_dirs), 1)
+    # Restore, to validate that the export was well-formed.
+    export_dir = export_dirs[model_fn_lib.ModeKeys.EVAL]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.EVAL], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('eval_multiplied' in graph_ops)
+        self.assertTrue('eval_mean/value' in graph_ops)
+        self.assertFalse('multiplied' in graph_ops)
+        self.assertTrue('feature_x' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_proto_roundtrip_no_serving(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn()
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    self.assertEqual(len(export_dirs), 2)
+    # Restore, to validate that the export was well-formed.
+    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.TRAINING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('multiplied' in graph_ops)
+        self.assertFalse('eval_multiplied' in graph_ops)
+        self.assertTrue('feature_x' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+    export_dir = export_dirs[model_fn_lib.ModeKeys.EVAL]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.EVAL], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('eval_multiplied' in graph_ops)
+        self.assertFalse('multiplied' in graph_ops)
+        # TODO(karmel): is this the desired behavior when names are shared?
+        self.assertTrue('feature_x_1' in graph_ops)
+        self.assertTrue('feature_y_1' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_proto_roundtrip_three_defs(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    # Restore, to validate that the export was well-formed.
+    for mode, tag_set in model_fn_lib.EXPORT_TAG_MAP.items():
+      export_dir = export_dirs[mode]
+      with ops.Graph().as_default() as graph:
+        with session.Session(graph=graph) as sess:
+          loader.load(sess, tag_set, export_dir)
+          graph_ops = [x.name for x in graph.get_operations()]
+          self.assertTrue('global_step/Assign' in graph_ops)
+          self.assertTrue('global_step/Initializer/zeros' in graph_ops)
+          self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_proto_roundtrip_all_vars(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.TRAINING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('later_var' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    export_dir = export_dirs[model_fn_lib.ModeKeys.PREDICT]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.SERVING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertFalse('later_var' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_name_collision(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.TRAINING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('name_collision' in graph_ops)
+        self.assertFalse('name_collision_1' in graph_ops)
+        collection_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+        self.assertEqual(3, collection_vars[-1].eval())
+
+    export_dir = export_dirs[model_fn_lib.ModeKeys.PREDICT]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.SERVING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('name_collision' in graph_ops)
+        self.assertFalse('name_collision_1' in graph_ops)
+        collection_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+        # This is a non-obvious detail: when we load the estimator spec
+        # for predict, name_collision gets set to 36. However, we then restore
+        # from checkpoint, which should overwrite that var and make it the 3
+        # from training. In practice, this would not be a good way to write
+        # a model_fn, but leaving this check in for now to ensure consistency
+        # with what would happen given our current order of spec, then
+        # checkpoint.
+        self.assertEqual(3, collection_vars[-1].eval())
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def _test_export_all_saved_models(self, input_receiver_fn_map):
+    tmpdir = tempfile.mkdtemp()
+    est = estimator.Estimator(model_fn=_model_fn_with_x_y)
+    est.train(input_fn=_x_y_input_fn, steps=1)
+
+    # Perform the export.
+    export_dir_base = os.path.join(
+        compat.as_bytes(tmpdir), compat.as_bytes('export'))
+    export_dirs = contrib_export.export_all_saved_models(
+        est, export_dir_base, input_receiver_fn_map)
+
+    # Check that all the files are in the right places.
+    self.assertTrue(gfile.Exists(export_dir_base))
+
+    for _, export_dir in export_dirs.items():
+      self._validate_exported_files(export_dir)
+
+    return export_dirs, tmpdir
+
+  def _validate_exported_files(self, export_dir):
+    self.assertTrue(gfile.Exists(export_dir))
+    self.assertTrue(gfile.Exists(os.path.join(
+        compat.as_bytes(export_dir),
+        compat.as_bytes('saved_model.pb'))))
+    self.assertTrue(gfile.Exists(os.path.join(
+        compat.as_bytes(export_dir),
+        compat.as_bytes('variables'))))
+    self.assertTrue(gfile.Exists(os.path.join(
+        compat.as_bytes(export_dir),
+        compat.as_bytes('variables/variables.index'))))
+    self.assertTrue(gfile.Exists(os.path.join(
+        compat.as_bytes(export_dir),
+        compat.as_bytes('variables/variables.data-00000-of-00001'))))
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD
index 56dec1eaa1f608..b25cc7aa2659bd 100644
--- a/tensorflow/python/estimator/BUILD
+++ b/tensorflow/python/estimator/BUILD
@@ -91,6 +91,7 @@ py_library(
         "//tensorflow/python:training",
         "//tensorflow/python:util",
         "//tensorflow/python/saved_model:signature_constants",
+        "//tensorflow/python/saved_model:tag_constants",
         "@six_archive//:six",
     ],
 )
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 530a4a24efc54f..9ae64d230ec26b 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -37,9 +37,8 @@
 from tensorflow.python.estimator import model_fn as model_fn_lib
 from tensorflow.python.estimator import run_config
 from tensorflow.python.estimator import util
-from tensorflow.python.estimator.export.export import build_all_signature_defs
-from tensorflow.python.estimator.export.export import get_temp_export_dir
-from tensorflow.python.estimator.export.export import get_timestamped_export_dir
+from tensorflow.python.estimator.export import export as export_helpers
+from tensorflow.python.estimator.export import export_output
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import random_seed
 from tensorflow.python.ops import array_ops
@@ -51,7 +50,6 @@
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.saved_model import builder as saved_model_builder
 from tensorflow.python.saved_model import constants
-from tensorflow.python.saved_model import tag_constants
 from tensorflow.python.summary import summary
 from tensorflow.python.summary.writer import writer_cache
 from tensorflow.python.training import device_setter
@@ -609,73 +607,283 @@ def export_savedmodel(
           are provided, or no checkpoint can be found.
     """
     # pylint: enable=line-too-long
+    return self._export_saved_model_for_mode(
+        export_dir_base,
+        serving_input_receiver_fn,
+        assets_extra=assets_extra,
+        as_text=as_text,
+        checkpoint_path=checkpoint_path,
+        strip_default_attrs=strip_default_attrs,
+        mode=model_fn_lib.ModeKeys.PREDICT)
+
+  def _export_all_saved_models(
+      self, export_dir_base, input_receiver_fn_map,
+      assets_extra=None,
+      as_text=False,
+      checkpoint_path=None,
+      strip_default_attrs=False):
+    # pylint: disable=line-too-long
+    """Exports requested train/eval/predict graphs as separate SavedModels.
+
+    This is a wrapper around export_saved_model_for_mode that accepts
+    multiple modes simultaneously and creates directories for each under
+    export_dir_base. See `Estimator.export_saved_model_for_mode` for
+    further details as to how the export works for each mode.
+
+    See tf.contrib.estimator.export_all_saved_models for the currently
+    exposed version of this function.
+
+    Args:
+      export_dir_base: A string containing a directory in which to create
+        timestamped subdirectories containing exported SavedModels.
+      input_receiver_fn_map: dict of tf.estimator.ModeKeys to input_receiver_fn
+        mappings, where the input_receiver_fn is a function that takes no
+        argument and returns the appropriate subclass of `InputReceiver`.
+      assets_extra: A dict specifying how to populate the assets.extra directory
+        within the exported SavedModel, or `None` if no extra assets are needed.
+      as_text: whether to write the SavedModel proto in text format.
+      checkpoint_path: The checkpoint path to export.  If `None` (the default),
+        the most recent checkpoint found within the model directory is chosen.
+      strip_default_attrs: Boolean. If `True`, default-valued attributes will be
+        removed from the NodeDefs. For a detailed guide, see
+        [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
+
+    Returns:
+      A dict of tf.estimator.ModeKeys value to string path for each exported
+      directory.
+
+    Raises:
+      ValueError: if any input_receiver_fn is None, no export_outputs
+        are provided, or no checkpoint can be found.
+    """
+    # pylint: enable=line-too-long
+    # TODO(b/65561022): Consider allowing multiple input_receiver_fns per mode.
+    exported = {}
+    for mode, input_receiver_fn in input_receiver_fn_map.items():
+      export_mode_dir = os.path.join(
+          compat.as_bytes(export_dir_base),
+          compat.as_bytes(mode))
+      gfile.MakeDirs(export_mode_dir)
+
+      exported_path = self._export_saved_model_for_mode(
+          export_mode_dir,
+          input_receiver_fn,
+          assets_extra=assets_extra,
+          as_text=as_text,
+          checkpoint_path=checkpoint_path,
+          strip_default_attrs=strip_default_attrs,
+          mode=mode)
+
+      exported[mode] = exported_path
+
+    return exported
+
+  def _export_saved_model_for_mode(
+      self, export_dir_base, input_receiver_fn,
+      assets_extra=None,
+      as_text=False,
+      checkpoint_path=None,
+      strip_default_attrs=False,
+      mode=model_fn_lib.ModeKeys.PREDICT):
+    # pylint: disable=line-too-long
+    """Exports a single train/eval/predict graph as a SavedModel.
+
+    For a detailed guide, see
+    @{$saved_model#using_savedmodel_with_estimators$Using SavedModel with Estimators}.
+
+    See tf.contrib.estimator.export_saved_model_for_mode for the currently
+    exposed version of this function.
+
+    This method takes an input_receiver_fn and mode. For the mode passed in,
+    this method builds a new graph by calling the input_receiver_fn to obtain
+    feature and label `Tensor`s. Next, this method calls the `Estimator`'s
+    model_fn in the passed mode to generate the model graph based on
+    those features and labels, and restores the given checkpoint
+    (or, lacking that, the most recent checkpoint) into the graph.
+    Finally, it creates a timestamped export directory below the
+    export_dir_base, and writes a `SavedModel` into it containing
+    the `MetaGraphDef` for the given mode and its associated signatures.
+
+    For prediction, the exported `MetaGraphDef` will provide one `SignatureDef`
+    for each element of the export_outputs dict returned from the model_fn,
+    named using the same keys.  One of these keys is always
+    signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which
+    signature will be served when a serving request does not specify one.
+    For each signature, the outputs are provided by the corresponding
+    `ExportOutput`s, and the inputs are always the input receivers provided by
+    the serving_input_receiver_fn.
+
+    For training and evaluation, the train_op is stored in an extra collection,
+    and loss, metrics, and predictions are included in a SignatureDef for the
+    mode in question.
+
+    Extra assets may be written into the SavedModel via the assets_extra
+    argument.  This should be a dict, where each key gives a destination path
+    (including the filename) relative to the assets.extra directory.  The
+    corresponding value gives the full path of the source file to be copied.
+    For example, the simple case of copying a single file without renaming it
+    is specified as `{'my_asset_file.txt': '/path/to/my_asset_file.txt'}`.
+
+    Args:
+      export_dir_base: A string containing a directory in which to create
+        timestamped subdirectories containing exported SavedModels.
+      input_receiver_fn: a function that takes no argument and
+        returns the appropriate subclass of `InputReceiver`.
+      assets_extra: A dict specifying how to populate the assets.extra directory
+        within the exported SavedModel, or `None` if no extra assets are needed.
+      as_text: whether to write the SavedModel proto in text format.
+      checkpoint_path: The checkpoint path to export.  If `None` (the default),
+        the most recent checkpoint found within the model directory is chosen.
+      strip_default_attrs: Boolean. If `True`, default-valued attributes will be
+        removed from the NodeDefs. For a detailed guide, see
+        [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
+      mode: tf.estimator.ModeKeys value indicating with mode will be exported.
+
+    Returns:
+      The string path to the exported directory.
+
+    Raises:
+      ValueError: if input_receiver_fn is None, no export_outputs
+        are provided, or no checkpoint can be found.
+    """
+    # pylint: enable=line-too-long
     with context.graph_mode():
-      if serving_input_receiver_fn is None:
-        raise ValueError('serving_input_receiver_fn must be defined.')
+      if not input_receiver_fn:
+        raise ValueError('An input_receiver_fn must be defined.')
 
-      with ops.Graph().as_default() as g:
-        self._create_and_assert_global_step(g)
-        random_seed.set_random_seed(self._config.tf_random_seed)
-        serving_input_receiver = serving_input_receiver_fn()
+      if not checkpoint_path:
+        # Locate the latest checkpoint
+        checkpoint_path = saver.latest_checkpoint(self._model_dir)
+      if not checkpoint_path:
+        raise ValueError("Couldn't find trained model at %s." % self._model_dir)
 
-        # Call the model_fn and collect the export_outputs.
-        estimator_spec = self._call_model_fn(
-            features=serving_input_receiver.features,
-            labels=None,
-            mode=model_fn_lib.ModeKeys.PREDICT,
-            config=self.config)
-
-        # Build the SignatureDefs from receivers and all outputs
-        signature_def_map = build_all_signature_defs(
-            serving_input_receiver.receiver_tensors,
-            estimator_spec.export_outputs,
-            serving_input_receiver.receiver_tensors_alternatives)
-
-        if not checkpoint_path:
-          # Locate the latest checkpoint
-          checkpoint_path = saver.latest_checkpoint(self._model_dir)
-        if not checkpoint_path:
-          raise ValueError(
-              "Couldn't find trained model at %s." % self._model_dir)
-
-        export_dir = get_timestamped_export_dir(export_dir_base)
-        temp_export_dir = get_temp_export_dir(export_dir)
-
-        # TODO(soergel): Consider whether MonitoredSession makes sense here
-        with tf_session.Session(config=self._session_config) as session:
-
-          saver_for_restore = estimator_spec.scaffold.saver or saver.Saver(
-              sharded=True)
-          saver_for_restore.restore(session, checkpoint_path)
-
-          local_init_op = (
-              estimator_spec.scaffold.local_init_op or
-              monitored_session.Scaffold.default_local_init_op())
-
-          # Perform the export
-          builder = saved_model_builder.SavedModelBuilder(temp_export_dir)
-          builder.add_meta_graph_and_variables(
-              session, [tag_constants.SERVING],
-              signature_def_map=signature_def_map,
-              assets_collection=ops.get_collection(
-                  ops.GraphKeys.ASSET_FILEPATHS),
-              legacy_init_op=local_init_op,
-              strip_default_attrs=strip_default_attrs)
-          builder.save(as_text)
-
-        # Add the extra assets
-        if assets_extra:
-          assets_extra_path = os.path.join(compat.as_bytes(temp_export_dir),
-                                           compat.as_bytes('assets.extra'))
-          for dest_relative, source in assets_extra.items():
-            dest_absolute = os.path.join(compat.as_bytes(assets_extra_path),
-                                         compat.as_bytes(dest_relative))
-            dest_path = os.path.dirname(dest_absolute)
-            gfile.MakeDirs(dest_path)
-            gfile.Copy(source, dest_absolute)
-
-        gfile.Rename(temp_export_dir, export_dir)
-        return export_dir
+      export_dir = export_helpers.get_timestamped_export_dir(export_dir_base)
+      temp_export_dir = export_helpers.get_temp_export_dir(export_dir)
+
+      builder = saved_model_builder.SavedModelBuilder(temp_export_dir)
+
+      self._add_meta_graph_and_variables_for_mode(
+          builder, input_receiver_fn, checkpoint_path,
+          strip_default_attrs, mode)
+
+      builder.save(as_text)
+
+      # Add the extra assets
+      if assets_extra:
+        assets_extra_path = os.path.join(compat.as_bytes(temp_export_dir),
+                                         compat.as_bytes('assets.extra'))
+        for dest_relative, source in assets_extra.items():
+          dest_absolute = os.path.join(compat.as_bytes(assets_extra_path),
+                                       compat.as_bytes(dest_relative))
+          dest_path = os.path.dirname(dest_absolute)
+          gfile.MakeDirs(dest_path)
+          gfile.Copy(source, dest_absolute)
+
+      gfile.Rename(temp_export_dir, export_dir)
+      return export_dir
+
+  def _add_meta_graph_and_variables_for_mode(
+      self, builder, input_receiver_fn, checkpoint_path, strip_default_attrs,
+      mode=model_fn_lib.ModeKeys.PREDICT):
+    # pylint: disable=line-too-long
+    """Loads variables and adds them along with a MetaGraphDef for saving.
+
+    Args:
+      builder: instance of SavedModelBuilder that will be used for saving.
+      input_receiver_fn: a function that takes no argument and
+        returns the appropriate subclass of `InputReceiver`.
+      checkpoint_path: The checkpoint path to export.  If `None` (the default),
+        the most recent checkpoint found within the model directory is chosen.
+      strip_default_attrs: Boolean. If `True`, default-valued attributes will be
+        removed from the NodeDefs. For a detailed guide, see
+        [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
+      mode: tf.estimator.ModeKeys value indicating which mode will be exported.
+    """
+    # pylint: enable=line-too-long
+    with ops.Graph().as_default() as g:
+      self._create_and_assert_global_step(g)
+      random_seed.set_random_seed(self._config.tf_random_seed)
+
+      input_receiver = input_receiver_fn()
+
+      # Call the model_fn and collect the export_outputs.
+      estimator_spec = self._call_model_fn(
+          features=input_receiver.features,
+          labels=getattr(input_receiver, 'labels', None),
+          mode=mode,
+          config=self.config)
+
+      export_outputs = self._get_export_outputs_for_spec(estimator_spec)
+
+      # Build the SignatureDefs from receivers and all outputs
+      signature_def_map = export_helpers.build_all_signature_defs(
+          input_receiver.receiver_tensors,
+          export_outputs,
+          getattr(input_receiver, 'receiver_tensors_alternatives', None),
+          serving_only=(mode == model_fn_lib.ModeKeys.PREDICT))
+
+      with tf_session.Session(config=self._session_config) as session:
+
+        export_tags = model_fn_lib.EXPORT_TAG_MAP[mode]
+
+        local_init_op = (
+            estimator_spec.scaffold.local_init_op or
+            monitored_session.Scaffold.default_local_init_op())
+
+        saver_for_restore = estimator_spec.scaffold.saver or saver.Saver(
+            sharded=True)
+        saver_for_restore.restore(session, checkpoint_path)
+
+        # We add the train op explicitly for now, so that we don't have to
+        # change the Builder public interface. Note that this is a no-op
+        # for prediction, where train_op is None.
+        builder._add_train_op(estimator_spec.train_op)  # pylint: disable=protected-access
+
+        builder.add_meta_graph_and_variables(
+            session,
+            tags=export_tags,
+            signature_def_map=signature_def_map,
+            assets_collection=ops.get_collection(
+                ops.GraphKeys.ASSET_FILEPATHS),
+            strip_default_attrs=strip_default_attrs,
+            legacy_init_op=local_init_op)
+
+  def _get_export_outputs_for_spec(self, estimator_spec):
+    """Given an EstimatorSpec, determine what our export outputs should be.
+
+    EstimatorSpecs contain export_outputs that are used for serving, but for
+    training and eval graphs, we must wrap the tensors of interest in
+    appropriate ExportOutput objects.
+
+    Args:
+      estimator_spec: EstimatorSpec object that will be exported.
+
+    Returns:
+      a dict mapping export_output_name to ExportOutput object.
+
+    Raises:
+      ValueError: if an appropriate ExportOutput cannot be found for the
+        passed EstimatorSpec.mode
+    """
+    mode = estimator_spec.mode
+    if mode == model_fn_lib.ModeKeys.PREDICT:
+      outputs = estimator_spec.export_outputs
+    else:
+      if mode == model_fn_lib.ModeKeys.TRAIN:
+        output_class = export_output.TrainOutput
+      elif mode == model_fn_lib.ModeKeys.EVAL:
+        output_class = export_output.EvalOutput
+      else:
+        raise ValueError(
+            'Export output type not found for mode: {}'.format(mode))
+
+      export_out = output_class(
+          loss=estimator_spec.loss,
+          predictions=estimator_spec.predictions,
+          metrics=estimator_spec.eval_metric_ops)
+      outputs = {mode: export_out}
+
+    return outputs
 
   def _get_features_from_input_fn(self, input_fn, mode):
     """Extracts the `features` from return values of `input_fn`."""
@@ -1544,3 +1752,5 @@ def _get_default_warm_start_settings(warm_start_from):
   else:
     raise ValueError('warm_start_from must be a string or a WarmStartSettings, '
                      'instead got {}'.format(type(warm_start_from)))
+
+
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 76b45b7f57633b..02088e5134f869 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -1865,6 +1865,41 @@ def _model_fn_for_export_tests(features, labels, mode):
           'test': export_output.ClassificationOutput(scores, classes)})
 
 
+def _x_y_input_fn():
+  return ({'x': constant_op.constant([[1], [1]]),
+           'y': constant_op.constant([[2], [2]])},
+          constant_op.constant([[1], [1]]))
+
+
+def _model_fn_with_x_y(features, labels, mode):
+  _ = labels
+  variables.Variable(1., name='weight')
+  scores = constant_op.constant([3.])
+  classes = constant_op.constant(['wumpus'])
+  if mode == model_fn_lib.ModeKeys.PREDICT:
+    variables.Variable(36., name='name_collision')
+    return model_fn_lib.EstimatorSpec(
+        mode,
+        predictions=constant_op.constant(10.),
+        export_outputs={
+            'test': export_output.ClassificationOutput(scores, classes)})
+  else:
+    prefix = 'eval_' if mode == model_fn_lib.ModeKeys.EVAL else ''
+
+    multiplied = math_ops.multiply(
+        features['x'], features['y'], name='{}multiplied'.format(prefix))
+    metrics = {'mean': metrics_lib.mean(features['x'] - features['y'],
+                                        name='{}mean'.format(prefix))}
+    variables.Variable(1., name='later_var')
+    variables.Variable(3., name='name_collision')
+    return model_fn_lib.EstimatorSpec(
+        mode,
+        predictions=multiplied,
+        loss=constant_op.constant(1.),
+        train_op=state_ops.assign_add(training.get_global_step(), 1),
+        eval_metric_ops=metrics)
+
+
 def _model_fn_with_saveables_for_export_tests(features, labels, mode):
   _, _ = features, labels
   table = saver_test_utils.CheckpointedOp(name='v2')
@@ -1881,21 +1916,41 @@ def _model_fn_with_saveables_for_export_tests(features, labels, mode):
           'test': export_output.PredictOutput({'prediction': prediction})})
 
 
+def _get_serving_input_receiver_fn():
+  feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),
+                  'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}
+  return export.build_parsing_serving_input_receiver_fn(feature_spec)
+
+
+def _get_supervised_input_receiver_fn():
+  feature_spec = {
+      'x': array_ops.placeholder(
+          dtype=dtypes.int64, shape=(2, 1), name='feature_x'),
+      'y': array_ops.placeholder(
+          dtype=dtypes.int64, shape=(2, 1), name='feature_y')
+      }
+  label_spec = array_ops.placeholder(
+      dtype=dtypes.float32, shape=[1], name='truth')
+
+  return export.build_raw_supervised_input_receiver_fn(feature_spec, label_spec)
+
+
 _VOCAB_FILE_CONTENT = 'emerson\nlake\npalmer\n'
 _EXTRA_FILE_CONTENT = 'kermit\npiggy\nralph\n'
 
 
 class EstimatorExportTest(test.TestCase):
 
-  def test_export_savedmodel_proto_roundtrip(self):
-    tmpdir = tempfile.mkdtemp()
-    est = estimator.Estimator(model_fn=_model_fn_for_export_tests)
-    est.train(input_fn=dummy_input_fn, steps=1)
+  def test_export_savedmodel_proto_roundtrip_raw_receiver(self):
     feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),
                     'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}
     serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
         feature_spec)
 
+    tmpdir = tempfile.mkdtemp()
+    est = estimator.Estimator(model_fn=_model_fn_for_export_tests)
+    est.train(input_fn=dummy_input_fn, steps=1)
+
     # Perform the export.
     export_dir_base = os.path.join(
         compat.as_bytes(tmpdir), compat.as_bytes('export'))
@@ -1904,6 +1959,266 @@ def test_export_savedmodel_proto_roundtrip(self):
 
     # Check that all the files are in the right places.
     self.assertTrue(gfile.Exists(export_dir_base))
+    self._validate_exported_files(export_dir)
+
+    # Restore, to validate that the export was well-formed.
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.SERVING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('input_example_tensor' in graph_ops)
+        self.assertTrue('ParseExample/ParseExample' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+  def test_export_saved_model_train(self):
+    self._test_export_saved_model_for_mode(
+        _get_supervised_input_receiver_fn(), model_fn_lib.ModeKeys.TRAIN)
+
+  def test_export_saved_model_eval(self):
+    self._test_export_saved_model_for_mode(
+        _get_supervised_input_receiver_fn(), model_fn_lib.ModeKeys.EVAL)
+
+  def test_export_saved_model_predict(self):
+    self._test_export_saved_model_for_mode(
+        _get_serving_input_receiver_fn(), model_fn_lib.ModeKeys.PREDICT)
+
+  def _test_export_saved_model_for_mode(self, input_receiver_fn, mode):
+    tmpdir = tempfile.mkdtemp()
+    est = estimator.Estimator(model_fn=_model_fn_for_export_tests)
+    est.train(input_fn=_x_y_input_fn, steps=1)
+
+    # Perform the export.
+    export_dir_base = os.path.join(
+        compat.as_bytes(tmpdir), compat.as_bytes('export'))
+    export_dir = est._export_saved_model_for_mode(
+        export_dir_base, input_receiver_fn, mode=mode)
+
+    # Check that all the files are in the right places.
+    self.assertTrue(gfile.Exists(export_dir_base))
+    self._validate_exported_files(export_dir)
+
+    # Restore, to validate that the export was well-formed.
+    tag_set = model_fn_lib.EXPORT_TAG_MAP[mode]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, tag_set, export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertFalse('name_collision_1' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_proto_roundtrip_receiver_map(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    self.assertEqual(len(export_dirs), 1)
+    # Restore, to validate that the export was well-formed.
+    export_dir = export_dirs[model_fn_lib.ModeKeys.PREDICT]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.SERVING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('input_example_tensor' in graph_ops)
+        self.assertTrue('ParseExample/ParseExample' in graph_ops)
+        self.assertFalse('feature_x' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_proto_roundtrip_train_only(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    self.assertEqual(len(export_dirs), 1)
+    # Restore, to validate that the export was well-formed.
+    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.TRAINING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('multiplied' in graph_ops)
+        self.assertTrue('mean/update_op' in graph_ops)
+        self.assertFalse('eval_multiplied' in graph_ops)
+        self.assertTrue('feature_x' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_proto_roundtrip_eval_only(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn()
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    self.assertEqual(len(export_dirs), 1)
+    # Restore, to validate that the export was well-formed.
+    export_dir = export_dirs[model_fn_lib.ModeKeys.EVAL]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.EVAL], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('eval_multiplied' in graph_ops)
+        self.assertTrue('eval_mean/value' in graph_ops)
+        self.assertFalse('multiplied' in graph_ops)
+        self.assertTrue('feature_x' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_proto_roundtrip_no_serving(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn()
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    self.assertEqual(len(export_dirs), 2)
+    # Restore, to validate that the export was well-formed.
+    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.TRAINING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('multiplied' in graph_ops)
+        self.assertFalse('eval_multiplied' in graph_ops)
+        self.assertTrue('feature_x' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+    export_dir = export_dirs[model_fn_lib.ModeKeys.EVAL]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.EVAL], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('eval_multiplied' in graph_ops)
+        self.assertFalse('multiplied' in graph_ops)
+        # TODO(karmel): is this the desired behavior when names are shared?
+        self.assertTrue('feature_x_1' in graph_ops)
+        self.assertTrue('feature_y_1' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_proto_roundtrip_three_defs(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    # Restore, to validate that the export was well-formed.
+    for mode, tag_set in model_fn_lib.EXPORT_TAG_MAP.items():
+      export_dir = export_dirs[mode]
+      with ops.Graph().as_default() as graph:
+        with session.Session(graph=graph) as sess:
+          loader.load(sess, tag_set, export_dir)
+          graph_ops = [x.name for x in graph.get_operations()]
+          self.assertTrue('global_step/Assign' in graph_ops)
+          self.assertTrue('global_step/Initializer/zeros' in graph_ops)
+          self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_proto_roundtrip_all_vars(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.TRAINING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('later_var' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    export_dir = export_dirs[model_fn_lib.ModeKeys.PREDICT]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.SERVING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertFalse('later_var' in graph_ops)
+        self.assertTrue('weight' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def test_export_all_saved_models_name_collision(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
+    }
+    export_dirs, tmpdir = self._test_export_all_saved_models(
+        input_receiver_fn_map)
+
+    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.TRAINING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('name_collision' in graph_ops)
+        self.assertFalse('name_collision_1' in graph_ops)
+        collection_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+        self.assertEqual(3, collection_vars[-1].eval())
+
+    export_dir = export_dirs[model_fn_lib.ModeKeys.PREDICT]
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.SERVING], export_dir)
+        graph_ops = [x.name for x in graph.get_operations()]
+        self.assertTrue('name_collision' in graph_ops)
+        self.assertFalse('name_collision_1' in graph_ops)
+        collection_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+        # This is a non-obvious detail: when we load the estimator spec
+        # for predict, name_collision gets set to 36. However, we then restore
+        # from checkpoint, which should overwrite that var and make it the 3
+        # from training. In practice, this would not be a good way to write
+        # a model_fn, but leaving this check in for now to ensure consistency
+        # with what would happen given our current order of spec, then
+        # checkpoint.
+        self.assertEqual(3, collection_vars[-1].eval())
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
+  def _test_export_all_saved_models(self, input_receiver_fn_map):
+    tmpdir = tempfile.mkdtemp()
+    est = estimator.Estimator(model_fn=_model_fn_with_x_y)
+    est.train(input_fn=_x_y_input_fn, steps=1)
+
+    # Perform the export.
+    export_dir_base = os.path.join(
+        compat.as_bytes(tmpdir), compat.as_bytes('export'))
+    export_dirs = est._export_all_saved_models(
+        export_dir_base, input_receiver_fn_map)
+
+    # Check that all the files are in the right places.
+    self.assertTrue(gfile.Exists(export_dir_base))
+
+    for _, export_dir in export_dirs.items():
+      self._validate_exported_files(export_dir)
+
+    return export_dirs, tmpdir
+
+  def _validate_exported_files(self, export_dir):
     self.assertTrue(gfile.Exists(export_dir))
     self.assertTrue(gfile.Exists(os.path.join(
         compat.as_bytes(export_dir),
@@ -1918,18 +2233,6 @@ def test_export_savedmodel_proto_roundtrip(self):
         compat.as_bytes(export_dir),
         compat.as_bytes('variables/variables.data-00000-of-00001'))))
 
-    # Restore, to validate that the export was well-formed.
-    with ops.Graph().as_default() as graph:
-      with session.Session(graph=graph) as sess:
-        loader.load(sess, [tag_constants.SERVING], export_dir)
-        graph_ops = [x.name for x in graph.get_operations()]
-        self.assertTrue('input_example_tensor' in graph_ops)
-        self.assertTrue('ParseExample/ParseExample' in graph_ops)
-        self.assertTrue('weight' in graph_ops)
-
-    # Clean up.
-    gfile.DeleteRecursively(tmpdir)
-
   def test_export_savedmodel_with_saveables_proto_roundtrip(self):
     tmpdir = tempfile.mkdtemp()
     est = estimator.Estimator(
@@ -2485,5 +2788,6 @@ def _model_fn(features, labels, mode):
                                        serving_input_receiver_fn)
     self.assertTrue(gfile.Exists(export_dir))
 
+
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/python/estimator/export/export.py b/tensorflow/python/estimator/export/export.py
index 41c1f5a2e25cd6..9aafb56679d219 100644
--- a/tensorflow/python/estimator/export/export.py
+++ b/tensorflow/python/estimator/export/export.py
@@ -40,6 +40,60 @@
 
 _SINGLE_FEATURE_DEFAULT_NAME = 'feature'
 _SINGLE_RECEIVER_DEFAULT_NAME = 'input'
+_SINGLE_LABEL_DEFAULT_NAME = 'label'
+
+
+def _wrap_and_check_receiver_tensors(receiver_tensors):
+  """Ensure that receiver_tensors is a dict of str to Tensor mappings.
+
+  Args:
+    receiver_tensors: dict of str to Tensors, or a single Tensor.
+
+  Returns:
+    dict of str to Tensors; this is the original dict if one was passed, or
+    the original tensor wrapped in a dictionary.
+
+  Raises:
+    ValueError: if receiver_tensors is None, or has non-string keys,
+      or non-Tensor values
+  """
+  if receiver_tensors is None:
+    raise ValueError('receiver_tensors must be defined.')
+  if not isinstance(receiver_tensors, dict):
+    receiver_tensors = {_SINGLE_RECEIVER_DEFAULT_NAME: receiver_tensors}
+  for name, tensor in receiver_tensors.items():
+    _check_tensor_key(name, error_label='receiver_tensors')
+    _check_tensor(tensor, name, error_label='receiver_tensor')
+  return receiver_tensors
+
+
+def _check_tensor(tensor, name, error_label='feature'):
+  """Check that passed `tensor` is a Tensor or SparseTensor."""
+  if not (isinstance(tensor, ops.Tensor)
+          or isinstance(tensor, sparse_tensor.SparseTensor)):
+    fmt_name = ' {}'.format(name) if name else ''
+    value_error = ValueError(
+        '{}{} must be a Tensor or SparseTensor.'.format(error_label, fmt_name))
+    # NOTE(ericmc): This if-else block is a specific carve-out for
+    # LabeledTensor, which has a `.tensor` attribute and which is
+    # convertible to tf.Tensor via ops.convert_to_tensor.
+    # Allowing all types convertible to tf.Tensor is considered by soergel@
+    # to be too permissive.
+    # TODO(soergel): accept any type convertible to Tensor,
+    # as in cl/193238295 snapshot #6.
+    if hasattr(tensor, 'tensor'):
+      try:
+        ops.convert_to_tensor(tensor)
+      except TypeError:
+        raise value_error
+    else:
+      raise value_error
+
+
+def _check_tensor_key(name, error_label='feature'):
+  if not isinstance(name, six.string_types):
+    raise ValueError(
+        '{} keys must be strings: {}.'.format(error_label, name))
 
 
 @tf_export('estimator.export.ServingInputReceiver')
@@ -51,16 +105,18 @@ class ServingInputReceiver(collections.namedtuple(
   The expected return values are:
     features: A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or
       `SparseTensor`, specifying the features to be passed to the model.
-    receiver_tensors: a `Tensor`, or a dict of string to `Tensor`, specifying
-      input nodes where this receiver expects to be fed by default.  Typically,
-      this is a single placeholder expecting serialized `tf.Example` protos.
+    receiver_tensors: A `Tensor`, `SparseTensor`, or dict of string to `Tensor`
+      or `SparseTensor`, specifying input nodes where this receiver expects to
+      be fed by default.  Typically, this is a single placeholder expecting
+      serialized `tf.Example` protos.
     receiver_tensors_alternatives: a dict of string to additional
-      groups of receiver tensors, each of which may be a `Tensor` or a dict of
-      string to `Tensor`.  These named receiver tensor alternatives generate
-      additional serving signatures, which may be used to feed inputs at
-      different points within the input receiver subgraph.  A typical usage is
-      to allow feeding raw feature `Tensor`s *downstream* of the
-      tf.parse_example() op.  Defaults to None.
+      groups of receiver tensors, each of which may be a `Tensor`,
+      `SparseTensor`, or dict of string to `Tensor` or`SparseTensor`.
+      These named receiver tensor alternatives generate additional serving
+      signatures, which may be used to feed inputs at different points within
+      the input receiver subgraph.  A typical usage is to allow feeding raw
+      feature `Tensor`s *downstream* of the tf.parse_example() op.
+      Defaults to None.
   """
 
   def __new__(cls, features, receiver_tensors,
@@ -70,36 +126,10 @@ def __new__(cls, features, receiver_tensors,
     if not isinstance(features, dict):
       features = {_SINGLE_FEATURE_DEFAULT_NAME: features}
     for name, tensor in features.items():
-      if not isinstance(name, six.string_types):
-        raise ValueError('feature keys must be strings: {}.'.format(name))
-      if not (isinstance(tensor, ops.Tensor)
-              or isinstance(tensor, sparse_tensor.SparseTensor)):
-        value_error = ValueError(
-            'feature {} must be a Tensor or SparseTensor.'.format(name))
-        # NOTE(ericmc): This if-else block is a specific carve-out for
-        # LabeledTensor, which has a `.tensor` attribute and which is
-        # convertible to tf.Tensor via ops.convert_to_tensor.
-        # Allowing all types convertible to tf.Tensor is considered by soergel@
-        # to be too permissive.
-        if hasattr(tensor, 'tensor'):
-          try:
-            ops.convert_to_tensor(tensor)
-          except TypeError:
-            raise value_error
-        else:
-          raise value_error
-
-    if receiver_tensors is None:
-      raise ValueError('receiver_tensors must be defined.')
-    if not isinstance(receiver_tensors, dict):
-      receiver_tensors = {_SINGLE_RECEIVER_DEFAULT_NAME: receiver_tensors}
-    for name, tensor in receiver_tensors.items():
-      if not isinstance(name, six.string_types):
-        raise ValueError(
-            'receiver_tensors keys must be strings: {}.'.format(name))
-      if not isinstance(tensor, ops.Tensor):
-        raise ValueError(
-            'receiver_tensor {} must be a Tensor.'.format(name))
+      _check_tensor_key(name)
+      _check_tensor(tensor, name)
+
+    receiver_tensors = _wrap_and_check_receiver_tensors(receiver_tensors)
 
     if receiver_tensors_alternatives is not None:
       if not isinstance(receiver_tensors_alternatives, dict):
@@ -115,14 +145,9 @@ def __new__(cls, features, receiver_tensors,
           receiver_tensors_alternatives[alternative_name] = (
               receiver_tensors_alt)
         for name, tensor in receiver_tensors_alt.items():
-          if not isinstance(name, six.string_types):
-            raise ValueError(
-                'receiver_tensors keys must be strings: {}.'.format(name))
-          if not (isinstance(tensor, ops.Tensor)
-                  or isinstance(tensor, sparse_tensor.SparseTensor)):
-            raise ValueError(
-                'receiver_tensor {} must be a Tensor or SparseTensor.'.format(
-                    name))
+          _check_tensor_key(name, error_label='receiver_tensors_alternative')
+          _check_tensor(
+              tensor, name, error_label='receiver_tensors_alternative')
 
     return super(ServingInputReceiver, cls).__new__(
         cls,
@@ -155,25 +180,25 @@ class TensorServingInputReceiver(collections.namedtuple(
   The expected return values are:
     features: A single `Tensor` or `SparseTensor`, representing the feature
       to be passed to the model.
-    receiver_tensors: a `Tensor`, or a dict of string to `Tensor`, specifying
-      input nodes where this receiver expects to be fed by default.  Typically,
-      this is a single placeholder expecting serialized `tf.Example` protos.
+    receiver_tensors: A `Tensor`, `SparseTensor`, or dict of string to `Tensor`
+      or `SparseTensor`, specifying input nodes where this receiver expects to
+      be fed by default.  Typically, this is a single placeholder expecting
+      serialized `tf.Example` protos.
     receiver_tensors_alternatives: a dict of string to additional
-      groups of receiver tensors, each of which may be a `Tensor` or a dict of
-      string to `Tensor`.  These named receiver tensor alternatives generate
-      additional serving signatures, which may be used to feed inputs at
-      different points within the input receiver subgraph.  A typical usage is
-      to allow feeding raw feature `Tensor`s *downstream* of the
-      tf.parse_example() op.  Defaults to None.
+      groups of receiver tensors, each of which may be a `Tensor`,
+      `SparseTensor`, or dict of string to `Tensor` or`SparseTensor`.
+      These named receiver tensor alternatives generate additional serving
+      signatures, which may be used to feed inputs at different points within
+      the input receiver subgraph.  A typical usage is to allow feeding raw
+      feature `Tensor`s *downstream* of the tf.parse_example() op.
+      Defaults to None.
   """
 
   def __new__(cls, features, receiver_tensors,
               receiver_tensors_alternatives=None):
     if features is None:
       raise ValueError('features must be defined.')
-    if not (isinstance(features, ops.Tensor)
-            or isinstance(features, sparse_tensor.SparseTensor)):
-      raise ValueError('feature must be a Tensor or SparseTensor.')
+    _check_tensor(features, None)
 
     receiver = ServingInputReceiver(
         features=features,
@@ -187,6 +212,49 @@ def __new__(cls, features, receiver_tensors,
         receiver_tensors_alternatives=receiver.receiver_tensors_alternatives)
 
 
+class SupervisedInputReceiver(collections.namedtuple(
+    'SupervisedInputReceiver',
+    ['features', 'labels', 'receiver_tensors'])):
+  """A return type for a training_input_receiver_fn or eval_input_receiver_fn.
+
+  This differs from a ServingInputReceiver in that (1) this receiver expects
+  a set of labels to be passed in with features, and (2) this receiver does
+  not support receiver_tensors_alternatives, which are primarily used for
+  serving.
+
+  The expected return values are:
+    features: A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or
+      `SparseTensor`, specifying the features to be passed to the model.
+    labels: A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or
+      `SparseTensor`, specifying the labels to be passed to the model.
+    receiver_tensors: A `Tensor`, `SparseTensor`, or dict of string to `Tensor`
+      or `SparseTensor`, specifying input nodes where this receiver expects to
+      be fed by default.  Typically, this is a single placeholder expecting
+      serialized `tf.Example` protos.
+
+  """
+
+  def __new__(cls, features, labels, receiver_tensors):
+    # Both features and labels can be dicts or raw tensors.
+    for input_vals, error_label in ((features, 'feature'), (labels, 'label')):
+      if input_vals is None:
+        raise ValueError('{}s must be defined.'.format(error_label))
+      if isinstance(input_vals, dict):
+        for name, tensor in input_vals.items():
+          _check_tensor_key(name, error_label=error_label)
+          _check_tensor(tensor, name, error_label=error_label)
+      else:
+        _check_tensor(input_vals, None, error_label=error_label)
+
+    receiver_tensors = _wrap_and_check_receiver_tensors(receiver_tensors)
+
+    return super(SupervisedInputReceiver, cls).__new__(
+        cls,
+        features=features,
+        labels=labels,
+        receiver_tensors=receiver_tensors)
+
+
 @tf_export('estimator.export.build_parsing_serving_input_receiver_fn')
 def build_parsing_serving_input_receiver_fn(feature_spec,
                                             default_batch_size=None):
@@ -216,6 +284,23 @@ def serving_input_receiver_fn():
   return serving_input_receiver_fn
 
 
+def _placeholder_from_tensor(t, default_batch_size=None):
+  shape_list = t.get_shape().as_list()
+  shape_list[0] = default_batch_size
+  shape = tensor_shape.TensorShape(shape_list)
+
+  # Reuse the feature tensor's op name (t.op.name) for the placeholder,
+  # excluding the index from the tensor's name (t.name):
+  # t.name = "%s:%d" % (t.op.name, t._value_index)
+  return array_ops.placeholder(dtype=t.dtype, shape=shape, name=t.op.name)
+
+
+def _placeholders_from_receiver_tensors_dict(
+    input_vals, default_batch_size=None):
+  return {name: _placeholder_from_tensor(t, default_batch_size)
+          for name, t in input_vals.items()}
+
+
 @tf_export('estimator.export.build_raw_serving_input_receiver_fn')
 def build_raw_serving_input_receiver_fn(features, default_batch_size=None):
   """Build a serving_input_receiver_fn expecting feature Tensors.
@@ -233,17 +318,9 @@ def build_raw_serving_input_receiver_fn(features, default_batch_size=None):
   """
   def serving_input_receiver_fn():
     """A serving_input_receiver_fn that expects features to be fed directly."""
-    receiver_tensors = {}
-    for name, t in features.items():
-      shape_list = t.get_shape().as_list()
-      shape_list[0] = default_batch_size
-      shape = tensor_shape.TensorShape(shape_list)
-
-      # Reuse the feature tensor's op name (t.op.name) for the placeholder,
-      # excluding the index from the tensor's name (t.name):
-      # t.name = "%s:%d" % (t.op.name, t._value_index)
-      receiver_tensors[name] = array_ops.placeholder(
-          dtype=t.dtype, shape=shape, name=t.op.name)
+    receiver_tensors = _placeholders_from_receiver_tensors_dict(
+        features, default_batch_size)
+
     # TODO(b/34885899): remove the unnecessary copy
     # The features provided are simply the placeholders, but we defensively copy
     # the dict because it may be mutated.
@@ -252,13 +329,100 @@ def serving_input_receiver_fn():
   return serving_input_receiver_fn
 
 
+def build_raw_supervised_input_receiver_fn(
+    features, labels, default_batch_size=None):
+  """Build a supervised_input_receiver_fn for raw features and labels.
+
+  This function wraps tensor placeholders in a supervised_receiver_fn
+  with the expectation that the features and labels appear precisely as
+  the model_fn expects them. Features and labels can therefore be dicts of
+  tensors, or raw tensors.
+
+  Args:
+    features: a dict of string to `Tensor` or `Tensor`.
+    labels: a dict of string to `Tensor` or `Tensor`.
+    default_batch_size: the number of query examples expected per batch.
+        Leave unset for variable batch size (recommended).
+
+  Returns:
+    A supervised_input_receiver_fn.
+
+  Raises:
+    ValueError: if features and labels have overlapping keys.
+  """
+  # Check for overlapping keys before beginning.
+  try:
+    feat_keys = features.keys()
+  except AttributeError:
+    feat_keys = [_SINGLE_RECEIVER_DEFAULT_NAME]
+  try:
+    label_keys = labels.keys()
+  except AttributeError:
+    label_keys = [_SINGLE_LABEL_DEFAULT_NAME]
+
+  overlap_keys = set(feat_keys) & set(label_keys)
+  if overlap_keys:
+    raise ValueError('Features and labels must have distinct keys. '
+                     'Found overlapping keys: {}'.format(overlap_keys))
+
+  def supervised_input_receiver_fn():
+    """A receiver_fn that expects pass-through features and labels."""
+    if not isinstance(features, dict):
+      features_cp = _placeholder_from_tensor(features, default_batch_size)
+      receiver_features = {_SINGLE_RECEIVER_DEFAULT_NAME: features_cp}
+    else:
+      receiver_features = _placeholders_from_receiver_tensors_dict(
+          features, default_batch_size)
+      features_cp = receiver_features
+
+    if not isinstance(labels, dict):
+      labels_cp = _placeholder_from_tensor(labels, default_batch_size)
+      receiver_labels = {_SINGLE_LABEL_DEFAULT_NAME: labels_cp}
+    else:
+      receiver_labels = _placeholders_from_receiver_tensors_dict(
+          labels, default_batch_size)
+      labels_cp = receiver_labels
+
+    receiver_tensors = dict(receiver_features)
+    receiver_tensors.update(receiver_labels)
+    return SupervisedInputReceiver(features_cp, labels_cp, receiver_tensors)
+
+  return supervised_input_receiver_fn
+
+
 ### Below utilities are specific to SavedModel exports.
 
 
 def build_all_signature_defs(receiver_tensors,
                              export_outputs,
-                             receiver_tensors_alternatives=None):
-  """Build `SignatureDef`s for all export outputs."""
+                             receiver_tensors_alternatives=None,
+                             serving_only=True):
+  """Build `SignatureDef`s for all export outputs.
+
+  Args:
+    receiver_tensors: a `Tensor`, or a dict of string to `Tensor`, specifying
+      input nodes where this receiver expects to be fed by default.  Typically,
+      this is a single placeholder expecting serialized `tf.Example` protos.
+    export_outputs: a dict of ExportOutput instances, each of which has
+      an as_signature_def instance method that will be called to retrieve
+      the signature_def for all export output tensors.
+    receiver_tensors_alternatives: a dict of string to additional
+      groups of receiver tensors, each of which may be a `Tensor` or a dict of
+      string to `Tensor`.  These named receiver tensor alternatives generate
+      additional serving signatures, which may be used to feed inputs at
+      different points within the input receiver subgraph.  A typical usage is
+      to allow feeding raw feature `Tensor`s *downstream* of the
+      tf.parse_example() op.  Defaults to None.
+    serving_only: boolean; if true, resulting signature defs will only include
+      valid serving signatures. If false, all requested signatures will be
+      returned.
+
+  Returns:
+    signature_def representing all passed args.
+
+  Raises:
+    ValueError: if export_outputs is not a dict
+  """
   if not isinstance(receiver_tensors, dict):
     receiver_tensors = {_SINGLE_RECEIVER_DEFAULT_NAME: receiver_tensors}
   if export_outputs is None or not isinstance(export_outputs, dict):
@@ -293,17 +457,24 @@ def build_all_signature_defs(receiver_tensors,
   _log_signature_report(signature_def_map, excluded_signatures)
 
   # The above calls to export_output.as_signature_def should return only
-  # valid signatures; if there is a validity problem, they raise ValueError,
-  # which we ignore above. Consequently the call to is_valid_signature here
-  # should not remove anything else; it's just an extra sanity check.
-  return {k: v for k, v in signature_def_map.items()
-          if signature_def_utils.is_valid_signature(v)}
+  # valid signatures; if there is a validity problem, they raise a ValueError,
+  # in which case we exclude that signature from signature_def_map above.
+  # The is_valid_signature check ensures that the signatures produced are
+  # valid for serving, and acts as an additional sanity check for export
+  # signatures produced for serving. We skip this check for training and eval
+  # signatures, which are not intended for serving.
+  if serving_only:
+    signature_def_map = {k: v for k, v in signature_def_map.items()
+                         if signature_def_utils.is_valid_signature(v)}
+  return signature_def_map
 
 
 _FRIENDLY_METHOD_NAMES = {
     signature_constants.CLASSIFY_METHOD_NAME: 'Classify',
     signature_constants.REGRESS_METHOD_NAME: 'Regress',
     signature_constants.PREDICT_METHOD_NAME: 'Predict',
+    signature_constants.SUPERVISED_TRAIN_METHOD_NAME: 'Train',
+    signature_constants.SUPERVISED_EVAL_METHOD_NAME: 'Eval',
 }
 
 
diff --git a/tensorflow/python/estimator/export/export_output.py b/tensorflow/python/estimator/export/export_output.py
index 87b964be37197d..d387ea2940e7a4 100644
--- a/tensorflow/python/estimator/export/export_output.py
+++ b/tensorflow/python/estimator/export/export_output.py
@@ -38,6 +38,8 @@ class ExportOutput(object):
 
   __metaclass__ = abc.ABCMeta
 
+  _SEPARATOR_CHAR = '/'
+
   @abc.abstractmethod
   def as_signature_def(self, receiver_tensors):
     """Generate a SignatureDef proto for inclusion in a MetaGraphDef.
@@ -51,6 +53,52 @@ def as_signature_def(self, receiver_tensors):
     """
     pass
 
+  def _check_output_key(self, key, error_label):
+    # For multi-head models, the key can be a tuple.
+    if isinstance(key, tuple):
+      key = self._SEPARATOR_CHAR.join(key)
+
+    if not isinstance(key, six.string_types):
+      raise ValueError(
+          '{} output key must be a string; got {}.'.format(error_label, key))
+    return key
+
+  def _wrap_and_check_outputs(
+      self, outputs, single_output_default_name, error_label=None):
+    """Wraps raw tensors as dicts and checks type.
+
+    Note that we create a new dict here so that we can overwrite the keys
+    if necessary.
+
+    Args:
+      outputs: A `Tensor` or a dict of string to `Tensor`.
+      single_output_default_name: A string key for use in the output dict
+        if the provided `outputs` is a raw tensor.
+      error_label: descriptive string for use in error messages. If none,
+        single_output_default_name will be used.
+
+    Returns:
+      A dict of tensors
+
+    Raises:
+      ValueError: if the outputs dict keys are not strings or tuples of strings
+        or the values are not Tensors.
+    """
+    if not isinstance(outputs, dict):
+      outputs = {single_output_default_name: outputs}
+
+    output_dict = {}
+    for key, value in outputs.items():
+      error_name = error_label or single_output_default_name
+      key = self._check_output_key(key, error_name)
+      if not isinstance(value, ops.Tensor):
+        raise ValueError(
+            '{} output value must be a Tensor; got {}.'.format(
+                error_name, value))
+
+      output_dict[key] = value
+    return output_dict
+
 
 @tf_export('estimator.export.ClassificationOutput')
 class ClassificationOutput(ExportOutput):
@@ -154,9 +202,6 @@ def as_signature_def(self, receiver_tensors):
     return signature_def_utils.regression_signature_def(examples, self.value)
 
 
-_SINGLE_OUTPUT_DEFAULT_NAME = 'output'
-
-
 @tf_export('estimator.export.PredictOutput')
 class PredictOutput(ExportOutput):
   """Represents the output of a generic prediction head.
@@ -165,6 +210,7 @@ class PredictOutput(ExportOutput):
 
   Named outputs must be provided as a dict from string to `Tensor`,
   """
+  _SINGLE_OUTPUT_DEFAULT_NAME = 'output'
 
   def __init__(self, outputs):
     """Constructor for PredictOutput.
@@ -177,16 +223,9 @@ def __init__(self, outputs):
       ValueError: if the outputs is not dict, or any of its keys are not
           strings, or any of its values are not `Tensor`s.
     """
-    if not isinstance(outputs, dict):
-      outputs = {_SINGLE_OUTPUT_DEFAULT_NAME: outputs}
-    for key, value in outputs.items():
-      if not isinstance(key, six.string_types):
-        raise ValueError(
-            'Prediction output key must be a string; got {}.'.format(key))
-      if not isinstance(value, ops.Tensor):
-        raise ValueError(
-            'Prediction output value must be a Tensor; got {}.'.format(value))
-    self._outputs = outputs
+
+    self._outputs = self._wrap_and_check_outputs(
+        outputs, self._SINGLE_OUTPUT_DEFAULT_NAME, error_label='Prediction')
 
   @property
   def outputs(self):
@@ -195,3 +234,161 @@ def outputs(self):
   def as_signature_def(self, receiver_tensors):
     return signature_def_utils.predict_signature_def(receiver_tensors,
                                                      self.outputs)
+
+
+class _SupervisedOutput(ExportOutput):
+  """Represents the output of a supervised training or eval process."""
+  __metaclass__ = abc.ABCMeta
+
+  LOSS_NAME = 'loss'
+  PREDICTIONS_NAME = 'predictions'
+  METRICS_NAME = 'metrics'
+
+  METRIC_VALUE_SUFFIX = 'value'
+  METRIC_UPDATE_SUFFIX = 'update_op'
+
+  _loss = None
+  _predictions = None
+  _metrics = None
+
+  def __init__(self, loss=None, predictions=None, metrics=None):
+    """Constructor for SupervisedOutput (ie, Train or Eval output).
+
+    Args:
+      loss: dict of Tensors or single Tensor representing calculated loss.
+      predictions: dict of Tensors or single Tensor representing model
+        predictions.
+      metrics: dict of (metric_value, update_op) tuples, or a single tuple.
+        metric_value must be a Tensor, and update_op must be a Tensor or Op.
+
+    Raises:
+      ValueError: if any of the outputs' dict keys are not strings or tuples of
+        strings or the values are not Tensors (or Operations in the case of
+        update_op).
+    """
+
+    if loss is not None:
+      loss_dict = self._wrap_and_check_outputs(loss, self.LOSS_NAME)
+      self._loss = self._prefix_output_keys(loss_dict, self.LOSS_NAME)
+    if predictions is not None:
+      pred_dict = self._wrap_and_check_outputs(
+          predictions, self.PREDICTIONS_NAME)
+      self._predictions = self._prefix_output_keys(
+          pred_dict, self.PREDICTIONS_NAME)
+    if metrics is not None:
+      self._metrics = self._wrap_and_check_metrics(metrics)
+
+  def _prefix_output_keys(self, output_dict, output_name):
+    """Prepend output_name to the output_dict keys if it doesn't exist.
+
+    This produces predictable prefixes for the pre-determined outputs
+    of SupervisedOutput.
+
+    Args:
+      output_dict: dict of string to Tensor, assumed valid.
+      output_name: prefix string to prepend to existing keys.
+
+    Returns:
+      dict with updated keys and existing values.
+    """
+
+    new_outputs = {}
+    for key, val in output_dict.items():
+      key = self._prefix_key(key, output_name)
+      new_outputs[key] = val
+    return new_outputs
+
+  def _prefix_key(self, key, output_name):
+    if key.find(output_name) != 0:
+      key = output_name + self._SEPARATOR_CHAR + key
+    return key
+
+  def _wrap_and_check_metrics(self, metrics):
+    """Handle the saving of metrics.
+
+    Metrics is either a tuple of (value, update_op), or a dict of such tuples.
+    Here, we separate out the tuples and create a dict with names to tensors.
+
+    Args:
+      metrics: dict of (metric_value, update_op) tuples, or a single tuple.
+
+    Returns:
+      dict of output_names to tensors
+
+    Raises:
+      ValueError: if the dict key is not a string, or the metric values or ops
+        are not tensors.
+    """
+    if not isinstance(metrics, dict):
+      metrics = {self.METRICS_NAME: metrics}
+
+    outputs = {}
+    for key, (metric_val, metric_op) in metrics.items():
+      key = self._check_output_key(key, self.METRICS_NAME)
+      key = self._prefix_key(key, self.METRICS_NAME)
+
+      val_name = key + self._SEPARATOR_CHAR + self.METRIC_VALUE_SUFFIX
+      op_name = key + self._SEPARATOR_CHAR + self.METRIC_UPDATE_SUFFIX
+      if not isinstance(metric_val, ops.Tensor):
+        raise ValueError(
+            '{} output value must be a Tensor; got {}.'.format(
+                key, metric_val))
+      if (not isinstance(metric_op, ops.Tensor) and
+          not isinstance(metric_op, ops.Operation)):
+        raise ValueError(
+            '{} update_op must be a Tensor or Operation; got {}.'.format(
+                key, metric_op))
+      outputs[val_name] = metric_val
+      outputs[op_name] = metric_op
+
+    return outputs
+
+  @property
+  def loss(self):
+    return self._loss
+
+  @property
+  def predictions(self):
+    return self._predictions
+
+  @property
+  def metrics(self):
+    return self._metrics
+
+  @abc.abstractmethod
+  def _get_signature_def_fn(self):
+    """Returns a function that produces a SignatureDef given desired outputs."""
+    pass
+
+  def as_signature_def(self, receiver_tensors):
+    signature_def_fn = self._get_signature_def_fn()
+    return signature_def_fn(
+        receiver_tensors, self.loss, self.predictions, self.metrics)
+
+
+class TrainOutput(_SupervisedOutput):
+  """Represents the output of a supervised training process.
+
+  This class generates the appropriate signature def for exporting
+  training output by type-checking and wrapping loss, predictions, and metrics
+  values.
+  """
+
+  def _get_signature_def_fn(self):
+    return signature_def_utils.supervised_train_signature_def
+
+
+class EvalOutput(_SupervisedOutput):
+  """Represents the output of a supervised eval process.
+
+  This class generates the appropriate signature def for exporting
+  eval output by type-checking and wrapping loss, predictions, and metrics
+  values.
+  """
+
+  def _get_signature_def_fn(self):
+    return signature_def_utils.supervised_eval_signature_def
+
+
+
+
diff --git a/tensorflow/python/estimator/export/export_output_test.py b/tensorflow/python/estimator/export/export_output_test.py
index 7090e53d807817..b21ba91b0fbb7e 100644
--- a/tensorflow/python/estimator/export/export_output_test.py
+++ b/tensorflow/python/estimator/export/export_output_test.py
@@ -225,5 +225,115 @@ def test_predict_outputs_invalid(self):
       })
 
 
+class MockSupervisedOutput(export_output_lib._SupervisedOutput):
+  """So that we can test the abstract class methods directly."""
+
+  def _get_signature_def_fn(self):
+    pass
+
+
+class SupervisedOutputTest(test.TestCase):
+
+  def test_supervised_outputs_valid(self):
+    """Tests that no errors are raised when provided outputs are valid."""
+    loss = {"my_loss": constant_op.constant([0])}
+    predictions = {u"output1": constant_op.constant(["foo"])}
+    metrics = {"metrics": (constant_op.constant([0]),
+                           constant_op.constant([10])),
+               "metrics2": (constant_op.constant([0]),
+                            constant_op.constant([10]))}
+
+    outputter = MockSupervisedOutput(loss, predictions, metrics)
+    self.assertEqual(outputter.loss["loss/my_loss"], loss["my_loss"])
+    self.assertEqual(
+        outputter.predictions["predictions/output1"], predictions["output1"])
+    self.assertEqual(outputter.metrics["metrics/value"], metrics["metrics"][0])
+    self.assertEqual(
+        outputter.metrics["metrics2/update_op"], metrics["metrics2"][1])
+
+    # Single Tensor is OK too
+    outputter = MockSupervisedOutput(
+        loss["my_loss"], predictions["output1"], metrics["metrics"])
+    self.assertEqual(outputter.loss, {"loss": loss["my_loss"]})
+    self.assertEqual(
+        outputter.predictions, {"predictions": predictions["output1"]})
+    self.assertEqual(outputter.metrics["metrics/value"], metrics["metrics"][0])
+
+  def test_supervised_outputs_none(self):
+    outputter = MockSupervisedOutput(
+        constant_op.constant([0]), None, None)
+    self.assertEqual(len(outputter.loss), 1)
+    self.assertEqual(outputter.predictions, None)
+    self.assertEqual(outputter.metrics, None)
+
+  def test_supervised_outputs_invalid(self):
+    with self.assertRaisesRegexp(ValueError, "predictions output value must"):
+      MockSupervisedOutput(constant_op.constant([0]), [3], None)
+    with self.assertRaisesRegexp(ValueError, "loss output value must"):
+      MockSupervisedOutput("str", None, None)
+    with self.assertRaisesRegexp(ValueError, "metrics output value must"):
+      MockSupervisedOutput(None, None, (15.3, 4))
+    with self.assertRaisesRegexp(ValueError, "loss output key must"):
+      MockSupervisedOutput({25: "Tensor"}, None, None)
+
+  def test_supervised_outputs_tuples(self):
+    """Tests that no errors are raised when provided outputs are valid."""
+    loss = {("my", "loss"): constant_op.constant([0])}
+    predictions = {(u"output1", "2"): constant_op.constant(["foo"])}
+    metrics = {("metrics", "twice"): (constant_op.constant([0]),
+                                      constant_op.constant([10]))}
+
+    outputter = MockSupervisedOutput(loss, predictions, metrics)
+    self.assertEqual(set(outputter.loss.keys()), set(["loss/my/loss"]))
+    self.assertEqual(set(outputter.predictions.keys()),
+                     set(["predictions/output1/2"]))
+    self.assertEqual(set(outputter.metrics.keys()),
+                     set(["metrics/twice/value", "metrics/twice/update_op"]))
+
+  def test_supervised_outputs_no_prepend(self):
+    """Tests that no errors are raised when provided outputs are valid."""
+    loss = {"loss": constant_op.constant([0])}
+    predictions = {u"predictions": constant_op.constant(["foo"])}
+    metrics = {u"metrics": (constant_op.constant([0]),
+                            constant_op.constant([10]))}
+
+    outputter = MockSupervisedOutput(loss, predictions, metrics)
+    self.assertEqual(set(outputter.loss.keys()), set(["loss"]))
+    self.assertEqual(set(outputter.predictions.keys()), set(["predictions"]))
+    self.assertEqual(set(outputter.metrics.keys()),
+                     set(["metrics/value", "metrics/update_op"]))
+
+  def test_train_signature_def(self):
+    loss = {"my_loss": constant_op.constant([0])}
+    predictions = {u"output1": constant_op.constant(["foo"])}
+    metrics = {"metrics": (constant_op.constant([0]),
+                           constant_op.constant([10]))}
+
+    outputter = export_output_lib.TrainOutput(loss, predictions, metrics)
+
+    receiver = {u"features": constant_op.constant(100, shape=(100, 2)),
+                "labels": constant_op.constant(100, shape=(100, 1))}
+    sig_def = outputter.as_signature_def(receiver)
+
+    self.assertTrue("loss/my_loss" in sig_def.outputs)
+    self.assertTrue("metrics/value" in sig_def.outputs)
+    self.assertTrue("predictions/output1" in sig_def.outputs)
+    self.assertTrue("features" in sig_def.inputs)
+
+  def test_eval_signature_def(self):
+    loss = {"my_loss": constant_op.constant([0])}
+    predictions = {u"output1": constant_op.constant(["foo"])}
+
+    outputter = export_output_lib.EvalOutput(loss, predictions, None)
+
+    receiver = {u"features": constant_op.constant(100, shape=(100, 2)),
+                "labels": constant_op.constant(100, shape=(100, 1))}
+    sig_def = outputter.as_signature_def(receiver)
+
+    self.assertTrue("loss/my_loss" in sig_def.outputs)
+    self.assertFalse("metrics/value" in sig_def.outputs)
+    self.assertTrue("predictions/output1" in sig_def.outputs)
+    self.assertTrue("features" in sig_def.inputs)
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/estimator/export/export_test.py b/tensorflow/python/estimator/export/export_test.py
index c203be7dacf800..0af587f2a850df 100644
--- a/tensorflow/python/estimator/export/export_test.py
+++ b/tensorflow/python/estimator/export/export_test.py
@@ -54,7 +54,7 @@ def _convert_labeled_tensor_mock_to_tensor(value, *args, **kwargs):
                                         _convert_labeled_tensor_mock_to_tensor)
 
 
-class ExportTest(test_util.TensorFlowTestCase):
+class ServingInputReceiverTest(test_util.TensorFlowTestCase):
 
   def test_serving_input_receiver_constructor(self):
     """Tests that no errors are raised when input is expected."""
@@ -161,6 +161,165 @@ def test_receiver_wrong_type(self):
     with self.assertRaises(ValueError):
       _ = export.ServingInputReceiver(feature, receiver_tensor)
 
+
+class SupervisedInputReceiverTest(test_util.TensorFlowTestCase):
+
+  def test_input_receiver_constructor(self):
+    """Tests that no errors are raised when input is expected."""
+    features = {
+        "feature0": constant_op.constant([0]),
+        u"feature1": constant_op.constant([1]),
+        "feature2": sparse_tensor.SparseTensor(
+            indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
+    }
+    labels = {
+        "classes": constant_op.constant([0] * 100),
+    }
+
+    receiver_tensors = {
+        "example0": array_ops.placeholder(dtypes.string, name="example0"),
+        u"example1": array_ops.placeholder(dtypes.string, name="example1"),
+    }
+    export.SupervisedInputReceiver(features, labels, receiver_tensors)
+
+  def test_input_receiver_raw_values(self):
+    """Tests that no errors are raised when input is expected."""
+    features = {
+        "feature0": constant_op.constant([0]),
+        u"feature1": constant_op.constant([1]),
+        "feature2": sparse_tensor.SparseTensor(
+            indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
+    }
+
+    labels = {
+        "classes": constant_op.constant([0] * 100),
+    }
+
+    receiver_tensors = {
+        "example0": array_ops.placeholder(dtypes.string, name="example0"),
+        u"example1": array_ops.placeholder(dtypes.string, name="example1"),
+    }
+    rec = export.SupervisedInputReceiver(
+        features["feature2"], labels, receiver_tensors)
+    self.assertIsInstance(rec.features, sparse_tensor.SparseTensor)
+
+    rec = export.SupervisedInputReceiver(
+        features, labels["classes"], receiver_tensors)
+    self.assertIsInstance(rec.labels, ops.Tensor)
+
+  def test_input_receiver_features_invalid(self):
+    features = constant_op.constant([0] * 100)
+    labels = constant_op.constant([0])
+    receiver_tensors = {
+        "example0": array_ops.placeholder(dtypes.string, name="example0"),
+        u"example1": array_ops.placeholder(dtypes.string, name="example1"),
+    }
+
+    with self.assertRaisesRegexp(ValueError, "features must be defined"):
+      export.SupervisedInputReceiver(
+          features=None,
+          labels=labels,
+          receiver_tensors=receiver_tensors)
+
+    with self.assertRaisesRegexp(ValueError, "feature keys must be strings"):
+      export.SupervisedInputReceiver(
+          features={1: constant_op.constant([1])},
+          labels=labels,
+          receiver_tensors=receiver_tensors)
+
+    with self.assertRaisesRegexp(ValueError, "label keys must be strings"):
+      export.SupervisedInputReceiver(
+          features=features,
+          labels={1: constant_op.constant([1])},
+          receiver_tensors=receiver_tensors)
+
+    with self.assertRaisesRegexp(
+        ValueError, "feature feature1 must be a Tensor or SparseTensor"):
+      export.SupervisedInputReceiver(
+          features={"feature1": [1]},
+          labels=labels,
+          receiver_tensors=receiver_tensors)
+
+    with self.assertRaisesRegexp(
+        ValueError, "feature must be a Tensor or SparseTensor"):
+      export.SupervisedInputReceiver(
+          features=[1],
+          labels=labels,
+          receiver_tensors=receiver_tensors)
+
+    with self.assertRaisesRegexp(
+        ValueError, "label must be a Tensor or SparseTensor"):
+      export.SupervisedInputReceiver(
+          features=features,
+          labels=100,
+          receiver_tensors=receiver_tensors)
+
+  def test_input_receiver_receiver_tensors_invalid(self):
+    features = {
+        "feature0": constant_op.constant([0]),
+        u"feature1": constant_op.constant([1]),
+        "feature2": sparse_tensor.SparseTensor(
+            indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
+    }
+    labels = constant_op.constant([0])
+
+    with self.assertRaisesRegexp(
+        ValueError, "receiver_tensors must be defined"):
+      export.SupervisedInputReceiver(
+          features=features,
+          labels=labels,
+          receiver_tensors=None)
+
+    with self.assertRaisesRegexp(
+        ValueError, "receiver_tensors keys must be strings"):
+      export.SupervisedInputReceiver(
+          features=features,
+          labels=labels,
+          receiver_tensors={
+              1: array_ops.placeholder(dtypes.string, name="example0")})
+
+    with self.assertRaisesRegexp(
+        ValueError, "receiver_tensor example1 must be a Tensor"):
+      export.SupervisedInputReceiver(
+          features=features,
+          labels=labels,
+          receiver_tensors={"example1": [1]})
+
+  def test_single_feature_single_receiver(self):
+    feature = constant_op.constant(5)
+    label = constant_op.constant(5)
+    receiver_tensor = array_ops.placeholder(dtypes.string)
+    input_receiver = export.SupervisedInputReceiver(
+        feature, label, receiver_tensor)
+
+    # single receiver is automatically named
+    receiver_key, = input_receiver.receiver_tensors.keys()
+    self.assertEqual("input", receiver_key)
+
+  def test_multi_feature_single_receiver(self):
+    features = {"foo": constant_op.constant(5),
+                "bar": constant_op.constant(6)}
+    labels = {"value": constant_op.constant(5)}
+    receiver_tensor = array_ops.placeholder(dtypes.string)
+    _ = export.SupervisedInputReceiver(features, labels, receiver_tensor)
+
+  def test_multi_feature_multi_receiver(self):
+    features = {"foo": constant_op.constant(5),
+                "bar": constant_op.constant(6)}
+    labels = {"value": constant_op.constant(5)}
+    receiver_tensors = {"baz": array_ops.placeholder(dtypes.int64),
+                        "qux": array_ops.placeholder(dtypes.float32)}
+    _ = export.SupervisedInputReceiver(features, labels, receiver_tensors)
+
+  def test_feature_labeled_tensor(self):
+    feature = LabeledTensorMock()
+    label = constant_op.constant(5)
+    receiver_tensor = array_ops.placeholder(dtypes.string)
+    _ = export.SupervisedInputReceiver(feature, label, receiver_tensor)
+
+
+class ExportTest(test_util.TensorFlowTestCase):
+
   def test_build_parsing_serving_input_receiver_fn(self):
     feature_spec = {"int_feature": parsing_ops.VarLenFeature(dtypes.int64),
                     "float_feature": parsing_ops.VarLenFeature(dtypes.float32)}
@@ -237,6 +396,69 @@ def test_build_raw_serving_input_receiver_fn(self):
           dtypes.int32,
           serving_input_receiver.receiver_tensors["feature_2"].dtype)
 
+  def test_build_raw_supervised_input_receiver_fn(self):
+    features = {"feature_1": constant_op.constant(["hello"]),
+                "feature_2": constant_op.constant([42])}
+    labels = {"foo": constant_op.constant([5]),
+              "bar": constant_op.constant([6])}
+    input_receiver_fn = export.build_raw_supervised_input_receiver_fn(
+        features, labels)
+    with ops.Graph().as_default():
+      input_receiver = input_receiver_fn()
+      self.assertEqual(set(["feature_1", "feature_2"]),
+                       set(input_receiver.features.keys()))
+      self.assertEqual(set(["foo", "bar"]),
+                       set(input_receiver.labels.keys()))
+      self.assertEqual(set(["feature_1", "feature_2", "foo", "bar"]),
+                       set(input_receiver.receiver_tensors.keys()))
+      self.assertEqual(
+          dtypes.string, input_receiver.receiver_tensors["feature_1"].dtype)
+      self.assertEqual(
+          dtypes.int32, input_receiver.receiver_tensors["feature_2"].dtype)
+
+  def test_build_raw_supervised_input_receiver_fn_raw_tensors(self):
+    features = {"feature_1": constant_op.constant(["hello"]),
+                "feature_2": constant_op.constant([42])}
+    labels = {"foo": constant_op.constant([5]),
+              "bar": constant_op.constant([6])}
+    input_receiver_fn1 = export.build_raw_supervised_input_receiver_fn(
+        features["feature_1"], labels)
+    input_receiver_fn2 = export.build_raw_supervised_input_receiver_fn(
+        features["feature_1"], labels["foo"])
+    with ops.Graph().as_default():
+      input_receiver = input_receiver_fn1()
+      self.assertIsInstance(input_receiver.features, ops.Tensor)
+      self.assertEqual(set(["foo", "bar"]),
+                       set(input_receiver.labels.keys()))
+      self.assertEqual(set(["input", "foo", "bar"]),
+                       set(input_receiver.receiver_tensors.keys()))
+
+      input_receiver = input_receiver_fn2()
+      self.assertIsInstance(input_receiver.features, ops.Tensor)
+      self.assertIsInstance(input_receiver.labels, ops.Tensor)
+      self.assertEqual(set(["input", "label"]),
+                       set(input_receiver.receiver_tensors.keys()))
+
+  def test_build_raw_supervised_input_receiver_fn_batch_size(self):
+    features = {"feature_1": constant_op.constant(["hello"]),
+                "feature_2": constant_op.constant([42])}
+    labels = {"foo": constant_op.constant([5]),
+              "bar": constant_op.constant([6])}
+    input_receiver_fn = export.build_raw_supervised_input_receiver_fn(
+        features, labels, default_batch_size=10)
+    with ops.Graph().as_default():
+      input_receiver = input_receiver_fn()
+      self.assertEqual([10], input_receiver.receiver_tensors["feature_1"].shape)
+      self.assertEqual([10], input_receiver.features["feature_1"].shape)
+
+  def test_build_raw_supervised_input_receiver_fn_overlapping_keys(self):
+    features = {"feature_1": constant_op.constant(["hello"]),
+                "feature_2": constant_op.constant([42])}
+    labels = {"feature_1": constant_op.constant([5]),
+              "bar": constant_op.constant([6])}
+    with self.assertRaises(ValueError):
+      export.build_raw_supervised_input_receiver_fn(features, labels)
+
   def test_build_all_signature_defs_without_receiver_alternatives(self):
     receiver_tensor = array_ops.placeholder(dtypes.string)
     output_1 = constant_op.constant([1.])
@@ -404,6 +626,35 @@ def test_get_timestamped_export_dir(self):
     self.assertTrue(int(time_1) < int(time_2))
     self.assertTrue(int(time_2) < int(time_3))
 
+  def test_build_all_signature_defs_serving_only(self):
+    receiver_tensor = {"input": array_ops.placeholder(dtypes.string)}
+    output_1 = constant_op.constant([1.])
+    export_outputs = {
+        signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
+            export_output.PredictOutput(outputs=output_1),
+        "train": export_output.TrainOutput(loss=output_1),
+    }
+
+    signature_defs = export.build_all_signature_defs(
+        receiver_tensor, export_outputs)
+
+    expected_signature_defs = {
+        "serving_default": signature_def_utils.predict_signature_def(
+            receiver_tensor, {"output": output_1})
+    }
+
+    self.assertDictEqual(expected_signature_defs, signature_defs)
+
+    signature_defs = export.build_all_signature_defs(
+        receiver_tensor, export_outputs, serving_only=False)
+
+    expected_signature_defs.update({
+        "train": signature_def_utils.supervised_train_signature_def(
+            receiver_tensor, loss={"loss": output_1})
+    })
+
+    self.assertDictEqual(expected_signature_defs, signature_defs)
+
 
 class TensorServingReceiverTest(test_util.TensorFlowTestCase):
 
diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py
index 8111ab564c0171..4ab2578769cf79 100644
--- a/tensorflow/python/estimator/model_fn.py
+++ b/tensorflow/python/estimator/model_fn.py
@@ -28,6 +28,7 @@
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
 from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.saved_model import tag_constants
 from tensorflow.python.training import monitored_session
 from tensorflow.python.training import session_run_hook
 from tensorflow.python.util import nest
@@ -53,6 +54,13 @@ class ModeKeys(object):
 LOSS_METRIC_KEY = 'loss'
 AVERAGE_LOSS_METRIC_KEY = 'average_loss'
 
+# Mapping of the modes to appropriate tag_constants that are used for saving.
+EXPORT_TAG_MAP = {
+    ModeKeys.PREDICT: [tag_constants.SERVING],
+    ModeKeys.TRAIN: [tag_constants.TRAINING],
+    ModeKeys.EVAL: [tag_constants.EVAL],
+}
+
 
 @tf_export('estimator.EstimatorSpec')
 class EstimatorSpec(
diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py
index 3447d917e9bf2d..071033b0669337 100644
--- a/tensorflow/python/saved_model/builder_impl.py
+++ b/tensorflow/python/saved_model/builder_impl.py
@@ -168,6 +168,25 @@ def _add_main_op(self, main_op):
         raise TypeError("main_op needs to be an Operation: %r" % main_op)
       ops.add_to_collection(constants.MAIN_OP_KEY, main_op)
 
+  def _add_train_op(self, train_op):
+    """Add train op to the SavedModel.
+
+    Note that this functionality is in development, and liable to be
+    moved elsewhere.
+
+    Args:
+      train_op: Op or group of ops that are used for training. These are
+        stored as a collection with key TRAIN_OP_KEY, but not executed.
+
+    Raises:
+      TypeError if Train op is not of type `Operation`.
+    """
+    if train_op is not None:
+      if (not isinstance(train_op, ops.Tensor) and
+          not isinstance(train_op, ops.Operation)):
+        raise TypeError("train_op needs to be a Tensor or Op: %r" % train_op)
+      ops.add_to_collection(constants.TRAIN_OP_KEY, train_op)
+
   def _tag_and_add_meta_graph(self, meta_graph_def, tags, signature_def_map):
     """Tags the meta graph def and adds it to the SavedModel.
 
@@ -238,6 +257,20 @@ def _validate_signature_def_map(self, signature_def_map):
         for outputs_key in outputs:
           self._validate_tensor_info(outputs[outputs_key])
 
+  def _add_collections(
+      self, assets_collection, legacy_init_op, main_op, train_op):
+    """Add asset and op collections to be saved."""
+    # Save asset files and write them to disk, if any.
+    self._save_and_write_assets(assets_collection)
+
+    if main_op is None:
+      # Add legacy init op to the SavedModel.
+      self._maybe_add_legacy_init_op(legacy_init_op)
+    else:
+      self._add_main_op(main_op)
+
+    self._add_train_op(train_op)
+
   def add_meta_graph(self,
                      tags,
                      signature_def_map=None,
@@ -285,14 +318,8 @@ def add_meta_graph(self,
     # properly populated.
     self._validate_signature_def_map(signature_def_map)
 
-    # Save asset files and write them to disk, if any.
-    self._save_and_write_assets(assets_collection)
-
-    if main_op is None:
-      # Add legacy init op to the SavedModel.
-      self._maybe_add_legacy_init_op(legacy_init_op)
-    else:
-      self._add_main_op(main_op)
+    # Add assets and ops
+    self._add_collections(assets_collection, legacy_init_op, main_op, None)
 
     # Initialize a saver to generate a sharded output for all saveables in the
     # current scope.
@@ -351,6 +378,7 @@ def add_meta_graph_and_variables(self,
       strip_default_attrs: Boolean. If `True`, default-valued attributes will be
         removed from the NodeDefs. For a detailed guide, see
         [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
+
     """
     # pylint: enable=line-too-long
     if self._has_saved_variables:
@@ -362,8 +390,8 @@ def add_meta_graph_and_variables(self,
     # properly populated.
     self._validate_signature_def_map(signature_def_map)
 
-    # Save asset files and write them to disk, if any.
-    self._save_and_write_assets(assets_collection)
+    # Add assets and ops
+    self._add_collections(assets_collection, legacy_init_op, main_op, None)
 
     # Create the variables sub-directory, if it does not exist.
     variables_dir = os.path.join(
@@ -376,12 +404,6 @@ def add_meta_graph_and_variables(self,
         compat.as_text(variables_dir),
         compat.as_text(constants.VARIABLES_FILENAME))
 
-    if main_op is None:
-      # Add legacy init op to the SavedModel.
-      self._maybe_add_legacy_init_op(legacy_init_op)
-    else:
-      self._add_main_op(main_op)
-
     # Initialize a saver to generate a sharded output for all saveables in the
     # current scope.
     saver = tf_saver.Saver(
diff --git a/tensorflow/python/saved_model/constants.py b/tensorflow/python/saved_model/constants.py
index 34206c6f6d49f1..61c6ffbd0d11ef 100644
--- a/tensorflow/python/saved_model/constants.py
+++ b/tensorflow/python/saved_model/constants.py
@@ -41,6 +41,10 @@
 tf_export("saved_model.constants.MAIN_OP_KEY").export_constant(
     __name__, "MAIN_OP_KEY")
 
+# CollectionDef key for the SavedModel train op.
+# Not exported while export_all_saved_models is in contrib.
+TRAIN_OP_KEY = "saved_model_train_op"
+
 # Schema version for SavedModel.
 SAVED_MODEL_SCHEMA_VERSION = 1
 tf_export("saved_model.constants.SAVED_MODEL_SCHEMA_VERSION").export_constant(
@@ -65,3 +69,5 @@
 VARIABLES_FILENAME = "variables"
 tf_export("saved_model.constants.VARIABLES_FILENAME").export_constant(
     __name__, "VARIABLES_FILENAME")
+
+
diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py
index 804255375e7c52..a4d994fd43fa5a 100644
--- a/tensorflow/python/saved_model/saved_model_test.py
+++ b/tensorflow/python/saved_model/saved_model_test.py
@@ -734,6 +734,96 @@ def testLegacyInitOpWithNonEmptyCollection(self):
         builder.add_meta_graph_and_variables(
             sess, ["foo"], legacy_init_op=legacy_init_op)
 
+  def testTrainOp(self):
+    export_dir = self._get_export_dir("test_train_op")
+    builder = saved_model_builder.SavedModelBuilder(export_dir)
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      # Add `v1` and `v2` variables to the graph.
+      v1 = variables.Variable(1, name="v1")
+      ops.add_to_collection("v", v1)
+      v2 = variables.Variable(2, name="v2")
+      ops.add_to_collection("v", v2)
+
+      sess.run(variables.global_variables_initializer())
+      train_op = state_ops.assign_add(v1, v2)
+
+      sess.run(train_op)
+      # TODO(karmel): remove explicit call when in the public method.
+      builder._add_train_op(train_op)
+      builder.add_meta_graph_and_variables(sess, ["foo"])
+
+    # Save the SavedModel to disk.
+    builder.save()
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      loader.load(sess, ["foo"], export_dir)
+      self.assertEqual(3, ops.get_collection("v")[0].eval())
+      self.assertEqual(2, ops.get_collection("v")[1].eval())
+      self.assertIsInstance(
+          ops.get_collection(constants.TRAIN_OP_KEY)[0], ops.Tensor)
+
+  def testTrainOpGroup(self):
+    export_dir = self._get_export_dir("test_train_op_group")
+    builder = saved_model_builder.SavedModelBuilder(export_dir)
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      # Add `v1` and `v2` variables to the graph.
+      v1 = variables.Variable(1, name="v1")
+      ops.add_to_collection("v", v1)
+      v2 = variables.Variable(2, name="v2")
+      ops.add_to_collection("v", v2)
+
+      sess.run(variables.global_variables_initializer())
+      train_op = control_flow_ops.group()
+
+      sess.run(train_op)
+      # TODO(karmel): remove explicit call when in the public method.
+      builder._add_train_op(train_op)
+      builder.add_meta_graph_and_variables(sess, ["foo"])
+
+    # Save the SavedModel to disk.
+    builder.save()
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      loader.load(sess, ["foo"], export_dir)
+      self.assertEqual(1, ops.get_collection("v")[0].eval())
+      self.assertEqual(2, ops.get_collection("v")[1].eval())
+      self.assertIsInstance(
+          ops.get_collection(constants.TRAIN_OP_KEY)[0], ops.Operation)
+
+  def testTrainOpAfterVariables(self):
+    export_dir = self._get_export_dir("test_train_op_after_variables")
+    builder = saved_model_builder.SavedModelBuilder(export_dir)
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      # Add `v1` and `v2` variables to the graph.
+      v1 = variables.Variable(1, name="v1")
+      ops.add_to_collection("v", v1)
+      v2 = variables.Variable(2, name="v2")
+      ops.add_to_collection("v", v2)
+
+      sess.run(variables.global_variables_initializer())
+      builder.add_meta_graph_and_variables(sess, ["pre_foo"])
+
+      train_op = state_ops.assign_add(v1, v2)
+      sess.run(train_op)
+      # TODO(karmel): remove explicit call when in the public method.
+      builder._add_train_op(train_op)
+      builder.add_meta_graph(["foo"])
+
+    # Save the SavedModel to disk.
+    builder.save()
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      loader.load(sess, ["foo"], export_dir)
+      self.assertIsInstance(
+          ops.get_collection(constants.TRAIN_OP_KEY)[0], ops.Tensor)
+
+    with self.test_session(graph=ops.Graph()) as sess:
+      loader.load(sess, ["pre_foo"], export_dir)
+      self.assertFalse(ops.get_collection(constants.TRAIN_OP_KEY))
+
   def testMultipleAssets(self):
     export_dir = self._get_export_dir("test_multiple_assets")
     builder = saved_model_builder.SavedModelBuilder(export_dir)
diff --git a/tensorflow/python/saved_model/signature_constants.py b/tensorflow/python/saved_model/signature_constants.py
index 819f351291f2bb..99007a9634b27d 100644
--- a/tensorflow/python/saved_model/signature_constants.py
+++ b/tensorflow/python/saved_model/signature_constants.py
@@ -94,3 +94,9 @@
     __name__, "REGRESS_OUTPUTS")
 
 ################################################################################
+# Train/Eval API constants.
+# Not exported while export_all_saved_models is in contrib.
+
+SUPERVISED_TRAIN_METHOD_NAME = "tensorflow/supervised/training"
+
+SUPERVISED_EVAL_METHOD_NAME = "tensorflow/supervised/eval"
diff --git a/tensorflow/python/saved_model/signature_def_utils.py b/tensorflow/python/saved_model/signature_def_utils.py
index ea0f52f17e56f9..27d6b70e9dce3f 100644
--- a/tensorflow/python/saved_model/signature_def_utils.py
+++ b/tensorflow/python/saved_model/signature_def_utils.py
@@ -26,6 +26,8 @@
 from tensorflow.python.saved_model.signature_def_utils_impl import is_valid_signature
 from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def
 from tensorflow.python.saved_model.signature_def_utils_impl import regression_signature_def
+from tensorflow.python.saved_model.signature_def_utils_impl import supervised_eval_signature_def
+from tensorflow.python.saved_model.signature_def_utils_impl import supervised_train_signature_def
 # pylint: enable=unused-import
 
 del absolute_import
diff --git a/tensorflow/python/saved_model/signature_def_utils_impl.py b/tensorflow/python/saved_model/signature_def_utils_impl.py
index d0331591889110..f8ad788f7775b6 100644
--- a/tensorflow/python/saved_model/signature_def_utils_impl.py
+++ b/tensorflow/python/saved_model/signature_def_utils_impl.py
@@ -185,6 +185,62 @@ def predict_signature_def(inputs, outputs):
   return signature_def
 
 
+def supervised_train_signature_def(
+    inputs, loss, predictions=None, metrics=None):
+  return _supervised_signature_def(
+      signature_constants.SUPERVISED_TRAIN_METHOD_NAME, inputs, loss=loss,
+      predictions=predictions, metrics=metrics)
+
+
+def supervised_eval_signature_def(
+    inputs, loss, predictions=None, metrics=None):
+  return _supervised_signature_def(
+      signature_constants.SUPERVISED_EVAL_METHOD_NAME, inputs, loss=loss,
+      predictions=predictions, metrics=metrics)
+
+
+def _supervised_signature_def(
+    method_name, inputs, loss=None, predictions=None,
+    metrics=None):
+  """Creates a signature for training and eval data.
+
+  This function produces signatures that describe the inputs and outputs
+  of a supervised process, such as training or evaluation, that
+  results in loss, metrics, and the like. Note that this function only requires
+  inputs to be not None.
+
+  Args:
+    method_name: Method name of the SignatureDef as a string.
+    inputs: dict of string to `Tensor`.
+    loss: dict of string to `Tensor` representing computed loss.
+    predictions: dict of string to `Tensor` representing the output predictions.
+    metrics: dict of string to `Tensor` representing metric ops.
+
+  Returns:
+    A train- or eval-flavored signature_def.
+
+  Raises:
+    ValueError: If inputs or outputs is `None`.
+  """
+  if inputs is None or not inputs:
+    raise ValueError('{} inputs cannot be None or empty.'.format(method_name))
+
+  signature_inputs = {key: utils.build_tensor_info(tensor)
+                      for key, tensor in inputs.items()}
+
+  signature_outputs = {}
+  for output_set in (loss, predictions, metrics):
+    if output_set is not None:
+      sig_out = {key: utils.build_tensor_info(tensor)
+                 for key, tensor in output_set.items()}
+      signature_outputs.update(sig_out)
+
+  signature_def = build_signature_def(
+      signature_inputs, signature_outputs, method_name)
+
+  return signature_def
+
+
 @tf_export('saved_model.signature_def_utils.is_valid_signature')
 def is_valid_signature(signature_def):
   """Determine whether a SignatureDef can be served by TensorFlow Serving."""
diff --git a/tensorflow/python/saved_model/signature_def_utils_test.py b/tensorflow/python/saved_model/signature_def_utils_test.py
index b2bd14db8cdab3..ebc54506335d41 100644
--- a/tensorflow/python/saved_model/signature_def_utils_test.py
+++ b/tensorflow/python/saved_model/signature_def_utils_test.py
@@ -180,6 +180,101 @@ def testPredictionSignatureDef(self):
     self.assertEqual(types_pb2.DT_STRING, output2_tensor_info_actual.dtype)
     self.assertEqual(0, len(output2_tensor_info_actual.tensor_shape.dim))
 
+  def testTrainSignatureDef(self):
+    self._testSupervisedSignatureDef(
+        signature_def_utils_impl.supervised_train_signature_def,
+        signature_constants.SUPERVISED_TRAIN_METHOD_NAME)
+
+  def testEvalSignatureDef(self):
+    self._testSupervisedSignatureDef(
+        signature_def_utils_impl.supervised_eval_signature_def,
+        signature_constants.SUPERVISED_EVAL_METHOD_NAME)
+
+  def _testSupervisedSignatureDef(self, fn_to_test, method_name):
+    inputs = {
+        "input-1": constant_op.constant("a", name="input-1"),
+        "input-2": constant_op.constant("b", name="input-2"),
+    }
+    loss = {"loss-1": constant_op.constant(0.45, name="loss-1")}
+    predictions = {
+        "classes": constant_op.constant([100], name="classes"),
+    }
+    metrics_val = constant_op.constant(100.0, name="metrics_val")
+    metrics = {
+        "metrics/value": metrics_val,
+        "metrics/update_op": array_ops.identity(metrics_val, name="metrics_op"),
+    }
+
+    signature_def = fn_to_test(inputs, loss, predictions, metrics)
+
+    self.assertEqual(method_name, signature_def.method_name)
+
+    # Check inputs in signature def.
+    self.assertEqual(2, len(signature_def.inputs))
+    input1_tensor_info_actual = (signature_def.inputs["input-1"])
+    self.assertEqual("input-1:0", input1_tensor_info_actual.name)
+    self.assertEqual(types_pb2.DT_STRING, input1_tensor_info_actual.dtype)
+    self.assertEqual(0, len(input1_tensor_info_actual.tensor_shape.dim))
+    input2_tensor_info_actual = (signature_def.inputs["input-2"])
+    self.assertEqual("input-2:0", input2_tensor_info_actual.name)
+    self.assertEqual(types_pb2.DT_STRING, input2_tensor_info_actual.dtype)
+    self.assertEqual(0, len(input2_tensor_info_actual.tensor_shape.dim))
+
+    # Check outputs in signature def.
+    self.assertEqual(4, len(signature_def.outputs))
+    self.assertEqual("loss-1:0", signature_def.outputs["loss-1"].name)
+    self.assertEqual(types_pb2.DT_FLOAT, signature_def.outputs["loss-1"].dtype)
+
+    self.assertEqual("classes:0", signature_def.outputs["classes"].name)
+    self.assertEqual(1, len(signature_def.outputs["classes"].tensor_shape.dim))
+
+    self.assertEqual(
+        "metrics_val:0", signature_def.outputs["metrics/value"].name)
+    self.assertEqual(
+        types_pb2.DT_FLOAT, signature_def.outputs["metrics/value"].dtype)
+
+    self.assertEqual(
+        "metrics_op:0", signature_def.outputs["metrics/update_op"].name)
+    self.assertEqual(
+        types_pb2.DT_FLOAT, signature_def.outputs["metrics/value"].dtype)
+
+  def testTrainSignatureDefMissingInputs(self):
+    self._testSupervisedSignatureDefMissingInputs(
+        signature_def_utils_impl.supervised_train_signature_def,
+        signature_constants.SUPERVISED_TRAIN_METHOD_NAME)
+
+  def testEvalSignatureDefMissingInputs(self):
+    self._testSupervisedSignatureDefMissingInputs(
+        signature_def_utils_impl.supervised_eval_signature_def,
+        signature_constants.SUPERVISED_EVAL_METHOD_NAME)
+
+  def _testSupervisedSignatureDefMissingInputs(self, fn_to_test, method_name):
+    inputs = {
+        "input-1": constant_op.constant("a", name="input-1"),
+        "input-2": constant_op.constant("b", name="input-2"),
+    }
+    loss = {"loss-1": constant_op.constant(0.45, name="loss-1")}
+    predictions = {
+        "classes": constant_op.constant([100], name="classes"),
+    }
+    metrics_val = constant_op.constant(100, name="metrics_val")
+    metrics = {
+        "metrics/value": metrics_val,
+        "metrics/update_op": array_ops.identity(metrics_val, name="metrics_op"),
+    }
+
+    with self.assertRaises(ValueError):
+      signature_def = fn_to_test(
+          {}, loss=loss, predictions=predictions, metrics=metrics)
+
+    signature_def = fn_to_test(inputs, loss=loss)
+    self.assertEqual(method_name, signature_def.method_name)
+    self.assertEqual(1, len(signature_def.outputs))
+
+    signature_def = fn_to_test(inputs, metrics=metrics, loss=loss)
+    self.assertEqual(method_name, signature_def.method_name)
+    self.assertEqual(3, len(signature_def.outputs))
+
   def testGetShapeAndTypes(self):
     inputs = {
         "input-1": constant_op.constant(["a", "b"]),
diff --git a/tensorflow/python/saved_model/tag_constants.py b/tensorflow/python/saved_model/tag_constants.py
index 5a797da791c82d..c82154e7b93aae 100644
--- a/tensorflow/python/saved_model/tag_constants.py
+++ b/tensorflow/python/saved_model/tag_constants.py
@@ -32,6 +32,9 @@
 tf_export("saved_model.tag_constants.TRAINING").export_constant(
     __name__, "TRAINING")
 
+# Tag for the `eval` graph. Not exported while the export logic is in contrib.
+EVAL = "eval"
+
 # Tag for the `gpu` graph.
 GPU = "gpu"
 tf_export("saved_model.tag_constants.GPU").export_constant(__name__, "GPU")
@@ -39,3 +42,5 @@
 # Tag for the `tpu` graph.
 TPU = "tpu"
 tf_export("saved_model.tag_constants.TPU").export_constant(__name__, "TPU")
+
+

From 037e52e20157985d3f385f8e0426cdde3f5aae2b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 4 May 2018 16:37:27 -0700
Subject: [PATCH 0409/1691] Expose read-only versions of tensors in tflite.

PiperOrigin-RevId: 195491701
---
 tensorflow/contrib/lite/interpreter.h | 37 ++++++++++++++++++++++-----
 1 file changed, 31 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h
index 1074f64263b5d7..0450e86ae7f84e 100644
--- a/tensorflow/contrib/lite/interpreter.h
+++ b/tensorflow/contrib/lite/interpreter.h
@@ -201,7 +201,7 @@ class Interpreter {
   // Overrides execution plan. This bounds checks indices sent in.
   TfLiteStatus SetExecutionPlan(const std::vector& new_plan);
 
-  // Get a tensor data structure.
+  // Get a mutable tensor data structure.
   // TODO(aselle): Create a safe ArrayHandle interface to avoid exposing this
   // read/write access to structure
   TfLiteTensor* tensor(int tensor_index) {
@@ -210,9 +210,14 @@ class Interpreter {
     return &context_.tensors[tensor_index];
   }
 
+  // Get an immutable tensor data structure.
+  const TfLiteTensor* tensor(int tensor_index) const {
+    if (tensor_index >= context_.tensors_size || tensor_index < 0)
+      return nullptr;
+    return &context_.tensors[tensor_index];
+  }
+
   // Get a pointer to an operation and registration data structure if in bounds.
-  // TODO(aselle): Create a safe ArrayHandle interface to avoid exposing this
-  // read/write access to structure
   const std::pair* node_and_registration(
       int node_index) const {
     if (node_index >= nodes_and_registration_.size() || node_index < 0)
@@ -220,7 +225,8 @@ class Interpreter {
     return &nodes_and_registration_[node_index];
   }
 
-  // Perform a checked cast to the appropriate tensor type.
+  // Perform a checked cast to the appropriate tensor type (mutable pointer
+  // version).
   template 
   T* typed_tensor(int tensor_index) {
     if (TfLiteTensor* tensor_ptr = tensor(tensor_index)) {
@@ -231,6 +237,18 @@ class Interpreter {
     return nullptr;
   }
 
+  // Perform a checked cast to the appropriate tensor type (immutable pointer
+  // version).
+  template 
+  const T* typed_tensor(int tensor_index) const {
+    if (const TfLiteTensor* tensor_ptr = tensor(tensor_index)) {
+      if (tensor_ptr->type == typeToTfLiteType()) {
+        return reinterpret_cast(tensor_ptr->data.raw);
+      }
+    }
+    return nullptr;
+  }
+
   // Return a pointer into the data of a given input tensor. The given index
   // must be between 0 and inputs().size().
   template 
@@ -238,13 +256,20 @@ class Interpreter {
     return typed_tensor(inputs_[index]);
   }
 
-  // Return a pointer into the data of a given output tensor. The given index
-  // must be between 0 and outputs().size().
+  // Return a mutable pointer into the data of a given output tensor. The given
+  // index must be between 0 and outputs().size().
   template 
   T* typed_output_tensor(int index) {
     return typed_tensor(outputs_[index]);
   }
 
+  // Return an immutable pointer into the data of a given output tensor. The
+  // given index must be between 0 and outputs().size().
+  template 
+  const T* typed_output_tensor(int index) const {
+    return typed_tensor(outputs_[index]);
+  }
+
   // Change the dimensionality of a given tensor. Note, this is only acceptable
   // for tensor indices that are inputs.
   // Returns status of failure or success.

From fa1d92f70adf52d9258384e8528f9a7203a141dd Mon Sep 17 00:00:00 2001
From: Bjarke Hammersholt Roune 
Date: Fri, 4 May 2018 16:51:06 -0700
Subject: [PATCH 0410/1691] Add infrastructure for a backend-specific
 configuration for each op. This is intentionally not exposed in
 ComputationBuilder and is not intended for use or to be set at all prior to
 the last backend-specific part of compilation.

PiperOrigin-RevId: 195493500
---
 tensorflow/compiler/xla/service/hlo.proto     |   3 +
 .../compiler/xla/service/hlo_computation.cc   |  52 ++++-----
 .../compiler/xla/service/hlo_computation.h    |  20 ++--
 .../compiler/xla/service/hlo_graph_dumper.cc  |  43 +++++---
 .../compiler/xla/service/hlo_graph_dumper.h   |   5 +-
 .../compiler/xla/service/hlo_instruction.cc   | 100 +++++-------------
 .../compiler/xla/service/hlo_instruction.h    |  59 +++++++++--
 tensorflow/compiler/xla/service/hlo_module.cc |  12 +++
 tensorflow/compiler/xla/service/hlo_module.h  |  19 ++++
 .../compiler/xla/service/hlo_verifier.cc      |  71 ++++++-------
 tensorflow/compiler/xla/statusor.h            |  11 +-
 tensorflow/compiler/xla/statusor_test.cc      |   8 ++
 .../compiler/xla/tools/parser/hlo_parser.cc   |  10 +-
 .../xla/tools/parser/hlo_parser_test.cc       |  22 +++-
 14 files changed, 259 insertions(+), 176 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo.proto b/tensorflow/compiler/xla/service/hlo.proto
index aa6860880b7a13..1f7c1cffd324ad 100644
--- a/tensorflow/compiler/xla/service/hlo.proto
+++ b/tensorflow/compiler/xla/service/hlo.proto
@@ -147,6 +147,9 @@ message HloInstructionProto {
   repeated int64 called_computation_ids = 38;
 
   xla.OpSharding sharding = 40;
+
+  // Backend configuration for the instruction. Has backend-specific meaning.
+  string backend_config = 43;
 }
 
 // Serialization of HloComputation.
diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc
index 594413e88fb26e..17e43c3cb826aa 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation.cc
@@ -347,6 +347,11 @@ std::list HloComputation::MakeEmbeddedComputationsList()
   // To avoid special handling of this computation, cast away const of
   // 'this'. 'this' is immediately removed from the post order after
   // construction.
+  //
+  // TODO(b/78350259): This violates const-correctness, since while the original
+  // computation is not returned, we still retrieve non-const computations from
+  // a const one. Consider also avoiding const for HloComputation, or review XLA
+  // for const-correctness of non-HloInstruction* types like this.
   ComputeComputationPostOrder(const_cast(this), &visited,
                               &post_order);
 
@@ -723,18 +728,25 @@ Status HloComputation::Accept(
   return this->Accept(&visitor);
 }
 
-std::unique_ptr HloComputation::Clone(const string& suffix,
-                                                      HloModule* module) {
+std::unique_ptr HloComputation::Clone(
+    const string& suffix, HloModule* module,
+    HloInstruction::CloneMap* clone_map) {
   return CloneWithReplacements(
       /*replacements=*/std::unordered_map>(),
-      module, suffix);
+      module, clone_map, suffix);
 }
 
 std::unique_ptr HloComputation::CloneWithReplacements(
     std::unordered_map>
         replacements,
-    HloModule* module, const string& suffix) {
+    HloModule* module, HloInstruction::CloneMap* clone_map,
+    const string& suffix) {
+  HloInstruction::CloneMap local_clone_map;
+  if (clone_map == nullptr) {
+    clone_map = &local_clone_map;
+  }
+
   // Look up instr in the replacements map, and return either the replacement,
   // or instr, if the replacement isn't present.
   //
@@ -756,24 +768,19 @@ std::unique_ptr HloComputation::CloneWithReplacements(
     }
   }
 
-  std::unordered_map clone_map;
   std::vector> instructions;
   std::unique_ptr new_instr = nullptr;
   for (auto instr : postorder) {
     std::vector new_operands;
     for (auto operand : instr->operands()) {
       auto replaced_operand = replace(operand);
-      // If replaced_operand is null, that means 'replacements' asked us not to
-      // include operand in the new computation.  But we can't do that, because
-      // operand is used by instr.
       CHECK_NE(replaced_operand, nullptr)
-          << "replacements map tried to eliminate a used instruction "
-          << operand->ToString() << ", used by " << instr->ToString();
-      new_operands.push_back(FindOrDie(clone_map, replaced_operand));
+          << "Replacements map specifies to leave out " << operand->ToString()
+          << ", but it is used by " << instr->ToString() << ".";
+      new_operands.push_back(FindOrDie(*clone_map, replaced_operand));
     }
-    new_instr =
-        instr->CloneWithNewOperands(instr->shape(), new_operands, module);
-    InsertOrDie(&clone_map, instr, new_instr.get());
+    new_instr = instr->CloneWithNewOperands(instr->shape(), new_operands,
+                                            module, clone_map);
     instructions.push_back(std::move(new_instr));
   }
   Builder builder(name() + "." + suffix);
@@ -781,27 +788,24 @@ std::unique_ptr HloComputation::CloneWithReplacements(
     builder.AddInstruction(std::move(instr));
   }
   auto result = builder.Build(
-      /*root_instruction=*/FindOrDie(clone_map, replace(root_instruction())));
+      /*root_instruction=*/FindOrDie(*clone_map, replace(root_instruction())));
 
   // Clone control dependencies.
   for (auto instr : postorder) {
-    HloInstruction* new_instr = FindOrDie(clone_map, instr);
+    HloInstruction* new_instr = FindOrDie(*clone_map, instr);
     for (auto successor : instr->control_successors()) {
       auto replaced_successor = replace(successor);
-
-      // successor may not be in clone_map, because it might have been
-      // removed by the replacements map.
-      if (replaced_successor == nullptr) {
-        continue;
-      }
+      CHECK_NE(replaced_successor, nullptr)
+          << "Replacements map specifies to leave out " << successor->ToString()
+          << ", but it is control-depended-on by " << instr->ToString() << ".";
 
       TF_CHECK_OK(new_instr->AddControlDependencyTo(
-          FindOrDie(clone_map, replaced_successor)));
+          FindOrDie(*clone_map, replaced_successor)));
     }
   }
 
   // We cloned the elements of 'replacements', so they're all going to be
-  // destroyed.  HloInstructions need to be detached from their operands before
+  // destroyed. HloInstructions need to be detached from their operands before
   // they're destroyed, otherwise they stick around in the operands' users lists
   // and cause use-after-frees.
   for (auto& kv : replacements) {
diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h
index 9d3f6e9a2c2efd..98983556256cec 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.h
+++ b/tensorflow/compiler/xla/service/hlo_computation.h
@@ -291,11 +291,17 @@ class HloComputation {
       const std::function& visitor_func) const;
 
   // Returns a deep copy of this computation including all instructions.
-  // If the module pointer is not nullptr, it will be the module where
-  // the cloned computations will be added to (in order to support deep
-  // cloning).
-  std::unique_ptr Clone(const string& suffix = "clone",
-                                        HloModule* module = nullptr);
+  //
+  // If the module pointer is not nullptr, then the cloned computations will be
+  // added to this module in order to support deep cloning. Otherwise the module
+  // of the computation is used.
+  //
+  // If clone_map is not nullptr, then each original instruction that is cloned
+  // will be inserted and map to its clone. clone_map should not already contain
+  // any of the instructions to clone.
+  std::unique_ptr Clone(
+      const string& suffix = "clone", HloModule* module = nullptr,
+      HloInstruction::CloneMap* clone_map = nullptr);
 
   // Like Clone(), but if an instruction is present in replacement_map, we use
   // the map's value to replace that instruction in the cloned computation.
@@ -305,7 +311,9 @@ class HloComputation {
   std::unique_ptr CloneWithReplacements(
       std::unordered_map>
           replacements,
-      HloModule* module = nullptr, const string& suffix = "clone");
+      HloModule* module = nullptr,
+      HloInstruction::CloneMap* clone_map = nullptr,
+      const string& suffix = "clone");
 
   // Returns true if the given instruction can be removed from the computation.
   // Parameter instructions cannot be removed without violating invariants of
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index bb4db89f0a242c..794f1b46829206 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -322,11 +322,13 @@ class HloDotDumper {
  public:
   HloDotDumper(const HloComputation* computation, tensorflow::StringPiece label,
                const DebugOptions& debug_options, bool show_metadata,
-               const HloExecutionProfile* profile, NodeFilter filter)
+               bool show_backend_config, const HloExecutionProfile* profile,
+               NodeFilter filter)
       : computation_(computation),
         label_(label.ToString()),
         debug_options_(debug_options),
         show_metadata_(show_metadata),
+        show_backend_config_(show_backend_config),
         profile_(profile),
         filter_(std::move(filter)) {}
 
@@ -365,6 +367,7 @@ class HloDotDumper {
   string GetInstructionNodeShape(const HloInstruction* instr);
   string GetInstructionNodeLabel(const HloInstruction* instr);
   string GetInstructionNodeMetadata(const HloInstruction* instr);
+  string GetInstructionNodeBackendConfig(const HloInstruction* instr);
   string GetInstructionNodeExtraInfo(const HloInstruction* instr);
   string GetInstructionNodeInlinedOperands(const HloInstruction* instr);
   void AddInstructionIncomingEdges(const HloInstruction* instr);
@@ -393,6 +396,7 @@ class HloDotDumper {
   const string label_;                 // overall name for the graph
   const DebugOptions& debug_options_;
   const bool show_metadata_;
+  const bool show_backend_config_;
   const HloExecutionProfile* profile_;  // may be null
   const NodeFilter filter_;
 
@@ -611,6 +615,10 @@ tooltip = " ";
     if (!extra_info.empty()) {
       StrAppend(&subcomp_label, "
", extra_info);
     }
+    string node_backend_config = GetInstructionNodeBackendConfig(parent_instr);
+    if (!node_backend_config.empty()) {
+      StrAppend(&subcomp_label, "
", node_backend_config);
+    }
 
     bool highlight = filter_.Highlight(parent_instr);
     const char* fillcolor;
@@ -765,6 +773,7 @@ string HloDotDumper::DumpInstruction(const HloInstruction* instr) {
   string node_shape = GetInstructionNodeShape(instr);
   string node_label = GetInstructionNodeLabel(instr);
   string node_metadata = GetInstructionNodeMetadata(instr);
+  string node_backend_config = GetInstructionNodeBackendConfig(instr);
   string extra_info = GetInstructionNodeExtraInfo(instr);
   string inlined_constants = GetInstructionNodeInlinedOperands(instr);
   string trivial_subcomputation = GetInstructionTrivialComputationStr(instr);
@@ -782,8 +791,8 @@ string HloDotDumper::DumpInstruction(const HloInstruction* instr) {
   }
   // Build the text that will be displayed inside the node.
   string node_body = node_label;
-  for (const string& s :
-       {trivial_subcomputation, node_metadata, extra_info, inlined_constants}) {
+  for (const string& s : {trivial_subcomputation, node_metadata,
+                          node_backend_config, extra_info, inlined_constants}) {
     if (!s.empty()) {
       StrAppend(&node_body, "
", s);
     }
@@ -1078,6 +1087,15 @@ string HloDotDumper::GetInstructionNodeMetadata(const HloInstruction* instr) {
   return Join(lines, "
");
 }
 
+string HloDotDumper::GetInstructionNodeBackendConfig(
+    const HloInstruction* instr) {
+  if (!show_backend_config_ || instr->backend_config().empty()) {
+    return "";
+  }
+
+  return StrCat("backend_config=\"", instr->backend_config(), "\"");
+}
+
 string HloDotDumper::GetInstructionNodeExtraInfo(const HloInstruction* instr) {
   std::vector lines;
 
@@ -1404,7 +1422,7 @@ string ExportGraph(const string& graph,
 string DumpGraph(const HloComputation& computation, const string& label,
                  const DebugOptions& debug_options,
                  const HloExecutionProfile* hlo_execution_profile,
-                 bool show_metadata) {
+                 bool show_metadata, bool show_backend_config) {
   GraphRendererInterface::GraphKind graph_kind;
   string graph;
   if (debug_options.xla_hlo_dump_as_graphdef()) {
@@ -1414,9 +1432,10 @@ string DumpGraph(const HloComputation& computation, const string& label,
                                                           &graph));
     graph_kind = GraphRendererInterface::TF_GRAPHDEF;
   } else {
-    graph = HloDotDumper(&computation, label, debug_options, show_metadata,
-                         hlo_execution_profile, NodeFilter())
-                .Dump();
+    graph =
+        HloDotDumper(&computation, label, debug_options, show_metadata,
+                     show_backend_config, hlo_execution_profile, NodeFilter())
+            .Dump();
     graph_kind = GraphRendererInterface::DOT_GRAPH;
   }
 
@@ -1427,15 +1446,15 @@ string DumpGraph(const HloComputation& computation, const string& label,
 }
 
 string DumpNeighborhoodAround(const HloInstruction& node, int radius,
-                              bool show_metadata) {
+                              bool show_metadata, bool show_backend_config) {
   auto debug_options = node.GetModule()->config().debug_options();
   string label =
       StrCat("Neighborhood of ", radius, " nodes around ", node.name());
   NodeFilter filter = MakeNodeFilter(&node, radius);
-  string graph =
-      HloDotDumper(node.parent(), label, debug_options, show_metadata,
-                   /*profile=*/nullptr, filter)
-          .Dump();
+  string graph = HloDotDumper(node.parent(), label, debug_options,
+                              show_metadata, show_backend_config,
+                              /*profile=*/nullptr, filter)
+                     .Dump();
   return ExportGraph(graph, GraphRendererInterface::DOT_GRAPH, debug_options);
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.h b/tensorflow/compiler/xla/service/hlo_graph_dumper.h
index 2704aae1e3ba7f..fc8e1468aca9c2 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.h
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.h
@@ -56,7 +56,7 @@ string MaybeDumpHloModule(const HloModule& module, const string& label,
 string DumpGraph(const HloComputation& computation, const string& label,
                  const DebugOptions& debug_options,
                  const HloExecutionProfile* hlo_execution_profile = nullptr,
-                 bool show_metadata = false);
+                 bool show_metadata = false, bool show_backend_config = false);
 
 // Like DumpGraph, but renders only nodes "near" the given node in the graph.
 //
@@ -64,7 +64,8 @@ string DumpGraph(const HloComputation& computation, const string& label,
 // (roughly) corresponds to the max distance a node may be from the primary node
 // before it's omitted from the graph.
 string DumpNeighborhoodAround(const HloInstruction& node, int radius,
-                              bool show_metadata = false);
+                              bool show_metadata = false,
+                              bool show_backend_config = false);
 
 // Dumps the HloModule::ToString() as a file into the provided directory path
 // suffixed with the provided label.
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index a714d0e1142450..2c733726a6f68b 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -109,6 +109,7 @@ StatusOr> HloInstruction::CreateFromProto(
   instruction->name_ = proto.name();
 
   instruction->metadata_ = proto.metadata();
+  instruction->set_backend_config(proto.backend_config());
   if (proto.has_literal()) {
     TF_ASSIGN_OR_RETURN(instruction->literal_,
                         Literal::CreateFromProto(proto.literal()));
@@ -1231,12 +1232,15 @@ bool HloInstruction::HasSideEffect() const {
 std::unique_ptr HloInstruction::CloneWithNewOperands(
     const Shape& shape,
     tensorflow::gtl::ArraySlice new_operands,
-    HloModule* module) const {
+    HloModule* module, CloneMap* clone_map) const {
   VLOG(3) << "CloneWithNewOperands:\n  " << ToString();
   VLOG(3) << "  new operands:";
   for (const HloInstruction* new_operand : new_operands) {
     VLOG(3) << "    %" << new_operand->name();
   }
+  if (module == nullptr) {
+    module = GetModule();
+  }
 
   std::unique_ptr clone;
 
@@ -1342,7 +1346,8 @@ std::unique_ptr HloInstruction::CloneWithNewOperands(
       break;
     case HloOpcode::kFft:
       CHECK_EQ(new_operands.size(), 1);
-      return CreateFft(shape, new_operands[0], fft_type_, fft_length_);
+      clone = CreateFft(shape, new_operands[0], fft_type_, fft_length_);
+      break;
     case HloOpcode::kCrossReplicaSum:
       clone = CreateCrossReplicaSum(shape, new_operands);
       break;
@@ -1415,9 +1420,15 @@ std::unique_ptr HloInstruction::CloneWithNewOperands(
     case HloOpcode::kConstant:
       clone = CreateConstant(literal_->CloneToUnique());
       break;
-    case HloOpcode::kFusion:
-      clone = CloneFusionWithNewOperands(shape, new_operands, module);
+    case HloOpcode::kFusion: {
+      CHECK_NE(module, nullptr);
+      auto new_fused_computation = module->AddEmbeddedComputation(
+          fused_instructions_computation()->Clone("clone", module, clone_map));
+      clone = CreateFusion(/*shape=*/shape, /*fusion_kind=*/fusion_kind(),
+                           /*operands=*/new_operands,
+                           /*fusion_computation=*/new_fused_computation);
       break;
+    }
     case HloOpcode::kParameter:
       clone = CreateParameter(parameter_number_, shape, name_);
       break;
@@ -1481,15 +1492,19 @@ std::unique_ptr HloInstruction::CloneWithNewOperands(
   }
   SetupDerivedInstruction(clone.get());
   clone->set_parent(parent_);
+  clone->set_backend_config(backend_config());
+  if (clone_map != nullptr) {
+    InsertOrDie(clone_map, this, clone.get());
+  }
   return clone;
 }
 
 HloInstruction::~HloInstruction() {}
 
-std::unique_ptr HloInstruction::Clone(const string& suffix,
-                                                      HloModule* module) const {
+std::unique_ptr HloInstruction::Clone(
+    const string& suffix, HloModule* module, CloneMap* clone_map) const {
   std::unique_ptr clone =
-      CloneWithNewOperands(shape_, operands_, module);
+      CloneWithNewOperands(shape_, operands_, module, clone_map);
   if (suffix.empty()) {
     clone->name_ = name();
   } else {
@@ -1526,71 +1541,6 @@ std::unique_ptr HloInstruction::Clone(const string& suffix,
   return clone;
 }
 
-std::unique_ptr HloInstruction::CloneFusionWithNewOperands(
-    const Shape& shape, tensorflow::gtl::ArraySlice operands,
-    HloModule* module) const {
-  CHECK_EQ(opcode_, HloOpcode::kFusion);
-  CHECK(parent() != nullptr);
-
-  auto new_instruction =
-      WrapUnique(new HloInstruction(HloOpcode::kFusion, shape));
-  // Add the operands to our new fusion instruction.
-  for (HloInstruction* new_operand : operands) {
-    new_instruction->AppendOperand(new_operand);
-  }
-  // Clone all the fused instructions for the new fusion instruction.
-  HloInstructionMap old_to_new;
-  std::list> new_fused_instructions;
-  // Create the list of fused parameters by mapping through the cloned,
-  // fused instructions.
-  for (HloInstruction* old_fused_parameter :
-       fused_instructions_computation()->parameter_instructions()) {
-    new_fused_instructions.push_back(
-        old_fused_parameter->Clone("clone", module));
-    HloInstruction* new_fusion_parameter = new_fused_instructions.back().get();
-    InsertOrDie(&old_to_new, old_fused_parameter, new_fusion_parameter);
-  }
-  for (auto old_fused_instruction :
-       fused_instructions_computation()->MakeInstructionPostOrder()) {
-    if (old_fused_instruction->opcode() == HloOpcode::kParameter) {
-      FindOrDie(old_to_new, old_fused_instruction);
-      continue;
-    }
-    std::vector new_operands;
-    for (int64 operand_idx = 0;
-         operand_idx < old_fused_instruction->operand_count(); ++operand_idx) {
-      HloInstruction* old_operand =
-          old_fused_instruction->mutable_operand(operand_idx);
-      new_operands.push_back(FindOrDie(old_to_new, old_operand));
-    }
-    new_fused_instructions.push_back(
-        old_fused_instruction->CloneWithNewOperands(
-            old_fused_instruction->shape(), new_operands, module));
-    HloInstruction* new_fused_instruction = new_fused_instructions.back().get();
-    new_fused_instruction->set_parent(parent_);
-    InsertOrDie(&old_to_new, old_fused_instruction, new_fused_instruction);
-  }
-  new_instruction->fusion_kind_ = fusion_kind_;
-  auto computation_builder = HloComputation::Builder(
-      fused_instructions_computation()->name() + ".clone",
-      new_instruction.get());
-  // We iterated the fusion instructions in reverse post order which means
-  // that we must reverse our new list of fusion instructions.
-  for (auto new_fused_instruction_iter = new_fused_instructions.rbegin();
-       new_fused_instruction_iter != new_fused_instructions.rend();
-       ++new_fused_instruction_iter) {
-    computation_builder.AddInstruction(std::move(*new_fused_instruction_iter));
-  }
-  if (module == nullptr) {
-    module = GetModule();
-  }
-  auto fused_root_ = fused_expression_root();
-  new_instruction->called_computations_.push_back(
-      CHECK_NOTNULL(module)->AddEmbeddedComputation(
-          computation_builder.Build(FindOrDie(old_to_new, fused_root_))));
-  return new_instruction;
-}
-
 std::pair
 HloInstruction::LatestNonGteAncestorAndIndex() const {
   const HloInstruction* hlo = this;
@@ -2172,6 +2122,9 @@ string HloInstruction::ToString(const HloPrintOptions& options) const {
        !metadata_.source_file().empty())) {
     StrAppend(&result, ", metadata={", xla::OpMetadataToString(metadata_), "}");
   }
+  if (options.print_backend_config() && !backend_config().empty()) {
+    StrAppend(&result, ", backend_config=\"", CEscape(backend_config()), "\"");
+  }
   return result;
 }
 
@@ -2357,6 +2310,7 @@ std::vector HloInstruction::ExtraAttributesToString(
     extra.push_back(
         StrCat("custom_call_target=\"", CEscape(custom_call_target_), "\""));
   }
+
   return extra;
 }
 
@@ -2386,6 +2340,7 @@ HloInstructionProto HloInstruction::ToProto() const {
   }
 
   *proto.mutable_metadata() = metadata_;
+  proto.set_backend_config(backend_config());
   if (literal_ != nullptr) {
     *proto.mutable_literal() = literal_->ToProto();
   }
@@ -2971,6 +2926,7 @@ Status HloInstruction::AcceptOrdered(
       continue;
     }
 
+    // TODO(b/78350259): Eliminate const laundering.
     HloInstruction* instruction =
         const_cast(const_instruction);
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index a5e9aecb9e7f52..19c8c1145317c6 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -66,6 +66,7 @@ class HloPrintOptions {
       : print_large_constants_(false),
         print_subcomputation_references_(true),
         print_metadata_(true),
+        print_backend_config_(true),
         compact_operands_(false),
         print_operand_shape_(true),
         print_program_shape_(true),
@@ -77,6 +78,7 @@ class HloPrintOptions {
         .set_print_large_constants(true)
         .set_print_subcomputation_references(true)
         .set_print_metadata(false)
+        .set_print_backend_config(false)
         .set_print_operand_shape(false)
         .set_print_program_shape(false)
         .set_print_percent(false);
@@ -99,12 +101,18 @@ class HloPrintOptions {
     return *this;
   }
 
-  // If true, metatdata will be printed.
+  // If true, metadata will be printed.
   HloPrintOptions& set_print_metadata(bool value) {
     print_metadata_ = value;
     return *this;
   }
 
+  // If true, backend_config will be printed.
+  HloPrintOptions& set_print_backend_config(bool value) {
+    print_backend_config_ = value;
+    return *this;
+  }
+
   // If true, operands' shapes will be printed.
   HloPrintOptions& set_print_operand_shape(bool value) {
     print_operand_shape_ = value;
@@ -141,6 +149,7 @@ class HloPrintOptions {
     return print_subcomputation_references_;
   }
   bool print_metadata() const { return print_metadata_; }
+  bool print_backend_config() const { return print_metadata_; }
   bool compact_operands() const { return compact_operands_; }
   bool print_operand_shape() const { return print_operand_shape_; }
   bool print_program_shape() const { return print_program_shape_; }
@@ -151,6 +160,7 @@ class HloPrintOptions {
   bool print_large_constants_;
   bool print_subcomputation_references_;
   bool print_metadata_;
+  bool print_backend_config_;
   bool compact_operands_;
   bool print_operand_shape_;
   bool print_program_shape_;
@@ -643,6 +653,8 @@ class HloInstruction {
   // Detaches an instruction from its operands. That is, remove the instruction
   // from each operand's user set. This should only be called prior to
   // deallocating the instruction.
+  //
+  // TODO(b/78305363): Make this automatic when deleting an instruction.
   void DetachFromOperands();
 
   // Performs a postorder DFS visit using this node as the root. If
@@ -1157,23 +1169,30 @@ class HloInstruction {
   // Precondition: opcode() == HloOpcode::kRng
   RandomDistribution random_distribution() const;
 
+  // See documentation for Clone().
+  using CloneMap = std::unordered_map;
+
   // Clones the HLO instruction. The clone will have the same opcode, shape, and
   // operands. After creation the clone has no uses. "this" (the instruction
   // cloned from) is not changed. Suffix is the string to append to the name of
-  // the instruction to form the name of the cloned instruction.  If the module
-  // pointer is not nullptr, it will be the module where the cloned computations
-  // will be added to (in order to support deep cloning).  Ignores the control
-  // predecessors and successors of this HLO instruction.
+  // the instruction to form the name of the cloned instruction. Ignores the
+  // control predecessors and successors of this HLO instruction.
+  //
+  // If the module pointer is not nullptr, then any cloned computations will be
+  // added to this module in order to support deep cloning. Otherwise the module
+  // of the instruction is used.
+  //
+  // If clone_map is not nullptr, then each original instruction that is cloned
+  // will be inserted and map to its clone. clone_map should not already contain
+  // any of the instructions to clone.
   std::unique_ptr Clone(const string& suffix = "clone",
-                                        HloModule* module = nullptr) const;
+                                        HloModule* module = nullptr,
+                                        CloneMap* clone_map = nullptr) const;
 
-  // Clones the HLO instruction as above but with new shape and operands.  If
-  // the module pointer is not nullptr, it will be the module where the cloned
-  // computations will be added to (in order to support deep cloning).  Ignores
-  // the control predecessors and successors of this HLO instruction.
+  // Clones the HLO instruction as above but with new shape and operands.
   std::unique_ptr CloneWithNewOperands(
       const Shape& shape, tensorflow::gtl::ArraySlice operands,
-      HloModule* module = nullptr) const;
+      HloModule* module = nullptr, CloneMap* clone_map = nullptr) const;
 
   // Returns the computations this instruction directly calls (if any).
   const std::vector& called_computations() const {
@@ -1262,6 +1281,19 @@ class HloInstruction {
   // if no id has been assigned yet).
   int unique_id() const { return unique_id_; }
 
+  // Returns the backend-specific configuration for how a backend should compile
+  // this HLO. The meaning of the field is backend specific. Not for use before
+  // or during general HLO optimization, since HLO optimizations do not preserve
+  // this field and they cannot interpret it due to its meaning being backend
+  // specific.
+  //
+  // TODO(b/78194644): Introduce structured configuration format as per
+  // go/xla-heuristics.
+  const string& backend_config() const { return backend_config_; }
+  void set_backend_config(string backend_config) {
+    backend_config_ = std::move(backend_config);
+  }
+
   // Sets the debug metadata for this instruction.
   void set_metadata(const OpMetadata& metadata) { metadata_ = metadata; }
   const OpMetadata& metadata() const { return metadata_; }
@@ -1283,6 +1315,7 @@ class HloInstruction {
   // Get/Set the number of partitions per outer dimension (in order, starting
   // with outer-most dimension first). Currently used by the parallel cpu
   // backend to partition HLOs into parallel tasks.
+  //
   // TODO(b/62783254) Replace these methods with a more general way to
   // annotate HLOs with backend-specific information.
   const std::vector& outer_dimension_partitions() const {
@@ -1510,6 +1543,10 @@ class HloInstruction {
   // The string representation of the infeed configuration.
   string infeed_config_;
 
+  // The backend-specific configuration for how a backend should compile this
+  // HLO. See the documentation on backend_config().
+  string backend_config_;
+
   // String identifier for instruction.
   string name_;
 
diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc
index c7a71928675391..5308fb5848341b 100644
--- a/tensorflow/compiler/xla/service/hlo_module.cc
+++ b/tensorflow/compiler/xla/service/hlo_module.cc
@@ -46,6 +46,18 @@ HloModule::HloModule(const string& name, const HloModuleConfig& config)
       config_(config),
       unique_id_(next_unique_module_id_++) {}
 
+StatusOr HloModule::LaunderConstInstructionFromModule(
+    const HloInstruction* hlo) {
+  if (hlo == nullptr) {
+    return nullptr;
+  }
+
+  TF_RET_CHECK(hlo->GetModule() == this);
+
+  // TODO(b/78350259): Eliminate const laundering.
+  return const_cast(hlo);
+}
+
 HloComputation* HloModule::AddComputationInternal(
     std::unique_ptr computation, bool is_entry,
     bool uniquify_names) {
diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h
index f9674df812dbbc..1604a7261240e5 100644
--- a/tensorflow/compiler/xla/service/hlo_module.h
+++ b/tensorflow/compiler/xla/service/hlo_module.h
@@ -217,6 +217,25 @@ class HloModule {
   // the lifetime of this process.
   int unique_id() const { return unique_id_; }
 
+  // Returns a non-const version of the passed-in const HloInstruction*. This is
+  // safe on the argument that if you have a non-const module, then you can
+  // access all instructions in the module as non-const.
+  //
+  // Returns an error if the passed-in instruction is not from this module,
+  // except that it is allowed to pass in a null pointer.
+  //
+  // TODO(b/78350259): Eliminate const laundering. The argument above is not
+  // reliable since at any time someone could add or discover a way for a
+  // non-const module to transitively contain a const HloInstruction. The
+  // reliable way to do this would be to create a const laundering map from a
+  // module, mapping each encountered HloInstruction to its non-const version
+  // and then look up each instruction in need of laundering in that map, but
+  // this is much more expensive and complicated. This returns a Status instead
+  // of doing a CHECK-failure in part to make it strongly apparent that this is
+  // something that can fail.
+  StatusOr LaunderConstInstructionFromModule(
+      const HloInstruction* hlo);
+
  private:
   HloComputation* AddComputationInternal(
       std::unique_ptr computation, bool is_entry,
diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc
index 8a30cbf9cd622f..096ebb7946e08b 100644
--- a/tensorflow/compiler/xla/service/hlo_verifier.cc
+++ b/tensorflow/compiler/xla/service/hlo_verifier.cc
@@ -116,7 +116,7 @@ Status ShapeVerifier::HandleOutfeed(HloInstruction* outfeed) {
   // produces no HLO value in the graph.
   if (!ShapeUtil::Compatible(outfeed->outfeed_shape(),
                              outfeed->operand(0)->shape())) {
-    return InvalidArgument(
+    return InternalError(
         "Expected outfeed to have shape compatible with operand's shape %s, "
         "actual shape is %s:\n%s",
         ShapeUtil::HumanString(outfeed->operand(0)->shape()).c_str(),
@@ -200,7 +200,7 @@ Status ShapeVerifier::HandleTranspose(HloInstruction* transpose) {
                      transpose->operand(0)->shape(), transpose->dimensions()));
 }
 
-Status ShapeVerifier::HandleParameter(HloInstruction*) {
+Status ShapeVerifier::HandleParameter(HloInstruction* hlo) {
   return tensorflow::Status::OK();
 }
 
@@ -410,7 +410,7 @@ Status CheckMixedPrecisionOperands(const HloInstruction* instruction) {
               if (fp_type == PRIMITIVE_TYPE_INVALID) {
                 fp_type = subshape.element_type();
               } else if (fp_type != subshape.element_type()) {
-                return FailedPrecondition(
+                return InternalError(
                     "Seen floating point types of different precisions in "
                     "%s, but mixed precision is disallowed.",
                     instruction->ToString().c_str());
@@ -490,7 +490,7 @@ Status ShapeVerifier::CheckShape(const HloInstruction* instruction,
       }
   }
   if (!compatible) {
-    return InvalidArgument(
+    return InternalError(
         "Expected instruction to have shape compatible with %s, actual "
         "shape is %s:\n%s",
         ShapeUtil::HumanString(inferred_shape).c_str(),
@@ -541,7 +541,7 @@ Status ShapeVerifier::CheckVariadicShape(const HloInstruction* instruction) {
 Status ShapeVerifier::CheckSameChannel(const HloInstruction* instr1,
                                        const HloInstruction* instr2) {
   if (instr1->channel_id() != instr2->channel_id()) {
-    return FailedPrecondition(
+    return InternalError(
         "Expected to have the same channel id, actual channel ids are: %s "
         "(%lld), %s (%lld)",
         instr1->ToString().c_str(), instr1->channel_id(),
@@ -571,22 +571,22 @@ string ComputationsToString(
 Status VerifyHloStructure(HloModule* module) {
   for (const HloComputation* computation : module->computations()) {
     if (computation->parent() == nullptr) {
-      return FailedPrecondition("Computation %s has a null parent pointer",
-                                computation->name().c_str());
+      return InternalError("Computation %s has a null parent pointer",
+                           computation->name().c_str());
     }
     if (computation->parent() != module) {
-      return FailedPrecondition(
+      return InternalError(
           "Computation %s parent() does not point to parent module",
           computation->name().c_str());
     }
 
     for (const HloInstruction* instruction : computation->instructions()) {
       if (instruction->parent() == nullptr) {
-        return FailedPrecondition("Instruction %s has a null parent pointer",
-                                  instruction->name().c_str());
+        return InternalError("Instruction %s has a null parent pointer",
+                             instruction->name().c_str());
       }
       if (instruction->parent() != computation) {
-        return FailedPrecondition(
+        return InternalError(
             "Instruction %s parent() does not point to parent computation",
             instruction->name().c_str());
       }
@@ -602,7 +602,7 @@ Status VerifyHloStructure(HloModule* module) {
       for (int i = 0; i < instruction->operand_count(); ++i) {
         const HloInstruction* operand = instruction->operand(i);
         if (operand->parent() != instruction->parent()) {
-          return FailedPrecondition(
+          return InternalError(
               "Operand %d (%s) of instruction %s is in a different "
               "computation: %s vs %s",
               i, operand->name().c_str(), instruction->name().c_str(),
@@ -619,7 +619,7 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const {
   // The parent fusion instruction of the fusion computation must be 'fusion'.
   HloComputation* fused_computation = fusion->fused_instructions_computation();
   if (fusion != fused_computation->FusionInstruction()) {
-    return FailedPrecondition(
+    return InternalError(
         "Instruction of fused computation does not match expected instruction "
         "%s.",
         fusion->ToString().c_str());
@@ -635,37 +635,37 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const {
   for (auto* instruction : fused_computation->instructions()) {
     if (fused_root == instruction) {
       if (root_owned) {
-        return FailedPrecondition("Root appears more than once in %s.",
-                                  fusion->ToString().c_str());
+        return InternalError("Root appears more than once in %s.",
+                             fusion->ToString().c_str());
       }
       root_owned = true;
     }
     for (int i = 0; i < fused_parameters.size(); ++i) {
       if (fused_parameters[i] == instruction) {
         if (parameter_owned[i]) {
-          return FailedPrecondition("Parameter appears more than once in %s.",
-                                    fusion->ToString().c_str());
+          return InternalError("Parameter appears more than once in %s.",
+                               fusion->ToString().c_str());
         }
         parameter_owned[i] = true;
       }
     }
   }
   if (!root_owned) {
-    return FailedPrecondition("Root not found in computation of %s.",
-                              fusion->ToString().c_str());
+    return InternalError("Root not found in computation of %s.",
+                         fusion->ToString().c_str());
   }
   // Make sure all the parameter_owned entries are set
   for (int i = 0; i < parameter_owned.size(); i++) {
     if (!parameter_owned[i]) {
-      return FailedPrecondition("Parameter %d not found in computation of %s.",
-                                i, fusion->ToString().c_str());
+      return InternalError("Parameter %d not found in computation of %s.", i,
+                           fusion->ToString().c_str());
     }
   }
 
   // Fused root must have no users.
   if (fused_root->user_count() != 0) {
-    return FailedPrecondition("Root of %s may not have users.",
-                              fusion->ToString().c_str());
+    return InternalError("Root of %s may not have users.",
+                         fusion->ToString().c_str());
   }
 
   // All uses of fused instructions must be in the fusion computation, and every
@@ -674,13 +674,13 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const {
        fusion->fused_instructions_computation()->instructions()) {
     if (instruction != fused_root) {
       if (instruction->user_count() == 0) {
-        return FailedPrecondition(
-            "Non-root instruction %s in %s must have users.",
-            instruction->ToString().c_str(), fusion->ToString().c_str());
+        return InternalError("Non-root instruction %s in %s must have users.",
+                             instruction->ToString().c_str(),
+                             fusion->ToString().c_str());
       }
       for (auto& user : instruction->users()) {
         if (fused_computation != user->parent()) {
-          return FailedPrecondition(
+          return InternalError(
               "Non-root instruction %s in %s may not have external users.",
               instruction->ToString().c_str(), fusion->ToString().c_str());
         }
@@ -695,34 +695,33 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const {
   for (auto fused_param : fused_parameters) {
     int64 param_no = fused_param->parameter_number();
     if (param_no < 0) {
-      return FailedPrecondition(
-          "Unexpected negative parameter number %lld in %s.", param_no,
-          fusion->ToString().c_str());
+      return InternalError("Unexpected negative parameter number %lld in %s.",
+                           param_no, fusion->ToString().c_str());
     }
     if (param_no >= fused_parameters.size()) {
-      return FailedPrecondition(
+      return InternalError(
           "Unexpected parameter number %lld in %s: higher then number of "
           "parameters %lu.",
           param_no, fusion->ToString().c_str(), fused_parameters.size());
     }
     if (parameter_numbers[param_no]) {
-      return FailedPrecondition(
+      return InternalError(
           "Did not expect parameter number %lld more than once in %s.",
           param_no, fusion->ToString().c_str());
     }
     parameter_numbers[param_no] = true;
     if (!ShapeUtil::Compatible(fused_param->shape(),
                                fusion->operand(param_no)->shape())) {
-      return FailedPrecondition(
+      return InternalError(
           "Shape mismatch between parameter number %lld and its operand in %s.",
           param_no, fusion->ToString().c_str());
     }
   }
-  // Make sure all the parameter_numbers entries were seen
+  // Make sure all the parameter_numbers entries were seen.
   for (int i = 0; i < parameter_numbers.size(); i++) {
     if (!parameter_numbers[i]) {
-      return FailedPrecondition("Did not see parameter number %d in %s.", i,
-                                fusion->ToString().c_str());
+      return InternalError("Did not see parameter number %d in %s.", i,
+                           fusion->ToString().c_str());
     }
   }
 
diff --git a/tensorflow/compiler/xla/statusor.h b/tensorflow/compiler/xla/statusor.h
index cccbce5fc83af8..0e1387c93938fa 100644
--- a/tensorflow/compiler/xla/statusor.h
+++ b/tensorflow/compiler/xla/statusor.h
@@ -13,13 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-// StatusOr is the union of a Status object and a T
-// object. StatusOr models the concept of an object that is either a
-// usable value, or an error Status explaining why such a value is
-// not present. To this end, StatusOr does not allow its Status
-// value to be Status::OK. Furthermore, the value of a StatusOr
-// must not be null. This is enforced by a debug check in most cases,
-// but even when it is not, clients must not set the value to null.
+// StatusOr is the union of a Status object and a T object. StatusOr models
+// the concept of an object that is either a value, or an error Status
+// explaining why such a value is not present. To this end, StatusOr does not
+// allow its Status value to be Status::OK.
 //
 // The primary use-case for StatusOr is as the return value of a
 // function which may fail.
diff --git a/tensorflow/compiler/xla/statusor_test.cc b/tensorflow/compiler/xla/statusor_test.cc
index f9d25945bc6175..7d76370e85d57f 100644
--- a/tensorflow/compiler/xla/statusor_test.cc
+++ b/tensorflow/compiler/xla/statusor_test.cc
@@ -75,6 +75,14 @@ TEST(StatusOr, ElementType) {
   static_assert(std::is_same::element_type, char>(), "");
 }
 
+TEST(StatusOr, NullPointerStatusOr) {
+  // As a very special case, null-plain-pointer StatusOr used to be an
+  // error. Test that it no longer is.
+  StatusOr null_status(nullptr);
+  EXPECT_TRUE(null_status.ok());
+  EXPECT_EQ(null_status.ValueOrDie(), nullptr);
+}
+
 TEST(StatusOr, TestNoDefaultConstructorInitialization) {
   // Explicitly initialize it with an error code.
   StatusOr statusor(tensorflow::errors::Cancelled(""));
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
index 40dc0730ce25ea..156a06c596c3f1 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
@@ -440,6 +440,10 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder,
   optional metadata;
   attrs["metadata"] = {/*required=*/false, AttrTy::kMetadata, &metadata};
 
+  optional backend_config;
+  attrs["backend_config"] = {/*required=*/false, AttrTy::kString,
+                             &backend_config};
+
   HloInstruction* instruction;
   switch (opcode) {
     case HloOpcode::kParameter: {
@@ -1094,8 +1098,7 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder,
 
   instruction->set_name(name);
 
-  // Add common attrs (sharding, control predecessors) to the instruction, if
-  // they were seen.
+  // Add shared attributes like metadata to the instruction, if they were seen.
   if (sharding) {
     instruction->set_sharding(
         HloSharding::FromProto(sharding.value()).ValueOrDie());
@@ -1112,6 +1115,9 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder,
   if (metadata) {
     instruction->set_metadata(*metadata);
   }
+  if (backend_config) {
+    instruction->set_backend_config(std::move(*backend_config));
+  }
   return AddInstruction(name, instruction, name_loc);
 }  // NOLINT(readability/fn_size)
 
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
index d38d8907a60538..e100d8cda14eab 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
@@ -65,7 +65,7 @@ ENTRY %axpy.v5 (alpha: f32[], x: f32[2,4], y: f32[2,4]) -> f32[2,4] {
 R"(HloModule constant_pred_module
 
 ENTRY %constant_pred () -> pred[] {
-  ROOT %constant = pred[] constant(true), metadata={op_type="const" op_name="\"it\'s not a problem\n" source_file="path/to/test.cc" source_line=68}
+  ROOT %constant = pred[] constant(true), metadata={op_type="const" op_name="\"it\'s not a problem\n" source_file="path/to/test.cc" source_line=68}, backend_config="foo\" bar"
 }
 
 )"
@@ -81,13 +81,14 @@ ENTRY %constant_s32 () -> s32[] {
 
 )"
 },
-// f32 constant, but the value is not a decimal
+// f32 constant, but the value is not a decimal and there is a backend
+// configuration
 {
 "ConstantF32",
 R"(HloModule ConstantF32_module
 
 ENTRY %ConstantF32.v4 () -> f32[] {
-  ROOT %constant = f32[] constant(42)
+  ROOT %constant = f32[] constant(42), backend_config="this is a configuration"
 }
 
 )"
@@ -1013,6 +1014,19 @@ ENTRY %SelectScalarS32True.v4 () -> s32[] {
   // but the constant names will not be exactly the same.
 }
 
+TEST_F(HloParserTest, ConfigurationField) {
+  const string original = R"(HloModule AModule
+ENTRY %configuration_test() -> s32[] {
+  %constant = s32[] constant(42), backend_config="foo bar"
+})";
+  auto result = Parse(original);
+  TF_ASSERT_OK(result.status());
+  EXPECT_EQ("foo bar", result.ValueOrDie()
+                           ->entry_computation()
+                           ->root_instruction()
+                           ->backend_config());
+}
+
 TEST_F(HloParserTest, LiteralDimensionsMismatch_1) {
   const string original = R"(HloModule some_2_module
 
@@ -1092,7 +1106,7 @@ ENTRY %Convolve1D1Window_0.v3 (input: f32[1,2,1], filter: f32[1,1,1]) -> f32[1,2
   %input = f32[1,2,1]{2,1,0} parameter(0)
   %copy = f32[1,2,1]{2,0,1} copy(f32[1,2,1]{2,1,0} %input)
   %filter = f32[1,1,1]{2,1,0} parameter(1)
-  ROOT %convolution = f32[1,2,1]{2,0,1} convolution(f32[1,2,1]{2,0,1} %copy, f32[1,1,1]{2,1,0} %filter), sharding={maximal device=1}, dim_labels=b0f_0io->b0f, window={pad=1_1 size=2}
+  ROOT %convolution = f32[1,2,1]{2,0,1} convolution(f32[1,2,1]{2,0,1} %copy, f32[1,1,1]{2,1,0} %filter), sharding={maximal device=1}, backend_config="foo", dim_labels=b0f_0io->b0f, window={pad=1_1 size=2}
 }
 
 )";

From bf228e1435da0032d2529de93661b742ee8a7048 Mon Sep 17 00:00:00 2001
From: Jiri Simsa 
Date: Fri, 4 May 2018 17:03:52 -0700
Subject: [PATCH 0411/1691] [tf.data] Adding `num_parallel_calls` to
 `map_and_batch`.

PiperOrigin-RevId: 195495206
---
 .../kernel_tests/batch_dataset_op_test.py     |  44 +-
 .../contrib/data/python/ops/batching.py       |  47 +-
 .../base_api/api_def_MapAndBatchDataset.pbtxt |  35 +-
 .../api_def_MapAndBatchDatasetV2.pbtxt        |  54 ++
 .../kernels/data/map_and_batch_dataset_op.cc  | 773 +++++++++---------
 tensorflow/core/ops/dataset_ops.cc            |  13 +
 6 files changed, 538 insertions(+), 428 deletions(-)
 create mode 100644 tensorflow/core/api_def/base_api/api_def_MapAndBatchDatasetV2.pbtxt

diff --git a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py
index 6588fd04acb027..2568b899d7ea1b 100644
--- a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py
@@ -427,7 +427,9 @@ def testBatchAndDropRemainderShapeInference(self):
     self.assertEqual([None], dataset.output_shapes[1][0].as_list())
     self.assertEqual([None, 30], dataset.output_shapes[1][1].as_list())
 
-  def _testMapAndBatchDatasetHelper(self, num_parallel_batches=1):
+  def _testMapAndBatchDatasetHelper(self,
+                                    num_parallel_calls=None,
+                                    num_parallel_batches=None):
     """Test a dataset that maps a TF function across its input elements."""
     # The pipeline is TensorSliceDataset ->
     # RepeatDataset(count) -> MapAndBatchDataset(square_3, batch_size).
@@ -446,6 +448,7 @@ def _map_fn(x, y, z):
             batching.map_and_batch(
                 map_func=_map_fn,
                 batch_size=batch_size,
+                num_parallel_calls=num_parallel_calls,
                 num_parallel_batches=num_parallel_batches))
         .make_initializable_iterator())
     init_op = iterator.initializer
@@ -497,12 +500,18 @@ def _map_fn(x, y, z):
       with self.assertRaises(errors.InvalidArgumentError):
         sess.run(init_op, feed_dict={count: 14, batch_size: 0})
 
-  def testMapAndBatchDataset(self):
+  def testMapAndBatch(self):
     return self._testMapAndBatchDatasetHelper()
 
-  def testMapAndBatchDatasetWithParallelBatching(self):
+  def testMapAndBatchWithParallelBatches(self):
     return self._testMapAndBatchDatasetHelper(num_parallel_batches=10)
 
+  def testMapAndBatchWithSequentialCalls(self):
+    return self._testMapAndBatchDatasetHelper(num_parallel_calls=1)
+
+  def testMapAndBatchWithParallelCalls(self):
+    return self._testMapAndBatchDatasetHelper(num_parallel_calls=2)
+
   def _testMapAndBatchPartialBatchHelper(self, drop_remainder=False):
     iterator = (
         dataset_ops.Dataset.range(10).apply(
@@ -682,7 +691,7 @@ def testCore(self):
 class MapAndBatchDatasetSerializationTest(
     dataset_serialization_test_base.DatasetSerializationTestBase):
 
-  def testSerializationCore(self):
+  def testNumParallelBatches(self):
     range_size = 11
     num_repeats = 2
     batch_size = 5
@@ -709,6 +718,33 @@ def _map_fn(x):
     self.run_core_tests(lambda: build_ds(10, True), lambda: build_ds(15, True),
                         num_outputs_drop_remainder)
 
+  def testNumParallelCalls(self):
+    range_size = 11
+    num_repeats = 2
+    batch_size = 5
+    total_outputs = range_size * num_repeats
+    num_outputs_drop_remainder = total_outputs // batch_size
+    num_outputs_keep_remainder = int(math.ceil(total_outputs / batch_size))
+    num_parallel_calls = 7
+
+    def build_ds(range_start, drop_remainder=False):
+
+      def _map_fn(x):
+        return math_ops.square(x)
+
+      return dataset_ops.Dataset.range(
+          range_start, range_start + range_size).repeat(num_repeats).apply(
+              batching.map_and_batch(
+                  map_func=_map_fn,
+                  batch_size=batch_size,
+                  num_parallel_calls=num_parallel_calls,
+                  drop_remainder=drop_remainder))
+
+    self.run_core_tests(lambda: build_ds(10), lambda: build_ds(15),
+                        num_outputs_keep_remainder)
+    self.run_core_tests(lambda: build_ds(10, True), lambda: build_ds(15, True),
+                        num_outputs_drop_remainder)
+
 
 class PaddedBatchDatasetSerializationTest(
     dataset_serialization_test_base.DatasetSerializationTestBase):
diff --git a/tensorflow/contrib/data/python/ops/batching.py b/tensorflow/contrib/data/python/ops/batching.py
index 42ec2b0b017973..b9393de4e90ae2 100644
--- a/tensorflow/contrib/data/python/ops/batching.py
+++ b/tensorflow/contrib/data/python/ops/batching.py
@@ -466,14 +466,14 @@ def _apply_fn(dataset):
 class _MapAndBatchDataset(dataset_ops.MapDataset):
   """A `Dataset` that maps a function over a batch of elements."""
 
-  def __init__(self, input_dataset, map_func, batch_size, num_parallel_batches,
+  def __init__(self, input_dataset, map_func, batch_size, num_parallel_calls,
                drop_remainder):
     """See `Dataset.map()` for details."""
     super(_MapAndBatchDataset, self).__init__(input_dataset, map_func)
     self._batch_size_t = ops.convert_to_tensor(
         batch_size, dtype=dtypes.int64, name="batch_size")
-    self._num_parallel_batches_t = ops.convert_to_tensor(
-        num_parallel_batches, dtype=dtypes.int64, name="num_parallel_batches")
+    self._num_parallel_calls_t = ops.convert_to_tensor(
+        num_parallel_calls, dtype=dtypes.int64, name="num_parallel_calls")
     self._drop_remainder_t = ops.convert_to_tensor(
         drop_remainder, dtype=dtypes.bool, name="drop_remainder")
 
@@ -483,12 +483,12 @@ def __init__(self, input_dataset, map_func, batch_size, num_parallel_batches,
   def _as_variant_tensor(self):
     # pylint: disable=protected-access
     input_resource = self._input_dataset._as_variant_tensor()
-    return gen_dataset_ops.map_and_batch_dataset(
+    return gen_dataset_ops.map_and_batch_dataset_v2(
         input_resource,
         self._map_func.captured_inputs,
         f=self._map_func,
         batch_size=self._batch_size_t,
-        num_parallel_batches=self._num_parallel_batches_t,
+        num_parallel_calls=self._num_parallel_calls_t,
         drop_remainder=self._drop_remainder_t,
         output_types=nest.flatten(
             sparse.as_dense_types(self.output_types, self.output_classes)),
@@ -511,8 +511,9 @@ def output_types(self):
 
 def map_and_batch(map_func,
                   batch_size,
-                  num_parallel_batches=1,
-                  drop_remainder=False):
+                  num_parallel_batches=None,
+                  drop_remainder=False,
+                  num_parallel_calls=None):
   """Fused implementation of `map` and `batch`.
 
   Maps `map_func` across `batch_size` consecutive elements of this dataset
@@ -528,21 +529,37 @@ def map_and_batch(map_func,
       nested structure of tensors.
     batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
       consecutive elements of this dataset to combine in a single batch.
-    num_parallel_batches: A `tf.int64` scalar `tf.Tensor`, representing the
-      number of batches to create in parallel. On one hand, higher values can
-      help mitigate the effect of stragglers. On the other hand, higher values
-      can increase contention if CPU is scarce.
-    drop_remainder: A `tf.bool` scalar `tf.Tensor`, representing whether the
-      last batch should be dropped in case its size is smaller than desired;
-      the default behavior is not to drop the smaller batch.
+    num_parallel_batches: (Optional.) A `tf.int64` scalar `tf.Tensor`,
+      representing the number of batches to create in parallel. On one hand,
+      higher values can help mitigate the effect of stragglers. On the other
+      hand, higher values can increase contention if CPU is scarce.
+    drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing
+      whether the last batch should be dropped in case its size is smaller than
+      desired; the default behavior is not to drop the smaller batch.
+    num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`,
+        representing the number of elements to process in parallel. If not
+        specified, `batch_size * num_parallel_batches` elements will be
+        processed in parallel.
 
   Returns:
     A `Dataset` transformation function, which can be passed to
     @{tf.data.Dataset.apply}.
+
+  Raises:
+    ValueError: If both `num_parallel_batches` and `num_parallel_calls` are
+      specified.
   """
 
+  if num_parallel_batches is None and num_parallel_calls is None:
+    num_parallel_calls = batch_size
+  elif num_parallel_batches is not None and num_parallel_calls is None:
+    num_parallel_calls = batch_size * num_parallel_batches
+  elif num_parallel_batches is not None and num_parallel_calls is not None:
+    raise ValueError("The `num_parallel_batches` and `num_parallel_calls` "
+                     "arguments are mutually exclusive.")
+
   def _apply_fn(dataset):
     return _MapAndBatchDataset(dataset, map_func, batch_size,
-                               num_parallel_batches, drop_remainder)
+                               num_parallel_calls, drop_remainder)
 
   return _apply_fn
diff --git a/tensorflow/core/api_def/base_api/api_def_MapAndBatchDataset.pbtxt b/tensorflow/core/api_def/base_api/api_def_MapAndBatchDataset.pbtxt
index bf544703de5599..e230c51edfe935 100644
--- a/tensorflow/core/api_def/base_api/api_def_MapAndBatchDataset.pbtxt
+++ b/tensorflow/core/api_def/base_api/api_def_MapAndBatchDataset.pbtxt
@@ -1,5 +1,19 @@
 op {
   graph_op_name: "MapAndBatchDataset"
+  visibility: HIDDEN
+  in_arg {
+    name: "input_dataset"
+    description: <
 Stack Overflow Link Error Message 
 Link to GitHub or Stack Overflow Error Message 

 
 

   36159194 
 
 
 

 

 
-Follow [this link]("https://www.tensorflow.org/images/embedding-mnist.mp4" )
+Follow [this link](https://www.tensorflow.org/images/embedding-mnist.mp4)
 to see a fun example of thumbnail images in the Embedding Projector.
 
 

From b2888c66e67d584756bb50850ae77acede7ba8bf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 10:47:35 -0700
Subject: [PATCH 0440/1691] Add EvaluateNodes to HoistFactorDiv test.

PiperOrigin-RevId: 195685340
---
 .../grappler/optimizers/arithmetic_optimizer_test.cc   | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
index e109e666331675..741cc135a101d0 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
@@ -696,6 +696,9 @@ TEST_F(ArithmeticOptimizerTest, HoistFactorDiv) {
         item.fetch = {"id"};
         TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+        auto tensors_expected = EvaluateNodes(item.graph, item.fetch);
+        EXPECT_EQ(1, tensors_expected.size());
+
         ArithmeticOptimizer optimizer;
         EnableOnlyHoistCommonFactor(&optimizer);
 
@@ -734,6 +737,13 @@ TEST_F(ArithmeticOptimizerTest, HoistFactorDiv) {
           EXPECT_EQ("id", id_node->name());
           EXPECT_EQ(HoistDivName("add"), id_node->input(0));
         }
+        auto tensors = EvaluateNodes(output, item.fetch);
+        EXPECT_EQ(1, tensors.size());
+        if (use_ints) {
+          test::ExpectTensorEqual(tensors_expected[0], tensors[0]);
+        } else {
+          test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
+        }
       }
     }
   }

From 9ba26ca0d59989592051fdb5c7a2caabe4f399f3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 10:49:26 -0700
Subject: [PATCH 0441/1691] Extend block sparsity support for TPUs

PiperOrigin-RevId: 195685740
---
 .../contrib/model_pruning/python/pruning.py   | 30 +++++----
 .../model_pruning/python/pruning_utils.py     | 51 +++++++++++++++
 .../python/pruning_utils_test.py              | 62 ++++++++++++++-----
 3 files changed, 116 insertions(+), 27 deletions(-)

diff --git a/tensorflow/contrib/model_pruning/python/pruning.py b/tensorflow/contrib/model_pruning/python/pruning.py
index ea6032e588cf39..4b7af18b331695 100644
--- a/tensorflow/contrib/model_pruning/python/pruning.py
+++ b/tensorflow/contrib/model_pruning/python/pruning.py
@@ -396,14 +396,19 @@ def _maybe_update_block_mask(self, weights, threshold):
                        self._block_pooling_function)
 
     with ops.name_scope(weights.op.name + '_pruning_ops'):
-      abs_weights = math_ops.abs(
-          array_ops.reshape(weights, [
-              1,
-              squeezed_weights.get_shape()[0],
-              squeezed_weights.get_shape()[1], 1
-          ]))
+      abs_weights = math_ops.abs(squeezed_weights)
+
       pool_window = [self._block_dim[0], self._block_dim[1]]
-      pooled_weights = nn_ops.pool(
+      pool_fn = pruning_utils.factorized_pool
+
+      if not self._spec.use_tpu:
+        pool_fn = nn_ops.pool
+        abs_weights = array_ops.reshape(
+            abs_weights,
+            [1, abs_weights.get_shape()[0],
+             abs_weights.get_shape()[1], 1])
+
+      pooled_weights = pool_fn(
           abs_weights,
           window_shape=pool_window,
           pooling_type=self._block_pooling_function,
@@ -411,19 +416,18 @@ def _maybe_update_block_mask(self, weights, threshold):
           padding='SAME',
           name=weights.op.name + '_pooled')
 
+      if pooled_weights.get_shape().ndims != 2:
+        pooled_weights = array_ops.squeeze(pooled_weights)
+
       smoothed_threshold, new_mask = self._update_mask(pooled_weights,
                                                        threshold)
-
-      reshaped_mask = array_ops.reshape(
-          new_mask,
-          [pooled_weights.get_shape()[1],
-           pooled_weights.get_shape()[2]])
       updated_mask = pruning_utils.kronecker_product(
-          reshaped_mask, array_ops.ones(self._block_dim))
+          new_mask, array_ops.ones(self._block_dim))
       sliced_mask = array_ops.slice(
           updated_mask, [0, 0],
           [squeezed_weights.get_shape()[0],
            squeezed_weights.get_shape()[1]])
+
     return smoothed_threshold, array_ops.reshape(sliced_mask,
                                                  array_ops.shape(weights))
 
diff --git a/tensorflow/contrib/model_pruning/python/pruning_utils.py b/tensorflow/contrib/model_pruning/python/pruning_utils.py
index 56d3dcef20d1b1..ef6c6a3f5d7aa2 100644
--- a/tensorflow/contrib/model_pruning/python/pruning_utils.py
+++ b/tensorflow/contrib/model_pruning/python/pruning_utils.py
@@ -29,6 +29,7 @@
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope
 
@@ -221,6 +222,56 @@ def loop_body(loop_count, cdf):
     return math_ops.div(cdf, math_ops.reduce_max(cdf))
 
 
+def factorized_pool(input_tensor,
+                    window_shape,
+                    pooling_type,
+                    strides,
+                    padding,
+                    name=None):
+  """Performs m x n pooling through a combination of 1xm and 1xn pooling.
+
+  Args:
+    input_tensor: Input tensor. Must be rank 2
+    window_shape: Pooling window shape
+    pooling_type: Either 'MAX' or 'AVG'
+    strides: The stride of the pooling window
+    padding: 'SAME' or 'VALID'.
+    name: Name of the op
+
+  Returns:
+    A rank 2 tensor containing the pooled output
+
+  Raises:
+    ValueError: if the input tensor is not rank 2
+  """
+  if input_tensor.get_shape().ndims != 2:
+    raise ValueError('factorized_pool() accepts tensors of rank 2 only')
+
+  [height, width] = input_tensor.get_shape()
+  with ops.name_scope(name, 'factorized_pool'):
+    input_tensor_aligned = array_ops.reshape(
+        input_tensor, [1, 1, height, width],
+        name=input_tensor.op.name + '_aligned')
+
+    height_pooling = nn_ops.pool(
+        input_tensor_aligned,
+        window_shape=[1, window_shape[0]],
+        pooling_type=pooling_type,
+        strides=[1, strides[0]],
+        padding=padding)
+    swap_height_width = array_ops.transpose(height_pooling, perm=[0, 1, 3, 2])
+
+    width_pooling = nn_ops.pool(
+        swap_height_width,
+        window_shape=[1, window_shape[1]],
+        pooling_type=pooling_type,
+        strides=[1, strides[1]],
+        padding=padding)
+
+  return array_ops.squeeze(
+      array_ops.transpose(width_pooling, perm=[0, 1, 3, 2]))
+
+
 def determine_partitioned_axis(partitioned_variable):
   partitioned_axis = 0
   concatenated_variable_shape = partitioned_variable.get_shape()
diff --git a/tensorflow/contrib/model_pruning/python/pruning_utils_test.py b/tensorflow/contrib/model_pruning/python/pruning_utils_test.py
index 10e1dd0a8eee88..ccde5b4e8a86fc 100644
--- a/tensorflow/contrib/model_pruning/python/pruning_utils_test.py
+++ b/tensorflow/contrib/model_pruning/python/pruning_utils_test.py
@@ -22,8 +22,10 @@
 
 from tensorflow.contrib.model_pruning.python import pruning_utils
 from tensorflow.python.framework import constant_op
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
@@ -31,6 +33,30 @@
 
 class PruningUtilsTest(test.TestCase):
 
+  def _compare_cdf(self, values):
+    abs_values = math_ops.abs(values)
+    max_value = math_ops.reduce_max(abs_values)
+    with self.test_session():
+      variables.global_variables_initializer().run()
+      cdf_from_histogram = pruning_utils.compute_cdf_from_histogram(
+          abs_values, [0.0, max_value], nbins=pruning_utils._NBINS)
+      cdf = pruning_utils.compute_cdf(abs_values, [0.0, max_value])
+      self.assertAllEqual(cdf.eval(), cdf_from_histogram.eval())
+
+  def _compare_pooling_methods(self, weights, pooling_kwargs):
+    with self.test_session():
+      variables.global_variables_initializer().run()
+      pooled_weights_tf = array_ops.squeeze(
+          nn_ops.pool(
+              array_ops.reshape(
+                  weights,
+                  [1, weights.get_shape()[0],
+                   weights.get_shape()[1], 1]), **pooling_kwargs))
+      pooled_weights_factorized_pool = pruning_utils.factorized_pool(
+          weights, **pooling_kwargs)
+      self.assertAllClose(pooled_weights_tf.eval(),
+                          pooled_weights_factorized_pool.eval())
+
   def testHistogram(self):
     width = 10
     height = 10
@@ -59,27 +85,35 @@ def testCDF(self):
       self.assertAllEqual(len(norm_cdf_val), nbins)
       self.assertAllEqual(expected_cdf, norm_cdf_val)
 
-  def _compare_cdf(self, values):
-    abs_values = math_ops.abs(values)
-    max_value = math_ops.reduce_max(abs_values)
-    with self.test_session():
-      variables.global_variables_initializer().run()
-      cdf_from_histogram = pruning_utils.compute_cdf_from_histogram(
-          abs_values, [0.0, max_value], nbins=pruning_utils._NBINS)
-      cdf = pruning_utils.compute_cdf(abs_values, [0.0, max_value])
-      return cdf.eval(), cdf_from_histogram.eval()
-
   def testCDFEquivalence2D(self):
     width = 100
     height = 100
     weights = variable_scope.get_variable("weights", shape=[width, height])
-    cdf_val, cdf_from_histogram_val = self._compare_cdf(weights)
-    self.assertAllEqual(cdf_val, cdf_from_histogram_val)
+    self._compare_cdf(weights)
 
   def testCDFEquivalence4D(self):
     weights = variable_scope.get_variable("weights", shape=[5, 5, 128, 128])
-    cdf_val, cdf_from_histogram_val = self._compare_cdf(weights)
-    self.assertAllEqual(cdf_val, cdf_from_histogram_val)
+    self._compare_cdf(weights)
+
+  def testFactorizedAvgPool(self):
+    weights = variable_scope.get_variable("weights", shape=[1024, 2048])
+    pooling_kwargs = {
+        "window_shape": [2, 4],
+        "pooling_type": "AVG",
+        "strides": [2, 4],
+        "padding": "SAME"
+    }
+    self._compare_pooling_methods(weights, pooling_kwargs)
+
+  def testFactorizedMaxPool(self):
+    weights = variable_scope.get_variable("weights", shape=[1024, 2048])
+    pooling_kwargs = {
+        "window_shape": [2, 4],
+        "pooling_type": "MAX",
+        "strides": [2, 4],
+        "padding": "SAME"
+    }
+    self._compare_pooling_methods(weights, pooling_kwargs)
 
 
 if __name__ == "__main__":

From 170634d5a10a94d3bd12cc794c284eafcf47fa54 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 11:05:56 -0700
Subject: [PATCH 0442/1691] Replaced calls to tensorflow::StringPiece::ToString
 with std::string conversions. That is, instances of sp.ToString() are
 replaced with std::string(sp).

This will allow tensorflow::StringPiece::ToString to be removed, which is necessary before it can be replaced with absl::string_view.

PiperOrigin-RevId: 195689392
---
 .../compiler/xla/service/hlo_creation_utils.cc  |  2 +-
 .../compiler/xla/service/hlo_graph_dumper.cc    |  2 +-
 .../compiler/xla/service/hlo_instruction.cc     |  6 +++---
 .../compiler/xla/service/hlo_instruction.h      |  2 +-
 .../compiler/xla/service/hlo_pass_pipeline.cc   | 10 +++++-----
 .../service/human_readable_profile_builder.h    |  9 +++++----
 tensorflow/compiler/xla/service/name_uniquer.cc |  2 +-
 .../compiler/xla/service/shape_inference.cc     |  4 ++--
 tensorflow/core/framework/function.cc           |  2 +-
 tensorflow/core/framework/node_def_builder.cc   | 17 +++++++++--------
 tensorflow/core/framework/node_def_util.cc      |  6 +++---
 tensorflow/core/framework/op_def_builder.cc     |  4 ++--
 tensorflow/core/framework/op_gen_lib.cc         |  2 +-
 tensorflow/core/framework/op_kernel.cc          |  2 +-
 .../core/framework/shape_inference_testutil.h   |  2 +-
 tensorflow/core/graph/graph.cc                  |  2 +-
 tensorflow/core/graph/graph_constructor.cc      | 10 +++++-----
 tensorflow/core/graph/graph_constructor_test.cc |  2 +-
 tensorflow/core/graph/graph_def_builder.cc      |  4 ++--
 tensorflow/core/graph/graph_def_builder.h       |  2 +-
 tensorflow/core/graph/graph_partition.cc        |  2 +-
 tensorflow/core/graph/node_builder.cc           |  2 +-
 tensorflow/core/graph/while_context.cc          |  2 +-
 23 files changed, 50 insertions(+), 48 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_creation_utils.cc b/tensorflow/compiler/xla/service/hlo_creation_utils.cc
index 9a89888480b8c7..ed3b654851ab93 100644
--- a/tensorflow/compiler/xla/service/hlo_creation_utils.cc
+++ b/tensorflow/compiler/xla/service/hlo_creation_utils.cc
@@ -269,7 +269,7 @@ StatusOr BroadcastZeros(
 StatusOr> CreateComputationWithSignature(
     ArraySlice domain, const Shape& range,
     tensorflow::StringPiece name) {
-  HloComputation::Builder b(name.ToString());
+  HloComputation::Builder b{std::string(name)};
   int64 param_idx = 0;
   for (const Shape* param_shape : domain) {
     b.AddInstruction(HloInstruction::CreateParameter(
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 794f1b46829206..b6b03876725e4d 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -325,7 +325,7 @@ class HloDotDumper {
                bool show_backend_config, const HloExecutionProfile* profile,
                NodeFilter filter)
       : computation_(computation),
-        label_(label.ToString()),
+        label_(std::string(label)),
         debug_options_(debug_options),
         show_metadata_(show_metadata),
         show_backend_config_(show_backend_config),
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 2c733726a6f68b..f9189077a1b0fc 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -438,7 +438,7 @@ HloInstruction::CreateCrossReplicaSum(
       << "Outfeed shape " << shape << " must be compatible with operand shape "
       << operand->shape();
   instruction->AppendOperand(operand);
-  instruction->outfeed_config_ = outfeed_config.ToString();
+  instruction->outfeed_config_ = std::string(outfeed_config);
   instruction->outfeed_shape_ = shape;
   return instruction;
 }
@@ -1168,7 +1168,7 @@ bool HloInstruction::HasSideEffect() const {
   for (auto operand : operands) {
     instruction->AppendOperand(operand);
   }
-  instruction->custom_call_target_ = custom_call_target.ToString();
+  instruction->custom_call_target_ = std::string(custom_call_target);
   return instruction;
 }
 
@@ -1180,7 +1180,7 @@ bool HloInstruction::HasSideEffect() const {
   for (auto operand : operands) {
     instruction->AppendOperand(operand);
   }
-  instruction->channel_name_ = channel_name.ToString();
+  instruction->channel_name_ = std::string(channel_name);
   instruction->cost_estimate_ns_ = cost_estimate_ns;
   return instruction;
 }
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 19c8c1145317c6..0bf2c589e4bb2f 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -1264,7 +1264,7 @@ class HloInstruction {
 
   // Gets/sets the string identifier for this instruction.
   const string& name() const { return name_; }
-  void set_name(tensorflow::StringPiece name) { name_ = name.ToString(); }
+  void set_name(tensorflow::StringPiece name) { name_ = std::string(name); }
 
   // Use the given NameUniquer to select a unique name for the instruction based
   // on the instruction's existing name.
diff --git a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc
index 5120775737bfa3..d8f1ab916b5c5c 100644
--- a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc
+++ b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc
@@ -90,7 +90,7 @@ StatusOr HloPassPipeline::Run(HloModule* module) {
     return Status::OK();
   };
 
-  string prefix = name().ToString() + ": pipeline start";
+  string prefix = std::string(name()) + ": pipeline start";
   bool changed = false;
   string message;
   TF_RETURN_IF_ERROR(
@@ -98,12 +98,12 @@ StatusOr HloPassPipeline::Run(HloModule* module) {
   const string xla_dump_per_pass_hlo_proto_to =
       module->config().debug_options().xla_dump_per_pass_hlo_proto_to();
   if (!xla_dump_per_pass_hlo_proto_to.empty()) {
-    DumpModuleProto(*module, xla_dump_per_pass_hlo_proto_to, name().ToString(),
-                    "pipeline_start");
+    DumpModuleProto(*module, xla_dump_per_pass_hlo_proto_to,
+                    std::string(name()), "pipeline_start");
   }
 
   for (auto& pass : passes_) {
-    if (disabled_passes.count(pass->name().ToString()) > 0) {
+    if (disabled_passes.count(std::string(pass->name())) > 0) {
       VLOG(1) << "  Skipping HLO pass " << pass->name()
               << ", disabled by --xla_disable_hlo_passes";
       continue;
@@ -121,7 +121,7 @@ StatusOr HloPassPipeline::Run(HloModule* module) {
         run_invariant_checkers(StrCat("after running pass: ", pass->name())));
     if (!xla_dump_per_pass_hlo_proto_to.empty()) {
       DumpModuleProto(*module, xla_dump_per_pass_hlo_proto_to,
-                      name().ToString(), pass->name().ToString());
+                      std::string(name()), std::string(pass->name()));
     }
 
     changed |= changed_this_pass;
diff --git a/tensorflow/compiler/xla/service/human_readable_profile_builder.h b/tensorflow/compiler/xla/service/human_readable_profile_builder.h
index fc24acd2713f4c..fb36d3a0d6532b 100644
--- a/tensorflow/compiler/xla/service/human_readable_profile_builder.h
+++ b/tensorflow/compiler/xla/service/human_readable_profile_builder.h
@@ -32,7 +32,7 @@ class HumanReadableProfileBuilder {
   explicit HumanReadableProfileBuilder(tensorflow::StringPiece computation_name,
                                        int64 total_cycles,
                                        double clock_rate_ghz)
-      : computation_name_(computation_name.ToString()),
+      : computation_name_(std::string(computation_name)),
         total_cycles_(total_cycles),
         clock_rate_ghz_(clock_rate_ghz) {
     CHECK_GE(clock_rate_ghz, 1e-9);
@@ -47,9 +47,10 @@ class HumanReadableProfileBuilder {
              tensorflow::StringPiece category, int64 cycles, int64 flop_count,
              int64 transcendental_count, int64 bytes_accessed,
              float optimal_seconds) {
-    op_infos_.push_back(
-        {op_name.ToString(), short_name.ToString(), category.ToString(), cycles,
-         flop_count, transcendental_count, bytes_accessed, optimal_seconds});
+    op_infos_.push_back({std::string(op_name), std::string(short_name),
+                         std::string(category), cycles, flop_count,
+                         transcendental_count, bytes_accessed,
+                         optimal_seconds});
   }
 
   // Gets the human-readable profile.
diff --git a/tensorflow/compiler/xla/service/name_uniquer.cc b/tensorflow/compiler/xla/service/name_uniquer.cc
index f74bcb0b79355c..3a6a7c25f4b727 100644
--- a/tensorflow/compiler/xla/service/name_uniquer.cc
+++ b/tensorflow/compiler/xla/service/name_uniquer.cc
@@ -53,7 +53,7 @@ NameUniquer::NameUniquer(const string& separator) {
 }
 
 string NameUniquer::GetUniqueName(tensorflow::StringPiece prefix) {
-  string root = GetSanitizedName(prefix.empty() ? "name" : prefix.ToString());
+  string root = GetSanitizedName(prefix.empty() ? "name" : std::string(prefix));
 
   // Strip away numeric suffix (if any). Only recognize separator if it is in
   // the middle of the name.
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index 48b2922e77b787..c493547d9e83e1 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -172,11 +172,11 @@ tensorflow::Status ExpectNotTupleOrOpaque(const Shape& shape,
                                           tensorflow::StringPiece op_type) {
   if (ShapeUtil::IsTuple(shape)) {
     return InvalidArgument("Expected non-tuple argument for %s, but got %s.",
-                           op_type.ToString().c_str(),
+                           std::string(op_type).c_str(),
                            ShapeUtil::HumanString(shape).c_str());
   } else if (ShapeUtil::IsOpaque(shape)) {
     return InvalidArgument("Expected non-opaque argument for %s, but got %s.",
-                           op_type.ToString().c_str(),
+                           std::string(op_type).c_str(),
                            ShapeUtil::HumanString(shape).c_str());
   } else {
     return tensorflow::Status::OK();
diff --git a/tensorflow/core/framework/function.cc b/tensorflow/core/framework/function.cc
index bdc1af9fdaeb2e..647c66099cfa99 100644
--- a/tensorflow/core/framework/function.cc
+++ b/tensorflow/core/framework/function.cc
@@ -504,7 +504,7 @@ string Print(const NodeDef& n) {
   std::vector dep;
   for (StringPiece s : n.input()) {
     if (str_util::ConsumePrefix(&s, "^")) {
-      dep.push_back(s.ToString());
+      dep.push_back(std::string(s));
     } else {
       dat.push_back(s);
     }
diff --git a/tensorflow/core/framework/node_def_builder.cc b/tensorflow/core/framework/node_def_builder.cc
index f9cf6ce87359d6..8e00bfe4f89420 100644
--- a/tensorflow/core/framework/node_def_builder.cc
+++ b/tensorflow/core/framework/node_def_builder.cc
@@ -24,22 +24,23 @@ limitations under the License.
 namespace tensorflow {
 
 NodeDefBuilder::NodeOut::NodeOut(StringPiece n, int i, DataType dt)
-    : node(n.ToString()), index(i), data_type(dt) {}
+    : node(std::string(n)), index(i), data_type(dt) {}
 
 NodeDefBuilder::NodeOut::NodeOut() {
   // uninitialized, call Reset() before use.
 }
 
 void NodeDefBuilder::NodeOut::Reset(StringPiece n, int i, DataType dt) {
-  node = n.ToString();
+  node = std::string(n);
   index = i;
   data_type = dt;
 }
 
 NodeDefBuilder::NodeDefBuilder(StringPiece name, StringPiece op_name,
                                const OpRegistryInterface* op_registry) {
-  node_def_.set_name(name.ToString());
-  const Status status = op_registry->LookUpOpDef(op_name.ToString(), &op_def_);
+  node_def_.set_name(std::string(name));
+  const Status status =
+      op_registry->LookUpOpDef(std::string(op_name), &op_def_);
   if (status.ok()) {
     Initialize();
   } else {
@@ -50,7 +51,7 @@ NodeDefBuilder::NodeDefBuilder(StringPiece name, StringPiece op_name,
 
 NodeDefBuilder::NodeDefBuilder(StringPiece name, const OpDef* op_def)
     : op_def_(op_def) {
-  node_def_.set_name(name.ToString());
+  node_def_.set_name(std::string(name));
   Initialize();
 }
 
@@ -170,7 +171,7 @@ void NodeDefBuilder::AddInput(StringPiece src_node, int src_index) {
   } else if (src_index > 0) {
     node_def_.add_input(strings::StrCat(src_node, ":", src_index));
   } else {
-    node_def_.add_input(src_node.ToString());
+    node_def_.add_input(std::string(src_node));
   }
 }
 
@@ -193,12 +194,12 @@ void NodeDefBuilder::VerifyInputRef(const OpDef::ArgDef* input_arg,
 }
 
 NodeDefBuilder& NodeDefBuilder::ControlInput(StringPiece src_node) {
-  control_inputs_.push_back(src_node.ToString());
+  control_inputs_.push_back(std::string(src_node));
   return *this;
 }
 
 NodeDefBuilder& NodeDefBuilder::Device(StringPiece device_spec) {
-  node_def_.set_device(device_spec.ToString());
+  node_def_.set_device(std::string(device_spec));
   return *this;
 }
 
diff --git a/tensorflow/core/framework/node_def_util.cc b/tensorflow/core/framework/node_def_util.cc
index bad92ca9b3d8c9..5798333dfefab6 100644
--- a/tensorflow/core/framework/node_def_util.cc
+++ b/tensorflow/core/framework/node_def_util.cc
@@ -245,7 +245,7 @@ DEFINE_GET_ATTR(NameAttrList, func, "func", emplace_back, v, ;);
 #undef DEFINE_GET_ATTR
 
 bool HasNodeAttr(const NodeDef& node_def, StringPiece attr_name) {
-  return node_def.attr().find(attr_name.ToString()) != node_def.attr().end();
+  return node_def.attr().find(std::string(attr_name)) != node_def.attr().end();
 }
 
 static const string& kEmptyString = *new string();
@@ -639,7 +639,7 @@ Status AttachDef(const Status& status, const Node& node) {
 
 void AddNodeAttr(StringPiece name, const AttrValue& value, NodeDef* node_def) {
   node_def->mutable_attr()->insert(
-      AttrValueMap::value_type(name.ToString(), value));
+      AttrValueMap::value_type(std::string(name), value));
 }
 
 #define ADD_NODE_ATTR(T)                                           \
@@ -677,7 +677,7 @@ ADD_NODE_ATTR(gtl::ArraySlice)
 #undef ADD_NODE_ATTR
 
 void AddAttr(StringPiece name, const AttrValue& value, AttrValueMap* map) {
-  map->insert(AttrValueMap::value_type(name.ToString(), value));
+  map->insert(AttrValueMap::value_type(std::string(name), value));
 }
 
 #define ADD_ATTR(T)                                            \
diff --git a/tensorflow/core/framework/op_def_builder.cc b/tensorflow/core/framework/op_def_builder.cc
index 403bd0b5e22a31..91eb6c0672d93e 100644
--- a/tensorflow/core/framework/op_def_builder.cc
+++ b/tensorflow/core/framework/op_def_builder.cc
@@ -527,7 +527,7 @@ void FinalizeDoc(const string& text, OpDef* op_def,
 }  // namespace
 
 OpDefBuilder::OpDefBuilder(StringPiece op_name) {
-  op_def()->set_name(op_name.ToString());  // NOLINT
+  op_def()->set_name(std::string(op_name));  // NOLINT
 }
 
 OpDefBuilder& OpDefBuilder::Attr(StringPiece spec) {
@@ -584,7 +584,7 @@ OpDefBuilder& OpDefBuilder::Deprecated(int version, StringPiece explanation) {
   } else {
     OpDeprecation* deprecation = op_def()->mutable_deprecation();
     deprecation->set_version(version);
-    deprecation->set_explanation(explanation.ToString());
+    deprecation->set_explanation(std::string(explanation));
   }
   return *this;
 }
diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc
index 7f23272871abe9..5e1404362595d9 100644
--- a/tensorflow/core/framework/op_gen_lib.cc
+++ b/tensorflow/core/framework/op_gen_lib.cc
@@ -185,7 +185,7 @@ static bool FindMultiline(StringPiece line, size_t colon, string* end) {
   while (str_util::ConsumePrefix(&line, " ")) {
   }
   if (str_util::ConsumePrefix(&line, "<<")) {
-    *end = line.ToString();
+    *end = std::string(line);
     return true;
   }
   return false;
diff --git a/tensorflow/core/framework/op_kernel.cc b/tensorflow/core/framework/op_kernel.cc
index ca91d68f79f9e3..c71bcb26abc025 100644
--- a/tensorflow/core/framework/op_kernel.cc
+++ b/tensorflow/core/framework/op_kernel.cc
@@ -923,7 +923,7 @@ void OpKernelContext::clear_recorded_memory() {
 struct KernelRegistration {
   KernelRegistration(const KernelDef& d, StringPiece c,
                      kernel_factory::OpKernelRegistrar::Factory f)
-      : def(d), kernel_class_name(c.ToString()), factory(f) {}
+      : def(d), kernel_class_name(std::string(c)), factory(f) {}
   const KernelDef def;
   const string kernel_class_name;
   const kernel_factory::OpKernelRegistrar::Factory factory;
diff --git a/tensorflow/core/framework/shape_inference_testutil.h b/tensorflow/core/framework/shape_inference_testutil.h
index 2a99af7659d9be..f6656b3b456388 100644
--- a/tensorflow/core/framework/shape_inference_testutil.h
+++ b/tensorflow/core/framework/shape_inference_testutil.h
@@ -32,7 +32,7 @@ class Tensor;
 
 struct ShapeInferenceTestOp {
   typedef std::pair ShapeAndType;
-  explicit ShapeInferenceTestOp(StringPiece name) : name(name.ToString()) {}
+  explicit ShapeInferenceTestOp(StringPiece name) : name(std::string(name)) {}
   string name;
   NodeDef node_def;
   std::vector input_tensors;
diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index eeb6c60f717523..71d0637dc23842 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -695,7 +695,7 @@ Status Graph::AddWhileContext(StringPiece frame_name,
                               std::vector body_outputs,
                               WhileContext** result) {
   auto pair = while_ctxs_.insert(std::pair(
-      frame_name.ToString(),
+      std::string(frame_name),
       WhileContext(frame_name, std::move(enter_nodes), std::move(exit_nodes),
                    cond_output, std::move(body_inputs),
                    std::move(body_outputs))));
diff --git a/tensorflow/core/graph/graph_constructor.cc b/tensorflow/core/graph/graph_constructor.cc
index c678283fce1768..2fd32c0bd4319f 100644
--- a/tensorflow/core/graph/graph_constructor.cc
+++ b/tensorflow/core/graph/graph_constructor.cc
@@ -489,7 +489,7 @@ Status GraphConstructor::InitFromEdges() {
           num_control_edges++;
         } else {
           TensorId id(ParseTensorName(input_name));
-          if (next_iteration_nodes_.find(id.first.ToString()) !=
+          if (next_iteration_nodes_.find(std::string(id.first)) !=
               next_iteration_nodes_.end()) {
             has_loop_back_edge = true;
           }
@@ -811,7 +811,7 @@ void GraphConstructor::UniquifyNames(
     // We require that UniquifyNames() is called on all NodeDefs in topological
     // order. This guarantees that node_def's inputs will already be uniquified
     // if necessary.
-    auto iter = uniquified_names_.find(id.first.ToString());
+    auto iter = uniquified_names_.find(std::string(id.first));
     if (iter == uniquified_names_.end()) continue;
     id.first = iter->second;
     node_def->set_input(i, id.ToString());
@@ -830,7 +830,7 @@ void GraphConstructor::UpdateUniquifiedColocationNames() {
     for (int i = 0; i < coloc_values.size(); ++i) {
       StringPiece val(coloc_values[i]);
       if (str_util::ConsumePrefix(&val, kColocationGroupPrefix)) {
-        const auto& name_pair = uniquified_names_.find(val.ToString());
+        const auto& name_pair = uniquified_names_.find(std::string(val));
         if (name_pair == uniquified_names_.end()) continue;
         updated = true;
         coloc_values[i] =
@@ -856,7 +856,7 @@ bool GraphConstructor::NameExistsInGraphDef(StringPiece name) {
 }
 
 string GraphConstructor::FindUniqueName(StringPiece original_name) {
-  string name = original_name.ToString();
+  string name = std::string(original_name);
   int count = 0;
   // Check that any generated names don't collide with imported NodeDefs (as
   // well as nodes in g_).
@@ -989,7 +989,7 @@ Status GraphConstructor::Convert() {
             src_node->num_outputs(), " outputs");
       }
 
-      inputs.emplace_back(id.first.ToString(), src_node, src_index);
+      inputs.emplace_back(std::string(id.first), src_node, src_index);
     }
 
     if (has_data_back_edge && !IsMerge(*node_def)) {
diff --git a/tensorflow/core/graph/graph_constructor_test.cc b/tensorflow/core/graph/graph_constructor_test.cc
index b513778de9c86e..c54b4fa269eaf2 100644
--- a/tensorflow/core/graph/graph_constructor_test.cc
+++ b/tensorflow/core/graph/graph_constructor_test.cc
@@ -157,7 +157,7 @@ class GraphConstructorTest : public ::testing::Test {
     }
     StringPiece loc(value[0]);
     return str_util::ConsumePrefix(&loc, kColocationGroupPrefix)
-               ? loc.ToString()
+               ? std::string(loc)
                : "";
   }
 
diff --git a/tensorflow/core/graph/graph_def_builder.cc b/tensorflow/core/graph/graph_def_builder.cc
index 7a58347bd1ba44..dd84c4f7c7269d 100644
--- a/tensorflow/core/graph/graph_def_builder.cc
+++ b/tensorflow/core/graph/graph_def_builder.cc
@@ -44,12 +44,12 @@ GraphDefBuilder::Options GraphDefBuilder::Options::WithControlInputs(
 }
 GraphDefBuilder::Options GraphDefBuilder::Options::WithNameImpl(
     StringPiece name) {
-  name_ = name.ToString();
+  name_ = std::string(name);
   return *this;
 }
 GraphDefBuilder::Options GraphDefBuilder::Options::WithDeviceImpl(
     StringPiece device) {
-  device_ = device.ToString();
+  device_ = std::string(device);
   return *this;
 }
 GraphDefBuilder::Options GraphDefBuilder::Options::WithControlInputImpl(
diff --git a/tensorflow/core/graph/graph_def_builder.h b/tensorflow/core/graph/graph_def_builder.h
index 776a74c6d8821e..0d6aae43556920 100644
--- a/tensorflow/core/graph/graph_def_builder.h
+++ b/tensorflow/core/graph/graph_def_builder.h
@@ -128,7 +128,7 @@ class GraphDefBuilder {
     Options WithControlInputsImpl(gtl::ArraySlice control_inputs);
     template 
     Options WithAttrImpl(StringPiece name, T&& value) {
-      attrs_.emplace_back(name.ToString(), AttrValue());
+      attrs_.emplace_back(std::string(name), AttrValue());
       SetAttrValue(std::forward(value), &attrs_.back().second);
       return *this;
     }
diff --git a/tensorflow/core/graph/graph_partition.cc b/tensorflow/core/graph/graph_partition.cc
index 877e4f1b44e005..1b1941f9c19cf6 100644
--- a/tensorflow/core/graph/graph_partition.cc
+++ b/tensorflow/core/graph/graph_partition.cc
@@ -785,7 +785,7 @@ Status TopologicalSortNodesWithTimePriority(
   for (int n = 0; n < gdef->node_size(); ++n) {
     const NodeDef* ndef = &gdef->node(n);
     for (int i = 0; i < ndef->input_size(); ++i) {
-      node_to_output_nodes[ParseTensorName(ndef->input(i)).first.ToString()]
+      node_to_output_nodes[std::string(ParseTensorName(ndef->input(i)).first)]
           .push_back(ndef);
     }
     int64 start_time;
diff --git a/tensorflow/core/graph/node_builder.cc b/tensorflow/core/graph/node_builder.cc
index 114962c0e4f296..03f3bbd6634b8a 100644
--- a/tensorflow/core/graph/node_builder.cc
+++ b/tensorflow/core/graph/node_builder.cc
@@ -30,7 +30,7 @@ NodeBuilder::NodeOut::NodeOut(Node* n, int32 i)  // NOLINT(runtime/explicit)
       dt(SafeGetOutput(node, i, &error)) {}
 
 NodeBuilder::NodeOut::NodeOut(StringPiece n, int32 i, DataType t)
-    : node(nullptr), error(false), name(n.ToString()), index(i), dt(t) {}
+    : node(nullptr), error(false), name(std::string(n)), index(i), dt(t) {}
 
 NodeBuilder::NodeOut::NodeOut()
     : node(nullptr), error(true), index(0), dt(DT_FLOAT) {}
diff --git a/tensorflow/core/graph/while_context.cc b/tensorflow/core/graph/while_context.cc
index 10a2b67f378174..1b38aac35db9f5 100644
--- a/tensorflow/core/graph/while_context.cc
+++ b/tensorflow/core/graph/while_context.cc
@@ -23,7 +23,7 @@ WhileContext::WhileContext(StringPiece frame_name,
                            OutputTensor cond_output,
                            std::vector body_inputs,
                            std::vector body_outputs)
-    : frame_name_(frame_name.ToString()),
+    : frame_name_(std::string(frame_name)),
       enter_nodes_(std::move(enter_nodes)),
       exit_nodes_(std::move(exit_nodes)),
       cond_output_(cond_output),

From f6a55cc344cd96098cabd500144aad266e692598 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 11:09:47 -0700
Subject: [PATCH 0443/1691] Add tests for broadcasting KL divergence
 calculations.

PiperOrigin-RevId: 195690035
---
 .../kernel_tests/mvn_full_covariance_test.py  | 31 ++++++++++++++-
 .../python/kernel_tests/mvn_tril_test.py      | 39 ++++++++++++++++---
 2 files changed, 62 insertions(+), 8 deletions(-)

diff --git a/tensorflow/contrib/distributions/python/kernel_tests/mvn_full_covariance_test.py b/tensorflow/contrib/distributions/python/kernel_tests/mvn_full_covariance_test.py
index 7435bcbc684c16..b003526392709b 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/mvn_full_covariance_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/mvn_full_covariance_test.py
@@ -131,8 +131,8 @@ def _random_mu_and_sigma(self, batch_shape, event_shape):
     return mu, sigma
 
   def testKLBatch(self):
-    batch_shape = (2,)
-    event_shape = (3,)
+    batch_shape = [2]
+    event_shape = [3]
     with self.test_session():
       mu_a, sigma_a = self._random_mu_and_sigma(batch_shape, event_shape)
       mu_b, sigma_b = self._random_mu_and_sigma(batch_shape, event_shape)
@@ -156,6 +156,33 @@ def testKLBatch(self):
       self.assertAllClose(expected_kl_0, kl_v[0])
       self.assertAllClose(expected_kl_1, kl_v[1])
 
+  def testKLBatchBroadcast(self):
+    batch_shape = [2]
+    event_shape = [3]
+    with self.test_session():
+      mu_a, sigma_a = self._random_mu_and_sigma(batch_shape, event_shape)
+      # No batch shape.
+      mu_b, sigma_b = self._random_mu_and_sigma([], event_shape)
+      mvn_a = ds.MultivariateNormalFullCovariance(
+          loc=mu_a,
+          covariance_matrix=sigma_a,
+          validate_args=True)
+      mvn_b = ds.MultivariateNormalFullCovariance(
+          loc=mu_b,
+          covariance_matrix=sigma_b,
+          validate_args=True)
+
+      kl = ds.kl_divergence(mvn_a, mvn_b)
+      self.assertEqual(batch_shape, kl.get_shape())
+
+      kl_v = kl.eval()
+      expected_kl_0 = _compute_non_batch_kl(mu_a[0, :], sigma_a[0, :, :],
+                                            mu_b, sigma_b)
+      expected_kl_1 = _compute_non_batch_kl(mu_a[1, :], sigma_a[1, :, :],
+                                            mu_b, sigma_b)
+      self.assertAllClose(expected_kl_0, kl_v[0])
+      self.assertAllClose(expected_kl_1, kl_v[1])
+
 
 def _compute_non_batch_kl(mu_a, sigma_a, mu_b, sigma_b):
   """Non-batch KL for N(mu_a, sigma_a), N(mu_b, sigma_b)."""
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/mvn_tril_test.py b/tensorflow/contrib/distributions/python/kernel_tests/mvn_tril_test.py
index 685f32883dae5b..b556d06123800f 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/mvn_tril_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/mvn_tril_test.py
@@ -235,8 +235,8 @@ def _random_mu_and_sigma(self, batch_shape, event_shape):
     return mu, sigma
 
   def testKLNonBatch(self):
-    batch_shape = ()
-    event_shape = (2,)
+    batch_shape = []
+    event_shape = [2]
     with self.test_session():
       mu_a, sigma_a = self._random_mu_and_sigma(batch_shape, event_shape)
       mu_b, sigma_b = self._random_mu_and_sigma(batch_shape, event_shape)
@@ -257,8 +257,8 @@ def testKLNonBatch(self):
       self.assertAllClose(expected_kl, kl_v)
 
   def testKLBatch(self):
-    batch_shape = (2,)
-    event_shape = (3,)
+    batch_shape = [2]
+    event_shape = [3]
     with self.test_session():
       mu_a, sigma_a = self._random_mu_and_sigma(batch_shape, event_shape)
       mu_b, sigma_b = self._random_mu_and_sigma(batch_shape, event_shape)
@@ -282,9 +282,36 @@ def testKLBatch(self):
       self.assertAllClose(expected_kl_0, kl_v[0])
       self.assertAllClose(expected_kl_1, kl_v[1])
 
+  def testKLBatchBroadcast(self):
+    batch_shape = [2]
+    event_shape = [3]
+    with self.test_session():
+      mu_a, sigma_a = self._random_mu_and_sigma(batch_shape, event_shape)
+      # No batch shape.
+      mu_b, sigma_b = self._random_mu_and_sigma([], event_shape)
+      mvn_a = ds.MultivariateNormalTriL(
+          loc=mu_a,
+          scale_tril=np.linalg.cholesky(sigma_a),
+          validate_args=True)
+      mvn_b = ds.MultivariateNormalTriL(
+          loc=mu_b,
+          scale_tril=np.linalg.cholesky(sigma_b),
+          validate_args=True)
+
+      kl = ds.kl_divergence(mvn_a, mvn_b)
+      self.assertEqual(batch_shape, kl.get_shape())
+
+      kl_v = kl.eval()
+      expected_kl_0 = _compute_non_batch_kl(mu_a[0, :], sigma_a[0, :, :],
+                                            mu_b, sigma_b)
+      expected_kl_1 = _compute_non_batch_kl(mu_a[1, :], sigma_a[1, :, :],
+                                            mu_b, sigma_b)
+      self.assertAllClose(expected_kl_0, kl_v[0])
+      self.assertAllClose(expected_kl_1, kl_v[1])
+
   def testKLTwoIdenticalDistributionsIsZero(self):
-    batch_shape = (2,)
-    event_shape = (3,)
+    batch_shape = [2]
+    event_shape = [3]
     with self.test_session():
       mu_a, sigma_a = self._random_mu_and_sigma(batch_shape, event_shape)
       mvn_a = ds.MultivariateNormalTriL(

From 93846eccdfa9dd6da34b37778e5f3b1a46739933 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 11:11:28 -0700
Subject: [PATCH 0444/1691] Extracts PartialConcatConstFolding into a method.

PiperOrigin-RevId: 195690333
---
 .../grappler/optimizers/constant_folding.cc   | 203 +++++++++---------
 .../grappler/optimizers/constant_folding.h    |   5 +
 2 files changed, 111 insertions(+), 97 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 47d882768634c4..e6a74dbdcd539e 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -2370,115 +2370,124 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
       }
     }
 
-    // Partial constant folding for Concat which is not commutative, so
-    // we have to preserve order and can only push consecutive runs of constant
-    // inputs into sub-nodes.
-    if (IsConcat(*node) && num_non_control_inputs > 3 &&
-        node->name().rfind("_partial_split_") == string::npos) {
-      int axis_arg = -1;
-      int begin = 0;
-      int end = num_non_control_inputs;
-      if (node->op() == "Concat") {
-        begin = 1;
-        axis_arg = 0;
-      } else if (node->op() == "ConcatV2") {
-        end = num_non_control_inputs - 1;
-        axis_arg = num_non_control_inputs - 1;
-      } else {
-        continue;
-      }
+    if (PartialConcatConstFolding(optimized_graph, properties, node)) {
+      graph_modified_ = true;
+      continue;
+    }
+  }
 
-      const NodeDef* axis_arg_node =
-          node_map_->GetNode(NodeName(node->input(axis_arg)));
-      if (axis_arg_node == nullptr || !IsReallyConstant(*axis_arg_node)) {
-        // We cannot constant fold Concat unless we the axis argument is
-        // constant. Skip node.
-        continue;
-      }
+  return Status::OK();
+}
 
-      // We search for consecutive runs of constant inputs in the range
-      // [begin:end[ and push then down into child nodes.
-      std::vector> constant_input_runs;
-      int first = begin;
-      int last = begin;
-      while (last < end) {
-        while (first < end && !IsReallyConstant(*node_map_->GetNode(
-                                  NodeName(node->input(first))))) {
-          ++first;
-        }
-        // Invariant: node[first] is constant || first >= end.
-        last = first + 1;
-        while (last < end && IsReallyConstant(*node_map_->GetNode(
-                                 NodeName(node->input(last))))) {
-          ++last;
-        }
-        // Invariant: node[last] is not constant || last >= end
-        // Discard intervals shorter than 2 elements.
-        if (first < end && (last - first) > 1) {
-          constant_input_runs.emplace_back(first, last);
-        }
-        first = last;
+bool ConstantFolding::PartialConcatConstFolding(GraphDef* optimized_graph,
+                                                GraphProperties* properties,
+                                                NodeDef* node) {
+  // Partial constant folding for Concat which is not commutative, so
+  // we have to preserve order and can only push consecutive runs of constant
+  // inputs into sub-nodes.
+  const int num_non_control_inputs = NumNonControlInputs(*node);
+  if (IsConcat(*node) && num_non_control_inputs > 3 &&
+      node->name().rfind("_partial_split_") == string::npos) {
+    int axis_arg = -1;
+    int begin = 0;
+    int end = num_non_control_inputs;
+    if (node->op() == "Concat") {
+      begin = 1;
+      axis_arg = 0;
+    } else if (node->op() == "ConcatV2") {
+      end = num_non_control_inputs - 1;
+      axis_arg = num_non_control_inputs - 1;
+    } else {
+      return false;
+    }
+
+    const NodeDef* axis_arg_node =
+        node_map_->GetNode(NodeName(node->input(axis_arg)));
+    if (axis_arg_node == nullptr || !IsReallyConstant(*axis_arg_node)) {
+      // We cannot constant fold Concat unless we the axis argument is
+      // constant. Skip node.
+      return false;
+    }
+
+    // We search for consecutive runs of constant inputs in the range
+    // [begin:end[ and push then down into child nodes.
+    std::vector> constant_input_runs;
+    int first = begin;
+    int last = begin;
+    while (last < end) {
+      while (first < end && !IsReallyConstant(*node_map_->GetNode(
+                                NodeName(node->input(first))))) {
+        ++first;
+      }
+      // Invariant: node[first] is constant || first >= end.
+      last = first + 1;
+      while (last < end && IsReallyConstant(*node_map_->GetNode(
+                               NodeName(node->input(last))))) {
+        ++last;
       }
+      // Invariant: node[last] is not constant || last >= end
+      // Discard intervals shorter than 2 elements.
+      if (first < end && (last - first) > 1) {
+        constant_input_runs.emplace_back(first, last);
+      }
+      first = last;
+    }
 
-      // Skip if all inputs are constant, and let constant folding take over.
-      if (constant_input_runs.size() == 1 &&
-          constant_input_runs[0].first == begin &&
-          constant_input_runs[0].second == end) {
-        continue;
+    // Skip if all inputs are constant, and let constant folding take over.
+    if (constant_input_runs.size() == 1 &&
+        constant_input_runs[0].first == begin &&
+        constant_input_runs[0].second == end) {
+      return false;
+    }
+    std::set inputs_to_delete;
+    for (auto interval : constant_input_runs) {
+      // Push the constant inputs in the interval to a child node than can be
+      // constant folded.
+      const string new_node_name = OptimizedNodeName(
+          *node, strings::StrCat("_partial_split_", interval.first));
+      if (node_map_->NodeExists(new_node_name)) {
+        break;
       }
-      std::set inputs_to_delete;
-      for (auto interval : constant_input_runs) {
-        // Push the constant inputs in the interval to a child node than can be
-        // constant folded.
-        const string new_node_name = OptimizedNodeName(
-            *node, strings::StrCat("_partial_split_", interval.first));
-        if (node_map_->NodeExists(new_node_name)) {
-          break;
-        }
-        NodeDef* added_node = optimized_graph->add_node();
-        *added_node = *node;
-        added_node->set_name(new_node_name);
-        node_map_->AddNode(added_node->name(), added_node);
-        added_node->clear_input();
-        for (int i = interval.first; i < interval.second; ++i) {
-          added_node->add_input(node->input(i));
-          node_map_->UpdateOutput(NodeName(node->input(i)), node->name(),
-                                  added_node->name());
-          if (i != interval.first) {
-            inputs_to_delete.insert(i);
-          }
+      NodeDef* added_node = optimized_graph->add_node();
+      *added_node = *node;
+      added_node->set_name(new_node_name);
+      node_map_->AddNode(added_node->name(), added_node);
+      added_node->clear_input();
+      for (int i = interval.first; i < interval.second; ++i) {
+        added_node->add_input(node->input(i));
+        node_map_->UpdateOutput(NodeName(node->input(i)), node->name(),
+                                added_node->name());
+        if (i != interval.first) {
+          inputs_to_delete.insert(i);
         }
-        added_node->add_input(node->input(axis_arg));
-        (*added_node->mutable_attr())["N"].set_i(interval.second -
-                                                 interval.first);
-        node_map_->AddOutput(NodeName(node->input(axis_arg)),
-                             added_node->name());
-
-        // Overwrite the first constant input with the result of the added
-        // child node.
-        node->set_input(interval.first, added_node->name());
-        node_map_->AddOutput(added_node->name(), node->name());
       }
-      if (!constant_input_runs.empty()) {
-        graph_modified_ = true;
-        if (!inputs_to_delete.empty()) {
-          // Fix up the inputs to the original node.
-          std::vector tmp(node->input().begin(), node->input().end());
-          node->clear_input();
-          for (int i = 0; i < tmp.size(); ++i) {
-            if (inputs_to_delete.find(i) == inputs_to_delete.end()) {
-              node->add_input(tmp[i]);
-            }
+      added_node->add_input(node->input(axis_arg));
+      (*added_node->mutable_attr())["N"].set_i(interval.second -
+                                               interval.first);
+      node_map_->AddOutput(NodeName(node->input(axis_arg)), added_node->name());
+
+      // Overwrite the first constant input with the result of the added
+      // child node.
+      node->set_input(interval.first, added_node->name());
+      node_map_->AddOutput(added_node->name(), node->name());
+    }
+    if (!constant_input_runs.empty()) {
+      if (!inputs_to_delete.empty()) {
+        // Fix up the inputs to the original node.
+        std::vector tmp(node->input().begin(), node->input().end());
+        node->clear_input();
+        for (int i = 0; i < tmp.size(); ++i) {
+          if (inputs_to_delete.find(i) == inputs_to_delete.end()) {
+            node->add_input(tmp[i]);
           }
-          (*node->mutable_attr())["N"].set_i(node->input_size() - 1);
-          properties->ClearInputProperties(node->name());
         }
-        continue;
+        (*node->mutable_attr())["N"].set_i(node->input_size() - 1);
+        properties->ClearInputProperties(node->name());
       }
+      return true;
     }
   }
-
-  return Status::OK();
+  return false;
 }
 
 Status ConstantFolding::RunOptimizationPass(Cluster* cluster,
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index a694f1721ad416..20965765385411 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -101,6 +101,11 @@ class ConstantFolding : public GraphOptimizer {
   Status RunOptimizationPass(Cluster* cluster, const GrapplerItem& item,
                              GraphDef* output);
 
+  // Applies partial constant folding for Concat which is not commutative.
+  // Returns true if the transformation applied successfully.
+  bool PartialConcatConstFolding(GraphDef* optimized_graph,
+                                 GraphProperties* properties, NodeDef* node);
+
   // Points to an externally provided device or to owned_device_;
   RewriterConfig::Toggle opt_level_;
   DeviceBase* cpu_device_;

From eb5ee79cb3108bb036fc4a6d465f6ef6e12f4a3a Mon Sep 17 00:00:00 2001
From: Yu-Cheng Ling 
Date: Mon, 7 May 2018 11:27:02 -0700
Subject: [PATCH 0445/1691] Release notes for TensorFlow Lite.

PiperOrigin-RevId: 195693362
---
 tensorflow/contrib/lite/RELEASE.md | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 tensorflow/contrib/lite/RELEASE.md

diff --git a/tensorflow/contrib/lite/RELEASE.md b/tensorflow/contrib/lite/RELEASE.md
new file mode 100644
index 00000000000000..8fd63d5cee7db3
--- /dev/null
+++ b/tensorflow/contrib/lite/RELEASE.md
@@ -0,0 +1,8 @@
+# Release 0.1.7
+
+* TensorFlow Lite 0.1.7 is based on tag `tflite-v0.1.7` (git commit
+  fa1db5eb0da85b5baccc2a46d534fdeb3bb473d0).
+* To reproduce the iOS library, it's required to cherry pick git commit
+  f1f1d5172fe5bfeaeb2cf657ffc43ba744187bee to fix a dependency issue.
+* The code is based on TensorFlow 1.8.0 release candidate and it's very close
+  to TensorFlow 1.8.0 release.

From f14123dc19be468b6776f057d45ddd4d40fef9b2 Mon Sep 17 00:00:00 2001
From: Igor Saprykin 
Date: Mon, 7 May 2018 11:48:31 -0700
Subject: [PATCH 0446/1691] Generalize the input to TPU distribution strategy. 
 Add cross-shard-replica sum.

TPUStrategy passes tests in minimize_loss_test.  That caused me to add a capability to have `iterations x cores` inputs of any structure.  I also resolved a big number of small issues and uncovered more things to resolve that are documented as todos.

PiperOrigin-RevId: 195696833
---
 .../contrib/distribute/python/combinations.py |   4 +
 .../distribute/python/minimize_loss_test.py   | 115 ++++++++++++------
 .../distribute/python/single_loss_example.py  |  20 ++-
 .../contrib/distribute/python/tpu_strategy.py |  13 +-
 .../contrib/distribute/python/values.py       |  57 +++++++--
 tensorflow/python/training/distribute.py      |   1 +
 6 files changed, 158 insertions(+), 52 deletions(-)

diff --git a/tensorflow/contrib/distribute/python/combinations.py b/tensorflow/contrib/distribute/python/combinations.py
index 946310aa6fc210..45d191127ee734 100644
--- a/tensorflow/contrib/distribute/python/combinations.py
+++ b/tensorflow/contrib/distribute/python/combinations.py
@@ -265,6 +265,10 @@ def required_tpu(self):
 one_device_strategy = NamedDistribution(
     "OneDeviceCPU", one_device_strategy.OneDeviceStrategy("/cpu:0"),
     None)
+tpu_strategy_single_iteration = NamedDistribution(
+    "TPUSingleIteration",
+    tpu_strategy.TPUStrategy(iterations_per_step=1),
+    required_tpu=True)
 tpu_strategy = NamedDistribution(
     "TPU", tpu_strategy.TPUStrategy(), required_tpu=True)
 mirrored_strategy_with_gpu_and_cpu = NamedDistribution(
diff --git a/tensorflow/contrib/distribute/python/minimize_loss_test.py b/tensorflow/contrib/distribute/python/minimize_loss_test.py
index e134fe34e10be4..d2054715f11c47 100644
--- a/tensorflow/contrib/distribute/python/minimize_loss_test.py
+++ b/tensorflow/contrib/distribute/python/minimize_loss_test.py
@@ -44,13 +44,16 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase):
           combinations.distributions_and_v1_optimizers(),
           combinations.combine(mode=["graph"], use_callable_loss=[True, False])
           + combinations.combine(mode=["eager"], use_callable_loss=[True]),
-          combinations.combine(is_tpu=[False])) +
-      combinations.combine(
-          distribution=[combinations.tpu_strategy],
-          optimizer_fn=[combinations.adam_optimizer_v1_fn],
-          mode=["graph"],
-          use_callable_loss=[False],
-          is_tpu=[True]))
+          combinations.combine(is_tpu=[False])) + combinations.combine(
+              distribution=[combinations.tpu_strategy],
+              optimizer_fn=[
+                  combinations.adam_optimizer_v1_fn,
+                  # TODO(isaprykin):  Make Adam v2 work with while_loops
+                  # and TPUs.
+              ],
+              mode=["graph"],
+              use_callable_loss=[False],
+              is_tpu=[True]))
   def testTrainNetwork(self, distribution, optimizer_fn, use_callable_loss,
                        is_tpu):
     with distribution.scope():
@@ -101,7 +104,8 @@ def run_step():
           distribution=[combinations.tpu_strategy],
           optimizer_fn=[
               combinations.adam_optimizer_v1_fn,
-              combinations.gradient_descent_optimizer_v1_fn
+              combinations.gradient_descent_optimizer_v1_fn,
+              combinations.gradient_descent_optimizer_v2_fn,
           ],
           mode=["graph"],
           is_tpu=[True]))
@@ -171,13 +175,28 @@ def get_expected_variables(optimizer_fn, num_parameter_devices):
           set(created_variables))
 
   @combinations.generate(
-      combinations.times(combinations.distributions_and_v1_optimizers(),
-                         combinations.combine(
-                             mode=["graph", "eager"],
-                             momentum=[0.8, 0.9, 0.99],
-                             renorm=[False, True])))
+      combinations.times(
+          combinations.combine(momentum=[0.8, 0.9, 0.99], renorm=[False, True]),
+          combinations.times(
+              combinations.distributions_and_v1_optimizers(),
+              combinations.combine(
+                  mode=["graph", "eager"],
+                  is_tpu=[False],
+                  # TODO(isaprykin):  Allow False here.  Currently subsequent
+                  # towers will re-execute UPDATE_OPS of previous towers.
+                  update_ops_in_cross_tower_mode=[True])) +
+          combinations.combine(
+              distribution=[combinations.tpu_strategy_single_iteration],
+              optimizer_fn=[
+                  combinations.gradient_descent_optimizer_v1_fn,
+                  combinations.gradient_descent_optimizer_v2_fn
+              ],
+              mode=["graph"],
+              is_tpu=[True],
+              update_ops_in_cross_tower_mode=[False])))
   def testTrainNetworkWithBatchNorm(self, distribution, optimizer_fn, momentum,
-                                    renorm):
+                                    renorm, is_tpu,
+                                    update_ops_in_cross_tower_mode):
     """Verifies that moving mean updates are reduced across towers."""
     with distribution.scope():
       num_towers = len(distribution.worker_devices)
@@ -185,7 +204,8 @@ def testTrainNetworkWithBatchNorm(self, distribution, optimizer_fn, momentum,
           optimizer_fn,
           batch_per_epoch=num_towers,
           momentum=momentum,
-          renorm=renorm)
+          renorm=renorm,
+          update_ops_in_tower_mode=not update_ops_in_cross_tower_mode)
 
       # Disable prefetching since that makes the specific input on each device
       # to be non deterministic, and this test relies on specific input being
@@ -196,16 +216,18 @@ def testTrainNetworkWithBatchNorm(self, distribution, optimizer_fn, momentum,
           dataset_fn).make_one_shot_iterator()
 
       def run_step():
-        return control_flow_ops.group(
-            distribution.unwrap(
-                distribution.call_for_each_tower(
-                    model_fn,
-                    iterator.get_next(),
-                    run_concurrently=batchnorm.built)) +
-            ops.get_collection(ops.GraphKeys.UPDATE_OPS))
+        fetches = distribution.unwrap(
+            distribution.call_for_each_tower(
+                model_fn, iterator.get_next(),
+                run_concurrently=batchnorm.built))
+        if update_ops_in_cross_tower_mode:
+          fetches += ops.get_collection(ops.GraphKeys.UPDATE_OPS)
+        return control_flow_ops.group(fetches)
 
       if not context.executing_eagerly():
         with self.test_session() as sess:
+          if is_tpu:
+            sess.run(tpu.initialize_system())
           run_step = sess.make_callable(run_step())
         self.evaluate(variables_lib.global_variables_initializer())
 
@@ -229,22 +251,40 @@ def averaged_batch_mean(i):
               expected_moving_mean - averaged_batch_mean(i)) * (1.0 - momentum))
           self.assertNear(expected_moving_means[i], moving_means[i], 0.0001)
 
+      if is_tpu:
+        with self.test_session() as sess:
+          sess.run(tpu.shutdown_system())
+
   @combinations.generate(
       combinations.times(
           combinations.combine(
-              distribution=[combinations.one_device_strategy,
-                            combinations.mirrored_strategy_with_gpu_and_cpu,
-                            combinations.mirrored_strategy_with_two_gpus],
-              optimizer_fn=[combinations.gradient_descent_optimizer_v1_fn,
-                            combinations.gradient_descent_optimizer_v2_fn],
-              loss_reduction=[losses_impl.Reduction.SUM,
-                              losses_impl.Reduction.MEAN,
-                              losses_impl.Reduction.SUM_OVER_BATCH_SIZE,
-                              losses_impl.Reduction.SUM_OVER_NONZERO_WEIGHTS]),
-          combinations.combine(mode=["graph"], use_callable_loss=[True, False])
-          + combinations.combine(mode=["eager"], use_callable_loss=[True])))
+              optimizer_fn=[
+                  combinations.gradient_descent_optimizer_v1_fn,
+                  combinations.gradient_descent_optimizer_v2_fn
+              ],
+              loss_reduction=[
+                  losses_impl.Reduction.SUM, losses_impl.Reduction.MEAN,
+                  losses_impl.Reduction.SUM_OVER_BATCH_SIZE,
+                  losses_impl.Reduction.SUM_OVER_NONZERO_WEIGHTS
+              ]),
+          combinations.times(
+              combinations.combine(
+                  distribution=[
+                      combinations.one_device_strategy,
+                      combinations.mirrored_strategy_with_gpu_and_cpu,
+                      combinations.mirrored_strategy_with_two_gpus
+                  ],
+                  is_tpu=[False]),
+              combinations.combine(
+                  mode=["graph"], use_callable_loss=[True, False]) +
+              combinations.combine(mode=["eager"], use_callable_loss=[True])) +
+          combinations.combine(
+              distribution=[combinations.tpu_strategy_single_iteration],
+              is_tpu=[True],
+              mode=["graph"],
+              use_callable_loss=[True, False])))
   def testMeanVsSum(self, distribution, optimizer_fn, loss_reduction,
-                    use_callable_loss):
+                    use_callable_loss, is_tpu):
     with distribution.scope():
       all_vars = []
 
@@ -280,12 +320,13 @@ def run_step():
 
       if not context.executing_eagerly():
         with self.test_session() as sess:
+          if is_tpu:
+            sess.run(tpu.initialize_system())
           run_step = sess.make_callable(run_step())
         self.evaluate(variables_lib.global_variables_initializer())
 
       run_step()
 
-      self.assertEqual(distribution.num_towers, len(all_vars))
       v = all_vars[0]
       self.assertTrue(all([v is vi for vi in all_vars[1:]]))
       weight = numpy.squeeze(self.evaluate(distribution.fetch(v)))
@@ -312,6 +353,10 @@ def run_step():
         # One of the mean loss reductions.
         self.assertNear(weight, 2 + 10.6, 0.0001)
 
+      if is_tpu:
+        with self.test_session() as sess:
+          sess.run(tpu.shutdown_system())
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/distribute/python/single_loss_example.py b/tensorflow/contrib/distribute/python/single_loss_example.py
index 0db0b59fcacee2..d1fdb3279cf2a7 100644
--- a/tensorflow/contrib/distribute/python/single_loss_example.py
+++ b/tensorflow/contrib/distribute/python/single_loss_example.py
@@ -22,6 +22,7 @@
 from tensorflow.contrib.distribute.python import step_fn
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import ops
 from tensorflow.python.layers import core
 from tensorflow.python.layers import normalization
 from tensorflow.python.ops import array_ops
@@ -59,7 +60,7 @@ def dataset_fn():
     # TODO(isaprykin): map_and_batch with drop_remainder causes shapes to be
     # fully defined for TPU.  Remove this when XLA supports dynamic shapes.
     return dataset.apply(
-        batching.map_and_batch(lambda x: x, batch_size=2, drop_remainder=True))
+        batching.map_and_batch(lambda x: x, batch_size=1, drop_remainder=True))
 
   # An Optimizer instance is created either outside or inside model_fn.
   outer_optimizer = None
@@ -68,11 +69,10 @@ def dataset_fn():
 
   layer = core.Dense(1, use_bias=use_bias)
 
-  def model_fn(xs):
+  def model_fn(x):
     """A very simple model written by the user."""
 
     def loss_fn():
-      x = math_ops.reduce_mean(xs, keepdims=True)
       y = array_ops.reshape(layer(x), []) - constant_op.constant(1.)
       return y * y
 
@@ -89,7 +89,8 @@ def loss_fn():
 def batchnorm_example(optimizer_fn,
                       batch_per_epoch=1,
                       momentum=0.9,
-                      renorm=False):
+                      renorm=False,
+                      update_ops_in_tower_mode=False):
   """Example of non-distribution-aware legacy code with batch normalization."""
 
   def dataset_fn():
@@ -103,12 +104,19 @@ def dataset_fn():
   optimizer = optimizer_fn()
   batchnorm = normalization.BatchNormalization(
       renorm=renorm, momentum=momentum, fused=False)
+  layer = core.Dense(1, use_bias=False)
 
   def model_fn(x):
+    """A model that uses batchnorm."""
 
     def loss_fn():
-      y = math_ops.reduce_sum(batchnorm(x, training=True), axis=1)
-      loss = math_ops.reduce_mean(y - constant_op.constant(1.))
+      y = batchnorm(x, training=True)
+      with ops.control_dependencies(
+          ops.get_collection(ops.GraphKeys.UPDATE_OPS)
+          if update_ops_in_tower_mode else []):
+        loss = math_ops.reduce_mean(
+            math_ops.reduce_sum(layer(y)) - constant_op.constant(1.))
+      # `x` and `y` will be fetched by the gradient computation, but not `loss`.
       return loss
 
     # Callable loss.
diff --git a/tensorflow/contrib/distribute/python/tpu_strategy.py b/tensorflow/contrib/distribute/python/tpu_strategy.py
index a7e4fe80f3e659..75441786a615fc 100644
--- a/tensorflow/contrib/distribute/python/tpu_strategy.py
+++ b/tensorflow/contrib/distribute/python/tpu_strategy.py
@@ -33,7 +33,6 @@
 from tensorflow.python.util import nest
 
 
-# TODO(isaprykin):  Consider whether inheriting is really appropriate.
 class TPUStrategy(one_device_strategy.OneDeviceStrategy):
   """Experimental TPU distribution strategy implementation."""
 
@@ -73,7 +72,6 @@ def _call_for_each_tower(self, fn, *args, **kwargs):
     def infeed_input(i):
       """Get input, split it and then enqueue."""
       iteration_inputs = [f.get(i) for f in feeds()]
-
       infeed_inputs = [[inputs_per_core[core_id]
                         for inputs_per_core in iteration_inputs]
                        for core_id in range(self._num_cores_per_host)]
@@ -117,3 +115,14 @@ def iterate_on_tpu():
           iterate_on_tpu, [], num_shards=self._num_cores_per_host)
 
     return control_flow_ops.group(tpu_result, enqueue_ops)
+
+  def _reduce(self, method_string, value, destinations):
+    del destinations  # TPU is graph mode only.  Rely on implicit Send/Recv.
+    if method_string == 'mean':
+      # TODO(jhseu):  Revisit once we support model-parallelism.
+      value *= (1. / self._num_cores_per_host)
+    return tpu_ops.cross_replica_sum(value)
+
+  @property
+  def num_towers(self):
+    return self._num_cores_per_host
diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py
index aaf177d07ead69..b04734f1a39749 100644
--- a/tensorflow/contrib/distribute/python/values.py
+++ b/tensorflow/contrib/distribute/python/values.py
@@ -672,11 +672,12 @@ def make_initializable_iterator(self):
     return MultiWorkerDataIterator(iterators, self._worker_device_map)
 
 
-class PerIteration(object):
-  """Holds input for multiple iterations at once."""
+class _PerKey(object):
+  """Holds data associated by keys."""
 
-  def __init__(self, index):
-    self._index = index
+  def __init__(self, *index):
+    # pylint: disable=protected-access
+    self._index = list(index)
 
   def get(self, iteration):
     return array_ops.gather(self._index, iteration)
@@ -687,6 +688,24 @@ def get_shape(self):
   def get_dtype(self):
     return self._index[-1][-1].dtype
 
+  def __str__(self):
+    return "%s:%s" % (self.__class__.__name__, self._index)
+
+  def __repr__(self):
+    return "%s(%r)" % (self.__class__.__name__, self._index)
+
+
+class PerIteration(_PerKey):
+  """Holds input for multiple iterations at once."""
+
+  def __init__(self, *index):
+    # pylint: disable=protected-access
+    super(PerIteration, self).__init__(*[batch._index for batch in index])
+
+
+class Batches(_PerKey):
+  pass
+
 
 class MultiIterator(object):
   """Iterator that returns results of multiple get_next()s."""
@@ -697,11 +716,31 @@ def __init__(self, dataset_iterator, iterations, batches_per_iteration):
     self._batches_per_iteration = batches_per_iteration
 
   def get_next(self, name=None):
-    return PerIteration([[
-        self._dataset_iterator.get_next(name=name)
-        for _ in range(self._batches_per_iteration)
-    ]
-                         for _ in range(self._iterations)])
+    """Return PerIteration with `iterations x batches_per_iteration` inputs."""
+    data = []
+    for _ in range(self._batches_per_iteration):
+      batch = []
+      for _ in range(self._iterations):
+        batch.append(self._dataset_iterator.get_next(name=name))
+      data.append(batch)
+
+    # Here is an example.  Suppose each get_next returns a tuple of two tensors.
+    # For 3 `iterations` and 2 `batches_per_iteration`, the `data` is:
+    # [[(a,z), (b,y), (c,x)], [(A,Z), (B,Y), (C,X)]]
+    #
+    # After the first `map_structure` it gets transformed to:
+    #  [(Batches(a, A), Batches(z, Z)),
+    #   (Batches(b, B), Batches(y, Y)),
+    #   (Batches(c, C), Batches(x, X))]
+    #
+    # After the second `map_structure` it gets transformed to a tuple of:
+    # (PerIteration([Batches(a, A), Batches(b, B), Batches(c, C)]),
+    #  PerIteration([Batches(z, Z), Batches(y, Y), Batches(x, X)]))
+
+    data = nest.map_structure(Batches, *data)
+    data = nest.map_structure(PerIteration, *data)
+
+    return data
 
   @property
   def initializer(self):
diff --git a/tensorflow/python/training/distribute.py b/tensorflow/python/training/distribute.py
index 21f81ee1878254..b60f87c05fa1f2 100644
--- a/tensorflow/python/training/distribute.py
+++ b/tensorflow/python/training/distribute.py
@@ -816,6 +816,7 @@ def reduce(self, method_string, value, destinations=None):
     # TODO(josh11b): Return an unwrapped value if colocate_with is a
     # single device.
     _require_cross_tower_context(self)
+    assert method_string in ("sum", "mean")
     return self._reduce(method_string, value, destinations)
 
   def _reduce(self, method_string, value, destinations):

From aa57960b545ca25223568e366d99b0a4be7a03da Mon Sep 17 00:00:00 2001
From: Alexandre Passos 
Date: Mon, 7 May 2018 11:49:08 -0700
Subject: [PATCH 0447/1691] Register bool scatter_update for resource variables
 Fixes #17784

PiperOrigin-RevId: 195696915
---
 tensorflow/core/kernels/resource_variable_ops.cc         | 9 +++++++++
 tensorflow/core/kernels/scatter_functor_gpu.cu.cc        | 2 ++
 .../python/kernel_tests/resource_variable_ops_test.py    | 9 +++++++++
 3 files changed, 20 insertions(+)

diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc
index a8bcc7f7dc2677..03cc414905c66a 100644
--- a/tensorflow/core/kernels/resource_variable_ops.cc
+++ b/tensorflow/core/kernels/resource_variable_ops.cc
@@ -703,6 +703,8 @@ TF_CALL_REAL_NUMBER_TYPES(REGISTER_SCATTER_MINMAX_CPU);
 
 REGISTER_SCATTER_KERNEL(string, CPU, "ResourceScatterUpdate",
                         scatter_op::UpdateOp::ASSIGN);
+REGISTER_SCATTER_KERNEL(bool, CPU, "ResourceScatterUpdate",
+                        scatter_op::UpdateOp::ASSIGN);
 REGISTER_SCATTER_KERNEL(Variant, CPU, "ResourceScatterUpdate",
                         scatter_op::UpdateOp::ASSIGN);
 
@@ -725,6 +727,13 @@ REGISTER_KERNEL_BUILDER(Name("ResourceScatterUpdate")
                             .TypeConstraint("Tindices"),
                         ResourceScatterUpdateOp)
+REGISTER_KERNEL_BUILDER(Name("ResourceScatterUpdate")
+                            .Device(DEVICE_GPU)
+                            .HostMemory("resource")
+                            .TypeConstraint("dtype")
+                            .TypeConstraint("Tindices"),
+                        ResourceScatterUpdateOp)
 REGISTER_KERNEL_BUILDER(Name("ResourceScatterUpdate")
                             .Device(DEVICE_GPU)
                             .HostMemory("resource")
diff --git a/tensorflow/core/kernels/scatter_functor_gpu.cu.cc b/tensorflow/core/kernels/scatter_functor_gpu.cu.cc
index 59911bf0d26afe..bdc878594a3862 100644
--- a/tensorflow/core/kernels/scatter_functor_gpu.cu.cc
+++ b/tensorflow/core/kernels/scatter_functor_gpu.cu.cc
@@ -42,6 +42,8 @@ typedef Eigen::GpuDevice GPUDevice;
 
 DEFINE_GPU_SPECS(float);
 DEFINE_GPU_SPECS(double);
+DEFINE_GPU_SPECS_OP(bool, int32, scatter_op::UpdateOp::ASSIGN);
+DEFINE_GPU_SPECS_OP(bool, int64, scatter_op::UpdateOp::ASSIGN);
 // TODO(b/27222123): The following fails to compile due to lack of support for
 // fp16.
 // TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS);
diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py
index 984192258c9724..3daf07ea6343d3 100644
--- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py
+++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py
@@ -400,6 +400,15 @@ def testGPU(self):
               resource_variable_ops.var_is_initialized_op(abc.handle)),
           True)
 
+  def testScatterBool(self):
+    with context.eager_mode():
+      ref = resource_variable_ops.ResourceVariable(
+          [False, True, False], trainable=False)
+      indices = math_ops.range(3)
+      updates = constant_op.constant([True, True, True])
+      state_ops.scatter_update(ref, indices, updates)
+      self.assertAllEqual(ref.read_value(), [True, True, True])
+
   @test_util.run_in_graph_and_eager_modes()
   def testConstraintArg(self):
     constraint = lambda x: x

From abe83fe35ed3b4b245471d58811b03170fda857d Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy 
Date: Mon, 7 May 2018 11:52:46 -0700
Subject: [PATCH 0448/1691] Disable autograph cfg_test in windows.

PiperOrigin-RevId: 195697446
---
 tensorflow/contrib/autograph/pyct/static_analysis/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/BUILD b/tensorflow/contrib/autograph/pyct/static_analysis/BUILD
index 68fbdf6953051c..8064a967cd389e 100644
--- a/tensorflow/contrib/autograph/pyct/static_analysis/BUILD
+++ b/tensorflow/contrib/autograph/pyct/static_analysis/BUILD
@@ -48,6 +48,7 @@ py_test(
     name = "cfg_test",
     srcs = ["cfg_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["no_windows"],
     deps = [
         ":static_analysis",
         "//tensorflow/contrib/autograph/pyct",

From 0297d9c1a64270e266a7aeb48f81c78f0a31f63b Mon Sep 17 00:00:00 2001
From: Shivani Agrawal 
Date: Mon, 7 May 2018 12:03:20 -0700
Subject: [PATCH 0449/1691] [tf.data] Patch to unref iterator_resource in
 DeserializeIteratorOp.

PiperOrigin-RevId: 195698980
---
 tensorflow/core/kernels/data/iterator_ops.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/data/iterator_ops.cc b/tensorflow/core/kernels/data/iterator_ops.cc
index a2f6c5fe2c3a4b..b6bf0ecd096f15 100644
--- a/tensorflow/core/kernels/data/iterator_ops.cc
+++ b/tensorflow/core/kernels/data/iterator_ops.cc
@@ -1051,7 +1051,7 @@ class DeserializeIteratorOp : public OpKernel {
     IteratorResource* iterator_resource;
     OP_REQUIRES_OK(
         ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &iterator_resource));
-
+    core::ScopedUnref unref_iterator(iterator_resource);
     Variant variant = ctx->input(1).scalar()();
     auto* wrapper = variant.get();
     OP_REQUIRES(ctx, wrapper != nullptr,

From fb6d927a06a1cff15a71f6b47c207fafbaad6a57 Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Mon, 7 May 2018 12:10:15 -0700
Subject: [PATCH 0450/1691] [XLA] Add FusionKind matcher to pattern_matcher.h.

PiperOrigin-RevId: 195700319
---
 .../compiler/xla/service/pattern_matcher.h    | 34 +++++++++++++++++++
 .../xla/service/pattern_matcher_test.cc       | 23 +++++++++++++
 2 files changed, 57 insertions(+)

diff --git a/tensorflow/compiler/xla/service/pattern_matcher.h b/tensorflow/compiler/xla/service/pattern_matcher.h
index 586f6ef7a9c4f1..d3bc47e61e0e75 100644
--- a/tensorflow/compiler/xla/service/pattern_matcher.h
+++ b/tensorflow/compiler/xla/service/pattern_matcher.h
@@ -702,6 +702,30 @@ class HloInstructionPatternOperandImpl {
   HloInstructionPattern operand_;
 };
 
+// An HloInstructionPattern implementation that matches only if the instruction
+// is a fusion node with a particular kind.
+template 
+class HloInstructionPatternFusionKindImpl {
+ public:
+  explicit constexpr HloInstructionPatternFusionKindImpl(
+      const Previous& previous, ::xla::HloInstruction::FusionKind kind)
+      : previous_(previous), kind_(kind) {}
+
+  bool Match(const ::xla::HloInstruction* inst) const {
+    return previous_.Match(inst) && inst->opcode() == HloOpcode::kFusion &&
+           inst->fusion_kind() == kind_;
+  }
+
+  bool Match(::xla::HloInstruction* inst) const {
+    return previous_.Match(inst) && inst->opcode() == HloOpcode::kFusion &&
+           inst->fusion_kind() == kind_;
+  }
+
+ private:
+  Previous previous_;
+  ::xla::HloInstruction::FusionKind kind_;
+};
+
 // A pattern that matches HloInstructions.
 template 
 class HloInstructionPattern {
@@ -807,6 +831,16 @@ class HloInstructionPattern {
         matched_inst_);
   }
 
+  // Modifies the pattern to match only if the instruction is a fusion node with
+  // the given kind.
+  constexpr HloInstructionPattern>
+  WithFusionKind(HloInstruction::FusionKind kind) const {
+    return HloInstructionPattern>(
+        HloInstructionPatternFusionKindImpl(impl_, kind), matched_inst_);
+  }
+
  private:
   Impl impl_;
   HloInstructionType** matched_inst_;
diff --git a/tensorflow/compiler/xla/service/pattern_matcher_test.cc b/tensorflow/compiler/xla/service/pattern_matcher_test.cc
index c88157c312524f..204e8c99209fa9 100644
--- a/tensorflow/compiler/xla/service/pattern_matcher_test.cc
+++ b/tensorflow/compiler/xla/service/pattern_matcher_test.cc
@@ -170,5 +170,28 @@ TEST(PatternMatcherTest, TupleShape) {
       Match(&tuple_shape, match::Shape().WithSubshape({0, 0}, match::Shape())));
 }
 
+TEST(PatternMatcherTest, FusionKind) {
+  constexpr char kModuleStr[] = R"(
+    HloModule test_module
+
+    fused_computation {
+      ROOT fp0 = f32[] parameter(0)
+    }
+
+    ENTRY while.v11 {
+      p0 = f32[] parameter(0)
+      ROOT fusion = f32[] fusion(p0), kind=kLoop, calls=fused_computation
+    })";
+  TF_ASSERT_OK_AND_ASSIGN(auto hlo_module, tools::Parse(kModuleStr));
+
+  auto* root = hlo_module->entry_computation()->root_instruction();
+  EXPECT_TRUE(Match(
+      root, match::Op().WithFusionKind(HloInstruction::FusionKind::kLoop)));
+  EXPECT_FALSE(Match(
+      root, match::Op().WithFusionKind(HloInstruction::FusionKind::kInput)));
+  EXPECT_FALSE(Match(root->operand(0), match::Op().WithFusionKind(
+                                           HloInstruction::FusionKind::kLoop)));
+}
+
 }  // namespace
 }  // namespace xla

From a75f3f533f8e769af3cc11b5125ceb5db8c14479 Mon Sep 17 00:00:00 2001
From: Bixia Zheng 
Date: Mon, 7 May 2018 12:15:52 -0700
Subject: [PATCH 0451/1691] [TF:XLA:GPU] Allow the use of linear address when
 there are size one dimensions in a tensor.

The current implementation of EmitArrayElementAddress incorrectly concludes
that having a size one dimension in a tensor indicates broadcasting is needed
and the linear address can't be used to access the tensor. We fix this by
leaving LinearValidOnShape to decide whether the linear address can be used to
access the tensor. This enables the vectorization of loads/stores in unrolled
elementwise op kernels when other criteria are met.

Add a test case.

PiperOrigin-RevId: 195701194
---
 .../compiler/xla/service/llvm_ir/ir_array.cc  | 22 +++++++++----------
 1 file changed, 10 insertions(+), 12 deletions(-)

diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
index 3312a888443233..7323abeb207715 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
@@ -333,18 +333,7 @@ llvm::Value* IrArray::EmitArrayElementAddress(
   }
   CHECK_EQ(index.size(), ShapeUtil::Rank(*shape_));
 
-  std::vector actual_index;
-  bool is_implicit_broadcast = false;
-  // We perform broadcasting when the operand shape has dimension(s) of size
-  // 1. In this case we fix the index value for that dimension to zero. This
-  // effectively broadcasts along this dimension.
-  for (int64 i = 0; i < index.size(); ++i) {
-    auto dim = shape_->dimensions(i);
-    actual_index.push_back(dim == 1 ? ir_builder->getInt64(0) : index[i]);
-    is_implicit_broadcast |= dim == 1;
-  }
-
-  if (!is_implicit_broadcast && index.LinearValidOnShape(*shape_)) {
+  if (index.LinearValidOnShape(*shape_)) {
     llvm::Module* module =
         ir_builder->GetInsertBlock()->getParent()->getParent();
     return ir_builder->CreateInBoundsGEP(
@@ -354,6 +343,15 @@ llvm::Value* IrArray::EmitArrayElementAddress(
         {index.linear()}, llvm_ir::AsStringRef(name));
   }
 
+  std::vector actual_index;
+  for (int64 i = 0; i < index.size(); ++i) {
+    // When dimension i is of size 1, LLVM optimization is able to replace
+    // index[i] with 0. However, setting index[i] to 0 here still allows LLVM to
+    // produce better code in some cases.
+    auto dim = shape_->dimensions(i);
+    actual_index.push_back(dim == 1 ? ir_builder->getInt64(0) : index[i]);
+  }
+
   // "base_ptr_" has the type of "*"
   // (e.g. [3 x [2 x float]]*). Therefore, the address of the indexed element
   // should be computed by

From c3fef21c4ddf34fd68ab2cd44b0be497b5303b4e Mon Sep 17 00:00:00 2001
From: Ian Langmore 
Date: Mon, 7 May 2018 12:17:02 -0700
Subject: [PATCH 0452/1691] Add 'optonly' directive to
 linear_operator_circulant tests.

PiperOrigin-RevId: 195701399
---
 tensorflow/python/kernel_tests/linalg/BUILD | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/kernel_tests/linalg/BUILD b/tensorflow/python/kernel_tests/linalg/BUILD
index 052f11f92e90b0..91be80322c3779 100644
--- a/tensorflow/python/kernel_tests/linalg/BUILD
+++ b/tensorflow/python/kernel_tests/linalg/BUILD
@@ -85,7 +85,10 @@ cuda_py_test(
         "//tensorflow/python:platform_test",
     ],
     shard_count = 5,
-    tags = ["noasan"],  # times out b/63678675
+    tags = [
+        "noasan",  # times out, b/63678675
+        "optonly",  # times out, b/79171797
+    ],
 )
 
 cuda_py_test(

From 6f3a890d91e6dbeb811aed23d0eb59abaa8c469f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 12:37:36 -0700
Subject: [PATCH 0453/1691] Adding Greater/GreaterEqual/LessEqual ops to
 complement Less.

PiperOrigin-RevId: 195704492
---
 tensorflow/contrib/lite/builtin_ops.h         |   3 +
 .../lite/g3doc/tf_ops_compatibility.md        |  39 ++
 .../contrib/lite/kernels/comparisons.cc       | 160 +++++---
 .../contrib/lite/kernels/comparisons_test.cc  | 207 ++++++++++-
 .../contrib/lite/kernels/internal/BUILD       |   1 +
 .../contrib/lite/kernels/internal/common.h    |  14 +
 .../internal/optimized/optimized_ops.h        |  11 +
 .../internal/reference/reference_ops.h        | 191 +++++++---
 tensorflow/contrib/lite/kernels/register.cc   |   6 +
 tensorflow/contrib/lite/model.cc              |   5 +-
 tensorflow/contrib/lite/nnapi_delegate.cc     |   3 +
 tensorflow/contrib/lite/schema/schema.fbs     |  15 +
 .../contrib/lite/schema/schema_generated.h    | 348 +++++++++++++++++-
 tensorflow/contrib/lite/testing/BUILD         |   3 +
 .../contrib/lite/testing/generate_examples.py | 102 +++++
 .../testing/generated_examples_zip_test.cc    |   3 +
 .../contrib/lite/toco/export_tensorflow.cc    |  21 ++
 .../propagate_fixed_sizes.cc                  |   6 +-
 .../toco/graph_transformations/quantize.cc    |   9 +-
 .../contrib/lite/toco/tflite/operator.cc      |   7 +
 20 files changed, 1051 insertions(+), 103 deletions(-)

diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h
index d66b72843a862e..778933f5693ed4 100644
--- a/tensorflow/contrib/lite/builtin_ops.h
+++ b/tensorflow/contrib/lite/builtin_ops.h
@@ -86,6 +86,9 @@ typedef enum {
   kTfLiteBuiltinLess = 58,
   kTfLiteBuiltinNeg = 59,
   kTfLiteBuiltinPadv2 = 60,
+  kTfLiteBuiltinGreater = 61,
+  kTfLiteBuiltinGreaterEqual = 62,
+  kTfLiteBuiltinLessEqual = 63,
 } TfLiteBuiltinOperator;
 
 #ifdef __cplusplus
diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
index 0051ee84ec38f8..fc57b8f28bef8b 100644
--- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
+++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
@@ -281,6 +281,32 @@ Options {
 }
 ```
 
+**GREATER**
+
+```
+Inputs {
+  0: a tensor
+  1: a tensor
+}
+Outputs {
+  0: a tensor of type bool, true whenever an element of the first tensor is
+  greater than the corresponding element of the second tensor.
+}
+```
+
+**GREATER_EQUAL**
+
+```
+Inputs {
+  0: a tensor
+  1: a tensor
+}
+Outputs {
+  0: a tensor of type bool, true whenever an element of the first tensor is
+  greater than or equal to the corresponding element of the second tensor.
+}
+```
+
 **L2_NORMALIZATION**
 
 ```
@@ -325,6 +351,19 @@ Outputs {
 }
 ```
 
+**LESS_EQUAL**
+
+```
+Inputs {
+  0: a tensor
+  1: a tensor
+}
+Outputs {
+  0: a tensor of type bool, true whenever an element of the first tensor is less
+  than or equal to the corresponding element of the second tensor.
+}
+```
+
 **LOCAL_RESPONSE_NORMALIZATION**
 
 ```
diff --git a/tensorflow/contrib/lite/kernels/comparisons.cc b/tensorflow/contrib/lite/kernels/comparisons.cc
index 87c413cb982daf..2885ce032b4b6a 100644
--- a/tensorflow/contrib/lite/kernels/comparisons.cc
+++ b/tensorflow/contrib/lite/kernels/comparisons.cc
@@ -28,7 +28,7 @@ constexpr int kInputTensor1 = 0;
 constexpr int kInputTensor2 = 1;
 constexpr int kOutputTensor = 0;
 
-TfLiteStatus LessPrepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ComparisonPrepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
@@ -56,61 +56,139 @@ TfLiteStatus LessPrepare(TfLiteContext* context, TfLiteNode* node) {
   return context->ResizeTensor(context, output, output_size);
 }
 
-TfLiteStatus LessEval(TfLiteContext* context, TfLiteNode* node) {
+#define TF_LITE_COMPARISON(type, opname, requires_broadcast)    \
+  requires_broadcast                                            \
+      ? reference_ops::Broadcast##opname(                       \
+            GetTensorData(input1), GetTensorDims(input1), \
+            GetTensorData(input2), GetTensorDims(input2), \
+            GetTensorData(output), GetTensorDims(output)) \
+      : reference_ops::opname(                                  \
+            GetTensorData(input1), GetTensorDims(input1), \
+            GetTensorData(input2), GetTensorDims(input2), \
+            GetTensorData(output), GetTensorDims(output));
+
+TfLiteStatus GreaterEval(TfLiteContext* context, TfLiteNode* node) {
   TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
   TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+  bool requires_broadcast = !HaveSameShapes(input1, input2);
+  // TODO(renjieliu): Support quantized data.
+  switch (input1->type) {
+    case kTfLiteFloat32:
+      TF_LITE_COMPARISON(float, Greater, requires_broadcast);
+      break;
+    case kTfLiteInt32:
+      TF_LITE_COMPARISON(int32_t, Greater, requires_broadcast);
+      break;
+    case kTfLiteInt64:
+      TF_LITE_COMPARISON(int64_t, Greater, requires_broadcast);
+      break;
+    default:
+      context->ReportError(context,
+                           "Does not support type other than float|int");
+      return kTfLiteError;
+  }
+  return kTfLiteOk;
+}
 
+TfLiteStatus GreaterEqualEval(TfLiteContext* context, TfLiteNode* node) {
+  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
   bool requires_broadcast = !HaveSameShapes(input1, input2);
+  // TODO(renjieliu): Support quantized data.
+  switch (input1->type) {
+    case kTfLiteFloat32:
+      TF_LITE_COMPARISON(float, GreaterEqual, requires_broadcast);
+      break;
+    case kTfLiteInt32:
+      TF_LITE_COMPARISON(int32_t, GreaterEqual, requires_broadcast);
+      break;
+    case kTfLiteInt64:
+      TF_LITE_COMPARISON(int64_t, GreaterEqual, requires_broadcast);
+      break;
+    default:
+      context->ReportError(context,
+                           "Does not support type other than float|int");
+      return kTfLiteError;
+  }
+  return kTfLiteOk;
+}
 
-#define TF_LITE_LESS(type, opname)                                          \
-  reference_ops::opname(GetTensorData(input1), GetTensorDims(input1), \
-                        GetTensorData(input2), GetTensorDims(input2), \
-                        GetTensorData(output), GetTensorDims(output));
+TfLiteStatus LessEval(TfLiteContext* context, TfLiteNode* node) {
+  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+  bool requires_broadcast = !HaveSameShapes(input1, input2);
+  // TODO(renjieliu): Support quantized data.
+  switch (input1->type) {
+    case kTfLiteFloat32:
+      TF_LITE_COMPARISON(float, Less, requires_broadcast);
+      break;
+    case kTfLiteInt32:
+      TF_LITE_COMPARISON(int32_t, Less, requires_broadcast);
+      break;
+    case kTfLiteInt64:
+      TF_LITE_COMPARISON(int64_t, Less, requires_broadcast);
+      break;
+    default:
+      context->ReportError(context,
+                           "Does not support type other than float|int");
+      return kTfLiteError;
+  }
+  return kTfLiteOk;
+}
 
+TfLiteStatus LessEqualEval(TfLiteContext* context, TfLiteNode* node) {
+  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+  bool requires_broadcast = !HaveSameShapes(input1, input2);
   // TODO(renjieliu): Support quantized data.
-  if (requires_broadcast) {
-    switch (input1->type) {
-      case kTfLiteFloat32:
-        TF_LITE_LESS(float, BroadcastLess);
-        break;
-      case kTfLiteInt32:
-        TF_LITE_LESS(int32_t, BroadcastLess);
-        break;
-      case kTfLiteInt64:
-        TF_LITE_LESS(int64_t, BroadcastLess);
-        break;
-      default:
-        context->ReportError(context,
-                             "Does not support type other than float|int");
-        return kTfLiteError;
-    }
-  } else {
-    switch (input1->type) {
-      case kTfLiteFloat32:
-        TF_LITE_LESS(float, Less);
-        break;
-      case kTfLiteInt32:
-        TF_LITE_LESS(int32_t, Less);
-        break;
-      case kTfLiteInt64:
-        TF_LITE_LESS(int64_t, Less);
-        break;
-      default:
-        context->ReportError(context,
-                             "Does not support type other than float|int");
-        return kTfLiteError;
-    }
+  switch (input1->type) {
+    case kTfLiteFloat32:
+      TF_LITE_COMPARISON(float, LessEqual, requires_broadcast);
+      break;
+    case kTfLiteInt32:
+      TF_LITE_COMPARISON(int32_t, LessEqual, requires_broadcast);
+      break;
+    case kTfLiteInt64:
+      TF_LITE_COMPARISON(int64_t, LessEqual, requires_broadcast);
+      break;
+    default:
+      context->ReportError(context,
+                           "Does not support type other than float|int");
+      return kTfLiteError;
   }
-#undef TF_LITE_LESS
   return kTfLiteOk;
 }
 
 }  // namespace comparisons
 
+TfLiteRegistration* Register_GREATER() {
+  static TfLiteRegistration r = {nullptr, nullptr,
+                                 comparisons::ComparisonPrepare,
+                                 comparisons::GreaterEval};
+  return &r;
+}
+
+TfLiteRegistration* Register_GREATER_EQUAL() {
+  static TfLiteRegistration r = {nullptr, nullptr,
+                                 comparisons::ComparisonPrepare,
+                                 comparisons::GreaterEqualEval};
+  return &r;
+}
+
 TfLiteRegistration* Register_LESS() {
-  static TfLiteRegistration r = {nullptr, nullptr, comparisons::LessPrepare,
-                                 comparisons::LessEval};
+  static TfLiteRegistration r = {
+      nullptr, nullptr, comparisons::ComparisonPrepare, comparisons::LessEval};
+  return &r;
+}
+
+TfLiteRegistration* Register_LESS_EQUAL() {
+  static TfLiteRegistration r = {nullptr, nullptr,
+                                 comparisons::ComparisonPrepare,
+                                 comparisons::LessEqualEval};
   return &r;
 }
 
diff --git a/tensorflow/contrib/lite/kernels/comparisons_test.cc b/tensorflow/contrib/lite/kernels/comparisons_test.cc
index da2d7f858984a4..835d238d36d175 100644
--- a/tensorflow/contrib/lite/kernels/comparisons_test.cc
+++ b/tensorflow/contrib/lite/kernels/comparisons_test.cc
@@ -23,6 +23,139 @@ namespace {
 
 using ::testing::ElementsAreArray;
 
+class GreaterOpModel : public SingleOpModel {
+ public:
+  GreaterOpModel(std::initializer_list input1_shape,
+                 std::initializer_list input2_shape,
+                 TensorType input_type) {
+    input1_ = AddInput(input_type);
+    input2_ = AddInput(input_type);
+    output_ = AddOutput(TensorType_BOOL);
+    SetBuiltinOp(BuiltinOperator_GREATER, BuiltinOptions_GreaterOptions,
+                 CreateGreaterOptions(builder_).Union());
+    BuildInterpreter({input1_shape, input2_shape});
+  }
+
+  int input1() { return input1_; }
+  int input2() { return input2_; }
+
+  std::vector GetOutput() { return ExtractVector(output_); }
+  std::vector GetOutputShape() { return GetTensorShape(output_); }
+
+ private:
+  int input1_;
+  int input2_;
+  int output_;
+};
+
+TEST(ComparisonsTest, GreaterFloat) {
+  GreaterOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_FLOAT32);
+  model.PopulateTensor(model.input1(), {0.1, 0.9, 0.7, 0.3});
+  model.PopulateTensor(model.input2(), {0.1, 0.2, 0.6, 0.5});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({false, true, true, false}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(ComparisonsTest, GreaterInt) {
+  GreaterOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_INT32);
+  model.PopulateTensor(model.input1(), {-1, 9, 7, 3});
+  model.PopulateTensor(model.input2(), {1, 2, 7, 5});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({false, true, false, false}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(ComparisonsTest, GreaterBroadcast) {
+  GreaterOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, TensorType_INT32);
+  model.PopulateTensor(model.input1(), {-1, 9, 7, 3});
+  model.PopulateTensor(model.input2(), {7});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({false, true, false, false}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(ComparisonsTest, GreaterBroadcastTwoD) {
+  GreaterOpModel model({1, 1, 2, 4}, {1, 1, 1, 4}, TensorType_INT32);
+  model.PopulateTensor(model.input1(), {-1, 9, 7, 3, 2, 4, 2, 8});
+  model.PopulateTensor(model.input2(), {7, 1, 2, 4});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({false, true, true, false,
+                                                   false, true, false, true}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 2, 4}));
+}
+
+class GreaterEqualOpModel : public SingleOpModel {
+ public:
+  GreaterEqualOpModel(std::initializer_list input1_shape,
+                      std::initializer_list input2_shape,
+                      TensorType input_type) {
+    input1_ = AddInput(input_type);
+    input2_ = AddInput(input_type);
+    output_ = AddOutput(TensorType_BOOL);
+    SetBuiltinOp(BuiltinOperator_GREATER_EQUAL,
+                 BuiltinOptions_GreaterEqualOptions,
+                 CreateGreaterEqualOptions(builder_).Union());
+    BuildInterpreter({input1_shape, input2_shape});
+  }
+
+  int input1() { return input1_; }
+  int input2() { return input2_; }
+
+  std::vector GetOutput() { return ExtractVector(output_); }
+  std::vector GetOutputShape() { return GetTensorShape(output_); }
+
+ private:
+  int input1_;
+  int input2_;
+  int output_;
+};
+
+TEST(ComparisonsTest, GreaterEqualFloat) {
+  GreaterEqualOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_FLOAT32);
+  model.PopulateTensor(model.input1(), {0.1, 0.9, 0.7, 0.3});
+  model.PopulateTensor(model.input2(), {0.1, 0.2, 0.6, 0.5});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({true, true, true, false}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(ComparisonsTest, GreaterEqualInt) {
+  GreaterEqualOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_INT32);
+  model.PopulateTensor(model.input1(), {-1, 9, 7, 3});
+  model.PopulateTensor(model.input2(), {1, 2, 7, 5});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({false, true, true, false}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(ComparisonsTest, GreaterEqualBroadcast) {
+  GreaterEqualOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, TensorType_INT32);
+  model.PopulateTensor(model.input1(), {-1, 9, 7, 3});
+  model.PopulateTensor(model.input2(), {7});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({false, true, true, false}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(ComparisonsTest, GreaterEqualBroadcastTwoD) {
+  GreaterEqualOpModel model({1, 1, 2, 4}, {1, 1, 1, 4}, TensorType_INT32);
+  model.PopulateTensor(model.input1(), {-1, 9, 7, 3, 2, 4, 2, 8});
+  model.PopulateTensor(model.input2(), {7, 1, 2, 4});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({false, true, true, false,
+                                                   false, true, true, true}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 2, 4}));
+}
+
 class LessOpModel : public SingleOpModel {
  public:
   LessOpModel(std::initializer_list input1_shape,
@@ -47,7 +180,7 @@ class LessOpModel : public SingleOpModel {
   int output_;
 };
 
-TEST(ArgMaxOpTest, LessFloat) {
+TEST(ComparisonsTest, LessFloat) {
   LessOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_FLOAT32);
   model.PopulateTensor(model.input1(), {0.1, 0.9, 0.7, 0.3});
   model.PopulateTensor(model.input2(), {0.1, 0.2, 0.6, 0.5});
@@ -57,7 +190,7 @@ TEST(ArgMaxOpTest, LessFloat) {
   EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
 }
 
-TEST(ArgMaxOpTest, LessInt) {
+TEST(ComparisonsTest, LessInt) {
   LessOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_INT32);
   model.PopulateTensor(model.input1(), {-1, 9, 7, 3});
   model.PopulateTensor(model.input2(), {1, 2, 6, 5});
@@ -67,7 +200,7 @@ TEST(ArgMaxOpTest, LessInt) {
   EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
 }
 
-TEST(ArgMaxOpTest, LessBroadcast) {
+TEST(ComparisonsTest, LessBroadcast) {
   LessOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, TensorType_INT32);
   model.PopulateTensor(model.input1(), {-1, 9, 7, 3});
   model.PopulateTensor(model.input2(), {7});
@@ -77,7 +210,7 @@ TEST(ArgMaxOpTest, LessBroadcast) {
   EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
 }
 
-TEST(ArgMaxOpTest, LessBroadcastTwoD) {
+TEST(ComparisonsTest, LessBroadcastTwoD) {
   LessOpModel model({1, 1, 2, 4}, {1, 1, 1, 4}, TensorType_INT32);
   model.PopulateTensor(model.input1(), {-1, 9, 7, 3, 2, 4, 6, 8});
   model.PopulateTensor(model.input2(), {7, 1, 2, 4});
@@ -88,6 +221,72 @@ TEST(ArgMaxOpTest, LessBroadcastTwoD) {
   EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 2, 4}));
 }
 
+class LessEqualOpModel : public SingleOpModel {
+ public:
+  LessEqualOpModel(std::initializer_list input1_shape,
+                   std::initializer_list input2_shape,
+                   TensorType input_type) {
+    input1_ = AddInput(input_type);
+    input2_ = AddInput(input_type);
+    output_ = AddOutput(TensorType_BOOL);
+    SetBuiltinOp(BuiltinOperator_LESS_EQUAL, BuiltinOptions_LessEqualOptions,
+                 CreateLessEqualOptions(builder_).Union());
+    BuildInterpreter({input1_shape, input2_shape});
+  }
+
+  int input1() { return input1_; }
+  int input2() { return input2_; }
+
+  std::vector GetOutput() { return ExtractVector(output_); }
+  std::vector GetOutputShape() { return GetTensorShape(output_); }
+
+ private:
+  int input1_;
+  int input2_;
+  int output_;
+};
+
+TEST(ComparisonsTest, LessEqualFloat) {
+  LessEqualOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_FLOAT32);
+  model.PopulateTensor(model.input1(), {0.1, 0.9, 0.7, 0.3});
+  model.PopulateTensor(model.input2(), {0.1, 0.2, 0.6, 0.5});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({true, false, false, true}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(ComparisonsTest, LessEqualInt) {
+  LessEqualOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_INT32);
+  model.PopulateTensor(model.input1(), {-1, 9, 7, 3});
+  model.PopulateTensor(model.input2(), {1, 2, 7, 5});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({true, false, true, true}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(ComparisonsTest, LessEqualBroadcast) {
+  LessEqualOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, TensorType_INT32);
+  model.PopulateTensor(model.input1(), {-1, 9, 7, 3});
+  model.PopulateTensor(model.input2(), {7});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({true, false, true, true}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(ComparisonsTest, LessEqualBroadcastTwoD) {
+  LessEqualOpModel model({1, 1, 2, 4}, {1, 1, 1, 4}, TensorType_INT32);
+  model.PopulateTensor(model.input1(), {-1, 9, 7, 3, 2, 4, 2, 8});
+  model.PopulateTensor(model.input2(), {7, 1, 2, 4});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({true, false, false, true,
+                                                   true, false, true, false}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 2, 4}));
+}
+
 }  // namespace
 }  // namespace tflite
 
diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD
index df29172f83a4b1..7ec4782f96e200 100644
--- a/tensorflow/contrib/lite/kernels/internal/BUILD
+++ b/tensorflow/contrib/lite/kernels/internal/BUILD
@@ -157,6 +157,7 @@ cc_library(
         ":quantization_util",
         ":strided_slice_logic",
         ":types",
+        ":reference_base",
         ":round",
         "//third_party/eigen3",
         "@gemmlowp",
diff --git a/tensorflow/contrib/lite/kernels/internal/common.h b/tensorflow/contrib/lite/kernels/internal/common.h
index 18601df22c1894..ede95dfee069fa 100644
--- a/tensorflow/contrib/lite/kernels/internal/common.h
+++ b/tensorflow/contrib/lite/kernels/internal/common.h
@@ -113,6 +113,20 @@ inline int32 MultiplyByQuantizedMultiplier(int32 x, int32 quantized_multiplier,
                              right_shift);
 }
 
+template 
+int CountLeadingZeros(T integer_input) {
+  static_assert(std::is_unsigned::value,
+                "Only unsigned integer types handled.");
+  const T one_in_leading_positive = static_cast(1)
+                                    << (std::numeric_limits::digits - 1);
+  int leading_zeros = 0;
+  while (integer_input < one_in_leading_positive) {
+    integer_input <<= 1;
+    ++leading_zeros;
+  }
+  return leading_zeros;
+}
+
 }  // namespace tflite
 
 #endif  // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_COMMON_H_
diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index e2a1a6996d5462..c506c5636c3986 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -31,6 +31,7 @@ limitations under the License.
 #include "public/gemmlowp.h"
 #include "tensorflow/contrib/lite/kernels/internal/common.h"
 #include "tensorflow/contrib/lite/kernels/internal/quantization_util.h"
+#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
 #include "tensorflow/contrib/lite/kernels/internal/round.h"
 #include "tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h"
 #include "tensorflow/contrib/lite/kernels/internal/types.h"
@@ -38,6 +39,16 @@ limitations under the License.
 namespace tflite {
 namespace optimized_ops {
 
+// Unoptimized reference ops:
+using reference_ops::BroadcastGreater;
+using reference_ops::BroadcastGreaterEqual;
+using reference_ops::BroadcastLess;
+using reference_ops::BroadcastLessEqual;
+using reference_ops::Greater;
+using reference_ops::GreaterEqual;
+using reference_ops::Less;
+using reference_ops::LessEqual;
+
 // Make a local VectorMap typedef allowing to map a float array
 // as a Eigen vector expression. The std::conditional here is to
 // construct the suitable Eigen type for the constness of the
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index 05e6ca8e7e097a..93dba1cc8e6c86 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -35,35 +35,6 @@ limitations under the License.
 namespace tflite {
 namespace reference_ops {
 
-inline int32 MultiplyByQuantizedMultiplierSmallerThanOne(
-    int32 x, int32 quantized_multiplier, int right_shift) {
-  using gemmlowp::RoundingDivideByPOT;
-  using gemmlowp::SaturatingRoundingDoublingHighMul;
-  return RoundingDivideByPOT(
-      SaturatingRoundingDoublingHighMul(x, quantized_multiplier), right_shift);
-}
-
-inline int32 MultiplyByQuantizedMultiplierGreaterThanOne(
-    int32 x, int32 quantized_multiplier, int left_shift) {
-  using gemmlowp::SaturatingRoundingDoublingHighMul;
-  return SaturatingRoundingDoublingHighMul(x * (1 << left_shift),
-                                           quantized_multiplier);
-}
-
-template 
-int CountLeadingZeros(T integer_input) {
-  static_assert(std::is_unsigned::value,
-                "Only unsigned integer types handled.");
-  const T one_in_leading_positive = static_cast(1)
-                                    << (std::numeric_limits::digits - 1);
-  int leading_zeros = 0;
-  while (integer_input < one_in_leading_positive) {
-    integer_input <<= 1;
-    ++leading_zeros;
-  }
-  return leading_zeros;
-}
-
 // DO NOT USE THIS STRUCT FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING ELEMENT-WISE
 // BROADCASTING.
 //
@@ -3614,17 +3585,29 @@ inline void TransposeConv(const float* input_data, const Dims<4>& input_dims,
 }
 
 template 
-inline void Less(int64_t num_elements, const T* input1, const T* input2,
-                 bool* output) {
-  for (int64_t i = 0; i < num_elements; ++i) {
-    output[i] = input1[i] < input2[i];
-  }
+inline bool GreaterFn(T lhs, T rhs) {
+  return lhs > rhs;
+}
+template 
+inline bool GreaterEqualFn(T lhs, T rhs) {
+  return lhs >= rhs;
+}
+template 
+inline bool LessFn(T lhs, T rhs) {
+  return lhs < rhs;
+}
+template 
+inline bool LessEqualFn(T lhs, T rhs) {
+  return lhs <= rhs;
 }
 
 template 
-inline void Less(const T* input1_data, const Dims<4>& input1_dims,
-                 const T* input2_data, const Dims<4>& input2_dims,
-                 bool* output_data, const Dims<4>& output_dims) {
+using ComparisonFn = bool (*)(T, T);
+
+template  F>
+inline void Comparison(const T* input1_data, const Dims<4>& input1_dims,
+                       const T* input2_data, const Dims<4>& input2_dims,
+                       bool* output_data, const Dims<4>& output_dims) {
   const int64_t batches =
       MatchingArraySize(input1_dims, 3, input2_dims, 3, output_dims, 3);
   const int64_t height =
@@ -3633,31 +3616,149 @@ inline void Less(const T* input1_data, const Dims<4>& input1_dims,
       MatchingArraySize(input1_dims, 1, input2_dims, 1, output_dims, 1);
   const int64_t depth =
       MatchingArraySize(input1_dims, 0, input2_dims, 0, output_dims, 0);
-  Less(batches * height * width * depth, input1_data, input2_data, output_data);
+  for (int64_t i = 0; i < batches * height * width * depth; ++i) {
+    output_data[i] = F(input1_data[i], input2_data[i]);
+  }
 }
 
-template 
-inline void BroadcastLess(T1* input1_data, const Dims<4>& input1_dims,
-                          T2* input2_data, const Dims<4>& input2_dims,
-                          bool* output_data, const Dims<4>& output_dims) {
-  gemmlowp::ScopedProfilingLabel label("BroadcastLess");
+template  F>
+inline void Comparison(int left_shift, const T* input1_data,
+                       const Dims<4>& input1_dims, int32 input1_offset,
+                       int32 input1_multiplier, int input1_shift,
+                       const T* input2_data, const Dims<4>& input2_dims,
+                       int32 input2_offset, int32 input2_multiplier,
+                       int input2_shift, bool* output_data,
+                       const Dims<4>& output_dims) {
+  const int64_t batches =
+      MatchingArraySize(input1_dims, 3, input2_dims, 3, output_dims, 3);
+  const int64_t height =
+      MatchingArraySize(input1_dims, 2, input2_dims, 2, output_dims, 2);
+  const int64_t width =
+      MatchingArraySize(input1_dims, 1, input2_dims, 1, output_dims, 1);
+  const int64_t depth =
+      MatchingArraySize(input1_dims, 0, input2_dims, 0, output_dims, 0);
+  for (int64_t i = 0; i < batches * height * width * depth; ++i) {
+    const int32 input1_val = input1_offset + input1_data[i];
+    const int32 input2_val = input2_offset + input2_data[i];
+    const int32 shifted_input1_val = input1_val * (1 << left_shift);
+    const int32 shifted_input2_val = input2_val * (1 << left_shift);
+    const int32 scaled_input1_val = MultiplyByQuantizedMultiplierSmallerThanOne(
+        shifted_input1_val, input1_multiplier, input1_shift);
+    const int32 scaled_input2_val = MultiplyByQuantizedMultiplierSmallerThanOne(
+        shifted_input2_val, input2_multiplier, input2_shift);
+    output_data[i] = F(scaled_input1_val, scaled_input2_val);
+  }
+}
+
+template  F>
+inline void BroadcastComparison(const T* input1_data,
+                                const Dims<4>& input1_dims,
+                                const T* input2_data,
+                                const Dims<4>& input2_dims, bool* output_data,
+                                const Dims<4>& output_dims) {
   NdArrayDesc<4> desc1;
   NdArrayDesc<4> desc2;
   NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2);
+  for (int b = 0; b < ArraySize(output_dims, 3); ++b) {
+    for (int y = 0; y < ArraySize(output_dims, 2); ++y) {
+      for (int x = 0; x < ArraySize(output_dims, 1); ++x) {
+        for (int c = 0; c < ArraySize(output_dims, 0); ++c) {
+          output_data[Offset(output_dims, c, x, y, b)] =
+              F(input1_data[SubscriptToIndex(desc1, c, x, y, b)],
+                input2_data[SubscriptToIndex(desc2, c, x, y, b)]);
+        }
+      }
+    }
+  }
+}
 
+template  F>
+inline void BroadcastComparison(int left_shift, const T* input1_data,
+                                const Dims<4>& input1_dims, int32 input1_offset,
+                                int32 input1_multiplier, int input1_shift,
+                                const T* input2_data,
+                                const Dims<4>& input2_dims, int32 input2_offset,
+                                int32 input2_multiplier, int input2_shift,
+                                bool* output_data, const Dims<4>& output_dims) {
+  NdArrayDesc<4> desc1;
+  NdArrayDesc<4> desc2;
+  NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2);
   for (int b = 0; b < ArraySize(output_dims, 3); ++b) {
     for (int y = 0; y < ArraySize(output_dims, 2); ++y) {
       for (int x = 0; x < ArraySize(output_dims, 1); ++x) {
         for (int c = 0; c < ArraySize(output_dims, 0); ++c) {
+          const int32 input1_val =
+              input1_offset + input1_data[SubscriptToIndex(desc1, c, x, y, b)];
+          const int32 input2_val =
+              input2_offset + input2_data[SubscriptToIndex(desc2, c, x, y, b)];
+          const int32 shifted_input1_val = input1_val * (1 << left_shift);
+          const int32 shifted_input2_val = input2_val * (1 << left_shift);
+          const int32 scaled_input1_val =
+              MultiplyByQuantizedMultiplierSmallerThanOne(
+                  shifted_input1_val, input1_multiplier, input1_shift);
+          const int32 scaled_input2_val =
+              MultiplyByQuantizedMultiplierSmallerThanOne(
+                  shifted_input2_val, input2_multiplier, input2_shift);
           output_data[Offset(output_dims, c, x, y, b)] =
-              input1_data[SubscriptToIndex(desc1, c, x, y, b)] <
-              input2_data[SubscriptToIndex(desc2, c, x, y, b)];
+              F(scaled_input1_val, scaled_input2_val);
         }
       }
     }
   }
 }
 
+#define TFLITE_COMPARISON_OP(name)                                            \
+  template                                                        \
+  inline void name(const T* input1_data, const Dims<4>& input1_dims,          \
+                   const T* input2_data, const Dims<4>& input2_dims,          \
+                   bool* output_data, const Dims<4>& output_dims) {           \
+    gemmlowp::ScopedProfilingLabel label(#name);                              \
+    Comparison(input1_data, input1_dims, input2_data,            \
+                            input2_dims, output_data, output_dims);           \
+  }                                                                           \
+  template                                                        \
+  inline void name(                                                           \
+      int left_shift, const T* input1_data, const Dims<4>& input1_dims,       \
+      int32 input1_offset, int32 input1_multiplier, int input1_shift,         \
+      const T* input2_data, const Dims<4>& input2_dims, int32 input2_offset,  \
+      int32 input2_multiplier, int input2_shift, bool* output_data,           \
+      const Dims<4>& output_dims) {                                           \
+    gemmlowp::ScopedProfilingLabel label(#name "/8bit");                      \
+    BroadcastComparison(left_shift, input1_data, input1_dims,    \
+                                     input1_offset, input1_multiplier,        \
+                                     input1_shift, input2_data, input2_dims,  \
+                                     input2_offset, input2_multiplier,        \
+                                     input2_shift, output_data, output_dims); \
+  }                                                                           \
+  template                                                        \
+  inline void Broadcast##name(                                                \
+      const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, \
+      const Dims<4>& input2_dims, bool* output_data,                          \
+      const Dims<4>& output_dims) {                                           \
+    gemmlowp::ScopedProfilingLabel label("Broadcast" #name);                  \
+    BroadcastComparison(input1_data, input1_dims, input2_data,   \
+                                     input2_dims, output_data, output_dims);  \
+  }                                                                           \
+  template                                                        \
+  inline void Broadcast##name(                                                \
+      int left_shift, const T* input1_data, const Dims<4>& input1_dims,       \
+      int32 input1_offset, int32 input1_multiplier, int input1_shift,         \
+      const T* input2_data, const Dims<4>& input2_dims, int32 input2_offset,  \
+      int32 input2_multiplier, int input2_shift, bool* output_data,           \
+      const Dims<4>& output_dims) {                                           \
+    gemmlowp::ScopedProfilingLabel label("Broadcast" #name "/8bit");          \
+    BroadcastComparison(left_shift, input1_data, input1_dims,    \
+                                     input1_offset, input1_multiplier,        \
+                                     input1_shift, input2_data, input2_dims,  \
+                                     input2_offset, input2_multiplier,        \
+                                     input2_shift, output_data, output_dims); \
+  }
+TFLITE_COMPARISON_OP(Greater);
+TFLITE_COMPARISON_OP(GreaterEqual);
+TFLITE_COMPARISON_OP(Less);
+TFLITE_COMPARISON_OP(LessEqual);
+#undef TFLITE_COMPARISON_OP
+
 }  // namespace reference_ops
 }  // namespace tflite
 
diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc
index a6ea874546f455..40855891a66e71 100644
--- a/tensorflow/contrib/lite/kernels/register.cc
+++ b/tensorflow/contrib/lite/kernels/register.cc
@@ -80,7 +80,10 @@ TfLiteRegistration* Register_PRELU();
 TfLiteRegistration* Register_MAXIMUM();
 TfLiteRegistration* Register_MINIMUM();
 TfLiteRegistration* Register_ARG_MAX();
+TfLiteRegistration* Register_GREATER();
+TfLiteRegistration* Register_GREATER_EQUAL();
 TfLiteRegistration* Register_LESS();
+TfLiteRegistration* Register_LESS_EQUAL();
 TfLiteRegistration* Register_FLOOR();
 TfLiteRegistration* Register_NEG();
 
@@ -144,7 +147,10 @@ BuiltinOpResolver::BuiltinOpResolver() {
   AddBuiltin(BuiltinOperator_MAXIMUM, Register_MAXIMUM());
   AddBuiltin(BuiltinOperator_MINIMUM, Register_MINIMUM());
   AddBuiltin(BuiltinOperator_ARG_MAX, Register_ARG_MAX());
+  AddBuiltin(BuiltinOperator_GREATER, Register_GREATER());
+  AddBuiltin(BuiltinOperator_GREATER_EQUAL, Register_GREATER_EQUAL());
   AddBuiltin(BuiltinOperator_LESS, Register_LESS());
+  AddBuiltin(BuiltinOperator_LESS_EQUAL, Register_LESS_EQUAL());
   AddBuiltin(BuiltinOperator_FLOOR, Register_FLOOR());
   AddBuiltin(BuiltinOperator_NEG, Register_NEG());
 
diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc
index 6253570fa26791..21c218137797b5 100644
--- a/tensorflow/contrib/lite/model.cc
+++ b/tensorflow/contrib/lite/model.cc
@@ -672,7 +672,10 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type,
       *builtin_data = reinterpret_cast(params);
       break;
     }
-    case BuiltinOperator_LESS: {
+    case BuiltinOperator_GREATER:
+    case BuiltinOperator_GREATER_EQUAL:
+    case BuiltinOperator_LESS:
+    case BuiltinOperator_LESS_EQUAL: {
       break;
     }
     case BuiltinOperator_DELEGATE: {
diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc
index b4c46917bf9ca2..e903af87b71ee2 100644
--- a/tensorflow/contrib/lite/nnapi_delegate.cc
+++ b/tensorflow/contrib/lite/nnapi_delegate.cc
@@ -372,7 +372,10 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       case tflite::BuiltinOperator_MAXIMUM:
       case tflite::BuiltinOperator_MINIMUM:
       case tflite::BuiltinOperator_ARG_MAX:
+      case tflite::BuiltinOperator_GREATER:
+      case tflite::BuiltinOperator_GREATER_EQUAL:
       case tflite::BuiltinOperator_LESS:
+      case tflite::BuiltinOperator_LESS_EQUAL:
       case tflite::BuiltinOperator_NEG:
         FATAL("Op code %d is currently not delegated to NNAPI", builtin);
         nn_op_type = -1;  // set to invalid
diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs
index 84ff3b16bd248a..9409e762338660 100644
--- a/tensorflow/contrib/lite/schema/schema.fbs
+++ b/tensorflow/contrib/lite/schema/schema.fbs
@@ -138,6 +138,9 @@ enum BuiltinOperator : byte {
   LESS = 58,
   NEG = 59,
   PADV2 = 60,
+  GREATER = 61,
+  GREATER_EQUAL = 62,
+  LESS_EQUAL = 63,
 }
 
 // Options for the builtin operators.
@@ -183,7 +186,10 @@ union BuiltinOptions {
   DequantizeOptions,
   MaximumMinimumOptions,
   ArgMaxOptions,
+  GreaterOptions,
+  GreaterEqualOptions,
   LessOptions,
+  LessEqualOptions,
   NegOptions,
 }
 
@@ -410,9 +416,18 @@ table ArgMaxOptions {
   output_type : TensorType;
 }
 
+table GreaterOptions {
+}
+
+table GreaterEqualOptions {
+}
+
 table LessOptions {
 }
 
+table LessEqualOptions {
+}
+
 table NegOptions {
 }
 
diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h
index 8855e4ad5857ed..ae3b33063e460c 100755
--- a/tensorflow/contrib/lite/schema/schema_generated.h
+++ b/tensorflow/contrib/lite/schema/schema_generated.h
@@ -154,9 +154,18 @@ struct MaximumMinimumOptionsT;
 struct ArgMaxOptions;
 struct ArgMaxOptionsT;
 
+struct GreaterOptions;
+struct GreaterOptionsT;
+
+struct GreaterEqualOptions;
+struct GreaterEqualOptionsT;
+
 struct LessOptions;
 struct LessOptionsT;
 
+struct LessEqualOptions;
+struct LessEqualOptionsT;
+
 struct NegOptions;
 struct NegOptionsT;
 
@@ -280,11 +289,14 @@ enum BuiltinOperator {
   BuiltinOperator_LESS = 58,
   BuiltinOperator_NEG = 59,
   BuiltinOperator_PADV2 = 60,
+  BuiltinOperator_GREATER = 61,
+  BuiltinOperator_GREATER_EQUAL = 62,
+  BuiltinOperator_LESS_EQUAL = 63,
   BuiltinOperator_MIN = BuiltinOperator_ADD,
-  BuiltinOperator_MAX = BuiltinOperator_PADV2
+  BuiltinOperator_MAX = BuiltinOperator_LESS_EQUAL
 };
 
-inline BuiltinOperator (&EnumValuesBuiltinOperator())[60] {
+inline BuiltinOperator (&EnumValuesBuiltinOperator())[63] {
   static BuiltinOperator values[] = {
     BuiltinOperator_ADD,
     BuiltinOperator_AVERAGE_POOL_2D,
@@ -345,7 +357,10 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[60] {
     BuiltinOperator_MINIMUM,
     BuiltinOperator_LESS,
     BuiltinOperator_NEG,
-    BuiltinOperator_PADV2
+    BuiltinOperator_PADV2,
+    BuiltinOperator_GREATER,
+    BuiltinOperator_GREATER_EQUAL,
+    BuiltinOperator_LESS_EQUAL
   };
   return values;
 }
@@ -413,6 +428,9 @@ inline const char **EnumNamesBuiltinOperator() {
     "LESS",
     "NEG",
     "PADV2",
+    "GREATER",
+    "GREATER_EQUAL",
+    "LESS_EQUAL",
     nullptr
   };
   return names;
@@ -466,13 +484,16 @@ enum BuiltinOptions {
   BuiltinOptions_DequantizeOptions = 39,
   BuiltinOptions_MaximumMinimumOptions = 40,
   BuiltinOptions_ArgMaxOptions = 41,
-  BuiltinOptions_LessOptions = 42,
-  BuiltinOptions_NegOptions = 43,
+  BuiltinOptions_GreaterOptions = 42,
+  BuiltinOptions_GreaterEqualOptions = 43,
+  BuiltinOptions_LessOptions = 44,
+  BuiltinOptions_LessEqualOptions = 45,
+  BuiltinOptions_NegOptions = 46,
   BuiltinOptions_MIN = BuiltinOptions_NONE,
   BuiltinOptions_MAX = BuiltinOptions_NegOptions
 };
 
-inline BuiltinOptions (&EnumValuesBuiltinOptions())[44] {
+inline BuiltinOptions (&EnumValuesBuiltinOptions())[47] {
   static BuiltinOptions values[] = {
     BuiltinOptions_NONE,
     BuiltinOptions_Conv2DOptions,
@@ -516,7 +537,10 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[44] {
     BuiltinOptions_DequantizeOptions,
     BuiltinOptions_MaximumMinimumOptions,
     BuiltinOptions_ArgMaxOptions,
+    BuiltinOptions_GreaterOptions,
+    BuiltinOptions_GreaterEqualOptions,
     BuiltinOptions_LessOptions,
+    BuiltinOptions_LessEqualOptions,
     BuiltinOptions_NegOptions
   };
   return values;
@@ -566,7 +590,10 @@ inline const char **EnumNamesBuiltinOptions() {
     "DequantizeOptions",
     "MaximumMinimumOptions",
     "ArgMaxOptions",
+    "GreaterOptions",
+    "GreaterEqualOptions",
     "LessOptions",
+    "LessEqualOptions",
     "NegOptions",
     nullptr
   };
@@ -746,10 +773,22 @@ template<> struct BuiltinOptionsTraits {
   static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions;
 };
 
+template<> struct BuiltinOptionsTraits {
+  static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions;
+};
+
+template<> struct BuiltinOptionsTraits {
+  static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions;
+};
+
 template<> struct BuiltinOptionsTraits {
   static const BuiltinOptions enum_value = BuiltinOptions_LessOptions;
 };
 
+template<> struct BuiltinOptionsTraits {
+  static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions;
+};
+
 template<> struct BuiltinOptionsTraits {
   static const BuiltinOptions enum_value = BuiltinOptions_NegOptions;
 };
@@ -1113,6 +1152,22 @@ struct BuiltinOptionsUnion {
     return type == BuiltinOptions_ArgMaxOptions ?
       reinterpret_cast(value) : nullptr;
   }
+  GreaterOptionsT *AsGreaterOptions() {
+    return type == BuiltinOptions_GreaterOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
+  const GreaterOptionsT *AsGreaterOptions() const {
+    return type == BuiltinOptions_GreaterOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
+  GreaterEqualOptionsT *AsGreaterEqualOptions() {
+    return type == BuiltinOptions_GreaterEqualOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
+  const GreaterEqualOptionsT *AsGreaterEqualOptions() const {
+    return type == BuiltinOptions_GreaterEqualOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
   LessOptionsT *AsLessOptions() {
     return type == BuiltinOptions_LessOptions ?
       reinterpret_cast(value) : nullptr;
@@ -1121,6 +1176,14 @@ struct BuiltinOptionsUnion {
     return type == BuiltinOptions_LessOptions ?
       reinterpret_cast(value) : nullptr;
   }
+  LessEqualOptionsT *AsLessEqualOptions() {
+    return type == BuiltinOptions_LessEqualOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
+  const LessEqualOptionsT *AsLessEqualOptions() const {
+    return type == BuiltinOptions_LessEqualOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
   NegOptionsT *AsNegOptions() {
     return type == BuiltinOptions_NegOptions ?
       reinterpret_cast(value) : nullptr;
@@ -4056,6 +4119,86 @@ inline flatbuffers::Offset CreateArgMaxOptions(
 
 flatbuffers::Offset CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
 
+struct GreaterOptionsT : public flatbuffers::NativeTable {
+  typedef GreaterOptions TableType;
+  GreaterOptionsT() {
+  }
+};
+
+struct GreaterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef GreaterOptionsT NativeTableType;
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           verifier.EndTable();
+  }
+  GreaterOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct GreaterOptionsBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  explicit GreaterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  GreaterOptionsBuilder &operator=(const GreaterOptionsBuilder &);
+  flatbuffers::Offset Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset CreateGreaterOptions(
+    flatbuffers::FlatBufferBuilder &_fbb) {
+  GreaterOptionsBuilder builder_(_fbb);
+  return builder_.Finish();
+}
+
+flatbuffers::Offset CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct GreaterEqualOptionsT : public flatbuffers::NativeTable {
+  typedef GreaterEqualOptions TableType;
+  GreaterEqualOptionsT() {
+  }
+};
+
+struct GreaterEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef GreaterEqualOptionsT NativeTableType;
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           verifier.EndTable();
+  }
+  GreaterEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct GreaterEqualOptionsBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  explicit GreaterEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  GreaterEqualOptionsBuilder &operator=(const GreaterEqualOptionsBuilder &);
+  flatbuffers::Offset Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset CreateGreaterEqualOptions(
+    flatbuffers::FlatBufferBuilder &_fbb) {
+  GreaterEqualOptionsBuilder builder_(_fbb);
+  return builder_.Finish();
+}
+
+flatbuffers::Offset CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
 struct LessOptionsT : public flatbuffers::NativeTable {
   typedef LessOptions TableType;
   LessOptionsT() {
@@ -4096,6 +4239,46 @@ inline flatbuffers::Offset CreateLessOptions(
 
 flatbuffers::Offset CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
 
+struct LessEqualOptionsT : public flatbuffers::NativeTable {
+  typedef LessEqualOptions TableType;
+  LessEqualOptionsT() {
+  }
+};
+
+struct LessEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef LessEqualOptionsT NativeTableType;
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           verifier.EndTable();
+  }
+  LessEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct LessEqualOptionsBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  explicit LessEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  LessEqualOptionsBuilder &operator=(const LessEqualOptionsBuilder &);
+  flatbuffers::Offset Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset CreateLessEqualOptions(
+    flatbuffers::FlatBufferBuilder &_fbb) {
+  LessEqualOptionsBuilder builder_(_fbb);
+  return builder_.Finish();
+}
+
+flatbuffers::Offset CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
 struct NegOptionsT : public flatbuffers::NativeTable {
   typedef NegOptions TableType;
   NegOptionsT() {
@@ -4376,9 +4559,18 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
   const ArgMaxOptions *builtin_options_as_ArgMaxOptions() const {
     return builtin_options_type() == BuiltinOptions_ArgMaxOptions ? static_cast(builtin_options()) : nullptr;
   }
+  const GreaterOptions *builtin_options_as_GreaterOptions() const {
+    return builtin_options_type() == BuiltinOptions_GreaterOptions ? static_cast(builtin_options()) : nullptr;
+  }
+  const GreaterEqualOptions *builtin_options_as_GreaterEqualOptions() const {
+    return builtin_options_type() == BuiltinOptions_GreaterEqualOptions ? static_cast(builtin_options()) : nullptr;
+  }
   const LessOptions *builtin_options_as_LessOptions() const {
     return builtin_options_type() == BuiltinOptions_LessOptions ? static_cast(builtin_options()) : nullptr;
   }
+  const LessEqualOptions *builtin_options_as_LessEqualOptions() const {
+    return builtin_options_type() == BuiltinOptions_LessEqualOptions ? static_cast(builtin_options()) : nullptr;
+  }
   const NegOptions *builtin_options_as_NegOptions() const {
     return builtin_options_type() == BuiltinOptions_NegOptions ? static_cast(builtin_options()) : nullptr;
   }
@@ -4572,10 +4764,22 @@ template<> inline const ArgMaxOptions *Operator::builtin_options_as inline const GreaterOptions *Operator::builtin_options_as() const {
+  return builtin_options_as_GreaterOptions();
+}
+
+template<> inline const GreaterEqualOptions *Operator::builtin_options_as() const {
+  return builtin_options_as_GreaterEqualOptions();
+}
+
 template<> inline const LessOptions *Operator::builtin_options_as() const {
   return builtin_options_as_LessOptions();
 }
 
+template<> inline const LessEqualOptions *Operator::builtin_options_as() const {
+  return builtin_options_as_LessEqualOptions();
+}
+
 template<> inline const NegOptions *Operator::builtin_options_as() const {
   return builtin_options_as_NegOptions();
 }
@@ -6206,6 +6410,52 @@ inline flatbuffers::Offset CreateArgMaxOptions(flatbuffers::FlatB
       _output_type);
 }
 
+inline GreaterOptionsT *GreaterOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new GreaterOptionsT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void GreaterOptions::UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+}
+
+inline flatbuffers::Offset GreaterOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateGreaterOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  return tflite::CreateGreaterOptions(
+      _fbb);
+}
+
+inline GreaterEqualOptionsT *GreaterEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new GreaterEqualOptionsT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void GreaterEqualOptions::UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+}
+
+inline flatbuffers::Offset GreaterEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateGreaterEqualOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  return tflite::CreateGreaterEqualOptions(
+      _fbb);
+}
+
 inline LessOptionsT *LessOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
   auto _o = new LessOptionsT();
   UnPackTo(_o, _resolver);
@@ -6229,6 +6479,29 @@ inline flatbuffers::Offset CreateLessOptions(flatbuffers::FlatBuffe
       _fbb);
 }
 
+inline LessEqualOptionsT *LessEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new LessEqualOptionsT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void LessEqualOptions::UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+}
+
+inline flatbuffers::Offset LessEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateLessEqualOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  return tflite::CreateLessEqualOptions(
+      _fbb);
+}
+
 inline NegOptionsT *NegOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
   auto _o = new NegOptionsT();
   UnPackTo(_o, _resolver);
@@ -6599,10 +6872,22 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob
       auto ptr = reinterpret_cast(obj);
       return verifier.VerifyTable(ptr);
     }
+    case BuiltinOptions_GreaterOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return verifier.VerifyTable(ptr);
+    }
+    case BuiltinOptions_GreaterEqualOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return verifier.VerifyTable(ptr);
+    }
     case BuiltinOptions_LessOptions: {
       auto ptr = reinterpret_cast(obj);
       return verifier.VerifyTable(ptr);
     }
+    case BuiltinOptions_LessEqualOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return verifier.VerifyTable(ptr);
+    }
     case BuiltinOptions_NegOptions: {
       auto ptr = reinterpret_cast(obj);
       return verifier.VerifyTable(ptr);
@@ -6789,10 +7074,22 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c
       auto ptr = reinterpret_cast(obj);
       return ptr->UnPack(resolver);
     }
+    case BuiltinOptions_GreaterOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return ptr->UnPack(resolver);
+    }
+    case BuiltinOptions_GreaterEqualOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return ptr->UnPack(resolver);
+    }
     case BuiltinOptions_LessOptions: {
       auto ptr = reinterpret_cast(obj);
       return ptr->UnPack(resolver);
     }
+    case BuiltinOptions_LessEqualOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return ptr->UnPack(resolver);
+    }
     case BuiltinOptions_NegOptions: {
       auto ptr = reinterpret_cast(obj);
       return ptr->UnPack(resolver);
@@ -6967,10 +7264,22 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff
       auto ptr = reinterpret_cast(value);
       return CreateArgMaxOptions(_fbb, ptr, _rehasher).Union();
     }
+    case BuiltinOptions_GreaterOptions: {
+      auto ptr = reinterpret_cast(value);
+      return CreateGreaterOptions(_fbb, ptr, _rehasher).Union();
+    }
+    case BuiltinOptions_GreaterEqualOptions: {
+      auto ptr = reinterpret_cast(value);
+      return CreateGreaterEqualOptions(_fbb, ptr, _rehasher).Union();
+    }
     case BuiltinOptions_LessOptions: {
       auto ptr = reinterpret_cast(value);
       return CreateLessOptions(_fbb, ptr, _rehasher).Union();
     }
+    case BuiltinOptions_LessEqualOptions: {
+      auto ptr = reinterpret_cast(value);
+      return CreateLessEqualOptions(_fbb, ptr, _rehasher).Union();
+    }
     case BuiltinOptions_NegOptions: {
       auto ptr = reinterpret_cast(value);
       return CreateNegOptions(_fbb, ptr, _rehasher).Union();
@@ -7145,10 +7454,22 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL
       value = new ArgMaxOptionsT(*reinterpret_cast(u.value));
       break;
     }
+    case BuiltinOptions_GreaterOptions: {
+      value = new GreaterOptionsT(*reinterpret_cast(u.value));
+      break;
+    }
+    case BuiltinOptions_GreaterEqualOptions: {
+      value = new GreaterEqualOptionsT(*reinterpret_cast(u.value));
+      break;
+    }
     case BuiltinOptions_LessOptions: {
       value = new LessOptionsT(*reinterpret_cast(u.value));
       break;
     }
+    case BuiltinOptions_LessEqualOptions: {
+      value = new LessEqualOptionsT(*reinterpret_cast(u.value));
+      break;
+    }
     case BuiltinOptions_NegOptions: {
       value = new NegOptionsT(*reinterpret_cast(u.value));
       break;
@@ -7365,11 +7686,26 @@ inline void BuiltinOptionsUnion::Reset() {
       delete ptr;
       break;
     }
+    case BuiltinOptions_GreaterOptions: {
+      auto ptr = reinterpret_cast(value);
+      delete ptr;
+      break;
+    }
+    case BuiltinOptions_GreaterEqualOptions: {
+      auto ptr = reinterpret_cast(value);
+      delete ptr;
+      break;
+    }
     case BuiltinOptions_LessOptions: {
       auto ptr = reinterpret_cast(value);
       delete ptr;
       break;
     }
+    case BuiltinOptions_LessEqualOptions: {
+      auto ptr = reinterpret_cast(value);
+      delete ptr;
+      break;
+    }
     case BuiltinOptions_NegOptions: {
       auto ptr = reinterpret_cast(value);
       delete ptr;
diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD
index ca1390fdeb0bee..6749e63552993f 100644
--- a/tensorflow/contrib/lite/testing/BUILD
+++ b/tensorflow/contrib/lite/testing/BUILD
@@ -33,9 +33,12 @@ gen_zipped_test_files(
         "fused_batch_norm.zip",
         "gather.zip",
         "global_batch_norm.zip",
+        "greater.zip",
+        "greater_equal.zip",
         "l2_pool.zip",
         "l2norm.zip",
         "less.zip",
+        "less_equal.zip",
         "local_response_norm.zip",
         "log_softmax.zip",
         "max_pool.zip",
diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index 6fe0f491d05352..7a658d43d358a6 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -2055,6 +2055,74 @@ def build_inputs(parameters, sess, inputs, outputs):
   make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
 
 
+def make_greater_tests(zip_path):
+  """Make a set of tests to do greater."""
+
+  test_parameters = [{
+      "input_dtype": [tf.float32, tf.int32, tf.int64],
+      "input_shape_pair": [([1, 1, 1, 3], [1, 1, 1, 3]),
+                           ([2, 3, 4, 5], [2, 3, 4, 5]), ([2, 3, 3], [2, 3]),
+                           ([5, 5], [1]), ([10], [2, 4, 10])],
+  }]
+
+  def build_graph(parameters):
+    """Build the greater op testing graph."""
+    input_value1 = tf.placeholder(
+        dtype=parameters["input_dtype"],
+        name="input1",
+        shape=parameters["input_shape_pair"][0])
+    input_value2 = tf.placeholder(
+        dtype=parameters["input_dtype"],
+        name="input2",
+        shape=parameters["input_shape_pair"][1])
+    out = tf.greater(input_value1, input_value2)
+    return [input_value1, input_value2], [out]
+
+  def build_inputs(parameters, sess, inputs, outputs):
+    input_value1 = create_tensor_data(parameters["input_dtype"],
+                                      parameters["input_shape_pair"][0])
+    input_value2 = create_tensor_data(parameters["input_dtype"],
+                                      parameters["input_shape_pair"][1])
+    return [input_value1, input_value2], sess.run(
+        outputs, feed_dict=dict(zip(inputs, [input_value1, input_value2])))
+
+  make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
+
+
+def make_greater_equal_tests(zip_path):
+  """Make a set of tests to do greater_equal."""
+
+  test_parameters = [{
+      "input_dtype": [tf.float32, tf.int32, tf.int64],
+      "input_shape_pair": [([1, 1, 1, 3], [1, 1, 1, 3]),
+                           ([2, 3, 4, 5], [2, 3, 4, 5]), ([2, 3, 3], [2, 3]),
+                           ([5, 5], [1]), ([10], [2, 4, 10])],
+  }]
+
+  def build_graph(parameters):
+    """Build the greater_equal op testing graph."""
+    input_value1 = tf.placeholder(
+        dtype=parameters["input_dtype"],
+        name="input1",
+        shape=parameters["input_shape_pair"][0])
+    input_value2 = tf.placeholder(
+        dtype=parameters["input_dtype"],
+        name="input2",
+        shape=parameters["input_shape_pair"][1])
+    out = tf.greater_equal(input_value1, input_value2)
+    return [input_value1, input_value2], [out]
+
+  def build_inputs(parameters, sess, inputs, outputs):
+    input_value1 = create_tensor_data(parameters["input_dtype"],
+                                      parameters["input_shape_pair"][0])
+    input_value2 = create_tensor_data(parameters["input_dtype"],
+                                      parameters["input_shape_pair"][1])
+    return [input_value1, input_value2], sess.run(
+        outputs, feed_dict=dict(zip(inputs, [input_value1, input_value2])))
+
+  make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
+
+
 def make_less_tests(zip_path):
   """Make a set of tests to do less."""
 
@@ -2089,6 +2157,40 @@ def build_inputs(parameters, sess, inputs, outputs):
   make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
 
 
+def make_less_equal_tests(zip_path):
+  """Make a set of tests to do less_equal."""
+
+  test_parameters = [{
+      "input_dtype": [tf.float32, tf.int32, tf.int64],
+      "input_shape_pair": [([1, 1, 1, 3], [1, 1, 1, 3]),
+                           ([2, 3, 4, 5], [2, 3, 4, 5]), ([2, 3, 3], [2, 3]),
+                           ([5, 5], [1]), ([10], [2, 4, 10])],
+  }]
+
+  def build_graph(parameters):
+    """Build the less_equal op testing graph."""
+    input_value1 = tf.placeholder(
+        dtype=parameters["input_dtype"],
+        name="input1",
+        shape=parameters["input_shape_pair"][0])
+    input_value2 = tf.placeholder(
+        dtype=parameters["input_dtype"],
+        name="input2",
+        shape=parameters["input_shape_pair"][1])
+    out = tf.less_equal(input_value1, input_value2)
+    return [input_value1, input_value2], [out]
+
+  def build_inputs(parameters, sess, inputs, outputs):
+    input_value1 = create_tensor_data(parameters["input_dtype"],
+                                      parameters["input_shape_pair"][0])
+    input_value2 = create_tensor_data(parameters["input_dtype"],
+                                      parameters["input_shape_pair"][1])
+    return [input_value1, input_value2], sess.run(
+        outputs, feed_dict=dict(zip(inputs, [input_value1, input_value2])))
+
+  make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
+
+
 def make_floor_tests(zip_path):
   """Make a set of tests to do floor."""
 
diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
index 96681952c947b4..2ce14f3b38dda1 100644
--- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
+++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
@@ -258,9 +258,12 @@ INSTANTIATE_TESTS(fully_connected)
 INSTANTIATE_TESTS(fused_batch_norm)
 INSTANTIATE_TESTS(gather)
 INSTANTIATE_TESTS(global_batch_norm)
+INSTANTIATE_TESTS(greater)
+INSTANTIATE_TESTS(greater_equal)
 INSTANTIATE_TESTS(l2_pool)
 INSTANTIATE_TESTS(l2norm)
 INSTANTIATE_TESTS(less)
+INSTANTIATE_TESTS(less_equal)
 INSTANTIATE_TESTS(local_response_norm)
 INSTANTIATE_TESTS(log_softmax)
 INSTANTIATE_TESTS(max_pool)
diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc
index 9e899cf97750a4..53df1987b30ae0 100644
--- a/tensorflow/contrib/lite/toco/export_tensorflow.cc
+++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc
@@ -1702,6 +1702,19 @@ void ConvertRandomUniformOperator(const Model& model,
   (*new_op->mutable_attr())["seed2"].set_i(src_op.seed2);
 }
 
+void ConvertComparisonOperator(const Model& model, const Operator& src_op,
+                               const char* op_name,
+                               GraphDef* tensorflow_graph) {
+  auto* comparison_op = tensorflow_graph->add_node();
+  comparison_op->set_op(op_name);
+  comparison_op->set_name(src_op.outputs[0]);
+  CHECK_EQ(src_op.inputs.size(), 2);
+  *comparison_op->add_input() = src_op.inputs[0];
+  *comparison_op->add_input() = src_op.inputs[1];
+  const auto data_type = GetTensorFlowDataType(model, src_op.inputs[0]);
+  (*comparison_op->mutable_attr())["T"].set_type(data_type);
+}
+
 void ConvertOperator(const Model& model, const Operator& src_op,
                      GraphDef* tensorflow_graph) {
   if (src_op.fused_activation_function != FusedActivationFunctionType::kNone) {
@@ -1893,6 +1906,14 @@ void ConvertOperator(const Model& model, const Operator& src_op,
     ConvertRandomUniformOperator(
         model, static_cast(src_op),
         tensorflow_graph);
+  } else if (src_op.type == OperatorType::kTensorFlowGreater) {
+    ConvertComparisonOperator(model, src_op, "Greater", tensorflow_graph);
+  } else if (src_op.type == OperatorType::kTensorFlowGreaterEqual) {
+    ConvertComparisonOperator(model, src_op, "GreaterEqual", tensorflow_graph);
+  } else if (src_op.type == OperatorType::kTensorFlowLess) {
+    ConvertComparisonOperator(model, src_op, "Less", tensorflow_graph);
+  } else if (src_op.type == OperatorType::kTensorFlowLessEqual) {
+    ConvertComparisonOperator(model, src_op, "LessEqual", tensorflow_graph);
   } else {
     LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(src_op.type);
   }
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
index 9b0e2321327b6c..a081abea559542 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
@@ -499,8 +499,8 @@ void ProcessTensorFlowReshapeOperator(Model* model,
       << op->outputs[0] << "\". Are your input shapes correct?";
 }
 
-void ProcessSimpleOperator(Model* model, Operator* op) {
-  const auto& input_array = model->GetArray(op->inputs[0]);
+void ProcessSimpleOperator(Model* model, Operator* op, int input_index) {
+  const auto& input_array = model->GetArray(op->inputs[input_index]);
   // Yield until input dims have been resolved.
   if (!input_array.has_shape()) {
     return;
@@ -1499,7 +1499,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) {
     case OperatorType::kCast:
     case OperatorType::kFloor:
     case OperatorType::kExp:
-      ProcessSimpleOperator(model, op);
+      ProcessSimpleOperator(model, op, 0);
       break;
     case OperatorType::kGather:
       ProcessGatherOperator(model, static_cast(op));
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc
index 58e214b76ba5e6..a1ca7371c87f4c 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc
@@ -55,7 +55,11 @@ bool SupportsQuantization(const Operator& op) {
          type == OperatorType::kStridedSlice ||
          type == OperatorType::kDepthToSpace ||
          type == OperatorType::kLstmCell || type == OperatorType::kGather ||
-         type == OperatorType::kTranspose || type == OperatorType::kMean;
+         type == OperatorType::kTranspose || type == OperatorType::kMean ||
+         type == OperatorType::kTensorFlowGreater ||
+         type == OperatorType::kTensorFlowGreaterEqual ||
+         type == OperatorType::kTensorFlowLess ||
+         type == OperatorType::kTensorFlowLessEqual;
 }
 
 const MinMax& GetOrComputeMinMax(Model* model, const string& array_name) {
@@ -257,8 +261,7 @@ bool ChooseHardcodedQuantizationForOperatorOutput(
         IsExactlyRepresentable(0., *quantized_data_type, *quantization_params));
     return true;
   }
-  if ((op.type == OperatorType::kLogistic) ||
-      (op.type == OperatorType::kSoftmax)) {
+  if (op.type == OperatorType::kLogistic || op.type == OperatorType::kSoftmax) {
     // Logistic and Softmax have range: [0, 1].
     //
     // For Logistic, 0.5 should be exactly representable, as implementations
diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc
index df784a2a76ee51..a008e633512760 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator.cc
+++ b/tensorflow/contrib/lite/toco/tflite/operator.cc
@@ -915,9 +915,16 @@ std::vector> BuildOperatorList() {
       "MAXIMUM", OperatorType::kTensorFlowMaximum));
   ops.emplace_back(new SimpleOperator(
       "MINIMUM", OperatorType::kTensorFlowMinimum));
+  ops.emplace_back(new SimpleOperator(
+      "GREATER", OperatorType::kTensorFlowGreater));
+  ops.emplace_back(new SimpleOperator(
+      "GREATER_EQUAL", OperatorType::kTensorFlowGreaterEqual));
   ops.emplace_back(new SimpleOperator(
       "LESS", OperatorType::kTensorFlowLess));
+  ops.emplace_back(new SimpleOperator(
+      "LESS_EQUAL", OperatorType::kTensorFlowLessEqual));
   ops.emplace_back(new SimpleOperator("NEG", OperatorType::kNeg));
+
   return ops;
 }
 }  // namespace

From a0496a1646fd13188cc985889ca325c004674a17 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 12:41:07 -0700
Subject: [PATCH 0454/1691] Make test tensorflow/python/keras:resnet50_test be
 size "medium"

This test sometimes runs longer than 60s, and has been getting flaky timeouts
as a result.  With a longer timeout, it succeeds reliably.

PiperOrigin-RevId: 195704998
---
 tensorflow/python/keras/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD
index 1b66f589397527..37b24841bdd4db 100755
--- a/tensorflow/python/keras/BUILD
+++ b/tensorflow/python/keras/BUILD
@@ -395,7 +395,7 @@ py_test(
 
 py_test(
     name = "resnet50_test",
-    size = "small",
+    size = "medium",
     srcs = ["_impl/keras/applications/resnet50_test.py"],
     srcs_version = "PY2AND3",
     deps = [

From 91fb950c266345ca5b689038adad5e1c31d36b57 Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Mon, 7 May 2018 12:48:38 -0700
Subject: [PATCH 0455/1691] Rename HloDotWithContractDimsMatcher to
 HloDotWithContractingDimsMatcher

This is a typo I introduced in cr/195514907.

PiperOrigin-RevId: 195706006
---
 tensorflow/compiler/xla/service/hlo_matchers.cc | 4 ++--
 tensorflow/compiler/xla/service/hlo_matchers.h  | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_matchers.cc b/tensorflow/compiler/xla/service/hlo_matchers.cc
index 41ce9c17625131..7e4b8834357d39 100644
--- a/tensorflow/compiler/xla/service/hlo_matchers.cc
+++ b/tensorflow/compiler/xla/service/hlo_matchers.cc
@@ -198,7 +198,7 @@ void HloShardingMatcher::DescribeTo(std::ostream* os) const {
   }
 }
 
-bool HloDotWithContractDimsMatcher::MatchAndExplain(
+bool HloDotWithContractingDimsMatcher::MatchAndExplain(
     const HloInstruction* instruction,
     ::testing::MatchResultListener* listener) const {
   if (!HloMatcher::MatchAndExplain(instruction, listener)) {
@@ -227,7 +227,7 @@ bool HloDotWithContractDimsMatcher::MatchAndExplain(
   return true;
 }
 
-void HloDotWithContractDimsMatcher::DescribeTo(std::ostream* os) const {
+void HloDotWithContractingDimsMatcher::DescribeTo(std::ostream* os) const {
   HloMatcher::DescribeTo(os);
   *os << " with lhs_contracting_dims={" << lhs_contracting_dim_
       << "} and rhs_contracting_dims={" << rhs_contracting_dim_ << "}";
diff --git a/tensorflow/compiler/xla/service/hlo_matchers.h b/tensorflow/compiler/xla/service/hlo_matchers.h
index 75231beac7500c..c33bdadf1c7145 100644
--- a/tensorflow/compiler/xla/service/hlo_matchers.h
+++ b/tensorflow/compiler/xla/service/hlo_matchers.h
@@ -133,9 +133,9 @@ class HloShardingMatcher
 
 // Matches a Dot HLO instruction with specific LHS and RHS contracting
 // dimensions.
-class HloDotWithContractDimsMatcher : public HloMatcher {
+class HloDotWithContractingDimsMatcher : public HloMatcher {
  public:
-  explicit HloDotWithContractDimsMatcher(
+  explicit HloDotWithContractingDimsMatcher(
       ::testing::Matcher lhs,
       ::testing::Matcher rhs, int64 lhs_contracting_dim,
       int64 rhs_contracting_dim)
@@ -350,7 +350,7 @@ inline ::testing::Matcher Dot(
     ::testing::Matcher rhs_matcher,
     int64 lhs_contracting_dim, int64 rhs_contracting_dim) {
   return ::testing::MakeMatcher(
-      new ::xla::testing::HloDotWithContractDimsMatcher(
+      new ::xla::testing::HloDotWithContractingDimsMatcher(
           lhs_matcher, rhs_matcher, lhs_contracting_dim, rhs_contracting_dim));
 }
 

From 914c971c7b690661754e83549325c5deadd9e62d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 13:18:33 -0700
Subject: [PATCH 0456/1691] Specialize functions only once per unique context.

PiperOrigin-RevId: 195710562
---
 .../grappler/optimizers/function_optimizer.cc | 140 +++++++++++++++++-
 .../optimizers/function_optimizer_test.cc     | 117 +++++++++++++++
 .../optimizers/meta_optimizer_test.cc         |  19 ++-
 tensorflow/core/grappler/utils/functions.cc   |  57 +++++++
 tensorflow/core/grappler/utils/functions.h    |  21 ++-
 .../core/grappler/utils/functions_test.cc     |  38 +++++
 6 files changed, 376 insertions(+), 16 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc
index 1bec9086f7151f..a44e1ee7f939c2 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc
@@ -14,10 +14,13 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/core/grappler/optimizers/function_optimizer.h"
+
 #include 
+
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/common_runtime/process_function_library_runtime.h"
+#include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/function.h"
 #include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/graph_def_util.h"
@@ -74,6 +77,73 @@ string UniqueSpecializedFunctionName(const FunctionDef& func,
   return unique_name;
 }
 
+// Specialized function instantiation type parameters, body parameters, and
+// const inputs.
+struct FunctionSpecializationSignature {
+  string func_name;
+  std::unordered_map type_parameters;
+  std::unordered_map body_parameters;
+  std::unordered_map const_inputs;
+
+  bool operator==(const FunctionSpecializationSignature& other) const {
+    bool equals = func_name == other.func_name &&
+                  type_parameters == other.type_parameters &&
+                  const_inputs == other.const_inputs;
+
+    if (!equals) return false;
+
+    // Equality is not defined for AttrValue.
+    if (body_parameters.size() != other.body_parameters.size()) return false;
+
+    for (const auto& lhs : body_parameters) {
+      auto it = other.body_parameters.find(lhs.first);
+      if (it == other.body_parameters.end()) return false;
+      if (!AreAttrValuesEqual(lhs.second, (*it).second)) return false;
+    }
+
+    return true;
+  }
+
+  struct Hash {
+    uint64 operator()(FunctionSpecializationSignature const& s) const {
+      uint64 h = Hash64(s.func_name);
+
+      // Use std::map for deterministic iteration order.
+
+      std::map types(s.type_parameters.begin(),
+                                       s.type_parameters.end());
+      for (const auto& pair : types) {
+        AttrValue attr_value;
+        attr_value.set_type(pair.second);
+        h = Hash64Combine(Hash64(pair.first), h);
+        h = Hash64Combine(AttrValueHash(attr_value), h);
+      }
+
+      std::map body(s.body_parameters.begin(),
+                                       s.body_parameters.end());
+      for (const auto& pair : body) {
+        h = Hash64Combine(Hash64(pair.first), h);
+        h = Hash64Combine(AttrValueHash(pair.second), h);
+      }
+
+      std::map inputs(s.const_inputs.begin(),
+                                   s.const_inputs.end());
+      for (const auto& pair : inputs) {
+        h = Hash64Combine(std::hash()(pair.first), h);
+        h = Hash64Combine(Hash64(pair.second), h);
+      }
+
+      return h;
+    }
+  };
+};
+
+struct FunctionSpecialization {
+  string specialized_func_name;
+  std::unordered_set const_inputs;
+  std::unordered_set control_deps;
+};
+
 class FunctionOptimizerContext {
  public:
   explicit FunctionOptimizerContext(RewriterConfig::Toggle opt_level,
@@ -108,6 +178,16 @@ class FunctionOptimizerContext {
     return gtl::FindWithDefault(inlined_functions_, name, nullptr);
   }
 
+  const FunctionSpecialization* FindFunctionSpecialization(
+      const FunctionSpecializationSignature& sig) const {
+    return gtl::FindOrNull(specialized_functions_, sig);
+  }
+
+  void AddSpecializedFunction(const FunctionSpecializationSignature& sig,
+                              const FunctionSpecialization& specialized_func) {
+    specialized_functions_.emplace(sig, specialized_func);
+  }
+
  private:
   void InitializeTrulyConstNodes(const GrapplerItem& item) {
     std::unordered_set feed_nodes;
@@ -148,6 +228,12 @@ class FunctionOptimizerContext {
   // Nodes that are Const and not in feed.
   std::unordered_map truly_const_nodes_;
 
+  // Specialized functions.
+  std::unordered_map
+      specialized_functions_;
+
   TF_DISALLOW_COPY_AND_ASSIGN(FunctionOptimizerContext);
 };
 
@@ -303,14 +389,34 @@ void RemovePushedDownConstInputs(const std::unordered_set& const_inputs,
 
     for (const string& ctrl : control_deps) {
       if (existing_control_deps.find(ctrl) == existing_control_deps.end()) {
-        VLOG(3) << "Forward control dependency to function caller node: input="
-                << ctrl;
+        VLOG(3) << "Forward control dependency: input=" << ctrl;
         specialized_func_node->add_input(ctrl);
       }
     }
   }
 }
 
+Status InitializeFunctionSpecializationSignature(
+    const NodeDef& func_node, const FunctionDef& func,
+    const AttrValueMap& func_attr, const FunctionOptimizerContext& ctx,
+    FunctionSpecializationSignature* sig) {
+  sig->func_name = func.signature().name();
+
+  TF_RETURN_IF_ERROR(
+      InstantiationTypeParameters(func, func_attr, &sig->type_parameters));
+  TF_RETURN_IF_ERROR(
+      InstantiationBodyParameters(func, func_attr, &sig->body_parameters));
+
+  for (int i = 0; i < func_node.input_size(); ++i) {
+    const string& input = func_node.input(i);
+    if (ctx.IsTrulyConst(input)) {
+      sig->const_inputs.emplace(i, input);
+    }
+  }
+
+  return Status::OK();
+}
+
 Status SpecializeFunction(const NodeDef& func_node, const FunctionDef& func,
                           FunctionOptimizerContext* ctx,
                           GraphDef* optimized_graph) {
@@ -320,6 +426,32 @@ Status SpecializeFunction(const NodeDef& func_node, const FunctionDef& func,
   const std::unordered_map func_attr(
       func_node.attr().begin(), func_node.attr().end());
 
+  FunctionSpecializationSignature signature;
+  TF_RETURN_IF_ERROR(InitializeFunctionSpecializationSignature(
+      func_node, func, func_attr, *ctx, &signature));
+
+  // Check if function was already specialized for identical context.
+  const FunctionSpecialization* already_specialized =
+      ctx->FindFunctionSpecialization(signature);
+
+  if (already_specialized) {
+    VLOG(2) << "Function was already specialized in identical context: "
+               "specialized_name="
+            << already_specialized->specialized_func_name;
+
+    // Add a function call node for the specialized function.
+    NodeDef* specialized_func_node = optimized_graph->add_node();
+    *specialized_func_node = func_node;
+    specialized_func_node->set_op(already_specialized->specialized_func_name);
+
+    RemovePushedDownConstInputs(already_specialized->const_inputs,
+                                already_specialized->control_deps,
+                                specialized_func_node);
+
+    return Status::OK();
+  }
+
+  // Add a new specialized function definition to the library.
   const auto& flib = ctx->function_library();
 
   // Make a GrapplerFunctionItem and convert it back to FunctionDef after
@@ -358,6 +490,10 @@ Status SpecializeFunction(const NodeDef& func_node, const FunctionDef& func,
   // Update specialized node to remove inputs for pushed down consts.
   RemovePushedDownConstInputs(const_inputs, control_deps,
                               specialized_func_node);
+
+  ctx->AddSpecializedFunction(
+      signature, {specialized_func_name, const_inputs, control_deps});
+
   return Status::OK();
 }
 
diff --git a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
index 147a2644212e23..a2dbab3dedd61f 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
@@ -718,5 +718,122 @@ TEST_F(FunctionOptimizerTest, SpecializeFunction_PushDownConstInput) {
   test::ExpectTensorEqual(tensors_expected[0], tensors[0]);
 }
 
+TEST_F(FunctionOptimizerTest, SpecializeFunction_OncePerUniqueContext) {
+  using test::function::NDef;
+
+  FunctionOptimizer optimizer(RewriterConfig::DEFAULT);
+
+  // Mark MyMul as noinline.
+  FunctionDef mul_func = FunctionDefHelper::Create(
+      "MyMul", {"x:T", "y:T"}, {"z:T"}, {"T: {float, int32}"},
+      {{{"output"}, "Mul", {"x", "y"}, {{"T", "$T"}}}},
+      /* Mapping between function returns and function node outputs. */
+      {{"z", "output:z:0"}});
+  (*mul_func.mutable_attr())["_noinline"].set_b(true);
+  std::vector function_library = {mul_func};
+
+  const Tensor kTwo = test::AsScalar(2.0);
+  const Tensor kThree = test::AsScalar(3.0);
+
+  GrapplerItem item;
+  item.graph = test::function::GDef(
+      {NDef("init", "NoOp", {}, {}, kDevice),
+
+       // Float placeholders.
+       NDef("xf", "Placeholder", {}, {{"dtype", DT_FLOAT}}, kDevice),
+       NDef("yf", "Placeholder", {}, {{"dtype", DT_FLOAT}}, kDevice),
+
+       // Int32 placeholders.
+       NDef("xi", "Placeholder", {}, {{"dtype", DT_INT32}}, kDevice),
+       NDef("yi", "Placeholder", {}, {{"dtype", DT_INT32}}, kDevice),
+
+       // Consts. Control inputs has to be attached to specialized func calls.
+       NDef("two", "Const", {"^init", "^xf"},
+            {{"dtype", DT_FLOAT}, {"value", kTwo}}, kDevice),
+       NDef("three", "Const", {"^init", "^xf"},
+            {{"dtype", DT_FLOAT}, {"value", kThree}}, kDevice),
+
+       // Specialization #1: DT_FLOAT type parameter.
+       NDef("mul_1", "MyMul", {"xf", "yf"}, {{"T", DT_FLOAT}}, kDevice),
+       NDef("mul_2", "MyMul", {"yf", "xf"}, {{"T", DT_FLOAT}}, kDevice),
+
+       // Specialization #2: DT_INT32 type parameter.
+       NDef("mul_3", "MyMul", {"xi", "yi"}, {{"T", DT_INT32}}, kDevice),
+
+       // Specialization #3: DT_FLOAT type parameter + const input kTwo.
+       NDef("mul_4", "MyMul", {"xf", "two"}, {{"T", DT_FLOAT}}, kDevice),
+       NDef("mul_5", "MyMul", {"yf", "two"}, {{"T", DT_FLOAT}}, kDevice),
+
+       // Specialization #4: DT_FLOAT type parameter + const input kThree.
+       NDef("mul_6", "MyMul", {"three", "xf"}, {{"T", DT_FLOAT}}, kDevice)},
+      function_library);
+
+  GraphDef output;
+  TF_EXPECT_OK(optimizer.Optimize(nullptr, item, &output));
+
+  // Make sure that MyMul was specialized once per unique context.
+  EXPECT_EQ(4, output.library().function_size());
+
+  // And graph nodes calling specialized functions.
+  int count = 0;
+  for (const NodeDef& node : output.node()) {
+    if (node.name() == "mul_1" && count++) {
+      EXPECT_EQ("MyMul_specialized_for_mul_1", node.op());
+      ASSERT_EQ(2, node.input_size());
+      EXPECT_EQ("xf", node.input(0));
+      EXPECT_EQ("yf", node.input(1));
+
+    } else if (node.name() == "mul_2" && count++) {
+      EXPECT_EQ("MyMul_specialized_for_mul_1", node.op());
+      ASSERT_EQ(2, node.input_size());
+      EXPECT_EQ("yf", node.input(0));
+      EXPECT_EQ("xf", node.input(1));
+
+    } else if (node.name() == "mul_3" && count++) {
+      EXPECT_EQ("MyMul_specialized_for_mul_3", node.op());
+      ASSERT_EQ(2, node.input_size());
+      EXPECT_EQ("xi", node.input(0));
+      EXPECT_EQ("yi", node.input(1));
+
+    } else if (node.name() == "mul_4" && count++) {
+      EXPECT_EQ("MyMul_specialized_for_mul_4", node.op());
+      ASSERT_EQ(2, node.input_size());
+      EXPECT_EQ("xf", node.input(0));
+      EXPECT_EQ("^init", node.input(1));
+
+    } else if (node.name() == "mul_5" && count++) {
+      EXPECT_EQ("MyMul_specialized_for_mul_4", node.op());
+      ASSERT_EQ(3, node.input_size());
+      EXPECT_EQ("yf", node.input(0));
+      EXPECT_EQ("^init", node.input(1));
+      EXPECT_EQ("^xf", node.input(2));
+
+    } else if (node.name() == "mul_6" && count++) {
+      EXPECT_EQ("MyMul_specialized_for_mul_6", node.op());
+      ASSERT_EQ(2, node.input_size());
+      EXPECT_EQ("xf", node.input(0));
+      EXPECT_EQ("^init", node.input(1));
+    }
+  }
+  EXPECT_EQ(6, count);
+
+  // And that graph evaluation yields the same result.
+  Tensor pi = test::AsScalar(3.14f);
+  Tensor four = test::AsScalar(4);
+  item.fetch = {"mul_1", "mul_2", "mul_3", "mul_4", "mul_5", "mul_6"};
+  item.feed = {{"xf", pi}, {"yf", pi}, {"xi", four}, {"yi", four}};
+
+  auto tensors_expected = EvaluateFetchNodes(item);
+  GrapplerItem optimized(item, std::move(output));
+  auto tensors = EvaluateFetchNodes(optimized);
+
+  test::ExpectTensorEqual(tensors_expected[0], tensors[0]);
+  test::ExpectTensorEqual(tensors_expected[1], tensors[1]);
+  test::ExpectTensorEqual(tensors_expected[2], tensors[2]);
+  test::ExpectTensorEqual(tensors_expected[3], tensors[3]);
+  test::ExpectTensorEqual(tensors_expected[4], tensors[4]);
+  test::ExpectTensorEqual(tensors_expected[5], tensors[5]);
+}
+
 }  // namespace grappler
 }  // namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc b/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc
index 887a988af9afed..8247cce33922e6 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc
@@ -163,30 +163,28 @@ TEST_F(MetaOptimizerTest, OptimizeFunctionLibrary) {
                                            output.library());
 
   // Specialized and optimized functions should be added to the graph.
-  EXPECT_EQ(6, optimized_flib.num_functions());
+  EXPECT_EQ(5, optimized_flib.num_functions());
 
   // MyQuadratic should be specialized once:
   //   0. 'quadratic' node in the main graph
   const string optimized_0 = "MyQuadratic_specialized_for_quadratic";
 
   // MySquare should be specialized and optimized for 3 instantiations:
-  //   1. 'square' node in the main graph
-  //   2. 'square' node in the MyQuadratic specialization
-  //   3. 'quadratic' node in the MyQuadratic specialization
+  //   1.  'square' node in the main graph
+  //   2.  'square' node in the MyQuadratic specialization
+  //   3*. 'quadratic' node in the MyQuadratic specialization
+  //        has identical instantiation context to #2
 
   const string optimized_1 = "MySquare_specialized_for_square";
   const string optimized_2 = "MySquare_specialized_for_square_1";
-  const string optimized_3 = "MySquare_specialized_for_quadratic";
 
   const FunctionDef* optimized_func_0 = optimized_flib.Find(optimized_0);
   const FunctionDef* optimized_func_1 = optimized_flib.Find(optimized_1);
   const FunctionDef* optimized_func_2 = optimized_flib.Find(optimized_2);
-  const FunctionDef* optimized_func_3 = optimized_flib.Find(optimized_3);
 
   ASSERT_NE(optimized_func_0, nullptr);
   ASSERT_NE(optimized_func_1, nullptr);
   ASSERT_NE(optimized_func_2, nullptr);
-  ASSERT_NE(optimized_func_3, nullptr);
 
   // Graph should call optimized function.
   int count = 0;
@@ -205,13 +203,14 @@ TEST_F(MetaOptimizerTest, OptimizeFunctionLibrary) {
     if (node.name() == "square" && count++) {
       EXPECT_EQ(optimized_2, node.op());
     } else if (node.name() == "quadratic" && count++) {
-      EXPECT_EQ(optimized_3, node.op());
+      // Share specialized function with the 'square' node.
+      EXPECT_EQ(optimized_2, node.op());
     }
   }
   EXPECT_EQ(2, count);
 
-  const std::vector optimized_funcs = {
-      optimized_func_1, optimized_func_1, optimized_func_3};
+  const std::vector optimized_funcs = {optimized_func_1,
+                                                           optimized_func_2};
 
   // MyMul should be inlined into all optimized versions of MySquare.
   for (const FunctionDef* optimized_func : optimized_funcs) {
diff --git a/tensorflow/core/grappler/utils/functions.cc b/tensorflow/core/grappler/utils/functions.cc
index 79b823fa2da129..34603f98693b07 100644
--- a/tensorflow/core/grappler/utils/functions.cc
+++ b/tensorflow/core/grappler/utils/functions.cc
@@ -417,6 +417,63 @@ bool IsParametrized(const FunctionDef& func) {
   return HasParametrizedType(func) || HasParametrizedBody(func);
 }
 
+Status InstantiationTypeParameters(
+    const FunctionDef& func, const AttrValueMap& func_instantiation_attr,
+    std::unordered_map* type_parameters) {
+  if (!type_parameters->empty()) {
+    return errors::InvalidArgument("Type parameters output map must be empty");
+  }
+
+  GrapplerFunctionItemInstantiation instantiation(&func_instantiation_attr);
+
+  const auto resolve_type_attr = [&](const OpDef::ArgDef& arg) {
+    // Check if it's unknown and unresolved type.
+    if (arg.type() == DT_INVALID &&
+        type_parameters->find(arg.type_attr()) == type_parameters->end()) {
+      DataType data_type;
+      TF_RETURN_IF_ERROR(instantiation.GetArgType(arg, &data_type));
+      type_parameters->insert({arg.type_attr(), data_type});
+    }
+    return Status::OK();
+  };
+
+  for (const auto& input : func.signature().input_arg())
+    TF_RETURN_IF_ERROR(resolve_type_attr(input));
+  for (const auto& output : func.signature().output_arg())
+    TF_RETURN_IF_ERROR(resolve_type_attr(output));
+
+  return Status::OK();
+}
+
+Status InstantiationBodyParameters(
+    const FunctionDef& func, const AttrValueMap& func_instantiation_attr,
+    std::unordered_map* body_parameters) {
+  if (!body_parameters->empty()) {
+    return errors::InvalidArgument("Body parameters output map must be empty");
+  }
+
+  for (const NodeDef& func_body_node : func.node_def()) {
+    for (auto& attr : func_body_node.attr()) {
+      const string& placeholder = attr.second.placeholder();
+
+      if (placeholder.empty() ||
+          body_parameters->find(placeholder) != body_parameters->end()) {
+        continue;
+      }
+
+      auto it = func_instantiation_attr.find(placeholder);
+      if (it != func_instantiation_attr.end()) {
+        body_parameters->emplace(placeholder, it->second);
+      } else {
+        return errors::InvalidArgument("Can't resolve placeholder: ",
+                                       placeholder);
+      }
+    }
+  }
+
+  return Status::OK();
+}
+
 Status MakeGrapplerFunctionItem(const FunctionDef& func,
                                 const AttrValueMap& func_instantiation_attr,
                                 const FunctionLibraryDefinition& flib,
diff --git a/tensorflow/core/grappler/utils/functions.h b/tensorflow/core/grappler/utils/functions.h
index d9d71b80ebcf4d..4641bf5252f67d 100644
--- a/tensorflow/core/grappler/utils/functions.h
+++ b/tensorflow/core/grappler/utils/functions.h
@@ -191,6 +191,19 @@ bool HasParametrizedBody(const FunctionDef& func);
 // Check if function has parametrized type or body.
 bool IsParametrized(const FunctionDef& func);
 
+// Resolve function instantiation type parameters from the attributes of the
+// caller node. Return error if type can't be resolved.
+Status InstantiationTypeParameters(
+    const FunctionDef& func, const AttrValueMap& func_instantiation_attr,
+    std::unordered_map* type_parameters);
+
+// Resolve function instantiation body parameters (values for the function body
+// attr placeholders) from the attributes of the caller node. Return error if
+// type can't be resolved.
+Status InstantiationBodyParameters(
+    const FunctionDef& func, const AttrValueMap& func_instantiation_attr,
+    std::unordered_map* body_parameters);
+
 // Register GrapplerFunctionItem input arg expansion and function body outputs
 // in the GrapplerFunctionConnectivity. Use function library definition to
 // lookup function body nodes output names and ranges.
@@ -205,10 +218,10 @@ Status ReplaceInputWithConst(const NodeDef& input_const, int input_position,
 // Make a GrapplerFunctionItem from the function definition and function
 // instantiation attributes (caller node attributes). Returns error if the given
 // function def cannot be converted (e.g. not all attributes are defined).
-Status MakeGrapplerFunctionItem(
-    const FunctionDef& func,
-    const std::unordered_map& func_instantiation_attr,
-    const FunctionLibraryDefinition& flib, GrapplerFunctionItem* item);
+Status MakeGrapplerFunctionItem(const FunctionDef& func,
+                                const AttrValueMap& func_instantiation_attr,
+                                const FunctionLibraryDefinition& flib,
+                                GrapplerFunctionItem* item);
 
 // Make a GrapplerFunction item from the function definition. Function must be
 // fully defined (no type or body parametrization).
diff --git a/tensorflow/core/grappler/utils/functions_test.cc b/tensorflow/core/grappler/utils/functions_test.cc
index fa6fec70ff9744..15d84374384f5e 100644
--- a/tensorflow/core/grappler/utils/functions_test.cc
+++ b/tensorflow/core/grappler/utils/functions_test.cc
@@ -54,6 +54,44 @@ TEST_F(FunctionsTest, IsParametrized) {
   EXPECT_FALSE(IsParametrized(non_parametrized_func));
 }
 
+TEST_F(FunctionsTest, InstantiationParameters) {
+  // Function definition is invalid, only type/body parameters are important.
+  FunctionDef func = FunctionDefHelper::Create(
+      "ParametrizedFunc",
+      /* inputs */
+      {"input1:A", "input2:B", "input3:float"},
+      /* outputs */
+      {"output1: A", "output2:C"},
+      /* type parameters */
+      {"A: {float, double}", "B: {float, int32}", "C: {float, double}"},
+      /* function body*/
+      {{{"output"}, "FakeOp", {"input1", "input2"}, {{"key", "$key"}}}},
+      /* Mapping between function returns and function node outputs. */
+      {{"x", "cx:output:0"}, {"y", "cy:output:0"}});
+
+  std::unordered_map func_instantiation_attr;
+  func_instantiation_attr["key"].set_s("key-value");
+  func_instantiation_attr["A"].set_type(DT_FLOAT);
+  func_instantiation_attr["B"].set_type(DT_INT32);
+  func_instantiation_attr["C"].set_type(DT_DOUBLE);
+
+  std::unordered_map type_parameters;
+  TF_EXPECT_OK(InstantiationTypeParameters(func, func_instantiation_attr,
+                                           &type_parameters));
+
+  ASSERT_EQ(3, type_parameters.size());
+  EXPECT_EQ(DT_FLOAT, type_parameters["A"]);
+  EXPECT_EQ(DT_INT32, type_parameters["B"]);
+  EXPECT_EQ(DT_DOUBLE, type_parameters["C"]);
+
+  std::unordered_map body_parameters;
+  TF_EXPECT_OK(InstantiationBodyParameters(func, func_instantiation_attr,
+                                           &body_parameters));
+
+  ASSERT_EQ(1, body_parameters.size());
+  EXPECT_EQ("key-value", body_parameters["key"].s());
+}
+
 TEST_F(FunctionsTest, GrapplerFunctionConnectivity_ExpandFunctionDefInput) {
   GrapplerFunctionConnectivity connectivity;
 

From bd8d7440d7121dc1e92c4794ca1d18d0e9eb0a17 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 13:24:12 -0700
Subject: [PATCH 0457/1691] Fixes for accessing variables with a
 MirroredStrategy in a cross-tower context:

* only provide read-only access to variables via get()

* don't fail if use the variable isn't copied to the current device in
  get()

* make _as_graph_element() return the aggregate value for tower-local
  variables (instead of the incorrect previous behavior of returning
  the primary)

PiperOrigin-RevId: 195711474
---
 .../python/mirrored_strategy_multigpu_test.py | 33 ++++++++------
 .../contrib/distribute/python/values.py       | 44 ++++++++++++++++---
 2 files changed, 56 insertions(+), 21 deletions(-)

diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py
index 6c5c055070c0fc..3635bd2e34f88a 100644
--- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py
+++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py
@@ -370,22 +370,27 @@ def model_fn(device_id):
       expected_sum = 0.0
       expected_mean = 0.0
       for i, d in enumerate(dist.worker_devices):
-        # Test access within a device scope, should see different values.
-        with ops.device(d):
-          v_sum_value = self.evaluate(ret_v_sum.read_value())
-          v_mean_value = self.evaluate(ret_v_mean.read_value())
-          expected = i + 3.0
-          self.assertEqual(expected, v_sum_value)
-          expected_sum += expected
-          expected = i * 6.0
-          self.assertEqual(expected, v_mean_value)
-          expected_mean += expected
-
-      # fetch() should return the value you get by applying the
-      # reduction across all towers.
-      self.assertEqual(expected_sum, self.evaluate(dist.fetch(ret_v_sum)))
+        # Should see different values on different devices.
+        v_sum_value = self.evaluate(ret_v_sum.get(d).read_value())
+        v_mean_value = self.evaluate(ret_v_mean.get(d).read_value())
+        expected = i + 3.0
+        self.assertEqual(expected, v_sum_value)
+        expected_sum += expected
+        expected = i * 6.0
+        self.assertEqual(expected, v_mean_value)
+        expected_mean += expected
       expected_mean /= len(dist.worker_devices)
+
+      # Without get(device), should return the value you get by
+      # applying the reduction across all towers (whether you use
+      # fetch(), get(), or nothing).
+      self.assertEqual(expected_sum, self.evaluate(dist.fetch(ret_v_sum)))
       self.assertEqual(expected_mean, self.evaluate(dist.fetch(ret_v_mean)))
+      self.assertEqual(expected_sum, self.evaluate(ret_v_sum.get()))
+      self.assertEqual(expected_mean, self.evaluate(ret_v_mean.get()))
+      if not context.executing_eagerly():
+        self.assertEqual(expected_sum, self.evaluate(ret_v_sum))
+        self.assertEqual(expected_mean, self.evaluate(ret_v_mean))
 
   # NOTE(priyag): Names and name scopes are ignored in eager, hence we are not
   # testing this in eager mode.
diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py
index b04734f1a39749..759f3c359975ba 100644
--- a/tensorflow/contrib/distribute/python/values.py
+++ b/tensorflow/contrib/distribute/python/values.py
@@ -34,6 +34,7 @@
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.training import checkpointable
 from tensorflow.python.training import device_util
 from tensorflow.python.training import distribute as distribute_lib
@@ -60,7 +61,7 @@ def get(self, device=None):
       else:
         device = distribute_lib.get_update_device()
         if device is None:
-          device = device_util.current()
+          return self._get_cross_tower()
     device = device_util.canonicalize(device)
     try:
       return self._index[device]
@@ -231,12 +232,6 @@ def op(self):
                               self._primary_var.op.type)
     return self.get().op
 
-  def _as_graph_element(self):
-    # pylint: disable=protected-access
-    if distribute_lib.get_cross_tower_context():
-      return self._primary_var._as_graph_element()
-    return self.get()._as_graph_element()
-
   def _should_act_as_resource_variable(self):
     """Pass resource_variable_ops.is_resource_variable check."""
     pass
@@ -320,6 +315,18 @@ def assign_add(self, *args, **kwargs):
   def assign(self, *args, **kwargs):
     return self.get(device=_get_update_device()).assign(*args, **kwargs)
 
+  def _get_cross_tower(self):
+    device = device_util.canonicalize(device_util.current())
+    if device in self._index:
+      return array_ops.identity(self._index[device])
+    return array_ops.identity(self._primary_var)
+
+  def _as_graph_element(self):
+    # pylint: disable=protected-access
+    if distribute_lib.get_cross_tower_context():
+      return self._primary_var._as_graph_element()
+    return self.get()._as_graph_element()
+
   def _gather_saveables_for_checkpoint(self):
     """Overrides CheckpointableBase method.
 
@@ -364,6 +371,12 @@ def restore(self, restored_tensors, restored_shapes):
         for d, v in six.iteritems(self._tower_local_variable._index)])  # pylint: disable=protected-access
 
 
+def _assert_tower_context():
+  if not distribute_lib.get_tower_context():
+    raise RuntimeError(
+        "Tower-local variables may only be assigned in a tower context.")
+
+
 class TowerLocalVariable(DistributedVariable, PerDevice,
                          checkpointable.CheckpointableBase):
   """Holds a map from device to variables whose values are reduced on save."""
@@ -374,18 +387,35 @@ def __init__(self, index, primary_var, reduce_method):
     super(TowerLocalVariable, self).__init__(index)
 
   def assign_sub(self, *args, **kwargs):
+    _assert_tower_context()
     return self.get().assign_sub(*args, **kwargs)
 
   def assign_add(self, *args, **kwargs):
+    _assert_tower_context()
     return self.get().assign_add(*args, **kwargs)
 
   def assign(self, *args, **kwargs):
+    _assert_tower_context()
     return self.get().assign(*args, **kwargs)
 
   @property
   def reduce_method(self):
     return self._reduce_method
 
+  def _get_cross_tower(self):
+    all_components = tuple(self._index.values())
+    # TODO(josh11b): Use a strategy-specific method.
+    total = math_ops.add_n(all_components)
+    if self._reduce_method == "mean":
+      return total * (1./ len(all_components))
+    return total
+
+  def _as_graph_element(self):
+    # pylint: disable=protected-access
+    if distribute_lib.get_cross_tower_context():
+      return self._get_cross_tower()
+    return self.get()._as_graph_element()
+
   def _gather_saveables_for_checkpoint(self):
     """Overrides CheckpointableBase method.
 

From 9a0c8453e4d12cb8cca6e72a8e6a19a4a3ba21b2 Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Mon, 7 May 2018 14:00:09 -0700
Subject: [PATCH 0458/1691] [TF:XLA] Bump open source llvm revision to r331624

PiperOrigin-RevId: 195717497
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index b67c4bf2ac20d6..8f499976de83a2 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -452,11 +452,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/b3f6a6a61625296bb532a65c0bf51b91b05b3361.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/b3f6a6a61625296bb532a65c0bf51b91b05b3361.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/7b8a8728fbd27086efbf3c57cf2bb35a557108c9.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/7b8a8728fbd27086efbf3c57cf2bb35a557108c9.tar.gz",
       ],
-      sha256 = "93895b289a78a47a1e75652e12a1b9a6c119f086a509b00e0084cf2bb944b709",
-      strip_prefix = "llvm-b3f6a6a61625296bb532a65c0bf51b91b05b3361",
+      sha256 = "c620859c3ae5818f316de4837f340b3bba1646f8add0a28e6d4da34ce47e3969",
+      strip_prefix = "llvm-7b8a8728fbd27086efbf3c57cf2bb35a557108c9",
       build_file = clean_dep("//third_party/llvm:llvm.BUILD"),
   )
 

From 92acc302beefd2b596b505d9278f8b0b46239cea Mon Sep 17 00:00:00 2001
From: Anna R 
Date: Mon, 7 May 2018 14:03:15 -0700
Subject: [PATCH 0459/1691] Change deprecation_version field in api_def.proto
 to a string.

PiperOrigin-RevId: 195718061
---
 tensorflow/core/framework/api_def.proto      | 6 ++++--
 tensorflow/core/framework/op_gen_lib.cc      | 3 ---
 tensorflow/core/framework/op_gen_lib_test.cc | 1 -
 3 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/framework/api_def.proto b/tensorflow/core/framework/api_def.proto
index 98c38efc0e9a8e..e878ab620bfe3c 100644
--- a/tensorflow/core/framework/api_def.proto
+++ b/tensorflow/core/framework/api_def.proto
@@ -55,8 +55,10 @@ message ApiDef {
     // use a snake_case convention instead of CamelCase.
     string name = 1;
 
-    // First GraphDef version at which the op is disallowed.
-    int32 deprecation_version = 2;
+    // If this endpoint is deprecated, set deprecation_message to a
+    // message that should be logged when the endpoint is used.
+    // The message should indicate alternative endpoint to use, if any.
+    string deprecation_message = 2;
   }
   repeated Endpoint endpoint = 3;
 
diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc
index 5e1404362595d9..3d7920a6e29f0b 100644
--- a/tensorflow/core/framework/op_gen_lib.cc
+++ b/tensorflow/core/framework/op_gen_lib.cc
@@ -306,9 +306,6 @@ void InitApiDefFromOpDef(const OpDef& op_def, ApiDef* api_def) {
 
   auto* endpoint = api_def->add_endpoint();
   endpoint->set_name(op_def.name());
-  if (op_def.has_deprecation()) {
-    endpoint->set_deprecation_version(op_def.deprecation().version());
-  }
 
   for (const auto& op_in_arg : op_def.input_arg()) {
     auto* api_in_arg = api_def->add_in_arg();
diff --git a/tensorflow/core/framework/op_gen_lib_test.cc b/tensorflow/core/framework/op_gen_lib_test.cc
index 857b1c8dbcac66..e0e77c74495d62 100644
--- a/tensorflow/core/framework/op_gen_lib_test.cc
+++ b/tensorflow/core/framework/op_gen_lib_test.cc
@@ -189,7 +189,6 @@ TEST(OpGenLibTest, ApiDefInitializedFromOpDef) {
 visibility: VISIBLE
 endpoint {
   name: "testop"
-  deprecation_version: 123
 }
 in_arg {
   name: "arg_a"

From fa2132ab65f92ea40c94152dba105a9f86a0a555 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 14:13:23 -0700
Subject: [PATCH 0460/1691] Use 64bit aggregation for gradients and hessians
 since the 32 bit version is numerically unstable for large minibatches.

PiperOrigin-RevId: 195719795
---
 .../lib/learner/batch/ordinal_split_handler.py      | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py
index 9d6cc9245aa463..f06b73c00d0beb 100644
--- a/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py
+++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py
@@ -501,11 +501,18 @@ def quantiles_ready():
         example_partition_ids)
 
     # Compute aggregate stats for each partition.
+    # Since unsorted_segment_sum can be numerically unstable, use 64bit
+    # operation.
+    gradients64 = math_ops.cast(gradients, dtypes.float64)
+    hessians64 = math_ops.cast(hessians, dtypes.float64)
     per_partition_gradients = math_ops.unsorted_segment_sum(
-        gradients, mapped_partitions, array_ops.size(unique_partitions))
+        gradients64, mapped_partitions, array_ops.size(unique_partitions))
     per_partition_hessians = math_ops.unsorted_segment_sum(
-        hessians, mapped_partitions, array_ops.size(unique_partitions))
-
+        hessians64, mapped_partitions, array_ops.size(unique_partitions))
+    per_partition_gradients = math_ops.cast(per_partition_gradients,
+                                            dtypes.float32)
+    per_partition_hessians = math_ops.cast(per_partition_hessians,
+                                           dtypes.float32)
     # Prepend a bias feature per partition that accumulates the stats for all
     # examples in that partition.
     bias_feature_ids = array_ops.fill(

From 544dcc5092a7bf49a5d3a43e25c0f29f087062dd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 14:15:37 -0700
Subject: [PATCH 0461/1691] Move PadV2Options to the end, in order to maintain
 schema compatibility.

PiperOrigin-RevId: 195720133
---
 tensorflow/contrib/lite/schema/schema.fbs     |   6 +-
 .../contrib/lite/schema/schema_generated.h    | 304 +++++++++---------
 2 files changed, 155 insertions(+), 155 deletions(-)

diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs
index 9409e762338660..3ec91e505db746 100644
--- a/tensorflow/contrib/lite/schema/schema.fbs
+++ b/tensorflow/contrib/lite/schema/schema.fbs
@@ -167,7 +167,6 @@ union BuiltinOptions {
   EmbeddingLookupSparseOptions,
   MulOptions,
   PadOptions,
-  PadV2Options,
   GatherOptions,
   BatchToSpaceNDOptions,
   SpaceToBatchNDOptions,
@@ -186,11 +185,12 @@ union BuiltinOptions {
   DequantizeOptions,
   MaximumMinimumOptions,
   ArgMaxOptions,
+  LessOptions,
+  NegOptions,
+  PadV2Options,
   GreaterOptions,
   GreaterEqualOptions,
-  LessOptions,
   LessEqualOptions,
-  NegOptions,
 }
 
 enum Padding : byte { SAME, VALID }
diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h
index ae3b33063e460c..c6e4dab4548360 100755
--- a/tensorflow/contrib/lite/schema/schema_generated.h
+++ b/tensorflow/contrib/lite/schema/schema_generated.h
@@ -465,32 +465,32 @@ enum BuiltinOptions {
   BuiltinOptions_EmbeddingLookupSparseOptions = 20,
   BuiltinOptions_MulOptions = 21,
   BuiltinOptions_PadOptions = 22,
-  BuiltinOptions_PadV2Options = 23,
-  BuiltinOptions_GatherOptions = 24,
-  BuiltinOptions_BatchToSpaceNDOptions = 25,
-  BuiltinOptions_SpaceToBatchNDOptions = 26,
-  BuiltinOptions_TransposeOptions = 27,
-  BuiltinOptions_MeanOptions = 28,
-  BuiltinOptions_SubOptions = 29,
-  BuiltinOptions_DivOptions = 30,
-  BuiltinOptions_SqueezeOptions = 31,
-  BuiltinOptions_SequenceRNNOptions = 32,
-  BuiltinOptions_StridedSliceOptions = 33,
-  BuiltinOptions_ExpOptions = 34,
-  BuiltinOptions_TopKV2Options = 35,
-  BuiltinOptions_SplitOptions = 36,
-  BuiltinOptions_LogSoftmaxOptions = 37,
-  BuiltinOptions_CastOptions = 38,
-  BuiltinOptions_DequantizeOptions = 39,
-  BuiltinOptions_MaximumMinimumOptions = 40,
-  BuiltinOptions_ArgMaxOptions = 41,
-  BuiltinOptions_GreaterOptions = 42,
-  BuiltinOptions_GreaterEqualOptions = 43,
-  BuiltinOptions_LessOptions = 44,
-  BuiltinOptions_LessEqualOptions = 45,
-  BuiltinOptions_NegOptions = 46,
+  BuiltinOptions_GatherOptions = 23,
+  BuiltinOptions_BatchToSpaceNDOptions = 24,
+  BuiltinOptions_SpaceToBatchNDOptions = 25,
+  BuiltinOptions_TransposeOptions = 26,
+  BuiltinOptions_MeanOptions = 27,
+  BuiltinOptions_SubOptions = 28,
+  BuiltinOptions_DivOptions = 29,
+  BuiltinOptions_SqueezeOptions = 30,
+  BuiltinOptions_SequenceRNNOptions = 31,
+  BuiltinOptions_StridedSliceOptions = 32,
+  BuiltinOptions_ExpOptions = 33,
+  BuiltinOptions_TopKV2Options = 34,
+  BuiltinOptions_SplitOptions = 35,
+  BuiltinOptions_LogSoftmaxOptions = 36,
+  BuiltinOptions_CastOptions = 37,
+  BuiltinOptions_DequantizeOptions = 38,
+  BuiltinOptions_MaximumMinimumOptions = 39,
+  BuiltinOptions_ArgMaxOptions = 40,
+  BuiltinOptions_LessOptions = 41,
+  BuiltinOptions_NegOptions = 42,
+  BuiltinOptions_PadV2Options = 43,
+  BuiltinOptions_GreaterOptions = 44,
+  BuiltinOptions_GreaterEqualOptions = 45,
+  BuiltinOptions_LessEqualOptions = 46,
   BuiltinOptions_MIN = BuiltinOptions_NONE,
-  BuiltinOptions_MAX = BuiltinOptions_NegOptions
+  BuiltinOptions_MAX = BuiltinOptions_LessEqualOptions
 };
 
 inline BuiltinOptions (&EnumValuesBuiltinOptions())[47] {
@@ -518,7 +518,6 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[47] {
     BuiltinOptions_EmbeddingLookupSparseOptions,
     BuiltinOptions_MulOptions,
     BuiltinOptions_PadOptions,
-    BuiltinOptions_PadV2Options,
     BuiltinOptions_GatherOptions,
     BuiltinOptions_BatchToSpaceNDOptions,
     BuiltinOptions_SpaceToBatchNDOptions,
@@ -537,11 +536,12 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[47] {
     BuiltinOptions_DequantizeOptions,
     BuiltinOptions_MaximumMinimumOptions,
     BuiltinOptions_ArgMaxOptions,
+    BuiltinOptions_LessOptions,
+    BuiltinOptions_NegOptions,
+    BuiltinOptions_PadV2Options,
     BuiltinOptions_GreaterOptions,
     BuiltinOptions_GreaterEqualOptions,
-    BuiltinOptions_LessOptions,
-    BuiltinOptions_LessEqualOptions,
-    BuiltinOptions_NegOptions
+    BuiltinOptions_LessEqualOptions
   };
   return values;
 }
@@ -571,7 +571,6 @@ inline const char **EnumNamesBuiltinOptions() {
     "EmbeddingLookupSparseOptions",
     "MulOptions",
     "PadOptions",
-    "PadV2Options",
     "GatherOptions",
     "BatchToSpaceNDOptions",
     "SpaceToBatchNDOptions",
@@ -590,11 +589,12 @@ inline const char **EnumNamesBuiltinOptions() {
     "DequantizeOptions",
     "MaximumMinimumOptions",
     "ArgMaxOptions",
+    "LessOptions",
+    "NegOptions",
+    "PadV2Options",
     "GreaterOptions",
     "GreaterEqualOptions",
-    "LessOptions",
     "LessEqualOptions",
-    "NegOptions",
     nullptr
   };
   return names;
@@ -697,10 +697,6 @@ template<> struct BuiltinOptionsTraits {
   static const BuiltinOptions enum_value = BuiltinOptions_PadOptions;
 };
 
-template<> struct BuiltinOptionsTraits {
-  static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options;
-};
-
 template<> struct BuiltinOptionsTraits {
   static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions;
 };
@@ -773,6 +769,18 @@ template<> struct BuiltinOptionsTraits {
   static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions;
 };
 
+template<> struct BuiltinOptionsTraits {
+  static const BuiltinOptions enum_value = BuiltinOptions_LessOptions;
+};
+
+template<> struct BuiltinOptionsTraits {
+  static const BuiltinOptions enum_value = BuiltinOptions_NegOptions;
+};
+
+template<> struct BuiltinOptionsTraits {
+  static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options;
+};
+
 template<> struct BuiltinOptionsTraits {
   static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions;
 };
@@ -781,18 +789,10 @@ template<> struct BuiltinOptionsTraits {
   static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions;
 };
 
-template<> struct BuiltinOptionsTraits {
-  static const BuiltinOptions enum_value = BuiltinOptions_LessOptions;
-};
-
 template<> struct BuiltinOptionsTraits {
   static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions;
 };
 
-template<> struct BuiltinOptionsTraits {
-  static const BuiltinOptions enum_value = BuiltinOptions_NegOptions;
-};
-
 struct BuiltinOptionsUnion {
   BuiltinOptions type;
   void *value;
@@ -1000,14 +1000,6 @@ struct BuiltinOptionsUnion {
     return type == BuiltinOptions_PadOptions ?
       reinterpret_cast(value) : nullptr;
   }
-  PadV2OptionsT *AsPadV2Options() {
-    return type == BuiltinOptions_PadV2Options ?
-      reinterpret_cast(value) : nullptr;
-  }
-  const PadV2OptionsT *AsPadV2Options() const {
-    return type == BuiltinOptions_PadV2Options ?
-      reinterpret_cast(value) : nullptr;
-  }
   GatherOptionsT *AsGatherOptions() {
     return type == BuiltinOptions_GatherOptions ?
       reinterpret_cast(value) : nullptr;
@@ -1152,6 +1144,30 @@ struct BuiltinOptionsUnion {
     return type == BuiltinOptions_ArgMaxOptions ?
       reinterpret_cast(value) : nullptr;
   }
+  LessOptionsT *AsLessOptions() {
+    return type == BuiltinOptions_LessOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
+  const LessOptionsT *AsLessOptions() const {
+    return type == BuiltinOptions_LessOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
+  NegOptionsT *AsNegOptions() {
+    return type == BuiltinOptions_NegOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
+  const NegOptionsT *AsNegOptions() const {
+    return type == BuiltinOptions_NegOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
+  PadV2OptionsT *AsPadV2Options() {
+    return type == BuiltinOptions_PadV2Options ?
+      reinterpret_cast(value) : nullptr;
+  }
+  const PadV2OptionsT *AsPadV2Options() const {
+    return type == BuiltinOptions_PadV2Options ?
+      reinterpret_cast(value) : nullptr;
+  }
   GreaterOptionsT *AsGreaterOptions() {
     return type == BuiltinOptions_GreaterOptions ?
       reinterpret_cast(value) : nullptr;
@@ -1168,14 +1184,6 @@ struct BuiltinOptionsUnion {
     return type == BuiltinOptions_GreaterEqualOptions ?
       reinterpret_cast(value) : nullptr;
   }
-  LessOptionsT *AsLessOptions() {
-    return type == BuiltinOptions_LessOptions ?
-      reinterpret_cast(value) : nullptr;
-  }
-  const LessOptionsT *AsLessOptions() const {
-    return type == BuiltinOptions_LessOptions ?
-      reinterpret_cast(value) : nullptr;
-  }
   LessEqualOptionsT *AsLessEqualOptions() {
     return type == BuiltinOptions_LessEqualOptions ?
       reinterpret_cast(value) : nullptr;
@@ -1184,14 +1192,6 @@ struct BuiltinOptionsUnion {
     return type == BuiltinOptions_LessEqualOptions ?
       reinterpret_cast(value) : nullptr;
   }
-  NegOptionsT *AsNegOptions() {
-    return type == BuiltinOptions_NegOptions ?
-      reinterpret_cast(value) : nullptr;
-  }
-  const NegOptionsT *AsNegOptions() const {
-    return type == BuiltinOptions_NegOptions ?
-      reinterpret_cast(value) : nullptr;
-  }
 };
 
 bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type);
@@ -4502,9 +4502,6 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
   const PadOptions *builtin_options_as_PadOptions() const {
     return builtin_options_type() == BuiltinOptions_PadOptions ? static_cast(builtin_options()) : nullptr;
   }
-  const PadV2Options *builtin_options_as_PadV2Options() const {
-    return builtin_options_type() == BuiltinOptions_PadV2Options ? static_cast(builtin_options()) : nullptr;
-  }
   const GatherOptions *builtin_options_as_GatherOptions() const {
     return builtin_options_type() == BuiltinOptions_GatherOptions ? static_cast(builtin_options()) : nullptr;
   }
@@ -4559,21 +4556,24 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
   const ArgMaxOptions *builtin_options_as_ArgMaxOptions() const {
     return builtin_options_type() == BuiltinOptions_ArgMaxOptions ? static_cast(builtin_options()) : nullptr;
   }
+  const LessOptions *builtin_options_as_LessOptions() const {
+    return builtin_options_type() == BuiltinOptions_LessOptions ? static_cast(builtin_options()) : nullptr;
+  }
+  const NegOptions *builtin_options_as_NegOptions() const {
+    return builtin_options_type() == BuiltinOptions_NegOptions ? static_cast(builtin_options()) : nullptr;
+  }
+  const PadV2Options *builtin_options_as_PadV2Options() const {
+    return builtin_options_type() == BuiltinOptions_PadV2Options ? static_cast(builtin_options()) : nullptr;
+  }
   const GreaterOptions *builtin_options_as_GreaterOptions() const {
     return builtin_options_type() == BuiltinOptions_GreaterOptions ? static_cast(builtin_options()) : nullptr;
   }
   const GreaterEqualOptions *builtin_options_as_GreaterEqualOptions() const {
     return builtin_options_type() == BuiltinOptions_GreaterEqualOptions ? static_cast(builtin_options()) : nullptr;
   }
-  const LessOptions *builtin_options_as_LessOptions() const {
-    return builtin_options_type() == BuiltinOptions_LessOptions ? static_cast(builtin_options()) : nullptr;
-  }
   const LessEqualOptions *builtin_options_as_LessEqualOptions() const {
     return builtin_options_type() == BuiltinOptions_LessEqualOptions ? static_cast(builtin_options()) : nullptr;
   }
-  const NegOptions *builtin_options_as_NegOptions() const {
-    return builtin_options_type() == BuiltinOptions_NegOptions ? static_cast(builtin_options()) : nullptr;
-  }
   const flatbuffers::Vector *custom_options() const {
     return GetPointer *>(VT_CUSTOM_OPTIONS);
   }
@@ -4688,10 +4688,6 @@ template<> inline const PadOptions *Operator::builtin_options_as() c
   return builtin_options_as_PadOptions();
 }
 
-template<> inline const PadV2Options *Operator::builtin_options_as() const {
-  return builtin_options_as_PadV2Options();
-}
-
 template<> inline const GatherOptions *Operator::builtin_options_as() const {
   return builtin_options_as_GatherOptions();
 }
@@ -4764,6 +4760,18 @@ template<> inline const ArgMaxOptions *Operator::builtin_options_as inline const LessOptions *Operator::builtin_options_as() const {
+  return builtin_options_as_LessOptions();
+}
+
+template<> inline const NegOptions *Operator::builtin_options_as() const {
+  return builtin_options_as_NegOptions();
+}
+
+template<> inline const PadV2Options *Operator::builtin_options_as() const {
+  return builtin_options_as_PadV2Options();
+}
+
 template<> inline const GreaterOptions *Operator::builtin_options_as() const {
   return builtin_options_as_GreaterOptions();
 }
@@ -4772,18 +4780,10 @@ template<> inline const GreaterEqualOptions *Operator::builtin_options_as inline const LessOptions *Operator::builtin_options_as() const {
-  return builtin_options_as_LessOptions();
-}
-
 template<> inline const LessEqualOptions *Operator::builtin_options_as() const {
   return builtin_options_as_LessEqualOptions();
 }
 
-template<> inline const NegOptions *Operator::builtin_options_as() const {
-  return builtin_options_as_NegOptions();
-}
-
 struct OperatorBuilder {
   flatbuffers::FlatBufferBuilder &fbb_;
   flatbuffers::uoffset_t start_;
@@ -6796,10 +6796,6 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob
       auto ptr = reinterpret_cast(obj);
       return verifier.VerifyTable(ptr);
     }
-    case BuiltinOptions_PadV2Options: {
-      auto ptr = reinterpret_cast(obj);
-      return verifier.VerifyTable(ptr);
-    }
     case BuiltinOptions_GatherOptions: {
       auto ptr = reinterpret_cast(obj);
       return verifier.VerifyTable(ptr);
@@ -6872,6 +6868,18 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob
       auto ptr = reinterpret_cast(obj);
       return verifier.VerifyTable(ptr);
     }
+    case BuiltinOptions_LessOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return verifier.VerifyTable(ptr);
+    }
+    case BuiltinOptions_NegOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return verifier.VerifyTable(ptr);
+    }
+    case BuiltinOptions_PadV2Options: {
+      auto ptr = reinterpret_cast(obj);
+      return verifier.VerifyTable(ptr);
+    }
     case BuiltinOptions_GreaterOptions: {
       auto ptr = reinterpret_cast(obj);
       return verifier.VerifyTable(ptr);
@@ -6880,18 +6888,10 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob
       auto ptr = reinterpret_cast(obj);
       return verifier.VerifyTable(ptr);
     }
-    case BuiltinOptions_LessOptions: {
-      auto ptr = reinterpret_cast(obj);
-      return verifier.VerifyTable(ptr);
-    }
     case BuiltinOptions_LessEqualOptions: {
       auto ptr = reinterpret_cast(obj);
       return verifier.VerifyTable(ptr);
     }
-    case BuiltinOptions_NegOptions: {
-      auto ptr = reinterpret_cast(obj);
-      return verifier.VerifyTable(ptr);
-    }
     default: return false;
   }
 }
@@ -6998,10 +6998,6 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c
       auto ptr = reinterpret_cast(obj);
       return ptr->UnPack(resolver);
     }
-    case BuiltinOptions_PadV2Options: {
-      auto ptr = reinterpret_cast(obj);
-      return ptr->UnPack(resolver);
-    }
     case BuiltinOptions_GatherOptions: {
       auto ptr = reinterpret_cast(obj);
       return ptr->UnPack(resolver);
@@ -7074,6 +7070,18 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c
       auto ptr = reinterpret_cast(obj);
       return ptr->UnPack(resolver);
     }
+    case BuiltinOptions_LessOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return ptr->UnPack(resolver);
+    }
+    case BuiltinOptions_NegOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return ptr->UnPack(resolver);
+    }
+    case BuiltinOptions_PadV2Options: {
+      auto ptr = reinterpret_cast(obj);
+      return ptr->UnPack(resolver);
+    }
     case BuiltinOptions_GreaterOptions: {
       auto ptr = reinterpret_cast(obj);
       return ptr->UnPack(resolver);
@@ -7082,18 +7090,10 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c
       auto ptr = reinterpret_cast(obj);
       return ptr->UnPack(resolver);
     }
-    case BuiltinOptions_LessOptions: {
-      auto ptr = reinterpret_cast(obj);
-      return ptr->UnPack(resolver);
-    }
     case BuiltinOptions_LessEqualOptions: {
       auto ptr = reinterpret_cast(obj);
       return ptr->UnPack(resolver);
     }
-    case BuiltinOptions_NegOptions: {
-      auto ptr = reinterpret_cast(obj);
-      return ptr->UnPack(resolver);
-    }
     default: return nullptr;
   }
 }
@@ -7188,10 +7188,6 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff
       auto ptr = reinterpret_cast(value);
       return CreatePadOptions(_fbb, ptr, _rehasher).Union();
     }
-    case BuiltinOptions_PadV2Options: {
-      auto ptr = reinterpret_cast(value);
-      return CreatePadV2Options(_fbb, ptr, _rehasher).Union();
-    }
     case BuiltinOptions_GatherOptions: {
       auto ptr = reinterpret_cast(value);
       return CreateGatherOptions(_fbb, ptr, _rehasher).Union();
@@ -7264,6 +7260,18 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff
       auto ptr = reinterpret_cast(value);
       return CreateArgMaxOptions(_fbb, ptr, _rehasher).Union();
     }
+    case BuiltinOptions_LessOptions: {
+      auto ptr = reinterpret_cast(value);
+      return CreateLessOptions(_fbb, ptr, _rehasher).Union();
+    }
+    case BuiltinOptions_NegOptions: {
+      auto ptr = reinterpret_cast(value);
+      return CreateNegOptions(_fbb, ptr, _rehasher).Union();
+    }
+    case BuiltinOptions_PadV2Options: {
+      auto ptr = reinterpret_cast(value);
+      return CreatePadV2Options(_fbb, ptr, _rehasher).Union();
+    }
     case BuiltinOptions_GreaterOptions: {
       auto ptr = reinterpret_cast(value);
       return CreateGreaterOptions(_fbb, ptr, _rehasher).Union();
@@ -7272,18 +7280,10 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff
       auto ptr = reinterpret_cast(value);
       return CreateGreaterEqualOptions(_fbb, ptr, _rehasher).Union();
     }
-    case BuiltinOptions_LessOptions: {
-      auto ptr = reinterpret_cast(value);
-      return CreateLessOptions(_fbb, ptr, _rehasher).Union();
-    }
     case BuiltinOptions_LessEqualOptions: {
       auto ptr = reinterpret_cast(value);
       return CreateLessEqualOptions(_fbb, ptr, _rehasher).Union();
     }
-    case BuiltinOptions_NegOptions: {
-      auto ptr = reinterpret_cast(value);
-      return CreateNegOptions(_fbb, ptr, _rehasher).Union();
-    }
     default: return 0;
   }
 }
@@ -7378,10 +7378,6 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL
       value = new PadOptionsT(*reinterpret_cast(u.value));
       break;
     }
-    case BuiltinOptions_PadV2Options: {
-      value = new PadV2OptionsT(*reinterpret_cast(u.value));
-      break;
-    }
     case BuiltinOptions_GatherOptions: {
       value = new GatherOptionsT(*reinterpret_cast(u.value));
       break;
@@ -7454,6 +7450,18 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL
       value = new ArgMaxOptionsT(*reinterpret_cast(u.value));
       break;
     }
+    case BuiltinOptions_LessOptions: {
+      value = new LessOptionsT(*reinterpret_cast(u.value));
+      break;
+    }
+    case BuiltinOptions_NegOptions: {
+      value = new NegOptionsT(*reinterpret_cast(u.value));
+      break;
+    }
+    case BuiltinOptions_PadV2Options: {
+      value = new PadV2OptionsT(*reinterpret_cast(u.value));
+      break;
+    }
     case BuiltinOptions_GreaterOptions: {
       value = new GreaterOptionsT(*reinterpret_cast(u.value));
       break;
@@ -7462,18 +7470,10 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL
       value = new GreaterEqualOptionsT(*reinterpret_cast(u.value));
       break;
     }
-    case BuiltinOptions_LessOptions: {
-      value = new LessOptionsT(*reinterpret_cast(u.value));
-      break;
-    }
     case BuiltinOptions_LessEqualOptions: {
       value = new LessEqualOptionsT(*reinterpret_cast(u.value));
       break;
     }
-    case BuiltinOptions_NegOptions: {
-      value = new NegOptionsT(*reinterpret_cast(u.value));
-      break;
-    }
     default:
       break;
   }
@@ -7591,11 +7591,6 @@ inline void BuiltinOptionsUnion::Reset() {
       delete ptr;
       break;
     }
-    case BuiltinOptions_PadV2Options: {
-      auto ptr = reinterpret_cast(value);
-      delete ptr;
-      break;
-    }
     case BuiltinOptions_GatherOptions: {
       auto ptr = reinterpret_cast(value);
       delete ptr;
@@ -7686,28 +7681,33 @@ inline void BuiltinOptionsUnion::Reset() {
       delete ptr;
       break;
     }
-    case BuiltinOptions_GreaterOptions: {
-      auto ptr = reinterpret_cast(value);
+    case BuiltinOptions_LessOptions: {
+      auto ptr = reinterpret_cast(value);
       delete ptr;
       break;
     }
-    case BuiltinOptions_GreaterEqualOptions: {
-      auto ptr = reinterpret_cast(value);
+    case BuiltinOptions_NegOptions: {
+      auto ptr = reinterpret_cast(value);
       delete ptr;
       break;
     }
-    case BuiltinOptions_LessOptions: {
-      auto ptr = reinterpret_cast(value);
+    case BuiltinOptions_PadV2Options: {
+      auto ptr = reinterpret_cast(value);
       delete ptr;
       break;
     }
-    case BuiltinOptions_LessEqualOptions: {
-      auto ptr = reinterpret_cast(value);
+    case BuiltinOptions_GreaterOptions: {
+      auto ptr = reinterpret_cast(value);
       delete ptr;
       break;
     }
-    case BuiltinOptions_NegOptions: {
-      auto ptr = reinterpret_cast(value);
+    case BuiltinOptions_GreaterEqualOptions: {
+      auto ptr = reinterpret_cast(value);
+      delete ptr;
+      break;
+    }
+    case BuiltinOptions_LessEqualOptions: {
+      auto ptr = reinterpret_cast(value);
       delete ptr;
       break;
     }

From cd065ca7be11a4c87c9a5e68271cbc2d9aaaa260 Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Mon, 7 May 2018 14:23:19 -0700
Subject: [PATCH 0462/1691] [XLA] Shard compilation of HloEvaluator.

PiperOrigin-RevId: 195721404
---
 tensorflow/compiler/xla/service/BUILD         |   17 +-
 .../compiler/xla/service/hlo_evaluator.cc     | 2102 +----------------
 .../compiler/xla/service/hlo_evaluator.h      |   48 +-
 .../xla/service/hlo_evaluator_typed_visitor.h | 2102 +++++++++++++++++
 .../hlo_evaluator_typed_visitor_bfloat16.cc   |   22 +
 .../hlo_evaluator_typed_visitor_bool.cc       |   22 +
 .../hlo_evaluator_typed_visitor_complex64.cc  |   22 +
 .../hlo_evaluator_typed_visitor_double.cc     |   22 +
 .../hlo_evaluator_typed_visitor_float.cc      |   22 +
 .../hlo_evaluator_typed_visitor_half.cc       |   22 +
 .../hlo_evaluator_typed_visitor_int32.cc      |   22 +
 .../hlo_evaluator_typed_visitor_int64.cc      |   22 +
 .../hlo_evaluator_typed_visitor_int8.cc       |   22 +
 .../hlo_evaluator_typed_visitor_uint32.cc     |   22 +
 .../hlo_evaluator_typed_visitor_uint64.cc     |   22 +
 .../hlo_evaluator_typed_visitor_uint8.cc      |   22 +
 16 files changed, 2440 insertions(+), 2093 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_bfloat16.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_bool.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_complex64.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_double.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_float.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_half.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_int32.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_int64.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_int8.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_uint32.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_uint64.cc
 create mode 100644 tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_uint8.cc

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 714c1e8754cbd3..ec67e19b230ea2 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -200,7 +200,22 @@ tf_cc_test(
 
 cc_library(
     name = "hlo_evaluator",
-    srcs = ["hlo_evaluator.cc"],
+    srcs = [
+        "hlo_evaluator.cc",
+        "hlo_evaluator_typed_visitor.h",
+        "hlo_evaluator_typed_visitor_bfloat16.cc",
+        "hlo_evaluator_typed_visitor_bool.cc",
+        "hlo_evaluator_typed_visitor_complex64.cc",
+        "hlo_evaluator_typed_visitor_double.cc",
+        "hlo_evaluator_typed_visitor_float.cc",
+        "hlo_evaluator_typed_visitor_half.cc",
+        "hlo_evaluator_typed_visitor_int32.cc",
+        "hlo_evaluator_typed_visitor_int64.cc",
+        "hlo_evaluator_typed_visitor_int8.cc",
+        "hlo_evaluator_typed_visitor_uint32.cc",
+        "hlo_evaluator_typed_visitor_uint64.cc",
+        "hlo_evaluator_typed_visitor_uint8.cc",
+    ],
     hdrs = ["hlo_evaluator.h"],
     deps = [
         ":hlo",
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 8cf94123b71403..fffe1923ba9282 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
 #include "tensorflow/compiler/xla/service/hlo_query.h"
@@ -42,7 +43,6 @@ limitations under the License.
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
-#include "tensorflow/core/lib/gtl/optional.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/types.h"
@@ -53,19 +53,6 @@ namespace {
 
 using tensorflow::gtl::ArraySlice;
 using tensorflow::gtl::FlatSet;
-using tensorflow::gtl::optional;
-
-template 
-struct is_complex_t : public std::false_type {};
-
-template <>
-struct is_complex_t : public std::true_type {};
-
-template 
-struct is_complex64_t : public std::false_type {};
-
-template <>
-struct is_complex64_t : public std::true_type {};
 
 template 
 StatusOr> Compare(const Shape& shape, HloOpcode opcode,
@@ -147,2092 +134,47 @@ StatusOr> Compare(
   return std::move(result);
 }
 
-template 
-StatusOr> ElementWiseUnaryOpImpl(
-    HloInstruction* instruction,
-    const std::function& unary_op,
-    const Literal& operand_literal) {
-  const auto shape = instruction->shape();
-  const auto* operand = instruction->operand(0);
-
-  // TODO(b/35950897, b/27796129): add DCHECK back once implicit broadcast is
-  // removed.
-  if (!ShapeUtil::SameDimensions(shape, operand->shape())) {
-    return Unimplemented(
-        "Implicit broadcasting is currently unsupported in HLO evaluator "
-        "Shape Mismatch: %s vs %s",
-        ShapeUtil::HumanString(shape).c_str(),
-        ShapeUtil::HumanString(operand->shape()).c_str());
-  }
-
-  auto result = Literal::CreateFromShape(shape);
-
-  TF_RETURN_IF_ERROR(
-      result->Populate([&](ArraySlice multi_index) {
-        return unary_op(operand_literal.Get(multi_index));
-      }));
-  return std::move(result);
-}
-
-// For one particular placement of a window in a base shape (the placement is
-// represented as `window_count_index`), iterates inside the window. Translates
-// the window index into base index. If the base index is within bound, call `f`
-// with the base index.
-void IterateThroughWindow(
-    const Shape& window_shape, const Window& window, const Shape& base_shape,
-    const ArraySlice& window_count_index,
-    const std::function&)>& f) {
-  const int64 rank = ShapeUtil::Rank(base_shape);
-  DimensionVector window_index(rank);
-  std::fill(window_index.begin(), window_index.end(), 0);
-  do {
-    std::vector base_index(rank);
-    bool out_of_bound = false;
-    for (int64 i = 0; i < rank; ++i) {
-      base_index[i] = window_count_index[i] * window.dimensions(i).stride() +
-                      window_index[i] - window.dimensions(i).padding_low();
-      if (base_index[i] < 0 || base_index[i] >= base_shape.dimensions(i)) {
-        out_of_bound = true;
-        break;
-      }
-    }
-    if (!out_of_bound) {
-      f(base_index);
-    }
-  } while (IndexUtil::BumpIndices(window_shape, &window_index));
-}
-
-// Creates a vector of multipliers which can be used to create a linear index
-// into shape.
-//
-// Given the multidimensional index {i1, ..., iN} and
-// M = MakeDimMultipliers(shape), the corresponding linear index LI is simply
-//
-//   LI = i1 * M[1] + i2 * M[2] + ... + iN * M[N].
-//
-// This lets you calculate LI given the multidimensional indices in any order.
-DimensionVector MakeDimMultipliers(const Shape& shape) {
-  DimensionVector v(ShapeUtil::Rank(shape));
-  int64 scale = 1;
-  for (auto dim : LayoutUtil::MinorToMajor(shape)) {
-    v[dim] = scale;
-    scale *= shape.dimensions(dim);
-  }
-  return v;
-}
-
 }  // namespace
 
-template 
-class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
- public:
-  explicit TypedVisitor(HloEvaluator* p) : parent_(p) {}
-
-  // The following higher-order functions convert a function with ElementwiseT
-  // to a function with ReturnT.
-  std::function ConvertUnaryFunction(
-      const std::function& unary_op) {
-    return [&unary_op](ReturnT arg) {
-      return static_cast(unary_op(static_cast(arg)));
-    };
-  }
-  std::function ConvertBinaryFunction(
-      const std::function&
-          binary_op) {
-    return [&binary_op](ReturnT arg1, ReturnT arg2) {
-      return static_cast(binary_op(static_cast(arg1),
-                                            static_cast(arg2)));
-    };
-  }
-  std::function ConvertTernaryFunction(
-      const std::function& ternary_op) {
-    return [&ternary_op](ReturnT arg1, ReturnT arg2, ReturnT arg3) {
-      return static_cast(ternary_op(static_cast(arg1),
-                                             static_cast(arg2),
-                                             static_cast(arg3)));
-    };
-  }
-
-  Status DefaultAction(HloInstruction* hlo_instruction) override {
-    return Unimplemented("unhandled HLO ops for HloEvaluator: %s.",
-                         HloOpcodeString(hlo_instruction->opcode()).c_str());
-  }
-
-  // TODO(b/35950897): many of the stl functions used in the handlers are not
-  // overloaded for every XLA primitive types.
-
-  template ::value>::type* =
-                nullptr>
-  Status HandleAbs(HloInstruction* abs) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[abs],
-                        ElementWiseUnaryOp(abs, [](NativeT elem_operand) {
-                          return elem_operand;
-                        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleAbs(HloInstruction* abs) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[abs],
-                        ElementWiseUnaryOp(abs, [](NativeT elem_operand) {
-                          return std::abs(elem_operand);
-                        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleAbs(HloInstruction* abs) {
-    const Literal& operand_literal =
-        parent_->GetEvaluatedLiteralFor(abs->operand(0));
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[abs],
-        (ElementWiseUnaryOpImpl(
-            abs, [](NativeT elem_operand) { return std::abs(elem_operand); },
-            operand_literal)));
-
-    return Status::OK();
-  }
-
-  Status HandleAbs(HloInstruction* abs) override {
-    // If the operand is of C64 type, the return type of abs will be F32.
-    // However, ElementwiseT would still be the return type, F32, and thus
-    // specifying the ElementwiseT explicitly as C64 is needed below.
-    if (abs->operand(0)->shape().element_type() == C64) {
-      return HandleAbs(abs);
-    }
-    return HandleAbs(abs);
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleRound(HloInstruction* round) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[round],
-        ElementWiseUnaryOp(round, [](ElementwiseT elem_operand) {
-          return std::round(elem_operand);
-        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleRound(HloInstruction* round) {
-    return InvalidArgument("Unsupported type for Round");
-  }
-
-  Status HandleRound(HloInstruction* round) override {
-    return HandleRound(round);
-  }
-
-  Status HandleBroadcast(HloInstruction* broadcast) override {
-    parent_->evaluated_[broadcast] =
-        Literal::CreateFromShape(broadcast->shape());
-    auto output = parent_->evaluated_[broadcast].get();
-    const Literal& operand_to_broadcast =
-        parent_->GetEvaluatedLiteralFor(broadcast->operand(0));
-    std::vector broadcast_indices(
-        ShapeUtil::Rank(broadcast->operand(0)->shape()), 0);
-
-    TF_RET_CHECK(broadcast->dimensions().size() ==
-                 ShapeUtil::Rank(operand_to_broadcast.shape()))
-        << "broadcast dimensions is of size: " << broadcast->dimensions().size()
-        << " and rank of operand_to_broadcast is: "
-        << ShapeUtil::Rank(operand_to_broadcast.shape());
-    // Checks that operand's dimensions are the same as the broadcast's
-    // dimensions along the dimensions to be broadcasted.
-    for (int64 i = 0; i < broadcast->dimensions().size(); ++i) {
-      TF_RET_CHECK(broadcast->shape().dimensions(broadcast->dimensions(i)) ==
-                   operand_to_broadcast.shape().dimensions(i));
-    }
-
-    return output->Populate([&](ArraySlice multi_index) {
-      for (int64 i = 0; i < broadcast->dimensions().size(); ++i) {
-        broadcast_indices[i] = multi_index[broadcast->dimensions(i)];
-      }
-      return operand_to_broadcast.Get(broadcast_indices);
-    });
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleCeil(HloInstruction* ceil) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[ceil],
-                        ElementWiseUnaryOp(ceil, [](ElementwiseT elem_operand) {
-                          return std::ceil(elem_operand);
-                        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleCeil(HloInstruction* ceil) {
-    return InvalidArgument("Unsupported type for Ceil");
-  }
-
-  Status HandleCeil(HloInstruction* ceil) override {
-    return HandleCeil(ceil);
-  }
-
-  Status HandleConvert(HloInstruction* convert) override {
-    const HloInstruction* operand = convert->operand(0);
-    TF_RET_CHECK(ShapeUtil::SameDimensions(operand->shape(), convert->shape()));
-    TF_ASSIGN_OR_RETURN(std::unique_ptr result,
-                        parent_->GetEvaluatedLiteralFor(operand).Convert(
-                            convert->shape().element_type()));
-
-    if (LayoutUtil::LayoutsInShapesEqual(result->shape(), convert->shape())) {
-      parent_->evaluated_[convert] = std::move(result);
-    } else {
-      parent_->evaluated_[convert] =
-          result->Relayout(convert->shape().layout());
-    }
-    return Status::OK();
-  }
-
-  Status HandleBitcastConvert(HloInstruction* convert) override {
-    const HloInstruction* operand = convert->operand(0);
-    TF_RET_CHECK(ShapeUtil::SameDimensions(operand->shape(), convert->shape()));
-    TF_ASSIGN_OR_RETURN(std::unique_ptr result,
-                        parent_->GetEvaluatedLiteralFor(operand).BitcastConvert(
-                            convert->shape().element_type()));
-
-    if (LayoutUtil::LayoutsInShapesEqual(result->shape(), convert->shape())) {
-      parent_->evaluated_[convert] = std::move(result);
-    } else {
-      parent_->evaluated_[convert] =
-          result->Relayout(convert->shape().layout());
-    }
-    return Status::OK();
-  }
-
-  Status HandleExp(HloInstruction* exp) override {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[exp],
-                        ElementWiseUnaryOp(exp, [](ElementwiseT elem_operand) {
-                          return std::exp(elem_operand);
-                        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleFloor(HloInstruction* floor) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[floor],
-        ElementWiseUnaryOp(floor, [](ElementwiseT elem_operand) {
-          return std::floor(elem_operand);
-        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleFloor(HloInstruction* floor) {
-    return InvalidArgument("Unsupported type for Floor");
-  }
-
-  Status HandleFloor(HloInstruction* floor) override {
-    return HandleFloor(floor);
-  }
-
-  Status HandleLog(HloInstruction* log) override {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[log],
-                        ElementWiseUnaryOp(log, [](ElementwiseT elem_operand) {
-                          return std::log(elem_operand);
-                        }));
-    return Status::OK();
-  }
-
-  template ::value &&
-                !std::is_same::value>::type* = nullptr>
-  Status HandleNot(HloInstruction* not_) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[not_],
-                        ElementWiseUnaryOp(not_, [](ElementwiseT elem_operand) {
-                          return ~elem_operand;
-                        }));
-    return Status::OK();
-  }
-
-  template ::value>::type* = nullptr>
-  Status HandleNot(HloInstruction* not_) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[not_],
-                        ElementWiseUnaryOp(not_, [](ElementwiseT elem_operand) {
-                          return !elem_operand;
-                        }));
-    return Status::OK();
-  }
-
-  template ::value>::type* =
-                nullptr>
-  Status HandleNot(HloInstruction* not_) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[not_],
-                        ElementWiseUnaryOp(not_, [](ElementwiseT elem_operand) {
-                          return !elem_operand;
-                        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleNot(HloInstruction* not_) {
-    return InvalidArgument("Unsupported type for Not");
-  }
-
-  Status HandleNot(HloInstruction* not_) override {
-    return HandleNot(not_);
-  }
-
-  template ::value &&
-                !std::is_floating_point::value>::type* = nullptr>
-  Status HandleNegate(HloInstruction* negate) {
-    using type = typename std::make_unsigned::type;
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[negate],
-        ElementWiseUnaryOp(negate, [](ElementwiseT elem_operand) {
-          return NativeT(-type(elem_operand));
-        }));
-    return Status::OK();
-  }
-
-  template ::value ||
-                std::is_floating_point::value>::type* = nullptr>
-  Status HandleNegate(HloInstruction* negate) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[negate],
-        ElementWiseUnaryOp(
-            negate, [](ElementwiseT elem_operand) { return -elem_operand; }));
-    return Status::OK();
-  }
-
-  Status HandleNegate(HloInstruction* negate) override {
-    return HandleNegate(negate);
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleSign(HloInstruction* sign) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[sign],
-                        ElementWiseUnaryOp(sign, [](ElementwiseT elem_operand) {
-                          return (ElementwiseT(0) < elem_operand) -
-                                 (elem_operand < ElementwiseT(0));
-                        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleSign(HloInstruction* sign) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[sign],
-                        ElementWiseUnaryOp(sign, [](ElementwiseT elem_operand) {
-                          auto abs_val = std::abs(elem_operand);
-                          return 0 == abs_val ? ElementwiseT(0)
-                                              : elem_operand / abs_val;
-                        }));
-    return Status::OK();
-  }
-
-  Status HandleSign(HloInstruction* sign) override {
-    return HandleSign(sign);
-  }
-
-  template ::value>::type* = nullptr>
-  Status HandleAtan2(HloInstruction* atan2) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[atan2],
-                        ElementWiseBinaryOp(atan2, [](ElementwiseT lhs_elem,
-                                                      ElementwiseT rhs_elem) {
-                          return std::atan2(lhs_elem, rhs_elem);
-                        }));
-    return Status::OK();
-  }
-
-  template ::value>::type* = nullptr>
-  Status HandleAtan2(HloInstruction* atan2) {
-    return InvalidArgument("Unsupported type for Atan2");
-  }
-
-  Status HandleAtan2(HloInstruction* atan2) override {
-    return HandleAtan2(atan2);
-  }
-
-  Status HandleTanh(HloInstruction* tanh) override {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[tanh],
-                        ElementWiseUnaryOp(tanh, [](ElementwiseT elem_operand) {
-                          return std::tanh(elem_operand);
-                        }));
-    return Status::OK();
-  }
-
-  template ::value &&
-                !std::is_floating_point::value>::type* = nullptr>
-  Status HandleMultiply(HloInstruction* multiply) {
-    using type = typename std::make_unsigned::type;
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[multiply],
-        ElementWiseBinaryOp(multiply,
-                            [](ElementwiseT lhs_elem, ElementwiseT rhs_elem) {
-                              return NativeT(type(lhs_elem) * type(rhs_elem));
-                            }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value ||
-                              std::is_floating_point::value ||
-                              is_complex_t::value>::type* = nullptr>
-  Status HandleMultiply(HloInstruction* multiply) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[multiply],
-        ElementWiseBinaryOp(multiply,
-                            [](ElementwiseT lhs_elem, ElementwiseT rhs_elem) {
-                              return lhs_elem * rhs_elem;
-                            }));
-    return Status::OK();
-  }
-
-  Status HandleMultiply(HloInstruction* multiply) override {
-    return HandleMultiply(multiply);
-  }
-
-  Status HandleSubtract(HloInstruction* subtract) override {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[subtract],
-        ElementWiseBinaryOp(subtract,
-                            [](ElementwiseT lhs_elem, ElementwiseT rhs_elem) {
-                              return lhs_elem - rhs_elem;
-                            }));
-    return Status::OK();
-  }
-
-  Status HandleAdd(HloInstruction* add) override {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[add],
-                        ElementWiseBinaryOp(add, [](ElementwiseT lhs_elem,
-                                                    ElementwiseT rhs_elem) {
-                          return lhs_elem + rhs_elem;
-                        }));
-    return Status::OK();
-  }
-
-  Status HandleDivide(HloInstruction* divide) override {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[divide],
-                        ElementWiseBinaryOp(divide, [](ElementwiseT lhs_elem,
-                                                       ElementwiseT rhs_elem) {
-                          return lhs_elem / rhs_elem;
-                        }));
-    return Status::OK();
-  }
-
-  template ::value>::type* =
-                nullptr>
-  Status HandleMaximum(HloInstruction* maximum) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[maximum],
-        ElementWiseBinaryOp(maximum, [](ElementwiseT lhs, ElementwiseT rhs) {
-          return std::max(lhs, rhs);
-        }));
-    return Status::OK();
-  }
-
-  template ::value>::type* = nullptr>
-  Status HandleMaximum(HloInstruction* maximum) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[maximum],
-        ElementWiseBinaryOp(maximum, [](ElementwiseT lhs, ElementwiseT rhs) {
-          return ((lhs >= rhs) || std::isnan(lhs)) ? lhs : rhs;
-        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleMaximum(HloInstruction* maximum) {
-    return InvalidArgument("Unsupported type for Maximum");
-  }
-
-  Status HandleMaximum(HloInstruction* maximum) override {
-    return HandleMaximum(maximum);
-  }
-
-  template ::value>::type* =
-                nullptr>
-  Status HandleMinimum(HloInstruction* minimum) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[minimum],
-                        ElementWiseBinaryOp(minimum, [](ElementwiseT lhs_el,
-                                                        ElementwiseT rhs_el) {
-                          return std::min(lhs_el, rhs_el);
-                        }));
-    return Status::OK();
-  }
-
-  template ::value>::type* = nullptr>
-  Status HandleMinimum(HloInstruction* minimum) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[minimum],
-        ElementWiseBinaryOp(minimum, [](ElementwiseT lhs_el,
-                                        ElementwiseT rhs_el) {
-          return ((lhs_el <= rhs_el) || std::isnan(lhs_el)) ? lhs_el : rhs_el;
-        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleMinimum(HloInstruction* minimum) {
-    return InvalidArgument("Unsupported type for Minimum");
-  }
-
-  Status HandleMinimum(HloInstruction* minimum) override {
-    return HandleMinimum(minimum);
-  }
-
-  Status HandlePower(HloInstruction* power) override {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[power],
-                        ElementWiseBinaryOp(power, [](ElementwiseT lhs_el,
-                                                      ElementwiseT rhs_el) {
-                          return std::pow(lhs_el, rhs_el);
-                        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleRemainder(HloInstruction* remainder) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[remainder],
-                        ElementWiseBinaryOp(remainder, [](ElementwiseT lhs_el,
-                                                          ElementwiseT rhs_el) {
-                          return std::fmod(lhs_el, rhs_el);
-                        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleRemainder(HloInstruction* remainder) {
-    return InvalidArgument("Unsupported type for Remainder");
-  }
-
-  Status HandleRemainder(HloInstruction* remainder) override {
-    return HandleRemainder(remainder);
-  }
-
-  template ::value>::type* =
-                nullptr>
-  Status HandleAnd(HloInstruction* and_) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[and_],
-        ElementWiseBinaryOp(and_, [](ElementwiseT lhs_el, ElementwiseT rhs_el) {
-          return lhs_el & rhs_el;
-        }));
-    return Status::OK();
-  }
-
-  template ::value>::type* = nullptr>
-  Status HandleAnd(HloInstruction* and_) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[and_],
-        ElementWiseBinaryOp(and_, [](ElementwiseT lhs_el, ElementwiseT rhs_el) {
-          return lhs_el && rhs_el;
-        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleAnd(HloInstruction* and_) {
-    return InvalidArgument("Unsupported type for And");
-  }
-
-  Status HandleAnd(HloInstruction* and_) override {
-    return HandleAnd(and_);
-  }
-
-  template ::value>::type* =
-                nullptr>
-  Status HandleOr(HloInstruction* or_) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[or_],
-        ElementWiseBinaryOp(or_, [](ElementwiseT lhs_el, ElementwiseT rhs_el) {
-          return lhs_el | rhs_el;
-        }));
-    return Status::OK();
-  }
-
-  template ::value>::type* = nullptr>
-  Status HandleOr(HloInstruction* or_) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[or_],
-        ElementWiseBinaryOp(or_, [](ElementwiseT lhs_el, ElementwiseT rhs_el) {
-          return lhs_el || rhs_el;
-        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleOr(HloInstruction* or_) {
-    return InvalidArgument("Unsupported type for Or");
-  }
-
-  Status HandleOr(HloInstruction* or_) override {
-    return HandleOr(or_);
-  }
-
-  template ::value &&
-                !std::is_same::value>::type* = nullptr>
-  Status HandleShiftLeft(HloInstruction* shl) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[shl],
-        ElementWiseBinaryOp(shl, [](NativeT lhs_elem, NativeT rhs_elem) {
-          return IsShiftOutOfBounds(rhs_elem) ? 0
-                                                       : (lhs_elem << rhs_elem);
-        }));
-    return Status::OK();
-  }
-
-  template ::value ||
-                                    std::is_same::value>::type* =
-                nullptr>
-  Status HandleShiftLeft(HloInstruction*) {
-    return InvalidArgument("Unsupported type for ShiftLeft");
-  }
-
-  Status HandleShiftLeft(HloInstruction* shl) override {
-    return HandleShiftLeft(shl);
-  }
-  template ::value &&
-                !std::is_same::value>::type* = nullptr>
-  Status HandleShiftRightArithmetic(HloInstruction* shr) {
-    typedef typename std::make_signed::type SignedT;
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[shr],
-        ElementWiseBinaryOp(shr, [](NativeT lhs_elem, NativeT rhs_elem) {
-          SignedT lhs_signed = static_cast(lhs_elem);
-          if (IsShiftOutOfBounds(rhs_elem)) {
-            return lhs_signed < 0 ? static_cast(-1) : 0;
-          } else {
-            return lhs_signed >> rhs_elem;
-          }
-        }));
-    return Status::OK();
-  }
-
-  template ::value ||
-                                    std::is_same::value>::type* =
-                nullptr>
-  Status HandleShiftRightArithmetic(HloInstruction*) {
-    return InvalidArgument("Unsupported type for ShiftRightArithmetic");
-  }
-
-  Status HandleShiftRightArithmetic(HloInstruction* shra) override {
-    return HandleShiftRightArithmetic(shra);
-  }
-
-  template ::value &&
-                !std::is_same::value>::type* = nullptr>
-  Status HandleShiftRightLogical(HloInstruction* shr) {
-    typedef typename std::make_unsigned::type UnsignedT;
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[shr],
-        ElementWiseBinaryOp(shr, [](NativeT lhs_elem, NativeT rhs_elem) {
-          // If shift amount is greater than the number of bits, then return 0.
-          if (IsShiftOutOfBounds(rhs_elem)) {
-            return static_cast(0);
-          }
-          return static_cast(static_cast(lhs_elem) >>
-                                      rhs_elem);
-        }));
-    return Status::OK();
-  }
-
-  template ::value ||
-                                    std::is_same::value>::type* =
-                nullptr>
-  Status HandleShiftRightLogical(HloInstruction*) {
-    return InvalidArgument("Unsupported type for ShiftRightLogical");
-  }
-
-  Status HandleShiftRightLogical(HloInstruction* shrl) override {
-    return HandleShiftRightLogical(shrl);
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleClamp(HloInstruction* clamp) {
-    std::function
-        clamp_op = [](ElementwiseT low, ElementwiseT value, ElementwiseT high) {
-          return std::fmin(high, std::fmax(value, low));
-        };
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[clamp],
-        ElementwiseTernaryOp(clamp,
-                             std::move(ConvertTernaryFunction(clamp_op))));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value>::type* = nullptr>
-  Status HandleClamp(HloInstruction*) {
-    return InvalidArgument("Unsupported type for Clamp");
-  }
-
-  Status HandleClamp(HloInstruction* clamp) override {
-    return HandleClamp(clamp);
-  }
-
-  Status HandleSelect(HloInstruction* select) override {
-    CHECK(!ShapeUtil::IsScalar(select->operand(0)->shape()));
-    CHECK(!ShapeUtil::IsTuple(select->shape()));
-    std::function select_op =
-        [](bool pred, ReturnT on_true, ReturnT on_false) {
-          if (pred) {
-            return on_true;
-          }
-          return on_false;
-        };
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[select],
-                        ElementwiseTernaryOp(select, std::move(select_op)));
-    return Status::OK();
-  }
-
-  Status HandleReverse(HloInstruction* reverse) override {
-    const auto result_shape = reverse->shape();
-    const auto reverse_dimensions = reverse->dimensions();
-
-    auto operand = reverse->operand(0);
-    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
-                        ShapeInference::InferReverseShape(operand->shape(),
-                                                          reverse_dimensions));
-
-    TF_RET_CHECK(ShapeUtil::Compatible(result_shape, inferred_return_shape))
-        << "return shape set to: " << ShapeUtil::HumanString(result_shape)
-        << " but is inferred to be: "
-        << ShapeUtil::HumanString(inferred_return_shape);
-
-    const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand);
-    auto result = Literal::CreateFromShape(result_shape);
-
-    TF_RETURN_IF_ERROR(
-        result->Populate([&](ArraySlice out_index) {
-          std::vector from_index(out_index.begin(), out_index.end());
-          for (const int64 dim : reverse_dimensions) {
-            from_index[dim] = result_shape.dimensions(dim) - 1 - out_index[dim];
-          }
-          return operand_literal.Get(from_index);
-        }));
-
-    parent_->evaluated_[reverse] = std::move(result);
-    return Status::OK();
-  }
-
-  Status HandleConvolution(HloInstruction* conv) override {
-    auto lhs = conv->operand(0);
-    auto rhs = conv->operand(1);
-    const auto& window = conv->window();
-    const Shape& result_shape = conv->shape();
-    const Shape& lhs_shape = lhs->shape();
-    const Shape& rhs_shape = rhs->shape();
-
-    TF_CHECK_OK(ShapeUtil::ValidateShape(lhs_shape));
-    TF_CHECK_OK(ShapeUtil::ValidateShape(rhs_shape));
-    CHECK(ShapeUtil::IsArray(lhs_shape));
-    CHECK(ShapeUtil::IsArray(rhs_shape));
-    CHECK(ShapeUtil::SameElementType(lhs_shape, rhs_shape));
-    CHECK(ShapeUtil::SameElementType(lhs_shape, result_shape));
-
-    const auto& dnums = conv->convolution_dimension_numbers();
-    const int64 num_spatial_dims = dnums.output_spatial_dimensions_size();
-    CHECK_EQ(num_spatial_dims, dnums.input_spatial_dimensions_size());
-    CHECK_EQ(num_spatial_dims, dnums.kernel_spatial_dimensions_size());
-    CHECK_GE(num_spatial_dims, 0);
-    CHECK_EQ(window.dimensions_size(), num_spatial_dims);
-
-    const auto lhs_rank = ShapeUtil::Rank(lhs_shape);
-    const auto rhs_rank = ShapeUtil::Rank(rhs_shape);
-
-    CHECK_EQ(num_spatial_dims + 2, lhs_rank);
-    CHECK_EQ(num_spatial_dims + 2, rhs_rank);
-
-    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
-                        ShapeInference::InferConvolveShape(lhs_shape, rhs_shape,
-                                                           window, dnums));
-    CHECK(ShapeUtil::Compatible(result_shape, inferred_return_shape))
-        << "return shape set to: " << ShapeUtil::HumanString(result_shape)
-        << " but is inferred to be: "
-        << ShapeUtil::HumanString(inferred_return_shape);
-
-    const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs);
-    const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs);
-
-    std::vector window_dimension_sizes;
-    for (auto i : dnums.kernel_spatial_dimensions()) {
-      window_dimension_sizes.push_back(ShapeUtil::GetDimension(rhs_shape, i));
-    }
-
-    const Shape& window_shape =
-        ShapeUtil::MakeShape(rhs_shape.element_type(), window_dimension_sizes);
-
-    DimensionVector lhs_dim_multipliers = MakeDimMultipliers(lhs_shape);
-    DimensionVector rhs_dim_multipliers = MakeDimMultipliers(rhs_shape);
-
-    auto lhs_literal_data = lhs_literal.data();
-    auto rhs_literal_data = rhs_literal.data();
-
-    auto func = [&window_shape, &dnums, &lhs_shape, &rhs_shape, &window,
-                 &lhs_dim_multipliers, &rhs_dim_multipliers, lhs_literal_data,
-                 rhs_literal_data](ArraySlice out_index) {
-      // Dimension number applicable for input (lhs).
-      const int64 input_batch_dim = dnums.input_batch_dimension();
-      const int64 input_z_dim = dnums.input_feature_dimension();
-      // Dimension number applicable for kernel (rhs).
-      const int64 kernel_input_z_dim = dnums.kernel_input_feature_dimension();
-      const int64 kernel_output_z_dim = dnums.kernel_output_feature_dimension();
-      // Dimension number applicable for output.
-      const int64 output_batch_dim = dnums.output_batch_dimension();
-      const int64 output_z_dim = dnums.output_feature_dimension();
-
-      const int64 z_size = ShapeUtil::GetDimension(lhs_shape, input_z_dim);
-
-      ElementwiseT result_val = static_cast(0);
-      DimensionVector rhs_spatial_index(dnums.kernel_spatial_dimensions_size(),
-                                        0);
-
-      // Convolve input feature with kernel.
-      do {
-        for (int64 iz = 0; iz < z_size; ++iz) {
-          int64 lhs_linear_index = 0;
-          lhs_linear_index += out_index[output_batch_dim] *
-                              lhs_dim_multipliers[input_batch_dim];
-          lhs_linear_index += iz * lhs_dim_multipliers[input_z_dim];
-
-          int64 rhs_linear_index = 0;
-          rhs_linear_index += out_index[output_z_dim] *
-                              rhs_dim_multipliers[kernel_output_z_dim];
-          rhs_linear_index += iz * rhs_dim_multipliers[kernel_input_z_dim];
-
-          // Find corresponding spatial dimension index for input (lhs).
-          for (int64 ki = 0; ki < rhs_spatial_index.size(); ++ki) {
-            // Spatial dimension number for input (lhs) and output.
-            const int64 input_spatial_dim = dnums.input_spatial_dimensions(ki);
-            const int64 output_spatial_dim =
-                dnums.output_spatial_dimensions(ki);
-
-            // Calculate lhs (input) index without taking base dilation into
-            // account.
-            const auto& window_dim = window.dimensions(ki);
-            const int64 undilated_index =
-                out_index[output_spatial_dim] * window_dim.stride() -
-                window_dim.padding_low() +
-                rhs_spatial_index[ki] * window_dim.window_dilation();
-            // Skip if the lhs (input) index is to be dilated.  As an
-            // optimization, skip this mod if there's no dilation.
-            if (window_dim.base_dilation() > 1 &&
-                undilated_index % window_dim.base_dilation() != 0) {
-              goto cnt;
-            }
-
-            // Calculate the actual lhs (input) index after dilation.  As an
-            // optimization, skip this integer divide if there's no dilation.
-            int64 lhs_spatial_index;
-            if (window_dim.base_dilation() > 1) {
-              lhs_spatial_index = undilated_index / window_dim.base_dilation();
-            } else {
-              lhs_spatial_index = undilated_index;
-            }
-            lhs_linear_index +=
-                lhs_spatial_index * lhs_dim_multipliers[input_spatial_dim];
-
-            // Skip if input index is not in bounds.
-            if (!(lhs_spatial_index >= 0 &&
-                  lhs_spatial_index <
-                      lhs_shape.dimensions(input_spatial_dim))) {
-              goto cnt;
-            }
-
-            rhs_linear_index +=
-                (window_dim.window_reversal()
-                     ? ((window_dim.size() - 1) - rhs_spatial_index[ki])
-                     : rhs_spatial_index[ki]) *
-                rhs_dim_multipliers[dnums.kernel_spatial_dimensions(ki)];
-          }
-
-          result_val +=
-              static_cast(lhs_literal_data[lhs_linear_index]) *
-              static_cast(rhs_literal_data[rhs_linear_index]);
-        }
-      cnt : {}
-      } while (IndexUtil::BumpIndices(window_shape, &rhs_spatial_index));
-
-      return static_cast(result_val);
-    };
-
-    auto result = Literal::CreateFromShape(result_shape);
-    TF_RETURN_IF_ERROR(result->PopulateParallel(func));
-
-    parent_->evaluated_[conv] = std::move(result);
-    return Status::OK();
-  }
-
-  Status HandleDot(HloInstruction* dot) override {
-    auto lhs = dot->operand(0);
-    auto rhs = dot->operand(1);
-    CHECK(ShapeUtil::IsArray(dot->shape()));
-    CHECK(ShapeUtil::IsArray(lhs->shape()));
-    CHECK(ShapeUtil::IsArray(rhs->shape()));
-
-    const auto& dnums = dot->dot_dimension_numbers();
-
-    const auto lhs_rank = ShapeUtil::Rank(lhs->shape());
-    const auto rhs_rank = ShapeUtil::Rank(rhs->shape());
-
-    CHECK(ShapeUtil::SameElementType(lhs->shape(), rhs->shape()));
-    CHECK(ShapeUtil::SameElementType(lhs->shape(), dot->shape()));
-
-    // There must be 1 and only 1 Contracting dimension for lhs and rhs.
-    CHECK_EQ(dnums.lhs_contracting_dimensions_size(), 1);
-    CHECK_EQ(dnums.rhs_contracting_dimensions_size(), 1);
-    const int64 lhs_contracting_dimension = dnums.lhs_contracting_dimensions(0);
-    const int64 rhs_contracting_dimension = dnums.rhs_contracting_dimensions(0);
-    // Contracted dimension sizes must be the same.
-    CHECK_EQ(lhs->shape().dimensions(lhs_contracting_dimension),
-             rhs->shape().dimensions(rhs_contracting_dimension))
-        << "lhs contracted dimension: "
-        << lhs->shape().dimensions(lhs_contracting_dimension)
-        << " rhs contracted dimension: "
-        << rhs->shape().dimensions(rhs_contracting_dimension);
-    const int64 contracted_dimension_size =
-        lhs->shape().dimensions(lhs_contracting_dimension);
-
-    const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs);
-    const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs);
-
-    auto result = Literal::CreateFromShape(dot->shape());
-
-    CHECK_EQ(dnums.lhs_batch_dimensions_size(),
-             dnums.rhs_batch_dimensions_size());
-
-    std::vector lhs_non_contracting_dims;
-    for (int64 i = 0; i < lhs_rank; i++) {
-      if (i != lhs_contracting_dimension) {
-        lhs_non_contracting_dims.push_back(i);
-      }
-    }
-
-    std::vector rhs_non_batch_non_contracting_dims;
-    FlatSet batch_dims_set(dnums.rhs_batch_dimensions().begin(),
-                                  dnums.rhs_batch_dimensions().end());
-    for (int64 i = 0; i < rhs_rank; i++) {
-      if (i != rhs_contracting_dimension && batch_dims_set.count(i) == 0) {
-        rhs_non_batch_non_contracting_dims.push_back(i);
-      }
-    }
-
-    const int64 batch_dim_size = dnums.lhs_batch_dimensions_size();
-    const int64 lhs_non_contracting_size = lhs_non_contracting_dims.size();
-
-    DimensionVector lhs_index(lhs_rank);
-    DimensionVector rhs_index(rhs_rank);
-    TF_RETURN_IF_ERROR(
-        result->Populate([&](ArraySlice result_index) {
-          ElementwiseT result_val = static_cast(0);
-
-          // Find the corresponding non-contracting indices for lhs and rhs.
-          //
-          // For `result_index`, its batch dimension, if exists, will be at the
-          // same dimension as the batch dimension of lhs and rhs. More
-          // specifically:
-          // - For lhs, the non-contracting dimensions, including the batch
-          // dimension have the same index as the `result_index`.
-          // - For rhs, the batch dimension is set seperately from other
-          // non-contracting dimensions, since these other non-contracting
-          // dimensions in rhs follow the non-contracting dimensions of lhs in
-          // the resulting index.
-          //
-          // As an example, for a resulting index:
-          //  result_index [result_batch, result_x, result_y]
-          // the effecting lhs and rhs indices are:
-          //  lhs [result_batch, lhs_non_contracting_dim, contracting_dim
-          //  rhs [result_batch, contracting_dim, rhs_non_contracting_dim]
-          // `result_x` is only affected by the lhs_non_contracting_dim and
-          // likewise `result_y` only depends on rhs_non_contracting_dim.
-          //
-          // so we can look up the lhs and rhs indices by:
-          //
-          // lhs:
-          //  batch index is the same as `result_batch`.
-          //    non-contracting dimension is the same as
-          //    result_index[lhs_non_contracting_dim]
-          // rhs:
-          //  batch index: the same as `result_batch`.
-          //  non-contracting dimension index: *not* the same as
-          //    result_index[rhs_non_contractng_dim], since the
-          //    non-contracting dimensions of lhs are included in the
-          //    result_index first. Instead, the non_contracting_dim of rhs must
-          //    be calculated as following:
-          //      lhs_non_contracting_dimensions_size +
-          //      (rhs_non_batch_non_contracting_dim - batch_dim_size) - 1
-          //
-          //    Note that (rhs_non_batch_contracting_dim - batch_dim_size) is
-          //    the index offset to the result_index that only depends on
-          //    the non_batch and non-contracting dimensions of rhs. -1 at the
-          //    end translates size to index.
-          for (auto i : lhs_non_contracting_dims) {
-            lhs_index[i] = result_index[i];
-          }
-          for (auto i : dnums.rhs_batch_dimensions()) {
-            rhs_index[i] = result_index[i];
-          }
-          for (auto i : rhs_non_batch_non_contracting_dims) {
-            const int64 rhs_non_batch_non_contracting_dim =
-                lhs_non_contracting_size + (i - batch_dim_size) - 1;
-            rhs_index[i] = result_index[rhs_non_batch_non_contracting_dim];
-          }
-
-          // Accumulates resulting product along the contracted dimension.
-          for (int64 i = 0; i < contracted_dimension_size; ++i) {
-            lhs_index[lhs_contracting_dimension] = i;
-            rhs_index[rhs_contracting_dimension] = i;
-
-            result_val +=
-                static_cast(lhs_literal.Get(lhs_index)) *
-                static_cast(rhs_literal.Get(rhs_index));
-          }
-
-          return static_cast(result_val);
-        }));
-
-    parent_->evaluated_[dot] = std::move(result);
-    return Status::OK();
-  }
-
-  Status HandlePad(HloInstruction* pad) override {
-    CHECK(!ShapeUtil::IsTuple(pad->operand(0)->shape()));
-    // Padding value must be scalar.
-    CHECK(ShapeUtil::IsScalar(pad->operand(1)->shape()));
-    CHECK_EQ(ShapeUtil::Rank(pad->operand(0)->shape()),
-             pad->padding_config().dimensions_size());
-
-    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
-                        ShapeInference::InferPadShape(
-                            /*operand_shape=*/pad->operand(0)->shape(),
-                            /*padding_value_shape=*/pad->operand(1)->shape(),
-                            /*padding_config=*/pad->padding_config()));
-    CHECK(ShapeUtil::Compatible(pad->shape(), inferred_return_shape))
-        << "return shape is set to: " << ShapeUtil::HumanString(pad->shape())
-        << "but is inferred to be: "
-        << ShapeUtil::HumanString(inferred_return_shape);
-
-    // Create new HLO of padded shape with padding value.
-    ReturnT scalar =
-        parent_->GetEvaluatedLiteralFor(pad->operand(1)).Get({});
-    auto result = Literal::CreateFromShape(pad->shape());
-    TF_RETURN_IF_ERROR(result->Populate(
-        [&scalar](ArraySlice multi_index) { return scalar; }));
-
-    const Literal& evaluated_operand =
-        parent_->GetEvaluatedLiteralFor(pad->operand(0));
-
-    std::vector input_index(ShapeUtil::Rank(evaluated_operand.shape()),
-                                   0);
-    std::vector target_index(ShapeUtil::Rank(result->shape()), 0);
-
-    // Loop through each element of the operand, assign them to the
-    // corresponding index of the resulting padded literal.
-    const PaddingConfig& pad_config = pad->padding_config();
-
-    auto func = [&](ArraySlice input_index) {
-      for (auto i = 0; i < input_index.size(); ++i) {
-        // Interior padding occurs logically before edge padding, so in the case
-        // of negative edge padding elements are removed from the
-        // interior-padded operand.
-        target_index[i] =
-            pad_config.dimensions(i).edge_padding_low() +
-            input_index[i] * (pad_config.dimensions(i).interior_padding() + 1);
-
-        // Account for negative low and high padding: skip assignment if the
-        // any target index is out of range.
-        if (!(target_index[i] >= 0 &&
-              target_index[i] < pad->shape().dimensions(i))) {
-          return true;
-        }
-      }
-      result->Set(target_index,
-                           evaluated_operand.Get(input_index));
-      return true;
-    };
-
-    std::vector zero_base(evaluated_operand.shape().dimensions_size(),
-                                 0);
-    std::vector step(evaluated_operand.shape().dimensions_size(), 1);
-
-    ShapeUtil::ForEachIndex(
-        evaluated_operand.shape(), zero_base,
-        AsInt64Slice(evaluated_operand.shape().dimensions()), step, func);
-
-    parent_->evaluated_[pad] = std::move(result);
-    return Status::OK();
-  }
-
-  Status HandleDynamicSlice(HloInstruction* dynamic_slice) override {
-    auto operand = dynamic_slice->operand(0);
-    auto start_indices = dynamic_slice->operand(1);
-    auto result_shape = dynamic_slice->shape();
-    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
-                        ShapeInference::InferDynamicSliceShape(
-                            operand->shape(), start_indices->shape(),
-                            dynamic_slice->dynamic_slice_sizes()));
-    TF_RET_CHECK(ShapeUtil::Compatible(result_shape, inferred_return_shape))
-        << "return shape is set to: " << ShapeUtil::HumanString(result_shape)
-        << "but is inferred to be: "
-        << ShapeUtil::HumanString(inferred_return_shape);
-    TF_RET_CHECK(
-        primitive_util::IsIntegralType(start_indices->shape().element_type()));
-
-    const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand);
-    const Literal& start_indices_literal =
-        parent_->GetEvaluatedLiteralFor(start_indices);
-
-    switch (start_indices->shape().element_type()) {
-      case S32: {
-        TF_ASSIGN_OR_RETURN(
-            parent_->evaluated_[dynamic_slice],
-            DynamicSlice(operand_literal, start_indices_literal,
-                                result_shape));
-      } break;
-      case S64: {
-        TF_ASSIGN_OR_RETURN(
-            parent_->evaluated_[dynamic_slice],
-            DynamicSlice(operand_literal, start_indices_literal,
-                                result_shape));
-      } break;
-      case U32: {
-        TF_ASSIGN_OR_RETURN(
-            parent_->evaluated_[dynamic_slice],
-            DynamicSlice(operand_literal, start_indices_literal,
-                                 result_shape));
-      } break;
-      case U64: {
-        TF_ASSIGN_OR_RETURN(
-            parent_->evaluated_[dynamic_slice],
-            DynamicSlice(operand_literal, start_indices_literal,
-                                 result_shape));
-      } break;
-      default:
-        LOG(FATAL) << "HandleDynamicSlice: unhandled primitive type for "
-                      "start_indices: "
-                   << PrimitiveType_Name(start_indices->shape().element_type());
-    }
-
-    return Status::OK();
-  }
-
-  Status HandleDynamicUpdateSlice(
-      HloInstruction* dynamic_update_slice) override {
-    auto operand = dynamic_update_slice->operand(0);
-    auto update = dynamic_update_slice->operand(1);
-    auto start_indices = dynamic_update_slice->operand(2);
-    auto result_shape = dynamic_update_slice->shape();
-    TF_ASSIGN_OR_RETURN(
-        auto inferred_return_shape,
-        ShapeInference::InferDynamicUpdateSliceShape(
-            operand->shape(), update->shape(), start_indices->shape()));
-    TF_RET_CHECK(ShapeUtil::Compatible(result_shape, inferred_return_shape))
-        << "return shape is set to: " << ShapeUtil::HumanString(result_shape)
-        << "but is inferred to be: "
-        << ShapeUtil::HumanString(inferred_return_shape);
-    TF_RET_CHECK(
-        primitive_util::IsIntegralType(start_indices->shape().element_type()));
-    TF_RET_CHECK(ShapeUtil::Compatible(result_shape, operand->shape()));
-
-    const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand);
-    const Literal& update_literal = parent_->GetEvaluatedLiteralFor(update);
-    const Literal& start_indices_literal =
-        parent_->GetEvaluatedLiteralFor(start_indices);
-
-    switch (start_indices->shape().element_type()) {
-      case S32: {
-        TF_ASSIGN_OR_RETURN(
-            parent_->evaluated_[dynamic_update_slice],
-            DynamicUpdateSlice(operand_literal, update_literal,
-                                      start_indices_literal));
-      } break;
-      case S64: {
-        TF_ASSIGN_OR_RETURN(
-            parent_->evaluated_[dynamic_update_slice],
-            DynamicUpdateSlice(operand_literal, update_literal,
-                                      start_indices_literal));
-      } break;
-      case U32: {
-        TF_ASSIGN_OR_RETURN(
-            parent_->evaluated_[dynamic_update_slice],
-            DynamicUpdateSlice(operand_literal, update_literal,
-                                       start_indices_literal));
-      } break;
-      case U64: {
-        TF_ASSIGN_OR_RETURN(
-            parent_->evaluated_[dynamic_update_slice],
-            DynamicUpdateSlice(operand_literal, update_literal,
-                                       start_indices_literal));
-      } break;
-      default:
-        LOG(FATAL) << "HandleDynamicUpdateSlice: unhandled primitive type for "
-                      "start_indices: "
-                   << PrimitiveType_Name(start_indices->shape().element_type());
-    }
-
-    return Status::OK();
-  }
-
-  template 
-  StatusOr> MapImpl(HloInstruction* map) {
-    auto operands = map->operands();
-    HloComputation* computation = map->to_apply();
-
-    auto result = Literal::CreateFromShape(map->shape());
-
-    HloEvaluator embedded_evaluator(parent_->max_loop_iterations_);
-    TF_RETURN_IF_ERROR(
-        result->Populate([&](ArraySlice multi_index) {
-          std::vector> arg_literals;
-          arg_literals.reserve(operands.size());
-
-          // Construct scalar literal parameters to be passed to the map
-          // computation.
-          for (auto operand : operands) {
-            const Literal& arg_literal =
-                parent_->GetEvaluatedLiteralFor(operand);
-
-            auto curr_val = arg_literal.Get(multi_index);
-            auto curr_val_literal = Literal::CreateR0(curr_val);
-
-            arg_literals.push_back(std::move(curr_val_literal));
-          }
-
-          std::unique_ptr computed_result =
-              embedded_evaluator
-                  .Evaluate>(*computation,
-                                                      arg_literals)
-                  .ConsumeValueOrDie();
-          // Clear visit states so that the we can use the evaluate again on
-          // the same computation.
-          embedded_evaluator.ResetVisitStates();
-
-          return computed_result->Get({});
-        }));
-    return std::move(result);
-  }
-
-  Status HandleMap(HloInstruction* map) override {
-    switch (map->operand(0)->shape().element_type()) {
-      case PRED: {
-        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
-        break;
-      }
-      case U8: {
-        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
-        break;
-      }
-      case U32: {
-        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
-        break;
-      }
-      case U64: {
-        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
-        break;
-      }
-      case S8: {
-        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
-        break;
-      }
-      case S32: {
-        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
-        break;
-      }
-      case S64: {
-        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
-        break;
-      }
-      case F16: {
-        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map],
-                            MapImpl(map));
-        break;
-      }
-      case F32: {
-        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
-        break;
-      }
-      case F64: {
-        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
-        break;
-      }
-      case C64: {
-        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
-        break;
-      }
-      default:
-        LOG(FATAL) << "HandleMap: unhandled primitive type for "
-                      "input operand: "
-                   << PrimitiveType_Name(
-                          map->operand(0)->shape().element_type());
-    }
-
-    return Status::OK();
-  }
-
-  Status HandleReduce(HloInstruction* reduce) override {
-    auto arg = reduce->operand(0);
-    auto init_value = reduce->operand(1);
-    ArraySlice dimensions(reduce->dimensions());
-    HloComputation* function = reduce->to_apply();
-    TF_RET_CHECK(ShapeUtil::Rank(reduce->shape()) ==
-                 ShapeUtil::Rank(arg->shape()) - dimensions.size());
-    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
-                        ShapeInference::InferReduceShape(
-                            /*arg=*/arg->shape(),
-                            /*init_value=*/init_value->shape(),
-                            /*dimensions_to_reduce=*/dimensions,
-                            /*to_apply=*/function->ComputeProgramShape()));
-    TF_RET_CHECK(ShapeUtil::Compatible(reduce->shape(), inferred_return_shape))
-        << "return shape is set to: " << ShapeUtil::HumanString(reduce->shape())
-        << "but is inferred to be: "
-        << ShapeUtil::HumanString(inferred_return_shape);
-
-    const Literal& arg_literal = parent_->GetEvaluatedLiteralFor(arg);
-    VLOG(3) << "HandleReduce arg_literal: " << arg_literal.ToString();
-    const Literal& init_literal = parent_->GetEvaluatedLiteralFor(init_value);
-    VLOG(3) << "HandleReduce init_literal: " << init_literal.ToString();
-    TF_RET_CHECK(ShapeUtil::IsScalar(init_literal.shape()));
-    auto init_scalar = init_literal.Get({});
-
-    auto result = Literal::CreateFromShape(reduce->shape());
-
-    const auto arg_dimensions = AsInt64Slice(arg_literal.shape().dimensions());
-    std::vector arg_dim_steps(arg_dimensions.size());
-    std::vector arg_dim_counts(arg_dimensions.size());
-    for (const int64 dim : dimensions) {
-      arg_dim_steps[dim] = 1;
-      arg_dim_counts[dim] = arg_dimensions[dim];
-    }
-
-    // Map each dimension in the result to a dimension in arg that isn't
-    // being reduced.
-    std::vector result_to_arg_index;
-    for (int64 i = 0; i < arg_dimensions.size(); ++i) {
-      if (arg_dim_steps[i] == 0) {
-        result_to_arg_index.push_back(i);
-      }
-    }
-
-    HloEvaluator embedded_evaluator(parent_->max_loop_iterations_);
-    // For each resulting dimension, calculate and assign computed value.
-    TF_RETURN_IF_ERROR(
-        result->Populate([&](ArraySlice multi_index) {
-          ReturnT result_val = init_scalar;
-
-          std::vector base(arg_dimensions.size());
-          for (int64 i = 0; i < multi_index.size(); ++i) {
-            base[result_to_arg_index[i]] = multi_index[i];
-          }
-
-          // When the reduction is addition of floats, accumulate in a double
-          // for better precision. Also, avoid creating Literals for the
-          // intermediate results; it's much faster.
-          if (ShapeUtil::ElementIsFloating(init_literal.shape()) &&
-              IsScalarAdd(function)) {
-            double computed_result = 0;
-            auto func = [&](ArraySlice input_index) {
-              computed_result += arg_literal.Get(input_index);
-              return true;
-            };
-            ShapeUtil::ForEachIndex(arg_literal.shape(), base, arg_dim_counts,
-                                    arg_dim_steps, func);
-            return static_cast(computed_result);
-          }
-          auto func = [&](ArraySlice input_index) {
-            auto curr_val = arg_literal.Get(input_index);
-
-            // Evaluate computation with specified literal operands.
-            auto curr_val_literal = Literal::CreateR0(curr_val);
-            auto result_val_literal = Literal::CreateR0(result_val);
-            std::vector args = {result_val_literal.get(),
-                                                curr_val_literal.get()};
-
-            std::unique_ptr computed_result =
-                embedded_evaluator.Evaluate(*function, args)
-                    .ConsumeValueOrDie();
-            // Clear visit states so that we can use the evaluator again on
-            // the same computation.
-            embedded_evaluator.ResetVisitStates();
-            // Assign computed result to result_val.
-            result_val = computed_result->Get({});
-            return true;
-          };
-          // Computes one element of the result, reducing all dimensions that
-          // contribute to that element.
-          ShapeUtil::ForEachIndex(arg_literal.shape(), base, arg_dim_counts,
-                                  arg_dim_steps, func);
-          return result_val;
-        }));
-
-    parent_->evaluated_[reduce] = std::move(result);
-    return Status::OK();
-  }
-
-  bool IsScalarAdd(HloComputation* computation) {
-    HloInstruction* instruction = computation->root_instruction();
-    if (instruction->opcode() == HloOpcode::kAdd &&
-        computation->num_parameters() == 2) {
-      const HloInstruction* lhs = instruction->operand(0);
-      const HloInstruction* rhs = instruction->operand(1);
-      return lhs->opcode() == HloOpcode::kParameter &&
-             ShapeUtil::IsScalar(lhs->shape()) &&
-             rhs->opcode() == HloOpcode::kParameter &&
-             ShapeUtil::IsScalar(rhs->shape()) && lhs != rhs;
-    }
-    return false;
-  }
-
-  Status HandleSelectAndScatter(HloInstruction* select_and_scatter) override {
-    auto operand = select_and_scatter->operand(0);
-    auto source = select_and_scatter->operand(1);
-    const Window& window = select_and_scatter->window();
-
-    const Literal& init_literal =
-        parent_->GetEvaluatedLiteralFor(select_and_scatter->operand(2));
-    TF_RET_CHECK(ShapeUtil::IsScalar(init_literal.shape()));
-    auto init_scalar = init_literal.Get({});
-
-    auto result = Literal::CreateFromShape(select_and_scatter->shape());
-
-    // Initialize result array with the init value.
-    TF_RETURN_IF_ERROR(result->Populate(
-        [&](ArraySlice output_index) { return init_scalar; }));
-
-    std::vector window_dimension_sizes;
-    for (const auto& window_dimension : window.dimensions()) {
-      window_dimension_sizes.push_back(window_dimension.size());
-    }
-    const Shape window_shape = ShapeUtil::MakeShape(
-        operand->shape().element_type(), window_dimension_sizes);
-
-    HloComputation* select = select_and_scatter->select();
-    HloComputation* scatter = select_and_scatter->scatter();
-
-    const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand);
-    const Literal& source_literal = parent_->GetEvaluatedLiteralFor(source);
-
-    int64 rank = ShapeUtil::Rank(operand_literal.shape());
-
-    HloEvaluator embedded_evaluator(parent_->max_loop_iterations_);
-    DimensionVector source_index(rank);
-
-    std::fill(source_index.begin(), source_index.end(), 0);
-    do {
-      // For each element in `source`, we place a window in `operand`. For each
-      // window placement, we iterate inside the window twice:
-      //
-      // 1. Find the selected index by applying `select` function to all
-      // elements. E.g., If the `select` function is GreaterEqual, the first
-      // iteration through the window finds the biggest value and returns its
-      // index.
-      //
-      // 2. Using the selected index, scatter value from `source` to result. We
-      // do this by iterating through the window, and compare each index with
-      // the selected index.
-      optional selected_val;
-      optional> selected_index;
-
-      IterateThroughWindow(
-          window_shape, window, operand_literal.shape(), source_index,
-          [&](const std::vector& operand_index) {
-            auto curr_val = operand_literal.Get(operand_index);
-            if (!selected_val) {
-              selected_val = curr_val;
-              selected_index = operand_index;
-            }
-            const auto curr_val_literal = Literal::CreateR0(curr_val);
-            const auto selected_val_literal =
-                Literal::CreateR0(*selected_val);
-
-            const std::vector args = {
-                selected_val_literal.get(), curr_val_literal.get()};
-            std::unique_ptr computed_result =
-                embedded_evaluator.Evaluate(*select, args)
-                    .ConsumeValueOrDie();
-            bool selected = !computed_result->Get({});
-            if (selected) {
-              selected_val = curr_val;
-              selected_index = operand_index;
-            }
-            embedded_evaluator.ResetVisitStates();
-          });
-
-      IterateThroughWindow(
-          window_shape, window, operand_literal.shape(), source_index,
-          [&](const std::vector& operand_index) {
-            if (std::equal(operand_index.begin(), operand_index.end(),
-                           selected_index->begin())) {
-              auto source = source_literal.Get(source_index);
-              auto scattered = result->Get(operand_index);
-              const auto source_literal = Literal::CreateR0(source);
-              const auto scattered_literal =
-                  Literal::CreateR0(scattered);
-
-              const std::vector args = {
-                  source_literal.get(), scattered_literal.get()};
-              std::unique_ptr computed_result =
-                  embedded_evaluator.Evaluate(*scatter, args)
-                      .ConsumeValueOrDie();
-              result->Set(operand_index, computed_result->Get({}));
-              // Clear visit states so that the we can use the evaluator again
-              // on the same computation.
-              embedded_evaluator.ResetVisitStates();
-            }
-          });
-    } while (IndexUtil::BumpIndices(source->shape(), &source_index));
-
-    parent_->evaluated_[select_and_scatter] = std::move(result);
-    return Status::OK();
-  }
-
-  Status HandleReduceWindow(HloInstruction* reduce_window) override {
-    auto operand = reduce_window->operand(0);
-    const Window& window = reduce_window->window();
-    HloComputation* function = reduce_window->to_apply();
-    TF_ASSIGN_OR_RETURN(
-        auto inferred_return_shape,
-        ShapeInference::InferReduceWindowShape(
-            /*operand_shape=*/reduce_window->operand(0)->shape(),
-            /*init_value=*/reduce_window->operand(1)->shape(), window,
-            /*to_apply_shape=*/function->ComputeProgramShape()));
-    TF_RET_CHECK(
-        ShapeUtil::Compatible(reduce_window->shape(), inferred_return_shape))
-        << "return shape is set to: "
-        << ShapeUtil::HumanStringWithLayout(reduce_window->shape())
-        << "but is inferred to be: "
-        << ShapeUtil::HumanStringWithLayout(inferred_return_shape);
-
-    const Literal& operand_literal =
-        parent_->GetEvaluatedLiteralFor(reduce_window->operand(0));
-    VLOG(3) << "HandleReduceWindow arg_literal: " << operand_literal.ToString();
-    const Literal& init_literal =
-        parent_->GetEvaluatedLiteralFor(reduce_window->operand(1));
-    VLOG(3) << "HandleReduceWindow init_literal: " << init_literal.ToString();
-    TF_RET_CHECK(ShapeUtil::IsScalar(init_literal.shape()));
-    auto init_scalar = init_literal.Get({});
-
-    auto result = Literal::CreateFromShape(reduce_window->shape());
-
-    // Creates a Shape object from window, for iteration below.
-    std::vector window_dimension_sizes;
-    for (const auto& window_dimension : window.dimensions()) {
-      window_dimension_sizes.push_back(window_dimension.size());
-    }
-    const Shape window_shape = ShapeUtil::MakeShape(
-        operand->shape().element_type(), window_dimension_sizes);
-
-    DimensionVector window_index(window.dimensions_size());
-    DimensionVector operand_index(ShapeUtil::Rank(operand_literal.shape()));
-
-    HloEvaluator embedded_evaluator(parent_->max_loop_iterations_);
-    // For each resulting dimension, calculate and assign computed value.
-    TF_RETURN_IF_ERROR(
-        result->Populate([&](ArraySlice output_index) {
-          ReturnT result_val = init_scalar;
-
-          std::fill(window_index.begin(), window_index.end(), 0);
-          std::fill(operand_index.begin(), operand_index.end(), 0);
-
-          IterateThroughWindow(
-              window_shape, window, operand_literal.shape(), output_index,
-              [&](const std::vector& operand_index) {
-                auto curr_val = operand_literal.Get(operand_index);
-
-                // Evaluate computation with specified literal operands.
-                const auto curr_val_literal =
-                    Literal::CreateR0(curr_val);
-                const auto result_val_literal =
-                    Literal::CreateR0(result_val);
-                const std::vector args = {
-                    result_val_literal.get(), curr_val_literal.get()};
-                std::unique_ptr computed_result =
-                    embedded_evaluator.Evaluate(*function, args)
-                        .ConsumeValueOrDie();
-
-                // Clear visit states so that the we can use the evaluate again
-                // on the same computation.
-                embedded_evaluator.ResetVisitStates();
-
-                result_val = computed_result->Get({});
-              });
-
-          return result_val;
-        }));
-
-    parent_->evaluated_[reduce_window] = std::move(result);
-    return Status::OK();
-  }
-
-  Status HandleSlice(HloInstruction* slice) override {
-    auto operand = slice->operand(0);
-    const Shape& shape = slice->shape();
-    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
-                        ShapeInference::InferSliceShape(
-                            operand->shape(), slice->slice_starts(),
-                            slice->slice_limits(), slice->slice_strides()));
-    TF_RET_CHECK(ShapeUtil::Compatible(shape, inferred_return_shape))
-        << "return shape set to: " << ShapeUtil::HumanString(shape)
-        << " but is inferred to be: "
-        << ShapeUtil::HumanString(inferred_return_shape);
-
-    const int64 rank = ShapeUtil::Rank(operand->shape());
-    const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand);
-    auto func = [&](ArraySlice out_index) {
-      DimensionVector operand_index(rank);
-      for (int64 i = 0; i < rank; ++i) {
-        operand_index[i] =
-            slice->slice_starts(i) + out_index[i] * slice->slice_strides(i);
-      }
-      return operand_literal.Get(operand_index);
-    };
-
-    auto result = Literal::CreateFromDimensions(
-        shape.element_type(), AsInt64Slice(shape.dimensions()));
-    TF_RETURN_IF_ERROR(result->Populate(func));
-    parent_->evaluated_[slice] = std::move(result);
-    return Status::OK();
-  }
-
-  // Enable CLZ only for int32 and uint32.
-  template <
-      typename NativeT,
-      typename std::enable_if<
-          (std::is_floating_point::value ||
-           std::is_integral::value || is_complex_t::value) &&
-          !(std::is_same::value ||
-            std::is_same::value)>::type* = nullptr>
-  Status HandleClz(HloInstruction* clz) {
-    return InvalidArgument("Unsupported type for Clz");
-  }
-
-  template ::value ||
-                std::is_same::value>::type* = nullptr>
-  Status HandleClz(HloInstruction* clz) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[clz],
-                        ElementWiseUnaryOp(clz, [](ElementwiseT elem_operand) {
-                          return 31 - tensorflow::Log2Floor(elem_operand);
-                        }));
-    return Status::OK();
-  }
-
-  Status HandleClz(HloInstruction* clz) override {
-    return HandleClz(clz);
-  }
-
-  template ::value>::type* = nullptr>
-  Status HandleSin(HloInstruction* sin) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[sin],
-                        ElementWiseUnaryOp(sin, [](ElementwiseT elem_operand) {
-                          return std::sin(elem_operand);
-                        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value ||
-                              is_complex_t::value>::type* = nullptr>
-  Status HandleSin(HloInstruction* sin) {
-    return InvalidArgument("Unsupported type for Sin");
-  }
-
-  Status HandleSin(HloInstruction* sin) override {
-    return HandleSin(sin);
-  }
-
-  template ::value>::type* = nullptr>
-  Status HandleCos(HloInstruction* cos) {
-    TF_ASSIGN_OR_RETURN(parent_->evaluated_[cos],
-                        ElementWiseUnaryOp(cos, [](ElementwiseT elem_operand) {
-                          return std::cos(elem_operand);
-                        }));
-    return Status::OK();
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value ||
-                              is_complex_t::value>::type* = nullptr>
-  Status HandleCos(HloInstruction* cos) {
-    return InvalidArgument("Unsupported type for Cos");
-  }
-
-  Status HandleCos(HloInstruction* cos) override {
-    return HandleCos(cos);
-  }
-
-  template ::value>::type* = nullptr>
-  Status HandleReducePrecision(HloInstruction* reduce_precision) {
-    TF_ASSIGN_OR_RETURN(
-        parent_->evaluated_[reduce_precision],
-        ElementWiseUnaryOp(reduce_precision, [reduce_precision](
-                                                 ElementwiseT elem) {
-          uint32_t value_as_int = tensorflow::bit_cast(elem);
-          const uint32_t mantissa_bits = reduce_precision->mantissa_bits();
-          const uint32_t exponent_bits = reduce_precision->exponent_bits();
-
-          // Code is based on the CPU/GPU implementation in LLVM-emitting code.
-          //
-          // Bits in float type:
-          //   mantissa : bits [0:22]
-          //   exponent : bits [23:30]
-          //   sign     : bits [31]
-          if (mantissa_bits < 23) {
-            const uint32_t last_mantissa_bit_mask = 1u << (23 - mantissa_bits);
-
-            // Compute rounding bias for round-to-nearest with ties to even.
-            // This is equal to a base value of 0111... plus one bit if the last
-            // remaining mantissa bit is 1.
-            const uint32_t base_rounding_bias =
-                (last_mantissa_bit_mask >> 1) - 1;
-            const uint32_t x_last_mantissa_bit =
-                (value_as_int & last_mantissa_bit_mask) >> (23 - mantissa_bits);
-            const uint32_t x_rounding_bias =
-                x_last_mantissa_bit + base_rounding_bias;
-
-            // Add rounding bias, and mask out truncated bits.  Note that the
-            // case where adding the rounding bias overflows into the exponent
-            // bits is correct; the non-masked mantissa bits will all be zero,
-            // and the exponent will be incremented by one.
-            const uint32_t truncation_mask = ~(last_mantissa_bit_mask - 1);
-            value_as_int = value_as_int + x_rounding_bias;
-            value_as_int = value_as_int & truncation_mask;
-          }
-          if (exponent_bits < 8) {
-            // Masks for f32 values.
-            const uint32_t f32_sign_bit_mask = 1u << 31;
-            const uint32_t f32_exp_bits_mask = 0xffu << 23;
-
-            // An exponent of 2^(n-1)-1 -- that is, 0111... with the zero in the
-            // most- significant bit -- is equal to 1.0f for all exponent sizes.
-            // Adding 2^(n-1)-1 to this gives us the highest non-infinite
-            // exponent for a bit- size of n, and subtracting 2^(n-1)-1 from
-            // this gives us the lowest' exponent (corresponding to 0.0f).
-            //
-            // Thus, the f32 exponent corresponding to the highest non-infinite
-            // exponent for a bit size of n is (2^7-1) + 2^(n-1)-1, and the f32
-            // exponent corresponding to the lowest exponent for a bit size of n
-            // is (2^7-1) - 2^(n-1)-1.
-            //
-            // Note that we have already checked that exponents_bits >= 1.
-            const uint32_t f32_exponent_bias = (1 << 7) - 1;
-            const uint32_t reduced_exponent_bias =
-                (1 << (exponent_bits - 1)) - 1;
-            const uint32_t reduced_max_exponent =
-                f32_exponent_bias + reduced_exponent_bias;
-            const uint32_t reduced_min_exponent =
-                f32_exponent_bias - reduced_exponent_bias;
-
-            // Do we overflow or underflow?
-            const uint32_t x_exponent = value_as_int & f32_exp_bits_mask;
-            const bool x_overflows = x_exponent > (reduced_max_exponent << 23);
-            const bool x_underflows =
-                x_exponent <= (reduced_min_exponent << 23);
-
-            // Compute appropriately-signed values of zero and infinity.
-            const uint32_t x_signed_zero = value_as_int & f32_sign_bit_mask;
-            const uint32_t x_signed_inf = x_signed_zero | f32_exp_bits_mask;
-
-            // Force to zero or infinity if overflow or underflow.  (Note that
-            // this truncates all denormal values to zero, rather than rounding
-            // them.)
-            value_as_int = x_overflows ? x_signed_inf : value_as_int;
-            value_as_int = x_underflows ? x_signed_zero : value_as_int;
-          }
-
-          float reduced_result = tensorflow::bit_cast(value_as_int);
-          if (std::isnan(elem)) {
-            reduced_result = mantissa_bits > 0
-                                 ? elem
-                                 : std::numeric_limits::infinity();
-          }
-          return reduced_result;
-        }));
-    return Status::OK();
-  }
-
-  template ::value>::type* = nullptr>
-  Status HandleReducePrecision(HloInstruction* reduce_precision) {
-    return InvalidArgument("Double not supported for reduce precision");
-  }
-
-  template <
-      typename NativeT,
-      typename std::enable_if::value ||
-                              is_complex_t::value>::type* = nullptr>
-  Status HandleReducePrecision(HloInstruction* reduce_precision) {
-    return InvalidArgument("Unsupported type for reduce precision");
-  }
-
-  Status HandleReducePrecision(HloInstruction* reduce_precision) override {
-    return HandleReducePrecision(reduce_precision);
-  }
-
- private:
-  template 
-  StatusOr> DynamicSlice(
-      const Literal& operand_literal, const Literal& start_indices_literal,
-      const Shape& result_shape) {
-    auto start_indices_typed = start_indices_literal.data();
-    std::vector start(start_indices_typed.begin(),
-                             start_indices_typed.end());
-
-    std::vector operand_indices(start.size());
-
-    auto result = Literal::CreateFromShape(result_shape);
-    TF_RETURN_IF_ERROR(
-        result->Populate([&](ArraySlice multi_index) {
-          for (int64 i = 0; i < operand_indices.size(); ++i) {
-            CHECK_GE(multi_index[i] + start[i], 0);
-            // Mod is only used here to be consistent with the existing
-            // backends' behavior.
-            operand_indices[i] = (multi_index[i] + start[i]) %
-                                 operand_literal.shape().dimensions(i);
-          }
-
-          auto result = operand_literal.Get(operand_indices);
-          return result;
-        }));
-
-    return std::move(result);
-  }
-
-  template 
-  StatusOr> DynamicUpdateSlice(
-      const Literal& operand_literal, const Literal& update_literal,
-      const Literal& start_indices_literal) {
-    auto result = operand_literal.CloneToUnique();
-    auto start_indices_typed = start_indices_literal.data();
-    const auto rank = ShapeUtil::Rank(result->shape());
-    std::vector start(rank, 0);
-    for (int64 i = 0; i < rank; ++i) {
-      // All other implementations currently wrap-around the index, so this
-      // should do so as well.
-      start[i] = (start_indices_typed[i] % result->shape().dimensions(i));
-      start[i] += (start[i] < 0) * result->shape().dimensions(i);
-    }
-    std::vector result_index(rank, 0);
-
-    auto func = [&](ArraySlice update_index) {
-      std::transform(update_index.begin(), update_index.end(), start.begin(),
-                     result_index.begin(), std::plus());
-      // Same as above, wrap-around only to match other implementations'
-      // semantics.
-      std::transform(result_index.begin(), result_index.end(),
-                     result->shape().dimensions().begin(), result_index.begin(),
-                     std::modulus());
-      result->Set(result_index,
-                           update_literal.Get(update_index));
-      return true;
-    };
-
-    std::vector base(update_literal.shape().dimensions_size(), 0);
-    std::vector step(update_literal.shape().dimensions_size(), 1);
-    ShapeUtil::ForEachIndex(update_literal.shape(), base,
-                            AsInt64Slice(update_literal.shape().dimensions()),
-                            step, func);
-
-    return std::move(result);
-  }
-
-  StatusOr> ElementWiseUnaryOp(
-      HloInstruction* instruction,
-      const std::function& unary_op) {
-    const Literal& operand_literal =
-        parent_->GetEvaluatedLiteralFor(instruction->operand(0));
-    TF_ASSIGN_OR_RETURN(
-        auto result_literal,
-        (ElementWiseUnaryOpImpl(
-            instruction, ConvertUnaryFunction(unary_op), operand_literal)));
-
-    return std::move(result_literal);
-  }
-
-  StatusOr> ElementWiseBinaryOp(
-      HloInstruction* instruction,
-      const std::function&
-          binary_op) {
-    const auto shape = instruction->shape();
-    const auto* lhs = instruction->operand(0);
-    const auto* rhs = instruction->operand(1);
-
-    // TODO(b/35950897, b/27796129): add DCHECK back once implicit broadcast
-    // is removed.
-    if (!(ShapeUtil::SameDimensions(shape, rhs->shape()) &&
-          ShapeUtil::SameDimensions(lhs->shape(), rhs->shape()))) {
-      return Unimplemented(
-          "Implicit broadcasting is currently unsupported in HLO evaluator "
-          "Shape Mismatch: %s vs %s vs %s: ",
-          ShapeUtil::HumanString(shape).c_str(),
-          ShapeUtil::HumanString(lhs->shape()).c_str(),
-          ShapeUtil::HumanString(rhs->shape()).c_str());
-    }
-
-    const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs);
-    const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs);
-
-    auto result = Literal::CreateFromShape(shape);
-
-    TF_RETURN_IF_ERROR(
-        result->Populate([&](ArraySlice multi_index) {
-          return ConvertBinaryFunction(binary_op)(
-              lhs_literal.Get(multi_index),
-              rhs_literal.Get(multi_index));
-        }));
-    return std::move(result);
-  }
-
-  template 
-  StatusOr> ElementwiseTernaryOp(
-      HloInstruction* instruction,
-      const std::function& ternary_op) {
-    const auto shape = instruction->shape();
-    const auto* lhs = instruction->operand(0);
-    const auto* rhs = instruction->operand(1);
-    const auto* ehs = instruction->operand(2);
-
-    // TODO(b/35950897, b/27796129): add DCHECK back once implicit
-    // broadcast is removed.
-    if (!(ShapeUtil::SameDimensions(shape, lhs->shape()) &&
-          ShapeUtil::SameDimensions(lhs->shape(), rhs->shape()) &&
-          ShapeUtil::SameDimensions(rhs->shape(), ehs->shape()))) {
-      return Unimplemented(
-          "Implicit broadcasting is currently unsupported in HLO evaluator "
-          "Shape Mismatch: %s vs %s vs %s vs %s: ",
-          ShapeUtil::HumanString(shape).c_str(),
-          ShapeUtil::HumanString(lhs->shape()).c_str(),
-          ShapeUtil::HumanString(rhs->shape()).c_str(),
-          ShapeUtil::HumanString(ehs->shape()).c_str());
-    }
-
-    const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs);
-    const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs);
-    const Literal& ehs_literal = parent_->GetEvaluatedLiteralFor(ehs);
-
-    auto result = Literal::CreateFromShape(shape);
-
-    TF_RETURN_IF_ERROR(
-        result->Populate([&](ArraySlice multi_index) {
-          return ternary_op(lhs_literal.Get(multi_index),
-                            rhs_literal.Get(multi_index),
-                            ehs_literal.Get(multi_index));
-        }));
-
-    return std::move(result);
-  }
-
-  template 
-  static bool IsShiftOutOfBounds(NativeT rhs) {
-    typedef typename std::make_unsigned::type UnsignedT;
-    UnsignedT lhs_size_unsigned = sizeof(NativeT) * CHAR_BIT;
-    UnsignedT rhs_unsigned = static_cast(rhs);
-    return rhs_unsigned >= lhs_size_unsigned;
-  }
-
-  HloEvaluator* parent_;
-};  // class HloEvaluator::TypedVisitor
-
 HloEvaluator::HloEvaluator(int64 max_loop_iterations)
     : max_loop_iterations_(max_loop_iterations) {
-  typed_visitors_[PRED] = MakeUnique>(this);
-  typed_visitors_[U8] = MakeUnique>(this);
+  typed_visitors_[PRED] = MakeUnique>(this);
+  typed_visitors_[U8] = MakeUnique>(this);
   typed_visitors_[U16] = MakeUnique([](HloInstruction*) {
     return Unimplemented(
-        "HloEvaluator::TypedVisitor: unhandled primitive type: U16.");
+        "HloEvaluator::HloEvaluatorTypedVisitor: unhandled primitive type: "
+        "U16.");
   });
-  typed_visitors_[U32] = MakeUnique>(this);
-  typed_visitors_[U64] = MakeUnique>(this);
-  typed_visitors_[S8] = MakeUnique>(this);
+  typed_visitors_[U32] = MakeUnique>(this);
+  typed_visitors_[U64] = MakeUnique>(this);
+  typed_visitors_[S8] = MakeUnique>(this);
   typed_visitors_[S16] = MakeUnique([](HloInstruction*) {
     return Unimplemented(
-        "HloEvaluator::TypedVisitor: unhandled primitive type: S16.");
+        "HloEvaluator::HloEvaluatorTypedVisitor: unhandled primitive type: "
+        "S16.");
   });
-  typed_visitors_[S32] = MakeUnique>(this);
-  typed_visitors_[S64] = MakeUnique>(this);
-  typed_visitors_[F16] = MakeUnique>(this);
-  typed_visitors_[F32] = MakeUnique>(this);
-  typed_visitors_[F64] = MakeUnique>(this);
-  typed_visitors_[C64] = MakeUnique>(this);
+  typed_visitors_[S32] = MakeUnique>(this);
+  typed_visitors_[S64] = MakeUnique>(this);
+  typed_visitors_[F16] =
+      MakeUnique>(this);
+  typed_visitors_[F32] = MakeUnique>(this);
+  typed_visitors_[F64] = MakeUnique>(this);
+  typed_visitors_[C64] = MakeUnique>(this);
 
   // Most of the evaluator computations we use don't support BF16 (e.g.,
   // std::ceil, std::tanh). To make evaluator work with BF16, we set all
   // elementwise computations to be done in F32 and do BF16<->F32 conversion
   // around the input and the output of the computations.
-  typed_visitors_[BF16] = MakeUnique>(this);
+  typed_visitors_[BF16] =
+      MakeUnique>(this);
 
   typed_visitors_[TUPLE] = MakeUnique([](HloInstruction*) {
     return Unimplemented(
-        "HloEvaluator::TypedVistor: unhandled primitive type: TUPLE.");
+        "HloEvaluatorTypedVisitor: unhandled primitive type: TUPLE.");
   });
   typed_visitors_[OPAQUE] = MakeUnique([](HloInstruction*) {
     return Unimplemented(
-        "HloEvaluator::TypedVisitor: unhandled primitive type: OPAQUE.");
+        "HloEvaluatorTypedVisitor: unhandled primitive type: OPAQUE.");
   });
 }
 
@@ -3034,7 +976,7 @@ Status HloEvaluator::HandleSelect(HloInstruction* select) {
 
   // If predicate is of scalar type, no element-wise selection would be needed.
   // This would also handle output array of tuple types as the DefaultAction
-  // would go through the TypedVisitor which doesn't handle tuples.
+  // would go through the HloEvaluatorTypedVisitor which doesn't handle tuples.
   if (ShapeUtil::IsScalar(pred.shape())) {
     if (pred.Get({})) {
       evaluated_[select] = on_true.CloneToUnique();
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h
index c0dcee0c3e382f..cc5676ea7b05be 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.h
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.h
@@ -109,19 +109,16 @@ class HloEvaluator : public DfsHloVisitorWithDefault {
           substitutions);
 
  protected:
-  // Templated DfsHloVisitor. Typically ReturnT here indicates the resulting
-  // literal type of each evaluated Handle* method of a TypedVisitor.
-  // There are however a few notable exceptions to this rule, notably:
-  // - HandleCompare and HandleIsFinite: where the resulting literal type is
-  // always boolean.
-  // These operations are handled outside of the parent HloEvaluator handlers
-  // instead of from within TypedVisitor.
+  // Make HloEvaluatorTypedVisitor a friend because it is logically part of this
+  // class.
   //
-  // Type params:
-  //   - ReturnT: The type of input and output of each operation.
-  //   - ElementwiseT: The type in which internal computation are done.
-  template 
-  class TypedVisitor;
+  // A straightforward implementation would be to make it a nested class
+  // declared and defined in hlo_evaluator.cc.  Instead HloEvaluatorTypedVisitor
+  // lives as a separate class with its own header because its template gets
+  // instantiated many times and we want to use extern templates to shard out
+  // the compilation of those instantiations across multiple cc files.
+  template 
+  friend class HloEvaluatorTypedVisitor;
 
   // Wraps around instruction handling to infer types before dispatching to
   // the corresponding typed Visitor.
@@ -169,6 +166,33 @@ class HloEvaluator : public DfsHloVisitorWithDefault {
   Status HandleSelect(HloInstruction* select) override;
 
  private:
+  template 
+  static StatusOr> ElementWiseUnaryOpImpl(
+      HloInstruction* instruction,
+      const std::function& unary_op,
+      const Literal& operand_literal) {
+    const auto shape = instruction->shape();
+    const auto* operand = instruction->operand(0);
+
+    // TODO(b/35950897, b/27796129): add DCHECK back once implicit broadcast is
+    // removed.
+    if (!ShapeUtil::SameDimensions(shape, operand->shape())) {
+      return Unimplemented(
+          "Implicit broadcasting is currently unsupported in HLO evaluator "
+          "Shape Mismatch: %s vs %s",
+          ShapeUtil::HumanString(shape).c_str(),
+          ShapeUtil::HumanString(operand->shape()).c_str());
+    }
+
+    auto result = Literal::CreateFromShape(shape);
+
+    TF_RETURN_IF_ERROR(result->Populate(
+        [&](tensorflow::gtl::ArraySlice multi_index) {
+          return unary_op(operand_literal.Get(multi_index));
+        }));
+    return std::move(result);
+  }
+
   // Returns the already-evaluated literal result for the instruction.
   // A Constant instruction is considered evaluated and its literal will be
   // returned directly without looking up the cache.
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h
new file mode 100644
index 00000000000000..f1cb36347850a5
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h
@@ -0,0 +1,2102 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_EVALUATOR_TYPED_VISITOR_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_EVALUATOR_TYPED_VISITOR_H_
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+#include "tensorflow/compiler/xla/service/shape_inference.h"
+#include "tensorflow/core/lib/core/casts.h"
+#include "tensorflow/core/lib/gtl/optional.h"
+
+namespace xla {
+
+// TODO(b/79274244): We'd like these type traits to live inside of
+// HloEvaluatorTypedVisitor so they don't pollute namespace xla, but that
+// crashes clang in the frontend.
+//
+// Anyway this is relatively safe as-is because hlo_evaluator_typed_visitor.h is
+// a "private" header that's not exposed outside of hlo_evaluator.cc.
+template 
+using is_complex_t = std::is_same;
+template 
+using is_complex64_t = std::is_same;
+
+// Templated DfsHloVisitor for use by HloEvaluator.
+//
+// Typically ReturnT here indicates the resulting literal type of each evaluated
+// Handle* method of a TypedVisitor.  There are however a few notable exceptions
+// to this rule, notably:
+// - HandleCompare and HandleIsFinite: where the resulting literal type is
+//   always boolean.
+// These operations are handled outside of the parent HloEvaluator handlers
+// instead of from within TypedVisitor.
+//
+// Type params:
+//   - ReturnT: The type of input and output of each operation.
+//   - ElementwiseT: The type in which internal computation are done.
+//
+// This a logically a private part of HloEvaluator.  It lives in this header
+// file rather than in hlo_evaluator.cc because we use extern templates and a
+// bunch of independent cc files to speed up compiling the many instantiations
+// of this class.
+template 
+class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault {
+ public:
+  explicit HloEvaluatorTypedVisitor(HloEvaluator* p) : parent_(p) {}
+
+  // The following higher-order functions convert a function with ElementwiseT
+  // to a function with ReturnT.
+  std::function ConvertUnaryFunction(
+      const std::function& unary_op) {
+    return [&unary_op](ReturnT arg) {
+      return static_cast(unary_op(static_cast(arg)));
+    };
+  }
+  std::function ConvertBinaryFunction(
+      const std::function&
+          binary_op) {
+    return [&binary_op](ReturnT arg1, ReturnT arg2) {
+      return static_cast(binary_op(static_cast(arg1),
+                                            static_cast(arg2)));
+    };
+  }
+  std::function ConvertTernaryFunction(
+      const std::function& ternary_op) {
+    return [&ternary_op](ReturnT arg1, ReturnT arg2, ReturnT arg3) {
+      return static_cast(ternary_op(static_cast(arg1),
+                                             static_cast(arg2),
+                                             static_cast(arg3)));
+    };
+  }
+
+  Status DefaultAction(HloInstruction* hlo_instruction) override {
+    return Unimplemented("unhandled HLO ops for HloEvaluator: %s.",
+                         HloOpcodeString(hlo_instruction->opcode()).c_str());
+  }
+
+  // TODO(b/35950897): many of the stl functions used in the handlers are not
+  // overloaded for every XLA primitive type.
+
+  template ::value>::type* =
+                nullptr>
+  Status HandleAbs(HloInstruction* abs) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[abs],
+                        ElementWiseUnaryOp(abs, [](NativeT elem_operand) {
+                          return elem_operand;
+                        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleAbs(HloInstruction* abs) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[abs],
+                        ElementWiseUnaryOp(abs, [](NativeT elem_operand) {
+                          return std::abs(elem_operand);
+                        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleAbs(HloInstruction* abs) {
+    const Literal& operand_literal =
+        parent_->GetEvaluatedLiteralFor(abs->operand(0));
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[abs],
+        (HloEvaluator::ElementWiseUnaryOpImpl(
+            abs, [](NativeT elem_operand) { return std::abs(elem_operand); },
+            operand_literal)));
+
+    return Status::OK();
+  }
+
+  Status HandleAbs(HloInstruction* abs) override {
+    // If the operand is of C64 type, the return type of abs will be F32.
+    // However, ElementwiseT would still be the return type, F32, and thus
+    // specifying the ElementwiseT explicitly as C64 is needed below.
+    if (abs->operand(0)->shape().element_type() == C64) {
+      return HandleAbs(abs);
+    }
+    return HandleAbs(abs);
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleRound(HloInstruction* round) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[round],
+        ElementWiseUnaryOp(round, [](ElementwiseT elem_operand) {
+          return std::round(elem_operand);
+        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleRound(HloInstruction* round) {
+    return InvalidArgument("Unsupported type for Round");
+  }
+
+  Status HandleRound(HloInstruction* round) override {
+    return HandleRound(round);
+  }
+
+  Status HandleBroadcast(HloInstruction* broadcast) override {
+    parent_->evaluated_[broadcast] =
+        Literal::CreateFromShape(broadcast->shape());
+    auto output = parent_->evaluated_[broadcast].get();
+    const Literal& operand_to_broadcast =
+        parent_->GetEvaluatedLiteralFor(broadcast->operand(0));
+    std::vector broadcast_indices(
+        ShapeUtil::Rank(broadcast->operand(0)->shape()), 0);
+
+    TF_RET_CHECK(broadcast->dimensions().size() ==
+                 ShapeUtil::Rank(operand_to_broadcast.shape()))
+        << "broadcast dimensions is of size: " << broadcast->dimensions().size()
+        << " and rank of operand_to_broadcast is: "
+        << ShapeUtil::Rank(operand_to_broadcast.shape());
+    // Checks that operand's dimensions are the same as the broadcast's
+    // dimensions along the dimensions to be broadcasted.
+    for (int64 i = 0; i < broadcast->dimensions().size(); ++i) {
+      TF_RET_CHECK(broadcast->shape().dimensions(broadcast->dimensions(i)) ==
+                   operand_to_broadcast.shape().dimensions(i));
+    }
+
+    return output->Populate(
+        [&](tensorflow::gtl::ArraySlice multi_index) {
+          for (int64 i = 0; i < broadcast->dimensions().size(); ++i) {
+            broadcast_indices[i] = multi_index[broadcast->dimensions(i)];
+          }
+          return operand_to_broadcast.Get(broadcast_indices);
+        });
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleCeil(HloInstruction* ceil) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[ceil],
+                        ElementWiseUnaryOp(ceil, [](ElementwiseT elem_operand) {
+                          return std::ceil(elem_operand);
+                        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleCeil(HloInstruction* ceil) {
+    return InvalidArgument("Unsupported type for Ceil");
+  }
+
+  Status HandleCeil(HloInstruction* ceil) override {
+    return HandleCeil(ceil);
+  }
+
+  Status HandleConvert(HloInstruction* convert) override {
+    const HloInstruction* operand = convert->operand(0);
+    TF_RET_CHECK(ShapeUtil::SameDimensions(operand->shape(), convert->shape()));
+    TF_ASSIGN_OR_RETURN(std::unique_ptr result,
+                        parent_->GetEvaluatedLiteralFor(operand).Convert(
+                            convert->shape().element_type()));
+
+    if (LayoutUtil::LayoutsInShapesEqual(result->shape(), convert->shape())) {
+      parent_->evaluated_[convert] = std::move(result);
+    } else {
+      parent_->evaluated_[convert] =
+          result->Relayout(convert->shape().layout());
+    }
+    return Status::OK();
+  }
+
+  Status HandleBitcastConvert(HloInstruction* convert) override {
+    const HloInstruction* operand = convert->operand(0);
+    TF_RET_CHECK(ShapeUtil::SameDimensions(operand->shape(), convert->shape()));
+    TF_ASSIGN_OR_RETURN(std::unique_ptr result,
+                        parent_->GetEvaluatedLiteralFor(operand).BitcastConvert(
+                            convert->shape().element_type()));
+
+    if (LayoutUtil::LayoutsInShapesEqual(result->shape(), convert->shape())) {
+      parent_->evaluated_[convert] = std::move(result);
+    } else {
+      parent_->evaluated_[convert] =
+          result->Relayout(convert->shape().layout());
+    }
+    return Status::OK();
+  }
+
+  Status HandleExp(HloInstruction* exp) override {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[exp],
+                        ElementWiseUnaryOp(exp, [](ElementwiseT elem_operand) {
+                          return std::exp(elem_operand);
+                        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleFloor(HloInstruction* floor) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[floor],
+        ElementWiseUnaryOp(floor, [](ElementwiseT elem_operand) {
+          return std::floor(elem_operand);
+        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleFloor(HloInstruction* floor) {
+    return InvalidArgument("Unsupported type for Floor");
+  }
+
+  Status HandleFloor(HloInstruction* floor) override {
+    return HandleFloor(floor);
+  }
+
+  Status HandleLog(HloInstruction* log) override {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[log],
+                        ElementWiseUnaryOp(log, [](ElementwiseT elem_operand) {
+                          return std::log(elem_operand);
+                        }));
+    return Status::OK();
+  }
+
+  template ::value &&
+                !std::is_same::value>::type* = nullptr>
+  Status HandleNot(HloInstruction* not_) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[not_],
+                        ElementWiseUnaryOp(not_, [](ElementwiseT elem_operand) {
+                          return ~elem_operand;
+                        }));
+    return Status::OK();
+  }
+
+  template ::value>::type* = nullptr>
+  Status HandleNot(HloInstruction* not_) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[not_],
+                        ElementWiseUnaryOp(not_, [](ElementwiseT elem_operand) {
+                          return !elem_operand;
+                        }));
+    return Status::OK();
+  }
+
+  template ::value>::type* =
+                nullptr>
+  Status HandleNot(HloInstruction* not_) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[not_],
+                        ElementWiseUnaryOp(not_, [](ElementwiseT elem_operand) {
+                          return !elem_operand;
+                        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleNot(HloInstruction* not_) {
+    return InvalidArgument("Unsupported type for Not");
+  }
+
+  Status HandleNot(HloInstruction* not_) override {
+    return HandleNot(not_);
+  }
+
+  template ::value &&
+                !std::is_floating_point::value>::type* = nullptr>
+  Status HandleNegate(HloInstruction* negate) {
+    using type = typename std::make_unsigned::type;
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[negate],
+        ElementWiseUnaryOp(negate, [](ElementwiseT elem_operand) {
+          return NativeT(-type(elem_operand));
+        }));
+    return Status::OK();
+  }
+
+  template ::value ||
+                std::is_floating_point::value>::type* = nullptr>
+  Status HandleNegate(HloInstruction* negate) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[negate],
+        ElementWiseUnaryOp(
+            negate, [](ElementwiseT elem_operand) { return -elem_operand; }));
+    return Status::OK();
+  }
+
+  Status HandleNegate(HloInstruction* negate) override {
+    return HandleNegate(negate);
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleSign(HloInstruction* sign) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[sign],
+                        ElementWiseUnaryOp(sign, [](ElementwiseT elem_operand) {
+                          return (ElementwiseT(0) < elem_operand) -
+                                 (elem_operand < ElementwiseT(0));
+                        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleSign(HloInstruction* sign) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[sign],
+                        ElementWiseUnaryOp(sign, [](ElementwiseT elem_operand) {
+                          auto abs_val = std::abs(elem_operand);
+                          return 0 == abs_val ? ElementwiseT(0)
+                                              : elem_operand / abs_val;
+                        }));
+    return Status::OK();
+  }
+
+  Status HandleSign(HloInstruction* sign) override {
+    return HandleSign(sign);
+  }
+
+  template ::value>::type* = nullptr>
+  Status HandleAtan2(HloInstruction* atan2) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[atan2],
+                        ElementWiseBinaryOp(atan2, [](ElementwiseT lhs_elem,
+                                                      ElementwiseT rhs_elem) {
+                          return std::atan2(lhs_elem, rhs_elem);
+                        }));
+    return Status::OK();
+  }
+
+  template ::value>::type* = nullptr>
+  Status HandleAtan2(HloInstruction* atan2) {
+    return InvalidArgument("Unsupported type for Atan2");
+  }
+
+  Status HandleAtan2(HloInstruction* atan2) override {
+    return HandleAtan2(atan2);
+  }
+
+  Status HandleTanh(HloInstruction* tanh) override {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[tanh],
+                        ElementWiseUnaryOp(tanh, [](ElementwiseT elem_operand) {
+                          return std::tanh(elem_operand);
+                        }));
+    return Status::OK();
+  }
+
+  template ::value &&
+                !std::is_floating_point::value>::type* = nullptr>
+  Status HandleMultiply(HloInstruction* multiply) {
+    using type = typename std::make_unsigned::type;
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[multiply],
+        ElementWiseBinaryOp(multiply,
+                            [](ElementwiseT lhs_elem, ElementwiseT rhs_elem) {
+                              return NativeT(type(lhs_elem) * type(rhs_elem));
+                            }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value ||
+                              std::is_floating_point::value ||
+                              is_complex_t::value>::type* = nullptr>
+  Status HandleMultiply(HloInstruction* multiply) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[multiply],
+        ElementWiseBinaryOp(multiply,
+                            [](ElementwiseT lhs_elem, ElementwiseT rhs_elem) {
+                              return lhs_elem * rhs_elem;
+                            }));
+    return Status::OK();
+  }
+
+  Status HandleMultiply(HloInstruction* multiply) override {
+    return HandleMultiply(multiply);
+  }
+
+  Status HandleSubtract(HloInstruction* subtract) override {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[subtract],
+        ElementWiseBinaryOp(subtract,
+                            [](ElementwiseT lhs_elem, ElementwiseT rhs_elem) {
+                              return lhs_elem - rhs_elem;
+                            }));
+    return Status::OK();
+  }
+
+  Status HandleAdd(HloInstruction* add) override {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[add],
+                        ElementWiseBinaryOp(add, [](ElementwiseT lhs_elem,
+                                                    ElementwiseT rhs_elem) {
+                          return lhs_elem + rhs_elem;
+                        }));
+    return Status::OK();
+  }
+
+  Status HandleDivide(HloInstruction* divide) override {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[divide],
+                        ElementWiseBinaryOp(divide, [](ElementwiseT lhs_elem,
+                                                       ElementwiseT rhs_elem) {
+                          return lhs_elem / rhs_elem;
+                        }));
+    return Status::OK();
+  }
+
+  template ::value>::type* =
+                nullptr>
+  Status HandleMaximum(HloInstruction* maximum) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[maximum],
+        ElementWiseBinaryOp(maximum, [](ElementwiseT lhs, ElementwiseT rhs) {
+          return std::max(lhs, rhs);
+        }));
+    return Status::OK();
+  }
+
+  template ::value>::type* = nullptr>
+  Status HandleMaximum(HloInstruction* maximum) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[maximum],
+        ElementWiseBinaryOp(maximum, [](ElementwiseT lhs, ElementwiseT rhs) {
+          return ((lhs >= rhs) || std::isnan(lhs)) ? lhs : rhs;
+        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleMaximum(HloInstruction* maximum) {
+    return InvalidArgument("Unsupported type for Maximum");
+  }
+
+  Status HandleMaximum(HloInstruction* maximum) override {
+    return HandleMaximum(maximum);
+  }
+
+  template ::value>::type* =
+                nullptr>
+  Status HandleMinimum(HloInstruction* minimum) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[minimum],
+                        ElementWiseBinaryOp(minimum, [](ElementwiseT lhs_el,
+                                                        ElementwiseT rhs_el) {
+                          return std::min(lhs_el, rhs_el);
+                        }));
+    return Status::OK();
+  }
+
+  template ::value>::type* = nullptr>
+  Status HandleMinimum(HloInstruction* minimum) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[minimum],
+        ElementWiseBinaryOp(minimum, [](ElementwiseT lhs_el,
+                                        ElementwiseT rhs_el) {
+          return ((lhs_el <= rhs_el) || std::isnan(lhs_el)) ? lhs_el : rhs_el;
+        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleMinimum(HloInstruction* minimum) {
+    return InvalidArgument("Unsupported type for Minimum");
+  }
+
+  Status HandleMinimum(HloInstruction* minimum) override {
+    return HandleMinimum(minimum);
+  }
+
+  Status HandlePower(HloInstruction* power) override {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[power],
+                        ElementWiseBinaryOp(power, [](ElementwiseT lhs_el,
+                                                      ElementwiseT rhs_el) {
+                          return std::pow(lhs_el, rhs_el);
+                        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleRemainder(HloInstruction* remainder) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[remainder],
+                        ElementWiseBinaryOp(remainder, [](ElementwiseT lhs_el,
+                                                          ElementwiseT rhs_el) {
+                          return std::fmod(lhs_el, rhs_el);
+                        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleRemainder(HloInstruction* remainder) {
+    return InvalidArgument("Unsupported type for Remainder");
+  }
+
+  Status HandleRemainder(HloInstruction* remainder) override {
+    return HandleRemainder(remainder);
+  }
+
+  template ::value>::type* =
+                nullptr>
+  Status HandleAnd(HloInstruction* and_) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[and_],
+        ElementWiseBinaryOp(and_, [](ElementwiseT lhs_el, ElementwiseT rhs_el) {
+          return lhs_el & rhs_el;
+        }));
+    return Status::OK();
+  }
+
+  template ::value>::type* = nullptr>
+  Status HandleAnd(HloInstruction* and_) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[and_],
+        ElementWiseBinaryOp(and_, [](ElementwiseT lhs_el, ElementwiseT rhs_el) {
+          return lhs_el && rhs_el;
+        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleAnd(HloInstruction* and_) {
+    return InvalidArgument("Unsupported type for And");
+  }
+
+  Status HandleAnd(HloInstruction* and_) override {
+    return HandleAnd(and_);
+  }
+
+  template ::value>::type* =
+                nullptr>
+  Status HandleOr(HloInstruction* or_) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[or_],
+        ElementWiseBinaryOp(or_, [](ElementwiseT lhs_el, ElementwiseT rhs_el) {
+          return lhs_el | rhs_el;
+        }));
+    return Status::OK();
+  }
+
+  template ::value>::type* = nullptr>
+  Status HandleOr(HloInstruction* or_) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[or_],
+        ElementWiseBinaryOp(or_, [](ElementwiseT lhs_el, ElementwiseT rhs_el) {
+          return lhs_el || rhs_el;
+        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleOr(HloInstruction* or_) {
+    return InvalidArgument("Unsupported type for Or");
+  }
+
+  Status HandleOr(HloInstruction* or_) override {
+    return HandleOr(or_);
+  }
+
+  template ::value &&
+                !std::is_same::value>::type* = nullptr>
+  Status HandleShiftLeft(HloInstruction* shl) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[shl],
+        ElementWiseBinaryOp(shl, [](NativeT lhs_elem, NativeT rhs_elem) {
+          return IsShiftOutOfBounds(rhs_elem) ? 0
+                                                       : (lhs_elem << rhs_elem);
+        }));
+    return Status::OK();
+  }
+
+  template ::value ||
+                                    std::is_same::value>::type* =
+                nullptr>
+  Status HandleShiftLeft(HloInstruction*) {
+    return InvalidArgument("Unsupported type for ShiftLeft");
+  }
+
+  Status HandleShiftLeft(HloInstruction* shl) override {
+    return HandleShiftLeft(shl);
+  }
+  template ::value &&
+                !std::is_same::value>::type* = nullptr>
+  Status HandleShiftRightArithmetic(HloInstruction* shr) {
+    typedef typename std::make_signed::type SignedT;
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[shr],
+        ElementWiseBinaryOp(shr, [](NativeT lhs_elem, NativeT rhs_elem) {
+          SignedT lhs_signed = static_cast(lhs_elem);
+          if (IsShiftOutOfBounds(rhs_elem)) {
+            return lhs_signed < 0 ? static_cast(-1) : 0;
+          } else {
+            return lhs_signed >> rhs_elem;
+          }
+        }));
+    return Status::OK();
+  }
+
+  template ::value ||
+                                    std::is_same::value>::type* =
+                nullptr>
+  Status HandleShiftRightArithmetic(HloInstruction*) {
+    return InvalidArgument("Unsupported type for ShiftRightArithmetic");
+  }
+
+  Status HandleShiftRightArithmetic(HloInstruction* shra) override {
+    return HandleShiftRightArithmetic(shra);
+  }
+
+  template ::value &&
+                !std::is_same::value>::type* = nullptr>
+  Status HandleShiftRightLogical(HloInstruction* shr) {
+    typedef typename std::make_unsigned::type UnsignedT;
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[shr],
+        ElementWiseBinaryOp(shr, [](NativeT lhs_elem, NativeT rhs_elem) {
+          // If shift amount is greater than the number of bits, then return 0.
+          if (IsShiftOutOfBounds(rhs_elem)) {
+            return static_cast(0);
+          }
+          return static_cast(static_cast(lhs_elem) >>
+                                      rhs_elem);
+        }));
+    return Status::OK();
+  }
+
+  template ::value ||
+                                    std::is_same::value>::type* =
+                nullptr>
+  Status HandleShiftRightLogical(HloInstruction*) {
+    return InvalidArgument("Unsupported type for ShiftRightLogical");
+  }
+
+  Status HandleShiftRightLogical(HloInstruction* shrl) override {
+    return HandleShiftRightLogical(shrl);
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleClamp(HloInstruction* clamp) {
+    std::function
+        clamp_op = [](ElementwiseT low, ElementwiseT value, ElementwiseT high) {
+          return std::fmin(high, std::fmax(value, low));
+        };
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[clamp],
+        ElementwiseTernaryOp(clamp,
+                             std::move(ConvertTernaryFunction(clamp_op))));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleClamp(HloInstruction*) {
+    return InvalidArgument("Unsupported type for Clamp");
+  }
+
+  Status HandleClamp(HloInstruction* clamp) override {
+    return HandleClamp(clamp);
+  }
+
+  Status HandleSelect(HloInstruction* select) override {
+    CHECK(!ShapeUtil::IsScalar(select->operand(0)->shape()));
+    CHECK(!ShapeUtil::IsTuple(select->shape()));
+    std::function select_op =
+        [](bool pred, ReturnT on_true, ReturnT on_false) {
+          if (pred) {
+            return on_true;
+          }
+          return on_false;
+        };
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[select],
+                        ElementwiseTernaryOp(select, std::move(select_op)));
+    return Status::OK();
+  }
+
+  Status HandleReverse(HloInstruction* reverse) override {
+    const auto result_shape = reverse->shape();
+    const auto reverse_dimensions = reverse->dimensions();
+
+    auto operand = reverse->operand(0);
+    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
+                        ShapeInference::InferReverseShape(operand->shape(),
+                                                          reverse_dimensions));
+
+    TF_RET_CHECK(ShapeUtil::Compatible(result_shape, inferred_return_shape))
+        << "return shape set to: " << ShapeUtil::HumanString(result_shape)
+        << " but is inferred to be: "
+        << ShapeUtil::HumanString(inferred_return_shape);
+
+    const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand);
+    auto result = Literal::CreateFromShape(result_shape);
+
+    TF_RETURN_IF_ERROR(result->Populate(
+        [&](tensorflow::gtl::ArraySlice out_index) {
+          std::vector from_index(out_index.begin(), out_index.end());
+          for (const int64 dim : reverse_dimensions) {
+            from_index[dim] = result_shape.dimensions(dim) - 1 - out_index[dim];
+          }
+          return operand_literal.Get(from_index);
+        }));
+
+    parent_->evaluated_[reverse] = std::move(result);
+    return Status::OK();
+  }
+
+  Status HandleConvolution(HloInstruction* conv) override {
+    auto lhs = conv->operand(0);
+    auto rhs = conv->operand(1);
+    const auto& window = conv->window();
+    const Shape& result_shape = conv->shape();
+    const Shape& lhs_shape = lhs->shape();
+    const Shape& rhs_shape = rhs->shape();
+
+    TF_CHECK_OK(ShapeUtil::ValidateShape(lhs_shape));
+    TF_CHECK_OK(ShapeUtil::ValidateShape(rhs_shape));
+    CHECK(ShapeUtil::IsArray(lhs_shape));
+    CHECK(ShapeUtil::IsArray(rhs_shape));
+    CHECK(ShapeUtil::SameElementType(lhs_shape, rhs_shape));
+    CHECK(ShapeUtil::SameElementType(lhs_shape, result_shape));
+
+    const auto& dnums = conv->convolution_dimension_numbers();
+    const int64 num_spatial_dims = dnums.output_spatial_dimensions_size();
+    CHECK_EQ(num_spatial_dims, dnums.input_spatial_dimensions_size());
+    CHECK_EQ(num_spatial_dims, dnums.kernel_spatial_dimensions_size());
+    CHECK_GE(num_spatial_dims, 0);
+    CHECK_EQ(window.dimensions_size(), num_spatial_dims);
+
+    const auto lhs_rank = ShapeUtil::Rank(lhs_shape);
+    const auto rhs_rank = ShapeUtil::Rank(rhs_shape);
+
+    CHECK_EQ(num_spatial_dims + 2, lhs_rank);
+    CHECK_EQ(num_spatial_dims + 2, rhs_rank);
+
+    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
+                        ShapeInference::InferConvolveShape(lhs_shape, rhs_shape,
+                                                           window, dnums));
+    CHECK(ShapeUtil::Compatible(result_shape, inferred_return_shape))
+        << "return shape set to: " << ShapeUtil::HumanString(result_shape)
+        << " but is inferred to be: "
+        << ShapeUtil::HumanString(inferred_return_shape);
+
+    const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs);
+    const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs);
+
+    std::vector window_dimension_sizes;
+    for (auto i : dnums.kernel_spatial_dimensions()) {
+      window_dimension_sizes.push_back(ShapeUtil::GetDimension(rhs_shape, i));
+    }
+
+    const Shape& window_shape =
+        ShapeUtil::MakeShape(rhs_shape.element_type(), window_dimension_sizes);
+
+    DimensionVector lhs_dim_multipliers = MakeDimMultipliers(lhs_shape);
+    DimensionVector rhs_dim_multipliers = MakeDimMultipliers(rhs_shape);
+
+    auto lhs_literal_data = lhs_literal.data();
+    auto rhs_literal_data = rhs_literal.data();
+
+    auto func = [&window_shape, &dnums, &lhs_shape, &rhs_shape, &window,
+                 &lhs_dim_multipliers, &rhs_dim_multipliers, lhs_literal_data,
+                 rhs_literal_data](
+                    tensorflow::gtl::ArraySlice out_index) {
+      // Dimension number applicable for input (lhs).
+      const int64 input_batch_dim = dnums.input_batch_dimension();
+      const int64 input_z_dim = dnums.input_feature_dimension();
+      // Dimension number applicable for kernel (rhs).
+      const int64 kernel_input_z_dim = dnums.kernel_input_feature_dimension();
+      const int64 kernel_output_z_dim = dnums.kernel_output_feature_dimension();
+      // Dimension number applicable for output.
+      const int64 output_batch_dim = dnums.output_batch_dimension();
+      const int64 output_z_dim = dnums.output_feature_dimension();
+
+      const int64 z_size = ShapeUtil::GetDimension(lhs_shape, input_z_dim);
+
+      ElementwiseT result_val = static_cast(0);
+      DimensionVector rhs_spatial_index(dnums.kernel_spatial_dimensions_size(),
+                                        0);
+
+      // Convolve input feature with kernel.
+      do {
+        for (int64 iz = 0; iz < z_size; ++iz) {
+          int64 lhs_linear_index = 0;
+          lhs_linear_index += out_index[output_batch_dim] *
+                              lhs_dim_multipliers[input_batch_dim];
+          lhs_linear_index += iz * lhs_dim_multipliers[input_z_dim];
+
+          int64 rhs_linear_index = 0;
+          rhs_linear_index += out_index[output_z_dim] *
+                              rhs_dim_multipliers[kernel_output_z_dim];
+          rhs_linear_index += iz * rhs_dim_multipliers[kernel_input_z_dim];
+
+          // Find corresponding spatial dimension index for input (lhs).
+          for (int64 ki = 0; ki < rhs_spatial_index.size(); ++ki) {
+            // Spatial dimension number for input (lhs) and output.
+            const int64 input_spatial_dim = dnums.input_spatial_dimensions(ki);
+            const int64 output_spatial_dim =
+                dnums.output_spatial_dimensions(ki);
+
+            // Calculate lhs (input) index without taking base dilation into
+            // account.
+            const auto& window_dim = window.dimensions(ki);
+            const int64 undilated_index =
+                out_index[output_spatial_dim] * window_dim.stride() -
+                window_dim.padding_low() +
+                rhs_spatial_index[ki] * window_dim.window_dilation();
+            // Skip if the lhs (input) index is to be dilated.  As an
+            // optimization, skip this mod if there's no dilation.
+            if (window_dim.base_dilation() > 1 &&
+                undilated_index % window_dim.base_dilation() != 0) {
+              goto cnt;
+            }
+
+            // Calculate the actual lhs (input) index after dilation.  As an
+            // optimization, skip this integer divide if there's no dilation.
+            int64 lhs_spatial_index;
+            if (window_dim.base_dilation() > 1) {
+              lhs_spatial_index = undilated_index / window_dim.base_dilation();
+            } else {
+              lhs_spatial_index = undilated_index;
+            }
+            lhs_linear_index +=
+                lhs_spatial_index * lhs_dim_multipliers[input_spatial_dim];
+
+            // Skip if input index is not in bounds.
+            if (!(lhs_spatial_index >= 0 &&
+                  lhs_spatial_index <
+                      lhs_shape.dimensions(input_spatial_dim))) {
+              goto cnt;
+            }
+
+            rhs_linear_index +=
+                (window_dim.window_reversal()
+                     ? ((window_dim.size() - 1) - rhs_spatial_index[ki])
+                     : rhs_spatial_index[ki]) *
+                rhs_dim_multipliers[dnums.kernel_spatial_dimensions(ki)];
+          }
+
+          result_val +=
+              static_cast(lhs_literal_data[lhs_linear_index]) *
+              static_cast(rhs_literal_data[rhs_linear_index]);
+        }
+      cnt : {}
+      } while (IndexUtil::BumpIndices(window_shape, &rhs_spatial_index));
+
+      return static_cast(result_val);
+    };
+
+    auto result = Literal::CreateFromShape(result_shape);
+    TF_RETURN_IF_ERROR(result->PopulateParallel(func));
+
+    parent_->evaluated_[conv] = std::move(result);
+    return Status::OK();
+  }
+
+  Status HandleDot(HloInstruction* dot) override {
+    auto lhs = dot->operand(0);
+    auto rhs = dot->operand(1);
+    CHECK(ShapeUtil::IsArray(dot->shape()));
+    CHECK(ShapeUtil::IsArray(lhs->shape()));
+    CHECK(ShapeUtil::IsArray(rhs->shape()));
+
+    const auto& dnums = dot->dot_dimension_numbers();
+
+    const auto lhs_rank = ShapeUtil::Rank(lhs->shape());
+    const auto rhs_rank = ShapeUtil::Rank(rhs->shape());
+
+    CHECK(ShapeUtil::SameElementType(lhs->shape(), rhs->shape()));
+    CHECK(ShapeUtil::SameElementType(lhs->shape(), dot->shape()));
+
+    // There must be 1 and only 1 Contracting dimension for lhs and rhs.
+    CHECK_EQ(dnums.lhs_contracting_dimensions_size(), 1);
+    CHECK_EQ(dnums.rhs_contracting_dimensions_size(), 1);
+    const int64 lhs_contracting_dimension = dnums.lhs_contracting_dimensions(0);
+    const int64 rhs_contracting_dimension = dnums.rhs_contracting_dimensions(0);
+    // Contracted dimension sizes must be the same.
+    CHECK_EQ(lhs->shape().dimensions(lhs_contracting_dimension),
+             rhs->shape().dimensions(rhs_contracting_dimension))
+        << "lhs contracted dimension: "
+        << lhs->shape().dimensions(lhs_contracting_dimension)
+        << " rhs contracted dimension: "
+        << rhs->shape().dimensions(rhs_contracting_dimension);
+    const int64 contracted_dimension_size =
+        lhs->shape().dimensions(lhs_contracting_dimension);
+
+    const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs);
+    const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs);
+
+    auto result = Literal::CreateFromShape(dot->shape());
+
+    CHECK_EQ(dnums.lhs_batch_dimensions_size(),
+             dnums.rhs_batch_dimensions_size());
+
+    std::vector lhs_non_contracting_dims;
+    for (int64 i = 0; i < lhs_rank; i++) {
+      if (i != lhs_contracting_dimension) {
+        lhs_non_contracting_dims.push_back(i);
+      }
+    }
+
+    std::vector rhs_non_batch_non_contracting_dims;
+    tensorflow::gtl::FlatSet batch_dims_set(
+        dnums.rhs_batch_dimensions().begin(),
+        dnums.rhs_batch_dimensions().end());
+    for (int64 i = 0; i < rhs_rank; i++) {
+      if (i != rhs_contracting_dimension && batch_dims_set.count(i) == 0) {
+        rhs_non_batch_non_contracting_dims.push_back(i);
+      }
+    }
+
+    const int64 batch_dim_size = dnums.lhs_batch_dimensions_size();
+    const int64 lhs_non_contracting_size = lhs_non_contracting_dims.size();
+
+    DimensionVector lhs_index(lhs_rank);
+    DimensionVector rhs_index(rhs_rank);
+    TF_RETURN_IF_ERROR(result->Populate(
+        [&](tensorflow::gtl::ArraySlice result_index) {
+          ElementwiseT result_val = static_cast(0);
+
+          // Find the corresponding non-contracting indices for lhs and rhs.
+          //
+          // For `result_index`, its batch dimension, if exists, will be at the
+          // same dimension as the batch dimension of lhs and rhs. More
+          // specifically:
+          // - For lhs, the non-contracting dimensions, including the batch
+          // dimension have the same index as the `result_index`.
+          // - For rhs, the batch dimension is set seperately from other
+          // non-contracting dimensions, since these other non-contracting
+          // dimensions in rhs follow the non-contracting dimensions of lhs in
+          // the resulting index.
+          //
+          // As an example, for a resulting index:
+          //  result_index [result_batch, result_x, result_y]
+          // the effecting lhs and rhs indices are:
+          //  lhs [result_batch, lhs_non_contracting_dim, contracting_dim
+          //  rhs [result_batch, contracting_dim, rhs_non_contracting_dim]
+          // `result_x` is only affected by the lhs_non_contracting_dim and
+          // likewise `result_y` only depends on rhs_non_contracting_dim.
+          //
+          // so we can look up the lhs and rhs indices by:
+          //
+          // lhs:
+          //  batch index is the same as `result_batch`.
+          //    non-contracting dimension is the same as
+          //    result_index[lhs_non_contracting_dim]
+          // rhs:
+          //  batch index: the same as `result_batch`.
+          //  non-contracting dimension index: *not* the same as
+          //    result_index[rhs_non_contractng_dim], since the
+          //    non-contracting dimensions of lhs are included in the
+          //    result_index first. Instead, the non_contracting_dim of rhs must
+          //    be calculated as following:
+          //      lhs_non_contracting_dimensions_size +
+          //      (rhs_non_batch_non_contracting_dim - batch_dim_size) - 1
+          //
+          //    Note that (rhs_non_batch_contracting_dim - batch_dim_size) is
+          //    the index offset to the result_index that only depends on
+          //    the non_batch and non-contracting dimensions of rhs. -1 at the
+          //    end translates size to index.
+          for (auto i : lhs_non_contracting_dims) {
+            lhs_index[i] = result_index[i];
+          }
+          for (auto i : dnums.rhs_batch_dimensions()) {
+            rhs_index[i] = result_index[i];
+          }
+          for (auto i : rhs_non_batch_non_contracting_dims) {
+            const int64 rhs_non_batch_non_contracting_dim =
+                lhs_non_contracting_size + (i - batch_dim_size) - 1;
+            rhs_index[i] = result_index[rhs_non_batch_non_contracting_dim];
+          }
+
+          // Accumulates resulting product along the contracted dimension.
+          for (int64 i = 0; i < contracted_dimension_size; ++i) {
+            lhs_index[lhs_contracting_dimension] = i;
+            rhs_index[rhs_contracting_dimension] = i;
+
+            result_val +=
+                static_cast(lhs_literal.Get(lhs_index)) *
+                static_cast(rhs_literal.Get(rhs_index));
+          }
+
+          return static_cast(result_val);
+        }));
+
+    parent_->evaluated_[dot] = std::move(result);
+    return Status::OK();
+  }
+
+  Status HandlePad(HloInstruction* pad) override {
+    CHECK(!ShapeUtil::IsTuple(pad->operand(0)->shape()));
+    // Padding value must be scalar.
+    CHECK(ShapeUtil::IsScalar(pad->operand(1)->shape()));
+    CHECK_EQ(ShapeUtil::Rank(pad->operand(0)->shape()),
+             pad->padding_config().dimensions_size());
+
+    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
+                        ShapeInference::InferPadShape(
+                            /*operand_shape=*/pad->operand(0)->shape(),
+                            /*padding_value_shape=*/pad->operand(1)->shape(),
+                            /*padding_config=*/pad->padding_config()));
+    CHECK(ShapeUtil::Compatible(pad->shape(), inferred_return_shape))
+        << "return shape is set to: " << ShapeUtil::HumanString(pad->shape())
+        << "but is inferred to be: "
+        << ShapeUtil::HumanString(inferred_return_shape);
+
+    // Create new HLO of padded shape with padding value.
+    ReturnT scalar =
+        parent_->GetEvaluatedLiteralFor(pad->operand(1)).Get({});
+    auto result = Literal::CreateFromShape(pad->shape());
+    TF_RETURN_IF_ERROR(result->Populate(
+        [&scalar](tensorflow::gtl::ArraySlice multi_index) {
+          return scalar;
+        }));
+
+    const Literal& evaluated_operand =
+        parent_->GetEvaluatedLiteralFor(pad->operand(0));
+
+    std::vector input_index(ShapeUtil::Rank(evaluated_operand.shape()),
+                                   0);
+    std::vector target_index(ShapeUtil::Rank(result->shape()), 0);
+
+    // Loop through each element of the operand, assign them to the
+    // corresponding index of the resulting padded literal.
+    const PaddingConfig& pad_config = pad->padding_config();
+
+    auto func = [&](tensorflow::gtl::ArraySlice input_index) {
+      for (auto i = 0; i < input_index.size(); ++i) {
+        // Interior padding occurs logically before edge padding, so in the case
+        // of negative edge padding elements are removed from the
+        // interior-padded operand.
+        target_index[i] =
+            pad_config.dimensions(i).edge_padding_low() +
+            input_index[i] * (pad_config.dimensions(i).interior_padding() + 1);
+
+        // Account for negative low and high padding: skip assignment if the
+        // any target index is out of range.
+        if (!(target_index[i] >= 0 &&
+              target_index[i] < pad->shape().dimensions(i))) {
+          return true;
+        }
+      }
+      result->Set(target_index,
+                           evaluated_operand.Get(input_index));
+      return true;
+    };
+
+    std::vector zero_base(evaluated_operand.shape().dimensions_size(),
+                                 0);
+    std::vector step(evaluated_operand.shape().dimensions_size(), 1);
+
+    ShapeUtil::ForEachIndex(
+        evaluated_operand.shape(), zero_base,
+        AsInt64Slice(evaluated_operand.shape().dimensions()), step, func);
+
+    parent_->evaluated_[pad] = std::move(result);
+    return Status::OK();
+  }
+
+  Status HandleDynamicSlice(HloInstruction* dynamic_slice) override {
+    auto operand = dynamic_slice->operand(0);
+    auto start_indices = dynamic_slice->operand(1);
+    auto result_shape = dynamic_slice->shape();
+    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
+                        ShapeInference::InferDynamicSliceShape(
+                            operand->shape(), start_indices->shape(),
+                            dynamic_slice->dynamic_slice_sizes()));
+    TF_RET_CHECK(ShapeUtil::Compatible(result_shape, inferred_return_shape))
+        << "return shape is set to: " << ShapeUtil::HumanString(result_shape)
+        << "but is inferred to be: "
+        << ShapeUtil::HumanString(inferred_return_shape);
+    TF_RET_CHECK(
+        primitive_util::IsIntegralType(start_indices->shape().element_type()));
+
+    const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand);
+    const Literal& start_indices_literal =
+        parent_->GetEvaluatedLiteralFor(start_indices);
+
+    switch (start_indices->shape().element_type()) {
+      case S32: {
+        TF_ASSIGN_OR_RETURN(
+            parent_->evaluated_[dynamic_slice],
+            DynamicSlice(operand_literal, start_indices_literal,
+                                result_shape));
+      } break;
+      case S64: {
+        TF_ASSIGN_OR_RETURN(
+            parent_->evaluated_[dynamic_slice],
+            DynamicSlice(operand_literal, start_indices_literal,
+                                result_shape));
+      } break;
+      case U32: {
+        TF_ASSIGN_OR_RETURN(
+            parent_->evaluated_[dynamic_slice],
+            DynamicSlice(operand_literal, start_indices_literal,
+                                 result_shape));
+      } break;
+      case U64: {
+        TF_ASSIGN_OR_RETURN(
+            parent_->evaluated_[dynamic_slice],
+            DynamicSlice(operand_literal, start_indices_literal,
+                                 result_shape));
+      } break;
+      default:
+        LOG(FATAL) << "HandleDynamicSlice: unhandled primitive type for "
+                      "start_indices: "
+                   << PrimitiveType_Name(start_indices->shape().element_type());
+    }
+
+    return Status::OK();
+  }
+
+  Status HandleDynamicUpdateSlice(
+      HloInstruction* dynamic_update_slice) override {
+    auto operand = dynamic_update_slice->operand(0);
+    auto update = dynamic_update_slice->operand(1);
+    auto start_indices = dynamic_update_slice->operand(2);
+    auto result_shape = dynamic_update_slice->shape();
+    TF_ASSIGN_OR_RETURN(
+        auto inferred_return_shape,
+        ShapeInference::InferDynamicUpdateSliceShape(
+            operand->shape(), update->shape(), start_indices->shape()));
+    TF_RET_CHECK(ShapeUtil::Compatible(result_shape, inferred_return_shape))
+        << "return shape is set to: " << ShapeUtil::HumanString(result_shape)
+        << "but is inferred to be: "
+        << ShapeUtil::HumanString(inferred_return_shape);
+    TF_RET_CHECK(
+        primitive_util::IsIntegralType(start_indices->shape().element_type()));
+    TF_RET_CHECK(ShapeUtil::Compatible(result_shape, operand->shape()));
+
+    const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand);
+    const Literal& update_literal = parent_->GetEvaluatedLiteralFor(update);
+    const Literal& start_indices_literal =
+        parent_->GetEvaluatedLiteralFor(start_indices);
+
+    switch (start_indices->shape().element_type()) {
+      case S32: {
+        TF_ASSIGN_OR_RETURN(
+            parent_->evaluated_[dynamic_update_slice],
+            DynamicUpdateSlice(operand_literal, update_literal,
+                                      start_indices_literal));
+      } break;
+      case S64: {
+        TF_ASSIGN_OR_RETURN(
+            parent_->evaluated_[dynamic_update_slice],
+            DynamicUpdateSlice(operand_literal, update_literal,
+                                      start_indices_literal));
+      } break;
+      case U32: {
+        TF_ASSIGN_OR_RETURN(
+            parent_->evaluated_[dynamic_update_slice],
+            DynamicUpdateSlice(operand_literal, update_literal,
+                                       start_indices_literal));
+      } break;
+      case U64: {
+        TF_ASSIGN_OR_RETURN(
+            parent_->evaluated_[dynamic_update_slice],
+            DynamicUpdateSlice(operand_literal, update_literal,
+                                       start_indices_literal));
+      } break;
+      default:
+        LOG(FATAL) << "HandleDynamicUpdateSlice: unhandled primitive type for "
+                      "start_indices: "
+                   << PrimitiveType_Name(start_indices->shape().element_type());
+    }
+
+    return Status::OK();
+  }
+
+  template 
+  StatusOr> MapImpl(HloInstruction* map) {
+    auto operands = map->operands();
+    HloComputation* computation = map->to_apply();
+
+    auto result = Literal::CreateFromShape(map->shape());
+
+    HloEvaluator embedded_evaluator(parent_->max_loop_iterations_);
+    TF_RETURN_IF_ERROR(result->Populate(
+        [&](tensorflow::gtl::ArraySlice multi_index) {
+          std::vector> arg_literals;
+          arg_literals.reserve(operands.size());
+
+          // Construct scalar literal parameters to be passed to the map
+          // computation.
+          for (auto operand : operands) {
+            const Literal& arg_literal =
+                parent_->GetEvaluatedLiteralFor(operand);
+
+            auto curr_val = arg_literal.Get(multi_index);
+            auto curr_val_literal = Literal::CreateR0(curr_val);
+
+            arg_literals.push_back(std::move(curr_val_literal));
+          }
+
+          std::unique_ptr computed_result =
+              embedded_evaluator
+                  .Evaluate>(*computation,
+                                                      arg_literals)
+                  .ConsumeValueOrDie();
+          // Clear visit states so that the we can use the evaluate again on
+          // the same computation.
+          embedded_evaluator.ResetVisitStates();
+
+          return computed_result->Get({});
+        }));
+    return std::move(result);
+  }
+
+  Status HandleMap(HloInstruction* map) override {
+    switch (map->operand(0)->shape().element_type()) {
+      case PRED: {
+        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
+        break;
+      }
+      case U8: {
+        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
+        break;
+      }
+      case U32: {
+        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
+        break;
+      }
+      case U64: {
+        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
+        break;
+      }
+      case S8: {
+        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
+        break;
+      }
+      case S32: {
+        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
+        break;
+      }
+      case S64: {
+        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
+        break;
+      }
+      case F16: {
+        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map],
+                            MapImpl(map));
+        break;
+      }
+      case F32: {
+        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
+        break;
+      }
+      case F64: {
+        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
+        break;
+      }
+      case C64: {
+        TF_ASSIGN_OR_RETURN(parent_->evaluated_[map], MapImpl(map));
+        break;
+      }
+      default:
+        LOG(FATAL) << "HandleMap: unhandled primitive type for "
+                      "input operand: "
+                   << PrimitiveType_Name(
+                          map->operand(0)->shape().element_type());
+    }
+
+    return Status::OK();
+  }
+
+  Status HandleReduce(HloInstruction* reduce) override {
+    auto arg = reduce->operand(0);
+    auto init_value = reduce->operand(1);
+    tensorflow::gtl::ArraySlice dimensions(reduce->dimensions());
+    HloComputation* function = reduce->to_apply();
+    TF_RET_CHECK(ShapeUtil::Rank(reduce->shape()) ==
+                 ShapeUtil::Rank(arg->shape()) - dimensions.size());
+    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
+                        ShapeInference::InferReduceShape(
+                            /*arg=*/arg->shape(),
+                            /*init_value=*/init_value->shape(),
+                            /*dimensions_to_reduce=*/dimensions,
+                            /*to_apply=*/function->ComputeProgramShape()));
+    TF_RET_CHECK(ShapeUtil::Compatible(reduce->shape(), inferred_return_shape))
+        << "return shape is set to: " << ShapeUtil::HumanString(reduce->shape())
+        << "but is inferred to be: "
+        << ShapeUtil::HumanString(inferred_return_shape);
+
+    const Literal& arg_literal = parent_->GetEvaluatedLiteralFor(arg);
+    VLOG(3) << "HandleReduce arg_literal: " << arg_literal.ToString();
+    const Literal& init_literal = parent_->GetEvaluatedLiteralFor(init_value);
+    VLOG(3) << "HandleReduce init_literal: " << init_literal.ToString();
+    TF_RET_CHECK(ShapeUtil::IsScalar(init_literal.shape()));
+    auto init_scalar = init_literal.Get({});
+
+    auto result = Literal::CreateFromShape(reduce->shape());
+
+    const auto arg_dimensions = AsInt64Slice(arg_literal.shape().dimensions());
+    std::vector arg_dim_steps(arg_dimensions.size());
+    std::vector arg_dim_counts(arg_dimensions.size());
+    for (const int64 dim : dimensions) {
+      arg_dim_steps[dim] = 1;
+      arg_dim_counts[dim] = arg_dimensions[dim];
+    }
+
+    // Map each dimension in the result to a dimension in arg that isn't
+    // being reduced.
+    std::vector result_to_arg_index;
+    for (int64 i = 0; i < arg_dimensions.size(); ++i) {
+      if (arg_dim_steps[i] == 0) {
+        result_to_arg_index.push_back(i);
+      }
+    }
+
+    HloEvaluator embedded_evaluator(parent_->max_loop_iterations_);
+    // For each resulting dimension, calculate and assign computed value.
+    TF_RETURN_IF_ERROR(result->Populate(
+        [&](tensorflow::gtl::ArraySlice multi_index) {
+          ReturnT result_val = init_scalar;
+
+          std::vector base(arg_dimensions.size());
+          for (int64 i = 0; i < multi_index.size(); ++i) {
+            base[result_to_arg_index[i]] = multi_index[i];
+          }
+
+          // When the reduction is addition of floats, accumulate in a double
+          // for better precision. Also, avoid creating Literals for the
+          // intermediate results; it's much faster.
+          if (ShapeUtil::ElementIsFloating(init_literal.shape()) &&
+              IsScalarAdd(function)) {
+            double computed_result = 0;
+            auto func = [&](tensorflow::gtl::ArraySlice input_index) {
+              computed_result += arg_literal.Get(input_index);
+              return true;
+            };
+            ShapeUtil::ForEachIndex(arg_literal.shape(), base, arg_dim_counts,
+                                    arg_dim_steps, func);
+            return static_cast(computed_result);
+          }
+          auto func = [&](tensorflow::gtl::ArraySlice input_index) {
+            auto curr_val = arg_literal.Get(input_index);
+
+            // Evaluate computation with specified literal operands.
+            auto curr_val_literal = Literal::CreateR0(curr_val);
+            auto result_val_literal = Literal::CreateR0(result_val);
+            std::vector args = {result_val_literal.get(),
+                                                curr_val_literal.get()};
+
+            std::unique_ptr computed_result =
+                embedded_evaluator.Evaluate(*function, args)
+                    .ConsumeValueOrDie();
+            // Clear visit states so that we can use the evaluator again on
+            // the same computation.
+            embedded_evaluator.ResetVisitStates();
+            // Assign computed result to result_val.
+            result_val = computed_result->Get({});
+            return true;
+          };
+          // Computes one element of the result, reducing all dimensions that
+          // contribute to that element.
+          ShapeUtil::ForEachIndex(arg_literal.shape(), base, arg_dim_counts,
+                                  arg_dim_steps, func);
+          return result_val;
+        }));
+
+    parent_->evaluated_[reduce] = std::move(result);
+    return Status::OK();
+  }
+
+  bool IsScalarAdd(HloComputation* computation) {
+    HloInstruction* instruction = computation->root_instruction();
+    if (instruction->opcode() == HloOpcode::kAdd &&
+        computation->num_parameters() == 2) {
+      const HloInstruction* lhs = instruction->operand(0);
+      const HloInstruction* rhs = instruction->operand(1);
+      return lhs->opcode() == HloOpcode::kParameter &&
+             ShapeUtil::IsScalar(lhs->shape()) &&
+             rhs->opcode() == HloOpcode::kParameter &&
+             ShapeUtil::IsScalar(rhs->shape()) && lhs != rhs;
+    }
+    return false;
+  }
+
+  Status HandleSelectAndScatter(HloInstruction* select_and_scatter) override {
+    auto operand = select_and_scatter->operand(0);
+    auto source = select_and_scatter->operand(1);
+    const Window& window = select_and_scatter->window();
+
+    const Literal& init_literal =
+        parent_->GetEvaluatedLiteralFor(select_and_scatter->operand(2));
+    TF_RET_CHECK(ShapeUtil::IsScalar(init_literal.shape()));
+    auto init_scalar = init_literal.Get({});
+
+    auto result = Literal::CreateFromShape(select_and_scatter->shape());
+
+    // Initialize result array with the init value.
+    TF_RETURN_IF_ERROR(result->Populate(
+        [&](tensorflow::gtl::ArraySlice output_index) {
+          return init_scalar;
+        }));
+
+    std::vector window_dimension_sizes;
+    for (const auto& window_dimension : window.dimensions()) {
+      window_dimension_sizes.push_back(window_dimension.size());
+    }
+    const Shape window_shape = ShapeUtil::MakeShape(
+        operand->shape().element_type(), window_dimension_sizes);
+
+    HloComputation* select = select_and_scatter->select();
+    HloComputation* scatter = select_and_scatter->scatter();
+
+    const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand);
+    const Literal& source_literal = parent_->GetEvaluatedLiteralFor(source);
+
+    int64 rank = ShapeUtil::Rank(operand_literal.shape());
+
+    HloEvaluator embedded_evaluator(parent_->max_loop_iterations_);
+    DimensionVector source_index(rank);
+
+    std::fill(source_index.begin(), source_index.end(), 0);
+    do {
+      // For each element in `source`, we place a window in `operand`. For each
+      // window placement, we iterate inside the window twice:
+      //
+      // 1. Find the selected index by applying `select` function to all
+      // elements. E.g., If the `select` function is GreaterEqual, the first
+      // iteration through the window finds the biggest value and returns its
+      // index.
+      //
+      // 2. Using the selected index, scatter value from `source` to result. We
+      // do this by iterating through the window, and compare each index with
+      // the selected index.
+      tensorflow::gtl::optional selected_val;
+      tensorflow::gtl::optional> selected_index;
+
+      IterateThroughWindow(
+          window_shape, window, operand_literal.shape(), source_index,
+          [&](const std::vector& operand_index) {
+            auto curr_val = operand_literal.Get(operand_index);
+            if (!selected_val) {
+              selected_val = curr_val;
+              selected_index = operand_index;
+            }
+            const auto curr_val_literal = Literal::CreateR0(curr_val);
+            const auto selected_val_literal =
+                Literal::CreateR0(*selected_val);
+
+            const std::vector args = {
+                selected_val_literal.get(), curr_val_literal.get()};
+            std::unique_ptr computed_result =
+                embedded_evaluator.Evaluate(*select, args)
+                    .ConsumeValueOrDie();
+            bool selected = !computed_result->Get({});
+            if (selected) {
+              selected_val = curr_val;
+              selected_index = operand_index;
+            }
+            embedded_evaluator.ResetVisitStates();
+          });
+
+      IterateThroughWindow(
+          window_shape, window, operand_literal.shape(), source_index,
+          [&](const std::vector& operand_index) {
+            if (std::equal(operand_index.begin(), operand_index.end(),
+                           selected_index->begin())) {
+              auto source = source_literal.Get(source_index);
+              auto scattered = result->Get(operand_index);
+              const auto source_literal = Literal::CreateR0(source);
+              const auto scattered_literal =
+                  Literal::CreateR0(scattered);
+
+              const std::vector args = {
+                  source_literal.get(), scattered_literal.get()};
+              std::unique_ptr computed_result =
+                  embedded_evaluator.Evaluate(*scatter, args)
+                      .ConsumeValueOrDie();
+              result->Set(operand_index, computed_result->Get({}));
+              // Clear visit states so that the we can use the evaluator again
+              // on the same computation.
+              embedded_evaluator.ResetVisitStates();
+            }
+          });
+    } while (IndexUtil::BumpIndices(source->shape(), &source_index));
+
+    parent_->evaluated_[select_and_scatter] = std::move(result);
+    return Status::OK();
+  }
+
+  Status HandleReduceWindow(HloInstruction* reduce_window) override {
+    auto operand = reduce_window->operand(0);
+    const Window& window = reduce_window->window();
+    HloComputation* function = reduce_window->to_apply();
+    TF_ASSIGN_OR_RETURN(
+        auto inferred_return_shape,
+        ShapeInference::InferReduceWindowShape(
+            /*operand_shape=*/reduce_window->operand(0)->shape(),
+            /*init_value=*/reduce_window->operand(1)->shape(), window,
+            /*to_apply_shape=*/function->ComputeProgramShape()));
+    TF_RET_CHECK(
+        ShapeUtil::Compatible(reduce_window->shape(), inferred_return_shape))
+        << "return shape is set to: "
+        << ShapeUtil::HumanStringWithLayout(reduce_window->shape())
+        << "but is inferred to be: "
+        << ShapeUtil::HumanStringWithLayout(inferred_return_shape);
+
+    const Literal& operand_literal =
+        parent_->GetEvaluatedLiteralFor(reduce_window->operand(0));
+    VLOG(3) << "HandleReduceWindow arg_literal: " << operand_literal.ToString();
+    const Literal& init_literal =
+        parent_->GetEvaluatedLiteralFor(reduce_window->operand(1));
+    VLOG(3) << "HandleReduceWindow init_literal: " << init_literal.ToString();
+    TF_RET_CHECK(ShapeUtil::IsScalar(init_literal.shape()));
+    auto init_scalar = init_literal.Get({});
+
+    auto result = Literal::CreateFromShape(reduce_window->shape());
+
+    // Creates a Shape object from window, for iteration below.
+    std::vector window_dimension_sizes;
+    for (const auto& window_dimension : window.dimensions()) {
+      window_dimension_sizes.push_back(window_dimension.size());
+    }
+    const Shape window_shape = ShapeUtil::MakeShape(
+        operand->shape().element_type(), window_dimension_sizes);
+
+    DimensionVector window_index(window.dimensions_size());
+    DimensionVector operand_index(ShapeUtil::Rank(operand_literal.shape()));
+
+    HloEvaluator embedded_evaluator(parent_->max_loop_iterations_);
+    // For each resulting dimension, calculate and assign computed value.
+    TF_RETURN_IF_ERROR(result->Populate(
+        [&](tensorflow::gtl::ArraySlice output_index) {
+          ReturnT result_val = init_scalar;
+
+          std::fill(window_index.begin(), window_index.end(), 0);
+          std::fill(operand_index.begin(), operand_index.end(), 0);
+
+          IterateThroughWindow(
+              window_shape, window, operand_literal.shape(), output_index,
+              [&](const std::vector& operand_index) {
+                auto curr_val = operand_literal.Get(operand_index);
+
+                // Evaluate computation with specified literal operands.
+                const auto curr_val_literal =
+                    Literal::CreateR0(curr_val);
+                const auto result_val_literal =
+                    Literal::CreateR0(result_val);
+                const std::vector args = {
+                    result_val_literal.get(), curr_val_literal.get()};
+                std::unique_ptr computed_result =
+                    embedded_evaluator.Evaluate(*function, args)
+                        .ConsumeValueOrDie();
+
+                // Clear visit states so that the we can use the evaluate again
+                // on the same computation.
+                embedded_evaluator.ResetVisitStates();
+
+                result_val = computed_result->Get({});
+              });
+
+          return result_val;
+        }));
+
+    parent_->evaluated_[reduce_window] = std::move(result);
+    return Status::OK();
+  }
+
+  Status HandleSlice(HloInstruction* slice) override {
+    auto operand = slice->operand(0);
+    const Shape& shape = slice->shape();
+    TF_ASSIGN_OR_RETURN(auto inferred_return_shape,
+                        ShapeInference::InferSliceShape(
+                            operand->shape(), slice->slice_starts(),
+                            slice->slice_limits(), slice->slice_strides()));
+    TF_RET_CHECK(ShapeUtil::Compatible(shape, inferred_return_shape))
+        << "return shape set to: " << ShapeUtil::HumanString(shape)
+        << " but is inferred to be: "
+        << ShapeUtil::HumanString(inferred_return_shape);
+
+    const int64 rank = ShapeUtil::Rank(operand->shape());
+    const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand);
+    auto func = [&](tensorflow::gtl::ArraySlice out_index) {
+      DimensionVector operand_index(rank);
+      for (int64 i = 0; i < rank; ++i) {
+        operand_index[i] =
+            slice->slice_starts(i) + out_index[i] * slice->slice_strides(i);
+      }
+      return operand_literal.Get(operand_index);
+    };
+
+    auto result = Literal::CreateFromDimensions(
+        shape.element_type(), AsInt64Slice(shape.dimensions()));
+    TF_RETURN_IF_ERROR(result->Populate(func));
+    parent_->evaluated_[slice] = std::move(result);
+    return Status::OK();
+  }
+
+  // Enable CLZ only for int32 and uint32.
+  template <
+      typename NativeT,
+      typename std::enable_if<
+          (std::is_floating_point::value ||
+           std::is_integral::value || is_complex_t::value) &&
+          !(std::is_same::value ||
+            std::is_same::value)>::type* = nullptr>
+  Status HandleClz(HloInstruction* clz) {
+    return InvalidArgument("Unsupported type for Clz");
+  }
+
+  template ::value ||
+                std::is_same::value>::type* = nullptr>
+  Status HandleClz(HloInstruction* clz) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[clz],
+                        ElementWiseUnaryOp(clz, [](ElementwiseT elem_operand) {
+                          return 31 - tensorflow::Log2Floor(elem_operand);
+                        }));
+    return Status::OK();
+  }
+
+  Status HandleClz(HloInstruction* clz) override {
+    return HandleClz(clz);
+  }
+
+  template ::value>::type* = nullptr>
+  Status HandleSin(HloInstruction* sin) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[sin],
+                        ElementWiseUnaryOp(sin, [](ElementwiseT elem_operand) {
+                          return std::sin(elem_operand);
+                        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value ||
+                              is_complex_t::value>::type* = nullptr>
+  Status HandleSin(HloInstruction* sin) {
+    return InvalidArgument("Unsupported type for Sin");
+  }
+
+  Status HandleSin(HloInstruction* sin) override {
+    return HandleSin(sin);
+  }
+
+  template ::value>::type* = nullptr>
+  Status HandleCos(HloInstruction* cos) {
+    TF_ASSIGN_OR_RETURN(parent_->evaluated_[cos],
+                        ElementWiseUnaryOp(cos, [](ElementwiseT elem_operand) {
+                          return std::cos(elem_operand);
+                        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value ||
+                              is_complex_t::value>::type* = nullptr>
+  Status HandleCos(HloInstruction* cos) {
+    return InvalidArgument("Unsupported type for Cos");
+  }
+
+  Status HandleCos(HloInstruction* cos) override {
+    return HandleCos(cos);
+  }
+
+  template ::value>::type* = nullptr>
+  Status HandleReducePrecision(HloInstruction* reduce_precision) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[reduce_precision],
+        ElementWiseUnaryOp(reduce_precision, [reduce_precision](
+                                                 ElementwiseT elem) {
+          uint32_t value_as_int = tensorflow::bit_cast(elem);
+          const uint32_t mantissa_bits = reduce_precision->mantissa_bits();
+          const uint32_t exponent_bits = reduce_precision->exponent_bits();
+
+          // Code is based on the CPU/GPU implementation in LLVM-emitting code.
+          //
+          // Bits in float type:
+          //   mantissa : bits [0:22]
+          //   exponent : bits [23:30]
+          //   sign     : bits [31]
+          if (mantissa_bits < 23) {
+            const uint32_t last_mantissa_bit_mask = 1u << (23 - mantissa_bits);
+
+            // Compute rounding bias for round-to-nearest with ties to even.
+            // This is equal to a base value of 0111... plus one bit if the last
+            // remaining mantissa bit is 1.
+            const uint32_t base_rounding_bias =
+                (last_mantissa_bit_mask >> 1) - 1;
+            const uint32_t x_last_mantissa_bit =
+                (value_as_int & last_mantissa_bit_mask) >> (23 - mantissa_bits);
+            const uint32_t x_rounding_bias =
+                x_last_mantissa_bit + base_rounding_bias;
+
+            // Add rounding bias, and mask out truncated bits.  Note that the
+            // case where adding the rounding bias overflows into the exponent
+            // bits is correct; the non-masked mantissa bits will all be zero,
+            // and the exponent will be incremented by one.
+            const uint32_t truncation_mask = ~(last_mantissa_bit_mask - 1);
+            value_as_int = value_as_int + x_rounding_bias;
+            value_as_int = value_as_int & truncation_mask;
+          }
+          if (exponent_bits < 8) {
+            // Masks for f32 values.
+            const uint32_t f32_sign_bit_mask = 1u << 31;
+            const uint32_t f32_exp_bits_mask = 0xffu << 23;
+
+            // An exponent of 2^(n-1)-1 -- that is, 0111... with the zero in the
+            // most- significant bit -- is equal to 1.0f for all exponent sizes.
+            // Adding 2^(n-1)-1 to this gives us the highest non-infinite
+            // exponent for a bit- size of n, and subtracting 2^(n-1)-1 from
+            // this gives us the lowest' exponent (corresponding to 0.0f).
+            //
+            // Thus, the f32 exponent corresponding to the highest non-infinite
+            // exponent for a bit size of n is (2^7-1) + 2^(n-1)-1, and the f32
+            // exponent corresponding to the lowest exponent for a bit size of n
+            // is (2^7-1) - 2^(n-1)-1.
+            //
+            // Note that we have already checked that exponents_bits >= 1.
+            const uint32_t f32_exponent_bias = (1 << 7) - 1;
+            const uint32_t reduced_exponent_bias =
+                (1 << (exponent_bits - 1)) - 1;
+            const uint32_t reduced_max_exponent =
+                f32_exponent_bias + reduced_exponent_bias;
+            const uint32_t reduced_min_exponent =
+                f32_exponent_bias - reduced_exponent_bias;
+
+            // Do we overflow or underflow?
+            const uint32_t x_exponent = value_as_int & f32_exp_bits_mask;
+            const bool x_overflows = x_exponent > (reduced_max_exponent << 23);
+            const bool x_underflows =
+                x_exponent <= (reduced_min_exponent << 23);
+
+            // Compute appropriately-signed values of zero and infinity.
+            const uint32_t x_signed_zero = value_as_int & f32_sign_bit_mask;
+            const uint32_t x_signed_inf = x_signed_zero | f32_exp_bits_mask;
+
+            // Force to zero or infinity if overflow or underflow.  (Note that
+            // this truncates all denormal values to zero, rather than rounding
+            // them.)
+            value_as_int = x_overflows ? x_signed_inf : value_as_int;
+            value_as_int = x_underflows ? x_signed_zero : value_as_int;
+          }
+
+          float reduced_result = tensorflow::bit_cast(value_as_int);
+          if (std::isnan(elem)) {
+            reduced_result = mantissa_bits > 0
+                                 ? elem
+                                 : std::numeric_limits::infinity();
+          }
+          return reduced_result;
+        }));
+    return Status::OK();
+  }
+
+  template ::value>::type* = nullptr>
+  Status HandleReducePrecision(HloInstruction* reduce_precision) {
+    return InvalidArgument("Double not supported for reduce precision");
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value ||
+                              is_complex_t::value>::type* = nullptr>
+  Status HandleReducePrecision(HloInstruction* reduce_precision) {
+    return InvalidArgument("Unsupported type for reduce precision");
+  }
+
+  Status HandleReducePrecision(HloInstruction* reduce_precision) override {
+    return HandleReducePrecision(reduce_precision);
+  }
+
+ private:
+  // Creates a vector of multipliers which can be used to create a linear index
+  // into shape.
+  //
+  // Given the multidimensional index {i1, ..., iN} and
+  // M = MakeDimMultipliers(shape), the corresponding linear index LI is simply
+  //
+  //   LI = i1 * M[1] + i2 * M[2] + ... + iN * M[N].
+  //
+  // This lets you calculate LI given the multidimensional indices in any order.
+  static DimensionVector MakeDimMultipliers(const Shape& shape) {
+    DimensionVector v(ShapeUtil::Rank(shape));
+    int64 scale = 1;
+    for (auto dim : LayoutUtil::MinorToMajor(shape)) {
+      v[dim] = scale;
+      scale *= shape.dimensions(dim);
+    }
+    return v;
+  }
+
+  // For one particular placement of a window in a base shape (the placement is
+  // represented as `window_count_index`), iterates inside the window.
+  // Translates the window index into base index. If the base index is within
+  // bound, call `f` with the base index.
+  static void IterateThroughWindow(
+      const Shape& window_shape, const Window& window, const Shape& base_shape,
+      const tensorflow::gtl::ArraySlice& window_count_index,
+      const std::function&)>& f) {
+    const int64 rank = ShapeUtil::Rank(base_shape);
+    DimensionVector window_index(rank);
+    std::fill(window_index.begin(), window_index.end(), 0);
+    do {
+      std::vector base_index(rank);
+      bool out_of_bound = false;
+      for (int64 i = 0; i < rank; ++i) {
+        base_index[i] = window_count_index[i] * window.dimensions(i).stride() +
+                        window_index[i] - window.dimensions(i).padding_low();
+        if (base_index[i] < 0 || base_index[i] >= base_shape.dimensions(i)) {
+          out_of_bound = true;
+          break;
+        }
+      }
+      if (!out_of_bound) {
+        f(base_index);
+      }
+    } while (IndexUtil::BumpIndices(window_shape, &window_index));
+  }
+
+  template 
+  StatusOr> DynamicSlice(
+      const Literal& operand_literal, const Literal& start_indices_literal,
+      const Shape& result_shape) {
+    auto start_indices_typed = start_indices_literal.data();
+    std::vector start(start_indices_typed.begin(),
+                             start_indices_typed.end());
+
+    std::vector operand_indices(start.size());
+
+    auto result = Literal::CreateFromShape(result_shape);
+    TF_RETURN_IF_ERROR(result->Populate(
+        [&](tensorflow::gtl::ArraySlice multi_index) {
+          for (int64 i = 0; i < operand_indices.size(); ++i) {
+            CHECK_GE(multi_index[i] + start[i], 0);
+            // Mod is only used here to be consistent with the existing
+            // backends' behavior.
+            operand_indices[i] = (multi_index[i] + start[i]) %
+                                 operand_literal.shape().dimensions(i);
+          }
+
+          auto result = operand_literal.Get(operand_indices);
+          return result;
+        }));
+
+    return std::move(result);
+  }
+
+  template 
+  StatusOr> DynamicUpdateSlice(
+      const Literal& operand_literal, const Literal& update_literal,
+      const Literal& start_indices_literal) {
+    auto result = operand_literal.CloneToUnique();
+    auto start_indices_typed = start_indices_literal.data();
+    const auto rank = ShapeUtil::Rank(result->shape());
+    std::vector start(rank, 0);
+    for (int64 i = 0; i < rank; ++i) {
+      // All other implementations currently wrap-around the index, so this
+      // should do so as well.
+      start[i] = (start_indices_typed[i] % result->shape().dimensions(i));
+      start[i] += (start[i] < 0) * result->shape().dimensions(i);
+    }
+    std::vector result_index(rank, 0);
+
+    auto func = [&](tensorflow::gtl::ArraySlice update_index) {
+      std::transform(update_index.begin(), update_index.end(), start.begin(),
+                     result_index.begin(), std::plus());
+      // Same as above, wrap-around only to match other implementations'
+      // semantics.
+      std::transform(result_index.begin(), result_index.end(),
+                     result->shape().dimensions().begin(), result_index.begin(),
+                     std::modulus());
+      result->Set(result_index,
+                           update_literal.Get(update_index));
+      return true;
+    };
+
+    std::vector base(update_literal.shape().dimensions_size(), 0);
+    std::vector step(update_literal.shape().dimensions_size(), 1);
+    ShapeUtil::ForEachIndex(update_literal.shape(), base,
+                            AsInt64Slice(update_literal.shape().dimensions()),
+                            step, func);
+
+    return std::move(result);
+  }
+
+  StatusOr> ElementWiseUnaryOp(
+      HloInstruction* instruction,
+      const std::function& unary_op) {
+    const Literal& operand_literal =
+        parent_->GetEvaluatedLiteralFor(instruction->operand(0));
+    TF_ASSIGN_OR_RETURN(
+        auto result_literal,
+        (HloEvaluator::ElementWiseUnaryOpImpl(
+            instruction, ConvertUnaryFunction(unary_op), operand_literal)));
+
+    return std::move(result_literal);
+  }
+
+  StatusOr> ElementWiseBinaryOp(
+      HloInstruction* instruction,
+      const std::function&
+          binary_op) {
+    const auto shape = instruction->shape();
+    const auto* lhs = instruction->operand(0);
+    const auto* rhs = instruction->operand(1);
+
+    // TODO(b/35950897, b/27796129): add DCHECK back once implicit broadcast
+    // is removed.
+    if (!(ShapeUtil::SameDimensions(shape, rhs->shape()) &&
+          ShapeUtil::SameDimensions(lhs->shape(), rhs->shape()))) {
+      return Unimplemented(
+          "Implicit broadcasting is currently unsupported in HLO evaluator "
+          "Shape Mismatch: %s vs %s vs %s: ",
+          ShapeUtil::HumanString(shape).c_str(),
+          ShapeUtil::HumanString(lhs->shape()).c_str(),
+          ShapeUtil::HumanString(rhs->shape()).c_str());
+    }
+
+    const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs);
+    const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs);
+
+    auto result = Literal::CreateFromShape(shape);
+
+    TF_RETURN_IF_ERROR(result->Populate(
+        [&](tensorflow::gtl::ArraySlice multi_index) {
+          return ConvertBinaryFunction(binary_op)(
+              lhs_literal.Get(multi_index),
+              rhs_literal.Get(multi_index));
+        }));
+    return std::move(result);
+  }
+
+  template 
+  StatusOr> ElementwiseTernaryOp(
+      HloInstruction* instruction,
+      const std::function& ternary_op) {
+    const auto shape = instruction->shape();
+    const auto* lhs = instruction->operand(0);
+    const auto* rhs = instruction->operand(1);
+    const auto* ehs = instruction->operand(2);
+
+    // TODO(b/35950897, b/27796129): add DCHECK back once implicit
+    // broadcast is removed.
+    if (!(ShapeUtil::SameDimensions(shape, lhs->shape()) &&
+          ShapeUtil::SameDimensions(lhs->shape(), rhs->shape()) &&
+          ShapeUtil::SameDimensions(rhs->shape(), ehs->shape()))) {
+      return Unimplemented(
+          "Implicit broadcasting is currently unsupported in HLO evaluator "
+          "Shape Mismatch: %s vs %s vs %s vs %s: ",
+          ShapeUtil::HumanString(shape).c_str(),
+          ShapeUtil::HumanString(lhs->shape()).c_str(),
+          ShapeUtil::HumanString(rhs->shape()).c_str(),
+          ShapeUtil::HumanString(ehs->shape()).c_str());
+    }
+
+    const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs);
+    const Literal& rhs_literal = parent_->GetEvaluatedLiteralFor(rhs);
+    const Literal& ehs_literal = parent_->GetEvaluatedLiteralFor(ehs);
+
+    auto result = Literal::CreateFromShape(shape);
+
+    TF_RETURN_IF_ERROR(result->Populate(
+        [&](tensorflow::gtl::ArraySlice multi_index) {
+          return ternary_op(lhs_literal.Get(multi_index),
+                            rhs_literal.Get(multi_index),
+                            ehs_literal.Get(multi_index));
+        }));
+
+    return std::move(result);
+  }
+
+  template 
+  static bool IsShiftOutOfBounds(NativeT rhs) {
+    typedef typename std::make_unsigned::type UnsignedT;
+    UnsignedT lhs_size_unsigned = sizeof(NativeT) * CHAR_BIT;
+    UnsignedT rhs_unsigned = static_cast(rhs);
+    return rhs_unsigned >= lhs_size_unsigned;
+  }
+
+  HloEvaluator* parent_;
+};
+
+// These extern templates prevent users of this class from implicitly
+// instantiating it.  We explicitly instantiate this class in the various
+// hlo_evaluator_typed_visitor*.cc files.
+extern template class HloEvaluatorTypedVisitor;
+extern template class HloEvaluatorTypedVisitor;
+extern template class HloEvaluatorTypedVisitor;
+extern template class HloEvaluatorTypedVisitor;
+extern template class HloEvaluatorTypedVisitor;
+extern template class HloEvaluatorTypedVisitor;
+extern template class HloEvaluatorTypedVisitor;
+extern template class HloEvaluatorTypedVisitor;
+extern template class HloEvaluatorTypedVisitor;
+extern template class HloEvaluatorTypedVisitor;
+extern template class HloEvaluatorTypedVisitor;
+extern template class HloEvaluatorTypedVisitor;
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_EVALUATOR_TYPED_VISITOR_H_
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_bfloat16.cc b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_bfloat16.cc
new file mode 100644
index 00000000000000..39c352dfb966af
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_bfloat16.cc
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+namespace xla {
+template class HloEvaluatorTypedVisitor;
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_bool.cc b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_bool.cc
new file mode 100644
index 00000000000000..289b40fa06d37b
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_bool.cc
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+namespace xla {
+template class HloEvaluatorTypedVisitor;
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_complex64.cc b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_complex64.cc
new file mode 100644
index 00000000000000..9cb4eb921fd3af
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_complex64.cc
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+namespace xla {
+template class HloEvaluatorTypedVisitor;
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_double.cc b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_double.cc
new file mode 100644
index 00000000000000..5e6252fbf8c24a
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_double.cc
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+namespace xla {
+template class HloEvaluatorTypedVisitor;
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_float.cc b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_float.cc
new file mode 100644
index 00000000000000..ee793ae77b1b43
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_float.cc
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+namespace xla {
+template class HloEvaluatorTypedVisitor;
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_half.cc b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_half.cc
new file mode 100644
index 00000000000000..038d9d39e4a588
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_half.cc
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+namespace xla {
+template class HloEvaluatorTypedVisitor;
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_int32.cc b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_int32.cc
new file mode 100644
index 00000000000000..b1952ca6193958
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_int32.cc
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+namespace xla {
+template class HloEvaluatorTypedVisitor;
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_int64.cc b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_int64.cc
new file mode 100644
index 00000000000000..0cbaffb40b7128
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_int64.cc
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+namespace xla {
+template class HloEvaluatorTypedVisitor;
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_int8.cc b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_int8.cc
new file mode 100644
index 00000000000000..6f4bf2a392b51a
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_int8.cc
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+namespace xla {
+template class HloEvaluatorTypedVisitor;
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_uint32.cc b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_uint32.cc
new file mode 100644
index 00000000000000..10235447e0d266
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_uint32.cc
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+namespace xla {
+template class HloEvaluatorTypedVisitor;
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_uint64.cc b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_uint64.cc
new file mode 100644
index 00000000000000..8abeaa6ffca440
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_uint64.cc
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+namespace xla {
+template class HloEvaluatorTypedVisitor;
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_uint8.cc b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_uint8.cc
new file mode 100644
index 00000000000000..6dabd1c176eabc
--- /dev/null
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor_uint8.cc
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h"
+
+#include "tensorflow/compiler/xla/service/hlo_evaluator.h"
+
+namespace xla {
+template class HloEvaluatorTypedVisitor;
+}  // namespace xla

From dfae6ff29e95345c7c6c0ef50fd5f45bd458cfdc Mon Sep 17 00:00:00 2001
From: Alexandre Passos 
Date: Mon, 7 May 2018 14:29:03 -0700
Subject: [PATCH 0463/1691] Fix resource variable in cond gradient.

PiperOrigin-RevId: 195722449
---
 tensorflow/python/BUILD                   |  1 +
 tensorflow/python/ops/control_flow_ops.py |  7 +++++++
 tensorflow/python/ops/gradients_impl.py   |  3 ++-
 tensorflow/python/ops/gradients_test.py   | 15 +++++++++++++++
 4 files changed, 25 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 087b89b1250376..4057e3768144cc 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -1762,6 +1762,7 @@ py_library(
         ":logging_ops_gen",
         ":math_ops",
         ":platform",
+        ":resource_variable_ops_gen",
         ":sparse_tensor",
         ":tensor_array_ops",
         ":tf_should_use",
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 07d4ff7b02c70e..5f60dab6ac3613 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -43,6 +43,7 @@
 from tensorflow.python.ops import gen_control_flow_ops
 from tensorflow.python.ops import gen_data_flow_ops
 from tensorflow.python.ops import gen_logging_ops
+from tensorflow.python.ops import gen_resource_variable_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import tensor_array_ops
 # go/tf-wildcard-import
@@ -1433,6 +1434,8 @@ def ZerosLikeOutsideLoop(op, index):
   """Create zeros_like for the specified output of an op."""
   val = op.outputs[index]
   if not util.IsSwitch(op):
+    if val.dtype == dtypes.resource:
+      return array_ops.zeros(gen_resource_variable_ops.variable_shape(val))
     return array_ops.zeros_like(val, optimize=False)
   else:
     op_ctxt = op._get_control_flow_context()
@@ -1441,6 +1444,10 @@ def ZerosLikeOutsideLoop(op, index):
       pred = op_ctxt.pred
       branch = op_ctxt.branch
       switch_val = switch(op.inputs[0], pred)[1 - branch]
+      if val.dtype == dtypes.resource:
+        with ops.control_dependencies([switch_val]):
+          return array_ops.zeros(
+              gen_resource_variable_ops.variable_shape(switch_val))
       zeros_shape = array_ops.shape_internal(switch_val, optimize=False)
       # Ensure ops created within array_ops.zeros are dominated by switch in
       # cond context.
diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py
index 1448151fef4aab..a6b1e6df54381a 100644
--- a/tensorflow/python/ops/gradients_impl.py
+++ b/tensorflow/python/ops/gradients_impl.py
@@ -297,7 +297,8 @@ def _DefaultGradYs(grad_ys,
 def _IsTrainable(tensor):
   dtype = dtypes.as_dtype(tensor.dtype)
   return dtype.base_dtype in (dtypes.float16, dtypes.float32, dtypes.float64,
-                              dtypes.complex64, dtypes.complex128)
+                              dtypes.complex64, dtypes.complex128,
+                              dtypes.resource)
 
 
 def _IsBackpropagatable(tensor):
diff --git a/tensorflow/python/ops/gradients_test.py b/tensorflow/python/ops/gradients_test.py
index 5e8b8822efd606..e72995020152c3 100644
--- a/tensorflow/python/ops/gradients_test.py
+++ b/tensorflow/python/ops/gradients_test.py
@@ -944,6 +944,21 @@ def Grad(_):
       # Smoke test to ensure numpy inputs are accepted
       F(x)
 
+  def testRVGradientsDynamicCond(self):
+    with self.test_session():
+      alpha = resource_variable_ops.ResourceVariable(
+          np.random.random((1,)),
+          dtype="float32")
+
+      conditional = array_ops.placeholder_with_default(True, shape=())
+      output = control_flow_ops.cond(
+          conditional, lambda: alpha * 2, lambda: alpha * 3)
+
+      g, = gradients_impl.gradients(output, alpha)
+      variables.global_variables_initializer().run()
+      self.assertAllEqual(g.eval(), [2.0])
+      self.assertAllEqual(g.eval(feed_dict={conditional: False}), [3.0])
+
 
 if __name__ == "__main__":
   googletest.main()

From 84986720ee8c07112356bbcc0911629e9c4dafb6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 14:34:11 -0700
Subject: [PATCH 0464/1691] Allow output has a different shape from input in
 the image.transform (#17011).

PiperOrigin-RevId: 195723288
---
 tensorflow/contrib/image/kernels/image_ops.cc | 33 ++++++++---
 tensorflow/contrib/image/kernels/image_ops.h  |  2 +-
 tensorflow/contrib/image/ops/image_ops.cc     | 55 +++++++++++++++++--
 .../python/kernel_tests/image_ops_test.py     | 30 ++++++++++
 .../contrib/image/python/ops/image_ops.py     | 49 +++++++++++------
 5 files changed, 139 insertions(+), 30 deletions(-)

diff --git a/tensorflow/contrib/image/kernels/image_ops.cc b/tensorflow/contrib/image/kernels/image_ops.cc
index c2e32da133b32c..575c2004fb8aea 100644
--- a/tensorflow/contrib/image/kernels/image_ops.cc
+++ b/tensorflow/contrib/image/kernels/image_ops.cc
@@ -70,6 +70,7 @@ class ImageProjectiveTransform : public OpKernel {
   void Compute(OpKernelContext* ctx) override {
     const Tensor& images_t = ctx->input(0);
     const Tensor& transform_t = ctx->input(1);
+    const Tensor& shape_t = ctx->input(2);
     OP_REQUIRES(ctx, images_t.shape().dims() == 4,
                 errors::InvalidArgument("Input images must have rank 4"));
     OP_REQUIRES(ctx,
@@ -80,11 +81,28 @@ class ImageProjectiveTransform : public OpKernel {
                      ProjectiveGenerator::kNumParameters),
                 errors::InvalidArgument(
                     "Input transform should be num_images x 8 or 1 x 8"));
-    auto images = images_t.tensor();
-    auto transform = transform_t.matrix();
+    OP_REQUIRES(ctx, shape_t.dims() == 1,
+                errors::InvalidArgument("output shape must be 1-dimensional",
+                                        shape_t.shape().DebugString()));
+    OP_REQUIRES(ctx, shape_t.NumElements() == 2,
+                errors::InvalidArgument("output shape must have two elements",
+                                        shape_t.shape().DebugString()));
+    auto Svec = shape_t.vec();
+    int32 out_height = Svec(0);
+    int32 out_width = Svec(1);
+    OP_REQUIRES(ctx, out_height > 0 && out_width > 0,
+                errors::InvalidArgument("output dimensions must be positive"));
+
     Tensor* output_t;
-    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, images_t.shape(), &output_t));
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(
+                            0,
+                            TensorShape({images_t.dim_size(0), out_height,
+                                         out_width, images_t.dim_size(3)}),
+                            &output_t));
     auto output = output_t->tensor();
+    auto images = images_t.tensor();
+    auto transform = transform_t.matrix();
+
     (FillProjectiveTransform(interpolation_))(
         ctx->eigen_device(), &output, images, transform);
   }
@@ -127,10 +145,11 @@ TF_CALL_double(DECLARE_FUNCTOR);
 
 }  // end namespace functor
 
-#define REGISTER(TYPE)                                        \
-  REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransform")    \
-                              .Device(DEVICE_GPU)             \
-                              .TypeConstraint("dtype"), \
+#define REGISTER(TYPE)                                       \
+  REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransform")   \
+                              .Device(DEVICE_GPU)            \
+                              .TypeConstraint("dtype") \
+                              .HostMemory("output_shape"),   \
                           ImageProjectiveTransform)
 
 TF_CALL_uint8(REGISTER);
diff --git a/tensorflow/contrib/image/kernels/image_ops.h b/tensorflow/contrib/image/kernels/image_ops.h
index ad501330617be8..2320329b923fee 100644
--- a/tensorflow/contrib/image/kernels/image_ops.h
+++ b/tensorflow/contrib/image/kernels/image_ops.h
@@ -161,7 +161,7 @@ struct FillProjectiveTransform {
   void operator()(const Device& device, OutputType* output,
                   const InputType& images,
                   const TransformsType& transform) const {
-    output->device(device) = images.generate(
+    output->device(device) = output->generate(
         ProjectiveGenerator(images, transform, interpolation_));
   }
 };
diff --git a/tensorflow/contrib/image/ops/image_ops.cc b/tensorflow/contrib/image/ops/image_ops.cc
index ebdcaea7abae2a..fb62507174de31 100644
--- a/tensorflow/contrib/image/ops/image_ops.cc
+++ b/tensorflow/contrib/image/ops/image_ops.cc
@@ -19,9 +19,56 @@ limitations under the License.
 
 namespace tensorflow {
 
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
 using shape_inference::ShapeHandle;
 
+namespace {
+
+// Sets output[0] to shape [batch_dim,height,width,channel_dim], where
+// height and width come from the size_tensor.
+Status SetOutputToSizedImage(InferenceContext* c, DimensionHandle batch_dim,
+                             int size_input_idx, DimensionHandle channel_dim) {
+  // Verify shape of size input.
+  ShapeHandle size;
+  TF_RETURN_IF_ERROR(c->WithRank(c->input(size_input_idx), 1, &size));
+  DimensionHandle unused;
+  TF_RETURN_IF_ERROR(c->WithValue(c->Dim(size, 0), 2, &unused));
+
+  // Get size values from the size tensor.
+  const Tensor* size_tensor = c->input_tensor(size_input_idx);
+  DimensionHandle width;
+  DimensionHandle height;
+  if (size_tensor == nullptr) {
+    width = c->UnknownDim();
+    height = c->UnknownDim();
+  } else {
+    // TODO(petewarden) - Remove once we have constant evaluation in C++ only.
+    if (size_tensor->dtype() != DT_INT32) {
+      return errors::InvalidArgument(
+          "Bad size input type for SetOutputToSizedImage: Expected DT_INT32 "
+          "but got ",
+          DataTypeString(size_tensor->dtype()), " for input #", size_input_idx,
+          " in ", c->DebugString());
+    }
+    auto vec = size_tensor->vec();
+    height = c->MakeDim(vec(0));
+    width = c->MakeDim(vec(1));
+  }
+  c->set_output(0, c->MakeShape({batch_dim, height, width, channel_dim}));
+  return Status::OK();
+}
+
+// TODO(qyu): Move this to core/framework/common_shape_fns.h
+Status ResizeShapeFn(InferenceContext* c) {
+  ShapeHandle input;
+  TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
+  return SetOutputToSizedImage(c, c->Dim(input, 0), 2 /* size_input_idx */,
+                               c->Dim(input, 3));
+}
+
+}  // namespace
+
 // TODO(ringwalt): Add a "fill_mode" argument with "constant", "mirror", etc.
 // TODO(ringwalt): Add a "fill_constant" argument for constant mode (default 0).
 // TODO(ringwalt): Add an "output_shape" argument. This is sufficient to
@@ -29,13 +76,11 @@ using shape_inference::ShapeHandle;
 REGISTER_OP("ImageProjectiveTransform")
     .Input("images: dtype")
     .Input("transforms: float32")
+    .Input("output_shape: int32")
     .Attr("dtype: {uint8, int32, int64, float32, float64}")
     .Attr("interpolation: string")
     .Output("transformed_images: dtype")
-    .SetShapeFn([](InferenceContext* c) {
-      c->set_output(0, c->input(0));
-      return Status::OK();
-    })
+    .SetShapeFn(ResizeShapeFn)
     .Doc(R"doc(
 Applies the given transform to each of the images.
 
@@ -49,7 +94,7 @@ If one row of `transforms` is `[a0, a1, a2, b0, b1, b2, c0, c1]`, then it maps
 the *output* point `(x, y)` to a transformed *input* point
 `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`, where
 `k = c0 x + c1 y + 1`. If the transformed point lays outside of the input
-image, the output pixel is set to 0. The output is the same size as the input,
+image, the output pixel is set to 0.
 
 images: 4D `Tensor`, input image(s) in NHWC format.
 transforms: 2D `Tensor`, projective transform(s) to apply to the image(s).
diff --git a/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py b/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py
index b50177ae5651fb..c0151d320f98a7 100644
--- a/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py
+++ b/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py
@@ -195,10 +195,40 @@ def _test_grad(self, shape_to_test):
           x_init_value=test_image)
       self.assertLess(left_err, 1e-10)
 
+  def _test_grad_different_shape(self, input_shape, output_shape):
+    with self.test_session():
+      test_image_shape = input_shape
+      test_image = np.random.randn(*test_image_shape)
+      test_image_tensor = constant_op.constant(
+          test_image, shape=test_image_shape)
+      test_transform = image_ops.angles_to_projective_transforms(
+          np.pi / 2, 4, 4)
+
+      if len(output_shape) == 2:
+        resize_shape = output_shape
+      elif len(output_shape) == 3:
+        resize_shape = output_shape[0:2]
+      elif len(output_shape) == 4:
+        resize_shape = output_shape[1:3]
+      output = image_ops.transform(
+          images=test_image_tensor,
+          transforms=test_transform,
+          output_shape=resize_shape)
+      left_err = gradient_checker.compute_gradient_error(
+          test_image_tensor,
+          test_image_shape,
+          output,
+          output_shape,
+          x_init_value=test_image)
+      self.assertLess(left_err, 1e-10)
+
   def test_grad(self):
     self._test_grad([16, 16])
     self._test_grad([4, 12, 12])
     self._test_grad([3, 4, 12, 12])
+    self._test_grad_different_shape([16, 16], [8, 8])
+    self._test_grad_different_shape([4, 12, 3], [8, 24, 3])
+    self._test_grad_different_shape([3, 4, 12, 3], [3, 8, 24, 3])
 
 
 class BipartiteMatchTest(test_util.TensorFlowTestCase):
diff --git a/tensorflow/contrib/image/python/ops/image_ops.py b/tensorflow/contrib/image/python/ops/image_ops.py
index cd984c80543886..192571ced81fe5 100644
--- a/tensorflow/contrib/image/python/ops/image_ops.py
+++ b/tensorflow/contrib/image/python/ops/image_ops.py
@@ -23,6 +23,7 @@
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import linalg_ops
@@ -212,7 +213,11 @@ def translations_to_projective_transforms(translations, name=None):
         axis=1)
 
 
-def transform(images, transforms, interpolation="NEAREST", name=None):
+def transform(images,
+              transforms,
+              interpolation="NEAREST",
+              output_shape=None,
+              name=None):
   """Applies the given transform(s) to the image(s).
 
   Args:
@@ -229,6 +234,10 @@ def transform(images, transforms, interpolation="NEAREST", name=None):
        the transform mapping input points to output points. Note that gradients
        are not backpropagated into transformation parameters.
     interpolation: Interpolation mode. Supported values: "NEAREST", "BILINEAR".
+    output_shape: Output dimesion after the transform, [height, width].
+       If None, output is the same size as input image.
+
+    name: The name of the op.
 
   Returns:
     Image(s) with the same type and shape as `images`, with the given
@@ -237,6 +246,7 @@ def transform(images, transforms, interpolation="NEAREST", name=None):
 
   Raises:
     TypeError: If `image` is an invalid type.
+    ValueError: If output shape is not 1-D int32 Tensor.
   """
   with ops.name_scope(name, "transform"):
     image_or_images = ops.convert_to_tensor(images, name="images")
@@ -255,6 +265,17 @@ def transform(images, transforms, interpolation="NEAREST", name=None):
     else:
       raise TypeError("Images should have rank between 2 and 4.")
 
+    if output_shape is None:
+      output_shape = tensor_util.constant_value(
+          array_ops.shape(images)[1:3]) or array_ops.shape(images)[1:3]
+
+    output_shape = ops.convert_to_tensor(
+        output_shape, dtypes.int32, name="output_shape")
+
+    if not output_shape.get_shape().is_compatible_with([2]):
+      raise ValueError("output_shape must be a 1-D Tensor of 2 elements: "
+                       "new_height, new_width")
+
     if len(transform_or_transforms.get_shape()) == 1:
       transforms = transform_or_transforms[None]
     elif transform_or_transforms.get_shape().ndims is None:
@@ -264,8 +285,12 @@ def transform(images, transforms, interpolation="NEAREST", name=None):
       transforms = transform_or_transforms
     else:
       raise TypeError("Transforms should have rank 1 or 2.")
+
     output = gen_image_ops.image_projective_transform(
-        images, transforms, interpolation=interpolation.upper())
+        images,
+        output_shape=output_shape,
+        transforms=transforms,
+        interpolation=interpolation.upper())
     if len(image_or_images.get_shape()) == 2:
       return output[0, :, :, 0]
     elif len(image_or_images.get_shape()) == 3:
@@ -375,14 +400,6 @@ def _image_projective_transform_grad(op, grad):
 
   if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES:
     raise TypeError("Invalid dtype %s." % image_or_images.dtype)
-  if len(image_or_images.get_shape()) == 2:
-    images = image_or_images[None, :, :, None]
-  elif len(image_or_images.get_shape()) == 3:
-    images = image_or_images[None, :, :, :]
-  elif len(image_or_images.get_shape()) == 4:
-    images = image_or_images
-  else:
-    raise TypeError("Images should have rank between 2 and 4")
   if len(transform_or_transforms.get_shape()) == 1:
     transforms = transform_or_transforms[None]
   elif len(transform_or_transforms.get_shape()) == 2:
@@ -395,13 +412,11 @@ def _image_projective_transform_grad(op, grad):
   inverse = linalg_ops.matrix_inverse(transforms)
   transforms = matrices_to_flat_transforms(inverse)
   output = gen_image_ops.image_projective_transform(
-      grad, transforms, interpolation=interpolation)
-  if len(image_or_images.get_shape()) == 2:
-    return [output[0, :, :, 0], None]
-  elif len(image_or_images.get_shape()) == 3:
-    return [output[0, :, :, :], None]
-  else:
-    return [output, None]
+      images=grad,
+      transforms=transforms,
+      output_shape=array_ops.shape(image_or_images)[1:3],
+      interpolation=interpolation)
+  return [output, None, None]
 
 
 def bipartite_match(distance_mat,

From 80c7ebc32324d689ae3feb17a0cb4cf32d736f19 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 14:55:26 -0700
Subject: [PATCH 0465/1691] Disable automated testing of
 tensorflow/compiler/tests:extract_image_patches_op_test_cpu_ondemand

A recent change has made this test flaky.

PiperOrigin-RevId: 195726647
---
 tensorflow/compiler/tests/BUILD | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index a94b298f878320..aaea83ae9cbd21 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -300,6 +300,10 @@ tf_xla_py_test(
     name = "extract_image_patches_op_test",
     size = "small",
     srcs = ["extract_image_patches_op_test.py"],
+    tags = [
+        "manual",
+        "notap",
+    ],
     deps = [
         ":xla_test",
         "//tensorflow/python:array_ops",

From 860452f3346e3a450782046081dc4d3263544344 Mon Sep 17 00:00:00 2001
From: Igor Ganichev 
Date: Mon, 7 May 2018 15:15:01 -0700
Subject: [PATCH 0466/1691] Replace references to TensorInfo with XlaTensor

PiperOrigin-RevId: 195730139
---
 tensorflow/compiler/jit/xla_tensor.h | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/jit/xla_tensor.h b/tensorflow/compiler/jit/xla_tensor.h
index 922a9189731209..6b29c82ec11e39 100644
--- a/tensorflow/compiler/jit/xla_tensor.h
+++ b/tensorflow/compiler/jit/xla_tensor.h
@@ -54,7 +54,7 @@ class XlaTensor {
   // Some Tensors can have complex on-device shapes, including tuple shapes. To
   // manage the memory for these tensors a ShapedBuffer may be required.
 
-  // Return true if this TensorInfo contains a ShapedBuffer.
+  // Return true if this XlaTensor contains a ShapedBuffer.
   bool has_shaped_buffer() const { return shaped_buffer_ != nullptr; }
   // Return the contained ShapedBuffer.
   // REQUIRES: has_shaped_buffer()
@@ -62,7 +62,7 @@ class XlaTensor {
     CHECK(has_shaped_buffer());
     return *shaped_buffer_;
   }
-  // Mutates the TensorInfo to set the ShapedBuffer.
+  // Mutates the XlaTensor to set the ShapedBuffer.
   void set_shaped_buffer(xla::ScopedShapedBuffer shaped_buffer) {
     shaped_buffer_ =
         xla::MakeUnique(std::move(shaped_buffer));
@@ -72,7 +72,7 @@ class XlaTensor {
   // in on-demand mode to avoid re-copying values from the device if we know the
   // host value already.
 
-  // Return true if this TensorInfo contains a host tensor.
+  // Return true if this XlaTensor contains a host tensor.
   bool has_host_tensor() const { return host_tensor_ != nullptr; }
   // Return the contained host tensor.
   // REQUIRES: has_host_tensor()

From de177c4c73a2eb3e72709a76940c1cc50341e18c Mon Sep 17 00:00:00 2001
From: Pavithra Vijay 
Date: Mon, 7 May 2018 15:16:59 -0700
Subject: [PATCH 0467/1691] Add support for tf.data.Dataset iterators in model
 training/eval methods in eager-mode

PiperOrigin-RevId: 195730534
---
 .../keras/_impl/keras/engine/base_layer.py    |   2 +-
 .../_impl/keras/engine/sequential_test.py     |  39 +-
 .../keras/_impl/keras/engine/training.py      | 234 +++--
 .../_impl/keras/engine/training_arrays.py     |   4 +-
 .../_impl/keras/engine/training_eager.py      | 932 +++++++++++++-----
 .../keras/_impl/keras/engine/training_test.py |  96 +-
 .../_impl/keras/engine/training_utils.py      |  91 +-
 .../_impl/keras/model_subclassing_test.py     |  21 +
 8 files changed, 1049 insertions(+), 370 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/engine/base_layer.py b/tensorflow/python/keras/_impl/keras/engine/base_layer.py
index 3af4eaabe90217..16ee2952b27979 100644
--- a/tensorflow/python/keras/_impl/keras/engine/base_layer.py
+++ b/tensorflow/python/keras/_impl/keras/engine/base_layer.py
@@ -1658,7 +1658,7 @@ class DeferredTensor(object):
   """Tensor-like object used to build graphs of layers in Eager mode.
 
   When calling a layer on a DeferredTensor, the layer will not perform any
-  computation and will simply perfom shape inference to return new
+  computation and will simply perform shape inference to return new
   DeferredTensors with appropriate shape information. Thus DeferredTensor
   behaves like a graph-mode Tensor when manipulated by layers.
   """
diff --git a/tensorflow/python/keras/_impl/keras/engine/sequential_test.py b/tensorflow/python/keras/_impl/keras/engine/sequential_test.py
index 8aba16aef3e187..a90ad131a51e3c 100644
--- a/tensorflow/python/keras/_impl/keras/engine/sequential_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/sequential_test.py
@@ -20,8 +20,11 @@
 
 import numpy as np
 
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.eager import context
 from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
+from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import test
 from tensorflow.python.training import rmsprop
 
@@ -75,7 +78,7 @@ def test_sequential_pop(self):
       model.pop()
 
   @tf_test_util.run_in_graph_and_eager_modes()
-  def test_sequential_deferred_build(self):
+  def test_sequential_deferred_build_with_np_arrays(self):
     num_hidden = 5
     input_dim = 3
     batch_size = 5
@@ -99,6 +102,40 @@ def test_sequential_deferred_build(self):
                      [None, num_classes])
     self.assertEqual(len(model.weights), 2 * 2)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
+  def test_sequential_deferred_build_with_dataset_iterators(self):
+    if not context.executing_eagerly():
+      # TODO(psv/fchollet): Add support for this use case in graph mode.
+      return
+    num_hidden = 5
+    input_dim = 3
+    num_classes = 2
+    num_samples = 50
+    steps_per_epoch = 10
+
+    model = keras.models.Sequential()
+    # We don't specify the input shape.
+    model.add(keras.layers.Dense(num_hidden))
+    model.add(keras.layers.Dense(num_classes))
+    model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3))
+    self.assertEqual(len(model.layers), 2)
+    self.assertEqual(len(model.weights), 0)
+    self.assertFalse(model.built)
+
+    x = array_ops.ones((num_samples, input_dim))
+    y = array_ops.zeros((num_samples, num_classes))
+    dataset = dataset_ops.Dataset.from_tensor_slices((x, y))
+    dataset = dataset.repeat(100)
+    dataset = dataset.batch(10)
+    iterator = dataset.make_one_shot_iterator()
+
+    model.fit(iterator, epochs=1, steps_per_epoch=steps_per_epoch)
+    self.assertTrue(model.built)
+    self.assertEqual(model.inputs[0].get_shape().as_list(), [None, input_dim])
+    self.assertEqual(model.outputs[0].get_shape().as_list(),
+                     [None, num_classes])
+    self.assertEqual(len(model.weights), 2 * 2)
+
   @tf_test_util.run_in_graph_and_eager_modes()
   def test_invalid_use_cases(self):
     # Added objects must be layer instances
diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py
index 5f9b3e8c7d7a93..c7623d2b524fa3 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training.py
@@ -18,11 +18,13 @@
 from __future__ import division
 from __future__ import print_function
 
+import weakref
 import numpy as np
 
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.eager import context
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras._impl.keras import backend as K
@@ -106,6 +108,11 @@ def call(self, inputs, training=False):
   ```
   """
 
+  def __init__(self, *args, **kwargs):
+    super(Model, self).__init__(*args, **kwargs)
+    # Create a cache for iterator get_next op.
+    self._iterator_get_next = weakref.WeakKeyDictionary()
+
   def compile(self,
               optimizer,
               loss=None,
@@ -623,12 +630,23 @@ def _make_predict_function(self):
           **kwargs)
     self._post_build_cleanup()
 
+  def _get_iterator_get_next_tensors(self, iterator):
+    get_next_op = self._iterator_get_next.get(iterator, None)
+    if get_next_op is None:
+      get_next_op = iterator.get_next()
+      self._iterator_get_next[iterator] = get_next_op
+    return get_next_op
+
   def _standardize_user_data(self,
                              x,
                              y=None,
                              sample_weight=None,
                              class_weight=None,
-                             batch_size=None):
+                             batch_size=None,
+                             check_steps=False,
+                             steps_name='steps',
+                             steps=None,
+                             validation_split=0):
     """Runs validation checks on input and target data passed by the user.
 
     Also standardizes the data to lists of arrays, in order.
@@ -660,6 +678,16 @@ def _standardize_user_data(self,
         to, as conveyed by `y`.
       batch_size: Integer batch size. If provided, it is used to run additional
         validation checks on stateful models.
+      check_steps: boolean, True if we want to check for validity of `steps` and
+        False, otherwise. For example, when we are standardizing one batch of
+        data for train_on_batch/predict_on_batch/test_on_batch APIs, `steps`
+        value is not required and we should not check for its validity in these
+        cases.
+      steps_name: The public API's parameter name for `steps`.
+      steps: Integer or `None`. Total number of steps (batches of samples) to
+        execute.
+      validation_split: Float between 0 and 1.
+        Fraction of the training data to be used as validation data.
 
     Returns:
       A tuple of 3 lists: input arrays, target arrays, sample-weight arrays.
@@ -671,33 +699,54 @@ def _standardize_user_data(self,
       ValueError: In case of invalid user-provided data.
       RuntimeError: If the model was never compiled.
     """
-    # First, we build/compile the model on the fly if necessary.
     if isinstance(x, dataset_ops.Dataset):
       raise ValueError('You passed a `Dataset` instance to your model (%s), '
                        'which is not supported. Instead, pass an `Iterator`, '
                        'which you can obtain e.g. via '
                        '`dataset.make_one_shot_iterator()` (the exact method '
                        'to use will depend on your specific dataset).' % x)
-    if isinstance(x, iterator_ops.Iterator):
-      if y is not None:
-        raise ValueError('You passed a dataset iterator (%s) as input `x` to '
-                         'your model. In that case, you should not specify '
-                         'a target (`y`) argument, since the dataset iterator '
-                         'generates both input data and target data. '
-                         'Received: %s' % (x, y))
-      if not context.executing_eagerly():
-        x, y = x.get_next()
-        # TODO(fchollet): handle case of `get_next` not returning 2 tensors?
-      else:
-        # TODO(psv): implement this. The way to support it will be to typecheck
-        # for `iterator` before `_standardize_user_data` is called and redirect
-        # to new training/eval functions in `training_eager.py`. The model
-        # may need to get built using the specs of the data from the first batch
-        # drawn from the iterator.
-        raise ValueError('Dataset iterators are not supported '
-                         'with eager execution yet.')
 
+    # Validates `steps` argument based on x's type.
+    if check_steps:
+      training_utils.check_steps_argument(x, steps, steps_name)
+
+    is_x_eager_iterator = isinstance(x, iterator_ops.EagerIterator)
+    is_x_iterator = isinstance(x, iterator_ops.Iterator)
+
+    # Validate user inputs when data is given as a dataset iterator.
+    if is_x_iterator or is_x_eager_iterator:
+      training_utils.validate_iterator_input(x, y, sample_weight,
+                                             validation_split)
+
+    # For eager iterators, when we have to process multiple batches of samples,
+    # we will standardize the data when we actually loop over iterator and get
+    # the batches. For now, we just return the iterator as is.
+    if is_x_eager_iterator and steps is not None:
+      return x, y, sample_weight
+
+    # If input data is a dataset iterator in graph mode or if it is an eager
+    # iterator and only one batch of samples is required, we fetch the data
+    # tensors from the iterator and then standardize them.
+    if is_x_iterator or is_x_eager_iterator:
+      try:
+        if is_x_iterator:
+          next_element = self._get_iterator_get_next_tensors(x)
+        else:
+          next_element = x.get_next()
+      except errors.OutOfRangeError:
+        raise RuntimeError('Your dataset iterator ran out of data; '
+                           'Make sure that your dataset can generate '
+                           'required number of samples.')
+
+      if not isinstance(next_element, (list, tuple)) or len(next_element) != 2:
+        raise ValueError('Please provide data as a list or tuple of 2 elements '
+                         ' - input and target pair. Received %s' % next_element)
+      x, y = next_element
+
+    # First, we build/compile the model on the fly if necessary.
     all_inputs = []
+    is_build_called = False
+    is_compile_called = False
     if not self.built:
       # We need to use `x` to set the model inputs.
       # We type-check that `x` and `y` are either single arrays
@@ -720,6 +769,7 @@ def _standardize_user_data(self,
       # If values, then in symbolic-mode placeholders will be created
       # to match the value shapes.
       if not self.inputs:
+        is_build_called = True
         self._set_inputs(x)
 
     if y is not None:
@@ -736,6 +786,7 @@ def _standardize_user_data(self,
             raise ValueError('Please provide as model targets either a single '
                              'array or a list of arrays. '
                              'You passed: y=' + str(y))
+          all_inputs += list(y)
         elif isinstance(y, dict):
           raise ValueError('Please do not pass a dictionary as model targets.')
         else:
@@ -743,14 +794,10 @@ def _standardize_user_data(self,
             raise ValueError('Please provide as model targets either a single '
                              'array or a list of arrays. '
                              'You passed: y=' + str(y))
+          all_inputs.append(y)
 
         # Typecheck that all inputs are *either* value *or* symbolic.
         # TODO(fchollet): this check could be removed in Eager mode?
-        if y is not None:
-          if isinstance(y, (list, tuple)):
-            all_inputs += list(y)
-          else:
-            all_inputs.append(y)
         if any(tensor_util.is_tensor(v) for v in all_inputs):
           if not all(tensor_util.is_tensor(v) for v in all_inputs):
             raise ValueError('Do not pass inputs that mix Numpy arrays and '
@@ -764,17 +811,22 @@ def _standardize_user_data(self,
           if not isinstance(y, (list, tuple)):
             y = [y]
           target_tensors = [v for v in y if tensor_util.is_tensor(v)]
+        is_compile_called = True
         self.compile(optimizer=self.optimizer,
                      loss=self.loss,
                      metrics=self.metrics,
                      loss_weights=self.loss_weights,
                      target_tensors=target_tensors)
 
-    # If `x` and `y` were all symbolic, then no model should not be fed any
-    # inputs and targets.
+    # In graph mode, if we had just set inputs and targets as symbolic tensors
+    # by invoking build and compile on the model respectively, we do not have to
+    # feed anything to the model. Model already has input and target data as
+    # part of the graph.
     # Note: in this case, `any` and `all` are equivalent since we disallow
     # mixed symbolic/value inputs.
-    if any(tensor_util.is_tensor(v) for v in all_inputs):
+    if (not context.executing_eagerly() and is_build_called and
+        is_compile_called and
+        any(tensor_util.is_tensor(v) for v in all_inputs)):
       return [], [], []
 
     # What follows is input validation and standardization to list format,
@@ -904,7 +956,12 @@ def _set_inputs(self, inputs, training=None):
       if isinstance(inputs, list):
         assert len(inputs) == 1
         inputs = inputs[0]
-      self.build(input_shape=(None,) + inputs.shape[1:])
+
+      if tensor_util.is_tensor(inputs):
+        input_shape = (None,) + tuple(inputs.get_shape().as_list()[1:])
+      else:
+        input_shape = (None,) + inputs.shape[1:]
+      self.build(input_shape=input_shape)
     elif context.executing_eagerly():
       self._eager_set_inputs(inputs)
     else:
@@ -931,12 +988,18 @@ def _eager_set_inputs(self, inputs):
     # On-the-fly setting of model inputs/outputs as DeferredTensors,
     # to keep track of number of inputs and outputs and their ndim.
     if isinstance(inputs, (list, tuple)):
-      dummy_output_values = self.call(
-          [ops.convert_to_tensor(v, dtype=K.floatx()) for v in inputs])
+      if tensor_util.is_tensor(inputs[0]):
+        dummy_output_values = self.call(inputs)
+      else:
+        dummy_output_values = self.call(
+            [ops.convert_to_tensor(v, dtype=K.floatx()) for v in inputs])
       dummy_input_values = list(inputs)
     else:
-      dummy_output_values = self.call(
-          ops.convert_to_tensor(inputs, dtype=K.floatx()))
+      if tensor_util.is_tensor(inputs):
+        dummy_output_values = self.call(inputs)
+      else:
+        dummy_output_values = self.call(
+            ops.convert_to_tensor(inputs, dtype=K.floatx()))
       dummy_input_values = [inputs]
     if isinstance(dummy_output_values, (list, tuple)):
       dummy_output_values = list(dummy_output_values)
@@ -1071,7 +1134,7 @@ def fit(self,
         batch_size: Integer or `None`.
             Number of samples per gradient update.
             If unspecified, `batch_size` will default to 32.
-            Do not specify the `batch_size` is your data is in the
+            Do not specify the `batch_size` if your data is in the
             form of symbolic tensors or dataset iterators (since they generate
             batches).
         epochs: Integer. Number of epochs to train the model.
@@ -1094,7 +1157,8 @@ def fit(self,
             the loss and any model metrics
             on this data at the end of each epoch.
             The validation data is selected from the last samples
-            in the `x` and `y` data provided, before shuffling.
+            in the `x` and `y` data provided, before shuffling. This argument is
+            not supported when `x` is a dataset iterator.
         validation_data: Data on which to evaluate
             the loss and any model metrics at the end of each epoch.
             The model will not be trained on this data.
@@ -1124,7 +1188,8 @@ def fit(self,
             `(samples, sequence_length)`,
             to apply a different weight to every timestep of every sample.
             In this case you should make sure to specify
-            `sample_weight_mode="temporal"` in `compile()`.
+            `sample_weight_mode="temporal"` in `compile()`. This argument is not
+            supported when `x` is a dataset iterator.
         initial_epoch: Integer.
             Epoch at which to start training
             (useful for resuming a previous training run).
@@ -1165,21 +1230,23 @@ def fit(self,
       epochs = kwargs.pop('nb_epoch')
     if kwargs:
       raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))
-    if x is None and y is None and steps_per_epoch is None:
-      raise ValueError('If fitting from data tensors, '
-                       'you should specify the `steps_per_epoch` '
-                       'argument.')
 
-    # Validate user data.
+    # Validate and standardize user data.
     x, y, sample_weights = self._standardize_user_data(
         x,
         y,
         sample_weight=sample_weight,
         class_weight=class_weight,
-        batch_size=batch_size)
+        batch_size=batch_size,
+        check_steps=True,
+        steps_name='steps_per_epoch',
+        steps=steps_per_epoch,
+        validation_split=validation_split)
+
     # Prepare validation data.
     if validation_data:
-      if isinstance(validation_data, iterator_ops.Iterator):
+      if (isinstance(validation_data, iterator_ops.Iterator) or
+          isinstance(validation_data, iterator_ops.EagerIterator)):
         val_x = validation_data
         val_y = None
         val_sample_weight = None
@@ -1196,11 +1263,13 @@ def fit(self,
             'or alternatively it could be a dataset iterator. However we '
             'received `validation_data=%s`' % validation_data)
 
+      # Validate and standardize validation data.
       val_x, val_y, val_sample_weights = self._standardize_user_data(
           val_x,
           val_y,
           sample_weight=val_sample_weight,
-          batch_size=batch_size)
+          batch_size=batch_size,
+          steps=validation_steps)
 
     elif validation_split and 0. < validation_split < 1.:
       if training_utils.has_symbolic_tensors(x):
@@ -1229,6 +1298,7 @@ def fit(self,
           inputs=x,
           targets=y,
           sample_weights=sample_weights,
+          class_weight=class_weight,
           batch_size=batch_size,
           epochs=epochs,
           verbose=verbose,
@@ -1300,7 +1370,8 @@ def evaluate(self,
             `(samples, sequence_length)`,
             to apply a different weight to every timestep of every sample.
             In this case you should make sure to specify
-            `sample_weight_mode="temporal"` in `compile()`.
+            `sample_weight_mode="temporal"` in `compile()`. This argument is not
+            supported when `x` is a dataset iterator.
         steps: Integer or `None`.
             Total number of steps (batches of samples)
             before declaring the evaluation round finished.
@@ -1318,17 +1389,16 @@ def evaluate(self,
     # Backwards compatibility.
     if batch_size is None and steps is None:
       batch_size = 32
-    if x is None and y is None and steps is None:
-      raise ValueError('If evaluating from data tensors, '
-                       'you should specify the `steps` '
-                       'argument.')
 
-    # Validate user data.
+    # Validate and standardize user data.
     x, y, sample_weights = self._standardize_user_data(
         x,
         y,
         sample_weight=sample_weight,
-        batch_size=batch_size)
+        batch_size=batch_size,
+        check_steps=True,
+        steps_name='steps',
+        steps=steps)
 
     if context.executing_eagerly():
       return training_eager.test_loop(
@@ -1345,7 +1415,12 @@ def predict(self, x, batch_size=None, verbose=0, steps=None):
     Computation is done in batches.
 
     Arguments:
-        x: Input samples, as Numpy array(s) or tensor(s).
+         x: Input samples. It could be:
+          - A Numpy array (or array-like), or a list of arrays
+            (in case the model has multiple inputs).
+          - A TensorFlow tensor, or a list of tensors
+            (in case the model has multiple inputs).
+          - A `tf.data` dataset iterator.
         batch_size: Integer or `None`.
             Number of samples per gradient update.
             If unspecified, `batch_size` will default to 32.
@@ -1369,11 +1444,10 @@ def predict(self, x, batch_size=None, verbose=0, steps=None):
     # Backwards compatibility.
     if batch_size is None and steps is None:
       batch_size = 32
-    if x is None and steps is None:
-      raise ValueError('If predicting from data tensors, '
-                       'you should specify the `steps` '
-                       'argument.')
-    x, _, _ = self._standardize_user_data(x)
+
+    # Validate and standardize user data.
+    x, _, _ = self._standardize_user_data(
+        x, check_steps=True, steps_name='steps', steps=steps)
 
     if context.executing_eagerly():
       return training_eager.predict_loop(
@@ -1406,7 +1480,9 @@ def train_on_batch(self, x, y=None, sample_weight=None, class_weight=None):
             with shape (samples, sequence_length),
             to apply a different weight to every timestep of every sample.
             In this case you should make sure to specify
-            sample_weight_mode="temporal" in compile().
+            sample_weight_mode="temporal" in compile(). This argument is not
+            supported when `x` is a dataset iterator.
+
         class_weight: Optional dictionary mapping
             class indices (integers) to
             a weight (float) to apply to the model's loss for the samples
@@ -1424,11 +1500,9 @@ class indices (integers) to
     Raises:
       ValueError: In case of invalid user-provided arguments.
     """
+    # Validate and standardize user data.
     x, y, sample_weights = self._standardize_user_data(
-        x,
-        y,
-        sample_weight=sample_weight,
-        class_weight=class_weight)
+        x, y, sample_weight=sample_weight, class_weight=class_weight)
 
     if context.executing_eagerly():
       outputs = training_eager.train_on_batch(
@@ -1470,7 +1544,8 @@ def test_on_batch(self, x, y=None, sample_weight=None):
             with shape (samples, sequence_length),
             to apply a different weight to every timestep of every sample.
             In this case you should make sure to specify
-            sample_weight_mode="temporal" in compile().
+            sample_weight_mode="temporal" in compile(). This argument is not
+            supported when `x` is a dataset iterator.
 
     Returns:
         Scalar test loss (if the model has a single output and no metrics)
@@ -1481,6 +1556,7 @@ def test_on_batch(self, x, y=None, sample_weight=None):
     Raises:
         ValueError: In case of invalid user-provided arguments.
     """
+    # Validate and standardize user data.
     x, y, sample_weights = self._standardize_user_data(
         x, y, sample_weight=sample_weight)
 
@@ -1503,23 +1579,34 @@ def predict_on_batch(self, x):
     """Returns predictions for a single batch of samples.
 
     Arguments:
-        x: Input samples, as Numpy array(s) or tensor(s).
+        x: Input data. It could be:
+          - A Numpy array (or array-like), or a list of arrays
+            (in case the model has multiple inputs).
+          - A TensorFlow tensor, or a list of tensors
+            (in case the model has multiple inputs).
+          - A `tf.data` dataset iterator.
 
     Returns:
         Numpy array(s) of predictions.
 
+    Raises:
+        ValueError: In case of mismatch between given number of inputs and
+          expectations of the model.
     """
-    x, _, _ = self._standardize_user_data(x)
-
+    # Validate and standardize user data.
+    inputs, _, _ = self._standardize_user_data(x)
     if context.executing_eagerly():
-      inputs = [ops.convert_to_tensor(val, dtype=K.floatx()) for val in x]
+      if not isinstance(inputs, iterator_ops.EagerIterator):
+        inputs = [
+            ops.convert_to_tensor(val, dtype=K.floatx()) for val in inputs
+        ]
       return self(inputs)  # pylint: disable=not-callable
 
     if not context.executing_eagerly():
       if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-        ins = x + [0]
+        ins = inputs + [0]
       else:
-        ins = x
+        ins = inputs
 
       self._make_predict_function()
       outputs = self.predict_function(ins)
@@ -1631,8 +1718,7 @@ def generate_arrays_from_file(path):
                             steps_per_epoch=10000, epochs=10)
     ```
     Raises:
-        ValueError: In case the generator yields
-            data in an invalid format.
+        ValueError: In case the generator yields data in an invalid format.
     """
     if not self.built and not self._is_graph_network:
       raise NotImplementedError(
@@ -1697,8 +1783,7 @@ def evaluate_generator(self,
         ValueError: in case of invalid arguments.
 
     Raises:
-        ValueError: In case the generator yields
-            data in an invalid format.
+        ValueError: In case the generator yields data in an invalid format.
     """
     if not self.built and not self._is_graph_network:
       raise NotImplementedError(
@@ -1751,8 +1836,7 @@ def predict_generator(self,
         Numpy array(s) of predictions.
 
     Raises:
-        ValueError: In case the generator yields
-            data in an invalid format.
+        ValueError: In case the generator yields data in an invalid format.
     """
     if not self.built and not self._is_graph_network:
       raise NotImplementedError(
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_arrays.py b/tensorflow/python/keras/_impl/keras/engine/training_arrays.py
index 4164cae864c7f8..12e74ef51df9f7 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_arrays.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_arrays.py
@@ -108,8 +108,8 @@ def fit_loop(model,
   do_validation = False
   if val_inputs:
     do_validation = True
-    if verbose and inputs and hasattr(inputs[0], 'shape') and hasattr(
-        val_inputs[0], 'shape'):
+    if (steps_per_epoch is None and verbose and inputs and
+        hasattr(inputs[0], 'shape') and hasattr(val_inputs[0], 'shape')):
       print('Train on %d samples, validate on %d samples' %
             (inputs[0].shape[0], val_inputs[0].shape[0]))
   if validation_steps:
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
index 34adeb7599d657..526ae65321adc0 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
@@ -23,7 +23,9 @@
 
 import numpy as np
 
+from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.eager.backprop import GradientTape
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras._impl.keras import backend
@@ -177,6 +179,550 @@ def _model_loss(model, inputs, targets, sample_weights=None, training=False):
   return outs, total_loss, loss_metrics
 
 
+def iterator_fit_loop(model,
+                      inputs,
+                      class_weight,
+                      steps_per_epoch,
+                      callback_model,
+                      out_labels,
+                      epoch_logs,
+                      val_inputs=None,
+                      val_targets=None,
+                      val_sample_weights=None,
+                      epochs=1,
+                      verbose=1,
+                      callbacks=None,
+                      callback_metrics=None,
+                      validation_steps=None,
+                      do_validation=False):
+  """Fit function for eager execution when input is given as dataset iterator.
+
+  Updates the given epoch logs.
+
+  Arguments:
+      model: Instance of the `Model`.
+      inputs: Input dataset iterator.
+      class_weight: Optional class-weight array to weight the importance of
+          samples in `inputs` based on the class they belong to, as conveyed by
+          the targets from the `inputs` iterator.
+      steps_per_epoch: Total number of steps (batches of samples)
+          before declaring one epoch finished and starting the
+          next epoch.
+      callback_model: Instance of `Model` to callback.
+      out_labels: Output labels generated from model metric names.
+      epoch_logs: Dictionary of logs from every epoch.
+      val_inputs: Input data for validation.
+      val_targets: Target data for validation.
+      val_sample_weights: Sample weight data for validation.
+      epochs: Number of times to iterate over the data
+      verbose: Verbosity mode, 0, 1 or 2
+      callbacks: List of callbacks to be called during training
+      callback_metrics: List of strings, the display names of the metrics
+          passed to the callbacks. They should be the
+          concatenation of list the display names of the outputs of
+           `f` and the list of display names of the outputs of `f_val`.
+      validation_steps: Number of steps to run validation for (only if doing
+        validation from data tensors). Ignored with default value of `None`.
+      do_validation: Boolean value indicating whether we should do validation.
+
+  Raises:
+      ValueError: In case of mismatch between given number of inputs and
+        expectations of the model.
+  """
+  assert isinstance(inputs, iterator_ops.EagerIterator)
+  for step_index in range(steps_per_epoch):
+    batch_logs = {}
+    batch_logs['batch'] = step_index
+    batch_logs['size'] = 1
+    callbacks.on_batch_begin(step_index, batch_logs)
+
+    # Get data from the iterator.
+    try:
+      next_element = inputs.get_next()
+    except errors.OutOfRangeError:
+      logging.warning(
+          'Your dataset iterator ran out of data; '
+          'interrupting training. Make sure that your dataset'
+          ' can generate at least `steps_per_epoch * epochs` '
+          'batches (in this case, %d batches).' % steps_per_epoch * epochs)
+      break
+
+    if not isinstance(next_element, (list, tuple)) or len(next_element) != 2:
+      raise ValueError('Please provide data as a list or tuple of 2 elements '
+                       ' - input and target pair. Received %s' % next_element)
+    x, y = next_element
+
+    # Validate and standardize data.
+    x, y, sample_weights = model._standardize_user_data(
+        x, y, class_weight=class_weight)
+    if sample_weights:
+      sample_weights = [
+          ops.convert_to_tensor(val, dtype=backend.floatx())
+          if val is not None else None for val in sample_weights
+      ]
+
+    if step_index == 0 and not callback_metrics:
+      out_labels = model.metrics_names
+      if do_validation:
+        callback_metrics = copy.copy(out_labels) + [
+            'val_' + n for n in out_labels
+        ]
+      else:
+        callback_metrics = copy.copy(out_labels)
+      callbacks.set_params({
+          'epochs': epochs,
+          'steps': steps_per_epoch,
+          'verbose': verbose,
+          'do_validation': do_validation,
+          'metrics': callback_metrics or [],
+      })
+
+    # Train model.
+    outs, loss, loss_metrics = _process_single_batch(
+        model, x, y, sample_weights=sample_weights, training=True)
+    if not isinstance(outs, list):
+      outs = [outs]
+
+    # Calculate metrics.
+    for l, o in zip(out_labels, outs):
+      batch_logs[l] = o
+    # Required for eager execution
+    metrics_results = _eager_metrics_fn(model, outs, y)
+    batch_logs['loss'] = tensor_util.constant_value(backend.mean(loss))
+
+    for k, v in zip(model.metrics_names,
+                    [backend.mean(loss)] + loss_metrics + metrics_results):
+      batch_logs[k] = tensor_util.constant_value(v)
+    callbacks.on_batch_end(step_index, batch_logs)
+    if callback_model.stop_training:
+      break
+
+    if step_index == steps_per_epoch - 1:
+      if do_validation:
+        val_outs = test_loop(
+            model,
+            val_inputs,
+            val_targets,
+            sample_weights=val_sample_weights,
+            steps=validation_steps,
+            verbose=0)
+        if not isinstance(val_outs, list):
+          val_outs = [val_outs]
+        # Same labels assumed.
+        for l, o in zip(out_labels, val_outs):
+          epoch_logs['val_' + l] = o
+
+
+def batch_fit_loop(model,
+                   inputs,
+                   targets,
+                   epoch_logs,
+                   index_array,
+                   out_labels,
+                   callback_model,
+                   batch_size,
+                   sample_weights=None,
+                   val_inputs=None,
+                   val_targets=None,
+                   val_sample_weights=None,
+                   callbacks=None,
+                   shuffle=True,
+                   num_train_samples=None,
+                   do_validation=False):
+  """Fit function for eager execution when input is given as arrays or tensors.
+
+  Updates the given epoch logs.
+
+  Arguments:
+      model: Instance of the `Model`.
+      inputs: List of input arrays.
+      targets: List of target arrays.
+      epoch_logs: Dictionary of logs from every epoch.
+      index_array: Index array generated from number of training samples.
+      out_labels: Output labels generated from model metric names.
+      callback_model: Instance of `Model` to callback.
+      batch_size: Integer batch size or None if unknown.
+      sample_weights: Optional list of sample weight arrays.
+      val_inputs: Input data for validation.
+      val_targets: Target data for validation.
+      val_sample_weights: Sample weight data for validation.
+      callbacks: List of callbacks to be called during training.
+      shuffle: Whether to shuffle the data at the beginning of each epoch.
+      num_train_samples: Integer number of training samples.
+      do_validation: Boolean value indicating whether we should do validation.
+  """
+  # TODO(psv): Create a dataset iterator instead of manually creating batches
+  # here and in batch_test_loop, batch_predict_loop.
+  if shuffle == 'batch':
+    index_array = model._batch_shuffle(index_array, batch_size)
+  elif shuffle:
+    np.random.shuffle(index_array)
+
+  batches = generic_utils.make_batches(num_train_samples, batch_size)
+
+  for batch_index, (batch_start, batch_end) in enumerate(batches):
+    batch_ids = index_array[batch_start:batch_end]
+    inputs_batch = slice_arrays(inputs, batch_ids, contiguous=not shuffle)
+    targets_batch = slice_arrays(targets, batch_ids, contiguous=not shuffle)
+    if sample_weights:
+      sample_weights_batch = slice_arrays(
+          sample_weights, batch_ids, contiguous=not shuffle)
+    else:
+      sample_weights_batch = None
+    batch_logs = {}
+    batch_logs['batch'] = batch_index
+    batch_logs['size'] = len(batch_ids)
+
+    callbacks.on_batch_begin(batch_index, batch_logs)
+
+    inputs_batch = [
+        ops.convert_to_tensor(val, dtype=backend.floatx())
+        for val in inputs_batch
+    ]
+    targets_batch = [
+        ops.convert_to_tensor(val, dtype=backend.floatx())
+        for val in targets_batch
+    ]
+    if sample_weights:
+      sample_weights_batch = [
+          ops.convert_to_tensor(val, dtype=backend.floatx())
+          if val is not None else None for val in sample_weights_batch
+      ]
+
+    outs, loss, loss_metrics = _process_single_batch(
+        model,
+        inputs_batch,
+        targets_batch,
+        sample_weights=sample_weights_batch,
+        training=True)
+
+    if not isinstance(outs, list):
+      outs = [outs]
+
+    for l, o in zip(out_labels, outs):
+      batch_logs[l] = o
+    # Required for eager execution
+    metrics_results = _eager_metrics_fn(model, outs, targets_batch)
+    batch_logs['loss'] = tensor_util.constant_value(backend.mean(loss))
+
+    for k, v in zip(model.metrics_names,
+                    [backend.mean(loss)] + loss_metrics + metrics_results):
+      batch_logs[k] = tensor_util.constant_value(v)
+    callbacks.on_batch_end(batch_index, batch_logs)
+    if callback_model.stop_training:
+      break
+
+    if batch_index == len(batches) - 1:  # Last batch.
+      if do_validation:
+        val_outs = test_loop(
+            model,
+            val_inputs,
+            val_targets,
+            sample_weights=val_sample_weights,
+            batch_size=batch_size,
+            verbose=0)
+        if not isinstance(val_outs, list):
+          val_outs = [val_outs]
+        # Same labels assumed.
+        for l, o in zip(out_labels, val_outs):
+          epoch_logs['val_' + l] = o
+
+
+def iterator_test_loop(model, inputs, steps, verbose=0):
+  """Test function for eager execution when input is given as dataset iterator.
+
+  Arguments:
+      model: Model instance that is being evaluated in Eager mode.
+      inputs: Input dataset iterator.
+      steps: Total number of steps (batches of samples) before declaring
+      predictions finished.
+      verbose: Verbosity mode.
+
+  Returns:
+      Scalar loss (if the model has a single output and no metrics)
+      or list of scalars (if the model has multiple outputs
+      and/or metrics). The attribute `model.metrics_names` will give you
+      the display labels for the scalar outputs.
+
+  Raises:
+      ValueError: In case of mismatch between given number of inputs and
+        expectations of the model.
+  """
+  assert isinstance(inputs, iterator_ops.EagerIterator)
+  outs = []
+  num_samples = 0
+  if verbose == 1:
+    progbar = generic_utils.Progbar(target=steps)
+  for step_index in range(steps):
+    # Get data from the iterator.
+    try:
+      next_element = inputs.get_next()
+    except errors.OutOfRangeError:
+      logging.warning(
+          'Your dataset iterator ran out of data interrupting testing. '
+          'Make sure that your dataset can generate at least `steps` batches '
+          '(in this case, %d batches).', steps)
+      break
+
+    if not isinstance(next_element, (list, tuple)) or len(next_element) != 2:
+      raise ValueError('Please provide data as a list or tuple of 2 elements '
+                       ' - input and target pair. Received %s' % next_element)
+    x, y = next_element
+
+    # Validate and standardize data.
+    x, y, sample_weights = model._standardize_user_data(x, y)
+
+    # Calculate model output, loss values.
+    loss_outs, loss, loss_metrics = _model_loss(
+        model, x, y, sample_weights=sample_weights, training=False)
+    metrics_results = _eager_metrics_fn(model, loss_outs, y)
+    batch_outs = []
+    for _, v in zip(model.metrics_names,
+                    [backend.mean(loss)] + loss_metrics + metrics_results):
+      batch_outs.append(tensor_util.constant_value(v))
+
+    # Get current step size.
+    if isinstance(x, list):
+      step_size = x[0].get_shape().as_list()[0]
+    else:
+      step_size = x.get_shape().as_list()[0]
+
+    # Accumulate results in output array.
+    if not isinstance(batch_outs, list):
+      batch_outs = [batch_outs]
+    if step_index == 0:
+      for _ in enumerate(batch_outs):
+        outs.append(0.)
+    for i, batch_out in enumerate(batch_outs):
+      outs[i] += batch_out * step_size
+
+    # Calculate sample size.
+    num_samples += step_size
+    if verbose == 1:
+      progbar.update(step_index + 1)
+
+    for i in range(len(outs)):
+      outs[i] /= num_samples
+    if len(outs) == 1:
+      return outs[0]
+    return outs
+
+
+def batch_test_loop(model,
+                    inputs,
+                    targets,
+                    batch_size,
+                    sample_weights=None,
+                    verbose=0):
+  """Test function for eager execution when input is given as arrays or tensors.
+
+  Arguments:
+      model: Model instance that is being evaluated in Eager mode.
+      inputs: List of input arrays.
+      targets: List of target arrays.
+      batch_size: Integer batch size.
+      sample_weights: Optional list of sample weight arrays.
+      verbose: Verbosity mode.
+
+  Returns:
+      Scalar loss (if the model has a single output and no metrics)
+      or list of scalars (if the model has multiple outputs
+      and/or metrics). The attribute `model.metrics_names` will give you
+      the display labels for the scalar outputs.
+  """
+  outs = []
+  feed_data = inputs + targets
+  if sample_weights:
+    feed_data += sample_weights
+  num_samples = training_utils.check_num_samples(
+      feed_data, batch_size=batch_size)
+  if verbose == 1:
+    progbar = generic_utils.Progbar(target=num_samples)
+  batches = generic_utils.make_batches(num_samples, batch_size)
+  index_array = np.arange(num_samples)
+  for batch_index, (batch_start, batch_end) in enumerate(batches):
+    batch_ids = index_array[batch_start:batch_end]
+    inputs_batch = slice_arrays(inputs, batch_ids)
+    targets_batch = slice_arrays(targets, batch_ids)
+    if sample_weights:
+      sample_weights_batch = slice_arrays(sample_weights, batch_ids)
+    else:
+      sample_weights_batch = None
+
+    inputs_batch = [
+        ops.convert_to_tensor(val, dtype=backend.floatx())
+        for val in inputs_batch
+    ]
+    targets_batch = [
+        ops.convert_to_tensor(val, dtype=backend.floatx())
+        for val in targets_batch
+    ]
+    if sample_weights:
+      sample_weights_batch = [
+          ops.convert_to_tensor(val, dtype=backend.floatx())
+          if val is not None else None for val in sample_weights_batch
+      ]
+
+    loss_outs, loss, loss_metrics = _model_loss(
+        model,
+        inputs_batch,
+        targets_batch,
+        sample_weights=sample_weights_batch,
+        training=False)
+    metrics_results = _eager_metrics_fn(model, loss_outs, targets_batch)
+    batch_outs = []
+    for _, v in zip(model.metrics_names,
+                    [backend.mean(loss)] + loss_metrics + metrics_results):
+      batch_outs.append(tensor_util.constant_value(v))
+
+    if isinstance(batch_outs, list):
+      if batch_index == 0:
+        for _ in enumerate(batch_outs):
+          outs.append(0.)
+      for i, batch_out in enumerate(batch_outs):
+        outs[i] += batch_out * len(batch_ids)
+    else:
+      if batch_index == 0:
+        outs.append(0.)
+      outs[0] += batch_outs * len(batch_ids)
+
+    if verbose == 1:
+      progbar.update(batch_end)
+
+  for i in range(len(outs)):
+    outs[i] /= num_samples
+  if len(outs) == 1:
+    return outs[0]
+  return outs
+
+
+def iterator_predict_loop(model, inputs, steps, verbose=0):
+  """Predict function for eager execution when input is dataset iterator.
+
+  Arguments:
+      model: Instance of `Model`.
+      inputs: Input dataset iterator.
+      steps: Total number of steps (batches of samples) before declaring
+          `_predict_loop` finished.
+      verbose: Verbosity mode.
+
+  Returns:
+      Array of predictions (if the model has a single output)
+      or list of arrays of predictions (if the model has multiple outputs).
+
+  Raises:
+      ValueError: In case of mismatch between given number of inputs and
+        expectations of the model.
+  """
+  assert isinstance(inputs, iterator_ops.EagerIterator)
+  outs = []
+  if verbose == 1:
+    progbar = generic_utils.Progbar(target=steps)
+  for step_index in range(steps):
+    # Get data from the iterator.
+    try:
+      next_element = inputs.get_next()
+    except errors.OutOfRangeError:
+      logging.warning(
+          'Your dataset iterator ran out of data; '
+          'interrupting prediction. Make sure that your '
+          'dataset can generate at least `steps` '
+          'batches (in this case, %d batches).', steps)
+      break
+
+    if not isinstance(next_element, (list, tuple)) or len(next_element) != 2:
+      raise ValueError(
+          'Please provide data as a list or tuple of 2 elements '
+          ' - input and target pair. Received %s. We do not use the '
+          '`target` value here.' % next_element)
+    x, _ = next_element
+
+    # Validate and standardize data.
+    x, _, _ = model._standardize_user_data(x)
+
+    if model._expects_training_arg:
+      batch_outs = model.call(x[0] if len(x) == 1 else x, training=False)
+    else:
+      batch_outs = model.call(x[0] if len(x) == 1 else x)
+    if not isinstance(batch_outs, list):
+      batch_outs = [batch_outs]
+
+    # We collect the results from every step and then concatenate them once
+    # in the end. This is an expensive process. We are doing this because we
+    # do not know the number of samples beforehand.
+    if step_index == 0:
+      for _ in batch_outs:
+        outs.append([])
+    for i, batch_out in enumerate(batch_outs):
+      outs[i].append(backend.get_value(batch_out))
+
+    if verbose == 1:
+      progbar.update(step_index + 1)
+  for i, out in enumerate(outs):
+    outs[i] = np.concatenate(tuple(out), axis=0)
+  if len(outs) == 1:
+    return outs[0]
+  return outs
+
+
+def batch_predict_loop(model, inputs, batch_size, verbose=0):
+  """Predict function for eager execution when input is arrays or tensors.
+
+  Arguments:
+      model: Instance of `Model`.
+      inputs: List of input arrays.
+      batch_size: Integer batch size.
+      verbose: Verbosity mode.
+
+  Returns:
+      Array of predictions (if the model has a single output)
+      or list of arrays of predictions (if the model has multiple outputs).
+  """
+  outs = []
+  num_samples = training_utils.check_num_samples(inputs, batch_size)
+  if verbose == 1:
+    progbar = generic_utils.Progbar(target=num_samples)
+  batches = generic_utils.make_batches(num_samples, batch_size)
+  index_array = np.arange(num_samples)
+  for batch_index, (batch_start, batch_end) in enumerate(batches):
+    batch_ids = index_array[batch_start:batch_end]
+    inputs_batch = slice_arrays(inputs, batch_ids)
+
+    inputs_batch = [
+        ops.convert_to_tensor(val, dtype=backend.floatx())
+        for val in inputs_batch
+    ]
+
+    if len(inputs_batch) == 1:
+      if model._expects_training_arg:
+        batch_outs = model.call(inputs_batch[0], training=False)
+      else:
+        batch_outs = model.call(inputs_batch[0])
+    else:
+      if model._expects_training_arg:
+        batch_outs = model.call(inputs_batch, training=False)
+      else:
+        batch_outs = model.call(inputs_batch)
+
+    if not isinstance(batch_outs, list):
+      batch_outs = [batch_outs]
+    if batch_index == 0:
+      # Pre-allocate the results arrays.
+      for batch_out in batch_outs:
+        dims = batch_out.shape[1:].dims
+        dims_list = [d.value for d in dims]
+        shape = (num_samples,) + tuple(dims_list)
+        outs.append(np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype))
+    for i, batch_out in enumerate(batch_outs):
+      outs[i][batch_start:batch_end] = batch_out
+    if verbose == 1:
+      progbar.update(batch_end)
+
+  if len(outs) == 1:
+    return outs[0]
+  return outs
+
+
 def slice_arrays(arrays, indices, contiguous=True):
   """Slices batches out of provided arrays (workaround for eager tensors).
 
@@ -268,19 +814,24 @@ def train_on_batch(model, inputs, targets, sample_weights=None):
   Returns:
       total loss and the loss associated with each output.
   """
-  inputs = [
-      ops.convert_to_tensor(val, dtype=backend.floatx()) for val in inputs]
-  targets = [
-      ops.convert_to_tensor(val, dtype=backend.floatx()) for val in targets]
-  sample_weights = [
-      ops.convert_to_tensor(val, dtype=backend.floatx())
-      if val is not None else None for val in sample_weights]
+  if len(inputs) and not tensor_util.is_tensor(inputs[0]):
+    inputs = [
+        ops.convert_to_tensor(val, dtype=backend.floatx()) for val in inputs
+    ]
+    targets = [
+        ops.convert_to_tensor(val, dtype=backend.floatx()) for val in targets
+    ]
+  if sample_weights:
+    sample_weights = [
+        ops.convert_to_tensor(val, dtype=backend.floatx())
+        if val is not None else None for val in sample_weights
+    ]
+
   outs, loss, _ = _process_single_batch(
       model, inputs, targets, sample_weights=sample_weights, training=True)
   if not isinstance(outs, list):
     outs = [outs]
-  metrics_results = _eager_metrics_fn(
-      model, outs, targets)
+  metrics_results = _eager_metrics_fn(model, outs, targets)
   if not isinstance(loss, list):
     loss = [loss]
   return loss + metrics_results
@@ -298,48 +849,55 @@ def test_on_batch(model, inputs, targets, sample_weights=None):
   Returns:
       total loss, loss and metrics associated with each output.
   """
-  inputs = [
-      ops.convert_to_tensor(val, dtype=backend.floatx()) for val in inputs]
-  targets = [
-      ops.convert_to_tensor(val, dtype=backend.floatx()) for val in targets]
-  sample_weights = [
-      ops.convert_to_tensor(val, dtype=backend.floatx())
-      if val is not None else None for val in sample_weights]
-  outs, loss, loss_metrics = _process_single_batch(
+  if len(inputs) and not tensor_util.is_tensor(inputs[0]):
+    inputs = [
+        ops.convert_to_tensor(val, dtype=backend.floatx()) for val in inputs
+    ]
+    targets = [
+        ops.convert_to_tensor(val, dtype=backend.floatx()) for val in targets
+    ]
+  if sample_weights:
+    sample_weights = [
+        ops.convert_to_tensor(val, dtype=backend.floatx())
+        if val is not None else None for val in sample_weights
+    ]
+  outs, loss, loss_metrics = _model_loss(
       model, inputs, targets, sample_weights=sample_weights, training=False)
   if not isinstance(outs, list):
     outs = [outs]
-  metrics_results = _eager_metrics_fn(
-      model, outs, targets)
+  metrics_results = _eager_metrics_fn(model, outs, targets)
   if not isinstance(loss, list):
     loss = [loss]
   return loss + loss_metrics + metrics_results
 
 
-def fit_loop(
-    model,
-    inputs,
-    targets,
-    sample_weights=None,
-    val_inputs=None,
-    val_targets=None,
-    val_sample_weights=None,
-    batch_size=None,
-    epochs=100,
-    verbose=1,
-    callbacks=None,
-    shuffle=True,
-    callback_metrics=None,
-    initial_epoch=0,
-    steps_per_epoch=None,
-    validation_steps=None):
-  """Abstract fit function for eager execution.
+def fit_loop(model,
+             inputs,
+             targets,
+             sample_weights=None,
+             class_weight=None,
+             val_inputs=None,
+             val_targets=None,
+             val_sample_weights=None,
+             batch_size=None,
+             epochs=1,
+             verbose=1,
+             callbacks=None,
+             shuffle=True,
+             callback_metrics=None,
+             initial_epoch=0,
+             steps_per_epoch=None,
+             validation_steps=None):
+  """Fit function for eager execution.
 
   Arguments:
       model: Instance of the model that is being executed in Eager mode.
       inputs: List of input arrays.
       targets: List of target arrays.
       sample_weights: Optional list of sample weight arrays.
+      class_weight: Optional class-weight array to weight the importance of
+          samples in `inputs` based on the class they belong to, as conveyed by
+          `targets`.
       val_inputs: Input data for validation.
       val_targets: Target data for validation.
       val_sample_weights: Sample weight data for validation.
@@ -366,47 +924,40 @@ def fit_loop(
   Raises:
     ValueError: In case of invalid argument values.
   """
-  if not batch_size:
-    raise ValueError('With eager execution, `batch_size` should be specified.')
-  if steps_per_epoch or validation_steps:
-    raise ValueError('With eager execution, `steps_per_epoch` and '
-                     '`validation_steps` are not valid arguments '
-                     '(set `batch_size` instead).')
-  # Required for Eager mode
+  # Required for eager execution
   with backend.learning_phase_scope(1):
     do_validation = False
     if val_inputs:
       do_validation = True
-      if (verbose and inputs and hasattr(inputs[0], 'shape') and
-          hasattr(val_inputs[0], 'shape')):
+      if (steps_per_epoch is None and verbose and inputs and
+          hasattr(inputs[0], 'shape') and hasattr(val_inputs[0], 'shape')):
         print('Train on %d samples, validate on %d samples' %
               (inputs[0].shape[0], val_inputs[0].shape[0]))
-    if validation_steps:
-      if steps_per_epoch is None:
-        raise ValueError('Can only use `validation_steps` when doing step-wise '
-                         'training, i.e. `steps_per_epoch` must be set.')
-      do_validation = True
 
-    out_labels = model.metrics_names
-    if do_validation:
-      callback_metrics = copy.copy(out_labels) + [
-          'val_' + n for n in out_labels
-      ]
-    else:
-      callback_metrics = copy.copy(out_labels)
+    num_train_samples = None
+    out_labels = None
+    if steps_per_epoch is None or model._is_compiled:
+      out_labels = model.metrics_names
+      if do_validation:
+        callback_metrics = copy.copy(out_labels) + [
+            'val_' + n for n in out_labels
+        ]
+      else:
+        callback_metrics = copy.copy(out_labels)
 
-    if sample_weights:
-      feed_data = inputs + targets + sample_weights
-    else:
-      feed_data = inputs + targets
-    num_train_samples = training_utils.check_num_samples(
-        feed_data,
-        batch_size=batch_size,
-        steps=steps_per_epoch,
-        steps_name='steps_per_epoch')
+    if steps_per_epoch is None:
+      if sample_weights:
+        feed_data = inputs + targets + sample_weights
+      else:
+        feed_data = inputs + targets
+      num_train_samples = training_utils.check_num_samples(
+          feed_data,
+          batch_size=batch_size,
+          steps=steps_per_epoch,
+          steps_name='steps_per_epoch')
 
-    if num_train_samples is not None:
-      index_array = np.arange(num_train_samples)
+      if num_train_samples is not None:
+        index_array = np.arange(num_train_samples)
 
     model.history = cbks.History()
     callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history]
@@ -441,6 +992,8 @@ def fit_loop(
     for cbk in callbacks:
       if not val_inputs:
         cbk.validation_data = []
+      elif isinstance(val_inputs, iterator_ops.EagerIterator):
+        cbk.validation_data = val_inputs
       elif val_sample_weights:
         cbk.validation_data = val_inputs + val_targets + val_sample_weights
       else:
@@ -449,87 +1002,48 @@ def fit_loop(
     for epoch in range(initial_epoch, epochs):
       callbacks.on_epoch_begin(epoch)
       epoch_logs = {}
-      if shuffle == 'batch':
-        index_array = model._batch_shuffle(index_array, batch_size)
-      elif shuffle:
-        np.random.shuffle(index_array)
-
-      batches = generic_utils.make_batches(num_train_samples, batch_size)
-
-      for batch_index, (batch_start, batch_end) in enumerate(batches):
-        batch_ids = index_array[batch_start:batch_end]
-        try:
-          inputs_batch = slice_arrays(inputs, batch_ids,
-                                      contiguous=not shuffle)
-          targets_batch = slice_arrays(targets, batch_ids,
-                                       contiguous=not shuffle)
-          if sample_weights:
-            sample_weights_batch = slice_arrays(sample_weights, batch_ids,
-                                                contiguous=not shuffle)
-          else:
-            sample_weights_batch = None
-        except TypeError:
-          raise TypeError('TypeError while preparing batch. '
-                          'If using HDF5 input data, '
-                          'pass shuffle="batch".')
-        batch_logs = {}
-        batch_logs['batch'] = batch_index
-        batch_logs['size'] = len(batch_ids)
-
-        callbacks.on_batch_begin(batch_index, batch_logs)
-
-        inputs_batch = [
-            ops.convert_to_tensor(val, dtype=backend.floatx())
-            for val in inputs_batch]
-        targets_batch = [
-            ops.convert_to_tensor(val, dtype=backend.floatx())
-            for val in targets_batch]
-        if sample_weights:
-          sample_weights_batch = [
-              ops.convert_to_tensor(val, dtype=backend.floatx())
-              if val is not None else None
-              for val in sample_weights_batch]
-
-        outs, loss, loss_metrics = _process_single_batch(
+
+      if steps_per_epoch is not None:
+        iterator_fit_loop(
             model,
-            inputs_batch,
-            targets_batch,
-            sample_weights=sample_weights_batch,
-            training=True)
-
-        if not isinstance(outs, list):
-          outs = [outs]
-
-        for l, o in zip(out_labels, outs):
-          batch_logs[l] = o
-        # Required for Eager mode
-        metrics_results = _eager_metrics_fn(model, outs, targets_batch)
-        batch_logs['loss'] = tensor_util.constant_value(backend.mean(loss))
-
-        for k, v in zip(model.metrics_names,
-                        [backend.mean(loss)] + loss_metrics + metrics_results):
-          batch_logs[k] = tensor_util.constant_value(v)
-        callbacks.on_batch_end(batch_index, batch_logs)
-        if callback_model.stop_training:
-          break
-
-        if batch_index == len(batches) - 1:  # Last batch.
-          if do_validation:
-            val_outs = test_loop(
-                model, val_inputs, val_targets,
-                sample_weights=val_sample_weights,
-                batch_size=batch_size,
-                verbose=0)
-            if not isinstance(val_outs, list):
-              val_outs = [val_outs]
-            # Same labels assumed.
-            for l, o in zip(out_labels, val_outs):
-              epoch_logs['val_' + l] = o
+            inputs,
+            class_weight,
+            steps_per_epoch=steps_per_epoch,
+            callback_model=callback_model,
+            out_labels=out_labels,
+            epoch_logs=epoch_logs,
+            val_inputs=val_inputs,
+            val_targets=val_targets,
+            val_sample_weights=val_sample_weights,
+            epochs=epochs,
+            verbose=verbose,
+            callbacks=callbacks,
+            callback_metrics=callback_metrics,
+            validation_steps=validation_steps,
+            do_validation=do_validation)
+      else:
+        batch_fit_loop(
+            model,
+            inputs,
+            targets,
+            epoch_logs=epoch_logs,
+            index_array=index_array,
+            out_labels=out_labels,
+            callback_model=callback_model,
+            batch_size=batch_size,
+            sample_weights=sample_weights,
+            val_inputs=val_inputs,
+            val_targets=val_targets,
+            val_sample_weights=val_sample_weights,
+            callbacks=callbacks,
+            shuffle=shuffle,
+            num_train_samples=num_train_samples,
+            do_validation=do_validation)
       callbacks.on_epoch_end(epoch, epoch_logs)
       if callback_model.stop_training:
         break
-    callbacks.on_train_end()
-    return model.history
+  callbacks.on_train_end()
+  return model.history
 
 
 def test_loop(model, inputs, targets,
@@ -537,7 +1051,7 @@ def test_loop(model, inputs, targets,
               batch_size=None,
               verbose=0,
               steps=None):
-  """Abstract method to loop over some data in batches.
+  """Test function for eager execution.
 
   Arguments:
       model: Model instance that is being evaluated in Eager mode.
@@ -557,77 +1071,26 @@ def test_loop(model, inputs, targets,
       the display labels for the scalar outputs.
   """
   with backend.learning_phase_scope(0):
-    feed_data = inputs + targets
-    if sample_weights:
-      feed_data += sample_weights
-    num_samples = training_utils.check_num_samples(
-        feed_data, batch_size=batch_size, steps=steps, steps_name='steps')
-    outs = []
-    if verbose == 1:
-      progbar = generic_utils.Progbar(target=num_samples)
-    batches = generic_utils.make_batches(num_samples, batch_size)
-    index_array = np.arange(num_samples)
-    for batch_index, (batch_start, batch_end) in enumerate(batches):
-      batch_ids = index_array[batch_start:batch_end]
-      inputs_batch = slice_arrays(inputs, batch_ids)
-      targets_batch = slice_arrays(targets, batch_ids)
-      if sample_weights:
-        sample_weights_batch = slice_arrays(sample_weights, batch_ids)
-      else:
-        sample_weights_batch = None
-
-      inputs_batch = [
-          ops.convert_to_tensor(val, dtype=backend.floatx())
-          for val in inputs_batch]
-      targets_batch = [
-          ops.convert_to_tensor(val, dtype=backend.floatx())
-          for val in targets_batch]
-      if sample_weights:
-        sample_weights_batch = [
-            ops.convert_to_tensor(val, dtype=backend.floatx())
-            if val is not None else None
-            for val in sample_weights_batch]
-
-      loss_outs, loss, loss_metrics = _model_loss(
+    if steps is not None:
+      return iterator_test_loop(model, inputs, steps, verbose=verbose)
+    else:
+      return batch_test_loop(
           model,
-          inputs_batch,
-          targets_batch,
-          sample_weights=sample_weights_batch,
-          training=False)
-      metrics_results = _eager_metrics_fn(model, loss_outs, targets_batch)
-      batch_outs = []
-      for _, v in zip(model.metrics_names,
-                      [backend.mean(loss)] + loss_metrics + metrics_results):
-        batch_outs.append(tensor_util.constant_value(v))
-
-      if isinstance(batch_outs, list):
-        if batch_index == 0:
-          for batch_out in enumerate(batch_outs):
-            outs.append(0.)
-        for i, batch_out in enumerate(batch_outs):
-          outs[i] += batch_out * len(batch_ids)
-      else:
-        if batch_index == 0:
-          outs.append(0.)
-        outs[0] += batch_outs * len(batch_ids)
-
-      if verbose == 1:
-        progbar.update(batch_end)
-    for i in range(len(outs)):
-      outs[i] /= num_samples
-    if len(outs) == 1:
-      return outs[0]
-    return outs
+          inputs,
+          targets,
+          batch_size=batch_size,
+          sample_weights=sample_weights,
+          verbose=verbose)
 
 
 def predict_loop(model, inputs,
                  batch_size=32,
                  verbose=0,
                  steps=None):
-  """Abstract method to loop over some data in batches.
+  """Predict function for eager execution.
 
   Arguments:
-      model:
+      model: Instance of `Model`.
       inputs: List of input arrays.
       batch_size: integer batch size.
       verbose: verbosity mode.
@@ -641,49 +1104,8 @@ def predict_loop(model, inputs,
       (if the model has multiple outputs).
   """
   with backend.learning_phase_scope(0):
-    num_samples = training_utils.check_num_samples(
-        inputs, batch_size, steps, 'steps')
-    if verbose == 1:
-      if steps is not None:
-        progbar = generic_utils.Progbar(target=steps)
-      else:
-        progbar = generic_utils.Progbar(target=num_samples)
-
-    outs = []
-    batches = generic_utils.make_batches(num_samples, batch_size)
-    index_array = np.arange(num_samples)
-    for batch_index, (batch_start, batch_end) in enumerate(batches):
-      batch_ids = index_array[batch_start:batch_end]
-      inputs_batch = slice_arrays(inputs, batch_ids)
-
-      inputs_batch = [
-          ops.convert_to_tensor(val, dtype=backend.floatx())
-          for val in inputs_batch]
-
-      if len(inputs_batch) == 1:
-        if model._expects_training_arg:
-          batch_outs = model.call(inputs_batch[0], training=False)
-        else:
-          batch_outs = model.call(inputs_batch[0])
-      else:
-        if model._expects_training_arg:
-          batch_outs = model.call(inputs_batch, training=False)
-        else:
-          batch_outs = model.call(inputs_batch)
-
-      if not isinstance(batch_outs, list):
-        batch_outs = [batch_outs]
-      if batch_index == 0:
-        # Pre-allocate the results arrays.
-        for batch_out in batch_outs:
-          dims = batch_out.shape[1:].dims
-          dims_list = [d.value for d in dims]
-          shape = (num_samples,) + tuple(dims_list)
-          outs.append(np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype))
-      for i, batch_out in enumerate(batch_outs):
-        outs[i][batch_start:batch_end] = batch_out
-      if verbose == 1:
-        progbar.update(batch_end)
-    if len(outs) == 1:
-      return outs[0]
-    return outs
+    if steps is not None:
+      return iterator_predict_loop(model, inputs, steps, verbose=verbose)
+    else:
+      return batch_predict_loop(
+          model, inputs, batch_size=batch_size, verbose=verbose)
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_test.py b/tensorflow/python/keras/_impl/keras/engine/training_test.py
index 58011a141268e5..cc2386a5bd872b 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_test.py
@@ -24,6 +24,7 @@
 import numpy as np
 
 from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
@@ -1340,16 +1341,12 @@ def test_model_with_input_feed_tensor(self):
                                  output_a_np)
 
       # test fit
-      out = model.fit(None,
-                      output_a_np, epochs=1, batch_size=10)
-      out = model.fit(None,
-                      output_a_np, epochs=1, batch_size=10)
+      _ = model.fit(None, output_a_np, epochs=1, steps_per_epoch=3)
+      _ = model.fit(None, output_a_np, epochs=1, steps_per_epoch=3)
 
       # test evaluate
-      out = model.evaluate(None,
-                           output_a_np, batch_size=10)
-      out = model.evaluate(None,
-                           output_a_np, batch_size=10)
+      _ = model.evaluate(None, output_a_np, steps=3)
+      _ = model.evaluate(None, output_a_np, steps=3)
 
       # test predict
       out = model.predict(None, steps=3)
@@ -1383,16 +1380,12 @@ def test_model_with_input_feed_tensor(self):
                                  output_a_np)
 
       # test fit
-      out = model.fit(None,
-                      output_a_np, epochs=1, batch_size=10)
-      out = model.fit(None,
-                      output_a_np, epochs=1, batch_size=10)
+      _ = model.fit(None, output_a_np, epochs=1, steps_per_epoch=10)
+      _ = model.fit(None, output_a_np, epochs=1, steps_per_epoch=10)
 
       # test evaluate
-      out = model.evaluate(None,
-                           output_a_np, batch_size=10)
-      out = model.evaluate(None,
-                           output_a_np, batch_size=10)
+      _ = model.evaluate(None, output_a_np, steps=10)
+      _ = model.evaluate(None, output_a_np, steps=10)
 
       # test predict
       out = model.predict(None, steps=3)
@@ -1715,40 +1708,56 @@ def test_metric_names_are_identical_in_graph_and_eager(self):
 
 class TestTrainingWithDatasetIterators(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_training_and_eval_methods_on_iterators_single_io(self):
     with self.test_session():
       x = keras.layers.Input(shape=(3,), name='input')
       y = keras.layers.Dense(4, name='dense')(x)
       model = keras.Model(x, y)
 
-      optimizer = 'rmsprop'
+      optimizer = RMSPropOptimizer(learning_rate=0.001)
       loss = 'mse'
       metrics = ['mae']
       model.compile(optimizer, loss, metrics=metrics)
 
-      inputs = np.zeros((10, 3))
-      targets = np.zeros((10, 4))
+      inputs = np.zeros((10, 3), dtype=np.float32)
+      targets = np.zeros((10, 4), dtype=np.float32)
       dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets))
       dataset = dataset.repeat(100)
       dataset = dataset.batch(10)
       iterator = dataset.make_one_shot_iterator()
 
-      model.fit(iterator, epochs=1, steps_per_epoch=2, verbose=0)
-      model.evaluate(iterator, steps=2, verbose=0)
+      model.fit(iterator, epochs=1, steps_per_epoch=2, verbose=1)
+      model.evaluate(iterator, steps=2, verbose=1)
       model.predict(iterator, steps=2)
       model.train_on_batch(iterator)
       model.test_on_batch(iterator)
+      model.predict_on_batch(iterator)
+
       # Test with validation data
       model.fit(iterator,
                 epochs=1, steps_per_epoch=2, verbose=0,
                 validation_data=iterator, validation_steps=2)
       # Test with validation split
-      with self.assertRaisesRegexp(ValueError,
-                                   'you cannot use `validation_split`'):
+      with self.assertRaisesRegexp(
+          ValueError, '`validation_split` argument is not supported '
+          'when input `x` is a dataset iterator'):
         model.fit(iterator,
                   epochs=1, steps_per_epoch=2, verbose=0,
                   validation_split=0.5, validation_steps=2)
 
+      # Test with sample weight.
+      sample_weight = np.random.random((10,))
+      with self.assertRaisesRegexp(
+          ValueError, '`sample_weight` argument is not supported '
+          'when input `x` is a dataset iterator'):
+        model.fit(
+            iterator,
+            epochs=1,
+            steps_per_epoch=2,
+            verbose=0,
+            sample_weight=sample_weight)
+
       # Test invalid usage
       with self.assertRaisesRegexp(ValueError,
                                    'Instead, pass an `Iterator`'):
@@ -1759,19 +1768,54 @@ def test_training_and_eval_methods_on_iterators_single_io(self):
         model.fit(iterator, iterator,
                   epochs=1, steps_per_epoch=2, verbose=0)
 
+      with self.assertRaisesRegexp(
+          ValueError, 'you should specify the `steps_per_epoch` argument'):
+        model.fit(iterator, epochs=1, verbose=0)
+      with self.assertRaisesRegexp(ValueError,
+                                   'you should specify the `steps` argument'):
+        model.evaluate(iterator, verbose=0)
+      with self.assertRaisesRegexp(ValueError,
+                                   'you should specify the `steps` argument'):
+        model.predict(iterator, verbose=0)
+
+  def test_get_next_op_created_once(self):
+    with self.test_session():
+      x = keras.layers.Input(shape=(3,), name='input')
+      y = keras.layers.Dense(4, name='dense')(x)
+      model = keras.Model(x, y)
+
+      optimizer = RMSPropOptimizer(learning_rate=0.001)
+      loss = 'mse'
+      metrics = ['mae']
+      model.compile(optimizer, loss, metrics=metrics)
+
+      inputs = np.zeros((10, 3), dtype=np.float32)
+      targets = np.zeros((10, 4), dtype=np.float32)
+      dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets))
+      dataset = dataset.repeat(100)
+      dataset = dataset.batch(10)
+      iterator = dataset.make_one_shot_iterator()
+
+      model.fit(iterator, epochs=1, steps_per_epoch=2, verbose=1)
+      # Finalize graph to make sure we are not appending another iterator
+      # get_next op in the graph.
+      ops.get_default_graph().finalize()
+      model.fit(iterator, epochs=1, steps_per_epoch=2, verbose=1)
+
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_iterators_running_out_of_data(self):
     with self.test_session():
       x = keras.layers.Input(shape=(3,), name='input')
       y = keras.layers.Dense(4, name='dense')(x)
       model = keras.Model(x, y)
 
-      optimizer = 'rmsprop'
+      optimizer = RMSPropOptimizer(learning_rate=0.001)
       loss = 'mse'
       metrics = ['mae']
       model.compile(optimizer, loss, metrics=metrics)
 
-      inputs = np.zeros((10, 3))
-      targets = np.zeros((10, 4))
+      inputs = np.zeros((10, 3), dtype=np.float32)
+      targets = np.zeros((10, 4), dtype=np.float32)
       dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets))
       dataset = dataset.repeat(2)
       dataset = dataset.batch(10)
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_utils.py b/tensorflow/python/keras/_impl/keras/engine/training_utils.py
index 662938f421b3a3..04d80c891ff2a1 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_utils.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_utils.py
@@ -22,6 +22,7 @@
 
 import numpy as np
 
+from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.eager import context
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras._impl.keras import backend as K
@@ -65,14 +66,7 @@ def check_num_samples(ins,
   if steps is not None and batch_size is not None:
     raise ValueError(
         'If ' + steps_name + ' is set, the `batch_size` must be None.')
-
-  if not ins or has_symbolic_tensors(ins):
-    if steps is None:
-      raise ValueError('If your data is in the form of symbolic tensors, '
-                       'you should specify the `' + steps_name + '` argument '
-                       '(instead of the `batch_size` argument, '
-                       'because symbolic tensors are expected to produce '
-                       'batches of input data).')
+  if check_steps_argument(ins, steps, steps_name):
     return None
   if hasattr(ins[0], 'shape'):
     return int(ins[0].shape[0])
@@ -551,8 +545,11 @@ def standardize_weights(y,
 
 
 def has_symbolic_tensors(ls):
-  return (any(tensor_util.is_tensor(v) for v in ls)
-          and not context.executing_eagerly())
+  if context.executing_eagerly():
+    return False
+  if isinstance(ls, (list, tuple)):
+    return any(tensor_util.is_tensor(v) for v in ls)
+  return tensor_util.is_tensor(ls)
 
 
 def populate_metric_names(model):
@@ -614,3 +611,77 @@ def add_metric_name(model, metric_name, index):
     metric_name = '%s_%d' % (base_metric_name, j)
     j += 1
   model.metrics_names.append(metric_name)
+
+
+def validate_iterator_input(x, y, sample_weight, validation_split=None):
+  """Validates user input arguments when a dataset iterator is passed.
+
+  Arguments:
+    x: Input data. A `tf.data` dataset iterator.
+    y: Target data. It could be either Numpy array(s) or TensorFlow tensor(s).
+        Expected to be `None` when `x` is a dataset iterator.
+    sample_weight: An optional sample-weight array passed by the user to
+        weight the importance of each sample in `x`. Expected to be `None` when
+        `x` is a dataset iterator
+    validation_split: Float between 0 and 1. Fraction of the training data to
+        be used as validation data. Expected to be `None` when `x` is a dataset
+        iterator.
+
+  Raises:
+    ValueError: if argument `y` or `sample_weight` or `validation_split` are
+        provided by user.
+  """
+  if y is not None:
+    raise ValueError('You passed a dataset iterator (%s) as input `x` to '
+                     'your model. In that case, you should not specify '
+                     'a target (`y`) argument, since the dataset iterator '
+                     'generates both input data and target data. '
+                     'Received: %s' % (x, y))
+  if sample_weight is not None:
+    raise ValueError('`sample_weight` argument is not supported when input'
+                     ' `x` is a dataset iterator. '
+                     'Received: x=%s, sample_weight=%s' % (x, sample_weight))
+  if validation_split is not None and validation_split != 0.0:
+    raise ValueError(
+        '`validation_split` argument is not supported when '
+        'input `x` is a dataset iterator. '
+        'Received: x=%s, validation_split=%f' % (x, validation_split))
+
+
+def check_steps_argument(input_data, steps, steps_name):
+  """Validates `steps` argument based on input data's type.
+
+  The cases when `steps` value must be provided are when
+    1. input data passed is an iterator.
+    2. model was built on top of symbolic tensors, input data is not
+       required and is `None`.
+    3. input data passed is a symbolic tensor.
+
+  Arguments:
+      input_data: Input data. Can be Numpy array(s) or TensorFlow tensor(s) or
+        tf.data.Dataset iterator or `None`.
+      steps: Integer or `None`. Total number of steps (batches of samples) to
+        execute.
+      steps_name: The public API's parameter name for `steps`.
+
+  Returns:
+    boolean, True if `steps` argument is required, else False.
+
+  Raises:
+      ValueError: if `steps` argument is required for given input data type
+        but not provided.
+  """
+
+  is_x_iterator = (
+      isinstance(input_data, iterator_ops.Iterator) or
+      isinstance(input_data, iterator_ops.EagerIterator))
+
+  if (input_data is None or is_x_iterator or has_symbolic_tensors(input_data) or
+      (isinstance(input_data, list) and not input_data)):
+    if steps is None:
+      input_type_str = 'iterators' if is_x_iterator else 'data tensors'
+      raise ValueError('When using {input_type} as input to a model, you should'
+                       ' specify the `{steps_name}` argument.'.format(
+                           input_type=input_type_str, steps_name=steps_name))
+    return True
+  return False
diff --git a/tensorflow/python/keras/_impl/keras/model_subclassing_test.py b/tensorflow/python/keras/_impl/keras/model_subclassing_test.py
index 3f850e57aa3d7d..1e88dc09fb410d 100644
--- a/tensorflow/python/keras/_impl/keras/model_subclassing_test.py
+++ b/tensorflow/python/keras/_impl/keras/model_subclassing_test.py
@@ -23,6 +23,7 @@
 import numpy as np
 import six
 
+from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.eager import context
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import test_util
@@ -250,6 +251,26 @@ def test_multi_io_workflow_with_tensors(self):
       model.fit([x1, x2], [y1, y2], epochs=2, steps_per_epoch=10, verbose=0)
       _ = model.evaluate(steps=10, verbose=0)
 
+  @test_util.run_in_graph_and_eager_modes()
+  def test_single_io_workflow_with_dataset_iterators(self):
+    num_classes = 2
+    num_samples = 10
+    input_dim = 50
+
+    with self.test_session():
+      model = SimpleTestModel(num_classes=num_classes, use_dp=True, use_bn=True)
+      model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
+
+      x = np.ones((num_samples, input_dim))
+      y = np.zeros((num_samples, num_classes))
+      dataset = dataset_ops.Dataset.from_tensor_slices((x, y))
+      dataset = dataset.repeat(100)
+      dataset = dataset.batch(10)
+      iterator = dataset.make_one_shot_iterator()
+
+      model.fit(iterator, epochs=2, steps_per_epoch=10, verbose=0)
+      _ = model.evaluate(iterator, steps=10, verbose=0)
+
   def test_multi_io_workflow_with_numpy_arrays_and_custom_placeholders(self):
 
     num_classes = (2, 3)

From ba0584a5da7e0ff59486bd77b63eab417fbff352 Mon Sep 17 00:00:00 2001
From: Michael Case 
Date: Mon, 7 May 2018 15:20:49 -0700
Subject: [PATCH 0468/1691] Fix TypeError in update_version.py

PiperOrigin-RevId: 195731183
---
 tensorflow/tools/ci_build/update_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/ci_build/update_version.py b/tensorflow/tools/ci_build/update_version.py
index 9ddb2190487c26..00bfcfd49bd1d9 100755
--- a/tensorflow/tools/ci_build/update_version.py
+++ b/tensorflow/tools/ci_build/update_version.py
@@ -250,7 +250,7 @@ def update_md_files(old_version, new_version):
 
   # Update any links to colab notebooks.
   def colab_url(version):
-    version_string = "%d.%d.%d" % (version.major, version.minor, version.patch)
+    version_string = "%s.%s.%s" % (version.major, version.minor, version.patch)
     prefix = "https://colab.research.google.com/github/tensorflow/models/blob/r"
     return prefix + version_string + "/"
 

From 3a2f1cfb73fa6a21eba077485bdc08aa05646ad1 Mon Sep 17 00:00:00 2001
From: Michael Case 
Date: Mon, 7 May 2018 15:24:02 -0700
Subject: [PATCH 0469/1691] Internal Change.

PiperOrigin-RevId: 195731675
---
 .../estimator/python/estimator/head.py        |  26 ++-
 .../contrib/tpu/python/tpu/tpu_estimator.py   |  26 +--
 tensorflow/python/estimator/canned/dnn.py     |  68 +++++-
 .../estimator/canned/dnn_testing_utils.py     |  20 +-
 tensorflow/python/estimator/canned/head.py    | 216 ++++++++++++++----
 .../python/estimator/canned/head_test.py      |  92 ++++++++
 tensorflow/python/estimator/model_fn.py       |  51 +++++
 7 files changed, 406 insertions(+), 93 deletions(-)

diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py
index 5d19bf4714ff6f..109fdd3883427a 100644
--- a/tensorflow/contrib/estimator/python/estimator/head.py
+++ b/tensorflow/contrib/estimator/python/estimator/head.py
@@ -560,10 +560,10 @@ def create_loss(self, features, mode, logits, labels):
         weights=weights,
         processed_labels=processed_labels)
 
-  def create_estimator_spec(
+  def _create_tpu_estimator_spec(
       self, features, mode, logits, labels=None, optimizer=None,
       train_op_fn=None, regularization_losses=None):
-    """Returns an `EstimatorSpec`.
+    """Returns an `model_fn._TPUEstimatorSpec`.
 
     Args:
       features: Input `dict` of `Tensor` or `SparseTensor` objects.
@@ -586,7 +586,7 @@ def create_estimator_spec(
         `loss_reduction=SUM_OVER_NONZERO_WEIGHTS` when creating the head to
         avoid scaling errors.
     Returns:
-      `EstimatorSpec`.
+      `model_fn._TPUEstimatorSpec`.
     Raises:
       ValueError: If both `train_op_fn` and `optimizer` are `None` in TRAIN
         mode, or if both are set.
@@ -606,7 +606,7 @@ def create_estimator_spec(
         classifier_output = head_lib._classification_output(  # pylint:disable=protected-access
             scores=probabilities, n_classes=self._n_classes,
             label_vocabulary=self._label_vocabulary)
-        return model_fn.EstimatorSpec(
+        return model_fn._TPUEstimatorSpec(  # pylint:disable=protected-access
             mode=model_fn.ModeKeys.PREDICT,
             predictions=predictions,
             export_outputs={
@@ -629,16 +629,18 @@ def create_estimator_spec(
 
       # Eval.
       if mode == model_fn.ModeKeys.EVAL:
-        return model_fn.EstimatorSpec(
+        return model_fn._TPUEstimatorSpec(  # pylint:disable=protected-access
             mode=model_fn.ModeKeys.EVAL,
             predictions=predictions,
             loss=regularized_training_loss,
-            eval_metric_ops=self._eval_metric_ops(
-                labels=processed_labels,
-                probabilities=probabilities,
-                weights=weights,
-                unreduced_loss=unreduced_loss,
-                regularization_loss=regularization_loss))
+            eval_metrics=head_lib._create_eval_metrics_tuple(  # pylint:disable=protected-access
+                self._eval_metric_ops, {
+                    'labels': processed_labels,
+                    'probabilities': probabilities,
+                    'weights': weights,
+                    'unreduced_loss': unreduced_loss,
+                    'regularization_loss': regularization_loss,
+                }))
 
       # Train.
       if optimizer is not None:
@@ -672,7 +674,7 @@ def create_estimator_spec(
         summary.scalar(
             head_lib._summary_key(self._name, keys.LOSS_REGULARIZATION),  # pylint:disable=protected-access
             regularization_loss)
-    return model_fn.EstimatorSpec(
+    return model_fn._TPUEstimatorSpec(  # pylint:disable=protected-access
         mode=model_fn.ModeKeys.TRAIN,
         predictions=predictions,
         loss=regularized_training_loss,
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index a69bfa9a20bed7..a624eceed9a65c 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -175,17 +175,7 @@ class _SIGNAL(object):
   STOP = -2
 
 
-class TPUEstimatorSpec(
-    collections.namedtuple('TPUEstimatorSpec', [
-        'mode',
-        'predictions',
-        'loss',
-        'train_op',
-        'eval_metrics',
-        'export_outputs',
-        'scaffold_fn',
-        'host_call'
-    ])):
+class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec):  # pylint: disable=protected-access
   """Ops and objects returned from a `model_fn` and passed to `TPUEstimator`.
 
   See `EstimatorSpec` for `mode`, 'predictions, 'loss', 'train_op', and
@@ -1156,7 +1146,7 @@ def train_step(loss):
           self._call_model_fn(features, labels))
       loss, train_op = estimator_spec.loss, estimator_spec.train_op
 
-      if isinstance(estimator_spec, TPUEstimatorSpec):
+      if isinstance(estimator_spec, model_fn_lib._TPUEstimatorSpec):  # pylint: disable=protected-access
         captured_scaffold_fn.capture(estimator_spec.scaffold_fn)
       else:
         captured_scaffold_fn.capture(None)
@@ -1165,8 +1155,8 @@ def train_step(loss):
       # outfeed.
       with ops.control_dependencies([train_op]):
         host_call_outfeed_ops = []
-        if (isinstance(estimator_spec, TPUEstimatorSpec) and
-            estimator_spec.host_call is not None):
+        if (isinstance(estimator_spec, model_fn_lib._TPUEstimatorSpec)  # pylint: disable=protected-access
+            and estimator_spec.host_call is not None):
           host_call.record({'host_call': estimator_spec.host_call})
           host_call_outfeed_ops = host_call.create_enqueue_op()
         with ops.control_dependencies(host_call_outfeed_ops):
@@ -1209,7 +1199,7 @@ def eval_step(total_loss):
       features, labels = inputs.features_and_labels()
 
       tpu_estimator_spec = self._call_model_fn(features, labels)
-      if not isinstance(tpu_estimator_spec, TPUEstimatorSpec):
+      if not isinstance(tpu_estimator_spec, model_fn_lib._TPUEstimatorSpec):  # pylint: disable=protected-access
         raise RuntimeError(
             'estimator_spec used by TPU evaluation must have type'
             '`TPUEstimatorSpec`. Got {}'.format(type(tpu_estimator_spec)))
@@ -1254,7 +1244,7 @@ def predict_step(unused_scalar_stopping_signal):
 
       tpu_estimator_spec = self._call_model_fn(
           features, labels, is_export_mode=False)
-      if not isinstance(tpu_estimator_spec, TPUEstimatorSpec):
+      if not isinstance(tpu_estimator_spec, model_fn_lib._TPUEstimatorSpec):  # pylint: disable=protected-access
         raise RuntimeError(
             'estimator_spec used by TPU prediction must have type'
             '`TPUEstimatorSpec`. Got {}'.format(type(tpu_estimator_spec)))
@@ -1316,7 +1306,7 @@ def _call_model_fn(self, features, labels, is_export_mode=False):
 
     estimator_spec = self._model_fn(features=features, **kwargs)
     if (self._ctx.is_running_on_cpu(is_export_mode) and
-        isinstance(estimator_spec, TPUEstimatorSpec)):
+        isinstance(estimator_spec, model_fn_lib._TPUEstimatorSpec)):  # pylint: disable=protected-access
       # The estimator_spec will be passed to `Estimator` directly, which expects
       # type `EstimatorSpec`.
       return estimator_spec.as_estimator_spec()
@@ -1325,7 +1315,7 @@ def _call_model_fn(self, features, labels, is_export_mode=False):
 
   def _verify_estimator_spec(self, estimator_spec):
     """Validates the estimator_spec."""
-    if isinstance(estimator_spec, TPUEstimatorSpec):
+    if isinstance(estimator_spec, model_fn_lib._TPUEstimatorSpec):  # pylint: disable=protected-access
       return estimator_spec
 
     err_msg = '{} returned by EstimatorSpec is not supported in TPUEstimator.'
diff --git a/tensorflow/python/estimator/canned/dnn.py b/tensorflow/python/estimator/canned/dnn.py
index 973a6ec74777de..e7fbf8eb7220b6 100644
--- a/tensorflow/python/estimator/canned/dnn.py
+++ b/tensorflow/python/estimator/canned/dnn.py
@@ -151,6 +151,59 @@ def _dnn_model_fn(features,
   Returns:
     An `EstimatorSpec` instance.
 
+  Raises:
+    ValueError: If features has the wrong type.
+  """
+  tpu_estimator_spec = _tpu_dnn_model_fn(
+      features=features,
+      labels=labels,
+      mode=mode,
+      head=head,
+      hidden_units=hidden_units,
+      feature_columns=feature_columns,
+      optimizer=optimizer,
+      activation_fn=activation_fn,
+      dropout=dropout,
+      input_layer_partitioner=input_layer_partitioner,
+      config=config)
+  return tpu_estimator_spec.as_estimator_spec()
+
+
+def _tpu_dnn_model_fn(features,
+                      labels,
+                      mode,
+                      head,
+                      hidden_units,
+                      feature_columns,
+                      optimizer='Adagrad',
+                      activation_fn=nn.relu,
+                      dropout=None,
+                      input_layer_partitioner=None,
+                      config=None):
+  """Deep Neural Net model_fn for TPUEstimator.
+
+  Args:
+    features: dict of `Tensor`.
+    labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of
+      dtype `int32` or `int64` in the range `[0, n_classes)`.
+    mode: Defines whether this is training, evaluation or prediction.
+      See `ModeKeys`.
+    head: A `head_lib._Head` instance.
+    hidden_units: Iterable of integer number of hidden units per layer.
+    feature_columns: Iterable of `feature_column._FeatureColumn` model inputs.
+    optimizer: String, `tf.Optimizer` object, or callable that creates the
+      optimizer to use for training. If not specified, will use the Adagrad
+      optimizer with a default learning rate of 0.05.
+    activation_fn: Activation function applied to each layer.
+    dropout: When not `None`, the probability we will drop out a given
+      coordinate.
+    input_layer_partitioner: Partitioner for input layer. Defaults
+      to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
+    config: `RunConfig` object to configure the runtime settings.
+
+  Returns:
+    A `model_fn.TPUEstimatorSpec` instance.
+
   Raises:
     ValueError: If features has the wrong type.
   """
@@ -182,7 +235,7 @@ def _dnn_model_fn(features,
         input_layer_partitioner=input_layer_partitioner)
     logits = logit_fn(features=features, mode=mode)
 
-    return head.create_estimator_spec(
+    return head._create_tpu_estimator_spec(  # pylint: disable=protected-access
         features=features,
         mode=mode,
         labels=labels,
@@ -320,17 +373,8 @@ def __init__(
       loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how
         to reduce training loss over batch. Defaults to `SUM`.
     """
-    if n_classes == 2:
-      head = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss(  # pylint: disable=protected-access
-          weight_column=weight_column,
-          label_vocabulary=label_vocabulary,
-          loss_reduction=loss_reduction)
-    else:
-      head = head_lib._multi_class_head_with_softmax_cross_entropy_loss(  # pylint: disable=protected-access
-          n_classes, weight_column=weight_column,
-          label_vocabulary=label_vocabulary,
-          loss_reduction=loss_reduction)
-
+    head = head_lib._binary_logistic_or_multi_class_head(  # pylint: disable=protected-access
+        n_classes, weight_column, label_vocabulary, loss_reduction)
     def _model_fn(features, labels, mode, config):
       """Call the defined shared _dnn_model_fn."""
       return _dnn_model_fn(
diff --git a/tensorflow/python/estimator/canned/dnn_testing_utils.py b/tensorflow/python/estimator/canned/dnn_testing_utils.py
index 62b13c3200dd78..06a648777f8f73 100644
--- a/tensorflow/python/estimator/canned/dnn_testing_utils.py
+++ b/tensorflow/python/estimator/canned/dnn_testing_utils.py
@@ -134,7 +134,7 @@ def mock_head(testcase, hidden_units, logits_dimension, expected_logits):
       hidden_weights_names + hidden_biases_names +
       [LOGITS_WEIGHTS_NAME + '/part_0:0', LOGITS_BIASES_NAME + '/part_0:0'])
 
-  def _create_estimator_spec(
+  def _create_tpu_estimator_spec(
       features, mode, logits, labels, train_op_fn=None, optimizer=None):
     del features, labels  # Not used.
     trainable_vars = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
@@ -149,19 +149,29 @@ def _create_estimator_spec(
           train_op = train_op_fn(loss)
         elif optimizer is not None:
           train_op = optimizer.minimize(loss, global_step=None)
-        return model_fn.EstimatorSpec(
+        return model_fn._TPUEstimatorSpec(
             mode=mode, loss=loss, train_op=train_op)
       elif mode == model_fn.ModeKeys.EVAL:
-        return model_fn.EstimatorSpec(mode=mode, loss=array_ops.identity(loss))
+        return model_fn._TPUEstimatorSpec(
+            mode=mode, loss=array_ops.identity(loss))
       elif mode == model_fn.ModeKeys.PREDICT:
-        return model_fn.EstimatorSpec(
+        return model_fn._TPUEstimatorSpec(
             mode=mode, predictions={'logits': array_ops.identity(logits)})
       else:
         testcase.fail('Invalid mode: {}'.format(mode))
 
+  def _create_estimator_spec(
+      features, mode, logits, labels, train_op_fn=None, optimizer=None):
+    tpu_spec = _create_tpu_estimator_spec(
+        features, mode, logits, labels, train_op_fn, optimizer)
+    return tpu_spec.as_estimator_spec()
+
   head = test.mock.NonCallableMagicMock(spec=head_lib._Head)
   head.logits_dimension = logits_dimension
-  head.create_estimator_spec = test.mock.MagicMock(wraps=_create_estimator_spec)
+  head._create_tpu_estimator_spec = test.mock.MagicMock(
+      wraps=_create_tpu_estimator_spec)
+  head.create_estimator_spec = test.mock.MagicMock(
+      wraps=_create_estimator_spec)
 
   return head
 
diff --git a/tensorflow/python/estimator/canned/head.py b/tensorflow/python/estimator/canned/head.py
index 48f448d7f5f917..232637314d25b3 100644
--- a/tensorflow/python/estimator/canned/head.py
+++ b/tensorflow/python/estimator/canned/head.py
@@ -32,6 +32,7 @@
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
@@ -69,6 +70,35 @@ def _summary_key(head_name, val):
   return '%s/%s' % (val, head_name) if head_name else val
 
 
+def _create_eval_metrics_tuple(fn, kwargs):
+  """Creates TPU eval metrics tuple.
+
+  Helper function to make eval_metric tuple (eval_metric_fn, fn_kwargs) used
+  by `TPUEstimator`. TPUEstimator requires that `eval_metric_fn` take
+  exclusively Tensor arguments. This helper can help create such a function from
+  a more generic function that can take both Tensor and non-Tensor arguments.
+
+  Args:
+    fn: A eval_metric_fn that takes both Tensor and non-Tensor arguments.
+        This function must return a dict of form
+        {'metric name': (metric_tensor, eval_op)}
+    kwargs: Dict of arguments for `fn`.
+
+  Returns:
+    `eval_metric` tuple that can be passed to a `model_fn._TPUEstimatorSpec`.
+  """
+  tensor_kwargs = {}
+  nontensor_kwargs = {}
+  for k, v in six.iteritems(kwargs):
+    if tensor_util.is_tensor(v):
+      tensor_kwargs[k] = v
+    else:
+      nontensor_kwargs[k] = v
+  def _fn(**tensors):
+    return fn(**dict(nontensor_kwargs, **tensors))
+  return (_fn, tensor_kwargs)
+
+
 class _Head(object):
   """Interface for the head/top of a model.
 
@@ -174,7 +204,6 @@ def create_loss(self, features, mode, logits, labels):
 
   # TODO(b/65403806): By default, collect regularization_losses from
   # GraphKeys.REGULARIZATION_LOSSES collection.
-  @abc.abstractmethod
   def create_estimator_spec(
       self, features, mode, logits, labels=None, optimizer=None,
       train_op_fn=None, regularization_losses=None):
@@ -203,7 +232,47 @@ def create_estimator_spec(
     Returns:
       `EstimatorSpec`.
     """
-    raise NotImplementedError('Calling an abstract method.')
+    try:
+      tpu_estimator_spec = (
+          self._create_tpu_estimator_spec(
+              features, mode, logits, labels, optimizer, train_op_fn,
+              regularization_losses))
+      return tpu_estimator_spec.as_estimator_spec()
+    except NotImplementedError:
+      # Not all subclasses of _Head will have implemented
+      # _create_tpu_estimator_spec. If it is implemented, we can use it to
+      # create our `EstimatorSpec` here.
+      raise NotImplementedError(
+          'Subclasses of _Head must implement `create_estimator_spec()` or '
+          '_create_tpu_estimator_spec().')
+
+  def _create_tpu_estimator_spec(
+      self, features, mode, logits, labels=None, optimizer=None,
+      train_op_fn=None, regularization_losses=None):
+    """Returns `model_fn._TPUEstimatorSpec` that a model_fn can return.
+
+    Args:
+      features: Input `dict` of `Tensor` or `SparseTensor` objects.
+      mode: Estimator's `ModeKeys`.
+      logits: logits `Tensor` to be used by the head.
+      labels: Labels `Tensor`, or `dict` of same.
+      optimizer: `Optimizer` instance to optimize the loss in TRAIN mode.
+        Namely, sets `train_op = optimizer.minimize(loss, global_step)`, which
+        updates variables and increments `global_step`.
+      train_op_fn: Function that takes a scalar loss `Tensor` and returns an op
+        to optimize the model with the loss in TRAIN mode. Used if `optimizer`
+        is `None`. Exactly one of `train_op_fn` and `optimizer` must be set in
+        TRAIN mode. None is allowed in other modes. If you want to optimize loss
+        yourself you can pass `lambda _: tf.no_op()` and then use
+        EstimatorSpec.loss to compute and apply gradients.
+      regularization_losses: A list of additional scalar losses to be added to
+        the training loss, such as regularization losses.
+
+    Returns:
+      A `model_fn._TPUEstimatorSpec' instance.
+    """
+    raise NotImplementedError(
+        'TPUEstimatorSpec not available for this model head.')
 
 
 def _check_dense_labels_match_logits_and_reshape(
@@ -702,10 +771,10 @@ def create_loss(self, features, mode, logits, labels):
         weights=weights,
         processed_labels=label_ids)
 
-  def create_estimator_spec(
+  def _create_tpu_estimator_spec(
       self, features, mode, logits, labels=None, optimizer=None,
       train_op_fn=None, regularization_losses=None):
-    """Returns an `EstimatorSpec`.
+    """Returns a `model_fn._TPUEstimatorSpec`.
 
     Args:
       features: Input `dict` of `Tensor` or `SparseTensor` objects.
@@ -727,7 +796,7 @@ def create_estimator_spec(
         `loss_reduction=SUM_OVER_NONZERO_WEIGHTS` when creating the head to
         avoid scaling errors.
     Returns:
-      `EstimatorSpec`.
+      A `model_fn._TPUEstimatorSpec` instance.
     Raises:
       ValueError: If both `train_op_fn` and `optimizer` are `None` in TRAIN
         mode, or if both are set.
@@ -761,7 +830,7 @@ def create_estimator_spec(
         classifier_output = _classification_output(
             scores=probabilities, n_classes=self._n_classes,
             label_vocabulary=self._label_vocabulary)
-        return model_fn.EstimatorSpec(
+        return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
             mode=model_fn.ModeKeys.PREDICT,
             predictions=predictions,
             export_outputs={
@@ -781,16 +850,17 @@ def create_estimator_spec(
         regularized_training_loss = training_loss
       # Eval.
       if mode == model_fn.ModeKeys.EVAL:
-        return model_fn.EstimatorSpec(
+        return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
             mode=model_fn.ModeKeys.EVAL,
             predictions=predictions,
             loss=regularized_training_loss,
-            eval_metric_ops=self._eval_metric_ops(
-                labels=label_ids,
-                class_ids=class_ids,
-                weights=weights,
-                unreduced_loss=unreduced_loss,
-                regularization_loss=regularization_loss))
+            eval_metrics=_create_eval_metrics_tuple(self._eval_metric_ops, {
+                'labels': label_ids,
+                'class_ids': class_ids,
+                'weights': weights,
+                'unreduced_loss': unreduced_loss,
+                'regularization_loss': regularization_loss
+            }))
 
       # Train.
       if optimizer is not None:
@@ -824,7 +894,7 @@ def create_estimator_spec(
         summary.scalar(
             _summary_key(self._name, keys.LOSS_REGULARIZATION),
             regularization_loss)
-    return model_fn.EstimatorSpec(
+    return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
         mode=model_fn.ModeKeys.TRAIN,
         predictions=predictions,
         loss=regularized_training_loss,
@@ -1060,7 +1130,7 @@ def create_loss(self, features, mode, logits, labels):
         weights=weights,
         processed_labels=labels)
 
-  def create_estimator_spec(
+  def _create_tpu_estimator_spec(
       self, features, mode, logits, labels=None, optimizer=None,
       train_op_fn=None, regularization_losses=None):
     """Returns an `EstimatorSpec`.
@@ -1122,7 +1192,7 @@ def create_estimator_spec(
         classifier_output = _classification_output(
             scores=probabilities, n_classes=2,
             label_vocabulary=self._label_vocabulary)
-        return model_fn.EstimatorSpec(
+        return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
             mode=model_fn.ModeKeys.PREDICT,
             predictions=predictions,
             export_outputs={
@@ -1146,18 +1216,22 @@ def create_estimator_spec(
 
       # Eval.
       if mode == model_fn.ModeKeys.EVAL:
-        return model_fn.EstimatorSpec(
+        return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
             mode=model_fn.ModeKeys.EVAL,
             predictions=predictions,
             loss=regularized_training_loss,
-            eval_metric_ops=self._eval_metric_ops(
-                labels=processed_labels,
-                logits=logits,
-                logistic=logistic,
-                class_ids=class_ids,
-                weights=weights,
-                unreduced_loss=unreduced_loss,
-                regularization_loss=regularization_loss))
+            eval_metrics=_create_eval_metrics_tuple(
+                self._eval_metric_ops,
+                {
+                    'labels': processed_labels,
+                    'logits': logits,
+                    'logistic': logistic,
+                    'class_ids': class_ids,
+                    'weights': weights,
+                    'unreduced_loss': unreduced_loss,
+                    'regularization_loss': regularization_loss
+                }
+            ))
 
       # Train.
       if optimizer is not None:
@@ -1190,7 +1264,7 @@ def create_estimator_spec(
         summary.scalar(
             _summary_key(self._name, keys.LOSS_REGULARIZATION),
             regularization_loss)
-    return model_fn.EstimatorSpec(
+    return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
         mode=model_fn.ModeKeys.TRAIN,
         predictions=predictions,
         loss=regularized_training_loss,
@@ -1322,7 +1396,25 @@ def create_loss(self, features, mode, logits, labels):
         weights=weights,
         processed_labels=labels)
 
-  def create_estimator_spec(
+  def _eval_metric_ops(self, weights, unreduced_loss, regularization_loss):
+    """Returns the Eval metric ops."""
+    keys = metric_keys.MetricKeys
+    # Estimator already adds a metric for loss.
+    eval_metric_ops = {
+        _summary_key(self._name, keys.LOSS_MEAN):
+            metrics_lib.mean(
+                values=unreduced_loss,
+                weights=weights)
+    }
+    if regularization_loss is not None:
+      regularization_loss_key = _summary_key(
+          self._name, keys.LOSS_REGULARIZATION)
+      eval_metric_ops[regularization_loss_key] = metrics_lib.mean(
+          values=regularization_loss,
+          name=keys.LOSS_REGULARIZATION)
+    return eval_metric_ops
+
+  def _create_tpu_estimator_spec(
       self, features, mode, logits, labels=None, optimizer=None,
       train_op_fn=None, regularization_losses=None):
     """Returns an `EstimatorSpec`.
@@ -1348,7 +1440,7 @@ def create_estimator_spec(
         `loss_reduction=SUM_OVER_NONZERO_WEIGHTS` when creating the head to
         avoid scaling errors.
     Returns:
-      `EstimatorSpec`.
+      A `model_fn._TPUEstimatorSpec` instance.
     Raises:
       ValueError: If both `train_op_fn` and `optimizer` are `None` in TRAIN
         mode, or if both are set.
@@ -1369,7 +1461,7 @@ def create_estimator_spec(
       if mode == model_fn.ModeKeys.PREDICT:
         regression_output = export_output.RegressionOutput(
             value=predicted_value)
-        return model_fn.EstimatorSpec(
+        return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
             mode=model_fn.ModeKeys.PREDICT,
             predictions=predictions,
             export_outputs={
@@ -1390,25 +1482,18 @@ def create_estimator_spec(
 
       # Eval.
       if mode == model_fn.ModeKeys.EVAL:
-        keys = metric_keys.MetricKeys
-        # Estimator already adds a metric for loss.
-        eval_metric_ops = {
-            _summary_key(self._name, keys.LOSS_MEAN):
-                metrics_lib.mean(
-                    values=unreduced_loss,
-                    weights=weights)
-        }
-        if regularization_loss is not None:
-          regularization_loss_key = _summary_key(
-              self._name, keys.LOSS_REGULARIZATION)
-          eval_metric_ops[regularization_loss_key] = metrics_lib.mean(
-              values=regularization_loss,
-              name=keys.LOSS_REGULARIZATION)
-        return model_fn.EstimatorSpec(
+        return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
             mode=model_fn.ModeKeys.EVAL,
             predictions=predictions,
             loss=regularized_training_loss,
-            eval_metric_ops=eval_metric_ops)
+            eval_metrics=_create_eval_metrics_tuple(
+                self._eval_metric_ops,
+                {
+                    'weights': weights,
+                    'unreduced_loss': unreduced_loss,
+                    'regularization_loss': regularization_loss,
+                }
+            ))
 
       # Train.
       if optimizer is not None:
@@ -1441,7 +1526,7 @@ def create_estimator_spec(
         summary.scalar(
             _summary_key(self._name, keys.LOSS_REGULARIZATION),
             regularization_loss)
-    return model_fn.EstimatorSpec(
+    return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
         mode=model_fn.ModeKeys.TRAIN,
         predictions=predictions,
         loss=regularized_training_loss,
@@ -1478,3 +1563,42 @@ def _weights(features, weight_column):
       raise ValueError('Weight column should be castable to float. '
                        'Given dtype: {}'.format(weights.dtype))
     return math_ops.to_float(weights, name='weights')
+
+
+def _binary_logistic_or_multi_class_head(
+    n_classes, weight_column, label_vocabulary, loss_reduction):
+  """Creates either binary or multi-class head.
+
+  Args:
+    n_classes: Number of label classes.
+    weight_column: A string or a `_NumericColumn` created by
+      `tf.feature_column.numeric_column` defining feature column representing
+      weights. It is used to down weight or boost examples during training. It
+      will be multiplied by the loss of the example. If it is a string, it is
+      used as a key to fetch weight tensor from the `features`. If it is a
+      `_NumericColumn`, raw tensor is fetched by key `weight_column.key`,
+      then weight_column.normalizer_fn is applied on it to get weight tensor.
+    label_vocabulary: A list of strings represents possible label values. If
+      given, labels must be string type and have any value in
+      `label_vocabulary`. If it is not given, that means labels are
+      already encoded as integer or float within [0, 1] for `n_classes=2` and
+      encoded as integer values in {0, 1,..., n_classes-1} for `n_classes`>2 .
+      Also there will be errors if vocabulary is not provided and labels are
+      string.
+    loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how
+      to reduce training loss over batch. Defaults to `SUM`.
+
+  Returns:
+    `head._Head` instance.
+  """
+  if n_classes == 2:
+    head = _binary_logistic_head_with_sigmoid_cross_entropy_loss(
+        weight_column=weight_column,
+        label_vocabulary=label_vocabulary,
+        loss_reduction=loss_reduction)
+  else:
+    head = _multi_class_head_with_softmax_cross_entropy_loss(
+        n_classes, weight_column=weight_column,
+        label_vocabulary=label_vocabulary,
+        loss_reduction=loss_reduction)
+  return head
diff --git a/tensorflow/python/estimator/canned/head_test.py b/tensorflow/python/estimator/canned/head_test.py
index 32a63399362811..ecca3e8b0d8286 100644
--- a/tensorflow/python/estimator/canned/head_test.py
+++ b/tensorflow/python/estimator/canned/head_test.py
@@ -86,6 +86,98 @@ def _sigmoid(logits):
   return 1 / (1 + np.exp(-logits))
 
 
+class CreateEstimatorSpecTest(test.TestCase):
+
+  class _HeadWithTPUSupport(head_lib._Head):
+    """Head that overrides _create_tpu_estimator_spec."""
+
+    def name(self):
+      return 'HeadWithTPUSupport'
+
+    def logits_dimension(self):
+      return None
+
+    def create_loss(self, features, mode, logits, labels):
+      return None
+
+    def _create_tpu_estimator_spec(self, features, mode, logits, labels=None,
+                                   optimizer=None, train_op_fn=None,
+                                   regularization_losses=None):
+      return model_fn._TPUEstimatorSpec(
+          mode=model_fn.ModeKeys.EVAL,
+          loss=constant_op.constant(0.0, dtype=dtypes.float32))
+
+  class _HeadWithOutTPUSupport(head_lib._Head):
+    """Head that overrides create_estimator_spec."""
+
+    def name(self):
+      return 'HeadWithOutTPUSupport'
+
+    def logits_dimension(self):
+      return None
+
+    def create_loss(self, features, mode, logits, labels):
+      return None
+
+    def create_estimator_spec(self, features, mode, logits, labels=None,
+                              optimizer=None, train_op_fn=None,
+                              regularization_losses=None):
+      return model_fn.EstimatorSpec(
+          mode=model_fn.ModeKeys.EVAL,
+          loss=constant_op.constant(0.0, dtype=dtypes.float32))
+
+  class _InvalidHead(head_lib._Head):
+    """Head that overrides neither estimator_spec functions."""
+
+    def name(self):
+      return 'InvalidHead'
+
+    def logits_dimension(self):
+      return None
+
+    def create_loss(self, features, mode, logits, labels):
+      return None
+
+  def test_head_override_tpu_estimator_spec(self):
+    """Test for `_Head` that overrides _create_tpu_estimator_spec."""
+    head = self._HeadWithTPUSupport()
+
+    tpu_spec = head._create_tpu_estimator_spec(
+        features=None, mode=None, logits=None)
+    self.assertTrue(isinstance(tpu_spec, model_fn._TPUEstimatorSpec))
+    est_spec = head.create_estimator_spec(
+        features=None, mode=None, logits=None)
+    self.assertTrue(isinstance(est_spec, model_fn.EstimatorSpec))
+
+  def test_head_override_estimator_spec(self):
+    """Test for `_Head` that overrides create_estimator_spec."""
+    head = self._HeadWithOutTPUSupport()
+
+    with self.assertRaisesRegexp(
+        NotImplementedError,
+        'TPUEstimatorSpec not available for this model head.'):
+      _ = head._create_tpu_estimator_spec(
+          features=None, mode=None, logits=None)
+    est_spec = head.create_estimator_spec(
+        features=None, mode=None, logits=None)
+    self.assertTrue(isinstance(est_spec, model_fn.EstimatorSpec))
+
+  def test_invalid_head_class(self):
+    head = self._InvalidHead()
+
+    with self.assertRaisesRegexp(
+        NotImplementedError,
+        'TPUEstimatorSpec not available for this model head.'):
+      _ = head._create_tpu_estimator_spec(
+          features=None, mode=None, logits=None)
+    with self.assertRaisesRegexp(
+        NotImplementedError,
+        r'Subclasses of _Head must implement `create_estimator_spec\(\)` or '
+        r'_create_tpu_estimator_spec\(\).'):
+      _ = head.create_estimator_spec(
+          features=None, mode=None, logits=None)
+
+
 class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase):
 
   def setUp(self):
diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py
index 4ab2578769cf79..3edf9fe940b19c 100644
--- a/tensorflow/python/estimator/model_fn.py
+++ b/tensorflow/python/estimator/model_fn.py
@@ -334,6 +334,57 @@ def _replace(self, **kwds):
     return EstimatorSpec(*new_fields)
 
 
+class _TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [
+    'mode',
+    'predictions',
+    'loss',
+    'train_op',
+    'eval_metrics',
+    'export_outputs',
+    'scaffold_fn',
+    'host_call'])):
+  """Ops and objects returned from a `model_fn` and passed to `TPUEstimator`.
+
+  This is a simplified implementation of `tf.contrib.tpu.EstimatorSpec`. See
+  tensorflow/contrib/tpu/python/tpu/tpu_estimator.py for more detailed
+  documentation.
+  """
+
+  def __new__(cls,
+              mode,
+              predictions=None,
+              loss=None,
+              train_op=None,
+              eval_metrics=None,
+              export_outputs=None,
+              scaffold_fn=None,
+              host_call=None):
+    """Creates a `_TPUEstimatorSpec` instance."""
+    return super(_TPUEstimatorSpec, cls).__new__(cls,
+                                                 mode=mode,
+                                                 predictions=predictions,
+                                                 loss=loss,
+                                                 train_op=train_op,
+                                                 eval_metrics=eval_metrics,
+                                                 export_outputs=export_outputs,
+                                                 scaffold_fn=scaffold_fn,
+                                                 host_call=host_call)
+
+  def as_estimator_spec(self):
+    """Creates an equivalent `EstimatorSpec` used by CPU train/eval."""
+    if not self.eval_metrics:
+      eval_metric_ops = None
+    else:
+      metric_fn, tensors = self.eval_metrics
+      eval_metric_ops = metric_fn(**tensors)
+    return EstimatorSpec(mode=self.mode,
+                         predictions=self.predictions,
+                         loss=self.loss,
+                         train_op=self.train_op,
+                         eval_metric_ops=eval_metric_ops,
+                         export_outputs=self.export_outputs)
+
+
 def _check_is_tensor_or_operation(x, name):
   if not (isinstance(x, ops.Operation) or isinstance(x, ops.Tensor)):
     raise TypeError('{} must be Operation or Tensor, given: {}'.format(name, x))

From fc7f0b296dd53d1b72af21d36d36b6bcc5291ea7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 15:41:22 -0700
Subject: [PATCH 0470/1691] Add support for select (via tf.where) support to
 tflite.

PiperOrigin-RevId: 195734246
---
 tensorflow/contrib/lite/builtin_ops.h         |   1 +
 .../lite/g3doc/tf_ops_compatibility.md        |  14 ++
 tensorflow/contrib/lite/kernels/BUILD         |  18 +++
 .../internal/optimized/optimized_ops.h        |  53 +++++++
 .../internal/reference/reference_ops.h        |  52 +++++++
 tensorflow/contrib/lite/kernels/register.cc   |   2 +
 tensorflow/contrib/lite/kernels/select.cc     | 125 +++++++++++++++
 .../contrib/lite/kernels/select_test.cc       | 143 ++++++++++++++++++
 tensorflow/contrib/lite/model.cc              |   3 +-
 tensorflow/contrib/lite/nnapi_delegate.cc     |   1 +
 tensorflow/contrib/lite/schema/schema.fbs     |   5 +
 .../contrib/lite/schema/schema_generated.h    | 124 ++++++++++++++-
 tensorflow/contrib/lite/testing/BUILD         |   1 +
 .../contrib/lite/testing/generate_examples.py |  33 +++-
 .../testing/generated_examples_zip_test.cc    |   1 +
 .../contrib/lite/toco/export_tensorflow.cc    |  16 ++
 .../propagate_array_data_types.cc             |  11 ++
 .../propagate_fixed_sizes.cc                  |  19 ++-
 .../contrib/lite/toco/import_tensorflow.cc    |  15 ++
 tensorflow/contrib/lite/toco/model.h          |  13 ++
 .../contrib/lite/toco/tflite/operator.cc      |   2 +
 .../contrib/lite/toco/tflite/operator_test.cc |   1 +
 tensorflow/contrib/lite/toco/tooling_util.cc  |   3 +
 tensorflow/contrib/lite/toco/types.proto      |   3 +
 24 files changed, 650 insertions(+), 9 deletions(-)
 create mode 100644 tensorflow/contrib/lite/kernels/select.cc
 create mode 100644 tensorflow/contrib/lite/kernels/select_test.cc
 mode change 100755 => 100644 tensorflow/contrib/lite/schema/schema_generated.h

diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h
index 778933f5693ed4..a038acf2848b21 100644
--- a/tensorflow/contrib/lite/builtin_ops.h
+++ b/tensorflow/contrib/lite/builtin_ops.h
@@ -89,6 +89,7 @@ typedef enum {
   kTfLiteBuiltinGreater = 61,
   kTfLiteBuiltinGreaterEqual = 62,
   kTfLiteBuiltinLessEqual = 63,
+  kTfLiteBuiltinSelect = 64,
 } TfLiteBuiltinOperator;
 
 #ifdef __cplusplus
diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
index fc57b8f28bef8b..f45fcceb2e6152 100644
--- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
+++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
@@ -639,6 +639,20 @@ Outputs {
 }
 ```
 
+**SELECT**
+
+```
+Inputs {
+  0: tensor
+  1: tensor
+  2: tensor
+}
+Outputs {
+  0: tensor that contains the elementwise values of 'tensor 1' if the
+  corresponding value of 'tensor 0' is true or the value of 'tensor 2' if false.
+}
+```
+
 And these are TensorFlow Lite operations that are present but not ready for
 custom models yet:
 
diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD
index feab18b5c23b43..79e3c9f2664594 100644
--- a/tensorflow/contrib/lite/kernels/BUILD
+++ b/tensorflow/contrib/lite/kernels/BUILD
@@ -164,6 +164,7 @@ cc_library(
         "register.cc",
         "reshape.cc",
         "resize_bilinear.cc",
+        "select.cc",
         "skip_gram.cc",
         "space_to_batch_nd.cc",
         "space_to_depth.cc",
@@ -870,6 +871,23 @@ tf_cc_test(
     ],
 )
 
+tf_cc_test(
+    name = "select_test",
+    size = "small",
+    srcs = [
+        "select_test.cc",
+    ],
+    tags = [
+        "tflite_not_portable_ios",
+    ],
+    deps = [
+        ":builtin_ops",
+        "//tensorflow/contrib/lite:framework",
+        "//tensorflow/contrib/lite/kernels:test_util",
+        "@com_google_googletest//:gtest",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index c506c5636c3986..8ab6f19b710e10 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -6318,6 +6318,59 @@ inline void TransposeConv(const float* input_data, const Dims<4>& input_dims,
   }
 }
 
+// UNOPTIMIZED COPY of Select from reference_ops.h.
+template 
+inline void Select(const D* input_condition_data,
+                   const Dims<4>& input_condition_dims, const T* input_x_data,
+                   const Dims<4>& input_x_dims, const T* input_y_data,
+                   const Dims<4>& input_y_dims, T* output_data,
+                   const Dims<4>& output_dims) {
+  const int64_t batches =
+      MatchingArraySize(input_condition_dims, 3, input_x_dims, 3, input_y_dims,
+                        3, output_dims, 3);
+  const int64_t height =
+      MatchingArraySize(input_condition_dims, 2, input_x_dims, 2, input_y_dims,
+                        2, output_dims, 2);
+  const int64_t width = MatchingArraySize(input_condition_dims, 1, input_x_dims,
+                                          1, input_y_dims, 1, output_dims, 1);
+  const int64_t depth = MatchingArraySize(input_condition_dims, 0, input_x_dims,
+                                          0, input_y_dims, 0, output_dims, 0);
+
+  const int64_t num_elements = batches * height * width * depth;
+  for (int64_t i = 0; i < num_elements; ++i) {
+    output_data[i] =
+        input_condition_data[i] ? input_x_data[i] : input_y_data[i];
+  }
+}
+
+// UNOPTIMIZED COPY of RankOneSelect from reference_ops.h.
+template 
+inline void RankOneSelect(const D* input_condition_data,
+                          const Dims<4>& input_condition_dims,
+                          const T* input_x_data, const Dims<4>& input_x_dims,
+                          const T* input_y_data, const Dims<4>& input_y_dims,
+                          T* output_data, const Dims<4>& output_dims) {
+  const int64_t rank = ArraySize(input_condition_dims, 0);
+
+  const int64_t batches =
+      MatchingArraySize(input_x_dims, 3, input_y_dims, 3, output_dims, 3);
+  const int64_t height =
+      MatchingArraySize(input_x_dims, 2, input_y_dims, 2, output_dims, 2);
+  const int64_t width =
+      MatchingArraySize(input_x_dims, 1, input_y_dims, 1, output_dims, 1);
+  const int64_t depth =
+      MatchingArraySize(input_x_dims, 0, input_y_dims, 0, output_dims, 0);
+
+  TFLITE_DCHECK_EQ(rank, batches);
+
+  int64_t offset = 0;
+  int64_t size = depth * height * width;
+  for (int64_t i = 0; i < rank; i++) {
+    const T* input_data = input_condition_data[i] ? input_x_data : input_y_data;
+    memcpy(output_data + offset, input_data + offset, size * sizeof(T));
+  }
+}
+
 }  // namespace optimized_ops
 }  // namespace tflite
 
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index 93dba1cc8e6c86..c3aff1093f0299 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -3759,6 +3759,58 @@ TFLITE_COMPARISON_OP(Less);
 TFLITE_COMPARISON_OP(LessEqual);
 #undef TFLITE_COMPARISON_OP
 
+template 
+inline void Select(const D* input_condition_data,
+                   const Dims<4>& input_condition_dims, const T* input_x_data,
+                   const Dims<4>& input_x_dims, const T* input_y_data,
+                   const Dims<4>& input_y_dims, T* output_data,
+                   const Dims<4>& output_dims) {
+  const int64_t batches =
+      MatchingArraySize(input_condition_dims, 3, input_x_dims, 3, input_y_dims,
+                        3, output_dims, 3);
+  const int64_t height =
+      MatchingArraySize(input_condition_dims, 2, input_x_dims, 2, input_y_dims,
+                        2, output_dims, 2);
+  const int64_t width = MatchingArraySize(input_condition_dims, 1, input_x_dims,
+                                          1, input_y_dims, 1, output_dims, 1);
+  const int64_t depth = MatchingArraySize(input_condition_dims, 0, input_x_dims,
+                                          0, input_y_dims, 0, output_dims, 0);
+
+  const int64_t num_elements = batches * height * width * depth;
+  for (int64_t i = 0; i < num_elements; ++i) {
+    output_data[i] =
+        input_condition_data[i] ? input_x_data[i] : input_y_data[i];
+  }
+}
+
+template 
+inline void RankOneSelect(const D* input_condition_data,
+                          const Dims<4>& input_condition_dims,
+                          const T* input_x_data, const Dims<4>& input_x_dims,
+                          const T* input_y_data, const Dims<4>& input_y_dims,
+                          T* output_data, const Dims<4>& output_dims) {
+  const int64_t rank = ArraySize(input_condition_dims, 0);
+
+  const int64_t batches =
+      MatchingArraySize(input_x_dims, 3, input_y_dims, 3, output_dims, 3);
+  const int64_t height =
+      MatchingArraySize(input_x_dims, 2, input_y_dims, 2, output_dims, 2);
+  const int64_t width =
+      MatchingArraySize(input_x_dims, 1, input_y_dims, 1, output_dims, 1);
+  const int64_t depth =
+      MatchingArraySize(input_x_dims, 0, input_y_dims, 0, output_dims, 0);
+
+  TFLITE_DCHECK_EQ(rank, batches);
+
+  int64_t offset = 0;
+  int64_t size = depth * height * width;
+  for (int64_t i = 0; i < rank; i++) {
+    const T* input_data = input_condition_data[i] ? input_x_data : input_y_data;
+    memcpy(output_data + offset, input_data + offset, size * sizeof(T));
+    offset += size;
+  }
+}
+
 }  // namespace reference_ops
 }  // namespace tflite
 
diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc
index 40855891a66e71..5df35aac62141f 100644
--- a/tensorflow/contrib/lite/kernels/register.cc
+++ b/tensorflow/contrib/lite/kernels/register.cc
@@ -86,6 +86,7 @@ TfLiteRegistration* Register_LESS();
 TfLiteRegistration* Register_LESS_EQUAL();
 TfLiteRegistration* Register_FLOOR();
 TfLiteRegistration* Register_NEG();
+TfLiteRegistration* Register_SELECT();
 
 BuiltinOpResolver::BuiltinOpResolver() {
   AddBuiltin(BuiltinOperator_RELU, Register_RELU());
@@ -153,6 +154,7 @@ BuiltinOpResolver::BuiltinOpResolver() {
   AddBuiltin(BuiltinOperator_LESS_EQUAL, Register_LESS_EQUAL());
   AddBuiltin(BuiltinOperator_FLOOR, Register_FLOOR());
   AddBuiltin(BuiltinOperator_NEG, Register_NEG());
+  AddBuiltin(BuiltinOperator_SELECT, Register_SELECT());
 
   // TODO(andrewharp, ahentz): Move these somewhere more appropriate so that
   // custom ops aren't always included by default.
diff --git a/tensorflow/contrib/lite/kernels/select.cc b/tensorflow/contrib/lite/kernels/select.cc
new file mode 100644
index 00000000000000..029ad9a709c514
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/select.cc
@@ -0,0 +1,125 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/contrib/lite/context.h"
+#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
+#include "tensorflow/contrib/lite/kernels/internal/tensor.h"
+#include "tensorflow/contrib/lite/kernels/kernel_util.h"
+#include "tensorflow/contrib/lite/kernels/op_macros.h"
+#include "tensorflow/contrib/lite/string_util.h"
+
+namespace tflite {
+namespace ops {
+namespace builtin {
+namespace select {
+
+constexpr int kInputTensorCondition = 0;
+constexpr int kInputTensorX = 1;
+constexpr int kInputTensorY = 2;
+constexpr int kOutputTensor = 0;
+
+TfLiteStatus SelectPrepare(TfLiteContext* context, TfLiteNode* node) {
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+
+  TfLiteTensor* input_condition =
+      GetInput(context, node, kInputTensorCondition);
+  TfLiteTensor* input_x = GetInput(context, node, kInputTensorX);
+  TfLiteTensor* input_y = GetInput(context, node, kInputTensorY);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  // Input must be bool.
+  TF_LITE_ENSURE(context, input_condition->type == kTfLiteBool);
+
+  // Input tensors must have the same type and size
+  TF_LITE_ENSURE_EQ(context, input_x->type, input_y->type);
+  TF_LITE_ENSURE(context, HaveSameShapes(input_x, input_y));
+  output->type = input_x->type;
+
+  // Either the same shape, or input_condition must be Rank 1 and match over the
+  // first dimension.
+  bool same_shape = HaveSameShapes(input_condition, input_x);
+  if (!same_shape && NumDimensions(input_condition) == 1) {
+    same_shape =
+        SizeOfDimension(input_condition, 0) == SizeOfDimension(input_x, 0);
+  }
+
+  TF_LITE_ENSURE(context, same_shape);
+
+  TfLiteIntArray* output_size = TfLiteIntArrayCopy(input_x->dims);
+  return context->ResizeTensor(context, output, output_size);
+}
+
+TfLiteStatus SelectEval(TfLiteContext* context, TfLiteNode* node) {
+  TfLiteTensor* input_condition =
+      GetInput(context, node, kInputTensorCondition);
+  TfLiteTensor* input_x = GetInput(context, node, kInputTensorX);
+  TfLiteTensor* input_y = GetInput(context, node, kInputTensorY);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  bool is_rank_one = !HaveSameShapes(input_condition, input_x);
+
+#define TF_LITE_SELECT(type, op)                                          \
+  reference_ops::op(GetTensorData(input_condition),                 \
+                    GetTensorDims(input_condition),                       \
+                    GetTensorData(input_x), GetTensorDims(input_x), \
+                    GetTensorData(input_y), GetTensorDims(input_y), \
+                    GetTensorData(output), GetTensorDims(output));
+
+#define TF_LITE_SWITCH(type, op)                                               \
+  switch (type) {                                                              \
+    break;                                                                     \
+    case kTfLiteBool:                                                          \
+      TF_LITE_SELECT(bool, op);                                                \
+      break;                                                                   \
+    case kTfLiteFloat32:                                                       \
+      TF_LITE_SELECT(float, op);                                               \
+      break;                                                                   \
+    case kTfLiteUInt8:                                                         \
+      TF_LITE_SELECT(uint8_t, op);                                             \
+      break;                                                                   \
+    case kTfLiteInt32:                                                         \
+      TF_LITE_SELECT(int32_t, op);                                             \
+      break;                                                                   \
+    case kTfLiteInt64:                                                         \
+      TF_LITE_SELECT(int64_t, op);                                             \
+      break;                                                                   \
+    default:                                                                   \
+      context->ReportError(context,                                            \
+                           "Does not support type other than bool|float|int"); \
+      return kTfLiteError;                                                     \
+  }
+
+  if (is_rank_one) {
+    TF_LITE_SWITCH(input_x->type, RankOneSelect);
+  } else {
+    TF_LITE_SWITCH(input_x->type, Select);
+  }
+
+#undef TF_LITE_SELECT
+#undef TF_LITE_SWITCH
+  return kTfLiteOk;
+}
+
+}  // namespace select
+
+TfLiteRegistration* Register_SELECT() {
+  static TfLiteRegistration r = {nullptr, nullptr, select::SelectPrepare,
+                                 select::SelectEval};
+  return &r;
+}
+
+}  // namespace builtin
+}  // namespace ops
+}  // namespace tflite
diff --git a/tensorflow/contrib/lite/kernels/select_test.cc b/tensorflow/contrib/lite/kernels/select_test.cc
new file mode 100644
index 00000000000000..cfe24a5fc92765
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/select_test.cc
@@ -0,0 +1,143 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include 
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/kernels/test_util.h"
+#include "tensorflow/contrib/lite/model.h"
+
+namespace tflite {
+namespace {
+
+using ::testing::ElementsAreArray;
+
+class SelectOpModel : public SingleOpModel {
+ public:
+  SelectOpModel(std::initializer_list input1_shape,
+                std::initializer_list input2_shape,
+                std::initializer_list input3_shape,
+                TensorType input_type) {
+    input1_ = AddInput(TensorType_BOOL);
+    input2_ = AddInput(input_type);
+    input3_ = AddInput(input_type);
+    output_ = AddOutput(input_type);
+    SetBuiltinOp(BuiltinOperator_SELECT, BuiltinOptions_SelectOptions,
+                 CreateSelectOptions(builder_).Union());
+    BuildInterpreter({input1_shape, input2_shape, input3_shape});
+  }
+
+  int input1() { return input1_; }
+  int input2() { return input2_; }
+  int input3() { return input3_; }
+
+  template 
+  std::vector GetOutput() {
+    return ExtractVector(output_);
+  }
+
+  std::vector GetOutputShape() { return GetTensorShape(output_); }
+
+ private:
+  int input1_;
+  int input2_;
+  int input3_;
+  int output_;
+};
+
+TEST(SelectOpTest, SelectBool) {
+  SelectOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, {1, 1, 1, 4},
+                      TensorType_BOOL);
+
+  model.PopulateTensor(model.input1(), {true, false, true, false});
+  model.PopulateTensor(model.input2(), {false, false, false, false});
+  model.PopulateTensor(model.input3(), {true, true, true, true});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(),
+              ElementsAreArray({false, true, false, true}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(SelectOpTest, SelectFloat) {
+  SelectOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, {1, 1, 1, 4},
+                      TensorType_FLOAT32);
+
+  model.PopulateTensor(model.input1(), {true, false, true, false});
+  model.PopulateTensor(model.input2(), {0.1, 0.2, 0.3, 0.4});
+  model.PopulateTensor(model.input3(), {0.5, 0.6, 0.7, 0.8});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({0.1, 0.6, 0.3, 0.8}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(SelectOpTest, SelectUInt8) {
+  SelectOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, {1, 1, 1, 4},
+                      TensorType_UINT8);
+
+  model.PopulateTensor(model.input1(), {false, true, false, false});
+  model.PopulateTensor(model.input2(), {1, 2, 3, 4});
+  model.PopulateTensor(model.input3(), {5, 6, 7, 8});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 2, 7, 8}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(SelectOpTest, SelectInt32) {
+  SelectOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, {1, 1, 1, 4},
+                      TensorType_INT32);
+
+  model.PopulateTensor(model.input1(), {false, true, false, false});
+  model.PopulateTensor(model.input2(), {1, 2, 3, 4});
+  model.PopulateTensor(model.input3(), {5, 6, 7, 8});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 2, 7, 8}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(SelectOpTest, RankOneSelectInt32) {
+  SelectOpModel model({2}, {2, 1, 2, 1}, {2, 1, 2, 1}, TensorType_INT32);
+
+  model.PopulateTensor(model.input1(), {false, true});
+  model.PopulateTensor(model.input2(), {1, 2, 3, 4});
+  model.PopulateTensor(model.input3(), {5, 6, 7, 8});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 6, 3, 4}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 1, 2, 1}));
+}
+
+TEST(SelectOpTest, RankZeroSelectInt32) {
+  SelectOpModel model({1}, {1, 2, 2, 1}, {1, 2, 2, 1}, TensorType_INT32);
+
+  model.PopulateTensor(model.input1(), {false});
+  model.PopulateTensor(model.input2(), {1, 2, 3, 4});
+  model.PopulateTensor(model.input3(), {5, 6, 7, 8});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 6, 7, 8}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 2, 2, 1}));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}
diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc
index 21c218137797b5..e89036ce730dd7 100644
--- a/tensorflow/contrib/lite/model.cc
+++ b/tensorflow/contrib/lite/model.cc
@@ -675,7 +675,8 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type,
     case BuiltinOperator_GREATER:
     case BuiltinOperator_GREATER_EQUAL:
     case BuiltinOperator_LESS:
-    case BuiltinOperator_LESS_EQUAL: {
+    case BuiltinOperator_LESS_EQUAL:
+    case BuiltinOperator_SELECT: {
       break;
     }
     case BuiltinOperator_DELEGATE: {
diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc
index e903af87b71ee2..6a231dc6bcb8d5 100644
--- a/tensorflow/contrib/lite/nnapi_delegate.cc
+++ b/tensorflow/contrib/lite/nnapi_delegate.cc
@@ -377,6 +377,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       case tflite::BuiltinOperator_LESS:
       case tflite::BuiltinOperator_LESS_EQUAL:
       case tflite::BuiltinOperator_NEG:
+      case tflite::BuiltinOperator_SELECT:
         FATAL("Op code %d is currently not delegated to NNAPI", builtin);
         nn_op_type = -1;  // set to invalid
         break;
diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs
index 3ec91e505db746..9de6180874742a 100644
--- a/tensorflow/contrib/lite/schema/schema.fbs
+++ b/tensorflow/contrib/lite/schema/schema.fbs
@@ -141,6 +141,7 @@ enum BuiltinOperator : byte {
   GREATER = 61,
   GREATER_EQUAL = 62,
   LESS_EQUAL = 63,
+  SELECT = 64,
 }
 
 // Options for the builtin operators.
@@ -191,6 +192,7 @@ union BuiltinOptions {
   GreaterOptions,
   GreaterEqualOptions,
   LessEqualOptions,
+  SelectOptions,
 }
 
 enum Padding : byte { SAME, VALID }
@@ -431,6 +433,9 @@ table LessEqualOptions {
 table NegOptions {
 }
 
+table SelectOptions {
+}
+
 // An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
 // builtin, or a string if the operator is custom.
 table OperatorCode {
diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h
old mode 100755
new mode 100644
index c6e4dab4548360..a2f0c8cdd28934
--- a/tensorflow/contrib/lite/schema/schema_generated.h
+++ b/tensorflow/contrib/lite/schema/schema_generated.h
@@ -169,6 +169,9 @@ struct LessEqualOptionsT;
 struct NegOptions;
 struct NegOptionsT;
 
+struct SelectOptions;
+struct SelectOptionsT;
+
 struct OperatorCode;
 struct OperatorCodeT;
 
@@ -292,11 +295,12 @@ enum BuiltinOperator {
   BuiltinOperator_GREATER = 61,
   BuiltinOperator_GREATER_EQUAL = 62,
   BuiltinOperator_LESS_EQUAL = 63,
+  BuiltinOperator_SELECT = 64,
   BuiltinOperator_MIN = BuiltinOperator_ADD,
-  BuiltinOperator_MAX = BuiltinOperator_LESS_EQUAL
+  BuiltinOperator_MAX = BuiltinOperator_SELECT
 };
 
-inline BuiltinOperator (&EnumValuesBuiltinOperator())[63] {
+inline BuiltinOperator (&EnumValuesBuiltinOperator())[64] {
   static BuiltinOperator values[] = {
     BuiltinOperator_ADD,
     BuiltinOperator_AVERAGE_POOL_2D,
@@ -360,7 +364,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[63] {
     BuiltinOperator_PADV2,
     BuiltinOperator_GREATER,
     BuiltinOperator_GREATER_EQUAL,
-    BuiltinOperator_LESS_EQUAL
+    BuiltinOperator_LESS_EQUAL,
+    BuiltinOperator_SELECT
   };
   return values;
 }
@@ -431,6 +436,7 @@ inline const char **EnumNamesBuiltinOperator() {
     "GREATER",
     "GREATER_EQUAL",
     "LESS_EQUAL",
+    "SELECT",
     nullptr
   };
   return names;
@@ -489,11 +495,12 @@ enum BuiltinOptions {
   BuiltinOptions_GreaterOptions = 44,
   BuiltinOptions_GreaterEqualOptions = 45,
   BuiltinOptions_LessEqualOptions = 46,
+  BuiltinOptions_SelectOptions = 47,
   BuiltinOptions_MIN = BuiltinOptions_NONE,
-  BuiltinOptions_MAX = BuiltinOptions_LessEqualOptions
+  BuiltinOptions_MAX = BuiltinOptions_SelectOptions
 };
 
-inline BuiltinOptions (&EnumValuesBuiltinOptions())[47] {
+inline BuiltinOptions (&EnumValuesBuiltinOptions())[48] {
   static BuiltinOptions values[] = {
     BuiltinOptions_NONE,
     BuiltinOptions_Conv2DOptions,
@@ -541,7 +548,8 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[47] {
     BuiltinOptions_PadV2Options,
     BuiltinOptions_GreaterOptions,
     BuiltinOptions_GreaterEqualOptions,
-    BuiltinOptions_LessEqualOptions
+    BuiltinOptions_LessEqualOptions,
+    BuiltinOptions_SelectOptions
   };
   return values;
 }
@@ -595,6 +603,7 @@ inline const char **EnumNamesBuiltinOptions() {
     "GreaterOptions",
     "GreaterEqualOptions",
     "LessEqualOptions",
+    "SelectOptions",
     nullptr
   };
   return names;
@@ -793,6 +802,10 @@ template<> struct BuiltinOptionsTraits {
   static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions;
 };
 
+template<> struct BuiltinOptionsTraits {
+  static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions;
+};
+
 struct BuiltinOptionsUnion {
   BuiltinOptions type;
   void *value;
@@ -1192,6 +1205,14 @@ struct BuiltinOptionsUnion {
     return type == BuiltinOptions_LessEqualOptions ?
       reinterpret_cast(value) : nullptr;
   }
+  SelectOptionsT *AsSelectOptions() {
+    return type == BuiltinOptions_SelectOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
+  const SelectOptionsT *AsSelectOptions() const {
+    return type == BuiltinOptions_SelectOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
 };
 
 bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type);
@@ -4319,6 +4340,46 @@ inline flatbuffers::Offset CreateNegOptions(
 
 flatbuffers::Offset CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
 
+struct SelectOptionsT : public flatbuffers::NativeTable {
+  typedef SelectOptions TableType;
+  SelectOptionsT() {
+  }
+};
+
+struct SelectOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef SelectOptionsT NativeTableType;
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           verifier.EndTable();
+  }
+  SelectOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct SelectOptionsBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  explicit SelectOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  SelectOptionsBuilder &operator=(const SelectOptionsBuilder &);
+  flatbuffers::Offset Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset CreateSelectOptions(
+    flatbuffers::FlatBufferBuilder &_fbb) {
+  SelectOptionsBuilder builder_(_fbb);
+  return builder_.Finish();
+}
+
+flatbuffers::Offset CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
 struct OperatorCodeT : public flatbuffers::NativeTable {
   typedef OperatorCode TableType;
   BuiltinOperator builtin_code;
@@ -4574,6 +4635,9 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
   const LessEqualOptions *builtin_options_as_LessEqualOptions() const {
     return builtin_options_type() == BuiltinOptions_LessEqualOptions ? static_cast(builtin_options()) : nullptr;
   }
+  const SelectOptions *builtin_options_as_SelectOptions() const {
+    return builtin_options_type() == BuiltinOptions_SelectOptions ? static_cast(builtin_options()) : nullptr;
+  }
   const flatbuffers::Vector *custom_options() const {
     return GetPointer *>(VT_CUSTOM_OPTIONS);
   }
@@ -4784,6 +4848,10 @@ template<> inline const LessEqualOptions *Operator::builtin_options_as inline const SelectOptions *Operator::builtin_options_as() const {
+  return builtin_options_as_SelectOptions();
+}
+
 struct OperatorBuilder {
   flatbuffers::FlatBufferBuilder &fbb_;
   flatbuffers::uoffset_t start_;
@@ -6525,6 +6593,29 @@ inline flatbuffers::Offset CreateNegOptions(flatbuffers::FlatBufferB
       _fbb);
 }
 
+inline SelectOptionsT *SelectOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new SelectOptionsT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void SelectOptions::UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+}
+
+inline flatbuffers::Offset SelectOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateSelectOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  return tflite::CreateSelectOptions(
+      _fbb);
+}
+
 inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
   auto _o = new OperatorCodeT();
   UnPackTo(_o, _resolver);
@@ -6892,6 +6983,10 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob
       auto ptr = reinterpret_cast(obj);
       return verifier.VerifyTable(ptr);
     }
+    case BuiltinOptions_SelectOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return verifier.VerifyTable(ptr);
+    }
     default: return false;
   }
 }
@@ -7094,6 +7189,10 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c
       auto ptr = reinterpret_cast(obj);
       return ptr->UnPack(resolver);
     }
+    case BuiltinOptions_SelectOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return ptr->UnPack(resolver);
+    }
     default: return nullptr;
   }
 }
@@ -7284,6 +7383,10 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff
       auto ptr = reinterpret_cast(value);
       return CreateLessEqualOptions(_fbb, ptr, _rehasher).Union();
     }
+    case BuiltinOptions_SelectOptions: {
+      auto ptr = reinterpret_cast(value);
+      return CreateSelectOptions(_fbb, ptr, _rehasher).Union();
+    }
     default: return 0;
   }
 }
@@ -7474,6 +7577,10 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL
       value = new LessEqualOptionsT(*reinterpret_cast(u.value));
       break;
     }
+    case BuiltinOptions_SelectOptions: {
+      value = new SelectOptionsT(*reinterpret_cast(u.value));
+      break;
+    }
     default:
       break;
   }
@@ -7711,6 +7818,11 @@ inline void BuiltinOptionsUnion::Reset() {
       delete ptr;
       break;
     }
+    case BuiltinOptions_SelectOptions: {
+      auto ptr = reinterpret_cast(value);
+      delete ptr;
+      break;
+    }
     default: break;
   }
   value = nullptr;
diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD
index 6749e63552993f..f89c0d28d37b66 100644
--- a/tensorflow/contrib/lite/testing/BUILD
+++ b/tensorflow/contrib/lite/testing/BUILD
@@ -64,6 +64,7 @@ gen_zipped_test_files(
         "sub.zip",
         "topk.zip",
         "transpose.zip",
+        "where.zip",
     ],
 )
 
diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index 7a658d43d358a6..05d099a82c7349 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -2242,10 +2242,41 @@ def build_inputs(parameters, sess, inputs, outputs):
   make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
 
 
+def make_where_tests(zip_path):
+  """Make a set of tests to do where."""
+
+  test_parameters = [{
+      "input_dtype": [tf.float32, tf.int32],
+      "input_shape_set": [([1, 2, 3, 4], [1, 2, 3, 4]),],
+  }]
+
+  def build_graph(parameters):
+    """Build the where op testing graph."""
+    input_value1 = tf.placeholder(
+        dtype=parameters["input_dtype"],
+        name="input2",
+        shape=parameters["input_shape_set"][0])
+    input_value2 = tf.placeholder(
+        dtype=parameters["input_dtype"],
+        name="input3",
+        shape=parameters["input_shape_set"][1])
+    less = tf.less(input_value1, input_value2)
+    out = tf.where(less, input_value1, input_value2)
+    return [input_value1, input_value2], [out]
+
+  def build_inputs(parameters, sess, inputs, outputs):
+    input_value1 = create_tensor_data(parameters["input_dtype"],
+                                      parameters["input_shape_set"][0])
+    input_value2 = create_tensor_data(parameters["input_dtype"],
+                                      parameters["input_shape_set"][1])
+    return [input_value1, input_value2], sess.run(
+        outputs, feed_dict=dict(zip(inputs, [input_value1, input_value2])))
+
+  make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
+
 # Toco binary path provided by the generate rule.
 bin_path = None
 
-
 def main(unused_args):
   global bin_path
   def mkdir_if_not_exist(x):
diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
index 2ce14f3b38dda1..49762bdfe7139c 100644
--- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
+++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
@@ -289,6 +289,7 @@ INSTANTIATE_TESTS(squeeze)
 INSTANTIATE_TESTS(strided_slice)
 INSTANTIATE_TESTS(sub)
 INSTANTIATE_TESTS(transpose)
+INSTANTIATE_TESTS(where)
 
 }  // namespace testing
 }  // namespace tflite
diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc
index 53df1987b30ae0..f5157149afca17 100644
--- a/tensorflow/contrib/lite/toco/export_tensorflow.cc
+++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc
@@ -1674,6 +1674,19 @@ void ConvertTensorFlowMaximumOperator(const Model& model,
   (*sub_op->mutable_attr())["T"].set_type(data_type);
 }
 
+void ConvertSelectOperator(const Model& model, const SelectOperator& src_op,
+                           GraphDef* tensorflow_graph) {
+  auto* sub_op = tensorflow_graph->add_node();
+  sub_op->set_op("Select");
+  sub_op->set_name(src_op.outputs[0]);
+  CHECK_EQ(src_op.inputs.size(), 3);
+  *sub_op->add_input() = src_op.inputs[0];
+  *sub_op->add_input() = src_op.inputs[1];
+  *sub_op->add_input() = src_op.inputs[2];
+  const auto data_type = GetTensorFlowDataType(model, src_op.inputs[1]);
+  (*sub_op->mutable_attr())["T"].set_type(data_type);
+}
+
 void ConvertTopKV2Operator(const Model& model, const TopKV2Operator& src_op,
                            GraphDef* tensorflow_graph) {
   auto* topk_op = tensorflow_graph->add_node();
@@ -1914,6 +1927,9 @@ void ConvertOperator(const Model& model, const Operator& src_op,
     ConvertComparisonOperator(model, src_op, "Less", tensorflow_graph);
   } else if (src_op.type == OperatorType::kTensorFlowLessEqual) {
     ConvertComparisonOperator(model, src_op, "LessEqual", tensorflow_graph);
+  } else if (src_op.type == OperatorType::kSelect) {
+    ConvertSelectOperator(model, static_cast(src_op),
+                          tensorflow_graph);
   } else {
     LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(src_op.type);
   }
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc
index c1cf79f62614c4..6342cf3e8af4d8 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc
@@ -152,6 +152,17 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) {
       // Yield on ExpandDim until it is converted to Reshape
       return false;
     }
+    case OperatorType::kSelect: {
+      // Select produces outputs with the same type as their 2nd input
+      CHECK_EQ(op->inputs.size(), 3);
+      const ArrayDataType data_type_x =
+          model->GetArray(op->inputs[1]).data_type;
+      const ArrayDataType data_type_y =
+          model->GetArray(op->inputs[2]).data_type;
+      CHECK(data_type_x == data_type_y);
+      SetDataTypeForAllOutputs(model, op, data_type_x);
+      break;
+    }
     default: {
       // These operators produce outputs with the same type as their 1st input
       CHECK_GT(op->inputs.size(), 0);
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
index a081abea559542..52b739c5e27536 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
@@ -529,6 +529,21 @@ void ProcessSimpleBinaryOperator(Model* model, Operator* op) {
                                   &output_array);
 }
 
+void ProcessSelectOperator(Model* model, SelectOperator* op) {
+  // Yield until all input dims have been resolved.
+  for (const auto& input : op->inputs) {
+    const auto& input_array = model->GetArray(input);
+    if (!input_array.has_shape()) {
+      return;
+    }
+  }
+
+  // Select's output matches the second and third output.
+  const auto& input1_array = model->GetArray(op->inputs[1]);
+  auto& output_array = model->GetArray(op->outputs[0]);
+  output_array.copy_shape(input1_array.shape());
+}
+
 void ProcessAddNOperator(Model* model, Operator* op) {
   // Yield until all input dims have been resolved.
   //
@@ -1570,7 +1585,9 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) {
     case OperatorType::kMean:
       ProcessTensorFlowReductionOperator(model, op);
       break;
-
+    case OperatorType::kSelect:
+      ProcessSelectOperator(model, static_cast(op));
+      break;
     case OperatorType::kSlice:
       ProcessSliceOperator(model, static_cast(op));
       break;
diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc
index 532fcdd808c136..52757ca748f12a 100644
--- a/tensorflow/contrib/lite/toco/import_tensorflow.cc
+++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc
@@ -1344,6 +1344,19 @@ void ConvertUnsupportedOperator(const NodeDef& node,
   }
 }
 
+void ConvertSelectOperator(const NodeDef& node,
+                           const TensorFlowImportFlags& tf_import_flags,
+                           Model* model) {
+  CheckInputsCount(node, tf_import_flags, 3);
+
+  auto* op = new SelectOperator;
+  for (const auto& input : node.input()) {
+    op->inputs.push_back(input);
+  }
+  op->outputs.push_back(node.name());
+  model->operators.emplace_back(op);
+}
+
 void ConvertStridedSliceOperator(const NodeDef& node,
                                  const TensorFlowImportFlags& tf_import_flags,
                                  Model* model) {
@@ -2254,6 +2267,8 @@ Status ImportTensorFlowNode(const tensorflow::NodeDef& node,
     ConvertDynamicStitchOperator(node, tf_import_flags, model);
   } else if (node.op() == "RandomUniform") {
     ConvertRandomUniform(node, tf_import_flags, model);
+  } else if (node.op() == "Select") {
+    ConvertSelectOperator(node, tf_import_flags, model);
   } else {
     ConvertUnsupportedOperator(node, tf_import_flags, model);
   }
diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h
index 7ee7841511ac9c..47f8db597846bf 100644
--- a/tensorflow/contrib/lite/toco/model.h
+++ b/tensorflow/contrib/lite/toco/model.h
@@ -133,6 +133,7 @@ enum class OperatorType {
   // instead of being given as plain constant arrays. So we need to insert
   // special nodes in the graph to shuffle axes.
   kReorderAxes,
+  kSelect,
 };
 
 // Helper to deal with TensorFlow arrays using a different ordering of
@@ -1087,6 +1088,18 @@ struct NegOperator : Operator {
   NegOperator() : Operator(OperatorType::kNeg) {}
 };
 
+// Element-wise select operator choosing elements from inputs[1] or input[2]
+//
+// Inputs:
+//  inputs[0]: required: boolean mask per index
+//  inputs[1]: required: tensor of values if true
+//  inputs[2]: required: tensor of values if false
+//
+//  TensorFlow equivalent: Select
+struct SelectOperator : Operator {
+  SelectOperator() : Operator(OperatorType::kSelect) {}
+};
+
 // Element-wise reciprocal-square-root (x^-0.5) operator.
 //
 // Inputs:
diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc
index a008e633512760..90e24aa104f5b0 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator.cc
+++ b/tensorflow/contrib/lite/toco/tflite/operator.cc
@@ -924,6 +924,8 @@ std::vector> BuildOperatorList() {
   ops.emplace_back(new SimpleOperator(
       "LESS_EQUAL", OperatorType::kTensorFlowLessEqual));
   ops.emplace_back(new SimpleOperator("NEG", OperatorType::kNeg));
+  ops.emplace_back(
+      new SimpleOperator("SELECT", OperatorType::kSelect));
 
   return ops;
 }
diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc
index 2b6c32b07c4a2e..a4fff9974a6421 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc
+++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc
@@ -116,6 +116,7 @@ TEST_F(OperatorTest, SimpleOperators) {
   CheckSimpleOperator("LESS",
                                               OperatorType::kTensorFlowLess);
   CheckSimpleOperator("NEG", OperatorType::kNeg);
+  CheckSimpleOperator("SELECT", OperatorType::kSelect);
 }
 
 TEST_F(OperatorTest, BuiltinAdd) {
diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index f82bb335356e94..1f56fe5c833add 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -391,6 +391,7 @@ const char* OperatorTypeName(OperatorType type) {
     HANDLE_OPERATORTYPENAME_CASE(Exp)
     HANDLE_OPERATORTYPENAME_CASE(DynamicPartition)
     HANDLE_OPERATORTYPENAME_CASE(DynamicStitch)
+    HANDLE_OPERATORTYPENAME_CASE(Select)
     default:
       LOG(FATAL) << "Unhandled op type";
 #undef HANDLE_OPERATORTYPENAME_CASE
@@ -2097,6 +2098,8 @@ ArrayDataType ConvertIODataTypeToArrayDataType(IODataType type) {
       return ArrayDataType::kInt32;
     case INT64:
       return ArrayDataType::kInt64;
+    case BOOL:
+      return ArrayDataType::kBool;
     default:
       return ArrayDataType::kNone;
   }
diff --git a/tensorflow/contrib/lite/toco/types.proto b/tensorflow/contrib/lite/toco/types.proto
index 03bd6150bc86bb..421667a83c14a7 100644
--- a/tensorflow/contrib/lite/toco/types.proto
+++ b/tensorflow/contrib/lite/toco/types.proto
@@ -37,4 +37,7 @@ enum IODataType {
 
   // Int16, quantized
   QUANTIZED_INT16 = 6;
+
+  // Boolean
+  BOOL = 7;
 }

From 37b8860e302d73845e74e1bfb6c3cb59207f2d77 Mon Sep 17 00:00:00 2001
From: Michael Kuperstein 
Date: Mon, 7 May 2018 15:41:52 -0700
Subject: [PATCH 0471/1691] [XLA] Fix a "we're we're" in the operation
 semantics.

PiperOrigin-RevId: 195734316
---
 tensorflow/docs_src/performance/xla/operation_semantics.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md
index f530fe1206c0e9..21e4c71a60f596 100644
--- a/tensorflow/docs_src/performance/xla/operation_semantics.md
+++ b/tensorflow/docs_src/performance/xla/operation_semantics.md
@@ -1049,8 +1049,8 @@ For a more intuitive description, see the "Informal Description" section below.
 :                  :                         : from.                           :
 |`gather_indices`  | `ComputationDataHandle` | Tensor containing the starting  |
 :                  :                         : indices of the slices we're     :
-:                  :                         : we're stitching together into   :
-:                  :                         : the output tensor.              :
+:                  :                         : stitching together into the     :
+:                  :                         : output tensor.                  :
 |`index_vector_dim`  | `int64`               | The dimension in                |
 :                  :                         : `gather_indices` that contains  :
 :                  :                         : the starting indices.           :

From 4a9beef315c3e456e7f087b5b3205df99f4a0876 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 15:47:57 -0700
Subject: [PATCH 0472/1691] Add EvaluateNodes to tests:
 RemoveIdentityTransposesMultipleOutputs,
 RemoveTransposesWithControlDependency, CombineBitcasts,
 CombineAndRemoveBitcasts, RemoveRedundantCast

PiperOrigin-RevId: 195735234
---
 .../optimizers/arithmetic_optimizer_test.cc   | 45 +++++++++++++++++++
 1 file changed, 45 insertions(+)

diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
index 741cc135a101d0..067adb359c70a4 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
@@ -1166,6 +1166,11 @@ TEST_F(ArithmeticOptimizerTest, RemoveIdentityTransposesMultipleOutputs) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto x_t = GenerateRandomTensor(TensorShape({8, 12, 28, 28}));
+  item.feed = {{"inputs", x_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyRemoveIdentityTranspose(&optimizer);
@@ -1178,6 +1183,10 @@ TEST_F(ArithmeticOptimizerTest, RemoveIdentityTransposesMultipleOutputs) {
       EXPECT_EQ(node.input(2), "Split:2");
     }
   }
+
+  auto tensors = EvaluateNodes(output, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
 TEST_F(ArithmeticOptimizerTest, RemoveTransposesWithControlDependency) {
@@ -1194,6 +1203,11 @@ TEST_F(ArithmeticOptimizerTest, RemoveTransposesWithControlDependency) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto x_t = GenerateRandomTensor(TensorShape({2, 3}));
+  item.feed = {{"Placeholder", x_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyRemoveIdentityTranspose(&optimizer);
@@ -1204,6 +1218,10 @@ TEST_F(ArithmeticOptimizerTest, RemoveTransposesWithControlDependency) {
   EXPECT_EQ(2, outputs_node->input_size());
   EXPECT_EQ(outputs_node->input(0), "outputs_const");
   EXPECT_EQ(outputs_node->input(1), "^Placeholder");
+
+  auto tensors = EvaluateNodes(output, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
 TEST_F(ArithmeticOptimizerTest, NotRemoveTransposes) {
@@ -1450,6 +1468,11 @@ TEST_F(ArithmeticOptimizerTest, CombineBitcasts) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto x_t = GenerateRandomTensor(TensorShape({2, 3}));
+  item.feed = {{"inputs", x_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyRemoveRedundantBitcast(&optimizer);
@@ -1461,6 +1484,10 @@ TEST_F(ArithmeticOptimizerTest, CombineBitcasts) {
   EXPECT_EQ(3, output.node_size());
   EXPECT_EQ(1, CountOpNodes(output, "Bitcast"));
   EXPECT_TRUE(IsNodesDirectlyConnected(node_map, "inputs", "bc2"));
+
+  auto tensors = EvaluateNodes(output, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorEqual(tensors_expected[0], tensors[0]);
 }
 
 TEST_F(ArithmeticOptimizerTest, CombineAndRemoveBitcasts) {
@@ -1475,6 +1502,11 @@ TEST_F(ArithmeticOptimizerTest, CombineAndRemoveBitcasts) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto x_t = GenerateRandomTensor(TensorShape({2, 3}));
+  item.feed = {{"inputs", x_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyRemoveRedundantBitcast(&optimizer);
@@ -1486,6 +1518,10 @@ TEST_F(ArithmeticOptimizerTest, CombineAndRemoveBitcasts) {
   EXPECT_EQ(2, output.node_size());
   EXPECT_EQ(0, CountOpNodes(output, "Bitcast"));
   EXPECT_TRUE(IsNodesDirectlyConnected(node_map, "inputs", "outputs"));
+
+  auto tensors = EvaluateNodes(output, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorEqual(tensors_expected[0], tensors[0]);
 }
 
 TEST_F(ArithmeticOptimizerTest, RemoveRedundantCast) {
@@ -1499,6 +1535,11 @@ TEST_F(ArithmeticOptimizerTest, RemoveRedundantCast) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto x_t = GenerateRandomTensor(TensorShape({2, 3}));
+  item.feed = {{"inputs", x_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyRemoveRedundantCast(&optimizer);
@@ -1510,6 +1551,10 @@ TEST_F(ArithmeticOptimizerTest, RemoveRedundantCast) {
   EXPECT_EQ(2, output.node_size());
   EXPECT_EQ(0, CountOpNodes(output, "Cast"));
   EXPECT_TRUE(IsNodesDirectlyConnected(node_map, "inputs", "outputs"));
+
+  auto tensors = EvaluateNodes(output, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorEqual(tensors_expected[0], tensors[0]);
 }
 
 TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_AddOpsOfIdenticalShape) {

From 27e6ab7c8b33d7f5e5795d31226b596ec70642fd Mon Sep 17 00:00:00 2001
From: Derek Murray 
Date: Mon, 7 May 2018 15:51:05 -0700
Subject: [PATCH 0473/1691] [Remote functions] Only set the default runner
 *after* resolving the remote FLR.

Previously, if the `runner` was not specified for a function
execution, we would immediately set it to the default runner of the
*local* FLR, even if the function was to be executed remotely. This
change postpones the resolution of the default runner until after the
function invocation has been routed to the FLR that will actually
execute it.

As a result, we avoid the pathological case where a GPU device using a
private threadpool (TF_GPU_THREAD_MODE=gpu_private) ends up running
all of the ops for the CPU-side input pipeline on the private
threadpool.

PiperOrigin-RevId: 195735734
---
 tensorflow/core/common_runtime/function.cc | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc
index a6f637b48837a0..bf05f6f1d95fa0 100644
--- a/tensorflow/core/common_runtime/function.cc
+++ b/tensorflow/core/common_runtime/function.cc
@@ -795,16 +795,16 @@ void FunctionLibraryRuntimeImpl::Run(const Options& opts, Handle handle,
     };
   }
 
-  if (run_opts.runner == nullptr) {
-    run_opts.runner = &default_runner_;
-  }
-  DCHECK(run_opts.runner != nullptr);
-
   if (!parent_->IsInstantiatedOnDevice(device_name_, handle)) {
     parent_->Run(run_opts, handle, args, rets, done);
     return;
   }
 
+  if (run_opts.runner == nullptr) {
+    run_opts.runner = &default_runner_;
+  }
+  DCHECK(run_opts.runner != nullptr);
+
   Executor::Args* exec_args = new Executor::Args;
   // Inherit the step_id from the caller.
   exec_args->step_id = run_opts.step_id;

From 94b0b2fbce60100c4fe81bf92f5c927626ed66b6 Mon Sep 17 00:00:00 2001
From: Blake Hechtman 
Date: Mon, 7 May 2018 15:58:29 -0700
Subject: [PATCH 0474/1691] [XLA] Make post order a possible schedule as it
 sometimes uses less memory than the DFS or list scheduler and it is very
 simple.

PiperOrigin-RevId: 195736916
---
 .../compiler/xla/service/cpu/cpu_compiler.cc  |  3 ++-
 .../compiler/xla/service/hlo_scheduling.cc    | 26 ++++++++++++++++++-
 .../compiler/xla/service/hlo_scheduling.h     |  6 +++++
 tensorflow/compiler/xla/tests/BUILD           |  1 +
 4 files changed, 34 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 91ed6e427ac7c2..3d2e24ca14eacd 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -535,7 +535,8 @@ StatusOr> CpuCompiler::RunBackend(
   // and reduced memory usage (as compared to using DependencyHloOrdering).
   TF_ASSIGN_OR_RETURN(
       SequentialHloOrdering::HloModuleSequence module_sequence,
-      CreateMemoryMinimizingSequence(*module, BufferSizeBytesFunction()));
+      CreateMemoryMinimizingSequence(*module, BufferSizeBytesFunction(),
+                                     DFSMemoryScheduler));
 
   // Run buffer analysis on the HLO graph. This analysis figures out which
   // temporary buffers are required to run the computation.
diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc
index 1a767628f6e2d3..23ace5afeab30d 100644
--- a/tensorflow/compiler/xla/service/hlo_scheduling.cc
+++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc
@@ -430,6 +430,15 @@ StatusOr> ListMemoryScheduler(
   return ListScheduler::Run(computation, points_to_analysis, size_function);
 }
 
+StatusOr> PostOrderMemoryScheduler(
+    const HloComputation& computation,
+    const TuplePointsToAnalysis& points_to_analysis,
+    const LogicalBuffer::SizeFunction& size_function) {
+  const auto& post_order = computation.MakeInstructionPostOrder();
+  return std::vector{post_order.begin(),
+                                            post_order.end()};
+}
+
 StatusOr> DefaultMemoryScheduler(
     const HloComputation& computation,
     const TuplePointsToAnalysis& points_to_analysis,
@@ -459,7 +468,22 @@ StatusOr> DefaultMemoryScheduler(
                                   size_function));
   VLOG(2) << "Min-memory dfs sequence: " << HumanReadableNumBytes(dfs_memory);
 
-  if (list_memory <= dfs_memory) {
+  TF_ASSIGN_OR_RETURN(
+      std::vector post_order_sequence,
+      PostOrderMemoryScheduler(computation, points_to_analysis, size_function));
+  TF_ASSIGN_OR_RETURN(
+      const int64 post_order_memory,
+      MinimumMemoryForComputation(computation, post_order_sequence,
+                                  points_to_analysis, size_function));
+  VLOG(2) << "Min-memory post order sequence: "
+          << HumanReadableNumBytes(post_order_memory);
+
+  if (post_order_memory < std::min(list_memory, dfs_memory)) {
+    VLOG(2) << "Chose min-memory post_order sequence: "
+            << HumanReadableNumBytes(post_order_memory);
+    return post_order_sequence;
+
+  } else if (list_memory <= dfs_memory) {
     VLOG(2) << "Chose min-memory list sequence: "
             << HumanReadableNumBytes(list_memory);
     return list_sequence;
diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.h b/tensorflow/compiler/xla/service/hlo_scheduling.h
index 068e68383deb17..fcb006f818fd1d 100644
--- a/tensorflow/compiler/xla/service/hlo_scheduling.h
+++ b/tensorflow/compiler/xla/service/hlo_scheduling.h
@@ -55,6 +55,12 @@ StatusOr> DFSMemoryScheduler(
     const TuplePointsToAnalysis& points_to_analysis,
     const LogicalBuffer::SizeFunction& size_function);
 
+// Naive Post Order scheduler
+StatusOr> PostOrderMemoryScheduler(
+    const HloComputation& computation,
+    const TuplePointsToAnalysis& points_to_analysis,
+    const LogicalBuffer::SizeFunction& size_function);
+
 // The default scheduling algorithm. Runs both the list scheduler
 // and the DFS scheduler, and chooses whichever returns a lower min-memory,
 // not accounting for fragmentation.
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 0571ff50554c5d..1c29abcb80d434 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -1868,6 +1868,7 @@ xla_test(
 xla_test(
     name = "local_client_execute_test",
     srcs = ["local_client_execute_test.cc"],
+    shard_count = 30,
     tags = ["optonly"],
     deps = [
         "//tensorflow/compiler/xla:literal_util",

From 5802096c267c805f6a69798aac10aefef759bb9f Mon Sep 17 00:00:00 2001
From: Akshay Agrawal 
Date: Mon, 7 May 2018 16:16:24 -0700
Subject: [PATCH 0475/1691] Refactor TensorArray to avoid copies and memory
 allocations when executing eagerly.

With this change, writes to TensorArrays when eager execution is enabled take O(1) time instead of O(n). Additionally, whereas writing to a TensorArray when constructing a graph results in allocating a new Python TensorArray object, writing to a TensorArray with eager enabled no longer performs that allocation (graph construction uses these allocations to ensure correctness of control flow and gradients, but this isn't necessary when executing eagerly). Finally, this change also removes the artificial write-once semantics of TensorArrays when executing eagerly.

PiperOrigin-RevId: 195739572
---
 .../kernel_tests/tensor_array_ops_test.py     |   1 -
 tensorflow/python/ops/tensor_array_ops.py     | 196 ++++++++----------
 2 files changed, 81 insertions(+), 116 deletions(-)

diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py
index 918bbd38edfd18..c0b36f143d109e 100644
--- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py
+++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py
@@ -438,7 +438,6 @@ def testTensorArrayReadWrongIndexOrDataTypeFails(self):
           "Tried to read from index 3 but array size is: 3"):
         self.evaluate(ta.read(3))
 
-  @test_util.run_in_graph_and_eager_modes()
   def testTensorArrayWriteMultipleFails(self):
     with self.test_session(use_gpu=True):
       ta = tensor_array_ops.TensorArray(
diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py
index d2f45ce37bbbbb..cc92da4fd7afd4 100644
--- a/tensorflow/python/ops/tensor_array_ops.py
+++ b/tensorflow/python/ops/tensor_array_ops.py
@@ -20,6 +20,7 @@
 from __future__ import print_function
 
 import contextlib
+import weakref
 
 from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
@@ -395,69 +396,8 @@ def close(self, name=None):
 # pylint: enable=protected-access
 
 
-# pylint: disable=protected-access
-def _eager_write_no_copy(ta, index, value):
-  """Writes value into an _EagerTensorArray without creating a new TensorArray.
-
-  Args:
-    ta: _EagerTensorArray into which to write value.
-    index: 0-D.  int32 scalar with the index to write to.
-    value: N-D.  Tensor of type `dtype`.  The Tensor to write to this index.
-
-  Raises:
-    errors_impl.AlreadyExistsError: attempting to overwrite an entry.
-    errors_impl.InvalidArgumentError: value dtype does not match `ta`'s dtype.
-    errors_impl.OutOfRangeError: `index` is out of bounds.
-    ValueError: shape of `value` is not consistent with inferred shape.
-  """
-
-  if isinstance(index, ops.EagerTensor):
-    index = index.numpy()
-
-  if index < 0:
-    raise errors_impl.OutOfRangeError(
-        None, None,
-        "Writing to negative indices (index %d) is not allowed." % index)
-
-  tensor_array = ta._tensor_array
-  size = len(tensor_array)
-  if index >= size:
-    if not ta._dynamic_size:
-      raise errors_impl.OutOfRangeError(
-          None, None,
-          "Tried to write to index %d but array is not resizeable and size "
-          "is: %d" % (index, size))
-    tensor_array.extend([None for _ in range(index - size + 1)])
-
-  if not isinstance(value, ops.EagerTensor):
-    value = constant_op.constant(value)
-
-  if ta._infer_shape:
-    if ta._element_shape is None:
-      ta._element_shape = value.shape
-    elif ta._element_shape != value.shape:
-      raise ValueError("Incompatible shape for value (%s), expected (%s)" %
-                       (value.shape.as_list(), ta._element_shape.as_list()))
-
-  if ta._dtype != value.dtype:
-    raise errors_impl.InvalidArgumentError(
-        None, None,
-        "TensorArray dtype is %s but Op is trying to write dtype %s" %
-        (ta._dtype.name, value.dtype.name))
-
-  if ta._tensor_array[index] is not None:
-    raise errors_impl.AlreadyExistsError(
-        None, None,
-        "Could not write to TensorArray index %d because it has already been "
-        "written to." % index)
-
-  tensor_array[index] = value
-
-# pylint: enable=protected-access
-
-
 class _EagerTensorArray(object):
-  """Eager-mode implementation of TensorArray.
+  """Eager-compatible implementation of TensorArray.
   """
 
   def __init__(self,
@@ -472,7 +412,7 @@ def __init__(self,
                element_shape=None,
                colocate_with_first_write_call=True,
                name=None):
-    """Constructs an Eager mode TensorArray.
+    """Constructs a TensorArray compatible with eager execution.
 
     Args:
       dtype: (required) data type of the TensorArray.
@@ -495,16 +435,19 @@ def __init__(self,
       ValueError: handle or flow are supplied, or if size is not supplied.
     """
 
-    del (flow, tensor_array_name, name)  # not meaningful in Eager
+    del (flow, tensor_array_name, name)  # Unused.
 
     if handle is not None:
-      raise ValueError("TensorArray handles are not supported in Eager mode.")
+      raise ValueError("TensorArray handles are not supported when eager "
+                       "execution is enabled.")
     if size is None:
-      raise ValueError("Size must be declared for TensorArrays in Eager mode.")
+      raise ValueError("Size must be declared for TensorArrays when eager "
+                       "execution is enabled.")
 
-    # These attributes are not meaningful in Eager, but some library functions
-    # (e.g., those in control_flow_ops.py) access them to create new tensor
-    # arrays; as such, we define them for the sake of compatibility.
+    # These attributes are not meaningful when eager is enabled, but some
+    # library functions (e.g., those in control_flow_ops.py) access them to
+    # create new tensor arrays; as such, we define them for the sake of
+    # compatibility.
     self._handle = None
     # we assign a dummy value to _flow in case other code assumes it to be
     # a Tensor
@@ -525,7 +468,7 @@ def __init__(self,
 
   @property
   def flow(self):
-    """Flows are not meaningful in Eager; this exists for compatibility."""
+    """For compatibility; flows are not meaningful when eager is enabled."""
     return self._flow
 
   @property
@@ -534,42 +477,22 @@ def dtype(self):
 
   @property
   def handle(self):
-    """Handles are not meaningful in Eager; this exists for compatibility."""
+    """For compatibility; handles are not meaningful when eager is enabled."""
     return self._handle
 
-  def _identity_without_array(self):
-    """Returns a new TensorArray with the same properties as this Eager one.
-
-    NB: Does not set the underlying _tensor_array attribute.
-    """
-    ta = TensorArray(
-        dtype=self._dtype,
-        size=len(self._tensor_array),
-        dynamic_size=self._dynamic_size,
-        clear_after_read=self._clear_after_read,
-        handle=self._handle,
-        flow=self._flow,
-        infer_shape=self._infer_shape,
-        element_shape=self._element_shape,
-        colocate_with_first_write_call=self._colocate_with_first_write_call)
-    ta._implementation._previously_read_indices = self._previously_read_indices  # pylint: disable=protected-access
-    return ta
-
   def identity(self):
     """See TensorArray."""
-    ta = self._identity_without_array()
-    ta._implementation._tensor_array = [t for t in self._tensor_array]  # pylint: disable=protected-access
-    return ta
+    return self.parent()
 
   def grad(self, source, flow=None, name=None):
     raise NotImplementedError(
-        "TensorArray.grad is not supported in Eager mode; Eager's gradient "
-        "implementation does not use/need this function to compute gradients "
-        "of operations that use TensorArrays.")
+        "TensorArray.grad is not supported when executing eagerly; eager's "
+        "gradient implementation does not use/need this function to compute "
+        "gradients of operations that use TensorArrays.")
 
   def read(self, index, name=None):
     """See TensorArray."""
-    del name  # not meaningful in Eager mode
+    del name  # not meaningful when executing eagerly.
 
     if isinstance(index, ops.EagerTensor):
       index = index.numpy()
@@ -600,12 +523,58 @@ def read(self, index, name=None):
       self._previously_read_indices.append(index)
     return tensor
 
+  def _write(self, index, value):
+    """Writes `value` into index named by `index`.
+
+    Args:
+      index: 0-D.  int32 scalar with the index to write to.
+      value: N-D.  Tensor of type `dtype`.  The `Tensor` to write to `index`.
+
+    Raises:
+      errors_impl.InvalidArgumentError: `value` dtype does not match dtype.
+      errors_impl.OutOfRangeError: `index` is out of bounds.
+      ValueError: shape of `value` is not consistent with inferred shape.
+    """
+
+    if isinstance(index, ops.EagerTensor):
+      index = index.numpy()
+
+    if index < 0:
+      raise errors_impl.OutOfRangeError(
+          None, None,
+          "Writing to negative indices (index %d) is not allowed." % index)
+
+    size = len(self._tensor_array)
+    if index >= size:
+      if not self._dynamic_size:
+        raise errors_impl.OutOfRangeError(
+            None, None,
+            "Tried to write to index %d but array is not resizeable and size "
+            "is: %d" % (index, size))
+      self._tensor_array.extend([None for _ in range(index - size + 1)])
+
+    if not isinstance(value, ops.EagerTensor):
+      value = constant_op.constant(value)
+
+    if self._infer_shape:
+      if self._element_shape is None:
+        self._element_shape = value.shape
+      elif self._element_shape != value.shape:
+        raise ValueError("Incompatible shape for value (%s), expected (%s)" %
+                         (value.shape.as_list(), self._element_shape.as_list()))
+
+    if self._dtype != value.dtype:
+      raise errors_impl.InvalidArgumentError(
+          None, None,
+          "TensorArray dtype is %s but Op is trying to write dtype %s" %
+          (self._dtype.name, value.dtype.name))
+    self._tensor_array[index] = value
+
   def write(self, index, value, name=None):
     """See TensorArray."""
-    del name  # not meaningful in Eager mode
-    ta = self.identity()
-    _eager_write_no_copy(ta._implementation, index, value)  # pylint: disable=protected-access
-    return ta
+    del name  # not meaningful when executing eagerly.
+    self._write(index, value)
+    return self.parent()
 
   def _maybe_zero(self, ix):
     val = self._tensor_array[ix]
@@ -623,7 +592,7 @@ def stack(self, name=None):
 
   def gather(self, indices, name=None):
     """See TensorArray."""
-    del name  # not meaningful in Eager mode
+    del name  # not meaningful when executing eagerly.
     return array_ops.stack([self._maybe_zero(i) for i in indices.numpy()])
 
   def concat(self, name=None):
@@ -651,17 +620,15 @@ def unstack(self, value, name=None):
       raise ValueError(
           "Cannot unstack %d tensors into a TensorArray of static size %d" %
           (len(tensors), len(self._tensor_array)))
-    ta = self._identity_without_array()
-    ta._implementation._tensor_array = tensors  # pylint: disable=protected-access
-    return ta
+    self._tensor_array = tensors
+    return self.parent()
 
   def scatter(self, indices, value, name=None):
     """See TensorArray."""
-    del name  # unused in Eager
-    ta = self.identity()
+    del name  # not meaningful when executing eagerly.
     for index, val in zip(indices.numpy(), array_ops.unstack(value)):
-      _eager_write_no_copy(ta._implementation, index, val)  # pylint: disable=protected-access
-    return ta
+      self._write(index, val)  # pylint: disable=protected-access
+    return self.parent()
 
   def split(self, value, lengths, name=None):
     """See TensorArray."""
@@ -690,20 +657,17 @@ def split(self, value, lengths, name=None):
           "dynamically resizeable" % (len(self._tensor_array),
                                       lengths.shape[0]))
     else:
-      ta = self._identity_without_array()
-      tensor_array = array_ops.split(value, lengths, name=name)
-      ta._implementation._tensor_array = tensor_array  # pylint: disable=protected-access
-      return ta
+      self._tensor_array = array_ops.split(value, lengths, name=name)
+      return self.parent()
 
   def size(self, name=None):
     """See TensorArray."""
-    del name  # not meaningful in Eager mode
+    del name  # not meaningful when executing eagerly.
     return constant_op.constant(len(self._tensor_array))
 
   def close(self, name=None):
-    del name  # not meaningful in Eager mode
+    del name  # not meaningful when executing eagerly.
     del self._tensor_array[:]
-    return
 
 
 # TensorArray is designed to hide an underlying implementation object
@@ -789,6 +753,8 @@ def __init__(self,
         colocate_with_first_write_call=colocate_with_first_write_call,
         name=name)
 
+    self._implementation.parent = weakref.ref(self)
+
   @property
   def flow(self):
     """The flow `Tensor` forcing ops leading to this TensorArray state."""

From 6e1784b6b4e0542de0ac3ebd790633c6db9cfe46 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne 
Date: Mon, 7 May 2018 16:16:32 -0700
Subject: [PATCH 0476/1691] ShapeRefiner fix: some variant-type tensors have
 handle data.

ShapeRefiner::AddNode() would only propagate handle data for
DT_RESOURCE tensors, but not DT_VARIANT. The Python shape inference
logic in common_shapes.py handled this correct, which is why we didn't
notice this earlier. In particular, list ops use DT_VARIANT with
handle data.
PiperOrigin-RevId: 195739586
---
 tensorflow/core/common_runtime/shape_refiner.cc | 13 ++++++-------
 tensorflow/python/kernel_tests/list_ops_test.py |  1 +
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/common_runtime/shape_refiner.cc b/tensorflow/core/common_runtime/shape_refiner.cc
index 06dbe049868b2f..a0772713d4c67e 100644
--- a/tensorflow/core/common_runtime/shape_refiner.cc
+++ b/tensorflow/core/common_runtime/shape_refiner.cc
@@ -232,13 +232,12 @@ Status ShapeRefiner::AddNode(const Node* node) {
     input_nodes[e->dst_input()] = input;
     input_shapes[e->dst_input()] = c->output(e->src_output());
 
-    // Only propagate handle data of edges which are carrying resource handles.
-    if (e->src()->output_type(e->src_output()) == DT_RESOURCE) {
-      const auto* in_v = c->output_handle_shapes_and_types(e->src_output());
-      if (in_v != nullptr) {
-        input_handle_shapes_and_types[e->dst_input()].reset(
-            new std::vector(*in_v));
-      }
+    const auto* in_v = c->output_handle_shapes_and_types(e->src_output());
+    if (in_v != nullptr) {
+      DataType input_type = e->src()->output_type(e->src_output());
+      DCHECK(input_type == DT_RESOURCE || input_type == DT_VARIANT);
+      input_handle_shapes_and_types[e->dst_input()].reset(
+          new std::vector(*in_v));
     }
   }
 
diff --git a/tensorflow/python/kernel_tests/list_ops_test.py b/tensorflow/python/kernel_tests/list_ops_test.py
index 098f9724a2a65f..49855200c2427a 100644
--- a/tensorflow/python/kernel_tests/list_ops_test.py
+++ b/tensorflow/python/kernel_tests/list_ops_test.py
@@ -43,6 +43,7 @@ def scalar_shape():
   return ops.convert_to_tensor([], dtype=dtypes.int32)
 
 
+@test_util.with_c_shapes
 class ListOpsTest(test_util.TensorFlowTestCase):
 
   @test_util.run_in_graph_and_eager_modes()

From 97fea64e69fbac87867343d92d2b47a2a582a79f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 16:31:07 -0700
Subject: [PATCH 0477/1691] Reorder executor NodeItem variable length data
 section so that all multi-byte aligned types precede all byte-aligned types
 so that alignment is satisfied without padding.

PiperOrigin-RevId: 195741712
---
 tensorflow/core/common_runtime/executor.cc | 26 ++++++++++------------
 1 file changed, 12 insertions(+), 14 deletions(-)

diff --git a/tensorflow/core/common_runtime/executor.cc b/tensorflow/core/common_runtime/executor.cc
index e389eb9b2a8b5c..7d63626b95d08b 100644
--- a/tensorflow/core/common_runtime/executor.cc
+++ b/tensorflow/core/common_runtime/executor.cc
@@ -272,9 +272,9 @@ struct NodeItem {
   // (uint8 is enough for DataType).
   //   EdgeInfo            out_edges[num_out_edges];
   //   AllocatorAttributes output_attr[num_outputs];
+  //   int                 forward_from[num_outputs];
   //   uint8               input_type[num_inputs];
   //   uint8               output_type[num_outputs];
-  //   int                 forward_from[num_outputs];
 
   // Return pointer to variable length section.
   char* var() const {
@@ -289,22 +289,20 @@ struct NodeItem {
     return reinterpret_cast(var() + sizeof(EdgeInfo) *
                                                               num_output_edges);
   }
+  int* forward_from_base() const {
+    return reinterpret_cast(var() + sizeof(EdgeInfo) * num_output_edges +
+                                  sizeof(AllocatorAttributes) * num_outputs);
+  }
   uint8* input_type_base() const {
-    return reinterpret_cast(var() +
-                                    sizeof(EdgeInfo) * num_output_edges +
-                                    sizeof(AllocatorAttributes) * num_outputs);
+    return reinterpret_cast(
+        var() + sizeof(EdgeInfo) * num_output_edges +
+        sizeof(AllocatorAttributes) * num_outputs + sizeof(int) * num_outputs);
   }
   uint8* output_type_base() const {
     return reinterpret_cast(
         var() + sizeof(EdgeInfo) * num_output_edges +
-        sizeof(AllocatorAttributes) * num_outputs + sizeof(uint8) * num_inputs);
-  }
-
-  int* forward_from_base() const {
-    return reinterpret_cast(var() + sizeof(EdgeInfo) * num_output_edges +
-                                  sizeof(AllocatorAttributes) * num_outputs +
-                                  sizeof(uint8) * num_inputs +
-                                  sizeof(uint8) * num_outputs);
+        sizeof(AllocatorAttributes) * num_outputs + sizeof(int) * num_outputs +
+        sizeof(uint8) * num_inputs);
   }
 
   TF_DISALLOW_COPY_AND_ASSIGN(NodeItem);
@@ -481,9 +479,9 @@ size_t GraphView::NodeItemBytes(const Node* n) {
       sizeof(NodeItem)                             // Fixed
       + num_output_edges * sizeof(EdgeInfo)        // output_edges[...]
       + num_outputs * sizeof(AllocatorAttributes)  // output_attr[...]
+      + num_outputs * sizeof(int)                  // forward_from[num_outputs]
       + num_inputs * sizeof(uint8)                 // input_type[num_inputs]
-      + num_outputs * sizeof(uint8)                // output_type[num_outputs]
-      + num_outputs * sizeof(int);                 // forward_from[num_outputs]
+      + num_outputs * sizeof(uint8);               // output_type[num_outputs]
   static constexpr size_t kItemAlignment = sizeof(NodeItem*);
   static_assert(kItemAlignment % alignof(NodeItem) == 0,
                 "NodeItem must be aligned with kItemAlignment");

From f64b16aa146aada0b2d20cafc0036a71f7460228 Mon Sep 17 00:00:00 2001
From: Billy Lamberta 
Date: Mon, 7 May 2018 16:38:02 -0700
Subject: [PATCH 0478/1691] Add TFX section. Add Ecosystem page and dropdown
 menu.

PiperOrigin-RevId: 195742728
---
 tensorflow/docs_src/deploy/index.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/docs_src/deploy/index.md b/tensorflow/docs_src/deploy/index.md
index 61edba04b46b7a..33220041895acd 100644
--- a/tensorflow/docs_src/deploy/index.md
+++ b/tensorflow/docs_src/deploy/index.md
@@ -15,3 +15,7 @@ the following documents:
     out-of-the-box integration with TensorFlow models.
     [Source code for TensorFlow Serving](https://github.com/tensorflow/serving)
     is available on GitHub.
+
+[TensorFlow Extended (TFX)](/tfx) is an end-to-end machine learning platform for
+TensorFlow. Implemented at Google, we've open sourced some TFX libraries with the
+rest of the system to come.

From a72ee2f74061cdd72f1197eed4c90a8216d39d74 Mon Sep 17 00:00:00 2001
From: Alexandre Passos 
Date: Mon, 7 May 2018 16:49:44 -0700
Subject: [PATCH 0479/1691] Fast-path to VarHandleOp

PiperOrigin-RevId: 195744374
---
 tensorflow/core/framework/resource_mgr.h | 18 ++++++++++++++----
 1 file changed, 14 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/framework/resource_mgr.h b/tensorflow/core/framework/resource_mgr.h
index c84ea3b034cc20..3cc17e1ca6b4a8 100644
--- a/tensorflow/core/framework/resource_mgr.h
+++ b/tensorflow/core/framework/resource_mgr.h
@@ -338,6 +338,9 @@ class ResourceHandleOp : public OpKernel {
  private:
   string container_;
   string name_;
+  mutex mutex_;
+  Tensor resource_ GUARDED_BY(mutex_);
+  std::atomic initialized_{false};
 };
 
 // Registers a kernel for an op which produces a handle to a resource of the
@@ -511,10 +514,17 @@ ResourceHandleOp::ResourceHandleOp(OpKernelConstruction* context)
 
 template 
 void ResourceHandleOp::Compute(OpKernelContext* ctx) {
-  Tensor* output = nullptr;
-  OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &output));
-  output->scalar()() =
-      MakeResourceHandle(ctx, container_, name_);
+  if (!initialized_.load()) {
+    mutex_lock ml(mutex_);
+    AllocatorAttributes attr;
+    attr.set_on_host(true);
+    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_RESOURCE, TensorShape({}),
+                                           &resource_, attr));
+    resource_.scalar()() =
+        MakeResourceHandle(ctx, container_, name_);
+    initialized_.store(true);
+  }
+  ctx->set_output(0, resource_);
 }
 
 }  //  end namespace tensorflow

From 3964bdeef88cb9f7824bbfc8ca4f44c7a4bd4dbd Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Mon, 7 May 2018 16:55:10 -0700
Subject: [PATCH 0480/1691] Delete kTransposeDot (it is no longer in use)

PiperOrigin-RevId: 195745124
---
 .../xla/service/cpu/cpu_layout_assignment.cc  | 10 ++---
 .../xla/service/cpu/dot_op_emitter.cc         | 13 ------
 .../compiler/xla/service/cpu/ir_emitter.cc    |  2 -
 .../service/cpu/parallel_task_assignment.cc   |  2 +-
 .../xla/service/gpu/ir_emission_utils.cc      |  4 --
 .../compiler/xla/service/hlo_instruction.cc   | 23 +---------
 .../compiler/xla/service/hlo_instruction.h    |  1 -
 .../xla/service/hlo_instruction_test.cc       | 11 +----
 .../compiler/xla/service/liveness_util.cc     | 22 ++++------
 .../xla/service/liveness_util_test.cc         | 42 -------------------
 10 files changed, 15 insertions(+), 115 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.cc b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.cc
index e8117377e61a4e..6c642080c34e72 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.cc
@@ -139,13 +139,9 @@ Status CpuLayoutAssignment::AddBackendConstraints(
       Shape lhs_shape(RowMajorShape(lhs_instruction->shape()));
       TF_RETURN_IF_ERROR(constraints->SetOperandLayout(lhs_shape, dot, 0));
 
-      // dot is a kDot or a kTransposeDot fusion node.  In the latter case, if
-      // it represents X @ X, it may have just one operand.
-      if (dot->operand_count() > 1) {
-        const HloInstruction* rhs_instruction = dot->operand(1);
-        Shape rhs_shape(RowMajorShape(rhs_instruction->shape()));
-        TF_RETURN_IF_ERROR(constraints->SetOperandLayout(rhs_shape, dot, 1));
-      }
+      const HloInstruction* rhs_instruction = dot->operand(1);
+      Shape rhs_shape(RowMajorShape(rhs_instruction->shape()));
+      TF_RETURN_IF_ERROR(constraints->SetOperandLayout(rhs_shape, dot, 1));
 
       // Set layouts of the instructions' shapes.
       TF_RETURN_IF_ERROR(constraints->SetInstructionLayout(output_shape, dot));
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
index e5ac2a33e1756e..8db4a0650d2867 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
@@ -1098,19 +1098,6 @@ bool PotentiallyImplementedAsEigenDot(const HloInstruction& hlo) {
     }
   }
 
-  if (hlo.opcode() == HloOpcode::kFusion &&
-      hlo.fusion_kind() == HloInstruction::FusionKind::kTransposeDot &&
-      hlo.fused_expression_root()->opcode() == HloOpcode::kDot) {
-    auto* dot = hlo.fused_expression_root();
-    const Shape& lhs_shape = dot->operand(0)->shape();
-    const Shape& rhs_shape = dot->operand(1)->shape();
-    if (ShapeUtil::HasZeroElements(lhs_shape) ||
-        ShapeUtil::HasZeroElements(rhs_shape)) {
-      return false;
-    }
-    return true;
-  }
-
   return false;
 }
 
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 12f50e00b5a330..55e5aa5063d0ed 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -2077,8 +2077,6 @@ static const HloInstruction* StripTranspose(const HloInstruction& hlo) {
 }
 
 Status IrEmitter::HandleFusion(HloInstruction* fusion) {
-  CHECK_NE(fusion->fusion_kind(), HloInstruction::FusionKind::kTransposeDot);
-
   auto* root = fusion->fused_expression_root();
   if (llvm_ir::CanEmitFusedDynamicUpdateSliceInPlace(fusion, assignment_)) {
     VLOG(3) << "HandleFusion FusedDynamicUpdateSliceInPlace";
diff --git a/tensorflow/compiler/xla/service/cpu/parallel_task_assignment.cc b/tensorflow/compiler/xla/service/cpu/parallel_task_assignment.cc
index fb28280fade307..47e8405ff2ea2c 100644
--- a/tensorflow/compiler/xla/service/cpu/parallel_task_assignment.cc
+++ b/tensorflow/compiler/xla/service/cpu/parallel_task_assignment.cc
@@ -127,7 +127,7 @@ int64 ParallelTaskAssignment::GetTargetParallelTaskCount(
   // Currently, we do not assign parallel tasks to instructions with at least
   // one of the following properties:
   // *) Internal threading (library calls to kConv, kDot, kFft, kCustomCall).
-  // *) Emit custom loops (kSelectAndScatter, FusionKind::kTransposeDot).
+  // *) Emit custom loops (kSelectAndScatter).
   // *) Operations that are not thread safe (like infeed and rng).
   // *) Tuple-shaped.
   // TODO(b/27458679) Parallelize instructions which are skipped here.
diff --git a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc
index 777345722cf846..96199035b9e6d3 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc
+++ b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc
@@ -85,10 +85,6 @@ bool ImplementedAsGemm(const HloInstruction& hlo) {
     }
   }
 
-  if (hlo.opcode() == HloOpcode::kFusion) {
-    CHECK_NE(hlo.fusion_kind(), HloInstruction::FusionKind::kTransposeDot);
-  }
-
   if (hlo.opcode() == HloOpcode::kFusion &&
       hlo.fusion_kind() == HloInstruction::FusionKind::kOutput &&
       hlo.fused_expression_root()->opcode() == HloOpcode::kMultiply) {
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index f9189077a1b0fc..857cd39adb8d32 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -793,23 +793,11 @@ HloInstruction::CreateBroadcastSequence(
   return instruction;
 }
 
-// We put the fusion kind into the instruction's name for transpose-dot fusions,
-// since those fusions are really just describing a type of dot rather than
-// generating a novel computation.
-static string FusionNodeName(HloInstruction::FusionKind fusion_kind) {
-  switch (fusion_kind) {
-    case HloInstruction::FusionKind::kTransposeDot:
-      return "dot_fusion";
-    default:
-      return "fusion";
-  }
-}
-
 /* static */ std::unique_ptr HloInstruction::CreateFusion(
     const Shape& shape, FusionKind fusion_kind, HloInstruction* fused_root) {
   auto instruction = WrapUnique(new HloInstruction(HloOpcode::kFusion, shape));
   instruction->fusion_kind_ = fusion_kind;
-  instruction->name_ = FusionNodeName(fusion_kind);
+  instruction->name_ = "fusion";
   instruction->set_parent(fused_root->parent());
   instruction->set_metadata(fused_root->metadata());
   instruction->CloneAndFuseInternal(fused_root);
@@ -825,7 +813,7 @@ static string FusionNodeName(HloInstruction::FusionKind fusion_kind) {
     instruction->AppendOperand(operand);
   }
   instruction->fusion_kind_ = fusion_kind;
-  instruction->name_ = FusionNodeName(fusion_kind);
+  instruction->name_ = "fusion";
   instruction->called_computations_.push_back(fusion_computation);
   fusion_computation->SetFusionInstruction(instruction.get());
   return instruction;
@@ -2442,8 +2430,6 @@ string HloInstruction::ToCategory() const {
         return "input fusion";
       case FusionKind::kOutput:
         return "output fusion";
-      case FusionKind::kTransposeDot:
-        return "dot";
       case FusionKind::kCustom:
         return "custom fusion";
     }
@@ -3226,8 +3212,6 @@ string ToString(HloInstruction::FusionKind kind) {
       return "kInput";
     case HloInstruction::FusionKind::kOutput:
       return "kOutput";
-    case HloInstruction::FusionKind::kTransposeDot:
-      return "kTransposeDot";
     case HloInstruction::FusionKind::kCustom:
       return "kCustom";
   }
@@ -3244,9 +3228,6 @@ StatusOr StringToFusionKind(
   if (kind_name == "kOutput") {
     return HloInstruction::FusionKind::kOutput;
   }
-  if (kind_name == "kTransposeDot") {
-    return HloInstruction::FusionKind::kTransposeDot;
-  }
   if (kind_name == "kCustom") {
     return HloInstruction::FusionKind::kCustom;
   }
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 0bf2c589e4bb2f..14be58d069e0d8 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -177,7 +177,6 @@ class HloInstruction {
     kOutput,        // Op's output is fused into the op itself.
                     // REQUIRES: At least one operand buffer must be able
                     // to alias the output buffer.
-    kTransposeDot,  // Fused into a dot with transposed operands.
     kCustom,        // Custom category for backend-specific fusions that
                     // do not match any of the more specific ones.
   };
diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
index 5b65b1152c8298..909cdc0b6269ed 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
@@ -1102,7 +1102,7 @@ TEST_F(HloInstructionTest, CloneOfFusionPreservesShape) {
   auto module = CreateNewModule();
   auto* computation = module->AddEntryComputation(builder.Build());
   HloInstruction* fusion = computation->CreateFusionInstruction(
-      {dot, reshape}, HloInstruction::FusionKind::kTransposeDot);
+      {dot, reshape}, HloInstruction::FusionKind::kLoop);
 
   auto fusion2 = fusion->Clone();
   const HloInstruction* root = fusion->fused_expression_root();
@@ -1169,7 +1169,7 @@ TEST_F(HloInstructionTest, NestedFusionEquality) {
   auto computation = module->AddEntryComputation(builder.Build());
 
   auto nested_fusion = computation->CreateFusionInstruction(
-      {dot, b_t}, HloInstruction::FusionKind::kTransposeDot);
+      {dot, b_t}, HloInstruction::FusionKind::kLoop);
 
   auto fusion = computation->CreateFusionInstruction(
       {add, nested_fusion}, HloInstruction::FusionKind::kOutput);
@@ -1246,13 +1246,6 @@ TEST_F(HloInstructionTest, Stringification) {
 
   auto module = CreateNewModule();
   auto* computation = module->AddEntryComputation(builder.Build());
-  HloInstruction* fusion = computation->CreateFusionInstruction(
-      {dot, reshape}, HloInstruction::FusionKind::kTransposeDot);
-
-  EXPECT_EQ(
-      fusion->ToString(options),
-      "%dot_fusion = f32[5,20]{1,0} fusion(f32[5,10]{1,0} %x, "
-      "f32[20,10]{1,0} %y), kind=kTransposeDot, calls=%fused_computation");
 
   HloInstruction* loop = builder.AddInstruction(
       HloInstruction::CreateWhile(sout, computation, computation, x));
diff --git a/tensorflow/compiler/xla/service/liveness_util.cc b/tensorflow/compiler/xla/service/liveness_util.cc
index 68c99256a246ed..79dfd1e409f155 100644
--- a/tensorflow/compiler/xla/service/liveness_util.cc
+++ b/tensorflow/compiler/xla/service/liveness_util.cc
@@ -173,9 +173,9 @@ bool HasUniqueFusedUseOfOperandAt(
 // (2) Is a loop fusion instruction where the only use of 'operand' at 'index'
 //     in the set 'user.fused_instructions' is a DynamicUpdateSlice fused root
 //     at operand 0. Or...
-// (3) Is a kDot -> kAdd (or fused kTransposeDot -> kAdd) output fusion
-//     instruction where the only use of 'operand' at 'index' in the set
-//     'user.fused_instructions' is a kAdd fused root at operand 0 or 1. Or...
+// (3) Is a kDot -> kAdd output fusion instruction where the only use of
+//     'operand' at 'index' in the set 'user.fused_instructions' is a kAdd fused
+//     root at operand 0 or 1. Or...
 // (4) The 'user' of 'operand' is DynamicUpdateSlice or While at operand index
 //     0.
 //
@@ -209,17 +209,13 @@ bool CanShareOperandBufferWithUser(
                user->fused_expression_root()->opcode() == HloOpcode::kAdd) {
       // Output fusion with kAdd fused root.
 
-      // Check if one operand of kAdd fused root is either kDot, or nested
-      // kFusion of kind kTransposeDot.
+      // Check if one operand of kAdd fused root is kDot or kConvolution.
       auto* add = user->fused_expression_root();
       auto add_operand_it =
           std::find_if(add->operands().begin(), add->operands().end(),
                        [&](HloInstruction* operand) {
                          return operand->opcode() == HloOpcode::kConvolution ||
-                                operand->opcode() == HloOpcode::kDot ||
-                                (operand->opcode() == HloOpcode::kFusion &&
-                                 operand->fusion_kind() ==
-                                     HloInstruction::FusionKind::kTransposeDot);
+                                operand->opcode() == HloOpcode::kDot;
                        });
       if (add_operand_it == add->operands().end()) {
         return false;
@@ -314,17 +310,13 @@ bool CanShareOperandBufferWithUser(HloInstruction* operand,
                user->fused_expression_root()->opcode() == HloOpcode::kAdd) {
       // Output fusion with kAdd fused root.
 
-      // Check if one operand of kAdd fused root is either kDot, or nested
-      // kFusion of kind kTransposeDot.
+      // Check if one operand of kAdd fused root is kDot, or kConvolution.
       auto* add = user->fused_expression_root();
       auto add_operand_it =
           std::find_if(add->operands().begin(), add->operands().end(),
                        [&](HloInstruction* operand) {
                          return operand->opcode() == HloOpcode::kConvolution ||
-                                operand->opcode() == HloOpcode::kDot ||
-                                (operand->opcode() == HloOpcode::kFusion &&
-                                 operand->fusion_kind() ==
-                                     HloInstruction::FusionKind::kTransposeDot);
+                                operand->opcode() == HloOpcode::kDot;
                        });
       if (add_operand_it == add->operands().end()) {
         return false;
diff --git a/tensorflow/compiler/xla/service/liveness_util_test.cc b/tensorflow/compiler/xla/service/liveness_util_test.cc
index f8b309488eeb53..c01b52df62ee67 100644
--- a/tensorflow/compiler/xla/service/liveness_util_test.cc
+++ b/tensorflow/compiler/xla/service/liveness_util_test.cc
@@ -303,48 +303,6 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedDotAdd) {
                                             *dataflow_analysis_));
 }
 
-TEST_F(CanShareOperandBufferWithUserTest, FusedTransposeDotAdd) {
-  auto builder = HloComputation::Builder(TestName());
-  Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2});
-
-  auto a = builder.AddInstruction(HloInstruction::CreateConstant(
-      Literal::CreateR2({{1.0, 0.0}, {0.0, 1.0}})));
-  auto b = builder.AddInstruction(HloInstruction::CreateConstant(
-      Literal::CreateR2({{2.0, 2.0}, {2.0, 2.0}})));
-  auto b_t = builder.AddInstruction(
-      HloInstruction::CreateTranspose(data_shape, b, {1, 0}));
-
-  DotDimensionNumbers dot_dnums;
-  dot_dnums.add_lhs_contracting_dimensions(1);
-  dot_dnums.add_rhs_contracting_dimensions(0);
-  auto dot = builder.AddInstruction(
-      HloInstruction::CreateDot(data_shape, a, b_t, dot_dnums));
-
-  auto one = builder.AddInstruction(
-      HloInstruction::CreateConstant(Literal::CreateR0(1.0)));
-  auto add_operand = builder.AddInstruction(
-      HloInstruction::CreateBroadcast(data_shape, one, {1}));
-
-  auto add = builder.AddInstruction(HloInstruction::CreateBinary(
-      data_shape, HloOpcode::kAdd, dot, add_operand));
-
-  BuildModule(builder.Build());
-
-  auto nested_fusion = computation_->CreateFusionInstruction(
-      {dot, b_t}, HloInstruction::FusionKind::kTransposeDot);
-
-  auto fusion = computation_->CreateFusionInstruction(
-      {add, nested_fusion}, HloInstruction::FusionKind::kOutput);
-  RunAnalysis();
-
-  // Output fused transpose-dot-add should be share buffer with 'add_operand'.
-  EXPECT_TRUE(CanShareOperandBufferWithUser(add_operand, {}, fusion, {},
-                                            *points_to_analysis_));
-
-  EXPECT_TRUE(CanShareOperandBufferWithUser(add_operand, {}, fusion, {},
-                                            *dataflow_analysis_));
-}
-
 TEST_F(CanShareOperandBufferWithUserTest, OutputFusionCantAliasOperandBuffer) {
   auto builder = HloComputation::Builder(TestName());
   Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2});

From db63348bf14d911f2eebeb418a0b570b65b64f92 Mon Sep 17 00:00:00 2001
From: Jacques Pienaar 
Date: Mon, 7 May 2018 16:59:41 -0700
Subject: [PATCH 0481/1691] Add test with tf.cond.

PiperOrigin-RevId: 195745718
---
 tensorflow/compiler/aot/tests/BUILD           | 14 +++++++++
 .../compiler/aot/tests/make_test_graphs.py    | 29 ++++++++++++-------
 .../aot/tests/test_graph_tfcond.config.pbtxt  | 20 +++++++++++++
 .../compiler/aot/tests/tfcompile_test.cc      | 26 +++++++++++++++++
 4 files changed, 79 insertions(+), 10 deletions(-)
 create mode 100644 tensorflow/compiler/aot/tests/test_graph_tfcond.config.pbtxt

diff --git a/tensorflow/compiler/aot/tests/BUILD b/tensorflow/compiler/aot/tests/BUILD
index 222e26810ac115..fd2cf2b67d4618 100644
--- a/tensorflow/compiler/aot/tests/BUILD
+++ b/tensorflow/compiler/aot/tests/BUILD
@@ -15,6 +15,7 @@ test_suite(
         ":test_graph_tfadd_with_ckpt_saver_test",
         ":test_graph_tfadd_with_ckpt_test",
         ":test_graph_tfassert_eq_test",
+        ":test_graph_tfcond_test",
         ":test_graph_tffunction_test",
         ":test_graph_tfgather_test",
         ":test_graph_tfmatmul_test",
@@ -55,6 +56,7 @@ genrule(
         "test_graph_tfadd_with_ckpt_saver.pb",
         "test_graph_tfadd_with_ckpt_saver.saver",
         "test_graph_tfassert_eq.pb",
+        "test_graph_tfcond.pb",
         "test_graph_tffunction.pb",
         "test_graph_tfgather.pb",
         "test_graph_tfmatmul.pb",
@@ -118,6 +120,17 @@ tf_library(
     ],
 )
 
+tf_library(
+    name = "test_graph_tfcond",
+    testonly = 1,
+    config = "test_graph_tfcond.config.pbtxt",
+    cpp_class = "CondComp",
+    graph = "test_graph_tfcond.pb",
+    tags = [
+        "manual",
+    ],
+)
+
 tf_library(
     name = "test_graph_tffunction",
     testonly = 1,
@@ -194,6 +207,7 @@ tf_cc_test(
         ":test_graph_tfadd_with_ckpt",
         ":test_graph_tfadd_with_ckpt_saver",
         ":test_graph_tfassert_eq",
+        ":test_graph_tfcond",
         ":test_graph_tffunction",
         ":test_graph_tfgather",
         ":test_graph_tfmatmul",
diff --git a/tensorflow/compiler/aot/tests/make_test_graphs.py b/tensorflow/compiler/aot/tests/make_test_graphs.py
index 67767f55dae9b1..9ec7df163b1425 100644
--- a/tensorflow/compiler/aot/tests/make_test_graphs.py
+++ b/tensorflow/compiler/aot/tests/make_test_graphs.py
@@ -78,6 +78,22 @@ def tfadd_with_ckpt_saver(out_dir):
       f.write(saver.as_saver_def().SerializeToString())
 
 
+def tfassert_eq(_):
+  x = array_ops.placeholder(dtypes.int32, name='x_hold')
+  y = array_ops.placeholder(dtypes.int32, name='y_hold')
+  control_flow_ops.Assert(
+      math_ops.equal(x, y), ['Expected x == y.'], name='assert_eq')
+  math_ops.add(x, math_ops.negative(y), name='x_y_diff')
+
+
+def tfcond(_):
+  p = array_ops.placeholder(dtypes.bool, name='p_hold')
+  x = array_ops.placeholder(dtypes.int32, name='x_hold')
+  y = array_ops.placeholder(dtypes.int32, name='y_hold')
+  z = control_flow_ops.cond(p, lambda: x, lambda: y)
+  array_ops.identity(z, name='result')
+
+
 def tfgather(_):
   params = array_ops.placeholder(dtypes.float32, name='params')
   indices = array_ops.placeholder(dtypes.int32, name='indices')
@@ -126,14 +142,6 @@ def tfsplits(_):
   array_ops.identity(y, name='result')
 
 
-def tfassert_eq(_):
-  x = array_ops.placeholder(dtypes.int32, name='x_hold')
-  y = array_ops.placeholder(dtypes.int32, name='y_hold')
-  control_flow_ops.Assert(
-      math_ops.equal(x, y), ['Expected x == y.'], name='assert_eq')
-  math_ops.add(x, math_ops.negative(y), name='x_y_diff')
-
-
 def write_graph(build_graph, out_dir):
   """Build a graph using build_graph and write it out."""
   g = ops.Graph()
@@ -148,12 +156,13 @@ def main(_):
   write_graph(tfadd, FLAGS.out_dir)
   write_graph(tfadd_with_ckpt, FLAGS.out_dir)
   write_graph(tfadd_with_ckpt_saver, FLAGS.out_dir)
+  write_graph(tfassert_eq, FLAGS.out_dir)
+  write_graph(tfcond, FLAGS.out_dir)
+  write_graph(tffunction, FLAGS.out_dir)
   write_graph(tfgather, FLAGS.out_dir)
   write_graph(tfmatmul, FLAGS.out_dir)
   write_graph(tfmatmulandadd, FLAGS.out_dir)
-  write_graph(tffunction, FLAGS.out_dir)
   write_graph(tfsplits, FLAGS.out_dir)
-  write_graph(tfassert_eq, FLAGS.out_dir)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/compiler/aot/tests/test_graph_tfcond.config.pbtxt b/tensorflow/compiler/aot/tests/test_graph_tfcond.config.pbtxt
new file mode 100644
index 00000000000000..94a01ad4abfaab
--- /dev/null
+++ b/tensorflow/compiler/aot/tests/test_graph_tfcond.config.pbtxt
@@ -0,0 +1,20 @@
+# Text form of tensorflow.tf2xla.Config proto.
+feed {
+  id { node_name: "p_hold" }
+  shape {}
+}
+feed {
+  id { node_name: "x_hold" }
+  shape {
+    dim { size: 1 }
+  }
+}
+feed {
+  id { node_name: "y_hold" }
+  shape {
+    dim { size: 1 }
+  }
+}
+fetch {
+  id { node_name: "result" }
+}
diff --git a/tensorflow/compiler/aot/tests/tfcompile_test.cc b/tensorflow/compiler/aot/tests/tfcompile_test.cc
index 27ba42b31fc250..309a991fc11ab7 100644
--- a/tensorflow/compiler/aot/tests/tfcompile_test.cc
+++ b/tensorflow/compiler/aot/tests/tfcompile_test.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/compiler/aot/tests/test_graph_tfadd_with_ckpt.h"
 #include "tensorflow/compiler/aot/tests/test_graph_tfadd_with_ckpt_saver.h"
 #include "tensorflow/compiler/aot/tests/test_graph_tfassert_eq.h"
+#include "tensorflow/compiler/aot/tests/test_graph_tfcond.h"
 #include "tensorflow/compiler/aot/tests/test_graph_tffunction.h"
 #include "tensorflow/compiler/aot/tests/test_graph_tfgather.h"
 #include "tensorflow/compiler/aot/tests/test_graph_tfmatmul.h"
@@ -150,6 +151,31 @@ TEST(TFCompileTest, AddWithCkptSaver) {
   EXPECT_EQ(add_const.result0_data(), add_const.results()[0]);
 }
 
+TEST(TFCompileTest, Cond) {
+  CondComp cond;
+  EXPECT_EQ(cond.arg0_data(), cond.args()[0]);
+  EXPECT_EQ(cond.arg1_data(), cond.args()[1]);
+  EXPECT_EQ(cond.arg2_data(), cond.args()[2]);
+  cond.arg1() = 10;
+  cond.arg2() = 20;
+  {
+    cond.arg0() = true;
+    const int32 expected_result = cond.arg1();
+    EXPECT_TRUE(cond.Run());
+    EXPECT_EQ(cond.result0(), expected_result);
+    EXPECT_EQ(cond.result0_data()[0], expected_result);
+    EXPECT_EQ(cond.result0_data(), cond.results()[0]);
+  }
+  {
+    cond.arg0() = false;
+    const int32 expected_result = cond.arg2();
+    EXPECT_TRUE(cond.Run());
+    EXPECT_EQ(cond.result0(), expected_result);
+    EXPECT_EQ(cond.result0_data()[0], expected_result);
+    EXPECT_EQ(cond.result0_data(), cond.results()[0]);
+  }
+}
+
 TEST(TFCompileTest, Gather) {
   GatherComp gather;
   EXPECT_EQ(gather.arg0_data(), gather.args()[0]);

From b67d8b278d48a046491b42eccbd5c5c23975d054 Mon Sep 17 00:00:00 2001
From: Blake Hechtman 
Date: Mon, 7 May 2018 17:00:27 -0700
Subject: [PATCH 0482/1691] Internal change

PiperOrigin-RevId: 195745819
---
 tensorflow/compiler/xla/tests/BUILD | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 1c29abcb80d434..b982cf0dbc4ed0 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -1867,6 +1867,8 @@ xla_test(
 
 xla_test(
     name = "local_client_execute_test",
+    # TODO(b/79375911): Test times out in LLVM at normal size.
+    size = "large",
     srcs = ["local_client_execute_test.cc"],
     shard_count = 30,
     tags = ["optonly"],

From 482ed8eb666d8bc1e5c3f47e5c1e61cc19e0fdb1 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne 
Date: Mon, 7 May 2018 17:21:39 -0700
Subject: [PATCH 0483/1691] Raise an error if we try to take the gradient wrt
 to the initial value of a loop variable.

Fixes #14101

PiperOrigin-RevId: 195748688
---
 .../kernel_tests/control_flow_ops_py_test.py  | 17 ++++++++
 tensorflow/python/ops/gradients_impl.py       | 39 +++++++++++++++++++
 2 files changed, 56 insertions(+)

diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
index 77e6f5f1a0d645..843759fed08446 100644
--- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
+++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
@@ -1847,6 +1847,23 @@ def fn1():
       r = control_flow_ops.cond(math_ops.less(1, 2), fn1, lambda: x)
       self.assertAllClose(9.0, r.eval(feed_dict={x: 1.0}))
 
+  def testGradInWhileWrtInitialLoopVal(self):
+    with self.test_session():
+      x = array_ops.placeholder(dtypes.float32, shape=(), name="x")
+      y = x + 1
+
+      def body(i, v):
+        z = v * 2
+        return i + 1, gradients_impl.gradients(z, x)[0]
+
+      with self.assertRaisesRegexp(
+          ValueError,
+          "Cannot compute gradient inside while loop with respect to op 'x'. "
+          "We do not support taking the gradient wrt or through the initial "
+          "value of a loop variable. Gradients can be computed through "
+          "loop invariants or wrt the input parameters to the loop body."):
+        control_flow_ops.while_loop(lambda i, x: i < 3, body, [0, y])
+
   def testWhileGradInWhile(self):
     with self.test_session():
       n = ops.convert_to_tensor(1.0, name="n")
diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py
index a6b1e6df54381a..069b5a43086100 100644
--- a/tensorflow/python/ops/gradients_impl.py
+++ b/tensorflow/python/ops/gradients_impl.py
@@ -418,6 +418,30 @@ def _MaybeCompile(scope, op, func, grad_fn):
     return grad_fn()
 
 
+def _RaiseNoGradWrtInitialLoopValError(op, from_ops):
+  """Raises an error if we backprop through a loop var."""
+  # Find the nearest 'to_op' reachable from 'op' to provide a more helpful error
+  # message.
+  target_op = None
+  queue = collections.deque([op])
+  visited = set()
+  while queue:
+    curr_op = queue.popleft()
+    if curr_op in visited: continue
+    visited.add(curr_op)
+    if curr_op in from_ops:
+      target_op = curr_op
+      break
+    queue.extend(t.op for t in curr_op.inputs)
+  assert target_op
+  raise ValueError(
+      "Cannot compute gradient inside while loop with respect to op '%s'. "
+      "We do not support taking the gradient wrt or through the initial value "
+      "of a loop variable. Gradients can be computed through loop invariants "
+      "or wrt the input parameters to the loop body."
+      % target_op.name)
+
+
 @tf_export("gradients")
 def gradients(ys,
               xs,
@@ -630,6 +654,21 @@ def _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops,
                   (op.name, op.type))
         if loop_state:
           loop_state.EnterGradWhileContext(op, before=False)
+
+        # NOTE(skyewm): We don't support computing gradients wrt a loop variable
+        # unless it's within the context of a single iteration (i.e. the
+        # gradient is wrt to the loop parameter in the body function, not wrt or
+        # through the initial value). This means if we're in a while loop
+        # context, we should never see a switch node from this context.
+        # pylint: disable=protected-access
+        if (control_flow_util.IsSwitch(op) and
+            op._control_flow_context is not None and
+            op._control_flow_context.IsWhileContext() and
+            op._control_flow_context ==
+            ops.get_default_graph()._get_control_flow_context()):
+          _RaiseNoGradWrtInitialLoopValError(op, from_ops)
+        # pylint: enable=protected-access
+
         if (grad_fn or is_func_call) and has_out_grads:
           # NOTE: If _AggregatedGrads didn't compute a value for the i'th
           # output, it means that the cost does not depend on output[i],

From b6906d19bacffa25fec074216a5c281e5689ef03 Mon Sep 17 00:00:00 2001
From: Igor Ganichev 
Date: Mon, 7 May 2018 17:21:53 -0700
Subject: [PATCH 0484/1691] Make eager functions runable on TPU

PiperOrigin-RevId: 195748721
---
 tensorflow/compiler/jit/BUILD                 |  22 ++
 .../compiler/jit/create_xla_launch_op.cc      | 206 ++++++++++++++----
 .../compiler/jit/create_xla_launch_op.h       |  35 +++
 .../compiler/jit/create_xla_launch_op_test.cc | 144 ++++++++++++
 .../compiler/jit/kernels/xla_launch_op.cc     |  90 ++++++--
 .../compiler/jit/kernels/xla_launch_op.h      |  51 +++--
 .../compiler/jit/xla_compile_on_demand_op.cc  |   3 +-
 tensorflow/compiler/jit/xla_launch_util.cc    |  18 +-
 tensorflow/compiler/jit/xla_launch_util.h     |  15 +-
 tensorflow/compiler/tests/BUILD               |   4 +
 tensorflow/compiler/tests/eager_test.py       | 112 +++++++++-
 .../python/examples/resnet50/resnet50_test.py |  55 +++--
 tensorflow/python/eager/function.py           | 127 +++++++----
 13 files changed, 718 insertions(+), 164 deletions(-)
 create mode 100644 tensorflow/compiler/jit/create_xla_launch_op.h
 create mode 100644 tensorflow/compiler/jit/create_xla_launch_op_test.cc

diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index 07136d6a746604..e942b46086c717 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -261,6 +261,7 @@ cc_library(
     name = "create_xla_launch_op",
     srcs = [
         "create_xla_launch_op.cc",
+        "create_xla_launch_op.h",
     ],
     deps = [
         ":common",
@@ -270,6 +271,27 @@ cc_library(
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+    ],
+    alwayslink = 1,
+)
+
+tf_cc_test(
+    name = "create_xla_launch_op_test",
+    srcs = [
+        "create_xla_launch_op.h",
+        "create_xla_launch_op_test.cc",
+    ],
+    deps = [
+        ":create_xla_launch_op",
+        "//tensorflow/core:core_cpu_internal",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:session_options",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
     ],
 )
 
diff --git a/tensorflow/compiler/jit/create_xla_launch_op.cc b/tensorflow/compiler/jit/create_xla_launch_op.cc
index 18d901323f1085..6ac84dc19ce40b 100644
--- a/tensorflow/compiler/jit/create_xla_launch_op.cc
+++ b/tensorflow/compiler/jit/create_xla_launch_op.cc
@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
+#include "tensorflow/compiler/jit/create_xla_launch_op.h"
 
 #include "tensorflow/compiler/jit/defs.h"
 #include "tensorflow/compiler/jit/kernels/xla_launch_op.h"
@@ -25,78 +26,189 @@ limitations under the License.
 namespace tensorflow {
 namespace {
 
-// Givens a NodeDef 'ndef' and the function library runtime 'flr', if
-// 'ndef' is a call to a compilable function defined in 'flr', returns OK
-// and fills in 'kernel' with a XlaLaunchOp kernel which computes the
-// node. Otherwise, returns a non-OK.
+// Utility which searches for values in a sorted list by scanning over it once.
+// No matter how many times ScanForValue is called, the list is scanned at most
+// once. However, if a call to ScanForValue skips over a value, that value is
+// not revisited in future calls to ScanForValue, so callers must take
+// care to order their calls.
 //
-// This routine is here so that FunctionLibraryRuntime can jit a
-// specific function call as requested.
-Status CreateXlaLaunchOp(FunctionLibraryRuntime* flr, const NodeDef& ndef,
-                         std::unique_ptr* kernel) {
-  bool xla_compile = false;
-  if (!flr->GetFunctionLibraryDefinition()
-           ->GetAttr(ndef, kXlaCompileAttr, &xla_compile)
-           .ok() ||
-      !xla_compile) {
-    // Not marked as _XlaCompile=true.
-    return errors::InvalidArgument("No ", kXlaCompileAttr, " for ", ndef.op());
+// Useful for merging multiple sorted lists in O(n) time.
+class SinglePassSearch {
+ public:
+  // Creates a SinglePassSearch object that can be used to search in `values`.
+  // Does not take ownership of `values`. `values` must outlive this.
+  // `values` must be sorted.
+  explicit SinglePassSearch(const std::vector* values)
+      : current_index_(0), values_(values) {}
+
+  // Scans forward in the vector looking for "value", updating the internal
+  // position in to the vector.
+  // Returns true iff the vector contains the given value at or after current
+  // position.
+  // Not thread-safe.
+  bool ScanForValue(int value) {
+    while (current_index_ < values_->size() &&
+           (*values_)[current_index_] <= value) {
+      if ((*values_)[current_index_] == value) {
+        current_index_++;
+        return true;
+      }
+      current_index_++;
+    }
+    return false;
   }
-  // Make sure that kernels have been registered on the JIT device.
-  XlaOpRegistry::RegisterCompilationKernels();
-  if (!IsCompilable(flr, ndef)) {
-    // ndef is calling a function that XLA can't compile.
-    return errors::InvalidArgument("Not compilable: ", ndef.ShortDebugString());
+
+ private:
+  int current_index_;
+  const std::vector* values_;
+};
+
+Status CompilationRequested(const FunctionLibraryRuntime& flr,
+                            const NodeDef& node_def) {
+  bool xla_compile = false;
+  // Check if op is marked _XlaCompile=true.
+  Status status = flr.GetFunctionLibraryDefinition()->GetAttr(
+      node_def, kXlaCompileAttr, &xla_compile);
+  if (!status.ok() || !xla_compile) {
+    if (VLOG_IS_ON(3)) {
+      if (!status.ok()) {
+        VLOG(3) << "No " << kXlaCompileAttr << " attr defined for "
+                << node_def.op() << ". status=" << status.ToString();
+      } else {
+        VLOG(3) << node_def.op() << " is explicitly marked not to be compiled";
+      }
+    }
+    return Status(error::INVALID_ARGUMENT, "");
   }
+  return Status::OK();
+}
+
+// Given a FunctionLibraryRuntime and a NodeDef calling a function in the
+// runtime, returns this function's body in `fbody` as well as the indices
+// of its constant and resource arguments.
+// `fbody` is owned by `flr`.
+// `constant_arg_indices` and `resource_arg_indices` should be empty vector.
+// They are sorted in ascending order on this function's return.
+Status GetBodyAndConstantsAndResources(FunctionLibraryRuntime* flr,
+                                       const NodeDef& node_def,
+                                       const FunctionBody** fbody,
+                                       std::vector* constant_arg_indices,
+                                       std::vector* resource_arg_indices) {
   FunctionLibraryRuntime::Handle handle;
-  // If ndef is not instantiable, e.g., the function does not exist,
+  // If node_def is not instantiable, e.g., the function does not exist,
   // simply bail out.
   TF_RETURN_IF_ERROR(
-      flr->Instantiate(ndef.op(), AttrSlice(&ndef.attr()), &handle));
-  const FunctionBody* fbody = flr->GetFunctionBody(handle);
-  CHECK(fbody);  // Can't be nullptr since we just instantiated it.
-  std::vector const_args(fbody->arg_types.size());
+      flr->Instantiate(node_def.op(), AttrSlice(&node_def.attr()), &handle));
+  *fbody = flr->GetFunctionBody(handle);
+  CHECK(*fbody);  // Can't be nullptr since we just instantiated it.
+  const DataTypeVector& arg_types = (*fbody)->arg_types;
+  std::vector const_args(arg_types.size());
   // If we can't analyze the const args. Bail out.
-  TF_RETURN_IF_ERROR(BackwardsConstAnalysis(*(fbody->graph), &const_args));
+  TF_RETURN_IF_ERROR(BackwardsConstAnalysis(*((*fbody)->graph), &const_args));
 
   for (int i = 0; i < const_args.size(); ++i) {
     if (const_args[i]) {
-      // There is a const arg. Bail out.
-      return errors::InvalidArgument("Const arg: ", i, " in ",
-                                     DebugString(fbody->fdef));
+      constant_arg_indices->push_back(i);
+    }
+  }
+
+  // There can be hundreds of resource variables. Reserve the space for them.
+  // We don't reserve for constants above as they are usually few.
+  resource_arg_indices->reserve(arg_types.size());
+  for (int i = 0; i < arg_types.size(); ++i) {
+    if (arg_types[i] == DT_RESOURCE) {
+      resource_arg_indices->push_back(i);
     }
   }
 
-  NodeDef launch_def;
-  launch_def.set_name(ndef.name());
-  launch_def.set_op("_XlaLaunch");
-  launch_def.set_device(flr->device()->name());
-  AddNodeAttr("Tconstants", DataTypeVector{}, &launch_def);
-  AddNodeAttr("Nresources", 0, &launch_def);
-  AddNodeAttr("Targs", fbody->arg_types, &launch_def);
-  AddNodeAttr("Tresults", fbody->ret_types, &launch_def);
-  NameAttrList func;
-  func.set_name(ndef.op());
-  *(func.mutable_attr()) = ndef.attr();
-  AddNodeAttr("function", func, &launch_def);
-
-  // TODO(b/32387911): Handles the host memory types across function
-  // calls properly. For now, we assume all inputs and outputs are on
-  // the device memory.
+  return Status::OK();
+}
+
+}  // namespace
+
+Status CreateXlaLaunchOp(FunctionLibraryRuntime* flr, const NodeDef& node_def,
+                         std::unique_ptr* kernel) {
+  TF_RETURN_IF_ERROR(CompilationRequested(*flr, node_def));
+
+  VLOG(3) << "Creating XlaLaunchOp for " << node_def.DebugString();
+
+  // Make sure that kernels have been registered on the JIT device.
+  XlaOpRegistry::RegisterCompilationKernels();
+  if (!IsCompilable(flr, node_def)) {
+    // node_def is calling a function that XLA can't compile.
+    return errors::InvalidArgument("Not compilable: ",
+                                   node_def.ShortDebugString());
+  }
+
+  // Get function body, constant args, and resource args.
+  const FunctionBody* fbody = nullptr;
+  std::vector constant_arg_indices;
+  std::vector resource_arg_indices;
+  TF_RETURN_IF_ERROR(GetBodyAndConstantsAndResources(
+      flr, node_def, &fbody, &constant_arg_indices, &resource_arg_indices));
+
+  // Set input and output memory types.
   MemoryTypeVector input_memory_types(fbody->arg_types.size(), DEVICE_MEMORY);
+  // These indices are used only for optimization purposes. They allow us
+  // to loop over constant_arg_indices and resource_arg_indices only once
+  // while iterating over all the function arguments checking if it is a
+  // resource or a constant.
+  // The reason we optimized this code is because functions can have a lot of
+  // captured arguments. For example, the backward pass of ResNet50 takes in all
+  // 214 variables and a similar number of activations.
+  SinglePassSearch constants_search(&constant_arg_indices);
+  SinglePassSearch resources_search(&resource_arg_indices);
+  for (int i = 0; i < fbody->arg_types.size(); ++i) {
+    if (resources_search.ScanForValue(i) || constants_search.ScanForValue(i)) {
+      // Compile-time constants and resource handles are expected to be in
+      // host memory.
+      input_memory_types[i] = HOST_MEMORY;
+    }
+  }
+  // One might wonder, about the case where a compile-time constant argument
+  // (which must be in host memory) is also used as an input into an op,
+  // e.g. Add, that expects its inputs in device memory. Here is how it
+  // works now.
+  // First, what do we mean by "op expects an input in XYZ memory"?
+  // There are two types of "ops" here: the tf2xla kernel and the HLO
+  // computation it builds. The tf2xla kernel needs to retrieve the actual
+  // numeric value of the compile-time constant tensors, so it really expects
+  // them to be on in host memory. However, for other inputs, it refers to them
+  // using xla::ComputationDataHandle, which is just a symbolic handle that
+  // xla::ComputationBuilder assigns. How does this handle gets assigned for
+  // constant arguments? Even constant arguments get an _Arg node in the graph
+  // instatiated for Function compilation. The tf2xla kernel for constant _Arg
+  // nodes takes the constant value, converts it to XlaLiteral, and feeds it
+  // to xla::ComputationBuilder.ConstantLiteral, which returns the handle. This
+  // constant XlaLiteral is included in the HLO graph, and subsequently, in
+  // the actual executable, which is copied to the device before being
+  // executed. Thus, when this executable runs, the constant is available in
+  // device memory.
+
+  // XlaLaunch kernel keeps all outputs (including constants, which it copies),
+  // in device memory
   MemoryTypeVector output_memory_types(fbody->ret_types.size(), DEVICE_MEMORY);
 
+  // Create the kernel.
+  NameAttrList function;
+  function.set_name(node_def.op());
+  *(function.mutable_attr()) = node_def.attr();
+
   Device* dev = flr->device();
   Status s;
   OpKernelConstruction construction(
       DeviceType(dev->device_type()), dev,
-      dev->GetAllocator(AllocatorAttributes()), &launch_def,
+      dev->GetAllocator(AllocatorAttributes()), &node_def,
       &fbody->fdef.signature(), flr, fbody->arg_types, input_memory_types,
       fbody->ret_types, output_memory_types, flr->graph_def_version(), &s);
-  kernel->reset(new XlaLocalLaunchOp(&construction));
+
+  *kernel = absl::make_unique(
+      &construction, constant_arg_indices, resource_arg_indices, function);
   return s;
 }
 
+namespace {
+
 bool RegisterLaunchOpCreator() {
   RegisterDefaultCustomKernelCreator(CreateXlaLaunchOp);
   return true;
diff --git a/tensorflow/compiler/jit/create_xla_launch_op.h b/tensorflow/compiler/jit/create_xla_launch_op.h
new file mode 100644
index 00000000000000..98a22e351532c1
--- /dev/null
+++ b/tensorflow/compiler/jit/create_xla_launch_op.h
@@ -0,0 +1,35 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_COMPILER_JIT_CREATE_XLA_LAUNCH_OP_H_
+#define TENSORFLOW_COMPILER_JIT_CREATE_XLA_LAUNCH_OP_H_
+
+#include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+
+class FunctionLibraryRuntime;
+class OpKernel;
+
+// Given a NodeDef 'node_def' and the function library runtime 'flr', if
+// 'node_def' is a call to a compilable function defined in 'flr', returns OK
+// and fills in 'kernel' with a XlaLaunchOp kernel which computes the
+// node. Otherwise, returns a non-OK.
+Status CreateXlaLaunchOp(FunctionLibraryRuntime* flr, const NodeDef& node_def,
+                         std::unique_ptr* kernel);
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_COMPILER_JIT_CREATE_XLA_LAUNCH_OP_H_
diff --git a/tensorflow/compiler/jit/create_xla_launch_op_test.cc b/tensorflow/compiler/jit/create_xla_launch_op_test.cc
new file mode 100644
index 00000000000000..c222824eda8306
--- /dev/null
+++ b/tensorflow/compiler/jit/create_xla_launch_op_test.cc
@@ -0,0 +1,144 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/jit/create_xla_launch_op.h"
+
+#include "tensorflow/core/common_runtime/device_factory.h"
+#include "tensorflow/core/common_runtime/function.h"
+#include "tensorflow/core/framework/function_testlib.h"
+#include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/public/session_options.h"
+#include "tensorflow/core/public/version.h"
+
+namespace tensorflow {
+
+NodeDef ToNodeDef(const string& text) {
+  NodeDef node_def;
+  EXPECT_TRUE(protobuf::TextFormat::MergeFromString(text, &node_def));
+  return node_def;
+}
+
+// Create a FunctionDef that takes one resource and one regular param
+FunctionDef XTimesY() {
+  return FunctionDefHelper::Define(
+      // Name
+      "XTimesY",
+      // Args
+      {"x: float", "y: resource"},
+      // Return values
+      {"z: float"},
+      // Attr def
+      {},
+      // Nodes
+      {
+          {{"y0"}, "ReadVariableOp", {"y"}, {{"dtype", DT_FLOAT}}},
+          {{"z"}, "Mul", {"x", "y0"}, {{"T", DT_FLOAT}}},
+      });
+}
+
+class CreateXlaLaunchOpTest : public ::testing::Test {
+ protected:
+  void Init(const std::vector& flib) {
+    SessionOptions options;
+    auto* device_count = options.config.mutable_device_count();
+    device_count->insert({"CPU", 1});
+    TF_CHECK_OK(DeviceFactory::AddDevices(
+        options, "/job:localhost/replica:0/task:0", &devices_));
+
+    FunctionDefLibrary proto;
+    for (const auto& fdef : flib) {
+      *(proto.add_function()) = fdef;
+    }
+    lib_def_ = absl::make_unique(
+        OpRegistry::Global(), proto);
+    OptimizerOptions opts;
+    device_mgr_ = absl::make_unique(devices_);
+    pflr_ = absl::make_unique(
+        device_mgr_.get(), Env::Default(), TF_GRAPH_DEF_VERSION, lib_def_.get(),
+        opts, /*default_thread_pool=*/nullptr, /*cluster_flr=*/nullptr);
+    flr_ = pflr_->GetFLR("/job:localhost/replica:0/task:0/cpu:0");
+  }
+
+  FunctionLibraryRuntime* flr_;
+  std::vector devices_;
+  std::unique_ptr device_mgr_;
+  std::unique_ptr lib_def_;
+  std::unique_ptr pflr_;
+
+  std::unique_ptr kernel_;
+};
+
+AttrValue BoolAttr(bool b) {
+  AttrValue v;
+  v.set_b(b);
+  return v;
+}
+
+TEST_F(CreateXlaLaunchOpTest, OneFloatOneResourceArgument) {
+  FunctionDef fdef = XTimesY();
+  (*fdef.mutable_attr())["_XlaCompile"] = BoolAttr(true);
+  Init({fdef});
+
+  Status status = CreateXlaLaunchOp(
+      flr_, ToNodeDef(R"pb(
+        name: 'XTimesY' op: 'XTimesY' input: 'a' input: 'b'
+      )pb"), &kernel_);
+  ASSERT_TRUE(status.ok()) << status.ToString();
+
+  EXPECT_EQ("XTimesY", kernel_->name());
+  EXPECT_EQ("XTimesY", kernel_->type_string());
+
+  EXPECT_EQ(2, kernel_->num_inputs());
+  EXPECT_EQ(DT_FLOAT, kernel_->input_type(0));
+  EXPECT_EQ(DT_RESOURCE, kernel_->input_type(1));
+  EXPECT_EQ(DEVICE_MEMORY, kernel_->input_memory_types()[0]);
+  EXPECT_EQ(HOST_MEMORY, kernel_->input_memory_types()[1]);
+
+  EXPECT_EQ(1, kernel_->num_outputs());
+  EXPECT_EQ(DT_FLOAT, kernel_->output_type(0));
+  EXPECT_EQ(DEVICE_MEMORY, kernel_->output_memory_types()[0]);
+}
+
+TEST_F(CreateXlaLaunchOpTest, FailsIfXlaCompileAttrNotSet) {
+  FunctionDef fdef = XTimesY();
+  Init({fdef});
+
+  Status status = CreateXlaLaunchOp(flr_, ToNodeDef(R"proto(
+                                      name: 'XTimesY'
+                                      op: 'XTimesY'
+                                      input: 'a'
+                                      input: 'b'
+                                    )proto"), &kernel_);
+  EXPECT_TRUE(errors::IsInvalidArgument(status)) << status.ToString();
+}
+
+TEST_F(CreateXlaLaunchOpTest, FailsIfXlaCompileAttrIsSetToFalse) {
+  FunctionDef fdef = XTimesY();
+  (*fdef.mutable_attr())["_XlaCompile"] = BoolAttr(false);
+  Init({fdef});
+
+  Status status = CreateXlaLaunchOp(flr_, ToNodeDef(R"proto(
+                                      name: 'XTimesY'
+                                      op: 'XTimesY'
+                                      input: 'a'
+                                      input: 'b'
+                                    )proto"), &kernel_);
+  EXPECT_TRUE(errors::IsInvalidArgument(status)) << status.ToString();
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.cc b/tensorflow/compiler/jit/kernels/xla_launch_op.cc
index 049d170fa48928..86a9fd3b8e124e 100644
--- a/tensorflow/compiler/jit/kernels/xla_launch_op.cc
+++ b/tensorflow/compiler/jit/kernels/xla_launch_op.cc
@@ -39,15 +39,15 @@ limitations under the License.
 
 namespace tensorflow {
 
-XlaLocalLaunchOp::XlaLocalLaunchOp(OpKernelConstruction* ctx)
-    : OpKernel(ctx), device_type_(ctx->device_type()) {
-  const NameAttrList* func;
-  OP_REQUIRES_OK(ctx, ctx->GetAttr("function", &func));
-  function_ = *func;
-  DataTypeVector constant_types;
-  OP_REQUIRES_OK(ctx, ctx->GetAttr("Tconstants", &constant_types));
-  num_constant_args_ = constant_types.size();
-  OP_REQUIRES_OK(ctx, ctx->GetAttr("Nresources", &num_resource_args_));
+XlaLocalLaunchBase::XlaLocalLaunchBase(OpKernelConstruction* ctx,
+                                       const std::vector& constants,
+                                       const std::vector& resources,
+                                       const NameAttrList& function)
+    : OpKernel(ctx),
+      constants_(constants),
+      resources_(resources),
+      device_type_(ctx->device_type()),
+      function_(function) {
   if (device_type_ == DeviceType(DEVICE_CPU)) {
     platform_id_ = se::host::kHostPlatformId;
   } else if (device_type_ == DeviceType(DEVICE_GPU)) {
@@ -57,8 +57,8 @@ XlaLocalLaunchOp::XlaLocalLaunchOp(OpKernelConstruction* ctx)
   }
 }
 
-Status XlaLocalLaunchOp::BuildCompilationCache(OpKernelContext* ctx,
-                                               XlaCompilationCache** cache) {
+Status XlaLocalLaunchBase::BuildCompilationCache(OpKernelContext* ctx,
+                                                 XlaCompilationCache** cache) {
   const XlaDevice::Metadata* metadata;
   Status s = XlaDevice::GetMetadata(ctx, &metadata);
   if (s.ok()) {
@@ -90,8 +90,8 @@ Status XlaLocalLaunchOp::BuildCompilationCache(OpKernelContext* ctx,
   return Status::OK();
 }
 
-void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) {
-  VLOG(1) << "XlaLocalLaunchOp::Compute "
+void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) {
+  VLOG(1) << "XlaLocalLaunchOpBase::Compute "
           << Canonicalize(function_.name(), AttrSlice(&function_.attr()));
   // We store information about the JIT-compiled XLA computation
   // in the ResourceMgr.
@@ -124,7 +124,7 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) {
   }
 
   std::map variables =
-      SnapshotResourceVariables(ctx, num_resource_args_);
+      SnapshotResourceVariables(ctx, resources_);
 
   xla::LocalClient* client = static_cast(cache->client());
 
@@ -161,7 +161,7 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) {
   xla::LocalExecutable* executable;
 
   std::map constant_args;
-  for (int i = 0; i < num_constant_args_; ++i) {
+  for (int i : constants_) {
     constant_args.insert({i, ctx->input(i)});
   }
   OP_REQUIRES_OK(ctx, cache->Compile(options, function_, constant_args,
@@ -170,8 +170,8 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) {
 
   VLOG(1) << "Executing XLA Computation...";
 
-  XlaComputationLaunchContext launch_context(
-      num_resource_args_, client, xla_allocator, allocate_xla_tensors);
+  XlaComputationLaunchContext launch_context(client, xla_allocator,
+                                             allocate_xla_tensors);
   launch_context.PopulateInputs(ctx, kernel, variables);
 
   // Execute the computation.
@@ -194,6 +194,62 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) {
   VLOG(1) << "Done";
 }
 
+namespace {
+
+// OP_REQUIRES_OK_RETURN is the same as OP_REQUIRES_OK except that
+// in error case, it returns RET instead of void.
+#define OP_REQUIRES_OK_RETURN(CTX, RET, ...)                \
+  do {                                                      \
+    ::tensorflow::Status _s(__VA_ARGS__);                   \
+    if (!TF_PREDICT_TRUE(_s.ok())) {                        \
+      (CTX)->CtxFailureWithWarning(__FILE__, __LINE__, _s); \
+      return RET;                                           \
+    }                                                       \
+  } while (0)
+
+// Helper static functions to construct parameters for
+// XlaLocalLaunchBase constructor from OpKernelConstruction.
+std::vector ConstantsVector(OpKernelConstruction* ctx) {
+  DataTypeVector constant_types;
+  OP_REQUIRES_OK_RETURN(ctx, std::vector(),
+                        ctx->GetAttr("Tconstants", &constant_types));
+  std::vector constants(constant_types.size());
+  std::iota(constants.begin(), constants.end(), 0);
+  return constants;
+}
+
+std::vector ResourcesVector(OpKernelConstruction* ctx) {
+  DataTypeVector constant_types;
+  OP_REQUIRES_OK_RETURN(ctx, std::vector(),
+                        ctx->GetAttr("Tconstants", &constant_types));
+
+  DataTypeVector arg_types;
+  OP_REQUIRES_OK_RETURN(ctx, std::vector(),
+                        ctx->GetAttr("Targs", &arg_types));
+
+  int num_resources;
+  OP_REQUIRES_OK_RETURN(ctx, std::vector(),
+                        ctx->GetAttr("Nresources", &num_resources));
+
+  std::vector resources(num_resources);
+  std::iota(resources.begin(), resources.end(),
+            constant_types.size() + arg_types.size());
+  return resources;
+}
+
+NameAttrList FunctionAttr(OpKernelConstruction* ctx) {
+  const NameAttrList* func;
+  OP_REQUIRES_OK_RETURN(ctx, NameAttrList(), ctx->GetAttr("function", &func));
+  return *func;
+}
+
+#undef OP_REQUIRES_OK_RETURN
+}  // namespace
+
+XlaLocalLaunchOp::XlaLocalLaunchOp(OpKernelConstruction* ctx)
+    : XlaLocalLaunchBase(ctx, ConstantsVector(ctx), ResourcesVector(ctx),
+                         FunctionAttr(ctx)) {}
+
 XlaLocalLaunchOp::~XlaLocalLaunchOp() {
   VLOG(1) << "XlaLocalLaunchOp destroyed";
 }
diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.h b/tensorflow/compiler/jit/kernels/xla_launch_op.h
index 8f8e646f0ff6d9..8dfc4b382d5115 100644
--- a/tensorflow/compiler/jit/kernels/xla_launch_op.h
+++ b/tensorflow/compiler/jit/kernels/xla_launch_op.h
@@ -26,6 +26,41 @@ limitations under the License.
 
 namespace tensorflow {
 
+// XlaLocalLaunchBase is almost the same as XlaLocalLaunchOp.
+// The only difference is that it does not require arguments to follow
+// the "constants, then regular args, then resources" order.
+// It takes vectors of constant and resource arguments explicitly.
+// It does not have corresponding OpDef because it is never present
+// in the GraphDef.
+// Currently, it is used by eager runtime. FunctionLibraryRuntime creates
+// this kernel when asked to create a kernel for an XLA-compiled function.
+class XlaLocalLaunchBase : public OpKernel {
+ public:
+  XlaLocalLaunchBase(OpKernelConstruction* ctx,
+                     const std::vector& constants,
+                     const std::vector& resources,
+                     const NameAttrList& function);
+  XlaLocalLaunchBase(const XlaLocalLaunchBase&) = delete;
+  XlaLocalLaunchBase& operator=(const XlaLocalLaunchBase&) = delete;
+  ~XlaLocalLaunchBase() override = default;
+
+  void Compute(OpKernelContext* ctx) override;
+
+ protected:
+  // Builds a XlaCompilationCache class suitable for the current device.
+  Status BuildCompilationCache(OpKernelContext* ctx,
+                               XlaCompilationCache** cache);
+
+  // Indexes of compile-time constant inputs
+  std::vector constants_;
+  // Indexes of resource inputs
+  std::vector resources_;
+
+  DeviceType device_type_;
+  NameAttrList function_;
+  se::Platform::Id platform_id_;
+};
+
 // XlaLocalLaunchOp is used to replace a region of the TensorFlow graph
 // which will be compiled and executed using XLA.  The XlaLocalLaunchOp is
 // responsible for handling interactions with the TensorFlow executor.
@@ -35,26 +70,12 @@ namespace tensorflow {
 // XlaLocalLaunchOp uses xla::LocalClient::Compile() and
 // xla::LocalExecutable::Run(), and passes arguments into/out of XLA in device
 // memory.
-class XlaLocalLaunchOp : public OpKernel {
+class XlaLocalLaunchOp : public XlaLocalLaunchBase {
  public:
   explicit XlaLocalLaunchOp(OpKernelConstruction* ctx);
   ~XlaLocalLaunchOp() override;
 
-  void Compute(OpKernelContext* ctx) override;
-
  private:
-  // Builds a XlaCompilationCache class suitable for the current device.
-  Status BuildCompilationCache(OpKernelContext* ctx,
-                               XlaCompilationCache** compiler);
-
-  DeviceType device_type_;
-  NameAttrList function_;
-  int num_constant_args_;
-  // Number of resource variable arguments.
-  int num_resource_args_;
-
-  se::Platform::Id platform_id_;
-
   TF_DISALLOW_COPY_AND_ASSIGN(XlaLocalLaunchOp);
 };
 
diff --git a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc
index 60458f6f3314b2..6b83cf67ffc571 100644
--- a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc
+++ b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc
@@ -48,13 +48,12 @@ Status XlaCompileOnDemandOp::Run(OpKernelContext* ctx,
                                  const XlaCompiler::CompilationResult* result,
                                  xla::LocalExecutable* executable) {
   std::map variables = GetVariables(ctx);
-  int64 num_resource_args = variables.size();
 
   xla::LocalClient* client = metadata.client();
 
   // Builds an XLA allocator for the device.
   XlaComputationLaunchContext launch_context(
-      num_resource_args, client, client->backend().memory_allocator(), true);
+      client, client->backend().memory_allocator(), true);
 
   launch_context.PopulateInputs(ctx, result, variables);
 
diff --git a/tensorflow/compiler/jit/xla_launch_util.cc b/tensorflow/compiler/jit/xla_launch_util.cc
index 33e53612b91315..0223f97a032cf9 100644
--- a/tensorflow/compiler/jit/xla_launch_util.cc
+++ b/tensorflow/compiler/jit/xla_launch_util.cc
@@ -38,14 +38,13 @@ using xla::ScopedShapedBuffer;
 using xla::ShapedBuffer;
 }  // anonymous namespace
 
-std::map SnapshotResourceVariables(OpKernelContext* ctx,
-                                                        int num_variables) {
+std::map SnapshotResourceVariables(
+    OpKernelContext* ctx, const std::vector& variables) {
   std::map snapshot;
-  int first_variable = ctx->num_inputs() - num_variables;
-  for (int i = 0; i < num_variables; ++i) {
+  for (int i : variables) {
     Var* variable = nullptr;
-    ResourceHandle handle = HandleFromInput(ctx, first_variable + i);
-    OptionalTensor& tensor = snapshot[first_variable + i];
+    ResourceHandle handle = HandleFromInput(ctx, i);
+    OptionalTensor& tensor = snapshot[i];
     if (LookupResource(ctx, handle, &variable).ok()) {
       tf_shared_lock lock(*variable->mu());
       tensor.name = handle.name();
@@ -112,10 +111,9 @@ ScopedShapedBuffer ExtractSubShapedBuffer(
 using internal::ExtractSubShapedBuffer;
 
 XlaComputationLaunchContext::XlaComputationLaunchContext(
-    int64 num_resource_args, xla::LocalClient* client,
-    xla::DeviceMemoryAllocator* xla_allocator, bool allocate_xla_tensors)
-    : num_resource_args_(num_resource_args),
-      client_(client),
+    xla::LocalClient* client, xla::DeviceMemoryAllocator* xla_allocator,
+    bool allocate_xla_tensors)
+    : client_(client),
       xla_allocator_(xla_allocator),
       allocate_xla_tensors_(allocate_xla_tensors) {}
 
diff --git a/tensorflow/compiler/jit/xla_launch_util.h b/tensorflow/compiler/jit/xla_launch_util.h
index 38291b0bd429b2..a2431253f8c44b 100644
--- a/tensorflow/compiler/jit/xla_launch_util.h
+++ b/tensorflow/compiler/jit/xla_launch_util.h
@@ -31,15 +31,17 @@ limitations under the License.
 namespace tensorflow {
 class XlaAllocator;
 
-// Takes a snapshot of the values of resource variable arguments, which are
-// the last `num_variables` arguments. We snapshot tensors that back
+// Takes a snapshot of the values of resource variable arguments, whose
+// indices are specified in `variables` argument. We snapshot tensors that back
 // resource variables since concurrent updates may modify the shape, and it is
 // important that the shapes used for compilation match the true shapes of the
 // buffers.
 //
-// Returns a map of TensorFlow argument index to resource variable.
-std::map SnapshotResourceVariables(OpKernelContext* ctx,
-                                                        int num_variables);
+// Returns a map of TensorFlow argument index to resource variable. If a
+// resource variable is not initialized, the corresponding OptionalTensor
+// will have its `present` field set to false.
+std::map SnapshotResourceVariables(
+    OpKernelContext* ctx, const std::vector& variables);
 
 // Adapter class that wraps a Tensorflow allocator as an XLA allocator.
 // Assumes that the Tensorflow allocator permits asynchronous deallocation:
@@ -72,7 +74,7 @@ class XlaComputationLaunchContext {
   // Create a new launch context. 'allocate_xla_tensors' is true if allocated
   // output tensors and variables are always XlaTensors. If false they are
   // assumed to be "normal" device pointers.
-  XlaComputationLaunchContext(int64 num_resource_args, xla::LocalClient* client,
+  XlaComputationLaunchContext(xla::LocalClient* client,
                               xla::DeviceMemoryAllocator* xla_allocator,
                               bool allocate_xla_tensors);
 
@@ -92,7 +94,6 @@ class XlaComputationLaunchContext {
   const std::vector& arguments() const { return arg_ptrs_; }
 
  private:
-  int64 num_resource_args_;
   xla::LocalClient* client_;
   xla::DeviceMemoryAllocator* xla_allocator_;
   bool allocate_xla_tensors_;
diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index aaea83ae9cbd21..9791792f29ca05 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -327,7 +327,11 @@ tf_xla_py_test(
         ":xla_test",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:layers",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn",
         "//tensorflow/python:platform_test",
+        "//tensorflow/python/eager:function",
     ],
 )
 
diff --git a/tensorflow/compiler/tests/eager_test.py b/tensorflow/compiler/tests/eager_test.py
index bdd0185dfe4abe..5ab1585f8c6e07 100644
--- a/tensorflow/compiler/tests/eager_test.py
+++ b/tensorflow/compiler/tests/eager_test.py
@@ -24,10 +24,16 @@
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.eager import backprop
 from tensorflow.python.eager import context
+from tensorflow.python.eager import function
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.layers import convolutional
+from tensorflow.python.layers import pooling
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.platform import googletest
 
@@ -43,7 +49,7 @@ def testBasic(self):
 
   def testExecuteListOutputLen0(self):
     with self.test_scope():
-      empty = constant_op.constant([], dtype=dtypes.int32)
+      empty = constant_op.constant([], dtype=dtypes.float32)
       result = array_ops.unstack(empty, 0)
       self.assertTrue(isinstance(result, list))
       self.assertEqual(0, len(result))
@@ -51,7 +57,7 @@ def testExecuteListOutputLen0(self):
   def testExecuteListOutputLen1(self):
     with self.test_scope():
       split_dim = constant_op.constant(1)
-      value = constant_op.constant([[0, 1, 2], [3, 4, 5]])
+      value = constant_op.constant([[0., 1., 2.], [3., 4., 5.]])
       result = array_ops.split(value, 1, axis=split_dim)
       self.assertTrue(isinstance(result, list))
       self.assertEqual(1, len(result))
@@ -60,7 +66,7 @@ def testExecuteListOutputLen1(self):
   def testExecuteListOutputLen3(self):
     with self.test_scope():
       split_dim = constant_op.constant(1)
-      value = constant_op.constant([[0, 1, 2], [3, 4, 5]])
+      value = constant_op.constant([[0., 1., 2.], [3., 4., 5.]])
       result = array_ops.split(value, 3, axis=split_dim)
       self.assertTrue(isinstance(result, list))
       self.assertEqual(3, len(result))
@@ -131,7 +137,105 @@ def f():
     self.assertEqual(2., grads[0][0].numpy())
 
 
-if __name__ == "__main__":
+class EagerFunctionTest(XLATestCase):
+
+  def testBasic(self):
+    with self.test_scope():
+      matmul = function.defun(math_ops.matmul, compiled=True)
+      t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
+      sq = matmul(t, t, transpose_a=True)
+      self.assertAllEqual(sq.numpy().reshape(-1), [10, 14, 14, 20])
+
+  def testConv(self):
+    if 'GPU' in self.device:
+      # TODO(b/32333178)
+      self.skipTest('Current implementation of RandomStandardNormal kernel '
+                    'is very slow on GPU, and has been blacklisted.')
+    with self.test_scope():
+      data_format = 'channels_last'
+      conv = convolutional.Conv2D(
+          filters=1, kernel_size=2, padding='VALID',
+          data_format=data_format, activation=nn_ops.relu,
+          kernel_initializer=init_ops.ones_initializer(),
+          bias_initializer=init_ops.zeros_initializer())
+      pool = pooling.MaxPooling2D(2, 2, data_format=data_format)
+
+      def model(x):
+        x = conv(x)
+        return pool(x)
+      model = function.defun(model, compiled=True)
+
+      x = array_ops.ones([1, 4, 4, 1])
+      y = model(x)
+      self.assertAllEqual(y.numpy(), [[[[4.]]]])
+
+  def testReadVariable(self):
+    with self.test_scope():
+      v = resource_variable_ops.ResourceVariable(1.0)
+
+      @function.defun(compiled=True)
+      def f():
+        return v.read_value()
+
+      var = f()
+      self.assertEqual(1.0, var.numpy())
+
+  def testUpdateVariable(self):
+    with self.test_scope():
+      v = resource_variable_ops.ResourceVariable(1.0)
+
+      def f(v):
+        v.assign_add(1.0)
+        return v
+
+      f = function.defun(f, compiled=True)
+
+      var = f(v)
+      self.assertEqual(2.0, var.numpy())
+
+  def testAllArgumentKinds(self):
+    """Test a complex function that takes different argument kinds.
+
+    tf2xla machinery that translates, compiles, and runs defuns
+    classifies arguments into: compile-time constants, regular tensors,
+    and resources. This test creates a function with a mix of all these
+    kinds. Moreover, the order of function arguments is intentionally mixed up.
+
+    This also tests the case when the same argument is a compile-time constant
+    as well as used in an operation that normally expects its inputs to be
+    in device memory - addition in this case.
+    """
+    with self.test_scope():
+      def foo(c1, r1, v1, c2, v2, r2):
+        # c1 and c2 are compile-time constants
+        # r1 and r2 are regular tensors
+        # v1 and v2 are resource variables
+        a = c1 + r1
+        b = math_ops.cast(c2, dtypes.float32) + v2
+        c = array_ops.slice(v1, c1, c2)
+        d = r2 * v2
+        return a, b, c, d
+
+      foo = function.defun(foo, compiled=True)
+
+      c1 = [0, 0]
+      c2 = array_ops.ones([2], dtype=dtypes.int32)
+
+      r1 = array_ops.ones([2])
+      r2 = [[2., 2.], [3., 3.]]
+
+      v1 = resource_variable_ops.ResourceVariable([[1., 2.], [3., 4.]])
+      v2 = resource_variable_ops.ResourceVariable([[10., 20.], [30., 40.]])
+
+      a, b, c, d = foo(c1, r1, v1, c2, v2, r2)
+
+      self.assertAllEqual([1, 1], a.numpy())
+      self.assertAllEqual([[11., 21.], [31., 41.]], b.numpy())
+      self.assertAllEqual([[1.]], c.numpy())
+      self.assertAllEqual([[20., 40.], [90., 120.]], d.numpy())
+
+
+if __name__ == '__main__':
   ops.enable_eager_execution(
       config=config_pb2.ConfigProto(log_device_placement=True))
   googletest.main()
diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
index 8517a3bf7b6aeb..b8f352d5f5b72f 100644
--- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
+++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
@@ -36,9 +36,7 @@ def device_and_data_format():
                                                               'channels_last')
 
 
-def random_batch(batch_size, device_and_format=None):
-  _, data_format = device_and_format or device_and_data_format()
-
+def random_batch(batch_size, data_format):
   shape = (3, 224, 224) if data_format == 'channels_first' else (224, 224, 3)
   shape = (batch_size,) + shape
 
@@ -70,7 +68,7 @@ def _apply(self, defun=False, execution_mode=None):
     if defun:
       model.call = tfe.defun(model.call)
     with tf.device(device), tfe.execution_mode(execution_mode):
-      images, _ = random_batch(2)
+      images, _ = random_batch(2, data_format)
       output = model(images, training=False)
       tfe.async_wait()
     self.assertEqual((2, 1000), output.shape)
@@ -91,7 +89,7 @@ def test_apply_no_top(self):
     device, data_format = device_and_data_format()
     model = resnet50.ResNet50(data_format, include_top=False)
     with tf.device(device):
-      images, _ = random_batch(2)
+      images, _ = random_batch(2, data_format)
       output = model(images, training=False)
     output_shape = ((2, 2048, 1, 1)
                     if data_format == 'channels_first' else (2, 1, 1, 2048))
@@ -101,7 +99,7 @@ def test_apply_with_pooling(self):
     device, data_format = device_and_data_format()
     model = resnet50.ResNet50(data_format, include_top=False, pooling='avg')
     with tf.device(device):
-      images, _ = random_batch(2)
+      images, _ = random_batch(2, data_format)
       output = model(images, training=False)
     self.assertEqual((2, 2048), output.shape)
 
@@ -115,7 +113,7 @@ def _test_train(self, execution_mode=None):
         name='t0').as_default(), tf.contrib.summary.always_record_summaries():
       with tf.device(device), tfe.execution_mode(execution_mode):
         optimizer = tf.train.GradientDescentOptimizer(0.1)
-        images, labels = random_batch(2)
+        images, labels = random_batch(2, data_format)
         train_one_step(model, images, labels, optimizer)
         self.assertEqual(320, len(model.variables))
         tfe.async_wait()
@@ -134,7 +132,7 @@ def test_no_garbage(self):
     model = resnet50.ResNet50(data_format)
     optimizer = tf.train.GradientDescentOptimizer(0.1)
     with tf.device(device):
-      images, labels = random_batch(2)
+      images, labels = random_batch(2, data_format)
       gc.disable()
       # Warm up. Note that this first run does create significant amounts of
       # garbage to be collected. The hope is that this is a build-only effect,
@@ -202,18 +200,18 @@ def _force_device_sync(self):
     # which forces a sync. This is a roundabout way, yes.
     tf.constant(1.).cpu()
 
-  def _benchmark_eager_apply(self, label, defun=False, execution_mode=None,
-                             device_and_format=None):
+  def _benchmark_eager_apply(self, label, device_and_format, defun=False,
+                             execution_mode=None, compiled=False):
     with tfe.execution_mode(execution_mode):
-      device, data_format = device_and_format or device_and_data_format()
+      device, data_format = device_and_format
       model = resnet50.ResNet50(data_format)
       if defun:
-        model.call = tfe.defun(model.call)
+        model.call = tfe.defun(model.call, compiled=compiled)
       batch_size = 64
       num_burn = 5
       num_iters = 30
       with tf.device(device):
-        images, _ = random_batch(batch_size, device_and_format)
+        images, _ = random_batch(batch_size, data_format)
         for _ in xrange(num_burn):
           model(images, training=False).cpu()
         if execution_mode:
@@ -227,30 +225,34 @@ def _benchmark_eager_apply(self, label, defun=False, execution_mode=None,
         self._report(label, start, num_iters, device, batch_size, data_format)
 
   def benchmark_eager_apply_sync(self):
-    self._benchmark_eager_apply('eager_apply', defun=False)
+    self._benchmark_eager_apply('eager_apply', device_and_data_format(),
+                                defun=False)
 
   def benchmark_eager_apply_async(self):
     self._benchmark_eager_apply(
-        'eager_apply_async', defun=False, execution_mode=tfe.ASYNC)
+        'eager_apply_async', device_and_data_format(), defun=False,
+        execution_mode=tfe.ASYNC)
 
   def benchmark_eager_apply_with_defun(self):
-    self._benchmark_eager_apply('eager_apply_with_defun', defun=True)
+    self._benchmark_eager_apply('eager_apply_with_defun',
+                                device_and_data_format(), defun=True)
 
   def _benchmark_eager_train(self,
                              label,
                              make_iterator,
+                             device_and_format,
                              defun=False,
                              execution_mode=None,
-                             device_and_format=None):
+                             compiled=False):
     with tfe.execution_mode(execution_mode):
-      device, data_format = device_and_format or device_and_data_format()
+      device, data_format = device_and_format
       for batch_size in self._train_batch_sizes():
-        (images, labels) = random_batch(batch_size, device_and_format)
+        (images, labels) = random_batch(batch_size, data_format)
         num_burn = 3
         num_iters = 10
         model = resnet50.ResNet50(data_format)
         if defun:
-          model.call = tfe.defun(model.call)
+          model.call = tfe.defun(model.call, compiled=compiled)
         optimizer = tf.train.GradientDescentOptimizer(0.1)
 
         with tf.device(device):
@@ -273,18 +275,21 @@ def _benchmark_eager_train(self,
           self._report(label, start, num_iters, device, batch_size, data_format)
 
   def benchmark_eager_train_sync(self):
-    self._benchmark_eager_train('eager_train', MockIterator, defun=False)
+    self._benchmark_eager_train('eager_train', MockIterator,
+                                device_and_data_format(), defun=False)
 
   def benchmark_eager_train_async(self):
     self._benchmark_eager_train(
         'eager_train_async',
         MockIterator,
+        device_and_data_format(),
         defun=False,
         execution_mode=tfe.ASYNC)
 
   def benchmark_eager_train_with_defun(self):
     self._benchmark_eager_train(
-        'eager_train_with_defun', MockIterator, defun=True)
+        'eager_train_with_defun', MockIterator,
+        device_and_data_format(), defun=True)
 
   def benchmark_eager_train_datasets(self):
 
@@ -294,7 +299,8 @@ def make_iterator(tensors):
       return tfe.Iterator(ds)
 
     self._benchmark_eager_train(
-        'eager_train_dataset', make_iterator, defun=False)
+        'eager_train_dataset', make_iterator,
+        device_and_data_format(), defun=False)
 
   def benchmark_eager_train_datasets_with_defun(self):
 
@@ -304,7 +310,8 @@ def make_iterator(tensors):
       return tfe.Iterator(ds)
 
     self._benchmark_eager_train(
-        'eager_train_dataset_with_defun', make_iterator, defun=True)
+        'eager_train_dataset_with_defun', make_iterator,
+        device_and_data_format(), defun=True)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py
index 741bd2ac9c911f..60cfacc14114d4 100644
--- a/tensorflow/python/eager/function.py
+++ b/tensorflow/python/eager/function.py
@@ -23,6 +23,7 @@
 
 import numpy as np
 
+from tensorflow.core.framework import attr_value_pb2
 from tensorflow.core.framework import function_pb2
 from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.eager import context
@@ -225,7 +226,7 @@ def _inference_name(n):
 class _EagerDefinedFunction(object):
   """Function object with the interface of tf _DefinedFunction."""
 
-  def __init__(self, name, graph, operations, inputs, outputs):
+  def __init__(self, name, graph, operations, inputs, outputs, attrs):
     """Initializes an eager defined function.
 
     Args:
@@ -235,6 +236,7 @@ def __init__(self, name, graph, operations, inputs, outputs):
         which will be in the function
       inputs: the tensors in the graph to be used as inputs to the function
       outputs: the tensors in the graph which will be outputs to the function
+      attrs: dict mapping names of attributes to their AttrValue values
     """
     fn = pywrap_tensorflow.TF_GraphToFunction_wrapper(
         graph._c_graph,  # pylint: disable=protected-access
@@ -246,6 +248,14 @@ def __init__(self, name, graph, operations, inputs, outputs):
         [],
         None,
         compat.as_str(""))
+
+    for name, attr_value in attrs.items():
+      serialized = attr_value.SerializeToString()
+      # TODO(iga): this creates and deletes a new TF_Status for every attr.
+      # It might be worth creating a convenient way to re-use status.
+      pywrap_tensorflow.TF_FunctionSetAttrValueProto(
+          fn, compat.as_str(name), serialized)
+
     # TODO(apassos) avoid creating a FunctionDef (specially to grab the
     # signature, but also in general it's nice not to depend on it.
     with c_api_util.tf_buffer() as buffer_:
@@ -287,25 +297,6 @@ def _flatten(sequence):
 
 class GraphModeFunction(object):
   """Callable object representing a graph-mode function.
-
-  Args:
-    name: str the name of the created function
-    input_placeholders: list of placeholder values (tensors) to feed when
-      calling the wrapped function.
-    extra_inputs: Tensor inputs this function definition closed over which
-      are passed as arguments. Need to track so gradients are supported
-      correctly.
-    graph: the Graph from which the operations will be pulled. Used as
-      a context when computing gradients.
-    operations: the subset of Operations in the graph used in the function
-      definition.
-    outputs: a flat list of the Tensors in the graph used as outputs to the
-      function
-    func_outputs: a possibly nested python object which will be returned by
-      this function. The Tensors in this structure will be replaced by their
-      corresponding values in outputs.
-    output_shapes: List of shapes of all tensors in outputs
-    variables: (optional) List of variables to watch during function execution.
   """
 
   def __init__(self,
@@ -317,9 +308,36 @@ def __init__(self,
                outputs,
                func_outputs,
                output_shapes,
-               variables=None):
+               variables=None,
+               attrs=None):
+    """Initialize a GraphModeFunction.
+
+    Args:
+      name: str the name of the created function
+      input_placeholders: list of placeholder values (tensors) to feed when
+        calling the wrapped function.
+      extra_inputs: Tensor inputs this function definition closed over which
+        are passed as arguments. Need to track so gradients are supported
+        correctly.
+      graph: the Graph from which the operations will be pulled. Used as
+        a context when computing gradients.
+      operations: the subset of Operations in the graph used in the function
+        definition.
+      outputs: a flat list of the Tensors in the graph used as outputs to the
+        function
+      func_outputs: a possibly nested python object which will be returned by
+        this function. The Tensors in this structure will be replaced by their
+        corresponding values in outputs.
+      output_shapes: List of shapes of all tensors in outputs
+      variables: (optional) List of variables to watch during function
+        execution.
+      attrs: (optional) dict mapping names of attributes to their AttrValue
+        values. Attributes in `attrs` will be included in this function's
+        definition.
+    """
+    self._attrs = attrs or {}
     defined_function = _EagerDefinedFunction(
-        name, graph, operations, input_placeholders, outputs)
+        name, graph, operations, input_placeholders, outputs, self._attrs)
     if len(input_placeholders) != len(defined_function.signature.input_arg):
       raise ValueError("Internal error: invalid lengths. %s %s" % (
           len(input_placeholders), len(defined_function.signature.input_arg)))
@@ -372,7 +390,7 @@ def _construct_backprop_function(self):
     forward_name = _forward_name(self._func_name)
     self._forward_fdef = _EagerDefinedFunction(
         forward_name, self._graph, self._ops, self._input_placeholders,
-        filtered_outputs + captures)
+        filtered_outputs + captures, self._attrs)
     all_inputs = self._out_grad_placeholders + captures
     # Excluding input ops from the body as we do not intend to execute these
     # operations when the function is executed.
@@ -386,7 +404,7 @@ def _construct_backprop_function(self):
     bname = _backward_name(self._func_name)
     self._backward_function = GraphModeFunction(
         bname, all_inputs, [], self._graph, function_def_ops,
-        backward_outputs, in_gradients, output_shapes)
+        backward_outputs, in_gradients, output_shapes, attrs=self._attrs)
 
   def _backprop_call(self, args):
     """Calls the wrapped function and records the result on a tape."""
@@ -560,7 +578,7 @@ def _get_defun_inputs(args):
   return nest.pack_sequence_as(args, ret)
 
 
-def _defun_internal(name, func, args, kwds):
+def _defun_internal(name, func, compiled, args, kwds):
   """Defines and returns graph-mode version of func."""
   graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
   with context.graph_mode():
@@ -625,9 +643,14 @@ def convert(x):
     for f in tmp_graph._functions.values():  # pylint: disable=protected-access
       # TODO(ashankar): What about the gradient registry?
       _register(f._c_func.func)  # pylint: disable=protected-access
+
+  attrs = {}
+  if compiled:
+    attrs["_XlaCompile"] = attr_value_pb2.AttrValue(b=True)
+
   return GraphModeFunction(
       fname, all_inputs, extra_inputs, tmp_graph, operations, func_def_outputs,
-      func_outputs, output_shapes, variables)
+      func_outputs, output_shapes, variables, attrs)
 
 
 # Defun uses this instead of Tensor as a cache key. Using dtype because
@@ -669,7 +692,7 @@ def _register(fn):
 
 
 # TODO(apassos): better error messages for non-hashable arguments.
-def named_defun(func, name):
+def named_defun(func, name, compiled=False):
   """Defines a function with a given name.
 
   See the documentation for `defun` for more information on the semantics of the
@@ -678,6 +701,7 @@ def named_defun(func, name):
   Args:
     func: the function to be wrapped.
     name: the name given to it.
+    compiled: if true, the framework will attempt to compile func with XLA.
 
   Returns:
     the wrapped function.
@@ -694,13 +718,13 @@ def decorated(*args, **kwds):
 
     if cache_key not in arguments_to_functions:
       arguments_to_functions[cache_key] = _defun_internal(
-          name, func, args, kwds)
+          name, func, compiled, args, kwds)
     return arguments_to_functions[cache_key](*args)
 
   return decorated
 
 
-def defun(func):
+def defun(func=None, compiled=False):
   """Decorator to compile func into graph_mode.
 
   `defun` converts a function that constructs a TensorFlow graph into a function
@@ -743,18 +767,45 @@ def g(x, y):
   ```
 
   Args:
-    func: function to be compiled.
+    func: function to be compiled. If `func` is None, returns a
+      decorator that can be invoked with a single argument - `func`. The
+      end result is equivalent to providing all the arguments up front.
+      In other words, defun(compiled=True)(func) is equivalent to
+      defun(func, compiled=True). The former allows the following use case:
+        @tfe.defun(compiled=True)
+        def foo(...):
+          ...
+    compiled: If True, an attempt to compile `func` with XLA will be made.
+      If it fails, function will be run normally. Experimental.
+      Currently, supported only for execution on TPUs.
 
   Returns:
-     A callable that will execute the compiled function (and return zero
-     or more `tf.Tensor` objects).
+     If `func` is not None, returns callable that will execute the compiled
+     function (and return zero or more `tf.Tensor` objects).
+     If `func` is None, returns a decorator that, when invoked with a single
+     `func` argument, returns a callable equivalent to the case above.
   """
   # TODO(apassos): deal with captured global state. Deal with control flow.
-  try:
-    name = func.__name__
-  except AttributeError:
-    name = "function"
-  return tf_decorator.make_decorator(func, named_defun(func, name))
+  def decorated(function):
+    try:
+      name = function.__name__
+    except AttributeError:
+      name = "function"
+    return tf_decorator.make_decorator(
+        function, named_defun(function, name, compiled=compiled))
+
+  # This code path is for the `foo = tfe.defun(foo, ...)` use case
+  if func is not None:
+    return decorated(func)
+
+  # This code path is for the
+  #
+  # @tfe.defun(...)
+  # def foo(...):
+  #    ...
+  #
+  # use case, which is equivalent to `foo = tfe.defun(...)(foo)`
+  return decorated
 
 
 def make_defun_op(func, *args, **kwds):
@@ -806,7 +857,7 @@ def g(x, y):
   name = func.__name__
   if any(isinstance(x, ops.EagerTensor) for x in kwds.values()):
     raise ValueError("Tensor keyword arguments are not supported.")
-  return _defun_internal(name, func, args, kwds)
+  return _defun_internal(name, func, False, args, kwds)
 
 
 class AutomaticControlDependencies(object):

From 2585a8181904b39c71fc314940587c02b30a68a6 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne 
Date: Mon, 7 May 2018 17:24:28 -0700
Subject: [PATCH 0485/1691] Make conv2d_tranpose_test.py work with C API shapes
 enabled.

The C API provides more accurate shape information in many cases.

PiperOrigin-RevId: 195749030
---
 tensorflow/python/kernel_tests/conv2d_transpose_test.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/kernel_tests/conv2d_transpose_test.py b/tensorflow/python/kernel_tests/conv2d_transpose_test.py
index b692d3da609fd9..27804be65ca9a5 100644
--- a/tensorflow/python/kernel_tests/conv2d_transpose_test.py
+++ b/tensorflow/python/kernel_tests/conv2d_transpose_test.py
@@ -23,6 +23,7 @@
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import nn_ops
@@ -292,6 +293,7 @@ def testConv2DTransposeValidNCHW(self):
 
         self.assertAllClose(cache_values, value)
 
+  @test_util.enable_c_shapes
   def testConv2DTransposeShapeInference(self):
     # Test case for 8972
     initializer = random_ops.truncated_normal(
@@ -301,7 +303,8 @@ def testConv2DTransposeShapeInference(self):
     f_shape = array_ops.stack([array_ops.shape(x)[0], 10, 5, 5])
     output = nn_ops.conv2d_transpose(
         x, f, f_shape, strides=[1, 1, 1, 1], padding="SAME")
-    self.assertEqual(output.get_shape().as_list(), [None, 10, 5, 5])
+    self.assertEqual(output.get_shape().as_list(), [3, 10, 5, 5])
+
 
 if __name__ == "__main__":
   test.main()

From 1af09b57ef663d4ab0c02a00e2af1f1e2819d32f Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne 
Date: Mon, 7 May 2018 17:28:41 -0700
Subject: [PATCH 0486/1691] Add logic for StridedSlice ops in
 ShapeRefiner::ConstantPartialShape().

This mimics the logic in tensor_util.constant_value_as_shape, allowing
the C++ shape inference code to infer more shapes than it could before.

This change also adds an optional stride argument to InferenceContext::Subshape().

PiperOrigin-RevId: 195749522
---
 .../core/common_runtime/shape_refiner.cc      | 113 ++++++++++++++++--
 .../core/common_runtime/shape_refiner.h       |  14 +++
 .../core/common_runtime/shape_refiner_test.cc | 100 ++++++++++++++++
 tensorflow/core/framework/shape_inference.cc  |  29 ++++-
 tensorflow/core/framework/shape_inference.h   |   7 ++
 5 files changed, 245 insertions(+), 18 deletions(-)

diff --git a/tensorflow/core/common_runtime/shape_refiner.cc b/tensorflow/core/common_runtime/shape_refiner.cc
index a0772713d4c67e..fa4d1eda625fa7 100644
--- a/tensorflow/core/common_runtime/shape_refiner.cc
+++ b/tensorflow/core/common_runtime/shape_refiner.cc
@@ -421,6 +421,28 @@ Status ShapeRefiner::EvaluateConstantTensorForEdge(const Node* node,
                                 kMaxTensorSize, disable_constant_propagation_);
 }
 
+Status ShapeRefiner::EvaluateConstantIntScalarEdge(const Node* node,
+                                                   int dst_idx, bool* evaluated,
+                                                   int64* result) {
+  Tensor scalar;
+  TF_RETURN_IF_ERROR(
+      EvaluateConstantTensorForEdge(node, dst_idx, evaluated, &scalar));
+  if (*evaluated) {
+    DCHECK_EQ(scalar.NumElements(), 1)
+        << "EvaluateConstantIntScalarEdge called on non-scalar edge: "
+        << scalar.NumElements();
+    if (scalar.dtype() == DT_INT32) {
+      *result = scalar.scalar()();
+    } else {
+      DCHECK_EQ(scalar.dtype(), DT_INT64)
+          << "EvaluateConstantIntScalarEdge called on non-integer edge: "
+          << scalar.dtype();
+      *result = scalar.scalar()();
+    }
+  }
+  return Status::OK();
+}
+
 Status ShapeRefiner::ConstantPartialShape(InferenceContext* target_context,
                                           const Node* node, int dst_idx,
                                           ShapeHandle* result) {
@@ -471,19 +493,11 @@ Status ShapeRefiner::ConstantPartialShape(InferenceContext* target_context,
     std::vector dims;
     // Pack is concatenating its input scalars to form the shape tensor vector.
     for (int i = 0; i < src_context->num_inputs(); ++i) {
-      Tensor scalar;
-      bool evaluated = false;
-      TF_RETURN_IF_ERROR(EvaluateConstantTensorForEdge(input_edge->src(), i,
-                                                       &evaluated, &scalar));
+      int64 size;
+      bool evaluated;
+      TF_RETURN_IF_ERROR(EvaluateConstantIntScalarEdge(input_edge->src(), i,
+                                                       &evaluated, &size));
       if (evaluated) {
-        int64 size;
-        if (scalar.dtype() == DT_INT32) {
-          size = scalar.scalar()();
-        } else if (scalar.dtype() == DT_INT64) {
-          size = scalar.scalar()();
-        } else {
-          return errors::InvalidArgument("Pack input must be int32 or int64");
-        }
         dims.push_back(size < 0 ? target_context->UnknownDim()
                                 : target_context->MakeDim(size));
       } else {
@@ -513,6 +527,9 @@ Status ShapeRefiner::ConstantPartialShape(InferenceContext* target_context,
       TF_RETURN_IF_ERROR(
           target_context->Concatenate(*result, sub_result, result));
     }
+  } else if (src_op == "StridedSlice") {
+    TF_RETURN_IF_ERROR(
+        PartialStridedSliceShape(input_edge->src(), src_context, result));
   } else {
     Tensor t;
     bool evaluated = false;
@@ -524,6 +541,78 @@ Status ShapeRefiner::ConstantPartialShape(InferenceContext* target_context,
   return Status::OK();
 }
 
+Status ShapeRefiner::PartialStridedSliceShape(Node* slice_node,
+                                              InferenceContext* ctx,
+                                              ShapeHandle* result) {
+  // Only attempt to evaluate if begin/end/strides all are scalars.
+  for (int i = 1; i <= 3; ++i) {
+    ShapeHandle input_shape = ctx->input(i);
+    if (ctx->Value(ctx->Dim(input_shape, 0)) != 1) {
+      *result = ctx->UnknownShape();
+      return Status::OK();
+    }
+  }
+
+  int begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask;
+  TF_RETURN_IF_ERROR(
+      GetNodeAttr(slice_node->attrs(), "begin_mask", &begin_mask));
+  TF_RETURN_IF_ERROR(GetNodeAttr(slice_node->attrs(), "end_mask", &end_mask));
+  TF_RETURN_IF_ERROR(
+      GetNodeAttr(slice_node->attrs(), "ellipsis_mask", &ellipsis_mask));
+  TF_RETURN_IF_ERROR(
+      GetNodeAttr(slice_node->attrs(), "new_axis_mask", &new_axis_mask));
+  TF_RETURN_IF_ERROR(
+      GetNodeAttr(slice_node->attrs(), "shrink_axis_mask", &shrink_axis_mask));
+
+  // Only attempt to evaluate if there are no special masks set (note that we
+  // can handle begin/end_mask == 1).
+  if (!(begin_mask == 0 || begin_mask == 1) ||
+      !(end_mask == 0 || end_mask == 1) || ellipsis_mask != 0 ||
+      new_axis_mask != 0 || shrink_axis_mask != 0) {
+    *result = ctx->UnknownShape();
+    return Status::OK();
+  }
+
+  bool evaluated;
+  int64 begin;
+  if (begin_mask == 1) {
+    begin = 0;
+  } else {
+    TF_RETURN_IF_ERROR(
+        EvaluateConstantIntScalarEdge(slice_node, 1, &evaluated, &begin));
+    if (!evaluated) {
+      *result = ctx->UnknownShape();
+      return Status::OK();
+    }
+  }
+
+  int64 end;
+  if (end_mask == 1) {
+    end = std::numeric_limits::max();
+  } else {
+    TF_RETURN_IF_ERROR(
+        EvaluateConstantIntScalarEdge(slice_node, 2, &evaluated, &end));
+    if (!evaluated) {
+      *result = ctx->UnknownShape();
+      return Status::OK();
+    }
+  }
+
+  int64 stride;
+  TF_RETURN_IF_ERROR(
+      EvaluateConstantIntScalarEdge(slice_node, 3, &evaluated, &stride));
+  if (!evaluated) {
+    *result = ctx->UnknownShape();
+    return Status::OK();
+  }
+
+  // Apply stride to input interpreted as a partial shape.
+  ShapeHandle input;
+  TF_RETURN_IF_ERROR(ConstantPartialShape(ctx, slice_node, 0, &input));
+  TF_RETURN_IF_ERROR(ctx->Subshape(input, begin, end, stride, result));
+  return Status::OK();
+}
+
 Status ShapeRefiner::RunShapeFn(const Node* node,
                                 const OpRegistrationData* op_reg_data,
                                 ExtendedInferenceContext* ec) {
diff --git a/tensorflow/core/common_runtime/shape_refiner.h b/tensorflow/core/common_runtime/shape_refiner.h
index d49c4373f0b8c8..9c96dcbc206ae8 100644
--- a/tensorflow/core/common_runtime/shape_refiner.h
+++ b/tensorflow/core/common_runtime/shape_refiner.h
@@ -215,9 +215,18 @@ class ShapeRefiner {
                                 bool keep_nested_shapes,
                                 ExtendedInferenceContext* outer_context);
 
+  // Attempts to evaluate the 'dst_idx'-th input to 'node'. If the input edge
+  // value can be evaluated, 'evaluated' is set to true and the value returned
+  // in 'result'. Otherwise 'evaluated' is set to false.
   Status EvaluateConstantTensorForEdge(const Node* node, int dst_idx,
                                        bool* evaluated, Tensor* result);
 
+  // Wrapper around EvaluateConstantTensorForEdge for scalar int32/int64 input
+  // tensors. The caller is responsible for checking that the specified edge is
+  // scalar and int32 or int64.
+  Status EvaluateConstantIntScalarEdge(const Node* node, int dst_idx,
+                                       bool* evaluated, int64* result);
+
   // This function tries to materialize as much information about the 'node''s
   // dst_idx input as a statically computable shape, and the result may be
   // partially known, depending on what is statically inferable.
@@ -243,6 +252,11 @@ class ShapeRefiner {
                               const Node* node, int dst_idx,
                               shape_inference::ShapeHandle* result);
 
+  // Implementation of ConstantPartialShape for StridedSlice nodes.
+  Status PartialStridedSliceShape(Node* slice_node,
+                                  shape_inference::InferenceContext* ctx,
+                                  shape_inference::ShapeHandle* result);
+
   Status RunShapeFn(const Node* node, const OpRegistrationData* op_reg_data,
                     ExtendedInferenceContext* ec);
 
diff --git a/tensorflow/core/common_runtime/shape_refiner_test.cc b/tensorflow/core/common_runtime/shape_refiner_test.cc
index f48638afc0f602..8b9657eec88db6 100644
--- a/tensorflow/core/common_runtime/shape_refiner_test.cc
+++ b/tensorflow/core/common_runtime/shape_refiner_test.cc
@@ -60,6 +60,39 @@ class ShapeRefinerTest : public ::testing::Test {
   }
 
   static constexpr int64 kMaxTensorSize = ShapeRefiner::kMaxTensorSize;
+
+  void TestStridedSlice(const PartialTensorShape& input_shape, int begin,
+                        int end, int stride, const char* expected,
+                        int begin_mask = 0, int end_mask = 0,
+                        int ellipsis_mask = 0) {
+    Scope root = Scope::DisabledShapeInferenceScope();
+    auto placeholder =
+        ops::Placeholder(root, DT_INT32, ops::Placeholder::Shape(input_shape));
+    auto input = ops::Shape(root, placeholder);
+    auto begin_op = ops::Const(root, {begin});
+    auto end_op = ops::Const(root, {end});
+    auto stride_op = ops::Const(root, {stride});
+    auto slice = ops::StridedSlice(root, input, begin_op, end_op, stride_op,
+                                   ops::StridedSlice::BeginMask(begin_mask)
+                                       .EndMask(end_mask)
+                                       .EllipsisMask(ellipsis_mask));
+    Node* result;
+    TF_ASSERT_OK(NodeBuilder("test", "TensorAsShapeInt32")
+                     .Input(slice.node())
+                     .Finalize(root.graph(), &result));
+
+    ShapeRefiner m(TF_GRAPH_DEF_VERSION, OpRegistry::Global());
+    TF_ASSERT_OK(m.AddNode(placeholder.node()));
+    TF_ASSERT_OK(m.AddNode(input.node()));
+    TF_ASSERT_OK(m.AddNode(begin_op.node()));
+    TF_ASSERT_OK(m.AddNode(end_op.node()));
+    TF_ASSERT_OK(m.AddNode(stride_op.node()));
+    TF_ASSERT_OK(m.AddNode(slice.node()));
+    TF_ASSERT_OK(m.AddNode(result));
+
+    shape_inference::InferenceContext* ctx = m.GetContext(result);
+    EXPECT_EQ(ctx->DebugString(ctx->output(0)), expected);
+  }
 };
 
 namespace {
@@ -1156,6 +1189,73 @@ TEST_F(ShapeRefinerTest, ConstantValueAsShape_ConcatInvalidDimValue) {
             m.AddNode(result).error_message());
 }
 
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_StridedSlice) {
+  TestStridedSlice(
+      /*input_shape=*/{1, -1, 3, -1, 5},
+      /*begin=*/2,
+      /*end=*/5,
+      /*stride=*/1,
+      /*expected=*/"[3,?,5]");
+}
+
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_StridedSliceNegativeStride) {
+  // clang-format off
+  TestStridedSlice(
+      /*input_shape=*/{1, -1, 3, -1, 5},
+      /*begin=*/10,
+      /*end=*/0,
+      /*stride=*/-1,
+      /*expected=*/"[5,?,3,?]");
+  // clang-format on
+}
+
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_StridedSliceMasks) {
+  TestStridedSlice(
+      /*input_shape=*/{1, -1, 3, -1, 5},
+      /*begin=*/3,
+      /*end=*/4,
+      /*stride=*/1,
+      /*expected=*/"[1,?,3,?,5]",
+      /*begin_mask=*/1,
+      /*end_mask=*/1);
+}
+
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_StridedSliceInvalidMask) {
+  TestStridedSlice(
+      /*input_shape=*/{1, -1, 3},
+      /*begin=*/2,
+      /*end=*/3,
+      /*stride=*/1,
+      /*expected=*/"[?,?,?]",
+      /*begin_mask=*/0,
+      /*end_mask=*/0,
+      /*ellipsis_mask=*/1);
+}
+
+TEST_F(ShapeRefinerTest, ConstantValueAsShape_StridedSliceMulti) {
+  Scope root = Scope::DisabledShapeInferenceScope();
+  auto input = ops::Placeholder(root, DT_INT32);
+  auto begin = ops::Const(root, {0, 0});
+  auto end = ops::Const(root, {2, 2});
+  auto stride = ops::Const(root, {1, 1});
+  auto slice = ops::StridedSlice(root, input, begin, end, stride);
+  Node* result;
+  TF_ASSERT_OK(NodeBuilder("test", "TensorAsShapeInt32")
+                   .Input(slice.node())
+                   .Finalize(root.graph(), &result));
+
+  ShapeRefiner m(TF_GRAPH_DEF_VERSION, OpRegistry::Global());
+  TF_ASSERT_OK(m.AddNode(input.node()));
+  TF_ASSERT_OK(m.AddNode(begin.node()));
+  TF_ASSERT_OK(m.AddNode(end.node()));
+  TF_ASSERT_OK(m.AddNode(stride.node()));
+  TF_ASSERT_OK(m.AddNode(slice.node()));
+  TF_ASSERT_OK(m.AddNode(result));
+
+  shape_inference::InferenceContext* ctx = m.GetContext(result);
+  EXPECT_EQ(ctx->DebugString(ctx->output(0)), "?");
+}
+
 namespace {
 
 // Dummy op to test ShapeRefiner util functions
diff --git a/tensorflow/core/framework/shape_inference.cc b/tensorflow/core/framework/shape_inference.cc
index 2b995e8b5e8c84..3185875e3bcb3b 100644
--- a/tensorflow/core/framework/shape_inference.cc
+++ b/tensorflow/core/framework/shape_inference.cc
@@ -605,10 +605,16 @@ Status InferenceContext::Subshape(ShapeHandle s, int64 start,
   return Subshape(s, start, std::numeric_limits::max() /* end */, out);
 }
 
-Status InferenceContext::Subshape(ShapeHandle s, int64 start_in, int64 end_in,
+Status InferenceContext::Subshape(ShapeHandle s, int64 start, int64 end,
                                   ShapeHandle* out) {
-  int64 start = start_in;
-  int64 end = end_in;
+  return Subshape(s, start, end, 1 /* stride */, out);
+}
+
+Status InferenceContext::Subshape(ShapeHandle s, int64 start, int64 end,
+                                  int64 stride, ShapeHandle* out) {
+  int64 start_in = start;
+  int64 end_in = end;
+
   const int32 rank = Rank(s);
   if (start == 0 && ((RankKnown(s) && end >= rank) ||
                      end == std::numeric_limits::max())) {
@@ -621,6 +627,9 @@ Status InferenceContext::Subshape(ShapeHandle s, int64 start_in, int64 end_in,
 
   if (start > rank) start = rank;
   if (end > rank) end = rank;
+
+  if (stride < 0 && start == rank) --start;
+
   if (start < 0) {
     start = rank + start;
     if (start < 0) {
@@ -638,16 +647,24 @@ Status InferenceContext::Subshape(ShapeHandle s, int64 start_in, int64 end_in,
                                      ", for shape with rank ", rank);
     }
   }
-  if (start > end) {
+  if (stride > 0 && start > end) {
     *out = nullptr;
     return errors::InvalidArgument(
         "Subshape must have computed start <= end, but is ", start, " and ",
         end, " (computed from start ", start_in, " and end ", end_in,
         " over shape with rank ", rank, ")");
+  } else if (stride < 0 && start < end) {
+    *out = nullptr;
+    return errors::InvalidArgument(
+        "Subshape must have computed start >= end since stride is negative, "
+        "but is ",
+        start, " and ", end, " (computed from start ", start_in, " and end ",
+        end_in, " over shape with rank ", rank, " and stride", stride, ")");
   }
+
   std::vector dims;
-  dims.reserve(end - start);
-  for (int i = start; i < end; ++i) {
+  dims.reserve((end - start) / stride);
+  for (int i = start; stride > 0 ? i < end : i > end; i += stride) {
     dims.push_back(Dim(s, i));
   }
   return ReturnCreatedShape(dims, out);
diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h
index 9431a62abefd1a..3f3729dcf97e4d 100644
--- a/tensorflow/core/framework/shape_inference.h
+++ b/tensorflow/core/framework/shape_inference.h
@@ -434,6 +434,13 @@ class InferenceContext {
   Status Subshape(ShapeHandle s, int64 start, int64 end,
                   ShapeHandle* out) TF_MUST_USE_RESULT;
 
+  // Returns in <*out> a sub-shape of , with dimensions [start:end:stride].
+  //  and  can be negative, to index from the end of the shape.
+  //  and  are set to the rank of  if > rank of .
+  //  can be negative, to reverse the .
+  Status Subshape(ShapeHandle s, int64 start, int64 end, int64 stride,
+                  ShapeHandle* out) TF_MUST_USE_RESULT;
+
   // Returns in <*out> the result of appending the dimensions of  to those
   // of .
   Status Concatenate(ShapeHandle s1, ShapeHandle s2,

From 9ecbb5574fb86d9f5280315141a11acd47e50dee Mon Sep 17 00:00:00 2001
From: wangsiyu 
Date: Tue, 8 May 2018 10:54:04 +0800
Subject: [PATCH 0487/1691] refine unit test case coding style and move
 _should_add_regularizer function into add_weight

---
 tensorflow/python/layers/base.py      | 23 ++++++++++++-----------
 tensorflow/python/layers/base_test.py |  4 ++--
 2 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index f7b2e471b27f72..78db47681a87c5 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -191,6 +191,18 @@ def add_weight(self, name, shape, dtype=None,
       RuntimeError: If called with partioned variable regularization and
         eager execution is enabled.
     """
+    
+    def _should_add_regularizer(variable, existing_variable_set):
+      result = True
+      if isinstance(variable, tf_variables.PartitionedVariable):
+        for var in variable:
+          if var in existing_variable_set:
+            result = False
+            break
+      else:
+        result = variable not in existing_variable_set
+      return result
+
     init_graph = None
     if not context.executing_eagerly():
       default_graph = ops.get_default_graph()
@@ -354,14 +366,3 @@ def _add_elements_to_collection(elements, collection_list):
     for element in elements:
       if element not in collection_set:
         collection.append(element)
-
-def _should_add_regularizer(variable, existing_variable_set):
-  result = True
-  if isinstance(variable, tf_variables.PartitionedVariable):
-    for var in variable:
-      if var in existing_variable_set:
-        result = False
-        break
-  else:
-    result = variable not in existing_variable_set
-  return result
diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py
index 361e3de7aa5afc..7158fd42e1c5ba 100644
--- a/tensorflow/python/layers/base_test.py
+++ b/tensorflow/python/layers/base_test.py
@@ -99,10 +99,10 @@ def testAddWeight(self):
   def testReusePartitionedVaraiblesAndRegularizers(self):
     regularizer = lambda x: math_ops.reduce_sum(x) * 1e-3
     partitioner = partitioned_variables.fixed_size_partitioner(3)
-    for i in xrange(2):
+    for reuse in [False, True]:
       with variable_scope.variable_scope(variable_scope.get_variable_scope(),
                                          partitioner=partitioner,
-                                         reuse=False if i == 0 else True):
+                                         reuse=reuse):
         layer = base_layers.Layer(name='my_layer')
         variable = layer.add_variable(
             'reg_part_var', [4, 4],

From 263c094c1d4f9509c4428e97fdd83957d8225c25 Mon Sep 17 00:00:00 2001
From: wangsiyu 
Date: Tue, 8 May 2018 12:58:27 +0800
Subject: [PATCH 0488/1691] eliminate result variable in
 _should_add_regularizer to make code clean

---
 tensorflow/python/layers/base.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index 78db47681a87c5..aa416d1ff6483a 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -193,15 +193,12 @@ def add_weight(self, name, shape, dtype=None,
     """
     
     def _should_add_regularizer(variable, existing_variable_set):
-      result = True
       if isinstance(variable, tf_variables.PartitionedVariable):
         for var in variable:
           if var in existing_variable_set:
-            result = False
-            break
+            return False
       else:
-        result = variable not in existing_variable_set
-      return result
+        return variable not in existing_variable_set
 
     init_graph = None
     if not context.executing_eagerly():

From f32699406f31e0b6a38a15c9a3d580d1ef9d6204 Mon Sep 17 00:00:00 2001
From: wangsiyu 
Date: Tue, 8 May 2018 15:46:38 +0800
Subject: [PATCH 0489/1691] fix bug of return value in _should_add_regularizer
 function and refine code in base_test.py to make it no more than 80 columns

---
 tensorflow/python/layers/base.py      | 2 +-
 tensorflow/python/layers/base_test.py | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index aa416d1ff6483a..e122d6533ce501 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -197,6 +197,7 @@ def _should_add_regularizer(variable, existing_variable_set):
         for var in variable:
           if var in existing_variable_set:
             return False
+        return True
       else:
         return variable not in existing_variable_set
 
@@ -240,7 +241,6 @@ def _should_add_regularizer(variable, existing_variable_set):
             partitioner=partitioner,
             use_resource=use_resource,
             getter=vs.get_variable)
-
         if regularizer:
           if context.executing_eagerly() or _should_add_regularizer(
               variable, existing_variables):
diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py
index 7158fd42e1c5ba..ab49e37b90e183 100644
--- a/tensorflow/python/layers/base_test.py
+++ b/tensorflow/python/layers/base_test.py
@@ -108,7 +108,8 @@ def testReusePartitionedVaraiblesAndRegularizers(self):
             'reg_part_var', [4, 4],
             initializer=init_ops.zeros_initializer(),
             regularizer=regularizer)
-    self.assertEqual(len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 3)
+    self.assertEqual(
+        len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 3)
 
   def testNoEagerActivityRegularizer(self):
     with context.eager_mode():

From 334d8bfb594caafe5ab7ecaf007b1bf9ca062590 Mon Sep 17 00:00:00 2001
From: wangsiyu 
Date: Tue, 8 May 2018 15:50:36 +0800
Subject: [PATCH 0490/1691] remove type

---
 tensorflow/python/layers/base.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index e122d6533ce501..aa43a153c2951f 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -241,6 +241,7 @@ def _should_add_regularizer(variable, existing_variable_set):
             partitioner=partitioner,
             use_resource=use_resource,
             getter=vs.get_variable)
+
         if regularizer:
           if context.executing_eagerly() or _should_add_regularizer(
               variable, existing_variables):

From 7a9e695d82ef75b3619177da245842fdddc3b8a8 Mon Sep 17 00:00:00 2001
From: Brennan Saeta 
Date: Mon, 7 May 2018 18:31:47 -0700
Subject: [PATCH 0491/1691] [tf.data] Move
 tensorflow::dataset::MakeIteratorContext to core/framework

PiperOrigin-RevId: 195756342
---
 tensorflow/core/framework/dataset.cc          | 19 +++++++++++++++++++
 tensorflow/core/framework/dataset.h           |  6 ++++++
 tensorflow/core/kernels/data/dataset_utils.cc | 12 ------------
 tensorflow/core/kernels/data/dataset_utils.h  |  2 --
 4 files changed, 25 insertions(+), 14 deletions(-)

diff --git a/tensorflow/core/framework/dataset.cc b/tensorflow/core/framework/dataset.cc
index 4145ef7bc9d226..62a9d5751d6d4e 100644
--- a/tensorflow/core/framework/dataset.cc
+++ b/tensorflow/core/framework/dataset.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 #include "tensorflow/core/framework/dataset.h"
 
+#include "tensorflow/core/framework/device_base.h"
 #include "tensorflow/core/graph/graph_def_builder.h"
 #include "tensorflow/core/graph/node_builder.h"
 
@@ -269,4 +270,22 @@ const char GraphDatasetBase::kDatasetGraphKey[] = "_DATASET_GRAPH";
 const char GraphDatasetBase::kDatasetGraphOutputNodeKey[] =
     "_DATASET_GRAPH_OUTPUT_NODE";
 
+namespace dataset {
+
+IteratorContext MakeIteratorContext(OpKernelContext* ctx) {
+  IteratorContext::Params params;
+  params.env = ctx->env();
+  params.runner = *(ctx->runner());
+  params.lib = ctx->function_library();
+  // Note: must use reinterpret_cast because function.h forward-declares Device.
+  DeviceBase* device =
+      reinterpret_cast(ctx->function_library()->device());
+  params.allocator_getter = [device](AllocatorAttributes attrs) {
+    return device->GetAllocator(attrs);
+  };
+  return IteratorContext(params);
+}
+
+}  // namespace dataset
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/dataset.h b/tensorflow/core/framework/dataset.h
index 775d9f6eb6a4e2..8624af9bf56e7e 100644
--- a/tensorflow/core/framework/dataset.h
+++ b/tensorflow/core/framework/dataset.h
@@ -619,6 +619,12 @@ Status GetDatasetFromVariantTensor(const Tensor& tensor,
 // The ownership of `dataset` is transferred to `tensor`.
 Status StoreDatasetInVariantTensor(DatasetBase* dataset, Tensor* tensor);
 
+namespace dataset {
+
+IteratorContext MakeIteratorContext(OpKernelContext* ctx);
+
+}  // namespace dataset
+
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_CORE_FRAMEWORK_DATASET_H_
diff --git a/tensorflow/core/kernels/data/dataset_utils.cc b/tensorflow/core/kernels/data/dataset_utils.cc
index 67ddb52d577edb..c608f9e1c67097 100644
--- a/tensorflow/core/kernels/data/dataset_utils.cc
+++ b/tensorflow/core/kernels/data/dataset_utils.cc
@@ -46,18 +46,6 @@ Status MakeIteratorFromInputElement(
   return Status::OK();
 }
 
-IteratorContext MakeIteratorContext(OpKernelContext* ctx) {
-  IteratorContext::Params params;
-  params.env = ctx->env();
-  params.runner = *(ctx->runner());
-  params.lib = ctx->function_library();
-  DeviceBase* device = ctx->function_library()->device();
-  params.allocator_getter = [device](AllocatorAttributes attrs) {
-    return device->GetAllocator(attrs);
-  };
-  return IteratorContext(params);
-}
-
 }  // namespace dataset
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/data/dataset_utils.h b/tensorflow/core/kernels/data/dataset_utils.h
index e5ca71dd99d7f5..6c4191c2be6c55 100644
--- a/tensorflow/core/kernels/data/dataset_utils.h
+++ b/tensorflow/core/kernels/data/dataset_utils.h
@@ -28,8 +28,6 @@ Status MakeIteratorFromInputElement(
     int64 thread_index, CapturedFunction* captured_func, StringPiece prefix,
     std::unique_ptr* out_iterator);
 
-IteratorContext MakeIteratorContext(OpKernelContext* ctx);
-
 }  // namespace dataset
 
 }  // namespace tensorflow

From 069f3124eedab44b4e884c3c64ba8d5eccadfe93 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 7 May 2018 19:56:26 -0700
Subject: [PATCH 0492/1691] Temporarily disable concat rewrite.

PiperOrigin-RevId: 195762860
---
 tensorflow/core/grappler/optimizers/arithmetic_optimizer.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h
index 3f9feac55f62f0..1f6f5636873fb3 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h
@@ -65,7 +65,7 @@ class ArithmeticOptimizer : public GraphOptimizer {
     bool remove_redundant_bitcast = true;
     bool remove_redundant_cast = true;
     bool remove_negation = true;
-    bool hoist_cwise_unary_chains = true;
+    bool hoist_cwise_unary_chains = false;
     bool convert_sqrt_div_to_rsqrt_mul = false;
     bool remove_idempotent = true;
 

From a799cdbe78ca2c2e9c41f2b1bf8a3f57162fbcea Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 02:11:52 -0700
Subject: [PATCH 0493/1691] Automated g4 rollback of changelist 195748721

PiperOrigin-RevId: 195790581
---
 tensorflow/compiler/jit/BUILD                 |  22 --
 .../compiler/jit/create_xla_launch_op.cc      | 206 ++++--------------
 .../compiler/jit/create_xla_launch_op.h       |  35 ---
 .../compiler/jit/create_xla_launch_op_test.cc | 144 ------------
 .../compiler/jit/kernels/xla_launch_op.cc     |  90 ++------
 .../compiler/jit/kernels/xla_launch_op.h      |  51 ++---
 .../compiler/jit/xla_compile_on_demand_op.cc  |   3 +-
 tensorflow/compiler/jit/xla_launch_util.cc    |  18 +-
 tensorflow/compiler/jit/xla_launch_util.h     |  15 +-
 tensorflow/compiler/tests/BUILD               |   4 -
 tensorflow/compiler/tests/eager_test.py       | 112 +---------
 .../python/examples/resnet50/resnet50_test.py |  55 ++---
 tensorflow/python/eager/function.py           | 127 ++++-------
 13 files changed, 164 insertions(+), 718 deletions(-)
 delete mode 100644 tensorflow/compiler/jit/create_xla_launch_op.h
 delete mode 100644 tensorflow/compiler/jit/create_xla_launch_op_test.cc

diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index e942b46086c717..07136d6a746604 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -261,7 +261,6 @@ cc_library(
     name = "create_xla_launch_op",
     srcs = [
         "create_xla_launch_op.cc",
-        "create_xla_launch_op.h",
     ],
     deps = [
         ":common",
@@ -271,27 +270,6 @@ cc_library(
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
-        "//tensorflow/core:protos_all_cc",
-    ],
-    alwayslink = 1,
-)
-
-tf_cc_test(
-    name = "create_xla_launch_op_test",
-    srcs = [
-        "create_xla_launch_op.h",
-        "create_xla_launch_op_test.cc",
-    ],
-    deps = [
-        ":create_xla_launch_op",
-        "//tensorflow/core:core_cpu_internal",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:protos_all_cc",
-        "//tensorflow/core:session_options",
-        "//tensorflow/core:test",
-        "//tensorflow/core:test_main",
-        "//tensorflow/core:testlib",
     ],
 )
 
diff --git a/tensorflow/compiler/jit/create_xla_launch_op.cc b/tensorflow/compiler/jit/create_xla_launch_op.cc
index 6ac84dc19ce40b..18d901323f1085 100644
--- a/tensorflow/compiler/jit/create_xla_launch_op.cc
+++ b/tensorflow/compiler/jit/create_xla_launch_op.cc
@@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-#include "tensorflow/compiler/jit/create_xla_launch_op.h"
 
 #include "tensorflow/compiler/jit/defs.h"
 #include "tensorflow/compiler/jit/kernels/xla_launch_op.h"
@@ -26,189 +25,78 @@ limitations under the License.
 namespace tensorflow {
 namespace {
 
-// Utility which searches for values in a sorted list by scanning over it once.
-// No matter how many times ScanForValue is called, the list is scanned at most
-// once. However, if a call to ScanForValue skips over a value, that value is
-// not revisited in future calls to ScanForValue, so callers must take
-// care to order their calls.
+// Givens a NodeDef 'ndef' and the function library runtime 'flr', if
+// 'ndef' is a call to a compilable function defined in 'flr', returns OK
+// and fills in 'kernel' with a XlaLaunchOp kernel which computes the
+// node. Otherwise, returns a non-OK.
 //
-// Useful for merging multiple sorted lists in O(n) time.
-class SinglePassSearch {
- public:
-  // Creates a SinglePassSearch object that can be used to search in `values`.
-  // Does not take ownership of `values`. `values` must outlive this.
-  // `values` must be sorted.
-  explicit SinglePassSearch(const std::vector* values)
-      : current_index_(0), values_(values) {}
-
-  // Scans forward in the vector looking for "value", updating the internal
-  // position in to the vector.
-  // Returns true iff the vector contains the given value at or after current
-  // position.
-  // Not thread-safe.
-  bool ScanForValue(int value) {
-    while (current_index_ < values_->size() &&
-           (*values_)[current_index_] <= value) {
-      if ((*values_)[current_index_] == value) {
-        current_index_++;
-        return true;
-      }
-      current_index_++;
-    }
-    return false;
-  }
-
- private:
-  int current_index_;
-  const std::vector* values_;
-};
-
-Status CompilationRequested(const FunctionLibraryRuntime& flr,
-                            const NodeDef& node_def) {
+// This routine is here so that FunctionLibraryRuntime can jit a
+// specific function call as requested.
+Status CreateXlaLaunchOp(FunctionLibraryRuntime* flr, const NodeDef& ndef,
+                         std::unique_ptr* kernel) {
   bool xla_compile = false;
-  // Check if op is marked _XlaCompile=true.
-  Status status = flr.GetFunctionLibraryDefinition()->GetAttr(
-      node_def, kXlaCompileAttr, &xla_compile);
-  if (!status.ok() || !xla_compile) {
-    if (VLOG_IS_ON(3)) {
-      if (!status.ok()) {
-        VLOG(3) << "No " << kXlaCompileAttr << " attr defined for "
-                << node_def.op() << ". status=" << status.ToString();
-      } else {
-        VLOG(3) << node_def.op() << " is explicitly marked not to be compiled";
-      }
-    }
-    return Status(error::INVALID_ARGUMENT, "");
+  if (!flr->GetFunctionLibraryDefinition()
+           ->GetAttr(ndef, kXlaCompileAttr, &xla_compile)
+           .ok() ||
+      !xla_compile) {
+    // Not marked as _XlaCompile=true.
+    return errors::InvalidArgument("No ", kXlaCompileAttr, " for ", ndef.op());
+  }
+  // Make sure that kernels have been registered on the JIT device.
+  XlaOpRegistry::RegisterCompilationKernels();
+  if (!IsCompilable(flr, ndef)) {
+    // ndef is calling a function that XLA can't compile.
+    return errors::InvalidArgument("Not compilable: ", ndef.ShortDebugString());
   }
-  return Status::OK();
-}
-
-// Given a FunctionLibraryRuntime and a NodeDef calling a function in the
-// runtime, returns this function's body in `fbody` as well as the indices
-// of its constant and resource arguments.
-// `fbody` is owned by `flr`.
-// `constant_arg_indices` and `resource_arg_indices` should be empty vector.
-// They are sorted in ascending order on this function's return.
-Status GetBodyAndConstantsAndResources(FunctionLibraryRuntime* flr,
-                                       const NodeDef& node_def,
-                                       const FunctionBody** fbody,
-                                       std::vector* constant_arg_indices,
-                                       std::vector* resource_arg_indices) {
   FunctionLibraryRuntime::Handle handle;
-  // If node_def is not instantiable, e.g., the function does not exist,
+  // If ndef is not instantiable, e.g., the function does not exist,
   // simply bail out.
   TF_RETURN_IF_ERROR(
-      flr->Instantiate(node_def.op(), AttrSlice(&node_def.attr()), &handle));
-  *fbody = flr->GetFunctionBody(handle);
-  CHECK(*fbody);  // Can't be nullptr since we just instantiated it.
-  const DataTypeVector& arg_types = (*fbody)->arg_types;
-  std::vector const_args(arg_types.size());
+      flr->Instantiate(ndef.op(), AttrSlice(&ndef.attr()), &handle));
+  const FunctionBody* fbody = flr->GetFunctionBody(handle);
+  CHECK(fbody);  // Can't be nullptr since we just instantiated it.
+  std::vector const_args(fbody->arg_types.size());
   // If we can't analyze the const args. Bail out.
-  TF_RETURN_IF_ERROR(BackwardsConstAnalysis(*((*fbody)->graph), &const_args));
+  TF_RETURN_IF_ERROR(BackwardsConstAnalysis(*(fbody->graph), &const_args));
 
   for (int i = 0; i < const_args.size(); ++i) {
     if (const_args[i]) {
-      constant_arg_indices->push_back(i);
-    }
-  }
-
-  // There can be hundreds of resource variables. Reserve the space for them.
-  // We don't reserve for constants above as they are usually few.
-  resource_arg_indices->reserve(arg_types.size());
-  for (int i = 0; i < arg_types.size(); ++i) {
-    if (arg_types[i] == DT_RESOURCE) {
-      resource_arg_indices->push_back(i);
+      // There is a const arg. Bail out.
+      return errors::InvalidArgument("Const arg: ", i, " in ",
+                                     DebugString(fbody->fdef));
     }
   }
 
-  return Status::OK();
-}
-
-}  // namespace
-
-Status CreateXlaLaunchOp(FunctionLibraryRuntime* flr, const NodeDef& node_def,
-                         std::unique_ptr* kernel) {
-  TF_RETURN_IF_ERROR(CompilationRequested(*flr, node_def));
-
-  VLOG(3) << "Creating XlaLaunchOp for " << node_def.DebugString();
-
-  // Make sure that kernels have been registered on the JIT device.
-  XlaOpRegistry::RegisterCompilationKernels();
-  if (!IsCompilable(flr, node_def)) {
-    // node_def is calling a function that XLA can't compile.
-    return errors::InvalidArgument("Not compilable: ",
-                                   node_def.ShortDebugString());
-  }
-
-  // Get function body, constant args, and resource args.
-  const FunctionBody* fbody = nullptr;
-  std::vector constant_arg_indices;
-  std::vector resource_arg_indices;
-  TF_RETURN_IF_ERROR(GetBodyAndConstantsAndResources(
-      flr, node_def, &fbody, &constant_arg_indices, &resource_arg_indices));
-
-  // Set input and output memory types.
+  NodeDef launch_def;
+  launch_def.set_name(ndef.name());
+  launch_def.set_op("_XlaLaunch");
+  launch_def.set_device(flr->device()->name());
+  AddNodeAttr("Tconstants", DataTypeVector{}, &launch_def);
+  AddNodeAttr("Nresources", 0, &launch_def);
+  AddNodeAttr("Targs", fbody->arg_types, &launch_def);
+  AddNodeAttr("Tresults", fbody->ret_types, &launch_def);
+  NameAttrList func;
+  func.set_name(ndef.op());
+  *(func.mutable_attr()) = ndef.attr();
+  AddNodeAttr("function", func, &launch_def);
+
+  // TODO(b/32387911): Handles the host memory types across function
+  // calls properly. For now, we assume all inputs and outputs are on
+  // the device memory.
   MemoryTypeVector input_memory_types(fbody->arg_types.size(), DEVICE_MEMORY);
-  // These indices are used only for optimization purposes. They allow us
-  // to loop over constant_arg_indices and resource_arg_indices only once
-  // while iterating over all the function arguments checking if it is a
-  // resource or a constant.
-  // The reason we optimized this code is because functions can have a lot of
-  // captured arguments. For example, the backward pass of ResNet50 takes in all
-  // 214 variables and a similar number of activations.
-  SinglePassSearch constants_search(&constant_arg_indices);
-  SinglePassSearch resources_search(&resource_arg_indices);
-  for (int i = 0; i < fbody->arg_types.size(); ++i) {
-    if (resources_search.ScanForValue(i) || constants_search.ScanForValue(i)) {
-      // Compile-time constants and resource handles are expected to be in
-      // host memory.
-      input_memory_types[i] = HOST_MEMORY;
-    }
-  }
-  // One might wonder, about the case where a compile-time constant argument
-  // (which must be in host memory) is also used as an input into an op,
-  // e.g. Add, that expects its inputs in device memory. Here is how it
-  // works now.
-  // First, what do we mean by "op expects an input in XYZ memory"?
-  // There are two types of "ops" here: the tf2xla kernel and the HLO
-  // computation it builds. The tf2xla kernel needs to retrieve the actual
-  // numeric value of the compile-time constant tensors, so it really expects
-  // them to be on in host memory. However, for other inputs, it refers to them
-  // using xla::ComputationDataHandle, which is just a symbolic handle that
-  // xla::ComputationBuilder assigns. How does this handle gets assigned for
-  // constant arguments? Even constant arguments get an _Arg node in the graph
-  // instatiated for Function compilation. The tf2xla kernel for constant _Arg
-  // nodes takes the constant value, converts it to XlaLiteral, and feeds it
-  // to xla::ComputationBuilder.ConstantLiteral, which returns the handle. This
-  // constant XlaLiteral is included in the HLO graph, and subsequently, in
-  // the actual executable, which is copied to the device before being
-  // executed. Thus, when this executable runs, the constant is available in
-  // device memory.
-
-  // XlaLaunch kernel keeps all outputs (including constants, which it copies),
-  // in device memory
   MemoryTypeVector output_memory_types(fbody->ret_types.size(), DEVICE_MEMORY);
 
-  // Create the kernel.
-  NameAttrList function;
-  function.set_name(node_def.op());
-  *(function.mutable_attr()) = node_def.attr();
-
   Device* dev = flr->device();
   Status s;
   OpKernelConstruction construction(
       DeviceType(dev->device_type()), dev,
-      dev->GetAllocator(AllocatorAttributes()), &node_def,
+      dev->GetAllocator(AllocatorAttributes()), &launch_def,
       &fbody->fdef.signature(), flr, fbody->arg_types, input_memory_types,
       fbody->ret_types, output_memory_types, flr->graph_def_version(), &s);
-
-  *kernel = absl::make_unique(
-      &construction, constant_arg_indices, resource_arg_indices, function);
+  kernel->reset(new XlaLocalLaunchOp(&construction));
   return s;
 }
 
-namespace {
-
 bool RegisterLaunchOpCreator() {
   RegisterDefaultCustomKernelCreator(CreateXlaLaunchOp);
   return true;
diff --git a/tensorflow/compiler/jit/create_xla_launch_op.h b/tensorflow/compiler/jit/create_xla_launch_op.h
deleted file mode 100644
index 98a22e351532c1..00000000000000
--- a/tensorflow/compiler/jit/create_xla_launch_op.h
+++ /dev/null
@@ -1,35 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_COMPILER_JIT_CREATE_XLA_LAUNCH_OP_H_
-#define TENSORFLOW_COMPILER_JIT_CREATE_XLA_LAUNCH_OP_H_
-
-#include "tensorflow/core/framework/node_def.pb.h"
-#include "tensorflow/core/lib/core/status.h"
-
-namespace tensorflow {
-
-class FunctionLibraryRuntime;
-class OpKernel;
-
-// Given a NodeDef 'node_def' and the function library runtime 'flr', if
-// 'node_def' is a call to a compilable function defined in 'flr', returns OK
-// and fills in 'kernel' with a XlaLaunchOp kernel which computes the
-// node. Otherwise, returns a non-OK.
-Status CreateXlaLaunchOp(FunctionLibraryRuntime* flr, const NodeDef& node_def,
-                         std::unique_ptr* kernel);
-
-}  // namespace tensorflow
-
-#endif  // TENSORFLOW_COMPILER_JIT_CREATE_XLA_LAUNCH_OP_H_
diff --git a/tensorflow/compiler/jit/create_xla_launch_op_test.cc b/tensorflow/compiler/jit/create_xla_launch_op_test.cc
deleted file mode 100644
index c222824eda8306..00000000000000
--- a/tensorflow/compiler/jit/create_xla_launch_op_test.cc
+++ /dev/null
@@ -1,144 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/compiler/jit/create_xla_launch_op.h"
-
-#include "tensorflow/core/common_runtime/device_factory.h"
-#include "tensorflow/core/common_runtime/function.h"
-#include "tensorflow/core/framework/function_testlib.h"
-#include "tensorflow/core/framework/node_def_builder.h"
-#include "tensorflow/core/framework/tensor_testutil.h"
-#include "tensorflow/core/lib/core/status.h"
-#include "tensorflow/core/platform/test.h"
-#include "tensorflow/core/public/session_options.h"
-#include "tensorflow/core/public/version.h"
-
-namespace tensorflow {
-
-NodeDef ToNodeDef(const string& text) {
-  NodeDef node_def;
-  EXPECT_TRUE(protobuf::TextFormat::MergeFromString(text, &node_def));
-  return node_def;
-}
-
-// Create a FunctionDef that takes one resource and one regular param
-FunctionDef XTimesY() {
-  return FunctionDefHelper::Define(
-      // Name
-      "XTimesY",
-      // Args
-      {"x: float", "y: resource"},
-      // Return values
-      {"z: float"},
-      // Attr def
-      {},
-      // Nodes
-      {
-          {{"y0"}, "ReadVariableOp", {"y"}, {{"dtype", DT_FLOAT}}},
-          {{"z"}, "Mul", {"x", "y0"}, {{"T", DT_FLOAT}}},
-      });
-}
-
-class CreateXlaLaunchOpTest : public ::testing::Test {
- protected:
-  void Init(const std::vector& flib) {
-    SessionOptions options;
-    auto* device_count = options.config.mutable_device_count();
-    device_count->insert({"CPU", 1});
-    TF_CHECK_OK(DeviceFactory::AddDevices(
-        options, "/job:localhost/replica:0/task:0", &devices_));
-
-    FunctionDefLibrary proto;
-    for (const auto& fdef : flib) {
-      *(proto.add_function()) = fdef;
-    }
-    lib_def_ = absl::make_unique(
-        OpRegistry::Global(), proto);
-    OptimizerOptions opts;
-    device_mgr_ = absl::make_unique(devices_);
-    pflr_ = absl::make_unique(
-        device_mgr_.get(), Env::Default(), TF_GRAPH_DEF_VERSION, lib_def_.get(),
-        opts, /*default_thread_pool=*/nullptr, /*cluster_flr=*/nullptr);
-    flr_ = pflr_->GetFLR("/job:localhost/replica:0/task:0/cpu:0");
-  }
-
-  FunctionLibraryRuntime* flr_;
-  std::vector devices_;
-  std::unique_ptr device_mgr_;
-  std::unique_ptr lib_def_;
-  std::unique_ptr pflr_;
-
-  std::unique_ptr kernel_;
-};
-
-AttrValue BoolAttr(bool b) {
-  AttrValue v;
-  v.set_b(b);
-  return v;
-}
-
-TEST_F(CreateXlaLaunchOpTest, OneFloatOneResourceArgument) {
-  FunctionDef fdef = XTimesY();
-  (*fdef.mutable_attr())["_XlaCompile"] = BoolAttr(true);
-  Init({fdef});
-
-  Status status = CreateXlaLaunchOp(
-      flr_, ToNodeDef(R"pb(
-        name: 'XTimesY' op: 'XTimesY' input: 'a' input: 'b'
-      )pb"), &kernel_);
-  ASSERT_TRUE(status.ok()) << status.ToString();
-
-  EXPECT_EQ("XTimesY", kernel_->name());
-  EXPECT_EQ("XTimesY", kernel_->type_string());
-
-  EXPECT_EQ(2, kernel_->num_inputs());
-  EXPECT_EQ(DT_FLOAT, kernel_->input_type(0));
-  EXPECT_EQ(DT_RESOURCE, kernel_->input_type(1));
-  EXPECT_EQ(DEVICE_MEMORY, kernel_->input_memory_types()[0]);
-  EXPECT_EQ(HOST_MEMORY, kernel_->input_memory_types()[1]);
-
-  EXPECT_EQ(1, kernel_->num_outputs());
-  EXPECT_EQ(DT_FLOAT, kernel_->output_type(0));
-  EXPECT_EQ(DEVICE_MEMORY, kernel_->output_memory_types()[0]);
-}
-
-TEST_F(CreateXlaLaunchOpTest, FailsIfXlaCompileAttrNotSet) {
-  FunctionDef fdef = XTimesY();
-  Init({fdef});
-
-  Status status = CreateXlaLaunchOp(flr_, ToNodeDef(R"proto(
-                                      name: 'XTimesY'
-                                      op: 'XTimesY'
-                                      input: 'a'
-                                      input: 'b'
-                                    )proto"), &kernel_);
-  EXPECT_TRUE(errors::IsInvalidArgument(status)) << status.ToString();
-}
-
-TEST_F(CreateXlaLaunchOpTest, FailsIfXlaCompileAttrIsSetToFalse) {
-  FunctionDef fdef = XTimesY();
-  (*fdef.mutable_attr())["_XlaCompile"] = BoolAttr(false);
-  Init({fdef});
-
-  Status status = CreateXlaLaunchOp(flr_, ToNodeDef(R"proto(
-                                      name: 'XTimesY'
-                                      op: 'XTimesY'
-                                      input: 'a'
-                                      input: 'b'
-                                    )proto"), &kernel_);
-  EXPECT_TRUE(errors::IsInvalidArgument(status)) << status.ToString();
-}
-
-}  // namespace tensorflow
diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.cc b/tensorflow/compiler/jit/kernels/xla_launch_op.cc
index 86a9fd3b8e124e..049d170fa48928 100644
--- a/tensorflow/compiler/jit/kernels/xla_launch_op.cc
+++ b/tensorflow/compiler/jit/kernels/xla_launch_op.cc
@@ -39,15 +39,15 @@ limitations under the License.
 
 namespace tensorflow {
 
-XlaLocalLaunchBase::XlaLocalLaunchBase(OpKernelConstruction* ctx,
-                                       const std::vector& constants,
-                                       const std::vector& resources,
-                                       const NameAttrList& function)
-    : OpKernel(ctx),
-      constants_(constants),
-      resources_(resources),
-      device_type_(ctx->device_type()),
-      function_(function) {
+XlaLocalLaunchOp::XlaLocalLaunchOp(OpKernelConstruction* ctx)
+    : OpKernel(ctx), device_type_(ctx->device_type()) {
+  const NameAttrList* func;
+  OP_REQUIRES_OK(ctx, ctx->GetAttr("function", &func));
+  function_ = *func;
+  DataTypeVector constant_types;
+  OP_REQUIRES_OK(ctx, ctx->GetAttr("Tconstants", &constant_types));
+  num_constant_args_ = constant_types.size();
+  OP_REQUIRES_OK(ctx, ctx->GetAttr("Nresources", &num_resource_args_));
   if (device_type_ == DeviceType(DEVICE_CPU)) {
     platform_id_ = se::host::kHostPlatformId;
   } else if (device_type_ == DeviceType(DEVICE_GPU)) {
@@ -57,8 +57,8 @@ XlaLocalLaunchBase::XlaLocalLaunchBase(OpKernelConstruction* ctx,
   }
 }
 
-Status XlaLocalLaunchBase::BuildCompilationCache(OpKernelContext* ctx,
-                                                 XlaCompilationCache** cache) {
+Status XlaLocalLaunchOp::BuildCompilationCache(OpKernelContext* ctx,
+                                               XlaCompilationCache** cache) {
   const XlaDevice::Metadata* metadata;
   Status s = XlaDevice::GetMetadata(ctx, &metadata);
   if (s.ok()) {
@@ -90,8 +90,8 @@ Status XlaLocalLaunchBase::BuildCompilationCache(OpKernelContext* ctx,
   return Status::OK();
 }
 
-void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) {
-  VLOG(1) << "XlaLocalLaunchOpBase::Compute "
+void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) {
+  VLOG(1) << "XlaLocalLaunchOp::Compute "
           << Canonicalize(function_.name(), AttrSlice(&function_.attr()));
   // We store information about the JIT-compiled XLA computation
   // in the ResourceMgr.
@@ -124,7 +124,7 @@ void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) {
   }
 
   std::map variables =
-      SnapshotResourceVariables(ctx, resources_);
+      SnapshotResourceVariables(ctx, num_resource_args_);
 
   xla::LocalClient* client = static_cast(cache->client());
 
@@ -161,7 +161,7 @@ void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) {
   xla::LocalExecutable* executable;
 
   std::map constant_args;
-  for (int i : constants_) {
+  for (int i = 0; i < num_constant_args_; ++i) {
     constant_args.insert({i, ctx->input(i)});
   }
   OP_REQUIRES_OK(ctx, cache->Compile(options, function_, constant_args,
@@ -170,8 +170,8 @@ void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) {
 
   VLOG(1) << "Executing XLA Computation...";
 
-  XlaComputationLaunchContext launch_context(client, xla_allocator,
-                                             allocate_xla_tensors);
+  XlaComputationLaunchContext launch_context(
+      num_resource_args_, client, xla_allocator, allocate_xla_tensors);
   launch_context.PopulateInputs(ctx, kernel, variables);
 
   // Execute the computation.
@@ -194,62 +194,6 @@ void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) {
   VLOG(1) << "Done";
 }
 
-namespace {
-
-// OP_REQUIRES_OK_RETURN is the same as OP_REQUIRES_OK except that
-// in error case, it returns RET instead of void.
-#define OP_REQUIRES_OK_RETURN(CTX, RET, ...)                \
-  do {                                                      \
-    ::tensorflow::Status _s(__VA_ARGS__);                   \
-    if (!TF_PREDICT_TRUE(_s.ok())) {                        \
-      (CTX)->CtxFailureWithWarning(__FILE__, __LINE__, _s); \
-      return RET;                                           \
-    }                                                       \
-  } while (0)
-
-// Helper static functions to construct parameters for
-// XlaLocalLaunchBase constructor from OpKernelConstruction.
-std::vector ConstantsVector(OpKernelConstruction* ctx) {
-  DataTypeVector constant_types;
-  OP_REQUIRES_OK_RETURN(ctx, std::vector(),
-                        ctx->GetAttr("Tconstants", &constant_types));
-  std::vector constants(constant_types.size());
-  std::iota(constants.begin(), constants.end(), 0);
-  return constants;
-}
-
-std::vector ResourcesVector(OpKernelConstruction* ctx) {
-  DataTypeVector constant_types;
-  OP_REQUIRES_OK_RETURN(ctx, std::vector(),
-                        ctx->GetAttr("Tconstants", &constant_types));
-
-  DataTypeVector arg_types;
-  OP_REQUIRES_OK_RETURN(ctx, std::vector(),
-                        ctx->GetAttr("Targs", &arg_types));
-
-  int num_resources;
-  OP_REQUIRES_OK_RETURN(ctx, std::vector(),
-                        ctx->GetAttr("Nresources", &num_resources));
-
-  std::vector resources(num_resources);
-  std::iota(resources.begin(), resources.end(),
-            constant_types.size() + arg_types.size());
-  return resources;
-}
-
-NameAttrList FunctionAttr(OpKernelConstruction* ctx) {
-  const NameAttrList* func;
-  OP_REQUIRES_OK_RETURN(ctx, NameAttrList(), ctx->GetAttr("function", &func));
-  return *func;
-}
-
-#undef OP_REQUIRES_OK_RETURN
-}  // namespace
-
-XlaLocalLaunchOp::XlaLocalLaunchOp(OpKernelConstruction* ctx)
-    : XlaLocalLaunchBase(ctx, ConstantsVector(ctx), ResourcesVector(ctx),
-                         FunctionAttr(ctx)) {}
-
 XlaLocalLaunchOp::~XlaLocalLaunchOp() {
   VLOG(1) << "XlaLocalLaunchOp destroyed";
 }
diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.h b/tensorflow/compiler/jit/kernels/xla_launch_op.h
index 8dfc4b382d5115..8f8e646f0ff6d9 100644
--- a/tensorflow/compiler/jit/kernels/xla_launch_op.h
+++ b/tensorflow/compiler/jit/kernels/xla_launch_op.h
@@ -26,41 +26,6 @@ limitations under the License.
 
 namespace tensorflow {
 
-// XlaLocalLaunchBase is almost the same as XlaLocalLaunchOp.
-// The only difference is that it does not require arguments to follow
-// the "constants, then regular args, then resources" order.
-// It takes vectors of constant and resource arguments explicitly.
-// It does not have corresponding OpDef because it is never present
-// in the GraphDef.
-// Currently, it is used by eager runtime. FunctionLibraryRuntime creates
-// this kernel when asked to create a kernel for an XLA-compiled function.
-class XlaLocalLaunchBase : public OpKernel {
- public:
-  XlaLocalLaunchBase(OpKernelConstruction* ctx,
-                     const std::vector& constants,
-                     const std::vector& resources,
-                     const NameAttrList& function);
-  XlaLocalLaunchBase(const XlaLocalLaunchBase&) = delete;
-  XlaLocalLaunchBase& operator=(const XlaLocalLaunchBase&) = delete;
-  ~XlaLocalLaunchBase() override = default;
-
-  void Compute(OpKernelContext* ctx) override;
-
- protected:
-  // Builds a XlaCompilationCache class suitable for the current device.
-  Status BuildCompilationCache(OpKernelContext* ctx,
-                               XlaCompilationCache** cache);
-
-  // Indexes of compile-time constant inputs
-  std::vector constants_;
-  // Indexes of resource inputs
-  std::vector resources_;
-
-  DeviceType device_type_;
-  NameAttrList function_;
-  se::Platform::Id platform_id_;
-};
-
 // XlaLocalLaunchOp is used to replace a region of the TensorFlow graph
 // which will be compiled and executed using XLA.  The XlaLocalLaunchOp is
 // responsible for handling interactions with the TensorFlow executor.
@@ -70,12 +35,26 @@ class XlaLocalLaunchBase : public OpKernel {
 // XlaLocalLaunchOp uses xla::LocalClient::Compile() and
 // xla::LocalExecutable::Run(), and passes arguments into/out of XLA in device
 // memory.
-class XlaLocalLaunchOp : public XlaLocalLaunchBase {
+class XlaLocalLaunchOp : public OpKernel {
  public:
   explicit XlaLocalLaunchOp(OpKernelConstruction* ctx);
   ~XlaLocalLaunchOp() override;
 
+  void Compute(OpKernelContext* ctx) override;
+
  private:
+  // Builds a XlaCompilationCache class suitable for the current device.
+  Status BuildCompilationCache(OpKernelContext* ctx,
+                               XlaCompilationCache** compiler);
+
+  DeviceType device_type_;
+  NameAttrList function_;
+  int num_constant_args_;
+  // Number of resource variable arguments.
+  int num_resource_args_;
+
+  se::Platform::Id platform_id_;
+
   TF_DISALLOW_COPY_AND_ASSIGN(XlaLocalLaunchOp);
 };
 
diff --git a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc
index 6b83cf67ffc571..60458f6f3314b2 100644
--- a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc
+++ b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc
@@ -48,12 +48,13 @@ Status XlaCompileOnDemandOp::Run(OpKernelContext* ctx,
                                  const XlaCompiler::CompilationResult* result,
                                  xla::LocalExecutable* executable) {
   std::map variables = GetVariables(ctx);
+  int64 num_resource_args = variables.size();
 
   xla::LocalClient* client = metadata.client();
 
   // Builds an XLA allocator for the device.
   XlaComputationLaunchContext launch_context(
-      client, client->backend().memory_allocator(), true);
+      num_resource_args, client, client->backend().memory_allocator(), true);
 
   launch_context.PopulateInputs(ctx, result, variables);
 
diff --git a/tensorflow/compiler/jit/xla_launch_util.cc b/tensorflow/compiler/jit/xla_launch_util.cc
index 0223f97a032cf9..33e53612b91315 100644
--- a/tensorflow/compiler/jit/xla_launch_util.cc
+++ b/tensorflow/compiler/jit/xla_launch_util.cc
@@ -38,13 +38,14 @@ using xla::ScopedShapedBuffer;
 using xla::ShapedBuffer;
 }  // anonymous namespace
 
-std::map SnapshotResourceVariables(
-    OpKernelContext* ctx, const std::vector& variables) {
+std::map SnapshotResourceVariables(OpKernelContext* ctx,
+                                                        int num_variables) {
   std::map snapshot;
-  for (int i : variables) {
+  int first_variable = ctx->num_inputs() - num_variables;
+  for (int i = 0; i < num_variables; ++i) {
     Var* variable = nullptr;
-    ResourceHandle handle = HandleFromInput(ctx, i);
-    OptionalTensor& tensor = snapshot[i];
+    ResourceHandle handle = HandleFromInput(ctx, first_variable + i);
+    OptionalTensor& tensor = snapshot[first_variable + i];
     if (LookupResource(ctx, handle, &variable).ok()) {
       tf_shared_lock lock(*variable->mu());
       tensor.name = handle.name();
@@ -111,9 +112,10 @@ ScopedShapedBuffer ExtractSubShapedBuffer(
 using internal::ExtractSubShapedBuffer;
 
 XlaComputationLaunchContext::XlaComputationLaunchContext(
-    xla::LocalClient* client, xla::DeviceMemoryAllocator* xla_allocator,
-    bool allocate_xla_tensors)
-    : client_(client),
+    int64 num_resource_args, xla::LocalClient* client,
+    xla::DeviceMemoryAllocator* xla_allocator, bool allocate_xla_tensors)
+    : num_resource_args_(num_resource_args),
+      client_(client),
       xla_allocator_(xla_allocator),
       allocate_xla_tensors_(allocate_xla_tensors) {}
 
diff --git a/tensorflow/compiler/jit/xla_launch_util.h b/tensorflow/compiler/jit/xla_launch_util.h
index a2431253f8c44b..38291b0bd429b2 100644
--- a/tensorflow/compiler/jit/xla_launch_util.h
+++ b/tensorflow/compiler/jit/xla_launch_util.h
@@ -31,17 +31,15 @@ limitations under the License.
 namespace tensorflow {
 class XlaAllocator;
 
-// Takes a snapshot of the values of resource variable arguments, whose
-// indices are specified in `variables` argument. We snapshot tensors that back
+// Takes a snapshot of the values of resource variable arguments, which are
+// the last `num_variables` arguments. We snapshot tensors that back
 // resource variables since concurrent updates may modify the shape, and it is
 // important that the shapes used for compilation match the true shapes of the
 // buffers.
 //
-// Returns a map of TensorFlow argument index to resource variable. If a
-// resource variable is not initialized, the corresponding OptionalTensor
-// will have its `present` field set to false.
-std::map SnapshotResourceVariables(
-    OpKernelContext* ctx, const std::vector& variables);
+// Returns a map of TensorFlow argument index to resource variable.
+std::map SnapshotResourceVariables(OpKernelContext* ctx,
+                                                        int num_variables);
 
 // Adapter class that wraps a Tensorflow allocator as an XLA allocator.
 // Assumes that the Tensorflow allocator permits asynchronous deallocation:
@@ -74,7 +72,7 @@ class XlaComputationLaunchContext {
   // Create a new launch context. 'allocate_xla_tensors' is true if allocated
   // output tensors and variables are always XlaTensors. If false they are
   // assumed to be "normal" device pointers.
-  XlaComputationLaunchContext(xla::LocalClient* client,
+  XlaComputationLaunchContext(int64 num_resource_args, xla::LocalClient* client,
                               xla::DeviceMemoryAllocator* xla_allocator,
                               bool allocate_xla_tensors);
 
@@ -94,6 +92,7 @@ class XlaComputationLaunchContext {
   const std::vector& arguments() const { return arg_ptrs_; }
 
  private:
+  int64 num_resource_args_;
   xla::LocalClient* client_;
   xla::DeviceMemoryAllocator* xla_allocator_;
   bool allocate_xla_tensors_;
diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 9791792f29ca05..aaea83ae9cbd21 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -327,11 +327,7 @@ tf_xla_py_test(
         ":xla_test",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:layers",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:nn",
         "//tensorflow/python:platform_test",
-        "//tensorflow/python/eager:function",
     ],
 )
 
diff --git a/tensorflow/compiler/tests/eager_test.py b/tensorflow/compiler/tests/eager_test.py
index 5ab1585f8c6e07..bdd0185dfe4abe 100644
--- a/tensorflow/compiler/tests/eager_test.py
+++ b/tensorflow/compiler/tests/eager_test.py
@@ -24,16 +24,10 @@
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.eager import backprop
 from tensorflow.python.eager import context
-from tensorflow.python.eager import function
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
-from tensorflow.python.layers import convolutional
-from tensorflow.python.layers import pooling
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import init_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.platform import googletest
 
@@ -49,7 +43,7 @@ def testBasic(self):
 
   def testExecuteListOutputLen0(self):
     with self.test_scope():
-      empty = constant_op.constant([], dtype=dtypes.float32)
+      empty = constant_op.constant([], dtype=dtypes.int32)
       result = array_ops.unstack(empty, 0)
       self.assertTrue(isinstance(result, list))
       self.assertEqual(0, len(result))
@@ -57,7 +51,7 @@ def testExecuteListOutputLen0(self):
   def testExecuteListOutputLen1(self):
     with self.test_scope():
       split_dim = constant_op.constant(1)
-      value = constant_op.constant([[0., 1., 2.], [3., 4., 5.]])
+      value = constant_op.constant([[0, 1, 2], [3, 4, 5]])
       result = array_ops.split(value, 1, axis=split_dim)
       self.assertTrue(isinstance(result, list))
       self.assertEqual(1, len(result))
@@ -66,7 +60,7 @@ def testExecuteListOutputLen1(self):
   def testExecuteListOutputLen3(self):
     with self.test_scope():
       split_dim = constant_op.constant(1)
-      value = constant_op.constant([[0., 1., 2.], [3., 4., 5.]])
+      value = constant_op.constant([[0, 1, 2], [3, 4, 5]])
       result = array_ops.split(value, 3, axis=split_dim)
       self.assertTrue(isinstance(result, list))
       self.assertEqual(3, len(result))
@@ -137,105 +131,7 @@ def f():
     self.assertEqual(2., grads[0][0].numpy())
 
 
-class EagerFunctionTest(XLATestCase):
-
-  def testBasic(self):
-    with self.test_scope():
-      matmul = function.defun(math_ops.matmul, compiled=True)
-      t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
-      sq = matmul(t, t, transpose_a=True)
-      self.assertAllEqual(sq.numpy().reshape(-1), [10, 14, 14, 20])
-
-  def testConv(self):
-    if 'GPU' in self.device:
-      # TODO(b/32333178)
-      self.skipTest('Current implementation of RandomStandardNormal kernel '
-                    'is very slow on GPU, and has been blacklisted.')
-    with self.test_scope():
-      data_format = 'channels_last'
-      conv = convolutional.Conv2D(
-          filters=1, kernel_size=2, padding='VALID',
-          data_format=data_format, activation=nn_ops.relu,
-          kernel_initializer=init_ops.ones_initializer(),
-          bias_initializer=init_ops.zeros_initializer())
-      pool = pooling.MaxPooling2D(2, 2, data_format=data_format)
-
-      def model(x):
-        x = conv(x)
-        return pool(x)
-      model = function.defun(model, compiled=True)
-
-      x = array_ops.ones([1, 4, 4, 1])
-      y = model(x)
-      self.assertAllEqual(y.numpy(), [[[[4.]]]])
-
-  def testReadVariable(self):
-    with self.test_scope():
-      v = resource_variable_ops.ResourceVariable(1.0)
-
-      @function.defun(compiled=True)
-      def f():
-        return v.read_value()
-
-      var = f()
-      self.assertEqual(1.0, var.numpy())
-
-  def testUpdateVariable(self):
-    with self.test_scope():
-      v = resource_variable_ops.ResourceVariable(1.0)
-
-      def f(v):
-        v.assign_add(1.0)
-        return v
-
-      f = function.defun(f, compiled=True)
-
-      var = f(v)
-      self.assertEqual(2.0, var.numpy())
-
-  def testAllArgumentKinds(self):
-    """Test a complex function that takes different argument kinds.
-
-    tf2xla machinery that translates, compiles, and runs defuns
-    classifies arguments into: compile-time constants, regular tensors,
-    and resources. This test creates a function with a mix of all these
-    kinds. Moreover, the order of function arguments is intentionally mixed up.
-
-    This also tests the case when the same argument is a compile-time constant
-    as well as used in an operation that normally expects its inputs to be
-    in device memory - addition in this case.
-    """
-    with self.test_scope():
-      def foo(c1, r1, v1, c2, v2, r2):
-        # c1 and c2 are compile-time constants
-        # r1 and r2 are regular tensors
-        # v1 and v2 are resource variables
-        a = c1 + r1
-        b = math_ops.cast(c2, dtypes.float32) + v2
-        c = array_ops.slice(v1, c1, c2)
-        d = r2 * v2
-        return a, b, c, d
-
-      foo = function.defun(foo, compiled=True)
-
-      c1 = [0, 0]
-      c2 = array_ops.ones([2], dtype=dtypes.int32)
-
-      r1 = array_ops.ones([2])
-      r2 = [[2., 2.], [3., 3.]]
-
-      v1 = resource_variable_ops.ResourceVariable([[1., 2.], [3., 4.]])
-      v2 = resource_variable_ops.ResourceVariable([[10., 20.], [30., 40.]])
-
-      a, b, c, d = foo(c1, r1, v1, c2, v2, r2)
-
-      self.assertAllEqual([1, 1], a.numpy())
-      self.assertAllEqual([[11., 21.], [31., 41.]], b.numpy())
-      self.assertAllEqual([[1.]], c.numpy())
-      self.assertAllEqual([[20., 40.], [90., 120.]], d.numpy())
-
-
-if __name__ == '__main__':
+if __name__ == "__main__":
   ops.enable_eager_execution(
       config=config_pb2.ConfigProto(log_device_placement=True))
   googletest.main()
diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
index b8f352d5f5b72f..8517a3bf7b6aeb 100644
--- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
+++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
@@ -36,7 +36,9 @@ def device_and_data_format():
                                                               'channels_last')
 
 
-def random_batch(batch_size, data_format):
+def random_batch(batch_size, device_and_format=None):
+  _, data_format = device_and_format or device_and_data_format()
+
   shape = (3, 224, 224) if data_format == 'channels_first' else (224, 224, 3)
   shape = (batch_size,) + shape
 
@@ -68,7 +70,7 @@ def _apply(self, defun=False, execution_mode=None):
     if defun:
       model.call = tfe.defun(model.call)
     with tf.device(device), tfe.execution_mode(execution_mode):
-      images, _ = random_batch(2, data_format)
+      images, _ = random_batch(2)
       output = model(images, training=False)
       tfe.async_wait()
     self.assertEqual((2, 1000), output.shape)
@@ -89,7 +91,7 @@ def test_apply_no_top(self):
     device, data_format = device_and_data_format()
     model = resnet50.ResNet50(data_format, include_top=False)
     with tf.device(device):
-      images, _ = random_batch(2, data_format)
+      images, _ = random_batch(2)
       output = model(images, training=False)
     output_shape = ((2, 2048, 1, 1)
                     if data_format == 'channels_first' else (2, 1, 1, 2048))
@@ -99,7 +101,7 @@ def test_apply_with_pooling(self):
     device, data_format = device_and_data_format()
     model = resnet50.ResNet50(data_format, include_top=False, pooling='avg')
     with tf.device(device):
-      images, _ = random_batch(2, data_format)
+      images, _ = random_batch(2)
       output = model(images, training=False)
     self.assertEqual((2, 2048), output.shape)
 
@@ -113,7 +115,7 @@ def _test_train(self, execution_mode=None):
         name='t0').as_default(), tf.contrib.summary.always_record_summaries():
       with tf.device(device), tfe.execution_mode(execution_mode):
         optimizer = tf.train.GradientDescentOptimizer(0.1)
-        images, labels = random_batch(2, data_format)
+        images, labels = random_batch(2)
         train_one_step(model, images, labels, optimizer)
         self.assertEqual(320, len(model.variables))
         tfe.async_wait()
@@ -132,7 +134,7 @@ def test_no_garbage(self):
     model = resnet50.ResNet50(data_format)
     optimizer = tf.train.GradientDescentOptimizer(0.1)
     with tf.device(device):
-      images, labels = random_batch(2, data_format)
+      images, labels = random_batch(2)
       gc.disable()
       # Warm up. Note that this first run does create significant amounts of
       # garbage to be collected. The hope is that this is a build-only effect,
@@ -200,18 +202,18 @@ def _force_device_sync(self):
     # which forces a sync. This is a roundabout way, yes.
     tf.constant(1.).cpu()
 
-  def _benchmark_eager_apply(self, label, device_and_format, defun=False,
-                             execution_mode=None, compiled=False):
+  def _benchmark_eager_apply(self, label, defun=False, execution_mode=None,
+                             device_and_format=None):
     with tfe.execution_mode(execution_mode):
-      device, data_format = device_and_format
+      device, data_format = device_and_format or device_and_data_format()
       model = resnet50.ResNet50(data_format)
       if defun:
-        model.call = tfe.defun(model.call, compiled=compiled)
+        model.call = tfe.defun(model.call)
       batch_size = 64
       num_burn = 5
       num_iters = 30
       with tf.device(device):
-        images, _ = random_batch(batch_size, data_format)
+        images, _ = random_batch(batch_size, device_and_format)
         for _ in xrange(num_burn):
           model(images, training=False).cpu()
         if execution_mode:
@@ -225,34 +227,30 @@ def _benchmark_eager_apply(self, label, device_and_format, defun=False,
         self._report(label, start, num_iters, device, batch_size, data_format)
 
   def benchmark_eager_apply_sync(self):
-    self._benchmark_eager_apply('eager_apply', device_and_data_format(),
-                                defun=False)
+    self._benchmark_eager_apply('eager_apply', defun=False)
 
   def benchmark_eager_apply_async(self):
     self._benchmark_eager_apply(
-        'eager_apply_async', device_and_data_format(), defun=False,
-        execution_mode=tfe.ASYNC)
+        'eager_apply_async', defun=False, execution_mode=tfe.ASYNC)
 
   def benchmark_eager_apply_with_defun(self):
-    self._benchmark_eager_apply('eager_apply_with_defun',
-                                device_and_data_format(), defun=True)
+    self._benchmark_eager_apply('eager_apply_with_defun', defun=True)
 
   def _benchmark_eager_train(self,
                              label,
                              make_iterator,
-                             device_and_format,
                              defun=False,
                              execution_mode=None,
-                             compiled=False):
+                             device_and_format=None):
     with tfe.execution_mode(execution_mode):
-      device, data_format = device_and_format
+      device, data_format = device_and_format or device_and_data_format()
       for batch_size in self._train_batch_sizes():
-        (images, labels) = random_batch(batch_size, data_format)
+        (images, labels) = random_batch(batch_size, device_and_format)
         num_burn = 3
         num_iters = 10
         model = resnet50.ResNet50(data_format)
         if defun:
-          model.call = tfe.defun(model.call, compiled=compiled)
+          model.call = tfe.defun(model.call)
         optimizer = tf.train.GradientDescentOptimizer(0.1)
 
         with tf.device(device):
@@ -275,21 +273,18 @@ def _benchmark_eager_train(self,
           self._report(label, start, num_iters, device, batch_size, data_format)
 
   def benchmark_eager_train_sync(self):
-    self._benchmark_eager_train('eager_train', MockIterator,
-                                device_and_data_format(), defun=False)
+    self._benchmark_eager_train('eager_train', MockIterator, defun=False)
 
   def benchmark_eager_train_async(self):
     self._benchmark_eager_train(
         'eager_train_async',
         MockIterator,
-        device_and_data_format(),
         defun=False,
         execution_mode=tfe.ASYNC)
 
   def benchmark_eager_train_with_defun(self):
     self._benchmark_eager_train(
-        'eager_train_with_defun', MockIterator,
-        device_and_data_format(), defun=True)
+        'eager_train_with_defun', MockIterator, defun=True)
 
   def benchmark_eager_train_datasets(self):
 
@@ -299,8 +294,7 @@ def make_iterator(tensors):
       return tfe.Iterator(ds)
 
     self._benchmark_eager_train(
-        'eager_train_dataset', make_iterator,
-        device_and_data_format(), defun=False)
+        'eager_train_dataset', make_iterator, defun=False)
 
   def benchmark_eager_train_datasets_with_defun(self):
 
@@ -310,8 +304,7 @@ def make_iterator(tensors):
       return tfe.Iterator(ds)
 
     self._benchmark_eager_train(
-        'eager_train_dataset_with_defun', make_iterator,
-        device_and_data_format(), defun=True)
+        'eager_train_dataset_with_defun', make_iterator, defun=True)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py
index 60cfacc14114d4..741bd2ac9c911f 100644
--- a/tensorflow/python/eager/function.py
+++ b/tensorflow/python/eager/function.py
@@ -23,7 +23,6 @@
 
 import numpy as np
 
-from tensorflow.core.framework import attr_value_pb2
 from tensorflow.core.framework import function_pb2
 from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.eager import context
@@ -226,7 +225,7 @@ def _inference_name(n):
 class _EagerDefinedFunction(object):
   """Function object with the interface of tf _DefinedFunction."""
 
-  def __init__(self, name, graph, operations, inputs, outputs, attrs):
+  def __init__(self, name, graph, operations, inputs, outputs):
     """Initializes an eager defined function.
 
     Args:
@@ -236,7 +235,6 @@ def __init__(self, name, graph, operations, inputs, outputs, attrs):
         which will be in the function
       inputs: the tensors in the graph to be used as inputs to the function
       outputs: the tensors in the graph which will be outputs to the function
-      attrs: dict mapping names of attributes to their AttrValue values
     """
     fn = pywrap_tensorflow.TF_GraphToFunction_wrapper(
         graph._c_graph,  # pylint: disable=protected-access
@@ -248,14 +246,6 @@ def __init__(self, name, graph, operations, inputs, outputs, attrs):
         [],
         None,
         compat.as_str(""))
-
-    for name, attr_value in attrs.items():
-      serialized = attr_value.SerializeToString()
-      # TODO(iga): this creates and deletes a new TF_Status for every attr.
-      # It might be worth creating a convenient way to re-use status.
-      pywrap_tensorflow.TF_FunctionSetAttrValueProto(
-          fn, compat.as_str(name), serialized)
-
     # TODO(apassos) avoid creating a FunctionDef (specially to grab the
     # signature, but also in general it's nice not to depend on it.
     with c_api_util.tf_buffer() as buffer_:
@@ -297,6 +287,25 @@ def _flatten(sequence):
 
 class GraphModeFunction(object):
   """Callable object representing a graph-mode function.
+
+  Args:
+    name: str the name of the created function
+    input_placeholders: list of placeholder values (tensors) to feed when
+      calling the wrapped function.
+    extra_inputs: Tensor inputs this function definition closed over which
+      are passed as arguments. Need to track so gradients are supported
+      correctly.
+    graph: the Graph from which the operations will be pulled. Used as
+      a context when computing gradients.
+    operations: the subset of Operations in the graph used in the function
+      definition.
+    outputs: a flat list of the Tensors in the graph used as outputs to the
+      function
+    func_outputs: a possibly nested python object which will be returned by
+      this function. The Tensors in this structure will be replaced by their
+      corresponding values in outputs.
+    output_shapes: List of shapes of all tensors in outputs
+    variables: (optional) List of variables to watch during function execution.
   """
 
   def __init__(self,
@@ -308,36 +317,9 @@ def __init__(self,
                outputs,
                func_outputs,
                output_shapes,
-               variables=None,
-               attrs=None):
-    """Initialize a GraphModeFunction.
-
-    Args:
-      name: str the name of the created function
-      input_placeholders: list of placeholder values (tensors) to feed when
-        calling the wrapped function.
-      extra_inputs: Tensor inputs this function definition closed over which
-        are passed as arguments. Need to track so gradients are supported
-        correctly.
-      graph: the Graph from which the operations will be pulled. Used as
-        a context when computing gradients.
-      operations: the subset of Operations in the graph used in the function
-        definition.
-      outputs: a flat list of the Tensors in the graph used as outputs to the
-        function
-      func_outputs: a possibly nested python object which will be returned by
-        this function. The Tensors in this structure will be replaced by their
-        corresponding values in outputs.
-      output_shapes: List of shapes of all tensors in outputs
-      variables: (optional) List of variables to watch during function
-        execution.
-      attrs: (optional) dict mapping names of attributes to their AttrValue
-        values. Attributes in `attrs` will be included in this function's
-        definition.
-    """
-    self._attrs = attrs or {}
+               variables=None):
     defined_function = _EagerDefinedFunction(
-        name, graph, operations, input_placeholders, outputs, self._attrs)
+        name, graph, operations, input_placeholders, outputs)
     if len(input_placeholders) != len(defined_function.signature.input_arg):
       raise ValueError("Internal error: invalid lengths. %s %s" % (
           len(input_placeholders), len(defined_function.signature.input_arg)))
@@ -390,7 +372,7 @@ def _construct_backprop_function(self):
     forward_name = _forward_name(self._func_name)
     self._forward_fdef = _EagerDefinedFunction(
         forward_name, self._graph, self._ops, self._input_placeholders,
-        filtered_outputs + captures, self._attrs)
+        filtered_outputs + captures)
     all_inputs = self._out_grad_placeholders + captures
     # Excluding input ops from the body as we do not intend to execute these
     # operations when the function is executed.
@@ -404,7 +386,7 @@ def _construct_backprop_function(self):
     bname = _backward_name(self._func_name)
     self._backward_function = GraphModeFunction(
         bname, all_inputs, [], self._graph, function_def_ops,
-        backward_outputs, in_gradients, output_shapes, attrs=self._attrs)
+        backward_outputs, in_gradients, output_shapes)
 
   def _backprop_call(self, args):
     """Calls the wrapped function and records the result on a tape."""
@@ -578,7 +560,7 @@ def _get_defun_inputs(args):
   return nest.pack_sequence_as(args, ret)
 
 
-def _defun_internal(name, func, compiled, args, kwds):
+def _defun_internal(name, func, args, kwds):
   """Defines and returns graph-mode version of func."""
   graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
   with context.graph_mode():
@@ -643,14 +625,9 @@ def convert(x):
     for f in tmp_graph._functions.values():  # pylint: disable=protected-access
       # TODO(ashankar): What about the gradient registry?
       _register(f._c_func.func)  # pylint: disable=protected-access
-
-  attrs = {}
-  if compiled:
-    attrs["_XlaCompile"] = attr_value_pb2.AttrValue(b=True)
-
   return GraphModeFunction(
       fname, all_inputs, extra_inputs, tmp_graph, operations, func_def_outputs,
-      func_outputs, output_shapes, variables, attrs)
+      func_outputs, output_shapes, variables)
 
 
 # Defun uses this instead of Tensor as a cache key. Using dtype because
@@ -692,7 +669,7 @@ def _register(fn):
 
 
 # TODO(apassos): better error messages for non-hashable arguments.
-def named_defun(func, name, compiled=False):
+def named_defun(func, name):
   """Defines a function with a given name.
 
   See the documentation for `defun` for more information on the semantics of the
@@ -701,7 +678,6 @@ def named_defun(func, name, compiled=False):
   Args:
     func: the function to be wrapped.
     name: the name given to it.
-    compiled: if true, the framework will attempt to compile func with XLA.
 
   Returns:
     the wrapped function.
@@ -718,13 +694,13 @@ def decorated(*args, **kwds):
 
     if cache_key not in arguments_to_functions:
       arguments_to_functions[cache_key] = _defun_internal(
-          name, func, compiled, args, kwds)
+          name, func, args, kwds)
     return arguments_to_functions[cache_key](*args)
 
   return decorated
 
 
-def defun(func=None, compiled=False):
+def defun(func):
   """Decorator to compile func into graph_mode.
 
   `defun` converts a function that constructs a TensorFlow graph into a function
@@ -767,45 +743,18 @@ def g(x, y):
   ```
 
   Args:
-    func: function to be compiled. If `func` is None, returns a
-      decorator that can be invoked with a single argument - `func`. The
-      end result is equivalent to providing all the arguments up front.
-      In other words, defun(compiled=True)(func) is equivalent to
-      defun(func, compiled=True). The former allows the following use case:
-        @tfe.defun(compiled=True)
-        def foo(...):
-          ...
-    compiled: If True, an attempt to compile `func` with XLA will be made.
-      If it fails, function will be run normally. Experimental.
-      Currently, supported only for execution on TPUs.
+    func: function to be compiled.
 
   Returns:
-     If `func` is not None, returns callable that will execute the compiled
-     function (and return zero or more `tf.Tensor` objects).
-     If `func` is None, returns a decorator that, when invoked with a single
-     `func` argument, returns a callable equivalent to the case above.
+     A callable that will execute the compiled function (and return zero
+     or more `tf.Tensor` objects).
   """
   # TODO(apassos): deal with captured global state. Deal with control flow.
-  def decorated(function):
-    try:
-      name = function.__name__
-    except AttributeError:
-      name = "function"
-    return tf_decorator.make_decorator(
-        function, named_defun(function, name, compiled=compiled))
-
-  # This code path is for the `foo = tfe.defun(foo, ...)` use case
-  if func is not None:
-    return decorated(func)
-
-  # This code path is for the
-  #
-  # @tfe.defun(...)
-  # def foo(...):
-  #    ...
-  #
-  # use case, which is equivalent to `foo = tfe.defun(...)(foo)`
-  return decorated
+  try:
+    name = func.__name__
+  except AttributeError:
+    name = "function"
+  return tf_decorator.make_decorator(func, named_defun(func, name))
 
 
 def make_defun_op(func, *args, **kwds):
@@ -857,7 +806,7 @@ def g(x, y):
   name = func.__name__
   if any(isinstance(x, ops.EagerTensor) for x in kwds.values()):
     raise ValueError("Tensor keyword arguments are not supported.")
-  return _defun_internal(name, func, False, args, kwds)
+  return _defun_internal(name, func, args, kwds)
 
 
 class AutomaticControlDependencies(object):

From 392ce20dccefe86b5ef38ef8ac2bf6534ca17cd8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 03:26:06 -0700
Subject: [PATCH 0494/1691] Fix a test expectation.

PiperOrigin-RevId: 195796348
---
 tensorflow/compiler/xla/service/instruction_fusion_test.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/service/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/instruction_fusion_test.cc
index b4b1955fe24fe7..6dd8fa1ab08737 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion_test.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion_test.cc
@@ -126,7 +126,7 @@ TEST_F(InstructionFusionTest, FuseCheapNonDuplicatableOps) {
   EXPECT_EQ(Count(*module, HloOpcode::kFusion), 1) << module->ToString();
 
   // Make sure the add hasn't been duplicated.
-  EXPECT_EQ(Count(*module, HloOpcode::kFusion), 1) << module->ToString();
+  EXPECT_EQ(Count(*module, HloOpcode::kAdd), 1) << module->ToString();
 }
 
 TEST_F(InstructionFusionTest, AvoidDuplicationIfNotAllFusableRecursively) {

From 42115bdf2b9d3bc2d544d19e2c822879cc634379 Mon Sep 17 00:00:00 2001
From: Asim Shankar 
Date: Tue, 8 May 2018 07:28:43 -0700
Subject: [PATCH 0495/1691] ProfileHandler: Remove unnecessary interface
 method. PiperOrigin-RevId: 195815565

---
 tensorflow/core/common_runtime/profile_handler.h | 16 ----------------
 1 file changed, 16 deletions(-)

diff --git a/tensorflow/core/common_runtime/profile_handler.h b/tensorflow/core/common_runtime/profile_handler.h
index 9d31b1aecbce21..391dc8c19878b7 100644
--- a/tensorflow/core/common_runtime/profile_handler.h
+++ b/tensorflow/core/common_runtime/profile_handler.h
@@ -29,22 +29,6 @@ class ProfileHandler {
   ProfileHandler() {}
   virtual ~ProfileHandler() {}
 
-  // Records that a miscellaneous activity occurred in the current step.
-  //
-  // Implementations of this method must be thread-safe.
-  //
-  // Args:
-  // - device: The device on which the activity occurred.
-  // - start: The time at which the activity started.
-  // - limit: The time at which the activity finished.
-  // - label: A label for the op, which may be used in visualization.
-  // - op_type: A type string for the op, which may be used in visualization.
-  // - details: A details string, which may be used in visualization.
-  // from time "start" to "limit" with "op_type" and "details".
-  virtual void RecordActivity(const string& device, Microseconds start,
-                              Microseconds limit, StringPiece label,
-                              StringPiece op_type, StringPiece details) = 0;
-
   // Records that a single Op was executed in the current step.
   //
   // Implementations of this method must be thread-safe.

From 0bd1408a2d95d0d30bf9412dc64edc45c71b915f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 07:57:12 -0700
Subject: [PATCH 0496/1691] Add missing #include for OpResponse. This class
 currently happens to be forward declared by xla.proto.h, but that proto
 doesn't actually need this type anywhere and we are working on removing such
 unneeded forward declarations.

PiperOrigin-RevId: 195818397
---
 tensorflow/compiler/xla/BUILD               | 1 +
 tensorflow/compiler/xla/service_interface.h | 1 +
 2 files changed, 2 insertions(+)

diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index 1af9cb6d2ab15a..dbf14f32bc3e54 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -99,6 +99,7 @@ cc_library(
     hdrs = ["service_interface.h"],
     visibility = [":friends"],
     deps = [
+        ":xla_data_proto",
         ":xla_proto",
         "//tensorflow/core:lib",
     ],
diff --git a/tensorflow/compiler/xla/service_interface.h b/tensorflow/compiler/xla/service_interface.h
index 5b44c26b7c7b08..4f64fe8f835017 100644
--- a/tensorflow/compiler/xla/service_interface.h
+++ b/tensorflow/compiler/xla/service_interface.h
@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_SERVICE_INTERFACE_H_
 
 #include "tensorflow/compiler/xla/xla.pb.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/core/status.h"
 
 namespace xla {

From 07fdb697d33478d7a72d09fc2371fa834e870b83 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 08:04:07 -0700
Subject: [PATCH 0497/1691] Automated g4 rollback of changelist 195723288

PiperOrigin-RevId: 195819297
---
 tensorflow/contrib/image/kernels/image_ops.cc | 33 +++--------
 tensorflow/contrib/image/kernels/image_ops.h  |  2 +-
 tensorflow/contrib/image/ops/image_ops.cc     | 55 ++-----------------
 .../python/kernel_tests/image_ops_test.py     | 30 ----------
 .../contrib/image/python/ops/image_ops.py     | 49 ++++++-----------
 5 files changed, 30 insertions(+), 139 deletions(-)

diff --git a/tensorflow/contrib/image/kernels/image_ops.cc b/tensorflow/contrib/image/kernels/image_ops.cc
index 575c2004fb8aea..c2e32da133b32c 100644
--- a/tensorflow/contrib/image/kernels/image_ops.cc
+++ b/tensorflow/contrib/image/kernels/image_ops.cc
@@ -70,7 +70,6 @@ class ImageProjectiveTransform : public OpKernel {
   void Compute(OpKernelContext* ctx) override {
     const Tensor& images_t = ctx->input(0);
     const Tensor& transform_t = ctx->input(1);
-    const Tensor& shape_t = ctx->input(2);
     OP_REQUIRES(ctx, images_t.shape().dims() == 4,
                 errors::InvalidArgument("Input images must have rank 4"));
     OP_REQUIRES(ctx,
@@ -81,28 +80,11 @@ class ImageProjectiveTransform : public OpKernel {
                      ProjectiveGenerator::kNumParameters),
                 errors::InvalidArgument(
                     "Input transform should be num_images x 8 or 1 x 8"));
-    OP_REQUIRES(ctx, shape_t.dims() == 1,
-                errors::InvalidArgument("output shape must be 1-dimensional",
-                                        shape_t.shape().DebugString()));
-    OP_REQUIRES(ctx, shape_t.NumElements() == 2,
-                errors::InvalidArgument("output shape must have two elements",
-                                        shape_t.shape().DebugString()));
-    auto Svec = shape_t.vec();
-    int32 out_height = Svec(0);
-    int32 out_width = Svec(1);
-    OP_REQUIRES(ctx, out_height > 0 && out_width > 0,
-                errors::InvalidArgument("output dimensions must be positive"));
-
-    Tensor* output_t;
-    OP_REQUIRES_OK(ctx, ctx->allocate_output(
-                            0,
-                            TensorShape({images_t.dim_size(0), out_height,
-                                         out_width, images_t.dim_size(3)}),
-                            &output_t));
-    auto output = output_t->tensor();
     auto images = images_t.tensor();
     auto transform = transform_t.matrix();
-
+    Tensor* output_t;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, images_t.shape(), &output_t));
+    auto output = output_t->tensor();
     (FillProjectiveTransform(interpolation_))(
         ctx->eigen_device(), &output, images, transform);
   }
@@ -145,11 +127,10 @@ TF_CALL_double(DECLARE_FUNCTOR);
 
 }  // end namespace functor
 
-#define REGISTER(TYPE)                                       \
-  REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransform")   \
-                              .Device(DEVICE_GPU)            \
-                              .TypeConstraint("dtype") \
-                              .HostMemory("output_shape"),   \
+#define REGISTER(TYPE)                                        \
+  REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransform")    \
+                              .Device(DEVICE_GPU)             \
+                              .TypeConstraint("dtype"), \
                           ImageProjectiveTransform)
 
 TF_CALL_uint8(REGISTER);
diff --git a/tensorflow/contrib/image/kernels/image_ops.h b/tensorflow/contrib/image/kernels/image_ops.h
index 2320329b923fee..ad501330617be8 100644
--- a/tensorflow/contrib/image/kernels/image_ops.h
+++ b/tensorflow/contrib/image/kernels/image_ops.h
@@ -161,7 +161,7 @@ struct FillProjectiveTransform {
   void operator()(const Device& device, OutputType* output,
                   const InputType& images,
                   const TransformsType& transform) const {
-    output->device(device) = output->generate(
+    output->device(device) = images.generate(
         ProjectiveGenerator(images, transform, interpolation_));
   }
 };
diff --git a/tensorflow/contrib/image/ops/image_ops.cc b/tensorflow/contrib/image/ops/image_ops.cc
index fb62507174de31..ebdcaea7abae2a 100644
--- a/tensorflow/contrib/image/ops/image_ops.cc
+++ b/tensorflow/contrib/image/ops/image_ops.cc
@@ -19,56 +19,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
 using shape_inference::ShapeHandle;
 
-namespace {
-
-// Sets output[0] to shape [batch_dim,height,width,channel_dim], where
-// height and width come from the size_tensor.
-Status SetOutputToSizedImage(InferenceContext* c, DimensionHandle batch_dim,
-                             int size_input_idx, DimensionHandle channel_dim) {
-  // Verify shape of size input.
-  ShapeHandle size;
-  TF_RETURN_IF_ERROR(c->WithRank(c->input(size_input_idx), 1, &size));
-  DimensionHandle unused;
-  TF_RETURN_IF_ERROR(c->WithValue(c->Dim(size, 0), 2, &unused));
-
-  // Get size values from the size tensor.
-  const Tensor* size_tensor = c->input_tensor(size_input_idx);
-  DimensionHandle width;
-  DimensionHandle height;
-  if (size_tensor == nullptr) {
-    width = c->UnknownDim();
-    height = c->UnknownDim();
-  } else {
-    // TODO(petewarden) - Remove once we have constant evaluation in C++ only.
-    if (size_tensor->dtype() != DT_INT32) {
-      return errors::InvalidArgument(
-          "Bad size input type for SetOutputToSizedImage: Expected DT_INT32 "
-          "but got ",
-          DataTypeString(size_tensor->dtype()), " for input #", size_input_idx,
-          " in ", c->DebugString());
-    }
-    auto vec = size_tensor->vec();
-    height = c->MakeDim(vec(0));
-    width = c->MakeDim(vec(1));
-  }
-  c->set_output(0, c->MakeShape({batch_dim, height, width, channel_dim}));
-  return Status::OK();
-}
-
-// TODO(qyu): Move this to core/framework/common_shape_fns.h
-Status ResizeShapeFn(InferenceContext* c) {
-  ShapeHandle input;
-  TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
-  return SetOutputToSizedImage(c, c->Dim(input, 0), 2 /* size_input_idx */,
-                               c->Dim(input, 3));
-}
-
-}  // namespace
-
 // TODO(ringwalt): Add a "fill_mode" argument with "constant", "mirror", etc.
 // TODO(ringwalt): Add a "fill_constant" argument for constant mode (default 0).
 // TODO(ringwalt): Add an "output_shape" argument. This is sufficient to
@@ -76,11 +29,13 @@ Status ResizeShapeFn(InferenceContext* c) {
 REGISTER_OP("ImageProjectiveTransform")
     .Input("images: dtype")
     .Input("transforms: float32")
-    .Input("output_shape: int32")
     .Attr("dtype: {uint8, int32, int64, float32, float64}")
     .Attr("interpolation: string")
     .Output("transformed_images: dtype")
-    .SetShapeFn(ResizeShapeFn)
+    .SetShapeFn([](InferenceContext* c) {
+      c->set_output(0, c->input(0));
+      return Status::OK();
+    })
     .Doc(R"doc(
 Applies the given transform to each of the images.
 
@@ -94,7 +49,7 @@ If one row of `transforms` is `[a0, a1, a2, b0, b1, b2, c0, c1]`, then it maps
 the *output* point `(x, y)` to a transformed *input* point
 `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`, where
 `k = c0 x + c1 y + 1`. If the transformed point lays outside of the input
-image, the output pixel is set to 0.
+image, the output pixel is set to 0. The output is the same size as the input,
 
 images: 4D `Tensor`, input image(s) in NHWC format.
 transforms: 2D `Tensor`, projective transform(s) to apply to the image(s).
diff --git a/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py b/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py
index c0151d320f98a7..b50177ae5651fb 100644
--- a/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py
+++ b/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py
@@ -195,40 +195,10 @@ def _test_grad(self, shape_to_test):
           x_init_value=test_image)
       self.assertLess(left_err, 1e-10)
 
-  def _test_grad_different_shape(self, input_shape, output_shape):
-    with self.test_session():
-      test_image_shape = input_shape
-      test_image = np.random.randn(*test_image_shape)
-      test_image_tensor = constant_op.constant(
-          test_image, shape=test_image_shape)
-      test_transform = image_ops.angles_to_projective_transforms(
-          np.pi / 2, 4, 4)
-
-      if len(output_shape) == 2:
-        resize_shape = output_shape
-      elif len(output_shape) == 3:
-        resize_shape = output_shape[0:2]
-      elif len(output_shape) == 4:
-        resize_shape = output_shape[1:3]
-      output = image_ops.transform(
-          images=test_image_tensor,
-          transforms=test_transform,
-          output_shape=resize_shape)
-      left_err = gradient_checker.compute_gradient_error(
-          test_image_tensor,
-          test_image_shape,
-          output,
-          output_shape,
-          x_init_value=test_image)
-      self.assertLess(left_err, 1e-10)
-
   def test_grad(self):
     self._test_grad([16, 16])
     self._test_grad([4, 12, 12])
     self._test_grad([3, 4, 12, 12])
-    self._test_grad_different_shape([16, 16], [8, 8])
-    self._test_grad_different_shape([4, 12, 3], [8, 24, 3])
-    self._test_grad_different_shape([3, 4, 12, 3], [3, 8, 24, 3])
 
 
 class BipartiteMatchTest(test_util.TensorFlowTestCase):
diff --git a/tensorflow/contrib/image/python/ops/image_ops.py b/tensorflow/contrib/image/python/ops/image_ops.py
index 192571ced81fe5..cd984c80543886 100644
--- a/tensorflow/contrib/image/python/ops/image_ops.py
+++ b/tensorflow/contrib/image/python/ops/image_ops.py
@@ -23,7 +23,6 @@
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import linalg_ops
@@ -213,11 +212,7 @@ def translations_to_projective_transforms(translations, name=None):
         axis=1)
 
 
-def transform(images,
-              transforms,
-              interpolation="NEAREST",
-              output_shape=None,
-              name=None):
+def transform(images, transforms, interpolation="NEAREST", name=None):
   """Applies the given transform(s) to the image(s).
 
   Args:
@@ -234,10 +229,6 @@ def transform(images,
        the transform mapping input points to output points. Note that gradients
        are not backpropagated into transformation parameters.
     interpolation: Interpolation mode. Supported values: "NEAREST", "BILINEAR".
-    output_shape: Output dimesion after the transform, [height, width].
-       If None, output is the same size as input image.
-
-    name: The name of the op.
 
   Returns:
     Image(s) with the same type and shape as `images`, with the given
@@ -246,7 +237,6 @@ def transform(images,
 
   Raises:
     TypeError: If `image` is an invalid type.
-    ValueError: If output shape is not 1-D int32 Tensor.
   """
   with ops.name_scope(name, "transform"):
     image_or_images = ops.convert_to_tensor(images, name="images")
@@ -265,17 +255,6 @@ def transform(images,
     else:
       raise TypeError("Images should have rank between 2 and 4.")
 
-    if output_shape is None:
-      output_shape = tensor_util.constant_value(
-          array_ops.shape(images)[1:3]) or array_ops.shape(images)[1:3]
-
-    output_shape = ops.convert_to_tensor(
-        output_shape, dtypes.int32, name="output_shape")
-
-    if not output_shape.get_shape().is_compatible_with([2]):
-      raise ValueError("output_shape must be a 1-D Tensor of 2 elements: "
-                       "new_height, new_width")
-
     if len(transform_or_transforms.get_shape()) == 1:
       transforms = transform_or_transforms[None]
     elif transform_or_transforms.get_shape().ndims is None:
@@ -285,12 +264,8 @@ def transform(images,
       transforms = transform_or_transforms
     else:
       raise TypeError("Transforms should have rank 1 or 2.")
-
     output = gen_image_ops.image_projective_transform(
-        images,
-        output_shape=output_shape,
-        transforms=transforms,
-        interpolation=interpolation.upper())
+        images, transforms, interpolation=interpolation.upper())
     if len(image_or_images.get_shape()) == 2:
       return output[0, :, :, 0]
     elif len(image_or_images.get_shape()) == 3:
@@ -400,6 +375,14 @@ def _image_projective_transform_grad(op, grad):
 
   if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES:
     raise TypeError("Invalid dtype %s." % image_or_images.dtype)
+  if len(image_or_images.get_shape()) == 2:
+    images = image_or_images[None, :, :, None]
+  elif len(image_or_images.get_shape()) == 3:
+    images = image_or_images[None, :, :, :]
+  elif len(image_or_images.get_shape()) == 4:
+    images = image_or_images
+  else:
+    raise TypeError("Images should have rank between 2 and 4")
   if len(transform_or_transforms.get_shape()) == 1:
     transforms = transform_or_transforms[None]
   elif len(transform_or_transforms.get_shape()) == 2:
@@ -412,11 +395,13 @@ def _image_projective_transform_grad(op, grad):
   inverse = linalg_ops.matrix_inverse(transforms)
   transforms = matrices_to_flat_transforms(inverse)
   output = gen_image_ops.image_projective_transform(
-      images=grad,
-      transforms=transforms,
-      output_shape=array_ops.shape(image_or_images)[1:3],
-      interpolation=interpolation)
-  return [output, None, None]
+      grad, transforms, interpolation=interpolation)
+  if len(image_or_images.get_shape()) == 2:
+    return [output[0, :, :, 0], None]
+  elif len(image_or_images.get_shape()) == 3:
+    return [output[0, :, :, :], None]
+  else:
+    return [output, None]
 
 
 def bipartite_match(distance_mat,

From a6a862e90d1b336570ab67816ca14e191f5acb32 Mon Sep 17 00:00:00 2001
From: Peter Hawkins 
Date: Tue, 8 May 2018 08:07:08 -0700
Subject: [PATCH 0498/1691] [TF:XLA] Fix NaN in StatelessRandomNormal if the
 underlying uniform distribution returned -1.

PiperOrigin-RevId: 195819645
---
 tensorflow/compiler/tests/stateless_random_ops_test.py   | 9 +++++++++
 .../compiler/tf2xla/kernels/stateless_random_ops.cc      | 3 ++-
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/tests/stateless_random_ops_test.py b/tensorflow/compiler/tests/stateless_random_ops_test.py
index 4336ebdbd184a0..b6f8390a45d43b 100644
--- a/tensorflow/compiler/tests/stateless_random_ops_test.py
+++ b/tensorflow/compiler/tests/stateless_random_ops_test.py
@@ -86,6 +86,15 @@ def testDistributionOfStatelessRandomUniform(self):
         # seed were not fixed.
         self.assertTrue(self._chi_squared(y, 10) < 16.92)
 
+  def testRandomNormalIsFinite(self):
+    with self.test_session() as sess, self.test_scope():
+      for dtype in self._random_types():
+        seed_t = array_ops.placeholder(dtypes.int32, shape=[2])
+        x = stateless.stateless_random_uniform(
+            shape=[10000], seed=seed_t, dtype=dtype)
+        y = sess.run(x, {seed_t: [0x12345678, 0xabcdef12]})
+        self.assertTrue(np.all(np.isfinite(y)))
+
   def _normal_cdf(self, x):
     """Cumulative distribution function for a standard normal distribution."""
     return 0.5 + 0.5 * np.vectorize(math.erf)(x / math.sqrt(2))
diff --git a/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc b/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc
index 6340c225185e68..a99d4ddc7c4956 100644
--- a/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc
@@ -255,7 +255,8 @@ class StatelessRandomNormalOp : public XlaOpKernel {
                                         seed_shape.DebugString()));
     xla::XlaOp seed = ctx->Input(1);
     xla::XlaBuilder* builder = ctx->builder();
-    auto uniform = RandomUniform(builder, seed, shape, -1.0, 1.0);
+    auto uniform =
+        RandomUniform(builder, seed, shape, std::nextafter(-1.0f, 0.0f), 1.0);
     // Convert uniform distribution to normal distribution by computing
     // sqrt(2) * erfinv(x)
     auto normal = builder->Mul(builder->ConstantR0(std::sqrt(2.0)),

From 4a6e6632eb866a2910396c6bc78d601b5b9b550e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 08:57:45 -0700
Subject: [PATCH 0499/1691] Update comment clarifying continuous eval behavior.

PiperOrigin-RevId: 195826025
---
 tensorflow/contrib/learn/python/learn/experiment.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index 3744abd860e7f4..dfc6a393d069fc 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -468,10 +468,15 @@ def _continuous_eval(self,
         on which that evaluation was based.
         At the beginning of evaluation, the passed `eval_results` will be None
         so it's expected that the predicate function handles that gracefully.
-        When `predicate_fn` is not specified, continuous eval will run in an
-        infinite loop (if `train_steps` is None). or exit once global step
-        reaches `train_steps`.
-
+        Continuous eval behavior under different conditions:
+          * When `predicate_fn` is specified:
+            + if `train_steps` is None, run until `predicate_fn` returns False.
+            + if `train_steps` is specified, run until either global step
+              reaches `train_steps` or `predicate_fn` returns False.
+          * When `predicate_fn` is not specified:
+            + if `train_steps` is None, run in an infinite loop.
+            + if `train_steps` is specified, run until global step reaches
+              `train_steps`.
       export: Whether to export from this step. Default is 'True'.
 
     Raises:

From bd606508ebb0e1dbb3215c3ad1d0a41da3507766 Mon Sep 17 00:00:00 2001
From: Shanqing Cai 
Date: Tue, 8 May 2018 09:04:17 -0700
Subject: [PATCH 0500/1691] Minor formatting tweaks to distribute.py and
 simple_tfkeras_example.py

PiperOrigin-RevId: 195827029
---
 .../python/examples/simple_tfkeras_example.py | 33 ++++++++++++-------
 tensorflow/python/training/distribute.py      | 16 ++++-----
 2 files changed, 29 insertions(+), 20 deletions(-)

diff --git a/tensorflow/contrib/distribute/python/examples/simple_tfkeras_example.py b/tensorflow/contrib/distribute/python/examples/simple_tfkeras_example.py
index b87224251ca384..2b05884b9b9347 100644
--- a/tensorflow/contrib/distribute/python/examples/simple_tfkeras_example.py
+++ b/tensorflow/contrib/distribute/python/examples/simple_tfkeras_example.py
@@ -12,11 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""An example tf.keras model that is trained using MirroredStrategy."""
+"""An example of training tf.keras Model using MirroredStrategy."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-from sys import argv
+
+import sys
+
 import numpy as np
 import tensorflow as tf
 
@@ -33,30 +35,37 @@ def input_fn():
 
 def main(args):
   if len(args) < 2:
-    print('You must specify  model_dir for checkpoints such as'
-          ' /tmp/tfkeras_example./')
+    print('You must specify model_dir for checkpoints such as'
+          ' /tmp/tfkeras_example/.')
     return
 
-  print('Using %s to store checkpoints.' % args[1])
-
-  strategy = tf.contrib.distribute.MirroredStrategy(
-      ['/device:GPU:0', '/device:GPU:1'])
-  config = tf.estimator.RunConfig(train_distribute=strategy)
-  optimizer = tf.train.GradientDescentOptimizer(0.2)
+  model_dir = args[1]
+  print('Using %s to store checkpoints.' % model_dir)
 
+  # Define tf.keras Model.
   model = tf.keras.Sequential()
   model.add(tf.keras.layers.Dense(16, activation='relu', input_shape=(10,)))
   model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
 
+  # Compile tf.keras Model.
+  optimizer = tf.train.GradientDescentOptimizer(0.2)
   model.compile(loss='binary_crossentropy', optimizer=optimizer)
   model.summary()
   tf.keras.backend.set_learning_phase(True)
+
+  # Define a DistributionStrategy and convert the tf.keras Model to a
+  # tf.Estimator that utilizes the DistributionStrategy.
+  strategy = tf.contrib.distribute.MirroredStrategy(
+      ['/device:GPU:0', '/device:GPU:1'])
+  config = tf.estimator.RunConfig(train_distribute=strategy)
   keras_estimator = tf.keras.estimator.model_to_estimator(
-      keras_model=model, config=config, model_dir=args[1])
+      keras_model=model, config=config, model_dir=model_dir)
 
+  # Train and evaluate the tf.Estimator.
   keras_estimator.train(input_fn=input_fn, steps=10)
   eval_result = keras_estimator.evaluate(input_fn=input_fn)
   print('Eval result: {}'.format(eval_result))
 
+
 if __name__ == '__main__':
-  tf.app.run(argv=argv)
+  tf.app.run(argv=sys.argv)
diff --git a/tensorflow/python/training/distribute.py b/tensorflow/python/training/distribute.py
index b60f87c05fa1f2..6d05a2ee29ada7 100644
--- a/tensorflow/python/training/distribute.py
+++ b/tensorflow/python/training/distribute.py
@@ -357,14 +357,14 @@ class DistributionStrategy(object):
     on different slices of the input data. This is in contrast to
     _model parallelism_ where we divide up a single copy of a model
     across multiple devices.
-    Note: for now we only support data parallelism at this time, but
+    Note: we only support data parallelism for now, but
     hope to add support for model parallelism in the future.
   * A _tower_ is one copy of the model, running on one slice of the
     input data.
-  * _Synchronous_, or more commonly _sync_, training is when the
+  * _Synchronous_, or more commonly _sync_, training is where the
     updates from each tower are aggregated together before updating
     the model variables. This is in contrast to _asynchronous_, or
-    _async_ training where each tower updates the model variables
+    _async_ training, where each tower updates the model variables
     independently.
   * Furthermore you might run your computation on multiple devices
     on one machine (or "host"), or on multiple machines/hosts.
@@ -386,11 +386,11 @@ class DistributionStrategy(object):
   * Reductions and Allreduce: A _reduction_ is some method of
     aggregating multiple values into one value, like "sum" or
     "mean". If doing sync training, we will perform a reduction on the
-    gradients to a parameter from each tower before applying the
+    gradients to a parameter from all towers before applying the
     update. Allreduce is an algorithm for performing a reduction on
     values from multiple devices and making the result available on
     all of those devices.
-  * In the future we will have support for TensorFlows' partitioned
+  * In the future we will have support for TensorFlow's partitioned
     variables, where a single variable is split across multiple
     devices.
 
@@ -419,9 +419,9 @@ class DistributionStrategy(object):
     `tower_fn` can use the `get_tower_context()` API to get enhanced
     behavior in this case.
 
-    You can also create an initializable iterator instead of one shot iterator.
-    In that case, you will need to ensure that you initialize the iterator
-    before calling get_next.
+    You can also create an initializable iterator instead of a one-shot
+    iterator. In that case, you will need to ensure that you initialize the
+    iterator before calling get_next.
     ```
     iterator = my_distribution.distribute_dataset(
         dataset).make_initializable_iterator())

From 77bb984c23aa7ec347c981c31f650598c9624304 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 09:46:45 -0700
Subject: [PATCH 0501/1691] Free ANeuralNetworksCompilation object in
 NNAPIDelegate destructor

PiperOrigin-RevId: 195832807
---
 tensorflow/contrib/lite/nnapi_delegate.cc | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc
index 6a231dc6bcb8d5..eb451397bd8eff 100644
--- a/tensorflow/contrib/lite/nnapi_delegate.cc
+++ b/tensorflow/contrib/lite/nnapi_delegate.cc
@@ -61,6 +61,10 @@ NNAPIAllocation::~NNAPIAllocation() {
 }
 
 NNAPIDelegate::~NNAPIDelegate() {
+  if (nn_compiled_model_) {
+    ANeuralNetworksCompilation_free(nn_compiled_model_);
+    nn_compiled_model_ = nullptr;
+  }
   if (nn_model_) {
     ANeuralNetworksModel_free(nn_model_);
     nn_model_ = nullptr;

From 074d2901e2f6b9807394f300e5ccbc65defcf161 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 10:25:30 -0700
Subject: [PATCH 0502/1691] Add cost model of depthwiseConv2dNative. Tensorflow
 computes depthwise separable convolutions as depthwiseConv2dNative followed
 by 1x1 Conv2D

PiperOrigin-RevId: 195838887
---
 .../grappler/costs/op_level_cost_estimator.cc | 68 +++++++++++++++----
 .../costs/op_level_cost_estimator_test.cc     | 26 +++++++
 2 files changed, 81 insertions(+), 13 deletions(-)

diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
index 199b69452f5d46..2542fa2d675364 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
@@ -32,6 +32,11 @@ constexpr char kConv2d[] = "Conv2D";
 constexpr char kConv2dBackpropFilter[] = "Conv2DBackpropFilter";
 constexpr char kConv2dBackpropInput[] = "Conv2DBackpropInput";
 constexpr char kFusedConv2dBiasActivation[] = "FusedConv2DBiasActivation";
+constexpr char kDepthwiseConv2dNative[] = "DepthwiseConv2dNative";
+constexpr char kDepthwiseConv2dNativeBackpropFilter[] =
+    "DepthwiseConv2dNativeBackpropFilter";
+constexpr char kDepthwiseConv2dNativeBackpropInput[] =
+    "DepthwiseConv2dNativeBackpropInput";
 constexpr char kMatMul[] = "MatMul";
 constexpr char kSparseMatMul[] = "SparseMatMul";
 constexpr char kPlaceholder[] = "Placeholder";
@@ -201,6 +206,14 @@ OpLevelCostEstimator::OpLevelCostEstimator() {
        wrap(&OpLevelCostEstimator::PredictConv2DBackpropInput)},
       {kFusedConv2dBiasActivation,
        wrap(&OpLevelCostEstimator::PredictFusedConv2DBiasActivation)},
+      // reuse Conv2D for DepthwiseConv2dNative because the caculation is the
+      // same although the actual meaning of the parameters are different. See
+      // comments in PredictConv2D and related functions
+      {kDepthwiseConv2dNative, wrap(&OpLevelCostEstimator::PredictConv2D)},
+      {kDepthwiseConv2dNativeBackpropFilter,
+       wrap(&OpLevelCostEstimator::PredictConv2DBackpropFilter)},
+      {kDepthwiseConv2dNativeBackpropInput,
+       wrap(&OpLevelCostEstimator::PredictConv2DBackpropInput)},
       {kMatMul, wrap(&OpLevelCostEstimator::PredictMatMul)},
       {kSparseMatMul, wrap(&OpLevelCostEstimator::PredictMatMul)},
       {kBatchMatMul, wrap(&OpLevelCostEstimator::PredictBatchMatMul)},
@@ -539,18 +552,30 @@ OpLevelCostEstimator::ConvolutionDimensionsFromInputs(
 int64 OpLevelCostEstimator::CountConv2DOperations(
     const OpInfo& op_features, ConvolutionDimensions* conv_info,
     bool* found_unknown_shapes) const {
-  if (op_features.op() != kConv2d) {
-    LOG(ERROR) << "Invalid Operation";
-    return 0;
-  }
+  DCHECK(op_features.op() == kConv2d ||
+         op_features.op() == kDepthwiseConv2dNative)
+      << "Invalid Operation: not Conv2D nor DepthwiseConv2dNative";
+
   ConvolutionDimensions conv_dims = ConvolutionDimensionsFromInputs(
       op_features.inputs(0).shape(), op_features.inputs(1).shape(), op_features,
       found_unknown_shapes);
 
+  //  in DepthwiseConv2dNative conv_dims.oz is actually the channel depth
+  //  multiplier; The effective output channel depth oz_effective is
+  //  conv_dims.iz * conv_dims.oz. thus # ops = N x H x W x oz_effective x 2RS.
+  //  Compare to Conv2D where # ops =  N x H x W x iz x oz x 2RS,
+  //  oz = oz_effective,  then Conv2D_ops / Depthwise_conv2d_native_ops = iz.
   int64 ops = conv_dims.batch;
   ops *= conv_dims.ox * conv_dims.oy;
   ops *= conv_dims.kx * conv_dims.ky;
-  ops *= conv_dims.iz * conv_dims.oz;
+  if (op_features.op() == kConv2d) {
+    ops *= conv_dims.iz * conv_dims.oz;
+  } else {
+    // To ensure output tensor dims to be correct for DepthwiseConv2DNative,
+    // although ops are the same as Conv2D.
+    conv_dims.oz *= conv_dims.iz;
+    ops *= conv_dims.oz;
+  }
   ops *= kOpsPerMac;
 
   if (conv_info != nullptr) {
@@ -797,7 +822,10 @@ int64 OpLevelCostEstimator::CountConv2DBackpropInputOperations(
     bool* found_unknown_shapes) const {
   int64 ops = 0;
 
-  DCHECK_EQ(kConv2dBackpropInput, op_features.op());
+  DCHECK(op_features.op() == kConv2dBackpropInput ||
+         op_features.op() == kDepthwiseConv2dNativeBackpropInput)
+      << "Invalid Operation: not kConv2dBackpropInput nor"
+         "kDepthwiseConv2dNativeBackpropInput";
 
   if (op_features.inputs_size() < 2) {
     *found_unknown_shapes = true;
@@ -830,10 +858,15 @@ int64 OpLevelCostEstimator::CountConv2DBackpropInputOperations(
   ops = conv_dims.batch;
   ops *= conv_dims.ox * conv_dims.oy;
   ops *= conv_dims.kx * conv_dims.ky;
-  ops *= conv_dims.iz * conv_dims.oz;
-  ops *= kOpsPerMac;
+  if (op_features.op() == kConv2dBackpropInput) {
+    ops *= conv_dims.iz * conv_dims.oz;
+  } else {
+    // conv_dims always use forward path definition regardless
+    conv_dims.oz *= conv_dims.iz;
+    ops *= conv_dims.oz;
+  }
 
-  VLOG(1) << "Operations for Conv2DBackpropInput " << ops;
+  VLOG(1) << "Operations for" << op_features.op() << "  " << ops;
 
   if (returned_conv_dims != nullptr) {
     *returned_conv_dims = conv_dims;
@@ -845,7 +878,11 @@ int64 OpLevelCostEstimator::CountConv2DBackpropFilterOperations(
     const OpInfo& op_features, ConvolutionDimensions* returned_conv_dims,
     bool* found_unknown_shapes) const {
   int64 ops = 0;
-  DCHECK_EQ(kConv2dBackpropFilter, op_features.op());
+
+  DCHECK(op_features.op() == kConv2dBackpropFilter ||
+         op_features.op() == kDepthwiseConv2dNativeBackpropFilter)
+      << "Invalid Operation: not kConv2dBackpropFilter nor"
+         "kDepthwiseConv2dNativeBackpropFilter";
 
   TensorShapeProto filter_shape;
   bool shape_found = false;
@@ -877,10 +914,15 @@ int64 OpLevelCostEstimator::CountConv2DBackpropFilterOperations(
   ops = conv_dims.batch;
   ops *= conv_dims.ox * conv_dims.oy;
   ops *= conv_dims.kx * conv_dims.ky;
-  ops *= conv_dims.iz * conv_dims.oz;
-  ops *= kOpsPerMac;
+  if (op_features.op() == kConv2dBackpropFilter) {
+    ops *= conv_dims.iz * conv_dims.oz;
+  } else {
+    // conv_dims always use forward path definition regardless
+    conv_dims.oz *= conv_dims.iz;
+    ops *= conv_dims.oz;
+  }
 
-  VLOG(1) << "Operations for Conv2DBackpropFilter" << ops;
+  VLOG(1) << "Operations for" << op_features.op() << "  " << ops;
 
   if (returned_conv_dims != nullptr) {
     *returned_conv_dims = conv_dims;
diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc
index 13ea43bed69282..b2c021b73ac4c3 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc
@@ -128,6 +128,23 @@ OpContext DescribeConvolution(int batch, int ix, int iy, int iz1, int iz2,
   return op_context;
 }
 
+// Describe DepthwiseConvolution constructs an OpContext for a
+// DepthwiseConv2dNative applied to an input
+// tensor with shape (batch, ix, iy, iz1) and a kernel tensor with shape
+// (kx, ky, iz2, cm). cm is channel multiplier
+
+OpContext DescribeDepthwiseConv2dNative(int batch, int ix, int iy, int iz1,
+                                        int iz2, int kx, int ky, int cm) {
+  OpContext op_context;
+  SetCpuDevice(&op_context.op_info);
+  op_context.op_info.set_op("DepthwiseConv2dNative");
+
+  DescribeTensor4D(batch, ix, iy, iz1, op_context.op_info.add_inputs());
+  DescribeTensor4D(kx, ky, iz2, cm, op_context.op_info.add_inputs());
+
+  return op_context;
+}
+
 // DescribeFusedConv2DBiasActivation constructs an OpContext for a
 // FusedConv2DBiasActivation applied to a convolution input tensor with shape
 // (batch, ix, iy, iz1), a kernel tensor with shape (kx, ky, iz2, oz), a
@@ -505,6 +522,15 @@ TEST_F(OpLevelCostEstimatorTest, Conv2DExecutionTime) {
   EXPECT_FALSE(cost.inaccurate);
 }
 
+TEST_F(OpLevelCostEstimatorTest, DepthwiseConv2dNativeExecutionTime) {
+  auto cost =
+      PredictCosts(DescribeDepthwiseConv2dNative(16, 19, 19, 48, 48, 5, 5, 3));
+  EXPECT_EQ(Costs::Duration(112340), cost.memory_time);
+  EXPECT_EQ(Costs::Duration(4158720), cost.compute_time);
+  EXPECT_EQ(Costs::Duration(4271060), cost.execution_time);
+  EXPECT_FALSE(cost.inaccurate);
+}
+
 TEST_F(OpLevelCostEstimatorTest, DummyExecutionTime) {
   auto cost = PredictCosts(DescribeBinaryOp("Dummy", 1000, 1));
   EXPECT_EQ(Costs::Duration(2000), cost.memory_time);

From 83aa3239b45175fff56e85b07a68caf1e182b455 Mon Sep 17 00:00:00 2001
From: Akshay Agrawal 
Date: Tue, 8 May 2018 11:07:45 -0700
Subject: [PATCH 0503/1691] When building functions, capture tensors in
 `internal_convert_to_tensor`.

This change is motivated by the fact that, when eager execution is disabled, library functions assume that tensors returned from `internal_convert_to_tensor` are in fact `Tensor`s and not `EagerTensor`s.

PiperOrigin-RevId: 195846039
---
 tensorflow/python/eager/function.py      | 16 +++++++++-------
 tensorflow/python/eager/function_test.py | 15 +++++++++++++++
 tensorflow/python/framework/function.py  |  9 +++++----
 tensorflow/python/framework/ops.py       | 17 +++++++++++++----
 4 files changed, 42 insertions(+), 15 deletions(-)

diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py
index 741bd2ac9c911f..89257bb20a688e 100644
--- a/tensorflow/python/eager/function.py
+++ b/tensorflow/python/eager/function.py
@@ -102,13 +102,15 @@ def _use_c_api_hack(self):
   def clear_resource_control_flow_state(self):
     self._last_op_using_resource_tensor = {}
 
-  def maybe_capture_tensor(self, tensor):
+  def capture(self, tensor, name=None):
     if isinstance(tensor, ops.EagerTensor):
-      return capture_value(
-          self.captures, tensor, tensor.dtype, str(ops.uid()))
+      if name is None:
+        name = str(ops.uid())
+      return capture_value(self.captures, tensor, tensor.dtype, name)
     if tensor.graph is not self:
-      return capture_value(
-          self.captures, tensor, tensor.dtype, tensor.op.name)
+      if name is None:
+        name = tensor.op.name
+      return capture_value(self.captures, tensor, tensor.dtype, name)
     return tensor
 
   def create_op(
@@ -126,7 +128,7 @@ def create_op(
     # forward the resources such as Identity and Switch can cause serialization
     # to fail.
     for i, inp in enumerate(inputs):
-      inputs[i] = self.maybe_capture_tensor(inp)
+      inputs[i] = self.capture(inp)
     return super(CapturingGraph, self).create_op(
         op_type, inputs, dtypes, input_types, name, attrs, op_def,
         compute_shapes, compute_device)
@@ -598,7 +600,7 @@ def convert(x):
       # call to convert_to_tensor, so we manually capture all such tensors.
       outputs_list = _flatten(func_outputs)
       func_def_outputs = [
-          tmp_graph.maybe_capture_tensor(x) for x in outputs_list
+          tmp_graph.capture(x) for x in outputs_list
           if x is not None
       ]
 
diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py
index 185f6d981cb36a..f53d6c26083cad 100644
--- a/tensorflow/python/eager/function_test.py
+++ b/tensorflow/python/eager/function_test.py
@@ -771,6 +771,21 @@ def false_fn():
       self.assertAllEqual(val.eval(feed_dict={p: False}), 10.0)
       self.assertAllEqual(val.eval(feed_dict={p: True}), 20.0)
 
+  def testDefunWhileLoopWithCapturedLoopVars(self):
+    n = 3
+    x = constant_op.constant(list(range(n)))
+
+    @function.defun
+    def loop():
+      c = lambda i, x: i < n
+      b = lambda i, x: (i + 1, x + 1)
+      i, out = control_flow_ops.while_loop(c, b, (0, x))
+      return i, out
+
+    i, out = loop()
+    self.assertEqual(int(i), 3)
+    self.assertAllEqual(out, [3, 4, 5])
+
   def testDecorator(self):
     with context.graph_mode(), self.test_session():
       v = resource_variable_ops.ResourceVariable(1.0)
diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py
index e7f9e590af8421..f82e94b1a3aba4 100644
--- a/tensorflow/python/framework/function.py
+++ b/tensorflow/python/framework/function.py
@@ -696,7 +696,7 @@ def create_op(self, op_type, inputs, data_types, **kwargs):
     return super(_FuncGraph, self).create_op(op_type, inputs, data_types,
                                              **kwargs)
 
-  def capture(self, tensor):
+  def capture(self, tensor, name=None):
     """Adds the given tensor to this graph and returns the captured tensor."""
     if tensor in self._captured:
       # Captured already.
@@ -704,15 +704,16 @@ def capture(self, tensor):
     elif self._capture_by_value:
       return self._add_tensor_and_parents(tensor)
     else:
-      return self._capture_tensor_as_extra_input(tensor)
+      return self._capture_tensor_as_extra_input(tensor, name)
 
-  def _capture_tensor_as_extra_input(self, tensor):
+  def _capture_tensor_as_extra_input(self, tensor, name=None):
     # Substitute with a placeholder.
     self.extra_inputs.append(tensor)
     # Hoist the new input placeholder out of any control flow context
     # we're currently in.
     with ops.control_dependencies(None):
-      ph = array_ops.placeholder(tensor.dtype, shape=tensor.get_shape())
+      ph = array_ops.placeholder(
+          tensor.dtype, shape=tensor.get_shape(), name=name)
     # pylint: disable=protected-access
     if ops._USE_C_SHAPES:
       handle_data = c_api.GetResourceHandleShapeAndType(tensor.graph._c_graph,
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index dd9acdd9ebb817..bf27647d2796b4 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -1057,13 +1057,19 @@ def internal_convert_to_tensor(value,
 
   """
   if ctx is None: ctx = context.context()
-  if ctx.executing_eagerly():
-    # Fast path for EagerTensors that don't need any conversion.
-    if isinstance(value, EagerTensor):
+  if isinstance(value, EagerTensor):
+    if ctx.executing_eagerly():
+      # Fast path for EagerTensors that don't need any conversion.
       # Note that we don't check that value's dtype matches the dtype
       # argument.  We expect that the C runtime will do that checking
       # when we execute the kernel.
       return value
+    else:
+      graph = get_default_graph()
+      if not graph.building_function:
+        raise RuntimeError("Attempting to capture an EagerTensor without "
+                           "building a function.")
+      return graph.capture(value, name=name)
 
   if dtype is not None:
     dtype = dtypes.as_dtype(dtype)
@@ -1251,7 +1257,10 @@ def internal_convert_to_tensor_or_indexed_slices(value,
   Raises:
     ValueError: If `dtype` does not match the element type of `value`.
   """
-  if isinstance(value, _TensorLike):
+  if isinstance(value, EagerTensor) and not context.executing_eagerly():
+    return internal_convert_to_tensor(
+        value, dtype=dtype, name=name, as_ref=as_ref)
+  elif isinstance(value, _TensorLike):
     if dtype and not dtypes.as_dtype(dtype).is_compatible_with(value.dtype):
       raise ValueError(
           "Tensor conversion requested dtype %s for Tensor with dtype %s: %r" %

From f0a506f67fe316c3adb282b58b7087e11d7c493f Mon Sep 17 00:00:00 2001
From: Andrew Selle 
Date: Tue, 8 May 2018 11:10:23 -0700
Subject: [PATCH 0504/1691] Fix Raspberry Pi build by making PNG not try to use
 Neon (by autodetect).

This involves patching to override the png neon option. In the future
it might be worth enabling PNG optimization.

PiperOrigin-RevId: 195846513
---
 tensorflow/workspace.bzl      |  1 +
 third_party/png_fix_rpi.patch | 16 ++++++++++++++++
 2 files changed, 17 insertions(+)
 create mode 100644 third_party/png_fix_rpi.patch

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 8f499976de83a2..01d424f20bfb4f 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -228,6 +228,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "e45ce5f68b1d80e2cb9a2b601605b374bdf51e1798ef1c2c2bd62131dfcf9eef",
       strip_prefix = "libpng-1.6.34",
       build_file = clean_dep("//third_party:png.BUILD"),
+      patch_file = clean_dep("//third_party:png_fix_rpi.patch"),
   )
 
   tf_http_archive(
diff --git a/third_party/png_fix_rpi.patch b/third_party/png_fix_rpi.patch
new file mode 100644
index 00000000000000..80da7b3c06444f
--- /dev/null
+++ b/third_party/png_fix_rpi.patch
@@ -0,0 +1,16 @@
+diff -r -u /tmp/libpng-1.6.34/scripts/pnglibconf.h.prebuilt ./scripts/pnglibconf.h.prebuilt
+--- /tmp/libpng-1.6.34/scripts/pnglibconf.h.prebuilt	2017-09-29 01:42:33.000000000 -0700
++++ ./scripts/pnglibconf.h.prebuilt	2018-05-01 09:51:24.719318242 -0700
+@@ -20,6 +20,12 @@
+ #define PNG_ALIGNED_MEMORY_SUPPORTED
+ /*#undef PNG_ARM_NEON_API_SUPPORTED*/
+ /*#undef PNG_ARM_NEON_CHECK_SUPPORTED*/
++
++/* Workaround not having a great build file by forcing
++ * png filter optimization to be disabled on arm */
++#define PNG_ARM_NEON_OPT 0
++
++
+ /*#undef PNG_POWERPC_VSX_API_SUPPORTED*/
+ /*#undef PNG_POWERPC_VSX_CHECK_SUPPORTED*/
+ #define PNG_BENIGN_ERRORS_SUPPORTED

From 4ca46dd6dda433e622e4a382123f9a81487aeef5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 11:12:07 -0700
Subject: [PATCH 0505/1691] Increase size of test
 //third_party/tensorflow/python:saver_large_variable_test from "small" to
 "medium" to prevent flaky timeouts.

PiperOrigin-RevId: 195846802
---
 tensorflow/python/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 4057e3768144cc..a865e8ca75744c 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -4135,7 +4135,7 @@ cuda_py_test(
 
 py_test(
     name = "saver_large_variable_test",
-    size = "small",
+    size = "medium",
     srcs = ["training/saver_large_variable_test.py"],
     srcs_version = "PY2AND3",
     tags = [

From b62573f37b1040311b520d55715492df32cac0cf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 11:15:53 -0700
Subject: [PATCH 0506/1691] Add affinity binding functionality and
 documentation to OVIC benchmarker.

PiperOrigin-RevId: 195847378
---
 tensorflow/contrib/lite/java/ovic/README.md   |  58 +++++++++-
 .../demo/app/OvicBenchmarkerActivity.java     | 100 ++++++++++++++++--
 2 files changed, 149 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/lite/java/ovic/README.md b/tensorflow/contrib/lite/java/ovic/README.md
index 373a50854c1497..77799b35691813 100644
--- a/tensorflow/contrib/lite/java/ovic/README.md
+++ b/tensorflow/contrib/lite/java/ovic/README.md
@@ -6,7 +6,7 @@ This folder contains building code for track one of the [Low Power ImageNet Reco
 
 Follow the steps [here](https://www.tensorflow.org/mobile/tflite/demo_android) to install Tensorflow, Bazel, and the Android NDK and SDK.
 
-## To test the benchmarker:
+## Test the benchmarker:
 
 The testing utilities helps the developers (you) to make sure that your submissions in TfLite format will be processed as expected in the competition's benchmarking system.
 
@@ -80,3 +80,59 @@ Change `TEST_IMAGE_PATH` to `my_test_image.jpg`. Change either `FLOAT_MODEL_PATH
 Now you can run the bazel tests to catch any runtime issues with the submission.
 
 Note: Please make sure that your submission passes the test. If a submission fails to pass the test it will not be processed by the submission server.
+
+## Measure on-device latency
+
+We provide two ways to measure the on-device latency of your submission. The first is through our competition server, which is reliable and repeatable, but is limited to a few trials per day. The second is through the benchmarker Apk, which requires a device and may not be as accurate as the server, but has a fast turn-around and no access limitations. We recommend that the participants use the benchmarker apk for early development, and reserve the competition server for evaluating promising submissions.
+
+### Running the benchmarker app
+
+Make sure that you have followed instructions in [Test your submissions](#test-your-submissions) to add your model to the testdata folder and to the corresponding build rules.
+
+Modify `tensorflow/contrib/lite/java/ovic/demo/app/OvicBenchmarkerActivity.java`:
+
+* Add your model to the benchmarker apk by changing `MODEL_PATH` and `TEST_IMAGE_PATH` below to your submission and test image.
+
+```
+  private static final String TEST_IMAGE_PATH = "my_test_image.jpg";
+  private static final String MODEL_PATH = "my_model.lite";
+```
+
+* Adjust the benchmark parameters when needed:
+
+You can chnage the length of each experiment, and the processor affinity below. `BIG_CORE_MASK` is an integer whose binary encoding represents the set of used cores. This number is phone-specific. For example, Pixel 2 has 8 cores: the 4 little cores are represented by the 4 less significant bits, and the 4 big cores by the 4 more significant bits. Therefore a mask value of 16, or in binary `00010000`, represents using only the first big core. The mask 32, or in binary `00100000` uses the second big core and should deliver identical results as the mask 16 because the big cores are interchangeable.
+
+```
+  /** Wall time for each benchmarking experiment. */
+  private static final double WALL_TIME = 3000;
+  /** Maximum number of iterations in each benchmarking experiment. */
+  private static final int MAX_ITERATIONS = 100;
+  /** Mask for binding to a single big core. Pixel 1 (4), Pixel 2 (16). */
+  private static final int BIG_CORE_MASK = 16;
+```
+
+Note: You'll need ROOT access to the phone to change processor affinity.
+
+* Build and install the app.
+
+```
+bazel build -c opt --cxxopt=--std=c++11 --cxxopt=-Wno-all //tensorflow/contrib/lite/java/ovic/demo/app:ovic_benchmarker_binary
+adb install -r bazel-bin/tensorflow/contrib/lite/java/ovic/demo/app/ovic_benchmarker_binary.apk
+```
+
+Start the app and click the `Start` button in dark green. The button should turn bright green, signaling that the experiment is running. The benchmarking results will be displayed after about the `WALL_TIME` you specified above. For example:
+
+```
+my_model.lite: Average latency=158.6ms after 20 runs.
+```
+
+### Sample latencies
+
+Note: the benchmarking results can be quite different depending on the background processes running on the phone. A few things that help stabilize the app's readings are placing the phone on a cooling plate, restarting the phone, and shutting down internet access.
+
+| Model                | Pixel 1 latency (ms)  | Pixel 2 latency (ms) |
+| -------------------- |:---------------------:| --------------------:|
+|  float_model.lite    | 120                   | 155                  |
+| quantized_model.lite | 85                    | 74                   |
+|  low_res_model.lite  | 4.2                   | 4.0                  |
+
diff --git a/tensorflow/contrib/lite/java/ovic/demo/app/OvicBenchmarkerActivity.java b/tensorflow/contrib/lite/java/ovic/demo/app/OvicBenchmarkerActivity.java
index a871b869b00959..59457c308ad7ca 100644
--- a/tensorflow/contrib/lite/java/ovic/demo/app/OvicBenchmarkerActivity.java
+++ b/tensorflow/contrib/lite/java/ovic/demo/app/OvicBenchmarkerActivity.java
@@ -20,10 +20,15 @@
 import android.graphics.Bitmap;
 import android.graphics.BitmapFactory;
 import android.os.Bundle;
+import android.os.Process;
+import android.os.SystemClock;
 import android.util.Log;
 import android.view.View;
 import android.widget.TextView;
+import java.io.BufferedReader;
+import java.io.File;
 import java.io.FileInputStream;
+import java.io.FileReader;
 import java.io.IOException;
 import java.io.InputStream;
 import java.nio.MappedByteBuffer;
@@ -50,6 +55,10 @@ public class OvicBenchmarkerActivity extends Activity {
   private static final double WALL_TIME = 3000;
   /** Maximum number of iterations in each benchmarking experiment. */
   private static final int MAX_ITERATIONS = 100;
+  /** Mask for binding to a single big core. Pixel 1 (4), Pixel 2 (16). */
+  private static final int BIG_CORE_MASK = 16;
+  /** Amount of time in milliseconds to wait for affinity to set. */
+  private static final int WAIT_TIME_FOR_AFFINITY = 1000;
 
   /* The model to be benchmarked. */
   private MappedByteBuffer model = null;
@@ -123,6 +132,13 @@ public void startPressed(View view) throws IOException {
       Log.e(TAG, "Can't initialize benchmarker.", e);
       throw e;
     }
+    String displayText = "";
+    try {
+      setProcessorAffinity(BIG_CORE_MASK);
+    } catch (IOException e) {
+      Log.e(TAG, e.getMessage());
+      displayText = e.getMessage() + "\n";
+    }
     Log.i(TAG, "Successfully initialized benchmarker.");
     int testIter = 0;
     Boolean iterSuccess = false;
@@ -147,17 +163,85 @@ public void startPressed(View view) throws IOException {
 
     if (textView != null) {
       if (testIter > 0) {
-        textView
-            .setText(
-                MODEL_PATH
-                    + ": Average latency="
-                    + df2.format(totalLatency / testIter)
-                    + "ms after "
-                    + testIter
-                    + " runs.");
+        textView.setText(
+            displayText
+                + MODEL_PATH
+                + ": Average latency="
+                + df2.format(totalLatency / testIter)
+                + "ms after "
+                + testIter
+                + " runs.");
       } else {
         textView.setText("Benchmarker failed to run on more than one images.");
       }
     }
   }
+
+  private static void setProcessorAffinity(int mask) throws IOException {
+    int myPid = Process.myPid();
+    Log.i(TAG, String.format("Setting processor affinity to 0x%02x", mask));
+
+    String command = String.format("taskset -a -p %x %d", mask, myPid);
+    try {
+      Runtime.getRuntime().exec(command).waitFor();
+    } catch (InterruptedException e) {
+      throw new IOException("Interrupted: " + e);
+    }
+
+    // Make sure set took effect - try for a second to confirm the change took.  If not then fail.
+    long startTimeMs = SystemClock.elapsedRealtime();
+    while (true) {
+      int readBackMask = readCpusAllowedMask();
+      if (readBackMask == mask) {
+        Log.i(TAG, String.format("Successfully set affinity to 0x%02x", mask));
+        return;
+      }
+      if (SystemClock.elapsedRealtime() > startTimeMs + WAIT_TIME_FOR_AFFINITY) {
+        throw new IOException(
+            String.format(
+                "Core-binding failed: affinity set to 0x%02x but read back as 0x%02x\n"
+                    + "please root device.",
+                mask, readBackMask));
+      }
+
+      try {
+        Thread.sleep(50);
+      } catch (InterruptedException e) {
+        // Ignore sleep interrupted, will sleep again and compare is final cross-check.
+      }
+    }
+  }
+
+  public static int readCpusAllowedMask() throws IOException {
+    // Determine how many CPUs there are total
+    final String pathname = "/proc/self/status";
+    final String resultPrefix = "Cpus_allowed:";
+    File file = new File(pathname);
+    String line = "";
+    String allowedCPU = "";
+    Integer allowedMask = null;
+    BufferedReader bufReader = null;
+    try {
+      bufReader = new BufferedReader(new FileReader(file));
+      while ((line = bufReader.readLine()) != null) {
+        if (line.startsWith(resultPrefix)) {
+          allowedMask = Integer.valueOf(line.substring(resultPrefix.length()).trim(), 16);
+          allowedCPU = bufReader.readLine();
+          break;
+        }
+      }
+    } catch (RuntimeException e) {
+      throw new IOException(
+          "Invalid number in " + pathname + " line: \"" + line + "\": " + e.getMessage());
+    } finally {
+      if (bufReader != null) {
+        bufReader.close();
+      }
+    }
+    if (allowedMask == null) {
+      throw new IOException(pathname + " missing " + resultPrefix + " line");
+    }
+    Log.i(TAG, allowedCPU);
+    return allowedMask;
+  }
 }

From 26749309690949cf355fd51f17e818b7450d3f7f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 11:19:46 -0700
Subject: [PATCH 0507/1691] Change visibility of hlo_proto.

PiperOrigin-RevId: 195848035
---
 tensorflow/compiler/xla/service/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index ec67e19b230ea2..aa3a6261e0117c 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -26,6 +26,7 @@ xla_proto_library(
 xla_proto_library(
     name = "hlo_proto",
     srcs = ["hlo.proto"],
+    visibility = ["//visibility:public"],
     deps = ["//tensorflow/compiler/xla:xla_data_proto"],
 )
 

From 211e3a20016cd1dd29883d57576eecd477a3dcac Mon Sep 17 00:00:00 2001
From: Ilya Biryukov 
Date: Tue, 8 May 2018 11:25:50 -0700
Subject: [PATCH 0508/1691] Update version of downloadable clang toolchain

PiperOrigin-RevId: 195849091
---
 third_party/clang_toolchain/download_clang.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/third_party/clang_toolchain/download_clang.bzl b/third_party/clang_toolchain/download_clang.bzl
index 54d383d7d76513..cfd8bfe98d7851 100644
--- a/third_party/clang_toolchain/download_clang.bzl
+++ b/third_party/clang_toolchain/download_clang.bzl
@@ -35,18 +35,18 @@ def download_clang(repo_ctx, out_folder):
 
   # Latest CLANG_REVISION and CLANG_SUB_REVISION of the Chromiums's release
   # can be found in https://chromium.googlesource.com/chromium/src/tools/clang/+/master/scripts/update.py
-  CLANG_REVISION = '321529'
+  CLANG_REVISION = '330570'
   CLANG_SUB_REVISION = 2
 
   package_version = '%s-%s' % (CLANG_REVISION, CLANG_SUB_REVISION)
 
   checksums = {
       'Linux_x64':
-          '76d4eb1ad011e3127c4a9de9b9f5d4ac624b5a9395c4d7395c9e0a487b13daf6',
+          '2108e172e05d4904c3c46125a33ab4a1175b36ec2a2226619a243e1d8f397e97',
       'Mac':
-          '4b2a7a65ac1ee892b318c723eec8771f514bb306f346aa8216bb0006f19d87b7',
+          '481b5c6909f0ea250216061bd45e9c982b4befff65cbfca2ee1090c21a109eac',
       'Win':
-          'eba51bb8f84af41a85903113666bd21c22709010c39c4cb19dc20cf1ed14581b',
+          '8f04a3ac99d463d4179eb2f68a13575408c3dddc62887a1e441c77123e35e301',
   }
 
   platform_folder = _get_platform_folder(repo_ctx.os.name)

From 4f7a0bc8c11827dde6986ad29e9fd21c48597367 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 11:45:53 -0700
Subject: [PATCH 0509/1691] Fix docstring for flush() method

PiperOrigin-RevId: 195852402
---
 tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py b/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
index 1b184d296b329c..50cc00afdcc77f 100644
--- a/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
+++ b/tensorflow/contrib/boosted_trees/python/ops/quantile_ops.py
@@ -187,7 +187,7 @@ def flush(self, stamp_token, next_stamp_token):
       stamp_token: Expected current token.
       next_stamp_token: Next value for the token.
     Returns:
-      A list of quantiles or approximate boundaries.
+      The flush operation.
     """
     return gen_quantile_ops.quantile_accumulator_flush(
         quantile_accumulator_handle=self._quantile_accumulator_handle,

From 59bffb7051231c7e0f8020892db8c3d584c555f4 Mon Sep 17 00:00:00 2001
From: Alina Sbirlea 
Date: Tue, 8 May 2018 11:54:03 -0700
Subject: [PATCH 0510/1691] Re-land: Optimize dot(DynamicSlice(ConstA),
 ConstantB) by memoizing dot(ConstA, ConstB)

Make transformation when ConstA and ConstB are 2D, and DynamicSlice is slicing a full row, column respectively.
Handle:
dot(DynamicSlice(Index, ConstA), ConstB) => DynamicSlice(Index, dot*(ConstA, ConstB));
and
dot(ConstA, DynamicSlice(Index, ConstB)) => DynamicSlice(Index, dot*(ConstA, ConstB));

Reason to roll forward: Previous issue of getting out of memory errors when generating LLVM constants was resolved by CSE-ing constants before allocation.

PiperOrigin-RevId: 195853680
---
 .../xla/service/algebraic_simplifier.cc       | 141 ++++++++++
 .../xla/service/algebraic_simplifier_test.cc  | 203 +++++++++++++++
 .../compiler/xla/tests/dot_operation_test.cc  | 245 ++++++++++++++++++
 3 files changed, 589 insertions(+)

diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index 8e785de68cb1fb..4ec79a024463b5 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -291,6 +291,8 @@ class AlgebraicSimplifierVisitor : public DfsHloVisitorWithDefault {
       const Shape& dot_shape, HloInstruction* lhs, int64 lhs_contracting_dim,
       HloInstruction* rhs, int64 rhs_contracting_dim, bool swapped);
 
+  StatusOr OptimizeDotOfGather(HloInstruction* dot);
+
   // Current HloComputation instance the AlgebraicSimplifierVisitor is
   // traversing.
   HloComputation* computation_;
@@ -912,6 +914,134 @@ StatusOr AlgebraicSimplifierVisitor::OptimizeDotOfConcatHelper(
   return add_result;
 }
 
+StatusOr AlgebraicSimplifierVisitor::OptimizeDotOfGather(
+    HloInstruction* dot) {
+  const DotDimensionNumbers& dnums = dot->dot_dimension_numbers();
+  if (dnums.lhs_contracting_dimensions_size() != 1 ||
+      dnums.rhs_contracting_dimensions_size() != 1 ||
+      dnums.lhs_batch_dimensions_size() != 0 ||
+      dnums.rhs_batch_dimensions_size() != 0 ||
+      dot->shape().dimensions_size() != 2) {  // dot output 2D
+    VLOG(10) << "DotOfGather: Can only optimize 2D, non-batch dot operations.";
+    return nullptr;
+  }
+
+  // Optimize either dot(DS(ctA), ctB)) or dot(ctB, DS(ctA)).
+  // Currently a Gather is a DynamicSlice.
+  auto is_dynamic_slice_constant_combination =
+      [](HloInstruction* a, HloInstruction* b, int a_contracting_dimension) {
+        // First operand is a DynamicSlice(Constant).
+        if (a->opcode() != HloOpcode::kDynamicSlice) {
+          return false;
+        }
+        auto* dynamic_slice_op = a->operand(0);
+        if (dynamic_slice_op->opcode() != HloOpcode::kConstant) {
+          return false;
+        }
+        // Second operand is a Constant.
+        if (b->opcode() != HloOpcode::kConstant) {
+          return false;
+        }
+        // The DynamicSlice output is a vector.
+        const Shape& dynamic_slice_shape = a->shape();
+        if (dynamic_slice_shape.dimensions(1 - a_contracting_dimension) != 1) {
+          return false;
+        }
+        // Constant size is the same before and after slice in the contracting
+        // dimension, otherwise we either must precompute for all possible slice
+        // indices or dot is invalid.
+        const Shape& dynamic_slice_op_shape = dynamic_slice_op->shape();
+        if (dynamic_slice_op_shape.dimensions(a_contracting_dimension) !=
+            dynamic_slice_shape.dimensions(a_contracting_dimension)) {
+          return false;
+        }
+        return true;
+      };
+
+  HloInstruction* lhs = dot->mutable_operand(0);
+  HloInstruction* rhs = dot->mutable_operand(1);
+  int lhs_contracting_dimension = dnums.lhs_contracting_dimensions(0);
+  int rhs_contracting_dimension = dnums.rhs_contracting_dimensions(0);
+
+  if (!is_dynamic_slice_constant_combination(
+          lhs, rhs, /*a_contracting_dimension=*/lhs_contracting_dimension) &&
+      !is_dynamic_slice_constant_combination(
+          rhs, lhs, /*a_contracting_dimension=*/rhs_contracting_dimension)) {
+    VLOG(10) << "DotOfGather: Can only optimize dot(DS(ctA), ctB)) or "
+                "dot(ctB, DS(ctA)), where the two constants have equal "
+                "contracting dimensions.";
+    return nullptr;
+  }
+
+  // LHS is DynamicSlice:
+  // input: dot(DS(ctA), ctB))
+  // where DS(ctA) = DS({M x K}, {start, 0}, {1, K}) and ctB = {K x N}.
+  // => input dimensions: dot({1 x K}, {K x N}) => {1 x N}.
+  // output: DS(dot(ctA, ctB))
+  // => output dimensions: DS ({M x N}, {start, 0}, {1, N}) => {1 x N}.
+
+  // RHS is DynamicSlice:
+  // input: dot(ctA, DS(ctB))
+  // where ctA = {M x K} and DS(ctB) = DS({K x N}, {0, start}, {K, 1}).
+  // => input dimensions: dot({M x K}, {K x 1}) => {M x 1}.
+  // output: DS(dot(ctA, ctB))
+  // => output dimensions: DS ({M x N}, {0, start}, {M, 1}) => {M x 1}.
+
+  bool lhs_is_dynamic_slice = lhs->opcode() == HloOpcode::kDynamicSlice;
+
+  // ctA:
+  HloInstruction* left_operand =
+      lhs_is_dynamic_slice ? lhs->mutable_operand(0) : lhs;
+  // ctB:
+  HloInstruction* right_operand =
+      lhs_is_dynamic_slice ? rhs : rhs->mutable_operand(0);
+  // Build ctA x ctB.
+  const int m = left_operand->shape().dimensions(1 - lhs_contracting_dimension);
+  const int n =
+      right_operand->shape().dimensions(1 - rhs_contracting_dimension);
+  auto memoized_shape = ShapeUtil::MakeShape(F32, {m, n});
+  auto* memoized_inst = computation_->AddInstruction(HloInstruction::CreateDot(
+      memoized_shape, left_operand, right_operand, dnums));
+  // Get pair {start, 0} or {0, start}.
+  HloInstruction* original_start_indices =
+      lhs_is_dynamic_slice ? lhs->mutable_operand(1) : rhs->mutable_operand(1);
+  // Position of start:
+  int index_of_non_zero_start = lhs_is_dynamic_slice
+                                    ? 1 - lhs_contracting_dimension
+                                    : 1 - rhs_contracting_dimension;
+  // Position of zero:
+  int index_of_zero_start = 1 - index_of_non_zero_start;
+
+  // Slice out start and 0 components and reorder if necessary.
+  auto indices_type = original_start_indices->shape().element_type();
+  Shape s_shape = ShapeUtil::MakeShape(indices_type, {1});
+  Shape d_shape = ShapeUtil::MakeShape(indices_type, {2});
+  HloInstruction* non_zero_start =
+      computation_->AddInstruction(HloInstruction::CreateSlice(
+          s_shape, original_start_indices, {index_of_non_zero_start},
+          {index_of_non_zero_start + 1}, {1}));
+  HloInstruction* zero_start =
+      computation_->AddInstruction(HloInstruction::CreateSlice(
+          s_shape, original_start_indices, {index_of_zero_start},
+          {index_of_zero_start + 1}, {1}));
+  HloInstruction* new_start_indices =
+      lhs_is_dynamic_slice
+          ? computation_->AddInstruction(HloInstruction::CreateConcatenate(
+                d_shape, {non_zero_start, zero_start}, 0))
+          : computation_->AddInstruction(HloInstruction::CreateConcatenate(
+                d_shape, {zero_start, non_zero_start}, 0));
+
+  // Build DynamicSlice(ctA x ctB).
+  const int new_slice_m = lhs_is_dynamic_slice ? 1 : m;
+  const int new_slice_n = lhs_is_dynamic_slice ? n : 1;
+  auto* memoized_lookup =
+      computation_->AddInstruction(HloInstruction::CreateDynamicSlice(
+          dot->shape(), memoized_inst, new_start_indices,
+          {new_slice_m, new_slice_n}));
+
+  return memoized_lookup;
+}
+
 Status AlgebraicSimplifierVisitor::HandleDot(HloInstruction* dot) {
   HloInstruction *lhs, *rhs;
   CHECK(Match(dot, m::Dot(m::Op(&lhs), m::Op(&rhs))));
@@ -941,6 +1071,17 @@ Status AlgebraicSimplifierVisitor::HandleDot(HloInstruction* dot) {
     return ReplaceInstruction(dot, dot_of_concat_optimized);
   }
 
+  // Simplify dot(ConstA, Gather(Index, ConstB)) to:
+  // Gather(Index, dot*(ConstA, ConstB)), where dot* is an appropriately
+  // batched version of dot.
+  TF_ASSIGN_OR_RETURN(HloInstruction * dot_of_gather_optimized,
+                      OptimizeDotOfGather(dot));
+  if (dot_of_gather_optimized) {
+    VLOG(10) << "Replaced dot(constA, gather(i, constB)) with "
+                "gather(i, dot*(constA, constB))";
+    return ReplaceInstruction(dot, dot_of_gather_optimized);
+  }
+
   if (enable_dot_strength_reduction_ && !is_layout_sensitive_) {
     TF_ASSIGN_OR_RETURN(bool did_strength_reduction,
                         HandleDotStrengthReduction(dot));
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
index d0c99bf818cd54..4e082877c776c3 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
@@ -2963,5 +2963,208 @@ TEST_F(AlgebraicSimplifierTest, DynamicUpdateSliceZeroUpdate) {
 INSTANTIATE_TEST_CASE_P(DotOfConcatSimplificationTestInstantiation,
                         DotOfConcatSimplificationTest,
                         ::testing::ValuesIn(kDotOfConcatTestSpecs));
+
+struct DotOfGatherTestSpec {
+  int64 m;
+  int64 k;
+  int64 n;
+  int s;      // start index for dynamic slice on the non-contracting dimension
+  int64 lcd;  // left contracting dimension
+  int64 rcd;  // right contracting dimension
+  bool neg;   // is negative testcase
+};
+
+class DotOfGatherSimplificationTest
+    : public HloVerifiedTestBase,
+      public ::testing::WithParamInterface {};
+
+// input: dot(DS(ctA), ctB))
+// where DS(ctA) = DS({M x K}, {s, 0}, {1, K}) and ctB = {K x N}.
+// => input dimensions: dot({1 x K}, {K x N}) => {1 x N}.
+// output: DS(dot(ctA, ctB))
+// => output dimensions: DS ({M x N}, {s, 0}, {1, N}) => {1 x N}.
+TEST_P(DotOfGatherSimplificationTest, ConstantRHS) {
+  HloComputation::Builder builder(TestName());
+
+  DotOfGatherTestSpec spec = GetParam();
+
+  ASSERT_LE(spec.s, spec.m);
+
+  // For negative tests, increase k of the dynamic slice argument to prevent the
+  // optimization (constants ctA, ctB must have equal contracting dimensions).
+  int64 k_increase = spec.neg ? 5 : 0;
+  int64 lhs_rows = (spec.lcd == 0) ? (spec.k + k_increase) : spec.m;
+  int64 lhs_cols = (spec.lcd == 0) ? spec.m : (spec.k + k_increase);
+  Shape lhs_shape = ShapeUtil::MakeShape(F32, {lhs_rows, lhs_cols});
+  auto* lhs = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+          /*from=*/10.0, /*to=*/10000.0, /*rows=*/lhs_rows,
+          /*cols=*/lhs_cols)));
+
+  int32 start_row = (spec.lcd == 0) ? 0 : spec.s;
+  int32 start_col = (spec.lcd == 0) ? spec.s : 0;
+  const auto start_indices =
+      builder.AddInstruction(HloInstruction::CreateConstant(
+          Literal::CreateR1({start_row, start_col})));
+  int64 slice_row_size = (spec.lcd == 0) ? spec.k : 1;
+  int64 slice_col_size = (spec.lcd == 0) ? 1 : spec.k;
+  Shape ds_shape = ShapeUtil::MakeShape(F32, {slice_row_size, slice_col_size});
+  auto* ds = builder.AddInstruction(HloInstruction::CreateDynamicSlice(
+      ds_shape, lhs, start_indices, {slice_row_size, slice_col_size}));
+
+  int64 rhs_rows = (spec.rcd == 0) ? spec.k : spec.n;
+  int64 rhs_cols = (spec.rcd == 0) ? spec.n : spec.k;
+  Shape rhs_shape = ShapeUtil::MakeShape(F32, {rhs_rows, rhs_cols});
+  auto* rhs = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+          /*from=*/10.0, /*to=*/10000.0, /*rows=*/rhs_rows,
+          /*cols=*/rhs_cols)));
+
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(spec.lcd);
+  dot_dnums.add_rhs_contracting_dimensions(spec.rcd);
+
+  int64 dot_row_size = 1;
+  int64 dot_col_size = spec.n;
+  Shape dot_shape = ShapeUtil::MakeShape(F32, {dot_row_size, dot_col_size});
+  builder.AddInstruction(
+      HloInstruction::CreateDot(dot_shape, ds, rhs, dot_dnums));
+
+  auto computation = module().AddEntryComputation(builder.Build());
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  TF_ASSERT_OK_AND_ASSIGN(bool run_successful, simplifier.Run(&module()));
+  ASSERT_TRUE(run_successful);
+  EXPECT_TRUE(
+      ShapeUtil::Equal(computation->root_instruction()->shape(), dot_shape));
+
+  if (spec.neg) {
+    EXPECT_NE(computation->root_instruction()->opcode(),
+              HloOpcode::kDynamicSlice);
+  } else {
+    EXPECT_THAT(computation->root_instruction(),
+                op::DynamicSlice(op::Dot(op::Constant(), op::Constant()),
+                                 op::Concatenate()));
+  }
+}
+
+// input: dot(ctA, DS(ctB))
+// where ctA = {M x K} and DS(ctB) = DS({K x N}, {0, s}, {K, 1}).
+// => input dimensions: dot({M x K}, {K x 1}) => {M x 1}.
+// output: DS(dot(ctA, ctB))
+// => output dimensions: DS ({M x N}, {0, s}, {M, 1}) => {M x 1}.
+TEST_P(DotOfGatherSimplificationTest, ConstantLHS) {
+  HloComputation::Builder builder(TestName());
+
+  DotOfGatherTestSpec spec = GetParam();
+
+  ASSERT_LE(spec.s, spec.n);
+
+  int64 lhs_rows = (spec.lcd == 0) ? spec.k : spec.m;
+  int64 lhs_cols = (spec.lcd == 0) ? spec.m : spec.k;
+  Shape lhs_shape = ShapeUtil::MakeShape(F32, {lhs_rows, lhs_cols});
+  auto* lhs = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+          /*from=*/10.0, /*to=*/10000.0, /*rows=*/lhs_rows,
+          /*cols=*/lhs_cols)));
+
+  // For negative tests increase k of the dynamic slice argument to prevent the
+  // optimization
+  int64 k_increase = spec.neg ? 5 : 0;
+  int64 rhs_rows = (spec.rcd == 0) ? (spec.k + k_increase) : spec.n;
+  int64 rhs_cols = (spec.rcd == 0) ? spec.n : (spec.k + k_increase);
+  Shape rhs_shape = ShapeUtil::MakeShape(F32, {rhs_rows, rhs_cols});
+  auto* rhs = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR2F32Linspace(
+          /*from=*/10.0, /*to=*/10000.0, /*rows=*/rhs_rows,
+          /*cols=*/rhs_cols)));
+
+  int32 start_row = (spec.rcd == 0) ? 0 : spec.s;
+  int32 start_col = (spec.rcd == 0) ? spec.s : 0;
+  const auto start_indices =
+      builder.AddInstruction(HloInstruction::CreateConstant(
+          Literal::CreateR1({start_row, start_col})));
+  int64 slice_row_size = (spec.rcd == 0) ? spec.k : 1;
+  int64 slice_col_size = (spec.rcd == 0) ? 1 : spec.k;
+  Shape ds_shape = ShapeUtil::MakeShape(F32, {slice_row_size, slice_col_size});
+  auto* ds = builder.AddInstruction(HloInstruction::CreateDynamicSlice(
+      ds_shape, rhs, start_indices, {slice_row_size, slice_col_size}));
+
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(spec.lcd);
+  dot_dnums.add_rhs_contracting_dimensions(spec.rcd);
+
+  int64 dot_row_size = spec.m;
+  int64 dot_col_size = 1;
+  Shape dot_shape = ShapeUtil::MakeShape(F32, {dot_row_size, dot_col_size});
+  builder.AddInstruction(
+      HloInstruction::CreateDot(dot_shape, lhs, ds, dot_dnums));
+
+  auto computation = module().AddEntryComputation(builder.Build());
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  TF_ASSERT_OK_AND_ASSIGN(bool run_successful, simplifier.Run(&module()));
+  ASSERT_TRUE(run_successful);
+  EXPECT_TRUE(
+      ShapeUtil::Equal(computation->root_instruction()->shape(), dot_shape));
+
+  if (spec.neg) {
+    EXPECT_NE(computation->root_instruction()->opcode(),
+              HloOpcode::kDynamicSlice);
+  } else {
+    EXPECT_THAT(computation->root_instruction(),
+                op::DynamicSlice(op::Dot(op::Constant(), op::Constant()),
+                                 op::Concatenate()));
+  }
+}
+
+std::vector DotOfGatherPositiveNegativeTests() {
+  std::vector positives = {
+      // "Classical dot", i.e. matrix multiply:
+      {/*m=*/10, /*k=*/10, /*n=*/5, /*s=*/0, /*lcd=*/1, /*rcd=*/0,
+       /*neg=*/false},
+      {/*m=*/20, /*k=*/20, /*n=*/3, /*s=*/2, /*lcd=*/1, /*rcd=*/0,
+       /*neg=*/false},
+      {/*m=*/10, /*k=*/3, /*n=*/10, /*s=*/9, /*lcd=*/1, /*rcd=*/0,
+       /*neg=*/false},
+      // Note: testing for m=1 and n=1 is unnecessary, as this optimizes to
+      // dot(ct, ct) before DotOfGather optimization kicks in.
+      // Contract on rows:
+      {/*m=*/10, /*k=*/10, /*n=*/5, /*s=*/0, /*lcd=*/0, /*rcd=*/0,
+       /*neg=*/false},
+      {/*m=*/20, /*k=*/20, /*n=*/3, /*s=*/2, /*lcd=*/0, /*rcd=*/0,
+       /*neg=*/false},
+      {/*m=*/10, /*k=*/3, /*n=*/10, /*s=*/9, /*lcd=*/0, /*rcd=*/0,
+       /*neg=*/false},
+      // Reverse matrix multiply:
+      {/*m=*/10, /*k=*/10, /*n=*/5, /*s=*/0, /*lcd=*/0, /*rcd=*/1,
+       /*neg=*/false},
+      {/*m=*/20, /*k=*/20, /*n=*/3, /*s=*/2, /*lcd=*/0, /*rcd=*/1,
+       /*neg=*/false},
+      {/*m=*/10, /*k=*/3, /*n=*/10, /*s=*/9, /*lcd=*/0, /*rcd=*/1,
+       /*neg=*/false},
+      // Contract on columns:
+      {/*m=*/10, /*k=*/10, /*n=*/5, /*s=*/0, /*lcd=*/1, /*rcd=*/1,
+       /*neg=*/false},
+      {/*m=*/20, /*k=*/20, /*n=*/3, /*s=*/2, /*lcd=*/1, /*rcd=*/1,
+       /*neg=*/false},
+      {/*m=*/10, /*k=*/3, /*n=*/10, /*s=*/9, /*lcd=*/1, /*rcd=*/1,
+       /*neg=*/false},
+  };
+  std::vector all;
+  for (int i = 0; i < positives.size(); i++) {
+    DotOfGatherTestSpec positive_test = positives[i];
+    all.push_back(positive_test);
+    DotOfGatherTestSpec negative_test = positive_test;
+    negative_test.neg = true;
+    all.push_back(negative_test);
+  }
+  return all;
+}
+
+INSTANTIATE_TEST_CASE_P(
+    DotOfGatherSimplificationTestInstantiation, DotOfGatherSimplificationTest,
+    ::testing::ValuesIn(DotOfGatherPositiveNegativeTests()));
+
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index 6b3efba4f80e45..efa5aed2d1af8e 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -798,5 +798,250 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64,
       this->error_spec_);
 }
 
+TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstRHSClassicMM) {
+  std::unique_ptr> constant_lhs_array(new Array2D(
+      {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}}));
+  std::unique_ptr> constant_rhs_array(
+      new Array2D({{1.0, 2.0, 3.0},
+                          {4.0, 5.0, 6.0},
+                          {7.0, 8.0, 9.0},
+                          {9.0, 8.0, 7.0},
+                          {6.0, 5.0, 4.0},
+                          {3.0, 2.0, 1.0}}));
+  // Dot result to slice from: {{114, 105, 96}, {96, 105, 114}}
+
+  XlaBuilder builder(TestName());
+  auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array);
+  auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array);
+  auto start_constant = builder.ConstantR1({1, 0});
+  auto dynamic_slice =
+      builder.DynamicSlice(lhs_constant, start_constant, {1, 6});
+
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(1);
+  dot_dnums.add_rhs_contracting_dimensions(0);
+  auto result = builder.DotGeneral(dynamic_slice, rhs_constant, dot_dnums);
+
+  Array2D expected({{96.0, 105.0, 114.0}});
+  ComputeAndCompareR2(&builder, expected, {}, error_spec_);
+}
+
+TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstLHSClassicMM) {
+  std::unique_ptr> constant_lhs_array(new Array2D(
+      {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}}));
+  std::unique_ptr> constant_rhs_array(
+      new Array2D({{1.0, 2.0, 3.0},
+                          {4.0, 5.0, 6.0},
+                          {7.0, 8.0, 9.0},
+                          {9.0, 8.0, 7.0},
+                          {6.0, 5.0, 4.0},
+                          {3.0, 2.0, 1.0}}));
+  // Dot result to slice from: {{114, 105, 96}, {96, 105, 114}}
+
+  XlaBuilder builder(TestName());
+  auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array);
+  auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array);
+  auto start_constant = builder.ConstantR1({0, 1});
+  auto dynamic_slice =
+      builder.DynamicSlice(rhs_constant, start_constant, {6, 1});
+
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(1);
+  dot_dnums.add_rhs_contracting_dimensions(0);
+  auto result = builder.DotGeneral(lhs_constant, dynamic_slice, dot_dnums);
+
+  Array2D expected({{105.0}, {105.0}});
+  ComputeAndCompareR2(&builder, expected, {}, error_spec_);
+}
+
+// TODO (b/69062148) Enable when Dot implements general contracting dimensions.
+TEST_F(DotOperationTest,
+       DISABLED_ON_CPU(DISABLED_ON_GPU(DISABLED_ON_INTERPRETER(
+           DotOfGatherOptimizationWithConstRHSReverseMM)))) {
+  std::unique_ptr> constant_lhs_array(
+      new Array2D({{1.0, 2.0, 3.0},
+                          {4.0, 5.0, 6.0},
+                          {7.0, 8.0, 9.0},
+                          {9.0, 8.0, 7.0},
+                          {6.0, 5.0, 4.0},
+                          {3.0, 2.0, 1.0}}));
+  std::unique_ptr> constant_rhs_array(new Array2D(
+      {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}}));
+  // Dot result to slice from: {{114, 96}, {105, 105}, {96, 114}}
+
+  XlaBuilder builder(TestName());
+  auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array);
+  auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array);
+  auto start_constant = builder.ConstantR1({0, 1});
+  auto dynamic_slice =
+      builder.DynamicSlice(lhs_constant, start_constant, {6, 1});
+
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(0);
+  dot_dnums.add_rhs_contracting_dimensions(1);
+  auto result = builder.DotGeneral(dynamic_slice, rhs_constant, dot_dnums);
+
+  Array2D expected({{105.0, 105.0}});
+  ComputeAndCompareR2(&builder, expected, {}, error_spec_);
+}
+
+// TODO (b/69062148) Enable when Dot implements general contracting dimensions.
+TEST_F(DotOperationTest,
+       DISABLED_ON_CPU(DISABLED_ON_GPU(DISABLED_ON_INTERPRETER(
+           DotOfGatherOptimizationWithConstLHSReverseMM)))) {
+  std::unique_ptr> constant_lhs_array(
+      new Array2D({{1.0, 2.0, 3.0},
+                          {4.0, 5.0, 6.0},
+                          {7.0, 8.0, 9.0},
+                          {9.0, 8.0, 7.0},
+                          {6.0, 5.0, 4.0},
+                          {3.0, 2.0, 1.0}}));
+  std::unique_ptr> constant_rhs_array(new Array2D(
+      {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}}));
+  // Dot result to slice from: {{114, 96}, {105, 105}, {96, 114}}
+
+  XlaBuilder builder(TestName());
+  auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array);
+  auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array);
+  auto start_constant = builder.ConstantR1({1, 0});
+  auto dynamic_slice =
+      builder.DynamicSlice(rhs_constant, start_constant, {1, 6});
+
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(0);
+  dot_dnums.add_rhs_contracting_dimensions(1);
+  auto result = builder.DotGeneral(lhs_constant, dynamic_slice, dot_dnums);
+
+  Array2D expected({{96.0}, {105.0}, {114.0}});
+  ComputeAndCompareR2(&builder, expected, {}, error_spec_);
+}
+
+// TODO (b/69062148) Enable when Dot implements general contracting dimensions.
+TEST_F(DotOperationTest,
+       DISABLED_ON_CPU(DISABLED_ON_GPU(
+           DISABLED_ON_INTERPRETER(DotOfGatherOptimizationWithConstRHSRows)))) {
+  std::unique_ptr> constant_lhs_array(
+      new Array2D({{1.0, 2.0},
+                          {3.0, 4.0},
+                          {5.0, 6.0},
+                          {6.0, 5.0},
+                          {4.0, 3.0},
+                          {2.0, 1.0}}));
+  std::unique_ptr> constant_rhs_array(
+      new Array2D({{1.0, 2.0, 3.0},
+                          {4.0, 5.0, 6.0},
+                          {7.0, 8.0, 9.0},
+                          {9.0, 8.0, 7.0},
+                          {6.0, 5.0, 4.0},
+                          {3.0, 2.0, 1.0}}));
+  // Dot result to slice from: {{132, 129, 126}, {126, 129, 132}}
+
+  XlaBuilder builder(TestName());
+  auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array);
+  auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array);
+  auto start_constant = builder.ConstantR1({0, 1});
+  auto dynamic_slice =
+      builder.DynamicSlice(lhs_constant, start_constant, {6, 1});
+
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(0);
+  dot_dnums.add_rhs_contracting_dimensions(0);
+  auto result = builder.DotGeneral(dynamic_slice, rhs_constant, dot_dnums);
+
+  Array2D expected({{126.0, 129.0, 132.0}});
+  ComputeAndCompareR2(&builder, expected, {}, error_spec_);
+}
+
+// TODO (b/69062148) Enable when Dot implements general contracting dimensions.
+TEST_F(DotOperationTest,
+       DISABLED_ON_CPU(DISABLED_ON_GPU(
+           DISABLED_ON_INTERPRETER(DotOfGatherOptimizationWithConstLHSRows)))) {
+  std::unique_ptr> constant_lhs_array(
+      new Array2D({{1.0, 2.0},
+                          {3.0, 4.0},
+                          {5.0, 6.0},
+                          {6.0, 5.0},
+                          {4.0, 3.0},
+                          {2.0, 1.0}}));
+  std::unique_ptr> constant_rhs_array(
+      new Array2D({{1.0, 2.0, 3.0},
+                          {4.0, 5.0, 6.0},
+                          {7.0, 8.0, 9.0},
+                          {9.0, 8.0, 7.0},
+                          {6.0, 5.0, 4.0},
+                          {3.0, 2.0, 1.0}}));
+  // Dot result to slice from: {{132, 129, 126}, {126, 129, 132}}
+
+  XlaBuilder builder(TestName());
+  auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array);
+  auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array);
+  auto start_constant = builder.ConstantR1({0, 1});
+  auto dynamic_slice =
+      builder.DynamicSlice(rhs_constant, start_constant, {6, 1});
+
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(0);
+  dot_dnums.add_rhs_contracting_dimensions(0);
+  auto result = builder.DotGeneral(lhs_constant, dynamic_slice, dot_dnums);
+
+  Array2D expected({{129.0}, {129.0}});
+  ComputeAndCompareR2(&builder, expected, {}, error_spec_);
+}
+
+// TODO (b/69062148) Enable when Dot implements general contracting dimensions.
+TEST_F(DotOperationTest,
+       DISABLED_ON_CPU(DISABLED_ON_GPU(
+           DISABLED_ON_INTERPRETER(DotOfGatherOptimizationWithConstRHSCols)))) {
+  std::unique_ptr> constant_lhs_array(new Array2D(
+      {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}}));
+  std::unique_ptr> constant_rhs_array(
+      new Array2D({{1.0, 2.0, 3.0, 4.0, 5.0, 6.0},
+                          {7.0, 8.0, 9.0, 9.0, 8.0, 7.0},
+                          {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}}));
+  // Dot result to slice from: {{91, 168, 56}, {56, 168, 91}}
+
+  XlaBuilder builder(TestName());
+  auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array);
+  auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array);
+  auto start_constant = builder.ConstantR1({1, 0});
+  auto dynamic_slice =
+      builder.DynamicSlice(lhs_constant, start_constant, {1, 6});
+
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(1);
+  dot_dnums.add_rhs_contracting_dimensions(1);
+  auto result = builder.DotGeneral(dynamic_slice, rhs_constant, dot_dnums);
+
+  Array2D expected({{56.0, 168.0, 91.0}});
+  ComputeAndCompareR2(&builder, expected, {}, error_spec_);
+}
+
+// TODO (b/69062148) Enable when Dot implements general contracting dimensions.
+TEST_F(DotOperationTest,
+       DISABLED_ON_CPU(DISABLED_ON_GPU(
+           DISABLED_ON_INTERPRETER(DotOfGatherOptimizationWithConstLHSCols)))) {
+  std::unique_ptr> constant_lhs_array(new Array2D(
+      {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}}));
+  std::unique_ptr> constant_rhs_array(
+      new Array2D({{1.0, 2.0, 3.0, 4.0, 5.0, 6.0},
+                          {7.0, 8.0, 9.0, 9.0, 8.0, 7.0},
+                          {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}}));
+  // Dot result to slice from: {{91, 168, 56}, {56, 168, 91}}
+
+  XlaBuilder builder(TestName());
+  auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array);
+  auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array);
+  auto start_constant = builder.ConstantR1({1, 0});
+  auto dynamic_slice =
+      builder.DynamicSlice(rhs_constant, start_constant, {1, 6});
+
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(1);
+  dot_dnums.add_rhs_contracting_dimensions(1);
+  auto result = builder.DotGeneral(lhs_constant, dynamic_slice, dot_dnums);
+
+  Array2D expected({{168.0}, {168.0}});
+  ComputeAndCompareR2(&builder, expected, {}, error_spec_);
+}
 }  // namespace
 }  // namespace xla

From b15500be31f29850c73804b8694e4f0f01b82305 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 12:04:38 -0700
Subject: [PATCH 0511/1691] Remove outdated CUDA SDK string (the text is now
 consistent with other version choices, and the '9.0' format is already
 present in the default).

PiperOrigin-RevId: 195855416
---
 configure.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/configure.py b/configure.py
index b745e374a2baaf..7d04d3a14f9734 100644
--- a/configure.py
+++ b/configure.py
@@ -845,8 +845,8 @@ def reformat_version_sequence(version_str, sequence_count):
 def set_tf_cuda_version(environ_cp):
   """Set CUDA_TOOLKIT_PATH and TF_CUDA_VERSION."""
   ask_cuda_version = (
-      'Please specify the CUDA SDK version you want to use, '
-      'e.g. 7.0. [Leave empty to default to CUDA %s]: ') % _DEFAULT_CUDA_VERSION
+      'Please specify the CUDA SDK version you want to use. '
+      '[Leave empty to default to CUDA %s]: ') % _DEFAULT_CUDA_VERSION
 
   for _ in range(_DEFAULT_PROMPT_ASK_ATTEMPTS):
     # Configure the Cuda SDK version to use.

From 8fcf64732bb43d6df5df99171346e9de6c15e7ed Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 12:10:36 -0700
Subject: [PATCH 0512/1691] Better wrapping of stream executor's cuDNN API
 calls. Replacing mutex locking and setting the cuDNN stream followed by
 calling wrap::cudnn... with an RAII CudnnHandle object that handles the
 former two operations.

Distinguish three different API types:

A) APIs that don't take a cudnnHandle_t: These are thread-safe APIs that don't enqueue any CUDA work on a stream. They can be called directly without any extra precautions.

B) APIs that take a cudnnHandle_t and perform CUDA work. The CUDA context needs to be acquired and the stream needs to be set beforehand, calls need to be serialized. A CudnnHandle instance guarantees that this work has been performed before calling cuDNN.

C) APIs that do take a cudnnHandle_t, but (presumably, the API makes no guarantees) still don't perform any CUDA work. This is limited to the API to setup RNN descriptors. Calls need to be serialized, but most likely we wouldn't need to acquire the CUDA context or set the stream. We still do though using the legacy default stream, because there are no guarantees.

PiperOrigin-RevId: 195856300
---
 tensorflow/core/platform/default/mutex.h      |    4 +-
 .../stream_executor/cuda/cuda_activation.cc   |    6 +
 .../stream_executor/cuda/cuda_activation.h    |    3 +-
 tensorflow/stream_executor/cuda/cuda_dnn.cc   | 1448 +++++++----------
 tensorflow/stream_executor/cuda/cuda_dnn.h    |   51 +-
 5 files changed, 596 insertions(+), 916 deletions(-)

diff --git a/tensorflow/core/platform/default/mutex.h b/tensorflow/core/platform/default/mutex.h
index a12d92795e1466..89e57d58a00546 100644
--- a/tensorflow/core/platform/default/mutex.h
+++ b/tensorflow/core/platform/default/mutex.h
@@ -77,9 +77,7 @@ class SCOPED_LOCKABLE mutex_lock {
 
   // Manually nulls out the source to prevent double-free.
   // (std::move does not null the source pointer by default.)
-  explicit mutex_lock(mutex_lock&& ml) noexcept : mu_(ml.mu_) {
-    ml.mu_ = nullptr;
-  }
+  mutex_lock(mutex_lock&& ml) noexcept : mu_(ml.mu_) { ml.mu_ = nullptr; }
   ~mutex_lock() UNLOCK_FUNCTION() {
     if (mu_ != nullptr) {
       mu_->unlock();
diff --git a/tensorflow/stream_executor/cuda/cuda_activation.cc b/tensorflow/stream_executor/cuda/cuda_activation.cc
index cf6b9e2c6e4b32..02371c3c3ab403 100644
--- a/tensorflow/stream_executor/cuda/cuda_activation.cc
+++ b/tensorflow/stream_executor/cuda/cuda_activation.cc
@@ -38,5 +38,11 @@ ScopedActivateExecutorContext::~ScopedActivateExecutorContext() {
   delete static_cast(driver_scoped_activate_context_);
 }
 
+ScopedActivateExecutorContext::ScopedActivateExecutorContext(
+    ScopedActivateExecutorContext &&other)
+    : driver_scoped_activate_context_(other.driver_scoped_activate_context_) {
+  other.driver_scoped_activate_context_ = nullptr;
+}
+
 }  // namespace cuda
 }  // namespace stream_executor
diff --git a/tensorflow/stream_executor/cuda/cuda_activation.h b/tensorflow/stream_executor/cuda/cuda_activation.h
index 04ffaef3646bb3..ef9807820fda49 100644
--- a/tensorflow/stream_executor/cuda/cuda_activation.h
+++ b/tensorflow/stream_executor/cuda/cuda_activation.h
@@ -44,10 +44,11 @@ class ScopedActivateExecutorContext {
   // fatal failure if it is not CUDA inside.
   explicit ScopedActivateExecutorContext(StreamExecutor* stream_exec);
 
+  ScopedActivateExecutorContext(ScopedActivateExecutorContext&& other);
+
   ~ScopedActivateExecutorContext();
 
  private:
-
   // The cuda.h-using datatype that we wrap.
   ScopedActivateContext* driver_scoped_activate_context_;
 
diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc
index 316f4c4f1e51e3..af78efe81db160 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.cc
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc
@@ -46,8 +46,20 @@ limitations under the License.
 #include "cuda/include/cudnn.h"
 // clang-format on
 
+namespace stream_executor {
+namespace cuda {
+
+PLUGIN_REGISTRY_DEFINE_PLUGIN_ID(kCuDnnPlugin);
+
 namespace {
 
+// TODO(csigg): remove dnn namespace qualifier from the RNN code below.
+using ::stream_executor::dnn::BatchDescriptor;
+using ::stream_executor::dnn::ConvolutionDescriptor;
+using ::stream_executor::dnn::FilterDescriptor;
+using ::stream_executor::dnn::NormalizeDescriptor;
+using ::stream_executor::dnn::PoolingDescriptor;
+
 // Converts (via narrowing) a type T value to a type U, and checks that the
 // value has no value change due to the conversion.
 template 
@@ -58,20 +70,6 @@ NarrowT CheckedNarrowing(const WideT& wide) {
   return narrow;
 }
 
-}  // namespace
-
-namespace stream_executor {
-
-using dnn::BatchDescriptor;
-using dnn::FilterDescriptor;
-using dnn::ConvolutionDescriptor;
-using dnn::PoolingDescriptor;
-using dnn::NormalizeDescriptor;
-
-namespace cuda {
-
-PLUGIN_REGISTRY_DEFINE_PLUGIN_ID(kCuDnnPlugin);
-
 string ToString(cudnnStatus_t status) {
   switch (status) {
     case CUDNN_STATUS_SUCCESS:
@@ -136,208 +134,82 @@ cudnnDataType_t GetCudnnDataType() {
   return CUDNN_DATA_HALF;
 }
 
-namespace wrap {
-
-#define STREAM_EXECUTOR_CUDNN_WRAP(__name)                         \
-  struct WrapperShim__##__name {                                   \
-    template                                     \
-    cudnnStatus_t operator()(CUDAExecutor* parent, Args... args) { \
-      cuda::ScopedActivateExecutorContext sac{parent};             \
-      cudnnStatus_t retval = ::__name(args...);                    \
-      return retval;                                               \
-    }                                                              \
-  } __name;
-
-#define STREAM_EXECUTOR_CUDNN_WRAP_WITH_CHECKED_STREAM(__name)           \
-  struct WrapperShim__##__name {                                         \
-    template                                           \
-    cudnnStatus_t operator()(CudnnSupport* dnn, Stream* s, Args... args) \
-        SHARED_LOCKS_REQUIRED(dnn->dnn_handle_mutex_) {                  \
-      CHECK_NOTNULL(s);                                                  \
-      CHECK_EQ(s, dnn->GetCurrentDnnStream())                            \
-          << "Stream is not set correctly!";                             \
-      cuda::ScopedActivateExecutorContext sac{dnn->GetParentExecutor()}; \
-      cudnnStatus_t retval = ::__name(args...);                          \
-      return retval;                                                     \
-    }                                                                    \
-  } __name;
-
-// Handles cudnnSetStream differently in order to add debug information.
-// It stores a reference to 'stream' in 'dnn', and checks that all calls from
-// that dnn instance use the same stream (see
-// STREAM_EXECUTOR_CUDNN_WRAP_WITH_CHECKED_STREAM macro).
-struct WrapperShim__cudnnSetStream {
-  cudnnStatus_t operator()(CudnnSupport* dnn, Stream* stream,
-                           cudnnHandle_t handle)
-      EXCLUSIVE_LOCKS_REQUIRED(dnn->dnn_handle_mutex_) {
-    dnn->SetCurrentDnnStream(stream);
-    cuda::ScopedActivateExecutorContext sac{dnn->GetParentExecutor()};
-    cudnnStatus_t retval = ::cudnnSetStream(handle, AsCUDAStreamValue(stream));
-    return retval;
-  }
-} cudnnSetStream;
-
-// clang-format off
-#define CUDNN_DNN_ROUTINE_EACH(__macro)                   \
-  __macro(cudnnGetConvolutionNdForwardOutputDim)          \
-  __macro(cudnnGetConvolutionForwardAlgorithm)            \
-  __macro(cudnnCreateTensorDescriptor)                    \
-  __macro(cudnnDestroyTensorDescriptor)                   \
-  __macro(cudnnCreateFilterDescriptor)                    \
-  __macro(cudnnSetPoolingNdDescriptor)                    \
-  __macro(cudnnSetLRNDescriptor)                          \
-  __macro(cudnnDestroyFilterDescriptor)                   \
-  __macro(cudnnCreateConvolutionDescriptor)               \
-  __macro(cudnnCreatePoolingDescriptor)                   \
-  __macro(cudnnDestroyPoolingDescriptor)                  \
-  __macro(cudnnCreateLRNDescriptor)                       \
-  __macro(cudnnDestroyLRNDescriptor)                      \
-  __macro(cudnnDestroyConvolutionDescriptor)              \
-  __macro(cudnnCreate)                                    \
-  __macro(cudnnDestroy)                                   \
-  __macro(cudnnGetConvolutionForwardWorkspaceSize)        \
-  __macro(cudnnSetConvolutionNdDescriptor)                \
-  __macro(cudnnSetTensor4dDescriptor)                     \
-  __macro(cudnnSetTensorNdDescriptor)                     \
-  __macro(cudnnSetFilterNdDescriptor)
-
-// clang-format on
-CUDNN_DNN_ROUTINE_EACH(STREAM_EXECUTOR_CUDNN_WRAP)
-#undef CUDNN_DNN_ROUTINE_EACH
-
-// clang-format off
-#define CUDNN_DNN_ROUTINE_EACH_WITH_STREAM(__macro)       \
-  __macro(cudnnBatchNormalizationBackward)                \
-  __macro(cudnnBatchNormalizationForwardInference)        \
-  __macro(cudnnBatchNormalizationForwardTraining)         \
-  __macro(cudnnActivationForward)                         \
-  __macro(cudnnConvolutionForward)                        \
-  __macro(cudnnConvolutionBackwardBias)                   \
-  __macro(cudnnTransformTensor)                           \
-  __macro(cudnnPoolingForward)                            \
-  __macro(cudnnPoolingBackward)                           \
-  __macro(cudnnLRNCrossChannelForward)                    \
-  __macro(cudnnLRNCrossChannelBackward)                   \
-  __macro(cudnnAddTensor)                                 \
-  __macro(cudnnConvolutionBackwardData)                   \
-  __macro(cudnnConvolutionBackwardFilter)
-
-// clang-format on
-CUDNN_DNN_ROUTINE_EACH_WITH_STREAM(
-    STREAM_EXECUTOR_CUDNN_WRAP_WITH_CHECKED_STREAM)
-#undef CUDNN_DNN_ROUTINE_EACH_WITH_STREAM
-
-// APIs available after R3:
-#if CUDNN_VERSION >= 3000
-#define CUDNN_DNN_ROUTINE_EACH_AFTER_R3(__macro)              \
-  __macro(cudnnGetConvolutionBackwardFilterWorkspaceSize)     \
-  __macro(cudnnGetConvolutionBackwardDataAlgorithm)           \
-  __macro(cudnnGetConvolutionBackwardFilterAlgorithm)         \
-  __macro(cudnnGetConvolutionBackwardDataWorkspaceSize)
-CUDNN_DNN_ROUTINE_EACH_AFTER_R3(STREAM_EXECUTOR_CUDNN_WRAP)
-#undef CUDNN_DNN_ROUTINE_EACH_AFTER_R3
-#endif
-
-// APIs in R3 but not in R5
-// clang-format off
-#if CUDNN_VERSION >= 3000 && CUDNN_VERSION < 5000
-#define CUDNN_DNN_ROUTINE_EACH_R3_WITH_STREAM(__macro)        \
-  __macro(cudnnAddTensor_v3)                                  \
-  __macro(cudnnConvolutionBackwardData_v3)                    \
-  __macro(cudnnConvolutionBackwardFilter_v3)
-// clang-format on
-
-CUDNN_DNN_ROUTINE_EACH_R3_WITH_STREAM(
-    STREAM_EXECUTOR_CUDNN_WRAP_WITH_CHECKED_STREAM)
-#undef CUDNN_DNN_ROUTINE_EACH_R3_WITH_STREAM
-#endif
-
-// APIs in R5
-// clang-format off
-#if CUDNN_VERSION >= 5000
-#define CUDNN_DNN_ROUTINE_EACH_R5(__macro)                    \
-  __macro(cudnnCreateActivationDescriptor)                    \
-  __macro(cudnnSetActivationDescriptor)                       \
-  __macro(cudnnGetActivationDescriptor)                       \
-  __macro(cudnnDestroyActivationDescriptor)                   \
-  __macro(cudnnCreateDropoutDescriptor)                       \
-  __macro(cudnnDestroyDropoutDescriptor)                      \
-  __macro(cudnnSetDropoutDescriptor)                          \
-  __macro(cudnnDropoutGetStatesSize)                          \
-  __macro(cudnnCreateRNNDescriptor)                           \
-  __macro(cudnnDestroyRNNDescriptor)                          \
-  __macro(cudnnGetRNNParamsSize)                              \
-  __macro(cudnnGetRNNWorkspaceSize)                           \
-  __macro(cudnnGetRNNTrainingReserveSize)                     \
-  __macro(cudnnGetRNNLinLayerMatrixParams)                    \
-  __macro(cudnnGetRNNLinLayerBiasParams)                      \
-  __macro(cudnnSetRNNDescriptor)                              \
-  __macro(cudnnGetFilterNdDescriptor)
-
-// clang-format on
-CUDNN_DNN_ROUTINE_EACH_R5(STREAM_EXECUTOR_CUDNN_WRAP)
-#undef CUDNN_DNN_ROUTINE_EACH_R5
-
-// clang-format off
-#define CUDNN_DNN_ROUTINE_EACH_R5_WITH_STREAM(__macro)        \
-  __macro(cudnnRNNForwardInference)                           \
-  __macro(cudnnRNNForwardTraining)                            \
-  __macro(cudnnRNNBackwardData)                               \
-  __macro(cudnnRNNBackwardWeights)
+// RAII wrapper for all calls to cuDNN with a cuDNN handle argument.
+//
+// See CudnnAccess::GetHandle() for details.
+class CudnnHandle {
+ public:
+  // Takes ownership of the executor context and the lock to access cuDNN
+  // using handle.
+  CudnnHandle(cuda::ScopedActivateExecutorContext context, mutex_lock lock,
+              cudnnHandle_t handle)
+      : context_(std::move(context)), lock_(std::move(lock)), handle_(handle) {}
 
-// clang-format on
-CUDNN_DNN_ROUTINE_EACH_R5_WITH_STREAM(
-    STREAM_EXECUTOR_CUDNN_WRAP_WITH_CHECKED_STREAM)
-#undef CUDNN_DNN_ROUTINE_EACH_R5_WITH_STREAM
-#endif
+  // Returns cuDNN handle. To be passed directly to cuDNN APIs, don't keep
+  // a copy.
+  cudnnHandle_t handle() const { return handle_; }
 
-// APIs in R6
-// clang-format off
-#if CUDNN_VERSION >= 6000
-#define CUDNN_DNN_ROUTINE_EACH_R6(__macro)                    \
-  __macro(cudnnSetRNNDescriptor_v6)                           \
-  __macro(cudnnCreatePersistentRNNPlan)                       \
-  __macro(cudnnDestroyPersistentRNNPlan)                      \
-  __macro(cudnnSetPersistentRNNPlan)
+ private:
+  cuda::ScopedActivateExecutorContext context_;
+  mutex_lock lock_;
+  cudnnHandle_t handle_;  // Not owned.
+};
 
-// clang-format on
-CUDNN_DNN_ROUTINE_EACH_R6(STREAM_EXECUTOR_CUDNN_WRAP)
-#undef CUDNN_DNN_ROUTINE_EACH_R6
+}  // namespace
 
-// clang-format off
-#define CUDNN_DNN_ROUTINE_EACH_R6_WITH_STREAM(__macro)        \
-  __macro(cudnnConvolutionBiasActivationForward)
+// Wraps a cuDNN handle and provides access to it through CudnnHandle instances,
+// which also locks a mutex, acquires the CUDA context, and sets the stream
+// that cuDNN should use to enqueue any work.
+//
+// Note: CudnnSupport::cudnn_ should be the only instantiation of this class.
+class CudnnAccess {
+ public:
+  // Takes ownership of the handle.
+  explicit CudnnAccess(cudnnHandle_t handle) : handle_(handle) {}
 
-// clang-format on
-CUDNN_DNN_ROUTINE_EACH_R6_WITH_STREAM(
-    STREAM_EXECUTOR_CUDNN_WRAP_WITH_CHECKED_STREAM)
-#undef CUDNN_DNN_ROUTINE_EACH_R6_WITH_STREAM
-#endif
+  ~CudnnAccess() {
+    mutex_lock lock(mutex_);
+    cudnnDestroy(handle_);
+  }
 
-// APIs in R7
-// clang-format off
-#if CUDNN_VERSION >= 7000
-#define CUDNN_DNN_ROUTINE_EACH_R7(__macro)                    \
-  __macro(cudnnSetConvolutionMathType)                        \
-  __macro(cudnnSetRNNMatrixMathType)                          \
-  __macro(cudnnSetConvolutionGroupCount)                      \
-  __macro(cudnnGetConvolutionGroupCount)
+  // Creates a CudnnHandle instance for stream.
+  //
+  // cuDNN API calls using the same handle instance need to be serialized across
+  // threads. This is guaranteed by CudnnHandle instances locking the mutex
+  // owned by this class.
+  //
+  // Most cuDNN APIs taking a handle perform work on a CUDA stream. The
+  // CudnnHandle instance acquires the executor's CUDA context and sets cuDNN to
+  // use the provided stream.
+  //
+  // The stream argument may be null, which translates to the legacy default
+  // stream. See
+  // https://docs.nvidia.com/cuda/cuda-driver-api/stream-sync-behavior.html.
+  // The legacy default stream synchronizes with all other streams and it is
+  // therefore a bad idea (performance wise) to call any cuDNN APIs that
+  // enqueue work in the stream.
+  CudnnHandle GetHandle(CUDAExecutor* executor, Stream* stream) {
+    mutex_lock lock(mutex_);
+    cuda::ScopedActivateExecutorContext context(executor);
+    CUstream cu_stream = stream ? AsCUDAStreamValue(stream) : cudaStreamLegacy;
+    auto status = cudnnSetStream(handle_, cu_stream);
+    CHECK_EQ(status, CUDNN_STATUS_SUCCESS) << "Failed to set cuDNN stream.";
+    using my_mutex_lock = mutex_lock;
+    return CudnnHandle(std::move(context), std::move(lock), handle_);
+  }
 
-// clang-format on
-CUDNN_DNN_ROUTINE_EACH_R7(STREAM_EXECUTOR_CUDNN_WRAP)
-#undef CUDNN_DNN_ROUTINE_EACH_R7
-#endif
+ private:
+  // Guards the enqueueing of cuDNN operations via the handle_ below.
+  mutex mutex_;
 
-}  // namespace wrap
+  // cuDNN library handle.
+  cudnnHandle_t handle_ GUARDED_BY(mutex_);  // Owned.
+};
 
 namespace {
 
 cudnnDataType_t GetRnnComputeType(dnn::DataType data_type);
 
-cudnnHandle_t ToHandle(void* opaque_handle) {
-  return static_cast(opaque_handle);
-}
-
 cudnnConvolutionFwdAlgo_t ToConvForwardAlgo(dnn::AlgorithmDesc algorithm) {
   cudnnConvolutionFwdAlgo_t algo =
       cudnnConvolutionFwdAlgo_t(algorithm.algo_id());
@@ -414,7 +286,7 @@ port::Status GetCudnnProperty(libraryPropertyType type, int* value) {
         port::StrCat("cudnnGetProperty failed for type: ", ToString(type),
                      " with status: ", ToString(status));
     LOG(ERROR) << error;
-    return port::Status{port::error::INTERNAL, error};
+    return port::Status(port::error::INTERNAL, error);
   }
   return port::Status::OK();
 }
@@ -453,19 +325,11 @@ port::Status GetLoadedCudnnVersion(CudnnVersion* version) {
 
 }  // namespace
 
-CudnnSupport::CudnnSupport(CUDAExecutor* parent)
-    : parent_(parent), dnn_handle_(nullptr), current_dnn_stream_(nullptr) {}
-
-CudnnSupport::~CudnnSupport() {
-  auto status = wrap::cudnnDestroy(parent_, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "could not destroy cudnn handle: " << ToString(status);
-  }
-}
+CudnnSupport::CudnnSupport(CUDAExecutor* parent) : parent_(parent) {}
 
 port::Status CudnnSupport::Init() {
-  auto status = wrap::cudnnCreate(
-      parent_, reinterpret_cast(&dnn_handle_));
+  cudnnHandle_t cudnn_handle = nullptr;
+  auto status = cudnnCreate(&cudnn_handle);
   if (status == CUDNN_STATUS_SUCCESS) {
     CudnnVersion source_version(CUDNN_MAJOR, CUDNN_MINOR, CUDNN_PATCHLEVEL);
 
@@ -481,9 +345,10 @@ port::Status CudnnSupport::Init() {
           "from sources, make sure the library loaded at runtime is compatible "
           "with the version specified during compile configuration.");
       LOG(ERROR) << error;
-      return port::Status{port::error::INTERNAL, error};
+      return port::Status(port::error::INTERNAL, error);
     }
 
+    cudnn_.reset(new CudnnAccess(cudnn_handle));
     return port::Status::OK();
   }
 
@@ -507,9 +372,9 @@ port::Status CudnnSupport::Init() {
     }
   }
 
-  return port::Status{port::error::INTERNAL,
+  return port::Status(port::error::INTERNAL,
                       port::StrCat("cudnn library could not create a handle: ",
-                                   ToString(status))};
+                                   ToString(status)));
 }
 
 port::StatusOr
@@ -520,14 +385,15 @@ CudnnSupport::GetVersion() {
       version.major_version, version.minor_version, version.patch_level);
 }
 
+namespace {
+
 // Turns a BatchDescriptor structure into a cudnn tensor handle within a scope.
 class ScopedTensorDescriptor {
  public:
-  ScopedTensorDescriptor(CUDAExecutor* parent,
-                         const BatchDescriptor& batch_descriptor,
+  ScopedTensorDescriptor(const BatchDescriptor& batch_descriptor,
                          cudnnDataType_t elem_type)
-      : parent_(parent), handle_(nullptr) {
-    cudnnStatus_t status = wrap::cudnnCreateTensorDescriptor(parent_, &handle_);
+      : handle_(nullptr) {
+    cudnnStatus_t status = cudnnCreateTensorDescriptor(&handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(FATAL) << "could not create cudnn tensor descriptor: "
                  << ToString(status);
@@ -550,8 +416,8 @@ class ScopedTensorDescriptor {
                        &CheckedNarrowing);
         std::transform(dims64.cbegin(), dims64.cend(), dims.begin(),
                        &CheckedNarrowing);
-        status = wrap::cudnnSetTensorNdDescriptor(
-            parent_, handle_, elem_type, nd, dims.data(), strides.data());
+        status = cudnnSetTensorNdDescriptor(handle_, elem_type, nd, dims.data(),
+                                            strides.data());
 
         if (status != CUDNN_STATUS_SUCCESS) {
           LOG(FATAL) << "could not convert BatchDescriptor "
@@ -561,8 +427,8 @@ class ScopedTensorDescriptor {
       } break;
 #if CUDNN_VERSION >= 6000
       case dnn::DataLayout::kBatchDepthYX4: {
-        status = wrap::cudnnSetTensor4dDescriptor(
-            parent_, handle_, CUDNN_TENSOR_NCHW_VECT_C, elem_type,
+        status = cudnnSetTensor4dDescriptor(
+            handle_, CUDNN_TENSOR_NCHW_VECT_C, elem_type,
             batch_descriptor.count(), batch_descriptor.feature_map_count(),
             batch_descriptor.height(), batch_descriptor.width());
         if (status != CUDNN_STATUS_SUCCESS) {
@@ -580,7 +446,7 @@ class ScopedTensorDescriptor {
   }
 
   ~ScopedTensorDescriptor() {
-    cudnnStatus_t status = wrap::cudnnDestroyTensorDescriptor(parent_, handle_);
+    cudnnStatus_t status = cudnnDestroyTensorDescriptor(handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(ERROR) << "could not destroy cudnn tensor descriptor: "
                  << ToString(status);
@@ -590,7 +456,6 @@ class ScopedTensorDescriptor {
   cudnnTensorDescriptor_t handle() const { return handle_; }
 
  private:
-  CUDAExecutor* parent_;            // Parent executor. Not owned.
   cudnnTensorDescriptor_t handle_;  // Owned.
 
   SE_DISALLOW_COPY_AND_ASSIGN(ScopedTensorDescriptor);
@@ -599,12 +464,10 @@ class ScopedTensorDescriptor {
 // Turns a FilterDescriptor structure into a cudnn filter handle within a scope.
 class ScopedFilterDescriptor {
  public:
-  ScopedFilterDescriptor(CUDAExecutor* parent,
-                         const FilterDescriptor& filter_descriptor,
-                         const BatchDescriptor& batch_descriptor,
+  ScopedFilterDescriptor(const FilterDescriptor& filter_descriptor,
                          cudnnDataType_t elem_type)
-      : parent_(parent), handle_(nullptr) {
-    cudnnStatus_t status = wrap::cudnnCreateFilterDescriptor(parent_, &handle_);
+      : handle_(nullptr) {
+    cudnnStatus_t status = cudnnCreateFilterDescriptor(&handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(FATAL) << "could not create cudnn filter descriptor: "
                  << ToString(status);
@@ -638,11 +501,11 @@ class ScopedFilterDescriptor {
     const auto& spatial_dims = filter_descriptor.input_filter_dims();
     std::copy(spatial_dims.begin(), spatial_dims.end(), dims.begin() + 2);
 
-    status = wrap::cudnnSetFilterNdDescriptor(parent_, handle_, elem_type,
+    status = cudnnSetFilterNdDescriptor(handle_, elem_type,
 #if CUDNN_VERSION >= 5000
-                                              format,
+                                        format,
 #endif
-                                              dims.size(), dims.data());
+                                        dims.size(), dims.data());
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(FATAL) << "could not set cudnn filter descriptor: "
                  << ToString(status);
@@ -650,7 +513,7 @@ class ScopedFilterDescriptor {
   }
 
   ~ScopedFilterDescriptor() {
-    cudnnStatus_t status = wrap::cudnnDestroyFilterDescriptor(parent_, handle_);
+    cudnnStatus_t status = cudnnDestroyFilterDescriptor(handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(ERROR) << "could not destroy cudnn filter descriptor: "
                  << ToString(status);
@@ -660,11 +523,7 @@ class ScopedFilterDescriptor {
   cudnnFilterDescriptor_t handle() const { return handle_; }
 
  private:
-  // Parent executor object. Not owned.
-  CUDAExecutor* parent_;
-
-  // cudnn filter descriptor this object creates. Owned.
-  cudnnFilterDescriptor_t handle_;
+  cudnnFilterDescriptor_t handle_;  // Owned.
 
   SE_DISALLOW_COPY_AND_ASSIGN(ScopedFilterDescriptor);
 };
@@ -718,11 +577,10 @@ static bool BatchnormSpatialPersistentEnabled() {
 class ScopedConvolutionDescriptor {
  public:
   ScopedConvolutionDescriptor(
-      CUDAExecutor* parent, const ConvolutionDescriptor& convolution_descriptor,
+      const ConvolutionDescriptor& convolution_descriptor,
       cudnnDataType_t data_type)
-      : parent_(parent), handle_(nullptr) {
-    cudnnStatus_t status =
-        wrap::cudnnCreateConvolutionDescriptor(parent_, &handle_);
+      : handle_(nullptr) {
+    cudnnStatus_t status = cudnnCreateConvolutionDescriptor(&handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(FATAL) << "could not create cudnn convolution descriptor: "
                  << ToString(status);
@@ -748,9 +606,9 @@ class ScopedConvolutionDescriptor {
     std::transform(dilations64.cbegin(), dilations64.cend(), dilations.begin(),
                    &CheckedNarrowing);
 
-    status = wrap::cudnnSetConvolutionNdDescriptor(
-        parent_, handle_, convolution_descriptor.ndims(), padding.data(),
-        strides.data(), dilations.data(),
+    status = cudnnSetConvolutionNdDescriptor(
+        handle_, convolution_descriptor.ndims(), padding.data(), strides.data(),
+        dilations.data(),
         // NOTE(keveman): cuDNN supports convolution and cross correlation.
         // However, almost all the use cases do cross correlation, so just
         // hard coding it here.
@@ -767,8 +625,8 @@ class ScopedConvolutionDescriptor {
 #if CUDNN_MAJOR >= 7
     VLOG(2) << "Requesting grouped convolution: "
             << convolution_descriptor.group_count();
-    status = wrap::cudnnSetConvolutionGroupCount(
-        parent_, handle_, convolution_descriptor.group_count());
+    status = cudnnSetConvolutionGroupCount(
+        handle_, convolution_descriptor.group_count());
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(FATAL) << "could not set cudnn convolution group count: "
                  << ToString(status);
@@ -784,8 +642,7 @@ class ScopedConvolutionDescriptor {
     cudnnMathType_t math_type =
         (use_tensor_op_math ? CUDNN_TENSOR_OP_MATH : CUDNN_DEFAULT_MATH);
     if (TensorOpMathEnabled()) {
-      cudnnStatus_t status =
-          wrap::cudnnSetConvolutionMathType(parent_, handle_, math_type);
+      cudnnStatus_t status = cudnnSetConvolutionMathType(handle_, math_type);
       if (status != CUDNN_STATUS_SUCCESS) {
         LOG(FATAL) << "could not set cudnn convolution math type: "
                    << ToString(status);
@@ -795,8 +652,7 @@ class ScopedConvolutionDescriptor {
   }
 
   ~ScopedConvolutionDescriptor() {
-    cudnnStatus_t status =
-        wrap::cudnnDestroyConvolutionDescriptor(parent_, handle_);
+    cudnnStatus_t status = cudnnDestroyConvolutionDescriptor(handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(ERROR) << "could not destroy cudnn convolution descriptor: "
                  << ToString(status);
@@ -806,7 +662,6 @@ class ScopedConvolutionDescriptor {
   cudnnConvolutionDescriptor_t handle() const { return handle_; }
 
  private:
-  CUDAExecutor* parent_;                 // Parent executor. Not owned.
   cudnnConvolutionDescriptor_t handle_;  // Owned.
 
   SE_DISALLOW_COPY_AND_ASSIGN(ScopedConvolutionDescriptor);
@@ -816,11 +671,9 @@ class ScopedConvolutionDescriptor {
 // within a scope.
 class ScopedPoolingDescriptor {
  public:
-  ScopedPoolingDescriptor(CUDAExecutor* parent,
-                          const PoolingDescriptor& pooling_descriptor)
-      : parent_(parent), handle_(nullptr) {
-    cudnnStatus_t status =
-        wrap::cudnnCreatePoolingDescriptor(parent_, &handle_);
+  explicit ScopedPoolingDescriptor(const PoolingDescriptor& pooling_descriptor)
+      : handle_(nullptr) {
+    cudnnStatus_t status = cudnnCreatePoolingDescriptor(&handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(FATAL) << "could not create cudnn pooling descriptor: "
                  << ToString(status);
@@ -840,8 +693,8 @@ class ScopedPoolingDescriptor {
     std::transform(shape64.cbegin(), shape64.cend(), shape.begin(),
                    &CheckedNarrowing);
     bool propagate_nans = pooling_descriptor.propagate_nans();
-    status = wrap::cudnnSetPoolingNdDescriptor(
-        parent_, handle_,
+    status = cudnnSetPoolingNdDescriptor(
+        handle_,
         (pooling_descriptor.mode() == dnn::PoolingMode::kMaximum
              ? CUDNN_POOLING_MAX
              : CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING),
@@ -855,8 +708,7 @@ class ScopedPoolingDescriptor {
     }
   }
   ~ScopedPoolingDescriptor() {
-    cudnnStatus_t status =
-        wrap::cudnnDestroyPoolingDescriptor(parent_, handle_);
+    cudnnStatus_t status = cudnnDestroyPoolingDescriptor(handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(ERROR) << "could not destroy cudnn pooling descriptor: "
                  << ToString(status);
@@ -866,7 +718,6 @@ class ScopedPoolingDescriptor {
   cudnnPoolingDescriptor_t handle() const { return handle_; }
 
  private:
-  CUDAExecutor* parent_;             // Parent executor. Not owned.
   cudnnPoolingDescriptor_t handle_;  // Owned.
 
   SE_DISALLOW_COPY_AND_ASSIGN(ScopedPoolingDescriptor);
@@ -875,10 +726,10 @@ class ScopedPoolingDescriptor {
 // Turns a NormalizeDescriptor structure into a cudnn LRN descriptor handle.
 class ScopedNormalizeDescriptor {
  public:
-  ScopedNormalizeDescriptor(CUDAExecutor* parent,
-                            const NormalizeDescriptor& normalize_descriptor)
-      : parent_(parent), handle_(nullptr) {
-    cudnnStatus_t status = wrap::cudnnCreateLRNDescriptor(parent_, &handle_);
+  explicit ScopedNormalizeDescriptor(
+      const NormalizeDescriptor& normalize_descriptor)
+      : handle_(nullptr) {
+    cudnnStatus_t status = cudnnCreateLRNDescriptor(&handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(FATAL) << "could not create cudnn LRN descriptor: "
                  << ToString(status);
@@ -904,15 +755,14 @@ class ScopedNormalizeDescriptor {
 
     double lrnBeta = normalize_descriptor.beta();
     double lrnK = normalize_descriptor.bias();
-    status = wrap::cudnnSetLRNDescriptor(parent_, handle_, lrnN, lrnAlpha,
-                                         lrnBeta, lrnK);
+    status = cudnnSetLRNDescriptor(handle_, lrnN, lrnAlpha, lrnBeta, lrnK);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(FATAL) << "could not set cudnn LRN descriptor: " << ToString(status);
     }
   }
 
   ~ScopedNormalizeDescriptor() {
-    cudnnStatus_t status = wrap::cudnnDestroyLRNDescriptor(parent_, handle_);
+    cudnnStatus_t status = cudnnDestroyLRNDescriptor(handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(ERROR) << "could not destroy cudnn LRN descriptor: "
                  << ToString(status);
@@ -922,7 +772,6 @@ class ScopedNormalizeDescriptor {
   cudnnLRNDescriptor_t handle() const { return handle_; }
 
  private:
-  CUDAExecutor* parent_;         // Parent executor. Not owned.
   cudnnLRNDescriptor_t handle_;  // Owned.
 
   SE_DISALLOW_COPY_AND_ASSIGN(ScopedNormalizeDescriptor);
@@ -933,13 +782,11 @@ class ScopedNormalizeDescriptor {
 // descriptor handle within a scope.
 class ScopedActivationDescriptor {
  public:
-  ScopedActivationDescriptor(CUDAExecutor* parent,
-                             dnn::ActivationMode activation_mode,
+  ScopedActivationDescriptor(dnn::ActivationMode activation_mode,
                              cudnnNanPropagation_t nan_propagation,
                              double value_max)
-      : parent_(parent), handle_(nullptr) {
-    cudnnStatus_t status =
-        wrap::cudnnCreateActivationDescriptor(parent_, &handle_);
+      : handle_(nullptr) {
+    cudnnStatus_t status = cudnnCreateActivationDescriptor(&handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(FATAL) << "could not create cudnn activation descriptor: "
                  << ToString(status);
@@ -970,8 +817,8 @@ class ScopedActivationDescriptor {
                    << static_cast(activation_mode);
     }
 
-    status = wrap::cudnnSetActivationDescriptor(parent_, handle_, mode,
-                                                nan_propagation, relu_ceiling);
+    status = cudnnSetActivationDescriptor(handle_, mode, nan_propagation,
+                                          relu_ceiling);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(FATAL) << "could not set cudnn activation descriptor: "
                  << ToString(status);
@@ -979,8 +826,7 @@ class ScopedActivationDescriptor {
   }
 
   ~ScopedActivationDescriptor() {
-    cudnnStatus_t status =
-        wrap::cudnnDestroyActivationDescriptor(parent_, handle_);
+    cudnnStatus_t status = cudnnDestroyActivationDescriptor(handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(ERROR) << "could not destroy cudnn activation descriptor: "
                  << ToString(status);
@@ -990,14 +836,12 @@ class ScopedActivationDescriptor {
   cudnnActivationDescriptor_t handle() const { return handle_; }
 
  private:
-  CUDAExecutor* parent_;                // Parent executor. Not owned.
   cudnnActivationDescriptor_t handle_;  // Owned.
 
   SE_DISALLOW_COPY_AND_ASSIGN(ScopedActivationDescriptor);
 };
 #endif
 
-namespace {
 cudnnDataType_t ToCudnnDataType(
     dnn::DataType data_type,
     dnn::DataLayout data_layout = dnn::DataLayout::kBatchDepthYX) {
@@ -1072,8 +916,6 @@ class MixinBase : public Base {};
 template <>
 class MixinBase {};
 
-}  // namespace
-
 #if CUDNN_VERSION >= 5000
 
 #define CUDNN_RETURN_IF_FAIL(STATUS, ...)                                \
@@ -1084,6 +926,7 @@ class MixinBase {};
     return;                                                              \
   }
 
+// TODO(csigg): Remove inheritance for code reuse.
 template 
 class CudnnDescriptorCommon : public MixinBase {
  public:
@@ -1097,12 +940,11 @@ class CudnnDescriptorCommon : public MixinBase {
 
 class CudnnDropoutDescriptor : public CudnnDescriptorCommon {
  public:
-  CudnnDropoutDescriptor(CUDAExecutor* parent, cudnnHandle_t cudnn_handle,
-                         float dropout, uint64 seed,
+  CudnnDropoutDescriptor(const CudnnHandle& cudnn, float dropout, uint64 seed,
                          ScratchAllocator* state_allocator)
-      : parent_(parent), handle_(nullptr) {
+      : handle_(nullptr) {
     cudnnStatus_t status;
-    status = wrap::cudnnCreateDropoutDescriptor(parent_, &handle_);
+    status = cudnnCreateDropoutDescriptor(&handle_);
     CUDNN_RETURN_IF_FAIL(status, "Failed to create dropout descriptor");
 
     if (dropout == 0.f) {
@@ -1112,8 +954,7 @@ class CudnnDropoutDescriptor : public CudnnDescriptorCommon {
     DeviceMemory state_memory;
     if (state_allocator) {
       size_t state_sizes_in_bytes = 0;
-      status = wrap::cudnnDropoutGetStatesSize(parent_, cudnn_handle,
-                                               &state_sizes_in_bytes);
+      status = cudnnDropoutGetStatesSize(cudnn.handle(), &state_sizes_in_bytes);
       CUDNN_RETURN_IF_FAIL(status, "Failed to query dropout state sizes");
 
       auto allocated =
@@ -1128,9 +969,9 @@ class CudnnDropoutDescriptor : public CudnnDescriptorCommon {
         return;
       }
     }
-    status = wrap::cudnnSetDropoutDescriptor(parent_, handle_, cudnn_handle,
-                                             dropout, state_memory.opaque(),
-                                             state_memory.size(), seed);
+    status = cudnnSetDropoutDescriptor(handle_, cudnn.handle(), dropout,
+                                       state_memory.opaque(),
+                                       state_memory.size(), seed);
     CUDNN_RETURN_IF_FAIL(
         status, port::StrCat(
                     "Failed to set dropout descriptor with state memory size: ",
@@ -1138,11 +979,9 @@ class CudnnDropoutDescriptor : public CudnnDescriptorCommon {
   }
 
   ~CudnnDropoutDescriptor() {
-    if (handle_) {
-      cudnnStatus_t status =
-          wrap::cudnnDestroyDropoutDescriptor(parent_, handle_);
-      CUDNN_RETURN_IF_FAIL(status, "Failed to destroy Cudnn dropout handle: ");
-    }
+    cudnnStatus_t status = cudnnDestroyDropoutDescriptor(handle_);
+    // TODO(csigg): This is a no-op (error is not reported). Same below.
+    CUDNN_RETURN_IF_FAIL(status, "Failed to destroy Cudnn dropout handle: ");
   }
 
   cudnnDropoutDescriptor_t handle() const {
@@ -1151,8 +990,7 @@ class CudnnDropoutDescriptor : public CudnnDescriptorCommon {
   }
 
  private:
-  CUDAExecutor* parent_;
-  cudnnDropoutDescriptor_t handle_;
+  cudnnDropoutDescriptor_t handle_;  // Owned.
   float dropout_;
   uint64 seed_;
   SE_DISALLOW_COPY_AND_ASSIGN(CudnnDropoutDescriptor);
@@ -1162,10 +1000,10 @@ class CudnnRnnParamsDescriptor : public CudnnDescriptorCommon {
  public:
   typedef dnn::RnnDescriptor::ParamsRegion ParamsRegion;
   typedef dnn::RnnDescriptor::ParamsRegions ParamsRegions;
-  CudnnRnnParamsDescriptor(CUDAExecutor* parent, cudnnHandle_t cudnn_handle,
+  CudnnRnnParamsDescriptor(const CudnnHandle& cudnn,
                            const CudnnRnnDescriptor& rnn_desc);
   ~CudnnRnnParamsDescriptor() {
-    cudnnStatus_t status = wrap::cudnnDestroyFilterDescriptor(parent_, handle_);
+    cudnnStatus_t status = cudnnDestroyFilterDescriptor(handle_);
     CUDNN_RETURN_IF_FAIL(status, "Failed to destroy RNN filter descriptor");
   }
   cudnnFilterDescriptor_t handle() const {
@@ -1184,7 +1022,6 @@ class CudnnRnnParamsDescriptor : public CudnnDescriptorCommon {
 
  private:
   int GetRegionCountPerLayer() const;
-  CUDAExecutor* parent_;
   cudnnFilterDescriptor_t handle_;
   const CudnnRnnDescriptor* rnn_desc_;
   int64 params_size_in_bytes_;
@@ -1193,19 +1030,20 @@ class CudnnRnnParamsDescriptor : public CudnnDescriptorCommon {
   SE_DISALLOW_COPY_AND_ASSIGN(CudnnRnnParamsDescriptor);
 };
 
+}  // namespace
+
 class CudnnRnnDescriptor : public CudnnDescriptorCommon {
  public:
-  CudnnRnnDescriptor(CUDAExecutor* parent, cudnnHandle_t cudnn_handle,
-                     int num_layers, int hidden_size, int input_size,
-                     int batch_size, cudnnRNNInputMode_t input_mode,
+  CudnnRnnDescriptor(const CudnnHandle& cudnn, int num_layers, int hidden_size,
+                     int input_size, int batch_size,
+                     cudnnRNNInputMode_t input_mode,
                      cudnnDirectionMode_t direction_mode,
                      cudnnRNNMode_t rnn_mode, cudnnDataType_t data_type,
                      cudnnDataType_t compute_type,
                      const dnn::AlgorithmConfig& algorithm_config,
                      float dropout, uint64 seed,
                      ScratchAllocator* state_allocator)
-      : parent_(parent),
-        rnn_desc_(nullptr),
+      : rnn_desc_(nullptr),
         num_layers_(num_layers),
         hidden_size_(hidden_size),
         input_size_(input_size),
@@ -1220,21 +1058,21 @@ class CudnnRnnDescriptor : public CudnnDescriptorCommon {
         compute_type_(compute_type),
         algorithm_config_(algorithm_config) {
     // Create the dropout handle.
-    cudnn_dropout_desc_.reset(new CudnnDropoutDescriptor(
-        parent, cudnn_handle, dropout, seed, state_allocator));
+    cudnn_dropout_desc_.reset(
+        new CudnnDropoutDescriptor(cudnn, dropout, seed, state_allocator));
     if (!cudnn_dropout_desc_->ok()) {
       SetFailure(cudnn_dropout_desc_->Status());
       return;
     }
 
     // Create the RNN handle
-    cudnnStatus_t status = wrap::cudnnCreateRNNDescriptor(parent_, &rnn_desc_);
+    cudnnStatus_t status = cudnnCreateRNNDescriptor(&rnn_desc_);
     CUDNN_RETURN_IF_FAIL(status, "Unable to create RNN descriptor");
 #if CUDNN_VERSION >= 6000
     // TODO: allow the user to choose an algorithm.
     rnn_algo_ = ToCudnnRNNAlgo(algorithm_config_.algorithm());
-    status = wrap::cudnnSetRNNDescriptor_v6(
-        parent, cudnn_handle, /*rnnDesc=*/rnn_desc_, /*hiddenSize=*/hidden_size,
+    status = cudnnSetRNNDescriptor_v6(
+        cudnn.handle(), /*rnnDesc=*/rnn_desc_, /*hiddenSize=*/hidden_size,
         /*numLayers=*/num_layers, /*dropoutDesc=*/dropout_handle(),
         /*inputMode=*/input_mode, /*direction=*/direction_mode,
         /*mode=*/rnn_mode, /*algo=*/rnn_algo_, /*dataType=*/compute_type);
@@ -1246,26 +1084,25 @@ class CudnnRnnDescriptor : public CudnnDescriptorCommon {
 
     if (rnn_algo_ == CUDNN_RNN_ALGO_PERSIST_DYNAMIC) {
       CHECK_GE(batch_size_, 0);
-      status = wrap::cudnnCreatePersistentRNNPlan(
-          parent, rnn_desc_, batch_size_, data_type_, &rnn_plan_);
+      status = cudnnCreatePersistentRNNPlan(rnn_desc_, batch_size_, data_type_,
+                                            &rnn_plan_);
       CUDNN_RETURN_IF_FAIL(status, "Unable to create persistent RNN plan.");
-      status = wrap::cudnnSetPersistentRNNPlan(parent, rnn_desc_, rnn_plan_);
+      status = cudnnSetPersistentRNNPlan(rnn_desc_, rnn_plan_);
       CUDNN_RETURN_IF_FAIL(status, "Unable to update persistent RNN plan.");
     }
 #else
     CHECK(algorithm_config_.is_default())
         << "Non-default algorithm not supported for CUDA version < 6.0";
-    status = wrap::cudnnSetRNNDescriptor(
-        parent, rnn_desc_ /*rnnDesc*/, hidden_size /*hiddenSize*/,
-        num_layers /*numLayers*/, dropout_handle() /*dropoutDesc*/,
-        input_mode /*inputMode*/, direction_mode /*direction*/,
-        rnn_mode /*mode*/, compute_type /*dataType*/);
+    status = cudnnSetRNNDescriptor(
+        /*rnnDesc=*/rnn_desc_, /*hiddenSize=*/hidden_size,
+        /*numLayers=*/num_layers, /*dropoutDesc=*/dropout_handle(),
+        /*inputMode=*/input_mode, /*direction=*/direction_mode,
+        /*mode=*/rnn_mode, /*dataType=*/compute_type);
     CUDNN_RETURN_IF_FAIL(status, "Unable to update RNN descriptor");
 #endif
 
     // Create the params handle.
-    cudnn_params_desc_.reset(
-        new CudnnRnnParamsDescriptor(parent, cudnn_handle, *this));
+    cudnn_params_desc_.reset(new CudnnRnnParamsDescriptor(cudnn, *this));
     if (!cudnn_params_desc_->ok()) {
       SetFailure(cudnn_params_desc_->Status());
       return;
@@ -1277,11 +1114,11 @@ class CudnnRnnDescriptor : public CudnnDescriptorCommon {
       cudnnStatus_t status;
 #if CUDNN_VERSION >= 6000
       if (rnn_algo_ == CUDNN_RNN_ALGO_PERSIST_DYNAMIC && rnn_plan_) {
-        status = wrap::cudnnDestroyPersistentRNNPlan(parent_, rnn_plan_);
+        status = cudnnDestroyPersistentRNNPlan(rnn_plan_);
         CUDNN_RETURN_IF_FAIL(status, "Unable to destroy persistent RNN plan.");
       }
 #endif
-      status = wrap::cudnnDestroyRNNDescriptor(parent_, rnn_desc_);
+      status = cudnnDestroyRNNDescriptor(rnn_desc_);
       CUDNN_RETURN_IF_FAIL(status, "Unable to destroy RNN descriptor");
     }
   }
@@ -1290,11 +1127,9 @@ class CudnnRnnDescriptor : public CudnnDescriptorCommon {
     cudnnMathType_t math_type =
         (use_tensor_op_math ? CUDNN_TENSOR_OP_MATH : CUDNN_DEFAULT_MATH);
     if (RnnTensorOpMathEnabled()) {
-      cudnnStatus_t status =
-          wrap::cudnnSetRNNMatrixMathType(parent_, rnn_desc_, math_type);
+      cudnnStatus_t status = cudnnSetRNNMatrixMathType(rnn_desc_, math_type);
       if (status != CUDNN_STATUS_SUCCESS) {
-        LOG(FATAL) << "could not set cudnn RNN math type: "
-                   << ToString(status);
+        LOG(FATAL) << "could not set cudnn RNN math type: " << ToString(status);
       }
     }
 #endif
@@ -1336,7 +1171,6 @@ class CudnnRnnDescriptor : public CudnnDescriptorCommon {
   }
 
  private:
-  CUDAExecutor* parent_;
   cudnnRNNDescriptor_t rnn_desc_;
   int num_layers_;
   int hidden_size_;
@@ -1359,30 +1193,28 @@ class CudnnRnnDescriptor : public CudnnDescriptorCommon {
   SE_DISALLOW_COPY_AND_ASSIGN(CudnnRnnDescriptor);
 };
 
+namespace {
+
 CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor(
-    CUDAExecutor* parent, cudnnHandle_t cudnn_handle,
-    const CudnnRnnDescriptor& rnn_desc)
-    : parent_(parent),
-      handle_(nullptr),
-      rnn_desc_(&rnn_desc),
-      params_size_in_bytes_(0) {
+    const CudnnHandle& cudnn, const CudnnRnnDescriptor& rnn_desc)
+    : handle_(nullptr), rnn_desc_(&rnn_desc), params_size_in_bytes_(0) {
   cudnnTensorDescriptor_t input_desc = nullptr;
   {
     // Query the params size.
-    auto status = wrap::cudnnCreateTensorDescriptor(parent, &input_desc);
+    auto status = cudnnCreateTensorDescriptor(&input_desc);
     CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to create tensor descriptor");
     int dims[] = {1, rnn_desc.input_size(), 1};
     int strides[] = {dims[1] * dims[2], dims[2], 1};
-    status = wrap::cudnnSetTensorNdDescriptor(
-        parent, input_desc /*tensorDesc*/, rnn_desc.data_type() /*dataType*/,
-        sizeof(dims) / sizeof(dims[0]) /*nbDims*/, dims /*dimA*/,
-        strides /*strideA*/);
+    status = cudnnSetTensorNdDescriptor(
+        /*tensorDesc=*/input_desc, rnn_desc.data_type() /*dataType*/,
+        sizeof(dims) / sizeof(dims[0]) /*nbDims*/, /*dimA=*/dims,
+        /*strideA=*/strides);
     CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to set tensor descriptor");
 
     size_t params_size = 0;
-    status = wrap::cudnnGetRNNParamsSize(
-        parent, cudnn_handle /*handle*/, rnn_desc.handle() /*rnnDesc*/,
-        input_desc /*xDesc*/, ¶ms_size /*sizeInBytes*/,
+    status = cudnnGetRNNParamsSize(
+        cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
+        /*xDesc=*/input_desc, /*sizeInBytes=*/¶ms_size,
         rnn_desc.data_type() /*dataType*/);
     CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to get RNN parameter size");
     params_size_in_bytes_ = static_cast(params_size);
@@ -1390,13 +1222,13 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor(
 
   {
     // Create the params descriptor.
-    auto status = wrap::cudnnCreateFilterDescriptor(parent, &handle_);
+    auto status = cudnnCreateFilterDescriptor(&handle_);
     CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to create RNN filter descriptor");
     int dims[] = {static_cast(params_size_in_bytes_), 1, 1};
-    status = wrap::cudnnSetFilterNdDescriptor(
-        parent, handle_ /*filterDesc*/, rnn_desc.data_type() /*dataType*/,
-        CUDNN_TENSOR_NCHW /*format*/, sizeof(dims) / sizeof(dims[0]) /*nbDims*/,
-        dims /*filterDimA*/);
+    status = cudnnSetFilterNdDescriptor(
+        /*filterDesc=*/handle_, rnn_desc.data_type() /*dataType*/,
+        /*format=*/CUDNN_TENSOR_NCHW, sizeof(dims) / sizeof(dims[0]) /*nbDims*/,
+        /*filterDimA=*/dims);
     CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to update RNN filter descriptor");
   }
 
@@ -1404,8 +1236,7 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor(
     // Create the weights and biases into the params buffer
     int region_count_per_layer = GetRegionCountPerLayer();
     cudnnFilterDescriptor_t region_desc_handle = nullptr;
-    auto status =
-        wrap::cudnnCreateFilterDescriptor(parent, ®ion_desc_handle);
+    auto status = cudnnCreateFilterDescriptor(®ion_desc_handle);
     CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to create filter descriptor");
     const int layer_count = rnn_desc.direction_mode() == CUDNN_UNIDIRECTIONAL
                                 ? rnn_desc.num_layers()
@@ -1415,21 +1246,21 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor(
         for (int type = 0; type < 2; type++) {
           void* offset = nullptr;
           if (type == 0) {
-            status = wrap::cudnnGetRNNLinLayerMatrixParams(
-                parent, cudnn_handle /*handle*/, rnn_desc.handle() /*rnnDesc*/,
-                layer /*layer*/, input_desc /*xDesc*/, handle_ /*wDesc*/,
-                nullptr /*w*/, region /*linLayerID*/,
-                region_desc_handle /*linLayerMatDesc*/,
-                &offset /*linLayerMat*/);
+            status = cudnnGetRNNLinLayerMatrixParams(
+                cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
+                /*layer=*/layer, /*xDesc=*/input_desc, /*wDesc=*/handle_,
+                /*w=*/nullptr, /*linLayerID=*/region,
+                /*linLayerMatDesc=*/region_desc_handle,
+                /*linLayerMat=*/&offset);
             CUDNN_RETURN_IF_FAIL(
                 status, "Cudnn fails to call cudnnGetRNNLinLayerMatrixParams");
           } else {
-            status = wrap::cudnnGetRNNLinLayerBiasParams(
-                parent, cudnn_handle /*rnnDesc*/, rnn_desc.handle() /*rnnDesc*/,
-                layer /*layer*/, input_desc /*xDesc*/, handle_ /*wDesc*/,
-                nullptr /*w*/, region /*linLayerID*/,
-                region_desc_handle /*linLayerBiasDesc*/,
-                &offset /*linLayerBias*/);
+            status = cudnnGetRNNLinLayerBiasParams(
+                cudnn.handle() /*rnnDesc*/, rnn_desc.handle() /*rnnDesc*/,
+                /*layer=*/layer, /*xDesc=*/input_desc, /*wDesc=*/handle_,
+                /*w=*/nullptr, /*linLayerID=*/region,
+                /*linLayerBiasDesc=*/region_desc_handle,
+                /*linLayerBias=*/&offset);
             CUDNN_RETURN_IF_FAIL(
                 status, "Cudnn fails to call cudnnGetRNNLinLayerBiasParams");
           }
@@ -1437,15 +1268,15 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor(
           cudnnDataType_t data_type;
           cudnnTensorFormat_t tensor_format;
           int n_dims;
-          status = wrap::cudnnGetFilterNdDescriptor(
-              parent, region_desc_handle /*filterDesc*/,
+          status = cudnnGetFilterNdDescriptor(
+              /*filterDesc=*/region_desc_handle,
               sizeof(dims) / sizeof(dims[0]) /*nbDimsRequested*/,
-              &data_type /*dataType*/, &tensor_format /*format*/,
-              &n_dims /*nbDims*/, dims /*filterDimA*/);
+              /*dataType=*/&data_type, /*format=*/&tensor_format,
+              /*nbDims=*/&n_dims, /*filterDimA=*/dims);
           CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to get filter description");
           int64 size = dims[0] * dims[1] * dims[2] *
                        CudnnDataTypeToByteSize(rnn_desc.data_type());
-          auto region = ParamsRegion{reinterpret_cast(offset), size};
+          ParamsRegion region = {reinterpret_cast(offset), size};
           if (type == 0) {
             weights_.push_back(region);
           } else {
@@ -1454,13 +1285,13 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor(
         }
       }
     }
-    status = wrap::cudnnDestroyFilterDescriptor(parent, region_desc_handle);
+    status = cudnnDestroyFilterDescriptor(region_desc_handle);
     CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to destroy filter descriptor");
   }
 
   {
     // Release the dummy input tensor descriptor.
-    auto status = wrap::cudnnDestroyTensorDescriptor(parent, input_desc);
+    auto status = cudnnDestroyTensorDescriptor(input_desc);
     CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to destroy tensor descriptor");
   }
 }
@@ -1480,6 +1311,8 @@ int CudnnRnnParamsDescriptor::GetRegionCountPerLayer() const {
   }
 }
 
+}  // namespace
+
 class CudnnRnnSequenceTensorDescriptor
     : public CudnnDescriptorCommon {
  public:
@@ -1499,14 +1332,14 @@ class CudnnRnnSequenceTensorDescriptor
       SetFailure(port::Status(port::error::UNKNOWN, error_msg));
       return;
     }
-    cudnnStatus_t status = wrap::cudnnCreateTensorDescriptor(parent, &handle);
+    cudnnStatus_t status = cudnnCreateTensorDescriptor(&handle);
     CUDNN_RETURN_IF_FAIL(status, "Failed to create tensor descriptor");
     int dims[] = {batch_size, data_size, 1};
     int strides[] = {dims[1] * dims[2], dims[2], 1};
-    status = wrap::cudnnSetTensorNdDescriptor(
-        parent, handle /*tensorDesc*/, data_type /*dataType*/,
-        sizeof(dims) / sizeof(dims[0]) /*nbDims*/, dims /*dimA*/,
-        strides /*strideA*/);
+    status = cudnnSetTensorNdDescriptor(
+        /*tensorDesc=*/handle, /*dataType=*/data_type,
+        sizeof(dims) / sizeof(dims[0]) /*nbDims*/, /*dimA=*/dims,
+        /*strideA=*/strides);
     CUDNN_RETURN_IF_FAIL(status, "Failed to update tensor descriptor");
     // Replicate handle across the number of steps.
     handles_.assign(seq_length, handle);
@@ -1514,8 +1347,7 @@ class CudnnRnnSequenceTensorDescriptor
 
   ~CudnnRnnSequenceTensorDescriptor() override {
     // Only the first one needs to be destroyed. All others are the same.
-    cudnnStatus_t status =
-        wrap::cudnnDestroyTensorDescriptor(parent_, handles_[0]);
+    cudnnStatus_t status = cudnnDestroyTensorDescriptor(handles_[0]);
     CUDNN_RETURN_IF_FAIL(status,
                          "Failed to destroy sequence tensor descriptor");
   }
@@ -1552,21 +1384,20 @@ class CudnnRnnStateTensorDescriptor
         batch_size_(batch_size),
         data_size_(data_size),
         data_type_(data_type) {
-    cudnnStatus_t status = wrap::cudnnCreateTensorDescriptor(parent, &handle_);
+    cudnnStatus_t status = cudnnCreateTensorDescriptor(&handle_);
     CUDNN_RETURN_IF_FAIL(status, "Failed to create tensor descriptor");
     int dims[] = {num_layers, batch_size, data_size};
     int strides[] = {dims[1] * dims[2], dims[2], 1};
-    status = wrap::cudnnSetTensorNdDescriptor(
-        parent, handle_ /*tensorDesc*/, data_type /*dataType*/,
-        sizeof(dims) / sizeof(dims[0]) /*nbDims*/, dims /*dimA*/,
-        strides /*strideA*/);
+    status = cudnnSetTensorNdDescriptor(
+        /*tensorDesc=*/handle_, /*dataType=*/data_type,
+        sizeof(dims) / sizeof(dims[0]) /*nbDims*/, /*dimA=*/dims,
+        /*strideA=*/strides);
     CUDNN_RETURN_IF_FAIL(status, "Failed to update tensor descriptor");
   }
 
   ~CudnnRnnStateTensorDescriptor() override {
     if (!handle_) {
-      cudnnStatus_t status =
-          wrap::cudnnDestroyTensorDescriptor(parent_, handle_);
+      cudnnStatus_t status = cudnnDestroyTensorDescriptor(handle_);
       CUDNN_RETURN_IF_FAIL(status, "Unable to destroy RNN state tensor");
     }
   }
@@ -1661,13 +1492,13 @@ bool ExtractAndCheckRnnForward(
   return true;
 }
 
-bool CheckRNNParameterSize(CUDAExecutor* parent, cudnnHandle_t cudnn_handle,
+bool CheckRNNParameterSize(const CudnnHandle& cudnn,
                            const CudnnRnnDescriptor& rnn_desc,
                            const CudnnRnnSequenceTensorDescriptor& input_desc) {
   size_t params_size_in_bytes = 0;
-  cudnnStatus_t status = wrap::cudnnGetRNNParamsSize(
-      parent, cudnn_handle /*handle*/, rnn_desc.handle() /*rnnDesc*/,
-      input_desc.handles()[0] /*xDesc*/, ¶ms_size_in_bytes /*sizeInBytes*/,
+  cudnnStatus_t status = cudnnGetRNNParamsSize(
+      /*handle=*/cudnn.handle(), rnn_desc.handle() /*rnnDesc*/,
+      input_desc.handles()[0] /*xDesc*/, /*sizeInBytes=*/¶ms_size_in_bytes,
       rnn_desc.data_type() /*dataType*/);
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "Unable to check RNN param size: " << ToString(status);
@@ -1677,18 +1508,17 @@ bool CheckRNNParameterSize(CUDAExecutor* parent, cudnnHandle_t cudnn_handle,
          rnn_desc.ParamsSizeInBytes();
 }
 
-bool CreateRnnWorkspace(Stream* stream, CUDAExecutor* parent,
-                        cudnnHandle_t cudnn_handle,
+bool CreateRnnWorkspace(Stream* stream, const CudnnHandle& cudnn,
                         const CudnnRnnDescriptor& rnn_desc,
                         const CudnnRnnSequenceTensorDescriptor& input_desc,
                         ScratchAllocator* workspace_allocator,
                         DeviceMemory* workspace) {
   // Query the workspace size.
   size_t workspace_size_in_bytes = 0;
-  cudnnStatus_t status = wrap::cudnnGetRNNWorkspaceSize(
-      parent, cudnn_handle /*handle*/, rnn_desc.handle() /*rnnDesc*/,
-      input_desc.seq_length() /*seqLength*/, input_desc.handles() /*xDesc*/,
-      &workspace_size_in_bytes /*sizeInBytes*/);
+  cudnnStatus_t status = cudnnGetRNNWorkspaceSize(
+      /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(),
+      /*seqLength=*/input_desc.seq_length(), /*xDesc=*/input_desc.handles(),
+      /*sizeInBytes=*/&workspace_size_in_bytes);
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "Unable to query workspace size: " << ToString(status);
     return false;
@@ -1740,25 +1570,18 @@ bool CudnnSupport::DoRnnForwardImpl(
     return false;
   }
 
-  // check params size
-  mutex_lock lock{dnn_handle_mutex_};
-  auto set_stream_status =
-      wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (set_stream_status != CUDNN_STATUS_SUCCESS) {
-    LOG(FATAL) << "failed to set stream for cudnn handle: "
-               << ToString(set_stream_status);
-  }
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
 
-  if (!CheckRNNParameterSize(parent_, ToHandle(dnn_handle_), rnn_desc,
-                             input_desc)) {
+  // check params size
+  if (!CheckRNNParameterSize(cudnn, rnn_desc, input_desc)) {
     LOG(ERROR) << "Invalid parameters";
     return false;
   }
 
   // create the workspace
   DeviceMemory workspace;
-  if (!CreateRnnWorkspace(stream, parent_, ToHandle(dnn_handle_), rnn_desc,
-                          input_desc, workspace_allocator, &workspace)) {
+  if (!CreateRnnWorkspace(stream, cudnn, rnn_desc, input_desc,
+                          workspace_allocator, &workspace)) {
     LOG(ERROR) << "Unable to create rnn workspace";
     return false;
   }
@@ -1768,11 +1591,10 @@ bool CudnnSupport::DoRnnForwardImpl(
   DeviceMemory reserve_space;
   if (is_training) {
     size_t reserve_space_size_in_bytes = 0;
-    cudnnStatus_t status = wrap::cudnnGetRNNTrainingReserveSize(
-        parent_, ToHandle(dnn_handle_) /*handle*/,
-        rnn_desc.handle() /*rnnDesc*/, model_dims.seq_length /*seqLength*/,
-        input_desc.handles() /*xDesc*/,
-        &reserve_space_size_in_bytes /*sizeInBytes*/);
+    cudnnStatus_t status = cudnnGetRNNTrainingReserveSize(
+        cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
+        /*seqLength=*/model_dims.seq_length, input_desc.handles() /*xDesc*/,
+        /*sizeInBytes=*/&reserve_space_size_in_bytes);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(ERROR) << "Unable to query reserve space size: " << ToString(status);
       return false;
@@ -1807,30 +1629,28 @@ bool CudnnSupport::DoRnnForwardImpl(
   // make the forward call
   cudnnStatus_t status;
   if (!is_training) {
-    status = wrap::cudnnRNNForwardInference(
-        this, stream, ToHandle(dnn_handle_) /*handle*/,
-        rnn_desc.handle() /*rnnDesc*/, model_dims.seq_length /*seqLength*/,
-        input_desc.handles() /*xDesc*/, input_data.opaque() /*x*/,
-        input_h_desc.handle() /*hxDesc*/, input_h_data.opaque() /*hx*/,
-        input_c_desc.handle() /*cxDesc*/, input_c_data.opaque() /*cx*/,
-        rnn_desc.params_handle() /*wDesc*/, params.opaque() /*w*/,
-        output_desc.handles() /*yDesc*/, output_data->opaque() /*y*/,
-        output_h_desc.handle() /*hyDesc*/, output_h_data->opaque() /*hy*/,
-        output_c_desc.handle() /*cyDesc*/, output_c_data->opaque() /*cy*/,
-        workspace.opaque() /*workspace*/,
+    status = cudnnRNNForwardInference(
+        cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
+        model_dims.seq_length /*seqLength*/, input_desc.handles() /*xDesc*/,
+        input_data.opaque() /*x*/, input_h_desc.handle() /*hxDesc*/,
+        input_h_data.opaque() /*hx*/, input_c_desc.handle() /*cxDesc*/,
+        input_c_data.opaque() /*cx*/, rnn_desc.params_handle() /*wDesc*/,
+        params.opaque() /*w*/, output_desc.handles() /*yDesc*/,
+        output_data->opaque() /*y*/, output_h_desc.handle() /*hyDesc*/,
+        output_h_data->opaque() /*hy*/, output_c_desc.handle() /*cyDesc*/,
+        output_c_data->opaque() /*cy*/, workspace.opaque() /*workspace*/,
         workspace.size() /*workSpaceSizeInBytes*/);
   } else {
-    status = wrap::cudnnRNNForwardTraining(
-        this, stream, ToHandle(dnn_handle_) /*handle*/,
-        rnn_desc.handle() /*rnnDesc*/, model_dims.seq_length /*seqLength*/,
-        input_desc.handles() /*xDesc*/, input_data.opaque() /*x*/,
-        input_h_desc.handle() /*hxDesc*/, input_h_data.opaque() /*hx*/,
-        input_c_desc.handle() /*cxDesc*/, input_c_data.opaque() /*cx*/,
-        rnn_desc.params_handle() /*wDesc*/, params.opaque() /*w*/,
-        output_desc.handles() /*yDesc*/, output_data->opaque() /*y*/,
-        output_h_desc.handle() /*hyDesc*/, output_h_data->opaque() /*hy*/,
-        output_c_desc.handle() /*cyDesc*/, output_c_data->opaque() /*cy*/,
-        workspace.opaque() /*workspace*/,
+    status = cudnnRNNForwardTraining(
+        cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
+        model_dims.seq_length /*seqLength*/, input_desc.handles() /*xDesc*/,
+        input_data.opaque() /*x*/, input_h_desc.handle() /*hxDesc*/,
+        input_h_data.opaque() /*hx*/, input_c_desc.handle() /*cxDesc*/,
+        input_c_data.opaque() /*cx*/, rnn_desc.params_handle() /*wDesc*/,
+        params.opaque() /*w*/, output_desc.handles() /*yDesc*/,
+        output_data->opaque() /*y*/, output_h_desc.handle() /*hyDesc*/,
+        output_h_data->opaque() /*hy*/, output_c_desc.handle() /*cyDesc*/,
+        output_c_data->opaque() /*cy*/, workspace.opaque() /*workspace*/,
         workspace.size() /*workSpaceSizeInBytes*/,
         reserve_space.opaque() /*reserveSpace*/,
         reserve_space.size() /*reserveSpaceSizeInBytes*/);
@@ -1896,25 +1716,18 @@ bool CudnnSupport::DoRnnBackwardImpl(
     return false;
   }
 
-  // check params size
-  mutex_lock lock{dnn_handle_mutex_};
-  auto set_stream_status =
-      wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (set_stream_status != CUDNN_STATUS_SUCCESS) {
-    LOG(FATAL) << "failed to set stream for cudnn handle: "
-               << ToString(set_stream_status);
-  }
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
 
-  if (!CheckRNNParameterSize(parent_, ToHandle(dnn_handle_), rnn_desc,
-                             input_desc)) {
+  // check params size
+  if (!CheckRNNParameterSize(cudnn, rnn_desc, input_desc)) {
     LOG(ERROR) << "Invalid parameters";
     return false;
   }
 
   // create the workspace
   DeviceMemory workspace;
-  if (!CreateRnnWorkspace(stream, parent_, ToHandle(dnn_handle_), rnn_desc,
-                          input_desc, workspace_allocator, &workspace)) {
+  if (!CreateRnnWorkspace(stream, cudnn, rnn_desc, input_desc,
+                          workspace_allocator, &workspace)) {
     LOG(ERROR) << "Unable to create rnn workspace";
     return false;
   }
@@ -1934,12 +1747,11 @@ bool CudnnSupport::DoRnnBackwardImpl(
     }
   }
   // make the backward data call
-  cudnnStatus_t status = wrap::cudnnRNNBackwardData(
-      this, stream, ToHandle(dnn_handle_) /*handle*/,
-      rnn_desc.handle() /*rnnDesc*/, model_dims.seq_length /*seqLength*/,
-      output_desc.handles() /*yDesc*/, output_data.opaque() /*y*/,
-      output_desc.handles() /*dyDesc*/, output_backprop_data.opaque() /*dy*/,
-      output_h_desc.handle() /*dhyDesc*/,
+  cudnnStatus_t status = cudnnRNNBackwardData(
+      cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
+      model_dims.seq_length /*seqLength*/, output_desc.handles() /*yDesc*/,
+      output_data.opaque() /*y*/, output_desc.handles() /*dyDesc*/,
+      output_backprop_data.opaque() /*dy*/, output_h_desc.handle() /*dhyDesc*/,
       output_h_backprop_data.opaque() /*dhy*/,
       output_c_desc.handle() /*dcyDesc*/,
       output_c_backprop_data.opaque() /*dcy*/,
@@ -1967,13 +1779,12 @@ bool CudnnSupport::DoRnnBackwardImpl(
     // Clear the dw to zeros.
     stream->ThenMemZero(params_backprop_data, params_backprop_data->size());
     // make the backward weight call
-    status = wrap::cudnnRNNBackwardWeights(
-        this, stream, ToHandle(dnn_handle_) /*handle*/,
-        rnn_desc.handle() /*rnnDesc*/, model_dims.seq_length /*seqLength*/,
-        input_desc.handles() /*xDesc*/, input_data.opaque() /*x*/,
-        input_h_desc.handle() /*hxDesc*/, input_h_data.opaque() /*hx*/,
-        output_desc.handles() /*yDesc*/, output_data.opaque() /*y*/,
-        workspace.opaque() /*workspace*/,
+    status = cudnnRNNBackwardWeights(
+        cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
+        model_dims.seq_length /*seqLength*/, input_desc.handles() /*xDesc*/,
+        input_data.opaque() /*x*/, input_h_desc.handle() /*hxDesc*/,
+        input_h_data.opaque() /*hx*/, output_desc.handles() /*yDesc*/,
+        output_data.opaque() /*y*/, workspace.opaque() /*workspace*/,
         workspace.size() /*workSpaceSizeInBytes*/,
         rnn_desc.params_handle() /*dwDesc*/,
         params_backprop_data->opaque() /*dw*/,
@@ -2011,13 +1822,15 @@ CudnnSupport::createRnnDescriptor(
     const dnn::AlgorithmConfig& algorithm_config, float dropout, uint64 seed,
     ScratchAllocator* state_allocator) {
 #if CUDNN_VERSION >= 5000
-  mutex_lock lock{dnn_handle_mutex_};
+  // Setting up a cudnnRNNDescriptor requires a cuDNN handle, but because it's
+  // not enqueueing anything into a stream, we pass in the null stream.
+  auto cudnn = cudnn_->GetHandle(parent_, /*stream=*/nullptr);
   std::unique_ptr rnn_desc(new CudnnRnnDescriptor(
-      parent_, ToHandle(dnn_handle_), num_layers, hidden_size, input_size,
-      batch_size, ToCudnnRnnInputMode(input_mode),
-      ToCudnnRnnDirectionMode(direction_mode), ToCudnnRnnMode(rnn_mode),
-      ToCudnnDataType(data_type), GetRnnComputeType(data_type),
-      algorithm_config, dropout, seed, state_allocator));
+      cudnn, num_layers, hidden_size, input_size, batch_size,
+      ToCudnnRnnInputMode(input_mode), ToCudnnRnnDirectionMode(direction_mode),
+      ToCudnnRnnMode(rnn_mode), ToCudnnDataType(data_type),
+      GetRnnComputeType(data_type), algorithm_config, dropout, seed,
+      state_allocator));
   if (!rnn_desc->ok()) {
     return rnn_desc->Status();
   }
@@ -2028,7 +1841,7 @@ CudnnSupport::createRnnDescriptor(
       port::StrCat("createRnnDescriptor needs at least Cudnn 5.0 to work. ",
                    "Current Cudnn version: ", CUDNN_VERSION, ". ");
   LOG(ERROR) << error_msg;
-  return port::Status{port::error::UNIMPLEMENTED, error_msg};
+  return port::Status(port::error::UNIMPLEMENTED, error_msg);
 #endif  // CUDNN_VERSION
 }
 
@@ -2051,7 +1864,7 @@ CudnnSupport::createRnnSequenceTensorDescriptor(int seq_length, int batch_size,
       "createRnnSequenceTensorDescriptor needs at least Cudnn 5.0 to work. ",
       "Current Cudnn version: ", CUDNN_VERSION, ". ");
   LOG(ERROR) << error_msg;
-  return port::Status{port::error::UNIMPLEMENTED, error_msg};
+  return port::Status(port::error::UNIMPLEMENTED, error_msg);
 #endif  // CUDNN_VERSION
 }
 
@@ -2073,7 +1886,7 @@ CudnnSupport::createRnnStateTensorDescriptor(int num_layer, int batch_size,
       "createRnnStateTensorDescriptor needs at least Cudnn 5.0 to work. ",
       "Current Cudnn version: ", CUDNN_VERSION, ". ");
   LOG(ERROR) << error_msg;
-  return port::Status{port::error::UNIMPLEMENTED, error_msg};
+  return port::Status(port::error::UNIMPLEMENTED, error_msg);
 #endif  // CUDNN_VERSION
 }
 
@@ -2375,35 +2188,26 @@ bool CudnnSupport::DoRnnBackward(
 namespace {
 
 inline cudnnConvolutionFwdAlgo_t GetCudnnConvolutionForwardAlgo(
-    Stream* stream, CUDAExecutor* parent, void* dnn_handle,
-    const ScopedTensorDescriptor& input_nd,
+    const CudnnHandle& cudnn, const ScopedTensorDescriptor& input_nd,
     const ScopedFilterDescriptor& filter,
     const ScopedConvolutionDescriptor& conv,
     const ScopedTensorDescriptor& output_nd, bool specify_workspace_limit,
-    ScratchAllocator* scratch_allocator) {
+    size_t memory_limit_bytes) {
   cudnnConvolutionFwdPreference_t preference =
       specify_workspace_limit ? CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT
                               : CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
-  auto memory_limit_bytes =
-      scratch_allocator == nullptr
-          ? 0
-          : scratch_allocator->GetMemoryLimitInBytes(stream);
-  if (memory_limit_bytes < 0) {
-    memory_limit_bytes = 0;
-  }
 
   cudnnConvolutionFwdAlgo_t algo_to_use;
-  auto status = wrap::cudnnGetConvolutionForwardAlgorithm(
-      parent, ToHandle(dnn_handle), input_nd.handle(), filter.handle(),
-      conv.handle(), output_nd.handle(), preference, memory_limit_bytes,
-      &algo_to_use);
+  auto status = cudnnGetConvolutionForwardAlgorithm(
+      cudnn.handle(), input_nd.handle(), filter.handle(), conv.handle(),
+      output_nd.handle(), preference, memory_limit_bytes, &algo_to_use);
   CHECK_EQ(status, CUDNN_STATUS_SUCCESS)
       << "Unable to find a suitable algorithm for doing forward convolution";
   return algo_to_use;
 }
 
 dnn::AlgorithmDesc GetCudnnConvolutionForwardAlgorithm(
-    Stream* stream, CUDAExecutor* parent, void* dnn_handle,
+    Stream* stream, const CudnnHandle& cudnn,
     const dnn::AlgorithmConfig& algorithm_config, bool is_profiling,
     const ScopedTensorDescriptor& input_nd,
     const ScopedFilterDescriptor& filter,
@@ -2414,19 +2218,29 @@ dnn::AlgorithmDesc GetCudnnConvolutionForwardAlgorithm(
   bool use_tensor_ops;
   if (algorithm_config.algorithm().is_default()) {
     use_tensor_ops = true;
+
+    auto memory_limit_bytes =
+        scratch_allocator == nullptr
+            ? 0
+            : scratch_allocator->GetMemoryLimitInBytes(stream);
+    if (memory_limit_bytes < 0) {
+      memory_limit_bytes = 0;
+    }
+
     algo = GetCudnnConvolutionForwardAlgo(
-        stream, parent, dnn_handle, input_nd, filter, conv, output_nd,
+        cudnn, input_nd, filter, conv, output_nd,
         /*specify_workspace_limit=*/scratch_allocator != nullptr,
-        scratch_allocator);
+        memory_limit_bytes);
   } else {
     use_tensor_ops = algorithm_config.algorithm().tensor_ops_enabled();
     algo = ToConvForwardAlgo(algorithm_config.algorithm());
   }
   size_t size_in_bytes;
-  auto status = wrap::cudnnGetConvolutionForwardWorkspaceSize(
-      parent, ToHandle(dnn_handle), /*srcDesc=*/input_nd.handle(),
-      /*filterDesc=*/filter.handle(), /*convDesc=*/conv.handle(),
-      /*destDesc=*/output_nd.handle(), /*algo=*/algo,
+  auto status = cudnnGetConvolutionForwardWorkspaceSize(
+      cudnn.handle(),
+      /*xDesc=*/input_nd.handle(),
+      /*wDesc=*/filter.handle(), /*convDesc=*/conv.handle(),
+      /*yDesc=*/output_nd.handle(), /*algo=*/algo,
       /*sizeInBytes=*/&size_in_bytes);
   int64 size_in_bytes_int64 = size_in_bytes;
   if (TF_PREDICT_FALSE(status != CUDNN_STATUS_SUCCESS)) {
@@ -2466,8 +2280,8 @@ dnn::AlgorithmDesc GetCudnnConvolutionForwardAlgorithm(
       if (algorithm_config.algorithm_no_scratch().is_default()) {
         use_tensor_ops = true;
         algo = GetCudnnConvolutionForwardAlgo(
-            stream, parent, dnn_handle, input_nd, filter, conv, output_nd,
-            /*specify_workspace_limit=*/false, nullptr);
+            cudnn, input_nd, filter, conv, output_nd,
+            /*specify_workspace_limit=*/false, 0);
       } else {
         use_tensor_ops = algorithm_config.algorithm().tensor_ops_enabled();
         algo = ToConvForwardAlgo(algorithm_config.algorithm_no_scratch());
@@ -2596,11 +2410,12 @@ cudnnDataType_t GetRnnComputeType(dnn::DataType data_type) {
       LOG(FATAL) << "Invalid RNN data type: " << static_cast(data_type);
   }
 }
+
 }  // namespace
 
 template 
 bool CudnnSupport::DoConvolveImpl(
-    Stream* stream, const BatchDescriptor& batch_descriptor,
+    Stream* stream, const BatchDescriptor& input_descriptor,
     const DeviceMemory& input_data,
     const FilterDescriptor& filter_descriptor,
     const DeviceMemory& filter_data,
@@ -2610,18 +2425,13 @@ bool CudnnSupport::DoConvolveImpl(
     const dnn::AlgorithmConfig& algorithm_config,
     dnn::ProfileResult* output_profile_result) {
   cudnnDataType_t cudnn_type = GetCudnnDataType();
-  ScopedTensorDescriptor input_nd{parent_, batch_descriptor, cudnn_type};
-  ScopedTensorDescriptor output_nd{parent_, output_descriptor, cudnn_type};
-  ScopedFilterDescriptor filter{parent_, filter_descriptor, batch_descriptor,
-                                cudnn_type};
-  ScopedConvolutionDescriptor conv{parent_, convolution_descriptor,
-                                   GetConvComputeType()};
-
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(FATAL) << "failed to set stream for cudnn handle: " << ToString(status);
-  }
+  ScopedTensorDescriptor input_nd(input_descriptor, cudnn_type);
+  ScopedTensorDescriptor output_nd(output_descriptor, cudnn_type);
+  ScopedFilterDescriptor filter(filter_descriptor, cudnn_type);
+  ScopedConvolutionDescriptor conv(convolution_descriptor,
+                                   GetConvComputeType());
+
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
   // Alpha is the scaling factor for input.
   float falpha = 1.0;
   double dalpha = 1.0;
@@ -2642,42 +2452,41 @@ bool CudnnSupport::DoConvolveImpl(
   //   GetCudnnConvolutionForwardAlgorithm().
   if (algorithm_config.algorithm().is_default()) {
     // With the default algorithm, use Cudnn's heuristics.
-    auto get_algorithm =
-        [&](bool specify_limit) SHARED_LOCKS_REQUIRED(dnn_handle_mutex_) {
-          cudnnConvolutionFwdPreference_t preference =
-              specify_limit ? CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT
-                            : CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
-
-          auto memory_limit_bytes =
-              scratch_allocator == nullptr
-                  ? 0
-                  : scratch_allocator->GetMemoryLimitInBytes(stream);
-          if (memory_limit_bytes < 0) {
-            memory_limit_bytes = 0;
-          }
+    auto get_algorithm = [&](bool specify_limit) {
+      cudnnConvolutionFwdPreference_t preference =
+          specify_limit ? CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT
+                        : CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
 
-          cudnnConvolutionFwdAlgo_t algo_to_use;
-          status = wrap::cudnnGetConvolutionForwardAlgorithm(
-              parent_, ToHandle(dnn_handle_), input_nd.handle(),
-              filter.handle(), conv.handle(), output_nd.handle(),
-              /*preference=*/preference,
-              /*memoryLimitInBytes=*/memory_limit_bytes,
-              /*algo=*/&algo_to_use);
-          CHECK_EQ(status, CUDNN_STATUS_SUCCESS)
-              << "Unable to find a suitable "
-                 "algorithm for doing forward "
-                 "convolution";
-          return algo_to_use;
-        };
+      auto memory_limit_bytes =
+          scratch_allocator == nullptr
+              ? 0
+              : scratch_allocator->GetMemoryLimitInBytes(stream);
+      if (memory_limit_bytes < 0) {
+        memory_limit_bytes = 0;
+      }
+
+      cudnnConvolutionFwdAlgo_t algo_to_use;
+      auto status = cudnnGetConvolutionForwardAlgorithm(
+          cudnn.handle(), input_nd.handle(), filter.handle(), conv.handle(),
+          output_nd.handle(),
+          /*preference=*/preference,
+          /*memoryLimitInBytes=*/memory_limit_bytes,
+          /*algo=*/&algo_to_use);
+      CHECK_EQ(status, CUDNN_STATUS_SUCCESS) << "Unable to find a suitable "
+                                                "algorithm for doing forward "
+                                                "convolution";
+      return algo_to_use;
+    };
 
     algo = get_algorithm(/*specify_limit=*/scratch_allocator != nullptr);
     use_tensor_ops = true;
     if (scratch_allocator != nullptr) {
       size_t size_in_bytes;
-      status = wrap::cudnnGetConvolutionForwardWorkspaceSize(
-          parent_, ToHandle(dnn_handle_), /*srcDesc=*/input_nd.handle(),
-          /*filterDesc=*/filter.handle(), /*convDesc=*/conv.handle(),
-          /*destDesc=*/output_nd.handle(), /*algo=*/algo,
+      auto status = cudnnGetConvolutionForwardWorkspaceSize(
+          cudnn.handle(),
+          /*xDesc=*/input_nd.handle(),
+          /*wDesc=*/filter.handle(), /*convDesc=*/conv.handle(),
+          /*yDesc=*/output_nd.handle(), /*algo=*/algo,
           /*sizeInBytes=*/&size_in_bytes);
       int64 size_in_bytes_int64 = size_in_bytes;
       if (status == CUDNN_STATUS_SUCCESS && size_in_bytes_int64 != 0) {
@@ -2709,10 +2518,11 @@ bool CudnnSupport::DoConvolveImpl(
     use_tensor_ops = algotype.tensor_ops_enabled();
     conv.set_use_tensor_op_math(use_tensor_ops);
     size_t size_in_bytes;
-    status = wrap::cudnnGetConvolutionForwardWorkspaceSize(
-        parent_, ToHandle(dnn_handle_), /*srcDesc=*/input_nd.handle(),
-        /*filterDesc=*/filter.handle(), /*convDesc=*/conv.handle(),
-        /*destDesc=*/output_nd.handle(), /*algo=*/algo,
+    auto status = cudnnGetConvolutionForwardWorkspaceSize(
+        cudnn.handle(),
+        /*xDesc=*/input_nd.handle(),
+        /*wDesc=*/filter.handle(), /*convDesc=*/conv.handle(),
+        /*yDesc=*/output_nd.handle(), /*algo=*/algo,
         /*sizeInBytes=*/&size_in_bytes);
     if (status != CUDNN_STATUS_SUCCESS) {
       if (is_profiling) {
@@ -2767,8 +2577,8 @@ bool CudnnSupport::DoConvolveImpl(
       return false;
     }
   }
-  status = wrap::cudnnConvolutionForward(
-      this, stream, ToHandle(dnn_handle_),
+  auto status = cudnnConvolutionForward(
+      cudnn.handle(),
       /*alpha=*/alpha, /*srcDesc=*/input_nd.handle(),
       /*srcData=*/input_data.opaque(), /*filterDesc=*/filter.handle(),
       /*filterData=*/filter_data.opaque(), /*convDesc=*/conv.handle(),
@@ -2822,30 +2632,22 @@ bool CudnnSupport::DoFusedConvolveImpl(
                 "supported for cuDNN version >= 6";
   return false;
 #else
-  ScopedTensorDescriptor conv_input_nd{
-      parent_, conv_input_descriptor,
-      static_cast(cudnn_data_type)};
-  ScopedTensorDescriptor output_nd{
-      parent_, output_descriptor,
-      static_cast(cudnn_data_type)};
-  ScopedFilterDescriptor filter{parent_, filter_descriptor,
-                                conv_input_descriptor,
-                                static_cast(cudnn_data_type)};
-  ScopedTensorDescriptor bias_nd{parent_, bias_descriptor, CUDNN_DATA_FLOAT};
-  ScopedConvolutionDescriptor conv{
-      parent_, convolution_descriptor,
-      static_cast(cudnn_compute_type)};
-
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  CHECK(status == CUDNN_STATUS_SUCCESS)
-      << "failed to set stream for cudnn handle: " << ToString(status);
-
+  ScopedTensorDescriptor conv_input_nd(
+      conv_input_descriptor, static_cast(cudnn_data_type));
+  ScopedTensorDescriptor output_nd(
+      output_descriptor, static_cast(cudnn_data_type));
+  ScopedFilterDescriptor filter(filter_descriptor,
+                                static_cast(cudnn_data_type));
+  ScopedTensorDescriptor bias_nd(bias_descriptor, CUDNN_DATA_FLOAT);
+  ScopedConvolutionDescriptor conv(
+      convolution_descriptor, static_cast(cudnn_compute_type));
+
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
   const bool is_profiling = output_profile_result != nullptr;
   DeviceMemory scratch;
   dnn::AlgorithmDesc algotype = GetCudnnConvolutionForwardAlgorithm(
-      stream, parent_, dnn_handle_, algorithm_config, is_profiling,
-      conv_input_nd, filter, conv, output_nd, scratch_allocator, &scratch);
+      stream, cudnn, algorithm_config, is_profiling, conv_input_nd, filter,
+      conv, output_nd, scratch_allocator, &scratch);
   if (algotype.is_default()) {
     if (!is_profiling) {
       LOG(ERROR) << "No suitable algorithm found";
@@ -2879,9 +2681,8 @@ bool CudnnSupport::DoFusedConvolveImpl(
   // activation descriptor. Note that this will change the nan propagation
   // behavior from separate conv, bias, and relu (which by default is
   // CUDNN_PROPAGATE_NAN.
-  ScopedActivationDescriptor activation_desc{parent_, activation_mode,
-                                             CUDNN_NOT_PROPAGATE_NAN,
-                                             output_descriptor.value_max()};
+  ScopedActivationDescriptor activation_desc(
+      activation_mode, CUDNN_NOT_PROPAGATE_NAN, output_descriptor.value_max());
   auto side_input_data_ptr = (side_input_scale == 0) ? output_data->opaque()
                                                      : side_input_data.opaque();
 
@@ -2902,8 +2703,9 @@ bool CudnnSupport::DoFusedConvolveImpl(
           << "\noutput_nd.handle() = " << output_nd.handle()
           << "\noutput_data->opaque() = " << output_data->opaque();
 
-  status = wrap::cudnnConvolutionBiasActivationForward(
-      this, stream, ToHandle(dnn_handle_), /*alpha1=*/&conv_input_scale,
+  auto status = cudnnConvolutionBiasActivationForward(
+      cudnn.handle(),
+      /*alpha1=*/&conv_input_scale,
       /*srcDesc=*/conv_input_nd.handle(), /*srcData=*/conv_input_data.opaque(),
       /*filterDesc=*/filter.handle(), /*filterData=*/filter_data.opaque(),
       /*convDesc=*/conv.handle(), algo, /*workSpace=*/scratch.opaque(),
@@ -3107,17 +2909,9 @@ bool CudnnSupport::DoBatchNormalizationForwardImpl(
     DeviceMemory* saved_mean, DeviceMemory* saved_inv_var,
     bool is_training, std::function&()> var_to_inv_var,
     std::function inv_var_to_var) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "failed to set stream for cudnn handle: " << ToString(status);
-    return false;
-  }
-
-  ScopedTensorDescriptor x_descriptor{parent_, x_desc,
-                                      ToCudnnDataType(input_data_type)};
-  ScopedTensorDescriptor scale_offset_descriptor{
-      parent_, scale_offset_desc, ToCudnnDataType(scale_data_type)};
+  ScopedTensorDescriptor x_descriptor(x_desc, ToCudnnDataType(input_data_type));
+  ScopedTensorDescriptor scale_offset_descriptor(
+      scale_offset_desc, ToCudnnDataType(scale_data_type));
   cudnnBatchNormMode_t mode = CUDNN_BATCHNORM_SPATIAL;
 #if CUDNN_VERSION >= 7000
   if (BatchnormSpatialPersistentEnabled() && is_training) {
@@ -3126,7 +2920,9 @@ bool CudnnSupport::DoBatchNormalizationForwardImpl(
 #endif
   float one = 1.0;
   float zero = 0.0;
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
 
+  auto status = CUDNN_STATUS_SUCCESS;
   if (is_training) {
     CHECK_EQ(batch_mean->is_null(), batch_var->is_null())
         << "batch_mean and batch_var must both be null or both be non-null";
@@ -3143,11 +2939,11 @@ bool CudnnSupport::DoBatchNormalizationForwardImpl(
       batch_var_opaque = nullptr;
     }
 
-    status = wrap::cudnnBatchNormalizationForwardTraining(
-        this, stream, ToHandle(dnn_handle_), mode, &one, &zero,
-        x_descriptor.handle(), x.opaque(), x_descriptor.handle(), y->opaque(),
-        scale_offset_descriptor.handle(), scale.opaque(), offset.opaque(), 1.0,
-        batch_mean_opaque, batch_var_opaque, epsilon, saved_mean->opaque(),
+    status = cudnnBatchNormalizationForwardTraining(
+        cudnn.handle(), mode, &one, &zero, x_descriptor.handle(), x.opaque(),
+        x_descriptor.handle(), y->opaque(), scale_offset_descriptor.handle(),
+        scale.opaque(), offset.opaque(), 1.0, batch_mean_opaque,
+        batch_var_opaque, epsilon, saved_mean->opaque(),
         saved_inv_var->opaque());
 #if CUDNN_VERSION < 5000
     CHECK(inv_var_to_var);
@@ -3160,11 +2956,11 @@ bool CudnnSupport::DoBatchNormalizationForwardImpl(
 #else
     const void* maybe_inv_var = estimated_variance.opaque();
 #endif
-    status = wrap::cudnnBatchNormalizationForwardInference(
-        this, stream, ToHandle(dnn_handle_), mode, &one, &zero,
-        x_descriptor.handle(), x.opaque(), x_descriptor.handle(), y->opaque(),
-        scale_offset_descriptor.handle(), scale.opaque(), offset.opaque(),
-        estimated_mean.opaque(), maybe_inv_var, epsilon);
+    status = cudnnBatchNormalizationForwardInference(
+        cudnn.handle(), mode, &one, &zero, x_descriptor.handle(), x.opaque(),
+        x_descriptor.handle(), y->opaque(), scale_offset_descriptor.handle(),
+        scale.opaque(), offset.opaque(), estimated_mean.opaque(), maybe_inv_var,
+        epsilon);
   }
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "failed to enqueue forward batch normalization on stream: "
@@ -3211,18 +3007,10 @@ bool CudnnSupport::DoBatchNormalizationBackwardImpl(
     const dnn::BatchDescriptor& scale_offset_desc, const double epsilon,
     DeviceMemory* x_backprop, DeviceMemory* scale_backprop,
     DeviceMemory* offset_backprop) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "failed to set stream for cudnn handle: " << ToString(status);
-    return false;
-  }
-
-  ScopedTensorDescriptor x_descriptor{
-      parent_, x_desc, static_cast(cudnn_input_type)};
-  ScopedTensorDescriptor scale_offset_descriptor{
-      parent_, scale_offset_desc,
-      static_cast(cudnn_scale_type)};
+  ScopedTensorDescriptor x_descriptor(
+      x_desc, static_cast(cudnn_input_type));
+  ScopedTensorDescriptor scale_offset_descriptor(
+      scale_offset_desc, static_cast(cudnn_scale_type));
   cudnnBatchNormMode_t mode = CUDNN_BATCHNORM_SPATIAL;
 #if CUDNN_VERSION >= 7000
   if (BatchnormSpatialPersistentEnabled()) {
@@ -3232,10 +3020,12 @@ bool CudnnSupport::DoBatchNormalizationBackwardImpl(
   float one = 1.0;
   float zero = 0.0;
 
-  status = wrap::cudnnBatchNormalizationBackward(
-      this, stream, ToHandle(dnn_handle_), mode, &one, &zero, &one, &zero,
-      x_descriptor.handle(), x.opaque(), x_descriptor.handle(),
-      y_backprop.opaque(), x_descriptor.handle(), x_backprop->opaque(),
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+
+  auto status = cudnnBatchNormalizationBackward(
+      cudnn.handle(), mode, &one, &zero, &one, &zero, x_descriptor.handle(),
+      x.opaque(), x_descriptor.handle(), y_backprop.opaque(),
+      x_descriptor.handle(), x_backprop->opaque(),
       scale_offset_descriptor.handle(), scale.opaque(),
       scale_backprop->opaque(), offset_backprop->opaque(), epsilon,
       mean.opaque(), inv_var.opaque());
@@ -3398,11 +3188,21 @@ bool CudnnSupport::DoFusedConvolve(
 #endif
 }
 
-template
-DeviceMemory CudnnSupport::MaybeTransformLayout(
-    Stream* stream,
-    BatchDescriptor* output_descriptor,
-    DeviceMemory backward_output_data,
+namespace {
+// NOTE(keveman): Temporary data layout transformation until cuDNN supports
+// kBatchYXDepth for backward pass. This function allocates temporary memory,
+// lays out the source data into the temporary but in the kBatchDepthXY
+// layout, and returns the temporary memory. The caller is responsible for
+// deallocating the temporary. Since the allocation is done using Stream's
+// AllocateTemporaryMemory, a later BlockHostUntilDone could be used for
+// deallocation.
+//
+// transform_scratch is populated with a legitimate temporary allocation iff
+// the original output data needs to be transformed.
+template 
+DeviceMemory MaybeTransformLayout(
+    Stream* stream, const CudnnHandle& cudnn,
+    BatchDescriptor* output_descriptor, DeviceMemory backward_output_data,
     std::unique_ptr>* transform_scratch) {
   if (output_descriptor->layout() == dnn::DataLayout::kBatchDepthYX) {
     return backward_output_data;
@@ -3415,15 +3215,14 @@ DeviceMemory CudnnSupport::MaybeTransformLayout(
   transformed_output_descriptor.CloneFrom(*output_descriptor);
   transformed_output_descriptor.set_layout(dnn::DataLayout::kBatchDepthYX);
   cudnnDataType_t cudnn_type = GetCudnnDataType();
-  ScopedTensorDescriptor orig_out_back_nd{parent_, *output_descriptor,
-                                          cudnn_type};
-  ScopedTensorDescriptor transformed_out_back_nd{
-      parent_, transformed_output_descriptor, cudnn_type};
+  ScopedTensorDescriptor orig_out_back_nd(*output_descriptor, cudnn_type);
+  ScopedTensorDescriptor transformed_out_back_nd(transformed_output_descriptor,
+                                                 cudnn_type);
 
   float alpha = 1.0f;
   float beta = 0.0f;
-  auto status = wrap::cudnnTransformTensor(
-      this, stream, ToHandle(dnn_handle_), &alpha, orig_out_back_nd.handle(),
+  auto status = cudnnTransformTensor(
+      cudnn.handle(), &alpha, orig_out_back_nd.handle(),
       backward_output_data.opaque(), &beta, transformed_out_back_nd.handle(),
       (*transform_scratch)->mutable_device_memory()->opaque());
 
@@ -3433,6 +3232,7 @@ DeviceMemory CudnnSupport::MaybeTransformLayout(
   output_descriptor->set_layout(dnn::DataLayout::kBatchDepthYX);
   return (*transform_scratch)->device_memory();
 }
+}  // namespace
 
 bool CudnnSupport::DoTransformTensor(Stream* stream,
                                      const dnn::BatchDescriptor& input_desc,
@@ -3441,21 +3241,15 @@ bool CudnnSupport::DoTransformTensor(Stream* stream,
                                      const dnn::BatchDescriptor& output_desc,
                                      dnn::DataType output_type, float scale,
                                      DeviceMemoryBase* output_data) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(FATAL) << "failed to set stream for cudnn handle: " << ToString(status);
-  }
-
   float beta = 0.0f;
   ScopedTensorDescriptor input_tensor_desc(
-      parent_, input_desc, ToCudnnDataType(input_type, input_desc.layout()));
+      input_desc, ToCudnnDataType(input_type, input_desc.layout()));
   ScopedTensorDescriptor output_tensor_desc(
-      parent_, output_desc, ToCudnnDataType(output_type, output_desc.layout()));
-  status = wrap::cudnnTransformTensor(
-      this, stream, ToHandle(dnn_handle_), &scale, input_tensor_desc.handle(),
-      input_data.opaque(), &beta, output_tensor_desc.handle(),
-      output_data->opaque());
+      output_desc, ToCudnnDataType(output_type, output_desc.layout()));
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+  auto status = cudnnTransformTensor(
+      cudnn.handle(), &scale, input_tensor_desc.handle(), input_data.opaque(),
+      &beta, output_tensor_desc.handle(), output_data->opaque());
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "Could not transform a tensor with layout "
                << input_desc.ToString() << " and data type "
@@ -3469,8 +3263,7 @@ bool CudnnSupport::DoTransformTensor(Stream* stream,
 
 template 
 bool CudnnSupport::DoConvolveBackwardDataImpl(
-    Stream* stream,
-    const FilterDescriptor& filter_descriptor,
+    Stream* stream, const FilterDescriptor& filter_descriptor,
     const DeviceMemory& filter_data,
     const BatchDescriptor& output_descriptor_in,
     DeviceMemory backward_output_data,
@@ -3479,12 +3272,6 @@ bool CudnnSupport::DoConvolveBackwardDataImpl(
     DeviceMemory* backward_input_data, ScratchAllocator* scratch_allocator,
     const dnn::AlgorithmConfig& algorithm_config,
     dnn::ProfileResult* output_profile_result) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(FATAL) << "failed to set stream for cudnn handle: " << ToString(status);
-  }
-
   cudnnDataType_t cudnn_type = GetCudnnDataType();
   // Alpha is the scaling factor for input.
   float falpha = 1.0;
@@ -3497,19 +3284,21 @@ bool CudnnSupport::DoConvolveBackwardDataImpl(
   void* beta = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dbeta)
                                                : static_cast(&fbeta);
 
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+
   // TBD(keveman): remove once cuDNN supports kBatchYXDepth for backward pass.
   BatchDescriptor output_descriptor;
   output_descriptor.CloneFrom(output_descriptor_in);
   std::unique_ptr> transform_scratch;
-  backward_output_data = MaybeTransformLayout(
-      stream, &output_descriptor, backward_output_data, &transform_scratch);
+  backward_output_data =
+      MaybeTransformLayout(stream, cudnn, &output_descriptor,
+                           backward_output_data, &transform_scratch);
 
-  ScopedTensorDescriptor out_back_nd{parent_, output_descriptor, cudnn_type};
-  ScopedTensorDescriptor in_back_nd{parent_, input_descriptor, cudnn_type};
-  ScopedFilterDescriptor filter{parent_, filter_descriptor, input_descriptor,
-                                cudnn_type};
-  ScopedConvolutionDescriptor conv{parent_, convolution_descriptor,
-                                   GetConvComputeType()};
+  ScopedTensorDescriptor out_back_nd(output_descriptor, cudnn_type);
+  ScopedTensorDescriptor in_back_nd(input_descriptor, cudnn_type);
+  ScopedFilterDescriptor filter(filter_descriptor, cudnn_type);
+  ScopedConvolutionDescriptor conv(convolution_descriptor,
+                                   GetConvComputeType());
 
   const bool is_profiling = output_profile_result != nullptr;
   cudnnConvolutionBwdDataAlgo_t algo;
@@ -3517,8 +3306,8 @@ bool CudnnSupport::DoConvolveBackwardDataImpl(
 
   if (algorithm_config.algorithm().is_default()) {
     // With the default algorithm, use Cudnn's heuristics.
-    auto get_algorithm = [&](bool specify_limit) SHARED_LOCKS_REQUIRED(
-        dnn_handle_mutex_) -> cudnnConvolutionBwdDataAlgo_t {
+    auto get_algorithm =
+        [&](bool specify_limit) -> cudnnConvolutionBwdDataAlgo_t {
       cudnnConvolutionBwdDataPreference_t preference =
           specify_limit ? CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT
                         : CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE;
@@ -3531,8 +3320,8 @@ bool CudnnSupport::DoConvolveBackwardDataImpl(
         memory_limit_bytes = 0;
       }
       cudnnConvolutionBwdDataAlgo_t algo_to_use;
-      cudnnStatus_t status = wrap::cudnnGetConvolutionBackwardDataAlgorithm(
-          parent_, ToHandle(dnn_handle_),
+      cudnnStatus_t status = cudnnGetConvolutionBackwardDataAlgorithm(
+          cudnn.handle(),
           /*filterDesc=*/filter.handle(),
           /*diffDesc=*/out_back_nd.handle(),
           /*convDesc=*/conv.handle(),
@@ -3550,8 +3339,8 @@ bool CudnnSupport::DoConvolveBackwardDataImpl(
 
     if (scratch_allocator != nullptr) {
       size_t size_in_bytes;
-      status = wrap::cudnnGetConvolutionBackwardDataWorkspaceSize(
-          parent_, ToHandle(dnn_handle_),
+      auto status = cudnnGetConvolutionBackwardDataWorkspaceSize(
+          cudnn.handle(),
           /*filterDesc=*/filter.handle(),
           /*diffDesc=*/out_back_nd.handle(),
           /*convDesc=*/conv.handle(),
@@ -3587,8 +3376,8 @@ bool CudnnSupport::DoConvolveBackwardDataImpl(
     algo = ToConvBackwardDataAlgo(algotype);
     conv.set_use_tensor_op_math(algotype.tensor_ops_enabled());
     size_t size_in_bytes;
-    status = wrap::cudnnGetConvolutionBackwardDataWorkspaceSize(
-        parent_, ToHandle(dnn_handle_),
+    auto status = cudnnGetConvolutionBackwardDataWorkspaceSize(
+        cudnn.handle(),
         /*filterDesc=*/filter.handle(),
         /*diffDesc=*/out_back_nd.handle(),
         /*convDesc=*/conv.handle(),
@@ -3645,23 +3434,24 @@ bool CudnnSupport::DoConvolveBackwardDataImpl(
   }
 
 #if CUDNN_VERSION >= 5000
-  status = wrap::cudnnConvolutionBackwardData(
+  auto status =
+      cudnnConvolutionBackwardData(cudnn.handle(),
 #else
-  status = wrap::cudnnConvolutionBackwardData_v3(
+  auto status =
+      cudnnConvolutionBackwardData_v3(cudnn.handle(),
 #endif
-      this, stream, ToHandle(dnn_handle_),
-      /*alpha=*/alpha,
-      /*filterDesc=*/filter.handle(),
-      /*filterData=*/filter_data.opaque(),
-      /*diffDesc=*/out_back_nd.handle(),
-      /*diffData=*/backward_output_data.opaque(),
-      /*convDesc=*/conv.handle(),
-      /*algo=*/algo,
-      /*workSpace=*/scratch.opaque(),
-      /*workSpaceSizeInBytes=*/scratch.size(),
-      /*beta=*/beta,
-      /*gradDesc=*/in_back_nd.handle(),
-      /*gradData=*/backward_input_data->opaque());
+                                   /*alpha=*/alpha,
+                                   /*wDesc=*/filter.handle(),
+                                   /*w=*/filter_data.opaque(),
+                                   /*dyDesc=*/out_back_nd.handle(),
+                                   /*dy=*/backward_output_data.opaque(),
+                                   /*convDesc=*/conv.handle(),
+                                   /*algo=*/algo,
+                                   /*workSpace=*/scratch.opaque(),
+                                   /*workSpaceSizeInBytes=*/scratch.size(),
+                                   /*beta=*/beta,
+                                   /*dxDesc=*/in_back_nd.handle(),
+                                   /*dx=*/backward_input_data->opaque());
   if (is_profiling) {
     timer->Stop(AsCUDAStream(stream));
     if (status == CUDNN_STATUS_SUCCESS) {
@@ -3749,12 +3539,6 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl(
     DeviceMemory* backward_filter_data, ScratchAllocator* scratch_allocator,
     const dnn::AlgorithmConfig& algorithm_config,
     dnn::ProfileResult* output_profile_result) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(FATAL) << "failed to set stream for cudnn handle: " << ToString(status);
-  }
-
   cudnnDataType_t cudnn_type = GetCudnnDataType();
   // Alpha is the scaling factor for input.
   float falpha = 1.0;
@@ -3767,19 +3551,21 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl(
   void* beta = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dbeta)
                                                : static_cast(&fbeta);
 
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+
   // TBD(keveman): remove once cuDNN supports kBatchYXDepth for backward pass.
   BatchDescriptor output_descriptor;
   output_descriptor.CloneFrom(output_descriptor_in);
   std::unique_ptr> transform_scratch;
-  backward_output_data = MaybeTransformLayout(
-      stream, &output_descriptor, backward_output_data, &transform_scratch);
+  backward_output_data =
+      MaybeTransformLayout(stream, cudnn, &output_descriptor,
+                           backward_output_data, &transform_scratch);
 
-  ScopedTensorDescriptor out_back_nd{parent_, output_descriptor, cudnn_type};
-  ScopedTensorDescriptor input_nd{parent_, input_descriptor, cudnn_type};
-  ScopedFilterDescriptor filter{parent_, filter_descriptor, input_descriptor,
-                                cudnn_type};
-  ScopedConvolutionDescriptor conv{parent_, convolution_descriptor,
-                                   GetConvComputeType()};
+  ScopedTensorDescriptor out_back_nd(output_descriptor, cudnn_type);
+  ScopedTensorDescriptor input_nd(input_descriptor, cudnn_type);
+  ScopedFilterDescriptor filter(filter_descriptor, cudnn_type);
+  ScopedConvolutionDescriptor conv(convolution_descriptor,
+                                   GetConvComputeType());
 
   const bool is_profiling = output_profile_result != nullptr;
   cudnnConvolutionBwdFilterAlgo_t algo;
@@ -3791,8 +3577,7 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl(
     // Lambda that retrieves the algorithm.
     // specify_limit will occur when we have a scratch allocator and it succeeds
     // in allocating; otherwise, we'll fall back to the "no workspace" version.
-    auto get_algorithm = [&](bool specify_limit) SHARED_LOCKS_REQUIRED(
-        dnn_handle_mutex_) {
+    auto get_algorithm = [&](bool specify_limit) {
       cudnnConvolutionBwdFilterPreference_t preference =
           specify_limit ? CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT
                         : CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE;
@@ -3806,8 +3591,8 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl(
       }
 
       cudnnConvolutionBwdFilterAlgo_t algo_to_use;
-      cudnnStatus_t status = wrap::cudnnGetConvolutionBackwardFilterAlgorithm(
-          parent_, ToHandle(dnn_handle_),
+      cudnnStatus_t status = cudnnGetConvolutionBackwardFilterAlgorithm(
+          cudnn.handle(),
           /*srcDesc=*/input_nd.handle(),
           /*diffDesc=*/out_back_nd.handle(),
           /*convDesc=*/conv.handle(),
@@ -3825,9 +3610,10 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl(
 
     if (scratch_allocator != nullptr) {
       size_t size_in_bytes;
-      status = wrap::cudnnGetConvolutionBackwardFilterWorkspaceSize(
-          parent_, ToHandle(dnn_handle_), /*srcDesc=*/input_nd.handle(),
-          /*diffDesc=*/out_back_nd.handle(), /*convDesc=*/conv.handle(),
+      auto status = cudnnGetConvolutionBackwardFilterWorkspaceSize(
+          cudnn.handle(),
+          /*xDesc=*/input_nd.handle(),
+          /*dyDesc=*/out_back_nd.handle(), /*convDesc=*/conv.handle(),
           /*gradDesc=*/filter.handle(), /*algo=*/algo,
           /*sizeInBytes=*/&size_in_bytes);
       int64 size_in_bytes_int64 = size_in_bytes;
@@ -3860,9 +3646,10 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl(
     conv.set_use_tensor_op_math(algotype.tensor_ops_enabled());
 
     size_t size_in_bytes;
-    status = wrap::cudnnGetConvolutionBackwardFilterWorkspaceSize(
-        parent_, ToHandle(dnn_handle_), /*srcDesc=*/input_nd.handle(),
-        /*diffDesc=*/out_back_nd.handle(), /*convDesc=*/conv.handle(),
+    auto status = cudnnGetConvolutionBackwardFilterWorkspaceSize(
+        cudnn.handle(),
+        /*xDesc=*/input_nd.handle(),
+        /*dyDesc=*/out_back_nd.handle(), /*convDesc=*/conv.handle(),
         /*gradDesc=*/filter.handle(), /*algo=*/algo,
         /*sizeInBytes=*/&size_in_bytes);
     if (status != CUDNN_STATUS_SUCCESS) {
@@ -3916,11 +3703,13 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl(
   }
 
 #if CUDNN_VERSION >= 5000
-  status = wrap::cudnnConvolutionBackwardFilter(
+  auto status = cudnnConvolutionBackwardFilter(
+      cudnn.handle(),
 #else
-  status = wrap::cudnnConvolutionBackwardFilter_v3(
+  auto status = cudnnConvolutionBackwardFilter_v3(
+      cudnn.handle(),
 #endif
-      this, stream, ToHandle(dnn_handle_), /*alpha=*/alpha,
+      /*alpha=*/alpha,
       /*srcDesc=*/input_nd.handle(),
       /*srcData=*/input_data.opaque(),
       /*diffDesc=*/out_back_nd.handle(),
@@ -4015,25 +3804,19 @@ bool CudnnSupport::DoConvolveBackwardBiasImpl(
     const DeviceMemory& input_data,
     const dnn::BatchDescriptor& bias_descriptor,
     DeviceMemory* backward_bias_data) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(FATAL) << "failed to set stream for cudnn handle: " << ToString(status);
-  }
-
   cudnnDataType_t cudnn_type = GetCudnnDataType();
-  ScopedTensorDescriptor input_nd{parent_, input_descriptor, cudnn_type};
-  ScopedTensorDescriptor bias_nd{parent_, bias_descriptor, cudnn_type};
+  ScopedTensorDescriptor input_nd(input_descriptor, cudnn_type);
+  ScopedTensorDescriptor bias_nd(bias_descriptor, cudnn_type);
 
   // Alpha is the scaling factor for input.
   float alpha = 1.0;
   // Beta is the scaling factor for output.
   float beta = 0.0;
 
-  status = wrap::cudnnConvolutionBackwardBias(
-      this, stream, ToHandle(dnn_handle_), &alpha, input_nd.handle(),
-      input_data.opaque(), &beta, bias_nd.handle(),
-      backward_bias_data->opaque());
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+  auto status = cudnnConvolutionBackwardBias(
+      cudnn.handle(), &alpha, input_nd.handle(), input_data.opaque(), &beta,
+      bias_nd.handle(), backward_bias_data->opaque());
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "failed to enqueue backward convolution on stream: "
                << ToString(status);
@@ -4209,8 +3992,7 @@ bool CudnnSupport::DoBiasAdd(Stream* stream,
                              const DeviceMemory& biases,
                              const dnn::BatchDescriptor& dimensions,
                              DeviceMemory* output_data) {
-  ScopedTensorDescriptor input_descriptor{parent_, dimensions,
-                                          CUDNN_DATA_FLOAT};
+  ScopedTensorDescriptor input_descriptor(dimensions, CUDNN_DATA_FLOAT);
 
   BatchDescriptor bias_dimensions;
   bias_dimensions.set_count(1)
@@ -4218,8 +4000,7 @@ bool CudnnSupport::DoBiasAdd(Stream* stream,
       .set_height(1)
       .set_width(1)
       .set_layout(dnn::DataLayout::kBatchYXDepth);
-  ScopedTensorDescriptor bias_descriptor{parent_, bias_dimensions,
-                                         CUDNN_DATA_FLOAT};
+  ScopedTensorDescriptor bias_descriptor(bias_dimensions, CUDNN_DATA_FLOAT);
 
   // cudnnAddTensor after R3 is in-place, so we need to copy input_data to
   // output_data before doing the addition, unless the input and
@@ -4235,23 +4016,18 @@ bool CudnnSupport::DoBiasAdd(Stream* stream,
     }
   }
 
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "failed to set stream for cudnn handle: " << ToString(status);
-    return false;
-  }
-
   const float alpha = 1.0f;
   const float beta = 1.0f;
 
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+
 #if CUDNN_VERSION >= 5000
-  status = wrap::cudnnAddTensor(
+  auto status = cudnnAddTensor(
 #else
-  status = wrap::cudnnAddTensor_v3(
+  auto status = cudnnAddTensor_v3(
 #endif
-      this, stream, ToHandle(dnn_handle_), &alpha, bias_descriptor.handle(),
-      biases.opaque(), &beta, input_descriptor.handle(), output_data->opaque());
+      cudnn.handle(), &alpha, bias_descriptor.handle(), biases.opaque(), &beta,
+      input_descriptor.handle(), output_data->opaque());
 
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "stream " << stream << " could not enqueue bias addition.";
@@ -4267,16 +4043,9 @@ bool CudnnSupport::DoActivate(Stream* stream,
                               const DeviceMemory& input_data,
                               DeviceMemory* output_data,
                               uint64 options) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "failed to set stream for cudnn handle: " << ToString(status);
-    return false;
-  }
-
 #if CUDNN_VERSION >= 5000
-  ScopedActivationDescriptor activation_desc{
-      parent_, activation_mode, CUDNN_PROPAGATE_NAN, dimensions.value_max()};
+  ScopedActivationDescriptor activation_desc(
+      activation_mode, CUDNN_PROPAGATE_NAN, dimensions.value_max());
 #else
   cudnnActivationMode_t mode;
   switch (activation_mode) {
@@ -4306,20 +4075,22 @@ bool CudnnSupport::DoActivate(Stream* stream,
   }
 #endif
 
-  ScopedTensorDescriptor input_nd{parent_, dimensions, CUDNN_DATA_FLOAT};
+  ScopedTensorDescriptor input_nd(dimensions, CUDNN_DATA_FLOAT);
   // Alpha is the input scaling factor.
   float alpha = 1.0;
   // Beta is the output scaling factor.
   float beta = 0.0;
-  status = wrap::cudnnActivationForward(
-      this, stream, ToHandle(dnn_handle_),
+
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+  auto status =
+      cudnnActivationForward(cudnn.handle(),
 #if CUDNN_VERSION >= 5000
-      activation_desc.handle(),
+                             activation_desc.handle(),
 #else
-      mode,
+                             mode,
 #endif
-      &alpha, input_nd.handle(), input_data.opaque(), &beta, input_nd.handle(),
-      output_data->opaque());
+                             &alpha, input_nd.handle(), input_data.opaque(),
+                             &beta, input_nd.handle(), output_data->opaque());
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "stream " << stream
                << " could not enqueue activation: " << ToString(status);
@@ -4335,26 +4106,19 @@ bool CudnnSupport::DoPoolForward(
     const DeviceMemory& input_data,
     const dnn::BatchDescriptor& output_dimensions,
     DeviceMemory* output_data) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "failed to set stream for cudnn handle: " << ToString(status);
-    return false;
-  }
-
   // Alpha is the scaling factor for input.
   double alpha = 1.0;
   // Beta is the scaling factor for output.
   double beta = 0.0;
 
-  ScopedTensorDescriptor src_desc{parent_, input_dimensions, CUDNN_DATA_DOUBLE};
-  ScopedTensorDescriptor dest_desc{parent_, output_dimensions,
-                                   CUDNN_DATA_DOUBLE};
-  ScopedPoolingDescriptor pooling_desc{parent_, pooling_dimensions};
-  status = wrap::cudnnPoolingForward(
-      this, stream, ToHandle(dnn_handle_), pooling_desc.handle(), &alpha,
-      src_desc.handle(), input_data.opaque(), &beta, dest_desc.handle(),
-      output_data->opaque());
+  ScopedTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_DOUBLE);
+  ScopedTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_DOUBLE);
+  ScopedPoolingDescriptor pooling_desc(pooling_dimensions);
+
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+  auto status = cudnnPoolingForward(
+      cudnn.handle(), pooling_desc.handle(), &alpha, src_desc.handle(),
+      input_data.opaque(), &beta, dest_desc.handle(), output_data->opaque());
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "failed to enqueue forward pooling on stream: "
                << ToString(status);
@@ -4369,26 +4133,19 @@ bool CudnnSupport::DoPoolForward(
     const DeviceMemory& input_data,
     const dnn::BatchDescriptor& output_dimensions,
     DeviceMemory* output_data) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "failed to set stream for cudnn handle: " << ToString(status);
-    return false;
-  }
-
   // Alpha is the scaling factor for input.
   float alpha = 1.0;
   // Beta is the scaling factor for output.
   float beta = 0.0;
 
-  ScopedTensorDescriptor src_desc{parent_, input_dimensions, CUDNN_DATA_FLOAT};
-  ScopedTensorDescriptor dest_desc{parent_, output_dimensions,
-                                   CUDNN_DATA_FLOAT};
-  ScopedPoolingDescriptor pooling_desc{parent_, pooling_dimensions};
-  status = wrap::cudnnPoolingForward(
-      this, stream, ToHandle(dnn_handle_), pooling_desc.handle(), &alpha,
-      src_desc.handle(), input_data.opaque(), &beta, dest_desc.handle(),
-      output_data->opaque());
+  ScopedTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_FLOAT);
+  ScopedTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_FLOAT);
+  ScopedPoolingDescriptor pooling_desc(pooling_dimensions);
+
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+  auto status = cudnnPoolingForward(
+      cudnn.handle(), pooling_desc.handle(), &alpha, src_desc.handle(),
+      input_data.opaque(), &beta, dest_desc.handle(), output_data->opaque());
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "failed to enqueue forward pooling on stream: "
                << ToString(status);
@@ -4403,25 +4160,18 @@ bool CudnnSupport::DoPoolForward(
     const DeviceMemory& input_data,
     const dnn::BatchDescriptor& output_dimensions,
     DeviceMemory* output_data) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "failed to set stream for cudnn handle: " << ToString(status);
-    return false;
-  }
-
   // Alpha is the scaling factor for input.
   float alpha = 1.0;
   // Beta is the scaling factor for output.
   float beta = 0.0;
 
-  ScopedTensorDescriptor src_desc{parent_, input_dimensions, CUDNN_DATA_HALF};
-  ScopedTensorDescriptor dest_desc{parent_, output_dimensions, CUDNN_DATA_HALF};
-  ScopedPoolingDescriptor pooling_desc{parent_, pooling_dimensions};
-  status = wrap::cudnnPoolingForward(
-      this, stream, ToHandle(dnn_handle_), pooling_desc.handle(), &alpha,
-      src_desc.handle(), input_data.opaque(), &beta, dest_desc.handle(),
-      output_data->opaque());
+  ScopedTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_HALF);
+  ScopedTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_HALF);
+  ScopedPoolingDescriptor pooling_desc(pooling_dimensions);
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+  auto status = cudnnPoolingForward(
+      cudnn.handle(), pooling_desc.handle(), &alpha, src_desc.handle(),
+      input_data.opaque(), &beta, dest_desc.handle(), output_data->opaque());
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "failed to enqueue forward pooling on stream: "
                << ToString(status);
@@ -4438,27 +4188,21 @@ bool CudnnSupport::DoPoolBackward(
     const DeviceMemory& output_data,
     const DeviceMemory& input_diff_data,
     DeviceMemory* output_diff_data) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "failed to set stream for cudnn handle: " << ToString(status);
-    return false;
-  }
-
   // Alpha is the scaling factor for input.
   double alpha = 1.0;
   // Beta is the scaling factor for output.
   double beta = 0.0;
 
-  ScopedTensorDescriptor src_desc{parent_, input_dimensions, CUDNN_DATA_DOUBLE};
-  ScopedTensorDescriptor dest_desc{parent_, output_dimensions,
-                                   CUDNN_DATA_DOUBLE};
-  ScopedPoolingDescriptor pooling_desc{parent_, pooling_dimensions};
-  status = wrap::cudnnPoolingBackward(
-      this, stream, ToHandle(dnn_handle_), pooling_desc.handle(), &alpha,
-      dest_desc.handle(), output_data.opaque(), dest_desc.handle(),
-      input_diff_data.opaque(), src_desc.handle(), input_data.opaque(), &beta,
-      src_desc.handle(), output_diff_data->opaque());
+  ScopedTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_DOUBLE);
+  ScopedTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_DOUBLE);
+  ScopedPoolingDescriptor pooling_desc(pooling_dimensions);
+
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+  auto status = cudnnPoolingBackward(
+      cudnn.handle(), pooling_desc.handle(), &alpha, dest_desc.handle(),
+      output_data.opaque(), dest_desc.handle(), input_diff_data.opaque(),
+      src_desc.handle(), input_data.opaque(), &beta, src_desc.handle(),
+      output_diff_data->opaque());
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "failed to enqueue backward pooling on stream: "
                << ToString(status);
@@ -4475,27 +4219,21 @@ bool CudnnSupport::DoPoolBackward(
     const DeviceMemory& output_data,
     const DeviceMemory& input_diff_data,
     DeviceMemory* output_diff_data) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "failed to set stream for cudnn handle: " << ToString(status);
-    return false;
-  }
-
   // Alpha is the scaling factor for input.
   float alpha = 1.0;
   // Beta is the scaling factor for output.
   float beta = 0.0;
 
-  ScopedTensorDescriptor src_desc{parent_, input_dimensions, CUDNN_DATA_FLOAT};
-  ScopedTensorDescriptor dest_desc{parent_, output_dimensions,
-                                   CUDNN_DATA_FLOAT};
-  ScopedPoolingDescriptor pooling_desc{parent_, pooling_dimensions};
-  status = wrap::cudnnPoolingBackward(
-      this, stream, ToHandle(dnn_handle_), pooling_desc.handle(), &alpha,
-      dest_desc.handle(), output_data.opaque(), dest_desc.handle(),
-      input_diff_data.opaque(), src_desc.handle(), input_data.opaque(), &beta,
-      src_desc.handle(), output_diff_data->opaque());
+  ScopedTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_FLOAT);
+  ScopedTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_FLOAT);
+  ScopedPoolingDescriptor pooling_desc(pooling_dimensions);
+
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+  auto status = cudnnPoolingBackward(
+      cudnn.handle(), pooling_desc.handle(), &alpha, dest_desc.handle(),
+      output_data.opaque(), dest_desc.handle(), input_diff_data.opaque(),
+      src_desc.handle(), input_data.opaque(), &beta, src_desc.handle(),
+      output_diff_data->opaque());
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "failed to enqueue backward pooling on stream: "
                << ToString(status);
@@ -4512,26 +4250,21 @@ bool CudnnSupport::DoPoolBackward(
     const DeviceMemory& output_data,
     const DeviceMemory& input_diff_data,
     DeviceMemory* output_diff_data) {
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "failed to set stream for cudnn handle: " << ToString(status);
-    return false;
-  }
-
   // Alpha is the scaling factor for input.
   float alpha = 1.0;
   // Beta is the scaling factor for output.
   float beta = 0.0;
 
-  ScopedTensorDescriptor src_desc{parent_, input_dimensions, CUDNN_DATA_HALF};
-  ScopedTensorDescriptor dest_desc{parent_, output_dimensions, CUDNN_DATA_HALF};
-  ScopedPoolingDescriptor pooling_desc{parent_, pooling_dimensions};
-  status = wrap::cudnnPoolingBackward(
-      this, stream, ToHandle(dnn_handle_), pooling_desc.handle(), &alpha,
-      dest_desc.handle(), output_data.opaque(), dest_desc.handle(),
-      input_diff_data.opaque(), src_desc.handle(), input_data.opaque(), &beta,
-      src_desc.handle(), output_diff_data->opaque());
+  ScopedTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_HALF);
+  ScopedTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_HALF);
+  ScopedPoolingDescriptor pooling_desc(pooling_dimensions);
+
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+  auto status = cudnnPoolingBackward(
+      cudnn.handle(), pooling_desc.handle(), &alpha, dest_desc.handle(),
+      output_data.opaque(), dest_desc.handle(), input_diff_data.opaque(),
+      src_desc.handle(), input_data.opaque(), &beta, src_desc.handle(),
+      output_diff_data->opaque());
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "failed to enqueue backward pooling on stream: "
                << ToString(status);
@@ -4553,7 +4286,7 @@ bool CudnnSupport::DoNormalizeWithDimensions(
     const DeviceMemory& input_data, DeviceMemory* output_data) {
   // Check for unsupported modes.
   if (normalize_descriptor.wrap_around()) {
-    LOG(ERROR) << "CUDA LRN does not support wrap-around mode";
+    LOG(ERROR) << "CUDA LRN does not support cudnn-around mode";
     return false;
   }
   if (normalize_descriptor.segment_size()) {
@@ -4561,26 +4294,21 @@ bool CudnnSupport::DoNormalizeWithDimensions(
     return false;
   }
 
-  // Launch the normalization.
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "failed to set stream for cudnn handle: " << ToString(status);
-    return false;
-  }
-
-  ScopedTensorDescriptor dims{parent_, dimensions, CUDNN_DATA_FLOAT};
-  ScopedNormalizeDescriptor normalize{parent_, normalize_descriptor};
+  ScopedTensorDescriptor dims(dimensions, CUDNN_DATA_FLOAT);
+  ScopedNormalizeDescriptor normalize(normalize_descriptor);
 
   // Alpha is the scaling factor for input.
   float alpha = 1.0f;
   // Beta is the scaling factor for output.
   float beta = 0.0f;
 
-  status = wrap::cudnnLRNCrossChannelForward(
-      this, stream, ToHandle(dnn_handle_), normalize.handle(),
-      CUDNN_LRN_CROSS_CHANNEL_DIM1, &alpha, dims.handle(), input_data.opaque(),
-      &beta, dims.handle(), output_data->opaque());
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+
+  // Launch the normalization.
+  auto status = cudnnLRNCrossChannelForward(
+      cudnn.handle(), normalize.handle(), CUDNN_LRN_CROSS_CHANNEL_DIM1, &alpha,
+      dims.handle(), input_data.opaque(), &beta, dims.handle(),
+      output_data->opaque());
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "failed to run cudnnLRNCrossChannelForward";
     return false;
@@ -4596,7 +4324,7 @@ bool CudnnSupport::DoNormalizeBackwardWithDimensions(
     DeviceMemory* raw_variable_gradient) {
   // Check for unsupported modes.
   if (normalize_descriptor.wrap_around()) {
-    LOG(ERROR) << "CUDA LRN does not support wrap-around mode";
+    LOG(ERROR) << "CUDA LRN does not support cudnn-around mode";
     return false;
   }
   if (normalize_descriptor.segment_size()) {
@@ -4604,23 +4332,16 @@ bool CudnnSupport::DoNormalizeBackwardWithDimensions(
     return false;
   }
 
-  mutex_lock lock{dnn_handle_mutex_};
-  auto status = wrap::cudnnSetStream(this, stream, ToHandle(dnn_handle_));
-  if (status != CUDNN_STATUS_SUCCESS) {
-    LOG(ERROR) << "failed to set stream for cudnn handle: " << ToString(status);
-    return false;
-  }
-
-  ScopedTensorDescriptor dims{parent_, dimensions, CUDNN_DATA_FLOAT};
-  ScopedNormalizeDescriptor normalize{parent_, normalize_descriptor};
+  ScopedTensorDescriptor dims(dimensions, CUDNN_DATA_FLOAT);
+  ScopedNormalizeDescriptor normalize(normalize_descriptor);
 
   float alpha = 1.0f;
   float beta = 0.0f;
 
-  status = wrap::cudnnLRNCrossChannelBackward(
-      this, stream, ToHandle(dnn_handle_), normalize.handle(),
-      CUDNN_LRN_CROSS_CHANNEL_DIM1, &alpha, dims.handle(),
-      normalized_data.opaque(), dims.handle(),
+  auto cudnn = cudnn_->GetHandle(parent_, stream);
+  auto status = cudnnLRNCrossChannelBackward(
+      cudnn.handle(), normalize.handle(), CUDNN_LRN_CROSS_CHANNEL_DIM1, &alpha,
+      dims.handle(), normalized_data.opaque(), dims.handle(),
       normalized_variable_gradient.opaque(), dims.handle(), raw_data.opaque(),
       &beta, dims.handle(), raw_variable_gradient->opaque());
   if (status != CUDNN_STATUS_SUCCESS) {
@@ -4736,17 +4457,14 @@ bool CudnnSupport::DeriveOutputBatchDescriptor(
     const FilterDescriptor& filter_descriptor,
     const dnn::ConvolutionDescriptor& convolution_descriptor,
     dnn::BatchDescriptor* output_batch_descriptor) {
-  ScopedTensorDescriptor input_nd{parent_, batch_descriptor, CUDNN_DATA_FLOAT};
-  ScopedFilterDescriptor filter{parent_, filter_descriptor, batch_descriptor,
-                                CUDNN_DATA_FLOAT};
-  ScopedConvolutionDescriptor conv{parent_, convolution_descriptor,
-                                   CUDNN_DATA_FLOAT};
+  ScopedTensorDescriptor input_nd(batch_descriptor, CUDNN_DATA_FLOAT);
+  ScopedFilterDescriptor filter(filter_descriptor, CUDNN_DATA_FLOAT);
+  ScopedConvolutionDescriptor conv(convolution_descriptor, CUDNN_DATA_FLOAT);
 
   int dn = batch_descriptor.ndims() + 2;
   std::vector dims(dn);  // in BDYX
-  auto status = wrap::cudnnGetConvolutionNdForwardOutputDim(
-      parent_, conv.handle(), input_nd.handle(), filter.handle(), dn,
-      dims.data());
+  auto status = cudnnGetConvolutionNdForwardOutputDim(
+      conv.handle(), input_nd.handle(), filter.handle(), dn, dims.data());
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "could not get output tensor for convolution: "
                << ToString(status);
diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.h b/tensorflow/stream_executor/cuda/cuda_dnn.h
index 8a0458bc802cab..e2de3c62d81ae5 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.h
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.h
@@ -19,6 +19,7 @@ limitations under the License.
 #ifndef TENSORFLOW_STREAM_EXECUTOR_CUDA_CUDA_DNN_H_
 #define TENSORFLOW_STREAM_EXECUTOR_CUDA_CUDA_DNN_H_
 
+#include "tensorflow/stream_executor/cuda/cuda_activation.h"
 #include "tensorflow/stream_executor/dnn.h"
 #include "tensorflow/stream_executor/lib/status.h"
 #include "tensorflow/stream_executor/platform/mutex.h"
@@ -42,7 +43,6 @@ extern const PluginId kCuDnnPlugin;
 class CudnnSupport : public dnn::DnnSupport {
  public:
   explicit CudnnSupport(CUDAExecutor* parent);
-  ~CudnnSupport() override;
 
   port::Status Init() override;
   port::StatusOr GetVersion() override;
@@ -624,54 +624,11 @@ class CudnnSupport : public dnn::DnnSupport {
                          dnn::DataType output_type, float scale,
                          DeviceMemoryBase* output_data) override;
 
-  const Stream* GetCurrentDnnStream() const
-      SHARED_LOCKS_REQUIRED(dnn_handle_mutex_) {
-    return current_dnn_stream_;
-  }
-
-  void SetCurrentDnnStream(Stream* stream)
-      EXCLUSIVE_LOCKS_REQUIRED(dnn_handle_mutex_) {
-    current_dnn_stream_ = stream;
-  }
-
-  CUDAExecutor* GetParentExecutor() { return parent_; }
-
-  // Guards the enqueueing of DNN operations via the dnn_handle_ below, and
-  // access to current_dnn_stream_.
-  //
-  // This is a public member because we need to add thread safty annotations in
-  // the cudnn wrapper functions in the cc file, which need to access this
-  // mutex (the annotations require C++ permission checks).
-  mutex dnn_handle_mutex_;
-
  private:
   CUDAExecutor* parent_;  // Parent executor object. Not owned.
 
-  // cudnn library handle. cudnnHandle_t type is not present in this header to
-  // prevent third-party library header inclusions from leaking outside the
-  // single cuda_dnn translation unit.
-  void* dnn_handle_ GUARDED_BY(dnn_handle_mutex_);
-
-  // The current cudnn stream that is set by SetCurrentDnnStream().
-  Stream* current_dnn_stream_ GUARDED_BY(dnn_handle_mutex_);
-
-  // NOTE(keveman): Temporary data layout transformation until cuDNN supports
-  // kBatchYXDepth for backward pass. This function allocates temporary memory,
-  // lays out the source data into the temporary but in the kBatchDepthXY
-  // layout, and returns the temporary memory. The caller is responsible for
-  // deallocating the temporary. Since the allocation is done using Stream's
-  // AllocateTemporaryMemory, a later BlockHostUntilDone could be used for
-  // deallocation.
-  //
-  // transform_scratch is populated with a legitimate temporary allocation iff
-  // the original output data needs to be transformed.
-  template
-  DeviceMemory MaybeTransformLayout(
-      Stream* stream,
-      dnn::BatchDescriptor* output_descriptor,
-      DeviceMemory backward_output_data,
-      std::unique_ptr>* transform_scratch)
-      EXCLUSIVE_LOCKS_REQUIRED(dnn_handle_mutex_);
+  // Provides access to the cuDNN handle.
+  std::unique_ptr cudnn_;
 
   template 
   bool DoBatchNormalizationForwardImpl(
@@ -700,7 +657,7 @@ class CudnnSupport : public dnn::DnnSupport {
 
   template 
   bool DoConvolveImpl(Stream* stream,
-                      const dnn::BatchDescriptor& batch_descriptor,
+                      const dnn::BatchDescriptor& input_descriptor,
                       const DeviceMemory& input_data,
                       const dnn::FilterDescriptor& filter_descriptor,
                       const DeviceMemory& filter_data,

From 62c50e197e25c661048fe90fdd177a87eda47376 Mon Sep 17 00:00:00 2001
From: Allen Lavoie 
Date: Tue, 8 May 2018 14:00:30 -0700
Subject: [PATCH 0513/1691] Avoid string formatting in assert_same_float_dtype
 unless there's an error

Especially helpful when executing eagerly

PiperOrigin-RevId: 195871887
---
 tensorflow/python/ops/check_ops.py | 30 +++++++++++++++++++++++-------
 1 file changed, 23 insertions(+), 7 deletions(-)

diff --git a/tensorflow/python/ops/check_ops.py b/tensorflow/python/ops/check_ops.py
index 306055d2025f17..cabc1e724cdb66 100644
--- a/tensorflow/python/ops/check_ops.py
+++ b/tensorflow/python/ops/check_ops.py
@@ -1169,19 +1169,35 @@ def _assert_same_base_type(items, expected_type=None):
   Raises:
     ValueError: If any types do not match.
   """
-  original_item_str = None
+  original_expected_type = expected_type
+  mismatch = False
   for item in items:
     if item is not None:
       item_type = item.dtype.base_dtype
       if not expected_type:
         expected_type = item_type
-        original_item_str = item.name if hasattr(item, 'name') else str(item)
       elif expected_type != item_type:
-        raise ValueError('%s, type=%s, must be of the same type (%s)%s.' % (
-            item.name if hasattr(item, 'name') else str(item),
-            item_type, expected_type,
-            (' as %s' % original_item_str) if original_item_str else ''))
-  return expected_type
+        mismatch = True
+        break
+  if mismatch:
+    # Loop back through and build up an informative error message (this is very
+    # slow, so we don't do it unless we found an error above).
+    expected_type = original_expected_type
+    original_item_str = None
+    for item in items:
+      if item is not None:
+        item_type = item.dtype.base_dtype
+        if not expected_type:
+          expected_type = item_type
+          original_item_str = item.name if hasattr(item, 'name') else str(item)
+        elif expected_type != item_type:
+          raise ValueError('%s, type=%s, must be of the same type (%s)%s.' % (
+              item.name if hasattr(item, 'name') else str(item),
+              item_type, expected_type,
+              (' as %s' % original_item_str) if original_item_str else ''))
+    return expected_type  # Should be unreachable
+  else:
+    return expected_type
 
 
 @tf_export('assert_same_float_dtype')

From 1d94ed775417bad963a91cd6831a51e7538d797b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 14:00:48 -0700
Subject: [PATCH 0514/1691] Increase size of test
 tensorflow/contrib/layers:rev_block_lib_test to medium to avoid flaky
 timeouts.

PiperOrigin-RevId: 195871947
---
 tensorflow/contrib/layers/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/layers/BUILD b/tensorflow/contrib/layers/BUILD
index d5b3b279a1b732..7355a403aeef78 100644
--- a/tensorflow/contrib/layers/BUILD
+++ b/tensorflow/contrib/layers/BUILD
@@ -381,7 +381,7 @@ py_test(
 
 py_test(
     name = "rev_block_lib_test",
-    size = "small",
+    size = "medium",
     srcs = ["python/layers/rev_block_lib_test.py"],
     srcs_version = "PY2AND3",
     deps = [

From d3f3fb5b5f2db18f890838b29cac94ba88335f0a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 14:41:48 -0700
Subject: [PATCH 0515/1691] Increase shard count of
 tensorflow/contrib/distributions:mixture_test to avoid flaky timeouts in asan
 mode

PiperOrigin-RevId: 195878809
---
 tensorflow/contrib/distributions/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index 47f2ebca773b46..8021ec6141225a 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -372,6 +372,7 @@ cuda_py_test(
         "//tensorflow/python:random_ops",
         "//tensorflow/python:variables",
     ],
+    shard_count = 4,
 )
 
 cuda_py_test(

From f58effe44dea9e8c7bf092c6779cd430994f7a72 Mon Sep 17 00:00:00 2001
From: Alexandre Passos 
Date: Tue, 8 May 2018 14:42:35 -0700
Subject: [PATCH 0516/1691] Do not differentiage integers in the eager API.

This is similar to the change made in:
https://github.com/tensorflow/tensorflow/commit/f63750645826df65b05cad505546a86f0e347674
for backpropagation during graph construction via tf.gradients()

PiperOrigin-RevId: 195878952
---
 tensorflow/c/eager/tape.h                   | 36 +++++++++---
 tensorflow/contrib/eager/python/tfe_test.py |  6 +-
 tensorflow/python/eager/backprop.py         |  5 ++
 tensorflow/python/eager/backprop_test.py    | 10 +++-
 tensorflow/python/eager/pywrap_tensor.cc    |  6 ++
 tensorflow/python/eager/pywrap_tensor.h     |  1 +
 tensorflow/python/eager/pywrap_tfe_src.cc   | 62 ++++++++++++++++++---
 7 files changed, 107 insertions(+), 19 deletions(-)

diff --git a/tensorflow/c/eager/tape.h b/tensorflow/c/eager/tape.h
index 8026076b9ef3bf..e9ed3395c44830 100644
--- a/tensorflow/c/eager/tape.h
+++ b/tensorflow/c/eager/tape.h
@@ -130,13 +130,15 @@ class GradientTape {
     }
   }
 
-  bool ShouldRecord(gtl::ArraySlice tensor_ids);
+  bool ShouldRecord(gtl::ArraySlice tensor_ids,
+                    gtl::ArraySlice dtypes);
 
   void Watch(int64 tensor_id);
 
   void RecordOperation(const string& op_type,
                        gtl::ArraySlice output_tensors,
                        gtl::ArraySlice input_tensor_id,
+                       gtl::ArraySlice input_dtypes,
                        BackwardFunction* backward_function,
                        const std::function& backward_function_deleter);
 
@@ -170,12 +172,30 @@ class GradientTape {
 
 // Template instantiations here
 
+inline bool IsDtypeTrainable(DataType dtype) {
+  switch (dtype) {
+    case DT_HALF:
+    case DT_BFLOAT16:
+    case DT_FLOAT:
+    case DT_DOUBLE:
+    case DT_COMPLEX64:
+    case DT_COMPLEX128:
+    case DT_RESOURCE:
+    case DT_VARIANT:
+      return true;
+    default:
+      return false;
+  }
+}
+
 template 
 bool GradientTape::ShouldRecord(
-    gtl::ArraySlice tensor_ids) {
-  for (int64 i : tensor_ids) {
-    if (tensor_tape_.find(i) != tensor_tape_.end()) {
-      return true;
+    gtl::ArraySlice tensor_ids,
+    gtl::ArraySlice dtypes) {
+  CHECK_EQ(tensor_ids.size(), dtypes.size());
+  for (int i = 0; i < tensor_ids.size(); ++i) {
+    if (tensor_tape_.find(tensor_ids[i]) != tensor_tape_.end()) {
+      return IsDtypeTrainable(dtypes[i]);
     }
   }
   return false;
@@ -189,9 +209,11 @@ void GradientTape::Watch(int64 tensor_id) {
 template 
 void GradientTape::RecordOperation(
     const string& op_type, gtl::ArraySlice output_tensors,
-    gtl::ArraySlice input_tensor_id, BackwardFunction* backward_function,
+    gtl::ArraySlice input_tensor_id,
+    gtl::ArraySlice input_dtypes,
+    BackwardFunction* backward_function,
     const std::function& backward_function_deleter) {
-  if (!ShouldRecord(input_tensor_id)) {
+  if (!ShouldRecord(input_tensor_id, input_dtypes)) {
     backward_function_deleter();
     return;
   }
diff --git a/tensorflow/contrib/eager/python/tfe_test.py b/tensorflow/contrib/eager/python/tfe_test.py
index e80ccbb74d8623..db50b33af2e4f1 100644
--- a/tensorflow/contrib/eager/python/tfe_test.py
+++ b/tensorflow/contrib/eager/python/tfe_test.py
@@ -57,7 +57,7 @@ def square(x):
       return math_ops.multiply(x, x)
 
     grad = tfe.gradients_function(square)
-    self.assertEquals([6], [x.numpy() for x in grad(3)])
+    self.assertEquals([6], [x.numpy() for x in grad(3.)])
 
   def testGradOfGrad(self):
 
@@ -66,7 +66,7 @@ def square(x):
 
     grad = tfe.gradients_function(square)
     gradgrad = tfe.gradients_function(lambda x: grad(x)[0])
-    self.assertEquals([2], [x.numpy() for x in gradgrad(3)])
+    self.assertEquals([2], [x.numpy() for x in gradgrad(3.)])
 
   def testCustomGrad(self):
 
@@ -80,7 +80,7 @@ def grad_fn(_):
       return y, grad_fn
 
     grad = tfe.gradients_function(f)
-    self.assertEquals([12], [x.numpy() for x in grad(3)])
+    self.assertEquals([12], [x.numpy() for x in grad(3.)])
 
   def testGPU(self):
     if tfe.num_gpus() <= 0:
diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py
index d04b004451223a..967c12828043f8 100644
--- a/tensorflow/python/eager/backprop.py
+++ b/tensorflow/python/eager/backprop.py
@@ -358,6 +358,8 @@ def f(x, y):
   assert y_grad.numpy() == (2 ** 3) - 2 * 2 * 3
   ```
 
+  Note that only tensors with real or complex dtypes are differentiable.
+
   Args:
    f: function to be differentiated. If `f` returns a scalar, this scalar will
      be differentiated. If `f` returns a tensor or list of tensors, by default
@@ -700,6 +702,9 @@ class GradientTape(object):
   dz_dx = g.gradient(z, x)  # 108.0 (4*x^3 at x = 3)
   dy_dx = g.gradient(y, x)  # 6.0
   del g  # Drop the reference to the tape
+  ```
+
+  Note that only tensors with real or complex dtypes are differentiable.
   """
 
   def __init__(self, persistent=False):
diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py
index 8d9959fe20768c..be674487f1f74a 100644
--- a/tensorflow/python/eager/backprop_test.py
+++ b/tensorflow/python/eager/backprop_test.py
@@ -124,6 +124,14 @@ def f(x):
     grad_fn = backprop.gradients_function(f)
     self.assertAllEqual(2., grad_fn(1., dy=2.)[0])
 
+  def testGradientInteger(self):
+
+    def f(x):
+      return x + x
+
+    int_tensor = constant_op.constant(1)
+    self.assertEqual(backprop.gradients_function(f)(int_tensor)[0], None)
+
   def testErrors(self):
 
     @custom_gradient.custom_gradient
@@ -753,7 +761,7 @@ def grad(dr):
       return result, grad
 
     x = resource_variable_ops.ResourceVariable(
-        initial_value=3, name='X.' + self.id())
+        initial_value=3., name='X.' + self.id())
 
     def f():
       return my_square(x)
diff --git a/tensorflow/python/eager/pywrap_tensor.cc b/tensorflow/python/eager/pywrap_tensor.cc
index b5b4e394e33bd3..b3aadd55ce7805 100644
--- a/tensorflow/python/eager/pywrap_tensor.cc
+++ b/tensorflow/python/eager/pywrap_tensor.cc
@@ -650,6 +650,12 @@ tensorflow::int64 EagerTensor_id(const PyObject* tensor) {
   return reinterpret_cast(tensor)->id;
 }
 
+tensorflow::DataType EagerTensor_dtype(const PyObject* tensor) {
+  CHECK(EagerTensor_CheckExact(tensor));
+  return static_cast(TFE_TensorHandleDataType(
+      reinterpret_cast(tensor)->handle));
+}
+
 PyObject* TFE_Py_InitEagerTensor(PyObject* base_class) {
   if (!PyType_Check(base_class)) {
     PyErr_SetString(
diff --git a/tensorflow/python/eager/pywrap_tensor.h b/tensorflow/python/eager/pywrap_tensor.h
index 63ab1ed84d5ba3..88982b0c8562c5 100644
--- a/tensorflow/python/eager/pywrap_tensor.h
+++ b/tensorflow/python/eager/pywrap_tensor.h
@@ -21,6 +21,7 @@ limitations under the License.
 
 bool EagerTensor_CheckExact(const PyObject* o);
 tensorflow::int64 EagerTensor_id(const PyObject* tensor);
+tensorflow::DataType EagerTensor_dtype(const PyObject* tensor);
 
 namespace tensorflow {
 TFE_TensorHandle* ConvertToEagerTensor(PyObject* value, PyObject* dtype);
diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc
index 4ecba1a46be8ff..48a5b21dc7fba9 100644
--- a/tensorflow/python/eager/pywrap_tfe_src.cc
+++ b/tensorflow/python/eager/pywrap_tfe_src.cc
@@ -843,6 +843,24 @@ static tensorflow::int64 FastTensorId(PyObject* tensor) {
   return id;
 }
 
+static tensorflow::DataType FastTensorDtype(PyObject* tensor) {
+  if (EagerTensor_CheckExact(tensor)) {
+    return EagerTensor_dtype(tensor);
+  }
+  PyObject* dtype_field = PyObject_GetAttrString(tensor, "dtype");
+  if (dtype_field == nullptr) {
+    return tensorflow::DT_INVALID;
+  }
+  PyObject* enum_field = PyObject_GetAttrString(dtype_field, "_type_enum");
+  Py_DECREF(dtype_field);
+  if (dtype_field == nullptr) {
+    return tensorflow::DT_INVALID;
+  }
+  tensorflow::int64 id = MakeInt(enum_field);
+  Py_DECREF(enum_field);
+  return static_cast(id);
+}
+
 class GradientTape
     : public tensorflow::eager::GradientTape {
  public:
@@ -1053,15 +1071,18 @@ PyObject* TFE_Py_TapeSetShouldRecord(PyObject* tensors) {
   // TODO(apassos) consider not building a list and changing the API to check
   // each tensor individually.
   std::vector tensor_ids;
+  std::vector dtypes;
   tensor_ids.reserve(len);
+  dtypes.reserve(len);
   for (int i = 0; i < len; ++i) {
     PyObject* item = PySequence_Fast_GET_ITEM(seq, i);
     tensor_ids.push_back(FastTensorId(item));
+    dtypes.push_back(FastTensorDtype(item));
   }
   Py_DECREF(seq);
   auto tape_set = *tape_set_ptr;
   for (TFE_Py_Tape* tape : tape_set) {
-    if (tape->tape->ShouldRecord(tensor_ids)) {
+    if (tape->tape->ShouldRecord(tensor_ids, dtypes)) {
       Py_RETURN_TRUE;
     }
   }
@@ -1169,9 +1190,27 @@ PyObject* TFE_Py_TapeWatchedVariables(PyObject* tape) {
 }
 
 namespace {
-void TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors,
-                            const std::vector& input_ids,
-                            PyObject* backward_function) {
+std::vector MakeTensorDtypeList(PyObject* tensors) {
+  PyObject* seq = PySequence_Fast(tensors, "expected a sequence");
+  if (seq == nullptr) {
+    return {};
+  }
+  int len = PySequence_Fast_GET_SIZE(seq);
+  std::vector list;
+  list.reserve(len);
+  for (int i = 0; i < len; ++i) {
+    PyObject* tensor = PySequence_Fast_GET_ITEM(seq, i);
+    list.push_back(FastTensorDtype(tensor));
+  }
+  Py_DECREF(seq);
+  return list;
+}
+
+void TapeSetRecordOperation(
+    PyObject* op_type, PyObject* output_tensors,
+    const std::vector& input_ids,
+    const std::vector& input_dtypes,
+    PyObject* backward_function) {
   std::vector output_info;
   PyObject* seq = PySequence_Fast(output_tensors,
                                   "expected a sequence of integer tensor ids");
@@ -1206,7 +1245,7 @@ void TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors,
   for (TFE_Py_Tape* tape : SafeTapeSet()) {
     Py_INCREF(backward_function);
     tape->tape->RecordOperation(
-        op_type_str, output_info, input_ids, backward_function,
+        op_type_str, output_info, input_ids, input_dtypes, backward_function,
         [backward_function]() { Py_DECREF(backward_function); });
   }
 }
@@ -1221,7 +1260,11 @@ void TFE_Py_TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors,
   std::vector input_ids = MakeTensorIDList(input_tensors);
   if (PyErr_Occurred()) return;
 
-  TapeSetRecordOperation(op_type, output_tensors, input_ids, backward_function);
+  std::vector input_dtypes =
+      MakeTensorDtypeList(input_tensors);
+  if (PyErr_Occurred()) return;
+  TapeSetRecordOperation(op_type, output_tensors, input_ids, input_dtypes,
+                         backward_function);
 }
 
 void TFE_Py_TapeSetDeleteTrace(tensorflow::int64 tensor_id) {
@@ -1710,10 +1753,12 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs,
                          PyObject* results, PyObject* name) {
   std::vector input_ids = MakeTensorIDList(inputs);
   if (PyErr_Occurred()) return nullptr;
+  std::vector input_dtypes = MakeTensorDtypeList(inputs);
+  if (PyErr_Occurred()) return nullptr;
 
   bool should_record = false;
   for (TFE_Py_Tape* tape : SafeTapeSet()) {
-    if (tape->tape->ShouldRecord(input_ids)) {
+    if (tape->tape->ShouldRecord(input_ids, input_dtypes)) {
       should_record = true;
       break;
     }
@@ -1744,7 +1789,8 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs,
   Py_DECREF(callback_args);
   if (backward_function == nullptr) return nullptr;
 
-  TapeSetRecordOperation(op_name, results, input_ids, backward_function);
+  TapeSetRecordOperation(op_name, results, input_ids, input_dtypes,
+                         backward_function);
 
   Py_DECREF(backward_function);
 

From 96fa17d853149f9bdf33c09b89abdd8c6521044d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 14:45:01 -0700
Subject: [PATCH 0517/1691] Increase shard_count of
 tensorflow/python/estimator:estimator_test to avoid flaky asan timeouts

PiperOrigin-RevId: 195879364
---
 tensorflow/python/estimator/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD
index b25cc7aa2659bd..2d9a084bc6bea9 100644
--- a/tensorflow/python/estimator/BUILD
+++ b/tensorflow/python/estimator/BUILD
@@ -489,6 +489,7 @@ py_library(
 py_test(
     name = "estimator_test",
     srcs = ["estimator_test.py"],
+    shard_count = 4,
     srcs_version = "PY2AND3",
     tags = ["notsan"],  # b/67510291
     deps = [

From 34f6241fd822b15c66085dbd1cbec092196d0225 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 14:50:24 -0700
Subject: [PATCH 0518/1691] Add missing ":haswell" match to list of platform
 selectors.

PiperOrigin-RevId: 195880275
---
 tensorflow/contrib/lite/kernels/internal/BUILD | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD
index 7ec4782f96e200..54188217d957bc 100644
--- a/tensorflow/contrib/lite/kernels/internal/BUILD
+++ b/tensorflow/contrib/lite/kernels/internal/BUILD
@@ -387,6 +387,9 @@ cc_library(
         ":armv7a": [
             ":neon_tensor_utils",
         ],
+        ":haswell": [
+            ":neon_tensor_utils",
+        ],
         ":ios_armv7": [
             ":neon_tensor_utils",
         ],

From 71387153307a7df94bcdc5307de95e6e228a95a9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 15:26:44 -0700
Subject: [PATCH 0519/1691] Increase shard count of
 tensorflow/python/keras:lstm_test to avoid flaky timeouts

PiperOrigin-RevId: 195886372
---
 tensorflow/python/keras/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD
index 37b24841bdd4db..77db07b86b601a 100755
--- a/tensorflow/python/keras/BUILD
+++ b/tensorflow/python/keras/BUILD
@@ -604,6 +604,7 @@ py_test(
     name = "lstm_test",
     size = "medium",
     srcs = ["_impl/keras/layers/lstm_test.py"],
+    shard_count = 4,
     srcs_version = "PY2AND3",
     tags = [
         "noasan",  # times out b/63678675

From 24d9492f07e8cba89ae94cf01a1bcae22fcf438b Mon Sep 17 00:00:00 2001
From: jjsjann123 
Date: Tue, 8 May 2018 16:30:08 -0700
Subject: [PATCH 0520/1691] [tftrt update] (#19135)

* [tftrt update]
  code cleaning, removed some boilerplate code

* addressing comments
---
 .../contrib/tensorrt/convert/convert_nodes.cc | 396 ++++++------------
 1 file changed, 130 insertions(+), 266 deletions(-)

diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
index 3767596f8c20a3..be559d30e00417 100644
--- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
+++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
@@ -346,11 +346,10 @@ void ReorderCKtoKC(const TRT_ShapedWeights& iweights,
       break;
     }
     case tensorflow::DataType::DT_HALF: {
-      Reorder2(
-          {k, c}, static_cast(iweights.GetValues()),
-          istrides,
-          static_cast(const_cast(oweights->GetValues())),
-          ostrides);
+      Reorder2({k, c}, static_cast(iweights.GetValues()),
+               istrides, static_cast(
+                             const_cast(oweights->GetValues())),
+               ostrides);
       break;
     }
     default:
@@ -1159,9 +1158,9 @@ tensorflow::Status BinaryTensorOpTensor(
   CHECK_EQ_TYPE(tensor_r->getType(), dtype);
   auto op_pair = ops.find(node_def.op());
   if (op_pair == ops.end())
-    return tensorflow::errors::Unimplemented(
-        "binary op: " + node_def.op() +
-        " not supported at: " + node_def.name());
+    return tensorflow::errors::Unimplemented("binary op: " + node_def.op() +
+                                             " not supported at: " +
+                                             node_def.name());
 
   nvinfer1::IElementWiseLayer* layer = ctx.network()->addElementWise(
       *const_cast(tensor_l),
@@ -2214,64 +2213,63 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode(
   return tensorflow::Status::OK();
 }
 
-tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) {
-  // Visit nodes in reverse topological order and construct the TRT network.
-
-  // Toposort
+tensorflow::Status ReverseTopologicalSort(
+    const tensorrt::convert::SubGraphParams& s,
+    std::list* order) {
   std::vector order_vec;
   tensorflow::GetPostOrder(s.graph, &order_vec);
   // Select just the subgraph
-  std::list order;
   for (tensorflow::Node* node : order_vec) {
     if (s.subgraph_node_ids.count(node->id())) {
-      order.push_front(node);  // we want topological order to construct the
+      // We want topological order to contstruct the
       // network layer by layer
+      order->push_front(node);
     }
   }
-  // topological order is needed to build TRT network
-  static int static_id = 0;
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status SetInputList(
+    const tensorrt::convert::SubGraphParams& s,
+    tensorflow::NodeDefBuilder* op_builder,
+    const std::vector* input_names,
+    std::vector* input_dtypes) {
+  std::vector income_edges;
+  VLOG(2) << "input edge size: " << input_names->size();
+  for (size_t i = 0; i < input_names->size(); ++i) {
+    VLOG(2) << "input edges: " << i << " " << input_names->at(i);
+    int output_idx = s.input_inds.at(i).second;
+    // we wired up the input here already, it is redundant to do it again in
+    //  ConvertSubGraphToTensorRT(convert_graph.cc)
+    auto incoming_edge = tensorflow::NodeDefBuilder::NodeOut(
+        input_names->at(i), output_idx, input_dtypes->at(i));
+    income_edges.push_back(incoming_edge);
+  }
+  tensorflow::gtl::ArraySlice input_list(
+      income_edges);
+  op_builder->Input(input_list);
+  return tensorflow::Status::OK();
+}
+
+string SubgraphNameScopeGenerator(const std::list* order) {
   string subgraph_name_scope;
-  if (!order.empty()) {
-    subgraph_name_scope = order.front()->name();
+  if (!order->empty()) {
+    subgraph_name_scope = order->front()->name();
   }
-  for (const tensorflow::Node* node : order) {
+  for (const tensorflow::Node* node : *order) {
     subgraph_name_scope = GetCommonNameScope(subgraph_name_scope, node->name());
   }
   // TODO(sami,ben,jie): proper naming!
-  string calib_op_name =
-      StrCat(subgraph_name_scope, "my_trt_calib_op_", static_id);
-  string engine_name = StrCat(subgraph_name_scope, "my_trt_op", static_id);
-  static_id++;
-  auto trt_rmgr = tensorflow::tensorrt::TRTResourceManager::instance();
-  auto op_rmgr = trt_rmgr->getManager("TRTCalibOps");
-  auto op_res = new tensorflow::tensorrt::TRTCalibrationResource();
-  TF_CHECK_OK(op_rmgr->Create(calib_op_name, calib_op_name, op_res));
-  op_res->logger_ = new tensorflow::tensorrt::Logger();
-  cudaSetDevice(s.cuda_gpu_id_);
-  op_res->builder_ = nvinfer1::createInferBuilder(*(op_res->logger_));
-  op_res->allocator_ = s.allocator_;
-#if NV_TENSORRT_MAJOR > 3
-  op_res->builder_->setGpuAllocator(s.allocator_.get());
-#endif
-  if (!op_res->builder_) {
-    return tensorflow::errors::Internal(
-        "failed to create TensorRT builder object");
-  }
-
-  op_res->network_ = op_res->builder_->createNetwork();
-  if (!op_res->network_) {
-    return tensorflow::errors::Internal(
-        "failed to create TensorRT network object");
-  }
-
-  // Build the network
-  auto weight_rmgr = trt_rmgr->getManager("WeightStore");
-  auto ws = new tensorflow::tensorrt::TRTWeightStore();
-  TF_CHECK_OK(weight_rmgr->Create(calib_op_name, calib_op_name, ws));
-  Converter converter(op_res->network_, ws, s.precision_mode == FP16MODE);
+  return subgraph_name_scope;
+}
 
-  std::vector input_names;
-  std::vector input_dtypes;
+tensorflow::Status ConvertSubgraph(
+    Converter& converter, tensorrt::convert::SubGraphParams& s,
+    std::list* order, std::vector* input_names,
+    std::vector* input_dtypes,
+    std::vector* output_names,
+    std::vector* output_dtypes,
+    const string& engine_name) {
   for (const std::pair& input : s.input_inds) {
     VLOG(2) << "parsing input. Node id= " << input.first;
     int node_id = input.first;
@@ -2314,19 +2312,18 @@ tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) {
 
     auto op_info = op_info_vec.at(shape_inference_output_idx);
     tensorflow::DataType tf_dtype = op_info.dtype();
-    input_dtypes.push_back(tf_dtype);
+    input_dtypes->push_back(tf_dtype);
 
     nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT);
     auto type_status = ConvertDType(tf_dtype, &dtype);
     if (type_status != tensorflow::Status::OK()) {
-      LOG(WARNING) << "Data type conversion for input '" << node_name
-                   << "' failed";
+      LOG(WARNING) << "Type conversion failed for " << node_name;
       return type_status;
     }
 
-    VLOG(2) << "accessing output index of: " << output_idx
+    VLOG(2) << "Accessing output index of: " << output_idx
             << ", at node: " << node_name
-            << "with output entry from shape_map: " << op_info_vec.size();
+            << " with output entry from shape_map: " << op_info_vec.size();
     // TODO(ben,jie): update TRT input format/dimension
     nvinfer1::DimsCHW input_dim_pseudo_chw;
     for (int i = 0; i < 3; i++) input_dim_pseudo_chw.d[i] = 1;
@@ -2352,33 +2349,29 @@ tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) {
       input_tensor_name = StrCat(node_name, ":", output_idx);
     }
 
-    input_names.push_back(input_tensor_name);
+    input_names->push_back(input_tensor_name);
     nvinfer1::ITensor* input_tensor = converter.network()->addInput(
         input_tensor_name.c_str(), dtype, input_dim_pseudo_chw);
 
     if (!input_tensor)
       return tensorflow::errors::InvalidArgument(
           "Failed to create Input layer");
-    VLOG(2) << "input tensor name :" << input_tensor_name;
+    VLOG(2) << "Input tensor name :" << input_tensor_name;
 
     if (!converter.insert_input_tensor(input_tensor_name, input_tensor))
       return tensorflow::errors::AlreadyExists(
-          "output tensor already exists for op: " + input_tensor_name);
+          "Output tensor already exists for op: " + input_tensor_name);
   }
 
-  VLOG(2) << "finished sorting";
-
-  for (const tensorflow::Node* node : order) {
+  for (const tensorflow::Node* node : *order) {
     const tensorflow::NodeDef& node_def = node->def();
-    VLOG(2) << "converting node: " << node_def.name() << " , " << node_def.op();
+    VLOG(2) << "Converting node: " << node_def.name() << " , " << node_def.op();
     TF_RETURN_IF_ERROR(converter.convert_node(node_def));
   }
 
-  VLOG(2) << "finished conversion";
+  VLOG(2) << "Finished conversion";
 
   // Gather output metadata
-  std::vector output_names;
-  std::vector output_dtypes;
   int trt_engine_op_output_idx = 0;
   for (const std::pair& output : s.output_inds) {
     int node_id = output.first;
@@ -2393,14 +2386,13 @@ tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) {
              : StrCat(engine_name, ":", trt_engine_op_output_idx),
          {output_idx, tensor_name}});
     trt_engine_op_output_idx++;
-    if (output_idx != 0) {
-      tensor_name = StrCat(tensor_name, ":", output_idx);
-    }
-    VLOG(1) << "output tensor name: " << tensor_name;
-    output_names.push_back(tensor_name);
+    if (output_idx != 0)
+      tensorflow::strings::StrAppend(&tensor_name, ":", output_idx);
+    VLOG(2) << "Output tensor name: " << tensor_name;
+    output_names->push_back(tensor_name);
     auto tensor_or_weights = converter.get_tensor(tensor_name);
     if (!tensor_or_weights.is_tensor()) {
-      return tensorflow::errors::InvalidArgument("Output node'" + tensor_name +
+      return tensorflow::errors::InvalidArgument("Output node '" + tensor_name +
                                                  "' is weights not tensor");
     }
     nvinfer1::ITensor* tensor = tensor_or_weights.tensor();
@@ -2410,12 +2402,65 @@ tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) {
     }
     converter.network()->markOutput(*tensor);
     tensorflow::DataType tf_dtype = node->output_type(output_idx);
-    output_dtypes.push_back(tf_dtype);
+    output_dtypes->push_back(tf_dtype);
     nvinfer1::DataType trt_dtype = nvinfer1::DataType::kFLOAT;
     TF_RETURN_IF_ERROR(ConvertDType(tf_dtype, &trt_dtype));
     tensor->setType(trt_dtype);
   }
 
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) {
+  // Visit nodes in reverse topological order and construct the TRT network.
+  // Toposort
+  std::list order;
+  TF_RETURN_IF_ERROR(ReverseTopologicalSort(s, &order));
+
+  static int static_id = 0;
+  string subgraph_name_scope = SubgraphNameScopeGenerator(&order);
+  // TODO(sami,ben,jie): proper naming!
+  string calib_op_name =
+      StrCat(subgraph_name_scope, "my_trt_calib_op_", static_id);
+  string engine_name = StrCat(subgraph_name_scope, "my_trt_op", static_id);
+  static_id++;
+
+  auto trt_rmgr = tensorflow::tensorrt::TRTResourceManager::instance();
+  auto op_rmgr = trt_rmgr->getManager("TRTCalibOps");
+  auto op_res = new tensorflow::tensorrt::TRTCalibrationResource();
+  TF_CHECK_OK(op_rmgr->Create(calib_op_name, calib_op_name, op_res));
+  op_res->logger_ = new tensorflow::tensorrt::Logger();
+  cudaSetDevice(s.cuda_gpu_id_);
+  op_res->builder_ = nvinfer1::createInferBuilder(*(op_res->logger_));
+  op_res->allocator_ = s.allocator_;
+#if NV_TENSORRT_MAJOR > 3
+  op_res->builder_->setGpuAllocator(s.allocator_.get());
+#endif
+  if (!op_res->builder_) {
+    return tensorflow::errors::Internal(
+        "failed to create TensorRT builder object");
+  }
+
+  op_res->network_ = op_res->builder_->createNetwork();
+  if (!op_res->network_) {
+    return tensorflow::errors::Internal(
+        "failed to create TensorRT network object");
+  }
+
+  // Build the network
+  auto weight_rmgr = trt_rmgr->getManager("WeightStore");
+  auto ws = new tensorflow::tensorrt::TRTWeightStore();
+  TF_CHECK_OK(weight_rmgr->Create(calib_op_name, calib_op_name, ws));
+  Converter converter(op_res->network_, ws, s.precision_mode == FP16MODE);
+
+  std::vector input_names;
+  std::vector input_dtypes;
+  std::vector output_names;
+  std::vector output_dtypes;
+  TF_RETURN_IF_ERROR(ConvertSubgraph(converter, s, &order, &input_names,
+                                     &input_dtypes, &output_names,
+                                     &output_dtypes, engine_name));
+
   VLOG(2) << "Finished processing outputs";
 
   // Build the engine
@@ -2427,21 +2472,8 @@ tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) {
   // Build the TRT op
   // TODO(sami,ben,jie): proper naming!
   tensorflow::NodeDefBuilder op_builder(calib_op_name, "TRTCalibOp");
-  std::vector income_edges;
-  for (size_t i = 0; i < input_names.size(); ++i) {
-    int output_idx = s.input_inds.at(i).second;
-    // we wired up the input here already, it is redundant to do it again in
-    //  ConvertSubGraphToTensorRT(convert_graph.cc)
-    auto incoming_edge = tensorflow::NodeDefBuilder::NodeOut(
-        input_names.at(i), output_idx, input_dtypes.at(i));
-    VLOG(1) << calib_op_name << " input " << i << " = " << input_names.at(i)
-            << ":" << output_idx
-            << " dType= " << tensorflow::DataTypeString(input_dtypes.at(i));
-    income_edges.push_back(incoming_edge);
-  }
-  tensorflow::gtl::ArraySlice input_list(
-      income_edges);
-  op_builder.Input(input_list);
+  SetInputList(s, &op_builder, &input_names, &input_dtypes);
+
   std::vector segment_names;
   segment_names.reserve(s.subgraph_node_ids.size());
   for (int i : s.subgraph_node_ids) {
@@ -2465,20 +2497,12 @@ tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) {
 tensorflow::Status ConvertSubGraphToTensorRTNodeDef(
     tensorrt::convert::SubGraphParams& s) {
   // Visit nodes in reverse topological order and construct the TRT network.
-
-  // Toposort
-  std::vector order_vec;
-  tensorflow::GetPostOrder(s.graph, &order_vec);
-  // Select just the subgraph
   std::list order;
-  for (tensorflow::Node* node : order_vec) {
-    if (s.subgraph_node_ids.count(node->id())) {
-      // We want topological order to contstruct the
-      // network layer by layer
-      order.push_front(node);
-    }
-  }
-  // Topological order is needed to build TRT network
+  TF_RETURN_IF_ERROR(ReverseTopologicalSort(s, &order));
+
+  static int static_id = 0;
+  string subgraph_name_scope = SubgraphNameScopeGenerator(&order);
+  string engine_name = StrCat(subgraph_name_scope, "my_trt_op", static_id++);
 
   tensorflow::tensorrt::Logger trt_logger;
   cudaSetDevice(s.cuda_gpu_id_);
@@ -2496,17 +2520,6 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef(
         "Failed to create TensorRT network object");
   }
 
-  string subgraph_name_scope;
-  if (!order.empty()) {
-    subgraph_name_scope = order.front()->name();
-  }
-  for (const tensorflow::Node* node : order) {
-    subgraph_name_scope = GetCommonNameScope(subgraph_name_scope, node->name());
-  }
-  static int static_id = 0;
-  // TODO(sami,ben,jie): proper naming!
-  string engine_name = StrCat(subgraph_name_scope, "my_trt_op");
-  engine_name = StrCat(engine_name, static_id++);
   auto trt_rmgr = tensorflow::tensorrt::TRTResourceManager::instance();
   auto weight_rmgr = trt_rmgr->getManager("WeightStore");
   auto ws = new tensorflow::tensorrt::TRTWeightStore();
@@ -2517,147 +2530,11 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef(
 
   std::vector input_names;
   std::vector input_dtypes;
-  for (const std::pair& input : s.input_inds) {
-    VLOG(2) << "parsing input. Node id= " << input.first;
-    int node_id = input.first;
-    int output_idx = input.second;
-    tensorflow::Node* node = s.graph.FindNodeId(node_id);
-    auto node_name = node->name();
-    // input_names should use the node name in the graph
-    // here it should be the input tensor name -> matching the binding
-    // insert original node name without port
-    auto tensor_name = node_name;
-    if (output_idx != 0) {
-      tensor_name = StrCat(tensor_name, ":", output_idx);
-    }
-
-    VLOG(2) << "input name: " << node_name << " tensor_name: " << tensor_name
-            << " idx: " << output_idx;
-
-    auto shape_inference_node_name = node_name;
-    auto shape_inference_output_idx = output_idx;
-    // rewire the shape inference to original node in the graph
-    if (s.output_edge_map->count(tensor_name)) {
-      shape_inference_node_name = s.output_edge_map->at(tensor_name).second;
-      shape_inference_output_idx = s.output_edge_map->at(tensor_name).first;
-    }
-    if (shape_inference_output_idx < 0) continue;
-    VLOG(2) << "shapeinference name: " << shape_inference_node_name
-            << " idx: " << shape_inference_output_idx;
-
-    if (!s.graph_properties.HasOutputProperties(shape_inference_node_name))
-      return tensorflow::errors::Internal("failed to find input node: " +
-                                          shape_inference_node_name);
-
-    auto op_info_vec =
-        s.graph_properties.GetOutputProperties(shape_inference_node_name);
-    if (static_cast(op_info_vec.size()) <= shape_inference_output_idx)
-      return tensorflow::errors::Internal(
-          "accessing output index of: ", shape_inference_output_idx,
-          ", at node: ", shape_inference_node_name,
-          " with output entry from shape_map: ", op_info_vec.size());
-
-    auto op_info = op_info_vec.at(shape_inference_output_idx);
-    tensorflow::DataType tf_dtype = op_info.dtype();
-    input_dtypes.push_back(tf_dtype);
-
-    nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT);
-    auto type_status = ConvertDType(tf_dtype, &dtype);
-    if (type_status != tensorflow::Status::OK()) {
-      LOG(WARNING) << "Type conversion failed for " << node_name;
-      return type_status;
-    }
-
-    VLOG(2) << "Accessing output index of: " << output_idx
-            << ", at node: " << node_name
-            << " with output entry from shape_map: " << op_info_vec.size();
-    // TODO(ben,jie): update TRT input format/dimension
-    nvinfer1::DimsCHW input_dim_pseudo_chw;
-    for (int i = 0; i < 3; i++) input_dim_pseudo_chw.d[i] = 1;
-
-    // TODO(jie): TRT 3.x only support 4 dimensional input tensor.
-    //            update the code once TRT 4.0 comes out.
-    if (op_info.shape().dim_size() != 4) {
-      string err_str = "Require 4 dimensional input.";
-      StrAppend(&err_str, " Got ", op_info.shape().dim_size(), " ",
-                shape_inference_node_name);
-      return tensorflow::errors::Unimplemented(err_str);
-    }
-
-    for (int i = 1; i < op_info.shape().dim_size(); i++) {
-      VLOG(2) << "dimension: " << i
-              << " , size: " << op_info.shape().dim(i).size();
-      input_dim_pseudo_chw.d[i - 1] = op_info.shape().dim(i).size();
-    }
-
-    // TODO(ben,jie): proper way to restore input tensor name?
-    auto input_tensor_name = node_name;
-    if (output_idx != 0) {
-      input_tensor_name = StrCat(node_name, ":", output_idx);
-    }
-
-    input_names.push_back(input_tensor_name);
-    nvinfer1::ITensor* input_tensor = converter.network()->addInput(
-        input_tensor_name.c_str(), dtype, input_dim_pseudo_chw);
-
-    if (!input_tensor)
-      return tensorflow::errors::InvalidArgument(
-          "Failed to create Input layer");
-    VLOG(2) << "Input tensor name :" << input_tensor_name;
-
-    if (!converter.insert_input_tensor(input_tensor_name, input_tensor))
-      return tensorflow::errors::AlreadyExists(
-          "Output tensor already exists for op: " + input_tensor_name);
-  }
-
-  VLOG(2) << "Finished sorting";
-
-  for (const tensorflow::Node* node : order) {
-    const tensorflow::NodeDef& node_def = node->def();
-    VLOG(2) << "Converting node: " << node_def.name() << " , " << node_def.op();
-    TF_RETURN_IF_ERROR(converter.convert_node(node_def));
-  }
-
-  VLOG(2) << "Finished conversion";
-
-  // Gather output metadata
   std::vector output_names;
   std::vector output_dtypes;
-  int trt_engine_op_output_idx = 0;
-  for (const std::pair& output : s.output_inds) {
-    int node_id = output.first;
-    int output_idx = output.second;
-    tensorflow::Node* node = s.graph.FindNodeId(node_id);
-    string op_name = node->name();
-    string tensor_name = op_name;
-
-    s.output_edge_map->insert(
-        {trt_engine_op_output_idx == 0
-             ? engine_name
-             : StrCat(engine_name, ":", trt_engine_op_output_idx),
-         {output_idx, tensor_name}});
-    trt_engine_op_output_idx++;
-    if (output_idx != 0)
-      tensorflow::strings::StrAppend(&tensor_name, ":", output_idx);
-    VLOG(2) << "Output tensor name: " << tensor_name;
-    output_names.push_back(tensor_name);
-    auto tensor_or_weights = converter.get_tensor(tensor_name);
-    if (!tensor_or_weights.is_tensor()) {
-      return tensorflow::errors::InvalidArgument("Output node '" + tensor_name +
-                                                 "' is weights not tensor");
-    }
-    nvinfer1::ITensor* tensor = tensor_or_weights.tensor();
-    if (!tensor) {
-      return tensorflow::errors::NotFound("Output tensor not found: " +
-                                          tensor_name);
-    }
-    converter.network()->markOutput(*tensor);
-    tensorflow::DataType tf_dtype = node->output_type(output_idx);
-    output_dtypes.push_back(tf_dtype);
-    nvinfer1::DataType trt_dtype = nvinfer1::DataType::kFLOAT;
-    TF_RETURN_IF_ERROR(ConvertDType(tf_dtype, &trt_dtype));
-    tensor->setType(trt_dtype);
-  }
+  TF_RETURN_IF_ERROR(ConvertSubgraph(converter, s, &order, &input_names,
+                                     &input_dtypes, &output_names,
+                                     &output_dtypes, engine_name));
 
   VLOG(2) << "Finished output";
 
@@ -2693,20 +2570,7 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef(
 
   // Build the TRT op
   tensorflow::NodeDefBuilder op_builder(engine_name, "TRTEngineOp");
-  std::vector income_edges;
-  VLOG(2) << "input edge size: " << input_names.size();
-  for (size_t i = 0; i < input_names.size(); ++i) {
-    VLOG(2) << "input edges: " << i << " " << input_names.at(i);
-    int output_idx = s.input_inds.at(i).second;
-    // we wired up the input here already, it is redundant to do it again in
-    //  ConvertSubGraphToTensorRT(convert_graph.cc)
-    auto incoming_edge = tensorflow::NodeDefBuilder::NodeOut(
-        input_names.at(i), output_idx, input_dtypes.at(i));
-    income_edges.push_back(incoming_edge);
-  }
-  tensorflow::gtl::ArraySlice input_list(
-      income_edges);
-  op_builder.Input(input_list);
+  SetInputList(s, &op_builder, &input_names, &input_dtypes);
 
   VLOG(0) << "Finished op preparation";
 

From c0fb9413914d983cad2ea6bb4997033a1f0dd722 Mon Sep 17 00:00:00 2001
From: Lukas Geiger 
Date: Wed, 9 May 2018 01:31:39 +0200
Subject: [PATCH 0521/1691] [tfgan] Allow to add custom eval metrics to
 GANEstimator (#19133)

---
 .../estimator/python/gan_estimator_impl.py    |  7 ++++-
 .../estimator/python/gan_estimator_test.py    |  9 +++++++
 .../gan/python/estimator/python/head_impl.py  | 27 ++++++++++++++-----
 .../gan/python/estimator/python/head_test.py  |  7 ++++-
 4 files changed, 42 insertions(+), 8 deletions(-)

diff --git a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py
index e3fc6bf0f03405..4092b320042162 100644
--- a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py
+++ b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py
@@ -112,6 +112,7 @@ def __init__(self,
                generator_optimizer=None,
                discriminator_optimizer=None,
                get_hooks_fn=None,
+               get_eval_metric_ops_fn=None,
                add_summaries=None,
                use_loss_summaries=True,
                config=None):
@@ -146,6 +147,9 @@ def __init__(self,
         list of hooks. These hooks are run on the generator and discriminator
         train ops, and can be used to implement the GAN training scheme.
         Defaults to `train.get_sequential_train_hooks()`.
+      get_eval_metric_ops_fn: A function that takes a `GANModel`, and returns a
+        dict of metric results keyed by name. The output of this function is
+        passed into `tf.estimator.EstimatorSpec` during evaluation.
       add_summaries: `None`, a single `SummaryType`, or a list of `SummaryType`.
       use_loss_summaries: If `True`, add loss summaries. If `False`, does not.
         If `None`, uses defaults.
@@ -160,7 +164,8 @@ def _model_fn(features, labels, mode):
               else discriminator_optimizer)
       gan_head = head_lib.gan_head(
           generator_loss_fn, discriminator_loss_fn, gopt, dopt,
-          use_loss_summaries, get_hooks_fn=get_hooks_fn)
+          use_loss_summaries, get_hooks_fn=get_hooks_fn,
+          get_eval_metric_ops_fn=get_eval_metric_ops_fn)
       return _gan_model_fn(
           features, labels, mode, generator_fn, discriminator_fn, gan_head,
           add_summaries)
diff --git a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py
index 6bbd173f86d7fe..955482599b372b 100644
--- a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py
+++ b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py
@@ -38,6 +38,7 @@
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import metrics as metrics_lib
 from tensorflow.python.ops import parsing_ops
 from tensorflow.python.platform import test
 from tensorflow.python.summary.writer import writer_cache
@@ -194,6 +195,12 @@ def make_opt():
       lr = learning_rate_decay.exponential_decay(1.0, gstep, 10, 0.9)
       return training.GradientDescentOptimizer(lr)
 
+    def get_metrics(gan_model):
+      return {
+          'mse_custom_metric': metrics_lib.mean_squared_error(
+              gan_model.real_data, gan_model.generated_data)
+      }
+
     gopt = make_opt if lr_decay else training.GradientDescentOptimizer(1.0)
     dopt = make_opt if lr_decay else training.GradientDescentOptimizer(1.0)
     est = estimator.GANEstimator(
@@ -203,6 +210,7 @@ def make_opt():
         discriminator_loss_fn=losses.wasserstein_discriminator_loss,
         generator_optimizer=gopt,
         discriminator_optimizer=dopt,
+        get_eval_metric_ops_fn=get_metrics,
         model_dir=self._model_dir)
 
     # TRAIN
@@ -215,6 +223,7 @@ def make_opt():
     self.assertIn('loss', six.iterkeys(scores))
     self.assertEqual(scores['discriminator_loss'] + scores['generator_loss'],
                      scores['loss'])
+    self.assertIn('mse_custom_metric', six.iterkeys(scores))
 
     # PREDICT
     predictions = np.array([x for x in est.predict(predict_input_fn)])
diff --git a/tensorflow/contrib/gan/python/estimator/python/head_impl.py b/tensorflow/contrib/gan/python/estimator/python/head_impl.py
index d174cb3bb2a9c5..ff903a78cc36c1 100644
--- a/tensorflow/contrib/gan/python/estimator/python/head_impl.py
+++ b/tensorflow/contrib/gan/python/estimator/python/head_impl.py
@@ -39,7 +39,7 @@ def _summary_key(head_name, val):
 def gan_head(generator_loss_fn, discriminator_loss_fn, generator_optimizer,
              discriminator_optimizer, use_loss_summaries=True,
              get_hooks_fn=tfgan_train.get_sequential_train_hooks(),
-             name=None):
+             get_eval_metric_ops_fn=None, name=None):
   """Creates a `GANHead`.
 
   Args:
@@ -51,9 +51,12 @@ def gan_head(generator_loss_fn, discriminator_loss_fn, generator_optimizer,
     discriminator_optimizer: Same as `generator_optimizer`, but for the
       discriminator updates.
     use_loss_summaries: If `True`, add loss summaries. If `False`, does not.
-        If `None`, uses defaults.
-    get_hooks_fn: A function that takes a GANTrainOps tuple and returns a list
-        of hooks.
+      If `None`, uses defaults.
+    get_hooks_fn: A function that takes a `GANTrainOps` tuple and returns a
+      list of hooks.
+    get_eval_metric_ops_fn: A function that takes a `GANModel`, and returns a
+      dict of metric results keyed by name. The output of this function is
+      passed into `tf.estimator.EstimatorSpec` during evaluation.
     name: name of the head. If provided, summary and metrics keys will be
       suffixed by `"/" + name`.
 
@@ -66,6 +69,7 @@ def gan_head(generator_loss_fn, discriminator_loss_fn, generator_optimizer,
                  discriminator_optimizer=discriminator_optimizer,
                  use_loss_summaries=use_loss_summaries,
                  get_hooks_fn=get_hooks_fn,
+                 get_eval_metric_ops_fn=get_eval_metric_ops_fn,
                  name=name)
 
 
@@ -76,6 +80,7 @@ def __init__(self, generator_loss_fn, discriminator_loss_fn,
                generator_optimizer, discriminator_optimizer,
                use_loss_summaries=True,
                get_hooks_fn=None,
+               get_eval_metric_ops_fn=None,
                name=None):
     """`Head` for GAN training.
 
@@ -89,8 +94,11 @@ def __init__(self, generator_loss_fn, discriminator_loss_fn,
         discriminator updates.
       use_loss_summaries: If `True`, add loss summaries. If `False`, does not.
         If `None`, uses defaults.
-      get_hooks_fn: A function that takes a GANTrainOps tuple and returns a list
-        of hooks. Defaults to `train.get_sequential_train_hooks()`
+      get_hooks_fn: A function that takes a `GANTrainOps` tuple and returns a
+        list of hooks. Defaults to `train.get_sequential_train_hooks()`
+      get_eval_metric_ops_fn: A function that takes a `GANModel`, and returns a
+        dict of metric results keyed by name. The output of this function is
+        passed into `tf.estimator.EstimatorSpec` during evaluation.
       name: name of the head. If provided, summary and metrics keys will be
         suffixed by `"/" + name`.
     """
@@ -108,6 +116,7 @@ def __init__(self, generator_loss_fn, discriminator_loss_fn,
     self._generator_optimizer = generator_optimizer
     self._discriminator_optimizer = discriminator_optimizer
     self._get_hooks_fn = get_hooks_fn
+    self._get_eval_metric_ops_fn = get_eval_metric_ops_fn
     self._name = name
 
   @property
@@ -187,6 +196,12 @@ def create_estimator_spec(
               _summary_key(self._name, 'discriminator_loss'):
                   metrics_lib.mean(gan_loss.discriminator_loss)
           }
+          if self._get_eval_metric_ops_fn is not None:
+            custom_eval_metric_ops = self._get_eval_metric_ops_fn(gan_model)
+            if not isinstance(custom_eval_metric_ops, dict):
+              raise TypeError('get_eval_metric_ops_fn must return a dict, '
+                              'received: {}'.format(custom_eval_metric_ops))
+            eval_metric_ops.update(custom_eval_metric_ops)
         return model_fn_lib.EstimatorSpec(
             mode=model_fn_lib.ModeKeys.EVAL,
             predictions=gan_model.generated_data,
diff --git a/tensorflow/contrib/gan/python/estimator/python/head_test.py b/tensorflow/contrib/gan/python/estimator/python/head_test.py
index 8168f005cd1105..6587f1fc600b94 100644
--- a/tensorflow/contrib/gan/python/estimator/python/head_test.py
+++ b/tensorflow/contrib/gan/python/estimator/python/head_test.py
@@ -62,9 +62,14 @@ def setUp(self):
         generator_loss_fn=dummy_loss,
         discriminator_loss_fn=dummy_loss,
         generator_optimizer=training.GradientDescentOptimizer(1.0),
-        discriminator_optimizer=training.GradientDescentOptimizer(1.0))
+        discriminator_optimizer=training.GradientDescentOptimizer(1.0),
+        get_eval_metric_ops_fn=self.get_metrics)
     self.assertTrue(isinstance(self.gan_head, head.GANHead))
 
+  def get_metrics(self, gan_model):
+    self.assertTrue(isinstance(gan_model, tfgan_tuples.GANModel))
+    return {}
+
   def _test_modes_helper(self, mode):
     self.gan_head.create_estimator_spec(
         features=None,

From 8039c947c3a2e0f3d780d0a1458bd40c6acd2145 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 15:33:37 -0700
Subject: [PATCH 0522/1691] Increase size of
 tensorflow/contrib/distributions:batch_reshape_test to medium to avoid flaky
 timeouts

PiperOrigin-RevId: 195887374
---
 tensorflow/contrib/distributions/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index 8021ec6141225a..a1d56066b417dd 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -460,7 +460,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "batch_reshape_test",
-    size = "small",
+    size = "medium",
     srcs = ["python/kernel_tests/batch_reshape_test.py"],
     additional_deps = [
         ":distributions_py",

From d4d97591d036bed4ddedc48d66b55500a31b4ab5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 15:34:40 -0700
Subject: [PATCH 0523/1691] Increase shard count of
 tensorflow/contrib/learn:state_saving_rnn_estimator_test to avoid flaky
 timeouts

PiperOrigin-RevId: 195887546
---
 tensorflow/contrib/learn/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index 3b053cd4c66952..4a360711f83435 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -485,6 +485,7 @@ py_test(
     name = "state_saving_rnn_estimator_test",
     size = "medium",
     srcs = ["python/learn/estimators/state_saving_rnn_estimator_test.py"],
+    shard_count = 4,
     srcs_version = "PY2AND3",
     tags = ["noasan"],
     deps = [

From 241e828794162436d1eb08c42e072249388f171f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 15:43:34 -0700
Subject: [PATCH 0524/1691] Add test to test suite.

PiperOrigin-RevId: 195888932
---
 tensorflow/contrib/lite/kernels/internal/BUILD                | 4 ++++
 .../contrib/lite/kernels/internal/quantization_util_test.cc   | 4 ++++
 2 files changed, 8 insertions(+)

diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD
index 54188217d957bc..d8340d426ae0bd 100644
--- a/tensorflow/contrib/lite/kernels/internal/BUILD
+++ b/tensorflow/contrib/lite/kernels/internal/BUILD
@@ -5,6 +5,7 @@ package(default_visibility = [
 licenses(["notice"])  # Apache 2.0
 
 load("//tensorflow/contrib/lite:build_def.bzl", "tflite_copts")
+load("//tensorflow/contrib/lite:special_rules.bzl", "tflite_portable_test_suite")
 
 tflite_deps_intel = [
     "@arm_neon_2_x86_sse",
@@ -428,6 +429,7 @@ cc_test(
         "//conditions:default": [],
     }),
     linkstatic = 1,
+    tags = ["tflite_not_portable_ios"],
     deps = [
         ":tensor_utils",
         "//tensorflow/contrib/lite:builtin_op_data",
@@ -462,3 +464,5 @@ cc_test(
 )
 
 exports_files(["optimized/eigen_tensor_reduced_instantiations_oss.h"])
+
+tflite_portable_test_suite()
diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc b/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc
index 3e9a3c29ee26e9..2d74b3d3849812 100644
--- a/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc
+++ b/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc
@@ -167,6 +167,7 @@ TEST(QuantizationUtilTest, ChooseQuantizationParamsZeroPointOnMinBoundary) {
   EXPECT_EQ(qp.zero_point, 0);
 }
 
+#ifdef GTEST_HAS_DEATH_TEST
 TEST(QuantizationUtilTest, ChooseQuantizationParamsZeroNotInRange) {
   // Assumption is that zero is within the range.
   EXPECT_DEATH(ChooseQuantizationParams(10.0, 30.0), "");
@@ -176,6 +177,7 @@ TEST(QuantizationUtilTest, ChooseQuantizationParamsEmptyRangePositive) {
   // Assumption is that zero is within the range.
   EXPECT_DEATH(ChooseQuantizationParams(30.0, 30.0), "");
 }
+#endif  // GTEST_HAS_DEATH_TEST
 
 TEST(QuantizationUtilTest, ChooseQuantizationParamsEmptyRangeZero) {
   QuantizationParams qp = ChooseQuantizationParams(0.0, 0.0);
@@ -189,6 +191,7 @@ TEST(QuantizationUtilTest, ChooseQuantizationParamsZeroPointOnMaxBoundary) {
   EXPECT_EQ(qp.zero_point, 255);
 }
 
+#ifdef GTEST_HAS_DEATH_TEST
 TEST(QuantizationUtilTest, ChooseQuantizationParamsInvalidRange) {
   EXPECT_DEATH(ChooseQuantizationParams(10.0, -30.0), "");
 }
@@ -261,6 +264,7 @@ TEST(QuantizationUtilTest, PreprocessSoftmaxScaling) {
   EXPECT_THAT(quantize(2.0, 16.0, 5), Pair(2147483647, 31));
   EXPECT_THAT(quantize(2.0, 8.0, 5), Pair(1073741824, 31));
 }
+#endif  // GTEST_HAS_DEATH_TEST
 
 TEST(QuantizationUtilTest, CalculateInputRadius) {
   EXPECT_EQ(CalculateInputRadius(4, 27), 15);

From 0028bf843d8846bd16b25bf5447b1649fde10fb7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 16:16:57 -0700
Subject: [PATCH 0525/1691] add test for pruning useless function lib in graph.

PiperOrigin-RevId: 195893756
---
 .../optimizers/function_optimizer_test.cc     | 25 +++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
index a2dbab3dedd61f..0aaf57e947f2c2 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
@@ -835,5 +835,30 @@ TEST_F(FunctionOptimizerTest, SpecializeFunction_OncePerUniqueContext) {
   test::ExpectTensorEqual(tensors_expected[5], tensors[5]);
 }
 
+TEST_F(FunctionOptimizerTest, PruningUselessLibraryFunctions) {
+  using test::function::NDef;
+  FunctionOptimizer optimizer(RewriterConfig::DEFAULT);
+  DisableFunctionSpecialization(&optimizer);
+  auto func = test::function::XTimesTwo();
+  (*func.mutable_attr())["_noinline"].set_b(true);
+  GrapplerItem item;
+  item.graph = test::function::GDef(
+      {NDef("x", "Placeholder", {}, {{"dtype", DT_FLOAT}}, "/device:CPU:0"),
+       NDef("y", "XTimesTwo", {"x"}, {{"T", DT_FLOAT}}, "/device:CPU:0"),
+       NDef("z", "Identity", {"y"}, {{"T", DT_FLOAT}}, "/device:CPU:0")},
+      // FunctionLib
+      {
+          func,
+          test::function::XTimesTwoInt32(),
+          test::function::XTimes16(),
+      });
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  EXPECT_EQ(output.library().function().size(), 1);
+  EXPECT_EQ(output.library().function(0).signature().name(), "XTimesTwo");
+}
+
 }  // namespace grappler
 }  // namespace tensorflow

From bbebae04db61e137e4013a031f429543422ae373 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 16:20:02 -0700
Subject: [PATCH 0526/1691] Only use integer values for event_ndims.

event_ndims have the semantics of being an integer. However, other code paths (such as const_value)
can return back numpy wrapped arrays, which can mess with how values are cached. Instead extract
everything as an integer.

PiperOrigin-RevId: 195894216
---
 .../kernel_tests/bijectors/chain_test.py      | 10 ++++
 .../python/ops/bijectors/chain.py             | 44 ++++++++---------
 .../kernel_tests/distributions/util_test.py   | 26 ++++++++++
 .../python/ops/distributions/bijector_impl.py | 49 ++++++++++---------
 tensorflow/python/ops/distributions/util.py   | 24 +++++++++
 5 files changed, 106 insertions(+), 47 deletions(-)

diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/chain_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/chain_test.py
index ca20442c394066..dc45114b1c23b5 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/chain_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/chain_test.py
@@ -26,6 +26,7 @@
 from tensorflow.contrib.distributions.python.ops.bijectors.softmax_centered import SoftmaxCentered
 from tensorflow.contrib.distributions.python.ops.bijectors.softplus import Softplus
 from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops.distributions import bijector
 from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency
 from tensorflow.python.platform import test
@@ -188,6 +189,15 @@ def testChainAffineExp(self):
         -np.log(6, dtype=np.float32) - np.sum(x),
         self.evaluate(chain.inverse_log_det_jacobian(y, event_ndims=1)))
 
+  def testChainIldjWithPlaceholder(self):
+    chain = Chain((Exp(), Exp()))
+    samples = array_ops.placeholder(
+        dtype=np.float32, shape=[None, 10], name="samples")
+    ildj = chain.inverse_log_det_jacobian(samples, event_ndims=0)
+    self.assertTrue(ildj is not None)
+    with self.test_session():
+      ildj.eval({samples: np.zeros([2, 10], np.float32)})
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/chain.py b/tensorflow/contrib/distributions/python/ops/bijectors/chain.py
index 85ad23e4133ef0..b158a51bb022b5 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/chain.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/chain.py
@@ -20,10 +20,9 @@
 
 import itertools
 
-from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import bijector
 
 
@@ -36,15 +35,6 @@ def _use_static_shape(input_tensor, ndims):
   return input_tensor.shape.is_fully_defined() and isinstance(ndims, int)
 
 
-def _maybe_get_event_ndims_statically(event_ndims):
-  static_event_ndims = (event_ndims if isinstance(event_ndims, int)
-                        else tensor_util.constant_value(event_ndims))
-  if static_event_ndims is not None:
-    return static_event_ndims
-
-  return event_ndims
-
-
 def _compute_min_event_ndims(bijector_list, compute_forward=True):
   """Computes the min_event_ndims associated with the give list of bijectors.
 
@@ -238,13 +228,13 @@ def _inverse(self, y, **kwargs):
     return y
 
   def _inverse_log_det_jacobian(self, y, **kwargs):
-    ildj = constant_op.constant(
-        0., dtype=y.dtype.base_dtype, name="inverse_log_det_jacobian")
+    y = ops.convert_to_tensor(y, name="y")
+    ildj = math_ops.cast(0., dtype=y.dtype.base_dtype)
 
     if not self.bijectors:
       return ildj
 
-    event_ndims = _maybe_get_event_ndims_statically(
+    event_ndims = self._maybe_get_event_ndims_statically(
         self.inverse_min_event_ndims)
 
     if _use_static_shape(y, event_ndims):
@@ -258,11 +248,12 @@ def _inverse_log_det_jacobian(self, y, **kwargs):
 
       if _use_static_shape(y, event_ndims):
         event_shape = b.inverse_event_shape(event_shape)
-        event_ndims = _maybe_get_event_ndims_statically(event_shape.ndims)
+        event_ndims = self._maybe_get_event_ndims_statically(
+            event_shape.ndims)
       else:
         event_shape = b.inverse_event_shape_tensor(event_shape)
-        event_ndims = _maybe_get_event_ndims_statically(
-            array_ops.rank(event_shape))
+        event_ndims = self._maybe_get_event_ndims_statically(
+            array_ops.size(event_shape))
       y = b.inverse(y, **kwargs.get(b.name, {}))
     return ildj
 
@@ -274,13 +265,12 @@ def _forward(self, x, **kwargs):
   def _forward_log_det_jacobian(self, x, **kwargs):
     x = ops.convert_to_tensor(x, name="x")
 
-    fldj = constant_op.constant(
-        0., dtype=x.dtype, name="inverse_log_det_jacobian")
+    fldj = math_ops.cast(0., dtype=x.dtype.base_dtype)
 
     if not self.bijectors:
       return fldj
 
-    event_ndims = _maybe_get_event_ndims_statically(
+    event_ndims = self._maybe_get_event_ndims_statically(
         self.forward_min_event_ndims)
 
     if _use_static_shape(x, event_ndims):
@@ -293,13 +283,21 @@ def _forward_log_det_jacobian(self, x, **kwargs):
           x, event_ndims=event_ndims, **kwargs.get(b.name, {}))
       if _use_static_shape(x, event_ndims):
         event_shape = b.forward_event_shape(event_shape)
-        event_ndims = _maybe_get_event_ndims_statically(event_shape.ndims)
+        event_ndims = self._maybe_get_event_ndims_statically(event_shape.ndims)
       else:
         event_shape = b.forward_event_shape_tensor(event_shape)
-        event_ndims = _maybe_get_event_ndims_statically(
-            array_ops.rank(event_shape))
+        event_ndims = self._maybe_get_event_ndims_statically(
+            array_ops.size(event_shape))
 
       x = b.forward(x, **kwargs.get(b.name, {}))
 
     return fldj
 
+  def _maybe_get_event_ndims_statically(self, event_ndims):
+    event_ndims_ = super(Chain, self)._maybe_get_event_ndims_statically(
+        event_ndims)
+    if event_ndims_ is None:
+      return event_ndims
+    return event_ndims_
+
+
diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py
index f54f146e0ac102..b9fe1976792711 100644
--- a/tensorflow/python/kernel_tests/distributions/util_test.py
+++ b/tensorflow/python/kernel_tests/distributions/util_test.py
@@ -147,6 +147,32 @@ def testAssertIntegerForm(self):
           array_ops.identity(w).eval(feed_dict=feed_dict)
 
 
+class MaybeGetStaticTest(test.TestCase):
+
+  def testGetStaticInt(self):
+    x = 2
+    self.assertEqual(x, du.maybe_get_static_value(x))
+    self.assertAllClose(
+        np.array(2.), du.maybe_get_static_value(x, dtype=np.float64))
+
+  def testGetStaticNumpyArray(self):
+    x = np.array(2, dtype=np.int32)
+    self.assertEqual(x, du.maybe_get_static_value(x))
+    self.assertAllClose(
+        np.array(2.), du.maybe_get_static_value(x, dtype=np.float64))
+
+  def testGetStaticConstant(self):
+    x = constant_op.constant(2, dtype=dtypes.int32)
+    self.assertEqual(np.array(2, dtype=np.int32), du.maybe_get_static_value(x))
+    self.assertAllClose(
+        np.array(2.), du.maybe_get_static_value(x, dtype=np.float64))
+
+  def testGetStaticPlaceholder(self):
+    x = array_ops.placeholder(dtype=dtypes.int32, shape=[1])
+    self.assertEqual(None, du.maybe_get_static_value(x))
+    self.assertEqual(None, du.maybe_get_static_value(x, dtype=np.float64))
+
+
 @test_util.with_c_api
 class GetLogitsAndProbsTest(test.TestCase):
 
diff --git a/tensorflow/python/ops/distributions/bijector_impl.py b/tensorflow/python/ops/distributions/bijector_impl.py
index 36eee5ce78f010..caceadf53a0a08 100644
--- a/tensorflow/python/ops/distributions/bijector_impl.py
+++ b/tensorflow/python/ops/distributions/bijector_impl.py
@@ -33,6 +33,7 @@
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.distributions import util as distribution_util
 
 
 __all__ = [
@@ -527,8 +528,6 @@ def __init__(self,
       ValueError:  If a member of `graph_parents` is not a `Tensor`.
     """
     self._graph_parents = graph_parents or []
-    forward_min_event_ndims = get_static_value(forward_min_event_ndims)
-    inverse_min_event_ndims = get_static_value(inverse_min_event_ndims)
 
     if forward_min_event_ndims is None and inverse_min_event_ndims is None:
       raise ValueError("Must specify at least one of `forward_min_event_ndims` "
@@ -538,12 +537,23 @@ def __init__(self,
     elif forward_min_event_ndims is None:
       forward_min_event_ndims = inverse_min_event_ndims
 
+    if not isinstance(forward_min_event_ndims, int):
+      raise TypeError("Expected forward_min_event_ndims to be of "
+                      "type int, got {}".format(
+                          type(forward_min_event_ndims).__name__))
+
+    if not isinstance(inverse_min_event_ndims, int):
+      raise TypeError("Expected inverse_min_event_ndims to be of "
+                      "type int, got {}".format(
+                          type(inverse_min_event_ndims).__name__))
+
     if forward_min_event_ndims < 0:
       raise ValueError("forward_min_event_ndims must be a non-negative "
                        "integer.")
     if inverse_min_event_ndims < 0:
       raise ValueError("inverse_min_event_ndims must be a non-negative "
                        "integer.")
+
     self._forward_min_event_ndims = forward_min_event_ndims
     self._inverse_min_event_ndims = inverse_min_event_ndims
     self._is_constant_jacobian = is_constant_jacobian
@@ -994,7 +1004,6 @@ def _lookup(self, x=None, y=None, kwargs=None):
   def _reduce_jacobian_det_over_event(
       self, y, ildj, min_event_ndims, event_ndims):
     """Reduce jacobian over event_ndims - min_event_ndims."""
-    assert_static(min_event_ndims)
 
     if not self.is_constant_jacobian:
       return math_ops.reduce_sum(
@@ -1012,7 +1021,7 @@ def _reduce_jacobian_det_over_event(
         axis=self._get_event_reduce_dims(min_event_ndims, event_ndims))
     # The multiplication by ones can change the inferred static shape so we try
     # to recover as much as possible.
-    event_ndims_ = get_static_value(event_ndims)
+    event_ndims_ = self._maybe_get_event_ndims_statically(event_ndims)
     if (event_ndims_ is not None and
         y.shape.ndims is not None and
         ildj.shape.ndims is not None):
@@ -1027,8 +1036,7 @@ def _reduce_jacobian_det_over_event(
 
   def _get_event_reduce_dims(self, min_event_ndims, event_ndims):
     """Compute the reduction dimensions given event_ndims."""
-    assert_static(min_event_ndims)
-    event_ndims_ = get_static_value(event_ndims, np.int32)
+    event_ndims_ = self._maybe_get_event_ndims_statically(event_ndims)
 
     if event_ndims_ is not None:
       return [-index for index in range(1, event_ndims_ - min_event_ndims + 1)]
@@ -1038,8 +1046,7 @@ def _get_event_reduce_dims(self, min_event_ndims, event_ndims):
 
   def _check_valid_event_ndims(self, min_event_ndims, event_ndims):
     """Check whether event_ndims is atleast min_event_ndims."""
-    assert_static(min_event_ndims)
-    event_ndims_ = get_static_value(event_ndims, np.int32)
+    event_ndims_ = self._maybe_get_event_ndims_statically(event_ndims)
     assertions = []
     if event_ndims_ is not None:
       if min_event_ndims > event_ndims_:
@@ -1051,21 +1058,15 @@ def _check_valid_event_ndims(self, min_event_ndims, event_ndims):
           check_ops.assert_greater_equal(event_ndims, min_event_ndims)]
     return assertions
 
+  def _maybe_get_event_ndims_statically(self, event_ndims):
+    """Helper which returns tries to return an integer static value."""
+    event_ndims_ = distribution_util.maybe_get_static_value(event_ndims)
 
-def get_static_value(x, dtype=None):
-  """Helper which returns static value; casting when dtype is preferred."""
-  if x is None:
-    return x
-  try:
-    x_ = tensor_util.constant_value(x)
-  except TypeError:
-    x_ = x
-  if x_ is None or dtype is None:
-    return x_
-  return np.array(x_, dtype)
-
+    if isinstance(event_ndims_, np.ndarray):
+      if (event_ndims_.dtype not in (np.int32, np.int64) or
+          len(event_ndims_.shape)):
+        raise ValueError("Expected a scalar integer, got {}".format(
+            event_ndims_))
+      event_ndims_ = event_ndims_.tolist()
 
-def assert_static(x):
-  """Helper which asserts that input arg is known statically."""
-  if x is None or type(x) != type(get_static_value(x)):  # pylint: disable=unidiomatic-typecheck
-    raise TypeError("Input must be known statically.")
+    return event_ndims_
diff --git a/tensorflow/python/ops/distributions/util.py b/tensorflow/python/ops/distributions/util.py
index 2e067eab459050..3afa85fda013ba 100644
--- a/tensorflow/python/ops/distributions/util.py
+++ b/tensorflow/python/ops/distributions/util.py
@@ -162,6 +162,30 @@ def all_shapes_equal():
       lambda: constant_op.constant(False))
 
 
+def maybe_get_static_value(x, dtype=None):
+  """Helper which tries to return a static value.
+
+  Given `x`, extract it's value statically, optionally casting to a specific
+  dtype. If this is not possible, None is returned.
+
+  Args:
+    x: `Tensor` for which to extract a value statically.
+    dtype: Optional dtype to cast to.
+
+  Returns:
+    Statically inferred value if possible, otherwise None.
+  """
+  if x is None:
+    return x
+  try:
+    x_ = tensor_util.constant_value(x)
+  except TypeError:
+    x_ = x
+  if x_ is None or dtype is None:
+    return x_
+  return np.array(x_, dtype)
+
+
 def get_logits_and_probs(logits=None,
                          probs=None,
                          multidimensional=False,

From 79b773a4395caf7f0b17ce9ac84a1f34dd277bb9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 16:23:27 -0700
Subject: [PATCH 0527/1691] Set size of
 tensorflow/python/keras:normalization_test to medium to avoid flaky timeouts

PiperOrigin-RevId: 195894737
---
 tensorflow/python/keras/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD
index 77db07b86b601a..523eb679352c2b 100755
--- a/tensorflow/python/keras/BUILD
+++ b/tensorflow/python/keras/BUILD
@@ -563,7 +563,7 @@ py_test(
 
 py_test(
     name = "normalization_test",
-    size = "small",
+    size = "medium",
     srcs = ["_impl/keras/layers/normalization_test.py"],
     srcs_version = "PY2AND3",
     tags = ["notsan"],

From 14d5f219f33b1ab8e0a67b84d97204d046adb91f Mon Sep 17 00:00:00 2001
From: Igor Ganichev 
Date: Tue, 8 May 2018 16:43:54 -0700
Subject: [PATCH 0528/1691] Make eager functions runable on TPU

PiperOrigin-RevId: 195897321
---
 tensorflow/compiler/jit/BUILD                 |  24 ++
 .../compiler/jit/create_xla_launch_op.cc      | 207 ++++++++++++++----
 .../compiler/jit/create_xla_launch_op.h       |  35 +++
 .../compiler/jit/create_xla_launch_op_test.cc | 145 ++++++++++++
 .../compiler/jit/kernels/xla_launch_op.cc     |  90 ++++++--
 .../compiler/jit/kernels/xla_launch_op.h      |  51 +++--
 .../compiler/jit/xla_compile_on_demand_op.cc  |   3 +-
 tensorflow/compiler/jit/xla_launch_util.cc    |  18 +-
 tensorflow/compiler/jit/xla_launch_util.h     |  15 +-
 tensorflow/compiler/tests/BUILD               |   4 +
 tensorflow/compiler/tests/eager_test.py       | 112 +++++++++-
 .../python/examples/resnet50/resnet50_test.py |  55 +++--
 tensorflow/python/eager/function.py           | 127 +++++++----
 13 files changed, 722 insertions(+), 164 deletions(-)
 create mode 100644 tensorflow/compiler/jit/create_xla_launch_op.h
 create mode 100644 tensorflow/compiler/jit/create_xla_launch_op_test.cc

diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index 07136d6a746604..a6b3ce394c6859 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -261,6 +261,7 @@ cc_library(
     name = "create_xla_launch_op",
     srcs = [
         "create_xla_launch_op.cc",
+        "create_xla_launch_op.h",
     ],
     deps = [
         ":common",
@@ -270,6 +271,29 @@ cc_library(
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "@com_google_absl//absl/memory",
+    ],
+    alwayslink = 1,
+)
+
+tf_cc_test(
+    name = "create_xla_launch_op_test",
+    srcs = [
+        "create_xla_launch_op.h",
+        "create_xla_launch_op_test.cc",
+    ],
+    deps = [
+        ":create_xla_launch_op",
+        "//tensorflow/core:core_cpu_internal",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:session_options",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+        "@com_google_absl//absl/memory",
     ],
 )
 
diff --git a/tensorflow/compiler/jit/create_xla_launch_op.cc b/tensorflow/compiler/jit/create_xla_launch_op.cc
index 18d901323f1085..f35e916eb937fa 100644
--- a/tensorflow/compiler/jit/create_xla_launch_op.cc
+++ b/tensorflow/compiler/jit/create_xla_launch_op.cc
@@ -12,7 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
+#include "tensorflow/compiler/jit/create_xla_launch_op.h"
 
+#include "absl/memory/memory.h"
 #include "tensorflow/compiler/jit/defs.h"
 #include "tensorflow/compiler/jit/kernels/xla_launch_op.h"
 #include "tensorflow/compiler/jit/mark_for_compilation_pass.h"
@@ -25,78 +27,189 @@ limitations under the License.
 namespace tensorflow {
 namespace {
 
-// Givens a NodeDef 'ndef' and the function library runtime 'flr', if
-// 'ndef' is a call to a compilable function defined in 'flr', returns OK
-// and fills in 'kernel' with a XlaLaunchOp kernel which computes the
-// node. Otherwise, returns a non-OK.
+// Utility which searches for values in a sorted list by scanning over it once.
+// No matter how many times ScanForValue is called, the list is scanned at most
+// once. However, if a call to ScanForValue skips over a value, that value is
+// not revisited in future calls to ScanForValue, so callers must take
+// care to order their calls.
 //
-// This routine is here so that FunctionLibraryRuntime can jit a
-// specific function call as requested.
-Status CreateXlaLaunchOp(FunctionLibraryRuntime* flr, const NodeDef& ndef,
-                         std::unique_ptr* kernel) {
-  bool xla_compile = false;
-  if (!flr->GetFunctionLibraryDefinition()
-           ->GetAttr(ndef, kXlaCompileAttr, &xla_compile)
-           .ok() ||
-      !xla_compile) {
-    // Not marked as _XlaCompile=true.
-    return errors::InvalidArgument("No ", kXlaCompileAttr, " for ", ndef.op());
+// Useful for merging multiple sorted lists in O(n) time.
+class SinglePassSearch {
+ public:
+  // Creates a SinglePassSearch object that can be used to search in `values`.
+  // Does not take ownership of `values`. `values` must outlive this.
+  // `values` must be sorted.
+  explicit SinglePassSearch(const std::vector* values)
+      : current_index_(0), values_(values) {}
+
+  // Scans forward in the vector looking for "value", updating the internal
+  // position in to the vector.
+  // Returns true iff the vector contains the given value at or after current
+  // position.
+  // Not thread-safe.
+  bool ScanForValue(int value) {
+    while (current_index_ < values_->size() &&
+           (*values_)[current_index_] <= value) {
+      if ((*values_)[current_index_] == value) {
+        current_index_++;
+        return true;
+      }
+      current_index_++;
+    }
+    return false;
   }
-  // Make sure that kernels have been registered on the JIT device.
-  XlaOpRegistry::RegisterCompilationKernels();
-  if (!IsCompilable(flr, ndef)) {
-    // ndef is calling a function that XLA can't compile.
-    return errors::InvalidArgument("Not compilable: ", ndef.ShortDebugString());
+
+ private:
+  int current_index_;
+  const std::vector* values_;
+};
+
+Status CompilationRequested(const FunctionLibraryRuntime& flr,
+                            const NodeDef& node_def) {
+  bool xla_compile = false;
+  // Check if op is marked _XlaCompile=true.
+  Status status = flr.GetFunctionLibraryDefinition()->GetAttr(
+      node_def, kXlaCompileAttr, &xla_compile);
+  if (!status.ok() || !xla_compile) {
+    if (VLOG_IS_ON(3)) {
+      if (!status.ok()) {
+        VLOG(3) << "No " << kXlaCompileAttr << " attr defined for "
+                << node_def.op() << ". status=" << status.ToString();
+      } else {
+        VLOG(3) << node_def.op() << " is explicitly marked not to be compiled";
+      }
+    }
+    return Status(error::INVALID_ARGUMENT, "");
   }
+  return Status::OK();
+}
+
+// Given a FunctionLibraryRuntime and a NodeDef calling a function in the
+// runtime, returns this function's body in `fbody` as well as the indices
+// of its constant and resource arguments.
+// `fbody` is owned by `flr`.
+// `constant_arg_indices` and `resource_arg_indices` should be empty vector.
+// They are sorted in ascending order on this function's return.
+Status GetBodyAndConstantsAndResources(FunctionLibraryRuntime* flr,
+                                       const NodeDef& node_def,
+                                       const FunctionBody** fbody,
+                                       std::vector* constant_arg_indices,
+                                       std::vector* resource_arg_indices) {
   FunctionLibraryRuntime::Handle handle;
-  // If ndef is not instantiable, e.g., the function does not exist,
+  // If node_def is not instantiable, e.g., the function does not exist,
   // simply bail out.
   TF_RETURN_IF_ERROR(
-      flr->Instantiate(ndef.op(), AttrSlice(&ndef.attr()), &handle));
-  const FunctionBody* fbody = flr->GetFunctionBody(handle);
-  CHECK(fbody);  // Can't be nullptr since we just instantiated it.
-  std::vector const_args(fbody->arg_types.size());
+      flr->Instantiate(node_def.op(), AttrSlice(&node_def.attr()), &handle));
+  *fbody = flr->GetFunctionBody(handle);
+  CHECK(*fbody);  // Can't be nullptr since we just instantiated it.
+  const DataTypeVector& arg_types = (*fbody)->arg_types;
+  std::vector const_args(arg_types.size());
   // If we can't analyze the const args. Bail out.
-  TF_RETURN_IF_ERROR(BackwardsConstAnalysis(*(fbody->graph), &const_args));
+  TF_RETURN_IF_ERROR(BackwardsConstAnalysis(*((*fbody)->graph), &const_args));
 
   for (int i = 0; i < const_args.size(); ++i) {
     if (const_args[i]) {
-      // There is a const arg. Bail out.
-      return errors::InvalidArgument("Const arg: ", i, " in ",
-                                     DebugString(fbody->fdef));
+      constant_arg_indices->push_back(i);
+    }
+  }
+
+  // There can be hundreds of resource variables. Reserve the space for them.
+  // We don't reserve for constants above as they are usually few.
+  resource_arg_indices->reserve(arg_types.size());
+  for (int i = 0; i < arg_types.size(); ++i) {
+    if (arg_types[i] == DT_RESOURCE) {
+      resource_arg_indices->push_back(i);
     }
   }
 
-  NodeDef launch_def;
-  launch_def.set_name(ndef.name());
-  launch_def.set_op("_XlaLaunch");
-  launch_def.set_device(flr->device()->name());
-  AddNodeAttr("Tconstants", DataTypeVector{}, &launch_def);
-  AddNodeAttr("Nresources", 0, &launch_def);
-  AddNodeAttr("Targs", fbody->arg_types, &launch_def);
-  AddNodeAttr("Tresults", fbody->ret_types, &launch_def);
-  NameAttrList func;
-  func.set_name(ndef.op());
-  *(func.mutable_attr()) = ndef.attr();
-  AddNodeAttr("function", func, &launch_def);
-
-  // TODO(b/32387911): Handles the host memory types across function
-  // calls properly. For now, we assume all inputs and outputs are on
-  // the device memory.
+  return Status::OK();
+}
+
+}  // namespace
+
+Status CreateXlaLaunchOp(FunctionLibraryRuntime* flr, const NodeDef& node_def,
+                         std::unique_ptr* kernel) {
+  TF_RETURN_IF_ERROR(CompilationRequested(*flr, node_def));
+
+  VLOG(3) << "Creating XlaLaunchOp for " << node_def.DebugString();
+
+  // Make sure that kernels have been registered on the JIT device.
+  XlaOpRegistry::RegisterCompilationKernels();
+  if (!IsCompilable(flr, node_def)) {
+    // node_def is calling a function that XLA can't compile.
+    return errors::InvalidArgument("Not compilable: ",
+                                   node_def.ShortDebugString());
+  }
+
+  // Get function body, constant args, and resource args.
+  const FunctionBody* fbody = nullptr;
+  std::vector constant_arg_indices;
+  std::vector resource_arg_indices;
+  TF_RETURN_IF_ERROR(GetBodyAndConstantsAndResources(
+      flr, node_def, &fbody, &constant_arg_indices, &resource_arg_indices));
+
+  // Set input and output memory types.
   MemoryTypeVector input_memory_types(fbody->arg_types.size(), DEVICE_MEMORY);
+  // These indices are used only for optimization purposes. They allow us
+  // to loop over constant_arg_indices and resource_arg_indices only once
+  // while iterating over all the function arguments checking if it is a
+  // resource or a constant.
+  // The reason we optimized this code is because functions can have a lot of
+  // captured arguments. For example, the backward pass of ResNet50 takes in all
+  // 214 variables and a similar number of activations.
+  SinglePassSearch constants_search(&constant_arg_indices);
+  SinglePassSearch resources_search(&resource_arg_indices);
+  for (int i = 0; i < fbody->arg_types.size(); ++i) {
+    if (resources_search.ScanForValue(i) || constants_search.ScanForValue(i)) {
+      // Compile-time constants and resource handles are expected to be in
+      // host memory.
+      input_memory_types[i] = HOST_MEMORY;
+    }
+  }
+  // One might wonder, about the case where a compile-time constant argument
+  // (which must be in host memory) is also used as an input into an op,
+  // e.g. Add, that expects its inputs in device memory. Here is how it
+  // works now.
+  // First, what do we mean by "op expects an input in XYZ memory"?
+  // There are two types of "ops" here: the tf2xla kernel and the HLO
+  // computation it builds. The tf2xla kernel needs to retrieve the actual
+  // numeric value of the compile-time constant tensors, so it really expects
+  // them to be on in host memory. However, for other inputs, it refers to them
+  // using xla::ComputationDataHandle, which is just a symbolic handle that
+  // xla::ComputationBuilder assigns. How does this handle gets assigned for
+  // constant arguments? Even constant arguments get an _Arg node in the graph
+  // instatiated for Function compilation. The tf2xla kernel for constant _Arg
+  // nodes takes the constant value, converts it to XlaLiteral, and feeds it
+  // to xla::ComputationBuilder.ConstantLiteral, which returns the handle. This
+  // constant XlaLiteral is included in the HLO graph, and subsequently, in
+  // the actual executable, which is copied to the device before being
+  // executed. Thus, when this executable runs, the constant is available in
+  // device memory.
+
+  // XlaLaunch kernel keeps all outputs (including constants, which it copies),
+  // in device memory
   MemoryTypeVector output_memory_types(fbody->ret_types.size(), DEVICE_MEMORY);
 
+  // Create the kernel.
+  NameAttrList function;
+  function.set_name(node_def.op());
+  *(function.mutable_attr()) = node_def.attr();
+
   Device* dev = flr->device();
   Status s;
   OpKernelConstruction construction(
       DeviceType(dev->device_type()), dev,
-      dev->GetAllocator(AllocatorAttributes()), &launch_def,
+      dev->GetAllocator(AllocatorAttributes()), &node_def,
       &fbody->fdef.signature(), flr, fbody->arg_types, input_memory_types,
       fbody->ret_types, output_memory_types, flr->graph_def_version(), &s);
-  kernel->reset(new XlaLocalLaunchOp(&construction));
+
+  *kernel = absl::make_unique(
+      &construction, constant_arg_indices, resource_arg_indices, function);
   return s;
 }
 
+namespace {
+
 bool RegisterLaunchOpCreator() {
   RegisterDefaultCustomKernelCreator(CreateXlaLaunchOp);
   return true;
diff --git a/tensorflow/compiler/jit/create_xla_launch_op.h b/tensorflow/compiler/jit/create_xla_launch_op.h
new file mode 100644
index 00000000000000..98a22e351532c1
--- /dev/null
+++ b/tensorflow/compiler/jit/create_xla_launch_op.h
@@ -0,0 +1,35 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_COMPILER_JIT_CREATE_XLA_LAUNCH_OP_H_
+#define TENSORFLOW_COMPILER_JIT_CREATE_XLA_LAUNCH_OP_H_
+
+#include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+
+class FunctionLibraryRuntime;
+class OpKernel;
+
+// Given a NodeDef 'node_def' and the function library runtime 'flr', if
+// 'node_def' is a call to a compilable function defined in 'flr', returns OK
+// and fills in 'kernel' with a XlaLaunchOp kernel which computes the
+// node. Otherwise, returns a non-OK.
+Status CreateXlaLaunchOp(FunctionLibraryRuntime* flr, const NodeDef& node_def,
+                         std::unique_ptr* kernel);
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_COMPILER_JIT_CREATE_XLA_LAUNCH_OP_H_
diff --git a/tensorflow/compiler/jit/create_xla_launch_op_test.cc b/tensorflow/compiler/jit/create_xla_launch_op_test.cc
new file mode 100644
index 00000000000000..bcd5e75c7e4c02
--- /dev/null
+++ b/tensorflow/compiler/jit/create_xla_launch_op_test.cc
@@ -0,0 +1,145 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/jit/create_xla_launch_op.h"
+
+#include "absl/memory/memory.h"
+#include "tensorflow/core/common_runtime/device_factory.h"
+#include "tensorflow/core/common_runtime/function.h"
+#include "tensorflow/core/framework/function_testlib.h"
+#include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/public/session_options.h"
+#include "tensorflow/core/public/version.h"
+
+namespace tensorflow {
+
+NodeDef ToNodeDef(const string& text) {
+  NodeDef node_def;
+  EXPECT_TRUE(protobuf::TextFormat::MergeFromString(text, &node_def));
+  return node_def;
+}
+
+// Create a FunctionDef that takes one resource and one regular param
+FunctionDef XTimesY() {
+  return FunctionDefHelper::Define(
+      // Name
+      "XTimesY",
+      // Args
+      {"x: float", "y: resource"},
+      // Return values
+      {"z: float"},
+      // Attr def
+      {},
+      // Nodes
+      {
+          {{"y0"}, "ReadVariableOp", {"y"}, {{"dtype", DT_FLOAT}}},
+          {{"z"}, "Mul", {"x", "y0"}, {{"T", DT_FLOAT}}},
+      });
+}
+
+class CreateXlaLaunchOpTest : public ::testing::Test {
+ protected:
+  void Init(const std::vector& flib) {
+    SessionOptions options;
+    auto* device_count = options.config.mutable_device_count();
+    device_count->insert({"CPU", 1});
+    TF_CHECK_OK(DeviceFactory::AddDevices(
+        options, "/job:localhost/replica:0/task:0", &devices_));
+
+    FunctionDefLibrary proto;
+    for (const auto& fdef : flib) {
+      *(proto.add_function()) = fdef;
+    }
+    lib_def_ = absl::make_unique(
+        OpRegistry::Global(), proto);
+    OptimizerOptions opts;
+    device_mgr_ = absl::make_unique(devices_);
+    pflr_ = absl::make_unique(
+        device_mgr_.get(), Env::Default(), TF_GRAPH_DEF_VERSION, lib_def_.get(),
+        opts, /*default_thread_pool=*/nullptr, /*cluster_flr=*/nullptr);
+    flr_ = pflr_->GetFLR("/job:localhost/replica:0/task:0/cpu:0");
+  }
+
+  FunctionLibraryRuntime* flr_;
+  std::vector devices_;
+  std::unique_ptr device_mgr_;
+  std::unique_ptr lib_def_;
+  std::unique_ptr pflr_;
+
+  std::unique_ptr kernel_;
+};
+
+AttrValue BoolAttr(bool b) {
+  AttrValue v;
+  v.set_b(b);
+  return v;
+}
+
+TEST_F(CreateXlaLaunchOpTest, OneFloatOneResourceArgument) {
+  FunctionDef fdef = XTimesY();
+  (*fdef.mutable_attr())["_XlaCompile"] = BoolAttr(true);
+  Init({fdef});
+
+  Status status = CreateXlaLaunchOp(
+      flr_, ToNodeDef(R"pb(
+        name: 'XTimesY' op: 'XTimesY' input: 'a' input: 'b'
+      )pb"), &kernel_);
+  ASSERT_TRUE(status.ok()) << status.ToString();
+
+  EXPECT_EQ("XTimesY", kernel_->name());
+  EXPECT_EQ("XTimesY", kernel_->type_string());
+
+  EXPECT_EQ(2, kernel_->num_inputs());
+  EXPECT_EQ(DT_FLOAT, kernel_->input_type(0));
+  EXPECT_EQ(DT_RESOURCE, kernel_->input_type(1));
+  EXPECT_EQ(DEVICE_MEMORY, kernel_->input_memory_types()[0]);
+  EXPECT_EQ(HOST_MEMORY, kernel_->input_memory_types()[1]);
+
+  EXPECT_EQ(1, kernel_->num_outputs());
+  EXPECT_EQ(DT_FLOAT, kernel_->output_type(0));
+  EXPECT_EQ(DEVICE_MEMORY, kernel_->output_memory_types()[0]);
+}
+
+TEST_F(CreateXlaLaunchOpTest, FailsIfXlaCompileAttrNotSet) {
+  FunctionDef fdef = XTimesY();
+  Init({fdef});
+
+  Status status = CreateXlaLaunchOp(flr_, ToNodeDef(R"proto(
+                                      name: 'XTimesY'
+                                      op: 'XTimesY'
+                                      input: 'a'
+                                      input: 'b'
+                                    )proto"), &kernel_);
+  EXPECT_TRUE(errors::IsInvalidArgument(status)) << status.ToString();
+}
+
+TEST_F(CreateXlaLaunchOpTest, FailsIfXlaCompileAttrIsSetToFalse) {
+  FunctionDef fdef = XTimesY();
+  (*fdef.mutable_attr())["_XlaCompile"] = BoolAttr(false);
+  Init({fdef});
+
+  Status status = CreateXlaLaunchOp(flr_, ToNodeDef(R"proto(
+                                      name: 'XTimesY'
+                                      op: 'XTimesY'
+                                      input: 'a'
+                                      input: 'b'
+                                    )proto"), &kernel_);
+  EXPECT_TRUE(errors::IsInvalidArgument(status)) << status.ToString();
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.cc b/tensorflow/compiler/jit/kernels/xla_launch_op.cc
index 049d170fa48928..86a9fd3b8e124e 100644
--- a/tensorflow/compiler/jit/kernels/xla_launch_op.cc
+++ b/tensorflow/compiler/jit/kernels/xla_launch_op.cc
@@ -39,15 +39,15 @@ limitations under the License.
 
 namespace tensorflow {
 
-XlaLocalLaunchOp::XlaLocalLaunchOp(OpKernelConstruction* ctx)
-    : OpKernel(ctx), device_type_(ctx->device_type()) {
-  const NameAttrList* func;
-  OP_REQUIRES_OK(ctx, ctx->GetAttr("function", &func));
-  function_ = *func;
-  DataTypeVector constant_types;
-  OP_REQUIRES_OK(ctx, ctx->GetAttr("Tconstants", &constant_types));
-  num_constant_args_ = constant_types.size();
-  OP_REQUIRES_OK(ctx, ctx->GetAttr("Nresources", &num_resource_args_));
+XlaLocalLaunchBase::XlaLocalLaunchBase(OpKernelConstruction* ctx,
+                                       const std::vector& constants,
+                                       const std::vector& resources,
+                                       const NameAttrList& function)
+    : OpKernel(ctx),
+      constants_(constants),
+      resources_(resources),
+      device_type_(ctx->device_type()),
+      function_(function) {
   if (device_type_ == DeviceType(DEVICE_CPU)) {
     platform_id_ = se::host::kHostPlatformId;
   } else if (device_type_ == DeviceType(DEVICE_GPU)) {
@@ -57,8 +57,8 @@ XlaLocalLaunchOp::XlaLocalLaunchOp(OpKernelConstruction* ctx)
   }
 }
 
-Status XlaLocalLaunchOp::BuildCompilationCache(OpKernelContext* ctx,
-                                               XlaCompilationCache** cache) {
+Status XlaLocalLaunchBase::BuildCompilationCache(OpKernelContext* ctx,
+                                                 XlaCompilationCache** cache) {
   const XlaDevice::Metadata* metadata;
   Status s = XlaDevice::GetMetadata(ctx, &metadata);
   if (s.ok()) {
@@ -90,8 +90,8 @@ Status XlaLocalLaunchOp::BuildCompilationCache(OpKernelContext* ctx,
   return Status::OK();
 }
 
-void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) {
-  VLOG(1) << "XlaLocalLaunchOp::Compute "
+void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) {
+  VLOG(1) << "XlaLocalLaunchOpBase::Compute "
           << Canonicalize(function_.name(), AttrSlice(&function_.attr()));
   // We store information about the JIT-compiled XLA computation
   // in the ResourceMgr.
@@ -124,7 +124,7 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) {
   }
 
   std::map variables =
-      SnapshotResourceVariables(ctx, num_resource_args_);
+      SnapshotResourceVariables(ctx, resources_);
 
   xla::LocalClient* client = static_cast(cache->client());
 
@@ -161,7 +161,7 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) {
   xla::LocalExecutable* executable;
 
   std::map constant_args;
-  for (int i = 0; i < num_constant_args_; ++i) {
+  for (int i : constants_) {
     constant_args.insert({i, ctx->input(i)});
   }
   OP_REQUIRES_OK(ctx, cache->Compile(options, function_, constant_args,
@@ -170,8 +170,8 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) {
 
   VLOG(1) << "Executing XLA Computation...";
 
-  XlaComputationLaunchContext launch_context(
-      num_resource_args_, client, xla_allocator, allocate_xla_tensors);
+  XlaComputationLaunchContext launch_context(client, xla_allocator,
+                                             allocate_xla_tensors);
   launch_context.PopulateInputs(ctx, kernel, variables);
 
   // Execute the computation.
@@ -194,6 +194,62 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) {
   VLOG(1) << "Done";
 }
 
+namespace {
+
+// OP_REQUIRES_OK_RETURN is the same as OP_REQUIRES_OK except that
+// in error case, it returns RET instead of void.
+#define OP_REQUIRES_OK_RETURN(CTX, RET, ...)                \
+  do {                                                      \
+    ::tensorflow::Status _s(__VA_ARGS__);                   \
+    if (!TF_PREDICT_TRUE(_s.ok())) {                        \
+      (CTX)->CtxFailureWithWarning(__FILE__, __LINE__, _s); \
+      return RET;                                           \
+    }                                                       \
+  } while (0)
+
+// Helper static functions to construct parameters for
+// XlaLocalLaunchBase constructor from OpKernelConstruction.
+std::vector ConstantsVector(OpKernelConstruction* ctx) {
+  DataTypeVector constant_types;
+  OP_REQUIRES_OK_RETURN(ctx, std::vector(),
+                        ctx->GetAttr("Tconstants", &constant_types));
+  std::vector constants(constant_types.size());
+  std::iota(constants.begin(), constants.end(), 0);
+  return constants;
+}
+
+std::vector ResourcesVector(OpKernelConstruction* ctx) {
+  DataTypeVector constant_types;
+  OP_REQUIRES_OK_RETURN(ctx, std::vector(),
+                        ctx->GetAttr("Tconstants", &constant_types));
+
+  DataTypeVector arg_types;
+  OP_REQUIRES_OK_RETURN(ctx, std::vector(),
+                        ctx->GetAttr("Targs", &arg_types));
+
+  int num_resources;
+  OP_REQUIRES_OK_RETURN(ctx, std::vector(),
+                        ctx->GetAttr("Nresources", &num_resources));
+
+  std::vector resources(num_resources);
+  std::iota(resources.begin(), resources.end(),
+            constant_types.size() + arg_types.size());
+  return resources;
+}
+
+NameAttrList FunctionAttr(OpKernelConstruction* ctx) {
+  const NameAttrList* func;
+  OP_REQUIRES_OK_RETURN(ctx, NameAttrList(), ctx->GetAttr("function", &func));
+  return *func;
+}
+
+#undef OP_REQUIRES_OK_RETURN
+}  // namespace
+
+XlaLocalLaunchOp::XlaLocalLaunchOp(OpKernelConstruction* ctx)
+    : XlaLocalLaunchBase(ctx, ConstantsVector(ctx), ResourcesVector(ctx),
+                         FunctionAttr(ctx)) {}
+
 XlaLocalLaunchOp::~XlaLocalLaunchOp() {
   VLOG(1) << "XlaLocalLaunchOp destroyed";
 }
diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.h b/tensorflow/compiler/jit/kernels/xla_launch_op.h
index 8f8e646f0ff6d9..8dfc4b382d5115 100644
--- a/tensorflow/compiler/jit/kernels/xla_launch_op.h
+++ b/tensorflow/compiler/jit/kernels/xla_launch_op.h
@@ -26,6 +26,41 @@ limitations under the License.
 
 namespace tensorflow {
 
+// XlaLocalLaunchBase is almost the same as XlaLocalLaunchOp.
+// The only difference is that it does not require arguments to follow
+// the "constants, then regular args, then resources" order.
+// It takes vectors of constant and resource arguments explicitly.
+// It does not have corresponding OpDef because it is never present
+// in the GraphDef.
+// Currently, it is used by eager runtime. FunctionLibraryRuntime creates
+// this kernel when asked to create a kernel for an XLA-compiled function.
+class XlaLocalLaunchBase : public OpKernel {
+ public:
+  XlaLocalLaunchBase(OpKernelConstruction* ctx,
+                     const std::vector& constants,
+                     const std::vector& resources,
+                     const NameAttrList& function);
+  XlaLocalLaunchBase(const XlaLocalLaunchBase&) = delete;
+  XlaLocalLaunchBase& operator=(const XlaLocalLaunchBase&) = delete;
+  ~XlaLocalLaunchBase() override = default;
+
+  void Compute(OpKernelContext* ctx) override;
+
+ protected:
+  // Builds a XlaCompilationCache class suitable for the current device.
+  Status BuildCompilationCache(OpKernelContext* ctx,
+                               XlaCompilationCache** cache);
+
+  // Indexes of compile-time constant inputs
+  std::vector constants_;
+  // Indexes of resource inputs
+  std::vector resources_;
+
+  DeviceType device_type_;
+  NameAttrList function_;
+  se::Platform::Id platform_id_;
+};
+
 // XlaLocalLaunchOp is used to replace a region of the TensorFlow graph
 // which will be compiled and executed using XLA.  The XlaLocalLaunchOp is
 // responsible for handling interactions with the TensorFlow executor.
@@ -35,26 +70,12 @@ namespace tensorflow {
 // XlaLocalLaunchOp uses xla::LocalClient::Compile() and
 // xla::LocalExecutable::Run(), and passes arguments into/out of XLA in device
 // memory.
-class XlaLocalLaunchOp : public OpKernel {
+class XlaLocalLaunchOp : public XlaLocalLaunchBase {
  public:
   explicit XlaLocalLaunchOp(OpKernelConstruction* ctx);
   ~XlaLocalLaunchOp() override;
 
-  void Compute(OpKernelContext* ctx) override;
-
  private:
-  // Builds a XlaCompilationCache class suitable for the current device.
-  Status BuildCompilationCache(OpKernelContext* ctx,
-                               XlaCompilationCache** compiler);
-
-  DeviceType device_type_;
-  NameAttrList function_;
-  int num_constant_args_;
-  // Number of resource variable arguments.
-  int num_resource_args_;
-
-  se::Platform::Id platform_id_;
-
   TF_DISALLOW_COPY_AND_ASSIGN(XlaLocalLaunchOp);
 };
 
diff --git a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc
index 60458f6f3314b2..6b83cf67ffc571 100644
--- a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc
+++ b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc
@@ -48,13 +48,12 @@ Status XlaCompileOnDemandOp::Run(OpKernelContext* ctx,
                                  const XlaCompiler::CompilationResult* result,
                                  xla::LocalExecutable* executable) {
   std::map variables = GetVariables(ctx);
-  int64 num_resource_args = variables.size();
 
   xla::LocalClient* client = metadata.client();
 
   // Builds an XLA allocator for the device.
   XlaComputationLaunchContext launch_context(
-      num_resource_args, client, client->backend().memory_allocator(), true);
+      client, client->backend().memory_allocator(), true);
 
   launch_context.PopulateInputs(ctx, result, variables);
 
diff --git a/tensorflow/compiler/jit/xla_launch_util.cc b/tensorflow/compiler/jit/xla_launch_util.cc
index 33e53612b91315..0223f97a032cf9 100644
--- a/tensorflow/compiler/jit/xla_launch_util.cc
+++ b/tensorflow/compiler/jit/xla_launch_util.cc
@@ -38,14 +38,13 @@ using xla::ScopedShapedBuffer;
 using xla::ShapedBuffer;
 }  // anonymous namespace
 
-std::map SnapshotResourceVariables(OpKernelContext* ctx,
-                                                        int num_variables) {
+std::map SnapshotResourceVariables(
+    OpKernelContext* ctx, const std::vector& variables) {
   std::map snapshot;
-  int first_variable = ctx->num_inputs() - num_variables;
-  for (int i = 0; i < num_variables; ++i) {
+  for (int i : variables) {
     Var* variable = nullptr;
-    ResourceHandle handle = HandleFromInput(ctx, first_variable + i);
-    OptionalTensor& tensor = snapshot[first_variable + i];
+    ResourceHandle handle = HandleFromInput(ctx, i);
+    OptionalTensor& tensor = snapshot[i];
     if (LookupResource(ctx, handle, &variable).ok()) {
       tf_shared_lock lock(*variable->mu());
       tensor.name = handle.name();
@@ -112,10 +111,9 @@ ScopedShapedBuffer ExtractSubShapedBuffer(
 using internal::ExtractSubShapedBuffer;
 
 XlaComputationLaunchContext::XlaComputationLaunchContext(
-    int64 num_resource_args, xla::LocalClient* client,
-    xla::DeviceMemoryAllocator* xla_allocator, bool allocate_xla_tensors)
-    : num_resource_args_(num_resource_args),
-      client_(client),
+    xla::LocalClient* client, xla::DeviceMemoryAllocator* xla_allocator,
+    bool allocate_xla_tensors)
+    : client_(client),
       xla_allocator_(xla_allocator),
       allocate_xla_tensors_(allocate_xla_tensors) {}
 
diff --git a/tensorflow/compiler/jit/xla_launch_util.h b/tensorflow/compiler/jit/xla_launch_util.h
index 38291b0bd429b2..a2431253f8c44b 100644
--- a/tensorflow/compiler/jit/xla_launch_util.h
+++ b/tensorflow/compiler/jit/xla_launch_util.h
@@ -31,15 +31,17 @@ limitations under the License.
 namespace tensorflow {
 class XlaAllocator;
 
-// Takes a snapshot of the values of resource variable arguments, which are
-// the last `num_variables` arguments. We snapshot tensors that back
+// Takes a snapshot of the values of resource variable arguments, whose
+// indices are specified in `variables` argument. We snapshot tensors that back
 // resource variables since concurrent updates may modify the shape, and it is
 // important that the shapes used for compilation match the true shapes of the
 // buffers.
 //
-// Returns a map of TensorFlow argument index to resource variable.
-std::map SnapshotResourceVariables(OpKernelContext* ctx,
-                                                        int num_variables);
+// Returns a map of TensorFlow argument index to resource variable. If a
+// resource variable is not initialized, the corresponding OptionalTensor
+// will have its `present` field set to false.
+std::map SnapshotResourceVariables(
+    OpKernelContext* ctx, const std::vector& variables);
 
 // Adapter class that wraps a Tensorflow allocator as an XLA allocator.
 // Assumes that the Tensorflow allocator permits asynchronous deallocation:
@@ -72,7 +74,7 @@ class XlaComputationLaunchContext {
   // Create a new launch context. 'allocate_xla_tensors' is true if allocated
   // output tensors and variables are always XlaTensors. If false they are
   // assumed to be "normal" device pointers.
-  XlaComputationLaunchContext(int64 num_resource_args, xla::LocalClient* client,
+  XlaComputationLaunchContext(xla::LocalClient* client,
                               xla::DeviceMemoryAllocator* xla_allocator,
                               bool allocate_xla_tensors);
 
@@ -92,7 +94,6 @@ class XlaComputationLaunchContext {
   const std::vector& arguments() const { return arg_ptrs_; }
 
  private:
-  int64 num_resource_args_;
   xla::LocalClient* client_;
   xla::DeviceMemoryAllocator* xla_allocator_;
   bool allocate_xla_tensors_;
diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index aaea83ae9cbd21..9791792f29ca05 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -327,7 +327,11 @@ tf_xla_py_test(
         ":xla_test",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:layers",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn",
         "//tensorflow/python:platform_test",
+        "//tensorflow/python/eager:function",
     ],
 )
 
diff --git a/tensorflow/compiler/tests/eager_test.py b/tensorflow/compiler/tests/eager_test.py
index bdd0185dfe4abe..5ab1585f8c6e07 100644
--- a/tensorflow/compiler/tests/eager_test.py
+++ b/tensorflow/compiler/tests/eager_test.py
@@ -24,10 +24,16 @@
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.eager import backprop
 from tensorflow.python.eager import context
+from tensorflow.python.eager import function
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.layers import convolutional
+from tensorflow.python.layers import pooling
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.platform import googletest
 
@@ -43,7 +49,7 @@ def testBasic(self):
 
   def testExecuteListOutputLen0(self):
     with self.test_scope():
-      empty = constant_op.constant([], dtype=dtypes.int32)
+      empty = constant_op.constant([], dtype=dtypes.float32)
       result = array_ops.unstack(empty, 0)
       self.assertTrue(isinstance(result, list))
       self.assertEqual(0, len(result))
@@ -51,7 +57,7 @@ def testExecuteListOutputLen0(self):
   def testExecuteListOutputLen1(self):
     with self.test_scope():
       split_dim = constant_op.constant(1)
-      value = constant_op.constant([[0, 1, 2], [3, 4, 5]])
+      value = constant_op.constant([[0., 1., 2.], [3., 4., 5.]])
       result = array_ops.split(value, 1, axis=split_dim)
       self.assertTrue(isinstance(result, list))
       self.assertEqual(1, len(result))
@@ -60,7 +66,7 @@ def testExecuteListOutputLen1(self):
   def testExecuteListOutputLen3(self):
     with self.test_scope():
       split_dim = constant_op.constant(1)
-      value = constant_op.constant([[0, 1, 2], [3, 4, 5]])
+      value = constant_op.constant([[0., 1., 2.], [3., 4., 5.]])
       result = array_ops.split(value, 3, axis=split_dim)
       self.assertTrue(isinstance(result, list))
       self.assertEqual(3, len(result))
@@ -131,7 +137,105 @@ def f():
     self.assertEqual(2., grads[0][0].numpy())
 
 
-if __name__ == "__main__":
+class EagerFunctionTest(XLATestCase):
+
+  def testBasic(self):
+    with self.test_scope():
+      matmul = function.defun(math_ops.matmul, compiled=True)
+      t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
+      sq = matmul(t, t, transpose_a=True)
+      self.assertAllEqual(sq.numpy().reshape(-1), [10, 14, 14, 20])
+
+  def testConv(self):
+    if 'GPU' in self.device:
+      # TODO(b/32333178)
+      self.skipTest('Current implementation of RandomStandardNormal kernel '
+                    'is very slow on GPU, and has been blacklisted.')
+    with self.test_scope():
+      data_format = 'channels_last'
+      conv = convolutional.Conv2D(
+          filters=1, kernel_size=2, padding='VALID',
+          data_format=data_format, activation=nn_ops.relu,
+          kernel_initializer=init_ops.ones_initializer(),
+          bias_initializer=init_ops.zeros_initializer())
+      pool = pooling.MaxPooling2D(2, 2, data_format=data_format)
+
+      def model(x):
+        x = conv(x)
+        return pool(x)
+      model = function.defun(model, compiled=True)
+
+      x = array_ops.ones([1, 4, 4, 1])
+      y = model(x)
+      self.assertAllEqual(y.numpy(), [[[[4.]]]])
+
+  def testReadVariable(self):
+    with self.test_scope():
+      v = resource_variable_ops.ResourceVariable(1.0)
+
+      @function.defun(compiled=True)
+      def f():
+        return v.read_value()
+
+      var = f()
+      self.assertEqual(1.0, var.numpy())
+
+  def testUpdateVariable(self):
+    with self.test_scope():
+      v = resource_variable_ops.ResourceVariable(1.0)
+
+      def f(v):
+        v.assign_add(1.0)
+        return v
+
+      f = function.defun(f, compiled=True)
+
+      var = f(v)
+      self.assertEqual(2.0, var.numpy())
+
+  def testAllArgumentKinds(self):
+    """Test a complex function that takes different argument kinds.
+
+    tf2xla machinery that translates, compiles, and runs defuns
+    classifies arguments into: compile-time constants, regular tensors,
+    and resources. This test creates a function with a mix of all these
+    kinds. Moreover, the order of function arguments is intentionally mixed up.
+
+    This also tests the case when the same argument is a compile-time constant
+    as well as used in an operation that normally expects its inputs to be
+    in device memory - addition in this case.
+    """
+    with self.test_scope():
+      def foo(c1, r1, v1, c2, v2, r2):
+        # c1 and c2 are compile-time constants
+        # r1 and r2 are regular tensors
+        # v1 and v2 are resource variables
+        a = c1 + r1
+        b = math_ops.cast(c2, dtypes.float32) + v2
+        c = array_ops.slice(v1, c1, c2)
+        d = r2 * v2
+        return a, b, c, d
+
+      foo = function.defun(foo, compiled=True)
+
+      c1 = [0, 0]
+      c2 = array_ops.ones([2], dtype=dtypes.int32)
+
+      r1 = array_ops.ones([2])
+      r2 = [[2., 2.], [3., 3.]]
+
+      v1 = resource_variable_ops.ResourceVariable([[1., 2.], [3., 4.]])
+      v2 = resource_variable_ops.ResourceVariable([[10., 20.], [30., 40.]])
+
+      a, b, c, d = foo(c1, r1, v1, c2, v2, r2)
+
+      self.assertAllEqual([1, 1], a.numpy())
+      self.assertAllEqual([[11., 21.], [31., 41.]], b.numpy())
+      self.assertAllEqual([[1.]], c.numpy())
+      self.assertAllEqual([[20., 40.], [90., 120.]], d.numpy())
+
+
+if __name__ == '__main__':
   ops.enable_eager_execution(
       config=config_pb2.ConfigProto(log_device_placement=True))
   googletest.main()
diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
index 8517a3bf7b6aeb..b8f352d5f5b72f 100644
--- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
+++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
@@ -36,9 +36,7 @@ def device_and_data_format():
                                                               'channels_last')
 
 
-def random_batch(batch_size, device_and_format=None):
-  _, data_format = device_and_format or device_and_data_format()
-
+def random_batch(batch_size, data_format):
   shape = (3, 224, 224) if data_format == 'channels_first' else (224, 224, 3)
   shape = (batch_size,) + shape
 
@@ -70,7 +68,7 @@ def _apply(self, defun=False, execution_mode=None):
     if defun:
       model.call = tfe.defun(model.call)
     with tf.device(device), tfe.execution_mode(execution_mode):
-      images, _ = random_batch(2)
+      images, _ = random_batch(2, data_format)
       output = model(images, training=False)
       tfe.async_wait()
     self.assertEqual((2, 1000), output.shape)
@@ -91,7 +89,7 @@ def test_apply_no_top(self):
     device, data_format = device_and_data_format()
     model = resnet50.ResNet50(data_format, include_top=False)
     with tf.device(device):
-      images, _ = random_batch(2)
+      images, _ = random_batch(2, data_format)
       output = model(images, training=False)
     output_shape = ((2, 2048, 1, 1)
                     if data_format == 'channels_first' else (2, 1, 1, 2048))
@@ -101,7 +99,7 @@ def test_apply_with_pooling(self):
     device, data_format = device_and_data_format()
     model = resnet50.ResNet50(data_format, include_top=False, pooling='avg')
     with tf.device(device):
-      images, _ = random_batch(2)
+      images, _ = random_batch(2, data_format)
       output = model(images, training=False)
     self.assertEqual((2, 2048), output.shape)
 
@@ -115,7 +113,7 @@ def _test_train(self, execution_mode=None):
         name='t0').as_default(), tf.contrib.summary.always_record_summaries():
       with tf.device(device), tfe.execution_mode(execution_mode):
         optimizer = tf.train.GradientDescentOptimizer(0.1)
-        images, labels = random_batch(2)
+        images, labels = random_batch(2, data_format)
         train_one_step(model, images, labels, optimizer)
         self.assertEqual(320, len(model.variables))
         tfe.async_wait()
@@ -134,7 +132,7 @@ def test_no_garbage(self):
     model = resnet50.ResNet50(data_format)
     optimizer = tf.train.GradientDescentOptimizer(0.1)
     with tf.device(device):
-      images, labels = random_batch(2)
+      images, labels = random_batch(2, data_format)
       gc.disable()
       # Warm up. Note that this first run does create significant amounts of
       # garbage to be collected. The hope is that this is a build-only effect,
@@ -202,18 +200,18 @@ def _force_device_sync(self):
     # which forces a sync. This is a roundabout way, yes.
     tf.constant(1.).cpu()
 
-  def _benchmark_eager_apply(self, label, defun=False, execution_mode=None,
-                             device_and_format=None):
+  def _benchmark_eager_apply(self, label, device_and_format, defun=False,
+                             execution_mode=None, compiled=False):
     with tfe.execution_mode(execution_mode):
-      device, data_format = device_and_format or device_and_data_format()
+      device, data_format = device_and_format
       model = resnet50.ResNet50(data_format)
       if defun:
-        model.call = tfe.defun(model.call)
+        model.call = tfe.defun(model.call, compiled=compiled)
       batch_size = 64
       num_burn = 5
       num_iters = 30
       with tf.device(device):
-        images, _ = random_batch(batch_size, device_and_format)
+        images, _ = random_batch(batch_size, data_format)
         for _ in xrange(num_burn):
           model(images, training=False).cpu()
         if execution_mode:
@@ -227,30 +225,34 @@ def _benchmark_eager_apply(self, label, defun=False, execution_mode=None,
         self._report(label, start, num_iters, device, batch_size, data_format)
 
   def benchmark_eager_apply_sync(self):
-    self._benchmark_eager_apply('eager_apply', defun=False)
+    self._benchmark_eager_apply('eager_apply', device_and_data_format(),
+                                defun=False)
 
   def benchmark_eager_apply_async(self):
     self._benchmark_eager_apply(
-        'eager_apply_async', defun=False, execution_mode=tfe.ASYNC)
+        'eager_apply_async', device_and_data_format(), defun=False,
+        execution_mode=tfe.ASYNC)
 
   def benchmark_eager_apply_with_defun(self):
-    self._benchmark_eager_apply('eager_apply_with_defun', defun=True)
+    self._benchmark_eager_apply('eager_apply_with_defun',
+                                device_and_data_format(), defun=True)
 
   def _benchmark_eager_train(self,
                              label,
                              make_iterator,
+                             device_and_format,
                              defun=False,
                              execution_mode=None,
-                             device_and_format=None):
+                             compiled=False):
     with tfe.execution_mode(execution_mode):
-      device, data_format = device_and_format or device_and_data_format()
+      device, data_format = device_and_format
       for batch_size in self._train_batch_sizes():
-        (images, labels) = random_batch(batch_size, device_and_format)
+        (images, labels) = random_batch(batch_size, data_format)
         num_burn = 3
         num_iters = 10
         model = resnet50.ResNet50(data_format)
         if defun:
-          model.call = tfe.defun(model.call)
+          model.call = tfe.defun(model.call, compiled=compiled)
         optimizer = tf.train.GradientDescentOptimizer(0.1)
 
         with tf.device(device):
@@ -273,18 +275,21 @@ def _benchmark_eager_train(self,
           self._report(label, start, num_iters, device, batch_size, data_format)
 
   def benchmark_eager_train_sync(self):
-    self._benchmark_eager_train('eager_train', MockIterator, defun=False)
+    self._benchmark_eager_train('eager_train', MockIterator,
+                                device_and_data_format(), defun=False)
 
   def benchmark_eager_train_async(self):
     self._benchmark_eager_train(
         'eager_train_async',
         MockIterator,
+        device_and_data_format(),
         defun=False,
         execution_mode=tfe.ASYNC)
 
   def benchmark_eager_train_with_defun(self):
     self._benchmark_eager_train(
-        'eager_train_with_defun', MockIterator, defun=True)
+        'eager_train_with_defun', MockIterator,
+        device_and_data_format(), defun=True)
 
   def benchmark_eager_train_datasets(self):
 
@@ -294,7 +299,8 @@ def make_iterator(tensors):
       return tfe.Iterator(ds)
 
     self._benchmark_eager_train(
-        'eager_train_dataset', make_iterator, defun=False)
+        'eager_train_dataset', make_iterator,
+        device_and_data_format(), defun=False)
 
   def benchmark_eager_train_datasets_with_defun(self):
 
@@ -304,7 +310,8 @@ def make_iterator(tensors):
       return tfe.Iterator(ds)
 
     self._benchmark_eager_train(
-        'eager_train_dataset_with_defun', make_iterator, defun=True)
+        'eager_train_dataset_with_defun', make_iterator,
+        device_and_data_format(), defun=True)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py
index 89257bb20a688e..b478b6b0dbf881 100644
--- a/tensorflow/python/eager/function.py
+++ b/tensorflow/python/eager/function.py
@@ -23,6 +23,7 @@
 
 import numpy as np
 
+from tensorflow.core.framework import attr_value_pb2
 from tensorflow.core.framework import function_pb2
 from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.eager import context
@@ -227,7 +228,7 @@ def _inference_name(n):
 class _EagerDefinedFunction(object):
   """Function object with the interface of tf _DefinedFunction."""
 
-  def __init__(self, name, graph, operations, inputs, outputs):
+  def __init__(self, name, graph, operations, inputs, outputs, attrs):
     """Initializes an eager defined function.
 
     Args:
@@ -237,6 +238,7 @@ def __init__(self, name, graph, operations, inputs, outputs):
         which will be in the function
       inputs: the tensors in the graph to be used as inputs to the function
       outputs: the tensors in the graph which will be outputs to the function
+      attrs: dict mapping names of attributes to their AttrValue values
     """
     fn = pywrap_tensorflow.TF_GraphToFunction_wrapper(
         graph._c_graph,  # pylint: disable=protected-access
@@ -248,6 +250,14 @@ def __init__(self, name, graph, operations, inputs, outputs):
         [],
         None,
         compat.as_str(""))
+
+    for name, attr_value in attrs.items():
+      serialized = attr_value.SerializeToString()
+      # TODO(iga): this creates and deletes a new TF_Status for every attr.
+      # It might be worth creating a convenient way to re-use status.
+      pywrap_tensorflow.TF_FunctionSetAttrValueProto(
+          fn, compat.as_str(name), serialized)
+
     # TODO(apassos) avoid creating a FunctionDef (specially to grab the
     # signature, but also in general it's nice not to depend on it.
     with c_api_util.tf_buffer() as buffer_:
@@ -289,25 +299,6 @@ def _flatten(sequence):
 
 class GraphModeFunction(object):
   """Callable object representing a graph-mode function.
-
-  Args:
-    name: str the name of the created function
-    input_placeholders: list of placeholder values (tensors) to feed when
-      calling the wrapped function.
-    extra_inputs: Tensor inputs this function definition closed over which
-      are passed as arguments. Need to track so gradients are supported
-      correctly.
-    graph: the Graph from which the operations will be pulled. Used as
-      a context when computing gradients.
-    operations: the subset of Operations in the graph used in the function
-      definition.
-    outputs: a flat list of the Tensors in the graph used as outputs to the
-      function
-    func_outputs: a possibly nested python object which will be returned by
-      this function. The Tensors in this structure will be replaced by their
-      corresponding values in outputs.
-    output_shapes: List of shapes of all tensors in outputs
-    variables: (optional) List of variables to watch during function execution.
   """
 
   def __init__(self,
@@ -319,9 +310,36 @@ def __init__(self,
                outputs,
                func_outputs,
                output_shapes,
-               variables=None):
+               variables=None,
+               attrs=None):
+    """Initialize a GraphModeFunction.
+
+    Args:
+      name: str the name of the created function
+      input_placeholders: list of placeholder values (tensors) to feed when
+        calling the wrapped function.
+      extra_inputs: Tensor inputs this function definition closed over which
+        are passed as arguments. Need to track so gradients are supported
+        correctly.
+      graph: the Graph from which the operations will be pulled. Used as
+        a context when computing gradients.
+      operations: the subset of Operations in the graph used in the function
+        definition.
+      outputs: a flat list of the Tensors in the graph used as outputs to the
+        function
+      func_outputs: a possibly nested python object which will be returned by
+        this function. The Tensors in this structure will be replaced by their
+        corresponding values in outputs.
+      output_shapes: List of shapes of all tensors in outputs
+      variables: (optional) List of variables to watch during function
+        execution.
+      attrs: (optional) dict mapping names of attributes to their AttrValue
+        values. Attributes in `attrs` will be included in this function's
+        definition.
+    """
+    self._attrs = attrs or {}
     defined_function = _EagerDefinedFunction(
-        name, graph, operations, input_placeholders, outputs)
+        name, graph, operations, input_placeholders, outputs, self._attrs)
     if len(input_placeholders) != len(defined_function.signature.input_arg):
       raise ValueError("Internal error: invalid lengths. %s %s" % (
           len(input_placeholders), len(defined_function.signature.input_arg)))
@@ -374,7 +392,7 @@ def _construct_backprop_function(self):
     forward_name = _forward_name(self._func_name)
     self._forward_fdef = _EagerDefinedFunction(
         forward_name, self._graph, self._ops, self._input_placeholders,
-        filtered_outputs + captures)
+        filtered_outputs + captures, self._attrs)
     all_inputs = self._out_grad_placeholders + captures
     # Excluding input ops from the body as we do not intend to execute these
     # operations when the function is executed.
@@ -388,7 +406,7 @@ def _construct_backprop_function(self):
     bname = _backward_name(self._func_name)
     self._backward_function = GraphModeFunction(
         bname, all_inputs, [], self._graph, function_def_ops,
-        backward_outputs, in_gradients, output_shapes)
+        backward_outputs, in_gradients, output_shapes, attrs=self._attrs)
 
   def _backprop_call(self, args):
     """Calls the wrapped function and records the result on a tape."""
@@ -562,7 +580,7 @@ def _get_defun_inputs(args):
   return nest.pack_sequence_as(args, ret)
 
 
-def _defun_internal(name, func, args, kwds):
+def _defun_internal(name, func, compiled, args, kwds):
   """Defines and returns graph-mode version of func."""
   graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
   with context.graph_mode():
@@ -627,9 +645,14 @@ def convert(x):
     for f in tmp_graph._functions.values():  # pylint: disable=protected-access
       # TODO(ashankar): What about the gradient registry?
       _register(f._c_func.func)  # pylint: disable=protected-access
+
+  attrs = {}
+  if compiled:
+    attrs["_XlaCompile"] = attr_value_pb2.AttrValue(b=True)
+
   return GraphModeFunction(
       fname, all_inputs, extra_inputs, tmp_graph, operations, func_def_outputs,
-      func_outputs, output_shapes, variables)
+      func_outputs, output_shapes, variables, attrs)
 
 
 # Defun uses this instead of Tensor as a cache key. Using dtype because
@@ -671,7 +694,7 @@ def _register(fn):
 
 
 # TODO(apassos): better error messages for non-hashable arguments.
-def named_defun(func, name):
+def named_defun(func, name, compiled=False):
   """Defines a function with a given name.
 
   See the documentation for `defun` for more information on the semantics of the
@@ -680,6 +703,7 @@ def named_defun(func, name):
   Args:
     func: the function to be wrapped.
     name: the name given to it.
+    compiled: if true, the framework will attempt to compile func with XLA.
 
   Returns:
     the wrapped function.
@@ -696,13 +720,13 @@ def decorated(*args, **kwds):
 
     if cache_key not in arguments_to_functions:
       arguments_to_functions[cache_key] = _defun_internal(
-          name, func, args, kwds)
+          name, func, compiled, args, kwds)
     return arguments_to_functions[cache_key](*args)
 
   return decorated
 
 
-def defun(func):
+def defun(func=None, compiled=False):
   """Decorator to compile func into graph_mode.
 
   `defun` converts a function that constructs a TensorFlow graph into a function
@@ -745,18 +769,45 @@ def g(x, y):
   ```
 
   Args:
-    func: function to be compiled.
+    func: function to be compiled. If `func` is None, returns a
+      decorator that can be invoked with a single argument - `func`. The
+      end result is equivalent to providing all the arguments up front.
+      In other words, defun(compiled=True)(func) is equivalent to
+      defun(func, compiled=True). The former allows the following use case:
+        @tfe.defun(compiled=True)
+        def foo(...):
+          ...
+    compiled: If True, an attempt to compile `func` with XLA will be made.
+      If it fails, function will be run normally. Experimental.
+      Currently, supported only for execution on TPUs.
 
   Returns:
-     A callable that will execute the compiled function (and return zero
-     or more `tf.Tensor` objects).
+     If `func` is not None, returns callable that will execute the compiled
+     function (and return zero or more `tf.Tensor` objects).
+     If `func` is None, returns a decorator that, when invoked with a single
+     `func` argument, returns a callable equivalent to the case above.
   """
   # TODO(apassos): deal with captured global state. Deal with control flow.
-  try:
-    name = func.__name__
-  except AttributeError:
-    name = "function"
-  return tf_decorator.make_decorator(func, named_defun(func, name))
+  def decorated(function):
+    try:
+      name = function.__name__
+    except AttributeError:
+      name = "function"
+    return tf_decorator.make_decorator(
+        function, named_defun(function, name, compiled=compiled))
+
+  # This code path is for the `foo = tfe.defun(foo, ...)` use case
+  if func is not None:
+    return decorated(func)
+
+  # This code path is for the
+  #
+  # @tfe.defun(...)
+  # def foo(...):
+  #    ...
+  #
+  # use case, which is equivalent to `foo = tfe.defun(...)(foo)`
+  return decorated
 
 
 def make_defun_op(func, *args, **kwds):
@@ -808,7 +859,7 @@ def g(x, y):
   name = func.__name__
   if any(isinstance(x, ops.EagerTensor) for x in kwds.values()):
     raise ValueError("Tensor keyword arguments are not supported.")
-  return _defun_internal(name, func, args, kwds)
+  return _defun_internal(name, func, False, args, kwds)
 
 
 class AutomaticControlDependencies(object):

From d14a530533a049bb4096d1789c626f7c3f3e1d83 Mon Sep 17 00:00:00 2001
From: Yifei Feng 
Date: Tue, 8 May 2018 17:16:45 -0700
Subject: [PATCH 0529/1691] Hardcode EndpointSpec deprecated input to False for
 now after cl/195718061.

---
 tensorflow/java/src/gen/cc/op_specs.cc | 3 +--
 tensorflow/java/src/gen/cc/op_specs.h  | 6 +++---
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/tensorflow/java/src/gen/cc/op_specs.cc b/tensorflow/java/src/gen/cc/op_specs.cc
index 081062ceaf2d0b..4bcfc7fe011423 100644
--- a/tensorflow/java/src/gen/cc/op_specs.cc
+++ b/tensorflow/java/src/gen/cc/op_specs.cc
@@ -382,8 +382,7 @@ EndpointSpec CreateEndpoint(const OpDef& op_def, const ApiDef& api_def,
   return EndpointSpec(package,
       name,
       Javadoc::Create(ParseDocumentation(api_def.summary()))
-          .details(ParseDocumentation(api_def.description())),
-      endpoint_def.deprecation_version() > 0);
+          .details(ParseDocumentation(api_def.description())));
 }
 
 }  // namespace
diff --git a/tensorflow/java/src/gen/cc/op_specs.h b/tensorflow/java/src/gen/cc/op_specs.h
index 81582ea207fef9..034cf636ed071a 100644
--- a/tensorflow/java/src/gen/cc/op_specs.h
+++ b/tensorflow/java/src/gen/cc/op_specs.h
@@ -34,11 +34,11 @@ class EndpointSpec {
   // package: package of this endpoint (from which also derives its package)
   // name: name of this endpoint class
   // javadoc: the endpoint class documentation
-  // deprecated: true if this endpoint is now deprecated
+  // TODO(annarev): hardcode depcreated to false until deprecated is possible
   EndpointSpec(const string& package, const string& name,
-      const Javadoc& javadoc, bool deprecated)
+      const Javadoc& javadoc)
     : package_(package), name_(name), javadoc_(javadoc),
-      deprecated_(deprecated) {}
+      deprecated_(false) {}
 
   const string& package() const { return package_; }
   const string& name() const { return name_; }

From 1f03f829285ca0fbd47a99350e9f5d99aa10e9b9 Mon Sep 17 00:00:00 2001
From: Yifei Feng <1192265+yifeif@users.noreply.github.com>
Date: Tue, 8 May 2018 17:35:21 -0700
Subject: [PATCH 0530/1691] Switch to use str instead of number for colab_url

Fix nightly failure:
  File "tensorflow/tools/ci_build/update_version.py", line 253, in colab_url
    version_string = "%d.%d.%d" % (version.major, version.minor, version.patch)
TypeError: %d format: a number is required, not str
---
 tensorflow/tools/ci_build/update_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/ci_build/update_version.py b/tensorflow/tools/ci_build/update_version.py
index 9ddb2190487c26..00bfcfd49bd1d9 100755
--- a/tensorflow/tools/ci_build/update_version.py
+++ b/tensorflow/tools/ci_build/update_version.py
@@ -250,7 +250,7 @@ def update_md_files(old_version, new_version):
 
   # Update any links to colab notebooks.
   def colab_url(version):
-    version_string = "%d.%d.%d" % (version.major, version.minor, version.patch)
+    version_string = "%s.%s.%s" % (version.major, version.minor, version.patch)
     prefix = "https://colab.research.google.com/github/tensorflow/models/blob/r"
     return prefix + version_string + "/"
 

From c317afd07eb11abe416080cdced9ec00198dbbb0 Mon Sep 17 00:00:00 2001
From: Yong Tang 
Date: Wed, 9 May 2018 08:55:19 -0700
Subject: [PATCH 0531/1691] Enable test case for float64 with conv1d (#19179)

The float64 for conv2d support has been added to tensorflow
in e3468b56d323783fdfb79fa2d6c24effc58bcaa9. (Thanks brianwa84!)
Since conv1d implementation invokes conv2d, the float64 support
for conv1d is supported now as well.

This fix adds the test case for float64 support of conv1d and
removes the TODO.

This fix fixes 19175.

Signed-off-by: Yong Tang 
---
 tensorflow/python/kernel_tests/conv1d_test.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/tensorflow/python/kernel_tests/conv1d_test.py b/tensorflow/python/kernel_tests/conv1d_test.py
index e2e6205911caa0..fcba456004407b 100644
--- a/tensorflow/python/kernel_tests/conv1d_test.py
+++ b/tensorflow/python/kernel_tests/conv1d_test.py
@@ -31,9 +31,7 @@ class Conv1DTest(test.TestCase):
 
   def testBasic(self):
     """Test that argument passing to conv1d is handled properly."""
-    # TODO(yongtang): dtypes.float64 can only be enabled once conv2d support
-    # dtypes.float64, as conv1d implicitly calls conv2d after expand_dims.
-    for dtype in [dtypes.float16, dtypes.float32]:
+    for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
       x = constant_op.constant([1, 2, 3, 4], dtype=dtype)
       x = array_ops.expand_dims(x, 0)  # Add batch dimension
       x = array_ops.expand_dims(x, 2)  # And depth dimension

From 76e8a4ec287c11d5b1286244d1821994640dbecf Mon Sep 17 00:00:00 2001
From: ctiijima 
Date: Wed, 9 May 2018 09:50:48 -0700
Subject: [PATCH 0532/1691] Grammar fixes for Programmers guide FAQ (#19170)

---
 tensorflow/docs_src/programmers_guide/faq.md | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/programmers_guide/faq.md
index 51c1a1e032baae..b6291a9fface40 100644
--- a/tensorflow/docs_src/programmers_guide/faq.md
+++ b/tensorflow/docs_src/programmers_guide/faq.md
@@ -72,7 +72,7 @@ tensors in the execution of a step.
 
 If `t` is a @{tf.Tensor} object,
 @{tf.Tensor.eval} is shorthand for
-@{tf.Session.run} (where `sess` is the
+@{tf.Session.run}, where `sess` is the
 current @{tf.get_default_session}. The
 two following snippets of code are equivalent:
 
@@ -101,9 +101,8 @@ sessions, it may be more straightforward to make explicit calls to
 Sessions can own resources, such as
 @{tf.Variable},
 @{tf.QueueBase}, and
-@{tf.ReaderBase}; and these resources can use
-a significant amount of memory. These resources (and the associated memory) are
-released when the session is closed, by calling
+@{tf.ReaderBase}. These resources can sometimes use
+a significant amount of memory, and can be released when the session is closed by calling
 @{tf.Session.close}.
 
 The intermediate tensors that are created as part of a call to
@@ -137,7 +136,7 @@ TensorFlow also has a
 to help build support for more client languages.  We invite contributions of new
 language bindings.
 
-Bindings for various other languages (such as [C#](https://github.com/migueldeicaza/TensorFlowSharp), [Julia](https://github.com/malmaud/TensorFlow.jl), [Ruby](https://github.com/somaticio/tensorflow.rb) and [Scala](https://github.com/eaplatanios/tensorflow_scala)) created and supported by the opensource community build on top of the C API supported by the TensorFlow maintainers.
+Bindings for various other languages (such as [C#](https://github.com/migueldeicaza/TensorFlowSharp), [Julia](https://github.com/malmaud/TensorFlow.jl), [Ruby](https://github.com/somaticio/tensorflow.rb) and [Scala](https://github.com/eaplatanios/tensorflow_scala)) created and supported by the open source community build on top of the C API supported by the TensorFlow maintainers.
 
 #### Does TensorFlow make use of all the devices (GPUs and CPUs) available on my machine?
 
@@ -210,8 +209,8 @@ a new tensor with a different dynamic shape.
 
 #### How do I build a graph that works with variable batch sizes?
 
-It is often useful to build a graph that works with variable batch sizes, for
-example so that the same code can be used for (mini-)batch training, and
+It is often useful to build a graph that works with variable batch sizes 
+so that the same code can be used for (mini-)batch training, and
 single-instance inference. The resulting graph can be
 @{tf.Graph.as_graph_def$saved as a protocol buffer}
 and
@@ -260,7 +259,7 @@ See the how-to documentation for
 There are three main options for dealing with data in a custom format.
 
 The easiest option is to write parsing code in Python that transforms the data
-into a numpy array. Then use @{tf.data.Dataset.from_tensor_slices} to
+into a numpy array. Then, use @{tf.data.Dataset.from_tensor_slices} to
 create an input pipeline from the in-memory data.
 
 If your data doesn't fit in memory, try doing the parsing in the Dataset
@@ -274,7 +273,7 @@ If your data is not easily parsable with the built-in TensorFlow operations,
 consider converting it, offline, to a format that is easily parsable, such
 as @{tf.python_io.TFRecordWriter$`TFRecord`} format.
 
-The more efficient method to customize the parsing behavior is to
+The most efficient method to customize the parsing behavior is to
 @{$adding_an_op$add a new op written in C++} that parses your
 data format. The @{$new_data_formats$guide to handling new data formats} has
 more information about the steps for doing this.

From baeb356fbf209bd8ef325704fa9bd22e6f2a0887 Mon Sep 17 00:00:00 2001
From: Letian Feng 
Date: Wed, 9 May 2018 18:50:57 +0200
Subject: [PATCH 0533/1691] Fix 2 typos in documents (#19177)

* fix minor typo in doc: tf.layer to tf.layers

* removed a duplicated line
---
 tensorflow/docs_src/programmers_guide/variables.md | 2 +-
 tensorflow/docs_src/tutorials/layers.md            | 1 -
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/programmers_guide/variables.md
index e8cf7711552f4c..cd8c4b5b9a026f 100644
--- a/tensorflow/docs_src/programmers_guide/variables.md
+++ b/tensorflow/docs_src/programmers_guide/variables.md
@@ -237,7 +237,7 @@ TensorFlow supports two ways of sharing variables:
 While code which explicitly passes variables around is very clear, it is
 sometimes convenient to write TensorFlow functions that implicitly use
 variables in their implementations. Most of the functional layers from
-`tf.layer` use this approach, as well as all `tf.metrics`, and a few other
+`tf.layers` use this approach, as well as all `tf.metrics`, and a few other
 library utilities.
 
 Variable scopes allow you to control variable reuse when calling functions which
diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md
index 37cd2bb1397dea..496b1e4da9d3b8 100644
--- a/tensorflow/docs_src/tutorials/layers.md
+++ b/tensorflow/docs_src/tutorials/layers.md
@@ -209,7 +209,6 @@ for two-dimensional image data expect input tensors to have a shape of
 *   _`channels`_. Number of color channels in the example images. For color
     images, the number of channels is 3 (red, green, blue). For monochrome
     images, there is just 1 channel (black).
-*   _`image_height`_. Height of the example images.
 *   _`data_format`_. A string, one of `channels_last` (default) or `channels_first`.
       `channels_last` corresponds to inputs with shape
       `(batch, ..., channels)` while `channels_first` corresponds to

From 4fb125264c5394c9e4295ed437adb1d9711bd456 Mon Sep 17 00:00:00 2001
From: AG Ramesh 
Date: Wed, 9 May 2018 09:51:10 -0700
Subject: [PATCH 0534/1691] [INTEL MKL] Fixes a failure in
 //tensorflow/python/profiler:model_analyzer_test. (#19152)

* Modified  testComplexCodeView test

 Modified  testComplexCodeView to look for lower total_float_ops. The value of total_float_ops is lower when Tensorflow is compiled with Intel MKL.

* Added code to check if MKL is enabled

* Fixed Pylint errors
---
 tensorflow/python/profiler/model_analyzer_test.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/profiler/model_analyzer_test.py b/tensorflow/python/profiler/model_analyzer_test.py
index 75580fc6308345..9e49188c1ef353 100644
--- a/tensorflow/python/profiler/model_analyzer_test.py
+++ b/tensorflow/python/profiler/model_analyzer_test.py
@@ -232,7 +232,12 @@ def testComplexCodeView(self):
 
         self.assertLess(0, tfprof_node.total_exec_micros)
         self.assertEqual(2844, tfprof_node.total_parameters)
-        self.assertLess(145660, tfprof_node.total_float_ops)
+        #The graph is modifed when MKL is enabled,total_float_ops will
+        #be different
+        if test_util.IsMklEnabled():
+          self.assertLess(101600, tfprof_node.total_float_ops)
+        else:
+          self.assertLess(145660, tfprof_node.total_float_ops)
         self.assertEqual(8, len(tfprof_node.children))
         self.assertEqual('_TFProfRoot', tfprof_node.name)
         self.assertEqual(

From 8d494db5b34a55a8d8b8e4ffb835c38f5fbaa4cf Mon Sep 17 00:00:00 2001
From: Akshay Modi 
Date: Tue, 8 May 2018 17:03:10 -0700
Subject: [PATCH 0535/1691] Skip convert_to_tensor in r_binary_op_wrapper in
 eager mode.

Should fallback from C if its not convertible.

PiperOrigin-RevId: 195899829
---
 tensorflow/python/ops/math_ops.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index ab5997e85c6030..e65a4b80d3c99b 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -871,7 +871,8 @@ def binary_op_wrapper_sparse(sp_x, y):
 
   def r_binary_op_wrapper(y, x):
     with ops.name_scope(None, op_name, [x, y]) as name:
-      x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name="x")
+      if not context.executing_eagerly():
+        x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name="x")
       return func(x, y, name=name)
 
   # Propagate func.__doc__ to the wrappers

From 2340b93644981768534ae0831d0927898921a018 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 17:04:00 -0700
Subject: [PATCH 0536/1691] Fix a dropped line in the DepthwiseConv2dNative
 model

PiperOrigin-RevId: 195900021
---
 tensorflow/core/grappler/costs/op_level_cost_estimator.cc | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
index 2542fa2d675364..fbdd3113117128 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
@@ -865,6 +865,7 @@ int64 OpLevelCostEstimator::CountConv2DBackpropInputOperations(
     conv_dims.oz *= conv_dims.iz;
     ops *= conv_dims.oz;
   }
+  ops *= kOpsPerMac;
 
   VLOG(1) << "Operations for" << op_features.op() << "  " << ops;
 
@@ -921,7 +922,7 @@ int64 OpLevelCostEstimator::CountConv2DBackpropFilterOperations(
     conv_dims.oz *= conv_dims.iz;
     ops *= conv_dims.oz;
   }
-
+  ops *= kOpsPerMac;
   VLOG(1) << "Operations for" << op_features.op() << "  " << ops;
 
   if (returned_conv_dims != nullptr) {

From a768f270c15ded657c30fe9ef873251de3556e58 Mon Sep 17 00:00:00 2001
From: Tony Wang 
Date: Tue, 8 May 2018 17:24:02 -0700
Subject: [PATCH 0537/1691] Add two helper methods to the graphcycle class.

PiperOrigin-RevId: 195902659
---
 tensorflow/compiler/jit/graphcycles/graphcycles.cc | 14 ++++++++++++++
 tensorflow/compiler/jit/graphcycles/graphcycles.h  |  4 ++++
 .../compiler/jit/graphcycles/graphcycles_test.cc   | 14 ++++++++++++++
 3 files changed, 32 insertions(+)

diff --git a/tensorflow/compiler/jit/graphcycles/graphcycles.cc b/tensorflow/compiler/jit/graphcycles/graphcycles.cc
index bc68afb322b5cf..805bbc62c1e2e8 100644
--- a/tensorflow/compiler/jit/graphcycles/graphcycles.cc
+++ b/tensorflow/compiler/jit/graphcycles/graphcycles.cc
@@ -354,6 +354,16 @@ bool GraphCycles::IsReachableNonConst(int32 x, int32 y) {
   return reachable;
 }
 
+bool GraphCycles::CanContractEdge(int32 a, int32 b) {
+  CHECK(HasEdge(a, b)) << "No edge exists from " << a << " to " << b;
+  RemoveEdge(a, b);
+  bool reachable = IsReachableNonConst(a, b);
+  // Restore the graph to its original state.
+  InsertEdge(a, b);
+  // If reachable, then contracting edge will cause cycle.
+  return !reachable;
+}
+
 bool GraphCycles::ContractEdge(int32 a, int32 b) {
   CHECK(HasEdge(a, b));
   RemoveEdge(a, b);
@@ -388,4 +398,8 @@ std::unordered_set GraphCycles::Successors(int32 node) {
   return rep_->nodes_[node]->out;
 }
 
+std::unordered_set GraphCycles::Predecessors(int32 node) {
+  return rep_->nodes_[node]->in;
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/jit/graphcycles/graphcycles.h b/tensorflow/compiler/jit/graphcycles/graphcycles.h
index d11d6e27b1b7bb..44448fa3d787d0 100644
--- a/tensorflow/compiler/jit/graphcycles/graphcycles.h
+++ b/tensorflow/compiler/jit/graphcycles/graphcycles.h
@@ -85,6 +85,9 @@ class GraphCycles {
   // and returns false.
   bool ContractEdge(int32 a, int32 b);
 
+  // Return true if can contract edge, otherwise return false.
+  bool CanContractEdge(int32 a, int32 b);
+
   // Return whether dest_node is reachable from source_node
   // by following edges.
   bool IsReachable(int32 source_node, int32 dest_node) const;
@@ -115,6 +118,7 @@ class GraphCycles {
   bool CheckInvariants() const;
 
   std::unordered_set Successors(int32 node);
+  std::unordered_set Predecessors(int32 node);
 
   // ----------------------------------------------------
   struct Rep;
diff --git a/tensorflow/compiler/jit/graphcycles/graphcycles_test.cc b/tensorflow/compiler/jit/graphcycles/graphcycles_test.cc
index e47b782207e912..274f5938a1228b 100644
--- a/tensorflow/compiler/jit/graphcycles/graphcycles_test.cc
+++ b/tensorflow/compiler/jit/graphcycles/graphcycles_test.cc
@@ -494,6 +494,20 @@ TEST_F(GraphCyclesTest, ContractEdge) {
   EXPECT_TRUE(g_.HasEdge(1, 4));
 }
 
+TEST_F(GraphCyclesTest, CanContractEdge) {
+  ASSERT_TRUE(AddEdge(1, 2));
+  ASSERT_TRUE(AddEdge(1, 3));
+  ASSERT_TRUE(AddEdge(2, 3));
+  ASSERT_TRUE(AddEdge(2, 4));
+  ASSERT_TRUE(AddEdge(3, 4));
+
+  EXPECT_FALSE(g_.CanContractEdge(1, 3));
+  EXPECT_FALSE(g_.CanContractEdge(2, 4));
+  EXPECT_TRUE(g_.CanContractEdge(1, 2));
+  EXPECT_TRUE(g_.CanContractEdge(2, 3));
+  EXPECT_TRUE(g_.CanContractEdge(3, 4));
+}
+
 static void BM_StressTest(int iters, int num_nodes) {
   while (iters > 0) {
     tensorflow::GraphCycles g;

From ffe6ede215729f99764761c5acf6a3bdebf69ced Mon Sep 17 00:00:00 2001
From: Akshay Modi 
Date: Tue, 8 May 2018 17:27:33 -0700
Subject: [PATCH 0538/1691] Include tensorflow::DataType header file

PiperOrigin-RevId: 195903041
---
 tensorflow/python/eager/BUILD           | 1 +
 tensorflow/python/eager/pywrap_tensor.h | 1 +
 2 files changed, 2 insertions(+)

diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD
index b3268c9047e264..a0fc538ae1366a 100644
--- a/tensorflow/python/eager/BUILD
+++ b/tensorflow/python/eager/BUILD
@@ -25,6 +25,7 @@ cc_library(
         "//tensorflow/c/eager:c_api_internal",
         "//tensorflow/c/eager:tape",
         "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/python:ndarray_tensor",
         "//tensorflow/python:ndarray_tensor_bridge",
         "//tensorflow/python:numpy_lib",
diff --git a/tensorflow/python/eager/pywrap_tensor.h b/tensorflow/python/eager/pywrap_tensor.h
index 88982b0c8562c5..bc042eb19e6a91 100644
--- a/tensorflow/python/eager/pywrap_tensor.h
+++ b/tensorflow/python/eager/pywrap_tensor.h
@@ -16,6 +16,7 @@ limitations under the License.
 #define TENSORFLOW_PYTHON_EAGER_PYWRAP_TENSOR_H_
 
 #include "tensorflow/c/eager/c_api.h"
+#include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/platform/types.h"
 #include "tensorflow/python/lib/core/numpy.h"
 

From 15879526893886852b64d60b72c40bc6daeda22e Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Tue, 8 May 2018 17:29:01 -0700
Subject: [PATCH 0539/1691] [XLA:GPU] Disable multi-streaming by default.

Run all GPU work on one stream by default.  We've found experimentally
that multi-streaming creates significant additional memory pressure on
some models, and we don't have any good benchmarks where multi-streaming
helps on which to tune the stream-assignment heuristics.  So just
disable it for now.

PiperOrigin-RevId: 195903229
---
 .../compiler/xla/legacy_flags/debug_options_flags.cc     | 6 ++++++
 tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc | 9 +++++++++
 .../compiler/xla/service/gpu/stream_assignment_test.cc   | 9 +++++++++
 3 files changed, 24 insertions(+)

diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
index bc8405703b02dc..f42fb92359f40e 100644
--- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
+++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
@@ -47,6 +47,12 @@ void SetDebugOptionsDefaults(DebugOptions* flags) {
   // Set cudnn batchnorm off by default; it does not provide a performance win
   // on average.
   flags->set_xla_gpu_use_cudnn_batchnorm(false);
+
+  // Run all GPU work on one stream by default.  Using multiple streams
+  // increases memory usage and we lack strong motivating benchmarks for tuning
+  // the heuristics needed to decide when to run on multiple streams.  See
+  // b/77879207.
+  flags->set_xla_gpu_disable_multi_streaming(true);
 }
 
 // Allocates flag_values and flag_objects; this function must not be called more
diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc b/tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc
index 6436abc06cb9b0..e230d538cc2df8 100644
--- a/tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc
+++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc
@@ -42,6 +42,15 @@ class HloScheduleTest : public HloTestBase {
         .ConsumeValueOrDie();
   }
 
+  std::unique_ptr CreateNewModule() {
+    HloModuleConfig config;
+    auto debug_options = GetDebugOptionsForTest();
+    debug_options.set_xla_gpu_disable_multi_streaming(false);
+    config.set_debug_options(debug_options);
+    return MakeUnique("test_module", VersionedComputationHandle(),
+                                 config);
+  }
+
   HloVec RemoveHlo(const HloVec& input,
                    const std::unordered_set& remove) {
     HloVec result(input);
diff --git a/tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc b/tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc
index b42767dfd500bd..696fa7e0194032 100644
--- a/tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc
@@ -28,6 +28,15 @@ namespace gpu {
 
 class StreamAssignmentTest : public HloTestBase {
  protected:
+  std::unique_ptr CreateNewModule() {
+    HloModuleConfig config;
+    auto debug_options = GetDebugOptionsForTest();
+    debug_options.set_xla_gpu_disable_multi_streaming(false);
+    config.set_debug_options(debug_options);
+    return MakeUnique("test_module", VersionedComputationHandle(),
+                                 config);
+  }
+
   // Pre-canned shapes.
   Shape f32_2x2_ = ShapeUtil::MakeShape(F32, {2, 2});
 };

From d8cc88a19d8a8c61023c34395cce55593a498cbf Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Tue, 8 May 2018 18:16:47 -0700
Subject: [PATCH 0540/1691] [XLA] Make XlaAllocator obey retry_on_failure arg.

Previously we ignored it.

PiperOrigin-RevId: 195908178
---
 tensorflow/compiler/jit/xla_launch_util.cc | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/jit/xla_launch_util.cc b/tensorflow/compiler/jit/xla_launch_util.cc
index 0223f97a032cf9..e12e88fcc94c46 100644
--- a/tensorflow/compiler/jit/xla_launch_util.cc
+++ b/tensorflow/compiler/jit/xla_launch_util.cc
@@ -62,7 +62,10 @@ XlaAllocator::~XlaAllocator() {}
 
 xla::StatusOr XlaAllocator::Allocate(
     int device_ordinal, uint64 size, bool retry_on_failure) {
-  void* data = wrapped_->AllocateRaw(Allocator::kAllocatorAlignment, size);
+  AllocationAttributes attrs;
+  attrs.no_retry_on_failure = !retry_on_failure;
+  void* data =
+      wrapped_->AllocateRaw(Allocator::kAllocatorAlignment, size, attrs);
   if (data == nullptr) {
     return errors::ResourceExhausted("Out of memory while trying to allocate ",
                                      size, " bytes.");

From 7bd992b02c0a19ce7aa9c085ab5caa0e00fe2516 Mon Sep 17 00:00:00 2001
From: Akshay Modi 
Date: Tue, 8 May 2018 18:36:32 -0700
Subject: [PATCH 0541/1691] Delete old op gen code and replace with eager op
 gen.

PiperOrigin-RevId: 195909821
---
 tensorflow/contrib/cmake/tf_python.cmake      |   10 +-
 tensorflow/python/BUILD                       |    8 +-
 tensorflow/python/eager/BUILD                 |   16 -
 .../python/eager/python_eager_op_gen.cc       | 1047 ------------
 tensorflow/python/eager/python_eager_op_gen.h |   43 -
 tensorflow/python/framework/load_library.py   |    2 +-
 tensorflow/python/framework/python_op_gen.cc  | 1427 +++++++++--------
 tensorflow/python/framework/python_op_gen.h   |   19 +-
 tensorflow/python/framework/python_op_gen.i   |    8 +-
 .../framework/python_op_gen_internal.cc       |  800 +++++++++
 .../python/framework/python_op_gen_main.cc    |    9 +-
 11 files changed, 1599 insertions(+), 1790 deletions(-)
 delete mode 100644 tensorflow/python/eager/python_eager_op_gen.cc
 delete mode 100644 tensorflow/python/eager/python_eager_op_gen.h
 create mode 100644 tensorflow/python/framework/python_op_gen_internal.cc

diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index c4bdb69d828b26..8d24a7ae38f5b0 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -244,13 +244,11 @@ add_custom_command(TARGET tf_python_copy_scripts_to_destination PRE_BUILD
 # tf_python_op_gen_main library
 ########################################################
 set(tf_python_op_gen_main_srcs
-    "${tensorflow_source_dir}/tensorflow/python/eager/python_eager_op_gen.h"
-    "${tensorflow_source_dir}/tensorflow/python/eager/python_eager_op_gen.cc"
     "${tensorflow_source_dir}/tensorflow/python/framework/python_op_gen.cc"
-    "${tensorflow_source_dir}/tensorflow/python/framework/python_op_gen.cc"
-    "${tensorflow_source_dir}/tensorflow/python/framework/python_op_gen_main.cc"
     "${tensorflow_source_dir}/tensorflow/python/framework/python_op_gen.h"
+    "${tensorflow_source_dir}/tensorflow/python/framework/python_op_gen_internal.cc"
     "${tensorflow_source_dir}/tensorflow/python/framework/python_op_gen_internal.h"
+    "${tensorflow_source_dir}/tensorflow/python/framework/python_op_gen_main.cc"
 )
 
 add_library(tf_python_op_gen_main OBJECT ${tf_python_op_gen_main_srcs})
@@ -464,12 +462,12 @@ set (pywrap_tensorflow_internal_src
     "${tensorflow_source_dir}/tensorflow/python/eager/pywrap_tfe_src.cc"
     "${tensorflow_source_dir}/tensorflow/python/client/tf_session_helper.h"
     "${tensorflow_source_dir}/tensorflow/python/client/tf_session_helper.cc"
-    "${tensorflow_source_dir}/tensorflow/python/eager/python_eager_op_gen.h"
-    "${tensorflow_source_dir}/tensorflow/python/eager/python_eager_op_gen.cc"
     "${tensorflow_source_dir}/tensorflow/python/framework/cpp_shape_inference.h"
     "${tensorflow_source_dir}/tensorflow/python/framework/cpp_shape_inference.cc"
     "${tensorflow_source_dir}/tensorflow/python/framework/python_op_gen.h"
     "${tensorflow_source_dir}/tensorflow/python/framework/python_op_gen.cc"
+    "${tensorflow_source_dir}/tensorflow/python/framework/python_op_gen_internal.h"
+    "${tensorflow_source_dir}/tensorflow/python/framework/python_op_gen_internal.cc"
     "${tensorflow_source_dir}/tensorflow/python/lib/core/bfloat16.h"
     "${tensorflow_source_dir}/tensorflow/python/lib/core/bfloat16.cc"
     "${tensorflow_source_dir}/tensorflow/python/lib/core/numpy.h"
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index a865e8ca75744c..699f78edd2d69c 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -502,7 +502,10 @@ py_test(
 
 cc_library(
     name = "python_op_gen",
-    srcs = ["framework/python_op_gen.cc"],
+    srcs = [
+        "framework/python_op_gen.cc",
+        "framework/python_op_gen_internal.cc",
+    ],
     hdrs = [
         "framework/python_op_gen.h",
         "framework/python_op_gen_internal.h",
@@ -524,12 +527,12 @@ cc_library(
     srcs = ["framework/python_op_gen_main.cc"],
     visibility = ["//visibility:public"],
     deps = [
+        ":python_op_gen",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:op_gen_lib",
         "//tensorflow/core:protos_all_cc",
-        "//tensorflow/python/eager:python_eager_op_gen",
     ],
 )
 
@@ -3526,7 +3529,6 @@ tf_py_wrap_cc(
         "//tensorflow/core/profiler/internal:print_model_analysis",
         "//tensorflow/tools/graph_transforms:transform_graph_lib",
         "//tensorflow/python/eager:pywrap_tfe_lib",
-        "//tensorflow/python/eager:python_eager_op_gen",
         "//util/python:python_headers",
     ] + (tf_additional_lib_deps() +
          tf_additional_plugin_deps() +
diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD
index a0fc538ae1366a..5530193d4e1dd8 100644
--- a/tensorflow/python/eager/BUILD
+++ b/tensorflow/python/eager/BUILD
@@ -192,22 +192,6 @@ py_library(
     ],
 )
 
-cc_library(
-    name = "python_eager_op_gen",
-    srcs = ["python_eager_op_gen.cc"],
-    hdrs = ["python_eager_op_gen.h"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:lib_internal",
-        "//tensorflow/core:op_gen_lib",
-        "//tensorflow/core:proto_text",
-        "//tensorflow/core:protos_all_cc",
-        "//tensorflow/python:python_op_gen",
-    ],
-)
-
 py_library(
     name = "graph_only_ops",
     srcs = ["graph_only_ops.py"],
diff --git a/tensorflow/python/eager/python_eager_op_gen.cc b/tensorflow/python/eager/python_eager_op_gen.cc
deleted file mode 100644
index 9afab0077b666b..00000000000000
--- a/tensorflow/python/eager/python_eager_op_gen.cc
+++ /dev/null
@@ -1,1047 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/python/eager/python_eager_op_gen.h"
-
-#include 
-#include 
-#include 
-#include "tensorflow/core/framework/api_def.pb.h"
-#include "tensorflow/core/framework/attr_value.pb.h"
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/op_def.pb_text.h"
-#include "tensorflow/core/framework/op_def.pb.h"
-#include "tensorflow/core/framework/op_def_util.h"
-#include "tensorflow/core/framework/op_gen_lib.h"
-#include "tensorflow/core/framework/tensor.pb_text.h"
-#include "tensorflow/core/framework/types.h"
-#include "tensorflow/core/framework/types.pb.h"
-#include "tensorflow/core/lib/gtl/map_util.h"
-#include "tensorflow/core/lib/gtl/stl_util.h"
-#include "tensorflow/core/lib/strings/str_util.h"
-#include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/lib/strings/stringprintf.h"
-#include "tensorflow/core/platform/logging.h"
-#include "tensorflow/core/platform/macros.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/python/framework/python_op_gen_internal.h"
-
-namespace tensorflow {
-namespace {
-
-const int kRightMargin = 78;
-
-constexpr char kEagerFallbackSuffix[] = "_eager_fallback";
-
-string AttrVarName(const string& attr_name,
-                   std::unordered_map* attr_expressions) {
-  const string var = strings::StrCat("_attr_", attr_name);
-  if (attr_expressions != nullptr) (*attr_expressions)[attr_name] = var;
-  return var;
-}
-
-void AddInferredAttr(const string& indentation, const string& attr_name,
-                     const string& value_expression, string* result,
-                     std::unordered_map* attr_expressions) {
-  strings::StrAppend(result, indentation,
-                     AttrVarName(attr_name, attr_expressions), " = ",
-                     value_expression, "\n");
-}
-
-string VectorToTuple(const std::vector& l) {
-  if (l.size() == 1) return strings::StrCat("(", l.front(), ",)");
-  string ret = "(";
-  for (int i = 0; i < l.size(); ++i) {
-    if (i > 0) {
-      strings::StrAppend(&ret, ", ");
-    }
-    strings::StrAppend(&ret, l[i]);
-  }
-  strings::StrAppend(&ret, ")");
-  return ret;
-}
-
-void Unflatten(const string& prefix, const std::vector& output_sizes,
-               const string& var, string* result) {
-  for (int i = 0; i < output_sizes.size(); ++i) {
-    if (!output_sizes[i].empty()) {
-      strings::StrAppend(result, prefix, var, " = ");
-      if (i > 0) strings::StrAppend(result, var, "[:", i, "] + ");
-      if (i + 1 < output_sizes.size()) {
-        // Special case i == 0 to avoid "0 +" in the generated code.
-        if (i == 0) {
-          strings::StrAppend(result, "[", var, "[:", output_sizes[i], "]] + ",
-                             var, "[", output_sizes[i], ":]");
-        } else {
-          strings::StrAppend(result, "[", var, "[", i, ":", i, " + ",
-                             output_sizes[i], "]] + ", var, "[", i, " + ",
-                             output_sizes[i], ":]");
-        }
-      } else {
-        strings::StrAppend(result, "[", var, "[", i, ":]]");
-      }
-      strings::StrAppend(result, "\n");
-    }
-  }
-}
-
-string TensorPBString(const TensorProto& pb) {
-  // Note: This gets used in the argument list, and so must survive naive
-  // word wrapping.
-  return strings::StrCat("\"\"\"", ProtoShortDebugString(pb), "\"\"\"");
-}
-
-const ApiDef::Arg* FindInputArg(StringPiece name, const ApiDef& api_def) {
-  for (int i = 0; i < api_def.in_arg_size(); ++i) {
-    if (api_def.in_arg(i).name() == name) {
-      return &api_def.in_arg(i);
-    }
-  }
-  return nullptr;
-}
-
-class GenEagerPythonOp : public python_op_gen_internal::GenPythonOp {
- public:
-  GenEagerPythonOp(const OpDef& op_def, const ApiDef& api_def,
-                   const string& function_name)
-      : python_op_gen_internal::GenPythonOp(op_def, api_def, function_name) {
-    op_name_ = function_name_;
-    str_util::ConsumePrefix(&op_name_, "_");
-  }
-  ~GenEagerPythonOp() override {}
-
-  string Code() override;
-
- protected:
-  void HandleGraphMode(const string& function_setup);
-
-  string GetEagerNotAllowedError();
-  void ExpectListArg(const string& indentation, const string& arg_name,
-                     string* output);
-  bool GetEagerFunctionSetup(const string& indentation, string* function_setup);
-  void GetOutputSizesAndNumOutputsExpr(std::vector* output_sizes,
-                                       string* num_outputs_expr);
-
-  void AddEagerFunctionTeardown(const string& indentation,
-                                const std::vector& output_sizes,
-                                bool execute_record_gradient);
-
-  bool AddEagerFastPathAndGraphCode(const string& parameters,
-                                    const std::vector& output_sizes,
-                                    const string& eager_not_allowed_error);
-  bool AddEagerFallbackCode(const string& parameters,
-                            const std::vector& output_sizes,
-                            const string& num_outputs_expr,
-                            const string& eager_not_allowed_error);
-  void AddEagerFastPathExecute();
-
-  void AddEagerInferredAttrs(const string& indentation);
-  void AddEagerInputCasts(const string& indentation);
-  void AddEagerAttrs(const string& indentation);
-  void AddEagerExecute(const string& indentation,
-                       const string& num_outputs_expr);
-
-  void AddAttrForArg(const string& attr, int arg_index) {
-    gtl::InsertIfNotPresent(&inferred_attrs_, attr,
-                            op_def_.input_arg(arg_index).name());
-    auto iter = attr_to_args_.find(attr);
-    if (iter == attr_to_args_.end()) {
-      attr_to_args_.insert(AttrToArgMap::value_type(attr, {arg_index}));
-    } else {
-      iter->second.push_back(arg_index);
-    }
-  }
-
-  // Returns a string expression representing a flattened list of all
-  // the inputs given by `*input_indices` (or all inputs if
-  // `input_indices` is nullptr).  `*output_sizes` can be used to unflatten.
-  string FlattenInputs(const std::vector* input_indices,
-                       std::vector* output_sizes) const;
-
-  StringPiece op_name_;
-  typedef std::unordered_map> AttrToArgMap;
-  AttrToArgMap attr_to_args_;
-  std::unordered_map attr_expressions_;
-  // This has all the input args followed by those attrs that don't have
-  // defaults.
-  std::vector params_no_default_;
-  // The parameters with defaults (these have to be listed after those without).
-  // No input args are included, just attrs.
-  std::vector>
-      params_with_default_;
-};
-
-string GetEagerPythonOp(const OpDef& op_def, const ApiDef& api_def,
-                        const string& function_name) {
-  return GenEagerPythonOp(op_def, api_def, function_name).Code();
-}
-
-string GenEagerPythonOp::FlattenInputs(
-    const std::vector* input_indices,
-    std::vector* output_sizes) const {
-  string inputs;
-  enum { STARTING, WAS_LIST_INPUT, WAS_SOLO_INPUT } inputs_state = STARTING;
-  const int n = input_indices != nullptr ? input_indices->size()
-                                         : op_def_.input_arg_size();
-  for (int j = 0; j < n; ++j) {
-    const int i = input_indices ? (*input_indices)[j] : j;
-    const auto& arg(op_def_.input_arg(i));
-    const bool is_list =
-        !arg.type_list_attr().empty() || !arg.number_attr().empty();
-    if (is_list) {
-      if (inputs_state == WAS_SOLO_INPUT) {
-        strings::StrAppend(&inputs, "] + ");
-      } else if (inputs_state == WAS_LIST_INPUT) {
-        strings::StrAppend(&inputs, " + ");
-      }
-      strings::StrAppend(&inputs, "list(", param_names_[i].GetRenameTo(), ")");
-      inputs_state = WAS_LIST_INPUT;
-      if (output_sizes != nullptr) {
-        if (!arg.number_attr().empty()) {
-          output_sizes->emplace_back(AttrVarName(arg.number_attr(), nullptr));
-        } else {
-          output_sizes->emplace_back(
-              strings::StrCat("len(", param_names_[i].GetRenameTo(), ")"));
-        }
-      }
-    } else {
-      if (inputs_state == WAS_SOLO_INPUT) {
-        strings::StrAppend(&inputs, ", ");
-      } else if (inputs_state == WAS_LIST_INPUT) {
-        strings::StrAppend(&inputs, " + [");
-      } else {
-        strings::StrAppend(&inputs, "[");
-      }
-      strings::StrAppend(&inputs, param_names_[i].GetRenameTo());
-      inputs_state = WAS_SOLO_INPUT;
-      if (output_sizes != nullptr) output_sizes->emplace_back();
-    }
-  }
-  if (inputs_state == STARTING) return "[]";
-  if (inputs_state == WAS_SOLO_INPUT) {
-    strings::StrAppend(&inputs, "]");
-  }
-  return inputs;
-}
-
-string GenEagerPythonOp::Code() {
-  if (api_def_.visibility() == ApiDef::SKIP) {
-    return "";
-  }
-
-  for (int i = 0; i < api_def_.arg_order_size(); ++i) {
-    const auto& arg = *FindInputArg(api_def_.arg_order(i), op_def_);
-    const auto& api_def_arg = *FindInputArg(api_def_.arg_order(i), api_def_);
-    params_no_default_.emplace_back(api_def_arg.name(),
-                                    api_def_arg.rename_to());
-    if (!arg.type_attr().empty()) {
-      AddAttrForArg(arg.type_attr(), i);
-    } else if (!arg.type_list_attr().empty()) {
-      AddAttrForArg(arg.type_list_attr(), i);
-    }
-    if (!arg.number_attr().empty()) {
-      AddAttrForArg(arg.number_attr(), i);
-    }
-  }
-  for (int i = 0; i < op_def_.attr_size(); ++i) {
-    const auto& attr(op_def_.attr(i));
-    const auto& api_def_attr(api_def_.attr(i));
-    // Do not add inferred attrs to the Python function signature.
-    if (inferred_attrs_.find(attr.name()) == inferred_attrs_.end()) {
-      if (api_def_attr.has_default_value()) {
-        if (attr.type() == "tensor") {
-          params_with_default_.emplace_back(
-              python_op_gen_internal::ParamNames(api_def_attr.name(),
-                                                 api_def_attr.rename_to()),
-              strings::StrCat(
-                  "_execute.make_tensor(",
-                  TensorPBString(api_def_attr.default_value().tensor()), ", \"",
-                  api_def_attr.rename_to(), "\")"));
-        } else if (attr.type() == "list(tensor)") {
-          std::vector pbtxt;
-          for (const auto& pb : api_def_attr.default_value().list().tensor()) {
-            pbtxt.emplace_back(TensorPBString(pb));
-          }
-          params_with_default_.emplace_back(
-              python_op_gen_internal::ParamNames(api_def_attr.name(),
-                                                 api_def_attr.rename_to()),
-              strings::StrCat("[_execute.make_tensor(_pb, \"",
-                              api_def_attr.rename_to(), "\") for _pb in ",
-                              VectorToTuple(pbtxt), "]"));
-        } else {
-          params_with_default_.emplace_back(
-              python_op_gen_internal::ParamNames(api_def_attr.name(),
-                                                 api_def_attr.rename_to()),
-              python_op_gen_internal::AttrValueToPython(
-                  attr.type(), api_def_attr.default_value(), "_dtypes."));
-        }
-      } else {
-        params_no_default_.emplace_back(api_def_attr.name(),
-                                        api_def_attr.rename_to());
-      }
-    }
-  }
-
-  // Save the list of attr parameters (attrs that won't be inferred),
-  // those with defaults go at the end.
-  // Get the attrs in the order we want by taking the attrs without defaults
-  // from the end of params_no_default_, and adding params_no_default_.
-  attrs_.reserve(params_no_default_.size() - op_def_.input_arg_size() +
-                 params_with_default_.size());
-  for (int i = op_def_.input_arg_size(); i < params_no_default_.size(); ++i) {
-    attrs_.push_back(params_no_default_[i].GetName());
-  }
-  for (const auto& p : params_with_default_) {
-    attrs_.push_back(p.first.GetName());
-  }
-
-  param_names_.reserve(params_no_default_.size() + params_with_default_.size());
-  param_names_.insert(param_names_.begin(), params_no_default_.begin(),
-                      params_no_default_.end());
-  for (const auto& param_and_default : params_with_default_) {
-    param_names_.push_back(param_and_default.first);
-  }
-
-  string parameters;
-  for (const auto& param : params_no_default_) {
-    if (!parameters.empty()) strings::StrAppend(¶meters, ", ");
-    strings::StrAppend(¶meters, param.GetRenameTo());
-  }
-  for (const auto& param_and_default : params_with_default_) {
-    if (!parameters.empty()) strings::StrAppend(¶meters, ", ");
-    strings::StrAppend(¶meters, param_and_default.first.GetRenameTo(), "=",
-                       param_and_default.second);
-  }
-  if (!parameters.empty()) strings::StrAppend(¶meters, ", ");
-  strings::StrAppend(¶meters, "name=None");
-
-  // Add attr_expressions_ for attrs that are params.
-  for (int i = 0; i < attrs_.size(); ++i) {
-    const string& attr_name = attrs_[i];
-    const string& attr_api_name =
-        param_names_[i + op_def_.input_arg_size()].GetRenameTo();
-    attr_expressions_[attr_name] = attr_api_name;
-  }
-  // Add attr_expressions_ for attrs that are inferred.
-  for (int i = 0; i < op_def_.attr_size(); ++i) {
-    const auto& attr(op_def_.attr(i));
-    if (attr.type() == "int") {
-      auto arg_list = attr_to_args_.find(attr.name());
-      if (arg_list != attr_to_args_.end()) {
-        AttrVarName(attr.name(), &attr_expressions_);
-      }
-    }
-  }
-
-  string num_outputs_expr;
-  std::vector output_sizes(num_outs_);
-  GetOutputSizesAndNumOutputsExpr(&output_sizes, &num_outputs_expr);
-
-  string eager_not_allowed_error = GetEagerNotAllowedError();
-
-  if (!AddEagerFastPathAndGraphCode(parameters, output_sizes,
-                                    eager_not_allowed_error)) {
-    return result_;
-  }
-
-  if (!AddEagerFallbackCode(parameters, output_sizes, num_outputs_expr,
-                            eager_not_allowed_error)) {
-    return result_;
-  }
-
-  return prelude_ + result_;
-}
-
-void GenEagerPythonOp::HandleGraphMode(const string& function_setup) {
-  // Handle graph-mode case
-  strings::StrAppend(&result_,
-                     "  _ctx = _context._context\n"
-                     "  if _ctx is None or not _ctx._eager_context.is_eager:\n",
-                     function_setup,
-                     "    _, _, _op = _op_def_lib._apply_op_helper(\n");
-  AddBodyNoReturn("        ");
-  if (num_outs_ > 0) {
-    strings::StrAppend(&result_, "    _result = _op.outputs[:]\n");
-    // Special case handling for stateful op with single list output
-    // that might be empty.
-    if (num_outs_ == 1 && op_def_.is_stateful() &&
-        (!op_def_.output_arg(0).number_attr().empty() ||
-         !op_def_.output_arg(0).type_list_attr().empty())) {
-      // TODO(josh11b): Can skip this if the number_attr/type_list_attr has
-      // a constraint indicating that this can never be empty.
-      strings::StrAppend(&result_,
-                         "    if not _result:\n"
-                         "      return _op\n");
-    }
-    strings::StrAppend(&result_, "    _inputs_flat = _op.inputs\n");
-
-    // Compute graph-mode attrs.
-    if (op_def_.attr_size() > 0) {
-      string attr_values;
-      for (int i = 0; i < op_def_.attr_size(); ++i) {
-        if (i > 0) strings::StrAppend(&attr_values, ", ");
-        const auto& attr_name(op_def_.attr(i).name());
-        strings::StrAppend(&attr_values, "\"", attr_name, "\", _op.get_attr(\"",
-                           attr_name, "\")");
-      }
-      strings::StrAppend(&attr_values, ")");
-      strings::StrAppend(&result_,
-                         WordWrap("    _attrs = (", attr_values, kRightMargin),
-                         "\n");
-    } else {
-      strings::StrAppend(&result_, "    _attrs = None\n");
-    }
-  } else {
-    strings::StrAppend(&result_, "    return _op\n");
-  }
-}
-
-string GenEagerPythonOp::GetEagerNotAllowedError() {
-  bool eager_allowed = true;
-  string ref_arg;
-  for (int i = 0; i < op_def_.input_arg_size(); ++i) {
-    const auto& arg = op_def_.input_arg(i);
-    if (arg.is_ref()) {
-      eager_allowed = false;
-      DCHECK_EQ(op_def_.input_arg(i).name(), api_def_.in_arg(i).name());
-      ref_arg = api_def_.in_arg(i).rename_to();
-    }
-  }
-  for (int i = 0; i < op_def_.output_arg_size(); ++i) {
-    const auto& arg = op_def_.output_arg(i);
-    if (arg.is_ref()) {
-      eager_allowed = false;
-      DCHECK_EQ(op_def_.output_arg(i).name(), api_def_.out_arg(i).name());
-      ref_arg = api_def_.out_arg(i).rename_to();
-    }
-  }
-
-  if (eager_allowed) return "";
-
-  return strings::StrCat("raise RuntimeError(\"", op_name_,
-                         " op does not support eager execution. ", "Arg '",
-                         ref_arg, "' is a ref.\")\n");
-}
-
-void GenEagerPythonOp::ExpectListArg(const string& indentation,
-                                     const string& arg_name, string* output) {
-  strings::StrAppend(output, indentation, "if not isinstance(", arg_name,
-                     ", (list, tuple)):\n", indentation, "  raise TypeError(\n",
-                     indentation, "      \"Expected list for '", arg_name,
-                     "' argument to \"\n", indentation, "      \"'", op_name_,
-                     "' Op, not %r.\" % ", arg_name, ")\n");
-}
-
-bool GenEagerPythonOp::GetEagerFunctionSetup(const string& indentation,
-                                             string* function_setup) {
-  // Validate list inputs, infer length attrs.
-  for (int i = 0; i < op_def_.attr_size(); ++i) {
-    const auto& attr(op_def_.attr(i));
-    if (attr.type() == "int") {
-      auto arg_list = attr_to_args_.find(attr.name());
-      if (arg_list != attr_to_args_.end()) {
-        // Inferred int attrs are the lengths of inputs. Validate those
-        // inputs are lists and have the same length.
-        for (auto iter = arg_list->second.begin();
-             iter != arg_list->second.end(); ++iter) {
-          const string& arg_api_name = param_names_[*iter].GetRenameTo();
-          ExpectListArg(indentation, arg_api_name, function_setup);
-          if (iter == arg_list->second.begin()) {
-            AddInferredAttr(indentation, attr.name(),
-                            strings::StrCat("len(", arg_api_name, ")"),
-                            function_setup, &attr_expressions_);
-          } else {
-            const auto& attr_var = attr_expressions_[attr.name()];
-            strings::StrAppend(
-                function_setup, indentation, "if len(", arg_api_name,
-                ") != ", attr_var, ":\n", indentation, "  raise ValueError(\n",
-                indentation, "      \"List argument '", arg_api_name, "' to '",
-                op_name_, "' Op with length %d \"\n", indentation,
-                "      \"must match length %d of argument '",
-                inferred_attrs_[attr.name()], "'.\" %\n", indentation,
-                "      (len(", arg_api_name, "), ", attr_var, "))\n");
-          }
-        }
-      }
-    }
-  }
-
-  for (int i = 0; i < attrs_.size(); ++i) {
-    const string& attr_name = attrs_[i];
-    const auto& param = param_names_[i + op_def_.input_arg_size()];
-    const auto& attr = *FindAttr(attr_name, op_def_);
-    const string& attr_api_name = param.GetRenameTo();
-    StringPiece attr_type = attr.type();
-    attr_expressions_[attr_name] = attr_api_name;
-    const int default_index = i - (attrs_.size() - params_with_default_.size());
-    if (default_index >= 0) {
-      const string& default_value = params_with_default_[default_index].second;
-      strings::StrAppend(function_setup, indentation, "if ", attr_api_name,
-                         " is None:\n");
-      strings::StrAppend(function_setup, indentation, "  ", attr_api_name,
-                         " = ", default_value, "\n");
-    }
-    if (str_util::StartsWith(attr_type, "list(")) {
-      ExpectListArg(indentation, attr_api_name, function_setup);
-    }
-
-    if (attr_type == "string") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = _execute.make_str(", attr_api_name, ", \"",
-                         attr_api_name, "\")\n");
-    } else if (attr_type == "list(string)") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = [_execute.make_str(_s, \"", attr_api_name,
-                         "\") for _s in ", attr_api_name, "]\n");
-    } else if (attr_type == "int") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = _execute.make_int(", attr_api_name, ", \"",
-                         attr_api_name, "\")\n");
-    } else if (attr_type == "list(int)") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = [_execute.make_int(_i, \"", attr_api_name,
-                         "\") for _i in ", attr_api_name, "]\n");
-    } else if (attr_type == "float") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = _execute.make_float(", attr_api_name, ", \"",
-                         attr_api_name, "\")\n");
-    } else if (attr_type == "list(float)") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = [_execute.make_float(_f, \"", attr_api_name,
-                         "\") for _f in ", attr_api_name, "]\n");
-    } else if (attr_type == "bool") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = _execute.make_bool(", attr_api_name, ", \"",
-                         attr_api_name, "\")\n");
-    } else if (attr_type == "list(bool)") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = [_execute.make_bool(_b, \"", attr_api_name,
-                         "\") for _b in ", attr_api_name, "]\n");
-    } else if (attr_type == "type") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = _execute.make_type(", attr_api_name, ", \"",
-                         attr_api_name, "\")\n");
-    } else if (attr_type == "list(type)") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = [_execute.make_type(_t, \"", attr_api_name,
-                         "\") for _t in ", attr_api_name, "]\n");
-    } else if (attr_type == "shape") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = _execute.make_shape(", attr_api_name, ", \"",
-                         attr_api_name, "\")\n");
-    } else if (attr_type == "list(shape)") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = [_execute.make_shape(_s, \"", attr_api_name,
-                         "\") for _s in ", attr_api_name, "]\n");
-    } else if (attr_type == "tensor") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = _execute.make_tensor(", attr_api_name, ", \"",
-                         attr_api_name, "\")\n");
-    } else if (attr_type == "list(tensor)") {
-      strings::StrAppend(function_setup, indentation, attr_api_name,
-                         " = [_execute.make_tensor(_t, \"", attr_api_name,
-                         "\") for _t in ", attr_api_name, "]\n");
-    } else if (attr_type != "func") {
-      *function_setup =
-          strings::StrCat("# No definition for ", function_name_,
-                          " since we don't support attrs with type\n"
-                          "# '",
-                          attr_type, "' right now.\n\n");
-      return false;
-    }
-  }
-  return true;
-}
-
-// If output i is list output, output_sizes[i] will be set to a
-// string with the python expression that will evaluate to its
-// length. output_sizes[i] is empty for non-list outputs.
-void GenEagerPythonOp::GetOutputSizesAndNumOutputsExpr(
-    std::vector* output_sizes, string* num_outputs_expr) {
-  // Expression representing the number of outputs.
-  int num_fixed_outputs = 0;
-  for (int i = 0; i < num_outs_; ++i) {
-    const auto& arg(op_def_.output_arg(i));
-    if (!arg.number_attr().empty()) {
-      if (!num_outputs_expr->empty()) {
-        strings::StrAppend(num_outputs_expr, " + ");
-      }
-      (*output_sizes)[i] = attr_expressions_[arg.number_attr()];
-      strings::StrAppend(num_outputs_expr, (*output_sizes)[i]);
-    } else if (!arg.type_list_attr().empty()) {
-      if (!num_outputs_expr->empty()) {
-        strings::StrAppend(num_outputs_expr, " + ");
-      }
-      // Have to be careful to use an expression that works in both
-      // graph and eager paths here.
-      const auto iter = inferred_attrs_.find(arg.type_list_attr());
-      if (iter == inferred_attrs_.end()) {
-        (*output_sizes)[i] = strings::StrCat(
-            "len(", attr_expressions_[arg.type_list_attr()], ")");
-      } else {
-        (*output_sizes)[i] = strings::StrCat("len(", iter->second, ")");
-      }
-      strings::StrAppend(num_outputs_expr, (*output_sizes)[i]);
-    } else {
-      ++num_fixed_outputs;
-    }
-  }
-  if (num_fixed_outputs > 0) {
-    if (!num_outputs_expr->empty()) {
-      strings::StrAppend(num_outputs_expr, " + ");
-    }
-    strings::StrAppend(num_outputs_expr, num_fixed_outputs);
-  } else if (num_outputs_expr->empty()) {
-    *num_outputs_expr = "0";
-  }
-}
-
-void GenEagerPythonOp::AddEagerFunctionTeardown(
-    const string& indentation, const std::vector& output_sizes,
-    bool execute_record_gradient) {
-  if (num_outs_ > 0) {
-    if (execute_record_gradient) {
-      strings::StrAppend(&result_, indentation, "_execute.record_gradient(\n",
-                         "      \"", op_def_.name(),
-                         "\", _inputs_flat, _attrs, _result, name)\n");
-    }
-    if (num_outs_ == 1 && !output_sizes[0].empty()) {
-      // Single list result.
-    } else if (num_outs_ == 1) {
-      // Execute returns a single-element list which we need to destructure.
-      strings::StrAppend(&result_, indentation, "_result, = _result\n");
-    } else {
-      // Have multiple outputs, so we will need to reformat the return
-      // value of execute() to be a list with one entry per op output
-      // (that entry will be a list of tensors if that output is of list
-      // type).
-      // For list outputs, convert the right subrange of _result into a list.
-      Unflatten(indentation, output_sizes, "_result", &result_);
-      // Convert to a named tuple.
-      strings::StrAppend(&result_, indentation, "_result = _", op_def_.name(),
-                         "Output._make(_result)\n");
-    }
-  } else {
-    strings::StrAppend(&result_, indentation, "_result = None\n");
-  }
-  strings::StrAppend(&result_, indentation, "return _result\n\n");
-}
-
-bool GenEagerPythonOp::AddEagerFastPathAndGraphCode(
-    const string& parameters, const std::vector& output_sizes,
-    const string& eager_not_allowed_error) {
-  AddExport();
-  AddDefLine(function_name_, parameters);
-  AddDocStringDescription();
-  AddDocStringArgs();
-  AddDocStringInputs();
-  AddDocStringAttrs();
-  AddDocStringNameArg();
-  AddOutputGlobals();  // Added to prelude_
-  AddDocStringOutputs();
-  strings::StrAppend(&result_, "  \"\"\"\n");
-
-  // Handle graph-mode case
-  string function_setup;
-  if (!GetEagerFunctionSetup("    ", &function_setup)) {
-    result_ = function_setup;
-    return false;
-  }
-  HandleGraphMode(function_setup);
-  AddEagerFunctionTeardown("    ", output_sizes,
-                           true /* execute_record_gradient */);
-
-  // Handle eager-mode case
-  strings::StrAppend(&result_, "  else:\n");
-
-  if (eager_not_allowed_error.empty()) {
-    AddEagerFastPathExecute();
-  } else {
-    strings::StrAppend(&result_, "    ", eager_not_allowed_error);
-  }
-
-  strings::StrAppend(&result_, "\n\n");
-  return true;
-}
-
-bool GenEagerPythonOp::AddEagerFallbackCode(
-    const string& parameters, const std::vector& output_sizes,
-    const string& num_outputs_expr, const string& eager_not_allowed_error) {
-  if (!eager_not_allowed_error.empty()) {
-    strings::StrAppend(&result_, "  ", eager_not_allowed_error);
-    return true;
-  }
-
-  AddDefLine(strings::StrCat(function_name_, kEagerFallbackSuffix),
-             strings::StrCat(parameters, ", ctx=None"));
-  strings::StrAppend(
-      &result_, "  r\"\"\"This is the slowpath function for Eager mode.\n");
-  strings::StrAppend(&result_, "  This is for function ", function_name_,
-                     "\n  \"\"\"\n");
-
-  strings::StrAppend(&result_, "  _ctx = ctx if ctx else _context.context()\n");
-
-  string function_setup;
-  if (!GetEagerFunctionSetup("  ", &function_setup)) {
-    result_ = function_setup;
-    return false;
-  }
-  strings::StrAppend(&result_, function_setup);
-
-  AddEagerInferredAttrs("  ");
-  AddEagerInputCasts("  ");
-  strings::StrAppend(
-      &result_, "  _inputs_flat = ", FlattenInputs(nullptr, nullptr), "\n");
-  AddEagerAttrs("  ");
-  AddEagerExecute("  ", num_outputs_expr);
-
-  AddEagerFunctionTeardown("  ", output_sizes,
-                           true /* execute_record_gradient */);
-
-  return true;
-}
-
-void GenEagerPythonOp::AddEagerFastPathExecute() {
-  string fastpath_execute_params = strings::StrCat(
-      "_ctx._context_handle, _ctx._eager_context.device_name, \"",
-      op_def_.name(), "\", ", "name, _ctx._post_execution_callbacks");
-  string fallback_params;
-
-  for (int i = 0; i < api_def_.in_arg_size(); i++) {
-    const string param_name = param_names_[i].GetRenameTo();
-    strings::StrAppend(&fastpath_execute_params, ", ", param_name);
-    if (!fallback_params.empty()) strings::StrAppend(&fallback_params, ", ");
-    strings::StrAppend(&fallback_params, param_name);
-  }
-
-  for (const auto& attr : api_def_.attr()) {
-    if (inferred_attrs_.find(attr.name()) == inferred_attrs_.end()) {
-      strings::StrAppend(&fastpath_execute_params, ", \"", attr.name(), "\", ",
-                         attr.rename_to());
-
-      if (!fallback_params.empty()) strings::StrAppend(&fallback_params, ", ");
-      strings::StrAppend(&fallback_params, attr.rename_to(), "=",
-                         attr.rename_to());
-    }
-  }
-
-  if (!fallback_params.empty()) strings::StrAppend(&fallback_params, ", ");
-  strings::StrAppend(&fallback_params, "name=name");
-
-  strings::StrAppend(&result_, "    try:\n");
-  strings::StrAppend(
-      &result_, "      ",
-      "_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(\n",
-      WordWrap(strings::StrCat("        "),
-               strings::StrCat(fastpath_execute_params, ")"), kRightMargin),
-      "\n");
-
-  if (op_def_.output_arg_size() > 1) {
-    const string output_tuple_name =
-        strings::StrCat("_", op_def_.name(), "Output");
-    strings::StrAppend(&result_, "      ", "_result = ", output_tuple_name,
-                       "._make(_result)\n");
-  }
-  strings::StrAppend(&result_, "      ", "return _result\n");
-
-  // Handle fallback.
-  if (!fallback_params.empty()) strings::StrAppend(&fallback_params, ", ");
-  strings::StrAppend(&fallback_params, "ctx=_ctx");
-  strings::StrAppend(&result_, "    ", "except _core._FallbackException:\n");
-  strings::StrAppend(
-      &result_, "      ", "return ", function_name_, kEagerFallbackSuffix,
-      "(\n",
-      WordWrap(strings::StrCat("          "),
-               strings::StrCat(fallback_params, ")"), kRightMargin),
-      "\n");
-
-  // Any errors thrown from execute need to be unwrapped from
-  // _NotOkStatusException.
-  strings::StrAppend(&result_, "    ",
-                     "except _core._NotOkStatusException as e:\n");
-  strings::StrAppend(&result_, "      ", "if name is not None:\n");
-  strings::StrAppend(&result_, "        ",
-                     "message = e.message + \" name: \" + name\n");
-  strings::StrAppend(&result_, "      ", "else:\n");
-  strings::StrAppend(&result_, "        ", "message = e.message\n");
-  strings::StrAppend(
-      &result_, "      ",
-      "_six.raise_from(_core._status_to_exception(e.code, message), None)\n");
-}
-
-void GenEagerPythonOp::AddEagerInferredAttrs(const string& indentation) {
-  // Figure out values for inferred attrs, and cast to eager tensors.
-  for (int i = 0; i < op_def_.attr_size(); ++i) {
-    const auto& attr(op_def_.attr(i));
-    const auto& api_def_attr(api_def_.attr(i));
-    auto arg_list = attr_to_args_.find(attr.name());
-    if (arg_list != attr_to_args_.end()) {
-      if (attr.type() == "type") {
-        std::vector output_sizes;
-        const string flattened =
-            FlattenInputs(&arg_list->second, &output_sizes);
-        string conversion = strings::StrCat("_execute.args_to_matching_eager(",
-                                            flattened, ", _ctx");
-        if (attr.has_default_value()) {
-          strings::StrAppend(
-              &conversion, ", ",
-              python_op_gen_internal::AttrValueToPython(
-                  attr.type(), api_def_attr.default_value(), "_dtypes."));
-        }
-        strings::StrAppend(&conversion, ")");
-        const string var_name = AttrVarName(attr.name(), &attr_expressions_);
-        if (output_sizes.size() == 1) {
-          // Avoid creating a temporary variable in the case where
-          // we can easily assign to the right value directly.
-          const string inputs_var =
-              param_names_[arg_list->second.front()].GetRenameTo();
-          if (output_sizes.front().empty()) {
-            strings::StrAppend(&result_, indentation, var_name, ", (",
-                               inputs_var, ",) = ", conversion, "\n");
-          } else {
-            strings::StrAppend(&result_, indentation, var_name, ", ",
-                               inputs_var, " = ", conversion, "\n");
-          }
-        } else {
-          const string inputs_var = strings::StrCat("_inputs_", attr.name());
-          strings::StrAppend(&result_, indentation, var_name, ", ", inputs_var,
-                             " = ", conversion, "\n");
-          // Convert from a flat list of eager tensors back to the
-          // parameter variables.
-          Unflatten(indentation, output_sizes, inputs_var, &result_);
-          std::vector p;
-          for (int j : arg_list->second) {
-            p.emplace_back(param_names_[j].GetRenameTo());
-          }
-          strings::StrAppend(&result_, indentation, VectorToTuple(p), " = ",
-                             inputs_var, "\n");
-        }
-      } else if (attr.type() == "list(type)") {
-        // NOTE: We ignore default values for these attrs, since it is
-        // unclear how you would use it, and the one use case is
-        // parse_single_sequence_example which only needs it for
-        // backwards compatibility.
-        const string var_name = AttrVarName(attr.name(), &attr_expressions_);
-        string inputs_var;
-        string conversion;
-        if (arg_list->second.size() > 1) {
-          // If you have more than one list(tensor) argument, their types
-          // have to match.
-          std::vector lists;
-          for (auto iter = arg_list->second.begin();
-               iter != arg_list->second.end(); ++iter) {
-            lists.push_back(param_names_[*iter].GetRenameTo());
-          }
-          inputs_var = VectorToTuple(lists);
-          conversion = "_execute.args_to_mixed_eager_tensors";
-        } else {
-          // For one list(tensor) argument, we just convert every
-          // element of the list to an eager tensor.
-          inputs_var = param_names_[arg_list->second.front()].GetRenameTo();
-          conversion = "_execute.convert_to_mixed_eager_tensors";
-        }
-        strings::StrAppend(&result_, indentation, var_name, ", ", inputs_var,
-                           " = ", conversion, "(", inputs_var, ", _ctx)\n");
-      }
-    }
-  }
-}
-
-void GenEagerPythonOp::AddEagerInputCasts(const string& indentation) {
-  // Cast remaining args to eager tensors
-  for (int i = 0; i < op_def_.input_arg_size(); ++i) {
-    const auto& arg(op_def_.input_arg(i));
-    if (!arg.type_attr().empty() || !arg.type_list_attr().empty()) continue;
-    const string& param = param_names_[i].GetRenameTo();
-    const string fn = arg.number_attr().empty() ? "" : "n_";
-    const string dtype =
-        python_op_gen_internal::DataTypeToPython(arg.type(), "_dtypes.");
-    strings::StrAppend(&result_, indentation, param, " = _ops.convert_", fn,
-                       "to_tensor(", param, ", ", dtype, ")\n");
-  }
-}
-
-void GenEagerPythonOp::AddEagerAttrs(const string& indentation) {
-  // Compute eager attrs
-  if (op_def_.attr_size() > 0) {
-    string attr_values;
-    for (int i = 0; i < op_def_.attr_size(); ++i) {
-      if (i > 0) strings::StrAppend(&attr_values, ", ");
-      const auto& attr_name(op_def_.attr(i).name());
-      strings::StrAppend(&attr_values, "\"", attr_name, "\", ",
-                         attr_expressions_[attr_name]);
-    }
-    strings::StrAppend(&attr_values, ")");
-    strings::StrAppend(
-        &result_,
-        WordWrap(indentation, strings::StrCat("_attrs = (", attr_values),
-                 kRightMargin),
-        "\n");
-  } else {
-    strings::StrAppend(&result_, indentation, "_attrs = None\n");
-  }
-}
-
-void GenEagerPythonOp::AddEagerExecute(const string& indentation,
-                                       const string& num_outputs_expr) {
-  const string return_prefix =
-      strings::StrCat(indentation, "_result = _execute.execute(");
-  const string return_args = strings::StrCat(
-      "b\"", op_def_.name(), "\", ", num_outputs_expr,
-      ", inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name)");
-  strings::StrAppend(&result_,
-                     // Wrap the arguments, and indent to the (.
-                     WordWrap(return_prefix, return_args, kRightMargin), "\n");
-}
-
-string GetEagerPythonOps(const OpList& ops, const ApiDefMap& api_defs,
-                         const std::vector& hidden_ops,
-                         bool require_shapes,
-                         const string& source_file_name = "") {
-  string result;
-  // Header
-  // TODO(josh11b): Mention the library for which wrappers are being generated.
-  strings::StrAppend(&result, R"("""Python wrappers around TensorFlow ops.
-
-This file is MACHINE GENERATED! Do not edit.
-)");
-
-  // Mention the original source file so someone tracing back through
-  // generated Python code will know where to look next.
-  if (!source_file_name.empty()) {
-    strings::StrAppend(&result, "Original C++ source file: ");
-    strings::StrAppend(&result, source_file_name);
-    strings::StrAppend(&result, "\n");
-  }
-
-  strings::StrAppend(&result, R"("""
-
-import collections as _collections
-import six as _six
-
-from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
-from tensorflow.python.eager import context as _context
-from tensorflow.python.eager import core as _core
-from tensorflow.python.eager import execute as _execute
-from tensorflow.python.framework import dtypes as _dtypes
-from tensorflow.python.framework import errors as _errors
-from tensorflow.python.framework import tensor_shape as _tensor_shape
-
-from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
-# Needed to trigger the call to _set_call_cpp_shape_fn.
-from tensorflow.python.framework import common_shapes as _common_shapes
-from tensorflow.python.framework import op_def_registry as _op_def_registry
-from tensorflow.python.framework import ops as _ops
-from tensorflow.python.framework import op_def_library as _op_def_library
-from tensorflow.python.util.tf_export import tf_export
-
-)");
-
-  // We'll make a copy of ops that filters out descriptions.
-  OpList cleaned_ops;
-  auto out = cleaned_ops.mutable_op();
-  out->Reserve(ops.op_size());
-  for (const auto& op_def : ops.op()) {
-    const auto* api_def = api_defs.GetApiDef(op_def.name());
-
-    if (api_def->visibility() == ApiDef::SKIP) {
-      continue;
-    }
-    // An op is hidden if either its ApiDef visibility is HIDDEN
-    // or it is in the hidden_ops list.
-    bool is_hidden = api_def->visibility() == ApiDef::HIDDEN;
-    bool hidden_by_api_def = is_hidden;
-    if (!is_hidden) {
-      for (const string& hidden : hidden_ops) {
-        if (op_def.name() == hidden) {
-          is_hidden = true;
-          break;
-        }
-      }
-    }
-
-    string function_name;
-    python_op_gen_internal::GenerateLowerCaseOpName(op_def.name(),
-                                                    &function_name);
-    bool is_reserved = python_op_gen_internal::IsPythonReserved(function_name);
-
-    // Prefix an op with underscore if the op is listed in hidden_ops or
-    // name is reserved or it is of the exceptions in IsOpWithUnderscorePrefix.
-    // Do not add underscores to ops set to HIDDEN in ApiDef otherwise.
-    // TODO(annarev): don't prefix with underscores even if op is in hidden_ops.
-    if (is_hidden) {
-      if (!hidden_by_api_def || is_reserved ||
-          python_op_gen_internal::IsOpWithUnderscorePrefix(function_name)) {
-        function_name = strings::StrCat("_", function_name);
-      }
-    } else if (is_reserved) {
-      // When users create custom python wrappers, they may link in the
-      // default op registry by accident, and because they can't
-      // enumerate all 'hidden' symbols, this guard is to prevent
-      // instantiating a python reserved word in their wrapper.
-      continue;
-    }
-
-    strings::StrAppend(&result,
-                       GetEagerPythonOp(op_def, *api_def, function_name));
-
-    if (!require_shapes) {
-      strings::StrAppend(&result, "_ops.RegisterShape(\"", op_def.name(),
-                         "\")(None)\n\n");
-    }
-
-    auto added = out->Add();
-    *added = op_def;
-    RemoveNonDeprecationDescriptionsFromOpDef(added);
-  }
-
-  result.append(R"(def _InitOpDefLibrary(op_list_proto_bytes):
-  op_list = _op_def_pb2.OpList()
-  op_list.ParseFromString(op_list_proto_bytes)
-  _op_def_registry.register_op_list(op_list)
-  op_def_lib = _op_def_library.OpDefLibrary()
-  op_def_lib.add_op_list(op_list)
-  return op_def_lib
-)");
-
-  result.append("# ");
-  auto ops_text = ProtoDebugString(cleaned_ops);
-  str_util::StripTrailingWhitespace(&ops_text);
-  result.append(str_util::StringReplace(ops_text, "\n", "\n# ", true));
-  result.append("\n");
-  strings::Appendf(&result, "_op_def_lib = _InitOpDefLibrary(b\"%s\")\n",
-                   str_util::CEscape(cleaned_ops.SerializeAsString()).c_str());
-  return result;
-}
-
-}  // namespace
-
-void PrintEagerPythonOps(const OpList& ops, const ApiDefMap& api_defs,
-                         const std::vector& hidden_ops,
-                         bool require_shapes, const string& source_file_name) {
-  printf("%s", GetEagerPythonOps(ops, api_defs, hidden_ops, require_shapes,
-                                 source_file_name)
-                   .c_str());
-}
-
-string GetEagerPythonWrappers(const char* op_list_buf, size_t op_list_len) {
-  string op_list_str(op_list_buf, op_list_len);
-  OpList ops;
-  ops.ParseFromString(op_list_str);
-
-  ApiDefMap api_def_map(ops);
-  return GetEagerPythonOps(ops, api_def_map, {}, false);
-}
-
-}  // namespace tensorflow
diff --git a/tensorflow/python/eager/python_eager_op_gen.h b/tensorflow/python/eager/python_eager_op_gen.h
deleted file mode 100644
index d27b00139d129a..00000000000000
--- a/tensorflow/python/eager/python_eager_op_gen.h
+++ /dev/null
@@ -1,43 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_PYTHON_EAGER_PYTHON_EAGER_OP_GEN_H_
-#define TENSORFLOW_PYTHON_EAGER_PYTHON_EAGER_OP_GEN_H_
-
-#include 
-#include 
-#include "tensorflow/core/framework/op_def.pb.h"
-#include "tensorflow/core/framework/op_gen_lib.h"
-#include "tensorflow/core/platform/types.h"
-
-namespace tensorflow {
-
-// hidden_ops should be a list of Op names that should get a leading _
-// in the output. Prints the output to stdout.
-// Optional fourth argument is the name of the original C++ source file
-// where the ops' REGISTER_OP() calls reside.
-void PrintEagerPythonOps(const OpList& ops, const ApiDefMap& api_defs,
-                         const std::vector& hidden_ops,
-                         bool require_shapes,
-                         const string& source_file_name = "");
-
-// Get the python wrappers for a list of ops in a OpList.
-// `op_list_buf` should be a pointer to a buffer containing
-// the binary encoded OpList proto, and `op_list_len` should be the
-// length of that buffer.
-string GetEagerPythonWrappers(const char* op_list_buf, size_t op_list_len);
-
-}  // namespace tensorflow
-
-#endif  // TENSORFLOW_PYTHON_EAGER_PYTHON_EAGER_OP_GEN_H_
diff --git a/tensorflow/python/framework/load_library.py b/tensorflow/python/framework/load_library.py
index 9a8477debb05fd..535c6017f5fd0f 100644
--- a/tensorflow/python/framework/load_library.py
+++ b/tensorflow/python/framework/load_library.py
@@ -58,7 +58,7 @@ def load_op_library(library_filename):
   op_list_str = py_tf.TF_GetOpList(lib_handle)
   op_list = op_def_pb2.OpList()
   op_list.ParseFromString(compat.as_bytes(op_list_str))
-  wrappers = py_tf.GetEagerPythonWrappers(op_list_str)
+  wrappers = py_tf.GetPythonWrappers(op_list_str)
 
   # Delete the library handle to release any memory held in C
   # that are no longer needed.
diff --git a/tensorflow/python/framework/python_op_gen.cc b/tensorflow/python/framework/python_op_gen.cc
index ad6c36b4b1773e..ec3748b40ec538 100644
--- a/tensorflow/python/framework/python_op_gen.cc
+++ b/tensorflow/python/framework/python_op_gen.cc
@@ -1,4 +1,4 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-
 #include "tensorflow/python/framework/python_op_gen.h"
 
 #include 
@@ -26,8 +25,6 @@ limitations under the License.
 #include "tensorflow/core/framework/op_def_util.h"
 #include "tensorflow/core/framework/op_gen_lib.h"
 #include "tensorflow/core/framework/tensor.pb_text.h"
-#include "tensorflow/core/framework/tensor.pb.h"
-#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
@@ -41,792 +38,913 @@ limitations under the License.
 #include "tensorflow/python/framework/python_op_gen_internal.h"
 
 namespace tensorflow {
-namespace python_op_gen_internal {
+namespace {
 
 const int kRightMargin = 78;
 
-bool IsPythonReserved(const string& s) {
-  static const std::set* const kPythonReserved = new std::set(
-      {// Keywords in Python, from:
-       //   import keyword
-       //   print keyword.kwlist
-       "and", "as", "assert", "break", "class", "continue", "def", "del",
-       "elif", "else", "except", "exec", "finally", "for", "from", "global",
-       "if", "import", "in", "is", "lambda", "not", "or", "pass", "print",
-       "raise", "return", "try", "while", "with", "yield",
-       // Built-in functions and types in Python, from:
-       //   [x for x in dir(__builtins__) if not x[0].islower()]
-       "ArithmeticError", "AssertionError", "AttributeError", "BaseException",
-       "BufferError", "BytesWarning", "DeprecationWarning", "EOFError",
-       "Ellipsis", "EnvironmentError", "Exception", "False",
-       "FloatingPointError", "FutureWarning", "GeneratorExit", "IOError",
-       "ImportError", "ImportWarning", "IndentationError", "IndexError",
-       "KeyError", "KeyboardInterrupt", "LookupError", "MemoryError",
-       "NameError", "None", "NotImplemented", "NotImplementedError", "OSError",
-       "OverflowError", "PendingDeprecationWarning", "ReferenceError",
-       "RuntimeError", "RuntimeWarning", "StandardError", "StopIteration",
-       "SyntaxError", "SyntaxWarning", "SystemError", "SystemExit", "TabError",
-       "True", "TypeError", "UnboundLocalError", "UnicodeDecodeError",
-       "UnicodeEncodeError", "UnicodeError", "UnicodeTranslateError",
-       "UnicodeWarning", "UserWarning", "ValueError", "Warning",
-       "ZeroDivisionError", "__debug__", "__doc__", "__import__", "__name__",
-       "__package__"});
-
-  return kPythonReserved->count(s) > 0;
-}
+constexpr char kEagerFallbackSuffix[] = "_eager_fallback";
 
-bool IsOpWithUnderscorePrefix(const string& s) {
-  static const std::set* const kUnderscoreOps = new std::set(
-      {// Lowercase built-in functions and types in Python, from:
-       // [x for x in dir(__builtins__) if x[0].islower()] except "round".
-       // These need to be excluded so they don't conflict with actual built-in
-       // functions since we use '*' imports.
-       "abs", "all", "any", "apply", "bin", "bool", "buffer", "bytearray",
-       "bytes", "callable", "chr", "classmethod", "cmp", "coerce", "compile",
-       "complex", "copyright", "credits", "delattr", "dict", "dir", "divmod",
-       "enumerate", "eval", "execfile", "exit", "file", "filter", "float",
-       "format", "frozenset", "getattr", "globals", "hasattr", "hash", "help",
-       "hex", "id", "input", "int", "intern", "isinstance", "issubclass",
-       "iter", "len", "license", "list", "locals", "long", "map", "max",
-       "memoryview", "min", "next", "object", "oct", "open", "ord", "pow",
-       "print", "property", "quit", "range", "raw_input", "reduce", "reload",
-       "repr", "reversed", "set", "setattr", "slice", "sorted", "staticmethod",
-       "str", "sum", "super", "tuple", "type", "unichr", "unicode", "vars",
-       "xrange", "zip",
-       // These have the same name as ops defined in Python and might be used
-       // incorrectly depending on order of '*' imports.
-       // TODO(annarev): reduce usage of '*' imports and remove these from the
-       // list.
-       "fused_batch_norm", "histogram_fixed_width", "stack",
-       "batch_norm_with_global_normalization", "clip_by_value"});
-  return kUnderscoreOps->count(s) > 0;
+string AttrVarName(const string& attr_name,
+                   std::unordered_map* attr_expressions) {
+  const string var = strings::StrCat("_attr_", attr_name);
+  if (attr_expressions != nullptr) (*attr_expressions)[attr_name] = var;
+  return var;
 }
 
-string AvoidPythonReserved(const string& s) {
-  if (IsPythonReserved(s)) return strings::StrCat(s, "_");
-  return s;
+void AddInferredAttr(const string& indentation, const string& attr_name,
+                     const string& value_expression, string* result,
+                     std::unordered_map* attr_expressions) {
+  strings::StrAppend(result, indentation,
+                     AttrVarName(attr_name, attr_expressions), " = ",
+                     value_expression, "\n");
 }
 
-// Indent the first line by "initial" spaces and all following lines
-// by "rest" spaces.
-string Indent(int initial, int rest, StringPiece in) {
-  // TODO(josh11b): Also word-wrapping?
-  string copy(in.data(), in.size());
-  str_util::StripTrailingWhitespace(©);
-  std::vector v = str_util::Split(copy, '\n');
+string VectorToTuple(const std::vector& l) {
+  if (l.size() == 1) return strings::StrCat("(", l.front(), ",)");
+  string ret = "(";
+  for (int i = 0; i < l.size(); ++i) {
+    if (i > 0) {
+      strings::StrAppend(&ret, ", ");
+    }
+    strings::StrAppend(&ret, l[i]);
+  }
+  strings::StrAppend(&ret, ")");
+  return ret;
+}
 
-  string result;
-  bool first = true;
-  for (const string& line : v) {
-    if (first) {
-      result = strings::StrCat(Spaces(initial), line, "\n");
-      first = false;
-    } else {
-      if (line.empty()) {
-        strings::StrAppend(&result, "\n");
+void Unflatten(const string& prefix, const std::vector& output_sizes,
+               const string& var, string* result) {
+  for (int i = 0; i < output_sizes.size(); ++i) {
+    if (!output_sizes[i].empty()) {
+      strings::StrAppend(result, prefix, var, " = ");
+      if (i > 0) strings::StrAppend(result, var, "[:", i, "] + ");
+      if (i + 1 < output_sizes.size()) {
+        // Special case i == 0 to avoid "0 +" in the generated code.
+        if (i == 0) {
+          strings::StrAppend(result, "[", var, "[:", output_sizes[i], "]] + ",
+                             var, "[", output_sizes[i], ":]");
+        } else {
+          strings::StrAppend(result, "[", var, "[", i, ":", i, " + ",
+                             output_sizes[i], "]] + ", var, "[", i, " + ",
+                             output_sizes[i], ":]");
+        }
       } else {
-        strings::StrAppend(&result, Spaces(rest), line, "\n");
+        strings::StrAppend(result, "[", var, "[", i, ":]]");
       }
+      strings::StrAppend(result, "\n");
     }
   }
-  return result;
 }
 
-// Adds append to *dest, with a space if the first line will be <= width,
-// or a newline otherwise.
-void AppendWithinWidth(string* dest, StringPiece append, int width) {
-  auto first_line = append.find('\n');
-  if (first_line == string::npos) first_line = append.size();
-  if (dest->size() + first_line + 1 /* space */ > static_cast(width)) {
-    strings::StrAppend(dest, "\n", append);
-  } else {
-    strings::StrAppend(dest, " ", append);
-  }
+string TensorPBString(const TensorProto& pb) {
+  // Note: This gets used in the argument list, and so must survive naive
+  // word wrapping.
+  return strings::StrCat("\"\"\"", ProtoShortDebugString(pb), "\"\"\"");
 }
 
-// Like DataTypeString() but uses the Python names for the
-// float types.
-string PythonDataTypeString(DataType dtype) {
-  switch (dtype) {
-    case DT_FLOAT:
-      return "float32";
-    case DT_DOUBLE:
-      return "float64";
-    default:
-      return DataTypeString(dtype);
+const ApiDef::Arg* FindInputArg(StringPiece name, const ApiDef& api_def) {
+  for (int i = 0; i < api_def.in_arg_size(); ++i) {
+    if (api_def.in_arg(i).name() == name) {
+      return &api_def.in_arg(i);
+    }
   }
+  return nullptr;
 }
 
-string TypeString(DataType dtype, bool ref) {
-  if (ref) {
-    return strings::StrCat("mutable `", PythonDataTypeString(dtype), "`");
-  } else {
-    return strings::StrCat("`", PythonDataTypeString(dtype), "`");
+class GenEagerPythonOp : public python_op_gen_internal::GenPythonOp {
+ public:
+  GenEagerPythonOp(const OpDef& op_def, const ApiDef& api_def,
+                   const string& function_name)
+      : python_op_gen_internal::GenPythonOp(op_def, api_def, function_name) {
+    op_name_ = function_name_;
+    str_util::ConsumePrefix(&op_name_, "_");
   }
-}
-
-string TypeListString(const AttrValue& value) {
-  string ret;
-  for (int t : value.list().type()) {
-    if (!ret.empty()) strings::StrAppend(&ret, ", ");
-    DataType dtype = static_cast(t);
-    if (IsRefType(dtype)) {
-      strings::StrAppend(&ret, PythonDataTypeString(RemoveRefType(dtype)),
-                         " mutable");
+  ~GenEagerPythonOp() override {}
+
+  string Code() override;
+
+ protected:
+  void HandleGraphMode(const string& function_setup);
+
+  string GetEagerNotAllowedError();
+  void ExpectListArg(const string& indentation, const string& arg_name,
+                     string* output);
+  bool GetEagerFunctionSetup(const string& indentation, string* function_setup);
+  void GetOutputSizesAndNumOutputsExpr(std::vector* output_sizes,
+                                       string* num_outputs_expr);
+
+  void AddEagerFunctionTeardown(const string& indentation,
+                                const std::vector& output_sizes,
+                                bool execute_record_gradient);
+
+  bool AddEagerFastPathAndGraphCode(const string& parameters,
+                                    const std::vector& output_sizes,
+                                    const string& eager_not_allowed_error);
+  bool AddEagerFallbackCode(const string& parameters,
+                            const std::vector& output_sizes,
+                            const string& num_outputs_expr,
+                            const string& eager_not_allowed_error);
+  void AddEagerFastPathExecute();
+
+  void AddEagerInferredAttrs(const string& indentation);
+  void AddEagerInputCasts(const string& indentation);
+  void AddEagerAttrs(const string& indentation);
+  void AddEagerExecute(const string& indentation,
+                       const string& num_outputs_expr);
+
+  void AddAttrForArg(const string& attr, int arg_index) {
+    gtl::InsertIfNotPresent(&inferred_attrs_, attr,
+                            op_def_.input_arg(arg_index).name());
+    auto iter = attr_to_args_.find(attr);
+    if (iter == attr_to_args_.end()) {
+      attr_to_args_.insert(AttrToArgMap::value_type(attr, {arg_index}));
     } else {
-      strings::StrAppend(&ret, "`", PythonDataTypeString(dtype), "`");
+      iter->second.push_back(arg_index);
     }
   }
-  return ret;
-}
 
-string SingleTensorName(DataType dtype, bool is_ref) {
-  const string type_str = TypeString(dtype, is_ref);
-  return strings::StrCat("A `Tensor` of type ", type_str, ".");
-}
+  // Returns a string expression representing a flattened list of all
+  // the inputs given by `*input_indices` (or all inputs if
+  // `input_indices` is nullptr).  `*output_sizes` can be used to unflatten.
+  string FlattenInputs(const std::vector* input_indices,
+                       std::vector* output_sizes) const;
 
-const char kUnknownTensorType[] = {"A `Tensor`."};
-
-string ArgTypeName(const OpDef& op_def, const OpDef::ArgDef& arg,
-                   const std::unordered_map& inferred_attrs,
-                   bool is_output) {
-  if (!arg.number_attr().empty()) {
-    // N Tensors with the same type
-    const string* original_arg =
-        gtl::FindOrNull(inferred_attrs, arg.number_attr());
-    string prefix;
-    if (original_arg == nullptr) {
-      prefix = strings::StrCat("A list of `", arg.number_attr(), "`");
-    } else if (*original_arg == arg.name()) {
-      const OpDef::AttrDef* attr = FindAttr(arg.number_attr(), op_def);
-      if (attr->has_minimum() && attr->minimum() > 0) {
-        prefix = strings::StrCat("A list of at least ", attr->minimum());
-      } else {
-        prefix = "A list of";
-      }
-    } else {
-      prefix = strings::StrCat("A list with the same length as `",
-                               AvoidPythonReserved(*original_arg), "` of");
-    }
+  StringPiece op_name_;
+  typedef std::unordered_map> AttrToArgMap;
+  AttrToArgMap attr_to_args_;
+  std::unordered_map attr_expressions_;
+  // This has all the input args followed by those attrs that don't have
+  // defaults.
+  std::vector params_no_default_;
+  // The parameters with defaults (these have to be listed after those without).
+  // No input args are included, just attrs.
+  std::vector>
+      params_with_default_;
+};
 
-    if (arg.type() != DT_INVALID) {
-      return strings::StrCat(prefix, " `Tensor` objects with type ",
-                             TypeString(arg.type(), arg.is_ref()), ".");
-    } else {
-      original_arg = gtl::FindOrNull(inferred_attrs, arg.type_attr());
-      if (arg.is_ref()) {
-        strings::StrAppend(&prefix, " mutable");
+string GetEagerPythonOp(const OpDef& op_def, const ApiDef& api_def,
+                        const string& function_name) {
+  return GenEagerPythonOp(op_def, api_def, function_name).Code();
+}
+
+string GenEagerPythonOp::FlattenInputs(
+    const std::vector* input_indices,
+    std::vector* output_sizes) const {
+  string inputs;
+  enum { STARTING, WAS_LIST_INPUT, WAS_SOLO_INPUT } inputs_state = STARTING;
+  const int n = input_indices != nullptr ? input_indices->size()
+                                         : op_def_.input_arg_size();
+  for (int j = 0; j < n; ++j) {
+    const int i = input_indices ? (*input_indices)[j] : j;
+    const auto& arg(op_def_.input_arg(i));
+    const bool is_list =
+        !arg.type_list_attr().empty() || !arg.number_attr().empty();
+    if (is_list) {
+      if (inputs_state == WAS_SOLO_INPUT) {
+        strings::StrAppend(&inputs, "] + ");
+      } else if (inputs_state == WAS_LIST_INPUT) {
+        strings::StrAppend(&inputs, " + ");
       }
-      if (original_arg == nullptr) {
-        return strings::StrCat(prefix, " `Tensor` objects with type `",
-                               arg.type_attr(), "`.");
-      } else if (*original_arg == arg.name()) {
-        const OpDef::AttrDef* attr = FindAttr(arg.type_attr(), op_def);
-        if (attr->has_allowed_values()) {
-          return strings::StrCat(prefix,
-                                 " `Tensor` objects with the same type in: ",
-                                 TypeListString(attr->allowed_values()), ".");
+      strings::StrAppend(&inputs, "list(", param_names_[i].GetRenameTo(), ")");
+      inputs_state = WAS_LIST_INPUT;
+      if (output_sizes != nullptr) {
+        if (!arg.number_attr().empty()) {
+          output_sizes->emplace_back(AttrVarName(arg.number_attr(), nullptr));
         } else {
-          return strings::StrCat(prefix,
-                                 " `Tensor` objects with the same type.");
+          output_sizes->emplace_back(
+              strings::StrCat("len(", param_names_[i].GetRenameTo(), ")"));
         }
-      } else {
-        return strings::StrCat(prefix,
-                               " `Tensor` objects with the same type as `",
-                               AvoidPythonReserved(*original_arg), "`.");
       }
-    }
-  } else if (!arg.type_attr().empty() || !arg.type_list_attr().empty()) {
-    const bool is_list = !arg.type_list_attr().empty();
-    const string attr_name = is_list ? arg.type_list_attr() : arg.type_attr();
-    const OpDef::AttrDef* attr = FindAttr(attr_name, op_def);
-    const string mutable_str = arg.is_ref() ? "mutable " : "";
-    const string prefix =
-        is_list ? strings::StrCat("A list of ", mutable_str, "`Tensor` objects")
-                : strings::StrCat("A ", mutable_str, "`Tensor`");
-    const string* original_arg = gtl::FindOrNull(inferred_attrs, attr_name);
-    if (original_arg == nullptr) {
-      return strings::StrCat(prefix, " of type `", attr_name, "`.");
-    } else if (*original_arg == arg.name()) {
-      if (attr->has_allowed_values()) {
-        if (is_list) {
-          return strings::StrCat(prefix, " with types from: ",
-                                 TypeListString(attr->allowed_values()), ".");
-        } else {
-          return strings::StrCat(
-              prefix, is_output ? ". Has one of the following types: "
-                                : ". Must be one of the following types: ",
-              TypeListString(attr->allowed_values()), ".");
-        }
+    } else {
+      if (inputs_state == WAS_SOLO_INPUT) {
+        strings::StrAppend(&inputs, ", ");
+      } else if (inputs_state == WAS_LIST_INPUT) {
+        strings::StrAppend(&inputs, " + [");
       } else {
-        return strings::StrCat(prefix, ".");
+        strings::StrAppend(&inputs, "[");
       }
-    } else {
-      return strings::StrCat(prefix,
-                             is_output ? ". Has the same type as `"
-                                       : ". Must have the same type as `",
-                             AvoidPythonReserved(*original_arg), "`.");
+      strings::StrAppend(&inputs, param_names_[i].GetRenameTo());
+      inputs_state = WAS_SOLO_INPUT;
+      if (output_sizes != nullptr) output_sizes->emplace_back();
     }
-  } else {
-    return SingleTensorName(arg.type(), arg.is_ref());
   }
+  if (inputs_state == STARTING) return "[]";
+  if (inputs_state == WAS_SOLO_INPUT) {
+    strings::StrAppend(&inputs, "]");
+  }
+  return inputs;
 }
 
-string GetReturns(const OpDef& op_def,
-                  const std::vector& output_type_string) {
-  string result;
-  DCHECK_EQ(op_def.output_arg_size(), output_type_string.size());
-  const int num_outs = op_def.output_arg_size();
-  strings::StrAppend(&result, "\n  Returns:\n");
-  if (num_outs == 0) {
-    strings::StrAppend(&result, "    The created Operation.\n");
-  } else {
-    if (num_outs == 1) {
-      StringPiece description = op_def.output_arg(0).description();
-      if (ConsumeEquals(&description)) {  // Skip the generated type info.
-        strings::StrAppend(&result, Indent(4, 4, description));
-      } else {
-        // Special case of one output, don't use the name of the output unless
-        // there is no description.
-        string desc = output_type_string.empty() ? kUnknownTensorType
-                                                 : output_type_string[0];
-        if (desc == kUnknownTensorType) {
-          // Special case where we don't understand how the output tensor type
-          // depends on the input tensor types, just use the output arg
-          // description if we can.
-          if (!description.empty()) {
-            desc = op_def.output_arg(0).description();
-          } else if (!op_def.output_arg(0).name().empty()) {
-            desc = strings::StrCat(" The ", op_def.output_arg(0).name(),
-                                   " `Tensor`.");
+string GenEagerPythonOp::Code() {
+  if (api_def_.visibility() == ApiDef::SKIP) {
+    return "";
+  }
+
+  for (int i = 0; i < api_def_.arg_order_size(); ++i) {
+    const auto& arg = *FindInputArg(api_def_.arg_order(i), op_def_);
+    const auto& api_def_arg = *FindInputArg(api_def_.arg_order(i), api_def_);
+    params_no_default_.emplace_back(api_def_arg.name(),
+                                    api_def_arg.rename_to());
+    if (!arg.type_attr().empty()) {
+      AddAttrForArg(arg.type_attr(), i);
+    } else if (!arg.type_list_attr().empty()) {
+      AddAttrForArg(arg.type_list_attr(), i);
+    }
+    if (!arg.number_attr().empty()) {
+      AddAttrForArg(arg.number_attr(), i);
+    }
+  }
+  for (int i = 0; i < op_def_.attr_size(); ++i) {
+    const auto& attr(op_def_.attr(i));
+    const auto& api_def_attr(api_def_.attr(i));
+    // Do not add inferred attrs to the Python function signature.
+    if (inferred_attrs_.find(attr.name()) == inferred_attrs_.end()) {
+      if (api_def_attr.has_default_value()) {
+        if (attr.type() == "tensor") {
+          params_with_default_.emplace_back(
+              python_op_gen_internal::ParamNames(api_def_attr.name(),
+                                                 api_def_attr.rename_to()),
+              strings::StrCat(
+                  "_execute.make_tensor(",
+                  TensorPBString(api_def_attr.default_value().tensor()), ", \"",
+                  api_def_attr.rename_to(), "\")"));
+        } else if (attr.type() == "list(tensor)") {
+          std::vector pbtxt;
+          for (const auto& pb : api_def_attr.default_value().list().tensor()) {
+            pbtxt.emplace_back(TensorPBString(pb));
           }
-        } else if (!description.empty()) {
-          AppendWithinWidth(&desc, description, kRightMargin - 4 /* indent */);
-        }
-        strings::StrAppend(&result, Indent(4, 4, desc));
-      }
-    } else {
-      std::vector out_names(num_outs);
-      for (int i = 0; i < num_outs; ++i) {
-        if (!op_def.output_arg(i).name().empty()) {
-          out_names[i] = op_def.output_arg(i).name();
-        } else {
-          out_names[i] = strings::StrCat("output", i);
-        }
-      }
-      strings::StrAppend(&result, "    A tuple of `Tensor` objects (",
-                         str_util::Join(out_names, ", "), ").\n\n");
-      for (int i = 0; i < num_outs; ++i) {
-        string desc = strings::StrCat(out_names[i], ": ");
-        StringPiece description = op_def.output_arg(i).description();
-        if (ConsumeEquals(&description)) {  // Skip the generated type info.
-          strings::StrAppend(&desc, description);
+          params_with_default_.emplace_back(
+              python_op_gen_internal::ParamNames(api_def_attr.name(),
+                                                 api_def_attr.rename_to()),
+              strings::StrCat("[_execute.make_tensor(_pb, \"",
+                              api_def_attr.rename_to(), "\") for _pb in ",
+                              VectorToTuple(pbtxt), "]"));
         } else {
-          const string type = static_cast(i) < output_type_string.size()
-                                  ? output_type_string[i]
-                                  : kUnknownTensorType;
-          if (!description.empty()) {
-            if (type == kUnknownTensorType) {
-              // Special case where we don't understand how the output tensor
-              // type depends on the input tensor types, so we just use the
-              // output arg description.
-              strings::StrAppend(&desc, description);
-            } else {
-              strings::StrAppend(&desc, type, " ", description);
-            }
-          } else {
-            strings::StrAppend(&desc, type);
-          }
+          params_with_default_.emplace_back(
+              python_op_gen_internal::ParamNames(api_def_attr.name(),
+                                                 api_def_attr.rename_to()),
+              python_op_gen_internal::AttrValueToPython(
+                  attr.type(), api_def_attr.default_value(), "_dtypes."));
         }
-        strings::StrAppend(&result, Indent(4, 6, desc));
+      } else {
+        params_no_default_.emplace_back(api_def_attr.name(),
+                                        api_def_attr.rename_to());
       }
     }
   }
-  return result;
-}
 
-string StringToPython(const string& str) {
-  return strings::StrCat("\"", str_util::CEscape(str), "\"");
-}
+  // Save the list of attr parameters (attrs that won't be inferred),
+  // those with defaults go at the end.
+  // Get the attrs in the order we want by taking the attrs without defaults
+  // from the end of params_no_default_, and adding params_no_default_.
+  attrs_.reserve(params_no_default_.size() - op_def_.input_arg_size() +
+                 params_with_default_.size());
+  for (int i = op_def_.input_arg_size(); i < params_no_default_.size(); ++i) {
+    attrs_.push_back(params_no_default_[i].GetName());
+  }
+  for (const auto& p : params_with_default_) {
+    attrs_.push_back(p.first.GetName());
+  }
 
-string DataTypeToPython(DataType dtype, const string& dtype_module) {
-  return strings::StrCat(dtype_module, PythonDataTypeString(dtype));
-}
+  param_names_.reserve(params_no_default_.size() + params_with_default_.size());
+  param_names_.insert(param_names_.begin(), params_no_default_.begin(),
+                      params_no_default_.end());
+  for (const auto& param_and_default : params_with_default_) {
+    param_names_.push_back(param_and_default.first);
+  }
 
-string ShapeToPython(const TensorShapeProto& shape) {
-  if (shape.unknown_rank()) {
-    return "None";
+  string parameters;
+  for (const auto& param : params_no_default_) {
+    if (!parameters.empty()) strings::StrAppend(¶meters, ", ");
+    strings::StrAppend(¶meters, param.GetRenameTo());
   }
-  string python = "[";
-  for (const auto& dim : shape.dim()) {
-    if (python.size() > 1) strings::StrAppend(&python, ", ");
-    if (!dim.name().empty()) {
-      strings::StrAppend(&python, "(", StringToPython(dim.name()), ", ",
-                         dim.size(), ")");
-    } else {
-      strings::StrAppend(&python, dim.size());
+  for (const auto& param_and_default : params_with_default_) {
+    if (!parameters.empty()) strings::StrAppend(¶meters, ", ");
+    strings::StrAppend(¶meters, param_and_default.first.GetRenameTo(), "=",
+                       param_and_default.second);
+  }
+  if (!parameters.empty()) strings::StrAppend(¶meters, ", ");
+  strings::StrAppend(¶meters, "name=None");
+
+  // Add attr_expressions_ for attrs that are params.
+  for (int i = 0; i < attrs_.size(); ++i) {
+    const string& attr_name = attrs_[i];
+    const string& attr_api_name =
+        param_names_[i + op_def_.input_arg_size()].GetRenameTo();
+    attr_expressions_[attr_name] = attr_api_name;
+  }
+  // Add attr_expressions_ for attrs that are inferred.
+  for (int i = 0; i < op_def_.attr_size(); ++i) {
+    const auto& attr(op_def_.attr(i));
+    if (attr.type() == "int") {
+      auto arg_list = attr_to_args_.find(attr.name());
+      if (arg_list != attr_to_args_.end()) {
+        AttrVarName(attr.name(), &attr_expressions_);
+      }
     }
   }
-  strings::StrAppend(&python, "]");
-  return python;
-}
 
-string TensorToPython(const TensorProto& proto) {
-  return ProtoShortDebugString(proto);
-}
+  string num_outputs_expr;
+  std::vector output_sizes(num_outs_);
+  GetOutputSizesAndNumOutputsExpr(&output_sizes, &num_outputs_expr);
 
-string AttrListToPython(const AttrValue& value,
-                        const string& dtype_module = "tf.") {
-  string ret;
-  if (value.list().s_size() > 0) {
-    for (int i = 0; i < value.list().s_size(); ++i) {
-      if (i > 0) strings::StrAppend(&ret, ", ");
-      strings::StrAppend(&ret, StringToPython(value.list().s(i)));
-    }
-  } else if (value.list().i_size() > 0) {
-    for (int i = 0; i < value.list().i_size(); ++i) {
-      if (i > 0) strings::StrAppend(&ret, ", ");
-      strings::StrAppend(&ret, value.list().i(i));
-    }
-  } else if (value.list().f_size() > 0) {
-    for (int i = 0; i < value.list().f_size(); ++i) {
-      if (i > 0) strings::StrAppend(&ret, ", ");
-      strings::StrAppend(&ret, value.list().f(i));
-    }
-  } else if (value.list().b_size() > 0) {
-    for (int i = 0; i < value.list().b_size(); ++i) {
-      if (i > 0) strings::StrAppend(&ret, ", ");
-      strings::StrAppend(&ret, value.list().b(i) ? "True" : "False");
-    }
-  } else if (value.list().type_size() > 0) {
-    for (int i = 0; i < value.list().type_size(); ++i) {
-      if (i > 0) strings::StrAppend(&ret, ", ");
-      strings::StrAppend(&ret,
-                         DataTypeToPython(value.list().type(i), dtype_module));
-    }
-  } else if (value.list().shape_size() > 0) {
-    for (int i = 0; i < value.list().shape_size(); ++i) {
-      if (i > 0) strings::StrAppend(&ret, ", ");
-      strings::StrAppend(&ret, ShapeToPython(value.list().shape(i)));
-    }
-  } else if (value.list().tensor_size() > 0) {
-    for (int i = 0; i < value.list().tensor_size(); ++i) {
-      if (i > 0) strings::StrAppend(&ret, ", ");
-      strings::StrAppend(&ret, TensorToPython(value.list().tensor(i)));
-    }
-  } else if (value.list().func_size() > 0) {
-    for (int i = 0; i < value.list().func_size(); ++i) {
-      if (i > 0) strings::StrAppend(&ret, ", ");
-      strings::StrAppend(&ret, StringToPython(value.list().func(i).name()));
-    }
+  string eager_not_allowed_error = GetEagerNotAllowedError();
+
+  if (!AddEagerFastPathAndGraphCode(parameters, output_sizes,
+                                    eager_not_allowed_error)) {
+    return result_;
   }
-  return ret;
+
+  if (!AddEagerFallbackCode(parameters, output_sizes, num_outputs_expr,
+                            eager_not_allowed_error)) {
+    return result_;
+  }
+
+  return prelude_ + result_;
 }
 
-// NOTE: The return value may contain spaces (for example, it could be
-// a string "foo bar" with an embedded space) and is not safe to pass
-// to WordWrap().
-string AttrValueToPython(const string& type, const AttrValue& value,
-                         const string& dtype_module) {
-  if (type == "string") {
-    return StringToPython(value.s());
-  } else if (type == "int") {
-    return strings::StrCat(value.i());
-  } else if (type == "float") {
-    if (std::isnan(value.f()) || std::isinf(value.f())) {
-      return strings::StrCat("float('", value.f(), "')");
+void GenEagerPythonOp::HandleGraphMode(const string& function_setup) {
+  // Handle graph-mode case
+  strings::StrAppend(&result_,
+                     "  _ctx = _context._context\n"
+                     "  if _ctx is None or not _ctx._eager_context.is_eager:\n",
+                     function_setup,
+                     "    _, _, _op = _op_def_lib._apply_op_helper(\n");
+  AddBodyNoReturn("        ");
+  if (num_outs_ > 0) {
+    strings::StrAppend(&result_, "    _result = _op.outputs[:]\n");
+    // Special case handling for stateful op with single list output
+    // that might be empty.
+    if (num_outs_ == 1 && op_def_.is_stateful() &&
+        (!op_def_.output_arg(0).number_attr().empty() ||
+         !op_def_.output_arg(0).type_list_attr().empty())) {
+      // TODO(josh11b): Can skip this if the number_attr/type_list_attr has
+      // a constraint indicating that this can never be empty.
+      strings::StrAppend(&result_,
+                         "    if not _result:\n"
+                         "      return _op\n");
+    }
+    strings::StrAppend(&result_, "    _inputs_flat = _op.inputs\n");
+
+    // Compute graph-mode attrs.
+    if (op_def_.attr_size() > 0) {
+      string attr_values;
+      for (int i = 0; i < op_def_.attr_size(); ++i) {
+        if (i > 0) strings::StrAppend(&attr_values, ", ");
+        const auto& attr_name(op_def_.attr(i).name());
+        strings::StrAppend(&attr_values, "\"", attr_name, "\", _op.get_attr(\"",
+                           attr_name, "\")");
+      }
+      strings::StrAppend(&attr_values, ")");
+      strings::StrAppend(&result_,
+                         WordWrap("    _attrs = (", attr_values, kRightMargin),
+                         "\n");
     } else {
-      return strings::StrCat(value.f());
+      strings::StrAppend(&result_, "    _attrs = None\n");
     }
-  } else if (type == "bool") {
-    return value.b() ? "True" : "False";
-  } else if (type == "type") {
-    return DataTypeToPython(value.type(), dtype_module);
-  } else if (type == "shape") {
-    return ShapeToPython(value.shape());
-  } else if (type == "tensor") {
-    return TensorToPython(value.tensor());
-  } else if (type == "func") {
-    return StringToPython(value.func().name());
-  } else if (str_util::StartsWith(type, "list(")) {
-    return strings::StrCat("[", AttrListToPython(value, dtype_module), "]");
   } else {
-    return "?";
+    strings::StrAppend(&result_, "    return _op\n");
   }
 }
 
-void GenerateLowerCaseOpName(const string& str, string* result) {
-  const char joiner = '_';
-  const int last_index = str.size() - 1;
-  for (int i = 0; i <= last_index; ++i) {
-    const char c = str[i];
-    // Emit a joiner only if a previous-lower-to-now-upper or a
-    // now-upper-to-next-lower transition happens.
-    if (isupper(c) && (i > 0)) {
-      if (islower(str[i - 1]) || ((i < last_index) && islower(str[i + 1]))) {
-        result->push_back(joiner);
-      }
+string GenEagerPythonOp::GetEagerNotAllowedError() {
+  bool eager_allowed = true;
+  string ref_arg;
+  for (int i = 0; i < op_def_.input_arg_size(); ++i) {
+    const auto& arg = op_def_.input_arg(i);
+    if (arg.is_ref()) {
+      eager_allowed = false;
+      DCHECK_EQ(op_def_.input_arg(i).name(), api_def_.in_arg(i).name());
+      ref_arg = api_def_.in_arg(i).rename_to();
+    }
+  }
+  for (int i = 0; i < op_def_.output_arg_size(); ++i) {
+    const auto& arg = op_def_.output_arg(i);
+    if (arg.is_ref()) {
+      eager_allowed = false;
+      DCHECK_EQ(op_def_.output_arg(i).name(), api_def_.out_arg(i).name());
+      ref_arg = api_def_.out_arg(i).rename_to();
     }
-    result->push_back(tolower(c));
   }
+
+  if (eager_allowed) return "";
+
+  return strings::StrCat("raise RuntimeError(\"", op_name_,
+                         " op does not support eager execution. ", "Arg '",
+                         ref_arg, "' is a ref.\")\n");
 }
 
-static void AddDelimiter(string* append_to, const string& delim) {
-  if (!append_to->empty()) strings::StrAppend(append_to, delim);
+void GenEagerPythonOp::ExpectListArg(const string& indentation,
+                                     const string& arg_name, string* output) {
+  strings::StrAppend(output, indentation, "if not isinstance(", arg_name,
+                     ", (list, tuple)):\n", indentation, "  raise TypeError(\n",
+                     indentation, "      \"Expected list for '", arg_name,
+                     "' argument to \"\n", indentation, "      \"'", op_name_,
+                     "' Op, not %r.\" % ", arg_name, ")\n");
 }
 
-const ApiDef::Attr* FindAttr(StringPiece name, const ApiDef& api_def) {
-  for (int i = 0; i < api_def.attr_size(); ++i) {
-    if (api_def.attr(i).name() == name) {
-      return &api_def.attr(i);
+bool GenEagerPythonOp::GetEagerFunctionSetup(const string& indentation,
+                                             string* function_setup) {
+  // Validate list inputs, infer length attrs.
+  for (int i = 0; i < op_def_.attr_size(); ++i) {
+    const auto& attr(op_def_.attr(i));
+    if (attr.type() == "int") {
+      auto arg_list = attr_to_args_.find(attr.name());
+      if (arg_list != attr_to_args_.end()) {
+        // Inferred int attrs are the lengths of inputs. Validate those
+        // inputs are lists and have the same length.
+        for (auto iter = arg_list->second.begin();
+             iter != arg_list->second.end(); ++iter) {
+          const string& arg_api_name = param_names_[*iter].GetRenameTo();
+          ExpectListArg(indentation, arg_api_name, function_setup);
+          if (iter == arg_list->second.begin()) {
+            AddInferredAttr(indentation, attr.name(),
+                            strings::StrCat("len(", arg_api_name, ")"),
+                            function_setup, &attr_expressions_);
+          } else {
+            const auto& attr_var = attr_expressions_[attr.name()];
+            strings::StrAppend(
+                function_setup, indentation, "if len(", arg_api_name,
+                ") != ", attr_var, ":\n", indentation, "  raise ValueError(\n",
+                indentation, "      \"List argument '", arg_api_name, "' to '",
+                op_name_, "' Op with length %d \"\n", indentation,
+                "      \"must match length %d of argument '",
+                inferred_attrs_[attr.name()], "'.\" %\n", indentation,
+                "      (len(", arg_api_name, "), ", attr_var, "))\n");
+          }
+        }
+      }
     }
   }
-  return nullptr;
-}
 
-const ApiDef::Arg* FindInputArg(StringPiece name, const ApiDef& api_def) {
-  for (int i = 0; i < api_def.in_arg_size(); ++i) {
-    if (api_def.in_arg(i).name() == name) {
-      return &api_def.in_arg(i);
+  for (int i = 0; i < attrs_.size(); ++i) {
+    const string& attr_name = attrs_[i];
+    const auto& param = param_names_[i + op_def_.input_arg_size()];
+    const auto& attr = *FindAttr(attr_name, op_def_);
+    const string& attr_api_name = param.GetRenameTo();
+    StringPiece attr_type = attr.type();
+    attr_expressions_[attr_name] = attr_api_name;
+    const int default_index = i - (attrs_.size() - params_with_default_.size());
+    if (default_index >= 0) {
+      const string& default_value = params_with_default_[default_index].second;
+      strings::StrAppend(function_setup, indentation, "if ", attr_api_name,
+                         " is None:\n");
+      strings::StrAppend(function_setup, indentation, "  ", attr_api_name,
+                         " = ", default_value, "\n");
+    }
+    if (str_util::StartsWith(attr_type, "list(")) {
+      ExpectListArg(indentation, attr_api_name, function_setup);
+    }
+
+    if (attr_type == "string") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = _execute.make_str(", attr_api_name, ", \"",
+                         attr_api_name, "\")\n");
+    } else if (attr_type == "list(string)") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = [_execute.make_str(_s, \"", attr_api_name,
+                         "\") for _s in ", attr_api_name, "]\n");
+    } else if (attr_type == "int") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = _execute.make_int(", attr_api_name, ", \"",
+                         attr_api_name, "\")\n");
+    } else if (attr_type == "list(int)") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = [_execute.make_int(_i, \"", attr_api_name,
+                         "\") for _i in ", attr_api_name, "]\n");
+    } else if (attr_type == "float") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = _execute.make_float(", attr_api_name, ", \"",
+                         attr_api_name, "\")\n");
+    } else if (attr_type == "list(float)") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = [_execute.make_float(_f, \"", attr_api_name,
+                         "\") for _f in ", attr_api_name, "]\n");
+    } else if (attr_type == "bool") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = _execute.make_bool(", attr_api_name, ", \"",
+                         attr_api_name, "\")\n");
+    } else if (attr_type == "list(bool)") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = [_execute.make_bool(_b, \"", attr_api_name,
+                         "\") for _b in ", attr_api_name, "]\n");
+    } else if (attr_type == "type") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = _execute.make_type(", attr_api_name, ", \"",
+                         attr_api_name, "\")\n");
+    } else if (attr_type == "list(type)") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = [_execute.make_type(_t, \"", attr_api_name,
+                         "\") for _t in ", attr_api_name, "]\n");
+    } else if (attr_type == "shape") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = _execute.make_shape(", attr_api_name, ", \"",
+                         attr_api_name, "\")\n");
+    } else if (attr_type == "list(shape)") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = [_execute.make_shape(_s, \"", attr_api_name,
+                         "\") for _s in ", attr_api_name, "]\n");
+    } else if (attr_type == "tensor") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = _execute.make_tensor(", attr_api_name, ", \"",
+                         attr_api_name, "\")\n");
+    } else if (attr_type == "list(tensor)") {
+      strings::StrAppend(function_setup, indentation, attr_api_name,
+                         " = [_execute.make_tensor(_t, \"", attr_api_name,
+                         "\") for _t in ", attr_api_name, "]\n");
+    } else if (attr_type != "func") {
+      *function_setup =
+          strings::StrCat("# No definition for ", function_name_,
+                          " since we don't support attrs with type\n"
+                          "# '",
+                          attr_type, "' right now.\n\n");
+      return false;
     }
   }
-  return nullptr;
+  return true;
 }
 
-GenPythonOp::GenPythonOp(const OpDef& op_def, const ApiDef& api_def,
-                         const string& function_name)
-    : op_def_(op_def),
-      api_def_(api_def),
-      function_name_(function_name),
-      num_outs_(op_def.output_arg_size()) {}
-
-GenPythonOp::~GenPythonOp() {}
-
-string GenPythonOp::Code() {
-  // This has all the input args followed by those attrs that don't have
-  // defaults.
-  std::vector params_no_default;
-  // The parameters with defaults (these have to be listed after those without).
-  // No input args are included, just attrs.
-  std::vector params_with_default;
-
-  for (int i = 0; i < api_def_.arg_order_size(); ++i) {
-    const auto& arg = *FindInputArg(api_def_.arg_order(i), op_def_);
-    const auto& api_def_arg = *FindInputArg(api_def_.arg_order(i), api_def_);
-    params_no_default.emplace_back(api_def_arg.name(), api_def_arg.rename_to());
-    if (!arg.type_attr().empty()) {
-      gtl::InsertIfNotPresent(&inferred_attrs_, arg.type_attr(), arg.name());
-    } else if (!arg.type_list_attr().empty()) {
-      gtl::InsertIfNotPresent(&inferred_attrs_, arg.type_list_attr(),
-                              arg.name());
-    }
+// If output i is list output, output_sizes[i] will be set to a
+// string with the python expression that will evaluate to its
+// length. output_sizes[i] is empty for non-list outputs.
+void GenEagerPythonOp::GetOutputSizesAndNumOutputsExpr(
+    std::vector* output_sizes, string* num_outputs_expr) {
+  // Expression representing the number of outputs.
+  int num_fixed_outputs = 0;
+  for (int i = 0; i < num_outs_; ++i) {
+    const auto& arg(op_def_.output_arg(i));
     if (!arg.number_attr().empty()) {
-      gtl::InsertIfNotPresent(&inferred_attrs_, arg.number_attr(), arg.name());
-    }
-  }
-  for (int i = 0; i < api_def_.attr_size(); ++i) {
-    const auto& attr(api_def_.attr(i));
-    // Do not add inferred attrs to the Python function signature.
-    if (inferred_attrs_.find(attr.name()) == inferred_attrs_.end()) {
-      if (attr.has_default_value()) {
-        params_with_default.emplace_back(attr.name(), attr.rename_to());
+      if (!num_outputs_expr->empty()) {
+        strings::StrAppend(num_outputs_expr, " + ");
+      }
+      (*output_sizes)[i] = attr_expressions_[arg.number_attr()];
+      strings::StrAppend(num_outputs_expr, (*output_sizes)[i]);
+    } else if (!arg.type_list_attr().empty()) {
+      if (!num_outputs_expr->empty()) {
+        strings::StrAppend(num_outputs_expr, " + ");
+      }
+      // Have to be careful to use an expression that works in both
+      // graph and eager paths here.
+      const auto iter = inferred_attrs_.find(arg.type_list_attr());
+      if (iter == inferred_attrs_.end()) {
+        (*output_sizes)[i] = strings::StrCat(
+            "len(", attr_expressions_[arg.type_list_attr()], ")");
       } else {
-        params_no_default.emplace_back(attr.name(), attr.rename_to());
+        (*output_sizes)[i] = strings::StrCat("len(", iter->second, ")");
       }
+      strings::StrAppend(num_outputs_expr, (*output_sizes)[i]);
+    } else {
+      ++num_fixed_outputs;
     }
   }
-
-  // Save the list of attr parameters (attrs that won't be inferred),
-  // those with defaults go at the end.
-  // Get the attrs in the order we want by taking the attrs without defaults
-  // from the end of args_no_default, and adding args_no_default.
-  attrs_.reserve(params_no_default.size() - op_def_.input_arg_size() +
-                 params_with_default.size());
-  for (int i = op_def_.input_arg_size(); i < params_no_default.size(); ++i) {
-    attrs_.push_back(params_no_default[i].GetName());
-  }
-  for (int i = 0; i < params_with_default.size(); ++i) {
-    attrs_.push_back(params_with_default[i].GetName());
-  }
-
-  param_names_.reserve(params_no_default.size() + params_with_default.size());
-  param_names_.insert(param_names_.begin(), params_no_default.begin(),
-                      params_no_default.end());
-  for (const auto& param : params_with_default) {
-    param_names_.push_back(param);
+  if (num_fixed_outputs > 0) {
+    if (!num_outputs_expr->empty()) {
+      strings::StrAppend(num_outputs_expr, " + ");
+    }
+    strings::StrAppend(num_outputs_expr, num_fixed_outputs);
+  } else if (num_outputs_expr->empty()) {
+    *num_outputs_expr = "0";
   }
+}
 
-  string parameters;
-  for (const auto& param : params_no_default) {
-    AddDelimiter(¶meters, ", ");
-    strings::StrAppend(¶meters, param.GetRenameTo());
-  }
-  for (const auto& param_and_default : params_with_default) {
-    AddDelimiter(¶meters, ", ");
-    strings::StrAppend(¶meters, param_and_default.GetRenameTo(), "=None");
+void GenEagerPythonOp::AddEagerFunctionTeardown(
+    const string& indentation, const std::vector& output_sizes,
+    bool execute_record_gradient) {
+  if (num_outs_ > 0) {
+    if (execute_record_gradient) {
+      strings::StrAppend(&result_, indentation, "_execute.record_gradient(\n",
+                         "      \"", op_def_.name(),
+                         "\", _inputs_flat, _attrs, _result, name)\n");
+    }
+    if (num_outs_ == 1 && !output_sizes[0].empty()) {
+      // Single list result.
+    } else if (num_outs_ == 1) {
+      // Execute returns a single-element list which we need to destructure.
+      strings::StrAppend(&result_, indentation, "_result, = _result\n");
+    } else {
+      // Have multiple outputs, so we will need to reformat the return
+      // value of execute() to be a list with one entry per op output
+      // (that entry will be a list of tensors if that output is of list
+      // type).
+      // For list outputs, convert the right subrange of _result into a list.
+      Unflatten(indentation, output_sizes, "_result", &result_);
+      // Convert to a named tuple.
+      strings::StrAppend(&result_, indentation, "_result = _", op_def_.name(),
+                         "Output._make(_result)\n");
+    }
+  } else {
+    strings::StrAppend(&result_, indentation, "_result = None\n");
   }
-  AddDelimiter(¶meters, ", ");
-  strings::StrAppend(¶meters, "name=None");
+  strings::StrAppend(&result_, indentation, "return _result\n\n");
+}
 
+bool GenEagerPythonOp::AddEagerFastPathAndGraphCode(
+    const string& parameters, const std::vector& output_sizes,
+    const string& eager_not_allowed_error) {
   AddExport();
-  AddDefLine(parameters);
+  AddDefLine(function_name_, parameters);
   AddDocStringDescription();
   AddDocStringArgs();
   AddDocStringInputs();
   AddDocStringAttrs();
   AddDocStringNameArg();
-  AddOutputGlobals();
+  AddOutputGlobals();  // Added to prelude_
   AddDocStringOutputs();
   strings::StrAppend(&result_, "  \"\"\"\n");
-  AddBody("  ");
-  strings::StrAppend(&result_, "\n\n");
 
-  return prelude_ + result_;
+  // Handle graph-mode case
+  string function_setup;
+  if (!GetEagerFunctionSetup("    ", &function_setup)) {
+    result_ = function_setup;
+    return false;
+  }
+  HandleGraphMode(function_setup);
+  AddEagerFunctionTeardown("    ", output_sizes,
+                           true /* execute_record_gradient */);
+
+  // Handle eager-mode case
+  strings::StrAppend(&result_, "  else:\n");
+
+  if (eager_not_allowed_error.empty()) {
+    AddEagerFastPathExecute();
+  } else {
+    strings::StrAppend(&result_, "    ", eager_not_allowed_error);
+  }
+
+  strings::StrAppend(&result_, "\n\n");
+  return true;
 }
 
-void GenPythonOp::AddExport() {
-  if (api_def_.visibility() != ApiDef::VISIBLE) {
-    return;
+bool GenEagerPythonOp::AddEagerFallbackCode(
+    const string& parameters, const std::vector& output_sizes,
+    const string& num_outputs_expr, const string& eager_not_allowed_error) {
+  if (!eager_not_allowed_error.empty()) {
+    strings::StrAppend(&result_, "  ", eager_not_allowed_error);
+    return true;
   }
 
-  strings::StrAppend(&result_, "@tf_export(");
+  AddDefLine(strings::StrCat(function_name_, kEagerFallbackSuffix),
+             strings::StrCat(parameters, ", ctx=None"));
+  strings::StrAppend(
+      &result_, "  r\"\"\"This is the slowpath function for Eager mode.\n");
+  strings::StrAppend(&result_, "  This is for function ", function_name_,
+                     "\n  \"\"\"\n");
 
-  // Add all endpoint names to tf_export.
-  bool first_endpoint = true;
-  for (const auto& endpoint : api_def_.endpoint()) {
-    if (!first_endpoint) {
-      strings::StrAppend(&result_, ", ");
-    } else {
-      first_endpoint = false;
-    }
-    string endpoint_name;
-    python_op_gen_internal::GenerateLowerCaseOpName(endpoint.name(),
-                                                    &endpoint_name);
-    strings::StrAppend(&result_, "'", endpoint_name, "'");
+  strings::StrAppend(&result_, "  _ctx = ctx if ctx else _context.context()\n");
+
+  string function_setup;
+  if (!GetEagerFunctionSetup("  ", &function_setup)) {
+    result_ = function_setup;
+    return false;
   }
-  strings::StrAppend(&result_, ")\n");
-}
+  strings::StrAppend(&result_, function_setup);
 
-void GenPythonOp::AddDefLine(const string& function_name,
-                             const string& parameters) {
-  strings::StrAppend(&result_, "def ", function_name, "(", parameters, "):\n");
-}
+  AddEagerInferredAttrs("  ");
+  AddEagerInputCasts("  ");
+  strings::StrAppend(
+      &result_, "  _inputs_flat = ", FlattenInputs(nullptr, nullptr), "\n");
+  AddEagerAttrs("  ");
+  AddEagerExecute("  ", num_outputs_expr);
 
-void GenPythonOp::AddDefLine(const string& parameters) {
-  AddDefLine(function_name_, parameters);
+  AddEagerFunctionTeardown("  ", output_sizes,
+                           true /* execute_record_gradient */);
+
+  return true;
 }
 
-void GenPythonOp::AddDocStringDescription() {
-  string comment;
-  if (api_def_.summary().empty()) {
-    comment = "TODO: add doc.\n";
-  } else {
-    comment = strings::StrCat(api_def_.summary(), "\n");
-    if (!api_def_.description().empty()) {
-      strings::StrAppend(&comment, "\n", Indent(2, 2, api_def_.description()));
-    }
+void GenEagerPythonOp::AddEagerFastPathExecute() {
+  string fastpath_execute_params = strings::StrCat(
+      "_ctx._context_handle, _ctx._eager_context.device_name, \"",
+      op_def_.name(), "\", ", "name, _ctx._post_execution_callbacks");
+  string fallback_params;
+
+  for (int i = 0; i < api_def_.in_arg_size(); i++) {
+    const string param_name = param_names_[i].GetRenameTo();
+    strings::StrAppend(&fastpath_execute_params, ", ", param_name);
+    if (!fallback_params.empty()) strings::StrAppend(&fallback_params, ", ");
+    strings::StrAppend(&fallback_params, param_name);
   }
-  strings::StrAppend(&result_, "  r\"\"\"", comment, "\n");
-}
 
-void GenPythonOp::AddDocStringArgs() {
-  strings::StrAppend(&result_, "  Args:\n");
-}
+  for (const auto& attr : api_def_.attr()) {
+    if (inferred_attrs_.find(attr.name()) == inferred_attrs_.end()) {
+      strings::StrAppend(&fastpath_execute_params, ", \"", attr.name(), "\", ",
+                         attr.rename_to());
 
-void GenPythonOp::AddDocStringInputs() {
-  for (int i = 0; i < api_def_.arg_order_size(); ++i) {
-    const auto& arg = *FindInputArg(api_def_.arg_order(i), op_def_);
-    const auto& api_def_arg = *FindInputArg(api_def_.arg_order(i), api_def_);
-    StringPiece description = api_def_arg.description();
-    string desc;
-    if (ConsumeEquals(&description)) {  // Skip the generated type info.
-      desc = strings::StrCat(param_names_[i].GetRenameTo(), ": ");
-    } else {
-      desc = strings::StrCat(param_names_[i].GetRenameTo(), ": ",
-                             ArgTypeName(op_def_, arg, inferred_attrs_, false));
+      if (!fallback_params.empty()) strings::StrAppend(&fallback_params, ", ");
+      strings::StrAppend(&fallback_params, attr.rename_to(), "=",
+                         attr.rename_to());
     }
-    if (!description.empty()) {
-      AppendWithinWidth(&desc, description, kRightMargin - 4 /* indent */);
-    }
-    strings::StrAppend(&result_, Indent(4, 6, desc));
   }
+
+  if (!fallback_params.empty()) strings::StrAppend(&fallback_params, ", ");
+  strings::StrAppend(&fallback_params, "name=name");
+
+  strings::StrAppend(&result_, "    try:\n");
+  strings::StrAppend(
+      &result_, "      ",
+      "_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(\n",
+      WordWrap(strings::StrCat("        "),
+               strings::StrCat(fastpath_execute_params, ")"), kRightMargin),
+      "\n");
+
+  if (op_def_.output_arg_size() > 1) {
+    const string output_tuple_name =
+        strings::StrCat("_", op_def_.name(), "Output");
+    strings::StrAppend(&result_, "      ", "_result = ", output_tuple_name,
+                       "._make(_result)\n");
+  }
+  strings::StrAppend(&result_, "      ", "return _result\n");
+
+  // Handle fallback.
+  if (!fallback_params.empty()) strings::StrAppend(&fallback_params, ", ");
+  strings::StrAppend(&fallback_params, "ctx=_ctx");
+  strings::StrAppend(&result_, "    ", "except _core._FallbackException:\n");
+  strings::StrAppend(
+      &result_, "      ", "return ", function_name_, kEagerFallbackSuffix,
+      "(\n",
+      WordWrap(strings::StrCat("          "),
+               strings::StrCat(fallback_params, ")"), kRightMargin),
+      "\n");
+
+  // Any errors thrown from execute need to be unwrapped from
+  // _NotOkStatusException.
+  strings::StrAppend(&result_, "    ",
+                     "except _core._NotOkStatusException as e:\n");
+  strings::StrAppend(&result_, "      ", "if name is not None:\n");
+  strings::StrAppend(&result_, "        ",
+                     "message = e.message + \" name: \" + name\n");
+  strings::StrAppend(&result_, "      ", "else:\n");
+  strings::StrAppend(&result_, "        ", "message = e.message\n");
+  strings::StrAppend(
+      &result_, "      ",
+      "_six.raise_from(_core._status_to_exception(e.code, message), None)\n");
 }
 
-void GenPythonOp::AddDocStringAttrs() {
-  for (const string& name : attrs_) {
-    const auto& attr = *FindAttr(name, op_def_);
-    const auto& api_def_attr = *FindAttr(name, api_def_);
-    string desc =
-        strings::StrCat(AvoidPythonReserved(api_def_attr.rename_to()), ": ");
-
-    static const char* const kAttrTypeName[][2] = {
-        {"string", "`string`"},
-        {"list(string)", "list of `strings`"},
-        {"int", "`int`"},
-        {"list(int)", "list of `ints`"},
-        {"float", "`float`"},
-        {"list(float)", "list of `floats`"},
-        {"bool", "`bool`"},
-        {"list(bool)", "list of `bools`"},
-        {"type", "`tf.DType`"},
-        {"list(type)", "list of `tf.DTypes`"},
-        {"shape", "`tf.TensorShape` or list of `ints`"},
-        {"list(shape)",
-         "list of shapes (each a `tf.TensorShape` or list of `ints`)"},
-        {"tensor", "`tf.TensorProto`"},
-        {"list(tensor)", "list of `tf.TensorProto` objects"},
-        {"func", "function decorated with @Defun"},
-        {"list(func)", "list of functions decorated with @Defun"},
-    };
-    for (size_t i = 0; i < TF_ARRAYSIZE(kAttrTypeName); ++i) {
-      if (attr.type() == kAttrTypeName[i][0]) {
-        string s;
-        if (api_def_attr.has_default_value()) {
-          s = strings::StrCat("optional ", kAttrTypeName[i][1]);
+void GenEagerPythonOp::AddEagerInferredAttrs(const string& indentation) {
+  // Figure out values for inferred attrs, and cast to eager tensors.
+  for (int i = 0; i < op_def_.attr_size(); ++i) {
+    const auto& attr(op_def_.attr(i));
+    const auto& api_def_attr(api_def_.attr(i));
+    auto arg_list = attr_to_args_.find(attr.name());
+    if (arg_list != attr_to_args_.end()) {
+      if (attr.type() == "type") {
+        std::vector output_sizes;
+        const string flattened =
+            FlattenInputs(&arg_list->second, &output_sizes);
+        string conversion = strings::StrCat("_execute.args_to_matching_eager(",
+                                            flattened, ", _ctx");
+        if (attr.has_default_value()) {
+          strings::StrAppend(
+              &conversion, ", ",
+              python_op_gen_internal::AttrValueToPython(
+                  attr.type(), api_def_attr.default_value(), "_dtypes."));
+        }
+        strings::StrAppend(&conversion, ")");
+        const string var_name = AttrVarName(attr.name(), &attr_expressions_);
+        if (output_sizes.size() == 1) {
+          // Avoid creating a temporary variable in the case where
+          // we can easily assign to the right value directly.
+          const string inputs_var =
+              param_names_[arg_list->second.front()].GetRenameTo();
+          if (output_sizes.front().empty()) {
+            strings::StrAppend(&result_, indentation, var_name, ", (",
+                               inputs_var, ",) = ", conversion, "\n");
+          } else {
+            strings::StrAppend(&result_, indentation, var_name, ", ",
+                               inputs_var, " = ", conversion, "\n");
+          }
         } else {
-          s = kAttrTypeName[i][1];
+          const string inputs_var = strings::StrCat("_inputs_", attr.name());
+          strings::StrAppend(&result_, indentation, var_name, ", ", inputs_var,
+                             " = ", conversion, "\n");
+          // Convert from a flat list of eager tensors back to the
+          // parameter variables.
+          Unflatten(indentation, output_sizes, inputs_var, &result_);
+          std::vector p;
+          for (int j : arg_list->second) {
+            p.emplace_back(param_names_[j].GetRenameTo());
+          }
+          strings::StrAppend(&result_, indentation, VectorToTuple(p), " = ",
+                             inputs_var, "\n");
         }
-        if (s[0] == 'o' || (s[0] == '`' && (s[1] == 'i' || s[1] == 'o'))) {
-          strings::StrAppend(&desc, "An ", s);
+      } else if (attr.type() == "list(type)") {
+        // NOTE: We ignore default values for these attrs, since it is
+        // unclear how you would use it, and the one use case is
+        // parse_single_sequence_example which only needs it for
+        // backwards compatibility.
+        const string var_name = AttrVarName(attr.name(), &attr_expressions_);
+        string inputs_var;
+        string conversion;
+        if (arg_list->second.size() > 1) {
+          // If you have more than one list(tensor) argument, their types
+          // have to match.
+          std::vector lists;
+          for (auto iter = arg_list->second.begin();
+               iter != arg_list->second.end(); ++iter) {
+            lists.push_back(param_names_[*iter].GetRenameTo());
+          }
+          inputs_var = VectorToTuple(lists);
+          conversion = "_execute.args_to_mixed_eager_tensors";
         } else {
-          strings::StrAppend(&desc, "A ", s);
+          // For one list(tensor) argument, we just convert every
+          // element of the list to an eager tensor.
+          inputs_var = param_names_[arg_list->second.front()].GetRenameTo();
+          conversion = "_execute.convert_to_mixed_eager_tensors";
         }
-        break;
+        strings::StrAppend(&result_, indentation, var_name, ", ", inputs_var,
+                           " = ", conversion, "(", inputs_var, ", _ctx)\n");
       }
     }
-
-    if (attr.has_allowed_values()) {
-      strings::StrAppend(&desc, " from: `",
-                         AttrListToPython(attr.allowed_values()), "`");
-    }
-
-    if (attr.has_minimum()) {
-      if (attr.type() == "int") {
-        strings::StrAppend(&desc, " that is `>= ", attr.minimum(), "`");
-      } else if (attr.minimum() > 0) {
-        strings::StrAppend(&desc, " that has length `>= ", attr.minimum(), "`");
-      }
-    }
-
-    strings::StrAppend(&desc, ".");
-
-    if (api_def_attr.has_default_value()) {
-      strings::StrAppend(
-          &desc, " Defaults to `",
-          AttrValueToPython(attr.type(), api_def_attr.default_value()), "`.");
-    }
-    if (!api_def_attr.description().empty()) {
-      AppendWithinWidth(&desc, api_def_attr.description(),
-                        kRightMargin - 4 /* indent */);
-    }
-    strings::StrAppend(&result_, Indent(4, 6, desc));
   }
 }
 
-void GenPythonOp::AddDocStringNameArg() {
-  strings::StrAppend(&result_,
-                     "    name: A name for the operation (optional).\n");
+void GenEagerPythonOp::AddEagerInputCasts(const string& indentation) {
+  // Cast remaining args to eager tensors
+  for (int i = 0; i < op_def_.input_arg_size(); ++i) {
+    const auto& arg(op_def_.input_arg(i));
+    if (!arg.type_attr().empty() || !arg.type_list_attr().empty()) continue;
+    const string& param = param_names_[i].GetRenameTo();
+    const string fn = arg.number_attr().empty() ? "" : "n_";
+    const string dtype =
+        python_op_gen_internal::DataTypeToPython(arg.type(), "_dtypes.");
+    strings::StrAppend(&result_, indentation, param, " = _ops.convert_", fn,
+                       "to_tensor(", param, ", ", dtype, ")\n");
+  }
 }
 
-void GenPythonOp::AddOutputGlobals() {
-  // Prepare a NamedTuple type to hold the outputs, if there are multiple
-  if (num_outs_ > 1) {
-    // Prepare the list of output names
-    std::vector out_names(num_outs_);
-    for (int i = 0; i < num_outs_; ++i) {
-      if (!api_def_.out_arg(i).rename_to().empty()) {
-        out_names[i] = api_def_.out_arg(i).rename_to();
-      } else {
-        out_names[i] = strings::StrCat("output", i);
-      }
+void GenEagerPythonOp::AddEagerAttrs(const string& indentation) {
+  // Compute eager attrs
+  if (op_def_.attr_size() > 0) {
+    string attr_values;
+    for (int i = 0; i < op_def_.attr_size(); ++i) {
+      if (i > 0) strings::StrAppend(&attr_values, ", ");
+      const auto& attr_name(op_def_.attr(i).name());
+      strings::StrAppend(&attr_values, "\"", attr_name, "\", ",
+                         attr_expressions_[attr_name]);
     }
-    string out_names_list =
-        strings::StrCat("[\"", str_util::Join(out_names, "\", \""), "\"]");
-
-    // Provide the output names as a Python list
-    string lower_op_name_outputs =
-        strings::StrCat("_", function_name_, "_outputs");
-    const string outputs_prefix = strings::StrCat(lower_op_name_outputs, " = ");
-    strings::StrAppend(&prelude_, "\n",
-                       WordWrap(outputs_prefix, out_names_list, kRightMargin),
-                       "\n");
-
-    strings::StrAppend(&prelude_, "_", op_def_.name(),
-                       "Output = _collections.namedtuple(\n");
-    const string tuple_type_prefix = "    ";
-    const string tuple_type_suffix = strings::StrCat(
-        "\"", op_def_.name(), "\", ", lower_op_name_outputs, ")");
+    strings::StrAppend(&attr_values, ")");
     strings::StrAppend(
-        &prelude_, WordWrap(tuple_type_prefix, tuple_type_suffix, kRightMargin),
-        "\n\n");
-  }
-  strings::StrAppend(&prelude_, "\n");
-}
-
-void GenPythonOp::AddDocStringOutputs() {
-  std::vector output_type_string;
-  output_type_string.reserve(num_outs_);
-  for (int i = 0; i < num_outs_; ++i) {
-    output_type_string.push_back(
-        ArgTypeName(op_def_, op_def_.output_arg(i), inferred_attrs_, true));
-  }
-  strings::StrAppend(&result_, GetReturns(op_def_, output_type_string));
-}
-
-void GenPythonOp::AddBody(const string& prefix) {
-  const string apply_prefix =
-      strings::StrCat(prefix, "_result = _op_def_lib.apply_op(");
-  AddBodyNoReturn(apply_prefix);
-  if (num_outs_ > 1) {
-    strings::StrAppend(&result_, prefix, "_result = _", op_def_.name(),
-                       "Output._make(_result)\n");
+        &result_,
+        WordWrap(indentation, strings::StrCat("_attrs = (", attr_values),
+                 kRightMargin),
+        "\n");
+  } else {
+    strings::StrAppend(&result_, indentation, "_attrs = None\n");
   }
-  strings::StrAppend(&result_, prefix, "return _result\n");
 }
 
-void GenPythonOp::AddBodyNoReturn(const string& apply_prefix) {
-  string args = strings::StrCat("\"", op_def_.name(), "\", ");
-  for (size_t i = 0; i < param_names_.size(); ++i) {
-    strings::StrAppend(&args, AvoidPythonReserved(param_names_[i].GetName()),
-                       "=", param_names_[i].GetRenameTo(), ", ");
-  }
-  strings::StrAppend(&args, "name=name)");
-
+void GenEagerPythonOp::AddEagerExecute(const string& indentation,
+                                       const string& num_outputs_expr) {
+  const string return_prefix =
+      strings::StrCat(indentation, "_result = _execute.execute(");
+  const string return_args = strings::StrCat(
+      "b\"", op_def_.name(), "\", ", num_outputs_expr,
+      ", inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name)");
   strings::StrAppend(&result_,
                      // Wrap the arguments, and indent to the (.
-                     WordWrap(apply_prefix, args, kRightMargin), "\n");
-}
-
-}  // namespace python_op_gen_internal
-
-string GetPythonOp(const OpDef& op_def, const ApiDef& api_def,
-                   const string& function_name) {
-  return python_op_gen_internal::GenPythonOp(op_def, api_def, function_name)
-      .Code();
+                     WordWrap(return_prefix, return_args, kRightMargin), "\n");
 }
 
 string GetPythonOps(const OpList& ops, const ApiDefMap& api_defs,
-                    const std::vector& hidden_ops,
-                    bool require_shapes) {
+                    const std::vector& hidden_ops, bool require_shapes,
+                    const string& source_file_name = "") {
   string result;
   // Header
   // TODO(josh11b): Mention the library for which wrappers are being generated.
   strings::StrAppend(&result, R"("""Python wrappers around TensorFlow ops.
 
 This file is MACHINE GENERATED! Do not edit.
-"""
+)");
+
+  // Mention the original source file so someone tracing back through
+  // generated Python code will know where to look next.
+  if (!source_file_name.empty()) {
+    strings::StrAppend(&result, "Original C++ source file: ");
+    strings::StrAppend(&result, source_file_name);
+    strings::StrAppend(&result, "\n");
+  }
+
+  strings::StrAppend(&result, R"("""
 
 import collections as _collections
+import six as _six
 
-from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
+from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
+from tensorflow.python.eager import context as _context
+from tensorflow.python.eager import core as _core
+from tensorflow.python.eager import execute as _execute
+from tensorflow.python.framework import dtypes as _dtypes
+from tensorflow.python.framework import errors as _errors
+from tensorflow.python.framework import tensor_shape as _tensor_shape
 
+from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
 # Needed to trigger the call to _set_call_cpp_shape_fn.
 from tensorflow.python.framework import common_shapes as _common_shapes
-
 from tensorflow.python.framework import op_def_registry as _op_def_registry
 from tensorflow.python.framework import ops as _ops
 from tensorflow.python.framework import op_def_library as _op_def_library
 from tensorflow.python.util.tf_export import tf_export
+
 )");
 
   // We'll make a copy of ops that filters out descriptions.
@@ -839,7 +957,6 @@ from tensorflow.python.util.tf_export import tf_export
     if (api_def->visibility() == ApiDef::SKIP) {
       continue;
     }
-
     // An op is hidden if either its ApiDef visibility is HIDDEN
     // or it is in the hidden_ops list.
     bool is_hidden = api_def->visibility() == ApiDef::HIDDEN;
@@ -875,11 +992,12 @@ from tensorflow.python.util.tf_export import tf_export
       continue;
     }
 
-    strings::StrAppend(&result, GetPythonOp(op_def, *api_def, function_name));
+    strings::StrAppend(&result,
+                       GetEagerPythonOp(op_def, *api_def, function_name));
 
     if (!require_shapes) {
       strings::StrAppend(&result, "_ops.RegisterShape(\"", op_def.name(),
-                         "\")(None)\n");
+                         "\")(None)\n\n");
     }
 
     auto added = out->Add();
@@ -894,8 +1012,6 @@ from tensorflow.python.util.tf_export import tf_export
   op_def_lib = _op_def_library.OpDefLibrary()
   op_def_lib.add_op_list(op_list)
   return op_def_lib
-
-
 )");
 
   result.append("# ");
@@ -908,16 +1024,21 @@ from tensorflow.python.util.tf_export import tf_export
   return result;
 }
 
+}  // namespace
+
 void PrintPythonOps(const OpList& ops, const ApiDefMap& api_defs,
-                    const std::vector& hidden_ops,
-                    bool require_shapes) {
-  printf("%s", GetPythonOps(ops, api_defs, hidden_ops, require_shapes).c_str());
+                    const std::vector& hidden_ops, bool require_shapes,
+                    const string& source_file_name) {
+  printf("%s", GetPythonOps(ops, api_defs, hidden_ops, require_shapes,
+                            source_file_name)
+                   .c_str());
 }
 
 string GetPythonWrappers(const char* op_list_buf, size_t op_list_len) {
   string op_list_str(op_list_buf, op_list_len);
   OpList ops;
   ops.ParseFromString(op_list_str);
+
   ApiDefMap api_def_map(ops);
   return GetPythonOps(ops, api_def_map, {}, false);
 }
diff --git a/tensorflow/python/framework/python_op_gen.h b/tensorflow/python/framework/python_op_gen.h
index 4d20888dc63462..7e754fd12246bd 100644
--- a/tensorflow/python/framework/python_op_gen.h
+++ b/tensorflow/python/framework/python_op_gen.h
@@ -1,4 +1,4 @@
-/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -12,29 +12,24 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-
 #ifndef TENSORFLOW_PYTHON_FRAMEWORK_PYTHON_OP_GEN_H_
 #define TENSORFLOW_PYTHON_FRAMEWORK_PYTHON_OP_GEN_H_
 
 #include 
 #include 
-#include "tensorflow/core/framework/api_def.pb.h"
 #include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/core/framework/op_gen_lib.h"
 #include "tensorflow/core/platform/types.h"
 
 namespace tensorflow {
 
-// hidden_ops should be a vector of Op names that should get a leading _ in the
-// output.
-// The Print* version prints the output to stdout, Get* version returns the
-// output as a string.
+// hidden_ops should be a list of Op names that should get a leading _
+// in the output. Prints the output to stdout.
+// Optional fourth argument is the name of the original C++ source file
+// where the ops' REGISTER_OP() calls reside.
 void PrintPythonOps(const OpList& ops, const ApiDefMap& api_defs,
-                    const std::vector& hidden_ops, bool require_shapes);
-string GetPythonOps(const OpList& ops, const ApiDefMap& api_defs,
-                    const std::vector& hidden_ops, bool require_shapes);
-string GetPythonOp(const OpDef& op_def, const ApiDef& api_def,
-                   const string& function_name);
+                    const std::vector& hidden_ops, bool require_shapes,
+                    const string& source_file_name = "");
 
 // Get the python wrappers for a list of ops in a OpList.
 // `op_list_buf` should be a pointer to a buffer containing
diff --git a/tensorflow/python/framework/python_op_gen.i b/tensorflow/python/framework/python_op_gen.i
index efcce2f2094179..26ec4e8e66b5d4 100644
--- a/tensorflow/python/framework/python_op_gen.i
+++ b/tensorflow/python/framework/python_op_gen.i
@@ -16,10 +16,10 @@ limitations under the License.
 %include "tensorflow/python/platform/base.i"
 
 %{
-#include "tensorflow/python/eager/python_eager_op_gen.h"
+#include "tensorflow/python/framework/python_op_gen.h"
 %}
 
-// Input typemap for GetEagerPythonWrappers.
+// Input typemap for GetPythonWrappers.
 // Accepts a python object of 'bytes' type, and converts it to
 // a const char* pointer and size_t length. The default typemap
 // going from python bytes to const char* tries to decode the
@@ -37,5 +37,5 @@ limitations under the License.
 
 
 %ignoreall;
-%unignore tensorflow::GetEagerPythonWrappers;
-%include "tensorflow/python/eager/python_eager_op_gen.h"
+%unignore tensorflow::GetPythonWrappers;
+%include "tensorflow/python/framework/python_op_gen.h"
diff --git a/tensorflow/python/framework/python_op_gen_internal.cc b/tensorflow/python/framework/python_op_gen_internal.cc
new file mode 100644
index 00000000000000..940bffb906db75
--- /dev/null
+++ b/tensorflow/python/framework/python_op_gen_internal.cc
@@ -0,0 +1,800 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/python/framework/python_op_gen_internal.h"
+
+#include 
+#include 
+#include 
+#include "tensorflow/core/framework/api_def.pb.h"
+#include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/op_def.pb_text.h"
+#include "tensorflow/core/framework/op_def.pb.h"
+#include "tensorflow/core/framework/op_def_util.h"
+#include "tensorflow/core/framework/op_gen_lib.h"
+#include "tensorflow/core/framework/tensor.pb_text.h"
+#include "tensorflow/core/framework/tensor.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/framework/types.pb.h"
+#include "tensorflow/core/lib/gtl/map_util.h"
+#include "tensorflow/core/lib/gtl/stl_util.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace python_op_gen_internal {
+
+const int kRightMargin = 78;
+
+bool IsPythonReserved(const string& s) {
+  static const std::set* const kPythonReserved = new std::set(
+      {// Keywords in Python, from:
+       //   import keyword
+       //   print keyword.kwlist
+       "and", "as", "assert", "break", "class", "continue", "def", "del",
+       "elif", "else", "except", "exec", "finally", "for", "from", "global",
+       "if", "import", "in", "is", "lambda", "not", "or", "pass", "print",
+       "raise", "return", "try", "while", "with", "yield",
+       // Built-in functions and types in Python, from:
+       //   [x for x in dir(__builtins__) if not x[0].islower()]
+       "ArithmeticError", "AssertionError", "AttributeError", "BaseException",
+       "BufferError", "BytesWarning", "DeprecationWarning", "EOFError",
+       "Ellipsis", "EnvironmentError", "Exception", "False",
+       "FloatingPointError", "FutureWarning", "GeneratorExit", "IOError",
+       "ImportError", "ImportWarning", "IndentationError", "IndexError",
+       "KeyError", "KeyboardInterrupt", "LookupError", "MemoryError",
+       "NameError", "None", "NotImplemented", "NotImplementedError", "OSError",
+       "OverflowError", "PendingDeprecationWarning", "ReferenceError",
+       "RuntimeError", "RuntimeWarning", "StandardError", "StopIteration",
+       "SyntaxError", "SyntaxWarning", "SystemError", "SystemExit", "TabError",
+       "True", "TypeError", "UnboundLocalError", "UnicodeDecodeError",
+       "UnicodeEncodeError", "UnicodeError", "UnicodeTranslateError",
+       "UnicodeWarning", "UserWarning", "ValueError", "Warning",
+       "ZeroDivisionError", "__debug__", "__doc__", "__import__", "__name__",
+       "__package__"});
+
+  return kPythonReserved->count(s) > 0;
+}
+
+bool IsOpWithUnderscorePrefix(const string& s) {
+  static const std::set* const kUnderscoreOps = new std::set(
+      {// Lowercase built-in functions and types in Python, from:
+       // [x for x in dir(__builtins__) if x[0].islower()] except "round".
+       // These need to be excluded so they don't conflict with actual built-in
+       // functions since we use '*' imports.
+       "abs", "all", "any", "apply", "bin", "bool", "buffer", "bytearray",
+       "bytes", "callable", "chr", "classmethod", "cmp", "coerce", "compile",
+       "complex", "copyright", "credits", "delattr", "dict", "dir", "divmod",
+       "enumerate", "eval", "execfile", "exit", "file", "filter", "float",
+       "format", "frozenset", "getattr", "globals", "hasattr", "hash", "help",
+       "hex", "id", "input", "int", "intern", "isinstance", "issubclass",
+       "iter", "len", "license", "list", "locals", "long", "map", "max",
+       "memoryview", "min", "next", "object", "oct", "open", "ord", "pow",
+       "print", "property", "quit", "range", "raw_input", "reduce", "reload",
+       "repr", "reversed", "set", "setattr", "slice", "sorted", "staticmethod",
+       "str", "sum", "super", "tuple", "type", "unichr", "unicode", "vars",
+       "xrange", "zip",
+       // These have the same name as ops defined in Python and might be used
+       // incorrectly depending on order of '*' imports.
+       // TODO(annarev): reduce usage of '*' imports and remove these from the
+       // list.
+       "fused_batch_norm", "histogram_fixed_width", "stack",
+       "batch_norm_with_global_normalization", "clip_by_value"});
+  return kUnderscoreOps->count(s) > 0;
+}
+
+string AvoidPythonReserved(const string& s) {
+  if (IsPythonReserved(s)) return strings::StrCat(s, "_");
+  return s;
+}
+
+// Indent the first line by "initial" spaces and all following lines
+// by "rest" spaces.
+string Indent(int initial, int rest, StringPiece in) {
+  // TODO(josh11b): Also word-wrapping?
+  string copy(in.data(), in.size());
+  str_util::StripTrailingWhitespace(©);
+  std::vector v = str_util::Split(copy, '\n');
+
+  string result;
+  bool first = true;
+  for (const string& line : v) {
+    if (first) {
+      result = strings::StrCat(Spaces(initial), line, "\n");
+      first = false;
+    } else {
+      if (line.empty()) {
+        strings::StrAppend(&result, "\n");
+      } else {
+        strings::StrAppend(&result, Spaces(rest), line, "\n");
+      }
+    }
+  }
+  return result;
+}
+
+// Adds append to *dest, with a space if the first line will be <= width,
+// or a newline otherwise.
+void AppendWithinWidth(string* dest, StringPiece append, int width) {
+  auto first_line = append.find('\n');
+  if (first_line == string::npos) first_line = append.size();
+  if (dest->size() + first_line + 1 /* space */ > static_cast(width)) {
+    strings::StrAppend(dest, "\n", append);
+  } else {
+    strings::StrAppend(dest, " ", append);
+  }
+}
+
+// Like DataTypeString() but uses the Python names for the
+// float types.
+string PythonDataTypeString(DataType dtype) {
+  switch (dtype) {
+    case DT_FLOAT:
+      return "float32";
+    case DT_DOUBLE:
+      return "float64";
+    default:
+      return DataTypeString(dtype);
+  }
+}
+
+string TypeString(DataType dtype, bool ref) {
+  if (ref) {
+    return strings::StrCat("mutable `", PythonDataTypeString(dtype), "`");
+  } else {
+    return strings::StrCat("`", PythonDataTypeString(dtype), "`");
+  }
+}
+
+string TypeListString(const AttrValue& value) {
+  string ret;
+  for (int t : value.list().type()) {
+    if (!ret.empty()) strings::StrAppend(&ret, ", ");
+    DataType dtype = static_cast(t);
+    if (IsRefType(dtype)) {
+      strings::StrAppend(&ret, PythonDataTypeString(RemoveRefType(dtype)),
+                         " mutable");
+    } else {
+      strings::StrAppend(&ret, "`", PythonDataTypeString(dtype), "`");
+    }
+  }
+  return ret;
+}
+
+string SingleTensorName(DataType dtype, bool is_ref) {
+  const string type_str = TypeString(dtype, is_ref);
+  return strings::StrCat("A `Tensor` of type ", type_str, ".");
+}
+
+const char kUnknownTensorType[] = {"A `Tensor`."};
+
+string ArgTypeName(const OpDef& op_def, const OpDef::ArgDef& arg,
+                   const std::unordered_map& inferred_attrs,
+                   bool is_output) {
+  if (!arg.number_attr().empty()) {
+    // N Tensors with the same type
+    const string* original_arg =
+        gtl::FindOrNull(inferred_attrs, arg.number_attr());
+    string prefix;
+    if (original_arg == nullptr) {
+      prefix = strings::StrCat("A list of `", arg.number_attr(), "`");
+    } else if (*original_arg == arg.name()) {
+      const OpDef::AttrDef* attr = FindAttr(arg.number_attr(), op_def);
+      if (attr->has_minimum() && attr->minimum() > 0) {
+        prefix = strings::StrCat("A list of at least ", attr->minimum());
+      } else {
+        prefix = "A list of";
+      }
+    } else {
+      prefix = strings::StrCat("A list with the same length as `",
+                               AvoidPythonReserved(*original_arg), "` of");
+    }
+
+    if (arg.type() != DT_INVALID) {
+      return strings::StrCat(prefix, " `Tensor` objects with type ",
+                             TypeString(arg.type(), arg.is_ref()), ".");
+    } else {
+      original_arg = gtl::FindOrNull(inferred_attrs, arg.type_attr());
+      if (arg.is_ref()) {
+        strings::StrAppend(&prefix, " mutable");
+      }
+      if (original_arg == nullptr) {
+        return strings::StrCat(prefix, " `Tensor` objects with type `",
+                               arg.type_attr(), "`.");
+      } else if (*original_arg == arg.name()) {
+        const OpDef::AttrDef* attr = FindAttr(arg.type_attr(), op_def);
+        if (attr->has_allowed_values()) {
+          return strings::StrCat(prefix,
+                                 " `Tensor` objects with the same type in: ",
+                                 TypeListString(attr->allowed_values()), ".");
+        } else {
+          return strings::StrCat(prefix,
+                                 " `Tensor` objects with the same type.");
+        }
+      } else {
+        return strings::StrCat(prefix,
+                               " `Tensor` objects with the same type as `",
+                               AvoidPythonReserved(*original_arg), "`.");
+      }
+    }
+  } else if (!arg.type_attr().empty() || !arg.type_list_attr().empty()) {
+    const bool is_list = !arg.type_list_attr().empty();
+    const string attr_name = is_list ? arg.type_list_attr() : arg.type_attr();
+    const OpDef::AttrDef* attr = FindAttr(attr_name, op_def);
+    const string mutable_str = arg.is_ref() ? "mutable " : "";
+    const string prefix =
+        is_list ? strings::StrCat("A list of ", mutable_str, "`Tensor` objects")
+                : strings::StrCat("A ", mutable_str, "`Tensor`");
+    const string* original_arg = gtl::FindOrNull(inferred_attrs, attr_name);
+    if (original_arg == nullptr) {
+      return strings::StrCat(prefix, " of type `", attr_name, "`.");
+    } else if (*original_arg == arg.name()) {
+      if (attr->has_allowed_values()) {
+        if (is_list) {
+          return strings::StrCat(prefix, " with types from: ",
+                                 TypeListString(attr->allowed_values()), ".");
+        } else {
+          return strings::StrCat(
+              prefix, is_output ? ". Has one of the following types: "
+                                : ". Must be one of the following types: ",
+              TypeListString(attr->allowed_values()), ".");
+        }
+      } else {
+        return strings::StrCat(prefix, ".");
+      }
+    } else {
+      return strings::StrCat(prefix,
+                             is_output ? ". Has the same type as `"
+                                       : ". Must have the same type as `",
+                             AvoidPythonReserved(*original_arg), "`.");
+    }
+  } else {
+    return SingleTensorName(arg.type(), arg.is_ref());
+  }
+}
+
+string GetReturns(const OpDef& op_def,
+                  const std::vector& output_type_string) {
+  string result;
+  DCHECK_EQ(op_def.output_arg_size(), output_type_string.size());
+  const int num_outs = op_def.output_arg_size();
+  strings::StrAppend(&result, "\n  Returns:\n");
+  if (num_outs == 0) {
+    strings::StrAppend(&result, "    The created Operation.\n");
+  } else {
+    if (num_outs == 1) {
+      StringPiece description = op_def.output_arg(0).description();
+      if (ConsumeEquals(&description)) {  // Skip the generated type info.
+        strings::StrAppend(&result, Indent(4, 4, description));
+      } else {
+        // Special case of one output, don't use the name of the output unless
+        // there is no description.
+        string desc = output_type_string.empty() ? kUnknownTensorType
+                                                 : output_type_string[0];
+        if (desc == kUnknownTensorType) {
+          // Special case where we don't understand how the output tensor type
+          // depends on the input tensor types, just use the output arg
+          // description if we can.
+          if (!description.empty()) {
+            desc = op_def.output_arg(0).description();
+          } else if (!op_def.output_arg(0).name().empty()) {
+            desc = strings::StrCat(" The ", op_def.output_arg(0).name(),
+                                   " `Tensor`.");
+          }
+        } else if (!description.empty()) {
+          AppendWithinWidth(&desc, description, kRightMargin - 4 /* indent */);
+        }
+        strings::StrAppend(&result, Indent(4, 4, desc));
+      }
+    } else {
+      std::vector out_names(num_outs);
+      for (int i = 0; i < num_outs; ++i) {
+        if (!op_def.output_arg(i).name().empty()) {
+          out_names[i] = op_def.output_arg(i).name();
+        } else {
+          out_names[i] = strings::StrCat("output", i);
+        }
+      }
+      strings::StrAppend(&result, "    A tuple of `Tensor` objects (",
+                         str_util::Join(out_names, ", "), ").\n\n");
+      for (int i = 0; i < num_outs; ++i) {
+        string desc = strings::StrCat(out_names[i], ": ");
+        StringPiece description = op_def.output_arg(i).description();
+        if (ConsumeEquals(&description)) {  // Skip the generated type info.
+          strings::StrAppend(&desc, description);
+        } else {
+          const string type = static_cast(i) < output_type_string.size()
+                                  ? output_type_string[i]
+                                  : kUnknownTensorType;
+          if (!description.empty()) {
+            if (type == kUnknownTensorType) {
+              // Special case where we don't understand how the output tensor
+              // type depends on the input tensor types, so we just use the
+              // output arg description.
+              strings::StrAppend(&desc, description);
+            } else {
+              strings::StrAppend(&desc, type, " ", description);
+            }
+          } else {
+            strings::StrAppend(&desc, type);
+          }
+        }
+        strings::StrAppend(&result, Indent(4, 6, desc));
+      }
+    }
+  }
+  return result;
+}
+
+string StringToPython(const string& str) {
+  return strings::StrCat("\"", str_util::CEscape(str), "\"");
+}
+
+string DataTypeToPython(DataType dtype, const string& dtype_module) {
+  return strings::StrCat(dtype_module, PythonDataTypeString(dtype));
+}
+
+string ShapeToPython(const TensorShapeProto& shape) {
+  if (shape.unknown_rank()) {
+    return "None";
+  }
+  string python = "[";
+  for (const auto& dim : shape.dim()) {
+    if (python.size() > 1) strings::StrAppend(&python, ", ");
+    if (!dim.name().empty()) {
+      strings::StrAppend(&python, "(", StringToPython(dim.name()), ", ",
+                         dim.size(), ")");
+    } else {
+      strings::StrAppend(&python, dim.size());
+    }
+  }
+  strings::StrAppend(&python, "]");
+  return python;
+}
+
+string TensorToPython(const TensorProto& proto) {
+  return ProtoShortDebugString(proto);
+}
+
+string AttrListToPython(const AttrValue& value,
+                        const string& dtype_module = "tf.") {
+  string ret;
+  if (value.list().s_size() > 0) {
+    for (int i = 0; i < value.list().s_size(); ++i) {
+      if (i > 0) strings::StrAppend(&ret, ", ");
+      strings::StrAppend(&ret, StringToPython(value.list().s(i)));
+    }
+  } else if (value.list().i_size() > 0) {
+    for (int i = 0; i < value.list().i_size(); ++i) {
+      if (i > 0) strings::StrAppend(&ret, ", ");
+      strings::StrAppend(&ret, value.list().i(i));
+    }
+  } else if (value.list().f_size() > 0) {
+    for (int i = 0; i < value.list().f_size(); ++i) {
+      if (i > 0) strings::StrAppend(&ret, ", ");
+      strings::StrAppend(&ret, value.list().f(i));
+    }
+  } else if (value.list().b_size() > 0) {
+    for (int i = 0; i < value.list().b_size(); ++i) {
+      if (i > 0) strings::StrAppend(&ret, ", ");
+      strings::StrAppend(&ret, value.list().b(i) ? "True" : "False");
+    }
+  } else if (value.list().type_size() > 0) {
+    for (int i = 0; i < value.list().type_size(); ++i) {
+      if (i > 0) strings::StrAppend(&ret, ", ");
+      strings::StrAppend(&ret,
+                         DataTypeToPython(value.list().type(i), dtype_module));
+    }
+  } else if (value.list().shape_size() > 0) {
+    for (int i = 0; i < value.list().shape_size(); ++i) {
+      if (i > 0) strings::StrAppend(&ret, ", ");
+      strings::StrAppend(&ret, ShapeToPython(value.list().shape(i)));
+    }
+  } else if (value.list().tensor_size() > 0) {
+    for (int i = 0; i < value.list().tensor_size(); ++i) {
+      if (i > 0) strings::StrAppend(&ret, ", ");
+      strings::StrAppend(&ret, TensorToPython(value.list().tensor(i)));
+    }
+  } else if (value.list().func_size() > 0) {
+    for (int i = 0; i < value.list().func_size(); ++i) {
+      if (i > 0) strings::StrAppend(&ret, ", ");
+      strings::StrAppend(&ret, StringToPython(value.list().func(i).name()));
+    }
+  }
+  return ret;
+}
+
+// NOTE: The return value may contain spaces (for example, it could be
+// a string "foo bar" with an embedded space) and is not safe to pass
+// to WordWrap().
+string AttrValueToPython(const string& type, const AttrValue& value,
+                         const string& dtype_module) {
+  if (type == "string") {
+    return StringToPython(value.s());
+  } else if (type == "int") {
+    return strings::StrCat(value.i());
+  } else if (type == "float") {
+    if (std::isnan(value.f()) || std::isinf(value.f())) {
+      return strings::StrCat("float('", value.f(), "')");
+    } else {
+      return strings::StrCat(value.f());
+    }
+  } else if (type == "bool") {
+    return value.b() ? "True" : "False";
+  } else if (type == "type") {
+    return DataTypeToPython(value.type(), dtype_module);
+  } else if (type == "shape") {
+    return ShapeToPython(value.shape());
+  } else if (type == "tensor") {
+    return TensorToPython(value.tensor());
+  } else if (type == "func") {
+    return StringToPython(value.func().name());
+  } else if (str_util::StartsWith(type, "list(")) {
+    return strings::StrCat("[", AttrListToPython(value, dtype_module), "]");
+  } else {
+    return "?";
+  }
+}
+
+void GenerateLowerCaseOpName(const string& str, string* result) {
+  const char joiner = '_';
+  const int last_index = str.size() - 1;
+  for (int i = 0; i <= last_index; ++i) {
+    const char c = str[i];
+    // Emit a joiner only if a previous-lower-to-now-upper or a
+    // now-upper-to-next-lower transition happens.
+    if (isupper(c) && (i > 0)) {
+      if (islower(str[i - 1]) || ((i < last_index) && islower(str[i + 1]))) {
+        result->push_back(joiner);
+      }
+    }
+    result->push_back(tolower(c));
+  }
+}
+
+static void AddDelimiter(string* append_to, const string& delim) {
+  if (!append_to->empty()) strings::StrAppend(append_to, delim);
+}
+
+const ApiDef::Attr* FindAttr(StringPiece name, const ApiDef& api_def) {
+  for (int i = 0; i < api_def.attr_size(); ++i) {
+    if (api_def.attr(i).name() == name) {
+      return &api_def.attr(i);
+    }
+  }
+  return nullptr;
+}
+
+const ApiDef::Arg* FindInputArg(StringPiece name, const ApiDef& api_def) {
+  for (int i = 0; i < api_def.in_arg_size(); ++i) {
+    if (api_def.in_arg(i).name() == name) {
+      return &api_def.in_arg(i);
+    }
+  }
+  return nullptr;
+}
+
+GenPythonOp::GenPythonOp(const OpDef& op_def, const ApiDef& api_def,
+                         const string& function_name)
+    : op_def_(op_def),
+      api_def_(api_def),
+      function_name_(function_name),
+      num_outs_(op_def.output_arg_size()) {}
+
+GenPythonOp::~GenPythonOp() {}
+
+string GenPythonOp::Code() {
+  // This has all the input args followed by those attrs that don't have
+  // defaults.
+  std::vector params_no_default;
+  // The parameters with defaults (these have to be listed after those without).
+  // No input args are included, just attrs.
+  std::vector params_with_default;
+
+  for (int i = 0; i < api_def_.arg_order_size(); ++i) {
+    const auto& arg = *FindInputArg(api_def_.arg_order(i), op_def_);
+    const auto& api_def_arg = *FindInputArg(api_def_.arg_order(i), api_def_);
+    params_no_default.emplace_back(api_def_arg.name(), api_def_arg.rename_to());
+    if (!arg.type_attr().empty()) {
+      gtl::InsertIfNotPresent(&inferred_attrs_, arg.type_attr(), arg.name());
+    } else if (!arg.type_list_attr().empty()) {
+      gtl::InsertIfNotPresent(&inferred_attrs_, arg.type_list_attr(),
+                              arg.name());
+    }
+    if (!arg.number_attr().empty()) {
+      gtl::InsertIfNotPresent(&inferred_attrs_, arg.number_attr(), arg.name());
+    }
+  }
+  for (int i = 0; i < api_def_.attr_size(); ++i) {
+    const auto& attr(api_def_.attr(i));
+    // Do not add inferred attrs to the Python function signature.
+    if (inferred_attrs_.find(attr.name()) == inferred_attrs_.end()) {
+      if (attr.has_default_value()) {
+        params_with_default.emplace_back(attr.name(), attr.rename_to());
+      } else {
+        params_no_default.emplace_back(attr.name(), attr.rename_to());
+      }
+    }
+  }
+
+  // Save the list of attr parameters (attrs that won't be inferred),
+  // those with defaults go at the end.
+  // Get the attrs in the order we want by taking the attrs without defaults
+  // from the end of args_no_default, and adding args_no_default.
+  attrs_.reserve(params_no_default.size() - op_def_.input_arg_size() +
+                 params_with_default.size());
+  for (int i = op_def_.input_arg_size(); i < params_no_default.size(); ++i) {
+    attrs_.push_back(params_no_default[i].GetName());
+  }
+  for (int i = 0; i < params_with_default.size(); ++i) {
+    attrs_.push_back(params_with_default[i].GetName());
+  }
+
+  param_names_.reserve(params_no_default.size() + params_with_default.size());
+  param_names_.insert(param_names_.begin(), params_no_default.begin(),
+                      params_no_default.end());
+  for (const auto& param : params_with_default) {
+    param_names_.push_back(param);
+  }
+
+  string parameters;
+  for (const auto& param : params_no_default) {
+    AddDelimiter(¶meters, ", ");
+    strings::StrAppend(¶meters, param.GetRenameTo());
+  }
+  for (const auto& param_and_default : params_with_default) {
+    AddDelimiter(¶meters, ", ");
+    strings::StrAppend(¶meters, param_and_default.GetRenameTo(), "=None");
+  }
+  AddDelimiter(¶meters, ", ");
+  strings::StrAppend(¶meters, "name=None");
+
+  AddExport();
+  AddDefLine(parameters);
+  AddDocStringDescription();
+  AddDocStringArgs();
+  AddDocStringInputs();
+  AddDocStringAttrs();
+  AddDocStringNameArg();
+  AddOutputGlobals();
+  AddDocStringOutputs();
+  strings::StrAppend(&result_, "  \"\"\"\n");
+  AddBody("  ");
+  strings::StrAppend(&result_, "\n\n");
+
+  return prelude_ + result_;
+}
+
+void GenPythonOp::AddExport() {
+  if (api_def_.visibility() != ApiDef::VISIBLE) {
+    return;
+  }
+
+  strings::StrAppend(&result_, "@tf_export(");
+
+  // Add all endpoint names to tf_export.
+  bool first_endpoint = true;
+  for (const auto& endpoint : api_def_.endpoint()) {
+    if (!first_endpoint) {
+      strings::StrAppend(&result_, ", ");
+    } else {
+      first_endpoint = false;
+    }
+    string endpoint_name;
+    python_op_gen_internal::GenerateLowerCaseOpName(endpoint.name(),
+                                                    &endpoint_name);
+    strings::StrAppend(&result_, "'", endpoint_name, "'");
+  }
+  strings::StrAppend(&result_, ")\n");
+}
+
+void GenPythonOp::AddDefLine(const string& function_name,
+                             const string& parameters) {
+  strings::StrAppend(&result_, "def ", function_name, "(", parameters, "):\n");
+}
+
+void GenPythonOp::AddDefLine(const string& parameters) {
+  AddDefLine(function_name_, parameters);
+}
+
+void GenPythonOp::AddDocStringDescription() {
+  string comment;
+  if (api_def_.summary().empty()) {
+    comment = "TODO: add doc.\n";
+  } else {
+    comment = strings::StrCat(api_def_.summary(), "\n");
+    if (!api_def_.description().empty()) {
+      strings::StrAppend(&comment, "\n", Indent(2, 2, api_def_.description()));
+    }
+  }
+  strings::StrAppend(&result_, "  r\"\"\"", comment, "\n");
+}
+
+void GenPythonOp::AddDocStringArgs() {
+  strings::StrAppend(&result_, "  Args:\n");
+}
+
+void GenPythonOp::AddDocStringInputs() {
+  for (int i = 0; i < api_def_.arg_order_size(); ++i) {
+    const auto& arg = *FindInputArg(api_def_.arg_order(i), op_def_);
+    const auto& api_def_arg = *FindInputArg(api_def_.arg_order(i), api_def_);
+    StringPiece description = api_def_arg.description();
+    string desc;
+    if (ConsumeEquals(&description)) {  // Skip the generated type info.
+      desc = strings::StrCat(param_names_[i].GetRenameTo(), ": ");
+    } else {
+      desc = strings::StrCat(param_names_[i].GetRenameTo(), ": ",
+                             ArgTypeName(op_def_, arg, inferred_attrs_, false));
+    }
+    if (!description.empty()) {
+      AppendWithinWidth(&desc, description, kRightMargin - 4 /* indent */);
+    }
+    strings::StrAppend(&result_, Indent(4, 6, desc));
+  }
+}
+
+void GenPythonOp::AddDocStringAttrs() {
+  for (const string& name : attrs_) {
+    const auto& attr = *FindAttr(name, op_def_);
+    const auto& api_def_attr = *FindAttr(name, api_def_);
+    string desc =
+        strings::StrCat(AvoidPythonReserved(api_def_attr.rename_to()), ": ");
+
+    static const char* const kAttrTypeName[][2] = {
+        {"string", "`string`"},
+        {"list(string)", "list of `strings`"},
+        {"int", "`int`"},
+        {"list(int)", "list of `ints`"},
+        {"float", "`float`"},
+        {"list(float)", "list of `floats`"},
+        {"bool", "`bool`"},
+        {"list(bool)", "list of `bools`"},
+        {"type", "`tf.DType`"},
+        {"list(type)", "list of `tf.DTypes`"},
+        {"shape", "`tf.TensorShape` or list of `ints`"},
+        {"list(shape)",
+         "list of shapes (each a `tf.TensorShape` or list of `ints`)"},
+        {"tensor", "`tf.TensorProto`"},
+        {"list(tensor)", "list of `tf.TensorProto` objects"},
+        {"func", "function decorated with @Defun"},
+        {"list(func)", "list of functions decorated with @Defun"},
+    };
+    for (size_t i = 0; i < TF_ARRAYSIZE(kAttrTypeName); ++i) {
+      if (attr.type() == kAttrTypeName[i][0]) {
+        string s;
+        if (api_def_attr.has_default_value()) {
+          s = strings::StrCat("optional ", kAttrTypeName[i][1]);
+        } else {
+          s = kAttrTypeName[i][1];
+        }
+        if (s[0] == 'o' || (s[0] == '`' && (s[1] == 'i' || s[1] == 'o'))) {
+          strings::StrAppend(&desc, "An ", s);
+        } else {
+          strings::StrAppend(&desc, "A ", s);
+        }
+        break;
+      }
+    }
+
+    if (attr.has_allowed_values()) {
+      strings::StrAppend(&desc, " from: `",
+                         AttrListToPython(attr.allowed_values()), "`");
+    }
+
+    if (attr.has_minimum()) {
+      if (attr.type() == "int") {
+        strings::StrAppend(&desc, " that is `>= ", attr.minimum(), "`");
+      } else if (attr.minimum() > 0) {
+        strings::StrAppend(&desc, " that has length `>= ", attr.minimum(), "`");
+      }
+    }
+
+    strings::StrAppend(&desc, ".");
+
+    if (api_def_attr.has_default_value()) {
+      strings::StrAppend(
+          &desc, " Defaults to `",
+          AttrValueToPython(attr.type(), api_def_attr.default_value()), "`.");
+    }
+    if (!api_def_attr.description().empty()) {
+      AppendWithinWidth(&desc, api_def_attr.description(),
+                        kRightMargin - 4 /* indent */);
+    }
+    strings::StrAppend(&result_, Indent(4, 6, desc));
+  }
+}
+
+void GenPythonOp::AddDocStringNameArg() {
+  strings::StrAppend(&result_,
+                     "    name: A name for the operation (optional).\n");
+}
+
+void GenPythonOp::AddOutputGlobals() {
+  // Prepare a NamedTuple type to hold the outputs, if there are multiple
+  if (num_outs_ > 1) {
+    // Prepare the list of output names
+    std::vector out_names(num_outs_);
+    for (int i = 0; i < num_outs_; ++i) {
+      if (!api_def_.out_arg(i).rename_to().empty()) {
+        out_names[i] = api_def_.out_arg(i).rename_to();
+      } else {
+        out_names[i] = strings::StrCat("output", i);
+      }
+    }
+    string out_names_list =
+        strings::StrCat("[\"", str_util::Join(out_names, "\", \""), "\"]");
+
+    // Provide the output names as a Python list
+    string lower_op_name_outputs =
+        strings::StrCat("_", function_name_, "_outputs");
+    const string outputs_prefix = strings::StrCat(lower_op_name_outputs, " = ");
+    strings::StrAppend(&prelude_, "\n",
+                       WordWrap(outputs_prefix, out_names_list, kRightMargin),
+                       "\n");
+
+    strings::StrAppend(&prelude_, "_", op_def_.name(),
+                       "Output = _collections.namedtuple(\n");
+    const string tuple_type_prefix = "    ";
+    const string tuple_type_suffix = strings::StrCat(
+        "\"", op_def_.name(), "\", ", lower_op_name_outputs, ")");
+    strings::StrAppend(
+        &prelude_, WordWrap(tuple_type_prefix, tuple_type_suffix, kRightMargin),
+        "\n\n");
+  }
+  strings::StrAppend(&prelude_, "\n");
+}
+
+void GenPythonOp::AddDocStringOutputs() {
+  std::vector output_type_string;
+  output_type_string.reserve(num_outs_);
+  for (int i = 0; i < num_outs_; ++i) {
+    output_type_string.push_back(
+        ArgTypeName(op_def_, op_def_.output_arg(i), inferred_attrs_, true));
+  }
+  strings::StrAppend(&result_, GetReturns(op_def_, output_type_string));
+}
+
+void GenPythonOp::AddBody(const string& prefix) {
+  const string apply_prefix =
+      strings::StrCat(prefix, "_result = _op_def_lib.apply_op(");
+  AddBodyNoReturn(apply_prefix);
+  if (num_outs_ > 1) {
+    strings::StrAppend(&result_, prefix, "_result = _", op_def_.name(),
+                       "Output._make(_result)\n");
+  }
+  strings::StrAppend(&result_, prefix, "return _result\n");
+}
+
+void GenPythonOp::AddBodyNoReturn(const string& apply_prefix) {
+  string args = strings::StrCat("\"", op_def_.name(), "\", ");
+  for (size_t i = 0; i < param_names_.size(); ++i) {
+    strings::StrAppend(&args, AvoidPythonReserved(param_names_[i].GetName()),
+                       "=", param_names_[i].GetRenameTo(), ", ");
+  }
+  strings::StrAppend(&args, "name=name)");
+
+  strings::StrAppend(&result_,
+                     // Wrap the arguments, and indent to the (.
+                     WordWrap(apply_prefix, args, kRightMargin), "\n");
+}
+
+}  // namespace python_op_gen_internal
+}  // namespace tensorflow
diff --git a/tensorflow/python/framework/python_op_gen_main.cc b/tensorflow/python/framework/python_op_gen_main.cc
index ca6ed42beec4a3..8eb943b960800e 100644
--- a/tensorflow/python/framework/python_op_gen_main.cc
+++ b/tensorflow/python/framework/python_op_gen_main.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/python/eager/python_eager_op_gen.h"
+#include "tensorflow/python/framework/python_op_gen.h"
 
 #include 
 #include 
@@ -133,11 +133,10 @@ void PrintAllPythonOps(const std::vector& op_list,
         *pruned_ops.mutable_op()->Add() = op_def;
       }
     }
-    PrintEagerPythonOps(pruned_ops, api_def_map, {}, require_shapes,
-                        source_file_name);
+    PrintPythonOps(pruned_ops, api_def_map, {}, require_shapes,
+                   source_file_name);
   } else {
-    PrintEagerPythonOps(ops, api_def_map, op_list, require_shapes,
-                        source_file_name);
+    PrintPythonOps(ops, api_def_map, op_list, require_shapes, source_file_name);
   }
 }
 

From eac758802e66934a6fde4e23fd92023780a5c075 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 22:49:20 -0700
Subject: [PATCH 0542/1691] Implementation of Slice.

PiperOrigin-RevId: 195926057
---
 tensorflow/contrib/lite/builtin_ops.h         |   1 +
 .../lite/g3doc/tf_ops_compatibility.md        |  18 +-
 tensorflow/contrib/lite/kernels/BUILD         |  18 ++
 .../internal/optimized/optimized_ops.h        |   4 +-
 .../internal/reference/reference_ops.h        |   4 +-
 tensorflow/contrib/lite/kernels/register.cc   |   2 +
 tensorflow/contrib/lite/kernels/slice.cc      | 197 ++++++++++++++++++
 tensorflow/contrib/lite/kernels/slice_test.cc | 173 +++++++++++++++
 tensorflow/contrib/lite/model.cc              |   3 +
 tensorflow/contrib/lite/nnapi_delegate.cc     |   1 +
 tensorflow/contrib/lite/schema/schema.fbs     |   5 +
 .../contrib/lite/schema/schema_generated.h    | 124 ++++++++++-
 tensorflow/contrib/lite/testing/BUILD         |   1 +
 .../contrib/lite/testing/generate_examples.py |  57 ++++-
 .../testing/generated_examples_zip_test.cc    |   4 +
 .../contrib/lite/toco/tflite/operator.cc      |   2 +
 .../contrib/lite/toco/tflite/operator_test.cc |   1 +
 17 files changed, 601 insertions(+), 14 deletions(-)
 create mode 100644 tensorflow/contrib/lite/kernels/slice.cc
 create mode 100644 tensorflow/contrib/lite/kernels/slice_test.cc
 mode change 100644 => 100755 tensorflow/contrib/lite/schema/schema_generated.h

diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h
index a038acf2848b21..6783f18b79de05 100644
--- a/tensorflow/contrib/lite/builtin_ops.h
+++ b/tensorflow/contrib/lite/builtin_ops.h
@@ -90,6 +90,7 @@ typedef enum {
   kTfLiteBuiltinGreaterEqual = 62,
   kTfLiteBuiltinLessEqual = 63,
   kTfLiteBuiltinSelect = 64,
+  kTfLiteBuiltinSlice = 65,
 } TfLiteBuiltinOperator;
 
 #ifdef __cplusplus
diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
index f45fcceb2e6152..f52d0fb08f4b75 100644
--- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
+++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
@@ -134,7 +134,6 @@ following common ops are not supported at the moment:
 *   [tf.depth_to_space](https://www.tensorflow.org/api_docs/python/tf/depth_to_space)
 *   [tf.gather](https://www.tensorflow.org/api_docs/python/tf/gather)
 *   [tf.image.resize_bilinear](https://www.tensorflow.org/api_docs/python/tf/image/resize_bilinear)
-*   [tf.slice](https://www.tensorflow.org/api_docs/python/tf/slice)
 *   [tf.tanh](https://www.tensorflow.org/api_docs/python/tf/tanh)
 
 ## TensorFlow Lite Operations
@@ -523,6 +522,19 @@ Options {
 }
 ```
 
+**SLICE**
+
+```
+Inputs {
+  0: tensor
+  1: 1D tensor
+  2: 1D tensor
+}
+Outputs {
+  0: slice of the input tensor of the given size from the given begin index.
+}
+```
+
 **SOFTMAX**
 
 ```
@@ -608,7 +620,7 @@ Outputs {
   0: slice of the input tensor of the given size
 }
 Options {
-  begin_mask: mask for begin indicies
+  begin_mask: mask for begin indices
   end_mask: mask for end indices
   shrink_axis_mask: mask that indicates which dimensions to remove
 }
@@ -623,7 +635,7 @@ Inputs {
 }
 Outputs {
   0: k largest element along each last dimensional slice
-  1: indicies of values within the last dimension of the input ensor
+  1: indices of values within the last dimension of the input ensor
 }
 ```
 
diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD
index 79e3c9f2664594..885b580700f699 100644
--- a/tensorflow/contrib/lite/kernels/BUILD
+++ b/tensorflow/contrib/lite/kernels/BUILD
@@ -166,6 +166,7 @@ cc_library(
         "resize_bilinear.cc",
         "select.cc",
         "skip_gram.cc",
+        "slice.cc",
         "space_to_batch_nd.cc",
         "space_to_depth.cc",
         "split.cc",
@@ -888,6 +889,23 @@ tf_cc_test(
     ],
 )
 
+tf_cc_test(
+    name = "slice_test",
+    size = "small",
+    srcs = [
+        "slice_test.cc",
+    ],
+    tags = [
+        "tflite_not_portable_ios",
+    ],
+    deps = [
+        ":builtin_ops",
+        "//tensorflow/contrib/lite:framework",
+        "//tensorflow/contrib/lite/kernels:test_util",
+        "@com_google_googletest//:gtest",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index 8ab6f19b710e10..637b21e1be2596 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -6045,10 +6045,10 @@ inline void Slice(const T* input_data, const Dims<4>& input_dims,
       size[3] == -1 ? input_dims.sizes[3] - start_b : start_b + size[3];
   const int start_h = begin[2];
   const int stop_h =
-      size[2] == -1 ? input_dims.sizes[2] - start_b : start_b + size[2];
+      size[2] == -1 ? input_dims.sizes[2] - start_h : start_h + size[2];
   const int start_w = begin[1];
   const int stop_w =
-      size[1] == -1 ? input_dims.sizes[1] - start_b : start_b + size[1];
+      size[1] == -1 ? input_dims.sizes[1] - start_w : start_w + size[1];
   const int start_d = begin[0];
   const int stop_d =
       size[0] == -1 ? input_dims.sizes[0] - start_d : start_d + size[0];
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index c3aff1093f0299..319e36de0f6ae4 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -3256,10 +3256,10 @@ inline void Slice(const T* input_data, const Dims<4>& input_dims,
       size[3] == -1 ? input_dims.sizes[3] - start_b : start_b + size[3];
   const int start_h = begin[2];
   const int stop_h =
-      size[2] == -1 ? input_dims.sizes[2] - start_b : start_b + size[2];
+      size[2] == -1 ? input_dims.sizes[2] - start_h : start_h + size[2];
   const int start_w = begin[1];
   const int stop_w =
-      size[1] == -1 ? input_dims.sizes[1] - start_b : start_b + size[1];
+      size[1] == -1 ? input_dims.sizes[1] - start_w : start_w + size[1];
   const int start_d = begin[0];
   const int stop_d =
       size[0] == -1 ? input_dims.sizes[0] - start_d : start_d + size[0];
diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc
index 5df35aac62141f..4544f2d2928af3 100644
--- a/tensorflow/contrib/lite/kernels/register.cc
+++ b/tensorflow/contrib/lite/kernels/register.cc
@@ -87,6 +87,7 @@ TfLiteRegistration* Register_LESS_EQUAL();
 TfLiteRegistration* Register_FLOOR();
 TfLiteRegistration* Register_NEG();
 TfLiteRegistration* Register_SELECT();
+TfLiteRegistration* Register_SLICE();
 
 BuiltinOpResolver::BuiltinOpResolver() {
   AddBuiltin(BuiltinOperator_RELU, Register_RELU());
@@ -155,6 +156,7 @@ BuiltinOpResolver::BuiltinOpResolver() {
   AddBuiltin(BuiltinOperator_FLOOR, Register_FLOOR());
   AddBuiltin(BuiltinOperator_NEG, Register_NEG());
   AddBuiltin(BuiltinOperator_SELECT, Register_SELECT());
+  AddBuiltin(BuiltinOperator_SLICE, Register_SLICE());
 
   // TODO(andrewharp, ahentz): Move these somewhere more appropriate so that
   // custom ops aren't always included by default.
diff --git a/tensorflow/contrib/lite/kernels/slice.cc b/tensorflow/contrib/lite/kernels/slice.cc
new file mode 100644
index 00000000000000..82baf53e1d8543
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/slice.cc
@@ -0,0 +1,197 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include 
+#include 
+#include 
+#include "tensorflow/contrib/lite/builtin_op_data.h"
+#include "tensorflow/contrib/lite/context.h"
+#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
+#include "tensorflow/contrib/lite/kernels/internal/tensor.h"
+#include "tensorflow/contrib/lite/kernels/kernel_util.h"
+#include "tensorflow/contrib/lite/kernels/op_macros.h"
+
+namespace tflite {
+namespace ops {
+namespace builtin {
+namespace slice {
+
+constexpr int kInputTensor = 0;
+constexpr int kBeginTensor = 1;
+constexpr int kSizeTensor = 2;
+constexpr int kOutputTensor = 0;
+
+// This Op only supports 1-4D cases and since we use the optimized ops 4D
+// implementation, the 1-3D tensors are mapped to 4D.
+const int kMaxDim = 4;
+
+template 
+TfLiteStatus CalculateOutputShapeVector(
+    TfLiteContext* context, TfLiteTensor* input, TfLiteTensor* begin,
+    TfLiteTensor* size, std::vector* output_shape_vector) {
+  for (int idx = 0; idx < NumDimensions(input); ++idx) {
+    T size_value = GetTensorData(size)[idx];
+    if (size_value < 0) {
+      if (size_value != -1) {
+        context->ReportError(context, "Invalid size.");
+        return kTfLiteError;
+      }
+      size_value = SizeOfDimension(input, idx) - GetTensorData(begin)[idx];
+    } else {
+      if (SizeOfDimension(input, idx) <
+          GetTensorData(begin)[idx] + size_value) {
+        context->ReportError(context, "Invalid begin and size.");
+        return kTfLiteError;
+      }
+    }
+    output_shape_vector->push_back(size_value);
+  }
+  return kTfLiteOk;
+}
+
+template 
+void GetBeginAndSizeVectors(int dimensions, TfLiteTensor* begin,
+                            TfLiteTensor* size, std::vector* begins,
+                            std::vector* sizes) {
+  for (int idx = dimensions - 1; idx >= 0; --idx) {
+    begins->push_back(GetTensorData(begin)[idx]);
+    sizes->push_back(GetTensorData(size)[idx]);
+  }
+}
+
+TfLiteStatus ResizeOutputShape(TfLiteContext* context, TfLiteTensor* input,
+                               TfLiteTensor* begin, TfLiteTensor* size,
+                               TfLiteTensor* output) {
+  std::vector output_shape_vector;
+
+  if (begin->type == kTfLiteInt32) {
+    TF_LITE_ENSURE_STATUS(CalculateOutputShapeVector(
+        context, input, begin, size, &output_shape_vector));
+  } else if (begin->type == kTfLiteInt64) {
+    TF_LITE_ENSURE_STATUS(CalculateOutputShapeVector(
+        context, input, begin, size, &output_shape_vector));
+  } else {
+    context->ReportError(context, "Type is currently not supported by Slice.");
+    return kTfLiteError;
+  }
+
+  TfLiteIntArray* output_shape =
+      TfLiteIntArrayCreate(output_shape_vector.size());
+  std::copy(output_shape_vector.begin(), output_shape_vector.end(),
+            output_shape->data);
+  return context->ResizeTensor(context, output, output_shape);
+}
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TfLiteTensor* begin = GetInput(context, node, kBeginTensor);
+  TfLiteTensor* size = GetInput(context, node, kSizeTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  // Ensure validity of input tensor and its dimension.
+  TF_LITE_ENSURE_EQ(context, input->type, output->type);
+  TF_LITE_ENSURE(context,
+                 begin->type == kTfLiteInt32 || begin->type == kTfLiteInt64);
+  TF_LITE_ENSURE(context,
+                 size->type == kTfLiteInt32 || size->type == kTfLiteInt64);
+  TF_LITE_ENSURE(context, NumDimensions(begin) == NumDimensions(size) == 1);
+  TF_LITE_ENSURE_MSG(context, NumDimensions(input) <= kMaxDim,
+                     "Slice op only supports 1D-4D input arrays.");
+
+  // Postpone allocation of output if any of the indexing tensors is not
+  // constant
+  if (!(IsConstantTensor(begin) && IsConstantTensor(size))) {
+    SetTensorToDynamic(output);
+    return kTfLiteOk;
+  }
+
+  return ResizeOutputShape(context, input, begin, size, output);
+}
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TfLiteTensor* begin = GetInput(context, node, kBeginTensor);
+  TfLiteTensor* size = GetInput(context, node, kSizeTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  if (IsDynamicTensor(output)) {
+    TF_LITE_ENSURE_OK(context,
+                      ResizeOutputShape(context, input, begin, size, output));
+  }
+
+  std::vector begins;
+  begins.reserve(kMaxDim);
+  std::vector sizes;
+  sizes.reserve(kMaxDim);
+
+  if (begin->type == kTfLiteInt32) {
+    GetBeginAndSizeVectors(NumDimensions(input), begin, size, &begins,
+                                    &sizes);
+  } else if (begin->type == kTfLiteInt64) {
+    GetBeginAndSizeVectors(NumDimensions(input), begin, size, &begins,
+                                    &sizes);
+  } else {
+    context->ReportError(context, "Type is currently not supported by Slice.");
+    return kTfLiteError;
+  }
+
+  for (int i = NumDimensions(input); i < kMaxDim; ++i) {
+    begins.push_back(0);
+    sizes.push_back(1);
+  }
+
+#define TF_LITE_SLICE(data_type)                                            \
+  optimized_ops::Slice(                                          \
+      GetTensorData(input), GetTensorDims(input), begins, sizes, \
+      GetTensorData(output), GetTensorDims(output))
+
+  switch (input->type) {
+    case kTfLiteFloat32:
+      TF_LITE_SLICE(float);
+      break;
+    case kTfLiteInt32:
+      TF_LITE_SLICE(int32_t);
+      break;
+    case kTfLiteInt64:
+      TF_LITE_SLICE(int64_t);
+      break;
+    case kTfLiteUInt8:
+      TF_LITE_SLICE(uint8_t);
+      break;
+    case kTfLiteBool:
+      TF_LITE_SLICE(bool);
+      break;
+    default:
+      context->ReportError(context,
+                           "Type is currently not supported by Slice.");
+      return kTfLiteError;
+  }
+#undef TF_LITE_SLICE
+  return kTfLiteOk;
+}
+
+}  // namespace slice
+
+TfLiteRegistration* Register_SLICE() {
+  static TfLiteRegistration r = {nullptr, nullptr, slice::Prepare, slice::Eval};
+  return &r;
+}
+
+}  // namespace builtin
+}  // namespace ops
+}  // namespace tflite
diff --git a/tensorflow/contrib/lite/kernels/slice_test.cc b/tensorflow/contrib/lite/kernels/slice_test.cc
new file mode 100644
index 00000000000000..4828f88f36bc1e
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/slice_test.cc
@@ -0,0 +1,173 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include 
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/kernels/test_util.h"
+#include "tensorflow/contrib/lite/model.h"
+
+namespace tflite {
+namespace {
+
+using ::testing::ElementsAreArray;
+
+template 
+class SliceOpModel : public SingleOpModel {
+ public:
+  SliceOpModel(std::initializer_list input_shape,
+               std::initializer_list begin_shape,
+               std::initializer_list size_shape,
+               TensorType tensor_index_type, TensorType tensor_input_type) {
+    input_ = AddInput(tensor_input_type);
+    begin_ = AddInput(tensor_index_type);
+    size_ = AddInput(tensor_index_type);
+    output_ = AddOutput(tensor_input_type);
+    SetBuiltinOp(BuiltinOperator_SLICE, BuiltinOptions_SliceOptions,
+                 CreateSliceOptions(builder_).Union());
+    BuildInterpreter({input_shape, begin_shape, size_shape});
+  }
+
+  void SetInput(std::initializer_list data) {
+    PopulateTensor(input_, data);
+  }
+  void SetBegin(std::initializer_list data) {
+    PopulateTensor(begin_, data);
+  }
+  void SetSize(std::initializer_list data) {
+    PopulateTensor(size_, data);
+  }
+
+  std::vector GetOutput() {
+    return ExtractVector(output_);
+  }
+  std::vector GetOutputShape() { return GetTensorShape(output_); }
+
+ private:
+  int input_;
+  int begin_;
+  int size_;
+  int output_;
+};
+
+TEST(SliceOpTest, In1D) {
+  SliceOpModel m({4}, {1}, {1}, TensorType_INT32,
+                                 TensorType_FLOAT32);
+  m.SetInput({1, 2, 3, 4});
+  m.SetBegin({1});
+  m.SetSize({2});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray({2, 3}));
+}
+
+TEST(SliceOpTest, In2D) {
+  SliceOpModel m({2, 3}, {2}, {2}, TensorType_INT32,
+                                 TensorType_FLOAT32);
+  m.SetInput({1, 2, 3, 4, 5, 6});
+  m.SetBegin({1, 0});
+  m.SetSize({1, 2});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray({4, 5}));
+}
+
+TEST(SliceOpTest, In3D) {
+  SliceOpModel m({2, 3, 2}, {3}, {4}, TensorType_INT32,
+                                 TensorType_FLOAT32);
+  m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
+  m.SetBegin({0, 0, 0});
+  m.SetSize({2, 3, 2});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 3, 2}));
+  EXPECT_THAT(m.GetOutput(),
+              ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}));
+}
+
+TEST(SliceOpTest, InputFloat) {
+  SliceOpModel m({4, 1, 1, 1}, {4}, {4}, TensorType_INT32,
+                                 TensorType_FLOAT32);
+  m.SetInput({1, 2, 3, 4});
+  m.SetBegin({1, 0, 0, 0});
+  m.SetSize({3, 1, 1, 1});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1, 1, 1}));
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray({2, 3, 4}));
+}
+
+TEST(SliceOpTest, IndexInt64) {
+  SliceOpModel m({4, 1, 1, 1}, {4}, {4}, TensorType_INT64,
+                                 TensorType_FLOAT32);
+  m.SetInput({1, 2, 3, 4});
+  m.SetBegin({1, 0, 0, 0});
+  m.SetSize({3, 1, 1, 1});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1, 1, 1}));
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray({2, 3, 4}));
+}
+
+// See these test cases under:
+// https://www.tensorflow.org/versions/master/api_docs/python/tf/slice
+TEST(SliceOpTest, InputInteger1) {
+  SliceOpModel m({3, 2, 3, 1}, {4}, {4}, TensorType_INT32,
+                                   TensorType_INT32);
+  m.SetInput({1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6});
+  m.SetBegin({1, 0, 0, 0});
+  m.SetSize({1, 1, 3, 1});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 3, 1}));
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 3, 3}));
+}
+
+TEST(SliceOpTest, InputInteger2) {
+  SliceOpModel m({3, 2, 3, 1}, {4}, {4}, TensorType_INT32,
+                                   TensorType_INT32);
+  m.SetInput({1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6});
+  m.SetBegin({1, 0, 0, 0});
+  m.SetSize({1, 2, 3, 1});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2, 3, 1}));
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 3, 3, 4, 4, 4}));
+}
+
+TEST(SliceOpTest, InputInteger3) {
+  SliceOpModel m({3, 2, 3, 1}, {4}, {4}, TensorType_INT32,
+                                   TensorType_INT32);
+  m.SetInput({1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6});
+  m.SetBegin({1, 0, 0, 0});
+  m.SetSize({2, 1, 3, 1});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 1, 3, 1}));
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 3, 3, 5, 5, 5}));
+}
+
+TEST(SliceOpTest, SizeMinus1) {
+  SliceOpModel m({3, 2, 3, 1}, {4}, {4}, TensorType_INT32,
+                                   TensorType_INT32);
+  m.SetInput({1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6});
+  m.SetBegin({1, 0, 0, 0});
+  m.SetSize({2, 1, -1, 1});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 1, 3, 1}));
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 3, 3, 5, 5, 5}));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}
diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc
index e89036ce730dd7..8222b99ef4d5f2 100644
--- a/tensorflow/contrib/lite/model.cc
+++ b/tensorflow/contrib/lite/model.cc
@@ -679,6 +679,9 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type,
     case BuiltinOperator_SELECT: {
       break;
     }
+    case BuiltinOperator_SLICE: {
+      break;
+    }
     case BuiltinOperator_DELEGATE: {
       // TODO(ycling): Revisit when supporting saving delegated models.
       error_reporter->Report("DELEGATE op shouldn't exist in model.");
diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc
index eb451397bd8eff..5b59971442cb44 100644
--- a/tensorflow/contrib/lite/nnapi_delegate.cc
+++ b/tensorflow/contrib/lite/nnapi_delegate.cc
@@ -382,6 +382,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       case tflite::BuiltinOperator_LESS_EQUAL:
       case tflite::BuiltinOperator_NEG:
       case tflite::BuiltinOperator_SELECT:
+      case tflite::BuiltinOperator_SLICE:
         FATAL("Op code %d is currently not delegated to NNAPI", builtin);
         nn_op_type = -1;  // set to invalid
         break;
diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs
index 9de6180874742a..5eeea7a8fcc159 100644
--- a/tensorflow/contrib/lite/schema/schema.fbs
+++ b/tensorflow/contrib/lite/schema/schema.fbs
@@ -142,6 +142,7 @@ enum BuiltinOperator : byte {
   GREATER_EQUAL = 62,
   LESS_EQUAL = 63,
   SELECT = 64,
+  SLICE = 65,
 }
 
 // Options for the builtin operators.
@@ -193,6 +194,7 @@ union BuiltinOptions {
   GreaterEqualOptions,
   LessEqualOptions,
   SelectOptions,
+  SliceOptions,
 }
 
 enum Padding : byte { SAME, VALID }
@@ -436,6 +438,9 @@ table NegOptions {
 table SelectOptions {
 }
 
+table SliceOptions {
+}
+
 // An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
 // builtin, or a string if the operator is custom.
 table OperatorCode {
diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h
old mode 100644
new mode 100755
index a2f0c8cdd28934..803c8acafd1687
--- a/tensorflow/contrib/lite/schema/schema_generated.h
+++ b/tensorflow/contrib/lite/schema/schema_generated.h
@@ -172,6 +172,9 @@ struct NegOptionsT;
 struct SelectOptions;
 struct SelectOptionsT;
 
+struct SliceOptions;
+struct SliceOptionsT;
+
 struct OperatorCode;
 struct OperatorCodeT;
 
@@ -296,11 +299,12 @@ enum BuiltinOperator {
   BuiltinOperator_GREATER_EQUAL = 62,
   BuiltinOperator_LESS_EQUAL = 63,
   BuiltinOperator_SELECT = 64,
+  BuiltinOperator_SLICE = 65,
   BuiltinOperator_MIN = BuiltinOperator_ADD,
-  BuiltinOperator_MAX = BuiltinOperator_SELECT
+  BuiltinOperator_MAX = BuiltinOperator_SLICE
 };
 
-inline BuiltinOperator (&EnumValuesBuiltinOperator())[64] {
+inline BuiltinOperator (&EnumValuesBuiltinOperator())[65] {
   static BuiltinOperator values[] = {
     BuiltinOperator_ADD,
     BuiltinOperator_AVERAGE_POOL_2D,
@@ -365,7 +369,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[64] {
     BuiltinOperator_GREATER,
     BuiltinOperator_GREATER_EQUAL,
     BuiltinOperator_LESS_EQUAL,
-    BuiltinOperator_SELECT
+    BuiltinOperator_SELECT,
+    BuiltinOperator_SLICE
   };
   return values;
 }
@@ -437,6 +442,7 @@ inline const char **EnumNamesBuiltinOperator() {
     "GREATER_EQUAL",
     "LESS_EQUAL",
     "SELECT",
+    "SLICE",
     nullptr
   };
   return names;
@@ -496,11 +502,12 @@ enum BuiltinOptions {
   BuiltinOptions_GreaterEqualOptions = 45,
   BuiltinOptions_LessEqualOptions = 46,
   BuiltinOptions_SelectOptions = 47,
+  BuiltinOptions_SliceOptions = 48,
   BuiltinOptions_MIN = BuiltinOptions_NONE,
-  BuiltinOptions_MAX = BuiltinOptions_SelectOptions
+  BuiltinOptions_MAX = BuiltinOptions_SliceOptions
 };
 
-inline BuiltinOptions (&EnumValuesBuiltinOptions())[48] {
+inline BuiltinOptions (&EnumValuesBuiltinOptions())[49] {
   static BuiltinOptions values[] = {
     BuiltinOptions_NONE,
     BuiltinOptions_Conv2DOptions,
@@ -549,7 +556,8 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[48] {
     BuiltinOptions_GreaterOptions,
     BuiltinOptions_GreaterEqualOptions,
     BuiltinOptions_LessEqualOptions,
-    BuiltinOptions_SelectOptions
+    BuiltinOptions_SelectOptions,
+    BuiltinOptions_SliceOptions
   };
   return values;
 }
@@ -604,6 +612,7 @@ inline const char **EnumNamesBuiltinOptions() {
     "GreaterEqualOptions",
     "LessEqualOptions",
     "SelectOptions",
+    "SliceOptions",
     nullptr
   };
   return names;
@@ -806,6 +815,10 @@ template<> struct BuiltinOptionsTraits {
   static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions;
 };
 
+template<> struct BuiltinOptionsTraits {
+  static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions;
+};
+
 struct BuiltinOptionsUnion {
   BuiltinOptions type;
   void *value;
@@ -1213,6 +1226,14 @@ struct BuiltinOptionsUnion {
     return type == BuiltinOptions_SelectOptions ?
       reinterpret_cast(value) : nullptr;
   }
+  SliceOptionsT *AsSliceOptions() {
+    return type == BuiltinOptions_SliceOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
+  const SliceOptionsT *AsSliceOptions() const {
+    return type == BuiltinOptions_SliceOptions ?
+      reinterpret_cast(value) : nullptr;
+  }
 };
 
 bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type);
@@ -4380,6 +4401,46 @@ inline flatbuffers::Offset CreateSelectOptions(
 
 flatbuffers::Offset CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
 
+struct SliceOptionsT : public flatbuffers::NativeTable {
+  typedef SliceOptions TableType;
+  SliceOptionsT() {
+  }
+};
+
+struct SliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef SliceOptionsT NativeTableType;
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           verifier.EndTable();
+  }
+  SliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct SliceOptionsBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  explicit SliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  SliceOptionsBuilder &operator=(const SliceOptionsBuilder &);
+  flatbuffers::Offset Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset CreateSliceOptions(
+    flatbuffers::FlatBufferBuilder &_fbb) {
+  SliceOptionsBuilder builder_(_fbb);
+  return builder_.Finish();
+}
+
+flatbuffers::Offset CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
 struct OperatorCodeT : public flatbuffers::NativeTable {
   typedef OperatorCode TableType;
   BuiltinOperator builtin_code;
@@ -4638,6 +4699,9 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
   const SelectOptions *builtin_options_as_SelectOptions() const {
     return builtin_options_type() == BuiltinOptions_SelectOptions ? static_cast(builtin_options()) : nullptr;
   }
+  const SliceOptions *builtin_options_as_SliceOptions() const {
+    return builtin_options_type() == BuiltinOptions_SliceOptions ? static_cast(builtin_options()) : nullptr;
+  }
   const flatbuffers::Vector *custom_options() const {
     return GetPointer *>(VT_CUSTOM_OPTIONS);
   }
@@ -4852,6 +4916,10 @@ template<> inline const SelectOptions *Operator::builtin_options_as inline const SliceOptions *Operator::builtin_options_as() const {
+  return builtin_options_as_SliceOptions();
+}
+
 struct OperatorBuilder {
   flatbuffers::FlatBufferBuilder &fbb_;
   flatbuffers::uoffset_t start_;
@@ -6616,6 +6684,29 @@ inline flatbuffers::Offset CreateSelectOptions(flatbuffers::FlatB
       _fbb);
 }
 
+inline SliceOptionsT *SliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new SliceOptionsT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void SliceOptions::UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+}
+
+inline flatbuffers::Offset SliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateSliceOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  return tflite::CreateSliceOptions(
+      _fbb);
+}
+
 inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
   auto _o = new OperatorCodeT();
   UnPackTo(_o, _resolver);
@@ -6987,6 +7078,10 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob
       auto ptr = reinterpret_cast(obj);
       return verifier.VerifyTable(ptr);
     }
+    case BuiltinOptions_SliceOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return verifier.VerifyTable(ptr);
+    }
     default: return false;
   }
 }
@@ -7193,6 +7288,10 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c
       auto ptr = reinterpret_cast(obj);
       return ptr->UnPack(resolver);
     }
+    case BuiltinOptions_SliceOptions: {
+      auto ptr = reinterpret_cast(obj);
+      return ptr->UnPack(resolver);
+    }
     default: return nullptr;
   }
 }
@@ -7387,6 +7486,10 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff
       auto ptr = reinterpret_cast(value);
       return CreateSelectOptions(_fbb, ptr, _rehasher).Union();
     }
+    case BuiltinOptions_SliceOptions: {
+      auto ptr = reinterpret_cast(value);
+      return CreateSliceOptions(_fbb, ptr, _rehasher).Union();
+    }
     default: return 0;
   }
 }
@@ -7581,6 +7684,10 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL
       value = new SelectOptionsT(*reinterpret_cast(u.value));
       break;
     }
+    case BuiltinOptions_SliceOptions: {
+      value = new SliceOptionsT(*reinterpret_cast(u.value));
+      break;
+    }
     default:
       break;
   }
@@ -7823,6 +7930,11 @@ inline void BuiltinOptionsUnion::Reset() {
       delete ptr;
       break;
     }
+    case BuiltinOptions_SliceOptions: {
+      auto ptr = reinterpret_cast(value);
+      delete ptr;
+      break;
+    }
     default: break;
   }
   value = nullptr;
diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD
index f89c0d28d37b66..ce462e24344f77 100644
--- a/tensorflow/contrib/lite/testing/BUILD
+++ b/tensorflow/contrib/lite/testing/BUILD
@@ -55,6 +55,7 @@ gen_zipped_test_files(
         "reshape.zip",
         "resize_bilinear.zip",
         "sigmoid.zip",
+        "slice.zip",
         "softmax.zip",
         "space_to_batch_nd.zip",
         "space_to_depth.zip",
diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index 05d099a82c7349..d2790b62922d57 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -90,7 +90,6 @@
     r"fully_connected.*transpose_.=True": "67586970",
     # Softmax graphs are too complex.
     r"softmax.*dim=0": "67749831",
-    r"softmax.*input_shape=\[1,3,4,3\]": "67749831",
     # SpaceToDepth only supports float32.
     r"space_to_depth.*(float16|int32|uint8|int64)": "68018134",
     # BatchToSpaceND only supports 4D tensors.
@@ -2274,6 +2273,62 @@ def build_inputs(parameters, sess, inputs, outputs):
 
   make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
 
+
+def make_slice_tests(zip_path):
+  """Make a set of tests to do slice."""
+
+  # TODO(renjieliu): add test/support for uint8.
+  test_parameters = [
+      # 4-D
+      {
+          "dtype": [tf.float32, tf.int32, tf.int64],
+          "index_type": [tf.int32, tf.int64],
+          "input_shape": [[12, 2, 2, 5]],
+          "begin": [[0, 0, 0, 0], [1, 0, 1, 0]],
+          "size": [[8, 2, 2, 3], [11, 2, 1, 5]],
+      },
+      # 2-D
+      {
+          "dtype": [tf.float32, tf.int32, tf.int64],
+          "index_type": [tf.int32, tf.int64],
+          "input_shape": [[2, 3]],
+          "begin": [[0, 0], [1, 0]],
+          "size": [[2, 3], [2, 2]],
+      },
+  ]
+
+  def build_graph(parameters):
+    """Build graph for slice test."""
+    input_tensor = tf.placeholder(
+        dtype=parameters["dtype"],
+        name="input",
+        shape=parameters["input_shape"])
+    begin = tf.placeholder(
+        dtype=parameters["index_type"],
+        name="begin",
+        shape=[len(parameters["input_shape"])])
+    size = tf.placeholder(
+        dtype=parameters["index_type"],
+        name="size",
+        shape=[len(parameters["input_shape"])])
+    tensors = [input_tensor, begin, size]
+    out = tf.slice(input_tensor, begin, size)
+    return tensors, [out]
+
+  def build_inputs(parameters, sess, inputs, outputs):
+    """Build inputs for slice test."""
+    input_values = create_tensor_data(parameters["dtype"],
+                                      parameters["input_shape"])
+    index_type = _TF_TYPE_INFO[parameters["index_type"]][0]
+
+    begin_values = np.array(parameters["begin"]).astype(index_type)
+    size_values = np.array(parameters["size"]).astype(index_type)
+    values = [input_values, begin_values, size_values]
+
+    return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
+
+  make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
+
 # Toco binary path provided by the generate rule.
 bin_path = None
 
diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
index 49762bdfe7139c..e582cb31def987 100644
--- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
+++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
@@ -67,6 +67,9 @@ std::map kBrokenTests = {
     // non-const tensors as crops.
     {R"(^\/batch_to_space_nd.*crops=\[\[1,1\],\[1,1\]\])", "70594634"},
 
+    // Softmax graphs are too complex.
+    {R"(^\/softmax.*input_shape=\[1,3,4,3\])", "67749831"},
+
     // SpaceToBatchND only supports 4D tensors.
     {R"(^\/space_to_batch_nd.*input_shape=\[1,4,4,4,1,1\])", "70848787"},
 
@@ -281,6 +284,7 @@ INSTANTIATE_TESTS(relu6)
 INSTANTIATE_TESTS(reshape)
 INSTANTIATE_TESTS(resize_bilinear)
 INSTANTIATE_TESTS(sigmoid)
+INSTANTIATE_TESTS(slice)
 INSTANTIATE_TESTS(softmax)
 INSTANTIATE_TESTS(space_to_batch_nd)
 INSTANTIATE_TESTS(space_to_depth)
diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc
index 90e24aa104f5b0..4257a927b3864e 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator.cc
+++ b/tensorflow/contrib/lite/toco/tflite/operator.cc
@@ -926,6 +926,8 @@ std::vector> BuildOperatorList() {
   ops.emplace_back(new SimpleOperator("NEG", OperatorType::kNeg));
   ops.emplace_back(
       new SimpleOperator("SELECT", OperatorType::kSelect));
+  ops.emplace_back(
+      new SimpleOperator("SLICE", OperatorType::kSlice));
 
   return ops;
 }
diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc
index a4fff9974a6421..f99929c33f0575 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc
+++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc
@@ -117,6 +117,7 @@ TEST_F(OperatorTest, SimpleOperators) {
                                               OperatorType::kTensorFlowLess);
   CheckSimpleOperator("NEG", OperatorType::kNeg);
   CheckSimpleOperator("SELECT", OperatorType::kSelect);
+  CheckSimpleOperator("SLICE", OperatorType::kSlice);
 }
 
 TEST_F(OperatorTest, BuiltinAdd) {

From 4a42d16f9559f0e8bfcdc69386bef9c9bff3a9d6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 8 May 2018 22:57:35 -0700
Subject: [PATCH 0543/1691] Unifying argument documentation style in
 CudnnSupport.

PiperOrigin-RevId: 195926489
---
 tensorflow/stream_executor/cuda/cuda_dnn.cc | 132 ++++++++++----------
 1 file changed, 66 insertions(+), 66 deletions(-)

diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc
index af78efe81db160..a0640e1b9d2539 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.cc
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc
@@ -1206,16 +1206,16 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor(
     int dims[] = {1, rnn_desc.input_size(), 1};
     int strides[] = {dims[1] * dims[2], dims[2], 1};
     status = cudnnSetTensorNdDescriptor(
-        /*tensorDesc=*/input_desc, rnn_desc.data_type() /*dataType*/,
-        sizeof(dims) / sizeof(dims[0]) /*nbDims*/, /*dimA=*/dims,
+        /*tensorDesc=*/input_desc, /*dataType=*/rnn_desc.data_type(),
+        /*nbDims=*/sizeof(dims) / sizeof(dims[0]), /*dimA=*/dims,
         /*strideA=*/strides);
     CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to set tensor descriptor");
 
     size_t params_size = 0;
     status = cudnnGetRNNParamsSize(
-        cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
+        /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(),
         /*xDesc=*/input_desc, /*sizeInBytes=*/¶ms_size,
-        rnn_desc.data_type() /*dataType*/);
+        /*dataType=*/rnn_desc.data_type());
     CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to get RNN parameter size");
     params_size_in_bytes_ = static_cast(params_size);
   }
@@ -1226,8 +1226,8 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor(
     CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to create RNN filter descriptor");
     int dims[] = {static_cast(params_size_in_bytes_), 1, 1};
     status = cudnnSetFilterNdDescriptor(
-        /*filterDesc=*/handle_, rnn_desc.data_type() /*dataType*/,
-        /*format=*/CUDNN_TENSOR_NCHW, sizeof(dims) / sizeof(dims[0]) /*nbDims*/,
+        /*filterDesc=*/handle_, /*dataType=*/rnn_desc.data_type(),
+        /*format=*/CUDNN_TENSOR_NCHW, /*nbDims=*/sizeof(dims) / sizeof(dims[0]),
         /*filterDimA=*/dims);
     CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to update RNN filter descriptor");
   }
@@ -1247,7 +1247,7 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor(
           void* offset = nullptr;
           if (type == 0) {
             status = cudnnGetRNNLinLayerMatrixParams(
-                cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
+                /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(),
                 /*layer=*/layer, /*xDesc=*/input_desc, /*wDesc=*/handle_,
                 /*w=*/nullptr, /*linLayerID=*/region,
                 /*linLayerMatDesc=*/region_desc_handle,
@@ -1256,7 +1256,7 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor(
                 status, "Cudnn fails to call cudnnGetRNNLinLayerMatrixParams");
           } else {
             status = cudnnGetRNNLinLayerBiasParams(
-                cudnn.handle() /*rnnDesc*/, rnn_desc.handle() /*rnnDesc*/,
+                /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(),
                 /*layer=*/layer, /*xDesc=*/input_desc, /*wDesc=*/handle_,
                 /*w=*/nullptr, /*linLayerID=*/region,
                 /*linLayerBiasDesc=*/region_desc_handle,
@@ -1270,7 +1270,7 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor(
           int n_dims;
           status = cudnnGetFilterNdDescriptor(
               /*filterDesc=*/region_desc_handle,
-              sizeof(dims) / sizeof(dims[0]) /*nbDimsRequested*/,
+              /*nbDimsRequested=*/sizeof(dims) / sizeof(dims[0]),
               /*dataType=*/&data_type, /*format=*/&tensor_format,
               /*nbDims=*/&n_dims, /*filterDimA=*/dims);
           CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to get filter description");
@@ -1338,7 +1338,7 @@ class CudnnRnnSequenceTensorDescriptor
     int strides[] = {dims[1] * dims[2], dims[2], 1};
     status = cudnnSetTensorNdDescriptor(
         /*tensorDesc=*/handle, /*dataType=*/data_type,
-        sizeof(dims) / sizeof(dims[0]) /*nbDims*/, /*dimA=*/dims,
+        /*nbDims=*/sizeof(dims) / sizeof(dims[0]), /*dimA=*/dims,
         /*strideA=*/strides);
     CUDNN_RETURN_IF_FAIL(status, "Failed to update tensor descriptor");
     // Replicate handle across the number of steps.
@@ -1390,7 +1390,7 @@ class CudnnRnnStateTensorDescriptor
     int strides[] = {dims[1] * dims[2], dims[2], 1};
     status = cudnnSetTensorNdDescriptor(
         /*tensorDesc=*/handle_, /*dataType=*/data_type,
-        sizeof(dims) / sizeof(dims[0]) /*nbDims*/, /*dimA=*/dims,
+        /*nbDims=*/sizeof(dims) / sizeof(dims[0]), /*dimA=*/dims,
         /*strideA=*/strides);
     CUDNN_RETURN_IF_FAIL(status, "Failed to update tensor descriptor");
   }
@@ -1497,9 +1497,9 @@ bool CheckRNNParameterSize(const CudnnHandle& cudnn,
                            const CudnnRnnSequenceTensorDescriptor& input_desc) {
   size_t params_size_in_bytes = 0;
   cudnnStatus_t status = cudnnGetRNNParamsSize(
-      /*handle=*/cudnn.handle(), rnn_desc.handle() /*rnnDesc*/,
-      input_desc.handles()[0] /*xDesc*/, /*sizeInBytes=*/¶ms_size_in_bytes,
-      rnn_desc.data_type() /*dataType*/);
+      /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(),
+      /*xDesc=*/input_desc.handles()[0], /*sizeInBytes=*/¶ms_size_in_bytes,
+      /*dataType=*/rnn_desc.data_type());
   if (status != CUDNN_STATUS_SUCCESS) {
     LOG(ERROR) << "Unable to check RNN param size: " << ToString(status);
     return false;
@@ -1592,8 +1592,8 @@ bool CudnnSupport::DoRnnForwardImpl(
   if (is_training) {
     size_t reserve_space_size_in_bytes = 0;
     cudnnStatus_t status = cudnnGetRNNTrainingReserveSize(
-        cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
-        /*seqLength=*/model_dims.seq_length, input_desc.handles() /*xDesc*/,
+        /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(),
+        /*seqLength=*/model_dims.seq_length, /*xDesc=*/input_desc.handles(),
         /*sizeInBytes=*/&reserve_space_size_in_bytes);
     if (status != CUDNN_STATUS_SUCCESS) {
       LOG(ERROR) << "Unable to query reserve space size: " << ToString(status);
@@ -1630,30 +1630,30 @@ bool CudnnSupport::DoRnnForwardImpl(
   cudnnStatus_t status;
   if (!is_training) {
     status = cudnnRNNForwardInference(
-        cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
-        model_dims.seq_length /*seqLength*/, input_desc.handles() /*xDesc*/,
-        input_data.opaque() /*x*/, input_h_desc.handle() /*hxDesc*/,
-        input_h_data.opaque() /*hx*/, input_c_desc.handle() /*cxDesc*/,
-        input_c_data.opaque() /*cx*/, rnn_desc.params_handle() /*wDesc*/,
-        params.opaque() /*w*/, output_desc.handles() /*yDesc*/,
-        output_data->opaque() /*y*/, output_h_desc.handle() /*hyDesc*/,
-        output_h_data->opaque() /*hy*/, output_c_desc.handle() /*cyDesc*/,
-        output_c_data->opaque() /*cy*/, workspace.opaque() /*workspace*/,
-        workspace.size() /*workSpaceSizeInBytes*/);
+        /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(),
+        /*seqLength=*/model_dims.seq_length, /*xDesc=*/input_desc.handles(),
+        /*x=*/input_data.opaque(), /*hxDesc=*/input_h_desc.handle(),
+        /*hx=*/input_h_data.opaque(), /*cxDesc=*/input_c_desc.handle(),
+        /*cx=*/input_c_data.opaque(), /*wDesc=*/rnn_desc.params_handle(),
+        /*w=*/params.opaque(), /*yDesc=*/output_desc.handles(),
+        /*y=*/output_data->opaque(), /*hyDesc=*/output_h_desc.handle(),
+        /*hy=*/output_h_data->opaque(), /*cyDesc=*/output_c_desc.handle(),
+        /*cy=*/output_c_data->opaque(), /*workspace=*/workspace.opaque(),
+        /*workSpaceSizeInBytes=*/workspace.size());
   } else {
     status = cudnnRNNForwardTraining(
-        cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
-        model_dims.seq_length /*seqLength*/, input_desc.handles() /*xDesc*/,
-        input_data.opaque() /*x*/, input_h_desc.handle() /*hxDesc*/,
-        input_h_data.opaque() /*hx*/, input_c_desc.handle() /*cxDesc*/,
-        input_c_data.opaque() /*cx*/, rnn_desc.params_handle() /*wDesc*/,
-        params.opaque() /*w*/, output_desc.handles() /*yDesc*/,
-        output_data->opaque() /*y*/, output_h_desc.handle() /*hyDesc*/,
-        output_h_data->opaque() /*hy*/, output_c_desc.handle() /*cyDesc*/,
-        output_c_data->opaque() /*cy*/, workspace.opaque() /*workspace*/,
-        workspace.size() /*workSpaceSizeInBytes*/,
-        reserve_space.opaque() /*reserveSpace*/,
-        reserve_space.size() /*reserveSpaceSizeInBytes*/);
+        /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(),
+        /*seqLength=*/model_dims.seq_length, /*xDesc=*/input_desc.handles(),
+        /*x=*/input_data.opaque(), /*hxDesc=*/input_h_desc.handle(),
+        /*hx=*/input_h_data.opaque(), /*cxDesc=*/input_c_desc.handle(),
+        /*cx=*/input_c_data.opaque(), /*wDesc=*/rnn_desc.params_handle(),
+        /*w=*/params.opaque(), /*yDesc=*/output_desc.handles(),
+        /*y=*/output_data->opaque(), /*hyDesc=*/output_h_desc.handle(),
+        /*hy=*/output_h_data->opaque(), /*cyDesc=*/output_c_desc.handle(),
+        /*cy=*/output_c_data->opaque(), /*workspace=*/workspace.opaque(),
+        /*workSpaceSizeInBytes=*/workspace.size(),
+        /*reserveSpace=*/reserve_space.opaque(),
+        /*reserveSpaceSizeInBytes=*/reserve_space.size());
   }
   if (is_profiling) {
     if (!timer->Stop(AsCUDAStream(stream))) {
@@ -1748,24 +1748,24 @@ bool CudnnSupport::DoRnnBackwardImpl(
   }
   // make the backward data call
   cudnnStatus_t status = cudnnRNNBackwardData(
-      cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
-      model_dims.seq_length /*seqLength*/, output_desc.handles() /*yDesc*/,
-      output_data.opaque() /*y*/, output_desc.handles() /*dyDesc*/,
-      output_backprop_data.opaque() /*dy*/, output_h_desc.handle() /*dhyDesc*/,
-      output_h_backprop_data.opaque() /*dhy*/,
-      output_c_desc.handle() /*dcyDesc*/,
-      output_c_backprop_data.opaque() /*dcy*/,
-      rnn_desc.params_handle() /*wDesc*/, params.opaque() /*w*/,
-      input_h_desc.handle() /*hxDesc*/, input_h_data.opaque() /*hx*/,
-      input_c_desc.handle() /*cxDesc*/, input_c_data.opaque() /*cx*/,
-      input_desc.handles() /*dxDesc*/, input_backprop_data->opaque() /*dx*/,
-      input_h_desc.handle() /*dhxDesc*/,
-      input_h_backprop_data->opaque() /*dhx*/,
-      input_c_desc.handle() /*dcxDesc*/,
-      input_c_backprop_data->opaque() /*dcx*/, workspace.opaque() /*workspace*/,
-      workspace.size() /*workSpaceSizeInBytes*/,
-      reserve_space_data->opaque() /*reserveSpace*/,
-      reserve_space_data->size() /*reserveSpaceSizeInBytes*/);
+      /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(),
+      /*seqLength=*/model_dims.seq_length, /*yDesc=*/output_desc.handles(),
+      /*y=*/output_data.opaque(), /*dyDesc=*/output_desc.handles(),
+      /*dy=*/output_backprop_data.opaque(), /*dhyDesc=*/output_h_desc.handle(),
+      /*dhy=*/output_h_backprop_data.opaque(),
+      /*dcyDesc=*/output_c_desc.handle(),
+      /*dcy=*/output_c_backprop_data.opaque(),
+      /*wDesc=*/rnn_desc.params_handle(), /*w=*/params.opaque(),
+      /*hxDesc=*/input_h_desc.handle(), /*hx=*/input_h_data.opaque(),
+      /*cxDesc=*/input_c_desc.handle(), /*cx=*/input_c_data.opaque(),
+      /*dxDesc=*/input_desc.handles(), /*dx=*/input_backprop_data->opaque(),
+      /*dhxDesc=*/input_h_desc.handle(),
+      /*dhx=*/input_h_backprop_data->opaque(),
+      /*dcxDesc=*/input_c_desc.handle(),
+      /*dcx=*/input_c_backprop_data->opaque(), /*workspace=*/workspace.opaque(),
+      /*workSpaceSizeInBytes=*/workspace.size(),
+      /*reserveSpace=*/reserve_space_data->opaque(),
+      /*reserveSpaceSizeInBytes=*/reserve_space_data->size());
 
   if (status != CUDNN_STATUS_SUCCESS) {
     if (is_profiling) {
@@ -1780,16 +1780,16 @@ bool CudnnSupport::DoRnnBackwardImpl(
     stream->ThenMemZero(params_backprop_data, params_backprop_data->size());
     // make the backward weight call
     status = cudnnRNNBackwardWeights(
-        cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/,
-        model_dims.seq_length /*seqLength*/, input_desc.handles() /*xDesc*/,
-        input_data.opaque() /*x*/, input_h_desc.handle() /*hxDesc*/,
-        input_h_data.opaque() /*hx*/, output_desc.handles() /*yDesc*/,
-        output_data.opaque() /*y*/, workspace.opaque() /*workspace*/,
-        workspace.size() /*workSpaceSizeInBytes*/,
-        rnn_desc.params_handle() /*dwDesc*/,
-        params_backprop_data->opaque() /*dw*/,
-        reserve_space_data->opaque() /*reserveSpace*/,
-        reserve_space_data->size() /*reserveSpaceSizeInBytes*/);
+        /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(),
+        /*seqLength=*/model_dims.seq_length, /*xDesc=*/input_desc.handles(),
+        /*x=*/input_data.opaque(), /*hxDesc=*/input_h_desc.handle(),
+        /*hx=*/input_h_data.opaque(), /*yDesc=*/output_desc.handles(),
+        /*y=*/output_data.opaque(), /*workspace=*/workspace.opaque(),
+        /*workSpaceSizeInBytes=*/workspace.size(),
+        /*dwDesc=*/rnn_desc.params_handle(),
+        /*dw=*/params_backprop_data->opaque(),
+        /*reserveSpace=*/reserve_space_data->opaque(),
+        /*reserveSpaceSizeInBytes=*/reserve_space_data->size());
     if (status != CUDNN_STATUS_SUCCESS) {
       if (is_profiling) {
         timer->Stop(AsCUDAStream(stream));

From ee1b43f69d7a7aeb517e54150a3fff30f51933c4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 05:22:36 -0700
Subject: [PATCH 0544/1691] Run test
 tensorflow/python/kernel_tests:array_ops_test only when optimizing to avoid
 flaky timeouts

PiperOrigin-RevId: 195955576
---
 tensorflow/python/kernel_tests/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index c892b6ee9a0071..6bc129a6c72224 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -1222,6 +1222,7 @@ cuda_py_test(
     shard_count = 10,
     tags = [
         "noasan",  # times out
+        "optonly",  # times out
     ],
 )
 

From 72c55090f6365b8b3846b09bc749ce92bf43479a Mon Sep 17 00:00:00 2001
From: Peter Hawkins 
Date: Wed, 9 May 2018 07:27:30 -0700
Subject: [PATCH 0545/1691] Automated g4 rollback of changelist 195120627

PiperOrigin-RevId: 195966744
---
 tensorflow/core/common_runtime/device.h               | 11 +++++++++++
 tensorflow/core/common_runtime/device_mgr.cc          |  3 +++
 .../process_function_library_runtime.cc               |  3 ++-
 3 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/common_runtime/device.h b/tensorflow/core/common_runtime/device.h
index 5918cd9bbf35a7..b537666492ce29 100644
--- a/tensorflow/core/common_runtime/device.h
+++ b/tensorflow/core/common_runtime/device.h
@@ -51,6 +51,8 @@ limitations under the License.
 
 namespace tensorflow {
 
+class DeviceMgr;
+
 class Device : public DeviceBase {
  public:
   Device(Env* env, const DeviceAttributes& device_attributes);
@@ -133,6 +135,10 @@ class Device : public DeviceBase {
   // Returns the resource manager associated w/ this device.
   virtual ResourceMgr* resource_manager() { return rmgr_; }
 
+  // Returns the device manager that owns this device, or nullptr if this Device
+  // is not owned by a device manager.
+  DeviceMgr* device_mgr() const { return device_mgr_; }
+
   // Summarizes the status of this Device, for debugging.
   string DebugString() const { return ProtoDebugString(device_attributes_); }
 
@@ -158,6 +164,11 @@ class Device : public DeviceBase {
   }
 
  private:
+  friend class DeviceMgr;
+
+  // Pointer to the device manager that owns this device. Not owned.
+  DeviceMgr* device_mgr_ = nullptr;
+
   const DeviceAttributes device_attributes_;
   DeviceNameUtils::ParsedName parsed_name_;
 
diff --git a/tensorflow/core/common_runtime/device_mgr.cc b/tensorflow/core/common_runtime/device_mgr.cc
index a77601ba79bf29..470abc14312928 100644
--- a/tensorflow/core/common_runtime/device_mgr.cc
+++ b/tensorflow/core/common_runtime/device_mgr.cc
@@ -27,6 +27,9 @@ namespace tensorflow {
 DeviceMgr::DeviceMgr(const std::vector& devices)
     : name_backing_store_(128) {
   for (Device* d : devices) {
+    CHECK(d->device_mgr_ == nullptr);
+    d->device_mgr_ = this;
+
     devices_.push_back(d);
 
     // Register under the (1) full name and (2) canonical name.
diff --git a/tensorflow/core/common_runtime/process_function_library_runtime.cc b/tensorflow/core/common_runtime/process_function_library_runtime.cc
index e61ed8c4794883..668ce877493a06 100644
--- a/tensorflow/core/common_runtime/process_function_library_runtime.cc
+++ b/tensorflow/core/common_runtime/process_function_library_runtime.cc
@@ -144,7 +144,8 @@ Status ProcessFunctionLibraryRuntime::GetDeviceContext(
   }
   Device* device = flr->device();
   string device_type = device->parsed_name().type;
-  if (device_type == "CPU" || device_type == "TPU_SYSTEM") {
+  if (device_type == "CPU" || device_type == "TPU_SYSTEM" ||
+      device_type == "TPU") {
     // "TPU_SYSTEM" indicates that `device` is a CPU.
     return Status::OK();
   }

From ac6819ec7a82b52abbf80b0e3da644673c1c8629 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 08:33:33 -0700
Subject: [PATCH 0546/1691] Add a few CHECKs here and there.

PiperOrigin-RevId: 195974944
---
 .../contrib/lite/toco/import_tensorflow.cc       | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc
index 52757ca748f12a..8a183c2968423a 100644
--- a/tensorflow/contrib/lite/toco/import_tensorflow.cc
+++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc
@@ -189,6 +189,7 @@ Status ImportFloatArray(const TensorProto& input_tensor, Array* output_array) {
       output_array->GetMutableBuffer().data;
   output_float_data.resize(RequiredBufferSizeForShape(output_array->shape()),
                            0.f);
+  CHECK_GE(output_float_data.size(), input_flat_size);
   if (input_tensor.float_val_size() == 1) {
     for (int i = 0; i < input_flat_size; i++) {
       output_float_data[i] = input_tensor.float_val(0);
@@ -221,6 +222,7 @@ Status ImportQuint8Array(const TensorProto& input_tensor, Array* output_array) {
   auto& output_int_data =
       output_array->GetMutableBuffer().data;
   output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0);
+  CHECK_GE(output_int_data.size(), input_flat_size);
   if (input_tensor.int_val_size()) {
     for (int i = 0; i < input_tensor.int_val_size(); i++) {
       output_int_data[i] = input_tensor.int_val(i);
@@ -249,6 +251,7 @@ Status ImportInt32Array(const TensorProto& input_tensor, Array* output_array) {
   auto& output_int_data =
       output_array->GetMutableBuffer().data;
   output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0);
+  CHECK_GE(output_int_data.size(), input_flat_size);
   if (input_tensor.int_val_size()) {
     for (int i = 0; i < input_tensor.int_val_size(); i++) {
       output_int_data[i] = input_tensor.int_val(i);
@@ -277,6 +280,7 @@ Status ImportInt64Array(const TensorProto& input_tensor, Array* output_array) {
   auto& output_int_data =
       output_array->GetMutableBuffer().data;
   output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0);
+  CHECK_GE(output_int_data.size(), input_flat_size);
   if (input_tensor.int64_val_size()) {
     for (int i = 0; i < input_tensor.int64_val_size(); i++) {
       output_int_data[i] = input_tensor.int64_val(i);
@@ -306,6 +310,7 @@ Status ImportBoolArray(const TensorProto& input_tensor, Array* output_array) {
       output_array->GetMutableBuffer().data;
   output_bool_data.resize(RequiredBufferSizeForShape(output_array->shape()),
                           false);
+  CHECK_GE(output_bool_data.size(), input_flat_size);
   if (input_tensor.bool_val_size()) {
     for (int i = 0; i < input_tensor.bool_val_size(); i++) {
       output_bool_data[i] = input_tensor.bool_val(i);
@@ -340,13 +345,16 @@ Status ImportStringArray(const TensorProto& input_tensor, Array* output_array) {
                             output_array->mutable_shape());
   if (!status.ok()) return status;
 
+  if (input_flat_size != input_tensor.string_val_size()) {
+    return Status(false,
+                  "Input_content string_val doesn't have the right dimensions "
+                  "for this string tensor");
+  }
+
   auto& output_string_data =
       output_array->GetMutableBuffer().data;
   output_string_data.resize(RequiredBufferSizeForShape(output_array->shape()));
-  if (input_flat_size != input_tensor.string_val_size()) {
-    LOG(FATAL) << "Input_content string_val doesn't have the right "
-                  "dimensions for this string tensor.";
-  }
+  CHECK_GE(output_string_data.size(), input_flat_size);
   for (int i = 0; i < input_flat_size; ++i) {
     output_string_data[i] = input_tensor.string_val(i);
   }

From bcec296af809947145a6ebfa1e46b1cafe21ec06 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 09:05:59 -0700
Subject: [PATCH 0547/1691] Adds _DefinedFunction.stateful_ops.

PiperOrigin-RevId: 195979035
---
 tensorflow/python/framework/function.py      | 14 ++++++++++++++
 tensorflow/python/framework/function_test.py |  4 ++++
 2 files changed, 18 insertions(+)

diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py
index f82e94b1a3aba4..b7607ceacafea8 100644
--- a/tensorflow/python/framework/function.py
+++ b/tensorflow/python/framework/function.py
@@ -313,6 +313,16 @@ def captured_inputs(self):
     self._create_definition_if_needed()
     return self._extra_inputs
 
+  @property
+  def stateful_ops(self):
+    """Returns the list of stateful ops in function definition.
+
+    Returns:
+      A list of (op.name, op.type) pairs.
+    """
+    self._create_definition_if_needed()
+    return self._stateful_ops
+
   def _create_definition_if_needed(self):
     """Creates the function definition if it's not created yet."""
     with context.graph_mode():
@@ -424,6 +434,10 @@ def _create_definition_if_needed_impl(self):
       else:
         self._func_name = compat.as_str(self._op_def.name)
 
+    self._stateful_ops = [(op.name, op.type)
+                          for op in temp_graph.get_operations()
+                          if op.op_def.is_stateful]
+
   def _set_c_attrs(self, attrs):
     """Sets `attrs` as attributes of self._c_func.
 
diff --git a/tensorflow/python/framework/function_test.py b/tensorflow/python/framework/function_test.py
index a5c19f189ea5c4..caec39f3034597 100644
--- a/tensorflow/python/framework/function_test.py
+++ b/tensorflow/python/framework/function_test.py
@@ -182,6 +182,8 @@ def testDefineFunction2ArgsOutputName(self):
     def APlus2B(a, b):
       return a + b * 2
 
+    # APlus2B is stateless.
+    self.assertEqual([], APlus2B.stateful_ops)
     with ops.Graph().as_default():
       call = APlus2B([1.0], [2.0])
       self.assertEqual("APlus2B", call.op.name)
@@ -428,6 +430,8 @@ def Foo(x):
       with ops.control_dependencies([check]):
         return x * 2
 
+    # Foo contains a stateful op (Assert).
+    self.assertEqual([("Assert", "Assert")], Foo.stateful_ops)
     g = ops.Graph()
     with g.as_default(), self.test_session():
       self.assertAllEqual(Foo(constant_op.constant(3.0)).eval(), 6.0)

From 16986a1c9ed64c2312ededf733f20a137b521819 Mon Sep 17 00:00:00 2001
From: Derek Murray 
Date: Wed, 9 May 2018 09:42:18 -0700
Subject: [PATCH 0548/1691] [Functions] Fix unbounded memory growth in
 FunctionLibraryRuntime.

A recent change modified the behavior of `FunctionLibraryRuntimeImpl::ReleaseHandle()`
so that it no longer freed the memory associated with an instantiated function. Since
we rely on instantiating and releasing a potentially large number of instances of the
same function in tf.data to isolate the (e.g. random number generator) state in each
instance, this change meant that the memory consumption could grow without bound in
a simple program like:

```python

  ds = tf.data.Dataset.from_tensors(0).repeat(None)

  # The function `lambda y: y + 1` would be instantiated for each element in the input.
  ds = ds.flat_map(lambda x: tf.data.Dataset.from_tensors(x).map(
    lambda y: y + tf.random_uniform([], minval=0, maxval=10, dtype=tf.int32)))

  iterator = ds.make_one_shot_iterator()
  next_elem = iterator.get_next()

  with tf.Session() as sess:
    while True:
      sess.run(next_elem)
```

PiperOrigin-RevId: 195983977
---
 tensorflow/core/common_runtime/function.cc    | 66 ++++++++-----------
 .../core/common_runtime/function_test.cc      | 27 ++++++--
 .../function_threadpool_test.cc               | 14 +++-
 .../process_function_library_runtime.cc       | 17 +++--
 .../process_function_library_runtime.h        | 12 +++-
 .../process_function_library_runtime_test.cc  | 10 +--
 6 files changed, 94 insertions(+), 52 deletions(-)

diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc
index bf05f6f1d95fa0..d05564e9c49609 100644
--- a/tensorflow/core/common_runtime/function.cc
+++ b/tensorflow/core/common_runtime/function.cc
@@ -208,19 +208,19 @@ class FunctionLibraryRuntimeImpl : public FunctionLibraryRuntime {
 
   // The instantiated and transformed function is encoded as a Graph
   // object, and an executor is created for the graph.
-  struct Item : public core::RefCounted {
-    bool invalidated = false;
+  struct Item {
+    uint64 instantiation_counter = 0;
     const Graph* graph = nullptr;                            // Owned by exec.
     const FunctionLibraryDefinition* overlay_lib = nullptr;  // Not owned.
     FunctionBody* func_graph = nullptr;
     Executor* exec = nullptr;
 
-    ~Item() override {
+    ~Item() {
       delete this->func_graph;
       delete this->exec;
     }
   };
-  std::unordered_map items_ GUARDED_BY(mu_);
+  std::unordered_map> items_ GUARDED_BY(mu_);
 
   ProcessFunctionLibraryRuntime* parent_ = nullptr;  // not owned.
 
@@ -284,9 +284,7 @@ FunctionLibraryRuntimeImpl::FunctionLibraryRuntimeImpl(
   }
 }
 
-FunctionLibraryRuntimeImpl::~FunctionLibraryRuntimeImpl() {
-  for (auto p : items_) p.second->Unref();
-}
+FunctionLibraryRuntimeImpl::~FunctionLibraryRuntimeImpl() {}
 
 // An asynchronous op kernel which executes an instantiated function
 // defined in a library.
@@ -490,30 +488,24 @@ Status FunctionLibraryRuntimeImpl::Instantiate(
   options_copy.target = device_name_;
   const string key = Canonicalize(function_name, attrs, options_copy);
 
-  Handle found_handle = kInvalidHandle;
   {
     mutex_lock l(mu_);
-    found_handle = parent_->GetHandle(key);
-    if (found_handle != kInvalidHandle) {
+    *handle = parent_->GetHandle(key);
+    if (*handle != kInvalidHandle) {
       FunctionLibraryRuntime::LocalHandle handle_on_device =
-          parent_->GetHandleOnDevice(device_name_, found_handle);
+          parent_->GetHandleOnDevice(device_name_, *handle);
       if (handle_on_device == kInvalidLocalHandle) {
         return errors::Internal("LocalHandle not found for handle ", *handle,
                                 ".");
       }
-      auto iter = items_.find(handle_on_device);
-      if (iter == items_.end()) {
+      auto item_handle = items_.find(handle_on_device);
+      if (item_handle == items_.end()) {
         return errors::Internal("LocalHandle ", handle_on_device,
-                                " for handle ", found_handle,
+                                " for handle ", *handle,
                                 " not found in items.");
       }
-      Item* item = iter->second;
-      if (!item->invalidated) {
-        *handle = found_handle;
-        return Status::OK();
-      }
-      // *item is invalidated. Fall through and instantiate the given
-      // function_name/attrs/option again.
+      ++item_handle->second->instantiation_counter;
+      return Status::OK();
     }
   }
 
@@ -545,16 +537,18 @@ Status FunctionLibraryRuntimeImpl::Instantiate(
 
   {
     mutex_lock l(mu_);
-    Handle found_handle_again = parent_->GetHandle(key);
-    if (found_handle_again != found_handle) {
+    *handle = parent_->GetHandle(key);
+    if (*handle != kInvalidHandle) {
       delete fbody;
-      *handle = found_handle_again;
+      ++items_[parent_->GetHandleOnDevice(device_name_, *handle)]
+            ->instantiation_counter;
     } else {
       *handle = parent_->AddHandle(key, device_name_, next_handle_);
       Item* item = new Item;
       item->func_graph = fbody;
       item->overlay_lib = options.overlay_lib;
-      items_.insert({next_handle_, item});
+      item->instantiation_counter = 1;
+      items_.emplace(next_handle_, std::unique_ptr(item));
       next_handle_++;
     }
   }
@@ -565,12 +559,17 @@ Status FunctionLibraryRuntimeImpl::ReleaseHandle(Handle handle) {
   if (!parent_->IsInstantiatedOnDevice(device_name_, handle)) {
     return parent_->ReleaseHandle(handle);
   }
+
   LocalHandle h = parent_->GetHandleOnDevice(device_name_, handle);
   CHECK_NE(h, kInvalidLocalHandle);
   mutex_lock l(mu_);
   CHECK_EQ(1, items_.count(h));
-  Item* item = items_[h];
-  item->invalidated = true;  // Reinstantiate later.
+  std::unique_ptr& item = items_[h];
+  --item->instantiation_counter;
+  if (item->instantiation_counter == 0) {
+    items_.erase(h);
+    TF_RETURN_IF_ERROR(parent_->RemoveHandle(handle));
+  }
   return Status::OK();
 }
 
@@ -680,7 +679,7 @@ Status FunctionLibraryRuntimeImpl::GetOrCreateItem(Handle handle, Item** item) {
       return errors::NotFound("Function handle ", handle,
                               " is not valid. Likely an internal error.");
     }
-    *item = items_[local_handle];
+    *item = items_[local_handle].get();
     if ((*item)->exec != nullptr) {
       return Status::OK();
     }
@@ -731,7 +730,6 @@ void FunctionLibraryRuntimeImpl::RunRemote(const Options& opts, Handle handle,
   // computation is done and stored in *rets, we send the return values back
   // to the source_device (caller) so that the ProcFLR can receive them later.
   std::vector* remote_args = new std::vector;
-  item->Ref();
   ProcessFunctionLibraryRuntime::ReceiveTensorsAsync(
       source_device, target_device, "arg_", src_incarnation, args.size(),
       device_context, {}, rendezvous, remote_args,
@@ -743,7 +741,6 @@ void FunctionLibraryRuntimeImpl::RunRemote(const Options& opts, Handle handle,
           s = frame->SetArgs(*remote_args);
         }
         if (!s.ok()) {
-          item->Unref();
           delete frame;
           delete remote_args;
           delete exec_args;
@@ -751,10 +748,9 @@ void FunctionLibraryRuntimeImpl::RunRemote(const Options& opts, Handle handle,
           return;
         }
         item->exec->RunAsync(
-            *exec_args, [item, frame, rets, done, source_device, target_device,
+            *exec_args, [frame, rets, done, source_device, target_device,
                          target_incarnation, rendezvous, device_context,
                          remote_args, exec_args](const Status& status) {
-              core::ScopedUnref unref(item);
               Status s = status;
               if (s.ok()) {
                 s = frame->ConsumeRetvals(rets);
@@ -840,13 +836,11 @@ void FunctionLibraryRuntimeImpl::Run(const Options& opts, Handle handle,
     return;
   }
 
-  item->Ref();
   item->exec->RunAsync(
       // Executor args
       *exec_args,
       // Done callback.
-      [item, frame, rets, done, exec_args](const Status& status) {
-        core::ScopedUnref unref(item);
+      [frame, rets, done, exec_args](const Status& status) {
         Status s = status;
         if (s.ok()) {
           s = frame->ConsumeRetvals(rets);
@@ -906,7 +900,6 @@ void FunctionLibraryRuntimeImpl::Run(const Options& opts, Handle handle,
   exec_args->runner = *run_opts.runner;
   exec_args->call_frame = frame;
 
-  item->Ref();
   item->exec->RunAsync(
       // Executor args
       *exec_args,
@@ -915,7 +908,6 @@ void FunctionLibraryRuntimeImpl::Run(const Options& opts, Handle handle,
           [item, frame, exec_args](DoneCallback done,
                                    // Start unbound arguments.
                                    const Status& status) {
-            core::ScopedUnref unref(item);
             delete exec_args;
             done(status);
           },
diff --git a/tensorflow/core/common_runtime/function_test.cc b/tensorflow/core/common_runtime/function_test.cc
index 373fc64007e43e..61b2f0e60f7ea6 100644
--- a/tensorflow/core/common_runtime/function_test.cc
+++ b/tensorflow/core/common_runtime/function_test.cc
@@ -231,8 +231,19 @@ class FunctionLibraryRuntimeTest : public ::testing::Test {
       return status;
     }
     FunctionLibraryRuntime::Options opts;
-    TF_RETURN_IF_ERROR(Run(flr, handle, opts, args, rets, add_runner));
-    return flr->ReleaseHandle(handle);
+    status = Run(flr, handle, opts, args, rets, add_runner);
+    if (!status.ok()) return status;
+
+    // Release the handle and try running again. It should not succeed.
+    status = flr->ReleaseHandle(handle);
+    if (!status.ok()) return status;
+
+    Status status2 = Run(flr, handle, opts, args, std::move(rets));
+    EXPECT_TRUE(errors::IsInvalidArgument(status2));
+    EXPECT_TRUE(
+        str_util::StrContains(status2.error_message(), "remote execution."));
+
+    return status;
   }
 
   Status Run(FunctionLibraryRuntime* flr, FunctionLibraryRuntime::Handle handle,
@@ -293,8 +304,16 @@ class FunctionLibraryRuntimeTest : public ::testing::Test {
       *rets[i] = retvals[i];
     }
 
-    // Release the handle.
-    return flr->ReleaseHandle(handle);
+    // Release the handle and try running again. It should not succeed.
+    status = flr->ReleaseHandle(handle);
+    if (!status.ok()) return status;
+
+    Status status2 = Run(flr, handle, opts, args, std::move(rets));
+    EXPECT_TRUE(errors::IsInvalidArgument(status2));
+    EXPECT_TRUE(
+        str_util::StrContains(status2.error_message(), "remote execution."));
+
+    return status;
   }
 
   std::unique_ptr GetFuncBody(FunctionLibraryRuntime* flr,
diff --git a/tensorflow/core/common_runtime/function_threadpool_test.cc b/tensorflow/core/common_runtime/function_threadpool_test.cc
index 98dac38a8cb903..2d09e83d013591 100644
--- a/tensorflow/core/common_runtime/function_threadpool_test.cc
+++ b/tensorflow/core/common_runtime/function_threadpool_test.cc
@@ -144,7 +144,19 @@ class FunctionLibraryRuntimeTest : public ::testing::Test {
       return status;
     }
     FunctionLibraryRuntime::Options opts;
-    return Run(flr, handle, opts, args, std::move(rets), add_runner);
+    status = Run(flr, handle, opts, args, rets, add_runner);
+    if (!status.ok()) return status;
+
+    // Release the handle and try running again. It should not succeed.
+    status = flr->ReleaseHandle(handle);
+    if (!status.ok()) return status;
+
+    Status status2 = Run(flr, handle, opts, args, std::move(rets));
+    EXPECT_TRUE(errors::IsInvalidArgument(status2));
+    EXPECT_TRUE(
+        str_util::StrContains(status2.error_message(), "remote execution."));
+
+    return status;
   }
 
   Status Run(FunctionLibraryRuntime* flr, FunctionLibraryRuntime::Handle handle,
diff --git a/tensorflow/core/common_runtime/process_function_library_runtime.cc b/tensorflow/core/common_runtime/process_function_library_runtime.cc
index 668ce877493a06..729312a310cad4 100644
--- a/tensorflow/core/common_runtime/process_function_library_runtime.cc
+++ b/tensorflow/core/common_runtime/process_function_library_runtime.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/rendezvous_util.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/util/device_name_utils.h"
+#include "tensorflow/core/util/ptr_util.h"
 
 namespace tensorflow {
 
@@ -183,8 +184,8 @@ FunctionLibraryRuntime::Handle ProcessFunctionLibraryRuntime::AddHandle(
     FunctionLibraryRuntime::LocalHandle local_handle) {
   mutex_lock l(mu_);
   auto h = next_handle_;
-  FunctionData* fd = new FunctionData(device_name, local_handle);
-  function_data_[h] = std::unique_ptr(fd);
+  function_data_[h] = MakeUnique(
+      device_name, local_handle, function_key);
   table_[function_key] = h;
   next_handle_++;
   return h;
@@ -247,8 +248,8 @@ Status ProcessFunctionLibraryRuntime::Instantiate(
         gtl::FindWithDefault(table_, function_key, kInvalidHandle);
     if (h == kInvalidHandle || function_data_.count(h) == 0) {
       h = next_handle_;
-      FunctionData* fd = new FunctionData(options.target, kInvalidHandle);
-      function_data_[h] = std::unique_ptr(fd);
+      function_data_[h] = MakeUnique(
+          options.target, kInvalidHandle, function_key);
       table_[function_key] = h;
       next_handle_++;
     }
@@ -263,6 +264,14 @@ Status ProcessFunctionLibraryRuntime::Instantiate(
   return Status::OK();
 }
 
+Status ProcessFunctionLibraryRuntime::RemoveHandle(
+    FunctionLibraryRuntime::Handle handle) {
+  mutex_lock l(mu_);
+  table_.erase(function_data_[handle]->function_key());
+  function_data_.erase(handle);
+  return Status::OK();
+}
+
 Status ProcessFunctionLibraryRuntime::ReleaseHandle(
     FunctionLibraryRuntime::Handle handle) {
   FunctionLibraryRuntime* flr = nullptr;
diff --git a/tensorflow/core/common_runtime/process_function_library_runtime.h b/tensorflow/core/common_runtime/process_function_library_runtime.h
index 05e57708993a93..69381dd34d94ec 100644
--- a/tensorflow/core/common_runtime/process_function_library_runtime.h
+++ b/tensorflow/core/common_runtime/process_function_library_runtime.h
@@ -134,6 +134,9 @@ class ProcessFunctionLibraryRuntime {
   // of the device where the function is registered.
   string GetDeviceName(FunctionLibraryRuntime::Handle handle);
 
+  // Removes handle from the state owned by this object.
+  Status RemoveHandle(FunctionLibraryRuntime::Handle handle);
+
   Status Clone(Env* env, int graph_def_version,
                const OptimizerOptions& optimizer_options,
                CustomKernelCreator custom_kernel_creator,
@@ -147,10 +150,14 @@ class ProcessFunctionLibraryRuntime {
   class FunctionData {
    public:
     FunctionData(const string& target_device,
-                 FunctionLibraryRuntime::LocalHandle local_handle)
-        : target_device_(target_device), local_handle_(local_handle) {}
+                 FunctionLibraryRuntime::LocalHandle local_handle,
+                 const string& function_key)
+        : target_device_(target_device),
+          local_handle_(local_handle),
+          function_key_(function_key) {}
 
     string target_device() { return target_device_; }
+    const string& function_key() { return function_key_; }
 
     FunctionLibraryRuntime::LocalHandle local_handle() {
       mutex_lock l(mu_);
@@ -169,6 +176,7 @@ class ProcessFunctionLibraryRuntime {
 
     const string target_device_;
     FunctionLibraryRuntime::LocalHandle local_handle_ GUARDED_BY(mu_);
+    const string function_key_;
     bool init_started_ GUARDED_BY(mu_) = false;
     Status init_result_ GUARDED_BY(mu_);
     Notification init_done_;
diff --git a/tensorflow/core/common_runtime/process_function_library_runtime_test.cc b/tensorflow/core/common_runtime/process_function_library_runtime_test.cc
index cc10e77ad2e952..4fbf2abc6714bb 100644
--- a/tensorflow/core/common_runtime/process_function_library_runtime_test.cc
+++ b/tensorflow/core/common_runtime/process_function_library_runtime_test.cc
@@ -119,13 +119,12 @@ class ProcessFunctionLibraryRuntimeTest : public ::testing::Test {
 
     EXPECT_GE(call_count, 1);  // Test runner is used.
 
-    // Release the handle and then try running the function.  It
-    // should still succeed.
+    // Release the handle and then try running the function. It shouldn't
+    // succeed.
     status = proc_flr_->ReleaseHandle(handle);
     if (!status.ok()) {
       return status;
     }
-
     Notification done2;
     proc_flr_->Run(opts, handle, args, &out,
                    [&status, &done2](const Status& s) {
@@ -133,7 +132,10 @@ class ProcessFunctionLibraryRuntimeTest : public ::testing::Test {
                      done2.Notify();
                    });
     done2.WaitForNotification();
-    return status;
+    EXPECT_TRUE(errors::IsNotFound(status));
+    EXPECT_TRUE(str_util::StrContains(status.error_message(), "not found."));
+
+    return Status::OK();
   }
 
   std::vector devices_;

From 75bc01123ea658ee1165a195f49a915697f8eba7 Mon Sep 17 00:00:00 2001
From: Russell Power 
Date: Wed, 9 May 2018 10:23:15 -0700
Subject: [PATCH 0549/1691] Fix bug in handling of SAVERS collection for
 shutdown hook.

PiperOrigin-RevId: 195989954
---
 tensorflow/contrib/tpu/python/tpu/session_support.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/session_support.py b/tensorflow/contrib/tpu/python/tpu/session_support.py
index faf677a81d0827..3e91e2df32e6f1 100644
--- a/tensorflow/contrib/tpu/python/tpu/session_support.py
+++ b/tensorflow/contrib/tpu/python/tpu/session_support.py
@@ -292,14 +292,21 @@ def saver(self):
     if self._saver:
       return self._saver
 
-    savers = ops.get_collection(ops.GraphKeys.SAVERS)[0]
+    savers = ops.get_collection(ops.GraphKeys.SAVERS)
     if not savers:
       return None
 
     if not isinstance(savers, list):
       return savers
 
-    assert len(savers) == 1, 'Only one saver supported.'
+    if len(savers) > 1:
+      logging.error(
+          'Multiple savers in the SAVERS collection.  On-demand checkpointing '
+          'will be disabled. Pass an explicit `saver` to the constructor to '
+          'override this behavior.'
+      )
+      return None
+
     return savers[0]
 
   def after_run(self, run_context, run_values):

From 46b86643aad647a59e8acdd0bb174650740ac041 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 10:38:18 -0700
Subject: [PATCH 0550/1691] Fix a bug of literal prints in hlo_graph_dumper
 Sigterm was raised when no literal info is associated with constant
 instructions in HloProto.

PiperOrigin-RevId: 195992305
---
 tensorflow/compiler/xla/service/hlo_graph_dumper.cc | 2 +-
 tensorflow/compiler/xla/service/hlo_instruction.cc  | 2 ++
 tensorflow/compiler/xla/service/hlo_instruction.h   | 3 +++
 3 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index b6b03876725e4d..55911acc28a7a4 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -825,7 +825,7 @@ string HloDotDumper::GetInstructionNodeInlinedOperands(
         *elem_count *= dim;
       }
     }
-    if (elem_count.has_value() && *elem_count <= 8) {
+    if (elem_count.has_value() && *elem_count <= 8 && constant->HasLiteral()) {
       return Printf("%s (%s)", constant->literal().ToString(),
                     ShapeUtil::HumanString(constant->shape()));
     }
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 857cd39adb8d32..03e039107f6805 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1557,6 +1557,8 @@ const Literal& HloInstruction::literal() const {
   return *literal_;
 }
 
+bool HloInstruction::HasLiteral() const { return literal_ != nullptr; }
+
 bool HloInstruction::CanHaveDimensionsField() const {
   return (opcode() == HloOpcode::kReverse ||
           opcode() == HloOpcode::kConcatenate ||
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 14be58d069e0d8..511227a34c273f 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -706,6 +706,9 @@ class HloInstruction {
   // Note: only constant and parameter opcodes have an associated literal.
   const Literal& literal() const;
 
+  // Returns whether there is literal associated with this instruction.
+  bool HasLiteral() const;
+
   // Returns the parameter number associated with this instruction.
   //
   // Note: only parameter opcodes have an associated parameter number.

From d8d0be5bd371096403684a03e8bc3b386a59fddb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 10:47:06 -0700
Subject: [PATCH 0551/1691] Test
 tensorflow/contrib/timeseries/python/timeseries:estimators_test only in opt
 mode to avoid flaky timeouts

PiperOrigin-RevId: 195993828
---
 tensorflow/contrib/timeseries/python/timeseries/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD
index d2746032a04946..e4963596d38dbe 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD
@@ -110,6 +110,7 @@ py_test(
         "no_pip_gpu",  # b/63391119
         "nomsan",  # Takes too long to run.
         "notsan",  # b/67865658
+        "optonly",  # Takes too long to run without optimization.
     ],
     deps = [
         ":ar_model",

From 49fd93aba815f9f74f167c935da42d85e8de0ca0 Mon Sep 17 00:00:00 2001
From: Jacques Pienaar 
Date: Wed, 9 May 2018 11:06:45 -0700
Subject: [PATCH 0552/1691] Avoid rebuilding the graph for every run.

* Use placeholder to avoid building the graph for every run in testIf.
* Update file comment.

PiperOrigin-RevId: 195997713
---
 .../python/kernel_tests/functional_ops_test.py      | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/tensorflow/python/kernel_tests/functional_ops_test.py b/tensorflow/python/kernel_tests/functional_ops_test.py
index 35a274e75f51b7..d3cf671ff749ed 100644
--- a/tensorflow/python/kernel_tests/functional_ops_test.py
+++ b/tensorflow/python/kernel_tests/functional_ops_test.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for tensorflow.kernels.bcast_ops."""
+"""Tests for tensorflow.kernels.functional_ops."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -670,13 +670,12 @@ def Thrice(x):
 
     with self.test_session(use_gpu=False) as sess:
 
-      def Run(x):
-        return sess.run(
-            functional_ops.If(math_ops.greater(x, 0), [x], Twice, Thrice))[0]
+      x = array_ops.placeholder(dtypes.float32)
+      ret = functional_ops.If(math_ops.greater(x, 0), [x], Twice, Thrice)[0]
 
-      self.assertAllEqual(Run(9.), 18.)
-      self.assertAllEqual(Run(-8.), -23.)
-      self.assertAllEqual(Run(0.), 1.)
+      self.assertAllEqual(sess.run(ret, feed_dict={x: 9.}), 18.)
+      self.assertAllEqual(sess.run(ret, feed_dict={x: -8.}), -23.)
+      self.assertAllEqual(sess.run(ret, feed_dict={x: 0.}), 1.)
 
   def testWhile(self):
 

From 37e48e870c9f431dd10fd838ba066c8d6c7bd9dd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 11:11:32 -0700
Subject: [PATCH 0553/1691] Increase the shard count of
 tensorflow/python/keras:wrappers_test to avoid flaky timeouts

PiperOrigin-RevId: 195998578
---
 tensorflow/python/keras/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD
index 523eb679352c2b..f29de5c432105e 100755
--- a/tensorflow/python/keras/BUILD
+++ b/tensorflow/python/keras/BUILD
@@ -644,6 +644,7 @@ py_test(
     name = "wrappers_test",
     size = "medium",
     srcs = ["_impl/keras/layers/wrappers_test.py"],
+    shard_count = 4,
     srcs_version = "PY2AND3",
     tags = [
         "noasan",  # http://b/78599823

From a01d9f7dfb58c72ea78ed560c78f99e96223ea76 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 11:22:24 -0700
Subject: [PATCH 0554/1691] Benchmark for tf.scan in graph and eager modes. As
 of this writing, a simple tf.scan sum is ~80x faster in graph mode (including
 graph building time) for 32,000 nodes. Additionally, tf.scan exhibits
 quadratic scaling in eager mode but linear in graph.

PiperOrigin-RevId: 196000512
---
 .../contrib/eager/python/examples/scan/BUILD  | 25 ++++++++
 .../python/examples/scan/scan_graph_test.py   | 57 +++++++++++++++++++
 .../eager/python/examples/scan/scan_test.py   | 56 ++++++++++++++++++
 3 files changed, 138 insertions(+)
 create mode 100644 tensorflow/contrib/eager/python/examples/scan/BUILD
 create mode 100644 tensorflow/contrib/eager/python/examples/scan/scan_graph_test.py
 create mode 100644 tensorflow/contrib/eager/python/examples/scan/scan_test.py

diff --git a/tensorflow/contrib/eager/python/examples/scan/BUILD b/tensorflow/contrib/eager/python/examples/scan/BUILD
new file mode 100644
index 00000000000000..638c57d1c92c1d
--- /dev/null
+++ b/tensorflow/contrib/eager/python/examples/scan/BUILD
@@ -0,0 +1,25 @@
+licenses(["notice"])  # Apache 2.0
+
+package(default_visibility = ["//tensorflow:internal"])
+
+load("//tensorflow:tensorflow.bzl", "cuda_py_test")
+
+cuda_py_test(
+    name = "scan_test",
+    size = "small",
+    srcs = ["scan_test.py"],
+    additional_deps = [
+        "//third_party/py/numpy",
+        "//tensorflow:tensorflow_py",
+    ],
+)
+
+cuda_py_test(
+    name = "scan_graph_test",
+    size = "small",
+    srcs = ["scan_graph_test.py"],
+    additional_deps = [
+        "//third_party/py/numpy",
+        "//tensorflow:tensorflow_py",
+    ],
+)
diff --git a/tensorflow/contrib/eager/python/examples/scan/scan_graph_test.py b/tensorflow/contrib/eager/python/examples/scan/scan_graph_test.py
new file mode 100644
index 00000000000000..4661dafbed19c7
--- /dev/null
+++ b/tensorflow/contrib/eager/python/examples/scan/scan_graph_test.py
@@ -0,0 +1,57 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Unit test for tf.scan under graph mode execution."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+
+import numpy as np
+import tensorflow as tf
+
+
+class ScanBenchmark(tf.test.Benchmark):
+
+  def runScan(self, n):
+    elems = np.arange(n)
+    start_time = time.time()
+    sum_op = tf.scan(lambda a, x: a + x, elems, parallel_iterations=1)
+    with tf.Session() as sess:
+      sess.run(sum_op)
+    wall_time = time.time() - start_time
+
+    self.report_benchmark(
+        name='scan',
+        iters=n,
+        wall_time=wall_time)
+
+  def benchmarkScan32000(self):
+    self.runScan(32000)
+
+  def benchmarkScan1M(self):
+    self.runScan(1000000)
+
+  def benchmarkScan2M(self):
+    self.runScan(2000000)
+
+  def benchmarkScan4M(self):
+    self.runScan(4000000)
+
+  def benchmarkScan8M(self):
+    self.runScan(8000000)
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/tensorflow/contrib/eager/python/examples/scan/scan_test.py b/tensorflow/contrib/eager/python/examples/scan/scan_test.py
new file mode 100644
index 00000000000000..b8c7cf1fe5bcb7
--- /dev/null
+++ b/tensorflow/contrib/eager/python/examples/scan/scan_test.py
@@ -0,0 +1,56 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Unit test for tf.scan under eager execution."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+
+import numpy as np
+import tensorflow as tf
+
+
+class ScanBenchmark(tf.test.Benchmark):
+
+  def runScan(self, n):
+    elems = np.arange(n)
+    start_time = time.time()
+    _ = tf.scan(lambda a, x: a + x, elems, parallel_iterations=1)
+    wall_time = time.time() - start_time
+
+    self.report_benchmark(
+        name='scan',
+        iters=n,
+        wall_time=wall_time)
+
+  def benchmarkScan2000(self):
+    self.runScan(2000)
+
+  def benchmarkScan4000(self):
+    self.runScan(4000)
+
+  def benchmarkScan8000(self):
+    self.runScan(8000)
+
+  def benchmarkScan16000(self):
+    self.runScan(16000)
+
+  def benchmarkScan32000(self):
+    self.runScan(32000)
+
+if __name__ == '__main__':
+  tf.enable_eager_execution()
+  tf.test.main()

From 7baa9ffe735adfa11c987c435216943767530269 Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Wed, 9 May 2018 11:22:31 -0700
Subject: [PATCH 0555/1691] [XLA] Make XLA's memory allocator return an owning
 smart pointer.

Previously, xla::DeviceMemoryAllocator::Allocate returned a
stream_executor::DeviceMemoryBase.  This is morally equivalent to a raw
pointer: It's on you the user to call Deallocate().

Unfortunately we ~never got this right.  Essentially all users of
Allocate() call it in a loop, and TF_RETURN_IF_ERROR within the loop.
If any of these allocations fails (mostly commonly, due to OOM), we leak
everything we've allocated up until then.

This patch changes our API so that it returns an owning pointer.  Now
things mostly Just Work.

Also worth calling out: The lambda in CpuExecutable::ExecuteOnStream
passed to ExecuteComputeFunction almost certainly had multithreaded
use-after-free bugs.  This patch fixes them.

PiperOrigin-RevId: 196000535
---
 tensorflow/compiler/jit/BUILD                 |   1 +
 tensorflow/compiler/jit/xla_launch_util.cc    |  14 +-
 tensorflow/compiler/jit/xla_launch_util.h     |   8 +-
 .../compiler/jit/xla_launch_util_test.cc      |   6 +-
 tensorflow/compiler/jit/xla_tensor.cc         |  14 +-
 tensorflow/compiler/xla/map_util.h            |  16 +-
 tensorflow/compiler/xla/service/BUILD         |  10 +-
 .../xla/service/allocation_tracker.cc         |   9 +-
 .../compiler/xla/service/allocation_tracker.h |  10 +-
 .../xla/service/cpu/cpu_executable.cc         | 142 ++++++++----------
 .../compiler/xla/service/cpu/cpu_executable.h |  14 +-
 .../xla/service/device_memory_allocator.cc    |  19 +--
 .../xla/service/device_memory_allocator.h     |  26 ++--
 .../xla/service/gpu/buffer_allocations.cc     |  62 +++++---
 .../xla/service/gpu/buffer_allocations.h      |  16 +-
 .../gpu/cudnn_convolution_algorithm_picker.cc |  40 ++---
 .../compiler/xla/service/gpu/fft_thunk.cc     |  31 +---
 .../compiler/xla/service/gpu/fft_thunk.h      |   4 +-
 .../xla/service/gpu/gpu_executable.cc         |   7 +-
 .../xla/service/owning_device_memory.cc       |  35 +++++
 .../xla/service/owning_device_memory.h        | 131 ++++++++++++++++
 .../compiler/xla/service/shaped_buffer.cc     |  10 +-
 .../compiler/xla/service/shaped_buffer.h      |  24 ++-
 .../compiler/xla/service/transfer_manager.cc  |   4 +-
 .../xla/tests/local_client_test_base.cc       |   8 +-
 .../xla/tests/local_client_test_base.h        |   6 +-
 .../stream_executor/stream_executor_pimpl.h   |   3 +
 27 files changed, 410 insertions(+), 260 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/owning_device_memory.cc
 create mode 100644 tensorflow/compiler/xla/service/owning_device_memory.h

diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index a6b3ce394c6859..a6d0408a8fe1f7 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -217,6 +217,7 @@ cc_library(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla/client:client_library",
         "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/service:device_memory_allocator",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:gpu_runtime",
diff --git a/tensorflow/compiler/jit/xla_launch_util.cc b/tensorflow/compiler/jit/xla_launch_util.cc
index e12e88fcc94c46..6a0f557627d117 100644
--- a/tensorflow/compiler/jit/xla_launch_util.cc
+++ b/tensorflow/compiler/jit/xla_launch_util.cc
@@ -60,7 +60,7 @@ XlaAllocator::XlaAllocator(const se::Platform* platform, Allocator* wrapped)
 
 XlaAllocator::~XlaAllocator() {}
 
-xla::StatusOr XlaAllocator::Allocate(
+xla::StatusOr XlaAllocator::Allocate(
     int device_ordinal, uint64 size, bool retry_on_failure) {
   AllocationAttributes attrs;
   attrs.no_retry_on_failure = !retry_on_failure;
@@ -69,13 +69,13 @@ xla::StatusOr XlaAllocator::Allocate(
   if (data == nullptr) {
     return errors::ResourceExhausted("Out of memory while trying to allocate ",
                                      size, " bytes.");
-  } else {
-    return se::DeviceMemoryBase(data, size);
   }
+  return xla::OwningDeviceMemory(se::DeviceMemoryBase(data, size),
+                                 device_ordinal, this);
 }
 
-Status XlaAllocator::Deallocate(int device_ordinal, se::DeviceMemoryBase* mem) {
-  wrapped_->DeallocateRaw(mem->opaque());
+Status XlaAllocator::Deallocate(int device_ordinal, se::DeviceMemoryBase mem) {
+  wrapped_->DeallocateRaw(mem.opaque());
   return Status::OK();
 }
 
@@ -241,7 +241,7 @@ void XlaComputationLaunchContext::PopulateOutputs(
       } else {
         Tensor output_tensor = XlaTensorBuffer::MakeTensor(
             ctx->expected_output_dtype(i), shape, buffer, allocator);
-        output.set_buffer(se::DeviceMemoryBase(nullptr, 0), {output_num});
+        output.set_buffer(xla::OwningDeviceMemory(), {output_num});
         ctx->set_output(i, output_tensor);
       }
       ++output_num;
@@ -291,7 +291,7 @@ void XlaComputationLaunchContext::PopulateOutputs(
     } else {
       Tensor output_tensor = XlaTensorBuffer::MakeTensor(
           write.type, write.shape, buffer, allocator);
-      output.set_buffer(se::DeviceMemoryBase(nullptr, 0), {output_num});
+      output.set_buffer(xla::OwningDeviceMemory(), {output_num});
       *variable->tensor() = output_tensor;
     }
     ++output_num;
diff --git a/tensorflow/compiler/jit/xla_launch_util.h b/tensorflow/compiler/jit/xla_launch_util.h
index a2431253f8c44b..4390701ccbd0bc 100644
--- a/tensorflow/compiler/jit/xla_launch_util.h
+++ b/tensorflow/compiler/jit/xla_launch_util.h
@@ -22,6 +22,8 @@ limitations under the License.
 #include "tensorflow/compiler/jit/xla_tensor.h"
 #include "tensorflow/compiler/tf2xla/xla_compiler.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
+#include "tensorflow/compiler/xla/service/device_memory_allocator.h"
+#include "tensorflow/compiler/xla/service/owning_device_memory.h"
 #include "tensorflow/core/framework/allocation_description.pb.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/types.h"
@@ -50,9 +52,9 @@ class XlaAllocator : public xla::DeviceMemoryAllocator {
  public:
   XlaAllocator(const se::Platform* platform, Allocator* wrapped);
   ~XlaAllocator() override;
-  xla::StatusOr Allocate(int device_ordinal, uint64 size,
-                                               bool retry_on_failure) override;
-  Status Deallocate(int device_ordinal, se::DeviceMemoryBase* mem) override;
+  xla::StatusOr Allocate(
+      int device_ordinal, uint64 size, bool retry_on_failure) override;
+  Status Deallocate(int device_ordinal, se::DeviceMemoryBase mem) override;
 
   // The Tensorflow BFC allocator used on GPU allows host-side deallocation
   // before GPU execution takes place. Tensorflow uses the ordering of the main
diff --git a/tensorflow/compiler/jit/xla_launch_util_test.cc b/tensorflow/compiler/jit/xla_launch_util_test.cc
index 27813efc0bc0ae..a45932403ec176 100644
--- a/tensorflow/compiler/jit/xla_launch_util_test.cc
+++ b/tensorflow/compiler/jit/xla_launch_util_test.cc
@@ -36,9 +36,9 @@ void BM_ExtractSubBuffer(int iters, int depth, int fan_out) {
   for (int i = 0; i < iters; ++i) {
     // Extract a buffer from approximately the middle of the first level of the
     // tree.
-    tensorflow::internal::ExtractSubShapedBuffer(&shaped_buffer,
-                                                 /*index=*/fan_out / 2,
-                                                 /*allocator=*/nullptr)
+    (void)tensorflow::internal::ExtractSubShapedBuffer(&shaped_buffer,
+                                                       /*index=*/fan_out / 2,
+                                                       /*allocator=*/nullptr)
         .release();
   }
 }
diff --git a/tensorflow/compiler/jit/xla_tensor.cc b/tensorflow/compiler/jit/xla_tensor.cc
index ce6456880bc1b3..a7211c9c7e281a 100644
--- a/tensorflow/compiler/jit/xla_tensor.cc
+++ b/tensorflow/compiler/jit/xla_tensor.cc
@@ -52,20 +52,22 @@ Status XlaTensor::AllocateShapedBuffer(DataType dtype, const TensorShape& shape,
       client->backend().transfer_manager()->HostShapeToDeviceShape(
           on_host_shape);
 
-  xla::ShapedBuffer buffer(on_host_shape, on_device_shape, client->platform(),
-                           device_ordinal);
-  for (auto& index_to_buffer : buffer.buffers()) {
+  xla::ScopedShapedBuffer shaped_buffer(on_host_shape, on_device_shape,
+                                        client->backend().memory_allocator(),
+                                        device_ordinal);
+  for (auto& index_to_buffer : shaped_buffer.buffers()) {
     xla::Shape subshape =
         xla::ShapeUtil::GetSubshape(on_device_shape, index_to_buffer.first);
     uint64 size =
         client->backend().transfer_manager()->GetByteSizeRequirement(subshape);
-    TF_ASSIGN_OR_RETURN(index_to_buffer.second,
+    TF_ASSIGN_OR_RETURN(xla::OwningDeviceMemory buffer,
                         client->backend().memory_allocator()->Allocate(
                             device_ordinal, size, /*retry_on_failure=*/false));
+    // Move our buffer into shaped_buffer, which takes ownership of it.
+    index_to_buffer.second = buffer.Forget();
   }
 
-  set_shaped_buffer(xla::ScopedShapedBuffer(
-      std::move(buffer), client->backend().memory_allocator()));
+  set_shaped_buffer(std::move(shaped_buffer));
   return Status::OK();
 }
 
diff --git a/tensorflow/compiler/xla/map_util.h b/tensorflow/compiler/xla/map_util.h
index 8db8c6f3de84a6..3c74e070da529b 100644
--- a/tensorflow/compiler/xla/map_util.h
+++ b/tensorflow/compiler/xla/map_util.h
@@ -86,11 +86,10 @@ const typename Collection::value_type::second_type& FindOrDefault(
 
 // Inserts the key-value pair into the collection. Dies if key was already
 // present.
-template 
-void InsertOrDie(Collection* const collection,
-                 const typename Collection::value_type::first_type& key,
-                 const typename Collection::value_type::second_type& data) {
-  auto p = collection->insert(std::make_pair(key, data));
+template 
+void InsertOrDie(Collection* const collection, Key&& key, Value&& value) {
+  auto p = collection->insert(
+      std::make_pair(std::forward(key), std::forward(value)));
   CHECK(p.second) << "duplicate key: " << key;
 }
 
@@ -101,9 +100,10 @@ bool ContainsKey(const Collection& collection, const Key& key) {
 }
 
 // Inserts `value` into `set`. Dies if it was already present.
-template 
-void InsertOrDie(Set* const set, const typename Set::value_type& value) {
-  CHECK(set->insert(value).second) << "duplicate value: " << value;
+template 
+void InsertOrDie(Set* const set, Value&& value) {
+  CHECK(set->insert(std::forward(value)).second)
+      << "duplicate value: " << value;
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index aa3a6261e0117c..fecc257f85a5d1 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -2316,8 +2316,14 @@ tf_cc_test(
 
 cc_library(
     name = "device_memory_allocator",
-    srcs = ["device_memory_allocator.cc"],
-    hdrs = ["device_memory_allocator.h"],
+    srcs = [
+        "device_memory_allocator.cc",
+        "owning_device_memory.cc",
+    ],
+    hdrs = [
+        "device_memory_allocator.h",
+        "owning_device_memory.h",
+    ],
     deps = [
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
diff --git a/tensorflow/compiler/xla/service/allocation_tracker.cc b/tensorflow/compiler/xla/service/allocation_tracker.cc
index cf1231bcce4d00..eb528032411703 100644
--- a/tensorflow/compiler/xla/service/allocation_tracker.cc
+++ b/tensorflow/compiler/xla/service/allocation_tracker.cc
@@ -220,8 +220,10 @@ void AllocationTracker::AddAllocationOrIncrementRefCount(
   AllocationMap& allocation_map = opaque_to_allocation_map_[device_ordinal];
   auto it = allocation_map.find(device_memory.opaque());
   if (it == allocation_map.end()) {
-    allocation_map[device_memory.opaque()] = {device_memory, device_ordinal,
-                                              /*ref_count=*/1};
+    allocation_map[device_memory.opaque()] = {
+        OwningDeviceMemory(device_memory, device_ordinal,
+                           backend_->memory_allocator()),
+        /*ref_count=*/1};
   } else {
     it->second.ref_count++;
   }
@@ -235,8 +237,7 @@ Status AllocationTracker::DecrementRefCount(se::DeviceMemoryBase device_memory,
   Allocation& allocation = it->second;
   TF_RET_CHECK(allocation.ref_count >= 1);
   if (allocation.ref_count == 1) {
-    TF_RETURN_IF_ERROR(backend_->memory_allocator()->Deallocate(
-        device_ordinal, &device_memory));
+    allocation.device_memory.Free();
     allocation_map.erase(it);
   } else {
     allocation.ref_count--;
diff --git a/tensorflow/compiler/xla/service/allocation_tracker.h b/tensorflow/compiler/xla/service/allocation_tracker.h
index 1174fa641c06ae..a7d8927cf7e90d 100644
--- a/tensorflow/compiler/xla/service/allocation_tracker.h
+++ b/tensorflow/compiler/xla/service/allocation_tracker.h
@@ -76,10 +76,7 @@ class AllocationTracker {
   // Data structure encapsulating single memory allocation on the device.
   struct Allocation {
     // The pointer to this allocation.
-    se::DeviceMemoryBase device_memory;
-
-    // The device that the memory is allocated on.
-    int device_ordinal;
+    OwningDeviceMemory device_memory;
 
     // This is the number of times this memory allocation is referred to by
     // registered data handles.
@@ -126,7 +123,10 @@ class AllocationTracker {
   int64 next_handle_ GUARDED_BY(mutex_);
 
   // A map from device ordinal to AllocationMap.
-  tensorflow::gtl::FlatMap opaque_to_allocation_map_
+  //
+  // This is not a TF FlatMap because (currently) FlatMap (and therefore
+  // AllocationMap) is not movable.
+  std::unordered_map opaque_to_allocation_map_
       GUARDED_BY(mutex_);
 
   // A map from data handle to a vector of shaped buffers that represent the
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc
index 32613b86907830..cf43b74c699ca8 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc
@@ -73,7 +73,7 @@ CpuExecutable::CpuExecutable(
 
 Status CpuExecutable::AllocateBuffers(
     DeviceMemoryAllocator* memory_allocator, int device_ordinal,
-    std::vector* buffers) {
+    std::vector* buffers) {
   CHECK_EQ(buffers->size(), assignment_->Allocations().size());
   VLOG(3) << "Allocating " << assignment_->Allocations().size()
           << " allocations for module " << module().name();
@@ -201,60 +201,18 @@ Status CpuExecutable::ExecuteComputeFunction(
   return Status::OK();
 }
 
-static void LogLiveAddresses(
-    tensorflow::gtl::ArraySlice buffers,
-    const std::vector& buffers_in_result) {
-  if (!VLOG_IS_ON(3)) {
-    return;
-  }
-
-  CHECK_EQ(buffers.size(), buffers_in_result.size());
-  std::vector live_out_buffers;
-  for (int i = 0; i < buffers.size(); ++i) {
-    if (buffers_in_result[i]) {
-      live_out_buffers.push_back(buffers[i].opaque());
-    }
-  }
-  VLOG(3) << "Live addresses in output marking found "
-          << live_out_buffers.size() << " addresses:\n"
-          << tensorflow::str_util::Join(
-                 live_out_buffers, ", ", [](string* out, const void* address) {
-                   tensorflow::strings::StrAppend(
-                       out, tensorflow::strings::Printf("%p", address));
-                 });
-}
-
-static Status DeallocateTempBuffers(
-    DeviceMemoryAllocator* allocator, se::Stream* stream,
-    tensorflow::gtl::ArraySlice buffers,
-    const std::vector& buffers_in_result) {
-  // Keep those buffers in the output of the marked live because they are needed
-  // by the service. They will be deallocated by the service.
-  for (size_t i = 0; i < buffers.size(); ++i) {
-    se::DeviceMemoryBase alloc = buffers[i];
-    if (!buffers_in_result[i] && !alloc.is_null()) {
-      VLOG(3) << "CpuExecutable deallocating buffer #" << i << " ["
-              << alloc.opaque() << "]";
-      TF_RETURN_IF_ERROR(
-          allocator->Deallocate(stream->parent()->device_ordinal(), &alloc));
-    }
-  }
-
-  return Status::OK();
-}
-
 StatusOr CpuExecutable::CreateResultShapedBuffer(
     const ServiceExecutableRunOptions* run_options,
-    tensorflow::gtl::ArraySlice allocated_buffers,
-    std::vector* buffers_in_result) {
+    tensorflow::gtl::MutableArraySlice buffers) {
   se::Stream* stream = run_options->stream();
   ScopedShapedBuffer result_buffer(
       /*on_host_shape=*/host_result_shape(),
       /*on_device_shape=*/host_result_shape(), run_options->allocator(),
       stream->parent()->device_ordinal());
 
-  // Copy DeviceMemoryBase values which contain the array(s) of the result into
-  // the respective location in ShapedBuffer which is returned to the caller.
+  // Move OwningDeviceMemory values which contain the array(s) of the result
+  // into the respective location in ScopedShapedBuffer which is returned to the
+  // caller.
   TF_RETURN_IF_ERROR(result_buffer.buffers().ForEachMutableElementWithStatus(
       [&](const ShapeIndex& index, se::DeviceMemoryBase* device_memory) {
         const auto& sources = this->GetRootPointsToSet().element(index);
@@ -273,10 +231,9 @@ StatusOr CpuExecutable::CreateResultShapedBuffer(
         CHECK(!slice.allocation()->is_entry_computation_parameter());
 
         const BufferAllocation::Index buffer_index = slice.index();
-        const se::DeviceMemoryBase& buffer = allocated_buffers[buffer_index];
+        OwningDeviceMemory& buffer = buffers[buffer_index];
         CHECK(!buffer.is_null() || buffer.size() == 0);
-        *device_memory = buffer;
-        (*buffers_in_result)[buffer_index] = true;
+        *device_memory = buffer.Forget();
         return Status::OK();
       }));
   return std::move(result_buffer);
@@ -292,23 +249,21 @@ StatusOr CpuExecutable::ExecuteOnStream(
 
   se::Stream* stream = run_options->stream();
   DeviceMemoryAllocator* memory_allocator = run_options->allocator();
-  std::vector buffers(assignment_->Allocations().size());
+  std::vector buffers(assignment_->Allocations().size());
 
   TF_RETURN_IF_ERROR(AllocateBuffers(
       memory_allocator, stream->parent()->device_ordinal(), &buffers));
-  TF_RETURN_IF_ERROR(ExecuteComputeFunction(
-      &run_options->run_options(), arguments, buffers, hlo_execution_profile));
 
-  std::vector buffers_in_result(assignment_->Allocations().size(), false);
-  TF_ASSIGN_OR_RETURN(
-      ScopedShapedBuffer result_buffer,
-      CreateResultShapedBuffer(run_options, buffers, &buffers_in_result));
-
-  // Free all buffers not in the result.
-  TF_RETURN_IF_ERROR(DeallocateTempBuffers(memory_allocator, stream, buffers,
-                                           buffers_in_result));
+  std::vector unowning_buffers;
+  unowning_buffers.reserve(buffers.size());
+  for (auto& buffer : buffers) {
+    unowning_buffers.push_back(buffer.AsDeviceMemoryBase());
+  }
+  TF_RETURN_IF_ERROR(ExecuteComputeFunction(&run_options->run_options(),
+                                            arguments, unowning_buffers,
+                                            hlo_execution_profile));
 
-  return std::move(result_buffer);
+  return CreateResultShapedBuffer(run_options, &buffers);
 }
 
 StatusOr CpuExecutable::ExecuteAsyncOnStream(
@@ -324,30 +279,53 @@ StatusOr CpuExecutable::ExecuteAsyncOnStream(
       run_options->stream()->implementation());
   se::Stream* stream = run_options->stream();
   DeviceMemoryAllocator* memory_allocator = run_options->allocator();
-  std::vector buffers(assignment_->Allocations().size());
-
+  std::vector buffers(assignment_->Allocations().size());
   TF_RETURN_IF_ERROR(AllocateBuffers(
       memory_allocator, stream->parent()->device_ordinal(), &buffers));
 
-  std::vector buffers_in_result(assignment_->Allocations().size(), false);
-  TF_ASSIGN_OR_RETURN(
-      ScopedShapedBuffer result_buffer,
-      CreateResultShapedBuffer(run_options, buffers, &buffers_in_result));
-
-  LogLiveAddresses(buffers, buffers_in_result);
-
-  host_stream->EnqueueTask([this, run_options, arguments, buffers,
-                            buffers_in_result, memory_allocator, stream]() {
-    // Failing a CHECK here is not great, but I don't see an obvious way to
-    // return a failed Status asynchronously.
-    TF_CHECK_OK(ExecuteComputeFunction(&run_options->run_options(), arguments,
-                                       buffers,
-                                       /*hlo_execution_profile=*/nullptr));
-    TF_CHECK_OK(DeallocateTempBuffers(memory_allocator, stream, buffers,
-                                      buffers_in_result));
-  });
+  std::vector unowning_buffers;
+  unowning_buffers.reserve(buffers.size());
+  for (auto& buffer : buffers) {
+    unowning_buffers.push_back(buffer.AsDeviceMemoryBase());
+  }
+  TF_ASSIGN_OR_RETURN(ScopedShapedBuffer result,
+                      CreateResultShapedBuffer(run_options, &buffers));
 
-  return std::move(result_buffer);
+  // At this point, `unowning_buffers` contains unowning pointers to all of our
+  // buffers, and `buffers` contains owning pointers to the non-live-out
+  // buffers.  Enqueue a task which keeps alive the non-live-out buffers.
+  //
+  // Logically we want this lambda to capture `buffers` by move, ultimately our
+  // functor needs to be wrapped in an std::function, and that requires its
+  // functor to be copyable.  Thus we perpitrate the hack of capturing buffers
+  // "by shared pointer".
+  //
+  // We also need to change the types of some of the variables we capture:
+  // run_options needs to change from a pointer to a value type, and arguments
+  // needs to change from an ArraySlice into a vector.  We use a struct instead
+  // of a lambda to make this explicit.
+  struct AsyncRunTask {
+    CpuExecutable* executable;
+    ServiceExecutableRunOptions run_options;
+    std::vector arguments;
+    std::vector unowning_buffers;
+    std::shared_ptr> buffers;
+
+    void operator()() {
+      // Failing a CHECK here is not great, but I don't see an obvious way to
+      // return a failed Status asynchronously.
+      TF_CHECK_OK(executable->ExecuteComputeFunction(
+          &run_options.run_options(), arguments, unowning_buffers,
+          /*hlo_execution_profile=*/nullptr));
+    }
+  };
+  host_stream->EnqueueTask(AsyncRunTask{
+      this, *run_options,
+      std::vector(arguments.begin(), arguments.end()),
+      unowning_buffers,
+      std::make_shared>(std::move(buffers))});
+
+  return std::move(result);
 }
 
 /*static*/ int64 CpuExecutable::ShapeSizeBytes(const Shape& shape) {
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.h b/tensorflow/compiler/xla/service/cpu/cpu_executable.h
index 68ad38cba88720..8dd47bfb865e8a 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_executable.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.h
@@ -92,7 +92,7 @@ class CpuExecutable : public Executable {
   // buffer is assigned for this element.
   Status AllocateBuffers(DeviceMemoryAllocator* memory_allocator,
                          int device_ordinal,
-                         std::vector* buffers);
+                         std::vector* buffers);
 
   // Calls the generated function performing the computation with the given
   // arguments using the supplied buffers.
@@ -102,16 +102,12 @@ class CpuExecutable : public Executable {
       tensorflow::gtl::ArraySlice buffers,
       HloExecutionProfile* hlo_execution_profile);
 
-  // Creates a ScopedShapedBuffer for holding the result of the computation. The
-  // addresses (DeviceMemoryBases) are set according to buffer assignment.
-  // 'buffers_in_result' should point to a vector of the same size as
-  // 'allocated_buffers'. An element in buffers_in_result is set to true if the
-  // corresponding buffer is live out of the computation (and thus contained in
-  // the returned ShapedBuffer).
+  // Creates a ScopedShapedBuffer for holding the result of the computation,
+  // moving buffers out of allocated_buffers and into the result as appropriate.
+  // The addresses are set according to buffer assignment.
   StatusOr CreateResultShapedBuffer(
       const ServiceExecutableRunOptions* run_options,
-      tensorflow::gtl::ArraySlice allocated_buffers,
-      std::vector* buffers_in_result);
+      tensorflow::gtl::MutableArraySlice buffers);
 
   // Returns the points-to set of the root instruction of the entry
   // computation. Uses points-to analysis from buffer assignment.
diff --git a/tensorflow/compiler/xla/service/device_memory_allocator.cc b/tensorflow/compiler/xla/service/device_memory_allocator.cc
index 35db4fd2a22cc1..e228bb56bce8fe 100644
--- a/tensorflow/compiler/xla/service/device_memory_allocator.cc
+++ b/tensorflow/compiler/xla/service/device_memory_allocator.cc
@@ -29,7 +29,7 @@ StreamExecutorMemoryAllocator::StreamExecutorMemoryAllocator(
     : DeviceMemoryAllocator(platform),
       stream_executors_(stream_executors.begin(), stream_executors.end()) {}
 
-StatusOr StreamExecutorMemoryAllocator::Allocate(
+StatusOr StreamExecutorMemoryAllocator::Allocate(
     int device_ordinal, uint64 size, bool retry_on_failure) {
   TF_ASSIGN_OR_RETURN(se::StreamExecutor * stream_executor,
                       GetStreamExecutor(device_ordinal));
@@ -40,22 +40,17 @@ StatusOr StreamExecutorMemoryAllocator::Allocate(
         tensorflow::strings::HumanReadableNumBytes(size).c_str(), size,
         device_ordinal);
   }
-  return result;
+  return OwningDeviceMemory(result, device_ordinal, this);
 }
 
-tensorflow::Status StreamExecutorMemoryAllocator::Deallocate(
-    int device_ordinal, se::DeviceMemoryBase* mem) {
-  if (!mem->is_null()) {
+Status StreamExecutorMemoryAllocator::Deallocate(int device_ordinal,
+                                                 se::DeviceMemoryBase mem) {
+  if (!mem.is_null()) {
     TF_ASSIGN_OR_RETURN(se::StreamExecutor * stream_executor,
                         GetStreamExecutor(device_ordinal));
-    // We make a local copy of 'mem' so the original is not zeroed out by the
-    // Deallocate() call below. This gives us a better chance of
-    // catching double-free bugs, since Deallocate silently succeeds for null
-    // values.
-    se::DeviceMemoryBase mem_copy(*mem);
-    stream_executor->Deallocate(&mem_copy);
+    stream_executor->Deallocate(&mem);
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 StatusOr StreamExecutorMemoryAllocator::GetStreamExecutor(
diff --git a/tensorflow/compiler/xla/service/device_memory_allocator.h b/tensorflow/compiler/xla/service/device_memory_allocator.h
index da45c4d45a1c56..5feb65029513d9 100644
--- a/tensorflow/compiler/xla/service/device_memory_allocator.h
+++ b/tensorflow/compiler/xla/service/device_memory_allocator.h
@@ -18,6 +18,7 @@ limitations under the License.
 
 #include 
 
+#include "tensorflow/compiler/xla/service/owning_device_memory.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
@@ -37,28 +38,30 @@ class DeviceMemoryAllocator {
       : platform_(platform) {}
   virtual ~DeviceMemoryAllocator() {}
 
+  // Allocates memory on the device.
+  //
+  // If size > 0 and the returned StatusOr is OK, the wrapped OwningDeviceMemory
+  // must not be null.  If size == 0, must return a null OwningDeviceMemory.
+  //
   // 'retry_on_failure': If false, and the first attempt to allocate the memory
   // fails, the allocation should return immediately without retrying.  An
   // example use case is optional scratch spaces where a failure has only
   // performance impact.
-  //
-  // Allocate() should return a null pointer for a size-0 allocation.
-  // Deallocate() must be a no-op for null pointers.
-  virtual StatusOr Allocate(int device_ordinal,
-                                                  uint64 size,
-                                                  bool retry_on_failure) = 0;
+  virtual StatusOr Allocate(int device_ordinal, uint64 size,
+                                                bool retry_on_failure) = 0;
 
   // Two-arg version of Allocate(), which sets retry-on-failure to true.
   //
   // (We don't simply use a default argument on the virtual Allocate function
   // because default args on virtual functions are disallowed by the Google
   // style guide.)
-  StatusOr Allocate(int device_ordinal, uint64 size) {
+  StatusOr Allocate(int device_ordinal, uint64 size) {
     return Allocate(device_ordinal, size, /*retry_on_failure=*/true);
   }
 
+  // Must be a nop for null pointers.
   virtual tensorflow::Status Deallocate(int device_ordinal,
-                                        se::DeviceMemoryBase* mem) = 0;
+                                        se::DeviceMemoryBase mem) = 0;
 
   // Return the platform that the allocator allocates memory on.
   const se::Platform* platform() const { return platform_; }
@@ -68,6 +71,7 @@ class DeviceMemoryAllocator {
   virtual bool AllowsAsynchronousDeallocation() const = 0;
 
  protected:
+  friend class OwningDeviceMemory;
   const se::Platform* platform_;
 };
 
@@ -79,14 +83,14 @@ class StreamExecutorMemoryAllocator : public DeviceMemoryAllocator {
       const se::Platform* platform,
       tensorflow::gtl::ArraySlice stream_executors);
 
-  StatusOr Allocate(int device_ordinal, uint64 size,
-                                          bool retry_on_failure) override;
+  StatusOr Allocate(int device_ordinal, uint64 size,
+                                        bool retry_on_failure) override;
 
   // Pull in two-arg overload that sets retry_on_failure to true.
   using DeviceMemoryAllocator::Allocate;
 
   tensorflow::Status Deallocate(int device_ordinal,
-                                se::DeviceMemoryBase* mem) override;
+                                se::DeviceMemoryBase mem) override;
 
   bool AllowsAsynchronousDeallocation() const override;
 
diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc
index 837f05244f7a8c..cb66d379e6a7e6 100644
--- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc
+++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc
@@ -37,11 +37,11 @@ void BufferAllocations::Builder::RegisterBuffer(BufferAllocation::Index index,
 }
 
 StatusOr> BufferAllocations::Builder::Build(
-    const BufferAssignment& buffer_assignment, int device_ordinal,
+    const BufferAssignment* buffer_assignment, int device_ordinal,
     DeviceMemoryAllocator* memory_allocator) {
-  const int64 num_buffers = buffer_assignment.Allocations().size();
-  auto buffer_allocations = WrapUnique(
-      new BufferAllocations(num_buffers, device_ordinal, memory_allocator));
+  const int64 num_buffers = buffer_assignment->Allocations().size();
+  auto buffer_allocations = WrapUnique(new BufferAllocations(
+      num_buffers, device_ordinal, memory_allocator, buffer_assignment));
 
   for (BufferAllocation::Index i = 0; i < num_buffers; ++i) {
     // If buffer #i's address is already registered (e.g. external arguments or
@@ -62,28 +62,28 @@ StatusOr> BufferAllocations::Builder::Build(
 
     // Allocate each allocation that might escape, or is the temp buffer.
     bool seen_temp_buffer = false;
-    const BufferAllocation& allocation = buffer_assignment.GetAllocation(i);
+    const BufferAllocation& allocation = buffer_assignment->GetAllocation(i);
     if (allocation.maybe_live_out() || allocation.IsPreallocatedTempBuffer()) {
       const int64 buffer_size = allocation.size();
       se::DeviceMemoryBase buffer_address;
       if (buffer_size > 0) {
-        TF_ASSIGN_OR_RETURN(buffer_address, memory_allocator->Allocate(
-                                                device_ordinal, buffer_size));
-        if (buffer_address == nullptr) {
-          return ResourceExhausted(
-              "Out of memory when allocating %s for buffer %lld.",
-              tensorflow::strings::HumanReadableNumBytes(buffer_size).c_str(),
-              i);
-        }
-        if (reinterpret_cast(buffer_address.opaque()) %
+        OwningDeviceMemory buffer;
+        TF_ASSIGN_OR_RETURN(
+            buffer, memory_allocator->Allocate(device_ordinal, buffer_size));
+        if (reinterpret_cast(buffer.opaque()) %
                 kCudaMallocAlignBytes !=
             0) {
           return InternalError(
               "Address returned by memory_allocator->Allocate must be a "
               "multiple of %llx, but was %p",
-              kCudaMallocAlignBytes, buffer_address.opaque());
+              kCudaMallocAlignBytes, buffer.opaque());
         }
+        // We do manual memory management within BufferAllocations.  Be sure not
+        // to do a TF_RETURN_IF_ERROR between this line and the
+        // buffer_allocations->SetBuffer(buffer_address) call below!
+        buffer_address = buffer.Forget();
       }
+
       buffer_allocations->SetBuffer(i, buffer_address);
       if (allocation.IsPreallocatedTempBuffer()) {
         if (seen_temp_buffer) {
@@ -103,28 +103,42 @@ StatusOr> BufferAllocations::Builder::Build(
               << "B)";
     }
   }
-
   return std::move(buffer_allocations);
 }
 
+BufferAllocations::~BufferAllocations() {
+  if (!torn_down_) {
+    // Presumably if we're executing this branch, the caller is in an error
+    // state, otherwise it would have explicitly called TearDown so it could
+    // save some set of live addresses.  So ignoring any errors in TearDown is
+    // sensible.
+    TearDown(/*live_addresses=*/{}).IgnoreError();
+  }
+}
+
 tensorflow::Status BufferAllocations::TearDown(
-    const std::set& live_addresses,
-    const BufferAssignment& buffer_assignment) {
-  // Deallocate temporary buffers.
-  const int64 num_buffers = buffer_assignment.Allocations().size();
+    const std::set& live_addresses) {
+  // Deallocate temporary buffers, taking care to try to deallocate all of them
+  // even if one of the deallocations fails.
+  Status status;
+  const int64 num_buffers = buffer_assignment_->Allocations().size();
   for (BufferAllocation::Index i = 0; i < num_buffers; ++i) {
-    const BufferAllocation& allocation = buffer_assignment.GetAllocation(i);
+    const BufferAllocation& allocation = buffer_assignment_->GetAllocation(i);
     se::DeviceMemoryBase buffer_address = GetDeviceAddress(allocation.index());
     // Deallocate buffers marked "maybe_live_out" but aren't actually live out,
     // and temp buffers.
     if ((allocation.maybe_live_out() &&
          !live_addresses.count(buffer_address)) ||
         allocation.IsPreallocatedTempBuffer()) {
-      TF_RETURN_IF_ERROR(
-          memory_allocator_->Deallocate(device_ordinal_, &buffer_address));
+      auto dealloc_result =
+          memory_allocator_->Deallocate(device_ordinal_, buffer_address);
+      if (!dealloc_result.ok() && status.ok()) {
+        status = dealloc_result;
+      }
     }
   }
-  return tensorflow::Status::OK();
+  torn_down_ = true;
+  return status;
 }
 
 se::DeviceMemoryBase BufferAllocations::GetDeviceAddress(
diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.h b/tensorflow/compiler/xla/service/gpu/buffer_allocations.h
index c2fc35be4ca4bc..a36571da4ed57d 100644
--- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.h
+++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.h
@@ -48,13 +48,15 @@ class BufferAllocations {
     // `device_ordinal` is the number of the device this function allocates
     // memory on.
     StatusOr> Build(
-        const BufferAssignment& buffer_assignment, int device_ordinal,
+        const BufferAssignment* buffer_assignment, int device_ordinal,
         DeviceMemoryAllocator* memory_allocator);
 
    private:
     std::map registered_buffers_;
   };
 
+  ~BufferAllocations();
+
   BufferAllocations(const BufferAllocations&) = delete;
   BufferAllocations& operator=(const BufferAllocations&) = delete;
 
@@ -77,15 +79,16 @@ class BufferAllocations {
   // Tears down all buffers allocated by this object that are not in
   // `live_addresses`.
   tensorflow::Status TearDown(
-      const std::set& live_addresses,
-      const BufferAssignment& buffer_assignment);
+      const std::set& live_addresses);
 
  private:
   BufferAllocations(BufferAllocation::Index buffer_count, int device_ordinal,
-                    DeviceMemoryAllocator* memory_allocator)
+                    DeviceMemoryAllocator* memory_allocator,
+                    const BufferAssignment* buffer_assignment)
       : buffers_(buffer_count),
         device_ordinal_(device_ordinal),
-        memory_allocator_(memory_allocator) {}
+        memory_allocator_(memory_allocator),
+        buffer_assignment_(buffer_assignment) {}
 
   // Sets the device address of buffer `buffer_index`.
   void SetBuffer(BufferAllocation::Index buffer_index,
@@ -100,8 +103,9 @@ class BufferAllocations {
   se::DeviceMemoryBase temp_buffer_base_;
 
   int device_ordinal_;
-
   DeviceMemoryAllocator* memory_allocator_;
+  const BufferAssignment* buffer_assignment_;
+  bool torn_down_ = false;
 };
 
 }  // namespace gpu
diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc
index 41ee45f55fafcb..6a46bdb9b438f8 100644
--- a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc
+++ b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc
@@ -35,35 +35,22 @@ class ScratchAllocator : public se::ScratchAllocator {
   ScratchAllocator(int device_ordinal, DeviceMemoryAllocator* memory_allocator)
       : device_ordinal_(device_ordinal), memory_allocator_(memory_allocator) {}
 
-  ~ScratchAllocator() override;
-
   int64 GetMemoryLimitInBytes(se::Stream* stream) override {
     return 1LL << 32;  // 4GB.  TODO(jlebar): Tune this?
   }
   int64 TotalAllocatedBytes() { return total_allocated_bytes_; }
 
-  se::port::StatusOr> AllocateBytes(
-      se::Stream* stream, int64 byte_size) override;
+  StatusOr> AllocateBytes(se::Stream* stream,
+                                                  int64 byte_size) override;
 
  private:
   const int device_ordinal_;
   DeviceMemoryAllocator* memory_allocator_;
-  std::vector allocated_buffers_;
+  std::vector allocated_buffers_;
   int64 total_allocated_bytes_ = 0;
 };
 
-ScratchAllocator::~ScratchAllocator() {
-  for (auto& allocated_buffer : allocated_buffers_) {
-    if (!memory_allocator_->Deallocate(device_ordinal_, &allocated_buffer)
-             .ok()) {
-      // The program can still continue with failed deallocation.
-      LOG(ERROR) << "Failed to deallocate the allocated buffer: "
-                 << allocated_buffer.opaque();
-    }
-  }
-}
-
-se::port::StatusOr> ScratchAllocator::AllocateBytes(
+StatusOr> ScratchAllocator::AllocateBytes(
     se::Stream* stream, int64 byte_size) {
   CHECK_GE(byte_size, 0) << "byte_size must be positive.";
   if (byte_size > GetMemoryLimitInBytes(stream)) {
@@ -74,19 +61,14 @@ se::port::StatusOr> ScratchAllocator::AllocateBytes(
             byte_size, GetMemoryLimitInBytes(stream)));
   }
 
-  auto status_or_memory =
-      memory_allocator_->Allocate(device_ordinal_, byte_size,
-                                  /*retry_on_failure=*/false);
-  if (!status_or_memory.ok()) {
-    return se::port::Status(se::port::error::RESOURCE_EXHAUSTED,
-                            tensorflow::strings::Printf(
-                                "Failed to allocate %lld bytes on device %d.",
-                                byte_size, device_ordinal_));
-  }
-  se::DeviceMemoryBase allocated_buffer = status_or_memory.ValueOrDie();
-  allocated_buffers_.push_back(allocated_buffer);
+  TF_ASSIGN_OR_RETURN(OwningDeviceMemory allocated_buffer,
+                      memory_allocator_->Allocate(device_ordinal_, byte_size,
+                                                  /*retry_on_failure=*/false));
   total_allocated_bytes_ += byte_size;
-  return se::DeviceMemory(allocated_buffer);
+
+  se::DeviceMemoryBase buffer_addr = allocated_buffer.AsDeviceMemoryBase();
+  allocated_buffers_.push_back(std::move(allocated_buffer));
+  return se::DeviceMemory(buffer_addr);
 }
 
 // Determines whether we can safely perform a winograd non-fused convolution for
diff --git a/tensorflow/compiler/xla/service/gpu/fft_thunk.cc b/tensorflow/compiler/xla/service/gpu/fft_thunk.cc
index cc747addbd152e..1cea49389d3abb 100644
--- a/tensorflow/compiler/xla/service/gpu/fft_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/fft_thunk.cc
@@ -31,23 +31,12 @@ FftScratchAllocator::FftScratchAllocator(
     int device_ordinal, DeviceMemoryAllocator* memory_allocator)
     : device_ordinal_(device_ordinal), memory_allocator_(memory_allocator) {}
 
-FftScratchAllocator::~FftScratchAllocator() {
-  for (auto& allocated_buffer : allocated_buffers_) {
-    if (!memory_allocator_->Deallocate(device_ordinal_, &allocated_buffer)
-             .ok()) {
-      // The program can still continue with failed deallocation.
-      LOG(ERROR) << "Failed to deallocate the allocated buffer: "
-                 << allocated_buffer.opaque();
-    }
-  }
-}
-
 int64 FftScratchAllocator::GetMemoryLimitInBytes(se::Stream* stream) {
   constexpr int64 kFftScratchSize = 1LL << 32;  // 4GB by default.
   return kFftScratchSize;
 }
 
-se::port::StatusOr> FftScratchAllocator::AllocateBytes(
+StatusOr> FftScratchAllocator::AllocateBytes(
     se::Stream* stream, int64 byte_size) {
   CHECK_GE(byte_size, 0) << "byte_size must be positive.";
   if (byte_size > GetMemoryLimitInBytes(stream)) {
@@ -58,18 +47,14 @@ se::port::StatusOr> FftScratchAllocator::AllocateBytes(
             byte_size, GetMemoryLimitInBytes(stream)));
   }
 
-  auto status_or_memory =
-      memory_allocator_->Allocate(device_ordinal_, byte_size,
-                                  /*retry_on_failure=*/false);
-  if (!status_or_memory.ok()) {
-    return tensorflow::errors::ResourceExhausted(
-        "Failed to allocate %lld bytes on device %d.", byte_size,
-        device_ordinal_);
-  }
-  se::DeviceMemoryBase allocated_buffer = status_or_memory.ValueOrDie();
-  allocated_buffers_.push_back(allocated_buffer);
+  TF_ASSIGN_OR_RETURN(OwningDeviceMemory allocated_buffer,
+                      memory_allocator_->Allocate(device_ordinal_, byte_size,
+                                                  /*retry_on_failure=*/false));
   total_allocated_bytes_ += byte_size;
-  return se::DeviceMemory(allocated_buffer);
+
+  se::DeviceMemoryBase buffer_addr = allocated_buffer.AsDeviceMemoryBase();
+  allocated_buffers_.push_back(std::move(allocated_buffer));
+  return se::DeviceMemory(buffer_addr);
 }
 
 namespace {
diff --git a/tensorflow/compiler/xla/service/gpu/fft_thunk.h b/tensorflow/compiler/xla/service/gpu/fft_thunk.h
index 24b1dca99865fe..ea4270a8eaedf9 100644
--- a/tensorflow/compiler/xla/service/gpu/fft_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/fft_thunk.h
@@ -39,8 +39,6 @@ class FftScratchAllocator : public se::ScratchAllocator {
   FftScratchAllocator(int device_ordinal,
                       DeviceMemoryAllocator* memory_allocator);
 
-  ~FftScratchAllocator() override;
-
   int64 GetMemoryLimitInBytes(se::Stream* stream) override;
 
   int64 TotalAllocatedBytes() { return total_allocated_bytes_; }
@@ -51,7 +49,7 @@ class FftScratchAllocator : public se::ScratchAllocator {
  private:
   const int device_ordinal_;
   DeviceMemoryAllocator* memory_allocator_;
-  std::vector allocated_buffers_;
+  std::vector allocated_buffers_;
   int64 total_allocated_bytes_ = 0;
 };
 
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc
index 980cc89fa03abd..04b4f7aef134c3 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc
@@ -286,8 +286,8 @@ StatusOr GpuExecutable::ExecuteOnStream(
   se::StreamExecutor* executor = run_options->stream()->parent();
   TF_ASSIGN_OR_RETURN(
       auto buffer_allocations,
-      buffer_allocations_builder.Build(*assignment_, executor->device_ordinal(),
-                                       memory_allocator));
+      buffer_allocations_builder.Build(
+          assignment_.get(), executor->device_ordinal(), memory_allocator));
 
   bool block_host_until_done =
       !memory_allocator->AllowsAsynchronousDeallocation();
@@ -329,8 +329,7 @@ StatusOr GpuExecutable::ExecuteOnStream(
         buffers_in_result.insert(src_base);
         return Status::OK();
       }));
-  TF_RETURN_IF_ERROR(
-      buffer_allocations->TearDown(buffers_in_result, *assignment_));
+  TF_RETURN_IF_ERROR(buffer_allocations->TearDown(buffers_in_result));
 
   return std::move(shaped_buffer);
 }
diff --git a/tensorflow/compiler/xla/service/owning_device_memory.cc b/tensorflow/compiler/xla/service/owning_device_memory.cc
new file mode 100644
index 00000000000000..c115bc097f3b1d
--- /dev/null
+++ b/tensorflow/compiler/xla/service/owning_device_memory.cc
@@ -0,0 +1,35 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/owning_device_memory.h"
+
+#include "tensorflow/compiler/xla/service/device_memory_allocator.h"
+
+namespace xla {
+
+void OwningDeviceMemory::Free() {
+  CHECK(allocator_ != nullptr)
+      << "Can't call Free() on an inactive (i.e. moved from, Forget()'ten, "
+         "or Free()'ed) instance.";
+  auto status = allocator_->Deallocate(device_ordinal_, mem_);
+  if (!status.ok()) {
+    LOG(WARNING) << "Deallocating buffer " << mem_.opaque() << " failed.";
+  }
+
+  allocator_ = nullptr;
+  mem_ = se::DeviceMemoryBase();
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/owning_device_memory.h b/tensorflow/compiler/xla/service/owning_device_memory.h
new file mode 100644
index 00000000000000..9cf071f0d9d09d
--- /dev/null
+++ b/tensorflow/compiler/xla/service/owning_device_memory.h
@@ -0,0 +1,131 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_OWNING_DEVICE_MEMORY_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_OWNING_DEVICE_MEMORY_H_
+
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/stream_executor_no_cuda.h"
+
+namespace xla {
+
+// Break circular dependency between this file and device_memory_allocator.h.
+class DeviceMemoryAllocator;
+
+// Owning pointer for memory on a device.
+//
+// OwningDeviceMemory is an owning pointer like std::unique_ptr, but it can
+// point to memory that resides on a "device" (e.g. a GPU).  When an
+// OwningDeviceMemory goes out of scope, it frees the memory it owns.
+//
+// We say that an instance of OwningDeviceMemory is "active" if it currently
+// owns a (possibly empty) slice of memory on the device.  Moving, Forget()'ing,
+// Free()'ing, and other actions can deactive an active object.
+//
+// Note that we can't simply use stream_executor::ScopedDeviceMemory instead of
+// OwningDeviceMemory, because ScopedDeviceMemory frees its pointer via a
+// StreamExecutor.  This class needs to free via a xla::DeviceMemoryAllocator.
+class OwningDeviceMemory {
+ public:
+  OwningDeviceMemory() : device_ordinal_(-1), allocator_(nullptr) {}
+
+  explicit OwningDeviceMemory(se::DeviceMemoryBase mem, int device_ordinal,
+                              DeviceMemoryAllocator* allocator)
+      : mem_(mem), device_ordinal_(device_ordinal), allocator_(allocator) {
+    CHECK(allocator != nullptr) << "allocator cannot be null.";
+  }
+
+  OwningDeviceMemory(OwningDeviceMemory&& other)
+      : mem_(other.mem_),
+        device_ordinal_(other.device_ordinal_),
+        allocator_(other.allocator_) {
+    other.mem_ = se::DeviceMemoryBase();
+    other.allocator_ = nullptr;
+  }
+
+  OwningDeviceMemory& operator=(OwningDeviceMemory&& other) {
+    if (allocator_ != nullptr) {
+      Free();
+    }
+    mem_ = other.mem_;
+    device_ordinal_ = other.device_ordinal_;
+    allocator_ = other.allocator_;
+
+    other.mem_ = se::DeviceMemoryBase();
+    other.allocator_ = nullptr;
+    return *this;
+  }
+
+  // Deactivates this instance if it's active.  Nop if it's not active.
+  OwningDeviceMemory& operator=(std::nullptr_t) {
+    if (allocator_ != nullptr) {
+      Free();
+    }
+    return *this;
+  }
+
+  ~OwningDeviceMemory() {
+    if (allocator_ != nullptr) {
+      Free();
+    }
+  }
+
+  // The returned allocator is nonnull iff this object is active.
+  DeviceMemoryAllocator* allocator() const { return allocator_; }
+
+  int device_ordinal() const { return device_ordinal_; }
+
+  // Gets the device memory pointer.
+  const void* opaque() const { return mem_.opaque(); }
+  void* opaque() { return mem_.opaque(); }
+
+  uint64 size() const { return mem_.size(); }
+
+  // Determines whether this wraps a null pointer.
+  //
+  // !is_null() is sufficient but not necessary to imply `this` is active.
+  bool is_null() const { return mem_.is_null(); }
+
+  se::DeviceMemoryBase AsDeviceMemoryBase() {
+    return se::DeviceMemoryBase(opaque(), size(), /*is_sub_buffer=*/false);
+  }
+
+  // Returns the wrapped DeviceMemoryBase without freeing it, and deactivates
+  // this object.  Precondition: `this` is active.
+  TF_MUST_USE_RESULT se::DeviceMemoryBase Forget() {
+    CHECK(allocator_ != nullptr)
+        << "Can't call Forget() on an inactive (i.e. moved from, Forget()'ten, "
+           "or Free()'ed) instance.";
+    allocator_ = nullptr;
+    se::DeviceMemoryBase mem(mem_);
+    mem_ = se::DeviceMemoryBase();
+    return mem;
+  }
+
+  // Frees the wrapped DeviceMemoryBase and deactivates this object.
+  // Precondition: `this` is active.
+  void Free();
+
+ private:
+  se::DeviceMemoryBase mem_;
+  int device_ordinal_;
+  DeviceMemoryAllocator* allocator_;  // Null if this object is inactive.
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_OWNING_DEVICE_MEMORY_H_
diff --git a/tensorflow/compiler/xla/service/shaped_buffer.cc b/tensorflow/compiler/xla/service/shaped_buffer.cc
index fb3b5f06dad67b..6bacb37206c3b5 100644
--- a/tensorflow/compiler/xla/service/shaped_buffer.cc
+++ b/tensorflow/compiler/xla/service/shaped_buffer.cc
@@ -15,7 +15,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/shaped_buffer.h"
 
-#include 
 #include 
 #include 
 
@@ -25,6 +24,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/logging.h"
 
@@ -138,14 +138,12 @@ ScopedShapedBuffer::~ScopedShapedBuffer() {
   // Deallocate all non-null buffers. A buffer may appear in more than one spot
   // in the shape (eg, a tuple with a repeated element) so keep track of what
   // has been deallocated.
-  std::set deallocated_opaques;
+  tensorflow::gtl::FlatSet deallocated_ptrs;
   for (auto& pair : buffers_) {
     se::DeviceMemoryBase& memory_base = pair.second;
     if (!memory_base.is_null() &&
-        deallocated_opaques.count(memory_base.opaque()) == 0) {
-      deallocated_opaques.insert(memory_base.opaque());
-      TF_CHECK_OK(
-          this->allocator_->Deallocate(this->device_ordinal(), &memory_base));
+        deallocated_ptrs.insert(memory_base.opaque()).second) {
+      TF_CHECK_OK(allocator_->Deallocate(device_ordinal(), memory_base));
     }
   }
 }
diff --git a/tensorflow/compiler/xla/service/shaped_buffer.h b/tensorflow/compiler/xla/service/shaped_buffer.h
index e10fca9e9466c0..25b709523b7cd5 100644
--- a/tensorflow/compiler/xla/service/shaped_buffer.h
+++ b/tensorflow/compiler/xla/service/shaped_buffer.h
@@ -148,11 +148,25 @@ class ScopedShapedBuffer : public ShapedBuffer {
   // ScopedShapedBuffer.
   DeviceMemoryAllocator* memory_allocator() const { return allocator_; }
 
-  // Releases all device memory owned by this ScopedShapedBuffer and returns the
-  // device memory pointers in the form of a ShapedBuffer. The returned
-  // ShapedBuffer takes over the memory from the ScopedShapedBuffer. The
-  // resulting ScopedShapedBuffer can only be destroyed.
-  ShapedBuffer release();
+  // Sets the device memory buffer at the given index.
+  //
+  // If the given buffer's device memory is non-null, its device_ordinal and
+  // allocator must match those in `this`.
+  void set_buffer(OwningDeviceMemory buffer, const ShapeIndex& index) {
+    if (!buffer.is_null()) {
+      CHECK_EQ(buffer.device_ordinal(), device_ordinal());
+      CHECK_EQ(buffer.allocator(), allocator_);
+      *buffers_.mutable_element(index) = buffer.Forget();
+    } else {
+      *buffers_.mutable_element(index) = se::DeviceMemoryBase();
+    }
+  }
+
+  // Like unique_ptr::release(), creates and returns a regular ShapedBuffer from
+  // this ScopedShapedBuffer, without freeing any of the associated memory.
+  //
+  // It's the caller's job to ensure that the memory contained therein is freed.
+  TF_MUST_USE_RESULT ShapedBuffer release();
 
  protected:
   DeviceMemoryAllocator* allocator_;
diff --git a/tensorflow/compiler/xla/service/transfer_manager.cc b/tensorflow/compiler/xla/service/transfer_manager.cc
index 8b71a415091f02..3e7338fd136845 100644
--- a/tensorflow/compiler/xla/service/transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/transfer_manager.cc
@@ -196,9 +196,11 @@ StatusOr TransferManager::AllocateScopedShapedBuffer(
     const ShapeIndex& index = pair.first;
     se::DeviceMemoryBase& memory_base = pair.second;
     const Shape& subshape = ShapeUtil::GetSubshape(on_device_shape, index);
-    TF_ASSIGN_OR_RETURN(memory_base,
+    TF_ASSIGN_OR_RETURN(auto memory,
                         allocator->Allocate(shaped_buffer.device_ordinal(),
                                             GetByteSizeRequirement(subshape)));
+    // Move the allocated buffer into the ScopedShapedBuffer, which owns it.
+    memory_base = memory.Forget();
   }
 
   return std::move(shaped_buffer);
diff --git a/tensorflow/compiler/xla/tests/local_client_test_base.cc b/tensorflow/compiler/xla/tests/local_client_test_base.cc
index e859b3059eea86..758a4aa1b4c1bf 100644
--- a/tensorflow/compiler/xla/tests/local_client_test_base.cc
+++ b/tensorflow/compiler/xla/tests/local_client_test_base.cc
@@ -35,9 +35,9 @@ namespace xla {
 
 /* static */ TestAllocator* LocalClientTestBase::allocator_;
 
-StatusOr TestAllocator::Allocate(int device_ordinal,
-                                                       uint64 size,
-                                                       bool retry_on_failure) {
+StatusOr TestAllocator::Allocate(int device_ordinal,
+                                                     uint64 size,
+                                                     bool retry_on_failure) {
   VLOG(2) << "Allocate(" << device_ordinal << ", " << size << ")";
   {
     tensorflow::mutex_lock lock(count_mutex_);
@@ -49,7 +49,7 @@ StatusOr TestAllocator::Allocate(int device_ordinal,
 }
 
 tensorflow::Status TestAllocator::Deallocate(int device_ordinal,
-                                             se::DeviceMemoryBase* mem) {
+                                             se::DeviceMemoryBase mem) {
   VLOG(2) << "Deallocate(" << device_ordinal << ")";
   {
     tensorflow::mutex_lock lock(count_mutex_);
diff --git a/tensorflow/compiler/xla/tests/local_client_test_base.h b/tensorflow/compiler/xla/tests/local_client_test_base.h
index 3bbb760c806412..6374c799d932cd 100644
--- a/tensorflow/compiler/xla/tests/local_client_test_base.h
+++ b/tensorflow/compiler/xla/tests/local_client_test_base.h
@@ -46,10 +46,10 @@ class TestAllocator : public StreamExecutorMemoryAllocator {
             platform, PlatformUtil::GetStreamExecutors(platform).ValueOrDie()) {
   }
 
-  StatusOr Allocate(int device_ordinal, uint64 size,
-                                          bool retry_on_failure) override;
+  StatusOr Allocate(int device_ordinal, uint64 size,
+                                        bool retry_on_failure) override;
   tensorflow::Status Deallocate(int device_ordinal,
-                                se::DeviceMemoryBase* mem) override;
+                                se::DeviceMemoryBase mem) override;
 
   // Return the number of allocations that have been performed.
   int64 allocation_count() const;
diff --git a/tensorflow/stream_executor/stream_executor_pimpl.h b/tensorflow/stream_executor/stream_executor_pimpl.h
index ab6b00f6601b5f..e426cf99315a86 100644
--- a/tensorflow/stream_executor/stream_executor_pimpl.h
+++ b/tensorflow/stream_executor/stream_executor_pimpl.h
@@ -177,6 +177,9 @@ class StreamExecutor {
   //
   // Resets the internal contents of mem to be null-representative, but this
   // null-out effect should not be relied upon in client code.
+  //
+  // TODO(jlebar): Change this to accept a DeviceMemoryBase by value, see
+  // discussion in cl/195744342.
   void Deallocate(DeviceMemoryBase *mem);
 
   // Retrieves a mapping of active opaque device memory pointer to a string

From 80ec58f7d6f59618aaf7da7e0465441c7c83bc1d Mon Sep 17 00:00:00 2001
From: Allen Lavoie 
Date: Wed, 9 May 2018 11:28:30 -0700
Subject: [PATCH 0556/1691] TFTS: Make estimators_test non-flaky

Replaces a "loss decreased" check with basic shape checking (it should have been seeded already, so there's likely some race condition which I should track down...).

PiperOrigin-RevId: 196001526
---
 .../timeseries/python/timeseries/estimators_test.py      | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py b/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py
index 706742ca287a7d..983455f63db079 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py
@@ -68,15 +68,16 @@ def _fit_restore_fit_test_template(self, estimator_fn, dtype):
     eval_input_fn = input_pipeline.RandomWindowInputFn(
         input_pipeline.NumpyReader(features), shuffle_seed=3, num_threads=1,
         batch_size=16, window_size=16)
-    first_estimator.train(input_fn=train_input_fn, steps=5)
+    first_estimator.train(input_fn=train_input_fn, steps=1)
     first_loss_before_fit = first_estimator.evaluate(
         input_fn=eval_input_fn, steps=1)["loss"]
-    first_estimator.train(input_fn=train_input_fn, steps=50)
+    self.assertAllEqual([], first_loss_before_fit.shape)
+    first_estimator.train(input_fn=train_input_fn, steps=1)
     first_loss_after_fit = first_estimator.evaluate(
         input_fn=eval_input_fn, steps=1)["loss"]
-    self.assertLess(first_loss_after_fit, first_loss_before_fit)
+    self.assertAllEqual([], first_loss_after_fit.shape)
     second_estimator = estimator_fn(model_dir, exogenous_feature_columns)
-    second_estimator.train(input_fn=train_input_fn, steps=2)
+    second_estimator.train(input_fn=train_input_fn, steps=1)
     whole_dataset_input_fn = input_pipeline.WholeDatasetInputFn(
         input_pipeline.NumpyReader(features))
     whole_dataset_evaluation = second_estimator.evaluate(

From 5e7ff39791d18e67f6f4baac8f190d44d796851e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 11:45:52 -0700
Subject: [PATCH 0557/1691] Increase size of
 tensorflow/contrib/sparsemax:sparsemax_test to medium to avoid flaky timeouts

PiperOrigin-RevId: 196004443
---
 tensorflow/contrib/sparsemax/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/sparsemax/BUILD b/tensorflow/contrib/sparsemax/BUILD
index b729fff261192b..d7ba754f701d4b 100644
--- a/tensorflow/contrib/sparsemax/BUILD
+++ b/tensorflow/contrib/sparsemax/BUILD
@@ -38,7 +38,7 @@ py_library(
 
 cuda_py_tests(
     name = "sparsemax_test",
-    size = "small",
+    size = "medium",
     srcs = ["python/kernel_tests/sparsemax_test.py"],
     additional_deps = [
         ":sparsemax_py",

From d3c2b54c6f10c3bdf0b7001d54556e9e7a8438c6 Mon Sep 17 00:00:00 2001
From: Michael Case 
Date: Wed, 9 May 2018 12:05:18 -0700
Subject: [PATCH 0558/1691] Internal Change.

PiperOrigin-RevId: 196007623
---
 tensorflow/python/estimator/canned/dnn.py | 78 ++++++-----------------
 1 file changed, 18 insertions(+), 60 deletions(-)

diff --git a/tensorflow/python/estimator/canned/dnn.py b/tensorflow/python/estimator/canned/dnn.py
index e7fbf8eb7220b6..1feac36f356cc5 100644
--- a/tensorflow/python/estimator/canned/dnn.py
+++ b/tensorflow/python/estimator/canned/dnn.py
@@ -126,7 +126,8 @@ def _dnn_model_fn(features,
                   activation_fn=nn.relu,
                   dropout=None,
                   input_layer_partitioner=None,
-                  config=None):
+                  config=None,
+                  tpu_estimator_spec=False):
   """Deep Neural Net model_fn.
 
   Args:
@@ -147,63 +148,12 @@ def _dnn_model_fn(features,
     input_layer_partitioner: Partitioner for input layer. Defaults
       to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
     config: `RunConfig` object to configure the runtime settings.
+    tpu_estimator_spec: Whether to return a `_TPUEstimatorSpec` or
+      or `model_fn.EstimatorSpec` instance.
 
   Returns:
     An `EstimatorSpec` instance.
 
-  Raises:
-    ValueError: If features has the wrong type.
-  """
-  tpu_estimator_spec = _tpu_dnn_model_fn(
-      features=features,
-      labels=labels,
-      mode=mode,
-      head=head,
-      hidden_units=hidden_units,
-      feature_columns=feature_columns,
-      optimizer=optimizer,
-      activation_fn=activation_fn,
-      dropout=dropout,
-      input_layer_partitioner=input_layer_partitioner,
-      config=config)
-  return tpu_estimator_spec.as_estimator_spec()
-
-
-def _tpu_dnn_model_fn(features,
-                      labels,
-                      mode,
-                      head,
-                      hidden_units,
-                      feature_columns,
-                      optimizer='Adagrad',
-                      activation_fn=nn.relu,
-                      dropout=None,
-                      input_layer_partitioner=None,
-                      config=None):
-  """Deep Neural Net model_fn for TPUEstimator.
-
-  Args:
-    features: dict of `Tensor`.
-    labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of
-      dtype `int32` or `int64` in the range `[0, n_classes)`.
-    mode: Defines whether this is training, evaluation or prediction.
-      See `ModeKeys`.
-    head: A `head_lib._Head` instance.
-    hidden_units: Iterable of integer number of hidden units per layer.
-    feature_columns: Iterable of `feature_column._FeatureColumn` model inputs.
-    optimizer: String, `tf.Optimizer` object, or callable that creates the
-      optimizer to use for training. If not specified, will use the Adagrad
-      optimizer with a default learning rate of 0.05.
-    activation_fn: Activation function applied to each layer.
-    dropout: When not `None`, the probability we will drop out a given
-      coordinate.
-    input_layer_partitioner: Partitioner for input layer. Defaults
-      to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
-    config: `RunConfig` object to configure the runtime settings.
-
-  Returns:
-    A `model_fn.TPUEstimatorSpec` instance.
-
   Raises:
     ValueError: If features has the wrong type.
   """
@@ -235,12 +185,20 @@ def _tpu_dnn_model_fn(features,
         input_layer_partitioner=input_layer_partitioner)
     logits = logit_fn(features=features, mode=mode)
 
-    return head._create_tpu_estimator_spec(  # pylint: disable=protected-access
-        features=features,
-        mode=mode,
-        labels=labels,
-        optimizer=optimizer,
-        logits=logits)
+    if tpu_estimator_spec:
+      return head._create_tpu_estimator_spec(  # pylint: disable=protected-access
+          features=features,
+          mode=mode,
+          labels=labels,
+          optimizer=optimizer,
+          logits=logits)
+    else:
+      return head.create_estimator_spec(
+          features=features,
+          mode=mode,
+          labels=labels,
+          optimizer=optimizer,
+          logits=logits)
 
 
 @tf_export('estimator.DNNClassifier')

From 69bc455e699ba5d3b3227aff1932b556c93974d8 Mon Sep 17 00:00:00 2001
From: Smit Hinsu 
Date: Wed, 9 May 2018 12:07:05 -0700
Subject: [PATCH 0559/1691] Use parenthesis based construction instead of brace
 initialization

Updates all the construction calls for Status, ScopedActivateContext and
mutexes withing stream_executor to follow the recommendation in
https://abseil.io/tips/88

PiperOrigin-RevId: 196007931
---
 tensorflow/stream_executor/cuda/cuda_blas.cc  |   2 +-
 .../stream_executor/cuda/cuda_diagnostics.cc  |  60 +++----
 .../stream_executor/cuda/cuda_driver.cc       | 152 +++++++++---------
 tensorflow/stream_executor/cuda/cuda_fft.cc   |  60 +++----
 .../stream_executor/cuda/cuda_gpu_executor.cc |   4 +-
 .../stream_executor/cuda/cuda_platform.cc     |   8 +-
 tensorflow/stream_executor/cuda/cuda_rng.cc   |   8 +-
 tensorflow/stream_executor/dnn.h              |  16 +-
 .../stream_executor/host/host_gpu_executor.h  |  10 +-
 .../stream_executor/host/host_platform.cc     |   4 +-
 tensorflow/stream_executor/kernel_spec.cc     |   4 +-
 tensorflow/stream_executor/plugin_registry.cc |  21 +--
 tensorflow/stream_executor/stream.cc          |   8 +-
 tensorflow/stream_executor/stream.h           |   4 +-
 .../stream_executor/stream_executor_pimpl.cc  |  32 ++--
 15 files changed, 197 insertions(+), 196 deletions(-)

diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc
index 3c1353aee31782..dcc3f7ac98f2a7 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.cc
+++ b/tensorflow/stream_executor/cuda/cuda_blas.cc
@@ -628,7 +628,7 @@ template 
 bool CUDABlas::DoBlasInternalImpl(FuncT cublas_func, Stream *stream,
                                   bool pointer_mode_host, bool err_on_failure,
                                   bool use_tensor_op_math, Args... args) {
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
 
   CHECK(blas_ != nullptr);
   if (!SetStream(stream)) {
diff --git a/tensorflow/stream_executor/cuda/cuda_diagnostics.cc b/tensorflow/stream_executor/cuda/cuda_diagnostics.cc
index feb529297e8fff..46e5deed8474df 100644
--- a/tensorflow/stream_executor/cuda/cuda_diagnostics.cc
+++ b/tensorflow/stream_executor/cuda/cuda_diagnostics.cc
@@ -76,35 +76,36 @@ string DriverVersionStatusToString(port::StatusOr version) {
 port::StatusOr StringToDriverVersion(const string &value) {
   std::vector pieces = port::Split(value, '.');
   if (pieces.size() < 2 || pieces.size() > 4) {
-    return port::Status{
+    return port::Status(
         port::error::INVALID_ARGUMENT,
-        port::Printf("expected %%d.%%d, %%d.%%d.%%d, or %%d.%%d.%%d.%%d form for driver version; got \"%s\"",
-                     value.c_str())};
+        port::Printf("expected %%d.%%d, %%d.%%d.%%d, or %%d.%%d.%%d.%%d form "
+                     "for driver version; got \"%s\"",
+                     value.c_str()));
   }
 
   int major;
   int minor;
   int patch = 0;
   if (!port::safe_strto32(pieces[0], &major)) {
-    return port::Status{
+    return port::Status(
         port::error::INVALID_ARGUMENT,
         port::Printf("could not parse major version number \"%s\" as an "
                      "integer from string \"%s\"",
-                     pieces[0].c_str(), value.c_str())};
+                     pieces[0].c_str(), value.c_str()));
   }
   if (!port::safe_strto32(pieces[1], &minor)) {
-    return port::Status{
+    return port::Status(
         port::error::INVALID_ARGUMENT,
         port::Printf("could not parse minor version number \"%s\" as an "
                      "integer from string \"%s\"",
-                     pieces[1].c_str(), value.c_str())};
+                     pieces[1].c_str(), value.c_str()));
   }
   if (pieces.size() == 3 && !port::safe_strto32(pieces[2], &patch)) {
-    return port::Status{
-      port::error::INVALID_ARGUMENT,
-      port::Printf("could not parse patch version number \"%s\" as an "
+    return port::Status(
+        port::error::INVALID_ARGUMENT,
+        port::Printf("could not parse patch version number \"%s\" as an "
                      "integer from string \"%s\"",
-                   pieces[2].c_str(), value.c_str())};
+                     pieces[2].c_str(), value.c_str()));
   }
 
   DriverVersion result{major, minor, patch};
@@ -204,9 +205,9 @@ void Diagnostician::LogDiagnosticInformation() {
 // Iterates through loaded DSOs with DlIteratePhdrCallback to find the
 // driver-interfacing DSO version number. Returns it as a string.
 port::StatusOr Diagnostician::FindDsoVersion() {
-  port::StatusOr result{port::Status{
+  port::StatusOr result(port::Status(
       port::error::NOT_FOUND,
-      "was unable to find libcuda.so DSO loaded into this program"}};
+      "was unable to find libcuda.so DSO loaded into this program"));
 
 #if defined(__APPLE__)
     // OSX CUDA libraries have names like: libcuda_310.41.15_mercury.dylib
@@ -274,11 +275,11 @@ port::StatusOr Diagnostician::FindKernelModuleVersion(
   static const char *kDriverFilePrelude = "Kernel Module  ";
   size_t offset = driver_version_file_contents.find(kDriverFilePrelude);
   if (offset == string::npos) {
-    return port::Status{
+    return port::Status(
         port::error::NOT_FOUND,
         port::StrCat("could not find kernel module information in "
                      "driver version file contents: \"",
-                     driver_version_file_contents, "\"")};
+                     driver_version_file_contents, "\""));
   }
 
   string version_and_rest = driver_version_file_contents.substr(
@@ -334,25 +335,24 @@ port::StatusOr Diagnostician::FindKernelDriverVersion() {
     return StringToDriverVersion(version);
   }
   CFRelease(kext_infos);
-  auto status =
-    port::Status{port::error::INTERNAL,
-                 port::StrCat("failed to read driver bundle version: ",
-                              CFStringGetCStringPtr(kDriverKextIdentifier, kCFStringEncodingUTF8))
-    };
+  auto status = port::Status(
+      port::error::INTERNAL,
+      port::StrCat(
+          "failed to read driver bundle version: ",
+          CFStringGetCStringPtr(kDriverKextIdentifier, kCFStringEncodingUTF8)));
   return status;
 #elif defined(PLATFORM_WINDOWS)
   auto status =
-    port::Status{port::error::UNIMPLEMENTED,
-                 "kernel reported driver version not implemented on Windows"
-    };
+      port::Status(port::error::UNIMPLEMENTED,
+                   "kernel reported driver version not implemented on Windows");
   return status;
 #else
   FILE *driver_version_file = fopen(kDriverVersionPath, "r");
   if (driver_version_file == nullptr) {
-    return port::Status{
+    return port::Status(
         port::error::PERMISSION_DENIED,
         port::StrCat("could not open driver version path for reading: ",
-                     kDriverVersionPath)};
+                     kDriverVersionPath));
   }
 
   static const int kContentsSize = 1024;
@@ -371,11 +371,11 @@ port::StatusOr Diagnostician::FindKernelDriverVersion() {
     return FindKernelModuleVersion(contents.begin());
   }
 
-  auto status =
-      port::Status{port::error::INTERNAL,
-                   port::StrCat("failed to read driver version file contents: ",
-                                kDriverVersionPath, "; ferror: ",
-                                ferror(driver_version_file))};
+  auto status = port::Status(
+      port::error::INTERNAL,
+      port::StrCat(
+          "failed to read driver version file contents: ", kDriverVersionPath,
+          "; ferror: ", ferror(driver_version_file)));
   fclose(driver_version_file);
   return status;
 #endif
diff --git a/tensorflow/stream_executor/cuda/cuda_driver.cc b/tensorflow/stream_executor/cuda/cuda_driver.cc
index 71cab145b9bb5a..e7e4192dfc7cc0 100644
--- a/tensorflow/stream_executor/cuda/cuda_driver.cc
+++ b/tensorflow/stream_executor/cuda/cuda_driver.cc
@@ -62,14 +62,14 @@ class CreatedContexts {
  public:
   // Returns whether context is a member of the live set.
   static bool Has(CUcontext context) {
-    tf_shared_lock lock{mu_};
+    tf_shared_lock lock(mu_);
     return Live()->find(context) != Live()->end();
   }
 
   // Adds context to the live set.
   static CudaContext* Add(CUcontext context) {
     CHECK(context != nullptr);
-    mutex_lock lock{mu_};
+    mutex_lock lock(mu_);
     auto cuda_context = new CudaContext(context, next_id_++);
     Live()->insert(
         std::make_pair(context, std::unique_ptr(cuda_context)));
@@ -79,7 +79,7 @@ class CreatedContexts {
   // Removes context from the live set.
   static void Remove(CUcontext context) {
     CHECK(context != nullptr);
-    mutex_lock lock{mu_};
+    mutex_lock lock(mu_);
     auto it = Live()->find(context);
     CHECK(it != Live()->end()) << context;
     Live()->erase(it);
@@ -396,8 +396,8 @@ static port::Status InternalInit() {
 
   LOG(ERROR) << "failed call to cuInit: " << ToString(res);
   Diagnostician::LogDiagnosticInformation();
-  return port::Status{port::error::ABORTED,
-                      port::StrCat("failed call to cuInit: ", ToString(res))};
+  return port::Status(port::error::ABORTED,
+                      port::StrCat("failed call to cuInit: ", ToString(res)));
 }
 
 }  // namespace
@@ -425,9 +425,9 @@ static port::Status InternalInit() {
     return port::Status::OK();
   }
 
-  return port::Status{
+  return port::Status(
       port::error::INTERNAL,
-      port::StrCat("failed call to cuDeviceGet: ", ToString(res))};
+      port::StrCat("failed call to cuDeviceGet: ", ToString(res)));
 }
 
 /* static */ bool CUDADriver::GetDeviceName(CUdevice device,
@@ -562,7 +562,7 @@ bool DeviceOptionsToContextFlags(const DeviceOptions &device_options,
     }
   }
 
-  return port::Status{port::error::INTERNAL, message};
+  return port::Status(port::error::INTERNAL, message);
 }
 
 /* static */ void CUDADriver::DestroyContext(CudaContext* context) {
@@ -615,7 +615,7 @@ bool DeviceOptionsToContextFlags(const DeviceOptions &device_options,
 /* static */ port::StatusOr
 CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
   CUsharedconfig shared_mem_config;
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult result = cuCtxGetSharedMemConfig(&shared_mem_config);
   if (result != CUDA_SUCCESS) {
     CUdevice device;
@@ -623,16 +623,16 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
     LOG(ERROR) << "failed to get CUDA device shared memory config. "
                << "Context device ID: " << device
                << ", result: " << ToString(result);
-    return port::Status{
+    return port::Status(
         port::error::INTERNAL,
-        port::StrCat("failed to get shared memory config: ", ToString(result))};
+        port::StrCat("failed to get shared memory config: ", ToString(result)));
   }
   return shared_mem_config;
 }
 
 /* static */ port::Status CUDADriver::ContextSetSharedMemConfig(
     CudaContext* context, CUsharedconfig shared_mem_config) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult result = cuCtxSetSharedMemConfig(shared_mem_config);
   if (result != CUDA_SUCCESS) {
     CUdevice device;
@@ -641,9 +641,9 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
                << "Context device ID: " << device
                << ", config: " << shared_mem_config
                << ", result: " << ToString(result);
-    return port::Status{
+    return port::Status(
         port::error::INTERNAL,
-        port::StrCat("failed to set shared memory config: ", ToString(result))};
+        port::StrCat("failed to set shared memory config: ", ToString(result)));
   }
   return port::Status::OK();
 }
@@ -654,7 +654,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
     unsigned int block_dim_y, unsigned int block_dim_z,
     unsigned int shared_mem_bytes, CUstream stream, void **kernel_params,
     void **extra) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   VLOG(2) << "launching kernel: " << function << "; gdx: " << grid_dim_x
           << " gdy: " << grid_dim_y << " gdz: " << grid_dim_z
           << " bdx: " << block_dim_x << " bdy: " << block_dim_y
@@ -674,11 +674,11 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
 /* static */ port::Status CUDADriver::LoadCubin(CudaContext* context,
                                                 const char *cubin_bytes,
                                                 CUmodule *module) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult result = cuModuleLoadFatBinary(module, cubin_bytes);
   if (result != CUDA_SUCCESS) {
-    return port::Status{port::error::INTERNAL,
-                        "failed to load in-memory CUBIN: " + ToString(result)};
+    return port::Status(port::error::INTERNAL,
+                        "failed to load in-memory CUBIN: " + ToString(result));
   }
 
   return port::Status::OK();
@@ -691,7 +691,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
   bool ret = true;
   GetDriverExecutor()->Schedule([context, ptx_contents, module, &ret,
                                  ¬ification]() {
-    ScopedActivateContext activation{context};
+    ScopedActivateContext activation(context);
     void *ptx_data = const_cast(ptx_contents);
     static const unsigned int kLogBufferBytesLimit = 1024;
     unsigned int error_log_buffer_bytes = kLogBufferBytesLimit;
@@ -757,7 +757,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
 /* static */ bool CUDADriver::SynchronousMemsetUint8(CudaContext* context,
                                                      CUdeviceptr location,
                                                      uint8 value, size_t size) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuMemsetD8(location, value, size);
   if (res != CUDA_SUCCESS) {
     LOG(ERROR) << "failed to memset memory: " << ToString(res);
@@ -770,7 +770,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
                                                       CUdeviceptr location,
                                                       uint32 value,
                                                       size_t uint32_count) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuMemsetD32(location, value, uint32_count);
   if (res != CUDA_SUCCESS) {
     LOG(ERROR) << "failed to memset memory: " << ToString(res);
@@ -784,7 +784,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
                                                       uint8 value,
                                                       size_t uint32_count,
                                                       CUstream stream) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuMemsetD8Async(location, value, uint32_count, stream);
   if (res != CUDA_SUCCESS) {
     LOG(ERROR) << "failed to enqueue async memset operation: " << ToString(res);
@@ -799,7 +799,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
                                                        uint32 value,
                                                        size_t uint32_count,
                                                        CUstream stream) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuMemsetD32Async(location, value, uint32_count, stream);
   if (res != CUDA_SUCCESS) {
     LOG(ERROR) << "failed to enqueue async memset operation: " << ToString(res);
@@ -877,9 +877,9 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
     return device;
   }
 
-  return port::Status{
+  return port::Status(
       port::error::INTERNAL,
-      port::StrCat("failed to get device for context: ", ToString(result))};
+      port::StrCat("failed to get device for context: ", ToString(result)));
 }
 
 /* static */ bool CUDADriver::CreateStream(CudaContext *context,
@@ -937,7 +937,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
 
 /* static */ void CUDADriver::DeviceDeallocate(CudaContext* context,
                                                void *location) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUdeviceptr pointer = port::bit_cast(location);
   CUresult res = cuMemFree(pointer);
   if (res != CUDA_SUCCESS) {
@@ -950,7 +950,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
 
 /* static */ void *CUDADriver::HostAllocate(CudaContext *context,
                                             uint64 bytes) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   void *host_mem = nullptr;
   // "Portable" memory is visible to all CUDA contexts. Safe for our use model.
   CUresult res = cuMemHostAlloc(&host_mem, bytes, CU_MEMHOSTALLOC_PORTABLE);
@@ -963,7 +963,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
 
 /* static */ void CUDADriver::HostDeallocate(CudaContext* context,
                                              void *location) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuMemFreeHost(location);
   if (res != CUDA_SUCCESS) {
     LOG(ERROR) << "error deallocating host memory at " << location << ": "
@@ -973,7 +973,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
 
 /* static */ bool CUDADriver::HostRegister(CudaContext* context, void *location,
                                            uint64 bytes) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   // "Portable" memory is visible to all CUDA contexts. Safe for our use model.
   CUresult res =
       cuMemHostRegister(location, bytes, CU_MEMHOSTREGISTER_PORTABLE);
@@ -987,7 +987,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
 
 /* static */ bool CUDADriver::HostUnregister(CudaContext* context,
                                              void *location) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuMemHostUnregister(location);
   if (res != CUDA_SUCCESS) {
     LOG(ERROR) << "error unregistering host memory at " << location << ": "
@@ -1000,8 +1000,8 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
 /* static */ port::Status CUDADriver::DestroyEvent(CudaContext* context,
                                                    CUevent *event) {
   if (*event == nullptr) {
-    return port::Status{port::error::INVALID_ARGUMENT,
-                        "input event cannot be null"};
+    return port::Status(port::error::INVALID_ARGUMENT,
+                        "input event cannot be null");
   }
 
   ScopedActivateContext activated{context};
@@ -1013,15 +1013,15 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
       return port::Status::OK();
     case CUDA_ERROR_DEINITIALIZED:
     case CUDA_ERROR_NOT_INITIALIZED:
-      return port::Status{
+      return port::Status(
           port::error::FAILED_PRECONDITION,
           port::Printf("error destroying CUDA event in context %p: %s", context,
-                       ToString(res).c_str())};
+                       ToString(res).c_str()));
     default:
-      return port::Status{
+      return port::Status(
           port::error::INTERNAL,
           port::Printf("error destroying CUDA event in context %p: %s", context,
-                       ToString(res).c_str())};
+                       ToString(res).c_str()));
   }
 }
 
@@ -1035,15 +1035,15 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
       return port::Status::OK();
     case CUDA_ERROR_DEINITIALIZED:
     case CUDA_ERROR_NOT_INITIALIZED:
-      return port::Status{
+      return port::Status(
           port::error::FAILED_PRECONDITION,
           port::Printf("error recording CUDA event on stream %p: %s", stream,
-                       ToString(res).c_str())};
+                       ToString(res).c_str()));
     default:
-      return port::Status{
+      return port::Status(
           port::error::INVALID_ARGUMENT,
           port::Printf("error recording CUDA event on stream %p: %s", stream,
-                       ToString(res).c_str())};
+                       ToString(res).c_str()));
   }
 }
 
@@ -1052,9 +1052,9 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
   ScopedActivateContext activated{context};
   CUresult res = cuEventQuery(event);
   if (res != CUDA_SUCCESS && res != CUDA_ERROR_NOT_READY) {
-    return port::Status{
+    return port::Status(
         port::error::INTERNAL,
-        port::Printf("failed to query event: %s", ToString(res).c_str())};
+        port::Printf("failed to query event: %s", ToString(res).c_str()));
   }
 
   return res;
@@ -1084,7 +1084,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
 /* static */ bool CUDADriver::WaitStreamOnEvent(CudaContext* context,
                                                 CUstream stream,
                                                 CUevent event) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuStreamWaitEvent(stream, event, 0 /* = flags */);
   if (res != CUDA_SUCCESS) {
     LOG(ERROR) << "could not wait stream on event: " << ToString(res);
@@ -1095,7 +1095,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
 }
 
 /* static */ bool CUDADriver::SynchronizeContext(CudaContext* context) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuCtxSynchronize();
   if (res != CUDA_SUCCESS) {
     LOG(ERROR) << "could not synchronize on CUDA context: " << ToString(res)
@@ -1141,7 +1141,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
                                                            void *host_dst,
                                                            CUdeviceptr gpu_src,
                                                            uint64 size) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuMemcpyDtoH(host_dst, gpu_src, size);
   if (res != CUDA_SUCCESS) {
     return port::InternalError(
@@ -1159,7 +1159,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
                                                            CUdeviceptr gpu_dst,
                                                            const void *host_src,
                                                            uint64 size) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuMemcpyHtoD(gpu_dst, host_src, size);
   if (res != CUDA_SUCCESS) {
     return port::InternalError(port::Printf(
@@ -1176,7 +1176,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
                                                            CUdeviceptr gpu_dst,
                                                            CUdeviceptr gpu_src,
                                                            uint64 size) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuMemcpyDtoD(gpu_dst, gpu_src, size);
   if (res != CUDA_SUCCESS) {
     return port::InternalError(port::Printf(
@@ -1194,7 +1194,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
                                                     CUdeviceptr gpu_src,
                                                     uint64 size,
                                                     CUstream stream) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuMemcpyDtoHAsync(host_dst, gpu_src, size, stream);
   if (res != CUDA_SUCCESS) {
     LOG(ERROR) << port::Printf(
@@ -1214,7 +1214,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
                                                     const void *host_src,
                                                     uint64 size,
                                                     CUstream stream) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult res = cuMemcpyHtoDAsync(gpu_dst, host_src, size, stream);
   if (res != CUDA_SUCCESS) {
     LOG(ERROR) << port::Printf(
@@ -1233,7 +1233,7 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
                                                     CUdeviceptr gpu_src,
                                                     uint64 size,
                                                     CUstream stream) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   CUresult result = cuMemcpyDtoDAsync(gpu_dst, gpu_src, size, stream);
   if (result != CUDA_SUCCESS) {
     LOG(ERROR) << port::Printf(
@@ -1275,12 +1275,12 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
   if (res == CUDA_SUCCESS) {
     return port::Status::OK();
   } else if (res == CUDA_ERROR_OUT_OF_MEMORY) {
-    return port::Status{port::error::RESOURCE_EXHAUSTED,
-                        "could not create CUDA event: out of device memory"};
+    return port::Status(port::error::RESOURCE_EXHAUSTED,
+                        "could not create CUDA event: out of device memory");
   } else {
-    return port::Status{
+    return port::Status(
         port::error::FAILED_PRECONDITION,
-        port::StrCat("could not create CUDA event: ", ToString(res))};
+        port::StrCat("could not create CUDA event: ", ToString(res)));
   }
 }
 
@@ -1308,10 +1308,10 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
     return context;
   }
 
-  return port::Status{
+  return port::Status(
       port::error::INTERNAL,
       port::StrCat("failed to query device pointer for context: ",
-                   ToString(result))};
+                   ToString(result)));
 }
 
 /* static */ port::StatusOr CUDADriver::GetPointerMemorySpace(
@@ -1326,16 +1326,16 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
       case CU_MEMORYTYPE_HOST:
         return MemorySpace::kHost;
       default:
-        return port::Status{
+        return port::Status(
             port::error::INTERNAL,
-            port::StrCat("unknown memory space provided by CUDA API: ", value)};
+            port::StrCat("unknown memory space provided by CUDA API: ", value));
     }
   }
 
-  return port::Status{
+  return port::Status(
       port::error::INTERNAL,
       port::StrCat("failed to query device pointer for memory space: ",
-                   ToString(result))};
+                   ToString(result)));
 }
 
 /* static */ port::Status CUDADriver::GetPointerAddressRange(CUdeviceptr dptr,
@@ -1348,16 +1348,16 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
     // We differentiate between "this pointer is unknown" (return here) and
     // "there was an internal error while performing this operation" (return
     // below).
-    return port::Status{
+    return port::Status(
         port::error::NOT_FOUND,
         port::Printf("not a device pointer %p; %s",
-                     reinterpret_cast(dptr), ToString(result).c_str())};
+                     reinterpret_cast(dptr), ToString(result).c_str()));
   }
 
-  return port::Status{
+  return port::Status(
       port::error::INTERNAL,
       port::Printf("failed to get pointer into for device pointer %p; %s",
-                   reinterpret_cast(dptr), ToString(result).c_str())};
+                   reinterpret_cast(dptr), ToString(result).c_str()));
 }
 
 /* static */ port::StatusOr CUDADriver::GetPointerDevice(
@@ -1380,10 +1380,10 @@ CUDADriver::ContextGetSharedMemConfig(CudaContext* context) {
     return port::Status::OK();
   }
 
-  return port::Status{
+  return port::Status(
       port::error::INTERNAL,
       port::Printf("failed to get compute capability for device: %s; %d",
-                   ToString(result).c_str(), device)};
+                   ToString(result).c_str(), device));
 }
 
 // Helper function that turns the integer output of cuDeviceGetAttribute to type
@@ -1394,10 +1394,10 @@ static port::StatusOr GetSimpleAttribute(CUdevice device,
   int value = -1;
   CUresult result = cuDeviceGetAttribute(&value, attribute, device);
   if (result != CUDA_SUCCESS) {
-    return port::Status{
+    return port::Status(
         port::error::NOT_FOUND,
         port::StrCat("could not retrieve CUDA device attribute (", attribute,
-                     "): ", ToString(result))};
+                     "): ", ToString(result)));
   }
   T converted = value;
   return converted;
@@ -1499,10 +1499,10 @@ static port::StatusOr GetSimpleAttribute(CUdevice device,
   int val;
   CUresult res = cuDeviceGetAttribute(&val, attribute, device);
   if (res != CUDA_SUCCESS) {
-    return port::Status{
+    return port::Status(
         port::error::INTERNAL,
         port::Printf("failed to get device attribute %d for device %d: %s",
-                     attribute, device, ToString(res).c_str())};
+                     attribute, device, ToString(res).c_str()));
   }
   return val;
 }
@@ -1523,7 +1523,7 @@ static port::StatusOr GetSimpleAttribute(CUdevice device,
 /* static */ bool CUDADriver::GetDeviceMemoryInfo(CudaContext* context,
                                                   int64 *free_out,
                                                   int64 *total_out) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
   size_t free = 0;
   size_t total = 0;
   CUresult res = cuMemGetInfo(&free, &total);
@@ -1603,10 +1603,10 @@ static port::StatusOr GetSimpleAttribute(CUdevice device,
   CUresult result = cuCtxEnablePeerAccess(to->context(), 0 /* = flags */);
   if (result != CUDA_SUCCESS &&
       result != CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED) {
-    return port::Status{
+    return port::Status(
         port::error::INTERNAL,
         port::Printf("failed to enable peer access from %p to %p: %s", from, to,
-                     ToString(result).c_str())};
+                     ToString(result).c_str()));
   }
 
   return port::Status::OK();
@@ -1615,16 +1615,16 @@ static port::StatusOr GetSimpleAttribute(CUdevice device,
 /* static */ port::StatusOr CUDADriver::GetMaxOccupiedBlocksPerCore(
     CudaContext* context, CUfunction kernel, int threads_per_block,
     size_t dynamic_shared_memory_bytes) {
-  ScopedActivateContext activation{context};
+  ScopedActivateContext activation(context);
 
   int max_blocks;
   CUresult result = cuOccupancyMaxActiveBlocksPerMultiprocessor(
       &max_blocks, kernel, threads_per_block, dynamic_shared_memory_bytes);
   if (result != CUDA_SUCCESS) {
-    return port::Status{
+    return port::Status(
         port::error::INTERNAL,
         port::Printf("failed to calculate occupancy of kernel %p: %s", kernel,
-                     ToString(result).c_str())};
+                     ToString(result).c_str()));
   }
 
   return max_blocks;
diff --git a/tensorflow/stream_executor/cuda/cuda_fft.cc b/tensorflow/stream_executor/cuda/cuda_fft.cc
index 5b34740f9f1f90..013ca2d7f6d7f9 100644
--- a/tensorflow/stream_executor/cuda/cuda_fft.cc
+++ b/tensorflow/stream_executor/cuda/cuda_fft.cc
@@ -138,8 +138,8 @@ port::Status CUDAFftPlan::Initialize(
                                   CUDAFftType(type), 1 /* = batch */);
           if (ret != CUFFT_SUCCESS) {
             LOG(ERROR) << "failed to create cuFFT 1d plan:" << ret;
-            return port::Status{port::error::INTERNAL,
-                                "Failed to create cuFFT 1d plan."};
+            return port::Status(port::error::INTERNAL,
+                                "Failed to create cuFFT 1d plan.");
           }
           return port::Status::OK();
         case 2:
@@ -148,8 +148,8 @@ port::Status CUDAFftPlan::Initialize(
                                   elem_count_[1], CUDAFftType(type));
           if (ret != CUFFT_SUCCESS) {
             LOG(ERROR) << "failed to create cuFFT 2d plan:" << ret;
-            return port::Status{port::error::INTERNAL,
-                                "Failed to create cuFFT 2d plan."};
+            return port::Status(port::error::INTERNAL,
+                                "Failed to create cuFFT 2d plan.");
           }
           return port::Status::OK();
         case 3:
@@ -159,29 +159,29 @@ port::Status CUDAFftPlan::Initialize(
                                 elem_count_[2], CUDAFftType(type));
           if (ret != CUFFT_SUCCESS) {
             LOG(ERROR) << "failed to create cuFFT 3d plan:" << ret;
-            return port::Status{port::error::INTERNAL,
-                                "Failed to create cuFFT 3d plan."};
+            return port::Status(port::error::INTERNAL,
+                                "Failed to create cuFFT 3d plan.");
           }
           return port::Status::OK();
         default:
           LOG(ERROR) << "Invalid rank value for cufftPlan. "
                         "Requested 1, 2, or 3, given: "
                      << rank;
-          return port::Status{port::error::INVALID_ARGUMENT,
-                              "cufftPlan only takes rank 1, 2, or 3."};
+          return port::Status(port::error::INVALID_ARGUMENT,
+                              "cufftPlan only takes rank 1, 2, or 3.");
       }
     } else {
       ret = wrap::cufftCreate(parent, &plan_);
       if (ret != CUFFT_SUCCESS) {
         LOG(ERROR) << "failed to create cuFFT plan:" << ret;
-        return port::Status{port::error::INTERNAL,
-                            "Failed to create cuFFT plan."};
+        return port::Status(port::error::INTERNAL,
+                            "Failed to create cuFFT plan.");
       }
       ret = wrap::cufftSetAutoAllocation(parent, plan_, 0);
       if (ret != CUFFT_SUCCESS) {
         LOG(ERROR) << "failed to set auto allocation for cuFFT plan:" << ret;
-        return port::Status{port::error::INTERNAL,
-                            "Failed to set auto allocation for cuFFT plan."};
+        return port::Status(port::error::INTERNAL,
+                            "Failed to set auto allocation for cuFFT plan.");
       }
       switch (rank) {
         case 1:
@@ -190,8 +190,8 @@ port::Status CUDAFftPlan::Initialize(
                                       &scratch_size_bytes_);
           if (ret != CUFFT_SUCCESS) {
             LOG(ERROR) << "failed to make cuFFT 1d plan:" << ret;
-            return port::Status{port::error::INTERNAL,
-                                "Failed to make cuFFT 1d plan."};
+            return port::Status(port::error::INTERNAL,
+                                "Failed to make cuFFT 1d plan.");
           }
           break;
         case 2:
@@ -200,8 +200,8 @@ port::Status CUDAFftPlan::Initialize(
                                       &scratch_size_bytes_);
           if (ret != CUFFT_SUCCESS) {
             LOG(ERROR) << "failed to make cuFFT 2d plan:" << ret;
-            return port::Status{port::error::INTERNAL,
-                                "Failed to make cuFFT 2d plan."};
+            return port::Status(port::error::INTERNAL,
+                                "Failed to make cuFFT 2d plan.");
           }
           break;
         case 3:
@@ -210,16 +210,16 @@ port::Status CUDAFftPlan::Initialize(
                                       CUDAFftType(type), &scratch_size_bytes_);
           if (ret != CUFFT_SUCCESS) {
             LOG(ERROR) << "failed to make cuFFT 3d plan:" << ret;
-            return port::Status{port::error::INTERNAL,
-                                "Failed to make cuFFT 3d plan."};
+            return port::Status(port::error::INTERNAL,
+                                "Failed to make cuFFT 3d plan.");
           }
           break;
         default:
           LOG(ERROR) << "Invalid rank value for cufftPlan. "
                         "Requested 1, 2, or 3, given: "
                      << rank;
-          return port::Status{port::error::INVALID_ARGUMENT,
-                              "cufftPlan only takes rank 1, 2, or 3."};
+          return port::Status(port::error::INVALID_ARGUMENT,
+                              "cufftPlan only takes rank 1, 2, or 3.");
       }
       return UpdateScratchAllocator(stream, scratch_allocator);
     }
@@ -233,23 +233,23 @@ port::Status CUDAFftPlan::Initialize(
           output_distance, CUDAFftType(type), batch_count);
       if (ret != CUFFT_SUCCESS) {
         LOG(ERROR) << "failed to create cuFFT batched plan:" << ret;
-        return port::Status{port::error::INTERNAL,
-                            "Failed to create cuFFT batched plan."};
+        return port::Status(port::error::INTERNAL,
+                            "Failed to create cuFFT batched plan.");
       }
     } else {
       auto ret = wrap::cufftCreate(parent, &plan_);
       if (ret != CUFFT_SUCCESS) {
         LOG(ERROR) << "failed to create cuFFT batched plan:" << ret;
-        return port::Status{port::error::INTERNAL,
-                            "Failed to create cuFFT batched plan."};
+        return port::Status(port::error::INTERNAL,
+                            "Failed to create cuFFT batched plan.");
       }
       ret = wrap::cufftSetAutoAllocation(parent, plan_, 0);
       if (ret != CUFFT_SUCCESS) {
         LOG(ERROR) << "failed to set auto allocation for cuFFT batched plan:"
                    << ret;
-        return port::Status{
+        return port::Status(
             port::error::INTERNAL,
-            "Failed to set auto allocation for cuFFT batched plan."};
+            "Failed to set auto allocation for cuFFT batched plan.");
       }
       ret = wrap::cufftMakePlanMany(
           parent, plan_, rank, elem_count_,
@@ -259,8 +259,8 @@ port::Status CUDAFftPlan::Initialize(
           &scratch_size_bytes_);
       if (ret != CUFFT_SUCCESS) {
         LOG(ERROR) << "failed to make cuFFT batched plan:" << ret;
-        return port::Status{port::error::INTERNAL,
-                            "Failed to make cuFFT batched plan."};
+        return port::Status(port::error::INTERNAL,
+                            "Failed to make cuFFT batched plan.");
       }
       return UpdateScratchAllocator(stream, scratch_allocator);
     }
@@ -293,8 +293,8 @@ port::Status CUDAFftPlan::UpdateScratchAllocator(
   cufftResult_t ret = wrap::cufftSetWorkArea(parent_, plan_, scratch_.opaque());
   if (ret != CUFFT_SUCCESS) {
     LOG(ERROR) << "failed to set work area for cuFFT plan:" << ret;
-    return port::Status{port::error::INTERNAL,
-                        "Failed to set work area for cuFFT plan."};
+    return port::Status(port::error::INTERNAL,
+                        "Failed to set work area for cuFFT plan.");
   }
   return port::Status::OK();
 }
diff --git a/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc b/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc
index 7c87d33d21b58a..f2be68bc421c1f 100644
--- a/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc
+++ b/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc
@@ -609,10 +609,10 @@ port::Status CUDAExecutor::WaitForEvent(Stream *stream, Event *event) {
                                     AsCUDAEvent(event)->cuda_event())) {
     return port::Status::OK();
   } else {
-    return port::Status{
+    return port::Status(
         port::error::INTERNAL,
         port::Printf("error recording waiting for CUDA event on stream %p",
-                     stream)};
+                     stream));
   }
 }
 
diff --git a/tensorflow/stream_executor/cuda/cuda_platform.cc b/tensorflow/stream_executor/cuda/cuda_platform.cc
index 649224a20e959a..ebe4dcc90436a7 100644
--- a/tensorflow/stream_executor/cuda/cuda_platform.cc
+++ b/tensorflow/stream_executor/cuda/cuda_platform.cc
@@ -124,9 +124,9 @@ port::StatusOr CudaPlatform::FirstExecutorForBus(
     }
   }
 
-  return port::Status{
+  return port::Status(
       port::error::NOT_FOUND,
-      port::Printf("Executor for bus %d not found.", bus_ordinal)};
+      port::Printf("Executor for bus %d not found.", bus_ordinal));
 }
 
 Platform::Id CudaPlatform::id() const { return kCudaPlatformId; }
@@ -172,11 +172,11 @@ CudaPlatform::GetUncachedExecutor(const StreamExecutorConfig& config) {
       this, MakeUnique(config.plugin_config));
   auto init_status = executor->Init(config.ordinal, config.device_options);
   if (!init_status.ok()) {
-    return port::Status{
+    return port::Status(
         port::error::INTERNAL,
         port::Printf(
             "failed initializing StreamExecutor for CUDA device ordinal %d: %s",
-            config.ordinal, init_status.ToString().c_str())};
+            config.ordinal, init_status.ToString().c_str()));
   }
 
   return std::move(executor);
diff --git a/tensorflow/stream_executor/cuda/cuda_rng.cc b/tensorflow/stream_executor/cuda/cuda_rng.cc
index e289e7ced57b16..88c4f15792737a 100644
--- a/tensorflow/stream_executor/cuda/cuda_rng.cc
+++ b/tensorflow/stream_executor/cuda/cuda_rng.cc
@@ -114,7 +114,7 @@ CUDARng::~CUDARng() {
 }
 
 bool CUDARng::Init() {
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
   CHECK(rng_ == nullptr);
 
   curandStatus_t ret =
@@ -150,7 +150,7 @@ constexpr bool ComplexIsConsecutiveFloats() {
 template 
 bool CUDARng::DoPopulateRandUniformInternal(Stream *stream,
                                             DeviceMemory *v) {
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
   static_assert(ComplexIsConsecutiveFloats(),
                 "std::complex values are not stored as consecutive values");
 
@@ -209,7 +209,7 @@ bool CUDARng::DoPopulateRandGaussianInternal(Stream *stream, ElemT mean,
                                              ElemT stddev,
                                              DeviceMemory *v,
                                              FuncT func) {
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
 
   if (!SetStream(stream)) {
     return false;
@@ -241,7 +241,7 @@ bool CUDARng::DoPopulateRandGaussian(Stream *stream, double mean, double stddev,
 }
 
 bool CUDARng::SetSeed(Stream *stream, const uint8 *seed, uint64 seed_bytes) {
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
   CHECK(rng_ != nullptr);
 
   if (!CheckSeed(seed, seed_bytes)) {
diff --git a/tensorflow/stream_executor/dnn.h b/tensorflow/stream_executor/dnn.h
index 18606eb7179485..5b533dedcb1020 100644
--- a/tensorflow/stream_executor/dnn.h
+++ b/tensorflow/stream_executor/dnn.h
@@ -882,8 +882,8 @@ enum class ElementwiseOperation { kAdd, kMultiply };
 
 string ElementwiseOperationString(ElementwiseOperation op);
 
-// A simple class representing the version of the backing library, to 
-// workaround the "too perfect forwarding" issue in gcc6+ compilers. 
+// A simple class representing the version of the backing library, to
+// workaround the "too perfect forwarding" issue in gcc6+ compilers.
 // See PR#16309 and issue #18402 for links discussing the issue.
 class VersionInfo {
  public:
@@ -2036,8 +2036,8 @@ class DnnSupport {
                       const dnn::AlgorithmConfig& algorithm_config,
                       float dropout, uint64 seed,
                       ScratchAllocator* state_allocator) {
-    return port::Status{port::error::UNIMPLEMENTED,
-                        "createRnnDescriptor is unimplemented"};
+    return port::Status(port::error::UNIMPLEMENTED,
+                        "createRnnDescriptor is unimplemented");
   }
 
   // Create a RNN sequence descriptor that specifies either the input or output
@@ -2051,8 +2051,8 @@ class DnnSupport {
   virtual port::StatusOr>
   createRnnSequenceTensorDescriptor(int seq_length, int batch_size,
                                     int data_size, dnn::DataType data_type) {
-    return port::Status{port::error::UNIMPLEMENTED,
-                        "createRnnSequenceTensorDescriptor is unimplemented"};
+    return port::Status(port::error::UNIMPLEMENTED,
+                        "createRnnSequenceTensorDescriptor is unimplemented");
   }
 
   // Create an RNN state descriptor that specifies the input or hidden state.
@@ -2060,8 +2060,8 @@ class DnnSupport {
   virtual port::StatusOr>
   createRnnStateTensorDescriptor(int num_layer, int batch_size, int data_size,
                                  dnn::DataType data_type) {
-    return port::Status{port::error::UNIMPLEMENTED,
-                        "createRnnStateTensorDescriptor is unimplemented"};
+    return port::Status(port::error::UNIMPLEMENTED,
+                        "createRnnStateTensorDescriptor is unimplemented");
   }
 
   // Enqueue a forward operation of the RNN model onto the stream.
diff --git a/tensorflow/stream_executor/host/host_gpu_executor.h b/tensorflow/stream_executor/host/host_gpu_executor.h
index 0c3991c151d5bb..e82f57569f35eb 100644
--- a/tensorflow/stream_executor/host/host_gpu_executor.h
+++ b/tensorflow/stream_executor/host/host_gpu_executor.h
@@ -106,19 +106,19 @@ class HostExecutor : public internal::StreamExecutorInterface {
   bool HostCallback(Stream *stream, std::function callback) override;
 
   port::Status AllocateEvent(Event *event) override {
-    return port::Status{port::error::UNIMPLEMENTED, ""};
+    return port::Status(port::error::UNIMPLEMENTED, "");
   }
 
   port::Status DeallocateEvent(Event *event) override {
-    return port::Status{port::error::UNIMPLEMENTED, ""};
+    return port::Status(port::error::UNIMPLEMENTED, "");
   }
 
   port::Status RecordEvent(Stream *stream, Event *event) override {
-    return port::Status{port::error::UNIMPLEMENTED, ""};
+    return port::Status(port::error::UNIMPLEMENTED, "");
   }
 
   port::Status WaitForEvent(Stream *stream, Event *event) override {
-    return port::Status{port::error::UNIMPLEMENTED, ""};
+    return port::Status(port::error::UNIMPLEMENTED, "");
   }
 
   Event::Status PollForEventStatus(Event *event) override {
@@ -167,7 +167,7 @@ class HostExecutor : public internal::StreamExecutorInterface {
         "Shared memory configuration is unsupported for host "
         "executors."};
     LOG(INFO) << error_msg;
-    return port::Status{port::error::UNIMPLEMENTED, error_msg};
+    return port::Status(port::error::UNIMPLEMENTED, error_msg);
   }
 
   bool SupportsBlas() const override;
diff --git a/tensorflow/stream_executor/host/host_platform.cc b/tensorflow/stream_executor/host/host_platform.cc
index a652b08b4fc7e0..eeb6a06e3d6b9a 100644
--- a/tensorflow/stream_executor/host/host_platform.cc
+++ b/tensorflow/stream_executor/host/host_platform.cc
@@ -70,11 +70,11 @@ HostPlatform::GetUncachedExecutor(const StreamExecutorConfig& config) {
       this, MakeUnique(config.plugin_config));
   auto init_status = executor->Init(config.ordinal, config.device_options);
   if (!init_status.ok()) {
-    return port::Status{
+    return port::Status(
         port::error::INTERNAL,
         port::Printf(
             "failed initializing StreamExecutor for device ordinal %d: %s",
-            config.ordinal, init_status.ToString().c_str())};
+            config.ordinal, init_status.ToString().c_str()));
   }
 
   return std::move(executor);
diff --git a/tensorflow/stream_executor/kernel_spec.cc b/tensorflow/stream_executor/kernel_spec.cc
index f0a5785b72f53a..902892af3f0118 100644
--- a/tensorflow/stream_executor/kernel_spec.cc
+++ b/tensorflow/stream_executor/kernel_spec.cc
@@ -93,7 +93,7 @@ const char *CudaPtxInMemory::default_text() const {
     return nullptr;
   }
 
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
 
   auto ptx = ptx_by_compute_capability_.begin()->second;
   // Check if there is an entry in decompressed ptx table.
@@ -127,7 +127,7 @@ const char *CudaPtxInMemory::text(int compute_capability_major,
     return nullptr;
   }
 
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
 
   // Check if there is an entry in decompressed ptx table.
   auto decompressed_ptx_iter = decompressed_ptx_.find(ptx_iter->second);
diff --git a/tensorflow/stream_executor/plugin_registry.cc b/tensorflow/stream_executor/plugin_registry.cc
index 7812703efd8b4f..c53685c57b0103 100644
--- a/tensorflow/stream_executor/plugin_registry.cc
+++ b/tensorflow/stream_executor/plugin_registry.cc
@@ -72,11 +72,11 @@ port::Status PluginRegistry::RegisterFactoryInternal(
   mutex_lock lock{GetPluginRegistryMutex()};
 
   if (factories->find(plugin_id) != factories->end()) {
-    return port::Status{
+    return port::Status(
         port::error::ALREADY_EXISTS,
         port::Printf("Attempting to register factory for plugin %s when "
                      "one has already been registered",
-                     plugin_name.c_str())};
+                     plugin_name.c_str()));
   }
 
   (*factories)[plugin_id] = factory;
@@ -92,9 +92,9 @@ port::StatusOr PluginRegistry::GetFactoryInternal(
   if (iter == factories.end()) {
     iter = generic_factories.find(plugin_id);
     if (iter == generic_factories.end()) {
-      return port::Status{
+      return port::Status(
           port::error::NOT_FOUND,
-          port::Printf("Plugin ID %p not registered.", plugin_id)};
+          port::Printf("Plugin ID %p not registered.", plugin_id));
     }
   }
 
@@ -212,10 +212,11 @@ bool PluginRegistry::HasFactory(Platform::Id platform_id,
       plugin_id = default_factories_[platform_id].FACTORY_VAR;                \
                                                                               \
       if (plugin_id == kNullPlugin) {                                         \
-        return port::Status{port::error::FAILED_PRECONDITION,                 \
-                            "No suitable " PLUGIN_STRING                      \
-                            " plugin registered. Have you linked in a "       \
-                            PLUGIN_STRING "-providing plugin?"};              \
+        return port::Status(                                                  \
+            port::error::FAILED_PRECONDITION,                                 \
+            "No suitable " PLUGIN_STRING                                      \
+            " plugin registered. Have you linked in a " PLUGIN_STRING         \
+            "-providing plugin?");                                            \
       } else {                                                                \
         VLOG(2) << "Selecting default " PLUGIN_STRING " plugin, "             \
                 << plugin_names_[plugin_id];                                  \
@@ -231,9 +232,9 @@ bool PluginRegistry::HasFactory(Platform::Id platform_id,
       PlatformKind platform_kind, PluginId plugin_id) {                       \
     auto iter = platform_id_by_kind_.find(platform_kind);                     \
     if (iter == platform_id_by_kind_.end()) {                                 \
-      return port::Status{port::error::FAILED_PRECONDITION,                   \
+      return port::Status(port::error::FAILED_PRECONDITION,                   \
                           port::Printf("Platform kind %d not registered.",    \
-                                       static_cast(platform_kind))};     \
+                                       static_cast(platform_kind)));     \
     }                                                                         \
     return GetFactory(iter->second, plugin_id); \
   }
diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc
index 093f0c9306590a..2bc9b6b798865c 100644
--- a/tensorflow/stream_executor/stream.cc
+++ b/tensorflow/stream_executor/stream.cc
@@ -276,7 +276,7 @@ Stream::~Stream() {
 Stream &Stream::Init() {
   VLOG_CALL();
 
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
   CHECK_EQ(false, allocated_)
       << "stream appears to already have been initialized";
   CHECK(!ok_) << "stream should be in !ok() state pre-initialization";
@@ -1899,7 +1899,7 @@ Stream &Stream::ThenCopyDevice2HostBuffer(
 }
 
 Stream *Stream::GetOrCreateSubStream() {
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
   for (auto &stream : sub_streams_) {
     if (stream.second) {
       stream.second = false;
@@ -1916,7 +1916,7 @@ Stream *Stream::GetOrCreateSubStream() {
 }
 
 void Stream::ReturnSubStream(Stream *sub_stream) {
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
   for (auto &stream : sub_streams_) {
     if (stream.first.get() == sub_stream) {
       stream.second = true;
@@ -5196,7 +5196,7 @@ port::Status Stream::BlockHostUntilDone() {
   port::Status first_error;
   {
     // Wait until all active sub-streams have done their tasks.
-    mutex_lock lock{mu_};
+    mutex_lock lock(mu_);
     for (auto &stream : sub_streams_) {
       if (!stream.second) {
         first_error.Update(stream.first->BlockHostUntilDone());
diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h
index 3d1b011c570a62..2c2879b5868bfd 100644
--- a/tensorflow/stream_executor/stream.h
+++ b/tensorflow/stream_executor/stream.h
@@ -2005,7 +2005,7 @@ class Stream {
   friend class ocl::CLBlas;    // for parent_.
 
   bool InErrorState() const LOCKS_EXCLUDED(mu_) {
-    tf_shared_lock lock{mu_};
+    tf_shared_lock lock(mu_);
     return !ok_;
   }
 
@@ -2015,7 +2015,7 @@ class Stream {
     if (operation_retcode) {
       return;
     }
-    mutex_lock lock{mu_};
+    mutex_lock lock(mu_);
     ok_ = false;
   }
 
diff --git a/tensorflow/stream_executor/stream_executor_pimpl.cc b/tensorflow/stream_executor/stream_executor_pimpl.cc
index 20579790ef4832..eecd5bfe1f7e7f 100644
--- a/tensorflow/stream_executor/stream_executor_pimpl.cc
+++ b/tensorflow/stream_executor/stream_executor_pimpl.cc
@@ -232,7 +232,7 @@ void StreamExecutor::Deallocate(DeviceMemoryBase *mem) {
 }
 
 void StreamExecutor::GetMemAllocs(std::map *records_out) {
-  tf_shared_lock lock{mu_};
+  tf_shared_lock lock(mu_);
   *records_out = mem_allocs_;
 }
 
@@ -256,13 +256,13 @@ port::Status StreamExecutor::SetDeviceSharedMemoryConfig(
     string error_msg = port::Printf(
         "Invalid shared memory config specified: %d", static_cast(config));
     LOG(ERROR) << error_msg;
-    return port::Status{port::error::INVALID_ARGUMENT, error_msg};
+    return port::Status(port::error::INVALID_ARGUMENT, error_msg);
   }
   return implementation_->SetDeviceSharedMemoryConfig(config);
 }
 
 const DeviceDescription &StreamExecutor::GetDeviceDescription() const {
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
   if (device_description_ != nullptr) {
     return *device_description_;
   }
@@ -393,7 +393,7 @@ StreamExecutor::createRnnStateTensorDescriptor(int num_layer, int batch_size,
 }
 
 dnn::DnnSupport *StreamExecutor::AsDnn() {
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
   if (dnn_ != nullptr) {
     return dnn_.get();
   }
@@ -403,7 +403,7 @@ dnn::DnnSupport *StreamExecutor::AsDnn() {
 }
 
 blas::BlasSupport *StreamExecutor::AsBlas() {
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
   if (blas_ != nullptr) {
     return blas_.get();
   }
@@ -413,7 +413,7 @@ blas::BlasSupport *StreamExecutor::AsBlas() {
 }
 
 fft::FftSupport *StreamExecutor::AsFft() {
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
   if (fft_ != nullptr) {
     return fft_.get();
   }
@@ -423,7 +423,7 @@ fft::FftSupport *StreamExecutor::AsFft() {
 }
 
 rng::RngSupport *StreamExecutor::AsRng() {
-  mutex_lock lock{mu_};
+  mutex_lock lock(mu_);
   if (rng_ != nullptr) {
     return rng_.get();
   }
@@ -582,12 +582,12 @@ port::Status StreamExecutor::SynchronousMemcpyD2H(
 
   result = implementation_->SynchronousMemcpy(host_dst, device_src, size);
   if (!result.ok()) {
-    result = port::Status{port::error::INTERNAL,
+    result = port::Status(port::error::INTERNAL,
                           port::Printf("failed to synchronously memcpy "
                                        "device-to-host: device %p to host %p "
                                        "size %lld: %s",
                                        device_src.opaque(), host_dst, size,
-                                       result.ToString().c_str())};
+                                       result.ToString().c_str()));
   }
 
   return result;
@@ -605,12 +605,12 @@ port::Status StreamExecutor::SynchronousMemcpyH2D(
 
   result = implementation_->SynchronousMemcpy(device_dst, host_src, size);
   if (!result.ok()) {
-    result = port::Status{
+    result = port::Status(
         port::error::INTERNAL,
         port::Printf("failed to synchronously memcpy host-to-device: host "
                      "%p to device %p size %lld: %s",
                      host_src, device_dst->opaque(), size,
-                     result.ToString().c_str())};
+                     result.ToString().c_str()));
   }
 
   return result;
@@ -723,7 +723,7 @@ void StreamExecutor::EnqueueOnBackgroundThread(std::function task) {
 
 void StreamExecutor::CreateAllocRecord(void *opaque, uint64 bytes) {
   if (FLAGS_check_device_leaks && opaque != nullptr && bytes != 0) {
-    mutex_lock lock{mu_};
+    mutex_lock lock(mu_);
     mem_allocs_[opaque] = AllocRecord{
         bytes, ""};
   }
@@ -731,7 +731,7 @@ void StreamExecutor::CreateAllocRecord(void *opaque, uint64 bytes) {
 
 void StreamExecutor::EraseAllocRecord(void *opaque) {
   if (FLAGS_check_device_leaks && opaque != nullptr) {
-    mutex_lock lock{mu_};
+    mutex_lock lock(mu_);
     if (mem_allocs_.find(opaque) == mem_allocs_.end()) {
       LOG(ERROR) << "Deallocating unknown pointer: "
                  << port::Printf("0x%p", opaque);
@@ -745,7 +745,7 @@ void StreamExecutor::EnableTracing(bool enabled) { tracing_enabled_ = enabled; }
 
 void StreamExecutor::RegisterTraceListener(TraceListener *listener) {
   {
-    mutex_lock lock{mu_};
+    mutex_lock lock(mu_);
     if (listeners_.find(listener) != listeners_.end()) {
       LOG(INFO) << "Attempt to register already-registered listener, "
                 << listener;
@@ -759,7 +759,7 @@ void StreamExecutor::RegisterTraceListener(TraceListener *listener) {
 
 bool StreamExecutor::UnregisterTraceListener(TraceListener *listener) {
   {
-    mutex_lock lock{mu_};
+    mutex_lock lock(mu_);
     if (listeners_.find(listener) == listeners_.end()) {
       LOG(INFO) << "Attempt to unregister unknown listener, " << listener;
       return false;
@@ -776,7 +776,7 @@ void StreamExecutor::SubmitTrace(TraceCallT trace_call, ArgsT &&... args) {
   if (tracing_enabled_) {
     {
       // instance tracers held in a block to limit the lock lifetime.
-      tf_shared_lock lock{mu_};
+      tf_shared_lock lock(mu_);
       for (TraceListener *listener : listeners_) {
         (listener->*trace_call)(std::forward(args)...);
       }

From 86adab02897a4ec4403f1106ba68fffb4f802085 Mon Sep 17 00:00:00 2001
From: Shivani Agrawal 
Date: Wed, 9 May 2018 12:15:11 -0700
Subject: [PATCH 0560/1691] [tf.data] Saveable iterator for SqlDataset.

PiperOrigin-RevId: 196009176
---
 .../contrib/data/python/kernel_tests/BUILD    |  1 +
 .../kernel_tests/sql_dataset_op_test.py       | 28 +++++-
 .../core/kernels/data/sql_dataset_ops.cc      | 89 +++++++++++++++----
 3 files changed, 101 insertions(+), 17 deletions(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD
index 7643c2a9fc9ea9..9855688f2d1943 100644
--- a/tensorflow/contrib/data/python/kernel_tests/BUILD
+++ b/tensorflow/contrib/data/python/kernel_tests/BUILD
@@ -407,6 +407,7 @@ py_test(
     srcs = ["sql_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     deps = [
+        ":dataset_serialization_test",
         "//tensorflow/contrib/data/python/ops:readers",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
diff --git a/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py
index e26cef8ec522c7..4148addf2878c9 100644
--- a/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py
@@ -22,6 +22,7 @@
 
 import sqlite3
 
+from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base
 from tensorflow.contrib.data.python.ops import readers
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
@@ -29,7 +30,7 @@
 from tensorflow.python.platform import test
 
 
-class SqlDatasetTest(test.TestCase):
+class SqlDatasetTestBase(test.TestCase):
 
   def _createSqlDataset(self, output_types, num_repeats=1):
     dataset = readers.SqlDataset(self.driver_name, self.data_source_name,
@@ -92,6 +93,9 @@ def setUp(self):
     conn.commit()
     conn.close()
 
+
+class SqlDatasetTest(SqlDatasetTestBase):
+
   # Test that SqlDataset can read from a database table.
   def testReadResultSet(self):
     init_op, get_next = self._createSqlDataset((dtypes.string, dtypes.string,
@@ -652,5 +656,27 @@ def testReadResultSetFloat64LargestConsecutiveWholeNumbersNotEqual(self):
         sess.run(get_next)
 
 
+class SqlDatasetSerializationTest(
+    SqlDatasetTestBase,
+    dataset_serialization_test_base.DatasetSerializationTestBase):
+
+  def _build_dataset(self, num_repeats):
+    data_source_name = os.path.join(test.get_temp_dir(), "tftest.sqlite")
+    driver_name = array_ops.placeholder_with_default(
+        array_ops.constant("sqlite", dtypes.string), shape=[])
+    query = ("SELECT first_name, last_name, motto FROM students ORDER BY "
+             "first_name DESC")
+    output_types = (dtypes.string, dtypes.string, dtypes.string)
+    return readers.SqlDataset(driver_name, data_source_name, query,
+                              output_types).repeat(num_repeats)
+
+  def testSQLSaveable(self):
+    num_repeats = 4
+    num_outputs = num_repeats * 2
+    self.run_core_tests(lambda: self._build_dataset(num_repeats),
+                        lambda: self._build_dataset(num_repeats // 2),
+                        num_outputs)
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/core/kernels/data/sql_dataset_ops.cc b/tensorflow/core/kernels/data/sql_dataset_ops.cc
index d50e9c9cf97390..634b3c280fedab 100644
--- a/tensorflow/core/kernels/data/sql_dataset_ops.cc
+++ b/tensorflow/core/kernels/data/sql_dataset_ops.cc
@@ -70,17 +70,19 @@ class SqlDatasetOp : public DatasetOpKernel {
                     "The set of supported databases is: {'sqlite'}.",
                     driver_name.c_str())));
 
-    *output = new Dataset(driver_name, data_source_name, query, output_types_,
-                          output_shapes_);
+    *output = new Dataset(ctx, driver_name, data_source_name, query,
+                          output_types_, output_shapes_);
   }
 
  private:
-  class Dataset : public DatasetBase {
+  class Dataset : public GraphDatasetBase {
    public:
-    Dataset(const string& driver_name, const string& data_source_name,
-            const string& query, const DataTypeVector& output_types,
+    Dataset(OpKernelContext* ctx, const string& driver_name,
+            const string& data_source_name, const string& query,
+            const DataTypeVector& output_types,
             const std::vector& output_shapes)
-        : driver_name_(driver_name),
+        : GraphDatasetBase(ctx),
+          driver_name_(driver_name),
           data_source_name_(data_source_name),
           query_(query),
           output_types_(output_types),
@@ -102,6 +104,21 @@ class SqlDatasetOp : public DatasetOpKernel {
 
     string DebugString() override { return "SqlDatasetOp::Dataset"; }
 
+   protected:
+    Status AsGraphDefInternal(OpKernelContext* ctx, DatasetGraphDefBuilder* b,
+                              Node** output) const override {
+      Node* driver_name_node;
+      TF_RETURN_IF_ERROR(b->AddScalar(driver_name_, &driver_name_node));
+      Node* data_source_name_node;
+      TF_RETURN_IF_ERROR(
+          b->AddScalar(data_source_name_, &data_source_name_node));
+      Node* query_node;
+      TF_RETURN_IF_ERROR(b->AddScalar(query_, &query_node));
+      TF_RETURN_IF_ERROR(b->AddDataset(
+          this, {driver_name_node, data_source_name_node, query_node}, output));
+      return Status::OK();
+    }
+
    private:
     class Iterator : public DatasetIterator {
      public:
@@ -121,22 +138,62 @@ class SqlDatasetOp : public DatasetOpKernel {
                              bool* end_of_sequence) override {
         mutex_lock l(mu_);
         if (!query_connection_initialized_) {
-          query_connection_initialized_ = true;
-          query_connection_ = sql::DriverManager::CreateQueryConnection(
-              dataset()->driver_name_);
-          Status s = query_connection_->Open(dataset()->data_source_name_,
-                                             dataset()->query_,
-                                             dataset()->output_types_);
-          if (!s.ok()) {
-            LOG(WARNING) << "Failed to connect to database: " << s;
-            return s;
-          }
+          TF_RETURN_IF_ERROR(InitializeQueryConnection());
         }
+        next_calls_++;
         return query_connection_->GetNext(ctx, out_tensors, end_of_sequence);
       }
 
+     protected:
+      Status SaveInternal(IteratorStateWriter* writer) override {
+        mutex_lock l(mu_);
+        if (query_connection_initialized_) {
+          TF_RETURN_IF_ERROR(
+              writer->WriteScalar(full_name("next_calls"), next_calls_));
+        }
+        return Status::OK();
+      }
+
+      Status RestoreInternal(IteratorContext* ctx,
+                             IteratorStateReader* reader) override {
+        mutex_lock l(mu_);
+        if (reader->Contains(full_name("next_calls"))) {
+          TF_RETURN_IF_ERROR(InitializeQueryConnection());
+          TF_RETURN_IF_ERROR(
+              reader->ReadScalar(full_name("next_calls"), &next_calls_));
+          int64 rem_next_calls = next_calls_;
+          std::vector out_tensors;
+          bool end_of_sequence = false;
+          while (rem_next_calls--) {
+            TF_RETURN_IF_ERROR(query_connection_->GetNext(ctx, &out_tensors,
+                                                          &end_of_sequence));
+            out_tensors.clear();
+          }
+        } else {
+          query_connection_initialized_ = false;
+        }
+        return Status::OK();
+      }
+
      private:
+      Status InitializeQueryConnection() EXCLUSIVE_LOCKS_REQUIRED(mu_) {
+        query_connection_initialized_ = true;
+        query_connection_ =
+            sql::DriverManager::CreateQueryConnection(dataset()->driver_name_);
+        Status s = query_connection_->Open(dataset()->data_source_name_,
+                                           dataset()->query_,
+                                           dataset()->output_types_);
+        next_calls_ = 0;
+        if (!s.ok()) {
+          LOG(WARNING) << "Failed to connect to database: " << s;
+          return s;
+        }
+        return Status::OK();
+      }
+
       mutex mu_;
+      // TODO(shivaniagrawal): explore ways to seek into a SQLite databases.
+      int64 next_calls_ GUARDED_BY(mu_) = 0;
       std::unique_ptr query_connection_ GUARDED_BY(mu_);
       bool query_connection_initialized_ GUARDED_BY(mu_) = false;
     };

From 9a4f5682a9854c555bf2bf2c5ecbc5635c848447 Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Wed, 9 May 2018 12:15:17 -0700
Subject: [PATCH 0561/1691] [TF:XLA] Bump open source llvm revision to r331867

PiperOrigin-RevId: 196009199
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 01d424f20bfb4f..fc65f4407eacb2 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -453,11 +453,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/7b8a8728fbd27086efbf3c57cf2bb35a557108c9.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/7b8a8728fbd27086efbf3c57cf2bb35a557108c9.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/d80aa1ad9d98bf74aca1527475556bb0d3485386.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/d80aa1ad9d98bf74aca1527475556bb0d3485386.tar.gz",
       ],
-      sha256 = "c620859c3ae5818f316de4837f340b3bba1646f8add0a28e6d4da34ce47e3969",
-      strip_prefix = "llvm-7b8a8728fbd27086efbf3c57cf2bb35a557108c9",
+      sha256 = "4dfb3e8acb68b0557bc9ffb9745c922f0e9f7e299901af1bb69930a3b9806648",
+      strip_prefix = "llvm-d80aa1ad9d98bf74aca1527475556bb0d3485386",
       build_file = clean_dep("//third_party/llvm:llvm.BUILD"),
   )
 

From fa3a9bcabfea46bb3a4c63f559b50cc066d484e7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 12:26:06 -0700
Subject: [PATCH 0562/1691] Collective Ops Part 6

Distributed-mode implementations of CollectiveRemoteAccess.
Extend Worker interface with corresponding new methods.

This change is part of a series of changes introducing infrastructure
for collective ops and initial implementations of reduction and broadcast.

PiperOrigin-RevId: 196010718
---
 tensorflow/core/BUILD                         |   1 +
 tensorflow/core/distributed_runtime/BUILD     |  34 ++
 .../collective_param_resolver_distributed.cc  |   1 -
 .../collective_rma_distributed.cc             | 206 ++++++++++
 .../collective_rma_distributed.h              |  50 +++
 .../collective_rma_distributed_test.cc        | 356 ++++++++++++++++++
 tensorflow/core/distributed_runtime/rpc/BUILD |   1 +
 .../rpc/grpc_remote_worker.cc                 |   7 +
 .../rpc/grpc_worker_service.cc                |  98 ++++-
 .../rpc/grpc_worker_service.h                 |   3 +
 .../rpc/grpc_worker_service_impl.cc           |   2 +
 .../rpc/grpc_worker_service_impl.h            |   1 +
 .../core/distributed_runtime/test_utils.h     |   5 +
 tensorflow/core/distributed_runtime/worker.cc |   9 +
 tensorflow/core/distributed_runtime/worker.h  |   3 +
 .../distributed_runtime/worker_interface.h    |   3 +
 .../core/protobuf/transport_options.proto     |   8 +
 tensorflow/core/protobuf/worker.proto         |  54 +++
 tensorflow/core/protobuf/worker_service.proto |   4 +
 19 files changed, 840 insertions(+), 6 deletions(-)
 create mode 100644 tensorflow/core/distributed_runtime/collective_rma_distributed.cc
 create mode 100644 tensorflow/core/distributed_runtime/collective_rma_distributed.h
 create mode 100644 tensorflow/core/distributed_runtime/collective_rma_distributed_test.cc
 create mode 100644 tensorflow/core/protobuf/transport_options.proto

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 76ff372cd0099a..ccb84887e11432 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -224,6 +224,7 @@ ADDITIONAL_CORE_PROTO_SRCS = [
     "protobuf/named_tensor.proto",
     "protobuf/saved_model.proto",
     "protobuf/tensorflow_server.proto",
+    "protobuf/transport_options.proto",
     "util/test_log.proto",
 ]
 
diff --git a/tensorflow/core/distributed_runtime/BUILD b/tensorflow/core/distributed_runtime/BUILD
index 256ce527a423f3..18b7069dbe5e86 100644
--- a/tensorflow/core/distributed_runtime/BUILD
+++ b/tensorflow/core/distributed_runtime/BUILD
@@ -452,6 +452,40 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "collective_rma_distributed",
+    srcs = ["collective_rma_distributed.cc"],
+    hdrs = ["collective_rma_distributed.h"],
+    deps = [
+        ":worker_cache",
+        "//tensorflow/core:core_cpu_internal",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",  # protobuf::Any
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:worker_proto_cc",
+    ],
+)
+
+tf_cc_test(
+    name = "collective_rma_distributed_test",
+    size = "small",
+    srcs = ["collective_rma_distributed_test.cc"],
+    deps = [
+        ":collective_rma_distributed",
+        ":device_resolver_distributed",
+        ":test_utils",
+        "//tensorflow/core:core_cpu_lib",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+        "//tensorflow/core:worker_proto_cc",
+    ],
+)
+
 cc_library(
     name = "collective_param_resolver_distributed",
     srcs = ["collective_param_resolver_distributed.cc"],
diff --git a/tensorflow/core/distributed_runtime/collective_param_resolver_distributed.cc b/tensorflow/core/distributed_runtime/collective_param_resolver_distributed.cc
index ecf5db811073f2..7a93b54eae386f 100644
--- a/tensorflow/core/distributed_runtime/collective_param_resolver_distributed.cc
+++ b/tensorflow/core/distributed_runtime/collective_param_resolver_distributed.cc
@@ -284,7 +284,6 @@ void CollectiveParamResolverDistributed::CompleteGroupDistributed(
     const GroupRecCallback& done) {
   VLOG(1) << "CompleteGroupDistributed group_key=" << cp->group.group_key
           << " dev: " << device << " is_leader=" << (group_leader_.empty());
-  VLOG(0) << "cp: " << cp->ToString();
   if (group_leader_.empty()) {
     // This is the group leader, so resolution is local.
     return CompleteGroupLocal(device, cp, done);
diff --git a/tensorflow/core/distributed_runtime/collective_rma_distributed.cc b/tensorflow/core/distributed_runtime/collective_rma_distributed.cc
new file mode 100644
index 00000000000000..54adcb9408d097
--- /dev/null
+++ b/tensorflow/core/distributed_runtime/collective_rma_distributed.cc
@@ -0,0 +1,206 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/core/distributed_runtime/collective_rma_distributed.h"
+
+#include "tensorflow/core/common_runtime/base_collective_executor.h"
+#include "tensorflow/core/common_runtime/copy_tensor.h"
+#include "tensorflow/core/common_runtime/device_mgr.h"
+#include "tensorflow/core/common_runtime/dma_helper.h"
+#include "tensorflow/core/common_runtime/process_util.h"
+#include "tensorflow/core/distributed_runtime/worker_cache.h"
+#include "tensorflow/core/platform/protobuf_internal.h"
+#include "tensorflow/core/protobuf/transport_options.pb.h"
+#include "tensorflow/core/protobuf/worker.pb.h"
+
+namespace tensorflow {
+
+namespace {
+
+// Supports client side cancellation of WorkerInterface calls via
+// registration with a CancellationManager.
+//
+// TODO(tucker): Maybe unify this with CancellableCall in
+// collective_param_resolver_distributed.cc.
+class CancellableCall {
+ public:
+  CancellableCall(CancellationManager* cancel_mgr, const string& remote_worker,
+                  WorkerCacheInterface* wc)
+      : cancel_mgr_(cancel_mgr), remote_worker_(remote_worker), wc_(wc) {
+    wi_ = wc_->CreateWorker(remote_worker_);
+  }
+  virtual ~CancellableCall() { wc_->ReleaseWorker(remote_worker_, wi_); }
+
+  virtual void IssueCall(const StatusCallback& done) = 0;
+
+  void Start(const StatusCallback& done) {
+    CancellationToken token = cancel_mgr_->get_cancellation_token();
+    const bool not_yet_cancelled = cancel_mgr_->RegisterCallback(
+        token, [this, token]() { opts_.StartCancel(); });
+    if (not_yet_cancelled) {
+      IssueCall([this, token, done](const Status& s) {
+        cancel_mgr_->DeregisterCallback(token);
+        done(s);
+      });
+    } else {
+      done(errors::Cancelled("RPC Request was cancelled"));
+    }
+  }
+
+ protected:
+  mutable mutex mu_;
+  CancellationManager* cancel_mgr_;  // Not owned
+  const string remote_worker_;
+  WorkerCacheInterface* wc_;  // Not owned
+  WorkerInterface* wi_;       // Owned by wc_, must be released.
+  CallOptions opts_;
+};
+
+class RecvBufCall : public CancellableCall {
+ public:
+  RecvBufCall(int64 step_id, const string& peer_device, const string& peer_task,
+              const string& key, Device* to_device,
+              DeviceContext* to_device_ctx,
+              const AllocatorAttributes& to_alloc_attr, Tensor* to_tensor,
+              const DeviceLocality& client_locality,
+              const DeviceLocality& server_locality,
+              CancellationManager* cancel_mgr, WorkerCacheInterface* wc)
+      : CancellableCall(cancel_mgr, peer_task, wc) {
+    req_.set_step_id(step_id);
+    req_.set_buf_rendezvous_key(key);
+    *req_.mutable_client_locality() = client_locality;
+    *req_.mutable_server_locality() = server_locality;
+    req_.set_num_bytes(to_tensor->TotalBytes());
+    req_.set_buf_ptr(reinterpret_cast(DMAHelper::base(to_tensor)));
+    req_.set_src_device(peer_device);
+    req_.set_dst_device(to_device->name());
+  }
+
+  ~RecvBufCall() override {}
+
+  void IssueCall(const StatusCallback& done) override {
+    wi_->RecvBufAsync(&opts_, &req_, &resp_, done);
+  }
+
+  RecvBufRequest req_;
+  RecvBufResponse resp_;
+};
+
+}  // namespace
+
+void CollectiveRemoteAccessDistributed::RecvFromPeer(
+    const string& peer_device, const string& peer_task, bool peer_is_local,
+    const string& key, Device* to_device, DeviceContext* to_device_ctx,
+    const AllocatorAttributes& to_alloc_attr, Tensor* to_tensor,
+    const DeviceLocality& client_locality, const StatusCallback& done) {
+  if (peer_is_local) {
+    CollectiveRemoteAccessLocal::RecvFromPeer(
+        peer_device, peer_task, peer_is_local, key, to_device, to_device_ctx,
+        to_alloc_attr, to_tensor, client_locality, done);
+    return;
+  }
+
+  // State that needs to be threaded through a couple of async calls
+  // in order to make this function completely non-blocking.
+  struct State {
+    DeviceLocality server_locality;
+    std::unique_ptr call;
+  };
+  State* state = new State;
+
+  // Logic to be executed on the RecvBufferAsync callback.
+  auto recv_buf_callback = [this, state, peer_task, to_device, to_alloc_attr,
+                            to_device_ctx, to_tensor, done](const Status& s) {
+    std::unique_ptr del_on_exit(state);
+    if (s.ok()) {
+      // In this generic implementation the bytes come back in the
+      // RPC response protobuf rather than via RDMA so we need to copy
+      // them into the destination tensor here.
+      RecvBufRespExtra extra;
+      state->call->resp_.transport_options().UnpackTo(&extra);
+      int64 num_bytes = extra.tensor_content().size();
+      if (num_bytes != to_tensor->TotalBytes()) {
+        done(errors::Internal("RecvBufResponse returned ", num_bytes,
+                              " bytes where to_tensor expected ",
+                              to_tensor->TotalBytes()));
+        return;
+      }
+      if (to_device->tensorflow_gpu_device_info()) {
+        // Move the bytes into a CPU tensor then use tensor-to-tensor copy.
+        // Use GPU-registered memory for the CPU tensor so the transfer
+        // goes faster.
+        Device* cpu_dev = nullptr;
+        Status status = dev_mgr_->LookupDevice("CPU:0", &cpu_dev);
+        if (!status.ok()) {
+          done(status);
+          return;
+        }
+        AllocatorAttributes cpu_attr;
+        cpu_attr.set_gpu_compatible(true);
+        Tensor* cpu_tensor = new Tensor(cpu_dev->GetAllocator(cpu_attr),
+                                        to_tensor->dtype(), to_tensor->shape());
+        memcpy(DMAHelper::base(cpu_tensor), extra.tensor_content().data(),
+               num_bytes);
+        // Then copy it to the GPU.
+        CopyTensor::ViaDMA("",  // edge name (non-existent)
+                           nullptr /*send_dev_ctx*/, to_device_ctx, cpu_dev,
+                           to_device, cpu_attr, to_alloc_attr, cpu_tensor,
+                           to_tensor,
+                           [this, cpu_tensor, done](const Status& s) {
+                             delete cpu_tensor;
+                             // This callback must not block, so execute
+                             // done in another thread.
+                             SchedClosure([s, done] { done(s); });
+                           });
+        return;
+      } else {
+        // CPU device
+        memcpy(DMAHelper::base(to_tensor), extra.tensor_content().data(),
+               num_bytes);
+      }
+    }
+    if (!s.ok() && errors::IsFailedPrecondition(s)) {
+      dev_resolver_->ClearTask(peer_task);
+    }
+
+    done(s);
+  };
+
+  // Logic to execute once we have the device locality for the server-side
+  // device.
+  auto dev_locality_callback = [this, state, peer_device, peer_task, key,
+                                to_device, to_device_ctx, to_alloc_attr,
+                                to_tensor, client_locality,
+                                recv_buf_callback](const Status& s) {
+    if (!s.ok()) {
+      recv_buf_callback(s);
+    } else {
+      state->call.reset(new RecvBufCall(
+          step_id_, peer_device, peer_task, key, to_device, to_device_ctx,
+          to_alloc_attr, to_tensor, client_locality, state->server_locality,
+          &cancel_mgr_, worker_cache_));
+      state->call->Start(recv_buf_callback);
+    }
+  };
+
+  dev_resolver_->GetLocalityAsync(
+      peer_device, peer_task, &state->server_locality, dev_locality_callback);
+}
+
+void CollectiveRemoteAccessDistributed::StartAbort(const Status& s) {
+  CollectiveRemoteAccessLocal::StartAbort(s);
+  cancel_mgr_.StartCancel();
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/distributed_runtime/collective_rma_distributed.h b/tensorflow/core/distributed_runtime/collective_rma_distributed.h
new file mode 100644
index 00000000000000..cfa9110f473edc
--- /dev/null
+++ b/tensorflow/core/distributed_runtime/collective_rma_distributed.h
@@ -0,0 +1,50 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_COLLECTIVE_RMA_DISTRIBUTED_H_
+#define TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_COLLECTIVE_RMA_DISTRIBUTED_H_
+#include "tensorflow/core/common_runtime/collective_rma_local.h"
+#include "tensorflow/core/framework/tensor.h"
+
+namespace tensorflow {
+class WorkerCacheInterface;
+
+// Extend CollectiveRemoteAccessLocal with access to remote peers.
+class CollectiveRemoteAccessDistributed : public CollectiveRemoteAccessLocal {
+ public:
+  CollectiveRemoteAccessDistributed(const DeviceMgr* dev_mgr,
+                                    DeviceResolverInterface* dev_resolver,
+                                    WorkerCacheInterface* worker_cache,
+                                    int64 step_id)
+      : CollectiveRemoteAccessLocal(dev_mgr, dev_resolver, step_id),
+        worker_cache_(worker_cache) {}
+
+  ~CollectiveRemoteAccessDistributed() override {}
+
+  void RecvFromPeer(const string& peer_device, const string& peer_task,
+                    bool peer_is_local, const string& key, Device* to_device,
+                    DeviceContext* to_device_ctx,
+                    const AllocatorAttributes& to_alloc_attr, Tensor* to_tensor,
+                    const DeviceLocality& client_locality,
+                    const StatusCallback& done) override;
+
+  void StartAbort(const Status& s) override;
+
+ protected:
+  WorkerCacheInterface* worker_cache_;  // Not owned
+  CancellationManager cancel_mgr_;
+};
+
+}  // namespace tensorflow
+#endif  // TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_COLLECTIVE_RMA_DISTRIBUTED_H_
diff --git a/tensorflow/core/distributed_runtime/collective_rma_distributed_test.cc b/tensorflow/core/distributed_runtime/collective_rma_distributed_test.cc
new file mode 100644
index 00000000000000..a552f81f584cbc
--- /dev/null
+++ b/tensorflow/core/distributed_runtime/collective_rma_distributed_test.cc
@@ -0,0 +1,356 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/distributed_runtime/collective_rma_distributed.h"
+
+#include "google/protobuf/any.pb.h"
+#include "tensorflow/core/common_runtime/device_mgr.h"
+#include "tensorflow/core/common_runtime/dma_helper.h"
+#include "tensorflow/core/common_runtime/process_util.h"
+#include "tensorflow/core/distributed_runtime/device_resolver_distributed.h"
+#include "tensorflow/core/distributed_runtime/test_utils.h"
+#include "tensorflow/core/framework/cancellation.h"
+#include "tensorflow/core/lib/core/notification.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/protobuf/transport_options.pb.h"
+#include "tensorflow/core/protobuf/worker.pb.h"
+#include "tensorflow/core/util/device_name_utils.h"
+
+// The only interesting method on CollectiveRemoteAccessDistributed
+// that's not on CollectiveRemoteAccessLocal is RecvFromPeer which
+// issues a RecvBufAsync call against a WorkerInterface.  That's all
+// that's tested here.  Note that RecvFromPeer can do a
+// DeviceResolverInterface::GetDeviceLocalityAsync call in preparation
+// for the RecvBufAsync.
+
+namespace tensorflow {
+namespace {
+
+static Device* NewDevice(const string& type, const string& name) {
+  class FakeDevice : public Device {
+   public:
+    explicit FakeDevice(const DeviceAttributes& attr) : Device(nullptr, attr) {}
+    Status Sync() override { return Status::OK(); }
+    Allocator* GetAllocator(AllocatorAttributes) override { return nullptr; }
+  };
+  DeviceAttributes attr;
+  attr.set_name(name);
+  attr.set_device_type(type);
+  attr.mutable_locality()->set_numa_node(3);  // a non-default value
+  return new FakeDevice(attr);
+}
+
+static int64 kStepId = 123;
+
+class FakeWorker : public TestWorkerInterface {
+ public:
+  FakeWorker(const string& name, DeviceMgr* dev_mgr,
+             DeviceResolverDistributed* dres)
+      : name_(name),
+        device_mgr_(dev_mgr),
+        device_resolver_(dres),
+        buf_rendezvous_(kStepId) {}
+
+  // Direct access to a BufRendezvous that holds whatever the remote
+  // worker is supposed to have.
+  BufRendezvous* buf_rendezvous() { return &buf_rendezvous_; }
+
+  void GetStatusAsync(const GetStatusRequest* request,
+                      GetStatusResponse* response,
+                      StatusCallback done) override {
+    std::vector dev_attr;
+    device_mgr_->ListDeviceAttributes(&dev_attr);
+    for (const auto& da : dev_attr) {
+      *response->add_device_attributes() = da;
+    }
+    done(Status::OK());
+  }
+
+  void RecvBufAsync(CallOptions* opts, const RecvBufRequest* request,
+                    RecvBufResponse* response, StatusCallback done) override {
+    opts->SetCancelCallback([this]() {
+      // Within this test the call is satisfied by a process-local
+      // BufRendezvous table. In real application the BufRendezvous
+      // would be on the other side of a network hop, so call
+      // BufRendezvous::StartAbort() from a separate thread to be
+      // more consistent with that situation and avoid mutex deadlock.
+      SchedClosure([this]() {
+        Env::Default()->SleepForMicroseconds(100);
+        buf_rendezvous_.StartAbort(errors::Internal("Cancelled"));
+      });
+    });
+    buf_rendezvous_.ConsumeBuf(
+        request->buf_rendezvous_key(),
+        [this, opts, request, response, done](const Status& s,
+                                              BufRendezvous::Hook* h) {
+          if (s.ok()) {
+            opts->ClearCancelCallback();
+            // Since this is not really RDMA into pre-allocated memory send the
+            // bytes in the response.
+            RecvBufRespExtra extra;
+            int64 num_bytes = h->prod_value->TotalBytes();
+            extra.set_tensor_content(string(
+                reinterpret_cast(DMAHelper::base(h->prod_value)),
+                num_bytes));
+            response->mutable_transport_options()->PackFrom(extra);
+          }
+          done(s);
+          if (h) BufRendezvous::DoneWithHook(h);
+        });
+  }
+
+ private:
+  string name_;
+  DeviceMgr* device_mgr_;
+  DeviceResolverDistributed* device_resolver_;
+  BufRendezvous buf_rendezvous_;
+};
+
+class FakeCache : public TestWorkerCache {
+ public:
+  // Override the Locality methods to actually pass through to the
+  // worker.
+  bool GetDeviceLocalityNonBlocking(const string& device,
+                                    DeviceLocality* locality) override {
+    return false;
+  }
+
+  void GetDeviceLocalityAsync(const string& device, DeviceLocality* locality,
+                              StatusCallback done) override {
+    string task_name;
+    string dev_part;
+    if (!DeviceNameUtils::SplitDeviceName(device, &task_name, &dev_part)) {
+      done(errors::Internal("failed to parse device name"));
+      return;
+    }
+    auto it = workers_.find(task_name);
+    if (it == workers_.end()) {
+      done(errors::Internal("failed to find worker ", task_name));
+      return;
+    }
+    WorkerInterface* wi = it->second;
+    GetStatusRequest req;
+    GetStatusResponse resp;
+    Notification note;
+    Status status = wi->GetStatus(&req, &resp);
+    if (!status.ok()) {
+      done(status);
+      return;
+    }
+    for (const auto& it : resp.device_attributes()) {
+      if (it.name() == device) {
+        *locality = it.locality();
+        done(Status::OK());
+        return;
+      }
+    }
+    done(errors::Internal("device not found: ", device));
+  }
+};
+
+class CollRMADistTest : public ::testing::Test {
+ protected:
+  CollRMADistTest() {}
+
+  ~CollRMADistTest() override {
+    for (DeviceMgr* dm : device_mgrs_) {
+      delete dm;
+    }
+    for (auto it : dev_resolvers_) {
+      delete it.second;
+    }
+    for (FakeWorker* w : workers_) {
+      delete w;
+    }
+  }
+
+  void SetUp() override {
+    const int num_workers = 2;
+    const int num_devices = 1;
+    string device_type = "CPU";
+    ConfigProto config;
+    string dev0_worker_name;
+    for (int w = 0; w < num_workers; ++w) {
+      string name = strings::StrCat("/job:worker/replica:0/task:", w);
+      if (w == 0) {
+        dev0_worker_name = name;
+        // TODO(tucker): Change to use config when available.
+        // config.set_collective_group_leader(name);
+      }
+      DefineWorker(config, name, device_type, num_devices);
+    }
+    // All tests simulate requests from worker 0 to worker 1.
+    rma_.reset(new CollectiveRemoteAccessDistributed(
+        device_mgrs_[0], dev_resolvers_[dev0_worker_name], &wc_, kStepId));
+
+    const int kNumElts = 8;
+    expected_value_ = Tensor(DT_FLOAT, {kNumElts});
+    to_tensor_ = Tensor(DT_FLOAT, {kNumElts});
+    auto exp_alias = expected_value_.flat();
+    auto to_alias = to_tensor_.flat();
+    for (int i = 0; i < kNumElts; ++i) {
+      exp_alias(i) = i;
+      to_alias(i) = -1;
+    }
+  }
+
+  void DefineWorker(const ConfigProto& config, const string& worker_name,
+                    const string& device_type, int num_devices) {
+    std::vector devices;
+    for (int i = 0; i < num_devices; ++i) {
+      devices.push_back(NewDevice(
+          device_type,
+          strings::StrCat(worker_name, "/device:", device_type, ":", i)));
+    }
+    DeviceMgr* dev_mgr = new DeviceMgr(devices);
+    device_mgrs_.push_back(dev_mgr);
+    std::vector* dv = &dev_by_task_[worker_name];
+    for (auto d : devices) {
+      dv->push_back(d->name());
+    }
+    DeviceResolverDistributed* dev_res =
+        new DeviceResolverDistributed(dev_mgr, &wc_, worker_name);
+    dev_resolvers_[worker_name] = dev_res;
+    FakeWorker* fw = new FakeWorker(worker_name, dev_mgr, dev_res);
+    workers_.push_back(fw);
+    wc_.AddWorker(worker_name, fw);
+  }
+
+  void ValidateResultTensor() {
+    ASSERT_EQ(expected_value_.NumElements(), to_tensor_.NumElements());
+    for (int i = 0; i < to_tensor_.NumElements(); ++i) {
+      EXPECT_FLOAT_EQ(expected_value_.flat()(i),
+                      to_tensor_.flat()(i));
+    }
+  }
+
+  FakeCache wc_;
+  CancellationManager cm_;
+  std::vector device_mgrs_;
+  std::unordered_map dev_resolvers_;
+  std::unordered_map> dev_by_task_;
+  std::vector workers_;
+  std::unique_ptr rma_;
+  mutex mu_;
+  int num_done_ GUARDED_BY(mu_);
+  condition_variable done_;
+  Tensor expected_value_;
+  Tensor to_tensor_;
+  CallOptions opts_;
+  DeviceLocality device_locality_;
+  AllocatorAttributes alloc_attr_;
+};
+
+TEST_F(CollRMADistTest, ProdFirstOK) {
+  Notification consumer_note;
+  Notification producer_note;
+  Status consumer_status;
+  Status producer_status;
+  FakeWorker* wi = workers_[1];
+  const string kBufKey = "fake_buf_key";
+  wi->buf_rendezvous()->ProvideBuf(
+      kBufKey, nullptr /*device*/, nullptr /*dev_ctx*/, &expected_value_,
+      AllocatorAttributes(),
+      [this, &producer_note, &producer_status](const Status& s) {
+        producer_status.Update(s);
+        producer_note.Notify();
+      });
+  Status status;
+  Device* dst_device = nullptr;
+  string dev_name = "CPU:0";
+  TF_EXPECT_OK(device_mgrs_[0]->LookupDevice(dev_name, &dst_device));
+  DeviceContext* to_device_ctx = nullptr;
+  rma_->RecvFromPeer(
+      "/job:worker/replica:0/task:1/device:" + dev_name,  // peer_dev
+      "/job:worker/replica:0/task:1",                     // peer_task
+      false,                                              // peer_is_local
+      kBufKey, dst_device, to_device_ctx, alloc_attr_, &to_tensor_,
+      device_locality_,
+      [this, &consumer_status, &consumer_note](const Status& s) {
+        consumer_status = s;
+        consumer_note.Notify();
+      });
+  consumer_note.WaitForNotification();
+  TF_EXPECT_OK(consumer_status);
+  producer_note.WaitForNotification();
+  TF_EXPECT_OK(producer_status);
+  ValidateResultTensor();
+}
+
+TEST_F(CollRMADistTest, ConsFirstOK) {
+  Notification consumer_note;
+  Notification producer_note;
+  Status consumer_status;
+  Status producer_status;
+  FakeWorker* wi = workers_[1];
+  const string kBufKey = "fake_buf_key";
+  Status status;
+  Device* dst_device = nullptr;
+  string dev_name = "CPU:0";
+  TF_EXPECT_OK(device_mgrs_[0]->LookupDevice(dev_name, &dst_device));
+  DeviceContext* to_device_ctx = nullptr;
+  rma_->RecvFromPeer(
+      "/job:worker/replica:0/task:1/device:" + dev_name,  // peer_dev
+      "/job:worker/replica:0/task:1",                     // peer_task
+      false,                                              // peer_is_local
+      kBufKey, dst_device, to_device_ctx, alloc_attr_, &to_tensor_,
+      device_locality_,
+      [this, &consumer_status, &consumer_note](const Status& s) {
+        consumer_status = s;
+        consumer_note.Notify();
+      });
+  wi->buf_rendezvous()->ProvideBuf(
+      kBufKey, nullptr /*device*/, nullptr /*dev_ctx*/, &expected_value_,
+      AllocatorAttributes(),
+      [this, &producer_note, &producer_status](const Status& s) {
+        producer_status.Update(s);
+        producer_note.Notify();
+      });
+  consumer_note.WaitForNotification();
+  TF_EXPECT_OK(consumer_status);
+  producer_note.WaitForNotification();
+  TF_EXPECT_OK(producer_status);
+  ValidateResultTensor();
+}
+
+TEST_F(CollRMADistTest, ConsFirstAbort) {
+  Notification consumer_note;
+  Status consumer_status;
+  const string kBufKey = "fake_buf_key";
+  Status status;
+  Device* dst_device = nullptr;
+  string dev_name = "CPU:0";
+  TF_EXPECT_OK(device_mgrs_[0]->LookupDevice(dev_name, &dst_device));
+  DeviceContext* to_device_ctx = nullptr;
+  rma_->RecvFromPeer(
+      "/job:worker/replica:0/task:1/device:" + dev_name,  // peer_dev
+      "/job:worker/replica:0/task:1",                     // peer_task
+      false,                                              // peer_is_local
+      kBufKey, dst_device, to_device_ctx, alloc_attr_, &to_tensor_,
+      device_locality_,
+      [this, &consumer_status, &consumer_note](const Status& s) {
+        consumer_status = s;
+        consumer_note.Notify();
+      });
+  rma_->StartAbort(errors::Internal("Deliberate Failure"));
+  consumer_note.WaitForNotification();
+  EXPECT_EQ(consumer_status.error_message(), "Cancelled");
+}
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/core/distributed_runtime/rpc/BUILD b/tensorflow/core/distributed_runtime/rpc/BUILD
index c2719f54622b25..40028ee241b6e7 100644
--- a/tensorflow/core/distributed_runtime/rpc/BUILD
+++ b/tensorflow/core/distributed_runtime/rpc/BUILD
@@ -171,6 +171,7 @@ tf_cuda_library(
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
+        "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:worker_proto_cc",
         "//tensorflow/core/distributed_runtime:graph_mgr",
         "//tensorflow/core/distributed_runtime:recent_request_ids",
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc b/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc
index 5b7b74ce636dcb..1acf1fb4fc1ea9 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc
@@ -54,6 +54,7 @@ class GrpcRemoteWorker : public WorkerInterface {
         cleanupgraph_(Method(GrpcWorkerMethod::kCleanupGraph)),
         cleanupall_(Method(GrpcWorkerMethod::kCleanupAll)),
         recvtensor_(Method(GrpcWorkerMethod::kRecvTensor)),
+        recvbuf_(Method(GrpcWorkerMethod::kRecvBuf)),
         logging_(Method(GrpcWorkerMethod::kLogging)),
         tracing_(Method(GrpcWorkerMethod::kTracing)),
         completegroup_(Method(GrpcWorkerMethod::kCompleteGroup)),
@@ -118,6 +119,11 @@ class GrpcRemoteWorker : public WorkerInterface {
     IssueRequest(request, response, cleanupall_, std::move(done));
   }
 
+  void RecvBufAsync(CallOptions* call_opts, const RecvBufRequest* request,
+                    RecvBufResponse* response, StatusCallback done) override {
+    IssueRequest(request, response, recvbuf_, std::move(done), call_opts);
+  }
+
   void CompleteGroupAsync(CallOptions* call_opts,
                           const CompleteGroupRequest* request,
                           CompleteGroupResponse* response,
@@ -239,6 +245,7 @@ class GrpcRemoteWorker : public WorkerInterface {
   const ::grpc::string cleanupgraph_;
   const ::grpc::string cleanupall_;
   const ::grpc::string recvtensor_;
+  const ::grpc::string recvbuf_;
   const ::grpc::string logging_;
   const ::grpc::string tracing_;
   const ::grpc::string completegroup_;
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
index 26fad1fc3c92d5..4383e4154104ed 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "grpc++/alarm.h"
 #include "grpc++/server_builder.h"
 
+#include "tensorflow/core/common_runtime/buf_rendezvous.h"
 #include "tensorflow/core/common_runtime/device.h"
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
@@ -37,10 +38,12 @@ limitations under the License.
 #include "tensorflow/core/distributed_runtime/worker_cache.h"
 #include "tensorflow/core/distributed_runtime/worker_session.h"
 #include "tensorflow/core/framework/cancellation.h"
+#include "tensorflow/core/framework/collective.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/tracing.h"
+#include "tensorflow/core/protobuf/transport_options.pb.h"
 #include "tensorflow/core/protobuf/worker.pb.h"
 
 namespace tensorflow {
@@ -159,6 +162,9 @@ class GrpcWorkerService : public AsyncServiceInterface {
       for (int i = 0; i < 1000; ++i) {
         EnqueueRecvTensorRequestRaw();
       }
+      for (int i = 0; i < 500; ++i) {
+        ENQUEUE_REQUEST(RecvBuf, true);
+      }
       for (int i = 0; i < 100; ++i) {
         ENQUEUE_REQUEST(RunGraph, true);
       }
@@ -170,9 +176,9 @@ class GrpcWorkerService : public AsyncServiceInterface {
       ENQUEUE_REQUEST(Tracing, false);
 
       for (int i = 0; i < 10; ++i) {
-        ENQUEUE_REQUEST(CompleteGroup, false);
-        ENQUEUE_REQUEST(CompleteInstance, false);
-        ENQUEUE_REQUEST(GetStepSequence, false);
+        ENQUEUE_REQUEST(CompleteGroup, true);
+        ENQUEUE_REQUEST(CompleteInstance, true);
+        ENQUEUE_REQUEST(GetStepSequence, true);
       }
 
       void* tag;
@@ -322,6 +328,20 @@ class GrpcWorkerService : public AsyncServiceInterface {
       ENQUEUE_REQUEST(Tracing, false);
     }
 
+    void RecvBufHandler(WorkerCall* call) {
+      Schedule([this, call]() {
+        CallOptions* call_opts = new CallOptions;
+        call->SetCancelCallback([call_opts]() { call_opts->StartCancel(); });
+        worker_->RecvBufAsync(call_opts, &call->request, &call->response,
+                              [call, call_opts](const Status& s) {
+                                call->ClearCancelCallback();
+                                delete call_opts;
+                                call->SendResponse(ToGrpcStatus(s));
+                              });
+      });
+      ENQUEUE_REQUEST(RecvBuf, true);
+    }
+
     void CompleteGroupHandler(
         WorkerCall* call) {
       Schedule([this, call]() {
@@ -334,7 +354,7 @@ class GrpcWorkerService : public AsyncServiceInterface {
                                       call->SendResponse(ToGrpcStatus(s));
                                     });
       });
-      ENQUEUE_REQUEST(CompleteGroup, false);
+      ENQUEUE_REQUEST(CompleteGroup, true);
     }
 
     void CompleteInstanceHandler(
@@ -360,7 +380,7 @@ class GrpcWorkerService : public AsyncServiceInterface {
             &call->request, &call->response,
             [call](const Status& s) { call->SendResponse(ToGrpcStatus(s)); });
       });
-      ENQUEUE_REQUEST(GetStepSequence, false);
+      ENQUEUE_REQUEST(GetStepSequence, true);
     }
 #undef ENQUEUE_REQUEST
 
@@ -485,6 +505,74 @@ void GrpcWorker::GrpcRecvTensorAsync(CallOptions* opts,
       });
 }
 
+void GrpcWorker::RecvBufAsync(CallOptions* opts, const RecvBufRequest* request,
+                              RecvBufResponse* response, StatusCallback done) {
+  // This is a generic, low performance implementation appropriate for grpc.
+  CollectiveExecutor::Handle ce_handle(
+      env_->collective_executor_mgr->FindOrCreate(request->step_id()), true);
+  CollectiveRemoteAccess* rma = ce_handle.get()->remote_access();
+  rma->buf_rendezvous()->ConsumeBuf(
+      request->buf_rendezvous_key(),
+      [this, opts, request, response, done](const Status& status,
+                                            BufRendezvous::Hook* hook) {
+        Status s = status;
+        if (s.ok()) {
+          if (!DMAHelper::CanUseDMA(hook->prod_value)) {
+            s = errors::Internal("Tensor value for key ",
+                                 request->buf_rendezvous_key(),
+                                 " is not of a type supported by RecvBuf");
+          }
+        }
+        if (s.ok()) {
+          // The RPC source tensor needs to be in CPU RAM.  If not already
+          // there make a copy using memory appropriate to the purpose.
+          const size_t num_bytes = hook->prod_value->TotalBytes();
+          const bool on_host =
+              hook->prod_dev->attributes().device_type() == "CPU" ||
+              hook->prod_attr.on_host();
+          if ((!on_host) && (num_bytes > 0)) {
+            Device* cpu_dev = nullptr;
+            s = env_->device_mgr->LookupDevice("CPU:0", &cpu_dev);
+            if (s.ok()) {
+              AllocatorAttributes cpu_attr;
+              cpu_attr.set_gpu_compatible(true);
+              cpu_attr.set_nic_compatible(true);
+              Tensor* cpu_tensor = new Tensor(cpu_dev->GetAllocator(cpu_attr),
+                                              hook->prod_value->dtype(),
+                                              hook->prod_value->shape());
+              hook->prod_ctx->CopyDeviceTensorToCPU(
+                  hook->prod_value, "empty_name", hook->prod_dev, cpu_tensor,
+                  [this, num_bytes, response, done, hook,
+                   cpu_tensor](const Status& s) {
+                    if (s.ok()) {
+                      RecvBufRespExtra extra;
+                      extra.set_tensor_content(reinterpret_cast(
+                                                   DMAHelper::base(cpu_tensor)),
+                                               num_bytes);
+                      response->mutable_transport_options()->PackFrom(extra);
+                    }
+                    response->set_send_start_micros(env_->env->NowMicros());
+                    done(s);
+                    BufRendezvous::DoneWithHook(hook);
+                    delete cpu_tensor;
+                  });
+              return;
+            }
+          } else {
+            // Tensor is on CPU.
+            RecvBufRespExtra extra;
+            extra.set_tensor_content(reinterpret_cast(
+                                         DMAHelper::base(hook->prod_value)),
+                                     num_bytes);
+            response->mutable_transport_options()->PackFrom(extra);
+          }
+        }
+        response->set_send_start_micros(env_->env->NowMicros());
+        done(s);
+        BufRendezvous::DoneWithHook(hook);
+      });
+}
+
 void GrpcWorker::LoggingAsync(const LoggingRequest* request,
                               LoggingResponse* response, StatusCallback done) {
   auto env = this->env();
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h
index fbddbda9e6f9e5..c0ed0884bc5cfd 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h
@@ -43,6 +43,9 @@ class GrpcWorker : public Worker {
   virtual void LoggingAsync(const LoggingRequest* request,
                             LoggingResponse* response, StatusCallback done);
 
+  virtual void RecvBufAsync(CallOptions* opts, const RecvBufRequest* request,
+                            RecvBufResponse* response, StatusCallback done);
+
   WorkerEnv* env();
 
  private:
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc
index a91cc0692af71b..38cc2b81d30e5d 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc
@@ -46,6 +46,8 @@ const char* GrpcWorkerMethodName(GrpcWorkerMethod id) {
       return "/tensorflow.WorkerService/CleanupAll";
     case GrpcWorkerMethod::kRecvTensor:
       return "/tensorflow.WorkerService/RecvTensor";
+    case GrpcWorkerMethod::kRecvBuf:
+      return "/tensorflow.WorkerService/RecvBuf";
     case GrpcWorkerMethod::kLogging:
       return "/tensorflow.WorkerService/Logging";
     case GrpcWorkerMethod::kTracing:
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h
index c5104c6a50182a..da270835bd1ab8 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h
@@ -81,6 +81,7 @@ enum class GrpcWorkerMethod {
   kCleanupGraph,
   kCleanupAll,
   kRecvTensor,
+  kRecvBuf,
   kLogging,
   kTracing,
   kCompleteGroup,
diff --git a/tensorflow/core/distributed_runtime/test_utils.h b/tensorflow/core/distributed_runtime/test_utils.h
index 0ed078241f3a58..48d83845dd3b0e 100644
--- a/tensorflow/core/distributed_runtime/test_utils.h
+++ b/tensorflow/core/distributed_runtime/test_utils.h
@@ -93,6 +93,11 @@ class TestWorkerInterface : public WorkerInterface {
     done(errors::Unimplemented("RunGraphAsync"));
   }
 
+  void RecvBufAsync(CallOptions* opts, const RecvBufRequest* request,
+                    RecvBufResponse* response, StatusCallback done) override {
+    done(errors::Unimplemented("RecvBufAsync"));
+  }
+
   void CompleteGroupAsync(CallOptions* opts,
                           const CompleteGroupRequest* request,
                           CompleteGroupResponse* response,
diff --git a/tensorflow/core/distributed_runtime/worker.cc b/tensorflow/core/distributed_runtime/worker.cc
index d682ac8f34cd3e..4e6500fbc6baff 100644
--- a/tensorflow/core/distributed_runtime/worker.cc
+++ b/tensorflow/core/distributed_runtime/worker.cc
@@ -337,6 +337,15 @@ void Worker::TracingAsync(const TracingRequest* request,
   done(errors::Unimplemented("Tracing"));
 }
 
+void Worker::RecvBufAsync(CallOptions* opts, const RecvBufRequest* request,
+                          RecvBufResponse* response, StatusCallback done) {
+  // The base Worker class does not implement RecvBufAsync because
+  // it is not currently used for worker-to-worker communication. Use a
+  // transport-specific implementation (such as `GrpcWorker::RecvBufAsync()`)
+  // instead.
+  done(errors::Unimplemented("Worker::RecvBufAsync()"));
+}
+
 void Worker::CompleteGroupAsync(CallOptions* opts,
                                 const CompleteGroupRequest* request,
                                 CompleteGroupResponse* response,
diff --git a/tensorflow/core/distributed_runtime/worker.h b/tensorflow/core/distributed_runtime/worker.h
index b5a9ada502b201..91eb27c10ea140 100644
--- a/tensorflow/core/distributed_runtime/worker.h
+++ b/tensorflow/core/distributed_runtime/worker.h
@@ -90,6 +90,9 @@ class Worker : public WorkerInterface {
   void TracingAsync(const TracingRequest* request, TracingResponse* response,
                     StatusCallback done) override;
 
+  void RecvBufAsync(CallOptions* opts, const RecvBufRequest* request,
+                    RecvBufResponse* response, StatusCallback done) override;
+
   void CompleteGroupAsync(CallOptions* opts,
                           const CompleteGroupRequest* request,
                           CompleteGroupResponse* response,
diff --git a/tensorflow/core/distributed_runtime/worker_interface.h b/tensorflow/core/distributed_runtime/worker_interface.h
index bad31d27b231db..a50ac3b8ae51e9 100644
--- a/tensorflow/core/distributed_runtime/worker_interface.h
+++ b/tensorflow/core/distributed_runtime/worker_interface.h
@@ -112,6 +112,9 @@ class WorkerInterface {
   virtual void TracingAsync(const TracingRequest* request,
                             TracingResponse* response, StatusCallback done) = 0;
 
+  virtual void RecvBufAsync(CallOptions* opts, const RecvBufRequest* request,
+                            RecvBufResponse* response, StatusCallback done) = 0;
+
   virtual void CompleteGroupAsync(CallOptions* opts,
                                   const CompleteGroupRequest* request,
                                   CompleteGroupResponse* response,
diff --git a/tensorflow/core/protobuf/transport_options.proto b/tensorflow/core/protobuf/transport_options.proto
new file mode 100644
index 00000000000000..d7b1bddbbe3d7d
--- /dev/null
+++ b/tensorflow/core/protobuf/transport_options.proto
@@ -0,0 +1,8 @@
+syntax = "proto3";
+
+package tensorflow;
+
+// Extra data needed on a non-RDMA RecvBufResponse.
+message RecvBufRespExtra {
+  bytes tensor_content = 1;
+};
diff --git a/tensorflow/core/protobuf/worker.proto b/tensorflow/core/protobuf/worker.proto
index 602f6a1ef143e2..f7816e9a67358f 100644
--- a/tensorflow/core/protobuf/worker.proto
+++ b/tensorflow/core/protobuf/worker.proto
@@ -416,6 +416,60 @@ message TracingRequest {
 message TracingResponse {
 }
 
+////////////////////////////////////////////////////////////////////////////////
+//
+// Raw data transfers in support of Collective Ops.
+// These methods are experimental and subject to change.
+//
+// The intention is to allow collectives to take advantage of the most
+// efficient methods available on a platform, e.g. RDMA, and not be
+// constrained to use the RPC system in use by other methods.
+//
+////////////////////////////////////////////////////////////////////////////////
+
+message RecvBufRequest {
+  // Use of the fields below may vary by implementation.  For example
+  // the buf_ptr and num_bytes may be set only for local operations and
+  // not sent on the wire, or only sent on the wire in one direction.
+
+  // Used at server side to find the correct BufRendezvous.
+  int64 step_id = 1;
+
+  // Arbitrary string identifying a BufRendezvous entry.
+  string buf_rendezvous_key = 2;
+
+  // Size of value expected, must agree with BufRendezvous entry.
+  int64 num_bytes = 3;
+
+  // When RDMA is in use, address of destination field on client.
+  fixed64 buf_ptr = 4;
+
+  // Optional information on client-side device locality.
+  DeviceLocality client_locality = 5;
+
+  // Optional information on server-side device locality.
+  DeviceLocality server_locality = 6;
+
+  // Optional, implementation-specific data.
+  google.protobuf.Any transport_options = 7;
+  // Optional, for annotating the timeline.
+  string src_device = 8;
+  string dst_device = 9;
+}
+
+message RecvBufResponse {
+  // Use of the fields below may vary by implementation.  Comments give
+  // intended use.
+
+  fixed64 buf_ptr = 1;  // Address of source field on server.
+  int64 num_bytes = 2;  // Byte length of buf_ptr field, if set.
+  bool is_dead = 3;     // True if value is 'dead' like a tensor.
+  // Optional, implementation-specific data.
+  google.protobuf.Any transport_options = 4;
+  // Optional, for timeline.
+  int64 send_start_micros = 5;
+}
+
 ////////////////////////////////////////////////////////////////////////////////
 //
 // Collective Op dynamic group resolution messages.
diff --git a/tensorflow/core/protobuf/worker_service.proto b/tensorflow/core/protobuf/worker_service.proto
index 01c76c01a9215d..e0c27f394a9ca1 100644
--- a/tensorflow/core/protobuf/worker_service.proto
+++ b/tensorflow/core/protobuf/worker_service.proto
@@ -73,6 +73,10 @@ service WorkerService {
   // See worker.proto for details.
   rpc Tracing(TracingRequest) returns (TracingResponse);
 
+  // See worker.proto for details.
+  rpc RecvBuf(RecvBufRequest) returns (RecvBufResponse) {
+  }
+
   // See worker.proto for details.
   rpc GetStepSequence(GetStepSequenceRequest) returns (GetStepSequenceResponse);
 

From 52c26df56bd0a5244c400c2c655db388ba8b95ce Mon Sep 17 00:00:00 2001
From: Jacques Pienaar 
Date: Wed, 9 May 2018 13:03:45 -0700
Subject: [PATCH 0563/1691] Add IsCondSwitch.

* Switch nodes are not part of the cond contexts of the tf.cond that they are the switches for, so check the contexts of the outputs of the switch to determine if a cond switch.
* Include the pivot of a cond in its cond context (there is one pivot per CondContext)
* If a cond is nested in a while loop, then the switch nodes of the cond is in the control flow context of the while loop, so only return that it is a loop switch if it isn't a cond switch.

PiperOrigin-RevId: 196015879
---
 .../kernel_tests/control_flow_util_test.py    | 78 +++++++++++++++++++
 tensorflow/python/ops/control_flow_ops.py     |  6 +-
 tensorflow/python/ops/control_flow_util.py    | 23 +++++-
 3 files changed, 103 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/kernel_tests/control_flow_util_test.py b/tensorflow/python/kernel_tests/control_flow_util_test.py
index 39e96f74b0461d..5138ad5aba8220 100644
--- a/tensorflow/python/kernel_tests/control_flow_util_test.py
+++ b/tensorflow/python/kernel_tests/control_flow_util_test.py
@@ -19,9 +19,13 @@
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_ops
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import control_flow_util
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import gen_control_flow_ops
 from tensorflow.python.platform import test
 
@@ -66,6 +70,80 @@ def testIsLoopExit(self):
 
     self.assertFalse(control_flow_util.IsLoopExit(test_ops.int_output().op))
 
+  def build_test_graph(self):
+    g = ops.Graph()
+    with g.as_default():
+
+      def while_loop(x):
+
+        def b(x):
+          with ops.name_scope("NestedCond"):
+            return control_flow_ops.cond(
+                math_ops.less(x, 100), lambda: math_ops.add(x, 1),
+                lambda: math_ops.add(x, 2))
+
+        c = lambda x: math_ops.less(x, 10000)
+        with ops.name_scope("OuterWhile"):
+          return control_flow_ops.while_loop(c, b, [x])
+
+      x = array_ops.placeholder(dtypes.int32)
+      with ops.name_scope("OuterCond"):
+        control_flow_ops.cond(
+            math_ops.less(x, 1000), lambda: while_loop(x),
+            lambda: math_ops.add(x, 2))
+    return g
+
+  def testIsCondSwitch(self):
+    g = self.build_test_graph()
+
+    cond_switch = [
+        "OuterCond/cond/Switch",
+        "OuterCond/cond/OuterWhile/while/Switch",
+        "OuterCond/cond/OuterWhile/while/NestedCond/cond/Switch",
+        "OuterCond/cond/OuterWhile/while/NestedCond/cond/Add/Switch",
+        "OuterCond/cond/OuterWhile/while/NestedCond/cond/Add_1/Switch",
+        "OuterCond/cond/Add/Switch",
+    ]
+    for n in g.get_operations():
+      if control_flow_util.IsSwitch(n):
+        self.assertTrue(
+            control_flow_util.IsCondSwitch(n) != control_flow_util.IsLoopSwitch(
+                n))
+      if n.name in cond_switch:
+        self.assertTrue(control_flow_util.IsSwitch(n))
+        self.assertTrue(
+            control_flow_util.IsCondSwitch(n),
+            msg="Mismatch for {}".format(n.name))
+        self.assertFalse(
+            control_flow_util.IsLoopSwitch(n),
+            msg="Mismatch for {}".format(n.name))
+      else:
+        self.assertFalse(
+            control_flow_util.IsCondSwitch(n),
+            msg="Mismatch for {}".format(n.name))
+
+  def testIsLoopSwitch(self):
+    g = self.build_test_graph()
+
+    loop_switch = ["OuterCond/cond/OuterWhile/while/Switch_1"]
+    for n in g.get_operations():
+      if control_flow_util.IsSwitch(n):
+        self.assertTrue(
+            control_flow_util.IsCondSwitch(n) != control_flow_util.IsLoopSwitch(
+                n))
+      if n.name in loop_switch:
+        self.assertTrue(control_flow_util.IsSwitch(n))
+        self.assertFalse(
+            control_flow_util.IsCondSwitch(n),
+            msg="Mismatch for {}".format(n.name))
+        self.assertTrue(
+            control_flow_util.IsLoopSwitch(n),
+            msg="Mismatch for {}".format(n.name))
+      else:
+        self.assertFalse(
+            control_flow_util.IsLoopSwitch(n),
+            msg="Mismatch for {}".format(n.name))
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 5f60dab6ac3613..5ebdb190791eff 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1685,12 +1685,12 @@ def __init__(self,
       self._pivot = pivot  # The predicate tensor in this branch
       self._branch = branch  # 0 or 1 representing this branch
 
-      # Values considered to have been already seen in this context. They are
-      # not included in this context.
+      # Values considered to have been already seen in this context. pred is not
+      # included in this context.
       self._values.add(pred.name)
       self._external_values[pred.name] = pred
       self._values.add(pivot.name)
-      self._external_values[pivot.name] = pivot
+      pivot.op._set_control_flow_context(self)  # pylint: disable=protected-access
 
   def _init_from_proto(self, context_def, import_scope=None):
     """Creates a new `CondContext` from protocol buffer.
diff --git a/tensorflow/python/ops/control_flow_util.py b/tensorflow/python/ops/control_flow_util.py
index eee31102db57b4..41f16acc7dbd61 100644
--- a/tensorflow/python/ops/control_flow_util.py
+++ b/tensorflow/python/ops/control_flow_util.py
@@ -63,11 +63,32 @@ def IsLoopExit(op):
   return op.type == "Exit" or op.type == "RefExit"
 
 
+def IsCondSwitch(op):
+  """Return true if `op` is the Switch for a conditional."""
+  if not IsSwitch(op):
+    return False
+  if not op.outputs:
+    return False
+  # Switch nodes are not part of the cond control flow context that they
+  # represent, so consider the consumers of its outputs to determine if it is
+  # cond switch or not. A switch is a cond switch iff all its consumers are in
+  # cond contexts.
+  is_cond_switch = True
+  for o in op.outputs:
+    for c in o.consumers():
+      ctxt = c._get_control_flow_context()  # pylint: disable=protected-access
+      if IsLoopEnter(c):
+        ctxt = ctxt.outer_context
+      is_cond_switch = is_cond_switch and (ctxt is not None and
+                                           ctxt.IsCondContext())
+  return is_cond_switch
+
+
 def IsLoopSwitch(op):
   """Return true if `op` is the Switch for a while loop."""
   if IsSwitch(op):
     ctxt = op._get_control_flow_context()  # pylint: disable=protected-access
-    return ctxt and ctxt.IsWhileContext()
+    return ctxt is not None and ctxt.IsWhileContext() and not IsCondSwitch(op)
   return False
 
 

From a4afe20fb4663c0f3b7f1b0086fe1c97557fea7b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 13:06:50 -0700
Subject: [PATCH 0564/1691] Increase size of test
 tensorflow/python:basic_session_run_hooks_test to avoid flaky timeouts

PiperOrigin-RevId: 196016436
---
 tensorflow/python/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 699f78edd2d69c..f7cbaec6ab0b24 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -4219,7 +4219,7 @@ tf_py_test(
 
 py_test(
     name = "basic_session_run_hooks_test",
-    size = "small",
+    size = "medium",
     srcs = ["training/basic_session_run_hooks_test.py"],
     srcs_version = "PY2AND3",
     tags = [

From e1347ba769b98e260d36e895be2963af35c88d18 Mon Sep 17 00:00:00 2001
From: Kay Zhu 
Date: Wed, 9 May 2018 13:07:35 -0700
Subject: [PATCH 0565/1691] [XLA] First step in adding Literal slice classes,
 to improve interface safety and prepare for enabling more efficient
 interfacing from Tensor to Literal to reduce host to device latency.

More specically:
* Introducing a new LiteralBase abstract base class that contains all immutable
methods of from the old Literal class.

* Introducing a subclass LiteralSlice to replace original LiteralView class.
LiteralSlice class is read-only and does not own Shape nor any buffer through
the Pieces. Change a number of callers to use LiteralSlice directly.

* Change Literal class to explicitly own the underlying Shape as well as owning
the underlying buffer via Piece.

* Conversion from Literal to LiteralSlice is now done via an implicit
conversion constructor instead of inheritance.

* Decouple ShapeTree from Literal classes.

* Use copy-and-swap for assignment constructors.

* Other minor cleanups.

PiperOrigin-RevId: 196016576
---
 tensorflow/compiler/tf2xla/literal_util.cc    |    6 +-
 tensorflow/compiler/tf2xla/literal_util.h     |    6 +-
 .../xla/client/computation_builder.cc         |    2 +-
 .../compiler/xla/client/computation_builder.h |    2 +-
 .../xla/client/xla_client/xla_builder.cc      |    2 +-
 .../xla/client/xla_client/xla_builder.h       |    2 +-
 tensorflow/compiler/xla/literal_util.cc       |  809 +++++------
 tensorflow/compiler/xla/literal_util.h        | 1246 +++++++++--------
 tensorflow/compiler/xla/literal_util_test.cc  |   47 +-
 .../compiler/xla/python/numpy_bridge.cc       |    8 +-
 tensorflow/compiler/xla/python/numpy_bridge.h |    7 +-
 .../xla/service/algebraic_simplifier.cc       |    4 +-
 .../xla/service/cpu/cpu_transfer_manager.cc   |    4 +-
 .../xla/service/cpu/cpu_transfer_manager.h    |    2 +-
 .../xla/service/cpu/external_constant_pool.cc |    4 +-
 .../xla/service/cpu/external_constant_pool.h  |    2 +-
 .../xla/service/generic_transfer_manager.cc   |    4 +-
 .../xla/service/generic_transfer_manager.h    |    2 +-
 .../xla/service/gpu/gpu_transfer_manager.cc   |    4 +-
 .../xla/service/gpu/gpu_transfer_manager.h    |    2 +-
 .../compiler/xla/service/hlo_evaluator.cc     |    8 +-
 .../compiler/xla/service/transfer_manager.h   |    2 +-
 .../compiler/xla/tests/broadcast_test.cc      |    4 +-
 tensorflow/compiler/xla/tests/client_test.cc  |    4 +-
 .../compiler/xla/tests/constants_test.cc      |    4 +-
 .../compiler/xla/tests/literal_test_util.cc   |   58 +-
 .../compiler/xla/tests/literal_test_util.h    |   84 +-
 .../xla/tests/local_client_execute_test.cc    |   44 +-
 28 files changed, 1238 insertions(+), 1135 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/literal_util.cc b/tensorflow/compiler/tf2xla/literal_util.cc
index 2c3cd658e04623..43e1c1e9fecec1 100644
--- a/tensorflow/compiler/tf2xla/literal_util.cc
+++ b/tensorflow/compiler/tf2xla/literal_util.cc
@@ -40,7 +40,7 @@ Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal) {
   return Status::OK();
 }
 
-Status CopyLiteralToHostTensor(const xla::Literal& literal,
+Status CopyLiteralToHostTensor(const xla::LiteralSlice& literal,
                                Tensor* host_tensor) {
   TF_RET_CHECK(xla::ShapeUtil::IsArray(literal.shape()) &&
                xla::ShapeUtil::ElementsIn(literal.shape()) ==
@@ -63,8 +63,8 @@ Status CopyLiteralToHostTensor(const xla::Literal& literal,
   return Status::OK();
 }
 
-Status LiteralToHostTensor(const xla::Literal& literal, DataType target_type,
-                           Tensor* host_tensor) {
+Status LiteralToHostTensor(const xla::LiteralSlice& literal,
+                           DataType target_type, Tensor* host_tensor) {
   TensorShape shape;
   TF_RETURN_IF_ERROR(XLAShapeToTensorShape(literal.shape(), &shape));
   *host_tensor = Tensor(target_type, shape);
diff --git a/tensorflow/compiler/tf2xla/literal_util.h b/tensorflow/compiler/tf2xla/literal_util.h
index f283b0236811f8..220bec15538c36 100644
--- a/tensorflow/compiler/tf2xla/literal_util.h
+++ b/tensorflow/compiler/tf2xla/literal_util.h
@@ -36,13 +36,13 @@ Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal);
 // derivable from the type of , because multiple tensorflow types map
 // to the same XLA type (e.g. INT32 and QINT32 both map to INT32 in
 // XLA).
-Status LiteralToHostTensor(const xla::Literal& literal, DataType target_type,
-                           Tensor* host_tensor);
+Status LiteralToHostTensor(const xla::LiteralSlice& literal,
+                           DataType target_type, Tensor* host_tensor);
 
 // Copies the contents of 'literal' to a previously allocated tensor
 // 'host_tensor'. The tensor and the literal must have the same number of
 // elements and the same type.
-Status CopyLiteralToHostTensor(const xla::Literal& literal,
+Status CopyLiteralToHostTensor(const xla::LiteralSlice& literal,
                                Tensor* host_tensor);
 
 }  // namespace tensorflow
diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index 83c7cb17440213..f9f994482cb9a9 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -185,7 +185,7 @@ bool ComputationBuilder::MakeWindow(
 }
 
 ComputationDataHandle ComputationBuilder::ConstantLiteral(
-    const Literal& literal) {
+    const LiteralSlice& literal) {
   OpRequest op_request;
   ConstantRequest* request = op_request.mutable_constant_request();
   *request->mutable_literal() = literal.ToProto();
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index ac1eb915cc52df..176962b6f84333 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -108,7 +108,7 @@ class ComputationBuilder {
 
   // Enqueues a constant with the value of the given literal onto the
   // computation.
-  ComputationDataHandle ConstantLiteral(const Literal& literal);
+  ComputationDataHandle ConstantLiteral(const LiteralSlice& literal);
 
   // Enqueues a constant onto the computation. Methods are templated on the
   // native host type (NativeT) which corresponds to a specific XLA
diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc
index 1899983e442116..4c59d621af43be 100644
--- a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc
+++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc
@@ -437,7 +437,7 @@ XlaOp XlaBuilder::Mul(const XlaOp& lhs, const XlaOp& rhs,
   return BinaryOp(HloOpcode::kMultiply, lhs, rhs, broadcast_dimensions);
 }
 
-XlaOp XlaBuilder::ConstantLiteral(const Literal& literal) {
+XlaOp XlaBuilder::ConstantLiteral(const LiteralSlice& literal) {
   return NoteErrorOrReturn([&]() -> StatusOr {
     HloInstructionProto instr;
     *instr.mutable_shape() = literal.shape();
diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.h b/tensorflow/compiler/xla/client/xla_client/xla_builder.h
index 4955f1515d66af..e1920d658bac24 100644
--- a/tensorflow/compiler/xla/client/xla_client/xla_builder.h
+++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.h
@@ -139,7 +139,7 @@ class XlaBuilder {
 
   // Enqueues a constant with the value of the given literal onto the
   // computation.
-  XlaOp ConstantLiteral(const Literal& literal);
+  XlaOp ConstantLiteral(const LiteralSlice& literal);
 
   // Enqueues a constant onto the computation. Methods are templated on the
   // native host type (NativeT) which corresponds to a specific XLA
diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index b3b5e34ba220c7..e9b0e11885a590 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -64,6 +64,8 @@ void ConvertEndianShort(char* bytes, int64 size) {
 
 }  // namespace
 
+LiteralBase::~LiteralBase() {}
+
 std::ostream& operator<<(std::ostream& out, const Literal& literal) {
   out << literal.ToString();
   return out;
@@ -95,99 +97,90 @@ Literal::StrideConfig::StrideConfig(
 Literal::Literal(const Shape& shape)
     : Literal(shape, /*allocate_arrays=*/true) {}
 
-Literal::Literal(const Shape& shape, bool allocate_arrays)
-    : shape_(shape), pieces_(shape), owns_buffers_(true) {
-  CHECK(LayoutUtil::HasLayout(shape));
-  for (auto& pair : pieces_) {
-    const ShapeIndex& index = pair.first;
-    Piece& piece = pair.second;
-
-    piece.set_subshape(&ShapeUtil::GetSubshape(shape_, index));
-    const Shape& subshape = piece.subshape();
-    if (ShapeUtil::IsArray(subshape)) {
-      if (allocate_arrays) {
-        if (LayoutUtil::IsSparseArray(subshape)) {
-          // For sparse arrays, the buffer must be of the size of the maximum
-          // number of sparse elements possible.
-          const int64 max_sparse_elements =
-              LayoutUtil::MaxSparseElements(subshape.layout());
-          piece.set_buffer(
-              new char[max_sparse_elements * ShapeUtil::ByteSizeOfPrimitiveType(
-                                                 subshape.element_type())]);
-          piece.set_sparse_indices(new SparseIndexArray(
-              max_sparse_elements, ShapeUtil::Rank(subshape)));
-        } else {
-          piece.set_buffer(new char[piece.size_bytes()]);
-        }
+void Literal::SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays) {
+  if (ShapeUtil::IsTuple(shape)) {
+    for (int i = 0; i < ShapeUtil::TupleElementCount(shape); ++i) {
+      const Shape& subshape = shape.tuple_shapes(i);
+
+      auto child_piece = Piece();
+      child_piece.set_subshape(&subshape);
+
+      SetPiece(subshape, &child_piece, allocate_arrays);
+
+      piece->emplace_back(std::move(child_piece));
+    }
+  } else {
+    CHECK(ShapeUtil::IsArray(shape));
+    if (allocate_arrays) {
+      if (LayoutUtil::IsSparseArray(shape)) {
+        // For sparse arrays, the buffer must be of the size of the maximum
+        // number of sparse elements possible.
+        const int64 max_sparse_elements =
+            LayoutUtil::MaxSparseElements(shape.layout());
+        piece->set_buffer(
+            new char[max_sparse_elements *
+                     ShapeUtil::ByteSizeOfPrimitiveType(shape.element_type())]);
+        piece->set_sparse_indices(
+            new SparseIndexArray(max_sparse_elements, ShapeUtil::Rank(shape)));
       } else {
-        piece.set_buffer(nullptr);
+        piece->set_buffer(new char[piece->size_bytes()]);
       }
     }
   }
 }
 
-Literal::~Literal() { DeallocateBuffers(); }
+Literal::Literal(const Shape& shape, bool allocate_arrays)
+    : LiteralBase(), shape_(MakeUnique(shape)) {
+  CHECK(LayoutUtil::HasLayout(*shape_));
+  root_piece_ = new Piece();
+  root_piece_->set_subshape(shape_.get());
+  CHECK(&root_piece_->subshape() == shape_.get());
 
-void Literal::DeallocateBuffers() {
-  if (owns_buffers_) {
-    for (auto& pair : pieces_) {
-      Piece& piece = pair.second;
-      if (piece.buffer() != nullptr) {
-        delete[] piece.buffer();
-        delete piece.sparse_indices();
-      }
-    }
-  }
+  SetPiece(*shape_, root_piece_, allocate_arrays);
 }
 
-Literal::Literal(Literal&& other) {
-  shape_ = std::move(other.shape_);
-  pieces_ = std::move(other.pieces_);
-  // We need to iterate through the pieces to set the subshape pointer
-  // properly. It must refer to subshapes within shape_.
-  for (auto& pair : pieces_) {
-    const ShapeIndex& index = pair.first;
-    Piece& piece = pair.second;
-    piece.set_subshape(&ShapeUtil::GetSubshape(shape_, index));
+Literal::~Literal() {
+  if (root_piece_ != nullptr) {
+    DeallocateBuffers();
+    delete root_piece_;
   }
-  owns_buffers_ = other.owns_buffers_;
+}
 
-  other.shape_ = ShapeUtil::MakeNil();
-  other.pieces_ = ShapeTree(other.shape_);
-  other.piece({}).set_subshape(&other.shape_);
+void Literal::DeallocateBuffers() {
+  root_piece_->ForEachMutableSubpiece(
+      [&](const ShapeIndex& index, Piece* piece) {
+        if (piece->buffer() != nullptr) {
+          delete[] piece->buffer();
+          delete piece->sparse_indices();
+        }
+      });
 }
 
+Literal::Literal(Literal&& other) : LiteralBase() { *this = std::move(other); }
+
 Literal& Literal::operator=(Literal&& other) {
-  DeallocateBuffers();
-  shape_ = std::move(other.shape_);
-  pieces_ = std::move(other.pieces_);
-  // We need to iterate through the pieces to set the subshape pointer
-  // properly. It must refer to subshapes within shape_.
-  for (auto& pair : pieces_) {
-    const ShapeIndex& index = pair.first;
-    Piece& piece = pair.second;
-    piece.set_subshape(&ShapeUtil::GetSubshape(shape_, index));
-  }
-  owns_buffers_ = other.owns_buffers_;
-
-  other.shape_ = ShapeUtil::MakeNil();
-  other.pieces_ = ShapeTree(other.shape_);
-  other.piece({}).set_subshape(&other.shape_);
+  CHECK(&other.root_piece_->subshape() == other.shape_.get());
+
+  using std::swap;
+  swap(shape_, other.shape_);
+  swap(root_piece_, other.root_piece_);
+  CHECK(&root_piece_->subshape() == shape_.get());
+
   return *this;
 }
 
-std::unique_ptr Literal::CreateFromShape(const Shape& shape) {
+std::unique_ptr LiteralBase::CreateFromShape(const Shape& shape) {
   auto literal = MakeUnique(shape);
-  for (auto& pair : literal->pieces_) {
-    Piece& piece = pair.second;
-    if (ShapeUtil::IsArray(piece.subshape())) {
-      memset(piece.untyped_data(), 0, piece.size_bytes());
-    }
-  }
+  literal->root_piece_->ForEachMutableSubpiece(
+      [&](const ShapeIndex& index, Piece* piece) {
+        if (ShapeUtil::IsArray(piece->subshape())) {
+          memset(piece->untyped_data(), 0, piece->size_bytes());
+        }
+      });
   return literal;
 }
 
-const SparseIndexArray* Literal::sparse_indices(
+const SparseIndexArray* LiteralBase::sparse_indices(
     const ShapeIndex& shape_index) const {
   return piece(shape_index).sparse_indices();
 }
@@ -204,7 +197,7 @@ SparseIndexArray* Literal::sparse_indices(const ShapeIndex& shape_index) {
 
 template 
 Status Literal::CopySliceFromInternal(
-    const Literal& src_literal, tensorflow::gtl::ArraySlice src_base,
+    const LiteralBase& src_literal, tensorflow::gtl::ArraySlice src_base,
     tensorflow::gtl::ArraySlice dest_base,
     tensorflow::gtl::ArraySlice copy_size) {
   TF_RET_CHECK(ShapeUtil::Rank(src_literal.shape()) == src_base.size());
@@ -217,8 +210,8 @@ Status Literal::CopySliceFromInternal(
 
   if (ShapeUtil::Rank(src_literal.shape()) == 0 ||
       ShapeUtil::Rank(shape()) == 0) {
-    // If any of the two shapes are scalars, we can just call the StridedCopy()
-    // directly, and we know we will be copying only one value.
+    // If any of the two shapes are scalars, we can just call the
+    // StridedCopy() directly, and we know we will be copying only one value.
     TF_RET_CHECK(copy_size.empty());
     StridedCopy(data(), linear_index(shape(), dest_base), 0,
                 src_literal.data(),
@@ -264,7 +257,7 @@ Status Literal::CopySliceFromInternal(
   return Status::OK();
 }
 
-Status Literal::CopyElementFrom(const Literal& src_literal,
+Status Literal::CopyElementFrom(const LiteralSlice& src_literal,
                                 tensorflow::gtl::ArraySlice src_index,
                                 tensorflow::gtl::ArraySlice dest_index) {
   DCHECK_EQ(shape().element_type(), src_literal.shape().element_type());
@@ -293,22 +286,21 @@ std::vector Literal::DecomposeTuple() {
     elements.push_back(Literal(ShapeUtil::GetSubshape(shape(), {i}),
                                /*allocate_arrays=*/false));
     Literal& element = elements.back();
-    for (auto& pair : element.pieces_) {
-      const ShapeIndex& index = pair.first;
-      Piece& dest_piece = pair.second;
-      ShapeIndex src_index = {i};
-      for (int64 j : index) {
-        src_index.push_back(j);
-      }
-      Piece& src_piece = piece(src_index);
-
-      // Move the respective buffer and sparse indices over to the element
-      // Literal.
-      dest_piece.set_buffer(src_piece.buffer());
-      src_piece.set_buffer(nullptr);
-      dest_piece.set_sparse_indices(src_piece.sparse_indices());
-      src_piece.set_sparse_indices(nullptr);
-    }
+    element.root_piece_->ForEachMutableSubpiece(
+        [&](const ShapeIndex& index, Piece* dest_piece) {
+          ShapeIndex src_index = {i};
+          for (int64 j : index) {
+            src_index.push_back(j);
+          }
+          Piece& src_piece = piece(src_index);
+
+          // Move the respective buffer and sparse indices over to the element
+          // Literal.
+          dest_piece->set_buffer(src_piece.buffer());
+          src_piece.set_buffer(nullptr);
+          dest_piece->set_sparse_indices(src_piece.sparse_indices());
+          src_piece.set_sparse_indices(nullptr);
+        });
   }
   // Set this literal to be nil-shaped.
   *this = Literal();
@@ -331,9 +323,9 @@ std::vector Literal::DecomposeTuple() {
 }
 
 namespace {
-
-// Copies the elements in 'src' to 'dest'. The shape and layout of the data in
-// the array slices are indicated by dest_shape and src_shape respectively.
+// Copies the elements in 'src' to 'dest'. The shape and layout of the data
+// in the array slices are indicated by dest_shape and src_shape
+// respectively.
 template 
 void CopyElementsBetween(tensorflow::gtl::MutableArraySlice dest,
                          tensorflow::gtl::ArraySlice src,
@@ -351,7 +343,7 @@ void CopyElementsBetween(tensorflow::gtl::MutableArraySlice dest,
 
 }  // namespace
 
-Status Literal::Piece::CopyFrom(const Literal::Piece& src) {
+Status LiteralBase::Piece::CopyFrom(const LiteralBase::Piece& src) {
   if (ShapeUtil::Equal(subshape(), src.subshape())) {
     // If the layouts are equal it's faster just to memcpy.
     memcpy(buffer(), src.buffer(), src.size_bytes());
@@ -381,14 +373,15 @@ Status Literal::Piece::CopyFrom(const Literal::Piece& src) {
 #undef COPY_ELEMENTS
       default:
         return Unimplemented(
-            "Copying a Literal object with element type %s is not implemented.",
+            "Copying a Literal object with element type %s is not "
+            "implemented.",
             PrimitiveType_Name(subshape().element_type()).c_str());
     }
   }
   return Status::OK();
 }
 
-Status Literal::CopyFrom(const Literal& src_literal,
+Status Literal::CopyFrom(const LiteralSlice& src_literal,
                          const ShapeIndex& dest_shape_index,
                          const ShapeIndex& src_shape_index) {
   const Shape& dest_subshape =
@@ -402,36 +395,33 @@ Status Literal::CopyFrom(const Literal& src_literal,
         ShapeUtil::HumanString(src_subshape).c_str());
   }
 
-  for (auto& pair : pieces_) {
-    const ShapeIndex& index = pair.first;
-    Piece& piece = pair.second;
-    if (!ShapeUtil::IsArray(piece.subshape())) {
-      continue;
-    }
-
-    // Determine if this index is in the part of this literal that we want to
-    // copy over from src_literal.
-    bool in_subtree_to_copy = true;
-    for (int i = 0; i < dest_shape_index.size(); ++i) {
-      if (index[i] != dest_shape_index[i]) {
-        in_subtree_to_copy = false;
-        break;
-      }
-    }
-    if (!in_subtree_to_copy) {
-      continue;
-    }
-
-    // Construct the index of the corresponding piece in the source literal.
-    ShapeIndex src_piece_index = src_shape_index;
-    for (int64 i = dest_shape_index.size(); i < index.size(); ++i) {
-      src_piece_index.push_back(index[i]);
-    }
+  return root_piece_->ForEachMutableSubpieceWithStatus(
+      [&](const ShapeIndex& index, Piece* piece) {
+        if (!ShapeUtil::IsArray(piece->subshape())) {
+          return Status::OK();
+        }
 
-    TF_RETURN_IF_ERROR(piece.CopyFrom(src_literal.piece(src_piece_index)));
-  }
-  return Status::OK();
-}
+        // Determine if this index is in the part of this literal that we want
+        // to copy over from src_literal.
+        bool in_subtree_to_copy = true;
+        for (int i = 0; i < dest_shape_index.size(); ++i) {
+          if (index[i] != dest_shape_index[i]) {
+            in_subtree_to_copy = false;
+            break;
+          }
+        }
+        if (!in_subtree_to_copy) {
+          return Status::OK();
+        }
+        // Construct the index of the corresponding piece in the source literal.
+        ShapeIndex src_piece_index = src_shape_index;
+        for (int64 i = dest_shape_index.size(); i < index.size(); ++i) {
+          src_piece_index.push_back(index[i]);
+        }
+        TF_RETURN_IF_ERROR(piece->CopyFrom(src_literal.piece(src_piece_index)));
+        return Status::OK();
+      });
+}  // namespace xla
 
 Status Literal::MoveFrom(Literal&& src_literal,
                          const ShapeIndex& dest_shape_index) {
@@ -444,37 +434,32 @@ Status Literal::MoveFrom(Literal&& src_literal,
         ShapeUtil::HumanString(src_literal.shape()).c_str());
   }
 
-  if (!(owns_buffers_ && src_literal.owns_buffers_)) {
-    return InvalidArgument(
-        "Source and destination literals must both own their buffers (ie, not "
-        "be views)");
-  }
+  src_literal.root_piece_->ForEachSubpiece(
+      [&](const ShapeIndex& src_index, const Piece& src_piece) {
+        if (!ShapeUtil::IsArray(src_piece.subshape())) {
+          return;
+        }
 
-  for (auto& pair : src_literal.pieces_) {
-    const ShapeIndex& src_index = pair.first;
-    Piece& src_piece = pair.second;
-    if (!ShapeUtil::IsArray(src_piece.subshape())) {
-      continue;
-    }
+        ShapeIndex dest_index = dest_shape_index;
+        for (int64 i : src_index) {
+          dest_index.push_back(i);
+        }
+        Piece& dest_piece = piece(dest_index);
+        delete[] dest_piece.buffer();
+        dest_piece.set_buffer(src_piece.buffer());
+        delete dest_piece.sparse_indices();
+        dest_piece.set_sparse_indices(src_piece.sparse_indices());
+      });
 
-    ShapeIndex dest_index = dest_shape_index;
-    for (int64 i : src_index) {
-      dest_index.push_back(i);
-    }
-    Piece& dest_piece = piece(dest_index);
-    delete[] dest_piece.buffer();
-    dest_piece.set_buffer(src_piece.buffer());
-    delete dest_piece.sparse_indices();
-    dest_piece.set_sparse_indices(src_piece.sparse_indices());
-  }
+  src_literal.shape_ = MakeUnique(ShapeUtil::MakeNil());
+  delete src_literal.root_piece_;
+  src_literal.root_piece_ = new LiteralBase::Piece();
+  src_literal.root_piece_->set_subshape(src_literal.shape_.get());
 
-  src_literal.shape_ = ShapeUtil::MakeNil();
-  src_literal.pieces_ = ShapeTree(src_literal.shape_);
-  src_literal.piece({}).set_subshape(&src_literal.shape_);
   return Status::OK();
 }
 
-Status Literal::CopySliceFrom(const Literal& src_literal,
+Status Literal::CopySliceFrom(const LiteralSlice& src_literal,
                               tensorflow::gtl::ArraySlice src_base,
                               tensorflow::gtl::ArraySlice dest_base,
                               tensorflow::gtl::ArraySlice copy_size) {
@@ -743,7 +728,7 @@ void Literal::PopulateR1(const tensorflow::core::Bitmap& values) {
   return CreateR2FromArray2D(*value);
 }
 
-std::unique_ptr Literal::Relayout(
+std::unique_ptr LiteralBase::Relayout(
     const Layout& new_layout, const ShapeIndex& shape_index) const {
   // Create new shape with 'new_layout' set at the given shape index.
   Shape new_shape = shape();
@@ -755,7 +740,7 @@ std::unique_ptr Literal::Relayout(
   return result;
 }
 
-std::unique_ptr Literal::Relayout(
+std::unique_ptr LiteralBase::Relayout(
     const Shape& shape_with_layout) const {
   CHECK(ShapeUtil::Compatible(shape_with_layout, shape()))
       << "Given shape_with_layout " << ShapeUtil::HumanString(shape_with_layout)
@@ -774,7 +759,7 @@ std::unique_ptr Literal::Relayout(
   return result;
 }
 
-StatusOr> Literal::Reshape(
+StatusOr> LiteralBase::Reshape(
     tensorflow::gtl::ArraySlice dimensions) const {
   if (!ShapeUtil::IsArray(shape())) {
     return InvalidArgument("Reshape does not support tuples.");
@@ -788,7 +773,8 @@ StatusOr> Literal::Reshape(
   }
   // Because the layout is monotonic, we can simply reuse the same sequence of
   // values without changing their order.
-  output->shape_ = ShapeUtil::MakeShape(shape().element_type(), dimensions);
+  *output->mutable_shape_do_not_use() =
+      ShapeUtil::MakeShape(shape().element_type(), dimensions);
 
   int64 elements_before = ShapeUtil::ElementsIn(shape());
   int64 elements_after = ShapeUtil::ElementsIn(output->shape());
@@ -802,7 +788,7 @@ StatusOr> Literal::Reshape(
   return std::move(output);
 }
 
-std::unique_ptr Literal::Transpose(
+std::unique_ptr LiteralBase::Transpose(
     tensorflow::gtl::ArraySlice permutation) const {
   CHECK(ShapeUtil::IsArray(shape())) << "Tuple is not supported for transpose";
   CHECK(IsPermutation(permutation, ShapeUtil::Rank(shape())))
@@ -819,8 +805,8 @@ std::unique_ptr Literal::Transpose(
   // representation intact.
   // For example, consider the shape F32[11,8]{1,0} under a {1,0} permutation.
   // The shape with affine layout resulting from that operation will be
-  // F32[8,11]{0,1}, since it leaves the original most minor (the 8 sized), the
-  // most minor.
+  // F32[8,11]{0,1}, since it leaves the original most minor (the 8 sized),
+  // the most minor.
   //
   // Essentially, given MinMaj(Di) the position of the Di dimension within the
   // minor to major vector, and given T(Di) the index that the original Di
@@ -836,12 +822,11 @@ std::unique_ptr Literal::Transpose(
   std::unique_ptr new_literal = CreateFromShape(permuted_shape);
   DCHECK_GE(ShapeUtil::ByteSizeOf(new_literal->shape()),
             ShapeUtil::ByteSizeOf(shape()));
-  std::memcpy(new_literal->root_piece().buffer(), root_piece().buffer(),
-              root_piece().size_bytes());
+  std::memcpy(new_literal->untyped_data(), untyped_data(), size_bytes());
   return new_literal;
 }
 
-std::unique_ptr Literal::Slice(
+std::unique_ptr LiteralBase::Slice(
     tensorflow::gtl::ArraySlice start_indices,
     tensorflow::gtl::ArraySlice limit_indices) const {
   CHECK(ShapeUtil::IsArray(shape())) << "tuple is not supported for slice";
@@ -909,20 +894,20 @@ std::unique_ptr Literal::Slice(
   }
 }
 
-Literal Literal::Clone() const {
+Literal LiteralBase::Clone() const {
   Literal result(shape());
   TF_CHECK_OK(result.CopyFrom(*this));
   return result;
 }
 
-std::unique_ptr Literal::CloneToUnique() const {
+std::unique_ptr LiteralBase::CloneToUnique() const {
   auto result = MakeUnique(shape());
   TF_CHECK_OK(result->CopyFrom(*this));
   return result;
 }
 
-string Literal::GetAsString(tensorflow::gtl::ArraySlice multi_index,
-                            const ShapeIndex& shape_index) const {
+string LiteralBase::GetAsString(tensorflow::gtl::ArraySlice multi_index,
+                                const ShapeIndex& shape_index) const {
   const Shape& subshape = ShapeUtil::GetSubshape(shape(), shape_index);
   CHECK(LayoutUtil::IsDenseArray(subshape));
   switch (subshape.element_type()) {
@@ -962,8 +947,8 @@ string Literal::GetAsString(tensorflow::gtl::ArraySlice multi_index,
   }
 }
 
-string Literal::GetSparseElementAsString(int64 sparse_element_number,
-                                         const ShapeIndex& shape_index) const {
+string LiteralBase::GetSparseElementAsString(
+    int64 sparse_element_number, const ShapeIndex& shape_index) const {
   const Shape& subshape = ShapeUtil::GetSubshape(shape(), shape_index);
   CHECK(LayoutUtil::IsSparseArray(subshape));
   switch (subshape.element_type()) {
@@ -1017,7 +1002,7 @@ string Literal::GetSparseElementAsString(int64 sparse_element_number,
   }
 }
 
-StatusOr Literal::GetIntegralAsS64(
+StatusOr LiteralBase::GetIntegralAsS64(
     tensorflow::gtl::ArraySlice multi_index) const {
   CHECK(LayoutUtil::IsDenseArray(shape()));
   switch (shape().element_type()) {
@@ -1070,7 +1055,7 @@ Status Literal::SetIntegralAsS64(tensorflow::gtl::ArraySlice multi_index,
   return Status::OK();
 }
 
-tensorflow::gtl::ArraySlice Literal::GetSparseIndex(
+tensorflow::gtl::ArraySlice LiteralBase::GetSparseIndex(
     int64 sparse_element_number, const ShapeIndex& shape_index) const {
   const Piece& p = piece(shape_index);
   CHECK_GE(sparse_element_number, 0);
@@ -1082,10 +1067,10 @@ void Literal::SortSparseElements(const ShapeIndex& shape_index) {
   piece(shape_index).SortSparseElements();
 }
 
-Literal Literal::GetFirstScalarLiteral() const {
-  CHECK(ShapeUtil::IsArray(shape_));
-  CHECK_GT(ShapeUtil::ElementsIn(shape_), 0);
-  switch (shape_.element_type()) {
+Literal LiteralBase::GetFirstScalarLiteral() const {
+  CHECK(ShapeUtil::IsArray(shape()));
+  CHECK_GT(ShapeUtil::ElementsIn(shape()), 0);
+  switch (shape().element_type()) {
     case PRED:
       return std::move(*Literal::CreateR0(GetFirstElement()));
     // 8 bit types.
@@ -1121,11 +1106,11 @@ Literal Literal::GetFirstScalarLiteral() const {
     case U64:
       return std::move(*Literal::CreateR0(GetFirstElement()));
     default:
-      LOG(FATAL) << "Unhandled primitive type " << shape_.element_type();
+      LOG(FATAL) << "Unhandled primitive type " << shape().element_type();
   }
 }
 
-void Literal::Piece::SortSparseElements() {
+void LiteralBase::Piece::SortSparseElements() {
   switch (subshape().element_type()) {
     case PRED:
       SortSparseElementsInternal();
@@ -1176,7 +1161,7 @@ void Literal::Piece::SortSparseElements() {
 }
 
 template 
-void Literal::Piece::SortSparseElementsInternal() {
+void LiteralBase::Piece::SortSparseElementsInternal() {
   CHECK(LayoutUtil::IsSparseArray(subshape()));
   int64 num_elements = sparse_indices()->index_count();
   auto values = data();
@@ -1186,10 +1171,11 @@ void Literal::Piece::SortSparseElementsInternal() {
 }
 
 namespace {
-
-void ToStringHelper(const Literal& literal, const ShapeIndex& shape_index,
+void ToStringHelper(const LiteralBase& literal, const ShapeIndex& shape_index,
                     bool print_layout, std::vector* pieces) {
   const Shape& subshape = ShapeUtil::GetSubshape(literal.shape(), shape_index);
+  CHECK(LayoutUtil::HasLayout(literal.shape()));
+  CHECK(LayoutUtil::HasLayout(subshape));
 
   auto shape_to_string = [print_layout](const Shape& shape) {
     if (print_layout) {
@@ -1348,13 +1334,14 @@ void ToStringHelper(const Literal& literal, const ShapeIndex& shape_index,
 
 }  // namespace
 
-int64 Literal::sparse_element_count() const {
+int64 LiteralBase::sparse_element_count() const {
   CHECK(LayoutUtil::IsSparseArray(shape()));
   return sparse_indices()->index_count();
 }
 
-string Literal::ToString(bool print_layout) const {
+string LiteralBase::ToString(bool print_layout) const {
   std::vector pieces;
+  CHECK(LayoutUtil::HasLayout(this->shape()));
   ToStringHelper(*this, {}, print_layout, &pieces);
   return tensorflow::str_util::Join(pieces, "");
 }
@@ -1362,7 +1349,7 @@ string Literal::ToString(bool print_layout) const {
 /* static */ std::unique_ptr Literal::MakeTuple(
     tensorflow::gtl::ArraySlice elements) {
   std::vector element_shapes;
-  for (const Literal* element : elements) {
+  for (const auto* element : elements) {
     element_shapes.push_back(element->shape());
   }
   auto literal = MakeUnique(ShapeUtil::MakeTupleShape(element_shapes));
@@ -1372,6 +1359,19 @@ string Literal::ToString(bool print_layout) const {
   return literal;
 }
 
+/* static */ std::unique_ptr Literal::MakeTupleFromSlices(
+    tensorflow::gtl::ArraySlice elements) {
+  std::vector element_shapes;
+  for (const auto& element : elements) {
+    element_shapes.push_back(element.shape());
+  }
+  auto literal = MakeUnique(ShapeUtil::MakeTupleShape(element_shapes));
+  for (int i = 0; i < elements.size(); ++i) {
+    TF_CHECK_OK(literal->CopyFrom(elements[i], /*dest_shape_index=*/{i}));
+  }
+  return literal;
+}
+
 /* static */ std::unique_ptr Literal::MakeTupleOwned(
     std::vector> elements) {
   std::vector element_shapes;
@@ -1387,7 +1387,7 @@ string Literal::ToString(bool print_layout) const {
   return literal;
 }
 
-void Literal::EachCellAsString(
+void LiteralBase::EachCellAsString(
     const std::function indices,
                              const string& value)>& per_cell) const {
   if (ShapeUtil::HasZeroElements(shape())) {
@@ -1403,7 +1403,7 @@ void Literal::EachCellAsString(
 namespace {
 template 
 std::unique_ptr ConvertBetweenNativeTypesWithConverter(
-    const Literal& src_literal, const ConverterType& converter) {
+    const LiteralBase& src_literal, const ConverterType& converter) {
   CHECK(ShapeUtil::IsArray(src_literal.shape()));
   auto result_literal = MakeUnique(ShapeUtil::ChangeElementType(
       src_literal.shape(),
@@ -1419,7 +1419,8 @@ std::unique_ptr ConvertBetweenNativeTypesWithConverter(
 }
 
 template 
-std::unique_ptr ConvertBetweenNativeTypes(const Literal& src_literal) {
+std::unique_ptr ConvertBetweenNativeTypes(
+    const LiteralBase& src_literal) {
   auto converter = [](NativeSrcT src) { return static_cast(src); };
   return ConvertBetweenNativeTypesWithConverter(
       src_literal, converter);
@@ -1428,7 +1429,7 @@ std::unique_ptr ConvertBetweenNativeTypes(const Literal& src_literal) {
 template 
 typename std::enable_if<(sizeof(NativeSrcT) == sizeof(NativeDestT)),
                         std::unique_ptr>::type
-BitcastBetweenNativeTypes(const Literal& src_literal) {
+BitcastBetweenNativeTypes(const LiteralBase& src_literal) {
   auto converter = [](NativeSrcT src) {
     return tensorflow::bit_cast(src);
   };
@@ -1436,19 +1437,19 @@ BitcastBetweenNativeTypes(const Literal& src_literal) {
       src_literal, converter);
 }
 
-// This template specialization is here to make the compiler happy. bit_cast has
-// a static check that the types are the same size. This specialization should
-// never be used because the source and destination types are checked for
-// identical sizes higher up.
+// This template specialization is here to make the compiler happy. bit_cast
+// has a static check that the types are the same size. This specialization
+// should never be used because the source and destination types are checked
+// for identical sizes higher up.
 template 
 typename std::enable_if<(sizeof(NativeSrcT) != sizeof(NativeDestT)),
                         std::unique_ptr>::type
-BitcastBetweenNativeTypes(const Literal& src_literal) {
+BitcastBetweenNativeTypes(const LiteralBase& src_literal) {
   LOG(FATAL) << "Invalid bitcast between types of different sizes.";
 }
 
 template 
-std::unique_ptr ConvertToC64(const Literal& src_literal) {
+std::unique_ptr ConvertToC64(const LiteralBase& src_literal) {
   CHECK(ShapeUtil::IsArray(src_literal.shape()));
   auto result_literal = MakeUnique(
       ShapeUtil::ChangeElementType(src_literal.shape(), C64));
@@ -1466,7 +1467,7 @@ std::unique_ptr ConvertToC64(const Literal& src_literal) {
 }
 
 template 
-std::unique_ptr ConvertIfTypesMatch(const Literal& src_literal,
+std::unique_ptr ConvertIfTypesMatch(const LiteralBase& src_literal,
                                              bool bitcast) {
   CHECK_EQ(primitive_src_type, src_literal.shape().element_type());
   if (bitcast) {
@@ -1486,7 +1487,7 @@ std::unique_ptr ConvertIfTypesMatch(const Literal& src_literal,
 
 template 
 StatusOr> ConvertIfDestTypeMatches(
-    const Literal& src_literal, PrimitiveType primitive_dest_type,
+    const LiteralBase& src_literal, PrimitiveType primitive_dest_type,
     bool bitcast) {
   switch (primitive_dest_type) {
 #define CONVERT_IF_TYPES_MATCH(type)                                    \
@@ -1521,7 +1522,8 @@ StatusOr> ConvertIfDestTypeMatches(
 }
 
 StatusOr> ConvertSwitch(
-    const Literal& literal, PrimitiveType primitive_dest_type, bool bitcast) {
+    const LiteralBase& literal, PrimitiveType primitive_dest_type,
+    bool bitcast) {
   TF_RET_CHECK(ShapeUtil::IsArray(literal.shape()));
   if (literal.shape().element_type() == primitive_dest_type) {
     return literal.CloneToUnique();
@@ -1555,17 +1557,18 @@ StatusOr> ConvertSwitch(
 
 }  // namespace
 
-StatusOr> Literal::Convert(
+StatusOr> LiteralBase::Convert(
     PrimitiveType primitive_dest_type) const {
   return ConvertSwitch(*this, primitive_dest_type, /*bitcast=*/false);
 }
 
-StatusOr> Literal::BitcastConvert(
+StatusOr> LiteralBase::BitcastConvert(
     PrimitiveType primitive_dest_type) const {
   if (primitive_util::BitWidth(shape().element_type()) !=
       primitive_util::BitWidth(primitive_dest_type)) {
     return InvalidArgument(
-        "Cannot bitcast convert from %s to %s, bit widths are different: %d != "
+        "Cannot bitcast convert from %s to %s, bit widths are different: %d "
+        "!= "
         "%d",
         PrimitiveType_Name(shape().element_type()).c_str(),
         PrimitiveType_Name(primitive_dest_type).c_str(),
@@ -1575,7 +1578,7 @@ StatusOr> Literal::BitcastConvert(
   return ConvertSwitch(*this, primitive_dest_type, /*bitcast=*/true);
 }
 
-StatusOr> Literal::ConvertToShape(
+StatusOr> LiteralBase::ConvertToShape(
     const Shape& dest_shape, bool round_f32_to_bf16) const {
   if (!ShapeUtil::IsTuple(dest_shape)) {
     if (round_f32_to_bf16 && shape().element_type() == F32 &&
@@ -1590,7 +1593,7 @@ StatusOr> Literal::ConvertToShape(
   }
   std::vector elements;
   for (int i = 0; i < ShapeUtil::TupleElementCount(shape()); ++i) {
-    auto element = LiteralView::Create(*this, {i});
+    auto element = LiteralSlice(*this, {i});
     TF_ASSIGN_OR_RETURN(
         auto new_element,
         element.ConvertToShape(ShapeUtil::GetSubshape(dest_shape, {i})));
@@ -1602,8 +1605,8 @@ StatusOr> Literal::ConvertToShape(
 }
 
 template 
-bool Literal::Piece::EqualElementsInternal(
-    const Literal::Piece& other, std::vector* multi_index) const {
+bool LiteralBase::Piece::EqualElementsInternal(
+    const LiteralBase::Piece& other, std::vector* multi_index) const {
   if (multi_index->size() == ShapeUtil::Rank(subshape())) {
     return (Get(*multi_index) == other.Get(*multi_index));
   }
@@ -1617,7 +1620,7 @@ bool Literal::Piece::EqualElementsInternal(
   return true;
 }
 
-bool Literal::Piece::EqualElements(const Literal::Piece& other) const {
+bool LiteralBase::Piece::EqualElements(const LiteralBase::Piece& other) const {
   DCHECK(ShapeUtil::Compatible(subshape(), other.subshape()));
 
   std::vector multi_index;
@@ -1645,32 +1648,31 @@ bool Literal::Piece::EqualElements(const Literal::Piece& other) const {
     case C64:
       return EqualElementsInternal(other, &multi_index);
     default:
-      LOG(FATAL) << "Unimplemented: Literal::Piece::EqualElements for type "
+      LOG(FATAL) << "Unimplemented: LiteralBase::Piece::EqualElements for type "
                  << PrimitiveType_Name(subshape().element_type());
   }
 }
 
-bool Literal::operator==(const Literal& other) const {
+bool LiteralBase::operator==(const LiteralBase& other) const {
   if (!ShapeUtil::Compatible(shape(), other.shape())) {
     return false;
   }
-  for (const auto& pair : pieces_) {
-    const ShapeIndex& index = pair.first;
-    const Piece& piece = pair.second;
-    if (!ShapeUtil::IsArray(piece.subshape())) {
-      continue;
-    }
 
-    const Piece& other_piece = other.piece(index);
-    if (!piece.EqualElements(other_piece)) {
-      return false;
-    }
-  }
-  return true;
+  return root_piece().ForEachSubpieceWithBool(
+      [&](const ShapeIndex& index, const Piece& piece) {
+        if (!ShapeUtil::IsArray(piece.subshape())) {
+          return true;
+        }
+
+        const Piece& other_piece = other.piece(index);
+        if (!piece.EqualElements(other_piece)) {
+          return false;
+        }
+        return true;
+      });
 }
 
 namespace {
-
 template 
 static bool AllElementsEqualValue(tensorflow::gtl::ArraySlice data,
                                   NativeT value) {
@@ -1684,11 +1686,11 @@ static bool AllElementsEqualValue(tensorflow::gtl::ArraySlice data,
 
 }  // namespace
 
-bool Literal::IsAll(int8 value) const {
-  for (const auto& pair : pieces_) {
-    const Piece& piece = pair.second;
+bool LiteralBase::IsAll(int8 value) const {
+  return root_piece().ForEachSubpieceWithBool([&](const ShapeIndex& index,
+                                                  const Piece& piece) {
     if (!ShapeUtil::IsArray(piece.subshape())) {
-      continue;
+      return true;
     }
 
     auto piece_is_all = [&]() {
@@ -1741,41 +1743,41 @@ bool Literal::IsAll(int8 value) const {
     if (!piece_is_all()) {
       return false;
     }
-  }
-  return true;
-}
+    return true;
+  });
+}  // namespace xla
 
-bool Literal::IsAllFloat(float value) const {
-  for (const auto& pair : pieces_) {
-    const Piece& piece = pair.second;
-    if (!ShapeUtil::IsArray(piece.subshape())) {
-      continue;
-    }
+bool LiteralBase::IsAllFloat(float value) const {
+  return root_piece().ForEachSubpieceWithBool(
+      [&](const ShapeIndex& index, const Piece& piece) {
+        if (!ShapeUtil::IsArray(piece.subshape())) {
+          return true;
+        }
 
-    auto piece_is_all = [&]() {
-      switch (shape().element_type()) {
-        case F32:
-          return AllElementsEqualValue(piece.data(), value);
-        case F64:
-          return AllElementsEqualValue(piece.data(), value);
-        case F16:
-          return AllElementsEqualValue(piece.data(),
-                                             static_cast(value));
-        case BF16:
-          return AllElementsEqualValue(piece.data(),
-                                                 static_cast(value));
-        default:
+        auto piece_is_all = [&]() {
+          switch (shape().element_type()) {
+            case F32:
+              return AllElementsEqualValue(piece.data(), value);
+            case F64:
+              return AllElementsEqualValue(piece.data(), value);
+            case F16:
+              return AllElementsEqualValue(piece.data(),
+                                                 static_cast(value));
+            case BF16:
+              return AllElementsEqualValue(
+                  piece.data(), static_cast(value));
+            default:
+              return false;
+          }
+        };
+        if (!piece_is_all()) {
           return false;
-      }
-    };
-    if (!piece_is_all()) {
-      return false;
-    }
-  }
-  return true;
+        }
+        return true;
+      });
 }
 
-bool Literal::IsAllComplex(complex64 value) const {
+bool LiteralBase::IsAllComplex(complex64 value) const {
   switch (shape().element_type()) {
     case C64:
       return AllElementsEqualValue(root_piece().data(),
@@ -1785,93 +1787,93 @@ bool Literal::IsAllComplex(complex64 value) const {
   }
 }
 
-bool Literal::IsAllFirst() const {
-  for (const auto& pair : pieces_) {
-    const Piece& piece = pair.second;
-    if (!ShapeUtil::IsArray(piece.subshape())) {
-      continue;
-    }
-
-    // Empty shapes are not all the first element since there is no first
-    // element.
-    if (ShapeUtil::HasZeroElements(piece.subshape())) {
-      return false;
-    }
-    auto piece_is_all = [&]() {
-      switch (piece.subshape().element_type()) {
-        case PRED: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
+bool LiteralBase::IsAllFirst() const {
+  return root_piece().ForEachSubpieceWithBool(
+      [&](const ShapeIndex& index, const Piece& piece) {
+        if (!ShapeUtil::IsArray(piece.subshape())) {
+          return true;
         }
-        // 8 bit types
-        case S8: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        case U8: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        // 16 bit types
-        case BF16: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        case F16: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        case S16: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        case U16: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        // 32 bit types
-        case F32: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        case U32: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        case S32: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        // 64 bit types
-        case C64: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        case F64: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        case S64: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        case U64: {
-          auto data = piece.data();
-          return AllElementsEqualValue(data, data[0]);
-        }
-        default:
+
+        // Empty shapes are not all the first element since there is no first
+        // element.
+        if (ShapeUtil::HasZeroElements(piece.subshape())) {
           return false;
-      }
-    };
+        }
+        auto piece_is_all = [&]() {
+          switch (piece.subshape().element_type()) {
+            case PRED: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            // 8 bit types
+            case S8: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            case U8: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            // 16 bit types
+            case BF16: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            case F16: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            case S16: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            case U16: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            // 32 bit types
+            case F32: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            case U32: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            case S32: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            // 64 bit types
+            case C64: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            case F64: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            case S64: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            case U64: {
+              auto data = piece.data();
+              return AllElementsEqualValue(data, data[0]);
+            }
+            default:
+              return false;
+          }
+        };
 
-    if (!piece_is_all()) {
-      return false;
-    }
-  }
-  return true;
+        if (!piece_is_all()) {
+          return false;
+        }
+        return true;
+      });
 }
 
-bool Literal::IsZero(tensorflow::gtl::ArraySlice indices) const {
+bool LiteralBase::IsZero(tensorflow::gtl::ArraySlice indices) const {
   CHECK(ShapeUtil::IsArray(shape()));
   switch (shape().element_type()) {
     case U8:
@@ -1904,7 +1906,6 @@ bool Literal::IsZero(tensorflow::gtl::ArraySlice indices) const {
 }
 
 namespace {
-
 template 
 void CopyToRepeatedField(RepeatedFieldT* dest,
                          const tensorflow::gtl::ArraySlice src) {
@@ -1913,7 +1914,7 @@ void CopyToRepeatedField(RepeatedFieldT* dest,
 
 }  // namespace
 
-void Literal::Piece::WriteToProto(LiteralProto* proto) const {
+void LiteralBase::Piece::WriteToProto(LiteralProto* proto) const {
   *proto->mutable_shape() = subshape();
   switch (subshape().element_type()) {
     case PRED:
@@ -1969,18 +1970,17 @@ void Literal::Piece::WriteToProto(LiteralProto* proto) const {
   }
 }
 
-const void* Literal::Piece::untyped_data() const {
+const void* LiteralBase::Piece::untyped_data() const {
   CHECK(ShapeUtil::IsArray(subshape())) << ShapeUtil::HumanString(subshape());
   return buffer();
 }
 
-void* Literal::Piece::untyped_data() {
+void* LiteralBase::Piece::untyped_data() {
   CHECK(ShapeUtil::IsArray(subshape())) << ShapeUtil::HumanString(subshape());
   return buffer();
 }
 
 namespace {
-
 template 
 Status CopyFromRepeatedField(tensorflow::gtl::MutableArraySlice dest,
                              const RepeatedFieldT& src) {
@@ -1995,7 +1995,7 @@ Status CopyFromRepeatedField(tensorflow::gtl::MutableArraySlice dest,
 
 }  // namespace
 
-Status Literal::Piece::CopyFromProto(const LiteralProto& proto) {
+Status LiteralBase::Piece::CopyFromProto(const LiteralProto& proto) {
   // These conditions should have been checked in Literal::CreateFromProto.
   TF_RET_CHECK(proto.has_shape());
   TF_RET_CHECK(LayoutUtil::HasLayout(proto.shape()));
@@ -2062,21 +2062,19 @@ Status Literal::Piece::CopyFromProto(const LiteralProto& proto) {
   return Status::OK();
 }
 
-LiteralProto Literal::ToProto() const {
+LiteralProto LiteralBase::ToProto() const {
   LiteralProto proto;
-  for (const auto& pair : pieces_) {
-    const ShapeIndex& index = pair.first;
-    const Piece& piece = pair.second;
-
-    LiteralProto* proto_piece = &proto;
-    for (int64 i : index) {
-      while (proto_piece->tuple_literals_size() <= i) {
-        proto_piece->add_tuple_literals();
-      }
-      proto_piece = proto_piece->mutable_tuple_literals(i);
-    }
-    piece.WriteToProto(proto_piece);
-  }
+  root_piece().ForEachSubpiece(
+      [&](const ShapeIndex& index, const Piece& piece) {
+        LiteralProto* proto_piece = &proto;
+        for (int64 i : index) {
+          while (proto_piece->tuple_literals_size() <= i) {
+            proto_piece->add_tuple_literals();
+          }
+          proto_piece = proto_piece->mutable_tuple_literals(i);
+        }
+        piece.WriteToProto(proto_piece);
+      });
 
   if (LayoutUtil::IsSparseArray(shape())) {
     CopyToRepeatedField(proto.mutable_sparse_indices(),
@@ -2098,33 +2096,34 @@ StatusOr> Literal::CreateFromProto(
 
   auto literal = MakeUnique(proto.shape());
 
-  for (auto& pair : literal->pieces_) {
-    const ShapeIndex& index = pair.first;
-    Piece& piece = pair.second;
-    const LiteralProto* proto_element = &proto;
-    for (int64 i : index) {
-      TF_RET_CHECK(i < proto_element->tuple_literals_size());
-      proto_element = &proto_element->tuple_literals(i);
-    }
+  TF_RETURN_IF_ERROR(literal->root_piece_->ForEachMutableSubpieceWithStatus(
+      [&](const ShapeIndex& index, Piece* piece) {
+        const LiteralProto* proto_element = &proto;
+        for (int64 i : index) {
+          CHECK(i < proto_element->tuple_literals_size());
+          proto_element = &proto_element->tuple_literals(i);
+        }
 
-    if (ShapeUtil::IsTuple(piece.subshape())) {
-      if (proto_element->tuple_literals_size() !=
-          ShapeUtil::TupleElementCount(piece.subshape())) {
-        return InvalidArgument(
-            "Expected %lld tuple elements in LiteralProto, has %d",
-            ShapeUtil::TupleElementCount(piece.subshape()),
-            proto_element->tuple_literals_size());
-      }
-      continue;
-    }
+        if (ShapeUtil::IsTuple(piece->subshape())) {
+          if (proto_element->tuple_literals_size() !=
+              ShapeUtil::TupleElementCount(piece->subshape())) {
+            return InvalidArgument(
+                "Expected %lld tuple elements in LiteralProto, has %d",
+                ShapeUtil::TupleElementCount(piece->subshape()),
+                proto_element->tuple_literals_size());
+          }
+          return Status::OK();
+        }
 
-    TF_RET_CHECK(ShapeUtil::IsArray(piece.subshape()));
-    TF_RETURN_IF_ERROR(piece.CopyFromProto(*proto_element));
-  }
+        CHECK(ShapeUtil::IsArray(piece->subshape()));
+        TF_RETURN_IF_ERROR(piece->CopyFromProto(*proto_element));
+
+        return Status::OK();
+      }));
   return std::move(literal);
 }
 
-const void* Literal::untyped_data(const ShapeIndex& shape_index) const {
+const void* LiteralBase::untyped_data(const ShapeIndex& shape_index) const {
   return piece(shape_index).untyped_data();
 }
 
@@ -2132,11 +2131,11 @@ void* Literal::untyped_data(const ShapeIndex& shape_index) {
   return piece(shape_index).untyped_data();
 }
 
-int64 Literal::size_bytes(const ShapeIndex& shape_index) const {
+int64 LiteralBase::size_bytes(const ShapeIndex& shape_index) const {
   return piece(shape_index).size_bytes();
 }
 
-string Literal::GetR1U8AsString() const {
+string LiteralBase::GetR1U8AsString() const {
   CHECK(ShapeUtil::IsArray(shape()));
   CHECK_EQ(ShapeUtil::Rank(shape()), 1);
   CHECK_EQ(shape().element_type(), U8);
@@ -2144,12 +2143,14 @@ string Literal::GetR1U8AsString() const {
                 ShapeUtil::ElementsIn(shape()));
 }
 
-/* static */ const LiteralView LiteralView::Create(
-    const Literal& literal, const ShapeIndex& view_root) {
-  return LiteralView(literal, view_root);
-}
+LiteralSlice::LiteralSlice(const LiteralBase& literal)
+    : LiteralBase(), root_piece_(&literal.root_piece()) {}
+
+LiteralSlice::LiteralSlice(const LiteralBase& literal,
+                           const ShapeIndex& view_root)
+    : LiteralBase(), root_piece_(&literal.piece(view_root)) {}
 
-size_t Literal::Hash() const {
+size_t LiteralBase::Hash() const {
   using tensorflow::Hash64;
   using tensorflow::Hash64Combine;
 
@@ -2170,46 +2171,4 @@ size_t Literal::Hash() const {
   return hash_value;
 }
 
-LiteralView::LiteralView(const Literal& literal, const ShapeIndex& view_root) {
-  shape_ = ShapeUtil::GetSubshape(literal.shape(), view_root);
-  pieces_ = ShapeTree(shape_);
-  owns_buffers_ = false;
-  for (auto& pair : pieces_) {
-    const ShapeIndex& index = pair.first;
-    Piece& piece = pair.second;
-
-    ShapeIndex src_index = view_root;
-    for (int64 i : index) {
-      src_index.push_back(i);
-    }
-    const Piece& src_piece = literal.piece(src_index);
-    piece.set_buffer(src_piece.buffer());
-    piece.set_sparse_indices(src_piece.sparse_indices());
-    piece.set_subshape(&ShapeUtil::GetSubshape(shape_, index));
-  }
-}
-
-LiteralView::~LiteralView() {}
-
-LiteralView::LiteralView(const LiteralView& other) { CopyFrom(other); }
-
-LiteralView& LiteralView::operator=(const LiteralView& other) {
-  CopyFrom(other);
-  return *this;
-}
-
-void LiteralView::CopyFrom(const LiteralView& other) {
-  // We can't use the default copy-constructor/copy-assignment because
-  // Piece::subshape_ points to subshapes within the Shape of the owning
-  // Literal/LiteralView.
-  shape_ = other.shape();
-  pieces_ = other.pieces_;
-  for (auto& pair : pieces_) {
-    const ShapeIndex& index = pair.first;
-    Piece& piece = pair.second;
-    piece.set_subshape(&ShapeUtil::GetSubshape(shape_, index));
-  }
-  owns_buffers_ = false;
-}
-
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index 290f38807840f9..30442afcc6e8b8 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -34,7 +34,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
-#include "tensorflow/compiler/xla/shape_tree.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/sparse_index_array.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -52,14 +51,491 @@ limitations under the License.
 
 namespace xla {
 
+// Forward declare Literal and LiteralSlice class to be used by the creation
+// methods in the base class.
+class Literal;
+class LiteralSlice;
+
+// Abstract base class for literals.
+class LiteralBase {
+ public:
+  virtual ~LiteralBase() = 0;
+
+  // Literals are equal if they have compatible shapes and the same data
+  // values. Layout is not compared.
+  bool operator==(const LiteralBase& other) const;
+  bool operator!=(const LiteralBase& other) const { return !(*this == other); }
+
+  // Returns the shape of the literal.
+  const Shape& shape() const { return root_piece().subshape(); }
+
+  // Serialize to proto.
+  LiteralProto ToProto() const;
+
+  // Returns an ArraySlice of the array for this literal for the given NativeT
+  // (e.g., float). CHECKs if the subshape of the literal at the given
+  // ShapeIndex is not array. See primitive_util.h for the mapping from XLA type
+  // to native type.
+  template 
+  tensorflow::gtl::ArraySlice data(
+      const ShapeIndex& shape_index = {}) const;
+
+  // Returns a const pointer to the sparse index array. Returns nullptr if the
+  // literal is not a sparse array.
+  const SparseIndexArray* sparse_indices(
+      const ShapeIndex& shape_index = {}) const;
+
+  // Returns a const pointer to (or size of) the underlying buffer holding the
+  // array at the given shape index. CHECKs if the subshape of the literal at
+  // the given ShapeIndex is not array.
+  const void* untyped_data(const ShapeIndex& shape_index = {}) const;
+  int64 size_bytes(const ShapeIndex& shape_index = {}) const;
+
+  // Returns this literal's data as a string. This literal must be a rank-1 U8
+  // array.
+  string GetR1U8AsString() const;
+
+  // Returns a string representation of the literal value.
+  // Warning: this function can take minutes for multi-million element Literals.
+  string ToString(bool print_layout = false) const;
+
+  // Gets an element in the literal at the given index. The multi_index is
+  // CHECKed against the dimension sizes.
+  template 
+  NativeT Get(tensorflow::gtl::ArraySlice multi_index,
+              const ShapeIndex& shape_index) const;
+  // Overloads of Get for array literals. CHECKs if the literal is not
+  // array-shaped and dense.
+  template 
+  NativeT Get(tensorflow::gtl::ArraySlice multi_index) const;
+
+  // Returns the element value at index (0, ..., 0), however many zeroes are
+  // required for that index.
+  template 
+  NativeT GetFirstElement() const;
+
+  // As Get(), but determines the correct type and converts the value
+  // into text.
+  string GetAsString(tensorflow::gtl::ArraySlice multi_index,
+                     const ShapeIndex& shape_index = {}) const;
+  // As GetSparseElement(), but determines the correct type and converts the
+  // value into text.
+  string GetSparseElementAsString(int64 sparse_element_number,
+                                  const ShapeIndex& shape_index = {}) const;
+  // As Get(), but determines the correct type and converts the value into
+  // int64.  This literal must be an array.
+  StatusOr GetIntegralAsS64(
+      tensorflow::gtl::ArraySlice multi_index) const;
+
+  // Returns the multi-index of the element in a sparse literal at the given
+  // sparse element number.  The sparse element number is the position with in
+  // the sparse array's list of (index, value) pairs, and is checked against the
+  // total number of (index, value) pairs in the sparse array.
+  tensorflow::gtl::ArraySlice GetSparseIndex(
+      int64 sparse_element_number, const ShapeIndex& shape_index = {}) const;
+
+  // Returns the value of the element in a sparse literal at the given sparse
+  // element number.  The sparse element number is the position with in the
+  // sparse array's list of (index, value) pairs, and is checked against the
+  // total number of (index, value) pairs in the sparse array.
+  template 
+  NativeT GetSparseElement(int64 sparse_element_number,
+                           const ShapeIndex& shape_index = {}) const;
+
+  // Invokes the "per cell" callback for each element in the provided
+  // literal with the element's indices and a string representation of
+  // the element's value.
+  //
+  // This function is useful if you want a polymorphic representation
+  // of the tensor's elements (turning it to a string for something
+  // like representation in a protobuf).
+  //
+  // This literal must have a dense layout.
+  void EachCellAsString(
+      const std::function indices,
+                               const string& value)>& per_cell) const;
+  template 
+  void EachCell(std::function indices,
+                                   NativeT value)>
+                    per_cell) const;
+
+  // Returns whether every element in this literal is equal to value.
+  //
+  // value is an int8 because we expect this to be called with small
+  // compile-time constants (0, -1, etc.) and so that whatever value you pass
+  // can be represented exactly by floating-point types as small as 16 bits.
+  //
+  // If value doesn't fit in this literal's type, returns false.  Values of 1/0
+  // are considered equal to true/false; other values are not considered equal
+  // to true. Also if this literal is not array-shaped false is returned.
+  bool IsAll(int8 value) const;
+
+  // Like IsAll(const Literal&, int8), except we check whether the literal is
+  // equal to a particular floating-point number.
+  //
+  // If the literal is not a floating-point value, this always returns false.
+  //
+  // This casts value to the type of literal, then compares using ==.  The usual
+  // admonishments about floating-point equality checks apply.  We expect you to
+  // use this to check for values that can be expressed precisely as a float,
+  // e.g. -0.5.  Also if this literal is not array-shaped false is returned.
+  bool IsAllFloat(float value) const;
+
+  // Like IsAll(const Literal&, int8), except we check whether the literal is
+  // equal to a particular complex number.
+  //
+  // If the literal is not a complex value, this always returns false.
+  //
+  // This casts value to the type of literal, then compares using ==.  The usual
+  // admonishments about floating-point equality checks apply.  We expect you to
+  // use this to check for complex values that can be expressed precisely as
+  // float pairs e.g. (-0.5, 1.0).
+  //
+  // This literal must have a dense layout.
+  bool IsAllComplex(complex64 value) const;
+
+  // Literal consists entirely of the first element of the literal.
+  bool IsAllFirst() const;
+
+  // Returns whether this literal is zero at the specified index. This literal
+  // must be an array with a dense layout.
+  bool IsZero(tensorflow::gtl::ArraySlice indices) const;
+
+  // Returns the count of the elements in the array at the given shape index in
+  // this literal.
+  int64 element_count(const ShapeIndex& index = {}) const {
+    return ShapeUtil::ElementsIn(ShapeUtil::GetSubshape(shape(), index));
+  }
+
+  // Return the count of the elements in the sparse array at the given shape
+  // index in this literal, which will be no larger than
+  // LayoutUtil::MaxSparseElements(SetSubshape(shape(), index).layout()).
+  int64 sparse_element_count() const;
+
+  // Compute a hash for this literal.  This literal must not be a sparse tensor
+  // or a tuple containing a sparse tensor.
+  size_t Hash() const;
+
+  // Converts this literal to the given shape. Returns an error is the
+  // conversion is not possible.
+  //
+  // round_f32_to_bf16: if true, converting F32 elements to BF16 uses rounding
+  // instead of truncation; otherwise, truncation is used.
+  //
+  // TODO(b/69266521): remove the round_to_bfloat16 flag when rounding becomes
+  // the default behavior.
+  StatusOr> ConvertToShape(
+      const Shape& dest_shape, bool round_f32_to_bf16 = false) const;
+
+  // Converts this literal to another primitive type using a bitcast
+  // conversion. The to and from primitive types must have the same bit
+  // width. Returns an error if the conversion is not possible. This literal
+  // must be array-shaped.
+  StatusOr> BitcastConvert(
+      PrimitiveType primitive_dest_type) const;
+
+  // Converts this literal to another primitive type. Returns an error if the
+  // conversion is not possible. This literal must be array-shaped.
+  StatusOr> Convert(
+      PrimitiveType primitive_dest_type) const;
+
+  // Returns a literal scalar representing the first element.
+  Literal GetFirstScalarLiteral() const;
+
+  // Clones the underlying buffers into a new Literal, or new
+  // std::unique_ptr.
+  Literal Clone() const;
+  std::unique_ptr CloneToUnique() const;
+
+  // TODO(b/67651157): The methods below which perform computation on Literals
+  // (Reshape, Slice, etc) should be moved elsewhere, and perhaps combined with
+  // evaluator code which operates on Literals.
+  //
+  // Creates a new value that has the equivalent value as this
+  // literal, but conforms to new_layout; e.g. a literal matrix that was in {0,
+  // 1} minor-to-major dimension layout can be re-layed-out as {1, 0}
+  // minor-to-major dimension layout and the value in the cell at any given
+  // logical index (i0, i1) will be the same.
+  //
+  // For tuple shaped literals, shape_index should be used to select the inner
+  // array that the new layout applies to.
+  //
+  // Note: this is useful when the client wants to ensure that a value placed in
+  // the XLA allocation tracker has a particular layout; for efficiency
+  // purposes or avoiding unimplemented operation/layout combinations.
+  std::unique_ptr Relayout(const Layout& new_layout,
+                                    const ShapeIndex& shape_index = {}) const;
+
+  // An overload of Relayout which changes the layout of the entire shape rather
+  // than being limited to a single array within the shape.
+  std::unique_ptr Relayout(const Shape& shape_with_layout) const;
+
+  // Creates a new literal by reshaping this literal to have the given
+  // dimensions. The total number of elements must not change; The
+  // implementation currently only supports monotonic dim0-major layouts.
+  // This literal must be an array.
+  StatusOr> Reshape(
+      tensorflow::gtl::ArraySlice dimensions) const;
+
+  // Creates a new literal by reordering the dimensions of this literal.
+  // The given `permutation` must be a permutation of the dimension numbers
+  // in the original literal, and it specifies the order of the new dimensions
+  // in the result literal (i.e., new_order[i] = old_order[permutation[i]]).
+  // For example, a transpose call on a literal of shape [3 x 8 x 4] and
+  // `permutation` = {2, 0, 1} returns a new literal of shape [4 x 3 x 8].
+  // This literal must be an array.
+  std::unique_ptr Transpose(
+      tensorflow::gtl::ArraySlice permutation) const;
+
+  // Creates a sub-array from this literal by extracting the indices
+  // [start_index, limit_index) of each dimension. The result literal has the
+  // same rank and layout as for the given literal. The number of indices in
+  // start_indices and limit_indices must be the rank of the literal, and the
+  // indices follow the order of the dimensions.
+  // This literal must be an array.
+  std::unique_ptr Slice(
+      tensorflow::gtl::ArraySlice start_indices,
+      tensorflow::gtl::ArraySlice limit_indices) const;
+
+  // Creates a literal with a prepended dimension with bound "times"; e.g. a
+  // f32[3x2] with times=4 will produce a f32[4x3x2] with the 3x2 from this
+  // literal replicated four times.
+  // This literal must be an array.
+  template 
+  std::unique_ptr Replicate(int64 times) const;
+
+  // Creates a new Literal object with the shape specified as parameter.
+  // The content of the literal values is the default value of the primitive
+  // type of literal itself (0 for numeric types, and false for predicates).
+  static std::unique_ptr CreateFromShape(const Shape& shape);
+
+ protected:
+  // A data structure representing a subshape at a particular ShapeIndex within
+  // the literal. For array-shaped ShapeIndexes, this data structure holds the
+  // pointer to the memory allocated for the array data.
+  class Piece {
+   public:
+    // Returns the buffer holding the array data for this piece as an array
+    // slice. This piece must be array-shaped.
+    template 
+    tensorflow::gtl::ArraySlice data() const;
+    template 
+    tensorflow::gtl::MutableArraySlice data();
+
+    // Returns the buffer holding the array data for this piece as a void*. This
+    // piece must be array-shaped.
+    void* untyped_data();
+    const void* untyped_data() const;
+
+    // Gets or sets an element in the array at the given index. The multi_index
+    // is CHECKed against the dimension sizes of the array.  This piece must be
+    // array-shaped.
+    template 
+    NativeT Get(tensorflow::gtl::ArraySlice index) const;
+    template 
+    void Set(tensorflow::gtl::ArraySlice index, NativeT value);
+
+    // Gets/sets the buffer holding the array data.
+    char* buffer() const { return buffer_; }
+    void set_buffer(char* buffer) { buffer_ = buffer; }
+
+    // The array of multi-indices that provide the locations of non-zero
+    // elements in a sparse array.  Only used if
+    // LayoutUtil::IsSparseArray(shape()) is true.
+    SparseIndexArray* sparse_indices() const { return sparse_indices_; }
+    void set_sparse_indices(SparseIndexArray* sparse_indices) {
+      sparse_indices_ = sparse_indices;
+    }
+
+    // Gets or sets the subshape of this piece. This reference points to a
+    // subshape within the shape in the containing Literal (Literal::shape_).
+    const Shape& subshape() const { return *subshape_; }
+    void set_subshape(const Shape* subshape) { subshape_ = subshape; }
+
+    // Returns the size in bytes of the buffer holding the array data.
+    int64 size_bytes() const { return ShapeUtil::ByteSizeOf(subshape()); }
+
+    // Returns the number of elements in this piece's array.
+    int64 element_count() const {
+      // If this is a sparse array, use the number of elements represented by
+      // the indices in the associated SparseIndexArray.
+      return LayoutUtil::IsSparseArray(subshape())
+                 ? sparse_indices()->index_count()
+                 : ShapeUtil::ElementsIn(subshape());
+    }
+
+    // Returns the child piece at 'index' of this piece.
+    Piece& child(int64 index) { return children_[index]; }
+
+    // Adds a child piece to this piece's children.
+    void emplace_back(Piece child_piece) {
+      children_.emplace_back(std::move(child_piece));
+    }
+
+    // Returns the size of children pieces of this piece.
+    int64 children_size() { return children_.size(); }
+
+    // Visitor functions that recursively traverses the piece and calls the
+    // given function at each child piece. The function has the type:
+    //    void (const ShapeIndex& index, const Piece& piece)
+    template 
+    void ForEachSubpiece(const Fn& func) const {
+      ShapeIndex index;
+      return ForEachHelper(
+                 [&func](const ShapeIndex& index, const Piece& piece) {
+                   func(index, piece);
+                   return Status::OK();
+                 },
+                 *this, &index)
+          .IgnoreError();
+    }
+    // Same as above, but the function has the type:
+    //    Status (const ShapeIndex& index, const Piece& piece)
+    // The first non-OK return value is returned by the function.
+    template 
+    Status ForEachSubpieceWithStatus(const Fn& func) const {
+      ShapeIndex index;
+      return ForEachHelper(func, *this, &index);
+    }
+    // Same as above, but the function has the type:
+    //    Bool (const ShapeIndex& index, const Piece& piece)
+    // The first non-true return value is returned by the function.
+    template 
+    bool ForEachSubpieceWithBool(const Fn& func) const {
+      ShapeIndex index;
+      return ForEachHelperBool(func, *this, &index);
+    }
+    // Same as above, but the function has the type:
+    //    Void (const ShapeIndex& index, Piece& piece)
+    template 
+    void ForEachMutableSubpiece(const Fn& func) {
+      ShapeIndex index;
+      return ForEachMutableHelper(
+                 [&func](const ShapeIndex& index, Piece* piece) {
+                   func(index, piece);
+                   return Status::OK();
+                 },
+                 const_cast(this), &index)
+          .IgnoreError();
+    }
+    // Same as above, but the function has the type:
+    //    Status (const ShapeIndex& index, Piece& piece)
+    // The first non-OK return value is returned by the function.
+    template 
+    Status ForEachMutableSubpieceWithStatus(const Fn& func) {
+      ShapeIndex index;
+      return ForEachMutableHelper(
+          func, const_cast(this), &index);
+    }
+
+    // Returns true if this piece and 'other' contain the same data. This piece
+    // and 'other' must be array-shaped and compatible.
+    bool EqualElements(const Piece& other) const;
+
+    // Writes the shape and data (if array-shaped) into the given proto.
+    void WriteToProto(LiteralProto* proto) const;
+
+    // Copy the data from 'src' into this piece's buffer. Shapes of this piece
+    // and src must be compatible.
+    Status CopyFrom(const Piece& src);
+
+    // Copies the data from the given proto into this piece. The shape of this
+    // piece must be equal (not just compatible) to the shape of the proto.
+    Status CopyFromProto(const LiteralProto& proto);
+
+    // Sorts the elements in a sparse array.
+    void SortSparseElements();
+
+   private:
+    // Helpers for traversing the piece via ForEachSubpiece rooted at 'index'.
+    // The first non-OK (or non-true) value is returned by the function.
+    // The callable 'func' has the same signature as described above in
+    // ForEachSubpiece*.
+    template 
+    Status ForEachHelper(const Fn& func, const Piece& piece,
+                         ShapeIndex* index) const {
+      TF_RETURN_IF_ERROR(func(*index, piece));
+      for (int64 i = 0; i < piece.children_.size(); ++i) {
+        index->push_back(i);
+        TF_RETURN_IF_ERROR(ForEachHelper(func, piece.children_[i], index));
+        index->pop_back();
+      }
+      return Status::OK();
+    }
+    template 
+    bool ForEachHelperBool(const Fn& func, const Piece& piece,
+                           ShapeIndex* index) const {
+      if (!func(*index, piece)) {
+        return false;
+      }
+      for (int64 i = 0; i < piece.children_.size(); ++i) {
+        index->push_back(i);
+        if (!ForEachHelperBool(func, piece.children_[i], index)) {
+          return false;
+        }
+        index->pop_back();
+      }
+      return true;
+    }
+    template 
+    Status ForEachMutableHelper(const Fn& func, Piece* piece,
+                                ShapeIndex* index) {
+      TF_RETURN_IF_ERROR(func(*index, piece));
+      for (int64 i = 0; i < piece->children_.size(); ++i) {
+        index->push_back(i);
+        TF_RETURN_IF_ERROR(
+            ForEachMutableHelper(func, &piece->children_[i], index));
+        index->pop_back();
+      }
+      return Status::OK();
+    }
+
+    // Recursive helper for EqualElements.
+    template 
+    bool EqualElementsInternal(const Piece& other,
+                               std::vector* multi_index) const;
+
+    // Helper for SortSparseElements that has the element type as a template
+    // parameter.
+    template 
+    void SortSparseElementsInternal();
+
+    // For array-shaped pieces, this is the buffer holding the literal data.
+    char* buffer_ = nullptr;
+
+    // For sparse arrays, this is the array of indices.
+    SparseIndexArray* sparse_indices_ = nullptr;
+
+    // The shape of piece. This points into the shape of the containing Literal
+    // (Literal::shape_).
+    const Shape* subshape_ = nullptr;
+
+    // Children pieces for tuple shaped pieces.
+    std::vector children_ = {};
+  };  // class Piece
+
+  const Piece& piece(const ShapeIndex& shape_index) const {
+    Piece* piece = &const_cast(root_piece());
+    for (const auto i : shape_index) {
+      DCHECK_GE(i, 0);
+      DCHECK_LT(i, piece->children_size());
+      piece = &piece->child(i);
+    }
+    return *piece;
+  }
+
+  // Returns the piece at the root of the shape.
+  virtual const Piece& root_piece() const = 0;
+
+  // LiteralSlice and Literal must access Pieces of other Literals.
+  friend class LiteralSlice;
+  friend class Literal;
+};
+
 // Class representing literal values in XLA.
 //
-// TODO(b/67651157): The methods in this class should be reduced to a minimal
-// set of methods which construct Literals and accessors methods. Other methods
-// which perform computation on Literals (Reshape, Slice, etc) should be moved
-// elsewhere, and perhaps combined with evaluator code which operates on
-// Literals.
-class Literal {
+// The underlying buffer and shape is always owned by this class.
+class Literal : public LiteralBase {
  public:
   Literal() : Literal(ShapeUtil::MakeNil()) {}
 
@@ -80,46 +556,156 @@ class Literal {
   Literal(const Shape& shape, bool allocate_arrays);
   Literal& operator=(Literal&& other);
 
-  // Literals are equal if they have compatible shapes and the same data
-  // values. Layout is not compared.
-  bool operator==(const Literal& other) const;
-  bool operator!=(const Literal& other) const { return !(*this == other); }
+  // TODO(b/67651157): Remove this accessor. Literal users should not be able to
+  // mutate the shape as this can produce malformed Literals.
+  Shape* mutable_shape_do_not_use() { return shape_.get(); }
 
-  // Serialize to and from a proto.
-  static StatusOr> CreateFromProto(
-      const LiteralProto& proto);
-  LiteralProto ToProto() const;
+  // Returns a MutableArraySlice view of the array for this literal for the
+  // given NativeT (e.g., float). CHECKs if the subshape of the literal at the
+  // given ShapeIndex is not array. See primitive_util.h for the mapping from
+  // XLA type to native type.
+  template 
+  tensorflow::gtl::MutableArraySlice data(
+      const ShapeIndex& shape_index = {});
+  // Unhide const method from parent class.
+  using LiteralBase::data;
+
+  // Returns a pointer to the sparse index array. Returns nullptr if the literal
+  // is not a sparse array.
+  SparseIndexArray* sparse_indices(const ShapeIndex& shape_index = {});
+
+  // Returns a pointer to the underlying buffer holding the array at the given
+  // shape index. CHECKs if the subshape of the literal at the given ShapeIndex
+  // is not array.
+  void* untyped_data(const ShapeIndex& shape_index = {});
+  // Unhide const method from parent class.
+  using LiteralBase::untyped_data;
+
+  // Populates a literal with a sparse layout with the given indices and values.
+  // Each index in the indices array is CHECKed against the dimensions in the
+  // literal's shape.  If sort is true, then the indices and values will be
+  // sorted.  If sort is false, then the indices and values are assumed to
+  // already be in sorted order.  See CreateSparse for an example of how data
+  // are populated.
+  template 
+  void PopulateSparse(SparseIndexArray indices,
+                      tensorflow::gtl::ArraySlice values,
+                      bool sort = true);
+
+  // Copy values from 'src_literal' rooted at 'src_shape_index' into this
+  // literal rooted at 'dest_shape_index'. The subshape of this literal rooted
+  // at 'dest_shape_index' must be compatible with the subshape of 'src_literal'
+  // rooted at 'src_shape_index', but need not be arrays.
+  Status CopyFrom(const LiteralSlice& src_literal,
+                  const ShapeIndex& dest_shape_index = {},
+                  const ShapeIndex& src_shape_index = {});
+
+  // Similar to CopyFrom, but with move semantincs. The subshape of this literal
+  // rooted at 'dest_shape_index' must be *equal* to the shape 'src_literal'
+  // (layouts and shapes must match), but need not be arrays. The memory
+  // allocated in this literal for the subshape at dest_shape_index is
+  // deallocated, and the respective buffers are replaced with those in
+  // src_literal. Upon return, src_literal is set to a nil shape (empty tuple).
+  Status MoveFrom(Literal&& src_literal,
+                  const ShapeIndex& dest_shape_index = {});
+
+  // Copies the values from src_literal, starting at src_base shape indexes,
+  // to this literal, starting at dest_base, where the copy size in each
+  // dimension is specified by copy_size.
+  // The src_literal and this literal must have the same primitive type,
+  // src_base+copy_size must fit the source literal dimensions, as well as
+  // dest_base+copy_size must fit the destination literal dimensions.
+  // Note: if either src_literal or this literal contains dimensions with zero
+  // element, then copy_size must be 0 in these dimensions while the
+  // corresponding base indices being 0.
+  // This literal and 'src_literal' must be arrays.
+  Status CopySliceFrom(const LiteralSlice& src_literal,
+                       tensorflow::gtl::ArraySlice src_base,
+                       tensorflow::gtl::ArraySlice dest_base,
+                       tensorflow::gtl::ArraySlice copy_size);
+
+  // Copies one element from src_literal[src_index] to (*this)[dest_index].
+  Status CopyElementFrom(const LiteralSlice& src_literal,
+                         tensorflow::gtl::ArraySlice src_index,
+                         tensorflow::gtl::ArraySlice dest_index);
 
-  // Return the shape of the literal.
-  const Shape& shape() const { return shape_; }
+  // Sets an element in the literal at the given index. The multi_index is
+  // CHECKed against the dimension sizes.
+  template 
+  void Set(tensorflow::gtl::ArraySlice multi_index,
+           const ShapeIndex& shape_index, NativeT value);
+  // Overloads of Set for array literals. CHECKs if the literal is not
+  // array-shaped and dense.
+  template 
+  void Set(tensorflow::gtl::ArraySlice multi_index, NativeT value);
+
+  // Appends the given element to the literal.  If the elements are not appended
+  // in sorted order, then SortSparseElements should be called before calling
+  // other methods.  This literal must have a sparse layout.
+  template 
+  void AppendSparseElement(tensorflow::gtl::ArraySlice multi_index,
+                           NativeT value, const ShapeIndex& shape_index = {});
+
+  // Sorts the elements in a sparse array.
+  void SortSparseElements(const ShapeIndex& shape_index = {});
+
+  // As Set(), but truncates `value` to the literal element type before storing.
+  // This literal must be an array.
+  Status SetIntegralAsS64(tensorflow::gtl::ArraySlice multi_index,
+                          int64 value);
+
+  // Populate this literal with the given values. Examples:
+  //
+  //   // Populate with floats.
+  //   Array2D float_values = ...
+  //   literal.PopulateR2FromArray2D(values);
+  //
+  //   // Populate with int32s.
+  //   literal.PopulateR2({{1, 2}, {3, 4}});
+  //
+  // The shape and element type of this literal must match given values. For
+  // example, in the call above to literal.PopulateR2(), 'literal' must be a 2x2
+  // array of S32.
+  template 
+  void PopulateR1(tensorflow::gtl::ArraySlice values);
+  void PopulateR1(const tensorflow::core::Bitmap& values);
+  template 
+  void PopulateR2(std::initializer_list> values);
+  template 
+  void PopulateFromArray(const Array& values);
+  template 
+  void PopulateR2FromArray2D(const Array2D& values);
+  template 
+  void PopulateR3FromArray3D(const Array3D& values);
+  template 
+  void PopulateR4FromArray4D(const Array4D& values);
+
+  // Populates literal values by calling the generator function for every cell
+  // in this literal object.
+  //
+  // generator must be a callable of the type
+  // NativeT(tensorflow::gtl::ArraySlice indexes) or compatible.
+  //
+  // This literal must have a dense layout.
+  template 
+  Status Populate(const FnType& generator);
 
-  // TODO(b/67651157): Remove this accessor. Literal users should not be able to
-  // mutate the shape as this can produce malformed Literals.
-  Shape* mutable_shape_do_not_use() { return &shape_; }
+  // A parallel version of Populate(). This can be used if the generator is
+  // thread-safe and the values for the shape's different elements are
+  // independent.
+  template 
+  Status PopulateParallel(const FnType& generator);
 
-  // Returns a (Mutable)ArraySlice view of the array for this literal for the
-  // given NativeT (e.g., float). CHECKs if the subshape of the literal at the
-  // given ShapeIndex is not array. See primitive_util.h for the mapping from
-  // XLA type to native type.
-  template 
-  tensorflow::gtl::ArraySlice data(
-      const ShapeIndex& shape_index = {}) const;
+  // Fills this literal with the given value.
   template 
-  tensorflow::gtl::MutableArraySlice data(
-      const ShapeIndex& shape_index = {});
+  void PopulateWithValue(NativeT value);
 
-  // Returns a pointer to the sparse index array. Returns nullptr if the literal
-  // is not a sparse array.
-  const SparseIndexArray* sparse_indices(
-      const ShapeIndex& shape_index = {}) const;
-  SparseIndexArray* sparse_indices(const ShapeIndex& shape_index = {});
+  // Factory methods below.
+  //
 
-  // Returns a pointer to (or size of) the underlying buffer holding the array
-  // at the given shape index. CHECKs if the subshape of the literal at the
-  // given ShapeIndex is not array.
-  const void* untyped_data(const ShapeIndex& shape_index = {}) const;
-  void* untyped_data(const ShapeIndex& shape_index = {});
-  int64 size_bytes(const ShapeIndex& shape_index = {}) const;
+  // Serialize from a proto.
+  static StatusOr> CreateFromProto(
+      const LiteralProto& proto);
 
   // Creates a new literal of a given rank. To minimize ambiguity (for users
   // and the compiler) these CreateR[0-2] methods should explicitly specify the
@@ -167,10 +753,6 @@ class Literal {
           values,
       const Layout& layout);
 
-  // Returns this literal's data as a string. This literal must be a rank-1 U8
-  // array.
-  string GetR1U8AsString() const;
-
   // Creates a literal with a sparse layout and the given indices and values.
   // The shape is initialized from the given dimensions.  The minor dimension of
   // the indices array must equal the rank of the shape (i.e. size of the
@@ -210,171 +792,16 @@ class Literal {
       tensorflow::gtl::ArraySlice dimensions, SparseIndexArray indices,
       tensorflow::gtl::ArraySlice values, bool sort = true);
 
-  // Populates a literal with a sparse layout with the given indices and values.
-  // Each index in the indices array is CHECKed against the dimensions in the
-  // literal's shape.  If sort is true, then the indices and values will be
-  // sorted.  If sort is false, then the indices and values are assumed to
-  // already be in sorted order.  See CreateSparse for an example of how data
-  // are populated.
-  template 
-  void PopulateSparse(SparseIndexArray indices,
-                      tensorflow::gtl::ArraySlice values,
-                      bool sort = true);
-
-  // Creates a new Literal object with the shape specified as parameter.
-  // The content of the literal values is the default value of the primitive
-  // type of literal itself (0 for numeric types, and false for predicates).
-  static std::unique_ptr CreateFromShape(const Shape& shape);
-
-  // Creates a new Literal object with its values havings the primitive_type
-  // type, and with dimensions defined by the dimensions parameter.
-  // The content of the literal values is the default value of the primitive
-  // type of literal itself (0 for numeric types, and false for predicates).
-  static std::unique_ptr CreateFromDimensions(
-      PrimitiveType primitive_type,
-      tensorflow::gtl::ArraySlice dimensions);
-
-  // Copy values from 'src_literal' rooted at 'src_shape_index' into this
-  // literal rooted at 'dest_shape_index'. The subshape of this literal rooted
-  // at 'dest_shape_index' must be compatible with the subshape of 'src_literal'
-  // rooted at 'src_shape_index', but need not be arrays.
-  Status CopyFrom(const Literal& src_literal,
-                  const ShapeIndex& dest_shape_index = {},
-                  const ShapeIndex& src_shape_index = {});
-
-  // Similar to CopyFrom, but with move semantincs. The subshape of this literal
-  // rooted at 'dest_shape_index' must be *equal* to the shape 'src_literal'
-  // (layouts and shapes must match), but need not be arrays. The memory
-  // allocated in this literal for the subshape at dest_shape_index is
-  // deallocated, and the respective buffers are replaced with those in
-  // src_literal. Upon return, src_literal is set to a nil shape (empty tuple).
-  Status MoveFrom(Literal&& src_literal,
-                  const ShapeIndex& dest_shape_index = {});
-
-  // Copies the values from src_literal, starting at src_base shape indexes,
-  // to this literal, starting at dest_base, where the copy size in each
-  // dimension is specified by copy_size.
-  // The src_literal and this literal must have the same primitive type,
-  // src_base+copy_size must fit the source literal dimensions, as well as
-  // dest_base+copy_size must fit the destination literal dimensions.
-  // Note: if either src_literal or this literal contains dimensions with zero
-  // element, then copy_size must be 0 in these dimensions while the
-  // corresponding base indices being 0.
-  // This literal and 'src_literal' must be arrays.
-  Status CopySliceFrom(const Literal& src_literal,
-                       tensorflow::gtl::ArraySlice src_base,
-                       tensorflow::gtl::ArraySlice dest_base,
-                       tensorflow::gtl::ArraySlice copy_size);
-
-  // Copies one element from src_literal[src_index] to (*this)[dest_index].
-  Status CopyElementFrom(const Literal& src_literal,
-                         tensorflow::gtl::ArraySlice src_index,
-                         tensorflow::gtl::ArraySlice dest_index);
-
-  // Returns a vector containing the tuple elements of this Literal as separate
-  // Literals. This Literal must be tuple-shaped and can be a nested tuple. The
-  // elements are moved into the new Literals; no data is copied. Upon return
-  // this Literal is set to a nil shape (empty tuple)
-  std::vector DecomposeTuple();
-
-  // This operation is the inverse of DecomposeTuple. The given elements are
-  // moved into the tuple elements of a new tuple-shaped Literal which is
-  // returned. Upon return, each of the Literals in 'elements' is set to a nil
-  // shape (empty tuple).
-  static Literal MoveIntoTuple(
-      tensorflow::gtl::MutableArraySlice elements);
-
-  // Creates a new value that has the equivalent value as this literal, but
-  // conforms to new_layout; e.g. a literal matrix that was in {0, 1}
-  // minor-to-major dimension layout can be re-layed-out as {1, 0}
-  // minor-to-major dimension layout and the value in the cell at any given
-  // logical index (i0, i1) will be the same.
-  //
-  // For tuple shaped literals, shape_index should be used to select the inner
-  // array that the new layout applies to.
-  //
-  // Note: this is useful when the client wants to ensure that a value placed in
-  // the XLA allocation tracker has a particular layout; for efficiency
-  // purposes or avoiding unimplemented operation/layout combinations.
-  std::unique_ptr Relayout(const Layout& new_layout,
-                                    const ShapeIndex& shape_index = {}) const;
-
-  // An overload of Relayout which changes the layout of the entire shape rather
-  // than being limited to a single array within the shape.
-  std::unique_ptr Relayout(const Shape& shape_with_layout) const;
-
-  // Creates a new literal by reshaping this literal to have the given
-  // dimensions. The total number of elements must not change; The
-  // implementation currently only supports monotonic dim0-major layouts.
-  // This literal must be an array.
-  StatusOr> Reshape(
-      tensorflow::gtl::ArraySlice dimensions) const;
-
-  // Creates a new literal by reordering the dimensions of this literal.
-  // The given `permutation` must be a permutation of the dimension numbers
-  // in the original literal, and it specifies the order of the new dimensions
-  // in the result literal (i.e., new_order[i] = old_order[permutation[i]]).
-  // For example, a transpose call on a literal of shape [3 x 8 x 4] and
-  // `permutation` = {2, 0, 1} returns a new literal of shape [4 x 3 x 8].
-  // This literal must be an array.
-  std::unique_ptr Transpose(
-      tensorflow::gtl::ArraySlice permutation) const;
-
-  // Creates a sub-array from this literal by extracting the indices
-  // [start_index, limit_index) of each dimension. The result literal has the
-  // same rank and layout as for the given literal. The number of indices in
-  // start_indices and limit_indices must be the rank of the literal, and the
-  // indices follow the order of the dimensions.
-  // This literal must be an array.
-  std::unique_ptr Slice(
-      tensorflow::gtl::ArraySlice start_indices,
-      tensorflow::gtl::ArraySlice limit_indices) const;
-
-  // Creates a literal with a prepended dimension with bound "times"; e.g. a
-  // f32[3x2] with times=4 will produce a f32[4x3x2] with the 3x2 from this
-  // literal replicated four times.
-  // This literal must be an array.
-  template 
-  std::unique_ptr Replicate(int64 times) const;
-
-  // Converts this literal to another primitive type using
-  // static_cast<>. Returns an error if the conversion is not possible. This
-  // literal must be array-shaped.
-  StatusOr> Convert(
-      PrimitiveType primitive_dest_type) const;
-
-  // Converts this literal to another primitive type using a bitcast
-  // conversion. The to and from primitive types must have the same bit
-  // width. Returns an error if the conversion is not possible. This literal
-  // must be array-shaped.
-  StatusOr> BitcastConvert(
-      PrimitiveType primitive_dest_type) const;
-
-  // Converts this literal to the given shape. Returns an error is the
-  // conversion is not possible.
-  //
-  // round_f32_to_bf16: if true, converting F32 elements to BF16 uses rounding
-  // instead of truncation; otherwise, truncation is used.
-  //
-  // TODO(b/69266521): remove the round_to_bfloat16 flag when rounding becomes
-  // the default behavior.
-  StatusOr> ConvertToShape(
-      const Shape& dest_shape, bool round_f32_to_bf16 = false) const;
-
   // Creates a scalar literal value zero of the given primitive type.
   static Literal Zero(PrimitiveType primitive_type);
-
   // Creates a scalar literal value one of the given primitive type.
   static Literal One(PrimitiveType primitive_type);
-
   // Creates a scalar literal value containing the minimum value of the given
   // primitive type. For floating-point types, returns -inf.
   static Literal MinValue(PrimitiveType primitive_type);
-
   // Creates a scalar literal value containing the maximum value of the given
   // primitive type. For floating-point types, returns inf.
   static Literal MaxValue(PrimitiveType primitive_type);
-
   // Creates a literal of the given shape where each element is `value`.
   template 
   static std::unique_ptr CreateFullWithDescendingLayout(
@@ -419,88 +846,15 @@ class Literal {
   // the z dimension given by "projection".
   template 
   static std::unique_ptr CreateR3Projected(
-      std::initializer_list> values,
-      int64 projection);
-
-  // Creates a literal that projects the (x, y) dimensions given in values into
-  // the z and p dimensions given.
-  template 
-  static std::unique_ptr CreateR4Projected(
-      std::initializer_list> values,
-      int64 projection_p, int64 projection_z);
-
-  // Clones this literal into a new Literal, or new std::unique_ptr.
-  Literal Clone() const;
-  std::unique_ptr CloneToUnique() const;
-
-  // Gets or sets an element in the literal at the given index. The multi_index
-  // is CHECKed against the dimension sizes.
-  template 
-  NativeT Get(tensorflow::gtl::ArraySlice multi_index,
-              const ShapeIndex& shape_index) const;
-  template 
-  void Set(tensorflow::gtl::ArraySlice multi_index,
-           const ShapeIndex& shape_index, NativeT value);
-
-  // Overloads of Get and Set for array literals. CHECKs if the literal is not
-  // array-shaped and dense.
-  template 
-  NativeT Get(tensorflow::gtl::ArraySlice multi_index) const;
-  template 
-  void Set(tensorflow::gtl::ArraySlice multi_index, NativeT value);
-
-  // Returns the multi-index of the element in a sparse literal at the given
-  // sparse element number.  The sparse element number is the position with in
-  // the sparse array's list of (index, value) pairs, and is checked against the
-  // total number of (index, value) pairs in the sparse array.
-  tensorflow::gtl::ArraySlice GetSparseIndex(
-      int64 sparse_element_number, const ShapeIndex& shape_index = {}) const;
-
-  // Returns the value of the element in a sparse literal at the given sparse
-  // element number.  The sparse element number is the position with in the
-  // sparse array's list of (index, value) pairs, and is checked against the
-  // total number of (index, value) pairs in the sparse array.
-  template 
-  NativeT GetSparseElement(int64 sparse_element_number,
-                           const ShapeIndex& shape_index = {}) const;
-
-  // Appends the given element to the literal.  If the elements are not appended
-  // in sorted order, then SortSparseElements should be called before calling
-  // other methods.  This literal must have a sparse layout.
-  template 
-  void AppendSparseElement(tensorflow::gtl::ArraySlice multi_index,
-                           NativeT value, const ShapeIndex& shape_index = {});
-
-  // Sorts the elements in a sparse array.
-  void SortSparseElements(const ShapeIndex& shape_index = {});
-
-  // Returns the element value at index (0, ..., 0), however many zeroes are
-  // required for that index.
-  template 
-  NativeT GetFirstElement() const;
-
-  // Returns a literal scalar representing the first element.
-  Literal GetFirstScalarLiteral() const;
-
-  // As Get(), but determines the correct type and converts the value
-  // into text.
-  string GetAsString(tensorflow::gtl::ArraySlice multi_index,
-                     const ShapeIndex& shape_index = {}) const;
-
-  // As GetSparseElement(), but determines the correct type and converts the
-  // value into text.
-  string GetSparseElementAsString(int64 sparse_element_number,
-                                  const ShapeIndex& shape_index = {}) const;
-
-  // As Get(), but determines the correct type and converts the value into
-  // int64.  This literal must be an array.
-  StatusOr GetIntegralAsS64(
-      tensorflow::gtl::ArraySlice multi_index) const;
+      std::initializer_list> values,
+      int64 projection);
 
-  // As Set(), but truncates `value` to the literal element type before storing.
-  // This literal must be an array.
-  Status SetIntegralAsS64(tensorflow::gtl::ArraySlice multi_index,
-                          int64 value);
+  // Creates a literal that projects the (x, y) dimensions given in values into
+  // the z and p dimensions given.
+  template 
+  static std::unique_ptr CreateR4Projected(
+      std::initializer_list> values,
+      int64 projection_p, int64 projection_z);
 
   // Returns an identity matrix (rank 2) with the given row and column count.
   template 
@@ -511,6 +865,9 @@ class Literal {
   static std::unique_ptr MakeTuple(
       tensorflow::gtl::ArraySlice elements);
 
+  static std::unique_ptr MakeTupleFromSlices(
+      tensorflow::gtl::ArraySlice elements);
+
   // As above, but intended to be invoked with move semantics; i.e.
   //
   //  std::vector> elements = ...;
@@ -542,135 +899,48 @@ class Literal {
     return MakeTupleOwned(std::move(v));
   }
 
-  // Returns a string representation of the literal value.
-  // Warning: this function can take minutes for multi-million element Literals.
-  string ToString(bool print_layout = false) const;
-
-  // Invokes the "per cell" callback for each element in the provided
-  // literal with the element's indices and a string representation of
-  // the element's value.
-  //
-  // This function is useful if you want a polymorphic representation
-  // of the tensor's elements (turning it to a string for something
-  // like representation in a protobuf).
-  //
-  // This literal must have a dense layout.
-  void EachCellAsString(
-      const std::function indices,
-                               const string& value)>& per_cell) const;
-  template 
-  void EachCell(std::function indices,
-                                   NativeT value)>
-                    per_cell) const;
-
-  // Populate this literal with the given values. Examples:
-  //
-  //   // Populate with floats.
-  //   Array2D float_values = ...
-  //   literal.PopulateR2FromArray2D(values);
-  //
-  //   // Populate with int32s.
-  //   literal.PopulateR2({{1, 2}, {3, 4}});
-  //
-  // The shape and element type of this literal must match given values. For
-  // example, in the call above to literal.PopulateR2(), 'literal' must be a 2x2
-  // array of S32.
-  template 
-  void PopulateR1(tensorflow::gtl::ArraySlice values);
-  void PopulateR1(const tensorflow::core::Bitmap& values);
-  template 
-  void PopulateR2(std::initializer_list> values);
-  template 
-  void PopulateFromArray(const Array& values);
-  template 
-  void PopulateR2FromArray2D(const Array2D& values);
-  template 
-  void PopulateR3FromArray3D(const Array3D& values);
-  template 
-  void PopulateR4FromArray4D(const Array4D& values);
-
-  // Populates literal values by calling the generator function for every cell
-  // in this literal object.
-  //
-  // generator must be a callable of the type
-  // NativeT(tensorflow::gtl::ArraySlice indexes) or compatible.
-  //
-  // This literal must have a dense layout.
-  template 
-  Status Populate(const FnType& generator);
-
-  // A parallel version of Populate(). This can be used if the generator is
-  // thread-safe and the values for the shape's different elements are
-  // independent.
-  template 
-  Status PopulateParallel(const FnType& generator);
-
-  // Fills this literal with the given value.
-  template 
-  void PopulateWithValue(NativeT value);
+  // Returns a vector containing the tuple elements of this Literal as separate
+  // Literals. This Literal must be tuple-shaped and can be a nested tuple. The
+  // elements are moved into the new Literals; no data is copied. Upon return
+  // this Literal is set to a nil shape (empty tuple)
+  std::vector DecomposeTuple();
 
-  // Returns whether every element in this literal is equal to value.
-  //
-  // value is an int8 because we expect this to be called with small
-  // compile-time constants (0, -1, etc.) and so that whatever value you pass
-  // can be represented exactly by floating-point types as small as 16 bits.
-  //
-  // If value doesn't fit in this literal's type, returns false.  Values of 1/0
-  // are considered equal to true/false; other values are not considered equal
-  // to true. Also if this literal is not array-shaped false is returned.
-  bool IsAll(int8 value) const;
+  // This operation is the inverse of DecomposeTuple. The given elements are
+  // moved into the tuple elements of a new tuple-shaped Literal which is
+  // returned. Upon return, each of the Literals in 'elements' is set to a nil
+  // shape (empty tuple).
+  static Literal MoveIntoTuple(
+      tensorflow::gtl::MutableArraySlice elements);
 
-  // Like IsAll(const Literal&, int8), except we check whether the literal is
-  // equal to a particular floating-point number.
-  //
-  // If the literal is not a floating-point value, this always returns false.
-  //
-  // This casts value to the type of literal, then compares using ==.  The usual
-  // admonishments about floating-point equality checks apply.  We expect you to
-  // use this to check for values that can be expressed precisely as a float,
-  // e.g. -0.5.  Also if this literal is not array-shaped false is returned.
-  bool IsAllFloat(float value) const;
+  // Creates a new Literal object with its values havings the primitive_type
+  // type, and with dimensions defined by the dimensions parameter.
+  // The content of the literal values is the default value of the primitive
+  // type of literal itself (0 for numeric types, and false for predicates).
+  static std::unique_ptr CreateFromDimensions(
+      PrimitiveType primitive_type,
+      tensorflow::gtl::ArraySlice dimensions);
 
-  // Like IsAll(const Literal&, int8), except we check whether the literal is
-  // equal to a particular complex number.
-  //
-  // If the literal is not a complex value, this always returns false.
-  //
-  // This casts value to the type of literal, then compares using ==.  The usual
-  // admonishments about floating-point equality checks apply.  We expect you to
-  // use this to check for complex values that can be expressed precisely as
-  // float pairs e.g. (-0.5, 1.0).
   //
-  // This literal must have a dense layout.
-  bool IsAllComplex(complex64 value) const;
+  // End of factory methods.
 
-  // Literal consists entirely of the first element of the literal.
-  bool IsAllFirst() const;
-
-  // Returns whether this literal is zero at the specified index. This literal
-  // must be an array with a dense layout.
-  bool IsZero(tensorflow::gtl::ArraySlice indices) const;
+ protected:
+  // Recursively sets the subshapes and buffers of all subpieces rooted at
+  // 'piece'. If 'allocate_array' is true, memory is allocated for the arrays in
+  // the shape.
+  void SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays);
 
-  // Return the count of the elements in the array at the given shape index in
-  // this literal.
-  int64 element_count(const ShapeIndex& index = {}) const {
-    return ShapeUtil::ElementsIn(ShapeUtil::GetSubshape(shape(), index));
+  // Returns the piece at the given ShapeIndex.
+  Piece& piece(const ShapeIndex& shape_index) {
+    return const_cast(LiteralBase::piece(shape_index));
   }
 
-  // Return the count of the elements in the sparse array at the given shape
-  // index in this literal, which will be no larger than
-  // LayoutUtil::MaxSparseElements(SetSubshape(shape(), index).layout()).
-  int64 sparse_element_count() const;
-
-  // Compute a hash for this literal.  This literal must not be a sparse tensor
-  // or a tuple containing a sparse tensor.
-  size_t Hash() const;
+  Piece& root_piece() const override { return *root_piece_; };
 
- protected:
+ private:
   // Internal template helper for the Literal::CopySliceFrom(), matching its
   // arguments one by one.
   template 
-  Status CopySliceFromInternal(const Literal& src_literal,
+  Status CopySliceFromInternal(const LiteralBase& src_literal,
                                tensorflow::gtl::ArraySlice src_base,
                                tensorflow::gtl::ArraySlice dest_base,
                                tensorflow::gtl::ArraySlice copy_size);
@@ -698,162 +968,40 @@ class Literal {
     int64 minor_loop_size = 1;
   };
 
-  // A data structure representing a subshape at a particular ShapeIndex within
-  // the literal. For array-shaped ShapeIndexes, this data structure holds the
-  // pointer to the memory allocated for the array data.
-  class Piece {
-   public:
-    // Return the buffer holding the array data for this piece as an array
-    // slice. This piece must be array-shaped.
-    template 
-    tensorflow::gtl::ArraySlice data() const;
-    template 
-    tensorflow::gtl::MutableArraySlice data();
-
-    // Return the buffer holding the array data for this piece as a void*. This
-    // piece must be array-shaped.
-    void* untyped_data();
-    const void* untyped_data() const;
-
-    // Gets or sets an element in the array at the given index. The multi_index
-    // is CHECKed against the dimension sizes of the array.  This piece must be
-    // array-shaped.
-    template 
-    NativeT Get(tensorflow::gtl::ArraySlice index) const;
-    template 
-    void Set(tensorflow::gtl::ArraySlice index, NativeT value);
-
-    // Gets/sets the buffer holding the array data.
-    char* buffer() const { return buffer_; }
-    void set_buffer(char* buffer) { buffer_ = buffer; }
-
-    // The array of multi-indices that provide the locations of non-zero
-    // elements in a sparse array.  Only used if
-    // LayoutUtil::IsSparseArray(shape()) is true.
-    SparseIndexArray* sparse_indices() const { return sparse_indices_; }
-    void set_sparse_indices(SparseIndexArray* sparse_indices) {
-      sparse_indices_ = sparse_indices;
-    }
-
-    // Gets or sets the subshape of this piece. This reference points to a
-    // subshape within the shape in the containing Literal (Literal::shape_).
-    const Shape& subshape() const { return *subshape_; }
-    void set_subshape(const Shape* subshape) { subshape_ = subshape; }
-
-    // Returns the size in bytes of the buffer holding the array data.
-    int64 size_bytes() const { return ShapeUtil::ByteSizeOf(subshape()); }
-
-    // Returns the number of elements in this piece's array.
-    int64 element_count() const {
-      // If this is a sparse array, use the number of elements represented by
-      // the indices in the associated SparseIndexArray.
-      return LayoutUtil::IsSparseArray(subshape())
-                 ? sparse_indices()->index_count()
-                 : ShapeUtil::ElementsIn(subshape());
-    }
-
-    // Copy the data from 'src' into this piece's buffer. Shapes of this piece
-    // and src must be compatible.
-    Status CopyFrom(const Piece& src);
-
-    // Returns true if this piece and 'other' contain the same data. This piece
-    // and 'other' must be array-shaped and compatible.
-    bool EqualElements(const Piece& other) const;
-
-    // Writes the shape and data (if array-shaped) into the given proto.
-    void WriteToProto(LiteralProto* proto) const;
-
-    // Copies the data from the given proto into this piece. The shape of this
-    // piece must be equal (not just compatible) to the shape of the proto.
-    Status CopyFromProto(const LiteralProto& proto);
-
-    // Sorts the elements in a sparse array.
-    void SortSparseElements();
-
-   private:
-    // Recursive helper for EqualElements.
-    template 
-    bool EqualElementsInternal(const Piece& other,
-                               std::vector* multi_index) const;
-
-    // Helper for SortSparseElements that has the element type as a template
-    // parameter.
-    template 
-    void SortSparseElementsInternal();
-
-    // For array-shaped pieces, this is the buffer holding the literal data.
-    char* buffer_ = nullptr;
-
-    // For sparse arrays, this is the array of indices.
-    SparseIndexArray* sparse_indices_ = nullptr;
-
-    // The shape of piece. This points into the shape of the containing Literal
-    // (Literal::shape_).
-    const Shape* subshape_ = nullptr;
-  };
-
-  // Returns the piece at the given ShapeIndex.
-  Piece& piece(const ShapeIndex& shape_index) {
-    return *pieces_.mutable_element(shape_index);
-  }
-  const Piece& piece(const ShapeIndex& shape_index) const {
-    return pieces_.element(shape_index);
-  }
-
-  // Returns the piece at the root of the shape (empty ShapeIndex).
-  Piece& root_piece() { return piece({}); }
-  const Piece& root_piece() const { return piece({}); }
+  // Literal class always owns the shape. The parent class borrows this shape.
+  std::unique_ptr shape_;
 
-  // Deallocate the buffers held by this literal (if the literal owns the
-  // buffer).
-  void DeallocateBuffers();
+  Piece* root_piece_ = nullptr;
 
   // Implementation details shared between Populate() and PopulateParallel()
   template 
   Status PopulateInternal(const FnType& generator, bool parallel);
 
-  Shape shape_;
-  ShapeTree pieces_;
-
-  // Whether the buffers held in pieces_ are owned by this Literal.
-  bool owns_buffers_;
+  // Deallocate the buffers held by this literal.
+  void DeallocateBuffers();
 
-  // LiteralView must access and manipulate Pieces of other Literals.
-  friend class LiteralView;
-};  // namespace xla
+  friend class LiteralBase;
+};
 
 std::ostream& operator<<(std::ostream& out, const Literal& literal);
 
-// A read-only view of a Literal. A LiteralView contains pointers to buffers
-// owned by the viewed Literal.
-//
-// TODO(b/71550060): Replace LiteralView with Literal slice classes (immutable
-// and mutable) similar to (Mutable)ArraySlice.
-class LiteralView : public Literal {
+// A read-only view of a Literal. A LiteralSlice contains pointers to shape and
+// literal buffers always owned by others.
+class LiteralSlice : public LiteralBase {
  public:
-  // Create and return a view of the given literal rooted at the given shape
-  // index within the given literal. A factory is used rather than a public
-  // constructor because only const LiteralViews are supported. It's still
-  // possible to create non-const LiteralViews via the copy constructors, but
-  // the factory method makes it a bit less likely. Implementing literal slices
-  // will fix this undesirable situation (b/71550060).
-  static const LiteralView Create(const Literal& literal,
-                                  const ShapeIndex& view_root = {});
-
-  LiteralView(const LiteralView& other);
-  LiteralView& operator=(const LiteralView& other);
-
-  virtual ~LiteralView();
+  LiteralSlice() : LiteralBase() {}
+  // Implicit conversion constructor that can also accept Literal.
+  LiteralSlice(const LiteralBase& literal);
+  LiteralSlice(const LiteralBase& literal, const ShapeIndex& view_root);
 
  private:
-  LiteralView(const Literal& literal, const ShapeIndex& view_root);
+  const Piece& root_piece() const override { return *root_piece_; };
 
-  // Helper for the copy constructor and copy assignment operator.
-  void CopyFrom(const LiteralView& other);
+  const Piece* root_piece_;  // Not owned.
 };
 
 template 
-tensorflow::gtl::ArraySlice Literal::Piece::data() const {
+tensorflow::gtl::ArraySlice LiteralBase::Piece::data() const {
   CHECK(ShapeUtil::IsArray(subshape())) << ShapeUtil::HumanString(subshape());
   CHECK_EQ(subshape().element_type(),
            primitive_util::NativeToPrimitiveType())
@@ -866,7 +1014,7 @@ tensorflow::gtl::ArraySlice Literal::Piece::data() const {
 }
 
 template 
-tensorflow::gtl::MutableArraySlice Literal::Piece::data() {
+tensorflow::gtl::MutableArraySlice LiteralBase::Piece::data() {
   CHECK(ShapeUtil::IsArray(subshape())) << ShapeUtil::HumanString(subshape());
   CHECK_EQ(subshape().element_type(),
            primitive_util::NativeToPrimitiveType())
@@ -879,7 +1027,7 @@ tensorflow::gtl::MutableArraySlice Literal::Piece::data() {
 }
 
 template 
-NativeT Literal::Piece::Get(
+NativeT LiteralBase::Piece::Get(
     tensorflow::gtl::ArraySlice multi_index) const {
   CHECK(LayoutUtil::IsDenseArray(subshape()));
   return data()[IndexUtil::MultidimensionalIndexToLinearIndex(
@@ -887,15 +1035,15 @@ NativeT Literal::Piece::Get(
 }
 
 template 
-void Literal::Piece::Set(tensorflow::gtl::ArraySlice multi_index,
-                         NativeT value) {
+void LiteralBase::Piece::Set(tensorflow::gtl::ArraySlice multi_index,
+                             NativeT value) {
   CHECK(LayoutUtil::IsDenseArray(subshape()));
   data()[IndexUtil::MultidimensionalIndexToLinearIndex(
       subshape(), multi_index)] = value;
 }
 
 template 
-tensorflow::gtl::ArraySlice Literal::data(
+tensorflow::gtl::ArraySlice LiteralBase::data(
     const ShapeIndex& shape_index) const {
   return piece(shape_index).data();
 }
@@ -907,13 +1055,13 @@ tensorflow::gtl::MutableArraySlice Literal::data(
 }
 
 template 
-inline NativeT Literal::Get(tensorflow::gtl::ArraySlice multi_index,
-                            const ShapeIndex& shape_index) const {
+inline NativeT LiteralBase::Get(tensorflow::gtl::ArraySlice multi_index,
+                                const ShapeIndex& shape_index) const {
   return piece(shape_index).Get(multi_index);
 }
 
 template 
-inline NativeT Literal::Get(
+inline NativeT LiteralBase::Get(
     tensorflow::gtl::ArraySlice multi_index) const {
   return root_piece().Get(multi_index);
 }
@@ -1160,13 +1308,13 @@ template 
 }
 
 template 
-NativeT Literal::GetFirstElement() const {
+NativeT LiteralBase::GetFirstElement() const {
   return data().at(0);
 }
 
 template 
-NativeT Literal::GetSparseElement(int64 sparse_element_number,
-                                  const ShapeIndex& shape_index) const {
+NativeT LiteralBase::GetSparseElement(int64 sparse_element_number,
+                                      const ShapeIndex& shape_index) const {
   CHECK(
       LayoutUtil::IsSparseArray(ShapeUtil::GetSubshape(shape(), shape_index)));
   return data(shape_index)[sparse_element_number];
@@ -1199,7 +1347,7 @@ template 
 }
 
 template 
-void Literal::EachCell(
+void LiteralBase::EachCell(
     std::function indices,
                        NativeT value)>
         per_cell) const {
@@ -1375,7 +1523,7 @@ template 
 }
 
 template 
-std::unique_ptr Literal::Replicate(int64 times) const {
+std::unique_ptr LiteralBase::Replicate(int64 times) const {
   DimensionVector bounds = {times};
   bounds.reserve(shape().dimensions_size() + 1);
   for (int64 bound : shape().dimensions()) {
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index 61046784e05623..087d509f282de9 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -974,7 +974,7 @@ TEST_F(LiteralUtilTest, CopyFromTuples) {
                                    Literal::CreateR1({2.0, 4.0}).get(),
                                    &nil_literal});
 
-  EXPECT_EQ(*matrix, LiteralView::Create(*nested_tuple, {0}));
+  EXPECT_EQ(*matrix, LiteralSlice(*nested_tuple, {0}));
   EXPECT_EQ(nested_tuple->Get({}, {1, 0}), 42);
   EXPECT_EQ(nested_tuple->Get({0}, {1, 1}), 23.0);
   EXPECT_EQ(nested_tuple->Get({1}, {1, 1}), 44.0);
@@ -985,7 +985,7 @@ TEST_F(LiteralUtilTest, CopyFromTuples) {
                                       /*src_shape_index=*/{}));
 
   // The matrix element should be unchanged.
-  EXPECT_EQ(*matrix, LiteralView::Create(*nested_tuple, {0}));
+  EXPECT_EQ(*matrix, LiteralSlice(*nested_tuple, {0}));
 
   // The tuple element should have been copied from 'tuple'.
   EXPECT_EQ(nested_tuple->Get({}, {1, 0}), -5);
@@ -1373,36 +1373,36 @@ TEST_F(LiteralUtilTest, CopyFromProto_f16) {
   ASSERT_EQ(h1, r[3]);
 }
 
-TEST_F(LiteralUtilTest, LiteralViewTest) {
+TEST_F(LiteralUtilTest, LiteralSliceTest) {
   auto scalar = Literal::CreateR0(1.0);
   auto matrix = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}});
   auto tuple = Literal::MakeTuple({scalar.get(), matrix.get()});
   auto nested_tuple = Literal::MakeTuple({tuple.get(), scalar.get()});
   Literal nil(ShapeUtil::MakeNil());
 
-  EXPECT_EQ(LiteralView::Create(*scalar, {}), *scalar);
-  EXPECT_EQ(LiteralView::Create(*matrix, {}), *matrix);
-  EXPECT_EQ(LiteralView::Create(*tuple, {}), *tuple);
-  EXPECT_EQ(LiteralView::Create(*nested_tuple, {}), *nested_tuple);
-  EXPECT_EQ(LiteralView::Create(nil, {}), nil);
+  EXPECT_EQ(LiteralSlice(*scalar, {}), *scalar);
+  EXPECT_EQ(LiteralSlice(*matrix, {}), *matrix);
+  EXPECT_EQ(LiteralSlice(*tuple, {}), *tuple);
+  EXPECT_EQ(LiteralSlice(*nested_tuple, {}), *nested_tuple);
+  EXPECT_EQ(LiteralSlice(nil, {}), nil);
 
-  EXPECT_EQ(LiteralView::Create(*tuple, {0}), *scalar);
-  EXPECT_EQ(LiteralView::Create(*tuple, {1}), *matrix);
+  EXPECT_EQ(LiteralSlice(*tuple, {0}), *scalar);
+  EXPECT_EQ(LiteralSlice(*tuple, {1}), *matrix);
 
-  EXPECT_EQ(LiteralView::Create(*nested_tuple, {0}), *tuple);
-  EXPECT_EQ(LiteralView::Create(*nested_tuple, {0, 0}), *scalar);
-  EXPECT_EQ(LiteralView::Create(*nested_tuple, {0, 1}), *matrix);
-  EXPECT_EQ(LiteralView::Create(*nested_tuple, {1}), *scalar);
+  EXPECT_EQ(LiteralSlice(*nested_tuple, {0}), *tuple);
+  EXPECT_EQ(LiteralSlice(*nested_tuple, {0, 0}), *scalar);
+  EXPECT_EQ(LiteralSlice(*nested_tuple, {0, 1}), *matrix);
+  EXPECT_EQ(LiteralSlice(*nested_tuple, {1}), *scalar);
 }
 
-TEST_F(LiteralUtilTest, MutatingLiteralView) {
+TEST_F(LiteralUtilTest, MutatingLiteralSlice) {
   auto scalar = Literal::CreateR0(1.0);
   auto matrix = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}});
   auto tuple = Literal::MakeTuple({scalar.get(), matrix.get()});
   auto nested_tuple = Literal::MakeTuple({tuple.get(), scalar.get()});
   // Verify that changing the underlying data beneath the view changes the
   // data of the view itself.
-  const auto nested_tuple_view = LiteralView::Create(*nested_tuple);
+  const auto nested_tuple_view = LiteralSlice(*nested_tuple);
   EXPECT_EQ(
       nested_tuple->Get(/*multi_index=*/{}, /*shape_index=*/{0, 0}),
       1.0f);
@@ -1418,16 +1418,15 @@ TEST_F(LiteralUtilTest, MutatingLiteralView) {
             555.0f);
 }
 
-TEST_F(LiteralUtilTest, LiteralViewOfALiteralView) {
+TEST_F(LiteralUtilTest, LiteralSliceOfALiteralSlice) {
   auto scalar = Literal::CreateR0(1.0);
   auto matrix = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}});
   auto tuple = Literal::MakeTuple({scalar.get(), matrix.get()});
   auto nested_tuple = Literal::MakeTuple({tuple.get(), scalar.get()});
 
-  const auto nested_tuple_view = LiteralView::Create(*nested_tuple);
-  const auto tuple_view =
-      LiteralView::Create(nested_tuple_view, /*view_root=*/{0});
-  const auto matrix_view = LiteralView::Create(tuple_view, /*view_root=*/{1});
+  const auto nested_tuple_view = LiteralSlice(*nested_tuple);
+  const auto tuple_view = LiteralSlice(nested_tuple_view, /*view_root=*/{0});
+  const auto matrix_view = LiteralSlice(tuple_view, /*view_root=*/{1});
   EXPECT_EQ(matrix_view, *Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}));
 }
 
@@ -1533,11 +1532,11 @@ TEST_F(LiteralUtilTest, LiteralMoveAssignment) {
   EXPECT_EQ(literal.Get({1, 1}), 4.0);
 }
 
-TEST_F(LiteralUtilTest, LiteralViewCopy) {
+TEST_F(LiteralUtilTest, LiteralSliceCopy) {
   std::unique_ptr matrix =
       Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}});
-  const auto matrix_view = LiteralView::Create(*matrix);
-  LiteralView matrix_view_copy(matrix_view);
+  const auto matrix_view = LiteralSlice(*matrix);
+  LiteralSlice matrix_view_copy(matrix_view);
 
   EXPECT_EQ(matrix_view_copy.Get({0, 0}), 1.0);
   EXPECT_EQ(matrix_view_copy.Get({0, 1}), 2.0);
diff --git a/tensorflow/compiler/xla/python/numpy_bridge.cc b/tensorflow/compiler/xla/python/numpy_bridge.cc
index dc6f5fe5fcc067..68648a3a176363 100644
--- a/tensorflow/compiler/xla/python/numpy_bridge.cc
+++ b/tensorflow/compiler/xla/python/numpy_bridge.cc
@@ -340,13 +340,13 @@ StatusOr OpMetadataFromPyObject(PyObject* o) {
   return result;
 }
 
-PyObject* PyObjectFromXlaLiteral(const Literal& literal) {
+PyObject* PyObjectFromXlaLiteral(const LiteralSlice& literal) {
   if (ShapeUtil::IsTuple(literal.shape())) {
     int num_elements = ShapeUtil::TupleElementCount(literal.shape());
     PyObject* tuple = PyTuple_New(num_elements);
     for (int i = 0; i < num_elements; i++) {
-      PyTuple_SET_ITEM(
-          tuple, i, PyObjectFromXlaLiteral(LiteralView::Create(literal, {i})));
+      PyTuple_SET_ITEM(tuple, i,
+                       PyObjectFromXlaLiteral(LiteralSlice(literal, {i})));
     }
     return tuple;
   } else {
@@ -431,7 +431,7 @@ Status CopyNumpyArrayToLiteral(int np_type, PyArrayObject* py_array,
   return Status::OK();
 }
 
-void CopyLiteralToNumpyArray(int np_type, const Literal& literal,
+void CopyLiteralToNumpyArray(int np_type, const LiteralSlice& literal,
                              PyArrayObject* py_array) {
   switch (np_type) {
     case NPY_BOOL:
diff --git a/tensorflow/compiler/xla/python/numpy_bridge.h b/tensorflow/compiler/xla/python/numpy_bridge.h
index 9656cb1c31c39d..64f0aae0f9790f 100644
--- a/tensorflow/compiler/xla/python/numpy_bridge.h
+++ b/tensorflow/compiler/xla/python/numpy_bridge.h
@@ -74,7 +74,7 @@ StatusOr OpMetadataFromPyObject(PyObject* o);
 // array data.
 //
 // The return value is a new reference.
-PyObject* PyObjectFromXlaLiteral(const Literal& literal);
+PyObject* PyObjectFromXlaLiteral(const LiteralSlice& literal);
 
 // Converts a Numpy ndarray or a nested Python tuple thereof to a
 // corresponding XLA literal.
@@ -90,7 +90,7 @@ StatusOr > XlaLiteralFromPyObject(PyObject* o);
 Status CopyNumpyArrayToLiteral(int np_type, PyArrayObject* py_array,
                                Literal* literal);
 
-void CopyLiteralToNumpyArray(int np_type, const Literal& literal,
+void CopyLiteralToNumpyArray(int np_type, const LiteralSlice& literal,
                              PyArrayObject* py_array);
 
 template 
@@ -101,7 +101,8 @@ void CopyNumpyArrayToLiteral(PyArrayObject* py_array, Literal* literal) {
 }
 
 template 
-void CopyLiteralToNumpyArray(const Literal& literal, PyArrayObject* py_array) {
+void CopyLiteralToNumpyArray(const LiteralSlice& literal,
+                             PyArrayObject* py_array) {
   NativeT* dest = static_cast(PyArray_DATA(py_array));
   auto source = literal.data();
   std::copy(source.begin(), source.end(), dest);
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index 4ec79a024463b5..3ce80bba179e54 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -501,13 +501,13 @@ Status AlgebraicSimplifierVisitor::HandleConcatenate(
 }
 
 static HloInstruction* BuildTupleConstant(HloComputation* computation,
-                                          const Literal& literal) {
+                                          const LiteralSlice& literal) {
   if (ShapeUtil::IsTuple(literal.shape())) {
     std::vector elems;
     elems.reserve(ShapeUtil::TupleElementCount(literal.shape()));
     for (int i = 0; i < ShapeUtil::TupleElementCount(literal.shape()); ++i) {
       elems.push_back(
-          BuildTupleConstant(computation, LiteralView::Create(literal, {i})));
+          BuildTupleConstant(computation, LiteralSlice(literal, {i})));
     }
     return computation->AddInstruction(HloInstruction::CreateTuple(elems));
   } else {
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc
index 9b39e7f5765ae5..d97802ee45d6ad 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc
@@ -88,8 +88,8 @@ CpuTransferManager::CpuTransferManager()
     : GenericTransferManager(se::host::kHostPlatformId,
                              /*pointer_size=*/sizeof(void*)) {}
 
-Status CpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
-                                                   const Literal& literal) {
+Status CpuTransferManager::TransferLiteralToInfeed(
+    se::StreamExecutor* executor, const LiteralSlice& literal) {
   const Shape& shape = literal.shape();
   VLOG(2) << "Transferring literal to infeed with shape: "
           << ShapeUtil::HumanString(shape);
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.h b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.h
index 3ecb0d23649837..6dfc666f09dfa6 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.h
@@ -38,7 +38,7 @@ class CpuTransferManager : public GenericTransferManager {
   ~CpuTransferManager() override {}
 
   Status TransferLiteralToInfeed(se::StreamExecutor* executor,
-                                 const Literal& literal) override;
+                                 const LiteralSlice& literal) override;
   Status TransferBufferToInfeed(se::StreamExecutor* executor, int64 size,
                                 const void* source) override;
   Status TransferLiteralFromOutfeed(se::StreamExecutor* executor,
diff --git a/tensorflow/compiler/xla/service/cpu/external_constant_pool.cc b/tensorflow/compiler/xla/service/cpu/external_constant_pool.cc
index 7dcc4ca7fa08b4..c5628655915875 100644
--- a/tensorflow/compiler/xla/service/cpu/external_constant_pool.cc
+++ b/tensorflow/compiler/xla/service/cpu/external_constant_pool.cc
@@ -26,13 +26,13 @@ limitations under the License.
 
 namespace xla {
 namespace cpu {
-void ExternalConstantPool::Insert(string name, const Literal& literal,
+void ExternalConstantPool::Insert(string name, const LiteralSlice& literal,
                                   int64 alignment) {
   CHECK(!ShapeUtil::IsTuple(literal.shape()));
   CHECK(alignment > 0 && IsPowerOfTwo(static_cast(alignment)));
   CHECK(entries_.find(name) == entries_.end());
 
-  int64 literal_size = ShapeUtil::ByteSizeOf(literal.shape());
+  const int64 literal_size = ShapeUtil::ByteSizeOf(literal.shape());
   void* raw_pointer = tensorflow::port::AlignedMalloc(
       literal_size, std::max(alignment, sizeof(void*)));
   CHECK(raw_pointer != nullptr) << "failed to allocate " << literal_size
diff --git a/tensorflow/compiler/xla/service/cpu/external_constant_pool.h b/tensorflow/compiler/xla/service/cpu/external_constant_pool.h
index 8008a56df4dbf1..0677f5f0b58005 100644
--- a/tensorflow/compiler/xla/service/cpu/external_constant_pool.h
+++ b/tensorflow/compiler/xla/service/cpu/external_constant_pool.h
@@ -43,7 +43,7 @@ class ExternalConstantPool {
   // The constant pool copies out the contents of `literal` into a buffer it
   // owns -- it does not keep pointers to `literal`, or to memory owned by
   // `literal`.
-  void Insert(string name, const Literal& literal, int64 alignment);
+  void Insert(string name, const LiteralSlice& literal, int64 alignment);
 
   // Find the constant with name `name` in this constant pool.  If there isn't
   // such constant, return nullptr.
diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.cc b/tensorflow/compiler/xla/service/generic_transfer_manager.cc
index ddb687314ee822..dbf1ab669077fc 100644
--- a/tensorflow/compiler/xla/service/generic_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/generic_transfer_manager.cc
@@ -115,7 +115,7 @@ Status GenericTransferManager::TransferLiteralToDevice(
           TF_RET_CHECK(GetByteSizeRequirement(device_subshape) ==
                        device_memory.size());
           // Element is array-shaped: transfer array data to device buffer.
-          const auto subliteral = LiteralView::Create(literal, index);
+          const auto subliteral = LiteralSlice(literal, index);
           std::unique_ptr relayed_out_literal;
           const void* source;
           if (LayoutUtil::Equal(device_subshape.layout(),
@@ -137,7 +137,7 @@ Status GenericTransferManager::TransferLiteralToDevice(
 }
 
 Status GenericTransferManager::TransferLiteralToInfeed(
-    se::StreamExecutor* executor, const Literal& literal) {
+    se::StreamExecutor* executor, const LiteralSlice& literal) {
   return Unimplemented("Generic transfer to Infeed");
 }
 
diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.h b/tensorflow/compiler/xla/service/generic_transfer_manager.h
index 0579099de40ba3..3343eca8517a00 100644
--- a/tensorflow/compiler/xla/service/generic_transfer_manager.h
+++ b/tensorflow/compiler/xla/service/generic_transfer_manager.h
@@ -49,7 +49,7 @@ class GenericTransferManager : public TransferManager {
                                  const ShapedBuffer& device_buffer) override;
 
   Status TransferLiteralToInfeed(se::StreamExecutor* executor,
-                                 const Literal& literal) override;
+                                 const LiteralSlice& literal) override;
   Status TransferLiteralFromOutfeed(se::StreamExecutor* executor,
                                     const Shape& literal_shape,
                                     Literal* literal) override;
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc
index f13727ca9b6954..7bb8df6581b49b 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc
@@ -44,8 +44,8 @@ GpuTransferManager::GpuTransferManager()
           /*pointer_size=*/llvm::DataLayout(gpu::GpuCompiler::kDataLayout)
               .getPointerSize(0 /* default address space */)) {}
 
-Status GpuTransferManager::TransferLiteralToInfeed(se::StreamExecutor* executor,
-                                                   const Literal& literal) {
+Status GpuTransferManager::TransferLiteralToInfeed(
+    se::StreamExecutor* executor, const LiteralSlice& literal) {
   const Shape& shape = literal.shape();
   VLOG(2) << "Transferring literal to infeed with shape: "
           << ShapeUtil::HumanString(shape);
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.h b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.h
index d040a999752305..09f8227f508a31 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.h
+++ b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.h
@@ -37,7 +37,7 @@ class GpuTransferManager : public GenericTransferManager {
   ~GpuTransferManager() override {}
 
   Status TransferLiteralToInfeed(se::StreamExecutor* executor,
-                                 const Literal& literal) override;
+                                 const LiteralSlice& literal) override;
   Status TransferBufferToInfeed(se::StreamExecutor* executor, int64 size,
                                 const void* source) override;
 
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index fffe1923ba9282..63eaf6f17ba45d 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -56,8 +56,8 @@ using tensorflow::gtl::FlatSet;
 
 template 
 StatusOr> Compare(const Shape& shape, HloOpcode opcode,
-                                           const Literal& lhs_literal,
-                                           const Literal& rhs_literal) {
+                                           LiteralSlice lhs_literal,
+                                           LiteralSlice rhs_literal) {
   std::function compare_op;
   switch (opcode) {
     case HloOpcode::kEq:
@@ -106,8 +106,8 @@ StatusOr> Compare(const Shape& shape, HloOpcode opcode,
 
 template <>
 StatusOr> Compare(
-    const Shape& shape, HloOpcode opcode, const Literal& lhs_literal,
-    const Literal& rhs_literal) {
+    const Shape& shape, HloOpcode opcode, LiteralSlice lhs_literal,
+    LiteralSlice rhs_literal) {
   std::function compare_op;
   switch (opcode) {
     case HloOpcode::kEq:
diff --git a/tensorflow/compiler/xla/service/transfer_manager.h b/tensorflow/compiler/xla/service/transfer_manager.h
index d82b4f0f81b5da..55c544fcd2496b 100644
--- a/tensorflow/compiler/xla/service/transfer_manager.h
+++ b/tensorflow/compiler/xla/service/transfer_manager.h
@@ -81,7 +81,7 @@ class TransferManager {
   // Transfers the given literal into the Infeed interface of the device,
   // using the given executor.
   virtual Status TransferLiteralToInfeed(se::StreamExecutor* executor,
-                                         const Literal& literal) = 0;
+                                         const LiteralSlice& literal) = 0;
 
   // Transfers the given literal from the Outfeed interface of the device,
   // using the given executor.
diff --git a/tensorflow/compiler/xla/tests/broadcast_test.cc b/tensorflow/compiler/xla/tests/broadcast_test.cc
index 6ebbf7191833ef..a180cdd604d425 100644
--- a/tensorflow/compiler/xla/tests/broadcast_test.cc
+++ b/tensorflow/compiler/xla/tests/broadcast_test.cc
@@ -87,11 +87,11 @@ XLA_TEST_F(BroadcastTest, BroadcastVectorTo2D) {
 
   LiteralTestUtil::ExpectNear(
       *Literal::CreateR2({{1.0, 1.0}, {2.0, 2.0}, {3.0, 3.0}}),
-      LiteralView::Create(*result, {0}), error_spec_);
+      LiteralSlice(*result, {0}), error_spec_);
 
   LiteralTestUtil::ExpectNear(
       *Literal::CreateR2({{1.0, 2.0, 3.0}, {1.0, 2.0, 3.0}}),
-      LiteralView::Create(*result, {1}), error_spec_);
+      LiteralSlice(*result, {1}), error_spec_);
 }
 
 XLA_TEST_F(BroadcastTest, Broadcast2DTo2D) {
diff --git a/tensorflow/compiler/xla/tests/client_test.cc b/tensorflow/compiler/xla/tests/client_test.cc
index 0b425b93bb144e..abf7312f48430c 100644
--- a/tensorflow/compiler/xla/tests/client_test.cc
+++ b/tensorflow/compiler/xla/tests/client_test.cc
@@ -91,9 +91,9 @@ XLA_TEST_F(ClientTest, ExecuteWithTupleLayout) {
       auto result,
       client_->ExecuteAndTransfer(computation, {}, &execution_options));
   LiteralTestUtil::ExpectR2Equal({{1, 2}, {3, 4}},
-                                        LiteralView::Create(*result, {0}));
+                                        LiteralSlice(*result, {0}));
   LiteralTestUtil::ExpectR2Equal({{10, 20}, {30, 40}},
-                                        LiteralView::Create(*result, {1}));
+                                        LiteralSlice(*result, {1}));
 
   EXPECT_TRUE(ShapeUtil::IsTuple(result->shape()));
   EXPECT_EQ(2, ShapeUtil::TupleElementCount(result->shape()));
diff --git a/tensorflow/compiler/xla/tests/constants_test.cc b/tensorflow/compiler/xla/tests/constants_test.cc
index 4743673561a665..d518e4a16598ec 100644
--- a/tensorflow/compiler/xla/tests/constants_test.cc
+++ b/tensorflow/compiler/xla/tests/constants_test.cc
@@ -169,9 +169,9 @@ TEST_F(ConstantsTest, DISABLED_TupleConstant) {
       ExecuteAndTransfer(&builder, {}).ConsumeValueOrDie();
 
   LiteralTestUtil::ExpectR2Near(
-      {{1.0}, {2.0}}, LiteralView::Create(*result, {0}), error_spec_);
+      {{1.0}, {2.0}}, LiteralSlice(*result, {0}), error_spec_);
   LiteralTestUtil::ExpectR1Near(
-      {2.0, 42.0}, LiteralView::Create(*result, {1}), error_spec_);
+      {2.0, 42.0}, LiteralSlice(*result, {1}), error_spec_);
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.cc b/tensorflow/compiler/xla/tests/literal_test_util.cc
index c28f79ae386670..868876c72db68c 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.cc
+++ b/tensorflow/compiler/xla/tests/literal_test_util.cc
@@ -111,7 +111,7 @@ namespace {
 // Return a literal with all arrays of type FromNativeT converted to type
 // ToNativeT in the given literal.
 template 
-std::unique_ptr ConvertType(const Literal& literal) {
+std::unique_ptr ConvertType(LiteralSlice literal) {
   // First construct shape of the result.
   Shape result_shape(literal.shape());
   ShapeUtil::ForEachMutableSubshape(
@@ -150,12 +150,12 @@ std::unique_ptr ConvertType(const Literal& literal) {
 }  // namespace
 
 /* static */ std::unique_ptr LiteralTestUtil::ConvertBF16ToF32(
-    const Literal& literal) {
+    LiteralSlice literal) {
   return ConvertType(literal);
 }
 
 /* static */ std::unique_ptr LiteralTestUtil::ConvertF32ToBF16(
-    const Literal& literal) {
+    LiteralSlice literal) {
   return ConvertType(literal);
 }
 
@@ -237,7 +237,7 @@ ::testing::AssertionResult CompareEqual(complex64 lhs,
 // actual literal and compares their values elementwise. Returns true if all
 // elements are equal.
 template 
-bool ExpectLiteralsEqual(const Literal& expected, const Literal& actual,
+bool ExpectLiteralsEqual(LiteralSlice expected, LiteralSlice actual,
                          tensorflow::gtl::MutableArraySlice multi_index,
                          int64 dimension) {
   if (dimension == expected.shape().dimensions_size()) {
@@ -259,8 +259,8 @@ bool ExpectLiteralsEqual(const Literal& expected, const Literal& actual,
 
 }  // namespace
 
-/* static */ void LiteralTestUtil::ExpectEqual(const Literal& expected,
-                                               const Literal& actual,
+/* static */ void LiteralTestUtil::ExpectEqual(LiteralSlice expected,
+                                               LiteralSlice actual,
                                                const string& message) {
   EXPECT_TRUE(Equal(expected, actual))
       << "expected:\n"
@@ -269,13 +269,13 @@ bool ExpectLiteralsEqual(const Literal& expected, const Literal& actual,
       << (message.empty() ? "" : StrCat("\nmessage: ", message));
 }
 
-/* static */ void LiteralTestUtil::ExpectNotEqual(const Literal& expected,
-                                                  const Literal& actual) {
+/* static */ void LiteralTestUtil::ExpectNotEqual(LiteralSlice expected,
+                                                  LiteralSlice actual) {
   EXPECT_FALSE(Equal(expected, actual));
 }
 
 /* static */ ::testing::AssertionResult LiteralTestUtil::Equal(
-    const Literal& expected, const Literal& actual) {
+    LiteralSlice expected, LiteralSlice actual) {
   VLOG(1) << "expected:";
   XLA_VLOG_LINES(1, expected.ToString());
   VLOG(1) << "actual:";
@@ -324,9 +324,9 @@ bool ExpectLiteralsEqual(const Literal& expected, const Literal& actual,
         SCOPED_TRACE(StrCat("Tuple index ", i, " in ",
                             ShapeUtil::HumanString(expected.shape())));
 
-        // Create LiteralViews of the expected and actual elements.
-        auto result = Equal(LiteralView::Create(expected, {i}),
-                            LiteralView::Create(actual, {i}));
+        // Create LiteralSlices of the expected and actual elements.
+        auto result =
+            Equal(LiteralSlice(expected, {i}), LiteralSlice(actual, {i}));
         tuple_match = tuple_match ? !!result : false;
       }
       match = tuple_match;
@@ -368,7 +368,7 @@ int64 RecursiveElementCount(const Shape& shape) {
 // 3 minutes.  The utility of printing a literal with >1000 elements is
 // questionable, especially when writing the Literal proto to disk is orders
 // of magnitude faster.
-string TruncateHugeLiteral(const Literal& literal) {
+string TruncateHugeLiteral(LiteralSlice literal) {
   return RecursiveElementCount(literal.shape()) < 1000
              ? literal.ToString()
              : "[TRUNCATED, Literal with more than 1000 values]";
@@ -435,8 +435,8 @@ class NearComparator {
   // result. The assertion result is successful if all actual and expected
   // elements are within the given error bound. In case of error, the assertion
   // result contains a detailed error message in case of failure.
-  static ::testing::AssertionResult Compare(const Literal& expected,
-                                            const Literal& actual,
+  static ::testing::AssertionResult Compare(LiteralSlice expected,
+                                            LiteralSlice actual,
                                             ErrorSpec error,
                                             bool detailed_message) {
     NearComparator comparator(expected, actual, error,
@@ -472,7 +472,7 @@ class NearComparator {
     }
   };
 
-  explicit NearComparator(const Literal& expected, const Literal& actual,
+  explicit NearComparator(LiteralSlice expected, LiteralSlice actual,
                           ErrorSpec error, bool detailed_message)
       : expected_(expected),
         actual_(actual),
@@ -649,7 +649,7 @@ class NearComparator {
   }
 
   // Writes the given literal to a file in the test temporary directory.
-  void WriteLiteralToTempFile(const Literal& literal, const string& name) {
+  void WriteLiteralToTempFile(LiteralSlice literal, const string& name) {
     int64 now_usec = tensorflow::Env::Default()->NowMicros();
     string filename = tensorflow::io::JoinPath(
         tensorflow::testing::TmpDir(),
@@ -733,8 +733,8 @@ class NearComparator {
   }
 
   // 'actual' and 'expected' literals being compared.
-  const Literal& expected_;
-  const Literal& actual_;
+  LiteralSlice expected_;
+  LiteralSlice actual_;
 
   // The error bounds of the comparison.
   ErrorSpec error_;
@@ -794,8 +794,8 @@ constexpr std::array NearComparator::kErrorBucketBounds;
 // Helper function for comparing two literals for nearness. Handles tuple-shapes
 // via recursion. shape_index is the ShapeIndex of expected (or actual)
 // currently being compared.
-::testing::AssertionResult NearHelper(const Literal& expected,
-                                      const Literal& actual,
+::testing::AssertionResult NearHelper(LiteralSlice expected,
+                                      LiteralSlice actual,
                                       const ErrorSpec& error,
                                       bool detailed_message,
                                       const ShapeIndex& shape_index) {
@@ -807,8 +807,8 @@ ::testing::AssertionResult NearHelper(const Literal& expected,
 
   if (ShapeUtil::IsTuple(expected.shape())) {
     for (int64 i = 0; i < ShapeUtil::TupleElementCount(expected.shape()); ++i) {
-      const auto expected_element = LiteralView::Create(expected, {i});
-      const auto actual_element = LiteralView::Create(actual, {i});
+      const auto expected_element = LiteralSlice(expected, {i});
+      const auto actual_element = LiteralSlice(actual, {i});
       ShapeIndex element_index = shape_index;
       element_index.push_back(i);
       ::testing::AssertionResult res =
@@ -874,14 +874,14 @@ ::testing::AssertionResult NearHelper(const Literal& expected,
 }  // namespace
 
 /* static */ ::testing::AssertionResult LiteralTestUtil::Near(
-    const Literal& expected, const Literal& actual, const ErrorSpec& error,
+    LiteralSlice expected, LiteralSlice actual, const ErrorSpec& error,
     bool detailed_message) {
   return NearHelper(expected, actual, error, detailed_message,
                     /*shape_index=*/{});
 }
 
-/* static */ void LiteralTestUtil::ExpectNear(const Literal& expected,
-                                              const Literal& actual,
+/* static */ void LiteralTestUtil::ExpectNear(LiteralSlice expected,
+                                              LiteralSlice actual,
                                               const ErrorSpec& error,
                                               const string& message) {
   ::testing::AssertionResult res =
@@ -897,7 +897,7 @@ ::testing::AssertionResult NearHelper(const Literal& expected,
 }
 
 /*static*/ ::testing::AssertionResult LiteralTestUtil::NearOrEqual(
-    const Literal& expected, const Literal& actual,
+    LiteralSlice expected, LiteralSlice actual,
     const tensorflow::gtl::optional& error) {
   if (error.has_value()) {
     VLOG(1) << "Expects near";
@@ -908,7 +908,7 @@ ::testing::AssertionResult NearHelper(const Literal& expected,
 }
 
 /*static*/ void LiteralTestUtil::ExpectNearOrEqual(
-    const Literal& expected, const Literal& actual,
+    LiteralSlice expected, LiteralSlice actual,
     const tensorflow::gtl::optional& error) {
   EXPECT_TRUE(NearOrEqual(expected, actual, error));
 }
@@ -920,7 +920,7 @@ ::testing::AssertionResult NearHelper(const Literal& expected,
 
 /* static */ std::unique_ptr LiteralTestUtil::Reshape(
     tensorflow::gtl::ArraySlice new_dimensions,
-    tensorflow::gtl::ArraySlice minor_to_major, const Literal& literal) {
+    tensorflow::gtl::ArraySlice minor_to_major, LiteralSlice literal) {
   int64 new_num_elements = 1;
   for (int64 i = 0; i < new_dimensions.size(); ++i) {
     new_num_elements *= new_dimensions[i];
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.h b/tensorflow/compiler/xla/tests/literal_test_util.h
index a755568c0f098e..4983dddcff3284 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.h
+++ b/tensorflow/compiler/xla/tests/literal_test_util.h
@@ -69,53 +69,53 @@ class LiteralTestUtil {
   // If the given literal's data type is bfloat16, converts it to a float
   // literal; otherwise, returns a copy of it. If the literal is a tuple,
   // recursively converts its elements.
-  static std::unique_ptr ConvertBF16ToF32(const Literal& bf16_literal);
+  static std::unique_ptr ConvertBF16ToF32(LiteralSlice bf16_literal);
 
   // If the given literal's data type is float, converts it to a bfloat16
   // literal; otherwise, returns a copy of it. If the literal is a tuple,
   // recursively converts its elements.
-  static std::unique_ptr ConvertF32ToBF16(const Literal& f32_literal);
+  static std::unique_ptr ConvertF32ToBF16(LiteralSlice f32_literal);
 
   // Asserts that the expected and actual literals are (bitwise) equal for all
   // elements in the literal. Also, asserts that the rank, dimensions sizes, and
   // primitive type are equal.
   static ::testing::AssertionResult Equal(
-      const Literal& expected, const Literal& actual) TF_MUST_USE_RESULT;
+      LiteralSlice expected, LiteralSlice actual) TF_MUST_USE_RESULT;
 
   // Expects that expected and actual are Equal.
-  static void ExpectEqual(const Literal& expected, const Literal& actual,
+  static void ExpectEqual(LiteralSlice expected, LiteralSlice actual,
                           const string& message = "");
 
   // Expects that expected and actual are Not Equal.
-  static void ExpectNotEqual(const Literal& expected, const Literal& actual);
+  static void ExpectNotEqual(LiteralSlice expected, LiteralSlice actual);
 
   // Asserts the given literal are (bitwise) equal to given expected values.
   template 
-  static void ExpectR0Equal(NativeT expected, const Literal& actual);
+  static void ExpectR0Equal(NativeT expected, LiteralSlice actual);
   template 
   static void ExpectR1Equal(tensorflow::gtl::ArraySlice expected,
-                            const Literal& actual);
+                            LiteralSlice actual);
   template 
   static void ExpectR2Equal(
       std::initializer_list> expected,
-      const Literal& actual);
+      LiteralSlice actual);
   template 
   static void ExpectR3Equal(
       std::initializer_list<
           std::initializer_list>>
           expected,
-      const Literal& actual);
+      LiteralSlice actual);
 
   // Asserts the given literal are (bitwise) equal to given array.
   template 
   static void ExpectR2EqualArray2D(const Array2D& expected,
-                                   const Literal& actual);
+                                   LiteralSlice actual);
   template 
   static void ExpectR3EqualArray3D(const Array3D& expected,
-                                   const Literal& actual);
+                                   LiteralSlice actual);
   template 
   static void ExpectR4EqualArray4D(const Array4D& expected,
-                                   const Literal& actual);
+                                   LiteralSlice actual);
 
   // Asserts that the expected and actual literals are within the given error
   // bound for all elements. Also, asserts that the rank, dimensions sizes, and
@@ -133,64 +133,61 @@ class LiteralTestUtil {
   // If detailed_message is true, then the error message in the assertion result
   // will contain a more detailed breakdown of mismatches.
   static ::testing::AssertionResult Near(
-      const Literal& expected, const Literal& actual, const ErrorSpec& error,
+      LiteralSlice expected, LiteralSlice actual, const ErrorSpec& error,
       bool detailed_message = false) TF_MUST_USE_RESULT;
 
   // Expects expected and actual to be Near with the given error.
-  static void ExpectNear(const Literal& expected, const Literal& actual,
+  static void ExpectNear(LiteralSlice expected, LiteralSlice actual,
                          const ErrorSpec& error, const string& message = "");
 
   // Asserts the given literal are within the given error bound of the given
   // expected values. Only supported for floating point values.
   template 
-  static void ExpectR0Near(NativeT expected, const Literal& actual,
+  static void ExpectR0Near(NativeT expected, LiteralSlice actual,
                            const ErrorSpec& error);
   template 
   static void ExpectR1Near(tensorflow::gtl::ArraySlice expected,
-                           const Literal& actual, const ErrorSpec& error);
+                           LiteralSlice actual, const ErrorSpec& error);
   template 
   static void ExpectR2Near(
       std::initializer_list> expected,
-      const Literal& actual, const ErrorSpec& error);
+      LiteralSlice actual, const ErrorSpec& error);
   template 
   static void ExpectR3Near(
       std::initializer_list<
           std::initializer_list>>
           expected,
-      const Literal& actual, const ErrorSpec& error);
+      LiteralSlice actual, const ErrorSpec& error);
   template 
   static void ExpectR4Near(
       std::initializer_list>>>
           expected,
-      const Literal& actual, const ErrorSpec& error);
+      LiteralSlice actual, const ErrorSpec& error);
 
   // Asserts the given literal are within the given error bound to the given
   // array. Only supported for floating point values.
   template 
   static void ExpectR2NearArray2D(const Array2D& expected,
-                                  const Literal& actual,
-                                  const ErrorSpec& error);
+                                  LiteralSlice actual, const ErrorSpec& error);
   template 
   static void ExpectR3NearArray3D(const Array3D& expected,
-                                  const Literal& actual,
-                                  const ErrorSpec& error);
+                                  LiteralSlice actual, const ErrorSpec& error);
   template 
   static void ExpectR4NearArray4D(const Array4D& expected,
-                                  const Literal& actual,
-                                  const ErrorSpec& error);
+                                  LiteralSlice actual, const ErrorSpec& error);
 
   // If the error spec is given, returns whether the expected and the actual are
   // within the error bound; otherwise, returns whether they are equal. Tuples
   // will be compared recursively.
   static ::testing::AssertionResult NearOrEqual(
-      const Literal& expected, const Literal& actual,
+      LiteralSlice expected, LiteralSlice actual,
       const tensorflow::gtl::optional& error) TF_MUST_USE_RESULT;
 
   // If the error spec is given, expects the expected and the actual to be near;
   // otherwise, expects them to be equal. Tuples will be compared recursively.
   static void ExpectNearOrEqual(
-      const Literal& expected, const Literal& actual,
+      LiteralSlice expected, LiteralSlice actual,
       const tensorflow::gtl::optional& error);
 
   // Returns a multi-dimensional index as a string. For example: '{7, 8}' will
@@ -205,8 +202,7 @@ class LiteralTestUtil {
   // layout order.
   static std::unique_ptr Reshape(
       tensorflow::gtl::ArraySlice new_dimensions,
-      tensorflow::gtl::ArraySlice minor_to_major,
-      const Literal& literal);
+      tensorflow::gtl::ArraySlice minor_to_major, LiteralSlice literal);
 
   // Creates a literal with the supplied shape, and uses the provided value
   // generator to populate the literal's values.
@@ -244,20 +240,20 @@ class LiteralTestUtil {
 
 template 
 /* static */ void LiteralTestUtil::ExpectR0Equal(NativeT expected,
-                                                 const Literal& actual) {
+                                                 LiteralSlice actual) {
   ExpectEqual(*Literal::CreateR0(expected), actual);
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR1Equal(
-    tensorflow::gtl::ArraySlice expected, const Literal& actual) {
+    tensorflow::gtl::ArraySlice expected, LiteralSlice actual) {
   ExpectEqual(*Literal::CreateR1(expected), actual);
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR2Equal(
     std::initializer_list> expected,
-    const Literal& actual) {
+    LiteralSlice actual) {
   ExpectEqual(*Literal::CreateR2(expected), actual);
 }
 
@@ -265,38 +261,38 @@ template 
 /* static */ void LiteralTestUtil::ExpectR3Equal(
     std::initializer_list>>
         expected,
-    const Literal& actual) {
+    LiteralSlice actual) {
   ExpectEqual(*Literal::CreateR3(expected), actual);
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR2EqualArray2D(
-    const Array2D& expected, const Literal& actual) {
+    const Array2D& expected, LiteralSlice actual) {
   ExpectEqual(*Literal::CreateR2FromArray2D(expected), actual);
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR3EqualArray3D(
-    const Array3D& expected, const Literal& actual) {
+    const Array3D& expected, LiteralSlice actual) {
   ExpectEqual(*Literal::CreateR3FromArray3D(expected), actual);
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR4EqualArray4D(
-    const Array4D& expected, const Literal& actual) {
+    const Array4D& expected, LiteralSlice actual) {
   ExpectEqual(*Literal::CreateR4FromArray4D(expected), actual);
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR0Near(NativeT expected,
-                                                const Literal& actual,
+                                                LiteralSlice actual,
                                                 const ErrorSpec& error) {
   ExpectNear(*Literal::CreateR0(expected), actual, error);
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR1Near(
-    tensorflow::gtl::ArraySlice expected, const Literal& actual,
+    tensorflow::gtl::ArraySlice expected, LiteralSlice actual,
     const ErrorSpec& error) {
   ExpectNear(*Literal::CreateR1(expected), actual, error);
 }
@@ -304,7 +300,7 @@ template 
 template 
 /* static */ void LiteralTestUtil::ExpectR2Near(
     std::initializer_list> expected,
-    const Literal& actual, const ErrorSpec& error) {
+    LiteralSlice actual, const ErrorSpec& error) {
   ExpectNear(*Literal::CreateR2(expected), actual, error);
 }
 
@@ -312,7 +308,7 @@ template 
 /* static */ void LiteralTestUtil::ExpectR3Near(
     std::initializer_list>>
         expected,
-    const Literal& actual, const ErrorSpec& error) {
+    LiteralSlice actual, const ErrorSpec& error) {
   ExpectNear(*Literal::CreateR3(expected), actual, error);
 }
 
@@ -321,27 +317,27 @@ template 
     std::initializer_list>>>
         expected,
-    const Literal& actual, const ErrorSpec& error) {
+    LiteralSlice actual, const ErrorSpec& error) {
   ExpectNear(*Literal::CreateR4(expected), actual, error);
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR2NearArray2D(
-    const Array2D& expected, const Literal& actual,
+    const Array2D& expected, LiteralSlice actual,
     const ErrorSpec& error) {
   ExpectNear(*Literal::CreateR2FromArray2D(expected), actual, error);
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR3NearArray3D(
-    const Array3D& expected, const Literal& actual,
+    const Array3D& expected, LiteralSlice actual,
     const ErrorSpec& error) {
   ExpectNear(*Literal::CreateR3FromArray3D(expected), actual, error);
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR4NearArray4D(
-    const Array4D& expected, const Literal& actual,
+    const Array4D& expected, LiteralSlice actual,
     const ErrorSpec& error) {
   ExpectNear(*Literal::CreateR4FromArray4D(expected), actual, error);
 }
diff --git a/tensorflow/compiler/xla/tests/local_client_execute_test.cc b/tensorflow/compiler/xla/tests/local_client_execute_test.cc
index 44c6811df84f49..96858c00d6bbe5 100644
--- a/tensorflow/compiler/xla/tests/local_client_execute_test.cc
+++ b/tensorflow/compiler/xla/tests/local_client_execute_test.cc
@@ -210,12 +210,12 @@ XLA_TEST_F(LocalClientExecuteTest, TupleResult) {
 
   std::unique_ptr result_literal = ShapedBufferToLiteral(result);
   LiteralTestUtil::ExpectR2Equal(
-      {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralView::Create(*result_literal, {0}));
+      {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {0}));
   LiteralTestUtil::ExpectR2Equal(
       {{10.0f, 20.0f}, {30.0f, 40.0f}},
-      LiteralView::Create(*result_literal, {1}));
+      LiteralSlice(*result_literal, {1}));
   LiteralTestUtil::ExpectR2Equal(
-      {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralView::Create(*result_literal, {2}));
+      {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {2}));
 }
 
 XLA_TEST_F(LocalClientExecuteTest, NestedTupleResult) {
@@ -239,16 +239,16 @@ XLA_TEST_F(LocalClientExecuteTest, NestedTupleResult) {
 
   std::unique_ptr result_literal = ShapedBufferToLiteral(result);
   LiteralTestUtil::ExpectR2Equal(
-      {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralView::Create(*result_literal, {1}));
+      {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {1}));
   LiteralTestUtil::ExpectR2Equal(
       {{1.0f, 2.0f}, {3.0f, 4.0f}},
-      LiteralView::Create(*result_literal, {0, 0}));
+      LiteralSlice(*result_literal, {0, 0}));
   LiteralTestUtil::ExpectR2Equal(
       {{10.0f, 20.0f}, {30.0f, 40.0f}},
-      LiteralView::Create(*result_literal, {0, 1}));
+      LiteralSlice(*result_literal, {0, 1}));
   LiteralTestUtil::ExpectR2Equal(
       {{1.0f, 2.0f}, {3.0f, 4.0f}},
-      LiteralView::Create(*result_literal, {0, 2}));
+      LiteralSlice(*result_literal, {0, 2}));
 }
 
 XLA_TEST_F(LocalClientExecuteTest, TupleResultWithLayout) {
@@ -274,9 +274,9 @@ XLA_TEST_F(LocalClientExecuteTest, TupleResultWithLayout) {
 
   std::unique_ptr result_literal = ShapedBufferToLiteral(result);
   LiteralTestUtil::ExpectR2Equal(
-      {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralView::Create(*result_literal, {0}));
+      {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {0}));
   LiteralTestUtil::ExpectR2Equal(
-      {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralView::Create(*result_literal, {1}));
+      {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {1}));
 }
 
 XLA_TEST_F(LocalClientExecuteTest, TupleArguments) {
@@ -321,9 +321,9 @@ XLA_TEST_F(LocalClientExecuteTest, TupleArguments) {
   std::unique_ptr result_literal = ShapedBufferToLiteral(result);
   LiteralTestUtil::ExpectR2Equal(
       {{56.0f, 46.0f}, {36.0f, 26.0f}},
-      LiteralView::Create(*result_literal, {0}));
+      LiteralSlice(*result_literal, {0}));
   LiteralTestUtil::ExpectR1Equal(
-      {40.0f, 71.0f, 117.0f}, LiteralView::Create(*result_literal, {1}));
+      {40.0f, 71.0f, 117.0f}, LiteralSlice(*result_literal, {1}));
 }
 
 XLA_TEST_F(LocalClientExecuteTest, NestedTupleArgument) {
@@ -361,9 +361,9 @@ XLA_TEST_F(LocalClientExecuteTest, NestedTupleArgument) {
 
   std::unique_ptr result_literal = ShapedBufferToLiteral(result);
   LiteralTestUtil::ExpectR2Equal(
-      {{-1.0, -2.0}, {-3.0, -4}}, LiteralView::Create(*result_literal, {0}));
+      {{-1.0, -2.0}, {-3.0, -4}}, LiteralSlice(*result_literal, {0}));
   LiteralTestUtil::ExpectR1Equal(
-      {264.0, 73.0, 133.0}, LiteralView::Create(*result_literal, {1}));
+      {264.0, 73.0, 133.0}, LiteralSlice(*result_literal, {1}));
 }
 
 XLA_TEST_F(LocalClientExecuteTest, PassingTupleResultBackIntoComputation) {
@@ -391,16 +391,16 @@ XLA_TEST_F(LocalClientExecuteTest, PassingTupleResultBackIntoComputation) {
   std::unique_ptr result_0_literal = ShapedBufferToLiteral(result_0);
   LiteralTestUtil::ExpectR2Equal(
       {{-1.0, -2.0}, {-3.0, -4.0}},
-      LiteralView::Create(*result_0_literal, {0}));
+      LiteralSlice(*result_0_literal, {0}));
   LiteralTestUtil::ExpectR2Equal(
-      {{22.0, 6.0}, {8.0, 10}}, LiteralView::Create(*result_0_literal, {1}));
+      {{22.0, 6.0}, {8.0, 10}}, LiteralSlice(*result_0_literal, {1}));
 
   ScopedShapedBuffer result_1 = ExecuteLocallyOrDie(computation, {&result_0});
   std::unique_ptr result_1_literal = ShapedBufferToLiteral(result_1);
   LiteralTestUtil::ExpectR2Equal(
-      {{1.0, 2.0}, {3.0, 4.0}}, LiteralView::Create(*result_1_literal, {0}));
+      {{1.0, 2.0}, {3.0, 4.0}}, LiteralSlice(*result_1_literal, {0}));
   LiteralTestUtil::ExpectR2Equal(
-      {{44.0, 12.0}, {16.0, 20}}, LiteralView::Create(*result_1_literal, {1}));
+      {{44.0, 12.0}, {16.0, 20}}, LiteralSlice(*result_1_literal, {1}));
 }
 
 XLA_TEST_F(LocalClientExecuteTest, LargeTuple) {
@@ -447,7 +447,7 @@ XLA_TEST_F(LocalClientExecuteTest, LargeTuple) {
 
   for (int i = 0; i < kElementCount; ++i) {
     LiteralTestUtil::ExpectR1Near(
-        {2.0f * i, 0.0f}, LiteralView::Create(*result_literal, {i}),
+        {2.0f * i, 0.0f}, LiteralSlice(*result_literal, {i}),
         error_spec_);
   }
 }
@@ -502,7 +502,7 @@ XLA_TEST_F(LocalClientExecuteTest, LargeNestedTuple) {
   for (int i = 0; i < kFanout; ++i) {
     for (int j = 0; j < kFanout; ++j) {
       LiteralTestUtil::ExpectR0Near(
-          i + j + i * kFanout + j, LiteralView::Create(*result_literal, {i, j}),
+          i + j + i * kFanout + j, LiteralSlice(*result_literal, {i, j}),
           error_spec_);
     }
   }
@@ -548,7 +548,7 @@ XLA_TEST_F(LocalClientExecuteTest, DeepTuple) {
     index.push_back(0);
   }
   LiteralTestUtil::ExpectR0Equal(
-      165.0, LiteralView::Create(*result_literal, index));
+      165.0, LiteralSlice(*result_literal, index));
 }
 
 XLA_TEST_F(LocalClientExecuteTest, InvalidNumberOfArguments) {
@@ -754,9 +754,9 @@ XLA_TEST_F(LocalClientExecuteTest, SelectBetweenTuples) {
       ExecuteLocallyOrDie(builder.Build().ValueOrDie(), {});
   std::unique_ptr tuple_literal = ShapedBufferToLiteral(result);
   LiteralTestUtil::ExpectR1Equal(
-      {2.0f, 4.0f, 6.0f}, LiteralView::Create(*tuple_literal, {0}));
+      {2.0f, 4.0f, 6.0f}, LiteralSlice(*tuple_literal, {0}));
   LiteralTestUtil::ExpectR1Equal(
-      {1.0f, 2.0f, 3.0f}, LiteralView::Create(*tuple_literal, {1}));
+      {1.0f, 2.0f, 3.0f}, LiteralSlice(*tuple_literal, {1}));
 }
 
 XLA_TEST_F(LocalClientExecuteTest, CompileExecutable) {

From ed325becde6bf8f8c86cc39c977ac32b1ea7ef5d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 13:28:00 -0700
Subject: [PATCH 0566/1691] Update tf.nn.[max,avg]_pool to specify that it
 accepts list/tuple stride and kernel arguments, not tensor arguments.

If you actually specify a tensor argument here, you get the error:
TypeError: Expected list for 'ksize' argument to 'avg_pool' Op, not .
PiperOrigin-RevId: 196019507
---
 tensorflow/python/ops/nn_ops.py | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py
index cd07550d2ee31e..09a44254360d3e 100644
--- a/tensorflow/python/ops/nn_ops.py
+++ b/tensorflow/python/ops/nn_ops.py
@@ -2100,11 +2100,10 @@ def avg_pool(value, ksize, strides, padding, data_format="NHWC", name=None):
   Args:
     value: A 4-D `Tensor` of shape `[batch, height, width, channels]` and type
       `float32`, `float64`, `qint8`, `quint8`, or `qint32`.
-    ksize: A 1-D int Tensor of 4 elements.
-      The size of the window for each dimension of the input tensor.
-    strides: A 1-D int Tensor of 4 elements
-      The stride of the sliding window for each dimension of the
-      input tensor.
+    ksize: A list or tuple of 4 ints. The size of the window for each dimension
+      of the input tensor.
+    strides: A list or tuple of 4 ints. The stride of the sliding window for
+      each dimension of the input tensor.
     padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
       See the @{tf.nn.convolution$comment here}
     data_format: A string. 'NHWC' and 'NCHW' are supported.
@@ -2130,10 +2129,10 @@ def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None):
 
   Args:
     value: A 4-D `Tensor` of the format specified by `data_format`.
-    ksize: A 1-D int Tensor of 4 elements.  The size of the window for
+    ksize: A list or tuple of 4 ints. The size of the window for each dimension
+      of the input tensor.
+    strides: A list or tuple of 4 ints. The stride of the sliding window for
       each dimension of the input tensor.
-    strides: A 1-D int Tensor of 4 elements.  The stride of the sliding
-      window for each dimension of the input tensor.
     padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
       See the @{tf.nn.convolution$comment here}
     data_format: A string. 'NHWC', 'NCHW' and 'NCHW_VECT_C' are supported.

From cc290f8a570469951239d1753d73f731ded5ae45 Mon Sep 17 00:00:00 2001
From: Yifei Feng 
Date: Wed, 9 May 2018 13:31:31 -0700
Subject: [PATCH 0567/1691] Internal change.

PiperOrigin-RevId: 196020032
---
 .../contrib/eager/python/examples/spinn/LICENSE.bazel           | 0
 third_party/libxsmm.BUILD                                       | 2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename third_party/examples/eager/spinn/LICENSE => tensorflow/contrib/eager/python/examples/spinn/LICENSE.bazel (100%)

diff --git a/third_party/examples/eager/spinn/LICENSE b/tensorflow/contrib/eager/python/examples/spinn/LICENSE.bazel
similarity index 100%
rename from third_party/examples/eager/spinn/LICENSE
rename to tensorflow/contrib/eager/python/examples/spinn/LICENSE.bazel
diff --git a/third_party/libxsmm.BUILD b/third_party/libxsmm.BUILD
index 4124f2db637689..78ed1f4e168891 100644
--- a/third_party/libxsmm.BUILD
+++ b/third_party/libxsmm.BUILD
@@ -38,8 +38,8 @@ genrule(
         ":libxsmm_interface",
     ],
     visibility = [
-        "//tensorflow/core/kernels:__pkg__",
         "//third_party/eigen3:__pkg__",
+        "//tensorflow/core/kernels:__pkg__",
     ],
 )
 

From 705550357fb9f1955207b5953779e8a382744f30 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 13:43:14 -0700
Subject: [PATCH 0568/1691] Adding constant slice op support.

PiperOrigin-RevId: 196021899
---
 tensorflow/contrib/lite/toco/BUILD            |   1 +
 .../graph_transformations.h                   |   1 +
 .../resolve_constant_slice.cc                 | 165 ++++++++++++++++++
 tensorflow/contrib/lite/toco/toco_tooling.cc  |   1 +
 4 files changed, 168 insertions(+)
 create mode 100644 tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_slice.cc

diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD
index 01ce0d9db21222..b8acc9a8e0361a 100644
--- a/tensorflow/contrib/lite/toco/BUILD
+++ b/tensorflow/contrib/lite/toco/BUILD
@@ -273,6 +273,7 @@ cc_library(
         "graph_transformations/resolve_constant_range.cc",
         "graph_transformations/resolve_constant_reshape.cc",
         "graph_transformations/resolve_constant_shape_or_rank.cc",
+        "graph_transformations/resolve_constant_slice.cc",
         "graph_transformations/resolve_constant_stack.cc",
         "graph_transformations/resolve_constant_strided_slice.cc",
         "graph_transformations/resolve_constant_transpose.cc",
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h
index 4e3ea721820cc6..8da242aa9c2ca4 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h
+++ b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h
@@ -182,6 +182,7 @@ DECLARE_GRAPH_TRANSFORMATION(ResolveTransposeAttributes)
 DECLARE_GRAPH_TRANSFORMATION(ResolveConstantRandomUniform)
 DECLARE_GRAPH_TRANSFORMATION(ResolveConstantRange)
 DECLARE_GRAPH_TRANSFORMATION(ResolveConstantShapeOrRank)
+DECLARE_GRAPH_TRANSFORMATION(ResolveConstantSlice)
 DECLARE_GRAPH_TRANSFORMATION(ResolveConstantStack)
 DECLARE_GRAPH_TRANSFORMATION(ResolveConstantStridedSlice)
 DECLARE_GRAPH_TRANSFORMATION(ResolveConstantFill)
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_slice.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_slice.cc
new file mode 100644
index 00000000000000..b35c3e19c43b1c
--- /dev/null
+++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_slice.cc
@@ -0,0 +1,165 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include 
+
+#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h"
+#include "tensorflow/contrib/lite/toco/model.h"
+#include "tensorflow/contrib/lite/toco/tooling_util.h"
+#include "tensorflow/core/platform/logging.h"
+
+namespace toco {
+
+namespace {
+
+template 
+bool Slice(SliceOperator const& op, Array const& input_array,
+           Array* output_array) {
+  // Implementation is taken from the tflite kernel.
+
+  CHECK(input_array.data_type == Type);
+  CHECK(output_array->data_type == Type);
+  const auto& input_data = input_array.GetBuffer().data;
+
+  // Create a buffer for the output array.
+  std::vector>& output_data =
+      output_array->GetMutableBuffer().data;
+  output_data.resize(RequiredBufferSizeForShape(output_array->shape()));
+
+  std::vector size = op.size;
+  if (size.size() != op.begin.size()) {
+    // Broadcast the end positions.
+    CHECK_EQ(op.size.size(), 1);
+    int broadcast_size = size[0];
+    while (size.size() < op.begin.size()) size.push_back(broadcast_size);
+  }
+
+  // Calculate begin and end indices along each dimension.
+  CHECK_LE(op.begin.size(), 4);
+  CHECK_LE(size.size(), 4);
+  std::vector begin = op.begin;
+  std::vector end;
+  for (int i = 0; i < begin.size(); ++i) {
+    int dim_size = size[i];
+    if (dim_size == -1) {
+      // -1 means the rest of the dimension.
+      dim_size = input_array.shape().dims()[i] - begin[i];
+    }
+    CHECK_GE(dim_size, 1);
+    end.push_back(begin[i] + dim_size - 1);
+  }
+
+  // Pad out so that we always have 4 dims, makes this loop easier.
+  while (begin.size() < 4) begin.insert(begin.begin(), 0);
+  while (end.size() < 4) end.insert(end.begin(), 0);
+  Shape padded_shape = input_array.shape();
+  while (padded_shape.dimensions_count() < 4) {
+    padded_shape.mutable_dims()->insert(padded_shape.mutable_dims()->begin(),
+                                        1);
+  }
+
+  auto* out_ptr = output_data.data();
+  for (int in_b = begin[0]; in_b <= end[0]; ++in_b) {
+    for (int in_h = begin[1]; in_h <= end[1]; ++in_h) {
+      for (int in_w = begin[2]; in_w <= end[2]; ++in_w) {
+        for (int in_d = begin[3]; in_d <= end[3]; ++in_d) {
+          *out_ptr++ =
+              input_data[Offset(padded_shape, {in_b, in_h, in_w, in_d})];
+        }
+      }
+    }
+  }
+
+  return true;
+}
+
+}  // namespace
+
+bool ResolveConstantSlice::Run(Model* model, std::size_t op_index) {
+  const auto it = model->operators.begin() + op_index;
+  const auto* base_op = it->get();
+  if (base_op->type != OperatorType::kSlice) {
+    return false;
+  }
+
+  const SliceOperator* op = static_cast(base_op);
+
+  CHECK_EQ(op->outputs.size(), 1);
+  auto& output_array = model->GetArray(op->outputs[0]);
+  if (output_array.data_type == ArrayDataType::kNone) {
+    // Yield until the output type has been set by PropagateArrayDataTypes.
+    return false;
+  }
+
+  if (!output_array.has_shape()) {
+    // Yield until the output shape has been set by PropagateFixedShapes.
+    return false;
+  }
+
+  if (op->begin.empty() || op->size.empty()) {
+    // Attributes have not resolved yet.
+    return false;
+  }
+
+  const auto& input_array = model->GetArray(op->inputs[0]);
+  if (!input_array.has_shape()) {
+    // Yield until the value shape has been resolved.
+    return false;
+  }
+  if (!IsConstantParameterArray(*model, op->inputs[0])) {
+    // Yield until the value is constant.
+    return false;
+  }
+
+  CHECK(!output_array.buffer);
+  switch (output_array.data_type) {
+    case ArrayDataType::kFloat:
+      if (!Slice(*op, input_array, &output_array)) {
+        return false;
+      }
+      break;
+    case ArrayDataType::kUint8:
+      if (!Slice(*op, input_array, &output_array)) {
+        return false;
+      }
+      break;
+    case ArrayDataType::kInt32:
+      if (!Slice(*op, input_array, &output_array)) {
+        return false;
+      }
+      break;
+    case ArrayDataType::kInt64:
+      if (!Slice(*op, input_array, &output_array)) {
+        return false;
+      }
+      break;
+    default:
+      LOG(FATAL) << "Unsupported data type input to Slice op with output \""
+                 << op->outputs[0] << "\"";
+      break;
+  }
+
+  // Erase input array if no longer used.
+  if (IsDiscardableArray(*model, op->inputs[0]) &&
+      CountOpsWithInput(*model, op->inputs[0]) == 1) {
+    model->EraseArray(op->inputs[0]);
+  }
+
+  // Erase the operator
+  model->operators.erase(it);
+
+  return true;
+}
+
+}  // namespace toco
diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc
index 58c99051bd9424..d8949165971d38 100644
--- a/tensorflow/contrib/lite/toco/toco_tooling.cc
+++ b/tensorflow/contrib/lite/toco/toco_tooling.cc
@@ -86,6 +86,7 @@ void MakeGeneralGraphTransformationsSet(
   transformations->Add(new ResolveConstantRandomUniform);
   transformations->Add(new ResolveConstantRange);
   transformations->Add(new ResolveConstantReshape);
+  transformations->Add(new ResolveConstantSlice);
   transformations->Add(new ResolveConstantStack);
   transformations->Add(new ResolveConstantStridedSlice);
   transformations->Add(new ResolveConstantTranspose);

From ec0ef29835563b762ec9443a3c194c5c904fd6be Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 13:55:20 -0700
Subject: [PATCH 0569/1691] When using static_state_saving_rnn(..) in the
 following manner

    _, state = tf.nn.static_state_saving_rnn(..)

the runtime will be blocked after some time, because the save_state method of the state_saver object won't be executed as a part of the graph (that part depends only on output node in the current implementation).
Now it should depend on state as well, so the above implementation won't be blocked.

PiperOrigin-RevId: 196024050
---
 .../rnn/python/kernel_tests/core_rnn_test.py  | 137 ++++++++++++++----
 tensorflow/python/ops/rnn.py                  |   7 +
 2 files changed, 116 insertions(+), 28 deletions(-)

diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
index ba4933ddf793c5..c75593e35689c8 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
@@ -38,6 +38,7 @@
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import rnn
 from tensorflow.python.ops import rnn_cell
+from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as variables_lib
@@ -142,6 +143,47 @@ def save_state(self, name, state):
     self.saved_state[name] = state
     return array_ops.identity(state)
 
+  @property
+  def batch_size(self):
+    return self._batch_size
+
+  @property
+  def state_size(self):
+    return self._state_size
+
+
+class TestStateSaverWithCounters(TestStateSaver):
+  """Class wrapper around TestStateSaver.
+
+  A dummy class used for testing of static_state_saving_rnn. It helps test if
+  save_state and state functions got called same number of time when we
+  evaluate output of rnn cell and state or either of them separately. It
+  inherits from the TestStateSaver and adds the counters for calls of functions.
+  """
+
+  def __init__(self, batch_size, state_size):
+    super(TestStateSaverWithCounters, self).__init__(batch_size, state_size)
+    self._num_state_calls = variables_lib.Variable(0)
+    self._num_save_state_calls = variables_lib.Variable(0)
+
+  def state(self, name):
+    with ops_lib.control_dependencies(
+        [state_ops.assign_add(self._num_state_calls, 1)]):
+      return super(TestStateSaverWithCounters, self).state(name)
+
+  def save_state(self, name, state):
+    with ops_lib.control_dependencies([state_ops.assign_add(
+        self._num_save_state_calls, 1)]):
+      return super(TestStateSaverWithCounters, self).save_state(name, state)
+
+  @property
+  def num_state_calls(self):
+    return self._num_state_calls
+
+  @property
+  def num_save_state_calls(self):
+    return self._num_save_state_calls
+
 
 class RNNTest(test.TestCase):
 
@@ -1792,13 +1834,40 @@ def setUp(self):
     self._seed = 23489
     np.random.seed(self._seed)
 
-  def _testScope(self, factory, prefix="prefix", use_outer_scope=True):
+  def _factory(self, scope, state_saver):
+    num_units = state_saver.state_size // 2
+    batch_size = state_saver.batch_size
+    input_size = 5
+    max_length = 8
+    initializer = init_ops.random_uniform_initializer(
+        -0.01, 0.01, seed=self._seed)
+    cell = rnn_cell.LSTMCell(
+        num_units,
+        use_peepholes=False,
+        initializer=initializer,
+        state_is_tuple=False)
+    inputs = max_length * [
+        array_ops.zeros(dtype=dtypes.float32, shape=(batch_size, input_size))
+    ]
+    out, state = rnn.static_state_saving_rnn(
+        cell,
+        inputs,
+        state_saver=state_saver,
+        state_name="save_lstm",
+        scope=scope)
+    return out, state, state_saver
+
+  def _testScope(self, prefix="prefix", use_outer_scope=True):
+    num_units = 3
+    batch_size = 2
+    state_saver = TestStateSaver(batch_size, 2 * num_units)
+
     with self.test_session(use_gpu=True, graph=ops_lib.Graph()):
       if use_outer_scope:
         with variable_scope.variable_scope(prefix) as scope:
-          factory(scope)
+          self._factory(scope=scope, state_saver=state_saver)
       else:
-        factory(prefix)
+        self._factory(scope=prefix, state_saver=state_saver)
         variables_lib.global_variables_initializer()
 
       # check that all the variables names starts
@@ -1813,34 +1882,46 @@ def _testScope(self, factory, prefix="prefix", use_outer_scope=True):
       self.assertEqual(len(scope_vars), len(all_vars))
 
   def testStateSaverRNNScope(self):
-    num_units = 3
-    input_size = 5
-    batch_size = 2
-    max_length = 8
+    self._testScope(use_outer_scope=True)
+    self._testScope(use_outer_scope=False)
+    self._testScope(prefix=None, use_outer_scope=False)
 
-    def factory(scope):
-      initializer = init_ops.random_uniform_initializer(
-          -0.01, 0.01, seed=self._seed)
-      state_saver = TestStateSaver(batch_size, 2 * num_units)
-      cell = rnn_cell.LSTMCell(
-          num_units,
-          use_peepholes=False,
-          initializer=initializer,
-          state_is_tuple=False)
-      inputs = max_length * [
-          array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size))
-      ]
-      return rnn.static_state_saving_rnn(
-          cell,
-          inputs,
-          state_saver=state_saver,
-          state_name="save_lstm",
-          scope=scope)
+  def testStateSaverCallsSaveState(self):
+    """Test that number of calls to state and save_state is equal.
 
-    self._testScope(factory, use_outer_scope=True)
-    self._testScope(factory, use_outer_scope=False)
-    self._testScope(factory, prefix=None, use_outer_scope=False)
+    Test if the order of actual evaluating or skipping evaluation of out,
+    state tensors, which are the output tensors from static_state_saving_rnn,
+    have influence on number of calls to save_state and state methods of
+    state_saver object (the number of calls should be same.)
+    """
 
+    num_units = 3
+    batch_size = 2
+    state_saver = TestStateSaverWithCounters(batch_size, 2 * num_units)
+    out, state, state_saver = self._factory(scope=None, state_saver=state_saver)
+
+    with self.test_session() as sess:
+      sess.run(variables_lib.global_variables_initializer())
+      sess.run(variables_lib.local_variables_initializer())
+
+      _, _, num_state_calls, num_save_state_calls = sess.run([
+          out,
+          state,
+          state_saver.num_state_calls,
+          state_saver.num_save_state_calls])
+      self.assertEqual(num_state_calls, num_save_state_calls)
+
+      _, num_state_calls, num_save_state_calls = sess.run([
+          out,
+          state_saver.num_state_calls,
+          state_saver.num_save_state_calls])
+      self.assertEqual(num_state_calls, num_save_state_calls)
+
+      _, num_state_calls, num_save_state_calls = sess.run([
+          state,
+          state_saver.num_state_calls,
+          state_saver.num_save_state_calls])
+      self.assertEqual(num_state_calls, num_save_state_calls)
 
 class GRUTest(test.TestCase):
 
diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py
index e94ad90dfd7fa7..c77a18d89049f7 100644
--- a/tensorflow/python/ops/rnn.py
+++ b/tensorflow/python/ops/rnn.py
@@ -1401,6 +1401,13 @@ def static_state_saving_rnn(cell,
     outputs[-1] = nest.pack_sequence_as(
         structure=last_output, flat_sequence=flat_last_output)
 
+    if state_is_tuple:
+      state = nest.pack_sequence_as(
+          structure=state,
+          flat_sequence=[array_ops.identity(s) for s in flat_state])
+    else:
+      state = array_ops.identity(state)
+
   return (outputs, state)
 
 

From 5d47c53adbb597a62ae2ffcdbb3d6fd15a8d2a86 Mon Sep 17 00:00:00 2001
From: Anna R 
Date: Wed, 9 May 2018 13:55:47 -0700
Subject: [PATCH 0570/1691] Internal change.

PiperOrigin-RevId: 196024130
---
 tensorflow/tools/pip_package/build_pip_package.sh | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh
index 8f0cf8c3d19480..b66d5bdd37c03e 100755
--- a/tensorflow/tools/pip_package/build_pip_package.sh
+++ b/tensorflow/tools/pip_package/build_pip_package.sh
@@ -53,6 +53,7 @@ function main() {
   PKG_NAME_FLAG=""
   GPU_BUILD=0
   NIGHTLY_BUILD=0
+  PROJECT_NAME=""
   while true; do
     if [[ "$1" == "--nightly_flag" ]]; then
       NIGHTLY_BUILD=1
@@ -60,6 +61,12 @@ function main() {
       GPU_BUILD=1
     elif [[ "$1" == "--gpudirect" ]]; then
       PKG_NAME_FLAG="--project_name tensorflow_gpudirect"
+    elif [[ "$1" == "--project_name" ]]; then
+      shift
+      if [[ -z "$1" ]]; then
+        break
+      fi
+      PROJECT_NAME="$1"
     fi
     shift
 
@@ -68,7 +75,9 @@ function main() {
     fi
   done
 
-  if [[ ${NIGHTLY_BUILD} == "1" && ${GPU_BUILD} == "1" ]]; then
+  if [[ -n ${PROJECT_NAME} ]]; then
+    PKG_NAME_FLAG="--project_name ${PROJECT_NAME}"
+  elif [[ ${NIGHTLY_BUILD} == "1" && ${GPU_BUILD} == "1" ]]; then
     PKG_NAME_FLAG="--project_name tf_nightly_gpu"
   elif [[ ${NIGHTLY_BUILD} == "1" ]]; then
     PKG_NAME_FLAG="--project_name tf_nightly"

From 42ee0ef7bc1e72bd581b8def333cd9e6aee48858 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 14:07:17 -0700
Subject: [PATCH 0571/1691] Fix default direction to left when almost no
 sparsity for a sparse inequality split.

PiperOrigin-RevId: 196026149
---
 .../kernels/split_handler_ops.cc              |  9 ++-
 .../kernel_tests/split_handler_ops_test.py    | 61 +++++++++++++++++++
 2 files changed, 69 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
index 44a8ffaf4b2f5a..04e32267cc4a00 100644
--- a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
@@ -422,6 +422,10 @@ class BuildSparseInequalitySplitsOp : public BaseBuildSplitOp {
               GradientStats(*gradients_t, *hessians_t, bucket_idx);
         }
         present_gradient_stats *= normalizer_ratio;
+        GradientStats not_present =
+            root_gradient_stats - present_gradient_stats;
+        // If there was (almost) no sparsity, fix the default direction to LEFT.
+        bool fixed_default_direction = not_present.IsAlmostZero();
 
         GradientStats left_gradient_stats;
         for (int64 element_idx = start_index; element_idx < end_index;
@@ -441,6 +445,7 @@ class BuildSparseInequalitySplitsOp : public BaseBuildSplitOp {
           // backward pass gradients.
           GradientStats right_gradient_stats =
               present_gradient_stats - left_gradient_stats;
+
           {
             NodeStats left_stats_default_left =
                 ComputeNodeStats(root_gradient_stats - right_gradient_stats);
@@ -457,7 +462,9 @@ class BuildSparseInequalitySplitsOp : public BaseBuildSplitOp {
               best_dimension_idx = dimension_id;
             }
           }
-          {
+          // Consider calculating the default direction only when there were
+          // enough missing examples.
+          if (!fixed_default_direction) {
             NodeStats left_stats_default_right =
                 ComputeNodeStats(left_gradient_stats);
             NodeStats right_stats_default_right =
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
index 28834ef55bf8e1..5cd37ec67ec3bd 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py
@@ -18,6 +18,8 @@
 from __future__ import division
 from __future__ import print_function
 
+import random
+
 from tensorflow.contrib.boosted_trees.proto import learner_pb2
 from tensorflow.contrib.boosted_trees.proto import split_info_pb2
 from tensorflow.contrib.boosted_trees.python.ops import split_handler_ops
@@ -399,6 +401,65 @@ def testMakeSparseMultidimensionalSplit(self):
 
     self.assertAllClose(0.6, split_node.split.threshold)
 
+  def testMakeSparseSplitDefaultDirectionIsStable(self):
+    """Tests default direction is stable when no sparsity."""
+    random.seed(1123)
+    for _ in range(50):
+      with self.test_session() as sess:
+        grad = random.random()
+        hessian = random.random()
+        # The data looks like the following (divide by the num of steps 2).
+        # Gradients       | Partition | bucket ID       |
+        # (grad, hessian) |  0        | -1              |
+        # And then 100 buckets of
+        # (grad/100, hessian/100), so there is no sparsity.
+        n_buckets = 100
+
+        # 1 for the overall sum, and 100 buckets.
+        partition_ids = array_ops.constant(
+            [0] * (n_buckets + 1), dtype=dtypes.int32)
+        # We have only 1 dimension in our sparse feature column.
+
+        bucket_ids = [-1] + [n for n in range(100)]
+        bucket_ids = array_ops.constant(bucket_ids, dtype=dtypes.int64)
+        dimension_ids = array_ops.constant(
+            [0] * (n_buckets + 1), dtype=dtypes.int64)
+        bucket_ids = array_ops.stack([bucket_ids, dimension_ids], axis=1)
+
+        gradients = [grad] + [grad / n_buckets] * n_buckets
+        gradients = array_ops.constant(gradients)
+        hessians = [hessian] + [hessian / n_buckets] * n_buckets
+        hessians = array_ops.constant(hessians)
+
+        boundaries = [x * 1 for x in range(n_buckets + 1)]
+        bucket_boundaries = array_ops.constant(boundaries, dtype=dtypes.float32)
+
+        partitions, gains, splits = (
+            split_handler_ops.build_sparse_inequality_splits(
+                num_minibatches=2,
+                partition_ids=partition_ids,
+                bucket_ids=bucket_ids,
+                gradients=gradients,
+                hessians=hessians,
+                bucket_boundaries=bucket_boundaries,
+                l1_regularization=0,
+                l2_regularization=2,
+                tree_complexity_regularization=0,
+                min_node_weight=0,
+                feature_column_group_id=0,
+                bias_feature_id=-1,
+                class_id=-1,
+                multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS))
+        partitions, gains, splits = (sess.run([partitions, gains, splits]))
+      self.assertAllEqual([0], partitions)
+      self.assertEqual(1, len(splits))
+
+      split_info = split_info_pb2.SplitInfo()
+      split_info.ParseFromString(splits[0])
+      self.assertTrue(
+          split_info.split_node.HasField(
+              'sparse_float_binary_split_default_left'))
+
   def testMakeMulticlassSparseSplit(self):
     """Tests split handler op."""
     with self.test_session() as sess:

From d5000cd97f0d0152c28512ff5ea7b3daa67d8e56 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 14:14:48 -0700
Subject: [PATCH 0572/1691] Use easy_install for pip installation for RBE
 images.

We will remove python-pip deb packages from rbe-{debian8, ubuntu16_04}:
  https://github.com/bazelbuild/bazel-toolchains/pull/46
So that we don't we have pip install by deb packages and Python's own package system (and they conflict with each other)

We only install pip by easy_install.

PiperOrigin-RevId: 196027421
---
 .../tools/ci_build/install/install_pip_packages_remote.sh     | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/tools/ci_build/install/install_pip_packages_remote.sh b/tensorflow/tools/ci_build/install/install_pip_packages_remote.sh
index 0beabcf5ef8300..721590f4d6081d 100755
--- a/tensorflow/tools/ci_build/install/install_pip_packages_remote.sh
+++ b/tensorflow/tools/ci_build/install/install_pip_packages_remote.sh
@@ -20,8 +20,8 @@ if [ ! -f /usr/bin/x86_64-linux-gnu-gcc ]; then
   ln -s /usr/local/bin/clang /usr/bin/x86_64-linux-gnu-gcc
 fi
 
-pip2 install --upgrade setuptools
-pip3 install --upgrade setuptools
+easy_install -U pip==9.0.3
+easy_install3 -U pip==9.0.3
 
 # The rest of the pip packages will be installed in
 # `install_pip_packages.sh`

From 7518c4cdd0eee5882405c79ca67da712db0da48e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 14:20:39 -0700
Subject: [PATCH 0573/1691] [XLA] Allow HloInstructionMap and HloInstructionSet
 to contain null keys.

Null HloInstruction* keys may be useful for representing sentinel values.

PiperOrigin-RevId: 196028425
---
 tensorflow/compiler/xla/service/hlo_instruction.h | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 511227a34c273f..ea5fc5be7b8047 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -1579,13 +1579,20 @@ std::ostream& operator<<(std::ostream& os, HloInstruction::FusionKind kind);
 // an HloInstruction* or a const HloInstruction*.
 // To make the iteration order over the map deterministic, the comparator
 // should not be using the pointer values, but rather an intrinsic property of
-// the hlo.
+// the hlo. Exception: null pointer values compare less than non-null.
 //
 // Note that this cannot be used for HLO instructions across multiple modules
 // since the id of HLO instructions are only unique within each HLO module.
 struct HloPtrComparator {
   bool operator()(const HloInstruction* const& lhs,
                   const HloInstruction* const& rhs) const {
+    if (rhs == nullptr) {
+      // Nothing compares less than nullptr.
+      return false;
+    }
+    if (lhs == nullptr) {
+      return true;
+    }
     return lhs->unique_id() < rhs->unique_id();
   }
 };

From 294e9a1ba1916933b1f932381f082a7d20482ddb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 14:41:23 -0700
Subject: [PATCH 0574/1691] Run
 tensorflow/python/kernel_tests:conv2d_backprop_filter_grad_test only when
 omptimzing to avoid flaky timeouts

PiperOrigin-RevId: 196031762
---
 tensorflow/python/kernel_tests/BUILD | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 6bc129a6c72224..61f3f69e84578d 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -2364,6 +2364,9 @@ cuda_py_test(
         "//tensorflow/python:nn_grad",
         "//tensorflow/python:nn_ops",
     ],
+    tags = [
+        "optonly",  # flaky timeouts unless optimized
+    ],
 )
 
 cuda_py_test(

From 4a6ca8f3124333519b740abc1b265180ca3bdc5d Mon Sep 17 00:00:00 2001
From: mbhuiyan 
Date: Wed, 9 May 2018 14:44:27 -0700
Subject: [PATCH 0575/1691] adding MKLDNN switch only for parameters

---
 ...direct_session_with_tracking_alloc_test.cc | 28 +++++++++----------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
index 29c8c8daecfbbb..bd3f9e1dd144d2 100644
--- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
@@ -101,27 +101,27 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) {
         EXPECT_EQ(2, shape.dim_size());
         EXPECT_EQ(2, shape.dim(0).size());
         EXPECT_EQ(1, shape.dim(1).size());
-#ifdef INTEL_MKL
-        // if MKL is used, it goes through various additional 
-        // graph rewrite pass. In TF, everytime a graph pass 
-        // happens, "constant" nodes are allocated
-        // and deallocated. Each allocation calls the
-        // (FindChunkPtr of BFCAllocator),
-        // which increments the value of AllocationId. 
-        // Thus AllocationId becomes more than 3 and 4 if 
-        // MKL is used. Now they are 9 and 10 for MKL. 
         if (node->name() == y->name()) {
+#ifdef INTEL_MKL
+          // if MKL is used, it goes through various additional 
+          // graph rewrite pass. In TF, everytime a graph pass 
+          // happens, "constant" nodes are allocated
+          // and deallocated. Each allocation calls the
+          // (FindChunkPtr of BFCAllocator),
+          // which increments the value of AllocationId. 
+          // Thus AllocationId becomes more than 3 and 4 if 
+          // MKL is used. Now they are 9 and 10 for MKL. 
           EXPECT_EQ(9, cm->AllocationId(node, 0));
-        } else {
-          EXPECT_EQ(10, cm->AllocationId(node, 0));
-        }
 #else
-        if (node->name() == y->name()) {
           EXPECT_EQ(3, cm->AllocationId(node, 0));
+#endif 
         } else {
+#ifdef INTEL_MKL
+          EXPECT_EQ(10, cm->AllocationId(node, 0));
+#else
           EXPECT_EQ(4, cm->AllocationId(node, 0));
-        }
 #endif 
+        }
       }
       EXPECT_LE(0, cm->MaxExecutionTime(node));
       EXPECT_GE(run_duration_micros, cm->MaxExecutionTime(node));

From ff6ec5d65cc9285b28a98786ca27adca05e89d1f Mon Sep 17 00:00:00 2001
From: Michael Case 
Date: Wed, 9 May 2018 15:07:40 -0700
Subject: [PATCH 0576/1691] Add option to set more generic module name filter
 for API generation.

PiperOrigin-RevId: 196036164
---
 .../tools/api/generator/create_python_api.py  | 29 +++++++++++++------
 .../api/generator/create_python_api_test.py   |  9 ++++--
 2 files changed, 26 insertions(+), 12 deletions(-)

diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py
index 65baa6e4b45dc5..b6171ce777a3ab 100644
--- a/tensorflow/tools/api/generator/create_python_api.py
+++ b/tensorflow/tools/api/generator/create_python_api.py
@@ -29,6 +29,7 @@
 _API_CONSTANTS_ATTR = '_tf_api_constants'
 _API_NAMES_ATTR = '_tf_api_names'
 _API_DIR = '/api/'
+_DEFAULT_MODULE_FILTER = 'tensorflow.'
 _OUTPUT_MODULE = 'tensorflow.tools.api.generator.api'
 _GENERATED_FILE_HEADER = """\"\"\"Imports for Python API.
 
@@ -145,9 +146,12 @@ def build(self):
     return module_text_map
 
 
-def get_api_init_text():
+def get_api_init_text(module_filter):
   """Get a map from destination module to __init__.py code for that module.
 
+  Args:
+    module_filter: Substring used to filter module names to process.
+
   Returns:
     A dictionary where
       key: (string) destination module (for e.g. tf or tf.consts).
@@ -161,7 +165,7 @@ def get_api_init_text():
   for module in list(sys.modules.values()):
     # Only look at tensorflow modules.
     if (not module or not hasattr(module, '__name__') or
-        'tensorflow.' not in module.__name__):
+        module_filter not in module.__name__):
       continue
     # Do not generate __init__.py files for contrib modules for now.
     if '.contrib.' in module.__name__ or module.__name__.endswith('.contrib'):
@@ -214,12 +218,13 @@ def get_api_init_text():
   return module_code_builder.build()
 
 
-def create_api_files(output_files):
+def create_api_files(output_files, module_filter):
   """Creates __init__.py files for the Python API.
 
   Args:
     output_files: List of __init__.py file paths to create.
       Each file must be under api/ directory.
+    module_filter: Substring used to filter module names to process.
 
   Raises:
     ValueError: if an output file is not under api/ directory,
@@ -247,7 +252,7 @@ def create_api_files(output_files):
       os.makedirs(os.path.dirname(file_path))
     open(file_path, 'a').close()
 
-  module_text_map = get_api_init_text()
+  module_text_map = get_api_init_text(module_filter)
 
   # Add imports to output files.
   missing_output_files = []
@@ -269,10 +274,7 @@ def create_api_files(output_files):
         ',\n'.join(sorted(missing_output_files)))
 
 
-def main(output_files):
-  create_api_files(output_files)
-
-if __name__ == '__main__':
+def main():
   parser = argparse.ArgumentParser()
   parser.add_argument(
       'outputs', metavar='O', type=str, nargs='+',
@@ -280,7 +282,12 @@ def main(output_files):
       'semicolon-separated list of Python files that we expect this script to '
       'output. If multiple files are passed in, then we assume output files '
       'are listed directly as arguments.')
+  parser.add_argument(
+      '--module_filter', default=_DEFAULT_MODULE_FILTER, type=str,
+      help='Only processes modules with names containing this substring.'
+  )
   args = parser.parse_args()
+
   if len(args.outputs) == 1:
     # If we only get a single argument, then it must be a file containing
     # list of outputs.
@@ -288,4 +295,8 @@ def main(output_files):
       outputs = [line.strip() for line in output_list_file.read().split(';')]
   else:
     outputs = args.outputs
-  main(outputs)
+  create_api_files(outputs, args.module_filter)
+
+
+if __name__ == '__main__':
+  main()
diff --git a/tensorflow/tools/api/generator/create_python_api_test.py b/tensorflow/tools/api/generator/create_python_api_test.py
index 218c8120453c8d..5f1052249e4e45 100644
--- a/tensorflow/tools/api/generator/create_python_api_test.py
+++ b/tensorflow/tools/api/generator/create_python_api_test.py
@@ -56,7 +56,8 @@ def tearDown(self):
     del sys.modules[_MODULE_NAME]
 
   def testFunctionImportIsAdded(self):
-    imports = create_python_api.get_api_init_text()
+    imports = create_python_api.get_api_init_text(
+        module_filter=create_python_api._DEFAULT_MODULE_FILTER)
     expected_import = (
         'from test.tensorflow.test_module import test_op as test_op1')
     self.assertTrue(
@@ -69,14 +70,16 @@ def testFunctionImportIsAdded(self):
         msg='%s not in %s' % (expected_import, str(imports)))
 
   def testClassImportIsAdded(self):
-    imports = create_python_api.get_api_init_text()
+    imports = create_python_api.get_api_init_text(
+        module_filter=create_python_api._DEFAULT_MODULE_FILTER)
     expected_import = 'from test.tensorflow.test_module import TestClass'
     self.assertTrue(
         'TestClass' in str(imports),
         msg='%s not in %s' % (expected_import, str(imports)))
 
   def testConstantIsAdded(self):
-    imports = create_python_api.get_api_init_text()
+    imports = create_python_api.get_api_init_text(
+        module_filter=create_python_api._DEFAULT_MODULE_FILTER)
     expected = 'from test.tensorflow.test_module import _TEST_CONSTANT'
     self.assertTrue(expected in str(imports),
                     msg='%s not in %s' % (expected, str(imports)))

From cf04e06291d1902246ccf757c0be816d35212ea3 Mon Sep 17 00:00:00 2001
From: Francois Chollet 
Date: Wed, 9 May 2018 15:36:34 -0700
Subject: [PATCH 0577/1691] Fix bug in which the ConvLSTM2D layer could not be
 cloned.

PiperOrigin-RevId: 196040413
---
 .../keras/layers/convolutional_recurrent.py   | 25 +++++++++++++------
 .../layers/convolutional_recurrent_test.py    | 17 +++++++++++++
 2 files changed, 34 insertions(+), 8 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py
index be25bbc043a3be..5e2004266af260 100644
--- a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py
+++ b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py
@@ -609,16 +609,25 @@ def build(self, input_shape):
         name='recurrent_kernel',
         regularizer=self.recurrent_regularizer,
         constraint=self.recurrent_constraint)
+
     if self.use_bias:
-      self.bias = self.add_weight(shape=(self.filters * 4,),
-                                  initializer=self.bias_initializer,
-                                  name='bias',
-                                  regularizer=self.bias_regularizer,
-                                  constraint=self.bias_constraint)
       if self.unit_forget_bias:
-        bias_value = np.zeros((self.filters * 4,))
-        bias_value[self.filters: self.filters * 2] = 1.
-        K.set_value(self.bias, bias_value)
+
+        def bias_initializer(_, *args, **kwargs):
+          return K.concatenate([
+              self.bias_initializer((self.filters,), *args, **kwargs),
+              initializers.Ones()((self.filters,), *args, **kwargs),
+              self.bias_initializer((self.filters * 2,), *args, **kwargs),
+          ])
+      else:
+        bias_initializer = self.bias_initializer
+      self.bias = self.add_weight(
+          shape=(self.filters * 4,),
+          name='bias',
+          initializer=bias_initializer,
+          regularizer=self.bias_regularizer,
+          constraint=self.bias_constraint)
+
     else:
       self.bias = None
 
diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent_test.py b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent_test.py
index 9e768b4e9552d1..827a7ffbdae676 100644
--- a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent_test.py
@@ -180,6 +180,23 @@ def test_conv_lstm_dropout(self):
                   'recurrent_dropout': 0.1},
           input_shape=(1, 2, 5, 5, 2))
 
+  def test_conv_lstm_cloning(self):
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.ConvLSTM2D(5, 3, input_shape=(None, 5, 5, 3)))
+
+      test_inputs = np.random.random((2, 4, 5, 5, 3))
+      reference_outputs = model.predict(test_inputs)
+      weights = model.get_weights()
+
+    # Use a new graph to clone the model
+    with self.test_session():
+      clone = keras.models.clone_model(model)
+      clone.set_weights(weights)
+
+      outputs = clone.predict(test_inputs)
+      self.assertAllClose(reference_outputs, outputs, atol=1e-5)
+
 
 if __name__ == '__main__':
   test.main()

From 22b8b9a528c658144a16dce19ba506561abae2ee Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 15:44:13 -0700
Subject: [PATCH 0578/1691] Allowing trivial passthrough ops to be turned into
 reshapes when they otherwise cannot be removed.

PiperOrigin-RevId: 196041444
---
 .../remove_trivial_passthrough.cc                 | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc
index 3e021b819fc82d..971e4ff8e6de52 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc
@@ -95,10 +95,23 @@ bool RemoveTrivialPassthroughOp(GraphTransformation* transformation,
         "Cannot remove %s, neither its main input nor its output may be "
         "discarded",
         LogName(*passthru_op));
-    return false;
+    if (passthru_op->type != OperatorType::kTensorFlowReshape &&
+        model->GetArray(main_input_name).has_shape()) {
+      // We can't remove either array but we can remove the op. Converting it to
+      // a reshape gives us some hope of later on fixing that (either in the
+      // final runtime or as an additional fixup step).
+      //
+      // Note that we don't try to insert copies in place of reshapes as the
+      // copy itself is a trivial reshape and we'd go into an infinite loop!
+      transformation->AddMessageF("Replacing with a copy (reshape) instead");
+      InsertCopyOperator(model, main_input_name, output_name);
+    } else {
+      return false;
+    }
   }
 
   // Remove the pass-through node.
+  CHECK_EQ(passthru_it->get(), passthru_op);
   model->operators.erase(passthru_it);
 
   // Remove any array that is no longer used.

From 72da47bbf0f3251690039649775b199790f9249e Mon Sep 17 00:00:00 2001
From: Jie 
Date: Wed, 9 May 2018 15:58:31 -0700
Subject: [PATCH 0579/1691] clang-format

---
 tensorflow/contrib/tensorrt/convert/convert_nodes.cc | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
index b5d4b750721829..8c482c84d5688b 100644
--- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
+++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
@@ -300,7 +300,8 @@ nvinfer1::DataType TFAttrs::get(const string& key) const {
 }
 
 template <>
-tensorflow::DataType TFAttrs::get(const string& key) const {
+tensorflow::DataType TFAttrs::get(
+    const string& key) const {
   return this->at(key)->type();
 }
 

From ef58a46b730155717f1b03abb20767c1924ad05e Mon Sep 17 00:00:00 2001
From: Allen Lavoie 
Date: Wed, 9 May 2018 15:56:43 -0700
Subject: [PATCH 0580/1691] Support saving Python state with object-based
 checkpoints

Allows SaveableObjects to specify feed dict addition callbacks for object-based saving.

For now just saves get_config() with Layers. Doesn't do any loading, and there isn't quite enough information to reconstruct a Model yet (needs topology).

My plan is to get Models to the point where they can be reconstructed from object-based checkpoints (probably one more change), add in SavedModel export (assuming no dynamic control flow for now), then add this "SavedModel+Python" format to Model.save / load_model.

PiperOrigin-RevId: 196043183
---
 .../optimizer_v2/checkpointable_utils_test.py |  43 +++---
 tensorflow/python/BUILD                       |  15 ++
 .../python/keras/_impl/keras/engine/saving.py |  39 +----
 tensorflow/python/training/checkpointable.py  |  57 +++++++-
 .../python/training/checkpointable_utils.py   | 135 ++++++++++++++----
 .../training/checkpointable_utils_test.py     | 103 +++++++++----
 tensorflow/python/training/saver.py           | 132 +++++++++--------
 tensorflow/python/util/serialization.py       |  64 +++++++++
 tensorflow/python/util/serialization_test.py  |  76 ++++++++++
 9 files changed, 493 insertions(+), 171 deletions(-)
 create mode 100644 tensorflow/python/util/serialization.py
 create mode 100644 tensorflow/python/util/serialization_test.py

diff --git a/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py b/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py
index 9e2858d00ff192..87b2ecf565649d 100644
--- a/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py
+++ b/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py
@@ -31,7 +31,6 @@
 from tensorflow.python.eager import function
 from tensorflow.python.eager import test
 from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
 from tensorflow.python.keras._impl.keras.engine import training
@@ -139,8 +138,9 @@ def testNamingWithOptimizer(self):
       self.evaluate(checkpointable_utils.gather_initializers(
           root_checkpointable))
       self.evaluate(train_op)
-    named_variables, serialized_graph = (
-        checkpointable_utils._serialize_object_graph(root_checkpointable))
+    named_variables, serialized_graph, _ = (
+        checkpointable_utils._serialize_object_graph(
+            root_checkpointable, saveables_cache=None))
     expected_checkpoint_names = (
         # Created in the root node, so no prefix.
         "optimizer_step",
@@ -163,24 +163,29 @@ def testNamingWithOptimizer(self):
     suffix = "/.ATTRIBUTES/VARIABLE_VALUE"
     expected_checkpoint_names = [
         name + suffix for name in expected_checkpoint_names]
+    # The Dense layers also save get_config() JSON
+    expected_checkpoint_names.extend(
+        ["model/_second/.ATTRIBUTES/OBJECT_CONFIG_JSON",
+         "model/_named_dense/.ATTRIBUTES/OBJECT_CONFIG_JSON"])
+    named_variables = {v.name: v for v in named_variables}
     six.assertCountEqual(self, expected_checkpoint_names,
                          named_variables.keys())
     # Check that we've mapped to the right variable objects (not exhaustive)
     self.assertEqual(
-        "global_step:0",
-        named_variables["optimizer_step" + suffix].name)
+        "global_step",
+        named_variables["optimizer_step" + suffix].full_name)
     self.assertEqual(
-        "my_model/dense_1/kernel:0",
-        named_variables["model/_second/kernel" + suffix].name)
+        "my_model/dense_1/kernel",
+        named_variables["model/_second/kernel" + suffix].full_name)
     self.assertEqual(
-        "my_model/dense/kernel:0",
-        named_variables["model/_named_dense/kernel" + suffix].name)
+        "my_model/dense/kernel",
+        named_variables["model/_named_dense/kernel" + suffix].full_name)
     self.assertEqual(
-        "beta1_power:0",
-        named_variables["optimizer/beta1_power" + suffix].name)
+        "beta1_power",
+        named_variables["optimizer/beta1_power" + suffix].full_name)
     self.assertEqual(
-        "beta2_power:0",
-        named_variables["optimizer/beta2_power" + suffix].name)
+        "beta2_power",
+        named_variables["optimizer/beta2_power" + suffix].full_name)
     # Spot check the generated protocol buffers.
     self.assertEqual("optimizer",
                      serialized_graph.nodes[0].children[1].local_name)
@@ -205,7 +210,7 @@ def testNamingWithOptimizer(self):
     self.assertEqual(
         "my_model/dense/kernel/Adam:0",
         optimizer.get_slot(
-            var=named_variables["model/_named_dense/kernel" + suffix],
+            var=model._named_dense.kernel,
             name="m").name)
     self.assertEqual(
         "model/_named_dense/kernel" + suffix,
@@ -417,16 +422,6 @@ def _call_model(x):
                          self.evaluate(root.save_counter))
   # pylint: enable=cell-var-from-loop
 
-  def _get_checkpoint_name(self, name):
-    root = checkpointable.Checkpointable()
-    checkpointable_utils.add_variable(
-        root, name=name, shape=[1, 2], dtype=dtypes.float64)
-    named_variables, _ = checkpointable_utils._serialize_object_graph(root)
-    checkpoint_name, = named_variables.keys()
-    with ops.name_scope("root/" + checkpoint_name):
-      pass  # Make sure we can use this as an op name if we prefix it.
-    return checkpoint_name
-
   def testAnonymousVarsInInit(self):
 
     class Model(training.Model):
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index f7cbaec6ab0b24..8b904a16c7e33d 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3036,9 +3036,12 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":array_ops",
+        ":constant_op",
+        ":control_flow_ops",
         ":dtypes",
         ":io_ops_gen",
         ":ops",
+        ":saveable_object",
         ":util",
         "//tensorflow/python/eager:context",
     ],
@@ -3223,6 +3226,18 @@ py_test(
     ],
 )
 
+py_test(
+    name = "util_serialization_test",
+    size = "small",
+    srcs = ["util/serialization_test.py"],
+    main = "util/serialization_test.py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":client_testlib",
+        ":util",
+    ],
+)
+
 py_test(
     name = "future_api_test",
     size = "small",
diff --git a/tensorflow/python/keras/_impl/keras/engine/saving.py b/tensorflow/python/keras/_impl/keras/engine/saving.py
index a0b709a1a58436..ee6e320546068d 100644
--- a/tensorflow/python/keras/_impl/keras/engine/saving.py
+++ b/tensorflow/python/keras/_impl/keras/engine/saving.py
@@ -30,6 +30,7 @@
 from tensorflow.python.keras._impl.keras.utils import conv_utils
 from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.util import serialization
 from tensorflow.python.util.tf_export import tf_export
 
 # pylint: disable=g-import-not-at-top
@@ -74,40 +75,6 @@ def save_model(model, filepath, overwrite=True, include_optimizer=True):
   if h5py is None:
     raise ImportError('`save_model` requires h5py.')
 
-  def get_json_type(obj):
-    """Serializes any object to a JSON-serializable structure.
-
-    Arguments:
-        obj: the object to serialize
-
-    Returns:
-        JSON-serializable structure representing `obj`.
-
-    Raises:
-        TypeError: if `obj` cannot be serialized.
-    """
-    # if obj is a serializable Keras class instance
-    # e.g. optimizer, layer
-    if hasattr(obj, 'get_config'):
-      return {'class_name': obj.__class__.__name__, 'config': obj.get_config()}
-
-    # if obj is any numpy type
-    if type(obj).__module__ == np.__name__:
-      if isinstance(obj, np.ndarray):
-        return {'type': type(obj), 'value': obj.tolist()}
-      else:
-        return obj.item()
-
-    # misc functions (e.g. loss function)
-    if callable(obj):
-      return obj.__name__
-
-    # if obj is a python 'type'
-    if type(obj).__name__ == type.__name__:
-      return obj.__name__
-
-    raise TypeError('Not JSON Serializable:', obj)
-
   from tensorflow.python.keras._impl.keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top
 
   # If file exists and should not be overwritten.
@@ -124,7 +91,7 @@ def get_json_type(obj):
             'class_name': model.__class__.__name__,
             'config': model.get_config()
         },
-        default=get_json_type).encode('utf8')
+        default=serialization.get_json_type).encode('utf8')
 
     model_weights_group = f.create_group('model_weights')
     model_layers = model.layers
@@ -154,7 +121,7 @@ def get_json_type(obj):
                 'sample_weight_mode': model.sample_weight_mode,
                 'loss_weights': model.loss_weights,
             },
-            default=get_json_type).encode('utf8')
+            default=serialization.get_json_type).encode('utf8')
 
         # Save optimizer weights.
         symbolic_weights = getattr(model.optimizer, 'weights')
diff --git a/tensorflow/python/training/checkpointable.py b/tensorflow/python/training/checkpointable.py
index d00312a1f34b58..956dd66bee7038 100644
--- a/tensorflow/python/training/checkpointable.py
+++ b/tensorflow/python/training/checkpointable.py
@@ -18,14 +18,21 @@
 from __future__ import print_function
 
 import collections
+import functools
+import json
+import weakref
 
 from tensorflow.python.eager import context
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import gen_io_ops as io_ops
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import saveable_object
 from tensorflow.python.util import nest
+from tensorflow.python.util import serialization
 
 
 # Key where the object graph proto is saved in a TensorBundle
@@ -37,6 +44,7 @@
 # the object has no dependencies, then its value may be restored on object
 # creation (avoiding double assignment when executing eagerly).
 VARIABLE_VALUE_KEY = "VARIABLE_VALUE"
+OBJECT_CONFIG_JSON_KEY = "OBJECT_CONFIG_JSON"
 
 CheckpointableReference = collections.namedtuple(
     "CheckpointableReference",
@@ -85,6 +93,35 @@ def checkpoint_position(self):
     return self._checkpoint_position
 
 
+class PythonStringStateSaveable(saveable_object.SaveableObject):
+  """Saves Python state in a checkpoint."""
+
+  def __init__(self, name, state_callback):
+    """Configure saving.
+
+    Args:
+      name: The checkpoint key to write to.
+      state_callback: A function taking no arguments which returns a
+        string. This function is run every time a checkpoint is written.
+    """
+    if context.executing_eagerly():
+      self._save_string = (
+          lambda: constant_op.constant(state_callback(), dtype=dtypes.string))
+    else:
+      self._save_string = constant_op.constant("", dtype=dtypes.string)
+      self.feed_dict_additions = (
+          lambda: {self._save_string: state_callback()})
+    spec = saveable_object.SaveSpec(
+        self._save_string, "", name, dtype=dtypes.string)
+    super(PythonStringStateSaveable, self).__init__(
+        self._save_string, [spec], name)
+
+  def restore(self, restored_tensors, restored_shapes):
+    # TODO(allenl): Add a Python hook for state coming out of a checkpoint
+    # (currently PythonStringStateSaveable is write-only).
+    return control_flow_ops.no_op()
+
+
 class _CheckpointPosition(object):
   """Indicates a position within a `_Checkpoint`."""
 
@@ -604,7 +641,6 @@ def _single_restoration_from_checkpoint_position(
     # restoration on to our dependencies.
     if checkpoint.restore_uid > self._update_uid:
       restore_ops = checkpoint_position.restore_ops()
-      # TODO(allenl): Get a list of feeds for saving Python state
       self._update_uid = checkpoint.restore_uid
     else:
       restore_ops = ()
@@ -656,7 +692,24 @@ def _gather_saveables_for_checkpoint(self):
        lambda name="global_name_for_this_object":
        SaveableObject(name=name, ...)}
     """
-    return {}
+    if not hasattr(self, "get_config"):
+      return {}
+    try:
+      self.get_config()
+    except NotImplementedError:
+      return {}
+    weak_self = weakref.ref(self)
+    def _state_callback():
+      dereferenced_self = weak_self()
+      if dereferenced_self:
+        return json.dumps(self,
+                          default=serialization.get_json_type,
+                          sort_keys=True).encode("utf8")
+      else:
+        return ""
+    return {OBJECT_CONFIG_JSON_KEY: functools.partial(
+        PythonStringStateSaveable,
+        state_callback=_state_callback)}
 
 
 class NoDependency(object):
diff --git a/tensorflow/python/training/checkpointable_utils.py b/tensorflow/python/training/checkpointable_utils.py
index f2a2b411fdd1c0..1e69096706352b 100644
--- a/tensorflow/python/training/checkpointable_utils.py
+++ b/tensorflow/python/training/checkpointable_utils.py
@@ -36,6 +36,7 @@
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.training import checkpointable as checkpointable_lib
 from tensorflow.python.training import optimizer as optimizer_lib
+from tensorflow.python.training import saveable_object
 from tensorflow.python.training import saver as saver_lib
 from tensorflow.python.util import deprecation
 from tensorflow.python.util.tf_export import tf_export
@@ -303,42 +304,93 @@ def _serialize_slot_variables(checkpointable_objects, node_ids, object_names):
 
 
 def _serialize_checkpointables(
-    checkpointable_objects, node_ids, object_names, slot_variables):
+    checkpointable_objects, node_ids, object_names, slot_variables,
+    saveables_cache):
   """Name non-slot `Checkpointable`s and add them to `object_graph_proto`."""
   object_graph_proto = (
       checkpointable_object_graph_pb2.CheckpointableObjectGraph())
-  named_saveables = {}
-
+  named_saveables = []
+  feed_additions = {}
   for checkpoint_id, checkpointable in enumerate(checkpointable_objects):
     assert node_ids[checkpointable] == checkpoint_id
     object_proto = object_graph_proto.nodes.add()
     object_proto.slot_variables.extend(slot_variables.get(checkpointable, ()))
     object_name = object_names[checkpointable]
+    if saveables_cache is not None:
+      cached_attributes = saveables_cache.setdefault(checkpointable, {})
+    else:
+      cached_attributes = None
     for name, saveable_factory in (
         checkpointable._gather_saveables_for_checkpoint().items()):  # pylint: disable=protected-access
       attribute = object_proto.attributes.add()
       attribute.name = name
       attribute.checkpoint_key = "%s/%s/%s" % (
           object_name, _OBJECT_ATTRIBUTES_NAME, _escape_local_name(name))
-      if callable(saveable_factory):
-        saveable = saveable_factory(name=attribute.checkpoint_key)
+      if cached_attributes is None:
+        saveables = None
       else:
-        saveable = saveable_factory
-      # Figure out the name-based Saver's name for this variable.
-      saver_dict = saver_lib.BaseSaverBuilder.OpListToDict(
-          [saveable], convert_variable_to_tensor=False)
-      attribute.full_name, = saver_dict.keys()
-      named_saveables[attribute.checkpoint_key] = saveable
+        saveables = cached_attributes.get(name, None)
+        if saveables is not None:
+          for saveable in saveables:
+            if attribute.checkpoint_key not in saveable.name:
+              # The checkpoint key for this SaveableObject is different. We need
+              # to re-create it.
+              saveables = None
+              del cached_attributes[name]
+              break
+      if saveables is None:
+        if callable(saveable_factory):
+          maybe_saveable = saveable_factory(name=attribute.checkpoint_key)
+        else:
+          maybe_saveable = saveable_factory
+        if isinstance(maybe_saveable, saveable_object.SaveableObject):
+          saveables = (maybe_saveable,)
+        else:
+          # Figure out the name-based Saver's name for this variable. If it's
+          # already a SaveableObject we'd just get the checkpoint key back, so
+          # we leave full_name blank.
+          saver_dict = saver_lib.BaseSaverBuilder.OpListToDict(
+              [maybe_saveable], convert_variable_to_tensor=False)
+          full_name, = saver_dict.keys()
+          saveables = tuple(saver_lib.BaseSaverBuilder.SaveableObjectsForOp(
+              op=maybe_saveable, name=attribute.checkpoint_key))
+          for saveable in saveables:
+            saveable.full_name = full_name
+        for saveable in saveables:
+          if attribute.checkpoint_key not in saveable.name:
+            raise AssertionError(
+                ("The object %s produced a SaveableObject with name '%s' for "
+                 "attribute '%s'. Expected a name containing '%s'.")
+                % (checkpointable, name, saveable.name,
+                   attribute.checkpoint_key))
+        if cached_attributes is not None:
+          cached_attributes[name] = saveables
+
+      for saveable in saveables:
+        if hasattr(saveable, "full_name"):
+          attribute.full_name = saveable.full_name
+        saveable_feed_dict_fn = getattr(saveable, "feed_dict_additions", None)
+        if saveable_feed_dict_fn is not None:
+          saveable_feed_dict = saveable_feed_dict_fn()  # pylint: disable=not-callable
+          for new_feed_key in saveable_feed_dict.keys():
+            if new_feed_key in feed_additions:
+              raise AssertionError(
+                  ("The object %s tried to feed a value for the Tensor %s "
+                   "when saving, but another object is already feeding a "
+                   "value.")
+                  % (checkpointable, new_feed_key))
+          feed_additions.update(saveable_feed_dict)
+      named_saveables.extend(saveables)
 
     for child in checkpointable._checkpoint_dependencies:  # pylint: disable=protected-access
       child_proto = object_proto.children.add()
       child_proto.node_id = node_ids[child.ref]
       child_proto.local_name = child.name
 
-  return named_saveables, object_graph_proto
+  return named_saveables, object_graph_proto, feed_additions
 
 
-def _serialize_object_graph(root_checkpointable):
+def _serialize_object_graph(root_checkpointable, saveables_cache):
   """Determine checkpoint keys for variables and build a serialized graph.
 
   Non-slot variables are keyed based on a shortest path from the root saveable
@@ -351,12 +403,17 @@ def _serialize_object_graph(root_checkpointable):
   Args:
     root_checkpointable: A `Checkpointable` object whose variables (including
       the variables of dependencies, recursively) should be saved.
+    saveables_cache: A dictionary mapping `Checkpointable` objects -> attribute
+      names -> SaveableObjects, used to avoid re-creating SaveableObjects when
+      graph building.
 
   Returns:
-    A tuple of (named_variables, object_graph_proto):
+    A tuple of (named_variables, object_graph_proto, feed_additions):
       named_variables: A dictionary mapping names to variable objects.
       object_graph_proto: A CheckpointableObjectGraph protocol buffer containing
         the serialized object graph and variable references.
+      feed_additions: A dictionary mapping from Tensors to values which should
+        be fed when saving.
 
   Raises:
     ValueError: If there are invalid characters in an optimizer's slot names.
@@ -376,7 +433,8 @@ def _serialize_object_graph(root_checkpointable):
       checkpointable_objects=checkpointable_objects,
       node_ids=node_ids,
       object_names=object_names,
-      slot_variables=slot_variables)
+      slot_variables=slot_variables,
+      saveables_cache=saveables_cache)
 
 
 def list_objects(root_checkpointable):
@@ -728,6 +786,14 @@ def __init__(self, root_checkpointable):
     self._last_restore_object_graph = None
     self._last_restore_checkpoint = None
 
+    if context.executing_eagerly():
+      # SaveableObjects are always recreated when executing eagerly.
+      self._saveable_object_cache = None
+    else:
+      # Maps Checkpointable objects -> attribute names -> SaveableObjects, to
+      # avoid re-creating SaveableObjects when graph building.
+      self._saveable_object_cache = weakref.WeakKeyDictionary()
+
   @property
   def _root_checkpointable(self):
     if isinstance(self._root_checkpointable_ref, weakref.ref):
@@ -759,8 +825,9 @@ def save(self, file_prefix, checkpoint_number=None, session=None):
     Returns:
       The full path to the checkpoint.
     """
-    named_variables, graph_proto = _serialize_object_graph(
-        self._root_checkpointable)
+    named_variables, graph_proto, feed_additions = _serialize_object_graph(
+        self._root_checkpointable,
+        saveables_cache=self._saveable_object_cache)
     if not context.executing_eagerly():
       if session is None:
         session = ops.get_default_session()
@@ -769,15 +836,15 @@ def save(self, file_prefix, checkpoint_number=None, session=None):
           self._object_graph_feed_tensor = constant_op.constant(
               "", dtype=dtypes.string)
       object_graph_tensor = self._object_graph_feed_tensor
-      feed_additions = {object_graph_tensor: graph_proto.SerializeToString()}
+      feed_additions.update(
+          {object_graph_tensor: graph_proto.SerializeToString()})
     else:
       session = None
       with ops.device("/cpu:0"):
         object_graph_tensor = constant_op.constant(
             graph_proto.SerializeToString(), dtype=dtypes.string)
-      feed_additions = None
     assert checkpointable_lib.OBJECT_GRAPH_PROTO_KEY not in named_variables
-    named_variables[checkpointable_lib.OBJECT_GRAPH_PROTO_KEY] = (
+    named_variables.append(
         _NoRestoreSaveable(
             tensor=object_graph_tensor,
             name=checkpointable_lib.OBJECT_GRAPH_PROTO_KEY))
@@ -804,13 +871,23 @@ def save(self, file_prefix, checkpoint_number=None, session=None):
 
   def _global_variable_names(self):
     """Generate a `tf.train.Saver`-style `var_list` using `variable.name`s."""
-    named_saveables, graph_proto = _serialize_object_graph(
-        self._root_checkpointable)
+    named_saveables, graph_proto, _ = _serialize_object_graph(
+        self._root_checkpointable,
+        # We destructively modify SaveableObjects, so don't do any caching.
+        saveables_cache=None)
+    named_saveables = {v.name: v for v in named_saveables}
     saver_names = {}
     for object_proto in graph_proto.nodes:
       for attribute_proto in object_proto.attributes:
-        saver_names[attribute_proto.full_name] = named_saveables[
-            attribute_proto.checkpoint_key]
+        if attribute_proto.full_name:
+          # Ignore attributes, such as Python object JSON, which don't have a
+          # name-based Saver name.
+          saveable = named_saveables[attribute_proto.checkpoint_key]
+          saveable.name = attribute_proto.full_name
+          for spec in saveable.specs:
+            spec.name = spec.name.replace(attribute_proto.checkpoint_key,
+                                          attribute_proto.full_name)
+          saver_names[attribute_proto.full_name] = saveable
     return saver_names
 
   def restore(self, save_path):
@@ -1037,6 +1114,7 @@ def __init__(self, **kwargs):
             % (v,))
       setattr(self, k, v)
     self._save_counter = None  # Created lazily for restore-on-create.
+    self._save_assign_op = None
     self._saver = CheckpointableSaver(weakref.ref(self))
 
   def _maybe_create_save_counter(self):
@@ -1089,10 +1167,13 @@ def save(self, file_prefix, session=None):
         # needs to be initialized before assign_add. This is only an issue if
         # restore() has not been called first.
         session.run(self.save_counter.initializer)
-    with ops.colocate_with(self.save_counter):
-      assign_op = self.save_counter.assign_add(1)
+    if not in_graph_mode or self._save_assign_op is None:
+      with ops.colocate_with(self.save_counter):
+        assign_op = self.save_counter.assign_add(1, read_value=False)
+      if in_graph_mode:
+        self._save_assign_op = assign_op
     if in_graph_mode:
-      session.run(assign_op)
+      session.run(self._save_assign_op)
     return self._saver.save(
         file_prefix=file_prefix,
         checkpoint_number=self.save_counter,
diff --git a/tensorflow/python/training/checkpointable_utils_test.py b/tensorflow/python/training/checkpointable_utils_test.py
index 3b8166bf37a6a8..dead8fd37179cc 100644
--- a/tensorflow/python/training/checkpointable_utils_test.py
+++ b/tensorflow/python/training/checkpointable_utils_test.py
@@ -17,10 +17,12 @@
 from __future__ import print_function
 
 import functools
+import json
 import os
 
 import six
 
+from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.client import session as session_lib
 from tensorflow.python.eager import backprop
 from tensorflow.python.eager import context
@@ -120,7 +122,8 @@ def testAddVariable(self):
       # The .name attribute may be globally influenced, but the checkpoint name
       # won't be (tested below).
       self.assertEqual("duplicate_1:0", duplicate.name)
-    named_variables, _ = checkpointable_utils._serialize_object_graph(obj)
+    named_variables, _, _ = checkpointable_utils._serialize_object_graph(
+        obj, saveables_cache=None)
     expected_checkpoint_names = (
         "a_variable/.ATTRIBUTES/VARIABLE_VALUE",
         "bare_initializer/.ATTRIBUTES/VARIABLE_VALUE",
@@ -129,7 +132,7 @@ def testAddVariable(self):
         "ones_initializer/.ATTRIBUTES/VARIABLE_VALUE",
     )
     six.assertCountEqual(
-        self, expected_checkpoint_names, named_variables.keys())
+        self, expected_checkpoint_names, [v.name for v in named_variables])
 
   def testInitNotCalled(self):
 
@@ -245,8 +248,9 @@ def testNamingWithOptimizer(self):
       self.evaluate(checkpointable_utils.gather_initializers(
           root_checkpointable))
       self.evaluate(train_op)
-    named_variables, serialized_graph = (
-        checkpointable_utils._serialize_object_graph(root_checkpointable))
+    named_variables, serialized_graph, _ = (
+        checkpointable_utils._serialize_object_graph(
+            root_checkpointable, saveables_cache=None))
     expected_checkpoint_names = (
         # Created in the root node, so no prefix.
         "optimizer_step",
@@ -269,24 +273,29 @@ def testNamingWithOptimizer(self):
     suffix = "/.ATTRIBUTES/VARIABLE_VALUE"
     expected_checkpoint_names = [
         name + suffix for name in expected_checkpoint_names]
+    # The Dense layers also save get_config() JSON
+    expected_checkpoint_names.extend(
+        ["model/_second/.ATTRIBUTES/OBJECT_CONFIG_JSON",
+         "model/_named_dense/.ATTRIBUTES/OBJECT_CONFIG_JSON"])
+    named_variables = {v.name: v for v in named_variables}
     six.assertCountEqual(self, expected_checkpoint_names,
                          named_variables.keys())
     # Check that we've mapped to the right variable objects (not exhaustive)
     self.assertEqual(
-        "global_step:0",
-        named_variables["optimizer_step" + suffix].name)
+        "global_step",
+        named_variables["optimizer_step" + suffix].full_name)
     self.assertEqual(
-        "my_model/dense_1/kernel:0",
-        named_variables["model/_second/kernel" + suffix].name)
+        "my_model/dense_1/kernel",
+        named_variables["model/_second/kernel" + suffix].full_name)
     self.assertEqual(
-        "my_model/dense/kernel:0",
-        named_variables["model/_named_dense/kernel" + suffix].name)
+        "my_model/dense/kernel",
+        named_variables["model/_named_dense/kernel" + suffix].full_name)
     self.assertEqual(
-        "beta1_power:0",
-        named_variables["optimizer/beta1_power" + suffix].name)
+        "beta1_power",
+        named_variables["optimizer/beta1_power" + suffix].full_name)
     self.assertEqual(
-        "beta2_power:0",
-        named_variables["optimizer/beta2_power" + suffix].name)
+        "beta2_power",
+        named_variables["optimizer/beta2_power" + suffix].full_name)
     # Spot check the generated protocol buffers.
     self.assertEqual("optimizer",
                      serialized_graph.nodes[0].children[1].local_name)
@@ -311,7 +320,7 @@ def testNamingWithOptimizer(self):
     self.assertEqual(
         "my_model/dense/kernel/Adam:0",
         optimizer.get_slot(
-            var=named_variables["model/_named_dense/kernel" + suffix],
+            var=model._named_dense.kernel,
             name="m").name)
     self.assertEqual(
         "model/_named_dense/kernel" + suffix,
@@ -563,11 +572,11 @@ def _get_checkpoint_name(self, name):
     root = checkpointable.Checkpointable()
     checkpointable_utils.add_variable(
         root, name=name, shape=[1, 2], dtype=dtypes.float64)
-    named_variables, _ = checkpointable_utils._serialize_object_graph(root)
-    checkpoint_name, = named_variables.keys()
-    with ops.name_scope("root/" + checkpoint_name):
+    (named_variable,), _, _ = checkpointable_utils._serialize_object_graph(
+        root, saveables_cache=None)
+    with ops.name_scope("root/" + named_variable.name):
       pass  # Make sure we can use this as an op name if we prefix it.
-    return checkpoint_name
+    return named_variable.name
 
   @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
   def testVariableNameEscaping(self):
@@ -585,9 +594,9 @@ def testNumberedPath(self):
     leaf = checkpointable.Checkpointable()
     root.leaf = leaf
     checkpointable_utils.add_variable(leaf, name="v", shape=[])
-    named_variables, _ = checkpointable_utils._serialize_object_graph(root)
-    variable_name, = named_variables.keys()
-    self.assertEqual(r"leaf/v/.ATTRIBUTES/VARIABLE_VALUE", variable_name)
+    (named_variable,), _, _ = checkpointable_utils._serialize_object_graph(
+        root, saveables_cache=None)
+    self.assertEqual(r"leaf/v/.ATTRIBUTES/VARIABLE_VALUE", named_variable.name)
 
   @test_util.run_in_graph_and_eager_modes()
   def testLocalNameValidation(self):
@@ -596,9 +605,10 @@ def testLocalNameValidation(self):
     # Dots are escaped, which avoids conflicts with reserved names.
     root._track_checkpointable(leaf, name=".ATTRIBUTES")
     checkpointable_utils.add_variable(checkpointable=leaf, name="a", shape=[])
-    named_variables, _ = checkpointable_utils._serialize_object_graph(root)
-    name, = named_variables.keys()
-    self.assertEqual(name, "..ATTRIBUTES/a/.ATTRIBUTES/VARIABLE_VALUE")
+    (named_variable,), _, _ = checkpointable_utils._serialize_object_graph(
+        root, saveables_cache=None)
+    self.assertEqual("..ATTRIBUTES/a/.ATTRIBUTES/VARIABLE_VALUE",
+                     named_variable.name)
 
   def testAnonymousVarsInInit(self):
 
@@ -1395,5 +1405,48 @@ def testSaveEagerLoadGraph(self):
         root.restore(save_path).assert_consumed().run_restore_ops()
         self._check_sentinels(root)
 
+
+class PythonMetadataTests(test.TestCase):
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testSaveLoad(self):
+    checkpoint_directory = self.get_temp_dir()
+    checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+    dense = core.Dense(1)
+    checkpoint = checkpointable_utils.Checkpoint(dense=dense)
+    dense(constant_op.constant([[1.]]))
+    checkpoint.restore(None).initialize_or_restore()
+    save_path = checkpoint.save(checkpoint_prefix)
+
+    def _get_dense_node_from_object_graph(object_graph_proto):
+      root_node = object_graph_proto.nodes[0]
+      for child in root_node.children:
+        if child.local_name == "dense":
+          break
+      else:
+        raise AssertionError(
+            "Expected a 'dense' dependency of root, didn't find one.")
+      dense_node = object_graph_proto.nodes[child.node_id]  # pylint: disable=undefined-loop-variable
+      self.assertEqual(1, len(dense_node.attributes))
+      reader = pywrap_tensorflow.NewCheckpointReader(save_path)
+      layer_json = reader.get_tensor(dense_node.attributes[0].checkpoint_key)
+      return json.loads(layer_json.decode("utf-8"))
+
+    layer_data = _get_dense_node_from_object_graph(
+        checkpointable_utils.object_metadata(save_path))
+    self.assertEqual("Dense", layer_data["class_name"])
+    self.assertEqual(1, layer_data["config"]["units"])
+
+    # Check that no new ops are added to the graph the second time we save.
+    ops.get_default_graph().finalize()
+
+    dense.units = 42
+    save_path = checkpoint.save(checkpoint_prefix)
+    layer_data = _get_dense_node_from_object_graph(
+        checkpointable_utils.object_metadata(save_path))
+    self.assertEqual("Dense", layer_data["class_name"])
+    self.assertEqual(42, layer_data["config"]["units"])
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index 53e821c995900c..98e79a4b723a0f 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -569,6 +569,76 @@ def OpListToDict(op_list, convert_variable_to_tensor=True):
       # pylint: enable=protected-access
     return names_to_saveables
 
+  @staticmethod
+  def SaveableObjectsForOp(op, name):
+    """Create `SaveableObject`s from an operation.
+
+    Args:
+      op: A variable, operation, or SaveableObject to coerce into a
+        SaveableObject.
+      name: A string name for the SaveableObject.
+
+    Yields:
+      `SaveableObject`s which together save/restore `op`.
+
+    Raises:
+      TypeError: If `name` is not a string.
+      ValueError: For operations with no known conversion to SaveableObject.
+    """
+    if not isinstance(name, six.string_types):
+      raise TypeError(
+          "names_to_saveables must be a dict mapping string names to "
+          "checkpointable operations. Name is not a string: %s" % name)
+    if isinstance(op, BaseSaverBuilder.SaveableObject):
+      yield op
+    elif isinstance(op, (list, tuple, variables.PartitionedVariable)):
+      if isinstance(op, variables.PartitionedVariable):
+        op = list(op)
+      # A set of slices.
+      slice_name = None
+      # pylint: disable=protected-access
+      for variable in op:
+        if not isinstance(variable, variables.Variable):
+          raise ValueError("Slices must all be Variables: %s" % variable)
+        if not variable._save_slice_info:
+          raise ValueError("Slices must all be slices: %s" % variable)
+        if slice_name is None:
+          slice_name = variable._save_slice_info.full_name
+        elif slice_name != variable._save_slice_info.full_name:
+          raise ValueError(
+              "Slices must all be from the same tensor: %s != %s" %
+              (slice_name, variable._save_slice_info.full_name))
+        if variable.op.type in ["Variable", "VariableV2",
+                                "AutoReloadVariable"]:
+          yield BaseSaverBuilder.VariableSaveable(
+              variable, variable._save_slice_info.spec, name)
+        else:
+          yield BaseSaverBuilder.ResourceVariableSaveable(
+              variable, variable._save_slice_info.spec, name)
+      # pylint: enable=protected-access
+    else:
+      # A variable or tensor.
+      if context.executing_eagerly():
+        if not isinstance(op, resource_variable_ops.ResourceVariable):
+          raise ValueError("Can only save/restore ResourceVariable eager "
+                           "mode is enabled, type: %s." % type(op))
+        yield BaseSaverBuilder.ResourceVariableSaveable(op, "", name)
+      else:
+        if isinstance(op, resource_variable_ops.ResourceVariable):
+          variable = op._graph_element  # pylint: disable=protected-access
+        else:
+          variable = ops.internal_convert_to_tensor(op, as_ref=True)
+        if not BaseSaverBuilder._IsVariable(variable):
+          raise TypeError("names_to_saveables must be a dict mapping string "
+                          "names to Tensors/Variables. Not a variable: %s" %
+                          variable)
+        if variable.op.type in ["Variable", "VariableV2",
+                                "AutoReloadVariable"]:
+          yield BaseSaverBuilder.VariableSaveable(variable, "", name)
+        else:
+          yield BaseSaverBuilder.ResourceVariableSaveable(
+              variable, "", name)
+
   def _ValidateAndSliceInputs(self, names_to_saveables):
     """Returns the variables and names that will be used for a Saver.
 
@@ -590,63 +660,11 @@ def _ValidateAndSliceInputs(self, names_to_saveables):
 
     saveables = []
     seen_ops = set()
-    for name in sorted(names_to_saveables.keys()):
-      if not isinstance(name, six.string_types):
-        raise TypeError(
-            "names_to_saveables must be a dict mapping string names to "
-            "checkpointable operations. Name is not a string: %s" % name)
-      op = names_to_saveables[name]
-      if isinstance(op, BaseSaverBuilder.SaveableObject):
-        self._AddSaveable(saveables, seen_ops, op)
-      elif isinstance(op, (list, tuple, variables.PartitionedVariable)):
-        if isinstance(op, variables.PartitionedVariable):
-          op = list(op)
-        # A set of slices.
-        slice_name = None
-        # pylint: disable=protected-access
-        for variable in op:
-          if not isinstance(variable, variables.Variable):
-            raise ValueError("Slices must all be Variables: %s" % variable)
-          if not variable._save_slice_info:
-            raise ValueError("Slices must all be slices: %s" % variable)
-          if slice_name is None:
-            slice_name = variable._save_slice_info.full_name
-          elif slice_name != variable._save_slice_info.full_name:
-            raise ValueError(
-                "Slices must all be from the same tensor: %s != %s" %
-                (slice_name, variable._save_slice_info.full_name))
-          if variable.op.type in ["Variable", "VariableV2",
-                                  "AutoReloadVariable"]:
-            saveable = BaseSaverBuilder.VariableSaveable(
-                variable, variable._save_slice_info.spec, name)
-          else:
-            saveable = BaseSaverBuilder.ResourceVariableSaveable(
-                variable, variable._save_slice_info.spec, name)
-          self._AddSaveable(saveables, seen_ops, saveable)
-        # pylint: enable=protected-access
-      else:
-        # A variable or tensor.
-        if context.executing_eagerly():
-          if not isinstance(op, resource_variable_ops.ResourceVariable):
-            raise ValueError("Can only save/restore ResourceVariable eager "
-                             "mode is enabled, type: %s." % type(op))
-          saveable = BaseSaverBuilder.ResourceVariableSaveable(op, "", name)
-        else:
-          if isinstance(op, resource_variable_ops.ResourceVariable):
-            variable = op._graph_element  # pylint: disable=protected-access
-          else:
-            variable = ops.internal_convert_to_tensor(op, as_ref=True)
-          if not BaseSaverBuilder._IsVariable(variable):
-            raise TypeError("names_to_saveables must be a dict mapping string "
-                            "names to Tensors/Variables. Not a variable: %s" %
-                            variable)
-          if variable.op.type in ["Variable", "VariableV2",
-                                  "AutoReloadVariable"]:
-            saveable = BaseSaverBuilder.VariableSaveable(variable, "", name)
-          else:
-            saveable = BaseSaverBuilder.ResourceVariableSaveable(
-                variable, "", name)
-        self._AddSaveable(saveables, seen_ops, saveable)
+    for name, op in sorted(names_to_saveables.items(),
+                           # Avoid comparing ops, sort only by name.
+                           key=lambda x: x[0]):
+      for converted_saveable_object in self.SaveableObjectsForOp(op, name):
+        self._AddSaveable(saveables, seen_ops, converted_saveable_object)
     return saveables
 
   def _AddSaveable(self, saveables, seen_ops, saveable):
diff --git a/tensorflow/python/util/serialization.py b/tensorflow/python/util/serialization.py
new file mode 100644
index 00000000000000..faf5164faa7f1c
--- /dev/null
+++ b/tensorflow/python/util/serialization.py
@@ -0,0 +1,64 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for serializing Python objects."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.framework import tensor_shape
+
+
+def get_json_type(obj):
+  """Serializes any object to a JSON-serializable structure.
+
+  Arguments:
+      obj: the object to serialize
+
+  Returns:
+      JSON-serializable structure representing `obj`.
+
+  Raises:
+      TypeError: if `obj` cannot be serialized.
+  """
+  # if obj is a serializable Keras class instance
+  # e.g. optimizer, layer
+  if hasattr(obj, 'get_config'):
+    return {'class_name': obj.__class__.__name__, 'config': obj.get_config()}
+
+  # if obj is any numpy type
+  if type(obj).__module__ == np.__name__:
+    if isinstance(obj, np.ndarray):
+      return {'type': type(obj), 'value': obj.tolist()}
+    else:
+      return obj.item()
+
+  # misc functions (e.g. loss function)
+  if callable(obj):
+    return obj.__name__
+
+  # if obj is a python 'type'
+  if type(obj).__name__ == type.__name__:
+    return obj.__name__
+
+  if isinstance(obj, tensor_shape.Dimension):
+    return obj.value
+
+  if isinstance(obj, tensor_shape.TensorShape):
+    return obj.as_list()
+
+  raise TypeError('Not JSON Serializable:', obj)
diff --git a/tensorflow/python/util/serialization_test.py b/tensorflow/python/util/serialization_test.py
new file mode 100644
index 00000000000000..f16fa5377b5dcd
--- /dev/null
+++ b/tensorflow/python/util/serialization_test.py
@@ -0,0 +1,76 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for serialization functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import json
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import test_util
+from tensorflow.python.keras._impl.keras.engine import input_layer
+from tensorflow.python.keras._impl.keras.engine import sequential
+from tensorflow.python.keras._impl.keras.engine import training
+from tensorflow.python.keras._impl.keras.layers import core
+from tensorflow.python.platform import test
+from tensorflow.python.util import serialization
+
+
+class SerializationTests(test.TestCase):
+
+  def test_serialize_dense(self):
+    dense = core.Dense(3)
+    dense(constant_op.constant([[4.]]))
+    round_trip = json.loads(json.dumps(
+        dense, default=serialization.get_json_type))
+    self.assertEqual(3, round_trip["config"]["units"])
+
+  def test_serialize_shape(self):
+    round_trip = json.loads(json.dumps(
+        tensor_shape.TensorShape([None, 2, 3]),
+        default=serialization.get_json_type))
+    self.assertIs(round_trip[0], None)
+    self.assertEqual(round_trip[1], 2)
+
+  @test_util.run_in_graph_and_eager_modes()
+  def test_serialize_sequential(self):
+    model = sequential.Sequential()
+    model.add(core.Dense(4))
+    model.add(core.Dense(5))
+    model(constant_op.constant([[1.]]))
+    sequential_round_trip = json.loads(
+        json.dumps(model, default=serialization.get_json_type))
+    self.assertEqual(5, sequential_round_trip["config"][1]["config"]["units"])
+    input_round_trip = json.loads(
+        json.dumps(model._input_layers, default=serialization.get_json_type))
+    self.assertAllEqual([1, 1],
+                        input_round_trip[0]["config"]["batch_input_shape"])
+
+  @test_util.run_in_graph_and_eager_modes()
+  def test_serialize_model(self):
+    x = input_layer.Input(shape=[3])
+    y = core.Dense(10)(x)
+    model = training.Model(x, y)
+    model(constant_op.constant([[1., 1., 1.]]))
+    model_round_trip = json.loads(
+        json.dumps(model, default=serialization.get_json_type))
+    self.assertEqual(
+        10, model_round_trip["config"]["layers"][1]["config"]["units"])
+
+if __name__ == "__main__":
+  test.main()

From f1badb6664c290176864d1a1d4ab537b7332b730 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 15:58:28 -0700
Subject: [PATCH 0581/1691] Add missing update of node map in the Mul(x,x) =>
 Square(x) rewrite. This is what caused a failure in
 //photos/vision/object_detection/ranking:brain_embedder_test when the
 concat/split hoisting was enabled.

PiperOrigin-RevId: 196043455
---
 tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
index adfae2e1a34eb8..f46c30c92c077d 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -2233,6 +2233,9 @@ string ArithmeticOptimizer::TrySimplifyAndReplaceUses(
         new_square_node->set_input(i - 1, new_square_node->input(i));
       }
       new_square_node->mutable_input()->RemoveLast();
+      for (const string& input : new_square_node->input()) {
+        node_map_->AddOutput(NodeName(input), new_square_node->name());
+      }
       return new_square_node->name();
     }
   }

From b348209171a2fac38def122d2ee43bd2fc3d9b1d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 16:18:45 -0700
Subject: [PATCH 0582/1691] Increase shard count for
 tensorflow/contrib/distributions:vector_diffeomixture_test to avoid flaky
 timeouts

PiperOrigin-RevId: 196046333
---
 tensorflow/contrib/distributions/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index a1d56066b417dd..c7a24f20981569 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -710,6 +710,7 @@ cuda_py_test(
         "//tensorflow/contrib/linalg:linalg_py",
         "//tensorflow/python:client_testlib",
     ],
+    shard_count = 4,
     tags = ["noasan"],  # times out, http://b/78588814
 )
 

From c07b719ab030c46f19c8e5cdd92730eaec38a8fb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 16:40:03 -0700
Subject: [PATCH 0583/1691] [XLA] Make hlo deserialization stable for HloModule
 by sorting by ids when creating from proto.

Also, delete the HloModule parameter HloInstruction::CreateFromProto, it's not used anywhere.

Also, in ToProto, set sharding to proto if there is sharding.

PiperOrigin-RevId: 196049173
---
 .../compiler/xla/service/hlo_computation.cc   | 18 +++++++--
 .../compiler/xla/service/hlo_computation.h    |  4 +-
 .../compiler/xla/service/hlo_instruction.cc   |  6 ++-
 .../compiler/xla/service/hlo_instruction.h    |  4 +-
 tensorflow/compiler/xla/service/hlo_module.cc | 40 ++++++++++++++-----
 5 files changed, 51 insertions(+), 21 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc
index 17e43c3cb826aa..05dceb1dc0cb4a 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation.cc
@@ -407,27 +407,37 @@ HloComputationProto HloComputation::ToProto() const {
 
 /* static */ StatusOr>
 HloComputation::CreateFromProto(
-    HloModule* module, const HloComputationProto& proto,
+    const HloComputationProto& proto,
     const tensorflow::gtl::FlatMap& computation_map) {
-  std::vector> instructions;
   tensorflow::gtl::FlatMap instruction_map;
+  tensorflow::gtl::FlatMap to_proto_id;
+  std::vector> instructions;
   int64 parameter_count = 0;
   for (const HloInstructionProto& instruction_proto : proto.instructions()) {
     TF_ASSIGN_OR_RETURN(
         std::unique_ptr instruction,
-        HloInstruction::CreateFromProto(module, instruction_proto,
-                                        instruction_map, computation_map));
+        HloInstruction::CreateFromProto(instruction_proto, instruction_map,
+                                        computation_map));
     if (instruction->opcode() == HloOpcode::kParameter) {
       parameter_count++;
     }
     TF_RET_CHECK(!ContainsKey(instruction_map, instruction_proto.id()));
     instruction_map[instruction_proto.id()] = instruction.get();
+    to_proto_id[instruction.get()] = instruction_proto.id();
     instructions.push_back(std::move(instruction));
   }
 
   TF_RET_CHECK(proto.root_id() != -1);
   TF_RET_CHECK(ContainsKey(instruction_map, proto.root_id()));
   HloInstruction* root = instruction_map.at(proto.root_id());
+
+  // Sort the instructions in the proto id's order.
+  std::sort(instructions.begin(), instructions.end(),
+            [&](const std::unique_ptr& a,
+                const std::unique_ptr& b) {
+              return to_proto_id[a.get()] < to_proto_id[b.get()];
+            });
+
   return WrapUnique(new HloComputation(proto.name(), parameter_count,
                                        &instructions, root,
                                        /*fusion_instruction=*/nullptr));
diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h
index 98983556256cec..ba9d44a9ab8714 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.h
+++ b/tensorflow/compiler/xla/service/hlo_computation.h
@@ -157,14 +157,12 @@ class HloComputation {
 
   // Creates a computation from the given proto. Arguments:
   //
-  //   module: the module which will contain the computation. The newly created
-  //     computation is *not* added to the module, however.
   //   proto: the proto to convert from.
   //   computation_map: a map from computation id to HloComputation*. This map
   //     must contain all computations which the newly constructed computation
   //     calls.
   static StatusOr> CreateFromProto(
-      HloModule* module, const HloComputationProto& proto,
+      const HloComputationProto& proto,
       const tensorflow::gtl::FlatMap& computation_map);
 
   // Gets the instructions in this computation.
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 03e039107f6805..3ff1007277a238 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -51,7 +51,7 @@ using ::tensorflow::strings::StrCat;
 
 /* static */
 StatusOr> HloInstruction::CreateFromProto(
-    HloModule* module, const HloInstructionProto& proto,
+    const HloInstructionProto& proto,
     const tensorflow::gtl::FlatMap& instruction_map,
     const tensorflow::gtl::FlatMap& computation_map) {
   TF_RET_CHECK(!proto.opcode().empty());
@@ -2396,6 +2396,10 @@ HloInstructionProto HloInstruction::ToProto() const {
     proto.add_fft_length(fft_len);
   }
 
+  if (has_sharding()) {
+    *proto.mutable_sharding() = sharding().ToProto();
+  }
+
   proto.set_channel_name(channel_name_);
   proto.set_cost_estimate_ns(cost_estimate_ns_);
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index ea5fc5be7b8047..2e5895efce0760 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -185,8 +185,6 @@ class HloInstruction {
 
   // Creates an instruction from the given proto. Arguments:
   //
-  //   module: the module which will contain the instruction. The newly created
-  //     instruction is *not* added to the module or any computation, however.
   //   proto: the proto to convert from.
   //   instruction_map: a map from instruction id to HloInstruction*. This map
   //     must contain all operands of the newly constructed instruction.
@@ -194,7 +192,7 @@ class HloInstruction {
   //     must contain all computations which the newly constructed instruction
   //     calls.
   static StatusOr> CreateFromProto(
-      HloModule* module, const HloInstructionProto& proto,
+      const HloInstructionProto& proto,
       const tensorflow::gtl::FlatMap& instruction_map,
       const tensorflow::gtl::FlatMap& computation_map);
 
diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc
index 5308fb5848341b..fbf1d58007e318 100644
--- a/tensorflow/compiler/xla/service/hlo_module.cc
+++ b/tensorflow/compiler/xla/service/hlo_module.cc
@@ -266,24 +266,44 @@ StatusOr> HloModule::CreateFromProto(
       << ShapeUtil::HumanStringWithLayout(expected_program_shape.result())
       << ", actual: " << ShapeUtil::HumanStringWithLayout(result_shape);
 
-  auto module = MakeUnique(proto.name(), entry_computation_handle,
-                                      module_config);
-
   tensorflow::gtl::FlatMap computation_map;
+  tensorflow::gtl::FlatMap to_proto_id;
+  std::vector> computations;
+  HloComputation* entry = nullptr;
   for (const HloComputationProto& computation_proto : proto.computations()) {
-    TF_ASSIGN_OR_RETURN(std::unique_ptr computation,
-                        HloComputation::CreateFromProto(
-                            module.get(), computation_proto, computation_map));
+    TF_ASSIGN_OR_RETURN(
+        std::unique_ptr computation,
+        HloComputation::CreateFromProto(computation_proto, computation_map));
     CHECK_NE(computation.get(), nullptr);
     int64 computation_id = computation_proto.id();
     TF_RET_CHECK(computation_id != -1);
     TF_RET_CHECK(!ContainsKey(computation_map, computation_id));
+    computation_map[computation_id] = computation.get();
+    to_proto_id[computation.get()] = computation_id;
+    if (computation_id == proto.entry_computation_id()) {
+      entry = computation.get();
+    }
+    computations.push_back(std::move(computation));
+  }
+  TF_RET_CHECK(entry != nullptr);
+
+  auto module = MakeUnique(proto.name(), entry_computation_handle,
+                                      module_config);
+
+  // Sort the computations in the proto id's order.
+  std::sort(computations.begin(), computations.end(),
+            [&](const std::unique_ptr& a,
+                const std::unique_ptr& b) {
+              return to_proto_id[a.get()] < to_proto_id[b.get()];
+            });
+
+  // Add sorted computations to the module.
+  for (auto& computation : computations) {
+    bool is_entry = computation.get() == entry;
     // Don't uniquify names because we want names to be stable across
     // serialization and deserialization.
-    computation_map[computation_id] = module->AddComputationInternal(
-        std::move(computation),
-        /*is_entry=*/proto.entry_computation_id() == computation_id,
-        /*uniquify_names=*/false);
+    module->AddComputationInternal(std::move(computation), is_entry,
+                                   /*uniquify_names=*/false);
   }
   TF_RET_CHECK(module->entry_computation_ != nullptr);
 

From b8f034f56b3ed82c477afd6e91ca3b17d6322cd0 Mon Sep 17 00:00:00 2001
From: Jie 
Date: Wed, 9 May 2018 16:57:11 -0700
Subject: [PATCH 0584/1691] detecting SetAttribute failure

---
 tensorflow/contrib/tensorrt/convert/convert_nodes.cc | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
index 8c482c84d5688b..f043237ebd07d3 100644
--- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
+++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
@@ -1217,7 +1217,10 @@ tensorflow::Status ConvertPlugin(Converter& ctx,
     // TODO(jie): support only list of float for toy example here.
     auto data = attrs.get>(attr_key);
     size_t size_data = data.size() * sizeof(float);
-    plugin->SetAttribute(attr_key, static_cast(data.data()), size_data);
+    if (!plugin->SetAttribute(attr_key, static_cast(data.data()),
+                              size_data)) {
+      return tensorflow::errors::InvalidArgument("plugin SetAttribute failed");
+    }
   }
 
   nvinfer1::IPluginLayer* layer =

From 930974af4d8e24958c75286c31dc7e0ee67e75ba Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 16:58:54 -0700
Subject: [PATCH 0585/1691] Improve error status message in
 scoped_allocator_ops.cc.

PiperOrigin-RevId: 196051520
---
 tensorflow/core/kernels/scoped_allocator_ops.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/scoped_allocator_ops.cc b/tensorflow/core/kernels/scoped_allocator_ops.cc
index 1800ee8c1f975b..1d2fb6996a3fcf 100644
--- a/tensorflow/core/kernels/scoped_allocator_ops.cc
+++ b/tensorflow/core/kernels/scoped_allocator_ops.cc
@@ -113,7 +113,7 @@ class ScopedAllocatorConcatOp : public OpKernel {
     OP_REQUIRES(context, backing_tensor.NumElements() >= shape_.num_elements(),
                 errors::InvalidArgument("Backing tensor num elements ",
                                         backing_tensor.NumElements(),
-                                        " is not equal to expected ",
+                                        " is not >= to expected ",
                                         shape_.num_elements()));
     Tensor output(dtype_);
     if (reshape_) {

From 20387e460ad8b72cb4ac9f6bda00394f2a404f3f Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar 
Date: Wed, 9 May 2018 17:30:30 -0700
Subject: [PATCH 0586/1691] Fix FreezeSavedModel to handle traversal of
 operations with multiple outputs.

PiperOrigin-RevId: 196055377
---
 tensorflow/cc/tools/freeze_saved_model.cc     | 16 +++++++-----
 .../cc/tools/freeze_saved_model_test.cc       | 25 +++++++++++++++++++
 2 files changed, 35 insertions(+), 6 deletions(-)

diff --git a/tensorflow/cc/tools/freeze_saved_model.cc b/tensorflow/cc/tools/freeze_saved_model.cc
index 4ddddcb5863c9f..2a859d6472dadf 100644
--- a/tensorflow/cc/tools/freeze_saved_model.cc
+++ b/tensorflow/cc/tools/freeze_saved_model.cc
@@ -71,6 +71,12 @@ void GetNodeNameToNodeDefMap(
   }
 }
 
+// Strips off the tensor part of the tensor_name to get the node_name.
+const string GetNodeNameFromTensorName(const string& tensor_name) {
+  std::vector tensor_name_parts = str_util::Split(tensor_name, ':');
+  return tensor_name_parts[0];
+}
+
 // Gets the set of node names needed by `outputs` and the corresponding set of
 // variable nodes to convert.
 void GetReachableNodesAndVariables(
@@ -83,10 +89,8 @@ void GetReachableNodesAndVariables(
       new std::unordered_set({"Variable", "VariableV2", "VarHandleOp"});
 
   std::queue nodes_to_visit;
-  for (const string& tensor_name : outputs) {
-    // We need to strip off the tensor part to get the node name.
-    std::vector tensor_name_parts = str_util::Split(tensor_name, ':');
-    nodes_to_visit.push(tensor_name_parts[0]);
+  for (const string& output_tensor_name : outputs) {
+    nodes_to_visit.push(GetNodeNameFromTensorName(output_tensor_name));
   }
   // We do a traversal backwards from the outputs specified in the MetaGraphDef.
   while (!nodes_to_visit.empty()) {
@@ -100,8 +104,8 @@ void GetReachableNodesAndVariables(
     if (kVariableTypes->find(node->op()) != kVariableTypes->end()) {
       variable_node_names->insert(node->name());
     }
-    for (const string& input : node->input()) {
-      nodes_to_visit.push(input);
+    for (const string& input_tensor_name : node->input()) {
+      nodes_to_visit.push(GetNodeNameFromTensorName(input_tensor_name));
     }
   }
 }
diff --git a/tensorflow/cc/tools/freeze_saved_model_test.cc b/tensorflow/cc/tools/freeze_saved_model_test.cc
index cd35fd3b95deec..e265a68e545cc9 100644
--- a/tensorflow/cc/tools/freeze_saved_model_test.cc
+++ b/tensorflow/cc/tools/freeze_saved_model_test.cc
@@ -351,6 +351,31 @@ TEST_F(FreezeTest, GraphDefWithNoVariables) {
   GraphDefEqual(frozen_graph_def, graph_def);
 }
 
+TEST_F(FreezeTest, GraphDefWithMultiOutputOperation) {
+  // Tensors from operations with multiple outputs get tensor suffixes when used
+  // in input fields of following nodes, i.e. split:0, split:1.
+  // Test that we traverse those correctly.
+  SavedModelBundle saved_model_bundle;
+  GraphDef graph_def;
+  Scope scope = Scope::NewRootScope();
+  Output a = ops::Const(scope.WithOpName("a"), {10.0f, 10.0f}, {2});
+  Output axis = ops::Const(scope.WithOpName("axis"), 0, {});
+  OutputList split = ops::Split(scope.WithOpName("split"), axis, a, 2).output;
+  Output b = ops::Const(scope.WithOpName("b"), 10.0f, {});
+  Output c = ops::Mul(scope.WithOpName("c"), split[1], b);
+  TF_ASSERT_OK(scope.ToGraphDef(&graph_def));
+  TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle(graph_def, {"c:0"}, "",
+                                                        &saved_model_bundle));
+
+  GraphDef frozen_graph_def;
+  std::unordered_set inputs;
+  std::unordered_set outputs;
+  TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs,
+                                &outputs));
+
+  GraphDefEqual(frozen_graph_def, graph_def);
+}
+
 TEST_F(FreezeTest, GraphDefWithoutDependentVariables) {
   TestFreezeGraphWithoutDependentVariables(false);
 }

From 6450b7841d37a685a0b0a33e0e00b0ef14db72a9 Mon Sep 17 00:00:00 2001
From: Adam Roberts 
Date: Wed, 9 May 2018 17:38:41 -0700
Subject: [PATCH 0587/1691] Clarify error message.

PiperOrigin-RevId: 196056372
---
 tensorflow/core/kernels/cudnn_rnn_ops.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/cudnn_rnn_ops.cc b/tensorflow/core/kernels/cudnn_rnn_ops.cc
index 25560b7c28273f..02d4fc89c87378 100644
--- a/tensorflow/core/kernels/cudnn_rnn_ops.cc
+++ b/tensorflow/core/kernels/cudnn_rnn_ops.cc
@@ -571,7 +571,7 @@ Status ExtractForwardInput(OpKernelContext* context,
           : 1;
 
   if ((*input_h)->dims() != 3) {
-    return errors::InvalidArgument("RNN input must be a 3-D vector.");
+    return errors::InvalidArgument("RNN input_h must be a 3-D vector.");
   }
   model_shapes->num_layers = (*input_h)->dim_size(0) / model_shapes->dir_count;
   model_shapes->num_units = (*input_h)->dim_size(2);

From 1d0f6b2edbf6aace7efdca7842a4c5f6e18f6f76 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 17:41:58 -0700
Subject: [PATCH 0588/1691] [TF:XLA] Speed up HLO CSE.

Use a hash set to find equivalent instructions. This avoids worst-case n^2
instruction comparisons. Instead of checking all users of operand(0) for equivalent instructions, do a lookup in a hash set.

PiperOrigin-RevId: 196056689
---
 tensorflow/compiler/xla/service/hlo_cse.cc | 68 +++++++++++++---------
 1 file changed, 39 insertions(+), 29 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_cse.cc b/tensorflow/compiler/xla/service/hlo_cse.cc
index 3b22c93733af29..28f861aecc6a57 100644
--- a/tensorflow/compiler/xla/service/hlo_cse.cc
+++ b/tensorflow/compiler/xla/service/hlo_cse.cc
@@ -32,6 +32,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
 
 namespace xla {
@@ -88,6 +89,20 @@ bool CombineConstants(HloComputation* computation, bool is_layout_sensitive) {
   return changed;
 }
 
+// An instruction is considered to be equivalent to another only if they
+// share the exact same set of operands.
+int64 CseHash(const HloInstruction* instruction) {
+  int64 hash = std::hash()(static_cast(instruction->opcode()));
+  hash = tensorflow::Hash64Combine(
+      hash, instruction->opcode() == HloOpcode::kGetTupleElement
+                ? instruction->tuple_index()
+                : -1);
+  for (auto operand : instruction->operands()) {
+    hash = tensorflow::Hash64Combine(hash, operand->unique_id());
+  }
+  return hash;
+}
+
 }  // namespace
 
 StatusOr HloCSE::Run(HloModule* module) {
@@ -96,6 +111,12 @@ StatusOr HloCSE::Run(HloModule* module) {
       eq_instructions = std::equal_to();
   const std::function
       eq_computations = std::equal_to();
+
+  auto cse_equal = [&](const HloInstruction* lhs, const HloInstruction* rhs) {
+    return lhs->Identical(*rhs, eq_instructions, eq_computations,
+                          is_layout_sensitive_);
+  };
+
   for (auto* computation : module->computations()) {
     if (only_fusion_computations_ && !computation->IsFusionComputation()) {
       continue;
@@ -103,13 +124,17 @@ StatusOr HloCSE::Run(HloModule* module) {
 
     changed |= CombineConstants(computation, is_layout_sensitive_);
 
-    std::list post_order =
-        computation->MakeInstructionPostOrder();
-    std::set removed_instructions;
-    for (auto instruction : post_order) {
-      // If the instruction has already been removed by CSE skip over it.
-      if (removed_instructions.count(instruction) > 0 ||
-          instruction->operand_count() == 0) {
+    // HLO instructions are grouped into equivalency classes by using the
+    // cse_equal predicate defined above. This set holds a representative
+    // instruction for each class.
+    tensorflow::gtl::FlatSet
+        representatives(/*N=*/1024, &CseHash, cse_equal);
+
+    for (auto instruction : computation->MakeInstructionPostOrder()) {
+      // If the instruction has zero operands (constants, parameters, etc.) skip
+      // over it.
+      if (instruction->operand_count() == 0) {
         continue;
       }
 
@@ -118,31 +143,16 @@ StatusOr HloCSE::Run(HloModule* module) {
         continue;
       }
 
-      // An instruction is considered to be equivalent to another only if they
-      // share the exact same set of operands. So to find equivalent
-      // instructions, we just search among instructions which share operand(0)
-      // of this instruction.
-      const HloInstruction* operand = instruction->operand(0);
-
-      tensorflow::gtl::InlinedVector
-          equivalent_instructions;
-      for (HloInstruction* user : operand->users()) {
-        if (user != instruction && !user->HasSideEffect() &&
-            user->Identical(*instruction, eq_instructions, eq_computations,
-                            is_layout_sensitive_)) {
-          equivalent_instructions.push_back(user);
-        }
-      }
-
-      // Replace all equivalent instructions with this instruction.
-      for (HloInstruction* equivalent_instruction : equivalent_instructions) {
+      auto it = representatives.find(instruction);
+      if (it != representatives.end()) {
+        HloInstruction* equivalent_instruction = *it;
         TF_RETURN_IF_ERROR(
-            equivalent_instruction->ReplaceAllUsesWith(instruction));
-        TF_RETURN_IF_ERROR(
-            computation->RemoveInstruction(equivalent_instruction));
-        removed_instructions.insert(equivalent_instruction);
+            instruction->ReplaceAllUsesWith(equivalent_instruction));
+        TF_RETURN_IF_ERROR(computation->RemoveInstruction(instruction));
         changed = true;
+        continue;
       }
+      representatives.insert(instruction);
     }
   }
   return changed;

From 1bb72f944663a4bcad19f4241bf76f0c70fda356 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 18:14:41 -0700
Subject: [PATCH 0589/1691] Increase size of test
 tensorflow/contrib/distributions:mvn_tril_test to medium to avoid flaky
 timeouts

PiperOrigin-RevId: 196059863
---
 tensorflow/contrib/distributions/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index c7a24f20981569..fa7f603fe8e3b5 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -337,7 +337,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "mvn_tril_test",
-    size = "small",
+    size = "medium",
     srcs = ["python/kernel_tests/mvn_tril_test.py"],
     additional_deps = [
         ":distributions_py",

From 901035bbe15d8a20cf619a2dca6c46fa4f6e8a76 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 18:35:50 -0700
Subject: [PATCH 0590/1691] Increase shard count for
 //third_party/tensorflow/contrib/learn:kmeans_test to avoid flaky timeouts

PiperOrigin-RevId: 196061508
---
 tensorflow/contrib/learn/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index 4a360711f83435..3a2655204e82d3 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -434,6 +434,7 @@ py_test(
     name = "kmeans_test",
     size = "medium",
     srcs = ["python/learn/estimators/kmeans_test.py"],
+    shard_count = 4,
     srcs_version = "PY2AND3",
     tags = [
         "noasan",  # b/73741358

From 2e7329d75b1c8da9e12000cb15972f123438623c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 18:45:13 -0700
Subject: [PATCH 0591/1691] Implement sin operator

PiperOrigin-RevId: 196062186
---
 tensorflow/contrib/lite/builtin_ops.h         |  1 +
 tensorflow/contrib/lite/kernels/BUILD         | 14 ++++
 .../contrib/lite/kernels/elementwise.cc       | 67 +++++++++++++++++++
 .../contrib/lite/kernels/elementwise_test.cc  | 60 +++++++++++++++++
 tensorflow/contrib/lite/kernels/register.cc   |  2 +
 tensorflow/contrib/lite/model.cc              |  1 +
 tensorflow/contrib/lite/nnapi_delegate.cc     |  1 +
 tensorflow/contrib/lite/schema/schema.fbs     |  1 +
 .../contrib/lite/schema/schema_generated.h    |  9 ++-
 tensorflow/contrib/lite/testing/BUILD         |  1 +
 .../contrib/lite/testing/generate_examples.py | 26 +++++++
 .../testing/generated_examples_zip_test.cc    |  1 +
 .../propagate_fixed_sizes.cc                  |  1 +
 .../contrib/lite/toco/import_tensorflow.cc    | 15 +++++
 tensorflow/contrib/lite/toco/model.h          | 12 ++++
 .../contrib/lite/toco/tflite/operator.cc      |  1 +
 .../contrib/lite/toco/tflite/operator_test.cc |  1 +
 tensorflow/contrib/lite/toco/tooling_util.cc  |  1 +
 18 files changed, 212 insertions(+), 3 deletions(-)
 create mode 100644 tensorflow/contrib/lite/kernels/elementwise.cc
 create mode 100644 tensorflow/contrib/lite/kernels/elementwise_test.cc

diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h
index 6783f18b79de05..1d0ad2d2db383b 100644
--- a/tensorflow/contrib/lite/builtin_ops.h
+++ b/tensorflow/contrib/lite/builtin_ops.h
@@ -91,6 +91,7 @@ typedef enum {
   kTfLiteBuiltinLessEqual = 63,
   kTfLiteBuiltinSelect = 64,
   kTfLiteBuiltinSlice = 65,
+  kTfLiteBuiltinSin = 66,
 } TfLiteBuiltinOperator;
 
 #ifdef __cplusplus
diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD
index 885b580700f699..6e2e790517ba9f 100644
--- a/tensorflow/contrib/lite/kernels/BUILD
+++ b/tensorflow/contrib/lite/kernels/BUILD
@@ -143,6 +143,7 @@ cc_library(
         "depthwise_conv.cc",
         "dequantize.cc",
         "div.cc",
+        "elementwise.cc",
         "embedding_lookup.cc",
         "embedding_lookup_sparse.cc",
         "exp.cc",
@@ -455,6 +456,19 @@ tf_cc_test(
     ],
 )
 
+tf_cc_test(
+    name = "elementwise_test",
+    size = "small",
+    srcs = ["elementwise_test.cc"],
+    tags = ["tflite_not_portable_ios"],
+    deps = [
+        ":builtin_ops",
+        "//tensorflow/contrib/lite:framework",
+        "//tensorflow/contrib/lite/kernels:test_util",
+        "@com_google_googletest//:gtest",
+    ],
+)
+
 tf_cc_test(
     name = "unidirectional_sequence_lstm_test",
     size = "small",
diff --git a/tensorflow/contrib/lite/kernels/elementwise.cc b/tensorflow/contrib/lite/kernels/elementwise.cc
new file mode 100644
index 00000000000000..6588256df714a0
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/elementwise.cc
@@ -0,0 +1,67 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include 
+#include "tensorflow/contrib/lite/context.h"
+#include "tensorflow/contrib/lite/kernels/internal/tensor.h"
+#include "tensorflow/contrib/lite/kernels/kernel_util.h"
+
+namespace tflite {
+namespace ops {
+namespace builtin {
+namespace elementwise {
+
+TfLiteStatus SinPrepare(TfLiteContext* context, TfLiteNode* node) {
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+  TfLiteTensor* input = GetInput(context, node, 0);
+  TfLiteTensor* output = GetOutput(context, node, 0);
+  TF_LITE_ENSURE_EQ(context, input->type, output->type);
+  // Quantized float is not supported yet.
+  TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32);
+  return context->ResizeTensor(context, output,
+                               TfLiteIntArrayCopy(input->dims));
+}
+
+TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) {
+  TfLiteTensor* input = GetInput(context, node, 0);
+  TfLiteTensor* output = GetOutput(context, node, 0);
+  switch (input->type) {
+    case kTfLiteFloat32: {
+      size_t elements = NumElements(input);
+      float* in = GetTensorData(input);
+      float* in_end = in + elements;
+      float* out = output->data.f;
+      for (; in < in_end; in++, out++) *out = std::sin(*in);
+      return kTfLiteOk;
+    }
+    default: {
+      context->ReportError(context, "Only float32 is supported currently");
+      return kTfLiteError;
+    }
+  }
+}
+
+}  // namespace elementwise
+
+TfLiteRegistration* Register_SIN() {
+  static TfLiteRegistration r = {nullptr, nullptr, elementwise::SinPrepare,
+                                 elementwise::SinEval};
+  return &r;
+}
+
+}  // namespace builtin
+}  // namespace ops
+}  // namespace tflite
diff --git a/tensorflow/contrib/lite/kernels/elementwise_test.cc b/tensorflow/contrib/lite/kernels/elementwise_test.cc
new file mode 100644
index 00000000000000..412ffb04b90fbc
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/elementwise_test.cc
@@ -0,0 +1,60 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include 
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/kernels/test_util.h"
+#include "tensorflow/contrib/lite/model.h"
+
+namespace tflite {
+namespace {
+
+using ::testing::ElementsAreArray;
+
+class SinOpModel : public SingleOpModel {
+ public:
+  SinOpModel(std::initializer_list input_shape) {
+    input_ = AddInput(TensorType_FLOAT32);
+    output_ = AddOutput(TensorType_FLOAT32);
+    SetBuiltinOp(BuiltinOperator_SIN, BuiltinOptions_NONE, 0);
+    BuildInterpreter({input_shape});
+  }
+
+  int input() const { return input_; }
+  int output() const { return output_; }
+
+ private:
+  int input_;
+  int output_;
+};
+
+TEST(ElementWise, Sin) {
+  SinOpModel m({1, 1, 4, 1});
+  m.PopulateTensor(m.input(), {0, 3.1415926, -3.1415926, 1});
+  m.Invoke();
+  EXPECT_THAT(m.ExtractVector(m.output()),
+              ElementsAreArray(ArrayFloatNear({0, 0, 0, 0.84147})));
+  EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}
diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc
index 4544f2d2928af3..d7eed96db0193c 100644
--- a/tensorflow/contrib/lite/kernels/register.cc
+++ b/tensorflow/contrib/lite/kernels/register.cc
@@ -88,6 +88,7 @@ TfLiteRegistration* Register_FLOOR();
 TfLiteRegistration* Register_NEG();
 TfLiteRegistration* Register_SELECT();
 TfLiteRegistration* Register_SLICE();
+TfLiteRegistration* Register_SIN();
 
 BuiltinOpResolver::BuiltinOpResolver() {
   AddBuiltin(BuiltinOperator_RELU, Register_RELU());
@@ -157,6 +158,7 @@ BuiltinOpResolver::BuiltinOpResolver() {
   AddBuiltin(BuiltinOperator_NEG, Register_NEG());
   AddBuiltin(BuiltinOperator_SELECT, Register_SELECT());
   AddBuiltin(BuiltinOperator_SLICE, Register_SLICE());
+  AddBuiltin(BuiltinOperator_SIN, Register_SIN());
 
   // TODO(andrewharp, ahentz): Move these somewhere more appropriate so that
   // custom ops aren't always included by default.
diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc
index 8222b99ef4d5f2..1fbf9650044685 100644
--- a/tensorflow/contrib/lite/model.cc
+++ b/tensorflow/contrib/lite/model.cc
@@ -352,6 +352,7 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type,
     case BuiltinOperator_PRELU:
     case BuiltinOperator_FLOOR:
     case BuiltinOperator_NEG:
+    case BuiltinOperator_SIN:
       break;
     case BuiltinOperator_CAST: {
       TfLiteCastParams* params = MallocPOD();
diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc
index 5b59971442cb44..1810dfae32694a 100644
--- a/tensorflow/contrib/lite/nnapi_delegate.cc
+++ b/tensorflow/contrib/lite/nnapi_delegate.cc
@@ -383,6 +383,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       case tflite::BuiltinOperator_NEG:
       case tflite::BuiltinOperator_SELECT:
       case tflite::BuiltinOperator_SLICE:
+      case tflite::BuiltinOperator_SIN:
         FATAL("Op code %d is currently not delegated to NNAPI", builtin);
         nn_op_type = -1;  // set to invalid
         break;
diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs
index 5eeea7a8fcc159..f310a0585fe9ce 100644
--- a/tensorflow/contrib/lite/schema/schema.fbs
+++ b/tensorflow/contrib/lite/schema/schema.fbs
@@ -143,6 +143,7 @@ enum BuiltinOperator : byte {
   LESS_EQUAL = 63,
   SELECT = 64,
   SLICE = 65,
+  SIN = 66,
 }
 
 // Options for the builtin operators.
diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h
index 803c8acafd1687..e31481c18bc922 100755
--- a/tensorflow/contrib/lite/schema/schema_generated.h
+++ b/tensorflow/contrib/lite/schema/schema_generated.h
@@ -300,11 +300,12 @@ enum BuiltinOperator {
   BuiltinOperator_LESS_EQUAL = 63,
   BuiltinOperator_SELECT = 64,
   BuiltinOperator_SLICE = 65,
+  BuiltinOperator_SIN = 66,
   BuiltinOperator_MIN = BuiltinOperator_ADD,
-  BuiltinOperator_MAX = BuiltinOperator_SLICE
+  BuiltinOperator_MAX = BuiltinOperator_SIN
 };
 
-inline BuiltinOperator (&EnumValuesBuiltinOperator())[65] {
+inline BuiltinOperator (&EnumValuesBuiltinOperator())[66] {
   static BuiltinOperator values[] = {
     BuiltinOperator_ADD,
     BuiltinOperator_AVERAGE_POOL_2D,
@@ -370,7 +371,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[65] {
     BuiltinOperator_GREATER_EQUAL,
     BuiltinOperator_LESS_EQUAL,
     BuiltinOperator_SELECT,
-    BuiltinOperator_SLICE
+    BuiltinOperator_SLICE,
+    BuiltinOperator_SIN
   };
   return values;
 }
@@ -443,6 +445,7 @@ inline const char **EnumNamesBuiltinOperator() {
     "LESS_EQUAL",
     "SELECT",
     "SLICE",
+    "SIN",
     nullptr
   };
   return names;
diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD
index ce462e24344f77..34f1f1b6b0bc68 100644
--- a/tensorflow/contrib/lite/testing/BUILD
+++ b/tensorflow/contrib/lite/testing/BUILD
@@ -55,6 +55,7 @@ gen_zipped_test_files(
         "reshape.zip",
         "resize_bilinear.zip",
         "sigmoid.zip",
+        "sin.zip",
         "slice.zip",
         "softmax.zip",
         "space_to_batch_nd.zip",
diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index d2790b62922d57..1090e79287b2dc 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -2241,6 +2241,32 @@ def build_inputs(parameters, sess, inputs, outputs):
   make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
 
 
+def make_sin_tests(zip_path):
+  """Make a set of tests to do sin."""
+
+  test_parameters = [{
+      "input_dtype": [tf.float32],
+      "input_shape": [[1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]],
+  }]
+
+  def build_graph(parameters):
+    """Build the sin op testing graph."""
+    input_value = tf.placeholder(
+        dtype=parameters["input_dtype"],
+        name="input1",
+        shape=parameters["input_shape"])
+    out = tf.sin(input_value)
+    return [input_value], [out]
+
+  def build_inputs(parameters, sess, inputs, outputs):
+    input_value = create_tensor_data(parameters["input_dtype"],
+                                     parameters["input_shape"])
+    return [input_value], sess.run(
+        outputs, feed_dict={inputs[0]: input_value})
+
+  make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
+
+
 def make_where_tests(zip_path):
   """Make a set of tests to do where."""
 
diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
index e582cb31def987..860696ecdccf89 100644
--- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
+++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
@@ -284,6 +284,7 @@ INSTANTIATE_TESTS(relu6)
 INSTANTIATE_TESTS(reshape)
 INSTANTIATE_TESTS(resize_bilinear)
 INSTANTIATE_TESTS(sigmoid)
+INSTANTIATE_TESTS(sin)
 INSTANTIATE_TESTS(slice)
 INSTANTIATE_TESTS(softmax)
 INSTANTIATE_TESTS(space_to_batch_nd)
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
index 52b739c5e27536..9d1d27f3ef01a5 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
@@ -1514,6 +1514,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) {
     case OperatorType::kCast:
     case OperatorType::kFloor:
     case OperatorType::kExp:
+    case OperatorType::kSin:
       ProcessSimpleOperator(model, op, 0);
       break;
     case OperatorType::kGather:
diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc
index 8a183c2968423a..3002857d2f5d48 100644
--- a/tensorflow/contrib/lite/toco/import_tensorflow.cc
+++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc
@@ -1248,6 +1248,19 @@ void ConvertLessEqualOperator(const NodeDef& node,
   model->operators.emplace_back(op);
 }
 
+void ConvertSinOperator(const NodeDef& node,
+                        const TensorFlowImportFlags& tf_import_flags,
+                        Model* model) {
+  CHECK_EQ(node.op(), "Sin");
+  auto* op = new SinOperator;
+  const int num_inputs = GetInputsCount(node, tf_import_flags);
+  for (int i = 0; i < num_inputs; ++i) {
+    op->inputs.push_back(node.input(i));
+  }
+  op->outputs.push_back(node.name());
+  model->operators.emplace_back(op);
+}
+
 void ConvertGreaterOperator(const NodeDef& node,
                             const TensorFlowImportFlags& tf_import_flags,
                             Model* model) {
@@ -2275,6 +2288,8 @@ Status ImportTensorFlowNode(const tensorflow::NodeDef& node,
     ConvertDynamicStitchOperator(node, tf_import_flags, model);
   } else if (node.op() == "RandomUniform") {
     ConvertRandomUniform(node, tf_import_flags, model);
+  } else if (node.op() == "Sin") {
+    ConvertSinOperator(node, tf_import_flags, model);
   } else if (node.op() == "Select") {
     ConvertSelectOperator(node, tf_import_flags, model);
   } else {
diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h
index 47f8db597846bf..aefa9ac5cb32b9 100644
--- a/tensorflow/contrib/lite/toco/model.h
+++ b/tensorflow/contrib/lite/toco/model.h
@@ -78,6 +78,7 @@ enum class OperatorType {
   kFloor,
   kGather,
   kResizeBilinear,
+  kSin,
   kSpaceToBatchND,
   kStack,
   kBatchToSpaceND,
@@ -618,6 +619,17 @@ struct TanhOperator : Operator {
   TanhOperator() : Operator(OperatorType::kTanh) {}
 };
 
+// Element-wise Sin operator:
+//   x -> Sin(x) = sin(x)
+//
+// Inputs:
+//   inputs[0]: required: the input array
+//
+// TensorFlow equivalent: Sin
+struct SinOperator : Operator {
+  SinOperator() : Operator(OperatorType::kSin) {}
+};
+
 // Element-wise addition operator.
 //
 // Inputs:
diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc
index 4257a927b3864e..5a999439c6ee03 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator.cc
+++ b/tensorflow/contrib/lite/toco/tflite/operator.cc
@@ -928,6 +928,7 @@ std::vector> BuildOperatorList() {
       new SimpleOperator("SELECT", OperatorType::kSelect));
   ops.emplace_back(
       new SimpleOperator("SLICE", OperatorType::kSlice));
+  ops.emplace_back(new SimpleOperator("SIN", OperatorType::kSin));
 
   return ops;
 }
diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc
index f99929c33f0575..89da8538e41723 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc
+++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc
@@ -118,6 +118,7 @@ TEST_F(OperatorTest, SimpleOperators) {
   CheckSimpleOperator("NEG", OperatorType::kNeg);
   CheckSimpleOperator("SELECT", OperatorType::kSelect);
   CheckSimpleOperator("SLICE", OperatorType::kSlice);
+  CheckSimpleOperator("SIN", OperatorType::kSin);
 }
 
 TEST_F(OperatorTest, BuiltinAdd) {
diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index 1f56fe5c833add..7a048f5eef6cae 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -337,6 +337,7 @@ const char* OperatorTypeName(OperatorType type) {
     HANDLE_OPERATORTYPENAME_CASE(LogSoftmax)
     HANDLE_OPERATORTYPENAME_CASE(Div)
     HANDLE_OPERATORTYPENAME_CASE(Tanh)
+    HANDLE_OPERATORTYPENAME_CASE(Sin)
     HANDLE_OPERATORTYPENAME_CASE(TensorFlowAll)
     HANDLE_OPERATORTYPENAME_CASE(TensorFlowAssert)
     HANDLE_OPERATORTYPENAME_CASE(ExpandDims)

From f79dbc73c5b2c0debb916280e4436d98890ed03b Mon Sep 17 00:00:00 2001
From: Pavithra Vijay 
Date: Wed, 9 May 2018 18:51:06 -0700
Subject: [PATCH 0592/1691] Partial update of tf.keras to the Keras 2.1.6 API.

Changes included are:
- Update docs on preprocessing image and text.
- Allow shift_range to be 1-D array-like or int in ImageDataGenerator.
- Add a test for image preprocessing function for flow_from_directory.
- Fix for off by one error in TimeSeriesGenerator.
- Correct tokenization with multi-character `split` in text_to_word_sequence.

PiperOrigin-RevId: 196062625
---
 .../keras/_impl/keras/preprocessing/image.py  | 305 +++++++++++++++---
 .../_impl/keras/preprocessing/image_test.py   |  32 +-
 .../_impl/keras/preprocessing/sequence.py     |  15 +-
 .../keras/preprocessing/sequence_test.py      |  67 +++-
 .../keras/_impl/keras/preprocessing/text.py   |  58 ++--
 .../_impl/keras/preprocessing/text_test.py    |  10 +
 6 files changed, 406 insertions(+), 81 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/image.py b/tensorflow/python/keras/_impl/keras/preprocessing/image.py
index 6299445c34b99f..5dfbf0fca5e15c 100644
--- a/tensorflow/python/keras/_impl/keras/preprocessing/image.py
+++ b/tensorflow/python/keras/_impl/keras/preprocessing/image.py
@@ -217,6 +217,16 @@ def random_zoom(x,
 
 @tf_export('keras.preprocessing.image.random_channel_shift')
 def random_channel_shift(x, intensity, channel_axis=0):
+  """Perform a random channel shift.
+
+  Arguments:
+      x: Input tensor. Must be 3D.
+      intensity: Transformation intensity.
+      channel_axis: Index of axis for channels in the input tensor.
+
+  Returns:
+      Numpy image tensor.
+  """
   x = np.rollaxis(x, channel_axis, 0)
   min_x, max_x = np.min(x), np.max(x)
   channel_images = [
@@ -451,54 +461,149 @@ def list_pictures(directory, ext='jpg|jpeg|bmp|png|ppm'):
 
 @tf_export('keras.preprocessing.image.ImageDataGenerator')
 class ImageDataGenerator(object):
-  """Generate minibatches of image data with real-time data augmentation.
+  """Generates batches of tensor image data with real-time data augmentation.
+  The data will be looped over (in batches).
 
   Arguments:
-      featurewise_center: set input mean to 0 over the dataset.
-      samplewise_center: set each sample mean to 0.
-      featurewise_std_normalization: divide inputs by std of the dataset.
-      samplewise_std_normalization: divide each input by its std.
-      zca_whitening: apply ZCA whitening.
+      featurewise_center: boolean, set input mean to 0 over the dataset,
+          feature-wise.
+      samplewise_center: boolean, set each sample mean to 0.
+      featurewise_std_normalization: boolean, divide inputs by std
+          of the dataset, feature-wise.
+      samplewise_std_normalization: boolean, divide each input by its std.
       zca_epsilon: epsilon for ZCA whitening. Default is 1e-6.
-      rotation_range: degrees (0 to 180).
-      width_shift_range: fraction of total width, if < 1, or pixels if >= 1.
-      height_shift_range: fraction of total height, if < 1, or pixels if >= 1.
-      brightness_range: the range of brightness to apply
-      shear_range: shear intensity (shear angle in degrees).
-      zoom_range: amount of zoom. if scalar z, zoom will be randomly picked
-          in the range [1-z, 1+z]. A sequence of two can be passed instead
-          to select this range.
-      channel_shift_range: shift range for each channel.
-      fill_mode: points outside the boundaries are filled according to the
-          given mode ('constant', 'nearest', 'reflect' or 'wrap'). Default
-          is 'nearest'.
-          Points outside the boundaries of the input are filled according to the
-            given mode:
+      zca_whitening: boolean, apply ZCA whitening.
+      rotation_range: int, degree range for random rotations.
+      width_shift_range: float, 1-D array-like or int
+          float: fraction of total width, if < 1, or pixels if >= 1.
+          1-D array-like: random elements from the array.
+          int: integer number of pixels from interval
+              `(-width_shift_range, +width_shift_range)`
+          With `width_shift_range=2` possible values are integers [-1, 0, +1],
+          same as with `width_shift_range=[-1, 0, +1]`,
+          while with `width_shift_range=1.0` possible values are floats in
+          the interval [-1.0, +1.0).
+      shear_range: float, shear Intensity
+          (Shear angle in counter-clockwise direction in degrees)
+      zoom_range: float or [lower, upper], Range for random zoom.
+          If a float, `[lower, upper] = [1-zoom_range, 1+zoom_range]`.
+      channel_shift_range: float, range for random channel shifts.
+      fill_mode: One of {"constant", "nearest", "reflect" or "wrap"}.
+          Default is 'nearest'. Points outside the boundaries of the input
+          are filled according to the given mode:
               'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k)
               'nearest':  aaaaaaaa|abcd|dddddddd
               'reflect':  abcddcba|abcd|dcbaabcd
               'wrap':  abcdabcd|abcd|abcdabcd
-      cval: value used for points outside the boundaries when fill_mode is
-          'constant'. Default is 0.
-      horizontal_flip: whether to randomly flip images horizontally.
-      vertical_flip: whether to randomly flip images vertically.
-      rescale: rescaling factor. If None or 0, no rescaling is applied,
-          otherwise we multiply the data by the value provided. This is
-          applied after the `preprocessing_function` (if any provided)
-          but before any other transformation.
+      cval: float or int, value used for points outside the boundaries
+          when `fill_mode = "constant"`.
+      horizontal_flip: boolean, randomly flip inputs horizontally.
+      vertical_flip: boolean, randomly flip inputs vertically.
+      rescale: rescaling factor. Defaults to None. If None or 0, no rescaling
+          is applied, otherwise we multiply the data by the value provided
+          (before applying any other transformation).
       preprocessing_function: function that will be implied on each input.
-          The function will run before any other modification on it.
+          The function will run after the image is resized and augmented.
           The function should take one argument:
           one image (Numpy tensor with rank 3),
           and should output a Numpy tensor with the same shape.
-      data_format: 'channels_first' or 'channels_last'. In 'channels_first'
-        mode, the channels dimension
-          (the depth) is at index 1, in 'channels_last' mode it is at index 3.
+      data_format: One of {"channels_first", "channels_last"}.
+          "channels_last" mode means that the images should have shape
+              `(samples, height, width, channels)`,
+          "channels_first" mode means that the images should have shape
+              `(samples, channels, height, width)`.
           It defaults to the `image_data_format` value found in your
-          Keras config file at `~/.keras/keras.json`.
+              Keras config file at `~/.keras/keras.json`.
           If you never set it, then it will be "channels_last".
-      validation_split: fraction of images reserved for validation (strictly
-        between 0 and 1).
+      validation_split: float, fraction of images reserved for validation
+          (strictly between 0 and 1).
+
+  Examples:
+      Example of using `.flow(x, y)`:
+      ```python
+      (x_train, y_train), (x_test, y_test) = cifar10.load_data()
+      y_train = np_utils.to_categorical(y_train, num_classes)
+      y_test = np_utils.to_categorical(y_test, num_classes)
+      datagen = ImageDataGenerator(
+          featurewise_center=True,
+          featurewise_std_normalization=True,
+          rotation_range=20,
+          width_shift_range=0.2,
+          height_shift_range=0.2,
+          horizontal_flip=True)
+      # compute quantities required for featurewise normalization
+      # (std, mean, and principal components if ZCA whitening is applied)
+      datagen.fit(x_train)
+      # fits the model on batches with real-time data augmentation:
+      model.fit_generator(datagen.flow(x_train, y_train, batch_size=32),
+                          steps_per_epoch=len(x_train) / 32, epochs=epochs)
+      # here's a more "manual" example
+      for e in range(epochs):
+          print('Epoch', e)
+          batches = 0
+          for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):
+              model.fit(x_batch, y_batch)
+              batches += 1
+              if batches >= len(x_train) / 32:
+                  # we need to break the loop by hand because
+                  # the generator loops indefinitely
+                  break
+      ```
+      Example of using `.flow_from_directory(directory)`:
+      ```python
+      train_datagen = ImageDataGenerator(
+          rescale=1./255,
+          shear_range=0.2,
+          zoom_range=0.2,
+          horizontal_flip=True)
+      test_datagen = ImageDataGenerator(rescale=1./255)
+      train_generator = train_datagen.flow_from_directory(
+          'data/train',
+          target_size=(150, 150),
+          batch_size=32,
+          class_mode='binary')
+      validation_generator = test_datagen.flow_from_directory(
+          'data/validation',
+          target_size=(150, 150),
+          batch_size=32,
+          class_mode='binary')
+      model.fit_generator(
+          train_generator,
+          steps_per_epoch=2000,
+          epochs=50,
+          validation_data=validation_generator,
+          validation_steps=800)
+      ```
+      Example of transforming images and masks together.
+      ```python
+      # we create two instances with the same arguments
+      data_gen_args = dict(featurewise_center=True,
+                           featurewise_std_normalization=True,
+                           rotation_range=90.,
+                           width_shift_range=0.1,
+                           height_shift_range=0.1,
+                           zoom_range=0.2)
+      image_datagen = ImageDataGenerator(**data_gen_args)
+      mask_datagen = ImageDataGenerator(**data_gen_args)
+      # Provide the same seed and keyword arguments to the fit and flow methods
+      seed = 1
+      image_datagen.fit(images, augment=True, seed=seed)
+      mask_datagen.fit(masks, augment=True, seed=seed)
+      image_generator = image_datagen.flow_from_directory(
+          'data/images',
+          class_mode=None,
+          seed=seed)
+      mask_generator = mask_datagen.flow_from_directory(
+          'data/masks',
+          class_mode=None,
+          seed=seed)
+      # combine generators into one which yields image and masks
+      train_generator = zip(image_generator, mask_generator)
+      model.fit_generator(
+          train_generator,
+          steps_per_epoch=2000,
+          epochs=50)
+      ```
   """
 
   def __init__(self,
@@ -613,6 +718,31 @@ def flow(self,
            save_prefix='',
            save_format='png',
            subset=None):
+    """Generates batches of augmented/normalized data with given numpy arrays.
+
+    Arguments:
+        x: data. Should have rank 4.
+            In case of grayscale data, the channels axis should have value 1
+            and in case of RGB data, it should have value 3.
+        y: labels.
+        batch_size: int (default: 32).
+        shuffle: boolean (default: True).
+        seed: int (default: None).
+        save_to_dir: None or str (default: None).
+            This allows you to optionally specify a directory
+            to which to save the augmented pictures being generated
+            (useful for visualizing what you are doing).
+        save_prefix: str (default: `''`). Prefix to use for filenames of
+            saved pictures (only relevant if `save_to_dir` is set).
+        save_format: one of "png", "jpeg". Default: "png".
+            (only relevant if `save_to_dir` is set)
+        subset: Subset of data (`"training"` or `"validation"`) if
+            `validation_split` is set in `ImageDataGenerator`.
+
+    Returns:
+        An Iterator yielding tuples of `(x, y)` where `x` is a numpy array of
+          image data and `y` is a numpy array of corresponding labels.
+    """
     return NumpyArrayIterator(
         x,
         y,
@@ -641,6 +771,65 @@ def flow_from_directory(self,
                           follow_links=False,
                           subset=None,
                           interpolation='nearest'):
+    """Generates batches of augmented/normalized data given directory path.
+
+    Arguments:
+        directory: path to the target directory. It should contain one
+            subdirectory per class. Any PNG, JPG, BMP, PPM or TIF images
+            inside each of the subdirectories directory tree will be included
+            in the generator. See [this script]
+            (https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)
+            for more details.
+        target_size: tuple of integers `(height, width)`, default: `(256,
+            256)`. The dimensions to which all images found will be resized.
+        color_mode: one of "grayscale", "rbg". Default: "rgb". Whether the
+            images will be converted to have 1 or 3 color channels.
+        classes: optional list of class subdirectories (e.g. `['dogs',
+            'cats']`). Default: None. If not provided, the list of classes
+            will be automatically inferred from the subdirectory
+            names/structure under `directory`, where each subdirectory will be
+            treated as a different class (and the order of the classes, which
+            will map to the label indices, will be alphanumeric). The
+            dictionary containing the mapping from class names to class
+            indices can be obtained via the attribute `class_indices`.
+        class_mode: one of "categorical", "binary", "sparse", "input" or
+            None. Default: "categorical". Determines the type of label arrays
+            that are returned: "categorical" will be 2D one-hot encoded
+            labels, "binary" will be 1D binary labels, "sparse" will be 1D
+            integer labels, "input" will be images identical to input images
+            (mainly used to work with autoencoders). If None, no labels are
+            returned (the generator will only yield batches of image data,
+            which is useful to use `model.predict_generator()`,
+            `model.evaluate_generator()`, etc.). Please note that in case of
+            class_mode None, the data still needs to reside in a subdirectory
+            of `directory` for it to work correctly.
+        batch_size: size of the batches of data (default: 32).
+        shuffle: whether to shuffle the data (default: True)
+        seed: optional random seed for shuffling and transformations.
+        save_to_dir: None or str (default: None). This allows you to
+            optionally specify a directory to which to save the augmented
+            pictures being generated (useful for visualizing what you are doing)
+        save_prefix: str. Prefix to use for filenames of saved pictures
+            (only relevant if `save_to_dir` is set).
+        save_format: one of "png", "jpeg" (only relevant if `save_to_dir` is
+            set). Default: "png".
+        follow_links: whether to follow symlinks inside class subdirectories
+            (default: False).
+        subset: Subset of data (`"training"` or `"validation"`) if
+          ` validation_split` is set in `ImageDataGenerator`.
+        interpolation: Interpolation method used to resample the image if
+            the target size is different from that of the loaded image.
+            Supported methods are `"nearest"`, `"bilinear"`, and `"bicubic"`.
+            If PIL version 1.1.3 or newer is installed, `"lanczos"` is also
+            supported. If PIL version 3.4.0 or newer is installed, `"box"` and
+            `"hamming"` are also supported. By default, `"nearest"` is used.
+
+    Returns:
+        A DirectoryIterator yielding tuples of `(x, y)` where `x` is a
+        numpy array containing a batch of images with shape
+        `(batch_size, *target_size, channels)` and `y` is a numpy
+        array of corresponding labels.
+    """
     return DirectoryIterator(
         directory,
         self,
@@ -669,7 +858,7 @@ def standardize(self, x):
         The inputs, normalized.
     """
     if self.preprocessing_function:
-      x = self.image_data_generator.preprocessing_function(x)
+      x = self.preprocessing_function(x)
     if self.rescale:
       x *= self.rescale
     if self.samplewise_center:
@@ -737,15 +926,24 @@ def random_transform(self, x, seed=None):
       theta = 0
 
     if self.height_shift_range:
-      tx = np.random.uniform(-self.height_shift_range, self.height_shift_range)
-      if self.height_shift_range < 1:
+      try:  # 1-D array-like or int
+        tx = np.random.choice(self.height_shift_range)
+        tx *= np.random.choice([-1, 1])
+      except ValueError:  # floating point
+        tx = np.random.uniform(-self.height_shift_range,
+                               self.height_shift_range)
+      if np.max(self.height_shift_range) < 1:
         tx *= x.shape[img_row_axis]
     else:
       tx = 0
 
     if self.width_shift_range:
-      ty = np.random.uniform(-self.width_shift_range, self.width_shift_range)
-      if self.width_shift_range < 1:
+      try:  # 1-D array-like or int
+        ty = np.random.choice(self.width_shift_range)
+        ty *= np.random.choice([-1, 1])
+      except ValueError:  # floating point
+        ty = np.random.uniform(-self.width_shift_range, self.width_shift_range)
+      if np.max(self.width_shift_range) < 1:
         ty *= x.shape[img_col_axis]
     else:
       ty = 0
@@ -809,24 +1007,25 @@ def random_transform(self, x, seed=None):
     return x
 
   def fit(self, x, augment=False, rounds=1, seed=None):
-    """Fits internal statistics to some sample data.
+    """Computes the internal data statistics based on an array of sample data.
 
-    Required for featurewise_center, featurewise_std_normalization
-    and zca_whitening.
+    These are statistics related to the data-dependent transformations.
+    Only required if featurewise_center or featurewise_std_normalization or
+    zca_whitening.
 
     Arguments:
-        x: Numpy array, the data to fit on. Should have rank 4.
-            In case of grayscale data,
-            the channels axis should have value 1, and in case
-            of RGB data, it should have value 3.
-        augment: Whether to fit on randomly augmented samples
-        rounds: If `augment`,
-            how many augmentation passes to do over the data
-        seed: random seed.
+        x: sample data. Should have rank 4.
+            In case of grayscale data, the channels axis should have value 1
+            and in case of RGB data, it should have value 3.
+        augment: Boolean (default: False). Whether to fit on randomly
+            augmented samples.
+        rounds: int (default: 1). If augment, how many augmentation passes
+            over the data to use.
+        seed: int (default: None). Random seed.
 
     Raises:
-        ValueError: in case of invalid input `x`.
-        ImportError: if Scipy is not available.
+        ValueError: If input rank is not 4.
+        ImportError: If scipy is not imported.
     """
     x = np.asarray(x, dtype=K.floatx())
     if x.ndim != 4:
diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/image_test.py b/tensorflow/python/keras/_impl/keras/preprocessing/image_test.py
index 001fee91f9ed60..d2e8ac10ae5399 100644
--- a/tensorflow/python/keras/_impl/keras/preprocessing/image_test.py
+++ b/tensorflow/python/keras/_impl/keras/preprocessing/image_test.py
@@ -246,7 +246,37 @@ def test_directory_iterator(self):
     self.assertEqual(len(dir_iterator.class_indices), num_classes)
     self.assertEqual(len(dir_iterator.classes), count)
     self.assertEqual(set(dir_iterator.filenames), set(filenames))
-    _ = dir_iterator.next()
+
+    def preprocessing_function(x):
+      """This will fail if not provided by a Numpy array.
+
+      Note: This is made to enforce backward compatibility.
+
+      Args:
+          x: A numpy array.
+
+      Returns:
+          An array of zeros with the same shape as the given array.
+      """
+      self.assertEqual(x.shape, (26, 26, 3))
+      self.assertIs(type(x), np.ndarray)
+      return np.zeros_like(x)
+
+    # Test usage as Sequence
+    generator = keras.preprocessing.image.ImageDataGenerator(
+        preprocessing_function=preprocessing_function)
+    dir_seq = generator.flow_from_directory(
+        str(temp_dir),
+        target_size=(26, 26),
+        color_mode='rgb',
+        batch_size=3,
+        class_mode='categorical')
+    self.assertEqual(len(dir_seq), count // 3 + 1)
+    x1, y1 = dir_seq[1]
+    self.assertEqual(x1.shape, (3, 26, 26, 3))
+    self.assertEqual(y1.shape, (3, num_classes))
+    x1, y1 = dir_seq[5]
+    self.assertTrue((x1 == 0).all())
 
   def directory_iterator_with_validation_split_test_helper(
       self, validation_split):
diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py b/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py
index e68c171d9c7e33..49bb0b957a9422 100644
--- a/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py
+++ b/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py
@@ -357,9 +357,15 @@ def __init__(self,
     self.reverse = reverse
     self.batch_size = batch_size
 
+    if self.start_index > self.end_index:
+      raise ValueError('`start_index+length=%i > end_index=%i` '
+                       'is disallowed, as no part of the sequence '
+                       'would be left to be used as current step.' %
+                       (self.start_index, self.end_index))
+
   def __len__(self):
     length = int(
-        np.ceil((self.end_index - self.start_index) /
+        np.ceil((self.end_index - self.start_index + 1) /
                 (self.batch_size * self.stride)))
     return length if length >= 0 else 0
 
@@ -373,11 +379,12 @@ def _empty_batch(self, num_rows):
   def __getitem__(self, index):
     if self.shuffle:
       rows = np.random.randint(
-          self.start_index, self.end_index, size=self.batch_size)
+          self.start_index, self.end_index + 1, size=self.batch_size)
     else:
       i = self.start_index + self.batch_size * self.stride * index
-      rows = np.arange(i, min(i + self.batch_size * self.stride,
-                              self.end_index), self.stride)
+      rows = np.arange(
+          i, min(i + self.batch_size * self.stride, self.end_index + 1),
+          self.stride)
 
     samples, targets = self._empty_batch(len(rows))
     for j in range(len(rows)):
diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/sequence_test.py b/tensorflow/python/keras/_impl/keras/preprocessing/sequence_test.py
index b9bfdd00048466..0e7045f517d44e 100644
--- a/tensorflow/python/keras/_impl/keras/preprocessing/sequence_test.py
+++ b/tensorflow/python/keras/_impl/keras/preprocessing/sequence_test.py
@@ -18,6 +18,8 @@
 from __future__ import division
 from __future__ import print_function
 
+from math import ceil
+
 import numpy as np
 
 from tensorflow.python.keras._impl import keras
@@ -146,7 +148,7 @@ def test_TimeseriesGenerator(self):
         start_index=10,
         end_index=30,
         batch_size=2)
-    self.assertEqual(len(data_gen), 5)
+    self.assertEqual(len(data_gen), 6)
     self.assertAllClose(data_gen[0][0],
                         np.array([[[10], [12], [14], [16], [18]],
                                   [[11], [13], [15], [17], [19]]]))
@@ -163,13 +165,74 @@ def test_TimeseriesGenerator(self):
         end_index=30,
         batch_size=2)
 
-    self.assertEqual(len(data_gen), 5)
+    self.assertEqual(len(data_gen), 6)
     self.assertAllClose(data_gen[0][0],
                         np.array(
                             [np.array(data[10:19:2]),
                              np.array(data[11:20:2])]))
     self.assertAllClose(data_gen[0][1], np.array([targets[20], targets[21]]))
 
+    with self.assertRaises(ValueError) as context:
+      keras.preprocessing.sequence.TimeseriesGenerator(data, targets, length=50)
+    error = str(context.exception)
+    self.assertIn('`start_index+length=50 > end_index=49` is disallowed', error)
+
+  def test_TimeSeriesGenerator_doesnt_miss_any_sample(self):
+    x = np.array([[i] for i in range(10)])
+
+    for length in range(3, 10):
+      g = keras.preprocessing.sequence.TimeseriesGenerator(
+          x, x, length=length, batch_size=1)
+      expected = max(0, len(x) - length)
+      actual = len(g)
+      self.assertEqual(expected, actual)
+
+      if actual > 0:
+        # All elements in range(length, 10) should be used as current step
+        expected = np.arange(length, 10).reshape(-1, 1)
+
+        y = np.concatenate([g[ix][1] for ix in range(len(g))], axis=0)
+        self.assertAllClose(y, expected)
+
+    x = np.array([[i] for i in range(23)])
+
+    strides = (1, 1, 5, 7, 3, 5, 3)
+    lengths = (3, 3, 4, 3, 1, 3, 7)
+    batch_sizes = (6, 6, 6, 5, 6, 6, 6)
+    shuffles = (False, True, True, False, False, False, False)
+
+    for stride, length, batch_size, shuffle in zip(strides, lengths,
+                                                   batch_sizes, shuffles):
+      g = keras.preprocessing.sequence.TimeseriesGenerator(
+          x,
+          x,
+          length=length,
+          sampling_rate=1,
+          stride=stride,
+          start_index=0,
+          end_index=None,
+          shuffle=shuffle,
+          reverse=False,
+          batch_size=batch_size)
+      if shuffle:
+        # all batches have the same size when shuffle is True.
+        expected_sequences = ceil(
+            (23 - length) / float(batch_size * stride)) * batch_size
+      else:
+        # last batch will be different if `(samples - length) / stride`
+        # is not a multiple of `batch_size`.
+        expected_sequences = ceil((23 - length) / float(stride))
+
+      expected_batches = ceil(expected_sequences / float(batch_size))
+
+      y = [g[ix][1] for ix in range(len(g))]
+
+      actual_sequences = sum(len(iy) for iy in y)
+      actual_batches = len(y)
+
+      self.assertEqual(expected_sequences, actual_sequences)
+      self.assertEqual(expected_batches, actual_batches)
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/text.py b/tensorflow/python/keras/_impl/keras/preprocessing/text.py
index f652f318f3d6da..f3b57de257a586 100644
--- a/tensorflow/python/keras/_impl/keras/preprocessing/text.py
+++ b/tensorflow/python/keras/_impl/keras/preprocessing/text.py
@@ -42,13 +42,15 @@ def text_to_word_sequence(text,
                           filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',
                           lower=True,
                           split=' '):
-  """Converts a text to a sequence of words (or tokens).
+  r"""Converts a text to a sequence of words (or tokens).
 
   Arguments:
       text: Input text (string).
-      filters: Sequence of characters to filter out.
-      lower: Whether to convert the input to lowercase.
-      split: Sentence split marker (string).
+      filters: list (or concatenation) of characters to filter out, such as
+          punctuation. Default: '!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',
+          includes basic punctuation, tabs, and newlines.
+      lower: boolean, whether to convert the input to lowercase.
+      split: string, separator for word splitting.
 
   Returns:
       A list of words (or tokens).
@@ -56,12 +58,21 @@ def text_to_word_sequence(text,
   if lower:
     text = text.lower()
 
-  if sys.version_info < (3,) and isinstance(text, unicode):
-    translate_map = dict((ord(c), unicode(split)) for c in filters)
+  if sys.version_info < (3,):
+    if isinstance(text, unicode):
+      translate_map = dict((ord(c), unicode(split)) for c in filters)
+      text = text.translate(translate_map)
+    elif len(split) == 1:
+      translate_map = maketrans(filters, split * len(filters))
+      text = text.translate(translate_map)
+    else:
+      for c in filters:
+        text = text.replace(c, split)
   else:
-    translate_map = maketrans(filters, split * len(filters))
+    translate_dict = dict((c, split) for c in filters)
+    translate_map = maketrans(translate_dict)
+    text = text.translate(translate_map)
 
-  text = text.translate(translate_map)
   seq = text.split(split)
   return [i for i in seq if i]
 
@@ -72,20 +83,23 @@ def one_hot(text,
             filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',
             lower=True,
             split=' '):
-  """One-hot encodes a text into a list of word indexes of size n.
+  r"""One-hot encodes a text into a list of word indexes of size n.
 
   This is a wrapper to the `hashing_trick` function using `hash` as the
   hashing function; unicity of word to index mapping non-guaranteed.
 
   Arguments:
       text: Input text (string).
-      n: Dimension of the hashing space.
-      filters: Sequence of characters to filter out.
-      lower: Whether to convert the input to lowercase.
-      split: Sentence split marker (string).
+      n: int, size of vocabulary.
+      filters: list (or concatenation) of characters to filter out, such as
+          punctuation. Default: '!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',
+          includes basic punctuation, tabs, and newlines.
+      lower: boolean, whether to set the text to lowercase.
+      split: string, separator for word splitting.
 
   Returns:
-      A list of integer word indices (unicity non-guaranteed).
+      List of integers in [1, n].
+      Each integer encodes a word (unicity non-guaranteed).
   """
   return hashing_trick(
       text, n, hash_function=hash, filters=filters, lower=lower, split=split)
@@ -98,19 +112,21 @@ def hashing_trick(text,
                   filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',
                   lower=True,
                   split=' '):
-  """Converts a text to a sequence of indexes in a fixed-size hashing space.
+  r"""Converts a text to a sequence of indexes in a fixed-size hashing space.
 
   Arguments:
       text: Input text (string).
       n: Dimension of the hashing space.
-      hash_function: if `None` uses python `hash` function, can be 'md5' or
+      hash_function: defaults to python `hash` function, can be 'md5' or
           any function that takes in input a string and returns a int.
-          Note that `hash` is not a stable hashing function, so
+          Note that 'hash' is not a stable hashing function, so
           it is not consistent across different runs, while 'md5'
           is a stable hashing function.
-      filters: Sequence of characters to filter out.
-      lower: Whether to convert the input to lowercase.
-      split: Sentence split marker (string).
+      filters: list (or concatenation) of characters to filter out, such as
+          punctuation. Default: '!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',
+          includes basic punctuation, tabs, and newlines.
+      lower: boolean, whether to set the text to lowercase.
+      split: string, separator for word splitting.
 
   Returns:
       A list of integer word indices (unicity non-guaranteed).
@@ -150,7 +166,7 @@ class Tokenizer(object):
           filtered from the texts. The default is all punctuation, plus
           tabs and line breaks, minus the `'` character.
       lower: boolean. Whether to convert the texts to lowercase.
-      split: character or string to use for token splitting.
+      split: string, separator for word splitting.
       char_level: if True, every character will be treated as a token.
       oov_token: if given, it will be added to word_index and used to
           replace out-of-vocabulary words during text_to_sequence calls
diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/text_test.py b/tensorflow/python/keras/_impl/keras/preprocessing/text_test.py
index c6a267e57e4e2d..6cdc0a70cca863 100644
--- a/tensorflow/python/keras/_impl/keras/preprocessing/text_test.py
+++ b/tensorflow/python/keras/_impl/keras/preprocessing/text_test.py
@@ -114,11 +114,21 @@ def test_text_to_word_sequence(self):
     seq = keras.preprocessing.text.text_to_word_sequence(text)
     self.assertEqual(seq, ['hello', 'world'])
 
+  def test_text_to_word_sequence_multichar_split(self):
+    text = 'hello!stop?world!'
+    seq = keras.preprocessing.text.text_to_word_sequence(text, split='stop')
+    self.assertEqual(seq, ['hello', 'world'])
+
   def test_text_to_word_sequence_unicode(self):
     text = u'ali! veli? kırk dokuz elli'
     seq = keras.preprocessing.text.text_to_word_sequence(text)
     self.assertEqual(seq, [u'ali', u'veli', u'kırk', u'dokuz', u'elli'])
 
+  def test_text_to_word_sequence_unicode_multichar_split(self):
+    text = u'ali!stopveli?stopkırkstopdokuzstopelli'
+    seq = keras.preprocessing.text.text_to_word_sequence(text, split='stop')
+    self.assertEqual(seq, [u'ali', u'veli', u'kırk', u'dokuz', u'elli'])
+
   def test_tokenizer_unicode(self):
     texts = [
         u'ali veli kırk dokuz elli', u'ali veli kırk dokuz elli veli kırk dokuz'

From bb8315f0cf066266647c6eacdf575ac8f5e9989e Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Wed, 9 May 2018 19:39:58 -0700
Subject: [PATCH 0593/1691] Don't call into Eigen unless the input and output
 tensors are aligned

We teach TargetMachineFeatures about the alignment required for Eigen GEMM and
Conv and then pipe TargetMachineFeatures through the places that need to decide
whether a dot or a conv needs to be lowered to a call to Eigen.

I also had to fix a minor bug in our LLVM IR implementation for convolution.

PiperOrigin-RevId: 196065557
---
 tensorflow/compiler/xla/service/cpu/BUILD     | 32 +++++++
 .../xla/service/cpu/conv_canonicalization.cc  |  3 +-
 .../xla/service/cpu/conv_canonicalization.h   |  8 ++
 .../service/cpu/conv_canonicalization_test.cc | 13 ++-
 .../compiler/xla/service/cpu/cpu_compiler.cc  | 37 +++++---
 .../compiler/xla/service/cpu/cpu_compiler.h   |  4 +-
 .../cpu/cpu_eigen_tensor_alignment_test.cc    | 94 +++++++++++++++++++
 .../xla/service/cpu/cpu_layout_assignment.cc  |  6 +-
 .../xla/service/cpu/cpu_layout_assignment.h   |  9 +-
 .../service/cpu/cpu_layout_assignment_test.cc | 15 ++-
 .../xla/service/cpu/dot_op_emitter.cc         | 40 ++++++--
 .../compiler/xla/service/cpu/dot_op_emitter.h |  4 +-
 .../xla/service/cpu/ir_emission_utils.cc      | 32 ++++++-
 .../xla/service/cpu/ir_emission_utils.h       |  9 +-
 .../xla/service/cpu/ir_emission_utils_test.cc |  8 +-
 .../compiler/xla/service/cpu/ir_emitter.cc    | 48 +++-------
 .../compiler/xla/service/cpu/ir_emitter.h     |  7 +-
 .../service/cpu/parallel_task_assignment.cc   | 13 ++-
 .../service/cpu/parallel_task_assignment.h    | 13 ++-
 .../cpu/parallel_task_assignment_test.cc      | 30 +++---
 .../xla/service/cpu/simple_orc_jit.cc         | 25 +++--
 .../compiler/xla/service/cpu/simple_orc_jit.h |  6 ++
 .../service/cpu/target_machine_features.cc    | 27 +++++-
 .../xla/service/cpu/target_machine_features.h | 55 ++++++++---
 .../cpu/target_machine_features_fake.h        | 57 +++++++++++
 25 files changed, 476 insertions(+), 119 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/cpu/cpu_eigen_tensor_alignment_test.cc
 create mode 100644 tensorflow/compiler/xla/service/cpu/target_machine_features_fake.h

diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 7e6d58c7fa5cca..790163fca67c42 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -295,6 +295,15 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "target_machine_features_fake",
+    testonly = 1,
+    hdrs = ["target_machine_features_fake.h"],
+    deps = [
+        ":target_machine_features",
+    ],
+)
+
 cc_library(
     name = "ir_function",
     srcs = ["ir_function.cc"],
@@ -336,6 +345,7 @@ cc_library(
     deps = [
         ":cpu_options",
         ":cpu_runtime",
+        ":ir_emission_utils",
         ":target_machine_features",
         ":vector_support_library",
         "//tensorflow/compiler/xla:shape_util",
@@ -660,6 +670,7 @@ cc_library(
     hdrs = ["ir_emission_utils.h"],
     deps = [
         ":cpu_runtime",
+        ":target_machine_features",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:window_util",
         "//tensorflow/compiler/xla/service:hlo",
@@ -672,6 +683,7 @@ tf_cc_test(
     srcs = ["ir_emission_utils_test.cc"],
     deps = [
         ":ir_emission_utils",
+        ":target_machine_features_fake",
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:util",
@@ -690,6 +702,7 @@ cc_library(
     deps = [
         ":dot_op_emitter",
         ":ir_emission_utils",
+        ":target_machine_features",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/service:computation_layout",
         "//tensorflow/compiler/xla/service:layout_assignment",
@@ -703,6 +716,7 @@ tf_cc_test(
     srcs = ["cpu_layout_assignment_test.cc"],
     deps = [
         ":cpu_layout_assignment",
+        ":target_machine_features_fake",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_layout",
         "//tensorflow/compiler/xla:shape_util",
@@ -727,6 +741,7 @@ cc_library(
     deps = [
         ":cpu_runtime",
         ":ir_emission_utils",
+        ":target_machine_features",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
@@ -741,6 +756,7 @@ tf_cc_test(
     srcs = ["conv_canonicalization_test.cc"],
     deps = [
         ":conv_canonicalization",
+        ":target_machine_features_fake",
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:util",
@@ -779,6 +795,7 @@ cc_library(
         ":dot_op_emitter",
         ":ir_emission_utils",
         ":shape_partition",
+        ":target_machine_features",
         "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:hlo_cost_analysis",
         "//tensorflow/compiler/xla/service:hlo_pass",
@@ -791,6 +808,7 @@ tf_cc_test(
     deps = [
         ":cpu_executable",
         ":parallel_task_assignment",
+        ":target_machine_features_fake",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_layout",
         "//tensorflow/compiler/xla:shape_util",
@@ -913,3 +931,17 @@ tf_cc_test(
         "//tensorflow/core:test",
     ],
 )
+
+tf_cc_test(
+    name = "cpu_eigen_tensor_alignment_test",
+    size = "small",
+    srcs = ["cpu_eigen_tensor_alignment_test.cc"],
+    deps = [
+        ":dot_op_emitter",
+        ":ir_emission_utils",
+        ":target_machine_features_fake",
+        "//tensorflow/compiler/xla:test",
+        "//tensorflow/compiler/xla/tests:xla_internal_test_main",
+        "//tensorflow/compiler/xla/tools/parser:hlo_parser",
+    ],
+)
diff --git a/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc b/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
index 2136aeb3877685..0985b9297fe487 100644
--- a/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
+++ b/tensorflow/compiler/xla/service/cpu/conv_canonicalization.cc
@@ -33,7 +33,8 @@ StatusOr ConvCanonicalization::Run(HloModule* module) {
   for (HloInstruction* hlo :
        module->entry_computation()->MakeInstructionPostOrder()) {
     if (hlo->opcode() == HloOpcode::kConvolution &&
-        !PotentiallyImplementedAsEigenConvolution(*hlo)) {
+        !PotentiallyImplementedAsEigenConvolution(*hlo,
+                                                  target_machine_features_)) {
       const ConvolutionDimensionNumbers& dnums =
           hlo->convolution_dimension_numbers();
       auto input_batch_dim = dnums.input_batch_dimension();
diff --git a/tensorflow/compiler/xla/service/cpu/conv_canonicalization.h b/tensorflow/compiler/xla/service/cpu/conv_canonicalization.h
index 9b2c3d82eb673c..e6fd1499edd009 100644
--- a/tensorflow/compiler/xla/service/cpu/conv_canonicalization.h
+++ b/tensorflow/compiler/xla/service/cpu/conv_canonicalization.h
@@ -16,6 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CONV_CANONICALIZATION_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CONV_CANONICALIZATION_H_
 
+#include "tensorflow/compiler/xla/service/cpu/target_machine_features.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
@@ -32,12 +33,19 @@ namespace cpu {
 // convolutions can run faster.
 class ConvCanonicalization : public HloPassInterface {
  public:
+  explicit ConvCanonicalization(
+      const TargetMachineFeatures* target_machine_features)
+      : target_machine_features_(*target_machine_features) {}
+
   ~ConvCanonicalization() override {}
   tensorflow::StringPiece name() const override {
     return "convolution-canonicalization";
   }
 
   StatusOr Run(HloModule* module) override;
+
+ private:
+  const TargetMachineFeatures& target_machine_features_;
 };
 
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc b/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc
index 968f53d5c70665..375b017b09263c 100644
--- a/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include 
 
+#include "tensorflow/compiler/xla/service/cpu/target_machine_features_fake.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
@@ -89,7 +90,11 @@ TEST_F(ConvCanonicalizationTest, NonCanonicalToCanonical) {
   HloComputation* entry_computation =
       module->AddEntryComputation(builder.Build());
 
-  ConvCanonicalization conv_canonicalization;
+  cpu::TargetMachineFeaturesWithFakeAlignmentLogic target_machine_features(
+      [](int64 shape_size) {
+        return cpu::TargetMachineFeatures::kEigenExpectedTensorAlignment;
+      });
+  ConvCanonicalization conv_canonicalization(&target_machine_features);
   EXPECT_TRUE(conv_canonicalization.Run(module.get()).ValueOrDie());
 
   const HloInstruction* output_reshape = entry_computation->root_instruction();
@@ -146,7 +151,11 @@ TEST_F(ConvCanonicalizationTest, CanonicalStaysTheSame) {
   auto module = CreateNewModule();
   module->AddEntryComputation(builder.Build());
 
-  ConvCanonicalization conv_canonicalization;
+  cpu::TargetMachineFeaturesWithFakeAlignmentLogic target_machine_features(
+      [](int64 shape_size) {
+        return cpu::TargetMachineFeatures::kEigenExpectedTensorAlignment;
+      });
+  ConvCanonicalization conv_canonicalization(&target_machine_features);
   EXPECT_FALSE(conv_canonicalization.Run(module.get()).ValueOrDie());
 }
 
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 3d2e24ca14eacd..7c89debd6c8d07 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -231,7 +231,10 @@ class CollectProfileCandidates : public DfsHloVisitorWithDefault {
 };
 }  // namespace
 
-Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) {
+Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile,
+                                 llvm::TargetMachine* target_machine) {
+  LLVMTargetMachineFeatures target_machine_features(target_machine);
+
   // Optimization pipeline.
   HloPassPipeline pipeline("CPU");
   pipeline.AddInvariantChecker();
@@ -249,7 +252,7 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) {
   // pass.
   pipeline.AddPass();
   pipeline.AddPass();
-  pipeline.AddPass();
+  pipeline.AddPass(&target_machine_features);
   {
     auto& pass =
         pipeline.AddPass>("simplification");
@@ -279,9 +282,10 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) {
     pass.AddPass();
   }
   pipeline.AddPass(
-      [](const HloInstruction& dot,
-         const TransposeFolding::OperandIndices& candidate_operands) {
-        return PotentiallyImplementedAsEigenDot(dot)
+      [&target_machine_features](
+          const HloInstruction& dot,
+          const TransposeFolding::OperandIndices& candidate_operands) {
+        return PotentiallyImplementedAsEigenDot(dot, target_machine_features)
                    ? candidate_operands
                    : TransposeFolding::OperandIndices{};
       },
@@ -296,7 +300,7 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) {
       ReducePrecisionInsertion::PassTiming::AFTER_FUSION);
 
   pipeline.AddPass(
-      module->device_entry_computation_layout());
+      module->device_entry_computation_layout(), &target_machine_features);
   // The LayoutAssignment pass may leave behind kCopy instructions which are
   // duplicate or NOPs, so remove them with algebraic simplification and CSE.
   pipeline.AddPass>(
@@ -316,8 +320,8 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) {
     // and thread synchronization dependencies which would likely increase
     // binary size (and most AOT applications are single-threaded).
     // TODO(b/29630486) Support multi-threaded AOT.
-    pipeline.AddPass(max_parallelism,
-                                           ShapeSizeBytesFunction());
+    pipeline.AddPass(
+        max_parallelism, ShapeSizeBytesFunction(), &target_machine_features);
   }
   // Copy insertion should be performed immediately before IR emission to avoid
   // inserting unnecessary copies (later pass adds an instruction which
@@ -470,7 +474,13 @@ StatusOr> CpuCompiler::RunHloPasses(
   VLOG(2) << "Before optimization:";
   XLA_VLOG_LINES(2, module->ToString());
 
-  TF_RETURN_IF_ERROR(RunHloPasses(module.get(), /*is_aot_compile=*/false));
+  std::unique_ptr jit_target_machine =
+      SimpleOrcJIT::InferTargetMachineForJIT(
+          CompilerTargetOptions(module->config()),
+          CodeGenOptLevel(module->config()));
+
+  TF_RETURN_IF_ERROR(RunHloPasses(module.get(), /*is_aot_compile=*/false,
+                                  jit_target_machine.get()));
 
   VLOG(2) << "After optimization:";
   XLA_VLOG_LINES(2, module->ToString());
@@ -561,10 +571,11 @@ StatusOr> CpuCompiler::RunBackend(
   // GetEmbeddedComputations guarantees that a called computation occurs
   // before a caller computation.
 
+  LLVMTargetMachineFeatures target_machine_features(jit->target_machine());
   IrEmitter ir_emitter(*module, *assignment, llvm_module.get(),
                        std::move(instruction_to_profile_idx),
                        std::move(computation_to_profile_idx),
-                       jit->target_machine(), jit->external_constant_pool());
+                       &target_machine_features, jit->external_constant_pool());
 
   for (auto embedded_computation :
        entry_computation->MakeEmbeddedComputationsList()) {
@@ -706,7 +717,8 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules,
     VLOG(2) << "Before optimization:";
     XLA_VLOG_LINES(2, module->ToString());
 
-    TF_RETURN_IF_ERROR(RunHloPasses(module, /*is_aot_compile=*/true));
+    TF_RETURN_IF_ERROR(
+        RunHloPasses(module, /*is_aot_compile=*/true, target_machine.get()));
 
     VLOG(2) << "After optimization:";
     XLA_VLOG_LINES(2, module->ToString());
@@ -746,10 +758,11 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules,
           &hlo_profile_index_map, &hlo_profile_printer_data));
     }
 
+    LLVMTargetMachineFeatures target_machine_features(target_machine.get());
     IrEmitter ir_emitter(*module, *assignment, &llvm_module,
                          std::move(instruction_to_profile_idx),
                          std::move(computation_to_profile_idx),
-                         target_machine.get(),
+                         &target_machine_features,
                          /*external_constant_pool=*/nullptr);
     HloComputation* computation = module->entry_computation();
     for (auto embedded_computation :
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h
index 65b05f04fa8d9c..e56f9f01134f84 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h
@@ -18,6 +18,7 @@ limitations under the License.
 
 #include 
 
+#include "llvm/Target/TargetMachine.h"
 #include "tensorflow/compiler/xla/service/executable.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/llvm_compiler.h"
@@ -148,7 +149,8 @@ class CpuCompiler : public LLVMCompiler {
 
   // Runs the HLO passes which are necessary for both optimizations and
   // correctness.
-  Status RunHloPasses(HloModule* module, bool is_aot_compile);
+  Status RunHloPasses(HloModule* module, bool is_aot_compile,
+                      llvm::TargetMachine* target_machine);
 
   TF_DISALLOW_COPY_AND_ASSIGN(CpuCompiler);
 };
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_eigen_tensor_alignment_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_eigen_tensor_alignment_test.cc
new file mode 100644
index 00000000000000..d12fa6bb9ad205
--- /dev/null
+++ b/tensorflow/compiler/xla/service/cpu/cpu_eigen_tensor_alignment_test.cc
@@ -0,0 +1,94 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/cpu/dot_op_emitter.h"
+#include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
+#include "tensorflow/compiler/xla/service/cpu/target_machine_features_fake.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h"
+
+namespace xla {
+namespace cpu {
+namespace {
+
+// Test that we don't call into Eigen with tensors too small to be aligned
+// reliably.
+
+class CpuEigenTensorAlignmentTest : public ::testing::Test {};
+
+TEST_F(CpuEigenTensorAlignmentTest, EigenDotAlignment) {
+  string hlo_string = R"(
+HloModule DotOperation
+
+ENTRY DotOperation {
+  arg0 = f32[5,256] parameter(0)
+  arg1 = f32[256,1024] parameter(1)
+  ROOT dot = f32[5,1024] dot(arg0, arg1), lhs_contracting_dims={1}, rhs_contracting_dims={0}
+}
+)";
+
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module,
+                          tools::Parse(hlo_string));
+
+  HloInstruction* dot = module->entry_computation()->root_instruction();
+
+  TargetMachineFeaturesWithFakeAlignmentLogic target_machine_with_no_alignment(
+      [](int64 size) { return 1; });
+
+  EXPECT_FALSE(
+      PotentiallyImplementedAsEigenDot(*dot, target_machine_with_no_alignment));
+
+  TargetMachineFeaturesWithFakeAlignmentLogic
+      target_machine_with_full_alignment([](int64 size) {
+        return TargetMachineFeatures::kEigenExpectedTensorAlignment;
+      });
+
+  EXPECT_TRUE(PotentiallyImplementedAsEigenDot(
+      *dot, target_machine_with_full_alignment));
+}
+
+TEST_F(CpuEigenTensorAlignmentTest, EigenConvAlignment) {
+  string hlo_string = R"(
+HloModule ConvOperation
+
+ENTRY ConvOperation {
+  arg0 = f32[1,2,1] parameter(0)
+  arg1 = f32[1,1,1] parameter(1)
+  ROOT conv = f32[1,2,1] convolution(arg0, arg1), window={size=1}, dim_labels=b0f_0io->b0f
+}
+)";
+
+  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module,
+                          tools::Parse(hlo_string));
+
+  HloInstruction* conv = module->entry_computation()->root_instruction();
+
+  TargetMachineFeaturesWithFakeAlignmentLogic target_machine_with_no_alignment(
+      [](int64 size) { return 1; });
+
+  EXPECT_FALSE(PotentiallyImplementedAsEigenConvolution(
+      *conv, target_machine_with_no_alignment));
+
+  TargetMachineFeaturesWithFakeAlignmentLogic
+      target_machine_with_full_alignment([](int64 size) {
+        return TargetMachineFeatures::kEigenExpectedTensorAlignment;
+      });
+
+  EXPECT_TRUE(PotentiallyImplementedAsEigenConvolution(
+      *conv, target_machine_with_full_alignment));
+}
+}  // namespace
+}  // namespace cpu
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.cc b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.cc
index 6c642080c34e72..85c461e6a894f9 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.cc
@@ -100,7 +100,8 @@ Status CpuLayoutAssignment::AddBackendConstraints(
   const HloComputation* computation = constraints->computation();
   for (auto* instruction : computation->instructions()) {
     if (instruction->opcode() == HloOpcode::kConvolution &&
-        PotentiallyImplementedAsEigenConvolution(*instruction)) {
+        PotentiallyImplementedAsEigenConvolution(*instruction,
+                                                 target_machine_features_)) {
       const HloInstruction* convolution = instruction;
       const HloInstruction* lhs_instruction = convolution->operand(0);
       const HloInstruction* rhs_instruction = convolution->operand(1);
@@ -126,7 +127,8 @@ Status CpuLayoutAssignment::AddBackendConstraints(
       const HloInstruction* op = instruction->operand(*op_idx);
       TF_RETURN_IF_ERROR(constraints->SetOperandLayout(
           ColMajorShape(op->shape()), instruction, *op_idx));
-    } else if (PotentiallyImplementedAsEigenDot(*instruction)) {
+    } else if (PotentiallyImplementedAsEigenDot(*instruction,
+                                                target_machine_features_)) {
       const HloInstruction* dot = instruction;
       // In order to implement `dot` with Eigen dot, the layouts of the lhs,
       // rhs, and output need to be row-major.
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.h b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.h
index 09adb5cb02abba..53536a277cd513 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.h
@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_LAYOUT_ASSIGNMENT_H_
 
 #include "tensorflow/compiler/xla/service/computation_layout.h"
+#include "tensorflow/compiler/xla/service/cpu/target_machine_features.h"
 #include "tensorflow/compiler/xla/service/layout_assignment.h"
 #include "tensorflow/core/lib/core/status.h"
 
@@ -28,12 +29,16 @@ namespace cpu {
 class CpuLayoutAssignment : public LayoutAssignment {
  public:
   explicit CpuLayoutAssignment(
-      const ComputationLayout& entry_computation_layout)
-      : LayoutAssignment(entry_computation_layout) {}
+      const ComputationLayout& entry_computation_layout,
+      const TargetMachineFeatures* target_machine_features)
+      : LayoutAssignment(entry_computation_layout),
+        target_machine_features_(*target_machine_features) {}
   ~CpuLayoutAssignment() override {}
 
  protected:
   Status AddBackendConstraints(LayoutConstraints* constraints) override;
+
+  const TargetMachineFeatures& target_machine_features_;
 };
 
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment_test.cc
index ba4c5a23d3e043..f6c93d36f72d68 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment_test.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/service/algebraic_simplifier.h"
 #include "tensorflow/compiler/xla/service/computation_layout.h"
+#include "tensorflow/compiler/xla/service/cpu/target_machine_features_fake.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_matchers.h"
@@ -49,7 +50,12 @@ class CpuLayoutAssignmentTest : public HloTestBase {
  protected:
   void AssignLayouts(HloModule* module,
                      ComputationLayout* entry_computation_layout) {
-    cpu::CpuLayoutAssignment layout_assignment(*entry_computation_layout);
+    cpu::TargetMachineFeaturesWithFakeAlignmentLogic target_machine_features(
+        [](int64 shape_size) {
+          return cpu::TargetMachineFeatures::kEigenExpectedTensorAlignment;
+        });
+    cpu::CpuLayoutAssignment layout_assignment(*entry_computation_layout,
+                                               &target_machine_features);
     EXPECT_IS_OK(layout_assignment.Run(module).status());
   }
 };
@@ -311,7 +317,12 @@ static StatusOr RunDotOutputFusion(
   result.addend_fusion_param = fusion_instruction->operand(
       fused_add->operand(1 - dot_operand_idx_in_add)->parameter_number());
 
-  cpu::CpuLayoutAssignment layout_assignment(computation_layout);
+  cpu::TargetMachineFeaturesWithFakeAlignmentLogic target_machine_features(
+      [](int64 shape_size) {
+        return cpu::TargetMachineFeatures::kEigenExpectedTensorAlignment;
+      });
+  cpu::CpuLayoutAssignment layout_assignment(computation_layout,
+                                             &target_machine_features);
   TF_ASSIGN_OR_RETURN(result.layout_assignment_changed_something,
                       layout_assignment.Run(module));
 
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
index 8db4a0650d2867..81c0d67cf54ebf 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
@@ -23,6 +23,7 @@ limitations under the License.
 #include "llvm/IR/Module.h"
 #include "llvm/IR/Value.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
+#include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 #include "tensorflow/compiler/xla/service/cpu/target_machine_features.h"
 #include "tensorflow/compiler/xla/service/cpu/vector_support_library.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
@@ -734,7 +735,7 @@ tensorflow::Status DotOpEmitter::Emit() {
 
   CHECK_EQ(addend_array_, nullptr);
 
-  if (PotentiallyImplementedAsEigenDot(dot_)) {
+  if (PotentiallyImplementedAsEigenDot(dot_, target_machine_features_)) {
     return EmitCallToRuntime();
   }
 
@@ -1058,19 +1059,39 @@ static bool IsRank2WithNoPadding(const Shape& shape) {
 
 // In a gemm operation where output = lhs * rhs, check whether the given shapes
 // are valid for the operation.
-static bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape,
-                               const Shape& output_shape) {
+static bool AreValidGemmShapes(
+    const Shape& lhs_shape, const Shape& rhs_shape, const Shape& output_shape,
+    const TargetMachineFeatures& target_machine_features) {
   // The inputs and the output must
   // 1) be matrices with no padding, and
   // 2) have an allowed element type.
   PrimitiveType output_primitive_type = output_shape.element_type();
-  return (output_primitive_type == F64 || output_primitive_type == F32 ||
-          output_primitive_type == F16) &&
-         IsRank2WithNoPadding(lhs_shape) && IsRank2WithNoPadding(rhs_shape) &&
-         IsRank2WithNoPadding(output_shape);
+  if (!(output_primitive_type == F64 || output_primitive_type == F32 ||
+        output_primitive_type == F16)) {
+    return false;
+  }
+
+  if (!(IsRank2WithNoPadding(lhs_shape) && IsRank2WithNoPadding(rhs_shape) &&
+        IsRank2WithNoPadding(output_shape))) {
+    return false;
+  }
+
+  auto is_aligned = [&](const Shape& shape) {
+    return GetMinimumAlignmentForArray(shape, target_machine_features) >=
+           TargetMachineFeatures::kEigenExpectedTensorAlignment;
+  };
+
+  if (!is_aligned(lhs_shape) || !is_aligned(rhs_shape) ||
+      !is_aligned(output_shape)) {
+    return false;
+  }
+
+  return true;
 }
 
-bool PotentiallyImplementedAsEigenDot(const HloInstruction& hlo) {
+bool PotentiallyImplementedAsEigenDot(
+    const HloInstruction& hlo,
+    const TargetMachineFeatures& target_machine_features) {
   // For certain types of Dot, we can call Eigen
   if (hlo.opcode() == HloOpcode::kDot) {
     const Shape& lhs_shape = hlo.operand(0)->shape();
@@ -1087,7 +1108,8 @@ bool PotentiallyImplementedAsEigenDot(const HloInstruction& hlo) {
 
     // If gemm can accept the operand shapes, use it rather than a custom
     // kernel.
-    if (AreValidGemmShapes(lhs_shape, rhs_shape, hlo.shape())) {
+    if (AreValidGemmShapes(lhs_shape, rhs_shape, hlo.shape(),
+                           target_machine_features)) {
       const DotDimensionNumbers& dim_numbers = hlo.dot_dimension_numbers();
       // The size of the reduction dimension should match. The shape inference
       // guarantees this invariant, so the check here is for programming
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
index a20bf2f9db3ad3..e5ede066f211b3 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
@@ -31,7 +31,9 @@ limitations under the License.
 namespace xla {
 namespace cpu {
 
-bool PotentiallyImplementedAsEigenDot(const HloInstruction& hlo);
+bool PotentiallyImplementedAsEigenDot(
+    const HloInstruction& hlo,
+    const TargetMachineFeatures& target_machine_features);
 
 // Returns the index for an operand to `hlo` that should ideally be column
 // major.  Returns nullopt if there is no such operand or if `hlo` is not a dot
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
index f209a69e3cd0f8..b560b7531c0d24 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc
@@ -24,8 +24,25 @@ limitations under the License.
 namespace xla {
 namespace cpu {
 
+int64 GetMinimumAlignmentForArray(
+    const Shape& shape, const TargetMachineFeatures& target_machine_features) {
+  CHECK(ShapeUtil::IsArray(shape));
+  CHECK(!LayoutUtil::HasLayout(shape) || LayoutUtil::IsDense(shape.layout()));
+
+  // We don't require a layout to be set on `shape`.  This only works on CPU
+  // because we don't pad our tensors or otherwise have complicated data tiling
+  // schemes.
+
+  int64 allocation_size_bytes =
+      ShapeUtil::ElementsIn(shape) *
+      ShapeUtil::ByteSizeOfPrimitiveType(shape.element_type());
+  return target_machine_features.minimum_alignment_for_allocation(
+      allocation_size_bytes);
+}
+
 bool PotentiallyImplementedAsEigenConvolution(
-    const HloInstruction& convolution) {
+    const HloInstruction& convolution,
+    const TargetMachineFeatures& target_machine_features) {
   // The following conditions are necessary (but not sufficient) for
   // implementing `convolution` with Eigen convolution:
   // - the input and kernel have a non-zero number of elements.
@@ -35,6 +52,18 @@ bool PotentiallyImplementedAsEigenConvolution(
   // To be sufficient, certain layout constraints need to be satisfied as well.
   const Shape& input_shape = convolution.operand(0)->shape();
   const Shape& kernel_shape = convolution.operand(1)->shape();
+  const Shape& output_shape = convolution.shape();
+
+  auto is_aligned = [&](const Shape& shape) {
+    return GetMinimumAlignmentForArray(shape, target_machine_features) >=
+           TargetMachineFeatures::kEigenExpectedTensorAlignment;
+  };
+
+  if (!is_aligned(input_shape) || !is_aligned(kernel_shape) ||
+      !is_aligned(output_shape)) {
+    return false;
+  }
+
   if (ShapeUtil::HasZeroElements(input_shape) ||
       ShapeUtil::HasZeroElements(kernel_shape)) {
     return false;
@@ -71,7 +100,6 @@ bool PotentiallyImplementedAsEigenConvolution(
     }
   }
 
-  const Shape& output_shape = convolution.shape();
   return dnums.input_batch_dimension() == 0 &&
          dnums.input_feature_dimension() == input_shape.dimensions_size() - 1 &&
          dnums.output_batch_dimension() == 0 &&
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.h b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.h
index 34b2003916933f..68fbc7caaa9bfe 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.h
@@ -17,13 +17,20 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_IR_EMISSION_UTILS_H_
 
 #include "llvm/IR/Value.h"
+#include "tensorflow/compiler/xla/service/cpu/target_machine_features.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 
 namespace xla {
 namespace cpu {
 
 bool PotentiallyImplementedAsEigenConvolution(
-    const HloInstruction& convolution);
+    const HloInstruction& convolution,
+    const TargetMachineFeatures& target_machine_features);
+
+// Computes the minimum alignment guaranteed for a tensor of shape `shape` on
+// the target machine.
+int64 GetMinimumAlignmentForArray(
+    const Shape& shape, const TargetMachineFeatures& target_machine_features);
 
 // Dynamic loop bounds are specified as an array of dimension index
 // [start, limit) pairs of ir values (one for each partitioned outer dimension).
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emission_utils_test.cc b/tensorflow/compiler/xla/service/cpu/ir_emission_utils_test.cc
index 215f48c4cc1a1a..abb2471e6ae6b2 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emission_utils_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emission_utils_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
 
+#include "tensorflow/compiler/xla/service/cpu/target_machine_features_fake.h"
 #include "tensorflow/compiler/xla/test.h"
 #include "tensorflow/compiler/xla/tools/parser/hlo_parser.h"
 
@@ -39,7 +40,12 @@ ENTRY Conv {
   HloComputation* entry_computation = module->entry_computation();
 
   HloInstruction* conv_instr = entry_computation->root_instruction();
-  EXPECT_FALSE(cpu::PotentiallyImplementedAsEigenConvolution(*conv_instr));
+  cpu::TargetMachineFeaturesWithFakeAlignmentLogic target_machine_features(
+      [](int64 shape_size) {
+        return cpu::TargetMachineFeatures::kEigenExpectedTensorAlignment;
+      });
+  EXPECT_FALSE(cpu::PotentiallyImplementedAsEigenConvolution(
+      *conv_instr, target_machine_features));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 55e5aa5063d0ed..44cf9ac11076f3 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -83,7 +83,7 @@ IrEmitter::IrEmitter(
     llvm::Module* llvm_module,
     std::unordered_map instruction_to_profile_idx,
     std::unordered_map computation_to_profile_idx,
-    llvm::TargetMachine* target_machine,
+    const TargetMachineFeatures* target_machine_features,
     ExternalConstantPool* external_constant_pool)
     : assignment_(assignment),
       module_(llvm_module),
@@ -94,7 +94,7 @@ IrEmitter::IrEmitter(
       alias_analysis_(hlo_module, assignment, &llvm_module->getContext()),
       hlo_module_config_(hlo_module.config()),
       is_top_level_computation_(false),
-      target_machine_features_(target_machine),
+      target_machine_features_(*target_machine_features),
       external_constant_pool_(external_constant_pool) {
   ir_builder_.setFastMathFlags(llvm_ir::GetFastMathFlags(
       /*fast_math_enabled=*/hlo_module_config_.debug_options()
@@ -227,32 +227,6 @@ Status IrEmitter::HandleCopy(HloInstruction* copy) {
   }
 }
 
-// Calculate the alignment of a buffer with a particular size.
-int IrEmitter::MinimumAlignmentForBufferSize(int64 buffer_size) {
-  // GLibc returns a pointer with alignment 8 on 32-bit platforms and 16 on
-  // 64-bit platforms.  TCMalloc returns a pointer with alignment 8 for
-  // allocations smaller than kMallocAlignmentThreshold bytes and at least
-  // alignment 16 for allocations greater than or equal to
-  // kMallocAlignmentThreshold bytes.  N.B. We could improve on this lower bound
-  // by explicitly allocating the memory with posix_memalign.  This is
-  // complicated by our desire to allow parameter buffers created by clients to
-  // be consumed directly by the JIT.
-  if (buffer_size == 0) {
-    // No need to align empty buffers.
-    return 1;
-  }
-
-  const int64 kMallocAlignmentThreshold = 512;
-
-  int pointer_size = module_->getDataLayout().getPointerSize();
-  int buffer_alignment = buffer_size >= kMallocAlignmentThreshold
-                             ? 2 * pointer_size
-                             : pointer_size;
-  DCHECK_GT(buffer_alignment, 0);
-
-  return buffer_alignment;
-}
-
 // Calculate the alignment of a buffer allocated for a given primitive type.
 int IrEmitter::MinimumAlignmentForPrimitiveType(PrimitiveType primitive_type) {
   int64 byte_size = ShapeUtil::ByteSizeOfPrimitiveType(primitive_type);
@@ -277,7 +251,7 @@ int IrEmitter::MinimumAlignmentForShape(const Shape& shape) {
   DCHECK_GE(buffer_size, 0);
   DCHECK_LE(buffer_size, SIZE_MAX);
 
-  return MinimumAlignmentForBufferSize(buffer_size);
+  return target_machine_features_.minimum_alignment_for_allocation(buffer_size);
 }
 
 void IrEmitter::AttachAlignmentMetadataForLoad(llvm::LoadInst* load,
@@ -290,7 +264,8 @@ void IrEmitter::AttachAlignmentMetadataForLoad(llvm::LoadInst* load,
 
 void IrEmitter::AttachAlignmentMetadataForLoad(llvm::LoadInst* load,
                                                int64 buffer_size) {
-  int alignment = MinimumAlignmentForBufferSize(buffer_size);
+  int alignment =
+      target_machine_features_.minimum_alignment_for_allocation(buffer_size);
   if (alignment > 1) {
     llvm_ir::SetAlignmentMetadataForLoad(load, alignment);
   }
@@ -861,7 +836,8 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) {
 
   // TODO(tonywy): Add PotentiallyImplementedAsMKLCovolution to support
   // different data layouts.
-  if (PotentiallyImplementedAsEigenConvolution(*convolution)) {
+  if (PotentiallyImplementedAsEigenConvolution(*convolution,
+                                               target_machine_features_)) {
     const Shape& lhs_shape = lhs->shape();
     const Shape& rhs_shape = rhs->shape();
     const Shape& convolution_shape = convolution->shape();
@@ -1027,12 +1003,14 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) {
         // We will accumulate the products into this sum to calculate
         // the output entry at the given index.
         PrimitiveType lhs_element_type = lhs->shape().element_type();
+        llvm::Type* lhs_llvm_type =
+            llvm_ir::PrimitiveTypeToIrType(lhs_element_type, module_);
         llvm::Value* sum_address = llvm_ir::EmitAllocaAtFunctionEntry(
-            llvm_ir::PrimitiveTypeToIrType(lhs_element_type, module_),
-            "convolution_sum_address", &ir_builder_,
+            lhs_llvm_type, "convolution_sum_address", &ir_builder_,
             MinimumAlignmentForPrimitiveType(lhs_element_type));
-        ir_builder_.CreateStore(
-            llvm::ConstantFP::get(ir_builder_.getFloatTy(), 0.0), sum_address);
+        llvm::Value* constant_zero =
+            llvm::Constant::getNullValue(lhs_llvm_type);
+        ir_builder_.CreateStore(constant_zero, sum_address);
 
         llvm_ir::ForLoopNest loops(IrName(convolution, "inner"), &ir_builder_);
         std::vector kernel_spatial(num_spatial_dims);
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
index 5a040760804fa5..f49cfc1dc378bb 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h
@@ -76,7 +76,7 @@ class IrEmitter : public DfsHloVisitorWithDefault {
                 instruction_to_profile_idx,
             std::unordered_map
                 computation_to_profile_idx,
-            llvm::TargetMachine* target_machine,
+            const TargetMachineFeatures* target_machine,
             ExternalConstantPool* external_constant_pool);
   ~IrEmitter() override;
 
@@ -514,9 +514,6 @@ class IrEmitter : public DfsHloVisitorWithDefault {
   // Calculate the alignment of a buffer allocated for a given primitive type.
   int MinimumAlignmentForPrimitiveType(PrimitiveType primitive_type);
 
-  // Calculate the alignment of a buffer with a particular size.
-  int MinimumAlignmentForBufferSize(int64 buffer_size);
-
   // Returns the number of bytes within the shape.
   int64 ByteSizeOf(const Shape& shape) const;
 
@@ -536,7 +533,7 @@ class IrEmitter : public DfsHloVisitorWithDefault {
 
   bool is_top_level_computation_;
 
-  TargetMachineFeatures target_machine_features_;
+  const TargetMachineFeatures& target_machine_features_;
 
   int64 external_global_constant_counter_ = 0;
   ExternalConstantPool* external_constant_pool_;
diff --git a/tensorflow/compiler/xla/service/cpu/parallel_task_assignment.cc b/tensorflow/compiler/xla/service/cpu/parallel_task_assignment.cc
index 47e8405ff2ea2c..63d0f7b95c7e45 100644
--- a/tensorflow/compiler/xla/service/cpu/parallel_task_assignment.cc
+++ b/tensorflow/compiler/xla/service/cpu/parallel_task_assignment.cc
@@ -104,7 +104,9 @@ class DefaultCostModel : public ParallelCostModel {
 
 ParallelTaskAssignment::ParallelTaskAssignment(
     const int64 max_parallelism,
-    const HloCostAnalysis::ShapeSizeFunction& shape_size, HloModule* module) {
+    const HloCostAnalysis::ShapeSizeFunction& shape_size, HloModule* module,
+    const TargetMachineFeatures* target_machine_features)
+    : target_machine_features_(*target_machine_features) {
   VLOG(1) << "ParallelTaskAssignment max_parallelism: " << max_parallelism;
   // Run cost analysis on 'module'.
   auto cost_analysis = MakeUnique(shape_size);
@@ -139,8 +141,10 @@ int64 ParallelTaskAssignment::GetTargetParallelTaskCount(
       opcode == HloOpcode::kFft || opcode == HloOpcode::kInfeed ||
       opcode == HloOpcode::kOutfeed || opcode == HloOpcode::kRng ||
       (opcode == HloOpcode::kConvolution &&
-       PotentiallyImplementedAsEigenConvolution(*instruction)) ||
-      PotentiallyImplementedAsEigenDot(*instruction) ||
+       PotentiallyImplementedAsEigenConvolution(*instruction,
+                                                target_machine_features_)) ||
+      PotentiallyImplementedAsEigenDot(*instruction,
+                                       target_machine_features_) ||
       (opcode == HloOpcode::kFusion &&
        instruction->fusion_kind() != HloInstruction::FusionKind::kLoop) ||
       ShapeUtil::IsTuple(instruction->shape())) {
@@ -231,7 +235,8 @@ bool ParallelTaskAssigner::AssignParallelTasksHelper(
 void ParallelTaskAssigner::ComputeTargetParallelTasks(
     HloModule* module, HloToParallelTasks* hlo_to_parallel_tasks) {
   ParallelTaskAssignment parallel_task_assignment(max_parallelism_,
-                                                  shape_size_function_, module);
+                                                  shape_size_function_, module,
+                                                  &target_machine_features_);
 
   // Compute parallel task counts for all instructions in 'module'.
   for (auto* computation : module->computations()) {
diff --git a/tensorflow/compiler/xla/service/cpu/parallel_task_assignment.h b/tensorflow/compiler/xla/service/cpu/parallel_task_assignment.h
index 7140dabe516cd7..8becc8fa23424d 100644
--- a/tensorflow/compiler/xla/service/cpu/parallel_task_assignment.h
+++ b/tensorflow/compiler/xla/service/cpu/parallel_task_assignment.h
@@ -16,6 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_PARALLEL_TASK_ASSIGNMENT_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_PARALLEL_TASK_ASSIGNMENT_H_
 
+#include "tensorflow/compiler/xla/service/cpu/target_machine_features.h"
 #include "tensorflow/compiler/xla/service/hlo_cost_analysis.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
@@ -39,7 +40,8 @@ class ParallelTaskAssignment {
   // 'module': the containing HloModule.
   ParallelTaskAssignment(const int64 max_parallelism,
                          const HloCostAnalysis::ShapeSizeFunction& shape_size,
-                         HloModule* module);
+                         HloModule* module,
+                         const TargetMachineFeatures* target_machine_features);
   ~ParallelTaskAssignment() {}
 
   // Computes and returns the target parallel task count for 'instruction'.
@@ -47,6 +49,7 @@ class ParallelTaskAssignment {
 
  private:
   std::unique_ptr cost_model_;
+  const TargetMachineFeatures& target_machine_features_;
 };
 
 // ParallelTaskAssigner computes target parallel task counts for all HLOs
@@ -63,8 +66,11 @@ class ParallelTaskAssigner : public HloPassInterface {
   // 'shape_size': shape size function used by HloCostAnalysis during parallel
   //               task assignment.
   ParallelTaskAssigner(const int64 max_parallelism,
-                       const HloCostAnalysis::ShapeSizeFunction& shape_size)
-      : max_parallelism_(max_parallelism), shape_size_function_(shape_size) {}
+                       const HloCostAnalysis::ShapeSizeFunction& shape_size,
+                       const TargetMachineFeatures* target_machine_features)
+      : max_parallelism_(max_parallelism),
+        shape_size_function_(shape_size),
+        target_machine_features_(*target_machine_features) {}
   ~ParallelTaskAssigner() override {}
 
   tensorflow::StringPiece name() const override {
@@ -94,6 +100,7 @@ class ParallelTaskAssigner : public HloPassInterface {
 
   int64 max_parallelism_;
   HloCostAnalysis::ShapeSizeFunction shape_size_function_;
+  const TargetMachineFeatures& target_machine_features_;
 };
 
 }  // namespace cpu
diff --git a/tensorflow/compiler/xla/service/cpu/parallel_task_assignment_test.cc b/tensorflow/compiler/xla/service/cpu/parallel_task_assignment_test.cc
index 13eb75a57213b1..fc2efbaf9a22b0 100644
--- a/tensorflow/compiler/xla/service/cpu/parallel_task_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/parallel_task_assignment_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/cpu/parallel_task_assignment.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_executable.h"
+#include "tensorflow/compiler/xla/service/cpu/target_machine_features_fake.h"
 #include "tensorflow/compiler/xla/test.h"
 #include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
@@ -31,6 +32,19 @@ class ParallelTaskAssignmentTest : public HloVerifiedTestBase {
   // Use any value larger than 2 since we only test whether a module is
   // parallelized or not
   const int max_parallelism_ = 10;
+
+  cpu::TargetMachineFeaturesWithFakeAlignmentLogic target_machine_features_;
+
+  ParallelTaskAssignmentTest()
+      : target_machine_features_([](int64 shape_size) {
+          return cpu::TargetMachineFeatures::kEigenExpectedTensorAlignment;
+        }) {}
+
+  StatusOr RunParallelTaskAssigner(HloModule* module) {
+    return cpu::ParallelTaskAssigner(max_parallelism_, shape_size_func_,
+                                     &target_machine_features_)
+        .Run(module);
+  }
 };
 
 TEST_F(ParallelTaskAssignmentTest, DotOperationNotParallelized) {
@@ -45,9 +59,7 @@ TEST_F(ParallelTaskAssignmentTest, DotOperationNotParallelized) {
   )";
 
   ParseAndVerifyModule(hlo_string);
-  TF_ASSERT_OK_AND_ASSIGN(bool changed, cpu::ParallelTaskAssigner(
-                                            max_parallelism_, shape_size_func_)
-                                            .Run(&module()));
+  TF_ASSERT_OK_AND_ASSIGN(bool changed, RunParallelTaskAssigner(&module()));
   EXPECT_FALSE(changed);
 }
 
@@ -74,9 +86,7 @@ TEST_F(ParallelTaskAssignmentTest,
   )";
 
   ParseAndVerifyModule(hlo_string);
-  TF_ASSERT_OK_AND_ASSIGN(bool changed, cpu::ParallelTaskAssigner(
-                                            max_parallelism_, shape_size_func_)
-                                            .Run(&module()));
+  TF_ASSERT_OK_AND_ASSIGN(bool changed, RunParallelTaskAssigner(&module()));
   EXPECT_FALSE(changed);
 }
 
@@ -92,9 +102,7 @@ TEST_F(ParallelTaskAssignmentTest, RngOperationNotParallelized) {
   )";
 
   ParseAndVerifyModule(hlo_string);
-  TF_ASSERT_OK_AND_ASSIGN(bool changed, cpu::ParallelTaskAssigner(
-                                            max_parallelism_, shape_size_func_)
-                                            .Run(&module()));
+  TF_ASSERT_OK_AND_ASSIGN(bool changed, RunParallelTaskAssigner(&module()));
   EXPECT_FALSE(changed);
 }
 
@@ -108,9 +116,7 @@ TEST_F(ParallelTaskAssignmentTest, InfeedOutfeedOperationNotParallelized) {
   )";
 
   ParseAndVerifyModule(hlo_string);
-  TF_ASSERT_OK_AND_ASSIGN(bool changed, cpu::ParallelTaskAssigner(
-                                            max_parallelism_, shape_size_func_)
-                                            .Run(&module()));
+  TF_ASSERT_OK_AND_ASSIGN(bool changed, RunParallelTaskAssigner(&module()));
   EXPECT_FALSE(changed);
 }
 
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index ff6f0a9d4e443c..62c97e5641da7f 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -73,20 +73,29 @@ llvm::StringRef GetHostCpuName() {
 }
 }  // namespace
 
+/*static*/ std::unique_ptr
+SimpleOrcJIT::InferTargetMachineForJIT(
+    const llvm::TargetOptions& target_options,
+    llvm::CodeGenOpt::Level opt_level) {
+  std::unique_ptr target_machine(
+      llvm::EngineBuilder()
+          .setTargetOptions(target_options)
+          .setOptLevel(opt_level)
+          .selectTarget(
+              /*TargetTriple=*/llvm::Triple(), /*MArch=*/"",
+              /*MCPU=*/GetHostCpuName(),
+              /*MAttrs=*/DetectMachineAttributes()));
+  CHECK(target_machine != nullptr);
+  return target_machine;
+}
+
 SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions& target_options,
                            llvm::CodeGenOpt::Level opt_level,
                            bool optimize_for_size, bool enable_fast_math,
                            bool disable_expensive_passes,
                            LLVMCompiler::ModuleHook pre_optimization_hook,
                            LLVMCompiler::ModuleHook post_optimization_hook)
-    : target_machine_(
-          CHECK_NOTNULL(llvm::EngineBuilder()
-                            .setTargetOptions(target_options)
-                            .setOptLevel(opt_level)
-                            .selectTarget(
-                                /*TargetTriple=*/llvm::Triple(), /*MArch=*/"",
-                                /*MCPU=*/GetHostCpuName(),
-                                /*MAttrs=*/DetectMachineAttributes()))),
+    : target_machine_(InferTargetMachineForJIT(target_options, opt_level)),
       disassembler_(*target_machine_),
       data_layout_(target_machine_->createDataLayout()),
       symbol_resolver_(llvm::orc::createLegacyLookupResolver(
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
index f4260a95bc4555..1851a3ee0bb97b 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
@@ -95,6 +95,12 @@ class SimpleOrcJIT {
     return &external_constant_pool_;
   }
 
+  // Creates an llvm::TargetMachine suitable for JITting code that will run on
+  // the current machine.
+  static std::unique_ptr InferTargetMachineForJIT(
+      const llvm::TargetOptions& target_options,
+      llvm::CodeGenOpt::Level opt_level);
+
  private:
   llvm::JITSymbol ResolveRuntimeSymbol(const std::string& name);
 
diff --git a/tensorflow/compiler/xla/service/cpu/target_machine_features.cc b/tensorflow/compiler/xla/service/cpu/target_machine_features.cc
index eeb049737dddd1..a0cd8ee2d2be10 100644
--- a/tensorflow/compiler/xla/service/cpu/target_machine_features.cc
+++ b/tensorflow/compiler/xla/service/cpu/target_machine_features.cc
@@ -18,7 +18,7 @@ limitations under the License.
 namespace xla {
 namespace cpu {
 
-llvm::TargetTransformInfo* TargetMachineFeatures::GetTargetTransformInfoFor(
+llvm::TargetTransformInfo* LLVMTargetMachineFeatures::GetTargetTransformInfoFor(
     const llvm::Function& function) const {
   auto it = target_transform_info_cache_.find(&function);
   if (it == target_transform_info_cache_.end()) {
@@ -31,5 +31,30 @@ llvm::TargetTransformInfo* TargetMachineFeatures::GetTargetTransformInfoFor(
   return &it->second;
 }
 
+int64 LLVMTargetMachineFeatures::minimum_alignment_for_allocation(
+    int64 size_bytes) const {
+  // GLibc malloc returns a pointer with alignment 8 on 32-bit platforms and 16
+  // on 64-bit platforms.  TCMalloc returns a pointer with alignment 8 for
+  // allocations smaller than kMallocAlignmentThreshold bytes and at least
+  // alignment 16 for allocations greater than or equal to
+  // kMallocAlignmentThreshold bytes.  N.B. We could improve on this lower bound
+  // by explicitly allocating the memory with posix_memalign.  This is
+  // complicated by our desire to allow parameter buffers created by clients to
+  // be consumed directly by the JIT.
+  if (size_bytes == 0) {
+    // No need to align empty buffers.
+    return 1;
+  }
+
+  const int64 kMallocAlignmentThreshold = 512;
+
+  int pointer_size = target_machine_->getPointerSize(0);
+  int buffer_alignment =
+      size_bytes >= kMallocAlignmentThreshold ? 2 * pointer_size : pointer_size;
+  DCHECK_GT(buffer_alignment, 0);
+
+  return buffer_alignment;
+}
+
 }  // namespace cpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/target_machine_features.h b/tensorflow/compiler/xla/service/cpu/target_machine_features.h
index 703942615e552d..8b00ae9e47eeed 100644
--- a/tensorflow/compiler/xla/service/cpu/target_machine_features.h
+++ b/tensorflow/compiler/xla/service/cpu/target_machine_features.h
@@ -24,43 +24,68 @@ limitations under the License.
 namespace xla {
 namespace cpu {
 
-// Wraps an llvm::TargetMachine and parses out some information that feeds into
-// LLVM IR code generation decisions.
+// Abstract interface for classes providing information about the target we're
+// compiling for.
 class TargetMachineFeatures {
  public:
   static constexpr int kX86AvxVectorByteSize = 32;
 
-  TargetMachineFeatures(llvm::TargetMachine* target_machine)
-      : target_machine_(target_machine) {}
+  // Input and output tensor buffers must be aligned to this many bytes if we
+  // want to call an Eigen backed GEMM or Convolution.
+  static constexpr int kEigenExpectedTensorAlignment = 16;
 
   // Return the vectorization factor, which is the number of bytes of data
   // explicitly vectorized routines will try to process at once.
-  int vectorization_factor_in_bytes() const {
-    // Ideally this should be a function of the cache line size (which we can
-    // get from llvm::TargetTransformInfo::getCacheLineSize) of the target
-    // machine.  Guess a value of 128 bytes for now.
-    return 128;
-  }
+  virtual int vectorization_factor_in_bytes() const = 0;
 
   // Return the size of the largest vector size in bytes.  We need to pass in
   // "function" since llvm functions can contain annotations for specializing
   // them to specific micro-architectures (though currently XLA does not use
   // this functionality).
-  int vector_register_byte_size(const llvm::Function& function) const {
-    llvm::TargetTransformInfo* tti = GetTargetTransformInfoFor(function);
-    return tti->getRegisterBitWidth(/*Vector=*/true) / 8;
-  }
+  virtual int vector_register_byte_size(
+      const llvm::Function& function) const = 0;
 
   // Return the number of elements of type `type` that can fit into the largest
   // vector register available.  We need to pass in "function" since llvm
   // functions can contain annotations for specializing them to specific
   // micro-architectures (though currently XLA does not use this functionality).
+  virtual int vector_register_num_elements(const llvm::Function& function,
+                                           PrimitiveType type) const = 0;
+
+  // Returns the minimum alignment for a buffer of size size_bytes.
+  virtual int64 minimum_alignment_for_allocation(int64 size_bytes) const = 0;
+
+  virtual ~TargetMachineFeatures() = default;
+};
+
+// Implements the TargetMachineFeatures interface using an llvm::TargetMachine.
+class LLVMTargetMachineFeatures : public TargetMachineFeatures {
+ public:
+  static constexpr int kX86AvxVectorByteSize = 32;
+
+  LLVMTargetMachineFeatures(llvm::TargetMachine* target_machine)
+      : target_machine_(target_machine) {}
+
+  int vectorization_factor_in_bytes() const override {
+    // Ideally this should be a function of the cache line size (which we can
+    // get from llvm::TargetTransformInfo::getCacheLineSize) of the target
+    // machine.  Guess a value of 128 bytes for now.
+    return 128;
+  }
+
+  int vector_register_byte_size(const llvm::Function& function) const override {
+    llvm::TargetTransformInfo* tti = GetTargetTransformInfoFor(function);
+    return tti->getRegisterBitWidth(/*Vector=*/true) / 8;
+  }
+
   int vector_register_num_elements(const llvm::Function& function,
-                                   PrimitiveType type) const {
+                                   PrimitiveType type) const override {
     return vector_register_byte_size(function) /
            (primitive_util::BitWidth(type) / 8);
   }
 
+  int64 minimum_alignment_for_allocation(int64 size_bytes) const override;
+
  private:
   llvm::TargetTransformInfo* GetTargetTransformInfoFor(
       const llvm::Function& function) const;
diff --git a/tensorflow/compiler/xla/service/cpu/target_machine_features_fake.h b/tensorflow/compiler/xla/service/cpu/target_machine_features_fake.h
new file mode 100644
index 00000000000000..ffc6927cbe1a2b
--- /dev/null
+++ b/tensorflow/compiler/xla/service/cpu/target_machine_features_fake.h
@@ -0,0 +1,57 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_TARGET_MACHINE_FEATURES_FAKE_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_TARGET_MACHINE_FEATURES_FAKE_H_
+
+#include "tensorflow/compiler/xla/service/cpu/target_machine_features.h"
+
+namespace xla {
+namespace cpu {
+// Delegates calls to minimum_alignment_for_allocation to a user provided
+// std::function, crashes on all other methods.
+//
+// Primarily useful for testing.
+class TargetMachineFeaturesWithFakeAlignmentLogic
+    : public TargetMachineFeatures {
+ public:
+  explicit TargetMachineFeaturesWithFakeAlignmentLogic(
+      std::function fake_alignment_logic)
+      : fake_alignment_logic_(std::move(fake_alignment_logic)) {}
+
+  int vectorization_factor_in_bytes() const override {
+    LOG(FATAL) << "Unexpected call to " << __func__;
+  }
+
+  int vector_register_byte_size(const llvm::Function& function) const override {
+    LOG(FATAL) << "Unexpected call to " << __func__;
+  }
+
+  int vector_register_num_elements(const llvm::Function& function,
+                                   PrimitiveType type) const override {
+    LOG(FATAL) << "Unexpected call to " << __func__;
+  }
+
+  int64 minimum_alignment_for_allocation(int64 size_bytes) const override {
+    return fake_alignment_logic_(size_bytes);
+  }
+
+ private:
+  std::function fake_alignment_logic_;
+};
+}  // namespace cpu
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_TARGET_MACHINE_FEATURES_FAKE_H_

From 8c747a1a8f8c78475c5d5d99d95509c836684dcf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Wed, 9 May 2018 20:32:13 -0700
Subject: [PATCH 0594/1691] Increase size of test
 tensorflow/contrib/learn:graph_io_test to medium to avoid flaky timeouts

PiperOrigin-RevId: 196068593
---
 tensorflow/contrib/learn/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index 3a2655204e82d3..0fdbe8f6308e30 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -746,7 +746,7 @@ py_test(
 
 tf_py_test(
     name = "graph_io_test",
-    size = "small",
+    size = "medium",
     srcs = ["python/learn/learn_io/graph_io_test.py"],
     additional_deps = [
         ":learn",

From 11574c3b5aa8dbb9d7dbaf0e1b20ad3ae5a4bb46 Mon Sep 17 00:00:00 2001
From: David Majnemer 
Date: Wed, 9 May 2018 23:21:19 -0700
Subject: [PATCH 0595/1691] [XLA] Add log1p/expm1

A new HLO seems prudent as it allows implementations to use fancy techniques to
compute accurate results for small inputs.

PiperOrigin-RevId: 196078115
---
 tensorflow/compiler/tests/unary_ops_test.py   | 20 +++--
 .../compiler/tf2xla/kernels/unary_ops.cc      |  6 +-
 .../xla/client/computation_builder.cc         | 10 +++
 .../compiler/xla/client/computation_builder.h |  6 ++
 .../xla/client/xla_client/xla_builder.cc      |  8 ++
 .../xla/client/xla_client/xla_builder.h       |  6 ++
 .../compiler/xla/service/dfs_hlo_visitor.h    |  6 ++
 .../xla/service/elemental_ir_emitter.cc       | 81 +++++++++++++++++++
 .../xla/service/elemental_ir_emitter.h        |  6 ++
 .../xla/service/gpu/elemental_ir_emitter.cc   | 10 +++
 .../xla/service/gpu/elemental_ir_emitter.h    |  6 ++
 .../xla/service/hlo_evaluator_typed_visitor.h | 46 +++++++++++
 .../compiler/xla/service/hlo_graph_dumper.cc  |  2 +
 .../compiler/xla/service/hlo_instruction.cc   | 12 +++
 tensorflow/compiler/xla/service/hlo_opcode.h  |  2 +
 .../xla/service/instruction_fusion.cc         |  2 +
 .../compiler/xla/service/shape_inference.cc   |  6 ++
 .../compiler/xla/service/user_computation.cc  |  4 +
 .../compiler/xla/tools/parser/hlo_parser.cc   |  2 +
 tensorflow/compiler/xla/xla_data.proto        |  6 ++
 20 files changed, 235 insertions(+), 12 deletions(-)

diff --git a/tensorflow/compiler/tests/unary_ops_test.py b/tensorflow/compiler/tests/unary_ops_test.py
index ba79f393a8f9b2..57a1d9b9e4de74 100644
--- a/tensorflow/compiler/tests/unary_ops_test.py
+++ b/tensorflow/compiler/tests/unary_ops_test.py
@@ -209,7 +209,9 @@ def testFloatOps(self):
       self._assertOpOutputMatchesExpected(
           math_ops.expm1,
           np.array([[-1, 1]], dtype=dtype),
-          expected=np.array([[-0.63212056, 1.71828183]], dtype=dtype))
+          expected=np.array([[-0.63212056, 1.71828183]], dtype=dtype),
+          rtol=1e-5,
+          atol=1e-6)
 
       self._assertOpOutputMatchesExpected(
           math_ops.floor,
@@ -251,12 +253,12 @@ def testFloatOps(self):
           np.array([[1, 2]], dtype=dtype),
           expected=np.array([[0.540297, -0.41614]], dtype=dtype))
 
-      # TODO(b/34703906): improve log1p implementation and make tolerance
-      # tighter.
       self._assertOpOutputMatchesExpected(
           math_ops.log1p,
           np.array([[1e-14, 1e-15, 0.6]], dtype=dtype),
-          expected=np.log1p(np.array([[1e-14, 1e-15, 0.6]], dtype=dtype)))
+          expected=np.log1p(np.array([[1e-14, 1e-15, 0.6]], dtype=dtype)),
+          rtol=1e-4,
+          atol=1e-6)
 
       self._assertOpOutputMatchesExpected(
           math_ops.rint,
@@ -419,7 +421,9 @@ def testComplexOps(self):
       self._assertOpOutputMatchesExpected(
           math_ops.expm1,
           np.array([[-1 + 2j, 3j, 2 - 3j]], dtype=dtype),
-          expected=np.expm1(np.array([[-1 + 2j, 3j, 2 - 3j]], dtype=dtype)))
+          expected=np.expm1(np.array([[-1 + 2j, 3j, 2 - 3j]], dtype=dtype)),
+          rtol=1e-6,
+          atol=1e-6)
 
       self._assertOpOutputMatchesExpected(
           math_ops.reciprocal,
@@ -441,13 +445,13 @@ def testComplexOps(self):
           np.array([[5j, 3 - 2j]], dtype=dtype),
           expected=np.cos(np.array([[5j, 3 - 2j]], dtype=dtype)))
 
-      # TODO(b/34703906): improve log1p implementation and make tolerance
-      # tighter.
       self._assertOpOutputMatchesExpected(
           math_ops.log1p,
           np.array([[1e-14, 1e-15j, 0.6 - 0.3j]], dtype=dtype),
           expected=np.log1p(
-              np.array([[1e-14, 1e-15j, 0.6 - 0.3j]], dtype=dtype)))
+              np.array([[1e-14, 1e-15j, 0.6 - 0.3j]], dtype=dtype)),
+          rtol=1e-4,
+          atol=1e-6)
 
       val = np.array([1, 2j, 2 - 3j, 4 + 5j], dtype=dtype)
       self._assertOpOutputMatchesExpected(
diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
index a4f50f52ebe8b1..3f6e218bcc5033 100644
--- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
@@ -100,8 +100,7 @@ XLAJIT_MAKE_UNARY(Cosh,
 XLAJIT_MAKE_UNARY(Sin, b->Sin(x));
 XLAJIT_MAKE_UNARY(Exp, b->Exp(x));
 
-// TODO(b/34703906): use a more accurate implementation of expm1.
-XLAJIT_MAKE_UNARY(Expm1, b->Sub(b->Exp(x), XlaHelpers::One(b, input_type(0))));
+XLAJIT_MAKE_UNARY(Expm1, b->Expm1(x));
 
 XLAJIT_MAKE_UNARY(Floor, b->Floor(x));
 XLAJIT_MAKE_UNARY(IsFinite, b->IsFinite(x));
@@ -115,8 +114,7 @@ XLAJIT_MAKE_UNARY(Inv, b->Div(XlaHelpers::One(b, input_type(0)), x));
 XLAJIT_MAKE_UNARY(Reciprocal, b->Div(XlaHelpers::One(b, input_type(0)), x));
 XLAJIT_MAKE_UNARY(Log, b->Log(x));
 
-// TODO(b/34703906): use a more accurate implementation of log1p.
-XLAJIT_MAKE_UNARY(Log1p, b->Log(b->Add(XlaHelpers::One(b, input_type(0)), x)));
+XLAJIT_MAKE_UNARY(Log1p, b->Log1p(x));
 
 XLAJIT_MAKE_UNARY(Invert, b->Not(x));
 XLAJIT_MAKE_UNARY(LogicalNot, b->Not(x));
diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index f9f994482cb9a9..b58279b1637b04 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -895,6 +895,11 @@ ComputationDataHandle ComputationBuilder::Exp(
   return UnaryOp(UNOP_EXP, operand);
 }
 
+ComputationDataHandle ComputationBuilder::Expm1(
+    const ComputationDataHandle& operand) {
+  return UnaryOp(UNOP_EXPM1, operand);
+}
+
 ComputationDataHandle ComputationBuilder::Floor(
     const ComputationDataHandle& operand) {
   return UnaryOp(UNOP_FLOOR, operand);
@@ -915,6 +920,11 @@ ComputationDataHandle ComputationBuilder::Log(
   return UnaryOp(UNOP_LOG, operand);
 }
 
+ComputationDataHandle ComputationBuilder::Log1p(
+    const ComputationDataHandle& operand) {
+  return UnaryOp(UNOP_LOG1P, operand);
+}
+
 ComputationDataHandle ComputationBuilder::Sign(
     const ComputationDataHandle& operand) {
   return UnaryOp(UNOP_SIGN, operand);
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index 176962b6f84333..9ec43720623546 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -584,6 +584,9 @@ class ComputationBuilder {
   // Enqueues an exp instruction onto the computation.
   ComputationDataHandle Exp(const ComputationDataHandle& operand);
 
+  // Enqueues an expm1 instruction onto the computation.
+  ComputationDataHandle Expm1(const ComputationDataHandle& operand);
+
   // Enqueues a floor instruction onto the computation.
   ComputationDataHandle Floor(const ComputationDataHandle& operand);
 
@@ -597,6 +600,9 @@ class ComputationBuilder {
   // Enqueues an log instruction (natural logarithm) onto the computation.
   ComputationDataHandle Log(const ComputationDataHandle& operand);
 
+  // Enqueues an log1p instruction onto the computation.
+  ComputationDataHandle Log1p(const ComputationDataHandle& operand);
+
   // Enqueues a sign instruction onto the computation.
   ComputationDataHandle Sign(const ComputationDataHandle& operand);
 
diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc
index 4c59d621af43be..2c6b6c60bb9fa4 100644
--- a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc
+++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc
@@ -1173,6 +1173,10 @@ XlaOp XlaBuilder::Exp(const XlaOp& operand) {
   return UnaryOp(HloOpcode::kExp, operand);
 }
 
+XlaOp XlaBuilder::Expm1(const XlaOp& operand) {
+  return UnaryOp(HloOpcode::kExpm1, operand);
+}
+
 XlaOp XlaBuilder::Floor(const XlaOp& operand) {
   return UnaryOp(HloOpcode::kFloor, operand);
 }
@@ -1189,6 +1193,10 @@ XlaOp XlaBuilder::Log(const XlaOp& operand) {
   return UnaryOp(HloOpcode::kLog, operand);
 }
 
+XlaOp XlaBuilder::Log1p(const XlaOp& operand) {
+  return UnaryOp(HloOpcode::kLog1p, operand);
+}
+
 XlaOp XlaBuilder::Sign(const XlaOp& operand) {
   return UnaryOp(HloOpcode::kSign, operand);
 }
diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.h b/tensorflow/compiler/xla/client/xla_client/xla_builder.h
index e1920d658bac24..e5807033d31ef3 100644
--- a/tensorflow/compiler/xla/client/xla_client/xla_builder.h
+++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.h
@@ -571,6 +571,9 @@ class XlaBuilder {
   // Enqueues an exp instruction onto the computation.
   XlaOp Exp(const XlaOp& operand);
 
+  // Enqueues an expm1 instruction onto the computation.
+  XlaOp Expm1(const XlaOp& operand);
+
   // Enqueues a floor instruction onto the computation.
   XlaOp Floor(const XlaOp& operand);
 
@@ -584,6 +587,9 @@ class XlaBuilder {
   // Enqueues an log instruction (natural logarithm) onto the computation.
   XlaOp Log(const XlaOp& operand);
 
+  // Enqueues an log1p instruction (log(x+1)) onto the computation.
+  XlaOp Log1p(const XlaOp& operand);
+
   // Enqueues a sign instruction onto the computation.
   XlaOp Sign(const XlaOp& operand);
 
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index 0528b076027603..b9d7ec9c2e17e5 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -138,6 +138,9 @@ class DfsHloVisitorBase {
   virtual Status HandleExp(HloInstructionPtr hlo) {
     return HandleElementwiseUnary(hlo);
   }
+  virtual Status HandleExpm1(HloInstructionPtr hlo) {
+    return HandleElementwiseUnary(hlo);
+  }
   virtual Status HandleFloor(HloInstructionPtr hlo) {
     return HandleElementwiseUnary(hlo);
   }
@@ -150,6 +153,9 @@ class DfsHloVisitorBase {
   virtual Status HandleClz(HloInstructionPtr hlo) {
     return HandleElementwiseUnary(hlo);
   }
+  virtual Status HandleLog1p(HloInstructionPtr hlo) {
+    return HandleElementwiseUnary(hlo);
+  }
   virtual Status HandleCos(HloInstructionPtr hlo) {
     return HandleElementwiseUnary(hlo);
   }
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index ae32d33766093c..f2ad6eaf3ac405 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -418,8 +418,12 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp(
     }
     case HloOpcode::kExp:
       return EmitExp(op->shape().element_type(), operand_value);
+    case HloOpcode::kExpm1:
+      return EmitExpm1(op->shape().element_type(), operand_value);
     case HloOpcode::kLog:
       return EmitLog(op->shape().element_type(), operand_value);
+    case HloOpcode::kLog1p:
+      return EmitLog1p(op->shape().element_type(), operand_value);
     case HloOpcode::kCos:
       return EmitCos(op->shape().element_type(), operand_value);
     case HloOpcode::kSin:
@@ -493,6 +497,22 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp(
       return EmitComposeComplex(
           op, ir_builder_->CreateFMul(one_half, log_sum_sq), angle);
     }
+    case HloOpcode::kLog1p: {
+      // log1p(a+bi) = .5*log((a+1)^2+b^2) + i*atan2(b, a + 1)
+      auto a = EmitExtractReal(operand_value);
+      auto b = EmitExtractImag(operand_value);
+      llvm::Type* llvm_ty = a->getType();
+      auto one = llvm::ConstantFP::get(llvm_ty, 1.0);
+      auto a_plus_one = ir_builder_->CreateFAdd(a, one);
+      auto sum_sq = ir_builder_->CreateFAdd(
+          ir_builder_->CreateFMul(a_plus_one, a_plus_one),
+          ir_builder_->CreateFMul(b, b));
+      TF_ASSIGN_OR_RETURN(auto log_sum_sq, EmitLog(component_type, sum_sq));
+      TF_ASSIGN_OR_RETURN(auto angle, EmitAtan2(component_type, b, a_plus_one));
+      auto one_half = llvm::ConstantFP::get(llvm_ty, 0.5);
+      return EmitComposeComplex(
+          op, ir_builder_->CreateFMul(one_half, log_sum_sq), angle);
+    }
     case HloOpcode::kConvert: {
       PrimitiveType from_type = op->operand(0)->shape().element_type();
       TF_RET_CHECK(primitive_util::IsComplexType(from_type));
@@ -523,6 +543,20 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp(
       return EmitComposeComplex(op, ir_builder_->CreateFMul(exp_a, cos_b),
                                 ir_builder_->CreateFMul(exp_a, sin_b));
     }
+    case HloOpcode::kExpm1: {
+      // e^(a+bi)-1 = (e^a*cos(b)-1)+e^a*sin(b)i
+      TF_ASSIGN_OR_RETURN(
+          auto exp_a, EmitExp(component_type, EmitExtractReal(operand_value)));
+      TF_ASSIGN_OR_RETURN(
+          auto cos_b, EmitCos(component_type, EmitExtractImag(operand_value)));
+      TF_ASSIGN_OR_RETURN(
+          auto sin_b, EmitSin(component_type, EmitExtractImag(operand_value)));
+      auto one = llvm::ConstantFP::get(exp_a->getType(), 1.0);
+      auto real_result =
+          ir_builder_->CreateFSub(ir_builder_->CreateFMul(exp_a, cos_b), one);
+      auto imag_result = ir_builder_->CreateFMul(exp_a, sin_b);
+      return EmitComposeComplex(op, real_result, imag_result);
+    }
     case HloOpcode::kCos: {
       // cos(z) = .5(e^(iz) + e^(-iz))
       // cos(a+bi) = .5(e^(-b+ai) + e^(b-ai))
@@ -975,6 +1009,28 @@ StatusOr ElementalIrEmitter::EmitLog(PrimitiveType prim_type,
                                       {value->getType()}, ir_builder_);
 }
 
+StatusOr ElementalIrEmitter::EmitLog1p(PrimitiveType prim_type,
+                                                     llvm::Value* value) const {
+  auto x = value;
+  auto type = llvm_ir::PrimitiveTypeToIrType(prim_type, module_);
+  auto one = llvm::ConstantFP::get(type, 1.0);
+  auto negative_half = llvm::ConstantFP::get(type, -0.5);
+  // When x is large, the naive evaluation of ln(x + 1) is more
+  // accurate than the Taylor series.
+  TF_ASSIGN_OR_RETURN(auto for_large_x,
+                      EmitLog(prim_type, ir_builder_->CreateFAdd(x, one)));
+  // The Taylor series for ln(x+1) is x - x^2/2 - x^3/3 + ….
+  auto for_small_x = ir_builder_->CreateFMul(
+      ir_builder_->CreateFAdd(ir_builder_->CreateFMul(negative_half, x), one),
+      x);
+  const auto kAntilogarithmIsSmallThreshold = 1e-4;
+  auto abs_x = llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::fabs, {value},
+                                            {type}, ir_builder_);
+  auto x_is_small = ir_builder_->CreateFCmpOLT(
+      abs_x, llvm::ConstantFP::get(type, kAntilogarithmIsSmallThreshold));
+  return ir_builder_->CreateSelect(x_is_small, for_small_x, for_large_x);
+}
+
 StatusOr ElementalIrEmitter::EmitSin(PrimitiveType prim_type,
                                                    llvm::Value* value) const {
   return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::sin, {value},
@@ -993,6 +1049,29 @@ StatusOr ElementalIrEmitter::EmitExp(PrimitiveType prim_type,
                                       {value->getType()}, ir_builder_);
 }
 
+StatusOr ElementalIrEmitter::EmitExpm1(PrimitiveType prim_type,
+                                                     llvm::Value* value) const {
+  auto x = value;
+  auto type = llvm_ir::PrimitiveTypeToIrType(prim_type, module_);
+  auto one = llvm::ConstantFP::get(type, 1.0);
+  auto half = llvm::ConstantFP::get(type, 0.5);
+  // When the exponent is large, the naive evaluation of e^(x) - 1 is more
+  // accurate than the Taylor series.
+  TF_ASSIGN_OR_RETURN(auto exp_x, EmitExp(prim_type, value));
+  auto for_large_x = ir_builder_->CreateFSub(exp_x, one);
+  // The Taylor series for exp(x) is 1 + x + x^2/2 + x^3/6 + ….
+  // We want exp(x)-1 which is x + x^2/2 + x^3/6 + ….
+  auto x_squared = ir_builder_->CreateFAdd(x, x);
+  auto x_squared_over_two = ir_builder_->CreateFMul(x_squared, half);
+  auto for_small_x = ir_builder_->CreateFAdd(x, x_squared_over_two);
+  const auto kExponentIsSmallThreshold = 1e-5;
+  auto abs_x = llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::fabs, {value},
+                                            {type}, ir_builder_);
+  auto x_is_small = ir_builder_->CreateFCmpOLT(
+      abs_x, llvm::ConstantFP::get(type, kExponentIsSmallThreshold));
+  return ir_builder_->CreateSelect(x_is_small, for_small_x, for_large_x);
+}
+
 StatusOr ElementalIrEmitter::EmitPow(PrimitiveType prim_type,
                                                    llvm::Value* lhs,
                                                    llvm::Value* rhs) const {
@@ -1877,10 +1956,12 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator(
     case HloOpcode::kCopy:
     case HloOpcode::kCos:
     case HloOpcode::kExp:
+    case HloOpcode::kExpm1:
     case HloOpcode::kFloor:
     case HloOpcode::kImag:
     case HloOpcode::kIsFinite:
     case HloOpcode::kLog:
+    case HloOpcode::kLog1p:
     case HloOpcode::kNegate:
     case HloOpcode::kNot:
     case HloOpcode::kReal:
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/elemental_ir_emitter.h
index 26dff0d96f1d0f..d199473374ad39 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.h
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.h
@@ -105,6 +105,9 @@ class ElementalIrEmitter {
   virtual StatusOr EmitLog(PrimitiveType prim_type,
                                          llvm::Value* value) const;
 
+  virtual StatusOr EmitLog1p(PrimitiveType prim_type,
+                                           llvm::Value* value) const;
+
   virtual StatusOr EmitSin(PrimitiveType prim_type,
                                          llvm::Value* value) const;
 
@@ -114,6 +117,9 @@ class ElementalIrEmitter {
   virtual StatusOr EmitExp(PrimitiveType prim_type,
                                          llvm::Value* value) const;
 
+  virtual StatusOr EmitExpm1(PrimitiveType prim_type,
+                                           llvm::Value* value) const;
+
   virtual StatusOr EmitPow(PrimitiveType prim_type,
                                          llvm::Value* lhs,
                                          llvm::Value* rhs) const;
diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc
index 5af7a77ea85856..e5e2a0478a0659 100644
--- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc
@@ -227,6 +227,11 @@ StatusOr GpuElementalIrEmitter::EmitLog(
   return EmitLibdeviceMathCall("__nv_log", {value}, {prim_type}, prim_type);
 }
 
+StatusOr GpuElementalIrEmitter::EmitLog1p(
+    PrimitiveType prim_type, llvm::Value* value) const {
+  return EmitLibdeviceMathCall("__nv_log1p", {value}, {prim_type}, prim_type);
+}
+
 StatusOr GpuElementalIrEmitter::EmitSin(
     PrimitiveType prim_type, llvm::Value* value) const {
   return EmitLibdeviceMathCall("__nv_sin", {value}, {prim_type}, prim_type);
@@ -242,6 +247,11 @@ StatusOr GpuElementalIrEmitter::EmitExp(
   return EmitLibdeviceMathCall("__nv_exp", {value}, {prim_type}, prim_type);
 }
 
+StatusOr GpuElementalIrEmitter::EmitExpm1(
+    PrimitiveType prim_type, llvm::Value* value) const {
+  return EmitLibdeviceMathCall("__nv_expm1", {value}, {prim_type}, prim_type);
+}
+
 StatusOr GpuElementalIrEmitter::EmitPow(PrimitiveType prim_type,
                                                       llvm::Value* lhs,
                                                       llvm::Value* rhs) const {
diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h
index 77d4569b1e8e39..91f4d960aa62ff 100644
--- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h
+++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h
@@ -64,6 +64,9 @@ class GpuElementalIrEmitter : public ElementalIrEmitter {
   StatusOr EmitLog(PrimitiveType prim_type,
                                  llvm::Value* value) const override;
 
+  StatusOr EmitLog1p(PrimitiveType prim_type,
+                                   llvm::Value* value) const override;
+
   StatusOr EmitSin(PrimitiveType prim_type,
                                  llvm::Value* value) const override;
 
@@ -73,6 +76,9 @@ class GpuElementalIrEmitter : public ElementalIrEmitter {
   StatusOr EmitExp(PrimitiveType prim_type,
                                  llvm::Value* value) const override;
 
+  StatusOr EmitExpm1(PrimitiveType prim_type,
+                                   llvm::Value* value) const override;
+
   StatusOr EmitPow(PrimitiveType prim_type, llvm::Value* lhs,
                                  llvm::Value* rhs) const override;
 
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h
index f1cb36347850a5..0e4ef08ad34c02 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h
@@ -253,6 +253,29 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault {
     return Status::OK();
   }
 
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleExpm1(HloInstruction* expm1) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[expm1],
+        ElementWiseUnaryOp(expm1, [](ElementwiseT elem_operand) {
+          return std::expm1(elem_operand);
+        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleExpm1(HloInstruction* floor) {
+    return InvalidArgument("Unsupported type for Expm1");
+  }
+
+  Status HandleExpm1(HloInstruction* floor) override {
+    return HandleExpm1(floor);
+  }
+
   template <
       typename NativeT,
       typename std::enable_if::value>::type* = nullptr>
@@ -284,6 +307,29 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault {
     return Status::OK();
   }
 
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleLog1p(HloInstruction* expm1) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[expm1],
+        ElementWiseUnaryOp(expm1, [](ElementwiseT elem_operand) {
+          return std::log1p(elem_operand);
+        }));
+    return Status::OK();
+  }
+
+  template <
+      typename NativeT,
+      typename std::enable_if::value>::type* = nullptr>
+  Status HandleLog1p(HloInstruction* floor) {
+    return InvalidArgument("Unsupported type for Log1p");
+  }
+
+  Status HandleLog1p(HloInstruction* floor) override {
+    return HandleLog1p(floor);
+  }
+
   template ::value &&
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 55911acc28a7a4..8dc3b83eee27c7 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -925,6 +925,7 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) {
     case HloOpcode::kDivide:
     case HloOpcode::kEq:
     case HloOpcode::kExp:
+    case HloOpcode::kExpm1:
     case HloOpcode::kFloor:
     case HloOpcode::kGe:
     case HloOpcode::kGt:
@@ -932,6 +933,7 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) {
     case HloOpcode::kIsFinite:
     case HloOpcode::kLe:
     case HloOpcode::kLog:
+    case HloOpcode::kLog1p:
     case HloOpcode::kLt:
     case HloOpcode::kMaximum:
     case HloOpcode::kMinimum:
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 3ff1007277a238..8d0fd65eb983a3 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -257,10 +257,12 @@ HloInstruction::CreateGetTupleElement(const Shape& shape,
     case HloOpcode::kCos:
     case HloOpcode::kClz:
     case HloOpcode::kExp:
+    case HloOpcode::kExpm1:
     case HloOpcode::kFloor:
     case HloOpcode::kImag:
     case HloOpcode::kIsFinite:
     case HloOpcode::kLog:
+    case HloOpcode::kLog1p:
     case HloOpcode::kNot:
     case HloOpcode::kNegate:
     case HloOpcode::kReal:
@@ -1245,10 +1247,12 @@ std::unique_ptr HloInstruction::CloneWithNewOperands(
     case HloOpcode::kCopy:
     case HloOpcode::kCos:
     case HloOpcode::kExp:
+    case HloOpcode::kExpm1:
     case HloOpcode::kImag:
     case HloOpcode::kIsFinite:
     case HloOpcode::kFloor:
     case HloOpcode::kLog:
+    case HloOpcode::kLog1p:
     case HloOpcode::kNot:
     case HloOpcode::kNegate:
     case HloOpcode::kReal:
@@ -1699,6 +1703,7 @@ bool HloInstruction::IdenticalSlowPath(
     case HloOpcode::kDivide:
     case HloOpcode::kEq:
     case HloOpcode::kExp:
+    case HloOpcode::kExpm1:
     case HloOpcode::kFloor:
     case HloOpcode::kGe:
     case HloOpcode::kGt:
@@ -1706,6 +1711,7 @@ bool HloInstruction::IdenticalSlowPath(
     case HloOpcode::kIsFinite:
     case HloOpcode::kLe:
     case HloOpcode::kLog:
+    case HloOpcode::kLog1p:
     case HloOpcode::kAnd:
     case HloOpcode::kNot:
     case HloOpcode::kOr:
@@ -2620,6 +2626,8 @@ Status HloInstruction::Visit(DfsHloVisitorBase* visitor) {
       return visitor->HandleNegate(this);
     case HloOpcode::kExp:
       return visitor->HandleExp(this);
+    case HloOpcode::kExpm1:
+      return visitor->HandleExpm1(this);
     case HloOpcode::kFloor:
       return visitor->HandleFloor(this);
     case HloOpcode::kCeil:
@@ -2628,6 +2636,8 @@ Status HloInstruction::Visit(DfsHloVisitorBase* visitor) {
       return visitor->HandleClz(this);
     case HloOpcode::kLog:
       return visitor->HandleLog(this);
+    case HloOpcode::kLog1p:
+      return visitor->HandleLog1p(this);
     case HloOpcode::kTanh:
       return visitor->HandleTanh(this);
     case HloOpcode::kCos:
@@ -2974,10 +2984,12 @@ bool HloInstruction::IsElementwise() const {
     case HloOpcode::kCopy:
     case HloOpcode::kCos:
     case HloOpcode::kExp:
+    case HloOpcode::kExpm1:
     case HloOpcode::kFloor:
     case HloOpcode::kImag:
     case HloOpcode::kIsFinite:
     case HloOpcode::kLog:
+    case HloOpcode::kLog1p:
     case HloOpcode::kNot:
     case HloOpcode::kNegate:
     case HloOpcode::kReal:
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h
index ca763076a16af1..ac7cd2f2f517cf 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.h
+++ b/tensorflow/compiler/xla/service/hlo_opcode.h
@@ -74,6 +74,7 @@ namespace xla {
   V(kDynamicUpdateSlice, "dynamic-update-slice")             \
   V(kEq, "equal-to", kHloOpcodeIsComparison)                 \
   V(kExp, "exponential")                                     \
+  V(kExpm1, "exponential-minus-one")                         \
   V(kFft, "fft")                                             \
   V(kFloor, "floor")                                         \
   V(kFusion, "fusion", kHloOpcodeIsVariadic)                 \
@@ -87,6 +88,7 @@ namespace xla {
   V(kIsFinite, "is-finite")                                  \
   V(kLe, "less-than-or-equal-to", kHloOpcodeIsComparison)    \
   V(kLog, "log")                                             \
+  V(kLog1p, "log-plus-one")                                  \
   V(kAnd, "and")                                             \
   V(kNot, "not")                                             \
   V(kOr, "or")                                               \
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index 6bb2ca19fe235d..06b84cc145007f 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -120,11 +120,13 @@ bool IsAlwaysDuplicable(const HloInstruction& instruction) {
     case HloOpcode::kDivide:
     case HloOpcode::kDot:
     case HloOpcode::kExp:
+    case HloOpcode::kExpm1:
     case HloOpcode::kFft:
     case HloOpcode::kFusion:
     case HloOpcode::kGather:
     case HloOpcode::kHostCompute:
     case HloOpcode::kLog:
+    case HloOpcode::kLog1p:
     case HloOpcode::kMap:
     case HloOpcode::kParameter:
     case HloOpcode::kPower:
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index c493547d9e83e1..fedb42ac88601d 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -58,6 +58,8 @@ UnaryOperation OpcodeToUnaryOperation(HloOpcode opcode) {
       return UNOP_COS;
     case HloOpcode::kExp:
       return UNOP_EXP;
+    case HloOpcode::kExpm1:
+      return UNOP_EXPM1;
     case HloOpcode::kFloor:
       return UNOP_FLOOR;
     case HloOpcode::kImag:
@@ -66,6 +68,8 @@ UnaryOperation OpcodeToUnaryOperation(HloOpcode opcode) {
       return UNOP_IS_FINITE;
     case HloOpcode::kLog:
       return UNOP_LOG;
+    case HloOpcode::kLog1p:
+      return UNOP_LOG1P;
     case HloOpcode::kNot:
       return UNOP_NOT;
     case HloOpcode::kNegate:
@@ -337,7 +341,9 @@ StatusOr InferWindowOutputShape(const Shape& base_shape,
     case UNOP_COS:
     case UNOP_SIN:
     case UNOP_EXP:
+    case UNOP_EXPM1:
     case UNOP_LOG:
+    case UNOP_LOG1P:
     case UNOP_TANH:
       if (!ShapeUtil::ElementIsFloating(arg) &&
           !ShapeUtil::ElementIsComplex(arg)) {
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 0f16a592b68e20..9e62d0acfb9894 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -55,6 +55,8 @@ HloOpcode UnaryOperationToHloOpcode(UnaryOperation unop) {
       return HloOpcode::kCos;
     case UNOP_EXP:
       return HloOpcode::kExp;
+    case UNOP_EXPM1:
+      return HloOpcode::kExpm1;
     case UNOP_FLOOR:
       return HloOpcode::kFloor;
     case UNOP_IMAG:
@@ -63,6 +65,8 @@ HloOpcode UnaryOperationToHloOpcode(UnaryOperation unop) {
       return HloOpcode::kIsFinite;
     case UNOP_LOG:
       return HloOpcode::kLog;
+    case UNOP_LOG1P:
+      return HloOpcode::kLog1p;
     case UNOP_NOT:
       return HloOpcode::kNot;
     case UNOP_NEGATE:
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
index 156a06c596c3f1..d0e7af8844203d 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
@@ -481,10 +481,12 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder,
     case HloOpcode::kCopy:
     case HloOpcode::kCos:
     case HloOpcode::kExp:
+    case HloOpcode::kExpm1:
     case HloOpcode::kImag:
     case HloOpcode::kIsFinite:
     case HloOpcode::kFloor:
     case HloOpcode::kLog:
+    case HloOpcode::kLog1p:
     case HloOpcode::kNot:
     case HloOpcode::kNegate:
     case HloOpcode::kReal:
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 750d72d797b4f8..b895ac045c361b 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -814,6 +814,12 @@ enum UnaryOperation {
 
   // Elementwise, computes clz(x).
   UNOP_CLZ = 17;
+
+  // Elementwise, computes exp(x)-1.
+  UNOP_EXPM1 = 18;
+
+  // Elementwise, computes log(x+1).
+  UNOP_LOG1P = 19;
 }
 
 message UnaryOpRequest {

From 2b5ac9ab6f5cfb4a4d6427291ea6d79ac84a096e Mon Sep 17 00:00:00 2001
From: Zhixian Yan 
Date: Thu, 10 May 2018 04:38:15 -0700
Subject: [PATCH 0596/1691] Support differing dimensions for strided_slice

PiperOrigin-RevId: 196101232
---
 .../contrib/lite/testing/generate_examples.py | 16 ++++-
 .../resolve_strided_slice_attributes.cc       | 59 ++++++++++++++-----
 2 files changed, 57 insertions(+), 18 deletions(-)

diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index 1090e79287b2dc..c3cc1e28d7e655 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -96,8 +96,6 @@
     r"batch_to_space_nd.*input_shape=\[8,2,2,2,1,1\]": "70594733",
     # Div will use floordiv.
     r"div.*int32": "72051395",
-    # TOCO require matching dimensions in strided_slice.
-    r"strided_slice.*begin=\[0\].*end=\[1\].*": "73170889",
     # No support for SplitV
     r"split.*num_or_size_splits=\[2,2\]": "73377559",
     # Needs support for dimensions other than the last one in argmax.
@@ -1811,7 +1809,19 @@ def make_strided_slice_tests(zip_path):
           "shrink_axis_mask": [None, 1, 8, 11, 15, -1],
           "constant_indices": [False, True],
       },
-      # TODO(b/73170889) Restore test paramaters removed in cl/191608113.
+      # Begin, end, strides dim are different from input shape
+      {
+          "dtype": [tf.float32],
+          "index_type": [tf.int32],
+          "input_shape": [[12, 2, 2, 5]],
+          "begin": [[0]],
+          "end": [[1]],
+          "strides": [None, [1]],
+          "begin_mask": [0],
+          "end_mask": [0],
+          "shrink_axis_mask": [1],
+          "constant_indices": [True],
+      },
       # 2-D
       {
           "dtype": [tf.float32, tf.int32, tf.int64],
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_strided_slice_attributes.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_strided_slice_attributes.cc
index 021e9918f2cf22..65132d7d1ef062 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_strided_slice_attributes.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_strided_slice_attributes.cc
@@ -19,6 +19,24 @@ limitations under the License.
 
 namespace toco {
 
+int PadAttributeArray(Array* attribute_array, std::vector pad_values,
+                      int mask) {
+  int attribute_dim_count = attribute_array->shape().dims(0);
+  int dim_count = pad_values.size();
+  if (attribute_dim_count < dim_count) {
+    Shape strided_slice_shape = Shape({dim_count});
+    attribute_array->copy_shape(strided_slice_shape);
+    Buffer* buffer =
+        &(attribute_array->GetMutableBuffer());
+    buffer->data.resize(RequiredBufferSizeForShape(strided_slice_shape));
+    for (int i = attribute_dim_count; i < dim_count; i++) {
+      buffer->data[i] = pad_values[i];
+      mask |= 1 << i;
+    }
+  }
+  return mask;
+}
+
 bool ResolveStridedSliceAttributes::Run(Model* model, std::size_t op_index) {
   const auto slice_it = model->operators.begin() + op_index;
   auto* slice_op = slice_it->get();
@@ -37,52 +55,63 @@ bool ResolveStridedSliceAttributes::Run(Model* model, std::size_t op_index) {
     return false;
   }
 
-  const auto& start_array = model->GetArray(op->inputs[1]);
+  auto& start_array = model->GetArray(op->inputs[1]);
   if (!start_array.has_shape()) return false;
   if (toco::RequiredBufferSizeForShape(start_array.shape()) > 4) {
     // Only 1-4D arrays are supported for now.
     return false;
   }
 
-  const auto& stop_array = model->GetArray(op->inputs[2]);
+  auto& stop_array = model->GetArray(op->inputs[2]);
   if (!stop_array.has_shape()) return false;
 
-  const auto& stride_array = model->GetArray(op->inputs[3]);
+  auto& stride_array = model->GetArray(op->inputs[3]);
   if (!stride_array.has_shape()) return false;
 
   if (!IsConstantParameterArray(*model, op->inputs[1])) return false;
   if (!IsConstantParameterArray(*model, op->inputs[2])) return false;
   if (!IsConstantParameterArray(*model, op->inputs[3])) return false;
 
-  op->start_indices = start_array.GetBuffer().data;
-  op->stop_indices = stop_array.GetBuffer().data;
-  op->strides = stride_array.GetBuffer().data;
+  int num_input_axes = input_array.shape().dimensions_count();
+  int start_indices_size = start_array.shape().dims(0);
+  int stop_indices_size = stop_array.shape().dims(0);
+  int stride_indices_size = stride_array.shape().dims(0);
 
-  CHECK_GE(op->start_indices.size(), 1);
-  CHECK_LE(op->start_indices.size(), 4);
-  CHECK_EQ(op->stop_indices.size(), op->start_indices.size());
-  CHECK_EQ(op->strides.size(), op->stop_indices.size());
+  CHECK_GE(start_indices_size, 1);
+  CHECK_LE(start_indices_size, 4);
+  CHECK_LE(stop_indices_size, 4);
+  CHECK_LE(stride_indices_size, 4);
 
   // The TensorFlow documentation is not explicit on how it handles fewer
   // supplied indices than dimensions, but they are accepted. We emulate TF's
   // behavior by fully iterating over each omitted dimension.
-  int num_input_axes = input_array.shape().dimensions_count();
-  CHECK_LE(op->start_indices.size(), num_input_axes)
+  CHECK_LE(start_indices_size, num_input_axes)
       << "StridedSlice op requires no more than " << num_input_axes
       << " start indices";
-  CHECK_LE(op->stop_indices.size(), num_input_axes)
+  CHECK_LE(stop_indices_size, num_input_axes)
       << "StridedSlice op requires no more than " << num_input_axes
       << " stop indices";
-  CHECK_LE(op->strides.size(), num_input_axes)
+  CHECK_LE(stride_indices_size, num_input_axes)
       << "StridedSlice op requires no more than " << num_input_axes
       << " strides";
-  op->PadIndices(num_input_axes);
 
   // Ideally, we would remove the input arrays after they have been resolved.
   // However, we must then reconstitute these input arrays for all supported
   // export formats. For now, leave the arrays so we don't have to modify our
   // exporters. Ideally, we wouldn't have op attributes, and would work directly
   // with the input arrays.
+  std::vector begin_pad_values(num_input_axes, 0);
+  op->begin_mask =
+      PadAttributeArray(&start_array, begin_pad_values, op->begin_mask);
+  op->end_mask =
+      PadAttributeArray(&stop_array, input_array.shape().dims(), op->end_mask);
+  std::vector stride_pad_values(num_input_axes, 1);
+  PadAttributeArray(&stride_array, stride_pad_values, 0);
+
+  op->start_indices = start_array.GetBuffer().data;
+  op->stop_indices = stop_array.GetBuffer().data;
+  op->strides = stride_array.GetBuffer().data;
+
   return true;
 }
 }  // namespace toco

From 3039c30887c67aeb67867282eb5157ba38c766a5 Mon Sep 17 00:00:00 2001
From: Smit Shilu 
Date: Thu, 10 May 2018 09:19:31 -0400
Subject: [PATCH 0597/1691] Updated index.md

Tensorflow lite image was not visible. Added image file path
---
 tensorflow/docs_src/mobile/tflite/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/mobile/tflite/index.md b/tensorflow/docs_src/mobile/tflite/index.md
index 01881ccf3bb15b..56220348276399 100644
--- a/tensorflow/docs_src/mobile/tflite/index.md
+++ b/tensorflow/docs_src/mobile/tflite/index.md
@@ -155,7 +155,7 @@ retraining for both floating point and quantized inference.
 
 The following diagram shows the architectural design of TensorFlow Lite:
 
-TensorFlow Lite architecture diagram
 

From 1744b8c0519cec31764d205b813bd4fd6028cbf9 Mon Sep 17 00:00:00 2001
From: Yong Tang 
Date: Thu, 10 May 2018 09:07:44 -0700
Subject: [PATCH 0598/1691] Fix warning in python 3 with deprecated
 inspect.getargspec (#19199)

This fix tries to address the issue raised in 16152 where
a warning will show up in python 3 with:
```
import tensorflow as tf

import warnings
warnings.filterwarnings('error')

tf.reduce_sum(tf.placeholder(tf.float64))
......
DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()
```

This fixes the issue with getfullargspec in tf_export, which takes
into consideration the python 2 vs python3.

This fix fixes 16152.

Signed-off-by: Yong Tang 
---
 tensorflow/python/util/tf_inspect.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/util/tf_inspect.py b/tensorflow/python/util/tf_inspect.py
index 663036de8a01c3..9bad4a24814e9d 100644
--- a/tensorflow/python/util/tf_inspect.py
+++ b/tensorflow/python/util/tf_inspect.py
@@ -116,7 +116,7 @@ def getcallargs(func, *positional, **named):
   it. If no attached decorators modify argspec, the final unwrapped target's
   argspec will be used.
   """
-  argspec = getargspec(func)
+  argspec = getfullargspec(func)
   call_args = named.copy()
   this = getattr(func, 'im_self', None) or getattr(func, '__self__', None)
   if ismethod(func) and this:

From 909c8c2c90413d0013d941c75084645148c64b55 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama 
Date: Thu, 10 May 2018 09:08:43 -0700
Subject: [PATCH 0599/1691] Add citation (#19201)

---
 tensorflow/contrib/slim/README.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tensorflow/contrib/slim/README.md b/tensorflow/contrib/slim/README.md
index 746b9556423761..a688f0f2803a2f 100644
--- a/tensorflow/contrib/slim/README.md
+++ b/tensorflow/contrib/slim/README.md
@@ -909,3 +909,8 @@ slim.evaluation.evaluation_loop(
 
 ## Authors
 Sergio Guadarrama and Nathan Silberman
+
+## Citation
+"TensorFlow-Slim: a lightweight library for defining, training and evaluating complex models in TensorFlow"
+S. Guadarrama, N. Silberman
+https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim 2016

From 4522626aff528815bc4087ab5b43c88b2d17a832 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 09:20:55 -0700
Subject: [PATCH 0600/1691] Add EvaluateNodes to tests:
 AddOpsRewrite_AddOpsOfIdenticalShape, AddOpsRewrite_MultiplePasses,
 AddOpsRewrite_AddInputMultipleTimes,
 AddOpsRewrite_AddOpsOfSymbolicallyEqualShape, AddOpsRewrite_MinimizeBCast,
 AddOpsRewrite_MinimizeBCastWithSymbolicShapes, RemoveNegation,
 MinimizeBroadcasts_SimpleSwap, MinimizeBroadcasts_FlattenTallGraph,
 MinimizeBroadcasts_BuildTreeUp

PiperOrigin-RevId: 196125583
---
 .../optimizers/arithmetic_optimizer_test.cc   | 138 +++++++++++++++++-
 1 file changed, 130 insertions(+), 8 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
index 067adb359c70a4..d60c3124edcc89 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
@@ -1574,6 +1574,14 @@ TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_AddOpsOfIdenticalShape) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto a_t = GenerateRandomTensor(TensorShape({2, 2}));
+  auto b_t = GenerateRandomTensor(TensorShape({2, 2}));
+  auto c_t = GenerateRandomTensor(TensorShape({2, 2}));
+  std::vector> feed = {
+      {"a", a_t}, {"b", b_t}, {"c", c_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyAddToAddNCombining(&optimizer);
@@ -1607,6 +1615,10 @@ TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_AddOpsOfIdenticalShape) {
   ASSERT_NE(updated_outputs, nullptr);
 
   EXPECT_EQ(collapsed_add->name(), updated_outputs->input(0));
+
+  auto tensors = EvaluateNodes(output, item.fetch, feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
 TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_MultiplePasses) {
@@ -1631,6 +1643,17 @@ TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_MultiplePasses) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto a_t = GenerateRandomTensor(TensorShape({2, 2}));
+  auto b_t = GenerateRandomTensor(TensorShape({2, 2}));
+  auto c_t = GenerateRandomTensor(TensorShape({2, 2}));
+  auto x_t = GenerateRandomTensor(TensorShape({2, 2}));
+  auto y_t = GenerateRandomTensor(TensorShape({2, 2}));
+  auto z_t = GenerateRandomTensor(TensorShape({2, 2}));
+  std::vector> feed = {
+      {"a", a_t}, {"b", b_t}, {"c", c_t}, {"x", x_t}, {"y", y_t}, {"z", z_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyAddToAddNCombining(&optimizer);
@@ -1680,6 +1703,10 @@ TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_MultiplePasses) {
   EXPECT_EQ(2, updated_mul->input_size());
   EXPECT_EQ(collapsed_left->name(), updated_mul->input(0));
   EXPECT_EQ(collapsed_right->name(), updated_mul->input(1));
+
+  auto tensors = EvaluateNodes(output, item.fetch, feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
 TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_AddInputMultipleTimes) {
@@ -1697,6 +1724,14 @@ TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_AddInputMultipleTimes) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto a_t = GenerateRandomTensor(TensorShape({2, 2}));
+  auto b_t = GenerateRandomTensor(TensorShape({2, 2}));
+  auto c_t = GenerateRandomTensor(TensorShape({2, 2}));
+  std::vector> feed = {
+      {"a", a_t}, {"b", b_t}, {"c", c_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyAddToAddNCombining(&optimizer);
@@ -1725,6 +1760,10 @@ TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_AddInputMultipleTimes) {
   EXPECT_EQ("b", collapsed_add->input(1));
   EXPECT_EQ("b", collapsed_add->input(2));
   EXPECT_EQ("c", collapsed_add->input(3));
+
+  auto tensors = EvaluateNodes(output, item.fetch, feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
 TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_AddOpsOfSymbolicallyEqualShape) {
@@ -1748,6 +1787,11 @@ TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_AddOpsOfSymbolicallyEqualShape) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto x_t = GenerateRandomTensor(TensorShape({2, 2}));
+  std::vector> feed = {{"input", x_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyAddToAddNCombining(&optimizer);
@@ -1779,6 +1823,10 @@ TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_AddOpsOfSymbolicallyEqualShape) {
   const NodeDef* updated_outputs = node_map.GetNode("outputs");
   ASSERT_NE(updated_outputs, nullptr);
   EXPECT_EQ(collapsed_add->name(), updated_outputs->input(0));
+
+  auto tensors = EvaluateNodes(output, item.fetch, feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
 TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_MinimizeBCast) {
@@ -1803,6 +1851,17 @@ TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_MinimizeBCast) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto a_t = GenerateRandomTensor(TensorShape({32}));
+  auto b_t = GenerateRandomTensor(TensorShape({32, 32}));
+  auto c_t = GenerateRandomTensor(TensorShape({32, 32, 32}));
+  auto x_t = GenerateRandomTensor(TensorShape({32}));
+  auto y_t = GenerateRandomTensor(TensorShape({32, 32}));
+  auto z_t = GenerateRandomTensor(TensorShape({32, 32, 32}));
+  std::vector> feed = {
+      {"a", a_t}, {"b", b_t}, {"c", c_t}, {"x", x_t}, {"y", y_t}, {"z", z_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyAddToAddNCombining(&optimizer);
@@ -1875,18 +1934,22 @@ TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_MinimizeBCast) {
   const NodeDef* updated_outputs = node_map.GetNode("outputs");
   ASSERT_NE(updated_outputs, nullptr);
   EXPECT_EQ(outer_add_name, updated_outputs->input(0));
+
+  auto tensors = EvaluateNodes(output, item.fetch, feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
 TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_MinimizeBCastWithSymbolicShapes) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
   // We have a small input with one unknown dimension
-  auto small = ops::Variable(s.WithOpName("small"), {-1, 1, 1}, DT_FLOAT);
+  auto small = ops::Variable(s.WithOpName("small"), {-1, 1, 1}, DT_DOUBLE);
 
   // And second input which is larger, but has the same unknown dimension
   // device spec prevents this node from rewriting
-  auto d = "/job:do_not_rewrite_me";
-  auto v = ops::Variable(s.WithOpName("v"), {1, 32, 32}, DT_FLOAT);
+  auto d = "/device:CPU:0";
+  auto v = ops::Variable(s.WithOpName("v"), {1, 32, 32}, DT_DOUBLE);
   auto large = ops::Add(s.WithOpName("large").WithDevice(d), small, v);
 
   // [a, c] have {?, 1, 1} shape, [b] has {?, 32, 32}
@@ -1904,6 +1967,12 @@ TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_MinimizeBCastWithSymbolicShapes) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto s_t = GenerateRandomTensor(TensorShape({8, 1, 1}));
+  auto v_t = GenerateRandomTensor(TensorShape({1, 32, 32}));
+  std::vector> feed = {{"small", s_t}, {"v", v_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyAddToAddNCombining(&optimizer);
@@ -1942,6 +2011,10 @@ TEST_F(ArithmeticOptimizerTest, AddOpsRewrite_MinimizeBCastWithSymbolicShapes) {
   const NodeDef* updated_outputs = node_map.GetNode("outputs");
   ASSERT_NE(updated_outputs, nullptr);
   EXPECT_EQ(outer_add_name, updated_outputs->input(0));
+
+  auto tensors = EvaluateNodes(output, item.fetch, feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
 TEST_F(ArithmeticOptimizerTest, RemoveNegation) {
@@ -1966,6 +2039,12 @@ TEST_F(ArithmeticOptimizerTest, RemoveNegation) {
   item.fetch = {"add_all"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto x_t = GenerateRandomTensor(TensorShape({2, 2}));
+  auto y_t = GenerateRandomTensor(TensorShape({2, 2}));
+  std::vector> feed = {{"x", x_t}, {"y", y_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyRemoveNegation(&optimizer);
@@ -2014,6 +2093,10 @@ TEST_F(ArithmeticOptimizerTest, RemoveNegation) {
     }
   }
   EXPECT_EQ(5, found);
+
+  auto tensors = EvaluateNodes(output, item.fetch, feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
 TEST_F(ArithmeticOptimizerTest, ConvertSqrtDivToRsqrtMul) {
@@ -2069,6 +2152,14 @@ TEST_F(ArithmeticOptimizerTest, MinimizeBroadcasts_SimpleSwap) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto a_t = GenerateRandomTensor(TensorShape({32}));
+  auto b_t = GenerateRandomTensor(TensorShape({32, 32}));
+  auto c_t = GenerateRandomTensor(TensorShape({32}));
+  std::vector> feed = {
+      {"a", a_t}, {"b", b_t}, {"c", c_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyMinimizeBroadcasts(&optimizer);
@@ -2093,16 +2184,20 @@ TEST_F(ArithmeticOptimizerTest, MinimizeBroadcasts_SimpleSwap) {
   ASSERT_NE(mul2_node, nullptr);
   EXPECT_EQ("mul1", mul2_node->input(0));
   EXPECT_EQ("b", mul2_node->input(1));
+
+  auto tensors = EvaluateNodes(output, item.fetch, feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
 TEST_F(ArithmeticOptimizerTest, MinimizeBroadcasts_FlattenTallGraph) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
-  auto a = ops::Variable(s.WithOpName("a"), {32}, DT_FLOAT);
-  auto b = ops::Variable(s.WithOpName("b"), {32, 32}, DT_FLOAT);
-  auto c = ops::Variable(s.WithOpName("c"), {32}, DT_FLOAT);
-  auto d = ops::Variable(s.WithOpName("d"), {32}, DT_FLOAT);
-  auto e = ops::Variable(s.WithOpName("e"), {32}, DT_FLOAT);
+  auto a = ops::Variable(s.WithOpName("a"), {32}, DT_DOUBLE);
+  auto b = ops::Variable(s.WithOpName("b"), {32, 32}, DT_DOUBLE);
+  auto c = ops::Variable(s.WithOpName("c"), {32}, DT_DOUBLE);
+  auto d = ops::Variable(s.WithOpName("d"), {32}, DT_DOUBLE);
+  auto e = ops::Variable(s.WithOpName("e"), {32}, DT_DOUBLE);
 
   auto mul1 = ops::Mul(s.WithOpName("mul1"), a, b);
   auto mul2 = ops::Mul(s.WithOpName("mul2"), mul1, c);
@@ -2115,6 +2210,16 @@ TEST_F(ArithmeticOptimizerTest, MinimizeBroadcasts_FlattenTallGraph) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto a_t = GenerateRandomTensor(TensorShape({32}));
+  auto b_t = GenerateRandomTensor(TensorShape({32, 32}));
+  auto c_t = GenerateRandomTensor(TensorShape({32}));
+  auto d_t = GenerateRandomTensor(TensorShape({32}));
+  auto e_t = GenerateRandomTensor(TensorShape({32}));
+  std::vector> feed = {
+      {"a", a_t}, {"b", b_t}, {"c", c_t}, {"d", d_t}, {"e", e_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyMinimizeBroadcasts(&optimizer);
@@ -2154,6 +2259,10 @@ TEST_F(ArithmeticOptimizerTest, MinimizeBroadcasts_FlattenTallGraph) {
   ASSERT_NE(mul4_node, nullptr);
   EXPECT_EQ("mul3", mul4_node->input(0));
   EXPECT_EQ("b", mul4_node->input(1));
+
+  auto tensors = EvaluateNodes(output, item.fetch, feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
 TEST_F(ArithmeticOptimizerTest, MinimizeBroadcasts_BuildTreeUp) {
@@ -2175,6 +2284,15 @@ TEST_F(ArithmeticOptimizerTest, MinimizeBroadcasts_BuildTreeUp) {
   item.fetch = {"outputs"};
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
+  auto a_t = GenerateRandomTensor(TensorShape({32}));
+  auto b_t = GenerateRandomTensor(TensorShape({32}));
+  auto c_t = GenerateRandomTensor(TensorShape({32}));
+  auto d_t = GenerateRandomTensor(TensorShape({32, 32}));
+  std::vector> feed = {
+      {"a", a_t}, {"b", b_t}, {"c", c_t}, {"D", d_t}};
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
   GraphDef output;
   ArithmeticOptimizer optimizer;
   EnableOnlyMinimizeBroadcasts(&optimizer);
@@ -2206,6 +2324,10 @@ TEST_F(ArithmeticOptimizerTest, MinimizeBroadcasts_BuildTreeUp) {
   ASSERT_NE(mul3_node, nullptr);
   EXPECT_EQ("D", mul3_node->input(0));
   EXPECT_EQ("mul1", mul3_node->input(1));
+
+  auto tensors = EvaluateNodes(output, item.fetch, feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
 TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryFromConcat) {

From e696dc1bd07f62c6621a7224e15c8d3fbc160054 Mon Sep 17 00:00:00 2001
From: Asim Shankar 
Date: Thu, 10 May 2018 09:38:11 -0700
Subject: [PATCH 0601/1691] Automated g4 rollback of changelist 195878952

PiperOrigin-RevId: 196127751
---
 tensorflow/c/eager/tape.h                   | 36 +++---------
 tensorflow/contrib/eager/python/tfe_test.py |  6 +-
 tensorflow/python/eager/backprop.py         |  5 --
 tensorflow/python/eager/backprop_test.py    | 10 +---
 tensorflow/python/eager/pywrap_tensor.cc    |  6 --
 tensorflow/python/eager/pywrap_tensor.h     |  1 -
 tensorflow/python/eager/pywrap_tfe_src.cc   | 62 +++------------------
 7 files changed, 19 insertions(+), 107 deletions(-)

diff --git a/tensorflow/c/eager/tape.h b/tensorflow/c/eager/tape.h
index e9ed3395c44830..8026076b9ef3bf 100644
--- a/tensorflow/c/eager/tape.h
+++ b/tensorflow/c/eager/tape.h
@@ -130,15 +130,13 @@ class GradientTape {
     }
   }
 
-  bool ShouldRecord(gtl::ArraySlice tensor_ids,
-                    gtl::ArraySlice dtypes);
+  bool ShouldRecord(gtl::ArraySlice tensor_ids);
 
   void Watch(int64 tensor_id);
 
   void RecordOperation(const string& op_type,
                        gtl::ArraySlice output_tensors,
                        gtl::ArraySlice input_tensor_id,
-                       gtl::ArraySlice input_dtypes,
                        BackwardFunction* backward_function,
                        const std::function& backward_function_deleter);
 
@@ -172,30 +170,12 @@ class GradientTape {
 
 // Template instantiations here
 
-inline bool IsDtypeTrainable(DataType dtype) {
-  switch (dtype) {
-    case DT_HALF:
-    case DT_BFLOAT16:
-    case DT_FLOAT:
-    case DT_DOUBLE:
-    case DT_COMPLEX64:
-    case DT_COMPLEX128:
-    case DT_RESOURCE:
-    case DT_VARIANT:
-      return true;
-    default:
-      return false;
-  }
-}
-
 template 
 bool GradientTape::ShouldRecord(
-    gtl::ArraySlice tensor_ids,
-    gtl::ArraySlice dtypes) {
-  CHECK_EQ(tensor_ids.size(), dtypes.size());
-  for (int i = 0; i < tensor_ids.size(); ++i) {
-    if (tensor_tape_.find(tensor_ids[i]) != tensor_tape_.end()) {
-      return IsDtypeTrainable(dtypes[i]);
+    gtl::ArraySlice tensor_ids) {
+  for (int64 i : tensor_ids) {
+    if (tensor_tape_.find(i) != tensor_tape_.end()) {
+      return true;
     }
   }
   return false;
@@ -209,11 +189,9 @@ void GradientTape::Watch(int64 tensor_id) {
 template 
 void GradientTape::RecordOperation(
     const string& op_type, gtl::ArraySlice output_tensors,
-    gtl::ArraySlice input_tensor_id,
-    gtl::ArraySlice input_dtypes,
-    BackwardFunction* backward_function,
+    gtl::ArraySlice input_tensor_id, BackwardFunction* backward_function,
     const std::function& backward_function_deleter) {
-  if (!ShouldRecord(input_tensor_id, input_dtypes)) {
+  if (!ShouldRecord(input_tensor_id)) {
     backward_function_deleter();
     return;
   }
diff --git a/tensorflow/contrib/eager/python/tfe_test.py b/tensorflow/contrib/eager/python/tfe_test.py
index db50b33af2e4f1..e80ccbb74d8623 100644
--- a/tensorflow/contrib/eager/python/tfe_test.py
+++ b/tensorflow/contrib/eager/python/tfe_test.py
@@ -57,7 +57,7 @@ def square(x):
       return math_ops.multiply(x, x)
 
     grad = tfe.gradients_function(square)
-    self.assertEquals([6], [x.numpy() for x in grad(3.)])
+    self.assertEquals([6], [x.numpy() for x in grad(3)])
 
   def testGradOfGrad(self):
 
@@ -66,7 +66,7 @@ def square(x):
 
     grad = tfe.gradients_function(square)
     gradgrad = tfe.gradients_function(lambda x: grad(x)[0])
-    self.assertEquals([2], [x.numpy() for x in gradgrad(3.)])
+    self.assertEquals([2], [x.numpy() for x in gradgrad(3)])
 
   def testCustomGrad(self):
 
@@ -80,7 +80,7 @@ def grad_fn(_):
       return y, grad_fn
 
     grad = tfe.gradients_function(f)
-    self.assertEquals([12], [x.numpy() for x in grad(3.)])
+    self.assertEquals([12], [x.numpy() for x in grad(3)])
 
   def testGPU(self):
     if tfe.num_gpus() <= 0:
diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py
index 967c12828043f8..d04b004451223a 100644
--- a/tensorflow/python/eager/backprop.py
+++ b/tensorflow/python/eager/backprop.py
@@ -358,8 +358,6 @@ def f(x, y):
   assert y_grad.numpy() == (2 ** 3) - 2 * 2 * 3
   ```
 
-  Note that only tensors with real or complex dtypes are differentiable.
-
   Args:
    f: function to be differentiated. If `f` returns a scalar, this scalar will
      be differentiated. If `f` returns a tensor or list of tensors, by default
@@ -702,9 +700,6 @@ class GradientTape(object):
   dz_dx = g.gradient(z, x)  # 108.0 (4*x^3 at x = 3)
   dy_dx = g.gradient(y, x)  # 6.0
   del g  # Drop the reference to the tape
-  ```
-
-  Note that only tensors with real or complex dtypes are differentiable.
   """
 
   def __init__(self, persistent=False):
diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py
index be674487f1f74a..8d9959fe20768c 100644
--- a/tensorflow/python/eager/backprop_test.py
+++ b/tensorflow/python/eager/backprop_test.py
@@ -124,14 +124,6 @@ def f(x):
     grad_fn = backprop.gradients_function(f)
     self.assertAllEqual(2., grad_fn(1., dy=2.)[0])
 
-  def testGradientInteger(self):
-
-    def f(x):
-      return x + x
-
-    int_tensor = constant_op.constant(1)
-    self.assertEqual(backprop.gradients_function(f)(int_tensor)[0], None)
-
   def testErrors(self):
 
     @custom_gradient.custom_gradient
@@ -761,7 +753,7 @@ def grad(dr):
       return result, grad
 
     x = resource_variable_ops.ResourceVariable(
-        initial_value=3., name='X.' + self.id())
+        initial_value=3, name='X.' + self.id())
 
     def f():
       return my_square(x)
diff --git a/tensorflow/python/eager/pywrap_tensor.cc b/tensorflow/python/eager/pywrap_tensor.cc
index b3aadd55ce7805..b5b4e394e33bd3 100644
--- a/tensorflow/python/eager/pywrap_tensor.cc
+++ b/tensorflow/python/eager/pywrap_tensor.cc
@@ -650,12 +650,6 @@ tensorflow::int64 EagerTensor_id(const PyObject* tensor) {
   return reinterpret_cast(tensor)->id;
 }
 
-tensorflow::DataType EagerTensor_dtype(const PyObject* tensor) {
-  CHECK(EagerTensor_CheckExact(tensor));
-  return static_cast(TFE_TensorHandleDataType(
-      reinterpret_cast(tensor)->handle));
-}
-
 PyObject* TFE_Py_InitEagerTensor(PyObject* base_class) {
   if (!PyType_Check(base_class)) {
     PyErr_SetString(
diff --git a/tensorflow/python/eager/pywrap_tensor.h b/tensorflow/python/eager/pywrap_tensor.h
index bc042eb19e6a91..fb093824a52080 100644
--- a/tensorflow/python/eager/pywrap_tensor.h
+++ b/tensorflow/python/eager/pywrap_tensor.h
@@ -22,7 +22,6 @@ limitations under the License.
 
 bool EagerTensor_CheckExact(const PyObject* o);
 tensorflow::int64 EagerTensor_id(const PyObject* tensor);
-tensorflow::DataType EagerTensor_dtype(const PyObject* tensor);
 
 namespace tensorflow {
 TFE_TensorHandle* ConvertToEagerTensor(PyObject* value, PyObject* dtype);
diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc
index 48a5b21dc7fba9..4ecba1a46be8ff 100644
--- a/tensorflow/python/eager/pywrap_tfe_src.cc
+++ b/tensorflow/python/eager/pywrap_tfe_src.cc
@@ -843,24 +843,6 @@ static tensorflow::int64 FastTensorId(PyObject* tensor) {
   return id;
 }
 
-static tensorflow::DataType FastTensorDtype(PyObject* tensor) {
-  if (EagerTensor_CheckExact(tensor)) {
-    return EagerTensor_dtype(tensor);
-  }
-  PyObject* dtype_field = PyObject_GetAttrString(tensor, "dtype");
-  if (dtype_field == nullptr) {
-    return tensorflow::DT_INVALID;
-  }
-  PyObject* enum_field = PyObject_GetAttrString(dtype_field, "_type_enum");
-  Py_DECREF(dtype_field);
-  if (dtype_field == nullptr) {
-    return tensorflow::DT_INVALID;
-  }
-  tensorflow::int64 id = MakeInt(enum_field);
-  Py_DECREF(enum_field);
-  return static_cast(id);
-}
-
 class GradientTape
     : public tensorflow::eager::GradientTape {
  public:
@@ -1071,18 +1053,15 @@ PyObject* TFE_Py_TapeSetShouldRecord(PyObject* tensors) {
   // TODO(apassos) consider not building a list and changing the API to check
   // each tensor individually.
   std::vector tensor_ids;
-  std::vector dtypes;
   tensor_ids.reserve(len);
-  dtypes.reserve(len);
   for (int i = 0; i < len; ++i) {
     PyObject* item = PySequence_Fast_GET_ITEM(seq, i);
     tensor_ids.push_back(FastTensorId(item));
-    dtypes.push_back(FastTensorDtype(item));
   }
   Py_DECREF(seq);
   auto tape_set = *tape_set_ptr;
   for (TFE_Py_Tape* tape : tape_set) {
-    if (tape->tape->ShouldRecord(tensor_ids, dtypes)) {
+    if (tape->tape->ShouldRecord(tensor_ids)) {
       Py_RETURN_TRUE;
     }
   }
@@ -1190,27 +1169,9 @@ PyObject* TFE_Py_TapeWatchedVariables(PyObject* tape) {
 }
 
 namespace {
-std::vector MakeTensorDtypeList(PyObject* tensors) {
-  PyObject* seq = PySequence_Fast(tensors, "expected a sequence");
-  if (seq == nullptr) {
-    return {};
-  }
-  int len = PySequence_Fast_GET_SIZE(seq);
-  std::vector list;
-  list.reserve(len);
-  for (int i = 0; i < len; ++i) {
-    PyObject* tensor = PySequence_Fast_GET_ITEM(seq, i);
-    list.push_back(FastTensorDtype(tensor));
-  }
-  Py_DECREF(seq);
-  return list;
-}
-
-void TapeSetRecordOperation(
-    PyObject* op_type, PyObject* output_tensors,
-    const std::vector& input_ids,
-    const std::vector& input_dtypes,
-    PyObject* backward_function) {
+void TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors,
+                            const std::vector& input_ids,
+                            PyObject* backward_function) {
   std::vector output_info;
   PyObject* seq = PySequence_Fast(output_tensors,
                                   "expected a sequence of integer tensor ids");
@@ -1245,7 +1206,7 @@ void TapeSetRecordOperation(
   for (TFE_Py_Tape* tape : SafeTapeSet()) {
     Py_INCREF(backward_function);
     tape->tape->RecordOperation(
-        op_type_str, output_info, input_ids, input_dtypes, backward_function,
+        op_type_str, output_info, input_ids, backward_function,
         [backward_function]() { Py_DECREF(backward_function); });
   }
 }
@@ -1260,11 +1221,7 @@ void TFE_Py_TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors,
   std::vector input_ids = MakeTensorIDList(input_tensors);
   if (PyErr_Occurred()) return;
 
-  std::vector input_dtypes =
-      MakeTensorDtypeList(input_tensors);
-  if (PyErr_Occurred()) return;
-  TapeSetRecordOperation(op_type, output_tensors, input_ids, input_dtypes,
-                         backward_function);
+  TapeSetRecordOperation(op_type, output_tensors, input_ids, backward_function);
 }
 
 void TFE_Py_TapeSetDeleteTrace(tensorflow::int64 tensor_id) {
@@ -1753,12 +1710,10 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs,
                          PyObject* results, PyObject* name) {
   std::vector input_ids = MakeTensorIDList(inputs);
   if (PyErr_Occurred()) return nullptr;
-  std::vector input_dtypes = MakeTensorDtypeList(inputs);
-  if (PyErr_Occurred()) return nullptr;
 
   bool should_record = false;
   for (TFE_Py_Tape* tape : SafeTapeSet()) {
-    if (tape->tape->ShouldRecord(input_ids, input_dtypes)) {
+    if (tape->tape->ShouldRecord(input_ids)) {
       should_record = true;
       break;
     }
@@ -1789,8 +1744,7 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs,
   Py_DECREF(callback_args);
   if (backward_function == nullptr) return nullptr;
 
-  TapeSetRecordOperation(op_name, results, input_ids, input_dtypes,
-                         backward_function);
+  TapeSetRecordOperation(op_name, results, input_ids, backward_function);
 
   Py_DECREF(backward_function);
 

From 9c18251256a88e23c47f60f3597f9c764000fba4 Mon Sep 17 00:00:00 2001
From: Karmel Allison 
Date: Thu, 10 May 2018 09:47:37 -0700
Subject: [PATCH 0602/1691] For Estimators, SavedModels for multiple modes
 should be exported into the same file.

PiperOrigin-RevId: 196128943
---
 .../estimator/python/estimator/export.py      |  77 ++++----
 .../estimator/python/estimator/export_test.py |  42 ++---
 tensorflow/python/estimator/estimator.py      | 163 ++++++++++-------
 tensorflow/python/estimator/estimator_test.py | 170 ++++++++++++++----
 4 files changed, 295 insertions(+), 157 deletions(-)

diff --git a/tensorflow/contrib/estimator/python/estimator/export.py b/tensorflow/contrib/estimator/python/estimator/export.py
index e7e366a3f26fa6..03cf6f107c1c55 100644
--- a/tensorflow/contrib/estimator/python/estimator/export.py
+++ b/tensorflow/contrib/estimator/python/estimator/export.py
@@ -60,38 +60,16 @@ def export_saved_model_for_mode(
   with ops.Graph().as_default() as graph:
     with session.Session(graph=graph) as sess:
       loader.load(sess, [tag_constants.TRAINING], export_dir)
+      weights = graph.get_tensor_by_name(''linear/linear_model/age/weights')
       ...
   ```
 
-  This method takes an input_receiver_fn and mode. For the mode passed in,
-  this method builds a new graph by calling the input_receiver_fn to obtain
-  feature and label `Tensor`s. Next, this method calls the `Estimator`'s
-  model_fn in the passed mode to generate the model graph based on
-  those features and labels, and restores the given checkpoint
-  (or, lacking that, the most recent checkpoint) into the graph.
-  Finally, it creates a timestamped export directory below the
-  export_dir_base, and writes a `SavedModel` into it containing
-  the `MetaGraphDef` for the given mode and its associated signatures.
-
-  For prediction, the exported `MetaGraphDef` will provide one `SignatureDef`
-  for each element of the export_outputs dict returned from the model_fn,
-  named using the same keys.  One of these keys is always
-  signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which
-  signature will be served when a serving request does not specify one.
-  For each signature, the outputs are provided by the corresponding
-  `ExportOutput`s, and the inputs are always the input receivers provided by
-  the serving_input_receiver_fn.
+  This method is a wrapper for _export_all_saved_models, and wraps a raw
+  input_receiver_fn in a dictionary to pass in to that function.
+  See _export_all_saved_models for full docs.
 
-  For training and evaluation, the train_op is stored in an extra collection,
-  and loss, metrics, and predictions are included in a SignatureDef for the
-  mode in question.
-
-  Extra assets may be written into the SavedModel via the assets_extra
-  argument.  This should be a dict, where each key gives a destination path
-  (including the filename) relative to the assets.extra directory.  The
-  corresponding value gives the full path of the source file to be copied.
-  For example, the simple case of copying a single file without renaming it
-  is specified as `{'my_asset_file.txt': '/path/to/my_asset_file.txt'}`.
+  See tf.contrib.estimator.export_saved_model_for_mode for the currently
+  exposed version of this function.
 
   Args:
     estimator: an instance of tf.estimator.Estimator
@@ -138,10 +116,39 @@ def export_all_saved_models(
   # pylint: disable=line-too-long
   """Exports requested train/eval/predict graphs as separate SavedModels.
 
-  This is a wrapper around export_saved_model_for_mode that accepts
-  multiple modes simultaneously and creates directories for each under
-  export_dir_base. See `Estimator.export_saved_model_for_mode` for
-  further details as to how the export works for each mode.
+  See tf.contrib.estimator.export_all_saved_models for the currently
+  exposed version of this function.
+
+  For each mode passed in via the input_receiver_fn_map,
+  this method builds a new graph by calling the input_receiver_fn to obtain
+  feature and label `Tensor`s. Next, this method calls the `Estimator`'s
+  model_fn in the passed mode to generate the model graph based on
+  those features and labels, and restores the given checkpoint
+  (or, lacking that, the most recent checkpoint) into the graph.
+  Only one of the modes is used for saving variables to the SavedModel
+  (order of preference: TRAIN, EVAL, then PREDICT), such that up to three
+  MetaGraphDefs are saved with a single set of variables in a single
+  SavedModel directory.
+
+  For prediction, the exported `MetaGraphDef` will provide one `SignatureDef`
+  for each element of the export_outputs dict returned from the model_fn,
+  named using the same keys.  One of these keys is always
+  signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which
+  signature will be served when a serving request does not specify one.
+  For each signature, the outputs are provided by the corresponding
+  `ExportOutput`s, and the inputs are always the input receivers provided by
+  the serving_input_receiver_fn.
+
+  For training and evaluation, the train_op is stored in an extra collection,
+  and loss, metrics, and predictions are included in a SignatureDef for the
+  mode in question.
+
+  Extra assets may be written into the SavedModel via the assets_extra
+  argument.  This should be a dict, where each key gives a destination path
+  (including the filename) relative to the assets.extra directory.  The
+  corresponding value gives the full path of the source file to be copied.
+  For example, the simple case of copying a single file without renaming it
+  is specified as `{'my_asset_file.txt': '/path/to/my_asset_file.txt'}`.
 
   Sample usage:
   ```python
@@ -166,7 +173,7 @@ def export_all_saved_models(
       model_fn_lib.ModeKeys.PREDICT: serve_rcvr_fn,
   }
 
-  export_dirs = tf.contrib.estimator.export_all_saved_models(
+  export_dir = tf.contrib.estimator.export_all_saved_models(
       classifier,
       export_dir_base='my_model/',
       input_receiver_fn_map=rcvr_fn_map)
@@ -175,8 +182,8 @@ def export_all_saved_models(
   # can be used for serving, analysis with TFMA, or directly loaded in.
   with ops.Graph().as_default() as graph:
     with session.Session(graph=graph) as sess:
-      loader.load(sess, [tag_constants.TRAINING],
-                  export_dirs[tf.estimator.ModeKeys.TRAIN])
+      loader.load(sess, [tag_constants.TRAINING], export_dir)
+      weights = graph.get_tensor_by_name('linear/linear_model/age/weights')
       ...
   ```
 
diff --git a/tensorflow/contrib/estimator/python/estimator/export_test.py b/tensorflow/contrib/estimator/python/estimator/export_test.py
index 89d02582e18e39..050821ee672f30 100644
--- a/tensorflow/contrib/estimator/python/estimator/export_test.py
+++ b/tensorflow/contrib/estimator/python/estimator/export_test.py
@@ -166,12 +166,9 @@ def test_export_all_saved_models_proto_roundtrip_receiver_map(self):
     input_receiver_fn_map = {
         model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
-    self.assertEqual(len(export_dirs), 1)
-    # Restore, to validate that the export was well-formed.
-    export_dir = export_dirs[model_fn_lib.ModeKeys.PREDICT]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.SERVING], export_dir)
@@ -188,12 +185,9 @@ def test_export_all_saved_models_proto_roundtrip_train_only(self):
     input_receiver_fn_map = {
         model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
-    self.assertEqual(len(export_dirs), 1)
-    # Restore, to validate that the export was well-formed.
-    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.TRAINING], export_dir)
@@ -211,12 +205,9 @@ def test_export_all_saved_models_proto_roundtrip_eval_only(self):
     input_receiver_fn_map = {
         model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn()
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
-    self.assertEqual(len(export_dirs), 1)
-    # Restore, to validate that the export was well-formed.
-    export_dir = export_dirs[model_fn_lib.ModeKeys.EVAL]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.EVAL], export_dir)
@@ -235,12 +226,9 @@ def test_export_all_saved_models_proto_roundtrip_no_serving(self):
         model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
         model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn()
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
-    self.assertEqual(len(export_dirs), 2)
-    # Restore, to validate that the export was well-formed.
-    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.TRAINING], export_dir)
@@ -249,7 +237,7 @@ def test_export_all_saved_models_proto_roundtrip_no_serving(self):
         self.assertFalse('eval_multiplied' in graph_ops)
         self.assertTrue('feature_x' in graph_ops)
         self.assertTrue('weight' in graph_ops)
-    export_dir = export_dirs[model_fn_lib.ModeKeys.EVAL]
+
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.EVAL], export_dir)
@@ -270,12 +258,11 @@ def test_export_all_saved_models_proto_roundtrip_three_defs(self):
         model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn(),
         model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
     # Restore, to validate that the export was well-formed.
-    for mode, tag_set in model_fn_lib.EXPORT_TAG_MAP.items():
-      export_dir = export_dirs[mode]
+    for tag_set in model_fn_lib.EXPORT_TAG_MAP.values():
       with ops.Graph().as_default() as graph:
         with session.Session(graph=graph) as sess:
           loader.load(sess, tag_set, export_dir)
@@ -292,10 +279,9 @@ def test_export_all_saved_models_proto_roundtrip_all_vars(self):
         model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
         model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
-    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.TRAINING], export_dir)
@@ -303,7 +289,6 @@ def test_export_all_saved_models_proto_roundtrip_all_vars(self):
         self.assertTrue('later_var' in graph_ops)
         self.assertTrue('weight' in graph_ops)
 
-    export_dir = export_dirs[model_fn_lib.ModeKeys.PREDICT]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.SERVING], export_dir)
@@ -319,10 +304,9 @@ def test_export_all_saved_models_name_collision(self):
         model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
         model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
-    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.TRAINING], export_dir)
@@ -332,7 +316,6 @@ def test_export_all_saved_models_name_collision(self):
         collection_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
         self.assertEqual(3, collection_vars[-1].eval())
 
-    export_dir = export_dirs[model_fn_lib.ModeKeys.PREDICT]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.SERVING], export_dir)
@@ -360,16 +343,15 @@ def _test_export_all_saved_models(self, input_receiver_fn_map):
     # Perform the export.
     export_dir_base = os.path.join(
         compat.as_bytes(tmpdir), compat.as_bytes('export'))
-    export_dirs = contrib_export.export_all_saved_models(
+    export_dir = contrib_export.export_all_saved_models(
         est, export_dir_base, input_receiver_fn_map)
 
     # Check that all the files are in the right places.
     self.assertTrue(gfile.Exists(export_dir_base))
 
-    for _, export_dir in export_dirs.items():
-      self._validate_exported_files(export_dir)
+    self._validate_exported_files(export_dir)
 
-    return export_dirs, tmpdir
+    return export_dir, tmpdir
 
   def _validate_exported_files(self, export_dir):
     self.assertTrue(gfile.Exists(export_dir))
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 9ae64d230ec26b..99be13cb026a9d 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -39,6 +39,7 @@
 from tensorflow.python.estimator import util
 from tensorflow.python.estimator.export import export as export_helpers
 from tensorflow.python.estimator.export import export_output
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import random_seed
 from tensorflow.python.ops import array_ops
@@ -616,29 +617,28 @@ def export_savedmodel(
         strip_default_attrs=strip_default_attrs,
         mode=model_fn_lib.ModeKeys.PREDICT)
 
-  def _export_all_saved_models(
-      self, export_dir_base, input_receiver_fn_map,
+  def _export_saved_model_for_mode(
+      self, export_dir_base, input_receiver_fn,
       assets_extra=None,
       as_text=False,
       checkpoint_path=None,
-      strip_default_attrs=False):
+      strip_default_attrs=False,
+      mode=model_fn_lib.ModeKeys.PREDICT):
     # pylint: disable=line-too-long
-    """Exports requested train/eval/predict graphs as separate SavedModels.
+    """Exports a single train/eval/predict graph as a SavedModel.
 
-    This is a wrapper around export_saved_model_for_mode that accepts
-    multiple modes simultaneously and creates directories for each under
-    export_dir_base. See `Estimator.export_saved_model_for_mode` for
-    further details as to how the export works for each mode.
+    This method is a wrapper for _export_all_saved_models, and wraps a raw
+    input_receiver_fn in a dictionary to pass in to that function.
+    See _export_all_saved_models for full docs.
 
-    See tf.contrib.estimator.export_all_saved_models for the currently
+    See tf.contrib.estimator.export_saved_model_for_mode for the currently
     exposed version of this function.
 
     Args:
       export_dir_base: A string containing a directory in which to create
         timestamped subdirectories containing exported SavedModels.
-      input_receiver_fn_map: dict of tf.estimator.ModeKeys to input_receiver_fn
-        mappings, where the input_receiver_fn is a function that takes no
-        argument and returns the appropriate subclass of `InputReceiver`.
+      input_receiver_fn: a function that takes no argument and
+        returns the appropriate subclass of `InputReceiver`.
       assets_extra: A dict specifying how to populate the assets.extra directory
         within the exported SavedModel, or `None` if no extra assets are needed.
       as_text: whether to write the SavedModel proto in text format.
@@ -647,60 +647,53 @@ def _export_all_saved_models(
       strip_default_attrs: Boolean. If `True`, default-valued attributes will be
         removed from the NodeDefs. For a detailed guide, see
         [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
+      mode: tf.estimator.ModeKeys value indicating with mode will be exported.
 
     Returns:
-      A dict of tf.estimator.ModeKeys value to string path for each exported
-      directory.
+      The string path to the exported directory.
 
     Raises:
-      ValueError: if any input_receiver_fn is None, no export_outputs
+      ValueError: if input_receiver_fn is None, no export_outputs
         are provided, or no checkpoint can be found.
     """
     # pylint: enable=line-too-long
-    # TODO(b/65561022): Consider allowing multiple input_receiver_fns per mode.
-    exported = {}
-    for mode, input_receiver_fn in input_receiver_fn_map.items():
-      export_mode_dir = os.path.join(
-          compat.as_bytes(export_dir_base),
-          compat.as_bytes(mode))
-      gfile.MakeDirs(export_mode_dir)
-
-      exported_path = self._export_saved_model_for_mode(
-          export_mode_dir,
-          input_receiver_fn,
-          assets_extra=assets_extra,
-          as_text=as_text,
-          checkpoint_path=checkpoint_path,
-          strip_default_attrs=strip_default_attrs,
-          mode=mode)
+    if not input_receiver_fn:
+      raise ValueError('An input_receiver_fn must be defined.')
 
-      exported[mode] = exported_path
+    input_receiver_fn_map = {mode: input_receiver_fn}
 
-    return exported
+    return self._export_all_saved_models(
+        export_dir_base,
+        input_receiver_fn_map,
+        assets_extra=assets_extra,
+        as_text=as_text,
+        checkpoint_path=checkpoint_path,
+        strip_default_attrs=strip_default_attrs)
 
-  def _export_saved_model_for_mode(
-      self, export_dir_base, input_receiver_fn,
+  def _export_all_saved_models(
+      self, export_dir_base, input_receiver_fn_map,
       assets_extra=None,
       as_text=False,
       checkpoint_path=None,
-      strip_default_attrs=False,
-      mode=model_fn_lib.ModeKeys.PREDICT):
+      strip_default_attrs=False):
     # pylint: disable=line-too-long
-    """Exports a single train/eval/predict graph as a SavedModel.
+    """Exports a SavedModel containing MetaGraphDefs for each requested mode.
 
-    For a detailed guide, see
-    @{$saved_model#using_savedmodel_with_estimators$Using SavedModel with Estimators}.
-
-    See tf.contrib.estimator.export_saved_model_for_mode for the currently
+    See tf.contrib.estimator.export_all_saved_models for the currently
     exposed version of this function.
 
-    This method takes an input_receiver_fn and mode. For the mode passed in,
+    For each mode passed in via the input_receiver_fn_map,
     this method builds a new graph by calling the input_receiver_fn to obtain
     feature and label `Tensor`s. Next, this method calls the `Estimator`'s
     model_fn in the passed mode to generate the model graph based on
     those features and labels, and restores the given checkpoint
     (or, lacking that, the most recent checkpoint) into the graph.
-    Finally, it creates a timestamped export directory below the
+    Only one of the modes is used for saving variables to the SavedModel
+    (order of preference: TRAIN, EVAL, then PREDICT), such that up to three
+    MetaGraphDefs are saved with a single set of variables in a single
+    SavedModel directory.
+
+    For the variables and MetaGraphDefs, a timestamped export directory below
     export_dir_base, and writes a `SavedModel` into it containing
     the `MetaGraphDef` for the given mode and its associated signatures.
 
@@ -727,8 +720,9 @@ def _export_saved_model_for_mode(
     Args:
       export_dir_base: A string containing a directory in which to create
         timestamped subdirectories containing exported SavedModels.
-      input_receiver_fn: a function that takes no argument and
-        returns the appropriate subclass of `InputReceiver`.
+      input_receiver_fn_map: dict of tf.estimator.ModeKeys to input_receiver_fn
+        mappings, where the input_receiver_fn is a function that takes no
+        argument and returns the appropriate subclass of `InputReceiver`.
       assets_extra: A dict specifying how to populate the assets.extra directory
         within the exported SavedModel, or `None` if no extra assets are needed.
       as_text: whether to write the SavedModel proto in text format.
@@ -737,20 +731,18 @@ def _export_saved_model_for_mode(
       strip_default_attrs: Boolean. If `True`, default-valued attributes will be
         removed from the NodeDefs. For a detailed guide, see
         [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
-      mode: tf.estimator.ModeKeys value indicating with mode will be exported.
 
     Returns:
-      The string path to the exported directory.
+      A dict of tf.estimator.ModeKeys value to string path for each exported
+      directory.
 
     Raises:
-      ValueError: if input_receiver_fn is None, no export_outputs
+      ValueError: if any input_receiver_fn is None, no export_outputs
         are provided, or no checkpoint can be found.
     """
     # pylint: enable=line-too-long
+    # TODO(b/65561022): Consider allowing multiple input_receiver_fns per mode.
     with context.graph_mode():
-      if not input_receiver_fn:
-        raise ValueError('An input_receiver_fn must be defined.')
-
       if not checkpoint_path:
         # Locate the latest checkpoint
         checkpoint_path = saver.latest_checkpoint(self._model_dir)
@@ -762,9 +754,34 @@ def _export_saved_model_for_mode(
 
       builder = saved_model_builder.SavedModelBuilder(temp_export_dir)
 
-      self._add_meta_graph_and_variables_for_mode(
-          builder, input_receiver_fn, checkpoint_path,
-          strip_default_attrs, mode)
+      save_variables = True
+      # Note that the order in which we run here matters, as the first
+      # mode we pass through will be used to save the variables. We run TRAIN
+      # first, as that is also the mode used for checkpoints, and therefore
+      # we are not likely to have vars in PREDICT that are not in the checkpoint
+      # created by TRAIN.
+      if input_receiver_fn_map.get(model_fn_lib.ModeKeys.TRAIN):
+        self._add_meta_graph_for_mode(
+            builder, input_receiver_fn_map, checkpoint_path,
+            strip_default_attrs, save_variables,
+            mode=model_fn_lib.ModeKeys.TRAIN)
+        save_variables = False
+      if input_receiver_fn_map.get(model_fn_lib.ModeKeys.EVAL):
+        self._add_meta_graph_for_mode(
+            builder, input_receiver_fn_map, checkpoint_path,
+            strip_default_attrs, save_variables,
+            mode=model_fn_lib.ModeKeys.EVAL)
+        save_variables = False
+      if input_receiver_fn_map.get(model_fn_lib.ModeKeys.PREDICT):
+        self._add_meta_graph_for_mode(
+            builder, input_receiver_fn_map, checkpoint_path,
+            strip_default_attrs, save_variables,
+            mode=model_fn_lib.ModeKeys.PREDICT)
+        save_variables = False
+
+      if save_variables:
+        raise ValueError('No valid modes for exporting found. Got {}.'.format(
+            input_receiver_fn_map.keys()))
 
       builder.save(as_text)
 
@@ -782,24 +799,31 @@ def _export_saved_model_for_mode(
       gfile.Rename(temp_export_dir, export_dir)
       return export_dir
 
-  def _add_meta_graph_and_variables_for_mode(
-      self, builder, input_receiver_fn, checkpoint_path, strip_default_attrs,
+  def _add_meta_graph_for_mode(
+      self, builder, input_receiver_fn_map, checkpoint_path,
+      strip_default_attrs, save_variables=True,
       mode=model_fn_lib.ModeKeys.PREDICT):
     # pylint: disable=line-too-long
     """Loads variables and adds them along with a MetaGraphDef for saving.
 
     Args:
       builder: instance of SavedModelBuilder that will be used for saving.
-      input_receiver_fn: a function that takes no argument and
-        returns the appropriate subclass of `InputReceiver`.
+      input_receiver_fn_map: dict of tf.estimator.ModeKeys to input_receiver_fn
+        mappings, where the input_receiver_fn is a function that takes no
+        argument and returns the appropriate subclass of `InputReceiver`.
       checkpoint_path: The checkpoint path to export.  If `None` (the default),
         the most recent checkpoint found within the model directory is chosen.
       strip_default_attrs: Boolean. If `True`, default-valued attributes will be
         removed from the NodeDefs. For a detailed guide, see
         [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
+      save_variables: bool, whether variables should be saved. If False, just
+        the MetaGraphDef will be saved. Note that save_variables should only be
+        True for the first call to this function, and the SavedModelBuilder will
+        raise an error if that is not the case.
       mode: tf.estimator.ModeKeys value indicating which mode will be exported.
     """
     # pylint: enable=line-too-long
+    input_receiver_fn = input_receiver_fn_map[mode]
     with ops.Graph().as_default() as g:
       self._create_and_assert_global_step(g)
       random_seed.set_random_seed(self._config.tf_random_seed)
@@ -832,15 +856,24 @@ def _add_meta_graph_and_variables_for_mode(
 
         saver_for_restore = estimator_spec.scaffold.saver or saver.Saver(
             sharded=True)
-        saver_for_restore.restore(session, checkpoint_path)
+
+        try:
+          saver_for_restore.restore(session, checkpoint_path)
+        except errors.NotFoundError as e:
+          msg = ('Could not load all requested variables from the checkpoint. '
+                 'Please make sure your model_fn does not expect variables '
+                 'that were not saved in the checkpoint.\n\n'
+                 'Encountered error with mode `{}` while restoring checkpoint '
+                 'from: `{}`. Full Traceback:\n\n{}').format(
+                     mode, checkpoint_path, e)
+          raise ValueError(msg)
 
         # We add the train op explicitly for now, so that we don't have to
         # change the Builder public interface. Note that this is a no-op
         # for prediction, where train_op is None.
         builder._add_train_op(estimator_spec.train_op)  # pylint: disable=protected-access
 
-        builder.add_meta_graph_and_variables(
-            session,
+        meta_graph_kwargs = dict(
             tags=export_tags,
             signature_def_map=signature_def_map,
             assets_collection=ops.get_collection(
@@ -848,6 +881,12 @@ def _add_meta_graph_and_variables_for_mode(
             strip_default_attrs=strip_default_attrs,
             legacy_init_op=local_init_op)
 
+        if save_variables:
+          builder.add_meta_graph_and_variables(
+              session, **meta_graph_kwargs)
+        else:
+          builder.add_meta_graph(**meta_graph_kwargs)
+
   def _get_export_outputs_for_spec(self, estimator_spec):
     """Given an EstimatorSpec, determine what our export outputs should be.
 
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 02088e5134f869..c9c6bdfeb5f834 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -2013,12 +2013,9 @@ def test_export_all_saved_models_proto_roundtrip_receiver_map(self):
     input_receiver_fn_map = {
         model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
-    self.assertEqual(len(export_dirs), 1)
-    # Restore, to validate that the export was well-formed.
-    export_dir = export_dirs[model_fn_lib.ModeKeys.PREDICT]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.SERVING], export_dir)
@@ -2035,12 +2032,9 @@ def test_export_all_saved_models_proto_roundtrip_train_only(self):
     input_receiver_fn_map = {
         model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
-    self.assertEqual(len(export_dirs), 1)
-    # Restore, to validate that the export was well-formed.
-    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.TRAINING], export_dir)
@@ -2058,12 +2052,9 @@ def test_export_all_saved_models_proto_roundtrip_eval_only(self):
     input_receiver_fn_map = {
         model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn()
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
-    self.assertEqual(len(export_dirs), 1)
-    # Restore, to validate that the export was well-formed.
-    export_dir = export_dirs[model_fn_lib.ModeKeys.EVAL]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.EVAL], export_dir)
@@ -2082,12 +2073,9 @@ def test_export_all_saved_models_proto_roundtrip_no_serving(self):
         model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
         model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn()
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
-    self.assertEqual(len(export_dirs), 2)
-    # Restore, to validate that the export was well-formed.
-    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.TRAINING], export_dir)
@@ -2096,7 +2084,7 @@ def test_export_all_saved_models_proto_roundtrip_no_serving(self):
         self.assertFalse('eval_multiplied' in graph_ops)
         self.assertTrue('feature_x' in graph_ops)
         self.assertTrue('weight' in graph_ops)
-    export_dir = export_dirs[model_fn_lib.ModeKeys.EVAL]
+
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.EVAL], export_dir)
@@ -2117,12 +2105,11 @@ def test_export_all_saved_models_proto_roundtrip_three_defs(self):
         model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn(),
         model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
     # Restore, to validate that the export was well-formed.
-    for mode, tag_set in model_fn_lib.EXPORT_TAG_MAP.items():
-      export_dir = export_dirs[mode]
+    for tag_set in model_fn_lib.EXPORT_TAG_MAP.values():
       with ops.Graph().as_default() as graph:
         with session.Session(graph=graph) as sess:
           loader.load(sess, tag_set, export_dir)
@@ -2139,10 +2126,9 @@ def test_export_all_saved_models_proto_roundtrip_all_vars(self):
         model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
         model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
-    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.TRAINING], export_dir)
@@ -2150,7 +2136,6 @@ def test_export_all_saved_models_proto_roundtrip_all_vars(self):
         self.assertTrue('later_var' in graph_ops)
         self.assertTrue('weight' in graph_ops)
 
-    export_dir = export_dirs[model_fn_lib.ModeKeys.PREDICT]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.SERVING], export_dir)
@@ -2166,10 +2151,9 @@ def test_export_all_saved_models_name_collision(self):
         model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
         model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
     }
-    export_dirs, tmpdir = self._test_export_all_saved_models(
+    export_dir, tmpdir = self._test_export_all_saved_models(
         input_receiver_fn_map)
 
-    export_dir = export_dirs[model_fn_lib.ModeKeys.TRAIN]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.TRAINING], export_dir)
@@ -2179,7 +2163,6 @@ def test_export_all_saved_models_name_collision(self):
         collection_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
         self.assertEqual(3, collection_vars[-1].eval())
 
-    export_dir = export_dirs[model_fn_lib.ModeKeys.PREDICT]
     with ops.Graph().as_default() as graph:
       with session.Session(graph=graph) as sess:
         loader.load(sess, [tag_constants.SERVING], export_dir)
@@ -2207,16 +2190,15 @@ def _test_export_all_saved_models(self, input_receiver_fn_map):
     # Perform the export.
     export_dir_base = os.path.join(
         compat.as_bytes(tmpdir), compat.as_bytes('export'))
-    export_dirs = est._export_all_saved_models(
+    export_dir = est._export_all_saved_models(
         export_dir_base, input_receiver_fn_map)
 
     # Check that all the files are in the right places.
     self.assertTrue(gfile.Exists(export_dir_base))
 
-    for _, export_dir in export_dirs.items():
-      self._validate_exported_files(export_dir)
+    self._validate_exported_files(export_dir)
 
-    return export_dirs, tmpdir
+    return export_dir, tmpdir
 
   def _validate_exported_files(self, export_dir):
     self.assertTrue(gfile.Exists(export_dir))
@@ -2233,6 +2215,42 @@ def _validate_exported_files(self, export_dir):
         compat.as_bytes(export_dir),
         compat.as_bytes('variables/variables.data-00000-of-00001'))))
 
+  def test_export_all_saved_models_var_not_found(self):
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
+    }
+
+    def _model_fn_with_predict_only_vars(features, labels, mode):
+      _, _ = features, labels
+      if mode == model_fn_lib.ModeKeys.PREDICT:
+        variables.Variable(1., name='only_in_predict')
+      else:
+        variables.Variable(1., name='otherwise')
+
+      prediction = constant_op.constant(1.)
+      return model_fn_lib.EstimatorSpec(
+          mode,
+          predictions=prediction,
+          loss=constant_op.constant(1.),
+          train_op=state_ops.assign_add(training.get_global_step(), 1),
+          export_outputs={
+              'test': export_output.PredictOutput({'prediction': prediction})
+          })
+
+    tmpdir = tempfile.mkdtemp()
+    est = estimator.Estimator(model_fn=_model_fn_with_predict_only_vars)
+    est.train(input_fn=_x_y_input_fn, steps=1)
+
+    # Perform the export.
+    export_dir_base = os.path.join(
+        compat.as_bytes(tmpdir), compat.as_bytes('export'))
+
+    err_regex = r'Could not load all requested variables[\w\W]*infer'
+    with self.assertRaisesRegexp(ValueError, err_regex):
+      est._export_all_saved_models(export_dir_base, input_receiver_fn_map)
+
   def test_export_savedmodel_with_saveables_proto_roundtrip(self):
     tmpdir = tempfile.mkdtemp()
     est = estimator.Estimator(
@@ -2464,6 +2482,43 @@ def _model_fn_scaffold(features, labels, mode):
 
     self.assertTrue(self.mock_saver.restore.called)
 
+  def test_scaffold_is_used_for_saver_multiple_modes(self):
+    tmpdir = tempfile.mkdtemp()
+
+    def _model_fn_scaffold(features, labels, mode):
+      _, _ = features, labels
+      variables.Variable(1., name='weight')
+      real_saver = saver.Saver()
+      self.mock_saver = test.mock.Mock(
+          wraps=real_saver, saver_def=real_saver.saver_def)
+      scores = constant_op.constant([3.])
+      if mode == model_fn_lib.ModeKeys.PREDICT:
+        scaffold = training.Scaffold(saver=self.mock_saver)
+      else:
+        scaffold = training.Scaffold()
+      return model_fn_lib.EstimatorSpec(
+          mode=mode,
+          predictions=constant_op.constant([[1.]]),
+          loss=constant_op.constant(0.),
+          train_op=state_ops.assign_add(training.get_global_step(), 1),
+          scaffold=scaffold,
+          export_outputs={'test': export_output.ClassificationOutput(scores)})
+
+    est = estimator.Estimator(model_fn=_model_fn_scaffold)
+    est.train(dummy_input_fn, steps=1)
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
+    }
+
+    # Perform the export.
+    export_dir_base = os.path.join(
+        compat.as_bytes(tmpdir), compat.as_bytes('export'))
+    est._export_all_saved_models(export_dir_base, input_receiver_fn_map)
+
+    self.assertTrue(self.mock_saver.restore.called)
+
   def test_scaffold_is_used_for_local_init(self):
     tmpdir = tempfile.mkdtemp()
 
@@ -2509,6 +2564,61 @@ def _model_fn_scaffold(features, labels, mode):
         my_int_value = sess.run(my_int)
         self.assertEqual(12345, my_int_value)
 
+  def test_scaffold_is_used_for_local_init_multiple_modes(self):
+    tmpdir = tempfile.mkdtemp()
+
+    def _model_fn_scaffold(features, labels, mode):
+      _, _ = features, labels
+      my_int = variables.Variable(1, name='my_int',
+                                  collections=[ops.GraphKeys.LOCAL_VARIABLES])
+      scores = constant_op.constant([3.])
+      with ops.control_dependencies([
+          variables.local_variables_initializer(),
+          lookup_ops.tables_initializer()
+      ]):
+        assign_op = state_ops.assign(my_int, 12345)
+
+      custom_local_init_op = None
+      if mode == model_fn_lib.ModeKeys.PREDICT:
+        # local_initSop must be an Operation, not a Tensor.
+        custom_local_init_op = control_flow_ops.group(assign_op)
+
+      return model_fn_lib.EstimatorSpec(
+          mode=mode,
+          predictions=constant_op.constant([[1.]]),
+          loss=constant_op.constant(0.),
+          train_op=state_ops.assign_add(training.get_global_step(), 1),
+          scaffold=training.Scaffold(local_init_op=custom_local_init_op),
+          export_outputs={'test': export_output.ClassificationOutput(scores)})
+
+    est = estimator.Estimator(model_fn=_model_fn_scaffold)
+    est.train(dummy_input_fn, steps=1)
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.TRAIN: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn(),
+        model_fn_lib.ModeKeys.PREDICT: _get_serving_input_receiver_fn()
+    }
+
+    # Perform the export.
+    export_dir_base = os.path.join(
+        compat.as_bytes(tmpdir), compat.as_bytes('export'))
+    export_dir = est._export_all_saved_models(
+        export_dir_base, input_receiver_fn_map)
+
+    # Restore, to validate that the custom local_init_op runs.
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.SERVING], export_dir)
+        my_int = graph.get_tensor_by_name('my_int:0')
+        my_int_value = sess.run(my_int)
+        self.assertEqual(12345, my_int_value)
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.TRAINING], export_dir)
+        my_int = graph.get_tensor_by_name('my_int:0')
+        my_int_value = sess.run(my_int)
+        self.assertEqual(1, my_int_value)
+
   def test_features_labels_mode(self):
     given_features = {'test-features': constant_op.constant([[1], [1]])}
 

From ed2bfbe66486324550aee8038e0edf332f85efb1 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama 
Date: Thu, 10 May 2018 09:49:50 -0700
Subject: [PATCH 0603/1691] Add citation for TF-Slim.

PiperOrigin-RevId: 196129248
---
 tensorflow/contrib/slim/README.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tensorflow/contrib/slim/README.md b/tensorflow/contrib/slim/README.md
index 746b9556423761..f2bb458848fab5 100644
--- a/tensorflow/contrib/slim/README.md
+++ b/tensorflow/contrib/slim/README.md
@@ -909,3 +909,8 @@ slim.evaluation.evaluation_loop(
 
 ## Authors
 Sergio Guadarrama and Nathan Silberman
+
+## Citation
+"TensorFlow-Slim: a lightweight library for defining, training and evaluating complex models in TensorFlow"
+S. Guadarrama, N. Silberman, 2016.
+https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim

From c4d8097bcd4203d68ee0911ae3476304d6ce65d6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 09:50:54 -0700
Subject: [PATCH 0604/1691] Increase shard count yet more for
 tensorflow/contrib/metrics:metric_ops_test to avoid flaky timeouts

PiperOrigin-RevId: 196129385
---
 tensorflow/contrib/metrics/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/metrics/BUILD b/tensorflow/contrib/metrics/BUILD
index e050f3c8d4fc61..4f2c82ca230116 100644
--- a/tensorflow/contrib/metrics/BUILD
+++ b/tensorflow/contrib/metrics/BUILD
@@ -77,7 +77,7 @@ py_test(
 py_test(
     name = "metric_ops_test",
     srcs = ["python/ops/metric_ops_test.py"],
-    shard_count = 8,
+    shard_count = 16,
     srcs_version = "PY2AND3",
     tags = ["noasan"],  # times out b/63678675
     deps = [

From f59f87131867d2a5782740101a8ab4e6536fe72e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 10:21:02 -0700
Subject: [PATCH 0605/1691] Register XLA device kernel for IdentityN op.

PiperOrigin-RevId: 196133882
---
 tensorflow/compiler/jit/BUILD            | 1 +
 tensorflow/compiler/jit/xla_device_ops.h | 4 ++++
 2 files changed, 5 insertions(+)

diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index a6d0408a8fe1f7..df634ca3ccdcce 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -176,6 +176,7 @@ cc_library(
         "//tensorflow/core/kernels:cast_op",
         "//tensorflow/core/kernels:constant_op",
         "//tensorflow/core/kernels:control_flow_ops",
+        "//tensorflow/core/kernels:identity_n_op",
         "//tensorflow/core/kernels:identity_op",
         "//tensorflow/core/kernels:no_op",
         "//tensorflow/core/kernels:sendrecv_ops",
diff --git a/tensorflow/compiler/jit/xla_device_ops.h b/tensorflow/compiler/jit/xla_device_ops.h
index 498d25cf566a91..65c0e8577f1d03 100644
--- a/tensorflow/compiler/jit/xla_device_ops.h
+++ b/tensorflow/compiler/jit/xla_device_ops.h
@@ -23,6 +23,7 @@ limitations under the License.
 #include "tensorflow/core/kernels/cast_op.h"
 #include "tensorflow/core/kernels/constant_op.h"
 #include "tensorflow/core/kernels/control_flow_ops.h"
+#include "tensorflow/core/kernels/identity_n_op.h"
 #include "tensorflow/core/kernels/identity_op.h"
 #include "tensorflow/core/kernels/no_op.h"
 #include "tensorflow/core/kernels/sendrecv_ops.h"
@@ -63,6 +64,9 @@ class XlaDeviceDummyOp : public OpKernel {
       ConstantOp);                                                             \
   REGISTER_KERNEL_BUILDER(                                                     \
       Name("Identity").Device(DEVICE).TypeConstraint("T", TYPES), IdentityOp); \
+  REGISTER_KERNEL_BUILDER(                                                     \
+      Name("IdentityN").Device(DEVICE).TypeConstraint("T", TYPES),             \
+      IdentityNOp);                                                            \
   REGISTER_KERNEL_BUILDER(Name("Placeholder").Device(DEVICE), PlaceholderOp);  \
   REGISTER_KERNEL_BUILDER(Name("PlaceholderV2").Device(DEVICE),                \
                           PlaceholderOp);                                      \

From 2d8b1a448446f809ef2ae682b966cb090e227f6c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 10:26:06 -0700
Subject: [PATCH 0606/1691] Removing expected softmax test failure and
 improving logging.

PiperOrigin-RevId: 196134704
---
 .../contrib/lite/testing/generate_examples.py |  5 ++-
 .../testing/generated_examples_zip_test.cc    | 34 ++++++++++++++-----
 2 files changed, 29 insertions(+), 10 deletions(-)

diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index c3cc1e28d7e655..9b27199c76b60c 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -20,6 +20,9 @@
 generate_examples 
 
 bazel run //tensorflow/contrib/lite/testing:generate_examples
+
+To more easily debug failures use (or override) the --save_graphdefs flag to
+place text proto graphdefs into the generated zip files.
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -427,7 +430,7 @@ def build_example(label, param_dict_real):
         report["toco_log"] = toco_log
 
         if FLAGS.save_graphdefs:
-          archive.writestr(label + ".pb",
+          archive.writestr(label + ".pbtxt",
                            text_format.MessageToString(graph_def),
                            zipfile.ZIP_DEFLATED)
 
diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
index 860696ecdccf89..a8714afd83bb96 100644
--- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
+++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
@@ -67,9 +67,6 @@ std::map kBrokenTests = {
     // non-const tensors as crops.
     {R"(^\/batch_to_space_nd.*crops=\[\[1,1\],\[1,1\]\])", "70594634"},
 
-    // Softmax graphs are too complex.
-    {R"(^\/softmax.*input_shape=\[1,3,4,3\])", "67749831"},
-
     // SpaceToBatchND only supports 4D tensors.
     {R"(^\/space_to_batch_nd.*input_shape=\[1,4,4,4,1,1\])", "70848787"},
 
@@ -207,7 +204,7 @@ std::vector UnarchiveZipAndFindTestNames(const string& zip_file_name) {
 
 class OpsTest : public ::testing::TestWithParam {};
 
-TEST_P(OpsTest, RunStuff) {
+TEST_P(OpsTest, RunZipTests) {
   string test_path = GetParam();
   string tflite_test_case = test_path + "_tests.txt";
   string tflite_dir = test_path.substr(0, test_path.find_last_of("/"));
@@ -230,7 +227,9 @@ TEST_P(OpsTest, RunStuff) {
     EXPECT_TRUE(result) << test_driver.GetErrorMessage();
   } else {
     if (FLAGS_ignore_known_bugs) {
-      EXPECT_FALSE(result);
+      EXPECT_FALSE(result) << "Test was expected to fail but is now passing; "
+                              "you can mark http://b/"
+                           << bug_number << " as fixed! Yay!";
     } else {
       EXPECT_TRUE(result) << test_driver.GetErrorMessage()
                           << ": Possibly due to http://b/" << bug_number;
@@ -238,12 +237,29 @@ TEST_P(OpsTest, RunStuff) {
   }
 }
 
+struct ZipPathParamName {
+  template 
+  string operator()(const ::testing::TestParamInfo& info) const {
+    string param_name = info.param;
+    size_t last_slash = param_name.find_last_of("\\/");
+    if (last_slash != string::npos) {
+      param_name = param_name.substr(last_slash);
+    }
+    for (size_t index = 0; index < param_name.size(); ++index) {
+      if (!isalnum(param_name[index]) && param_name[index] != '_')
+        param_name[index] = '_';
+    }
+    return param_name;
+  }
+};
+
 // Instantiate a test. This assumes `zip_base`.zip is a declared data file
 // of this test.
-#define INSTANTIATE_TESTS(zip_base) \
-  INSTANTIATE_TEST_CASE_P(          \
-      zip_base, OpsTest,            \
-      ::testing::ValuesIn(UnarchiveZipAndFindTestNames(#zip_base ".zip")));
+#define INSTANTIATE_TESTS(zip_base)                                        \
+  INSTANTIATE_TEST_CASE_P(                                                 \
+      zip_base, OpsTest,                                                   \
+      ::testing::ValuesIn(UnarchiveZipAndFindTestNames(#zip_base ".zip")), \
+      ZipPathParamName());
 
 INSTANTIATE_TESTS(add)
 INSTANTIATE_TESTS(arg_max)

From e8a9224cd7351bb58080963f3db5932296398023 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 10:49:20 -0700
Subject: [PATCH 0607/1691] Update documentation of ServingInputReceiver when a
 non-dict is passed as argument.

PiperOrigin-RevId: 196138375
---
 tensorflow/python/estimator/export/export.py | 99 ++++++++++++--------
 1 file changed, 58 insertions(+), 41 deletions(-)

diff --git a/tensorflow/python/estimator/export/export.py b/tensorflow/python/estimator/export/export.py
index 9aafb56679d219..48ae8cd49791c2 100644
--- a/tensorflow/python/estimator/export/export.py
+++ b/tensorflow/python/estimator/export/export.py
@@ -14,7 +14,6 @@
 # ==============================================================================
 """Configuration and utilities for receiving inputs at serving time."""
 
-
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -37,7 +36,6 @@
 from tensorflow.python.util import compat
 from tensorflow.python.util.tf_export import tf_export
 
-
 _SINGLE_FEATURE_DEFAULT_NAME = 'feature'
 _SINGLE_RECEIVER_DEFAULT_NAME = 'input'
 _SINGLE_LABEL_DEFAULT_NAME = 'label'
@@ -69,11 +67,11 @@ def _wrap_and_check_receiver_tensors(receiver_tensors):
 
 def _check_tensor(tensor, name, error_label='feature'):
   """Check that passed `tensor` is a Tensor or SparseTensor."""
-  if not (isinstance(tensor, ops.Tensor)
-          or isinstance(tensor, sparse_tensor.SparseTensor)):
+  if not (isinstance(tensor, ops.Tensor) or
+          isinstance(tensor, sparse_tensor.SparseTensor)):
     fmt_name = ' {}'.format(name) if name else ''
-    value_error = ValueError(
-        '{}{} must be a Tensor or SparseTensor.'.format(error_label, fmt_name))
+    value_error = ValueError('{}{} must be a Tensor or SparseTensor.'.format(
+        error_label, fmt_name))
     # NOTE(ericmc): This if-else block is a specific carve-out for
     # LabeledTensor, which has a `.tensor` attribute and which is
     # convertible to tf.Tensor via ops.convert_to_tensor.
@@ -92,19 +90,23 @@ def _check_tensor(tensor, name, error_label='feature'):
 
 def _check_tensor_key(name, error_label='feature'):
   if not isinstance(name, six.string_types):
-    raise ValueError(
-        '{} keys must be strings: {}.'.format(error_label, name))
+    raise ValueError('{} keys must be strings: {}.'.format(error_label, name))
 
 
 @tf_export('estimator.export.ServingInputReceiver')
-class ServingInputReceiver(collections.namedtuple(
-    'ServingInputReceiver',
-    ['features', 'receiver_tensors', 'receiver_tensors_alternatives'])):
+class ServingInputReceiver(
+    collections.namedtuple(
+        'ServingInputReceiver',
+        ['features', 'receiver_tensors', 'receiver_tensors_alternatives'])):
   """A return type for a serving_input_receiver_fn.
 
   The expected return values are:
     features: A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or
-      `SparseTensor`, specifying the features to be passed to the model.
+      `SparseTensor`, specifying the features to be passed to the model. Note:
+      if `features` passed is not a dict, it will be wrapped in a dict with a
+      single entry, using 'feature' as the key.  Consequently, the model must
+      accept a feature dict of the form {'feature': tensor}.  You may use
+      `TensorServingInputReceiver` if you want the tensor to be passed as is.
     receiver_tensors: A `Tensor`, `SparseTensor`, or dict of string to `Tensor`
       or `SparseTensor`, specifying input nodes where this receiver expects to
       be fed by default.  Typically, this is a single placeholder expecting
@@ -119,7 +121,9 @@ class ServingInputReceiver(collections.namedtuple(
       Defaults to None.
   """
 
-  def __new__(cls, features, receiver_tensors,
+  def __new__(cls,
+              features,
+              receiver_tensors,
               receiver_tensors_alternatives=None):
     if features is None:
       raise ValueError('features must be defined.')
@@ -139,8 +143,9 @@ def __new__(cls, features, receiver_tensors,
       for alternative_name, receiver_tensors_alt in (
           six.iteritems(receiver_tensors_alternatives)):
         if not isinstance(receiver_tensors_alt, dict):
-          receiver_tensors_alt = {_SINGLE_RECEIVER_DEFAULT_NAME:
-                                  receiver_tensors_alt}
+          receiver_tensors_alt = {
+              _SINGLE_RECEIVER_DEFAULT_NAME: receiver_tensors_alt
+          }
           # Updating dict during iteration is OK in this case.
           receiver_tensors_alternatives[alternative_name] = (
               receiver_tensors_alt)
@@ -157,9 +162,10 @@ def __new__(cls, features, receiver_tensors,
 
 
 @tf_export('estimator.export.TensorServingInputReceiver')
-class TensorServingInputReceiver(collections.namedtuple(
-    'TensorServingInputReceiver',
-    ['features', 'receiver_tensors', 'receiver_tensors_alternatives'])):
+class TensorServingInputReceiver(
+    collections.namedtuple(
+        'TensorServingInputReceiver',
+        ['features', 'receiver_tensors', 'receiver_tensors_alternatives'])):
   """A return type for a serving_input_receiver_fn.
 
   This is for use with models that expect a single `Tensor` or `SparseTensor`
@@ -194,7 +200,9 @@ class TensorServingInputReceiver(collections.namedtuple(
       Defaults to None.
   """
 
-  def __new__(cls, features, receiver_tensors,
+  def __new__(cls,
+              features,
+              receiver_tensors,
               receiver_tensors_alternatives=None):
     if features is None:
       raise ValueError('features must be defined.')
@@ -212,9 +220,9 @@ def __new__(cls, features, receiver_tensors,
         receiver_tensors_alternatives=receiver.receiver_tensors_alternatives)
 
 
-class SupervisedInputReceiver(collections.namedtuple(
-    'SupervisedInputReceiver',
-    ['features', 'labels', 'receiver_tensors'])):
+class SupervisedInputReceiver(
+    collections.namedtuple('SupervisedInputReceiver',
+                           ['features', 'labels', 'receiver_tensors'])):
   """A return type for a training_input_receiver_fn or eval_input_receiver_fn.
 
   This differs from a ServingInputReceiver in that (1) this receiver expects
@@ -272,11 +280,13 @@ def build_parsing_serving_input_receiver_fn(feature_spec,
   Returns:
     A serving_input_receiver_fn suitable for use in serving.
   """
+
   def serving_input_receiver_fn():
     """An input_fn that expects a serialized tf.Example."""
-    serialized_tf_example = array_ops.placeholder(dtype=dtypes.string,
-                                                  shape=[default_batch_size],
-                                                  name='input_example_tensor')
+    serialized_tf_example = array_ops.placeholder(
+        dtype=dtypes.string,
+        shape=[default_batch_size],
+        name='input_example_tensor')
     receiver_tensors = {'examples': serialized_tf_example}
     features = parsing_ops.parse_example(serialized_tf_example, feature_spec)
     return ServingInputReceiver(features, receiver_tensors)
@@ -295,10 +305,12 @@ def _placeholder_from_tensor(t, default_batch_size=None):
   return array_ops.placeholder(dtype=t.dtype, shape=shape, name=t.op.name)
 
 
-def _placeholders_from_receiver_tensors_dict(
-    input_vals, default_batch_size=None):
-  return {name: _placeholder_from_tensor(t, default_batch_size)
-          for name, t in input_vals.items()}
+def _placeholders_from_receiver_tensors_dict(input_vals,
+                                             default_batch_size=None):
+  return {
+      name: _placeholder_from_tensor(t, default_batch_size)
+      for name, t in input_vals.items()
+  }
 
 
 @tf_export('estimator.export.build_raw_serving_input_receiver_fn')
@@ -316,6 +328,7 @@ def build_raw_serving_input_receiver_fn(features, default_batch_size=None):
   Returns:
     A serving_input_receiver_fn.
   """
+
   def serving_input_receiver_fn():
     """A serving_input_receiver_fn that expects features to be fed directly."""
     receiver_tensors = _placeholders_from_receiver_tensors_dict(
@@ -329,8 +342,9 @@ def serving_input_receiver_fn():
   return serving_input_receiver_fn
 
 
-def build_raw_supervised_input_receiver_fn(
-    features, labels, default_batch_size=None):
+def build_raw_supervised_input_receiver_fn(features,
+                                           labels,
+                                           default_batch_size=None):
   """Build a supervised_input_receiver_fn for raw features and labels.
 
   This function wraps tensor placeholders in a supervised_receiver_fn
@@ -443,11 +457,12 @@ def build_all_signature_defs(receiver_tensors,
     for receiver_name, receiver_tensors_alt in (
         six.iteritems(receiver_tensors_alternatives)):
       if not isinstance(receiver_tensors_alt, dict):
-        receiver_tensors_alt = {_SINGLE_RECEIVER_DEFAULT_NAME:
-                                receiver_tensors_alt}
+        receiver_tensors_alt = {
+            _SINGLE_RECEIVER_DEFAULT_NAME: receiver_tensors_alt
+        }
       for output_key, export_output in export_outputs.items():
-        signature_name = '{}:{}'.format(receiver_name or 'None',
-                                        output_key or 'None')
+        signature_name = '{}:{}'.format(receiver_name or 'None', output_key or
+                                        'None')
         try:
           signature = export_output.as_signature_def(receiver_tensors_alt)
           signature_def_map[signature_name] = signature
@@ -464,8 +479,11 @@ def build_all_signature_defs(receiver_tensors,
   # signatures produced for serving. We skip this check for training and eval
   # signatures, which are not intended for serving.
   if serving_only:
-    signature_def_map = {k: v for k, v in signature_def_map.items()
-                         if signature_def_utils.is_valid_signature(v)}
+    signature_def_map = {
+        k: v
+        for k, v in signature_def_map.items()
+        if signature_def_utils.is_valid_signature(v)
+    }
   return signature_def_map
 
 
@@ -506,8 +524,8 @@ def _log_signature_report(signature_def_map, excluded_signatures):
 
   if not signature_def_map:
     logging.warn('Export includes no signatures!')
-  elif (signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
-        not in signature_def_map):
+  elif (signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY not in
+        signature_def_map):
     logging.warn('Export includes no default signature!')
 
 
@@ -547,6 +565,5 @@ def get_temp_export_dir(timestamped_export_dir):
   """
   (dirname, basename) = os.path.split(timestamped_export_dir)
   temp_export_dir = os.path.join(
-      compat.as_bytes(dirname),
-      compat.as_bytes('temp-{}'.format(basename)))
+      compat.as_bytes(dirname), compat.as_bytes('temp-{}'.format(basename)))
   return temp_export_dir

From af4cd0e87cf59c5307546a9ca41bdd457634c58d Mon Sep 17 00:00:00 2001
From: Francois Chollet 
Date: Thu, 10 May 2018 10:51:23 -0700
Subject: [PATCH 0608/1691] Fix inaccurate docstring of Orthogonal initializer.

PiperOrigin-RevId: 196138675
---
 tensorflow/python/ops/init_ops.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py
index f93bf0a17f31b4..1f8d8dc4f3e7b8 100644
--- a/tensorflow/python/ops/init_ops.py
+++ b/tensorflow/python/ops/init_ops.py
@@ -488,9 +488,9 @@ class Orthogonal(Initializer):
 
   If the shape of the tensor to initialize is two-dimensional, it is initialized
   with an orthogonal matrix obtained from the QR decomposition of a matrix of
-  uniform random numbers. If the matrix has fewer rows than columns then the
-  output will have orthogonal rows. Otherwise, the output will have orthogonal
-  columns.
+  random numbers drawn from a normal distribution.
+  If the matrix has fewer rows than columns then the output will have orthogonal
+  rows. Otherwise, the output will have orthogonal columns.
 
   If the shape of the tensor to initialize is more than two-dimensional,
   a matrix of shape `(shape[0] * ... * shape[n - 2], shape[n - 1])`

From 0013b6953547fe17865c21155bdebe4cfe656e74 Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar 
Date: Thu, 10 May 2018 10:58:11 -0700
Subject: [PATCH 0609/1691] Traverse through control dependencies.

PiperOrigin-RevId: 196139886
---
 tensorflow/cc/tools/freeze_saved_model.cc     |  6 ++++-
 .../cc/tools/freeze_saved_model_test.cc       | 25 +++++++++++++++++++
 2 files changed, 30 insertions(+), 1 deletion(-)

diff --git a/tensorflow/cc/tools/freeze_saved_model.cc b/tensorflow/cc/tools/freeze_saved_model.cc
index 2a859d6472dadf..23e9dc40d23899 100644
--- a/tensorflow/cc/tools/freeze_saved_model.cc
+++ b/tensorflow/cc/tools/freeze_saved_model.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/cc/tools/freeze_saved_model.h"
 
+#include 
 #include 
 
 #include "tensorflow/core/framework/attr_value.pb.h"
@@ -72,7 +73,10 @@ void GetNodeNameToNodeDefMap(
 }
 
 // Strips off the tensor part of the tensor_name to get the node_name.
-const string GetNodeNameFromTensorName(const string& tensor_name) {
+const string GetNodeNameFromTensorName(string tensor_name) {
+  if (tensor_name[0] == '^') {
+    tensor_name.erase(0, 1);
+  }
   std::vector tensor_name_parts = str_util::Split(tensor_name, ':');
   return tensor_name_parts[0];
 }
diff --git a/tensorflow/cc/tools/freeze_saved_model_test.cc b/tensorflow/cc/tools/freeze_saved_model_test.cc
index e265a68e545cc9..979b23c3fc5f66 100644
--- a/tensorflow/cc/tools/freeze_saved_model_test.cc
+++ b/tensorflow/cc/tools/freeze_saved_model_test.cc
@@ -376,6 +376,31 @@ TEST_F(FreezeTest, GraphDefWithMultiOutputOperation) {
   GraphDefEqual(frozen_graph_def, graph_def);
 }
 
+TEST_F(FreezeTest, GraphDefWithControlDependency) {
+  // Inputs that are control dependencies get tensor prefixes,
+  // i.e. ^control_dependency.
+  // Test that we traverse those correctly.
+  SavedModelBundle saved_model_bundle;
+  GraphDef graph_def;
+  Scope scope = Scope::NewRootScope();
+  Output source = ops::Const(scope.WithOpName("source"), 10.0f, {});
+  Output a = ops::Const(scope.WithOpName("a").WithControlDependencies(source),
+                        {10.0f, 10.0f}, {2});
+  Output b = ops::Const(scope.WithOpName("b"), 10.0f, {});
+  Output c = ops::Mul(scope.WithOpName("c"), a, b);
+  TF_ASSERT_OK(scope.ToGraphDef(&graph_def));
+  TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle(graph_def, {"c:0"}, "",
+                                                        &saved_model_bundle));
+
+  GraphDef frozen_graph_def;
+  std::unordered_set inputs;
+  std::unordered_set outputs;
+  TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs,
+                                &outputs));
+
+  GraphDefEqual(frozen_graph_def, graph_def);
+}
+
 TEST_F(FreezeTest, GraphDefWithoutDependentVariables) {
   TestFreezeGraphWithoutDependentVariables(false);
 }

From f08f24cd559b5824a1874a0e76d339875e43f366 Mon Sep 17 00:00:00 2001
From: Ben Barsdell 
Date: Thu, 10 May 2018 11:06:01 -0700
Subject: [PATCH 0610/1691] Add GPU support for float16 batched matmul (#18436)

* Add GPU support for float16 batched matmul

- Uses cublasGemmBatchedEx introduced in CUDA 9.1.
- Includes support for Tensor Op math.
- Falls back to a loop over non-batched gemm calls on older CUDA
  versions or GPU architectures.

* Refactor GPU batched gemm into one internal func
---
 .../core/kernels/batch_matmul_op_impl.h       | 106 +++++++++++++++++-
 .../core/kernels/batch_matmul_op_real.cc      |   4 +
 tensorflow/stream_executor/blas.h             |  14 +++
 tensorflow/stream_executor/cuda/cuda_blas.cc  | 106 +++++++++++++++---
 tensorflow/stream_executor/cuda/cuda_blas.h   |   6 +-
 tensorflow/stream_executor/stream.cc          |  34 ++++++
 tensorflow/stream_executor/stream.h           |  14 +++
 7 files changed, 262 insertions(+), 22 deletions(-)

diff --git a/tensorflow/core/kernels/batch_matmul_op_impl.h b/tensorflow/core/kernels/batch_matmul_op_impl.h
index a1c03f99181a6c..475bda848db4a7 100644
--- a/tensorflow/core/kernels/batch_matmul_op_impl.h
+++ b/tensorflow/core/kernels/batch_matmul_op_impl.h
@@ -329,6 +329,8 @@ struct LaunchBatchMatMul {
       c_ptrs.push_back(&c_device_memory.back());
     }
 
+    typedef Scalar Coefficient;
+
     // Cublas does
     // C = A x B
     // where A, B and C are assumed to be in column major.
@@ -352,9 +354,9 @@ struct LaunchBatchMatMul {
         bool blas_launch_status =
             stream
                 ->ThenBlasGemv(gemv_trans_a, adj_x ? m : k, adj_x ? k : m,
-                               static_cast(1.0), *(a_ptrs[0]),
+                               static_cast(1.0), *(a_ptrs[0]),
                                adj_x ? m : k, *(b_ptrs[0]), 1,
-                               static_cast(0.0), c_ptrs[0], 1)
+                               static_cast(0.0), c_ptrs[0], 1)
                 .ok();
         if (!blas_launch_status) {
           context->SetStatus(errors::Internal(
@@ -366,9 +368,9 @@ struct LaunchBatchMatMul {
         bool blas_launch_status =
             stream
                 ->ThenBlasGemm(blas_transpose_b, blas_transpose_a, n, m, k,
-                               static_cast(1.0), *(b_ptrs[0]),
+                               static_cast(1.0), *(b_ptrs[0]),
                                adj_y ? k : n, *(a_ptrs[0]), adj_x ? m : k,
-                               static_cast(0.0), c_ptrs[0], n)
+                               static_cast(0.0), c_ptrs[0], n)
                 .ok();
         if (!blas_launch_status) {
           context->SetStatus(errors::Internal(
@@ -383,8 +385,8 @@ struct LaunchBatchMatMul {
           stream
               ->ThenBlasGemmBatchedWithScratch(
                   blas_transpose_b, blas_transpose_a, n, m, k,
-                  static_cast(1.0), b_ptrs, adj_y ? k : n, a_ptrs,
-                  adj_x ? m : k, static_cast(0.0), c_ptrs, n,
+                  static_cast(1.0), b_ptrs, adj_y ? k : n, a_ptrs,
+                  adj_x ? m : k, static_cast(0.0), c_ptrs, n,
                   batch_size, &scratch_allocator)
               .ok();
       if (!blas_launch_status) {
@@ -398,6 +400,98 @@ struct LaunchBatchMatMul {
   }
 };
 
+template <>
+struct LaunchBatchMatMul {
+  static void Launch(OpKernelContext* context, const Tensor& in_x,
+                     const Tensor& in_y, bool adj_x, bool adj_y, Tensor* out) {
+    typedef Eigen::half Scalar;
+    constexpr perftools::gputools::blas::Transpose kTranspose =
+        is_complex::value
+            ? perftools::gputools::blas::Transpose::kConjugateTranspose
+            : perftools::gputools::blas::Transpose::kTranspose;
+    perftools::gputools::blas::Transpose trans[] = {
+        perftools::gputools::blas::Transpose::kNoTranspose, kTranspose};
+    const uint64 m = in_x.dim_size(adj_x ? 2 : 1);
+    const uint64 k = in_x.dim_size(adj_x ? 1 : 2);
+    const uint64 n = in_y.dim_size(adj_y ? 1 : 2);
+    const uint64 batch_size = in_x.dim_size(0);
+    auto blas_transpose_a = trans[adj_x];
+    auto blas_transpose_b = trans[adj_y];
+
+    auto* stream = context->op_device_context()->stream();
+    OP_REQUIRES(context, stream, errors::Internal("No GPU stream available."));
+
+    typedef perftools::gputools::DeviceMemory DeviceMemoryType;
+    std::vector a_device_memory;
+    std::vector b_device_memory;
+    std::vector c_device_memory;
+    std::vector a_ptrs;
+    std::vector b_ptrs;
+    std::vector c_ptrs;
+    a_device_memory.reserve(batch_size);
+    b_device_memory.reserve(batch_size);
+    c_device_memory.reserve(batch_size);
+    a_ptrs.reserve(batch_size);
+    b_ptrs.reserve(batch_size);
+    c_ptrs.reserve(batch_size);
+    auto* a_base_ptr = in_x.template flat().data();
+    auto* b_base_ptr = in_y.template flat().data();
+    auto* c_base_ptr = out->template flat().data();
+    for (int64 i = 0; i < batch_size; ++i) {
+      a_device_memory.push_back(AsDeviceMemory(a_base_ptr + i * m * k));
+      b_device_memory.push_back(AsDeviceMemory(b_base_ptr + i * k * n));
+      c_device_memory.push_back(AsDeviceMemory(c_base_ptr + i * m * n));
+      a_ptrs.push_back(&a_device_memory.back());
+      b_ptrs.push_back(&b_device_memory.back());
+      c_ptrs.push_back(&c_device_memory.back());
+    }
+
+    typedef float Coefficient;
+
+    // Cublas does
+    // C = A x B
+    // where A, B and C are assumed to be in column major.
+    // We want the output to be in row-major, so we can compute
+    // C' = B' x A', where ' stands for transpose (not adjoint).
+    // TODO(yangzihao): Choose the best of the three strategies using autotune.
+    if (batch_size == 1) {
+      // This is a regular matrix*matrix or matrix*vector multiply. Avoid the
+      // overhead of the scratch allocator and the batch interface.
+      // TODO(benbarsdell): Use fp16 Gemv if it becomes supported by CUBLAS
+      bool blas_launch_status =
+          stream
+              ->ThenBlasGemm(blas_transpose_b, blas_transpose_a, n, m, k,
+                             static_cast(1.0), *(b_ptrs[0]),
+                             adj_y ? k : n, *(a_ptrs[0]), adj_x ? m : k,
+                             static_cast(0.0), c_ptrs[0], n)
+              .ok();
+      if (!blas_launch_status) {
+        context->SetStatus(errors::Internal(
+            "Blas xGEMM launch failed : a.shape=", in_x.shape().DebugString(),
+            ", b.shape=", in_y.shape().DebugString(), ", m=", m, ", n=", n,
+            ", k=", k));
+      }
+    } else {
+      CublasScratchAllocator scratch_allocator(context);
+      bool blas_launch_status =
+          stream
+              ->ThenBlasGemmBatchedWithScratch(
+                  blas_transpose_b, blas_transpose_a, n, m, k,
+                  static_cast(1.0), b_ptrs, adj_y ? k : n, a_ptrs,
+                  adj_x ? m : k, static_cast(0.0), c_ptrs, n,
+                  batch_size, &scratch_allocator)
+              .ok();
+      if (!blas_launch_status) {
+        context->SetStatus(
+            errors::Internal("Blas xGEMMBatched launch failed : a.shape=",
+                             in_x.shape().DebugString(), ", b.shape=",
+                             in_y.shape().DebugString(), ", m=", m, ", n=", n,
+                             ", k=", k, ", batch_size=", batch_size));
+      }
+    }
+  }
+};
+
 #endif  // GOOGLE_CUDA
 
 #ifdef TENSORFLOW_USE_SYCL
diff --git a/tensorflow/core/kernels/batch_matmul_op_real.cc b/tensorflow/core/kernels/batch_matmul_op_real.cc
index 7e1e2aa4ec1358..2bb22bbd4f66b6 100644
--- a/tensorflow/core/kernels/batch_matmul_op_real.cc
+++ b/tensorflow/core/kernels/batch_matmul_op_real.cc
@@ -15,6 +15,10 @@ limitations under the License.
 
 #include "tensorflow/core/kernels/batch_matmul_op_impl.h"
 
+#if GOOGLE_CUDA
+#include "cuda/include/cuda.h"
+#endif  // GOOGLE_CUDA
+
 namespace tensorflow {
 
 #if !defined(INTEL_MKL)
diff --git a/tensorflow/stream_executor/blas.h b/tensorflow/stream_executor/blas.h
index be0b0bf5fb20b2..ea87744b225215 100644
--- a/tensorflow/stream_executor/blas.h
+++ b/tensorflow/stream_executor/blas.h
@@ -1083,6 +1083,13 @@ class BlasSupport {
   // This is a batched version of DoBlasGemm.
   // The batched GEMM computes matrix product for each input/output in a, b,
   // and c, which contain batch_count DeviceMemory objects.
+  virtual bool DoBlasGemmBatched(
+      Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+      uint64 n, uint64 k, float alpha,
+      const port::ArraySlice *> &a, int lda,
+      const port::ArraySlice *> &b, int ldb,
+      float beta, const port::ArraySlice *> &c,
+      int ldc, int batch_count, ScratchAllocator *scratch_allocator) = 0;
   virtual bool DoBlasGemmBatched(
       Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
       uint64 n, uint64 k, float alpha,
@@ -1945,6 +1952,13 @@ class BlasSupport {
       DeviceMemory> *c, int ldc,                          \
       blas::ComputationType computation_type, blas::AlgorithmType algorithm,   \
       blas::ProfileResult *output_profile_result) override;                    \
+  bool DoBlasGemmBatched(                                                      \
+      Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
+      uint64 m, uint64 n, uint64 k, float alpha,                               \
+      const port::ArraySlice *> &a, int lda,         \
+      const port::ArraySlice *> &b, int ldb,         \
+      float beta, const port::ArraySlice *> &c,      \
+      int ldc, int batch_count, ScratchAllocator *scratch_allocator) override; \
   bool DoBlasGemmBatched(                                                      \
       Stream *stream, blas::Transpose transa, blas::Transpose transb,          \
       uint64 m, uint64 n, uint64 k, float alpha,                               \
diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc
index 3c1353aee31782..38e33d429b529a 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.cc
+++ b/tensorflow/stream_executor/cuda/cuda_blas.cc
@@ -292,6 +292,10 @@ STREAM_EXECUTOR_CUBLAS_WRAP(cublasGetMathMode)
 STREAM_EXECUTOR_CUBLAS_WRAP(cublasSetMathMode)
 #endif
 
+#if CUDA_VERSION >= 9010
+PERFTOOLS_GPUTOOLS_CUBLAS_WRAP(cublasGemmBatchedEx)
+#endif
+
 }  // namespace wrap
 
 static string ToString(cublasStatus_t status) {
@@ -2342,13 +2346,23 @@ bool CUDABlas::DoBlasGemmWithAlgorithm(
       computation_type, algorithm, output_profile_result);
 }
 
-template 
+template 
+struct HalfAsFloat {
+  typedef T type;
+};
+
+template <>
+struct HalfAsFloat {
+  typedef float type;
+};
+
+template 
 port::Status CUDABlas::DoBlasGemmBatchedInternal(
     FuncT cublas_func, Stream *stream, blas::Transpose transa,
-    blas::Transpose transb, uint64 m, uint64 n, uint64 k, T alpha,
+    blas::Transpose transb, uint64 m, uint64 n, uint64 k, Scalar alpha,
     const port::ArraySlice *> &a_ptrs_to_wrappers, int lda,
     const port::ArraySlice *> &b_ptrs_to_wrappers, int ldb,
-    T beta, const port::ArraySlice *> &c_ptrs_to_wrappers,
+    Scalar beta, const port::ArraySlice *> &c_ptrs_to_wrappers,
     int ldc, int batch_count, ScratchAllocator *scratch_allocator) {
   std::vector a_raw_ptrs, b_raw_ptrs, c_raw_ptrs;
   for (int i = 0; i < batch_count; ++i) {
@@ -2357,7 +2371,7 @@ port::Status CUDABlas::DoBlasGemmBatchedInternal(
     c_raw_ptrs.push_back(static_cast(c_ptrs_to_wrappers[i]->opaque()));
   }
 
-  typedef typename CUDAComplexT::type CUDA_T;
+  typedef typename HalfAsFloat::type>::type CUDA_T;
 
   const size_t size = batch_count * sizeof(CUDA_T *);
 
@@ -2409,18 +2423,84 @@ port::Status CUDABlas::DoBlasGemmBatchedInternal(
                         "CUDABlas::DoBlasGemmBatched");
   }
 
-  bool ok = DoBlasInternal(
-      cublas_func, stream, true /* = pointer_mode_host */,
-      CUDABlasTranspose(transa), CUDABlasTranspose(transb), m, n, k,
-      CUDAComplex(&alpha), const_cast(CUDAMemory(a)), lda,
-      const_cast(CUDAMemory(b)), ldb, CUDAComplex(&beta),
-      const_cast(CUDAMemory(c)), ldc, batch_count);
+  cudaDataType_t data_type = CUDADataType::type;
 
-  if (ok) {
+#if CUDA_VERSION >= 9010
+  int cc_major, cc_minor;
+  if (stream->parent()->GetDeviceDescription().cuda_compute_capability(
+          &cc_major, &cc_minor) &&
+      cc_major >= 5) {
+    bool use_tensor_ops = TensorOpMathEnabled() && data_type == CUDA_R_16F;
+    cublasGemmAlgo_t algo =
+        (use_tensor_ops ? CUBLAS_GEMM_DFALT_TENSOR_OP : CUBLAS_GEMM_DFALT);
+    cudaDataType_t compute_type =
+        (data_type == CUDA_R_16F ? CUDA_R_32F : data_type);
+    const void **a_void_ptrs = reinterpret_cast(
+        const_cast(CUDAMemory(a)));
+    const void **b_void_ptrs = reinterpret_cast(
+        const_cast(CUDAMemory(b)));
+    void **c_void_ptrs =
+        reinterpret_cast(const_cast(CUDAMemory(c)));
+    bool ok;
+    ok = DoBlasInternalImpl(
+        wrap::cublasGemmBatchedEx, stream, true /* = pointer_mode_host */,
+        true /* = err_on_failure */, use_tensor_ops, CUDABlasTranspose(transa),
+        CUDABlasTranspose(transb), m, n, k, &alpha, a_void_ptrs, data_type, lda,
+        b_void_ptrs, data_type, ldb, &beta, c_void_ptrs, data_type, ldc,
+        batch_count, compute_type, algo);
+    if (ok) {
+      return port::Status::OK();
+    }
+    return port::Status(port::error::INTERNAL,
+                        "failed BLAS call, see log for details");
+  }
+#endif
+  // either CUDA_VERSION < 9.1 or SM < 5.0
+  if (data_type != CUDA_R_16F) {
+    bool ok = DoBlasInternal(
+        cublas_func, stream, true /* = pointer_mode_host */,
+        CUDABlasTranspose(transa), CUDABlasTranspose(transb), m, n, k,
+        CUDAComplex(&alpha), const_cast(CUDAMemory(a)), lda,
+        const_cast(CUDAMemory(b)), ldb, CUDAComplex(&beta),
+        const_cast(CUDAMemory(c)), ldc, batch_count);
+    if (ok) {
+      return port::Status::OK();
+    }
+    return port::Status(port::error::INTERNAL,
+                        "failed BLAS call, see log for details");
+  } else {
+    // Fall back to a loop for fp16
+    for (int b = 0; b < batch_count; ++b) {
+      const DeviceMemory &a_matrix = *a_ptrs_to_wrappers[b];
+      const DeviceMemory &b_matrix = *b_ptrs_to_wrappers[b];
+      DeviceMemory *c_matrix = c_ptrs_to_wrappers[b];
+      bool ok = DoBlasGemm(stream, transa, transb, m, n, k, alpha, a_matrix,
+                           lda, b_matrix, ldb, beta, c_matrix, ldc);
+      if (!ok) {
+        return port::Status(port::error::INTERNAL,
+                            "failed BLAS call, see log for details");
+      }
+    }
     return port::Status::OK();
   }
-  return port::Status(port::error::INTERNAL,
-                      "failed BLAS call, see log for details");
+}
+
+bool CUDABlas::DoBlasGemmBatched(
+    Stream *stream, blas::Transpose transa, blas::Transpose transb, uint64 m,
+    uint64 n, uint64 k, float alpha,
+    const port::ArraySlice *> &a_array, int lda,
+    const port::ArraySlice *> &b_array, int ldb,
+    float beta, const port::ArraySlice *> &c_array,
+    int ldc, int batch_count, ScratchAllocator *scratch_allocator) {
+  // Note: The func passed here (cublasSgemmBatched) is not actually called,
+  // due to special handling of fp16 inside DoBlasGemmBatchedInternal.
+  port::Status status = DoBlasGemmBatchedInternal(
+      wrap::cublasSgemmBatched, stream, transa, transb, m, n, k, alpha, a_array,
+      lda, b_array, ldb, beta, c_array, ldc, batch_count, scratch_allocator);
+  if (!status.ok()) {
+    LOG(ERROR) << status;
+  }
+  return status.ok();
 }
 
 bool CUDABlas::DoBlasGemmBatched(
diff --git a/tensorflow/stream_executor/cuda/cuda_blas.h b/tensorflow/stream_executor/cuda/cuda_blas.h
index 12dc5e47fd1b9d..42b3fde5b0816f 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.h
+++ b/tensorflow/stream_executor/cuda/cuda_blas.h
@@ -107,12 +107,12 @@ class CUDABlas : public blas::BlasSupport {
 
   // A helper function to implement DoBlasGemmBatched interfaces for generic
   // types.
-  template 
+  template 
   port::Status DoBlasGemmBatchedInternal(
       FuncT cublas_func, Stream *stream, blas::Transpose transa,
-      blas::Transpose transb, uint64 m, uint64 n, uint64 k, T alpha,
+      blas::Transpose transb, uint64 m, uint64 n, uint64 k, Scalar alpha,
       const port::ArraySlice *> &a_array, int lda,
-      const port::ArraySlice *> &b_array, int ldb, T beta,
+      const port::ArraySlice *> &b_array, int ldb, Scalar beta,
       const port::ArraySlice *> &c_array, int ldc,
       int batch_count, ScratchAllocator *scratch_allocator);
 
diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc
index 093f0c9306590a..330320c758becd 100644
--- a/tensorflow/stream_executor/stream.cc
+++ b/tensorflow/stream_executor/stream.cc
@@ -4480,6 +4480,40 @@ Stream &Stream::ThenBlasTrsm(blas::Side side, blas::UpperLower uplo,
               n, alpha, a, lda, b, ldb);
 }
 
+Stream &Stream::ThenBlasGemmBatched(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, float alpha,
+    const port::ArraySlice *> &a, int lda,
+    const port::ArraySlice *> &b, int ldb, float beta,
+    const port::ArraySlice *> &c, int ldc,
+    int batch_count) {
+  return ThenBlasGemmBatchedWithScratch(transa, transb, m, n, k, alpha, a, lda,
+                                        b, ldb, beta, c, ldc, batch_count,
+                                        /*scratch_allocator=*/nullptr);
+}
+
+Stream &Stream::ThenBlasGemmBatchedWithScratch(
+    blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+    uint64 k, float alpha,
+    const port::ArraySlice *> &a, int lda,
+    const port::ArraySlice *> &b, int ldb, float beta,
+    const port::ArraySlice *> &c, int ldc,
+    int batch_count, ScratchAllocator *scratch_allocator) {
+  VLOG_CALL(PARAM(transa), PARAM(transb), PARAM(m), PARAM(n), PARAM(k),
+            PARAM(alpha), PARAM(a), PARAM(lda), PARAM(b), PARAM(ldb),
+            PARAM(beta), PARAM(c), PARAM(ldc), PARAM(batch_count));
+
+  ThenBlasImpl *> &, int,
+               const port::ArraySlice *> &, int,
+               float, const port::ArraySlice *> &,
+               int, int, ScratchAllocator *>
+      impl;
+  return impl(this, &blas::BlasSupport::DoBlasGemmBatched, transa, transb, m, n,
+              k, alpha, a, lda, b, ldb, beta, c, ldc, batch_count,
+              scratch_allocator);
+}
+
 Stream &Stream::ThenBlasGemmBatched(
     blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
     uint64 k, float alpha, const port::ArraySlice *> &a,
diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h
index 3d1b011c570a62..99d27b548638b0 100644
--- a/tensorflow/stream_executor/stream.h
+++ b/tensorflow/stream_executor/stream.h
@@ -1474,6 +1474,13 @@ class Stream {
       blas::ProfileResult *output_profile_result);
 
   // See BlasSupport::DoBlasGemmBatched.
+  Stream &ThenBlasGemmBatched(
+      blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+      uint64 k, float alpha,
+      const port::ArraySlice *> &a, int lda,
+      const port::ArraySlice *> &b, int ldb,
+      float beta, const port::ArraySlice *> &c,
+      int ldc, int batch_count);
   Stream &ThenBlasGemmBatched(blas::Transpose transa, blas::Transpose transb,
                               uint64 m, uint64 n, uint64 k, float alpha,
                               const port::ArraySlice *> &a,
@@ -1506,6 +1513,13 @@ class Stream {
       std::complex beta,
       const port::ArraySlice> *> &c, int ldc,
       int batch_count);
+  Stream &ThenBlasGemmBatchedWithScratch(
+      blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
+      uint64 k, float alpha,
+      const port::ArraySlice *> &a, int lda,
+      const port::ArraySlice *> &b, int ldb,
+      float beta, const port::ArraySlice *> &c,
+      int ldc, int batch_count, ScratchAllocator *scratch_allocator);
   Stream &ThenBlasGemmBatchedWithScratch(
       blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n,
       uint64 k, float alpha, const port::ArraySlice *> &a,

From 68ee0e153c5318a79dae612647f27a31f6c2f59c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 11:22:20 -0700
Subject: [PATCH 0611/1691] Implementation of the basic_rnn TFLite Op using the
 symmetric quantization.

PiperOrigin-RevId: 196144379
---
 tensorflow/contrib/lite/kernels/basic_rnn.cc  | 164 ++++++++++++++----
 .../contrib/lite/kernels/basic_rnn_test.cc    | 155 +++++++++++------
 .../lite/kernels/internal/kernel_utils.cc     |  74 ++++++++
 .../lite/kernels/internal/kernel_utils.h      |  17 ++
 4 files changed, 324 insertions(+), 86 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/basic_rnn.cc b/tensorflow/contrib/lite/kernels/basic_rnn.cc
index 2c5074eca3176c..a54ab8d5c30a14 100644
--- a/tensorflow/contrib/lite/kernels/basic_rnn.cc
+++ b/tensorflow/contrib/lite/kernels/basic_rnn.cc
@@ -12,18 +12,14 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-#include 
-#include 
-#include 
-#include 
-#include 
-#include 
-#include 
+#include 
+#include 
 
 #include "tensorflow/contrib/lite/builtin_op_data.h"
 #include "tensorflow/contrib/lite/context.h"
 #include "tensorflow/contrib/lite/kernels/activation_functor.h"
 #include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h"
+#include "tensorflow/contrib/lite/kernels/kernel_util.h"
 #include "tensorflow/contrib/lite/kernels/op_macros.h"
 
 namespace tflite {
@@ -35,20 +31,29 @@ constexpr int kInputTensor = 0;
 constexpr int kWeightsTensor = 1;
 constexpr int kRecurrentWeightsTensor = 2;
 constexpr int kBiasTensor = 3;
-constexpr int KHiddenStateTensor = 0;
+constexpr int kHiddenStateTensor = 0;
 constexpr int kOutputTensor = 1;
 
+void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+  auto* scratch_tensor_index = new int;
+  context->AddTensors(context, /*tensors_to_add=*/2, scratch_tensor_index);
+  return scratch_tensor_index;
+}
+
+void Free(TfLiteContext* context, void* buffer) {
+  delete reinterpret_cast(buffer);
+}
+
 TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   // Check we have all the inputs and outputs we need.
   TF_LITE_ENSURE_EQ(context, node->inputs->size, 4);
   TF_LITE_ENSURE_EQ(context, node->outputs->size, 2);
 
-  TfLiteTensor* input = &context->tensors[node->inputs->data[kInputTensor]];
-  TfLiteTensor* input_weights =
-      &context->tensors[node->inputs->data[kWeightsTensor]];
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
   TfLiteTensor* recurrent_weights =
-      &context->tensors[node->inputs->data[kRecurrentWeightsTensor]];
-  TfLiteTensor* bias = &context->tensors[node->inputs->data[kBiasTensor]];
+      GetInput(context, node, kRecurrentWeightsTensor);
+  TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
 
   // Check all the parameters of tensor match within themselves and match the
   // input configuration.
@@ -59,9 +64,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ASSERT_EQ(recurrent_weights->dims->data[0], bias->dims->data[0]);
   TF_LITE_ASSERT_EQ(recurrent_weights->dims->data[1], bias->dims->data[0]);
 
-  TfLiteTensor* hidden_state =
-      &context->tensors[node->outputs->data[KHiddenStateTensor]];
-  TfLiteTensor* output = &context->tensors[node->outputs->data[kOutputTensor]];
+  TfLiteTensor* hidden_state = GetOutput(context, node, kHiddenStateTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   // Resize state.
   TfLiteIntArray* hidden_state_size_array = TfLiteIntArrayCreate(2);
@@ -80,25 +84,44 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_OK(context,
                     context->ResizeTensor(context, output, output_size_array));
 
+  // Allocate temporary tensors to store quantized values of input and
+  // hidden_state tensors.
+  if (input->type == kTfLiteFloat32 && input_weights->type == kTfLiteUInt8) {
+    int* scratch_tensor_index = reinterpret_cast(node->user_data);
+    TfLiteIntArrayFree(node->temporaries);
+    node->temporaries = TfLiteIntArrayCreate(2);
+    node->temporaries->data[0] = *scratch_tensor_index;
+    TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/0);
+    input_quantized->type = kTfLiteUInt8;
+    input_quantized->allocation_type = kTfLiteArenaRw;
+    if (!TfLiteIntArrayEqual(input_quantized->dims, input->dims)) {
+      TfLiteIntArray* input_quantized_size = TfLiteIntArrayCopy(input->dims);
+      TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_quantized,
+                                                       input_quantized_size));
+    }
+    node->temporaries->data[1] = *scratch_tensor_index + 1;
+    TfLiteTensor* hidden_state_quantized =
+        GetTemporary(context, node, /*index=*/1);
+    hidden_state_quantized->type = kTfLiteUInt8;
+    hidden_state_quantized->allocation_type = kTfLiteArenaRw;
+    if (!TfLiteIntArrayEqual(hidden_state_quantized->dims,
+                             hidden_state->dims)) {
+      TfLiteIntArray* hidden_state_quantized_size =
+          TfLiteIntArrayCopy(hidden_state->dims);
+      TF_LITE_ENSURE_OK(context,
+                        context->ResizeTensor(context, hidden_state_quantized,
+                                              hidden_state_quantized_size));
+    }
+  }
+
   return kTfLiteOk;
 }
 
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  auto* params = reinterpret_cast(node->builtin_data);
-
-  TfLiteTensor* input = &context->tensors[node->inputs->data[kInputTensor]];
-  TfLiteTensor* input_weights =
-      &context->tensors[node->inputs->data[kWeightsTensor]];
-  TfLiteTensor* recurrent_weights =
-      &context->tensors[node->inputs->data[kRecurrentWeightsTensor]];
-  TfLiteTensor* bias = &context->tensors[node->inputs->data[kBiasTensor]];
-  TfLiteTensor* hidden_state =
-      &context->tensors[node->outputs->data[KHiddenStateTensor]];
-  TfLiteTensor* output = &context->tensors[node->outputs->data[kOutputTensor]];
-
-  // Initialize the pointer bias.
-  const float* bias_ptr = bias->data.f;
-
+TfLiteStatus EvalFloat(const TfLiteTensor* input,
+                       const TfLiteTensor* input_weights,
+                       const TfLiteTensor* recurrent_weights,
+                       const TfLiteTensor* bias, const TfLiteRNNParams* params,
+                       TfLiteTensor* hidden_state, TfLiteTensor* output) {
   const int batch_size = input->dims->data[0];
   const int num_units = input_weights->dims->data[0];
   const int input_size = input->dims->data[1];
@@ -108,9 +131,10 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   // Initialize the pointer to input and output.
   const float* input_ptr_batch = input->data.f;
   float* output_ptr_batch = output->data.f;
-  // Initialize input_weights and recurrent_weights.
+  // Initialize input_weights, recurrent_weights and bias.
   const float* input_weights_ptr = input_weights->data.f;
   const float* recurrent_weights_ptr = recurrent_weights->data.f;
+  const float* bias_ptr = bias->data.f;
 
   kernel_utils::RnnBatchStep(input_ptr_batch, input_weights_ptr,
                              recurrent_weights_ptr, bias_ptr, input_size,
@@ -119,11 +143,81 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   return kTfLiteOk;
 }
 
+TfLiteStatus EvalQuantized(const TfLiteTensor* input,
+                           const TfLiteTensor* input_weights,
+                           const TfLiteTensor* recurrent_weights,
+                           const TfLiteTensor* bias,
+                           const TfLiteRNNParams* params,
+                           TfLiteTensor* input_scratch,
+                           TfLiteTensor* hidden_state_scratch,
+                           TfLiteTensor* hidden_state, TfLiteTensor* output) {
+  const int batch_size = input->dims->data[0];
+  const int num_units = input_weights->dims->data[0];
+  const int input_size = input->dims->data[1];
+
+  // Initialize the pointer to hidden state.
+  float* hidden_state_ptr_batch = hidden_state->data.f;
+  // Initialize the pointer to input and output.
+  const float* input_ptr_batch = input->data.f;
+  float* output_ptr_batch = output->data.f;
+  // Initialize input_weights, recurrent_weights and bias.
+  const int8_t* input_weights_ptr =
+      reinterpret_cast(input_weights->data.uint8);
+  const int8_t* recurrent_weights_ptr =
+      reinterpret_cast(recurrent_weights->data.uint8);
+  const float* bias_ptr = bias->data.f;
+  // Get the scale of the quantized weights.
+  float input_weights_scale = input_weights->params.scale;
+  float recurrent_weights_scale = recurrent_weights->params.scale;
+  // Initialize temporary storage for quantized values.
+  int8_t* quantized_input_ptr =
+      reinterpret_cast(input_scratch->data.uint8);
+  int8_t* quantized_hidden_state_ptr =
+      reinterpret_cast(hidden_state_scratch->data.uint8);
+
+  kernel_utils::RnnBatchStep(
+      input_ptr_batch, input_weights_ptr, input_weights_scale,
+      recurrent_weights_ptr, recurrent_weights_scale, bias_ptr, input_size,
+      num_units, batch_size, params->activation, quantized_input_ptr,
+      quantized_hidden_state_ptr, hidden_state_ptr_batch, output_ptr_batch);
+  return kTfLiteOk;
+}
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  auto* params = reinterpret_cast(node->builtin_data);
+
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
+  TfLiteTensor* recurrent_weights =
+      GetInput(context, node, kRecurrentWeightsTensor);
+  TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
+  TfLiteTensor* hidden_state = GetOutput(context, node, kHiddenStateTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  switch (input_weights->type) {
+    case kTfLiteFloat32:
+      return EvalFloat(input, input_weights, recurrent_weights, bias, params,
+                       hidden_state, output);
+    case kTfLiteUInt8: {
+      // TODO(mirkov): implement eval with quantized inputs as well.
+      TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32);
+      TfLiteTensor* input_quantized = GetTemporary(context, node, 0);
+      TfLiteTensor* hidden_state_quantized = GetTemporary(context, node, 1);
+      return EvalQuantized(input, input_weights, recurrent_weights, bias,
+                           params, input_quantized, hidden_state_quantized,
+                           hidden_state, output);
+    }
+    default:
+      context->ReportError(context, "Type not currently supported.");
+      return kTfLiteError;
+  }
+  return kTfLiteOk;
+}
+
 }  // namespace rnn
 
 TfLiteRegistration* Register_RNN() {
-  static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr,
-                                 rnn::Prepare, rnn::Eval};
+  static TfLiteRegistration r = {rnn::Init, rnn::Free, rnn::Prepare, rnn::Eval};
   return &r;
 }
 
diff --git a/tensorflow/contrib/lite/kernels/basic_rnn_test.cc b/tensorflow/contrib/lite/kernels/basic_rnn_test.cc
index fa7ef525db47c9..96465fcaf0a785 100644
--- a/tensorflow/contrib/lite/kernels/basic_rnn_test.cc
+++ b/tensorflow/contrib/lite/kernels/basic_rnn_test.cc
@@ -14,7 +14,9 @@ limitations under the License.
 ==============================================================================*/
 // Unit test for TFLite RNN op.
 
-#include 
+#include 
+#include 
+#include 
 #include 
 
 #include 
@@ -122,13 +124,62 @@ static float rnn_golden_output[] = {
     0,          2.02616,    0,         0.728256,  0.84183,   0.0907453,
     0.628881,   3.58099,    1.49974,   0};
 
+static std::initializer_list rnn_weights = {
+    0.461459,    0.153381,   0.529743,    -0.00371218, 0.676267,   -0.211346,
+    0.317493,    0.969689,   -0.343251,   0.186423,    0.398151,   0.152399,
+    0.448504,    0.317662,   0.523556,    -0.323514,   0.480877,   0.333113,
+    -0.757714,   -0.674487,  -0.643585,   0.217766,    -0.0251462, 0.79512,
+    -0.595574,   -0.422444,  0.371572,    -0.452178,   -0.556069,  -0.482188,
+    -0.685456,   -0.727851,  0.841829,    0.551535,    -0.232336,  0.729158,
+    -0.00294906, -0.69754,   0.766073,    -0.178424,   0.369513,   -0.423241,
+    0.548547,    -0.0152023, -0.757482,   -0.85491,    0.251331,   -0.989183,
+    0.306261,    -0.340716,  0.886103,    -0.0726757,  -0.723523,  -0.784303,
+    0.0354295,   0.566564,   -0.485469,   -0.620498,   0.832546,   0.697884,
+    -0.279115,   0.294415,   -0.584313,   0.548772,    0.0648819,  0.968726,
+    0.723834,    -0.0080452, -0.350386,   -0.272803,   0.115121,   -0.412644,
+    -0.824713,   -0.992843,  -0.592904,   -0.417893,   0.863791,   -0.423461,
+    -0.147601,   -0.770664,  -0.479006,   0.654782,    0.587314,   -0.639158,
+    0.816969,    -0.337228,  0.659878,    0.73107,     0.754768,   -0.337042,
+    0.0960841,   0.368357,   0.244191,    -0.817703,   -0.211223,  0.442012,
+    0.37225,     -0.623598,  -0.405423,   0.455101,    0.673656,   -0.145345,
+    -0.511346,   -0.901675,  -0.81252,    -0.127006,   0.809865,   -0.721884,
+    0.636255,    0.868989,   -0.347973,   -0.10179,    -0.777449,  0.917274,
+    0.819286,    0.206218,   -0.00785118, 0.167141,    0.45872,    0.972934,
+    -0.276798,   0.837861,   0.747958,    -0.0151566,  -0.330057,  -0.469077,
+    0.277308,    0.415818};
+
+static std::initializer_list rnn_recurrent_weights = {
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1};
+
+static std::initializer_list rnn_bias = {
+    0.065691948, -0.69055247, 0.1107955,  -0.97084129, -0.23957068, -0.23566568,
+    -0.389184,   0.47481549,  -0.4791103, 0.29931796,  0.10463274,  0.83918178,
+    0.37197268,  0.61957061,  0.3956964,  -0.37609905};
+
 class RNNOpModel : public SingleOpModel {
  public:
-  RNNOpModel(int batches, int units, int size)
+  RNNOpModel(int batches, int units, int size,
+             const TensorType& weights = TensorType_FLOAT32,
+             const TensorType& recurrent_weights = TensorType_FLOAT32)
       : batches_(batches), units_(units), input_size_(size) {
     input_ = AddInput(TensorType_FLOAT32);
-    weights_ = AddInput(TensorType_FLOAT32);
-    recurrent_weights_ = AddInput(TensorType_FLOAT32);
+    weights_ = AddInput(weights);
+    recurrent_weights_ = AddInput(recurrent_weights);
     bias_ = AddInput(TensorType_FLOAT32);
     hidden_state_ = AddOutput(TensorType_FLOAT32);
     output_ = AddOutput(TensorType_FLOAT32);
@@ -173,7 +224,7 @@ class RNNOpModel : public SingleOpModel {
   int num_units() { return units_; }
   int num_batches() { return batches_; }
 
- private:
+ protected:
   int input_;
   int weights_;
   int recurrent_weights_;
@@ -186,53 +237,26 @@ class RNNOpModel : public SingleOpModel {
   int input_size_;
 };
 
-TEST(FullyConnectedOpTest, BlackBoxTest) {
+// The hybrid model has quantized weights and recurrent_weights.
+class HybridRNNOpModel : public RNNOpModel {
+ public:
+  HybridRNNOpModel(int batches, int units, int size)
+      : RNNOpModel(batches, units, size, TensorType_UINT8, TensorType_UINT8) {}
+
+  void SetWeights(std::initializer_list f) {
+    SymmetricQuantizeAndPopulate(weights_, f);
+  }
+
+  void SetRecurrentWeights(std::initializer_list f) {
+    SymmetricQuantizeAndPopulate(recurrent_weights_, f);
+  }
+};
+
+TEST(RnnOpTest, BlackBoxTest) {
   RNNOpModel rnn(2, 16, 8);
-  rnn.SetWeights(
-      {0.461459,    0.153381,   0.529743,    -0.00371218, 0.676267,   -0.211346,
-       0.317493,    0.969689,   -0.343251,   0.186423,    0.398151,   0.152399,
-       0.448504,    0.317662,   0.523556,    -0.323514,   0.480877,   0.333113,
-       -0.757714,   -0.674487,  -0.643585,   0.217766,    -0.0251462, 0.79512,
-       -0.595574,   -0.422444,  0.371572,    -0.452178,   -0.556069,  -0.482188,
-       -0.685456,   -0.727851,  0.841829,    0.551535,    -0.232336,  0.729158,
-       -0.00294906, -0.69754,   0.766073,    -0.178424,   0.369513,   -0.423241,
-       0.548547,    -0.0152023, -0.757482,   -0.85491,    0.251331,   -0.989183,
-       0.306261,    -0.340716,  0.886103,    -0.0726757,  -0.723523,  -0.784303,
-       0.0354295,   0.566564,   -0.485469,   -0.620498,   0.832546,   0.697884,
-       -0.279115,   0.294415,   -0.584313,   0.548772,    0.0648819,  0.968726,
-       0.723834,    -0.0080452, -0.350386,   -0.272803,   0.115121,   -0.412644,
-       -0.824713,   -0.992843,  -0.592904,   -0.417893,   0.863791,   -0.423461,
-       -0.147601,   -0.770664,  -0.479006,   0.654782,    0.587314,   -0.639158,
-       0.816969,    -0.337228,  0.659878,    0.73107,     0.754768,   -0.337042,
-       0.0960841,   0.368357,   0.244191,    -0.817703,   -0.211223,  0.442012,
-       0.37225,     -0.623598,  -0.405423,   0.455101,    0.673656,   -0.145345,
-       -0.511346,   -0.901675,  -0.81252,    -0.127006,   0.809865,   -0.721884,
-       0.636255,    0.868989,   -0.347973,   -0.10179,    -0.777449,  0.917274,
-       0.819286,    0.206218,   -0.00785118, 0.167141,    0.45872,    0.972934,
-       -0.276798,   0.837861,   0.747958,    -0.0151566,  -0.330057,  -0.469077,
-       0.277308,    0.415818});
-
-  rnn.SetBias({0.065691948, -0.69055247, 0.1107955, -0.97084129, -0.23957068,
-               -0.23566568, -0.389184, 0.47481549, -0.4791103, 0.29931796,
-               0.10463274, 0.83918178, 0.37197268, 0.61957061, 0.3956964,
-               -0.37609905});
-
-  rnn.SetRecurrentWeights({0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1});
+  rnn.SetWeights(rnn_weights);
+  rnn.SetBias(rnn_bias);
+  rnn.SetRecurrentWeights(rnn_recurrent_weights);
 
   rnn.ResetHiddenState();
   const int input_sequence_size = sizeof(rnn_input) / sizeof(float) /
@@ -256,6 +280,35 @@ TEST(FullyConnectedOpTest, BlackBoxTest) {
   }
 }
 
+TEST(HybridRnnOpTest, BlackBoxTest) {
+  HybridRNNOpModel rnn(2, 16, 8);
+  rnn.SetWeights(rnn_weights);
+  rnn.SetBias(rnn_bias);
+  rnn.SetRecurrentWeights(rnn_recurrent_weights);
+
+  rnn.ResetHiddenState();
+  const int input_sequence_size = sizeof(rnn_input) / sizeof(float) /
+                                  (rnn.input_size() * rnn.num_batches());
+
+  for (int i = 0; i < input_sequence_size; i++) {
+    float* batch_start = rnn_input + i * rnn.input_size();
+    float* batch_end = batch_start + rnn.input_size();
+    rnn.SetInput(0, batch_start, batch_end);
+    rnn.SetInput(rnn.input_size(), batch_start, batch_end);
+
+    rnn.Invoke();
+
+    float* golden_start = rnn_golden_output + i * rnn.num_units();
+    float* golden_end = golden_start + rnn.num_units();
+    std::vector expected;
+    expected.insert(expected.end(), golden_start, golden_end);
+    expected.insert(expected.end(), golden_start, golden_end);
+
+    EXPECT_THAT(rnn.GetOutput(), ElementsAreArray(ArrayFloatNear(
+                                     expected, /*max_abs_error=*/0.0104)));
+  }
+}
+
 }  // namespace
 }  // namespace tflite
 
diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc
index f142374269606b..5f9cfc450db1c2 100644
--- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc
+++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc
@@ -12,6 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
+#include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h"
+
+#include 
+
 #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h"
 
 namespace tflite {
@@ -40,6 +44,76 @@ void RnnBatchStep(const float* input_ptr_batch, const float* input_weights_ptr,
                                         hidden_state_ptr_batch);
 }
 
+void RnnBatchStep(const float* input_ptr_batch, const int8_t* input_weights_ptr,
+                  float input_weights_scale,
+                  const int8_t* recurrent_weights_ptr,
+                  float recurrent_weights_scale, const float* bias_ptr,
+                  int input_size, int num_units, int batch_size,
+                  TfLiteFusedActivation activation,
+                  int8_t* quantized_input_ptr_batch,
+                  int8_t* quantized_hidden_state_ptr_batch,
+                  float* hidden_state_ptr_batch, float* output_ptr_batch) {
+  // Output = bias
+  tensor_utils::VectorBatchVectorAssign(bias_ptr, num_units, batch_size,
+                                        output_ptr_batch);
+
+  // TODO(mirkov): change std::minmax_element with a vectorized call.
+  auto minmax_element = std::minmax_element(
+      input_ptr_batch, input_ptr_batch + batch_size * input_size);
+
+  // Save quantization and matmul computation for all zero input.
+  if (!(*minmax_element.first == 0.0 && *minmax_element.second == 0.0)) {
+    // Quantize input from float to uint8 + quantization params (scaling
+    // factor).
+    float unused_min, unused_max;
+    float* scaling_factors = new float[batch_size];
+    for (int b = 0; b < batch_size; ++b) {
+      const int offset = b * input_size;
+      tensor_utils::SymmetricQuantizeFloats(
+          input_ptr_batch + offset, input_size,
+          quantized_input_ptr_batch + offset, &unused_min, &unused_max,
+          &scaling_factors[b]);
+      scaling_factors[b] *= input_weights_scale;
+    }
+
+    // Output += input * input_weights
+    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+        input_weights_ptr, num_units, input_size, quantized_input_ptr_batch,
+        scaling_factors, batch_size, output_ptr_batch, /*result_stride=*/1);
+    delete[] scaling_factors;
+  }
+
+  minmax_element = std::minmax_element(
+      hidden_state_ptr_batch, hidden_state_ptr_batch + batch_size * num_units);
+  // Save quantization and matmul computation for all zero input.
+  if (!(*minmax_element.first == 0.0 && *minmax_element.second == 0.0)) {
+    // Quantize hidden_state
+    float unused_min, unused_max;
+    float* scaling_factors = new float[batch_size];
+    for (int b = 0; b < batch_size; ++b) {
+      const int offset = b * num_units;
+      tensor_utils::SymmetricQuantizeFloats(
+          hidden_state_ptr_batch + offset, num_units,
+          quantized_hidden_state_ptr_batch + offset, &unused_min, &unused_max,
+          &scaling_factors[b]);
+      scaling_factors[b] *= recurrent_weights_scale;
+    }
+
+    // Output += recurrent_weights * hidden_state
+    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+        recurrent_weights_ptr, num_units, num_units,
+        quantized_hidden_state_ptr_batch, scaling_factors, batch_size,
+        output_ptr_batch, /*result_stride=*/1);
+    delete[] scaling_factors;
+  }
+
+  // Output = activation(Output) and update hidden_state
+  tensor_utils::ApplyActivationToVector(
+      output_ptr_batch, num_units * batch_size, activation, output_ptr_batch);
+  tensor_utils::VectorBatchVectorAssign(output_ptr_batch, num_units, batch_size,
+                                        hidden_state_ptr_batch);
+}
+
 void LstmStep(
     const float* input_ptr_batch, const float* input_to_input_weights_ptr,
     const float* input_to_forget_weights_ptr,
diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h
index 3ec60ee57a8783..cbfbcbeefcd34f 100644
--- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h
+++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h
@@ -35,6 +35,23 @@ void RnnBatchStep(const float* input_ptr_batch, const float* input_weights_ptr,
                   TfLiteFusedActivation activation,
                   float* hidden_state_ptr_batch, float* output_ptr_batch);
 
+// Performs a quantized RNN batch inference step. Same as above, but for
+// quantization purposes, we also pass in quantized_hidden_state_ptr_batch and
+// quantized_input_ptr_batch pointers for temporary storage of the quantized
+// values of hidden_state_ptr_batch and input_ptr_batch, respectively.
+// These temporary storages are expected to be preallocated to the same size as
+// the respective pointers.
+// {input,recurrent}_weights_scale params are used for dequantization/recovery.
+void RnnBatchStep(const float* input_ptr_batch, const int8_t* input_weights_ptr,
+                  float input_weights_scale,
+                  const int8_t* recurrent_weights_ptr,
+                  float recurrent_weights_scale, const float* bias_ptr,
+                  int input_size, int num_units, int batch_size,
+                  TfLiteFusedActivation activation,
+                  int8_t* quantized_input_ptr_batch,
+                  int8_t* quantized_hidden_state_ptr_batch,
+                  float* hidden_state_ptr_batch, float* output_ptr_batch);
+
 // Performs an LSTM batch inference step for input specified by input_ptr_batch.
 // The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and
 // biases (*_bias_ptr), and buffers (*_scratch), along with additional

From d7596f58c8ab027df6b0419f2a9a3fa6d46dfdaa Mon Sep 17 00:00:00 2001
From: mbhuiyan 
Date: Wed, 4 Apr 2018 10:52:49 -0700
Subject: [PATCH 0612/1691]  Fixing a unit test failure for INTEL MKL where
 memeory allocation check failed because of use of INTEL MKL

---
 .../direct_session_with_tracking_alloc_test.cc      | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
index 695423b2cb1993..084253d9499158 100644
--- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
@@ -101,11 +101,24 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) {
         EXPECT_EQ(2, shape.dim_size());
         EXPECT_EQ(2, shape.dim(0).size());
         EXPECT_EQ(1, shape.dim(1).size());
+#ifndef INTEL_MKL
+        // if MKL is used, it goes through various additional 
+        // graph rewrite pass. In TF, everytime a graph pass 
+        // happens, "constant" nodes are allocated
+        // and deallocated. Each allocation calls the
+        // (FindChunkPtr of BFCAllocator)
+        // , which increments the value of AllocationId. 
+        // Thus AllocationId becomes more than 3 and 4 if 
+        // MKL is used, they can be 10 and 11 or 
+        // other numbers. If MKL is used
+        // following check will not hold. 
+        // Thus, skipping the check if MKL is used.
         if (node->name() == y->name()) {
           EXPECT_EQ(9, cm->AllocationId(node, 0));
         } else {
           EXPECT_EQ(10, cm->AllocationId(node, 0));
         }
+#endif 
       }
       EXPECT_LE(0, cm->MaxExecutionTime(node));
       EXPECT_GE(run_duration_micros, cm->MaxExecutionTime(node));

From ee78a3b96af4f56ceb41296195a47e5c416c796e Mon Sep 17 00:00:00 2001
From: mbhuiyan 
Date: Fri, 4 May 2018 12:02:28 -0700
Subject: [PATCH 0613/1691]  if MKL is used allocation id is set to 9 and 10

---
 .../direct_session_with_tracking_alloc_test.cc  | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
index 084253d9499158..0c9e1931b4af62 100644
--- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
@@ -101,18 +101,21 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) {
         EXPECT_EQ(2, shape.dim_size());
         EXPECT_EQ(2, shape.dim(0).size());
         EXPECT_EQ(1, shape.dim(1).size());
-#ifndef INTEL_MKL
+#ifdef INTEL_MKL
         // if MKL is used, it goes through various additional 
         // graph rewrite pass. In TF, everytime a graph pass 
         // happens, "constant" nodes are allocated
         // and deallocated. Each allocation calls the
-        // (FindChunkPtr of BFCAllocator)
-        // , which increments the value of AllocationId. 
+        // (FindChunkPtr of BFCAllocator),
+        // which increments the value of AllocationId. 
         // Thus AllocationId becomes more than 3 and 4 if 
-        // MKL is used, they can be 10 and 11 or 
-        // other numbers. If MKL is used
-        // following check will not hold. 
-        // Thus, skipping the check if MKL is used.
+        // MKL is used. Now they are 9 and 10 for MKL. 
+        if (node->name() == y->name()) {
+          EXPECT_EQ(9, cm->AllocationId(node, 0));
+        } else {
+          EXPECT_EQ(10, cm->AllocationId(node, 0));
+        }
+#else
         if (node->name() == y->name()) {
           EXPECT_EQ(9, cm->AllocationId(node, 0));
         } else {

From 5389a1e8bc9711f8686e5447205516cd88800eee Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 11:30:50 -0700
Subject: [PATCH 0614/1691] Optimizations for broadcast add operator.

PiperOrigin-RevId: 196145896
---
 .../internal/optimized/optimized_ops.h        | 129 +++++++++---------
 1 file changed, 63 insertions(+), 66 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index 637b21e1be2596..7f28c29bc6f4f7 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -2499,52 +2499,17 @@ inline void Add(const float* input1_data, const Dims<4>& input1_dims,
   }
 }
 
-// legacy, for compatibility with old checked-in code
-template 
-void Add(const float* input1_data, const Dims<4>& input1_dims,
-         const float* input2_data, const Dims<4>& input2_dims,
-         float* output_data, const Dims<4>& output_dims) {
-  float output_activation_min, output_activation_max;
-  GetActivationMinMax(Ac, &output_activation_min, &output_activation_max);
-
-  Add(input1_data, input1_dims, input2_data, input2_dims, output_activation_min,
-      output_activation_max, output_data, output_dims);
-}
-
-template 
-inline void Add(int left_shift, const uint8* input1_data,
-                const Dims<4>& input1_dims, int32 input1_offset,
-                int32 input1_multiplier, int input1_shift,
-                const uint8* input2_data, const Dims<4>& input2_dims,
-                int32 input2_offset, int32 input2_multiplier, int input2_shift,
-                int32 output_offset, int32 output_multiplier, int output_shift,
-                int32 output_activation_min, int32 output_activation_max,
-                uint8* output_data, const Dims<4>& output_dims) {
-  static_assert(Ac == FusedActivationFunctionType::kNone ||
-                    Ac == FusedActivationFunctionType::kRelu ||
-                    Ac == FusedActivationFunctionType::kRelu6 ||
-                    Ac == FusedActivationFunctionType::kRelu1,
-                "");
-  TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
-  if (Ac == FusedActivationFunctionType::kNone) {
-    TFLITE_DCHECK_EQ(output_activation_min, 0);
-    TFLITE_DCHECK_EQ(output_activation_max, 255);
-  }
-  gemmlowp::ScopedProfilingLabel label("Add/8bit");
-  /* const int batches = */ MatchingArraySize(input1_dims, 3, input2_dims, 3,
-                                              output_dims, 3);
-  /* const int height = */ MatchingArraySize(input1_dims, 2, input2_dims, 2,
-                                             output_dims, 2);
-  /* const int width = */ MatchingArraySize(input1_dims, 1, input2_dims, 1,
-                                            output_dims, 1);
-  /* const int depth = */ MatchingArraySize(input1_dims, 0, input2_dims, 0,
-                                            output_dims, 0);
-  TFLITE_DCHECK(IsPackedWithoutStrides(input1_dims));
-  TFLITE_DCHECK(IsPackedWithoutStrides(input2_dims));
-  TFLITE_DCHECK(IsPackedWithoutStrides(output_dims));
-
+// Element-wise add that can often be used for inner loop of broadcast add as
+// well as the non-broadcast add.
+inline void AddElementwise(int size, int left_shift, const uint8* input1_data,
+                           int32 input1_offset, int32 input1_multiplier,
+                           int input1_shift, const uint8* input2_data,
+                           int32 input2_offset, int32 input2_multiplier,
+                           int input2_shift, int32 output_offset,
+                           int32 output_multiplier, int output_shift,
+                           int32 output_activation_min,
+                           int32 output_activation_max, uint8* output_data) {
   int i = 0;
-  const int size = input1_dims.sizes[3] * input1_dims.strides[3];
   TFLITE_DCHECK_GT(input1_offset, -256);
   TFLITE_DCHECK_GT(input2_offset, -256);
   TFLITE_DCHECK_LT(input1_offset, 256);
@@ -2623,6 +2588,54 @@ inline void Add(int left_shift, const uint8* input1_data,
   }
 }
 
+// legacy, for compatibility with old checked-in code
+template 
+void Add(const float* input1_data, const Dims<4>& input1_dims,
+         const float* input2_data, const Dims<4>& input2_dims,
+         float* output_data, const Dims<4>& output_dims) {
+  float output_activation_min, output_activation_max;
+  GetActivationMinMax(Ac, &output_activation_min, &output_activation_max);
+
+  Add(input1_data, input1_dims, input2_data, input2_dims, output_activation_min,
+      output_activation_max, output_data, output_dims);
+}
+
+template 
+inline void Add(int left_shift, const uint8* input1_data,
+                const Dims<4>& input1_dims, int32 input1_offset,
+                int32 input1_multiplier, int input1_shift,
+                const uint8* input2_data, const Dims<4>& input2_dims,
+                int32 input2_offset, int32 input2_multiplier, int input2_shift,
+                int32 output_offset, int32 output_multiplier, int output_shift,
+                int32 output_activation_min, int32 output_activation_max,
+                uint8* output_data, const Dims<4>& output_dims) {
+  static_assert(Ac == FusedActivationFunctionType::kNone ||
+                    Ac == FusedActivationFunctionType::kRelu ||
+                    Ac == FusedActivationFunctionType::kRelu6 ||
+                    Ac == FusedActivationFunctionType::kRelu1,
+                "");
+  TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
+  if (Ac == FusedActivationFunctionType::kNone) {
+    TFLITE_DCHECK_EQ(output_activation_min, 0);
+    TFLITE_DCHECK_EQ(output_activation_max, 255);
+  }
+  gemmlowp::ScopedProfilingLabel label("Add/8bit");
+  const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims);
+  TFLITE_DCHECK(IsPackedWithoutStrides(input1_dims));
+  TFLITE_DCHECK(IsPackedWithoutStrides(input2_dims));
+  TFLITE_DCHECK(IsPackedWithoutStrides(output_dims));
+
+  TFLITE_DCHECK_GT(input1_offset, -256);
+  TFLITE_DCHECK_GT(input2_offset, -256);
+  TFLITE_DCHECK_LT(input1_offset, 256);
+  TFLITE_DCHECK_LT(input2_offset, 256);
+  AddElementwise(flat_size, left_shift, input1_data, input1_offset,
+                 input1_multiplier, input1_shift, input2_data, input2_offset,
+                 input2_multiplier, input2_shift, output_offset,
+                 output_multiplier, output_shift, output_activation_min,
+                 output_activation_max, output_data);
+}
+
 template 
 inline void Add(const int16* input1_data, const Dims<4>& input1_dims,
                 int input1_shift, const int16* input2_data,
@@ -2833,27 +2846,11 @@ inline void BroadcastAddFivefold(
       input2_data_ptr = input2_data_reset;
       for (int i2 = 0; i2 < y2; ++i2) {
         for (int i1 = 0; i1 < y1; ++i1) {
-          for (int i0 = 0; i0 < y0; ++i0) {
-            const int32 input1_val = input1_offset + input1_data_ptr[i0];
-            const int32 input2_val = input2_offset + input2_data_ptr[i0];
-            const int32 shifted_input1_val = input1_val * (1 << left_shift);
-            const int32 shifted_input2_val = input2_val * (1 << left_shift);
-            const int32 scaled_input1_val =
-                MultiplyByQuantizedMultiplierSmallerThanOne(
-                    shifted_input1_val, input1_multiplier, input1_shift);
-            const int32 scaled_input2_val =
-                MultiplyByQuantizedMultiplierSmallerThanOne(
-                    shifted_input2_val, input2_multiplier, input2_shift);
-            const int32 raw_sum = scaled_input1_val + scaled_input2_val;
-            const int32 raw_output =
-                MultiplyByQuantizedMultiplierSmallerThanOne(
-                    raw_sum, output_multiplier, output_shift) +
-                output_offset;
-            const int32 clamped_output =
-                std::min(output_activation_max,
-                         std::max(output_activation_min, raw_output));
-            output_data_ptr[i0] = static_cast(clamped_output);
-          }
+          AddElementwise(
+              y0, left_shift, input1_data_ptr, input1_offset, input1_multiplier,
+              input1_shift, input2_data_ptr, input2_offset, input2_multiplier,
+              input2_shift, output_offset, output_multiplier, output_shift,
+              output_activation_min, output_activation_max, output_data_ptr);
           input2_data_ptr += y0;
           output_data_ptr += y0;
         }

From 1d1ff22f1c0accfd5ff97f1543c1ba74c5dac380 Mon Sep 17 00:00:00 2001
From: P-Hidringer 
Date: Fri, 11 May 2018 03:36:57 +0900
Subject: [PATCH 0615/1691] Fix default value for parameter 'prefix' in
 slim.tfexample_decoder.BoundingBox.__init__ (#19190)

---
 tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
index f2d31dc8db5688..d877831fce99a3 100644
--- a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
+++ b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
@@ -102,7 +102,7 @@ class BoundingBox(ItemHandler):
   """An ItemHandler that concatenates a set of parsed Tensors to Bounding Boxes.
   """
 
-  def __init__(self, keys=None, prefix=None):
+  def __init__(self, keys=None, prefix=''):
     """Initialize the bounding box handler.
 
     Args:

From 2c133de38ea8ac0493265fe3bea267ec28ba8ecb Mon Sep 17 00:00:00 2001
From: Mahmoud Abuzaina 
Date: Thu, 10 May 2018 11:37:19 -0700
Subject: [PATCH 0616/1691] Fixing util_cuda_kernel_helper_test_gpu when
 building with MKL enabled (#19185)

---
 tensorflow/core/BUILD | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 4b86d6ef4752b1..277f27f2688812 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -3402,7 +3402,11 @@ tf_cuda_only_cc_test(
         ":test",
         ":test_main",
         "//third_party/eigen3",
-    ],
+    ] + if_mkl(
+        [
+            "//third_party/mkl:intel_binary_blob",
+        ],
+    ),
 )
 
 tf_cc_test_gpu(

From 4b5308ef4698ea47eec25cf93ae09ae0c49cff8b Mon Sep 17 00:00:00 2001
From: Yong Tang 
Date: Thu, 10 May 2018 11:38:04 -0700
Subject: [PATCH 0617/1691] Use "```" (backtick) for code blocks in
 adding_an_op.md (#19187)

* Use "```" (backtick) for code blocks in adding_an_op.md

In adding_an_op.md, most of the code blocks uses "```" (backtick)
and annotations are added automatically. Though there was one
place where the code block are done with manual html code. This
is really error-prune and hard to change if there is an update
in the future.

This fix converts to "```c++" (backticks) so that it is easy
to maintain in the future.

Signed-off-by: Yong Tang 

* Fix extra `\` at the beginning of the block

Signed-off-by: Yong Tang 

* Update adding_an_op.md

add new lines where the 
 tags were.
---
 tensorflow/docs_src/extend/adding_an_op.md | 63 ++++++++++++----------
 1 file changed, 34 insertions(+), 29 deletions(-)

diff --git a/tensorflow/docs_src/extend/adding_an_op.md b/tensorflow/docs_src/extend/adding_an_op.md
index c3795492cef7d6..1b028be4ea16af 100644
--- a/tensorflow/docs_src/extend/adding_an_op.md
+++ b/tensorflow/docs_src/extend/adding_an_op.md
@@ -863,48 +863,53 @@ REGISTER_OP("ZeroOut")
 Instead of writing another `OpKernel` with redundant code as above, often you
 will be able to use a C++ template instead.  You will still have one kernel
 registration (`REGISTER_KERNEL_BUILDER` call) per overload.
-
+    .Device(DEVICE_CPU)
+    .TypeConstraint("T"),
+    ZeroOutOp);
+```
 
 If you have more than a couple overloads, you can put the registration in a
 macro.

From 11569894f10243fda5f827510cc30a9e12fc1e3a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 11:35:22 -0700
Subject: [PATCH 0618/1691] Extracts PartialAssocOpConstFolding into a method.

PiperOrigin-RevId: 196146716
---
 .../grappler/optimizers/constant_folding.cc   | 155 +++++++++---------
 .../grappler/optimizers/constant_folding.h    |   5 +
 2 files changed, 86 insertions(+), 74 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index e6a74dbdcd539e..28fc5fdcb50745 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -2294,80 +2294,9 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
       }
     }
 
-    // Partial constant folding for associative operators:
-    // Split AddN/AccumulateNV2 to enable partial
-    // folding of ops when more than one but not all inputs are constant.
-    // For AddN and AccumulateNV2, we may furthermore reorder inputs, since
-    // addition is commutative.
-    const int num_non_control_inputs = NumNonControlInputs(*node);
-    if (IsAggregate(*node) && IsCommutative(*node) &&
-        num_non_control_inputs > 2) {
-      const int num_control_inputs =
-          node->input_size() - num_non_control_inputs;
-      std::vector const_inputs;
-      std::vector nonconst_inputs;
-      for (int i = 0; i < node->input_size(); ++i) {
-        const string& input = node->input(i);
-        const NodeDef* input_node = node_map_->GetNode(NodeName(input));
-        CHECK(input_node != nullptr) << input;
-        if (!IsControlInput(input) && IsReallyConstant(*input_node)) {
-          const_inputs.push_back(i);
-        } else {
-          // Non-const and control inputs.
-          nonconst_inputs.push_back(i);
-        }
-      }
-      // Promote AccumulateNV2 with all constant inputs to AddN, since it is
-      // a fake node that cannot be constant folded by itself.
-      if (const_inputs.size() == num_non_control_inputs &&
-          node->op() == "AccumulateNV2") {
-        node->set_op("AddN");
-        node->mutable_attr()->erase("shape");
-        graph_modified_ = true;
-        continue;
-      }
-      const string new_node_name = OptimizedNodeName(
-          *node, strings::StrCat("_partial_split_", const_inputs.size()));
-      if (1 < const_inputs.size() &&
-          const_inputs.size() < num_non_control_inputs &&
-          !node_map_->NodeExists(new_node_name)) {
-        NodeDef* added_node = optimized_graph->add_node();
-        *added_node = *node;
-        // Always use AddN for the constant node, since AccumulateNV2 is a fake
-        // node that cannot be constant folded, since it does not have a kernel.
-        added_node->set_op("AddN");
-        added_node->mutable_attr()->erase("shape");
-        added_node->set_name(new_node_name);
-        node_map_->AddNode(added_node->name(), added_node);
-        added_node->clear_input();
-        for (int i : const_inputs) {
-          added_node->add_input(node->input(i));
-          node_map_->UpdateOutput(NodeName(node->input(i)), node->name(),
-                                  added_node->name());
-        }
-
-        // Overwrite the first const input with the added node.
-        node->set_input(const_inputs[0], added_node->name());
-        node_map_->AddOutput(added_node->name(), node->name());
-        nonconst_inputs.push_back(const_inputs[0]);
-        // Compact the remaining inputs to the original node.
-        std::sort(nonconst_inputs.begin(), nonconst_inputs.end());
-        int idx = 0;
-        for (int i : nonconst_inputs) {
-          if (idx != i) {
-            node->set_input(idx, node->input(i));
-          }
-          ++idx;
-        }
-        node->mutable_input()->DeleteSubrange(nonconst_inputs.size(),
-                                              const_inputs.size() - 1);
-        (*node->mutable_attr())["N"].set_i(node->input_size() -
-                                           num_control_inputs);
-        properties->ClearInputProperties(node->name());
-        (*added_node->mutable_attr())["N"].set_i(const_inputs.size());
-        graph_modified_ = true;
-        continue;
-      }
+    if (PartialAssocOpConstFolding(optimized_graph, properties, node)) {
+      graph_modified_ = true;
+      continue;
     }
 
     if (PartialConcatConstFolding(optimized_graph, properties, node)) {
@@ -2379,6 +2308,84 @@ Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
   return Status::OK();
 }
 
+bool ConstantFolding::PartialAssocOpConstFolding(GraphDef* optimized_graph,
+                                                 GraphProperties* properties,
+                                                 NodeDef* node) {
+  // Partial constant folding for associative operators:
+  // Split AddN/AccumulateNV2 to enable partial
+  // folding of ops when more than one but not all inputs are constant.
+  // For AddN and AccumulateNV2, we may furthermore reorder inputs, since
+  // addition is commutative.
+  const int num_non_control_inputs = NumNonControlInputs(*node);
+  if (IsAggregate(*node) && IsCommutative(*node) &&
+      num_non_control_inputs > 2) {
+    const int num_control_inputs = node->input_size() - num_non_control_inputs;
+    std::vector const_inputs;
+    std::vector nonconst_inputs;
+    for (int i = 0; i < node->input_size(); ++i) {
+      const string& input = node->input(i);
+      const NodeDef* input_node = node_map_->GetNode(NodeName(input));
+      CHECK(input_node != nullptr) << input;
+      if (!IsControlInput(input) && IsReallyConstant(*input_node)) {
+        const_inputs.push_back(i);
+      } else {
+        // Non-const and control inputs.
+        nonconst_inputs.push_back(i);
+      }
+    }
+    // Promote AccumulateNV2 with all constant inputs to AddN, since it is
+    // a fake node that cannot be constant folded by itself.
+    if (const_inputs.size() == num_non_control_inputs &&
+        node->op() == "AccumulateNV2") {
+      node->set_op("AddN");
+      node->mutable_attr()->erase("shape");
+      return true;
+    }
+    const string new_node_name = OptimizedNodeName(
+        *node, strings::StrCat("_partial_split_", const_inputs.size()));
+    if (1 < const_inputs.size() &&
+        const_inputs.size() < num_non_control_inputs &&
+        !node_map_->NodeExists(new_node_name)) {
+      NodeDef* added_node = optimized_graph->add_node();
+      *added_node = *node;
+      // Always use AddN for the constant node, since AccumulateNV2 is a fake
+      // node that cannot be constant folded, since it does not have a kernel.
+      added_node->set_op("AddN");
+      added_node->mutable_attr()->erase("shape");
+      added_node->set_name(new_node_name);
+      node_map_->AddNode(added_node->name(), added_node);
+      added_node->clear_input();
+      for (int i : const_inputs) {
+        added_node->add_input(node->input(i));
+        node_map_->UpdateOutput(NodeName(node->input(i)), node->name(),
+                                added_node->name());
+      }
+
+      // Overwrite the first const input with the added node.
+      node->set_input(const_inputs[0], added_node->name());
+      node_map_->AddOutput(added_node->name(), node->name());
+      nonconst_inputs.push_back(const_inputs[0]);
+      // Compact the remaining inputs to the original node.
+      std::sort(nonconst_inputs.begin(), nonconst_inputs.end());
+      int idx = 0;
+      for (int i : nonconst_inputs) {
+        if (idx != i) {
+          node->set_input(idx, node->input(i));
+        }
+        ++idx;
+      }
+      node->mutable_input()->DeleteSubrange(nonconst_inputs.size(),
+                                            const_inputs.size() - 1);
+      (*node->mutable_attr())["N"].set_i(node->input_size() -
+                                         num_control_inputs);
+      properties->ClearInputProperties(node->name());
+      (*added_node->mutable_attr())["N"].set_i(const_inputs.size());
+      return true;
+    }
+  }
+  return false;
+}
+
 bool ConstantFolding::PartialConcatConstFolding(GraphDef* optimized_graph,
                                                 GraphProperties* properties,
                                                 NodeDef* node) {
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index 20965765385411..1c698ee6f4bffd 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -106,6 +106,11 @@ class ConstantFolding : public GraphOptimizer {
   bool PartialConcatConstFolding(GraphDef* optimized_graph,
                                  GraphProperties* properties, NodeDef* node);
 
+  // Applies partial constant folding for associative operators AddN and
+  // AccumulateNV2. Returns true if the transformation applied successfully.
+  bool PartialAssocOpConstFolding(GraphDef* optimized_graph,
+                                  GraphProperties* properties, NodeDef* node);
+
   // Points to an externally provided device or to owned_device_;
   RewriterConfig::Toggle opt_level_;
   DeviceBase* cpu_device_;

From d27e562ecc4967e17c053f1ae83eff969af0f695 Mon Sep 17 00:00:00 2001
From: mbhuiyan 
Date: Thu, 10 May 2018 11:38:47 -0700
Subject: [PATCH 0619/1691] rebasing with master and removing the conflict

---
 ...direct_session_with_tracking_alloc_test.cc | 30 +++++++++----------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
index 0c9e1931b4af62..2634ffccae9a53 100644
--- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
@@ -101,27 +101,27 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) {
         EXPECT_EQ(2, shape.dim_size());
         EXPECT_EQ(2, shape.dim(0).size());
         EXPECT_EQ(1, shape.dim(1).size());
-#ifdef INTEL_MKL
-        // if MKL is used, it goes through various additional 
-        // graph rewrite pass. In TF, everytime a graph pass 
-        // happens, "constant" nodes are allocated
-        // and deallocated. Each allocation calls the
-        // (FindChunkPtr of BFCAllocator),
-        // which increments the value of AllocationId. 
-        // Thus AllocationId becomes more than 3 and 4 if 
-        // MKL is used. Now they are 9 and 10 for MKL. 
         if (node->name() == y->name()) {
-          EXPECT_EQ(9, cm->AllocationId(node, 0));
-        } else {
-          EXPECT_EQ(10, cm->AllocationId(node, 0));
-        }
+#ifdef INTEL_MKL
+          // if MKL is used, it goes through various additional 
+          // graph rewrite pass. In TF, everytime a graph pass 
+          // happens, "constant" nodes are allocated
+          // and deallocated. Each allocation calls the
+          // (FindChunkPtr of BFCAllocator),
+          // which increments the value of AllocationId. 
+          // Thus AllocationId becomes more than 3 and 4 if 
+          // MKL is used. Now they are 9 and 10 for MKL. 
+          EXPECT_EQ(15, cm->AllocationId(node, 0));
 #else
-        if (node->name() == y->name()) {
           EXPECT_EQ(9, cm->AllocationId(node, 0));
+#endif 
         } else {
+#ifdef INTEL_MKL
+          EXPECT_EQ(16, cm->AllocationId(node, 0));
+#else
           EXPECT_EQ(10, cm->AllocationId(node, 0));
-        }
 #endif 
+        }
       }
       EXPECT_LE(0, cm->MaxExecutionTime(node));
       EXPECT_GE(run_duration_micros, cm->MaxExecutionTime(node));

From 5fc40446cbbef0c7f5b869e11dbbbe3413359ddc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 11:49:31 -0700
Subject: [PATCH 0620/1691] Adds metric_class_ids argument in multi_label_head.

PiperOrigin-RevId: 196149006
---
 .../estimator/python/estimator/head.py        | 69 +++++++++++++-
 .../estimator/python/estimator/head_test.py   | 90 +++++++++++++++++++
 .../python/estimator/canned/metric_keys.py    |  5 ++
 3 files changed, 161 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py
index 109fdd3883427a..fe6e5eaf60b389 100644
--- a/tensorflow/contrib/estimator/python/estimator/head.py
+++ b/tensorflow/contrib/estimator/python/estimator/head.py
@@ -18,6 +18,8 @@
 from __future__ import division
 from __future__ import print_function
 
+import six
+
 from tensorflow.python.estimator import model_fn
 from tensorflow.python.estimator.canned import head as head_lib
 from tensorflow.python.estimator.canned import metric_keys
@@ -41,6 +43,7 @@
 _DEFAULT_SERVING_KEY = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
 
 
+# TODO(roumposg): Add code examples in public factory methods.
 def multi_class_head(n_classes,
                      weight_column=None,
                      label_vocabulary=None,
@@ -375,6 +378,7 @@ def multi_label_head(n_classes,
                      label_vocabulary=None,
                      loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE,
                      loss_fn=None,
+                     classes_for_class_based_metrics=None,
                      name=None):
   """Creates a `_Head` for multi-label classification.
 
@@ -427,6 +431,10 @@ def multi_label_head(n_classes,
       reduce training loss over batch. Defaults to `SUM_OVER_BATCH_SIZE`, namely
       weighted sum of losses divided by batch size. See `tf.losses.Reduction`.
     loss_fn: Optional loss function.
+    classes_for_class_based_metrics: List of integer class IDs or string class
+      names for which per-class metrics are evaluated. If integers, all must be
+      in the range `[0, n_classes - 1]`. If strings, all must be in
+      `label_vocabulary`.
     name: name of the head. If provided, summary and metrics keys will be
       suffixed by `"/" + name`. Also used as `name_scope` when creating ops.
 
@@ -434,8 +442,8 @@ def multi_label_head(n_classes,
     An instance of `_Head` for multi-label classification.
 
   Raises:
-    ValueError: if `n_classes`, `thresholds`, `loss_reduction` or `loss_fn` is
-    invalid.
+    ValueError: if `n_classes`, `thresholds`, `loss_reduction`, `loss_fn` or
+    `metric_class_ids` is invalid.
   """
   thresholds = tuple(thresholds) if thresholds else tuple()
   if n_classes is None or n_classes < 2:
@@ -460,10 +468,31 @@ def multi_label_head(n_classes,
   if (loss_reduction not in losses.Reduction.all() or
       loss_reduction == losses.Reduction.NONE):
     raise ValueError('Invalid loss_reduction: {}'.format(loss_reduction))
+  classes_for_class_based_metrics = tuple(
+      [] if classes_for_class_based_metrics is None
+      else classes_for_class_based_metrics)
+  if classes_for_class_based_metrics:
+    if isinstance(classes_for_class_based_metrics[0], six.string_types):
+      if not label_vocabulary:
+        raise ValueError(
+            'label_vocabulary must be provided when '
+            'classes_for_class_based_metrics are sting.')
+      class_ids = []
+      for class_string in classes_for_class_based_metrics:
+        class_ids.append(label_vocabulary.index(class_string))
+      classes_for_class_based_metrics = tuple(class_ids)
+    else:
+      for class_id in classes_for_class_based_metrics:
+        if (class_id < 0) or (class_id >= n_classes):
+          raise ValueError(
+              'All classes_for_class_based_metrics must be in range [0, {}]. '
+              'Given: {}'.format(n_classes - 1, class_id))
   return _MultiLabelHead(
       n_classes=n_classes, weight_column=weight_column, thresholds=thresholds,
       label_vocabulary=label_vocabulary, loss_reduction=loss_reduction,
-      loss_fn=loss_fn, name=name)
+      loss_fn=loss_fn,
+      classes_for_class_based_metrics=classes_for_class_based_metrics,
+      name=name)
 
 
 class _MultiLabelHead(head_lib._Head):  # pylint:disable=protected-access
@@ -476,6 +505,7 @@ def __init__(self,
                label_vocabulary=None,
                loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE,
                loss_fn=None,
+               classes_for_class_based_metrics=None,
                name=None):
     self._n_classes = n_classes
     self._weight_column = weight_column
@@ -483,6 +513,7 @@ def __init__(self,
     self._label_vocabulary = label_vocabulary
     self._loss_reduction = loss_reduction
     self._loss_fn = loss_fn
+    self._classes_for_class_based_metrics = classes_for_class_based_metrics
     self._name = name
 
   @property
@@ -737,4 +768,36 @@ def _eval_metric_ops(
                 weights=weights,
                 threshold=threshold,
                 name=recall_key))
+      for class_id in self._classes_for_class_based_metrics:
+        batch_rank = array_ops.rank(probabilities) - 1
+        begin = array_ops.concat(
+            [array_ops.zeros([batch_rank], dtype=dtypes.int32), [class_id]],
+            axis=0)
+        size = array_ops.concat(
+            [-1 * array_ops.ones([batch_rank], dtype=dtypes.int32), [1]],
+            axis=0)
+        class_probabilities = array_ops.slice(
+            probabilities, begin=begin, size=size)
+        class_labels = array_ops.slice(labels, begin=begin, size=size)
+        prob_key = keys.PROBABILITY_MEAN_AT_CLASS % class_id
+        metric_ops[head_lib._summary_key(self._name, prob_key)] = (  # pylint:disable=protected-access
+            head_lib._predictions_mean(  # pylint:disable=protected-access
+                predictions=class_probabilities,
+                weights=weights,
+                name=prob_key))
+        auc_key = keys.AUC_AT_CLASS % class_id
+        metric_ops[head_lib._summary_key(self._name, auc_key)] = (  # pylint:disable=protected-access
+            head_lib._auc(  # pylint:disable=protected-access
+                labels=class_labels,
+                predictions=class_probabilities,
+                weights=weights,
+                name=auc_key))
+        auc_pr_key = keys.AUC_PR_AT_CLASS % class_id
+        metric_ops[head_lib._summary_key(self._name, auc_pr_key)] = (  # pylint:disable=protected-access
+            head_lib._auc(  # pylint:disable=protected-access
+                labels=class_labels,
+                predictions=class_probabilities,
+                weights=weights,
+                curve='PR',
+                name=auc_pr_key))
     return metric_ops
diff --git a/tensorflow/contrib/estimator/python/estimator/head_test.py b/tensorflow/contrib/estimator/python/estimator/head_test.py
index 19b86df5565a85..d6c158608b5c56 100644
--- a/tensorflow/contrib/estimator/python/estimator/head_test.py
+++ b/tensorflow/contrib/estimator/python/estimator/head_test.py
@@ -175,6 +175,21 @@ def _loss_fn(labels, logits, name=None):
         r'loss_fn has unexpected args: \[\'name\'\]'):
       head_lib.multi_label_head(n_classes=3, loss_fn=_loss_fn)
 
+  def test_classes_for_class_based_metrics_invalid(self):
+    with self.assertRaisesRegexp(
+        ValueError,
+        r'All classes_for_class_based_metrics must be in range \[0, 2\]\. '
+        r'Given: -1'):
+      head_lib.multi_label_head(
+          n_classes=3, classes_for_class_based_metrics=[2, -1])
+
+  def test_classes_for_class_based_metrics_string_invalid(self):
+    with self.assertRaisesRegexp(
+        ValueError, r'\'z\' is not in list'):
+      head_lib.multi_label_head(
+          n_classes=3, label_vocabulary=['a', 'b', 'c'],
+          classes_for_class_based_metrics=['c', 'z'])
+
   def test_name(self):
     head = head_lib.multi_label_head(n_classes=4, name='foo')
     self.assertEqual('foo', head.name)
@@ -591,6 +606,81 @@ def test_eval_with_thresholds(self):
         expected_loss=expected_loss,
         expected_metrics=expected_metrics)
 
+  def test_eval_with_classes_for_class_based_metrics(self):
+    head = head_lib.multi_label_head(
+        n_classes=2, classes_for_class_based_metrics=[0, 1])
+
+    logits = np.array([[-1., 1.], [-1.5, 1.5]], dtype=np.float32)
+    labels = np.array([[1, 0], [1, 1]], dtype=np.int64)
+    # loss = labels * -log(sigmoid(logits)) +
+    #        (1 - labels) * -log(1 - sigmoid(logits))
+    # Sum over examples, divide by batch_size.
+    expected_loss = 0.5 * np.sum(
+        _sigmoid_cross_entropy(labels=labels, logits=logits))
+
+    keys = metric_keys.MetricKeys
+    expected_metrics = {
+        # Average loss over examples.
+        keys.LOSS_MEAN: expected_loss,
+        # auc and auc_pr cannot be reliably calculated for only 4 samples, but
+        # this assert tests that the algorithm remains consistent.
+        keys.AUC: 0.3333,
+        keys.AUC_PR: 0.7639,
+        keys.PROBABILITY_MEAN_AT_CLASS % 0: np.sum(_sigmoid(logits[:, 0])) / 2.,
+        keys.AUC_AT_CLASS % 0: 0.,
+        keys.AUC_PR_AT_CLASS % 0: 1.,
+        keys.PROBABILITY_MEAN_AT_CLASS % 1: np.sum(_sigmoid(logits[:, 1])) / 2.,
+        keys.AUC_AT_CLASS % 1: 1.,
+        keys.AUC_PR_AT_CLASS % 1: 1.,
+    }
+
+    self._test_eval(
+        head=head,
+        logits=logits,
+        labels=labels,
+        expected_loss=expected_loss,
+        expected_metrics=expected_metrics)
+
+  def test_eval_with_classes_for_class_based_metrics_string(self):
+    head = head_lib.multi_label_head(
+        n_classes=2, label_vocabulary=['a', 'b'],
+        classes_for_class_based_metrics=['a', 'b'])
+
+    logits = np.array([[-1., 1.], [-1.5, 1.5]], dtype=np.float32)
+    labels = sparse_tensor.SparseTensor(
+        values=['a', 'a', 'b'],
+        indices=[[0, 0], [1, 0], [1, 1]],
+        dense_shape=[2, 2])
+    labels_onehot = np.array([[1, 0], [1, 1]], dtype=np.int64)
+    # loss = labels * -log(sigmoid(logits)) +
+    #        (1 - labels) * -log(1 - sigmoid(logits))
+    # Sum over examples, divide by batch_size.
+    expected_loss = 0.5 * np.sum(
+        _sigmoid_cross_entropy(labels=labels_onehot, logits=logits))
+
+    keys = metric_keys.MetricKeys
+    expected_metrics = {
+        # Average loss over examples.
+        keys.LOSS_MEAN: expected_loss,
+        # auc and auc_pr cannot be reliably calculated for only 4 samples, but
+        # this assert tests that the algorithm remains consistent.
+        keys.AUC: 0.3333,
+        keys.AUC_PR: 0.7639,
+        keys.PROBABILITY_MEAN_AT_CLASS % 0: np.sum(_sigmoid(logits[:, 0])) / 2.,
+        keys.AUC_AT_CLASS % 0: 0.,
+        keys.AUC_PR_AT_CLASS % 0: 1.,
+        keys.PROBABILITY_MEAN_AT_CLASS % 1: np.sum(_sigmoid(logits[:, 1])) / 2.,
+        keys.AUC_AT_CLASS % 1: 1.,
+        keys.AUC_PR_AT_CLASS % 1: 1.,
+    }
+
+    self._test_eval(
+        head=head,
+        logits=logits,
+        labels=labels,
+        expected_loss=expected_loss,
+        expected_metrics=expected_metrics)
+
   def test_eval_with_weights(self):
     n_classes = 2
     head = head_lib.multi_label_head(n_classes, weight_column='example_weights')
diff --git a/tensorflow/python/estimator/canned/metric_keys.py b/tensorflow/python/estimator/canned/metric_keys.py
index f374d3154982e3..4f7c849ba4b058 100644
--- a/tensorflow/python/estimator/canned/metric_keys.py
+++ b/tensorflow/python/estimator/canned/metric_keys.py
@@ -42,3 +42,8 @@ class MetricKeys(object):
   ACCURACY_AT_THRESHOLD = 'accuracy/positive_threshold_%g'
   PRECISION_AT_THRESHOLD = 'precision/positive_threshold_%g'
   RECALL_AT_THRESHOLD = 'recall/positive_threshold_%g'
+
+  # The following require a class id applied.
+  PROBABILITY_MEAN_AT_CLASS = 'probability_mean/class%d'
+  AUC_AT_CLASS = 'auc/class%d'
+  AUC_PR_AT_CLASS = 'auc_precision_recall/class%d'

From 71b88284d9834f83a5d73feda3cf67944b878362 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 11:54:00 -0700
Subject: [PATCH 0621/1691] Adds BaseLineEstimator, which accepts a
 user-specified head.

PiperOrigin-RevId: 196149694
---
 tensorflow/contrib/estimator/BUILD            |  44 ++
 tensorflow/contrib/estimator/__init__.py      |   2 +
 .../estimator/python/estimator/baseline.py    |  98 ++++
 .../python/estimator/baseline_test.py         | 430 ++++++++++++++++++
 .../contrib/estimator/python/estimator/dnn.py |   2 +-
 5 files changed, 575 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/contrib/estimator/python/estimator/baseline.py
 create mode 100644 tensorflow/contrib/estimator/python/estimator/baseline_test.py

diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD
index e9a68801efccc1..53bbafd4a76a11 100644
--- a/tensorflow/contrib/estimator/BUILD
+++ b/tensorflow/contrib/estimator/BUILD
@@ -14,6 +14,7 @@ py_library(
     srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
     deps = [
+        ":baseline",
         ":boosted_trees",
         ":dnn",
         ":dnn_linear_combined",
@@ -29,6 +30,49 @@ py_library(
     ],
 )
 
+py_library(
+    name = "baseline",
+    srcs = ["python/estimator/baseline.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python/estimator",
+        "//tensorflow/python/estimator:baseline",
+    ],
+)
+
+py_test(
+    name = "baseline_test",
+    size = "small",
+    srcs = ["python/estimator/baseline_test.py"],
+    srcs_version = "PY2AND3",
+    tags = [
+        "no_pip",
+        "notsan",
+    ],
+    deps = [
+        ":baseline",
+        ":head",
+        "//tensorflow/python:check_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:session",
+        "//tensorflow/python:summary",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variables",
+        "//tensorflow/python/estimator:export_export",
+        "//tensorflow/python/estimator:metric_keys",
+        "//tensorflow/python/estimator:numpy_io",
+        "//tensorflow/python/feature_column",
+        "//tensorflow/python/ops/losses",
+        "//third_party/py/numpy",
+        "@six_archive//:six",
+    ],
+)
+
 py_library(
     name = "boosted_trees",
     srcs = ["python/estimator/boosted_trees.py"],
diff --git a/tensorflow/contrib/estimator/__init__.py b/tensorflow/contrib/estimator/__init__.py
index ec502f86ddb724..32a0f2545dd0ea 100644
--- a/tensorflow/contrib/estimator/__init__.py
+++ b/tensorflow/contrib/estimator/__init__.py
@@ -19,6 +19,7 @@
 from __future__ import print_function
 
 # pylint: disable=unused-import,line-too-long,wildcard-import
+from tensorflow.contrib.estimator.python.estimator.baseline import *
 from tensorflow.contrib.estimator.python.estimator.boosted_trees import *
 from tensorflow.contrib.estimator.python.estimator.dnn import *
 from tensorflow.contrib.estimator.python.estimator.dnn_linear_combined import *
@@ -45,6 +46,7 @@
     'multi_label_head',
     'poisson_regression_head',
     'regression_head',
+    'BaselineEstimator',
     'DNNEstimator',
     'DNNLinearCombinedEstimator',
     'LinearEstimator',
diff --git a/tensorflow/contrib/estimator/python/estimator/baseline.py b/tensorflow/contrib/estimator/python/estimator/baseline.py
new file mode 100644
index 00000000000000..beffbee73064b9
--- /dev/null
+++ b/tensorflow/contrib/estimator/python/estimator/baseline.py
@@ -0,0 +1,98 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Baseline estimators."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.estimator import estimator
+from tensorflow.python.estimator.canned import baseline
+
+
+class BaselineEstimator(estimator.Estimator):
+  """An estimator that can establish a simple baseline.
+
+  The estimator uses a user-specified head.
+
+  This estimator ignores feature values and will learn to predict the average
+  value of each label. E.g. for single-label classification problems, this will
+  predict the probability distribution of the classes as seen in the labels.
+  For multi-label classification problems, it will predict the ratio of examples
+  that contain each class.
+
+  Example:
+
+  ```python
+
+  # Build baseline multi-label classifier.
+  estimator = BaselineEstimator(
+      head=tf.contrib.estimator.multi_label_head(n_classes=3))
+
+  # Input builders
+  def input_fn_train: # returns x, y (where y represents label's class index).
+    pass
+
+  def input_fn_eval: # returns x, y (where y represents label's class index).
+    pass
+
+  # Fit model.
+  estimator.train(input_fn=input_fn_train)
+
+  # Evaluates cross entropy between the test and train labels.
+  loss = classifier.evaluate(input_fn=input_fn_eval)["loss"]
+
+  # For each class, predicts the ratio of training examples that contain the
+  # class.
+  predictions = classifier.predict(new_samples)
+
+  ```
+
+  Input of `train` and `evaluate` should have following features,
+    otherwise there will be a `KeyError`:
+
+  * if `weight_column` passed to the `head` constructor is not `None`, a feature
+    with `key=weight_column` whose value is a `Tensor`.
+  """
+
+  def __init__(self,
+               head,
+               model_dir=None,
+               optimizer='Ftrl',
+               config=None):
+    """Initializes a BaselineEstimator instance.
+
+    Args:
+      head: A `_Head` instance constructed with a method such as
+        `tf.contrib.estimator.multi_label_head`.
+      model_dir: Directory to save model parameters, graph and etc. This can
+        also be used to load checkpoints from the directory into a estimator to
+        continue training a previously saved model.
+      optimizer: String, `tf.Optimizer` object, or callable that creates the
+        optimizer to use for training. If not specified, will use
+        `FtrlOptimizer` with a default learning rate of 0.3.
+      config: `RunConfig` object to configure the runtime settings.
+    """
+    def _model_fn(features, labels, mode, config):
+      return baseline._baseline_model_fn(  # pylint: disable=protected-access
+          features=features,
+          labels=labels,
+          mode=mode,
+          head=head,
+          optimizer=optimizer,
+          config=config)
+    super(BaselineEstimator, self).__init__(
+        model_fn=_model_fn,
+        model_dir=model_dir,
+        config=config)
diff --git a/tensorflow/contrib/estimator/python/estimator/baseline_test.py b/tensorflow/contrib/estimator/python/estimator/baseline_test.py
new file mode 100644
index 00000000000000..d0e3e670f73328
--- /dev/null
+++ b/tensorflow/contrib/estimator/python/estimator/baseline_test.py
@@ -0,0 +1,430 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for baseline.py."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import shutil
+import tempfile
+
+import numpy as np
+import six
+
+from tensorflow.contrib.estimator.python.estimator import baseline
+from tensorflow.contrib.estimator.python.estimator import head as head_lib
+from tensorflow.python.client import session as tf_session
+from tensorflow.python.estimator.canned import metric_keys
+from tensorflow.python.estimator.export import export
+from tensorflow.python.estimator.inputs import numpy_io
+from tensorflow.python.feature_column import feature_column as feature_column_lib
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.ops.losses import losses
+from tensorflow.python.platform import gfile
+from tensorflow.python.platform import test
+from tensorflow.python.summary.writer import writer_cache
+from tensorflow.python.training import checkpoint_utils
+from tensorflow.python.training import distribute as distribute_lib
+from tensorflow.python.training import optimizer
+from tensorflow.python.training import saver
+
+# Names of variables created by model.
+BIAS_NAME = 'baseline/bias'
+
+
+def assert_close(expected, actual, rtol=1e-04, name='assert_close'):
+  with ops.name_scope(name, 'assert_close', (expected, actual, rtol)) as scope:
+    expected = ops.convert_to_tensor(expected, name='expected')
+    actual = ops.convert_to_tensor(actual, name='actual')
+    rdiff = math_ops.abs(expected - actual, 'diff') / math_ops.abs(expected)
+    rtol = ops.convert_to_tensor(rtol, name='rtol')
+    return check_ops.assert_less(
+        rdiff,
+        rtol,
+        data=('Condition expected =~ actual did not hold element-wise:'
+              'expected = ', expected, 'actual = ', actual, 'rdiff = ', rdiff,
+              'rtol = ', rtol,),
+        name=scope)
+
+
+def save_variables_to_ckpt(model_dir):
+  init_all_op = [variables.global_variables_initializer()]
+  with tf_session.Session() as sess:
+    sess.run(init_all_op)
+    saver.Saver().save(sess, os.path.join(model_dir, 'model.ckpt'))
+
+
+def _baseline_estimator_fn(
+    weight_column=None, label_dimension=1, *args, **kwargs):
+  """Returns a BaselineEstimator that uses regression_head."""
+  return baseline.BaselineEstimator(
+      head=head_lib.regression_head(
+          weight_column=weight_column, label_dimension=label_dimension,
+          # Tests in core (from which this test inherits) test the sum loss.
+          loss_reduction=losses.Reduction.SUM),
+      *args, **kwargs)
+
+
+class BaselineEstimatorEvaluationTest(test.TestCase):
+
+  def setUp(self):
+    self._model_dir = tempfile.mkdtemp()
+
+  def tearDown(self):
+    if self._model_dir:
+      writer_cache.FileWriterCache.clear()
+      shutil.rmtree(self._model_dir)
+
+  def test_evaluation_batch(self):
+    """Tests evaluation for batch_size==2."""
+    with ops.Graph().as_default():
+      variables.Variable([13.0], name=BIAS_NAME)
+      variables.Variable(
+          100, name=ops.GraphKeys.GLOBAL_STEP, dtype=dtypes.int64)
+      save_variables_to_ckpt(self._model_dir)
+
+    baseline_estimator = _baseline_estimator_fn(model_dir=self._model_dir)
+    eval_metrics = baseline_estimator.evaluate(
+        input_fn=lambda: ({'age': ((1,), (1,))}, ((10.,), (10.,))), steps=1)
+
+    # Logit is bias = 13, while label is 10.
+    # Loss per example is 3**2 = 9.
+    # Training loss is the sum over batch = 9 + 9 = 18
+    # Average loss is the average over batch = 9
+    self.assertDictEqual({
+        metric_keys.MetricKeys.LOSS: 18.,
+        metric_keys.MetricKeys.LOSS_MEAN: 9.,
+        ops.GraphKeys.GLOBAL_STEP: 100
+    }, eval_metrics)
+
+  def test_evaluation_weights(self):
+    """Tests evaluation with weights."""
+    with ops.Graph().as_default():
+      variables.Variable([13.0], name=BIAS_NAME)
+      variables.Variable(
+          100, name=ops.GraphKeys.GLOBAL_STEP, dtype=dtypes.int64)
+      save_variables_to_ckpt(self._model_dir)
+
+    def _input_fn():
+      features = {'age': ((1,), (1,)), 'weights': ((1.,), (2.,))}
+      labels = ((10.,), (10.,))
+      return features, labels
+
+    baseline_estimator = _baseline_estimator_fn(
+        weight_column='weights',
+        model_dir=self._model_dir)
+    eval_metrics = baseline_estimator.evaluate(input_fn=_input_fn, steps=1)
+
+    # Logit is bias = 13, while label is 10.
+    # Loss per example is 3**2 = 9.
+    # Training loss is the weighted sum over batch = 9 + 2*9 = 27
+    # average loss is the weighted average = 9 + 2*9 / (1 + 2) = 9
+    self.assertDictEqual({
+        metric_keys.MetricKeys.LOSS: 27.,
+        metric_keys.MetricKeys.LOSS_MEAN: 9.,
+        ops.GraphKeys.GLOBAL_STEP: 100
+    }, eval_metrics)
+
+  def test_evaluation_for_multi_dimensions(self):
+    label_dim = 2
+    with ops.Graph().as_default():
+      variables.Variable([46.0, 58.0], name=BIAS_NAME)
+      variables.Variable(100, name='global_step', dtype=dtypes.int64)
+      save_variables_to_ckpt(self._model_dir)
+
+    baseline_estimator = _baseline_estimator_fn(
+        label_dimension=label_dim,
+        model_dir=self._model_dir)
+    input_fn = numpy_io.numpy_input_fn(
+        x={
+            'age': np.array([[2., 4., 5.]]),
+        },
+        y=np.array([[46., 58.]]),
+        batch_size=1,
+        num_epochs=None,
+        shuffle=False)
+    eval_metrics = baseline_estimator.evaluate(input_fn=input_fn, steps=1)
+
+    self.assertItemsEqual(
+        (metric_keys.MetricKeys.LOSS, metric_keys.MetricKeys.LOSS_MEAN,
+         ops.GraphKeys.GLOBAL_STEP), eval_metrics.keys())
+
+    # Logit is bias which is [46, 58]
+    self.assertAlmostEqual(0, eval_metrics[metric_keys.MetricKeys.LOSS])
+
+
+class BaselineEstimatorPredictTest(test.TestCase):
+
+  def setUp(self):
+    self._model_dir = tempfile.mkdtemp()
+
+  def tearDown(self):
+    if self._model_dir:
+      writer_cache.FileWriterCache.clear()
+      shutil.rmtree(self._model_dir)
+
+  def test_1d(self):
+    """Tests predict when all variables are one-dimensional."""
+    with ops.Graph().as_default():
+      variables.Variable([.2], name=BIAS_NAME)
+      variables.Variable(100, name='global_step', dtype=dtypes.int64)
+      save_variables_to_ckpt(self._model_dir)
+
+    baseline_estimator = _baseline_estimator_fn(model_dir=self._model_dir)
+
+    predict_input_fn = numpy_io.numpy_input_fn(
+        x={'x': np.array([[2.]])},
+        y=None,
+        batch_size=1,
+        num_epochs=1,
+        shuffle=False)
+    predictions = baseline_estimator.predict(input_fn=predict_input_fn)
+    predicted_scores = list([x['predictions'] for x in predictions])
+    # x * weight + bias = 2. * 10. + .2 = 20.2
+    self.assertAllClose([[.2]], predicted_scores)
+
+  def testMultiDim(self):
+    """Tests predict when all variables are multi-dimenstional."""
+    batch_size = 2
+    label_dimension = 3
+    with ops.Graph().as_default():
+      variables.Variable(  # shape=[label_dimension]
+          [.2, .4, .6], name=BIAS_NAME)
+      variables.Variable(100, name='global_step', dtype=dtypes.int64)
+      save_variables_to_ckpt(self._model_dir)
+
+    baseline_estimator = _baseline_estimator_fn(
+        label_dimension=label_dimension,
+        model_dir=self._model_dir)
+
+    predict_input_fn = numpy_io.numpy_input_fn(
+        # x shape=[batch_size, x_dim]
+        x={'x': np.array([[1., 2., 3., 4.], [5., 6., 7., 8.]])},
+        y=None,
+        batch_size=batch_size,
+        num_epochs=1,
+        shuffle=False)
+    predictions = baseline_estimator.predict(input_fn=predict_input_fn)
+    predicted_scores = list([x['predictions'] for x in predictions])
+    # score = bias, shape=[batch_size, label_dimension]
+    self.assertAllClose([[0.2, 0.4, 0.6], [0.2, 0.4, 0.6]],
+                        predicted_scores)
+
+
+class BaselineEstimatorIntegrationTest(test.TestCase):
+
+  def setUp(self):
+    self._model_dir = tempfile.mkdtemp()
+
+  def tearDown(self):
+    if self._model_dir:
+      writer_cache.FileWriterCache.clear()
+      shutil.rmtree(self._model_dir)
+
+  def _test_complete_flow(self, train_input_fn, eval_input_fn, predict_input_fn,
+                          input_dimension, label_dimension, prediction_length):
+    feature_columns = [
+        feature_column_lib.numeric_column('x', shape=(input_dimension,))
+    ]
+    est = _baseline_estimator_fn(
+        label_dimension=label_dimension,
+        model_dir=self._model_dir)
+
+    # TRAIN
+    # learn y = x
+    est.train(train_input_fn, steps=200)
+
+    # EVALUTE
+    scores = est.evaluate(eval_input_fn)
+    self.assertEqual(200, scores[ops.GraphKeys.GLOBAL_STEP])
+    self.assertIn(metric_keys.MetricKeys.LOSS, six.iterkeys(scores))
+
+    # PREDICT
+    predictions = np.array(
+        [x['predictions'] for x in est.predict(predict_input_fn)])
+    self.assertAllEqual((prediction_length, label_dimension), predictions.shape)
+
+    # EXPORT
+    feature_spec = feature_column_lib.make_parse_example_spec(feature_columns)
+    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
+        feature_spec)
+    export_dir = est.export_savedmodel(tempfile.mkdtemp(),
+                                       serving_input_receiver_fn)
+    self.assertTrue(gfile.Exists(export_dir))
+
+  def test_numpy_input_fn(self):
+    """Tests complete flow with numpy_input_fn."""
+    label_dimension = 2
+    input_dimension = label_dimension
+    batch_size = 10
+    prediction_length = batch_size
+    data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
+    data = data.reshape(batch_size, label_dimension)
+
+    train_input_fn = numpy_io.numpy_input_fn(
+        x={'x': data},
+        y=data,
+        batch_size=batch_size,
+        num_epochs=None,
+        shuffle=True)
+    eval_input_fn = numpy_io.numpy_input_fn(
+        x={'x': data},
+        y=data,
+        batch_size=batch_size,
+        num_epochs=1,
+        shuffle=False)
+    predict_input_fn = numpy_io.numpy_input_fn(
+        x={'x': data},
+        y=None,
+        batch_size=batch_size,
+        num_epochs=1,
+        shuffle=False)
+
+    self._test_complete_flow(
+        train_input_fn=train_input_fn,
+        eval_input_fn=eval_input_fn,
+        predict_input_fn=predict_input_fn,
+        input_dimension=input_dimension,
+        label_dimension=label_dimension,
+        prediction_length=prediction_length)
+
+
+class BaselineEstimatorTrainingTest(test.TestCase):
+
+  def setUp(self):
+    self._model_dir = tempfile.mkdtemp()
+
+  def tearDown(self):
+    if self._model_dir:
+      writer_cache.FileWriterCache.clear()
+      shutil.rmtree(self._model_dir)
+
+  def _mock_optimizer(self, expected_loss=None):
+    expected_var_names = [
+        '%s:0' % BIAS_NAME
+    ]
+
+    def _minimize(loss, global_step=None, var_list=None):
+      trainable_vars = var_list or ops.get_collection(
+          ops.GraphKeys.TRAINABLE_VARIABLES)
+      self.assertItemsEqual(expected_var_names,
+                            [var.name for var in trainable_vars])
+
+      # Verify loss. We can't check the value directly, so we add an assert op.
+      self.assertEquals(0, loss.shape.ndims)
+      if expected_loss is None:
+        if global_step is not None:
+          return distribute_lib.increment_var(global_step)
+        return control_flow_ops.no_op()
+      assert_loss = assert_close(
+          math_ops.to_float(expected_loss, name='expected'),
+          loss,
+          name='assert_loss')
+      with ops.control_dependencies((assert_loss,)):
+        if global_step is not None:
+          return distribute_lib.increment_var(global_step)
+        return control_flow_ops.no_op()
+
+    mock_optimizer = test.mock.NonCallableMock(
+        spec=optimizer.Optimizer,
+        wraps=optimizer.Optimizer(use_locking=False, name='my_optimizer'))
+    mock_optimizer.minimize = test.mock.MagicMock(wraps=_minimize)
+
+    # NOTE: Estimator.params performs a deepcopy, which wreaks havoc with mocks.
+    # So, return mock_optimizer itself for deepcopy.
+    mock_optimizer.__deepcopy__ = lambda _: mock_optimizer
+    return mock_optimizer
+
+  def _assert_checkpoint(self,
+                         label_dimension,
+                         expected_global_step,
+                         expected_bias=None):
+    shapes = {
+        name: shape
+        for (name, shape) in checkpoint_utils.list_variables(self._model_dir)
+    }
+
+    self.assertEqual([], shapes[ops.GraphKeys.GLOBAL_STEP])
+    self.assertEqual(expected_global_step,
+                     checkpoint_utils.load_variable(self._model_dir,
+                                                    ops.GraphKeys.GLOBAL_STEP))
+
+    self.assertEqual([label_dimension], shapes[BIAS_NAME])
+    if expected_bias is not None:
+      self.assertEqual(expected_bias,
+                       checkpoint_utils.load_variable(self._model_dir,
+                                                      BIAS_NAME))
+
+  def testFromScratch(self):
+    # Create BaselineRegressor.
+    label = 5.
+    age = 17
+    # loss = (logits - label)^2 = (0 - 5.)^2 = 25.
+    mock_optimizer = self._mock_optimizer(expected_loss=25.)
+    baseline_estimator = _baseline_estimator_fn(
+        model_dir=self._model_dir,
+        optimizer=mock_optimizer)
+    self.assertEqual(0, mock_optimizer.minimize.call_count)
+
+    # Train for a few steps, and validate optimizer and final checkpoint.
+    num_steps = 10
+    baseline_estimator.train(
+        input_fn=lambda: ({'age': ((age,),)}, ((label,),)), steps=num_steps)
+    self.assertEqual(1, mock_optimizer.minimize.call_count)
+    self._assert_checkpoint(
+        label_dimension=1,
+        expected_global_step=num_steps,
+        expected_bias=[0.])
+
+  def testFromCheckpoint(self):
+    # Create initial checkpoint.
+    bias = 7.0
+    initial_global_step = 100
+    with ops.Graph().as_default():
+      variables.Variable([bias], name=BIAS_NAME)
+      variables.Variable(
+          initial_global_step,
+          name=ops.GraphKeys.GLOBAL_STEP,
+          dtype=dtypes.int64)
+      save_variables_to_ckpt(self._model_dir)
+
+    # logits = bias = 6.
+    # loss = (logits - label)^2 = (7 - 5)^2 = 4
+    mock_optimizer = self._mock_optimizer(expected_loss=4.)
+    baseline_estimator = _baseline_estimator_fn(
+        model_dir=self._model_dir,
+        optimizer=mock_optimizer)
+    self.assertEqual(0, mock_optimizer.minimize.call_count)
+
+    # Train for a few steps, and validate optimizer and final checkpoint.
+    num_steps = 10
+    baseline_estimator.train(
+        input_fn=lambda: ({'age': ((17,),)}, ((5.,),)), steps=num_steps)
+    self.assertEqual(1, mock_optimizer.minimize.call_count)
+    self._assert_checkpoint(
+        label_dimension=1,
+        expected_global_step=initial_global_step + num_steps,
+        expected_bias=[bias])
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/estimator/python/estimator/dnn.py b/tensorflow/contrib/estimator/python/estimator/dnn.py
index cf6e3329d2e277..7ff25b95c079c7 100644
--- a/tensorflow/contrib/estimator/python/estimator/dnn.py
+++ b/tensorflow/contrib/estimator/python/estimator/dnn.py
@@ -93,7 +93,7 @@ def __init__(self,
                dropout=None,
                input_layer_partitioner=None,
                config=None):
-    """Initializes a `DNNClassifier` instance.
+    """Initializes a `DNNEstimator` instance.
 
     Args:
       head: A `_Head` instance constructed with a method such as

From 3ffa132c03ff02decc86a31d8bf888e9381278a7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 11:57:20 -0700
Subject: [PATCH 0622/1691] Use distribution_util.arguments instead of locals.
 This fixes a bug in newer python version where locals is a dynamic list.

PiperOrigin-RevId: 196150149
---
 .../python/ops/autoregressive.py              |  2 +-
 .../distributions/python/ops/batch_reshape.py |  3 +-
 .../distributions/python/ops/binomial.py      |  2 +-
 .../distributions/python/ops/cauchy.py        |  3 +-
 .../contrib/distributions/python/ops/chi2.py  |  5 +-
 .../distributions/python/ops/deterministic.py |  3 +-
 .../distributions/python/ops/geometric.py     |  2 +-
 .../distributions/python/ops/gumbel.py        |  3 +-
 .../distributions/python/ops/half_normal.py   |  3 +-
 .../distributions/python/ops/independent.py   |  3 +-
 .../distributions/python/ops/inverse_gamma.py |  4 +-
 .../distributions/python/ops/logistic.py      |  3 +-
 .../distributions/python/ops/mixture.py       |  2 +-
 .../python/ops/mixture_same_family.py         |  2 +-
 .../distributions/python/ops/mvn_diag.py      |  4 +-
 .../python/ops/mvn_diag_plus_low_rank.py      |  2 +-
 .../python/ops/mvn_full_covariance.py         |  3 +-
 .../python/ops/mvn_linear_operator.py         |  2 +-
 .../distributions/python/ops/mvn_tril.py      |  2 +-
 .../python/ops/negative_binomial.py           |  2 +-
 .../python/ops/onehot_categorical.py          |  2 +-
 .../distributions/python/ops/poisson.py       |  2 +-
 .../python/ops/poisson_lognormal.py           |  2 +-
 .../python/ops/quantized_distribution.py      |  2 +-
 .../python/ops/relaxed_bernoulli.py           |  2 +-
 .../python/ops/relaxed_onehot_categorical.py  |  2 +-
 .../distributions/python/ops/sinh_arcsinh.py  |  2 +-
 .../python/ops/vector_diffeomixture.py        |  2 +-
 .../python/ops/vector_exponential_diag.py     |  2 +-
 .../ops/vector_exponential_linear_operator.py |  2 +-
 .../python/ops/vector_laplace_diag.py         |  2 +-
 .../ops/vector_laplace_linear_operator.py     |  2 +-
 .../python/ops/vector_sinh_arcsinh_diag.py    |  2 +-
 .../python/ops/vector_student_t.py            |  2 +-
 .../distributions/python/ops/wishart.py       |  6 +-
 .../python/kernel_tests/distributions/BUILD   |  1 +
 .../kernel_tests/distributions/util_test.py   | 56 +++++++++++++++++++
 .../python/ops/distributions/bernoulli.py     |  2 +-
 tensorflow/python/ops/distributions/beta.py   |  4 +-
 .../python/ops/distributions/categorical.py   |  2 +-
 .../python/ops/distributions/dirichlet.py     |  2 +-
 .../distributions/dirichlet_multinomial.py    |  2 +-
 .../python/ops/distributions/distribution.py  |  3 +-
 .../python/ops/distributions/exponential.py   |  5 +-
 tensorflow/python/ops/distributions/gamma.py  |  4 +-
 .../python/ops/distributions/laplace.py       |  5 +-
 .../python/ops/distributions/multinomial.py   |  2 +-
 tensorflow/python/ops/distributions/normal.py |  5 +-
 .../python/ops/distributions/student_t.py     |  4 +-
 .../distributions/transformed_distribution.py |  2 +-
 .../python/ops/distributions/uniform.py       |  3 +-
 tensorflow/python/ops/distributions/util.py   | 38 +++++++++++++
 52 files changed, 169 insertions(+), 60 deletions(-)

diff --git a/tensorflow/contrib/distributions/python/ops/autoregressive.py b/tensorflow/contrib/distributions/python/ops/autoregressive.py
index 88ed0127841093..d813831bef803a 100644
--- a/tensorflow/contrib/distributions/python/ops/autoregressive.py
+++ b/tensorflow/contrib/distributions/python/ops/autoregressive.py
@@ -144,7 +144,7 @@ def __init__(self,
         `distribution_fn(sample0).event_shape.num_elements()` are both `None`.
       ValueError: if `num_steps < 1`.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name) as name:
       self._distribution_fn = distribution_fn
       self._sample0 = sample0
diff --git a/tensorflow/contrib/distributions/python/ops/batch_reshape.py b/tensorflow/contrib/distributions/python/ops/batch_reshape.py
index bf5590cd552a91..8a4041cf4364dc 100644
--- a/tensorflow/contrib/distributions/python/ops/batch_reshape.py
+++ b/tensorflow/contrib/distributions/python/ops/batch_reshape.py
@@ -28,6 +28,7 @@
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import distribution as distribution_lib
+from tensorflow.python.ops.distributions import util as distribution_util
 
 
 __all__ = [
@@ -104,7 +105,7 @@ def __init__(self,
       ValueError: if `batch_shape` size is not the same as a
         `distribution.batch_shape` size.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     name = name or "BatchReshape" + distribution.name
     self._distribution = distribution
     with ops.name_scope(name, values=[batch_shape]) as name:
diff --git a/tensorflow/contrib/distributions/python/ops/binomial.py b/tensorflow/contrib/distributions/python/ops/binomial.py
index 12d16031783b78..24b26bf124c78c 100644
--- a/tensorflow/contrib/distributions/python/ops/binomial.py
+++ b/tensorflow/contrib/distributions/python/ops/binomial.py
@@ -163,7 +163,7 @@ def __init__(self,
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[total_count, logits, probs]) as name:
       self._total_count = self._maybe_assert_valid_total_count(
           ops.convert_to_tensor(total_count, name="total_count"),
diff --git a/tensorflow/contrib/distributions/python/ops/cauchy.py b/tensorflow/contrib/distributions/python/ops/cauchy.py
index daacfe657fe154..f5ffdd873124d6 100644
--- a/tensorflow/contrib/distributions/python/ops/cauchy.py
+++ b/tensorflow/contrib/distributions/python/ops/cauchy.py
@@ -29,6 +29,7 @@
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
+from tensorflow.python.ops.distributions import util as distribution_util
 
 __all__ = [
     "Cauchy",
@@ -120,7 +121,7 @@ def __init__(self,
     Raises:
       TypeError: if `loc` and `scale` have different `dtype`.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[loc, scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(scale)]
                                     if validate_args else []):
diff --git a/tensorflow/contrib/distributions/python/ops/chi2.py b/tensorflow/contrib/distributions/python/ops/chi2.py
index c77c5fd20895a6..08cdc1582892cc 100644
--- a/tensorflow/contrib/distributions/python/ops/chi2.py
+++ b/tensorflow/contrib/distributions/python/ops/chi2.py
@@ -25,6 +25,7 @@
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import gamma
+from tensorflow.python.ops.distributions import util as distribution_util
 
 
 __all__ = [
@@ -83,7 +84,7 @@ def __init__(self,
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     # Even though all stats of chi2 are defined for valid parameters, this is
     # not true in the parent class "gamma."  therefore, passing
     # allow_nan_stats=True
@@ -119,7 +120,7 @@ def __init__(self,
                validate_args=False,
                allow_nan_stats=True,
                name="Chi2WithAbsDf"):
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[df]) as name:
       super(Chi2WithAbsDf, self).__init__(
           df=math_ops.floor(
diff --git a/tensorflow/contrib/distributions/python/ops/deterministic.py b/tensorflow/contrib/distributions/python/ops/deterministic.py
index a42350430e9851..6d7d6d307bd0f8 100644
--- a/tensorflow/contrib/distributions/python/ops/deterministic.py
+++ b/tensorflow/contrib/distributions/python/ops/deterministic.py
@@ -32,6 +32,7 @@
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import distribution
+from tensorflow.python.ops.distributions import util as distribution_util
 
 __all__ = [
     "Deterministic",
@@ -86,7 +87,7 @@ def __init__(self,
     Raises:
       ValueError:  If `loc` is a scalar.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[loc, atol, rtol]) as name:
       loc = ops.convert_to_tensor(loc, name="loc")
       if is_vector and validate_args:
diff --git a/tensorflow/contrib/distributions/python/ops/geometric.py b/tensorflow/contrib/distributions/python/ops/geometric.py
index 53dd42f4c83fce..446cff6ec242f2 100644
--- a/tensorflow/contrib/distributions/python/ops/geometric.py
+++ b/tensorflow/contrib/distributions/python/ops/geometric.py
@@ -85,7 +85,7 @@ def __init__(self,
       name: Python `str` name prefixed to Ops created by this class.
     """
 
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[logits, probs]) as name:
       self._logits, self._probs = distribution_util.get_logits_and_probs(
           logits, probs, validate_args=validate_args, name=name)
diff --git a/tensorflow/contrib/distributions/python/ops/gumbel.py b/tensorflow/contrib/distributions/python/ops/gumbel.py
index 2c261073ee1646..ed9ea6f4f3ffe1 100644
--- a/tensorflow/contrib/distributions/python/ops/gumbel.py
+++ b/tensorflow/contrib/distributions/python/ops/gumbel.py
@@ -29,6 +29,7 @@
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
+from tensorflow.python.ops.distributions import util as distribution_util
 
 
 class _Gumbel(distribution.Distribution):
@@ -124,7 +125,7 @@ def __init__(self,
     Raises:
       TypeError: if loc and scale are different dtypes.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[loc, scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(scale)] if
                                     validate_args else []):
diff --git a/tensorflow/contrib/distributions/python/ops/half_normal.py b/tensorflow/contrib/distributions/python/ops/half_normal.py
index d0df2befd6e46c..7e12767f6d8f6c 100644
--- a/tensorflow/contrib/distributions/python/ops/half_normal.py
+++ b/tensorflow/contrib/distributions/python/ops/half_normal.py
@@ -31,6 +31,7 @@
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
 from tensorflow.python.ops.distributions import special_math
+from tensorflow.python.ops.distributions import util as distribution_util
 
 
 __all__ = [
@@ -105,7 +106,7 @@ def __init__(self,
         if one or more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(scale)] if
                                     validate_args else []):
diff --git a/tensorflow/contrib/distributions/python/ops/independent.py b/tensorflow/contrib/distributions/python/ops/independent.py
index fbde55ef310de1..fa89fff3b7b2f8 100644
--- a/tensorflow/contrib/distributions/python/ops/independent.py
+++ b/tensorflow/contrib/distributions/python/ops/independent.py
@@ -29,6 +29,7 @@
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import distribution as distribution_lib
 from tensorflow.python.ops.distributions import kullback_leibler
+from tensorflow.python.ops.distributions import util as distribution_util
 
 
 class Independent(distribution_lib.Distribution):
@@ -116,7 +117,7 @@ def __init__(
       ValueError: if `reinterpreted_batch_ndims` exceeds
         `distribution.batch_ndims`
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     name = name or "Independent" + distribution.name
     self._distribution = distribution
     with ops.name_scope(name) as name:
diff --git a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py
index 502bd4f493337b..85e8e10466038e 100644
--- a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py
+++ b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py
@@ -125,7 +125,7 @@ def __init__(self,
     Raises:
       TypeError: if `concentration` and `rate` are different dtypes.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[concentration, rate]) as name:
       with ops.control_dependencies([
           check_ops.assert_positive(concentration),
@@ -280,7 +280,7 @@ def __init__(self,
                validate_args=False,
                allow_nan_stats=True,
                name="InverseGammaWithSoftplusConcentrationRate"):
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[concentration, rate]) as name:
       super(InverseGammaWithSoftplusConcentrationRate, self).__init__(
           concentration=nn.softplus(concentration,
diff --git a/tensorflow/contrib/distributions/python/ops/logistic.py b/tensorflow/contrib/distributions/python/ops/logistic.py
index c83b5bc2e3a8c5..0103283259b052 100644
--- a/tensorflow/contrib/distributions/python/ops/logistic.py
+++ b/tensorflow/contrib/distributions/python/ops/logistic.py
@@ -31,6 +31,7 @@
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
+from tensorflow.python.ops.distributions import util as distribution_util
 
 
 class Logistic(distribution.Distribution):
@@ -119,7 +120,7 @@ def __init__(self,
     Raises:
       TypeError: if loc and scale are different dtypes.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[loc, scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(scale)] if
                                     validate_args else []):
diff --git a/tensorflow/contrib/distributions/python/ops/mixture.py b/tensorflow/contrib/distributions/python/ops/mixture.py
index 2ef294af2e8bc9..d54f30dc634ab5 100644
--- a/tensorflow/contrib/distributions/python/ops/mixture.py
+++ b/tensorflow/contrib/distributions/python/ops/mixture.py
@@ -116,7 +116,7 @@ def __init__(self,
         matching static batch shapes, or all components do not
         have matching static event shapes.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     if not isinstance(cat, categorical.Categorical):
       raise TypeError("cat must be a Categorical distribution, but saw: %s" %
                       cat)
diff --git a/tensorflow/contrib/distributions/python/ops/mixture_same_family.py b/tensorflow/contrib/distributions/python/ops/mixture_same_family.py
index 0b1301e551728f..c7c90cf875484a 100644
--- a/tensorflow/contrib/distributions/python/ops/mixture_same_family.py
+++ b/tensorflow/contrib/distributions/python/ops/mixture_same_family.py
@@ -130,7 +130,7 @@ def __init__(self,
       ValueError: if `mixture_distribution` categories does not equal
         `components_distribution` rightmost batch shape.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name) as name:
       self._mixture_distribution = mixture_distribution
       self._components_distribution = components_distribution
diff --git a/tensorflow/contrib/distributions/python/ops/mvn_diag.py b/tensorflow/contrib/distributions/python/ops/mvn_diag.py
index e3236c2db93695..cad398582b9c93 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn_diag.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn_diag.py
@@ -193,7 +193,7 @@ def __init__(self,
     Raises:
       ValueError: if at most `scale_identity_multiplier` is specified.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name) as name:
       with ops.name_scope("init", values=[
           loc, scale_diag, scale_identity_multiplier]):
@@ -224,7 +224,7 @@ def __init__(self,
                validate_args=False,
                allow_nan_stats=True,
                name="MultivariateNormalDiagWithSoftplusScale"):
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[scale_diag]) as name:
       super(MultivariateNormalDiagWithSoftplusScale, self).__init__(
           loc=loc,
diff --git a/tensorflow/contrib/distributions/python/ops/mvn_diag_plus_low_rank.py b/tensorflow/contrib/distributions/python/ops/mvn_diag_plus_low_rank.py
index 2f6a6f198cbcfb..1c11594df3ad26 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn_diag_plus_low_rank.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn_diag_plus_low_rank.py
@@ -215,7 +215,7 @@ def __init__(self,
     Raises:
       ValueError: if at most `scale_identity_multiplier` is specified.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     def _convert_to_tensor(x, name):
       return None if x is None else ops.convert_to_tensor(x, name=name)
     with ops.name_scope(name) as name:
diff --git a/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py b/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py
index 5d06a396fe7a3b..47d7d13cf357f1 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py
@@ -24,6 +24,7 @@
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import linalg_ops
+from tensorflow.python.ops.distributions import util as distribution_util
 
 
 __all__ = [
@@ -155,7 +156,7 @@ def __init__(self,
     Raises:
       ValueError: if neither `loc` nor `covariance_matrix` are specified.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
 
     # Convert the covariance_matrix up to a scale_tril and call MVNTriL.
     with ops.name_scope(name) as name:
diff --git a/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py b/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py
index 44c92312c7dc75..79916fef8d7b75 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py
@@ -170,7 +170,7 @@ def __init__(self,
       ValueError: if `scale` is unspecified.
       TypeError: if not `scale.dtype.is_floating`
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     if scale is None:
       raise ValueError("Missing required `scale` parameter.")
     if not scale.dtype.is_floating:
diff --git a/tensorflow/contrib/distributions/python/ops/mvn_tril.py b/tensorflow/contrib/distributions/python/ops/mvn_tril.py
index d6f8b731cbeed5..d6b0ed994ec0a6 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn_tril.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn_tril.py
@@ -179,7 +179,7 @@ def __init__(self,
     Raises:
       ValueError: if neither `loc` nor `scale_tril` are specified.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     def _convert_to_tensor(x, name):
       return None if x is None else ops.convert_to_tensor(x, name=name)
     if loc is None and scale_tril is None:
diff --git a/tensorflow/contrib/distributions/python/ops/negative_binomial.py b/tensorflow/contrib/distributions/python/ops/negative_binomial.py
index eeaf9c0a5ebc13..1085c56dc86c8d 100644
--- a/tensorflow/contrib/distributions/python/ops/negative_binomial.py
+++ b/tensorflow/contrib/distributions/python/ops/negative_binomial.py
@@ -90,7 +90,7 @@ def __init__(self,
       name: Python `str` name prefixed to Ops created by this class.
     """
 
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[total_count, logits, probs]) as name:
       self._logits, self._probs = distribution_util.get_logits_and_probs(
           logits, probs, validate_args=validate_args, name=name)
diff --git a/tensorflow/contrib/distributions/python/ops/onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/onehot_categorical.py
index 305b138fdc2318..a4b9f3b78d4fdc 100644
--- a/tensorflow/contrib/distributions/python/ops/onehot_categorical.py
+++ b/tensorflow/contrib/distributions/python/ops/onehot_categorical.py
@@ -115,7 +115,7 @@ def __init__(
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[logits, probs]) as name:
       self._logits, self._probs = distribution_util.get_logits_and_probs(
           name=name, logits=logits, probs=probs, validate_args=validate_args,
diff --git a/tensorflow/contrib/distributions/python/ops/poisson.py b/tensorflow/contrib/distributions/python/ops/poisson.py
index a84aad6fc93723..b34539402102b8 100644
--- a/tensorflow/contrib/distributions/python/ops/poisson.py
+++ b/tensorflow/contrib/distributions/python/ops/poisson.py
@@ -93,7 +93,7 @@ def __init__(self,
       TypeError: if `rate` is not a float-type.
       TypeError: if `log_rate` is not a float-type.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[rate]) as name:
       if (rate is None) == (log_rate is None):
         raise ValueError("Must specify exactly one of `rate` and `log_rate`.")
diff --git a/tensorflow/contrib/distributions/python/ops/poisson_lognormal.py b/tensorflow/contrib/distributions/python/ops/poisson_lognormal.py
index 19c99dcee92978..fe72091d7d759e 100644
--- a/tensorflow/contrib/distributions/python/ops/poisson_lognormal.py
+++ b/tensorflow/contrib/distributions/python/ops/poisson_lognormal.py
@@ -255,7 +255,7 @@ def __init__(self,
       TypeError: if `quadrature_grid` and `quadrature_probs` have different base
         `dtype`.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[loc, scale]) as name:
       if loc is not None:
         loc = ops.convert_to_tensor(loc, name="loc")
diff --git a/tensorflow/contrib/distributions/python/ops/quantized_distribution.py b/tensorflow/contrib/distributions/python/ops/quantized_distribution.py
index eb94760ad71f5b..584d2c385fced9 100644
--- a/tensorflow/contrib/distributions/python/ops/quantized_distribution.py
+++ b/tensorflow/contrib/distributions/python/ops/quantized_distribution.py
@@ -263,7 +263,7 @@ def __init__(self,
           `Distribution` or continuous.
       NotImplementedError:  If the base distribution does not implement `cdf`.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     values = (
         list(distribution.parameters.values()) +
         [low, high])
diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
index 84c8d29072c2f1..0362996e684fb3 100644
--- a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
+++ b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
@@ -165,7 +165,7 @@ def __init__(self,
     Raises:
       ValueError: If both `probs` and `logits` are passed, or if neither.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[logits, probs, temperature]) as name:
       with ops.control_dependencies([check_ops.assert_positive(temperature)]
                                     if validate_args else []):
diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py
index 325f41e37c928b..910c430ae7f026 100644
--- a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py
+++ b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py
@@ -162,7 +162,7 @@ def __init__(
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[logits, probs, temperature]) as name:
 
       self._logits, self._probs = distribution_util.get_logits_and_probs(
diff --git a/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py b/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py
index 03828fa61277ee..f04dc8da391402 100644
--- a/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py
+++ b/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py
@@ -132,7 +132,7 @@ def __init__(self,
         if one or more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
 
     with ops.name_scope(name,
                         values=[loc, scale, skewness, tailweight]) as name:
diff --git a/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py b/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py
index af6ff8162b1730..cd6d7499595d88 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py
@@ -395,7 +395,7 @@ def __init__(self,
       ValueError: if `not distribution.is_scalar_batch`.
       ValueError: if `not distribution.is_scalar_event`.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[mix_loc, temperature]) as name:
       if not scale or len(scale) < 2:
         raise ValueError("Must specify list (or list-like object) of scale "
diff --git a/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py b/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py
index e265b5d0f7c10b..3465d66b30501e 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py
@@ -175,7 +175,7 @@ def __init__(self,
     Raises:
       ValueError: if at most `scale_identity_multiplier` is specified.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name) as name:
       with ops.name_scope("init", values=[
           loc, scale_diag, scale_identity_multiplier]):
diff --git a/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py
index 89136d6760bb66..2c31b019845d7e 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py
@@ -175,7 +175,7 @@ def __init__(self,
       ValueError: if `scale` is unspecified.
       TypeError: if not `scale.dtype.is_floating`
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     if scale is None:
       raise ValueError("Missing required `scale` parameter.")
     if not scale.dtype.is_floating:
diff --git a/tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py b/tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py
index 8dd983b750d9b3..6a36018d6f1b83 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py
@@ -210,7 +210,7 @@ def __init__(self,
     Raises:
       ValueError: if at most `scale_identity_multiplier` is specified.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name):
       with ops.name_scope("init", values=[
           loc, scale_diag, scale_identity_multiplier]):
diff --git a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
index ec485c95c15da2..97e5c76d800acd 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
@@ -191,7 +191,7 @@ def __init__(self,
       ValueError: if `scale` is unspecified.
       TypeError: if not `scale.dtype.is_floating`
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     if scale is None:
       raise ValueError("Missing required `scale` parameter.")
     if not scale.dtype.is_floating:
diff --git a/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py b/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py
index 1438ede26500bc..ff5ca4525700ae 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py
@@ -163,7 +163,7 @@ def __init__(self,
     Raises:
       ValueError: if at most `scale_identity_multiplier` is specified.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
 
     with ops.name_scope(
         name,
diff --git a/tensorflow/contrib/distributions/python/ops/vector_student_t.py b/tensorflow/contrib/distributions/python/ops/vector_student_t.py
index 7e78ded9df0756..4742f7521816d4 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_student_t.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_student_t.py
@@ -175,7 +175,7 @@ def __init__(self,
         if one or more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     graph_parents = [df, loc, scale_identity_multiplier, scale_diag,
                      scale_tril, scale_perturb_factor, scale_perturb_diag]
     with ops.name_scope(name) as name:
diff --git a/tensorflow/contrib/distributions/python/ops/wishart.py b/tensorflow/contrib/distributions/python/ops/wishart.py
index 91453fed5d2791..f555867e7f3c2a 100644
--- a/tensorflow/contrib/distributions/python/ops/wishart.py
+++ b/tensorflow/contrib/distributions/python/ops/wishart.py
@@ -107,7 +107,7 @@ def __init__(self,
       ValueError: if df < k, where scale operator event shape is
         `(k, k)`
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     self._cholesky_input_output_matrices = cholesky_input_output_matrices
     with ops.name_scope(name) as name:
       with ops.name_scope("init", values=[df, scale_operator]):
@@ -530,7 +530,7 @@ def __init__(self,
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[scale]) as name:
       with ops.name_scope("init", values=[scale]):
         scale = ops.convert_to_tensor(scale)
@@ -646,7 +646,7 @@ def __init__(self,
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name) as name:
       with ops.name_scope("init", values=[scale]):
         scale = ops.convert_to_tensor(scale)
diff --git a/tensorflow/python/kernel_tests/distributions/BUILD b/tensorflow/python/kernel_tests/distributions/BUILD
index f3cc9636f91f7d..cf2e8832fd5225 100644
--- a/tensorflow/python/kernel_tests/distributions/BUILD
+++ b/tensorflow/python/kernel_tests/distributions/BUILD
@@ -41,6 +41,7 @@ cuda_py_test(
         "//tensorflow/python:math_ops",
         "//tensorflow/python:platform_test",
     ],
+    shard_count = 3,
 )
 
 cuda_py_test(
diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py
index b9fe1976792711..8569b365395558 100644
--- a/tensorflow/python/kernel_tests/distributions/util_test.py
+++ b/tensorflow/python/kernel_tests/distributions/util_test.py
@@ -1017,6 +1017,62 @@ def testInverseSoftplusGradientFinite(self):
       self.assertAllEqual(
           np.ones_like(grads).astype(np.bool), np.isfinite(grads))
 
+class ArgumentsTest(test.TestCase):
+
+  def testNoArguments(self):
+    def foo():
+      return du.parent_frame_arguments()
+
+    self.assertEqual({}, foo())
+
+  def testPositionalArguments(self):
+    def foo(a, b, c, d):  # pylint: disable=unused-argument
+      return du.parent_frame_arguments()
+
+    self.assertEqual({"a": 1, "b": 2, "c": 3, "d": 4}, foo(1, 2, 3, 4))
+
+    # Tests that it does not matter where this function is called, and
+    # no other local variables are returned back.
+    def bar(a, b, c):
+      unused_x = a * b
+      unused_y = c * 3
+      return du.parent_frame_arguments()
+
+    self.assertEqual({"a": 1, "b": 2, "c": 3}, bar(1, 2, 3))
+
+  def testOverloadedArgumentValues(self):
+    def foo(a, b, c):  # pylint: disable=unused-argument
+      a = 42
+      b = 31
+      c = 42
+      return du.parent_frame_arguments()
+    self.assertEqual({"a": 42, "b": 31, "c": 42}, foo(1, 2, 3))
+
+  def testKeywordArguments(self):
+    def foo(**kwargs):  # pylint: disable=unused-argument
+      return du.parent_frame_arguments()
+
+    self.assertEqual({"a": 1, "b": 2, "c": 3, "d": 4}, foo(a=1, b=2, c=3, d=4))
+
+  def testPositionalKeywordArgs(self):
+    def foo(a, b, c, **kwargs):  # pylint: disable=unused-argument
+      return du.parent_frame_arguments()
+
+    self.assertEqual({"a": 1, "b": 2, "c": 3}, foo(a=1, b=2, c=3))
+    self.assertEqual({"a": 1, "b": 2, "c": 3, "unicorn": None},
+                     foo(a=1, b=2, c=3, unicorn=None))
+
+  def testNoVarargs(self):
+    def foo(a, b, c, *varargs, **kwargs):  # pylint: disable=unused-argument
+      return du.parent_frame_arguments()
+
+    self.assertEqual({"a": 1, "b": 2, "c": 3}, foo(a=1, b=2, c=3))
+    self.assertEqual({"a": 1, "b": 2, "c": 3}, foo(1, 2, 3, *[1, 2, 3]))
+    self.assertEqual({"a": 1, "b": 2, "c": 3, "unicorn": None},
+                     foo(1, 2, 3, unicorn=None))
+    self.assertEqual({"a": 1, "b": 2, "c": 3, "unicorn": None},
+                     foo(1, 2, 3, *[1, 2, 3], unicorn=None))
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/ops/distributions/bernoulli.py b/tensorflow/python/ops/distributions/bernoulli.py
index 2c9f0e9a32dd3f..d7fb3f1f783cce 100644
--- a/tensorflow/python/ops/distributions/bernoulli.py
+++ b/tensorflow/python/ops/distributions/bernoulli.py
@@ -71,7 +71,7 @@ def __init__(self,
     Raises:
       ValueError: If p and logits are passed, or if neither are passed.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name) as name:
       self._logits, self._probs = distribution_util.get_logits_and_probs(
           logits=logits,
diff --git a/tensorflow/python/ops/distributions/beta.py b/tensorflow/python/ops/distributions/beta.py
index 8beab99bf868cd..b6978486004aff 100644
--- a/tensorflow/python/ops/distributions/beta.py
+++ b/tensorflow/python/ops/distributions/beta.py
@@ -150,7 +150,7 @@ def __init__(self,
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[concentration1, concentration0]) as name:
       self._concentration1 = self._maybe_assert_valid_concentration(
           ops.convert_to_tensor(concentration1, name="concentration1"),
@@ -321,7 +321,7 @@ def __init__(self,
                validate_args=False,
                allow_nan_stats=True,
                name="BetaWithSoftplusConcentration"):
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[concentration1,
                                       concentration0]) as name:
       super(BetaWithSoftplusConcentration, self).__init__(
diff --git a/tensorflow/python/ops/distributions/categorical.py b/tensorflow/python/ops/distributions/categorical.py
index 8f25b1149c3c8b..bbdc8c455af66c 100644
--- a/tensorflow/python/ops/distributions/categorical.py
+++ b/tensorflow/python/ops/distributions/categorical.py
@@ -182,7 +182,7 @@ def __init__(
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[logits, probs]) as name:
       self._logits, self._probs = distribution_util.get_logits_and_probs(
           logits=logits,
diff --git a/tensorflow/python/ops/distributions/dirichlet.py b/tensorflow/python/ops/distributions/dirichlet.py
index eafcd5c78f7752..8d0d1d860bf4a7 100644
--- a/tensorflow/python/ops/distributions/dirichlet.py
+++ b/tensorflow/python/ops/distributions/dirichlet.py
@@ -154,7 +154,7 @@ def __init__(self,
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[concentration]) as name:
       self._concentration = self._maybe_assert_valid_concentration(
           ops.convert_to_tensor(concentration, name="concentration"),
diff --git a/tensorflow/python/ops/distributions/dirichlet_multinomial.py b/tensorflow/python/ops/distributions/dirichlet_multinomial.py
index fe0ed7e07d5965..3a35e0caa0f411 100644
--- a/tensorflow/python/ops/distributions/dirichlet_multinomial.py
+++ b/tensorflow/python/ops/distributions/dirichlet_multinomial.py
@@ -191,7 +191,7 @@ def __init__(self,
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[total_count, concentration]) as name:
       # Broadcasting works because:
       # * The broadcasting convention is to prepend dimensions of size [1], and
diff --git a/tensorflow/python/ops/distributions/distribution.py b/tensorflow/python/ops/distributions/distribution.py
index 3815abf72de1e2..fd08bda9b9e8b2 100644
--- a/tensorflow/python/ops/distributions/distribution.py
+++ b/tensorflow/python/ops/distributions/distribution.py
@@ -524,7 +524,8 @@ def dtype(self):
   def parameters(self):
     """Dictionary of parameters used to instantiate this `Distribution`."""
     # Remove "self", "__class__", or other special variables. These can appear
-    # if the subclass used `parameters = locals()`.
+    # if the subclass used:
+    # `parameters = distribution_util.parent_frame_arguments()`.
     return dict((k, v) for k, v in self._parameters.items()
                 if not k.startswith("__") and k != "self")
 
diff --git a/tensorflow/python/ops/distributions/exponential.py b/tensorflow/python/ops/distributions/exponential.py
index cf0e729e1a189d..1e08f48d529b16 100644
--- a/tensorflow/python/ops/distributions/exponential.py
+++ b/tensorflow/python/ops/distributions/exponential.py
@@ -27,6 +27,7 @@
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import gamma
+from tensorflow.python.ops.distributions import util as distribution_util
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -90,7 +91,7 @@ def __init__(self,
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     # Even though all statistics of are defined for valid inputs, this is not
     # true in the parent class "Gamma."  Therefore, passing
     # allow_nan_stats=True
@@ -143,7 +144,7 @@ def __init__(self,
                validate_args=False,
                allow_nan_stats=True,
                name="ExponentialWithSoftplusRate"):
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[rate]) as name:
       super(ExponentialWithSoftplusRate, self).__init__(
           rate=nn.softplus(rate, name="softplus_rate"),
diff --git a/tensorflow/python/ops/distributions/gamma.py b/tensorflow/python/ops/distributions/gamma.py
index d39f7c56d39ae1..7ca690d9d2f834 100644
--- a/tensorflow/python/ops/distributions/gamma.py
+++ b/tensorflow/python/ops/distributions/gamma.py
@@ -126,7 +126,7 @@ def __init__(self,
     Raises:
       TypeError: if `concentration` and `rate` are different dtypes.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[concentration, rate]) as name:
       with ops.control_dependencies([
           check_ops.assert_positive(concentration),
@@ -261,7 +261,7 @@ def __init__(self,
                validate_args=False,
                allow_nan_stats=True,
                name="GammaWithSoftplusConcentrationRate"):
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[concentration, rate]) as name:
       super(GammaWithSoftplusConcentrationRate, self).__init__(
           concentration=nn.softplus(concentration,
diff --git a/tensorflow/python/ops/distributions/laplace.py b/tensorflow/python/ops/distributions/laplace.py
index 3ccfc618d11577..ee3a6a40ff78fb 100644
--- a/tensorflow/python/ops/distributions/laplace.py
+++ b/tensorflow/python/ops/distributions/laplace.py
@@ -33,6 +33,7 @@
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
 from tensorflow.python.ops.distributions import special_math
+from tensorflow.python.ops.distributions import util as distribution_util
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -100,7 +101,7 @@ def __init__(self,
     Raises:
       TypeError: if `loc` and `scale` are of different dtype.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[loc, scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(scale)] if
                                     validate_args else []):
@@ -217,7 +218,7 @@ def __init__(self,
                validate_args=False,
                allow_nan_stats=True,
                name="LaplaceWithSoftplusScale"):
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[loc, scale]) as name:
       super(LaplaceWithSoftplusScale, self).__init__(
           loc=loc,
diff --git a/tensorflow/python/ops/distributions/multinomial.py b/tensorflow/python/ops/distributions/multinomial.py
index ab77f5c1f815f3..036ba45cccf499 100644
--- a/tensorflow/python/ops/distributions/multinomial.py
+++ b/tensorflow/python/ops/distributions/multinomial.py
@@ -182,7 +182,7 @@ def __init__(self,
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[total_count, logits, probs]) as name:
       self._total_count = ops.convert_to_tensor(total_count, name="total_count")
       if validate_args:
diff --git a/tensorflow/python/ops/distributions/normal.py b/tensorflow/python/ops/distributions/normal.py
index 20d4420e91886c..0620aae10d0d3b 100644
--- a/tensorflow/python/ops/distributions/normal.py
+++ b/tensorflow/python/ops/distributions/normal.py
@@ -32,6 +32,7 @@
 from tensorflow.python.ops.distributions import distribution
 from tensorflow.python.ops.distributions import kullback_leibler
 from tensorflow.python.ops.distributions import special_math
+from tensorflow.python.ops.distributions import util as distribution_util
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -131,7 +132,7 @@ def __init__(self,
     Raises:
       TypeError: if `loc` and `scale` have different `dtype`.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[loc, scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(scale)] if
                                     validate_args else []):
@@ -243,7 +244,7 @@ def __init__(self,
                validate_args=False,
                allow_nan_stats=True,
                name="NormalWithSoftplusScale"):
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[scale]) as name:
       super(NormalWithSoftplusScale, self).__init__(
           loc=loc,
diff --git a/tensorflow/python/ops/distributions/student_t.py b/tensorflow/python/ops/distributions/student_t.py
index 961b07a7bdac34..9330b930b5140b 100644
--- a/tensorflow/python/ops/distributions/student_t.py
+++ b/tensorflow/python/ops/distributions/student_t.py
@@ -157,7 +157,7 @@ def __init__(self,
     Raises:
       TypeError: if loc and scale are different dtypes.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[df, loc, scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(df)]
                                     if validate_args else []):
@@ -349,7 +349,7 @@ def __init__(self,
                validate_args=False,
                allow_nan_stats=True,
                name="StudentTWithAbsDfSoftplusScale"):
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[df, scale]) as name:
       super(StudentTWithAbsDfSoftplusScale, self).__init__(
           df=math_ops.floor(math_ops.abs(df)),
diff --git a/tensorflow/python/ops/distributions/transformed_distribution.py b/tensorflow/python/ops/distributions/transformed_distribution.py
index bc321900dcbcfe..9392464ec11613 100644
--- a/tensorflow/python/ops/distributions/transformed_distribution.py
+++ b/tensorflow/python/ops/distributions/transformed_distribution.py
@@ -252,7 +252,7 @@ def __init__(self,
       name: Python `str` name prefixed to Ops created by this class. Default:
         `bijector.name + distribution.name`.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     name = name or (("" if bijector is None else bijector.name) +
                     distribution.name)
     with ops.name_scope(name, values=[event_shape, batch_shape]) as name:
diff --git a/tensorflow/python/ops/distributions/uniform.py b/tensorflow/python/ops/distributions/uniform.py
index 087797c653bb3e..dfa10331e3e9d6 100644
--- a/tensorflow/python/ops/distributions/uniform.py
+++ b/tensorflow/python/ops/distributions/uniform.py
@@ -29,6 +29,7 @@
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
+from tensorflow.python.ops.distributions import util as distribution_util
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -102,7 +103,7 @@ def __init__(self,
     Raises:
       InvalidArgumentError: if `low >= high` and `validate_args=False`.
     """
-    parameters = locals()
+    parameters = distribution_util.parent_frame_arguments()
     with ops.name_scope(name, values=[low, high]) as name:
       with ops.control_dependencies([
           check_ops.assert_less(
diff --git a/tensorflow/python/ops/distributions/util.py b/tensorflow/python/ops/distributions/util.py
index 3afa85fda013ba..59c89d21f9142f 100644
--- a/tensorflow/python/ops/distributions/util.py
+++ b/tensorflow/python/ops/distributions/util.py
@@ -33,6 +33,7 @@
 from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
+from tensorflow.python.util import tf_inspect
 
 
 def assert_close(
@@ -1297,6 +1298,43 @@ def pad(x, axis, front=False, back=False, value=0, count=1, name=None):
     return x
 
 
+def parent_frame_arguments():
+  """Returns parent frame arguments.
+
+  When called inside a function, returns a dictionary with the caller's function
+  arguments. These are positional arguments and keyword arguments (**kwargs),
+  while variable arguments (*varargs) are excluded.
+
+  When called at global scope, this will return an empty dictionary, since there
+  are no arguments.
+
+  WARNING: If caller function argument names are overloaded before invoking
+  this method, then values will reflect the overloaded value. For this reason,
+  we recommend calling `parent_frame_arguments` at the beginning of the
+  function.
+  """
+  # All arguments and the names used for *varargs, and **kwargs
+  arg_names, variable_arg_name, keyword_arg_name, local_vars = (
+      tf_inspect._inspect.getargvalues(  # pylint: disable=protected-access
+          # Get the first frame of the caller of this method.
+          tf_inspect._inspect.stack()[1][0]))  # pylint: disable=protected-access
+
+  # Remove the *varargs, and flatten the **kwargs. Both are
+  # nested lists.
+  local_vars.pop(variable_arg_name, {})
+  keyword_args = local_vars.pop(keyword_arg_name, {})
+
+  final_args = {}
+  # Copy over arguments and their values. In general, local_vars
+  # may contain more than just the arguments, since this method
+  # can be called anywhere in a function.
+  for arg_name in arg_names:
+    final_args[arg_name] = local_vars.pop(arg_name)
+  final_args.update(keyword_args)
+
+  return final_args
+
+
 class AppendDocstring(object):
   """Helper class to promote private subclass docstring to public counterpart.
 

From bd95d55a2886677ba194351197d93c8b1408cc85 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 12:14:52 -0700
Subject: [PATCH 0623/1691] Implementation of the unidirectional_sequence_rnn
 TFLite Op using the symmetric quantization.

PiperOrigin-RevId: 196152754
---
 .../kernels/unidirectional_sequence_rnn.cc    | 184 +++++++++++--
 .../unidirectional_sequence_rnn_test.cc       | 243 ++++++++++--------
 2 files changed, 300 insertions(+), 127 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc
index ac00c37b67dcbe..5ae635bfdab3e2 100644
--- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc
+++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/contrib/lite/context.h"
 #include "tensorflow/contrib/lite/kernels/activation_functor.h"
 #include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h"
+#include "tensorflow/contrib/lite/kernels/kernel_util.h"
 #include "tensorflow/contrib/lite/kernels/op_macros.h"
 
 namespace tflite {
@@ -38,17 +39,26 @@ constexpr int kBiasTensor = 3;
 constexpr int kHiddenStateTensor = 0;
 constexpr int kOutputTensor = 1;
 
+void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+  auto* scratch_tensor_index = new int;
+  context->AddTensors(context, /*tensors_to_add=*/2, scratch_tensor_index);
+  return scratch_tensor_index;
+}
+
+void Free(TfLiteContext* context, void* buffer) {
+  delete reinterpret_cast(buffer);
+}
+
 TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   // Check we have all the inputs and outputs we need.
   TF_LITE_ENSURE_EQ(context, node->inputs->size, 4);
   TF_LITE_ENSURE_EQ(context, node->outputs->size, 2);
 
-  TfLiteTensor* input = &context->tensors[node->inputs->data[kInputTensor]];
-  TfLiteTensor* input_weights =
-      &context->tensors[node->inputs->data[kWeightsTensor]];
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
   TfLiteTensor* recurrent_weights =
-      &context->tensors[node->inputs->data[kRecurrentWeightsTensor]];
-  TfLiteTensor* bias = &context->tensors[node->inputs->data[kBiasTensor]];
+      GetInput(context, node, kRecurrentWeightsTensor);
+  TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
 
   // Check all the parameters of tensor match within themselves and match the
   // input configuration.
@@ -64,9 +74,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ASSERT_EQ(recurrent_weights->dims->data[0], bias->dims->data[0]);
   TF_LITE_ASSERT_EQ(recurrent_weights->dims->data[1], bias->dims->data[0]);
 
-  TfLiteTensor* hidden_state =
-      &context->tensors[node->outputs->data[kHiddenStateTensor]];
-  TfLiteTensor* output = &context->tensors[node->outputs->data[kOutputTensor]];
+  TfLiteTensor* hidden_state = GetOutput(context, node, kHiddenStateTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   // Resize state.
   TfLiteIntArray* hidden_state_size_array = TfLiteIntArrayCreate(2);
@@ -86,22 +95,44 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_OK(context,
                     context->ResizeTensor(context, output, output_size_array));
 
+  // Allocate temporary tensors to store quantized values of input and
+  // hidden_state tensors.
+  if (input->type == kTfLiteFloat32 && input_weights->type == kTfLiteUInt8) {
+    int* scratch_tensor_index = reinterpret_cast(node->user_data);
+    TfLiteIntArrayFree(node->temporaries);
+    node->temporaries = TfLiteIntArrayCreate(2);
+    node->temporaries->data[0] = *scratch_tensor_index;
+    TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/0);
+    input_quantized->type = kTfLiteUInt8;
+    input_quantized->allocation_type = kTfLiteArenaRw;
+    if (!TfLiteIntArrayEqual(input_quantized->dims, input->dims)) {
+      TfLiteIntArray* input_quantized_size = TfLiteIntArrayCopy(input->dims);
+      TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_quantized,
+                                                       input_quantized_size));
+    }
+    node->temporaries->data[1] = *scratch_tensor_index + 1;
+    TfLiteTensor* hidden_state_quantized =
+        GetTemporary(context, node, /*index=*/1);
+    hidden_state_quantized->type = kTfLiteUInt8;
+    hidden_state_quantized->allocation_type = kTfLiteArenaRw;
+    if (!TfLiteIntArrayEqual(hidden_state_quantized->dims,
+                             hidden_state->dims)) {
+      TfLiteIntArray* hidden_state_quantized_size =
+          TfLiteIntArrayCopy(hidden_state->dims);
+      TF_LITE_ENSURE_OK(context,
+                        context->ResizeTensor(context, hidden_state_quantized,
+                                              hidden_state_quantized_size));
+    }
+  }
   return kTfLiteOk;
 }
 
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  auto* params = reinterpret_cast(node->builtin_data);
-
-  TfLiteTensor* input = &context->tensors[node->inputs->data[kInputTensor]];
-  TfLiteTensor* input_weights =
-      &context->tensors[node->inputs->data[kWeightsTensor]];
-  TfLiteTensor* recurrent_weights =
-      &context->tensors[node->inputs->data[kRecurrentWeightsTensor]];
-  TfLiteTensor* bias = &context->tensors[node->inputs->data[kBiasTensor]];
-  TfLiteTensor* hidden_state =
-      &context->tensors[node->outputs->data[kHiddenStateTensor]];
-  TfLiteTensor* output = &context->tensors[node->outputs->data[kOutputTensor]];
-
+TfLiteStatus EvalFloat(const TfLiteTensor* input,
+                       const TfLiteTensor* input_weights,
+                       const TfLiteTensor* recurrent_weights,
+                       const TfLiteTensor* bias,
+                       const TfLiteSequenceRNNParams* params,
+                       TfLiteTensor* hidden_state, TfLiteTensor* output) {
   // Initialize the pointer bias.
   const float* bias_ptr = bias->data.f;
 
@@ -120,7 +151,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   if (time_major) {
     // Initialize the pointer to hidden state.
     float* hidden_state_ptr_batch = hidden_state->data.f;
-    // Unroll the sequence and use batch batch operations for efficiency.
+    // Unroll the sequence and use batch operations for efficiency.
     for (int s = 0; s < max_time; s++) {
       // Initialize the pointer to input and output.
       const float* input_ptr_batch =
@@ -154,12 +185,115 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   return kTfLiteOk;
 }
 
+TfLiteStatus EvalQuantized(const TfLiteTensor* input,
+                           const TfLiteTensor* input_weights,
+                           const TfLiteTensor* recurrent_weights,
+                           const TfLiteTensor* bias,
+                           const TfLiteSequenceRNNParams* params,
+                           TfLiteTensor* input_scratch,
+                           TfLiteTensor* hidden_state_scratch,
+                           TfLiteTensor* hidden_state, TfLiteTensor* output) {
+  const bool time_major = params->time_major;
+  const int batch_size =
+      (time_major) ? input->dims->data[1] : input->dims->data[0];
+  const int max_time =
+      (time_major) ? input->dims->data[0] : input->dims->data[1];
+  const int num_units = input_weights->dims->data[0];
+  const int input_size = input->dims->data[2];
+
+  // Initialize the pointer bias.
+  const float* bias_ptr = bias->data.f;
+  // Initialize input_weights and recurrent_weights.
+  const int8_t* input_weights_ptr =
+      reinterpret_cast(input_weights->data.uint8);
+  const int8_t* recurrent_weights_ptr =
+      reinterpret_cast(recurrent_weights->data.uint8);
+  // Get the scale of the quantized weights.
+  float input_weights_scale = input_weights->params.scale;
+  float recurrent_weights_scale = recurrent_weights->params.scale;
+  // Initialize temporary storage for quantized values.
+  int8_t* quantized_input_ptr =
+      reinterpret_cast(input_scratch->data.uint8);
+  int8_t* quantized_hidden_state_ptr =
+      reinterpret_cast(hidden_state_scratch->data.uint8);
+
+  if (time_major) {
+    // Initialize the pointer to hidden state.
+    float* hidden_state_ptr_batch = hidden_state->data.f;
+    // Unroll the sequence and use batch operations for efficiency.
+    for (int s = 0; s < max_time; s++) {
+      // Initialize the pointer to input and output.
+      const float* input_ptr_batch =
+          input->data.f + s * input_size * batch_size;
+      float* output_ptr_batch = output->data.f + s * num_units * batch_size;
+
+      kernel_utils::RnnBatchStep(
+          input_ptr_batch, input_weights_ptr, input_weights_scale,
+          recurrent_weights_ptr, recurrent_weights_scale, bias_ptr, input_size,
+          num_units, batch_size, params->activation, quantized_input_ptr,
+          quantized_hidden_state_ptr, hidden_state_ptr_batch, output_ptr_batch);
+    }
+  } else {
+    // For each batch
+    for (int b = 0; b < batch_size; b++) {
+      // Initialize the pointer to hidden state.
+      float* hidden_state_ptr_batch = hidden_state->data.f + b * num_units;
+      for (int s = 0; s < max_time; s++) {
+        // Initialize the pointer to input and output.
+        const float* input_ptr_batch =
+            input->data.f + b * input_size * max_time + s * input_size;
+        float* output_ptr_batch =
+            output->data.f + b * num_units * max_time + s * num_units;
+
+        kernel_utils::RnnBatchStep(
+            input_ptr_batch, input_weights_ptr, input_weights_scale,
+            recurrent_weights_ptr, recurrent_weights_scale, bias_ptr,
+            input_size, num_units, /*batch_size=*/1, params->activation,
+            quantized_input_ptr, quantized_hidden_state_ptr,
+            hidden_state_ptr_batch, output_ptr_batch);
+      }
+    }
+  }
+  return kTfLiteOk;
+}
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  auto* params = reinterpret_cast(node->builtin_data);
+
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
+  TfLiteTensor* recurrent_weights =
+      GetInput(context, node, kRecurrentWeightsTensor);
+  TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
+  TfLiteTensor* hidden_state = GetOutput(context, node, kHiddenStateTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  switch (input_weights->type) {
+    case kTfLiteFloat32:
+      return EvalFloat(input, input_weights, recurrent_weights, bias, params,
+                       hidden_state, output);
+    case kTfLiteUInt8: {
+      // TODO(mirkov): implement eval with quantized inputs as well.
+      TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32);
+      TfLiteTensor* input_quantized = GetTemporary(context, node, 0);
+      TfLiteTensor* hidden_state_quantized = GetTemporary(context, node, 1);
+      return EvalQuantized(input, input_weights, recurrent_weights, bias,
+                           params, input_quantized, hidden_state_quantized,
+                           hidden_state, output);
+    }
+    default:
+      context->ReportError(context, "Type not currently supported.");
+      return kTfLiteError;
+  }
+  return kTfLiteOk;
+}
+
 }  // namespace unidirectional_sequence_rnn
 
 TfLiteRegistration* Register_UNIDIRECTIONAL_SEQUENCE_RNN() {
-  static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr,
-                                 unidirectional_sequence_rnn::Prepare,
-                                 unidirectional_sequence_rnn::Eval};
+  static TfLiteRegistration r = {
+      unidirectional_sequence_rnn::Init, unidirectional_sequence_rnn::Free,
+      unidirectional_sequence_rnn::Prepare, unidirectional_sequence_rnn::Eval};
   return &r;
 }
 
diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn_test.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn_test.cc
index 7e32969763b596..0adab837b07a6d 100644
--- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn_test.cc
+++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn_test.cc
@@ -122,17 +122,66 @@ static float rnn_golden_output[] = {
     0,          2.02616,    0,         0.728256,  0.84183,   0.0907453,
     0.628881,   3.58099,    1.49974,   0};
 
+static std::initializer_list rnn_weights = {
+    0.461459,    0.153381,   0.529743,    -0.00371218, 0.676267,   -0.211346,
+    0.317493,    0.969689,   -0.343251,   0.186423,    0.398151,   0.152399,
+    0.448504,    0.317662,   0.523556,    -0.323514,   0.480877,   0.333113,
+    -0.757714,   -0.674487,  -0.643585,   0.217766,    -0.0251462, 0.79512,
+    -0.595574,   -0.422444,  0.371572,    -0.452178,   -0.556069,  -0.482188,
+    -0.685456,   -0.727851,  0.841829,    0.551535,    -0.232336,  0.729158,
+    -0.00294906, -0.69754,   0.766073,    -0.178424,   0.369513,   -0.423241,
+    0.548547,    -0.0152023, -0.757482,   -0.85491,    0.251331,   -0.989183,
+    0.306261,    -0.340716,  0.886103,    -0.0726757,  -0.723523,  -0.784303,
+    0.0354295,   0.566564,   -0.485469,   -0.620498,   0.832546,   0.697884,
+    -0.279115,   0.294415,   -0.584313,   0.548772,    0.0648819,  0.968726,
+    0.723834,    -0.0080452, -0.350386,   -0.272803,   0.115121,   -0.412644,
+    -0.824713,   -0.992843,  -0.592904,   -0.417893,   0.863791,   -0.423461,
+    -0.147601,   -0.770664,  -0.479006,   0.654782,    0.587314,   -0.639158,
+    0.816969,    -0.337228,  0.659878,    0.73107,     0.754768,   -0.337042,
+    0.0960841,   0.368357,   0.244191,    -0.817703,   -0.211223,  0.442012,
+    0.37225,     -0.623598,  -0.405423,   0.455101,    0.673656,   -0.145345,
+    -0.511346,   -0.901675,  -0.81252,    -0.127006,   0.809865,   -0.721884,
+    0.636255,    0.868989,   -0.347973,   -0.10179,    -0.777449,  0.917274,
+    0.819286,    0.206218,   -0.00785118, 0.167141,    0.45872,    0.972934,
+    -0.276798,   0.837861,   0.747958,    -0.0151566,  -0.330057,  -0.469077,
+    0.277308,    0.415818};
+
+static std::initializer_list rnn_recurrent_weights = {
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0.1};
+
+static std::initializer_list rnn_bias = {
+    0.065691948, -0.69055247, 0.1107955,  -0.97084129, -0.23957068, -0.23566568,
+    -0.389184,   0.47481549,  -0.4791103, 0.29931796,  0.10463274,  0.83918178,
+    0.37197268,  0.61957061,  0.3956964,  -0.37609905};
+
 class UnidirectionalRNNOpModel : public SingleOpModel {
  public:
-  UnidirectionalRNNOpModel(int batches, int sequence_len, int units, int size,
-                           bool time_major)
+  UnidirectionalRNNOpModel(
+      int batches, int sequence_len, int units, int size, bool time_major,
+      const TensorType& weights = TensorType_FLOAT32,
+      const TensorType& recurrent_weights = TensorType_FLOAT32)
       : batches_(batches),
         sequence_len_(sequence_len),
         units_(units),
         input_size_(size) {
     input_ = AddInput(TensorType_FLOAT32);
-    weights_ = AddInput(TensorType_FLOAT32);
-    recurrent_weights_ = AddInput(TensorType_FLOAT32);
+    weights_ = AddInput(weights);
+    recurrent_weights_ = AddInput(recurrent_weights);
     bias_ = AddInput(TensorType_FLOAT32);
     hidden_state_ = AddOutput(TensorType_FLOAT32);
     output_ = AddOutput(TensorType_FLOAT32);
@@ -187,7 +236,7 @@ class UnidirectionalRNNOpModel : public SingleOpModel {
   int num_batches() { return batches_; }
   int sequence_len() { return sequence_len_; }
 
- private:
+ protected:
   int input_;
   int weights_;
   int recurrent_weights_;
@@ -201,58 +250,31 @@ class UnidirectionalRNNOpModel : public SingleOpModel {
   int input_size_;
 };
 
-// TODO(mirkov): add another test which directly compares to TF once TOCO
-// supports the conversion from dynamic_rnn with BasicRNNCell.
-TEST(FullyConnectedOpTest, BlackBoxTest) {
+// The hybrid model has quantized weights and recurrent_weights.
+class HybridUnidirectionalRNNOpModel : public UnidirectionalRNNOpModel {
+ public:
+  HybridUnidirectionalRNNOpModel(int batches, int sequence_len, int units,
+                                 int size, bool time_major)
+      : UnidirectionalRNNOpModel(batches, sequence_len, units, size, time_major,
+                                 TensorType_UINT8, TensorType_UINT8) {}
+
+  void SetWeights(std::initializer_list f) {
+    SymmetricQuantizeAndPopulate(weights_, f);
+  }
+
+  void SetRecurrentWeights(std::initializer_list f) {
+    SymmetricQuantizeAndPopulate(recurrent_weights_, f);
+  }
+};
+
+TEST(UnidirectionalRNNOpTest, BlackBoxTest) {
   UnidirectionalRNNOpModel rnn(/*batches=*/2, /*sequence_len=*/16,
                                /*units=*/16, /*size=*/8, /*time_major=*/false);
-  rnn.SetWeights(
-      {0.461459,    0.153381,   0.529743,    -0.00371218, 0.676267,   -0.211346,
-       0.317493,    0.969689,   -0.343251,   0.186423,    0.398151,   0.152399,
-       0.448504,    0.317662,   0.523556,    -0.323514,   0.480877,   0.333113,
-       -0.757714,   -0.674487,  -0.643585,   0.217766,    -0.0251462, 0.79512,
-       -0.595574,   -0.422444,  0.371572,    -0.452178,   -0.556069,  -0.482188,
-       -0.685456,   -0.727851,  0.841829,    0.551535,    -0.232336,  0.729158,
-       -0.00294906, -0.69754,   0.766073,    -0.178424,   0.369513,   -0.423241,
-       0.548547,    -0.0152023, -0.757482,   -0.85491,    0.251331,   -0.989183,
-       0.306261,    -0.340716,  0.886103,    -0.0726757,  -0.723523,  -0.784303,
-       0.0354295,   0.566564,   -0.485469,   -0.620498,   0.832546,   0.697884,
-       -0.279115,   0.294415,   -0.584313,   0.548772,    0.0648819,  0.968726,
-       0.723834,    -0.0080452, -0.350386,   -0.272803,   0.115121,   -0.412644,
-       -0.824713,   -0.992843,  -0.592904,   -0.417893,   0.863791,   -0.423461,
-       -0.147601,   -0.770664,  -0.479006,   0.654782,    0.587314,   -0.639158,
-       0.816969,    -0.337228,  0.659878,    0.73107,     0.754768,   -0.337042,
-       0.0960841,   0.368357,   0.244191,    -0.817703,   -0.211223,  0.442012,
-       0.37225,     -0.623598,  -0.405423,   0.455101,    0.673656,   -0.145345,
-       -0.511346,   -0.901675,  -0.81252,    -0.127006,   0.809865,   -0.721884,
-       0.636255,    0.868989,   -0.347973,   -0.10179,    -0.777449,  0.917274,
-       0.819286,    0.206218,   -0.00785118, 0.167141,    0.45872,    0.972934,
-       -0.276798,   0.837861,   0.747958,    -0.0151566,  -0.330057,  -0.469077,
-       0.277308,    0.415818});
-
-  rnn.SetBias({0.065691948, -0.69055247, 0.1107955, -0.97084129, -0.23957068,
-               -0.23566568, -0.389184, 0.47481549, -0.4791103, 0.29931796,
-               0.10463274, 0.83918178, 0.37197268, 0.61957061, 0.3956964,
-               -0.37609905});
-
-  rnn.SetRecurrentWeights({0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1});
-
+  rnn.SetWeights(rnn_weights);
+  rnn.SetBias(rnn_bias);
+  rnn.SetRecurrentWeights(rnn_recurrent_weights);
   rnn.ResetHiddenState();
+
   const int input_sequence_size = rnn.input_size() * rnn.sequence_len();
   float* batch_start = rnn_input;
   float* batch_end = batch_start + input_sequence_size;
@@ -270,56 +292,42 @@ TEST(FullyConnectedOpTest, BlackBoxTest) {
   EXPECT_THAT(rnn.GetOutput(), ElementsAreArray(ArrayFloatNear(expected)));
 }
 
-TEST(FullyConnectedOpTest, TimeMajorBlackBoxTest) {
-  UnidirectionalRNNOpModel rnn(/*batches=*/2, /*sequence_len=*/16,
-                               /*units=*/16, /*size=*/8, /*time_major=*/true);
-  rnn.SetWeights(
-      {0.461459,    0.153381,   0.529743,    -0.00371218, 0.676267,   -0.211346,
-       0.317493,    0.969689,   -0.343251,   0.186423,    0.398151,   0.152399,
-       0.448504,    0.317662,   0.523556,    -0.323514,   0.480877,   0.333113,
-       -0.757714,   -0.674487,  -0.643585,   0.217766,    -0.0251462, 0.79512,
-       -0.595574,   -0.422444,  0.371572,    -0.452178,   -0.556069,  -0.482188,
-       -0.685456,   -0.727851,  0.841829,    0.551535,    -0.232336,  0.729158,
-       -0.00294906, -0.69754,   0.766073,    -0.178424,   0.369513,   -0.423241,
-       0.548547,    -0.0152023, -0.757482,   -0.85491,    0.251331,   -0.989183,
-       0.306261,    -0.340716,  0.886103,    -0.0726757,  -0.723523,  -0.784303,
-       0.0354295,   0.566564,   -0.485469,   -0.620498,   0.832546,   0.697884,
-       -0.279115,   0.294415,   -0.584313,   0.548772,    0.0648819,  0.968726,
-       0.723834,    -0.0080452, -0.350386,   -0.272803,   0.115121,   -0.412644,
-       -0.824713,   -0.992843,  -0.592904,   -0.417893,   0.863791,   -0.423461,
-       -0.147601,   -0.770664,  -0.479006,   0.654782,    0.587314,   -0.639158,
-       0.816969,    -0.337228,  0.659878,    0.73107,     0.754768,   -0.337042,
-       0.0960841,   0.368357,   0.244191,    -0.817703,   -0.211223,  0.442012,
-       0.37225,     -0.623598,  -0.405423,   0.455101,    0.673656,   -0.145345,
-       -0.511346,   -0.901675,  -0.81252,    -0.127006,   0.809865,   -0.721884,
-       0.636255,    0.868989,   -0.347973,   -0.10179,    -0.777449,  0.917274,
-       0.819286,    0.206218,   -0.00785118, 0.167141,    0.45872,    0.972934,
-       -0.276798,   0.837861,   0.747958,    -0.0151566,  -0.330057,  -0.469077,
-       0.277308,    0.415818});
-
-  rnn.SetBias({0.065691948, -0.69055247, 0.1107955, -0.97084129, -0.23957068,
-               -0.23566568, -0.389184, 0.47481549, -0.4791103, 0.29931796,
-               0.10463274, 0.83918178, 0.37197268, 0.61957061, 0.3956964,
-               -0.37609905});
-
-  rnn.SetRecurrentWeights({0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0.1});
+TEST(HybridUnidirectionalRNNOpModelOpTest, BlackBoxTest) {
+  HybridUnidirectionalRNNOpModel rnn(/*batches=*/2, /*sequence_len=*/16,
+                                     /*units=*/16, /*size=*/8,
+                                     /*time_major=*/false);
+  rnn.SetWeights(rnn_weights);
+  rnn.SetBias(rnn_bias);
+  rnn.SetRecurrentWeights(rnn_recurrent_weights);
+  rnn.ResetHiddenState();
+
+  const int input_sequence_size = rnn.input_size() * rnn.sequence_len();
+  float* batch_start = rnn_input;
+  float* batch_end = batch_start + input_sequence_size;
+  rnn.SetInput(0, batch_start, batch_end);
+  rnn.SetInput(input_sequence_size, batch_start, batch_end);
+
+  rnn.Invoke();
+
+  float* golden_start = rnn_golden_output;
+  float* golden_end = golden_start + rnn.num_units() * rnn.sequence_len();
+  std::vector expected;
+  expected.insert(expected.end(), golden_start, golden_end);
+  expected.insert(expected.end(), golden_start, golden_end);
+
+  EXPECT_THAT(rnn.GetOutput(), ElementsAreArray(ArrayFloatNear(
+                                   expected, /*max_abs_error=*/0.013)));
+}
 
+TEST(UnidirectionalRNNOpTest, TimeMajorBlackBoxTest) {
+  UnidirectionalRNNOpModel rnn(/*batches=*/2, /*sequence_len=*/16,
+                               /*units=*/16, /*size=*/8,
+                               /*time_major=*/true);
+  rnn.SetWeights(rnn_weights);
+  rnn.SetBias(rnn_bias);
+  rnn.SetRecurrentWeights(rnn_recurrent_weights);
   rnn.ResetHiddenState();
+
   for (int i = 0; i < rnn.sequence_len(); i++) {
     float* batch_start = rnn_input + i * rnn.input_size();
     float* batch_end = batch_start + rnn.input_size();
@@ -341,6 +349,37 @@ TEST(FullyConnectedOpTest, TimeMajorBlackBoxTest) {
   EXPECT_THAT(rnn.GetOutput(), ElementsAreArray(ArrayFloatNear(expected)));
 }
 
+TEST(HybridUnidirectionalRNNOpModelOpTest, TimeMajorBlackBoxTest) {
+  HybridUnidirectionalRNNOpModel rnn(/*batches=*/2, /*sequence_len=*/16,
+                                     /*units=*/16, /*size=*/8,
+                                     /*time_major=*/true);
+  rnn.SetWeights(rnn_weights);
+  rnn.SetBias(rnn_bias);
+  rnn.SetRecurrentWeights(rnn_recurrent_weights);
+  rnn.ResetHiddenState();
+
+  for (int i = 0; i < rnn.sequence_len(); i++) {
+    float* batch_start = rnn_input + i * rnn.input_size();
+    float* batch_end = batch_start + rnn.input_size();
+    // The two batches are identical.
+    rnn.SetInput(2 * i * rnn.input_size(), batch_start, batch_end);
+    rnn.SetInput((2 * i + 1) * rnn.input_size(), batch_start, batch_end);
+  }
+
+  rnn.Invoke();
+
+  std::vector expected;
+  for (int i = 0; i < rnn.sequence_len(); i++) {
+    float* golden_batch_start = rnn_golden_output + i * rnn.num_units();
+    float* golden_batch_end = golden_batch_start + rnn.num_units();
+    expected.insert(expected.end(), golden_batch_start, golden_batch_end);
+    expected.insert(expected.end(), golden_batch_start, golden_batch_end);
+  }
+
+  EXPECT_THAT(rnn.GetOutput(), ElementsAreArray(ArrayFloatNear(
+                                   expected, /*max_abs_error=*/0.013)));
+}
+
 }  // namespace
 }  // namespace tflite
 

From b17bd867aea8cadb3c6c0c9cc2ea2dee9c79686d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 12:16:29 -0700
Subject: [PATCH 0624/1691] Make sure default GPU context is used within
 CollectiveRemoteAccessLocal::MemCpyAsync when not explicitly set.

PiperOrigin-RevId: 196152927
---
 .../common_runtime/collective_rma_local.cc    | 23 +++++++++++++++++--
 1 file changed, 21 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/common_runtime/collective_rma_local.cc b/tensorflow/core/common_runtime/collective_rma_local.cc
index ad9b32ce3514dc..69f1a9f24cde88 100644
--- a/tensorflow/core/common_runtime/collective_rma_local.cc
+++ b/tensorflow/core/common_runtime/collective_rma_local.cc
@@ -54,9 +54,13 @@ void CollectiveRemoteAccessLocal::RecvFromPeer(
                       hook->prod_value,  // src Tensor*
                       to_tensor,         // dst Tensor*
                       [hook, done](const Status& s) {
+                        // This callback may be executing in the GPUEventMgr
+                        // pool in which case it must be very short duration
+                        // and non-blocking (except e.g. for queue insertion).
+                        // It would be safer, though expensive, to transfer
+                        // to another thread here.
                         done(s);
-                        hook->prod_cb(s);
-                        delete hook;
+                        BufRendezvous::DoneWithHook(hook);
                       });
         }
       });
@@ -91,6 +95,21 @@ void CollectiveRemoteAccessLocal::MemCpyAsync(
       dst_attr.on_host() ? DEVICE_CPU : dst_dev->attributes().device_type());
   const bool non_cpu_src = src_device_type != DeviceType(DEVICE_CPU);
   const bool non_cpu_dst = dst_device_type != DeviceType(DEVICE_CPU);
+  // For GPU devices when only one compute stream is used (the default)
+  // the OpKernelContext does not supply a DeviceContext.  It's assumed
+  // that all nodes use the default context.
+  if (src_dev_ctx == nullptr && src_device_type == DEVICE_GPU) {
+    const DeviceBase::GpuDeviceInfo* dev_info =
+        src_dev->tensorflow_gpu_device_info();
+    CHECK(dev_info);
+    src_dev_ctx = dev_info->default_context;
+  }
+  if (dst_dev_ctx == nullptr && dst_device_type == DEVICE_GPU) {
+    const DeviceBase::GpuDeviceInfo* dev_info =
+        src_dev->tensorflow_gpu_device_info();
+    CHECK(dev_info);
+    dst_dev_ctx = dev_info->default_context;
+  }
   if (non_cpu_src) CHECK(src_dev_ctx);
   if (non_cpu_dst) CHECK(dst_dev_ctx);
   if (non_cpu_src || non_cpu_dst) {

From 0172ce3504dc455198b67d9cdda19bce012af1a9 Mon Sep 17 00:00:00 2001
From: Rob Sloan 
Date: Thu, 10 May 2018 12:28:29 -0700
Subject: [PATCH 0625/1691] Break out node loop from
 ConstantFolding::SimplifyGraph.

PiperOrigin-RevId: 196154571
---
 .../grappler/optimizers/constant_folding.cc   | 1266 ++++++++---------
 .../grappler/optimizers/constant_folding.h    |    2 +
 2 files changed, 632 insertions(+), 636 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 28fc5fdcb50745..d5c583a8ed8933 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -1587,722 +1587,716 @@ Status ConstantFolding::ReplaceOperationWithConstant(
 Status ConstantFolding::SimplifyGraph(GraphDef* optimized_graph,
                                       GraphProperties* properties,
                                       bool use_shape_info) {
-  const bool is_aggressive = opt_level_ == RewriterConfig::AGGRESSIVE;
   for (int i = 0; i < optimized_graph->node_size(); ++i) {
-    NodeDef* node = optimized_graph->mutable_node(i);
+    TF_RETURN_IF_ERROR(SimplifyNode(optimized_graph->mutable_node(i),
+                                    optimized_graph, properties,
+                                    use_shape_info));
+  }
+  return Status::OK();
+}
 
-    if (IsSplit(*node) && node->attr().at("num_split").i() == 1) {
-      ReplaceOperationWithIdentity(1, *properties, node, optimized_graph);
-      continue;
-    }
+Status ConstantFolding::SimplifyNode(NodeDef* node, GraphDef* optimized_graph,
+                                     GraphProperties* properties,
+                                     bool use_shape_info) {
+  const bool is_aggressive = opt_level_ == RewriterConfig::AGGRESSIVE;
+  if (IsSplit(*node) && node->attr().at("num_split").i() == 1) {
+    ReplaceOperationWithIdentity(1, *properties, node, optimized_graph);
+    return Status::OK();
+  }
 
-    if (IsSplitV(*node) && node->attr().at("num_split").i() == 1) {
-      ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
-      continue;
-    }
+  if (IsSplitV(*node) && node->attr().at("num_split").i() == 1) {
+    ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
+    return Status::OK();
+  }
 
-    // Remove Shuffle or Transpose op over dimensions of size 1.
-    if (use_shape_info && (IsShuffle(*node) || IsTranspose(*node)) &&
-        properties->GetInputProperties(node->name()).size() >= 2) {
-      const auto& shape =
-          properties->GetInputProperties(node->name())[0].shape();
-      if (shape.unknown_rank()) {
-        // Not optimizable.
-        continue;
-      }
-      const auto& p = properties->GetInputProperties(node->name())[1];
-      if (TensorShape::IsValid(p.shape()) && p.has_value()) {
-        Tensor perm(p.dtype(), p.shape());
-        if (!perm.FromProto(p.value())) {
-          return errors::InvalidArgument("Cannot parse tensor from proto: ",
-                                         p.value().DebugString());
-        }
-        std::vector permutation;
-        for (int j = 0; j < perm.NumElements(); ++j) {
-          if (perm.dtype() == DT_INT64) {
-            permutation.push_back(perm.vec()(j));
-          } else {
-            permutation.push_back(perm.vec()(j));
-          }
-        }
-        if (permutation.size() != shape.dim_size()) {
-          // Number of elements in perm should be same as dim_size. Skip if not.
-          continue;
-        }
-        // The node is replaceable iff
-        // dim_size == 0 || all dims have size 1 ||
-        // all dims with > 1 size are not permuted.
-        bool replaceable = true;
-        for (int j = 0; replaceable && j < shape.dim_size(); ++j) {
-          replaceable &= shape.dim(j).size() == 1 || j == permutation[j];
-        }
-        if (replaceable) {
-          ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
-          continue;
+  // Remove Shuffle or Transpose op over dimensions of size 1.
+  if (use_shape_info && (IsShuffle(*node) || IsTranspose(*node)) &&
+      properties->GetInputProperties(node->name()).size() >= 2) {
+    const auto& shape = properties->GetInputProperties(node->name())[0].shape();
+    if (shape.unknown_rank()) {
+      // Not optimizable.
+      return Status::OK();
+    }
+    const auto& p = properties->GetInputProperties(node->name())[1];
+    if (TensorShape::IsValid(p.shape()) && p.has_value()) {
+      Tensor perm(p.dtype(), p.shape());
+      if (!perm.FromProto(p.value())) {
+        return errors::InvalidArgument("Cannot parse tensor from proto: ",
+                                       p.value().DebugString());
+      }
+      std::vector permutation;
+      for (int j = 0; j < perm.NumElements(); ++j) {
+        if (perm.dtype() == DT_INT64) {
+          permutation.push_back(perm.vec()(j));
+        } else {
+          permutation.push_back(perm.vec()(j));
         }
       }
-    }
-
-    // Remove RandomShuffle op if it is scalar or first dimension is of size 1.
-    if (use_shape_info && IsRandomShuffle(*node) &&
-        !properties->GetInputProperties(node->name()).empty()) {
-      const auto& shape =
-          properties->GetInputProperties(node->name())[0].shape();
+      if (permutation.size() != shape.dim_size()) {
+        // Number of elements in perm should be same as dim_size. Skip if not.
+        return Status::OK();
+      }
       // The node is replaceable iff
-      // unknown_rank == false && (dim_size == 0 || first dim is of size 1)
-      if (!shape.unknown_rank() &&
-          (shape.dim_size() == 0 || shape.dim(0).size() == 1)) {
+      // dim_size == 0 || all dims have size 1 ||
+      // all dims with > 1 size are not permuted.
+      bool replaceable = true;
+      for (int j = 0; replaceable && j < shape.dim_size(); ++j) {
+        replaceable &= shape.dim(j).size() == 1 || j == permutation[j];
+      }
+      if (replaceable) {
         ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
-        continue;
+        return Status::OK();
       }
     }
+  }
 
-    // Remove Reverse op over dimensions with size 1.
-    if (use_shape_info && node->op() == "ReverseV2" &&
-        properties->GetInputProperties(node->name()).size() >= 2) {
-      const auto& shape =
-          properties->GetInputProperties(node->name())[0].shape();
-      if (shape.unknown_rank()) {
-        // Not optimizable.
-        continue;
-      }
-      const auto& a = properties->GetInputProperties(node->name())[1];
-      if (TensorShape::IsValid(a.shape()) && a.has_value()) {
-        Tensor axis(a.dtype(), a.shape());
-        if (!axis.FromProto(a.value())) {
-          return errors::InvalidArgument("Cannot parse tensor from proto: ",
-                                         a.value().DebugString());
-        }
-        std::set target_axes;
-        for (int j = 0; j < axis.NumElements(); ++j) {
-          // value of axis can be negative.
-          if (axis.dtype() == DT_INT64) {
-            target_axes.insert((axis.vec()(j) + shape.dim_size()) %
-                               shape.dim_size());
-          } else {
-            target_axes.insert((axis.vec()(j) + shape.dim_size()) %
-                               shape.dim_size());
-          }
-        }
-
-        // The node is replaceable iff
-        // unknown_rank == false &&
-        // (dim_size == 0 || all dims have size 1 ||
-        //  all dims with > 1 size are not in target_axes)
-        bool replaceable = !shape.unknown_rank();
-        for (int j = 0; replaceable && j < shape.dim_size(); ++j) {
-          replaceable &= shape.dim(j).size() == 1 ||
-                         target_axes.find(j) == target_axes.end();
-        }
-        if (replaceable) {
-          ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
-          continue;
-        }
-      }
+  // Remove RandomShuffle op if it is scalar or first dimension is of size 1.
+  if (use_shape_info && IsRandomShuffle(*node) &&
+      !properties->GetInputProperties(node->name()).empty()) {
+    const auto& shape = properties->GetInputProperties(node->name())[0].shape();
+    // The node is replaceable iff
+    // unknown_rank == false && (dim_size == 0 || first dim is of size 1)
+    if (!shape.unknown_rank() &&
+        (shape.dim_size() == 0 || shape.dim(0).size() == 1)) {
+      ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
+      return Status::OK();
     }
+  }
 
-    if (use_shape_info && IsSlice(*node) &&
-        properties->GetInputProperties(node->name()).size() == 3) {
-      const auto& input = properties->GetInputProperties(node->name())[0];
-      const auto& b = properties->GetInputProperties(node->name())[1];
-      const auto& s = properties->GetInputProperties(node->name())[2];
-      if (TensorShape::IsValid(b.shape()) && b.has_value() &&
-          TensorShape::IsValid(s.shape()) && s.has_value()) {
-        Tensor begin(b.dtype(), b.shape());
-        if (!begin.FromProto(b.value())) {
-          return errors::InvalidArgument("Cannot parse tensor from proto: ",
-                                         b.value().DebugString());
-        }
-        Tensor size(s.dtype(), s.shape());
-        if (!size.FromProto(s.value())) {
-          return errors::InvalidArgument("Cannot parse tensor from proto: ",
-                                         s.value().DebugString());
-        }
-        // The node is replaceable iff unknown_rank == false &&
-        // begin == 0 && (size == -1 || size == input_shape) for all dimensions
-        bool replaceable = !input.shape().unknown_rank();
-        for (int j = 0; replaceable && j < input.shape().dim_size(); ++j) {
-          if (begin.dtype() == DT_INT32) {
-            replaceable &= begin.vec()(j) == 0;
-          } else {
-            replaceable &= begin.vec()(j) == 0;
-          }
-          if (size.dtype() == DT_INT32) {
-            replaceable &= (size.vec()(j) == -1 ||
-                            size.vec()(j) == input.shape().dim(j).size());
-          } else {
-            replaceable &=
-                (size.vec()(j) == -1 ||
-                 size.vec()(j) == input.shape().dim(j).size());
-          }
-        }
-        if (replaceable) {
-          ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
-          continue;
-        }
-      }
+  // Remove Reverse op over dimensions with size 1.
+  if (use_shape_info && node->op() == "ReverseV2" &&
+      properties->GetInputProperties(node->name()).size() >= 2) {
+    const auto& shape = properties->GetInputProperties(node->name())[0].shape();
+    if (shape.unknown_rank()) {
+      // Not optimizable.
+      return Status::OK();
     }
-
-    if (use_shape_info && IsTile(*node) &&
-        properties->GetInputProperties(node->name()).size() == 2) {
-      const auto& m = properties->GetInputProperties(node->name())[1];
-      if (TensorShape::IsValid(m.shape()) && m.has_value()) {
-        Tensor multiplies(m.dtype(), m.shape());
-        if (!multiplies.FromProto(m.value())) {
-          return errors::InvalidArgument("Cannot parse tensor from proto: ",
-                                         m.value().DebugString());
-        }
-        // The node is replaceable iff all values in multiplies are 1.
-        bool replaceable = true;
-        if (multiplies.dtype() == DT_INT32) {
-          for (int j = 0; replaceable && j < multiplies.vec().size();
-               ++j) {
-            replaceable &= multiplies.vec()(j) == 1;
-          }
+    const auto& a = properties->GetInputProperties(node->name())[1];
+    if (TensorShape::IsValid(a.shape()) && a.has_value()) {
+      Tensor axis(a.dtype(), a.shape());
+      if (!axis.FromProto(a.value())) {
+        return errors::InvalidArgument("Cannot parse tensor from proto: ",
+                                       a.value().DebugString());
+      }
+      std::set target_axes;
+      for (int j = 0; j < axis.NumElements(); ++j) {
+        // value of axis can be negative.
+        if (axis.dtype() == DT_INT64) {
+          target_axes.insert((axis.vec()(j) + shape.dim_size()) %
+                             shape.dim_size());
         } else {
-          for (int j = 0; replaceable && j < multiplies.vec().size();
-               ++j) {
-            replaceable &= multiplies.vec()(j) == 1;
-          }
-        }
-        if (replaceable) {
-          ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
-          continue;
+          target_axes.insert((axis.vec()(j) + shape.dim_size()) %
+                             shape.dim_size());
         }
       }
-    }
 
-    if (use_shape_info && IsPad(*node) &&
-        properties->GetInputProperties(node->name()).size() >= 2) {
-      const auto& p = properties->GetInputProperties(node->name())[1];
-      if (TensorShape::IsValid(p.shape()) && p.has_value()) {
-        Tensor paddings(p.dtype(), p.shape());
-        if (!paddings.FromProto(p.value())) {
-          return errors::InvalidArgument("Cannot parse tensor from proto: ",
-                                         p.value().DebugString());
-        }
-        // The node is replaceable iff all values in paddings are 0.
-        bool replaceable = true;
-        // The operation requires it to be int32 value so we don't check for
-        // 1nt64.
-        const auto flatten = paddings.flat();
-        for (int j = 0; replaceable && j < flatten.size(); ++j) {
-          replaceable &= flatten(j) == 0;
-        }
-        if (replaceable) {
-          ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
-          continue;
-        }
-      }
-    }
-
-    if (use_shape_info && IsSqueeze(*node) &&
-        !properties->GetInputProperties(node->name()).empty()) {
-      // https://www.tensorflow.org/api_docs/python/tf/squeeze mentions it's
-      // error to squeeze a dimension that is not 1, so we only need to check
-      // whether the input has > 1 size for each dimension.
-      const auto& shape =
-          properties->GetInputProperties(node->name())[0].shape();
       // The node is replaceable iff
-      // unknown_rank == false && (dim_size == 0 || all dims have size > 1)
+      // unknown_rank == false &&
+      // (dim_size == 0 || all dims have size 1 ||
+      //  all dims with > 1 size are not in target_axes)
       bool replaceable = !shape.unknown_rank();
       for (int j = 0; replaceable && j < shape.dim_size(); ++j) {
-        replaceable &= shape.dim(j).size() > 1;
+        replaceable &= shape.dim(j).size() == 1 ||
+                       target_axes.find(j) == target_axes.end();
       }
       if (replaceable) {
         ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
-        continue;
+        return Status::OK();
       }
     }
+  }
 
-    if (IsPack(*node) && NumNonControlInputs(*node) == 1 &&
-        !OptimizedNodeExists(*node, "_const_axis")) {
-      // Create constant axis node.
-      Tensor axis_t(DT_INT32, TensorShape({}));
-      NodeDef* axis_node = optimized_graph->add_node();
-      axis_node->set_name(OptimizedNodeName(*node, "_const_axis"));
-      const int axis = node->attr().at("axis").i();
-      if (!SetTensorValue(DT_INT32, axis, &axis_t).ok() ||
-          !CreateNodeDef(axis_node->name(), TensorValue(&axis_t), axis_node)
-               .ok()) {
-        continue;
+  if (use_shape_info && IsSlice(*node) &&
+      properties->GetInputProperties(node->name()).size() == 3) {
+    const auto& input = properties->GetInputProperties(node->name())[0];
+    const auto& b = properties->GetInputProperties(node->name())[1];
+    const auto& s = properties->GetInputProperties(node->name())[2];
+    if (TensorShape::IsValid(b.shape()) && b.has_value() &&
+        TensorShape::IsValid(s.shape()) && s.has_value()) {
+      Tensor begin(b.dtype(), b.shape());
+      if (!begin.FromProto(b.value())) {
+        return errors::InvalidArgument("Cannot parse tensor from proto: ",
+                                       b.value().DebugString());
       }
-      // Add a control dependency to make sure axis_node is in the right frame.
-      const string ctrl_dep = ConstantFolding::AddControlDependency(
-          node->input(0), graph_, node_map_.get());
-      axis_node->add_input(ctrl_dep);
-      axis_node->set_device(node->device());
-      node->set_op("ExpandDims");
-      if (node->attr().count("axis") != 0) {
-        node->mutable_attr()->erase("axis");
+      Tensor size(s.dtype(), s.shape());
+      if (!size.FromProto(s.value())) {
+        return errors::InvalidArgument("Cannot parse tensor from proto: ",
+                                       s.value().DebugString());
       }
-      if (node->attr().count("N") != 0) {
-        node->mutable_attr()->erase("N");
+      // The node is replaceable iff unknown_rank == false &&
+      // begin == 0 && (size == -1 || size == input_shape) for all dimensions
+      bool replaceable = !input.shape().unknown_rank();
+      for (int j = 0; replaceable && j < input.shape().dim_size(); ++j) {
+        if (begin.dtype() == DT_INT32) {
+          replaceable &= begin.vec()(j) == 0;
+        } else {
+          replaceable &= begin.vec()(j) == 0;
+        }
+        if (size.dtype() == DT_INT32) {
+          replaceable &= (size.vec()(j) == -1 ||
+                          size.vec()(j) == input.shape().dim(j).size());
+        } else {
+          replaceable &= (size.vec()(j) == -1 ||
+                          size.vec()(j) == input.shape().dim(j).size());
+        }
       }
-      (*node->mutable_attr())["Tdim"].set_type(DT_INT32);
-      node->add_input(axis_node->name());
-      if (node->input_size() > 2) {
-        node->mutable_input()->SwapElements(1, node->input_size() - 1);
+      if (replaceable) {
+        ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
+        return Status::OK();
       }
-      graph_modified_ = true;
-      continue;
     }
+  }
 
-    // Move constants past Enter.
-    if (IsEnter(*node) && node->input_size() > 0) {
-      if (node->attr().count("is_constant") == 0 ||
-          !node->attr().at("is_constant").b()) {
-        continue;
+  if (use_shape_info && IsTile(*node) &&
+      properties->GetInputProperties(node->name()).size() == 2) {
+    const auto& m = properties->GetInputProperties(node->name())[1];
+    if (TensorShape::IsValid(m.shape()) && m.has_value()) {
+      Tensor multiplies(m.dtype(), m.shape());
+      if (!multiplies.FromProto(m.value())) {
+        return errors::InvalidArgument("Cannot parse tensor from proto: ",
+                                       m.value().DebugString());
       }
-      const string& node_name = node->name();
-      const NodeDef* input = node_map_->GetNode(node->input(0));
-      if (input != nullptr && IsReallyConstant(*input) &&
-          !OptimizedNodeExists(*input, "_enter")) {
-        auto fanouts = node_map_->GetOutputs(node_name);
-        // Find non-constant nodes that consume the output of *node.
-        std::vector consumers;
-        for (NodeDef* fanout : fanouts) {
-          if (!IsConstant(*fanout)) {
-            for (int i = 0; i < fanout->input_size(); ++i) {
-              if (fanout->input(i) == node_name) {
-                consumers.push_back(fanout);
-                break;
-              }
-            }
-          }
+      // The node is replaceable iff all values in multiplies are 1.
+      bool replaceable = true;
+      if (multiplies.dtype() == DT_INT32) {
+        for (int j = 0; replaceable && j < multiplies.vec().size(); ++j) {
+          replaceable &= multiplies.vec()(j) == 1;
         }
-        if (!consumers.empty()) {
-          NodeDef* new_node = optimized_graph->add_node();
-          *new_node = *input;
-          new_node->set_name(OptimizedNodeName(*input, "_enter"));
-          new_node->set_device(node->device());
-          new_node->clear_input();
-          new_node->add_input(AsControlDependency(node_name));
-          node_map_->AddNode(new_node->name(), new_node);
-          node_map_->AddOutput(node_name, new_node->name());
-          for (NodeDef* consumer : consumers) {
-            for (int i = 0; i < consumer->input_size(); ++i) {
-              if (NodeName(consumer->input(i)) == node_name) {
-                node_map_->UpdateInput(consumer->name(), node_name,
-                                       new_node->name());
-                consumer->set_input(i, new_node->name());
-              }
-            }
-          }
-          graph_modified_ = true;
-          continue;
+      } else {
+        for (int j = 0; replaceable && j < multiplies.vec().size();
+             ++j) {
+          replaceable &= multiplies.vec()(j) == 1;
         }
       }
+      if (replaceable) {
+        ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
+        return Status::OK();
+      }
     }
+  }
 
-    // Switch(x, x) will always feed false to its false branch and true to
-    // its true branch. By rewriting the graph a bit, we can propagate these
-    // constants down the two output branches, and just use control dependencies
-    // to trigger the selected one at runtime. For example,
-    //
-    //     +------+
-    // x-->|Switch|-->a  (in practice there may be multiple consumers of each
-    // x-->|      |-->b   output branch.)
-    //     +------+
-    //
-    // Is rewritten as
-    //
-    //     +------+
-    // x-->|Switch|-->Identity--^>Const(false)-->a
-    // x-->|      |-->Identity--^>Const(true)-->b
-    //     +------+
-    if (node->op() == "Switch" && node->input(0) == node->input(1) &&
-        !OptimizedNodeExists(*node, "_const_false") &&
-        !OptimizedNodeExists(*node, "_const_true")) {
-      bool already_optimized = true;
-      // If the optimization was already applied, the switch would have exactly
-      // one Identity node consuming each of its outputs, each without any
-      // non-control outputs.
-      auto fanouts = node_map_->GetOutputs(node->name());
-      if (fanouts.size() == 2) {
-        for (NodeDef* fanout : fanouts) {
-          if (!IsIdentity(*fanout) ||
-              NumNonControlOutputs(*fanout, *node_map_) > 0) {
-            already_optimized = false;
-            break;
-          }
-        }
+  if (use_shape_info && IsPad(*node) &&
+      properties->GetInputProperties(node->name()).size() >= 2) {
+    const auto& p = properties->GetInputProperties(node->name())[1];
+    if (TensorShape::IsValid(p.shape()) && p.has_value()) {
+      Tensor paddings(p.dtype(), p.shape());
+      if (!paddings.FromProto(p.value())) {
+        return errors::InvalidArgument("Cannot parse tensor from proto: ",
+                                       p.value().DebugString());
       }
-      Tensor false_t(DT_BOOL, TensorShape({}));
-      Tensor true_t(DT_BOOL, TensorShape({}));
-      // Make sure we don't proceed if this switch node was already optimized.
-      if (!already_optimized && SetTensorValue(DT_BOOL, true, &true_t).ok() &&
-          SetTensorValue(DT_BOOL, false, &false_t).ok()) {
-        // Copy the set of consumers of the switch as they will be manipulated
-        // below.
-        const std::set& consumer_set =
-            node_map_->GetOutputs(node->name());
-        std::vector consumers(consumer_set.begin(),
-                                        consumer_set.end());
-        std::sort(consumers.begin(), consumers.end(),
-                  [](const NodeDef* n1, const NodeDef* n2) {
-                    return n1->name() < n2->name();
-                  });
-        // Create constant false & true nodes.
-        NodeDef* false_node = optimized_graph->add_node();
-        false_node->set_name(OptimizedNodeName(*node, "_const_false"));
-        if (!CreateNodeDef(false_node->name(), TensorValue(&false_t),
-                           false_node)
-                 .ok()) {
-          continue;
-        }
-        false_node->set_device(node->device());
+      // The node is replaceable iff all values in paddings are 0.
+      bool replaceable = true;
+      // The operation requires it to be int32 value so we don't check for
+      // 1nt64.
+      const auto flatten = paddings.flat();
+      for (int j = 0; replaceable && j < flatten.size(); ++j) {
+        replaceable &= flatten(j) == 0;
+      }
+      if (replaceable) {
+        ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
+        return Status::OK();
+      }
+    }
+  }
 
-        NodeDef* true_node = optimized_graph->add_node();
-        true_node->set_name(OptimizedNodeName(*node, "_const_true"));
-        if (!CreateNodeDef(true_node->name(), TensorValue(&true_t), true_node)
-                 .ok()) {
-          continue;
-        }
-        true_node->set_device(node->device());
-
-        // Add controls from the switch ports to the constants, and connect the
-        // constants to the original switch outputs.
-        const string false_port = node->name();
-        const string true_port = strings::StrCat(node->name(), ":1");
-        const string false_ctrl_dep =
-            AddControlDependency(false_port, optimized_graph, node_map_.get());
-        false_node->add_input(false_ctrl_dep);
-        const string true_ctrl_dep =
-            AddControlDependency(true_port, optimized_graph, node_map_.get());
-        true_node->add_input(true_ctrl_dep);
-
-        node_map_->AddNode(false_node->name(), false_node);
-        node_map_->AddNode(true_node->name(), true_node);
-        node_map_->AddOutput(NodeName(false_ctrl_dep), false_node->name());
-        node_map_->AddOutput(NodeName(true_ctrl_dep), true_node->name());
+  if (use_shape_info && IsSqueeze(*node) &&
+      !properties->GetInputProperties(node->name()).empty()) {
+    // https://www.tensorflow.org/api_docs/python/tf/squeeze mentions it's
+    // error to squeeze a dimension that is not 1, so we only need to check
+    // whether the input has > 1 size for each dimension.
+    const auto& shape = properties->GetInputProperties(node->name())[0].shape();
+    // The node is replaceable iff
+    // unknown_rank == false && (dim_size == 0 || all dims have size > 1)
+    bool replaceable = !shape.unknown_rank();
+    for (int j = 0; replaceable && j < shape.dim_size(); ++j) {
+      replaceable &= shape.dim(j).size() > 1;
+    }
+    if (replaceable) {
+      ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
+      return Status::OK();
+    }
+  }
+
+  if (IsPack(*node) && NumNonControlInputs(*node) == 1 &&
+      !OptimizedNodeExists(*node, "_const_axis")) {
+    // Create constant axis node.
+    Tensor axis_t(DT_INT32, TensorShape({}));
+    NodeDef* axis_node = optimized_graph->add_node();
+    axis_node->set_name(OptimizedNodeName(*node, "_const_axis"));
+    const int axis = node->attr().at("axis").i();
+    if (!SetTensorValue(DT_INT32, axis, &axis_t).ok() ||
+        !CreateNodeDef(axis_node->name(), TensorValue(&axis_t), axis_node)
+             .ok()) {
+      return Status::OK();
+    }
+    // Add a control dependency to make sure axis_node is in the right frame.
+    const string ctrl_dep = ConstantFolding::AddControlDependency(
+        node->input(0), graph_, node_map_.get());
+    axis_node->add_input(ctrl_dep);
+    axis_node->set_device(node->device());
+    node->set_op("ExpandDims");
+    if (node->attr().count("axis") != 0) {
+      node->mutable_attr()->erase("axis");
+    }
+    if (node->attr().count("N") != 0) {
+      node->mutable_attr()->erase("N");
+    }
+    (*node->mutable_attr())["Tdim"].set_type(DT_INT32);
+    node->add_input(axis_node->name());
+    if (node->input_size() > 2) {
+      node->mutable_input()->SwapElements(1, node->input_size() - 1);
+    }
+    graph_modified_ = true;
+    return Status::OK();
+  }
 
+  // Move constants past Enter.
+  if (IsEnter(*node) && node->input_size() > 0) {
+    if (node->attr().count("is_constant") == 0 ||
+        !node->attr().at("is_constant").b()) {
+      return Status::OK();
+    }
+    const string& node_name = node->name();
+    const NodeDef* input = node_map_->GetNode(node->input(0));
+    if (input != nullptr && IsReallyConstant(*input) &&
+        !OptimizedNodeExists(*input, "_enter")) {
+      auto fanouts = node_map_->GetOutputs(node_name);
+      // Find non-constant nodes that consume the output of *node.
+      std::vector consumers;
+      for (NodeDef* fanout : fanouts) {
+        if (!IsConstant(*fanout)) {
+          for (int i = 0; i < fanout->input_size(); ++i) {
+            if (fanout->input(i) == node_name) {
+              consumers.push_back(fanout);
+              break;
+            }
+          }
+        }
+      }
+      if (!consumers.empty()) {
+        NodeDef* new_node = optimized_graph->add_node();
+        *new_node = *input;
+        new_node->set_name(OptimizedNodeName(*input, "_enter"));
+        new_node->set_device(node->device());
+        new_node->clear_input();
+        new_node->add_input(AsControlDependency(node_name));
+        node_map_->AddNode(new_node->name(), new_node);
+        node_map_->AddOutput(node_name, new_node->name());
         for (NodeDef* consumer : consumers) {
           for (int i = 0; i < consumer->input_size(); ++i) {
-            const string& input = consumer->input(i);
-            if (input == false_port) {
-              consumer->set_input(i, false_node->name());
-              node_map_->UpdateInput(consumer->name(), false_port,
-                                     false_node->name());
-            } else if (input == true_port) {
-              consumer->set_input(i, true_node->name());
-              node_map_->UpdateInput(consumer->name(), true_port,
-                                     true_node->name());
+            if (NodeName(consumer->input(i)) == node_name) {
+              node_map_->UpdateInput(consumer->name(), node_name,
+                                     new_node->name());
+              consumer->set_input(i, new_node->name());
             }
           }
         }
         graph_modified_ = true;
-        continue;
+        return Status::OK();
       }
     }
-    if (IsSimplifiableReduction(*node)) {
-      // Replace the reduction node with an identity node, that can be further
-      // optimized by the model pruner.
-      DataType output_type;
-      if (node->attr().count("T") > 0) {
-        output_type = node->attr().at("T").type();
-      } else {
-        // This is an 'any' or 'all' reduction. The output is always boolean.
-        output_type = DT_BOOL;
+  }
+
+  // Switch(x, x) will always feed false to its false branch and true to
+  // its true branch. By rewriting the graph a bit, we can propagate these
+  // constants down the two output branches, and just use control dependencies
+  // to trigger the selected one at runtime. For example,
+  //
+  //     +------+
+  // x-->|Switch|-->a  (in practice there may be multiple consumers of each
+  // x-->|      |-->b   output branch.)
+  //     +------+
+  //
+  // Is rewritten as
+  //
+  //     +------+
+  // x-->|Switch|-->Identity--^>Const(false)-->a
+  // x-->|      |-->Identity--^>Const(true)-->b
+  //     +------+
+  if (node->op() == "Switch" && node->input(0) == node->input(1) &&
+      !OptimizedNodeExists(*node, "_const_false") &&
+      !OptimizedNodeExists(*node, "_const_true")) {
+    bool already_optimized = true;
+    // If the optimization was already applied, the switch would have exactly
+    // one Identity node consuming each of its outputs, each without any
+    // non-control outputs.
+    auto fanouts = node_map_->GetOutputs(node->name());
+    if (fanouts.size() == 2) {
+      for (NodeDef* fanout : fanouts) {
+        if (!IsIdentity(*fanout) ||
+            NumNonControlOutputs(*fanout, *node_map_) > 0) {
+          already_optimized = false;
+          break;
+        }
       }
-      node->set_op("Identity");
-      node->clear_attr();
-      (*node->mutable_attr())["T"].set_type(output_type);
-      *node->mutable_input(1) = AsControlDependency(node->input(1));
-      graph_modified_ = true;
-      continue;
     }
-    if (use_shape_info && IsSimplifiableReshape(*node, *properties)) {
-      DataType output_type = node->attr().at("T").type();
-      node->set_op("Identity");
-      node->clear_attr();
-      (*node->mutable_attr())["T"].set_type(output_type);
-      *node->mutable_input(1) = AsControlDependency(node->input(1));
-      graph_modified_ = true;
-      continue;
-    }
-
-    const bool is_mul = IsMul(*node) || IsLogicalAnd(*node);
-    const bool is_matmul = IsMatMul(*node);
-    const bool is_add = IsAdd(*node) || IsBiasAdd(*node) || IsLogicalOr(*node);
-    const bool is_sub = IsSub(*node);
-    const bool is_any_div = IsAnyDiv(*node);
-    // Simplify arithmetic operations with ones or zeros.
-    if (use_shape_info &&
-        (is_mul || is_matmul || is_add || is_sub || is_any_div) &&
-        properties->HasInputProperties(node->name()) &&
-        properties->HasOutputProperties(node->name())) {
-      const NodeDef* x = node_map_->GetNode(node->input(0));
-      const NodeDef* y = node_map_->GetNode(node->input(1));
-      if (x == nullptr || y == nullptr) {
-        return errors::InvalidArgument("Invalid inputs to node: ",
-                                       node->DebugString());
-      }
-      const TensorShapeProto& output_shape =
-          properties->GetOutputProperties(node->name())[0].shape();
-
-      // Simplify element-wise multiplication by ones or addition/subtraction
-      // of zeros.
-      const TensorShapeProto& y_shape =
-          properties->GetInputProperties(node->name())[1].shape();
-      const bool x_is_zero = IsZeros(*x);
-      const bool x_is_one = x_is_zero ? false : IsOnes(*x);
-      const bool y_matches_output_shape = ShapesEqual(output_shape, y_shape);
-      if (y_matches_output_shape &&
-          ((is_mul && x_is_one) || (is_add && x_is_zero))) {
-        // 1 * y = y or 0 + y = y.
-        ReplaceOperationWithSnapshot(1, *properties, node, optimized_graph);
-        continue;
+    Tensor false_t(DT_BOOL, TensorShape({}));
+    Tensor true_t(DT_BOOL, TensorShape({}));
+    // Make sure we don't proceed if this switch node was already optimized.
+    if (!already_optimized && SetTensorValue(DT_BOOL, true, &true_t).ok() &&
+        SetTensorValue(DT_BOOL, false, &false_t).ok()) {
+      // Copy the set of consumers of the switch as they will be manipulated
+      // below.
+      const std::set& consumer_set =
+          node_map_->GetOutputs(node->name());
+      std::vector consumers(consumer_set.begin(), consumer_set.end());
+      std::sort(consumers.begin(), consumers.end(),
+                [](const NodeDef* n1, const NodeDef* n2) {
+                  return n1->name() < n2->name();
+                });
+      // Create constant false & true nodes.
+      NodeDef* false_node = optimized_graph->add_node();
+      false_node->set_name(OptimizedNodeName(*node, "_const_false"));
+      if (!CreateNodeDef(false_node->name(), TensorValue(&false_t), false_node)
+               .ok()) {
+        return Status::OK();
       }
+      false_node->set_device(node->device());
 
-      if (y_matches_output_shape && (is_sub && x_is_zero)) {
-        // Replace 0 - y with Neg(y).
-        ReplaceSubtractionFromZeroByNegation(node, optimized_graph);
-        continue;
+      NodeDef* true_node = optimized_graph->add_node();
+      true_node->set_name(OptimizedNodeName(*node, "_const_true"));
+      if (!CreateNodeDef(true_node->name(), TensorValue(&true_t), true_node)
+               .ok()) {
+        return Status::OK();
       }
-
-      // Replace 1 / y with Reciprocal op.
-      if (y_matches_output_shape && is_any_div && x_is_one) {
-        DataType type = node->attr().at("T").type();
-        if (DataTypeIsFloating(type) || DataTypeIsComplex(type)) {
-          ReplaceDivisionOfOnesByReciprocal(node, optimized_graph);
-          continue;
+      true_node->set_device(node->device());
+
+      // Add controls from the switch ports to the constants, and connect the
+      // constants to the original switch outputs.
+      const string false_port = node->name();
+      const string true_port = strings::StrCat(node->name(), ":1");
+      const string false_ctrl_dep =
+          AddControlDependency(false_port, optimized_graph, node_map_.get());
+      false_node->add_input(false_ctrl_dep);
+      const string true_ctrl_dep =
+          AddControlDependency(true_port, optimized_graph, node_map_.get());
+      true_node->add_input(true_ctrl_dep);
+
+      node_map_->AddNode(false_node->name(), false_node);
+      node_map_->AddNode(true_node->name(), true_node);
+      node_map_->AddOutput(NodeName(false_ctrl_dep), false_node->name());
+      node_map_->AddOutput(NodeName(true_ctrl_dep), true_node->name());
+
+      for (NodeDef* consumer : consumers) {
+        for (int i = 0; i < consumer->input_size(); ++i) {
+          const string& input = consumer->input(i);
+          if (input == false_port) {
+            consumer->set_input(i, false_node->name());
+            node_map_->UpdateInput(consumer->name(), false_port,
+                                   false_node->name());
+          } else if (input == true_port) {
+            consumer->set_input(i, true_node->name());
+            node_map_->UpdateInput(consumer->name(), true_port,
+                                   true_node->name());
+          }
         }
       }
+      graph_modified_ = true;
+      return Status::OK();
+    }
+  }
+  if (IsSimplifiableReduction(*node)) {
+    // Replace the reduction node with an identity node, that can be further
+    // optimized by the model pruner.
+    DataType output_type;
+    if (node->attr().count("T") > 0) {
+      output_type = node->attr().at("T").type();
+    } else {
+      // This is an 'any' or 'all' reduction. The output is always boolean.
+      output_type = DT_BOOL;
+    }
+    node->set_op("Identity");
+    node->clear_attr();
+    (*node->mutable_attr())["T"].set_type(output_type);
+    *node->mutable_input(1) = AsControlDependency(node->input(1));
+    graph_modified_ = true;
+    return Status::OK();
+  }
+  if (use_shape_info && IsSimplifiableReshape(*node, *properties)) {
+    DataType output_type = node->attr().at("T").type();
+    node->set_op("Identity");
+    node->clear_attr();
+    (*node->mutable_attr())["T"].set_type(output_type);
+    *node->mutable_input(1) = AsControlDependency(node->input(1));
+    graph_modified_ = true;
+    return Status::OK();
+  }
 
-      const TensorShapeProto& x_shape =
-          properties->GetInputProperties(node->name())[0].shape();
-      const bool y_is_zero = IsZeros(*y);
-      const bool y_is_one = y_is_zero ? false : IsOnes(*y);
-      const bool x_matches_output_shape = ShapesEqual(output_shape, x_shape);
-      if (x_matches_output_shape && (((is_mul || is_any_div) && y_is_one) ||
-                                     ((is_add || is_sub) && y_is_zero))) {
-        // x * 1 = x or x / 1 = x or x +/- 0 = x
-        ReplaceOperationWithSnapshot(0, *properties, node, optimized_graph);
-        continue;
-      }
+  const bool is_mul = IsMul(*node) || IsLogicalAnd(*node);
+  const bool is_matmul = IsMatMul(*node);
+  const bool is_add = IsAdd(*node) || IsBiasAdd(*node) || IsLogicalOr(*node);
+  const bool is_sub = IsSub(*node);
+  const bool is_any_div = IsAnyDiv(*node);
+  // Simplify arithmetic operations with ones or zeros.
+  if (use_shape_info &&
+      (is_mul || is_matmul || is_add || is_sub || is_any_div) &&
+      properties->HasInputProperties(node->name()) &&
+      properties->HasOutputProperties(node->name())) {
+    const NodeDef* x = node_map_->GetNode(node->input(0));
+    const NodeDef* y = node_map_->GetNode(node->input(1));
+    if (x == nullptr || y == nullptr) {
+      return errors::InvalidArgument("Invalid inputs to node: ",
+                                     node->DebugString());
+    }
+    const TensorShapeProto& output_shape =
+        properties->GetOutputProperties(node->name())[0].shape();
+
+    // Simplify element-wise multiplication by ones or addition/subtraction
+    // of zeros.
+    const TensorShapeProto& y_shape =
+        properties->GetInputProperties(node->name())[1].shape();
+    const bool x_is_zero = IsZeros(*x);
+    const bool x_is_one = x_is_zero ? false : IsOnes(*x);
+    const bool y_matches_output_shape = ShapesEqual(output_shape, y_shape);
+    if (y_matches_output_shape &&
+        ((is_mul && x_is_one) || (is_add && x_is_zero))) {
+      // 1 * y = y or 0 + y = y.
+      ReplaceOperationWithSnapshot(1, *properties, node, optimized_graph);
+      return Status::OK();
+    }
 
-      // x OR true = true OR y = true.
-      const PartialTensorShape shp(output_shape);
-      if (shp.IsFullyDefined() && IsLogicalOr(*node) &&
-          (y_is_one || x_is_one)) {
-        TF_RETURN_IF_ERROR(ReplaceOperationWithConstant(
-            1, *properties, output_shape, node, optimized_graph));
-      }
-
-      // Simplify multiplication and matmul by zeros.
-      // Also optimize zeros divided by a tensor, but only if we are in
-      // aggressive mode, since we might get rid of divisions by zero.
-      bool optimize_zeros_divided_by_y =
-          is_any_div && x_is_zero && is_aggressive;
-      if ((x_is_zero || y_is_zero) &&
-          (is_mul || is_matmul || optimize_zeros_divided_by_y)) {
-        if (shp.IsFullyDefined()) {
-          TF_RETURN_IF_ERROR(ReplaceOperationWithConstant(
-              0, *properties, output_shape, node, optimized_graph));
-          continue;
-        }
-        // Even if an input shape is only partially known, we may known that it
-        // matches the output shape and thus forward the corresponding zero
-        // input.
-        if ((is_mul || is_any_div) && x_is_zero && x_matches_output_shape) {
-          ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
-          continue;
-        } else if (is_mul && y_is_zero && y_matches_output_shape) {
-          ReplaceOperationWithIdentity(1, *properties, node, optimized_graph);
-          continue;
-        }
-      }
+    if (y_matches_output_shape && (is_sub && x_is_zero)) {
+      // Replace 0 - y with Neg(y).
+      ReplaceSubtractionFromZeroByNegation(node, optimized_graph);
+      return Status::OK();
     }
 
-    // Strength reduce floating point division by a constant Div(x, const) to
-    // multiplication by the reciprocal Mul(x, Reciprocal(const)). This in turn
-    // will be constant folded to Mul(x, 1.0/const).
-    if (node->input_size() >= 2 && (IsRealDiv(*node) || IsDiv(*node))) {
-      const string& const_input = node->input(1);
-      const NodeDef* denom = node_map_->GetNode(const_input);
-      CHECK(denom != nullptr);
-      if (!IsReallyConstant(*denom)) {
-        continue;
-      }
-      if (node->attr().count("T") == 0) {
-        continue;
-      }
+    // Replace 1 / y with Reciprocal op.
+    if (y_matches_output_shape && is_any_div && x_is_one) {
       DataType type = node->attr().at("T").type();
-      if (IsDiv(*node) &&
-          !(DataTypeIsFloating(type) || DataTypeIsComplex(type))) {
-        continue;
+      if (DataTypeIsFloating(type) || DataTypeIsComplex(type)) {
+        ReplaceDivisionOfOnesByReciprocal(node, optimized_graph);
+        return Status::OK();
       }
-      // Insert new reciprocal op and change node from Div to Mul.
-      NodeDef* reciprocal_node = optimized_graph->add_node();
-      reciprocal_node->set_name(OptimizedNodeName(*node, "_recip"));
-      reciprocal_node->set_op("Reciprocal");
-      reciprocal_node->set_device(node->device());
-      node->set_op("Mul");
-      // Re-wire inputs and outputs.
-      reciprocal_node->add_input(const_input);
-      (*reciprocal_node->mutable_attr())["T"].set_type(type);
-      node->set_input(1, reciprocal_node->name());
-      node_map_->AddNode(reciprocal_node->name(), reciprocal_node);
-      node_map_->UpdateOutput(node->name(), const_input,
-                              reciprocal_node->name());
-      graph_modified_ = true;
-      continue;
     }
 
-    // Consider the transformation
-    //
-    //                      +                +       = parent
-    //                     / \              / \
-    //                    C   +    -- >    X   +     = children
-    //                       / \              / \
-    //                      X   Y            C   Y   = leaves
-    //
-    // where C is constant and X is non-constant, and '+' denotes an
-    // associative and commutative operator like addition or multiplication.
-    // This optimization pushes constants down in the tree to canonicalize it.
-    // Moreoever, in cases where the child node has a second constant input Y
-    // we will create a leaf node that can be folded, e.g.
-    //
-    //    Add(C1, Add(C2, X)) -> Add(X, Add(C1, C2)) -> Add(X, C1 + C2)
-    //
-    // TODO(rmlarsen): Handle non-associative/non-commutative operators like
-    // subtraction and division, as well as mixed subtraction/addition,
-    // division/multiplication.
-    // Don't touch BiasAdd since they can't handle vectors as their first
-    // inputs.
-    if (has_fetch_ && (IsAdd(*node) || is_mul) &&
-        NumNonControlInputs(*node) == 2) {
-      NodeDef* left_child = node_map_->GetNode(node->input(0));
-      NodeDef* right_child = node_map_->GetNode(node->input(1));
-      // One child must be constant, and the other the same op as the parent.
-      if (node->op() != left_child->op() && node->op() != right_child->op()) {
-        continue;
-      }
-      const bool left_child_is_constant = IsReallyConstant(*left_child);
-      const bool right_child_is_constant = IsReallyConstant(*right_child);
-      if (!left_child_is_constant && !right_child_is_constant) {
-        continue;
-      }
-      if (node->device() != left_child->device() ||
-          node->device() != right_child->device()) {
-        continue;
+    const TensorShapeProto& x_shape =
+        properties->GetInputProperties(node->name())[0].shape();
+    const bool y_is_zero = IsZeros(*y);
+    const bool y_is_one = y_is_zero ? false : IsOnes(*y);
+    const bool x_matches_output_shape = ShapesEqual(output_shape, x_shape);
+    if (x_matches_output_shape && (((is_mul || is_any_div) && y_is_one) ||
+                                   ((is_add || is_sub) && y_is_zero))) {
+      // x * 1 = x or x / 1 = x or x +/- 0 = x
+      ReplaceOperationWithSnapshot(0, *properties, node, optimized_graph);
+      return Status::OK();
+    }
+
+    // x OR true = true OR y = true.
+    const PartialTensorShape shp(output_shape);
+    if (shp.IsFullyDefined() && IsLogicalOr(*node) && (y_is_one || x_is_one)) {
+      TF_RETURN_IF_ERROR(ReplaceOperationWithConstant(
+          1, *properties, output_shape, node, optimized_graph));
+    }
+
+    // Simplify multiplication and matmul by zeros.
+    // Also optimize zeros divided by a tensor, but only if we are in
+    // aggressive mode, since we might get rid of divisions by zero.
+    bool optimize_zeros_divided_by_y = is_any_div && x_is_zero && is_aggressive;
+    if ((x_is_zero || y_is_zero) &&
+        (is_mul || is_matmul || optimize_zeros_divided_by_y)) {
+      if (shp.IsFullyDefined()) {
+        TF_RETURN_IF_ERROR(ReplaceOperationWithConstant(
+            0, *properties, output_shape, node, optimized_graph));
+        return Status::OK();
       }
-      NodeDef* op_child_node =
-          left_child_is_constant ? right_child : left_child;
-      NodeDef* const_child_node =
-          left_child_is_constant ? left_child : right_child;
-      // Make sure that it is safe to change the value of the child node->
-      if (op_child_node->input_size() < 2 ||
-          nodes_to_preserve_.find(op_child_node->name()) !=
-              nodes_to_preserve_.end() ||
-          NumNonControlOutputs(*op_child_node, *node_map_) > 1) {
-        continue;
+      // Even if an input shape is only partially known, we may known that it
+      // matches the output shape and thus forward the corresponding zero
+      // input.
+      if ((is_mul || is_any_div) && x_is_zero && x_matches_output_shape) {
+        ReplaceOperationWithIdentity(0, *properties, node, optimized_graph);
+        return Status::OK();
+      } else if (is_mul && y_is_zero && y_matches_output_shape) {
+        ReplaceOperationWithIdentity(1, *properties, node, optimized_graph);
+        return Status::OK();
       }
+    }
+  }
 
-      // Identify the nodes to swap.
-      NodeDef* left_leaf = node_map_->GetNode(op_child_node->input(0));
-      NodeDef* right_leaf = node_map_->GetNode(op_child_node->input(1));
-      const bool left_leaf_is_constant = IsReallyConstant(*left_leaf);
-      const bool right_leaf_is_constant = IsReallyConstant(*right_leaf);
-      if (left_leaf_is_constant && right_leaf_is_constant) {
-        // Child is already foldable, leave it alone.
-        continue;
-      }
-      const int non_const_leaf_input = left_leaf_is_constant ? 1 : 0;
-      const int parent_const_input = left_child_is_constant ? 0 : 1;
-      const auto& child_output = node_map_->GetOutputs(op_child_node->name());
-      if (child_output.find(const_child_node) != child_output.end()) {
-        // If there is a control edge from the child op to C, the transformation
-        // would create a cycle in the graph. We know that it must be a control
-        // edge. We can replace such a control edge with a control edge from A
-        // to C.
-        CHECK(MaybeRemoveControlInput(op_child_node->name(), const_child_node,
-                                      graph_, node_map_.get()));
-        NodeDef* other_leaf = left_leaf_is_constant ? left_leaf : right_leaf;
-        MaybeAddControlInput(other_leaf->name(), const_child_node, graph_,
-                             node_map_.get());
-      }
-
-      // Swap the constant child with a non-constant leaf node.
-      node_map_->UpdateInput(node->name(), node->input(parent_const_input),
-                             op_child_node->input(non_const_leaf_input));
-      node_map_->UpdateInput(op_child_node->name(),
-                             op_child_node->input(non_const_leaf_input),
-                             node->input(parent_const_input));
-      std::swap(*node->mutable_input(parent_const_input),
-                *op_child_node->mutable_input(non_const_leaf_input));
-      graph_modified_ = true;
-      continue;
+  // Strength reduce floating point division by a constant Div(x, const) to
+  // multiplication by the reciprocal Mul(x, Reciprocal(const)). This in turn
+  // will be constant folded to Mul(x, 1.0/const).
+  if (node->input_size() >= 2 && (IsRealDiv(*node) || IsDiv(*node))) {
+    const string& const_input = node->input(1);
+    const NodeDef* denom = node_map_->GetNode(const_input);
+    CHECK(denom != nullptr);
+    if (!IsReallyConstant(*denom)) {
+      return Status::OK();
     }
+    if (node->attr().count("T") == 0) {
+      return Status::OK();
+    }
+    DataType type = node->attr().at("T").type();
+    if (IsDiv(*node) &&
+        !(DataTypeIsFloating(type) || DataTypeIsComplex(type))) {
+      return Status::OK();
+    }
+    // Insert new reciprocal op and change node from Div to Mul.
+    NodeDef* reciprocal_node = optimized_graph->add_node();
+    reciprocal_node->set_name(OptimizedNodeName(*node, "_recip"));
+    reciprocal_node->set_op("Reciprocal");
+    reciprocal_node->set_device(node->device());
+    node->set_op("Mul");
+    // Re-wire inputs and outputs.
+    reciprocal_node->add_input(const_input);
+    (*reciprocal_node->mutable_attr())["T"].set_type(type);
+    node->set_input(1, reciprocal_node->name());
+    node_map_->AddNode(reciprocal_node->name(), reciprocal_node);
+    node_map_->UpdateOutput(node->name(), const_input, reciprocal_node->name());
+    graph_modified_ = true;
+    return Status::OK();
+  }
 
-    // Partial constant propagation through IdentityN.
-    if (IsIdentityN(*node) && NumNonControlInputs(*node) > 0) {
-      const std::set& tmp = node_map_->GetOutputs(node->name());
-      const std::vector consumers(tmp.begin(), tmp.end());
-      bool updated_graph = false;
-      for (int input_idx = 0; input_idx < node->input_size(); ++input_idx) {
-        const string& input = node->input(input_idx);
-        if (IsControlInput(input)) {
-          break;
-        }
-        const NodeDef* input_node = node_map_->GetNode(NodeName(input));
-        if (input_node == nullptr) {
-          LOG(ERROR) << "Bad input: " << input;
-          break;
-        }
-        // Forward constant inputs to outputs and add a control dependency on
-        // the IdentityN node.
-        if (IsReallyConstant(*input_node)) {
-          // Update each consumer.
-          for (NodeDef* consumer : consumers) {
-            bool add_dep = false;
-            for (int consumer_input_idx = 0;
-                 consumer_input_idx < consumer->input_size();
-                 ++consumer_input_idx) {
-              const string& consumer_input =
-                  consumer->input(consumer_input_idx);
-              if (IsControlInput(consumer_input)) {
-                break;
-              }
-              int output_idx;
-              const string input_node_name =
-                  ParseNodeName(consumer_input, &output_idx);
-              if (input_node_name == node->name() && output_idx == input_idx) {
-                consumer->set_input(consumer_input_idx, input);
-                // We will keep the input from IdentityN through a control
-                // dependency, so we only need to add the consumer as an output
-                // for the constant input node.
-                node_map_->AddOutput(NodeName(input), consumer->name());
-                add_dep = true;
-              }
+  // Consider the transformation
+  //
+  //                      +                +       = parent
+  //                     / \              / \
+  //                    C   +    -- >    X   +     = children
+  //                       / \              / \
+  //                      X   Y            C   Y   = leaves
+  //
+  // where C is constant and X is non-constant, and '+' denotes an
+  // associative and commutative operator like addition or multiplication.
+  // This optimization pushes constants down in the tree to canonicalize it.
+  // Moreoever, in cases where the child node has a second constant input Y
+  // we will create a leaf node that can be folded, e.g.
+  //
+  //    Add(C1, Add(C2, X)) -> Add(X, Add(C1, C2)) -> Add(X, C1 + C2)
+  //
+  // TODO(rmlarsen): Handle non-associative/non-commutative operators like
+  // subtraction and division, as well as mixed subtraction/addition,
+  // division/multiplication.
+  // Don't touch BiasAdd since they can't handle vectors as their first
+  // inputs.
+  if (has_fetch_ && (IsAdd(*node) || is_mul) &&
+      NumNonControlInputs(*node) == 2) {
+    NodeDef* left_child = node_map_->GetNode(node->input(0));
+    NodeDef* right_child = node_map_->GetNode(node->input(1));
+    // One child must be constant, and the other the same op as the parent.
+    if (node->op() != left_child->op() && node->op() != right_child->op()) {
+      return Status::OK();
+    }
+    const bool left_child_is_constant = IsReallyConstant(*left_child);
+    const bool right_child_is_constant = IsReallyConstant(*right_child);
+    if (!left_child_is_constant && !right_child_is_constant) {
+      return Status::OK();
+    }
+    if (node->device() != left_child->device() ||
+        node->device() != right_child->device()) {
+      return Status::OK();
+    }
+    NodeDef* op_child_node = left_child_is_constant ? right_child : left_child;
+    NodeDef* const_child_node =
+        left_child_is_constant ? left_child : right_child;
+    // Make sure that it is safe to change the value of the child node->
+    if (op_child_node->input_size() < 2 ||
+        nodes_to_preserve_.find(op_child_node->name()) !=
+            nodes_to_preserve_.end() ||
+        NumNonControlOutputs(*op_child_node, *node_map_) > 1) {
+      return Status::OK();
+    }
+
+    // Identify the nodes to swap.
+    NodeDef* left_leaf = node_map_->GetNode(op_child_node->input(0));
+    NodeDef* right_leaf = node_map_->GetNode(op_child_node->input(1));
+    const bool left_leaf_is_constant = IsReallyConstant(*left_leaf);
+    const bool right_leaf_is_constant = IsReallyConstant(*right_leaf);
+    if (left_leaf_is_constant && right_leaf_is_constant) {
+      // Child is already foldable, leave it alone.
+      return Status::OK();
+    }
+    const int non_const_leaf_input = left_leaf_is_constant ? 1 : 0;
+    const int parent_const_input = left_child_is_constant ? 0 : 1;
+    const auto& child_output = node_map_->GetOutputs(op_child_node->name());
+    if (child_output.find(const_child_node) != child_output.end()) {
+      // If there is a control edge from the child op to C, the transformation
+      // would create a cycle in the graph. We know that it must be a control
+      // edge. We can replace such a control edge with a control edge from A
+      // to C.
+      CHECK(MaybeRemoveControlInput(op_child_node->name(), const_child_node,
+                                    graph_, node_map_.get()));
+      NodeDef* other_leaf = left_leaf_is_constant ? left_leaf : right_leaf;
+      MaybeAddControlInput(other_leaf->name(), const_child_node, graph_,
+                           node_map_.get());
+    }
+
+    // Swap the constant child with a non-constant leaf node.
+    node_map_->UpdateInput(node->name(), node->input(parent_const_input),
+                           op_child_node->input(non_const_leaf_input));
+    node_map_->UpdateInput(op_child_node->name(),
+                           op_child_node->input(non_const_leaf_input),
+                           node->input(parent_const_input));
+    std::swap(*node->mutable_input(parent_const_input),
+              *op_child_node->mutable_input(non_const_leaf_input));
+    graph_modified_ = true;
+    return Status::OK();
+  }
+
+  // Partial constant propagation through IdentityN.
+  if (IsIdentityN(*node) && NumNonControlInputs(*node) > 0) {
+    const std::set& tmp = node_map_->GetOutputs(node->name());
+    const std::vector consumers(tmp.begin(), tmp.end());
+    bool updated_graph = false;
+    for (int input_idx = 0; input_idx < node->input_size(); ++input_idx) {
+      const string& input = node->input(input_idx);
+      if (IsControlInput(input)) {
+        break;
+      }
+      const NodeDef* input_node = node_map_->GetNode(NodeName(input));
+      if (input_node == nullptr) {
+        LOG(ERROR) << "Bad input: " << input;
+        break;
+      }
+      // Forward constant inputs to outputs and add a control dependency on
+      // the IdentityN node.
+      if (IsReallyConstant(*input_node)) {
+        // Update each consumer.
+        for (NodeDef* consumer : consumers) {
+          bool add_dep = false;
+          for (int consumer_input_idx = 0;
+               consumer_input_idx < consumer->input_size();
+               ++consumer_input_idx) {
+            const string& consumer_input = consumer->input(consumer_input_idx);
+            if (IsControlInput(consumer_input)) {
+              break;
             }
-            if (add_dep) {
-              consumer->add_input(AsControlDependency(node->name()));
-              updated_graph = true;
+            int output_idx;
+            const string input_node_name =
+                ParseNodeName(consumer_input, &output_idx);
+            if (input_node_name == node->name() && output_idx == input_idx) {
+              consumer->set_input(consumer_input_idx, input);
+              // We will keep the input from IdentityN through a control
+              // dependency, so we only need to add the consumer as an output
+              // for the constant input node.
+              node_map_->AddOutput(NodeName(input), consumer->name());
+              add_dep = true;
             }
           }
+          if (add_dep) {
+            consumer->add_input(AsControlDependency(node->name()));
+            updated_graph = true;
+          }
         }
       }
-
-      if (updated_graph) {
-        for (NodeDef* consumer : consumers) {
-          DedupControlInputs(consumer);
-        }
-        graph_modified_ = true;
-        continue;
-      }
     }
 
-    if (PartialAssocOpConstFolding(optimized_graph, properties, node)) {
+    if (updated_graph) {
+      for (NodeDef* consumer : consumers) {
+        DedupControlInputs(consumer);
+      }
       graph_modified_ = true;
-      continue;
+      return Status::OK();
     }
+  }
 
-    if (PartialConcatConstFolding(optimized_graph, properties, node)) {
-      graph_modified_ = true;
-      continue;
-    }
+  if (PartialAssocOpConstFolding(optimized_graph, properties, node)) {
+    graph_modified_ = true;
+    return Status::OK();
+  }
+
+  if (PartialConcatConstFolding(optimized_graph, properties, node)) {
+    graph_modified_ = true;
+    return Status::OK();
   }
 
   return Status::OK();
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index 1c698ee6f4bffd..7aad3a6ae1da35 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -97,6 +97,8 @@ class ConstantFolding : public GraphOptimizer {
                              const GraphProperties& properties) const;
   Status SimplifyGraph(GraphDef* output, GraphProperties* properties,
                        bool use_shape_info);
+  Status SimplifyNode(NodeDef* node, GraphDef* optimized_graph,
+                      GraphProperties* properties, bool use_shape_info);
 
   Status RunOptimizationPass(Cluster* cluster, const GrapplerItem& item,
                              GraphDef* output);

From f1d31a2d5eba253f6c9ade5a2cae2b6b84d7236a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 12:37:29 -0700
Subject: [PATCH 0626/1691] DT_TEXTREL set by -Wl,-z,notext is incompatible
 with indirect functions (IFUNC). NVFlex.o in cuda_9_0/lib64/libculibos.a has
 buggy .eh_frame, which overlaps with .rela.rodata R_X86_64_PC32 relocations
 and makes it not able to be linked with LLD.

PiperOrigin-RevId: 196155873
---
 tensorflow/tensorflow.bzl | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index b2cec7655faad6..4bfd8f5721454a 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -959,15 +959,6 @@ def tf_cuda_library(deps=None, cuda_deps=None, copts=tf_copts(), **kwargs):
   if not cuda_deps:
     cuda_deps = []
 
-  if 'linkstatic' not in kwargs or kwargs['linkstatic'] != 1:
-    enable_text_relocation_linkopt = select({
-          clean_dep("//tensorflow:darwin"): [],
-          clean_dep("//tensorflow:windows"): [],
-          "//conditions:default": ['-Wl,-z,notext'],})
-    if 'linkopts' in kwargs:
-      kwargs['linkopts'] += enable_text_relocation_linkopt
-    else:
-      kwargs['linkopts'] = enable_text_relocation_linkopt
   native.cc_library(
       deps=deps + if_cuda(cuda_deps + [
           clean_dep("//tensorflow/core:cuda"),

From d0f396bb89d9d02f51c0a6e3ad17dd08ae9b8cd4 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" 
Date: Thu, 10 May 2018 12:38:21 -0700
Subject: [PATCH 0627/1691] BUGFIX: correctly propagate dtype in
 distributions.special_math.

PiperOrigin-RevId: 196155994
---
 .../distributions/special_math_test.py        | 160 ++++++++++--------
 .../python/ops/distributions/special_math.py  |  45 ++---
 2 files changed, 113 insertions(+), 92 deletions(-)

diff --git a/tensorflow/python/kernel_tests/distributions/special_math_test.py b/tensorflow/python/kernel_tests/distributions/special_math_test.py
index 2d434a39c29338..d5d50a180a1df6 100644
--- a/tensorflow/python/kernel_tests/distributions/special_math_test.py
+++ b/tensorflow/python/kernel_tests/distributions/special_math_test.py
@@ -23,11 +23,14 @@
 
 import numpy as np
 
+from tensorflow.python.eager import backprop as tfe_backprop
+from tensorflow.python.eager import context as tfe_context
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import gradients_impl
-from tensorflow.python.ops import variables
 from tensorflow.python.ops.distributions import special_math
 from tensorflow.python.platform import test
 from tensorflow.python.platform import tf_logging
@@ -64,6 +67,16 @@ def _make_grid(dtype, grid_spec):
   return np.reshape(grid, grid_spec.shape)
 
 
+def _value_and_gradient(fn, *args):
+  """Calls `fn` and computes the gradient of the result wrt `arg`."""
+  if tfe_context.executing_eagerly():
+    v, g = tfe_backprop.val_and_grad_function(fn)(args)
+  else:
+    v = fn(*args)
+    g = gradients_impl.gradients(v, args)
+  return v, g
+
+
 GridSpec = collections.namedtuple("GridSpec", ["min", "max", "shape"])
 
 ErrorSpec = collections.namedtuple("ErrorSpec", ["rtol", "atol"])
@@ -71,11 +84,12 @@ def _make_grid(dtype, grid_spec):
 
 class NdtriTest(test.TestCase):
 
-  def assertAllFinite(self, tensor):
-    is_finite = np.isfinite(tensor.eval())
+  def assertAllFinite(self, x):
+    is_finite = np.isfinite(x)
     all_true = np.ones_like(is_finite, dtype=np.bool)
     self.assertAllEqual(all_true, is_finite)
 
+  @test_util.run_in_graph_and_eager_modes()
   def testNdtri(self):
     """Verifies that ndtri computation is correct."""
     with self.test_session():
@@ -89,7 +103,7 @@ def testNdtri(self):
                      np.exp(-2), 1. - np.exp(-2)))
       expected_x = special.ndtri(p)
       x = special_math.ndtri(p)
-      self.assertAllClose(expected_x, x.eval(), atol=0.)
+      self.assertAllClose(expected_x, self.evaluate(x), atol=0.)
 
   def testNdtriDynamicShape(self):
     """Verifies that ndtri computation is correct."""
@@ -108,23 +122,27 @@ def testNdtriDynamicShape(self):
 
   def _baseNdtriFiniteGradientTest(self, dtype):
     """Verifies that ndtri has finite gradients at interesting points."""
-    g = ops.Graph()
-    with g.as_default():
-      # Tests gradients at 0, 1, and piece-wise boundaries.
-      p = variables.Variable(
-          np.array([0.,
-                    np.exp(-32.), np.exp(-2.),
-                    1. - np.exp(-2.), 1. - np.exp(-32.),
-                    1.]).astype(dtype))
-    value = special_math.ndtri(p)
-    grads = gradients_impl.gradients(value, p)
-    with self.test_session(graph=g):
-      variables.global_variables_initializer().run()
-      self.assertAllFinite(grads[0])
-
+    # Tests gradients at 0, 1, and piece-wise boundaries.
+    p = constant_op.constant(
+        np.array([
+            0.,
+            np.exp(-32.),
+            np.exp(-2.),
+            1. - np.exp(-2.),
+            1. - np.exp(-32.),
+            1.,
+        ]).astype(dtype))
+    # Not having the lambda sanitzer means we'd get an `IndexError` whenever
+    # the user supplied function has default args.
+    _, grads = _value_and_gradient(
+        lambda x: special_math.ndtri(x), p)  # pylint: disable=unnecessary-lambda
+    self.assertAllFinite(self.evaluate(grads[0]))
+
+  @test_util.run_in_graph_and_eager_modes()
   def testNdtriFiniteGradientFloat32(self):
     self._baseNdtriFiniteGradientTest(np.float32)
 
+  @test_util.run_in_graph_and_eager_modes()
   def testNdtriFiniteGradientFloat64(self):
     self._baseNdtriFiniteGradientTest(np.float64)
 
@@ -147,55 +165,53 @@ def _test_grid_log(self, dtype, grid_spec, error_spec):
     if not special:
       return
 
-    with self.test_session():
-      grid = _make_grid(dtype, grid_spec)
-      actual = sm.log_ndtr(grid).eval()
-
-      # Basic tests.
-      # isfinite checks for NaN and Inf.
-      self.assertTrue(np.isfinite(actual).all())
-      # On the grid, -inf < log_cdf(x) < 0.  In this case, we should be able
-      # to use a huge grid because we have used tricks to escape numerical
-      # difficulties.
-      self.assertTrue((actual < 0).all())
-      _check_strictly_increasing(actual)
-
-      # Versus scipy.
-      expected = special.log_ndtr(grid)
-      # Scipy prematurely goes to zero at some places that we don't.  So don't
-      # include these in the comparison.
-      self.assertAllClose(
-          expected.astype(np.float64)[expected < 0],
-          actual.astype(np.float64)[expected < 0],
-          rtol=error_spec.rtol,
-          atol=error_spec.atol)
+    grid = _make_grid(dtype, grid_spec)
+    actual = self.evaluate(sm.log_ndtr(grid))
+
+    # Basic tests.
+    # isfinite checks for NaN and Inf.
+    self.assertTrue(np.isfinite(actual).all())
+    # On the grid, -inf < log_cdf(x) < 0.  In this case, we should be able
+    # to use a huge grid because we have used tricks to escape numerical
+    # difficulties.
+    self.assertTrue((actual < 0).all())
+    _check_strictly_increasing(actual)
+
+    # Versus scipy.
+    expected = special.log_ndtr(grid)
+    # Scipy prematurely goes to zero at some places that we don't.  So don't
+    # include these in the comparison.
+    self.assertAllClose(
+        expected.astype(np.float64)[expected < 0],
+        actual.astype(np.float64)[expected < 0],
+        rtol=error_spec.rtol,
+        atol=error_spec.atol)
 
   def _test_grid_no_log(self, dtype, grid_spec, error_spec):
     if not special:
       return
 
-    with self.test_session():
-      grid = _make_grid(dtype, grid_spec)
-      actual = sm.ndtr(grid).eval()
-
-      # Basic tests.
-      # isfinite checks for NaN and Inf.
-      self.assertTrue(np.isfinite(actual).all())
-      # On the grid, 0 < cdf(x) < 1.  The grid cannot contain everything due
-      # to numerical limitations of cdf.
-      self.assertTrue((actual > 0).all())
-      self.assertTrue((actual < 1).all())
-      _check_strictly_increasing(actual)
-
-      # Versus scipy.
-      expected = special.ndtr(grid)
-      # Scipy prematurely goes to zero at some places that we don't.  So don't
-      # include these in the comparison.
-      self.assertAllClose(
-          expected.astype(np.float64)[expected < 0],
-          actual.astype(np.float64)[expected < 0],
-          rtol=error_spec.rtol,
-          atol=error_spec.atol)
+    grid = _make_grid(dtype, grid_spec)
+    actual = self.evaluate(sm.ndtr(grid))
+
+    # Basic tests.
+    # isfinite checks for NaN and Inf.
+    self.assertTrue(np.isfinite(actual).all())
+    # On the grid, 0 < cdf(x) < 1.  The grid cannot contain everything due
+    # to numerical limitations of cdf.
+    self.assertTrue((actual > 0).all())
+    self.assertTrue((actual < 1).all())
+    _check_strictly_increasing(actual)
+
+    # Versus scipy.
+    expected = special.ndtr(grid)
+    # Scipy prematurely goes to zero at some places that we don't.  So don't
+    # include these in the comparison.
+    self.assertAllClose(
+        expected.astype(np.float64)[expected < 0],
+        actual.astype(np.float64)[expected < 0],
+        rtol=error_spec.rtol,
+        atol=error_spec.atol)
 
   def test_float32(self):
     self._test_grid(np.float32, self._grid32, self._error32)
@@ -254,14 +270,17 @@ def assert_all_false(self, v):
     self.assertAllEqual(np.zeros_like(v, dtype=np.bool), v)
 
   def _test_grad_finite(self, dtype):
-    with self.test_session():
-      x = variables.Variable([-100., 0., 100.], dtype=dtype)
-      output = (sm.log_ndtr(x) if self._use_log else sm.ndtr(x))
-      grad_output = gradients_impl.gradients(output, x)
-      variables.global_variables_initializer().run()
-      # isfinite checks for NaN and Inf.
-      self.assert_all_true(np.isfinite(output.eval()))
-      self.assert_all_true(np.isfinite(grad_output[0].eval()))
+    x = constant_op.constant([-100., 0., 100.], dtype=dtype)
+    output = (sm.log_ndtr(x) if self._use_log else sm.ndtr(x))
+    fn = sm.log_ndtr if self._use_log else sm.ndtr
+    # Not having the lambda sanitzer means we'd get an `IndexError` whenever
+    # the user supplied function has default args.
+    output, grad_output = _value_and_gradient(
+        lambda x_: fn(x_), x)  # pylint: disable=unnecessary-lambda
+    # isfinite checks for NaN and Inf.
+    output_, grad_output_ = self.evaluate([output, grad_output])
+    self.assert_all_true(np.isfinite(output_))
+    self.assert_all_true(np.isfinite(grad_output_[0]))
 
   def _test_grad_accuracy(self, dtype, grid_spec, error_spec):
     raw_grid = _make_grid(dtype, grid_spec)
@@ -357,7 +376,6 @@ def testErfInvIntegerInput(self):
         special_math.erfinv(x)
 
 
-
 class LogCDFLaplaceTest(test.TestCase):
   # Note that scipy.stats.laplace does not have a stable Log CDF, so we cannot
   # rely on scipy to cross check the extreme values.
diff --git a/tensorflow/python/ops/distributions/special_math.py b/tensorflow/python/ops/distributions/special_math.py
index 1d605c5dfcca9b..d1ee04dd1f7a3c 100644
--- a/tensorflow/python/ops/distributions/special_math.py
+++ b/tensorflow/python/ops/distributions/special_math.py
@@ -18,7 +18,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import math
 import numpy as np
 
 from tensorflow.python.framework import constant_op
@@ -42,15 +41,15 @@
 # then made more conservative just to be safe. (Conservative means use the
 # expansion more than we probably need to.) See `NdtrTest` in
 # special_math_test.py.
-LOGNDTR_FLOAT64_LOWER = -20
-LOGNDTR_FLOAT32_LOWER = -10
+LOGNDTR_FLOAT64_LOWER = np.array(-20, np.float64)
+LOGNDTR_FLOAT32_LOWER = np.array(-10, np.float32)
 
 # Upper bound values were chosen by examining for which values of 'x'
 # Log[cdf(x)] is 0, after which point we need to use the approximation
 # Log[cdf(x)] = Log[1 - cdf(-x)] approx -cdf(-x). We chose a value slightly
 # conservative, meaning we use the approximation earlier than needed.
-LOGNDTR_FLOAT64_UPPER = 8
-LOGNDTR_FLOAT32_UPPER = 5
+LOGNDTR_FLOAT64_UPPER = np.array(8, np.float64)
+LOGNDTR_FLOAT32_UPPER = np.array(5, np.float32)
 
 
 def ndtr(x, name="ndtr"):
@@ -91,7 +90,7 @@ def ndtr(x, name="ndtr"):
 def _ndtr(x):
   """Implements ndtr core logic."""
   half_sqrt_2 = constant_op.constant(
-      0.5 * math.sqrt(2.), dtype=x.dtype, name="half_sqrt_2")
+      0.5 * np.sqrt(2.), dtype=x.dtype, name="half_sqrt_2")
   w = x * half_sqrt_2
   z = math_ops.abs(w)
   y = array_ops.where(math_ops.less(z, half_sqrt_2),
@@ -190,18 +189,18 @@ def _ndtri(p):
 
   def _create_polynomial(var, coeffs):
     """Compute n_th order polynomial via Horner's method."""
-    if not coeffs:
-      return 0.
+    coeffs = np.array(coeffs, var.dtype.as_numpy_dtype)
+    if not coeffs.size:
+      return array_ops.zeros_like(var)
     return coeffs[0] + _create_polynomial(var, coeffs[1:]) * var
 
-  maybe_complement_p = array_ops.where(p > 1. - np.exp(-2.), 1. - p, p)
+  maybe_complement_p = array_ops.where(p > -np.expm1(-2.), 1. - p, p)
   # Write in an arbitrary value in place of 0 for p since 0 will cause NaNs
   # later on. The result from the computation when p == 0 is not used so any
   # number that doesn't result in NaNs is fine.
-  one_half = constant_op.constant(0.5, dtype=p.dtype)
   sanitized_mcp = array_ops.where(
       maybe_complement_p <= 0.,
-      array_ops.fill(array_ops.shape(p), one_half),
+      array_ops.fill(array_ops.shape(p), np.array(0.5, p.dtype.as_numpy_dtype)),
       maybe_complement_p)
 
   # Compute x for p > exp(-2): x/sqrt(2pi) = w + w**3 P0(w**2)/Q0(w**2).
@@ -216,10 +215,12 @@ def _create_polynomial(var, coeffs):
   # arrays based on whether p < exp(-32).
   z = math_ops.sqrt(-2. * math_ops.log(sanitized_mcp))
   first_term = z - math_ops.log(z) / z
-  second_term_small_p = (_create_polynomial(1. / z, p2)
-                         / _create_polynomial(1. / z, q2)) / z
-  second_term_otherwise = (_create_polynomial(1. / z, p1)
-                           / _create_polynomial(1. / z, q1)) / z
+  second_term_small_p = (
+      _create_polynomial(math_ops.reciprocal(z), p2) /
+      _create_polynomial(math_ops.reciprocal(z), q2) / z)
+  second_term_otherwise = (
+      _create_polynomial(math_ops.reciprocal(z), p1) /
+      _create_polynomial(math_ops.reciprocal(z), q1) / z)
   x_for_small_p = first_term - second_term_small_p
   x_otherwise = first_term - second_term_otherwise
 
@@ -330,23 +331,25 @@ def _log_ndtr_lower(x, series_order):
   """Asymptotic expansion version of `Log[cdf(x)]`, appropriate for `x<<-1`."""
   x_2 = math_ops.square(x)
   # Log of the term multiplying (1 + sum)
-  log_scale = -0.5 * x_2 - math_ops.log(-x) - 0.5 * math.log(2. * math.pi)
+  log_scale = -0.5 * x_2 - math_ops.log(-x) - 0.5 * np.log(2. * np.pi)
   return log_scale + math_ops.log(_log_ndtr_asymptotic_series(x, series_order))
 
 
 def _log_ndtr_asymptotic_series(x, series_order):
   """Calculates the asymptotic series used in log_ndtr."""
+  dtype = x.dtype.as_numpy_dtype
   if series_order <= 0:
-    return 1.
+    return np.array(1, dtype)
   x_2 = math_ops.square(x)
-  even_sum = 0.
-  odd_sum = 0.
+  even_sum = array_ops.zeros_like(x)
+  odd_sum = array_ops.zeros_like(x)
   x_2n = x_2  # Start with x^{2*1} = x^{2*n} with n = 1.
   for n in range(1, series_order + 1):
+    y = np.array(_double_factorial(2 * n - 1), dtype) / x_2n
     if n % 2:
-      odd_sum += _double_factorial(2 * n - 1) / x_2n
+      odd_sum += y
     else:
-      even_sum += _double_factorial(2 * n - 1) / x_2n
+      even_sum += y
     x_2n *= x_2
   return 1. + even_sum - odd_sum
 

From 9c5aaf325bac0b0e180e3b1fe1ed81a88ef2fd55 Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Thu, 10 May 2018 12:38:27 -0700
Subject: [PATCH 0628/1691] Make FlatSet and FlatMap movable.

PiperOrigin-RevId: 196156010
---
 tensorflow/core/lib/gtl/flatmap.h       | 11 +++++++++++
 tensorflow/core/lib/gtl/flatmap_test.cc | 26 +++++++++++++++++++------
 tensorflow/core/lib/gtl/flatrep.h       | 21 +++++++++++++++++++-
 tensorflow/core/lib/gtl/flatset.h       | 11 +++++++++++
 tensorflow/core/lib/gtl/flatset_test.cc | 20 ++++++++++++++++---
 5 files changed, 79 insertions(+), 10 deletions(-)

diff --git a/tensorflow/core/lib/gtl/flatmap.h b/tensorflow/core/lib/gtl/flatmap.h
index 889d2ddaa6be36..9dc439c163733c 100644
--- a/tensorflow/core/lib/gtl/flatmap.h
+++ b/tensorflow/core/lib/gtl/flatmap.h
@@ -76,6 +76,10 @@ class FlatMap {
 
   FlatMap(const FlatMap& src) : rep_(src.rep_) {}
 
+  // Move constructor leaves src in a valid but unspecified state (same as
+  // std::unordered_map).
+  FlatMap(FlatMap&& src) : rep_(std::move(src.rep_)) {}
+
   template 
   FlatMap(InputIter first, InputIter last, size_t N = 1,
           const Hash& hf = Hash(), const Eq& eq = Eq())
@@ -92,6 +96,13 @@ class FlatMap {
     return *this;
   }
 
+  // Move-assignment operator leaves src in a valid but unspecified state (same
+  // as std::unordered_map).
+  FlatMap& operator=(FlatMap&& src) {
+    rep_.MoveFrom(std::move(src.rep_));
+    return *this;
+  }
+
   ~FlatMap() {}
 
   void swap(FlatMap& x) { rep_.swap(x.rep_); }
diff --git a/tensorflow/core/lib/gtl/flatmap_test.cc b/tensorflow/core/lib/gtl/flatmap_test.cc
index 0901eba9265a48..0fd22ab37be6be 100644
--- a/tensorflow/core/lib/gtl/flatmap_test.cc
+++ b/tensorflow/core/lib/gtl/flatmap_test.cc
@@ -656,19 +656,33 @@ TEST(FlatMap, UniqueMap) {
   }
   EXPECT_EQ(map.size(), N);
 
+  // move constructor
+  UniqMap map2(std::move(map));
+
   // Lookups
   for (int i = 0; i < N; i++) {
-    EXPECT_EQ(*map.at(MakeUniq(i)), i + 100);
+    EXPECT_EQ(*map2.at(MakeUniq(i)), i + 100);
   }
 
+  // move assignment
+  UniqMap map3;
+  map3 = std::move(map2);
+
   // find+erase
-  EXPECT_EQ(map.count(MakeUniq(2)), 1);
-  map.erase(MakeUniq(2));
-  EXPECT_EQ(map.count(MakeUniq(2)), 0);
+  EXPECT_EQ(map3.count(MakeUniq(2)), 1);
+  map3.erase(MakeUniq(2));
+  EXPECT_EQ(map3.count(MakeUniq(2)), 0);
 
   // clear
-  map.clear();
-  EXPECT_EQ(map.size(), 0);
+  map3.clear();
+  EXPECT_EQ(map3.size(), 0);
+
+  // Check that moved-from maps are in a valid (though unspecified) state.
+  EXPECT_GE(map.size(), 0);
+  EXPECT_GE(map2.size(), 0);
+  // This insert should succeed no matter what state `map` is in, because
+  // MakeUniq(-1) is never called above: This key can't possibly exist.
+  EXPECT_TRUE(map.emplace(MakeUniq(-1), MakeUniq(-1)).second);
 }
 
 TEST(FlatMap, UniqueMapIter) {
diff --git a/tensorflow/core/lib/gtl/flatrep.h b/tensorflow/core/lib/gtl/flatrep.h
index 0d7e7487fc3335..65a076b0f39d5e 100644
--- a/tensorflow/core/lib/gtl/flatrep.h
+++ b/tensorflow/core/lib/gtl/flatrep.h
@@ -51,10 +51,23 @@ class FlatRep {
   FlatRep(size_t N, const Hash& hf, const Eq& eq) : hash_(hf), equal_(eq) {
     Init(N);
   }
-  explicit FlatRep(const FlatRep& src) : hash_(src.hash_), equal_(src.equal_) {
+  FlatRep(const FlatRep& src) : hash_(src.hash_), equal_(src.equal_) {
     Init(src.size());
     CopyEntries(src.array_, src.end_, CopyEntry());
   }
+
+  FlatRep(FlatRep&& src)
+      // Copy rather than move src.hash_ and src.equal_.  This is necessary to
+      // leave src in a valid state -- otherwise e.g. if hash_ is an
+      // std::function, moving it would null it out.
+      : hash_(src.hash_), equal_(src.equal_) {
+    // TODO(jlebar): Init(1) still allocates some memory, so this isn't as cheap
+    // as it could be.  The fundamental problem is that we need to leave src in
+    // a valid state, and FlatRep *always* owns a nonzero amount of memory.
+    Init(1);
+    swap(src);
+  }
+
   ~FlatRep() {
     clear_no_resize();
     delete[] array_;
@@ -78,6 +91,12 @@ class FlatRep {
     }
   }
 
+  void MoveFrom(FlatRep&& src) {
+    if (this != &src) {
+      swap(src);
+    }
+  }
+
   void clear_no_resize() {
     for (Bucket* b = array_; b != end_; b++) {
       for (uint32 i = 0; i < kWidth; i++) {
diff --git a/tensorflow/core/lib/gtl/flatset.h b/tensorflow/core/lib/gtl/flatset.h
index f31e3abe411588..311b7abe4da8ba 100644
--- a/tensorflow/core/lib/gtl/flatset.h
+++ b/tensorflow/core/lib/gtl/flatset.h
@@ -59,6 +59,10 @@ class FlatSet {
 
   FlatSet(const FlatSet& src) : rep_(src.rep_) {}
 
+  // Move constructor leaves src in a valid but unspecified state (same as
+  // std::unordered_set).
+  FlatSet(FlatSet&& src) : rep_(std::move(src.rep_)) {}
+
   template 
   FlatSet(InputIter first, InputIter last, size_t N = 1,
           const Hash& hf = Hash(), const Eq& eq = Eq())
@@ -75,6 +79,13 @@ class FlatSet {
     return *this;
   }
 
+  // Move-assignment operator leaves src in a valid but unspecified state (same
+  // as std::unordered_set).
+  FlatSet& operator=(FlatSet&& src) {
+    rep_.MoveFrom(std::move(src.rep_));
+    return *this;
+  }
+
   ~FlatSet() {}
 
   void swap(FlatSet& x) { rep_.swap(x.rep_); }
diff --git a/tensorflow/core/lib/gtl/flatset_test.cc b/tensorflow/core/lib/gtl/flatset_test.cc
index 010b4bb5df3337..8f8a9535680922 100644
--- a/tensorflow/core/lib/gtl/flatset_test.cc
+++ b/tensorflow/core/lib/gtl/flatset_test.cc
@@ -552,18 +552,32 @@ TEST(FlatSet, UniqueSet) {
   }
   EXPECT_EQ(set.size(), N);
 
+  // Move constructor
+  UniqSet set2(std::move(set));
+
   // Lookups
   for (int i = 0; i < N; i++) {
-    EXPECT_EQ(set.count(MakeUniq(i)), 1);
+    EXPECT_EQ(set2.count(MakeUniq(i)), 1);
   }
 
+  // Move-assignment operator
+  UniqSet set3;
+  set3 = std::move(set2);
+
   // erase
-  set.erase(MakeUniq(2));
-  EXPECT_EQ(set.count(MakeUniq(2)), 0);
+  set3.erase(MakeUniq(2));
+  EXPECT_EQ(set3.count(MakeUniq(2)), 0);
 
   // clear
   set.clear();
   EXPECT_EQ(set.size(), 0);
+
+  // Check that moved-from sets are in a valid (though unspecified) state.
+  EXPECT_GE(set.size(), 0);
+  EXPECT_GE(set2.size(), 0);
+  // This insert should succeed no matter what state `set` is in, because
+  // MakeUniq(-1) is never called above: This key can't possibly exist.
+  EXPECT_TRUE(set.emplace(MakeUniq(-1)).second);
 }
 
 TEST(FlatSet, UniqueSetIter) {

From 2a9eef3836c71a595c5c86645d54ff74ea3c1812 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 12:46:29 -0700
Subject: [PATCH 0629/1691] Fix a bug about getting arguments of partial
 functions.

PiperOrigin-RevId: 196157095
---
 tensorflow/contrib/learn/python/learn/estimators/head.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py
index e28e6854a5097d..339c4e0e360ed9 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/head.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/head.py
@@ -1862,12 +1862,12 @@ def _get_arguments(func):
   if hasattr(func, "__code__"):
     # Regular function.
     return tf_inspect.getargspec(func)
-  elif hasattr(func, "__call__"):
-    # Callable object.
-    return _get_arguments(func.__call__)
   elif hasattr(func, "func"):
     # Partial function.
     return _get_arguments(func.func)
+  elif hasattr(func, "__call__"):
+    # Callable object.
+    return _get_arguments(func.__call__)
 
 
 def _verify_loss_fn_args(loss_fn):

From 9f09b0a34850d1a41896fc067a229e5c6c8649b7 Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Thu, 10 May 2018 13:28:33 -0700
Subject: [PATCH 0630/1691] Add missing FlatSet::insert(Key&&) overload.

PiperOrigin-RevId: 196162544
---
 tensorflow/core/lib/gtl/flatset.h       | 6 ++++--
 tensorflow/core/lib/gtl/flatset_test.cc | 6 ++++++
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/lib/gtl/flatset.h b/tensorflow/core/lib/gtl/flatset.h
index 311b7abe4da8ba..bb4356e46de194 100644
--- a/tensorflow/core/lib/gtl/flatset.h
+++ b/tensorflow/core/lib/gtl/flatset.h
@@ -180,6 +180,7 @@ class FlatSet {
   }
 
   std::pair insert(const Key& k) { return Insert(k); }
+  std::pair insert(Key&& k) { return Insert(std::move(k)); }
   template 
   void insert(InputIter first, InputIter last) {
     for (; first != last; ++first) {
@@ -276,9 +277,10 @@ class FlatSet {
     }
   };
 
-  std::pair Insert(const Key& k) {
+  template 
+  std::pair Insert(K&& k) {
     rep_.MaybeResize();
-    auto r = rep_.FindOrInsert(k);
+    auto r = rep_.FindOrInsert(std::forward(k));
     const bool inserted = !r.found;
     return {iterator(r.b, rep_.limit(), r.index), inserted};
   }
diff --git a/tensorflow/core/lib/gtl/flatset_test.cc b/tensorflow/core/lib/gtl/flatset_test.cc
index 8f8a9535680922..7f0138404f131e 100644
--- a/tensorflow/core/lib/gtl/flatset_test.cc
+++ b/tensorflow/core/lib/gtl/flatset_test.cc
@@ -593,6 +593,12 @@ TEST(FlatSet, UniqueSetIter) {
   EXPECT_EQ(sum, (kCount * (kCount + 1)) / 2);
 }
 
+TEST(FlatSet, InsertUncopyable) {
+  UniqSet set;
+  EXPECT_TRUE(set.insert(MakeUniq(0)).second);
+  EXPECT_EQ(set.size(), 1);
+}
+
 /* This would be a good negative compilation test, if we could do that.
 
 TEST(FlatSet, MutableIterator_ShouldNotCompile) {

From e991d614aa148d24e0ae73c4da21c5ddd6597e23 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 13:53:29 -0700
Subject: [PATCH 0631/1691] Optimizations to DepthwiseConv

PiperOrigin-RevId: 196166118
---
 .../depthwiseconv_uint8_3x3_filter.h          | 6033 +++++------------
 1 file changed, 1653 insertions(+), 4380 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h
index 55e0d5c3aa9ebb..4834103241840e 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h
@@ -25,4386 +25,1631 @@ namespace optimized_ops {
 
 #ifdef __aarch64__
 
-inline void preload_l1_keep(const uint8* ptr) {
-#ifdef GEMMLOWP_ARM_64
-  asm volatile("prfm pldl1keep, [%[ptr]]\n" ::[ptr] "r"(ptr) :);
-#else
-  gemmlowp::Prefetch(ptr);
-#endif
-}
-
-// Implementation of quantized DepthwiseConv for 3x3 filters.
-
-// Below are helper structs to remove the use of arrays.
-// There is an llvm bug that causes significant slowdown when using arrays for
-// NEON intrinsics vector data types.
-// See: https://bugs.llvm.org/show_bug.cgi?id=34945
-
-struct Int32x8 {
-  int32x4_t low, high;
-};
-
-struct Filter3x3x8 {
-  int16x8_t f0, f1, f2, f3, f4, f5, f6, f7, f8;
-};
-
-// Loads 3x3 filter of depth 8 and adds filter offsets.
-inline Filter3x3x8 Load3x3Filter(const uint8* filter_ptr, int32 filter_offset,
-                                 int output_depth) {
-  Filter3x3x8 filter;
-
-  uint8x8_t temp_u8_0, temp_u8_1, temp_u8_2, temp_u8_3, temp_u8_4, temp_u8_5,
-      temp_u8_6, temp_u8_7, temp_u8_8;
-  int16x8_t filter_offset_vec = vdupq_n_s16(filter_offset);
-
-  temp_u8_0 = vld1_u8(filter_ptr + 0 * output_depth);
-  temp_u8_1 = vld1_u8(filter_ptr + 1 * output_depth);
-  temp_u8_2 = vld1_u8(filter_ptr + 2 * output_depth);
-  temp_u8_3 = vld1_u8(filter_ptr + 3 * output_depth);
-  temp_u8_4 = vld1_u8(filter_ptr + 4 * output_depth);
-  temp_u8_5 = vld1_u8(filter_ptr + 5 * output_depth);
-  temp_u8_6 = vld1_u8(filter_ptr + 6 * output_depth);
-  temp_u8_7 = vld1_u8(filter_ptr + 7 * output_depth);
-  temp_u8_8 = vld1_u8(filter_ptr + 8 * output_depth);
-
-  filter.f0 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_0));
-  filter.f1 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_1));
-  filter.f2 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_2));
-  filter.f3 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_3));
-  filter.f4 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_4));
-  filter.f5 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_5));
-  filter.f6 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_6));
-  filter.f7 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_7));
-  filter.f8 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_8));
-
-  filter.f0 = vaddq_s16(filter.f0, filter_offset_vec);
-  filter.f1 = vaddq_s16(filter.f1, filter_offset_vec);
-  filter.f2 = vaddq_s16(filter.f2, filter_offset_vec);
-  filter.f3 = vaddq_s16(filter.f3, filter_offset_vec);
-  filter.f4 = vaddq_s16(filter.f4, filter_offset_vec);
-  filter.f5 = vaddq_s16(filter.f5, filter_offset_vec);
-  filter.f6 = vaddq_s16(filter.f6, filter_offset_vec);
-  filter.f7 = vaddq_s16(filter.f7, filter_offset_vec);
-  filter.f8 = vaddq_s16(filter.f8, filter_offset_vec);
-
-  return filter;
-}
-
-// Applies activation, offset and downquantize on a set of accumulator
-// registers that correspond to a 2x2 output of depth 8.
-// Stores results to output.
-inline void DownquantizeAndStore2x2Output(
-    Int32x8 acc_0, Int32x8 acc_1, Int32x8 acc_2, Int32x8 acc_3,
-    int32 output_offset, int32 output_multiplier, int output_shift,
-    int32 output_activation_min, int32 output_activation_max, uint8* output_ptr,
-    int output_depth, int output_width) {
-  using gemmlowp::RoundingDivideByPOT;
-  const int32x4_t output_offset_vec = vdupq_n_s32(output_offset);
-  const int32x4_t output_activation_min_vec =
-      vdupq_n_s32(output_activation_min);
-  const int32x4_t output_activation_max_vec =
-      vdupq_n_s32(output_activation_max);
-
-  // Fixed-point multiplication.
-  acc_0.low = vqrdmulhq_n_s32(acc_0.low, output_multiplier);
-  acc_0.high = vqrdmulhq_n_s32(acc_0.high, output_multiplier);
-  acc_1.low = vqrdmulhq_n_s32(acc_1.low, output_multiplier);
-  acc_1.high = vqrdmulhq_n_s32(acc_1.high, output_multiplier);
-  acc_2.low = vqrdmulhq_n_s32(acc_2.low, output_multiplier);
-  acc_2.high = vqrdmulhq_n_s32(acc_2.high, output_multiplier);
-  acc_3.low = vqrdmulhq_n_s32(acc_3.low, output_multiplier);
-  acc_3.high = vqrdmulhq_n_s32(acc_3.high, output_multiplier);
-
-  acc_0.low = RoundingDivideByPOT(acc_0.low, output_shift);
-  acc_0.high = RoundingDivideByPOT(acc_0.high, output_shift);
-  acc_1.low = RoundingDivideByPOT(acc_1.low, output_shift);
-  acc_1.high = RoundingDivideByPOT(acc_1.high, output_shift);
-  acc_2.low = RoundingDivideByPOT(acc_2.low, output_shift);
-  acc_2.high = RoundingDivideByPOT(acc_2.high, output_shift);
-  acc_3.low = RoundingDivideByPOT(acc_3.low, output_shift);
-  acc_3.high = RoundingDivideByPOT(acc_3.high, output_shift);
-
-  // Add the output offset.
-  acc_0.low = vaddq_s32(acc_0.low, output_offset_vec);
-  acc_0.high = vaddq_s32(acc_0.high, output_offset_vec);
-  acc_1.low = vaddq_s32(acc_1.low, output_offset_vec);
-  acc_1.high = vaddq_s32(acc_1.high, output_offset_vec);
-  acc_2.low = vaddq_s32(acc_2.low, output_offset_vec);
-  acc_2.high = vaddq_s32(acc_2.high, output_offset_vec);
-  acc_3.low = vaddq_s32(acc_3.low, output_offset_vec);
-  acc_3.high = vaddq_s32(acc_3.high, output_offset_vec);
-
-  // Apply the activation function.
-  acc_0.low = vmaxq_s32(acc_0.low, output_activation_min_vec);
-  acc_0.high = vmaxq_s32(acc_0.high, output_activation_min_vec);
-  acc_1.low = vmaxq_s32(acc_1.low, output_activation_min_vec);
-  acc_1.high = vmaxq_s32(acc_1.high, output_activation_min_vec);
-  acc_2.low = vmaxq_s32(acc_2.low, output_activation_min_vec);
-  acc_2.high = vmaxq_s32(acc_2.high, output_activation_min_vec);
-  acc_3.low = vmaxq_s32(acc_3.low, output_activation_min_vec);
-  acc_3.high = vmaxq_s32(acc_3.high, output_activation_min_vec);
-
-  acc_0.low = vminq_s32(acc_0.low, output_activation_max_vec);
-  acc_0.high = vminq_s32(acc_0.high, output_activation_max_vec);
-  acc_1.low = vminq_s32(acc_1.low, output_activation_max_vec);
-  acc_1.high = vminq_s32(acc_1.high, output_activation_max_vec);
-  acc_2.low = vminq_s32(acc_2.low, output_activation_max_vec);
-  acc_2.high = vminq_s32(acc_2.high, output_activation_max_vec);
-  acc_3.low = vminq_s32(acc_3.low, output_activation_max_vec);
-  acc_3.high = vminq_s32(acc_3.high, output_activation_max_vec);
-
-  // Saturating cast to uint8 and store to destination.
-  int16x4_t acc_0_low_s16 = vqmovn_s32(acc_0.low);
-  int16x4_t acc_0_high_s16 = vqmovn_s32(acc_0.high);
-  int16x4_t acc_1_low_s16 = vqmovn_s32(acc_1.low);
-  int16x4_t acc_1_high_s16 = vqmovn_s32(acc_1.high);
-  int16x4_t acc_2_low_s16 = vqmovn_s32(acc_2.low);
-  int16x4_t acc_2_high_s16 = vqmovn_s32(acc_2.high);
-  int16x4_t acc_3_low_s16 = vqmovn_s32(acc_3.low);
-  int16x4_t acc_3_high_s16 = vqmovn_s32(acc_3.high);
-
-  int16x8_t res_0_s16 = vcombine_s16(acc_0_low_s16, acc_0_high_s16);
-  int16x8_t res_1_s16 = vcombine_s16(acc_1_low_s16, acc_1_high_s16);
-  int16x8_t res_2_s16 = vcombine_s16(acc_2_low_s16, acc_2_high_s16);
-  int16x8_t res_3_s16 = vcombine_s16(acc_3_low_s16, acc_3_high_s16);
-
-  uint8x8_t res_0_u8 = vqmovun_s16(res_0_s16);
-  uint8x8_t res_1_u8 = vqmovun_s16(res_1_s16);
-  uint8x8_t res_2_u8 = vqmovun_s16(res_2_s16);
-  uint8x8_t res_3_u8 = vqmovun_s16(res_3_s16);
-
-  vst1_u8(output_ptr, res_0_u8);
-  vst1_u8(output_ptr + output_depth, res_1_u8);
-  vst1_u8(output_ptr + output_depth * output_width, res_2_u8);
-  vst1_u8(output_ptr + output_depth * output_width + output_depth, res_3_u8);
-}
-
-inline void DownquantizeAndStore(Int32x8 acc, int32 output_offset,
-                                 int32 output_multiplier, int output_shift,
-                                 int32 output_activation_min,
-                                 int32 output_activation_max,
-                                 uint8* output_ptr) {
-  using gemmlowp::RoundingDivideByPOT;
-  const int32x4_t output_offset_vec = vdupq_n_s32(output_offset);
-  const int32x4_t output_activation_min_vec =
-      vdupq_n_s32(output_activation_min);
-  const int32x4_t output_activation_max_vec =
-      vdupq_n_s32(output_activation_max);
-
-  acc.low = vqrdmulhq_n_s32(acc.low, output_multiplier);
-  acc.high = vqrdmulhq_n_s32(acc.high, output_multiplier);
-
-  acc.low = RoundingDivideByPOT(acc.low, output_shift);
-  acc.high = RoundingDivideByPOT(acc.high, output_shift);
-
-  acc.low = vaddq_s32(acc.low, output_offset_vec);
-  acc.high = vaddq_s32(acc.high, output_offset_vec);
-
-  acc.low = vmaxq_s32(acc.low, output_activation_min_vec);
-  acc.high = vmaxq_s32(acc.high, output_activation_min_vec);
-
-  acc.low = vminq_s32(acc.low, output_activation_max_vec);
-  acc.high = vminq_s32(acc.high, output_activation_max_vec);
-
-  int16x4_t acc_low_s16 = vqmovn_s32(acc.low);
-  int16x4_t acc_high_s16 = vqmovn_s32(acc.high);
-
-  int16x8_t res_s16 = vcombine_s16(acc_low_s16, acc_high_s16);
-  uint8x8_t res_u8 = vqmovun_s16(res_s16);
-  vst1_u8(output_ptr, res_u8);
-}
-
-inline void DownquantizeAndStore2Output(
-    Int32x8 acc_0, Int32x8 acc_1, int32 output_offset, int32 output_multiplier,
-    int output_shift, int32 output_activation_min, int32 output_activation_max,
-    uint8* output_ptr, int output_ptr_offset) {
-  {
-    using gemmlowp::RoundingDivideByPOT;
-    const int32x4_t output_offset_vec = vdupq_n_s32(output_offset);
-    const int32x4_t output_activation_min_vec =
-        vdupq_n_s32(output_activation_min);
-    const int32x4_t output_activation_max_vec =
-        vdupq_n_s32(output_activation_max);
-
-    // Fixed-point multiplication.
-    acc_0.low = vqrdmulhq_n_s32(acc_0.low, output_multiplier);
-    acc_0.high = vqrdmulhq_n_s32(acc_0.high, output_multiplier);
-    acc_1.low = vqrdmulhq_n_s32(acc_1.low, output_multiplier);
-    acc_1.high = vqrdmulhq_n_s32(acc_1.high, output_multiplier);
-
-    acc_0.low = RoundingDivideByPOT(acc_0.low, output_shift);
-    acc_0.high = RoundingDivideByPOT(acc_0.high, output_shift);
-    acc_1.low = RoundingDivideByPOT(acc_1.low, output_shift);
-    acc_1.high = RoundingDivideByPOT(acc_1.high, output_shift);
-
-    // Add the output offset.
-    acc_0.low = vaddq_s32(acc_0.low, output_offset_vec);
-    acc_0.high = vaddq_s32(acc_0.high, output_offset_vec);
-    acc_1.low = vaddq_s32(acc_1.low, output_offset_vec);
-    acc_1.high = vaddq_s32(acc_1.high, output_offset_vec);
-
-    // Apply the activation function.
-    acc_0.low = vmaxq_s32(acc_0.low, output_activation_min_vec);
-    acc_0.high = vmaxq_s32(acc_0.high, output_activation_min_vec);
-    acc_1.low = vmaxq_s32(acc_1.low, output_activation_min_vec);
-    acc_1.high = vmaxq_s32(acc_1.high, output_activation_min_vec);
-
-    acc_0.low = vminq_s32(acc_0.low, output_activation_max_vec);
-    acc_0.high = vminq_s32(acc_0.high, output_activation_max_vec);
-    acc_1.low = vminq_s32(acc_1.low, output_activation_max_vec);
-    acc_1.high = vminq_s32(acc_1.high, output_activation_max_vec);
-  }
-
-  // Saturating cast to uint8 and store to destination.
-  int16x8_t res_0_s16;
-  {
-    int16x4_t acc_0_low_s16 = vqmovn_s32(acc_0.low);
-    int16x4_t acc_0_high_s16 = vqmovn_s32(acc_0.high);
-    res_0_s16 = vcombine_s16(acc_0_low_s16, acc_0_high_s16);
-  }
-
-  int16x8_t res_1_s16;
-  {
-    int16x4_t acc_1_low_s16 = vqmovn_s32(acc_1.low);
-    int16x4_t acc_1_high_s16 = vqmovn_s32(acc_1.high);
-    res_1_s16 = vcombine_s16(acc_1_low_s16, acc_1_high_s16);
-  }
-
-  uint8x8_t res_0_u8 = vqmovun_s16(res_0_s16);
-  uint8x8_t res_1_u8 = vqmovun_s16(res_1_s16);
-  vst1_u8(output_ptr, res_0_u8);
-  vst1_u8(output_ptr + output_ptr_offset, res_1_u8);
-}
-
-// Performs multiply accumulate on 3 inputs of depth 8.
-inline Int32x8 MultiplyAccumulateRow(Int32x8 accum, int16x8_t f0, int16x8_t f1,
-                                     int16x8_t f2, int16x8_t i0, int16x8_t i1,
-                                     int16x8_t i2) {
-  accum.low = vmlal_s16(accum.low, vget_low_s16(f0), vget_low_s16(i0));
-  accum.high = vmlal_s16(accum.high, vget_high_s16(f0), vget_high_s16(i0));
-  accum.low = vmlal_s16(accum.low, vget_low_s16(f1), vget_low_s16(i1));
-  accum.high = vmlal_s16(accum.high, vget_high_s16(f1), vget_high_s16(i1));
-  accum.low = vmlal_s16(accum.low, vget_low_s16(f2), vget_low_s16(i2));
-  accum.high = vmlal_s16(accum.high, vget_high_s16(f2), vget_high_s16(i2));
-  return accum;
-}
-
-// Performs multiply accumulate on 3 inputs of depth 8.
-inline Int32x8 MultiplyAccumulate3x3Filter(const Filter3x3x8& f, int16x8_t i0,
-                                           int16x8_t i1, int16x8_t i2,
-                                           int16x8_t i3, int16x8_t i4,
-                                           int16x8_t i5, int16x8_t i6,
-                                           int16x8_t i7, int16x8_t i8,
-                                           Int32x8 accum) {
-  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f0), vget_low_s16(i0));
-  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f0), vget_high_s16(i0));
-  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f1), vget_low_s16(i1));
-  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f1), vget_high_s16(i1));
-  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f2), vget_low_s16(i2));
-  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f2), vget_high_s16(i2));
-  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f3), vget_low_s16(i3));
-  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f3), vget_high_s16(i3));
-  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f4), vget_low_s16(i4));
-  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f4), vget_high_s16(i4));
-  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f5), vget_low_s16(i5));
-  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f5), vget_high_s16(i5));
-  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f6), vget_low_s16(i6));
-  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f6), vget_high_s16(i6));
-  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f7), vget_low_s16(i7));
-  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f7), vget_high_s16(i7));
-  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f8), vget_low_s16(i8));
-  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f8), vget_high_s16(i8));
-  return accum;
-}
-
-inline void DotProductAndStore(const Filter3x3x8& filter, int16x8_t i0,
-                               int16x8_t i1, int16x8_t i2, int16x8_t i3,
-                               int16x8_t i4, int16x8_t i5, int16x8_t i6,
-                               int16x8_t i7, int16x8_t i8,
-                               const int32* bias_ptr, int32 output_offset,
-                               int32 output_multiplier, int output_shift,
-                               int32 output_activation_min,
-                               int32 output_activation_max, uint8* output_ptr) {
-  Int32x8 acc;
-  acc.low = vld1q_s32(bias_ptr);
-  acc.high = vld1q_s32(bias_ptr + 4);
-
-  acc = MultiplyAccumulate3x3Filter(filter, i0, i1, i2, i3, i4, i5, i6, i7, i8,
-                                    acc);
-
-  DownquantizeAndStore(acc, output_offset, output_multiplier, output_shift,
-                       output_activation_min, output_activation_max,
-                       output_ptr);
-}
-
-// Performs multiply-accumulate on a 3x4 input for 2 horizontal outputs.
-inline void DotProductAndStore2xStride1(
-    const Filter3x3x8& filter, int16x8_t i0, int16x8_t i1, int16x8_t i2,
-    int16x8_t i3, int16x8_t i4, int16x8_t i5, int16x8_t i6, int16x8_t i7,
-    int16x8_t i8, int16x8_t i9, int16x8_t i10, int16x8_t i11,
-    const int32* bias_ptr, int32 output_offset, int32 output_multiplier,
-    int output_shift, int32 output_activation_min, int32 output_activation_max,
-    uint8* output_ptr, int output_ptr_offset) {
-  Int32x8 acc_0, acc_1;
-  acc_0.low = vld1q_s32(bias_ptr);
-  acc_1.low = vld1q_s32(bias_ptr);
-  acc_0.high = vld1q_s32(bias_ptr + 4);
-  acc_1.high = vld1q_s32(bias_ptr + 4);
-
-  acc_0 = MultiplyAccumulate3x3Filter(filter, i0, i1, i2, i4, i5, i6, i8, i9,
-                                      i10, acc_0);
-  acc_1 = MultiplyAccumulate3x3Filter(filter, i1, i2, i3, i5, i6, i7, i9, i10,
-                                      i11, acc_1);
-  DownquantizeAndStore2Output(acc_0, acc_1, output_offset, output_multiplier,
-                              output_shift, output_activation_min,
-                              output_activation_max, output_ptr,
-                              output_ptr_offset);
-}
-
-// Performs multiply-accumulate on a 4x3 input for 2 vertical outputs.
-inline void DotProductAndStore2yStride1(
-    const Filter3x3x8& filter, int16x8_t i0, int16x8_t i1, int16x8_t i2,
-    int16x8_t i3, int16x8_t i4, int16x8_t i5, int16x8_t i6, int16x8_t i7,
-    int16x8_t i8, int16x8_t i9, int16x8_t i10, int16x8_t i11,
-    const int32* bias_ptr, int32 output_offset, int32 output_multiplier,
-    int output_shift, int32 output_activation_min, int32 output_activation_max,
-    uint8* output_ptr, int output_ptr_offset) {
-  Int32x8 acc_0, acc_1;
-  acc_0.low = vld1q_s32(bias_ptr);
-  acc_1.low = vld1q_s32(bias_ptr);
-  acc_0.high = vld1q_s32(bias_ptr + 4);
-  acc_1.high = vld1q_s32(bias_ptr + 4);
-
-  acc_0 = MultiplyAccumulate3x3Filter(filter, i0, i1, i2, i3, i4, i5, i6, i7,
-                                      i8, acc_0);
-  acc_1 = MultiplyAccumulate3x3Filter(filter, i3, i4, i5, i6, i7, i8, i9, i10,
-                                      i11, acc_1);
-  DownquantizeAndStore2Output(acc_0, acc_1, output_offset, output_multiplier,
-                              output_shift, output_activation_min,
-                              output_activation_max, output_ptr,
-                              output_ptr_offset);
-}
-
-// A kernel that is optimized on the number of output cells in the x and y
-// direction, and the stride. Assumes 3x3 filters of 8 depth.
-template 
-struct ConvKernel3x3FilterDepth8 {};
-
-template <>
-struct ConvKernel3x3FilterDepth8<8, 8, 1, 1> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-    const int output_row_size = output_depth * output_width;
-
-    // To process 8x8 outputs using a 3x3 filter, we require 10x10 inputs.
-    // Load inputs for the first 2 filters on the top left, then slide to
-    // the right, down, left, down, right, etc. in a snake-like path. This
-    // minimizes the total number of loads.
-    //
-    //        INPUT                          OUTPUT
-    //   |\----------------\               |\------------\
-    //   | \                \              | \            \
-    //   |  \----------------\             |  \------------\
-    //   |  | 0    ...     9 |             |  | 0  ...   7 |
-    //   |  | 10   ...    19 |     --->    |  | 8  ...  15 |
-    //   |  | 20   ...    29 |              \ | .. ...  .. |
-    //    \ | ..   ...    .. |               \| 56 ...  63 |
-    //     \| 90   ...   109 |                |------------|
-    //      |----------------|
-    //
-    // The first set of loads corresponds to:
-    //
-    //        INPUT                          OUTPUT
-    //   |\-----------------                |\-----------
-    //   | \                                | \
-    //   |  \-----------------              |  \----------
-    //   |  | 0  1   2  3 ...               |  | 0  1 ...
-    //   |  | 10 11 12 13 ...     --->      |  | ..   ...
-    //   |  | 20 21 22 23 ...                  | ..   ...
-    //   |  | ..   ...    ...
-    //
-    // The next set of loads correspond to a sliding window to the right.
-    // It loads inputs 4, 5, 14, 15, 23, 24 and keeps 2, 3, 12, 13, and 22:
-    //
-    //        INPUT                          OUTPUT
-    //   |\-------------------                |\-------------
-    //   | \                                  | \
-    //   |  \-------------------              |  \------------
-    //   |  | .. 2  3   4  5 ...              |  | .. 2  3 ...
-    //   |  | .. 12 13 14 15 ...     --->     |  | ..      ...
-    //   |  | .. 21 22 23 24 ...                 | ..      ...
-    //   |  | ..    ...      ...
-    //
-    // And so on...
-
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11;
-
-    // Load inputs for 1x2 outputs starting from the top left. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (0) and (1).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-
-      ptr += input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-
-      ptr += input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr, output_depth);
-
-    // Slide to the right for outputs x = [2, 3], y = 0. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (2) and (3).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 4 * input_depth;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
-        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 2 * output_depth, output_depth);
-
-    // Slide to the right again for outputs x = [4, 5], y = 0. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (4) and (5).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 6 * input_depth;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 4 * output_depth, output_depth);
-
-    // Slide to the right one last time for outputs x = [6, 7], y = 0.
-    // Referring to the indexes in the diagram above, this corresponds to
-    // outputs (6) and (7).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 8 * input_depth;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
-        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 6 * output_depth, output_depth);
-
-    // Slide to down for outputs x = [6, 7], y = 1. Referring to the indexes in
-    // the diagram above, this corresponds to outputs (14) and (15).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 6 * input_depth + 3 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
-        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 6 * output_depth + output_row_size,
-        output_depth);
-
-    // Slide left for outputs x = [4, 5], y = 1. Referring to the indexes in
-    // the diagram above, this corresponds to outputs (12) and (13).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 4 * input_depth + input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
-        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 4 * output_depth + output_row_size,
-        output_depth);
-
-    // Slide left again for outputs x = [2, 3], y = 1. Referring to the indexes
-    // in the diagram above, this corresponds to outputs (10) and (11).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 2 * input_depth + input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
-        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 2 * output_depth + output_row_size,
-        output_depth);
-
-    // Slide left one more time for outputs x = [0, 1], y = 1. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (8) and (9).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
-        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + output_row_size, output_depth);
-
-    // Slide down for outputs x = [0, 1], y = 2. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (16) and (17).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 4 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_8, input_9, input_10, input_11, input_0, input_1, input_2,
-        input_3, input_4, input_5, input_6, input_7, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 2 * output_row_size, output_depth);
-
-    // Slide right for outputs x = [2, 3], y = 2. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (18) and (19).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 4 * input_depth + 2 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_10, input_11, input_8, input_9, input_2, input_3, input_0,
-        input_1, input_6, input_7, input_4, input_5, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 2 * output_depth + 2 * output_row_size, output_depth);
-
-    // Slide right for outputs x = [4, 5], y = 2. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (20) and (21).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 6 * input_depth + 2 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_8, input_9, input_10, input_11, input_0, input_1, input_2,
-        input_3, input_4, input_5, input_6, input_7, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 4 * output_depth + 2 * output_row_size, output_depth);
-
-    // Slide right one more time for outputs x = [6, 7], y = 2. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (22) and (23).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 8 * input_depth + 2 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_10, input_11, input_8, input_9, input_2, input_3, input_0,
-        input_1, input_6, input_7, input_4, input_5, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 6 * output_depth + 2 * output_row_size, output_depth);
-
-    // Slide down for outputs x = [6, 7], y = 3. Referring to the indexes in
-    // the diagram above, this corresponds to outputs (30) and (31).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 6 * input_depth + 5 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
-        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 6 * output_depth + 3 * output_row_size, output_depth);
-
-    // Slide left for outputs x = [4, 5], y = 3. Referring to the indexes in
-    // the diagram above, this corresponds to outputs (28) and (29).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 4 * input_depth + 3 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 4 * output_depth + 3 * output_row_size, output_depth);
-
-    // Slide left for outputs x = [2, 3], y = 3. Referring to the indexes in
-    // the diagram above, this corresponds to outputs (26) and (27).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 2 * input_depth + 3 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
-        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 2 * output_depth + 3 * output_row_size, output_depth);
-
-    // Slide left one more time for outputs x = [0, 1], y = 3. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (24) and (25).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 3 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 3 * output_row_size, output_depth);
-
-    // Slide down for outputs x = [0, 1], y = 4. Referring to the indexes in
-    // the diagram above, this corresponds to outputs (32) and (33).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 6 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
-        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 4 * output_row_size, output_depth);
-
-    // Slide right for outputs x = [2, 3], y = 4. Referring to the indexes in
-    // the diagram above, this corresponds to outputs (34) and (35).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 4 * input_depth + 4 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
-        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 2 * output_depth + 4 * output_row_size, output_depth);
-
-    // Slide right for outputs x = [4, 5], y = 4. Referring to the indexes in
-    // the diagram above, this corresponds to outputs (36) and (37).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 6 * input_depth + 4 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
-        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 4 * output_depth + 4 * output_row_size, output_depth);
-
-    // Slide right one more time for outputs x = [6, 7], y = 4. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (38) and (39).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 8 * input_depth + 4 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
-        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 6 * output_depth + 4 * output_row_size, output_depth);
-
-    // Slide down for outputs x = [6, 7], y = 5. Referring to the  indexes in
-    // the diagram above, this corresponds to outputs (46) and (47).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 6 * input_depth + 7 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_10, input_11, input_8, input_9, input_2, input_3, input_0,
-        input_1, input_6, input_7, input_4, input_5, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 6 * output_depth + 5 * output_row_size, output_depth);
-
-    // Slide left for outputs x = [4, 5], y = 5. Referring to the  indexes in
-    // the diagram above, this corresponds to outputs (44) and (45).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 4 * input_depth + 5 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_8, input_9, input_10, input_11, input_0, input_1, input_2,
-        input_3, input_4, input_5, input_6, input_7, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 4 * output_depth + 5 * output_row_size, output_depth);
-
-    // Slide left for outputs x = [2, 3], y = 5. Referring to the  indexes in
-    // the diagram above, this corresponds to outputs (42) and (43).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 2 * input_depth + 5 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_10, input_11, input_8, input_9, input_2, input_3, input_0,
-        input_1, input_6, input_7, input_4, input_5, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 2 * output_depth + 5 * output_row_size, output_depth);
-
-    // Slide left one more time for outputs x = [0, 1], y = 5. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (40) and (41).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 5 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_8, input_9, input_10, input_11, input_0, input_1, input_2,
-        input_3, input_4, input_5, input_6, input_7, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 5 * output_row_size, output_depth);
-
-    // Slide down for outputs x = [0, 1], y = 6. Referring to the  indexes in
-    // the diagram above, this corresponds to outputs (48) and (49).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 8 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 6 * output_row_size, output_depth);
-
-    // Slide right for outputs x = [2, 3], y = 6. Referring to the  indexes in
-    // the diagram above, this corresponds to outputs (50) and (51).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 4 * input_depth + 6 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
-        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 2 * output_depth + 6 * output_row_size, output_depth);
-
-    // Slide right for outputs x = [4, 5], y = 6. Referring to the  indexes in
-    // the diagram above, this corresponds to outputs (52) and (53).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 6 * input_depth + 6 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 4 * output_depth + 6 * output_row_size, output_depth);
-
-    // Slide right one more time for outputs x = [6, 7], y = 6. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (54) and (55).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 8 * input_depth + 6 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
-        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 6 * output_depth + 6 * output_row_size, output_depth);
-
-    // Slide down for outputs x = [6, 7], y = 7. Referring to the indexes in the
-    // diagram above, this corresponds to outputs (62) and (63).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 6 * input_depth + 9 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
-        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 6 * output_depth + 7 * output_row_size, output_depth);
-
-    // Slide left for outputs x = [4, 5], y = 7. Referring to the indexes in the
-    // diagram above, this corresponds to outputs (60) and (61).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 4 * input_depth + 7 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
-        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 4 * output_depth + 7 * output_row_size, output_depth);
-
-    // Slide left for outputs x = [2, 3], y = 7. Referring to the indexes in the
-    // diagram above, this corresponds to outputs (58) and (59).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 2 * input_depth + 7 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
-        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 2 * output_depth + 7 * output_row_size, output_depth);
-
-    // Slide left one more time for outputs x = [0, 1], y = 7. Referring to the
-    // indexes in the diagram above, this corresponds to outputs (56) and (57).
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 7 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
-        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 7 * output_row_size, output_depth);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<4, 4, 1, 1> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-    const int output_row_size = output_depth * output_width;
-
-    // To process 4x4 outputs using a 3x3 filter, we require 6x6 inputs.
-    // Load inputs for the first 2 filters on the top left, then slide to
-    // the right, down, left, down, right, etc. in a snake-like path. This
-    // minimizes the total number of loads.
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11;
-
-    // Load inputs for 1x2 outputs starting from the top left.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-
-      ptr += input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-
-      ptr += input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr, output_depth);
-
-    // Now load 1x2 inputs on the top right.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 4 * input_depth;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
-        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 2 * output_depth, output_depth);
-
-    // Now load next inputs when sliding window down.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 2 * input_depth + 3 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
-        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 2 * output_depth + output_row_size,
-        output_depth);
-
-    // Now load next inputs when sliding window left.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
-        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + output_row_size, output_depth);
-
-    // Now load next inputs when sliding window down.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 4 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_8, input_9, input_10, input_11, input_0, input_1, input_2,
-        input_3, input_4, input_5, input_6, input_7, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 2 * output_row_size, output_depth);
-
-    // Now load next inputs when sliding window right.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 4 * input_depth + 2 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_10, input_11, input_8, input_9, input_2, input_3, input_0,
-        input_1, input_6, input_7, input_4, input_5, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 2 * output_depth + 2 * output_row_size, output_depth);
-
-    // Now load next inputs when sliding window down.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 2 * input_depth + 5 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
-        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max,
-        output_ptr + 2 * output_depth + 3 * output_row_size, output_depth);
-
-    // Now load next inputs when sliding window left.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 3 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 3 * output_row_size, output_depth);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<4, 2, 1, 1> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-    const int output_row_size = output_depth * output_width;
-
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11;
-
-    // Load inputs for 1x2 outputs starting from the top.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-
-      ptr += input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-
-      ptr += input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr, output_depth);
-
-    output_ptr += output_row_size;
-
-    // Now load next inputs one row down.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 3 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
-        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr, output_depth);
-
-    output_ptr += output_row_size;
-
-    // Now load next row.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 4 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_8, input_9, input_10, input_11, input_0, input_1, input_2,
-        input_3, input_4, input_5, input_6, input_7, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr, output_depth);
-
-    output_ptr += output_row_size;
-
-    // Now load last row.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 5 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr, output_depth);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<4, 1, 1, 1> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-    const int output_row_size = output_depth * output_width;
-
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11;
-
-    // Load inputs for 2x1 outputs starting from the top.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      ptr += input_row_size;
-      temp_3 = vld1_u8(ptr);
-      temp_4 = vld1_u8(ptr + input_depth);
-      temp_5 = vld1_u8(ptr + 2 * input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-
-      ptr += input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      ptr += input_row_size;
-      temp_3 = vld1_u8(ptr);
-      temp_4 = vld1_u8(ptr + input_depth);
-      temp_5 = vld1_u8(ptr + 2 * input_depth);
-
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-    }
-
-    DotProductAndStore2yStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr, output_row_size);
-
-    // Load inputs for bottom 2 rows.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 4 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      ptr += input_row_size;
-      temp_3 = vld1_u8(ptr);
-      temp_4 = vld1_u8(ptr + input_depth);
-      temp_5 = vld1_u8(ptr + 2 * input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-    }
-
-    DotProductAndStore2yStride1(
-        filter, input_6, input_7, input_8, input_9, input_10, input_11, input_0,
-        input_1, input_2, input_3, input_4, input_5, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 2 * output_row_size,
-        output_row_size);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<2, 2, 1, 1> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    Int32x8 acc_0, acc_1, acc_2, acc_3;
-
-    acc_0.low = vld1q_s32(bias_ptr);
-    acc_1.low = vld1q_s32(bias_ptr);
-    acc_2.low = vld1q_s32(bias_ptr);
-    acc_3.low = vld1q_s32(bias_ptr);
-
-    bias_ptr += 4;
-    acc_0.high = vld1q_s32(bias_ptr);
-    acc_1.high = vld1q_s32(bias_ptr);
-    acc_2.high = vld1q_s32(bias_ptr);
-    acc_3.high = vld1q_s32(bias_ptr);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-
-    // Add scope for input registers to help the compiler know that it is
-    // not needed.
-    {
-      // To process 2x2 outputs using a 3x3 filter, we require 4x4 inputs.
-      // Load inputs for the top two filters first.
-      int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-          input_7, input_8, input_9, input_10, input_11;
-
-      const uint8* ptr = input_ptr;
-
-      // Load top 3 rows.
-      {
-        uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-        temp_0 = vld1_u8(ptr);
-        temp_1 = vld1_u8(ptr + input_depth);
-        temp_2 = vld1_u8(ptr + 2 * input_depth);
-        temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-        input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-        input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-        input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-        input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-        input_0 = vaddq_s16(input_0, input_offset_vec);
-        input_1 = vaddq_s16(input_1, input_offset_vec);
-        input_2 = vaddq_s16(input_2, input_offset_vec);
-        input_3 = vaddq_s16(input_3, input_offset_vec);
-
-        ptr += input_row_size;
-        temp_0 = vld1_u8(ptr);
-        temp_1 = vld1_u8(ptr + input_depth);
-        temp_2 = vld1_u8(ptr + 2 * input_depth);
-        temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-        input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-        input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-        input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-        input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-        input_4 = vaddq_s16(input_4, input_offset_vec);
-        input_5 = vaddq_s16(input_5, input_offset_vec);
-        input_6 = vaddq_s16(input_6, input_offset_vec);
-        input_7 = vaddq_s16(input_7, input_offset_vec);
-
-        ptr += input_row_size;
-        temp_0 = vld1_u8(ptr);
-        temp_1 = vld1_u8(ptr + input_depth);
-        temp_2 = vld1_u8(ptr + 2 * input_depth);
-        temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-        input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-        input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-        input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-        input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-        input_8 = vaddq_s16(input_8, input_offset_vec);
-        input_9 = vaddq_s16(input_9, input_offset_vec);
-        input_10 = vaddq_s16(input_10, input_offset_vec);
-        input_11 = vaddq_s16(input_11, input_offset_vec);
-      }
-
-      // Multiply-accum for top-left output.
-      acc_0 = MultiplyAccumulate3x3Filter(filter, input_0, input_1, input_2,
-                                          input_4, input_5, input_6, input_8,
-                                          input_9, input_10, acc_0);
-
-      // Multiply-accum for top-right output.
-      acc_1 = MultiplyAccumulate3x3Filter(filter, input_1, input_2, input_3,
-                                          input_5, input_6, input_7, input_9,
-                                          input_10, input_11, acc_1);
-
-      // Now load the bottom row.
-      {
-        uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-        ptr += input_row_size;
-        temp_0 = vld1_u8(ptr);
-        temp_1 = vld1_u8(ptr + input_depth);
-        temp_2 = vld1_u8(ptr + 2 * input_depth);
-        temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-        input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-        input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-        input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-        input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-        input_0 = vaddq_s16(input_0, input_offset_vec);
-        input_1 = vaddq_s16(input_1, input_offset_vec);
-        input_2 = vaddq_s16(input_2, input_offset_vec);
-        input_3 = vaddq_s16(input_3, input_offset_vec);
-      }
-
-      // Multiply-accum for bottom-left output.
-      acc_2 = MultiplyAccumulate3x3Filter(filter, input_4, input_5, input_6,
-                                          input_8, input_9, input_10, input_0,
-                                          input_1, input_2, acc_2);
-
-      // Multiply-accum for bottom-right output.
-      acc_3 = MultiplyAccumulate3x3Filter(filter, input_5, input_6, input_7,
-                                          input_9, input_10, input_11, input_1,
-                                          input_2, input_3, acc_3);
-    }
-
-    DownquantizeAndStore2x2Output(acc_0, acc_1, acc_2, acc_3, output_offset,
-                                  output_multiplier, output_shift,
-                                  output_activation_min, output_activation_max,
-                                  output_ptr, output_depth, output_width);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<2, 4, 1, 1> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-    const int output_row_size = output_depth * output_width;
-
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11;
-
-    // Load inputs for 1x2 outputs starting from the top left.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-
-      ptr += input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-
-      ptr += input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr, output_depth);
-
-    // Now load 1x2 inputs on the top right.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + 4 * input_depth;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
-        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 2 * output_depth, output_depth);
-
-    // Now load next inputs when sliding window down.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr + 2 * input_depth + 3 * input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
-        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 2 * output_depth + output_row_size,
-        output_depth);
-
-    // Now load next inputs when sliding window left.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
-        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + output_row_size, output_depth);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<1, 4, 1, 1> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11;
-
-    // Load inputs for 1x2 outputs starting from the left.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3;
-
-      const uint8* ptr = input_ptr;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-
-      ptr += input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-
-      ptr += input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      temp_3 = vld1_u8(ptr + 3 * input_depth);
-
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr, output_depth);
-
-    // Now load 1x2 inputs on the right.
-    {
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr + input_depth * 4;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_2 = vld1_u8(ptr);
-      temp_3 = vld1_u8(ptr + input_depth);
-
-      ptr += input_row_size;
-      temp_4 = vld1_u8(ptr);
-      temp_5 = vld1_u8(ptr + input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-    }
-
-    DotProductAndStore2xStride1(
-        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
-        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr + 2 * output_depth, output_depth);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<2, 1, 1, 1> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    // To process 2x1 outputs using a 3x3 filter, we require 4x3 inputs.
-    // Load all inputs at the beginning.
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11;
-
-    // Load inputs for 1x2 outputs starting from the top left.
-    {
-      const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
-
-      const uint8* ptr = input_ptr;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      ptr += input_row_size;
-      temp_3 = vld1_u8(ptr);
-      temp_4 = vld1_u8(ptr + input_depth);
-      temp_5 = vld1_u8(ptr + 2 * input_depth);
-
-      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_0 = vaddq_s16(input_0, input_offset_vec);
-      input_1 = vaddq_s16(input_1, input_offset_vec);
-      input_2 = vaddq_s16(input_2, input_offset_vec);
-      input_3 = vaddq_s16(input_3, input_offset_vec);
-      input_4 = vaddq_s16(input_4, input_offset_vec);
-      input_5 = vaddq_s16(input_5, input_offset_vec);
-
-      ptr += input_row_size;
-      temp_0 = vld1_u8(ptr);
-      temp_1 = vld1_u8(ptr + input_depth);
-      temp_2 = vld1_u8(ptr + 2 * input_depth);
-      ptr += input_row_size;
-      temp_3 = vld1_u8(ptr);
-      temp_4 = vld1_u8(ptr + input_depth);
-      temp_5 = vld1_u8(ptr + 2 * input_depth);
-
-      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-      input_6 = vaddq_s16(input_6, input_offset_vec);
-      input_7 = vaddq_s16(input_7, input_offset_vec);
-      input_8 = vaddq_s16(input_8, input_offset_vec);
-      input_9 = vaddq_s16(input_9, input_offset_vec);
-      input_10 = vaddq_s16(input_10, input_offset_vec);
-      input_11 = vaddq_s16(input_11, input_offset_vec);
-    }
-
-    DotProductAndStore2yStride1(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
-        output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_ptr, output_depth * output_width);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<4, 2, 2, 2> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    const int output_row_size = output_depth * output_width;
-
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    Int32x8 acc_0, acc_1;
-    acc_0.low = vld1q_s32(bias_ptr);
-    acc_1.low = vld1q_s32(bias_ptr);
-    acc_0.high = vld1q_s32(bias_ptr + 4);
-    acc_1.high = vld1q_s32(bias_ptr + 4);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, input_9;
-
-    const uint8* ptr = input_ptr;
-    uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4;
-
-    // Load first 2 rows.
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    temp_3 = vld1_u8(ptr + 3 * input_depth);
-    temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-
-    ptr += input_row_size;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    temp_3 = vld1_u8(ptr + 3 * input_depth);
-    temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-    input_9 = vaddq_s16(input_9, input_offset_vec);
-
-    acc_0 = MultiplyAccumulateRow(acc_0, filter.f0, filter.f1, filter.f2,
-                                  input_0, input_1, input_2);
-
-    acc_1 = MultiplyAccumulateRow(acc_1, filter.f0, filter.f1, filter.f2,
-                                  input_2, input_3, input_4);
-
-    acc_0 = MultiplyAccumulateRow(acc_0, filter.f3, filter.f4, filter.f5,
-                                  input_5, input_6, input_7);
-
-    acc_1 = MultiplyAccumulateRow(acc_1, filter.f3, filter.f4, filter.f5,
-                                  input_7, input_8, input_9);
-
-    // Load next 2 rows.
-    ptr += input_row_size;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    temp_3 = vld1_u8(ptr + 3 * input_depth);
-    temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-
-    ptr += input_row_size;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    temp_3 = vld1_u8(ptr + 3 * input_depth);
-    temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-    input_9 = vaddq_s16(input_9, input_offset_vec);
-
-    acc_0 = MultiplyAccumulateRow(acc_0, filter.f6, filter.f7, filter.f8,
-                                  input_0, input_1, input_2);
-
-    acc_1 = MultiplyAccumulateRow(acc_1, filter.f6, filter.f7, filter.f8,
-                                  input_2, input_3, input_4);
-
-    DownquantizeAndStore2Output(
-        acc_0, acc_1, output_offset, output_multiplier, output_shift,
-        output_activation_min, output_activation_max, output_ptr, output_depth);
-
-    output_ptr += output_row_size;
-
-    // Moving onto the next row of outputs.
-    acc_0.low = vld1q_s32(bias_ptr);
-    acc_1.low = vld1q_s32(bias_ptr);
-    acc_0.high = vld1q_s32(bias_ptr + 4);
-    acc_1.high = vld1q_s32(bias_ptr + 4);
-
-    acc_0 = MultiplyAccumulateRow(acc_0, filter.f0, filter.f1, filter.f2,
-                                  input_0, input_1, input_2);
-
-    acc_1 = MultiplyAccumulateRow(acc_1, filter.f0, filter.f1, filter.f2,
-                                  input_2, input_3, input_4);
-
-    acc_0 = MultiplyAccumulateRow(acc_0, filter.f3, filter.f4, filter.f5,
-                                  input_5, input_6, input_7);
-
-    acc_1 = MultiplyAccumulateRow(acc_1, filter.f3, filter.f4, filter.f5,
-                                  input_7, input_8, input_9);
-
-    // Load next 2 rows.
-    ptr += input_row_size;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    temp_3 = vld1_u8(ptr + 3 * input_depth);
-    temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-
-    ptr += input_row_size;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    temp_3 = vld1_u8(ptr + 3 * input_depth);
-    temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-    input_9 = vaddq_s16(input_9, input_offset_vec);
-
-    acc_0 = MultiplyAccumulateRow(acc_0, filter.f6, filter.f7, filter.f8,
-                                  input_0, input_1, input_2);
-
-    acc_1 = MultiplyAccumulateRow(acc_1, filter.f6, filter.f7, filter.f8,
-                                  input_2, input_3, input_4);
-
-    DownquantizeAndStore2Output(
-        acc_0, acc_1, output_offset, output_multiplier, output_shift,
-        output_activation_min, output_activation_max, output_ptr, output_depth);
-
-    output_ptr += output_row_size;
-
-    // Moving onto the next row of outputs.
-    acc_0.low = vld1q_s32(bias_ptr);
-    acc_1.low = vld1q_s32(bias_ptr);
-    acc_0.high = vld1q_s32(bias_ptr + 4);
-    acc_1.high = vld1q_s32(bias_ptr + 4);
-
-    acc_0 = MultiplyAccumulateRow(acc_0, filter.f0, filter.f1, filter.f2,
-                                  input_0, input_1, input_2);
-
-    acc_1 = MultiplyAccumulateRow(acc_1, filter.f0, filter.f1, filter.f2,
-                                  input_2, input_3, input_4);
-
-    acc_0 = MultiplyAccumulateRow(acc_0, filter.f3, filter.f4, filter.f5,
-                                  input_5, input_6, input_7);
-
-    acc_1 = MultiplyAccumulateRow(acc_1, filter.f3, filter.f4, filter.f5,
-                                  input_7, input_8, input_9);
-
-    // Load next 2 rows.
-    ptr += input_row_size;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    temp_3 = vld1_u8(ptr + 3 * input_depth);
-    temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-
-    ptr += input_row_size;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    temp_3 = vld1_u8(ptr + 3 * input_depth);
-    temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-    input_9 = vaddq_s16(input_9, input_offset_vec);
-
-    acc_0 = MultiplyAccumulateRow(acc_0, filter.f6, filter.f7, filter.f8,
-                                  input_0, input_1, input_2);
-
-    acc_1 = MultiplyAccumulateRow(acc_1, filter.f6, filter.f7, filter.f8,
-                                  input_2, input_3, input_4);
-
-    DownquantizeAndStore2Output(
-        acc_0, acc_1, output_offset, output_multiplier, output_shift,
-        output_activation_min, output_activation_max, output_ptr, output_depth);
-
-    output_ptr += output_row_size;
-
-    // Moving onto the next row of outputs.
-    acc_0.low = vld1q_s32(bias_ptr);
-    acc_1.low = vld1q_s32(bias_ptr);
-    acc_0.high = vld1q_s32(bias_ptr + 4);
-    acc_1.high = vld1q_s32(bias_ptr + 4);
-
-    acc_0 = MultiplyAccumulateRow(acc_0, filter.f0, filter.f1, filter.f2,
-                                  input_0, input_1, input_2);
-
-    acc_1 = MultiplyAccumulateRow(acc_1, filter.f0, filter.f1, filter.f2,
-                                  input_2, input_3, input_4);
-
-    acc_0 = MultiplyAccumulateRow(acc_0, filter.f3, filter.f4, filter.f5,
-                                  input_5, input_6, input_7);
-
-    acc_1 = MultiplyAccumulateRow(acc_1, filter.f3, filter.f4, filter.f5,
-                                  input_7, input_8, input_9);
-
-    // Load last row.
-    ptr += input_row_size;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    temp_3 = vld1_u8(ptr + 3 * input_depth);
-    temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-
-    acc_0 = MultiplyAccumulateRow(acc_0, filter.f6, filter.f7, filter.f8,
-                                  input_0, input_1, input_2);
-
-    acc_1 = MultiplyAccumulateRow(acc_1, filter.f6, filter.f7, filter.f8,
-                                  input_2, input_3, input_4);
-
-    DownquantizeAndStore2Output(
-        acc_0, acc_1, output_offset, output_multiplier, output_shift,
-        output_activation_min, output_activation_max, output_ptr, output_depth);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<4, 4, 2, 2> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    // Reuse 4x2 kernel twice.
-    ConvKernel3x3FilterDepth8<4, 2, 2, 2>::Run(
-        input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
-        filter_offset, bias_ptr, output_offset, output_multiplier, output_shift,
-        output_activation_min, output_activation_max, output_ptr, output_depth,
-        output_width);
-
-    ConvKernel3x3FilterDepth8<4, 2, 2, 2>::Run(
-        input_ptr + 4 * input_depth, input_depth, input_offset, input_row_size,
-        filter_ptr, filter_offset, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max,
-        output_ptr + 2 * output_depth, output_depth, output_width);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<4, 1, 2, 2> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    const int output_row_size = output_depth * output_width;
-
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8;
-    uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5, temp_6, temp_7,
-        temp_8;
-
-    const uint8* ptr = input_ptr;
-
-    // Load all inputs for top output.
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    ptr += input_row_size;
-    temp_3 = vld1_u8(ptr);
-    temp_4 = vld1_u8(ptr + input_depth);
-    temp_5 = vld1_u8(ptr + 2 * input_depth);
-    ptr += input_row_size;
-    temp_6 = vld1_u8(ptr);
-    temp_7 = vld1_u8(ptr + input_depth);
-    temp_8 = vld1_u8(ptr + 2 * input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-
-    // Second output.
-    output_ptr += output_row_size;
-
-    ptr += input_row_size;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    ptr += input_row_size;
-    temp_3 = vld1_u8(ptr);
-    temp_4 = vld1_u8(ptr + input_depth);
-    temp_5 = vld1_u8(ptr + 2 * input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_6, input_7, input_8, input_0, input_1, input_2, input_3,
-        input_4, input_5, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-
-    // Third output.
-    output_ptr += output_row_size;
-
-    ptr += input_row_size;
-    temp_6 = vld1_u8(ptr);
-    temp_7 = vld1_u8(ptr + input_depth);
-    temp_8 = vld1_u8(ptr + 2 * input_depth);
-    ptr += input_row_size;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_3, input_4, input_5, input_6, input_7, input_8, input_0,
-        input_1, input_2, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-
-    // Fourth output.
-    output_ptr += output_row_size;
-
-    ptr += input_row_size;
-    temp_3 = vld1_u8(ptr);
-    temp_4 = vld1_u8(ptr + input_depth);
-    temp_5 = vld1_u8(ptr + 2 * input_depth);
-    ptr += input_row_size;
-    temp_6 = vld1_u8(ptr);
-    temp_7 = vld1_u8(ptr + input_depth);
-    temp_8 = vld1_u8(ptr + 2 * input_depth);
-
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
-
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<2, 2, 2, 2> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    Int32x8 acc_0, acc_1, acc_2, acc_3;
-    acc_0.low = vld1q_s32(bias_ptr);
-    acc_1.low = vld1q_s32(bias_ptr);
-    acc_2.low = vld1q_s32(bias_ptr);
-    acc_3.low = vld1q_s32(bias_ptr);
-
-    bias_ptr += 4;
-    acc_0.high = vld1q_s32(bias_ptr);
-    acc_1.high = vld1q_s32(bias_ptr);
-    acc_2.high = vld1q_s32(bias_ptr);
-    acc_3.high = vld1q_s32(bias_ptr);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-
-    // Add scope for input registers to help the compiler know that it is
-    // not needed.
-    {
-      // To process 2x2 outputs using a 3x3 filter at stride 2, we require
-      // 5x5 inputs. We load the first 5x2 inputs at a time.
-      int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-          input_7, input_8, input_9;
-
-      const uint8* ptr = input_ptr;
-
-      // Load inputs.
-      {
-        uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4;
-
-        temp_0 = vld1_u8(ptr);
-        temp_1 = vld1_u8(ptr + input_depth);
-        temp_2 = vld1_u8(ptr + 2 * input_depth);
-        temp_3 = vld1_u8(ptr + 3 * input_depth);
-        temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-        input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-        input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-        input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-        input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-        input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-        input_0 = vaddq_s16(input_0, input_offset_vec);
-        input_1 = vaddq_s16(input_1, input_offset_vec);
-        input_2 = vaddq_s16(input_2, input_offset_vec);
-        input_3 = vaddq_s16(input_3, input_offset_vec);
-        input_4 = vaddq_s16(input_4, input_offset_vec);
-
-        ptr += input_row_size;
-        temp_0 = vld1_u8(ptr);
-        temp_1 = vld1_u8(ptr + input_depth);
-        temp_2 = vld1_u8(ptr + 2 * input_depth);
-        temp_3 = vld1_u8(ptr + 3 * input_depth);
-        temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-        input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-        input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-        input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-        input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-        input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-        input_5 = vaddq_s16(input_5, input_offset_vec);
-        input_6 = vaddq_s16(input_6, input_offset_vec);
-        input_7 = vaddq_s16(input_7, input_offset_vec);
-        input_8 = vaddq_s16(input_8, input_offset_vec);
-        input_9 = vaddq_s16(input_9, input_offset_vec);
-      }
-
-      acc_0 = MultiplyAccumulateRow(acc_0, filter.f0, filter.f1, filter.f2,
-                                    input_0, input_1, input_2);
-
-      acc_1 = MultiplyAccumulateRow(acc_1, filter.f0, filter.f1, filter.f2,
-                                    input_2, input_3, input_4);
-
-      acc_0 = MultiplyAccumulateRow(acc_0, filter.f3, filter.f4, filter.f5,
-                                    input_5, input_6, input_7);
-
-      acc_1 = MultiplyAccumulateRow(acc_1, filter.f3, filter.f4, filter.f5,
-                                    input_7, input_8, input_9);
-
-      // Load next inputs.
-      {
-        uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4;
-
-        ptr += input_row_size;
-        temp_0 = vld1_u8(ptr);
-        temp_1 = vld1_u8(ptr + input_depth);
-        temp_2 = vld1_u8(ptr + 2 * input_depth);
-        temp_3 = vld1_u8(ptr + 3 * input_depth);
-        temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-        input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-        input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-        input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-        input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-        input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-        input_0 = vaddq_s16(input_0, input_offset_vec);
-        input_1 = vaddq_s16(input_1, input_offset_vec);
-        input_2 = vaddq_s16(input_2, input_offset_vec);
-        input_3 = vaddq_s16(input_3, input_offset_vec);
-        input_4 = vaddq_s16(input_4, input_offset_vec);
-
-        ptr += input_row_size;
-        temp_0 = vld1_u8(ptr);
-        temp_1 = vld1_u8(ptr + input_depth);
-        temp_2 = vld1_u8(ptr + 2 * input_depth);
-        temp_3 = vld1_u8(ptr + 3 * input_depth);
-        temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-        input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-        input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-        input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-        input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-        input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-        input_5 = vaddq_s16(input_5, input_offset_vec);
-        input_6 = vaddq_s16(input_6, input_offset_vec);
-        input_7 = vaddq_s16(input_7, input_offset_vec);
-        input_8 = vaddq_s16(input_8, input_offset_vec);
-        input_9 = vaddq_s16(input_9, input_offset_vec);
-      }
-
-      acc_0 = MultiplyAccumulateRow(acc_0, filter.f6, filter.f7, filter.f8,
-                                    input_0, input_1, input_2);
-
-      acc_1 = MultiplyAccumulateRow(acc_1, filter.f6, filter.f7, filter.f8,
-                                    input_2, input_3, input_4);
-
-      // Moving onto the two bottom outputs.
-      acc_2 = MultiplyAccumulateRow(acc_2, filter.f0, filter.f1, filter.f2,
-                                    input_0, input_1, input_2);
-
-      acc_3 = MultiplyAccumulateRow(acc_3, filter.f0, filter.f1, filter.f2,
-                                    input_2, input_3, input_4);
-
-      acc_2 = MultiplyAccumulateRow(acc_2, filter.f3, filter.f4, filter.f5,
-                                    input_5, input_6, input_7);
-
-      acc_3 = MultiplyAccumulateRow(acc_3, filter.f3, filter.f4, filter.f5,
-                                    input_7, input_8, input_9);
-
-      // Load last input row.
-      {
-        uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4;
-
-        ptr += input_row_size;
-        temp_0 = vld1_u8(ptr);
-        temp_1 = vld1_u8(ptr + input_depth);
-        temp_2 = vld1_u8(ptr + 2 * input_depth);
-        temp_3 = vld1_u8(ptr + 3 * input_depth);
-        temp_4 = vld1_u8(ptr + 4 * input_depth);
-
-        input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-        input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-        input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-        input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-        input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-
-        input_0 = vaddq_s16(input_0, input_offset_vec);
-        input_1 = vaddq_s16(input_1, input_offset_vec);
-        input_2 = vaddq_s16(input_2, input_offset_vec);
-        input_3 = vaddq_s16(input_3, input_offset_vec);
-        input_4 = vaddq_s16(input_4, input_offset_vec);
-      }
-
-      acc_2 = MultiplyAccumulateRow(acc_2, filter.f6, filter.f7, filter.f8,
-                                    input_0, input_1, input_2);
-
-      acc_3 = MultiplyAccumulateRow(acc_3, filter.f6, filter.f7, filter.f8,
-                                    input_2, input_3, input_4);
-    }
-
-    DownquantizeAndStore2x2Output(acc_0, acc_1, acc_2, acc_3, output_offset,
-                                  output_multiplier, output_shift,
-                                  output_activation_min, output_activation_max,
-                                  output_ptr, output_depth, output_width);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<2, 4, 2, 2> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    // Reuse 2x2 kernel twice.
-    ConvKernel3x3FilterDepth8<2, 2, 2, 2>::Run(
-        input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
-        filter_offset, bias_ptr, output_offset, output_multiplier, output_shift,
-        output_activation_min, output_activation_max, output_ptr, output_depth,
-        output_width);
-
-    ConvKernel3x3FilterDepth8<2, 2, 2, 2>::Run(
-        input_ptr + 4 * input_depth, input_depth, input_offset, input_row_size,
-        filter_ptr, filter_offset, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max,
-        output_ptr + 2 * output_depth, output_depth, output_width);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<2, 1, 2, 2> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    const int output_row_size = output_depth * output_width;
-
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8;
-    uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5, temp_6, temp_7,
-        temp_8;
-
-    const uint8* ptr = input_ptr;
-
-    // Load all inputs for top output.
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    ptr += input_row_size;
-    temp_3 = vld1_u8(ptr);
-    temp_4 = vld1_u8(ptr + input_depth);
-    temp_5 = vld1_u8(ptr + 2 * input_depth);
-    ptr += input_row_size;
-    temp_6 = vld1_u8(ptr);
-    temp_7 = vld1_u8(ptr + input_depth);
-    temp_8 = vld1_u8(ptr + 2 * input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-
-    // Second output.
-    output_ptr += output_row_size;
-
-    ptr += input_row_size;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    ptr += input_row_size;
-    temp_3 = vld1_u8(ptr);
-    temp_4 = vld1_u8(ptr + input_depth);
-    temp_5 = vld1_u8(ptr + 2 * input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_6, input_7, input_8, input_0, input_1, input_2, input_3,
-        input_4, input_5, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<1, 2, 2, 2> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8;
-    uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5, temp_6, temp_7,
-        temp_8;
-
-    const uint8* ptr = input_ptr;
-
-    // Load all inputs for top output.
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    ptr += input_row_size;
-    temp_3 = vld1_u8(ptr);
-    temp_4 = vld1_u8(ptr + input_depth);
-    temp_5 = vld1_u8(ptr + 2 * input_depth);
-    ptr += input_row_size;
-    temp_6 = vld1_u8(ptr);
-    temp_7 = vld1_u8(ptr + input_depth);
-    temp_8 = vld1_u8(ptr + 2 * input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-
-    // Second output.
-    output_ptr += output_depth;
-
-    ptr = input_ptr + 3 * input_depth;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    ptr += input_row_size;
-    temp_3 = vld1_u8(ptr);
-    temp_4 = vld1_u8(ptr + input_depth);
-    ptr += input_row_size;
-    temp_6 = vld1_u8(ptr);
-    temp_7 = vld1_u8(ptr + input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_2, input_0, input_1, input_5, input_3, input_4, input_8,
-        input_6, input_7, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-  }
-};
-
-template <>
-struct ConvKernel3x3FilterDepth8<1, 4, 2, 2> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8;
-    uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5, temp_6, temp_7,
-        temp_8;
-
-    const uint8* ptr = input_ptr;
-
-    // Load all inputs for top output.
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    temp_2 = vld1_u8(ptr + 2 * input_depth);
-    ptr += input_row_size;
-    temp_3 = vld1_u8(ptr);
-    temp_4 = vld1_u8(ptr + input_depth);
-    temp_5 = vld1_u8(ptr + 2 * input_depth);
-    ptr += input_row_size;
-    temp_6 = vld1_u8(ptr);
-    temp_7 = vld1_u8(ptr + input_depth);
-    temp_8 = vld1_u8(ptr + 2 * input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-
-    // Second output.
-    output_ptr += output_depth;
-
-    ptr = input_ptr + 3 * input_depth;
-    temp_0 = vld1_u8(ptr);
-    temp_1 = vld1_u8(ptr + input_depth);
-    ptr += input_row_size;
-    temp_3 = vld1_u8(ptr);
-    temp_4 = vld1_u8(ptr + input_depth);
-    ptr += input_row_size;
-    temp_6 = vld1_u8(ptr);
-    temp_7 = vld1_u8(ptr + input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
-
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_2, input_0, input_1, input_5, input_3, input_4, input_8,
-        input_6, input_7, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-
-    // Third output.
-    output_ptr += output_depth;
-
-    ptr = input_ptr + 5 * input_depth;
-    temp_2 = vld1_u8(ptr);
-    temp_0 = vld1_u8(ptr + input_depth);
-    ptr += input_row_size;
-    temp_5 = vld1_u8(ptr);
-    temp_3 = vld1_u8(ptr + input_depth);
-    ptr += input_row_size;
-    temp_8 = vld1_u8(ptr);
-    temp_6 = vld1_u8(ptr + input_depth);
-
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
-
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_1, input_2, input_0, input_4, input_5, input_3, input_7,
-        input_8, input_6, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-
-    // Fourth output.
-    output_ptr += output_depth;
-
-    ptr = input_ptr + 7 * input_depth;
-    temp_1 = vld1_u8(ptr);
-    temp_2 = vld1_u8(ptr + input_depth);
-    ptr += input_row_size;
-    temp_4 = vld1_u8(ptr);
-    temp_5 = vld1_u8(ptr + input_depth);
-    ptr += input_row_size;
-    temp_7 = vld1_u8(ptr);
-    temp_8 = vld1_u8(ptr + input_depth);
-
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
-
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-  }
-};
-
-template 
-struct ConvKernel3x3FilterDepth8<1, 1, kFixedStrideWidth, kFixedStrideHeight> {
-  static inline void Run(const uint8* input_ptr, int input_depth,
-                         int32 input_offset, int input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int output_depth, int output_width) {
-    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
-
-    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8;
-
-    uint8x8_t temp_0 = vld1_u8(input_ptr);
-    uint8x8_t temp_1 = vld1_u8(input_ptr + input_depth);
-    uint8x8_t temp_2 = vld1_u8(input_ptr + 2 * input_depth);
-
-    input_ptr += input_row_size;
-    uint8x8_t temp_3 = vld1_u8(input_ptr);
-    uint8x8_t temp_4 = vld1_u8(input_ptr + input_depth);
-    uint8x8_t temp_5 = vld1_u8(input_ptr + 2 * input_depth);
-
-    input_ptr += input_row_size;
-    uint8x8_t temp_6 = vld1_u8(input_ptr);
-    uint8x8_t temp_7 = vld1_u8(input_ptr + input_depth);
-    uint8x8_t temp_8 = vld1_u8(input_ptr + 2 * input_depth);
-
-    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
-    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
-    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
-    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
-    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
-    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
-    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
-    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
-    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
-
-    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
-    input_0 = vaddq_s16(input_0, input_offset_vec);
-    input_1 = vaddq_s16(input_1, input_offset_vec);
-    input_2 = vaddq_s16(input_2, input_offset_vec);
-    input_3 = vaddq_s16(input_3, input_offset_vec);
-    input_4 = vaddq_s16(input_4, input_offset_vec);
-    input_5 = vaddq_s16(input_5, input_offset_vec);
-    input_6 = vaddq_s16(input_6, input_offset_vec);
-    input_7 = vaddq_s16(input_7, input_offset_vec);
-    input_8 = vaddq_s16(input_8, input_offset_vec);
-
-    DotProductAndStore(
-        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-        input_7, input_8, bias_ptr, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max, output_ptr);
-  }
-};
-
-inline void ShuffleInput(const uint8* input_ptr, int input_depth,
-                         int input_width, int input_height, int output_depth,
-                         int output_width, int output_height,
-                         uint8* output_ptr) {
-  const int input_row_size = input_depth * input_width;
-
-  for (int y = 0; y < output_height; y++) {
-    const uint8* ptr = input_ptr;
-    for (int x = 0; x < output_width; x++) {
-      memcpy(output_ptr, ptr, output_depth);
-      output_ptr += output_depth;
-      ptr += input_depth;
-    }
-    input_ptr += input_row_size;
-  }
-}
-
-template 
-struct ConvRow3x3FilterDepth8 {};
-
-template 
-struct ConvRow3x3FilterDepth8<1, kFixedStrideWidth, kFixedStrideHeight> {
-  static inline void Run(const uint8* input_data, int start_x, int start_y,
-                         int input_depth, int input_width, int input_height,
-                         int input_row_size, int32 input_offset,
-                         const uint8* filter_data, int32 filter_offset,
-                         const int32* bias_data, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_data,
-                         int output_depth, int output_width,
-                         uint8* shuffle_workspace) {
-    int out_x = start_x;
-
-    // 1x4 at a time.
-    for (; out_x <= output_width - 4; out_x += 4) {
-      const int32* bias_ptr = bias_data;
-      const uint8* filter_ptr = filter_data;
-
-      const uint8* input_ptr = input_data;
-      uint8* output_ptr = output_data;
-
-      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
-        ConvKernel3x3FilterDepth8<1, 4, kFixedStrideWidth, kFixedStrideHeight>::
-            Run(input_ptr, input_depth, input_offset, input_row_size,
-                filter_ptr, filter_offset, bias_ptr, output_offset,
-                output_multiplier, output_shift, output_activation_min,
-                output_activation_max, output_ptr, output_depth, output_width);
-
-        input_ptr += 8;
-        output_ptr += 8;
-        filter_ptr += 8;
-        bias_ptr += 8;
-      }
-
-      input_data += 4 * kFixedStrideWidth * input_depth;
-      output_data += 4 * output_depth;
-    }
-
-    // 1x1 at a time.
-    for (; out_x < output_width; out_x++) {
-      const int32* bias_ptr = bias_data;
-      const uint8* filter_ptr = filter_data;
-
-      const uint8* input_ptr = input_data;
-      uint8* output_ptr = output_data;
-
-      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
-        ConvKernel3x3FilterDepth8<1, 1, kFixedStrideWidth, kFixedStrideHeight>::
-            Run(input_ptr, input_depth, input_offset, input_row_size,
-                filter_ptr, filter_offset, bias_ptr, output_offset,
-                output_multiplier, output_shift, output_activation_min,
-                output_activation_max, output_ptr, output_depth, output_width);
+#define DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE 10 * 10 * 64
 
-        input_ptr += 8;
-        output_ptr += 8;
-        filter_ptr += 8;
-        bias_ptr += 8;
-      }
+template 
+struct DepthwiseConvWindow {};
 
-      input_data += kFixedStrideWidth * input_depth;
-      output_data += output_depth;
-    }
-  }
-};
+// clang-format gets confused with this file and ends up formatting lines to
+// be larger than 80 characters. Turn off here and back on at the end of the
+// file.
 
-template 
-struct ConvRow3x3FilterDepth8<2, kFixedStrideWidth, kFixedStrideHeight> {
-  static inline void Run(const uint8* input_data, int start_x, int start_y,
-                         int input_depth, int input_width, int input_height,
-                         int input_row_size, int32 input_offset,
-                         const uint8* filter_data, int32 filter_offset,
-                         const int32* bias_data, int32 output_offset,
+// clang-format off
+template <>
+struct DepthwiseConvWindow<8, 1, 1> {
+ public:
+  static inline void Run(const uint8* input_ptr, int64_t input_depth,
+                         int32 input_offset, int64_t input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
                          int32 output_multiplier, int output_shift,
                          int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_data,
-                         int output_depth, int output_width,
-                         uint8* shuffle_workspace) {
-    int out_x = start_x;
-
-    // 2x4 at a time.
-    for (; out_x <= output_width - 4; out_x += 4) {
-      const int32* bias_ptr = bias_data;
-      const uint8* filter_ptr = filter_data;
-
-      const uint8* input_ptr = input_data;
-      uint8* output_ptr = output_data;
-
-      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
-        ConvKernel3x3FilterDepth8<2, 4, kFixedStrideWidth, kFixedStrideHeight>::
-            Run(input_ptr, input_depth, input_offset, input_row_size,
-                filter_ptr, filter_offset, bias_ptr, output_offset,
-                output_multiplier, output_shift, output_activation_min,
-                output_activation_max, output_ptr, output_depth, output_width);
-
-        input_ptr += 8;
-        output_ptr += 8;
-        filter_ptr += 8;
-        bias_ptr += 8;
-      }
-
-      input_data += 4 * kFixedStrideWidth * input_depth;
-      output_data += 4 * output_depth;
-    }
-
-    // 2x2 at a time.
-    for (; out_x <= output_width - 2; out_x += 2) {
-      const int32* bias_ptr = bias_data;
-      const uint8* filter_ptr = filter_data;
-
-      const uint8* input_ptr = input_data;
-      uint8* output_ptr = output_data;
-
-      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
-        ConvKernel3x3FilterDepth8<2, 2, kFixedStrideWidth, kFixedStrideHeight>::
-            Run(input_ptr, input_depth, input_offset, input_row_size,
-                filter_ptr, filter_offset, bias_ptr, output_offset,
-                output_multiplier, output_shift, output_activation_min,
-                output_activation_max, output_ptr, output_depth, output_width);
-
-        input_ptr += 8;
-        output_ptr += 8;
-        filter_ptr += 8;
-        bias_ptr += 8;
-      }
-
-      input_data += 2 * kFixedStrideWidth * input_depth;
-      output_data += 2 * output_depth;
-    }
-
-    // 2x1 at a time.
-    for (; out_x < output_width; out_x++) {
-      const int32* bias_ptr = bias_data;
-      const uint8* filter_ptr = filter_data;
-
-      const uint8* input_ptr = input_data;
-      uint8* output_ptr = output_data;
-
-      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
-        ConvKernel3x3FilterDepth8<2, 1, kFixedStrideWidth, kFixedStrideHeight>::
-            Run(input_ptr, input_depth, input_offset, input_row_size,
-                filter_ptr, filter_offset, bias_ptr, output_offset,
-                output_multiplier, output_shift, output_activation_min,
-                output_activation_max, output_ptr, output_depth, output_width);
-
-        input_ptr += 8;
-        output_ptr += 8;
-        filter_ptr += 8;
-        bias_ptr += 8;
-      }
-
-      input_data += kFixedStrideWidth * input_depth;
-      output_data += output_depth;
-    }
+                         int32 output_activation_max, uint8* output_ptr,
+                         int64_t output_depth, int output_width,
+                         int output_window_height,
+                         int output_window_width) {
+    const int64_t output_row_size = output_depth * output_width;
+    const int64_t input_width_increment = 2 * input_depth;
+    const int64_t input_height_increment = 2 * input_row_size;
+    const int64_t output_height_increment = 2 * output_row_size;
+
+#define DEPTHWISECONV_LABEL_HEIGHT_2_LOOP "1"
+#define DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP "2"
+#define DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1 "3"
+#define DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP "4"
+#define DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP "5"
+#define DEPTHWISECONV_LABEL_HEIGHT_1 "6"
+#define DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP "7"
+#define DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1 "8"
+#define DEPTHWISECONV_LABEL_HEIGHT_1_END "9"
+
+    asm volatile(
+        // Performs depthwise convolutions for a window specified by
+        // |output_window_height| and |output_window_width|. The inner-most loop
+        // processes 2x2 outputs, and any leftovers at the end.
+        //
+        // Algorithm works as follows:
+        //
+        //   1. Load filters of 8 depth (8x3x3). Registers v0--v8 hold filter
+        //      values.
+        //   2. For 2 output heights at a time:
+        //        i.  For 2 output widths at a time, load inputs for a 2x1 (2
+        //            height, 1 width) output window (4x3 input window).
+        //            Registers v9--v20 hold input values. Mul-add with
+        //            accumulators v21--v24. Then run activation, downquantize
+        //            and store. Repeat for the next 2x1 output window,
+        //            leveraging overlapping inputs.
+        //        ii. Handle single leftover width if exists.
+        //   3. Handle single leftover height if exists.
+        //        i.  For 2 output widths at a time, load inputs for a 1x2 (1
+        //            height, 2 width) output window (3x4 input window).
+        //            Registers v9--v20 hold input values. Mul-add with
+        //            accumulators v21--v24. Then run activation, downquantize
+        //            and store. Repeat for the next 1x2 output window,
+        //            leveraging overlapping inputs.
+        //        ii. Handle single leftover width if exists.
+        //
+        // Loads are placed as soon as the register is no longer needed and
+        // interleaved with arithmetic operations to take advantage of
+        // dual-issue pipelines. We also add input offsets as far from the loads
+        // as possible to give loads enough cycles to fetch data from memory.
+
+        // Set "constant" registers. These registers may be replaced with temp
+        // values from time to time when there are not enough NEON registers.
+        "dup v26.8h, %w[input_offset]\n"
+        "cmp %w[output_window_height], #2\n"
+        "dup v27.4s, %w[output_multiplier]\n"
+
+        "neg w5, %w[output_shift]\n"
+        "dup v28.4s, w5\n"
+
+        "dup v29.4s, %w[output_offset]\n"
+        "dup v30.4s, %w[output_activation_min]\n"
+        "dup v31.4s, %w[output_activation_max]\n"
+
+        "add x5, %[bias_ptr], #16\n"
+        "dup v9.8h, %w[filter_offset]\n"
+
+        // Load filters and add offsets.
+        "ld1 {v0.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "ld1 {v1.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v0.8h, v9.8h, v0.8b\n"
+        "ld1 {v2.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v1.8h, v9.8h, v1.8b\n"
+        "ld1 {v3.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v2.8h, v9.8h, v2.8b\n"
+        "ld1 {v4.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v3.8h, v9.8h, v3.8b\n"
+        "ld1 {v5.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v4.8h, v9.8h, v4.8b\n"
+        "ld1 {v6.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v5.8h, v9.8h, v5.8b\n"
+        "ld1 {v7.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v6.8h, v9.8h, v6.8b\n"
+        "ld1 {v8.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v7.8h, v9.8h, v7.8b\n"
+        "uaddw v8.8h, v9.8h, v8.8b\n"
+
+        "blt " DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP "f\n"
+
+        //"loop_%=:\n"
+        DEPTHWISECONV_LABEL_HEIGHT_2_LOOP ":\n"
+          // This loop processes 2x2 outputs. To avoid register exhaustion,
+          // inputs for the left 2 outputs are loaded first, then the right
+          // two outputs.
+          "mov x6, %[input_ptr]\n"
+          "mov x4, x6\n"
+          "ld1 {v9.8b}, [x4], %[input_depth]\n"
+          "add x0, x6, %[input_row_size]\n"
+          "ld1 {v10.8b}, [x4], %[input_depth]\n"
+          "add x1, x0, %[input_row_size]\n"
+          "ld1 {v11.8b}, [x4], %[input_depth]\n"
+          "add x7, x1, %[input_row_size]\n"
+          "ld1 {v12.8b}, [x0], %[input_depth]\n"
+          "mov w8, %w[output_window_width]\n"
+          "ld1 {v13.8b}, [x0], %[input_depth]\n"
+          "mov x2, %[output_ptr]\n"
+          "ld1 {v14.8b}, [x0], %[input_depth]\n"
+          "add x3, %[output_ptr], %[output_row_size]\n"
+          "ld1 {v15.8b}, [x1], %[input_depth]\n"
+          "cmp w8, #2\n"
+          "ld1 {v16.8b}, [x1], %[input_depth]\n"
+          "ld1 {v17.8b}, [x1], %[input_depth]\n"
+          "ld1 {v18.8b}, [x7], %[input_depth]\n"
+          "ld1 {v19.8b}, [x7], %[input_depth]\n"
+          "ld1 {v20.8b}, [x7], %[input_depth]\n"
+          "ld1 {v21.4s}, [%[bias_ptr]]\n"
+          "ld1 {v22.4s}, [x5]\n"
+          "ld1 {v23.4s}, [%[bias_ptr]]\n"
+          "ld1 {v24.4s}, [x5]\n"
+
+          "uaddw v9.8h, v26.8h, v9.8b\n"
+          "uaddw v10.8h, v26.8h, v10.8b\n"
+          "uaddw v11.8h, v26.8h, v11.8b\n"
+          "uaddw v12.8h, v26.8h, v12.8b\n"
+          "uaddw v13.8h, v26.8h, v13.8b\n"
+          "uaddw v14.8h, v26.8h, v14.8b\n"
+          "uaddw v15.8h, v26.8h, v15.8b\n"
+          "uaddw v16.8h, v26.8h, v16.8b\n"
+          "uaddw v17.8h, v26.8h, v17.8b\n"
+          "uaddw v18.8h, v26.8h, v18.8b\n"
+          "uaddw v19.8h, v26.8h, v19.8b\n"
+          "uaddw v20.8h, v26.8h, v20.8b\n"
+
+          "blt " DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1 "f\n"
+
+          //"loop_%=:\n"
+          DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP ":\n"
+            // Mul-add left outputs.
+            "smlal v21.4s, v0.4h, v9.4h\n"
+            "subs w8, w8, #2\n"
+            "smlal2 v22.4s, v0.8h, v9.8h\n"
+            "cmp w8, #2\n"
+            "smlal v23.4s, v0.4h, v12.4h\n"
+            "ld1 {v9.8b}, [x4]\n"
+            "smlal2 v24.4s, v0.8h, v12.8h\n"
+            "smlal v21.4s, v1.4h, v10.4h\n"
+            "smlal2 v22.4s, v1.8h, v10.8h\n"
+            "smlal v23.4s, v1.4h, v13.4h\n"
+            "smlal2 v24.4s, v1.8h, v13.8h\n"
+            "smlal v21.4s, v2.4h, v11.4h\n"
+            "smlal2 v22.4s, v2.8h, v11.8h\n"
+            "smlal v23.4s, v2.4h, v14.4h\n"
+            "smlal2 v24.4s, v2.8h, v14.8h\n"
+            "smlal v21.4s, v3.4h, v12.4h\n"
+            "smlal2 v22.4s, v3.8h, v12.8h\n"
+            "ld1 {v12.8b}, [x0]\n"
+            "smlal v23.4s, v3.4h, v15.4h\n"
+            "smlal2 v24.4s, v3.8h, v15.8h\n"
+            "smlal v21.4s, v4.4h, v13.4h\n"
+            "smlal2 v22.4s, v4.8h, v13.8h\n"
+            "smlal v23.4s, v4.4h, v16.4h\n"
+            "smlal2 v24.4s, v4.8h, v16.8h\n"
+            "smlal v21.4s, v5.4h, v14.4h\n"
+            "smlal2 v22.4s, v5.8h, v14.8h\n"
+            "smlal v23.4s, v5.4h, v17.4h\n"
+            "smlal2 v24.4s, v5.8h, v17.8h\n"
+            "smlal v21.4s, v6.4h, v15.4h\n"
+            "smlal2 v22.4s, v6.8h, v15.8h\n"
+            "ld1 {v15.8b}, [x1]\n"
+            "smlal v23.4s, v6.4h, v18.4h\n"
+            "smlal2 v24.4s, v6.8h, v18.8h\n"
+            "ld1 {v18.8b}, [x7]\n"
+            "smlal v21.4s, v7.4h, v16.4h\n"
+            "smlal2 v22.4s, v7.8h, v16.8h\n"
+            "smlal v23.4s, v7.4h, v19.4h\n"
+            "smlal2 v24.4s, v7.8h, v19.8h\n"
+            "smlal v21.4s, v8.4h, v17.4h\n"
+            "smlal2 v22.4s, v8.8h, v17.8h\n"
+            "smlal v23.4s, v8.4h, v20.4h\n"
+            "smlal2 v24.4s, v8.8h, v20.8h\n"
+
+            "sqrdmulh v21.4s, v21.4s, v27.4s\n"
+            "sqrdmulh v22.4s, v22.4s, v27.4s\n"
+            "sqrdmulh v23.4s, v23.4s, v27.4s\n"
+            "sqrdmulh v24.4s, v24.4s, v27.4s\n"
+            "and v25.16b, v21.16b, v28.16b\n"
+            "and v29.16b, v22.16b, v28.16b\n"
+            "and v30.16b, v23.16b, v28.16b\n"
+            "and v31.16b, v24.16b, v28.16b\n"
+            "sshr v25.4s, v25.4s, #31\n"
+            "sshr v29.4s, v29.4s, #31\n"
+            "sshr v30.4s, v30.4s, #31\n"
+            "sshr v31.4s, v31.4s, #31\n"
+            "sqadd v21.4s, v21.4s, v25.4s\n"
+            "sqadd v22.4s, v22.4s, v29.4s\n"
+            "dup v29.4s, %w[output_offset]\n"
+            "sqadd v23.4s, v23.4s, v30.4s\n"
+            "dup v30.4s, %w[output_activation_min]\n"
+            "sqadd v24.4s, v24.4s, v31.4s\n"
+            "dup v31.4s, %w[output_activation_max]\n"
+            "srshl v21.4s, v21.4s, v28.4s\n"
+            "srshl v22.4s, v22.4s, v28.4s\n"
+            "srshl v23.4s, v23.4s, v28.4s\n"
+            "srshl v24.4s, v24.4s, v28.4s\n"
+            "add v21.4s, v21.4s, v29.4s\n"
+            "add v22.4s, v22.4s, v29.4s\n"
+            "add v23.4s, v23.4s, v29.4s\n"
+            "add v24.4s, v24.4s, v29.4s\n"
+            "smax v21.4s, v21.4s, v30.4s\n"
+            "smax v22.4s, v22.4s, v30.4s\n"
+            "smax v23.4s, v23.4s, v30.4s\n"
+            "smax v24.4s, v24.4s, v30.4s\n"
+            "smin v21.4s, v21.4s, v31.4s\n"
+            "smin v22.4s, v22.4s, v31.4s\n"
+            "smin v23.4s, v23.4s, v31.4s\n"
+            "smin v24.4s, v24.4s, v31.4s\n"
+            "sqxtn v21.4h, v21.4s\n"
+            "sqxtn v23.4h, v23.4s\n"
+            "sqxtn2 v21.8h, v22.4s\n"
+            "ld1 {v22.4s}, [x5]\n"
+            "sqxtn2 v23.8h, v24.4s\n"
+            "ld1 {v24.4s}, [x5]\n"
+            "sqxtun v21.8b, v21.8h\n"
+            "sqxtun v23.8b, v23.8h\n"
+            "uaddw v9.8h, v26.8h, v9.8b\n"
+            "st1 {v21.8b}, [x2], %[output_depth]\n"
+            "uaddw v12.8h, v26.8h, v12.8b\n"
+            "st1 {v23.8b}, [x3], %[output_depth]\n"
+            "uaddw v15.8h, v26.8h, v15.8b\n"
+            "ld1 {v21.4s}, [%[bias_ptr]]\n"
+            "uaddw v18.8h, v26.8h, v18.8b\n"
+            "ld1 {v23.4s}, [%[bias_ptr]]\n"
+
+            // Mul-add right outputs.
+            "smlal v21.4s, v0.4h, v10.4h\n"
+            "add x6, x6, %[input_width_increment]\n"
+            "smlal2 v22.4s, v0.8h, v10.8h\n"
+            "mov x4, x6\n"
+            "smlal v23.4s, v0.4h, v13.4h\n"
+            "add x0, x6, %[input_row_size]\n"
+            "smlal2 v24.4s, v0.8h, v13.8h\n"
+            "add x1, x0, %[input_row_size]\n"
+            "smlal v21.4s, v1.4h, v11.4h\n"
+            "add x7, x1, %[input_row_size]\n"
+            "smlal2 v22.4s, v1.8h, v11.8h\n"
+            "smlal v23.4s, v1.4h, v14.4h\n"
+            "smlal2 v24.4s, v1.8h, v14.8h\n"
+            "smlal v21.4s, v2.4h, v9.4h\n"
+            "smlal2 v22.4s, v2.8h, v9.8h\n"
+            "ld1 {v9.8b}, [x4], %[input_depth]\n"
+            "smlal v23.4s, v2.4h, v12.4h\n"
+            "ld1 {v10.8b}, [x4], %[input_depth]\n"
+            "smlal2 v24.4s, v2.8h, v12.8h\n"
+            "ld1 {v11.8b}, [x4], %[input_depth]\n"
+            "smlal v21.4s, v3.4h, v13.4h\n"
+            "smlal2 v22.4s, v3.8h, v13.8h\n"
+            "smlal v23.4s, v3.4h, v16.4h\n"
+            "smlal2 v24.4s, v3.8h, v16.8h\n"
+            "smlal v21.4s, v4.4h, v14.4h\n"
+            "smlal2 v22.4s, v4.8h, v14.8h\n"
+            "smlal v23.4s, v4.4h, v17.4h\n"
+            "smlal2 v24.4s, v4.8h, v17.8h\n"
+            "smlal v21.4s, v5.4h, v12.4h\n"
+            "smlal2 v22.4s, v5.8h, v12.8h\n"
+            "ld1 {v12.8b}, [x0], %[input_depth]\n"
+            "smlal v23.4s, v5.4h, v15.4h\n"
+            "ld1 {v13.8b}, [x0], %[input_depth]\n"
+            "smlal2 v24.4s, v5.8h, v15.8h\n"
+            "ld1 {v14.8b}, [x0], %[input_depth]\n"
+            "smlal v21.4s, v6.4h, v16.4h\n"
+            "smlal2 v22.4s, v6.8h, v16.8h\n"
+            "smlal v23.4s, v6.4h, v19.4h\n"
+            "smlal2 v24.4s, v6.8h, v19.8h\n"
+            "smlal v21.4s, v7.4h, v17.4h\n"
+            "smlal2 v22.4s, v7.8h, v17.8h\n"
+            "smlal v23.4s, v7.4h, v20.4h\n"
+            "smlal2 v24.4s, v7.8h, v20.8h\n"
+            "smlal v21.4s, v8.4h, v15.4h\n"
+            "smlal2 v22.4s, v8.8h, v15.8h\n"
+            "ld1 {v15.8b}, [x1], %[input_depth]\n"
+            "smlal v23.4s, v8.4h, v18.4h\n"
+            "ld1 {v16.8b}, [x1], %[input_depth]\n"
+            "smlal2 v24.4s, v8.8h, v18.8h\n"
+            "ld1 {v17.8b}, [x1], %[input_depth]\n"
+
+            "sqrdmulh v21.4s, v21.4s, v27.4s\n"
+            "ld1 {v18.8b}, [x7], %[input_depth]\n"
+            "sqrdmulh v22.4s, v22.4s, v27.4s\n"
+            "ld1 {v19.8b}, [x7], %[input_depth]\n"
+            "sqrdmulh v23.4s, v23.4s, v27.4s\n"
+            "ld1 {v20.8b}, [x7], %[input_depth]\n"
+            "sqrdmulh v24.4s, v24.4s, v27.4s\n"
+            "and v25.16b, v21.16b, v28.16b\n"
+            "and v29.16b, v22.16b, v28.16b\n"
+            "and v30.16b, v23.16b, v28.16b\n"
+            "and v31.16b, v24.16b, v28.16b\n"
+            "sshr v25.4s, v25.4s, #31\n"
+            "sshr v29.4s, v29.4s, #31\n"
+            "sshr v30.4s, v30.4s, #31\n"
+            "sshr v31.4s, v31.4s, #31\n"
+            "sqadd v21.4s, v21.4s, v25.4s\n"
+            "sqadd v22.4s, v22.4s, v29.4s\n"
+            "dup v29.4s, %w[output_offset]\n"
+            "sqadd v23.4s, v23.4s, v30.4s\n"
+            "dup v30.4s, %w[output_activation_min]\n"
+            "sqadd v24.4s, v24.4s, v31.4s\n"
+            "dup v31.4s, %w[output_activation_max]\n"
+            "srshl v21.4s, v21.4s, v28.4s\n"
+            "srshl v22.4s, v22.4s, v28.4s\n"
+            "srshl v23.4s, v23.4s, v28.4s\n"
+            "srshl v24.4s, v24.4s, v28.4s\n"
+            "add v21.4s, v21.4s, v29.4s\n"
+            "add v22.4s, v22.4s, v29.4s\n"
+            "add v23.4s, v23.4s, v29.4s\n"
+            "add v24.4s, v24.4s, v29.4s\n"
+            "smax v21.4s, v21.4s, v30.4s\n"
+            "smax v22.4s, v22.4s, v30.4s\n"
+            "smax v23.4s, v23.4s, v30.4s\n"
+            "smax v24.4s, v24.4s, v30.4s\n"
+            "smin v21.4s, v21.4s, v31.4s\n"
+            "smin v22.4s, v22.4s, v31.4s\n"
+            "smin v23.4s, v23.4s, v31.4s\n"
+            "smin v24.4s, v24.4s, v31.4s\n"
+            "sqxtn v21.4h, v21.4s\n"
+            "sqxtn v23.4h, v23.4s\n"
+            "sqxtn2 v21.8h, v22.4s\n"
+            "ld1 {v22.4s}, [x5]\n"
+            "sqxtn2 v23.8h, v24.4s\n"
+            "ld1 {v24.4s}, [x5]\n"
+            "sqxtun v21.8b, v21.8h\n"
+            "sqxtun v23.8b, v23.8h\n"
+            "uaddw v9.8h, v26.8h, v9.8b\n"
+            "st1 {v21.8b}, [x2], %[output_depth]\n"
+            "uaddw v10.8h, v26.8h, v10.8b\n"
+            "st1 {v23.8b}, [x3], %[output_depth]\n"
+            "uaddw v11.8h, v26.8h, v11.8b\n"
+            "uaddw v12.8h, v26.8h, v12.8b\n"
+            "uaddw v13.8h, v26.8h, v13.8b\n"
+            "uaddw v14.8h, v26.8h, v14.8b\n"
+            "uaddw v15.8h, v26.8h, v15.8b\n"
+            "ld1 {v21.4s}, [%[bias_ptr]]\n"
+            "uaddw v16.8h, v26.8h, v16.8b\n"
+            "ld1 {v23.4s}, [%[bias_ptr]]\n"
+            "uaddw v17.8h, v26.8h, v17.8b\n"
+            "uaddw v18.8h, v26.8h, v18.8b\n"
+            "uaddw v19.8h, v26.8h, v19.8b\n"
+            "uaddw v20.8h, v26.8h, v20.8b\n"
+
+            "bge " DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP "b\n"
+
+          // Do last width column if exists.
+          "cmp w8, #1\n"
+          "blt " DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP "f\n"
+
+          DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1 ":\n"
+          "smlal v21.4s, v0.4h, v9.4h\n"
+          "smlal2 v22.4s, v0.8h, v9.8h\n"
+          "smlal v23.4s, v0.4h, v12.4h\n"
+          "smlal2 v24.4s, v0.8h, v12.8h\n"
+          "smlal v21.4s, v1.4h, v10.4h\n"
+          "smlal2 v22.4s, v1.8h, v10.8h\n"
+          "smlal v23.4s, v1.4h, v13.4h\n"
+          "smlal2 v24.4s, v1.8h, v13.8h\n"
+          "smlal v21.4s, v2.4h, v11.4h\n"
+          "smlal2 v22.4s, v2.8h, v11.8h\n"
+          "smlal v23.4s, v2.4h, v14.4h\n"
+          "smlal2 v24.4s, v2.8h, v14.8h\n"
+          "smlal v21.4s, v3.4h, v12.4h\n"
+          "smlal2 v22.4s, v3.8h, v12.8h\n"
+          "smlal v23.4s, v3.4h, v15.4h\n"
+          "smlal2 v24.4s, v3.8h, v15.8h\n"
+          "smlal v21.4s, v4.4h, v13.4h\n"
+          "smlal2 v22.4s, v4.8h, v13.8h\n"
+          "smlal v23.4s, v4.4h, v16.4h\n"
+          "smlal2 v24.4s, v4.8h, v16.8h\n"
+          "smlal v21.4s, v5.4h, v14.4h\n"
+          "smlal2 v22.4s, v5.8h, v14.8h\n"
+          "smlal v23.4s, v5.4h, v17.4h\n"
+          "smlal2 v24.4s, v5.8h, v17.8h\n"
+          "smlal v21.4s, v6.4h, v15.4h\n"
+          "smlal2 v22.4s, v6.8h, v15.8h\n"
+          "smlal v23.4s, v6.4h, v18.4h\n"
+          "smlal2 v24.4s, v6.8h, v18.8h\n"
+          "smlal v21.4s, v7.4h, v16.4h\n"
+          "smlal2 v22.4s, v7.8h, v16.8h\n"
+          "smlal v23.4s, v7.4h, v19.4h\n"
+          "smlal2 v24.4s, v7.8h, v19.8h\n"
+          "smlal v21.4s, v8.4h, v17.4h\n"
+          "smlal2 v22.4s, v8.8h, v17.8h\n"
+          "smlal v23.4s, v8.4h, v20.4h\n"
+          "smlal2 v24.4s, v8.8h, v20.8h\n"
+
+          "sqrdmulh v21.4s, v21.4s, v27.4s\n"
+          "sqrdmulh v22.4s, v22.4s, v27.4s\n"
+          "sqrdmulh v23.4s, v23.4s, v27.4s\n"
+          "sqrdmulh v24.4s, v24.4s, v27.4s\n"
+          "and v9.16b, v21.16b, v28.16b\n"
+          "and v12.16b, v22.16b, v28.16b\n"
+          "and v15.16b, v23.16b, v28.16b\n"
+          "and v18.16b, v24.16b, v28.16b\n"
+          "sshr v9.4s, v9.4s, #31\n"
+          "sshr v12.4s, v12.4s, #31\n"
+          "sshr v15.4s, v15.4s, #31\n"
+          "sshr v18.4s, v18.4s, #31\n"
+          "sqadd v21.4s, v21.4s, v9.4s\n"
+          "sqadd v22.4s, v22.4s, v12.4s\n"
+          "sqadd v23.4s, v23.4s, v15.4s\n"
+          "sqadd v24.4s, v24.4s, v18.4s\n"
+          "srshl v21.4s, v21.4s, v28.4s\n"
+          "srshl v22.4s, v22.4s, v28.4s\n"
+          "srshl v23.4s, v23.4s, v28.4s\n"
+          "srshl v24.4s, v24.4s, v28.4s\n"
+          "add v21.4s, v21.4s, v29.4s\n"
+          "add v22.4s, v22.4s, v29.4s\n"
+          "add v23.4s, v23.4s, v29.4s\n"
+          "add v24.4s, v24.4s, v29.4s\n"
+          "smax v21.4s, v21.4s, v30.4s\n"
+          "smax v22.4s, v22.4s, v30.4s\n"
+          "smax v23.4s, v23.4s, v30.4s\n"
+          "smax v24.4s, v24.4s, v30.4s\n"
+          "smin v21.4s, v21.4s, v31.4s\n"
+          "smin v22.4s, v22.4s, v31.4s\n"
+          "smin v23.4s, v23.4s, v31.4s\n"
+          "smin v24.4s, v24.4s, v31.4s\n"
+          "sqxtn v21.4h, v21.4s\n"
+          "sqxtn v23.4h, v23.4s\n"
+          "sqxtn2 v21.8h, v22.4s\n"
+          "sqxtn2 v23.8h, v24.4s\n"
+          "sqxtun v21.8b, v21.8h\n"
+          "sqxtun v23.8b, v23.8h\n"
+          "st1 {v21.8b}, [x2], %[output_depth]\n"
+          "st1 {v23.8b}, [x3], %[output_depth]\n"
+
+          DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP ":\n"
+          "subs %w[output_window_height], %w[output_window_height], #2\n"
+          "add %[input_ptr], %[input_ptr], %[input_height_increment]\n"
+          "cmp %w[output_window_height], #2\n"
+          "add %[output_ptr], %[output_ptr], %[output_height_increment]\n"
+          "bge " DEPTHWISECONV_LABEL_HEIGHT_2_LOOP "b\n"
+
+        DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP ":\n"
+        "cmp %w[output_window_height], #1\n"
+        "blt " DEPTHWISECONV_LABEL_HEIGHT_1_END "f\n"
+
+        DEPTHWISECONV_LABEL_HEIGHT_1 ":\n"
+        // Load inputs for 3x4 input window which corresponds to a 1x2 output
+        // window.
+        "mov x4, %[input_ptr]\n"
+        "ld1 {v9.8b}, [x4], %[input_depth]\n"
+        "add x0, %[input_ptr], %[input_row_size]\n"
+        "ld1 {v10.8b}, [x4], %[input_depth]\n"
+        "add x1, x0, %[input_row_size]\n"
+        "ld1 {v11.8b}, [x4], %[input_depth]\n"
+        "add x7, x1, %[input_row_size]\n"
+        "ld1 {v12.8b}, [x4], %[input_depth]\n"
+        "mov w8, %w[output_window_width]\n"
+        "ld1 {v13.8b}, [x0], %[input_depth]\n"
+        "mov x2, %[output_ptr]\n"
+        "ld1 {v14.8b}, [x0], %[input_depth]\n"
+        "add x3, %[output_ptr], %[output_row_size]\n"
+        "ld1 {v15.8b}, [x0], %[input_depth]\n"
+        "cmp w8, #2\n"
+        "ld1 {v16.8b}, [x0], %[input_depth]\n"
+        "ld1 {v17.8b}, [x1], %[input_depth]\n"
+        "ld1 {v18.8b}, [x1], %[input_depth]\n"
+        "ld1 {v19.8b}, [x1], %[input_depth]\n"
+        "ld1 {v20.8b}, [x1], %[input_depth]\n"
+        "ld1 {v21.4s}, [%[bias_ptr]]\n"
+        "ld1 {v22.4s}, [x5]\n"
+        "ld1 {v23.4s}, [%[bias_ptr]]\n"
+        "ld1 {v24.4s}, [x5]\n"
+
+        "uaddw v9.8h, v26.8h, v9.8b\n"
+        "uaddw v10.8h, v26.8h, v10.8b\n"
+        "uaddw v11.8h, v26.8h, v11.8b\n"
+        "uaddw v12.8h, v26.8h, v12.8b\n"
+        "uaddw v13.8h, v26.8h, v13.8b\n"
+        "uaddw v14.8h, v26.8h, v14.8b\n"
+        "uaddw v15.8h, v26.8h, v15.8b\n"
+        "uaddw v16.8h, v26.8h, v16.8b\n"
+        "uaddw v17.8h, v26.8h, v17.8b\n"
+        "uaddw v18.8h, v26.8h, v18.8b\n"
+        "uaddw v19.8h, v26.8h, v19.8b\n"
+        "uaddw v20.8h, v26.8h, v20.8b\n"
+
+        "blt " DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1 "f\n"
+
+        //"loop_%=:\n"
+        DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP ":\n"
+          "smlal v21.4s, v0.4h, v9.4h\n"
+          "subs w8, w8, #2\n"
+          "smlal2 v22.4s, v0.8h, v9.8h\n"
+          "cmp w8, #2\n"
+          "smlal v23.4s, v0.4h, v10.4h\n"
+          "add %[input_ptr], %[input_ptr], %[input_width_increment]\n"
+          "smlal2 v24.4s, v0.8h, v10.8h\n"
+          "mov x4, %[input_ptr]\n"
+          "smlal v21.4s, v1.4h, v10.4h\n"
+          "ld1 {v9.8b}, [x4], %[input_depth]\n"
+          "smlal2 v22.4s, v1.8h, v10.8h\n"
+          "ld1 {v10.8b}, [x4], %[input_depth]\n"
+          "smlal v23.4s, v1.4h, v11.4h\n"
+          "add x0, %[input_ptr], %[input_row_size]\n"
+          "smlal2 v24.4s, v1.8h, v11.8h\n"
+          "add x1, x0, %[input_row_size]\n"
+          "smlal v21.4s, v2.4h, v11.4h\n"
+          "add x7, x1, %[input_row_size]\n"
+          "smlal2 v22.4s, v2.8h, v11.8h\n"
+          "ld1 {v11.8b}, [x4], %[input_depth]\n"
+          "smlal v23.4s, v2.4h, v12.4h\n"
+          "smlal2 v24.4s, v2.8h, v12.8h\n"
+          "ld1 {v12.8b}, [x4], %[input_depth]\n"
+          "smlal v21.4s, v3.4h, v13.4h\n"
+          "smlal2 v22.4s, v3.8h, v13.8h\n"
+          "ld1 {v13.8b}, [x0], %[input_depth]\n"
+          "smlal v23.4s, v3.4h, v14.4h\n"
+          "smlal2 v24.4s, v3.8h, v14.8h\n"
+          "smlal v21.4s, v4.4h, v14.4h\n"
+          "smlal2 v22.4s, v4.8h, v14.8h\n"
+          "ld1 {v14.8b}, [x0], %[input_depth]\n"
+          "smlal v23.4s, v4.4h, v15.4h\n"
+          "smlal2 v24.4s, v4.8h, v15.8h\n"
+          "smlal v21.4s, v5.4h, v15.4h\n"
+          "smlal2 v22.4s, v5.8h, v15.8h\n"
+          "ld1 {v15.8b}, [x0], %[input_depth]\n"
+          "smlal v23.4s, v5.4h, v16.4h\n"
+          "smlal2 v24.4s, v5.8h, v16.8h\n"
+          "ld1 {v16.8b}, [x0], %[input_depth]\n"
+          "smlal v21.4s, v6.4h, v17.4h\n"
+          "smlal2 v22.4s, v6.8h, v17.8h\n"
+          "ld1 {v17.8b}, [x1], %[input_depth]\n"
+          "smlal v23.4s, v6.4h, v18.4h\n"
+          "smlal2 v24.4s, v6.8h, v18.8h\n"
+          "smlal v21.4s, v7.4h, v18.4h\n"
+          "smlal2 v22.4s, v7.8h, v18.8h\n"
+          "ld1 {v18.8b}, [x1], %[input_depth]\n"
+          "smlal v23.4s, v7.4h, v19.4h\n"
+          "smlal2 v24.4s, v7.8h, v19.8h\n"
+          "smlal v21.4s, v8.4h, v19.4h\n"
+          "smlal2 v22.4s, v8.8h, v19.8h\n"
+          "ld1 {v19.8b}, [x1], %[input_depth]\n"
+          "smlal v23.4s, v8.4h, v20.4h\n"
+          "smlal2 v24.4s, v8.8h, v20.8h\n"
+          "ld1 {v20.8b}, [x1], %[input_depth]\n"
+
+          "sqrdmulh v21.4s, v21.4s, v27.4s\n"
+          "sqrdmulh v22.4s, v22.4s, v27.4s\n"
+          "sqrdmulh v23.4s, v23.4s, v27.4s\n"
+          "sqrdmulh v24.4s, v24.4s, v27.4s\n"
+          "and v25.16b, v21.16b, v28.16b\n"
+          "and v29.16b, v22.16b, v28.16b\n"
+          "and v30.16b, v23.16b, v28.16b\n"
+          "and v31.16b, v24.16b, v28.16b\n"
+          "sshr v25.4s, v25.4s, #31\n"
+          "sshr v29.4s, v29.4s, #31\n"
+          "sshr v30.4s, v30.4s, #31\n"
+          "sshr v31.4s, v31.4s, #31\n"
+          "sqadd v21.4s, v21.4s, v25.4s\n"
+          "sqadd v22.4s, v22.4s, v29.4s\n"
+          "dup v29.4s, %w[output_offset]\n"
+          "sqadd v23.4s, v23.4s, v30.4s\n"
+          "dup v30.4s, %w[output_activation_min]\n"
+          "sqadd v24.4s, v24.4s, v31.4s\n"
+          "dup v31.4s, %w[output_activation_max]\n"
+          "srshl v21.4s, v21.4s, v28.4s\n"
+          "srshl v22.4s, v22.4s, v28.4s\n"
+          "srshl v23.4s, v23.4s, v28.4s\n"
+          "srshl v24.4s, v24.4s, v28.4s\n"
+          "add v21.4s, v21.4s, v29.4s\n"
+          "add v22.4s, v22.4s, v29.4s\n"
+          "add v23.4s, v23.4s, v29.4s\n"
+          "add v24.4s, v24.4s, v29.4s\n"
+          "smax v21.4s, v21.4s, v30.4s\n"
+          "smax v22.4s, v22.4s, v30.4s\n"
+          "smax v23.4s, v23.4s, v30.4s\n"
+          "smax v24.4s, v24.4s, v30.4s\n"
+          "smin v21.4s, v21.4s, v31.4s\n"
+          "smin v22.4s, v22.4s, v31.4s\n"
+          "smin v23.4s, v23.4s, v31.4s\n"
+          "smin v24.4s, v24.4s, v31.4s\n"
+          "sqxtn v21.4h, v21.4s\n"
+          "sqxtn v23.4h, v23.4s\n"
+          "sqxtn2 v21.8h, v22.4s\n"
+          "ld1 {v22.4s}, [x5]\n"
+          "sqxtn2 v23.8h, v24.4s\n"
+          "ld1 {v24.4s}, [x5]\n"
+          "sqxtun v21.8b, v21.8h\n"
+          "sqxtun v23.8b, v23.8h\n"
+          "uaddw v9.8h, v26.8h, v9.8b\n"
+          "st1 {v21.8b}, [%[output_ptr]], %[output_depth]\n"
+          "uaddw v10.8h, v26.8h, v10.8b\n"
+          "st1 {v23.8b}, [%[output_ptr]], %[output_depth]\n"
+          "uaddw v11.8h, v26.8h, v11.8b\n"
+          "uaddw v12.8h, v26.8h, v12.8b\n"
+          "uaddw v13.8h, v26.8h, v13.8b\n"
+          "uaddw v14.8h, v26.8h, v14.8b\n"
+          "uaddw v15.8h, v26.8h, v15.8b\n"
+          "ld1 {v21.4s}, [%[bias_ptr]]\n"
+          "uaddw v16.8h, v26.8h, v16.8b\n"
+          "ld1 {v23.4s}, [%[bias_ptr]]\n"
+          "uaddw v17.8h, v26.8h, v17.8b\n"
+          "uaddw v18.8h, v26.8h, v18.8b\n"
+          "uaddw v19.8h, v26.8h, v19.8b\n"
+          "uaddw v20.8h, v26.8h, v20.8b\n"
+
+          "bge " DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP "b\n"
+
+        "cmp w8, #1\n"
+        "blt " DEPTHWISECONV_LABEL_HEIGHT_1_END "f\n"
+
+        // Do bottom right output if exists.
+        DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1 ":\n"
+        "smlal v21.4s, v0.4h, v9.4h\n"
+        "smlal2 v22.4s, v0.8h, v9.8h\n"
+        "smlal v21.4s, v1.4h, v10.4h\n"
+        "smlal2 v22.4s, v1.8h, v10.8h\n"
+        "smlal v21.4s, v2.4h, v11.4h\n"
+        "smlal2 v22.4s, v2.8h, v11.8h\n"
+        "smlal v21.4s, v3.4h, v13.4h\n"
+        "smlal2 v22.4s, v3.8h, v13.8h\n"
+        "smlal v21.4s, v4.4h, v14.4h\n"
+        "smlal2 v22.4s, v4.8h, v14.8h\n"
+        "smlal v21.4s, v5.4h, v15.4h\n"
+        "smlal2 v22.4s, v5.8h, v15.8h\n"
+        "smlal v21.4s, v6.4h, v17.4h\n"
+        "smlal2 v22.4s, v6.8h, v17.8h\n"
+        "smlal v21.4s, v7.4h, v18.4h\n"
+        "smlal2 v22.4s, v7.8h, v18.8h\n"
+        "smlal v21.4s, v8.4h, v19.4h\n"
+        "smlal2 v22.4s, v8.8h, v19.8h\n"
+
+        "sqrdmulh v21.4s, v21.4s, v27.4s\n"
+        "sqrdmulh v22.4s, v22.4s, v27.4s\n"
+        "and v9.16b, v21.16b, v28.16b\n"
+        "and v12.16b, v22.16b, v28.16b\n"
+        "sshr v9.4s, v9.4s, #31\n"
+        "sshr v12.4s, v12.4s, #31\n"
+        "sqadd v21.4s, v21.4s, v9.4s\n"
+        "sqadd v22.4s, v22.4s, v12.4s\n"
+        "srshl v21.4s, v21.4s, v28.4s\n"
+        "srshl v22.4s, v22.4s, v28.4s\n"
+        "add v21.4s, v21.4s, v29.4s\n"
+        "add v22.4s, v22.4s, v29.4s\n"
+        "smax v21.4s, v21.4s, v30.4s\n"
+        "smax v22.4s, v22.4s, v30.4s\n"
+        "smin v21.4s, v21.4s, v31.4s\n"
+        "smin v22.4s, v22.4s, v31.4s\n"
+        "sqxtn v21.4h, v21.4s\n"
+        "sqxtn2 v21.8h, v22.4s\n"
+        "sqxtun v21.8b, v21.8h\n"
+        "st1 {v21.8b}, [%[output_ptr]]\n"
+
+        DEPTHWISECONV_LABEL_HEIGHT_1_END ":\n"
+
+    :
+    // Outputs.
+    [filter_ptr] "+r"(filter_ptr), [input_ptr] "+r"(input_ptr),
+    [output_ptr] "+r"(output_ptr),
+    [output_window_height] "+r"(output_window_height)
+    :
+    // Inputs.
+    [bias_ptr] "r"(bias_ptr), [output_depth] "r"(output_depth),
+    [filter_offset] "r"(filter_offset), [input_row_size] "r"(input_row_size),
+    [input_depth] "r"(input_depth), [input_offset] "r"(input_offset),
+    [output_multiplier] "r"(output_multiplier),
+    [output_shift] "r"(output_shift), [output_offset] "r"(output_offset),
+    [output_activation_min] "r"(output_activation_min),
+    [output_activation_max] "r"(output_activation_max),
+    [output_row_size] "r"(output_row_size),
+    [output_window_width] "r"(output_window_width),
+    [input_width_increment] "r"(input_width_increment),
+    [input_height_increment] "r"(input_height_increment),
+    [output_height_increment] "r"(output_height_increment)
+    :
+    // Clobbers.
+    // We use these NEON registers.
+    "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11",
+    "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21",
+    "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31",
+    // We use these general-purpose registers.
+    "x0", "x1", "x2", "x3", "x4", "x5", "x6", "x7", "w8");
+
+#undef DEPTHWISECONV_LABEL_HEIGHT_1_END
+#undef DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1
+#undef DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP
+#undef DEPTHWISECONV_LABEL_HEIGHT_1
+#undef DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP
+#undef DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP
+#undef DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1
+#undef DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP
+#undef DEPTHWISECONV_LABEL_HEIGHT_2_LOOP
   }
 };
 
 template <>
-struct ConvRow3x3FilterDepth8<4, 1, 1> {
-  static inline void Run(const uint8* input_data, int start_x, int start_y,
-                         int input_depth, int input_width, int input_height,
-                         int input_row_size, int32 input_offset,
-                         const uint8* filter_data, int32 filter_offset,
-                         const int32* bias_data, int32 output_offset,
+struct DepthwiseConvWindow<8, 2, 2> {
+  static inline void Run(const uint8* input_ptr, int64_t input_depth,
+                         int32 input_offset, int64_t input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
                          int32 output_multiplier, int output_shift,
                          int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_data,
-                         int output_depth, int output_width,
-                         uint8* shuffle_workspace) {
-    int out_x = start_x;
-
-    // 4x4 at a time.
-    for (; out_x <= output_width - 4; out_x += 4) {
-      const int32* bias_ptr = bias_data;
-      const uint8* filter_ptr = filter_data;
-
-      const uint8* input_ptr = input_data;
-      uint8* output_ptr = output_data;
-
-      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
-        ConvKernel3x3FilterDepth8<4, 4, 1, 1>::Run(
-            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
-            filter_offset, bias_ptr, output_offset, output_multiplier,
-            output_shift, output_activation_min, output_activation_max,
-            output_ptr, output_depth, output_width);
-
-        input_ptr += 8;
-        output_ptr += 8;
-        filter_ptr += 8;
-        bias_ptr += 8;
-      }
-
-      input_data += 4 * input_depth;
-      output_data += 4 * output_depth;
-    }
-
-    // Handle the rest of the right side.
-    // 4x2 at a time.
-    for (; out_x <= output_width - 2; out_x += 2) {
-      const int32* bias_ptr = bias_data;
-      const uint8* filter_ptr = filter_data;
-
-      const uint8* input_ptr = input_data;
-      uint8* output_ptr = output_data;
-
-      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
-        ConvKernel3x3FilterDepth8<4, 2, 1, 1>::Run(
-            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
-            filter_offset, bias_ptr, output_offset, output_multiplier,
-            output_shift, output_activation_min, output_activation_max,
-            output_ptr, output_depth, output_width);
-
-        input_ptr += 8;
-        output_ptr += 8;
-        filter_ptr += 8;
-        bias_ptr += 8;
-      }
-
-      input_data += 2 * input_depth;
-      output_data += 2 * output_depth;
-    }
-
-    // 4x1 at a time.
-    for (; out_x < output_width; out_x++) {
-      const int32* bias_ptr = bias_data;
-      const uint8* filter_ptr = filter_data;
-
-      const uint8* input_ptr = input_data;
-      uint8* output_ptr = output_data;
-
-      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
-        ConvKernel3x3FilterDepth8<4, 1, 1, 1>::Run(
-            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
-            filter_offset, bias_ptr, output_offset, output_multiplier,
-            output_shift, output_activation_min, output_activation_max,
-            output_ptr, output_depth, output_width);
-
-        input_ptr += 8;
-        output_ptr += 8;
-        filter_ptr += 8;
-        bias_ptr += 8;
-      }
+                         int32 output_activation_max, uint8* output_ptr,
+                         int64_t output_depth, int output_width,
+                         int output_window_height, int output_window_width) {
+    const int64_t output_row_size = output_depth * output_width;
+    const int64_t input_width_increment = 4 * input_depth;
+    const int64_t input_height_increment = 4 * input_row_size;
+    const int64_t output_height_increment = 2 * output_row_size;
+
+#define DEPTHWISECONV_LABEL_HEIGHT_2_LOOP "1"
+#define DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP "2"
+#define DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1 "3"
+#define DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP "4"
+#define DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP "5"
+#define DEPTHWISECONV_LABEL_HEIGHT_1 "6"
+#define DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP "7"
+#define DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1 "8"
+#define DEPTHWISECONV_LABEL_HEIGHT_1_END "9"
+
+    asm volatile(
+        // Performs depthwise convolutions for a window specified by
+        // |output_window_height| and |output_window_width|. The inner-most loop
+        // processes 2x2 outputs, and any leftovers at the end.
+        //
+        // Algorithm works as follows:
+        //
+        //   1. Load filters of 8 depth (8x3x3). Registers v0--v8 hold filter
+        //      values.
+        //   2. For 2 output heights at a time:
+        //        i.  For 2 output widths at a time at stride 2, a 5x5 input
+        //            window is required. To avoid register exhaustion, we load
+        //            the first 2 rows of the 5x5 input window into registers
+        //            v9--v18, and use the same registers to load the next 2
+        //            rows, and finally v9--v13 to load the last row.
+        //            Accumulators for all 2x2 outputs are reserved by registers
+        //            v21-v22 (top left output), v23-v24 (top right output),
+        //            v19-v20 (bottom left output), v25-v26 (bottom right
+        //            output).
+        //        ii. Handle single leftover width if exists.
+        //   3. Handle single leftover height if exists.
+        //        i.  For 2 output widths at a time at stride 2, load inputs for
+        //            a 1x2 (1 height, 2 width) output window (3x5 input
+        //            window). Registers v9--v24 hold input values. Mul-add with
+        //            accumulators v24--v27.
+        //        ii. Handle single leftover width if exists.
+        //
+        // Loads are placed as soon as the register is no longer needed and
+        // interleaved with arithmetic operations to take advantage of
+        // dual-issue pipelines. We also add input offsets as far from the loads
+        // as possible to give loads enough cycles to fetch data from memory.
+
+        // Set "constant" registers. These registers may be replaced with temp
+        // values from time to time when there are not enough NEON registers.
+        "neg w7, %w[output_shift]\n"
+        "dup v26.4s, w7\n"
+        "cmp %w[output_window_height], #2\n"
+        "dup v27.4s, %w[output_multiplier]\n"
+        "dup v28.8h, %w[input_offset]\n"
+        "dup v29.4s, %w[output_offset]\n"
+        "dup v30.4s, %w[output_activation_min]\n"
+        "dup v31.4s, %w[output_activation_max]\n"
+
+        // Load filters and add offsets.
+        "add x5, %[bias_ptr], #16\n"
+        "ld1 {v0.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "dup v9.8h, %w[filter_offset]\n"
+        "ld1 {v1.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v0.8h, v9.8h, v0.8b\n"
+        "ld1 {v2.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v1.8h, v9.8h, v1.8b\n"
+        "ld1 {v3.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v2.8h, v9.8h, v2.8b\n"
+        "ld1 {v4.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v3.8h, v9.8h, v3.8b\n"
+        "ld1 {v5.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v4.8h, v9.8h, v4.8b\n"
+        "ld1 {v6.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v5.8h, v9.8h, v5.8b\n"
+        "ld1 {v7.8b}, [%[filter_ptr]], %[output_depth]\n"
+        "uaddw v6.8h, v9.8h, v6.8b\n"
+        "ld1 {v8.8b}, [%[filter_ptr]]\n"
+        "uaddw v7.8h, v9.8h, v7.8b\n"
+        "uaddw v8.8h, v9.8h, v8.8b\n"
+
+        "blt " DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP "f\n"
+
+        //"loop_%=:\n"
+        DEPTHWISECONV_LABEL_HEIGHT_2_LOOP ":\n"
+          // Load the first two rows of the 5x5 input window, then reuse the
+          // same registers to load subsequent rows as they become available.
+          "mov x6, %[input_ptr]\n"
+          "mov x0, x6\n"
+          "add x1, x0, %[input_row_size]\n"
+          "ld1 {v9.8b}, [x0], %[input_depth]\n"
+          "mov w4, %w[output_window_width]\n"
+          "ld1 {v10.8b}, [x0], %[input_depth]\n"
+          "cmp w4, #2\n"
+          "ld1 {v11.8b}, [x0], %[input_depth]\n"
+          "add x2, x1, %[input_row_size]\n"
+          "ld1 {v12.8b}, [x0], %[input_depth]\n"
+          "ld1 {v13.8b}, [x0]\n"
+          "add x0, x2, %[input_row_size]\n"
+          "ld1 {v14.8b}, [x1], %[input_depth]\n"
+          "mov x3, %[output_ptr]\n"
+          "ld1 {v15.8b}, [x1], %[input_depth]\n"
+          "add x10, %[output_ptr], %[output_row_size]\n"
+          "ld1 {v16.8b}, [x1], %[input_depth]\n"
+          "ld1 {v17.8b}, [x1], %[input_depth]\n"
+          "ld1 {v18.8b}, [x1]\n"
+          "add x1, x0, %[input_row_size]\n"
+
+          "uaddw v9.8h, v28.8h, v9.8b\n"
+          "uaddw v10.8h, v28.8h, v10.8b\n"
+          "uaddw v11.8h, v28.8h, v11.8b\n"
+          "ld1 {v21.4s}, [%[bias_ptr]]\n"
+          "uaddw v12.8h, v28.8h, v12.8b\n"
+          "ld1 {v22.4s}, [x5]\n"
+          "uaddw v13.8h, v28.8h, v13.8b\n"
+          "ld1 {v23.4s}, [%[bias_ptr]]\n"
+          "uaddw v14.8h, v28.8h, v14.8b\n"
+          "ld1 {v24.4s}, [x5]\n"
+          "uaddw v15.8h, v28.8h, v15.8b\n"
+          "ld1 {v19.4s}, [%[bias_ptr]]\n"
+          "uaddw v16.8h, v28.8h, v16.8b\n"
+          "ld1 {v20.4s}, [x5]\n"
+          "uaddw v17.8h, v28.8h, v17.8b\n"
+          "ld1 {v25.4s}, [%[bias_ptr]]\n"
+          "uaddw v18.8h, v28.8h, v18.8b\n"
+          "ld1 {v26.4s}, [x5]\n"
+
+          "blt " DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1 "f\n"
+
+          //"loop_%=:\n"
+          DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP ":\n"
+            "smlal v21.4s, v0.4h, v9.4h\n"
+            "subs w4, w4, #2\n"
+            "smlal2 v22.4s, v0.8h, v9.8h\n"
+            "ld1 {v9.8b}, [x2], %[input_depth]\n"
+            "smlal v23.4s, v0.4h, v11.4h\n"
+            "cmp w4, #2\n"
+            "smlal2 v24.4s, v0.8h, v11.8h\n"
+            "smlal v21.4s, v1.4h, v10.4h\n"
+            "smlal2 v22.4s, v1.8h, v10.8h\n"
+            "ld1 {v10.8b}, [x2], %[input_depth]\n"
+            "smlal v23.4s, v1.4h, v12.4h\n"
+            "smlal2 v24.4s, v1.8h, v12.8h\n"
+            "smlal v21.4s, v2.4h, v11.4h\n"
+            "smlal2 v22.4s, v2.8h, v11.8h\n"
+            "ld1 {v11.8b}, [x2], %[input_depth]\n"
+            "smlal v23.4s, v2.4h, v13.4h\n"
+            "ld1 {v12.8b}, [x2], %[input_depth]\n"
+            "smlal2 v24.4s, v2.8h, v13.8h\n"
+            "ld1 {v13.8b}, [x2]\n"
+
+            "smlal v21.4s, v3.4h, v14.4h\n"
+            "smlal2 v22.4s, v3.8h, v14.8h\n"
+            "ld1 {v14.8b}, [x0], %[input_depth]\n"
+            "smlal v23.4s, v3.4h, v16.4h\n"
+            "smlal2 v24.4s, v3.8h, v16.8h\n"
+            "smlal v21.4s, v4.4h, v15.4h\n"
+            "smlal2 v22.4s, v4.8h, v15.8h\n"
+            "ld1 {v15.8b}, [x0], %[input_depth]\n"
+            "smlal v23.4s, v4.4h, v17.4h\n"
+            "smlal2 v24.4s, v4.8h, v17.8h\n"
+            "smlal v21.4s, v5.4h, v16.4h\n"
+            "uaddw v9.8h, v28.8h, v9.8b\n"
+            "smlal2 v22.4s, v5.8h, v16.8h\n"
+            "ld1 {v16.8b}, [x0], %[input_depth]\n"
+            "smlal v23.4s, v5.4h, v18.4h\n"
+            "ld1 {v17.8b}, [x0], %[input_depth]\n"
+            "smlal2 v24.4s, v5.8h, v18.8h\n"
+            "ld1 {v18.8b}, [x0]\n"
+
+            "smlal v21.4s, v6.4h, v9.4h\n"
+            "uaddw v10.8h, v28.8h, v10.8b\n"
+            "smlal2 v22.4s, v6.8h, v9.8h\n"
+            "uaddw v11.8h, v28.8h, v11.8b\n"
+            "smlal v19.4s, v0.4h, v9.4h\n"
+            "uaddw v12.8h, v28.8h, v12.8b\n"
+            "smlal2 v20.4s, v0.8h, v9.8h\n"
+            "ld1 {v9.8b}, [x1], %[input_depth]\n"
+            "smlal v23.4s, v6.4h, v11.4h\n"
+            "uaddw v13.8h, v28.8h, v13.8b\n"
+            "smlal2 v24.4s, v6.8h, v11.8h\n"
+            "smlal v21.4s, v7.4h, v10.4h\n"
+            "smlal2 v22.4s, v7.8h, v10.8h\n"
+            "smlal v19.4s, v1.4h, v10.4h\n"
+            "smlal2 v20.4s, v1.8h, v10.8h\n"
+            "ld1 {v10.8b}, [x1], %[input_depth]\n"
+            "smlal v23.4s, v7.4h, v12.4h\n"
+            "smlal2 v24.4s, v7.8h, v12.8h\n"
+            "smlal v25.4s, v1.4h, v12.4h\n"
+            "smlal2 v26.4s, v1.8h, v12.8h\n"
+            "smlal v21.4s, v8.4h, v11.4h\n"
+            "smlal2 v22.4s, v8.8h, v11.8h\n"
+            "smlal v19.4s, v2.4h, v11.4h\n"
+            "add x6, x6, %[input_width_increment]\n"
+            "smlal2 v20.4s, v2.8h, v11.8h\n"
+            "mov x0, x6\n"
+
+            "smlal v25.4s, v0.4h, v11.4h\n"
+            "smlal2 v26.4s, v0.8h, v11.8h\n"
+            "ld1 {v11.8b}, [x1], %[input_depth]\n"
+            "smlal v23.4s, v8.4h, v13.4h\n"
+            "ld1 {v12.8b}, [x1], %[input_depth]\n"
+            "smlal2 v24.4s, v8.8h, v13.8h\n"
+            "smlal v25.4s, v2.4h, v13.4h\n"
+            "smlal2 v26.4s, v2.8h, v13.8h\n"
+            "ld1 {v13.8b}, [x1]\n"
+            "add x1, x0, %[input_row_size]\n"
+
+            "dup v28.4s, w7\n"
+            "add x2, x1, %[input_row_size]\n"
+            "sqrdmulh v21.4s, v21.4s, v27.4s\n"
+            "sqrdmulh v22.4s, v22.4s, v27.4s\n"
+            "sqrdmulh v23.4s, v23.4s, v27.4s\n"
+            "sqrdmulh v24.4s, v24.4s, v27.4s\n"
+            "and v27.16b, v21.16b, v28.16b\n"
+            "and v29.16b, v22.16b, v28.16b\n"
+            "and v30.16b, v23.16b, v28.16b\n"
+            "and v31.16b, v24.16b, v28.16b\n"
+            "sshr v27.4s, v27.4s, #31\n"
+            "sshr v29.4s, v29.4s, #31\n"
+            "sshr v30.4s, v30.4s, #31\n"
+            "sshr v31.4s, v31.4s, #31\n"
+            "sqadd v21.4s, v21.4s, v27.4s\n"
+            "dup v27.4s, %w[output_multiplier]\n"
+            "sqadd v22.4s, v22.4s, v29.4s\n"
+            "dup v29.4s, %w[output_offset]\n"
+            "sqadd v23.4s, v23.4s, v30.4s\n"
+            "dup v30.4s, %w[output_activation_min]\n"
+            "sqadd v24.4s, v24.4s, v31.4s\n"
+            "dup v31.4s, %w[output_activation_max]\n"
+            "srshl v21.4s, v21.4s, v28.4s\n"
+            "srshl v22.4s, v22.4s, v28.4s\n"
+            "srshl v23.4s, v23.4s, v28.4s\n"
+            "srshl v24.4s, v24.4s, v28.4s\n"
+            "dup v28.8h, %w[input_offset]\n"
+            "add v21.4s, v21.4s, v29.4s\n"
+            "add v22.4s, v22.4s, v29.4s\n"
+            "add v23.4s, v23.4s, v29.4s\n"
+            "add v24.4s, v24.4s, v29.4s\n"
+            "smax v21.4s, v21.4s, v30.4s\n"
+            "smax v22.4s, v22.4s, v30.4s\n"
+            "smax v23.4s, v23.4s, v30.4s\n"
+            "smax v24.4s, v24.4s, v30.4s\n"
+            "smin v21.4s, v21.4s, v31.4s\n"
+            "smin v22.4s, v22.4s, v31.4s\n"
+            "smin v23.4s, v23.4s, v31.4s\n"
+            "smin v24.4s, v24.4s, v31.4s\n"
+            "sqxtn v21.4h, v21.4s\n"
+            "sqxtn v23.4h, v23.4s\n"
+            "sqxtn2 v21.8h, v22.4s\n"
+            "ld1 {v22.4s}, [x5]\n"
+            "sqxtn2 v23.8h, v24.4s\n"
+            "ld1 {v24.4s}, [x5]\n"
+            "sqxtun v21.8b, v21.8h\n"
+            "sqxtun v23.8b, v23.8h\n"
+            "uaddw v9.8h, v28.8h, v9.8b\n"
+            "st1 {v21.8b}, [x3], %[output_depth]\n"
+            "uaddw v10.8h, v28.8h, v10.8b\n"
+            "st1 {v23.8b}, [x3], %[output_depth]\n"
+            "uaddw v11.8h, v28.8h, v11.8b\n"
+
+            "smlal v19.4s, v6.4h, v9.4h\n"
+            "uaddw v12.8h, v28.8h, v12.8b\n"
+            "smlal2 v20.4s, v6.8h, v9.8h\n"
+            "ld1 {v9.8b}, [x0], %[input_depth]\n"
+            "smlal v25.4s, v6.4h, v11.4h\n"
+            "uaddw v13.8h, v28.8h, v13.8b\n"
+            "smlal2 v26.4s, v6.8h, v11.8h\n"
+            "uaddw v14.8h, v28.8h, v14.8b\n"
+            "smlal v19.4s, v7.4h, v10.4h\n"
+            "uaddw v15.8h, v28.8h, v15.8b\n"
+            "smlal2 v20.4s, v7.8h, v10.8h\n"
+            "ld1 {v10.8b}, [x0], %[input_depth]\n"
+            "smlal v25.4s, v7.4h, v12.4h\n"
+            "uaddw v16.8h, v28.8h, v16.8b\n"
+            "smlal2 v26.4s, v7.8h, v12.8h\n"
+            "uaddw v17.8h, v28.8h, v17.8b\n"
+            "smlal v19.4s, v8.4h, v11.4h\n"
+            "uaddw v18.8h, v28.8h, v18.8b\n"
+            "smlal2 v20.4s, v8.8h, v11.8h\n"
+            "ld1 {v11.8b}, [x0], %[input_depth]\n"
+            "smlal v25.4s, v8.4h, v13.4h\n"
+            "ld1 {v12.8b}, [x0], %[input_depth]\n"
+            "smlal2 v26.4s, v8.8h, v13.8h\n"
+            "ld1 {v13.8b}, [x0]\n"
+            "add x0, x2, %[input_row_size]\n"
+
+            "smlal v19.4s, v3.4h, v14.4h\n"
+            "smlal2 v20.4s, v3.8h, v14.8h\n"
+            "ld1 {v14.8b}, [x1], %[input_depth]\n"
+            "smlal v25.4s, v3.4h, v16.4h\n"
+            "ld1 {v21.4s}, [%[bias_ptr]]\n"
+            "smlal2 v26.4s, v3.8h, v16.8h\n"
+            "ld1 {v23.4s}, [%[bias_ptr]]\n"
+            "smlal v19.4s, v4.4h, v15.4h\n"
+            "uaddw v9.8h, v28.8h, v9.8b\n"
+            "smlal2 v20.4s, v4.8h, v15.8h\n"
+            "ld1 {v15.8b}, [x1], %[input_depth]\n"
+            "smlal v25.4s, v4.4h, v17.4h\n"
+            "uaddw v10.8h, v28.8h, v10.8b\n"
+            "smlal2 v26.4s, v4.8h, v17.8h\n"
+            "uaddw v11.8h, v28.8h, v11.8b\n"
+            "smlal v19.4s, v5.4h, v16.4h\n"
+            "uaddw v12.8h, v28.8h, v12.8b\n"
+            "smlal2 v20.4s, v5.8h, v16.8h\n"
+            "ld1 {v16.8b}, [x1], %[input_depth]\n"
+            "smlal v25.4s, v5.4h, v18.4h\n"
+            "ld1 {v17.8b}, [x1], %[input_depth]\n"
+            "smlal2 v26.4s, v5.8h, v18.8h\n"
+            "ld1 {v18.8b}, [x1]\n"
+            "add x1, x0, %[input_row_size]\n"
+            "uaddw v13.8h, v28.8h, v13.8b\n"
+
+            "dup v28.4s, w7\n"
+            "sqrdmulh v19.4s, v19.4s, v27.4s\n"
+            "sqrdmulh v20.4s, v20.4s, v27.4s\n"
+            "sqrdmulh v25.4s, v25.4s, v27.4s\n"
+            "sqrdmulh v26.4s, v26.4s, v27.4s\n"
+            "and v27.16b, v19.16b, v28.16b\n"
+            "and v29.16b, v20.16b, v28.16b\n"
+            "and v30.16b, v25.16b, v28.16b\n"
+            "and v31.16b, v26.16b, v28.16b\n"
+            "sshr v27.4s, v27.4s, #31\n"
+            "sshr v29.4s, v29.4s, #31\n"
+            "sshr v30.4s, v30.4s, #31\n"
+            "sshr v31.4s, v31.4s, #31\n"
+            "sqadd v19.4s, v19.4s, v27.4s\n"
+            "dup v27.4s, %w[output_multiplier]\n"
+            "sqadd v20.4s, v20.4s, v29.4s\n"
+            "dup v29.4s, %w[output_offset]\n"
+            "sqadd v25.4s, v25.4s, v30.4s\n"
+            "dup v30.4s, %w[output_activation_min]\n"
+            "sqadd v26.4s, v26.4s, v31.4s\n"
+            "dup v31.4s, %w[output_activation_max]\n"
+            "srshl v19.4s, v19.4s, v28.4s\n"
+            "srshl v20.4s, v20.4s, v28.4s\n"
+            "srshl v25.4s, v25.4s, v28.4s\n"
+            "srshl v26.4s, v26.4s, v28.4s\n"
+            "dup v28.8h, %w[input_offset]\n"
+            "add v19.4s, v19.4s, v29.4s\n"
+            "add v20.4s, v20.4s, v29.4s\n"
+            "add v25.4s, v25.4s, v29.4s\n"
+            "add v26.4s, v26.4s, v29.4s\n"
+            "smax v19.4s, v19.4s, v30.4s\n"
+            "smax v20.4s, v20.4s, v30.4s\n"
+            "smax v25.4s, v25.4s, v30.4s\n"
+            "smax v26.4s, v26.4s, v30.4s\n"
+            "smin v19.4s, v19.4s, v31.4s\n"
+            "smin v20.4s, v20.4s, v31.4s\n"
+            "smin v25.4s, v25.4s, v31.4s\n"
+            "smin v26.4s, v26.4s, v31.4s\n"
+            "sqxtn v19.4h, v19.4s\n"
+            "sqxtn v25.4h, v25.4s\n"
+            "sqxtn2 v19.8h, v20.4s\n"
+            "ld1 {v20.4s}, [x5]\n"
+            "sqxtn2 v25.8h, v26.4s\n"
+            "ld1 {v26.4s}, [x5]\n"
+            "sqxtun v19.8b, v19.8h\n"
+            "sqxtun v25.8b, v25.8h\n"
+            "uaddw v14.8h, v28.8h, v14.8b\n"
+            "st1 {v19.8b}, [x10], %[output_depth]\n"
+            "uaddw v15.8h, v28.8h, v15.8b\n"
+            "st1 {v25.8b}, [x10], %[output_depth]\n"
+            "uaddw v16.8h, v28.8h, v16.8b\n"
+            "uaddw v17.8h, v28.8h, v17.8b\n"
+            "ld1 {v19.4s}, [%[bias_ptr]]\n"
+            "uaddw v18.8h, v28.8h, v18.8b\n"
+            "ld1 {v25.4s}, [%[bias_ptr]]\n"
+
+            "bge " DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP "b\n"
+
+          "cmp w4, #1\n"
+          "blt " DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP "f\n"
+
+          DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1 ":\n"
+          // Registers v9, v10, v11, v14, v15, and v16 have already been loaded
+          // with the correct values at this point. This corresponds to the
+          // first two input rows of the top left output. Now load the last
+          // input row for this output. Once these inputs are no longer needed,
+          // load the input rows for the bottom left output.
+          "ld1 {v12.8b}, [x2], %[input_depth]\n"
+          "smlal v21.4s, v0.4h, v9.4h\n"
+          "ld1 {v13.8b}, [x2], %[input_depth]\n"
+          "smlal2 v22.4s, v0.8h, v9.8h\n"
+          "ld1 {v17.8b}, [x2]\n"
+          "smlal v21.4s, v1.4h, v10.4h\n"
+          "ld1 {v9.8b}, [x0], %[input_depth]\n"
+          "smlal2 v22.4s, v1.8h, v10.8h\n"
+          "ld1 {v10.8b}, [x0], %[input_depth]\n"
+          "smlal v21.4s, v2.4h, v11.4h\n"
+          "smlal2 v22.4s, v2.8h, v11.8h\n"
+          "ld1 {v11.8b}, [x0]\n"
+          "smlal v21.4s, v3.4h, v14.4h\n"
+          "smlal2 v22.4s, v3.8h, v14.8h\n"
+          "ld1 {v14.8b}, [x1], %[input_depth]\n"
+          "smlal v21.4s, v4.4h, v15.4h\n"
+          "smlal2 v22.4s, v4.8h, v15.8h\n"
+          "ld1 {v15.8b}, [x1], %[input_depth]\n"
+          "smlal v21.4s, v5.4h, v16.4h\n"
+          "uaddw v12.8h, v28.8h, v12.8b\n"
+          "smlal2 v22.4s, v5.8h, v16.8h\n"
+          "uaddw v13.8h, v28.8h, v13.8b\n"
+          "ld1 {v16.8b}, [x1]\n"
+
+          "smlal v21.4s, v6.4h, v12.4h\n"
+          "smlal2 v22.4s, v6.8h, v12.8h\n"
+          "smlal v23.4s, v0.4h, v12.4h\n"
+          "uaddw v17.8h, v28.8h, v17.8b\n"
+          "smlal2 v24.4s, v0.8h, v12.8h\n"
+          "smlal v21.4s, v7.4h, v13.4h\n"
+          "smlal2 v22.4s, v7.8h, v13.8h\n"
+          "smlal v23.4s, v1.4h, v13.4h\n"
+          "smlal2 v24.4s, v1.8h, v13.8h\n"
+          "smlal v21.4s, v8.4h, v17.4h\n"
+          "smlal2 v22.4s, v8.8h, v17.8h\n"
+          "smlal v23.4s, v2.4h, v17.4h\n"
+          "smlal2 v24.4s, v2.8h, v17.8h\n"
+
+          "dup v26.4s, w7\n"
+          "sqrdmulh v21.4s, v21.4s, v27.4s\n"
+          "sqrdmulh v22.4s, v22.4s, v27.4s\n"
+          "and v18.16b, v21.16b, v26.16b\n"
+          "and v19.16b, v22.16b, v26.16b\n"
+          "sshr v18.4s, v18.4s, #31\n"
+          "sshr v19.4s, v19.4s, #31\n"
+          "sqadd v21.4s, v21.4s, v18.4s\n"
+          "sqadd v22.4s, v22.4s, v19.4s\n"
+          "srshl v21.4s, v21.4s, v26.4s\n"
+          "srshl v22.4s, v22.4s, v26.4s\n"
+          "add v21.4s, v21.4s, v29.4s\n"
+          "add v22.4s, v22.4s, v29.4s\n"
+          "smax v21.4s, v21.4s, v30.4s\n"
+          "smax v22.4s, v22.4s, v30.4s\n"
+          "smin v21.4s, v21.4s, v31.4s\n"
+          "smin v22.4s, v22.4s, v31.4s\n"
+          "sqxtn v21.4h, v21.4s\n"
+          "sqxtn2 v21.8h, v22.4s\n"
+          "sqxtun v21.8b, v21.8h\n"
+          "uaddw v9.8h, v28.8h, v9.8b\n"
+          "st1 {v21.8b}, [x3]\n"
+          "uaddw v10.8h, v28.8h, v10.8b\n"
+
+          "smlal v23.4s, v3.4h, v9.4h\n"
+          "uaddw v11.8h, v28.8h, v11.8b\n"
+          "smlal2 v24.4s, v3.8h, v9.8h\n"
+          "uaddw v14.8h, v28.8h, v14.8b\n"
+          "smlal v23.4s, v4.4h, v10.4h\n"
+          "uaddw v15.8h, v28.8h, v15.8b\n"
+          "smlal2 v24.4s, v4.8h, v10.8h\n"
+          "uaddw v16.8h, v28.8h, v16.8b\n"
+          "smlal v23.4s, v5.4h, v11.4h\n"
+          "smlal2 v24.4s, v5.8h, v11.8h\n"
+
+          "smlal v23.4s, v6.4h, v14.4h\n"
+          "smlal2 v24.4s, v6.8h, v14.8h\n"
+          "smlal v23.4s, v7.4h, v15.4h\n"
+          "smlal2 v24.4s, v7.8h, v15.8h\n"
+          "smlal v23.4s, v8.4h, v16.4h\n"
+          "smlal2 v24.4s, v8.8h, v16.8h\n"
+
+          "sqrdmulh v23.4s, v23.4s, v27.4s\n"
+          "sqrdmulh v24.4s, v24.4s, v27.4s\n"
+          "and v18.16b, v23.16b, v26.16b\n"
+          "and v19.16b, v24.16b, v26.16b\n"
+          "sshr v18.4s, v18.4s, #31\n"
+          "sshr v19.4s, v19.4s, #31\n"
+          "sqadd v23.4s, v23.4s, v18.4s\n"
+          "sqadd v24.4s, v24.4s, v19.4s\n"
+          "srshl v23.4s, v23.4s, v26.4s\n"
+          "srshl v24.4s, v24.4s, v26.4s\n"
+          "add v23.4s, v23.4s, v29.4s\n"
+          "add v24.4s, v24.4s, v29.4s\n"
+          "smax v23.4s, v23.4s, v30.4s\n"
+          "smax v24.4s, v24.4s, v30.4s\n"
+          "smin v23.4s, v23.4s, v31.4s\n"
+          "smin v24.4s, v24.4s, v31.4s\n"
+          "sqxtn v23.4h, v23.4s\n"
+          "sqxtn2 v23.8h, v24.4s\n"
+          "sqxtun v23.8b, v23.8h\n"
+          "st1 {v23.8b}, [x10]\n"
+
+          DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP ":\n"
+          "subs %w[output_window_height], %w[output_window_height], #2\n"
+          "add %[input_ptr], %[input_ptr], %[input_height_increment]\n"
+          "cmp %w[output_window_height], #2\n"
+          "add %[output_ptr], %[output_ptr], %[output_height_increment]\n"
+          "bge " DEPTHWISECONV_LABEL_HEIGHT_2_LOOP "b\n"
+
+        DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP ":\n"
+        "cmp %w[output_window_height], #1\n"
+        "blt " DEPTHWISECONV_LABEL_HEIGHT_1_END "f\n"
+
+        DEPTHWISECONV_LABEL_HEIGHT_1 ":\n"
+        "mov x6, %[input_ptr]\n"
+        "mov x0, x6\n"
+        "add x1, x0, %[input_row_size]\n"
+        "ld1 {v9.8b}, [x0], %[input_depth]\n"
+        "add x2, x1, %[input_row_size]\n"
+        "ld1 {v10.8b}, [x0], %[input_depth]\n"
+        "mov x3, %[output_ptr]\n"
+        "ld1 {v11.8b}, [x0], %[input_depth]\n"
+        "mov w4, %w[output_window_width]\n"
+        "ld1 {v18.8b}, [x0], %[input_depth]\n"
+        "cmp w4, #2\n"
+        "ld1 {v19.8b}, [x0]\n"
+        "ld1 {v12.8b}, [x1], %[input_depth]\n"
+        "ld1 {v13.8b}, [x1], %[input_depth]\n"
+        "ld1 {v14.8b}, [x1], %[input_depth]\n"
+        "ld1 {v20.8b}, [x1], %[input_depth]\n"
+        "ld1 {v21.8b}, [x1]\n"
+        "ld1 {v15.8b}, [x2], %[input_depth]\n"
+        "ld1 {v16.8b}, [x2], %[input_depth]\n"
+        "ld1 {v17.8b}, [x2], %[input_depth]\n"
+        "ld1 {v22.8b}, [x2], %[input_depth]\n"
+        "ld1 {v23.8b}, [x2]\n"
+
+        "uaddw v9.8h, v28.8h, v9.8b\n"
+        "ld1 {v24.4s}, [%[bias_ptr]]\n"
+        "uaddw v10.8h, v28.8h, v10.8b\n"
+        "ld1 {v25.4s}, [x5]\n"
+        "uaddw v11.8h, v28.8h, v11.8b\n"
+        "ld1 {v26.4s}, [%[bias_ptr]]\n"
+        "uaddw v18.8h, v28.8h, v18.8b\n"
+        "ld1 {v27.4s}, [x5]\n"
+        "uaddw v19.8h, v28.8h, v19.8b\n"
+        "uaddw v12.8h, v28.8h, v12.8b\n"
+        "uaddw v13.8h, v28.8h, v13.8b\n"
+        "uaddw v14.8h, v28.8h, v14.8b\n"
+        "uaddw v20.8h, v28.8h, v20.8b\n"
+        "uaddw v21.8h, v28.8h, v21.8b\n"
+        "uaddw v15.8h, v28.8h, v15.8b\n"
+        "uaddw v16.8h, v28.8h, v16.8b\n"
+        "uaddw v17.8h, v28.8h, v17.8b\n"
+        "uaddw v22.8h, v28.8h, v22.8b\n"
+        "uaddw v23.8h, v28.8h, v23.8b\n"
+
+        "blt " DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1 "f\n"
+
+        //"loop_%=:\n"
+        DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP ":\n"
+          "add x6, x6, %[input_width_increment]\n"
+          "smlal v24.4s, v0.4h, v9.4h\n"
+          "mov x0, x6\n"
+          "add x1, x0, %[input_row_size]\n"
+          "smlal2 v25.4s, v0.8h, v9.8h\n"
+          "ld1 {v9.8b}, [x0], %[input_depth]\n"
+          "smlal v26.4s, v0.4h, v11.4h\n"
+          "add x2, x1, %[input_row_size]\n"
+          "smlal2 v27.4s, v0.8h, v11.8h\n"
+          "subs w4, w4, #2\n"
+          "smlal v24.4s, v1.4h, v10.4h\n"
+          "cmp w4, #2\n"
+          "smlal2 v25.4s, v1.8h, v10.8h\n"
+          "ld1 {v10.8b}, [x0], %[input_depth]\n"
+          "smlal v26.4s, v1.4h, v18.4h\n"
+          "smlal2 v27.4s, v1.8h, v18.8h\n"
+          "smlal v24.4s, v2.4h, v11.4h\n"
+          "smlal2 v25.4s, v2.8h, v11.8h\n"
+          "ld1 {v11.8b}, [x0], %[input_depth]\n"
+          "smlal v26.4s, v2.4h, v19.4h\n"
+          "ld1 {v18.8b}, [x0], %[input_depth]\n"
+          "smlal2 v27.4s, v2.8h, v19.8h\n"
+          "ld1 {v19.8b}, [x0], %[input_depth]\n"
+          "smlal v24.4s, v3.4h, v12.4h\n"
+          "smlal2 v25.4s, v3.8h, v12.8h\n"
+          "ld1 {v12.8b}, [x1], %[input_depth]\n"
+          "smlal v26.4s, v3.4h, v14.4h\n"
+          "smlal2 v27.4s, v3.8h, v14.8h\n"
+          "smlal v24.4s, v4.4h, v13.4h\n"
+          "smlal2 v25.4s, v4.8h, v13.8h\n"
+          "ld1 {v13.8b}, [x1], %[input_depth]\n"
+          "smlal v26.4s, v4.4h, v20.4h\n"
+          "smlal2 v27.4s, v4.8h, v20.8h\n"
+          "smlal v24.4s, v5.4h, v14.4h\n"
+          "smlal2 v25.4s, v5.8h, v14.8h\n"
+          "ld1 {v14.8b}, [x1], %[input_depth]\n"
+          "smlal v26.4s, v5.4h, v21.4h\n"
+          "ld1 {v20.8b}, [x1], %[input_depth]\n"
+          "smlal2 v27.4s, v5.8h, v21.8h\n"
+          "ld1 {v21.8b}, [x1], %[input_depth]\n"
+          "smlal v24.4s, v6.4h, v15.4h\n"
+          "smlal2 v25.4s, v6.8h, v15.8h\n"
+          "ld1 {v15.8b}, [x2], %[input_depth]\n"
+          "smlal v26.4s, v6.4h, v17.4h\n"
+          "smlal2 v27.4s, v6.8h, v17.8h\n"
+          "smlal v24.4s, v7.4h, v16.4h\n"
+          "smlal2 v25.4s, v7.8h, v16.8h\n"
+          "ld1 {v16.8b}, [x2], %[input_depth]\n"
+          "smlal v26.4s, v7.4h, v22.4h\n"
+          "smlal2 v27.4s, v7.8h, v22.8h\n"
+          "smlal v24.4s, v8.4h, v17.4h\n"
+          "smlal2 v25.4s, v8.8h, v17.8h\n"
+          "ld1 {v17.8b}, [x2], %[input_depth]\n"
+          "smlal v26.4s, v8.4h, v23.4h\n"
+          "ld1 {v22.8b}, [x2], %[input_depth]\n"
+          "smlal2 v27.4s, v8.8h, v23.8h\n"
+          "ld1 {v23.8b}, [x2], %[input_depth]\n"
+
+          "dup v28.4s, %w[output_multiplier]\n"
+          "dup v29.4s, w7\n"
+          "sqrdmulh v24.4s, v24.4s, v28.4s\n"
+          "sqrdmulh v25.4s, v25.4s, v28.4s\n"
+          "sqrdmulh v26.4s, v26.4s, v28.4s\n"
+          "sqrdmulh v27.4s, v27.4s, v28.4s\n"
+          "dup v28.4s, %w[output_offset]\n"
+          "and v30.16b, v24.16b, v29.16b\n"
+          "and v31.16b, v25.16b, v29.16b\n"
+          "sshr v30.4s, v30.4s, #31\n"
+          "sshr v31.4s, v31.4s, #31\n"
+          "sqadd v24.4s, v24.4s, v30.4s\n"
+          "sqadd v25.4s, v25.4s, v31.4s\n"
+          "and v30.16b, v26.16b, v29.16b\n"
+          "and v31.16b, v27.16b, v29.16b\n"
+          "sshr v30.4s, v30.4s, #31\n"
+          "sshr v31.4s, v31.4s, #31\n"
+          "sqadd v26.4s, v26.4s, v30.4s\n"
+          "dup v30.4s, %w[output_activation_min]\n"
+          "sqadd v27.4s, v27.4s, v31.4s\n"
+          "dup v31.4s, %w[output_activation_max]\n"
+          "srshl v24.4s, v24.4s, v29.4s\n"
+          "srshl v25.4s, v25.4s, v29.4s\n"
+          "srshl v26.4s, v26.4s, v29.4s\n"
+          "srshl v27.4s, v27.4s, v29.4s\n"
+          "add v24.4s, v24.4s, v28.4s\n"
+          "add v25.4s, v25.4s, v28.4s\n"
+          "add v26.4s, v26.4s, v28.4s\n"
+          "add v27.4s, v27.4s, v28.4s\n"
+          "dup v28.8h, %w[input_offset]\n"
+          "smax v24.4s, v24.4s, v30.4s\n"
+          "smax v25.4s, v25.4s, v30.4s\n"
+          "smax v26.4s, v26.4s, v30.4s\n"
+          "smax v27.4s, v27.4s, v30.4s\n"
+          "smin v24.4s, v24.4s, v31.4s\n"
+          "smin v25.4s, v25.4s, v31.4s\n"
+          "smin v26.4s, v26.4s, v31.4s\n"
+          "smin v27.4s, v27.4s, v31.4s\n"
+          "sqxtn v24.4h, v24.4s\n"
+          "sqxtn v26.4h, v26.4s\n"
+          "sqxtn2 v24.8h, v25.4s\n"
+          "ld1 {v25.4s}, [x5]\n"
+          "sqxtn2 v26.8h, v27.4s\n"
+          "ld1 {v27.4s}, [x5]\n"
+          "sqxtun v24.8b, v24.8h\n"
+          "sqxtun v26.8b, v26.8h\n"
+          "uaddw v9.8h, v28.8h, v9.8b\n"
+          "st1 {v24.8b}, [x3], %[output_depth]\n"
+          "uaddw v10.8h, v28.8h, v10.8b\n"
+          "st1 {v26.8b}, [x3], %[output_depth]\n"
+          "uaddw v11.8h, v28.8h, v11.8b\n"
+          "uaddw v18.8h, v28.8h, v18.8b\n"
+          "uaddw v19.8h, v28.8h, v19.8b\n"
+          "uaddw v12.8h, v28.8h, v12.8b\n"
+          "uaddw v13.8h, v28.8h, v13.8b\n"
+          "uaddw v14.8h, v28.8h, v14.8b\n"
+          "uaddw v20.8h, v28.8h, v20.8b\n"
+          "uaddw v21.8h, v28.8h, v21.8b\n"
+          "ld1 {v24.4s}, [%[bias_ptr]]\n"
+          "uaddw v15.8h, v28.8h, v15.8b\n"
+          "ld1 {v26.4s}, [%[bias_ptr]]\n"
+          "uaddw v16.8h, v28.8h, v16.8b\n"
+          "uaddw v17.8h, v28.8h, v17.8b\n"
+          "uaddw v22.8h, v28.8h, v22.8b\n"
+          "uaddw v23.8h, v28.8h, v23.8b\n"
+
+          "bge " DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP "b\n"
+
+        "cmp w4, #1\n"
+        "blt " DEPTHWISECONV_LABEL_HEIGHT_1_END "f\n"
+
+        DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1 ":\n"
+        "dup v26.4s, w7\n"
+        "dup v27.4s, %w[output_multiplier]\n"
+        "dup v29.4s, %w[output_offset]\n"
+
+        "smlal v24.4s, v0.4h, v9.4h\n"
+        "smlal2 v25.4s, v0.8h, v9.8h\n"
+        "smlal v24.4s, v1.4h, v10.4h\n"
+        "smlal2 v25.4s, v1.8h, v10.8h\n"
+        "smlal v24.4s, v2.4h, v11.4h\n"
+        "smlal2 v25.4s, v2.8h, v11.8h\n"
+        "smlal v24.4s, v3.4h, v12.4h\n"
+        "smlal2 v25.4s, v3.8h, v12.8h\n"
+        "smlal v24.4s, v4.4h, v13.4h\n"
+        "smlal2 v25.4s, v4.8h, v13.8h\n"
+        "smlal v24.4s, v5.4h, v14.4h\n"
+        "smlal2 v25.4s, v5.8h, v14.8h\n"
+        "smlal v24.4s, v6.4h, v15.4h\n"
+        "smlal2 v25.4s, v6.8h, v15.8h\n"
+        "smlal v24.4s, v7.4h, v16.4h\n"
+        "smlal2 v25.4s, v7.8h, v16.8h\n"
+        "smlal v24.4s, v8.4h, v17.4h\n"
+        "smlal2 v25.4s, v8.8h, v17.8h\n"
+
+        "sqrdmulh v24.4s, v24.4s, v27.4s\n"
+        "sqrdmulh v25.4s, v25.4s, v27.4s\n"
+        "and v18.16b, v24.16b, v26.16b\n"
+        "and v19.16b, v25.16b, v26.16b\n"
+        "sshr v18.4s, v18.4s, #31\n"
+        "sshr v19.4s, v19.4s, #31\n"
+        "sqadd v24.4s, v24.4s, v18.4s\n"
+        "sqadd v25.4s, v25.4s, v19.4s\n"
+        "srshl v24.4s, v24.4s, v26.4s\n"
+        "srshl v25.4s, v25.4s, v26.4s\n"
+        "add v24.4s, v24.4s, v29.4s\n"
+        "add v25.4s, v25.4s, v29.4s\n"
+        "smax v24.4s, v24.4s, v30.4s\n"
+        "smax v25.4s, v25.4s, v30.4s\n"
+        "smin v24.4s, v24.4s, v31.4s\n"
+        "smin v25.4s, v25.4s, v31.4s\n"
+        "sqxtn v24.4h, v24.4s\n"
+        "sqxtn2 v24.8h, v25.4s\n"
+        "sqxtun v24.8b, v24.8h\n"
+        "st1 {v24.8b}, [x3]\n"
+
+        DEPTHWISECONV_LABEL_HEIGHT_1_END ":\n"
+    :
+    // Outputs.
+    [filter_ptr] "+r"(filter_ptr), [input_ptr] "+r"(input_ptr),
+    [output_ptr] "+r"(output_ptr),
+    [output_window_height] "+r"(output_window_height)
+    :
+    // Inputs.
+    [bias_ptr] "r"(bias_ptr), [output_depth] "r"(output_depth),
+    [filter_offset] "r"(filter_offset), [input_row_size] "r"(input_row_size),
+    [input_depth] "r"(input_depth), [input_offset] "r"(input_offset),
+    [output_multiplier] "r"(output_multiplier),
+    [output_shift] "r"(output_shift), [output_offset] "r"(output_offset),
+    [output_activation_min] "r"(output_activation_min),
+    [output_activation_max] "r"(output_activation_max),
+    [output_window_width] "r"(output_window_width),
+    [input_width_increment] "r"(input_width_increment),
+    [input_height_increment] "r"(input_height_increment),
+    [output_height_increment] "r"(output_height_increment),
+    [output_row_size] "r"(output_row_size)
+    :
+    // Clobbers.
+    // We use these NEON registers.
+    "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11",
+    "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21",
+    "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31",
+    // We use these general-purpose registers.
+    "x0", "x1", "x2", "x3", "w4", "x5", "x6", "w7", "x10");
+#undef DEPTHWISECONV_LABEL_HEIGHT_1_END
+#undef DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1
+#undef DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP
+#undef DEPTHWISECONV_LABEL_HEIGHT_1
+#undef DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP
+#undef DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP
+#undef DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1
+#undef DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP
+#undef DEPTHWISECONV_LABEL_HEIGHT_2_LOOP
+  }
+};
 
-      input_data += input_depth;
-      output_data += output_depth;
+// Copies a subset of the input designated by |input_ptr| into |output_ptr|
+// with the specified output dimensions. Supports output depths of 64 only as
+// this is the cache line size.
+inline void ShuffleInput(const uint8* input_ptr, int64_t input_depth,
+                         int input_width, int input_height,
+                         int64_t output_depth, int output_width,
+                         int output_height, uint8* output_ptr) {
+  const int64_t input_row_size = input_depth * input_width;
+  for (int y = 0; y < output_height; y++) {
+    const uint8* ptr = input_ptr;
+    for (int x = 0; x < output_width; x++) {
+      memcpy(output_ptr, ptr, output_depth);
+      output_ptr += output_depth;
+      ptr += input_depth;
     }
+    input_ptr += input_row_size;
   }
-};
+}
 
-template <>
-struct ConvRow3x3FilterDepth8<4, 2, 2> {
-  // The buffer size of the shuffled input.
-  static inline constexpr int ShuffleWorkspaceSize() { return 64 * 9 * 9; }
+template 
+struct DepthwiseConvMultiRow {
+ public:
+  constexpr static int kShuffleInputHeight =
+      kStrideHeight * (kShuffleOutputHeight - 1) + 3;
+  constexpr static int kShuffleInputWidth =
+      kStrideWidth * (kShuffleOutputWidth - 1) + 3;
 
   static inline void Run(const uint8* input_data, int start_x, int start_y,
-                         int input_depth, int input_width, int input_height,
-                         int input_row_size, int32 input_offset,
+                         int64_t input_depth, int input_width, int input_height,
+                         int64_t input_row_size, int32 input_offset,
                          const uint8* filter_data, int32 filter_offset,
                          const int32* bias_data, int32 output_offset,
                          int32 output_multiplier, int output_shift,
                          int32 output_activation_min,
                          int32 output_activation_max, uint8* output_data,
-                         int output_depth, int output_width,
+                         int64_t output_depth, int output_width,
                          uint8* shuffle_workspace) {
-    // Branch and cache misses increase substantially with stride 2 kernels.
-    // Adding prefetching reduces latency by as much as 2x.
-    const int i0 = 0;
-    const int i1 = input_depth;
-    const int i2 = 2 * input_depth;
-    const int i3 = 3 * input_depth;
-    const int i4 = 4 * input_depth;
-    const int i5 = 5 * input_depth;
-    const int i6 = 6 * input_depth;
-    const int i7 = 7 * input_depth;
-    const int i8 = 8 * input_depth;
-
-#define DEPTHWISECONV_PRELOAD_ROW(input_ptr, i)         \
-  preload_l1_keep(input_ptr + i * input_row_size + i0); \
-  preload_l1_keep(input_ptr + i * input_row_size + i1); \
-  preload_l1_keep(input_ptr + i * input_row_size + i2); \
-  preload_l1_keep(input_ptr + i * input_row_size + i3); \
-  preload_l1_keep(input_ptr + i * input_row_size + i4); \
-  preload_l1_keep(input_ptr + i * input_row_size + i5); \
-  preload_l1_keep(input_ptr + i * input_row_size + i6); \
-  preload_l1_keep(input_ptr + i * input_row_size + i7); \
-  preload_l1_keep(input_ptr + i * input_row_size + i8);
+    // Make sure shuffle parameters fall within the allowed workspace size.
+    static_assert(64 * kShuffleInputWidth * kShuffleInputHeight <=
+                  DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE,
+                  "Shuffle workspace size is too large.");
+
+    // Although it is possible to have kOutputRows != kShuffleOutputHeight, the
+    // below code assumes that they are the same.
+    static_assert(kOutputRows == kShuffleOutputHeight,
+                  "Output heights that are not equal to the shuffle output "
+                  "height are not supported.");
 
     int out_x = start_x;
-    // 4x4 at a time.
-    for (; out_x <= output_width - 4; out_x += 4) {
-      const int32* bias_ptr = bias_data;
-      const uint8* filter_ptr = filter_data;
+    // Run shuffling on inputs with sufficiently large depth and width. When
+    // these parameters are large enough, more time is taken to load inputs from
+    // memory. At this point, it becomes useful to prefetch and preshuffle the
+    // input data to maximize locality.
+    if (output_depth > 64 || (output_depth <= 64 && input_width > 150)) {
+      for (; out_x <= output_width - kShuffleOutputWidth;
+             out_x += kShuffleOutputWidth) {
+        const uint8* input_ptr = input_data;
+        const int32* bias_ptr = bias_data;
+        const uint8* filter_ptr = filter_data;
+        uint8* output_ptr = output_data;
+        int64_t depth = 0;
+        for (; depth <= output_depth - 64; depth += 64) {
+          // Preload.
+          const uint8* h_ptr = input_ptr;
+          for (int i = 0; i < kShuffleInputHeight; i++) {
+            const uint8* ptr = h_ptr;
+            for (int j = 0; j < kShuffleInputWidth; j++) {
+              asm volatile("prfm pldl1keep, [%[ptr]]\n" ::[ptr] "r"(ptr) :);
+              ptr += input_depth;
+            }
+            h_ptr += input_row_size;
+          }
+
+          // For a large enough input, shuffle into 64 x kShuffleInputWidth x
+          // kShuffleInputHeight buckets.
+          ShuffleInput(input_ptr, input_depth, input_width, input_height, 64,
+                       kShuffleInputWidth, kShuffleInputHeight,
+                       shuffle_workspace);
+          const uint8* shuffled_ptr = shuffle_workspace;
+
+          for (int micro_depth = 0; micro_depth <= 64 - 8; micro_depth += 8) {
+            DepthwiseConvWindow<8, kStrideWidth, kStrideHeight>::Run(
+                shuffled_ptr, 64, input_offset, 64 * kShuffleInputWidth,
+                filter_ptr, filter_offset, bias_ptr, output_offset,
+                output_multiplier, output_shift, output_activation_min,
+                output_activation_max, output_ptr, output_depth, output_width,
+               kShuffleOutputHeight, kShuffleOutputWidth);
+
+            shuffled_ptr += 8;
+            output_ptr += 8;
+            filter_ptr += 8;
+            bias_ptr += 8;
+          }
+          input_ptr += 64;
+        }
 
-      const uint8* input_ptr = input_data;
-      uint8* output_ptr = output_data;
+        // Preload.
+        const uint8* h_ptr = input_ptr;
+        for (int i = 0; i < kShuffleInputHeight; i++) {
+          const uint8* ptr = h_ptr;
+          for (int j = 0; j < kShuffleInputWidth; j++) {
+            asm volatile("prfm pldl1keep, [%[ptr]]\n" ::[ptr] "r"(ptr) :);
+            ptr += input_depth;
+          }
+          h_ptr += input_row_size;
+        }
+
+        // Handle leftover depth.
+        for (; depth <= output_depth - 8; depth += 8) {
+          DepthwiseConvWindow<8, kStrideWidth, kStrideHeight>::Run(input_ptr,
+              input_depth, input_offset, input_row_size, filter_ptr,
+              filter_offset, bias_ptr, output_offset, output_multiplier,
+              output_shift, output_activation_min, output_activation_max,
+              output_ptr, output_depth, output_width, kShuffleOutputHeight,
+              kShuffleOutputWidth);
 
-      int depth = 0;
-      for (; depth <= output_depth - 64; depth += 64) {
-        // Preload 9x9 input.
-        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 0);
-        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 1);
-        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 2);
-        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 3);
-        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 4);
-        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 5);
-        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 6);
-        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 7);
-        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 8);
-
-        // For a large input window (64x9x9) that is small enough to fit in L1
-        // cache, copy the input into a separate buffer and run the kernel on
-        // this new buffer. This reduces the likelihood of cache misses when
-        // the kernel is loading input data. If this size is ever changed,
-        // update the ShuffleWorkspaceSize() function to return the new size.
-        ShuffleInput(input_ptr, input_depth, input_width, input_height, 64, 9,
-                     9, shuffle_workspace);
-        const uint8* shuffled_ptr = &shuffle_workspace[0];
-
-        for (int micro_depth = 0; micro_depth <= 64 - 8; micro_depth += 8) {
-          ConvKernel3x3FilterDepth8<4, 4, 2, 2>::Run(
-              shuffled_ptr, 64, input_offset, 64 * 9, filter_ptr, filter_offset,
-              bias_ptr, output_offset, output_multiplier, output_shift,
-              output_activation_min, output_activation_max, output_ptr,
-              output_depth, output_width);
-
-          shuffled_ptr += 8;
+          input_ptr += 8;
           output_ptr += 8;
           filter_ptr += 8;
           bias_ptr += 8;
         }
-        input_ptr += 64;
-      }
-
-      // Preload 9x9 input one more time for the rest of the depth.
-      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 0);
-      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 1);
-      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 2);
-      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 3);
-      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 4);
-      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 5);
-      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 6);
-      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 7);
-      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 8);
-
-      for (; depth <= output_depth - 8; depth += 8) {
-        ConvKernel3x3FilterDepth8<4, 4, 2, 2>::Run(
-            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
-            filter_offset, bias_ptr, output_offset, output_multiplier,
-            output_shift, output_activation_min, output_activation_max,
-            output_ptr, output_depth, output_width);
-
-        input_ptr += 8;
-        output_ptr += 8;
-        filter_ptr += 8;
-        bias_ptr += 8;
-      }
-
-      input_data += 4 * 2 * input_depth;
-      output_data += 4 * output_depth;
-    }
-
-#undef DEPTHWISECONV_PRELOAD_ROW
-
-    // Handle the rest of the right side.
-    // 4x2 at a time.
-    for (; out_x <= output_width - 2; out_x += 2) {
-      const int32* bias_ptr = bias_data;
-      const uint8* filter_ptr = filter_data;
-
-      const uint8* input_ptr = input_data;
-      uint8* output_ptr = output_data;
-
-      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
-        ConvKernel3x3FilterDepth8<4, 2, 2, 2>::Run(
-            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
-            filter_offset, bias_ptr, output_offset, output_multiplier,
-            output_shift, output_activation_min, output_activation_max,
-            output_ptr, output_depth, output_width);
-
-        input_ptr += 8;
-        output_ptr += 8;
-        filter_ptr += 8;
-        bias_ptr += 8;
-      }
-
-      input_data += 2 * 2 * input_depth;
-      output_data += 2 * output_depth;
-    }
-
-    // 4x1 at a time.
-    for (; out_x < output_width; out_x++) {
-      const int32* bias_ptr = bias_data;
-      const uint8* filter_ptr = filter_data;
-
-      const uint8* input_ptr = input_data;
-      uint8* output_ptr = output_data;
 
-      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
-        ConvKernel3x3FilterDepth8<4, 1, 2, 2>::Run(
-            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
-            filter_offset, bias_ptr, output_offset, output_multiplier,
-            output_shift, output_activation_min, output_activation_max,
-            output_ptr, output_depth, output_width);
-
-        input_ptr += 8;
-        output_ptr += 8;
-        filter_ptr += 8;
-        bias_ptr += 8;
+        input_data += kShuffleOutputWidth * kStrideWidth * input_depth;
+        output_data += kShuffleOutputWidth * output_depth;
       }
-
-      input_data += 2 * input_depth;
-      output_data += output_depth;
     }
-  }
-};
-
-template <>
-struct ConvRow3x3FilterDepth8<8, 2, 2> {
-  static inline void Run(const uint8* input_data, int start_x, int start_y,
-                         int input_depth, int input_width, int input_height,
-                         int input_row_size, int32 input_offset,
-                         const uint8* filter_data, int32 filter_offset,
-                         const int32* bias_data, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_data,
-                         int output_depth, int output_width,
-                         uint8* shuffle_workspace) {
-    // Reuse 4 row kernels twice.
-    ConvRow3x3FilterDepth8<4, 2, 2>::Run(
-        input_data, start_x, start_y, input_depth, input_width, input_height,
-        input_row_size, input_offset, filter_data, filter_offset, bias_data,
-        output_offset, output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_data, output_depth, output_width,
-        shuffle_workspace);
-
-    ConvRow3x3FilterDepth8<4, 2, 2>::Run(
-        input_data + 2 * 4 * input_row_size, start_x, start_y + 4, input_depth,
-        input_width, input_height, input_row_size, input_offset, filter_data,
-        filter_offset, bias_data, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max,
-        output_data + 4 * output_depth * output_width, output_depth,
-        output_width, shuffle_workspace);
-  }
-};
-
-template <>
-struct ConvRow3x3FilterDepth8<8, 1, 1> {
-  // The buffer size of the shuffled input.
-  static inline constexpr int ShuffleWorkspaceSize() { return 64 * 10 * 10; }
 
-  static inline void Run(const uint8* input_data, int start_x, int start_y,
-                         int input_depth, int input_width, int input_height,
-                         int input_row_size, int32 input_offset,
-                         const uint8* filter_data, int32 filter_offset,
-                         const int32* bias_data, int32 output_offset,
-                         int32 output_multiplier, int output_shift,
-                         int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_data,
-                         int output_depth, int output_width,
-                         uint8* shuffle_workspace) {
-    int out_x = start_x;
-    // 8x8 at a time.
-    for (; out_x <= output_width - 8; out_x += 8) {
+    const int output_leftover_width = output_width - out_x;
+    if (output_leftover_width > 0) {
       const int32* bias_ptr = bias_data;
       const uint8* filter_ptr = filter_data;
-
       const uint8* input_ptr = input_data;
       uint8* output_ptr = output_data;
 
-      int depth = 0;
-      for (; depth <= output_depth - 64; depth += 64) {
-        // For a large input window (64x10x10) that is small enough to fit in L1
-        // cache, copy the input into a separate buffer and run the kernel on
-        // this new buffer. This reduces the likelihood of cache misses when
-        // the kernel is loading input data. If the size of the input window
-        // changes, update the function ShuffleWorkspaceSize() with the new
-        // size.
-        ShuffleInput(input_ptr, input_depth, input_width, input_height, 64, 10,
-                     10, shuffle_workspace);
-        const uint8* shuffled_ptr = shuffle_workspace;
-
-        for (int micro_depth = 0; micro_depth <= 64 - 8; micro_depth += 8) {
-          ConvKernel3x3FilterDepth8<8, 8, 1, 1>::Run(
-              shuffled_ptr, 64, input_offset, 64 * 10, filter_ptr,
-              filter_offset, bias_ptr, output_offset, output_multiplier,
-              output_shift, output_activation_min, output_activation_max,
-              output_ptr, output_depth, output_width);
-
-          shuffled_ptr += 8;
-          output_ptr += 8;
-          filter_ptr += 8;
-          bias_ptr += 8;
-        }
-        input_ptr += 64;
-      }
-
-      for (; depth <= output_depth - 8; depth += 8) {
-        ConvKernel3x3FilterDepth8<8, 8, 1, 1>::Run(
-            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
+      for (int64_t depth = 0; depth <= output_depth - 8; depth += 8) {
+        DepthwiseConvWindow<8, kStrideWidth, kStrideHeight>::Run(input_ptr,
+            input_depth, input_offset, input_row_size, filter_ptr,
             filter_offset, bias_ptr, output_offset, output_multiplier,
             output_shift, output_activation_min, output_activation_max,
-            output_ptr, output_depth, output_width);
+            output_ptr, output_depth, output_width, kShuffleOutputHeight,
+            output_leftover_width);
 
         input_ptr += 8;
         output_ptr += 8;
         filter_ptr += 8;
         bias_ptr += 8;
       }
-
-      input_data += 8 * input_depth;
-      output_data += 8 * output_depth;
     }
-
-    // Handle the rest of the right side by re-using 4 row kernels twice.
-    ConvRow3x3FilterDepth8<4, 1, 1>::Run(
-        input_data, out_x, start_y, input_depth, input_width, input_height,
-        input_row_size, input_offset, filter_data, filter_offset, bias_data,
-        output_offset, output_multiplier, output_shift, output_activation_min,
-        output_activation_max, output_data, output_depth, output_width,
-        shuffle_workspace);
-
-    ConvRow3x3FilterDepth8<4, 1, 1>::Run(
-        input_data + 4 * input_row_size, out_x, start_y + 4, input_depth,
-        input_width, input_height, input_row_size, input_offset, filter_data,
-        filter_offset, bias_data, output_offset, output_multiplier,
-        output_shift, output_activation_min, output_activation_max,
-        output_data + 4 * output_depth * output_width, output_depth,
-        output_width, shuffle_workspace);
   }
 };
 
@@ -4458,11 +1703,13 @@ inline void DepthwiseConv3x3Filter(
     int32 output_offset, int32 output_multiplier, int output_shift,
     int32 output_activation_min, int32 output_activation_max,
     uint8* output_data, const Dims<4>& output_dims) {
+  // 64-bit is used for types that will be added to 64-bit addresses in asm.
   const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
-  const int output_depth = MatchingArraySize(filter_dims, 0, output_dims, 0);
+  const int64_t output_depth =
+      MatchingArraySize(filter_dims, 0, output_dims, 0);
   const int input_height = ArraySize(input_dims, 2);
   const int input_width = ArraySize(input_dims, 1);
-  const int input_depth = ArraySize(input_dims, 0);
+  const int64_t input_depth = ArraySize(input_dims, 0);
   const int filter_height = ArraySize(filter_dims, 2);
   const int filter_width = ArraySize(filter_dims, 1);
   const int output_height = ArraySize(output_dims, 2);
@@ -4480,22 +1727,40 @@ inline void DepthwiseConv3x3Filter(
   TFLITE_DCHECK(stride_width == 1 || stride_width == 2);
   TFLITE_DCHECK(stride_width == stride_height);
 
-  const int input_row_size = input_depth * (input_width + 2 * pad_width);
-  const int output_row_size = output_depth * output_width;
-  const int input_batch_size = input_row_size * (input_height + 2 * pad_height);
-  const int output_batch_size = output_depth * output_width * output_height;
-
-  using conv_row_func_t = decltype(&ConvRow3x3FilterDepth8<1, 1, 1>::Run);
-  conv_row_func_t conv_1_output_row = ConvRow3x3FilterDepth8<1, 1, 1>::Run;
-  conv_row_func_t conv_2_output_rows = ConvRow3x3FilterDepth8<2, 1, 1>::Run;
-  conv_row_func_t conv_4_output_rows = ConvRow3x3FilterDepth8<4, 1, 1>::Run;
-  conv_row_func_t conv_8_output_rows = ConvRow3x3FilterDepth8<8, 1, 1>::Run;
-
-  if (stride_width == 2) {
-    conv_1_output_row = ConvRow3x3FilterDepth8<1, 2, 2>::Run;
-    conv_2_output_rows = ConvRow3x3FilterDepth8<2, 2, 2>::Run;
-    conv_4_output_rows = ConvRow3x3FilterDepth8<4, 2, 2>::Run;
-    conv_8_output_rows = ConvRow3x3FilterDepth8<8, 2, 2>::Run;
+  const int64_t input_row_size = input_depth * (input_width + 2 * pad_width);
+  const int64_t output_row_size = output_depth * output_width;
+  const int64_t input_batch_size =
+      input_row_size * (input_height + 2 * pad_height);
+  const int64_t output_batch_size = output_depth * output_width * output_height;
+
+  using conv_row_func_t = decltype(&DepthwiseConvMultiRow<1, 1, 1, 1, 1>::Run);
+  conv_row_func_t conv_1_output_row, conv_2_output_rows, conv_4_output_rows,
+      conv_8_output_rows;
+
+  int conv_2_shuffle_input_width = 0;
+  int conv_4_shuffle_input_width = 0;
+
+  if (stride_width == 1) {
+    conv_1_output_row = DepthwiseConvMultiRow<1, 1, 30, 1, 1>::Run;
+    conv_2_output_rows = DepthwiseConvMultiRow<2, 2, 22, 1, 1>::Run;
+    conv_4_output_rows = DepthwiseConvMultiRow<4, 4, 14, 1, 1>::Run;
+    conv_8_output_rows = DepthwiseConvMultiRow<8, 8, 8, 1, 1>::Run;
+
+    conv_2_shuffle_input_width =
+        DepthwiseConvMultiRow<2, 2, 22, 1, 1>::kShuffleInputWidth;
+    conv_4_shuffle_input_width =
+        DepthwiseConvMultiRow<4, 4, 14, 1, 1>::kShuffleInputWidth;
+
+  } else {
+    conv_1_output_row = DepthwiseConvMultiRow<1, 1, 14, 2, 2>::Run;
+    conv_2_output_rows = DepthwiseConvMultiRow<2, 2, 8, 2, 2>::Run;
+    conv_4_output_rows = DepthwiseConvMultiRow<4, 4, 4, 2, 2>::Run;
+    conv_8_output_rows = DepthwiseConvMultiRow<8, 8, 2, 2, 2>::Run;
+
+    conv_2_shuffle_input_width =
+        DepthwiseConvMultiRow<2, 2, 8, 2, 2>::kShuffleInputWidth;
+    conv_4_shuffle_input_width =
+        DepthwiseConvMultiRow<4, 4, 4, 2, 2>::kShuffleInputWidth;
   }
 
   // Allocate maximum memory needed for shuffled input.
@@ -4503,49 +1768,56 @@ inline void DepthwiseConv3x3Filter(
   // allocated on the stack. Eventually we will want to move it to the heap
   // and have it allocated outside of this function, like the im2col_array used
   // in gemmlowp.
-#define DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE 10 * 10 * 64
   uint8 shuffle_workspace[DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE];
 
-  // Make sure the kernels using this buffer will not run out of bounds.
-  static_assert(ConvRow3x3FilterDepth8<8, 1, 1>::ShuffleWorkspaceSize() <=
-                    DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE,
-                "Shuffle workspace size is too small.");
-  static_assert(ConvRow3x3FilterDepth8<4, 2, 2>::ShuffleWorkspaceSize() <=
-                    DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE,
-                "Shuffle workspace size is too small.");
-
-#undef DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE
-
   for (int b = 0; b < batches; ++b) {
     const uint8* input_ptr = input_data + b * input_batch_size;
     uint8* output_ptr = output_data + b * output_batch_size;
 
     int out_y = 0;
 
-    // Handle 8 rows at a time.
-    for (; out_y <= output_height - 8; out_y += 8) {
-      conv_8_output_rows(input_ptr, 0, out_y, input_depth, input_width,
-                         input_height, input_row_size, input_offset,
-                         filter_data, filter_offset, bias_data, output_offset,
-                         output_multiplier, output_shift, output_activation_min,
-                         output_activation_max, output_ptr, output_depth,
-                         output_width, shuffle_workspace);
+    // Shuffling shapes that maximize width over the shuffle workspace size
+    // perform better since the inputs are closer together, minimizing shuffling
+    // time.
+    //
+    // If the input shape has width large enough for the 2 height kernels
+    // |conv_2_output_rows|, we prefer to use this. The innermost loop of the
+    // kernels handle 2 height x 2 width so this is the fastest path.
+    //
+    // If the input shape has smaller width but larger height, shuffling is
+    // still useful and can benefit from kernels |conv_4_output_rows| and
+    // |conv_8_output_rows|.
 
-      input_ptr += 8 * stride_height * input_row_size;
-      output_ptr += 8 * output_row_size;
+    // Handle 8 rows at a time.
+    if (input_width < conv_4_shuffle_input_width) {
+      for (; out_y <= output_height - 8; out_y += 8) {
+        conv_8_output_rows(input_ptr, 0, out_y, input_depth, input_width,
+                           input_height, input_row_size, input_offset,
+                           filter_data, filter_offset, bias_data,
+                           output_offset, output_multiplier, output_shift,
+                           output_activation_min, output_activation_max,
+                           output_ptr, output_depth, output_width,
+                           shuffle_workspace);
+
+        input_ptr += 8 * stride_height * input_row_size;
+        output_ptr += 8 * output_row_size;
+      }
     }
 
     // Handle 4 rows at a time.
-    for (; out_y <= output_height - 4; out_y += 4) {
-      conv_4_output_rows(input_ptr, 0, out_y, input_depth, input_width,
-                         input_height, input_row_size, input_offset,
-                         filter_data, filter_offset, bias_data, output_offset,
-                         output_multiplier, output_shift, output_activation_min,
-                         output_activation_max, output_ptr, output_depth,
-                         output_width, shuffle_workspace);
-
-      input_ptr += 4 * stride_height * input_row_size;
-      output_ptr += 4 * output_row_size;
+    if (input_width < conv_2_shuffle_input_width) {
+      for (; out_y <= output_height - 4; out_y += 4) {
+        conv_4_output_rows(input_ptr, 0, out_y, input_depth, input_width,
+                           input_height, input_row_size, input_offset,
+                           filter_data, filter_offset, bias_data,
+                           output_offset, output_multiplier, output_shift,
+                           output_activation_min, output_activation_max,
+                           output_ptr, output_depth, output_width,
+                           shuffle_workspace);
+
+        input_ptr += 4 * stride_height * input_row_size;
+        output_ptr += 4 * output_row_size;
+      }
     }
 
     // Handle 2 rows at a time.
@@ -4575,6 +1847,7 @@ inline void DepthwiseConv3x3Filter(
     }
   }
 }
+// clang-format on
 
 #endif  // __aarch64__
 

From 48e436c091bad11a9a146a280a1cefbeff3ffc8e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 13:56:34 -0700
Subject: [PATCH 0632/1691] Increase size of test
 //third_party/tensorflow/contrib/distributions:distribution_test to avoid
 flaky timeouts

PiperOrigin-RevId: 196166582
---
 tensorflow/contrib/distributions/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index fa7f603fe8e3b5..6192f04c8b695d 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -94,7 +94,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "distribution_test",
-    size = "small",
+    size = "medium",
     srcs = ["python/kernel_tests/distribution_test.py"],
     additional_deps = [
         ":distributions_py",

From 2b42a0620f45cc40c3cc96552c565271bfed0c82 Mon Sep 17 00:00:00 2001
From: ManHyuk 
Date: Fri, 11 May 2018 06:11:43 +0900
Subject: [PATCH 0633/1691] Fix typo (#19106)

* fix typo
---
 tensorflow/compiler/xla/shape_util.h                            | 2 +-
 .../hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h
index cb8bf5a2b9e5d0..82c75f85d838f9 100644
--- a/tensorflow/compiler/xla/shape_util.h
+++ b/tensorflow/compiler/xla/shape_util.h
@@ -231,7 +231,7 @@ class ShapeUtil {
   }
 
   // Returns the higher-precision element type if a and b are both floating
-  // point types; otherwise, checks that that they have the same element type
+  // point types; otherwise, checks that they have the same element type
   // and returns it.
   static PrimitiveType HigherPrecisionElementType(const Shape& a,
                                                   const Shape& b) {
diff --git a/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc b/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
index 60281951dda940..66939fbb0f0d3b 100644
--- a/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
+++ b/tensorflow/contrib/hvx/hvx_ops_support_checker/hvx_ops_support_checker_main.cc
@@ -115,7 +115,7 @@ static void CheckOpsSupport(const GraphDef& graph_def,
       HexagonOpsDefinitions::getInstance();
   LOG(INFO) << "Checking " << graph_def.node_size() << " nodes";
   LOG(INFO) << "dump_all_nodes = " << dump_all_nodes
-            << ", dump_shape_and_tpye = " << dump_shape_and_type;
+            << ", dump_shape_and_type = " << dump_shape_and_type;
 
   std::unordered_set unsupported_ops;
   bool all_supported = true;

From 7a493376873e6c21a3fd8d0e04fa51057afaf7a8 Mon Sep 17 00:00:00 2001
From: Benoit Steiner 
Date: Thu, 10 May 2018 14:22:51 -0700
Subject: [PATCH 0634/1691] Started work on a shape optimizer

PiperOrigin-RevId: 196170800
---
 tensorflow/core/grappler/optimizers/BUILD     |  40 +++++-
 .../grappler/optimizers/meta_optimizer.cc     |   7 +-
 .../grappler/optimizers/shape_optimizer.cc    | 133 ++++++++++++++++++
 .../grappler/optimizers/shape_optimizer.h     |  54 +++++++
 .../optimizers/shape_optimizer_test.cc        | 105 ++++++++++++++
 .../grappler/optimizers/symbolic_shapes.cc    |  60 ++++++++
 .../grappler/optimizers/symbolic_shapes.h     |  14 ++
 .../optimizers/symbolic_shapes_test.cc        |  27 ++++
 .../core/protobuf/rewriter_config.proto       |   3 +
 9 files changed, 441 insertions(+), 2 deletions(-)
 create mode 100644 tensorflow/core/grappler/optimizers/shape_optimizer.cc
 create mode 100644 tensorflow/core/grappler/optimizers/shape_optimizer.h
 create mode 100644 tensorflow/core/grappler/optimizers/shape_optimizer_test.cc

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 900dfa95c59ec3..e1c2a64da10c18 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -508,7 +508,6 @@ cc_library(
         ":arithmetic_optimizer",
         ":auto_parallel",
         ":constant_folding",
-        ":custom_graph_optimizer",
         ":custom_graph_optimizer_registry",
         ":debug_stripper",
         ":dependency_optimizer",
@@ -518,6 +517,7 @@ cc_library(
         ":loop_optimizer",
         ":memory_optimizer",
         ":model_pruner",
+        ":shape_optimizer",
         "//tensorflow/core:core_cpu_base",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -629,6 +629,43 @@ tf_cuda_cc_test(
     ],
 )
 
+cc_library(
+    name = "shape_optimizer",
+    srcs = ["shape_optimizer.cc"],
+    hdrs = [
+        "shape_optimizer.h",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":graph_optimizer",
+        ":symbolic_shapes",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core/grappler:graph_view",
+        "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler:op_types",
+        "//tensorflow/core/grappler:utils",
+        "//tensorflow/core/grappler/costs:graph_properties",
+        "//tensorflow/core/grappler/utils:frame",
+    ],
+)
+
+tf_cc_test(
+    name = "shape_optimizer_test",
+    srcs = ["shape_optimizer_test.cc"],
+    deps = [
+        ":shape_optimizer",
+        "//tensorflow/cc:cc_ops",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+        "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler/utils:grappler_test",
+    ],
+)
+
 cc_library(
     name = "symbolic_shapes",
     srcs = ["symbolic_shapes.cc"],
@@ -636,6 +673,7 @@ cc_library(
     visibility = ["//visibility:public"],
     deps = [
         "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
     ] + tf_protos_grappler(),
 )
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index 0c8e18d7ab18b6..4435a8353b5810 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -24,11 +24,11 @@ limitations under the License.
 #include "tensorflow/core/grappler/optimizers/debug_stripper.h"
 #include "tensorflow/core/grappler/optimizers/dependency_optimizer.h"
 #include "tensorflow/core/grappler/optimizers/function_optimizer.h"
-#include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
 #include "tensorflow/core/grappler/optimizers/layout_optimizer.h"
 #include "tensorflow/core/grappler/optimizers/loop_optimizer.h"
 #include "tensorflow/core/grappler/optimizers/memory_optimizer.h"
 #include "tensorflow/core/grappler/optimizers/model_pruner.h"
+#include "tensorflow/core/grappler/optimizers/shape_optimizer.h"
 #include "tensorflow/core/grappler/utils/colocation.h"
 #include "tensorflow/core/grappler/utils/functions.h"
 #include "tensorflow/core/grappler/utils/topological_sort.h"
@@ -78,6 +78,7 @@ std::unique_ptr MetaOptimizer::MakeNewOptimizer(
   MK_OPT("pruning", new ModelPruner());
   MK_OPT("function", new FunctionOptimizer(cfg_.function_optimization()));
   MK_OPT("constfold", new ConstantFolding(cpu_device_));
+  MK_OPT("shape", new ShapeOptimizer());
   MK_OPT("layout", new LayoutOptimizer());
   MK_OPT("memory", new MemoryOptimizer(RewriterConfig::MANUAL));
   MK_OPT("arithmetic", new ArithmeticOptimizer(cfg_.arithmetic_optimization()));
@@ -107,6 +108,9 @@ Status MetaOptimizer::InitializeOptimizers(
     optimizers->emplace_back(
         new ConstantFolding(cfg_.constant_folding(), cpu_device_));
   }
+  if (cfg_.shape_optimization() == RewriterConfig::ON) {
+    optimizers->emplace_back(new ShapeOptimizer());
+  }
   if (cfg_.arithmetic_optimization() != RewriterConfig::OFF) {
     optimizers->emplace_back(
         new ArithmeticOptimizer(cfg_.arithmetic_optimization()));
@@ -344,6 +348,7 @@ bool MetaOptimizerEnabled(const RewriterConfig& cfg) {
          cfg.layout_optimizer() != RewriterConfig::OFF ||
          cfg.function_optimization() != RewriterConfig::OFF ||
          cfg.constant_folding() != RewriterConfig::OFF ||
+         cfg.shape_optimization() == RewriterConfig::ON ||
          cfg.arithmetic_optimization() != RewriterConfig::OFF ||
          cfg.loop_optimization() != RewriterConfig::OFF ||
          cfg.dependency_optimization() != RewriterConfig::OFF ||
diff --git a/tensorflow/core/grappler/optimizers/shape_optimizer.cc b/tensorflow/core/grappler/optimizers/shape_optimizer.cc
new file mode 100644
index 00000000000000..26c54df56b9e25
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/shape_optimizer.cc
@@ -0,0 +1,133 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/grappler/optimizers/shape_optimizer.h"
+
+#include "tensorflow/core/framework/tensor_shape.pb.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/grappler/graph_view.h"
+#include "tensorflow/core/grappler/grappler_item.h"
+#include "tensorflow/core/grappler/optimizers/symbolic_shapes.h"
+
+#include "tensorflow/core/grappler/op_types.h"
+#include "tensorflow/core/grappler/utils.h"
+#include "tensorflow/core/lib/core/errors.h"
+
+namespace tensorflow {
+namespace grappler {
+
+Status ShapeOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
+                                GraphDef* optimized_graph) {
+  *optimized_graph = item.graph;
+
+  GraphProperties properties(item);
+  TF_RETURN_IF_ERROR(properties.InferStatically(false));
+  GraphView graph(optimized_graph);
+
+  // The product of all the dimensions in a tensor shape can be expressed more
+  // simply as the size of the tensor.
+  for (auto& node : *optimized_graph->mutable_node()) {
+    if (!IsShape(node)) {
+      continue;
+    }
+    for (GraphView::InputPort fanout :
+         graph.GetFanout(GraphView::OutputPort(&node, 0))) {
+      if (fanout.node->op() != "Prod") {
+        continue;
+      }
+      if (fanout.node->attr().count("keep_dims") != 0 &&
+          fanout.node->attr().at("keep_dims").b()) {
+        // Keeping the reduced dimensions won't result in a scalar, so we can't
+        // rewrite the whole expression directly as a Size operation.
+        continue;
+      }
+      const GraphView::OutputPort reduce_indices =
+          graph.GetRegularFanin(GraphView::InputPort(fanout.node, 1));
+      const auto& prop =
+          properties.GetOutputProperties(reduce_indices.node->name());
+      if (prop.size() < reduce_indices.port_id) {
+        continue;
+      }
+      const TensorShapeProto& reduction_indices_shape =
+          prop[reduce_indices.port_id].shape();
+      if (NumCoefficients(reduction_indices_shape) == 1) {
+        const auto& input_props = properties.GetInputProperties(node.name());
+        if (input_props.size() != 1) {
+          continue;
+        }
+        // Rewrite the reduction of the shape dimensions as a Size operation.
+        const DataType type = input_props[0].dtype();
+        fanout.node->set_op("Size");
+        fanout.node->set_input(0, node.input(0));
+        fanout.node->set_input(1, AsControlDependency(node));
+        fanout.node->mutable_attr()->erase("Tidx");
+        fanout.node->mutable_attr()->erase("keep_dims");
+        (*fanout.node->mutable_attr())["out_type"] =
+            fanout.node->attr().at("T");
+        (*fanout.node->mutable_attr())["T"].set_type(type);
+      }
+    }
+  }
+  for (auto& node : *optimized_graph->mutable_node()) {
+    // Try to convert the ratio of 2 symbolic tensor sizes into a constant. This
+    // is possible whenever the symbolic dimensions in the numerator and
+    // denominator cancel each other.
+    if (node.op() == "Div") {
+      const GraphView::OutputPort input1 =
+          graph.GetRegularFanin(GraphView::InputPort(&node, 0));
+      const GraphView::OutputPort input2 =
+          graph.GetRegularFanin(GraphView::InputPort(&node, 1));
+      if (!IsSize(*input1.node) || !IsSize(*input2.node)) {
+        continue;
+      }
+      const auto& prop1 = properties.GetInputProperties(input1.node->name());
+      const auto& prop2 = properties.GetInputProperties(input2.node->name());
+      if (prop1.size() != 1 || prop2.size() != 1) {
+        continue;
+      }
+      const TensorShapeProto& shape1 = prop1[0].shape();
+      const TensorShapeProto& shape2 = prop2[0].shape();
+      int64 result = ComputeSizeRatio(shape1, shape2);
+      if (result >= 0) {
+        // Replace div with constant.
+        node.set_op("Const");
+        DataType dtype = node.attr().at("T").type();
+        node.mutable_attr()->erase("T");
+        (*node.mutable_attr())["dtype"].set_type(dtype);
+        TensorProto* t = (*node.mutable_attr())["value"].mutable_tensor();
+        t->set_dtype(dtype);
+        *t->mutable_tensor_shape() = TensorShapeProto();
+        if (dtype == DT_INT32) {
+          t->add_int_val(result);
+        } else {
+          t->add_int64_val(result);
+        }
+        node.set_input(0, AsControlDependency(node.input(0)));
+        node.set_input(1, AsControlDependency(node.input(1)));
+      }
+    }
+  }
+  return Status::OK();
+}
+
+void ShapeOptimizer::Feedback(Cluster* /*cluster*/,
+                              const GrapplerItem& /*item*/,
+                              const GraphDef& /*optimized_graph*/,
+                              double /*result*/) {
+  // Nothing to do for LoopOptimizer.
+}
+
+}  // end namespace grappler
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/shape_optimizer.h b/tensorflow/core/grappler/optimizers/shape_optimizer.h
new file mode 100644
index 00000000000000..b7f84a1e5dbe7d
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/shape_optimizer.h
@@ -0,0 +1,54 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_SHAPE_OPTIMIZER_H_
+#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_SHAPE_OPTIMIZER_H_
+
+#include 
+#include "tensorflow/core/grappler/costs/graph_properties.h"
+#include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
+#include "tensorflow/core/grappler/utils.h"
+#include "tensorflow/core/grappler/utils/frame.h"
+#include "tensorflow/core/protobuf/rewriter_config.pb.h"
+
+namespace tensorflow {
+namespace grappler {
+
+// Optimize TensorFlow subgraphs that operate on shape and shape related
+// information.
+class ShapeOptimizer : public GraphOptimizer {
+ public:
+  ShapeOptimizer() : opt_level_(RewriterConfig::ON) {}
+  explicit ShapeOptimizer(RewriterConfig::Toggle opt_level)
+      : opt_level_(opt_level) {}
+
+  ~ShapeOptimizer() override {}
+
+  string name() const override { return "shape_optimizer"; };
+
+  Status Optimize(Cluster* cluster, const GrapplerItem& item,
+                  GraphDef* optimized_graph) override;
+
+  void Feedback(Cluster* cluster, const GrapplerItem& item,
+                const GraphDef& optimized_graph, double result) override;
+
+ private:
+  RewriterConfig::Toggle opt_level_;
+};
+
+}  // end namespace grappler
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_SHAPE_OPTIMIZER_H_
diff --git a/tensorflow/core/grappler/optimizers/shape_optimizer_test.cc b/tensorflow/core/grappler/optimizers/shape_optimizer_test.cc
new file mode 100644
index 00000000000000..95a5eccd4f0ee8
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/shape_optimizer_test.cc
@@ -0,0 +1,105 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/grappler/optimizers/shape_optimizer.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/grappler/grappler_item.h"
+#include "tensorflow/core/grappler/utils/grappler_test.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace grappler {
+namespace {
+
+class ShapeOptimizerTest : public GrapplerTest {};
+
+TEST_F(ShapeOptimizerTest, OptimizeShapeProduct) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+  Output a = ops::Const(s.WithOpName("a"), 3.14f, {32, 16});
+  Output c = ops::Shape(s.WithOpName("c"), a);
+  Output d = ops::Const(s.WithOpName("d"), 0, {1});
+  ops::ReduceProd::Attrs attrs;
+  Output e = ops::ReduceProd(s.WithOpName("e"), c, d, attrs.KeepDims(false));
+  Output f = ops::ReduceProd(s.WithOpName("f"), c, d, attrs.KeepDims(true));
+
+  GrapplerItem item;
+  item.fetch = {"e", "f"};
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch);
+
+  GraphDef output;
+  ShapeOptimizer optimizer;
+  TF_EXPECT_OK(optimizer.Optimize(nullptr, item, &output));
+
+  int found = 0;
+  for (const NodeDef& node : output.node()) {
+    if (node.name() == "e") {
+      found++;
+      EXPECT_EQ("Size", node.op());
+      EXPECT_EQ("a", node.input(0));
+    } else if (node.name() == "f") {
+      found++;
+      EXPECT_EQ("Prod", node.op());
+      EXPECT_EQ("c", node.input(0));
+    }
+  }
+  EXPECT_EQ(2, found);
+
+  auto tensors_actual = EvaluateNodes(output, item.fetch);
+  EXPECT_NEAR(tensors_expected[0].scalar()(),
+              tensors_actual[0].scalar()(), 0);
+  EXPECT_NEAR(tensors_expected[1].scalar()(),
+              tensors_actual[1].scalar()(), 0);
+}
+
+TEST_F(ShapeOptimizerTest, OptimizeShapeRatio) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+  Output a = ops::Const(s.WithOpName("a"), 3.14f, {32, 32});
+  Output b = ops::Const(s.WithOpName("b"), 3.14f, {32, 16});
+  Output c = ops::Size(s.WithOpName("c"), a);
+  Output d = ops::Size(s.WithOpName("d"), b);
+  Output e = ops::Div(s.WithOpName("e"), c, d);
+
+  GrapplerItem item;
+  item.fetch = {"e"};
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch);
+
+  GraphDef output;
+  ShapeOptimizer optimizer;
+  TF_EXPECT_OK(optimizer.Optimize(nullptr, item, &output));
+
+  int found = 0;
+  for (const NodeDef& node : output.node()) {
+    if (node.name() == "e") {
+      found++;
+      EXPECT_EQ("Const", node.op());
+    }
+  }
+  EXPECT_EQ(1, found);
+
+  auto tensors_actual = EvaluateNodes(output, item.fetch);
+  EXPECT_NEAR(tensors_expected[0].scalar()(),
+              tensors_actual[0].scalar()(), 0);
+}
+
+}  // namespace
+}  // namespace grappler
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/symbolic_shapes.cc b/tensorflow/core/grappler/optimizers/symbolic_shapes.cc
index cfca2dc0d38480..32e86f82902c02 100644
--- a/tensorflow/core/grappler/optimizers/symbolic_shapes.cc
+++ b/tensorflow/core/grappler/optimizers/symbolic_shapes.cc
@@ -49,6 +49,27 @@ bool ShapeIsSymbolicallyDefined(const OpInfo::TensorProperties& properties) {
   return ShapeIsSymbolicallyDefined(properties.shape());
 }
 
+int Rank(const TensorShapeProto& shape) {
+  if (shape.unknown_rank()) {
+    return -1;
+  }
+  return shape.dim_size();
+}
+
+int64 NumCoefficients(const TensorShapeProto& shape) {
+  if (shape.unknown_rank()) {
+    return -1;
+  }
+  int64 num_coefficients = 1;
+  for (const auto& dim : shape.dim()) {
+    if (dim.size() < 0) {
+      return -1;
+    }
+    num_coefficients *= dim.size();
+  }
+  return num_coefficients;
+}
+
 bool ShapesSymbolicallyEqual(const TensorShapeProto& left,
                              const TensorShapeProto& right) {
   if (left.unknown_rank() || right.unknown_rank() ||
@@ -173,5 +194,44 @@ bool CompareSymbolicallyShapedTensorSizes(
   return CompareSymbolicallyShapedTensorSizes(left.shape(), right.shape());
 }
 
+int64 ComputeSizeRatio(const TensorShapeProto& numerator,
+                       const TensorShapeProto& denominator) {
+  if (numerator.unknown_rank() || denominator.unknown_rank()) {
+    return -1;
+  }
+  std::multiset symbolic_dims;
+  int64 num = 1;
+  for (const auto& dim : numerator.dim()) {
+    if (dim.size() == -1) {
+      return -1;
+    } else if (dim.size() < -1) {
+      symbolic_dims.insert(dim.size());
+    } else {
+      num *= dim.size();
+    }
+  }
+  int64 denom = 1;
+  for (const auto& dim : denominator.dim()) {
+    if (dim.size() == -1) {
+      return -1;
+    } else if (dim.size() < -1) {
+      auto it = symbolic_dims.find(dim.size());
+      if (it == symbolic_dims.end()) {
+        return -1;
+      }
+      symbolic_dims.erase(it);
+    } else {
+      denom *= dim.size();
+    }
+  }
+  if (denom == 0) {
+    return -1;
+  }
+  if (!symbolic_dims.empty()) {
+    return -1;
+  }
+  return num / denom;
+}
+
 }  // end namespace grappler
 }  // end namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/symbolic_shapes.h b/tensorflow/core/grappler/optimizers/symbolic_shapes.h
index eb79bab3141579..38d7fbf090f352 100644
--- a/tensorflow/core/grappler/optimizers/symbolic_shapes.h
+++ b/tensorflow/core/grappler/optimizers/symbolic_shapes.h
@@ -18,6 +18,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/grappler/costs/op_performance_data.pb.h"
+#include "tensorflow/core/platform/types.h"
 
 namespace tensorflow {
 namespace grappler {
@@ -31,6 +32,14 @@ bool IsUnknown(const TensorShapeProto::Dim& dim);
 bool ShapeIsSymbolicallyDefined(const TensorShapeProto& shape);
 bool ShapeIsSymbolicallyDefined(const OpInfo::TensorProperties& properties);
 
+// Returns the rank of the shape ir -1 if unknown
+int Rank(const TensorShapeProto& shape);
+
+// Returns the number of coefficients in the shape or -1 if unknown.
+// TODO(bsteiner) Add a function that computes the minimum size of the tensor,
+// ie the size assuming all the symbolic dimensions take the value 1.
+int64 NumCoefficients(const TensorShapeProto& shape);
+
 // Shapes are symbolically equal, if they have the same rank, they are known or
 // symbolically defined, and have matching dimensions.
 bool ShapesSymbolicallyEqual(const TensorShapeProto& left,
@@ -54,6 +63,11 @@ bool CompareSymbolicallyShapedTensorSizes(
     const OpInfo::TensorProperties& left,
     const OpInfo::TensorProperties& right);
 
+// Returns the ratio of the sizes of the 2 shapes if known statically, or -1
+// otherwise.
+int64 ComputeSizeRatio(const TensorShapeProto& numerator,
+                       const TensorShapeProto& denominator);
+
 }  // namespace grappler
 }  // end namespace tensorflow
 
diff --git a/tensorflow/core/grappler/optimizers/symbolic_shapes_test.cc b/tensorflow/core/grappler/optimizers/symbolic_shapes_test.cc
index 5ef9f659257106..5720fbd097fa2c 100644
--- a/tensorflow/core/grappler/optimizers/symbolic_shapes_test.cc
+++ b/tensorflow/core/grappler/optimizers/symbolic_shapes_test.cc
@@ -90,6 +90,33 @@ TEST_F(SymbolicShapesTest, CompareSymbolicallyShapedTensorSizes) {
   EXPECT_FALSE(MakeShape({-1, -1, 32}) < MakeShape({1, -1, 32}));
 }
 
+TEST_F(SymbolicShapesTest, RankAndNumCoeff) {
+  EXPECT_EQ(2, Rank(MakeShape({32, 32})));
+  EXPECT_EQ(32 * 32, NumCoefficients(MakeShape({32, 32})));
+  EXPECT_EQ(2, Rank(MakeShape({-2, 32})));
+  EXPECT_EQ(-1, NumCoefficients(MakeShape({-2, 32})));
+  TensorShapeProto shape;
+  shape.set_unknown_rank(true);
+  EXPECT_EQ(-1, Rank(shape));
+  EXPECT_EQ(-1, NumCoefficients(shape));
+}
+
+TEST_F(SymbolicShapesTest, SizeRatio) {
+  EXPECT_EQ(16, ComputeSizeRatio(MakeShape({32, 32}), MakeShape({32, 2})));
+  EXPECT_EQ(16, ComputeSizeRatio(MakeShape({-2, 32}), MakeShape({-2, 2})));
+  EXPECT_EQ(16,
+            ComputeSizeRatio(MakeShape({-2, -2, 32}), MakeShape({-2, 2, -2})));
+  EXPECT_EQ(-1,
+            ComputeSizeRatio(MakeShape({-2, -2, 32}), MakeShape({-2, 2, 2})));
+  EXPECT_EQ(-1,
+            ComputeSizeRatio(MakeShape({-2, 2, 32}), MakeShape({-2, 2, -2})));
+  EXPECT_EQ(-1, ComputeSizeRatio(MakeShape({-2, -2}), MakeShape({-2, 2})));
+  EXPECT_EQ(-1, ComputeSizeRatio(MakeShape({-2, 32}), MakeShape({-2, -2})));
+  EXPECT_EQ(1, ComputeSizeRatio(MakeShape({-2, -3}), MakeShape({-3, -2})));
+  EXPECT_EQ(-1, ComputeSizeRatio(MakeShape({-1, 32}), MakeShape({-2, 2})));
+  EXPECT_EQ(-1, ComputeSizeRatio(MakeShape({-1, 32}), MakeShape({-2, 0})));
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index 029b27cd043705..1f9b0c51c16958 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -46,6 +46,9 @@ message RewriterConfig {
   // Statically infer the value of tensors when possible, and materialize the
   // result using constants.
   Toggle constant_folding = 3;
+  // Shape optimizations (default is OFF)
+  // Simplify computations made on shapes;
+  Toggle shape_optimization = 13;
   // Arithmetic optimizations (default is ON)
   // e.g. Simplify arithmetic ops; merge ops with same value (like constants).
   Toggle arithmetic_optimization = 7;

From 1b67ccbe8006eacffd268553abd01310e8b187d6 Mon Sep 17 00:00:00 2001
From: Francois Chollet 
Date: Thu, 10 May 2018 14:27:40 -0700
Subject: [PATCH 0635/1691] Enable Model training/eval from generator in eager
 execution. Fixes #18287

PiperOrigin-RevId: 196171525
---
 .../_impl/keras/engine/training_eager_test.py  | 18 ++++++++++++++++++
 .../_impl/keras/engine/training_generator.py   |  7 -------
 2 files changed, 18 insertions(+), 7 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
index 5adb3ef94086f6..2375dffc335ed0 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
@@ -402,6 +402,24 @@ def test_model_methods_with_eager_tensors_single_io(self):
     model.train_on_batch(inputs, targets)
     model.test_on_batch(inputs, targets)
 
+  def test_generator_methods(self):
+    model = keras.Sequential()
+    model.add(keras.layers.Dense(4, input_shape=(3,)))
+    optimizer = RMSPropOptimizer(learning_rate=0.001)
+    model.compile(optimizer, 'mse', metrics=['mae'])
+
+    x = np.random.random((10, 3))
+    y = np.random.random((10, 4))
+
+    def iterator():
+      while 1:
+        yield x, y
+
+    model.fit_generator(iterator(), steps_per_epoch=3, epochs=1)
+    model.evaluate_generator(iterator(), steps=3)
+    out = model.predict_generator(iterator(), steps=3)
+    self.assertEqual(out.shape, (30, 4))
+
 
 class LossWeightingTest(test.TestCase):
 
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_generator.py b/tensorflow/python/keras/_impl/keras/engine/training_generator.py
index 58b5bc39c10ea0..a66e72072def5a 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_generator.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_generator.py
@@ -49,9 +49,6 @@ def fit_generator(model,
   epoch = initial_epoch
 
   do_validation = bool(validation_data)
-  model._make_train_function()
-  if do_validation:
-    model._make_test_function()
 
   is_sequence = isinstance(generator, Sequence)
   if not is_sequence and use_multiprocessing and workers > 1:
@@ -252,8 +249,6 @@ def evaluate_generator(model,
                        workers=1,
                        use_multiprocessing=False):
   """See docstring for `Model.evaluate_generator`."""
-  model._make_test_function()
-
   steps_done = 0
   wait_time = 0.01
   all_outs = []
@@ -346,8 +341,6 @@ def predict_generator(model,
                       use_multiprocessing=False,
                       verbose=0):
   """See docstring for `Model.predict_generator`."""
-  model._make_predict_function()
-
   steps_done = 0
   wait_time = 0.01
   all_outs = []

From 8786c16b860364e33be5f639dfcd9e70ccf4f991 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 14:34:37 -0700
Subject: [PATCH 0636/1691] Replace SymbolicGradientEnv with
 FunctionOptimizerContext.

Do not construct FunctionLibraryDefinition twice.

PiperOrigin-RevId: 196172648
---
 .../grappler/optimizers/function_optimizer.cc | 103 ++++++++----------
 1 file changed, 43 insertions(+), 60 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc
index a44e1ee7f939c2..2864d739f0ad90 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc
@@ -144,11 +144,18 @@ struct FunctionSpecialization {
   std::unordered_set control_deps;
 };
 
+class FakeCPUDevice : public Device {
+ public:
+  FakeCPUDevice(Env* env, const DeviceAttributes& attr) : Device(env, attr) {}
+  Status Sync() override { return Status::OK(); }
+};
+
 class FunctionOptimizerContext {
  public:
   explicit FunctionOptimizerContext(RewriterConfig::Toggle opt_level,
                                     const GrapplerItem& item)
-      : function_library_(OpRegistry::Global(), item.graph.library()) {
+      : graph_version_(item.graph.versions().producer()),
+        function_library_(OpRegistry::Global(), item.graph.library()) {
     InitializeTrulyConstNodes(item);
     InitializeInlinedFunctions(opt_level, item);
   }
@@ -161,6 +168,11 @@ class FunctionOptimizerContext {
     return &function_library_;
   }
 
+  FunctionLibraryRuntime* mutable_function_library_runtime() {
+    InitializeFunctionLibraryRuntime();
+    return flr_;
+  }
+
   bool IsInlinedFunction(const string& name) const {
     return inlined_functions_.count(name) > 0;
   }
@@ -222,12 +234,35 @@ class FunctionOptimizerContext {
     }
   }
 
+  void InitializeFunctionLibraryRuntime() {
+    if (!flr_) {
+      Env* env = Env::Default();
+      DeviceAttributes attr;
+      attr.set_name("/device:CPU:0");
+      attr.set_device_type("CPU");
+      Device* device = new FakeCPUDevice(env, attr);
+      device_mgr_.reset(new DeviceMgr({device}));
+      OptimizerOptions optimizer_opts;
+      optimizer_opts.set_do_function_inlining(true);
+      process_flr_.reset(new ProcessFunctionLibraryRuntime(
+          device_mgr_.get(), env, graph_version_, &function_library_,
+          optimizer_opts));
+      flr_ = process_flr_->GetFLR(device->name());
+    }
+  }
+
+  const int graph_version_;
   FunctionLibraryDefinition function_library_;
+
+  // These fields initialized lazily only if needed.
+  std::unique_ptr device_mgr_;
+  std::unique_ptr process_flr_;
+  FunctionLibraryRuntime* flr_ = nullptr;
+
   // Functions that can be inlined into optimized graph.
   std::unordered_map inlined_functions_;
   // Nodes that are Const and not in feed.
   std::unordered_map truly_const_nodes_;
-
   // Specialized functions.
   std::unordered_map devices;
-    devices.push_back(dev);
-    dvc_mgr_.reset(new DeviceMgr(devices));
-    fld_.reset(new FunctionLibraryDefinition(OpRegistry::Global(), library_));
-    OptimizerOptions optimizer_opts;
-    optimizer_opts.set_do_function_inlining(true);
-    pflr_.reset(new ProcessFunctionLibraryRuntime(
-        dvc_mgr_.get(), env, graph_version_, fld_.get(), optimizer_opts));
-    flr_ = pflr_->GetFLR(dev->name());
-  }
-
-  const int graph_version_;
-  const FunctionDefLibrary& library_;
-  std::unique_ptr dvc_mgr_;
-  std::unique_ptr fld_;
-  std::unique_ptr pflr_;
-  FunctionLibraryRuntime* flr_ = nullptr;
-};
-
-Status InlineSymbolicGradient(const NodeDef& node, SymbolicGradientEnv* env,
+Status InlineSymbolicGradient(const NodeDef& node,
+                              FunctionOptimizerContext* ctx,
                               GraphDef* inlined_graph) {
   VLOG(2) << "Inline symbolic gradient: " << SummarizeNodeDef(node);
 
@@ -732,15 +717,15 @@ Status InlineSymbolicGradient(const NodeDef& node, SymbolicGradientEnv* env,
   GraphConstructorOptions graph_ctor_opts;
   graph_ctor_opts.allow_internal_ops = true;
   graph_ctor_opts.expect_device_spec = false;
-  Graph graph(env->function_library());
+  Graph graph(ctx->function_library());
   TF_RETURN_IF_ERROR(
       ConvertGraphDefToGraph(graph_ctor_opts, graph_def, &graph));
 
   // Recursively inline the functions until there is nothing more to inline. We
   // should at least expand one function.
   int counter = 0;
-  while (counter < 50 &&
-         ExpandInlineFunctions(env->function_library_runtime(), &graph)) {
+  while (counter < 50 && ExpandInlineFunctions(
+                             ctx->mutable_function_library_runtime(), &graph)) {
     ++counter;
   }
 
@@ -801,8 +786,6 @@ Status FunctionOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
   }
 
   FunctionOptimizerContext ctx(opt_level_, item);
-  SymbolicGradientEnv env(item.graph.versions().producer(),
-                          item.graph.library());
 
   bool inline_gradients = options_.enable_symbolic_gradient_inlining;
   bool inline_func = options_.enable_function_inlining;
@@ -816,7 +799,7 @@ Status FunctionOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
       const auto* f_attr = gtl::FindOrNull(node.attr(), "f");
       string f_name = f_attr != nullptr ? f_attr->func().name() : "";
       if (ctx.IsInlinedFunction(f_name)) {
-        TF_RETURN_IF_ERROR(InlineSymbolicGradient(node, &env, optimized_graph));
+        TF_RETURN_IF_ERROR(InlineSymbolicGradient(node, &ctx, optimized_graph));
         continue;
       }
     }

From 6a4eb755a7c6cc858f5873e8a46477ede054b49e Mon Sep 17 00:00:00 2001
From: Akshay Modi 
Date: Thu, 10 May 2018 14:39:02 -0700
Subject: [PATCH 0637/1691] Automated g4 rollback of changelist 195899829

PiperOrigin-RevId: 196173343
---
 tensorflow/python/ops/distributions/special_math.py | 8 ++++----
 tensorflow/python/ops/math_ops.py                   | 3 +--
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/ops/distributions/special_math.py b/tensorflow/python/ops/distributions/special_math.py
index d1ee04dd1f7a3c..31b7a36fd3ae40 100644
--- a/tensorflow/python/ops/distributions/special_math.py
+++ b/tensorflow/python/ops/distributions/special_math.py
@@ -216,11 +216,11 @@ def _create_polynomial(var, coeffs):
   z = math_ops.sqrt(-2. * math_ops.log(sanitized_mcp))
   first_term = z - math_ops.log(z) / z
   second_term_small_p = (
-      _create_polynomial(math_ops.reciprocal(z), p2) /
-      _create_polynomial(math_ops.reciprocal(z), q2) / z)
+      _create_polynomial(1. / z, p2) /
+      _create_polynomial(1. / z, q2) / z)
   second_term_otherwise = (
-      _create_polynomial(math_ops.reciprocal(z), p1) /
-      _create_polynomial(math_ops.reciprocal(z), q1) / z)
+      _create_polynomial(1. / z, p1) /
+      _create_polynomial(1. / z, q1) / z)
   x_for_small_p = first_term - second_term_small_p
   x_otherwise = first_term - second_term_otherwise
 
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index e65a4b80d3c99b..ab5997e85c6030 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -871,8 +871,7 @@ def binary_op_wrapper_sparse(sp_x, y):
 
   def r_binary_op_wrapper(y, x):
     with ops.name_scope(None, op_name, [x, y]) as name:
-      if not context.executing_eagerly():
-        x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name="x")
+      x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name="x")
       return func(x, y, name=name)
 
   # Propagate func.__doc__ to the wrappers

From 878d34c786364323644d9751cc0a18afe4240c85 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 14:46:24 -0700
Subject: [PATCH 0638/1691] Removed duplicate implementation of Select, updated
 quant support for select.

PiperOrigin-RevId: 196174442
---
 .../internal/optimized/optimized_ops.h        | 55 +------------------
 .../internal/reference/reference_ops.h        | 14 ++---
 .../graph_transformations/hardcode_min_max.cc | 30 +++++++++-
 .../propagate_fake_quant_num_bits.cc          |  3 +
 .../toco/graph_transformations/quantize.cc    |  3 +-
 5 files changed, 43 insertions(+), 62 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index 7f28c29bc6f4f7..732e630aa8c583 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -48,6 +48,8 @@ using reference_ops::Greater;
 using reference_ops::GreaterEqual;
 using reference_ops::Less;
 using reference_ops::LessEqual;
+using reference_ops::RankOneSelect;
+using reference_ops::Select;
 
 // Make a local VectorMap typedef allowing to map a float array
 // as a Eigen vector expression. The std::conditional here is to
@@ -6315,59 +6317,6 @@ inline void TransposeConv(const float* input_data, const Dims<4>& input_dims,
   }
 }
 
-// UNOPTIMIZED COPY of Select from reference_ops.h.
-template 
-inline void Select(const D* input_condition_data,
-                   const Dims<4>& input_condition_dims, const T* input_x_data,
-                   const Dims<4>& input_x_dims, const T* input_y_data,
-                   const Dims<4>& input_y_dims, T* output_data,
-                   const Dims<4>& output_dims) {
-  const int64_t batches =
-      MatchingArraySize(input_condition_dims, 3, input_x_dims, 3, input_y_dims,
-                        3, output_dims, 3);
-  const int64_t height =
-      MatchingArraySize(input_condition_dims, 2, input_x_dims, 2, input_y_dims,
-                        2, output_dims, 2);
-  const int64_t width = MatchingArraySize(input_condition_dims, 1, input_x_dims,
-                                          1, input_y_dims, 1, output_dims, 1);
-  const int64_t depth = MatchingArraySize(input_condition_dims, 0, input_x_dims,
-                                          0, input_y_dims, 0, output_dims, 0);
-
-  const int64_t num_elements = batches * height * width * depth;
-  for (int64_t i = 0; i < num_elements; ++i) {
-    output_data[i] =
-        input_condition_data[i] ? input_x_data[i] : input_y_data[i];
-  }
-}
-
-// UNOPTIMIZED COPY of RankOneSelect from reference_ops.h.
-template 
-inline void RankOneSelect(const D* input_condition_data,
-                          const Dims<4>& input_condition_dims,
-                          const T* input_x_data, const Dims<4>& input_x_dims,
-                          const T* input_y_data, const Dims<4>& input_y_dims,
-                          T* output_data, const Dims<4>& output_dims) {
-  const int64_t rank = ArraySize(input_condition_dims, 0);
-
-  const int64_t batches =
-      MatchingArraySize(input_x_dims, 3, input_y_dims, 3, output_dims, 3);
-  const int64_t height =
-      MatchingArraySize(input_x_dims, 2, input_y_dims, 2, output_dims, 2);
-  const int64_t width =
-      MatchingArraySize(input_x_dims, 1, input_y_dims, 1, output_dims, 1);
-  const int64_t depth =
-      MatchingArraySize(input_x_dims, 0, input_y_dims, 0, output_dims, 0);
-
-  TFLITE_DCHECK_EQ(rank, batches);
-
-  int64_t offset = 0;
-  int64_t size = depth * height * width;
-  for (int64_t i = 0; i < rank; i++) {
-    const T* input_data = input_condition_data[i] ? input_x_data : input_y_data;
-    memcpy(output_data + offset, input_data + offset, size * sizeof(T));
-  }
-}
-
 }  // namespace optimized_ops
 }  // namespace tflite
 
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index 319e36de0f6ae4..6a36bb2c055520 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -3621,7 +3621,7 @@ inline void Comparison(const T* input1_data, const Dims<4>& input1_dims,
   }
 }
 
-template  F>
+template  F>
 inline void Comparison(int left_shift, const T* input1_data,
                        const Dims<4>& input1_dims, int32 input1_offset,
                        int32 input1_multiplier, int input1_shift,
@@ -3672,7 +3672,7 @@ inline void BroadcastComparison(const T* input1_data,
   }
 }
 
-template  F>
+template  F>
 inline void BroadcastComparison(int left_shift, const T* input1_data,
                                 const Dims<4>& input1_dims, int32 input1_offset,
                                 int32 input1_multiplier, int input1_shift,
@@ -3724,11 +3724,11 @@ inline void BroadcastComparison(int left_shift, const T* input1_data,
       int32 input2_multiplier, int input2_shift, bool* output_data,           \
       const Dims<4>& output_dims) {                                           \
     gemmlowp::ScopedProfilingLabel label(#name "/8bit");                      \
-    BroadcastComparison(left_shift, input1_data, input1_dims,    \
-                                     input1_offset, input1_multiplier,        \
-                                     input1_shift, input2_data, input2_dims,  \
-                                     input2_offset, input2_multiplier,        \
-                                     input2_shift, output_data, output_dims); \
+    Comparison(left_shift, input1_data, input1_dims,             \
+                            input1_offset, input1_multiplier, input1_shift,   \
+                            input2_data, input2_dims, input2_offset,          \
+                            input2_multiplier, input2_shift, output_data,     \
+                            output_dims);                                     \
   }                                                                           \
   template                                                        \
   inline void Broadcast##name(                                                \
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc
index 437e30a91803bf..d63ee7c9519d16 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc
@@ -188,6 +188,32 @@ bool HardcodeMinMaxFromFirstInput(Model* model, Operator* op) {
   return true;
 }
 
+bool HardcodeMinMaxForSelect(Model* model, Operator* op) {
+  auto& output_array = model->GetArray(op->outputs[0]);
+  if (output_array.minmax) {
+    return false;
+  }
+  const auto& input_array_1 = model->GetArray(op->inputs[1]);
+  if (!input_array_1.minmax) {
+    return false;
+  }
+  const auto& input_array_2 = model->GetArray(op->inputs[2]);
+  if (!input_array_2.minmax) {
+    return false;
+  }
+
+  const auto& input_minmax_1 = input_array_1.GetMinMax();
+  const auto& input_minmax_2 = input_array_2.GetMinMax();
+
+  CHECK_EQ(input_minmax_1.min, input_minmax_2.min);
+  CHECK_EQ(input_minmax_1.max, input_minmax_2.max);
+  CHECK(!output_array.minmax);
+  auto& output_minmax = output_array.GetOrCreateMinMax();
+  output_minmax.min = input_minmax_1.min;
+  output_minmax.max = input_minmax_1.max;
+  return true;
+}
+
 bool HardcodeMinMaxForOutput(Model* model, Operator* op, double min,
                              double max) {
   CHECK_EQ(op->outputs.size(), 1);
@@ -345,7 +371,9 @@ bool HardcodeMinMax::Run(Model* model, std::size_t op_index) {
     case OperatorType::kMean:
       changed = HardcodeMinMaxFromFirstInput(model, op);
       break;
-
+    case OperatorType::kSelect:
+      changed = HardcodeMinMaxForSelect(model, op);
+      break;
     case OperatorType::kLogistic:
       // We hardcode quantization_params to: zero_point=0, scale=1/256.
       // This choice of minmax is the one that is equivalent to that.
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc
index 0bce183c1897df..6d51fc8c31e6c8 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc
@@ -102,6 +102,7 @@ bool DoesOpBlockBackwardPropagation(const Operator& op) {
       // Gathers need their parameters changed to the appropriate data type.
     case OperatorType::kTensorFlowReshape:
     case OperatorType::kTranspose:
+    case OperatorType::kSelect:
       // Reshapes and transposes don't change values.
       return false;
     default:
@@ -113,6 +114,8 @@ bool DoesOpBlockBackwardPropagation(const Operator& op) {
 // propagation.
 bool DoesOpInputBlockBackwardPropagation(const Operator& op, int input_index) {
   switch (op.type) {
+    case OperatorType::kSelect:
+      return input_index == 0;
     case OperatorType::kGather:
       // Ignore gather indices.
       return input_index != 0;
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc
index a1ca7371c87f4c..142841fcc460e8 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc
@@ -59,7 +59,8 @@ bool SupportsQuantization(const Operator& op) {
          type == OperatorType::kTensorFlowGreater ||
          type == OperatorType::kTensorFlowGreaterEqual ||
          type == OperatorType::kTensorFlowLess ||
-         type == OperatorType::kTensorFlowLessEqual;
+         type == OperatorType::kTensorFlowLessEqual ||
+         type == OperatorType::kSelect;
 }
 
 const MinMax& GetOrComputeMinMax(Model* model, const string& array_name) {

From 349ad798de7f69423e8397c223285ad58238cc31 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 15:06:52 -0700
Subject: [PATCH 0639/1691] Add Nearest Neighbor sampling to
 tf.image.crop_and_resize() op - Prevent smearing when crop resize integer
 labels - Faster than Bilinear sampling

PiperOrigin-RevId: 196177762
---
 .../base_api/api_def_CropAndResize.pbtxt      |  27 +--
 tensorflow/core/kernels/crop_and_resize_op.cc | 151 +++++++++------
 tensorflow/core/kernels/crop_and_resize_op.h  |   5 +-
 .../core/kernels/crop_and_resize_op_gpu.cu.cc | 183 +++++++++++-------
 .../core/kernels/crop_and_resize_op_test.cc   | 166 ++++++++++++++--
 tensorflow/core/ops/image_ops.cc              |   4 +-
 tensorflow/python/ops/image_grad.py           |  18 +-
 7 files changed, 390 insertions(+), 164 deletions(-)

diff --git a/tensorflow/core/api_def/base_api/api_def_CropAndResize.pbtxt b/tensorflow/core/api_def/base_api/api_def_CropAndResize.pbtxt
index 629f575d0a25ce..e6609a16e125ad 100644
--- a/tensorflow/core/api_def/base_api/api_def_CropAndResize.pbtxt
+++ b/tensorflow/core/api_def/base_api/api_def_CropAndResize.pbtxt
@@ -47,8 +47,9 @@ END
   attr {
     name: "method"
     description: <GetAttr("method", &method));
-    OP_REQUIRES(context, method == "bilinear",
-                errors::InvalidArgument("method must be 'bilinear'", method));
+    OP_REQUIRES_OK(context, context->GetAttr("method", &method_));
+    OP_REQUIRES(context, method_ == "bilinear" || method_ == "nearest",
+                errors::InvalidArgument(
+                    "method must be 'bilinear' or 'nearest'", method_));
     OP_REQUIRES_OK(context, context->GetAttr("extrapolation_value",
                                              &extrapolation_value_));
   }
@@ -178,7 +178,7 @@ class CropAndResizeOp : public AsyncOpKernel {
       const Tensor& box_index = context->input(2);
       const bool status = functor::CropAndResize()(
           context, image.tensor(), boxes.tensor(),
-          box_index.tensor(), extrapolation_value_,
+          box_index.tensor(), method_, extrapolation_value_,
           output->tensor());
       if (!status) {
         context->SetStatus(
@@ -193,6 +193,7 @@ class CropAndResizeOp : public AsyncOpKernel {
 
  private:
   float extrapolation_value_;
+  string method_;
 };
 
 // Partial specialization of CropAndResize functor for a CPUDevice.
@@ -203,7 +204,7 @@ struct CropAndResize {
                   typename TTypes::ConstTensor image,
                   typename TTypes::ConstTensor boxes,
                   typename TTypes::ConstTensor box_index,
-                  float extrapolation_value,
+                  const string& method_name, float extrapolation_value,
                   typename TTypes::Tensor crops) {
     const int batch_size = image.dimension(0);
     const int image_height = image.dimension(1);
@@ -247,37 +248,57 @@ struct CropAndResize {
             }
             continue;
           }
-          const int top_y_index = floorf(in_y);
-          const int bottom_y_index = ceilf(in_y);
-          const float y_lerp = in_y - top_y_index;
-
-          for (int x = 0; x < crop_width; ++x) {
-            const float in_x = (crop_width > 1)
-                                   ? x1 * (image_width - 1) + x * width_scale
-                                   : 0.5 * (x1 + x2) * (image_width - 1);
-            if (in_x < 0 || in_x > image_width - 1) {
+          if (method_name == "bilinear") {
+            const int top_y_index = floorf(in_y);
+            const int bottom_y_index = ceilf(in_y);
+            const float y_lerp = in_y - top_y_index;
+
+            for (int x = 0; x < crop_width; ++x) {
+              const float in_x = (crop_width > 1)
+                                     ? x1 * (image_width - 1) + x * width_scale
+                                     : 0.5 * (x1 + x2) * (image_width - 1);
+              if (in_x < 0 || in_x > image_width - 1) {
+                for (int d = 0; d < depth; ++d) {
+                  crops(b, y, x, d) = extrapolation_value;
+                }
+                continue;
+              }
+              const int left_x_index = floorf(in_x);
+              const int right_x_index = ceilf(in_x);
+              const float x_lerp = in_x - left_x_index;
+
               for (int d = 0; d < depth; ++d) {
-                crops(b, y, x, d) = extrapolation_value;
+                const float top_left(static_cast(
+                    image(b_in, top_y_index, left_x_index, d)));
+                const float top_right(static_cast(
+                    image(b_in, top_y_index, right_x_index, d)));
+                const float bottom_left(static_cast(
+                    image(b_in, bottom_y_index, left_x_index, d)));
+                const float bottom_right(static_cast(
+                    image(b_in, bottom_y_index, right_x_index, d)));
+                const float top = top_left + (top_right - top_left) * x_lerp;
+                const float bottom =
+                    bottom_left + (bottom_right - bottom_left) * x_lerp;
+                crops(b, y, x, d) = top + (bottom - top) * y_lerp;
               }
-              continue;
             }
-            const int left_x_index = floorf(in_x);
-            const int right_x_index = ceilf(in_x);
-            const float x_lerp = in_x - left_x_index;
-
-            for (int d = 0; d < depth; ++d) {
-              const float top_left(static_cast(
-                  image(b_in, top_y_index, left_x_index, d)));
-              const float top_right(static_cast(
-                  image(b_in, top_y_index, right_x_index, d)));
-              const float bottom_left(static_cast(
-                  image(b_in, bottom_y_index, left_x_index, d)));
-              const float bottom_right(static_cast(
-                  image(b_in, bottom_y_index, right_x_index, d)));
-              const float top = top_left + (top_right - top_left) * x_lerp;
-              const float bottom =
-                  bottom_left + (bottom_right - bottom_left) * x_lerp;
-              crops(b, y, x, d) = top + (bottom - top) * y_lerp;
+          } else {  // method == "nearest"
+            for (int x = 0; x < crop_width; ++x) {
+              const float in_x = (crop_width > 1)
+                                     ? x1 * (image_width - 1) + x * width_scale
+                                     : 0.5 * (x1 + x2) * (image_width - 1);
+              if (in_x < 0 || in_x > image_width - 1) {
+                for (int d = 0; d < depth; ++d) {
+                  crops(b, y, x, d) = extrapolation_value;
+                }
+                continue;
+              }
+              const int closest_x_index = roundf(in_x);
+              const int closest_y_index = roundf(in_y);
+              for (int d = 0; d < depth; ++d) {
+                crops(b, y, x, d) = static_cast(
+                    image(b_in, closest_y_index, closest_x_index, d));
+              }
             }
           }
         }
@@ -285,12 +306,17 @@ struct CropAndResize {
     };
 
     // A rough estimation of the cost for each cropped box.
-    const double cost_per_pixel =
+    double cost_per_pixel =
         depth * (Eigen::TensorOpCost::AddCost() * 6 +
                  Eigen::TensorOpCost::MulCost() * 3 +
                  Eigen::TensorOpCost::CastCost() * 4) +
         (Eigen::TensorOpCost::AddCost() * 2 +
          Eigen::TensorOpCost::AddCost() * 3);
+    if (method_name == "nearest") {
+      cost_per_pixel = depth * Eigen::TensorOpCost::CastCost() +
+                       Eigen::TensorOpCost::AddCost() * 4 +
+                       Eigen::TensorOpCost::MulCost() * 4;
+    }
     const double cost_per_box = crop_height * crop_width * cost_per_pixel;
 
     const DeviceBase::CpuWorkerThreads& worker_threads =
@@ -309,10 +335,10 @@ class CropAndResizeGradImageOp : public AsyncOpKernel {
  public:
   explicit CropAndResizeGradImageOp(OpKernelConstruction* context)
       : AsyncOpKernel(context) {
-    string method;
-    OP_REQUIRES_OK(context, context->GetAttr("method", &method));
-    OP_REQUIRES(context, method == "bilinear",
-                errors::InvalidArgument("method must be 'bilinear'", method));
+    OP_REQUIRES_OK(context, context->GetAttr("method", &method_));
+    OP_REQUIRES(context, method_ == "bilinear" || method_ == "nearest",
+                errors::InvalidArgument(
+                    "method must be 'bilinear' or 'nearest'", method_));
   }
 
   void ComputeAsync(OpKernelContext* context, DoneCallback done) override {
@@ -372,14 +398,14 @@ class CropAndResizeGradImageOp : public AsyncOpKernel {
             &output),
         done);
 
-    auto compute_callback = [context, output]() {
+    auto compute_callback = [this, context, output]() {
       const Tensor& grads = context->input(0);
       const Tensor& boxes = context->input(1);
       const Tensor& box_index = context->input(2);
       const bool status = functor::CropAndResizeBackpropImage()(
           context->eigen_device(), grads.tensor(),
           boxes.tensor(), box_index.tensor(),
-          output->tensor());
+          output->tensor(), method_);
       if (!status) {
         context->SetStatus(errors::Internal(
             "Failed launch CropAndResizeBackpropImage kernel."));
@@ -390,6 +416,9 @@ class CropAndResizeGradImageOp : public AsyncOpKernel {
                                  batch_size, std::move(compute_callback),
                                  std::move(done));
   }
+
+ private:
+  string method_;
 };
 
 // Partial specialization of CropAndResizeBackpropImage functor for a CPUDevice.
@@ -400,7 +429,8 @@ struct CropAndResizeBackpropImage {
                   typename TTypes::ConstTensor grads,
                   typename TTypes::ConstTensor boxes,
                   typename TTypes::ConstTensor box_index,
-                  typename TTypes::Tensor grads_image) {
+                  typename TTypes::Tensor grads_image,
+                  const string& method_name) {
     const int batch_size = grads_image.dimension(0);
     const int image_height = grads_image.dimension(1);
     const int image_width = grads_image.dimension(2);
@@ -448,21 +478,30 @@ struct CropAndResizeBackpropImage {
           if (in_x < 0 || in_x > image_width - 1) {
             continue;
           }
-          const int left_x_index = floorf(in_x);
-          const int right_x_index = ceilf(in_x);
-          const float x_lerp = in_x - left_x_index;
+          if (method_name == "bilinear") {
+            const int left_x_index = floorf(in_x);
+            const int right_x_index = ceilf(in_x);
+            const float x_lerp = in_x - left_x_index;
 
-          for (int d = 0; d < depth; ++d) {
-            const float dtop = (1 - y_lerp) * grads(b, y, x, d);
-            grads_image(b_in, top_y_index, left_x_index, d) +=
-                static_cast((1 - x_lerp) * dtop);
-            grads_image(b_in, top_y_index, right_x_index, d) +=
-                static_cast(x_lerp * dtop);
-            const float dbottom = y_lerp * grads(b, y, x, d);
-            grads_image(b_in, bottom_y_index, left_x_index, d) +=
-                static_cast((1 - x_lerp) * dbottom);
-            grads_image(b_in, bottom_y_index, right_x_index, d) +=
-                static_cast(x_lerp * dbottom);
+            for (int d = 0; d < depth; ++d) {
+              const float dtop = (1 - y_lerp) * grads(b, y, x, d);
+              grads_image(b_in, top_y_index, left_x_index, d) +=
+                  static_cast((1 - x_lerp) * dtop);
+              grads_image(b_in, top_y_index, right_x_index, d) +=
+                  static_cast(x_lerp * dtop);
+              const float dbottom = y_lerp * grads(b, y, x, d);
+              grads_image(b_in, bottom_y_index, left_x_index, d) +=
+                  static_cast((1 - x_lerp) * dbottom);
+              grads_image(b_in, bottom_y_index, right_x_index, d) +=
+                  static_cast(x_lerp * dbottom);
+            }
+          } else {  // method_name == "nearest"
+            for (int d = 0; d < depth; ++d) {
+              int closest_x_index = roundf(in_x);
+              int closest_y_index = roundf(in_y);
+              grads_image(b_in, closest_y_index, closest_x_index, d) +=
+                  static_cast(grads(b, y, x, d));
+            }
           }
         }
       }
diff --git a/tensorflow/core/kernels/crop_and_resize_op.h b/tensorflow/core/kernels/crop_and_resize_op.h
index b6b1dbd7b0c3d4..61dc3f941f6c8c 100644
--- a/tensorflow/core/kernels/crop_and_resize_op.h
+++ b/tensorflow/core/kernels/crop_and_resize_op.h
@@ -31,7 +31,7 @@ struct CropAndResize {
                   typename TTypes::ConstTensor image,
                   typename TTypes::ConstTensor boxes,
                   typename TTypes::ConstTensor box_ind,
-                  float extrapolation_value,
+                  string method_name, float extrapolation_value,
                   typename TTypes::Tensor crops);
 };
 
@@ -41,7 +41,8 @@ struct CropAndResizeBackpropImage {
   bool operator()(const Device& d, typename TTypes::ConstTensor grads,
                   typename TTypes::ConstTensor boxes,
                   typename TTypes::ConstTensor box_ind,
-                  typename TTypes::Tensor grads_image);
+                  typename TTypes::Tensor grads_image,
+                  const string& method_name);
 };
 
 template 
diff --git a/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc b/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc
index d12787d5244d12..8ab08fb93aeef2 100644
--- a/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc
@@ -32,11 +32,16 @@ typedef Eigen::GpuDevice GPUDevice;
 
 namespace {
 
+enum InterpolationMethod {
+  BILINEAR = 0,
+  NEAREST = 1,
+};
+
 template 
 __global__ void CropAndResizeKernel(
     const int32 nthreads, const T* image_ptr, const float* boxes_ptr,
     const int32* box_ind_ptr, int num_boxes, int batch, int image_height,
-    int image_width, int crop_height, int crop_width, int depth,
+    int image_width, int crop_height, int crop_width, int depth, int method_id,
     float extrapolation_value, float* crops_ptr) {
   CUDA_1D_KERNEL_LOOP(out_idx, nthreads) {
     // out_idx = d + depth * (w + crop_width * (h + crop_height * b))
@@ -80,37 +85,47 @@ __global__ void CropAndResizeKernel(
       continue;
     }
 
-    const int top_y_index = floorf(in_y);
-    const int bottom_y_index = ceilf(in_y);
-    const float y_lerp = in_y - top_y_index;
-
-    const int left_x_index = floorf(in_x);
-    const int right_x_index = ceilf(in_x);
-    const float x_lerp = in_x - left_x_index;
-
-    const float top_left(static_cast(
-        image_ptr[((b_in * image_height + top_y_index) * image_width +
-                   left_x_index) *
-                      depth +
-                  d]));
-    const float top_right(static_cast(
-        image_ptr[((b_in * image_height + top_y_index) * image_width +
-                   right_x_index) *
-                      depth +
-                  d]));
-    const float bottom_left(static_cast(
-        image_ptr[((b_in * image_height + bottom_y_index) * image_width +
-                   left_x_index) *
-                      depth +
-                  d]));
-    const float bottom_right(static_cast(
-        image_ptr[((b_in * image_height + bottom_y_index) * image_width +
-                   right_x_index) *
-                      depth +
-                  d]));
-    const float top = top_left + (top_right - top_left) * x_lerp;
-    const float bottom = bottom_left + (bottom_right - bottom_left) * x_lerp;
-    crops_ptr[out_idx] = top + (bottom - top) * y_lerp;
+    if (method_id == BILINEAR) {
+      const int top_y_index = floorf(in_y);
+      const int bottom_y_index = ceilf(in_y);
+      const float y_lerp = in_y - top_y_index;
+
+      const int left_x_index = floorf(in_x);
+      const int right_x_index = ceilf(in_x);
+      const float x_lerp = in_x - left_x_index;
+
+      const float top_left(static_cast(
+          image_ptr[((b_in * image_height + top_y_index) * image_width +
+                     left_x_index) *
+                        depth +
+                    d]));
+      const float top_right(static_cast(
+          image_ptr[((b_in * image_height + top_y_index) * image_width +
+                     right_x_index) *
+                        depth +
+                    d]));
+      const float bottom_left(static_cast(
+          image_ptr[((b_in * image_height + bottom_y_index) * image_width +
+                     left_x_index) *
+                        depth +
+                    d]));
+      const float bottom_right(static_cast(
+          image_ptr[((b_in * image_height + bottom_y_index) * image_width +
+                     right_x_index) *
+                        depth +
+                    d]));
+      const float top = top_left + (top_right - top_left) * x_lerp;
+      const float bottom = bottom_left + (bottom_right - bottom_left) * x_lerp;
+      crops_ptr[out_idx] = top + (bottom - top) * y_lerp;
+    } else {  // method_id == kMethodNearestId
+      const int closest_x_index = roundf(in_x);
+      const int closest_y_index = roundf(in_y);
+      crops_ptr[out_idx] = static_cast(
+          image_ptr[((b_in * image_height + closest_y_index) * image_width +
+                     closest_x_index) *
+                        depth +
+                    d]);
+    }
   }
 }
 
@@ -119,7 +134,7 @@ __global__ void CropAndResizeBackpropImageKernel(
     const int32 nthreads, const float* grads_ptr, const float* boxes_ptr,
     const int32* box_ind_ptr, int num_boxes, int batch, int image_height,
     int image_width, int crop_height, int crop_width, int depth,
-    T* grads_image_ptr) {
+    T* grads_image_ptr, int method_id) {
   CUDA_1D_KERNEL_LOOP(out_idx, nthreads) {
     // out_idx = d + depth * (w + crop_width * (h + crop_height * b))
     int idx = out_idx;
@@ -160,41 +175,52 @@ __global__ void CropAndResizeBackpropImageKernel(
       continue;
     }
 
-    const int top_y_index = floorf(in_y);
-    const int bottom_y_index = ceilf(in_y);
-    const float y_lerp = in_y - top_y_index;
-
-    const int left_x_index = floorf(in_x);
-    const int right_x_index = ceilf(in_x);
-    const float x_lerp = in_x - left_x_index;
-
-    const float dtop = (1 - y_lerp) * grads_ptr[out_idx];
-    CudaAtomicAdd(
-        grads_image_ptr +
-            ((b_in * image_height + top_y_index) * image_width + left_x_index) *
-                depth +
-            d,
-        static_cast((1 - x_lerp) * dtop));
-    CudaAtomicAdd(grads_image_ptr +
-                      ((b_in * image_height + top_y_index) * image_width +
-                       right_x_index) *
-                          depth +
-                      d,
-                  static_cast(x_lerp * dtop));
-
-    const float dbottom = y_lerp * grads_ptr[out_idx];
-    CudaAtomicAdd(grads_image_ptr +
-                      ((b_in * image_height + bottom_y_index) * image_width +
-                       left_x_index) *
-                          depth +
-                      d,
-                  static_cast((1 - x_lerp) * dbottom));
-    CudaAtomicAdd(grads_image_ptr +
-                      ((b_in * image_height + bottom_y_index) * image_width +
-                       right_x_index) *
-                          depth +
-                      d,
-                  static_cast(x_lerp * dbottom));
+    if (method_id == BILINEAR) {
+      const int top_y_index = floorf(in_y);
+      const int bottom_y_index = ceilf(in_y);
+      const float y_lerp = in_y - top_y_index;
+
+      const int left_x_index = floorf(in_x);
+      const int right_x_index = ceilf(in_x);
+      const float x_lerp = in_x - left_x_index;
+
+      const float dtop = (1 - y_lerp) * grads_ptr[out_idx];
+      CudaAtomicAdd(grads_image_ptr +
+                        ((b_in * image_height + top_y_index) * image_width +
+                         left_x_index) *
+                            depth +
+                        d,
+                    static_cast((1 - x_lerp) * dtop));
+      CudaAtomicAdd(grads_image_ptr +
+                        ((b_in * image_height + top_y_index) * image_width +
+                         right_x_index) *
+                            depth +
+                        d,
+                    static_cast(x_lerp * dtop));
+
+      const float dbottom = y_lerp * grads_ptr[out_idx];
+      CudaAtomicAdd(grads_image_ptr +
+                        ((b_in * image_height + bottom_y_index) * image_width +
+                         left_x_index) *
+                            depth +
+                        d,
+                    static_cast((1 - x_lerp) * dbottom));
+      CudaAtomicAdd(grads_image_ptr +
+                        ((b_in * image_height + bottom_y_index) * image_width +
+                         right_x_index) *
+                            depth +
+                        d,
+                    static_cast(x_lerp * dbottom));
+    } else {  // method_id == NEAREST
+      const int closest_x_index = roundf(in_x);
+      const int closest_y_index = roundf(in_y);
+      CudaAtomicAdd(grads_image_ptr +
+                        ((b_in * image_height + closest_y_index) * image_width +
+                         closest_x_index) *
+                            depth +
+                        d,
+                    static_cast(grads_ptr[out_idx]));
+    }
   }
 }
 
@@ -324,7 +350,7 @@ struct CropAndResize {
                   typename TTypes::ConstTensor image,
                   typename TTypes::ConstTensor boxes,
                   typename TTypes::ConstTensor box_ind,
-                  float extrapolation_value,
+                  string method_name, float extrapolation_value,
                   typename TTypes::Tensor crops) {
     const int batch = image.dimension(0);
     const int image_height = image.dimension(1);
@@ -338,13 +364,19 @@ struct CropAndResize {
     const int total_count = num_boxes * crop_height * crop_width * depth;
     const GPUDevice& d = context->eigen_device();
 
+    InterpolationMethod method = BILINEAR;
+    if (method_name == "nearest") {
+      method = NEAREST;
+    }
+
     if (total_count > 0) {
       CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d);
       CropAndResizeKernel<<>>(
           config.virtual_thread_count, image.data(), boxes.data(),
           box_ind.data(), num_boxes, batch, image_height, image_width,
-          crop_height, crop_width, depth, extrapolation_value, crops.data());
+          crop_height, crop_width, depth, method, extrapolation_value,
+          crops.data());
     }
     return d.ok();
   }
@@ -356,7 +388,8 @@ struct CropAndResizeBackpropImage {
                   typename TTypes::ConstTensor grads,
                   typename TTypes::ConstTensor boxes,
                   typename TTypes::ConstTensor box_ind,
-                  typename TTypes::Tensor grads_image) {
+                  typename TTypes::Tensor grads_image,
+                  const string& method_name) {
     const int batch = grads_image.dimension(0);
     const int image_height = grads_image.dimension(1);
     const int image_width = grads_image.dimension(2);
@@ -377,6 +410,12 @@ struct CropAndResizeBackpropImage {
           config.virtual_thread_count, grads_image.data());
     }
 
+    // Configurate interpolation method.
+    InterpolationMethod method = BILINEAR;
+    if (method_name == "nearest") {
+      method = NEAREST;
+    }
+
     // Accumulate.
     total_count = num_boxes * crop_height * crop_width * depth;
     if (total_count > 0) {
@@ -385,7 +424,7 @@ struct CropAndResizeBackpropImage {
           config.block_count, config.thread_per_block, 0, d.stream()>>>(
           config.virtual_thread_count, grads.data(), boxes.data(),
           box_ind.data(), num_boxes, batch, image_height, image_width,
-          crop_height, crop_width, depth, grads_image.data());
+          crop_height, crop_width, depth, grads_image.data(), method);
     }
     return d.ok();
   }
diff --git a/tensorflow/core/kernels/crop_and_resize_op_test.cc b/tensorflow/core/kernels/crop_and_resize_op_test.cc
index 709082e79903d0..6921020d09e94f 100644
--- a/tensorflow/core/kernels/crop_and_resize_op_test.cc
+++ b/tensorflow/core/kernels/crop_and_resize_op_test.cc
@@ -34,13 +34,14 @@ namespace tensorflow {
 class CropAndResizeOpTest : public OpsTestBase {
  protected:
   template 
-  void MakeOp(float extrapolation_value) {
+  void MakeOp(float extrapolation_value, const string& method) {
     TF_EXPECT_OK(NodeDefBuilder("crop_and_resize_op", "CropAndResize")
                      .Input(FakeInput(DataTypeToEnum::value))
                      .Input(FakeInput(DT_FLOAT))
                      .Input(FakeInput(DT_INT32))
                      .Input(FakeInput(DT_INT32))
                      .Attr("extrapolation_value", extrapolation_value)
+                     .Attr("method", method)
                      .Finalize(node_def()));
     TF_EXPECT_OK(InitOp());
   }
@@ -48,7 +49,7 @@ class CropAndResizeOpTest : public OpsTestBase {
 
 #define REGISTER_TEST(T)                                               \
   TEST_F(CropAndResizeOpTest, TestCropAndResize##T) {                  \
-    MakeOp(0);                                                      \
+    MakeOp(0, "bilinear");                                          \
     AddInputFromArray(TensorShape({1, 2, 2, 1}), {1, 2, 3, 4});     \
     AddInputFromArray(TensorShape({1, 4}), {0, 0, 1, 1});       \
     AddInputFromArray(TensorShape({1}), {0});                   \
@@ -58,6 +59,19 @@ class CropAndResizeOpTest : public OpsTestBase {
     Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 1, 1, 1})); \
     test::FillValues(&expected, {2.5});                         \
     test::ExpectTensorEqual(expected, *GetOutput(0));           \
+  }                                                                    \
+                                                                       \
+  TEST_F(CropAndResizeOpTest, TestCropAndResize##T##nearest) {         \
+    MakeOp(0, "nearest");                                           \
+    AddInputFromArray(TensorShape({1, 2, 2, 1}), {1, 2, 3, 4});     \
+    AddInputFromArray(TensorShape({1, 4}), {0, 0, 1, 1});       \
+    AddInputFromArray(TensorShape({1}), {0});                   \
+    AddInputFromArray(TensorShape({2}), {1, 1});                \
+    TF_ASSERT_OK(RunOpKernel());                                       \
+                                                                       \
+    Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 1, 1, 1})); \
+    test::FillValues(&expected, {4.0});                         \
+    test::ExpectTensorEqual(expected, *GetOutput(0));           \
   }
 
 REGISTER_TEST(float)
@@ -72,7 +86,7 @@ REGISTER_TEST(int64)
 #undef REGISTER_TEST
 
 TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To1x1Uint8) {
-  MakeOp(0);
+  MakeOp(0, "bilinear");
   // Input:
   //  1, 2
   //  3, 4
@@ -87,8 +101,24 @@ TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To1x1Uint8) {
   test::ExpectTensorEqual(expected, *GetOutput(0));
 }
 
+TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To1x1Uint8NearestNeibor) {
+  MakeOp(0, "nearest");
+  // Input:
+  //  1, 2
+  //  3, 4
+  AddInputFromArray(TensorShape({1, 2, 2, 1}), {1, 2, 3, 4});
+  AddInputFromArray(TensorShape({1, 4}), {0, 0, 1, 1});
+  AddInputFromArray(TensorShape({1}), {0});
+  AddInputFromArray(TensorShape({2}), {1, 1});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 1, 1, 1}));
+  test::FillValues(&expected, {4.0});
+  test::ExpectTensorEqual(expected, *GetOutput(0));
+}
+
 TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To1x1Flipped) {
-  MakeOp(0);
+  MakeOp(0, "bilinear");
   // Input:
   //  1, 2
   //  3, 4
@@ -103,8 +133,24 @@ TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To1x1Flipped) {
   test::ExpectTensorEqual(expected, *GetOutput(0));
 }
 
+TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To1x1FlippedNearestNeighbor) {
+  MakeOp(0, "nearest");
+  // Input:
+  //  1, 2
+  //  3, 4
+  AddInputFromArray(TensorShape({1, 2, 2, 1}), {1, 2, 3, 4});
+  AddInputFromArray(TensorShape({1, 4}), {1, 1, 0, 0});
+  AddInputFromArray(TensorShape({1}), {0});
+  AddInputFromArray(TensorShape({2}), {1, 1});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 1, 1, 1}));
+  test::FillValues(&expected, {4.0});
+  test::ExpectTensorEqual(expected, *GetOutput(0));
+}
+
 TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To3x3) {
-  MakeOp(0);
+  MakeOp(0, "bilinear");
   // Input:
   //  1, 2
   //  3, 4
@@ -124,8 +170,29 @@ TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To3x3) {
   test::ExpectTensorEqual(expected, *GetOutput(0));
 }
 
+TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To3x3NearestNeighbor) {
+  MakeOp(0, "nearest");
+  // Input:
+  //  1, 2
+  //  3, 4
+  AddInputFromArray(TensorShape({1, 2, 2, 1}), {1, 2, 3, 4});
+  AddInputFromArray(TensorShape({1, 4}), {0, 0, 1, 1});
+  AddInputFromArray(TensorShape({1}), {0});
+  AddInputFromArray(TensorShape({2}), {3, 3});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 3, 3, 1}));
+  // clang-format off
+  test::FillValues(&expected,
+    {1,  2,  2,
+     3,  4,  4,
+     3,  4,  4});
+  // clang-format on
+  test::ExpectTensorEqual(expected, *GetOutput(0));
+}
+
 TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To3x3Flipped) {
-  MakeOp(0);
+  MakeOp(0, "bilinear");
   // Input:
   //  1, 2
   //  3, 4
@@ -145,8 +212,54 @@ TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To3x3Flipped) {
   test::ExpectTensorEqual(expected, *GetOutput(0));
 }
 
+TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To3x3FlippedNearestNeighbor) {
+  MakeOp(0, "nearest");
+  // Input:
+  //  1, 2
+  //  3, 4
+  AddInputFromArray(TensorShape({1, 2, 2, 1}), {1, 2, 3, 4});
+  AddInputFromArray(TensorShape({1, 4}), {1, 1, 0, 0});
+  AddInputFromArray(TensorShape({1}), {0});
+  AddInputFromArray(TensorShape({2}), {3, 3});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 3, 3, 1}));
+  // clang-format off
+  test::FillValues(&expected,
+    {4,  4,  3,
+     4,  4,  3,
+     2,  2,  1});
+  // clang-format on
+  test::ExpectTensorEqual(expected, *GetOutput(0));
+}
+
 TEST_F(CropAndResizeOpTest, TestCropAndResize3x3To2x2) {
-  MakeOp(0);
+  MakeOp(0, "bilinear");
+  // Input:
+  //  1, 2, 3
+  //  4, 5, 6
+  //  7, 8, 9
+  AddInputFromArray(TensorShape({1, 3, 3, 1}),
+                           {1, 2, 3, 4, 5, 6, 7, 8, 9});
+  AddInputFromArray(TensorShape({2, 4}), {0, 0, 1, 1, 0, 0, 0.5, 0.5});
+  AddInputFromArray(TensorShape({2}), {0, 0});
+  AddInputFromArray(TensorShape({2}), {2, 2});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 2, 2, 1}));
+
+  // clang-format off
+  test::FillValues(&expected,
+    {1,  3,
+     7,  9,
+     1,  2,
+     4,  5});
+  // clang-format on
+  test::ExpectTensorEqual(expected, *GetOutput(0));
+}
+
+TEST_F(CropAndResizeOpTest, TestCropAndResize3x3To2x2NearestNeighbor) {
+  MakeOp(0, "nearest");
   // Input:
   //  1, 2, 3
   //  4, 5, 6
@@ -171,7 +284,32 @@ TEST_F(CropAndResizeOpTest, TestCropAndResize3x3To2x2) {
 }
 
 TEST_F(CropAndResizeOpTest, TestCropAndResize3x3To2x2Flipped) {
-  MakeOp(0);
+  MakeOp(0, "bilinear");
+  // Input:
+  //  1, 2, 3
+  //  4, 5, 6
+  //  7, 8, 9
+  AddInputFromArray(TensorShape({1, 3, 3, 1}),
+                           {1, 2, 3, 4, 5, 6, 7, 8, 9});
+  AddInputFromArray(TensorShape({2, 4}), {1, 1, 0, 0, 0.5, 0.5, 0, 0});
+  AddInputFromArray(TensorShape({2}), {0, 0});
+  AddInputFromArray(TensorShape({2}), {2, 2});
+  TF_ASSERT_OK(RunOpKernel());
+
+  Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 2, 2, 1}));
+
+  // clang-format off
+  test::FillValues(&expected,
+    {9,  7,
+     3,  1,
+     5,  4,
+     2,  1});
+  // clang-format on
+  test::ExpectTensorEqual(expected, *GetOutput(0));
+}
+
+TEST_F(CropAndResizeOpTest, TestCropAndResize3x3To2x2FlippedNearestNeighbor) {
+  MakeOp(0, "nearest");
   // Input:
   //  1, 2, 3
   //  4, 5, 6
@@ -197,7 +335,7 @@ TEST_F(CropAndResizeOpTest, TestCropAndResize3x3To2x2Flipped) {
 
 TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To3x3Extrapolated) {
   const float v = -1;
-  MakeOp(v);
+  MakeOp(v, "bilinear");
   // Input:
   //  1, 2
   //  3, 4
@@ -218,7 +356,7 @@ TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To3x3Extrapolated) {
 }
 
 TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To3x3NoCrop) {
-  MakeOp(0);
+  MakeOp(0, "bilinear");
   // Input:
   //  1, 2
   //  3, 4
@@ -236,7 +374,7 @@ TEST_F(CropAndResizeOpTest, TestCropAndResize2x2To3x3NoCrop) {
 }
 
 TEST_F(CropAndResizeOpTest, TestInvalidInputShape) {
-  MakeOp(0);
+  MakeOp(0, "bilinear");
   AddInputFromArray(TensorShape({2, 2, 1}), {1, 2, 3, 4});
   AddInputFromArray(TensorShape({1, 4}), {0, 0, 1, 1});
   AddInputFromArray(TensorShape({1}), {0});
@@ -248,7 +386,7 @@ TEST_F(CropAndResizeOpTest, TestInvalidInputShape) {
 }
 
 TEST_F(CropAndResizeOpTest, TestInvalidBoxIndexShape) {
-  MakeOp(0);
+  MakeOp(0, "bilinear");
   AddInputFromArray(TensorShape({1, 2, 2, 1}), {1, 2, 3, 4});
   AddInputFromArray(TensorShape({1, 4}), {0, 0, 1, 1});
   AddInputFromArray(TensorShape({2}), {0, 0});
@@ -261,7 +399,7 @@ TEST_F(CropAndResizeOpTest, TestInvalidBoxIndexShape) {
 }
 
 TEST_F(CropAndResizeOpTest, TestInvalidBoxIndex) {
-  MakeOp(0);
+  MakeOp(0, "bilinear");
   AddInputFromArray(TensorShape({1, 2, 2, 1}), {1, 2, 3, 4});
   AddInputFromArray(TensorShape({1, 4}), {0, 0, 1, 1});
   AddInputFromArray(TensorShape({1}), {1});
@@ -274,7 +412,7 @@ TEST_F(CropAndResizeOpTest, TestInvalidBoxIndex) {
 }
 
 TEST_F(CropAndResizeOpTest, TestWithSharding) {
-  MakeOp(0);
+  MakeOp(0, "bilinear");
   // Generate a relatively large input (999x999) so that sharding happens.
   const int kLength = 999;  // Length of the input. Must use an odd number.
   const int kHalf = (kLength + 1) / 2;  // Half size for the cropped result.
diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc
index c3b08e067a2c35..0d0677b48c3911 100644
--- a/tensorflow/core/ops/image_ops.cc
+++ b/tensorflow/core/ops/image_ops.cc
@@ -548,7 +548,7 @@ REGISTER_OP("CropAndResize")
     .Input("crop_size: int32")
     .Output("crops: float")
     .Attr("T: {uint8, uint16, int8, int16, int32, int64, half, float, double}")
-    .Attr("method: {'bilinear'} = 'bilinear'")
+    .Attr("method: {'bilinear', 'nearest'} = 'bilinear'")
     .Attr("extrapolation_value: float = 0")
     .SetShapeFn([](InferenceContext* c) {
       // Get inputs and validate ranks.
@@ -579,7 +579,7 @@ REGISTER_OP("CropAndResizeGradImage")
     .Input("image_size: int32")
     .Output("output: T")
     .Attr("T: {float, half, double}")
-    .Attr("method: {'bilinear'} = 'bilinear'")
+    .Attr("method: {'bilinear', 'nearest'} = 'bilinear'")
     .SetShapeFn([](InferenceContext* c) {
       ShapeHandle out;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(3, &out));
diff --git a/tensorflow/python/ops/image_grad.py b/tensorflow/python/ops/image_grad.py
index 9f43e3f1466d90..102181e68b4d09 100644
--- a/tensorflow/python/ops/image_grad.py
+++ b/tensorflow/python/ops/image_grad.py
@@ -107,16 +107,20 @@ def _CropAndResizeGrad(op, grad):
   allowed_types = [dtypes.float16, dtypes.float32, dtypes.float64]
   if op.inputs[0].dtype in allowed_types:
     # pylint: disable=protected-access
-    grad0 = gen_image_ops.crop_and_resize_grad_image(grad,
-                                                     op.inputs[1],
-                                                     op.inputs[2],
-                                                     image_shape,
-                                                     T=op.get_attr("T"))
+    grad0 = gen_image_ops.crop_and_resize_grad_image(
+        grad, op.inputs[1], op.inputs[2], image_shape, T=op.get_attr("T"),
+        method=op.get_attr("method"))
     # pylint: enable=protected-access
   else:
     grad0 = None
 
-  grad1 = gen_image_ops.crop_and_resize_grad_boxes(grad, op.inputs[0],
-                                                   op.inputs[1], op.inputs[2])
+  # `grad0` is the gradient to the input image pixels and it
+  # has been implemented for nearest neighbor and bilinear sampling
+  # respectively. `grad1` is the gradient to the input crop boxes' coordinates.
+  # When using nearest neighbor sampling, the gradient to crop boxes'
+  # coordinates are not well defined. In practice, we still approximate
+  # grad1 using the gradient derived from bilinear sampling.
+  grad1 = gen_image_ops.crop_and_resize_grad_boxes(
+      grad, op.inputs[0], op.inputs[1], op.inputs[2])
 
   return [grad0, grad1, None, None]

From 8444f722ccebba5793642fa6241dab9c77ed5382 Mon Sep 17 00:00:00 2001
From: Pavithra Vijay 
Date: Thu, 10 May 2018 15:20:37 -0700
Subject: [PATCH 0640/1691] Fix bug due to incorrect nesting of return
 statement in eager iterator evaluation.

PiperOrigin-RevId: 196179837
---
 .../_impl/keras/engine/training_eager.py      | 10 ++--
 .../_impl/keras/engine/training_eager_test.py | 56 ++++++++++++++++++-
 2 files changed, 60 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
index 526ae65321adc0..adf0c9be79a61f 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
@@ -501,11 +501,11 @@ def iterator_test_loop(model, inputs, steps, verbose=0):
     if verbose == 1:
       progbar.update(step_index + 1)
 
-    for i in range(len(outs)):
-      outs[i] /= num_samples
-    if len(outs) == 1:
-      return outs[0]
-    return outs
+  for i in range(len(outs)):
+    outs[i] /= num_samples
+  if len(outs) == 1:
+    return outs[0]
+  return outs
 
 
 def batch_test_loop(model,
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
index 2375dffc335ed0..2031a8a3dc9731 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
@@ -20,6 +20,7 @@
 
 import numpy as np
 
+from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
@@ -94,7 +95,7 @@ def test_fit_on_arrays(self):
         verbose=2)
     model.train_on_batch([input_a_np, input_b_np], [output_d_np, output_e_np])
 
-  # Test with validation split
+    # Test with validation split
     model.fit(
         [input_a_np, input_b_np], [output_d_np, output_e_np],
         epochs=2,
@@ -688,6 +689,59 @@ def test_metrics_correctness(self):
     outs = model.evaluate(x, y)
     self.assertEqual(outs[1], 0.)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
+  def test_loss_correctness_with_iterator(self):
+    # Test that training loss is the same in eager and graph
+    # (by comparing it to a reference value in a deterministic case)
+    model = keras.Sequential()
+    model.add(
+        keras.layers.Dense(
+            3, activation='relu', input_dim=4, kernel_initializer='ones'))
+    model.add(
+        keras.layers.Dense(2, activation='softmax', kernel_initializer='ones'))
+    model.compile(
+        loss='sparse_categorical_crossentropy',
+        optimizer=RMSPropOptimizer(learning_rate=0.001))
+    x = np.ones((100, 4), dtype=np.float32)
+    np.random.seed(123)
+    y = np.random.randint(0, 1, size=(100, 1))
+    dataset = dataset_ops.Dataset.from_tensor_slices((x, y))
+    dataset = dataset.repeat(100)
+    dataset = dataset.batch(10)
+    iterator = dataset.make_one_shot_iterator()
+    history = model.fit(iterator, epochs=1, steps_per_epoch=10)
+    self.assertEqual(np.around(history.history['loss'][-1], decimals=4), 0.6173)
+
+  @tf_test_util.run_in_graph_and_eager_modes()
+  def test_metrics_correctness_with_iterator(self):
+    model = keras.Sequential()
+    model.add(
+        keras.layers.Dense(
+            8, activation='relu', input_dim=4, kernel_initializer='ones'))
+    model.add(
+        keras.layers.Dense(1, activation='sigmoid', kernel_initializer='ones'))
+    model.compile(
+        loss='binary_crossentropy',
+        metrics=['accuracy'],
+        optimizer=RMSPropOptimizer(learning_rate=0.001))
+    np.random.seed(123)
+    x = np.random.randint(10, size=(100, 4)).astype(np.float32)
+    y = np.random.randint(2, size=(100, 1)).astype(np.float32)
+    dataset = dataset_ops.Dataset.from_tensor_slices((x, y))
+    dataset = dataset.batch(10)
+    iterator = dataset.make_one_shot_iterator()
+    outs = model.evaluate(iterator, steps=10)
+    self.assertEqual(np.around(outs[1], decimals=1), 0.5)
+
+    y = np.zeros((100, 1), dtype=np.float32)
+    dataset = dataset_ops.Dataset.from_tensor_slices((x, y))
+    dataset = dataset.repeat(100)
+    dataset = dataset.batch(10)
+    iterator = dataset.make_one_shot_iterator()
+    outs = model.evaluate(iterator, steps=10)
+    self.assertEqual(outs[1], 0.)
+
+
 if __name__ == '__main__':
   ops.enable_eager_execution()
   test.main()

From ff7f7a566b356a7e2de2b8f174d0f09e673179f4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 15:20:53 -0700
Subject: [PATCH 0641/1691] Update ops-related pbtxt files.

PiperOrigin-RevId: 196179875
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 107 ++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 |   2 +
 2 files changed, 109 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 6880ceb50564f7..b4f215a2c0bab6 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -14641,6 +14641,66 @@ op {
     }
   }
 }
+op {
+  name: "CropAndResize"
+  input_arg {
+    name: "image"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "boxes"
+    type: DT_FLOAT
+  }
+  input_arg {
+    name: "box_ind"
+    type: DT_INT32
+  }
+  input_arg {
+    name: "crop_size"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "crops"
+    type: DT_FLOAT
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+      }
+    }
+  }
+  attr {
+    name: "method"
+    type: "string"
+    default_value {
+      s: "bilinear"
+    }
+    allowed_values {
+      list {
+        s: "bilinear"
+        s: "nearest"
+      }
+    }
+  }
+  attr {
+    name: "extrapolation_value"
+    type: "float"
+    default_value {
+      f: 0
+    }
+  }
+}
 op {
   name: "CropAndResizeGradBoxes"
   input_arg {
@@ -14790,6 +14850,53 @@ op {
     }
   }
 }
+op {
+  name: "CropAndResizeGradImage"
+  input_arg {
+    name: "grads"
+    type: DT_FLOAT
+  }
+  input_arg {
+    name: "boxes"
+    type: DT_FLOAT
+  }
+  input_arg {
+    name: "box_ind"
+    type: DT_INT32
+  }
+  input_arg {
+    name: "image_size"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_HALF
+        type: DT_DOUBLE
+      }
+    }
+  }
+  attr {
+    name: "method"
+    type: "string"
+    default_value {
+      s: "bilinear"
+    }
+    allowed_values {
+      list {
+        s: "bilinear"
+        s: "nearest"
+      }
+    }
+  }
+}
 op {
   name: "Cross"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index d741598b197909..6dd6ae475a83a7 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -6242,6 +6242,7 @@ op {
     allowed_values {
       list {
         s: "bilinear"
+        s: "nearest"
       }
     }
   }
@@ -6347,6 +6348,7 @@ op {
     allowed_values {
       list {
         s: "bilinear"
+        s: "nearest"
       }
     }
   }

From f7e24ab1113ae7094e4831a606a29e0d5b956bfe Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 15:43:55 -0700
Subject: [PATCH 0642/1691] Remove cancelling pairs of transposes that are
 separated by a non-branching chain of ops that preserve value, order, and
 shape. Off by default.

PiperOrigin-RevId: 196183111
---
 .../optimizers/arithmetic_optimizer.cc        | 62 ++++++++++++++-----
 .../optimizers/arithmetic_optimizer_test.cc   | 43 ++++++++++++-
 2 files changed, 89 insertions(+), 16 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
index f46c30c92c077d..26eca9b82004e7 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -254,6 +254,17 @@ NodeDef* GetTailOfValuePreservingChain(
                         is_value_preserving_non_branching);
 }
 
+NodeDef* GetTailOfIdempotentChain(
+    const NodeDef& node, const NodeMap& node_map,
+    const std::unordered_set& nodes_to_preserve) {
+  auto is_idempotent_non_branching = [&](const NodeDef& node) {
+    return nodes_to_preserve.find(node.name()) == nodes_to_preserve.end() &&
+           IsIdempotent(node) && NumNonControlOutputs(node, node_map) == 1;
+  };
+  return GetTailOfChain(node, node_map, /*follow_control_input=*/false,
+                        is_idempotent_non_branching);
+}
+
 // Graph optimizer context extension specific to ArithmeticOptimizer.
 struct ArithmeticOptimizerContext {
   explicit ArithmeticOptimizerContext(SetVector* nodes_to_simplify)
@@ -1149,21 +1160,27 @@ class MinimizeBroadcasts : public ArithmeticNodesGroupOptimizerStage {
 class RemoveIdentityTranspose : public ArithmeticOptimizerStage {
  public:
   explicit RemoveIdentityTranspose(const GraphOptimizerContext& ctx,
-                                   const ArithmeticOptimizerContext& ctx_ext)
-      : ArithmeticOptimizerStage("RemoveIdentityTranspose", ctx, ctx_ext) {}
+                                   const ArithmeticOptimizerContext& ctx_ext,
+                                   RewriterConfig::Toggle opt_level)
+      : ArithmeticOptimizerStage("RemoveIdentityTranspose", ctx, ctx_ext),
+        opt_level_(opt_level) {}
   ~RemoveIdentityTranspose() override = default;
 
   bool IsSupported(const NodeDef* node) const override {
     return IsTranspose(*node) || IsConjugateTranspose(*node);
   }
 
-  // TODO(rmlarsen): Forward control dependencies on the bypassed
-  // transpose nodes.
   Status TrySimplify(NodeDef* node, string* simplified_node_name) override {
     TF_RETURN_IF_ERROR(EnsureNodeIsSupported(node));
+    NodeDef* tail = node;
+    // TODO(rmlarsen): Enable in regular mode after May 15, 2018.
+    if (opt_level_ == RewriterConfig::AGGRESSIVE) {
+      tail = GetTailOfIdempotentChain(*tail, *ctx().node_map,
+                                      *ctx().nodes_to_preserve);
+    }
+    NodeDef* first_transpose;
+    TF_RETURN_IF_ERROR(GetInputNode(tail->input(0), &first_transpose));
 
-    NodeDef* input;
-    TF_RETURN_IF_ERROR(GetInputNode(node->input(0), &input));
     NodeDef* node_perm;
     TF_RETURN_IF_ERROR(GetInputNode(node->input(1), &node_perm));
     if (!IsConstant(*node_perm)) {
@@ -1171,17 +1188,30 @@ class RemoveIdentityTranspose : public ArithmeticOptimizerStage {
     }
     std::vector node_perm_values;
     TF_RETURN_IF_ERROR(GetPermutation(*node_perm, &node_perm_values));
-    if (input->op() == node->op()) {
+    if (first_transpose->op() == node->op()) {
       // Remove pairs of transposes that cancel each other.
-      NodeDef* input_perm;
-      TF_RETURN_IF_ERROR(GetInputNode(input->input(1), &input_perm));
-      if (!IsConstant(*input_perm)) {
+      NodeDef* first_transpose_perm;
+      TF_RETURN_IF_ERROR(
+          GetInputNode(first_transpose->input(1), &first_transpose_perm));
+      if (!IsConstant(*first_transpose_perm)) {
         return Status::OK();
       }
-      std::vector input_perm_values;
-      TF_RETURN_IF_ERROR(GetPermutation(*input_perm, &input_perm_values));
-      if (AreInversePermutations(node_perm_values, input_perm_values)) {
-        *simplified_node_name = input->input(0);
+      std::vector first_transpose_perm_values;
+      TF_RETURN_IF_ERROR(
+          GetPermutation(*first_transpose_perm, &first_transpose_perm_values));
+      if (AreInversePermutations(node_perm_values,
+                                 first_transpose_perm_values)) {
+        if (tail == node) {
+          // Bypass adjacent pair.
+          *simplified_node_name = first_transpose->input(0);
+        } else {
+          // Bypass pair connected through chain.
+          tail->set_input(0, first_transpose->input(0));
+          ctx().node_map->UpdateInput(tail->name(), first_transpose->name(),
+                                      first_transpose->input(0));
+          ForwardControlDependencies(tail, {first_transpose});
+          *simplified_node_name = node->input(0);
+        }
       }
     } else {
       // Remove simple identity transposes.
@@ -1231,6 +1261,8 @@ class RemoveIdentityTranspose : public ArithmeticOptimizerStage {
     }
     return true;
   }
+
+  RewriterConfig::Toggle opt_level_;
 };
 
 // Remove redundant Bitcasts.
@@ -2401,7 +2433,7 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) {
   if (options_.minimize_broadcasts && can_use_shapes)
     pipeline.AddStage(ctx, ctx_ext);
   if (options_.remove_identity_transpose && can_use_shapes)
-    pipeline.AddStage(ctx, ctx_ext);
+    pipeline.AddStage(ctx, ctx_ext, opt_level_);
   if (options_.remove_redundant_bitcast)
     pipeline.AddStage(ctx, ctx_ext);
   if (options_.remove_redundant_cast)
diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
index d60c3124edcc89..d648fa0787333c 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
@@ -1122,7 +1122,7 @@ TEST_F(ArithmeticOptimizerTest, RemoveIdentityTransposes) {
       ops::RandomUniform(s.WithOpName("inputs"), inputs_shape, DT_FLOAT);
   Output perm1 = ops::Const(s.WithOpName("perm1"), {0, 2, 3, 1}, {4});
   Output perm2 = ops::Const(s.WithOpName("perm2"), {0, 3, 1, 2}, {4});
-  Output perm3 = ops::Const(s.WithOpName("perm2"), {0, 1, 2, 3}, {4});
+  Output perm3 = ops::Const(s.WithOpName("perm3"), {0, 1, 2, 3}, {4});
   Output transpose1 = ops::Transpose(s.WithOpName("transpose1"), inputs, perm1);
   Output transpose2 =
       ops::Transpose(s.WithOpName("transpose2"), transpose1, perm2);
@@ -1248,6 +1248,47 @@ TEST_F(ArithmeticOptimizerTest, NotRemoveTransposes) {
   EXPECT_EQ(6, output.node_size());
 }
 
+TEST_F(ArithmeticOptimizerTest, RemoveIdentityTransposesThroughChain) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+  Output inputs_shape =
+      ops::Const(s.WithOpName("inputs_shape"), {8, 3, 28, 28}, {4});
+  Output inputs =
+      ops::RandomUniform(s.WithOpName("inputs"), inputs_shape, DT_FLOAT);
+  Output perm1 = ops::Const(s.WithOpName("perm1"), {0, 2, 3, 1}, {4});
+  Output perm2 = ops::Const(s.WithOpName("perm2"), {0, 3, 1, 2}, {4});
+  Output transpose1 = ops::Transpose(
+      s.WithOpName("transpose1").WithControlDependencies(perm2), inputs, perm1);
+  Output identity = ops::Identity(s.WithOpName("id"), transpose1);
+  Output transpose2 =
+      ops::Transpose(s.WithOpName("transpose2"), identity, perm2);
+  Output id1 = ops::Identity(s.WithOpName("id1"), transpose2);
+
+  GrapplerItem item;
+  item.fetch = {"id1"};
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+
+  GraphDef output;
+  ArithmeticOptimizer optimizer(RewriterConfig::AGGRESSIVE);
+  EnableOnlyRemoveIdentityTranspose(&optimizer);
+  OptimizeAndPrune(&optimizer, &item, &output);
+
+  std::set nodes_after_optimization;
+  for (const NodeDef& node : output.node()) {
+    nodes_after_optimization.insert(node.name());
+    if (node.name() == "id") {
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("inputs", node.input(0));
+      EXPECT_EQ("^perm2", node.input(1));
+    }
+    if (node.name() == "id1") {
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("id", node.input(0));
+    }
+  }
+  EXPECT_EQ(nodes_after_optimization,
+            std::set({"id", "id1", "inputs_shape", "inputs", "perm2"}));
+}
+
 TEST_F(ArithmeticOptimizerTest, FoldMulToTransposeConv) {
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
   Output inputs = ops::Placeholder(s.WithOpName("inputs"), DT_FLOAT,

From 8a8dddf8bd93946d02fa080f8103943a03a6a274 Mon Sep 17 00:00:00 2001
From: Alexandre Passos 
Date: Thu, 10 May 2018 15:54:13 -0700
Subject: [PATCH 0643/1691] Do not differentiate integers in the eager backprop
 API.

(with bugfix)

PiperOrigin-RevId: 196184587
---
 tensorflow/c/eager/tape.h                   | 38 ++++++++++---
 tensorflow/contrib/eager/python/tfe_test.py |  6 +-
 tensorflow/python/eager/backprop.py         |  5 ++
 tensorflow/python/eager/backprop_test.py    | 22 +++++++-
 tensorflow/python/eager/pywrap_tensor.cc    |  6 ++
 tensorflow/python/eager/pywrap_tensor.h     |  1 +
 tensorflow/python/eager/pywrap_tfe_src.cc   | 62 ++++++++++++++++++---
 7 files changed, 121 insertions(+), 19 deletions(-)

diff --git a/tensorflow/c/eager/tape.h b/tensorflow/c/eager/tape.h
index 8026076b9ef3bf..dcc2357b71a68b 100644
--- a/tensorflow/c/eager/tape.h
+++ b/tensorflow/c/eager/tape.h
@@ -130,13 +130,15 @@ class GradientTape {
     }
   }
 
-  bool ShouldRecord(gtl::ArraySlice tensor_ids);
+  bool ShouldRecord(gtl::ArraySlice tensor_ids,
+                    gtl::ArraySlice dtypes);
 
   void Watch(int64 tensor_id);
 
   void RecordOperation(const string& op_type,
                        gtl::ArraySlice output_tensors,
                        gtl::ArraySlice input_tensor_id,
+                       gtl::ArraySlice input_dtypes,
                        BackwardFunction* backward_function,
                        const std::function& backward_function_deleter);
 
@@ -170,12 +172,32 @@ class GradientTape {
 
 // Template instantiations here
 
+inline bool IsDtypeTrainable(DataType dtype) {
+  switch (dtype) {
+    case DT_HALF:
+    case DT_BFLOAT16:
+    case DT_FLOAT:
+    case DT_DOUBLE:
+    case DT_COMPLEX64:
+    case DT_COMPLEX128:
+    case DT_RESOURCE:
+    case DT_VARIANT:
+      return true;
+    default:
+      return false;
+  }
+}
+
 template 
 bool GradientTape::ShouldRecord(
-    gtl::ArraySlice tensor_ids) {
-  for (int64 i : tensor_ids) {
-    if (tensor_tape_.find(i) != tensor_tape_.end()) {
-      return true;
+    gtl::ArraySlice tensor_ids,
+    gtl::ArraySlice dtypes) {
+  CHECK_EQ(tensor_ids.size(), dtypes.size());
+  for (int i = 0; i < tensor_ids.size(); ++i) {
+    if (tensor_tape_.find(tensor_ids[i]) != tensor_tape_.end()) {
+      if (IsDtypeTrainable(dtypes[i])) {
+        return true;
+      }
     }
   }
   return false;
@@ -189,9 +211,11 @@ void GradientTape::Watch(int64 tensor_id) {
 template 
 void GradientTape::RecordOperation(
     const string& op_type, gtl::ArraySlice output_tensors,
-    gtl::ArraySlice input_tensor_id, BackwardFunction* backward_function,
+    gtl::ArraySlice input_tensor_id,
+    gtl::ArraySlice input_dtypes,
+    BackwardFunction* backward_function,
     const std::function& backward_function_deleter) {
-  if (!ShouldRecord(input_tensor_id)) {
+  if (!ShouldRecord(input_tensor_id, input_dtypes)) {
     backward_function_deleter();
     return;
   }
diff --git a/tensorflow/contrib/eager/python/tfe_test.py b/tensorflow/contrib/eager/python/tfe_test.py
index e80ccbb74d8623..db50b33af2e4f1 100644
--- a/tensorflow/contrib/eager/python/tfe_test.py
+++ b/tensorflow/contrib/eager/python/tfe_test.py
@@ -57,7 +57,7 @@ def square(x):
       return math_ops.multiply(x, x)
 
     grad = tfe.gradients_function(square)
-    self.assertEquals([6], [x.numpy() for x in grad(3)])
+    self.assertEquals([6], [x.numpy() for x in grad(3.)])
 
   def testGradOfGrad(self):
 
@@ -66,7 +66,7 @@ def square(x):
 
     grad = tfe.gradients_function(square)
     gradgrad = tfe.gradients_function(lambda x: grad(x)[0])
-    self.assertEquals([2], [x.numpy() for x in gradgrad(3)])
+    self.assertEquals([2], [x.numpy() for x in gradgrad(3.)])
 
   def testCustomGrad(self):
 
@@ -80,7 +80,7 @@ def grad_fn(_):
       return y, grad_fn
 
     grad = tfe.gradients_function(f)
-    self.assertEquals([12], [x.numpy() for x in grad(3)])
+    self.assertEquals([12], [x.numpy() for x in grad(3.)])
 
   def testGPU(self):
     if tfe.num_gpus() <= 0:
diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py
index d04b004451223a..967c12828043f8 100644
--- a/tensorflow/python/eager/backprop.py
+++ b/tensorflow/python/eager/backprop.py
@@ -358,6 +358,8 @@ def f(x, y):
   assert y_grad.numpy() == (2 ** 3) - 2 * 2 * 3
   ```
 
+  Note that only tensors with real or complex dtypes are differentiable.
+
   Args:
    f: function to be differentiated. If `f` returns a scalar, this scalar will
      be differentiated. If `f` returns a tensor or list of tensors, by default
@@ -700,6 +702,9 @@ class GradientTape(object):
   dz_dx = g.gradient(z, x)  # 108.0 (4*x^3 at x = 3)
   dy_dx = g.gradient(y, x)  # 6.0
   del g  # Drop the reference to the tape
+  ```
+
+  Note that only tensors with real or complex dtypes are differentiable.
   """
 
   def __init__(self, persistent=False):
diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py
index 8d9959fe20768c..73dbbedbe97c66 100644
--- a/tensorflow/python/eager/backprop_test.py
+++ b/tensorflow/python/eager/backprop_test.py
@@ -96,6 +96,18 @@ def fn():
     self.assertAllEqual(grads_and_vars[0][0], 1.0)
     self.assertAllEqual(id(grads_and_vars[0][1]), id(x))
 
+  def testWhereGradient(self):
+    # Note: where is special because only some of its arguments are of
+    # differentiable dtypes.
+
+    def f(x):
+      return array_ops.where(x < 10, x, x * x)
+
+    g = backprop.gradients_function(f)
+
+    self.assertAllEqual(g(5.)[0], 1.0)
+    self.assertAllEqual(g(50.)[0], 100.0)
+
   def testTwoTargets(self):
     with backprop.GradientTape() as t:
       x = constant_op.constant(3.0)
@@ -124,6 +136,14 @@ def f(x):
     grad_fn = backprop.gradients_function(f)
     self.assertAllEqual(2., grad_fn(1., dy=2.)[0])
 
+  def testGradientInteger(self):
+
+    def f(x):
+      return x + x
+
+    int_tensor = constant_op.constant(1)
+    self.assertEqual(backprop.gradients_function(f)(int_tensor)[0], None)
+
   def testErrors(self):
 
     @custom_gradient.custom_gradient
@@ -753,7 +773,7 @@ def grad(dr):
       return result, grad
 
     x = resource_variable_ops.ResourceVariable(
-        initial_value=3, name='X.' + self.id())
+        initial_value=3., name='X.' + self.id())
 
     def f():
       return my_square(x)
diff --git a/tensorflow/python/eager/pywrap_tensor.cc b/tensorflow/python/eager/pywrap_tensor.cc
index b5b4e394e33bd3..b3aadd55ce7805 100644
--- a/tensorflow/python/eager/pywrap_tensor.cc
+++ b/tensorflow/python/eager/pywrap_tensor.cc
@@ -650,6 +650,12 @@ tensorflow::int64 EagerTensor_id(const PyObject* tensor) {
   return reinterpret_cast(tensor)->id;
 }
 
+tensorflow::DataType EagerTensor_dtype(const PyObject* tensor) {
+  CHECK(EagerTensor_CheckExact(tensor));
+  return static_cast(TFE_TensorHandleDataType(
+      reinterpret_cast(tensor)->handle));
+}
+
 PyObject* TFE_Py_InitEagerTensor(PyObject* base_class) {
   if (!PyType_Check(base_class)) {
     PyErr_SetString(
diff --git a/tensorflow/python/eager/pywrap_tensor.h b/tensorflow/python/eager/pywrap_tensor.h
index fb093824a52080..bc042eb19e6a91 100644
--- a/tensorflow/python/eager/pywrap_tensor.h
+++ b/tensorflow/python/eager/pywrap_tensor.h
@@ -22,6 +22,7 @@ limitations under the License.
 
 bool EagerTensor_CheckExact(const PyObject* o);
 tensorflow::int64 EagerTensor_id(const PyObject* tensor);
+tensorflow::DataType EagerTensor_dtype(const PyObject* tensor);
 
 namespace tensorflow {
 TFE_TensorHandle* ConvertToEagerTensor(PyObject* value, PyObject* dtype);
diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc
index 4ecba1a46be8ff..48a5b21dc7fba9 100644
--- a/tensorflow/python/eager/pywrap_tfe_src.cc
+++ b/tensorflow/python/eager/pywrap_tfe_src.cc
@@ -843,6 +843,24 @@ static tensorflow::int64 FastTensorId(PyObject* tensor) {
   return id;
 }
 
+static tensorflow::DataType FastTensorDtype(PyObject* tensor) {
+  if (EagerTensor_CheckExact(tensor)) {
+    return EagerTensor_dtype(tensor);
+  }
+  PyObject* dtype_field = PyObject_GetAttrString(tensor, "dtype");
+  if (dtype_field == nullptr) {
+    return tensorflow::DT_INVALID;
+  }
+  PyObject* enum_field = PyObject_GetAttrString(dtype_field, "_type_enum");
+  Py_DECREF(dtype_field);
+  if (dtype_field == nullptr) {
+    return tensorflow::DT_INVALID;
+  }
+  tensorflow::int64 id = MakeInt(enum_field);
+  Py_DECREF(enum_field);
+  return static_cast(id);
+}
+
 class GradientTape
     : public tensorflow::eager::GradientTape {
  public:
@@ -1053,15 +1071,18 @@ PyObject* TFE_Py_TapeSetShouldRecord(PyObject* tensors) {
   // TODO(apassos) consider not building a list and changing the API to check
   // each tensor individually.
   std::vector tensor_ids;
+  std::vector dtypes;
   tensor_ids.reserve(len);
+  dtypes.reserve(len);
   for (int i = 0; i < len; ++i) {
     PyObject* item = PySequence_Fast_GET_ITEM(seq, i);
     tensor_ids.push_back(FastTensorId(item));
+    dtypes.push_back(FastTensorDtype(item));
   }
   Py_DECREF(seq);
   auto tape_set = *tape_set_ptr;
   for (TFE_Py_Tape* tape : tape_set) {
-    if (tape->tape->ShouldRecord(tensor_ids)) {
+    if (tape->tape->ShouldRecord(tensor_ids, dtypes)) {
       Py_RETURN_TRUE;
     }
   }
@@ -1169,9 +1190,27 @@ PyObject* TFE_Py_TapeWatchedVariables(PyObject* tape) {
 }
 
 namespace {
-void TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors,
-                            const std::vector& input_ids,
-                            PyObject* backward_function) {
+std::vector MakeTensorDtypeList(PyObject* tensors) {
+  PyObject* seq = PySequence_Fast(tensors, "expected a sequence");
+  if (seq == nullptr) {
+    return {};
+  }
+  int len = PySequence_Fast_GET_SIZE(seq);
+  std::vector list;
+  list.reserve(len);
+  for (int i = 0; i < len; ++i) {
+    PyObject* tensor = PySequence_Fast_GET_ITEM(seq, i);
+    list.push_back(FastTensorDtype(tensor));
+  }
+  Py_DECREF(seq);
+  return list;
+}
+
+void TapeSetRecordOperation(
+    PyObject* op_type, PyObject* output_tensors,
+    const std::vector& input_ids,
+    const std::vector& input_dtypes,
+    PyObject* backward_function) {
   std::vector output_info;
   PyObject* seq = PySequence_Fast(output_tensors,
                                   "expected a sequence of integer tensor ids");
@@ -1206,7 +1245,7 @@ void TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors,
   for (TFE_Py_Tape* tape : SafeTapeSet()) {
     Py_INCREF(backward_function);
     tape->tape->RecordOperation(
-        op_type_str, output_info, input_ids, backward_function,
+        op_type_str, output_info, input_ids, input_dtypes, backward_function,
         [backward_function]() { Py_DECREF(backward_function); });
   }
 }
@@ -1221,7 +1260,11 @@ void TFE_Py_TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors,
   std::vector input_ids = MakeTensorIDList(input_tensors);
   if (PyErr_Occurred()) return;
 
-  TapeSetRecordOperation(op_type, output_tensors, input_ids, backward_function);
+  std::vector input_dtypes =
+      MakeTensorDtypeList(input_tensors);
+  if (PyErr_Occurred()) return;
+  TapeSetRecordOperation(op_type, output_tensors, input_ids, input_dtypes,
+                         backward_function);
 }
 
 void TFE_Py_TapeSetDeleteTrace(tensorflow::int64 tensor_id) {
@@ -1710,10 +1753,12 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs,
                          PyObject* results, PyObject* name) {
   std::vector input_ids = MakeTensorIDList(inputs);
   if (PyErr_Occurred()) return nullptr;
+  std::vector input_dtypes = MakeTensorDtypeList(inputs);
+  if (PyErr_Occurred()) return nullptr;
 
   bool should_record = false;
   for (TFE_Py_Tape* tape : SafeTapeSet()) {
-    if (tape->tape->ShouldRecord(input_ids)) {
+    if (tape->tape->ShouldRecord(input_ids, input_dtypes)) {
       should_record = true;
       break;
     }
@@ -1744,7 +1789,8 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs,
   Py_DECREF(callback_args);
   if (backward_function == nullptr) return nullptr;
 
-  TapeSetRecordOperation(op_name, results, input_ids, backward_function);
+  TapeSetRecordOperation(op_name, results, input_ids, input_dtypes,
+                         backward_function);
 
   Py_DECREF(backward_function);
 

From 66b6dda1b77cbf075e94009718446511fa13dd41 Mon Sep 17 00:00:00 2001
From: Ruoxin Sang 
Date: Thu, 10 May 2018 15:56:54 -0700
Subject: [PATCH 0644/1691] Export GCS object statting streamz metrics. Fix the
 wrong #define Guard name in gcs_file_system.h.

PiperOrigin-RevId: 196184962
---
 .../core/platform/cloud/gcs_file_system.cc    |  4 +
 .../core/platform/cloud/gcs_file_system.h     | 10 +-
 .../platform/cloud/gcs_file_system_test.cc    | 98 ++++++++++++-------
 3 files changed, 75 insertions(+), 37 deletions(-)

diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc
index e44e89743485c6..0df5a57678c6a0 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system.cc
@@ -997,6 +997,10 @@ Status GcsFileSystem::StatForObject(const string& fname, const string& bucket,
     request->SetResultBuffer(&output_buffer);
     request->SetTimeouts(timeouts_.connect, timeouts_.idle, timeouts_.metadata);
 
+    if (stats_ != nullptr) {
+      stats_->RecordStatObjectRequest();
+    }
+
     TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(),
                                     " when reading metadata of gs://", bucket,
                                     "/", object);
diff --git a/tensorflow/core/platform/cloud/gcs_file_system.h b/tensorflow/core/platform/cloud/gcs_file_system.h
index 6250aa75948d22..d095773770cfde 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.h
+++ b/tensorflow/core/platform/cloud/gcs_file_system.h
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_CORE_PLATFORM_GCS_FILE_SYSTEM_H_
-#define TENSORFLOW_CORE_PLATFORM_GCS_FILE_SYSTEM_H_
+#ifndef TENSORFLOW_CORE_PLATFORM_CLOUD_GCS_FILE_SYSTEM_H_
+#define TENSORFLOW_CORE_PLATFORM_CLOUD_GCS_FILE_SYSTEM_H_
 
 #include 
 #include 
@@ -56,6 +56,10 @@ class GcsStatsInterface {
   virtual void RecordBlockRetrieved(const string& file, size_t offset,
                                     size_t bytes_transferred) = 0;
 
+  // RecordStatObjectRequest is called once a statting object request over GCS
+  // is about to be made.
+  virtual void RecordStatObjectRequest() = 0;
+
   /// HttpStats is called to optionally provide a RequestStats listener
   /// to be annotated on every HTTP request made to the GCS API.
   ///
@@ -264,4 +268,4 @@ class RetryingGcsFileSystem : public RetryingFileSystem {
 
 }  // namespace tensorflow
 
-#endif  // TENSORFLOW_CORE_PLATFORM_GCS_FILE_SYSTEM_H_
+#endif  // TENSORFLOW_CORE_PLATFORM_CLOUD_GCS_FILE_SYSTEM_H_
diff --git a/tensorflow/core/platform/cloud/gcs_file_system_test.cc b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
index 28be13869b6947..4b594e5e61b769 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system_test.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
@@ -2833,41 +2833,71 @@ TEST(GcsFileSystemTest, CreateHttpRequest) {
   TF_EXPECT_OK(request->Send());
 }
 
-TEST(GcsFileSystemTest, NewRandomAccessFile_StatsRecording) {
-  class TestGcsStats : public GcsStatsInterface {
-   public:
-    void Init(GcsFileSystem* fs, GcsThrottle* throttle,
-              const FileBlockCache* block_cache) override {
-      CHECK(fs_ == nullptr);
-      CHECK(throttle_ == nullptr);
-      CHECK(block_cache_ == nullptr);
-
-      fs_ = fs;
-      throttle_ = throttle;
-      block_cache_ = block_cache;
-    }
-
-    void RecordBlockLoadRequest(const string& file, size_t offset) override {
-      block_load_request_file_ = file;
-    }
-
-    void RecordBlockRetrieved(const string& file, size_t offset,
-                              size_t bytes_transferred) override {
-      block_retrieved_file_ = file;
-      block_retrieved_bytes_transferred_ = bytes_transferred;
-    }
-
-    HttpRequest::RequestStats* HttpStats() override { return nullptr; }
-
-    GcsFileSystem* fs_ = nullptr;
-    GcsThrottle* throttle_ = nullptr;
-    const FileBlockCache* block_cache_ = nullptr;
-
-    string block_load_request_file_;
-    string block_retrieved_file_;
-    size_t block_retrieved_bytes_transferred_ = 0;
-  };
+class TestGcsStats : public GcsStatsInterface {
+ public:
+  void Init(GcsFileSystem* fs, GcsThrottle* throttle,
+            const FileBlockCache* block_cache) override {
+    CHECK(fs_ == nullptr);
+    CHECK(throttle_ == nullptr);
+    CHECK(block_cache_ == nullptr);
+
+    fs_ = fs;
+    throttle_ = throttle;
+    block_cache_ = block_cache;
+  }
+
+  void RecordBlockLoadRequest(const string& file, size_t offset) override {
+    block_load_request_file_ = file;
+  }
+
+  void RecordBlockRetrieved(const string& file, size_t offset,
+                            size_t bytes_transferred) override {
+    block_retrieved_file_ = file;
+    block_retrieved_bytes_transferred_ = bytes_transferred;
+  }
+
+  void RecordStatObjectRequest() override { stat_object_request_count_++; }
+
+  HttpRequest::RequestStats* HttpStats() override { return nullptr; }
+
+  GcsFileSystem* fs_ = nullptr;
+  GcsThrottle* throttle_ = nullptr;
+  const FileBlockCache* block_cache_ = nullptr;
+
+  string block_load_request_file_;
+  string block_retrieved_file_;
+  size_t block_retrieved_bytes_transferred_ = 0;
+  int stat_object_request_count_ = 0;
+};
+
+TEST(GcsFileSystemTest, Stat_StatsRecording) {
+  std::vector requests({new FakeHttpRequest(
+      "Uri: https://www.googleapis.com/storage/v1/b/bucket/o/"
+      "file.txt?fields=size%2Cupdated\n"
+      "Auth Token: fake_token\n"
+      "Timeouts: 5 1 10\n",
+      strings::StrCat("{\"size\": \"1010\","
+                      "\"updated\": \"2016-04-29T23:15:24.896Z\"}"))});
+  GcsFileSystem fs(std::unique_ptr(new FakeAuthProvider),
+                   std::unique_ptr(
+                       new FakeHttpRequestFactory(&requests)),
+                   0 /* block size */, 0 /* max bytes */, 0 /* max staleness */,
+                   0 /* stat cache max age */, 0 /* stat cache max entries */,
+                   0 /* matching paths cache max age */,
+                   0 /* matching paths cache max entries */,
+                   0 /* initial retry delay */, kTestTimeoutConfig,
+                   nullptr /* gcs additional header */);
 
+  TestGcsStats stats;
+  fs.SetStats(&stats);
+  EXPECT_EQ(stats.fs_, &fs);
+
+  FileStatistics stat;
+  TF_EXPECT_OK(fs.Stat("gs://bucket/file.txt", &stat));
+  EXPECT_EQ(1, stats.stat_object_request_count_);
+}
+
+TEST(GcsFileSystemTest, NewRandomAccessFile_StatsRecording) {
   std::vector requests({new FakeHttpRequest(
       "Uri: https://storage.googleapis.com/bucket/random_access.txt\n"
       "Auth Token: fake_token\n"

From 874cf8e1d332175c8a90d7512f8385e98e2a7377 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 16:09:00 -0700
Subject: [PATCH 0645/1691] Enable support for crops in BatchToSpaceNd

PiperOrigin-RevId: 196186750
---
 .../contrib/lite/kernels/batch_to_space_nd.cc | 22 ++++++++++++-------
 .../lite/kernels/batch_to_space_nd_test.cc    |  8 +++----
 .../testing/generated_examples_zip_test.cc    |  4 ----
 3 files changed, 18 insertions(+), 16 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc b/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc
index 90edf4f9e3683f..bd4057556c775e 100644
--- a/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc
+++ b/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc
@@ -66,12 +66,10 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
   TF_LITE_ENSURE_EQ(context, NumDimensions(op_context->crops),
                     kSpatialDimensionNum);
 
-  // TODO(ycling): Add crops as part of calculation. Remove check for a crops
-  // containing all zeroes.
-  TF_LITE_ENSURE_EQ(context, crops[0], 0);
-  TF_LITE_ENSURE_EQ(context, crops[1], 0);
-  TF_LITE_ENSURE_EQ(context, crops[2], 0);
-  TF_LITE_ENSURE_EQ(context, crops[3], 0);
+  TF_LITE_ENSURE(context, crops[0] >= 0);
+  TF_LITE_ENSURE(context, crops[1] >= 0);
+  TF_LITE_ENSURE(context, crops[2] >= 0);
+  TF_LITE_ENSURE(context, crops[3] >= 0);
 
   // Number of batch must be multiple of (block_shape[0] * block_shape[1]).
   TF_LITE_ENSURE_EQ(context,
@@ -79,8 +77,16 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
 
   const int output_batch_size =
       input_size->data[0] / (block_shape[0] * block_shape[1]);
-  const int output_height = input_size->data[1] * block_shape[0];
-  const int output_width = input_size->data[2] * block_shape[1];
+
+  const int crops_top = crops[0];
+  const int crops_bottom = crops[1];
+  const int crops_left = crops[2];
+  const int crops_right = crops[3];
+  const int output_height =
+      input_size->data[1] * block_shape[0] - crops_top - crops_bottom;
+  const int output_width =
+      input_size->data[2] * block_shape[1] - crops_left - crops_right;
+
   const int output_channel_size = input_size->data[3];
 
   TfLiteIntArray* output_size = TfLiteIntArrayCopy(input_size);
diff --git a/tensorflow/contrib/lite/kernels/batch_to_space_nd_test.cc b/tensorflow/contrib/lite/kernels/batch_to_space_nd_test.cc
index 8485cde1b40066..95b025c1b30cc6 100644
--- a/tensorflow/contrib/lite/kernels/batch_to_space_nd_test.cc
+++ b/tensorflow/contrib/lite/kernels/batch_to_space_nd_test.cc
@@ -120,16 +120,16 @@ TEST(BatchToSpaceNDOpTest, InvalidShapeTest) {
 }
 
 TEST(BatchToSpaceNDOpTest, InvalidCropsConstTest) {
-  EXPECT_DEATH(BatchToSpaceNDOpConstModel({3, 2, 2, 1}, {2, 2}, {0, 0, 0, 1}),
-               "1 != 0");
+  EXPECT_DEATH(BatchToSpaceNDOpConstModel({3, 2, 2, 1}, {2, 2}, {0, 0, 0, -1}),
+               "crops.3. >= 0 was not true.");
 }
 
 TEST(BatchToSpaceNDOpTest, InvalidCropsDynamicTest) {
   BatchToSpaceNDOpDynamicModel m({4, 2, 2, 1});
   m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
   m.SetBlockShape({2, 2});
-  m.SetCrops({0, 0, 1, 0});
-  EXPECT_DEATH(m.Invoke(), "1 != 0");
+  m.SetCrops({0, 0, -1, 0});
+  EXPECT_DEATH(m.Invoke(), "crops.2. >= 0 was not true.");
 }
 
 }  // namespace
diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
index a8714afd83bb96..6ecaf2a355ec2f 100644
--- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
+++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
@@ -63,10 +63,6 @@ std::map kBrokenTests = {
     // L2Norm only supports tensors with 4D or fewer.
     {R"(^\/l2normdim=.*,epsilon=.*,input_shape=\[.,.,.,.,.*\])", "67963684"},
 
-    // BatchToSpaceND doesn't support cropping. This catches test cases with
-    // non-const tensors as crops.
-    {R"(^\/batch_to_space_nd.*crops=\[\[1,1\],\[1,1\]\])", "70594634"},
-
     // SpaceToBatchND only supports 4D tensors.
     {R"(^\/space_to_batch_nd.*input_shape=\[1,4,4,4,1,1\])", "70848787"},
 

From 587ff8f3068b012ae9993115726f733ccf857609 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 16:18:20 -0700
Subject: [PATCH 0646/1691] ring_reducer.cc errata: 1. Block in the current
 (blockable) thread when pre-copying input to output rather than continuing in
 the callback which cannot block. 2. Clear RingField array on exit to more
 promptly release Refs on output tensor buffer. 3. Properly set the
 forward_from_array parameter in SubContext.

PiperOrigin-RevId: 196188047
---
 .../core/common_runtime/ring_reducer.cc       | 24 ++++++++++++-------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/tensorflow/core/common_runtime/ring_reducer.cc b/tensorflow/core/common_runtime/ring_reducer.cc
index a17281835ea5f5..6b072f3cc9cc52 100644
--- a/tensorflow/core/common_runtime/ring_reducer.cc
+++ b/tensorflow/core/common_runtime/ring_reducer.cc
@@ -157,21 +157,27 @@ void RingReducer::Run(StatusCallback done) {
   // we're not computing in-place on the input tensor.
   if ((input_ != output_) &&
       (DMAHelper::base(input_) != DMAHelper::base(output_))) {
+    // We are running in a blockable thread and the callback can't block so
+    // just wait here on the copy.
+    Notification note;
     CollectiveRemoteAccessLocal::MemCpyAsync(
         ctx_->input_device_context(0), ctx_->op_device_context(), device_,
         device_, ctx_->input_alloc_attr(0), ctx_->output_alloc_attr(0), input_,
-        output_, [this](const Status& s) {
-          if (!s.ok()) {
-            done_(s);
-          } else {
-            ContinueAfterInputCopy();
-          }
+        output_, [this, ¬e, &status](const Status& s) {
+          status.Update(s);
+          note.Notify();
         });
-  } else {
-    ContinueAfterInputCopy();
+    note.WaitForNotification();
+    if (!status.ok()) {
+      done_(status);
+      return;
+    }
   }
+  ContinueAfterInputCopy();
 }
 
+// Note that this function is blocking and must not run in any thread
+// which cannot be blocked.
 void RingReducer::ContinueAfterInputCopy() {
   AllocatorAttributes attr = ctx_->output_alloc_attr(0);
   ca_.reset(MakeCollectiveAdapter(output_, group_size_ * num_subdivs_,
@@ -235,6 +241,7 @@ void RingReducer::Finish(bool ok) {
     mutex_lock l(status_mu_);
     s = status_;
   }
+  rfv_.clear();  // Give up Refs on output tensor.
   done_(s);
 }
 
@@ -252,6 +259,7 @@ RingReducer::SubContext::SubContext(OpKernelContext* ctx,
   sub_params_.input_device_contexts = &sub_input_dc_;
   sub_params_.eigen_gpu_device = nullptr;
   sub_params_.ensure_eigen_gpu_device();
+  sub_params_.forward_from_array = &forward_from_;
   sub_ctx_ = new OpKernelContext(&sub_params_, 1);
 }
 

From 0a814669f92737d01eaca7995eb895303250172b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 16:34:09 -0700
Subject: [PATCH 0647/1691] [XLA] Redesign: change the docs to describe the new
 interfaces. This change is simply about replacing keywords and formatting
 files. - s/ComputationDataHandle/XlaOp/ - s/ComputationBuilder/XlaBuilder/ -
 s/\/XlaComputation/ -
 s/client\/computation\.h/client\/xla_client\/xla_computation\.h/ -
 s/client\/computation_builder\.h/client\/xla_client\/xla_builder\.h/

PiperOrigin-RevId: 196189890
---
 .../docs_src/performance/xla/broadcasting.md  |   4 +-
 .../performance/xla/operation_semantics.md    | 655 +++++++++---------
 2 files changed, 318 insertions(+), 341 deletions(-)

diff --git a/tensorflow/docs_src/performance/xla/broadcasting.md b/tensorflow/docs_src/performance/xla/broadcasting.md
index ca3bddf758cf64..2b010184260174 100644
--- a/tensorflow/docs_src/performance/xla/broadcasting.md
+++ b/tensorflow/docs_src/performance/xla/broadcasting.md
@@ -97,9 +97,9 @@ shape is broadcast into a larger rank shape. For example, given a 2x3x4 cuboid
 and a 3x4 matrix, a broadcasting tuple (1,2) means matching the matrix to
 dimensions 1 and 2 of the cuboid.
 
-This type of broadcast is used in the binary ops in `ComputationBuilder`, if the
+This type of broadcast is used in the binary ops in `XlaBuilder`, if the
 `broadcast_dimensions` argument is given. For example, see
-[ComputationBuilder::Add](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.cc).
+[XlaBuilder::Add](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.cc).
 In the XLA source code, this type of broadcasting is sometimes called "InDim"
 broadcasting.
 
diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md
index 21e4c71a60f596..5887c3d88bf8c7 100644
--- a/tensorflow/docs_src/performance/xla/operation_semantics.md
+++ b/tensorflow/docs_src/performance/xla/operation_semantics.md
@@ -1,7 +1,7 @@
 # Operation Semantics
 
 The following describes the semantics of operations defined in the
-[`ComputationBuilder`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h)
+[`XlaBuilder`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h)
 interface. Typically, these operations map one-to-one to operations defined in
 the RPC interface in
 [`xla_data.proto`](https://www.tensorflow.org/code/tensorflow/compiler/xla/xla_data.proto).
@@ -16,7 +16,7 @@ and familiar names; for example a *vector* is a 1-dimensional array and a
 ## BatchNormGrad
 
 See also
-[`ComputationBuilder::BatchNormGrad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h)
+[`XlaBuilder::BatchNormGrad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h)
 and [the original batch normalization paper](https://arxiv.org/abs/1502.03167)
 for a detailed description of the algorithm.
 
@@ -26,14 +26,14 @@ Calculates gradients of batch norm.
 
 | Arguments       | Type                    | Semantics                        |
 | --------------- | ----------------------- | -------------------------------- |
-| `operand`       | `ComputationDataHandle` | n dimensional array to be        |
+| `operand`       | `XlaOp`                 | n dimensional array to be        |
 :                 :                         : normalized (x)                   :
-| `scale`         | `ComputationDataHandle` | 1 dimensional array              |
+| `scale`         | `XlaOp`                 | 1 dimensional array              |
 :                 :                         : (\\(\gamma\\))                   :
-| `mean`          | `ComputationDataHandle` | 1 dimensional array (\\(\mu\\))  |
-| `variance`      | `ComputationDataHandle` | 1 dimensional array              |
+| `mean`          | `XlaOp`                 | 1 dimensional array (\\(\mu\\))  |
+| `variance`      | `XlaOp`                 | 1 dimensional array              |
 :                 :                         : (\\(\sigma^2\\))                 :
-| `grad_output`   | `ComputationDataHandle` | Gradients passed to              |
+| `grad_output`   | `XlaOp`                 | Gradients passed to              |
 :                 :                         : `BatchNormTraining`              :
 :                 :                         : (\\( \nabla y\\))                :
 | `epsilon`       | `float`                 | Epsilon value (\\(\epsilon\\))   |
@@ -70,35 +70,33 @@ The output type is a tuple of three handles:
 
 | Outputs        | Type                    | Semantics                         |
 | -------------  | ----------------------- | --------------------------------- |
-| `grad_operand` | `ComputationDataHandle` | gradient with respect to input    |
+| `grad_operand` | `XlaOp`                 | gradient with respect to input    |
 :                :                         : `operand` (\\( \nabla x\\))       :
-| `grad_scale`   | `ComputationDataHandle` | gradient with respect to input    |
+| `grad_scale`   | `XlaOp`                 | gradient with respect to input    |
 :                :                         : `scale` (\\( \nabla \gamma\\))    :
-| `grad_offset`  | `ComputationDataHandle` | gradient with respect to input    |
+| `grad_offset`  | `XlaOp`                 | gradient with respect to input    |
 :                :                         : `offset`(\\( \nabla \beta\\))     :
 
 ## BatchNormInference
 
 See also
-[`ComputationBuilder::BatchNormInference`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h) and
-[the original batch normalization paper](https://arxiv.org/abs/1502.03167)
+[`XlaBuilder::BatchNormInference`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h)
+and [the original batch normalization paper](https://arxiv.org/abs/1502.03167)
 for a detailed description of the algorithm.
 
 Normalizes an array across batch and spatial dimensions.
 
  `BatchNormInference(operand, scale, offset, mean, variance, epsilon, feature_index)` 
 
-| Arguments       | Type                    | Semantics                       |
-| --------------  | ----------------------- | ------------------------------- |
-| `operand`       | `ComputationDataHandle` | n dimensional array to be       |
-:                 :                         : normalized                      :
-| `scale`         | `ComputationDataHandle` | 1 dimensional array             |
-| `offset`        | `ComputationDataHandle` | 1 dimensional array             |
-| `mean`          | `ComputationDataHandle` | 1 dimensional array             |
-| `variance`      | `ComputationDataHandle` | 1 dimensional array             |
-| `epsilon`       | `float`                 | Epsilon value                   |
-| `feature_index` | `int64`                 | Index to feature dimension in   |
-:                 :                         : `operand`                       :
+Arguments       | Type    | Semantics
+--------------- | ------- | ---------------------------------------
+`operand`       | `XlaOp` | n dimensional array to be normalized
+`scale`         | `XlaOp` | 1 dimensional array
+`offset`        | `XlaOp` | 1 dimensional array
+`mean`          | `XlaOp` | 1 dimensional array
+`variance`      | `XlaOp` | 1 dimensional array
+`epsilon`       | `float` | Epsilon value
+`feature_index` | `int64` | Index to feature dimension in `operand`
 
 For each feature in the feature dimension (`feature_index` is the index for the
 feature dimension in `operand`), the operation calculates the mean and variance
@@ -117,25 +115,21 @@ The output is an n-dimensional, normalized array with the same shape as input
 ## BatchNormTraining
 
 See also
-[`ComputationBuilder::BatchNormTraining`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h) and
-[`the original batch normalization paper`](https://arxiv.org/abs/1502.03167)
+[`XlaBuilder::BatchNormTraining`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h)
+and [`the original batch normalization paper`](https://arxiv.org/abs/1502.03167)
 for a detailed description of the algorithm.
 
 Normalizes an array across batch and spatial dimensions.
 
  `BatchNormTraining(operand, scale, offset, epsilon, feature_index)` 
 
-| Arguments       | Type                    | Semantics                        |
-| --------------- | ----------------------- | -------------------------------- |
-| `operand`       | `ComputationDataHandle` | n dimensional array to be        |
-:                 :                         : normalized (x)                   :
-| `scale`         | `ComputationDataHandle` | 1 dimensional array              |
-:                 :                         : (\\(\gamma\\))                   :
-| `offset`        | `ComputationDataHandle` | 1 dimensional array              |
-:                 :                         : (\\(\beta\\))                    :
-| `epsilon`       | `float`                 | Epsilon value (\\(\epsilon\\))   |
-| `feature_index` | `int64`                 | Index to feature dimension       |
-:                 :                         : in `operand`                     :
+Arguments       | Type    | Semantics
+--------------- | ------- | ----------------------------------------
+`operand`       | `XlaOp` | n dimensional array to be normalized (x)
+`scale`         | `XlaOp` | 1 dimensional array (\\(\gamma\\))
+`offset`        | `XlaOp` | 1 dimensional array (\\(\beta\\))
+`epsilon`       | `float` | Epsilon value (\\(\epsilon\\))
+`feature_index` | `int64` | Index to feature dimension in `operand`
 
 For each feature in the feature dimension (`feature_index` is the index for the
 feature dimension in `operand`), the operation calculates the mean and variance
@@ -158,14 +152,14 @@ contains `m` elements with `w` and `h` as the size of spatial dimensions
 
 The epsilon value, usually a small number, is added to avoid divide-by-zero errors.
 
-The output type is a tuple of three `ComputationDataHandle`s:
+The output type is a tuple of three `XlaOp`s:
 
 | Outputs      | Type                    | Semantics                            |
 | ------------ | ----------------------- | -------------------------------------|
-| `output`     | `ComputationDataHandle` | n dimensional array with the same    |
+| `output`     | `XlaOp`                 | n dimensional array with the same    |
 :              :                         : shape as input `operand` (y)         :
-| `batch_mean` | `ComputationDataHandle` | 1 dimensional array (\\(\mu\\))      |
-| `batch_var`  | `ComputationDataHandle` | 1 dimensional array (\\(\sigma^2\\)) |
+| `batch_mean` | `XlaOp`                 | 1 dimensional array (\\(\mu\\))      |
+| `batch_var`  | `XlaOp`                 | 1 dimensional array (\\(\sigma^2\\)) |
 
 The `batch_mean` and `batch_var` are moments calculated across the batch and
 spatial dimensions using the formulas above.
@@ -173,7 +167,7 @@ spatial dimensions using the formulas above.
 ## BitcastConvertType
 
 See also
-[`ComputationBuilder::BitcastConvertType`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::BitcastConvertType`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Similar to a `tf.bitcast` in TensorFlow, performs an element-wise bitcast
 operation from a data shape to a target shape. The dimensions must match, and
@@ -183,10 +177,10 @@ with different floating-point representations will give different results.
 
  `BitcastConvertType(operand, new_element_type)` 
 
-Arguments          | Type                    | Semantics
------------------- | ----------------------- | ---------------------------
-`operand`          | `ComputationDataHandle` | array of type T with dims D
-`new_element_type` | `PrimitiveType`         | type U
+Arguments          | Type            | Semantics
+------------------ | --------------- | ---------------------------
+`operand`          | `XlaOp`         | array of type T with dims D
+`new_element_type` | `PrimitiveType` | type U
 
 The dimensions of the operand and the target shape must match. The bit-width of
 the source and destination element types must be equal. The source
@@ -195,16 +189,16 @@ and destination element types must not be tuples.
 ## Broadcast
 
 See also
-[`ComputationBuilder::Broadcast`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Broadcast`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Adds dimensions to an array by duplicating the data in the array.
 
  `Broadcast(operand, broadcast_sizes)` 
 
-Arguments         | Type                    | Semantics
------------------ | ----------------------- | -------------------------------
-`operand`         | `ComputationDataHandle` | The array to duplicate
-`broadcast_sizes` | `ArraySlice`     | The sizes of the new dimensions
+Arguments         | Type                | Semantics
+----------------- | ------------------- | -------------------------------
+`operand`         | `XlaOp`             | The array to duplicate
+`broadcast_sizes` | `ArraySlice` | The sizes of the new dimensions
 
 The new dimensions are inserted on the left, i.e. if `broadcast_sizes` has
 values `{a0, ..., aN}` and the operand shape has dimensions `{b0, ..., bM}` then
@@ -223,19 +217,18 @@ For example, if `operand` is a scalar `f32` with value `2.0f`, and
 ## Call
 
 See also
-[`ComputationBuilder::Call`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Call`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Invokes a computation with the given arguments.
 
  `Call(computation, args...)` 
 
-| Arguments     | Type                     | Semantics                        |
-| ------------- | ------------------------ | -------------------------------- |
-| `computation` | `Computation`            | computation of type `T_0, T_1,   |
-:               :                          : ..., T_N -> S` with N parameters :
-:               :                          : of arbitrary type                :
-| `args`        | sequence of N            | N arguments of arbitrary type    |
-:               : `ComputationDataHandle`s :                                  :
+| Arguments     | Type                   | Semantics                           |
+| ------------- | ---------------------- | ----------------------------------- |
+| `computation` | `XlaComputation`       | computation of type `T_0, T_1, ..., |
+:               :                        : T_N -> S` with N parameters of      :
+:               :                        : arbitrary type                      :
+| `args`        | sequence of N `XlaOp`s | N arguments of arbitrary type       |
 
 The arity and types of the `args` must match the parameters of the
 `computation`. It is allowed to have no `args`.
@@ -243,17 +236,17 @@ The arity and types of the `args` must match the parameters of the
 ## Clamp
 
 See also
-[`ComputationBuilder::Clamp`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Clamp`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Clamps an operand to within the range between a minimum and maximum value.
 
  `Clamp(min, operand, max)` 
 
-| Arguments     | Type                    | Semantics                        |
-| ------------- | ----------------------- | -------------------------------- |
-| `min`         | `ComputationDataHandle` | array of type T                  |
-| `operand`     | `ComputationDataHandle` | array of type T                  |
-| `max`         | `ComputationDataHandle` | array of type T                  |
+Arguments | Type    | Semantics
+--------- | ------- | ---------------
+`min`     | `XlaOp` | array of type T
+`operand` | `XlaOp` | array of type T
+`max`     | `XlaOp` | array of type T
 
 Given an operand and minimum and maximum values, returns the operand if it is in
 the range between the minimum and maximum, else returns the minimum value if the
@@ -276,18 +269,17 @@ Clamp(min, operand, max) = s32[3]{0, 5, 6};
 ## Collapse
 
 See also
-[`ComputationBuilder::Collapse`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h)
+[`XlaBuilder::Collapse`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h)
 and the @{tf.reshape} operation.
 
 Collapses dimensions of an array into one dimension.
 
  `Collapse(operand, dimensions)` 
 
-| Arguments    | Type                    | Semantics                           |
-| ------------ | ----------------------- | ----------------------------------- |
-| `operand`    | `ComputationDataHandle` | array of type T                     |
-| `dimensions` | `int64` vector          | in-order, consecutive subset of T's |
-:              :                         : dimensions.                         :
+Arguments    | Type           | Semantics
+------------ | -------------- | -----------------------------------------------
+`operand`    | `XlaOp`        | array of type T
+`dimensions` | `int64` vector | in-order, consecutive subset of T's dimensions.
 
 Collapse replaces the given subset of the operand's dimensions by a single
 dimension. The input arguments are an arbitrary array of type T and a
@@ -340,7 +332,7 @@ then v12 == f32[8x3] {{10, 11, 12},
 ## Concatenate
 
 See also
-[`ComputationBuilder::ConcatInDim`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::ConcatInDim`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Concatenate composes an array from multiple array operands. The array is of the
 same rank as each of the input array operands (which must be of the same rank as
@@ -348,13 +340,13 @@ each other) and contains the arguments in the order that they were specified.
 
  `Concatenate(operands..., dimension)` 
 
-| Arguments   | Type                    | Semantics                            |
-| ----------- | ----------------------- | ------------------------------------ |
-| `operands`  | sequence of N           | N arrays of type T with dimensions   |
-:             : `ComputationDataHandle` : [L0, L1, ...]. Requires N >= 1.      :
-| `dimension` | `int64`                 | A value in the interval `[0, N)`     |
-:             :                         : that names the dimension to be       :
-:             :                         : concatenated between the `operands`. :
+| Arguments   | Type                  | Semantics                              |
+| ----------- | --------------------- | -------------------------------------- |
+| `operands`  | sequence of N `XlaOp` | N arrays of type T with dimensions     |
+:             :                       : [L0, L1, ...]. Requires N >= 1.        :
+| `dimension` | `int64`               | A value in the interval `[0, N)` that  |
+:             :                       : names the dimension to be concatenated :
+:             :                       : between the `operands`.                :
 
 With the exception of `dimension` all dimensions must be the same. This is
 because XLA does not support "ragged" arrays. Also note that rank-0 values
@@ -395,20 +387,19 @@ Diagram:
 
 ## Conditional
 
-See also [`ComputationBuilder::Conditional`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+See also
+[`XlaBuilder::Conditional`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
  `Conditional(pred, true_operand, true_computation, false_operand,
-    false_computation)` 
-
-| Arguments           | Type                    | Semantics                   |
-| ------------------- | ----------------------- | --------------------------- |
-| `pred`              | `ComputationDataHandle` | Scalar of type `PRED`       |
-| `true_operand`      | `ComputationDataHandle` | Argument of type `T_0`      |
-| `true_computation`  | `Computation`           | Computation of type `T_0 -> |
-:                     :                         : S`                          :
-| `false_operand`     | `ComputationDataHandle` | Argument of type `T_1`      |
-| `false_computation` | `Computation`           | Computation of type `T_1 -> |
-:                     :                         : S`                          :
+false_computation)` 

-template <typename T>
+```c++
+template 
 class ZeroOutOp : public OpKernel {
  public:
-  explicit ZeroOutOp(OpKernelConstruction\* context) : OpKernel(context) {}

-  void Compute(OpKernelContext\* context) override {
+  explicit ZeroOutOp(OpKernelConstruction* context) : OpKernel(context) {}
+  
+  void Compute(OpKernelContext* context) override {
     // Grab the input tensor
-    const Tensor& input\_tensor = context->input(0);
-    auto input = input\_tensor.flat<T>();

+    const Tensor& input_tensor = context->input(0);
+    auto input = input_tensor.flat();
+    
     // Create an output tensor
     Tensor* output = NULL;
-    OP\_REQUIRES\_OK(context,
-                   context->allocate\_output(0, input_tensor.shape(), &output));
-    auto output\_flat = output->template flat<T>();

+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, input_tensor.shape(), &output));
+    auto output_flat = output->template flat();
+    
     // Set all the elements of the output tensor to 0
     const int N = input.size();
-    for (int i = 0; i < N; i++) {
-      output\_flat(i) = 0;
-    }

+    for (int i = 0; i < N; i++) {
+      output_flat(i) = 0;
+    }
+    
     // Preserve the first input value
-    if (N > 0) output\_flat(0) = input(0);
+    if (N > 0) output_flat(0) = input(0);
   }
-};

-// Note that TypeConstraint<int32>("T") means that attr "T" (defined
+};
+
+// Note that TypeConstraint("T") means that attr "T" (defined
 // in the op registration above) must be "int32" to use this template
-// instantiation.
-REGISTER\_KERNEL\_BUILDER(
+// instantiation.
+REGISTER_KERNEL_BUILDER(
     Name("ZeroOut")
-    .Device(DEVICE\_CPU)
-    .TypeConstraint<int32>("T"),
-    ZeroOutOp<int32>);
-REGISTER\_KERNEL\_BUILDER(
+    .Device(DEVICE_CPU)
+    .TypeConstraint("T"),
+    ZeroOutOp);
+REGISTER_KERNEL_BUILDER(
     Name("ZeroOut")
-    .Device(DEVICE\_CPU)
-    .TypeConstraint<float>("T"),
-    ZeroOutOp<float>);
-REGISTER\_KERNEL\_BUILDER(
+    .Device(DEVICE_CPU)
+    .TypeConstraint("T"),
+    ZeroOutOp);
+REGISTER_KERNEL_BUILDER(
     Name("ZeroOut")
-    .Device(DEVICE\_CPU)
-    .TypeConstraint<double>("T"),
-    ZeroOutOp<double>);
-

+
+Arguments           | Type             | Semantics
+------------------- | ---------------- | ---------------------------------
+`pred`              | `XlaOp`          | Scalar of type `PRED`
+`true_operand`      | `XlaOp`          | Argument of type `T_0`
+`true_computation`  | `XlaComputation` | XlaComputation of type `T_0 -> S`
+`false_operand`     | `XlaOp`          | Argument of type `T_1`
+`false_computation` | `XlaComputation` | XlaComputation of type `T_1 -> S`
 
 Executes `true_computation` if `pred` is `true`, `false_computation` if `pred`
 is `false`, and returns the result.
@@ -425,7 +416,7 @@ executed depending on the value of `pred`.
 ## Conv (convolution)
 
 See also
-[`ComputationBuilder::Conv`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Conv`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 As ConvWithGeneralPadding, but the padding is specified in a short-hand way as
 either SAME or VALID. SAME padding pads the input (`lhs`) with zeroes so that
@@ -435,7 +426,7 @@ account. VALID padding simply means no padding.
 ## ConvWithGeneralPadding (convolution)
 
 See also
-[`ComputationBuilder::ConvWithGeneralPadding`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::ConvWithGeneralPadding`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Computes a convolution of the kind used in neural networks. Here, a convolution
 can be thought of as a n-dimensional window moving across a n-dimensional base
@@ -443,8 +434,8 @@ area and a computation is performed for each possible position of the window.
 
 | Arguments        | Type                    | Semantics                     |
 | ---------------- | ----------------------- | ----------------------------- |
-| `lhs`            | `ComputationDataHandle` | rank n+2 array of inputs      |
-| `rhs`            | `ComputationDataHandle` | rank n+2 array of kernel      |
+| `lhs`            | `XlaOp`                 | rank n+2 array of inputs      |
+| `rhs`            | `XlaOp`                 | rank n+2 array of kernel      |
 :                  :                         : weights                       :
 | `window_strides` | `ArraySlice`     | n-d array of kernel strides   |
 | `padding`        | `ArraySlice `ConvertElementType(operand, new_element_type)` 
 
-Arguments          | Type                    | Semantics
------------------- | ----------------------- | ---------------------------
-`operand`          | `ComputationDataHandle` | array of type T with dims D
-`new_element_type` | `PrimitiveType`         | type U
+Arguments          | Type            | Semantics
+------------------ | --------------- | ---------------------------
+`operand`          | `XlaOp`         | array of type T with dims D
+`new_element_type` | `PrimitiveType` | type U
 
 The dimensions of the operand and the target shape must match. The source and
 destination element types must not be tuples.
@@ -581,15 +572,15 @@ then b == f32[3]{0.0, 1.0, 2.0}
 ## CrossReplicaSum
 
 See also
-[`ComputationBuilder::CrossReplicaSum`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::CrossReplicaSum`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Computes a sum across replicas.
 
  `CrossReplicaSum(operand)` 
 
-| Arguments    | Type                    | Semantics                          |
-| ------------ | ----------------------- | ---------------------------------- |
-| `operand`    | `ComputationDataHandle` | Array to sum across replicas.      |
+Arguments | Type    | Semantics
+--------- | ------- | -----------------------------
+`operand` | `XlaOp` | Array to sum across replicas.
 
 The output shape is the same as the input shape. For example, if there are two
 replicas and the operand has the value `(1.0, 2.5)` and `(3.0, 5.25)`
@@ -607,21 +598,21 @@ than another.
 ## CustomCall
 
 See also
-[`ComputationBuilder::CustomCall`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::CustomCall`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Call a user-provided function within a computation.
 
  `CustomCall(target_name, args..., shape)` 
 
-| Arguments     | Type                     | Semantics                        |
-| ------------- | ------------------------ | -------------------------------- |
-| `target_name` | `string`                 | Name of the function. A call     |
-:               :                          : instruction will be emitted      :
-:               :                          : which targets this symbol name.  :
-| `args`        | sequence of N            | N arguments of arbitrary type,   |
-:               : `ComputationDataHandle`s : which will be passed to the      :
-:               :                          : function.                        :
-| `shape`       | `Shape`                  | Output shape of the function     |
+| Arguments     | Type                   | Semantics                         |
+| ------------- | ---------------------- | --------------------------------- |
+| `target_name` | `string`               | Name of the function. A call      |
+:               :                        : instruction will be emitted which :
+:               :                        : targets this symbol name.         :
+| `args`        | sequence of N `XlaOp`s | N arguments of arbitrary type,    |
+:               :                        : which will be passed to the       :
+:               :                        : function.                         :
+| `shape`       | `Shape`                | Output shape of the function      |
 
 The function signature is the same, regardless of the arity or type of args:
 
@@ -668,14 +659,14 @@ idempotent.
 ## Dot
 
 See also
-[`ComputationBuilder::Dot`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Dot`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
  `Dot(lhs, rhs)` 
 
-Arguments | Type                    | Semantics
---------- | ----------------------- | ---------------
-`lhs`     | `ComputationDataHandle` | array of type T
-`rhs`     | `ComputationDataHandle` | array of type T
+Arguments | Type    | Semantics
+--------- | ------- | ---------------
+`lhs`     | `XlaOp` | array of type T
+`rhs`     | `XlaOp` | array of type T
 
 The exact semantics of this operation depend on the ranks of the operands:
 
@@ -697,15 +688,15 @@ multiplications or matrix/matrix multiplications.
 ## DotGeneral
 
 See also
-[`ComputationBuilder::DotGeneral`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::DotGeneral`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
  `DotGeneral(lhs, rhs, dimension_numbers)` 
 
-| Arguments | Type                    | Semantics
-| --------- | ----------------------- | ---------------
-| `lhs`     | `ComputationDataHandle` | array of type T
-| `rhs`     | `ComputationDataHandle` | array of type T
-| `dimension_numbers` | `DotDimensionNumbers` | array of type T
+Arguments           | Type                  | Semantics
+------------------- | --------------------- | ---------------
+`lhs`               | `XlaOp`               | array of type T
+`rhs`               | `XlaOp`               | array of type T
+`dimension_numbers` | `DotDimensionNumbers` | array of type T
 
 As Dot, but allows contracting and batch dimension numbers to be specified for
 both the 'lhs' and 'rhs'.
@@ -784,7 +775,7 @@ non-contracting/non-batch dimension.
 ## DynamicSlice
 
 See also
-[`ComputationBuilder::DynamicSlice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::DynamicSlice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 DynamicSlice extracts a sub-array from the input array at dynamic
 `start_indices`. The size of the slice in each dimension is passed in
@@ -796,22 +787,21 @@ calculation of 'start_indices') is currently implementation-defined.
 
  `DynamicSlice(operand, start_indices, size_indices)` 
 
-| Arguments       | Type                    | Semantics                        |
-| --------------- | ----------------------- | -------------------------------- |
-| `operand`       | `ComputationDataHandle` | N dimensional array of type T    |
-| `start_indices` | `ComputationDataHandle` | Rank 1 array of N integers       |
-:                 :                         : containing the starting indices  :
-:                 :                         : of the slice for each dimension. :
-:                 :                         : Value must be greater than or    :
-:                 :                         : equal to zero.                   :
-| `size_indices`  | `ArraySlice`     | List of N integers containing    |
-:                 :                         : the slice size for each          :
-:                 :                         : dimension. Each value must be    :
-:                 :                         : strictly greater than zero, and  :
-:                 :                         : start + size must be less than   :
-:                 :                         : or equal to the size of the      :
-:                 :                         : dimension to avoid wrapping      :
-:                 :                         : modulo dimension size.           :
+| Arguments       | Type                | Semantics                           |
+| --------------- | ------------------- | ----------------------------------- |
+| `operand`       | `XlaOp`             | N dimensional array of type T       |
+| `start_indices` | `XlaOp`             | Rank 1 array of N integers          |
+:                 :                     : containing the starting indices of  :
+:                 :                     : the slice for each dimension. Value :
+:                 :                     : must be greater than or equal to    :
+:                 :                     : zero.                               :
+| `size_indices`  | `ArraySlice` | List of N integers containing the   |
+:                 :                     : slice size for each dimension. Each :
+:                 :                     : value must be strictly greater than :
+:                 :                     : zero, and start + size must be less :
+:                 :                     : than or equal to the size of the    :
+:                 :                     : dimension to avoid wrapping modulo  :
+:                 :                     : dimension size.                     :
 
 1-dimensional example:
 
@@ -840,7 +830,7 @@ DynamicSlice(b, s, {2, 2}) produces:
 ## DynamicUpdateSlice
 
 See also
-[`ComputationBuilder::DynamicUpdateSlice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::DynamicUpdateSlice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 DynamicUpdateSlice generates a result which is the value of the input array
 `operand`, with a slice `update` overwritten at `start_indices`.
@@ -853,23 +843,19 @@ calculation of 'start_indices') is currently implementation-defined.
 
  `DynamicUpdateSlice(operand, update, start_indices)` 
 
-| Arguments       | Type                    | Semantics                        |
-| --------------- | ----------------------- | -------------------------------- |
-| `operand`       | `ComputationDataHandle` | N dimensional array of type T    |
-| `update`        | `ComputationDataHandle` | N dimensional array of type T    |
-:                 :                         : containing the slice update.     :
-:                 :                         : Each dimension of update shape   :
-:                 :                         : must be strictly greater than    :
-:                 :                         : zero, and start + update must be :
-:                 :                         : less than or equal to the operand:
-:                 :                         : size for each dimension to avoid :
-:                 :                         : generating out-of-bounds update  :
-:                 :                         : indices.                         :
-| `start_indices` | `ComputationDataHandle` | Rank 1 array of N integers       |
-:                 :                         : containing the starting indices  :
-:                 :                         : of the slice for each dimension. :
-:                 :                         : Value must be greater than or    :
-:                 :                         : equal to zero.                   :
+| Arguments       | Type    | Semantics                                        |
+| --------------- | ------- | ------------------------------------------------ |
+| `operand`       | `XlaOp` | N dimensional array of type T                    |
+| `update`        | `XlaOp` | N dimensional array of type T containing the     |
+:                 :         : slice update. Each dimension of update shape     :
+:                 :         : must be strictly greater than zero, and start +  :
+:                 :         : update must be less than or equal to the operand :
+:                 :         : size for each dimension to avoid generating      :
+:                 :         : out-of-bounds update indices.                    :
+| `start_indices` | `XlaOp` | Rank 1 array of N integers containing the        |
+:                 :         : starting indices of the slice for each           :
+:                 :         : dimension. Value must be greater than or equal   :
+:                 :         : to zero.                                         :
 
 1-dimensional example:
 
@@ -907,7 +893,7 @@ DynamicUpdateSlice(b, u, s) produces:
 ## Element-wise binary arithmetic operations
 
 See also
-[`ComputationBuilder::Add`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Add`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 A set of element-wise binary arithmetic operations is supported.
 
@@ -917,10 +903,10 @@ Where `Op` is one of `Add` (addition), `Sub` (subtraction), `Mul`
 (multiplication), `Div` (division), `Rem` (remainder), `Max` (maximum), `Min`
 (minimum), `LogicalAnd` (logical AND), or `LogicalOr` (logical OR).
 
-Arguments | Type                    | Semantics
---------- | ----------------------- | ----------------------------------------
-`lhs`     | `ComputationDataHandle` | left-hand-side operand: array of type T
-`rhs`     | `ComputationDataHandle` | right-hand-side operand: array of type T
+Arguments | Type    | Semantics
+--------- | ------- | ----------------------------------------
+`lhs`     | `XlaOp` | left-hand-side operand: array of type T
+`rhs`     | `XlaOp` | right-hand-side operand: array of type T
 
 The arguments' shapes have to be either similar or compatible. See the
 @{$broadcasting$broadcasting} documentation about what it means for shapes to
@@ -952,7 +938,7 @@ shapes of both operands. The semantics are described in detail on the
 ## Element-wise comparison operations
 
 See also
-[`ComputationBuilder::Eq`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Eq`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 A set of standard element-wise binary comparison operations is supported. Note
 that standard IEEE 754 floating-point comparison semantics apply when comparing
@@ -964,10 +950,10 @@ Where `Op` is one of `Eq` (equal-to), `Ne` (not equal-to), `Ge`
 (greater-or-equal-than), `Gt` (greater-than), `Le` (less-or-equal-than), `Lt`
 (less-than).
 
-Arguments | Type                    | Semantics
---------- | ----------------------- | ----------------------------------------
-`lhs`     | `ComputationDataHandle` | left-hand-side operand: array of type T
-`rhs`     | `ComputationDataHandle` | right-hand-side operand: array of type T
+Arguments | Type    | Semantics
+--------- | ------- | ----------------------------------------
+`lhs`     | `XlaOp` | left-hand-side operand: array of type T
+`rhs`     | `XlaOp` | right-hand-side operand: array of type T
 
 The arguments' shapes have to be either similar or compatible. See the
 @{$broadcasting$broadcasting} documentation about what it means for shapes to
@@ -991,7 +977,7 @@ in detail on the @{$broadcasting$broadcasting page}.
 
 ## Element-wise unary functions
 
-ComputationBuilder supports these element-wise unary functions:
+XlaBuilder supports these element-wise unary functions:
 
 `Abs(operand)` Element-wise abs `x -> |x|`.
 
@@ -1023,9 +1009,9 @@ using the comparison operator of the element type of `operand`.
 `Tanh(operand)` Element-wise hyperbolic tangent `x -> tanh(x)`.
 
 
-Arguments | Type                    | Semantics
---------- | ----------------------- | ---------------------------
-`operand` | `ComputationDataHandle` | The operand to the function
+Arguments | Type    | Semantics
+--------- | ------- | ---------------------------
+`operand` | `XlaOp` | The operand to the function
 
 The function is applied to each element in the `operand` array, resulting in an
 array with the same shape. It is allowed for `operand` to be a scalar (rank 0).
@@ -1038,16 +1024,16 @@ potentially different runtime offset) of an input tensor into an output tensor.
 ### General Semantics
 
 See also
-[`ComputationBuilder::Gather`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Gather`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 For a more intuitive description, see the "Informal Description" section below.
 
  `gather(operand, gather_indices, output_window_dims, elided_window_dims, window_bounds, gather_dims_to_operand_dims)` 
 
 |Arguments         | Type                    | Semantics                       |
 |----------------- | ----------------------- | --------------------------------|
-|`operand`         | `ComputationDataHandle` | The tensor we’re gathering      |
+|`operand`         | `XlaOp`                 | The tensor we’re gathering      |
 :                  :                         : from.                           :
-|`gather_indices`  | `ComputationDataHandle` | Tensor containing the starting  |
+|`gather_indices`  | `XlaOp`                 | Tensor containing the starting  |
 :                  :                         : indices of the slices we're     :
 :                  :                         : stitching together into the     :
 :                  :                         : output tensor.                  :
@@ -1241,7 +1227,7 @@ concatenation of all these rows.
 ## GetTupleElement
 
 See also
-[`ComputationBuilder::GetTupleElement`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::GetTupleElement`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Indexes into a tuple with a compile-time-constant value.
 
@@ -1262,7 +1248,7 @@ See also @{tf.tuple}.
 ## Infeed
 
 See also
-[`ComputationBuilder::Infeed`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Infeed`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
  `Infeed(shape)` 
 
@@ -1275,7 +1261,7 @@ See also
 
 Reads a single data item from the implicit Infeed streaming interface of the
 device, interpreting the data as the given shape and its layout, and returns a
-`ComputationDataHandle` of the data. Multiple Infeed operations are allowed in a
+`XlaOp` of the data. Multiple Infeed operations are allowed in a
 computation, but there must be a total order among the Infeed operations. For
 example, two Infeeds in the code below have a total order since there is a
 dependency between the while loops.
@@ -1301,21 +1287,19 @@ Infeed of the device.
 ## Map
 
 See also
-[`ComputationBuilder::Map`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Map`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
  `Map(operands..., computation)` 
 
-| Arguments         | Type                     | Semantics                     |
-| ----------------- | ------------------------ | ----------------------------- |
-| `operands`        | sequence of N            | N arrays of types T_0..T_{N-1}|
-:                   : `ComputationDataHandle`s :                               :
-| `computation`     | `Computation`            | computation of type `T_0,     |
-:                   :                          : T_1, ..., T_{N + M -1} -> S`  :
-:                   :                          : with N parameters of type T   :
-:                   :                          : and M of arbitrary type       :
-| `dimensions`       | `int64` array           | array of map dimensions    |
-| `static_operands` | sequence of M            | M arrays of arbitrary type    |
-:                   : `ComputationDataHandle`s :                               :
+| Arguments         | Type                   | Semantics                      |
+| ----------------- | ---------------------- | ------------------------------ |
+| `operands`        | sequence of N `XlaOp`s | N arrays of types T_0..T_{N-1} |
+| `computation`     | `XlaComputation`        | computation of type `T_0, T_1, |
+:                   :                        : ..., T_{N + M -1} -> S` with N :
+:                   :                        : parameters of type T and M of  :
+:                   :                        : arbitrary type                 :
+| `dimensions`      | `int64` array          | array of map dimensions        |
+| `static_operands` | sequence of M `XlaOp`s | M arrays of arbitrary type     |
 
 Applies a scalar function over the given `operands` arrays, producing an array
 of the same dimensions where each element is the result of the mapped function
@@ -1334,18 +1318,18 @@ input arrays to produce the output array.
 ## Pad
 
 See also
-[`ComputationBuilder::Pad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Pad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
  `Pad(operand, padding_value, padding_config)` 
 
-| Arguments        | Type                    | Semantics                     |
-| ---------------- | ----------------------- | ----------------------------- |
-| `operand`        | `ComputationDataHandle` | array of type `T`             |
-| `padding_value`  | `ComputationDataHandle` | scalar of type `T` to fill in |
-:                  :                         : the added padding             :
-| `padding_config` | `PaddingConfig`         | padding amount on both edges  |
-:                  :                         : (low, high) and between the   :
-:                  :                         : elements of each dimension    :
+| Arguments        | Type            | Semantics                               |
+| ---------------- | --------------- | --------------------------------------- |
+| `operand`        | `XlaOp`         | array of type `T`                       |
+| `padding_value`  | `XlaOp`         | scalar of type `T` to fill in the added |
+:                  :                 : padding                                 :
+| `padding_config` | `PaddingConfig` | padding amount on both edges (low,      |
+:                  :                 : high) and between the elements of each  :
+:                  :                 : dimension                               :
 
 Expands the given `operand` array by padding around the array as well as between
 the elements of the array with the given `padding_value`. `padding_config`
@@ -1373,7 +1357,7 @@ are all 0. The figure below shows examples of different `edge_padding` and
 ## Recv
 
 See also
-[`ComputationBuilder::Recv`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Recv`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
  `Recv(shape, channel_handle)` 
 
@@ -1384,7 +1368,7 @@ See also
 
 Receives data of the given shape from a `Send` instruction in another
 computation that shares the same channel handle. Returns a
-ComputationDataHandle for the received data.
+XlaOp for the received data.
 
 The client API of `Recv` operation represents synchronous communication.
 However, the instruction is internally decomposed into 2 HLO instructions
@@ -1407,19 +1391,18 @@ complete and returns the received data.
 ## Reduce
 
 See also
-[`ComputationBuilder::Reduce`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Reduce`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Applies a reduction function to an array.
 
  `Reduce(operand, init_value, computation, dimensions)` 
 
-| Arguments     | Type                    | Semantics                        |
-| ------------- | ----------------------- | -------------------------------- |
-| `operand`     | `ComputationDataHandle` | array of type `T`                |
-| `init_value`  | `ComputationDataHandle` | scalar of type `T`               |
-| `computation` | `Computation`           | computation of type `T, T -> T`  |
-| `dimensions`  | `int64` array           | unordered array of dimensions to |
-:               :                         : reduce                           :
+Arguments     | Type             | Semantics
+------------- | ---------------- | ---------------------------------------
+`operand`     | `XlaOp`          | array of type `T`
+`init_value`  | `XlaOp`          | scalar of type `T`
+`computation` | `XlaComputation` | computation of type `T, T -> T`
+`dimensions`  | `int64` array    | unordered array of dimensions to reduce
 
 This operation reduces one or more dimensions of the input array into scalars.
 The rank of the returned array is `rank(operand) - len(dimensions)`.
@@ -1525,7 +1508,7 @@ Reducing the 3D array over all its dimensions produces the scalar `84`.
 ## ReducePrecision
 
 See also
-[`ComputationBuilder::ReducePrecision`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::ReducePrecision`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Models the effect of converting floating-point values to a lower-precision
 format (such as IEEE-FP16) and back to the original format.  The number of
@@ -1535,14 +1518,11 @@ implementations.
 
  `ReducePrecision(operand, mantissa_bits, exponent_bits)` 
 
-| Arguments           | Type                    | Semantics                    |
-| ------------------- | ----------------------- | ---------------------------- |
-| `operand`           | `ComputationDataHandle` | array of floating-point type |
-:                     :                         : `T`.                         :
-| `exponent_bits`     | `int32`                 | number of exponent bits in   |
-:                     :                         : lower-precision format       :
-| `mantissa_bits`     | `int32`                 | number of mantissa bits in   |
-:                     :                         : lower-precision format       :
+Arguments       | Type    | Semantics
+--------------- | ------- | -------------------------------------------------
+`operand`       | `XlaOp` | array of floating-point type `T`.
+`exponent_bits` | `int32` | number of exponent bits in lower-precision format
+`mantissa_bits` | `int32` | number of mantissa bits in lower-precision format
 
 The result is an array of type `T`.  The input values are rounded to the nearest
 value representable with the given number of mantissa bits (using "ties to even"
@@ -1559,7 +1539,7 @@ portion of the conversion is then simply a no-op.
 ## ReduceWindow
 
 See also
-[`ComputationBuilder::ReduceWindow`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::ReduceWindow`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Applies a reduction function to all elements in each window of the input
 multi-dimensional array, producing an output multi-dimensional array with the
@@ -1571,25 +1551,25 @@ on the left-hand side.
  `ReduceWindow(operand, init_value, computation, window_dimensions,
 window_strides, padding)` 
 
-| Arguments           | Type                    | Semantics                    |
-| ------------------- | ----------------------- | ---------------------------- |
-| `operand`           | `ComputationDataHandle` | N dimensional array          |
-:                     :                         : containing elements of type  :
-:                     :                         : T. This is the base area on  :
-:                     :                         : which the window is placed.  :
-| `init_value`        | `ComputationDataHandle` | Starting value for the       |
-:                     :                         : reduction. See [Reduce]      :
-:                     :                         : (#reduce) for details.       :
-| `computation`       | `Computation`           | Reduction function of type   |
-:                     :                         : `T, T -> T`, to apply to all :
-:                     :                         : elements in each window      :
-| `window_dimensions` | `ArraySlice`     | array of integers for window |
-:                     :                         : dimension values             :
-| `window_strides`    | `ArraySlice`     | array of integers for window |
-:                     :                         : stride values                :
-| `padding`           | `Padding`               | padding type for window      |
-:                     :                         : (Padding\:\:kSame or         :
-:                     :                         : Padding\:\:kValid)           :
+| Arguments           | Type                | Semantics                        |
+| ------------------- | ------------------- | -------------------------------- |
+| `operand`           | `XlaOp`             | N dimensional array containing   |
+:                     :                     : elements of type T. This is the  :
+:                     :                     : base area on which the window is :
+:                     :                     : placed.                          :
+| `init_value`        | `XlaOp`             | Starting value for the           |
+:                     :                     : reduction. See [Reduce](#reduce) :
+:                     :                     : for details.                     :
+| `computation`       | `XlaComputation`    | Reduction function of type `T, T |
+:                     :                     : -> T`, to apply to all elements  :
+:                     :                     : in each window                   :
+| `window_dimensions` | `ArraySlice` | array of integers for window     |
+:                     :                     : dimension values                 :
+| `window_strides`    | `ArraySlice` | array of integers for window     |
+:                     :                     : stride values                    :
+| `padding`           | `Padding`           | padding type for window          |
+:                     :                     : (Padding\:\:kSame or             :
+:                     :                     : Padding\:\:kValid)               :
 
 Below code and figure shows an example of using `ReduceWindow`. Input is a
 matrix of size [4x6] and both window_dimensions and window_stride_dimensions are
@@ -1597,9 +1577,9 @@ matrix of size [4x6] and both window_dimensions and window_stride_dimensions are
 
 ```
 // Create a computation for the reduction (maximum).
-Computation max;
+XlaComputation max;
 {
-  ComputationBuilder builder(client_, "max");
+  XlaBuilder builder(client_, "max");
   auto y = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "y");
   auto x = builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "x");
   builder.Max(y, x);
@@ -1607,7 +1587,7 @@ Computation max;
 }
 
 // Create a ReduceWindow computation with the max reduction computation.
-ComputationBuilder builder(client_, "reduce_window_2x3");
+XlaBuilder builder(client_, "reduce_window_2x3");
 auto shape = ShapeUtil::MakeShape(F32, {4, 6});
 auto input = builder.Parameter(0, shape, "input");
 builder.ReduceWindow(
@@ -1642,7 +1622,7 @@ context of [`Reduce`](#reduce) for more details.
 ## Reshape
 
 See also
-[`ComputationBuilder::Reshape`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h)
+[`XlaBuilder::Reshape`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h)
 and the [`Collapse`](#collapse) operation.
 
 Reshapes the dimensions of an array into a new configuration.
@@ -1650,11 +1630,11 @@ Reshapes the dimensions of an array into a new configuration.
  `Reshape(operand, new_sizes)` 
  `Reshape(operand, dimensions, new_sizes)` 
 
-Arguments    | Type                    | Semantics
------------- | ----------------------- | ---------------------------------------
-`operand`    | `ComputationDataHandle` | array of type T
-`dimensions` | `int64` vector          | order in which dimensions are collapsed
-`new_sizes`  | `int64` vector          | vector of sizes of new dimensions
+Arguments    | Type           | Semantics
+------------ | -------------- | ---------------------------------------
+`operand`    | `XlaOp`        | array of type T
+`dimensions` | `int64` vector | order in which dimensions are collapsed
+`new_sizes`  | `int64` vector | vector of sizes of new dimensions
 
 Conceptually, reshape first flattens an array into a one-dimensional vector of
 data values, and then refines this vector into a new shape. The input arguments
@@ -1723,14 +1703,14 @@ Reshape(5, {}, {1,1}) == f32[1x1] {{5}};
 ## Rev (reverse)
 
 See also
-[`ComputationBuilder::Rev`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Rev`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 `Rev(operand, dimensions)`
 
-Arguments    | Type                    | Semantics
------------- | ----------------------- | ---------------------
-`operand`    | `ComputationDataHandle` | array of type T
-`dimensions` | `ArraySlice`     | dimensions to reverse
+Arguments    | Type                | Semantics
+------------ | ------------------- | ---------------------
+`operand`    | `XlaOp`             | array of type T
+`dimensions` | `ArraySlice` | dimensions to reverse
 
 Reverses the order of elements in the `operand` array along the specified
 `dimensions`, generating an output array of the same shape. Each element of the
@@ -1745,7 +1725,7 @@ the two window dimensions during the gradient computation in neural networks.
 ## RngNormal
 
 See also
-[`ComputationBuilder::RngNormal`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::RngNormal`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Constructs an output of a given shape with random numbers generated following
 the $$N(\mu, \sigma)$$ normal distribution. The parameters `mu` and `sigma`, and
@@ -1754,18 +1734,18 @@ be scalar valued.
 
 `RngNormal(mean, sigma, shape)`
 
-| Arguments | Type                    | Semantics                              |
-| --------- | ----------------------- | -------------------------------------- |
-| `mu`      | `ComputationDataHandle` | Scalar of type F32 specifying mean of  |
-:           :                         : generated numbers                      :
-| `sigma`   | `ComputationDataHandle` | Scalar of type F32 specifying standard |
-:           :                         : deviation of generated numbers         :
-| `shape`   | `Shape`                 | Output shape of type F32               |
+| Arguments | Type    | Semantics                                           |
+| --------- | ------- | --------------------------------------------------- |
+| `mu`      | `XlaOp` | Scalar of type F32 specifying mean of generated     |
+:           :         : numbers                                             :
+| `sigma`   | `XlaOp` | Scalar of type F32 specifying standard deviation of |
+:           :         : generated numbers                                   :
+| `shape`   | `Shape` | Output shape of type F32                            |
 
 ## RngUniform
 
 See also
-[`ComputationBuilder::RngUniform`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::RngUniform`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Constructs an output of a given shape with random numbers generated following
 the uniform distribution over the interval $$[a,b)$$. The parameters and output
@@ -1777,27 +1757,27 @@ is implementation-defined.
 
 | Arguments | Type                    | Semantics                         |
 | --------- | ----------------------- | --------------------------------- |
-| `a`       | `ComputationDataHandle` | Scalar of type T specifying lower |
+| `a`       | `XlaOp`                 | Scalar of type T specifying lower |
 :           :                         : limit of interval                 :
-| `b`       | `ComputationDataHandle` | Scalar of type T specifying upper |
+| `b`       | `XlaOp`                 | Scalar of type T specifying upper |
 :           :                         : limit of interval                 :
 | `shape`   | `Shape`                 | Output shape of type T            |
 
 ## Select
 
 See also
-[`ComputationBuilder::Select`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Select`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Constructs an output array from elements of two input arrays, based on the
 values of a predicate array.
 
  `Select(pred, on_true, on_false)` 
 
-Arguments  | Type                    | Semantics
----------- | ----------------------- | ------------------
-`pred`     | `ComputationDataHandle` | array of type PRED
-`on_true`  | `ComputationDataHandle` | array of type T
-`on_false` | `ComputationDataHandle` | array of type T
+Arguments  | Type    | Semantics
+---------- | ------- | ------------------
+`pred`     | `XlaOp` | array of type PRED
+`on_true`  | `XlaOp` | array of type T
+`on_false` | `XlaOp` | array of type T
 
 The arrays `on_true` and `on_false` must have the same shape. This is also the
 shape of the output array. The array `pred` must have the same dimensionality as
@@ -1837,7 +1817,7 @@ the same shape!) then `pred` has to be a scalar of type `PRED`.
 ## SelectAndScatter
 
 See also
-[`ComputationBuilder::SelectAndScatter`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::SelectAndScatter`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 This operation can be considered as a composite operation that first computes
 `ReduceWindow` on the `operand` array to select an element from each window, and
@@ -1870,33 +1850,32 @@ backpropagate the gradient values for a pooling layer in a neural network.
 `SelectAndScatter(operand, select, window_dimensions, window_strides,
 padding, source, init_value, scatter)`
 
-| Arguments           | Type                    | Semantics                    |
-| ------------------- | ----------------------- | ---------------------------- |
-| `operand`           | `ComputationDataHandle` | array of type T over which   |
-:                     :                         : the windows slide            :
-| `select`            | `Computation`           | binary computation of type   |
-:                     :                         : `T, T -> PRED`, to apply to  :
-:                     :                         : all elements in each window; :
-:                     :                         : returns `true` if the first  :
-:                     :                         : parameter is selected and    :
-:                     :                         : returns `false` if the       :
-:                     :                         : second parameter is selected :
-| `window_dimensions` | `ArraySlice`     | array of integers for window |
-:                     :                         : dimension values             :
-| `window_strides`    | `ArraySlice`     | array of integers for window |
-:                     :                         : stride values                :
-| `padding`           | `Padding`               | padding type for window      |
-:                     :                         : (Padding\:\:kSame or         :
-:                     :                         : Padding\:\:kValid)           :
-| `source`            | `ComputationDataHandle` | array of type T with the     |
-:                     :                         : values to scatter            :
-| `init_value`        | `ComputationDataHandle` | scalar value of type T for   |
-:                     :                         : the initial value of the     :
-:                     :                         : output array                 :
-| `scatter`           | `Computation`           | binary computation of type   |
-:                     :                         : `T, T -> T`, to apply each   :
-:                     :                         : scatter source element with  :
-:                     :                         : its destination element      :
+| Arguments           | Type                | Semantics                        |
+| ------------------- | ------------------- | -------------------------------- |
+| `operand`           | `XlaOp`             | array of type T over which the   |
+:                     :                     : windows slide                    :
+| `select`            | `XlaComputation`    | binary computation of type `T, T |
+:                     :                     : -> PRED`, to apply to all        :
+:                     :                     : elements in each window; returns :
+:                     :                     : `true` if the first parameter is :
+:                     :                     : selected and returns `false` if  :
+:                     :                     : the second parameter is selected :
+| `window_dimensions` | `ArraySlice` | array of integers for window     |
+:                     :                     : dimension values                 :
+| `window_strides`    | `ArraySlice` | array of integers for window     |
+:                     :                     : stride values                    :
+| `padding`           | `Padding`           | padding type for window          |
+:                     :                     : (Padding\:\:kSame or             :
+:                     :                     : Padding\:\:kValid)               :
+| `source`            | `XlaOp`             | array of type T with the values  |
+:                     :                     : to scatter                       :
+| `init_value`        | `XlaOp`             | scalar value of type T for the   |
+:                     :                     : initial value of the output      :
+:                     :                     : array                            :
+| `scatter`           | `XlaComputation`    | binary computation of type `T, T |
+:                     :                     : -> T`, to apply each scatter     :
+:                     :                     : source element with its          :
+:                     :                     : destination element              :
 
 The figure below shows examples of using `SelectAndScatter`, with the `select`
 function computing the maximal value among its parameters. Note that when the
@@ -1918,14 +1897,14 @@ context of [`Reduce`](#reduce) for more details.
 ## Send
 
 See also
-[`ComputationBuilder::Send`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Send`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
  `Send(operand, channel_handle)` 
 
-| Arguments        | Type                    | Semantics                        |
-| ---------------- | ----------------------- | -------------------------------- |
-| `operand`        | `ComputationDataHandle` | data to send (array of type T)   |
-| `channel_handle` | `ChannelHandle`         | unique identifier for each send/recv pair |
+Arguments        | Type            | Semantics
+---------------- | --------------- | -----------------------------------------
+`operand`        | `XlaOp`         | data to send (array of type T)
+`channel_handle` | `ChannelHandle` | unique identifier for each send/recv pair
 
 Sends the given operand data to a `Recv` instruction in another computation
 that shares the same channel handle. Does not return any data.
@@ -1973,7 +1952,7 @@ computations. For example, below schedules lead to deadlocks.
 ## Slice
 
 See also
-[`ComputationBuilder::Slice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Slice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Slicing extracts a sub-array from the input array. The sub-array is of the same
 rank as the input and contains the values inside a bounding box within the input
@@ -1982,23 +1961,20 @@ arguments to the slice operation.
 
  `Slice(operand, start_indices, limit_indices)` 
 
-| Arguments       | Type                    | Semantics                        |
-| --------------- | ----------------------- | -------------------------------- |
-| `operand`       | `ComputationDataHandle` | N dimensional array of type T    |
-| `start_indices` | `ArraySlice`     | List of N integers containing    |
-:                 :                         : the starting indices of the      :
-:                 :                         : slice for each dimension. Values :
-:                 :                         : must be greater than or equal to :
-:                 :                         : zero.                            :
-| `limit_indices` | `ArraySlice`     | List of N integers containing    |
-:                 :                         : the ending indices (exclusive)   :
-:                 :                         : for the slice for each           :
-:                 :                         : dimension. Each value must be    :
-:                 :                         : strictly greater than the        :
-:                 :                         : respective `start_indices` value :
-:                 :                         : for the dimension and less than  :
-:                 :                         : or equal to the size of the      :
-:                 :                         : dimension.                       :
+| Arguments       | Type                | Semantics                            |
+| --------------- | ------------------- | ------------------------------------ |
+| `operand`       | `XlaOp`             | N dimensional array of type T        |
+| `start_indices` | `ArraySlice` | List of N integers containing the    |
+:                 :                     : starting indices of the slice for    :
+:                 :                     : each dimension. Values must be       :
+:                 :                     : greater than or equal to zero.       :
+| `limit_indices` | `ArraySlice` | List of N integers containing the    |
+:                 :                     : ending indices (exclusive) for the   :
+:                 :                     : slice for each dimension. Each value :
+:                 :                     : must be strictly greater than the    :
+:                 :                     : respective `start_indices` value for :
+:                 :                     : the dimension and less than or equal :
+:                 :                     : to the size of the dimension.        :
 
 1-dimensional example:
 
@@ -2025,15 +2001,15 @@ Slice(b, {2, 1}, {4, 3}) produces:
 ## Sort
 
 See also
-[`ComputationBuilder::Sort`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Sort`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 Sorts the elements in the operand.
 
 `Sort(operand)`
 
-Arguments | Type                    | Semantics
---------- | ----------------------- | -------------------
-`operand` | `ComputationDataHandle` | The operand to sort
+Arguments | Type    | Semantics
+--------- | ------- | -------------------
+`operand` | `XlaOp` | The operand to sort
 
 ## Transpose
 
@@ -2041,10 +2017,10 @@ See also the @{tf.reshape} operation.
 
 `Transpose(operand)`
 
-Arguments     | Type                    | Semantics
----------     | ----------------------- | -------------------------
-`operand`     | `ComputationDataHandle` | The operand to transpose.
-`permutation` | `ArraySlice`     | How to permute the dimensions.
+Arguments     | Type                | Semantics
+------------- | ------------------- | ------------------------------
+`operand`     | `XlaOp`             | The operand to transpose.
+`permutation` | `ArraySlice` | How to permute the dimensions.
 
 
 Permutes the operand dimensions with the given permutation, so
@@ -2056,7 +2032,7 @@ This is the same as Reshape(operand, permutation,
 ## Tuple
 
 See also
-[`ComputationBuilder::Tuple`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::Tuple`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
 A tuple containing a variable number of data handles, each of which has its own
 shape.
@@ -2075,18 +2051,19 @@ Tuples can be deconstructed (accessed) via the [`GetTupleElement`]
 ## While
 
 See also
-[`ComputationBuilder::While`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+[`XlaBuilder::While`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h).
 
  `While(condition, body, init)` 
 
-| Arguments   | Type          | Semantics                                      |
-| ----------- | ------------- | ---------------------------------------------- |
-| `condition` | `Computation` | Computation of type `T -> PRED` which defines  |
-:             :               : the termination condition of the loop.         :
-| `body`      | `Computation` | Computation of type `T -> T` which defines the |
-:             :               : body of the loop.                              :
-| `init`      | `T`           | Initial value for the parameter of `condition` |
-:             :               : and `body`.                                    :
+| Arguments   | Type             | Semantics                                |
+| ----------- | ---------------- | ---------------------------------------- |
+| `condition` | `XlaComputation` | XlaComputation of type `T -> PRED` which |
+:             :                  : defines the termination condition of the :
+:             :                  : loop.                                    :
+| `body`      | `XlaComputation` | XlaComputation of type `T -> T` which    |
+:             :                  : defines the body of the loop.            :
+| `init`      | `T`              | Initial value for the parameter of       |
+:             :                  : `condition` and `body`.                  :
 
 Sequentially executes the `body` until the `condition` fails. This is similar to
 a typical while loop in many other languages except for the differences and

From ac70125923a3315802f867837521377a6a18f283 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 16:56:13 -0700
Subject: [PATCH 0648/1691] Fix some races detected by the analysis tool.

collective_rma_distributed: Return WorkerInterface to cache
prior to invoking RecvFromPeer callback, instead of after.

broadcaster: put status_ updates inside mutex.
PiperOrigin-RevId: 196192631
---
 tensorflow/core/common_runtime/broadcaster.cc | 22 ++++++++-----------
 .../collective_rma_distributed.cc             |  5 ++++-
 2 files changed, 13 insertions(+), 14 deletions(-)

diff --git a/tensorflow/core/common_runtime/broadcaster.cc b/tensorflow/core/common_runtime/broadcaster.cc
index 5e8af8653dc011..30087a5b42dd91 100644
--- a/tensorflow/core/common_runtime/broadcaster.cc
+++ b/tensorflow/core/common_runtime/broadcaster.cc
@@ -134,7 +134,7 @@ void Broadcaster::TreeSendTo(const CollectiveParams& cp,
 // Execute a tree broadcast, i.e. each non-source device receives from
 // one other and sends to up-to two others.
 void Broadcaster::RunTree() {
-  mutex mu;
+  mutex mu;               // also guards status_ while callbacks are pending
   int pending_count = 0;  // GUARDED_BY(mu)
   condition_variable all_done;
   std::vector send_to_ranks;
@@ -164,13 +164,11 @@ void Broadcaster::RunTree() {
       DispatchSend(
           target_rank, output_,
           [this, target_rank, &mu, &pending_count, &all_done](const Status& s) {
+            mutex_lock l(mu);
             status_.Update(s);
-            {
-              mutex_lock l(mu);
-              --pending_count;
-              if (pending_count == 0) {
-                all_done.notify_all();
-              }
+            --pending_count;
+            if (pending_count == 0) {
+              all_done.notify_all();
             }
           });
     }
@@ -191,13 +189,11 @@ void Broadcaster::RunTree() {
           op_dev_ctx, op_dev_ctx, device_, device_, ctx_->input_alloc_attr(0),
           ctx_->output_alloc_attr(0), input, output_,
           [this, &mu, &pending_count, &all_done](const Status& s) {
+            mutex_lock l(mu);
             status_.Update(s);
-            {
-              mutex_lock l(mu);
-              --pending_count;
-              if (0 == pending_count) {
-                all_done.notify_all();
-              }
+            --pending_count;
+            if (0 == pending_count) {
+              all_done.notify_all();
             }
           });
     }
diff --git a/tensorflow/core/distributed_runtime/collective_rma_distributed.cc b/tensorflow/core/distributed_runtime/collective_rma_distributed.cc
index 54adcb9408d097..c15878bfd3a2ba 100644
--- a/tensorflow/core/distributed_runtime/collective_rma_distributed.cc
+++ b/tensorflow/core/distributed_runtime/collective_rma_distributed.cc
@@ -122,7 +122,6 @@ void CollectiveRemoteAccessDistributed::RecvFromPeer(
   // Logic to be executed on the RecvBufferAsync callback.
   auto recv_buf_callback = [this, state, peer_task, to_device, to_alloc_attr,
                             to_device_ctx, to_tensor, done](const Status& s) {
-    std::unique_ptr del_on_exit(state);
     if (s.ok()) {
       // In this generic implementation the bytes come back in the
       // RPC response protobuf rather than via RDMA so we need to copy
@@ -134,6 +133,7 @@ void CollectiveRemoteAccessDistributed::RecvFromPeer(
         done(errors::Internal("RecvBufResponse returned ", num_bytes,
                               " bytes where to_tensor expected ",
                               to_tensor->TotalBytes()));
+        delete state;
         return;
       }
       if (to_device->tensorflow_gpu_device_info()) {
@@ -144,6 +144,7 @@ void CollectiveRemoteAccessDistributed::RecvFromPeer(
         Status status = dev_mgr_->LookupDevice("CPU:0", &cpu_dev);
         if (!status.ok()) {
           done(status);
+          delete state;
           return;
         }
         AllocatorAttributes cpu_attr;
@@ -163,6 +164,7 @@ void CollectiveRemoteAccessDistributed::RecvFromPeer(
                              // done in another thread.
                              SchedClosure([s, done] { done(s); });
                            });
+        delete state;
         return;
       } else {
         // CPU device
@@ -174,6 +176,7 @@ void CollectiveRemoteAccessDistributed::RecvFromPeer(
       dev_resolver_->ClearTask(peer_task);
     }
 
+    delete state;
     done(s);
   };
 

From a888a0ab8cb20ca310a1eec9aab006eaf11309b7 Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Thu, 10 May 2018 17:06:27 -0700
Subject: [PATCH 0649/1691] Add a HLO evaluator test case for gather

PiperOrigin-RevId: 196193959
---
 .../xla/service/hlo_evaluator_test.cc         | 25 +++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
index cc16446778cbea..8e9688c7ab4e94 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
@@ -2005,6 +2005,31 @@ ENTRY main {
       *Evaluate({operand.get(), gather_indices.get()}));
 }
 
+TEST_P(HloEvaluatorTest, EvaluateGather_NoOutputWindowDims) {
+  const string hlo_text = R"(
+HloModule GatherXd
+
+ENTRY main {
+  operand = s32[3] parameter(0)
+  indices = s32[2,2,1] parameter(1)
+  ROOT gather = s32[2,2] gather(operand, indices),
+      output_window_dims={},
+      elided_window_dims={0},
+      gather_dims_to_operand_dims={0},
+      index_vector_dim=2,
+      window_bounds={1}
+}
+)";
+  ParseAndVerifyModule(hlo_text);
+
+  std::unique_ptr operand = Literal::CreateR1({0, 1, 2});
+  std::unique_ptr gather_indices =
+      Literal::CreateR3({{{0}, {1}}, {{2}, {1}}});
+  LiteralTestUtil::ExpectEqual(
+      *Literal::CreateR2({{0, 1}, {2, 1}}),
+      *Evaluate({operand.get(), gather_indices.get()}));
+}
+
 // Verifies that HloEvaluator evaluates a HLO instruction that performs
 // element-wise comparison with 2 bfloat16 operands.
 TEST_P(HloEvaluatorTest, DoesCompareBF16) {

From d774abfe3850b41b3883dd26e4f9c945c0ababb9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 17:07:21 -0700
Subject: [PATCH 0650/1691] Pipe through warm_start_from parameter

PiperOrigin-RevId: 196194069
---
 tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index a624eceed9a65c..afc8c7d5cc189d 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -1759,7 +1759,8 @@ def __init__(self,
                train_batch_size=None,
                eval_batch_size=None,
                predict_batch_size=None,
-               batch_axis=None):
+               batch_axis=None,
+               warm_start_from=None):
     """Constructs an `TPUEstimator` instance.
 
     Args:
@@ -1798,6 +1799,12 @@ def __init__(self,
         and per_host_input_for_training is True, batches will be sharded based
         on the major dimension. If tpu_config.per_host_input_for_training is
         False or `PER_HOST_V2`, batch_axis is ignored.
+      warm_start_from: Optional string filepath to a checkpoint or SavedModel to
+                       warm-start from, or a `tf.estimator.WarmStartSettings`
+                       object to fully configure warm-starting.  If the string
+                       filepath is provided instead of a `WarmStartSettings`,
+                       then all variables are warm-started, and it is assumed
+                       that vocabularies and Tensor names are unchanged.
 
     Raises:
       ValueError: `params` has reserved keys already.
@@ -1850,7 +1857,8 @@ def __init__(self,
         model_fn=model_function,
         model_dir=model_dir,
         config=config,
-        params=params)
+        params=params,
+        warm_start_from=warm_start_from)
     self._iterations_per_training_loop = (
         self._config.tpu_config.iterations_per_loop)
 

From 03d770b78d4cb799ce7945adcbc8ac10fe6f4d38 Mon Sep 17 00:00:00 2001
From: Brennan Saeta 
Date: Thu, 10 May 2018 17:32:40 -0700
Subject: [PATCH 0651/1691] [TPU]: If the $TPU_NAME env var is set, fallback to
 that.

PiperOrigin-RevId: 196196939
---
 .../python/training/tpu_cluster_resolver.py        | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py
index 1403483d287041..8ede28602fd6cf 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py
@@ -36,6 +36,7 @@
 
 
 _GKE_ENV_VARIABLE = 'KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS'
+_DEFAULT_ENV_VARIABLE = 'TPU_NAME'
 
 
 class TPUClusterResolver(ClusterResolver):
@@ -70,6 +71,12 @@ def _inGke():
   def _gkeMaster():
     return os.environ[_GKE_ENV_VARIABLE].split(',')[0]
 
+  @staticmethod
+  def _envVarFallback():
+    if _DEFAULT_ENV_VARIABLE in os.environ:
+      return os.environ[_DEFAULT_ENV_VARIABLE]
+    return None
+
   def __init__(self,
                tpu=None,
                zone=None,
@@ -123,8 +130,11 @@ def __init__(self,
 
     in_gke = self._inGke()
     # When using GKE with Cloud TPUs, the env variable will be set.
-    if tpu is None and in_gke:
-      tpu = self._gkeMaster()
+    if tpu is None:
+      if in_gke:
+        tpu = self._gkeMaster()
+      else:
+        tpu = self._envVarFallback()
 
     self._tpu = compat.as_bytes(tpu)  # self._tpu is always bytes
     self._job_name = job_name

From a543d9471047ca3f6881c87105fcbe2cdff9207d Mon Sep 17 00:00:00 2001
From: Achal Shah 
Date: Thu, 10 May 2018 17:43:30 -0700
Subject: [PATCH 0652/1691] Fix cublas wrap macro for cublasGemmBatchedEx
 (#19210)

---
 tensorflow/stream_executor/cuda/cuda_blas.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc
index 38e33d429b529a..3cc7f365e46ea8 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.cc
+++ b/tensorflow/stream_executor/cuda/cuda_blas.cc
@@ -293,7 +293,7 @@ STREAM_EXECUTOR_CUBLAS_WRAP(cublasSetMathMode)
 #endif
 
 #if CUDA_VERSION >= 9010
-PERFTOOLS_GPUTOOLS_CUBLAS_WRAP(cublasGemmBatchedEx)
+STREAM_EXECUTOR_CUBLAS_WRAP(cublasGemmBatchedEx)
 #endif
 
 }  // namespace wrap

From cf4cc8542fd71dcc05226c487329275cd6bf3e6a Mon Sep 17 00:00:00 2001
From: Francois Chollet 
Date: Thu, 10 May 2018 17:42:27 -0700
Subject: [PATCH 0653/1691] Partial update of tf.keras to the Keras 2.1.6 API.

This covers the following features and associated unit tests:
- multi-output layer where `compute_output_mask` returns `None`.
- saving to, and loading from, an existing hdf5 file.
- `verbose` argument (1/0) in `evaluate_generator`.
- stateful metrics with generator methods.
- `data_format` argument in `Flatten`.
- `constants` argument in Bidirectional's `__call__`.

PiperOrigin-RevId: 196198134
---
 tensorflow/python/keras/BUILD                 |   2 +-
 .../python/keras/_impl/keras/__init__.py      |   2 +-
 .../keras/_impl/keras/applications/vgg16.py   |  10 --
 .../keras/_impl/keras/applications/vgg19.py   |  10 --
 .../python/keras/_impl/keras/callbacks.py     |   3 -
 .../keras/_impl/keras/engine/network.py       |  21 ++-
 .../python/keras/_impl/keras/engine/saving.py | 145 +++++++++++-------
 .../keras/_impl/keras/engine/saving_test.py   |  55 +++++--
 .../keras/_impl/keras/engine/topology_test.py |  27 ++++
 .../keras/_impl/keras/engine/training.py      |  15 +-
 .../_impl/keras/engine/training_arrays.py     |  11 +-
 .../_impl/keras/engine/training_generator.py  |  27 +++-
 .../keras/_impl/keras/engine/training_test.py |   1 +
 .../keras/layers/convolutional_recurrent.py   |  12 +-
 .../python/keras/_impl/keras/layers/core.py   |  27 +++-
 .../keras/_impl/keras/layers/core_test.py     |  10 ++
 .../keras/_impl/keras/layers/recurrent.py     | 108 ++++++-------
 .../keras/_impl/keras/layers/wrappers.py      |  99 ++++++++----
 .../keras/_impl/keras/layers/wrappers_test.py | 135 ++++++++++++++++
 .../python/keras/_impl/keras/metrics_test.py  |  43 +++++-
 .../api/golden/tensorflow.keras.-model.pbtxt  |   2 +-
 .../golden/tensorflow.keras.-sequential.pbtxt |   2 +-
 ...nsorflow.keras.layers.-bidirectional.pbtxt |   2 +-
 .../tensorflow.keras.layers.-flatten.pbtxt    |   2 +-
 .../tensorflow.keras.models.-model.pbtxt      |   2 +-
 .../tensorflow.keras.models.-sequential.pbtxt |   2 +-
 .../golden/tensorflow.layers.-flatten.pbtxt   |   2 +-
 27 files changed, 568 insertions(+), 209 deletions(-)

diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD
index f29de5c432105e..295f23108b41da 100755
--- a/tensorflow/python/keras/BUILD
+++ b/tensorflow/python/keras/BUILD
@@ -316,7 +316,7 @@ py_test(
 
 py_test(
     name = "metrics_test",
-    size = "small",
+    size = "medium",
     srcs = ["_impl/keras/metrics_test.py"],
     srcs_version = "PY2AND3",
     tags = [
diff --git a/tensorflow/python/keras/_impl/keras/__init__.py b/tensorflow/python/keras/_impl/keras/__init__.py
index 53f5d31e9c5b86..3a58abe2ed548c 100644
--- a/tensorflow/python/keras/_impl/keras/__init__.py
+++ b/tensorflow/python/keras/_impl/keras/__init__.py
@@ -40,4 +40,4 @@
 from tensorflow.python.keras._impl.keras.models import Model
 from tensorflow.python.keras._impl.keras.models import Sequential
 
-__version__ = '2.1.5-tf'
+__version__ = '2.1.6-tf'
diff --git a/tensorflow/python/keras/_impl/keras/applications/vgg16.py b/tensorflow/python/keras/_impl/keras/applications/vgg16.py
index cefb25063e3050..25a15475eaa403 100644
--- a/tensorflow/python/keras/_impl/keras/applications/vgg16.py
+++ b/tensorflow/python/keras/_impl/keras/applications/vgg16.py
@@ -223,16 +223,6 @@ def VGG16(include_top=True,
           cache_subdir='models',
           file_hash='6d6bbae143d832006294945121d1f1fc')
     model.load_weights(weights_path)
-    if K.backend() == 'theano':
-      layer_utils.convert_all_kernels_in_model(model)
-
-    if K.image_data_format() == 'channels_first':
-      if include_top:
-        maxpool = model.get_layer(name='block5_pool')
-        shape = maxpool.output_shape[1:]
-        dense = model.get_layer(name='fc1')
-        layer_utils.convert_dense_weights_data_format(dense, shape,
-                                                      'channels_first')
 
   elif weights is not None:
     model.load_weights(weights)
diff --git a/tensorflow/python/keras/_impl/keras/applications/vgg19.py b/tensorflow/python/keras/_impl/keras/applications/vgg19.py
index dadaf4fdf0cc59..b09d0068b79738 100644
--- a/tensorflow/python/keras/_impl/keras/applications/vgg19.py
+++ b/tensorflow/python/keras/_impl/keras/applications/vgg19.py
@@ -232,16 +232,6 @@ def VGG19(include_top=True,
           cache_subdir='models',
           file_hash='253f8cb515780f3b799900260a226db6')
     model.load_weights(weights_path)
-    if K.backend() == 'theano':
-      layer_utils.convert_all_kernels_in_model(model)
-
-    if K.image_data_format() == 'channels_first':
-      if include_top:
-        maxpool = model.get_layer(name='block5_pool')
-        shape = maxpool.output_shape[1:]
-        dense = model.get_layer(name='fc1')
-        layer_utils.convert_dense_weights_data_format(dense, shape,
-                                                      'channels_first')
 
   elif weights is not None:
     model.load_weights(weights)
diff --git a/tensorflow/python/keras/_impl/keras/callbacks.py b/tensorflow/python/keras/_impl/keras/callbacks.py
index deb1e8867dba3d..a05e727d0e241b 100644
--- a/tensorflow/python/keras/_impl/keras/callbacks.py
+++ b/tensorflow/python/keras/_impl/keras/callbacks.py
@@ -268,9 +268,6 @@ class TerminateOnNaN(Callback):
   """Callback that terminates training when a NaN loss is encountered.
   """
 
-  def __init__(self):
-    super(TerminateOnNaN, self).__init__()
-
   def on_batch_end(self, batch, logs=None):
     logs = logs or {}
     loss = logs.get('loss')
diff --git a/tensorflow/python/keras/_impl/keras/engine/network.py b/tensorflow/python/keras/_impl/keras/engine/network.py
index 9e75096249fada..eb5805ba350d39 100644
--- a/tensorflow/python/keras/_impl/keras/engine/network.py
+++ b/tensorflow/python/keras/_impl/keras/engine/network.py
@@ -839,10 +839,14 @@ def _run_internal_graph(self, inputs, training=None, mask=None):
               output_tensors = nest.flatten(
                   layer.call(computed_tensor, **kwargs))
               if hasattr(layer, 'compute_mask'):
-                output_masks = nest.flatten(
-                    layer.compute_mask(computed_tensor, computed_mask))
+                output_masks = layer.compute_mask(computed_tensor,
+                                                  computed_mask)
+                if output_masks is None:
+                  output_masks = [None for _ in output_tensors]
+                else:
+                  output_masks = nest.flatten(output_masks)
               else:
-                output_masks = [None for _ in range(len(output_tensors))]
+                output_masks = [None for _ in output_tensors]
               computed_tensors = [computed_tensor]
               computed_masks = [computed_mask]
             else:
@@ -855,11 +859,16 @@ def _run_internal_graph(self, inputs, training=None, mask=None):
 
               output_tensors = nest.flatten(
                   layer.call(computed_tensors, **kwargs))
+
               if hasattr(layer, 'compute_mask'):
-                output_masks = nest.flatten(
-                    layer.compute_mask(computed_tensors, computed_masks))
+                output_masks = layer.compute_mask(computed_tensors,
+                                                  computed_masks)
+                if output_masks is None:
+                  output_masks = [None for _ in output_tensors]
+                else:
+                  output_masks = nest.flatten(output_masks)
               else:
-                output_masks = [None for _ in range(len(output_tensors))]
+                output_masks = [None for _ in output_tensors]
 
             if not context.executing_eagerly():
               if layer.activity_regularizer is not None:
diff --git a/tensorflow/python/keras/_impl/keras/engine/saving.py b/tensorflow/python/keras/_impl/keras/engine/saving.py
index ee6e320546068d..6a3ae3b20c11eb 100644
--- a/tensorflow/python/keras/_impl/keras/engine/saving.py
+++ b/tensorflow/python/keras/_impl/keras/engine/saving.py
@@ -62,7 +62,9 @@ def save_model(model, filepath, overwrite=True, include_optimizer=True):
 
   Arguments:
       model: Keras model instance to be saved.
-      filepath: String, path where to save the model.
+      filepath: One of the following:
+          - String, path where to save the model
+          - `h5py.File` object where to save the model
       overwrite: Whether we should overwrite any existing
           model at the target location, or instead
           ask the user with a manual prompt.
@@ -77,13 +79,20 @@ def save_model(model, filepath, overwrite=True, include_optimizer=True):
 
   from tensorflow.python.keras._impl.keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top
 
-  # If file exists and should not be overwritten.
-  if not overwrite and os.path.isfile(filepath):
-    proceed = ask_to_proceed_with_overwrite(filepath)
-    if not proceed:
-      return
+  if not isinstance(filepath, h5py.File):
+    # If file exists and should not be overwritten.
+    if not overwrite and os.path.isfile(filepath):
+      proceed = ask_to_proceed_with_overwrite(filepath)
+      if not proceed:
+        return
 
-  with h5py.File(filepath, mode='w') as f:
+    f = h5py.File(filepath, mode='w')
+    opened_new_file = True
+  else:
+    f = filepath
+    opened_new_file = False
+
+  try:
     f.attrs['keras_version'] = str(keras_version).encode('utf8')
     f.attrs['backend'] = K.backend().encode('utf8')
     f.attrs['model_config'] = json.dumps(
@@ -142,6 +151,9 @@ def save_model(model, filepath, overwrite=True, include_optimizer=True):
             else:
               param_dset[:] = val
     f.flush()
+  finally:
+    if opened_new_file:
+      f.close()
 
 
 @tf_export('keras.models.load_model')
@@ -149,7 +161,9 @@ def load_model(filepath, custom_objects=None, compile=True):  # pylint: disable=
   """Loads a model saved via `save_model`.
 
   Arguments:
-      filepath: String, path to the saved model.
+      filepath: One of the following:
+          - String, path to the saved model
+          - `h5py.File` object from which to load the model
       custom_objects: Optional dictionary mapping names
           (strings) to custom classes or functions to be
           considered during deserialization.
@@ -199,7 +213,14 @@ def convert_custom_objects(obj):
       return custom_objects[obj]
     return obj
 
-  with h5py.File(filepath, mode='r') as f:
+  opened_new_file = not isinstance(filepath, h5py.File)
+  if opened_new_file:
+    f = h5py.File(filepath, mode='r')
+  else:
+    f = filepath
+
+  model = None
+  try:
     # instantiate model
     model_config = f.attrs.get('model_config')
     if model_config is None:
@@ -210,54 +231,54 @@ def convert_custom_objects(obj):
     # set weights
     load_weights_from_hdf5_group(f['model_weights'], model.layers)
 
-    # Early return if compilation is not required.
-    if not compile:
-      return model
-
-    # instantiate optimizer
-    training_config = f.attrs.get('training_config')
-    if training_config is None:
-      logging.warning('No training configuration found in save file: '
-                      'the model was *not* compiled. Compile it manually.')
-      return model
-    training_config = json.loads(training_config.decode('utf-8'))
-    optimizer_config = training_config['optimizer_config']
-    optimizer = optimizers.deserialize(
-        optimizer_config, custom_objects=custom_objects)
-
-    # Recover loss functions and metrics.
-    loss = convert_custom_objects(training_config['loss'])
-    metrics = convert_custom_objects(training_config['metrics'])
-    sample_weight_mode = training_config['sample_weight_mode']
-    loss_weights = training_config['loss_weights']
-
-    # Compile model.
-    model.compile(
-        optimizer=optimizer,
-        loss=loss,
-        metrics=metrics,
-        loss_weights=loss_weights,
-        sample_weight_mode=sample_weight_mode)
-
-    # Set optimizer weights.
-    if 'optimizer_weights' in f:
-      # Build train function (to get weight updates).
-      model._make_train_function()
-      optimizer_weights_group = f['optimizer_weights']
-      optimizer_weight_names = [
-          n.decode('utf8')
-          for n in optimizer_weights_group.attrs['weight_names']
-      ]
-      optimizer_weight_values = [
-          optimizer_weights_group[n] for n in optimizer_weight_names
-      ]
-      try:
-        model.optimizer.set_weights(optimizer_weight_values)
-      except ValueError:
-        logging.warning('Error in loading the saved optimizer '
-                        'state. As a result, your model is '
-                        'starting with a freshly initialized '
-                        'optimizer.')
+    if compile:
+      # instantiate optimizer
+      training_config = f.attrs.get('training_config')
+      if training_config is None:
+        logging.warning('No training configuration found in save file: '
+                        'the model was *not* compiled. Compile it manually.')
+        return model
+      training_config = json.loads(training_config.decode('utf-8'))
+      optimizer_config = training_config['optimizer_config']
+      optimizer = optimizers.deserialize(
+          optimizer_config, custom_objects=custom_objects)
+
+      # Recover loss functions and metrics.
+      loss = convert_custom_objects(training_config['loss'])
+      metrics = convert_custom_objects(training_config['metrics'])
+      sample_weight_mode = training_config['sample_weight_mode']
+      loss_weights = training_config['loss_weights']
+
+      # Compile model.
+      model.compile(
+          optimizer=optimizer,
+          loss=loss,
+          metrics=metrics,
+          loss_weights=loss_weights,
+          sample_weight_mode=sample_weight_mode)
+
+      # Set optimizer weights.
+      if 'optimizer_weights' in f:
+        # Build train function (to get weight updates).
+        model._make_train_function()
+        optimizer_weights_group = f['optimizer_weights']
+        optimizer_weight_names = [
+            n.decode('utf8')
+            for n in optimizer_weights_group.attrs['weight_names']
+        ]
+        optimizer_weight_values = [
+            optimizer_weights_group[n] for n in optimizer_weight_names
+        ]
+        try:
+          model.optimizer.set_weights(optimizer_weight_values)
+        except ValueError:
+          logging.warning('Error in loading the saved optimizer '
+                          'state. As a result, your model is '
+                          'starting with a freshly initialized '
+                          'optimizer.')
+  finally:
+    if opened_new_file:
+      f.close()
   return model
 
 
@@ -636,6 +657,12 @@ def convert_gru_weights(weights, from_cudnn=True):
 
 
 def save_weights_to_hdf5_group(f, layers):
+  """Saves the weights of a list of layers to a HDF5 group.
+
+  Arguments:
+      f: HDF5 group.
+      layers: List of layer instances.
+  """
   from tensorflow.python.keras._impl.keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top
 
   save_attributes_to_hdf5_group(
@@ -710,7 +737,7 @@ def load_weights_from_hdf5_group(f, layers):
   for k, name in enumerate(layer_names):
     g = f[name]
     weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
-    weight_values = [g[weight_name] for weight_name in weight_names]
+    weight_values = [np.asarray(g[weight_name]) for weight_name in weight_names]
     layer = filtered_layers[k]
     symbolic_weights = layer.weights
     weight_values = preprocess_weights_for_loading(
@@ -766,7 +793,7 @@ def load_weights_from_hdf5_group_by_name(f, layers):
   for k, name in enumerate(layer_names):
     g = f[name]
     weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
-    weight_values = [g[weight_name] for weight_name in weight_names]
+    weight_values = [np.asarray(g[weight_name]) for weight_name in weight_names]
 
     for layer in index.get(name, []):
       symbolic_weights = layer.weights
diff --git a/tensorflow/python/keras/_impl/keras/engine/saving_test.py b/tensorflow/python/keras/_impl/keras/engine/saving_test.py
index 709a8e9fb1e1ba..e66844027d946e 100644
--- a/tensorflow/python/keras/_impl/keras/engine/saving_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/saving_test.py
@@ -253,7 +253,7 @@ class TestWholeModelSaving(test.TestCase):
 
   def test_sequential_model_saving(self):
     if h5py is None:
-      return  # Skip test if models cannot be saved.
+      self.skipTest('h5py required to run this test')
 
     with self.test_session():
       model = keras.models.Sequential()
@@ -290,7 +290,7 @@ def test_sequential_model_saving(self):
 
   def test_sequential_model_saving_2(self):
     if h5py is None:
-      return  # Skip test if models cannot be saved.
+      self.skipTest('h5py required to run this test')
 
     with self.test_session():
       # test with custom optimizer, loss
@@ -326,7 +326,7 @@ def custom_loss(y_true, y_pred):
 
   def test_functional_model_saving(self):
     if h5py is None:
-      return  # Skip test if models cannot be saved.
+      self.skipTest('h5py required to run this test')
 
     with self.test_session():
       inputs = keras.layers.Input(shape=(3,))
@@ -354,7 +354,7 @@ def test_functional_model_saving(self):
 
   def test_saving_without_compilation(self):
     if h5py is None:
-      return  # Skip test if models cannot be saved.
+      self.skipTest('h5py required to run this test')
 
     with self.test_session():
       model = keras.models.Sequential()
@@ -370,7 +370,7 @@ def test_saving_without_compilation(self):
 
   def test_saving_with_tf_optimizer(self):
     if h5py is None:
-      return  # Skip test if models cannot be saved.
+      self.skipTest('h5py required to run this test')
 
     with self.test_session():
       model = keras.models.Sequential()
@@ -388,7 +388,7 @@ def test_saving_with_tf_optimizer(self):
 
   def test_saving_right_after_compilation(self):
     if h5py is None:
-      return  # Skip test if models cannot be saved.
+      self.skipTest('h5py required to run this test')
 
     with self.test_session():
       model = keras.models.Sequential()
@@ -405,7 +405,7 @@ def test_saving_right_after_compilation(self):
 
   def test_saving_lambda_numpy_array_arguments(self):
     if h5py is None:
-      return  # Skip test if models cannot be saved.
+      self.skipTest('h5py required to run this test')
 
     mean = np.random.random((4, 2, 3))
     std = np.abs(np.random.random((4, 2, 3))) + 1e-5
@@ -427,7 +427,7 @@ def test_saving_lambda_numpy_array_arguments(self):
 
   def test_saving_model_with_long_layer_names(self):
     if h5py is None:
-      return  # Skip test if models cannot be saved.
+      self.skipTest('h5py required to run this test')
 
     with self.test_session():
       # This layer name will make the `layers_name` HDF5 attribute blow
@@ -468,7 +468,7 @@ def test_saving_model_with_long_layer_names(self):
 
   def test_saving_model_with_long_weights_names(self):
     if h5py is None:
-      return  # Skip test if models cannot be saved.
+      self.skipTest('h5py required to run this test')
 
     with self.test_session():
       x = keras.Input(shape=(2,), name='nested_model_input')
@@ -511,6 +511,43 @@ def test_saving_model_with_long_weights_names(self):
       os.close(fd)
       os.remove(fname)
 
+  def test_model_saving_to_pre_created_h5py_file(self):
+    if h5py is None:
+      self.skipTest('h5py required to run this test')
+
+    with self.test_session():
+      inputs = keras.Input(shape=(3,))
+      x = keras.layers.Dense(2)(inputs)
+      outputs = keras.layers.Dense(3)(x)
+
+      model = keras.Model(inputs, outputs)
+      model.compile(loss=keras.losses.MSE,
+                    optimizer=keras.optimizers.Adam(),
+                    metrics=[keras.metrics.categorical_accuracy])
+      x = np.random.random((1, 3))
+      y = np.random.random((1, 3))
+      model.train_on_batch(x, y)
+
+      out = model.predict(x)
+      fd, fname = tempfile.mkstemp('.h5')
+      with h5py.File(fname, mode='r+') as h5file:
+        keras.models.save_model(model, h5file)
+        loaded_model = keras.models.load_model(h5file)
+        out2 = loaded_model.predict(x)
+      self.assertAllClose(out, out2, atol=1e-05)
+
+      # Test non-default options in h5
+      with h5py.File('_', driver='core',
+                     backing_store=False) as h5file:
+        keras.models.save_model(model, h5file)
+        loaded_model = keras.models.load_model(h5file)
+        out2 = loaded_model.predict(x)
+      self.assertAllClose(out, out2, atol=1e-05)
+
+      # Cleanup
+      os.close(fd)
+      os.remove(fname)
+
 
 class SubclassedModel(training.Model):
 
diff --git a/tensorflow/python/keras/_impl/keras/engine/topology_test.py b/tensorflow/python/keras/_impl/keras/engine/topology_test.py
index 6993a042890088..635c446879a24a 100644
--- a/tensorflow/python/keras/_impl/keras/engine/topology_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/topology_test.py
@@ -883,6 +883,33 @@ def test_explicit_training_argument(self):
       preds = model.predict(x)
       self.assertEqual(np.min(preds), 0.)  # At least one unit was dropped.
 
+  def test_multi_output_model_with_none_masking(self):
+
+    with self.test_session():
+      def func(x):
+        return [x * 0.2, x * 0.3]
+
+      def output_shape(input_shape):
+        return [input_shape, input_shape]
+
+      i = keras.layers.Input(shape=(3, 2, 1))
+      o = keras.layers.Lambda(function=func, output_shape=output_shape)(i)
+
+      self.assertEqual(keras.backend.int_shape(o[0]), (None, 3, 2, 1))
+      self.assertEqual(keras.backend.int_shape(o[1]), (None, 3, 2, 1))
+
+      o = keras.layers.add(o)
+      model = keras.Model(i, o)
+
+      i2 = keras.layers.Input(shape=(3, 2, 1))
+      o2 = model(i2)
+      model2 = keras.Model(i2, o2)
+
+      x = np.random.random((4, 3, 2, 1))
+      out = model2.predict(x)
+      assert out.shape == (4, 3, 2, 1)
+      self.assertAllClose(out, x * 0.2 + x * 0.3, atol=1e-4)
+
 
 class DeferredModeTest(test.TestCase):
 
diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py
index c7623d2b524fa3..16d1b160e43ff7 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training.py
@@ -285,6 +285,10 @@ def compile(self,
           self.metrics_names.append(self.output_names[i] + '_loss')
       self.nested_metrics = training_utils.collect_metrics(metrics,
                                                            self.output_names)
+      # TODO(fchollet): support stateful metrics in eager execution.
+      self.stateful_metric_functions = []
+      self.stateful_metric_names = []
+
       with K.name_scope('metrics'):
         training_utils.populate_metric_names(self)
       self._feed_sample_weight_modes = []
@@ -461,6 +465,7 @@ def compile(self,
                                                              self.output_names)
     self.metrics_updates = []
     self.stateful_metric_names = []
+    self.stateful_metric_functions = []
     with K.name_scope('metrics'):
       for i in range(len(self.outputs)):
         if i in skip_target_indices:
@@ -516,8 +521,9 @@ def handle_metrics(metrics, weights=None):
 
             # Keep track of state updates created by
             # stateful metrics (i.e. metrics layers).
-            if isinstance(metric_fn, Layer):
+            if isinstance(metric_fn, Layer) and metric_fn.stateful:
               self.stateful_metric_names.append(metric_name)
+              self.stateful_metric_functions.append(metric_fn)
               self.metrics_updates += metric_fn.updates
 
         handle_metrics(output_metrics)
@@ -1745,7 +1751,8 @@ def evaluate_generator(self,
                          steps=None,
                          max_queue_size=10,
                          workers=1,
-                         use_multiprocessing=False):
+                         use_multiprocessing=False,
+                         verbose=0):
     """Evaluates the model on a data generator.
 
     The generator should return the same kind of data
@@ -1772,6 +1779,7 @@ def evaluate_generator(self,
             Note that because this implementation relies on multiprocessing,
             you should not pass non-picklable arguments to the generator
             as they can't be passed easily to children processes.
+        verbose: Verbosity mode, 0 or 1.
 
     Returns:
         Scalar test loss (if the model has a single output and no metrics)
@@ -1796,7 +1804,8 @@ def evaluate_generator(self,
         steps=steps,
         max_queue_size=max_queue_size,
         workers=workers,
-        use_multiprocessing=use_multiprocessing)
+        use_multiprocessing=use_multiprocessing,
+        verbose=verbose)
 
   def predict_generator(self,
                         generator,
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_arrays.py b/tensorflow/python/keras/_impl/keras/engine/training_arrays.py
index 12e74ef51df9f7..84f93da89839c3 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_arrays.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_arrays.py
@@ -27,7 +27,6 @@
 from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras import callbacks as cbks
 from tensorflow.python.keras._impl.keras.engine import training_utils
-from tensorflow.python.keras._impl.keras.engine.base_layer import Layer
 from tensorflow.python.keras._impl.keras.utils.generic_utils import make_batches
 from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar
 from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays
@@ -180,9 +179,8 @@ def fit_loop(model,
 
   for epoch in range(initial_epoch, epochs):
     # Reset stateful metrics
-    for m in model.metrics:
-      if isinstance(m, Layer):
-        m.reset_states()
+    for m in model.stateful_metric_functions:
+      m.reset_states()
     # Update callbacks
     callbacks.on_epoch_begin(epoch)
     epoch_logs = {}
@@ -413,9 +411,8 @@ def test_loop(model, inputs, targets,
     ins = inputs + targets + sample_weights
 
   if hasattr(model, 'metrics'):
-    for m in model.metrics:
-      if isinstance(m, Layer):
-        m.reset_states()
+    for m in model.stateful_metric_functions:
+      m.reset_states()
     stateful_metric_indices = [
         i for i, name in enumerate(model.metrics_names)
         if str(name) in model.stateful_metric_names
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_generator.py b/tensorflow/python/keras/_impl/keras/engine/training_generator.py
index a66e72072def5a..0de8297795877c 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_generator.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_generator.py
@@ -152,6 +152,8 @@ def fit_generator(model,
     # Construct epoch logs.
     epoch_logs = {}
     while epoch < epochs:
+      for m in model.stateful_metric_functions:
+        m.reset_states()
       callbacks.on_epoch_begin(epoch)
       steps_done = 0
       batch_index = 0
@@ -247,8 +249,19 @@ def evaluate_generator(model,
                        steps=None,
                        max_queue_size=10,
                        workers=1,
-                       use_multiprocessing=False):
+                       use_multiprocessing=False,
+                       verbose=0):
   """See docstring for `Model.evaluate_generator`."""
+  stateful_metric_indices = []
+  if hasattr(model, 'metrics'):
+    for m in model.stateful_metric_functions:
+      m.reset_states()
+    stateful_metric_indices = [
+        i for i, name in enumerate(model.metrics_names)
+        if str(name) in model.stateful_metric_names]
+  else:
+    stateful_metric_indices = []
+
   steps_done = 0
   wait_time = 0.01
   all_outs = []
@@ -288,6 +301,9 @@ def evaluate_generator(model,
       else:
         output_generator = generator
 
+    if verbose == 1:
+      progbar = Progbar(target=steps)
+
     while steps_done < steps:
       generator_output = next(output_generator)
       if not hasattr(generator_output, '__len__'):
@@ -318,6 +334,8 @@ def evaluate_generator(model,
 
       steps_done += 1
       batch_sizes.append(batch_size)
+      if verbose == 1:
+        progbar.update(steps_done)
 
   finally:
     if enqueuer is not None:
@@ -328,8 +346,11 @@ def evaluate_generator(model,
   else:
     averages = []
     for i in range(len(outs)):
-      averages.append(
-          np.average([out[i] for out in all_outs], weights=batch_sizes))
+      if i not in stateful_metric_indices:
+        averages.append(
+            np.average([out[i] for out in all_outs], weights=batch_sizes))
+      else:
+        averages.append(float(all_outs[-1][i]))
     return averages
 
 
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_test.py b/tensorflow/python/keras/_impl/keras/engine/training_test.py
index cc2386a5bd872b..4b01fbb165ace0 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_test.py
@@ -947,6 +947,7 @@ def custom_generator():
                                  steps=5,
                                  max_queue_size=10,
                                  workers=2,
+                                 verbose=1,
                                  use_multiprocessing=True)
         model.evaluate_generator(custom_generator(),
                                  steps=5,
diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py
index 5e2004266af260..9cad08274e58d6 100644
--- a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py
+++ b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py
@@ -29,6 +29,7 @@
 from tensorflow.python.keras._impl.keras.engine import InputSpec
 from tensorflow.python.keras._impl.keras.engine import Layer
 from tensorflow.python.keras._impl.keras.layers.recurrent import _generate_dropout_mask
+from tensorflow.python.keras._impl.keras.layers.recurrent import _standardize_args
 from tensorflow.python.keras._impl.keras.layers.recurrent import RNN
 from tensorflow.python.keras._impl.keras.utils import conv_utils
 from tensorflow.python.keras._impl.keras.utils import generic_utils
@@ -167,6 +168,7 @@ def __init__(self,
                                     **kwargs)
     self.input_spec = [InputSpec(ndim=5)]
     self.states = None
+    self._num_constants = None
 
   @tf_utils.shape_type_conversion
   def compute_output_shape(self, input_shape):
@@ -214,7 +216,7 @@ def build(self, input_shape):
     # Note input_shape will be list of shapes of initial states and
     # constants if these are passed in __call__.
     if self._num_constants is not None:
-      constants_shape = input_shape[-self._num_constants:]
+      constants_shape = input_shape[-self._num_constants:]  # pylint: disable=E1130
     else:
       constants_shape = None
 
@@ -279,8 +281,8 @@ def get_initial_state(self, inputs):
       return [initial_state]
 
   def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
-    inputs, initial_state, constants = self._standardize_args(
-        inputs, initial_state, constants)
+    inputs, initial_state, constants = _standardize_args(
+        inputs, initial_state, constants, self._num_constants)
 
     if initial_state is None and constants is None:
       return super(ConvRNN2D, self).__call__(inputs, **kwargs)
@@ -853,10 +855,10 @@ class ConvLSTM2D(ConvRNN2D):
   Input shape:
     - if data_format='channels_first'
         5D tensor with shape:
-        `(samples,time, channels, rows, cols)`
+        `(samples, time, channels, rows, cols)`
     - if data_format='channels_last'
         5D tensor with shape:
-        `(samples,time, rows, cols, channels)`
+        `(samples, time, rows, cols, channels)`
 
   Output shape:
     - if `return_sequences`
diff --git a/tensorflow/python/keras/_impl/keras/layers/core.py b/tensorflow/python/keras/_impl/keras/layers/core.py
index 9c4cb0f4fda681..30327781dffc67 100644
--- a/tensorflow/python/keras/_impl/keras/layers/core.py
+++ b/tensorflow/python/keras/_impl/keras/layers/core.py
@@ -33,6 +33,7 @@
 from tensorflow.python.keras._impl.keras import regularizers
 from tensorflow.python.keras._impl.keras.engine import InputSpec
 from tensorflow.python.keras._impl.keras.engine import Layer
+from tensorflow.python.keras._impl.keras.utils import conv_utils
 from tensorflow.python.keras._impl.keras.utils import generic_utils
 from tensorflow.python.keras._impl.keras.utils import tf_utils
 from tensorflow.python.ops import array_ops
@@ -501,6 +502,17 @@ def get_config(self):
 class Flatten(Layer):
   """Flattens the input. Does not affect the batch size.
 
+  Arguments:
+      data_format: A string,
+          one of `channels_last` (default) or `channels_first`.
+          The ordering of the dimensions in the inputs.
+          `channels_last` corresponds to inputs with shape
+          `(batch, ..., channels)` while `channels_first` corresponds to
+          inputs with shape `(batch, channels, ...)`.
+          It defaults to the `image_data_format` value found in your
+          Keras config file at `~/.keras/keras.json`.
+          If you never set it, then it will be "channels_last".
+
   Example:
 
   ```python
@@ -515,11 +527,19 @@ class Flatten(Layer):
   ```
   """
 
-  def __init__(self, **kwargs):
+  def __init__(self, data_format=None, **kwargs):
     super(Flatten, self).__init__(**kwargs)
+    self.data_format = conv_utils.normalize_data_format(data_format)
     self.input_spec = InputSpec(min_ndim=2)
 
   def call(self, inputs):
+    if self.data_format == 'channels_first':
+      permutation = [0]
+      permutation.extend([i for i in
+                          range(2, K.ndim(inputs))])
+      permutation.append(1)
+      inputs = array_ops.transpose(inputs, perm=permutation)
+
     outputs = array_ops.reshape(inputs, (array_ops.shape(inputs)[0], -1))
     if not context.executing_eagerly():
       outputs.set_shape(self.compute_output_shape(inputs.get_shape()))
@@ -534,6 +554,11 @@ def compute_output_shape(self, input_shape):
       output_shape += [None]
     return tensor_shape.TensorShape(output_shape)
 
+  def get_config(self):
+    config = {'data_format': self.data_format}
+    base_config = super(Flatten, self).get_config()
+    return dict(list(base_config.items()) + list(config.items()))
+
 
 @tf_export('keras.layers.RepeatVector')
 class RepeatVector(Layer):
diff --git a/tensorflow/python/keras/_impl/keras/layers/core_test.py b/tensorflow/python/keras/_impl/keras/layers/core_test.py
index d22d8d12dc4e76..9b360b65d6336d 100644
--- a/tensorflow/python/keras/_impl/keras/layers/core_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/core_test.py
@@ -124,6 +124,16 @@ def test_flatten(self):
     testing_utils.layer_test(
         keras.layers.Flatten, kwargs={}, input_shape=(3, 2, 4))
 
+    # Test channels_first
+    inputs = np.random.random((10, 3, 5, 5)).astype('float32')
+    outputs = testing_utils.layer_test(
+        keras.layers.Flatten,
+        kwargs={'data_format': 'channels_first'},
+        input_data=inputs)
+    target_outputs = np.reshape(
+        np.transpose(inputs, (0, 2, 3, 1)), (-1, 5 * 5 * 3))
+    self.assertAllClose(outputs, target_outputs)
+
   @tf_test_util.run_in_graph_and_eager_modes()
   def test_repeat_vector(self):
     testing_utils.layer_test(
diff --git a/tensorflow/python/keras/_impl/keras/layers/recurrent.py b/tensorflow/python/keras/_impl/keras/layers/recurrent.py
index caf9e6f46f51c7..93150b97fa87f5 100644
--- a/tensorflow/python/keras/_impl/keras/layers/recurrent.py
+++ b/tensorflow/python/keras/_impl/keras/layers/recurrent.py
@@ -519,9 +519,10 @@ def get_initial_state(self, inputs):
       return [K.tile(initial_state, [1, self.cell.state_size])]
 
   def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
-    inputs, initial_state, constants = self._standardize_args(
-        inputs, initial_state, constants)
-
+    inputs, initial_state, constants = _standardize_args(inputs,
+                                                         initial_state,
+                                                         constants,
+                                                         self._num_constants)
     if initial_state is None and constants is None:
       return super(RNN, self).__call__(inputs, **kwargs)
 
@@ -661,46 +662,6 @@ def step(inputs, states):
     else:
       return output
 
-  def _standardize_args(self, inputs, initial_state, constants):
-    """Standardize `__call__` to a single list of tensor inputs.
-
-    When running a model loaded from file, the input tensors
-    `initial_state` and `constants` can be passed to `RNN.__call__` as part
-    of `inputs` instead of by the dedicated keyword arguments. This method
-    makes sure the arguments are separated and that `initial_state` and
-    `constants` are lists of tensors (or None).
-
-    Arguments:
-        inputs: tensor or list/tuple of tensors
-        initial_state: tensor or list of tensors or None
-        constants: tensor or list of tensors or None
-
-    Returns:
-        inputs: tensor
-        initial_state: list of tensors or None
-        constants: list of tensors or None
-    """
-    if isinstance(inputs, list):
-      assert initial_state is None and constants is None
-      if self._num_constants is not None:
-        constants = inputs[-self._num_constants:]  # pylint: disable=invalid-unary-operand-type
-        inputs = inputs[:-self._num_constants]  # pylint: disable=invalid-unary-operand-type
-      if len(inputs) > 1:
-        initial_state = inputs[1:]
-      inputs = inputs[0]
-
-    def to_list_or_none(x):
-      if x is None or isinstance(x, list):
-        return x
-      if isinstance(x, tuple):
-        return list(x)
-      return [x]
-
-    initial_state = to_list_or_none(initial_state)
-    constants = to_list_or_none(constants)
-
-    return inputs, initial_state, constants
-
   def reset_states(self, states=None):
     if not self.stateful:
       raise AttributeError('Layer must be stateful.')
@@ -914,13 +875,13 @@ def call(self, inputs, states, training=None):
     prev_output = states[0]
     if 0 < self.dropout < 1 and self._dropout_mask is None:
       self._dropout_mask = _generate_dropout_mask(
-          _generate_dropout_ones(inputs, array_ops.shape(inputs)[-1]),
+          array_ops.ones_like(inputs),
           self.dropout,
           training=training)
     if (0 < self.recurrent_dropout < 1 and
         self._recurrent_dropout_mask is None):
       self._recurrent_dropout_mask = _generate_dropout_mask(
-          _generate_dropout_ones(inputs, self.units),
+          array_ops.ones_like(prev_output),
           self.recurrent_dropout,
           training=training)
 
@@ -1333,14 +1294,14 @@ def call(self, inputs, states, training=None):
 
     if 0 < self.dropout < 1 and self._dropout_mask is None:
       self._dropout_mask = _generate_dropout_mask(
-          _generate_dropout_ones(inputs, array_ops.shape(inputs)[-1]),
+          array_ops.ones_like(inputs),
           self.dropout,
           training=training,
           count=3)
     if (0 < self.recurrent_dropout < 1 and
         self._recurrent_dropout_mask is None):
       self._recurrent_dropout_mask = _generate_dropout_mask(
-          _generate_dropout_ones(inputs, self.units),
+          array_ops.ones_like(h_tm1),
           self.recurrent_dropout,
           training=training,
           count=3)
@@ -1873,14 +1834,14 @@ def bias_initializer(_, *args, **kwargs):
   def call(self, inputs, states, training=None):
     if 0 < self.dropout < 1 and self._dropout_mask is None:
       self._dropout_mask = _generate_dropout_mask(
-          _generate_dropout_ones(inputs, array_ops.shape(inputs)[-1]),
+          array_ops.ones_like(inputs),
           self.dropout,
           training=training,
           count=4)
     if (0 < self.recurrent_dropout < 1 and
         self._recurrent_dropout_mask is None):
       self._recurrent_dropout_mask = _generate_dropout_mask(
-          _generate_dropout_ones(inputs, self.units),
+          array_ops.ones_like(states[0]),
           self.recurrent_dropout,
           training=training,
           count=4)
@@ -2254,12 +2215,7 @@ def from_config(cls, config):
     return cls(**config)
 
 
-def _generate_dropout_ones(inputs, dims):
-  return K.ones((array_ops.shape(inputs)[0], dims))
-
-
 def _generate_dropout_mask(ones, rate, training=None, count=1):
-
   def dropped_inputs():
     return K.dropout(ones, rate)
 
@@ -2605,3 +2561,47 @@ def get_config(self):
     }
     base_config = super(Recurrent, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
+
+
+def _standardize_args(inputs, initial_state, constants, num_constants):
+  """Standardizes `__call__` to a single list of tensor inputs.
+
+  When running a model loaded from a file, the input tensors
+  `initial_state` and `constants` can be passed to `RNN.__call__()` as part
+  of `inputs` instead of by the dedicated keyword arguments. This method
+  makes sure the arguments are separated and that `initial_state` and
+  `constants` are lists of tensors (or None).
+
+  Arguments:
+      inputs: Tensor or list/tuple of tensors. which may include constants
+        and initial states. In that case `num_constant` must be specified.
+      initial_state: Tensor or list of tensors or None, initial states.
+      constants: Tensor or list of tensors or None, constant tensors.
+      num_constants: Expected number of constants (if constants are passed as
+        part of the `inputs` list.
+
+  Returns:
+      inputs: Single tensor.
+      initial_state: List of tensors or None.
+      constants: List of tensors or None.
+  """
+  if isinstance(inputs, list):
+    assert initial_state is None and constants is None
+    if num_constants is not None:
+      constants = inputs[-num_constants:]
+      inputs = inputs[:-num_constants]
+    if len(inputs) > 1:
+      initial_state = inputs[1:]
+    inputs = inputs[0]
+
+  def to_list_or_none(x):
+    if x is None or isinstance(x, list):
+      return x
+    if isinstance(x, tuple):
+      return list(x)
+    return [x]
+
+  initial_state = to_list_or_none(initial_state)
+  constants = to_list_or_none(constants)
+
+  return inputs, initial_state, constants
diff --git a/tensorflow/python/keras/_impl/keras/layers/wrappers.py b/tensorflow/python/keras/_impl/keras/layers/wrappers.py
index 91b8c1148bec56..d1d09bb4a2b99b 100644
--- a/tensorflow/python/keras/_impl/keras/layers/wrappers.py
+++ b/tensorflow/python/keras/_impl/keras/layers/wrappers.py
@@ -25,6 +25,7 @@
 from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras.engine import InputSpec
 from tensorflow.python.keras._impl.keras.engine import Layer
+from tensorflow.python.keras._impl.keras.layers.recurrent import _standardize_args
 from tensorflow.python.keras._impl.keras.utils import generic_utils
 from tensorflow.python.keras._impl.keras.utils import tf_utils
 from tensorflow.python.ops import array_ops
@@ -284,6 +285,7 @@ def __init__(self, layer, merge_mode='concat', weights=None, **kwargs):
     self.return_state = layer.return_state
     self.supports_masking = True
     self._trainable = True
+    self._num_constants = None
     super(Bidirectional, self).__init__(layer, **kwargs)
     self.input_spec = layer.input_spec
 
@@ -326,37 +328,51 @@ def compute_output_shape(self, input_shape):
       return [output_shape] + state_shape + copy.copy(state_shape)
     return output_shape
 
-  def __call__(self, inputs, initial_state=None, **kwargs):
+  def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
+    """`Bidirectional.__call__` implements the same API as the wrapped `RNN`."""
+    inputs, initial_state, constants = _standardize_args(
+        inputs, initial_state, constants, self._num_constants)
+
     if isinstance(inputs, list):
       if len(inputs) > 1:
         initial_state = inputs[1:]
       inputs = inputs[0]
 
-    if initial_state is None:
+    if initial_state is None and constants is None:
       return super(Bidirectional, self).__call__(inputs, **kwargs)
 
-    # Standardize `initial_state` into list
-    if isinstance(initial_state, tuple):
-      initial_state = list(initial_state)
-    elif not isinstance(initial_state, list):
-      initial_state = [initial_state]
-
-    # Check if `initial_state` can be splitted into half
-    num_states = len(initial_state)
-    if num_states % 2 > 0:
-      raise ValueError(
-          'When passing `initial_state` to a Bidirectional RNN, the state '
-          'should be a list containing the states of the underlying RNNs. '
-          'Found: ' + str(initial_state))
-
-    # Applies the same workaround as in `RNN.__call__`, without handling
-    # constants
-    kwargs['initial_state'] = initial_state
-    additional_inputs = initial_state
-    additional_specs = [InputSpec(shape=K.int_shape(state))
-                        for state in initial_state]
-    self.forward_layer.state_spec = additional_specs[:num_states // 2]
-    self.backward_layer.state_spec = additional_specs[num_states // 2:]
+    # Applies the same workaround as in `RNN.__call__`
+    additional_inputs = []
+    additional_specs = []
+    if initial_state is not None:
+      # Check if `initial_state` can be splitted into half
+      num_states = len(initial_state)
+      if num_states % 2 > 0:
+        raise ValueError(
+            'When passing `initial_state` to a Bidirectional RNN, '
+            'the state should be a list containing the states of '
+            'the underlying RNNs. '
+            'Found: ' + str(initial_state))
+
+      kwargs['initial_state'] = initial_state
+      additional_inputs += initial_state
+      state_specs = [InputSpec(shape=K.int_shape(state))
+                     for state in initial_state]
+      self.forward_layer.state_spec = state_specs[:num_states // 2]
+      self.backward_layer.state_spec = state_specs[num_states // 2:]
+      additional_specs += state_specs
+    if constants is not None:
+      kwargs['constants'] = constants
+      additional_inputs += constants
+      constants_spec = [InputSpec(shape=K.int_shape(constant))
+                        for constant in constants]
+      self.forward_layer.constants_spec = constants_spec
+      self.backward_layer.constants_spec = constants_spec
+      additional_specs += constants_spec
+
+      self._num_constants = len(constants)
+      self.forward_layer._num_constants = self._num_constants
+      self.backward_layer._num_constants = self._num_constants
 
     is_keras_tensor = K.is_keras_tensor(additional_inputs[0])
     for tensor in additional_inputs:
@@ -381,12 +397,19 @@ def __call__(self, inputs, initial_state=None, **kwargs):
     else:
       return super(Bidirectional, self).__call__(inputs, **kwargs)
 
-  def call(self, inputs, training=None, mask=None, initial_state=None):
+  def call(self, inputs,
+           training=None,
+           mask=None,
+           initial_state=None,
+           constants=None):
+    """`Bidirectional.call` implements the same API as the wrapped `RNN`."""
     kwargs = {}
     if generic_utils.has_arg(self.layer.call, 'training'):
       kwargs['training'] = training
     if generic_utils.has_arg(self.layer.call, 'mask'):
       kwargs['mask'] = mask
+    if generic_utils.has_arg(self.layer.call, 'constants'):
+      kwargs['constants'] = constants
 
     if initial_state is not None and generic_utils.has_arg(
         self.layer.call, 'initial_state'):
@@ -444,13 +467,23 @@ def build(self, input_shape):
     self.built = True
 
   def compute_mask(self, inputs, mask):
+    if isinstance(mask, list):
+      mask = mask[0]
     if self.return_sequences:
       if not self.merge_mode:
-        return [mask, mask]
+        output_mask = [mask, mask]
       else:
-        return mask
+        output_mask = mask
     else:
-      return None
+      output_mask = [None, None] if not self.merge_mode else None
+
+    if self.return_state:
+      states = self.forward_layer.states
+      state_mask = [None for _ in states]
+      if isinstance(output_mask, list):
+        return output_mask + state_mask * 2
+      return [output_mask] + state_mask * 2
+    return output_mask
 
   @property
   def trainable_weights(self):
@@ -488,5 +521,15 @@ def constraints(self):
 
   def get_config(self):
     config = {'merge_mode': self.merge_mode}
+    if self._num_constants is not None:
+      config['num_constants'] = self._num_constants
     base_config = super(Bidirectional, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
+
+  @classmethod
+  def from_config(cls, config, custom_objects=None):
+    num_constants = config.pop('num_constants', None)
+    layer = super(Bidirectional, cls).from_config(config,
+                                                  custom_objects=custom_objects)
+    layer._num_constants = num_constants
+    return layer
diff --git a/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py b/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py
index 8fcf66e90ff128..05b272a470df30 100644
--- a/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py
@@ -18,6 +18,8 @@
 from __future__ import division
 from __future__ import print_function
 
+import copy
+
 import numpy as np
 
 from tensorflow.python.framework import test_util as tf_test_util
@@ -26,6 +28,45 @@
 from tensorflow.python.training.rmsprop import RMSPropOptimizer
 
 
+class _RNNCellWithConstants(keras.layers.Layer):
+
+  def __init__(self, units, **kwargs):
+    self.units = units
+    self.state_size = units
+    super(_RNNCellWithConstants, self).__init__(**kwargs)
+
+  def build(self, input_shape):
+    [input_shape, constant_shape] = input_shape
+
+    self.input_kernel = self.add_weight(
+        shape=(input_shape[-1], self.units),
+        initializer='uniform',
+        name='kernel')
+    self.recurrent_kernel = self.add_weight(
+        shape=(self.units, self.units),
+        initializer='uniform',
+        name='recurrent_kernel')
+    self.constant_kernel = self.add_weight(
+        shape=(constant_shape[-1], self.units),
+        initializer='uniform',
+        name='constant_kernel')
+    self.built = True
+
+  def call(self, inputs, states, constants):
+    [prev_output] = states
+    [constant] = constants
+    h_input = keras.backend.dot(inputs, self.input_kernel)
+    h_state = keras.backend.dot(prev_output, self.recurrent_kernel)
+    h_const = keras.backend.dot(constant, self.constant_kernel)
+    output = h_input + h_state + h_const
+    return output, [output]
+
+  def get_config(self):
+    config = {'units': self.units}
+    base_config = super(_RNNCellWithConstants, self).get_config()
+    return dict(list(base_config.items()) + list(config.items()))
+
+
 class TimeDistributedTest(test.TestCase):
 
   @tf_test_util.run_in_graph_and_eager_modes()
@@ -383,6 +424,100 @@ def test_Bidirectional_trainable(self):
       layer.trainable = True
       assert len(layer.trainable_weights) == 6
 
+  def test_Bidirectional_with_constants(self):
+    with self.test_session():
+      # Test basic case.
+      x = keras.Input((5, 5))
+      c = keras.Input((3,))
+      cell = _RNNCellWithConstants(32)
+      custom_objects = {'_RNNCellWithConstants': _RNNCellWithConstants}
+      with keras.utils.CustomObjectScope(custom_objects):
+        layer = keras.layers.Bidirectional(keras.layers.RNN(cell))
+      y = layer(x, constants=c)
+      model = keras.Model([x, c], y)
+      model.compile(optimizer='rmsprop', loss='mse')
+      model.train_on_batch(
+          [np.zeros((6, 5, 5)), np.zeros((6, 3))],
+          np.zeros((6, 64))
+      )
+
+      # Test basic case serialization.
+      x_np = np.random.random((6, 5, 5))
+      c_np = np.random.random((6, 3))
+      y_np = model.predict([x_np, c_np])
+      weights = model.get_weights()
+      config = layer.get_config()
+
+      with keras.utils.CustomObjectScope(custom_objects):
+        layer = keras.layers.Bidirectional.from_config(copy.deepcopy(config))
+      y = layer(x, constants=c)
+      model = keras.Model([x, c], y)
+      model.set_weights(weights)
+      y_np_2 = model.predict([x_np, c_np])
+      self.assertAllClose(y_np, y_np_2, atol=1e-4)
+
+      # Test flat list inputs
+      with keras.utils.CustomObjectScope(custom_objects):
+        layer = keras.layers.Bidirectional.from_config(copy.deepcopy(config))
+      y = layer([x, c])
+      model = keras.Model([x, c], y)
+      model.set_weights(weights)
+      y_np_3 = model.predict([x_np, c_np])
+      self.assertAllClose(y_np, y_np_3, atol=1e-4)
+
+  def test_Bidirectional_with_constants_layer_passing_initial_state(self):
+    with self.test_session():
+      # Test basic case.
+      x = keras.Input((5, 5))
+      c = keras.Input((3,))
+      s_for = keras.Input((32,))
+      s_bac = keras.Input((32,))
+      cell = _RNNCellWithConstants(32)
+      custom_objects = {'_RNNCellWithConstants': _RNNCellWithConstants}
+      with keras.utils.CustomObjectScope(custom_objects):
+        layer = keras.layers.Bidirectional(keras.layers.RNN(cell))
+      y = layer(x, initial_state=[s_for, s_bac], constants=c)
+      model = keras.Model([x, s_for, s_bac, c], y)
+      model.compile(optimizer='rmsprop', loss='mse')
+      model.train_on_batch(
+          [np.zeros((6, 5, 5)),
+           np.zeros((6, 32)),
+           np.zeros((6, 32)),
+           np.zeros((6, 3))],
+          np.zeros((6, 64))
+      )
+
+      # Test basic case serialization.
+      x_np = np.random.random((6, 5, 5))
+      s_fw_np = np.random.random((6, 32))
+      s_bk_np = np.random.random((6, 32))
+      c_np = np.random.random((6, 3))
+      y_np = model.predict([x_np, s_fw_np, s_bk_np, c_np])
+      weights = model.get_weights()
+      config = layer.get_config()
+
+      with keras.utils.CustomObjectScope(custom_objects):
+        layer = keras.layers.Bidirectional.from_config(copy.deepcopy(config))
+      y = layer(x, initial_state=[s_for, s_bac], constants=c)
+      model = keras.Model([x, s_for, s_bac, c], y)
+      model.set_weights(weights)
+      y_np_2 = model.predict([x_np, s_fw_np, s_bk_np, c_np])
+      self.assertAllClose(y_np, y_np_2, atol=1e-4)
+
+      # Verify that state is used
+      y_np_2_different_s = model.predict(
+          [x_np, s_fw_np + 10., s_bk_np + 10., c_np])
+      assert np.mean(y_np - y_np_2_different_s) != 0
+
+      # Test flat list inputs
+      with keras.utils.CustomObjectScope(custom_objects):
+        layer = keras.layers.Bidirectional.from_config(copy.deepcopy(config))
+      y = layer([x, s_for, s_bac, c])
+      model = keras.Model([x, s_for, s_bac, c], y)
+      model.set_weights(weights)
+      y_np_3 = model.predict([x_np, s_fw_np, s_bk_np, c_np])
+      self.assertAllClose(y_np, y_np_3, atol=1e-4)
+
 
 def _to_list(ls):
   if isinstance(ls, list):
diff --git a/tensorflow/python/keras/_impl/keras/metrics_test.py b/tensorflow/python/keras/_impl/keras/metrics_test.py
index 13cef978127810..819bf602566fd2 100644
--- a/tensorflow/python/keras/_impl/keras/metrics_test.py
+++ b/tensorflow/python/keras/_impl/keras/metrics_test.py
@@ -92,6 +92,7 @@ class BinaryTruePositives(keras.layers.Layer):
         def __init__(self, name='true_positives', **kwargs):
           super(BinaryTruePositives, self).__init__(name=name, **kwargs)
           self.true_positives = keras.backend.variable(value=0, dtype='int32')
+          self.stateful = True
 
         def reset_states(self):
           keras.backend.set_value(self.true_positives, 0)
@@ -132,10 +133,17 @@ def __call__(self, y_true, y_pred):
                     metrics=['acc', metric_fn])
 
       # Test fit, evaluate
-      samples = 1000
+      samples = 100
       x = np.random.random((samples, 2))
       y = np.random.randint(2, size=(samples, 1))
-      model.fit(x, y, epochs=1, batch_size=10)
+      val_samples = 10
+      val_x = np.random.random((val_samples, 2))
+      val_y = np.random.randint(2, size=(val_samples, 1))
+
+      history = model.fit(x, y,
+                          epochs=1,
+                          batch_size=10,
+                          validation_data=(val_x, val_y))
       outs = model.evaluate(x, y, batch_size=10)
       preds = model.predict(x)
 
@@ -145,6 +153,37 @@ def ref_true_pos(y_true, y_pred):
       # Test correctness (e.g. updates should have been run)
       self.assertAllClose(outs[2], ref_true_pos(y, preds), atol=1e-5)
 
+      # Test correctness of the validation metric computation
+      val_preds = model.predict(val_x)
+      val_outs = model.evaluate(val_x, val_y, batch_size=10)
+      self.assertAllClose(
+          val_outs[2], ref_true_pos(val_y, val_preds), atol=1e-5)
+      self.assertAllClose(
+          val_outs[2], history.history['val_true_positives'][-1], atol=1e-5)
+
+      # Test with generators
+      gen = [(np.array([x0]), np.array([y0])) for x0, y0 in zip(x, y)]
+      val_gen = [(np.array([x0]), np.array([y0]))
+                 for x0, y0 in zip(val_x, val_y)]
+      history = model.fit_generator(iter(gen),
+                                    epochs=1,
+                                    steps_per_epoch=samples,
+                                    validation_data=iter(val_gen),
+                                    validation_steps=val_samples)
+      outs = model.evaluate_generator(iter(gen), steps=samples)
+      preds = model.predict_generator(iter(gen), steps=samples)
+
+      # Test correctness of the metric results
+      self.assertAllClose(outs[2], ref_true_pos(y, preds), atol=1e-5)
+
+      # Test correctness of the validation metric computation
+      val_preds = model.predict_generator(iter(val_gen), steps=val_samples)
+      val_outs = model.evaluate_generator(iter(val_gen), steps=val_samples)
+      self.assertAllClose(
+          val_outs[2], ref_true_pos(val_y, val_preds), atol=1e-5)
+      self.assertAllClose(
+          val_outs[2], history.history['val_true_positives'][-1], atol=1e-5)
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
index cee76bdc1db69d..1568c3175b6d8a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
@@ -155,7 +155,7 @@ tf_class {
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
index 02718cb5f9e3ca..10ddd5378b19f8 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
@@ -160,7 +160,7 @@ tf_class {
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt
index 5e5b04c7c695c6..63123c905c0e49 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt
@@ -119,7 +119,7 @@ tf_class {
   }
   member_method {
     name: "call"
-    argspec: "args=[\'self\', \'inputs\', \'training\', \'mask\', \'initial_state\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'inputs\', \'training\', \'mask\', \'initial_state\', \'constants\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
   }
   member_method {
     name: "compute_mask"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt
index 82dc878a8c7f7f..6be64be6ea29c3 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt
@@ -82,7 +82,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
+    argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
   }
   member_method {
     name: "add_loss"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
index dd78384005fce1..bbb15950aeca58 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
@@ -155,7 +155,7 @@ tf_class {
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
index 9fcb03f47e7701..8ba2aa00fb62f2 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
@@ -160,7 +160,7 @@ tf_class {
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt
index efa4419692993a..fa76e91d2c9923 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt
@@ -92,7 +92,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
+    argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
   }
   member_method {
     name: "add_loss"

From 5cef54072782a9a893eda69bec30fcf79cd0086b Mon Sep 17 00:00:00 2001
From: Younghee Kwon 
Date: Thu, 10 May 2018 18:17:33 -0700
Subject: [PATCH 0654/1691] A test fix on Windows.

PiperOrigin-RevId: 196201610
---
 .../python/kernel_tests/boosted_trees/training_ops_test.py      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/kernel_tests/boosted_trees/training_ops_test.py b/tensorflow/python/kernel_tests/boosted_trees/training_ops_test.py
index d6c004774746dd..13b804875e94a9 100644
--- a/tensorflow/python/kernel_tests/boosted_trees/training_ops_test.py
+++ b/tensorflow/python/kernel_tests/boosted_trees/training_ops_test.py
@@ -1379,7 +1379,7 @@ def testPostPruningOfAllNodes(self):
         }
         post_pruned_nodes_meta {
           new_node_id: 0
-          logit_change: -24.0143
+          logit_change: -24.014299
         }
       }
       tree_metadata {

From 56b46370ba08c76200711f4a8d25194af1235fd5 Mon Sep 17 00:00:00 2001
From: Allen Lavoie 
Date: Thu, 10 May 2018 18:28:24 -0700
Subject: [PATCH 0655/1691] Checkpointable: Have RNN wrappers add their cells
 as dependencies

Also marks _SlimRNNCell as not checkpointable, and adds a more convenient way to tag such classes.

Ideally adding a wrapper around a cell wouldn't break a checkpoint. This could look like RNN cell wrappers inheriting the dependencies of the cell they're wrapping. Possible to add that later if there's demand, or users can just add a dependency on wrapper._cell in addition to/instead of the wrapper when modifying programs.

Fixes #19208.

PiperOrigin-RevId: 196202366
---
 .../python/kernel_tests/core_rnn_cell_test.py | 14 +++++++++++--
 .../rnn/python/kernel_tests/core_rnn_test.py  |  3 +++
 tensorflow/python/ops/rnn_cell_impl.py        |  8 ++++++-
 tensorflow/python/training/checkpointable.py  | 11 ++++++++++
 .../python/training/checkpointable_utils.py   |  6 ++++++
 .../training/checkpointable_utils_test.py     | 21 +++++++++++++++++++
 6 files changed, 60 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
index d41fc0b3ac1cee..e512e8db53ed3f 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
@@ -483,7 +483,12 @@ def testResidualWrapper(self):
         base_cell = rnn_cell_impl.GRUCell(3)
         g, m_new = base_cell(x, m)
         variable_scope.get_variable_scope().reuse_variables()
-        g_res, m_new_res = rnn_cell_impl.ResidualWrapper(base_cell)(x, m)
+        wrapper_object = rnn_cell_impl.ResidualWrapper(base_cell)
+        (name, dep), = wrapper_object._checkpoint_dependencies
+        self.assertIs(dep, base_cell)
+        self.assertEqual("cell", name)
+
+        g_res, m_new_res = wrapper_object(x, m)
         sess.run([variables_lib.global_variables_initializer()])
         res = sess.run([g, g_res, m_new, m_new_res], {
             x: np.array([[1., 1., 1.]]),
@@ -526,7 +531,12 @@ def testDeviceWrapper(self):
         "root", initializer=init_ops.constant_initializer(0.5)):
       x = array_ops.zeros([1, 3])
       m = array_ops.zeros([1, 3])
-      cell = rnn_cell_impl.DeviceWrapper(rnn_cell_impl.GRUCell(3), "/cpu:14159")
+      wrapped = rnn_cell_impl.GRUCell(3)
+      cell = rnn_cell_impl.DeviceWrapper(wrapped, "/cpu:14159")
+      (name, dep), = cell._checkpoint_dependencies
+      self.assertIs(dep, wrapped)
+      self.assertEqual("cell", name)
+
       outputs, _ = cell(x, m)
       self.assertTrue("cpu:14159" in outputs.device.lower())
 
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
index c75593e35689c8..be99a5d67a3e49 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
@@ -228,6 +228,9 @@ def testDropout(self):
     cell = Plus1RNNCell()
     full_dropout_cell = rnn_cell.DropoutWrapper(
         cell, input_keep_prob=1e-12, seed=0)
+    (name, dep), = full_dropout_cell._checkpoint_dependencies
+    self.assertIs(dep, cell)
+    self.assertEqual("cell", name)
     batch_size = 2
     input_size = 5
     max_length = 8
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index 67f753485b8c30..68d22794d38175 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -1005,6 +1005,8 @@ def tensor_and_const_value(v):
 
     # Set cell, variational_recurrent, seed before running the code below
     self._cell = cell
+    if isinstance(cell, checkpointable.CheckpointableBase):
+      self._track_checkpointable(self._cell, name="cell")
     self._variational_recurrent = variational_recurrent
     self._seed = seed
 
@@ -1152,6 +1154,8 @@ def __init__(self, cell, residual_fn=None):
         and outputs.
     """
     self._cell = cell
+    if isinstance(cell, checkpointable.CheckpointableBase):
+      self._track_checkpointable(self._cell, name="cell")
     self._residual_fn = residual_fn
 
   @property
@@ -1207,6 +1211,8 @@ def __init__(self, cell, device):
       device: A device string or function, for passing to `tf.device`.
     """
     self._cell = cell
+    if isinstance(cell, checkpointable.CheckpointableBase):
+      self._track_checkpointable(self._cell, name="cell")
     self._device = device
 
   @property
@@ -1322,7 +1328,7 @@ def call(self, inputs, state):
     return cur_inp, new_states
 
 
-class _SlimRNNCell(RNNCell):
+class _SlimRNNCell(RNNCell, checkpointable.NotCheckpointable):
   """A simple wrapper for slim.rnn_cells."""
 
   def __init__(self, cell_fn):
diff --git a/tensorflow/python/training/checkpointable.py b/tensorflow/python/training/checkpointable.py
index 956dd66bee7038..a57bcaea691152 100644
--- a/tensorflow/python/training/checkpointable.py
+++ b/tensorflow/python/training/checkpointable.py
@@ -737,6 +737,17 @@ def __init__(self, value):
     self.value = value
 
 
+class NotCheckpointable(object):
+  """Marks instances of child classes as unsaveable using an object-based API.
+
+  Useful for marking objects which would otherwise look checkpointable because
+  of inheritance (e.g. through `Layer`) as not checkpointable. Inheriting from
+  `NotCheckpointable` does not prevent an object from being assigned to any
+  attributes, but will throw an error on save/restore.
+  """
+  pass
+
+
 class Checkpointable(CheckpointableBase):
   """Manages dependencies on other objects.
 
diff --git a/tensorflow/python/training/checkpointable_utils.py b/tensorflow/python/training/checkpointable_utils.py
index 1e69096706352b..72be434fb2ce09 100644
--- a/tensorflow/python/training/checkpointable_utils.py
+++ b/tensorflow/python/training/checkpointable_utils.py
@@ -205,6 +205,12 @@ def _breadth_first_checkpointable_traversal(root_checkpointable):
   path_to_root = {root_checkpointable: ()}
   while to_visit:
     current_checkpointable = to_visit.popleft()
+    if isinstance(current_checkpointable, checkpointable_lib.NotCheckpointable):
+      raise NotImplementedError(
+          ("The object %s does not support object-based saving. File a feature "
+           "request if this limitation bothers you. In the meantime, you can "
+           "remove the dependency on this object and save everything else.")
+          % (current_checkpointable,))
     current_checkpointable._maybe_initialize_checkpointable()  # pylint: disable=protected-access
     bfs_sorted.append(current_checkpointable)
     for child_checkpointable in (
diff --git a/tensorflow/python/training/checkpointable_utils_test.py b/tensorflow/python/training/checkpointable_utils_test.py
index dead8fd37179cc..84cacb6ed9109e 100644
--- a/tensorflow/python/training/checkpointable_utils_test.py
+++ b/tensorflow/python/training/checkpointable_utils_test.py
@@ -174,6 +174,27 @@ def testObjectMetadata(self):
         all_variable_names.append(attribute.full_name)
     self.assertIn("dense/kernel", all_variable_names)
 
+  def testNotCheckpointable(self):
+
+    class CallsFunctionalStuff(
+        checkpointable.NotCheckpointable, checkpointable.Checkpointable):
+      pass
+
+    test_dir = self.get_temp_dir()
+    prefix = os.path.join(test_dir, "ckpt")
+    checkpoint = checkpointable_utils.Checkpoint(x=CallsFunctionalStuff())
+    with self.assertRaises(NotImplementedError):
+      checkpoint.save(prefix)
+
+    class CallsFunctionalStuffOtherMRO(
+        checkpointable.Checkpointable, checkpointable.NotCheckpointable):
+      pass
+
+    checkpoint_reversed = checkpointable_utils.Checkpoint(
+        x=CallsFunctionalStuffOtherMRO())
+    with self.assertRaises(NotImplementedError):
+      checkpoint_reversed.save(prefix)
+
 
 class _MirroringSaveable(saver_lib.BaseSaverBuilder.SaveableObject):
 

From 2656548f3ef7653474f3f8ad4072778e9e3aee2f Mon Sep 17 00:00:00 2001
From: Smit Hinsu 
Date: Thu, 10 May 2018 19:05:45 -0700
Subject: [PATCH 0656/1691] Internal change

PiperOrigin-RevId: 196205436
---
 .../LICENSE.bazel => third_party/examples/eager/spinn/LICENSE     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename tensorflow/contrib/eager/python/examples/spinn/LICENSE.bazel => third_party/examples/eager/spinn/LICENSE (100%)

diff --git a/tensorflow/contrib/eager/python/examples/spinn/LICENSE.bazel b/third_party/examples/eager/spinn/LICENSE
similarity index 100%
rename from tensorflow/contrib/eager/python/examples/spinn/LICENSE.bazel
rename to third_party/examples/eager/spinn/LICENSE

From 5a492ef9bbfa4bb93fcf0e2b2f8afa34d25d5236 Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Thu, 10 May 2018 19:28:35 -0700
Subject: [PATCH 0657/1691] [XLA:GPU] Remove unused
 Thunk::ShouldBlockFutureThunks function.

PiperOrigin-RevId: 196206896
---
 .../xla/service/gpu/gpu_executable.cc         | 24 +------------------
 tensorflow/compiler/xla/service/gpu/thunk.h   | 10 --------
 2 files changed, 1 insertion(+), 33 deletions(-)

diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc
index 04b4f7aef134c3..e09bee0b941552 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc
@@ -164,9 +164,6 @@ Status GpuExecutable::ExecuteThunks(
                                 sub_streams, hlo_module_->entry_computation());
   uint64 start_micros = tensorflow::Env::Default()->NowMicros();
 
-  // The next event enqueued on stream N must not run until the thunk at
-  // last_blocking_thunk_for_stream[N] completes.
-  std::map last_blocking_thunk_for_stream;
   std::map> thunk_to_finish_event;
   for (Thunk* thunk : thunk_schedule_->TotalOrder()) {
     TF_RETURN_IF_ERROR(thunk->Initialize(*this));
@@ -179,18 +176,10 @@ Status GpuExecutable::ExecuteThunks(
       stream->ThenWaitFor(FindOrDie(thunk_to_finish_event, dependency).get());
     }
 
-    if (last_blocking_thunk_for_stream.count(stream_no)) {
-      stream->ThenWaitFor(FindOrDie(thunk_to_finish_event,
-                                    last_blocking_thunk_for_stream[stream_no])
-                              .get());
-      last_blocking_thunk_for_stream.erase(stream_no);
-    }
-
     // If this thunk requests it, wait for all currently-executing thunks to
     // finish.  This is useful e.g. if the thunk is about to perform autotuning.
     if (thunk->ShouldHaltAllActivityBeforeRunning(stream)) {
       TF_RETURN_IF_ERROR(main_stream->BlockHostUntilDone());
-      last_blocking_thunk_for_stream.clear();
     }
 
     profiler.StartOperation();
@@ -198,22 +187,11 @@ Status GpuExecutable::ExecuteThunks(
             << thunk->hlo_instruction()->ToString() << " on stream "
             << stream_no;
     TF_RETURN_IF_ERROR(thunk->ExecuteOnStream(buffer_allocations, stream));
-    if (thunk_schedule_->Depended(thunk) || thunk->ShouldBlockFutureThunks()) {
+    if (thunk_schedule_->Depended(thunk)) {
       auto finish_event = MakeUnique(main_stream->parent());
       finish_event->Init();
       stream->ThenRecordEvent(finish_event.get());
       thunk_to_finish_event[thunk] = std::move(finish_event);
-
-      if (thunk->ShouldBlockFutureThunks()) {
-        // Set last_blocking_thunk_for_stream on all streams other than this one
-        // so that all other streams will wait for this thunk to complete before
-        // executing any events that occur later in the total order.
-        for (int32 i = 0; i < sub_streams.size() + 1; ++i) {
-          if (i != stream_no) {
-            last_blocking_thunk_for_stream[i] = thunk;
-          }
-        }
-      }
     }
     profiler.FinishOperation(thunk->hlo_instruction());
   }
diff --git a/tensorflow/compiler/xla/service/gpu/thunk.h b/tensorflow/compiler/xla/service/gpu/thunk.h
index a0c785ed913109..57d921260909a3 100644
--- a/tensorflow/compiler/xla/service/gpu/thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/thunk.h
@@ -89,16 +89,6 @@ class Thunk {
     return false;
   }
 
-  // Indicates whether thunks scheduled after this one should wait for this one
-  // to complete before running. For example, a convolution thunk creates a
-  // scratch allocator, then kicks off a convolution in cudnn via the stream
-  // executor. When the stream executor call returns, the scratch allocator goes
-  // out of scope, and the scratch memory is deallocated. In this case, the
-  // convolution thunk needs to return true so that future thunks wait for the
-  // convolution thunk to avoid reusing the deallocated memory until the
-  // convolution thunk is done with it.
-  virtual bool ShouldBlockFutureThunks() { return false; }
-
   // Execute the kernel for the thunk on the given stream. This method must be
   // called after Initialize and can be called multiple times over Thunk's
   // lifetime. Stream argument must be non-null.

From 400dd49b4cbd44b0f1463cceb5ac42c457bdce32 Mon Sep 17 00:00:00 2001
From: Chris Leary 
Date: Thu, 10 May 2018 20:10:34 -0700
Subject: [PATCH 0658/1691] [XLA] Break out literal comparisons from testonly
 target.

Moves methods from LiteralTestUtil::* to Literal::* where they have nothing
to do with test infrastructure.

Pares down the "void" variants of the LiteralTestUtil methods and consolidates
to the version that return success/failure such that the values can be
EXPECT_TRUE / ASSERT_TRUE asserted in the caller test cases.

This way the literal comparison functionality can be used from cc_libraries
that are not test only / cc_binary.

PiperOrigin-RevId: 196209410
---
 .../compiler/tf2xla/xla_compiler_test.cc      |  13 +-
 tensorflow/compiler/xla/BUILD                 |  11 +
 tensorflow/compiler/xla/literal_comparison.cc | 226 ++++++++++
 tensorflow/compiler/xla/literal_comparison.h  |  40 ++
 tensorflow/compiler/xla/literal_util.cc       | 126 ++++++
 tensorflow/compiler/xla/literal_util.h        |  89 ++++
 .../compiler/xla/rpc/grpc_client_test.cc      |   4 +-
 .../xla/service/bfloat16_propagation_test.cc  |   8 +-
 .../xla/service/hlo_constant_folding_test.cc  |   4 +-
 .../compiler/xla/service/hlo_cse_test.cc      |   6 +-
 .../xla/service/hlo_evaluator_test.cc         | 136 +++---
 .../compiler/xla/service/inliner_test.cc      |   6 +-
 tensorflow/compiler/xla/tests/BUILD           |   1 +
 .../compiler/xla/tests/broadcast_test.cc      |  56 +--
 .../xla/tests/client_library_test_base.cc     |  25 +-
 .../xla/tests/client_library_test_base.h      |   8 +-
 tensorflow/compiler/xla/tests/client_test.cc  |   8 +-
 .../xla/tests/compilation_cache_test.cc       |   8 +-
 .../xla/tests/compute_constant_test.cc        |  10 +-
 tensorflow/compiler/xla/tests/copy_test.cc    |   4 +-
 tensorflow/compiler/xla/tests/fusion_test.cc  | 114 ++---
 .../xla/tests/gather_operation_test.cc        |   4 +-
 .../compiler/xla/tests/literal_test_util.cc   | 422 ++----------------
 .../compiler/xla/tests/literal_test_util.h    | 229 +++-------
 .../xla/tests/literal_test_util_test.cc       |  11 +-
 .../xla/tests/multioutput_fusion_test.cc      |   4 +-
 tensorflow/compiler/xla/tests/prng_test.cc    |  10 +-
 tensorflow/compiler/xla/tests/reshape_test.cc |  20 +-
 .../tests/round_trip_packed_literal_test.cc   |   4 +-
 .../xla/tests/round_trip_transfer_test.cc     |   2 +-
 .../xla/tests/scalar_computations_test.cc     |   4 +-
 .../xla/tests/transfer_manager_test.cc        |  10 +-
 32 files changed, 842 insertions(+), 781 deletions(-)
 create mode 100644 tensorflow/compiler/xla/literal_comparison.cc
 create mode 100644 tensorflow/compiler/xla/literal_comparison.h

diff --git a/tensorflow/compiler/tf2xla/xla_compiler_test.cc b/tensorflow/compiler/tf2xla/xla_compiler_test.cc
index 6b8918b2617973..4382ffe6ba3bfc 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler_test.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler_test.cc
@@ -225,7 +225,7 @@ TEST_F(XlaCompilerTest, Simple) {
       xla::Literal::CreateR1({4, 143});
   std::unique_ptr expected_literal =
       xla::Literal::MakeTuple({expected0.get()});
-  xla::LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal);
+  EXPECT_TRUE(xla::LiteralTestUtil::Equal(*expected_literal, *actual_literal));
 }
 
 TEST_F(XlaCompilerTest, HasSaneErrorOnNonCompileTimeConstantInputToReshape) {
@@ -320,7 +320,8 @@ TEST_F(XlaCompilerTest, ConstantOutputs) {
         xla::Literal::CreateR1({-7, -42});
     std::unique_ptr expected_literal =
         xla::Literal::MakeTuple({expected0.get()});
-    xla::LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal);
+    EXPECT_TRUE(
+        xla::LiteralTestUtil::Equal(*expected_literal, *actual_literal));
   }
 
   {
@@ -355,7 +356,7 @@ TEST_F(XlaCompilerTest, ConstantOutputs) {
         xla::Literal::CreateR1({-7, -42});
     std::unique_ptr expected =
         xla::Literal::MakeTuple({expected0.get(), expected1.get()});
-    xla::LiteralTestUtil::ExpectEqual(*expected, *actual_literal);
+    EXPECT_TRUE(xla::LiteralTestUtil::Equal(*expected, *actual_literal));
   }
 }
 
@@ -523,7 +524,7 @@ TEST_F(XlaCompilerTest, CanPassTensorArraysToAndFromComputation) {
       {output_base.get(), output_grad1.get(), output_grad2.get()});
   std::unique_ptr expected_literal =
       xla::Literal::MakeTuple({output_read.get(), output_resource.get()});
-  xla::LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal);
+  EXPECT_TRUE(xla::LiteralTestUtil::Equal(*expected_literal, *actual_literal));
 }
 
 // Tests compilation and execution of a graph that adds two tensors.
@@ -746,7 +747,7 @@ TEST_F(XlaCompilerTest, Variables) {
       xla::Literal::CreateR1({4, 143});
   std::unique_ptr expected_literal =
       xla::Literal::MakeTuple({expected0.get(), expected1.get()});
-  xla::LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal);
+  EXPECT_TRUE(xla::LiteralTestUtil::Equal(*expected_literal, *actual_literal));
 }
 
 // Tests a simple graph that reads and writes a variable, with a
@@ -811,7 +812,7 @@ TEST_F(XlaCompilerTest, VariableRepresentationShapeFunction) {
       xla::Literal::CreateR1({26, 66, 34, 401});
   std::unique_ptr expected_literal =
       xla::Literal::MakeTuple({expected0.get(), expected1.get()});
-  xla::LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal);
+  EXPECT_TRUE(xla::LiteralTestUtil::Equal(*expected_literal, *actual_literal));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index dbf14f32bc3e54..729480e80f8b3b 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -330,6 +330,17 @@ tf_cc_test(
     ],
 )
 
+cc_library(
+    name = "literal_comparison",
+    srcs = ["literal_comparison.cc"],
+    hdrs = ["literal_comparison.h"],
+    deps = [
+        ":literal_util",
+        ":util",
+        "//tensorflow/core:lib",
+    ],
+)
+
 cc_library(
     name = "metric_table_report",
     srcs = ["metric_table_report.cc"],
diff --git a/tensorflow/compiler/xla/literal_comparison.cc b/tensorflow/compiler/xla/literal_comparison.cc
new file mode 100644
index 00000000000000..df3f5af0a197a8
--- /dev/null
+++ b/tensorflow/compiler/xla/literal_comparison.cc
@@ -0,0 +1,226 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/literal_comparison.h"
+
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/core/casts.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+
+using tensorflow::strings::StrCat;
+
+namespace xla {
+namespace literal_comparison {
+namespace {
+
+// Helper function for comparing a floating point type, FloatT, bitwise equal
+// between the left-hand-side and right-hand-side, by bit-casting to UnsignedT
+// -- on miscompare, a nice error message is given in the AssertionFailure.
+template 
+Status CompareFloatsBitwiseEqual(FloatT lhs, FloatT rhs) {
+  auto ulhs = tensorflow::bit_cast(lhs);
+  auto urhs = tensorflow::bit_cast(rhs);
+  auto lhs_double = static_cast(lhs);
+  auto rhs_double = static_cast(rhs);
+  if (ulhs != urhs) {
+    return InvalidArgument(
+        "floating values are not bitwise-equal; and equality testing "
+        "was requested: %s=%g=%a vs %s=%g=%a",
+        StrCat(tensorflow::strings::Hex(ulhs)).c_str(), lhs_double, lhs_double,
+        StrCat(tensorflow::strings::Hex(urhs)).c_str(), rhs_double, rhs_double);
+  }
+  return Status::OK();
+}
+
+// Templated comparator that specializes for float equality comparison with the
+// bitwise helper above (this is the un-specialized fallback, to just use the
+// default gunit implementation).
+template 
+Status CompareEqual(NativeT lhs, NativeT rhs) {
+  if (lhs == rhs) {
+    return Status::OK();
+  }
+  return InvalidArgument("Expected equality of these values:\n  %s\n  %s",
+                         StrCat(lhs).c_str(), StrCat(rhs).c_str());
+}
+
+// Specializations for floating types that do bitwise comparisons when equality
+// comparison is requested.
+template <>
+Status CompareEqual(bfloat16 lhs, bfloat16 rhs) {
+  return CompareFloatsBitwiseEqual(lhs, rhs);
+}
+template <>
+Status CompareEqual(Eigen::half lhs, Eigen::half rhs) {
+  return CompareFloatsBitwiseEqual(lhs, rhs);
+}
+template <>
+Status CompareEqual(float lhs, float rhs) {
+  return CompareFloatsBitwiseEqual(lhs, rhs);
+}
+template <>
+Status CompareEqual(double lhs, double rhs) {
+  return CompareFloatsBitwiseEqual(lhs, rhs);
+}
+template <>
+Status CompareEqual(complex64 lhs, complex64 rhs) {
+  auto res = CompareEqual(lhs.real(), rhs.real());
+  if (!res.ok()) {
+    return res;
+  }
+  return CompareEqual(lhs.imag(), rhs.imag());
+}
+
+// A recursive function which iterates through every index of expected and
+// actual literal and compares their values elementwise. Returns true if all
+// elements are equal.
+template 
+Status Equal(LiteralSlice expected, LiteralSlice actual,
+             tensorflow::gtl::MutableArraySlice multi_index,
+             int64 dimension) {
+  if (dimension == expected.shape().dimensions_size()) {
+    NativeT expected_value = expected.Get(multi_index);
+    NativeT actual_value = actual.Get(multi_index);
+    return CompareEqual(expected_value, actual_value);
+  }
+
+  Status result;
+  for (int64 i = 0; i < expected.shape().dimensions(dimension); ++i) {
+    multi_index[dimension] = i;
+    result.Update(Equal(expected, actual, multi_index, dimension + 1));
+  }
+  return result;
+}
+
+}  // namespace
+
+Status EqualShapes(const Shape& expected, const Shape& actual) {
+  if (ShapeUtil::IsTuple(expected) != ShapeUtil::IsTuple(actual)) {
+    return InvalidArgument("tupleness-mismatch! want: %s got %s",
+                           ShapeUtil::HumanString(expected).c_str(),
+                           ShapeUtil::HumanString(actual).c_str());
+  }
+  if (ShapeUtil::IsTuple(expected)) {
+    if (ShapeUtil::TupleElementCount(expected) !=
+        ShapeUtil::TupleElementCount(actual)) {
+      return InvalidArgument(
+          "want tuple element count: %lld got tuple element count: %lld",
+          ShapeUtil::TupleElementCount(expected),
+          ShapeUtil::TupleElementCount(actual));
+    }
+    for (int i = 0; i < expected.tuple_shapes_size(); ++i) {
+      Status result =
+          EqualShapes(expected.tuple_shapes(i), actual.tuple_shapes(i));
+      if (!result.ok()) {
+        return AppendStatus(result, StrCat("mismatch in tuple index", i));
+      }
+    }
+  } else {
+    if (ShapeUtil::Rank(expected) != ShapeUtil::Rank(actual)) {
+      return InvalidArgument("want rank of %s got rank of %s",
+                             ShapeUtil::HumanString(expected).c_str(),
+                             ShapeUtil::HumanString(actual).c_str());
+    }
+    if (expected.element_type() != actual.element_type()) {
+      return InvalidArgument(
+          "mismatch in primitive type %s vs %s",
+          PrimitiveType_Name(expected.element_type()).c_str(),
+          PrimitiveType_Name(actual.element_type()).c_str());
+    }
+    if (expected.dimensions_size() != actual.dimensions_size()) {
+      return InvalidArgument("want dimensions_size %d got dimensions_size %d",
+                             expected.dimensions_size(),
+                             actual.dimensions_size());
+    }
+    for (int i = 0; i < expected.dimensions_size(); ++i) {
+      if (expected.dimensions(i) != actual.dimensions(i)) {
+        return InvalidArgument(
+            "mismatch in dimension #%d expected: %s actual: %s", i,
+            ShapeUtil::HumanString(expected).c_str(),
+            ShapeUtil::HumanString(actual).c_str());
+      }
+    }
+  }
+  return Status::OK();
+}
+
+Status Equal(const LiteralSlice& expected, const LiteralSlice& actual) {
+  VLOG(1) << "expected:";
+  XLA_VLOG_LINES(1, expected.ToString());
+  VLOG(1) << "actual:";
+  XLA_VLOG_LINES(1, actual.ToString());
+
+  TF_RETURN_IF_ERROR(EqualShapes(expected.shape(), actual.shape()));
+  std::vector multi_index(expected.shape().dimensions_size(), 0);
+  Status result;
+  switch (expected.shape().element_type()) {
+    case PRED:
+      result = Equal(expected, actual, &multi_index, 0);
+      break;
+    case U8:
+      result = Equal(expected, actual, &multi_index, 0);
+      break;
+    case S32:
+      result = Equal(expected, actual, &multi_index, 0);
+      break;
+    case S64:
+      result = Equal(expected, actual, &multi_index, 0);
+      break;
+    case U32:
+      result = Equal(expected, actual, &multi_index, 0);
+      break;
+    case U64:
+      result = Equal(expected, actual, &multi_index, 0);
+      break;
+    case BF16:
+      result = Equal(expected, actual, &multi_index, 0);
+      break;
+    case F16:
+      result = Equal(expected, actual, &multi_index, 0);
+      break;
+    case F32:
+      result = Equal(expected, actual, &multi_index, 0);
+      break;
+    case F64:
+      result = Equal(expected, actual, &multi_index, 0);
+      break;
+    case C64:
+      result = Equal(expected, actual, &multi_index, 0);
+      break;
+    case TUPLE: {
+      for (int i = 0; i < ShapeUtil::TupleElementCount(expected.shape()); ++i) {
+        result.Update(
+            Equal(LiteralSlice(expected, {i}), LiteralSlice(actual, {i})));
+      }
+      break;
+    }
+    default:
+      LOG(FATAL)
+          << "Unsupported primitive type in LiteralTestUtil::ExpectEqual: "
+          << PrimitiveType_Name(expected.shape().element_type());
+  }
+
+  if (result.ok()) {
+    return Status::OK();
+  }
+
+  return AppendStatus(result,
+                      tensorflow::strings::Printf("expected: %s\nactual:   %s",
+                                                  expected.ToString().c_str(),
+                                                  actual.ToString().c_str()));
+}
+
+}  // namespace literal_comparison
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/literal_comparison.h b/tensorflow/compiler/xla/literal_comparison.h
new file mode 100644
index 00000000000000..e667405b3e319a
--- /dev/null
+++ b/tensorflow/compiler/xla/literal_comparison.h
@@ -0,0 +1,40 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Library for comparing literals without taking a dependency on testing
+// libraries.
+
+#ifndef TENSORFLOW_COMPILER_XLA_LITERAL_COMPARISON_H_
+#define TENSORFLOW_COMPILER_XLA_LITERAL_COMPARISON_H_
+
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace xla {
+namespace literal_comparison {
+
+// Returns ok if the given shapes have the same rank, dimension sizes, and
+// primitive types.
+Status EqualShapes(const Shape& expected, const Shape& actual);
+
+// Returns ok if the expected and actual literals are (bitwise) equal for all
+// elements in the literal. Also, asserts that the rank, dimensions sizes, and
+// primitive type are equal.
+Status Equal(const LiteralSlice& expected, const LiteralSlice& actual);
+
+}  // namespace literal_comparison
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_LITERAL_COMPARISON_H_
diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index e9b0e11885a590..82a2bcad76f132 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -62,6 +62,45 @@ void ConvertEndianShort(char* bytes, int64 size) {
   }
 }
 
+// Return a literal with all arrays of type FromNativeT converted to type
+// ToNativeT in the given literal.
+template 
+std::unique_ptr ConvertType(LiteralSlice literal) {
+  // First construct shape of the result.
+  Shape result_shape(literal.shape());
+  ShapeUtil::ForEachMutableSubshape(
+      &result_shape, [](Shape* subshape, const ShapeIndex&) {
+        if (subshape->element_type() ==
+            primitive_util::NativeToPrimitiveType()) {
+          subshape->set_element_type(
+              primitive_util::NativeToPrimitiveType());
+        }
+      });
+  auto result = MakeUnique(result_shape);
+
+  // Then copy over the data from 'literal' converting FromNativeT values to
+  // ToNativeT values as necessary.
+  ShapeUtil::ForEachSubshape(
+      literal.shape(),
+      [&](const Shape& subshape, const ShapeIndex& shape_index) {
+        if (ShapeUtil::IsArray(subshape)) {
+          if (subshape.element_type() ==
+              primitive_util::NativeToPrimitiveType()) {
+            auto src = literal.data(shape_index);
+            auto dest = result->data(shape_index);
+            for (int64 i = 0; i < src.size(); ++i) {
+              dest[i] = static_cast(src[i]);
+            }
+          } else {
+            TF_CHECK_OK(result->CopyFrom(literal,
+                                         /*dest_shape_index=*/shape_index,
+                                         /*src_shape_index=*/shape_index));
+          }
+        }
+      });
+  return result;
+}
+
 }  // namespace
 
 LiteralBase::~LiteralBase() {}
@@ -195,6 +234,16 @@ SparseIndexArray* Literal::sparse_indices(const ShapeIndex& shape_index) {
   return CreateFromShape(ShapeUtil::MakeShape(primitive_type, dimensions));
 }
 
+/* static */ std::unique_ptr Literal::ConvertBF16ToF32(
+    const LiteralSlice& bf16_literal) {
+  return ConvertType(bf16_literal);
+}
+
+/* static */ std::unique_ptr Literal::ConvertF32ToBF16(
+    const LiteralSlice& f32_literal) {
+  return ConvertType(f32_literal);
+}
+
 template 
 Status Literal::CopySliceFromInternal(
     const LiteralBase& src_literal, tensorflow::gtl::ArraySlice src_base,
@@ -788,6 +837,78 @@ StatusOr> LiteralBase::Reshape(
   return std::move(output);
 }
 
+/* static */ std::unique_ptr Literal::ReshapeSlice(
+    tensorflow::gtl::ArraySlice new_dimensions,
+    tensorflow::gtl::ArraySlice minor_to_major,
+    const LiteralSlice& literal) {
+  int64 new_num_elements = 1;
+  for (int64 i = 0; i < new_dimensions.size(); ++i) {
+    new_num_elements *= new_dimensions[i];
+  }
+  CHECK_EQ(ShapeUtil::ElementsIn(literal.shape()), new_num_elements);
+  CHECK_EQ(new_dimensions.size(), minor_to_major.size());
+
+  auto new_literal = MakeUnique(
+      ShapeUtil::MakeShape(literal.shape().element_type(), new_dimensions));
+
+  // Create a new shape with the given minor-to-major layout. This shape is used
+  // solely for converting linear address to multi-dimensional addresses when
+  // writing elements to the new literal.
+  Shape shape_with_layout = new_literal->shape();
+  *shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout(minor_to_major);
+
+  // Copy data into new literal, element-by-element.
+  for (int64 i = 0; i < ShapeUtil::ElementsIn(literal.shape()); ++i) {
+    std::vector from_multi_index =
+        IndexUtil::LinearIndexToMultidimensionalIndex(literal.shape(), i);
+    std::vector to_multi_index =
+        IndexUtil::LinearIndexToMultidimensionalIndex(shape_with_layout, i);
+    switch (literal.shape().element_type()) {
+      case PRED:
+        new_literal->Set(to_multi_index,
+                               literal.Get(from_multi_index));
+        break;
+      case U8:
+        new_literal->Set(to_multi_index,
+                                literal.Get(from_multi_index));
+        break;
+      case U32:
+        new_literal->Set(to_multi_index,
+                                 literal.Get(from_multi_index));
+        break;
+      case S32:
+        new_literal->Set(to_multi_index,
+                                literal.Get(from_multi_index));
+        break;
+      case U64:
+        new_literal->Set(to_multi_index,
+                                 literal.Get(from_multi_index));
+        break;
+      case S64:
+        new_literal->Set(to_multi_index,
+                                literal.Get(from_multi_index));
+        break;
+      case F32:
+        new_literal->Set(to_multi_index,
+                                literal.Get(from_multi_index));
+        break;
+      case F64:
+        new_literal->Set(to_multi_index,
+                                 literal.Get(from_multi_index));
+        break;
+      case C64:
+        new_literal->Set(to_multi_index,
+                                    literal.Get(from_multi_index));
+        break;
+      default:
+        LOG(FATAL) << "Unhandled primitive element type: "
+                   << PrimitiveType_Name(literal.shape().element_type());
+    }
+  }
+
+  return new_literal;
+}
+
 std::unique_ptr LiteralBase::Transpose(
     tensorflow::gtl::ArraySlice permutation) const {
   CHECK(ShapeUtil::IsArray(shape())) << "Tuple is not supported for transpose";
@@ -2123,6 +2244,11 @@ StatusOr> Literal::CreateFromProto(
   return std::move(literal);
 }
 
+/* static */ string Literal::MultiIndexAsString(
+    tensorflow::gtl::ArraySlice multi_index) {
+  return StrCat("{", tensorflow::str_util::Join(multi_index, ","), "}");
+}
+
 const void* LiteralBase::untyped_data(const ShapeIndex& shape_index) const {
   return piece(shape_index).untyped_data();
 }
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index 30442afcc6e8b8..8d51aa3881449d 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -920,9 +920,66 @@ class Literal : public LiteralBase {
       PrimitiveType primitive_type,
       tensorflow::gtl::ArraySlice dimensions);
 
+  // If the given literal's data type is bfloat16, converts it to a float
+  // literal; otherwise, returns a copy of it. If the literal is a tuple,
+  // recursively converts its elements.
+  static std::unique_ptr ConvertBF16ToF32(
+      const LiteralSlice& bf16_literal);
+
+  // If the given literal's data type is float, converts it to a bfloat16
+  // literal; otherwise, returns a copy of it. If the literal is a tuple,
+  // recursively converts its elements.
+  static std::unique_ptr ConvertF32ToBF16(
+      const LiteralSlice& f32_literal);
+
+  // Creates a literal with a new shape with the given new dimensions using the
+  // data in the given input literal. For reshaping purposes the (flat) data
+  // buffer of the input literal is assumed to have the given minor_to_major
+  // layout order.
+  static std::unique_ptr ReshapeSlice(
+      tensorflow::gtl::ArraySlice new_dimensions,
+      tensorflow::gtl::ArraySlice minor_to_major,
+      const LiteralSlice& literal);
+
+  // Creates a literal with the supplied shape, and uses the provided value
+  // generator to populate the literal's values.
+  // Returns the new literal object, or an error Status if failed.
+  template <
+      PrimitiveType type,
+      typename T = typename primitive_util::PrimitiveTypeToNative::type>
+  static StatusOr> CreateRandomLiteral(
+      const Shape& shape,
+      const std::function)>& generator);
+
+  // Creates a literal with the supplied shape, and initializes the literal
+  // values using a normal distribution with given mean and stddev standard
+  // deviation, and using the engine as entropy generator.
+  // Returns the new literal object, or an error Status if failed.
+  template <
+      PrimitiveType type, typename E,
+      typename T = typename primitive_util::PrimitiveTypeToNative::type>
+  static StatusOr> CreateRandomLiteral(
+      const Shape& shape, E* engine, T mean, T stddev);
+
+  // Creates a literal with the supplied shape, and initializes the literal
+  // values using a normal distribution with given mean and stddev standard
+  // deviation.
+  // Returns the new literal object, or an error Status if failed.
+  template <
+      PrimitiveType type,
+      typename T = typename primitive_util::PrimitiveTypeToNative::type>
+  static StatusOr> CreateRandomLiteral(
+      const Shape& shape, T mean, T stddev);
+
   //
   // End of factory methods.
 
+  // Returns a multi-dimensional index as a string. For example: '{7, 8}' will
+  // be returned for a 2-dimensional index with dimension 0 index equal to 7,
+  // dimension 1 equal to 8.
+  static string MultiIndexAsString(
+      tensorflow::gtl::ArraySlice multi_index);
+
  protected:
   // Recursively sets the subshapes and buffers of all subpieces rooted at
   // 'piece'. If 'allocate_array' is true, memory is allocated for the arrays in
@@ -1558,6 +1615,38 @@ std::unique_ptr LiteralBase::Replicate(int64 times) const {
   return literal;
 }
 
+template 
+/* static */ StatusOr> Literal::CreateRandomLiteral(
+    const Shape& shape,
+    const std::function)>& generator) {
+  using NativeT = typename primitive_util::PrimitiveTypeToNative::type;
+  TF_RET_CHECK(shape.element_type() == type);
+  std::unique_ptr literal = Literal::CreateFromShape(shape);
+  TF_RETURN_IF_ERROR(literal.get()->Populate(
+      [&](tensorflow::gtl::ArraySlice indexes) {
+        return generator(indexes);
+      }));
+  return std::move(literal);
+}
+
+template 
+/* static */ StatusOr> Literal::CreateRandomLiteral(
+    const Shape& shape, E* engine, T mean, T stddev) {
+  using NativeT = typename primitive_util::PrimitiveTypeToNative::type;
+  std::normal_distribution generator(mean, stddev);
+  return CreateRandomLiteral(
+      shape, [&](tensorflow::gtl::ArraySlice /*indexes*/) {
+        return generator(*engine);
+      });
+}
+
+template 
+/* static */ StatusOr> Literal::CreateRandomLiteral(
+    const Shape& shape, T mean, T stddev) {
+  std::minstd_rand0 engine;
+  return CreateRandomLiteral(shape, &engine, mean, stddev);
+}
+
 }  // namespace xla
 
 #endif  // TENSORFLOW_COMPILER_XLA_LITERAL_UTIL_H_
diff --git a/tensorflow/compiler/xla/rpc/grpc_client_test.cc b/tensorflow/compiler/xla/rpc/grpc_client_test.cc
index 10997c0719dfb8..313f11a9a95715 100644
--- a/tensorflow/compiler/xla/rpc/grpc_client_test.cc
+++ b/tensorflow/compiler/xla/rpc/grpc_client_test.cc
@@ -101,8 +101,8 @@ TEST_F(GRPCClientTestBase, AxpyTenValues) {
   TF_ASSERT_OK_AND_ASSIGN(auto computation, builder.Build());
   TF_ASSERT_OK_AND_ASSIGN(auto result_literal, client_->ExecuteAndTransfer(
                                                    computation, {}, nullptr));
-  LiteralTestUtil::ExpectNear(*expected_literal, *result_literal,
-                              ErrorSpec(0.0001));
+  EXPECT_TRUE(LiteralTestUtil::Near(*expected_literal, *result_literal,
+                                    ErrorSpec(0.0001)));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
index 313910a861f7f4..5e1499ee6b6ef3 100644
--- a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
@@ -149,12 +149,12 @@ TEST_F(BFloat16PropagationTest, ConvertConstantLiteral) {
   EXPECT_TRUE(OutputsBF16(dot->operand(1)));
   EXPECT_EQ(dot->operand(0)->opcode(), HloOpcode::kConstant);
   EXPECT_EQ(dot->operand(1)->opcode(), HloOpcode::kConstant);
-  LiteralTestUtil::ExpectEqual(
+  EXPECT_TRUE(LiteralTestUtil::Equal(
       dot->operand(0)->literal(),
-      *LiteralTestUtil::ConvertF32ToBF16(*Literal::CreateFromArray(array_a)));
-  LiteralTestUtil::ExpectEqual(
+      *Literal::ConvertF32ToBF16(*Literal::CreateFromArray(array_a))));
+  EXPECT_TRUE(LiteralTestUtil::Equal(
       dot->operand(1)->literal(),
-      *LiteralTestUtil::ConvertF32ToBF16(*Literal::CreateFromArray(array_b)));
+      *Literal::ConvertF32ToBF16(*Literal::CreateFromArray(array_b))));
 }
 
 // Tests that BF16 can be propagated through nested tuples.
diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
index 7b552ee5b1798c..5d05ccfc0b223d 100644
--- a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
@@ -149,7 +149,7 @@ TEST_F(HloConstantFoldingTest, Slice) {
   const int64 slice_limits[] = {10, 8, 6, 5, 9};
   const int64 slice_strides[] = {1, 1, 1, 1, 1};
   TF_ASSERT_OK_AND_ASSIGN(auto literal,
-                          LiteralTestUtil::CreateRandomLiteral(
+                          Literal::CreateRandomLiteral(
                               ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
   HloInstruction* literal_instruction = builder.AddInstruction(
       HloInstruction::CreateConstant(std::move(literal)));
@@ -172,7 +172,7 @@ TEST_F(HloConstantFoldingTest, TransposeConstantFold) {
   HloComputation::Builder builder(TestName());
   const int64 dimensions[] = {11, 8, 7, 5, 9};
   TF_ASSERT_OK_AND_ASSIGN(auto literal,
-                          LiteralTestUtil::CreateRandomLiteral(
+                          Literal::CreateRandomLiteral(
                               ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
   auto literal_clone = literal->Literal::CloneToUnique();
   HloInstruction* literal_instruction = builder.AddInstruction(
diff --git a/tensorflow/compiler/xla/service/hlo_cse_test.cc b/tensorflow/compiler/xla/service/hlo_cse_test.cc
index df8853f34f6a72..a04b4f4dcf0272 100644
--- a/tensorflow/compiler/xla/service/hlo_cse_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_cse_test.cc
@@ -72,7 +72,7 @@ TEST_F(HloCseTest, CombineTwoConstants) {
 
   auto result = ExecuteAndTransfer(std::move(module), {});
   auto expected = Literal::CreateR0(84.0);
-  LiteralTestUtil::ExpectNear(*expected, *result, ErrorSpec(1e-4));
+  EXPECT_TRUE(LiteralTestUtil::Near(*expected, *result, ErrorSpec(1e-4)));
 }
 
 TEST_F(HloCseTest, CombineTwoConstantsDifferentLayoutsAndInsensitive) {
@@ -104,7 +104,7 @@ TEST_F(HloCseTest, CombineTwoConstantsDifferentLayoutsAndInsensitive) {
 
   auto result = ExecuteAndTransfer(std::move(module), {});
   auto expected = Literal::CreateR2({{2.0, 4.0}, {6.0, 8.0}});
-  LiteralTestUtil::ExpectNear(*expected, *result, ErrorSpec(1e-4));
+  EXPECT_TRUE(LiteralTestUtil::Near(*expected, *result, ErrorSpec(1e-4)));
 }
 
 TEST_F(HloCseTest, CombineTwoConstantsDifferentLayoutsAndSensitive) {
@@ -134,7 +134,7 @@ TEST_F(HloCseTest, CombineTwoConstantsDifferentLayoutsAndSensitive) {
 
   auto result = ExecuteAndTransfer(std::move(module), {});
   auto expected = Literal::CreateR2({{2.0, 4.0}, {6.0, 8.0}});
-  LiteralTestUtil::ExpectNear(*expected, *result, ErrorSpec(1e-4));
+  EXPECT_TRUE(LiteralTestUtil::Near(*expected, *result, ErrorSpec(1e-4)));
 }
 
 TEST_F(HloCseTest, ConstantsSameValueDifferentType) {
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
index 8e9688c7ab4e94..ae5b5e0412ef99 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc
@@ -82,9 +82,9 @@ class HloEvaluatorTest : public ::testing::WithParamInterface,
     auto element_type = expected->shape().element_type();
     if (element_type == F32 || element_type == F64) {
       ErrorSpec error(aabs);
-      LiteralTestUtil::ExpectNear(*expected, *result, error);
+      EXPECT_TRUE(LiteralTestUtil::Near(*expected, *result, error));
     } else {
-      LiteralTestUtil::ExpectEqual(*expected, *result);
+      EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
     }
   }
 
@@ -100,7 +100,7 @@ class HloEvaluatorTest : public ::testing::WithParamInterface,
 
     std::unique_ptr result = Evaluate();
 
-    LiteralTestUtil::ExpectEqual(*expected, *result);
+    EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
   }
 
   bool use_bfloat16_;
@@ -129,7 +129,7 @@ TEST_P(HloEvaluatorTest, DoesClamp) {
 
   auto expected = Literal::CreateR2({{0, 4}, {2, 4}});
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, DISABLED_DoesClampSpecialBroadcast) {
@@ -150,7 +150,7 @@ TEST_P(HloEvaluatorTest, DISABLED_DoesClampSpecialBroadcast) {
 
   auto expected = Literal::CreateR2({{0, 0}, {1, 1}});
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 // Verifies that HloEvaluator evaluates a HLO instruction that performs select
@@ -175,7 +175,7 @@ TEST_P(HloEvaluatorTest, DoesSelect) {
 
   auto expected = Literal::CreateR2({{2, 5}, {0, 4}});
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 // Verifies that HloEvaluator evaluates a HLO instruction that performs
@@ -307,7 +307,7 @@ TEST_P(HloEvaluatorTest, DoesTraverseInstructions) {
 
   auto expected = Literal::CreateR2({{4, -16}, {-196, 12}});
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 // Verifies Reshape operation is correctly evaluated.
@@ -315,7 +315,7 @@ TEST_P(HloEvaluatorTest, DoesReshape) {
   HloComputation::Builder b(TestName());
   const int64 dimensions[] = {11, 8, 7, 5, 9};
   TF_ASSERT_OK_AND_ASSIGN(auto literal,
-                          LiteralTestUtil::CreateRandomLiteral(
+                          Literal::CreateRandomLiteral(
                               ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
   auto literal_clone = literal->CloneToUnique();
   HloInstruction* literal_instruction =
@@ -351,7 +351,7 @@ TEST_P(HloEvaluatorTest, DoesBroadcast) {
 
   std::unique_ptr result = Evaluate({});
 
-  LiteralTestUtil::ExpectEqual(*result, *output_literal);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*result, *output_literal));
 }
 
 TEST_P(HloEvaluatorTest, DoesBroadcastScalar) {
@@ -370,7 +370,7 @@ TEST_P(HloEvaluatorTest, DoesBroadcastScalar) {
 
   std::unique_ptr result = Evaluate({});
 
-  LiteralTestUtil::ExpectEqual(*result, *output_literal);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*result, *output_literal));
 }
 
 TEST_P(HloEvaluatorTest, DoesConcatenateSimple) {
@@ -392,7 +392,7 @@ TEST_P(HloEvaluatorTest, DoesConcatenateSimple) {
 
   auto expected =
       Literal::CreateR2({{-1, -2}, {100, 200}, {-2, -3}, {-100, -200}});
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, ConcatenateHandlesShapeWithZeroElement) {
@@ -413,7 +413,7 @@ TEST_P(HloEvaluatorTest, ConcatenateHandlesShapeWithZeroElement) {
   std::unique_ptr result = Evaluate();
 
   auto expected = Literal::CreateR1({100, 200});
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, ConvertWithSameLayout) {
@@ -432,7 +432,7 @@ TEST_P(HloEvaluatorTest, ConvertWithSameLayout) {
 
   std::unique_ptr result = Evaluate();
 
-  LiteralTestUtil::ExpectEqual(*result, *expected);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*result, *expected));
 }
 
 TEST_P(HloEvaluatorTest, ConvertWithDifferentLayout) {
@@ -452,7 +452,7 @@ TEST_P(HloEvaluatorTest, ConvertWithDifferentLayout) {
 
   std::unique_ptr result = Evaluate();
 
-  LiteralTestUtil::ExpectEqual(*result, *expected);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*result, *expected));
 }
 
 PaddingConfig CreatePaddingConfig(
@@ -490,7 +490,7 @@ TEST_P(HloEvaluatorTest, Pad2DIntegerArrayWithZeroDimension) {
   auto expected = Literal::CreateR2(
       {{10, 10}, {10, 10}, {10, 10}, {10, 10}, {10, 10}});
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, Pad4DFloatArrayWithInteriorPadding) {
@@ -525,7 +525,7 @@ TEST_P(HloEvaluatorTest, Pad4DFloatArrayWithInteriorPadding) {
 
   auto expected = Literal::CreateR4FromArray4D(*expected_array);
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, NegativePadding2D) {
@@ -567,7 +567,7 @@ TEST_P(HloEvaluatorTest, NegativePadding2D) {
   (*expected_array)(0, 4) = 2.718f;
   auto expected = Literal::CreateR2FromArray2D(*expected_array);
 
-  LiteralTestUtil::ExpectNear(*expected, *result, ErrorSpec(0x1.0P-5));
+  EXPECT_TRUE(LiteralTestUtil::Near(*expected, *result, ErrorSpec(0x1.0P-5)));
 }
 
 TEST_P(HloEvaluatorTest, NegativeAndInteriorPadding2D) {
@@ -606,7 +606,7 @@ TEST_P(HloEvaluatorTest, NegativeAndInteriorPadding2D) {
   auto expected_array = MakeUnique>(0, 9);
   auto expected = Literal::CreateR2FromArray2D(*expected_array);
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, DotRank2AndRank1) {
@@ -651,7 +651,7 @@ TEST_P(HloEvaluatorTest, DotRank2AndRank1) {
   // clang-format on
   auto expected = Literal::CreateR2FromArray2D(expected_array);
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, DotRank1AndRank2) {
@@ -688,7 +688,7 @@ TEST_P(HloEvaluatorTest, DotRank1AndRank2) {
 
   auto expected = Literal::CreateR1({22.f, 28.f});
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, DotRank2AndRank2) {
@@ -737,7 +737,7 @@ TEST_P(HloEvaluatorTest, DotRank2AndRank2) {
   });
   auto expected = Literal::CreateR2FromArray2D(expected_array);
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, SimpleConv1D) {
@@ -785,7 +785,7 @@ TEST_P(HloEvaluatorTest, SimpleConv1D) {
   Array3D expected_array = {{{11.f, 18.f, 9.f}}};
   auto expected = Literal::CreateR3FromArray3D(expected_array);
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, Simple4x4Conv2DWith2x2Kernel) {
@@ -847,7 +847,7 @@ TEST_P(HloEvaluatorTest, Simple4x4Conv2DWith2x2Kernel) {
   // clang-format on
   auto expected = Literal::CreateR4FromArray4D(expected_array);
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, Conv2DGeneralDimensionsReversed) {
@@ -927,7 +927,7 @@ TEST_P(HloEvaluatorTest, Conv2DGeneralDimensionsReversed) {
   auto expected = Literal::CreateR4FromArray4D(
       use_bfloat16_ ? expected_array_bf16 : expected_array);
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, Conv2DGeneralDimensions) {
@@ -1004,7 +1004,7 @@ TEST_P(HloEvaluatorTest, Conv2DGeneralDimensions) {
   auto expected = Literal::CreateR4FromArray4D(
       use_bfloat16_ ? expected_array_bf16 : expected_array);
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, DilatedBaseConv2DWithHighPadding) {
@@ -1067,7 +1067,7 @@ TEST_P(HloEvaluatorTest, DilatedBaseConv2DWithHighPadding) {
   }));
   auto expected = Literal::CreateR4FromArray4D(expected_array);
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, DilatedBaseConv2DWithLowAndHighPadding) {
@@ -1131,7 +1131,7 @@ TEST_P(HloEvaluatorTest, DilatedBaseConv2DWithLowAndHighPadding) {
   }));
   auto expected = Literal::CreateR4FromArray4D(expected_array);
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest,
@@ -1203,7 +1203,7 @@ TEST_P(HloEvaluatorTest,
   }));
   auto expected = Literal::CreateR4FromArray4D(expected_array);
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 class HloEvaluatorPreciseReduceTest : public HloVerifiedTestBase {};
@@ -1319,7 +1319,7 @@ TEST_P(HloEvaluatorTest, ReduceAdd) {
 
   auto expected = Literal::CreateR1({6, 18});
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, ReduceWindowMax) {
@@ -1370,7 +1370,7 @@ TEST_P(HloEvaluatorTest, ReduceWindowMax) {
   std::unique_ptr result = Evaluate();
 
   auto expected = Literal::CreateR2({{6, 7}});
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, ReduceWindowAdd) {
@@ -1427,7 +1427,7 @@ TEST_P(HloEvaluatorTest, ReduceWindowAdd) {
   std::unique_ptr result = Evaluate();
 
   auto expected = Literal::CreateR2({{1, 3, 5}, {5, 11, 13}});
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, ReduceWindowAdd6D) {
@@ -1490,7 +1490,7 @@ TEST_P(HloEvaluatorTest, ReduceWindowAdd6D) {
   std::vector output_dims = {4, 3, 3, 3, 4, 4};
   std::unique_ptr result_literal =
       Literal::CreateFullWithDescendingLayout(output_dims, 8.0f);
-  LiteralTestUtil::ExpectEqual(*result_literal, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*result_literal, *result));
 }
 
 TEST_P(HloEvaluatorTest, StridedSlice) {
@@ -1523,7 +1523,7 @@ TEST_P(HloEvaluatorTest, StridedSlice) {
       {19},
   });
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, DynamicSlice) {
@@ -1556,7 +1556,7 @@ TEST_P(HloEvaluatorTest, DynamicSlice) {
       {6, 7, 8},
   });
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 // Verifies that the HloEvaluator's implementation goes along with existing
@@ -1591,7 +1591,7 @@ TEST_P(HloEvaluatorTest, DynamicSliceModSlice) {
       {6, 7, 8},
   });
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, DynamicSliceUpdate) {
@@ -1627,7 +1627,7 @@ TEST_P(HloEvaluatorTest, DynamicSliceUpdate) {
       {5, -6, -7},
   });
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, SetAndGetTuples) {
@@ -1662,7 +1662,7 @@ TEST_P(HloEvaluatorTest, SetAndGetTuples) {
       {5, 6, 7},
   });
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, SetAndGetNestedTuples) {
@@ -1703,7 +1703,7 @@ TEST_P(HloEvaluatorTest, SetAndGetNestedTuples) {
       result_inner_literal.get(),
   });
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, Reverse) {
@@ -1756,7 +1756,7 @@ TEST_P(HloEvaluatorTest, Reverse) {
   });
   // clang-format on
 
-  LiteralTestUtil::ExpectEqual(*expected, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result));
 }
 
 TEST_P(HloEvaluatorTest, EvaluateWithSubstitutions) {
@@ -1776,8 +1776,8 @@ TEST_P(HloEvaluatorTest, EvaluateWithSubstitutions) {
       add, {{param0, Literal::CreateR1({1, 2, 3, 4}).get()},
             {square, Literal::CreateR1({10, 20, 30, 40}).get()}});
   TF_ASSERT_OK(result.status());
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR1({11, 22, 33, 44}),
-                               *result.ValueOrDie());
+  EXPECT_TRUE(LiteralTestUtil::Equal(
+      *Literal::CreateR1({11, 22, 33, 44}), *result.ValueOrDie()));
 }
 
 // Check that EvaluateWithSubstitutions works if one of the operands to the op
@@ -1800,8 +1800,8 @@ TEST_P(HloEvaluatorTest, EvaluateWithSubstitutionsWithConstantOperand) {
   auto result = evaluator.EvaluateWithSubstitutions(
       add, {{square, Literal::CreateR1({10, 20, 30, 40}).get()}});
   TF_ASSERT_OK(result.status());
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR1({11, 22, 33, 44}),
-                               *result.ValueOrDie());
+  EXPECT_TRUE(LiteralTestUtil::Equal(
+      *Literal::CreateR1({11, 22, 33, 44}), *result.ValueOrDie()));
 }
 
 TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherV1) {
@@ -1823,9 +1823,9 @@ ENTRY main {
   std::unique_ptr operand =
       Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}});
   std::unique_ptr gather_indices = Literal::CreateR1({0, 2});
-  LiteralTestUtil::ExpectEqual(
-      *Literal::CreateR2({{1, 2, 3}, {7, 8, 9}}),
-      *Evaluate({operand.get(), gather_indices.get()}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR2({{1, 2, 3}, {7, 8, 9}}),
+                             *Evaluate({operand.get(), gather_indices.get()})));
 }
 
 TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherV2) {
@@ -1847,9 +1847,9 @@ ENTRY main {
   std::unique_ptr operand =
       Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}});
   std::unique_ptr gather_indices = Literal::CreateR1({0, 2});
-  LiteralTestUtil::ExpectEqual(
+  EXPECT_TRUE(LiteralTestUtil::Equal(
       *Literal::CreateR2({{1, 3}, {4, 6}, {7, 9}}),
-      *Evaluate({operand.get(), gather_indices.get()}));
+      *Evaluate({operand.get(), gather_indices.get()})));
 }
 
 TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherMultipleBatchDims) {
@@ -1872,10 +1872,10 @@ ENTRY main {
       Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}});
   std::unique_ptr gather_indices =
       Literal::CreateR2({{0, 2}, {2, 1}});
-  LiteralTestUtil::ExpectEqual(
+  EXPECT_TRUE(LiteralTestUtil::Equal(
       *Literal::CreateR3(
           {{{1, 3}, {4, 6}, {7, 9}}, {{3, 2}, {6, 5}, {9, 8}}}),
-      *Evaluate({operand.get(), gather_indices.get()}));
+      *Evaluate({operand.get(), gather_indices.get()})));
 }
 
 TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherNd) {
@@ -1900,9 +1900,9 @@ ENTRY main {
                                 {{-7, 7}, {-8, 8}, {-9, 9}}});
   std::unique_ptr gather_indices =
       Literal::CreateR2({{0, 0}, {1, 0}});
-  LiteralTestUtil::ExpectEqual(
-      *Literal::CreateR2({{-1, 1}, {-4, 4}}),
-      *Evaluate({operand.get(), gather_indices.get()}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR2({{-1, 1}, {-4, 4}}),
+                             *Evaluate({operand.get(), gather_indices.get()})));
 }
 
 TEST_P(HloEvaluatorTest,
@@ -1928,9 +1928,9 @@ ENTRY main {
                                 {{-7, 7}, {-8, 8}, {-9, 9}}});
   std::unique_ptr gather_indices =
       Literal::CreateR2({{0, 0}, {1, 0}});
-  LiteralTestUtil::ExpectEqual(
-      *Literal::CreateR2({{-2, 2}, {-1, 1}}),
-      *Evaluate({operand.get(), gather_indices.get()}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR2({{-2, 2}, {-1, 1}}),
+                             *Evaluate({operand.get(), gather_indices.get()})));
 }
 
 TEST_P(HloEvaluatorTest, EvaluateGather_DynamicSlice) {
@@ -1952,9 +1952,9 @@ ENTRY main {
   std::unique_ptr operand =
       Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}});
   std::unique_ptr gather_indices = Literal::CreateR1({1, 1});
-  LiteralTestUtil::ExpectEqual(
-      *Literal::CreateR2({{5}}),
-      *Evaluate({operand.get(), gather_indices.get()}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR2({{5}}),
+                             *Evaluate({operand.get(), gather_indices.get()})));
 }
 
 TEST_P(HloEvaluatorTest, EvaluateGather_BatchDynamicSlice) {
@@ -1977,9 +1977,9 @@ ENTRY main {
       Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}});
   std::unique_ptr gather_indices =
       Literal::CreateR2({{2, 1}, {1, 1}});
-  LiteralTestUtil::ExpectEqual(
-      *Literal::CreateR3({{{8}}, {{5}}}),
-      *Evaluate({operand.get(), gather_indices.get()}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR3({{{8}}, {{5}}}),
+                             *Evaluate({operand.get(), gather_indices.get()})));
 }
 
 TEST_P(HloEvaluatorTest, EvaluateGather_ZeroDimBounds) {
@@ -2000,9 +2000,9 @@ ENTRY main {
   ParseAndVerifyModule(hlo_text);
   std::unique_ptr operand = Literal::CreateR2({{}, {}, {}});
   std::unique_ptr gather_indices = Literal::CreateR1({0, 2});
-  LiteralTestUtil::ExpectEqual(
-      *Literal::CreateR2({{}, {}}),
-      *Evaluate({operand.get(), gather_indices.get()}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR2({{}, {}}),
+                             *Evaluate({operand.get(), gather_indices.get()})));
 }
 
 TEST_P(HloEvaluatorTest, EvaluateGather_NoOutputWindowDims) {
@@ -2025,9 +2025,9 @@ ENTRY main {
   std::unique_ptr operand = Literal::CreateR1({0, 1, 2});
   std::unique_ptr gather_indices =
       Literal::CreateR3({{{0}, {1}}, {{2}, {1}}});
-  LiteralTestUtil::ExpectEqual(
-      *Literal::CreateR2({{0, 1}, {2, 1}}),
-      *Evaluate({operand.get(), gather_indices.get()}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR2({{0, 1}, {2, 1}}),
+                             *Evaluate({operand.get(), gather_indices.get()})));
 }
 
 // Verifies that HloEvaluator evaluates a HLO instruction that performs
diff --git a/tensorflow/compiler/xla/service/inliner_test.cc b/tensorflow/compiler/xla/service/inliner_test.cc
index 7aa1c7c8358318..d2af261008f40e 100644
--- a/tensorflow/compiler/xla/service/inliner_test.cc
+++ b/tensorflow/compiler/xla/service/inliner_test.cc
@@ -71,7 +71,7 @@ TEST_F(InlinerTest, MapMax) {
   // Verify execution on CPU.
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
   auto expected = Literal::CreateR1({4, 3, 3, 4});
-  LiteralTestUtil::ExpectEqual(*result, *expected);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*result, *expected));
 }
 
 // Test that `constant` function is changed to `broadcast`.
@@ -105,7 +105,7 @@ TEST_F(InlinerTest, MapConstant) {
   // Verify execution on CPU.
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
   auto expected = Literal::CreateR2({{2, 2, 2, 2}, {2, 2, 2, 2}});
-  LiteralTestUtil::ExpectEqual(*result, *expected);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*result, *expected));
 }
 
 TEST_F(InlinerTest, MapSubtractOppositeOrder) {
@@ -143,7 +143,7 @@ TEST_F(InlinerTest, MapSubtractOppositeOrder) {
   // Verify execution on CPU.
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
   auto expected = Literal::CreateR1({3, 1, -1, -3});
-  LiteralTestUtil::ExpectEqual(*result, *expected);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*result, *expected));
 }
 
 
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index b982cf0dbc4ed0..4b0dfde5e23a41 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -87,6 +87,7 @@ cc_library(
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
         "//tensorflow/compiler/xla:array4d",
+        "//tensorflow/compiler/xla:literal_comparison",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:test",
diff --git a/tensorflow/compiler/xla/tests/broadcast_test.cc b/tensorflow/compiler/xla/tests/broadcast_test.cc
index a180cdd604d425..51b9f0d3e330e7 100644
--- a/tensorflow/compiler/xla/tests/broadcast_test.cc
+++ b/tensorflow/compiler/xla/tests/broadcast_test.cc
@@ -46,8 +46,8 @@ XLA_TEST_F(BroadcastTest, BroadcastScalarToScalar) {
   hlo_module->AddEntryComputation(builder.Build());
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
-  LiteralTestUtil::ExpectNear(*Literal::CreateR0(42.0), *result,
-                              error_spec_);
+  EXPECT_TRUE(LiteralTestUtil::Near(*Literal::CreateR0(42.0), *result,
+                                    error_spec_));
 }
 
 XLA_TEST_F(BroadcastTest, BroadcastScalarTo2D) {
@@ -62,9 +62,9 @@ XLA_TEST_F(BroadcastTest, BroadcastScalarTo2D) {
   hlo_module->AddEntryComputation(builder.Build());
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
-  LiteralTestUtil::ExpectNear(
+  EXPECT_TRUE(LiteralTestUtil::Near(
       *Literal::CreateR2({{42.0, 42.0}, {42.0, 42.0}}), *result,
-      error_spec_);
+      error_spec_));
 }
 
 XLA_TEST_F(BroadcastTest, BroadcastVectorTo2D) {
@@ -85,13 +85,13 @@ XLA_TEST_F(BroadcastTest, BroadcastVectorTo2D) {
   hlo_module->AddEntryComputation(builder.Build());
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
-  LiteralTestUtil::ExpectNear(
+  EXPECT_TRUE(LiteralTestUtil::Near(
       *Literal::CreateR2({{1.0, 1.0}, {2.0, 2.0}, {3.0, 3.0}}),
-      LiteralSlice(*result, {0}), error_spec_);
+      LiteralSlice(*result, {0}), error_spec_));
 
-  LiteralTestUtil::ExpectNear(
+  EXPECT_TRUE(LiteralTestUtil::Near(
       *Literal::CreateR2({{1.0, 2.0, 3.0}, {1.0, 2.0, 3.0}}),
-      LiteralSlice(*result, {1}), error_spec_);
+      LiteralSlice(*result, {1}), error_spec_));
 }
 
 XLA_TEST_F(BroadcastTest, Broadcast2DTo2D) {
@@ -106,9 +106,9 @@ XLA_TEST_F(BroadcastTest, Broadcast2DTo2D) {
   hlo_module->AddEntryComputation(builder.Build());
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
-  LiteralTestUtil::ExpectNear(
-      *Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}), *result,
-      error_spec_);
+  EXPECT_TRUE(
+      LiteralTestUtil::Near(*Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}),
+                            *result, error_spec_));
 }
 
 XLA_TEST_F(BroadcastTest, Broadcast2DTo2DTranspose) {
@@ -125,9 +125,9 @@ XLA_TEST_F(BroadcastTest, Broadcast2DTo2DTranspose) {
   hlo_module->AddEntryComputation(builder.Build());
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
-  LiteralTestUtil::ExpectNear(
-      *Literal::CreateR2({{1.0, 3.0}, {2.0, 4.0}}), *result,
-      error_spec_);
+  EXPECT_TRUE(
+      LiteralTestUtil::Near(*Literal::CreateR2({{1.0, 3.0}, {2.0, 4.0}}),
+                            *result, error_spec_));
 }
 
 XLA_TEST_F(BroadcastTest, Broadcast2DTo3D) {
@@ -142,10 +142,10 @@ XLA_TEST_F(BroadcastTest, Broadcast2DTo3D) {
   hlo_module->AddEntryComputation(builder.Build());
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
-  LiteralTestUtil::ExpectNear(
+  EXPECT_TRUE(LiteralTestUtil::Near(
       *Literal::CreateR3({{{1.0, 2.0}, {1.0, 2.0}, {1.0, 2.0}},
                                  {{3.0, 4.0}, {3.0, 4.0}, {3.0, 4.0}}}),
-      *result, error_spec_);
+      *result, error_spec_));
 }
 
 TEST_F(BroadcastTest, Broadcast_R1_2_To_R4_2x2x3x3) {
@@ -166,8 +166,8 @@ TEST_F(BroadcastTest, Broadcast_R1_2_To_R4_2x2x3x3) {
   Array2D pz({{1, 2}, {1, 2}});
   expected.FillWithPZ(pz);
 
-  LiteralTestUtil::ExpectNear(*Literal::CreateR4FromArray4D(expected),
-                              *result, error_spec_);
+  EXPECT_TRUE(LiteralTestUtil::Near(
+      *Literal::CreateR4FromArray4D(expected), *result, error_spec_));
 }
 
 TEST_F(BroadcastTest, Broadcast_R1_1025_To_R4_3x3x3x1025) {
@@ -196,8 +196,8 @@ TEST_F(BroadcastTest, Broadcast_R1_1025_To_R4_3x3x3x1025) {
   }
   expected.FillWithYX(yx);
 
-  LiteralTestUtil::ExpectNear(*Literal::CreateR4FromArray4D(expected),
-                              *result, error_spec_);
+  EXPECT_TRUE(LiteralTestUtil::Near(
+      *Literal::CreateR4FromArray4D(expected), *result, error_spec_));
 }
 
 XLA_TEST_F(BroadcastTest, Broadcast_R1_64_To_R4_32x64x7x7) {
@@ -218,8 +218,8 @@ XLA_TEST_F(BroadcastTest, Broadcast_R1_64_To_R4_32x64x7x7) {
   hlo_module->AddEntryComputation(builder.Build());
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
-  LiteralTestUtil::ExpectNear(*Literal::CreateR4FromArray4D(r4_array), *result,
-                              error_spec_);
+  EXPECT_TRUE(LiteralTestUtil::Near(*Literal::CreateR4FromArray4D(r4_array),
+                                    *result, error_spec_));
 }
 
 TEST_F(BroadcastTest, Broadcast_R0_to_R4_64x64x3x3) {
@@ -238,8 +238,8 @@ TEST_F(BroadcastTest, Broadcast_R0_to_R4_64x64x3x3) {
   Array4D expected(64, 64, 3, 3);
   expected.Fill(1.0f);
 
-  LiteralTestUtil::ExpectNear(*Literal::CreateR4FromArray4D(expected),
-                              *result, error_spec_);
+  EXPECT_TRUE(LiteralTestUtil::Near(
+      *Literal::CreateR4FromArray4D(expected), *result, error_spec_));
 }
 
 TEST_F(BroadcastTest, Broadcast_R2_2x2_To_R4_3x3x2x2) {
@@ -260,8 +260,8 @@ TEST_F(BroadcastTest, Broadcast_R2_2x2_To_R4_3x3x2x2) {
   Array4D expected(3, 3, 2, 2);
   expected.FillWithYX(to_broadcast);
 
-  LiteralTestUtil::ExpectNear(*Literal::CreateR4FromArray4D(expected),
-                              *result, error_spec_);
+  EXPECT_TRUE(LiteralTestUtil::Near(
+      *Literal::CreateR4FromArray4D(expected), *result, error_spec_));
 }
 
 TEST_F(BroadcastTest, Broadcast_R3_2x3x4_to_R4_2x3x4x5) {
@@ -291,8 +291,8 @@ TEST_F(BroadcastTest, Broadcast_R3_2x3x4_to_R4_2x3x4x5) {
   hlo_module->AddEntryComputation(builder.Build());
   auto result = ExecuteAndTransfer(std::move(hlo_module), {});
 
-  LiteralTestUtil::ExpectNear(*Literal::CreateR4FromArray4D(expected),
-                              *result, error_spec_);
+  EXPECT_TRUE(LiteralTestUtil::Near(
+      *Literal::CreateR4FromArray4D(expected), *result, error_spec_));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc
index 41f9a5f66649dd..be542c15c09902 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.cc
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc
@@ -297,7 +297,7 @@ tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
   std::unique_ptr converted_expected;
   Shape layout_shape;
   if (use_bfloat16_) {
-    converted_expected = LiteralTestUtil::ConvertF32ToBF16(expected);
+    converted_expected = Literal::ConvertF32ToBF16(expected);
     expected_ptr = converted_expected.get();
     if (shape_with_layout != nullptr) {
       layout_shape = *shape_with_layout;
@@ -311,7 +311,7 @@ tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
     }
   }
   auto expect_equal = [&](const Literal& actual, const string& error_message) {
-    LiteralTestUtil::ExpectEqual(*expected_ptr, actual, error_message);
+    EXPECT_TRUE(LiteralTestUtil::Equal(*expected_ptr, actual)) << error_message;
   };
   if (execution_options_.debug_options().xla_test_all_output_layouts()) {
     return ComputeAndCompareLiteralWithAllOutputLayouts(
@@ -323,7 +323,7 @@ tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
   }
   TF_ASSIGN_OR_RETURN(auto actual, ExecuteAndTransfer(computation, arguments,
                                                       shape_with_layout));
-  LiteralTestUtil::ExpectEqual(*expected_ptr, *actual);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected_ptr, *actual));
   return tensorflow::Status::OK();
 }
 
@@ -349,7 +349,7 @@ tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
   std::unique_ptr converted_expected;
   Shape layout_shape;
   if (use_bfloat16_) {
-    converted_expected = LiteralTestUtil::ConvertF32ToBF16(expected);
+    converted_expected = Literal::ConvertF32ToBF16(expected);
     expected_ptr = converted_expected.get();
     if (shape_with_layout != nullptr) {
       layout_shape = *shape_with_layout;
@@ -363,7 +363,8 @@ tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
     }
   }
   auto expect_near = [&](const Literal& actual, const string& error_message) {
-    LiteralTestUtil::ExpectNear(*expected_ptr, actual, error, error_message);
+    EXPECT_TRUE(LiteralTestUtil::Near(*expected_ptr, actual, error))
+        << error_message;
   };
   if (execution_options_.debug_options().xla_test_all_output_layouts()) {
     return ComputeAndCompareLiteralWithAllOutputLayouts(
@@ -375,7 +376,7 @@ tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
   }
   TF_ASSIGN_OR_RETURN(auto actual, ExecuteAndTransfer(computation, arguments,
                                                       shape_with_layout));
-  LiteralTestUtil::ExpectNear(*expected_ptr, *actual, error);
+  EXPECT_TRUE(LiteralTestUtil::Near(*expected_ptr, *actual, error));
   return tensorflow::Status::OK();
 }
 
@@ -407,7 +408,7 @@ void ClientLibraryTestBase::ComputeAndCompareTuple(
     return;
   }
   auto actual = actual_status.ConsumeValueOrDie();
-  LiteralTestUtil::ExpectEqual(expected, *actual);
+  EXPECT_TRUE(LiteralTestUtil::Equal(expected, *actual));
 }
 
 void ClientLibraryTestBase::ComputeAndCompareTuple(
@@ -419,7 +420,7 @@ void ClientLibraryTestBase::ComputeAndCompareTuple(
     return;
   }
   auto actual = actual_status.ConsumeValueOrDie();
-  LiteralTestUtil::ExpectNear(expected, *actual, error);
+  EXPECT_TRUE(LiteralTestUtil::Near(expected, *actual, error));
 }
 
 void ClientLibraryTestBase::ComputeAndCompare(
@@ -431,7 +432,7 @@ void ClientLibraryTestBase::ComputeAndCompare(
   }
   std::unique_ptr reference, result;
   std::tie(reference, result) = status_or_data.ConsumeValueOrDie();
-  LiteralTestUtil::ExpectEqual(*reference, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*reference, *result));
 }
 
 void ClientLibraryTestBase::ComputeAndCompare(
@@ -444,7 +445,7 @@ void ClientLibraryTestBase::ComputeAndCompare(
   }
   std::unique_ptr reference, result;
   std::tie(reference, result) = status_or_data.ConsumeValueOrDie();
-  LiteralTestUtil::ExpectNear(*reference, *result, error);
+  EXPECT_TRUE(LiteralTestUtil::Near(*reference, *result, error));
 }
 
 StatusOr, std::unique_ptr>>
@@ -562,7 +563,7 @@ XlaOp ClientLibraryTestBase::AddParam(const Literal& argument,
 XlaOp ClientLibraryTestBase::CreateConstantFromLiteral(const Literal& literal,
                                                        XlaBuilder* builder) {
   return builder->ConstantLiteral(
-      use_bfloat16_ ? *LiteralTestUtil::ConvertF32ToBF16(literal) : literal);
+      use_bfloat16_ ? *Literal::ConvertF32ToBF16(literal) : literal);
 }
 
 std::unique_ptr
@@ -583,7 +584,7 @@ ClientLibraryTestBase::CreateParameterAndTransferLiteral(
   const Literal* param_literal = &literal;
   std::unique_ptr converted_literal;
   if (use_bfloat16_) {
-    converted_literal = LiteralTestUtil::ConvertF32ToBF16(literal);
+    converted_literal = Literal::ConvertF32ToBF16(literal);
     param_literal = converted_literal.get();
   }
   std::unique_ptr data =
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h
index 16e838e60ffbd7..c8c3af0db300e2 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.h
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.h
@@ -541,7 +541,7 @@ std::unique_ptr ClientLibraryTestBase::CreateR0Parameter(
     XlaBuilder* builder, XlaOp* data_handle) {
   std::unique_ptr literal = Literal::CreateR0(value);
   if (use_bfloat16_ && literal->shape().element_type() == F32) {
-    literal = LiteralTestUtil::ConvertF32ToBF16(*literal);
+    literal = Literal::ConvertF32ToBF16(*literal);
   }
   std::unique_ptr data =
       client_->TransferToServer(*literal).ConsumeValueOrDie();
@@ -555,7 +555,7 @@ std::unique_ptr ClientLibraryTestBase::CreateR1Parameter(
     const string& name, XlaBuilder* builder, XlaOp* data_handle) {
   std::unique_ptr literal = Literal::CreateR1(values);
   if (use_bfloat16_ && literal->shape().element_type() == F32) {
-    literal = LiteralTestUtil::ConvertF32ToBF16(*literal);
+    literal = Literal::ConvertF32ToBF16(*literal);
   }
   std::unique_ptr data =
       client_->TransferToServer(*literal).ConsumeValueOrDie();
@@ -569,7 +569,7 @@ std::unique_ptr ClientLibraryTestBase::CreateR2Parameter(
     const string& name, XlaBuilder* builder, XlaOp* data_handle) {
   std::unique_ptr literal = Literal::CreateR2FromArray2D(array_2d);
   if (use_bfloat16_ && literal->shape().element_type() == F32) {
-    literal = LiteralTestUtil::ConvertF32ToBF16(*literal);
+    literal = Literal::ConvertF32ToBF16(*literal);
   }
   std::unique_ptr data =
       client_->TransferToServer(*literal).ConsumeValueOrDie();
@@ -583,7 +583,7 @@ std::unique_ptr ClientLibraryTestBase::CreateR3Parameter(
     const string& name, XlaBuilder* builder, XlaOp* data_handle) {
   std::unique_ptr literal = Literal::CreateR3FromArray3D(array_3d);
   if (use_bfloat16_ && literal->shape().element_type() == F32) {
-    literal = LiteralTestUtil::ConvertF32ToBF16(*literal);
+    literal = Literal::ConvertF32ToBF16(*literal);
   }
   std::unique_ptr data =
       client_->TransferToServer(*literal).ConsumeValueOrDie();
diff --git a/tensorflow/compiler/xla/tests/client_test.cc b/tensorflow/compiler/xla/tests/client_test.cc
index abf7312f48430c..08671cf6244582 100644
--- a/tensorflow/compiler/xla/tests/client_test.cc
+++ b/tensorflow/compiler/xla/tests/client_test.cc
@@ -62,9 +62,9 @@ XLA_TEST_F(ClientTest, ExecuteWithLayout) {
       TF_ASSERT_OK_AND_ASSIGN(
           auto computed, client_->Transfer(*data, &expected_literal->shape()));
 
-      LiteralTestUtil::AssertEqualShapesAndLayouts(expected_literal->shape(),
-                                                   computed->shape());
-      LiteralTestUtil::ExpectEqual(*expected_literal, *computed);
+      ASSERT_TRUE(LiteralTestUtil::EqualShapesAndLayouts(
+          expected_literal->shape(), computed->shape()));
+      EXPECT_TRUE(LiteralTestUtil::Equal(*expected_literal, *computed));
     }
   }
 }
@@ -142,7 +142,7 @@ XLA_TEST_F(ClientTest, DISABLED_ON_GPU(ExecuteParallel)) {
       auto result_literal,
       client_->Transfer(*results[0], &expected_result->shape()));
 
-  LiteralTestUtil::ExpectEqual(*expected_result, *result_literal);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected_result, *result_literal));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/tests/compilation_cache_test.cc b/tensorflow/compiler/xla/tests/compilation_cache_test.cc
index ecce599a8a3bd5..e1aa9d7b041506 100644
--- a/tensorflow/compiler/xla/tests/compilation_cache_test.cc
+++ b/tensorflow/compiler/xla/tests/compilation_cache_test.cc
@@ -50,8 +50,8 @@ class CompilationCacheTest : public ClientLibraryTestBase {
                                  /*execution_options=*/&execution_options_,
                                  &execution_profile)
             .ConsumeValueOrDie();
-    LiteralTestUtil::ExpectNear(*Literal::CreateR0(expected_result),
-                                *result, error_spec_);
+    EXPECT_TRUE(LiteralTestUtil::Near(
+        *Literal::CreateR0(expected_result), *result, error_spec_));
     EXPECT_EQ(expect_cache_hit, execution_profile.compilation_cache_hit());
   }
 
@@ -67,8 +67,8 @@ class CompilationCacheTest : public ClientLibraryTestBase {
                            .ConsumeValueOrDie();
     std::unique_ptr result =
         client_->Transfer(*data_handle).ConsumeValueOrDie();
-    LiteralTestUtil::ExpectNear(*Literal::CreateR2(expected_result),
-                                *result, error_spec_);
+    EXPECT_TRUE(LiteralTestUtil::Near(
+        *Literal::CreateR2(expected_result), *result, error_spec_));
     EXPECT_EQ(expect_cache_hit, execution_profile.compilation_cache_hit());
   }
 
diff --git a/tensorflow/compiler/xla/tests/compute_constant_test.cc b/tensorflow/compiler/xla/tests/compute_constant_test.cc
index bf4b8fb0bcf229..ba22530f1cfee5 100644
--- a/tensorflow/compiler/xla/tests/compute_constant_test.cc
+++ b/tensorflow/compiler/xla/tests/compute_constant_test.cc
@@ -208,7 +208,7 @@ TEST_F(ComputeConstantTest, NonScalarAdd) {
                             ComputeConstantLiteral(client, computation, &b));
     std::unique_ptr expected_literal =
         Literal::CreateR1({4, 6});
-    LiteralTestUtil::ExpectEqual(*expected_literal, *computed);
+    EXPECT_TRUE(LiteralTestUtil::Equal(*expected_literal, *computed));
   }
 }
 
@@ -222,7 +222,7 @@ TEST_F(ComputeConstantTest, IntegerDivide) {
     TF_ASSERT_OK_AND_ASSIGN(auto computed,
                             ComputeConstantLiteral(client, computation, &b));
     std::unique_ptr expected_literal = Literal::CreateR0(5);
-    LiteralTestUtil::ExpectEqual(*expected_literal, *computed);
+    EXPECT_TRUE(LiteralTestUtil::Equal(*expected_literal, *computed));
   }
 }
 
@@ -244,9 +244,9 @@ XLA_TEST_F(ComputeConstantTest, Layout) {
       std::unique_ptr expected_literal =
           Literal::CreateR2WithLayout({{11, 22}, {33, 44}},
                                              LayoutUtil::MakeLayout(layout));
-      LiteralTestUtil::AssertEqualShapesAndLayouts(expected_literal->shape(),
-                                                   computed->shape());
-      LiteralTestUtil::ExpectEqual(*expected_literal, *computed);
+      ASSERT_TRUE(LiteralTestUtil::EqualShapesAndLayouts(
+          expected_literal->shape(), computed->shape()));
+      EXPECT_TRUE(LiteralTestUtil::Equal(*expected_literal, *computed));
     }
   }
 }
diff --git a/tensorflow/compiler/xla/tests/copy_test.cc b/tensorflow/compiler/xla/tests/copy_test.cc
index 155fbacf58d81c..2b3390ca98cb29 100644
--- a/tensorflow/compiler/xla/tests/copy_test.cc
+++ b/tensorflow/compiler/xla/tests/copy_test.cc
@@ -49,7 +49,7 @@ class CopyOpTest : public HloTestBase {
     module->AddEntryComputation(std::move(computation));
 
     std::unique_ptr result = ExecuteAndTransfer(std::move(module), {});
-    LiteralTestUtil::ExpectEqual(literal, *result);
+    EXPECT_TRUE(LiteralTestUtil::Equal(literal, *result));
   }
 
   void TestCopyConstantLayout021(size_t n1, size_t n2, size_t n3);
@@ -253,7 +253,7 @@ XLA_TEST_F(CopyOpClientTest, Copy0x0) {
 
   auto actual = ExecuteAndTransfer(&builder, {input_data.get()}, &out_shape)
                     .ConsumeValueOrDie();
-  LiteralTestUtil::ExpectEqual(*empty, *actual);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*empty, *actual));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc
index b947f8208a5fa3..e6f79b5ac55ddd 100644
--- a/tensorflow/compiler/xla/tests/fusion_test.cc
+++ b/tensorflow/compiler/xla/tests/fusion_test.cc
@@ -118,9 +118,9 @@ class FusionTest : public HloTestBase {
     auto expected = Literal::CreateR2FromArray2D(answer_data);
     auto actual = ExecuteAndTransfer(std::move(hlo_module), {});
     if (primitive_util::IsFloatingPointType(prim_type)) {
-      LiteralTestUtil::ExpectNear(*expected, *actual, ErrorSpec(1e-4));
+      EXPECT_TRUE(LiteralTestUtil::Near(*expected, *actual, ErrorSpec(1e-4)));
     } else {
-      LiteralTestUtil::ExpectEqual(*expected, *actual);
+      EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *actual));
     }
   }
 
@@ -221,9 +221,9 @@ XLA_TEST_F(FusionTest, Test) {
            const4, reshape3, add2, const1, const0},
           HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectNear(*Literal::CreateR2({{0.5}, {2.72}}),
-                              *ExecuteAndTransfer(std::move(hlo_module), {}),
-                              ErrorSpec(1e-4));
+  EXPECT_TRUE(LiteralTestUtil::Near(
+      *Literal::CreateR2({{0.5}, {2.72}}),
+      *ExecuteAndTransfer(std::move(hlo_module), {}), ErrorSpec(1e-4)));
 }
 
 // Test whether we emit appropriate code for parameters of fusion instructions.
@@ -247,9 +247,9 @@ XLA_TEST_F(FusionTest, Parameter) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{add3, const2},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectNear(*Literal::CreateR2({{-1.0, 0.0, 1.0}}),
-                              *ExecuteAndTransfer(std::move(hlo_module), {}),
-                              ErrorSpec(1e-4));
+  EXPECT_TRUE(LiteralTestUtil::Near(
+      *Literal::CreateR2({{-1.0, 0.0, 1.0}}),
+      *ExecuteAndTransfer(std::move(hlo_module), {}), ErrorSpec(1e-4)));
 }
 
 XLA_TEST_F(FusionTest, RandomizedParallelPartition) {
@@ -307,9 +307,9 @@ XLA_TEST_F(FusionTest, BroadcastIntoBinaryOp) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{add2, broadcast},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectNear(
+  EXPECT_TRUE(LiteralTestUtil::Near(
       *Literal::CreateR2({{0.0, 0.0, -1.0}, {11.0, 22.0, 33.0}}),
-      *ExecuteAndTransfer(std::move(hlo_module), {}), ErrorSpec(1e-4));
+      *ExecuteAndTransfer(std::move(hlo_module), {}), ErrorSpec(1e-4)));
 }
 
 XLA_TEST_F(FusionTest, ReshapeToScalar) {
@@ -322,8 +322,9 @@ XLA_TEST_F(FusionTest, ReshapeToScalar) {
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR0(5),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR0(5),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, Reshape_3by2_1by2by3) {
@@ -336,9 +337,9 @@ XLA_TEST_F(FusionTest, Reshape_3by2_1by2by3) {
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(
+  EXPECT_TRUE(LiteralTestUtil::Equal(
       *Literal::CreateR3({{{1, 2, 3}, {4, 5, 6}}}),
-      *ExecuteAndTransfer(std::move(hlo_module), {}));
+      *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, Reshape_1by2by3_3by2) {
@@ -351,9 +352,9 @@ XLA_TEST_F(FusionTest, Reshape_1by2by3_3by2) {
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(
+  EXPECT_TRUE(LiteralTestUtil::Equal(
       *Literal::CreateR2({{1, 2}, {3, 4}, {5, 6}}),
-      *ExecuteAndTransfer(std::move(hlo_module), {}));
+      *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, Reshape_1by1by1_) {
@@ -366,8 +367,9 @@ XLA_TEST_F(FusionTest, Reshape_1by1by1_) {
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR0(7),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR0(7),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, Reshape__1by1by1) {
@@ -380,8 +382,9 @@ XLA_TEST_F(FusionTest, Reshape__1by1by1) {
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR3({{{7}}}),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR3({{{7}}}),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, Reshape__) {
@@ -394,8 +397,9 @@ XLA_TEST_F(FusionTest, Reshape__) {
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR0(7),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR0(7),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, Reshape_3by3_3by3) {
@@ -408,9 +412,9 @@ XLA_TEST_F(FusionTest, Reshape_3by3_3by3) {
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(
+  EXPECT_TRUE(LiteralTestUtil::Equal(
       *Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}),
-      *ExecuteAndTransfer(std::move(hlo_module), {}));
+      *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, Transpose_2by3) {
@@ -423,9 +427,9 @@ XLA_TEST_F(FusionTest, Transpose_2by3) {
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(
+  EXPECT_TRUE(LiteralTestUtil::Equal(
       *Literal::CreateR2({{1, 4}, {2, 5}, {3, 6}}),
-      *ExecuteAndTransfer(std::move(hlo_module), {}));
+      *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, Transpose_3by3) {
@@ -438,9 +442,9 @@ XLA_TEST_F(FusionTest, Transpose_3by3) {
   hlo_module->AddEntryComputation(builder.Build())
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1},
                                 HloInstruction::FusionKind::kLoop);
-  LiteralTestUtil::ExpectEqual(
+  EXPECT_TRUE(LiteralTestUtil::Equal(
       *Literal::CreateR2({{1, 4, 7}, {2, 5, 8}, {3, 6, 9}}),
-      *ExecuteAndTransfer(std::move(hlo_module), {}));
+      *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, Reverse) {
@@ -454,8 +458,9 @@ XLA_TEST_F(FusionTest, Reverse) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reverse1},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR1({3, 2, 1}),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR1({3, 2, 1}),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, ReverseNegate) {
@@ -471,8 +476,9 @@ XLA_TEST_F(FusionTest, ReverseNegate) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{negate2, reverse1},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR1({-3, -2, -1}),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR1({-3, -2, -1}),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, BroadcastNegate) {
@@ -488,8 +494,9 @@ XLA_TEST_F(FusionTest, BroadcastNegate) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{negate2, broadcast1},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR1({-1, -1}),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR1({-1, -1}),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, SliceNegate) {
@@ -505,8 +512,9 @@ XLA_TEST_F(FusionTest, SliceNegate) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{negate2, slice1},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR1({-1, -3}),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR1({-1, -3}),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, DynamicSliceNegate) {
@@ -526,8 +534,9 @@ XLA_TEST_F(FusionTest, DynamicSliceNegate) {
           /*instructions_to_fuse=*/{negate3, dynamic_slice2},
           HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR1({-2, -3}),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR1({-2, -3}),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, ReshapeNegate) {
@@ -543,8 +552,9 @@ XLA_TEST_F(FusionTest, ReshapeNegate) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{negate2, reshape1},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR2({{-1, -2}, {-3, -4}}),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR2({{-1, -2}, {-3, -4}}),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 // TODO(b/64070202): Investigate failure.
@@ -561,8 +571,9 @@ XLA_TEST_F(FusionTest, DISABLED_ON_GPU(TransposeNegate)) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{negate2, transpose1},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR2({{-1, -3}, {-2, -4}}),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR2({{-1, -3}, {-2, -4}}),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 std::unique_ptr MakeReduceTestComputation() {
@@ -591,8 +602,9 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(Reduce)) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reduce2},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR0(15),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR0(15),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceImplicitBroadcast)) {
@@ -612,8 +624,9 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceImplicitBroadcast)) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{negate3, reduce2},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR0(-15),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR0(-15),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceWindow)) {
@@ -661,9 +674,9 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceWindow)) {
       ->CreateFusionInstruction(/*instructions_to_fuse=*/{reduce_window2},
                                 HloInstruction::FusionKind::kLoop);
 
-  LiteralTestUtil::ExpectEqual(
+  EXPECT_TRUE(LiteralTestUtil::Equal(
       *Literal::CreateR2({{462, 2145}, {24871, 62491}}),
-      *ExecuteAndTransfer(std::move(hlo_module), {}));
+      *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 // When a constant (or other op) which has multiple users is imported
@@ -697,8 +710,9 @@ XLA_TEST_F(FusionTest, SharedConstant) {
   // fused instruction contains the constant(2), the parameter, and 4 adds
   EXPECT_EQ(entry_comp->root_instruction()->fused_instruction_count(), 6);
 
-  LiteralTestUtil::ExpectEqual(*Literal::CreateR1({8}),
-                               *ExecuteAndTransfer(std::move(hlo_module), {}));
+  EXPECT_TRUE(
+      LiteralTestUtil::Equal(*Literal::CreateR1({8}),
+                             *ExecuteAndTransfer(std::move(hlo_module), {})));
 }
 
 XLA_TEST_F(FusionTest, Add2D) { TestElementwise2D(HloOpcode::kAdd); }
diff --git a/tensorflow/compiler/xla/tests/gather_operation_test.cc b/tensorflow/compiler/xla/tests/gather_operation_test.cc
index 130456e61ca8a2..4854c649c15f2a 100644
--- a/tensorflow/compiler/xla/tests/gather_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/gather_operation_test.cc
@@ -629,8 +629,8 @@ XLA_TEST_F(GatherClientLibraryTest, DISABLED_ON_GPU(Basic)) {
       client_->ExecuteParallel(computation_instances));
   TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result_literal,
                           client_->Transfer(*(result_data[0])));
-  LiteralTestUtil::ExpectEqual(
-      *result_literal, *Literal::CreateR2({{1, 2, 3}, {7, 8, 9}}));
+  EXPECT_TRUE(LiteralTestUtil::Equal(
+      *result_literal, *Literal::CreateR2({{1, 2, 3}, {7, 8, 9}})));
 }
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.cc b/tensorflow/compiler/xla/tests/literal_test_util.cc
index 868876c72db68c..c38a78d5db7deb 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.cc
+++ b/tensorflow/compiler/xla/tests/literal_test_util.cc
@@ -21,6 +21,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/index_util.h"
 #include "tensorflow/compiler/xla/layout_util.h"
+#include "tensorflow/compiler/xla/literal_comparison.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -46,117 +47,21 @@ using ::tensorflow::strings::StrCat;
 
 /* static */ ::testing::AssertionResult LiteralTestUtil::EqualShapes(
     const Shape& expected, const Shape& actual) {
-  if (ShapeUtil::IsTuple(expected) != ShapeUtil::IsTuple(actual)) {
-    return ::testing::AssertionFailure()
-           << "tupleness-mismatch! want: " << ShapeUtil::HumanString(expected)
-           << " got: " << ShapeUtil::HumanString(actual);
-  }
-  if (ShapeUtil::IsTuple(expected)) {
-    if (ShapeUtil::TupleElementCount(expected) !=
-        ShapeUtil::TupleElementCount(actual)) {
-      return ::testing::AssertionFailure()
-             << "want tuple element count: "
-             << ShapeUtil::TupleElementCount(expected)
-             << " got tuple element count: "
-             << ShapeUtil::TupleElementCount(actual);
-    }
-    for (int i = 0; i < expected.tuple_shapes_size(); ++i) {
-      ::testing::AssertionResult result =
-          EqualShapes(expected.tuple_shapes(i), actual.tuple_shapes(i))
-          << "mismatch in tuple index " << i;
-      if (!result) {
-        return result;
-      }
-    }
-  } else {
-    if (ShapeUtil::Rank(expected) != ShapeUtil::Rank(actual)) {
-      return ::testing::AssertionFailure()
-             << "want rank of: " << ShapeUtil::HumanString(expected)
-             << " got rank of: " << ShapeUtil::HumanString(actual);
-    }
-    if (expected.element_type() != actual.element_type()) {
-      return ::testing::AssertionFailure()
-             << PrimitiveType_Name(expected.element_type()) << " vs "
-             << PrimitiveType_Name(actual.element_type());
-    }
-    if (expected.dimensions_size() != actual.dimensions_size()) {
-      return ::testing::AssertionFailure()
-             << "want dimensions_size " << expected.dimensions_size()
-             << " got dimensions_size " << actual.dimensions_size();
-    }
-    for (int i = 0; i < expected.dimensions_size(); ++i) {
-      if (expected.dimensions(i) != actual.dimensions(i)) {
-        return ::testing::AssertionFailure()
-               << "mismatch in dimension #" << i
-               << " expected: " << ShapeUtil::HumanString(expected)
-               << " actual: " << ShapeUtil::HumanString(actual);
-      }
-    }
+  Status result = literal_comparison::EqualShapes(expected, actual);
+  if (result.ok()) {
+    return ::testing::AssertionSuccess();
   }
-  return ::testing::AssertionSuccess();
-}
-
-/* static */ void LiteralTestUtil::AssertEqualShapes(const Shape& expected,
-                                                     const Shape& actual) {
-  ASSERT_TRUE(EqualShapes(expected, actual));
+  return ::testing::AssertionFailure() << result;
 }
 
-/* static */ void LiteralTestUtil::AssertEqualShapesAndLayouts(
+/* static */ ::testing::AssertionResult LiteralTestUtil::EqualShapesAndLayouts(
     const Shape& expected, const Shape& actual) {
-  ASSERT_EQ(expected.ShortDebugString(), actual.ShortDebugString());
-}
-
-namespace {
-
-// Return a literal with all arrays of type FromNativeT converted to type
-// ToNativeT in the given literal.
-template 
-std::unique_ptr ConvertType(LiteralSlice literal) {
-  // First construct shape of the result.
-  Shape result_shape(literal.shape());
-  ShapeUtil::ForEachMutableSubshape(
-      &result_shape, [](Shape* subshape, const ShapeIndex&) {
-        if (subshape->element_type() ==
-            primitive_util::NativeToPrimitiveType()) {
-          subshape->set_element_type(
-              primitive_util::NativeToPrimitiveType());
-        }
-      });
-  auto result = MakeUnique(result_shape);
-
-  // Then copy over the data from 'literal' converting FromNativeT values to
-  // ToNativeT values as necessary.
-  ShapeUtil::ForEachSubshape(
-      literal.shape(),
-      [&](const Shape& subshape, const ShapeIndex& shape_index) {
-        if (ShapeUtil::IsArray(subshape)) {
-          if (subshape.element_type() ==
-              primitive_util::NativeToPrimitiveType()) {
-            auto src = literal.data(shape_index);
-            auto dest = result->data(shape_index);
-            for (int64 i = 0; i < src.size(); ++i) {
-              dest[i] = static_cast(src[i]);
-            }
-          } else {
-            TF_CHECK_OK(result->CopyFrom(literal,
-                                         /*dest_shape_index=*/shape_index,
-                                         /*src_shape_index=*/shape_index));
-          }
-        }
-      });
-  return result;
-}
-
-}  // namespace
-
-/* static */ std::unique_ptr LiteralTestUtil::ConvertBF16ToF32(
-    LiteralSlice literal) {
-  return ConvertType(literal);
-}
-
-/* static */ std::unique_ptr LiteralTestUtil::ConvertF32ToBF16(
-    LiteralSlice literal) {
-  return ConvertType(literal);
+  if (expected.ShortDebugString() != actual.ShortDebugString()) {
+    return ::testing::AssertionFailure()
+           << "want: " << expected.ShortDebugString()
+           << " got: " << actual.ShortDebugString();
+  }
+  return ::testing::AssertionSuccess();
 }
 
 namespace {
@@ -168,183 +73,15 @@ string Hostname() {
   return string(hostname);
 }
 
-// Helper function for comparing a floating point type, FloatT, bitwise equal
-// between the left-hand-side and right-hand-side, by bit-casting to UnsignedT
-// -- on miscompare, a nice error message is given in the AssertionFailure.
-template 
-::testing::AssertionResult CompareFloatsBitwiseEqual(FloatT lhs, FloatT rhs) {
-  auto ulhs = tensorflow::bit_cast(lhs);
-  auto urhs = tensorflow::bit_cast(rhs);
-  auto lhs_double = static_cast(lhs);
-  auto rhs_double = static_cast(rhs);
-  if (ulhs != urhs) {
-    return ::testing::AssertionFailure() << Printf(
-               "floating values are not bitwise-equal; and equality testing "
-               "was requested: %s=%g=%a vs %s=%g=%a",
-               StrCat(tensorflow::strings::Hex(ulhs)).c_str(), lhs_double,
-               lhs_double, StrCat(tensorflow::strings::Hex(urhs)).c_str(),
-               rhs_double, rhs_double);
-  }
-  return ::testing::AssertionSuccess();
-}
-
-// Templated comparator that specializes for float equality comparison with the
-// bitwise helper above (this is the un-specialized fallback, to just use the
-// default gunit implementation).
-template 
-::testing::AssertionResult CompareEqual(NativeT lhs, NativeT rhs) {
-  if (lhs == rhs) {
-    return ::testing::AssertionSuccess();
-  }
-  ::testing::Message msg;
-  msg << "Expected equality of these values:";
-  msg << "\n  " << lhs;
-  msg << "\n  " << rhs;
-
-  return ::testing::AssertionFailure() << msg;
-}
-
-// Specializations for floating types that do bitwise comparisons when equality
-// comparison is requested.
-template <>
-::testing::AssertionResult CompareEqual(bfloat16 lhs, bfloat16 rhs) {
-  return CompareFloatsBitwiseEqual(lhs, rhs);
-}
-template <>
-::testing::AssertionResult CompareEqual(Eigen::half lhs,
-                                                     Eigen::half rhs) {
-  return CompareFloatsBitwiseEqual(lhs, rhs);
-}
-template <>
-::testing::AssertionResult CompareEqual(float lhs, float rhs) {
-  return CompareFloatsBitwiseEqual(lhs, rhs);
-}
-template <>
-::testing::AssertionResult CompareEqual(double lhs, double rhs) {
-  return CompareFloatsBitwiseEqual(lhs, rhs);
-}
-template <>
-::testing::AssertionResult CompareEqual(complex64 lhs,
-                                                   complex64 rhs) {
-  auto res = CompareEqual(lhs.real(), rhs.real());
-  if (!res) {
-    return res;
-  }
-  return CompareEqual(lhs.imag(), rhs.imag());
-}
-
-// A recursive function which iterates through every index of expected and
-// actual literal and compares their values elementwise. Returns true if all
-// elements are equal.
-template 
-bool ExpectLiteralsEqual(LiteralSlice expected, LiteralSlice actual,
-                         tensorflow::gtl::MutableArraySlice multi_index,
-                         int64 dimension) {
-  if (dimension == expected.shape().dimensions_size()) {
-    NativeT expected_value = expected.Get(multi_index);
-    NativeT actual_value = actual.Get(multi_index);
-    ::testing::AssertionResult result =
-        CompareEqual(expected_value, actual_value);
-    return result;  // Defines implicit coersion to bool.
-  }
-
-  bool all_match = true;
-  for (int64 i = 0; i < expected.shape().dimensions(dimension); ++i) {
-    multi_index[dimension] = i;
-    all_match = all_match && ExpectLiteralsEqual(
-                                 expected, actual, multi_index, dimension + 1);
-  }
-  return all_match;
-}
-
 }  // namespace
 
-/* static */ void LiteralTestUtil::ExpectEqual(LiteralSlice expected,
-                                               LiteralSlice actual,
-                                               const string& message) {
-  EXPECT_TRUE(Equal(expected, actual))
-      << "expected:\n"
-      << expected.ToString() << "\n\tvs actual:\n"
-      << actual.ToString()
-      << (message.empty() ? "" : StrCat("\nmessage: ", message));
-}
-
-/* static */ void LiteralTestUtil::ExpectNotEqual(LiteralSlice expected,
-                                                  LiteralSlice actual) {
-  EXPECT_FALSE(Equal(expected, actual));
-}
-
 /* static */ ::testing::AssertionResult LiteralTestUtil::Equal(
-    LiteralSlice expected, LiteralSlice actual) {
-  VLOG(1) << "expected:";
-  XLA_VLOG_LINES(1, expected.ToString());
-  VLOG(1) << "actual:";
-  XLA_VLOG_LINES(1, actual.ToString());
-
-  AssertEqualShapes(expected.shape(), actual.shape());
-  std::vector multi_index(expected.shape().dimensions_size(), 0);
-  bool match = false;
-  switch (expected.shape().element_type()) {
-    case PRED:
-      match = ExpectLiteralsEqual(expected, actual, &multi_index, 0);
-      break;
-    case U8:
-      match = ExpectLiteralsEqual(expected, actual, &multi_index, 0);
-      break;
-    case S32:
-      match = ExpectLiteralsEqual(expected, actual, &multi_index, 0);
-      break;
-    case S64:
-      match = ExpectLiteralsEqual(expected, actual, &multi_index, 0);
-      break;
-    case U32:
-      match = ExpectLiteralsEqual(expected, actual, &multi_index, 0);
-      break;
-    case U64:
-      match = ExpectLiteralsEqual(expected, actual, &multi_index, 0);
-      break;
-    case BF16:
-      match = ExpectLiteralsEqual(expected, actual, &multi_index, 0);
-      break;
-    case F16:
-      match = ExpectLiteralsEqual(expected, actual, &multi_index, 0);
-      break;
-    case F32:
-      match = ExpectLiteralsEqual(expected, actual, &multi_index, 0);
-      break;
-    case F64:
-      match = ExpectLiteralsEqual(expected, actual, &multi_index, 0);
-      break;
-    case C64:
-      match = ExpectLiteralsEqual(expected, actual, &multi_index, 0);
-      break;
-    case TUPLE: {
-      bool tuple_match = true;
-      for (int i = 0; i < ShapeUtil::TupleElementCount(expected.shape()); ++i) {
-        SCOPED_TRACE(StrCat("Tuple index ", i, " in ",
-                            ShapeUtil::HumanString(expected.shape())));
-
-        // Create LiteralSlices of the expected and actual elements.
-        auto result =
-            Equal(LiteralSlice(expected, {i}), LiteralSlice(actual, {i}));
-        tuple_match = tuple_match ? !!result : false;
-      }
-      match = tuple_match;
-      break;
-    }
-    default:
-      LOG(FATAL)
-          << "Unsupported primitive type in LiteralTestUtil::ExpectEqual: "
-          << PrimitiveType_Name(expected.shape().element_type());
-  }
-  ::testing::AssertionResult result = ::testing::AssertionSuccess();
-  if (!match) {
-    result = ::testing::AssertionFailure()
-             << "expected: " << expected.ToString()
-             << "\nactual:   " << actual.ToString();
-    VLOG(1) << result.message();
+    const LiteralSlice& expected, const LiteralSlice& actual) {
+  Status result = literal_comparison::Equal(expected, actual);
+  if (result.ok()) {
+    return ::testing::AssertionSuccess();
   }
-  return result;
+  return ::testing::AssertionFailure() << result;
 }
 
 namespace {
@@ -368,7 +105,7 @@ int64 RecursiveElementCount(const Shape& shape) {
 // 3 minutes.  The utility of printing a literal with >1000 elements is
 // questionable, especially when writing the Literal proto to disk is orders
 // of magnitude faster.
-string TruncateHugeLiteral(LiteralSlice literal) {
+string TruncateHugeLiteral(const LiteralSlice& literal) {
   return RecursiveElementCount(literal.shape()) < 1000
              ? literal.ToString()
              : "[TRUNCATED, Literal with more than 1000 values]";
@@ -435,8 +172,8 @@ class NearComparator {
   // result. The assertion result is successful if all actual and expected
   // elements are within the given error bound. In case of error, the assertion
   // result contains a detailed error message in case of failure.
-  static ::testing::AssertionResult Compare(LiteralSlice expected,
-                                            LiteralSlice actual,
+  static ::testing::AssertionResult Compare(const LiteralSlice& expected,
+                                            const LiteralSlice& actual,
                                             ErrorSpec error,
                                             bool detailed_message) {
     NearComparator comparator(expected, actual, error,
@@ -464,7 +201,7 @@ class NearComparator {
       return Printf(
           "actual %s, expected %s, index %s, rel error %8.3g, abs error %8.3g",
           FpValueToString(actual).c_str(), FpValueToString(expected).c_str(),
-          LiteralTestUtil::MultiIndexAsString(
+          Literal::MultiIndexAsString(
               IndexUtil::LinearIndexToMultidimensionalIndex(shape,
                                                             linear_index))
               .c_str(),
@@ -472,8 +209,9 @@ class NearComparator {
     }
   };
 
-  explicit NearComparator(LiteralSlice expected, LiteralSlice actual,
-                          ErrorSpec error, bool detailed_message)
+  explicit NearComparator(const LiteralSlice& expected,
+                          const LiteralSlice& actual, ErrorSpec error,
+                          bool detailed_message)
       : expected_(expected),
         actual_(actual),
         error_(error),
@@ -649,7 +387,7 @@ class NearComparator {
   }
 
   // Writes the given literal to a file in the test temporary directory.
-  void WriteLiteralToTempFile(LiteralSlice literal, const string& name) {
+  void WriteLiteralToTempFile(const LiteralSlice& literal, const string& name) {
     int64 now_usec = tensorflow::Env::Default()->NowMicros();
     string filename = tensorflow::io::JoinPath(
         tensorflow::testing::TmpDir(),
@@ -794,8 +532,8 @@ constexpr std::array NearComparator::kErrorBucketBounds;
 // Helper function for comparing two literals for nearness. Handles tuple-shapes
 // via recursion. shape_index is the ShapeIndex of expected (or actual)
 // currently being compared.
-::testing::AssertionResult NearHelper(LiteralSlice expected,
-                                      LiteralSlice actual,
+::testing::AssertionResult NearHelper(const LiteralSlice& expected,
+                                      const LiteralSlice& actual,
                                       const ErrorSpec& error,
                                       bool detailed_message,
                                       const ShapeIndex& shape_index) {
@@ -874,30 +612,14 @@ ::testing::AssertionResult NearHelper(LiteralSlice expected,
 }  // namespace
 
 /* static */ ::testing::AssertionResult LiteralTestUtil::Near(
-    LiteralSlice expected, LiteralSlice actual, const ErrorSpec& error,
-    bool detailed_message) {
+    const LiteralSlice& expected, const LiteralSlice& actual,
+    const ErrorSpec& error, bool detailed_message) {
   return NearHelper(expected, actual, error, detailed_message,
                     /*shape_index=*/{});
 }
 
-/* static */ void LiteralTestUtil::ExpectNear(LiteralSlice expected,
-                                              LiteralSlice actual,
-                                              const ErrorSpec& error,
-                                              const string& message) {
-  ::testing::AssertionResult res =
-      Near(expected, actual, error, /*detailed_message=*/false);
-  if (!res) {
-    res << "Expected: " << TruncateHugeLiteral(expected) << "\n";
-    res << "Actual: " << TruncateHugeLiteral(actual) << "\n";
-    if (!message.empty()) {
-      res << StrCat("\nmessage: ", message);
-    }
-  }
-  EXPECT_TRUE(res);
-}
-
-/*static*/ ::testing::AssertionResult LiteralTestUtil::NearOrEqual(
-    LiteralSlice expected, LiteralSlice actual,
+/* static */ ::testing::AssertionResult LiteralTestUtil::NearOrEqual(
+    const LiteralSlice& expected, const LiteralSlice& actual,
     const tensorflow::gtl::optional& error) {
   if (error.has_value()) {
     VLOG(1) << "Expects near";
@@ -907,86 +629,4 @@ ::testing::AssertionResult NearHelper(LiteralSlice expected,
   return Equal(expected, actual);
 }
 
-/*static*/ void LiteralTestUtil::ExpectNearOrEqual(
-    LiteralSlice expected, LiteralSlice actual,
-    const tensorflow::gtl::optional& error) {
-  EXPECT_TRUE(NearOrEqual(expected, actual, error));
-}
-
-/* static */ string LiteralTestUtil::MultiIndexAsString(
-    tensorflow::gtl::ArraySlice multi_index) {
-  return StrCat("{", tensorflow::str_util::Join(multi_index, ","), "}");
-}
-
-/* static */ std::unique_ptr LiteralTestUtil::Reshape(
-    tensorflow::gtl::ArraySlice new_dimensions,
-    tensorflow::gtl::ArraySlice minor_to_major, LiteralSlice literal) {
-  int64 new_num_elements = 1;
-  for (int64 i = 0; i < new_dimensions.size(); ++i) {
-    new_num_elements *= new_dimensions[i];
-  }
-  CHECK_EQ(ShapeUtil::ElementsIn(literal.shape()), new_num_elements);
-  CHECK_EQ(new_dimensions.size(), minor_to_major.size());
-
-  auto new_literal = MakeUnique(
-      ShapeUtil::MakeShape(literal.shape().element_type(), new_dimensions));
-
-  // Create a new shape with the given minor-to-major layout. This shape is used
-  // solely for converting linear address to multi-dimensional addresses when
-  // writing elements to the new literal.
-  Shape shape_with_layout = new_literal->shape();
-  *shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout(minor_to_major);
-
-  // Copy data into new literal, element-by-element.
-  for (int64 i = 0; i < ShapeUtil::ElementsIn(literal.shape()); ++i) {
-    std::vector from_multi_index =
-        IndexUtil::LinearIndexToMultidimensionalIndex(literal.shape(), i);
-    std::vector to_multi_index =
-        IndexUtil::LinearIndexToMultidimensionalIndex(shape_with_layout, i);
-    switch (literal.shape().element_type()) {
-      case PRED:
-        new_literal->Set(to_multi_index,
-                               literal.Get(from_multi_index));
-        break;
-      case U8:
-        new_literal->Set(to_multi_index,
-                                literal.Get(from_multi_index));
-        break;
-      case U32:
-        new_literal->Set(to_multi_index,
-                                 literal.Get(from_multi_index));
-        break;
-      case S32:
-        new_literal->Set(to_multi_index,
-                                literal.Get(from_multi_index));
-        break;
-      case U64:
-        new_literal->Set(to_multi_index,
-                                 literal.Get(from_multi_index));
-        break;
-      case S64:
-        new_literal->Set(to_multi_index,
-                                literal.Get(from_multi_index));
-        break;
-      case F32:
-        new_literal->Set(to_multi_index,
-                                literal.Get(from_multi_index));
-        break;
-      case F64:
-        new_literal->Set(to_multi_index,
-                                 literal.Get(from_multi_index));
-        break;
-      case C64:
-        new_literal->Set(to_multi_index,
-                                    literal.Get(from_multi_index));
-        break;
-      default:
-        LOG(FATAL) << "Unhandled primitive element type: "
-                   << PrimitiveType_Name(literal.shape().element_type());
-    }
-  }
-
-  return new_literal;
-}
-
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.h b/tensorflow/compiler/xla/tests/literal_test_util.h
index 4983dddcff3284..c9cb8514e67c3b 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.h
+++ b/tensorflow/compiler/xla/tests/literal_test_util.h
@@ -57,65 +57,47 @@ class LiteralTestUtil {
  public:
   // Asserts that the given shapes have the same rank, dimension sizes, and
   // primitive types.
-  static ::testing::AssertionResult EqualShapes(const Shape& expected,
-                                                const Shape& actual);
-  static void AssertEqualShapes(const Shape& expected, const Shape& actual);
+  static ::testing::AssertionResult EqualShapes(
+      const Shape& expected, const Shape& actual) MUST_USE_RESULT;
 
   // Asserts that the provided shapes are equal as defined in AssertEqualShapes
   // and that they have the same layout.
-  static void AssertEqualShapesAndLayouts(const Shape& expected,
-                                          const Shape& actual);
+  static ::testing::AssertionResult EqualShapesAndLayouts(
+      const Shape& expected, const Shape& actual) MUST_USE_RESULT;
 
-  // If the given literal's data type is bfloat16, converts it to a float
-  // literal; otherwise, returns a copy of it. If the literal is a tuple,
-  // recursively converts its elements.
-  static std::unique_ptr ConvertBF16ToF32(LiteralSlice bf16_literal);
-
-  // If the given literal's data type is float, converts it to a bfloat16
-  // literal; otherwise, returns a copy of it. If the literal is a tuple,
-  // recursively converts its elements.
-  static std::unique_ptr ConvertF32ToBF16(LiteralSlice f32_literal);
-
-  // Asserts that the expected and actual literals are (bitwise) equal for all
-  // elements in the literal. Also, asserts that the rank, dimensions sizes, and
-  // primitive type are equal.
-  static ::testing::AssertionResult Equal(
-      LiteralSlice expected, LiteralSlice actual) TF_MUST_USE_RESULT;
-
-  // Expects that expected and actual are Equal.
-  static void ExpectEqual(LiteralSlice expected, LiteralSlice actual,
-                          const string& message = "");
-
-  // Expects that expected and actual are Not Equal.
-  static void ExpectNotEqual(LiteralSlice expected, LiteralSlice actual);
+  static ::testing::AssertionResult Equal(const LiteralSlice& expected,
+                                          const LiteralSlice& actual)
+      TF_MUST_USE_RESULT;
 
   // Asserts the given literal are (bitwise) equal to given expected values.
   template 
-  static void ExpectR0Equal(NativeT expected, LiteralSlice actual);
+  static void ExpectR0Equal(NativeT expected, const LiteralSlice& actual);
+
   template 
   static void ExpectR1Equal(tensorflow::gtl::ArraySlice expected,
-                            LiteralSlice actual);
+                            const LiteralSlice& actual);
   template 
   static void ExpectR2Equal(
       std::initializer_list> expected,
-      LiteralSlice actual);
+      const LiteralSlice& actual);
+
   template 
   static void ExpectR3Equal(
       std::initializer_list<
           std::initializer_list>>
           expected,
-      LiteralSlice actual);
+      const LiteralSlice& actual);
 
   // Asserts the given literal are (bitwise) equal to given array.
   template 
   static void ExpectR2EqualArray2D(const Array2D& expected,
-                                   LiteralSlice actual);
+                                   const LiteralSlice& actual);
   template 
   static void ExpectR3EqualArray3D(const Array3D& expected,
-                                   LiteralSlice actual);
+                                   const LiteralSlice& actual);
   template 
   static void ExpectR4EqualArray4D(const Array4D& expected,
-                                   LiteralSlice actual);
+                                   const LiteralSlice& actual);
 
   // Asserts that the expected and actual literals are within the given error
   // bound for all elements. Also, asserts that the rank, dimensions sizes, and
@@ -133,183 +115,138 @@ class LiteralTestUtil {
   // If detailed_message is true, then the error message in the assertion result
   // will contain a more detailed breakdown of mismatches.
   static ::testing::AssertionResult Near(
-      LiteralSlice expected, LiteralSlice actual, const ErrorSpec& error,
-      bool detailed_message = false) TF_MUST_USE_RESULT;
-
-  // Expects expected and actual to be Near with the given error.
-  static void ExpectNear(LiteralSlice expected, LiteralSlice actual,
-                         const ErrorSpec& error, const string& message = "");
+      const LiteralSlice& expected, const LiteralSlice& actual,
+      const ErrorSpec& error, bool detailed_message = false) TF_MUST_USE_RESULT;
 
   // Asserts the given literal are within the given error bound of the given
   // expected values. Only supported for floating point values.
   template 
-  static void ExpectR0Near(NativeT expected, LiteralSlice actual,
+  static void ExpectR0Near(NativeT expected, const LiteralSlice& actual,
                            const ErrorSpec& error);
+
   template 
   static void ExpectR1Near(tensorflow::gtl::ArraySlice expected,
-                           LiteralSlice actual, const ErrorSpec& error);
+                           const LiteralSlice& actual, const ErrorSpec& error);
+
   template 
   static void ExpectR2Near(
       std::initializer_list> expected,
-      LiteralSlice actual, const ErrorSpec& error);
+      const LiteralSlice& actual, const ErrorSpec& error);
+
   template 
   static void ExpectR3Near(
       std::initializer_list<
           std::initializer_list>>
           expected,
-      LiteralSlice actual, const ErrorSpec& error);
+      const LiteralSlice& actual, const ErrorSpec& error);
+
   template 
   static void ExpectR4Near(
       std::initializer_list>>>
           expected,
-      LiteralSlice actual, const ErrorSpec& error);
+      const LiteralSlice& actual, const ErrorSpec& error);
 
   // Asserts the given literal are within the given error bound to the given
   // array. Only supported for floating point values.
   template 
   static void ExpectR2NearArray2D(const Array2D& expected,
-                                  LiteralSlice actual, const ErrorSpec& error);
+                                  const LiteralSlice& actual,
+                                  const ErrorSpec& error);
+
   template 
   static void ExpectR3NearArray3D(const Array3D& expected,
-                                  LiteralSlice actual, const ErrorSpec& error);
+                                  const LiteralSlice& actual,
+                                  const ErrorSpec& error);
+
   template 
   static void ExpectR4NearArray4D(const Array4D& expected,
-                                  LiteralSlice actual, const ErrorSpec& error);
+                                  const LiteralSlice& actual,
+                                  const ErrorSpec& error);
 
   // If the error spec is given, returns whether the expected and the actual are
   // within the error bound; otherwise, returns whether they are equal. Tuples
   // will be compared recursively.
   static ::testing::AssertionResult NearOrEqual(
-      LiteralSlice expected, LiteralSlice actual,
+      const LiteralSlice& expected, const LiteralSlice& actual,
       const tensorflow::gtl::optional& error) TF_MUST_USE_RESULT;
 
-  // If the error spec is given, expects the expected and the actual to be near;
-  // otherwise, expects them to be equal. Tuples will be compared recursively.
-  static void ExpectNearOrEqual(
-      LiteralSlice expected, LiteralSlice actual,
-      const tensorflow::gtl::optional& error);
-
-  // Returns a multi-dimensional index as a string. For example: '{7, 8}' will
-  // be returned for a 2-dimensional index with dimension 0 index equal to 7,
-  // dimension 1 equal to 8.
-  static string MultiIndexAsString(
-      tensorflow::gtl::ArraySlice multi_index);
-
-  // Creates a literal with a new shape with the given new dimensions using the
-  // data in the given input literal. For reshaping purposes the (flat) data
-  // buffer of the input literal is assumed to have the given minor_to_major
-  // layout order.
-  static std::unique_ptr Reshape(
-      tensorflow::gtl::ArraySlice new_dimensions,
-      tensorflow::gtl::ArraySlice minor_to_major, LiteralSlice literal);
-
-  // Creates a literal with the supplied shape, and uses the provided value
-  // generator to populate the literal's values.
-  // Returns the new literal object, or an error Status if failed.
-  template <
-      PrimitiveType type,
-      typename T = typename primitive_util::PrimitiveTypeToNative::type>
-  static StatusOr> CreateRandomLiteral(
-      const Shape& shape,
-      const std::function)>& generator);
-
-  // Creates a literal with the supplied shape, and initializes the literal
-  // values using a normal distribution with given mean and stddev standard
-  // deviation, and using the engine as entropy generator.
-  // Returns the new literal object, or an error Status if failed.
-  template <
-      PrimitiveType type, typename E,
-      typename T = typename primitive_util::PrimitiveTypeToNative::type>
-  static StatusOr> CreateRandomLiteral(
-      const Shape& shape, E* engine, T mean, T stddev);
-
-  // Creates a literal with the supplied shape, and initializes the literal
-  // values using a normal distribution with given mean and stddev standard
-  // deviation.
-  // Returns the new literal object, or an error Status if failed.
-  template <
-      PrimitiveType type,
-      typename T = typename primitive_util::PrimitiveTypeToNative::type>
-  static StatusOr> CreateRandomLiteral(
-      const Shape& shape, T mean, T stddev);
-
  private:
   TF_DISALLOW_COPY_AND_ASSIGN(LiteralTestUtil);
 };
 
 template 
 /* static */ void LiteralTestUtil::ExpectR0Equal(NativeT expected,
-                                                 LiteralSlice actual) {
-  ExpectEqual(*Literal::CreateR0(expected), actual);
+                                                 const LiteralSlice& actual) {
+  EXPECT_TRUE(Equal(*Literal::CreateR0(expected), actual));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR1Equal(
-    tensorflow::gtl::ArraySlice expected, LiteralSlice actual) {
-  ExpectEqual(*Literal::CreateR1(expected), actual);
+    tensorflow::gtl::ArraySlice expected, const LiteralSlice& actual) {
+  EXPECT_TRUE(Equal(*Literal::CreateR1(expected), actual));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR2Equal(
     std::initializer_list> expected,
-    LiteralSlice actual) {
-  ExpectEqual(*Literal::CreateR2(expected), actual);
+    const LiteralSlice& actual) {
+  EXPECT_TRUE(Equal(*Literal::CreateR2(expected), actual));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR3Equal(
     std::initializer_list>>
         expected,
-    LiteralSlice actual) {
-  ExpectEqual(*Literal::CreateR3(expected), actual);
+    const LiteralSlice& actual) {
+  EXPECT_TRUE(Equal(*Literal::CreateR3(expected), actual));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR2EqualArray2D(
-    const Array2D& expected, LiteralSlice actual) {
-  ExpectEqual(*Literal::CreateR2FromArray2D(expected), actual);
+    const Array2D& expected, const LiteralSlice& actual) {
+  EXPECT_TRUE(Equal(*Literal::CreateR2FromArray2D(expected), actual));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR3EqualArray3D(
-    const Array3D& expected, LiteralSlice actual) {
-  ExpectEqual(*Literal::CreateR3FromArray3D(expected), actual);
+    const Array3D& expected, const LiteralSlice& actual) {
+  EXPECT_TRUE(Equal(*Literal::CreateR3FromArray3D(expected), actual));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR4EqualArray4D(
-    const Array4D& expected, LiteralSlice actual) {
-  ExpectEqual(*Literal::CreateR4FromArray4D(expected), actual);
+    const Array4D& expected, const LiteralSlice& actual) {
+  EXPECT_TRUE(Equal(*Literal::CreateR4FromArray4D(expected), actual));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR0Near(NativeT expected,
-                                                LiteralSlice actual,
+                                                const LiteralSlice& actual,
                                                 const ErrorSpec& error) {
-  ExpectNear(*Literal::CreateR0(expected), actual, error);
+  EXPECT_TRUE(Near(*Literal::CreateR0(expected), actual, error));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR1Near(
-    tensorflow::gtl::ArraySlice expected, LiteralSlice actual,
+    tensorflow::gtl::ArraySlice expected, const LiteralSlice& actual,
     const ErrorSpec& error) {
-  ExpectNear(*Literal::CreateR1(expected), actual, error);
+  EXPECT_TRUE(Near(*Literal::CreateR1(expected), actual, error));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR2Near(
     std::initializer_list> expected,
-    LiteralSlice actual, const ErrorSpec& error) {
-  ExpectNear(*Literal::CreateR2(expected), actual, error);
+    const LiteralSlice& actual, const ErrorSpec& error) {
+  EXPECT_TRUE(Near(*Literal::CreateR2(expected), actual, error));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR3Near(
     std::initializer_list>>
         expected,
-    LiteralSlice actual, const ErrorSpec& error) {
-  ExpectNear(*Literal::CreateR3(expected), actual, error);
+    const LiteralSlice& actual, const ErrorSpec& error) {
+  EXPECT_TRUE(Near(*Literal::CreateR3(expected), actual, error));
 }
 
 template 
@@ -317,63 +254,29 @@ template 
     std::initializer_list>>>
         expected,
-    LiteralSlice actual, const ErrorSpec& error) {
-  ExpectNear(*Literal::CreateR4(expected), actual, error);
+    const LiteralSlice& actual, const ErrorSpec& error) {
+  EXPECT_TRUE(Near(*Literal::CreateR4(expected), actual, error));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR2NearArray2D(
-    const Array2D& expected, LiteralSlice actual,
+    const Array2D& expected, const LiteralSlice& actual,
     const ErrorSpec& error) {
-  ExpectNear(*Literal::CreateR2FromArray2D(expected), actual, error);
+  EXPECT_TRUE(Near(*Literal::CreateR2FromArray2D(expected), actual, error));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR3NearArray3D(
-    const Array3D& expected, LiteralSlice actual,
+    const Array3D& expected, const LiteralSlice& actual,
     const ErrorSpec& error) {
-  ExpectNear(*Literal::CreateR3FromArray3D(expected), actual, error);
+  EXPECT_TRUE(Near(*Literal::CreateR3FromArray3D(expected), actual, error));
 }
 
 template 
 /* static */ void LiteralTestUtil::ExpectR4NearArray4D(
-    const Array4D& expected, LiteralSlice actual,
+    const Array4D& expected, const LiteralSlice& actual,
     const ErrorSpec& error) {
-  ExpectNear(*Literal::CreateR4FromArray4D(expected), actual, error);
-}
-
-template 
-/* static */ StatusOr>
-LiteralTestUtil::CreateRandomLiteral(
-    const Shape& shape,
-    const std::function)>& generator) {
-  using NativeT = typename primitive_util::PrimitiveTypeToNative::type;
-  TF_RET_CHECK(shape.element_type() == type);
-  std::unique_ptr literal = Literal::CreateFromShape(shape);
-  TF_RETURN_IF_ERROR(literal.get()->Populate(
-      [&](tensorflow::gtl::ArraySlice indexes) {
-        return generator(indexes);
-      }));
-  return std::move(literal);
-}
-
-template 
-/* static */ StatusOr>
-LiteralTestUtil::CreateRandomLiteral(const Shape& shape, E* engine, T mean,
-                                     T stddev) {
-  using NativeT = typename primitive_util::PrimitiveTypeToNative::type;
-  std::normal_distribution generator(mean, stddev);
-  return CreateRandomLiteral(
-      shape, [&](tensorflow::gtl::ArraySlice /*indexes*/) {
-        return generator(*engine);
-      });
-}
-
-template 
-/* static */ StatusOr>
-LiteralTestUtil::CreateRandomLiteral(const Shape& shape, T mean, T stddev) {
-  std::minstd_rand0 engine;
-  return CreateRandomLiteral(shape, &engine, mean, stddev);
+  EXPECT_TRUE(Near(*Literal::CreateR4FromArray4D(expected), actual, error));
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/tests/literal_test_util_test.cc b/tensorflow/compiler/xla/tests/literal_test_util_test.cc
index 9d619a77c7e8d6..bbac7285aefbb1 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util_test.cc
+++ b/tensorflow/compiler/xla/tests/literal_test_util_test.cc
@@ -34,7 +34,7 @@ TEST(LiteralTestUtilTest, ComparesEqualTuplesEqual) {
   std::unique_ptr literal = Literal::MakeTuple({
       Literal::CreateR0(42).get(), Literal::CreateR0(64).get(),
   });
-  LiteralTestUtil::ExpectEqual(*literal, *literal);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *literal));
 }
 
 TEST(LiteralTestUtilTest, ComparesUnequalTuplesUnequal) {
@@ -97,6 +97,15 @@ TEST(LiteralTestUtilTest, ExpectNearFailurePlacesResultsInTemporaryDirectory) {
   }
 }
 
+TEST(LiteralTestUtilTest, NotEqualHasValuesInMessage) {
+  auto expected = Literal::CreateR1({1, 2, 3});
+  auto actual = Literal::CreateR1({4, 5, 6});
+  ::testing::AssertionResult result =
+      LiteralTestUtil::Equal(*expected, *actual);
+  EXPECT_THAT(result.message(), ::testing::HasSubstr("expected: {1, 2, 3}"));
+  EXPECT_THAT(result.message(), ::testing::HasSubstr("actual:   {4, 5, 6}"));
+}
+
 TEST(LiteralTestUtilTest, NearComparatorR1) {
   auto a =
       Literal::CreateR1({0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8});
diff --git a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc
index 0a603f4954badd..7778053fb4478f 100644
--- a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc
+++ b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc
@@ -108,7 +108,7 @@ class MultiOutputFusionTest : public HloTestBase {
     expect.PopulateWithValue(size * 1.5f * 3.5f);
     auto actual = ExecuteAndTransfer(
         std::move(hlo_module), {Literal::CreateR0(-9.0f).get(), &arg1});
-    LiteralTestUtil::ExpectNear(expect, *actual, error_spec_);
+    EXPECT_TRUE(LiteralTestUtil::Near(expect, *actual, error_spec_));
   }
 
   void RunTest1D(bool manual_fusion, int size) {
@@ -168,7 +168,7 @@ class MultiOutputFusionTest : public HloTestBase {
 
     Literal expect = std::move(*Literal::CreateR1({size * 1.5f * 3.5f}));
     auto actual = ExecuteAndTransfer(std::move(hlo_module), {&input0, &input1});
-    LiteralTestUtil::ExpectNear(expect, *actual, error_spec_);
+    EXPECT_TRUE(LiteralTestUtil::Near(expect, *actual, error_spec_));
   }
 };
 
diff --git a/tensorflow/compiler/xla/tests/prng_test.cc b/tensorflow/compiler/xla/tests/prng_test.cc
index 29a4f75001c688..1a2de6937c3e13 100644
--- a/tensorflow/compiler/xla/tests/prng_test.cc
+++ b/tensorflow/compiler/xla/tests/prng_test.cc
@@ -273,11 +273,11 @@ XLA_TEST_F(PrngTest, PassInGlobalRngSeed) {
                                              &execution_options_));
   }
 
-  LiteralTestUtil::ExpectEqual(*result1, *result2);
-  LiteralTestUtil::ExpectEqual(*result1, *result3);
-  LiteralTestUtil::ExpectNotEqual(*result1, *result4);
-  LiteralTestUtil::ExpectNotEqual(*result4, *result5);
-  LiteralTestUtil::ExpectNotEqual(*result5, *result6);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*result1, *result2));
+  EXPECT_TRUE(LiteralTestUtil::Equal(*result1, *result3));
+  EXPECT_FALSE(LiteralTestUtil::Equal(*result1, *result4));
+  EXPECT_FALSE(LiteralTestUtil::Equal(*result4, *result5));
+  EXPECT_FALSE(LiteralTestUtil::Equal(*result5, *result6));
 }
 
 XLA_TEST_F(PrngTest, TenValuesN01) {
diff --git a/tensorflow/compiler/xla/tests/reshape_test.cc b/tensorflow/compiler/xla/tests/reshape_test.cc
index d7462d581b8596..a4580cd71d46ad 100644
--- a/tensorflow/compiler/xla/tests/reshape_test.cc
+++ b/tensorflow/compiler/xla/tests/reshape_test.cc
@@ -656,9 +656,9 @@ XLA_TEST_P(ReshapeTest, R4Dim0MinorLayoutToR2Dim0MajorLayout) {
   std::unique_ptr expected =
       Literal::CreateR2FromArray2D(expected_array);
   if (use_bfloat16()) {
-    expected = LiteralTestUtil::ConvertF32ToBF16(*expected);
+    expected = Literal::ConvertF32ToBF16(*expected);
   }
-  LiteralTestUtil::ExpectEqual(*expected, *actual);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *actual));
 }
 
 XLA_TEST_P(ReshapeTest, R2ToR4_3x8_To_3x2x1x4) {
@@ -731,7 +731,7 @@ XLA_TEST_P(ReshapeTest, R4ToR2_2x1x1x1_To_2x1) {
   builder.Reshape(parameter, /*dimensions=*/{0, 1, 2, 3}, /*new_sizes=*/{2, 1});
 
   std::unique_ptr expected =
-      LiteralTestUtil::Reshape({2, 1}, {1, 0}, *input_literal);
+      Literal::ReshapeSlice({2, 1}, {1, 0}, *input_literal);
   ComputeAndCompareLiteral(&builder, *expected, {input_data.get()},
                            zero_error_spec_);
 }
@@ -753,7 +753,7 @@ XLA_TEST_P(ReshapeTest, R4ToR2_2x1x4x1_To_4x2) {
   builder.Reshape(parameter, /*dimensions=*/{0, 1, 2, 3}, /*new_sizes=*/{4, 2});
 
   std::unique_ptr expected =
-      LiteralTestUtil::Reshape({4, 2}, {1, 0}, *input_literal);
+      Literal::ReshapeSlice({4, 2}, {1, 0}, *input_literal);
   ComputeAndCompareLiteral(&builder, *expected, {input_data.get()},
                            zero_error_spec_);
 }
@@ -817,7 +817,7 @@ XLA_TEST_P(ReshapeTest, NoopReshape) {
   // Since the reshape is a no-op, verify that it does not change the underlying
   // data.
   if (use_bfloat16()) {
-    auto expected = LiteralTestUtil::ConvertF32ToBF16(*input_literal);
+    auto expected = Literal::ConvertF32ToBF16(*input_literal);
     EXPECT_EQ(expected->data(), output_literal->data());
   } else {
     EXPECT_EQ(input_literal->data(), output_literal->data());
@@ -886,7 +886,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeSimple) {
                   /*new_sizes=*/new_bounds);
 
   std::unique_ptr expected =
-      LiteralTestUtil::Reshape(new_bounds, {2, 3, 1, 0}, *input_literal)
+      Literal::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal)
           ->Relayout(LayoutUtil::MakeLayout({3, 2, 1, 0}));
 
   // Specify the requested output shape explicitly to ensure that this reshape
@@ -915,7 +915,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeMajorFirstEffectiveR2) {
                   /*new_sizes=*/new_bounds);
 
   std::unique_ptr expected =
-      LiteralTestUtil::Reshape(new_bounds, {2, 3, 1, 0}, *input_literal)
+      Literal::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal)
           ->Relayout(LayoutUtil::MakeLayout({3, 2, 1, 0}));
 
   // Specify the requested output shape explicitly to ensure that this reshape
@@ -944,7 +944,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeMajorFirstMinorEffectiveR1) {
                   /*new_sizes=*/new_bounds);
 
   std::unique_ptr expected =
-      LiteralTestUtil::Reshape(new_bounds, {2, 3, 1, 0}, *input_literal)
+      Literal::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal)
           ->Relayout(LayoutUtil::MakeLayout({3, 2, 1, 0}));
 
   // Specify the requested output shape explicitly to ensure that this reshape
@@ -974,7 +974,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeMajorFirstMinorEffectiveR1InR2) {
                   /*new_sizes=*/new_bounds);
 
   std::unique_ptr expected =
-      LiteralTestUtil::Reshape(new_bounds, {2, 3, 1, 0}, *input_literal)
+      Literal::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal)
           ->Relayout(LayoutUtil::MakeLayout({3, 2, 1, 0}));
 
   // Specify the requested output shape explicitly to ensure that this reshape
@@ -1003,7 +1003,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeTrivialR2) {
                   /*new_sizes=*/new_bounds);
 
   std::unique_ptr expected =
-      LiteralTestUtil::Reshape(new_bounds, {1, 0, 2, 3}, *input_literal)
+      Literal::ReshapeSlice(new_bounds, {1, 0, 2, 3}, *input_literal)
           ->Relayout(input_literal->shape().layout());
 
   // Specify the requested output shape explicitly to ensure that this reshape
diff --git a/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc b/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc
index 8cbfcc6f5c4272..7cfca781acda15 100644
--- a/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc
+++ b/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc
@@ -100,7 +100,7 @@ TEST_F(RoundTripPackedLiteralTest, RoundTripsR2F32Size2x2Dim0Minor) {
   EXPECT_EQ(46.0f, actual->Get({1, 1}));
 
   std::unique_ptr round_tripped = RoundTripToServer(*actual);
-  LiteralTestUtil::ExpectEqual(*round_tripped, *actual);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*round_tripped, *actual));
 }
 
 TEST_F(RoundTripPackedLiteralTest, RoundTripsR2F32Size2x2Dim1Minor) {
@@ -135,7 +135,7 @@ TEST_F(RoundTripPackedLiteralTest, RoundTripsR2F32Size2x2Dim1Minor) {
   EXPECT_EQ(46.0f, actual->Get({1, 1}));
 
   std::unique_ptr round_tripped = RoundTripToServer(*actual);
-  LiteralTestUtil::ExpectEqual(*round_tripped, *actual);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*round_tripped, *actual));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc b/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc
index 32db45f8a66266..f334a8c1318a59 100644
--- a/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc
+++ b/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc
@@ -41,7 +41,7 @@ class RoundTripTransferTest : public ClientLibraryTestBase {
         client_->TransferToServer(original).ConsumeValueOrDie();
     std::unique_ptr result =
         client_->Transfer(*data).ConsumeValueOrDie();
-    LiteralTestUtil::ExpectEqual(original, *result);
+    EXPECT_TRUE(LiteralTestUtil::Equal(original, *result));
   }
 };
 
diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
index f35bc43a495213..308d3fc78a51e6 100644
--- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc
+++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
@@ -390,7 +390,7 @@ XLA_TEST_F(ScalarComputationsTest, DivU32s) {
                                      &execution_options_)
                 .ConsumeValueOrDie();
         auto expected_literal = Literal::CreateR0(dividend / divisor);
-        LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal);
+        EXPECT_TRUE(LiteralTestUtil::Equal(*expected_literal, *actual_literal));
       }
     }
   }
@@ -431,7 +431,7 @@ XLA_TEST_F(ScalarComputationsTest, RemU32s) {
                                      &execution_options_)
                 .ConsumeValueOrDie();
         auto expected_literal = Literal::CreateR0(dividend % divisor);
-        LiteralTestUtil::ExpectEqual(*expected_literal, *actual_literal);
+        EXPECT_TRUE(LiteralTestUtil::Equal(*expected_literal, *actual_literal));
       }
     }
   }
diff --git a/tensorflow/compiler/xla/tests/transfer_manager_test.cc b/tensorflow/compiler/xla/tests/transfer_manager_test.cc
index e2067bc1b835a9..0063e7ad415e9b 100644
--- a/tensorflow/compiler/xla/tests/transfer_manager_test.cc
+++ b/tensorflow/compiler/xla/tests/transfer_manager_test.cc
@@ -175,7 +175,7 @@ XLA_TEST_F(TransferManagerTest, TransferTuple) {
                           transfer_manager_->TransferLiteralFromDevice(
                               stream_executor_, device_buffer));
 
-  LiteralTestUtil::ExpectEqual(*literal, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result));
 }
 
 XLA_TEST_F(TransferManagerTest, TransferEmptyTuple) {
@@ -189,7 +189,7 @@ XLA_TEST_F(TransferManagerTest, TransferEmptyTuple) {
                           transfer_manager_->TransferLiteralFromDevice(
                               stream_executor_, device_buffer));
 
-  LiteralTestUtil::ExpectEqual(*literal, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result));
 }
 
 XLA_TEST_F(TransferManagerTest, TransferNestedTuple) {
@@ -209,7 +209,7 @@ XLA_TEST_F(TransferManagerTest, TransferNestedTuple) {
                           transfer_manager_->TransferLiteralFromDevice(
                               stream_executor_, device_buffer));
 
-  LiteralTestUtil::ExpectEqual(*literal, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result));
 }
 
 XLA_TEST_F(TransferManagerTest, TransferComplexValue) {
@@ -224,7 +224,7 @@ XLA_TEST_F(TransferManagerTest, TransferComplexValue) {
                           transfer_manager_->TransferLiteralFromDevice(
                               stream_executor_, device_buffer));
 
-  LiteralTestUtil::ExpectEqual(*literal, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result));
 }
 
 XLA_TEST_F(TransferManagerTest, TransferComplexValueInTuple) {
@@ -243,7 +243,7 @@ XLA_TEST_F(TransferManagerTest, TransferComplexValueInTuple) {
                           transfer_manager_->TransferLiteralFromDevice(
                               stream_executor_, device_buffer));
 
-  LiteralTestUtil::ExpectEqual(*literal, *result);
+  EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result));
 }
 
 }  // namespace

From 0043a0eb7280fe0f0f5a06d9d59ed517b7a189a4 Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy 
Date: Thu, 10 May 2018 20:55:55 -0700
Subject: [PATCH 0659/1691] Disable flaky batch_dataset_op_test

PiperOrigin-RevId: 196212027
---
 tensorflow/contrib/data/python/kernel_tests/BUILD | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD
index 9855688f2d1943..a3668d1b96f2c2 100644
--- a/tensorflow/contrib/data/python/kernel_tests/BUILD
+++ b/tensorflow/contrib/data/python/kernel_tests/BUILD
@@ -11,7 +11,10 @@ py_test(
     size = "medium",
     srcs = ["batch_dataset_op_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["no_pip"],
+    tags = [
+        "no_oss",
+        "no_pip",
+    ],
     deps = [
         ":dataset_serialization_test",
         "//tensorflow/contrib/data/python/ops:batching",

From 85b9d787a2385e3963f60cecde1ad190bb6f7c97 Mon Sep 17 00:00:00 2001
From: Chris Leary 
Date: Thu, 10 May 2018 21:15:35 -0700
Subject: [PATCH 0660/1691] [XLA] Roll forward fix to use TF macro.

PiperOrigin-RevId: 196213299
---
 tensorflow/compiler/xla/tests/literal_test_util.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/literal_test_util.h b/tensorflow/compiler/xla/tests/literal_test_util.h
index c9cb8514e67c3b..391abb1f1bd70c 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.h
+++ b/tensorflow/compiler/xla/tests/literal_test_util.h
@@ -58,12 +58,12 @@ class LiteralTestUtil {
   // Asserts that the given shapes have the same rank, dimension sizes, and
   // primitive types.
   static ::testing::AssertionResult EqualShapes(
-      const Shape& expected, const Shape& actual) MUST_USE_RESULT;
+      const Shape& expected, const Shape& actual) TF_MUST_USE_RESULT;
 
   // Asserts that the provided shapes are equal as defined in AssertEqualShapes
   // and that they have the same layout.
   static ::testing::AssertionResult EqualShapesAndLayouts(
-      const Shape& expected, const Shape& actual) MUST_USE_RESULT;
+      const Shape& expected, const Shape& actual) TF_MUST_USE_RESULT;
 
   static ::testing::AssertionResult Equal(const LiteralSlice& expected,
                                           const LiteralSlice& actual)

From 6064844b1c8cc1822eb74093c947a4ae35a75225 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 22:05:13 -0700
Subject: [PATCH 0661/1691] Correct accidental code reversion.

PiperOrigin-RevId: 196216176
---
 .../internal/reference/reference_ops.h        | 39 ++++++-------------
 1 file changed, 12 insertions(+), 27 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index 6a36bb2c055520..273b57414795dd 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -1456,33 +1456,6 @@ inline void BroadcastMul(const uint8* input1_data, const Dims<4>& input1_dims,
                output_data, output_dims);
 }
 
-inline void Div(const float* input1_data, const Dims<4>& input1_dims,
-                const float* input2_data, const Dims<4>& input2_dims,
-                float output_activation_min, float output_activation_max,
-                float* output_data, const Dims<4>& output_dims) {
-  const int batches =
-      MatchingArraySize(input1_dims, 3, input2_dims, 3, output_dims, 3);
-  const int height =
-      MatchingArraySize(input1_dims, 2, input2_dims, 2, output_dims, 2);
-  const int width =
-      MatchingArraySize(input1_dims, 1, input2_dims, 1, output_dims, 1);
-  const int depth =
-      MatchingArraySize(input1_dims, 0, input2_dims, 0, output_dims, 0);
-  for (int b = 0; b < batches; ++b) {
-    for (int y = 0; y < height; ++y) {
-      for (int x = 0; x < width; ++x) {
-        for (int c = 0; c < depth; ++c) {
-          output_data[Offset(output_dims, c, x, y, b)] =
-              ActivationFunctionWithMinMax(
-                  input1_data[Offset(input1_dims, c, x, y, b)] /
-                      input2_data[Offset(input2_dims, c, x, y, b)],
-                  output_activation_min, output_activation_max);
-        }
-      }
-    }
-  }
-}
-
 // TODO(jiawen): We can implement BroadcastDiv on buffers of arbitrary
 // dimensionality if the runtime code does a single loop over one dimension
 // that handles broadcasting as the base case. The code generator would then
@@ -1524,6 +1497,18 @@ void BroadcastDiv(const T* input1_data, const Dims<4>& input1_dims,
   }
 }
 
+inline void Div(const float* input1_data, const Dims<4>& input1_dims,
+                const float* input2_data, const Dims<4>& input2_dims,
+                float output_activation_min, float output_activation_max,
+                float* output_data, const Dims<4>& output_dims) {
+  const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims);
+  for (int i = 0; i < flat_size; ++i) {
+    output_data[i] = ActivationFunctionWithMinMax(
+        input1_data[i] / input2_data[i], output_activation_min,
+        output_activation_max);
+  }
+}
+
 inline void Sub(const float* input1_data, const Dims<4>& input1_dims,
                 const float* input2_data, const Dims<4>& input2_dims,
                 float output_activation_min, float output_activation_max,

From 84121edc10d84dc5826518caf910e5688d5a1734 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Thu, 10 May 2018 22:34:52 -0700
Subject: [PATCH 0662/1691] Add missing #include. tensorflow::FunctionDef only
 happens to be available in this header because it happens to be
 forward-declared in one of the other .proto.h headers, but it's not actually
 used there and will go away.

PiperOrigin-RevId: 196217574
---
 tensorflow/c/c_test_util.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/c/c_test_util.h b/tensorflow/c/c_test_util.h
index cd19cf8d624d9b..c16aba666ee697 100644
--- a/tensorflow/c/c_test_util.h
+++ b/tensorflow/c/c_test_util.h
@@ -20,6 +20,7 @@ limitations under the License.
 
 #include 
 #include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/types.pb.h"

From 12638c1c24c387e7c5b95a20a4d0f7275fa9e43d Mon Sep 17 00:00:00 2001
From: Mustafa Ispir 
Date: Thu, 10 May 2018 22:46:15 -0700
Subject: [PATCH 0663/1691] Added eval_dir to Estimator so that user does not
 need to guess which directory contains evaluation summaries.

PiperOrigin-RevId: 196218167
---
 tensorflow/python/estimator/estimator.py      | 21 ++++++++++++++-----
 tensorflow/python/estimator/estimator_test.py | 11 +++++++++-
 ...rflow.estimator.-baseline-classifier.pbtxt |  4 ++++
 ...orflow.estimator.-baseline-regressor.pbtxt |  4 ++++
 ....estimator.-boosted-trees-classifier.pbtxt |  4 ++++
 ...w.estimator.-boosted-trees-regressor.pbtxt |  4 ++++
 ...nsorflow.estimator.-d-n-n-classifier.pbtxt |  4 ++++
 ...or.-d-n-n-linear-combined-classifier.pbtxt |  4 ++++
 ...tor.-d-n-n-linear-combined-regressor.pbtxt |  4 ++++
 ...ensorflow.estimator.-d-n-n-regressor.pbtxt |  4 ++++
 .../tensorflow.estimator.-estimator.pbtxt     |  4 ++++
 ...sorflow.estimator.-linear-classifier.pbtxt |  4 ++++
 ...nsorflow.estimator.-linear-regressor.pbtxt |  4 ++++
 13 files changed, 70 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 99be13cb026a9d..9cfc680789219d 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -371,6 +371,21 @@ def _convert_train_steps_to_hooks(self, steps, max_steps):
     else:
       return []
 
+  def eval_dir(self, name=None):
+    """Shows directory name where evaluation metrics are dumped.
+
+    Args:
+      name: Name of the evaluation if user needs to run multiple evaluations on
+        different data sets, such as on training data vs test data. Metrics for
+        different evaluations are saved in separate folders, and appear
+        separately in tensorboard.
+
+    Returns:
+      A string which is the path of directory contains evaluation metrics.
+    """
+    return os.path.join(self._model_dir, 'eval' if not name else
+                        'eval_' + name)
+
   def evaluate(self, input_fn, steps=None, hooks=None, checkpoint_path=None,
                name=None):
     """Evaluates the model given evaluation data input_fn.
@@ -1325,10 +1340,6 @@ def _evaluate_model(self,
                      'initialization to evaluate.'.format(self._model_dir))
       checkpoint_path = latest_path
 
-    # Setup output directory.
-    eval_dir = os.path.join(self._model_dir, 'eval' if not name else
-                            'eval_' + name)
-
     with ops.Graph().as_default() as g:
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
@@ -1372,7 +1383,7 @@ def _evaluate_model(self,
           config=self._session_config)
 
       _write_dict_to_summary(
-          output_dir=eval_dir,
+          output_dir=self.eval_dir(name),
           dictionary=eval_results,
           current_global_step=eval_results[ops.GraphKeys.GLOBAL_STEP])
 
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index c9c6bdfeb5f834..0f268f5df90307 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -1061,6 +1061,15 @@ def _model_fn(features, labels, mode):
 
 class EstimatorEvaluateTest(test.TestCase):
 
+  def test_eval_dir(self):
+    est = estimator.Estimator(
+        model_fn=model_fn_global_step_incrementer,
+        model_dir='some_path')
+    expected_eval_dir = os.path.join('some_path', 'eval')
+    self.assertEqual(expected_eval_dir, est.eval_dir())
+    expected_eval_dir_name = os.path.join('some_path', 'eval_a_name')
+    self.assertEqual(expected_eval_dir_name, est.eval_dir('a_name'))
+
   def test_input_fn_args(self):
     expected_mode = model_fn_lib.ModeKeys.EVAL
     expected_params = {'batch_size': 10}
@@ -1385,7 +1394,7 @@ def model_fn_global_step_incrementer_image(features, labels, mode):
     # Get last evaluation Event written.
     for key in ['foo/0', 'foo/1', 'foo/2']:
       self.assertTrue(
-          check_eventfile_for_keyword(key, os.path.join(est.model_dir, 'eval')),
+          check_eventfile_for_keyword(key, est.eval_dir()),
           '{} should be part of reported summaries.'.format(key))
 
 
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt
index be9ba4ce85bd5b..cf22e39d4c8ab9 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt
@@ -23,6 +23,10 @@ tf_class {
     name: "__init__"
     argspec: "args=[\'self\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'config\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Ftrl\', \'None\', \'weighted_sum\'], "
   }
+  member_method {
+    name: "eval_dir"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "evaluate"
     argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt
index 91fca67b6b5b11..a363bceae3b57d 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt
@@ -23,6 +23,10 @@ tf_class {
     name: "__init__"
     argspec: "args=[\'self\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'config\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Ftrl\', \'None\', \'weighted_sum\'], "
   }
+  member_method {
+    name: "eval_dir"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "evaluate"
     argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-classifier.pbtxt
index 53a903c239b5fc..099838fa65f6a5 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-classifier.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-classifier.pbtxt
@@ -23,6 +23,10 @@ tf_class {
     name: "__init__"
     argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\'], "
   }
+  member_method {
+    name: "eval_dir"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "evaluate"
     argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-regressor.pbtxt
index ba17c90de28899..87bd19a23a3db7 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-regressor.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-regressor.pbtxt
@@ -23,6 +23,10 @@ tf_class {
     name: "__init__"
     argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'label_dimension\', \'weight_column\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\'], "
   }
+  member_method {
+    name: "eval_dir"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "evaluate"
     argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt
index cd4f72fcf839fa..111914f643a3b1 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt
@@ -23,6 +23,10 @@ tf_class {
     name: "__init__"
     argspec: "args=[\'self\', \'hidden_units\', \'feature_columns\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'activation_fn\', \'dropout\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Adagrad\', \'\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\'], "
   }
+  member_method {
+    name: "eval_dir"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "evaluate"
     argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt
index 303fd74a64d0c7..67e4ee02d05812 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt
@@ -23,6 +23,10 @@ tf_class {
     name: "__init__"
     argspec: "args=[\'self\', \'model_dir\', \'linear_feature_columns\', \'linear_optimizer\', \'dnn_feature_columns\', \'dnn_optimizer\', \'dnn_hidden_units\', \'dnn_activation_fn\', \'dnn_dropout\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'Ftrl\', \'None\', \'Adagrad\', \'None\', \'\', \'None\', \'2\', \'None\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\'], "
   }
+  member_method {
+    name: "eval_dir"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "evaluate"
     argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt
index c97ea7969eff3e..e1289b975e721e 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt
@@ -23,6 +23,10 @@ tf_class {
     name: "__init__"
     argspec: "args=[\'self\', \'model_dir\', \'linear_feature_columns\', \'linear_optimizer\', \'dnn_feature_columns\', \'dnn_optimizer\', \'dnn_hidden_units\', \'dnn_activation_fn\', \'dnn_dropout\', \'label_dimension\', \'weight_column\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'Ftrl\', \'None\', \'Adagrad\', \'None\', \'\', \'None\', \'1\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\'], "
   }
+  member_method {
+    name: "eval_dir"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "evaluate"
     argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt
index 4b5b5bf0e3599a..d030b2f51f019e 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt
@@ -23,6 +23,10 @@ tf_class {
     name: "__init__"
     argspec: "args=[\'self\', \'hidden_units\', \'feature_columns\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'activation_fn\', \'dropout\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Adagrad\', \'\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\'], "
   }
+  member_method {
+    name: "eval_dir"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "evaluate"
     argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt
index 42a0d595216ad2..d72b5769778d2e 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt
@@ -22,6 +22,10 @@ tf_class {
     name: "__init__"
     argspec: "args=[\'self\', \'model_fn\', \'model_dir\', \'config\', \'params\', \'warm_start_from\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
   }
+  member_method {
+    name: "eval_dir"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "evaluate"
     argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt
index 2de52d6c57cc70..cb578759eee2ed 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt
@@ -23,6 +23,10 @@ tf_class {
     name: "__init__"
     argspec: "args=[\'self\', \'feature_columns\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'config\', \'partitioner\', \'warm_start_from\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Ftrl\', \'None\', \'None\', \'None\', \'weighted_sum\'], "
   }
+  member_method {
+    name: "eval_dir"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "evaluate"
     argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt
index e552f33720bb93..fcd01bb663c7af 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt
@@ -23,6 +23,10 @@ tf_class {
     name: "__init__"
     argspec: "args=[\'self\', \'feature_columns\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'config\', \'partitioner\', \'warm_start_from\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Ftrl\', \'None\', \'None\', \'None\', \'weighted_sum\'], "
   }
+  member_method {
+    name: "eval_dir"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "evaluate"
     argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "

From 256c1d173c09198cf24fa7029499dfbdcbf1ee65 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 02:38:54 -0700
Subject: [PATCH 0664/1691] Remove 'using' of dnn types in CudnnSupport
 implementation file.

PiperOrigin-RevId: 196233933
---
 tensorflow/stream_executor/cuda/cuda_dnn.cc | 113 ++++++++++----------
 1 file changed, 54 insertions(+), 59 deletions(-)

diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc
index a0640e1b9d2539..78dbd43c2dc6b0 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.cc
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc
@@ -53,13 +53,6 @@ PLUGIN_REGISTRY_DEFINE_PLUGIN_ID(kCuDnnPlugin);
 
 namespace {
 
-// TODO(csigg): remove dnn namespace qualifier from the RNN code below.
-using ::stream_executor::dnn::BatchDescriptor;
-using ::stream_executor::dnn::ConvolutionDescriptor;
-using ::stream_executor::dnn::FilterDescriptor;
-using ::stream_executor::dnn::NormalizeDescriptor;
-using ::stream_executor::dnn::PoolingDescriptor;
-
 // Converts (via narrowing) a type T value to a type U, and checks that the
 // value has no value change due to the conversion.
 template 
@@ -390,7 +383,7 @@ namespace {
 // Turns a BatchDescriptor structure into a cudnn tensor handle within a scope.
 class ScopedTensorDescriptor {
  public:
-  ScopedTensorDescriptor(const BatchDescriptor& batch_descriptor,
+  ScopedTensorDescriptor(const dnn::BatchDescriptor& batch_descriptor,
                          cudnnDataType_t elem_type)
       : handle_(nullptr) {
     cudnnStatus_t status = cudnnCreateTensorDescriptor(&handle_);
@@ -464,7 +457,7 @@ class ScopedTensorDescriptor {
 // Turns a FilterDescriptor structure into a cudnn filter handle within a scope.
 class ScopedFilterDescriptor {
  public:
-  ScopedFilterDescriptor(const FilterDescriptor& filter_descriptor,
+  ScopedFilterDescriptor(const dnn::FilterDescriptor& filter_descriptor,
                          cudnnDataType_t elem_type)
       : handle_(nullptr) {
     cudnnStatus_t status = cudnnCreateFilterDescriptor(&handle_);
@@ -577,7 +570,7 @@ static bool BatchnormSpatialPersistentEnabled() {
 class ScopedConvolutionDescriptor {
  public:
   ScopedConvolutionDescriptor(
-      const ConvolutionDescriptor& convolution_descriptor,
+      const dnn::ConvolutionDescriptor& convolution_descriptor,
       cudnnDataType_t data_type)
       : handle_(nullptr) {
     cudnnStatus_t status = cudnnCreateConvolutionDescriptor(&handle_);
@@ -671,7 +664,8 @@ class ScopedConvolutionDescriptor {
 // within a scope.
 class ScopedPoolingDescriptor {
  public:
-  explicit ScopedPoolingDescriptor(const PoolingDescriptor& pooling_descriptor)
+  explicit ScopedPoolingDescriptor(
+      const dnn::PoolingDescriptor& pooling_descriptor)
       : handle_(nullptr) {
     cudnnStatus_t status = cudnnCreatePoolingDescriptor(&handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
@@ -727,7 +721,7 @@ class ScopedPoolingDescriptor {
 class ScopedNormalizeDescriptor {
  public:
   explicit ScopedNormalizeDescriptor(
-      const NormalizeDescriptor& normalize_descriptor)
+      const dnn::NormalizeDescriptor& normalize_descriptor)
       : handle_(nullptr) {
     cudnnStatus_t status = cudnnCreateLRNDescriptor(&handle_);
     if (status != CUDNN_STATUS_SUCCESS) {
@@ -2415,12 +2409,12 @@ cudnnDataType_t GetRnnComputeType(dnn::DataType data_type) {
 
 template 
 bool CudnnSupport::DoConvolveImpl(
-    Stream* stream, const BatchDescriptor& input_descriptor,
+    Stream* stream, const dnn::BatchDescriptor& input_descriptor,
     const DeviceMemory& input_data,
-    const FilterDescriptor& filter_descriptor,
+    const dnn::FilterDescriptor& filter_descriptor,
     const DeviceMemory& filter_data,
-    const ConvolutionDescriptor& convolution_descriptor,
-    const BatchDescriptor& output_descriptor, DeviceMemory* output_data,
+    const dnn::ConvolutionDescriptor& convolution_descriptor,
+    const dnn::BatchDescriptor& output_descriptor, DeviceMemory* output_data,
     ScratchAllocator* scratch_allocator,
     const dnn::AlgorithmConfig& algorithm_config,
     dnn::ProfileResult* output_profile_result) {
@@ -3038,13 +3032,13 @@ bool CudnnSupport::DoBatchNormalizationBackwardImpl(
 }
 
 bool CudnnSupport::DoConvolve(
-    Stream* stream, const BatchDescriptor& batch_descriptor,
+    Stream* stream, const dnn::BatchDescriptor& batch_descriptor,
     const DeviceMemory& input_data,
-    const FilterDescriptor& filter_descriptor,
+    const dnn::FilterDescriptor& filter_descriptor,
     const DeviceMemory& filter_data,
-    const ConvolutionDescriptor& convolution_descriptor,
-    const BatchDescriptor& output_descriptor, DeviceMemory* output_data,
-    ScratchAllocator* scratch_allocator,
+    const dnn::ConvolutionDescriptor& convolution_descriptor,
+    const dnn::BatchDescriptor& output_descriptor,
+    DeviceMemory* output_data, ScratchAllocator* scratch_allocator,
     const dnn::AlgorithmConfig& algorithm_config,
     dnn::ProfileResult* output_profile_result) {
   return DoConvolveImpl(
@@ -3054,13 +3048,13 @@ bool CudnnSupport::DoConvolve(
 }
 
 bool CudnnSupport::DoConvolve(
-    Stream* stream, const BatchDescriptor& batch_descriptor,
+    Stream* stream, const dnn::BatchDescriptor& batch_descriptor,
     const DeviceMemory& input_data,
-    const FilterDescriptor& filter_descriptor,
+    const dnn::FilterDescriptor& filter_descriptor,
     const DeviceMemory& filter_data,
-    const ConvolutionDescriptor& convolution_descriptor,
-    const BatchDescriptor& output_descriptor, DeviceMemory* output_data,
-    ScratchAllocator* scratch_allocator,
+    const dnn::ConvolutionDescriptor& convolution_descriptor,
+    const dnn::BatchDescriptor& output_descriptor,
+    DeviceMemory* output_data, ScratchAllocator* scratch_allocator,
     const dnn::AlgorithmConfig& algorithm_config,
     dnn::ProfileResult* output_profile_result) {
   return DoConvolveImpl(
@@ -3070,12 +3064,12 @@ bool CudnnSupport::DoConvolve(
 }
 
 bool CudnnSupport::DoConvolve(
-    Stream* stream, const BatchDescriptor& batch_descriptor,
+    Stream* stream, const dnn::BatchDescriptor& batch_descriptor,
     const DeviceMemory& input_data,
-    const FilterDescriptor& filter_descriptor,
+    const dnn::FilterDescriptor& filter_descriptor,
     const DeviceMemory& filter_data,
-    const ConvolutionDescriptor& convolution_descriptor,
-    const BatchDescriptor& output_descriptor,
+    const dnn::ConvolutionDescriptor& convolution_descriptor,
+    const dnn::BatchDescriptor& output_descriptor,
     DeviceMemory* output_data, ScratchAllocator* scratch_allocator,
     const dnn::AlgorithmConfig& algorithm_config,
     dnn::ProfileResult* output_profile_result) {
@@ -3202,7 +3196,8 @@ namespace {
 template 
 DeviceMemory MaybeTransformLayout(
     Stream* stream, const CudnnHandle& cudnn,
-    BatchDescriptor* output_descriptor, DeviceMemory backward_output_data,
+    dnn::BatchDescriptor* output_descriptor,
+    DeviceMemory backward_output_data,
     std::unique_ptr>* transform_scratch) {
   if (output_descriptor->layout() == dnn::DataLayout::kBatchDepthYX) {
     return backward_output_data;
@@ -3211,7 +3206,7 @@ DeviceMemory MaybeTransformLayout(
   *transform_scratch =
       stream->AllocateTemporaryArray(backward_output_data.ElementCount())
           .ConsumeValueOrDie();
-  BatchDescriptor transformed_output_descriptor;
+  dnn::BatchDescriptor transformed_output_descriptor;
   transformed_output_descriptor.CloneFrom(*output_descriptor);
   transformed_output_descriptor.set_layout(dnn::DataLayout::kBatchDepthYX);
   cudnnDataType_t cudnn_type = GetCudnnDataType();
@@ -3263,12 +3258,12 @@ bool CudnnSupport::DoTransformTensor(Stream* stream,
 
 template 
 bool CudnnSupport::DoConvolveBackwardDataImpl(
-    Stream* stream, const FilterDescriptor& filter_descriptor,
+    Stream* stream, const dnn::FilterDescriptor& filter_descriptor,
     const DeviceMemory& filter_data,
-    const BatchDescriptor& output_descriptor_in,
+    const dnn::BatchDescriptor& output_descriptor_in,
     DeviceMemory backward_output_data,
-    const ConvolutionDescriptor& convolution_descriptor,
-    const BatchDescriptor& input_descriptor,
+    const dnn::ConvolutionDescriptor& convolution_descriptor,
+    const dnn::BatchDescriptor& input_descriptor,
     DeviceMemory* backward_input_data, ScratchAllocator* scratch_allocator,
     const dnn::AlgorithmConfig& algorithm_config,
     dnn::ProfileResult* output_profile_result) {
@@ -3287,7 +3282,7 @@ bool CudnnSupport::DoConvolveBackwardDataImpl(
   auto cudnn = cudnn_->GetHandle(parent_, stream);
 
   // TBD(keveman): remove once cuDNN supports kBatchYXDepth for backward pass.
-  BatchDescriptor output_descriptor;
+  dnn::BatchDescriptor output_descriptor;
   output_descriptor.CloneFrom(output_descriptor_in);
   std::unique_ptr> transform_scratch;
   backward_output_data =
@@ -3475,12 +3470,12 @@ bool CudnnSupport::DoConvolveBackwardDataImpl(
 }
 
 bool CudnnSupport::DoConvolveBackwardData(
-    Stream* stream, const FilterDescriptor& filter_descriptor,
+    Stream* stream, const dnn::FilterDescriptor& filter_descriptor,
     const DeviceMemory& filter_data,
-    const BatchDescriptor& output_descriptor,
+    const dnn::BatchDescriptor& output_descriptor,
     DeviceMemory backward_output_data,
-    const ConvolutionDescriptor& convolution_descriptor,
-    const BatchDescriptor& input_descriptor,
+    const dnn::ConvolutionDescriptor& convolution_descriptor,
+    const dnn::BatchDescriptor& input_descriptor,
     DeviceMemory* backward_input_data,
     ScratchAllocator* scratch_allocator,
     const dnn::AlgorithmConfig& algorithm_config,
@@ -3493,12 +3488,12 @@ bool CudnnSupport::DoConvolveBackwardData(
 }
 
 bool CudnnSupport::DoConvolveBackwardData(
-    Stream* stream, const FilterDescriptor& filter_descriptor,
+    Stream* stream, const dnn::FilterDescriptor& filter_descriptor,
     const DeviceMemory& filter_data,
-    const BatchDescriptor& output_descriptor,
+    const dnn::BatchDescriptor& output_descriptor,
     DeviceMemory backward_output_data,
-    const ConvolutionDescriptor& convolution_descriptor,
-    const BatchDescriptor& input_descriptor,
+    const dnn::ConvolutionDescriptor& convolution_descriptor,
+    const dnn::BatchDescriptor& input_descriptor,
     DeviceMemory* backward_input_data,
     ScratchAllocator* scratch_allocator,
     const dnn::AlgorithmConfig& algorithm_config,
@@ -3511,12 +3506,12 @@ bool CudnnSupport::DoConvolveBackwardData(
 }
 
 bool CudnnSupport::DoConvolveBackwardData(
-    Stream* stream, const FilterDescriptor& filter_descriptor,
+    Stream* stream, const dnn::FilterDescriptor& filter_descriptor,
     const DeviceMemory& filter_data,
-    const BatchDescriptor& output_descriptor,
+    const dnn::BatchDescriptor& output_descriptor,
     DeviceMemory backward_output_data,
-    const ConvolutionDescriptor& convolution_descriptor,
-    const BatchDescriptor& input_descriptor,
+    const dnn::ConvolutionDescriptor& convolution_descriptor,
+    const dnn::BatchDescriptor& input_descriptor,
     DeviceMemory* backward_input_data,
     ScratchAllocator* scratch_allocator,
     const dnn::AlgorithmConfig& algorithm_config,
@@ -3554,7 +3549,7 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl(
   auto cudnn = cudnn_->GetHandle(parent_, stream);
 
   // TBD(keveman): remove once cuDNN supports kBatchYXDepth for backward pass.
-  BatchDescriptor output_descriptor;
+  dnn::BatchDescriptor output_descriptor;
   output_descriptor.CloneFrom(output_descriptor_in);
   std::unique_ptr> transform_scratch;
   backward_output_data =
@@ -3826,27 +3821,27 @@ bool CudnnSupport::DoConvolveBackwardBiasImpl(
 }
 
 bool CudnnSupport::DoConvolveBackwardBias(
-    Stream* stream, const BatchDescriptor& input_descriptor,
+    Stream* stream, const dnn::BatchDescriptor& input_descriptor,
     const DeviceMemory& input_data,
-    const BatchDescriptor& bias_descriptor,
+    const dnn::BatchDescriptor& bias_descriptor,
     DeviceMemory* backward_bias_data) {
   return DoConvolveBackwardBiasImpl(stream, input_descriptor, input_data,
                                     bias_descriptor, backward_bias_data);
 }
 
 bool CudnnSupport::DoConvolveBackwardBias(
-    Stream* stream, const BatchDescriptor& input_descriptor,
+    Stream* stream, const dnn::BatchDescriptor& input_descriptor,
     const DeviceMemory& input_data,
-    const BatchDescriptor& bias_descriptor,
+    const dnn::BatchDescriptor& bias_descriptor,
     DeviceMemory* backward_bias_data) {
   return DoConvolveBackwardBiasImpl(stream, input_descriptor, input_data,
                                     bias_descriptor, backward_bias_data);
 }
 
 bool CudnnSupport::DoConvolveBackwardBias(
-    Stream* stream, const BatchDescriptor& input_descriptor,
+    Stream* stream, const dnn::BatchDescriptor& input_descriptor,
     const DeviceMemory& input_data,
-    const BatchDescriptor& bias_descriptor,
+    const dnn::BatchDescriptor& bias_descriptor,
     DeviceMemory* backward_bias_data) {
   return DoConvolveBackwardBiasImpl(stream, input_descriptor, input_data,
                                     bias_descriptor, backward_bias_data);
@@ -3994,7 +3989,7 @@ bool CudnnSupport::DoBiasAdd(Stream* stream,
                              DeviceMemory* output_data) {
   ScopedTensorDescriptor input_descriptor(dimensions, CUDNN_DATA_FLOAT);
 
-  BatchDescriptor bias_dimensions;
+  dnn::BatchDescriptor bias_dimensions;
   bias_dimensions.set_count(1)
       .set_feature_map_count(dimensions.feature_map_count())
       .set_height(1)
@@ -4453,8 +4448,8 @@ bool CudnnSupport::DoMemcpyH2DQuantized(
 }
 
 bool CudnnSupport::DeriveOutputBatchDescriptor(
-    const BatchDescriptor& batch_descriptor,
-    const FilterDescriptor& filter_descriptor,
+    const dnn::BatchDescriptor& batch_descriptor,
+    const dnn::FilterDescriptor& filter_descriptor,
     const dnn::ConvolutionDescriptor& convolution_descriptor,
     dnn::BatchDescriptor* output_batch_descriptor) {
   ScopedTensorDescriptor input_nd(batch_descriptor, CUDNN_DATA_FLOAT);

From 20b3d4d297318874fd9b94b6bbeb3f90064ca9d4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 02:39:15 -0700
Subject: [PATCH 0665/1691] Fixing 'nothing to do' test in depthwise backward
 filter kernel for GPU.

PiperOrigin-RevId: 196233957
---
 tensorflow/core/kernels/depthwise_conv_grad_op.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/depthwise_conv_grad_op.cc b/tensorflow/core/kernels/depthwise_conv_grad_op.cc
index 7afa21acb919e1..42a4832910eb66 100644
--- a/tensorflow/core/kernels/depthwise_conv_grad_op.cc
+++ b/tensorflow/core/kernels/depthwise_conv_grad_op.cc
@@ -1076,7 +1076,7 @@ class DepthwiseConv2dNativeBackpropFilterOp : public OpKernel {
                                 {1}, 0, filter_shape, &filter_backprop));
 
     // If there is nothing to compute, return.
-    if (filter_shape.num_elements() == 0) {
+    if (out_backprop.shape().num_elements() == 0) {
       return;
     }
 

From 56646a1f5e6773c6637b2477670fcbc4385cf21b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 04:33:38 -0700
Subject: [PATCH 0666/1691] Add NNAPI 1.1 Div/Mul/Pad/Mean nodes.

PiperOrigin-RevId: 196240584
---
 .../contrib/lite/nnapi/NeuralNetworksShim.h   | 981 +-----------------
 tensorflow/contrib/lite/nnapi_delegate.cc     |  63 +-
 2 files changed, 69 insertions(+), 975 deletions(-)

diff --git a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h
index 4a648e42837fbf..becd1f615f04a8 100644
--- a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h
+++ b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h
@@ -65,7 +65,8 @@ inline bool NNAPIExists() {
   return nnapi_is_available;
 }
 
-// nn api types
+// NN api types based on NNAPI header file
+// https://developer.android.com/ndk/reference/group/neural-networks
 
 /**
  * Operand types.
@@ -77,31 +78,11 @@ inline bool NNAPIExists() {
  * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, and ANEURALNETWORKS_INT32.
  */
 enum {
-  /** The following entries are used to declare scalars. */
-
-  /** A 32 bit floating point scalar value. */
   ANEURALNETWORKS_FLOAT32 = 0,
-  /** A signed 32 bit integer scalar value. */
   ANEURALNETWORKS_INT32 = 1,
-  /** An unsigned 32 bit integer scalar value. */
   ANEURALNETWORKS_UINT32 = 2,
-
-  /** The following entries are used to declare tensors. */
-
-  /** A tensor of 32 bit floating point values. */
   ANEURALNETWORKS_TENSOR_FLOAT32 = 3,
-  /** A tensor of 32 bit integer values. */
   ANEURALNETWORKS_TENSOR_INT32 = 4,
-  /** A tensor of 8 bit integers that represent real numbers.
-   *
-   * Attached to this tensor are two numbers that can be used to convert
-   * the 8 bit integer to the real value and vice versa.  These two numbers are:
-   * - scale: a 32 bit floating point value
-   * - zero_value: an 32 bit integer
-   *
-   * The formula is:
-   * real_value = (integer_value - zero_value) * scale.
-   */
   ANEURALNETWORKS_TENSOR_QUANT8_ASYMM = 5,
 };
 
@@ -111,968 +92,44 @@ enum {
  * The type of operations that can be added to a model.
  */
 enum {
-  /** Adds two tensors, element-wise.
-   *
-   * Takes two input tensors of identical type and compatible dimensions. The
-   * output is the sum of both input tensors, optionally modified by an
-   * activation function.
-   *
-   * Two dimensions are compatible when:
-   *     1. they are equal, or
-   *     2. one of them is 1
-   *
-   * The size of the output is the maximum size along each dimension of the
-   * input operands. It starts with the trailing dimensions, and works its way
-   * forward.
-   *
-   * Example:
-   *
-   *     input1.dimension = {4, 1, 2}
-   *     input2.dimension = {5, 4, 3, 1}
-   *     output.dimension = {5, 4, 3, 2}
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   *
-   * Supported tensor rank: up to 4
-   *
-   * Inputs:
-   * * 0: A tensor.
-   * * 1: A tensor of the same type, and compatible dimensions as input0.
-   * * 2: An INT32 value, and has to be one of the {@link FuseCode} values.
-   *      Specifies the activation to invoke on the result of each addition.
-   *
-   * Outputs:
-   * * 0: The sum, a tensor of the same type as input0.
-   */
   ANEURALNETWORKS_ADD = 0,
-  /** Performs a 2-D average pooling operation.
-   *
-   * The output dimensions are functions of the filter dimensions, stride, and
-   * padding.
-   *
-   * The values in the output tensor are computed as:
-   *
-   *     output[batch, row, col, channel] =
-   *         sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: 4, with "NHWC" data layout.
-   *
-   * Inputs:
-   * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the
-   * input.
-   * * 1: An INT32 value, specifying the padding on the left, in the ‘width’
-   * dimension.
-   * * 2: An INT32 value, specifying the padding on the right,in the ‘width’
-   * dimension.
-   * * 3: An INT32 value, specifying the padding on the top, in the ‘height’
-   * dimension.
-   * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’
-   * dimension.
-   * * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
-   * * 6: An INT32 value, specifying the output stride in the ‘height’
-   * dimension.
-   * * 7: An INT32 value, specifying the filter width.
-   * * 8: An INT32 value, specifying the filter height.
-   * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
-   *      Specifies the activation to invoke on the result of each addition.
-   *
-   * Outputs:
-   * * 0: The output 4-D tensor, of shape [batches, out_height, out_width,
-   * depth].
-   */
   ANEURALNETWORKS_AVERAGE_POOL_2D = 1,
-  /** Concatenates the input tensors along the given dimension.
-   *
-   * The input tensors must have identical type and the same dimensions except
-   * the dimension along the concatenation axis.
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: up to 4
-   *
-   * Inputs:
-   * 0 ~ n: The list on n input tensors, of shape [D0, D1, ..., Daxis(i), ...,
-   * Dm] n+1: An INT32 value, specifying the concatenation axis. n+2: An INT32
-   * value, and has to be one of the {@link FuseCode} values. Specifies the
-   * activation to invoke on the result of each addition.
-   *
-   * Outputs:
-   * * 0: The output, a tensor of the same type as the input tensors.
-   *      The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
-   */
   ANEURALNETWORKS_CONCATENATION = 2,
-  /** Performs an 2-D convolution operation.
-   *
-   * The CONV_2D op sweeps a 2-D filter that can mix channels together over a
-   * batch of images, applying the filter to each window of each image of the
-   * appropriate size.
-   *
-   * The output dimensions are functions of the filter dimensions, stride, and
-   * padding.
-   *
-   * The values in the output tensor are computed as:
-   *
-   *     output[batch, row, col, channel] =
-   *         sum_{i, j} (
-   *             input[batch, row + i, col + j, k] *
-   *             filter[channel, row + i, col + j, k] +
-   *             bias[channel]
-   *         )
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: 4, with "NHWC" data layout.
-   *
-   * Inputs:
-   * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying
-   * the input.
-   * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width,
-   * depth_in], specifying the filter.
-   * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
-   *      For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the
-   * bias should also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input
-   * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias should
-   * be of {@link ANEURALNETWORKS_TENSOR_INT32}.
-   * * 3: An INT32 value, specifying the padding on the left, in the ‘width’
-   * dimension.
-   * * 4: An INT32 value, specifying the padding on the right,in the ‘width’
-   * dimension.
-   * * 5: An INT32 value, specifying the padding on the top, in the ‘height’
-   * dimension.
-   * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’
-   * dimension.
-   * * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
-   * * 8: An INT32 value, specifying the output stride in the ‘height’
-   * dimension.
-   * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
-   *      Specifies the activation to invoke on the result of each addition.
-   *
-   * Outputs:
-   * * 0: The output 4-D tensor, of shape [batches, out_height, out_width,
-   * depth_out].
-   */
   ANEURALNETWORKS_CONV_2D = 3,
-  /** Performs a depthwise 2-D convolution operation.
-   *
-   * Given an input tensor of shape [batches, height, width, depth_in] and a
-   * filter tensor of shape [depth_out, filter_height, filter_width, depth_in]
-   * containing in_channels convolutional filters of depth 1, DEPTHWISE_CONV
-   * applies a different filter to each input channel (expanding from 1 channel
-   * to channel_multiplier channels for each), then concatenates the results
-   * together.
-   *
-   * The output has depth_out = depth_in * depth_multiplier channels.
-   * The output dimensions are functions of the filter dimensions, stride, and
-   * padding.
-   *
-   * The values in the output tensor are computed as:
-   *
-   *     output[b, i, j, k * channel_multiplier + q] =
-   *         sum_{di, dj} (
-   *             input[b, strides[1] * i + di, strides[2] * j + dj, k] *
-   *             filter[di, dj, k, q]
-   *         )
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: 4, with "NHWC" data layout.
-   *
-   * Inputs:
-   * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying
-   * the input.
-   * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width,
-   * depth_in], specifying the filter.
-   * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
-   *      For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the
-   * bias should also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input
-   * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias should
-   * be of {@link ANEURALNETWORKS_TENSOR_INT32}.
-   * * 3: An INT32 value, specifying the padding on the left, in the ‘width’
-   * dimension.
-   * * 4: An INT32 value, specifying the padding on the right,in the ‘width’
-   * dimension.
-   * * 5: An INT32 value, specifying the padding on the top, in the ‘height’
-   * dimension.
-   * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’
-   * dimension.
-   * * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
-   * * 8: An INT32 value, specifying the output stride in the ‘height’
-   * dimension.
-   * * 9: An INT32 value, specifying the depthwise multiplier.
-   * * 10: An INT32 value, and has to be one of the {@link FuseCode} values.
-   *       Specifies the activation to invoke on the result of each addition.
-   *
-   * Outputs:
-   * * 0: The output 4-D tensor, of shape [batches, out_height, out_width,
-   * depth_out].
-   */
   ANEURALNETWORKS_DEPTHWISE_CONV_2D = 4,
-  /** Rearranges data from depth into blocks of spatial data.
-   *
-   * More specifically, this op outputs a copy of the input tensor where values
-   * from the depth dimension are moved in spatial blocks to the height and
-   * width dimensions. The value block_size indicates the input block size and
-   * how the data is moved.
-   *
-   * Chunks of data of size block_size * block_size from depth are rearranged
-   * into non-overlapping blocks of size block_size x block_size.
-   *
-   * The width of the output tensor is input_depth * block_size, whereas the
-   * height is input_height * block_size. The depth of the input tensor must be
-   * divisible by block_size * block_size
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: 4, with "NHWC" data layout.
-   *
-   * Inputs:
-   * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying
-   * the input.
-   * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
-   *      block_size * block_size must be a divisor of the input depth.
-   *
-   * Outputs:
-   * * 0: The output 4-D tensor, of shape [batch, height*block_size,
-   * width*block_size, depth/(block_size*block_size)].
-   */
   ANEURALNETWORKS_DEPTH_TO_SPACE = 5,
-  /** Dequantizes the input tensor.
-   *
-   * The formula is:
-   *
-   *     output = (input - zero_value) * scale.
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: up to 4
-   *
-   * Inputs:
-   * * 0: A tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}.
-   *
-   * Outputs:
-   * * 0: The output tensor of same shape as input0, but with type
-   *      {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
-   */
   ANEURALNETWORKS_DEQUANTIZE = 6,
-
-  /**
-   * Looks up items from a given tensor.
-   *
-   * Each item in the output is a raw copy of the corresponding item in
-   * the input “values”. If the given “lookup” indices are out of bounds,
-   * the op will fail and an error will be reported.
-   *
-   * Inputs:
-   * * 0: Values. An n-D tensor of any type X (where n >= 2). E.g., if n is 2,
-   *      then the shape would be [lookup_dimension, values_dimension], where
-   *      “lookup_dimension” corresponds to the indexing dimension in the lookup
-   *      table, and “values_dimension” to the contents.
-   * * 1: Lookups. An 1-D tensor of type T, of shape [lookup_size], where
-   *      “lookup_size” is the number of elements to look for, and each entry
-   *      corresponds to the first dimension of the “values” tensor.
-   *
-   * Output:
-   * * 0: A n-D tensor of type X and the same rank and shape as the “values”
-   *      tensor, except for the first dimension which has size “lookup_size”.
-   */
   ANEURALNETWORKS_EMBEDDING_LOOKUP = 7,
-
-  /** Computes element-wise floor() on the input tensor.
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   *
-   * Supported tensor rank: up to 4
-   *
-   * Inputs:
-   * * 0: A tensor.
-   *
-   * Outputs:
-   * * 0: The output, a tensor of the same type and dimensions as input0.
-   */
   ANEURALNETWORKS_FLOOR = 8,
-  /** Denotes a fully (densely) connected layer, which connects all elements in
-   * the input tensor with each element in the output tensor.
-   *
-   * This layer implements the operation:
-   *
-   *     outputs = activation(inputs * weights’ + bias)
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: up to 4.
-   *
-   * Inputs:
-   * * 0: A tensor, specifying the input. If rank is greater than 2, then it
-   * gets flattened to a 2-D Tensor. The 2-D Tensor is handled as if dimensions
-   * corresponded to shape [batch_size, input_size], where “batch_size”
-   * corresponds to the batching dimension, and “input_size” is the size of the
-   * input.
-   * * 1: A 2-D tensor, specifying the weights, of shape [num_units,
-   * input_size], where "num_units" corresponds to the number of output nodes.
-   * * 2: A 1-D tensor, of shape [num_units], specifying the bias.
-   *      For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the
-   * bias should also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input
-   * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias should
-   * be of {@link ANEURALNETWORKS_TENSOR_INT32}.
-   * * 3: An INT32 value, and has to be one of the {@link FuseCode} values.
-   *      Specifies the activation to invoke on the result of each addition.
-   *
-   * Outputs:
-   * * 0: The output tensor, of shape [batch_size, num_units].
-   */
   ANEURALNETWORKS_FULLY_CONNECTED = 9,
-
-  /**
-   * Looks up values of a hash table with given keys.
-   *
-   * Inputs:
-   * * 0: Lookups. A 1-D int32 tensor with shape [ k ].
-   * * 1: Keys. A 1-D int32 tensor with shape [ n ], *MUST* be sorted in
-   *      ascending order.
-   * * 2: Values. A tensor with shape [ n … ].
-   *
-   * Outputs:
-   * * 0: Output. A tensor with shape [ k …].
-   * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup
-   *      hits or not.
-   */
   ANEURALNETWORKS_HASHTABLE_LOOKUP = 10,
-
-  /** Applies L2 normalization along the depth dimension.
-   *
-   * The values in the output tensor are computed as:
-   *
-   *     output[batch, row, col, channel] =
-   *         input[batch, row, col, channel] /
-   *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
-   *
-   * For x with more dimensions, independently normalizes each 1-D slice along
-   * dimension dim.
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   *
-   * Supported tensor rank: 4, with "NHWC" data layout.
-   *
-   * Inputs:
-   * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the
-   * input.
-   *
-   * Outputs:
-   * * 0: The output 4-D tensor, of shape [batches, out_height, out_width,
-   * depth].
-   */
   ANEURALNETWORKS_L2_NORMALIZATION = 11,
-
-  /** Performs an 2-D L2 pooling operation.
-   *
-   * The output dimensions are functions of the filter dimensions, stride, and
-   * padding.
-   *
-   * The values in the output tensor are computed as:
-   *
-   *     output[batch, row, col, channel] =
-   *         sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) /
-   * sum(1))
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   *
-   * Supported tensor rank: 4, with "NHWC" data layout.
-   *
-   * Inputs:
-   * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the
-   * input.
-   * * 1: An INT32 value, specifying the padding on the left, in the ‘width’
-   * dimension.
-   * * 2: An INT32 value, specifying the padding on the right,in the ‘width’
-   * dimension.
-   * * 3: An INT32 value, specifying the padding on the top, in the ‘height’
-   * dimension.
-   * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’
-   * dimension.
-   * * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
-   * * 6: An INT32 value, specifying the output stride in the ‘height’
-   * dimension.
-   * * 7: An INT32 value, specifying the filter width.
-   * * 8: An INT32 value, specifying the filter height.
-   * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
-   *      Specifies the activation to invoke on the result of each addition.
-   *
-   * Outputs:
-   * * 0: The output 4-D tensor, of shape [batches, out_height, out_width,
-   * depth].
-   */
   ANEURALNETWORKS_L2_POOL_2D = 12,
-  /** Applies Local Response Normalization along the depth dimension.
-   *
-   * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the
-   * last dimension), and each vector is normalized independently. Within a
-   * given vector, each component is divided by the weighted, squared sum of
-   * inputs within depth_radius.
-   *
-   * The output is calculated using this formula:
-   *
-   *     sqr_sum[a, b, c, d] =
-   *         sum(pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2)
-   *     output = input / pow((bias + alpha * sqr_sum), beta)
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   *
-   * Supported tensor rank: 4, with "NHWC" data layout.
-   *
-   * Inputs:
-   * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the
-   * input.
-   * * 1: An INT32 value, specifying the radius of the normalization window.
-   * * 2: A FLOAT32 value, specifying the bias, must not be zero.
-   * * 3: A FLOAT32 value, specifying the scale factor, alpha.
-   * * 4: A FLOAT32 value, specifying the exponent, beta.
-   *
-   * Outputs:
-   * * 0: The output tensor of same shape as input0.
-   */
   ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION = 13,
-  /** Computes sigmoid activation on the input tensor element-wise.
-   *
-   * The output is calculated using this formula:
-   *
-   *     output = 1 / (1 + exp(-input))
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: up to 4.
-   *
-   * Inputs:
-   * * 0: A tensor, specifying the input.
-   *
-   * Outputs:
-   * * 0: The output tensor of same shape as input0.
-   */
   ANEURALNETWORKS_LOGISTIC = 14,
-
-  /**
-   * Projects an input to a bit vector via locality sensitive hashing.
-   *
-   * Inputs:
-   * * 0: Hash functions. Dim.size == 2, DataType: Float.
-   *            Tensor[0].Dim[0]: Number of hash functions.
-   *            Tensor[0].Dim[1]: Number of seeds per hash functions.
-   *            Tensor[0].Dim[1] <= 32 in sparse case.
-   *
-   * * 1: Input. Dim.size >= 1, no restriction on DataType.
-   * * 2: Weight. Optional. Dim.size == 1, DataType: Float.
-   *     If not set, each input element is considered to have the same weight of
-   *     1.0.
-   *     Tensor[1].Dim[0] == Tensor[2].Dim[0]
-   * * 3: Type:
-   *        Sparse: Value LSHProjectionType_SPARSE(=1).
-   *          Computed bit vector is considered to be sparse.
-   *          Each output element is an int32 made up of multiple bits computed
-   * from hash functions.
-   *
-   *        Dense: Value LSHProjectionType_DENSE(=2).
-   *          Computed bit vector is considered to be dense. Each output element
-   *          represents a bit and can take the value of either 0 or 1.
-   *
-   * Outputs:
-   * * 0: If the projection type is sparse:
-   *        Output.Dim == { Tensor[0].Dim[0] }
-   *        A tensor of int32 that represents hash signatures.
-   *      If the projection type is Dense:
-   *        Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
-   *        A flattened tensor that represents projected bit vectors.
-   */
   ANEURALNETWORKS_LSH_PROJECTION = 15,
-
-  /**
-   * Long short-term memory unit (LSTM) recurrent network layer.
-   *
-   * The default non-peephole implementation is based on:
-   * http://www.bioinf.jku.at/publications/older/2604.pdf
-   * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
-   * Computation, 9(8):1735-1780, 1997.
-   *
-   * The peephole implementation is based on:
-   * https://research.google.com/pubs/archive/43905.pdf
-   * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
-   * recurrent neural network architectures for large scale acoustic modeling."
-   * INTERSPEECH, 2014.
-   *
-   * The coupling of input and forget gate (CIFG) is based on:
-   * http://arxiv.org/pdf/1503.04069.pdf
-   * Greff et al. "LSTM: A Search Space Odyssey"
-   *
-   * The class has the following independently optional inputs:
-   * * If input gate (if CIFG): “input_to_forget_weights”,
-   *   “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”.
-   * * If no peephole connections: “cell_to_input_weights”,
-   *   “cell_to_forget_weights”, “cell_to_output_weights”.
-   * * If no projection layer: “projection_weights” and “projection_bias”.
-   * * If no projection bias: “projection_bias”.
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   *
-   * Inputs:
-   * * 0: Input.
-   *      A 2-D tensor of type T, of shape [batch_size, input_size], where
-   *      “batch_size” corresponds to the batching dimension, and “input_size”
-   *      is the size of the input.
-   * * 1: input_to_input_weights.
-   *      A 2-D tensor of type T, of shape [num_units, input_size], where
-   *      “num_units” corresponds to the number of cell units.
-   * * 2: input_to_forget_weights.
-   *      A 2-D tensor of type T, of shape [num_units, input_size].
-   * * 3: input_to_cell_weights.
-   *      A 2-D tensor of type T, of shape [num_units, input_size].
-   * * 4: input_to_output_weights.
-   *      A 2-D tensor of type T, of shape [num_units, input_size].
-   * * 5: recurrent_to_input_weights.
-   *      A 2-D tensor of type T, of shape [num_units, output_size], where
-   *      “output_size” corresponds to either the number of cell units (i.e.,
-   *      “num_units”), or the second dimension of the “projection_weights”, if
-   *      defined.
-   * * 6: recurrent_to_forget_weights.
-   *      A 2-D tensor of type T, of shape [num_units, output_size].
-   * * 7: recurrent_to_cell_weights.
-   *      A 2-D tensor of type T, of shape [num_units, output_size].
-   * * 8: recurrent_to_output_weights.
-   *      A 2-D tensor of type T, of shape [num_units, output_size].
-   * * 9: cell_to_input_weights.
-   *      A 1-D tensor of type T, of shape [num_units].
-   * * 10:cell_to_forget_weights.
-   *      A 1-D tensor of type T, of shape [num_units].
-   * * 11:cell_to_output_weights.
-   *      A 1-D tensor of type T, of shape [num_units].
-   * * 12:input_gate_bias.
-   *      A 1-D tensor of type T, of shape [num_units].
-   * * 13:forget_gate_bias.
-   *      A 1-D tensor of type T, of shape [num_units].
-   * * 14:cell_bias.
-   *      A 1-D tensor of type T, of shape [num_units].
-   * * 15:output_gate_bias.
-   *      A 1-D tensor of type T, of shape [num_units].
-   * * 16:projection_weights.
-   *      A 2-D tensor of type T, of shape [output_size, num_units].
-   * * 17:projection_bias.
-   *      A 1-D tensor of type T, of shape [output_size].
-   *
-   * Parameters:
-   * * 18:fused_activation_function.
-   *      An (optional) ActivationFunctionType indicating the activation
-   *      function.
-   *      If “NONE” is specified then it results in a linear activation.
-   * * 19:cell_clip.
-   *      A clipping threshold for the cell state, such that values are bound
-   *      within [-cell_clip, cell_clip]. If set to 0.0 then clipping is
-   *      disabled.
-   * * 20:proj_clip.
-   *      A clipping threshold for the output from the projection layer, such
-   *      that values are bound within [-proj_clip, proj_clip]. If set to 0.0
-   *      then clipping is disabled.
-   *
-   * Outputs:
-   * * 0: scratch_buffer.
-   *      A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
-   * * 1: output_state.
-   *      A 2-D tensor of type T, of shape [batch_size, output_size].
-   * * 2: cell_state.
-   *      A 2-D tensor of type T, of shape [batch_size, num_units].
-   * * 3: output.
-   *      A 2-D tensor of type T, of shape [batch_size, output_size]. This is
-   *      effectively the same as the current “output_state” value.
-   */
   ANEURALNETWORKS_LSTM = 16,
-
-  /** Performs an 2-D max pooling operation.
-   *
-   * The output dimensions are functions of the filter dimensions, stride, and
-   * padding.
-   *
-   * The values in the output tensor are computed as:
-   *
-   *     output[batch, row, col, channel] =
-   *         max_{i, j} (input[batch, row + i, col + j, channel])
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: 4, with "NHWC" data layout.
-   *
-   * Inputs:
-   * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the
-   * input.
-   * * 1: An INT32 value, specifying the padding on the left, in the ‘width’
-   * dimension.
-   * * 2: An INT32 value, specifying the padding on the right,in the ‘width’
-   * dimension.
-   * * 3: An INT32 value, specifying the padding on the top, in the ‘height’
-   * dimension.
-   * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’
-   * dimension.
-   * * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
-   * * 6: An INT32 value, specifying the output stride in the ‘height’
-   * dimension.
-   * * 7: An INT32 value, specifying the filter width.
-   * * 8: An INT32 value, specifying the filter height.
-   * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
-   *      Specifies the activation to invoke on the result of each addition.
-   *
-   * Outputs:
-   * * 0: The output 4-D tensor, of shape [batches, out_height, out_width,
-   * depth].
-   */
   ANEURALNETWORKS_MAX_POOL_2D = 17,
-
-  /** Multiplies two tensors, element-wise.
-   *
-   * Takes two input tensors of identical type and compatible dimensions. The
-   * output is the product of both input tensors, optionally modified by an
-   * activation function.
-   *
-   * Two dimensions are compatible when:
-   *     1. they are equal, or
-   *     2. one of them is 1
-   *
-   * The size of the resulting output is the maximum size along each dimension
-   * of the input operands. It starts with the trailing dimensions, and works
-   * its way forward.
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   *
-   * Supported tensor rank: up to 4
-   *
-   * Inputs:
-   * * 0: A tensor.
-   * * 1: A tensor of the same type, and compatible dimensions as input0.
-   * * 2: An INT32 value, and has to be one of the {@link FuseCode} values.
-   *      Specifies the activation to invoke on the result of each addition.
-   *
-   * Outputs:
-   * * 0: The product, a tensor of the same type as input0.
-   */
   ANEURALNETWORKS_MUL = 18,
-  /** Computes rectified linear activation on the input tensor element-wise.
-   *
-   * The output is calculated using this formula:
-   *
-   *     output = max(0, input)
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: up to 4.
-   *
-   * Inputs:
-   * * 0: A tensor, specifying the input.
-   *
-   * Outputs:
-   * * 0: The output tensor of same shape as input0.
-   */
   ANEURALNETWORKS_RELU = 19,
-  /** Computes rectified linear 1 activation on the input tensor element-wise.
-   *
-   * The output is calculated using this formula:
-   *
-   *     output = min(1.f, max(-1.f, input))
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: up to 4.
-   *
-   * Inputs:
-   * * 0: A tensor, specifying the input.
-   *
-   * Outputs:
-   * * 0: The output tensor of same shape as input0.
-   */
   ANEURALNETWORKS_RELU1 = 20,
-  /** Computes rectified linear 6 activation on the input tensor element-wise.
-   *
-   * The output is calculated using this formula:
-   *
-   *     output = min(6, max(0, input))
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: up to 4.
-   *
-   * Inputs:
-   * * 0: A tensor, specifying the input.
-   *
-   * Outputs:
-   * * 0: The output tensor of same shape as input0.
-   */
   ANEURALNETWORKS_RELU6 = 21,
-  /** Reshapes a tensor.
-   *
-   * Given tensor, this operation returns a tensor that has the same values as
-   * tensor, but with a newly specified shape.
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: up to 4.
-   *
-   * Inputs:
-   * * 0: A tensor, specifying the tensor to be reshaped.
-   * * 1: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32}, defining
-   * the shape of the output tensor. The number of elements implied by shape
-   * must be the same as the number of elements in the input tensor.
-   *
-   * Outputs:
-   * * 0: The output tensor, of shape specified by the input shape.
-   */
   ANEURALNETWORKS_RESHAPE = 22,
-  /** Resizes images to given size using the bilinear interpretation.
-   *
-   * Resized images will be distorted if their original aspect ratio is not the
-   * same as input.
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   *
-   * Supported tensor rank: 4, with "NHWC" data layout.
-   *
-   * Inputs:
-   * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the
-   * input.
-   * * 1: An INT32 value, specifying the output width of the output tensor.
-   * * 2: An INT32 value, specifying the output height of the output tensor.
-   *
-   * Outputs:
-   * * 0: The output 4-D tensor, of shape [batches, new_height, new_width,
-   * depth].
-   */
   ANEURALNETWORKS_RESIZE_BILINEAR = 23,
-
-  /**
-   * A basic recurrent neural network layer.
-   *
-   * This layer implements the operation:
-   * outputs = state = activation(inputs * input_weights + state *
-   * recurrent_weights + bias)
-   *
-   * Where:
-   * * “input_weights” is a weight matrix that multiplies the inputs;
-   * * “recurrent_weights” is a weight matrix that multiplies the current
-   *    “state” which itself is the output from the previous time step
-   *    computation;
-   * * “bias” is a bias vector (added to each output vector in the batch);
-   * * “activation” is the function passed as the “fused_activation_function”
-   *   argument (if not “NONE”).
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   *
-   * Inputs:
-   * * 0: input.
-   *      A 2-D tensor of type T, of shape [batch_size, input_size], where
-   *      “batch_size” corresponds to the batching dimension, and “input_size”
-   * is the size of the input.
-   * * 1: weights.
-   *      A 2-D tensor of type T, of shape [num_units, input_size], where
-   *      “num_units” corresponds to the number of units.
-   * * 2: recurrent_weights.
-   *      A 2-D tensor of type T, of shape [num_units, num_units], with columns
-   *      corresponding to the weights from each unit.
-   * * 3: bias.
-   *      A 1-D tensor of type T, of shape [num_units].
-   *
-   *    For FLOAT32 input tensor, bias must also be FLOAT32.
-   *    For UINT8 input tensor, bias must be INT32.
-   *
-   * Parameters
-   * * 4: fused_activation_function.
-   *      An (optional) ActivationFunctionType indicating the activation
-   *      function. If “NONE” is specified then it results in a linear
-   *      activation.
-   *
-   * * 5: Hidden state.
-   *      A 2-D tensor of type T, of shape [batch_size, num_units].
-   *
-   * Outputs:
-   * * 0: output.
-   *      A 2-D tensor of type T, of shape [batch_size, num_units]. This is
-   *      effectively the same as the current state value.
-   */
   ANEURALNETWORKS_RNN = 24,
-
-  /** Computes the softmax activation on the input tensor element-wise, per
-   * batch, by normalizing the input vector so the maximum coefficient is zero.
-   *
-   * The output is calculated using this formula:
-   *
-   *     output[batch, i] =
-   *         exp((input[batch, i] - max(input[batch, :])) * beta) /
-   *         sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: 2 or 4.
-   *
-   * Inputs:
-   * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
-   * * 1: A FLOAT32 value, specifying the scaling factor for the exponent, beta.
-   *
-   * Outputs:
-   * * 0: The output tensor of same shape as input0.
-   */
   ANEURALNETWORKS_SOFTMAX = 25,
-
-  /** Rearranges blocks of spatial data, into depth.
-   *
-   * More specifically, this op outputs a copy of the input tensor where values
-   * from the height and width dimensions are moved to the depth dimension. The
-   * value block_size indicates the input block size and how the data is moved.
-   *
-   * Chunks of data of size block_size * block_size from depth are rearranged
-   * into non-overlapping blocks of size block_size x block_size.
-   *
-   * The depth of the output tensor is input_depth * block_size * block_size.
-   * The input tensor's height and width must be divisible by block_size.
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-   *
-   * Supported tensor rank: 4, with "NHWC" data layout.
-   *
-   * Inputs:
-   * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying
-   * the input.
-   * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
-   *      block_size must be a divisor of both the input height and width.
-   *
-   * Outputs:
-   * * 0: The output 4-D tensor, of shape [batch, height/block_size,
-   * width/block_size, depth*block_size*block_size].
-   */
   ANEURALNETWORKS_SPACE_TO_DEPTH = 26,
-
-  /**
-   * SVDF op is a kind of stateful layer derived from the notion that a
-   * densely connected layer that's processing a sequence of input frames can
-   * be approximated by using a singular value decomposition of each of its
-   * nodes. The implementation is based on:
-   *
-   * https://research.google.com/pubs/archive/43813.pdf
-   *
-   * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
-   * “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
-   * INTERSPEECH, 2015.
-   *
-   * It processes the incoming input using a 2-stage filtering mechanism:
-   * * stage 1 performs filtering on the "features" dimension, whose outputs get
-   *   pushed into a memory of fixed-size memory_size.
-   * * stage 2 performs filtering on the "time" dimension of the memory_size
-   *   memoized outputs of stage 1.
-   *
-   * Specifically, for rank 1, this layer implements the operation:
-   *
-   *    memory = push(conv1d(inputs, weights_feature, feature_dim, "VALID"));
-   *    outputs = activation(memory * weights_time + bias);
-   *
-   * Where:
-   * * “weights_feature” is a weights matrix that processes the inputs (by
-   *   convolving the input with every “feature filter”), and whose outputs get
-   *   pushed, stacked in order, into the fixed-size “memory” (the oldest entry
-   *   gets dropped);
-   * * “weights_time” is a weights matrix that processes the “memory” (by a
-   *   batched matrix multiplication on the num_units);
-   * * “bias” is an optional bias vector (added to each output vector in the
-   *   batch); and
-   * * “activation” is the function passed as the “fused_activation_function”
-   *   argument (if not “NONE”).
-   *
-   * Each rank adds a dimension to the weights matrices by means of stacking
-   * the filters.
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   *
-   * Inputs:
-   * * 0: input.
-   *      A 2-D tensor of type T, of shape [batch_size, input_size], where
-   *      “batch_size” corresponds to the batching dimension, and “input_size”
-   * is the size of the input.
-   * * 1: weights_feature.
-   *      A 2-D tensor of type T, of shape [num_units, input_size], where
-   *      “num_units” corresponds to the number of units.
-   * * 2: weights_time.
-   *      A 2-D tensor of type T, of shape [num_units, memory_size], where
-   *      “memory_size” corresponds to the fixed-size of the memory.
-   * * 3: bias.
-   *      A optional 1-D tensor of type T, of shape [num_units].
-   *
-   *    For FLOAT32 input tensor, bias must also be FLOAT32.
-   *    For UINT8 input tensor, bias must be INT32.
-   *
-   * Parameters:
-   * * 4: rank.
-   *      The rank of the SVD approximation.
-   * * 5: fused_activation_function.
-   *      An (optional) ActivationFunctionType indicating the activation
-   * function. If “NONE” is specified then it results in a linear activation.
-   *
-   * Outputs:
-   * * 0: state.
-   *      A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) *
-   * num_units * rank].
-   * * 1: output.
-   *      A 2-D tensor of type T, of shape [batch_size, num_units].
-   */
   ANEURALNETWORKS_SVDF = 27,
-
-  /** Computes hyperbolic tangent of input tensor element-wise.
-   *
-   * The output is calculated using this formula:
-   *
-   *     output = tanh(input)
-   *
-   * Supported tensor types:
-   * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
-   *
-   * Supported tensor rank: up to 4.
-   *
-   * Inputs:
-   * * 0: A tensor, specifying the input.
-   *
-   * Outputs:
-   * * 0: The output tensor of same shape as input0.
-   */
   ANEURALNETWORKS_TANH = 28,
+  ANEURALNETWORKS_BATCH_TO_SPACE_ND = 29,
+  ANEURALNETWORKS_DIV = 30,
+  ANEURALNETWORKS_MEAN = 31,
+  ANEURALNETWORKS_PAD = 32,
+  ANEURALNETWORKS_SPACE_TO_BATCH_ND = 33,
+  ANEURALNETWORKS_SQUEEZE = 34,
+  ANEURALNETWORKS_STRIDED_SLICE = 35,
+  ANEURALNETWORKS_SUB = 36,
+  ANEURALNETWORKS_TRANSPOSE = 37,
 };
 
 /**
@@ -1080,13 +137,9 @@ enum {
  *
  */
 enum {
-  /** NO fused activation function. */
   ANEURALNETWORKS_FUSED_NONE = 0,
-  /** Fused ReLU activation function. */
   ANEURALNETWORKS_FUSED_RELU = 1,
-  /** Fused ReLU1 activation function. */
   ANEURALNETWORKS_FUSED_RELU1 = 2,
-  /** Fused ReLU6 activation function. */
   ANEURALNETWORKS_FUSED_RELU6 = 3,
 };
 
@@ -1094,20 +147,8 @@ enum {
  * Execution preferences.
  */
 enum {
-  /**
-   * Prefer executing in a way that minimizes battery drain.
-   * This is desirable for compilations that will be executed often.
-   */
   ANEURALNETWORKS_PREFER_LOW_POWER = 0,
-  /**
-   * Prefer returning a single answer as fast as possible, even if this causes
-   * more power consumption.
-   */
   ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1,
-  /**
-   * Prefer maximizing the throughput of successive frames, for example when
-   * processing successive frames coming from the camera.
-   */
   ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2,
 };
 
diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc
index 1810dfae32694a..d99c88a26d9e32 100644
--- a/tensorflow/contrib/lite/nnapi_delegate.cc
+++ b/tensorflow/contrib/lite/nnapi_delegate.cc
@@ -23,6 +23,10 @@ limitations under the License.
 #include "tensorflow/contrib/lite/model.h"
 #include "tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h"
 
+#ifdef __ANDROID__
+#include 
+#endif
+
 namespace tflite {
 
 // TODO(aselle): FATAL leaves resources hanging.
@@ -46,6 +50,32 @@ void FATAL(const char* format, ...) {
     FATAL("Aborting since tflite returned failure."); \
   }
 
+namespace {
+
+int32_t GetAndroidSdkVersion() {
+#ifdef __ANDROID__
+  const char* sdkProp = "ro.build.version.sdk";
+  char sdkVersion[PROP_VALUE_MAX];
+  int length = __system_property_get(sdkProp, sdkVersion);
+  if (length != 0) {
+    for (int i = 0; i < length; ++i) {
+      int digit = sdkVersion[i] - '0';
+      if (digit < 0 || digit > 9) {
+        // Non-numeric SDK version, assume it's higher then expected;
+        return 0xFFFF;
+      }
+    }
+    return atoi(sdkVersion);
+  }
+  FATAL("No %s prop", sdkProp);
+#endif  // __ANDROID__
+  return 0;
+}
+
+static const int32_t kAndroidSdkVersion = GetAndroidSdkVersion();
+
+}  // namespace
+
 NNAPIAllocation::NNAPIAllocation(const char* filename,
                                  ErrorReporter* error_reporter)
     : MMAPAllocation(filename, error_reporter) {
@@ -245,6 +275,11 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       add_scalar_float32(builtin->proj_clip);
     };
 
+    auto add_mean_params = [&add_scalar_int32](void* data) {
+      auto builtin = reinterpret_cast(data);
+      add_scalar_int32(builtin->keep_dims);
+    };
+
 #if 0
     auto add_reshape_params = [&](void* data) {
       auto builtin = reinterpret_cast(data);
@@ -262,8 +297,9 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       augmented_inputs.push_back(next_id++);
     };
 #endif
-
+    int nnapi_version = 10;
     ANeuralNetworksOperationType nn_op_type;
+
     switch (builtin) {
       case tflite::BuiltinOperator_ADD:
         nn_op_type = ANEURALNETWORKS_ADD;
@@ -337,6 +373,23 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
         nn_op_type = ANEURALNETWORKS_LSTM;
         break;
       }
+      case tflite::BuiltinOperator_PAD:
+        nnapi_version = 11;  // require NNAPI 1.1
+        nn_op_type = ANEURALNETWORKS_PAD;
+        break;
+      case tflite::BuiltinOperator_MEAN:
+        nnapi_version = 11;  // require NNAPI 1.1
+        add_mean_params(node.builtin_data);
+        nn_op_type = ANEURALNETWORKS_MEAN;
+        break;
+      case tflite::BuiltinOperator_DIV:
+        nnapi_version = 11;  // require NNAPI 1.1
+        nn_op_type = ANEURALNETWORKS_DIV;
+        break;
+      case tflite::BuiltinOperator_SUB:
+        nnapi_version = 11;  // require NNAPI 1.1
+        nn_op_type = ANEURALNETWORKS_SUB;
+        break;
       case tflite::BuiltinOperator_CONCAT_EMBEDDINGS:
       case tflite::BuiltinOperator_LSH_PROJECTION:
       case tflite::BuiltinOperator_SVDF:
@@ -350,7 +403,6 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM:
       case tflite::BuiltinOperator_L2_NORMALIZATION:
       case tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION:
-      case tflite::BuiltinOperator_PAD:
       case tflite::BuiltinOperator_PADV2:
       case tflite::BuiltinOperator_RESIZE_BILINEAR:
       case tflite::BuiltinOperator_CALL:
@@ -361,9 +413,6 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       case tflite::BuiltinOperator_BATCH_TO_SPACE_ND:
       case tflite::BuiltinOperator_TOPK_V2:
       case tflite::BuiltinOperator_TRANSPOSE:
-      case tflite::BuiltinOperator_MEAN:
-      case tflite::BuiltinOperator_DIV:
-      case tflite::BuiltinOperator_SUB:
       case tflite::BuiltinOperator_SPLIT:
       case tflite::BuiltinOperator_SQUEEZE:
       case tflite::BuiltinOperator_STRIDED_SLICE:
@@ -393,6 +442,10 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
         break;
     }
 
+    if (nnapi_version == 11 && kAndroidSdkVersion < 28) {
+      FATAL("Op %d needs NNAPI1.1", builtin);
+    }
+
     // Add the operation.
     CHECK_NN(ANeuralNetworksModel_addOperation(
         nn_model, nn_op_type, static_cast(augmented_inputs.size()),

From 6a43945520afbf4a6e54923402ae65c1e8361dfa Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 07:51:14 -0700
Subject: [PATCH 0667/1691] Make core:device_tracer private to core/BUILD.

PiperOrigin-RevId: 196254936
---
 tensorflow/core/BUILD       | 1 +
 tensorflow/core/debug/BUILD | 1 -
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index ccb84887e11432..2f5f6ae17b50ba 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -2566,6 +2566,7 @@ tf_cuda_library(
     ],
     copts = tf_copts(),
     cuda_deps = tf_additional_cupti_wrapper_deps() + tf_additional_device_tracer_cuda_deps(),
+    visibility = ["//visibility:private"],
     deps = [
         ":core_cpu_internal",
         ":lib",
diff --git a/tensorflow/core/debug/BUILD b/tensorflow/core/debug/BUILD
index 5fab740e920519..1528c7f130657c 100644
--- a/tensorflow/core/debug/BUILD
+++ b/tensorflow/core/debug/BUILD
@@ -90,7 +90,6 @@ tf_cuda_library(
     deps = [
         ":debug",
         "//tensorflow/core:core_cpu_internal",
-        "//tensorflow/core:device_tracer",
         "//tensorflow/core:direct_session_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",

From 4aa456ef505f60fed357b9e321703468471304c7 Mon Sep 17 00:00:00 2001
From: Jingyue Wu 
Date: Fri, 11 May 2018 09:27:13 -0700
Subject: [PATCH 0668/1691] ArithmeticOptimizer assumes valid feeds in
 aggressive mode.

ArithmeticOptimizer depends heavily on shapes in some stages.

PiperOrigin-RevId: 196264319
---
 .../optimizers/arithmetic_optimizer.cc        |  3 +-
 .../optimizers/arithmetic_optimizer_test.cc   | 61 +++++++++++++++++++
 2 files changed, 63 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
index 26eca9b82004e7..30da23d212b0ff 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -2526,7 +2526,8 @@ Status ArithmeticOptimizer::Optimize(Cluster* /*cluster*/,
   TF_RETURN_IF_ERROR(TopologicalSort(optimized_graph_));
 
   graph_properties_.reset(new GraphProperties(optimized_item));
-  const Status status = graph_properties_->InferStatically(false);
+  const bool assume_valid_feeds = opt_level_ == RewriterConfig::AGGRESSIVE;
+  const Status status = graph_properties_->InferStatically(assume_valid_feeds);
   const bool can_use_shapes = status.ok();
   if (!can_use_shapes) {
     VLOG(1) << "Shape inference failed." << status.error_message();
diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
index d648fa0787333c..27c0dde41938c3 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc
@@ -964,6 +964,67 @@ TEST_F(ArithmeticOptimizerTest, IdentityReshape) {
   test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
 }
 
+TEST_F(ArithmeticOptimizerTest, NotAssumeValidFeeds) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+  Output inputs =
+      ops::Placeholder(s, DT_FLOAT, ops::Placeholder::Shape({4, 3, 28, 28}));
+  Output target_shape = ops::Const(s, {4, 3, 28, 28}, {4});
+  Output reshape = ops::Reshape(s, inputs, target_shape);
+  Output outputs = ops::Identity(s.WithOpName("outputs"), reshape);
+
+  auto x_t = GenerateRandomTensor(TensorShape({4, 3, 28, 28}));
+  GrapplerItem item;
+  item.fetch = {"outputs"};
+  item.feed = {{"Placeholder", x_t}};
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors_expected.size());
+
+  GraphDef output;
+  TF_EXPECT_OK(ArithmeticOptimizer().Optimize(nullptr, item, &output));
+
+  item.graph.Swap(&output);
+  TF_EXPECT_OK(ModelPruner().Optimize(nullptr, item, &output));
+
+  // The reshape is preserved because the shape of the placeholder can be
+  // different from the shape of the actual feed.
+  EXPECT_EQ(1, CountOpNodes(output, "Reshape"));
+
+  auto tensors = EvaluateNodes(output, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
+}
+
+TEST_F(ArithmeticOptimizerTest, AssumeValidFeedsInAggressiveMode) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+  Output inputs =
+      ops::Placeholder(s, DT_FLOAT, ops::Placeholder::Shape({4, 3, 28, 28}));
+  Output target_shape = ops::Const(s, {4, 3, 28, 28}, {4});
+  Output reshape = ops::Reshape(s, inputs, target_shape);
+  Output outputs = ops::Identity(s.WithOpName("outputs"), reshape);
+
+  auto x_t = GenerateRandomTensor(TensorShape({4, 3, 28, 28}));
+  GrapplerItem item;
+  item.fetch = {"outputs"};
+  item.feed = {{"Placeholder", x_t}};
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+
+  auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors_expected.size());
+  GraphDef output;
+  TF_EXPECT_OK(ArithmeticOptimizer(RewriterConfig::AGGRESSIVE)
+                   .Optimize(nullptr, item, &output));
+
+  item.graph.Swap(&output);
+  TF_EXPECT_OK(ModelPruner().Optimize(nullptr, item, &output));
+
+  EXPECT_EQ(0, CountOpNodes(output, "Reshape"));
+  auto tensors = EvaluateNodes(output, item.fetch, item.feed);
+  EXPECT_EQ(1, tensors.size());
+  test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6);
+}
+
 TEST_F(ArithmeticOptimizerTest, NotIdentityReshape) {
   // Reshape from [-1,3,28,28] to [8,-1,28,28] is not identity, because it can
   // be from [4,3,28,28] to [8,6,28,28].

From b03008bdbb4dbcdecc3eb1505669e49094267b67 Mon Sep 17 00:00:00 2001
From: David Norman 
Date: Fri, 11 May 2018 09:40:34 -0700
Subject: [PATCH 0669/1691] Allow for disabling of some tests (#19202)

---
 .../compiler/xla/tests/dot_operation_test.cc     | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index efa5aed2d1af8e..b236cf00a8053a 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -798,7 +798,7 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64,
       this->error_spec_);
 }
 
-TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstRHSClassicMM) {
+XLA_TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstRHSClassicMM) {
   std::unique_ptr> constant_lhs_array(new Array2D(
       {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}}));
   std::unique_ptr> constant_rhs_array(
@@ -826,7 +826,7 @@ TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstRHSClassicMM) {
   ComputeAndCompareR2(&builder, expected, {}, error_spec_);
 }
 
-TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstLHSClassicMM) {
+XLA_TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstLHSClassicMM) {
   std::unique_ptr> constant_lhs_array(new Array2D(
       {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}}));
   std::unique_ptr> constant_rhs_array(
@@ -855,7 +855,7 @@ TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstLHSClassicMM) {
 }
 
 // TODO (b/69062148) Enable when Dot implements general contracting dimensions.
-TEST_F(DotOperationTest,
+XLA_TEST_F(DotOperationTest,
        DISABLED_ON_CPU(DISABLED_ON_GPU(DISABLED_ON_INTERPRETER(
            DotOfGatherOptimizationWithConstRHSReverseMM)))) {
   std::unique_ptr> constant_lhs_array(
@@ -886,7 +886,7 @@ TEST_F(DotOperationTest,
 }
 
 // TODO (b/69062148) Enable when Dot implements general contracting dimensions.
-TEST_F(DotOperationTest,
+XLA_TEST_F(DotOperationTest,
        DISABLED_ON_CPU(DISABLED_ON_GPU(DISABLED_ON_INTERPRETER(
            DotOfGatherOptimizationWithConstLHSReverseMM)))) {
   std::unique_ptr> constant_lhs_array(
@@ -917,7 +917,7 @@ TEST_F(DotOperationTest,
 }
 
 // TODO (b/69062148) Enable when Dot implements general contracting dimensions.
-TEST_F(DotOperationTest,
+XLA_TEST_F(DotOperationTest,
        DISABLED_ON_CPU(DISABLED_ON_GPU(
            DISABLED_ON_INTERPRETER(DotOfGatherOptimizationWithConstRHSRows)))) {
   std::unique_ptr> constant_lhs_array(
@@ -953,7 +953,7 @@ TEST_F(DotOperationTest,
 }
 
 // TODO (b/69062148) Enable when Dot implements general contracting dimensions.
-TEST_F(DotOperationTest,
+XLA_TEST_F(DotOperationTest,
        DISABLED_ON_CPU(DISABLED_ON_GPU(
            DISABLED_ON_INTERPRETER(DotOfGatherOptimizationWithConstLHSRows)))) {
   std::unique_ptr> constant_lhs_array(
@@ -989,7 +989,7 @@ TEST_F(DotOperationTest,
 }
 
 // TODO (b/69062148) Enable when Dot implements general contracting dimensions.
-TEST_F(DotOperationTest,
+XLA_TEST_F(DotOperationTest,
        DISABLED_ON_CPU(DISABLED_ON_GPU(
            DISABLED_ON_INTERPRETER(DotOfGatherOptimizationWithConstRHSCols)))) {
   std::unique_ptr> constant_lhs_array(new Array2D(
@@ -1017,7 +1017,7 @@ TEST_F(DotOperationTest,
 }
 
 // TODO (b/69062148) Enable when Dot implements general contracting dimensions.
-TEST_F(DotOperationTest,
+XLA_TEST_F(DotOperationTest,
        DISABLED_ON_CPU(DISABLED_ON_GPU(
            DISABLED_ON_INTERPRETER(DotOfGatherOptimizationWithConstLHSCols)))) {
   std::unique_ptr> constant_lhs_array(new Array2D(

From ff6be80a1ec3c353ebd0d17e2f0b46d9097310db Mon Sep 17 00:00:00 2001
From: Yong Tang 
Date: Fri, 11 May 2018 09:48:32 -0700
Subject: [PATCH 0670/1691] Improve the shape function for
 ParameterizedTruncatedNormal (#19215)

The parameters of ParameterizedTruncatedNormal should
be 0-D or 1-D, which is checked in ther kernel functions.
There is no check in the shape function of the ops.

This fix improves the shape function and checks the
parameters of ParameterizedTruncatedNormal whever possible.

Signed-off-by: Yong Tang 
---
 tensorflow/core/ops/random_ops.cc | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/ops/random_ops.cc b/tensorflow/core/ops/random_ops.cc
index 416ce9c0d82ca0..80ffae579655d5 100644
--- a/tensorflow/core/ops/random_ops.cc
+++ b/tensorflow/core/ops/random_ops.cc
@@ -72,7 +72,15 @@ REGISTER_OP("ParameterizedTruncatedNormal")
     .Attr("seed2: int = 0")
     .Attr("dtype: {half,bfloat16,float,double}")
     .Attr("T: {int32, int64}")
-    .SetShapeFn(shape_inference::RandomShape);
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused;
+      // Parameters must be 0-d or 1-d.
+      TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(1), 1, &unused));
+      TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(2), 1, &unused));
+      TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(3), 1, &unused));
+      TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(4), 1, &unused));
+      return shape_inference::RandomShape(c);
+    });
 
 REGISTER_OP("TruncatedNormal")
     .Input("shape: T")

From 346998b968d8a97852c775538a98db4473e46115 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 10:25:02 -0700
Subject: [PATCH 0671/1691] Adds code examples in public head methods.

PiperOrigin-RevId: 196272143
---
 .../estimator/python/estimator/head.py        | 163 +++++++++++++++++-
 1 file changed, 162 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py
index fe6e5eaf60b389..8b97f86db19a1b 100644
--- a/tensorflow/contrib/estimator/python/estimator/head.py
+++ b/tensorflow/contrib/estimator/python/estimator/head.py
@@ -43,7 +43,6 @@
 _DEFAULT_SERVING_KEY = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
 
 
-# TODO(roumposg): Add code examples in public factory methods.
 def multi_class_head(n_classes,
                      weight_column=None,
                      label_vocabulary=None,
@@ -75,6 +74,33 @@ def multi_class_head(n_classes,
   shape `[D0, D1, ... DN, 1]`. Namely, the head applies `label_vocabulary` to
   the input labels before passing them to `loss_fn`.
 
+  The head can be used with a canned estimator. Example:
+
+  ```python
+  my_head = tf.contrib.estimator.multi_class_head(n_classes=3)
+  my_estimator = tf.contrib.estimator.DNNEstimator(
+      head=my_head,
+      hidden_units=...,
+      feature_columns=...)
+  ```
+
+  It can also be used with a custom `model_fn`. Example:
+
+  ```python
+  def _my_model_fn(features, labels, mode):
+    my_head = tf.contrib.estimator.multi_class_head(n_classes=3)
+    logits = tf.keras.Model(...)(features)
+
+    return my_head.create_estimator_spec(
+        features=features,
+        mode=mode,
+        labels=labels,
+        optimizer=tf.AdagradOptimizer(learning_rate=0.1),
+        logits=logits)
+
+  my_estimator = tf.estimator.Estimator(model_fn=_my_model_fn)
+  ```
+
   Args:
     n_classes: Number of classes, must be greater than 2 (for 2 classes, use
       `binary_classification_head`).
@@ -142,6 +168,33 @@ def binary_classification_head(
   shape `[D0, D1, ... DN, 1]`. Namely, the head applies `label_vocabulary` to
   the input labels before passing them to `loss_fn`.
 
+  The head can be used with a canned estimator. Example:
+
+  ```python
+  my_head = tf.contrib.estimator.binary_classification_head()
+  my_estimator = tf.contrib.estimator.DNNEstimator(
+      head=my_head,
+      hidden_units=...,
+      feature_columns=...)
+  ```
+
+  It can also be used with a custom `model_fn`. Example:
+
+  ```python
+  def _my_model_fn(features, labels, mode):
+    my_head = tf.contrib.estimator.binary_classification_head()
+    logits = tf.keras.Model(...)(features)
+
+    return my_head.create_estimator_spec(
+        features=features,
+        mode=mode,
+        labels=labels,
+        optimizer=tf.AdagradOptimizer(learning_rate=0.1),
+        logits=logits)
+
+  my_estimator = tf.estimator.Estimator(model_fn=_my_model_fn)
+  ```
+
   Args:
     weight_column: A string or a `_NumericColumn` created by
       `tf.feature_column.numeric_column` defining feature column representing
@@ -214,6 +267,33 @@ def regression_head(weight_column=None,
   https://en.wikipedia.org/wiki/Generalized_linear_model#Link_function
   Namely, for poisson regression, set `inverse_link_fn=tf.exp`.
 
+  The head can be used with a canned estimator. Example:
+
+  ```python
+  my_head = tf.contrib.estimator.regression_head()
+  my_estimator = tf.contrib.estimator.DNNEstimator(
+      head=my_head,
+      hidden_units=...,
+      feature_columns=...)
+  ```
+
+  It can also be used with a custom `model_fn`. Example:
+
+  ```python
+  def _my_model_fn(features, labels, mode):
+    my_head = tf.contrib.estimator.regression_head()
+    logits = tf.keras.Model(...)(features)
+
+    return my_head.create_estimator_spec(
+        features=features,
+        mode=mode,
+        labels=labels,
+        optimizer=tf.AdagradOptimizer(learning_rate=0.1),
+        logits=logits)
+
+  my_estimator = tf.estimator.Estimator(model_fn=_my_model_fn)
+  ```
+
   Args:
     weight_column: A string or a `_NumericColumn` created by
       `tf.feature_column.numeric_column` defining feature column representing
@@ -273,6 +353,33 @@ def poisson_regression_head(
   This is implemented as a generalized linear model, see
   https://en.wikipedia.org/wiki/Generalized_linear_model.
 
+  The head can be used with a canned estimator. Example:
+
+  ```python
+  my_head = tf.contrib.estimator.poisson_regression_head()
+  my_estimator = tf.contrib.estimator.DNNEstimator(
+      head=my_head,
+      hidden_units=...,
+      feature_columns=...)
+  ```
+
+  It can also be used with a custom `model_fn`. Example:
+
+  ```python
+  def _my_model_fn(features, labels, mode):
+    my_head = tf.contrib.estimator.poisson_regression_head()
+    logits = tf.keras.Model(...)(features)
+
+    return my_head.create_estimator_spec(
+        features=features,
+        mode=mode,
+        labels=labels,
+        optimizer=tf.AdagradOptimizer(learning_rate=0.1),
+        logits=logits)
+
+  my_estimator = tf.estimator.Estimator(model_fn=_my_model_fn)
+  ```
+
   Args:
     weight_column: A string or a `_NumericColumn` created by
       `tf.feature_column.numeric_column` defining feature column representing
@@ -340,6 +447,33 @@ def logistic_regression_head(
   This is implemented as a generalized linear model, see
   https://en.wikipedia.org/wiki/Generalized_linear_model.
 
+  The head can be used with a canned estimator. Example:
+
+  ```python
+  my_head = tf.contrib.estimator.logistic_regression_head()
+  my_estimator = tf.contrib.estimator.DNNEstimator(
+      head=my_head,
+      hidden_units=...,
+      feature_columns=...)
+  ```
+
+  It can also be used with a custom `model_fn`. Example:
+
+  ```python
+  def _my_model_fn(features, labels, mode):
+    my_head = tf.contrib.estimator.logistic_regression_head()
+    logits = tf.keras.Model(...)(features)
+
+    return my_head.create_estimator_spec(
+        features=features,
+        mode=mode,
+        labels=labels,
+        optimizer=tf.AdagradOptimizer(learning_rate=0.1),
+        logits=logits)
+
+  my_estimator = tf.estimator.Estimator(model_fn=_my_model_fn)
+  ```
+
   Args:
     weight_column: A string or a `_NumericColumn` created by
       `tf.feature_column.numeric_column` defining feature column representing
@@ -410,6 +544,33 @@ def multi_label_head(n_classes,
   shape `[D0, D1, ... DN, n_classes]`. Namely, the head applies
   `label_vocabulary` to the input labels before passing them to `loss_fn`.
 
+  The head can be used with a canned estimator. Example:
+
+  ```python
+  my_head = tf.contrib.estimator.multi_label_head(n_classes=3)
+  my_estimator = tf.contrib.estimator.DNNEstimator(
+      head=my_head,
+      hidden_units=...,
+      feature_columns=...)
+  ```
+
+  It can also be used with a custom `model_fn`. Example:
+
+  ```python
+  def _my_model_fn(features, labels, mode):
+    my_head = tf.contrib.estimator.multi_label_head(n_classes=3)
+    logits = tf.keras.Model(...)(features)
+
+    return my_head.create_estimator_spec(
+        features=features,
+        mode=mode,
+        labels=labels,
+        optimizer=tf.AdagradOptimizer(learning_rate=0.1),
+        logits=logits)
+
+  my_estimator = tf.estimator.Estimator(model_fn=_my_model_fn)
+  ```
+
   Args:
     n_classes: Number of classes, must be greater than 1 (for 1 class, use
       `binary_classification_head`).

From b125f6ad1f94be7541d56e6edf9235b3cf68f76e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 10:42:50 -0700
Subject: [PATCH 0672/1691] [XLA] Redesign: delete ComputationBuilder.

PiperOrigin-RevId: 196275032
---
 tensorflow/compiler/tf2xla/lib/BUILD          |    1 -
 tensorflow/compiler/xla/client/BUILD          |   25 -
 .../xla/client/computation_builder.cc         | 1584 -----------------
 .../compiler/xla/client/computation_builder.h | 1073 -----------
 tensorflow/compiler/xla/service/BUILD         |    1 -
 tensorflow/compiler/xla/tests/BUILD           |   69 -
 tensorflow/compiler/xla/tests/call_test.cc    |    1 -
 .../xla/tests/client_library_test_base.cc     |    1 -
 .../xla/tests/compilation_cache_test.cc       |    1 -
 .../compiler/xla/tests/constants_test.cc      |    1 -
 .../xla/tests/convolution_variants_test.cc    |    1 -
 .../compiler/xla/tests/deallocation_test.cc   |    1 -
 .../xla/tests/deconstruct_tuple_test.cc       |    1 -
 .../xla/tests/matrix_ops_simple_test.cc       |    1 -
 .../xla/tests/multioutput_fusion_test.cc      |    1 -
 tensorflow/compiler/xla/tests/params_test.cc  |    1 -
 tensorflow/compiler/xla/tests/reduce_test.cc  |    1 -
 tensorflow/compiler/xla/tests/tuple_test.cc   |    1 -
 18 files changed, 2765 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/client/computation_builder.cc
 delete mode 100644 tensorflow/compiler/xla/client/computation_builder.h

diff --git a/tensorflow/compiler/tf2xla/lib/BUILD b/tensorflow/compiler/tf2xla/lib/BUILD
index 04ad3694a0c0df..ef12b1618b8649 100644
--- a/tensorflow/compiler/tf2xla/lib/BUILD
+++ b/tensorflow/compiler/tf2xla/lib/BUILD
@@ -141,7 +141,6 @@ xla_test(
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
diff --git a/tensorflow/compiler/xla/client/BUILD b/tensorflow/compiler/xla/client/BUILD
index aac3273d5fd144..989cd61d9fc2f1 100644
--- a/tensorflow/compiler/xla/client/BUILD
+++ b/tensorflow/compiler/xla/client/BUILD
@@ -178,31 +178,6 @@ cc_library(
     ],
 )
 
-cc_library(
-    name = "computation_builder",
-    srcs = ["computation_builder.cc"],
-    hdrs = ["computation_builder.h"],
-    deps = [
-        ":client",
-        ":computation",
-        ":global_data",
-        ":padding",
-        "//tensorflow/compiler/xla:array",
-        "//tensorflow/compiler/xla:array2d",
-        "//tensorflow/compiler/xla:array3d",
-        "//tensorflow/compiler/xla:array4d",
-        "//tensorflow/compiler/xla:literal_util",
-        "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla:status_macros",
-        "//tensorflow/compiler/xla:statusor",
-        "//tensorflow/compiler/xla:types",
-        "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla:xla_proto",
-        "//tensorflow/core:lib",
-    ],
-)
-
 cc_library(
     name = "sharding_builder",
     srcs = ["sharding_builder.cc"],
diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
deleted file mode 100644
index b58279b1637b04..00000000000000
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ /dev/null
@@ -1,1584 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/compiler/xla/client/computation_builder.h"
-
-#include 
-#include 
-#include 
-#include 
-#include 
-
-#include "tensorflow/compiler/xla/ptr_util.h"
-#include "tensorflow/compiler/xla/shape_util.h"
-#include "tensorflow/compiler/xla/status_macros.h"
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/compiler/xla/util.h"
-#include "tensorflow/compiler/xla/xla.pb.h"
-#include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/platform/logging.h"
-#include "tensorflow/core/platform/protobuf.h"
-
-namespace xla {
-
-ComputationBuilder::ComputationBuilder(Client* client,
-                                       const string& computation_name)
-    : name_(computation_name), client_(client) {}
-
-ComputationBuilder::~ComputationBuilder() {}
-
-void ComputationBuilder::NoteError(const Status& error) {
-  if (die_immediately_on_error_) {
-    LOG(FATAL) << "error building computation: " << error;
-  }
-
-  if (first_error_.ok()) {
-    first_error_ = error;
-    first_error_backtrace_.CreateCurrent(/*skip_count=*/1);
-  }
-}
-
-std::unique_ptr ComputationBuilder::CreateSubBuilder(
-    const string& computation_name) {
-  auto sub_builder = MakeUnique(client_, computation_name);
-  sub_builder->parent_builder_ = this;
-  sub_builder->die_immediately_on_error_ = die_immediately_on_error_;
-  return sub_builder;
-}
-
-Status ComputationBuilder::PrepareComputation() {
-  TF_RETURN_IF_ERROR(first_error_);
-
-  if (!computation_.IsNull()) {
-    return Status::OK();
-  }
-
-  ComputationRequest request;
-  request.set_name(name_);
-  ComputationResponse response;
-
-  VLOG(2) << "making computation request";
-  Status s = client_->stub()->Computation(&request, &response);
-  VLOG(2) << "done with computation request";
-
-  if (!s.ok()) {
-    NoteError(s);
-    return first_error_;
-  }
-
-  computation_ = Computation(client_->stub(), response.computation());
-  return Status::OK();
-}
-
-Status ComputationBuilder::RunOp(OpRequest* op_request,
-                                 OpResponse* op_response) {
-  TF_RETURN_IF_ERROR(first_error_);
-  TF_RETURN_IF_ERROR(PrepareComputation());
-
-  // Fill in fields that are set on every OpRequest.
-  *op_request->mutable_computation() = computation_.handle();
-  *op_request->mutable_metadata() = metadata_;
-  if (sharding_) {
-    *op_request->mutable_sharding() = *sharding_;
-  }
-
-  const string& op_name =
-      OpRequest::descriptor()->FindFieldByNumber(op_request->op_case())->name();
-  VLOG(2) << "running op request: " << op_name;
-  Status status = client_->stub()->Op(op_request, op_response);
-  VLOG(2) << "done with op request: " << op_name;
-  return status;
-}
-
-void ComputationBuilder::RunOpAndNoteError(OpRequest* op_request) {
-  OpResponse op_response;
-  Status status = RunOp(op_request, &op_response);
-  if (!status.ok()) {
-    NoteError(status);
-  }
-}
-
-ComputationDataHandle ComputationBuilder::RunOpAndParseResponse(
-    OpRequest* op_request) {
-  OpResponse op_response;
-  Status status = RunOp(op_request, &op_response);
-  if (!status.ok()) {
-    NoteError(status);
-    return ComputationDataHandle();
-  }
-  if (op_response.output().handle() == 0) {
-    NoteError(InternalError("No output handle"));
-    return ComputationDataHandle();
-  }
-  return op_response.output();
-}
-
-bool ComputationBuilder::MakeWindow(
-    tensorflow::gtl::ArraySlice window_dimensions,
-    tensorflow::gtl::ArraySlice window_strides,
-    tensorflow::gtl::ArraySlice> padding,
-    tensorflow::gtl::ArraySlice lhs_dilation,
-    tensorflow::gtl::ArraySlice rhs_dilation, Window* window) {
-  const auto verify_size = [&](const size_t x, const char* x_name) {
-    if (x == 0 || x == window_dimensions.size()) {
-      return true;
-    } else {
-      NoteError(InvalidArgument(
-          "%s", tensorflow::strings::StrCat(
-                    "Window has different number of window dimensions than of ",
-                    x_name, "\nNumber of window dimensions: ",
-                    window_dimensions.size(), "\nNumber of ", x_name, ": ", x,
-                    "\n")
-                    .c_str()));  //
-      return false;
-    }
-  };
-  if (!verify_size(window_strides.size(), "window strides") ||
-      !verify_size(padding.size(), "padding entries") ||
-      !verify_size(lhs_dilation.size(), "lhs dilation factors") ||
-      !verify_size(rhs_dilation.size(), "rhs dilation factors")) {
-    return false;
-  }
-
-  window->Clear();
-  for (size_t i = 0; i < window_dimensions.size(); i++) {
-    auto dim = window->add_dimensions();
-    dim->set_size(window_dimensions[i]);
-    if (!window_strides.empty()) {
-      dim->set_stride(window_strides[i]);
-    } else {
-      dim->set_stride(1);
-    }
-    if (!padding.empty()) {
-      dim->set_padding_low(padding[i].first);
-      dim->set_padding_high(padding[i].second);
-    } else {
-      dim->set_padding_low(0);
-      dim->set_padding_high(0);
-    }
-    if (!lhs_dilation.empty()) {
-      dim->set_base_dilation(lhs_dilation[i]);
-    } else {
-      dim->set_base_dilation(1);
-    }
-    if (!rhs_dilation.empty()) {
-      dim->set_window_dilation(rhs_dilation[i]);
-    } else {
-      dim->set_window_dilation(1);
-    }
-    dim->set_window_reversal(false);
-  }
-  return true;
-}
-
-ComputationDataHandle ComputationBuilder::ConstantLiteral(
-    const LiteralSlice& literal) {
-  OpRequest op_request;
-  ConstantRequest* request = op_request.mutable_constant_request();
-  *request->mutable_literal() = literal.ToProto();
-  VLOG(3) << "created constant: " << request->literal().ShortDebugString();
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Parameter(int64 parameter_number,
-                                                    const Shape& shape,
-                                                    const string& name) {
-  OpRequest op_request;
-  ParameterRequest* request = op_request.mutable_parameter_request();
-  *request->mutable_shape() = shape;
-  request->set_parameter(parameter_number);
-  request->set_name(name);
-  return RunOpAndParseResponse(&op_request);
-}
-
-StatusOr> ComputationBuilder::GetShapeWithoutNoteError(
-    const ComputationDataHandle& operand) {
-  GetLocalShapeRequest request;
-  *request.mutable_computation() = computation_.handle();
-  *request.mutable_operand() = operand;
-  GetLocalShapeResponse response;
-
-  VLOG(2) << "making get-shape request";
-  TF_RETURN_IF_ERROR(client_->stub()->GetLocalShape(&request, &response));
-  VLOG(2) << "done with request";
-
-  TF_RET_CHECK(response.has_shape());
-  std::unique_ptr shape = WrapUnique(response.release_shape());
-  TF_RET_CHECK(shape != nullptr);
-  return std::move(shape);
-}
-
-StatusOr> ComputationBuilder::GetShape(
-    const ComputationDataHandle& operand) {
-  TF_RETURN_IF_ERROR(first_error_);
-
-  auto status_or_shape = GetShapeWithoutNoteError(operand);
-  if (!status_or_shape.ok()) {
-    NoteError(status_or_shape.status());
-    return first_error_;
-  }
-  return status_or_shape;
-}
-
-StatusOr ComputationBuilder::GetProgramShape() {
-  TF_RETURN_IF_ERROR(first_error_);
-
-  GetComputationShapeRequest request;
-  *request.mutable_computation() = computation_.handle();
-  GetComputationShapeResponse response;
-
-  VLOG(2) << "making get-program-shape-request";
-  Status status = client_->stub()->GetComputationShape(&request, &response);
-  VLOG(2) << "done with get-program-shape-request";
-
-  if (!status.ok()) {
-    first_error_ = status;
-    return status;
-  }
-
-  TF_RET_CHECK(response.has_program_shape());
-  return std::move(*response.mutable_program_shape());
-}
-
-ComputationDataHandle ComputationBuilder::Slice(
-    const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice start_indices,
-    tensorflow::gtl::ArraySlice limit_indices,
-    tensorflow::gtl::ArraySlice strides) {
-  OpRequest op_request;
-  SliceRequest* request = op_request.mutable_slice_request();
-  *request->mutable_operand() = operand;
-  for (int64 index : start_indices) {
-    request->add_start_indices(index);
-  }
-  for (int64 index : limit_indices) {
-    request->add_limit_indices(index);
-  }
-  for (int64 index : strides) {
-    request->add_strides(index);
-  }
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::SliceInDim(
-    const ComputationDataHandle& operand, int64 start_index, int64 limit_index,
-    int64 stride, int64 dimno) {
-  StatusOr> shape_status = GetShape(operand);
-  if (!shape_status.ok()) {
-    NoteError(shape_status.status());
-    return ComputationDataHandle{};
-  }
-  const Shape& shape = *shape_status.ValueOrDie();
-  std::vector starts(ShapeUtil::Rank(shape), 0);
-  std::vector limits(shape.dimensions().begin(),
-                            shape.dimensions().end());
-  std::vector strides(ShapeUtil::Rank(shape), 1);
-  starts[dimno] = start_index;
-  limits[dimno] = limit_index;
-  strides[dimno] = stride;
-  return Slice(operand, starts, limits, strides);
-}
-
-ComputationDataHandle ComputationBuilder::DynamicSlice(
-    const ComputationDataHandle& operand,
-    const ComputationDataHandle& start_indices,
-    tensorflow::gtl::ArraySlice slice_sizes) {
-  OpRequest op_request;
-  DynamicSliceRequest* request = op_request.mutable_dynamic_slice_request();
-  *request->mutable_operand() = operand;
-  *request->mutable_start_indices() = start_indices;
-  for (int64 index : slice_sizes) {
-    request->add_slice_sizes(index);
-  }
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::DynamicUpdateSlice(
-    const ComputationDataHandle& operand, const ComputationDataHandle& update,
-    const ComputationDataHandle& start_indices) {
-  OpRequest op_request;
-  DynamicUpdateSliceRequest* request =
-      op_request.mutable_dynamic_update_slice_request();
-  *request->mutable_operand() = operand;
-  *request->mutable_update() = update;
-  *request->mutable_start_indices() = start_indices;
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::ConcatInDim(
-    tensorflow::gtl::ArraySlice operands,
-    int64 dimension) {
-  OpRequest op_request;
-  ConcatenateRequest* request = op_request.mutable_concatenate_request();
-  for (const ComputationDataHandle& operand : operands) {
-    *request->add_operands() = operand;
-  }
-  request->set_dimension(dimension);
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Broadcast(
-    const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice broadcast_sizes) {
-  OpRequest op_request;
-  BroadcastRequest* request = op_request.mutable_broadcast_request();
-  *request->mutable_operand() = operand;
-  for (int64 size : broadcast_sizes) {
-    request->add_broadcast_sizes(size);
-  }
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Pad(
-    const ComputationDataHandle& operand,
-    const ComputationDataHandle& padding_value,
-    const PaddingConfig& padding_config) {
-  OpRequest op_request;
-  PadRequest* request = op_request.mutable_pad_request();
-  *request->mutable_operand() = operand;
-  *request->mutable_padding_value() = padding_value;
-  *request->mutable_padding_config() = padding_config;
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Reshape(
-    const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice dimensions,
-    tensorflow::gtl::ArraySlice new_sizes) {
-  OpRequest op_request;
-  ReshapeRequest* request = op_request.mutable_reshape_request();
-  *request->mutable_operand() = operand;
-  for (int64 dimension : dimensions) {
-    request->add_dimensions(dimension);
-  }
-  for (int64 new_size : new_sizes) {
-    request->add_new_sizes(new_size);
-  }
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Reshape(
-    const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice new_sizes) {
-  if (!first_error_.ok()) {
-    return ComputationDataHandle();
-  }
-
-  StatusOr> shape = GetShape(operand);
-  if (!shape.ok()) {
-    return ComputationDataHandle();
-  }
-  std::vector dimensions(shape.ValueOrDie()->dimensions().size());
-  std::iota(dimensions.begin(), dimensions.end(), 0);
-  return Reshape(operand, dimensions, new_sizes);
-}
-
-ComputationDataHandle ComputationBuilder::Collapse(
-    const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice dimensions) {
-  if (!first_error_.ok()) {
-    return ComputationDataHandle();
-  }
-
-  // Don't support out-of-order collapse here.
-  // Checks that the collapsed dimensions are in order and consecutive.
-  for (tensorflow::gtl::ArraySlice::size_type i = 1;
-       i < dimensions.size(); ++i) {
-    if (dimensions[i] - 1 != dimensions[i - 1]) {
-      NoteError(InvalidArgument(
-          "Collapsed dimensions are not in order and consecutive."));
-      return ComputationDataHandle();
-    }
-  }
-
-  // Create a new sizes vector from the old shape, replacing the collapsed
-  // dimensions by the product of their sizes.
-  StatusOr> shape_or_status = GetShape(operand);
-  if (!shape_or_status.ok()) {
-    return ComputationDataHandle();
-  }
-  std::unique_ptr original_shape = shape_or_status.ConsumeValueOrDie();
-
-  VLOG(3) << "original shape: " << ShapeUtil::HumanString(*original_shape);
-  VLOG(3) << "dims to collapse: "
-          << tensorflow::str_util::Join(dimensions, ",");
-
-  if (dimensions.size() <= 1) {
-    // Not collapsing anything, trivially we can return the operand versus
-    // enqueueing a trivial reshape.
-    return operand;
-  }
-
-  std::vector new_sizes;
-  for (int i = 0; i < ShapeUtil::Rank(*original_shape); ++i) {
-    if (i <= dimensions.front() || i > dimensions.back()) {
-      new_sizes.push_back(original_shape->dimensions(i));
-    } else {
-      new_sizes.back() *= original_shape->dimensions(i);
-    }
-  }
-
-  VLOG(3) << "new sizes: [" << tensorflow::str_util::Join(new_sizes, ",")
-          << "]";
-
-  return Reshape(operand, new_sizes);
-}
-
-void ComputationBuilder::Trace(const string& tag,
-                               const ComputationDataHandle& operand) {
-  OpRequest op_request;
-  TraceRequest* request = op_request.mutable_trace_request();
-  request->set_tag(tag);
-  *request->mutable_operand() = operand;
-  RunOpAndNoteError(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Select(
-    const ComputationDataHandle& pred, const ComputationDataHandle& on_true,
-    const ComputationDataHandle& on_false) {
-  return TernaryOp(TRIOP_SELECT, pred, on_true, on_false);
-}
-
-ComputationDataHandle ComputationBuilder::Tuple(
-    tensorflow::gtl::ArraySlice elements) {
-  OpRequest op_request;
-  VariadicOpRequest* request = op_request.mutable_variadic_op_request();
-  request->set_varop(VAROP_TUPLE);
-  for (const ComputationDataHandle& operand : elements) {
-    *request->add_operands() = operand;
-  }
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::GetTupleElement(
-    const ComputationDataHandle& tuple_data, int64 index) {
-  OpRequest op_request;
-  GetTupleElementRequest* request =
-      op_request.mutable_get_tuple_element_request();
-  *request->mutable_operand() = tuple_data;
-  request->set_index(index);
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Eq(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_EQ, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Ne(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_NE, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Ge(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_GE, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Gt(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_GT, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Le(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_LE, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Lt(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_LT, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Dot(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs) {
-  StatusOr> lhs_shape_or_status = GetShape(lhs);
-  if (!lhs_shape_or_status.ok()) {
-    return ComputationDataHandle();
-  }
-  std::unique_ptr lhs_shape = lhs_shape_or_status.ConsumeValueOrDie();
-
-  DotDimensionNumbers dimension_numbers;
-  dimension_numbers.add_lhs_contracting_dimensions(
-      lhs_shape->dimensions_size() == 1 ? 0 : 1);
-  dimension_numbers.add_rhs_contracting_dimensions(0);
-  return DotGeneral(lhs, rhs, dimension_numbers);
-}
-
-ComputationDataHandle ComputationBuilder::DotGeneral(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    const DotDimensionNumbers& dimension_numbers) {
-  OpRequest op_request;
-  DotRequest* request = op_request.mutable_dot_request();
-  *request->mutable_lhs() = lhs;
-  *request->mutable_rhs() = rhs;
-  *request->mutable_dimension_numbers() = dimension_numbers;
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Conv(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice window_strides, Padding padding) {
-  return ConvWithGeneralDimensions(
-      lhs, rhs, window_strides, padding,
-      CreateDefaultConvDimensionNumbers(window_strides.size()));
-}
-
-ComputationDataHandle ComputationBuilder::ConvWithGeneralPadding(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice window_strides,
-    tensorflow::gtl::ArraySlice> padding) {
-  return ConvGeneral(lhs, rhs, window_strides, padding,
-                     CreateDefaultConvDimensionNumbers(window_strides.size()));
-}
-
-bool ComputationBuilder::VerifyConvolution(
-    const Shape& lhs_shape, const Shape& rhs_shape,
-    const ConvolutionDimensionNumbers& dimension_numbers) {
-  if (ShapeUtil::Rank(lhs_shape) != ShapeUtil::Rank(rhs_shape)) {
-    NoteError(
-        InvalidArgument("Convolution arguments must have same number of "
-                        "dimensions. Got: %s and %s",
-                        ShapeUtil::HumanString(lhs_shape).c_str(),
-                        ShapeUtil::HumanString(rhs_shape).c_str()));
-    return false;
-  }
-  int num_dims = ShapeUtil::Rank(lhs_shape);
-  if (num_dims < 2) {
-    NoteError(InvalidArgument(
-        "Convolution expects argument arrays with >= 3 dimensions. "
-        "Got: %s and %s",
-        ShapeUtil::HumanString(lhs_shape).c_str(),
-        ShapeUtil::HumanString(rhs_shape).c_str()));
-    return false;
-  }
-  int num_spatial_dims = num_dims - 2;
-
-  const auto check_spatial_dimensions =
-      [&](const char* const field_name,
-          const tensorflow::protobuf::RepeatedField&
-              numbers) {
-        if (numbers.size() != num_spatial_dims) {
-          NoteError(InvalidArgument("Expected %d elements for %s, but got %d.",
-                                    num_spatial_dims, field_name,
-                                    numbers.size()));
-          return false;
-        }
-        for (int i = 0; i < numbers.size(); ++i) {
-          if (numbers.Get(i) < 0 || numbers.Get(i) >= num_dims) {
-            NoteError(
-                InvalidArgument("Convolution %s[%d] is out of bounds: %lld",
-                                field_name, i, numbers.Get(i)));
-            return false;
-          }
-        }
-        return true;
-      };
-  return check_spatial_dimensions(
-             "input_spatial_dimensions",
-             dimension_numbers.input_spatial_dimensions()) &&
-         check_spatial_dimensions(
-             "kernel_spatial_dimensions",
-             dimension_numbers.kernel_spatial_dimensions()) &&
-         check_spatial_dimensions(
-             "output_spatial_dimensions",
-             dimension_numbers.output_spatial_dimensions());
-}
-
-ComputationDataHandle ComputationBuilder::ConvWithGeneralDimensions(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice window_strides, Padding padding,
-    const ConvolutionDimensionNumbers& dimension_numbers) {
-  if (!first_error_.ok() || !PrepareComputation().ok()) {
-    return ComputationDataHandle();
-  }
-
-  StatusOr> lhs_shape_or_status = GetShape(lhs);
-  if (!lhs_shape_or_status.ok()) {
-    return ComputationDataHandle();
-  }
-
-  StatusOr> rhs_shape_or_status = GetShape(rhs);
-  if (!rhs_shape_or_status.ok()) {
-    return ComputationDataHandle();
-  }
-
-  std::unique_ptr lhs_shape = lhs_shape_or_status.ConsumeValueOrDie();
-  std::unique_ptr rhs_shape = rhs_shape_or_status.ConsumeValueOrDie();
-
-  if (!VerifyConvolution(*lhs_shape, *rhs_shape, dimension_numbers)) {
-    NoteError(InternalError("failed to verify convolution"));
-    return ComputationDataHandle();
-  }
-
-  std::vector base_area_dimensions(
-      dimension_numbers.input_spatial_dimensions_size());
-  for (std::vector::size_type i = 0; i < base_area_dimensions.size();
-       ++i) {
-    base_area_dimensions[i] =
-        lhs_shape->dimensions(dimension_numbers.input_spatial_dimensions(i));
-  }
-
-  std::vector window_dimensions(
-      dimension_numbers.kernel_spatial_dimensions_size());
-  for (std::vector::size_type i = 0; i < window_dimensions.size(); ++i) {
-    window_dimensions[i] =
-        rhs_shape->dimensions(dimension_numbers.kernel_spatial_dimensions(i));
-  }
-
-  return ConvGeneral(lhs, rhs, window_strides,
-                     MakePadding(base_area_dimensions, window_dimensions,
-                                 window_strides, padding),
-                     dimension_numbers);
-}
-
-ComputationDataHandle ComputationBuilder::ConvGeneral(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice window_strides,
-    tensorflow::gtl::ArraySlice> padding,
-    const ConvolutionDimensionNumbers& dimension_numbers) {
-  return ConvGeneralDilated(lhs, rhs, window_strides, padding, {}, {},
-                            dimension_numbers);
-}
-
-ComputationDataHandle ComputationBuilder::ConvGeneralDilated(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice window_strides,
-    tensorflow::gtl::ArraySlice> padding,
-    tensorflow::gtl::ArraySlice lhs_dilation,
-    tensorflow::gtl::ArraySlice rhs_dilation,
-    const ConvolutionDimensionNumbers& dimension_numbers) {
-  if (!first_error_.ok() || !PrepareComputation().ok()) {
-    return ComputationDataHandle();
-  }
-
-  StatusOr> lhs_shape_or_status = GetShape(lhs);
-  if (!lhs_shape_or_status.ok()) {
-    return ComputationDataHandle();
-  }
-
-  StatusOr> rhs_shape_or_status = GetShape(rhs);
-  if (!rhs_shape_or_status.ok()) {
-    return ComputationDataHandle();
-  }
-
-  std::unique_ptr lhs_shape = lhs_shape_or_status.ConsumeValueOrDie();
-  std::unique_ptr rhs_shape = rhs_shape_or_status.ConsumeValueOrDie();
-  if (!VerifyConvolution(*lhs_shape, *rhs_shape, dimension_numbers)) {
-    // Error is recorded in VerifyConvolution.
-    return ComputationDataHandle();
-  }
-
-  std::vector window_dimensions(
-      dimension_numbers.kernel_spatial_dimensions_size());
-  for (std::vector::size_type i = 0; i < window_dimensions.size(); ++i) {
-    window_dimensions[i] =
-        rhs_shape->dimensions(dimension_numbers.kernel_spatial_dimensions(i));
-  }
-
-  OpRequest op_request;
-  ConvolveRequest* request = op_request.mutable_convolve_request();
-  *request->mutable_lhs() = lhs;
-  *request->mutable_rhs() = rhs;
-  *request->mutable_dimension_numbers() = dimension_numbers;
-
-  if (!MakeWindow(window_dimensions, window_strides, padding, lhs_dilation,
-                  rhs_dilation, request->mutable_window())) {
-    // Error is recorded in MakeWindow.
-    return ComputationDataHandle();
-  }
-
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Fft(
-    const ComputationDataHandle& operand, const FftType fft_type,
-    const tensorflow::gtl::ArraySlice fft_length) {
-  OpRequest op_request;
-  FftRequest* request = op_request.mutable_fft_request();
-  *request->mutable_operand() = operand;
-  request->set_fft_type(fft_type);
-  for (int64 dim_len : fft_length) {
-    request->add_fft_length(dim_len);
-  }
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Infeed(const Shape& shape,
-                                                 const string& config) {
-  OpRequest op_request;
-  InfeedRequest* request = op_request.mutable_infeed_request();
-  *request->mutable_shape() = shape;
-  *request->mutable_config() = config;
-  return RunOpAndParseResponse(&op_request);
-}
-
-void ComputationBuilder::Outfeed(const ComputationDataHandle& operand,
-                                 const Shape& shape_with_layout,
-                                 const string& outfeed_config) {
-  OpRequest op_request;
-  OutfeedRequest* request = op_request.mutable_outfeed_request();
-  request->set_outfeed_config(outfeed_config);
-  *request->mutable_operand() = operand;
-  *request->mutable_shape() = shape_with_layout;
-  RunOpAndNoteError(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Call(
-    const Computation& computation,
-    tensorflow::gtl::ArraySlice operands) {
-  OpRequest op_request;
-  CallRequest* request = op_request.mutable_call_request();
-  *request->mutable_to_apply() = computation.handle();
-  for (const ComputationDataHandle& operand : operands) {
-    *request->add_operands() = operand;
-  }
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::CustomCall(
-    const string& call_target_name,
-    tensorflow::gtl::ArraySlice operands,
-    const Shape& shape) {
-  OpRequest op_request;
-  CustomCallRequest* request = op_request.mutable_custom_call_request();
-  request->set_call_target_name(call_target_name);
-  for (const ComputationDataHandle& operand : operands) {
-    *request->add_operands() = operand;
-  }
-  *request->mutable_shape() = shape;
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::HostCompute(
-    tensorflow::gtl::ArraySlice operands,
-    const string& channel_name, int64 cost_estimate_ns, const Shape& shape) {
-  OpRequest op_request;
-  HostComputeRequest* request = op_request.mutable_host_compute_request();
-  for (const ComputationDataHandle& operand : operands) {
-    *request->add_operands() = operand;
-  }
-  *request->mutable_shape() = shape;
-  request->set_channel_name(channel_name);
-  request->set_cost_estimate_ns(cost_estimate_ns);
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Complex(
-    const ComputationDataHandle& real, const ComputationDataHandle& imag,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_COMPLEX, real, imag, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Conj(
-    const ComputationDataHandle& operand) {
-  return Complex(Real(operand), Neg(Imag(operand)));
-}
-
-ComputationDataHandle ComputationBuilder::Add(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_ADD, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Sub(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_SUB, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Mul(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_MUL, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Div(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_DIV, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Rem(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_REM, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Max(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_MAX, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Min(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_MIN, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::And(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_AND, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Or(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_OR, lhs, rhs, broadcast_dimensions);
-}
-
-// TODO(b/65209188): Create a dedicated lowering for Xor
-ComputationDataHandle ComputationBuilder::Xor(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return Or(And(Not(lhs), rhs, broadcast_dimensions),
-            And(lhs, Not(rhs), broadcast_dimensions));
-}
-
-ComputationDataHandle ComputationBuilder::Not(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_NOT, operand);
-}
-
-ComputationDataHandle ComputationBuilder::ShiftLeft(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_SHIFT_LEFT, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::ShiftRightArithmetic(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_SHIFT_RIGHT_ARITHMETIC, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::ShiftRightLogical(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_SHIFT_RIGHT_LOGICAL, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Abs(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_ABS, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Atan2(
-    const ComputationDataHandle& y, const ComputationDataHandle& x,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_ATAN2, y, x, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::Exp(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_EXP, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Expm1(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_EXPM1, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Floor(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_FLOOR, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Ceil(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_CEIL, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Round(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_ROUND_NEAREST_AFZ, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Log(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_LOG, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Log1p(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_LOG1P, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Sign(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_SIGN, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Cos(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_COS, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Sin(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_SIN, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Tanh(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_TANH, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Real(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_REAL, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Imag(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_IMAG, operand);
-}
-
-ComputationDataHandle ComputationBuilder::IsFinite(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_IS_FINITE, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Transpose(
-    const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice permutation) {
-  OpRequest op_request;
-  TransposeRequest* request = op_request.mutable_transpose_request();
-  *request->mutable_operand() = operand;
-  for (int64 dimension : permutation) {
-    request->add_dimensions(dimension);
-  }
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Rev(
-    const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice dimensions) {
-  OpRequest op_request;
-  ReverseRequest* request = op_request.mutable_reverse_request();
-  *request->mutable_operand() = operand;
-  for (int64 dimension : dimensions) {
-    request->add_dimensions(dimension);
-  }
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Sort(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_SORT, operand);
-}
-
-ComputationDataHandle ComputationBuilder::SqrtF32(
-    const ComputationDataHandle& operand) {
-  return BinaryOp(BINOP_POW, operand, ConstantR0(0.5),
-                  /*broadcast_dimensions=*/{});
-}
-
-ComputationDataHandle ComputationBuilder::Pow(
-    const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  return BinaryOp(BINOP_POW, lhs, rhs, broadcast_dimensions);
-}
-
-ComputationDataHandle ComputationBuilder::ConvertElementType(
-    const ComputationDataHandle& operand, PrimitiveType new_element_type) {
-  if (!first_error_.ok() || !PrepareComputation().ok()) {
-    return ComputationDataHandle();
-  }
-
-  StatusOr> shape_status = GetShape(operand);
-  if (!shape_status.ok()) {
-    return ComputationDataHandle();
-  }
-  std::unique_ptr original = shape_status.ConsumeValueOrDie();
-
-  OpRequest op_request;
-  ConvertRequest* request = op_request.mutable_convert_request();
-  *request->mutable_operand() = operand;
-  request->set_new_element_type(new_element_type);
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::BitcastConvertType(
-    const ComputationDataHandle& operand, PrimitiveType new_element_type) {
-  if (!first_error_.ok() || !PrepareComputation().ok()) {
-    return ComputationDataHandle();
-  }
-
-  StatusOr> shape_status = GetShape(operand);
-  if (!shape_status.ok()) {
-    return ComputationDataHandle();
-  }
-  std::unique_ptr original = shape_status.ConsumeValueOrDie();
-
-  OpRequest op_request;
-  ConvertRequest* request = op_request.mutable_bitcast_convert_request();
-  *request->mutable_operand() = operand;
-  request->set_new_element_type(new_element_type);
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::SquareF32(
-    const ComputationDataHandle& operand) {
-  return BinaryOp(BINOP_POW, operand, ConstantR0(2.0),
-                  /*broadcast_dimensions=*/{});
-}
-
-ComputationDataHandle ComputationBuilder::ReciprocalF32(
-    const ComputationDataHandle& operand) {
-  return BinaryOp(BINOP_POW, operand, ConstantR0(-1.0),
-                  /*broadcast_dimensions=*/{});
-}
-
-ComputationDataHandle ComputationBuilder::Neg(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_NEGATE, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Clz(
-    const ComputationDataHandle& operand) {
-  return UnaryOp(UNOP_CLZ, operand);
-}
-
-ComputationDataHandle ComputationBuilder::Clamp(
-    const ComputationDataHandle& min, const ComputationDataHandle& operand,
-    const ComputationDataHandle& max) {
-  return TernaryOp(TRIOP_CLAMP, min, operand, max);
-}
-
-ComputationDataHandle ComputationBuilder::UnaryOp(
-    UnaryOperation unop, const ComputationDataHandle& operand) {
-  OpRequest op_request;
-  UnaryOpRequest* request = op_request.mutable_unary_op_request();
-  request->set_unop(unop);
-  *request->mutable_operand() = operand;
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::BinaryOp(
-    BinaryOperation binop, const ComputationDataHandle& lhs,
-    const ComputationDataHandle& rhs,
-    tensorflow::gtl::ArraySlice broadcast_dimensions) {
-  OpRequest op_request;
-  BinaryOpRequest* request = op_request.mutable_binary_op_request();
-  request->set_binop(binop);
-  *request->mutable_lhs() = lhs;
-  *request->mutable_rhs() = rhs;
-  for (int64 dimension : broadcast_dimensions) {
-    request->add_broadcast_dimensions(dimension);
-  }
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::RngOp(
-    RandomDistribution distribution,
-    tensorflow::gtl::ArraySlice parameters,
-    const Shape& shape) {
-  OpRequest op_request;
-  RngRequest* request = op_request.mutable_rng_request();
-  request->set_distribution(distribution);
-  for (const ComputationDataHandle& param : parameters) {
-    *request->add_parameter() = param;
-  }
-  *request->mutable_shape() = shape;
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::TernaryOp(
-    TernaryOperation triop, const ComputationDataHandle& lhs,
-    const ComputationDataHandle& rhs, const ComputationDataHandle& ehs) {
-  OpRequest op_request;
-  TernaryOpRequest* request = op_request.mutable_ternary_op_request();
-  request->set_triop(triop);
-  *request->mutable_lhs() = lhs;
-  *request->mutable_rhs() = rhs;
-  *request->mutable_ehs() = ehs;
-  return RunOpAndParseResponse(&op_request);
-}
-
-Status ComputationBuilder::SetReturnValue(
-    const ComputationDataHandle& operand) {
-  TF_RETURN_IF_ERROR(first_error_);
-
-  SetReturnValueRequest request;
-  *request.mutable_computation() = computation_.handle();
-  *request.mutable_operand() = operand;
-
-  SetReturnValueResponse response;
-
-  VLOG(2) << "making set-handle-to-execute request";
-  Status s = client_->stub()->SetReturnValue(&request, &response);
-  VLOG(2) << "done with request";
-
-  if (!s.ok()) {
-    NoteError(s);
-    return first_error_;
-  }
-
-  return Status::OK();
-}
-
-StatusOr ComputationBuilder::IsConstant(
-    const ComputationDataHandle& operand, int64 num_parameters) {
-  TF_RETURN_IF_ERROR(first_error_);
-
-  IsConstantRequest request;
-  *request.mutable_computation() = computation_.handle();
-  *request.mutable_operand() = operand;
-  request.set_num_parameters(num_parameters);
-  IsConstantResponse response;
-
-  VLOG(2) << "making IsConstant request";
-  Status s = client_->stub()->IsConstant(&request, &response);
-  VLOG(2) << "done with request";
-
-  if (!s.ok()) {
-    return s;
-  }
-  return response.is_constant();
-}
-
-StatusOr> ComputationBuilder::ComputeConstant(
-    const ComputationDataHandle& operand, const Layout* output_layout,
-    tensorflow::gtl::ArraySlice parameters) {
-  TF_RETURN_IF_ERROR(first_error_);
-
-  ComputeConstantRequest request;
-  *request.mutable_computation() = computation_.handle();
-  *request.mutable_operand() = operand;
-  if (output_layout != nullptr) {
-    *request.mutable_output_layout() = *output_layout;
-  }
-  for (const auto& param : parameters) {
-    *request.add_parameters() = param.ToProto();
-  }
-
-  ComputeConstantResponse response;
-
-  VLOG(2) << "making compute-constant request";
-  Status s = client_->stub()->ComputeConstant(&request, &response);
-  VLOG(2) << "done with request";
-
-  if (!s.ok()) {
-    return s;
-  }
-
-  VLOG(3) << "ComputeConstant: {" << response.DebugString() << "}";
-
-  if (!response.has_literal()) {
-    return InternalError(
-        "no computed literal in the provided response in ComputeConstant "
-        "request");
-  }
-  return Literal::CreateFromProto(response.literal());
-}
-
-ComputationDataHandle ComputationBuilder::Map(
-    tensorflow::gtl::ArraySlice operands,
-    const Computation& computation,
-    tensorflow::gtl::ArraySlice dimensions,
-    tensorflow::gtl::ArraySlice static_operands) {
-  OpRequest op_request;
-  MapRequest* request = op_request.mutable_map_request();
-  for (const ComputationDataHandle& operand : operands) {
-    *request->add_operands() = operand;
-  }
-  *request->mutable_to_apply() = computation.handle();
-  for (int64 dimension : dimensions) {
-    request->add_dimensions(dimension);
-  }
-  for (const ComputationDataHandle& sop : static_operands) {
-    *request->add_static_operands() = sop;
-  }
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::RngNormal(
-    const ComputationDataHandle& mu, const ComputationDataHandle& sigma,
-    const Shape& shape) {
-  return RngOp(RandomDistribution::RNG_NORMAL, {mu, sigma}, shape);
-}
-
-ComputationDataHandle ComputationBuilder::RngUniform(
-    const ComputationDataHandle& a, const ComputationDataHandle& b,
-    const Shape& shape) {
-  return RngOp(RandomDistribution::RNG_UNIFORM, {a, b}, shape);
-}
-
-ComputationDataHandle ComputationBuilder::While(
-    const Computation& condition, const Computation& body,
-    const ComputationDataHandle& init) {
-  OpRequest op_request;
-  WhileRequest* request = op_request.mutable_while_request();
-  *request->mutable_condition() = condition.handle();
-  *request->mutable_body() = body.handle();
-  *request->mutable_init() = init;
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Gather(
-    const ComputationDataHandle& input,
-    const ComputationDataHandle& gather_indices,
-    const GatherDimensionNumbers& dimension_numbers,
-    tensorflow::gtl::ArraySlice window_bounds) {
-  OpRequest op_request;
-  GatherRequest* gather_request = op_request.mutable_gather_request();
-  *gather_request->mutable_input() = input;
-  *gather_request->mutable_gather_indices() = gather_indices;
-  *gather_request->mutable_dimension_numbers() = dimension_numbers;
-  for (int64 window_bound : window_bounds) {
-    gather_request->add_window_bounds(window_bound);
-  }
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Conditional(
-    const ComputationDataHandle& predicate,
-    const ComputationDataHandle& true_operand,
-    const Computation& true_computation,
-    const ComputationDataHandle& false_operand,
-    const Computation& false_computation) {
-  OpRequest op_request;
-  ConditionalRequest* request = op_request.mutable_conditional_request();
-  *request->mutable_predicate() = predicate;
-  *request->mutable_true_operand() = true_operand;
-  *request->mutable_true_computation() = true_computation.handle();
-  *request->mutable_false_operand() = false_operand;
-  *request->mutable_false_computation() = false_computation.handle();
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Reduce(
-    const ComputationDataHandle& operand,
-    const ComputationDataHandle& init_value, const Computation& computation,
-    tensorflow::gtl::ArraySlice dimensions_to_reduce) {
-  OpRequest op_request;
-  ReduceRequest* request = op_request.mutable_reduce_request();
-  *request->mutable_operand() = operand;
-  *request->mutable_init_value() = init_value;
-  for (int64 dimension : dimensions_to_reduce) {
-    request->add_dimensions(dimension);
-  }
-  *request->mutable_to_apply() = computation.handle();
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::ReduceAll(
-    const ComputationDataHandle& operand,
-    const ComputationDataHandle& init_value, const Computation& computation) {
-  if (!first_error_.ok() || !PrepareComputation().ok()) {
-    return ComputationDataHandle();
-  }
-
-  StatusOr> shape = GetShape(operand);
-  if (!shape.ok()) {
-    return ComputationDataHandle();
-  }
-
-  std::vector all_dimnos(ShapeUtil::Rank(*shape.ValueOrDie()));
-  std::iota(all_dimnos.begin(), all_dimnos.end(), 0);
-  return Reduce(operand, init_value, computation, all_dimnos);
-}
-
-ComputationDataHandle ComputationBuilder::ReduceWindow(
-    const ComputationDataHandle& operand,
-    const ComputationDataHandle& init_value, const Computation& computation,
-    tensorflow::gtl::ArraySlice window_dimensions,
-    tensorflow::gtl::ArraySlice window_strides, Padding padding) {
-  if (!first_error_.ok()) {
-    return ComputationDataHandle();
-  }
-
-  StatusOr> shape = GetShape(operand);
-  if (!shape.ok()) {
-    return ComputationDataHandle();
-  }
-
-  Status padding_valid =
-      ValidatePaddingValues(AsInt64Slice(shape.ValueOrDie()->dimensions()),
-                            window_dimensions, window_strides);
-  if (!padding_valid.ok()) {
-    first_error_ = padding_valid;
-    return ComputationDataHandle();
-  }
-
-  std::vector> padding_values =
-      MakePadding(AsInt64Slice(shape.ValueOrDie()->dimensions()),
-                  window_dimensions, window_strides, padding);
-  return ReduceWindowWithGeneralPadding(operand, init_value, computation,
-                                        window_dimensions, window_strides,
-                                        padding_values);
-}
-
-ComputationDataHandle ComputationBuilder::ReduceWindowWithGeneralPadding(
-    const ComputationDataHandle& operand,
-    const ComputationDataHandle& init_value, const Computation& computation,
-    tensorflow::gtl::ArraySlice window_dimensions,
-    tensorflow::gtl::ArraySlice window_strides,
-    tensorflow::gtl::ArraySlice> padding) {
-  OpRequest op_request;
-  ReduceWindowRequest* request = op_request.mutable_reduce_window_request();
-  *request->mutable_operand() = operand;
-  *request->mutable_to_apply() = computation.handle();
-  *request->mutable_init_value() = init_value;
-
-  if (!MakeWindow(window_dimensions, window_strides, padding, {}, {},
-                  request->mutable_window())) {
-    NoteError(InternalError("failed to make window"));
-    return ComputationDataHandle();
-  }
-
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::BatchNormTraining(
-    const ComputationDataHandle& operand, const ComputationDataHandle& scale,
-    const ComputationDataHandle& offset, float epsilon, int64 feature_index) {
-  OpRequest op_request;
-  BatchNormTrainingRequest* request =
-      op_request.mutable_batch_norm_training_request();
-  *request->mutable_operand() = operand;
-  *request->mutable_scale() = scale;
-  *request->mutable_offset() = offset;
-  request->set_epsilon(epsilon);
-  request->set_feature_index(feature_index);
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::BatchNormInference(
-    const ComputationDataHandle& operand, const ComputationDataHandle& scale,
-    const ComputationDataHandle& offset, const ComputationDataHandle& mean,
-    const ComputationDataHandle& variance, float epsilon, int64 feature_index) {
-  OpRequest op_request;
-  BatchNormInferenceRequest* request =
-      op_request.mutable_batch_norm_inference_request();
-  *request->mutable_operand() = operand;
-  *request->mutable_scale() = scale;
-  *request->mutable_offset() = offset;
-  *request->mutable_mean() = mean;
-  *request->mutable_variance() = variance;
-  request->set_epsilon(epsilon);
-  request->set_feature_index(feature_index);
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::BatchNormGrad(
-    const ComputationDataHandle& operand, const ComputationDataHandle& scale,
-    const ComputationDataHandle& batch_mean,
-    const ComputationDataHandle& batch_var,
-    const ComputationDataHandle& grad_output, float epsilon,
-    int64 feature_index) {
-  OpRequest op_request;
-  BatchNormGradRequest* request = op_request.mutable_batch_norm_grad_request();
-  *request->mutable_operand() = operand;
-  *request->mutable_scale() = scale;
-  *request->mutable_mean() = batch_mean;
-  *request->mutable_variance() = batch_var;
-  *request->mutable_grad_output() = grad_output;
-  request->set_epsilon(epsilon);
-  request->set_feature_index(feature_index);
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::CrossReplicaSum(
-    const ComputationDataHandle& operand) {
-  OpRequest op_request;
-  CrossReplicaSumRequest* request =
-      op_request.mutable_cross_replica_sum_request();
-  *request->mutable_operand() = operand;
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::SelectAndScatter(
-    const ComputationDataHandle& operand, const Computation& select,
-    tensorflow::gtl::ArraySlice window_dimensions,
-    tensorflow::gtl::ArraySlice window_strides, Padding padding,
-    const ComputationDataHandle& source,
-    const ComputationDataHandle& init_value, const Computation& scatter) {
-  if (!first_error_.ok()) {
-    return ComputationDataHandle();
-  }
-
-  StatusOr> shape = GetShape(operand);
-  if (!shape.ok()) {
-    return ComputationDataHandle();
-  }
-  return SelectAndScatterWithGeneralPadding(
-      operand, select, window_dimensions, window_strides,
-      MakePadding(AsInt64Slice(shape.ValueOrDie()->dimensions()),
-                  window_dimensions, window_strides, padding),
-      source, init_value, scatter);
-}
-
-ComputationDataHandle ComputationBuilder::SelectAndScatterWithGeneralPadding(
-    const ComputationDataHandle& operand, const Computation& select,
-    tensorflow::gtl::ArraySlice window_dimensions,
-    tensorflow::gtl::ArraySlice window_strides,
-    tensorflow::gtl::ArraySlice> padding,
-    const ComputationDataHandle& source,
-    const ComputationDataHandle& init_value, const Computation& scatter) {
-  OpRequest op_request;
-  SelectAndScatterRequest* request =
-      op_request.mutable_select_and_scatter_request();
-  *request->mutable_operand() = operand;
-  *request->mutable_select() = select.handle();
-  *request->mutable_source() = source;
-  *request->mutable_init_value() = init_value;
-  *request->mutable_scatter() = scatter.handle();
-
-  if (!MakeWindow(window_dimensions, window_strides, padding, {}, {},
-                  request->mutable_window())) {
-    NoteError(InternalError("failed to make window"));
-    return ComputationDataHandle();
-  }
-
-  return RunOpAndParseResponse(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::ReducePrecision(
-    const ComputationDataHandle& operand, const int exponent_bits,
-    const int mantissa_bits) {
-  OpRequest op_request;
-  ReducePrecisionRequest* request =
-      op_request.mutable_reduce_precision_request();
-  *request->mutable_operand() = operand;
-  request->set_exponent_bits(exponent_bits);
-  request->set_mantissa_bits(mantissa_bits);
-  return RunOpAndParseResponse(&op_request);
-}
-
-void ComputationBuilder::Send(const ComputationDataHandle& operand,
-                              const ChannelHandle& handle) {
-  OpRequest op_request;
-  SendRequest* request = op_request.mutable_send_request();
-  *request->mutable_operand() = operand;
-  *request->mutable_channel_handle() = handle;
-  *op_request.mutable_computation() = computation_.handle();
-  RunOpAndNoteError(&op_request);
-}
-
-ComputationDataHandle ComputationBuilder::Recv(const Shape& shape,
-                                               const ChannelHandle& handle) {
-  OpRequest op_request;
-  RecvRequest* request = op_request.mutable_recv_request();
-  *request->mutable_shape() = shape;
-  *request->mutable_channel_handle() = handle;
-  return RunOpAndParseResponse(&op_request);
-}
-
-Computation ComputationBuilder::BuildAndNoteError() {
-  DCHECK(parent_builder_ != nullptr);
-  auto build_status = Build();
-  if (!build_status.ok()) {
-    parent_builder_->NoteError(
-        AddStatus(build_status.status(),
-                  tensorflow::strings::StrCat("error from: ", name_)));
-    return Computation();
-  }
-  return build_status.ConsumeValueOrDie();
-}
-
-StatusOr ComputationBuilder::Build() {
-  if (!first_error_.ok()) {
-    string backtrace;
-    first_error_backtrace_.Dump(tensorflow::DebugWriteToString, &backtrace);
-    return AppendStatus(first_error_, backtrace);
-  }
-
-  if (computation_.IsNull()) {
-    return FailedPrecondition("no computation was built");
-  }
-
-  return {std::move(computation_)};
-}
-
-/* static */ ConvolutionDimensionNumbers
-ComputationBuilder::CreateDefaultConvDimensionNumbers(int num_spatial_dims) {
-  ConvolutionDimensionNumbers dimension_numbers;
-  dimension_numbers.set_input_batch_dimension(kConvBatchDimension);
-  dimension_numbers.set_input_feature_dimension(kConvFeatureDimension);
-  dimension_numbers.set_output_batch_dimension(kConvBatchDimension);
-  dimension_numbers.set_output_feature_dimension(kConvFeatureDimension);
-  dimension_numbers.set_kernel_output_feature_dimension(
-      kConvKernelOutputDimension);
-  dimension_numbers.set_kernel_input_feature_dimension(
-      kConvKernelInputDimension);
-  for (int i = 0; i < num_spatial_dims; ++i) {
-    dimension_numbers.add_input_spatial_dimensions(i + 2);
-    dimension_numbers.add_kernel_spatial_dimensions(i + 2);
-    dimension_numbers.add_output_spatial_dimensions(i + 2);
-  }
-  return dimension_numbers;
-}
-
-/* static */ StatusOr
-ComputationBuilder::CreateConvDimensionNumbers(
-    int64 input_batch, int64 input_feature, int64 input_first_spatial,
-    int64 input_second_spatial, int64 output_batch, int64 output_feature,
-    int64 output_first_spatial, int64 output_second_spatial,
-    int64 kernel_output_feature, int64 kernel_input_feature,
-    int64 kernel_first_spatial, int64 kernel_second_spatial) {
-  if (std::set({input_batch, input_feature, input_first_spatial,
-                       input_second_spatial})
-          .size() != 4) {
-    return FailedPrecondition(
-        "dimension numbers for the input are not unique: (%lld, %lld, %lld, "
-        "%lld)",
-        input_batch, input_feature, input_first_spatial, input_second_spatial);
-  }
-  if (std::set({kernel_output_feature, kernel_input_feature,
-                       kernel_first_spatial, kernel_second_spatial})
-          .size() != 4) {
-    return FailedPrecondition(
-        "dimension numbers for the weight are not unique: (%lld, %lld, %lld, "
-        "%lld)",
-        kernel_output_feature, kernel_input_feature, kernel_first_spatial,
-        kernel_second_spatial);
-  }
-  if (std::set({output_batch, output_feature, output_first_spatial,
-                       output_second_spatial})
-          .size() != 4) {
-    return FailedPrecondition(
-        "dimension numbers for the output are not unique: (%lld, %lld, %lld, "
-        "%lld)",
-        output_batch, output_feature, output_first_spatial,
-        output_second_spatial);
-  }
-  ConvolutionDimensionNumbers dimension_numbers;
-  dimension_numbers.set_input_batch_dimension(input_batch);
-  dimension_numbers.set_input_feature_dimension(input_feature);
-  dimension_numbers.add_input_spatial_dimensions(input_first_spatial);
-  dimension_numbers.add_input_spatial_dimensions(input_second_spatial);
-  dimension_numbers.set_kernel_output_feature_dimension(kernel_output_feature);
-  dimension_numbers.set_kernel_input_feature_dimension(kernel_input_feature);
-  dimension_numbers.add_kernel_spatial_dimensions(kernel_first_spatial);
-  dimension_numbers.add_kernel_spatial_dimensions(kernel_second_spatial);
-  dimension_numbers.set_output_batch_dimension(output_batch);
-  dimension_numbers.set_output_feature_dimension(output_feature);
-  dimension_numbers.add_output_spatial_dimensions(output_first_spatial);
-  dimension_numbers.add_output_spatial_dimensions(output_second_spatial);
-  return dimension_numbers;
-}
-
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
deleted file mode 100644
index 9ec43720623546..00000000000000
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ /dev/null
@@ -1,1073 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_COMPUTATION_BUILDER_H_
-#define TENSORFLOW_COMPILER_XLA_CLIENT_COMPUTATION_BUILDER_H_
-
-#include 
-#include 
-#include 
-#include 
-#include 
-
-#include "tensorflow/compiler/xla/array.h"
-#include "tensorflow/compiler/xla/array2d.h"
-#include "tensorflow/compiler/xla/array3d.h"
-#include "tensorflow/compiler/xla/array4d.h"
-#include "tensorflow/compiler/xla/client/client.h"
-#include "tensorflow/compiler/xla/client/computation.h"
-#include "tensorflow/compiler/xla/client/global_data.h"
-#include "tensorflow/compiler/xla/client/padding.h"
-#include "tensorflow/compiler/xla/literal_util.h"
-#include "tensorflow/compiler/xla/statusor.h"
-#include "tensorflow/compiler/xla/types.h"
-#include "tensorflow/compiler/xla/xla_data.pb.h"
-#include "tensorflow/core/lib/core/bitmap.h"
-#include "tensorflow/core/lib/core/stringpiece.h"
-#include "tensorflow/core/lib/gtl/array_slice.h"
-#include "tensorflow/core/platform/macros.h"
-#include "tensorflow/core/platform/stacktrace.h"
-#include "tensorflow/core/platform/types.h"
-
-namespace xla {
-
-// Wraps an XLA client with a convenient interface for building up
-// computations. Any errors encountered in building up the computation are
-// deferred from being handled until Build() is called.
-//
-// Thread-compatible.
-//
-// TODO(b/74197823): Deprecated. Use XlaBuilder instead.
-class ComputationBuilder {
- public:
-  // client: client in which to build the computation.
-  // computation_name: name to use for the built computation.
-  ComputationBuilder(Client* client, const string& computation_name);
-
-  ~ComputationBuilder();
-
-  // Returns the client the builder was initialized with.
-  Client* client() const { return client_; }
-
-  // Returns the computation name.
-  const string& name() const { return name_; }
-
-  // Sets OpMetadata that will be added to all instructions until cleared.
-  //
-  // OpMetadata is often applied to a series of XLA HLO instructions. As a
-  // result, OpMetadata is set on the Computation Builder. All subsequent
-  // instructions generated via this Computation Builder will have the same
-  // OpMetadata attached until a call to ClearOpMetadata.
-  void SetOpMetadata(const OpMetadata& metadata) { metadata_ = metadata; }
-
-  // Clears the HloMetadata state.
-  void ClearOpMetadata() { metadata_.Clear(); }
-
-  // Sets an OpSharding that will be attached to all instructions until cleared.
-  void SetSharding(const OpSharding& sharding) { sharding_ = sharding; }
-
-  // Clears the sharding. Ops will be sharded according to the default placement
-  // policy.
-  void ClearSharding() { sharding_ = tensorflow::gtl::nullopt; }
-
-  // Returns the OpSharding that will be attached to all instructions.
-  const tensorflow::gtl::optional& sharding() const {
-    return sharding_;
-  }
-
-  // Sets the builder to a mode where it will die immediately when an error is
-  // encountered, rather than producing it in a deferred fashion when Build() is
-  // called (which is the default).
-  void set_die_immediately_on_error(bool enabled) {
-    die_immediately_on_error_ = enabled;
-  }
-
-  // Enqueues a "retrieve parameter value" instruction for a parameter that was
-  // passed to the computation.
-  ComputationDataHandle Parameter(int64 parameter_number, const Shape& shape,
-                                  const string& name);
-
-  // Retrieves the (inferred) shape of the operand in the computation.
-  StatusOr> GetShape(
-      const ComputationDataHandle& operand);
-
-  // Retrieves the (inferred) result for the current computation's shape.
-  StatusOr GetProgramShape();
-
-  // Enqueues a constant with the value of the given literal onto the
-  // computation.
-  ComputationDataHandle ConstantLiteral(const LiteralSlice& literal);
-
-  // Enqueues a constant onto the computation. Methods are templated on the
-  // native host type (NativeT) which corresponds to a specific XLA
-  // PrimitiveType as given in the following table:
-  //
-  //  Native Type   PrimitiveType
-  // -----------------------------
-  //   bool           PRED
-  //   int32          S32
-  //   int64          S64
-  //   uint32         U32
-  //   uint64         U64
-  //   float          F32
-  //   double         F64
-  //
-  // Note: not all primitive types defined in xla_data.proto have a
-  // corresponding native type yet.
-  template 
-  ComputationDataHandle ConstantR0(NativeT value);
-  template 
-  ComputationDataHandle ConstantR1(tensorflow::gtl::ArraySlice values);
-  ComputationDataHandle ConstantR1(const tensorflow::core::Bitmap& values);
-  template 
-  ComputationDataHandle ConstantR2(
-      std::initializer_list> values);
-  template 
-  ComputationDataHandle ConstantFromArrayWithLayout(
-      const Array& values, const Layout& layout);
-  template 
-  ComputationDataHandle ConstantFromArray(const Array& values);
-  template 
-  ComputationDataHandle ConstantR2FromArray2DWithLayout(
-      const Array2D& values, const Layout& layout);
-  template 
-  ComputationDataHandle ConstantR2FromArray2D(const Array2D& values);
-  template 
-  ComputationDataHandle ConstantR3FromArray3DWithLayout(
-      const Array3D& values, const Layout& layout);
-  template 
-  ComputationDataHandle ConstantR3FromArray3D(const Array3D& values);
-  template 
-  ComputationDataHandle ConstantR4FromArray4DWithLayout(
-      const Array4D& values, const Layout& layout);
-  template 
-  ComputationDataHandle ConstantR4FromArray4D(const Array4D& values);
-
-  // Enqueues a rank one constant (vector) onto the computation. The vector has
-  // size 'length' and every element has the value 'value'.
-  template 
-  ComputationDataHandle ConstantR1(int64 length, NativeT value);
-
-  // Adds dimensions to an array by duplicating the data in the array.
-  //
-  // The new dimensions are inserted on the left, i.e. if
-  // broadcast_sizes has values {a0, ..., aN} and the operand shape
-  // has dimensions {b0, ..., bM} then the shape of the output has
-  // dimensions {a0, ..., aN, b0, ..., bM}.
-  //
-  // The new dimensions index into copies of the operand, i.e.
-  //
-  //   output[i0, ..., iN, j0, ..., jM] = operand[j0, ..., jM]
-  ComputationDataHandle Broadcast(
-      const ComputationDataHandle& operand,
-      tensorflow::gtl::ArraySlice broadcast_sizes);
-
-  // Enqueues a pad operation onto the computation that pads the given value on
-  // the edges as well as between the elements of the input. padding_config
-  // specifies the padding amount for each dimension.
-  ComputationDataHandle Pad(const ComputationDataHandle& operand,
-                            const ComputationDataHandle& padding_value,
-                            const PaddingConfig& padding_config);
-
-  // Enqueues an operation onto the computation that flattens the operand based
-  // on the dimension order (major/slowest-varying to minor/fastest-varying)
-  // given, followed by reshaping it into the shape with the given dimension
-  // sizes (also major to minor). Conceptually, this is a limited form of
-  // "shape casting".
-  ComputationDataHandle Reshape(const ComputationDataHandle& operand,
-                                tensorflow::gtl::ArraySlice dimensions,
-                                tensorflow::gtl::ArraySlice new_sizes);
-
-  // Enqueues an operation onto the computation that collapses the operand, from
-  // first to last dimension (C order), then reshapes it to the given dimension
-  // sizes. Conceptually, this is a limited form of "shape casting".
-  ComputationDataHandle Reshape(const ComputationDataHandle& operand,
-                                tensorflow::gtl::ArraySlice new_sizes);
-
-  // Wrapper for Reshape.
-  // Enqueues an operation to collapse the provided dimensions; e.g. an
-  // operand with dimensions {x=256, y=2, z=2, p=32} can be collapsed to
-  // {x=1024, y=32} by collapsing dims {0, 1, 2}. Collapsing dimensions must
-  // be a consecutive, in-order subsequence of the operand dimensions.
-  //
-  // Note that collapsing a single dimension does nothing:
-  //
-  //    {256} collapsing {0} => {256}
-  //    {1} collapsing {0} => {1}
-  //
-  // Collapsing multiple dimensions produces a single result dimension:
-  //
-  //    {256, 2} collapsing {0,1} => {512}
-  //    {256, 2, 3} collapsing {0,1} => {512, 3}
-  //
-  // This could potentially cause data to be moved -- it provides a more
-  // structured form of reshaping than an arbitrary Reshape operation.
-  ComputationDataHandle Collapse(const ComputationDataHandle& operand,
-                                 tensorflow::gtl::ArraySlice dimensions);
-
-  // Enqueues a slice operation onto the computation that slices the operand
-  // from the start indices to the limit indices; e.g.
-  //
-  //        x
-  //   [ 0 1 2 3 ]
-  // y [ 4 5 6 7 ] => slice(start={1, 1}, limit={2, 3}) => [ 5 6 ]
-  //   [ 8 9 a b ]
-  //
-  // Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D
-  // range notation.
-  // The strides parameter determines the stride over the slice
-  ComputationDataHandle Slice(const ComputationDataHandle& operand,
-                              tensorflow::gtl::ArraySlice start_indices,
-                              tensorflow::gtl::ArraySlice limit_indices,
-                              tensorflow::gtl::ArraySlice strides);
-
-  // Enqueues a slice operation in a given dimension, taking all other
-  // dimensions as they are; e.g. if dimno is 1 from start_index 2 to
-  // limit_index 4 by 1, and the shape is f32[7,8,9], this call is short-hand
-  // for:
-  //
-  //  array[:, 2:4:1, :]
-  ComputationDataHandle SliceInDim(const ComputationDataHandle& operand,
-                                   int64 start_index, int64 limit_index,
-                                   int64 stride, int64 dimno);
-
-  // Enqueues a slice operation onto the computation that slices the 'operand'
-  // from dynamic start indices which are passed in 'start_indices'.
-  // The size of the slice in each dimension is passed in 'slice_sizes',
-  // which specify the end point of exclusive slice intervals in each
-  // dimension [start, start + size).
-  // The shape of 'start_indices' must be rank == 1, with dimension size
-  // equal to the rank of the 'operand'.
-  // Slice index calculations are computed modulo input dimension sizes to
-  // prevent dynamic start indices from generating out-of-bound array accesses.
-  ComputationDataHandle DynamicSlice(
-      const ComputationDataHandle& operand,
-      const ComputationDataHandle& start_indices,
-      tensorflow::gtl::ArraySlice slice_sizes);
-
-  // Enqueues a dynamic update slice operation onto the computation, which
-  // updates a slice of 'operand' with 'update' at dynamic 'start_indices'.
-  // The shape of 'update' determines the shape of the slice of 'operand'
-  // which is updated.
-  // The indices specified in 'start_indices' specify the offset of the slice
-  // of 'operand' which is updated.
-  //
-  //               update = {10, 11} // calculated at runtime.
-  //   [1 2 3]     start  = {1, 1}   // calculated at runtime.  [1 2  3 ]
-  //   [4 5 6]  => DynamicUpdateslice(data, update, start)   => [4 10 11]
-  //   [7 8 9]                                                  [7 8  9 ]
-  //
-  // The shape of 'start_indices' must be rank == 1, with dimension size
-  // equal to the rank of the 'operand'.
-  // Slice index calculations are computed modulo update dimension sizes to
-  // prevent dynamic start indices from generating out-of-bound array accesses.
-  ComputationDataHandle DynamicUpdateSlice(
-      const ComputationDataHandle& operand, const ComputationDataHandle& update,
-      const ComputationDataHandle& start_indices);
-
-  // Enqueues a concatenate instruction onto the computation. 'operands' must
-  // have >= 1 entry.
-  ComputationDataHandle ConcatInDim(
-      tensorflow::gtl::ArraySlice operands,
-      int64 dimension);
-
-  // Enqueue a tracing operation onto the computation; the computation will emit
-  // a logging message with the operand.
-  void Trace(const string& tag, const ComputationDataHandle& operand);
-
-  // Enqueues a conditional-move-like select operation onto the computation;
-  // predicated on pred, selects between on_true and on_false.
-  ComputationDataHandle Select(const ComputationDataHandle& pred,
-                               const ComputationDataHandle& on_true,
-                               const ComputationDataHandle& on_false);
-
-  // Enqueues a tuple-creation instruction onto the computation.
-  ComputationDataHandle Tuple(
-      tensorflow::gtl::ArraySlice elements);
-
-  // Enqueues a tuple-element-get instruction onto the computation.
-  ComputationDataHandle GetTupleElement(const ComputationDataHandle& tuple_data,
-                                        int64 index);
-
-  // Enqueues an equal-to comparison instruction onto the computation.
-  ComputationDataHandle Eq(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a not-equal comparison instruction onto the computation.
-  ComputationDataHandle Ne(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a greater-or-equal comparison instruction onto the computation.
-  ComputationDataHandle Ge(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a greater-than comparison instruction onto the computation.
-  ComputationDataHandle Gt(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a less-than comparison instruction onto the computation.
-  ComputationDataHandle Lt(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a less-or-equal comparison instruction onto the computation.
-  ComputationDataHandle Le(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a dot instruction onto the computation.
-  ComputationDataHandle Dot(const ComputationDataHandle& lhs,
-                            const ComputationDataHandle& rhs);
-
-  // Enqueues a general dot instruction onto the computation.
-  ComputationDataHandle DotGeneral(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      const DotDimensionNumbers& dimension_numbers);
-
-  // Default dimension numbers used for a 2D convolution.
-  static constexpr int64 kConvBatchDimension = 0;
-  static constexpr int64 kConvFeatureDimension = 1;
-  static constexpr int64 kConvFirstSpatialDimension = 2;
-  static constexpr int64 kConvSecondSpatialDimension = 3;
-  static constexpr int64 kConvKernelOutputDimension = 0;
-  static constexpr int64 kConvKernelInputDimension = 1;
-  static constexpr int64 kConvKernelFirstSpatialDimension = 2;
-  static constexpr int64 kConvKernelSecondSpatialDimension = 3;
-
-  // Creates a default ConvolutionDimensionNumbers. For a 2D convolution, for
-  // the input operand {batch, feature, height, width} = {0, 1, 2, 3} and for
-  // the kernel operand
-  // {output_feature, input_feature, height, width} = {0, 1, 2, 3}.
-  static ConvolutionDimensionNumbers CreateDefaultConvDimensionNumbers(
-      int num_spatial_dims = 2);
-
-  // Creates a ConvolutionDimensionNumbers with the given arguments. Returns an
-  // error if either the input or the weight dimension numbers have conflicts.
-  static StatusOr CreateConvDimensionNumbers(
-      int64 input_batch, int64 input_feature, int64 input_first_spatial,
-      int64 input_second_spatial, int64 output_batch, int64 output_feature,
-      int64 output_first_spatial, int64 output_second_spatial,
-      int64 kernel_output_feature, int64 kernel_input_feature,
-      int64 kernel_first_spatial, int64 kernel_second_spatial);
-
-  // Enqueues a convolution instruction onto the computation, which uses the
-  // default convolution dimension numbers.
-  ComputationDataHandle Conv(const ComputationDataHandle& lhs,
-                             const ComputationDataHandle& rhs,
-                             tensorflow::gtl::ArraySlice window_strides,
-                             Padding padding);
-
-  // Enqueues a convolution instruction onto the computation, with the caller
-  // provided padding configuration in the format returned by MakePadding().
-  ComputationDataHandle ConvWithGeneralPadding(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice window_strides,
-      tensorflow::gtl::ArraySlice> padding);
-
-  // Enqueues a convolution instruction onto the computation, with the caller
-  // provided dimension numbers configuration.
-  ComputationDataHandle ConvWithGeneralDimensions(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice window_strides, Padding padding,
-      const ConvolutionDimensionNumbers& dimension_numbers);
-
-  // Enqueues a convolution instruction onto the computation, with the caller
-  // provided padding configuration as well as the dimension numbers.
-  ComputationDataHandle ConvGeneral(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice window_strides,
-      tensorflow::gtl::ArraySlice> padding,
-      const ConvolutionDimensionNumbers& dimension_numbers);
-
-  // Enqueues a convolution instruction onto the computation, with the caller
-  // provided padding configuration, dilation factors and dimension numbers.
-  ComputationDataHandle ConvGeneralDilated(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice window_strides,
-      tensorflow::gtl::ArraySlice> padding,
-      tensorflow::gtl::ArraySlice lhs_dilation,
-      tensorflow::gtl::ArraySlice rhs_dilation,
-      const ConvolutionDimensionNumbers& dimension_numbers);
-
-  // Enqueues an FFT instruction onto the computation, of the given type and
-  // with the given FFT length.
-  ComputationDataHandle Fft(const ComputationDataHandle& operand,
-                            FftType fft_type,
-                            tensorflow::gtl::ArraySlice fft_length);
-
-  // Enqueues an infeed instruction onto the computation, which writes data of
-  // the given shape to the infeed buffer of the device.
-  ComputationDataHandle Infeed(const Shape& shape, const string& config = "");
-
-  // Enqueues an outfeed instruction onto the computation. This instruction
-  // generates outgoing data transfers for the given data.
-  //
-  // shape_with_layout communicates the laid out shape that we want to outfeed
-  // -- if !ShapeUtil::Compatible(GetShape(operand), shape_with_layout) an error
-  // will occur.
-  void Outfeed(const ComputationDataHandle& operand,
-               const Shape& shape_with_layout, const string& outfeed_config);
-
-  // Enqueues a call instruction onto the computation.
-  ComputationDataHandle Call(
-      const Computation& computation,
-      tensorflow::gtl::ArraySlice operands);
-
-  // Enqueues a custom call instruction onto the computation.
-  // During code generation, a call instruction is emitted which targets a
-  // symbol with the name |call_target_name|.  The |operands| are passed to the
-  // call instruction.  |shape| is the resultant shape.
-  ComputationDataHandle CustomCall(
-      const string& call_target_name,
-      tensorflow::gtl::ArraySlice operands,
-      const Shape& shape);
-
-  // Enqueues a pseudo-op to represent host-side computation data-dependencies.
-  // During code generation, host send and receive operations will be generated
-  // to transfer |operands| to the host and a single result of |shape| back to
-  // the device.  Host send/recv operations are emitted using |channel_name|.
-  // Dataflow dependencies and the |cost_estimate_ns| field may be used in HLO
-  // instruction scheduling.
-  ComputationDataHandle HostCompute(
-      tensorflow::gtl::ArraySlice operands,
-      const string& channel_name, int64 cost_estimate_ns, const Shape& shape);
-
-  // The following methods enqueue element-wise binary arithmetic operations
-  // onto the computation. The shapes of the operands have to match unless one
-  // of the operands is a scalar, or an explicit broadcast dimension is given
-  // (see g3doc for more details).
-
-  // Enqueues a complex compose instruction onto the computation.
-  ComputationDataHandle Complex(
-      const ComputationDataHandle& real, const ComputationDataHandle& imag,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a complex conjugate instruction onto the computation.
-  ComputationDataHandle Conj(const ComputationDataHandle& operand);
-
-  // Enqueues an add instruction onto the computation.
-  ComputationDataHandle Add(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a subtract instruction onto the computation.
-  ComputationDataHandle Sub(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a multiply instruction onto the computation.
-  ComputationDataHandle Mul(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a divide instruction onto the computation.
-  ComputationDataHandle Div(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a remainder instruction onto the computation.
-  ComputationDataHandle Rem(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a max instruction onto the computation.
-  ComputationDataHandle Max(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues a min instruction onto the computation.
-  ComputationDataHandle Min(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Element-wise logical operators
-  ComputationDataHandle And(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  ComputationDataHandle Or(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  ComputationDataHandle Xor(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  ComputationDataHandle Not(const ComputationDataHandle& operand);
-
-  ComputationDataHandle ShiftLeft(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-  ComputationDataHandle ShiftRightArithmetic(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-  ComputationDataHandle ShiftRightLogical(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Reduces an array among the provided dimensions, given "computation" as a
-  // reduction operator.
-  ComputationDataHandle Reduce(
-      const ComputationDataHandle& operand,
-      const ComputationDataHandle& init_value, const Computation& computation,
-      tensorflow::gtl::ArraySlice dimensions_to_reduce);
-
-  // Convenience wrapper around the above that reduces all the dimensions in the
-  // operand shape.
-  ComputationDataHandle ReduceAll(const ComputationDataHandle& operand,
-                                  const ComputationDataHandle& init_value,
-                                  const Computation& computation);
-
-  // Enqueues a windowed reduce instruction onto the computation.
-  ComputationDataHandle ReduceWindow(
-      const ComputationDataHandle& operand,
-      const ComputationDataHandle& init_value, const Computation& computation,
-      tensorflow::gtl::ArraySlice window_dimensions,
-      tensorflow::gtl::ArraySlice window_strides, Padding padding);
-
-  // As ReduceWindow(), but the padding is given in the format
-  // returned by MakePadding().
-  ComputationDataHandle ReduceWindowWithGeneralPadding(
-      const ComputationDataHandle& operand,
-      const ComputationDataHandle& init_value, const Computation& computation,
-      tensorflow::gtl::ArraySlice window_dimensions,
-      tensorflow::gtl::ArraySlice window_strides,
-      tensorflow::gtl::ArraySlice> padding);
-
-  // Returns the sum of the operand value across all replicas. All replicas
-  // supply one input to the sum and all replicas receive the resulting sum.
-  ComputationDataHandle CrossReplicaSum(const ComputationDataHandle& operand);
-
-  // Enqueues an operation that scatters the `source` array to the selected
-  // indices of each window.
-  ComputationDataHandle SelectAndScatter(
-      const ComputationDataHandle& operand, const Computation& select,
-      tensorflow::gtl::ArraySlice window_dimensions,
-      tensorflow::gtl::ArraySlice window_strides, Padding padding,
-      const ComputationDataHandle& source,
-      const ComputationDataHandle& init_value, const Computation& scatter);
-
-  // As SelectAndScatter(), but the padding is given in the format
-  // returned by MakePadding().
-  ComputationDataHandle SelectAndScatterWithGeneralPadding(
-      const ComputationDataHandle& operand, const Computation& select,
-      tensorflow::gtl::ArraySlice window_dimensions,
-      tensorflow::gtl::ArraySlice window_strides,
-      tensorflow::gtl::ArraySlice> padding,
-      const ComputationDataHandle& source,
-      const ComputationDataHandle& init_value, const Computation& scatter);
-
-  // Enqueues an abs instruction onto the computation.
-  ComputationDataHandle Abs(const ComputationDataHandle& operand);
-
-  // Enqueues a atan2 instruction onto the computation.
-  ComputationDataHandle Atan2(
-      const ComputationDataHandle& y, const ComputationDataHandle& x,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues an exp instruction onto the computation.
-  ComputationDataHandle Exp(const ComputationDataHandle& operand);
-
-  // Enqueues an expm1 instruction onto the computation.
-  ComputationDataHandle Expm1(const ComputationDataHandle& operand);
-
-  // Enqueues a floor instruction onto the computation.
-  ComputationDataHandle Floor(const ComputationDataHandle& operand);
-
-  // Enqueues a ceil instruction onto the computation.
-  ComputationDataHandle Ceil(const ComputationDataHandle& operand);
-
-  // Enqueues a round instruction onto the computation, rounding to nearest even
-  // with half-way cases rounding away from zero.
-  ComputationDataHandle Round(const ComputationDataHandle& operand);
-
-  // Enqueues an log instruction (natural logarithm) onto the computation.
-  ComputationDataHandle Log(const ComputationDataHandle& operand);
-
-  // Enqueues an log1p instruction onto the computation.
-  ComputationDataHandle Log1p(const ComputationDataHandle& operand);
-
-  // Enqueues a sign instruction onto the computation.
-  ComputationDataHandle Sign(const ComputationDataHandle& operand);
-
-  // Enqueues a cosine instruction onto the computation.
-  ComputationDataHandle Cos(const ComputationDataHandle& operand);
-
-  // Enqueues a sine instruction onto the computation.
-  ComputationDataHandle Sin(const ComputationDataHandle& operand);
-
-  // Enqueues a tanh instruction onto the computation.
-  ComputationDataHandle Tanh(const ComputationDataHandle& operand);
-
-  // Enqueues a real-part instruction onto the computation.
-  ComputationDataHandle Real(const ComputationDataHandle& operand);
-
-  // Enqueues an imaginary-part instruction onto the computation.
-  ComputationDataHandle Imag(const ComputationDataHandle& operand);
-
-  // Enqueues a float32 sqrt instruction onto the computation.
-  // (float32 is specified as there is an implicit float32 0.5f constant
-  // exponent).
-  ComputationDataHandle SqrtF32(const ComputationDataHandle& operand);
-
-  // Enqueues a float32 square instruction onto the computation.
-  // (float32 is specified as there is an implicit float32 2.0f constant
-  // exponent).
-  ComputationDataHandle SquareF32(const ComputationDataHandle& operand);
-
-  // Enqueues a lhs^rhs computation onto the computation.
-  ComputationDataHandle Pow(
-      const ComputationDataHandle& lhs, const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions = {});
-
-  // Enqueues an operator that tests if the operand's values are finite, i.e.,
-  // not Inf or NaN. Defined only for floating-point types. Returns an array of
-  // booleans with the same shape where entries are true iff the corresponding
-  // entry was NaN.
-  ComputationDataHandle IsFinite(const ComputationDataHandle& operand);
-
-  // Enqueues a convert instruction onto the computation that changes the
-  // element type of the operand array to primitive_type.
-  ComputationDataHandle ConvertElementType(const ComputationDataHandle& operand,
-                                           PrimitiveType new_element_type);
-
-  // Enqueues a no-op instruction onto the computation that changes
-  // the element type of the operand array to primitive_type. The
-  // bit-widths of the source and destination element types must be
-  // identical.
-  ComputationDataHandle BitcastConvertType(const ComputationDataHandle& operand,
-                                           PrimitiveType new_element_type);
-
-  // Enqueues a float32 reciprocal instruction onto the computation.
-  // (float32 is specified as there is an implicit float32 -1.0f constant
-  // exponent).
-  //
-  // TODO(b/34468990) axe F32 suffix, can be determined by reflecting on the
-  // shape of the operand.
-  ComputationDataHandle ReciprocalF32(const ComputationDataHandle& operand);
-
-  // Enqueues a negate instruction onto the computation.
-  ComputationDataHandle Neg(const ComputationDataHandle& operand);
-
-  // Enqueues a count-leading-zeros instruction onto the computation.
-  ComputationDataHandle Clz(const ComputationDataHandle& operand);
-
-  // Enqueues a transpose instruction onto the computation.
-  ComputationDataHandle Transpose(
-      const ComputationDataHandle& operand,
-      tensorflow::gtl::ArraySlice permutation);
-
-  // Enqueues a reverse instruction onto the computation. The order of the
-  // elements in the given dimensions is reversed (i.e., the element at index i
-  // is moved to index dimension_size - 1 - i).
-  ComputationDataHandle Rev(const ComputationDataHandle& operand,
-                            tensorflow::gtl::ArraySlice dimensions);
-
-  // Enqueues a sort (as increasing order) instruction onto the computation.
-  ComputationDataHandle Sort(const ComputationDataHandle& operand);
-
-  // Enqueues a clamp instruction onto the computation.
-  ComputationDataHandle Clamp(const ComputationDataHandle& min,
-                              const ComputationDataHandle& operand,
-                              const ComputationDataHandle& max);
-
-  // Enqueues a map instruction onto the computation.
-  ComputationDataHandle Map(
-      tensorflow::gtl::ArraySlice operands,
-      const Computation& computation,
-      tensorflow::gtl::ArraySlice dimensions,
-      tensorflow::gtl::ArraySlice static_operands = {});
-
-  // Enqueues a N(mu, sigma) random number generation instruction onto the
-  // computation.
-  ComputationDataHandle RngNormal(const ComputationDataHandle& mu,
-                                  const ComputationDataHandle& sigma,
-                                  const Shape& shape);
-
-  // Enqueues a U(a, b) random number generation instruction onto the
-  // computation. Returns values in the semi-open interval [a, b).
-  ComputationDataHandle RngUniform(const ComputationDataHandle& a,
-                                   const ComputationDataHandle& b,
-                                   const Shape& shape);
-
-  // Enqueues a while node onto the computation.
-  ComputationDataHandle While(const Computation& condition,
-                              const Computation& body,
-                              const ComputationDataHandle& init);
-
-  // Enqueues a conditional node onto the computation.
-  ComputationDataHandle Conditional(const ComputationDataHandle& predicate,
-                                    const ComputationDataHandle& true_operand,
-                                    const Computation& true_computation,
-                                    const ComputationDataHandle& false_operand,
-                                    const Computation& false_computation);
-
-  // Enqueues a ReducePrecision node onto the computation.
-  ComputationDataHandle ReducePrecision(const ComputationDataHandle& operand,
-                                        const int exponent_bits,
-                                        const int mantissa_bits);
-
-  // Enqueues a Gather node onto the computation.
-  ComputationDataHandle Gather(
-      const ComputationDataHandle& input,
-      const ComputationDataHandle& gather_indices,
-      const GatherDimensionNumbers& dimension_numbers,
-      tensorflow::gtl::ArraySlice window_bounds);
-
-  // Enqueues a Send node onto the computation, to send the given operand to
-  // a Recv instruction that shares the same channel handle.
-  void Send(const ComputationDataHandle& operand, const ChannelHandle& handle);
-
-  // Enqueues a Recv node onto the computation. The data comes from a Send
-  // instruction that shares the same channel handle and its shape must
-  // be the same as the given shape.
-  ComputationDataHandle Recv(const Shape& shape, const ChannelHandle& handle);
-
-  // Returns true if 'operand' is a compile-time constant. A compile-time
-  // constant does not depend on parameters with index greater than or equal to
-  // `num_parameters`, or on stateful operators such as `RngNormal` or `Infeed`.
-  // Unlike `ComputeConstant`, `IsConstant` tests whether a computation is a
-  // compile-time constant without evaluating the computation.
-  StatusOr IsConstant(const ComputationDataHandle& operand,
-                            int64 num_parameters = 0);
-
-  // Normalizes operand across spatial and batch dimensions for each feature.
-  //
-  // Returns a tuple (normalized, batch_mean, batch_var) where `normalized`
-  // is the normalized result and batch_mean and batch_var are the mean and
-  // variance, respectively, across batch for the operand.
-  ComputationDataHandle BatchNormTraining(const ComputationDataHandle& operand,
-                                          const ComputationDataHandle& scale,
-                                          const ComputationDataHandle& offset,
-                                          float epsilon, int64 feature_index);
-
-  // Normalizes operand across spatial and batch dimensions for each feature.
-  //
-  // `BatchNormInference` is equivalent to calling `BatchNormTraining` without
-  // computing `mean` and `variance` for each batch inside the operation. It
-  // uses the input `mean` and `variance` instead as estimated values. The
-  // purpose of this op is to reduce latency in inference, hence the name
-  // `BatchNormInference`.
-  //
-  // The output has the same shape as `operand`, and contains the normalized
-  // values for each batch.
-  ComputationDataHandle BatchNormInference(
-      const ComputationDataHandle& operand, const ComputationDataHandle& scale,
-      const ComputationDataHandle& offset, const ComputationDataHandle& mean,
-      const ComputationDataHandle& variance, float epsilon,
-      int64 feature_index);
-
-  // Calculates the gradients of a batch norm op.
-  //
-  // The inputs `batch_mean` and `batch_var` represent the mean and variance
-  // across the batch.
-  //
-  // Returns a tuple of three elements:
-  //   - grad_operand: Gradient with respect to input `operand`
-  //   - grad_offset: Gradient with respect to input `offset`
-  //   - grad_scale: Gradient with respect to input `scale`
-  ComputationDataHandle BatchNormGrad(const ComputationDataHandle& operand,
-                                      const ComputationDataHandle& scale,
-                                      const ComputationDataHandle& batch_mean,
-                                      const ComputationDataHandle& batch_var,
-                                      const ComputationDataHandle& grad_output,
-                                      float epsilon, int64 feature_index);
-
-  // Computes the value of a constant indicated by a
-  // ComputationDataHandle using a non-optimized interpreter on the host.
-  //
-  // The operand must be from the computation currently being built -
-  // i.e., returned from this builder with no intervening call to
-  // Build(). This happens to currently work regardless of that, but
-  // that may stop working at any time.
-  //
-  // The operand must represent a constant value, which in this case
-  // means that it must not statically depend on any parameter of the
-  // computation that is being built other then the ones specified on the
-  // parameter list. The parameters in the list will be indexed by their
-  // parameter id property so the number of parameters specified should be at
-  // least as many as the largest used parameter index.
-  //
-  // `IsConstant` can be used to test whether a computation is a compile-time
-  // constant without evaluation it. `ComputeConstant` only succeeds for
-  // computations where `IsConstant` returns true.
-  //
-  // This functionality can be useful when translating a computation
-  // into XLA where something that looked dynamic is required by
-  // XLA to be specified as a constant. E.g. the source
-  // computation (outside of XLA) may include a dynamic
-  // computation of the shape of something and ComputeConstant lets
-  // you determine what the value of that computation is in the case
-  // where the value can be determined at compile time.
-  //
-  // If output_layout is non-null, then the output of the computation
-  // will be stored using that layout.
-  StatusOr> ComputeConstant(
-      const ComputationDataHandle& operand,
-      const Layout* output_layout = nullptr,
-      tensorflow::gtl::ArraySlice parameters = {});
-
-  // Returns a new ComputationBuilder whose resultant Computation is used only
-  // by this ComputationBuilder. The sub-ComputationBuilder has the same
-  // die_immediately_on_error behavior as the parent.
-  std::unique_ptr CreateSubBuilder(
-      const string& computation_name);
-
-  // Modifies the computation being built so that executions of it
-  // will return the value associated with operand, rather than the
-  // last expression enqueued on the ComputationBuilder. Any subsequent
-  // operations added to the ComputationBuilder will not have any effect unless
-  // SetReturnValue is called again.
-  Status SetReturnValue(const ComputationDataHandle& operand);
-
-  // Builds the computation with the requested operations, or returns a non-ok
-  // status.
-  StatusOr Build();
-
-  // Builds the computation with the requested operations, or notes an error in
-  // the parent ComputationBuilder and returns an empty computation if building
-  // failed. This function is intended to be used where the returned
-  // Computation is only used by the parent ComputationBuilder and hence further
-  // operation on the returned Computation will simply be error'ed out if an
-  // error occurred while building this computation. If the built computation is
-  // to be used by a ComputationBuilder other than the parent ComputationBuilder
-  // then Build() should be used instead.
-  Computation BuildAndNoteError();
-
-  // Returns the first error that was encountered while building the
-  // computation. When an error is encountered, by default we return a vacuous
-  // ComputationDataHandle and inform the user of the error that occurred while
-  // building the computation when they make a final call to Build().
-  //
-  // See also set_die_immediately_on_error().
-  Status first_error() const { return first_error_; }
-
- private:
-  // Limited checking of convolution parameters. Returns false on
-  // error.
-  bool VerifyConvolution(const Shape& lhs_shape, const Shape& rhs_shape,
-                         const ConvolutionDimensionNumbers& dimension_numbers);
-
-  // The parent ComputationBuilder of a sub-ComputationBuilder. The
-  // parent_builder_ will be the nullptr if not a sub-ComputationBuilder.
-  ComputationBuilder* parent_builder_{nullptr};
-
-  // Helper function for creating a Window proto from user-supplied
-  // data. Returns true if the user-supplied data was valid.
-  bool MakeWindow(tensorflow::gtl::ArraySlice window_dimensions,
-                  tensorflow::gtl::ArraySlice window_strides,
-                  tensorflow::gtl::ArraySlice> padding,
-                  tensorflow::gtl::ArraySlice lhs_dilation,
-                  tensorflow::gtl::ArraySlice rhs_dilation,
-                  Window* window);
-
-  // Internal helper method that does the building for an arbitrary unary op.
-  ComputationDataHandle UnaryOp(UnaryOperation unop,
-                                const ComputationDataHandle& operand);
-
-  // Internal helper method that does the building for an arbitrary binary op.
-  // broadcast_dimensions specifies which dimensions to use for broadcasting
-  // when the operation is between tensors of different ranks.
-  ComputationDataHandle BinaryOp(
-      BinaryOperation binop, const ComputationDataHandle& lhs,
-      const ComputationDataHandle& rhs,
-      tensorflow::gtl::ArraySlice broadcast_dimensions);
-
-  // Internal helper method that does the building for an arbitrary ternary op.
-  ComputationDataHandle TernaryOp(TernaryOperation triop,
-                                  const ComputationDataHandle& lhs,
-                                  const ComputationDataHandle& rhs,
-                                  const ComputationDataHandle& ehs);
-
-  // Internal helper method that does the building for a random number generator
-  // of a given distribution with an explicitly specified shape.
-  ComputationDataHandle RngOp(
-      RandomDistribution distribution,
-      tensorflow::gtl::ArraySlice parameters,
-      const Shape& shape);
-
-  // Populates computation_ with a valid object or returns a failing status.
-  // This is used before any given operation is enqueued.
-  Status PrepareComputation();
-
-  // Notes that the error occurred by:
-  // * storing it internally and capturing a backtrace if it's the first error
-  //   (this deferred value will be produced on the call to Build())
-  // * dying if die_immediately_on_error_ is true
-  void NoteError(const Status& error);
-
-  // Helper function that runs the given op_request, filling in op_response.
-  // Before the op is run, PrepareComputation is called, and common fields in
-  // the op_request are filled in.
-  Status RunOp(OpRequest* op_request, OpResponse* op_response);
-
-  // Helper function that calls RunOp and calls NoteError on failures.
-  void RunOpAndNoteError(OpRequest* op_request);
-
-  // Helper function that calls RunOp and either returns the output computation
-  // data handle (on success) or a vacuous computation data handle (on failure).
-  ComputationDataHandle RunOpAndParseResponse(OpRequest* op_request);
-
-  // Helper function that implements GetShape without noting errors. This makes
-  // it easier to ensure the real GetShape will note errors on every error path.
-  StatusOr> GetShapeWithoutNoteError(
-      const ComputationDataHandle& operand);
-
-  string name_;  // Name to use for the built computation.
-
-  // The first error encountered while building the computation.
-  // This is OK until the first error is encountered.
-  Status first_error_;
-
-  // The saved stack trace from the point at which the first error occurred.
-  tensorflow::SavedStackTrace first_error_backtrace_;
-
-  // The computation that operations are enqueued onto.
-  Computation computation_;
-
-  // The client that the computation is created in. Not owned.
-  Client* client_;
-
-  // Mode bit that indicates whether to die when a first error is encountered.
-  bool die_immediately_on_error_ = false;
-
-  // The metadata to attach to each op. This is structured as a "modal"-like
-  // operation, in order to simplify client code (and not sprinkle this metadata
-  // throughout the TensorFlow op kernel implementations).
-  OpMetadata metadata_;
-
-  // Sharding for this operator. This is structured as a "model"-like operation,
-  // in order to simplify client code, similar to metadata_.
-  tensorflow::gtl::optional sharding_;
-
-  TF_DISALLOW_COPY_AND_ASSIGN(ComputationBuilder);
-};
-
-template 
-ComputationDataHandle ComputationBuilder::ConstantR0(NativeT value) {
-  return ConstantLiteral(*Literal::CreateR0(value));
-}
-
-template 
-ComputationDataHandle ComputationBuilder::ConstantR1(
-    tensorflow::gtl::ArraySlice values) {
-  return ConstantLiteral(*Literal::CreateR1(values));
-}
-
-template 
-ComputationDataHandle ComputationBuilder::ConstantR1(int64 length,
-                                                     NativeT value) {
-  Literal literal(ShapeUtil::MakeShape(
-      primitive_util::NativeToPrimitiveType(), {length}));
-  literal.PopulateWithValue(value);
-  return ConstantLiteral(literal);
-}
-
-inline ComputationDataHandle ComputationBuilder::ConstantR1(
-    const tensorflow::core::Bitmap& values) {
-  return ConstantLiteral(*Literal::CreateR1(values));
-}
-
-template 
-ComputationDataHandle ComputationBuilder::ConstantR2(
-    std::initializer_list> values) {
-  return ConstantLiteral(*Literal::CreateR2(values));
-}
-
-template 
-ComputationDataHandle ComputationBuilder::ConstantFromArrayWithLayout(
-    const Array& values, const Layout& layout) {
-  return ConstantLiteral(
-      *Literal::CreateFromArrayWithLayout(values, layout));
-}
-
-template 
-ComputationDataHandle ComputationBuilder::ConstantFromArray(
-    const Array& values) {
-  return ConstantLiteral(*Literal::CreateFromArray(values));
-}
-
-template 
-ComputationDataHandle ComputationBuilder::ConstantR2FromArray2DWithLayout(
-    const Array2D& values, const Layout& layout) {
-  return ConstantLiteral(
-      *Literal::CreateFromArrayWithLayout(values, layout));
-}
-
-template 
-ComputationDataHandle ComputationBuilder::ConstantR2FromArray2D(
-    const Array2D& values) {
-  return ConstantLiteral(*Literal::CreateR2FromArray2D(values));
-}
-
-template 
-ComputationDataHandle ComputationBuilder::ConstantR3FromArray3DWithLayout(
-    const Array3D& values, const Layout& layout) {
-  return ConstantLiteral(
-      *Literal::CreateR3FromArray3DWithLayout(values, layout));
-}
-
-template 
-ComputationDataHandle ComputationBuilder::ConstantR3FromArray3D(
-    const Array3D& values) {
-  return ConstantFromArray(values);
-}
-
-template 
-ComputationDataHandle ComputationBuilder::ConstantR4FromArray4DWithLayout(
-    const Array4D& values, const Layout& layout) {
-  return ConstantFromArrayWithLayout(values, layout);
-}
-
-template 
-ComputationDataHandle ComputationBuilder::ConstantR4FromArray4D(
-    const Array4D& values) {
-  return ConstantFromArray(values);
-}
-
-// RAII-style object: sets the current sharding assignment in builder on
-// construction, and sets back to the previous assignment on destruction.
-class ScopedShardingAssignment {
- public:
-  ScopedShardingAssignment(xla::ComputationBuilder* builder,
-                           tensorflow::gtl::optional sharding)
-      : builder_(builder), prev_sharding_(builder->sharding()) {
-    SetSharding(sharding);
-  }
-
-  ~ScopedShardingAssignment() { SetSharding(prev_sharding_); }
-
- private:
-  void SetSharding(const tensorflow::gtl::optional& sharding) {
-    if (sharding.has_value()) {
-      builder_->SetSharding(sharding.value());
-    } else {
-      builder_->ClearSharding();
-    }
-  }
-
-  xla::ComputationBuilder* const builder_;
-  tensorflow::gtl::optional prev_sharding_;
-
-  TF_DISALLOW_COPY_AND_ASSIGN(ScopedShardingAssignment);
-};
-
-}  // namespace xla
-
-#endif  // TENSORFLOW_COMPILER_XLA_CLIENT_COMPUTATION_BUILDER_H_
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index fecc257f85a5d1..b3e598f65becc2 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -2535,7 +2535,6 @@ tf_cc_test(
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 4b0dfde5e23a41..dfaf9c063f7c5a 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -153,7 +153,6 @@ tf_cc_binary(
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/client:client_library",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
         "//tensorflow/compiler/xla/service/cpu:cpu_compiler",
@@ -189,8 +188,6 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:client_library",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -289,8 +286,6 @@ xla_test(
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -314,7 +309,6 @@ xla_test(
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -336,7 +330,6 @@ xla_test(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -379,7 +372,6 @@ xla_test(
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -399,7 +391,6 @@ xla_test(
         "enable_for_xla_interpreter",
     ],
     deps = [
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -423,8 +414,6 @@ xla_test(
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla:xla_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
@@ -451,8 +440,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -473,7 +460,6 @@ xla_test(
     ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -492,7 +478,6 @@ xla_test(
     ],
     deps = [
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -529,7 +514,6 @@ xla_test(
     tags = ["enable_for_xla_interpreter"],
     deps = [
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
@@ -553,7 +537,6 @@ xla_test(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -573,8 +556,6 @@ xla_test(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -599,8 +580,6 @@ xla_test(
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -627,7 +606,6 @@ xla_test(
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -698,7 +676,6 @@ xla_test(
         "//tensorflow/compiler/xla:array3d",
         "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -742,7 +719,6 @@ xla_test(
         "//tensorflow/compiler/xla:array3d",
         "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -767,7 +743,6 @@ xla_test(
         "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -791,7 +766,6 @@ xla_test(
         "//tensorflow/compiler/xla:array4d",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -844,7 +818,6 @@ xla_test(
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:reference_util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -869,7 +842,6 @@ xla_test(
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:util",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client:padding",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -931,8 +903,6 @@ xla_test(
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
@@ -961,8 +931,6 @@ xla_test(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:test_helpers",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -1003,7 +971,6 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1056,8 +1023,6 @@ xla_test(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1079,7 +1044,6 @@ xla_test(
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -1109,8 +1073,6 @@ xla_test(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
@@ -1241,8 +1203,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
@@ -1282,7 +1242,6 @@ xla_test(
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array4d",
         "//tensorflow/compiler/xla:reference_util",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1305,7 +1264,6 @@ xla_test(
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:test",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1345,7 +1303,6 @@ xla_test(
         "enable_for_xla_interpreter",
     ],
     deps = [
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1363,7 +1320,6 @@ xla_test(
         "enable_for_xla_interpreter",
     ],
     deps = [
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1389,8 +1345,6 @@ xla_test(
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1412,7 +1366,6 @@ xla_test(
         "//tensorflow/compiler/xla:test",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1484,8 +1437,6 @@ xla_test(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/lib:arithmetic",
@@ -1533,7 +1484,6 @@ xla_test(
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1575,8 +1525,6 @@ xla_test(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla:xla_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -1597,7 +1545,6 @@ xla_test(
         "enable_for_xla_interpreter",
     ],
     deps = [
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1621,8 +1568,6 @@ xla_test(
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:client_library",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1643,7 +1588,6 @@ xla_test(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -1662,7 +1606,6 @@ xla_test(
     srcs = ["execution_profile_test.cc"],
     deps = [
         ":client_library_test_base",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1677,7 +1620,6 @@ xla_test(
     args = ["--xla_hlo_profile"],
     deps = [
         ":client_library_test_base",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1783,8 +1725,6 @@ xla_test(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:client_library",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1812,8 +1752,6 @@ xla_test(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:client_library",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1851,8 +1789,6 @@ xla_test(
     deps = [
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:statusor",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1881,8 +1817,6 @@ xla_test(
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:client_library",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
@@ -1950,8 +1884,6 @@ xla_test(
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/client:computation",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:global_data",
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
@@ -2052,7 +1984,6 @@ xla_test(
         ":local_client_test_base",
         ":test_utils",
         "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_builder",
         "//tensorflow/compiler/xla/client/xla_client:xla_computation",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
diff --git a/tensorflow/compiler/xla/tests/call_test.cc b/tensorflow/compiler/xla/tests/call_test.cc
index a43ca3d5ca2ba3..5fd33b50c94356 100644
--- a/tensorflow/compiler/xla/tests/call_test.cc
+++ b/tensorflow/compiler/xla/tests/call_test.cc
@@ -16,7 +16,6 @@ limitations under the License.
 #include 
 #include 
 
-#include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_computation.h"
 #include "tensorflow/compiler/xla/literal_util.h"
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc
index be542c15c09902..b68f3093a3838e 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.cc
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc
@@ -18,7 +18,6 @@ limitations under the License.
 #include 
 
 #include "tensorflow/compiler/xla/client/client_library.h"
-#include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/execution_options_util.h"
 #include "tensorflow/compiler/xla/literal_util.h"
diff --git a/tensorflow/compiler/xla/tests/compilation_cache_test.cc b/tensorflow/compiler/xla/tests/compilation_cache_test.cc
index e1aa9d7b041506..50a006964869b3 100644
--- a/tensorflow/compiler/xla/tests/compilation_cache_test.cc
+++ b/tensorflow/compiler/xla/tests/compilation_cache_test.cc
@@ -17,7 +17,6 @@ limitations under the License.
 #include 
 #include 
 
-#include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
diff --git a/tensorflow/compiler/xla/tests/constants_test.cc b/tensorflow/compiler/xla/tests/constants_test.cc
index d518e4a16598ec..fa963b175fcd10 100644
--- a/tensorflow/compiler/xla/tests/constants_test.cc
+++ b/tensorflow/compiler/xla/tests/constants_test.cc
@@ -21,7 +21,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/array2d.h"
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/array4d.h"
-#include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
 #include "tensorflow/compiler/xla/literal_util.h"
diff --git a/tensorflow/compiler/xla/tests/convolution_variants_test.cc b/tensorflow/compiler/xla/tests/convolution_variants_test.cc
index 50d6e25d868c49..fea850dc135e33 100644
--- a/tensorflow/compiler/xla/tests/convolution_variants_test.cc
+++ b/tensorflow/compiler/xla/tests/convolution_variants_test.cc
@@ -25,7 +25,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/array3d.h"
 #include "tensorflow/compiler/xla/array4d.h"
-#include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/padding.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
diff --git a/tensorflow/compiler/xla/tests/deallocation_test.cc b/tensorflow/compiler/xla/tests/deallocation_test.cc
index c76e5aabf4b8a3..bfe688e20d182d 100644
--- a/tensorflow/compiler/xla/tests/deallocation_test.cc
+++ b/tensorflow/compiler/xla/tests/deallocation_test.cc
@@ -15,7 +15,6 @@ limitations under the License.
 
 #include 
 
-#include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
index d0ada247483039..12789fe66530fe 100644
--- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
+++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
@@ -16,7 +16,6 @@ limitations under the License.
 #include 
 #include 
 
-#include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
index 464cc012140d48..27fd36e06acdc5 100644
--- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
@@ -18,7 +18,6 @@ limitations under the License.
 #include 
 
 #include "tensorflow/compiler/xla/array2d.h"
-#include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_computation.h"
diff --git a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc
index 7778053fb4478f..b745522ff00fea 100644
--- a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc
+++ b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc
@@ -19,7 +19,6 @@ limitations under the License.
 #include 
 #include 
 
-#include "tensorflow/compiler/xla/client/computation_builder.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
diff --git a/tensorflow/compiler/xla/tests/params_test.cc b/tensorflow/compiler/xla/tests/params_test.cc
index 97dab860c06bdd..f04db776e6eca7 100644
--- a/tensorflow/compiler/xla/tests/params_test.cc
+++ b/tensorflow/compiler/xla/tests/params_test.cc
@@ -19,7 +19,6 @@ limitations under the License.
 #include 
 
 #include "tensorflow/compiler/xla/array2d.h"
-#include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
diff --git a/tensorflow/compiler/xla/tests/reduce_test.cc b/tensorflow/compiler/xla/tests/reduce_test.cc
index bcc05c2d41d843..d671d40456a276 100644
--- a/tensorflow/compiler/xla/tests/reduce_test.cc
+++ b/tensorflow/compiler/xla/tests/reduce_test.cc
@@ -34,7 +34,6 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/array2d.h"
 #include "tensorflow/compiler/xla/array4d.h"
-#include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/global_data.h"
 #include "tensorflow/compiler/xla/client/lib/arithmetic.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc
index 5c287bac6a7cab..e950c681e64f55 100644
--- a/tensorflow/compiler/xla/tests/tuple_test.cc
+++ b/tensorflow/compiler/xla/tests/tuple_test.cc
@@ -17,7 +17,6 @@ limitations under the License.
 #include 
 
 #include "tensorflow/compiler/xla/array2d.h"
-#include "tensorflow/compiler/xla/client/computation.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
 #include "tensorflow/compiler/xla/client/xla_client/xla_computation.h"

From 227eee585118e942e5fefa8f949562749c482f7a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 10:43:30 -0700
Subject: [PATCH 0673/1691] Use Identity instead of Snapshot when the graph
 does not contain ops that modify their inputs.

PiperOrigin-RevId: 196275133
---
 tensorflow/core/grappler/op_types.cc          |  15 ++
 tensorflow/core/grappler/op_types.h           |   4 +
 .../grappler/optimizers/constant_folding.cc   |  21 +++
 .../grappler/optimizers/constant_folding.h    |   1 +
 .../optimizers/constant_folding_test.cc       | 159 ++++++++++--------
 5 files changed, 127 insertions(+), 73 deletions(-)

diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index e633ecf78989f7..07f826beedfe8f 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -408,6 +408,21 @@ bool IsPersistent(const NodeDef& node) {
   return IsConstant(node) || IsVariable(node);
 }
 
+bool MaybeHasRefInput(const NodeDef& node) {
+  const OpDef* op_def;
+  Status status = OpRegistry::Global()->LookUpOpDef(node.op(), &op_def);
+  if (!status.ok()) {
+    return true;
+  }
+  // Nodes such as Assign or AssignAdd modify one of their inputs.
+  for (const auto& input : op_def->input_arg()) {
+    if (input.is_ref()) {
+      return true;
+    }
+  }
+  return false;
+}
+
 bool IsFreeOfSideEffect(const NodeDef& node) {
   // Placeholders must be preserved to keep the graph feedable.
   if (IsPlaceholder(node)) {
diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h
index f6105d710e41c0..a5599eb22ec5cc 100644
--- a/tensorflow/core/grappler/op_types.h
+++ b/tensorflow/core/grappler/op_types.h
@@ -166,6 +166,10 @@ bool IsPersistent(const NodeDef& node);
 
 bool IsFreeOfSideEffect(const NodeDef& node);
 
+// Returns true if the takes a tensor reference as input, or if looking up its
+// OpDef failed.
+bool MaybeHasRefInput(const NodeDef& node);
+
 bool ModifiesFrameInfo(const NodeDef& node);
 
 // Returns true if the op is known to write to one or more of its inputs.
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index d5c583a8ed8933..171d4923bc55b5 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -1514,6 +1514,16 @@ void ConstantFolding::ReplaceOperationWithIdentity(
 void ConstantFolding::ReplaceOperationWithSnapshot(
     int input_to_forward, const GraphProperties& properties, NodeDef* node,
     GraphDef* graph) {
+  // If the graph contains no ops that mutate their inputs, we can
+  // use Identity insted of Snapshot.
+
+  // TODO(rmlarsen): Enable in regular mode after May 15, 2018.
+  if (opt_level_ == RewriterConfig::AGGRESSIVE &&
+      !graph_contains_assign_or_inplace_op_) {
+    ReplaceOperationWithIdentity(input_to_forward, properties, node, graph);
+    return;
+  }
+
   const DataType dtype = GetDataTypeFromNodeOrProps(*node, properties);
   if (dtype == DT_INVALID) return;
 
@@ -2546,6 +2556,17 @@ Status ConstantFolding::Optimize(Cluster* cluster, const GrapplerItem& item,
     cpu_device_ = owned_device_.get();
   }
 
+  graph_contains_assign_or_inplace_op_ = false;
+  // TODO(rmlarsen): Enable in regular mode after May 15, 2018.
+  if (opt_level_ == RewriterConfig::AGGRESSIVE) {
+    for (const NodeDef& node : item.graph.node()) {
+      if (ModifiesInputsInPlace(node) || MaybeHasRefInput(node)) {
+        graph_contains_assign_or_inplace_op_ = true;
+        break;
+      }
+    }
+  }
+
   has_fetch_ = !item.fetch.empty();
   GrapplerItem item_to_optimize = item;
   *optimized_graph = item.graph;
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index 7aad3a6ae1da35..f92f755d8915d5 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -126,6 +126,7 @@ class ConstantFolding : public GraphOptimizer {
   std::unordered_set feed_nodes_;
   bool has_fetch_;
   bool graph_modified_;
+  bool graph_contains_assign_or_inplace_op_;
 };
 
 }  // end namespace grappler
diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index f018b217e66365..0bf51c48f72554 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -33,77 +33,89 @@ class ConstantFoldingTest : public GrapplerTest {
  protected:
   template 
   void SimpleNeutralElementTest() {
-    typedef typename EnumToDataType::Type T;
-    tensorflow::Scope s = tensorflow::Scope::NewRootScope();
-    Output x = ops::Placeholder(s.WithOpName("x"), DTYPE,
-                                ops::Placeholder::Shape(TensorShape({2, 2})));
-    Tensor zeros_t(DTYPE, TensorShape({2, 2}));
-    Tensor ones_t(DTYPE, TensorShape({2, 2}));
-    Tensor x_t(DTYPE, TensorShape({2, 2}));
-    for (int i = 0; i < 4; ++i) {
-      zeros_t.flat()(i) = T(0);
-      ones_t.flat()(i) = T(1);
-      x_t.flat()(i) = T(i + 1);
-    }
-    Output zeros = ops::Const(s.WithOpName("zeros"), zeros_t);
-    Output ones = ops::Const(s.WithOpName("ones"), ones_t);
-    Output mul1;
-    Output mul2;
-    Output add1;
-    Output add2;
-    if (DTYPE == DT_BOOL) {
-      mul1 = ops::LogicalAnd(s.WithOpName("mul1"), x, zeros);
-      mul2 = ops::LogicalAnd(s.WithOpName("mul2"), x, ones);
-      add1 = ops::LogicalOr(s.WithOpName("add1"), x, zeros);
-      add2 = ops::LogicalOr(s.WithOpName("add2"), x, ones);
-    } else {
-      mul1 = ops::Mul(s.WithOpName("mul1"), x, zeros);
-      mul2 = ops::Mul(s.WithOpName("mul2"), x, ones);
-      add1 = ops::Add(s.WithOpName("add1"), x, zeros);
-      add1 = ops::Add(s.WithOpName("add2"), x, ones);
-    }
-    GrapplerItem item;
-    TF_CHECK_OK(s.ToGraphDef(&item.graph));
-    item.fetch = {"mul1", "mul2", "add1", "add2"};
-    ConstantFolding optimizer(nullptr /* cpu_device */);
-    GraphDef output;
-    Status status = optimizer.Optimize(nullptr, item, &output);
-    TF_EXPECT_OK(status);
-
-    EXPECT_EQ(7, output.node_size());
-    for (int i = 0; i < output.node_size(); ++i) {
-      const NodeDef& node = output.node(i);
-      const string& name = node.name();
-      if (name == "mul1") {
-        EXPECT_EQ("Const", node.op());
-        EXPECT_EQ("^x", node.input(0));
-        EXPECT_EQ("^zeros", node.input(1));
-      } else if (name == "mul2") {
-        EXPECT_EQ("Snapshot", node.op());
-        EXPECT_EQ("x", node.input(0));
-        EXPECT_EQ("^ones", node.input(1));
-      } else if (name == "add1") {
-        EXPECT_EQ("Snapshot", node.op());
-        EXPECT_EQ("x", node.input(0));
-        EXPECT_EQ("^zeros", node.input(1));
-      } else if (name == "add2") {
-        if (DTYPE == DT_BOOL) {
+    for (bool use_snapshot : {false, true}) {
+      typedef typename EnumToDataType::Type T;
+      tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+      Output x = ops::Placeholder(s.WithOpName("x"), DTYPE,
+                                  ops::Placeholder::Shape(TensorShape({2, 2})));
+      Output v = ops::Variable(s.WithOpName("v"), {2, 2}, DTYPE);
+      Tensor zeros_t(DTYPE, TensorShape({2, 2}));
+      Tensor ones_t(DTYPE, TensorShape({2, 2}));
+      Tensor x_t(DTYPE, TensorShape({2, 2}));
+      for (int i = 0; i < 4; ++i) {
+        zeros_t.flat()(i) = T(0);
+        ones_t.flat()(i) = T(1);
+        x_t.flat()(i) = T(i + 1);
+      }
+      Output zeros = ops::Const(s.WithOpName("zeros"), zeros_t);
+      Output ones = ops::Const(s.WithOpName("ones"), ones_t);
+      Output mul1;
+      Output mul2;
+      Output add1;
+      Output add2;
+      if (DTYPE == DT_BOOL) {
+        mul1 = ops::LogicalAnd(s.WithOpName("mul1"), x, zeros);
+        mul2 = ops::LogicalAnd(s.WithOpName("mul2"), x, ones);
+        add1 = ops::LogicalOr(s.WithOpName("add1"), x, zeros);
+        add2 = ops::LogicalOr(s.WithOpName("add2"), x, ones);
+      } else {
+        mul1 = ops::Mul(s.WithOpName("mul1"), x, zeros);
+        mul2 = ops::Mul(s.WithOpName("mul2"), x, ones);
+        add1 = ops::Add(s.WithOpName("add1"), x, zeros);
+        add1 = ops::Add(s.WithOpName("add2"), x, ones);
+      }
+      if (use_snapshot) {
+        // Add an op with ref input to prevent Snapshot from being
+        // turned into Identity.
+        ops::Assign(s.WithOpName("assign"), v, ones);
+      }
+      GrapplerItem item;
+      TF_CHECK_OK(s.ToGraphDef(&item.graph));
+      item.fetch = {"mul1", "mul2", "add1", "add2"};
+      ConstantFolding optimizer(RewriterConfig::AGGRESSIVE,
+                                nullptr /* cpu_device */);
+      GraphDef output;
+      Status status = optimizer.Optimize(nullptr, item, &output);
+      TF_EXPECT_OK(status);
+
+      EXPECT_EQ(7, output.node_size());
+      const string snapshot_or_identity =
+          use_snapshot ? "Snapshot" : "Identity";
+      for (int i = 0; i < output.node_size(); ++i) {
+        const NodeDef& node = output.node(i);
+        const string& name = node.name();
+        if (name == "mul1") {
           EXPECT_EQ("Const", node.op());
           EXPECT_EQ("^x", node.input(0));
+          EXPECT_EQ("^zeros", node.input(1));
+        } else if (name == "mul2") {
+          EXPECT_EQ(snapshot_or_identity, node.op());
+          EXPECT_EQ("x", node.input(0));
           EXPECT_EQ("^ones", node.input(1));
-        } else {
-          EXPECT_EQ("Add", node.op());
+        } else if (name == "add1") {
+          EXPECT_EQ(snapshot_or_identity, node.op());
           EXPECT_EQ("x", node.input(0));
-          EXPECT_EQ("ones", node.input(1));
+          EXPECT_EQ("^zeros", node.input(1));
+        } else if (name == "add2") {
+          if (DTYPE == DT_BOOL) {
+            EXPECT_EQ("Const", node.op());
+            EXPECT_EQ("^x", node.input(0));
+            EXPECT_EQ("^ones", node.input(1));
+          } else {
+            EXPECT_EQ("Add", node.op());
+            EXPECT_EQ("x", node.input(0));
+            EXPECT_EQ("ones", node.input(1));
+          }
         }
       }
-    }
-    auto tensors_expected = EvaluateNodes(item.graph, item.fetch, {{"x", x_t}});
-    auto tensors = EvaluateNodes(output, item.fetch, {{"x", x_t}});
-    EXPECT_EQ(4, tensors_expected.size());
-    EXPECT_EQ(4, tensors.size());
-    for (int i = 0; i < item.fetch.size(); ++i) {
-      test::ExpectTensorEqual(tensors_expected[i], tensors[i]);
+      auto tensors_expected =
+          EvaluateNodes(item.graph, item.fetch, {{"x", x_t}});
+      auto tensors = EvaluateNodes(output, item.fetch, {{"x", x_t}});
+      EXPECT_EQ(4, tensors_expected.size());
+      EXPECT_EQ(4, tensors.size());
+      for (int i = 0; i < item.fetch.size(); ++i) {
+        test::ExpectTensorEqual(tensors_expected[i], tensors[i]);
+      }
     }
   }
 };
@@ -284,7 +296,8 @@ TEST_F(ConstantFoldingTest, NeutralElement) {
     TF_CHECK_OK(s.ToGraphDef(&item.graph));
     item.fetch = {"stack", "matmul3", "matmul4"};
 
-    ConstantFolding optimizer(nullptr /* cpu_device */);
+    ConstantFolding optimizer(RewriterConfig::AGGRESSIVE,
+                              nullptr /* cpu_device */);
     GraphDef output;
     Status status = optimizer.Optimize(nullptr, item, &output);
     TF_EXPECT_OK(status);
@@ -309,11 +322,11 @@ TEST_F(ConstantFoldingTest, NeutralElement) {
         EXPECT_EQ(ctrl_zeros_name, node.input(0));
         EXPECT_EQ("^y", node.input(1));
       } else if (name == "mul3") {
-        EXPECT_EQ("Snapshot", node.op());
+        EXPECT_EQ("Identity", node.op());
         EXPECT_EQ("x", node.input(0));
         EXPECT_EQ(ctrl_ones_name, node.input(1));
       } else if (name == "mul4") {
-        EXPECT_EQ("Snapshot", node.op());
+        EXPECT_EQ("Identity", node.op());
         EXPECT_EQ("y", node.input(0));
         EXPECT_EQ(ctrl_ones_name, node.input(1));
       } else if (name == "mul5") {
@@ -325,7 +338,7 @@ TEST_F(ConstantFoldingTest, NeutralElement) {
         EXPECT_EQ("^zeros_1d", node.input(0));
         EXPECT_EQ("^y", node.input(1));
       } else if (name == "div1") {
-        EXPECT_EQ("Snapshot", node.op());
+        EXPECT_EQ("Identity", node.op());
         EXPECT_EQ("x", node.input(0));
         EXPECT_EQ(ctrl_ones_name, node.input(1));
       } else if (name == "div2") {
@@ -361,15 +374,15 @@ TEST_F(ConstantFoldingTest, NeutralElement) {
         EXPECT_EQ(2, t.tensor_shape().dim(0).size());
         EXPECT_EQ(3, t.tensor_shape().dim(1).size());
       } else if (name == "add1") {
-        EXPECT_EQ("Snapshot", node.op());
+        EXPECT_EQ("Identity", node.op());
         EXPECT_EQ("x", node.input(0));
         EXPECT_EQ(ctrl_zeros_name, node.input(1));
       } else if (name == "add2") {
-        EXPECT_EQ("Snapshot", node.op());
+        EXPECT_EQ("Identity", node.op());
         EXPECT_EQ("y", node.input(0));
         EXPECT_EQ(ctrl_zeros_name, node.input(1));
       } else if (name == "bias_add1") {
-        EXPECT_EQ("Snapshot", node.op());
+        EXPECT_EQ("Identity", node.op());
         EXPECT_EQ("x", node.input(0));
         EXPECT_EQ("^zeros_1d", node.input(1));
       } else if (name == "bias_add2") {
@@ -378,7 +391,7 @@ TEST_F(ConstantFoldingTest, NeutralElement) {
         EXPECT_EQ(zeros_name, node.input(0));
         EXPECT_EQ("bias", node.input(1));
       } else if (name == "sub1") {
-        EXPECT_EQ("Snapshot", node.op());
+        EXPECT_EQ("Identity", node.op());
         EXPECT_EQ("x", node.input(0));
         EXPECT_EQ(ctrl_zeros_name, node.input(1));
       } else if (name == "sub2") {

From 1aa40a1ce7869b6557049bcc623dad452a69ef6c Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar 
Date: Fri, 11 May 2018 10:51:24 -0700
Subject: [PATCH 0674/1691] Introduce ordered_inputs option to graph_matcher to
 allow simpler matching of commutative operations.

#18919

PiperOrigin-RevId: 196276502
---
 .../contrib/quantize/python/graph_matcher.py  | 35 ++++++++---
 .../quantize/python/graph_matcher_test.py     | 39 ++++++++++++
 .../contrib/quantize/python/quantize.py       | 59 ++++++++-----------
 3 files changed, 91 insertions(+), 42 deletions(-)

diff --git a/tensorflow/contrib/quantize/python/graph_matcher.py b/tensorflow/contrib/quantize/python/graph_matcher.py
index bacc707a3abb55..aa3ca991c060b2 100644
--- a/tensorflow/contrib/quantize/python/graph_matcher.py
+++ b/tensorflow/contrib/quantize/python/graph_matcher.py
@@ -19,6 +19,7 @@
 from __future__ import print_function
 
 import abc
+import itertools
 
 
 class Pattern(object):
@@ -33,7 +34,7 @@ def match(self, op, tensor):
 class OpTypePattern(Pattern):
   """A tree pattern that matches TF expressions with certain op types."""
 
-  def __init__(self, op_type, name=None, inputs=None):
+  def __init__(self, op_type, name=None, inputs=None, ordered_inputs=True):
     """Initializes an OpTypePattern.
 
     Args:
@@ -48,16 +49,25 @@ def __init__(self, op_type, name=None, inputs=None):
       inputs: Optional list of `Pattern`s or strings that specify the
         patterns for the inputs of a matching op. If None, this pattern accepts
         any inputs of a matching op.
+      ordered_inputs: Defaults to True. If False, will match any op that
+        matches a permutation of the inputs.
+
+    Raises:
+      ValueError: if too many inputs are provided when order_inputs is False.
     """
     self._op_type = op_type
     self._name = name
     if inputs is None:
       inputs = []
+    if len(inputs) > 8:
+      raise ValueError(
+          'Only < 8 inputs are allowed when ordered_inputs is False.')
     self._inputs = [
         input_pattern
         if isinstance(input_pattern, Pattern) else OpTypePattern(input_pattern)
         for input_pattern in inputs
     ]
+    self._ordered_inputs = ordered_inputs
 
   @property
   def name(self):
@@ -78,12 +88,23 @@ def match(self, op, tensor):
     if len(op.inputs) != len(self._inputs):
       return None
 
-    for input_tensor, input_pattern in zip(op.inputs, self._inputs):
-      input_match_result = input_pattern.match(input_tensor.op, input_tensor)
-      if input_match_result is None:
-        return None
-      match_result.merge_from(input_match_result)
-    return match_result
+    input_patterns_list = [self._inputs]
+    # If order doesn't matter for the inputs, then make sure we match at least
+    # one permutation of the inputs.
+    if not self._ordered_inputs:
+      input_patterns_list = list(itertools.permutations(self._inputs))
+
+    for input_patterns in input_patterns_list:
+      match_failed = False
+      for input_tensor, input_pattern in zip(op.inputs, input_patterns):
+        input_match_result = input_pattern.match(input_tensor.op, input_tensor)
+        if input_match_result is None:
+          match_failed = True
+          break
+        match_result.merge_from(input_match_result)
+      if not match_failed:
+        return match_result
+    return None
 
 
 class OneofPattern(Pattern):
diff --git a/tensorflow/contrib/quantize/python/graph_matcher_test.py b/tensorflow/contrib/quantize/python/graph_matcher_test.py
index 6d587572181c12..be741644b61541 100644
--- a/tensorflow/contrib/quantize/python/graph_matcher_test.py
+++ b/tensorflow/contrib/quantize/python/graph_matcher_test.py
@@ -22,6 +22,7 @@
 from tensorflow.contrib.layers.python.layers import initializers
 from tensorflow.contrib.layers.python.layers import layers
 from tensorflow.contrib.quantize.python import graph_matcher
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
@@ -163,6 +164,44 @@ def test_oneof_pattern(self):
       self.assertEqual(match_result.get_tensor('slice'), slicing)
       self.assertEqual(match_result.get_op('transpose'), transpose.op)
 
+  def test_ordered_pattern(self):
+    #   +            +
+    #  / \          / \
+    # x   y  and   y   x  should both match when ordered inputs is False.
+    # Even when x and y are different operations.
+    g = ops.Graph()
+    with g.as_default():
+      x = array_ops.placeholder(dtypes.float32, shape=[], name='x')
+      y = constant_op.constant(1.0, dtype=dtypes.float32)
+      plus = x + y
+
+    add_pattern_a = graph_matcher.OpTypePattern(
+        'Add', inputs=['Const', 'Placeholder'], ordered_inputs=False)
+    add_pattern_b = graph_matcher.OpTypePattern(
+        'Add', inputs=['Placeholder', 'Const'], ordered_inputs=False)
+    add_pattern_fail = graph_matcher.OpTypePattern(
+        'Add', inputs=['Const', 'Placeholder'], ordered_inputs=True)
+    # Both add_pattern_a and add_pattern_b should match the graph since
+    # ordered_input was set False.
+    matcher_a = graph_matcher.GraphMatcher(add_pattern_a)
+    self.assertEqual([
+        match_result.get_op(add_pattern_a)
+        for match_result in matcher_a.match_graph(g)
+    ], [plus.op])
+    matcher_b = graph_matcher.GraphMatcher(add_pattern_b)
+    self.assertEqual([
+        match_result.get_op(add_pattern_b)
+        for match_result in matcher_b.match_graph(g)
+    ], [plus.op])
+    # But if ordered_inputs is True, the inputs list match should fail if not
+    # specified in the right order.
+    matcher_fail = graph_matcher.GraphMatcher(add_pattern_fail)
+    self.assertEqual(
+        len([
+            match_result.get_op(add_pattern_fail)
+            for match_result in matcher_fail.match_graph(g)
+        ]), 0)
+
 
 if __name__ == '__main__':
   googletest.main()
diff --git a/tensorflow/contrib/quantize/python/quantize.py b/tensorflow/contrib/quantize/python/quantize.py
index 60616ea749cd3f..4e0de24e0e7205 100644
--- a/tensorflow/contrib/quantize/python/quantize.py
+++ b/tensorflow/contrib/quantize/python/quantize.py
@@ -233,37 +233,37 @@ def _FindLayersToQuantize(graph):
               weight_identity_pattern, weight_resource_var_pattern,
               folded_weight_pattern
           ])
-      ])
+      ],
+      ordered_inputs=False)
 
   folded_bias_mul_pattern = graph_matcher.OpTypePattern(
-      'Mul', inputs=[graph_matcher.OpTypePattern('*'), layer_pattern])
+      'Mul',
+      inputs=[graph_matcher.OpTypePattern('*'), layer_pattern],
+      ordered_inputs=False)
   post_layer_op_correction_pattern = graph_matcher.OpTypePattern(
-      'Add', inputs=[folded_bias_mul_pattern,
-                     graph_matcher.OpTypePattern('*')])
+      'Add',
+      inputs=[folded_bias_mul_pattern,
+              graph_matcher.OpTypePattern('*')],
+      ordered_inputs=False)
   folded_bias_add_pattern = graph_matcher.OpTypePattern(
       'Add',
       inputs=[
           post_layer_op_correction_pattern,
           graph_matcher.OpTypePattern('*')
-      ])
+      ],
+      ordered_inputs=False)
 
   bias_add_pattern = graph_matcher.OpTypePattern(
-      'Add|BiasAdd', inputs=[layer_pattern, '*'])
+      'Add|BiasAdd', inputs=[layer_pattern, '*'], ordered_inputs=False)
 
   # The bias can come from the bias add or the folded bias add.
-  bypass_pattern_a = graph_matcher.OpTypePattern(
+  bypass_pattern = graph_matcher.OpTypePattern(
       'Add',
       inputs=[
           graph_matcher.OneofPattern(
               [bias_add_pattern, folded_bias_add_pattern]), '*'
-      ])
-  bypass_pattern_b = graph_matcher.OpTypePattern(
-      'Add',
-      inputs=[
-          '*',
-          graph_matcher.OneofPattern(
-              [bias_add_pattern, folded_bias_add_pattern])
-      ])
+      ],
+      ordered_inputs=False)
 
   # The input to the activation can come from bias add, fold bias add, the
   # bypasses.
@@ -273,15 +273,14 @@ def _FindLayersToQuantize(graph):
       '|'.join(_ACTIVATION_TYPES) + '|Identity',
       inputs=[
           graph_matcher.OneofPattern([
-              bias_add_pattern, folded_bias_add_pattern, bypass_pattern_a,
-              bypass_pattern_b
+              bias_add_pattern,
+              folded_bias_add_pattern,
+              bypass_pattern,
           ])
       ])
 
-  post_activation_bypass_pattern_a = graph_matcher.OpTypePattern(
-      'Add', inputs=['*', activation_pattern])
-  post_activation_bypass_pattern_b = graph_matcher.OpTypePattern(
-      'Add', inputs=[activation_pattern, '*'])
+  post_activation_bypass_pattern = graph_matcher.OpTypePattern(
+      'Add', inputs=['*', activation_pattern], ordered_inputs=False)
 
   # The order of the following matching blocks is very important. Since matches
   # aren't guaranteed to be disjoint, we structure matches from largest to
@@ -297,10 +296,7 @@ def _FindLayersToQuantize(graph):
   # to ensure we don't match only the first part of this layer, missing the
   # post activation bypass node.
   post_activation_bypass_layer_matcher = graph_matcher.GraphMatcher(
-      graph_matcher.OneofPattern([
-          post_activation_bypass_pattern_a,
-          post_activation_bypass_pattern_b,
-      ]))
+      post_activation_bypass_pattern)
   for match_result in post_activation_bypass_layer_matcher.match_graph(graph):
     layer_op = match_result.get_op(layer_pattern)
     weight_tensor = match_result.get_tensor(weight_identity_pattern)
@@ -312,14 +308,9 @@ def _FindLayersToQuantize(graph):
     bias_add_op = match_result.get_op(bias_add_pattern)
     if bias_add_op is None:
       bias_add_op = match_result.get_op(folded_bias_add_pattern)
-    bypass_op = match_result.get_op(bypass_pattern_a)
-    if bypass_op is None:
-      bypass_op = match_result.get_op(bypass_pattern_b)
+    bypass_op = match_result.get_op(bypass_pattern)
     post_activation_bypass_op = match_result.get_op(
-        post_activation_bypass_pattern_a)
-    if post_activation_bypass_op is None:
-      post_activation_bypass_op = match_result.get_op(
-          post_activation_bypass_pattern_b)
+        post_activation_bypass_pattern)
     if layer_op not in matched_layer_set:
       matched_layer_set.add(layer_op)
       layer_matches.append(
@@ -340,9 +331,7 @@ def _FindLayersToQuantize(graph):
     bias_add_op = match_result.get_op(bias_add_pattern)
     if bias_add_op is None:
       bias_add_op = match_result.get_op(folded_bias_add_pattern)
-    bypass_op = match_result.get_op(bypass_pattern_a)
-    if bypass_op is None:
-      bypass_op = match_result.get_op(bypass_pattern_b)
+    bypass_op = match_result.get_op(bypass_pattern)
     if layer_op not in matched_layer_set:
       matched_layer_set.add(layer_op)
       layer_matches.append(

From 9c82788d12037fc10b60b06092e94d513eb4aa14 Mon Sep 17 00:00:00 2001
From: Michael Case 
Date: Fri, 11 May 2018 10:58:17 -0700
Subject: [PATCH 0675/1691] Move fn_args utility into core TensorFlow from
 Estimator.

Working on untangling TF/Estimator deps. Some core TF code depends on Estimator
by using the fn_args utility function within Estimator.

PiperOrigin-RevId: 196277612
---
 tensorflow/contrib/eager/python/network.py    |  6 +-
 tensorflow/contrib/estimator/BUILD            |  2 +-
 .../estimator/python/estimator/extenders.py   |  6 +-
 .../estimator/python/estimator/logit_fns.py   |  4 +-
 .../python/estimator/replicate_model_fn.py    |  4 +-
 .../contrib/learn/python/learn/experiment.py  |  4 +-
 .../contrib/tpu/python/tpu/tpu_estimator.py   |  8 +--
 tensorflow/python/BUILD                       | 10 ++++
 tensorflow/python/estimator/BUILD             | 12 +---
 tensorflow/python/estimator/canned/head.py    |  6 +-
 tensorflow/python/estimator/estimator.py      |  8 +--
 tensorflow/python/estimator/estimator_test.py |  6 +-
 tensorflow/python/estimator/run_config.py     |  4 +-
 tensorflow/python/estimator/util.py           | 40 +------------
 .../keras/_impl/keras/engine/base_layer.py    |  7 ++-
 tensorflow/python/layers/base.py              |  4 +-
 tensorflow/python/ops/variable_scope.py       |  4 +-
 .../python/training/monitored_session.py      |  4 +-
 tensorflow/python/util/function_utils.py      | 57 +++++++++++++++++++
 .../function_utils_test.py}                   | 18 +++---
 20 files changed, 119 insertions(+), 95 deletions(-)
 create mode 100644 tensorflow/python/util/function_utils.py
 rename tensorflow/python/{estimator/util_test.py => util/function_utils_test.py} (85%)

diff --git a/tensorflow/contrib/eager/python/network.py b/tensorflow/contrib/eager/python/network.py
index 44828bea50c660..9af50ee1464c7c 100644
--- a/tensorflow/contrib/eager/python/network.py
+++ b/tensorflow/contrib/eager/python/network.py
@@ -23,7 +23,6 @@
 import weakref
 
 from tensorflow.python.eager import context
-from tensorflow.python.estimator import util as estimator_util
 from tensorflow.python.framework import ops
 from tensorflow.python.keras._impl.keras.engine import base_layer as keras_base_layer
 from tensorflow.python.layers import base
@@ -33,6 +32,7 @@
 from tensorflow.python.training import saver as saver_lib
 from tensorflow.python.training import training_util
 from tensorflow.python.util import deprecation
+from tensorflow.python.util import function_utils
 
 # pylint: disable=protected-access
 # Explanation for protected-access disable: Network has lots of same-class and
@@ -545,10 +545,10 @@ def __init__(self, layers_funcs=None, name=None):
 
   def add(self, layer_func):
     if isinstance(layer_func, base.Layer):
-      args = estimator_util.fn_args(layer_func.call)
+      args = function_utils.fn_args(layer_func.call)
       self.track_layer(layer_func)
     elif callable(layer_func):
-      args = estimator_util.fn_args(layer_func)
+      args = function_utils.fn_args(layer_func)
     else:
       raise TypeError(
           "Sequential.add() takes only tf.layers.Layer objects or callables; "
diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD
index 53bbafd4a76a11..df08dc2be65037 100644
--- a/tensorflow/contrib/estimator/BUILD
+++ b/tensorflow/contrib/estimator/BUILD
@@ -366,9 +366,9 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:framework_ops",
+        "//tensorflow/python:util",
         "//tensorflow/python/estimator:dnn",
         "//tensorflow/python/estimator:linear",
-        "//tensorflow/python/estimator:util",
     ],
 )
 
diff --git a/tensorflow/contrib/estimator/python/estimator/extenders.py b/tensorflow/contrib/estimator/python/estimator/extenders.py
index 201699ed775f70..bf08be09e7baf6 100644
--- a/tensorflow/contrib/estimator/python/estimator/extenders.py
+++ b/tensorflow/contrib/estimator/python/estimator/extenders.py
@@ -22,12 +22,12 @@
 
 from tensorflow.python.estimator import estimator as estimator_lib
 from tensorflow.python.estimator import model_fn as model_fn_lib
-from tensorflow.python.estimator import util as estimator_util
 from tensorflow.python.estimator.export.export_output import PredictOutput
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
 from tensorflow.python.ops import clip_ops
 from tensorflow.python.training import optimizer as optimizer_lib
+from tensorflow.python.util import function_utils
 
 
 _VALID_METRIC_FN_ARGS = set(['features', 'labels', 'predictions', 'config'])
@@ -330,7 +330,7 @@ def get_slot_names(self, *args, **kwargs):
 
 
 def _verify_metric_fn_args(metric_fn):
-  args = set(estimator_util.fn_args(metric_fn))
+  args = set(function_utils.fn_args(metric_fn))
   invalid_args = list(args - _VALID_METRIC_FN_ARGS)
   if invalid_args:
     raise ValueError('metric_fn (%s) has following not expected args: %s' %
@@ -339,7 +339,7 @@ def _verify_metric_fn_args(metric_fn):
 
 def _call_metric_fn(metric_fn, features, labels, predictions, config):
   """Calls metric fn with proper arguments."""
-  metric_fn_args = estimator_util.fn_args(metric_fn)
+  metric_fn_args = function_utils.fn_args(metric_fn)
   kwargs = {}
   if 'features' in metric_fn_args:
     kwargs['features'] = features
diff --git a/tensorflow/contrib/estimator/python/estimator/logit_fns.py b/tensorflow/contrib/estimator/python/estimator/logit_fns.py
index 09c2862ccd3f90..c8b0dd62970e34 100644
--- a/tensorflow/contrib/estimator/python/estimator/logit_fns.py
+++ b/tensorflow/contrib/estimator/python/estimator/logit_fns.py
@@ -41,10 +41,10 @@
 
 import six
 
-from tensorflow.python.estimator import util
 from tensorflow.python.estimator.canned import dnn as dnn_core
 from tensorflow.python.estimator.canned import linear as linear_core
 from tensorflow.python.framework import ops
+from tensorflow.python.util import function_utils
 
 # pylint: disable=protected-access
 dnn_logit_fn_builder = dnn_core._dnn_logit_fn_builder
@@ -72,7 +72,7 @@ def call_logit_fn(logit_fn, features, mode, params, config):
     ValueError: if logit_fn does not return a Tensor or a dictionary mapping
       strings to Tensors.
   """
-  logit_fn_args = util.fn_args(logit_fn)
+  logit_fn_args = function_utils.fn_args(logit_fn)
   kwargs = {}
   if 'mode' in logit_fn_args:
     kwargs['mode'] = mode
diff --git a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py
index f8564446e5da3e..cda23aa437f954 100644
--- a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py
+++ b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py
@@ -32,7 +32,6 @@
 from tensorflow.core.framework import node_def_pb2
 from tensorflow.python.client import device_lib
 from tensorflow.python.estimator import model_fn as model_fn_lib
-from tensorflow.python.estimator import util
 from tensorflow.python.estimator.export import export_output as export_output_lib
 from tensorflow.python.framework import device as framework_device
 from tensorflow.python.framework import ops as ops_lib
@@ -48,6 +47,7 @@
 from tensorflow.python.training import device_setter as device_setter_lib
 from tensorflow.python.training import optimizer as optimizer_lib
 from tensorflow.python.util import deprecation
+from tensorflow.python.util import function_utils
 
 
 @deprecation.deprecated(
@@ -521,7 +521,7 @@ def _get_loss_towers(model_fn,
   """Replicate the loss computation across devices."""
   tower_specs = []
 
-  model_fn_args = util.fn_args(model_fn)
+  model_fn_args = function_utils.fn_args(model_fn)
   optional_params = {}
   if 'params' in model_fn_args:
     optional_params['params'] = copy.deepcopy(params)
diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index dfc6a393d069fc..541da9061732ad 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -38,19 +38,19 @@
 from tensorflow.contrib.learn.python.learn.estimators import run_config
 from tensorflow.contrib.tpu.python.tpu import tpu_estimator
 from tensorflow.python.estimator import estimator as core_estimator
-from tensorflow.python.estimator import util as estimator_util
 from tensorflow.python.framework import ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import basic_session_run_hooks
 from tensorflow.python.training import saver
 from tensorflow.python.training import server_lib
 from tensorflow.python.util import compat
+from tensorflow.python.util import function_utils
 
 __all__ = ["Experiment"]
 
 
 def _get_standardized_predicate_fn(predicate_fn):
-  pred_fn_args = estimator_util.fn_args(predicate_fn)
+  pred_fn_args = function_utils.fn_args(predicate_fn)
   if "checkpoint_path" not in pred_fn_args:
     # pylint: disable=unused-argument
     def _pred_fn_wrapper(eval_results, checkpoint_path):
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index afc8c7d5cc189d..1bf2fc5dea7af7 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -46,7 +46,6 @@
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.estimator import estimator as estimator_lib
 from tensorflow.python.estimator import model_fn as model_fn_lib
-from tensorflow.python.estimator import util
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
@@ -68,6 +67,7 @@
 from tensorflow.python.training import session_run_hook
 from tensorflow.python.training import training
 from tensorflow.python.training import training_util
+from tensorflow.python.util import function_utils
 from tensorflow.python.util import nest
 from tensorflow.python.util import tf_inspect
 
@@ -1269,7 +1269,7 @@ def predict_step(unused_scalar_stopping_signal):
 
   def _call_model_fn(self, features, labels, is_export_mode=False):
     """Calls the model_fn with required parameters."""
-    model_fn_args = util.fn_args(self._model_fn)
+    model_fn_args = function_utils.fn_args(self._model_fn)
     kwargs = {}
 
     # Makes deep copy with `config` and params` in case user mutates them.
@@ -1361,7 +1361,7 @@ def validate(host_calls):
 
       if isinstance(host_call[1], (tuple, list)):
         fullargspec = tf_inspect.getfullargspec(host_call[0])
-        fn_args = util.fn_args(host_call[0])
+        fn_args = function_utils.fn_args(host_call[0])
         # wrapped_hostcall_with_global_step uses varargs, so we allow that.
         if fullargspec.varargs is None and len(host_call[1]) != len(fn_args):
           raise RuntimeError(
@@ -1938,7 +1938,7 @@ def _call_input_fn(self, input_fn, mode):
     Raises:
       ValueError: if input_fn takes invalid arguments or does not have `params`.
     """
-    input_fn_args = util.fn_args(input_fn)
+    input_fn_args = function_utils.fn_args(input_fn)
     config = self.config  # a deep copy.
     kwargs = {}
     if 'params' in input_fn_args:
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 8b904a16c7e33d..cc96d5aee5fe79 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3249,6 +3249,16 @@ py_test(
     ],
 )
 
+py_test(
+    name = "function_utils_test",
+    srcs = ["util/function_utils_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":client_testlib",
+        ":util",
+    ],
+)
+
 py_test(
     name = "tf_contextlib_test",
     size = "small",
diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD
index 2d9a084bc6bea9..a498e855724535 100644
--- a/tensorflow/python/estimator/BUILD
+++ b/tensorflow/python/estimator/BUILD
@@ -445,16 +445,6 @@ py_library(
     ],
 )
 
-py_test(
-    name = "util_test",
-    srcs = ["util_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":util",
-        "//tensorflow/python:client_testlib",
-    ],
-)
-
 py_library(
     name = "estimator",
     srcs = [
@@ -645,7 +635,6 @@ py_library(
         ":metric_keys",
         ":model_fn",
         ":prediction_keys",
-        ":util",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:check_ops",
         "//tensorflow/python:control_flow_ops",
@@ -659,6 +648,7 @@ py_library(
         "//tensorflow/python:string_ops",
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
+        "//tensorflow/python:util",
         "//tensorflow/python:weights_broadcast_ops",
         "//tensorflow/python/feature_column",
         "//tensorflow/python/ops/losses",
diff --git a/tensorflow/python/estimator/canned/head.py b/tensorflow/python/estimator/canned/head.py
index 232637314d25b3..dcf8b15dad5b66 100644
--- a/tensorflow/python/estimator/canned/head.py
+++ b/tensorflow/python/estimator/canned/head.py
@@ -24,7 +24,6 @@
 import six
 
 from tensorflow.python.estimator import model_fn
-from tensorflow.python.estimator import util
 from tensorflow.python.estimator.canned import metric_keys
 from tensorflow.python.estimator.canned import prediction_keys
 from tensorflow.python.estimator.export import export_output
@@ -46,6 +45,7 @@
 from tensorflow.python.saved_model import signature_constants
 from tensorflow.python.summary import summary
 from tensorflow.python.training import training_util
+from tensorflow.python.util import function_utils
 
 _DEFAULT_SERVING_KEY = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
 
@@ -461,7 +461,7 @@ def _validate_loss_fn_args(loss_fn):
   Raises:
     ValueError: If the signature is unexpected.
   """
-  loss_fn_args = util.fn_args(loss_fn)
+  loss_fn_args = function_utils.fn_args(loss_fn)
   for required_arg in ['labels', 'logits']:
     if required_arg not in loss_fn_args:
       raise ValueError(
@@ -484,7 +484,7 @@ def _call_loss_fn(loss_fn, labels, logits, features, expected_loss_dim=1):
   Returns:
     Loss Tensor with shape [D0, D1, ... DN, expected_loss_dim].
   """
-  loss_fn_args = util.fn_args(loss_fn)
+  loss_fn_args = function_utils.fn_args(loss_fn)
   kwargs = {}
   if 'features' in loss_fn_args:
     kwargs['features'] = features
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 9cfc680789219d..5fdda0427f2d0c 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -36,7 +36,6 @@
 from tensorflow.python.eager import context
 from tensorflow.python.estimator import model_fn as model_fn_lib
 from tensorflow.python.estimator import run_config
-from tensorflow.python.estimator import util
 from tensorflow.python.estimator.export import export as export_helpers
 from tensorflow.python.estimator.export import export_output
 from tensorflow.python.framework import errors
@@ -63,6 +62,7 @@
 from tensorflow.python.training import warm_starting_util
 from tensorflow.python.util import compat
 from tensorflow.python.util import compat_internal
+from tensorflow.python.util import function_utils
 from tensorflow.python.util import nest
 from tensorflow.python.util.tf_export import tf_export
 
@@ -1052,7 +1052,7 @@ def _call_input_fn(self, input_fn, mode):
     Raises:
       ValueError: if input_fn takes invalid arguments.
     """
-    input_fn_args = util.fn_args(input_fn)
+    input_fn_args = function_utils.fn_args(input_fn)
     kwargs = {}
     if 'mode' in input_fn_args:
       kwargs['mode'] = mode
@@ -1078,7 +1078,7 @@ def _call_model_fn(self, features, labels, mode, config):
     Raises:
       ValueError: if model_fn returns invalid objects.
     """
-    model_fn_args = util.fn_args(self._model_fn)
+    model_fn_args = function_utils.fn_args(self._model_fn)
     kwargs = {}
     if 'labels' in model_fn_args:
       kwargs['labels'] = labels
@@ -1483,7 +1483,7 @@ def _get_replica_device_setter(config):
 
 def _verify_model_fn_args(model_fn, params):
   """Verifies model fn arguments."""
-  args = set(util.fn_args(model_fn))
+  args = set(function_utils.fn_args(model_fn))
   if 'features' not in args:
     raise ValueError('model_fn (%s) must include features argument.' % model_fn)
   if params is not None and 'params' not in args:
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 0f268f5df90307..1b701899487ede 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -33,7 +33,6 @@
 from tensorflow.python.estimator import estimator
 from tensorflow.python.estimator import model_fn as model_fn_lib
 from tensorflow.python.estimator import run_config
-from tensorflow.python.estimator import util
 from tensorflow.python.estimator.export import export
 from tensorflow.python.estimator.export import export_output
 from tensorflow.python.estimator.inputs import numpy_io
@@ -72,6 +71,7 @@
 from tensorflow.python.training import session_run_hook
 from tensorflow.python.training import training
 from tensorflow.python.util import compat
+from tensorflow.python.util import function_utils
 
 _TMP_DIR = '/tmp'
 _ANOTHER_TMP_DIR = '/another_tmp'
@@ -332,7 +332,7 @@ def model_fn(features, labels, mode, config, params):
       _, _, _, _, _ = features, labels, mode, config, params
 
     est = estimator.Estimator(model_fn=model_fn)
-    model_fn_args = util.fn_args(est.model_fn)
+    model_fn_args = function_utils.fn_args(est.model_fn)
     self.assertEqual(
         set(['features', 'labels', 'mode', 'config']), set(model_fn_args))
 
@@ -342,7 +342,7 @@ def model_fn(features, labels):
       _, _ = features, labels
 
     est = estimator.Estimator(model_fn=model_fn)
-    model_fn_args = util.fn_args(est.model_fn)
+    model_fn_args = function_utils.fn_args(est.model_fn)
     self.assertEqual(
         set(['features', 'labels', 'mode', 'config']), set(model_fn_args))
 
diff --git a/tensorflow/python/estimator/run_config.py b/tensorflow/python/estimator/run_config.py
index 8162b249f1f0be..c7707be8397d95 100644
--- a/tensorflow/python/estimator/run_config.py
+++ b/tensorflow/python/estimator/run_config.py
@@ -27,8 +27,8 @@
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import server_lib
-from tensorflow.python.estimator import util
 from tensorflow.python.util import compat_internal
+from tensorflow.python.util import function_utils
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -283,7 +283,7 @@ def _validate(property_name, cond, message):
             message='tf_random_seed must be integer.')
 
   _validate('device_fn', lambda device_fn: six.callable(device_fn) and
-            set(util.fn_args(device_fn)) == _VALID_DEVICE_FN_ARGS,
+            set(function_utils.fn_args(device_fn)) == _VALID_DEVICE_FN_ARGS,
             message='device_fn must be callable with exactly'
                     ' one argument "op".')
 
diff --git a/tensorflow/python/estimator/util.py b/tensorflow/python/estimator/util.py
index bb4bdd3fdfb2e1..e4e1d37f74330c 100644
--- a/tensorflow/python/estimator/util.py
+++ b/tensorflow/python/estimator/util.py
@@ -13,55 +13,21 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Utility to retrieve function args."""
+"""Utilities for Estimators."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import functools
 import os
 import time
 
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import compat
-from tensorflow.python.util import tf_decorator
-from tensorflow.python.util import tf_inspect
-
-
-def _is_bounded_method(fn):
-  _, fn = tf_decorator.unwrap(fn)
-  return tf_inspect.ismethod(fn) and (fn.__self__ is not None)
-
-
-def _is_callable_object(obj):
-  return hasattr(obj, '__call__') and tf_inspect.ismethod(obj.__call__)
-
-
-def fn_args(fn):
-  """Get argument names for function-like object.
-
-  Args:
-    fn: Function, or function-like object (e.g., result of `functools.partial`).
-
-  Returns:
-    `tuple` of string argument names.
-
-  Raises:
-    ValueError: if partial function has positionally bound arguments
-  """
-  if isinstance(fn, functools.partial):
-    args = fn_args(fn.func)
-    args = [a for a in args[len(fn.args):] if a not in (fn.keywords or [])]
-  else:
-    if _is_callable_object(fn):
-      fn = fn.__call__
-    args = tf_inspect.getfullargspec(fn).args
-    if _is_bounded_method(fn):
-      args.remove('self')
-  return tuple(args)
+from tensorflow.python.util import function_utils
 
+fn_args = function_utils.fn_args
 
 # When we create a timestamped directory, there is a small chance that the
 # directory already exists because another process is also creating these
diff --git a/tensorflow/python/keras/_impl/keras/engine/base_layer.py b/tensorflow/python/keras/_impl/keras/engine/base_layer.py
index 16ee2952b27979..72ab77fbbda068 100644
--- a/tensorflow/python/keras/_impl/keras/engine/base_layer.py
+++ b/tensorflow/python/keras/_impl/keras/engine/base_layer.py
@@ -25,7 +25,7 @@
 from six.moves import zip  # pylint: disable=redefined-builtin
 
 from tensorflow.python.eager import context
-from tensorflow.python.estimator import util as estimator_util
+from tensorflow.python.estimator import util as function_utils
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
@@ -44,6 +44,7 @@
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.training import checkpointable
+from tensorflow.python.util import function_utils
 from tensorflow.python.util import nest
 from tensorflow.python.util import tf_decorator
 from tensorflow.python.util import tf_inspect
@@ -146,7 +147,7 @@ def __init__(self, trainable=True, name=None, dtype=None, **kwargs):
     # return tensors. When using graph execution, _losses is a list of ops.
     self._losses = []
     self._dtype = None if dtype is None else dtypes.as_dtype(dtype).name
-    self._call_fn_args = estimator_util.fn_args(self.call)
+    self._call_fn_args = function_utils.fn_args(self.call)
     self._compute_previous_mask = ('mask' in self._call_fn_args or
                                    hasattr(self, 'compute_mask'))
     self._uses_inputs_arg = True
@@ -644,7 +645,7 @@ def __call__(self, inputs, *args, **kwargs):
         self._compute_previous_mask):
       previous_mask = collect_previous_mask(inputs)
       if not hasattr(self, '_call_fn_args'):
-        self._call_fn_args = estimator_util.fn_args(self.call)
+        self._call_fn_args = function_utils.fn_args(self.call)
       if ('mask' in self._call_fn_args and 'mask' not in kwargs and
           not generic_utils.is_all_none(previous_mask)):
         # The previous layer generated a mask, and mask was not explicitly pass
diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index 64db49c900c21d..2040e0081e93f0 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -20,12 +20,12 @@
 import copy
 
 from tensorflow.python.eager import context
-from tensorflow.python.estimator import util as estimator_util
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.keras._impl.keras.engine import base_layer
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.ops import variables as tf_variables
+from tensorflow.python.util import function_utils
 from tensorflow.python.util import nest
 from tensorflow.python.util.tf_export import tf_export
 
@@ -308,7 +308,7 @@ def __call__(self, inputs, *args, **kwargs):
       try:
         call_has_scope_arg = self._call_has_scope_arg
       except AttributeError:
-        self._call_fn_args = estimator_util.fn_args(self.call)
+        self._call_fn_args = function_utils.fn_args(self.call)
         self._call_has_scope_arg = 'scope' in self._call_fn_args
         call_has_scope_arg = self._call_has_scope_arg
       if call_has_scope_arg:
diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index adb0f59948a9be..f5970fdbb29f75 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -32,7 +32,6 @@
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
 from tensorflow.python.eager import context
-from tensorflow.python.estimator import util as estimator_util
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
@@ -41,6 +40,7 @@
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.util import function_utils
 from tensorflow.python.util import tf_contextlib
 from tensorflow.python.util.tf_export import tf_export
 
@@ -422,7 +422,7 @@ def _true_getter(name, shape=None, dtype=dtypes.float32,  # pylint: disable=miss
           "use_resource": use_resource,
       }
       # `fn_args` can handle functions, `functools.partial`, `lambda`.
-      if "constraint" in estimator_util.fn_args(custom_getter):
+      if "constraint" in function_utils.fn_args(custom_getter):
         custom_getter_kwargs["constraint"] = constraint
       return custom_getter(**custom_getter_kwargs)
     else:
diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py
index f584a009d946a1..fece3370f34317 100644
--- a/tensorflow/python/training/monitored_session.py
+++ b/tensorflow/python/training/monitored_session.py
@@ -25,7 +25,6 @@
 import six
 
 from tensorflow.core.protobuf import config_pb2
-from tensorflow.python.estimator import util
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
@@ -41,6 +40,7 @@
 from tensorflow.python.training import saver as training_saver
 from tensorflow.python.training import session_manager as sm
 from tensorflow.python.training import session_run_hook
+from tensorflow.python.util import function_utils
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -620,7 +620,7 @@ def step_fn(step_context):
         `step_context`. It may also optionally have `self` for cases when it
         belongs to an object.
     """
-    step_fn_arguments = util.fn_args(step_fn)
+    step_fn_arguments = function_utils.fn_args(step_fn)
     if step_fn_arguments != ('step_context',) and step_fn_arguments != (
         'self',
         'step_context',
diff --git a/tensorflow/python/util/function_utils.py b/tensorflow/python/util/function_utils.py
new file mode 100644
index 00000000000000..7bbbde3cd288a7
--- /dev/null
+++ b/tensorflow/python/util/function_utils.py
@@ -0,0 +1,57 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility to retrieve function args."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+
+from tensorflow.python.util import tf_decorator
+from tensorflow.python.util import tf_inspect
+
+
+def _is_bounded_method(fn):
+  _, fn = tf_decorator.unwrap(fn)
+  return tf_inspect.ismethod(fn) and (fn.__self__ is not None)
+
+
+def _is_callable_object(obj):
+  return hasattr(obj, '__call__') and tf_inspect.ismethod(obj.__call__)
+
+
+def fn_args(fn):
+  """Get argument names for function-like object.
+
+  Args:
+    fn: Function, or function-like object (e.g., result of `functools.partial`).
+
+  Returns:
+    `tuple` of string argument names.
+
+  Raises:
+    ValueError: if partial function has positionally bound arguments
+  """
+  if isinstance(fn, functools.partial):
+    args = fn_args(fn.func)
+    args = [a for a in args[len(fn.args):] if a not in (fn.keywords or [])]
+  else:
+    if _is_callable_object(fn):
+      fn = fn.__call__
+    args = tf_inspect.getfullargspec(fn).args
+    if _is_bounded_method(fn):
+      args.remove('self')
+  return tuple(args)
diff --git a/tensorflow/python/estimator/util_test.py b/tensorflow/python/util/function_utils_test.py
similarity index 85%
rename from tensorflow/python/estimator/util_test.py
rename to tensorflow/python/util/function_utils_test.py
index 4b2c8d7637e2e6..e78cf6a5b02af3 100644
--- a/tensorflow/python/estimator/util_test.py
+++ b/tensorflow/python/util/function_utils_test.py
@@ -20,8 +20,8 @@
 
 import functools
 
-from tensorflow.python.estimator import util
 from tensorflow.python.platform import test
+from tensorflow.python.util import function_utils
 
 
 class FnArgsTest(test.TestCase):
@@ -29,7 +29,7 @@ class FnArgsTest(test.TestCase):
   def test_simple_function(self):
     def fn(a, b):
       return a + b
-    self.assertEqual(('a', 'b'), util.fn_args(fn))
+    self.assertEqual(('a', 'b'), function_utils.fn_args(fn))
 
   def test_callable(self):
 
@@ -38,7 +38,7 @@ class Foo(object):
       def __call__(self, a, b):
         return a + b
 
-    self.assertEqual(('a', 'b'), util.fn_args(Foo()))
+    self.assertEqual(('a', 'b'), function_utils.fn_args(Foo()))
 
   def test_bounded_method(self):
 
@@ -47,7 +47,7 @@ class Foo(object):
       def bar(self, a, b):
         return a + b
 
-    self.assertEqual(('a', 'b'), util.fn_args(Foo().bar))
+    self.assertEqual(('a', 'b'), function_utils.fn_args(Foo().bar))
 
   def test_partial_function(self):
     expected_test_arg = 123
@@ -59,7 +59,7 @@ def fn(a, test_arg):
 
     wrapped_fn = functools.partial(fn, test_arg=123)
 
-    self.assertEqual(('a',), util.fn_args(wrapped_fn))
+    self.assertEqual(('a',), function_utils.fn_args(wrapped_fn))
 
   def test_partial_function_with_positional_args(self):
     expected_test_arg = 123
@@ -71,7 +71,7 @@ def fn(test_arg, a):
 
     wrapped_fn = functools.partial(fn, 123)
 
-    self.assertEqual(('a',), util.fn_args(wrapped_fn))
+    self.assertEqual(('a',), function_utils.fn_args(wrapped_fn))
 
     self.assertEqual(3, wrapped_fn(3))
     self.assertEqual(3, wrapped_fn(a=3))
@@ -88,7 +88,7 @@ def fn(a, test_arg1, test_arg2):
     wrapped_fn = functools.partial(fn, test_arg2=456)
     double_wrapped_fn = functools.partial(wrapped_fn, test_arg1=123)
 
-    self.assertEqual(('a',), util.fn_args(double_wrapped_fn))
+    self.assertEqual(('a',), function_utils.fn_args(double_wrapped_fn))
 
   def test_double_partial_with_positional_args_in_outer_layer(self):
     expected_test_arg1 = 123
@@ -102,7 +102,7 @@ def fn(test_arg1, a, test_arg2):
     wrapped_fn = functools.partial(fn, test_arg2=456)
     double_wrapped_fn = functools.partial(wrapped_fn, 123)
 
-    self.assertEqual(('a',), util.fn_args(double_wrapped_fn))
+    self.assertEqual(('a',), function_utils.fn_args(double_wrapped_fn))
 
     self.assertEqual(3, double_wrapped_fn(3))
     self.assertEqual(3, double_wrapped_fn(a=3))
@@ -119,7 +119,7 @@ def fn(test_arg1, test_arg2, a):
     wrapped_fn = functools.partial(fn, 123)  # binds to test_arg1
     double_wrapped_fn = functools.partial(wrapped_fn, 456)  # binds to test_arg2
 
-    self.assertEqual(('a',), util.fn_args(double_wrapped_fn))
+    self.assertEqual(('a',), function_utils.fn_args(double_wrapped_fn))
 
     self.assertEqual(3, double_wrapped_fn(3))
     self.assertEqual(3, double_wrapped_fn(a=3))

From 8480a96e1fb43edd26846a6c6d986f9408f8a2db Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 11:01:30 -0700
Subject: [PATCH 0676/1691] [XLA] Fix a doc that still mentioned
 computation_builder.

PiperOrigin-RevId: 196278086
---
 tensorflow/docs_src/performance/xla/broadcasting.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/performance/xla/broadcasting.md b/tensorflow/docs_src/performance/xla/broadcasting.md
index 2b010184260174..eaa709c2f84245 100644
--- a/tensorflow/docs_src/performance/xla/broadcasting.md
+++ b/tensorflow/docs_src/performance/xla/broadcasting.md
@@ -99,7 +99,7 @@ dimensions 1 and 2 of the cuboid.
 
 This type of broadcast is used in the binary ops in `XlaBuilder`, if the
 `broadcast_dimensions` argument is given. For example, see
-[XlaBuilder::Add](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.cc).
+[XlaBuilder::Add](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.cc).
 In the XLA source code, this type of broadcasting is sometimes called "InDim"
 broadcasting.
 

From e1562e72c197ec830547a051ddfe0f720acb9f67 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 11:04:22 -0700
Subject: [PATCH 0677/1691] Allow communicating instructions within a kCall
 computation.

PiperOrigin-RevId: 196278635
---
 .../xla/service/hlo_module_group_metadata.cc  | 38 +++++++++++--------
 .../xla/service/hlo_module_group_metadata.h   |  5 +++
 2 files changed, 28 insertions(+), 15 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc
index 54c34ce1166516..67f4c37413f47b 100644
--- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc
+++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc
@@ -47,6 +47,9 @@ string HloModuleGroupMetadata::TrackedInstruction::ToString() const {
     case ComputationKind::kConditionalFalse:
       repr += ":CONDITIONAL_FALSE";
       break;
+    case ComputationKind::kCallFunction:
+      repr += ":CALL";
+      break;
   }
   return repr;
 }
@@ -206,6 +209,9 @@ Status HloModuleGroupMetadata::RecordInstructions() {
           TrackedInstruction(hlo, ComputationKind::kConditionalTrue);
       tracked_instructions_[hlo->false_computation()] =
           TrackedInstruction(hlo, ComputationKind::kConditionalFalse);
+    } else if (hlo->opcode() == HloOpcode::kCall) {
+      tracked_instructions_[hlo->to_apply()] =
+          TrackedInstruction(hlo, ComputationKind::kCallFunction);
     }
     if (!IsChannelInstruction(hlo)) {
       return Status::OK();
@@ -258,7 +264,8 @@ Status HloModuleGroupMetadata::RecordInstructions() {
 Status HloModuleGroupMetadata::AddCompanion(HloInstruction* instruction1,
                                             HloInstruction* instruction2) {
   TF_RET_CHECK(instruction1->opcode() == HloOpcode::kWhile ||
-               instruction1->opcode() == HloOpcode::kConditional);
+               instruction1->opcode() == HloOpcode::kConditional ||
+               instruction1->opcode() == HloOpcode::kCall);
   VLOG(2) << "adding as companions:" << instruction1->ToString() << " and "
           << instruction2->ToString();
 
@@ -336,21 +343,11 @@ Status HloModuleGroupMetadata::VerifyChannelInstructions() {
     }
   }
 
-  // Check if channel instructions are used only in allowed computations.
-  const auto allowed = [this](HloInstruction* hlo) {
-    HloComputation* computation = hlo->parent();
-    const HloModule* module = computation->parent();
-    if (module->entry_computation() == computation ||
-        tracked_instructions_.count(computation) > 0) {
-      return true;
-    }
-    return false;
-  };
   for (const Channel& channel : channels_) {
-    if (!allowed(channel.send) || !allowed(channel.send_done) ||
-        !allowed(channel.recv) || !allowed(channel.recv_done)) {
-      return FailedPrecondition("channel is used in disallowed computation");
-    }
+    TF_RETURN_IF_ERROR(CheckCommunicatingInstruction(channel.send));
+    TF_RETURN_IF_ERROR(CheckCommunicatingInstruction(channel.send_done));
+    TF_RETURN_IF_ERROR(CheckCommunicatingInstruction(channel.recv));
+    TF_RETURN_IF_ERROR(CheckCommunicatingInstruction(channel.recv_done));
   }
   // Check if the nest levels match for each channel.
   for (const Channel& channel : channels_) {
@@ -368,4 +365,15 @@ Status HloModuleGroupMetadata::VerifyChannelInstructions() {
   return Status::OK();
 }
 
+Status HloModuleGroupMetadata::CheckCommunicatingInstruction(
+    HloInstruction* instruction) const {
+  HloComputation* computation = instruction->parent();
+  const HloModule* module = computation->parent();
+  if (module->entry_computation() == computation ||
+      tracked_instructions_.count(computation) > 0) {
+    return Status::OK();
+  }
+  return FailedPrecondition("channel is used in disallowed computation");
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h
index c48a7ab0b59269..88ed9a2ecc70aa 100644
--- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h
+++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h
@@ -60,6 +60,7 @@ class HloModuleGroupMetadata {
     kWhileBody,
     kConditionalTrue,
     kConditionalFalse,
+    kCallFunction,
   };
 
   // Tracks the instruction mapped to a given computation, and the computation
@@ -202,6 +203,10 @@ class HloModuleGroupMetadata {
   Status AddCompanion(HloInstruction* instruction1,
                       HloInstruction* instruction2);
 
+  // Checks whether a communicating instruction is placed in a valid position
+  // within the graph.
+  Status CheckCommunicatingInstruction(HloInstruction* instruction) const;
+
   // Retrieves a pointer to the stored TrackedInstruction associated with a
   // tracked computation, or nullptr in case such computation is not tracked.
   const TrackedInstruction* GetTrackedInstruction(

From 1d6973d68b5d617e3a2dbf935643d0c0e4dcdac5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 11:04:33 -0700
Subject: [PATCH 0678/1691] RELNOTES: This allows the use of '.' in variables
 (e.g. "hparams.parse('a.b=1.0')"), which would previously raise an error.
 This will correspond to an attribute name with an embedded '.' symbol (e.g.
 'a.b'), which can only be accessed indirectly (e.g. through getattr and
 setattr).  To set this up the user will first need to explicitly add the
 variable to the hparam object (e.g. "hparams.add_hparam(name='a.b',
 value=0.0)").

NOTE: the use of '.' in variable names is now allowed, but it is not recommended.
PiperOrigin-RevId: 196278660
---
 .../contrib/training/python/training/hparam.py    |  9 ++++++++-
 .../training/python/training/hparam_test.py       | 15 +++++++++++++++
 2 files changed, 23 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/training/python/training/hparam.py b/tensorflow/contrib/training/python/training/hparam.py
index f0418f04ba2c5c..3beb7bfe3048a8 100644
--- a/tensorflow/contrib/training/python/training/hparam.py
+++ b/tensorflow/contrib/training/python/training/hparam.py
@@ -34,7 +34,7 @@
 # where  is either a single token or [] enclosed list of tokens.
 # For example:  "var[1] = a" or "x = [1,2,3]"
 PARAM_RE = re.compile(r"""
-  (?P[a-zA-Z][\w]*)      # variable name: "var" or "x"
+  (?P[a-zA-Z][\w\.]*)      # variable name: "var" or "x"
   (\[\s*(?P\d+)\s*\])?  # (optional) index: "1" or None
   \s*=\s*
   ((?P[^,\[]*)            # single value: "a" or None
@@ -200,6 +200,13 @@ def parse_values(values, type_map):
   If a hyperparameter name in both an index assignment and scalar assignment,
   a ValueError is raised.  (e.g. 'a=[1,2,3],a[0] = 1').
 
+  The hyperparameter name may contain '.' symbols, which will result in an
+  attribute name that is only accessible through the getattr and setattr
+  functions.  (And must be first explicit added through add_hparam.)
+
+  WARNING: Use of '.' in your variable names is allowed, but is not well
+  supported and not recommended.
+
   The `value` in `name=value` must follows the syntax according to the
   type of the parameter:
 
diff --git a/tensorflow/contrib/training/python/training/hparam_test.py b/tensorflow/contrib/training/python/training/hparam_test.py
index 11fd15b5275a3c..660c97f25e8458 100644
--- a/tensorflow/contrib/training/python/training/hparam_test.py
+++ b/tensorflow/contrib/training/python/training/hparam_test.py
@@ -118,6 +118,21 @@ def testSomeValues(self):
     self.assertEqual('2.3"', hparams2.c_c)
     self.assertEqual('/a=b/c/d', hparams2.d)
 
+  def testWithPeriodInVariableName(self):
+    hparams = hparam.HParams()
+    hparams.add_hparam(name='a.b', value=0.0)
+    hparams.parse('a.b=1.0')
+    self.assertEqual(1.0, getattr(hparams, 'a.b'))
+    hparams.add_hparam(name='c.d', value=0.0)
+    with self.assertRaisesRegexp(ValueError, 'Could not parse'):
+      hparams.parse('c.d=abc')
+    hparams.add_hparam(name='e.f', value='')
+    hparams.parse('e.f=abc')
+    self.assertEqual('abc', getattr(hparams, 'e.f'))
+    hparams.add_hparam(name='d..', value=0.0)
+    hparams.parse('d..=10.0')
+    self.assertEqual(10.0, getattr(hparams, 'd..'))
+
   def testSetFromMap(self):
     hparams = hparam.HParams(a=1, b=2.0, c='tanh')
     hparams.override_from_dict({'a': -2, 'c': 'identity'})

From c72dbeaedc8db265a074c47cbbf0b19aa03b7a69 Mon Sep 17 00:00:00 2001
From: Amit Patankar 
Date: Fri, 11 May 2018 12:27:40 -0700
Subject: [PATCH 0679/1691] Updating the descriptions for TensorFlow.

PiperOrigin-RevId: 196291844
---
 tensorflow/tools/pip_package/setup.py | 22 +++++++++++++++++++---
 1 file changed, 19 insertions(+), 3 deletions(-)

diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 937d41c36ca33a..f7385e59912fa0 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -33,6 +33,21 @@
 # result for pip.
 _VERSION = '1.8.0-rc1'
 
+_SHORT_DESCRIPTION = ('TensorFlow is an open source machine learning framework '
+                      'for everyone.')
+
+_LONG_DESCRIPTION = ('TensorFlow is an open source software library for high '
+                     'performance numerical computation. Its flexible '
+                     'architecture allows easy deployment of computation across'
+                     ' a variety of platforms (CPUs, GPUs, TPUs), and from '
+                     'desktops to clusters of servers to mobile and edge '
+                     'devices. Originally developed by researchers and '
+                     'engineers from the Google Brain team within Google\'s AI '
+                     'organization, it comes with strong support for machine '
+                     'learning and deep learning and the flexible numerical '
+                     'computation core is used across many other scientific '
+                     'domains.')
+
 REQUIRED_PACKAGES = [
     'absl-py >= 0.1.6',
     'astor >= 0.6.0',
@@ -214,8 +229,8 @@ def find_files(pattern, root):
 setup(
     name=project_name,
     version=_VERSION.replace('-', ''),
-    description='TensorFlow helps the tensors flow',
-    long_description='',
+    description=_SHORT_DESCRIPTION,
+    long_description=_LONG_DESCRIPTION,
     url='https://www.tensorflow.org/',
     author='Google Inc.',
     author_email='opensource@google.com',
@@ -261,4 +276,5 @@ def find_files(pattern, root):
         'Topic :: Software Development :: Libraries :: Python Modules',
     ],
     license='Apache 2.0',
-    keywords='tensorflow tensor machine learning',)
+    keywords='tensorflow tensor machine learning',
+)

From 3ac41829fbfe4c1c75967df3d1b39115ca420359 Mon Sep 17 00:00:00 2001
From: Shashi Shekhar 
Date: Fri, 11 May 2018 12:36:40 -0700
Subject: [PATCH 0680/1691] Change default number of threads to 1.

PiperOrigin-RevId: 196293227
---
 tensorflow/contrib/lite/tools/benchmark_model.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/lite/tools/benchmark_model.cc b/tensorflow/contrib/lite/tools/benchmark_model.cc
index 93c80e0f5e021f..671ee8359e1d24 100644
--- a/tensorflow/contrib/lite/tools/benchmark_model.cc
+++ b/tensorflow/contrib/lite/tools/benchmark_model.cc
@@ -354,7 +354,7 @@ int Main(int argc, char** argv) {
   string output_layer_string;  // e.g.: output
   int num_runs = 50;
   string run_delay = "-1.0";
-  int num_threads = -1;
+  int num_threads = 1;
   string benchmark_name = "";
   string output_prefix = "";
   int warmup_runs = 1;

From b6fac88897cb2c70890b0f03baa89785379768b0 Mon Sep 17 00:00:00 2001
From: Jeremy Lau 
Date: Fri, 11 May 2018 12:39:40 -0700
Subject: [PATCH 0681/1691] Update HeapSimulator to use BufferValue.

PiperOrigin-RevId: 196293610
---
 tensorflow/compiler/xla/service/BUILD         | 17 +++-
 .../compiler/xla/service/buffer_assignment.cc | 16 +++-
 .../xla/service/buffer_value_containers.h     | 55 +++++++++++++
 .../compiler/xla/service/heap_simulator.cc    | 81 ++++++++++---------
 .../compiler/xla/service/heap_simulator.h     | 55 ++++++-------
 .../xla/service/heap_simulator_test.cc        | 66 +++++++--------
 6 files changed, 184 insertions(+), 106 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/buffer_value_containers.h

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index b3e598f65becc2..f6af8163154d03 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1010,6 +1010,7 @@ cc_library(
     ],
     deps = [
         ":buffer_liveness",
+        ":buffer_value_containers",
         ":heap_simulator",
         ":hlo",
         ":hlo_proto",
@@ -1098,11 +1099,12 @@ cc_library(
     srcs = ["heap_simulator.cc"],
     hdrs = ["heap_simulator.h"],
     deps = [
+        ":buffer_value",
+        ":buffer_value_containers",
         ":hlo",
         ":hlo_ordering",
         ":hlo_proto",
         ":liveness_util",
-        ":logical_buffer",
         ":tuple_points_to_analysis",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:util",
@@ -1118,7 +1120,7 @@ tf_cc_test(
         ":heap_simulator",
         ":hlo",
         ":hlo_ordering",
-        ":logical_buffer",
+        ":hlo_value",
         ":tuple_points_to_analysis",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:status_macros",
@@ -1785,6 +1787,17 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "buffer_value_containers",
+    hdrs = ["buffer_value_containers.h"],
+    deps = [
+        ":buffer_value",
+        ":logical_buffer",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+)
+
 cc_library(
     name = "logical_buffer",
     srcs = ["logical_buffer.cc"],
diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index 94ccfedf6289b4..c0b8bf903923a3 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -24,6 +24,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/buffer_value_containers.h"
 #include "tensorflow/compiler/xla/service/heap_simulator.h"
 #include "tensorflow/compiler/xla/service/hlo.pb.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
@@ -699,7 +700,7 @@ BufferAssignmentProto BufferAssignment::ToProto() const {
         BufferAssignmentProto::BufferAlias* proto_alias =
             proto.add_buffer_aliases();
         LogicalBufferProto::Location proto_alias_location =
-            LogicalBuffer::ToLocationProto(*alias.instruction(), alias.index());
+            BufferValue::ToLocationProto(*alias.instruction(), alias.index());
         proto_alias->set_source_buffer_id(buffer.id());
         proto_alias->mutable_location()->Swap(&proto_alias_location);
       }
@@ -1083,7 +1084,9 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering(
       VLOG(2) << "Simulating heap for color " << color;
       int64 alignment = assignment->color_alignment_(color);
       HeapSimulator::Options options;
-      options.buffers_to_assign = &single_colored_set.second;
+      BufferValueFlatSet buffer_value_set =
+          ToBufferValueFlatSet(single_colored_set.second);
+      options.buffers_to_assign = &buffer_value_set;
       TF_ASSIGN_OR_RETURN(
           const HeapSimulator::Result result,
           HeapSimulator::Run(MakeUnique(
@@ -1111,7 +1114,9 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering(
         VLOG(2) << "Simulating heap for color " << color;
         int64 alignment = assignment->color_alignment_(color);
         HeapSimulator::Options options;
-        options.buffers_to_assign = &single_colored_set.second;
+        BufferValueFlatSet buffer_value_set =
+            ToBufferValueFlatSet(single_colored_set.second);
+        options.buffers_to_assign = &buffer_value_set;
         TF_ASSIGN_OR_RETURN(
             const HeapSimulator::Result result,
             HeapSimulator::Run(MakeUnique(
@@ -1224,7 +1229,10 @@ void BufferAssigner::AssignBuffersFromHeapSimulator(
   BufferAllocation* allocation = assignment->NewEmptyAllocation(
       result.heap_size, /*is_thread_local=*/false, /*is_reusable=*/true, color);
   for (const auto& buffer_chunk : result.chunk_map) {
-    const LogicalBuffer& buffer = *buffer_chunk.first;
+    // TODO(lauj) Remove this down_cast after downstream users of
+    // BufferAllocation::assigned_buffers() are updated to use BufferValue.
+    const LogicalBuffer& buffer =
+        *CHECK_NOTNULL(dynamic_cast(buffer_chunk.first));
     const HeapSimulator::Chunk& chunk = buffer_chunk.second;
     assignment->AddAssignment(allocation, buffer, chunk.offset, chunk.size);
   }
diff --git a/tensorflow/compiler/xla/service/buffer_value_containers.h b/tensorflow/compiler/xla/service/buffer_value_containers.h
new file mode 100644
index 00000000000000..305914fca828f1
--- /dev/null
+++ b/tensorflow/compiler/xla/service/buffer_value_containers.h
@@ -0,0 +1,55 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_BUFFER_VALUE_CONTAINERS_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_BUFFER_VALUE_CONTAINERS_H_
+
+#include "tensorflow/compiler/xla/service/buffer_value.h"
+#include "tensorflow/compiler/xla/service/logical_buffer.h"
+#include "tensorflow/core/lib/gtl/compactptrset.h"
+#include "tensorflow/core/lib/gtl/flatset.h"
+
+namespace xla {
+
+// Define various containers of BufferValues, and utilities to convert from
+// containers of LogicalBuffers to containers of BufferValues.
+
+using BufferValueCompactPointerSet =
+    tensorflow::gtl::CompactPointerSet;
+template 
+BufferValueCompactPointerSet ToBufferValueCompactPointerSet(
+    const LogicalBufferContainerT& logical_buffer_container) {
+  BufferValueCompactPointerSet output;
+  for (const LogicalBuffer* buffer : logical_buffer_container) {
+    output.insert(buffer);
+  }
+  return output;
+}
+
+using BufferValueFlatSet = tensorflow::gtl::FlatSet;
+template 
+BufferValueFlatSet ToBufferValueFlatSet(
+    const LogicalBufferContainerT& logical_buffer_container) {
+  BufferValueFlatSet output;
+  output.reserve(logical_buffer_container.size());
+  for (const LogicalBuffer* buffer : logical_buffer_container) {
+    output.insert(buffer);
+  }
+  return output;
+}
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_BUFFER_VALUE_CONTAINERS_H_
diff --git a/tensorflow/compiler/xla/service/heap_simulator.cc b/tensorflow/compiler/xla/service/heap_simulator.cc
index 3dd4c4a0794e5c..9a07ee36838346 100644
--- a/tensorflow/compiler/xla/service/heap_simulator.cc
+++ b/tensorflow/compiler/xla/service/heap_simulator.cc
@@ -32,7 +32,7 @@ StatusOr HeapSimulator::Run(
     std::unique_ptr algorithm, const HloModule& module,
     const SequentialHloOrdering::HloModuleSequence& module_sequence,
     const TuplePointsToAnalysis& points_to_analysis,
-    const LogicalBuffer::SizeFunction& size_fn, const Options& options) {
+    const BufferValue::SizeFunction& size_fn, const Options& options) {
   HeapSimulator heap(std::move(algorithm), size_fn, options, &module_sequence);
   const HloComputation* entry_computation = module.entry_computation();
   const std::vector& instruction_sequence =
@@ -47,7 +47,7 @@ StatusOr HeapSimulator::Run(
     std::unique_ptr algorithm, const HloComputation& computation,
     const std::vector& instruction_sequence,
     const TuplePointsToAnalysis& points_to_analysis,
-    const LogicalBuffer::SizeFunction& size_fn, const Options& options) {
+    const BufferValue::SizeFunction& size_fn, const Options& options) {
   HeapSimulator heap(std::move(algorithm), size_fn, options,
                      /*module_sequence=*/nullptr);
   TF_RETURN_IF_ERROR(heap.RunComputation(computation, instruction_sequence,
@@ -73,11 +73,11 @@ Status HeapSimulator::RunComputation(
   // 'used_buffers' is the reverse map - it tracks which buffers were used by an
   // instruction, so that we can remove the instructions from a buffer's live
   // set after they are visited.
-  FlatMap> live_buffers;
-  FlatMap> used_buffers;
+  FlatMap> live_buffers;
+  FlatMap> used_buffers;
   auto add_user_to_buffer = [this, &live_buffers, &used_buffers](
                                 const HloInstruction* user,
-                                const LogicalBuffer* buffer) {
+                                const BufferValue* buffer) {
     if (!IgnoreBuffer(buffer)) {
       VLOG(4) << "  Adding user " << user->name() << " to buffer "
               << buffer->ToString();
@@ -96,7 +96,7 @@ Status HeapSimulator::RunComputation(
     const PointsToSet::BufferSet& buffer_set = points_to.CreateFlattenedSet();
     for (const HloInstruction* user : instruction->users()) {
       if (user->opcode() != HloOpcode::kGetTupleElement) {
-        for (const LogicalBuffer* buffer : buffer_set) {
+        for (const BufferValue* buffer : buffer_set) {
           add_user_to_buffer(user, buffer);
         }
       } else {
@@ -104,12 +104,12 @@ Status HeapSimulator::RunComputation(
         // alive. It only needs the buffers that relate to the element its
         // extracting, and the tuple it's extracting from, but not the buffers
         // for the other elements.
-        for (const LogicalBuffer* buffer : points_to.element({})) {
+        for (const BufferValue* buffer : points_to.element({})) {
           add_user_to_buffer(user, buffer);
         }
         const PointsToSet& gte_points_to =
             points_to_analysis.GetPointsToSet(user);
-        for (const LogicalBuffer* buffer : gte_points_to.CreateFlattenedSet()) {
+        for (const BufferValue* buffer : gte_points_to.CreateFlattenedSet()) {
           add_user_to_buffer(user, buffer);
         }
       }
@@ -117,24 +117,25 @@ Status HeapSimulator::RunComputation(
   }
 
   const HloInstruction* root = computation.root_instruction();
-  auto output_source_buffers =
-      points_to_analysis.GetPointsToSet(root).CreateFlattenedSet();
+  BufferValueCompactPointerSet output_source_buffers =
+      ToBufferValueCompactPointerSet(
+          points_to_analysis.GetPointsToSet(root).CreateFlattenedSet());
 
-  std::vector dead_buffers_to_free;
-  std::vector operand_buffers_to_free;
+  std::vector dead_buffers_to_free;
+  std::vector operand_buffers_to_free;
   for (const HloInstruction* instruction : instruction_sequence) {
     const TuplePointsToAnalysis::BufferDefinitionVector&
         buffers_defined_by_instruction =
             points_to_analysis.GetBuffersDefinedByInstruction(instruction);
 
     VLOG(3) << "Instruction: " << instruction->ToString();
-    for (const LogicalBuffer* buffer : buffers_defined_by_instruction) {
+    for (const BufferValue* buffer : buffers_defined_by_instruction) {
       VLOG(4) << "  Defines: " << buffer->ToString()
               << (IgnoreBuffer(buffer) ? " (Ignored)" : "");
     }
 
     dead_buffers_to_free.clear();
-    for (const LogicalBuffer* buffer : buffers_defined_by_instruction) {
+    for (const BufferValue* buffer : buffers_defined_by_instruction) {
       if (IgnoreBuffer(buffer)) {
         continue;
       }
@@ -161,7 +162,7 @@ Status HeapSimulator::RunComputation(
     // have no instructions left to visit are moved from live_buffers to
     // operand_buffers_to_free.
     operand_buffers_to_free.clear();
-    for (const LogicalBuffer* operand_buffer : used_buffers[instruction]) {
+    for (const BufferValue* operand_buffer : used_buffers[instruction]) {
       if (IgnoreBuffer(operand_buffer)) {
         continue;
       }
@@ -177,7 +178,7 @@ Status HeapSimulator::RunComputation(
     }
     // Sort to get a deterministic iteration order.
     std::sort(operand_buffers_to_free.begin(), operand_buffers_to_free.end(),
-              [](const LogicalBuffer* x, const LogicalBuffer* y) {
+              [](const BufferValue* x, const BufferValue* y) {
                 return x->id() < y->id();
               });
 
@@ -188,7 +189,7 @@ Status HeapSimulator::RunComputation(
     //
     // INVARIANT: Either Alloc or ShareBuffer will be called for each buffer
     // that we should assign.
-    for (const LogicalBuffer* buffer : buffers_defined_by_instruction) {
+    for (const BufferValue* buffer : buffers_defined_by_instruction) {
       if (IgnoreBuffer(buffer)) {
         continue;
       }
@@ -199,7 +200,7 @@ Status HeapSimulator::RunComputation(
       // we must be the last user of the buffer.
       bool shared = false;
       if (options_.may_reuse_operand_buffers) {
-        for (const LogicalBuffer* operand_buffer : operand_buffers_to_free) {
+        for (const BufferValue* operand_buffer : operand_buffers_to_free) {
           if (buffer->instruction()->IsUserOf(operand_buffer->instruction()) &&
               buffer->instruction()->opcode() != HloOpcode::kCopy &&
               CanShareOperandBufferWithUser(
@@ -248,11 +249,11 @@ Status HeapSimulator::RunComputation(
 
     // Free buffers that are no longer live.  This is the earliest point that we
     // can de-allocate; right after the last use of the buffer.
-    for (const LogicalBuffer* buffer : dead_buffers_to_free) {
+    for (const BufferValue* buffer : dead_buffers_to_free) {
       VLOG(3) << "  Freeing dead: " << buffer->ToString();
       Free(buffer, instruction);
     }
-    for (const LogicalBuffer* buffer : operand_buffers_to_free) {
+    for (const BufferValue* buffer : operand_buffers_to_free) {
       VLOG(3) << "  Freeing operand: " << buffer->ToString();
       Free(buffer, instruction);
     }
@@ -261,10 +262,10 @@ Status HeapSimulator::RunComputation(
   // Any remaining live buffers must be entry parameters or output source
   // buffers, which had a nullptr sentry added.  Free them now, in a
   // deterministic order.
-  std::vector to_free;
+  std::vector to_free;
   to_free.reserve(live_buffers.size());
   for (const auto& buffer_pending : live_buffers) {
-    const LogicalBuffer* buffer = buffer_pending.first;
+    const BufferValue* buffer = buffer_pending.first;
     const FlatSet& pending = buffer_pending.second;
     CHECK_EQ(pending.size(), 1) << *buffer;
     CHECK(*pending.begin() == nullptr) << *buffer;
@@ -272,10 +273,10 @@ Status HeapSimulator::RunComputation(
   }
 
   std::sort(to_free.begin(), to_free.end(),
-            [](const LogicalBuffer* x, const LogicalBuffer* y) {
+            [](const BufferValue* x, const BufferValue* y) {
               return x->id() < y->id();
             });
-  for (const LogicalBuffer* buffer : to_free) {
+  for (const BufferValue* buffer : to_free) {
     VLOG(3) << "Freeing pending: " << buffer->ToString();
     Free(buffer, root);
   }
@@ -285,7 +286,7 @@ Status HeapSimulator::RunComputation(
 
 HeapSimulator::HeapSimulator(
     std::unique_ptr algorithm,
-    const LogicalBuffer::SizeFunction& size_fn, const Options& options,
+    const BufferValue::SizeFunction& size_fn, const Options& options,
     const SequentialHloOrdering::HloModuleSequence* module_sequence)
     : no_fragmentation_stats_(MakeUnique()),
       algorithm_(std::move(algorithm)),
@@ -297,7 +298,7 @@ HeapSimulator::HeapSimulator(
 
 HeapSimulator::~HeapSimulator() {}
 
-bool HeapSimulator::IgnoreBuffer(const LogicalBuffer* buffer) const {
+bool HeapSimulator::IgnoreBuffer(const BufferValue* buffer) const {
   // Buffers for constants are ignored unless the alloc_constants option is
   // set. Also ignore buffers that we're not meant to assign.
   //
@@ -311,7 +312,7 @@ bool HeapSimulator::IgnoreBuffer(const LogicalBuffer* buffer) const {
 }
 
 // Alloc always calls the underlying heap algorithm.
-void HeapSimulator::Alloc(const LogicalBuffer* buffer,
+void HeapSimulator::Alloc(const BufferValue* buffer,
                           const HloInstruction* instruction) {
   CHECK(allocated_buffers_.count(buffer) == 0)
       << "Alloc called on allocated buffer: " << *buffer;
@@ -331,7 +332,7 @@ void HeapSimulator::Alloc(const LogicalBuffer* buffer,
 // buffers whose group liveness has expired.  Shared group liveness is tracked
 // by maintaining a refcount; the Free call on the last buffer in the group
 // causes Free to be called on the underlying algorithm.
-void HeapSimulator::Free(const LogicalBuffer* buffer,
+void HeapSimulator::Free(const BufferValue* buffer,
                          const HloInstruction* instruction) {
   auto shared_it = shared_buffers_.find(buffer);
   if (shared_it != shared_buffers_.end()) {
@@ -362,8 +363,8 @@ void HeapSimulator::Free(const LogicalBuffer* buffer,
 // The 'buffer' must be a non-allocated, non-freed buffer, just like in calls to
 // Alloc.  The 'shared' buffer must be a previously allocated or shared buffer.
 // Both 'buffer' and 'shared' will be associated with the same SharedGroup.
-void HeapSimulator::ShareBuffer(const LogicalBuffer* buffer,
-                                const LogicalBuffer* shared,
+void HeapSimulator::ShareBuffer(const BufferValue* buffer,
+                                const BufferValue* shared,
                                 const HloInstruction* instruction) {
   CHECK_LE(size_fn_(*buffer), size_fn_(*shared))
       << "ShareBuffer oversized buffer" << *buffer << " shared: " << *shared;
@@ -374,7 +375,7 @@ void HeapSimulator::ShareBuffer(const LogicalBuffer* buffer,
   CHECK(freed_buffers_.count(shared) == 0)
       << "ShareBuffer called on freed shared buffer: " << *shared;
 
-  const LogicalBuffer* canonical = nullptr;
+  const BufferValue* canonical = nullptr;
   auto shared_it = shared_buffers_.find(shared);
   if (shared_it != shared_buffers_.end()) {
     // The 'shared' buffer already has a group; it might be the canonical, but
@@ -408,7 +409,7 @@ HeapSimulator::Result HeapSimulator::Finish() {
   // collecting statistics, e.g. NoFragmentationStatsHeap.
   if (!result.chunk_map.empty()) {
     for (const auto& share_pair : shared_buffers_) {
-      const LogicalBuffer* buffer = share_pair.first;
+      const BufferValue* buffer = share_pair.first;
       std::shared_ptr group = share_pair.second;
       if (buffer != group->canonical) {
         // The canonical must already exist in the chunk_map, since we called
@@ -437,9 +438,9 @@ HeapSimulator::Result HeapSimulator::Finish() {
 }
 
 void HeapSimulator::FillDebugTrace(HeapSimulatorTrace::Event::Kind kind,
-                                   const LogicalBuffer* buffer,
+                                   const BufferValue* buffer,
                                    const HloInstruction* instruction,
-                                   const LogicalBuffer* share_with_canonical) {
+                                   const BufferValue* share_with_canonical) {
   HeapSimulatorTrace::Event* event = debug_trace_.add_events();
   event->set_kind(kind);
   event->set_buffer_id(buffer->id());
@@ -453,14 +454,14 @@ void HeapSimulator::FillDebugTrace(HeapSimulatorTrace::Event::Kind kind,
   }
 }
 
-void NoFragmentationStatsHeap::Alloc(const LogicalBuffer* buffer, int64 size) {
+void NoFragmentationStatsHeap::Alloc(const BufferValue* buffer, int64 size) {
   current_heap_size_ += size;
   if (current_heap_size_ > max_heap_size_) {
     max_heap_size_ = current_heap_size_;
   }
 }
 
-void NoFragmentationStatsHeap::Free(const LogicalBuffer* buffer, int64 size) {
+void NoFragmentationStatsHeap::Free(const BufferValue* buffer, int64 size) {
   current_heap_size_ -= size;
 }
 
@@ -472,12 +473,12 @@ HeapSimulator::Result NoFragmentationStatsHeap::Finish() {
   return result;
 }
 
-void DecreasingSizeRunsHeap::Alloc(const LogicalBuffer* buffer, int64 size) {
+void DecreasingSizeRunsHeap::Alloc(const BufferValue* buffer, int64 size) {
   SetMode(kAlloc);
   run_.emplace_back(Op{buffer, size});
 }
 
-void DecreasingSizeRunsHeap::Free(const LogicalBuffer* buffer, int64 size) {
+void DecreasingSizeRunsHeap::Free(const BufferValue* buffer, int64 size) {
   CHECK(mode_ != kInit) << "Free called on empty heap: " << *buffer;
   SetMode(kFree);
   run_.emplace_back(Op{buffer, size});
@@ -518,7 +519,7 @@ void DecreasingSizeRunsHeap::CallAndDrainRun() {
   run_.clear();
 }
 
-void LazyBestFitHeap::Alloc(const LogicalBuffer* buffer, int64 size) {
+void LazyBestFitHeap::Alloc(const BufferValue* buffer, int64 size) {
   // Degenerate case: 0-sized buffers are always allocated at offset 0.
   if (size == 0) {
     result_.chunk_map.emplace(buffer, Chunk{0, 0});
@@ -586,7 +587,7 @@ void LazyBestFitHeap::Alloc(const LogicalBuffer* buffer, int64 size) {
   result_.chunk_map.emplace(buffer, Chunk{kLazyAllocOffset, size});
 }
 
-void LazyBestFitHeap::Free(const LogicalBuffer* buffer, int64 size) {
+void LazyBestFitHeap::Free(const BufferValue* buffer, int64 size) {
   auto alloc_it = result_.chunk_map.find(buffer);
   CHECK(alloc_it != result_.chunk_map.end())
       << "Free called on non-allocated buffer: " << *buffer;
diff --git a/tensorflow/compiler/xla/service/heap_simulator.h b/tensorflow/compiler/xla/service/heap_simulator.h
index 636f19dd39f097..8b2b43a37a5c41 100644
--- a/tensorflow/compiler/xla/service/heap_simulator.h
+++ b/tensorflow/compiler/xla/service/heap_simulator.h
@@ -21,11 +21,12 @@ limitations under the License.
 #include 
 #include 
 
+#include "tensorflow/compiler/xla/service/buffer_value.h"
+#include "tensorflow/compiler/xla/service/buffer_value_containers.h"
 #include "tensorflow/compiler/xla/service/hlo.pb.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_ordering.h"
-#include "tensorflow/compiler/xla/service/logical_buffer.h"
 #include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/core/lib/gtl/flatmap.h"
@@ -43,7 +44,7 @@ class HeapAlgorithm;
 // don't need to return the assignment of buffer offsets until the very end.
 class HeapSimulator {
  public:
-  // Chunk represents a contiguous piece of memory.  Each LogicalBuffer will be
+  // Chunk represents a contiguous piece of memory.  Each BufferValue will be
   // associated with a chunk in the assignment result.
   struct Chunk {
     int64 offset;
@@ -55,7 +56,7 @@ class HeapSimulator {
   // Result represents the result of the heap simulation.
   struct Result {
     // The assignment of buffers to chunks.
-    tensorflow::gtl::FlatMap chunk_map;
+    tensorflow::gtl::FlatMap chunk_map;
 
     // The total size in bytes of the heap, containing all assigned chunks.
     int64 heap_size = 0;
@@ -81,7 +82,7 @@ class HeapSimulator {
     bool alloc_constants;
     // If 'buffers_to_assign' is provided, only those buffers are assigned
     // offsets, otherwise all buffers defined by the instructions are assigned.
-    const tensorflow::gtl::FlatSet* buffers_to_assign;
+    const BufferValueFlatSet* buffers_to_assign;
   };
 
   // Run the heap simulation with the given algorithm, assuming the given
@@ -97,7 +98,7 @@ class HeapSimulator {
       std::unique_ptr algorithm, const HloModule& module,
       const SequentialHloOrdering::HloModuleSequence& module_sequence,
       const TuplePointsToAnalysis& points_to_analysis,
-      const LogicalBuffer::SizeFunction& size_fn,
+      const BufferValue::SizeFunction& size_fn,
       const Options& options = Options());
 
   // Same as above, but runs on a single computation. The 'instruction_sequence'
@@ -109,7 +110,7 @@ class HeapSimulator {
       const HloComputation& computation,
       const std::vector& instruction_sequence,
       const TuplePointsToAnalysis& points_to_analysis,
-      const LogicalBuffer::SizeFunction& size_fn,
+      const BufferValue::SizeFunction& size_fn,
       const Options& options = Options());
 
  private:
@@ -118,7 +119,7 @@ class HeapSimulator {
   // be run recursively. I.e. the simulation is run over the whole module.
   HeapSimulator(
       std::unique_ptr algorithm,
-      const LogicalBuffer::SizeFunction& size_fn, const Options& options,
+      const BufferValue::SizeFunction& size_fn, const Options& options,
       const SequentialHloOrdering::HloModuleSequence* module_sequence);
   ~HeapSimulator();
 
@@ -127,21 +128,21 @@ class HeapSimulator {
       const std::vector& instruction_sequence,
       const TuplePointsToAnalysis& points_to_analysis);
 
-  bool IgnoreBuffer(const LogicalBuffer* buffer) const;
-  void Alloc(const LogicalBuffer* buffer, const HloInstruction* instruction);
-  void Free(const LogicalBuffer* buffer, const HloInstruction* instruction);
-  void ShareBuffer(const LogicalBuffer* buffer, const LogicalBuffer* shared,
+  bool IgnoreBuffer(const BufferValue* buffer) const;
+  void Alloc(const BufferValue* buffer, const HloInstruction* instruction);
+  void Free(const BufferValue* buffer, const HloInstruction* instruction);
+  void ShareBuffer(const BufferValue* buffer, const BufferValue* shared,
                    const HloInstruction* instruction);
   Result Finish();
 
   void FillDebugTrace(HeapSimulatorTrace::Event::Kind kind,
-                      const LogicalBuffer* buffer,
+                      const BufferValue* buffer,
                       const HloInstruction* instruction,
-                      const LogicalBuffer* shared_with_canonical);
+                      const BufferValue* shared_with_canonical);
 
   const std::unique_ptr no_fragmentation_stats_;
   const std::unique_ptr algorithm_;
-  const LogicalBuffer::SizeFunction size_fn_;
+  const BufferValue::SizeFunction size_fn_;
   const Options options_;
   const SequentialHloOrdering::HloModuleSequence* module_sequence_;
 
@@ -160,15 +161,15 @@ class HeapSimulator {
   // The shared_buffers_ map associates each shared buffer (including the
   // canonical) to its SharedGroup control block.
   struct SharedGroup {
-    const LogicalBuffer* canonical = nullptr;
+    const BufferValue* canonical = nullptr;
     int64 refcount = 0;
   };
-  tensorflow::gtl::FlatMap>
+  tensorflow::gtl::FlatMap>
       shared_buffers_;
 
   // Hold some sets for error-checking the sequence of Alloc and Free calls.
-  tensorflow::gtl::FlatSet allocated_buffers_;
-  tensorflow::gtl::FlatSet freed_buffers_;
+  tensorflow::gtl::FlatSet allocated_buffers_;
+  tensorflow::gtl::FlatSet freed_buffers_;
 
   // Debugging information filled in while the heap simulator runs.
   HeapSimulatorTrace debug_trace_;
@@ -186,10 +187,10 @@ class HeapAlgorithm {
   virtual ~HeapAlgorithm() = default;
 
   // Alloc allocates a buffer of 'size' bytes.
-  virtual void Alloc(const LogicalBuffer* buffer, int64 size) = 0;
+  virtual void Alloc(const BufferValue* buffer, int64 size) = 0;
 
   // Free de-allocates a previously allocated buffer.
-  virtual void Free(const LogicalBuffer* buffer, int64 size) = 0;
+  virtual void Free(const BufferValue* buffer, int64 size) = 0;
 
   // Finish collects the buffer offset assignment results.  Free may only be
   // called once, after the Alloc and Free calls.
@@ -205,8 +206,8 @@ class NoFragmentationStatsHeap : public HeapAlgorithm {
   NoFragmentationStatsHeap() = default;
   ~NoFragmentationStatsHeap() override = default;
 
-  void Alloc(const LogicalBuffer* buffer, int64 size) override;
-  void Free(const LogicalBuffer* buffer, int64 size) override;
+  void Alloc(const BufferValue* buffer, int64 size) override;
+  void Free(const BufferValue* buffer, int64 size) override;
   Result Finish() override;
 
  private:
@@ -223,14 +224,14 @@ class DecreasingSizeRunsHeap : public HeapAlgorithm {
       : algorithm_(std::move(algorithm)) {}
   ~DecreasingSizeRunsHeap() override {}
 
-  void Alloc(const LogicalBuffer* buffer, int64 size) override;
-  void Free(const LogicalBuffer* buffer, int64 size) override;
+  void Alloc(const BufferValue* buffer, int64 size) override;
+  void Free(const BufferValue* buffer, int64 size) override;
   Result Finish() override;
 
  private:
   // A single Alloc or Free operation that we've buffered in run_.
   struct Op {
-    const LogicalBuffer* buffer;
+    const BufferValue* buffer;
     int64 size;
   };
 
@@ -266,8 +267,8 @@ class LazyBestFitHeap : public HeapAlgorithm {
   LazyBestFitHeap(int64 alignment) : alignment_(alignment) {}
   ~LazyBestFitHeap() override {}
 
-  void Alloc(const LogicalBuffer* buffer, int64 size) override;
-  void Free(const LogicalBuffer* buffer, int64 size) override;
+  void Alloc(const BufferValue* buffer, int64 size) override;
+  void Free(const BufferValue* buffer, int64 size) override;
   Result Finish() override;
 
  private:
diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc
index fd56a603bb6f84..6271652412c297 100644
--- a/tensorflow/compiler/xla/service/heap_simulator_test.cc
+++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc
@@ -25,7 +25,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/hlo_ordering.h"
-#include "tensorflow/compiler/xla/service/logical_buffer.h"
+#include "tensorflow/compiler/xla/service/hlo_value.h"
 #include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h"
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/tests/hlo_test_base.h"
@@ -39,7 +39,7 @@ const char kFree[] = "Free";
 const char kFinish[] = "Finish";
 
 // CallSequence records a sequence of Alloc/Free/Finish calls.
-using CallSequence = std::vector>;
+using CallSequence = std::vector>;
 
 // HeapCallRecorder is a dummy heap algorithm that simply records its calls.
 class HeapCallRecorder : public HeapAlgorithm {
@@ -47,7 +47,7 @@ class HeapCallRecorder : public HeapAlgorithm {
   explicit HeapCallRecorder(CallSequence* calls) : calls_(calls) {}
   ~HeapCallRecorder() override {}
 
-  void Alloc(const LogicalBuffer* buffer, int64 size) override {
+  void Alloc(const BufferValue* buffer, int64 size) override {
     calls_->emplace_back(kAlloc, buffer);
     // Instead of assigning a real offset, we set the cardinality of the Alloc
     // call.  This isn't a valid assignment, but allows us to easily test for
@@ -55,7 +55,7 @@ class HeapCallRecorder : public HeapAlgorithm {
     const int64 offset = result_.chunk_map.size();
     result_.chunk_map.emplace(buffer, Chunk{offset, size});
   }
-  void Free(const LogicalBuffer* buffer, int64 size) override {
+  void Free(const BufferValue* buffer, int64 size) override {
     calls_->emplace_back(kFree, buffer);
   }
   Result Finish() override {
@@ -118,7 +118,7 @@ class HeapSimulatorTracker {
 
     // Hack the size_fn so that it returns a decreasing value as we step through
     // the sequence. This lets us ensure the Alloc calls are in the sequence
-    // order. The Free calls are sorted by LogicalBuffer.id, which is at least
+    // order. The Free calls are sorted by BufferValue.id, which is at least
     // deterministic.
     auto size_fn = [&reverse_position](const BufferValue& buffer) {
       return reverse_position[buffer.instruction()];
@@ -133,8 +133,8 @@ class HeapSimulatorTracker {
   HloModule* module() { return module_.get(); }
 
   // Returns the buffer defined at the given instruction and index.
-  const LogicalBuffer* BufferAt(const HloInstruction* instruction,
-                                const ShapeIndex& index) const {
+  const BufferValue* BufferAt(const HloInstruction* instruction,
+                              const ShapeIndex& index) const {
     return points_to_analysis_->GetBufferDefinedAt(instruction, index)
         .ConsumeValueOrDie();
   }
@@ -150,8 +150,8 @@ class HeapSimulatorTracker {
                            const ShapeIndex& index_a,
                            const HloInstruction* instruction_b,
                            const ShapeIndex& index_b) {
-    const LogicalBuffer* a = BufferAt(instruction_a, index_a);
-    const LogicalBuffer* b = BufferAt(instruction_b, index_b);
+    const BufferValue* a = BufferAt(instruction_a, index_a);
+    const BufferValue* b = BufferAt(instruction_b, index_b);
     EXPECT_EQ(result_.chunk_map[a].offset, result_.chunk_map[b].offset)
         << *a << ", " << *b;
   }
@@ -525,7 +525,7 @@ TEST_F(HeapSimulatorTest, WholeModule) {
       // Now the final cond less-than buffer is allocated.
       {kAlloc, tracker.BufferAt(cond_lt, {})},
 
-      // The order of the remaining Free calls is based on the LogicalBuffer.id,
+      // The order of the remaining Free calls is based on the BufferValue.id,
       // which is deterministic, but not obvious.
       {kFree, tracker.BufferAt(param, {})},
       {kFree, tracker.BufferAt(param, {0})},
@@ -547,40 +547,40 @@ TEST_F(HeapSimulatorTest, WholeModule) {
 class HeapAlgorithmTestBase : public ::testing::Test {
  protected:
   HeapAlgorithmTestBase() : builder_("heap_simulator_test") {
-    buffer_a_ = DummyLogicalBuffer();
-    buffer_b_ = DummyLogicalBuffer();
-    buffer_c_ = DummyLogicalBuffer();
-    buffer_d_ = DummyLogicalBuffer();
-    buffer_e_ = DummyLogicalBuffer();
-    buffer_f_ = DummyLogicalBuffer();
-    buffer_g_ = DummyLogicalBuffer();
-    buffer_h_ = DummyLogicalBuffer();
-    buffer_i_ = DummyLogicalBuffer();
+    buffer_a_ = DummyBufferValue();
+    buffer_b_ = DummyBufferValue();
+    buffer_c_ = DummyBufferValue();
+    buffer_d_ = DummyBufferValue();
+    buffer_e_ = DummyBufferValue();
+    buffer_f_ = DummyBufferValue();
+    buffer_g_ = DummyBufferValue();
+    buffer_h_ = DummyBufferValue();
+    buffer_i_ = DummyBufferValue();
   }
   ~HeapAlgorithmTestBase() override {}
 
-  const LogicalBuffer* buffer_a_;
-  const LogicalBuffer* buffer_b_;
-  const LogicalBuffer* buffer_c_;
-  const LogicalBuffer* buffer_d_;
-  const LogicalBuffer* buffer_e_;
-  const LogicalBuffer* buffer_f_;
-  const LogicalBuffer* buffer_g_;
-  const LogicalBuffer* buffer_h_;
-  const LogicalBuffer* buffer_i_;
+  const BufferValue* buffer_a_;
+  const BufferValue* buffer_b_;
+  const BufferValue* buffer_c_;
+  const BufferValue* buffer_d_;
+  const BufferValue* buffer_e_;
+  const BufferValue* buffer_f_;
+  const BufferValue* buffer_g_;
+  const BufferValue* buffer_h_;
+  const BufferValue* buffer_i_;
 
  private:
-  // Create a dummy LogicalBuffer to pass to the heap algorithm.
-  const LogicalBuffer* DummyLogicalBuffer() {
-    const LogicalBuffer::Id id = buffers_.size();
+  // Create a dummy BufferValue to pass to the heap algorithm.
+  const BufferValue* DummyBufferValue() {
+    const BufferValue::Id id = buffers_.size();
     auto const0 = builder_.AddInstruction(
         HloInstruction::CreateConstant(Literal::CreateR0(1.0)));
-    buffers_.emplace_back(MakeUnique(const0, ShapeIndex{}, id));
+    buffers_.emplace_back(MakeUnique(id, const0, ShapeIndex{}));
     return buffers_.back().get();
   }
 
   HloComputation::Builder builder_;
-  std::vector> buffers_;
+  std::vector> buffers_;
 };
 
 class NoFragmentationStatsHeapTest : public HeapAlgorithmTestBase {};

From 398a62037eb5f0aa049d3243818d16f2b3a10dec Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 12:55:55 -0700
Subject: [PATCH 0682/1691] Reads the L2 and L3 cache sizes from the system
 instead of using hard-coded constants.

PiperOrigin-RevId: 196296096
---
 tensorflow/core/kernels/conv_grad_filter_ops.cc |  3 +--
 tensorflow/core/kernels/conv_grad_input_ops.cc  |  5 ++---
 tensorflow/core/kernels/deep_conv2d.cc          | 10 ++++------
 3 files changed, 7 insertions(+), 11 deletions(-)

diff --git a/tensorflow/core/kernels/conv_grad_filter_ops.cc b/tensorflow/core/kernels/conv_grad_filter_ops.cc
index aca75176a565dd..bdd08222d40a76 100644
--- a/tensorflow/core/kernels/conv_grad_filter_ops.cc
+++ b/tensorflow/core/kernels/conv_grad_filter_ops.cc
@@ -404,10 +404,9 @@ class Conv2DCustomBackpropFilterOp : public OpKernel {
     // image ('work_unit_size').
 
     // TODO(andydavis)
-    // *) Get L3 cache size from device at runtime (30MB is from ivybridge).
     // *) Consider reducing 'target_working_set_size' if L3 is shared by
     //    other concurrently running tensorflow ops.
-    const size_t target_working_set_size = (30LL << 20) / sizeof(T);
+    const size_t target_working_set_size = Eigen::l3CacheSize() / sizeof(T);
 
     const size_t size_A = output_image_size * filter_total_size;
 
diff --git a/tensorflow/core/kernels/conv_grad_input_ops.cc b/tensorflow/core/kernels/conv_grad_input_ops.cc
index 63a775afa8bd69..95301b170fb6f2 100644
--- a/tensorflow/core/kernels/conv_grad_input_ops.cc
+++ b/tensorflow/core/kernels/conv_grad_input_ops.cc
@@ -420,9 +420,8 @@ class Conv2DCustomBackpropInputOp : public OpKernel {
     const int output_image_size =
         dims.spatial_dims[0].output_size * dims.spatial_dims[1].output_size;
 
-    // TODO(andydavis) Get L2/L3 cache sizes from device.
-    const size_t l2_cache_size = 256LL << 10;
-    const size_t l3_cache_size = 30LL << 20;
+    const size_t l2_cache_size = Eigen::l2CacheSize();
+    const size_t l3_cache_size = Eigen::l3CacheSize();
 
     // Use L3 cache size as target working set size.
     const size_t target_working_set_size = l3_cache_size / sizeof(T);
diff --git a/tensorflow/core/kernels/deep_conv2d.cc b/tensorflow/core/kernels/deep_conv2d.cc
index 829155fb313bd3..014684de642764 100644
--- a/tensorflow/core/kernels/deep_conv2d.cc
+++ b/tensorflow/core/kernels/deep_conv2d.cc
@@ -393,9 +393,8 @@ struct TransformFilters {
 
     // Calculate filter transform batch based on cache/filter sizes.
 
-    // Cache budget (based on L2 cache size = 256KB).
-    // TODO(andydavis) Read cache size from system.
-    const int64 cache_size = (256LL << 10) / sizeof(T);
+    // Cache budget (based on L2 cache size).
+    const int64 cache_size = Eigen::l2CacheSize() / sizeof(T);
 
     // Fixed cost.
     const int64 filter_transform_matrix_size =
@@ -1017,9 +1016,8 @@ struct DeepConv2D {
       const int64 filter_shard_size = filter_shards_row * filter_shards_col;
       const int64 out_tile_spatial_size = out_tile_rows * out_tile_cols;
 
-      // Cache budget (based on L2 cache size = 256KB).
-      // TODO(andydavis) Read cache size from the system.
-      const int64 cache_size = (256LL << 10) / sizeof(T);
+      // Cache budget (based on L2 cache size).
+      const int64 cache_size = Eigen::l2CacheSize() / sizeof(T);
 
       // Fixed costs.
       const int64 tile_transform_matrix_size =

From 815e02963bbec52626bf86b88773cdbb0aeb25a6 Mon Sep 17 00:00:00 2001
From: Alexandre Passos 
Date: Fri, 11 May 2018 13:42:31 -0700
Subject: [PATCH 0683/1691] Allow zero initializer by default for string
 variables (no reason not to)

PiperOrigin-RevId: 196302302
---
 tensorflow/python/kernel_tests/variable_scope_test.py | 7 +++++++
 tensorflow/python/ops/variable_scope.py               | 3 ++-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/kernel_tests/variable_scope_test.py b/tensorflow/python/kernel_tests/variable_scope_test.py
index 51aa671098905e..9dc4ec0f9625cc 100644
--- a/tensorflow/python/kernel_tests/variable_scope_test.py
+++ b/tensorflow/python/kernel_tests/variable_scope_test.py
@@ -40,6 +40,7 @@
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as variables_lib
 from tensorflow.python.platform import test
+from tensorflow.python.util import compat
 
 
 class VariableScopeTest(test.TestCase):
@@ -110,6 +111,12 @@ def testVarScopeConstraint(self):
         w = variable_scope.get_variable("w", [])
         self.assertEqual(w.constraint, constraint)
 
+  def testStringDefaultInitializer(self):
+    with self.test_session():
+      v = variable_scope.get_variable("string", shape=[], dtype=dtypes.string)
+      variables_lib.global_variables_initializer().run()
+      self.assertAllEqual(compat.as_bytes(v.eval()), b"")
+
   @test_util.run_in_graph_and_eager_modes()
   def testVarScopeDType(self):
     with variable_scope.variable_scope("tower2") as tower:
diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index f5970fdbb29f75..d79d8c8babd309 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -840,7 +840,8 @@ def _get_default_initializer(self, name, shape=None, dtype=dtypes.float32):
       initializing_from_value = False
     # If dtype is DT_INT/DT_UINT, provide a default value `zero`
     # If dtype is DT_BOOL, provide a default value `FALSE`
-    elif dtype.is_integer or dtype.is_unsigned or dtype.is_bool:
+    elif (dtype.is_integer or dtype.is_unsigned or dtype.is_bool
+          or dtype == dtypes.string):
       initializer = init_ops.zeros_initializer()
       initializing_from_value = False
     # NOTES:Do we need to support for handling DT_STRING and DT_COMPLEX here?

From e8dbaff96389ecefd8f84d4c3ce3fce18e876cca Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Fri, 11 May 2018 14:05:38 -0700
Subject: [PATCH 0684/1691] Make the elemental ir emitter for dot operations
 respect contraction dims

PiperOrigin-RevId: 196305803
---
 tensorflow/compiler/xla/service/BUILD         | 19 ++++++
 .../xla/service/elemental_ir_emitter.cc       | 16 +++--
 .../xla/service/elemental_ir_emitter_test.cc  | 65 +++++++++++++++++++
 3 files changed, 94 insertions(+), 6 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/elemental_ir_emitter_test.cc

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index f6af8163154d03..f1e57f3b6f3608 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -12,6 +12,7 @@ package_group(
     ],
 )
 
+load("//tensorflow/compiler/xla/tests:build_defs.bzl", "xla_test")
 load("//tensorflow/compiler/xla:xla.bzl", "xla_proto_library")
 load("//tensorflow:tensorflow.bzl", "tf_cc_test")
 load("//tensorflow:tensorflow.bzl", "tf_cc_binary")
@@ -2371,6 +2372,24 @@ cc_library(
     ],
 )
 
+xla_test(
+    name = "elemental_ir_emitter_test",
+    srcs = ["elemental_ir_emitter_test.cc"],
+    backends = [
+        "cpu",
+        "gpu",
+    ],
+    deps = [
+        "//tensorflow/compiler/xla:execution_options_util",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:test",
+        "//tensorflow/compiler/xla/tests:client_library_test_base",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
+        "//tensorflow/compiler/xla/tests:xla_internal_test_main",
+        "//tensorflow/compiler/xla/tools/parser:hlo_parser",
+    ],
+)
+
 cc_library(
     name = "hlo_module_config",
     srcs = ["hlo_module_config.cc"],
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index f2ad6eaf3ac405..0a400e982ad50e 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -1863,8 +1863,13 @@ StatusOr ElementalIrEmitter::EmitElementalDot(
     const llvm_ir::IrArray::Index& dot_result_index) const {
   auto lhs_generator = operand_to_generator.at(hlo->operand(0));
   auto rhs_generator = operand_to_generator.at(hlo->operand(1));
-  int64 contracted_dim_size = hlo->operand(0)->shape().dimensions(
-      hlo->operand(0)->shape().dimensions_size() - 1);
+
+  const DotDimensionNumbers& dim_numbers = hlo->dot_dimension_numbers();
+  int64 lhs_contracting_dim = dim_numbers.lhs_contracting_dimensions(0);
+  int64 rhs_contracting_dim = dim_numbers.rhs_contracting_dimensions(0);
+
+  int64 contracted_dim_size =
+      hlo->operand(0)->shape().dimensions(lhs_contracting_dim);
   int64 lhs_dims = hlo->operand(0)->shape().dimensions_size();
   int64 rhs_dims = hlo->operand(1)->shape().dimensions_size();
 
@@ -1895,13 +1900,12 @@ StatusOr ElementalIrEmitter::EmitElementalDot(
   for (int64 i = 0; i < lhs_dims - 1; i++) {
     lhs_index.push_back(dot_result_index[i]);
   }
-  lhs_index.push_back(inner_loop->GetIndVarValue());
+  lhs_index.InsertAt(lhs_contracting_dim, inner_loop->GetIndVarValue());
 
-  for (int64 i = 0; i < rhs_dims - 2; i++) {
+  for (int64 i = 0; i < rhs_dims - 1; i++) {
     rhs_index.push_back(dot_result_index[lhs_dims - 1 + i]);
   }
-  rhs_index.push_back(inner_loop->GetIndVarValue());
-  rhs_index.push_back(dot_result_index.back());
+  rhs_index.InsertAt(rhs_contracting_dim, inner_loop->GetIndVarValue());
 
   llvm::Value* current_accumulator =
       ir_builder_->CreateLoad(accumulator_alloca);
diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter_test.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter_test.cc
new file mode 100644
index 00000000000000..b43dc0c65d9b6e
--- /dev/null
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter_test.cc
@@ -0,0 +1,65 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/execution_options_util.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/tests/client_library_test_base.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+#include "tensorflow/compiler/xla/tests/test_macros.h"
+#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h"
+
+namespace xla {
+namespace {
+
+using tensorflow::gtl::nullopt;
+
+class ElementalIrEmitterExecutionTest : public HloTestBase {
+ protected:
+  void RunTest(const string& hlo_text,
+               tensorflow::gtl::ArraySlice args) {
+    HloModuleConfig config;
+    config.set_debug_options(GetDebugOptionsForTest());
+    TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module,
+                            tools::Parse(hlo_text, config));
+    EXPECT_TRUE(RunAndCompareNoHloPasses(std::move(module), args, nullopt));
+  }
+};
+
+XLA_TEST_F(ElementalIrEmitterExecutionTest, DotFusion) {
+  const string hlo_text = R"(
+HloModule FusedDot
+
+fused_computation {
+  arg0 = s32[1,2,1]{2,1,0} parameter(0)
+  reshape.lhs = s32[2,1]{1,0} reshape(arg0)
+  arg1 = s32[1,2,1]{2,1,0} parameter(1)
+  reshape.rhs = s32[2,1]{1,0} reshape(arg1)
+  ROOT dot = s32[1,1]{1,0} dot(reshape.lhs, reshape.rhs), lhs_contracting_dims={0}, rhs_contracting_dims={0}
+}
+
+ENTRY main {
+  entry_arg0 = s32[1,2,1]{2,1,0} parameter(0)
+  entry_arg1 = s32[1,2,1]{2,1,0} parameter(1)
+  ROOT fusion = s32[1,1]{1,0} fusion(entry_arg0, entry_arg1), kind=kLoop, calls=fused_computation
+}
+)";
+
+  std::unique_ptr lhs = Literal::CreateR3({{{1}, {2}}});
+  std::unique_ptr rhs = Literal::CreateR3({{{3}, {4}}});
+  RunTest(hlo_text, {lhs.get(), rhs.get()});
+}
+}  // namespace
+}  // namespace xla

From ddb8fe491faccfdf219a5d9b7ba959c98ae38f33 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 14:24:47 -0700
Subject: [PATCH 0685/1691] Add some python wrapper for TF_ApiDefMap.

PiperOrigin-RevId: 196308677
---
 tensorflow/python/BUILD                       | 13 +++++
 tensorflow/python/framework/c_api_util.py     | 46 ++++++++++++++++
 .../python/framework/c_api_util_test.py       | 55 +++++++++++++++++++
 3 files changed, 114 insertions(+)
 create mode 100644 tensorflow/python/framework/c_api_util_test.py

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index cc96d5aee5fe79..ea11b701ba16c9 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -627,6 +627,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":pywrap_tensorflow",
+        "//tensorflow/core:protos_all_py",
     ],
 )
 
@@ -3971,6 +3972,18 @@ cuda_py_test(
     tags = ["noguitar"],
 )
 
+py_test(
+    name = "c_api_util_test",
+    size = "small",
+    srcs = ["framework/c_api_util_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":c_api_util",
+        ":framework_test_lib",
+        ":platform_test",
+    ],
+)
+
 py_test(
     name = "graph_util_test",
     size = "small",
diff --git a/tensorflow/python/framework/c_api_util.py b/tensorflow/python/framework/c_api_util.py
index 7bbe3183dfa376..aff289f7be08e2 100644
--- a/tensorflow/python/framework/c_api_util.py
+++ b/tensorflow/python/framework/c_api_util.py
@@ -19,6 +19,8 @@
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.core.framework import api_def_pb2
+from tensorflow.core.framework import op_def_pb2
 from tensorflow.python import pywrap_tensorflow as c_api
 from tensorflow.python.util import compat
 from tensorflow.python.util import tf_contextlib
@@ -89,6 +91,50 @@ def __del__(self):
       c_api.TF_DeleteFunction(self.func)
 
 
+class ApiDefMap(object):
+  """Wrapper around Tf_ApiDefMap that handles querying and deletion.
+
+  The OpDef protos are also stored in this class so that they could
+  be queried by op name.
+  """
+
+  def __init__(self):
+    op_def_proto = op_def_pb2.OpList()
+    buf = c_api.TF_GetAllOpList()
+    try:
+      op_def_proto.ParseFromString(c_api.TF_GetBuffer(buf))
+      self._api_def_map = c_api.TF_NewApiDefMap(buf)
+    finally:
+      c_api.TF_DeleteBuffer(buf)
+
+    self._op_per_name = {}
+    for op in op_def_proto.op:
+      self._op_per_name[op.name] = op
+
+  def __del__(self):
+    # Note: when we're destructing the global context (i.e when the process is
+    # terminating) we can have already deleted other modules.
+    if c_api is not None and c_api.TF_DeleteApiDefMap is not None:
+      c_api.TF_DeleteApiDefMap(self._api_def_map)
+
+  def put_api_def(self, text):
+    c_api.TF_ApiDefMapPut(self._api_def_map, text, len(text))
+
+  def get_api_def(self, op_name):
+    api_def_proto = api_def_pb2.ApiDef()
+    buf = c_api.TF_ApiDefMapGet(self._api_def_map, op_name, len(op_name))
+    try:
+      api_def_proto.ParseFromString(c_api.TF_GetBuffer(buf))
+    finally:
+      c_api.TF_DeleteBuffer(buf)
+    return api_def_proto
+
+  def get_op_def(self, op_name):
+    if op_name in self._op_per_name:
+      return self._op_per_name[op_name]
+    raise ValueError("No entry found for " + op_name + ".")
+
+
 @tf_contextlib.contextmanager
 def tf_buffer(data=None):
   """Context manager that creates and deletes TF_Buffer.
diff --git a/tensorflow/python/framework/c_api_util_test.py b/tensorflow/python/framework/c_api_util_test.py
new file mode 100644
index 00000000000000..e0bc9ee531669e
--- /dev/null
+++ b/tensorflow/python/framework/c_api_util_test.py
@@ -0,0 +1,55 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for c_api utils."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import c_api_util
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import googletest
+
+
+class ApiDefMapTest(test_util.TensorFlowTestCase):
+
+  def testApiDefMapGet(self):
+    api_def_map = c_api_util.ApiDefMap()
+    op_def = api_def_map.get_op_def("Add")
+    self.assertEqual(op_def.name, "Add")
+    api_def = api_def_map.get_api_def("Add")
+    self.assertEqual(api_def.graph_op_name, "Add")
+
+  def testApiDefMapPutThenGet(self):
+    api_def_map = c_api_util.ApiDefMap()
+    api_def_text = """
+op {
+  graph_op_name: "Add"
+  summary: "Returns x + y element-wise."
+  description: <
Date: Fri, 11 May 2018 14:45:36 -0700
Subject: [PATCH 0686/1691] Checkpointable: Add UniqueNameTracker for managing
 dependencies on arbitrarily named objects

Makes generating object-unique dependency names easier, which will hopefully discourage people from using Graph-global names with Checkpointable.

PiperOrigin-RevId: 196311633
---
 tensorflow/contrib/checkpoint/__init__.py     |  11 +-
 tensorflow/contrib/checkpoint/python/BUILD    |  23 ++++
 .../contrib/checkpoint/python/containers.py   |  77 ++++++++++++++
 .../checkpoint/python/containers_test.py      | 100 ++++++++++++++++++
 4 files changed, 208 insertions(+), 3 deletions(-)
 create mode 100644 tensorflow/contrib/checkpoint/python/containers.py
 create mode 100644 tensorflow/contrib/checkpoint/python/containers_test.py

diff --git a/tensorflow/contrib/checkpoint/__init__.py b/tensorflow/contrib/checkpoint/__init__.py
index e529b25b3caa1e..c5f7072aea955f 100644
--- a/tensorflow/contrib/checkpoint/__init__.py
+++ b/tensorflow/contrib/checkpoint/__init__.py
@@ -14,22 +14,27 @@
 # ==============================================================================
 """Tools for working with object-based checkpoints.
 
-
-For creating and managing dependencies:
-@@CheckpointableObjectGraph
+Visualization and inspection:
 @@dot_graph_from_checkpoint
 @@object_metadata
+
+Creating and managing dependencies:
+@@Checkpointable
+@@CheckpointableObjectGraph
 @@NoDependency
 @@split_dependency
+@@UniqueNameTracker
 """
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.checkpoint.python.containers import UniqueNameTracker
 from tensorflow.contrib.checkpoint.python.split_dependency import split_dependency
 from tensorflow.contrib.checkpoint.python.visualize import dot_graph_from_checkpoint
 from tensorflow.core.protobuf.checkpointable_object_graph_pb2 import CheckpointableObjectGraph
+from tensorflow.python.training.checkpointable import Checkpointable
 from tensorflow.python.training.checkpointable import NoDependency
 from tensorflow.python.training.checkpointable_utils import object_metadata
 
diff --git a/tensorflow/contrib/checkpoint/python/BUILD b/tensorflow/contrib/checkpoint/python/BUILD
index a5681ffa61d07e..cbb9852ccf24aa 100644
--- a/tensorflow/contrib/checkpoint/python/BUILD
+++ b/tensorflow/contrib/checkpoint/python/BUILD
@@ -8,11 +8,34 @@ py_library(
     name = "checkpoint",
     srcs_version = "PY2AND3",
     deps = [
+        ":containers",
         ":split_dependency",
         ":visualize",
     ],
 )
 
+py_library(
+    name = "containers",
+    srcs = ["containers.py"],
+    srcs_version = "PY2AND3",
+    visibility = ["//tensorflow:internal"],
+    deps = ["//tensorflow/python:checkpointable"],
+)
+
+py_test(
+    name = "containers_test",
+    srcs = ["containers_test.py"],
+    deps = [
+        ":containers",
+        "//tensorflow/python:checkpointable",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:resource_variable_ops",
+        "//tensorflow/python:training",
+        "@six_archive//:six",
+    ],
+)
+
 py_library(
     name = "split_dependency",
     srcs = ["split_dependency.py"],
diff --git a/tensorflow/contrib/checkpoint/python/containers.py b/tensorflow/contrib/checkpoint/python/containers.py
new file mode 100644
index 00000000000000..82aa04e38fb6f3
--- /dev/null
+++ b/tensorflow/contrib/checkpoint/python/containers.py
@@ -0,0 +1,77 @@
+"""Checkpointable data structures."""
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.training import checkpointable as checkpointable_lib
+
+
+class UniqueNameTracker(checkpointable_lib.CheckpointableBase):
+  """Adds dependencies on checkpointable objects with name hints.
+
+  Useful for creating dependencies with locally unique names.
+
+  Example usage:
+  ```python
+  class SlotManager(tf.contrib.checkpoint.Checkpointable):
+
+    def __init__(self):
+      # Create a dependency named "slotdeps" on the container.
+      self.slotdeps = tf.contrib.checkpoint.UniqueNameTracker()
+      slotdeps = self.slotdeps
+      slots = []
+      slots.append(slotdeps.track(tfe.Variable(3.), "x"))  # Named "x"
+      slots.append(slotdeps.track(tfe.Variable(4.), "y"))
+      slots.append(slotdeps.track(tfe.Variable(5.), "x"))  # Named "x_1"
+  ```
+  """
+
+  def __init__(self):
+    self._maybe_initialize_checkpointable()
+    self._name_counts = {}
+
+  def track(self, checkpointable, base_name):
+    """Add a dependency on `checkpointable`.
+
+    Args:
+      checkpointable: An object to add a checkpoint dependency on.
+      base_name: A name hint, which is uniquified to determine the dependency
+        name.
+    Returns:
+      `checkpointable`, for chaining.
+    Raises:
+      ValueError: If `checkpointable` is not a checkpointable object.
+    """
+
+    if not isinstance(checkpointable, checkpointable_lib.CheckpointableBase):
+      raise ValueError(
+          ("Expected a checkpointable value, got %s which does not inherit "
+           "from CheckpointableBase.") % (checkpointable,))
+
+    def _format_name(prefix, number):
+      if number > 0:
+        return "%s_%d" % (prefix, number)
+      else:
+        return prefix
+
+    count = self._name_counts.get(base_name, 0)
+    candidate = _format_name(base_name, count)
+    while self._lookup_dependency(candidate) is not None:
+      count += 1
+      candidate = _format_name(base_name, count)
+    self._name_counts[base_name] = count + 1
+    return self._track_checkpointable(checkpointable, name=candidate)
diff --git a/tensorflow/contrib/checkpoint/python/containers_test.py b/tensorflow/contrib/checkpoint/python/containers_test.py
new file mode 100644
index 00000000000000..15775f4cb3fd33
--- /dev/null
+++ b/tensorflow/contrib/checkpoint/python/containers_test.py
@@ -0,0 +1,100 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+import six
+
+from tensorflow.contrib.checkpoint.python import containers
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import resource_variable_ops
+from tensorflow.python.platform import test
+from tensorflow.python.training import checkpointable
+from tensorflow.python.training import checkpointable_utils
+from tensorflow.python.training.checkpointable_utils import object_metadata
+
+
+class UniqueNameTrackerTests(test.TestCase):
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testNames(self):
+    checkpoint_directory = self.get_temp_dir()
+    checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+
+    x1 = resource_variable_ops.ResourceVariable(2.)
+    x2 = resource_variable_ops.ResourceVariable(3.)
+    x3 = resource_variable_ops.ResourceVariable(4.)
+    y = resource_variable_ops.ResourceVariable(5.)
+    slots = containers.UniqueNameTracker()
+    slots.track(x1, "x")
+    slots.track(x2, "x")
+    slots.track(x3, "x_1")
+    slots.track(y, "y")
+    self.evaluate((x1.initializer, x2.initializer, x3.initializer,
+                   y.initializer))
+    save_root = checkpointable_utils.Checkpoint(slots=slots)
+    save_path = save_root.save(checkpoint_prefix)
+
+    restore_slots = checkpointable.Checkpointable()
+    restore_root = checkpointable_utils.Checkpoint(
+        slots=restore_slots)
+    status = restore_root.restore(save_path)
+    restore_slots.x = resource_variable_ops.ResourceVariable(0.)
+    restore_slots.x_1 = resource_variable_ops.ResourceVariable(0.)
+    restore_slots.x_1_1 = resource_variable_ops.ResourceVariable(0.)
+    restore_slots.y = resource_variable_ops.ResourceVariable(0.)
+    status.assert_consumed().run_restore_ops()
+    self.assertEqual(2., self.evaluate(restore_slots.x))
+    self.assertEqual(3., self.evaluate(restore_slots.x_1))
+    self.assertEqual(4., self.evaluate(restore_slots.x_1_1))
+    self.assertEqual(5., self.evaluate(restore_slots.y))
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testExample(self):
+    class SlotManager(checkpointable.Checkpointable):
+
+      def __init__(self):
+        self.slotdeps = containers.UniqueNameTracker()
+        slotdeps = self.slotdeps
+        slots = []
+        slots.append(slotdeps.track(
+            resource_variable_ops.ResourceVariable(3.), "x"))
+        slots.append(slotdeps.track(
+            resource_variable_ops.ResourceVariable(4.), "y"))
+        slots.append(slotdeps.track(
+            resource_variable_ops.ResourceVariable(5.), "x"))
+        self.slots = slots
+
+    manager = SlotManager()
+    self.evaluate([v.initializer for v in manager.slots])
+    checkpoint = checkpointable_utils.Checkpoint(slot_manager=manager)
+    checkpoint_directory = self.get_temp_dir()
+    checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+    save_path = checkpoint.save(checkpoint_prefix)
+    metadata = object_metadata(save_path)
+    dependency_names = []
+    for node in metadata.nodes:
+      for child in node.children:
+        dependency_names.append(child.local_name)
+    six.assertCountEqual(
+        self,
+        dependency_names,
+        ["x", "x_1", "y", "slot_manager", "slotdeps", "save_counter"])
+
+if __name__ == "__main__":
+  test.main()

From 81a162301830a02d72184a996c2abdde9b9b149a Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Fri, 11 May 2018 15:02:15 -0700
Subject: [PATCH 0687/1691] [TF:XLA] Bump open source llvm revision to r332085

PiperOrigin-RevId: 196314181
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index fc65f4407eacb2..ea31df0e06df04 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -453,11 +453,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/d80aa1ad9d98bf74aca1527475556bb0d3485386.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/d80aa1ad9d98bf74aca1527475556bb0d3485386.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/a915f005cd63fd111bbca510236a5163a7e83576.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/a915f005cd63fd111bbca510236a5163a7e83576.tar.gz",
       ],
-      sha256 = "4dfb3e8acb68b0557bc9ffb9745c922f0e9f7e299901af1bb69930a3b9806648",
-      strip_prefix = "llvm-d80aa1ad9d98bf74aca1527475556bb0d3485386",
+      sha256 = "1c81ec0f843ea2c9369ccfa1c1b20023dc9a999bf075ae192fcb89e23896d929",
+      strip_prefix = "llvm-a915f005cd63fd111bbca510236a5163a7e83576",
       build_file = clean_dep("//third_party/llvm:llvm.BUILD"),
   )
 

From 95f12f9bd5e8f73a67d534a608a384fe73729dad Mon Sep 17 00:00:00 2001
From: Sanjoy Das 
Date: Fri, 11 May 2018 15:02:33 -0700
Subject: [PATCH 0688/1691] Remove degenerate batch dimensions form batch dot

The way things are set up today this specific optimization isn't particularly
important, but I want to implement a follow-on optimization in
BatchDotSimplification to transform (non-degenerate) batch GEMV operations into
GEMM which I'm expecting to help us a bit.

This would normally be in the algebraic simplifier, but we want to fixpoint this
pass before we run DotDecomposer.  This will become more important when we
implement the (non-degenerate) batch GEMV operations -> GEMM transform.

PiperOrigin-RevId: 196314230
---
 tensorflow/compiler/xla/service/BUILD         |  42 +++++
 .../xla/service/batch_dot_simplification.cc   |  99 +++++++++++
 .../xla/service/batch_dot_simplification.h    |  39 ++++
 .../service/batch_dot_simplification_test.cc  | 168 ++++++++++++++++++
 tensorflow/compiler/xla/service/cpu/BUILD     |   1 +
 .../compiler/xla/service/cpu/cpu_compiler.cc  |   2 +
 .../xla/service/hlo_creation_utils.cc         |  11 ++
 .../compiler/xla/service/hlo_creation_utils.h |   5 +
 8 files changed, 367 insertions(+)
 create mode 100644 tensorflow/compiler/xla/service/batch_dot_simplification.cc
 create mode 100644 tensorflow/compiler/xla/service/batch_dot_simplification.h
 create mode 100644 tensorflow/compiler/xla/service/batch_dot_simplification_test.cc

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index f1e57f3b6f3608..5b70bf31957775 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1362,6 +1362,48 @@ tf_cc_test(
     ],
 )
 
+cc_library(
+    name = "batch_dot_simplification",
+    srcs = ["batch_dot_simplification.cc"],
+    hdrs = ["batch_dot_simplification.h"],
+    deps = [
+        ":hlo",
+        ":hlo_creation_utils",
+        ":hlo_pass",
+        "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:window_util",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/core:lib",
+    ],
+)
+
+tf_cc_test(
+    name = "batch_dot_simplification_test",
+    srcs = ["batch_dot_simplification_test.cc"],
+    deps = [
+        ":batch_dot_simplification",
+        ":hlo",
+        ":hlo_matchers",
+        ":hlo_pass",
+        "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:test",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:window_util",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
+        "//tensorflow/compiler/xla/tests:hlo_verified_test_base",
+        "//tensorflow/compiler/xla/tests:xla_internal_test_main",  # fixdeps: keep
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+    ],
+)
+
 tf_cc_test(
     name = "gather_expander_test",
     srcs = ["gather_expander_test.cc"],
diff --git a/tensorflow/compiler/xla/service/batch_dot_simplification.cc b/tensorflow/compiler/xla/service/batch_dot_simplification.cc
new file mode 100644
index 00000000000000..2099916509acdb
--- /dev/null
+++ b/tensorflow/compiler/xla/service/batch_dot_simplification.cc
@@ -0,0 +1,99 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/batch_dot_simplification.h"
+
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_creation_utils.h"
+
+namespace xla {
+StatusOr
+BatchDotSimplification::ElideDegenerateBatchDimensionFromBatchDot(
+    HloInstruction* batch_dot) {
+  const DotDimensionNumbers& dim_numbers = batch_dot->dot_dimension_numbers();
+  HloInstruction *lhs = batch_dot->mutable_operand(0),
+                 *rhs = batch_dot->mutable_operand(1);
+  const Shape& lhs_shape = lhs->shape();
+
+  std::vector degenerate_dims;
+  for (int64 batch_dim : dim_numbers.lhs_batch_dimensions()) {
+    if (lhs_shape.dimensions(batch_dim) == 1) {
+      degenerate_dims.push_back(batch_dim);
+    }
+  }
+
+  if (degenerate_dims.empty()) {
+    return false;
+  }
+
+  TF_ASSIGN_OR_RETURN(HloInstruction * new_lhs,
+                      ElideDegenerateDims(lhs, degenerate_dims));
+  TF_ASSIGN_OR_RETURN(HloInstruction * new_rhs,
+                      ElideDegenerateDims(rhs, degenerate_dims));
+
+  DotDimensionNumbers new_dim_numbers = dim_numbers;
+  new_dim_numbers.clear_lhs_batch_dimensions();
+  new_dim_numbers.clear_rhs_batch_dimensions();
+
+  for (int64 i = 0, e = dim_numbers.lhs_batch_dimensions_size() -
+                        degenerate_dims.size();
+       i < e; i++) {
+    new_dim_numbers.add_lhs_batch_dimensions(i);
+    new_dim_numbers.add_rhs_batch_dimensions(i);
+  }
+
+  new_dim_numbers.set_lhs_contracting_dimensions(
+      0,
+      new_dim_numbers.lhs_contracting_dimensions(0) - degenerate_dims.size());
+  new_dim_numbers.set_rhs_contracting_dimensions(
+      0,
+      new_dim_numbers.rhs_contracting_dimensions(0) - degenerate_dims.size());
+
+  TF_ASSIGN_OR_RETURN(HloInstruction * new_dot,
+                      MakeDotHlo(new_lhs, new_rhs, new_dim_numbers));
+
+  TF_ASSIGN_OR_RETURN(HloInstruction * new_dot_reshaped,
+                      MakeReshapeHlo(batch_dot->shape(), new_dot));
+
+  VLOG(2) << "Replaced " << batch_dot->ToString() << " with "
+          << new_dot->ToString();
+
+  TF_RETURN_IF_ERROR(
+      batch_dot->parent()->ReplaceInstruction(batch_dot, new_dot_reshaped));
+
+  return true;
+}
+
+tensorflow::StringPiece BatchDotSimplification::name() const {
+  return "batch-dot-simplification";
+}
+
+StatusOr BatchDotSimplification::Run(HloModule* module) {
+  bool changed = false;
+  std::vector dot_instrs;
+  for (HloComputation* computation : module->MakeNonfusionComputations()) {
+    c_copy_if(computation->instructions(), std::back_inserter(dot_instrs),
+              [](HloInstruction* instr) {
+                return instr->opcode() == HloOpcode::kDot;
+              });
+  }
+  for (HloInstruction* dot_instr : dot_instrs) {
+    TF_ASSIGN_OR_RETURN(bool elided_batch_dim_from_one,
+                        ElideDegenerateBatchDimensionFromBatchDot(dot_instr));
+    changed |= elided_batch_dim_from_one;
+  }
+  return changed;
+}
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/batch_dot_simplification.h b/tensorflow/compiler/xla/service/batch_dot_simplification.h
new file mode 100644
index 00000000000000..c0ca8d8ebac1a3
--- /dev/null
+++ b/tensorflow/compiler/xla/service/batch_dot_simplification.h
@@ -0,0 +1,39 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_BATCH_DOT_SIMPLIFICATION_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_BATCH_DOT_SIMPLIFICATION_H_
+
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
+
+namespace xla {
+// Simplifies batch dot operations.
+//
+// Normally these would live in the algebraic simplifier, but we want to run
+// this to fixpoint (this pass reaches fixed point in one execution) before we
+// run the DotDecomposer.
+class BatchDotSimplification : public HloPassInterface {
+ public:
+  StatusOr Run(HloModule* module) override;
+  tensorflow::StringPiece name() const override;
+
+ private:
+  StatusOr ElideDegenerateBatchDimensionFromBatchDot(
+      HloInstruction* batch_dot);
+};
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_BATCH_DOT_SIMPLIFICATION_H_
diff --git a/tensorflow/compiler/xla/service/batch_dot_simplification_test.cc b/tensorflow/compiler/xla/service/batch_dot_simplification_test.cc
new file mode 100644
index 00000000000000..38f1a5d3a645f9
--- /dev/null
+++ b/tensorflow/compiler/xla/service/batch_dot_simplification_test.cc
@@ -0,0 +1,168 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/batch_dot_simplification.h"
+#include "tensorflow/compiler/xla/service/hlo_matchers.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+#include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h"
+
+namespace xla {
+namespace {
+
+namespace op = xla::testing::opcode_matchers;
+
+class BatchDotSimplificationTest : public HloVerifiedTestBase {};
+
+TEST_F(BatchDotSimplificationTest,
+       ElideSingleDegenerateBatchDotDim_VectorVector) {
+  const string hlo_text = R"(
+HloModule BatchDot
+
+main {
+  a = f32[1,3] parameter(0)
+  b = f32[1,3] parameter(1)
+  ROOT dot = f32[1] dot(a, b), lhs_batch_dims={0}, rhs_batch_dims={0}, lhs_contracting_dims={1}, rhs_contracting_dims={1}
+}
+)";
+
+  ParseAndVerifyModule(hlo_text);
+  BatchDotSimplification pass;
+  ASSERT_TRUE(pass.Run(&module()).ValueOrDie());
+
+  HloInstruction* root = module().entry_computation()->root_instruction();
+  EXPECT_THAT(root,
+              op::Reshape(op::Dot(
+                  op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)),
+                  /*lhs_contracting_dim=*/0, /*rhs_contracting_dim=*/0)));
+}
+
+TEST_F(BatchDotSimplificationTest,
+       ElideSingleDegenerateBatchDotDim_MatrixVector) {
+  const string hlo_text = R"(
+HloModule BatchDot
+
+main {
+  a = f32[1,9,3] parameter(0)
+  b = f32[1,3] parameter(1)
+  ROOT dot = f32[1,9] dot(a, b), lhs_batch_dims={0}, rhs_batch_dims={0}, lhs_contracting_dims={2}, rhs_contracting_dims={1}
+}
+)";
+
+  ParseAndVerifyModule(hlo_text);
+  BatchDotSimplification pass;
+  ASSERT_TRUE(pass.Run(&module()).ValueOrDie());
+
+  HloInstruction* root = module().entry_computation()->root_instruction();
+  EXPECT_THAT(root,
+              op::Reshape(op::Dot(
+                  op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)),
+                  /*lhs_contracting_dim=*/1, /*rhs_contracting_dim=*/0)));
+}
+
+TEST_F(BatchDotSimplificationTest,
+       ElideSingleDegenerateBatchDotDim_MatrixMatrix) {
+  const string hlo_text = R"(
+HloModule BatchDot
+
+main {
+  a = f32[1,9,3] parameter(0)
+  b = f32[1,3,7] parameter(1)
+  ROOT dot = f32[1,9,7] dot(a, b), lhs_batch_dims={0}, rhs_batch_dims={0}, lhs_contracting_dims={2}, rhs_contracting_dims={1}
+}
+)";
+
+  ParseAndVerifyModule(hlo_text);
+  BatchDotSimplification pass;
+  ASSERT_TRUE(pass.Run(&module()).ValueOrDie());
+
+  HloInstruction* root = module().entry_computation()->root_instruction();
+  EXPECT_THAT(root,
+              op::Reshape(op::Dot(
+                  op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)),
+                  /*lhs_contracting_dim=*/1, /*rhs_contracting_dim=*/0)));
+}
+
+TEST_F(BatchDotSimplificationTest,
+       ElideMultipleDegenerateBatchDotDims_VectorVector) {
+  const string hlo_text = R"(
+HloModule BatchDot
+
+main {
+  a = f32[9,1,7,1,3] parameter(0)
+  b = f32[9,1,7,1,3] parameter(1)
+  ROOT dot = f32[9,1,7,1] dot(a, b), lhs_batch_dims={0,1,2,3}, rhs_batch_dims={0,1,2,3}, lhs_contracting_dims={4}, rhs_contracting_dims={4}
+}
+)";
+
+  ParseAndVerifyModule(hlo_text);
+  BatchDotSimplification pass;
+  ASSERT_TRUE(pass.Run(&module()).ValueOrDie());
+
+  HloInstruction* root = module().entry_computation()->root_instruction();
+  EXPECT_THAT(root,
+              op::Reshape(op::Dot(
+                  op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)),
+                  /*lhs_contracting_dim=*/2, /*rhs_contracting_dim=*/2)));
+}
+
+TEST_F(BatchDotSimplificationTest,
+       ElideMultipleDegenerateBatchDotDims_VectorMatrix) {
+  const string hlo_text = R"(
+HloModule BatchDot
+
+main {
+  a = f32[9,1,7,1,3] parameter(0)
+  b = f32[9,1,7,1,20,3] parameter(1)
+  ROOT dot = f32[9,1,7,1,20] dot(a, b), lhs_batch_dims={0,1,2,3}, rhs_batch_dims={0,1,2,3}, lhs_contracting_dims={4}, rhs_contracting_dims={5}
+}
+)";
+
+  ParseAndVerifyModule(hlo_text);
+  BatchDotSimplification pass;
+  ASSERT_TRUE(pass.Run(&module()).ValueOrDie());
+
+  HloInstruction* root = module().entry_computation()->root_instruction();
+  EXPECT_THAT(root,
+              op::Reshape(op::Dot(
+                  op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)),
+                  /*lhs_contracting_dim=*/2, /*rhs_contracting_dim=*/3)));
+}
+
+TEST_F(BatchDotSimplificationTest,
+       ElideMultipleDegenerateBatchDotDims_MatrixMatrix) {
+  const string hlo_text = R"(
+HloModule BatchDot
+
+main {
+  a = f32[9,1,7,1,19,3] parameter(0)
+  b = f32[9,1,7,1,3,20] parameter(1)
+  ROOT dot = f32[9,1,7,1,19,20] dot(a, b), lhs_batch_dims={0,1,2,3}, rhs_batch_dims={0,1,2,3}, lhs_contracting_dims={5}, rhs_contracting_dims={4}
+}
+)";
+
+  ParseAndVerifyModule(hlo_text);
+  BatchDotSimplification pass;
+  ASSERT_TRUE(pass.Run(&module()).ValueOrDie());
+
+  HloInstruction* root = module().entry_computation()->root_instruction();
+  EXPECT_THAT(root,
+              op::Reshape(op::Dot(
+                  op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)),
+                  /*lhs_contracting_dim=*/3, /*rhs_contracting_dim=*/2)));
+}
+
+}  // namespace
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 790163fca67c42..5f5b81686adc72 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -103,6 +103,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/service:algebraic_simplifier",
+        "//tensorflow/compiler/xla/service:batch_dot_simplification",
         "//tensorflow/compiler/xla/service:batchnorm_expander",
         "//tensorflow/compiler/xla/service:buffer_assignment",
         "//tensorflow/compiler/xla/service:buffer_liveness",
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 7c89debd6c8d07..beeb826747d103 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -43,6 +43,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/protobuf_util.h"
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/algebraic_simplifier.h"
+#include "tensorflow/compiler/xla/service/batch_dot_simplification.h"
 #include "tensorflow/compiler/xla/service/batchnorm_expander.h"
 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
 #include "tensorflow/compiler/xla/service/buffer_liveness.h"
@@ -251,6 +252,7 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile,
   // TODO(b/65775800): Fix wrong output bug in Call and remove the CallInliner
   // pass.
   pipeline.AddPass();
+  pipeline.AddPass();
   pipeline.AddPass();
   pipeline.AddPass(&target_machine_features);
   {
diff --git a/tensorflow/compiler/xla/service/hlo_creation_utils.cc b/tensorflow/compiler/xla/service/hlo_creation_utils.cc
index ed3b654851ab93..0fb65c845a6d44 100644
--- a/tensorflow/compiler/xla/service/hlo_creation_utils.cc
+++ b/tensorflow/compiler/xla/service/hlo_creation_utils.cc
@@ -162,6 +162,17 @@ StatusOr MakeConcatHlo(ArraySlice operands,
       HloInstruction::CreateConcatenate(concat_shape, operands, dimension));
 }
 
+StatusOr MakeDotHlo(HloInstruction* lhs, HloInstruction* rhs,
+                                     const DotDimensionNumbers& dim_numbers) {
+  HloComputation* computation = lhs->parent();
+  CHECK_EQ(computation, rhs->parent());
+  TF_ASSIGN_OR_RETURN(
+      Shape dot_shape,
+      ShapeInference::InferDotOpShape(lhs->shape(), rhs->shape(), dim_numbers));
+  return computation->AddInstruction(
+      HloInstruction::CreateDot(dot_shape, lhs, rhs, dim_numbers));
+}
+
 StatusOr CollapseFirstNDims(HloInstruction* operand, int64 n) {
   CHECK_GT(n, 0);
 
diff --git a/tensorflow/compiler/xla/service/hlo_creation_utils.h b/tensorflow/compiler/xla/service/hlo_creation_utils.h
index c9a7361a6af0c2..49b1402d689a74 100644
--- a/tensorflow/compiler/xla/service/hlo_creation_utils.h
+++ b/tensorflow/compiler/xla/service/hlo_creation_utils.h
@@ -97,6 +97,11 @@ StatusOr MakeGetTupleElementHlo(HloInstruction* operand,
 StatusOr MakeConcatHlo(
     tensorflow::gtl::ArraySlice operands, int64 dimension);
 
+// Creates a Dot HLO instruction and adds it to the computation containing `lhs`
+// and `rhs` (both must be in the same computation).
+StatusOr MakeDotHlo(HloInstruction* lhs, HloInstruction* rhs,
+                                     const DotDimensionNumbers& dim_numbers);
+
 // -----------------------------------------------------------------------------
 // Some other miscellaneous helpers to generate common HLO patterns.  All of
 // these add all the instructions they generate into the computation containing

From cd9ac6414531a8f7308a7698f0954084443d5120 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 15:03:34 -0700
Subject: [PATCH 0689/1691] Modify the python interface to toco to provide
 arithmetic operations used by the model.

PiperOrigin-RevId: 196314416
---
 tensorflow/contrib/lite/toco/model.h                 |  4 ++++
 tensorflow/contrib/lite/toco/python/toco.i           |  7 +++++--
 .../contrib/lite/toco/python/toco_python_api.cc      | 12 +++++++++++-
 .../contrib/lite/toco/python/toco_python_api.h       |  7 +++++--
 tensorflow/contrib/lite/toco/toco_tooling.cc         |  1 +
 5 files changed, 26 insertions(+), 5 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h
index aefa9ac5cb32b9..d878ac54e4d819 100644
--- a/tensorflow/contrib/lite/toco/model.h
+++ b/tensorflow/contrib/lite/toco/model.h
@@ -1829,6 +1829,8 @@ class Model {
   }
   const ArrayMap& GetArrayMap() const { return arrays; }
 
+  int64 ArithmeticOpsCount() const { return ops_count; }
+
   // Optional arrays are used for optional tensors,
   // these tensors do not have data, but with reserved names as op inputs.
   std::set optional_arrays;
@@ -1845,6 +1847,8 @@ class Model {
   std::size_t transient_data_size = 0;
   // For code-generation only: required alignment of the transient_data buffer
   std::size_t transient_data_alignment = 0;
+  // Arithmatic operations performed in the model.
+  int64 ops_count = 0;
 
  private:
   // The associative array mapping names to Array's.
diff --git a/tensorflow/contrib/lite/toco/python/toco.i b/tensorflow/contrib/lite/toco/python/toco.i
index 3787cba4a371f1..0d2fbdd67b3aa5 100644
--- a/tensorflow/contrib/lite/toco/python/toco.i
+++ b/tensorflow/contrib/lite/toco/python/toco.i
@@ -24,9 +24,12 @@ namespace toco {
 // Convert a model represented in `input_contents`. `model_flags_proto`
 // describes model parameters. `toco_flags_proto` describes conversion
 // parameters (see relevant .protos for more information). Returns a string
-// representing the contents of the converted model.
+// representing the contents of the converted model. When extended_return
+// flag is set to true returns a dictionary that contains string representation
+// of the converted model and some statitics like arithmetic ops count.
 PyObject* TocoConvert(PyObject* model_flags_proto_txt_raw,
                         PyObject* toco_flags_proto_txt_raw,
-                        PyObject* input_contents_txt_raw);
+                        PyObject* input_contents_txt_raw,
+                        bool extended_return = false);
 
 } // namespace toco
\ No newline at end of file
diff --git a/tensorflow/contrib/lite/toco/python/toco_python_api.cc b/tensorflow/contrib/lite/toco/python/toco_python_api.cc
index 153c117d17e456..5b1db852b4f8e8 100644
--- a/tensorflow/contrib/lite/toco/python/toco_python_api.cc
+++ b/tensorflow/contrib/lite/toco/python/toco_python_api.cc
@@ -37,7 +37,7 @@ namespace toco {
 // sure we input and output bytes rather than unicode strings for Python3.
 PyObject* TocoConvert(PyObject* model_flags_proto_txt_raw,
                       PyObject* toco_flags_proto_txt_raw,
-                      PyObject* input_contents_txt_raw) {
+                      PyObject* input_contents_txt_raw, bool extended_return) {
   // Use Python C API to validate and convert arguments. In py3 (bytes),
   // in py2 (str).
   auto ConvertArg = [&](PyObject* obj, bool* error) {
@@ -78,6 +78,16 @@ PyObject* TocoConvert(PyObject* model_flags_proto_txt_raw,
   Export(toco_flags, *model, toco_flags.allow_custom_ops(),
          &output_file_contents_txt);
 
+  if (extended_return) {
+    PyObject* dict = PyDict_New();
+    PyDict_SetItemString(
+        dict, "flatbuffer",
+        TOCO_FROM_CPPSTRING_TO_PY(output_file_contents_txt.data(),
+                                  output_file_contents_txt.size()));
+    PyDict_SetItemString(dict, "arithmetic_ops",
+                         PyLong_FromLong(model->ArithmeticOpsCount()));
+    return dict;
+  }
   // Convert arguments back to byte (py3) or str (py2)
   return TOCO_FROM_CPPSTRING_TO_PY(output_file_contents_txt.data(),
                                    output_file_contents_txt.size());
diff --git a/tensorflow/contrib/lite/toco/python/toco_python_api.h b/tensorflow/contrib/lite/toco/python/toco_python_api.h
index dc378353f79945..9af38e937c2980 100644
--- a/tensorflow/contrib/lite/toco/python/toco_python_api.h
+++ b/tensorflow/contrib/lite/toco/python/toco_python_api.h
@@ -23,10 +23,13 @@ namespace toco {
 // Convert a model represented in `input_contents`. `model_flags_proto`
 // describes model parameters. `toco_flags_proto` describes conversion
 // parameters (see relevant .protos for more information). Returns a string
-// representing the contents of the converted model.
+// representing the contents of the converted model. When extended_return
+// flag is set to true returns a dictionary that contains string representation
+// of the converted model and some statitics like arithmetic ops count.
 PyObject* TocoConvert(PyObject* model_flags_proto_txt_raw,
                       PyObject* toco_flags_proto_txt_raw,
-                      PyObject* input_contents_txt_raw);
+                      PyObject* input_contents_txt_raw,
+                      bool extended_return = false);
 
 }  // namespace toco
 
diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc
index d8949165971d38..b5531ca2f4785e 100644
--- a/tensorflow/contrib/lite/toco/toco_tooling.cc
+++ b/tensorflow/contrib/lite/toco/toco_tooling.cc
@@ -373,6 +373,7 @@ void Transform(const TocoFlags& toco_flags, Model* model) {
     LOG(INFO) << "Estimated count of arithmetic ops: " << 1e-9 * ops_count
               << " billion (note that a multiply-add is counted as 2 ops).";
   }
+  model->ops_count = ops_count;
 }
 
 void Export(const TocoFlags& toco_flags, const Model& model,

From b24dec71a9d88a4d2c48b5fc4dbb87cc0db4aaa9 Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Fri, 11 May 2018 15:04:41 -0700
Subject: [PATCH 0690/1691] [XLA:GPU] Load kernel thunks' kernels before
 running them.

The motivation here is that with --xla_hlo_profile, we count the time
spent in Thunk::ExecuteOnStream, but we don't want to count the time
spent loading the CUDA code into the GPU as time spent in the first
kernel thunk we try to run.

PiperOrigin-RevId: 196314733
---
 .../xla/service/gpu/conditional_thunk.cc      |  7 +--
 .../xla/service/gpu/conditional_thunk.h       |  3 +-
 .../compiler/xla/service/gpu/for_thunk.cc     |  5 +-
 .../compiler/xla/service/gpu/for_thunk.h      |  3 +-
 .../xla/service/gpu/gpu_executable.cc         | 10 ++--
 .../compiler/xla/service/gpu/kernel_thunk.cc  | 49 +++++++++++--------
 .../compiler/xla/service/gpu/kernel_thunk.h   |  6 ++-
 .../xla/service/gpu/sequential_thunk.cc       |  6 +--
 .../xla/service/gpu/sequential_thunk.h        |  3 +-
 tensorflow/compiler/xla/service/gpu/thunk.h   | 13 +++--
 .../compiler/xla/service/gpu/while_thunk.cc   |  8 +--
 .../compiler/xla/service/gpu/while_thunk.h    |  3 +-
 12 files changed, 70 insertions(+), 46 deletions(-)

diff --git a/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc b/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc
index dce8de2e301ecf..77a48965e03134 100644
--- a/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc
@@ -35,9 +35,10 @@ ConditionalThunk::ConditionalThunk(
       true_thunk_(std::move(true_thunk_sequence), hlo),
       false_thunk_(std::move(false_thunk_sequence), hlo) {}
 
-Status ConditionalThunk::Initialize(const GpuExecutable& executable) {
-  TF_RETURN_IF_ERROR(true_thunk_.Initialize(executable));
-  TF_RETURN_IF_ERROR(false_thunk_.Initialize(executable));
+Status ConditionalThunk::Initialize(const GpuExecutable& executable,
+                                    se::StreamExecutor* executor) {
+  TF_RETURN_IF_ERROR(true_thunk_.Initialize(executable, executor));
+  TF_RETURN_IF_ERROR(false_thunk_.Initialize(executable, executor));
   return Status::OK();
 }
 
diff --git a/tensorflow/compiler/xla/service/gpu/conditional_thunk.h b/tensorflow/compiler/xla/service/gpu/conditional_thunk.h
index e40872688fdad2..ee03865d174469 100644
--- a/tensorflow/compiler/xla/service/gpu/conditional_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/conditional_thunk.h
@@ -47,7 +47,8 @@ class ConditionalThunk : public Thunk {
   ConditionalThunk(const ConditionalThunk&) = delete;
   ConditionalThunk& operator=(const ConditionalThunk&) = delete;
 
-  Status Initialize(const GpuExecutable& executable) override;
+  Status Initialize(const GpuExecutable& executable,
+                    se::StreamExecutor* executor) override;
   Status ExecuteOnStream(const BufferAllocations& buffer_allocations,
                          se::Stream* stream) override;
 
diff --git a/tensorflow/compiler/xla/service/gpu/for_thunk.cc b/tensorflow/compiler/xla/service/gpu/for_thunk.cc
index 6e6966df3987ee..c49c273587045e 100644
--- a/tensorflow/compiler/xla/service/gpu/for_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/for_thunk.cc
@@ -30,8 +30,9 @@ ForThunk::ForThunk(const int64 loop_limit,
       body_thunk_sequence_(
           MakeUnique(std::move(*body_thunk_sequence), hlo)) {}
 
-tensorflow::Status ForThunk::Initialize(const GpuExecutable& executable) {
-  TF_RETURN_IF_ERROR(body_thunk_sequence_->Initialize(executable));
+tensorflow::Status ForThunk::Initialize(const GpuExecutable& executable,
+                                        se::StreamExecutor* executor) {
+  TF_RETURN_IF_ERROR(body_thunk_sequence_->Initialize(executable, executor));
   return tensorflow::Status::OK();
 }
 
diff --git a/tensorflow/compiler/xla/service/gpu/for_thunk.h b/tensorflow/compiler/xla/service/gpu/for_thunk.h
index c78d1c50686297..56c5c4985ac42a 100644
--- a/tensorflow/compiler/xla/service/gpu/for_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/for_thunk.h
@@ -36,7 +36,8 @@ class ForThunk : public Thunk {
   ForThunk(const ForThunk&) = delete;
   ForThunk& operator=(const ForThunk&) = delete;
 
-  tensorflow::Status Initialize(const GpuExecutable& executable) override;
+  tensorflow::Status Initialize(const GpuExecutable& executable,
+                                se::StreamExecutor* executor) override;
   tensorflow::Status ExecuteOnStream(
       const BufferAllocations& buffer_allocations, se::Stream* stream) override;
 
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc
index e09bee0b941552..f8766474a81ab7 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc
@@ -134,9 +134,10 @@ Status GpuExecutable::ExecuteThunks(
     const BufferAllocations& buffer_allocations, bool block_host_until_done,
     HloExecutionProfile* hlo_execution_profile) {
   se::Stream* main_stream = run_options->stream();
+  se::StreamExecutor* executor = main_stream->parent();
 
   std::pair stream_compute_compatibility;
-  main_stream->parent()->GetDeviceDescription().cuda_compute_capability(
+  executor->GetDeviceDescription().cuda_compute_capability(
       &stream_compute_compatibility.first,
       &stream_compute_compatibility.second);
   TF_RET_CHECK(stream_compute_compatibility == compute_capability_)
@@ -155,9 +156,8 @@ Status GpuExecutable::ExecuteThunks(
   sub_streams.reserve(thunk_schedule_->StreamCount() - 1);
   while (sub_streams.size() + 1 < thunk_schedule_->StreamCount()) {
     sub_streams.emplace_back();
-    TF_ASSIGN_OR_RETURN(
-        sub_streams.back(),
-        run_options->BorrowStream(main_stream->parent()->device_ordinal()));
+    TF_ASSIGN_OR_RETURN(sub_streams.back(),
+                        run_options->BorrowStream(executor->device_ordinal()));
   }
 
   HloExecutionProfiler profiler(do_profile, hlo_execution_profile, main_stream,
@@ -166,7 +166,7 @@ Status GpuExecutable::ExecuteThunks(
 
   std::map> thunk_to_finish_event;
   for (Thunk* thunk : thunk_schedule_->TotalOrder()) {
-    TF_RETURN_IF_ERROR(thunk->Initialize(*this));
+    TF_RETURN_IF_ERROR(thunk->Initialize(*this, executor));
     int32 stream_no =
         thunk_schedule_->StreamNumberForHlo(*thunk->hlo_instruction());
     se::Stream* stream =
diff --git a/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc b/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc
index d376ef7a245eb9..3baee228cf861c 100644
--- a/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc
@@ -35,23 +35,35 @@ KernelThunk::KernelThunk(
       kernel_name_(kernel_name),
       unroll_factor_(unroll_factor) {}
 
-tensorflow::Status KernelThunk::Initialize(const GpuExecutable& executable) {
+tensorflow::Status KernelThunk::Initialize(const GpuExecutable& executable,
+                                           se::StreamExecutor* executor) {
   tensorflow::mutex_lock lock(mutex_);
-  if (loader_spec_) {
-    // Already initialized by another thread.
-    return tensorflow::Status::OK();
-  }
+  if (!loader_spec_) {
+    loader_spec_.reset(new se::MultiKernelLoaderSpec(args_.size()));
+    tensorflow::StringPiece ptx = executable.ptx();
+    // Convert tensorflow::StringPiece to se::port::StringPiece because
+    // StreamExecutor uses the latter.
+    loader_spec_->AddCudaPtxInMemory(
+        se::port::StringPiece(ptx.data(), ptx.size()), kernel_name_);
 
-  loader_spec_.reset(new se::MultiKernelLoaderSpec(args_.size()));
-  tensorflow::StringPiece ptx = executable.ptx();
-  // Convert tensorflow::StringPiece to se::port::StringPiece because
-  // StreamExecutor uses the latter.
-  loader_spec_->AddCudaPtxInMemory(
-      se::port::StringPiece(ptx.data(), ptx.size()), kernel_name_);
+    if (!executable.cubin().empty()) {
+      loader_spec_->AddCudaCubinInMemory(
+          reinterpret_cast(executable.cubin().data()),
+          kernel_name_);
+    }
+  }
 
-  if (!executable.cubin().empty()) {
-    loader_spec_->AddCudaCubinInMemory(
-        reinterpret_cast(executable.cubin().data()), kernel_name_);
+  // Load the kernel into the device if necessary.
+  //
+  // We could alternatively do this within ExecuteOnStream, but doing it here
+  // lets the time spent loading the kernel not count towards our execution
+  // profiles.
+  auto it = kernel_cache_.find(executor);
+  if (kernel_cache_.end() == it) {
+    it = kernel_cache_.emplace(executor, se::KernelBase(executor)).first;
+    if (!executor->GetKernel(*loader_spec_, &it->second)) {
+      return InternalError("Unable to load kernel %s", kernel_name_.c_str());
+    }
   }
 
   return tensorflow::Status::OK();
@@ -68,15 +80,12 @@ tensorflow::Status KernelThunk::ExecuteOnStream(
   se::StreamExecutor* executor = stream->parent();
   LaunchDimensions launch_dimensions;
   const se::KernelBase* kernel = nullptr;
+
   {
     tensorflow::mutex_lock lock(mutex_);
     auto it = kernel_cache_.find(executor);
-    if (kernel_cache_.end() == it) {
-      it = kernel_cache_.emplace(executor, se::KernelBase(executor)).first;
-      if (!executor->GetKernel(*loader_spec_, &it->second)) {
-        return InternalError("Unable to load kernel %s", kernel_name_.c_str());
-      }
-    }
+    CHECK(it != kernel_cache_.end())
+        << "Initialize() not called for StreamExecutor " << executor;
     launch_dimensions = launch_dimensions_;
     kernel = &it->second;
   }
diff --git a/tensorflow/compiler/xla/service/gpu/kernel_thunk.h b/tensorflow/compiler/xla/service/gpu/kernel_thunk.h
index b556befe66b6be..532f15ee3ab8eb 100644
--- a/tensorflow/compiler/xla/service/gpu/kernel_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/kernel_thunk.h
@@ -57,7 +57,8 @@ class KernelThunk : public Thunk {
   int unroll_factor() const { return unroll_factor_; }
   void SetLaunchDimensions(const LaunchDimensions& launch_dims);
 
-  tensorflow::Status Initialize(const GpuExecutable& executable) override;
+  tensorflow::Status Initialize(const GpuExecutable& executable,
+                                se::StreamExecutor* executor) override;
 
   // Executes the kernel for the thunk on "stream", which must be non-null.
   tensorflow::Status ExecuteOnStream(
@@ -83,7 +84,8 @@ class KernelThunk : public Thunk {
   mutable tensorflow::mutex mutex_;
   std::unique_ptr loader_spec_ GUARDED_BY(mutex_);
 
-  // Loaded kernels for each `StreamExecutor`
+  // Loaded kernels for each `StreamExecutor`.  Requires pointer stability of
+  // values.
   std::unordered_map kernel_cache_
       GUARDED_BY(mutex_);
 };
diff --git a/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc b/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc
index c8510808f10a73..849eff2c88178b 100644
--- a/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc
@@ -24,10 +24,10 @@ SequentialThunk::SequentialThunk(std::vector>&& thunks,
                                  const HloInstruction* hlo)
     : Thunk(Kind::kSequential, hlo), thunks_(std::move(thunks)) {}
 
-tensorflow::Status SequentialThunk::Initialize(
-    const GpuExecutable& executable) {
+tensorflow::Status SequentialThunk::Initialize(const GpuExecutable& executable,
+                                               se::StreamExecutor* executor) {
   for (auto& thunk : thunks_) {
-    TF_RETURN_IF_ERROR(thunk->Initialize(executable));
+    TF_RETURN_IF_ERROR(thunk->Initialize(executable, executor));
   }
   return tensorflow::Status::OK();
 }
diff --git a/tensorflow/compiler/xla/service/gpu/sequential_thunk.h b/tensorflow/compiler/xla/service/gpu/sequential_thunk.h
index df17b8d67b8032..83057913319f71 100644
--- a/tensorflow/compiler/xla/service/gpu/sequential_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/sequential_thunk.h
@@ -38,7 +38,8 @@ class SequentialThunk : public Thunk {
 
   const std::vector>& thunks() const { return thunks_; }
 
-  tensorflow::Status Initialize(const GpuExecutable& executable) override;
+  tensorflow::Status Initialize(const GpuExecutable& executable,
+                                se::StreamExecutor* executor) override;
   tensorflow::Status ExecuteOnStream(
       const BufferAllocations& buffer_allocations, se::Stream* stream) override;
 
diff --git a/tensorflow/compiler/xla/service/gpu/thunk.h b/tensorflow/compiler/xla/service/gpu/thunk.h
index 57d921260909a3..ff9b6087e0fc66 100644
--- a/tensorflow/compiler/xla/service/gpu/thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/thunk.h
@@ -70,10 +70,13 @@ class Thunk {
   Kind kind() const { return kind_; }
   const HloInstruction* hlo_instruction() const { return hlo_instruction_; }
 
-  // Prepares for executing the thunk. This method is called only once over
-  // Thunk's lifetime. For example, KernelThunk::Initialize loads the PTX of a
-  // kernel, which is the same in every execution.
-  virtual tensorflow::Status Initialize(const GpuExecutable& executable) {
+  // Prepares the thunk for execution on the given StreamExecutor.
+  //
+  // This may be called multiple times.  Its main purpose is to give us a chance
+  // to do initialization outside of ExecuteOnStream() so that the
+  // time spent initializing doesn't count towards our execution profile.
+  virtual tensorflow::Status Initialize(const GpuExecutable& /*executable*/,
+                                        se::StreamExecutor* /*executor*/) {
     return tensorflow::Status::OK();
   }
 
@@ -92,6 +95,8 @@ class Thunk {
   // Execute the kernel for the thunk on the given stream. This method must be
   // called after Initialize and can be called multiple times over Thunk's
   // lifetime. Stream argument must be non-null.
+  //
+  // Precondition: Initialize(stream->parent()) has been called.
   virtual tensorflow::Status ExecuteOnStream(
       const BufferAllocations& buffer_allocations, se::Stream* stream) = 0;
 
diff --git a/tensorflow/compiler/xla/service/gpu/while_thunk.cc b/tensorflow/compiler/xla/service/gpu/while_thunk.cc
index a9f3d619a3ffd6..30b9640c4c75da 100644
--- a/tensorflow/compiler/xla/service/gpu/while_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/while_thunk.cc
@@ -34,9 +34,11 @@ WhileThunk::WhileThunk(
       body_thunk_sequence_(
           MakeUnique(std::move(*body_thunk_sequence), hlo)) {}
 
-Status WhileThunk::Initialize(const GpuExecutable& executable) {
-  TF_RETURN_IF_ERROR(condition_thunk_sequence_->Initialize(executable));
-  TF_RETURN_IF_ERROR(body_thunk_sequence_->Initialize(executable));
+Status WhileThunk::Initialize(const GpuExecutable& executable,
+                              se::StreamExecutor* executor) {
+  TF_RETURN_IF_ERROR(
+      condition_thunk_sequence_->Initialize(executable, executor));
+  TF_RETURN_IF_ERROR(body_thunk_sequence_->Initialize(executable, executor));
   return Status::OK();
 }
 
diff --git a/tensorflow/compiler/xla/service/gpu/while_thunk.h b/tensorflow/compiler/xla/service/gpu/while_thunk.h
index e589ca78a7ea00..22176685a92df9 100644
--- a/tensorflow/compiler/xla/service/gpu/while_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/while_thunk.h
@@ -45,7 +45,8 @@ class WhileThunk : public Thunk {
   WhileThunk(const WhileThunk&) = delete;
   WhileThunk& operator=(const WhileThunk&) = delete;
 
-  Status Initialize(const GpuExecutable& executable) override;
+  Status Initialize(const GpuExecutable& executable,
+                    se::StreamExecutor* executor) override;
   Status ExecuteOnStream(const BufferAllocations& buffer_allocations,
                          se::Stream* stream) override;
 

From 9d59278f2d284fc88a95a0f3d894427e905bfe93 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 15:07:24 -0700
Subject: [PATCH 0691/1691] Implement constant-only ListDiff Op in XLA to
 support dense layer.

PiperOrigin-RevId: 196315170
---
 tensorflow/compiler/tests/BUILD               |  15 +++
 tensorflow/compiler/tests/listdiff_op_test.py | 101 +++++++++++++++
 tensorflow/compiler/tf2xla/kernels/BUILD      |   1 +
 .../compiler/tf2xla/kernels/listdiff_op.cc    | 120 ++++++++++++++++++
 4 files changed, 237 insertions(+)
 create mode 100644 tensorflow/compiler/tests/listdiff_op_test.py
 create mode 100644 tensorflow/compiler/tf2xla/kernels/listdiff_op.cc

diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD
index 9791792f29ca05..96dfc8d8f1cb94 100644
--- a/tensorflow/compiler/tests/BUILD
+++ b/tensorflow/compiler/tests/BUILD
@@ -409,6 +409,21 @@ tf_xla_py_test(
     ],
 )
 
+tf_xla_py_test(
+    name = "listdiff_op_test",
+    size = "small",
+    srcs = ["listdiff_op_test.py"],
+    deps = [
+        ":xla_test",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:data_flow_ops",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:platform_test",
+        "@six_archive//:six",
+    ],
+)
+
 tf_xla_py_test(
     name = "lrn_ops_test",
     size = "medium",
diff --git a/tensorflow/compiler/tests/listdiff_op_test.py b/tensorflow/compiler/tests/listdiff_op_test.py
new file mode 100644
index 00000000000000..45a04f0cf56e88
--- /dev/null
+++ b/tensorflow/compiler/tests/listdiff_op_test.py
@@ -0,0 +1,101 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for XLA listdiff operator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
+from tensorflow.compiler.tests import xla_test
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.platform import test
+
+
+class ListDiffTest(xla_test.XLATestCase):
+
+  def _testListDiff(self, x, y, out, idx):
+    for dtype in [dtypes.int32, dtypes.int64]:
+      for index_dtype in [dtypes.int32, dtypes.int64]:
+        with self.test_session() as sess:
+          x_tensor = ops.convert_to_tensor(x, dtype=dtype)
+          y_tensor = ops.convert_to_tensor(y, dtype=dtype)
+          with self.test_scope():
+            out_tensor, idx_tensor = array_ops.listdiff(
+                x_tensor, y_tensor, out_idx=index_dtype)
+            tf_out, tf_idx = sess.run([out_tensor, idx_tensor])
+        self.assertAllEqual(out, tf_out)
+        self.assertAllEqual(idx, tf_idx)
+        self.assertEqual(1, out_tensor.get_shape().ndims)
+        self.assertEqual(1, idx_tensor.get_shape().ndims)
+
+  def testBasic1(self):
+    self._testListDiff(x=[1, 2, 3, 4], y=[1, 2], out=[3, 4], idx=[2, 3])
+
+  def testBasic2(self):
+    self._testListDiff(x=[1, 2, 3, 4], y=[2], out=[1, 3, 4], idx=[0, 2, 3])
+
+  def testBasic3(self):
+    self._testListDiff(x=[1, 4, 3, 2], y=[4, 2], out=[1, 3], idx=[0, 2])
+
+  def testDuplicates(self):
+    self._testListDiff(x=[1, 2, 4, 3, 2, 3, 3, 1],
+                       y=[4, 2],
+                       out=[1, 3, 3, 3, 1],
+                       idx=[0, 3, 5, 6, 7])
+
+  def testRandom(self):
+    num_random_tests = 10
+    int_low = -7
+    int_high = 8
+    max_size = 50
+    for _ in xrange(num_random_tests):
+      x_size = np.random.randint(max_size + 1)
+      x = np.random.randint(int_low, int_high, size=x_size)
+      y_size = np.random.randint(max_size + 1)
+      y = np.random.randint(int_low, int_high, size=y_size)
+      out_idx = [(entry, pos) for pos, entry in enumerate(x) if entry not in y]
+      if out_idx:
+        out, idx = map(list, zip(*out_idx))
+      else:
+        out = []
+        idx = []
+      self._testListDiff(list(x), list(y), out, idx)
+
+  def testFullyOverlapping(self):
+    self._testListDiff(x=[1, 2, 3, 4], y=[1, 2, 3, 4], out=[], idx=[])
+
+  def testNonOverlapping(self):
+    self._testListDiff(x=[1, 2, 3, 4],
+                       y=[5, 6],
+                       out=[1, 2, 3, 4],
+                       idx=[0, 1, 2, 3])
+
+  def testEmptyX(self):
+    self._testListDiff(x=[], y=[1, 2], out=[], idx=[])
+
+  def testEmptyY(self):
+    self._testListDiff(x=[1, 2, 3, 4], y=[], out=[1, 2, 3, 4], idx=[0, 1, 2, 3])
+
+  def testEmptyXY(self):
+    self._testListDiff(x=[], y=[], out=[], idx=[])
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD
index 85ab4c41bf6a75..e6da157c111ad9 100644
--- a/tensorflow/compiler/tf2xla/kernels/BUILD
+++ b/tensorflow/compiler/tf2xla/kernels/BUILD
@@ -45,6 +45,7 @@ tf_kernel_library(
         "image_resize_ops.cc",
         "index_ops.cc",
         "l2loss_op.cc",
+        "listdiff_op.cc",
         "lrn_ops.cc",
         "matmul_op.cc",
         "matrix_band_part_op.cc",
diff --git a/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc b/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc
new file mode 100644
index 00000000000000..0388b4c830702e
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc
@@ -0,0 +1,120 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// XLA-specific ListDiff Op. This only supports constant DT_INT32 and DT_INT64
+// input.
+
+#include 
+
+#include "tensorflow/compiler/tf2xla/type_util.h"
+#include "tensorflow/compiler/tf2xla/xla_helpers.h"
+#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
+#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
+#include "tensorflow/core/framework/kernel_def_builder.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/lib/core/errors.h"
+
+namespace tensorflow {
+namespace {
+
+constexpr std::array kListDiffTypes = {DT_INT32, DT_INT64};
+
+// ListDiffOp is an XLA kernel that supports constant-only x and y input.
+class ListDiffOp : public XlaOpKernel {
+ public:
+  explicit ListDiffOp(OpKernelConstruction* context) : XlaOpKernel(context) {}
+
+  void Compile(XlaOpKernelContext* context) override {
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(context->InputShape(0)),
+                errors::InvalidArgument("ListDiff expects x as a vector, not ",
+                                        context->InputShape(0).DebugString()));
+
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(context->InputShape(1)),
+                errors::InvalidArgument("ListDiff expects y as a vector, not ",
+                                        context->InputShape(1).DebugString()));
+
+    DataType val_type = context->expected_output_dtype(0);
+    DataType idx_type = context->expected_output_dtype(1);
+
+    Status status;
+    switch (val_type) {
+      case DT_INT32:
+        status = ListDiffWithIndexType(context, idx_type);
+        break;
+      case DT_INT64:
+        status = ListDiffWithIndexType(context, idx_type);
+        break;
+      default:
+        // This should never happen since we restrict this kernel to only match
+        // inputs with supported Tensor datatype.
+        status = errors::InvalidArgument("ListDiff expects x and y as either ",
+                                         "int32 or int64, not ",
+                                         DataTypeString(val_type));
+    }
+    OP_REQUIRES_OK(context, status);
+  }
+
+ private:
+  template 
+  Status ListDiff(XlaOpKernelContext* context) {
+    std::vector x_input, y_input;
+    TF_RETURN_IF_ERROR(context->ConstantInputAsIntVector(0, &x_input));
+    TF_RETURN_IF_ERROR(context->ConstantInputAsIntVector(1, &y_input));
+
+    std::unordered_set y_input_set;
+    y_input_set.reserve(y_input.size());
+    for (auto y : y_input) {
+      y_input_set.insert(y);
+    }
+
+    std::vector val_output;
+    std::vector idx_output;
+    auto x_size = x_input.size();
+    for (Tidx i = 0; i < x_size; ++i) {
+      if (y_input_set.count(x_input[i]) > 0) {
+        continue;
+      }
+      val_output.push_back(x_input[i]);
+      idx_output.push_back(i);
+    }
+
+    context->SetOutput(0, context->builder()->ConstantR1(val_output));
+    context->SetOutput(1, context->builder()->ConstantR1(idx_output));
+    return Status::OK();
+  }
+
+  template 
+  Status ListDiffWithIndexType(XlaOpKernelContext* context, DataType idx_type) {
+    switch (idx_type) {
+      case DT_INT32:
+        return ListDiff(context);
+      case DT_INT64:
+        return ListDiff(context);
+      default:
+        return errors::InvalidArgument(
+            "ListDiff expects idx_out as either int32 or int64, not ",
+            DataTypeString(idx_type));
+    }
+  }
+};
+
+REGISTER_XLA_OP(Name("ListDiff")
+                    .TypeConstraint("T", kListDiffTypes)
+                    .CompileTimeConstInput("x")
+                    .CompileTimeConstInput("y"),
+                ListDiffOp);
+
+}  // namespace
+}  // namespace tensorflow

From 640e0baf6e69b037ecc8c3044a11441f18afd180 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 15:07:47 -0700
Subject: [PATCH 0692/1691] Introduce an indirection to access posix/error.h,
 so implementations don't have to worry about platform details.

PiperOrigin-RevId: 196315234
---
 tensorflow/core/BUILD                         |  1 +
 tensorflow/core/platform/error.h              | 30 +++++++++++++++++++
 .../platform/hadoop/hadoop_file_system.cc     |  2 +-
 3 files changed, 32 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/core/platform/error.h

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 2f5f6ae17b50ba..8be43aade74a76 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -303,6 +303,7 @@ PLATFORM_OTHER_HDRS = [
     "platform/cpu_info.h",
     "platform/cpu_feature_guard.h",
     "platform/dynamic_annotations.h",
+    "platform/error.h",
     "platform/env.h",
     "platform/file_system.h",
     "platform/file_system_helper.h",
diff --git a/tensorflow/core/platform/error.h b/tensorflow/core/platform/error.h
new file mode 100644
index 00000000000000..ae965b6c773e26
--- /dev/null
+++ b/tensorflow/core/platform/error.h
@@ -0,0 +1,30 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CORE_PLATFORM_ERROR_H_
+#define TENSORFLOW_CORE_PLATFORM_ERROR_H_
+
+#include "tensorflow/core/platform/platform.h"
+
+#if defined(PLATFORM_GOOGLE) || defined(PLATFORM_POSIX) || \
+    defined(PLATFORM_POSIX_ANDROID) || defined(PLATFORM_GOOGLE_ANDROID)
+#include "tensorflow/core/platform/posix/error.h"
+#elif defined(PLATFORM_WINDOWS)
+#include "tensorflow/core/platform/windows/error.h"
+#else
+#error Define the appropriate PLATFORM_ macro for this platform
+#endif
+
+#endif  // TENSORFLOW_CORE_PLATFORM_ERROR_H_
diff --git a/tensorflow/core/platform/hadoop/hadoop_file_system.cc b/tensorflow/core/platform/hadoop/hadoop_file_system.cc
index a8cb40502c1185..72c12318cac883 100644
--- a/tensorflow/core/platform/hadoop/hadoop_file_system.cc
+++ b/tensorflow/core/platform/hadoop/hadoop_file_system.cc
@@ -21,11 +21,11 @@ limitations under the License.
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/error.h"
 #include "tensorflow/core/platform/file_system.h"
 #include "tensorflow/core/platform/file_system_helper.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/mutex.h"
-#include "tensorflow/core/platform/posix/error.h"
 #include "third_party/hadoop/hdfs.h"
 
 namespace tensorflow {

From 06ff12d06e85888701a2dba441e982e34a7db6ec Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 15:07:48 -0700
Subject: [PATCH 0693/1691] Expose MaybeGetMinimumShape for use in cost
 estimators other than OpLevelCostEstimator.

PiperOrigin-RevId: 196315239
---
 .../grappler/costs/op_level_cost_estimator.cc | 54 +++++++++----------
 .../grappler/costs/op_level_cost_estimator.h  |  2 +
 2 files changed, 29 insertions(+), 27 deletions(-)

diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
index fbdd3113117128..b8e337582c93a8 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
@@ -129,33 +129,6 @@ int64 GetOutputSize(const int64 input, const int64 filter, const int64 stride,
   }
 }
 
-// Return a minimum shape if the shape is unknown. If known, return the original
-// shape.
-TensorShapeProto MaybeGetMinimumShape(const TensorShapeProto& original_shape,
-                                      int rank, bool* found_unknown_shapes) {
-  auto shape = original_shape;
-  if (shape.unknown_rank() || shape.dim_size() < rank) {
-    *found_unknown_shapes = true;
-    TensorShapeProto::Dim dim;
-    VLOG(2) << "Use minimum shape because the rank is unknown.";
-    // The size of each dimension is at least 1, if unknown.
-    dim.set_size(1);
-    for (int i = 0; i < rank; i++) {
-      *shape.add_dim() = dim;
-    }
-  } else {
-    for (int i = 0; i < shape.dim_size(); i++) {
-      if (shape.dim(i).size() < 0) {
-        *found_unknown_shapes = true;
-        VLOG(2) << "Use minimum dim size 1 because the shape is unknown.";
-        // The size of each dimension is at least 1, if unknown.
-        shape.mutable_dim(i)->set_size(1);
-      }
-    }
-  }
-  return shape;
-}
-
 // Return the output element count of a binary element-wise op considering
 // broadcasting.
 int64 CwiseOutputElementCount(const TensorShapeProto& input_shape_1,
@@ -187,6 +160,33 @@ int64 CwiseOutputElementCount(const TensorShapeProto& input_shape_1,
 
 }  // namespace
 
+// Return a minimum shape if the shape is unknown. If known, return the original
+// shape.
+TensorShapeProto MaybeGetMinimumShape(const TensorShapeProto& original_shape,
+                                      int rank, bool* found_unknown_shapes) {
+  auto shape = original_shape;
+  if (shape.unknown_rank() || shape.dim_size() < rank) {
+    *found_unknown_shapes = true;
+    TensorShapeProto::Dim dim;
+    VLOG(2) << "Use minimum shape because the rank is unknown.";
+    // The size of each dimension is at least 1, if unknown.
+    dim.set_size(1);
+    for (int i = 0; i < rank; i++) {
+      *shape.add_dim() = dim;
+    }
+  } else {
+    for (int i = 0; i < shape.dim_size(); i++) {
+      if (shape.dim(i).size() < 0) {
+        *found_unknown_shapes = true;
+        VLOG(2) << "Use minimum dim size 1 because the shape is unknown.";
+        // The size of each dimension is at least 1, if unknown.
+        shape.mutable_dim(i)->set_size(1);
+      }
+    }
+  }
+  return shape;
+}
+
 OpLevelCostEstimator::OpLevelCostEstimator() {
   // Syntactic sugar to build and return a lambda that takes an OpInfo and
   // returns a cost.
diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.h b/tensorflow/core/grappler/costs/op_level_cost_estimator.h
index 35649f7ee959a2..d384f5727965bc 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.h
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.h
@@ -30,6 +30,8 @@ namespace grappler {
 
 bool GetTensorShapeProtoFromTensorProto(const TensorProto& tensor_proto,
                                         TensorShapeProto* tensor_shape_proto);
+TensorShapeProto MaybeGetMinimumShape(const TensorShapeProto& original_shape,
+                                      int rank, bool* found_unknown_shapes);
 
 class OpLevelCostEstimator {
  public:

From 13b1b433c8e2f6fa2d4d88e6f55209571a15607a Mon Sep 17 00:00:00 2001
From: Mark Daoust 
Date: Fri, 11 May 2018 15:17:58 -0700
Subject: [PATCH 0694/1691] Add `` to the call to `Tensor`

PiperOrigin-RevId: 196316735
---
 tensorflow/docs_src/community/swift.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/community/swift.md b/tensorflow/docs_src/community/swift.md
index e5a0f02a8c3633..ba0bae4702b260 100644
--- a/tensorflow/docs_src/community/swift.md
+++ b/tensorflow/docs_src/community/swift.md
@@ -18,7 +18,7 @@ with the full performance of TensorFlow Sessions on CPU, GPU and
 ```swift
 import TensorFlow
 
-var x = Tensor([[1, 2], [3, 4]])
+var x = Tensor([[1, 2], [3, 4]])
 
 for i in 1...5 {
   x += x ⊗ x

From 5740942769e9a3a0e68775d19f139ca7f7aa61c4 Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy 
Date: Fri, 11 May 2018 15:44:14 -0700
Subject: [PATCH 0695/1691] Update how build statuses and artifacts are demoed
 in README.md (#19232)

---
 README.md | 38 ++++++++++++++++++++++++++------------
 1 file changed, 26 insertions(+), 12 deletions(-)

diff --git a/README.md b/README.md
index e1a50c87e26d49..e7f4080cf44f9a 100644
--- a/README.md
+++ b/README.md
@@ -5,9 +5,9 @@
 -----------------
 
 
-| **`Documentation`** | **`Linux CPU`** | **`Linux GPU`** | **`Mac OS CPU`** | **`Windows CPU`** | **`Android`** |
-|-----------------|---------------------|------------------|-------------------|---------------|---------------|
-| [![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/api_docs/) | ![Build Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-cc.png) | ![Build Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-gpu-cc.png) | ![Build Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/macos-py2-cc.png) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-win-cmake-py)](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-android)](https://ci.tensorflow.org/job/tensorflow-master-android) [ ![Download](https://api.bintray.com/packages/google/tensorflow/tensorflow/images/download.svg) ](https://bintray.com/google/tensorflow/tensorflow/_latestVersion)
+| **`Documentation`** |
+|-----------------|
+| [![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/api_docs/) |
 
 **TensorFlow** is an open source software library for numerical computation using
 data flow graphs.  The graph nodes represent mathematical operations, while
@@ -40,15 +40,6 @@ environment to install the nightly TensorFlow build. We support CPU and GPU
 packages on Linux, Mac, and Windows.
 
 
-**Individual whl files**
-* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tf_nightly-1.head-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/)) / [Python 3.4](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tf_nightly-1.head-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tf_nightly-1.head-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=cpu-slave/)) / [Python 3.6](http://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.6,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tf_nightly-1.head-cp36-cp36m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.6,label=cpu-slave/))
-* Linux GPU: [Python 2](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/42/artifact/pip_test/whl/tf_nightly_gpu-1.head-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tf_nightly_gpu-1.head-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tf_nightly_gpu-1.head-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/)) / [Python 3.6](http://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.6,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tf_nightly_gpu-1.head-cp36-cp36m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.6,label=gpu-linux/))
-* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-mac/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tf_nightly-1.head-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-mac/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-mac/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tf_nightly-1.head-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-mac/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
-* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-windows/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tf_nightly-1.head-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-windows/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-windows/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tf_nightly-1.head-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-windows/M=windows,PY=36/))
-* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-windows/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tf_nightly_gpu-1.head-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-windows/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-windows/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tf_nightly_gpu-1.head-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-windows/M=windows-gpu,PY=36/))
-* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
-([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))
-
 #### *Try your first TensorFlow program*
 ```shell
 $ python
@@ -82,6 +73,29 @@ The TensorFlow project strives to abide by generally accepted best practices in
 
 [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/1486/badge)](https://bestpractices.coreinfrastructure.org/projects/1486)
 
+
+## Continuous build status
+
+### Official Builds
+
+| Build Type      | Status | Artifacts |
+| ---             | ---    | ---       |
+| **Linux CPU**   | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-cc.png) | [pypi](https://pypi.org/project/tf-nightly/) |
+| **Linux GPU**   | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-gpu-cc.png) | [pypi](https://pypi.org/project/tf-nightly-gpu/) |
+| **Linux XLA**   | TBA | TBA |
+| **MacOS**       | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/macos-py2-cc.png) | [pypi](https://pypi.org/project/tf-nightly/) |
+| **Windows CPU** | [![Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-win-cmake-py)](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [pypi](https://pypi.org/project/tf-nightly/) |
+| **Windows GPU** | [![Status](http://ci.tensorflow.org/job/tf-master-win-gpu-cmake/badge/icon)](http://ci.tensorflow.org/job/tf-master-win-gpu-cmake/) | [pypi](https://pypi.org/project/tf-nightly-gpu/) |
+| **Android**     | [![Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-android)](https://ci.tensorflow.org/job/tensorflow-master-android) | [![Download](https://api.bintray.com/packages/google/tensorflow/tensorflow/images/download.svg)](https://bintray.com/google/tensorflow/tensorflow/_latestVersion) [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/) [build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/) |
+
+
+### Community Supported Builds
+
+| Build Type    | Status | Artifacts |
+| ---           | ---    | ---       |
+| **IBM s390x** | [![Build Status](http://ibmz-ci.osuosl.org/job/TensorFlow_IBMZ_CI/badge/icon)](http://ibmz-ci.osuosl.org/job/TensorFlow_IBMZ_CI/) | TBA |
+
+
 ## For more information
 
 * [TensorFlow Website](https://www.tensorflow.org)

From 4ca7a9157863a6d57879c598cc370583d60018d3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 15:44:39 -0700
Subject: [PATCH 0696/1691] In broadcaster.cc send from the input tensor, not
 the output, since it may not have been forwarded.  Add non-forwarding cases
 to unittest.

PiperOrigin-RevId: 196320304
---
 tensorflow/core/common_runtime/broadcaster.cc |   2 +-
 .../core/common_runtime/broadcaster_test.cc   | 102 +++++++++---------
 2 files changed, 53 insertions(+), 51 deletions(-)

diff --git a/tensorflow/core/common_runtime/broadcaster.cc b/tensorflow/core/common_runtime/broadcaster.cc
index 30087a5b42dd91..9ceff866787f26 100644
--- a/tensorflow/core/common_runtime/broadcaster.cc
+++ b/tensorflow/core/common_runtime/broadcaster.cc
@@ -162,7 +162,7 @@ void Broadcaster::RunTree() {
         ++pending_count;
       }
       DispatchSend(
-          target_rank, output_,
+          target_rank, (is_source_ ? &ctx_->input(0) : output_),
           [this, target_rank, &mu, &pending_count, &all_done](const Status& s) {
             mutex_lock l(mu);
             status_.Update(s);
diff --git a/tensorflow/core/common_runtime/broadcaster_test.cc b/tensorflow/core/common_runtime/broadcaster_test.cc
index 89d39144b3d212..959b93d56e7fd4 100644
--- a/tensorflow/core/common_runtime/broadcaster_test.cc
+++ b/tensorflow/core/common_runtime/broadcaster_test.cc
@@ -314,11 +314,11 @@ class BroadcasterTest : public ::testing::Test {
 
   typedef std::function InitFunc;
 
-  void Broadcast() {
+  void Broadcast(bool forward_input) {
     std::atomic done(0);
     for (auto di : instances_) {
-      SchedClosure([di, &done] {
-        di->DoBroadcast();
+      SchedClosure([di, forward_input, &done] {
+        di->DoBroadcast(forward_input);
         ++done;
       });
     }
@@ -380,7 +380,8 @@ class BroadcasterTest : public ::testing::Test {
 
   template 
   void RunTest(DataType dtype, const DeviceType& device_type, int num_workers,
-               int num_devices, int tensor_len, int fail_after) {
+               int num_devices, int tensor_len, int fail_after,
+               bool forward_input) {
     Init(num_workers, num_devices, dtype, device_type, fail_after);
 
     // Initialize each instance tensor with distinct values.
@@ -423,7 +424,7 @@ class BroadcasterTest : public ::testing::Test {
       expected[i] = t->flat()(i);
     }
 
-    Broadcast();
+    Broadcast(forward_input);
 
     // At this point all of the ops have terminated.
     for (int di = 0; di < instances_.size(); ++di) {
@@ -573,7 +574,7 @@ class BroadcasterTest : public ::testing::Test {
       }
     }
 
-    void DoBroadcast() {
+    void DoBroadcast(bool forward_input) {
       // Prepare an OpKernelContext.
       OpKernelContext::Params op_params;
       op_params.step_id = parent_->step_id_;
@@ -596,7 +597,8 @@ class BroadcasterTest : public ::testing::Test {
       input_dc.push_back(dev_ctx);
       op_params.input_device_contexts = &input_dc;
       op_params.op_device_context = dev_ctx;
-      int forward_from[] = {0};
+      int forward_from[] = {OpKernelContext::Params::kNeverForward};
+      if (forward_input) forward_from[0] = 0;
       if (col_params_.is_source) {
         op_params.forward_from_array = &forward_from[0];
       }
@@ -680,61 +682,61 @@ class BroadcasterTest : public ::testing::Test {
 // D = number of devices per worker
 // L = tensor length
 // A = abort after count
-#define DEF_TEST(B, T, W, D, L, A)                                 \
-  TEST_F(BroadcasterTest,                                          \
-         DaTy##B##_DevTy##T##_Wkr##W##_Dev##D##_Len##L##_Abt##A) { \
-    DataType dtype = DT_##B;                                       \
-    switch (dtype) {                                               \
-      case DT_FLOAT: {                                             \
-        RunTest(dtype, DEVICE_##T, W, D, L, A);             \
-      } break;                                                     \
-      case DT_DOUBLE: {                                            \
-        RunTest(dtype, DEVICE_##T, W, D, L, A);            \
-      } break;                                                     \
-      case DT_INT32: {                                             \
-        RunTest(dtype, DEVICE_##T, W, D, L, A);             \
-      } break;                                                     \
-      case DT_INT64: {                                             \
-        RunTest(dtype, DEVICE_##T, W, D, L, A);             \
-      } break;                                                     \
-      default:                                                     \
-        LOG(FATAL) << "Unimplemented";                             \
-    }                                                              \
+#define DEF_TEST(B, T, W, D, L, A, F)                                      \
+  TEST_F(BroadcasterTest,                                                  \
+         DaTy##B##_DevTy##T##_Wkr##W##_Dev##D##_Len##L##_Abt##A##_Fw##F) { \
+    DataType dtype = DT_##B;                                               \
+    switch (dtype) {                                                       \
+      case DT_FLOAT: {                                                     \
+        RunTest(dtype, DEVICE_##T, W, D, L, A, F);                  \
+      } break;                                                             \
+      case DT_DOUBLE: {                                                    \
+        RunTest(dtype, DEVICE_##T, W, D, L, A, F);                 \
+      } break;                                                             \
+      case DT_INT32: {                                                     \
+        RunTest(dtype, DEVICE_##T, W, D, L, A, F);                  \
+      } break;                                                             \
+      case DT_INT64: {                                                     \
+        RunTest(dtype, DEVICE_##T, W, D, L, A, F);                  \
+      } break;                                                             \
+      default:                                                             \
+        LOG(FATAL) << "Unimplemented";                                     \
+    }                                                                      \
   }
 
 #ifndef GOOGLE_CUDA
-//       B      T    W  D  L  A
-DEF_TEST(FLOAT, CPU, 1, 2, 1, 0)
-DEF_TEST(FLOAT, CPU, 1, 2, 1001, 0)
-DEF_TEST(FLOAT, CPU, 2, 1, 128, 0)
-DEF_TEST(FLOAT, CPU, 2, 4, 128, 0)
-DEF_TEST(FLOAT, CPU, 2, 8, 4095, 0)
-DEF_TEST(FLOAT, CPU, 4, 4, 1045991, 0)
-
-DEF_TEST(DOUBLE, CPU, 2, 4, 128, 0)
-DEF_TEST(INT32, CPU, 2, 4, 128, 0)
-DEF_TEST(INT64, CPU, 2, 4, 128, 0)
+//       B      T    W  D  L  A  F
+DEF_TEST(FLOAT, CPU, 1, 2, 1, 0, false)
+DEF_TEST(FLOAT, CPU, 1, 2, 1001, 0, true)
+DEF_TEST(FLOAT, CPU, 2, 1, 128, 0, false)
+DEF_TEST(FLOAT, CPU, 2, 4, 128, 0, true)
+DEF_TEST(FLOAT, CPU, 2, 8, 4095, 0, false)
+DEF_TEST(FLOAT, CPU, 4, 4, 1045991, 0, true)
+
+DEF_TEST(DOUBLE, CPU, 2, 4, 128, 0, false)
+DEF_TEST(INT32, CPU, 2, 4, 128, 0, true)
+DEF_TEST(INT64, CPU, 2, 4, 128, 0, false)
 
 // Failure cases
-DEF_TEST(FLOAT, CPU, 2, 4, 128, 1)
-DEF_TEST(FLOAT, CPU, 2, 4, 128, 5)
+DEF_TEST(FLOAT, CPU, 2, 4, 128, 1, true)
+DEF_TEST(FLOAT, CPU, 2, 4, 128, 5, false)
 #endif
 
 #ifdef GOOGLE_CUDA
 // Can only set W=1 for GPU tests.
-//       B      T    W  D  L  A
-DEF_TEST(FLOAT, GPU, 1, 2, 1, 0)
-DEF_TEST(FLOAT, GPU, 1, 2, 33, 0)
-DEF_TEST(FLOAT, GPU, 1, 3, 64, 0)
-DEF_TEST(FLOAT, GPU, 1, 8, 1001, 0)
-DEF_TEST(FLOAT, GPU, 1, 8, 4095, 0)
-DEF_TEST(FLOAT, GPU, 1, 8, 1045991, 0)
+//       B      T    W  D  L  A  F
+DEF_TEST(FLOAT, GPU, 1, 2, 1, 0, true)
+DEF_TEST(FLOAT, GPU, 1, 2, 33, 0, false)
+DEF_TEST(FLOAT, GPU, 1, 3, 64, 0, true)
+DEF_TEST(FLOAT, GPU, 1, 8, 1001, 0, false)
+DEF_TEST(FLOAT, GPU, 1, 8, 4095, 0, true)
+DEF_TEST(FLOAT, GPU, 1, 8, 1045991, 0, false)
 
-DEF_TEST(DOUBLE, GPU, 1, 8, 1001, 0)
-DEF_TEST(INT64, GPU, 1, 8, 1001, 0)
+DEF_TEST(DOUBLE, GPU, 1, 8, 1001, 0, true)
+DEF_TEST(INT64, GPU, 1, 8, 1001, 0, false)
 
 // Failure cases
-DEF_TEST(FLOAT, GPU, 1, 8, 128, 6)
+DEF_TEST(FLOAT, GPU, 1, 8, 128, 6, true)
 #endif
 
 }  // namespace

From 5828842e5956825a65a5423b1ca503f72b084e62 Mon Sep 17 00:00:00 2001
From: Allen Lavoie 
Date: Fri, 11 May 2018 15:58:39 -0700
Subject: [PATCH 0697/1691] Checkpointable: Remove overzealous error checking
 from tf.make_template

It was checking that all variables in the Template's scope were dependencies, but Optimizer slot variables are created with the same prefix (and should not be dependencies).

Conversely, eager execution's eager slot variable creation meant that Templates create unnecessary/somewhat harmful dependencies on restored slot variables. Fixes that.

PiperOrigin-RevId: 196321999
---
 .../optimizer_v2/checkpointable_utils_test.py | 45 +++++++++++++++++++
 .../contrib/optimizer_v2/optimizer_v2.py      | 11 ++++-
 tensorflow/python/ops/template.py             | 36 ---------------
 .../training/checkpointable_utils_test.py     | 17 +++++--
 tensorflow/python/training/optimizer.py       | 11 ++++-
 5 files changed, 78 insertions(+), 42 deletions(-)

diff --git a/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py b/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py
index 87b2ecf565649d..b1f2e9d8609a89 100644
--- a/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py
+++ b/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py
@@ -36,8 +36,10 @@
 from tensorflow.python.keras._impl.keras.engine import training
 from tensorflow.python.keras._impl.keras.layers import core
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import template
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.training import checkpointable
 from tensorflow.python.training import checkpointable_utils
@@ -612,6 +614,49 @@ def testMultipleGraphsNonSlotVariables(self):
         self.assertAllEqual(3., self.evaluate(beta1_power))
 
 
+class TemplateTests(test.TestCase):
+
+  @test_util.run_in_graph_and_eager_modes()
+  def test_checkpointable_save_restore(self):
+
+    def _templated():
+      v = variable_scope.get_variable(
+          "v", shape=[1], initializer=init_ops.zeros_initializer(),
+          use_resource=True)
+      v2 = variable_scope.get_variable(
+          "v2", shape=[1], initializer=init_ops.zeros_initializer(),
+          use_resource=True)
+      return v, v + 1., v2
+
+    save_template = template.make_template("s1", _templated)
+    v1_save, _, v2_save = save_template()
+    optimizer = adam.AdamOptimizer(0.0)
+    save_root = checkpointable_utils.Checkpoint(
+        my_template=save_template, optimizer=optimizer)
+    optimizer.minimize(v1_save.read_value)
+    self.evaluate([v.initializer for v in optimizer.variables()])
+    self.evaluate(v1_save.assign([12.]))
+    self.evaluate(v2_save.assign([14.]))
+    checkpoint_directory = self.get_temp_dir()
+    checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+    save_path = save_root.save(checkpoint_prefix)
+
+    load_template = template.make_template("s2", _templated)
+    load_optimizer = adam.AdamOptimizer(0.0)
+    load_root = checkpointable_utils.Checkpoint(
+        my_template=load_template, optimizer=load_optimizer)
+    status = load_root.restore(save_path)
+    var, var_plus_one, var2 = load_template()
+    load_optimizer.minimize(var.read_value)
+    self.assertEqual(2, len(load_template._checkpoint_dependencies))
+    self.assertEqual("v", load_template._checkpoint_dependencies[0].name)
+    self.assertEqual("v2", load_template._checkpoint_dependencies[1].name)
+    status.assert_consumed().run_restore_ops()
+    self.assertAllEqual([12.], self.evaluate(var))
+    self.assertAllEqual([13.], self.evaluate(var_plus_one))
+    self.assertAllEqual([14.], self.evaluate(var2))
+
+
 class CheckpointCompatibilityTests(test.TestCase):
 
   def _initialized_model(self):
diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2.py b/tensorflow/contrib/optimizer_v2/optimizer_v2.py
index 46bfbb729fa9cd..694a3cebd662a4 100644
--- a/tensorflow/contrib/optimizer_v2/optimizer_v2.py
+++ b/tensorflow/contrib/optimizer_v2/optimizer_v2.py
@@ -360,7 +360,16 @@ def _create_or_restore_slot_variable(
     """
     slot_variable = self.get_slot(var=variable, name=slot_name)
     if (slot_variable is None and context.executing_eagerly() and
-        slot_variable_position.is_simple_variable()):
+        slot_variable_position.is_simple_variable()
+        # Defer slot variable creation if there is an active variable creator
+        # scope. Generally we'd like to eagerly create/restore slot variables
+        # when possible, but this may mean that scopes intended to catch
+        # `variable` also catch its eagerly created slot variable
+        # unintentionally (specifically make_template would add a dependency on
+        # a slot variable if not for this case). Deferring is mostly harmless
+        # (aside from double initialization), and makes variable creator scopes
+        # behave the same way they do when graph building.
+        and not ops.get_default_graph()._variable_creator_stack):  # pylint: disable=protected-access
       initializer = checkpointable.CheckpointInitialValue(
           checkpoint_position=slot_variable_position)
       slot_variable = self.create_slot(
diff --git a/tensorflow/python/ops/template.py b/tensorflow/python/ops/template.py
index 9b6b8c508fcd7e..b46c46d871a667 100644
--- a/tensorflow/python/ops/template.py
+++ b/tensorflow/python/ops/template.py
@@ -295,42 +295,6 @@ def __init__(self, name, func, create_scope_now=False, unique_name=None,
     # which is not the same as whether the scope has been created.
     self._variables_created = False
 
-  @property
-  def _checkpoint_dependencies(self):
-    """Sanity checking for object-based saving.
-
-    Does not override Checkpointable dependency tracking, but checks that
-    variables accessible through Checkpointable dependencies on other `Template`
-    objects include all of the variable_scope-filtered `Template.variables`.
-
-    Returns:
-      A list of checkpointable.CheckpointableReference objects.
-    Raises:
-      ValueError: If this object is not compatible with object-based saving.
-    """
-    dependencies = super(Template, self)._checkpoint_dependencies
-    dependency_variables = []
-    for _, dependency in dependencies:
-      if isinstance(dependency, Template):
-        dependency_variables.extend(dependency.variables)
-      else:
-        dependency_variables.append(dependency)
-    dependency_variables = set(dependency_variables)
-    not_included_variables = []
-    for expected_variable in sorted(self.variables, key=lambda v: v.name):
-      if expected_variable not in dependency_variables:
-        not_included_variables.append(expected_variable)
-    if not_included_variables:
-      # Trying to save a Template which improperly tracks its variables.
-      raise ValueError(
-          ("The Template '%s' references variables which are not included via "
-           "object-based dependency tracking. Most likely a custom "
-           "getter/creator was registered which does not call Template's "
-           "custom variable creator (which is responsible for tracking "
-           "dependencies).\n\nExpected these variables to be dependencies: %s")
-          % (self, not_included_variables))
-    return dependencies
-
   def _checkpointable_custom_creator(self, next_creator, name, initial_value,
                                      checkpointable_parent=None, **kwargs):
     """A variable creation hook which adds Checkpointable dependencies.
diff --git a/tensorflow/python/training/checkpointable_utils_test.py b/tensorflow/python/training/checkpointable_utils_test.py
index 84cacb6ed9109e..d94cdcfc063e81 100644
--- a/tensorflow/python/training/checkpointable_utils_test.py
+++ b/tensorflow/python/training/checkpointable_utils_test.py
@@ -1250,14 +1250,20 @@ def test_checkpointable_save_restore(self):
 
     def _templated():
       v = variable_scope.get_variable(
-          "v", shape=[1], initializer=init_ops.zeros_initializer())
+          "v", shape=[1], initializer=init_ops.zeros_initializer(),
+          use_resource=True)
       v2 = variable_scope.get_variable(
-          "v2", shape=[1], initializer=init_ops.zeros_initializer())
+          "v2", shape=[1], initializer=init_ops.zeros_initializer(),
+          use_resource=True)
       return v, v + 1., v2
 
     save_template = template.make_template("s1", _templated)
-    save_root = checkpointable_utils.Checkpoint(my_template=save_template)
     v1_save, _, v2_save = save_template()
+    optimizer = adam.AdamOptimizer(0.0)
+    save_root = checkpointable_utils.Checkpoint(
+        my_template=save_template, optimizer=optimizer)
+    optimizer.minimize(v1_save.read_value)
+    self.evaluate([v.initializer for v in optimizer.variables()])
     self.evaluate(v1_save.assign([12.]))
     self.evaluate(v2_save.assign([14.]))
     checkpoint_directory = self.get_temp_dir()
@@ -1265,9 +1271,12 @@ def _templated():
     save_path = save_root.save(checkpoint_prefix)
 
     load_template = template.make_template("s2", _templated)
-    load_root = checkpointable_utils.Checkpoint(my_template=load_template)
+    load_optimizer = adam.AdamOptimizer(0.0)
+    load_root = checkpointable_utils.Checkpoint(
+        my_template=load_template, optimizer=load_optimizer)
     status = load_root.restore(save_path)
     var, var_plus_one, var2 = load_template()
+    load_optimizer.minimize(var.read_value)
     self.assertEqual(2, len(load_template._checkpoint_dependencies))
     self.assertEqual("v", load_template._checkpoint_dependencies[0].name)
     self.assertEqual("v2", load_template._checkpoint_dependencies[1].name)
diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py
index 66914bacf35c75..a676ef9a12e09e 100644
--- a/tensorflow/python/training/optimizer.py
+++ b/tensorflow/python/training/optimizer.py
@@ -1175,7 +1175,16 @@ def _create_or_restore_slot_variable(
     variable_key = _var_key(variable)
     slot_variable = named_slots.get(variable_key, None)
     if (slot_variable is None and context.executing_eagerly() and
-        slot_variable_position.is_simple_variable()):
+        slot_variable_position.is_simple_variable()
+        # Defer slot variable creation if there is an active variable creator
+        # scope. Generally we'd like to eagerly create/restore slot variables
+        # when possible, but this may mean that scopes intended to catch
+        # `variable` also catch its eagerly created slot variable
+        # unintentionally (specifically make_template would add a dependency on
+        # a slot variable if not for this case). Deferring is mostly harmless
+        # (aside from double initialization), and makes variable creator scopes
+        # behave the same way they do when graph building.
+        and not ops.get_default_graph()._variable_creator_stack):  # pylint: disable=protected-access
       initializer = checkpointable.CheckpointInitialValue(
           checkpoint_position=slot_variable_position)
       slot_variable = self._get_or_make_slot(

From 2f5f2cb4253b4eaf7953cf7ed28f76e0bdee6fcc Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Fri, 11 May 2018 16:04:54 -0700
Subject: [PATCH 0698/1691] [XLA] s/tensorflow::Status/Status/.

These are type aliases of one another; we'd like to be consistent and
use the shorter one.

PiperOrigin-RevId: 196322955
---
 tensorflow/compiler/xla/BUILD                 |   3 +-
 tensorflow/compiler/xla/client/client.cc      |   6 +-
 tensorflow/compiler/xla/client/global_data.cc |   2 +-
 .../compiler/xla/client/local_client.cc       |   8 +-
 tensorflow/compiler/xla/client/local_client.h |   8 +-
 tensorflow/compiler/xla/layout_util.cc        |  22 +-
 tensorflow/compiler/xla/layout_util.h         |  11 +-
 tensorflow/compiler/xla/rpc/grpc_service.cc   |   4 +-
 tensorflow/compiler/xla/rpc/grpc_stub.cc      | 116 +++++-----
 tensorflow/compiler/xla/rpc/grpc_stub.h       | 121 +++++------
 .../xla/service/allocation_tracker.cc         |   6 +-
 .../compiler/xla/service/buffer_liveness.cc   |   4 +-
 .../compiler/xla/service/buffer_liveness.h    |   2 +-
 .../xla/service/compile_only_service.h        |  40 ++--
 .../xla/service/cpu/cpu_layout_assignment.cc  |   2 +-
 .../xla/service/cpu/dot_op_emitter.cc         |  14 +-
 .../compiler/xla/service/cpu/dot_op_emitter.h |   8 +-
 .../xla/service/device_memory_allocator.h     |   6 +-
 .../compiler/xla/service/execution_tracker.cc |   8 +-
 .../compiler/xla/service/execution_tracker.h  |   4 +-
 .../xla/service/gpu/buffer_allocations.cc     |   2 +-
 .../xla/service/gpu/buffer_allocations.h      |   3 +-
 .../compiler/xla/service/gpu/copy_thunk.cc    |   8 +-
 .../compiler/xla/service/gpu/copy_thunk.h     |   8 +-
 .../compiler/xla/service/gpu/fft_thunk.cc     |   6 +-
 .../compiler/xla/service/gpu/fft_thunk.h      |   4 +-
 .../compiler/xla/service/gpu/for_thunk.cc     |  12 +-
 .../compiler/xla/service/gpu/for_thunk.h      |   8 +-
 .../compiler/xla/service/gpu/gemm_thunk.cc    |   6 +-
 .../compiler/xla/service/gpu/gemm_thunk.h     |   4 +-
 .../compiler/xla/service/gpu/gpu_compiler.cc  |   9 +-
 .../compiler/xla/service/gpu/kernel_thunk.cc  |  12 +-
 .../compiler/xla/service/gpu/kernel_thunk.h   |   8 +-
 .../gpu/llvm_gpu_backend/gpu_backend_lib.cc   |  13 +-
 .../xla/service/gpu/sequential_thunk.cc       |  10 +-
 .../xla/service/gpu/sequential_thunk.h        |   8 +-
 tensorflow/compiler/xla/service/gpu/thunk.h   |  10 +-
 .../compiler/xla/service/gpu/tuple_thunk.cc   |   6 +-
 .../compiler/xla/service/gpu/tuple_thunk.h    |   4 +-
 .../xla/service/gpu/while_transformer.cc      |  12 +-
 .../compiler/xla/service/hlo_verifier.cc      |  38 ++--
 .../compiler/xla/service/hlo_verifier.h       |   4 +-
 .../xla/service/layout_assignment_test.cc     |  13 +-
 .../xla/service/llvm_ir/fused_ir_emitter.cc   |   2 +-
 .../xla/service/llvm_ir/loop_emitter.cc       |   9 +-
 .../xla/service/llvm_ir/loop_emitter.h        |   5 +-
 tensorflow/compiler/xla/service/service.cc    | 200 +++++++++---------
 tensorflow/compiler/xla/service/service.h     | 133 +++++-------
 .../compiler/xla/service/shape_inference.cc   |  30 +--
 .../compiler/xla/service/transpose_folding.cc |   2 +-
 tensorflow/compiler/xla/service_interface.h   | 114 +++++-----
 tensorflow/compiler/xla/shape_layout.cc       |   8 +-
 tensorflow/compiler/xla/shape_layout.h        |   4 +-
 tensorflow/compiler/xla/status.h              |   2 +-
 tensorflow/compiler/xla/statusor_test.cc      |   2 +-
 tensorflow/compiler/xla/test_helpers.h        |  29 +--
 .../xla/tests/client_library_test_base.cc     |  26 ++-
 .../xla/tests/client_library_test_base.h      |   8 +-
 .../xla/tests/local_client_test_base.cc       |   3 +-
 .../xla/tests/local_client_test_base.h        |   3 +-
 tensorflow/compiler/xla/tests/params_test.cc  |   2 +-
 .../compiler/xla/text_literal_writer.cc       |   4 +-
 tensorflow/compiler/xla/text_literal_writer.h |   4 +-
 .../xla/tools/parser/hlo_parser_test.cc       |  20 +-
 64 files changed, 558 insertions(+), 655 deletions(-)

diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index 729480e80f8b3b..43040459c1dbea 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -99,9 +99,9 @@ cc_library(
     hdrs = ["service_interface.h"],
     visibility = [":friends"],
     deps = [
+        ":status",
         ":xla_data_proto",
         ":xla_proto",
-        "//tensorflow/core:lib",
     ],
 )
 
@@ -245,6 +245,7 @@ cc_library(
     visibility = ["//visibility:public"],
     deps = [
         ":protobuf_util",
+        ":status",
         ":status_macros",
         ":statusor",
         ":types",
diff --git a/tensorflow/compiler/xla/client/client.cc b/tensorflow/compiler/xla/client/client.cc
index 328e1b8fa84e7b..0a79b3cf279e25 100644
--- a/tensorflow/compiler/xla/client/client.cc
+++ b/tensorflow/compiler/xla/client/client.cc
@@ -336,7 +336,7 @@ StatusOr>> Client::ExecuteParallel(
 
   ExecuteParallelResponse response;
   VLOG(1) << "making execute-parallel request: " << request.ShortDebugString();
-  tensorflow::Status s = stub_->ExecuteParallel(&request, &response);
+  Status s = stub_->ExecuteParallel(&request, &response);
   VLOG(1) << "done with request";
 
   if (!s.ok()) {
@@ -372,7 +372,7 @@ StatusOr>> Client::ExecuteParallel(
   ExecuteParallelResponse response;
   VLOG(1) << "making execute-graph-parallel request: "
           << request.ShortDebugString();
-  tensorflow::Status s = stub_->ExecuteGraphParallel(&request, &response);
+  Status s = stub_->ExecuteGraphParallel(&request, &response);
   VLOG(1) << "done with request";
 
   if (!s.ok()) {
@@ -401,7 +401,7 @@ StatusOr> Client::GetDeviceHandles(
 
   GetDeviceHandlesResponse response;
   VLOG(1) << "making get device request: " << request.ShortDebugString();
-  tensorflow::Status s = stub_->GetDeviceHandles(&request, &response);
+  Status s = stub_->GetDeviceHandles(&request, &response);
   VLOG(1) << "done with request";
 
   if (!s.ok()) {
diff --git a/tensorflow/compiler/xla/client/global_data.cc b/tensorflow/compiler/xla/client/global_data.cc
index 40f59eaa68ebeb..2986d406001370 100644
--- a/tensorflow/compiler/xla/client/global_data.cc
+++ b/tensorflow/compiler/xla/client/global_data.cc
@@ -31,7 +31,7 @@ GlobalData::~GlobalData() {
   *request.mutable_data() = handle_;
   UnregisterResponse response;
   VLOG(1) << "requesting to unregister " << handle_.ShortDebugString();
-  tensorflow::Status s = parent_->Unregister(&request, &response);
+  Status s = parent_->Unregister(&request, &response);
   VLOG(1) << "done with request";
 
   if (!s.ok()) {
diff --git a/tensorflow/compiler/xla/client/local_client.cc b/tensorflow/compiler/xla/client/local_client.cc
index 1acc6f86860e52..9d44d3ad7d52b9 100644
--- a/tensorflow/compiler/xla/client/local_client.cc
+++ b/tensorflow/compiler/xla/client/local_client.cc
@@ -48,7 +48,7 @@ LocalExecutable::LocalExecutable(std::unique_ptr executable,
       << "Must have a valid device ordinal that the executable was built for.";
 }
 
-tensorflow::Status LocalExecutable::ValidateExecutionOptions(
+Status LocalExecutable::ValidateExecutionOptions(
     const tensorflow::gtl::ArraySlice arguments,
     const ExecutableRunOptions& run_options, const Backend& backend) {
   const ComputationLayout& host_computation_layout =
@@ -207,7 +207,7 @@ StatusOr LocalExecutable::ExecuteAndDump(
   return std::move(result);
 }
 
-tensorflow::Status LocalExecutable::RecordArguments(
+Status LocalExecutable::RecordArguments(
     const tensorflow::gtl::ArraySlice arguments,
     SessionModule* session_module) {
   session_module->clear_arguments();
@@ -219,8 +219,8 @@ tensorflow::Status LocalExecutable::RecordArguments(
   return Status::OK();
 }
 
-tensorflow::Status LocalExecutable::RecordResult(
-    const ShapedBuffer* result, SessionModule* session_module) {
+Status LocalExecutable::RecordResult(const ShapedBuffer* result,
+                                     SessionModule* session_module) {
   session_module->clear_result();
   TF_ASSIGN_OR_RETURN(std::unique_ptr literal,
                       LiteralFromShapedBuffer(*result));
diff --git a/tensorflow/compiler/xla/client/local_client.h b/tensorflow/compiler/xla/client/local_client.h
index d8fd7a5623d1fe..31950377f4c70c 100644
--- a/tensorflow/compiler/xla/client/local_client.h
+++ b/tensorflow/compiler/xla/client/local_client.h
@@ -59,7 +59,7 @@ class LocalExecutable {
 
   // Validates that the given arguments and options satisfy various constraints
   // of the computation.
-  tensorflow::Status ValidateExecutionOptions(
+  Status ValidateExecutionOptions(
       const tensorflow::gtl::ArraySlice arguments,
       const ExecutableRunOptions& run_options, const Backend& backend);
 
@@ -71,13 +71,13 @@ class LocalExecutable {
 
   // Records the arguments used to invoke the computation in a SessionModule
   // proto.
-  tensorflow::Status RecordArguments(
+  Status RecordArguments(
       const tensorflow::gtl::ArraySlice arguments,
       SessionModule* session_module);
 
   // Records the result of the computation in a SessionModule proto.
-  tensorflow::Status RecordResult(const ShapedBuffer* result,
-                                  SessionModule* session_module);
+  Status RecordResult(const ShapedBuffer* result,
+                      SessionModule* session_module);
 
   // Returns a literal containing the contents of the given ShapedBuffer.
   StatusOr> LiteralFromShapedBuffer(
diff --git a/tensorflow/compiler/xla/layout_util.cc b/tensorflow/compiler/xla/layout_util.cc
index c6f8f6766e9d01..a76fdcda250168 100644
--- a/tensorflow/compiler/xla/layout_util.cc
+++ b/tensorflow/compiler/xla/layout_util.cc
@@ -140,8 +140,7 @@ Layout CreateDefaultLayoutForRank(int64 rank) {
   LayoutUtil::SetToDefaultLayout(program_shape->mutable_result());
 }
 
-/* static */ tensorflow::Status LayoutUtil::ValidateLayoutInShape(
-    const Shape& shape) {
+/* static */ Status LayoutUtil::ValidateLayoutInShape(const Shape& shape) {
   if (ShapeUtil::IsTuple(shape)) {
     // Tuple shape.
     if (shape.has_layout()) {
@@ -150,12 +149,12 @@ Layout CreateDefaultLayoutForRank(int64 rank) {
     for (auto& element_shape : shape.tuple_shapes()) {
       TF_RETURN_IF_ERROR(ValidateLayoutInShape(element_shape));
     }
-    return tensorflow::Status::OK();
+    return Status::OK();
   } else if (ShapeUtil::IsOpaque(shape)) {
     if (shape.has_layout()) {
       return InvalidArgument("opaque should not have a layout field");
     }
-    return tensorflow::Status::OK();
+    return Status::OK();
   } else {
     // Array shape.
     if (!shape.has_layout()) {
@@ -166,14 +165,14 @@ Layout CreateDefaultLayoutForRank(int64 rank) {
   }
 }
 
-/* static */ tensorflow::Status LayoutUtil::ValidateLayoutForShape(
-    const Layout& layout, const Shape& shape) {
+/* static */ Status LayoutUtil::ValidateLayoutForShape(const Layout& layout,
+                                                       const Shape& shape) {
   if (ShapeUtil::IsTuple(shape)) {
     return InvalidArgument("a single Layout is not valid for tuple shapes");
   }
 
   if (ShapeUtil::IsOpaque(shape)) {
-    return tensorflow::Status::OK();
+    return Status::OK();
   }
 
   if (layout.format() == INVALID_FORMAT) {
@@ -225,7 +224,7 @@ Layout CreateDefaultLayoutForRank(int64 rank) {
     }
   }
 
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 /* static */ void LayoutUtil::ClearLayout(Shape* shape) {
@@ -384,7 +383,7 @@ Layout CreateDefaultLayoutForRank(int64 rank) {
 namespace {
 
 // Internal helper for recursively copying layouts.
-tensorflow::Status CopyLayoutInternal(const Shape& src, Shape* dst) {
+Status CopyLayoutInternal(const Shape& src, Shape* dst) {
   if (ShapeUtil::IsTuple(src) != ShapeUtil::IsTuple(*dst)) {
     return InvalidArgument(
         "cannot copy layout from shape: shape structure differs");
@@ -411,14 +410,13 @@ tensorflow::Status CopyLayoutInternal(const Shape& src, Shape* dst) {
       dst->clear_layout();
     }
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 }  // namespace
 
 /* static */
-tensorflow::Status LayoutUtil::CopyLayoutBetweenShapes(const Shape& src,
-                                                       Shape* dst) {
+Status LayoutUtil::CopyLayoutBetweenShapes(const Shape& src, Shape* dst) {
   return CopyLayoutInternal(src, dst);
 }
 
diff --git a/tensorflow/compiler/xla/layout_util.h b/tensorflow/compiler/xla/layout_util.h
index 6cec7501015e2d..d3d6a2cc94012f 100644
--- a/tensorflow/compiler/xla/layout_util.h
+++ b/tensorflow/compiler/xla/layout_util.h
@@ -20,9 +20,9 @@ limitations under the License.
 
 #include 
 
+#include "tensorflow/compiler/xla/status.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
-#include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/types.h"
@@ -61,12 +61,12 @@ class LayoutUtil {
   static void SetToDefaultLayout(ProgramShape* program_shape);
 
   // Validates that the layout within the given shape is correct.
-  static tensorflow::Status ValidateLayoutInShape(const Shape& shape);
+  static Status ValidateLayoutInShape(const Shape& shape);
 
   // Validates that the provided layout satisfies invariants for the given
   // shape.
-  static tensorflow::Status ValidateLayoutForShape(const Layout& layout,
-                                                   const Shape& shape);
+  static Status ValidateLayoutForShape(const Layout& layout,
+                                       const Shape& shape);
 
   // Clears the layout in the given Shape. After this function is called,
   // HasLayout will return false for the shape.
@@ -179,8 +179,7 @@ class LayoutUtil {
   // tuples.  'src' and 'dst' need not be compatible but the two shapes must
   // have the same tuple structure (if any) and arrays must have the same
   // rank. within the shapes must have the same number of dimensions.
-  static tensorflow::Status CopyLayoutBetweenShapes(const Shape& src,
-                                                    Shape* dst);
+  static Status CopyLayoutBetweenShapes(const Shape& src, Shape* dst);
 
   // Returns true if the layouts of lhs and rhs are equal, false
   // otherwise. Recursively compares layouts of tuples.
diff --git a/tensorflow/compiler/xla/rpc/grpc_service.cc b/tensorflow/compiler/xla/rpc/grpc_service.cc
index ffb72fc73c5bc1..5f4dc6bd08f18b 100644
--- a/tensorflow/compiler/xla/rpc/grpc_service.cc
+++ b/tensorflow/compiler/xla/rpc/grpc_service.cc
@@ -27,8 +27,8 @@ namespace xla {
   return std::move(grpc_service);
 }
 
-::grpc::Status DelegateRPC(std::function op) {
-  tensorflow::Status s = op();
+::grpc::Status DelegateRPC(std::function op) {
+  Status s = op();
   return tensorflow::ToGrpcStatus(s);
 }
 
diff --git a/tensorflow/compiler/xla/rpc/grpc_stub.cc b/tensorflow/compiler/xla/rpc/grpc_stub.cc
index e1f2b0abe39b10..620ac6cec4f76d 100644
--- a/tensorflow/compiler/xla/rpc/grpc_stub.cc
+++ b/tensorflow/compiler/xla/rpc/grpc_stub.cc
@@ -20,53 +20,49 @@ namespace xla {
 
 GRPCStub::~GRPCStub() = default;
 
-tensorflow::Status MakeRPC(
+Status MakeRPC(
     const std::function<::grpc::Status(::grpc::ClientContext*)>& rpc_method) {
   ::grpc::ClientContext context;
   ::grpc::Status s = rpc_method(&context);
   return tensorflow::FromGrpcStatus(s);
 }
 
-tensorflow::Status GRPCStub::TransferToClient(
-    const TransferToClientRequest* request,
-    TransferToClientResponse* response) {
+Status GRPCStub::TransferToClient(const TransferToClientRequest* request,
+                                  TransferToClientResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->TransferToClient(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::TransferToServer(
-    const TransferToServerRequest* request,
-    TransferToServerResponse* response) {
+Status GRPCStub::TransferToServer(const TransferToServerRequest* request,
+                                  TransferToServerResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->TransferToServer(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::TransferToInfeed(
-    const TransferToInfeedRequest* request,
-    TransferToInfeedResponse* response) {
+Status GRPCStub::TransferToInfeed(const TransferToInfeedRequest* request,
+                                  TransferToInfeedResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->TransferToInfeed(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::TransferFromOutfeed(
-    const TransferFromOutfeedRequest* request,
-    TransferFromOutfeedResponse* response) {
+Status GRPCStub::TransferFromOutfeed(const TransferFromOutfeedRequest* request,
+                                     TransferFromOutfeedResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->TransferFromOutfeed(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::ResetDevice(const ResetDeviceRequest* request,
-                                         ResetDeviceResponse* response) {
+Status GRPCStub::ResetDevice(const ResetDeviceRequest* request,
+                             ResetDeviceResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->ResetDevice(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::LoadComputationSnapshot(
+Status GRPCStub::LoadComputationSnapshot(
     const LoadComputationSnapshotRequest* request,
     LoadComputationSnapshotResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
@@ -74,28 +70,28 @@ tensorflow::Status GRPCStub::LoadComputationSnapshot(
   });
 }
 
-tensorflow::Status GRPCStub::Execute(const ExecuteRequest* request,
-                                     ExecuteResponse* response) {
+Status GRPCStub::Execute(const ExecuteRequest* request,
+                         ExecuteResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->Execute(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::ExecuteGraph(const ExecuteGraphRequest* request,
-                                          ExecuteResponse* response) {
+Status GRPCStub::ExecuteGraph(const ExecuteGraphRequest* request,
+                              ExecuteResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->ExecuteGraph(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::ExecuteParallel(
-    const ExecuteParallelRequest* request, ExecuteParallelResponse* response) {
+Status GRPCStub::ExecuteParallel(const ExecuteParallelRequest* request,
+                                 ExecuteParallelResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->ExecuteParallel(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::ExecuteGraphParallel(
+Status GRPCStub::ExecuteGraphParallel(
     const ExecuteGraphParallelRequest* request,
     ExecuteParallelResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
@@ -103,38 +99,35 @@ tensorflow::Status GRPCStub::ExecuteGraphParallel(
   });
 }
 
-tensorflow::Status GRPCStub::ExecuteAsync(const ExecuteAsyncRequest* request,
-                                          ExecuteAsyncResponse* response) {
+Status GRPCStub::ExecuteAsync(const ExecuteAsyncRequest* request,
+                              ExecuteAsyncResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->ExecuteAsync(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::WaitForExecution(
-    const WaitForExecutionRequest* request,
-    WaitForExecutionResponse* response) {
+Status GRPCStub::WaitForExecution(const WaitForExecutionRequest* request,
+                                  WaitForExecutionResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->WaitForExecution(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::DeconstructTuple(
-    const DeconstructTupleRequest* request,
-    DeconstructTupleResponse* response) {
+Status GRPCStub::DeconstructTuple(const DeconstructTupleRequest* request,
+                                  DeconstructTupleResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->DeconstructTuple(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::GetComputationStats(
-    const ComputationStatsRequest* request,
-    ComputationStatsResponse* response) {
+Status GRPCStub::GetComputationStats(const ComputationStatsRequest* request,
+                                     ComputationStatsResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->GetComputationStats(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::GetComputationGraphStats(
+Status GRPCStub::GetComputationGraphStats(
     const ComputationGraphStatsRequest* request,
     ComputationStatsResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
@@ -142,81 +135,77 @@ tensorflow::Status GRPCStub::GetComputationGraphStats(
   });
 }
 
-tensorflow::Status GRPCStub::GetComputationShape(
-    const GetComputationShapeRequest* request,
-    GetComputationShapeResponse* response) {
+Status GRPCStub::GetComputationShape(const GetComputationShapeRequest* request,
+                                     GetComputationShapeResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->GetComputationShape(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::GetShape(const GetShapeRequest* request,
-                                      GetShapeResponse* response) {
+Status GRPCStub::GetShape(const GetShapeRequest* request,
+                          GetShapeResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->GetShape(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::GetDeviceHandles(
-    const GetDeviceHandlesRequest* request,
-    GetDeviceHandlesResponse* response) {
+Status GRPCStub::GetDeviceHandles(const GetDeviceHandlesRequest* request,
+                                  GetDeviceHandlesResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->GetDeviceHandles(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::CreateChannelHandle(
-    const CreateChannelHandleRequest* request,
-    CreateChannelHandleResponse* response) {
+Status GRPCStub::CreateChannelHandle(const CreateChannelHandleRequest* request,
+                                     CreateChannelHandleResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->CreateChannelHandle(context, *request, response);
   });
 }
 
 // Methods used by ComputationBuilder.
-tensorflow::Status GRPCStub::Computation(const ComputationRequest* request,
-                                         ComputationResponse* response) {
+Status GRPCStub::Computation(const ComputationRequest* request,
+                             ComputationResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->Computation(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::Op(const OpRequest* request,
-                                OpResponse* response) {
+Status GRPCStub::Op(const OpRequest* request, OpResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->CreateOp(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::GetLocalShape(const GetLocalShapeRequest* request,
-                                           GetLocalShapeResponse* response) {
+Status GRPCStub::GetLocalShape(const GetLocalShapeRequest* request,
+                               GetLocalShapeResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->GetLocalShape(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::SetReturnValue(
-    const SetReturnValueRequest* request, SetReturnValueResponse* responses) {
+Status GRPCStub::SetReturnValue(const SetReturnValueRequest* request,
+                                SetReturnValueResponse* responses) {
   return MakeRPC([this, request, responses](::grpc::ClientContext* context) {
     return grpc_stub_->SetReturnValue(context, *request, responses);
   });
 }
 
-tensorflow::Status GRPCStub::IsConstant(const IsConstantRequest* request,
-                                        IsConstantResponse* response) {
+Status GRPCStub::IsConstant(const IsConstantRequest* request,
+                            IsConstantResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->IsConstant(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::ComputeConstant(
-    const ComputeConstantRequest* request, ComputeConstantResponse* response) {
+Status GRPCStub::ComputeConstant(const ComputeConstantRequest* request,
+                                 ComputeConstantResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->ComputeConstant(context, *request, response);
   });
 }
 
-tensorflow::Status GRPCStub::ComputeConstantGraph(
+Status GRPCStub::ComputeConstantGraph(
     const ComputeConstantGraphRequest* request,
     ComputeConstantResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
@@ -225,17 +214,16 @@ tensorflow::Status GRPCStub::ComputeConstantGraph(
 }
 
 // Methods used by Computation.
-tensorflow::Status GRPCStub::SnapshotComputation(
-    const SnapshotComputationRequest* request,
-    SnapshotComputationResponse* response) {
+Status GRPCStub::SnapshotComputation(const SnapshotComputationRequest* request,
+                                     SnapshotComputationResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->SnapshotComputation(context, *request, response);
   });
 }
 
 // Methods used by GlobalData.
-tensorflow::Status GRPCStub::Unregister(const UnregisterRequest* request,
-                                        UnregisterResponse* response) {
+Status GRPCStub::Unregister(const UnregisterRequest* request,
+                            UnregisterResponse* response) {
   return MakeRPC([this, request, response](::grpc::ClientContext* context) {
     return grpc_stub_->Unregister(context, *request, response);
   });
diff --git a/tensorflow/compiler/xla/rpc/grpc_stub.h b/tensorflow/compiler/xla/rpc/grpc_stub.h
index fd9810d4f1a5e0..5906d45769b574 100644
--- a/tensorflow/compiler/xla/rpc/grpc_stub.h
+++ b/tensorflow/compiler/xla/rpc/grpc_stub.h
@@ -28,105 +28,90 @@ class GRPCStub : public ServiceInterface {
   explicit GRPCStub(grpc::XlaService::Stub* stub) : grpc_stub_(stub) {}
   ~GRPCStub() override;
 
-  tensorflow::Status TransferToClient(
-      const TransferToClientRequest* arg,
-      TransferToClientResponse* result) override;
+  Status TransferToClient(const TransferToClientRequest* arg,
+                          TransferToClientResponse* result) override;
 
-  tensorflow::Status TransferToServer(
-      const TransferToServerRequest* arg,
-      TransferToServerResponse* result) override;
+  Status TransferToServer(const TransferToServerRequest* arg,
+                          TransferToServerResponse* result) override;
 
-  tensorflow::Status TransferToInfeed(
-      const TransferToInfeedRequest* arg,
-      TransferToInfeedResponse* result) override;
+  Status TransferToInfeed(const TransferToInfeedRequest* arg,
+                          TransferToInfeedResponse* result) override;
 
-  tensorflow::Status TransferFromOutfeed(
-      const TransferFromOutfeedRequest* arg,
-      TransferFromOutfeedResponse* result) override;
+  Status TransferFromOutfeed(const TransferFromOutfeedRequest* arg,
+                             TransferFromOutfeedResponse* result) override;
 
-  tensorflow::Status ResetDevice(const ResetDeviceRequest* arg,
-                                 ResetDeviceResponse* result) override;
+  Status ResetDevice(const ResetDeviceRequest* arg,
+                     ResetDeviceResponse* result) override;
 
-  tensorflow::Status LoadComputationSnapshot(
+  Status LoadComputationSnapshot(
       const LoadComputationSnapshotRequest* request,
       LoadComputationSnapshotResponse* result) override;
 
-  tensorflow::Status Execute(const ExecuteRequest* arg,
-                             ExecuteResponse* result) override;
+  Status Execute(const ExecuteRequest* arg, ExecuteResponse* result) override;
 
-  tensorflow::Status ExecuteGraph(const ExecuteGraphRequest* request,
-                                  ExecuteResponse* response) override;
+  Status ExecuteGraph(const ExecuteGraphRequest* request,
+                      ExecuteResponse* response) override;
 
-  tensorflow::Status ExecuteParallel(const ExecuteParallelRequest* arg,
-                                     ExecuteParallelResponse* result) override;
+  Status ExecuteParallel(const ExecuteParallelRequest* arg,
+                         ExecuteParallelResponse* result) override;
 
-  tensorflow::Status ExecuteGraphParallel(
-      const ExecuteGraphParallelRequest* request,
-      ExecuteParallelResponse* response) override;
+  Status ExecuteGraphParallel(const ExecuteGraphParallelRequest* request,
+                              ExecuteParallelResponse* response) override;
 
-  tensorflow::Status ExecuteAsync(const ExecuteAsyncRequest* arg,
-                                  ExecuteAsyncResponse* result) override;
+  Status ExecuteAsync(const ExecuteAsyncRequest* arg,
+                      ExecuteAsyncResponse* result) override;
 
-  tensorflow::Status WaitForExecution(
-      const WaitForExecutionRequest* arg,
-      WaitForExecutionResponse* result) override;
+  Status WaitForExecution(const WaitForExecutionRequest* arg,
+                          WaitForExecutionResponse* result) override;
 
-  tensorflow::Status DeconstructTuple(
-      const DeconstructTupleRequest* arg,
-      DeconstructTupleResponse* result) override;
+  Status DeconstructTuple(const DeconstructTupleRequest* arg,
+                          DeconstructTupleResponse* result) override;
 
-  tensorflow::Status GetComputationStats(
-      const ComputationStatsRequest* arg,
-      ComputationStatsResponse* result) override;
+  Status GetComputationStats(const ComputationStatsRequest* arg,
+                             ComputationStatsResponse* result) override;
 
-  tensorflow::Status GetComputationGraphStats(
-      const ComputationGraphStatsRequest* request,
-      ComputationStatsResponse* response) override;
+  Status GetComputationGraphStats(const ComputationGraphStatsRequest* request,
+                                  ComputationStatsResponse* response) override;
 
-  tensorflow::Status GetComputationShape(
-      const GetComputationShapeRequest* arg,
-      GetComputationShapeResponse* result) override;
+  Status GetComputationShape(const GetComputationShapeRequest* arg,
+                             GetComputationShapeResponse* result) override;
 
-  tensorflow::Status GetShape(const GetShapeRequest* arg,
-                              GetShapeResponse* result) override;
+  Status GetShape(const GetShapeRequest* arg,
+                  GetShapeResponse* result) override;
 
-  tensorflow::Status GetDeviceHandles(
-      const GetDeviceHandlesRequest* arg,
-      GetDeviceHandlesResponse* result) override;
+  Status GetDeviceHandles(const GetDeviceHandlesRequest* arg,
+                          GetDeviceHandlesResponse* result) override;
 
-  tensorflow::Status CreateChannelHandle(
-      const CreateChannelHandleRequest* arg,
-      CreateChannelHandleResponse* result) override;
+  Status CreateChannelHandle(const CreateChannelHandleRequest* arg,
+                             CreateChannelHandleResponse* result) override;
 
   // Methods used by ComputationBuilder.
-  tensorflow::Status Computation(const ComputationRequest* arg,
-                                 ComputationResponse* result) override;
+  Status Computation(const ComputationRequest* arg,
+                     ComputationResponse* result) override;
 
-  tensorflow::Status Op(const OpRequest* arg, OpResponse* result) override;
-  tensorflow::Status GetLocalShape(const GetLocalShapeRequest* arg,
-                                   GetLocalShapeResponse* result) override;
+  Status Op(const OpRequest* arg, OpResponse* result) override;
+  Status GetLocalShape(const GetLocalShapeRequest* arg,
+                       GetLocalShapeResponse* result) override;
 
-  tensorflow::Status SetReturnValue(const SetReturnValueRequest* arg,
-                                    SetReturnValueResponse* results) override;
+  Status SetReturnValue(const SetReturnValueRequest* arg,
+                        SetReturnValueResponse* results) override;
 
-  tensorflow::Status IsConstant(const IsConstantRequest* arg,
-                                IsConstantResponse* result) override;
+  Status IsConstant(const IsConstantRequest* arg,
+                    IsConstantResponse* result) override;
 
-  tensorflow::Status ComputeConstant(const ComputeConstantRequest* arg,
-                                     ComputeConstantResponse* result) override;
+  Status ComputeConstant(const ComputeConstantRequest* arg,
+                         ComputeConstantResponse* result) override;
 
-  tensorflow::Status ComputeConstantGraph(
-      const ComputeConstantGraphRequest* arg,
-      ComputeConstantResponse* result) override;
+  Status ComputeConstantGraph(const ComputeConstantGraphRequest* arg,
+                              ComputeConstantResponse* result) override;
 
   // Methods used by Computation.
-  tensorflow::Status SnapshotComputation(
-      const SnapshotComputationRequest* ag,
-      SnapshotComputationResponse* result) override;
+  Status SnapshotComputation(const SnapshotComputationRequest* ag,
+                             SnapshotComputationResponse* result) override;
 
   // Methods used by GlobalData.
-  tensorflow::Status Unregister(const UnregisterRequest* arg,
-                                UnregisterResponse* result) override;
+  Status Unregister(const UnregisterRequest* arg,
+                    UnregisterResponse* result) override;
 
   grpc::XlaService::Stub* service() { return grpc_stub_; }
 
diff --git a/tensorflow/compiler/xla/service/allocation_tracker.cc b/tensorflow/compiler/xla/service/allocation_tracker.cc
index eb528032411703..95b4cb6d2e6940 100644
--- a/tensorflow/compiler/xla/service/allocation_tracker.cc
+++ b/tensorflow/compiler/xla/service/allocation_tracker.cc
@@ -101,7 +101,7 @@ StatusOr AllocationTracker::RegisterInternal(
   return result;
 }
 
-tensorflow::Status AllocationTracker::Unregister(const GlobalDataHandle& data) {
+Status AllocationTracker::Unregister(const GlobalDataHandle& data) {
   tensorflow::mutex_lock lock(mutex_);
   VLOG(2) << "Unregister("
           << "handle: " << data.handle() << ")";
@@ -130,7 +130,7 @@ tensorflow::Status AllocationTracker::Unregister(const GlobalDataHandle& data) {
   for (auto& shaped_buffer : it->second) {
     shaped_buffer.reset();
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 StatusOr> AllocationTracker::DeconstructTuple(
@@ -242,7 +242,7 @@ Status AllocationTracker::DecrementRefCount(se::DeviceMemoryBase device_memory,
   } else {
     allocation.ref_count--;
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/buffer_liveness.cc b/tensorflow/compiler/xla/service/buffer_liveness.cc
index 37982aaef9eddd..acb546a0a1278b 100644
--- a/tensorflow/compiler/xla/service/buffer_liveness.cc
+++ b/tensorflow/compiler/xla/service/buffer_liveness.cc
@@ -44,7 +44,7 @@ StatusOr> BufferLiveness::Run(
   return std::move(liveness);
 }
 
-tensorflow::Status BufferLiveness::Analyze() {
+Status BufferLiveness::Analyze() {
   TF_ASSIGN_OR_RETURN(points_to_analysis_, TuplePointsToAnalysis::Run(module_));
   for (auto* computation : module_->computations()) {
     if (computation->IsFusionComputation()) {
@@ -71,7 +71,7 @@ tensorflow::Status BufferLiveness::Analyze() {
   }
 
   XLA_VLOG_LINES(3, ToString());
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 string BufferLiveness::ToString() const {
diff --git a/tensorflow/compiler/xla/service/buffer_liveness.h b/tensorflow/compiler/xla/service/buffer_liveness.h
index 11834a5127e383..cdd3cf4032ef69 100644
--- a/tensorflow/compiler/xla/service/buffer_liveness.h
+++ b/tensorflow/compiler/xla/service/buffer_liveness.h
@@ -89,7 +89,7 @@ class BufferLiveness {
 
   // Perform buffer liveness analysis. This method must be called prior to
   // MayInterfere or MaybeLiveOut.
-  tensorflow::Status Analyze();
+  Status Analyze();
 
   // Returns true if the live range of the buffer of 'a' is strictly before the
   // live range of the buffer of 'b' (they do not overlap).
diff --git a/tensorflow/compiler/xla/service/compile_only_service.h b/tensorflow/compiler/xla/service/compile_only_service.h
index c10609e67fcdec..7f2ce0e8974c01 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.h
+++ b/tensorflow/compiler/xla/service/compile_only_service.h
@@ -75,48 +75,42 @@ class CompileOnlyService : public Service {
   // Override Service methods that require or imply the existence of an
   // execute backend.  Note that this does not include TransferToClient, as
   // computing constants produces global data that we may wish to transfer.
-  tensorflow::Status Execute(const ExecuteRequest* arg,
-                             ExecuteResponse* result) override {
+  Status Execute(const ExecuteRequest* arg, ExecuteResponse* result) override {
     return Unimplemented("CompileOnlyService does not support execution.");
   }
-  tensorflow::Status ExecuteParallel(const ExecuteParallelRequest* arg,
-                                     ExecuteParallelResponse* result) override {
+  Status ExecuteParallel(const ExecuteParallelRequest* arg,
+                         ExecuteParallelResponse* result) override {
     return Unimplemented("CompileOnlyService does not support execution.");
   }
-  tensorflow::Status GetDeviceHandles(
-      const GetDeviceHandlesRequest* arg,
-      GetDeviceHandlesResponse* result) override {
+  Status GetDeviceHandles(const GetDeviceHandlesRequest* arg,
+                          GetDeviceHandlesResponse* result) override {
     return Unimplemented("CompileOnlyService does not support devices.");
   }
-  tensorflow::Status ExecuteAsync(const ExecuteAsyncRequest* arg,
-                                  ExecuteAsyncResponse* result) override {
+  Status ExecuteAsync(const ExecuteAsyncRequest* arg,
+                      ExecuteAsyncResponse* result) override {
     return Unimplemented("CompileOnlyService does not support execution.");
   }
-  tensorflow::Status WaitForExecution(
-      const WaitForExecutionRequest* arg,
-      WaitForExecutionResponse* result) override {
+  Status WaitForExecution(const WaitForExecutionRequest* arg,
+                          WaitForExecutionResponse* result) override {
     return Unimplemented("CompileOnlyService does not support execution.");
   }
-  tensorflow::Status TransferToServer(
-      const TransferToServerRequest* arg,
-      TransferToServerResponse* result) override {
+  Status TransferToServer(const TransferToServerRequest* arg,
+                          TransferToServerResponse* result) override {
     return Unimplemented(
         "CompileOnlyService does not support device data transfers.");
   }
-  tensorflow::Status TransferToInfeed(
-      const TransferToInfeedRequest* arg,
-      TransferToInfeedResponse* result) override {
+  Status TransferToInfeed(const TransferToInfeedRequest* arg,
+                          TransferToInfeedResponse* result) override {
     return Unimplemented(
         "CompileOnlyService does not support device data transfers.");
   }
-  tensorflow::Status TransferFromOutfeed(
-      const TransferFromOutfeedRequest* arg,
-      TransferFromOutfeedResponse* result) override {
+  Status TransferFromOutfeed(const TransferFromOutfeedRequest* arg,
+                             TransferFromOutfeedResponse* result) override {
     return Unimplemented(
         "CompileOnlyService does not support device data transfers.");
   }
-  tensorflow::Status ResetDevice(const ResetDeviceRequest* arg,
-                                 ResetDeviceResponse* result) override {
+  Status ResetDevice(const ResetDeviceRequest* arg,
+                     ResetDeviceResponse* result) override {
     return Unimplemented("CompileOnlyService does not support devices.");
   }
 
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.cc b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.cc
index 85c461e6a894f9..aa872d5ec9e759 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment.cc
@@ -179,7 +179,7 @@ Status CpuLayoutAssignment::AddBackendConstraints(
       }
     }
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 }  // namespace cpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
index 81c0d67cf54ebf..5cdfc110affb85 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
@@ -542,7 +542,7 @@ DotOpEmitter::DotOpEmitter(const HloInstruction& dot,
       hlo_module_config_(hlo_module_config),
       target_machine_features_(target_machine_features) {}
 
-/* static */ tensorflow::Status DotOpEmitter::EmitDotOperation(
+/* static */ Status DotOpEmitter::EmitDotOperation(
     const HloInstruction& dot, const llvm_ir::IrArray& target_array,
     const llvm_ir::IrArray& lhs_array, const llvm_ir::IrArray& rhs_array,
     const llvm_ir::IrArray* addend_array,
@@ -691,7 +691,7 @@ bool DotOpEmitter::EmitLlvmIrDotIfProfitable() {
   return true;
 }
 
-tensorflow::Status DotOpEmitter::Emit() {
+Status DotOpEmitter::Emit() {
   // The dot operation performs a sum of products over dimension 0 of the left
   // hand side operand and dimension 1 of the right hand side operand.
   //
@@ -869,10 +869,10 @@ tensorflow::Status DotOpEmitter::Emit() {
   // loop.
   ir_builder_->SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock());
 
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status DotOpEmitter::EmitScalarDot() {
+Status DotOpEmitter::EmitScalarDot() {
   // A scalar dot is just a scalar multiply.
   llvm::Value* result;
   llvm::Value* lhs_value =
@@ -897,10 +897,10 @@ tensorflow::Status DotOpEmitter::EmitScalarDot() {
     result = ir_builder_->CreateFMul(lhs_value, rhs_value);
   }
   target_array_.EmitWriteArrayElement(/*index=*/{}, result, ir_builder_);
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status DotOpEmitter::EmitCallToRuntime() {
+Status DotOpEmitter::EmitCallToRuntime() {
   // The signature of the Eigen runtime matmul function is:
   //
   //   (void)(void* run_options, float* out, float* lhs, float* rhs,
@@ -1002,7 +1002,7 @@ tensorflow::Status DotOpEmitter::EmitCallToRuntime() {
        ir_builder_->getInt64(mat_mult_dims.k),
        ir_builder_->getInt32(transpose_lhs),
        ir_builder_->getInt32(transpose_rhs)});
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 DotOpEmitter::MatMultDims DotOpEmitter::GetMatMultDims() const {
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
index e5ede066f211b3..566f07ba75b3d5 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h
@@ -57,7 +57,7 @@ class DotOpEmitter {
   // dimensions as the result, and the result is computed as `addend_array` +
   // dot(`lhs_array`, `rhs_array`).  A non-null `addend_array` is only supported
   // for Matrix-vector products.
-  static tensorflow::Status EmitDotOperation(
+  static Status EmitDotOperation(
       const HloInstruction& dot, const llvm_ir::IrArray& target_array,
       const llvm_ir::IrArray& lhs_array, const llvm_ir::IrArray& rhs_array,
       const llvm_ir::IrArray* addend_array,
@@ -76,18 +76,18 @@ class DotOpEmitter {
                const TargetMachineFeatures& target_machine_features);
 
   // Emits the IR to perform the dot operation.
-  tensorflow::Status Emit();
+  Status Emit();
 
   // Emits instructions to perform a scalar dot product (a multiply of the
   // LHS and RHS) and store the results in the target.
-  tensorflow::Status EmitScalarDot();
+  Status EmitScalarDot();
 
   // Emit an LLVM IR implementation of the dot operation if we can.  Returns
   // true if an LLVM IR implementation was emitted.
   bool EmitLlvmIrDotIfProfitable();
 
   // Emits a call to the CPU runtime to perform the matrix multiply.
-  tensorflow::Status EmitCallToRuntime();
+  Status EmitCallToRuntime();
 
   // Emits a series of nested loops for iterating over an operand array in the
   // dot operation. Loops are constructed in major to minor dimension layout
diff --git a/tensorflow/compiler/xla/service/device_memory_allocator.h b/tensorflow/compiler/xla/service/device_memory_allocator.h
index 5feb65029513d9..d87b86caf0d3ac 100644
--- a/tensorflow/compiler/xla/service/device_memory_allocator.h
+++ b/tensorflow/compiler/xla/service/device_memory_allocator.h
@@ -60,8 +60,7 @@ class DeviceMemoryAllocator {
   }
 
   // Must be a nop for null pointers.
-  virtual tensorflow::Status Deallocate(int device_ordinal,
-                                        se::DeviceMemoryBase mem) = 0;
+  virtual Status Deallocate(int device_ordinal, se::DeviceMemoryBase mem) = 0;
 
   // Return the platform that the allocator allocates memory on.
   const se::Platform* platform() const { return platform_; }
@@ -89,8 +88,7 @@ class StreamExecutorMemoryAllocator : public DeviceMemoryAllocator {
   // Pull in two-arg overload that sets retry_on_failure to true.
   using DeviceMemoryAllocator::Allocate;
 
-  tensorflow::Status Deallocate(int device_ordinal,
-                                se::DeviceMemoryBase mem) override;
+  Status Deallocate(int device_ordinal, se::DeviceMemoryBase mem) override;
 
   bool AllowsAsynchronousDeallocation() const override;
 
diff --git a/tensorflow/compiler/xla/service/execution_tracker.cc b/tensorflow/compiler/xla/service/execution_tracker.cc
index 2f0b9ed2bd98fb..6794cfe297b0fb 100644
--- a/tensorflow/compiler/xla/service/execution_tracker.cc
+++ b/tensorflow/compiler/xla/service/execution_tracker.cc
@@ -37,11 +37,11 @@ AsyncExecution::AsyncExecution(Backend* backend,
   }
 }
 
-tensorflow::Status AsyncExecution::BlockUntilDone() const {
+Status AsyncExecution::BlockUntilDone() const {
   for (auto& stream : streams_) {
     TF_RETURN_IF_ERROR(stream->BlockHostUntilDone());
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 ExecutionTracker::ExecutionTracker() : next_handle_(1) {}
@@ -61,7 +61,7 @@ ExecutionHandle ExecutionTracker::Register(
   return execution_handle;
 }
 
-tensorflow::Status ExecutionTracker::Unregister(const ExecutionHandle& handle) {
+Status ExecutionTracker::Unregister(const ExecutionHandle& handle) {
   tensorflow::mutex_lock lock(execution_mutex_);
   auto it = handle_to_execution_.find(handle.handle());
   if (it == handle_to_execution_.end()) {
@@ -69,7 +69,7 @@ tensorflow::Status ExecutionTracker::Unregister(const ExecutionHandle& handle) {
                     handle.handle());
   }
   handle_to_execution_.erase(handle.handle());
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 StatusOr ExecutionTracker::Resolve(
diff --git a/tensorflow/compiler/xla/service/execution_tracker.h b/tensorflow/compiler/xla/service/execution_tracker.h
index 5b6bddf9f16a85..4458152dd9a988 100644
--- a/tensorflow/compiler/xla/service/execution_tracker.h
+++ b/tensorflow/compiler/xla/service/execution_tracker.h
@@ -43,7 +43,7 @@ class AsyncExecution {
   AsyncExecution(Backend* backend, std::vector streams,
                  const ExecutionProfile& profile, GlobalDataHandle result);
 
-  tensorflow::Status BlockUntilDone() const;
+  Status BlockUntilDone() const;
 
   const GlobalDataHandle& result() const { return result_; }
 
@@ -77,7 +77,7 @@ class ExecutionTracker {
                            GlobalDataHandle data);
 
   // Unregisters the execution for the given handle.
-  tensorflow::Status Unregister(const ExecutionHandle& handle);
+  Status Unregister(const ExecutionHandle& handle);
 
   // Resolves the given ExecutionHandle to an AsyncExecution. Returns an
   // error status if the given handle is not found, which means that the
diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc
index cb66d379e6a7e6..ab5149dcdb0929 100644
--- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc
+++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc
@@ -116,7 +116,7 @@ BufferAllocations::~BufferAllocations() {
   }
 }
 
-tensorflow::Status BufferAllocations::TearDown(
+Status BufferAllocations::TearDown(
     const std::set& live_addresses) {
   // Deallocate temporary buffers, taking care to try to deallocate all of them
   // even if one of the deallocations fails.
diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.h b/tensorflow/compiler/xla/service/gpu/buffer_allocations.h
index a36571da4ed57d..636623502597b3 100644
--- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.h
+++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.h
@@ -78,8 +78,7 @@ class BufferAllocations {
 
   // Tears down all buffers allocated by this object that are not in
   // `live_addresses`.
-  tensorflow::Status TearDown(
-      const std::set& live_addresses);
+  Status TearDown(const std::set& live_addresses);
 
  private:
   BufferAllocations(BufferAllocation::Index buffer_count, int device_ordinal,
diff --git a/tensorflow/compiler/xla/service/gpu/copy_thunk.cc b/tensorflow/compiler/xla/service/gpu/copy_thunk.cc
index bf912fbd14de58..ee38c0318a878c 100644
--- a/tensorflow/compiler/xla/service/gpu/copy_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/copy_thunk.cc
@@ -29,12 +29,12 @@ HostToDeviceCopyThunk::HostToDeviceCopyThunk(
       destination_buffer_(destination_buffer),
       mem_size_(mem_size) {}
 
-tensorflow::Status HostToDeviceCopyThunk::ExecuteOnStream(
+Status HostToDeviceCopyThunk::ExecuteOnStream(
     const BufferAllocations& buffer_allocations, se::Stream* stream) {
   se::DeviceMemoryBase destination_data =
       buffer_allocations.GetDeviceAddress(destination_buffer_);
   stream->ThenMemcpy(&destination_data, source_address_, mem_size_);
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 DeviceToDeviceCopyThunk::DeviceToDeviceCopyThunk(
@@ -46,14 +46,14 @@ DeviceToDeviceCopyThunk::DeviceToDeviceCopyThunk(
       destination_buffer_(destination_buffer),
       mem_size_(mem_size) {}
 
-tensorflow::Status DeviceToDeviceCopyThunk::ExecuteOnStream(
+Status DeviceToDeviceCopyThunk::ExecuteOnStream(
     const BufferAllocations& buffer_allocations, se::Stream* stream) {
   se::DeviceMemoryBase destination_data =
       buffer_allocations.GetDeviceAddress(destination_buffer_);
   se::DeviceMemoryBase source_data =
       buffer_allocations.GetDeviceAddress(source_buffer_);
   stream->ThenMemcpy(&destination_data, source_data, mem_size_);
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 }  // namespace gpu
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/gpu/copy_thunk.h b/tensorflow/compiler/xla/service/gpu/copy_thunk.h
index 2e7eb5f3445bc9..8b128386f61636 100644
--- a/tensorflow/compiler/xla/service/gpu/copy_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/copy_thunk.h
@@ -39,8 +39,8 @@ class HostToDeviceCopyThunk : public Thunk {
   HostToDeviceCopyThunk(const HostToDeviceCopyThunk&) = delete;
   HostToDeviceCopyThunk& operator=(const HostToDeviceCopyThunk&) = delete;
 
-  tensorflow::Status ExecuteOnStream(
-      const BufferAllocations& buffer_allocations, se::Stream* stream) override;
+  Status ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                         se::Stream* stream) override;
 
  private:
   const void* source_address_;
@@ -62,8 +62,8 @@ class DeviceToDeviceCopyThunk : public Thunk {
   DeviceToDeviceCopyThunk(const DeviceToDeviceCopyThunk&) = delete;
   DeviceToDeviceCopyThunk& operator=(const DeviceToDeviceCopyThunk&) = delete;
 
-  tensorflow::Status ExecuteOnStream(
-      const BufferAllocations& buffer_allocations, se::Stream* stream) override;
+  Status ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                         se::Stream* stream) override;
 
  private:
   const BufferAllocation::Slice source_buffer_;
diff --git a/tensorflow/compiler/xla/service/gpu/fft_thunk.cc b/tensorflow/compiler/xla/service/gpu/fft_thunk.cc
index 1cea49389d3abb..e14ee6918bf148 100644
--- a/tensorflow/compiler/xla/service/gpu/fft_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/fft_thunk.cc
@@ -106,8 +106,8 @@ FftThunk::FftThunk(FftType fft_type,
       input_shape_(input_shape),
       output_shape_(output_shape) {}
 
-tensorflow::Status FftThunk::ExecuteOnStream(
-    const BufferAllocations& buffer_allocations, se::Stream* stream) {
+Status FftThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                                 se::Stream* stream) {
   VLOG(3) << "FFT type: " << FftTypeToString(fft_type_);
   VLOG(3) << "Input shape: " << ShapeUtil::HumanStringWithLayout(input_shape_);
   VLOG(3) << "Output shape: "
@@ -207,7 +207,7 @@ tensorflow::Status FftThunk::ExecuteOnStream(
       LOG(FATAL) << "unsupported fft type";
   }
   if (launch_ok) {
-    return tensorflow::Status::OK();
+    return Status::OK();
   }
   return InternalError("Unable to launch fft for thunk %p with type %s", this,
                        FftTypeToString(fft_type_).c_str());
diff --git a/tensorflow/compiler/xla/service/gpu/fft_thunk.h b/tensorflow/compiler/xla/service/gpu/fft_thunk.h
index ea4270a8eaedf9..b0a22564f3a09b 100644
--- a/tensorflow/compiler/xla/service/gpu/fft_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/fft_thunk.h
@@ -71,8 +71,8 @@ class FftThunk : public Thunk {
   FftThunk& operator=(const FftThunk&) = delete;  // Cannot share fft_plan_
 
   // Does the FFT for the thunk on "stream".
-  tensorflow::Status ExecuteOnStream(
-      const BufferAllocations& buffer_allocations, se::Stream* stream) override;
+  Status ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                         se::Stream* stream) override;
 
  private:
   const se::fft::Type fft_type_;
diff --git a/tensorflow/compiler/xla/service/gpu/for_thunk.cc b/tensorflow/compiler/xla/service/gpu/for_thunk.cc
index c49c273587045e..b36539e0cb8d0a 100644
--- a/tensorflow/compiler/xla/service/gpu/for_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/for_thunk.cc
@@ -30,20 +30,20 @@ ForThunk::ForThunk(const int64 loop_limit,
       body_thunk_sequence_(
           MakeUnique(std::move(*body_thunk_sequence), hlo)) {}
 
-tensorflow::Status ForThunk::Initialize(const GpuExecutable& executable,
-                                        se::StreamExecutor* executor) {
+Status ForThunk::Initialize(const GpuExecutable& executable,
+                            se::StreamExecutor* executor) {
   TF_RETURN_IF_ERROR(body_thunk_sequence_->Initialize(executable, executor));
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status ForThunk::ExecuteOnStream(
-    const BufferAllocations& buffer_allocations, se::Stream* stream) {
+Status ForThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                                 se::Stream* stream) {
   for (int64 i = 0; i < loop_limit_; ++i) {
     // Invoke loop body thunk sequence.
     TF_RETURN_IF_ERROR(
         body_thunk_sequence_->ExecuteOnStream(buffer_allocations, stream));
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 }  // namespace gpu
diff --git a/tensorflow/compiler/xla/service/gpu/for_thunk.h b/tensorflow/compiler/xla/service/gpu/for_thunk.h
index 56c5c4985ac42a..41ddfe0ceb1d05 100644
--- a/tensorflow/compiler/xla/service/gpu/for_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/for_thunk.h
@@ -36,10 +36,10 @@ class ForThunk : public Thunk {
   ForThunk(const ForThunk&) = delete;
   ForThunk& operator=(const ForThunk&) = delete;
 
-  tensorflow::Status Initialize(const GpuExecutable& executable,
-                                se::StreamExecutor* executor) override;
-  tensorflow::Status ExecuteOnStream(
-      const BufferAllocations& buffer_allocations, se::Stream* stream) override;
+  Status Initialize(const GpuExecutable& executable,
+                    se::StreamExecutor* executor) override;
+  Status ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                         se::Stream* stream) override;
 
  private:
   const int64 loop_limit_;
diff --git a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc
index f996fe486d1fe6..2ebb40a44e8722 100644
--- a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc
@@ -232,8 +232,8 @@ GemmThunk::GemmThunk(const BufferAllocation::Slice& lhs_buffer,
       output_shape_(output_shape),
       alpha_(alpha) {}
 
-tensorflow::Status GemmThunk::ExecuteOnStream(
-    const BufferAllocations& buffer_allocations, se::Stream* stream) {
+Status GemmThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                                  se::Stream* stream) {
   VLOG(2) << "Executing a GemmThunk";
 
   se::DeviceMemoryBase lhs_data =
@@ -350,7 +350,7 @@ tensorflow::Status GemmThunk::ExecuteOnStream(
   if (!launch_ok) {
     return InternalError("Unable to launch cuBLAS gemm on stream %p", stream);
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 }  // namespace gpu
diff --git a/tensorflow/compiler/xla/service/gpu/gemm_thunk.h b/tensorflow/compiler/xla/service/gpu/gemm_thunk.h
index f42cbf9e9483b5..7a4830d64e7cae 100644
--- a/tensorflow/compiler/xla/service/gpu/gemm_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/gemm_thunk.h
@@ -47,8 +47,8 @@ class GemmThunk : public Thunk {
   GemmThunk& operator=(const GemmThunk&) = delete;
 
   // Does the gemm operation for the thunk on "stream", which must be non-null.
-  tensorflow::Status ExecuteOnStream(
-      const BufferAllocations& buffer_allocations, se::Stream* stream) override;
+  Status ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                         se::Stream* stream) override;
 
   // Returns true if we'll perform autotuning if run on the given stream.  If
   // so, we want the GPU to be quiescent during autotuning, so as not to
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
index 4fdc4c89618bc0..df494a1aa961c3 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
@@ -128,9 +128,8 @@ string GetLibdeviceDir(const string& config_cuda_data_dir) {
 }
 
 // Runs optimization passes on the given HLO module.
-tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
-                                     se::StreamExecutor* stream_exec,
-                                     DeviceMemoryAllocator* device_allocator) {
+Status OptimizeHloModule(HloModule* hlo_module, se::StreamExecutor* stream_exec,
+                         DeviceMemoryAllocator* device_allocator) {
   {
     HloPassPipeline pipeline("optimization");
     pipeline.AddInvariantChecker();
@@ -283,12 +282,12 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
       TF_RETURN_IF_ERROR(fusion.Run(hlo_module).status());
     }
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 // Modifies the given HLO module so that it will be accepted by IrEmitter.
 // Unlike optimization passes, the passes are necessary for correctness.
-tensorflow::Status PrepareHloModuleForIrEmitting(HloModule* hlo_module) {
+Status PrepareHloModuleForIrEmitting(HloModule* hlo_module) {
   // In some cases, we have to place the result of an instruction in a temporary
   // buffer. For instance, the buffer that holds an external parameter is
   // assumed immutable at this point, and should not be reused for output
diff --git a/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc b/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc
index 3baee228cf861c..f56c1ce69f11ed 100644
--- a/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc
@@ -35,8 +35,8 @@ KernelThunk::KernelThunk(
       kernel_name_(kernel_name),
       unroll_factor_(unroll_factor) {}
 
-tensorflow::Status KernelThunk::Initialize(const GpuExecutable& executable,
-                                           se::StreamExecutor* executor) {
+Status KernelThunk::Initialize(const GpuExecutable& executable,
+                               se::StreamExecutor* executor) {
   tensorflow::mutex_lock lock(mutex_);
   if (!loader_spec_) {
     loader_spec_.reset(new se::MultiKernelLoaderSpec(args_.size()));
@@ -66,7 +66,7 @@ tensorflow::Status KernelThunk::Initialize(const GpuExecutable& executable,
     }
   }
 
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 void KernelThunk::SetLaunchDimensions(const LaunchDimensions& launch_dims) {
@@ -74,8 +74,8 @@ void KernelThunk::SetLaunchDimensions(const LaunchDimensions& launch_dims) {
   launch_dimensions_ = launch_dims;
 }
 
-tensorflow::Status KernelThunk::ExecuteOnStream(
-    const BufferAllocations& buffer_allocations, se::Stream* stream) {
+Status KernelThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                                    se::Stream* stream) {
   // Load the kernel.
   se::StreamExecutor* executor = stream->parent();
   LaunchDimensions launch_dimensions;
@@ -106,7 +106,7 @@ tensorflow::Status KernelThunk::ExecuteOnStream(
           *kernel_args)) {
     return InternalError("Unable to launch kernel %s", kernel_name_.c_str());
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 }  // namespace gpu
diff --git a/tensorflow/compiler/xla/service/gpu/kernel_thunk.h b/tensorflow/compiler/xla/service/gpu/kernel_thunk.h
index 532f15ee3ab8eb..7def27e189b667 100644
--- a/tensorflow/compiler/xla/service/gpu/kernel_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/kernel_thunk.h
@@ -57,12 +57,12 @@ class KernelThunk : public Thunk {
   int unroll_factor() const { return unroll_factor_; }
   void SetLaunchDimensions(const LaunchDimensions& launch_dims);
 
-  tensorflow::Status Initialize(const GpuExecutable& executable,
-                                se::StreamExecutor* executor) override;
+  Status Initialize(const GpuExecutable& executable,
+                    se::StreamExecutor* executor) override;
 
   // Executes the kernel for the thunk on "stream", which must be non-null.
-  tensorflow::Status ExecuteOnStream(
-      const BufferAllocations& buffer_allocations, se::Stream* stream) override;
+  Status ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                         se::Stream* stream) override;
 
  private:
   // Buffers passed to the kernel as arguments.
diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc
index d70cb07c57d48c..917c57682345d0 100644
--- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc
+++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc
@@ -77,8 +77,7 @@ static string GetLibdeviceFilename(const string& libdevice_dir_path,
   // Since CUDA 9.0, all GPU versions are included in a single file
   const char* unified_libdevice_filename = "libdevice.10.bc";
   std::vector unified_libdevice_files;
-  const tensorflow::Status status =
-    tensorflow::Env::Default()->GetMatchingPaths(
+  const Status status = tensorflow::Env::Default()->GetMatchingPaths(
       tensorflow::io::JoinPath(libdevice_dir_path, unified_libdevice_filename),
       &unified_libdevice_files);
   if (status.ok() && unified_libdevice_files.size() == 1) {
@@ -311,11 +310,11 @@ bool CouldNeedLibdevice(const llvm::Module& module) {
 }
 
 // Links libdevice into the given module if the module needs libdevice.
-tensorflow::Status LinkLibdeviceIfNecessary(
-    llvm::Module* module, std::pair compute_capability,
-    const string& libdevice_dir_path) {
+Status LinkLibdeviceIfNecessary(llvm::Module* module,
+                                std::pair compute_capability,
+                                const string& libdevice_dir_path) {
   if (!CouldNeedLibdevice(*module)) {
-    return tensorflow::Status::OK();
+    return Status::OK();
   }
 
   llvm::Linker linker(*module);
@@ -336,7 +335,7 @@ tensorflow::Status LinkLibdeviceIfNecessary(
     return tensorflow::errors::Internal(tensorflow::strings::StrCat(
         "Error linking libdevice from ", libdevice_path));
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 StatusOr CompileModuleToPtx(llvm::Module* module,
diff --git a/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc b/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc
index 849eff2c88178b..b50f5b5a903e6a 100644
--- a/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc
@@ -24,20 +24,20 @@ SequentialThunk::SequentialThunk(std::vector>&& thunks,
                                  const HloInstruction* hlo)
     : Thunk(Kind::kSequential, hlo), thunks_(std::move(thunks)) {}
 
-tensorflow::Status SequentialThunk::Initialize(const GpuExecutable& executable,
-                                               se::StreamExecutor* executor) {
+Status SequentialThunk::Initialize(const GpuExecutable& executable,
+                                   se::StreamExecutor* executor) {
   for (auto& thunk : thunks_) {
     TF_RETURN_IF_ERROR(thunk->Initialize(executable, executor));
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status SequentialThunk::ExecuteOnStream(
+Status SequentialThunk::ExecuteOnStream(
     const BufferAllocations& buffer_allocations, se::Stream* stream) {
   for (const auto& thunk : thunks_) {
     TF_RETURN_IF_ERROR(thunk->ExecuteOnStream(buffer_allocations, stream));
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 }  // namespace gpu
diff --git a/tensorflow/compiler/xla/service/gpu/sequential_thunk.h b/tensorflow/compiler/xla/service/gpu/sequential_thunk.h
index 83057913319f71..3537110bb5c252 100644
--- a/tensorflow/compiler/xla/service/gpu/sequential_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/sequential_thunk.h
@@ -38,10 +38,10 @@ class SequentialThunk : public Thunk {
 
   const std::vector>& thunks() const { return thunks_; }
 
-  tensorflow::Status Initialize(const GpuExecutable& executable,
-                                se::StreamExecutor* executor) override;
-  tensorflow::Status ExecuteOnStream(
-      const BufferAllocations& buffer_allocations, se::Stream* stream) override;
+  Status Initialize(const GpuExecutable& executable,
+                    se::StreamExecutor* executor) override;
+  Status ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                         se::Stream* stream) override;
 
  private:
   // The list of sub-thunks.
diff --git a/tensorflow/compiler/xla/service/gpu/thunk.h b/tensorflow/compiler/xla/service/gpu/thunk.h
index ff9b6087e0fc66..931c0bffab8503 100644
--- a/tensorflow/compiler/xla/service/gpu/thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/thunk.h
@@ -75,9 +75,9 @@ class Thunk {
   // This may be called multiple times.  Its main purpose is to give us a chance
   // to do initialization outside of ExecuteOnStream() so that the
   // time spent initializing doesn't count towards our execution profile.
-  virtual tensorflow::Status Initialize(const GpuExecutable& /*executable*/,
-                                        se::StreamExecutor* /*executor*/) {
-    return tensorflow::Status::OK();
+  virtual Status Initialize(const GpuExecutable& /*executable*/,
+                            se::StreamExecutor* /*executor*/) {
+    return Status::OK();
   }
 
   // Users of Thunk should call ShouldHaltAllActivityBeforeRunning(stream)
@@ -97,8 +97,8 @@ class Thunk {
   // lifetime. Stream argument must be non-null.
   //
   // Precondition: Initialize(stream->parent()) has been called.
-  virtual tensorflow::Status ExecuteOnStream(
-      const BufferAllocations& buffer_allocations, se::Stream* stream) = 0;
+  virtual Status ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                                 se::Stream* stream) = 0;
 
  private:
   Kind kind_;
diff --git a/tensorflow/compiler/xla/service/gpu/tuple_thunk.cc b/tensorflow/compiler/xla/service/gpu/tuple_thunk.cc
index ecb54857ccc40e..97cb04c38fbf18 100644
--- a/tensorflow/compiler/xla/service/gpu/tuple_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/tuple_thunk.cc
@@ -20,8 +20,8 @@ limitations under the License.
 namespace xla {
 namespace gpu {
 
-tensorflow::Status TupleThunk::ExecuteOnStream(
-    const BufferAllocations& buffer_allocations, se::Stream* stream) {
+Status TupleThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                                   se::Stream* stream) {
   std::vector tuple_element_buffer_addresses;
   for (BufferAllocation::Slice tuple_element_buffer : tuple_element_buffers_) {
     tuple_element_buffer_addresses.push_back(
@@ -40,7 +40,7 @@ tensorflow::Status TupleThunk::ExecuteOnStream(
         tuple_element_buffer_addresses.data(), dest_buffer_address.opaque(),
         sizeof(void*) * tuple_element_buffer_addresses.size());
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 }  // namespace gpu
diff --git a/tensorflow/compiler/xla/service/gpu/tuple_thunk.h b/tensorflow/compiler/xla/service/gpu/tuple_thunk.h
index 8b459c29a136a6..951f809b51937c 100644
--- a/tensorflow/compiler/xla/service/gpu/tuple_thunk.h
+++ b/tensorflow/compiler/xla/service/gpu/tuple_thunk.h
@@ -45,8 +45,8 @@ class TupleThunk : public Thunk {
   TupleThunk(const TupleThunk&) = delete;
   TupleThunk& operator=(const TupleThunk&) = delete;
 
-  tensorflow::Status ExecuteOnStream(
-      const BufferAllocations& buffer_allocations, se::Stream* stream) override;
+  Status ExecuteOnStream(const BufferAllocations& buffer_allocations,
+                         se::Stream* stream) override;
 
  private:
   const std::vector tuple_element_buffers_;
diff --git a/tensorflow/compiler/xla/service/gpu/while_transformer.cc b/tensorflow/compiler/xla/service/gpu/while_transformer.cc
index e6caec8625f0d6..ad55728c45599c 100644
--- a/tensorflow/compiler/xla/service/gpu/while_transformer.cc
+++ b/tensorflow/compiler/xla/service/gpu/while_transformer.cc
@@ -144,7 +144,7 @@ class ExprTree {
       TF_RETURN_IF_ERROR(pair.second->Match(instruction->operand(pair.first),
                                             tagged_instructions));
     }
-    return tensorflow::Status::OK();
+    return Status::OK();
   }
 
  private:
@@ -169,7 +169,7 @@ class MatcherBase {
 
   // Attempts to match each ExprTree in 'expr_trees_'.
   // Returns OK on the first successful match, error status otherwise.
-  virtual tensorflow::Status Run() {
+  virtual Status Run() {
     Status status;
     for (const ExprTree& expr_tree : expr_trees_) {
       status = MatchExprTree(expr_tree);
@@ -201,7 +201,7 @@ class MatcherBase {
     } else if (type == S64) {
       *const_value = literal.GetFirstElement();
     }
-    return tensorflow::Status::OK();
+    return Status::OK();
   }
 
   StatusOr GetTaggedInstruction(
@@ -315,7 +315,7 @@ class WhileConditionComputationMatcher : public MatcherBase {
                              gte_fusion_param0->name().c_str());
     }
 
-    return tensorflow::Status::OK();
+    return Status::OK();
   }
 
   const HloComputation* computation_;
@@ -379,7 +379,7 @@ class WhileInitOperandMatcher : public MatcherBase {
         GetTaggedInstruction("loop_start", tagged_instructions));
     TF_RETURN_IF_ERROR(ParseConstInteger(const_hlo, &loop_start_));
 
-    return tensorflow::Status::OK();
+    return Status::OK();
   }
 
   const HloInstruction* while_hlo_;
@@ -477,7 +477,7 @@ class WhileBodyComputationMatcher : public MatcherBase {
         }
       }
     }
-    return tensorflow::Status::OK();
+    return Status::OK();
   }
 
   const HloComputation* computation_;
diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc
index 096ebb7946e08b..7d6d0d9eaf7096 100644
--- a/tensorflow/compiler/xla/service/hlo_verifier.cc
+++ b/tensorflow/compiler/xla/service/hlo_verifier.cc
@@ -106,9 +106,7 @@ Status ShapeVerifier::HandleReducePrecision(HloInstruction* reduce_precision) {
                                           reduce_precision->mantissa_bits()));
 }
 
-Status ShapeVerifier::HandleInfeed(HloInstruction*) {
-  return tensorflow::Status::OK();
-}
+Status ShapeVerifier::HandleInfeed(HloInstruction*) { return Status::OK(); }
 
 Status ShapeVerifier::HandleOutfeed(HloInstruction* outfeed) {
   // Outfeed has a separate shape field for the value which is outfed to the
@@ -127,12 +125,10 @@ Status ShapeVerifier::HandleOutfeed(HloInstruction* outfeed) {
 }
 
 Status ShapeVerifier::HandleHostCompute(HloInstruction*) {
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-Status ShapeVerifier::HandleRng(HloInstruction*) {
-  return tensorflow::Status::OK();
-}
+Status ShapeVerifier::HandleRng(HloInstruction*) { return Status::OK(); }
 
 Status ShapeVerifier::HandleReverse(HloInstruction* reverse) {
   return CheckShape(
@@ -164,7 +160,7 @@ Status ShapeVerifier::HandleReduce(HloInstruction* reduce) {
 }
 
 Status ShapeVerifier::HandleBitcast(HloInstruction* bitcast) {
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 Status ShapeVerifier::HandleBroadcast(HloInstruction* broadcast) {
@@ -183,7 +179,7 @@ Status ShapeVerifier::HandleBroadcast(HloInstruction* broadcast) {
                  operand_shape.dimensions(operand_dimension))
         << broadcast->ToString() << " operand shape " << operand_shape;
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 Status ShapeVerifier::HandleReshape(HloInstruction* reshape) {
@@ -191,7 +187,7 @@ Status ShapeVerifier::HandleReshape(HloInstruction* reshape) {
   TF_RETURN_IF_ERROR(CheckShape(reshape, reshape->shape()));
   TF_RET_CHECK(ShapeUtil::ElementsIn(reshape->shape()) ==
                ShapeUtil::ElementsIn(reshape->operand(0)->shape()));
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 Status ShapeVerifier::HandleTranspose(HloInstruction* transpose) {
@@ -201,21 +197,17 @@ Status ShapeVerifier::HandleTranspose(HloInstruction* transpose) {
 }
 
 Status ShapeVerifier::HandleParameter(HloInstruction* hlo) {
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-Status ShapeVerifier::HandleFusion(HloInstruction*) {
-  return tensorflow::Status::OK();
-}
+Status ShapeVerifier::HandleFusion(HloInstruction*) { return Status::OK(); }
 
 Status ShapeVerifier::HandleCall(HloInstruction* call) {
   // The shape of kCall should match the shape of the computation it calls.
   return CheckShape(call, call->to_apply()->ComputeProgramShape().result());
 }
 
-Status ShapeVerifier::HandleCustomCall(HloInstruction*) {
-  return tensorflow::Status::OK();
-}
+Status ShapeVerifier::HandleCustomCall(HloInstruction*) { return Status::OK(); }
 
 Status ShapeVerifier::HandleSlice(HloInstruction* slice) {
   return CheckShape(slice,
@@ -497,7 +489,7 @@ Status ShapeVerifier::CheckShape(const HloInstruction* instruction,
         ShapeUtil::HumanString(instruction->shape()).c_str(),
         instruction->ToString().c_str());
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 Status ShapeVerifier::CheckShape(const HloInstruction* instruction,
@@ -547,7 +539,7 @@ Status ShapeVerifier::CheckSameChannel(const HloInstruction* instr1,
         instr1->ToString().c_str(), instr1->channel_id(),
         instr2->ToString().c_str(), instr2->channel_id());
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 string ComputationsToString(
@@ -612,7 +604,7 @@ Status VerifyHloStructure(HloModule* module) {
       }
     }
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const {
@@ -728,7 +720,7 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const {
   // TODO(b/65423525): We'd like to check that all operands are distinct.
   // This is currently disabled due to the invariant being violated by
   // multi-output fusion.
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 Status HloVerifier::CheckWhileInstruction(HloInstruction* instruction) {
@@ -777,7 +769,7 @@ Status HloVerifier::CheckWhileInstruction(HloInstruction* instruction) {
         "init: %s, body: %s",
         init->ToString().c_str(), body_root->ToString().c_str());
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 Status HloVerifier::CheckElementwiseInstruction(HloInstruction* instruction) {
@@ -795,7 +787,7 @@ Status HloVerifier::CheckElementwiseInstruction(HloInstruction* instruction) {
           ShapeUtil::HumanString(operand_shape).c_str());
     }
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 StatusOr HloVerifier::Run(HloModule* module) {
diff --git a/tensorflow/compiler/xla/service/hlo_verifier.h b/tensorflow/compiler/xla/service/hlo_verifier.h
index 6208887547a14d..1392a78097aa02 100644
--- a/tensorflow/compiler/xla/service/hlo_verifier.h
+++ b/tensorflow/compiler/xla/service/hlo_verifier.h
@@ -82,9 +82,7 @@ class ShapeVerifier : public DfsHloVisitor {
   Status HandleBatchNormGrad(HloInstruction* batch_norm_grad) override;
   Status HandleGather(HloInstruction* gather) override;
 
-  Status FinishVisit(HloInstruction*) override {
-    return tensorflow::Status::OK();
-  }
+  Status FinishVisit(HloInstruction*) override { return Status::OK(); }
 
  protected:
   // Check the instruction's shape against the shape given by ShapeInference
diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc
index 7e1bb11eaada0e..986e177406b634 100644
--- a/tensorflow/compiler/xla/service/layout_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc
@@ -660,13 +660,12 @@ TEST_F(LayoutAssignmentTest, TransposeWithinFusionDoesNotCrash) {
                          /*device_allocator=*/nullptr)
           .ConsumeValueOrDie();
 
-  EXPECT_EQ(
-      ::tensorflow::Status::OK(),
-      backend()
-          .compiler()
-          ->RunBackend(std::move(module), backend().default_stream_executor(),
-                       /*device_allocator=*/nullptr)
-          .status());
+  EXPECT_EQ(Status::OK(), backend()
+                              .compiler()
+                              ->RunBackend(std::move(module),
+                                           backend().default_stream_executor(),
+                                           /*device_allocator=*/nullptr)
+                              .status());
 }
 
 // A GTE inside of a fusion node inherits the layout of its operand (which
diff --git a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc
index bc683a1880b010..f172b1d87c8702 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc
@@ -151,7 +151,7 @@ Status FusedIrEmitter::HandleTuple(HloInstruction* tuple) {
 
 Status FusedIrEmitter::FinishVisit(HloInstruction* root) {
   fused_root_ = root;
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 FusedIrEmitter::Generator FusedIrEmitter::GetRootGenerator() const {
diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc
index 3978acc132f34b..0728ccfff7b85e 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc
@@ -39,14 +39,13 @@ LoopEmitter::LoopEmitter(const BodyEmitter& body_emitter, const Shape& shape,
 LoopEmitter::LoopEmitter(const ElementGenerator& target_element_generator,
                          const IrArray& target_array,
                          llvm::IRBuilder<>* ir_builder)
-    : body_emitter_([=](const llvm_ir::IrArray::Index array_index)
-                        -> ::tensorflow::Status {
+    : body_emitter_([=](const llvm_ir::IrArray::Index array_index) -> Status {
         // Convert target_element_generator to a BodyEmitter.
         TF_ASSIGN_OR_RETURN(llvm::Value * target_element,
                             target_element_generator(array_index));
         target_array.EmitWriteArrayElement(array_index, target_element,
                                            ir_builder);
-        return tensorflow::Status::OK();
+        return Status::OK();
       }),
       shape_(target_array.GetShape()),
       ir_builder_(ir_builder) {}
@@ -124,7 +123,7 @@ std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock(
   return {array_index};
 }
 
-tensorflow::Status LoopEmitter::EmitLoop(tensorflow::StringPiece loop_name) {
+Status LoopEmitter::EmitLoop(tensorflow::StringPiece loop_name) {
   for (const IrArray::Index& array_index :
        EmitIndexAndSetExitBasicBlock(loop_name)) {
     TF_RETURN_IF_ERROR(body_emitter_(array_index));
@@ -135,7 +134,7 @@ tensorflow::Status LoopEmitter::EmitLoop(tensorflow::StringPiece loop_name) {
   if (exit_bb_ != nullptr) {
     ir_builder_->SetInsertPoint(exit_bb_);
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 }  // namespace llvm_ir
diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h
index 9ff497aecd0bc9..b70d28ecd3033e 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h
+++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h
@@ -38,8 +38,7 @@ using ElementGenerator =
 // Emits a loop for every element in the given shape.
 class LoopEmitter {
  public:
-  using BodyEmitter =
-      std::function;
+  using BodyEmitter = std::function;
 
   LoopEmitter(const BodyEmitter& body_emitter, const Shape& shape,
               llvm::IRBuilder<>* ir_builder);
@@ -72,7 +71,7 @@ class LoopEmitter {
       tensorflow::StringPiece loop_name);
 
   // Emits a complete loop nest for every element in the given shape.
-  tensorflow::Status EmitLoop(tensorflow::StringPiece loop_name = "");
+  Status EmitLoop(tensorflow::StringPiece loop_name = "");
 
  protected:
   // An IR emitter that generates the loop body.
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 495f8801ba82ec..047cadb3d9d5a3 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -64,7 +64,7 @@ namespace {
 
 // Records the arguments used to invoke a computation in a SessionModule
 // proto.
-tensorflow::Status RecordArguments(
+Status RecordArguments(
     const tensorflow::gtl::ArraySlice arguments,
     se::StreamExecutor* executor, TransferManager* transfer_manager,
     SessionModule* module) {
@@ -75,24 +75,22 @@ tensorflow::Status RecordArguments(
         transfer_manager->TransferLiteralFromDevice(executor, *argument));
     *module->add_arguments() = literal->ToProto();
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 // Records the result of a computation in a SessionModule proto.
-tensorflow::Status RecordResult(const ShapedBuffer& result,
-                                se::StreamExecutor* executor,
-                                TransferManager* transfer_manager,
-                                SessionModule* module) {
+Status RecordResult(const ShapedBuffer& result, se::StreamExecutor* executor,
+                    TransferManager* transfer_manager, SessionModule* module) {
   module->clear_result();
   TF_ASSIGN_OR_RETURN(
       std::unique_ptr literal,
       transfer_manager->TransferLiteralFromDevice(executor, result));
   *module->mutable_result() = literal->ToProto();
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 // Records the arguments used to invoke a computation in an HloSnapshot proto.
-tensorflow::Status RecordArguments(
+Status RecordArguments(
     const tensorflow::gtl::ArraySlice arguments,
     se::StreamExecutor* executor, TransferManager* transfer_manager,
     HloSnapshot* module) {
@@ -103,20 +101,18 @@ tensorflow::Status RecordArguments(
         transfer_manager->TransferLiteralFromDevice(executor, *argument));
     *module->add_arguments() = literal->ToProto();
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 // Records the result of a computation in a HloSnapshot proto.
-tensorflow::Status RecordResult(const ShapedBuffer& result,
-                                se::StreamExecutor* executor,
-                                TransferManager* transfer_manager,
-                                HloSnapshot* module) {
+Status RecordResult(const ShapedBuffer& result, se::StreamExecutor* executor,
+                    TransferManager* transfer_manager, HloSnapshot* module) {
   module->clear_result();
   TF_ASSIGN_OR_RETURN(
       std::unique_ptr literal,
       transfer_manager->TransferLiteralFromDevice(executor, result));
   *module->mutable_result() = literal->ToProto();
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 }  // namespace
@@ -199,8 +195,8 @@ Service::Service(const ServiceOptions& options,
   }
 }
 
-tensorflow::Status Service::Computation(const ComputationRequest* arg,
-                                        ComputationResponse* result) {
+Status Service::Computation(const ComputationRequest* arg,
+                            ComputationResponse* result) {
   if (arg->name().empty()) {
     return InvalidArgument("computation request needs a name");
   }
@@ -210,24 +206,23 @@ tensorflow::Status Service::Computation(const ComputationRequest* arg,
   VLOG(1) << Printf("Created new computation %s on service %p, name %s",
                     result->computation().ShortDebugString().c_str(), this,
                     arg->name().c_str());
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::CreateChannelHandle(
-    const CreateChannelHandleRequest* arg,
-    CreateChannelHandleResponse* result) {
+Status Service::CreateChannelHandle(const CreateChannelHandleRequest* arg,
+                                    CreateChannelHandleResponse* result) {
   *result->mutable_channel() = channel_tracker_.NewChannel();
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::Unregister(const UnregisterRequest* arg,
-                                       UnregisterResponse* result) {
+Status Service::Unregister(const UnregisterRequest* arg,
+                           UnregisterResponse* result) {
   return allocation_tracker_.Unregister(arg->data());
 }
 
 // Deconstructs a previously-allocated global handle.
-tensorflow::Status Service::DeconstructTuple(const DeconstructTupleRequest* arg,
-                                             DeconstructTupleResponse* result) {
+Status Service::DeconstructTuple(const DeconstructTupleRequest* arg,
+                                 DeconstructTupleResponse* result) {
   TF_ASSIGN_OR_RETURN(
       std::vector elements,
       allocation_tracker_.DeconstructTuple(arg->tuple_handle()));
@@ -235,11 +230,11 @@ tensorflow::Status Service::DeconstructTuple(const DeconstructTupleRequest* arg,
   for (auto& element : elements) {
     *result->add_element_handles() = element;
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::ValidateResultShapeWithLayout(
-    const Shape& shape_with_layout, const Shape& result_shape) const {
+Status Service::ValidateResultShapeWithLayout(const Shape& shape_with_layout,
+                                              const Shape& result_shape) const {
   if (!ShapeUtil::Compatible(shape_with_layout, result_shape)) {
     return InvalidArgument(
         "Shape used to set computation result layout %s is not compatible "
@@ -511,7 +506,7 @@ Status Service::ValidateEntryComputationLayout(HloModule* module) {
       module->device_entry_computation_layout().result_shape(),
       execute_backend_->transfer_manager()->HostShapeToDeviceShape(
           module->host_entry_computation_layout().result_shape())));
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 StatusOr> Service::BuildExecutable(
@@ -801,8 +796,8 @@ StatusOr Service::ExecuteAndRegisterResult(
                                                        result_tag);
 }
 
-tensorflow::Status Service::SetReturnValue(const SetReturnValueRequest* arg,
-                                           SetReturnValueResponse* results) {
+Status Service::SetReturnValue(const SetReturnValueRequest* arg,
+                               SetReturnValueResponse* results) {
   TF_ASSIGN_OR_RETURN(UserComputation * computation,
                       computation_tracker_.Resolve(arg->computation()));
   return computation->SetReturnValue(arg->operand());
@@ -849,8 +844,8 @@ StatusOr>> Service::GetArguments(
   return replicated_arguments;
 }
 
-tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg,
-                                            ExecuteParallelResponse* result) {
+Status Service::ExecuteParallel(const ExecuteParallelRequest* arg,
+                                ExecuteParallelResponse* result) {
   VLOG(1) << "running execute-parallel request: " << arg->ShortDebugString();
 
   std::vector>> all_arguments;
@@ -957,11 +952,11 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg,
   }
 
   VLOG(1) << "successfully completed 'execute-parallel' request";
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::ExecuteGraphParallel(
-    const ExecuteGraphParallelRequest* arg, ExecuteParallelResponse* result) {
+Status Service::ExecuteGraphParallel(const ExecuteGraphParallelRequest* arg,
+                                     ExecuteParallelResponse* result) {
   VLOG(1) << "running execute-graph-parallel request";
 
   std::vector>> all_arguments;
@@ -1058,11 +1053,11 @@ tensorflow::Status Service::ExecuteGraphParallel(
   }
 
   VLOG(1) << "successfully completed 'execute-graph-parallel' request";
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::GetDeviceHandles(const GetDeviceHandlesRequest* arg,
-                                             GetDeviceHandlesResponse* result) {
+Status Service::GetDeviceHandles(const GetDeviceHandlesRequest* arg,
+                                 GetDeviceHandlesResponse* result) {
   const int64 available_device_count = execute_backend_->device_count();
   const int64 replica_count = options_.number_of_replicas();
   if (replica_count <= 0) {
@@ -1082,11 +1077,11 @@ tensorflow::Status Service::GetDeviceHandles(const GetDeviceHandlesRequest* arg,
     *result->add_device_handles() = device_handle;
   }
 
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::ExecuteOneToN(const ExecuteRequest* arg,
-                                          ExecuteResponse* result) {
+Status Service::ExecuteOneToN(const ExecuteRequest* arg,
+                              ExecuteResponse* result) {
   ExecuteParallelRequest parallel_arg;
   *parallel_arg.add_requests() = *arg;
   ExecuteParallelResponse parallel_result;
@@ -1094,8 +1089,8 @@ tensorflow::Status Service::ExecuteOneToN(const ExecuteRequest* arg,
   return PickParallelResponse(parallel_result, result);
 }
 
-tensorflow::Status Service::ExecuteOneToN(const ExecuteGraphRequest* arg,
-                                          ExecuteResponse* result) {
+Status Service::ExecuteOneToN(const ExecuteGraphRequest* arg,
+                              ExecuteResponse* result) {
   ExecuteGraphParallelRequest parallel_arg;
   *parallel_arg.add_requests() = *arg;
   ExecuteParallelResponse parallel_result;
@@ -1103,7 +1098,7 @@ tensorflow::Status Service::ExecuteOneToN(const ExecuteGraphRequest* arg,
   return PickParallelResponse(parallel_result, result);
 }
 
-tensorflow::Status Service::PickParallelResponse(
+Status Service::PickParallelResponse(
     const ExecuteParallelResponse& parallel_result, ExecuteResponse* result) {
   // The "result device" selection is a bit hacky, but better than assuming it
   // is device 0. We have b/76035356 for restructuring the client API to clean
@@ -1126,8 +1121,7 @@ tensorflow::Status Service::PickParallelResponse(
   return Status::OK();
 }
 
-tensorflow::Status Service::Execute(const ExecuteRequest* arg,
-                                    ExecuteResponse* result) {
+Status Service::Execute(const ExecuteRequest* arg, ExecuteResponse* result) {
   VLOG(1) << "running execute request: " << arg->ShortDebugString();
 
   TF_ASSIGN_OR_RETURN(UserComputation * user_computation,
@@ -1198,7 +1192,7 @@ tensorflow::Status Service::Execute(const ExecuteRequest* arg,
   }
 
   VLOG(1) << "successfully completed 'execute' request";
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 StatusOr> Service::BuildExecutable(
@@ -1243,8 +1237,8 @@ StatusOr> Service::BuildExecutable(
   return std::move(executable);
 }
 
-tensorflow::Status Service::ExecuteGraph(const ExecuteGraphRequest* arg,
-                                         ExecuteResponse* result) {
+Status Service::ExecuteGraph(const ExecuteGraphRequest* arg,
+                             ExecuteResponse* result) {
   VLOG(1) << "running execute-graph request";
 
   if (!arg->has_computation()) {
@@ -1303,11 +1297,11 @@ tensorflow::Status Service::ExecuteGraph(const ExecuteGraphRequest* arg,
   }
 
   VLOG(1) << "successfully completed 'execute-graph' request";
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg,
-                                         ExecuteAsyncResponse* result) {
+Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg,
+                             ExecuteAsyncResponse* result) {
   VLOG(1) << "running execute-async request: " << arg->ShortDebugString();
 
   TF_ASSIGN_OR_RETURN(UserComputation * user_computation,
@@ -1383,11 +1377,11 @@ tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg,
   streams.clear();
 
   VLOG(1) << "successfully completed 'execute-async' request";
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::WaitForExecution(const WaitForExecutionRequest* arg,
-                                             WaitForExecutionResponse* result) {
+Status Service::WaitForExecution(const WaitForExecutionRequest* arg,
+                                 WaitForExecutionResponse* result) {
   TF_ASSIGN_OR_RETURN(const auto execution,
                       execution_tracker_.Resolve(arg->execution()));
 
@@ -1398,11 +1392,11 @@ tensorflow::Status Service::WaitForExecution(const WaitForExecutionRequest* arg,
 
   TF_RETURN_IF_ERROR(execution_tracker_.Unregister(arg->execution()));
   VLOG(1) << "successfully completed 'wait-for-execution' request";
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::TransferToClient(const TransferToClientRequest* arg,
-                                             TransferToClientResponse* result) {
+Status Service::TransferToClient(const TransferToClientRequest* arg,
+                                 TransferToClientResponse* result) {
   TF_ASSIGN_OR_RETURN(const ShapedBuffer* shaped_buffer,
                       allocation_tracker_.ResolveForReplica(arg->data(), 0));
 
@@ -1432,7 +1426,7 @@ tensorflow::Status Service::TransferToClient(const TransferToClientRequest* arg,
     *result->mutable_literal() =
         result_literal->Relayout(*return_shape)->ToProto();
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 namespace {
@@ -1450,8 +1444,8 @@ std::unique_ptr CloneShapedBufferOnDevice(
 
 }  // namespace
 
-tensorflow::Status Service::TransferToServer(const TransferToServerRequest* arg,
-                                             TransferToServerResponse* result) {
+Status Service::TransferToServer(const TransferToServerRequest* arg,
+                                 TransferToServerResponse* result) {
   TF_ASSIGN_OR_RETURN(std::unique_ptr literal,
                       Literal::CreateFromProto(arg->literal()));
   const Shape& shape = literal->shape();
@@ -1484,11 +1478,11 @@ tensorflow::Status Service::TransferToServer(const TransferToServerRequest* arg,
                           StrCat("TransferToServer literal of shape ",
                                  ShapeUtil::HumanString(shape))));
 
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::TransferToInfeed(const TransferToInfeedRequest* arg,
-                                             TransferToInfeedResponse* result) {
+Status Service::TransferToInfeed(const TransferToInfeedRequest* arg,
+                                 TransferToInfeedResponse* result) {
   const int64 replica_count = options_.number_of_replicas();
   if (arg->replica_id() < 0 || arg->replica_id() >= replica_count) {
     return FailedPrecondition(
@@ -1517,9 +1511,8 @@ tensorflow::Status Service::TransferToInfeed(const TransferToInfeedRequest* arg,
       executor, *literal);
 }
 
-tensorflow::Status Service::TransferFromOutfeed(
-    const TransferFromOutfeedRequest* arg,
-    TransferFromOutfeedResponse* result) {
+Status Service::TransferFromOutfeed(const TransferFromOutfeedRequest* arg,
+                                    TransferFromOutfeedResponse* result) {
   const int64 replica_count = options_.number_of_replicas();
   if (arg->replica_id() < 0 || arg->replica_id() >= replica_count) {
     return FailedPrecondition(
@@ -1545,16 +1538,16 @@ tensorflow::Status Service::TransferFromOutfeed(
       execute_backend_->transfer_manager()->TransferLiteralFromOutfeed(
           executor, arg->shape_with_layout(), &literal));
   *result->mutable_literal() = literal.ToProto();
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::ResetDevice(const ResetDeviceRequest* arg,
-                                        ResetDeviceResponse* result) {
+Status Service::ResetDevice(const ResetDeviceRequest* arg,
+                            ResetDeviceResponse* result) {
   return execute_backend_->ResetDevices();
 }
 
-tensorflow::Status Service::IsConstant(const IsConstantRequest* arg,
-                                       IsConstantResponse* result) {
+Status Service::IsConstant(const IsConstantRequest* arg,
+                           IsConstantResponse* result) {
   TF_ASSIGN_OR_RETURN(UserComputation * user_computation,
                       computation_tracker_.Resolve(arg->computation()));
 
@@ -1570,11 +1563,11 @@ tensorflow::Status Service::IsConstant(const IsConstantRequest* arg,
       user_computation->IsConstant(arg->operand(), arg->num_parameters()));
 
   result->set_is_constant(is_constant);
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::ComputeConstant(const ComputeConstantRequest* arg,
-                                            ComputeConstantResponse* result) {
+Status Service::ComputeConstant(const ComputeConstantRequest* arg,
+                                ComputeConstantResponse* result) {
   TF_ASSIGN_OR_RETURN(UserComputation * user_computation,
                       computation_tracker_.Resolve(arg->computation()));
 
@@ -1661,11 +1654,11 @@ tensorflow::Status Service::ComputeConstant(const ComputeConstantRequest* arg,
   }
   *result->mutable_literal() = result_literal->ToProto();
 
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::ComputeConstantGraph(
-    const ComputeConstantGraphRequest* arg, ComputeConstantResponse* result) {
+Status Service::ComputeConstantGraph(const ComputeConstantGraphRequest* arg,
+                                     ComputeConstantResponse* result) {
   if (!arg->has_computation()) {
     return InvalidArgument("computations may not be empty");
   }
@@ -1703,20 +1696,18 @@ tensorflow::Status Service::ComputeConstantGraph(
   }
   *result->mutable_literal() = result_literal->ToProto();
 
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::GetShape(const GetShapeRequest* arg,
-                                     GetShapeResponse* result) {
+Status Service::GetShape(const GetShapeRequest* arg, GetShapeResponse* result) {
   TF_ASSIGN_OR_RETURN(const ShapedBuffer* buffer,
                       allocation_tracker_.ResolveForReplica(arg->data(), 0));
   *result->mutable_shape() = buffer->on_host_shape();
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::GetComputationShape(
-    const GetComputationShapeRequest* arg,
-    GetComputationShapeResponse* result) {
+Status Service::GetComputationShape(const GetComputationShapeRequest* arg,
+                                    GetComputationShapeResponse* result) {
   TF_ASSIGN_OR_RETURN(UserComputation * computation,
                       computation_tracker_.Resolve(arg->computation()));
 
@@ -1726,21 +1717,21 @@ tensorflow::Status Service::GetComputationShape(
   TF_ASSIGN_OR_RETURN(auto program_shape, computation->ComputeProgramShape(
                                               versioned_handle.version));
   *result->mutable_program_shape() = *program_shape;
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::GetLocalShape(const GetLocalShapeRequest* arg,
-                                          GetLocalShapeResponse* result) {
+Status Service::GetLocalShape(const GetLocalShapeRequest* arg,
+                              GetLocalShapeResponse* result) {
   TF_ASSIGN_OR_RETURN(UserComputation * computation,
                       computation_tracker_.Resolve(arg->computation()));
 
   TF_ASSIGN_OR_RETURN(*result->mutable_shape(),
                       computation->GetShape(arg->operand()));
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::GetComputationStats(
-    const ComputationStatsRequest* arg, ComputationStatsResponse* result) {
+Status Service::GetComputationStats(const ComputationStatsRequest* arg,
+                                    ComputationStatsResponse* result) {
   TF_ASSIGN_OR_RETURN(UserComputation * user_computation,
                       computation_tracker_.Resolve(arg->computation()));
 
@@ -1766,10 +1757,10 @@ tensorflow::Status Service::GetComputationStats(
   stats.set_flop_count(analysis.flop_count());
   stats.set_transcendental_count(analysis.transcendental_count());
   *result->mutable_stats() = stats;
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::GetComputationGraphStats(
+Status Service::GetComputationGraphStats(
     const ComputationGraphStatsRequest* arg, ComputationStatsResponse* result) {
   if (!arg->has_computation()) {
     return InvalidArgument("Computations may not be empty.");
@@ -1796,11 +1787,11 @@ tensorflow::Status Service::GetComputationGraphStats(
   stats.set_flop_count(analysis.flop_count());
   stats.set_transcendental_count(analysis.transcendental_count());
   *result->mutable_stats() = stats;
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 template 
-tensorflow::Status Service::AddInstruction(
+Status Service::AddInstruction(
     const RequestT* arg, ResponseT* result,
     const std::function(UserComputation*)>&
         adder) {
@@ -1808,10 +1799,10 @@ tensorflow::Status Service::AddInstruction(
                       computation_tracker_.Resolve(arg->computation()));
 
   TF_ASSIGN_OR_RETURN(*result->mutable_output(), adder(computation));
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::Op(const OpRequest* arg, OpResponse* result) {
+Status Service::Op(const OpRequest* arg, OpResponse* result) {
   TF_ASSIGN_OR_RETURN(UserComputation * computation,
                       computation_tracker_.Resolve(arg->computation()));
   StatusOr handle_status;
@@ -2033,27 +2024,26 @@ tensorflow::Status Service::Op(const OpRequest* arg, OpResponse* result) {
   if (arg->has_sharding()) {
     TF_RETURN_IF_ERROR(computation->SetOpSharding(handle, arg->sharding()));
   }
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::SnapshotComputation(
-    const SnapshotComputationRequest* arg,
-    SnapshotComputationResponse* result) {
+Status Service::SnapshotComputation(const SnapshotComputationRequest* arg,
+                                    SnapshotComputationResponse* result) {
   TF_ASSIGN_OR_RETURN(
       std::unique_ptr module,
       computation_tracker_.SnapshotComputation(arg->computation()));
 
   result->set_allocated_module(module.release());
 
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status Service::LoadComputationSnapshot(
+Status Service::LoadComputationSnapshot(
     const LoadComputationSnapshotRequest* arg,
     LoadComputationSnapshotResponse* result) {
   TF_ASSIGN_OR_RETURN(*result->mutable_computation(),
                       computation_tracker_.LoadSessionModule(arg->module()));
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 DeviceHandle Service::SingleComputationDeviceHandle() const {
diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h
index f84fe407e05da3..81fbd41957887a 100644
--- a/tensorflow/compiler/xla/service/service.h
+++ b/tensorflow/compiler/xla/service/service.h
@@ -85,55 +85,52 @@ class Service : public ServiceInterface {
 
   // Creates a new computation with the given name.
   // A unique ComputationHandle is returned.
-  tensorflow::Status Computation(const ComputationRequest* arg,
-                                 ComputationResponse* result) override;
+  Status Computation(const ComputationRequest* arg,
+                     ComputationResponse* result) override;
 
   // Unregisters a previously-allocated global handle.
   //
   // If the handle given is not currently allocated, a NOT_FOUND status is
   // returned.
-  tensorflow::Status Unregister(const UnregisterRequest* arg,
-                                UnregisterResponse* result) override;
+  Status Unregister(const UnregisterRequest* arg,
+                    UnregisterResponse* result) override;
 
   // Deconstructs a tuple. Returns a newly created GlobalDataHandle for each
   // element in the tuple.
-  tensorflow::Status DeconstructTuple(
-      const DeconstructTupleRequest* arg,
-      DeconstructTupleResponse* result) override;
+  Status DeconstructTuple(const DeconstructTupleRequest* arg,
+                          DeconstructTupleResponse* result) override;
 
   // Modifies the provided computation so that subsequent executions
   // will compute the provided ComputationDataHandle, rather than the
   // last expression enqueued on that Computation.
-  tensorflow::Status SetReturnValue(const SetReturnValueRequest* arg,
-                                    SetReturnValueResponse* results) override;
+  Status SetReturnValue(const SetReturnValueRequest* arg,
+                        SetReturnValueResponse* results) override;
 
   // Executes a computation with the provided global data passed as
   // immutable arguments. Returns global data output and execution timing.
-  tensorflow::Status Execute(const ExecuteRequest* arg,
-                             ExecuteResponse* result) override;
+  Status Execute(const ExecuteRequest* arg, ExecuteResponse* result) override;
 
   // Executes a computation with the provided global data passed as
   // immutable arguments. The request contains the whole computation graph.
   // Returns global data output and execution timing.
   //
   // TODO(b/74197823): This is a part of a NOT YET ready refactor.
-  tensorflow::Status ExecuteGraph(const ExecuteGraphRequest* arg,
-                                  ExecuteResponse* result) override;
+  Status ExecuteGraph(const ExecuteGraphRequest* arg,
+                      ExecuteResponse* result) override;
 
   // Executes one or more computations in parallel with the provided global data
   // passed as immutable arguments. Returns global data output for each
   // computation.
-  tensorflow::Status ExecuteParallel(const ExecuteParallelRequest* arg,
-                                     ExecuteParallelResponse* result) override;
+  Status ExecuteParallel(const ExecuteParallelRequest* arg,
+                         ExecuteParallelResponse* result) override;
 
   // Executes one or more computations in parallel with the provided global data
   // passed as immutable arguments. Returns global data output for each
   // computation.
   //
   // TODO(b/74197823): This is a part of a NOT YET ready refactor.
-  tensorflow::Status ExecuteGraphParallel(
-      const ExecuteGraphParallelRequest* arg,
-      ExecuteParallelResponse* result) override;
+  Status ExecuteGraphParallel(const ExecuteGraphParallelRequest* arg,
+                              ExecuteParallelResponse* result) override;
 
   // Requests one or more device handles from the target.
   //
@@ -143,9 +140,8 @@ class Service : public ServiceInterface {
   // the first set of replicas, and the next R devices to the second set of
   // replicas, etc. Each returned device handle represents the device with the
   // replica id 0.
-  tensorflow::Status GetDeviceHandles(
-      const GetDeviceHandlesRequest* arg,
-      GetDeviceHandlesResponse* result) override;
+  Status GetDeviceHandles(const GetDeviceHandlesRequest* arg,
+                          GetDeviceHandlesResponse* result) override;
 
   // Asynchronously executes a computation with provided arguments. Invokes
   // the provided computation with the provided global data passed as
@@ -154,38 +150,33 @@ class Service : public ServiceInterface {
   // (Note: The corresponding function in xla::Client was removed as part of
   // b/64116060, in an attempt to simplify our API.  We're keeping this around
   // for now in case we want to expose this to clients in a different way.)
-  tensorflow::Status ExecuteAsync(const ExecuteAsyncRequest* arg,
-                                  ExecuteAsyncResponse* result) override;
+  Status ExecuteAsync(const ExecuteAsyncRequest* arg,
+                      ExecuteAsyncResponse* result) override;
 
   // Waits until the specified execution is complete and returns the result.
   // Calling this API multiple times with the same execution handle returns the
   // method with an error since the execution handle is destroyed after the
   // first call.
-  tensorflow::Status WaitForExecution(
-      const WaitForExecutionRequest* arg,
-      WaitForExecutionResponse* result) override;
+  Status WaitForExecution(const WaitForExecutionRequest* arg,
+                          WaitForExecutionResponse* result) override;
 
   // Requests that global data be transferred to the client in literal form.
-  tensorflow::Status TransferToClient(
-      const TransferToClientRequest* arg,
-      TransferToClientResponse* result) override;
+  Status TransferToClient(const TransferToClientRequest* arg,
+                          TransferToClientResponse* result) override;
 
   // Transfers data from a literal provided by the client, into device memory.
-  tensorflow::Status TransferToServer(
-      const TransferToServerRequest* arg,
-      TransferToServerResponse* result) override;
+  Status TransferToServer(const TransferToServerRequest* arg,
+                          TransferToServerResponse* result) override;
 
   // Transfers data from a literal provided by the client, into the Infeed
   // buffer of the device.
-  tensorflow::Status TransferToInfeed(
-      const TransferToInfeedRequest* arg,
-      TransferToInfeedResponse* result) override;
+  Status TransferToInfeed(const TransferToInfeedRequest* arg,
+                          TransferToInfeedResponse* result) override;
 
   // Transfers data from the Outfeed othe device to the literal provided by the
   // client.
-  tensorflow::Status TransferFromOutfeed(
-      const TransferFromOutfeedRequest* arg,
-      TransferFromOutfeedResponse* result) override;
+  Status TransferFromOutfeed(const TransferFromOutfeedRequest* arg,
+                             TransferFromOutfeedResponse* result) override;
 
   // Resets devices, clearing all existing state on all the devices associated
   // with this service (including memory allocated on the devices).
@@ -196,71 +187,65 @@ class Service : public ServiceInterface {
   // ResetDevice should be called before an Execution that expect the device to
   // be in the reset state. For example, if the prior Execution modifies device
   // state (e.g., architectural state) that the next Execution depends on.
-  tensorflow::Status ResetDevice(const ResetDeviceRequest* arg,
-                                 ResetDeviceResponse* result) override;
+  Status ResetDevice(const ResetDeviceRequest* arg,
+                     ResetDeviceResponse* result) override;
 
   // Tests if an expression is a compile-time constant.
-  tensorflow::Status IsConstant(const IsConstantRequest* arg,
-                                IsConstantResponse* result) override;
+  Status IsConstant(const IsConstantRequest* arg,
+                    IsConstantResponse* result) override;
 
   // Computes the value of a constant expression.
-  tensorflow::Status ComputeConstant(const ComputeConstantRequest* arg,
-                                     ComputeConstantResponse* result) override;
-  tensorflow::Status ComputeConstantGraph(
-      const ComputeConstantGraphRequest* arg,
-      ComputeConstantResponse* result) override;
+  Status ComputeConstant(const ComputeConstantRequest* arg,
+                         ComputeConstantResponse* result) override;
+  Status ComputeConstantGraph(const ComputeConstantGraphRequest* arg,
+                              ComputeConstantResponse* result) override;
 
   // Returns the shape (with layout) of an array associated with a given data
   // handle.
-  tensorflow::Status GetShape(const GetShapeRequest* arg,
-                              GetShapeResponse* result) override;
+  Status GetShape(const GetShapeRequest* arg,
+                  GetShapeResponse* result) override;
 
   // Returns the program shape of the computation associated with the given
   // handle.
-  tensorflow::Status GetComputationShape(
-      const GetComputationShapeRequest* arg,
-      GetComputationShapeResponse* result) override;
+  Status GetComputationShape(const GetComputationShapeRequest* arg,
+                             GetComputationShapeResponse* result) override;
 
   /////
   // Computation-oriented methods.
 
   // Enqueues an Op on the computation.
-  tensorflow::Status Op(const OpRequest* arg, OpResponse* result) override;
+  Status Op(const OpRequest* arg, OpResponse* result) override;
 
   // Retrieves the inferred shape for a value within a computation.
-  tensorflow::Status GetLocalShape(const GetLocalShapeRequest* arg,
-                                   GetLocalShapeResponse* result) override;
+  Status GetLocalShape(const GetLocalShapeRequest* arg,
+                       GetLocalShapeResponse* result) override;
 
   // Retrieves the statistics of a computation.
-  tensorflow::Status GetComputationStats(
-      const ComputationStatsRequest* arg,
-      ComputationStatsResponse* result) override;
+  Status GetComputationStats(const ComputationStatsRequest* arg,
+                             ComputationStatsResponse* result) override;
 
   // Retrieves the statistics of a computation.
   //
   // TODO(b/74197823): This is a part of a NOT YET ready refactor.
-  tensorflow::Status GetComputationGraphStats(
-      const ComputationGraphStatsRequest* arg,
-      ComputationStatsResponse* result) override;
+  Status GetComputationGraphStats(const ComputationGraphStatsRequest* arg,
+                                  ComputationStatsResponse* result) override;
 
   // Snapshots the current state of a computation handle into a serializable
   // protocol buffer form, so it can be loaded via
   // LoadComputationSnapshot.
-  tensorflow::Status SnapshotComputation(
-      const SnapshotComputationRequest* arg,
-      SnapshotComputationResponse* result) override;
+  Status SnapshotComputation(const SnapshotComputationRequest* arg,
+                             SnapshotComputationResponse* result) override;
 
   // Loads a computation from a serialized protocol buffer created via
   // SnapshotComputation.
-  tensorflow::Status LoadComputationSnapshot(
+  Status LoadComputationSnapshot(
       const LoadComputationSnapshotRequest* arg,
       LoadComputationSnapshotResponse* result) override;
 
   // Creates a unique channel handle that can be used for Send/Recv
   // instructions.
-  tensorflow::Status CreateChannelHandle(
-      const CreateChannelHandleRequest* arg,
-      CreateChannelHandleResponse* result) override;
+  Status CreateChannelHandle(const CreateChannelHandleRequest* arg,
+                             CreateChannelHandleResponse* result) override;
 
   // Returns the ComputationTracker of the current service instance.
   // Only used in unit tests to access user computations from client.
@@ -389,7 +374,7 @@ class Service : public ServiceInterface {
 
   // Convenience function for adding a function to a user computation.
   template 
-  tensorflow::Status AddInstruction(
+  Status AddInstruction(
       const RequestT* arg, ResponseT* result,
       const std::function(UserComputation*)>&
           adder);
@@ -397,16 +382,14 @@ class Service : public ServiceInterface {
   // Executes a single computation which has more than one target device.
   // The N devices are expected to all return an empty tuple, but one, which
   // will be the result of this computation.
-  tensorflow::Status ExecuteOneToN(const ExecuteRequest* arg,
-                                   ExecuteResponse* result);
-  tensorflow::Status ExecuteOneToN(const ExecuteGraphRequest* arg,
-                                   ExecuteResponse* result);
+  Status ExecuteOneToN(const ExecuteRequest* arg, ExecuteResponse* result);
+  Status ExecuteOneToN(const ExecuteGraphRequest* arg, ExecuteResponse* result);
 
   // Convenience function which checks whether the given shape_with_layout
   // (presumably passed by the client to set the result layout) is valid for the
   // given computation result shape.
-  tensorflow::Status ValidateResultShapeWithLayout(
-      const Shape& shape_with_layout, const Shape& result_shape) const;
+  Status ValidateResultShapeWithLayout(const Shape& shape_with_layout,
+                                       const Shape& result_shape) const;
 
   // Returns the stream executors assigned to the replicas represented by the
   // given device handle. Each device_handle is a virtual replicated device that
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index fedb42ac88601d..3500978bdd808f 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -172,8 +172,8 @@ bool AllUnique(tensorflow::gtl::ArraySlice slice) {
   return std::set(slice.begin(), slice.end()).size() == slice.size();
 }
 
-tensorflow::Status ExpectNotTupleOrOpaque(const Shape& shape,
-                                          tensorflow::StringPiece op_type) {
+Status ExpectNotTupleOrOpaque(const Shape& shape,
+                              tensorflow::StringPiece op_type) {
   if (ShapeUtil::IsTuple(shape)) {
     return InvalidArgument("Expected non-tuple argument for %s, but got %s.",
                            std::string(op_type).c_str(),
@@ -183,13 +183,13 @@ tensorflow::Status ExpectNotTupleOrOpaque(const Shape& shape,
                            std::string(op_type).c_str(),
                            ShapeUtil::HumanString(shape).c_str());
   } else {
-    return tensorflow::Status::OK();
+    return Status::OK();
   }
 }
 
-tensorflow::Status VerifyReducerShape(const ProgramShape& reducer_shape,
-                                      const Shape& init_value_shape,
-                                      const PrimitiveType& input_element_type) {
+Status VerifyReducerShape(const ProgramShape& reducer_shape,
+                          const Shape& init_value_shape,
+                          const PrimitiveType& input_element_type) {
   if (reducer_shape.parameters_size() != 2) {
     return InvalidArgument(
         "Reduction function must take 2 parameters, but "
@@ -249,7 +249,7 @@ tensorflow::Status VerifyReducerShape(const ProgramShape& reducer_shape,
         ShapeUtil::HumanString(accumulator_shape).c_str());
   }
 
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 StatusOr InferWindowOutputShape(const Shape& base_shape,
@@ -1218,11 +1218,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
       scale_shape, "scale input of batch norm training"));
 
   TF_RET_CHECK(ShapeUtil::ValidateShapeWithOptionalLayout(operand_shape) ==
-               tensorflow::Status::OK());
+               Status::OK());
   TF_RET_CHECK(ShapeUtil::ValidateShapeWithOptionalLayout(offset_shape) ==
-               tensorflow::Status::OK());
+               Status::OK());
   TF_RET_CHECK(ShapeUtil::ValidateShapeWithOptionalLayout(scale_shape) ==
-               tensorflow::Status::OK());
+               Status::OK());
 
   if (feature_index >= ShapeUtil::Rank(operand_shape)) {
     return InvalidArgument(
@@ -1324,15 +1324,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
       scale_shape, "scale input of batch norm inference"));
 
   TF_RET_CHECK(ShapeUtil::ValidateShapeWithOptionalLayout(operand_shape) ==
-               tensorflow::Status::OK());
+               Status::OK());
   TF_RET_CHECK(ShapeUtil::ValidateShapeWithOptionalLayout(offset_shape) ==
-               tensorflow::Status::OK());
+               Status::OK());
   TF_RET_CHECK(ShapeUtil::ValidateShapeWithOptionalLayout(scale_shape) ==
-               tensorflow::Status::OK());
+               Status::OK());
   TF_RET_CHECK(ShapeUtil::ValidateShapeWithOptionalLayout(mean_shape) ==
-               tensorflow::Status::OK());
+               Status::OK());
   TF_RET_CHECK(ShapeUtil::ValidateShapeWithOptionalLayout(variance_shape) ==
-               tensorflow::Status::OK());
+               Status::OK());
 
   if (feature_index >= ShapeUtil::Rank(operand_shape)) {
     return InvalidArgument(
diff --git a/tensorflow/compiler/xla/service/transpose_folding.cc b/tensorflow/compiler/xla/service/transpose_folding.cc
index f7a5512fec47f7..ba16dc640e2d29 100644
--- a/tensorflow/compiler/xla/service/transpose_folding.cc
+++ b/tensorflow/compiler/xla/service/transpose_folding.cc
@@ -215,7 +215,7 @@ StatusOr TransposeFolding::Run(HloModule* module) {
             std::make_pair(instruction, operand_indices));
       }
     }
-    return tensorflow::Status::OK();
+    return Status::OK();
   };
 
   for (auto* comp : module->MakeNonfusionComputations()) {
diff --git a/tensorflow/compiler/xla/service_interface.h b/tensorflow/compiler/xla/service_interface.h
index 4f64fe8f835017..141347a792c23a 100644
--- a/tensorflow/compiler/xla/service_interface.h
+++ b/tensorflow/compiler/xla/service_interface.h
@@ -16,9 +16,9 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_INTERFACE_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_INTERFACE_H_
 
+#include "tensorflow/compiler/xla/status.h"
 #include "tensorflow/compiler/xla/xla.pb.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
-#include "tensorflow/core/lib/core/status.h"
 
 namespace xla {
 
@@ -32,99 +32,93 @@ class ServiceInterface {
   virtual ~ServiceInterface() = default;
 
   // TODO(b/31824348): Convert to use StatusOr.
-  virtual tensorflow::Status TransferToClient(
-      const TransferToClientRequest* arg, TransferToClientResponse* result) = 0;
+  virtual Status TransferToClient(const TransferToClientRequest* arg,
+                                  TransferToClientResponse* result) = 0;
 
-  virtual tensorflow::Status TransferToServer(
-      const TransferToServerRequest* arg, TransferToServerResponse* result) = 0;
+  virtual Status TransferToServer(const TransferToServerRequest* arg,
+                                  TransferToServerResponse* result) = 0;
 
-  virtual tensorflow::Status TransferToInfeed(
-      const TransferToInfeedRequest* arg, TransferToInfeedResponse* result) = 0;
+  virtual Status TransferToInfeed(const TransferToInfeedRequest* arg,
+                                  TransferToInfeedResponse* result) = 0;
 
-  virtual tensorflow::Status TransferFromOutfeed(
-      const TransferFromOutfeedRequest* arg,
-      TransferFromOutfeedResponse* result) = 0;
+  virtual Status TransferFromOutfeed(const TransferFromOutfeedRequest* arg,
+                                     TransferFromOutfeedResponse* result) = 0;
 
-  virtual tensorflow::Status ResetDevice(const ResetDeviceRequest* arg,
-                                         ResetDeviceResponse* result) = 0;
+  virtual Status ResetDevice(const ResetDeviceRequest* arg,
+                             ResetDeviceResponse* result) = 0;
 
-  virtual tensorflow::Status LoadComputationSnapshot(
+  virtual Status LoadComputationSnapshot(
       const LoadComputationSnapshotRequest* request,
       LoadComputationSnapshotResponse* result) = 0;
 
-  virtual tensorflow::Status Execute(const ExecuteRequest* arg,
-                                     ExecuteResponse* result) = 0;
+  virtual Status Execute(const ExecuteRequest* arg,
+                         ExecuteResponse* result) = 0;
 
-  virtual tensorflow::Status ExecuteGraph(const ExecuteGraphRequest* arg,
-                                          ExecuteResponse* result) = 0;
+  virtual Status ExecuteGraph(const ExecuteGraphRequest* arg,
+                              ExecuteResponse* result) = 0;
 
-  virtual tensorflow::Status ExecuteParallel(
-      const ExecuteParallelRequest* arg, ExecuteParallelResponse* result) = 0;
+  virtual Status ExecuteParallel(const ExecuteParallelRequest* arg,
+                                 ExecuteParallelResponse* result) = 0;
 
-  virtual tensorflow::Status ExecuteGraphParallel(
-      const ExecuteGraphParallelRequest* arg,
-      ExecuteParallelResponse* result) = 0;
+  virtual Status ExecuteGraphParallel(const ExecuteGraphParallelRequest* arg,
+                                      ExecuteParallelResponse* result) = 0;
 
-  virtual tensorflow::Status ExecuteAsync(const ExecuteAsyncRequest* arg,
-                                          ExecuteAsyncResponse* result) = 0;
+  virtual Status ExecuteAsync(const ExecuteAsyncRequest* arg,
+                              ExecuteAsyncResponse* result) = 0;
 
-  virtual tensorflow::Status WaitForExecution(
-      const WaitForExecutionRequest* arg, WaitForExecutionResponse* result) = 0;
+  virtual Status WaitForExecution(const WaitForExecutionRequest* arg,
+                                  WaitForExecutionResponse* result) = 0;
 
-  virtual tensorflow::Status DeconstructTuple(
-      const DeconstructTupleRequest* arg, DeconstructTupleResponse* result) = 0;
+  virtual Status DeconstructTuple(const DeconstructTupleRequest* arg,
+                                  DeconstructTupleResponse* result) = 0;
 
-  virtual tensorflow::Status GetComputationStats(
-      const ComputationStatsRequest* arg, ComputationStatsResponse* result) = 0;
+  virtual Status GetComputationStats(const ComputationStatsRequest* arg,
+                                     ComputationStatsResponse* result) = 0;
 
-  virtual tensorflow::Status GetComputationGraphStats(
+  virtual Status GetComputationGraphStats(
       const ComputationGraphStatsRequest* arg,
       ComputationStatsResponse* result) = 0;
 
-  virtual tensorflow::Status GetComputationShape(
-      const GetComputationShapeRequest* arg,
-      GetComputationShapeResponse* result) = 0;
+  virtual Status GetComputationShape(const GetComputationShapeRequest* arg,
+                                     GetComputationShapeResponse* result) = 0;
 
-  virtual tensorflow::Status GetShape(const GetShapeRequest* arg,
-                                      GetShapeResponse* result) = 0;
+  virtual Status GetShape(const GetShapeRequest* arg,
+                          GetShapeResponse* result) = 0;
 
-  virtual tensorflow::Status CreateChannelHandle(
-      const CreateChannelHandleRequest* arg,
-      CreateChannelHandleResponse* result) = 0;
+  virtual Status CreateChannelHandle(const CreateChannelHandleRequest* arg,
+                                     CreateChannelHandleResponse* result) = 0;
 
-  virtual tensorflow::Status GetDeviceHandles(
-      const GetDeviceHandlesRequest* arg, GetDeviceHandlesResponse* result) = 0;
+  virtual Status GetDeviceHandles(const GetDeviceHandlesRequest* arg,
+                                  GetDeviceHandlesResponse* result) = 0;
 
   // Methods used by ComputationBuilder.
-  virtual tensorflow::Status Computation(const ComputationRequest* arg,
-                                         ComputationResponse* result) = 0;
+  virtual Status Computation(const ComputationRequest* arg,
+                             ComputationResponse* result) = 0;
 
-  virtual tensorflow::Status Op(const OpRequest* arg, OpResponse* result) = 0;
+  virtual Status Op(const OpRequest* arg, OpResponse* result) = 0;
 
-  virtual tensorflow::Status GetLocalShape(const GetLocalShapeRequest* arg,
-                                           GetLocalShapeResponse* result) = 0;
+  virtual Status GetLocalShape(const GetLocalShapeRequest* arg,
+                               GetLocalShapeResponse* result) = 0;
 
-  virtual tensorflow::Status SetReturnValue(
-      const SetReturnValueRequest* arg, SetReturnValueResponse* results) = 0;
+  virtual Status SetReturnValue(const SetReturnValueRequest* arg,
+                                SetReturnValueResponse* results) = 0;
 
-  virtual tensorflow::Status IsConstant(const IsConstantRequest* arg,
-                                        IsConstantResponse* result) = 0;
+  virtual Status IsConstant(const IsConstantRequest* arg,
+                            IsConstantResponse* result) = 0;
 
-  virtual tensorflow::Status ComputeConstant(
-      const ComputeConstantRequest* arg, ComputeConstantResponse* result) = 0;
+  virtual Status ComputeConstant(const ComputeConstantRequest* arg,
+                                 ComputeConstantResponse* result) = 0;
 
-  virtual tensorflow::Status ComputeConstantGraph(
-      const ComputeConstantGraphRequest* arg,
-      ComputeConstantResponse* result) = 0;
+  virtual Status ComputeConstantGraph(const ComputeConstantGraphRequest* arg,
+                                      ComputeConstantResponse* result) = 0;
 
   // Methods used by Computation.
-  virtual tensorflow::Status SnapshotComputation(
-      const SnapshotComputationRequest* ag,
-      SnapshotComputationResponse* result) = 0;
+  virtual Status SnapshotComputation(const SnapshotComputationRequest* ag,
+                                     SnapshotComputationResponse* result) = 0;
 
   // Methods used by GlobalData.
-  virtual tensorflow::Status Unregister(const UnregisterRequest* arg,
-                                        UnregisterResponse* result) = 0;
+  virtual Status Unregister(const UnregisterRequest* arg,
+                            UnregisterResponse* result) = 0;
 };
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/shape_layout.cc b/tensorflow/compiler/xla/shape_layout.cc
index 789eba5780d37e..7ee366b27a82bd 100644
--- a/tensorflow/compiler/xla/shape_layout.cc
+++ b/tensorflow/compiler/xla/shape_layout.cc
@@ -22,24 +22,24 @@ limitations under the License.
 
 namespace xla {
 
-tensorflow::Status ShapeLayout::CopyLayoutFromShape(const Shape& other_shape) {
+Status ShapeLayout::CopyLayoutFromShape(const Shape& other_shape) {
   if (!ShapeUtil::Compatible(other_shape, shape_)) {
     return InvalidArgument("Shape %s is not compatible with shape %s",
                            ShapeUtil::HumanString(other_shape).c_str(),
                            ShapeUtil::HumanString(shape()).c_str());
   }
   shape_ = other_shape;
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status ShapeLayout::AssignLayoutToShape(Shape* to_shape) const {
+Status ShapeLayout::AssignLayoutToShape(Shape* to_shape) const {
   if (!ShapeUtil::Compatible(*to_shape, shape_)) {
     return InvalidArgument("Shape %s is not compatible with shape %s",
                            ShapeUtil::HumanString(*to_shape).c_str(),
                            ShapeUtil::HumanString(shape()).c_str());
   }
   *to_shape = shape_;
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 void ShapeLayout::SetToDefaultLayout() {
diff --git a/tensorflow/compiler/xla/shape_layout.h b/tensorflow/compiler/xla/shape_layout.h
index a1dce758cd3ab3..36806da599cc9b 100644
--- a/tensorflow/compiler/xla/shape_layout.h
+++ b/tensorflow/compiler/xla/shape_layout.h
@@ -40,7 +40,7 @@ class ShapeLayout {
   // Assigns the layouts in this ShapeLayout to the Layout fields of the given
   // shape. 'to_shape' and the shape of the ShapeLayout object must be
   // compatible.
-  tensorflow::Status AssignLayoutToShape(Shape* to_shape) const;
+  Status AssignLayoutToShape(Shape* to_shape) const;
 
   // Returns true if the Layouts in this ShapeLayout match the layouts in the
   // given shape. Returns false otherwise. If the given shape is not compatible
@@ -49,7 +49,7 @@ class ShapeLayout {
 
   // Copies the layout from the given shape into this ShapeLayout. 'other_shape'
   // must be compatible with the ShapeLayout's shape.
-  tensorflow::Status CopyLayoutFromShape(const Shape& other_shape);
+  Status CopyLayoutFromShape(const Shape& other_shape);
 
   // Clears (Layout::Clear) all the Layouts stored in this object.
   void Clear();
diff --git a/tensorflow/compiler/xla/status.h b/tensorflow/compiler/xla/status.h
index 4eb3bf3766412d..69abb51852ac09 100644
--- a/tensorflow/compiler/xla/status.h
+++ b/tensorflow/compiler/xla/status.h
@@ -21,7 +21,7 @@ limitations under the License.
 
 namespace xla {
 
-using tensorflow::Status;
+using tensorflow::Status;  // TENSORFLOW_STATUS_OK
 
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/statusor_test.cc b/tensorflow/compiler/xla/statusor_test.cc
index 7d76370e85d57f..377a618ffbd993 100644
--- a/tensorflow/compiler/xla/statusor_test.cc
+++ b/tensorflow/compiler/xla/statusor_test.cc
@@ -413,7 +413,7 @@ TEST(StatusOr, TestPointerValueConst) {
   EXPECT_EQ(&kI, thing.ValueOrDie());
 }
 
-// NOTE(tucker): tensorflow::StatusOr does not support this kind
+// NOTE(tucker): StatusOr does not support this kind
 // of resize op.
 // TEST(StatusOr, StatusOrVectorOfUniquePointerCanResize) {
 //   using EvilType = std::vector>;
diff --git a/tensorflow/compiler/xla/test_helpers.h b/tensorflow/compiler/xla/test_helpers.h
index 17bae2e4f61126..8918350135fbb8 100644
--- a/tensorflow/compiler/xla/test_helpers.h
+++ b/tensorflow/compiler/xla/test_helpers.h
@@ -40,13 +40,10 @@ class Literal;
 namespace testing {
 
 namespace internal_status {
-inline const ::tensorflow::Status& GetStatus(
-    const ::tensorflow::Status& status) {
-  return status;
-}
+inline const Status& GetStatus(const Status& status) { return status; }
 
 template 
-inline const ::tensorflow::Status& GetStatus(const StatusOr& status) {
+inline const Status& GetStatus(const StatusOr& status) {
   return status.status();
 }
 }  // namespace internal_status
@@ -57,21 +54,17 @@ inline const ::tensorflow::Status& GetStatus(const StatusOr& status) {
 // The following macros are similar to macros in gmock, but deliberately named
 // differently in order to avoid conflicts in files which include both.
 
-// Macros for testing the results of functions that return tensorflow::Status or
+// Macros for testing the results of functions that return Status or
 // StatusOr (for any type T).
-#define EXPECT_IS_OK(expression)      \
-  EXPECT_EQ(tensorflow::Status::OK(), \
-            xla::testing::internal_status::GetStatus(expression))
-#define EXPECT_IS_NOT_OK(expression)  \
-  EXPECT_NE(tensorflow::Status::OK(), \
-            xla::testing::internal_status::GetStatus(expression))
+#define EXPECT_IS_OK(expression) \
+  EXPECT_EQ(Status::OK(), xla::testing::internal_status::GetStatus(expression))
+#define EXPECT_IS_NOT_OK(expression) \
+  EXPECT_NE(Status::OK(), xla::testing::internal_status::GetStatus(expression))
 #undef ASSERT_IS_OK
-#define ASSERT_IS_OK(expression)      \
-  ASSERT_EQ(tensorflow::Status::OK(), \
-            xla::testing::internal_status::GetStatus(expression))
+#define ASSERT_IS_OK(expression) \
+  ASSERT_EQ(Status::OK(), xla::testing::internal_status::GetStatus(expression))
 #undef ASSERT_IS_NOT_OK
-#define ASSERT_IS_NOT_OK(expression)  \
-  ASSERT_NE(tensorflow::Status::OK(), \
-            xla::testing::internal_status::GetStatus(expression))
+#define ASSERT_IS_NOT_OK(expression) \
+  ASSERT_NE(Status::OK(), xla::testing::internal_status::GetStatus(expression))
 
 #endif  // TENSORFLOW_COMPILER_XLA_TEST_HELPERS_H_
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc
index b68f3093a3838e..bf8ed4d9fb0bc6 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.cc
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc
@@ -177,8 +177,7 @@ void ClientLibraryTestBase::ComputeAndCompareLiteral(
                                                   error, shape_with_layout));
 }
 
-tensorflow::Status
-ClientLibraryTestBase::ComputeAndCompareLiteralWithAllOutputLayouts(
+Status ClientLibraryTestBase::ComputeAndCompareLiteralWithAllOutputLayouts(
     const xla::XlaComputation& computation, const Literal& expected,
     tensorflow::gtl::ArraySlice arguments,
     const std::function arguments,
     const std::function choose;
-  choose = [&, this](int64 index) -> tensorflow::Status {
+  std::function choose;
+  choose = [&, this](int64 index) -> Status {
     if (index < arguments.size()) {
       // Try out all layouts for the operand.
       TF_ASSIGN_OR_RETURN(auto literal,
@@ -229,7 +227,7 @@ ClientLibraryTestBase::ComputeAndCompareLiteralWithAllInputLayouts(
         TF_RETURN_IF_ERROR(choose(index + 1));
         arguments_with_layout.pop_back();
         layout_strings.pop_back();
-        return tensorflow::Status::OK();
+        return Status::OK();
       }
 
       std::vector minor_to_major(ShapeUtil::Rank(literal->shape()));
@@ -247,7 +245,7 @@ ClientLibraryTestBase::ComputeAndCompareLiteralWithAllInputLayouts(
         layout_strings.pop_back();
       } while (
           std::next_permutation(minor_to_major.begin(), minor_to_major.end()));
-      return tensorflow::Status::OK();
+      return Status::OK();
     }
 
     // Every argument has an assigned layout.
@@ -262,13 +260,13 @@ ClientLibraryTestBase::ComputeAndCompareLiteralWithAllInputLayouts(
       tensorflow::strings::StrAppend(&error_message, str, " ");
     }
     verify_output(*actual, error_message);
-    return tensorflow::Status::OK();
+    return Status::OK();
   };
 
   return choose(0);
 }
 
-tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
+Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
     XlaBuilder* builder, const Literal& expected,
     tensorflow::gtl::ArraySlice arguments_passed_in,
     const Shape* shape_with_layout) {
@@ -323,10 +321,10 @@ tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
   TF_ASSIGN_OR_RETURN(auto actual, ExecuteAndTransfer(computation, arguments,
                                                       shape_with_layout));
   EXPECT_TRUE(LiteralTestUtil::Equal(*expected_ptr, *actual));
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
-tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
+Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
     XlaBuilder* builder, const Literal& expected,
     tensorflow::gtl::ArraySlice arguments_passed_in,
     ErrorSpec error, const Shape* shape_with_layout) {
@@ -376,7 +374,7 @@ tensorflow::Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus(
   TF_ASSIGN_OR_RETURN(auto actual, ExecuteAndTransfer(computation, arguments,
                                                       shape_with_layout));
   EXPECT_TRUE(LiteralTestUtil::Near(*expected_ptr, *actual, error));
-  return tensorflow::Status::OK();
+  return Status::OK();
 }
 
 void ClientLibraryTestBase::ComputeAndCompareR1U8(
diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h
index c8c3af0db300e2..0499fec5898a42 100644
--- a/tensorflow/compiler/xla/tests/client_library_test_base.h
+++ b/tensorflow/compiler/xla/tests/client_library_test_base.h
@@ -188,11 +188,11 @@ class ClientLibraryTestBase : public ::testing::Test {
       const Shape* shape_with_layout = nullptr);
 
   // ComputeAndCompare variant which returns an error status.
-  tensorflow::Status ComputeAndCompareLiteralWithStatus(
+  Status ComputeAndCompareLiteralWithStatus(
       XlaBuilder* builder, const Literal& expected,
       tensorflow::gtl::ArraySlice arguments,
       const Shape* shape_with_layout = nullptr);
-  tensorflow::Status ComputeAndCompareLiteralWithStatus(
+  Status ComputeAndCompareLiteralWithStatus(
       XlaBuilder* builder, const Literal& expected,
       tensorflow::gtl::ArraySlice arguments, ErrorSpec error,
       const Shape* shape_with_layout = nullptr);
@@ -378,12 +378,12 @@ class ClientLibraryTestBase : public ::testing::Test {
   ExecutionOptions execution_options_;
 
  private:
-  tensorflow::Status ComputeAndCompareLiteralWithAllOutputLayouts(
+  Status ComputeAndCompareLiteralWithAllOutputLayouts(
       const xla::XlaComputation& computation, const Literal& expected,
       tensorflow::gtl::ArraySlice arguments,
       const std::function& verify_output);
-  tensorflow::Status ComputeAndCompareLiteralWithAllInputLayouts(
+  Status ComputeAndCompareLiteralWithAllInputLayouts(
       const xla::XlaComputation& computation, const Literal& expected,
       tensorflow::gtl::ArraySlice arguments,
       const std::function TestAllocator::Allocate(int device_ordinal,
                                                  retry_on_failure);
 }
 
-tensorflow::Status TestAllocator::Deallocate(int device_ordinal,
-                                             se::DeviceMemoryBase mem) {
+Status TestAllocator::Deallocate(int device_ordinal, se::DeviceMemoryBase mem) {
   VLOG(2) << "Deallocate(" << device_ordinal << ")";
   {
     tensorflow::mutex_lock lock(count_mutex_);
diff --git a/tensorflow/compiler/xla/tests/local_client_test_base.h b/tensorflow/compiler/xla/tests/local_client_test_base.h
index 6374c799d932cd..258226523d830b 100644
--- a/tensorflow/compiler/xla/tests/local_client_test_base.h
+++ b/tensorflow/compiler/xla/tests/local_client_test_base.h
@@ -48,8 +48,7 @@ class TestAllocator : public StreamExecutorMemoryAllocator {
 
   StatusOr Allocate(int device_ordinal, uint64 size,
                                         bool retry_on_failure) override;
-  tensorflow::Status Deallocate(int device_ordinal,
-                                se::DeviceMemoryBase mem) override;
+  Status Deallocate(int device_ordinal, se::DeviceMemoryBase mem) override;
 
   // Return the number of allocations that have been performed.
   int64 allocation_count() const;
diff --git a/tensorflow/compiler/xla/tests/params_test.cc b/tensorflow/compiler/xla/tests/params_test.cc
index f04db776e6eca7..838f1b4e2f0f0e 100644
--- a/tensorflow/compiler/xla/tests/params_test.cc
+++ b/tensorflow/compiler/xla/tests/params_test.cc
@@ -160,7 +160,7 @@ XLA_TEST_F(ParamsTest, MissingParameter) {
   auto p = builder.Parameter(2, ShapeUtil::MakeShape(F32, {}), "param2");
   auto computation_status = builder.Build();
 
-  ASSERT_NE(computation_status.status(), tensorflow::Status::OK());
+  ASSERT_NE(computation_status.status(), Status::OK());
 }
 
 XLA_TEST_F(ParamsTest, UnusedParameter) {
diff --git a/tensorflow/compiler/xla/text_literal_writer.cc b/tensorflow/compiler/xla/text_literal_writer.cc
index 6e3061b78a554f..373c0d2d8d8ab0 100644
--- a/tensorflow/compiler/xla/text_literal_writer.cc
+++ b/tensorflow/compiler/xla/text_literal_writer.cc
@@ -30,7 +30,7 @@ limitations under the License.
 
 namespace xla {
 
-/* static */ tensorflow::Status TextLiteralWriter::WriteToPath(
+/* static */ Status TextLiteralWriter::WriteToPath(
     const Literal& literal, tensorflow::StringPiece path) {
   std::unique_ptr f;
   auto s = tensorflow::Env::Default()->NewWritableFile(std::string(path), &f);
@@ -43,7 +43,7 @@ namespace xla {
     return s;
   }
 
-  tensorflow::Status status;
+  Status status;
   tensorflow::WritableFile* f_ptr = f.get();
   literal.EachCellAsString(
       [f_ptr, &status](tensorflow::gtl::ArraySlice indices,
diff --git a/tensorflow/compiler/xla/text_literal_writer.h b/tensorflow/compiler/xla/text_literal_writer.h
index 7375493f4309c9..0a1235b5e04675 100644
--- a/tensorflow/compiler/xla/text_literal_writer.h
+++ b/tensorflow/compiler/xla/text_literal_writer.h
@@ -37,8 +37,8 @@ namespace xla {
 // This should be readable by xla::TextLiteralReader.
 class TextLiteralWriter {
  public:
-  static tensorflow::Status WriteToPath(const Literal& literal,
-                                        tensorflow::StringPiece path);
+  static Status WriteToPath(const Literal& literal,
+                            tensorflow::StringPiece path);
 
  private:
   TF_DISALLOW_COPY_AND_ASSIGN(TextLiteralWriter);
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
index e100d8cda14eab..131aded95ab04c 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
@@ -938,13 +938,13 @@ INSTANTIATE_TEST_CASE_P(HloParserTestSuccessInstantiation, HloParserShortTest,
 TEST_F(HloParserTest, Empty) {
   const string original = "";
   auto result = Parse(original);
-  EXPECT_NE(tensorflow::Status::OK(), result.status());
+  EXPECT_NE(Status::OK(), result.status());
 }
 
 TEST_F(HloParserTest, Garbage) {
   const string original = "HloModule thi$ str1ng makes# N0 sen$e @all!*&^%$";
   auto result = Parse(original);
-  EXPECT_NE(tensorflow::Status::OK(), result.status());
+  EXPECT_NE(Status::OK(), result.status());
 }
 
 TEST_F(HloParserTest, WrongOpcode) {
@@ -958,7 +958,7 @@ ENTRY %blabla (x: f32[], y: f32[]) -> f32[] {
 
 )";
   auto result = Parse(original);
-  EXPECT_NE(tensorflow::Status::OK(), result.status());
+  EXPECT_NE(Status::OK(), result.status());
 }
 
 TEST_F(HloParserTest, WrongShape) {
@@ -970,7 +970,7 @@ ENTRY %blabla (x: g32[]) -> g32[] {
 
 )";
   auto result = Parse(original);
-  EXPECT_NE(tensorflow::Status::OK(), result.status());
+  EXPECT_NE(Status::OK(), result.status());
 }
 
 TEST_F(HloParserTest, WrongOperandsSize) {
@@ -983,7 +983,7 @@ ENTRY %blabla (x: f32[]) -> pred[] {
 
 )";
   auto result = Parse(original);
-  EXPECT_NE(tensorflow::Status::OK(), result.status());
+  EXPECT_NE(Status::OK(), result.status());
 }
 
 TEST_F(HloParserTest, OperandNotFound) {
@@ -994,7 +994,7 @@ ENTRY %blabla (x: f32[]) -> pred[] {
 }
 )";
   auto result = Parse(original);
-  EXPECT_NE(tensorflow::Status::OK(), result.status());
+  EXPECT_NE(Status::OK(), result.status());
 }
 
 TEST_F(HloParserTest, MoreConstants) {
@@ -1036,7 +1036,7 @@ ENTRY %some_2 () -> f32[2] {
 
 )";
   auto result = Parse(original);
-  EXPECT_NE(tensorflow::Status::OK(), result.status());
+  EXPECT_NE(Status::OK(), result.status());
   ExpectHasSubstr(result.status().error_message(),
                   "expects nested array in rank 1, but sees larger");
 }
@@ -1050,7 +1050,7 @@ ENTRY %some_2x3 () -> f32[2,3] {
 
 )";
   auto result = Parse(original);
-  EXPECT_NE(tensorflow::Status::OK(), result.status());
+  EXPECT_NE(Status::OK(), result.status());
   ExpectHasSubstr(result.status().error_message(),
                   "expects nested array in rank 2, but sees 1");
 }
@@ -1064,7 +1064,7 @@ ENTRY %some_2x3x2 () -> f32[2,3,2] {
 
 )";
   auto result = Parse(original);
-  EXPECT_NE(tensorflow::Status::OK(), result.status());
+  EXPECT_NE(Status::OK(), result.status());
   ExpectHasSubstr(result.status().error_message(),
                   "expects 3 elements in the [0]th element");
 }
@@ -1079,7 +1079,7 @@ ENTRY %ConstantF16Overflow.v4 () -> f16[] {
 
 )";
   auto result = Parse(original);
-  EXPECT_NE(tensorflow::Status::OK(), result.status());
+  EXPECT_NE(Status::OK(), result.status());
   ExpectHasSubstr(result.status().error_message(),
                   "is out of range for literal's primitive type F16");
 }

From 3c8adb12b0779cbc81555224f37bdb4cfaf6d6fa Mon Sep 17 00:00:00 2001
From: Yong Tang 
Date: Sat, 12 May 2018 00:09:26 +0000
Subject: [PATCH 0699/1691] Fix incorrect link for nvidia drivers

This fix fixes the incorrect link for nvidia drivers (previously
the link points to `Page Not Found`).

Signed-off-by: Yong Tang 
---
 tensorflow/docs_src/install/install_linux.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index e1948c71fdf45b..199b91503741a2 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -517,7 +517,7 @@ on your system:
   from source. To use the TensorFlow binaries, version 3.5 or higher is required.
   See the [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a
   list of supported GPU cards.
-* [GPU drivers](http://nvidia.com/driver) that support your version of the CUDA
+* [GPU drivers](http://nvidia.com/drivers) that support your version of the CUDA
   Toolkit.
 * The `libcupti-dev` library is the NVIDIA CUDA Profile Tools Interface. This
   library provides advanced profiling support. To install this library,

From d43cb8d7358fecacef076fdab42dae03911edfc5 Mon Sep 17 00:00:00 2001
From: Saurabh Saxena 
Date: Fri, 11 May 2018 17:14:29 -0700
Subject: [PATCH 0700/1691] Add hook for checkpointing input pipeline while
 training with Estimator.

PiperOrigin-RevId: 196331223
---
 tensorflow/contrib/data/__init__.py           |   1 +
 tensorflow/contrib/data/python/ops/BUILD      |  21 +++
 .../contrib/data/python/ops/iterator_ops.py   | 169 +++++++++++++++++-
 .../data/python/ops/iterator_ops_test.py      | 123 +++++++++++++
 tensorflow/python/data/ops/iterator_ops.py    |   7 +-
 5 files changed, 314 insertions(+), 7 deletions(-)
 create mode 100644 tensorflow/contrib/data/python/ops/iterator_ops_test.py

diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py
index 077cbba9d2ae41..4f2c72b6606ccd 100644
--- a/tensorflow/contrib/data/__init__.py
+++ b/tensorflow/contrib/data/__init__.py
@@ -72,6 +72,7 @@
 from tensorflow.contrib.data.python.ops.interleave_ops import parallel_interleave
 from tensorflow.contrib.data.python.ops.interleave_ops import sample_from_datasets
 from tensorflow.contrib.data.python.ops.interleave_ops import sloppy_interleave
+from tensorflow.contrib.data.python.ops.iterator_ops import CheckpointInputPipelineHook
 from tensorflow.contrib.data.python.ops.iterator_ops import make_saveable_from_iterator
 from tensorflow.contrib.data.python.ops.prefetching_ops import prefetch_to_device
 from tensorflow.contrib.data.python.ops.readers import make_batched_features_dataset
diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD
index 5b04c5316cfbb7..144460fde06401 100644
--- a/tensorflow/contrib/data/python/ops/BUILD
+++ b/tensorflow/contrib/data/python/ops/BUILD
@@ -45,6 +45,27 @@ py_library(
         "//tensorflow/python:dataset_ops_gen",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:training",
+        "//tensorflow/python/data/ops:iterator_ops",
+    ],
+)
+
+py_test(
+    name = "iterator_ops_test",
+    size = "small",
+    srcs = ["iterator_ops_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":iterator_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variables",
+        "//tensorflow/python/data/ops:dataset_ops",
+        "//tensorflow/python/estimator",
+        "//tensorflow/python/estimator:model_fn",
     ],
 )
 
diff --git a/tensorflow/contrib/data/python/ops/iterator_ops.py b/tensorflow/contrib/data/python/ops/iterator_ops.py
index d736029fb035e5..f1d0e5cddc2d75 100644
--- a/tensorflow/contrib/data/python/ops/iterator_ops.py
+++ b/tensorflow/contrib/data/python/ops/iterator_ops.py
@@ -16,10 +16,12 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-
+from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import gen_dataset_ops
-from tensorflow.python.training import saver
+from tensorflow.python.training import basic_session_run_hooks
+from tensorflow.python.training import saver as saver_lib
+from tensorflow.python.training import session_run_hook
 
 
 def make_saveable_from_iterator(iterator):
@@ -60,14 +62,14 @@ def make_saveable_from_iterator(iterator):
   return _Saveable(iterator._iterator_resource)  # pylint: disable=protected-access
 
 
-class _Saveable(saver.BaseSaverBuilder.SaveableObject):
+class _Saveable(saver_lib.BaseSaverBuilder.SaveableObject):
   """SaveableObject for saving/restoring iterator state."""
 
   def __init__(self, iterator_resource):
     serialized_iterator = gen_dataset_ops.serialize_iterator(iterator_resource)
     specs = [
-        saver.BaseSaverBuilder.SaveSpec(serialized_iterator, "",
-                                        iterator_resource.name + "-state")
+        saver_lib.BaseSaverBuilder.SaveSpec(serialized_iterator, "",
+                                            iterator_resource.name + "-state")
     ]
     super(_Saveable, self).__init__(iterator_resource, specs,
                                     iterator_resource.name)
@@ -75,3 +77,160 @@ def __init__(self, iterator_resource):
   def restore(self, restored_tensors, unused_restored_shapes):
     with ops.colocate_with(self.op):
       return gen_dataset_ops.deserialize_iterator(self.op, restored_tensors[0])
+
+
+class CheckpointInputPipelineHook(session_run_hook.SessionRunHook):
+  """Checkpoints input pipeline state every N steps or seconds.
+
+  This hook saves the state of the iterators in the `Graph` so that when
+  training is resumed the input pipeline continues from where it left off.
+  This could potentially avoid overfitting in certain pipelines where the
+  number of training steps per eval are small compared to the dataset
+  size or if the training pipeline is pre-empted.
+
+  Differences from `CheckpointSaverHook`:
+  1. Saves only the input pipelines in the "iterators" collection and not the
+     global variables or other saveable objects.
+  2. Does not write the `GraphDef` and `MetaGraphDef` to the summary.
+
+  Example of checkpointing the training pipeline:
+
+  ```python
+  est = tf.estimator.Estimator(model_fn)
+  while True:
+    est.train(
+        train_input_fn,
+        hooks=[tf.contrib.data.CheckpointInputPipelineHook(est)],
+        steps=train_steps_per_eval)
+    # Note: We do not pass the hook here.
+    metrics = est.evaluate(eval_input_fn)
+    if should_stop_the_training(metrics):
+      break
+  ```
+
+  This hook should be used if the input pipeline state needs to be saved
+  separate from the model checkpoint. Doing so may be useful for a few reasons:
+  1. The input pipeline checkpoint may be large, if there are large shuffle
+     or prefetch buffers for instance, and may bloat the checkpoint size.
+  2. If the input pipeline is shared between training and validation, restoring
+     the checkpoint during validation may override the validation input
+     pipeline.
+
+  For saving the input pipeline checkpoint alongside the model weights use
+  @{tf.contrib.data.make_saveable_from_iterator} directly to create a
+  `SaveableObject` and add to the `SAVEABLE_OBJECTS` collection. Note, however,
+  that you will need to be careful not to restore the training iterator during
+  eval. You can do that by not adding the iterator to the SAVEABLE_OBJECTS
+  collector when building the eval graph.
+  """
+
+  def __init__(self, estimator):
+    """Initializes a `CheckpointInputPipelineHook`.
+
+    Args:
+      estimator: Estimator.
+
+    Raises:
+      ValueError: One of `save_steps` or `save_secs` should be set.
+      ValueError: At most one of saver or scaffold should be set.
+    """
+    # `checkpoint_basename` is "input.ckpt" for non-distributed pipelines or
+    # of the form "input__.ckpt" for distributed pipelines.
+    # Note: The default `checkpoint_basename` used by `CheckpointSaverHook` is
+    # "model.ckpt". We intentionally choose the input pipeline checkpoint prefix
+    # to be different to avoid conflicts with the model checkpoint.
+
+    # pylint: disable=protected-access
+    checkpoint_prefix = "input"
+    if estimator._config.num_worker_replicas > 1:
+      # Distributed setting.
+      suffix = "_{}_{}".format(estimator._config.task_type,
+                               estimator._config.task_id)
+      checkpoint_prefix += suffix
+    # pylint: enable=protected-access
+
+    # We use a composition paradigm instead of inheriting from
+    # `CheckpointSaverHook` because `Estimator` does an `isinstance` check
+    # to check whether a `CheckpointSaverHook` is already present in the list
+    # of hooks and if not, adds one. Inheriting from `CheckpointSaverHook`
+    # would thwart this behavior. This hook checkpoints *only the iterators*
+    # and not the graph variables.
+    self._checkpoint_saver_hook = basic_session_run_hooks.CheckpointSaverHook(
+        estimator.model_dir,
+        save_secs=estimator._config.save_checkpoints_secs,  # pylint: disable=protected-access
+        save_steps=estimator._config.save_checkpoints_steps,  # pylint: disable=protected-access
+        checkpoint_basename=checkpoint_prefix + ".ckpt")
+
+    # Name for the protocol buffer file that will contain the list of most
+    # recent checkpoints stored as a `CheckpointState` protocol buffer.
+    # This file, kept in the same directory as the checkpoint files, is
+    # automatically managed by the `Saver` to keep track of recent checkpoints.
+    # The default name used by the `Saver` for this file is "checkpoint". Here
+    # we use the name "checkpoint_" so that in case the
+    # `checkpoint_dir` is the same as the model checkpoint directory, there are
+    # no conflicts during restore.
+    self._latest_filename = "checkpoint_" + checkpoint_prefix
+
+  def begin(self):
+    # Build a Saver that saves all iterators in the `GLOBAL_ITERATORS`
+    # collection if no `Saver` or `Scaffold` is provided.
+    # pylint: disable=protected-access
+    if (self._checkpoint_saver_hook._saver is None and
+        self._checkpoint_saver_hook._scaffold is None):
+      iterators = ops.get_collection(iterator_ops.GLOBAL_ITERATORS)
+      saveables = [_Saveable(i) for i in iterators]
+      self._checkpoint_saver_hook._saver = _CustomSaver(saveables,
+                                                        self._latest_filename)
+    # pylint: enable=protected-access
+    self._checkpoint_saver_hook.begin()
+
+  def after_create_session(self, session, coord):
+    # Check if there is an existing checkpoint. If so, restore from it.
+    # pylint: disable=protected-access
+    latest_checkpoint_path = saver_lib.latest_checkpoint(
+        self._checkpoint_saver_hook._checkpoint_dir,
+        latest_filename=self._latest_filename)
+    if latest_checkpoint_path:
+      self._checkpoint_saver_hook._get_saver().restore(session,
+                                                       latest_checkpoint_path)
+    else:
+      # The checkpoint saved here is the state at step "global_step".
+      # Note: We do not save the GraphDef or MetaGraphDef here.
+      global_step = session.run(self._checkpoint_saver_hook._global_step_tensor)
+      self._checkpoint_saver_hook._save(session, global_step)
+      self._checkpoint_saver_hook._timer.update_last_triggered_step(global_step)
+    # pylint: enable=protected-access
+
+  def before_run(self, run_context):
+    return self._checkpoint_saver_hook.before_run(run_context)
+
+  def after_run(self, run_context, run_values):
+    self._checkpoint_saver_hook.after_run(run_context, run_values)
+
+  def end(self, session):
+    self._checkpoint_saver_hook.end(session)
+
+
+class _CustomSaver(saver_lib.Saver):
+  """`Saver` with a different default `latest_filename`.
+
+  This is used in the `CheckpointInputPipelineHook` to avoid conflicts with
+  the model ckpt saved by the `CheckpointSaverHook`.
+  """
+
+  def __init__(self, var_list, latest_filename):
+    super(_CustomSaver, self).__init__(var_list)
+    self._latest_filename = latest_filename
+
+  def save(self,
+           sess,
+           save_path,
+           global_step=None,
+           latest_filename=None,
+           meta_graph_suffix="meta",
+           write_meta_graph=True,
+           write_state=True,
+           strip_default_attrs=False):
+    return super(_CustomSaver, self).save(
+        sess, save_path, global_step, latest_filename or self._latest_filename,
+        meta_graph_suffix, write_meta_graph, write_state, strip_default_attrs)
diff --git a/tensorflow/contrib/data/python/ops/iterator_ops_test.py b/tensorflow/contrib/data/python/ops/iterator_ops_test.py
new file mode 100644
index 00000000000000..30a993b1f7056b
--- /dev/null
+++ b/tensorflow/contrib/data/python/ops/iterator_ops_test.py
@@ -0,0 +1,123 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for experimental iterator_ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.data.python.ops import iterator_ops
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.estimator import estimator
+from tensorflow.python.estimator import model_fn
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+from tensorflow.python.training import saver as saver_lib
+from tensorflow.python.training import training_util
+
+
+class CheckpointInputPipelineHookTest(test.TestCase):
+
+  @staticmethod
+  def _model_fn(features, labels, mode, config):
+    del labels
+    del mode
+    del config
+    global_step = training_util.get_or_create_global_step()
+    update_global_step_op = global_step.assign_add(1)
+    latest_feature = variables.Variable(
+        0, name='latest_feature', dtype=dtypes.int64)
+    store_latest_feature_op = latest_feature.assign(features)
+    ops.add_to_collection('my_vars', global_step)
+    ops.add_to_collection('my_vars', latest_feature)
+    return model_fn.EstimatorSpec(
+        mode='train',
+        train_op=control_flow_ops.group(
+            [update_global_step_op, store_latest_feature_op]),
+        loss=constant_op.constant(2.0))
+
+  def _read_vars(self, model_dir):
+    """Returns (global_step, latest_feature)."""
+    with ops.Graph().as_default() as g:
+      ckpt_path = saver_lib.latest_checkpoint(model_dir)
+      meta_filename = ckpt_path + '.meta'
+      saver_lib.import_meta_graph(meta_filename)
+      saver = saver_lib.Saver()
+      with self.test_session(graph=g) as sess:
+        saver.restore(sess, ckpt_path)
+        return sess.run(ops.get_collection('my_vars'))
+
+  def _build_iterator_saver_hook(self, est):
+    return iterator_ops.CheckpointInputPipelineHook(est)
+
+  def testReturnDatasetFromInputFn(self):
+
+    def _input_fn():
+      return dataset_ops.Dataset.range(10)
+
+    est = estimator.Estimator(model_fn=self._model_fn)
+
+    est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
+    self.assertSequenceEqual(self._read_vars(est.model_dir), (2, 1))
+    est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
+    self.assertSequenceEqual(self._read_vars(est.model_dir), (4, 3))
+
+  def testBuildIteratorInInputFn(self):
+
+    def _input_fn():
+      ds = dataset_ops.Dataset.range(10)
+      iterator = ds.make_one_shot_iterator()
+      return iterator.get_next()
+
+    est = estimator.Estimator(model_fn=self._model_fn)
+
+    est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
+    self.assertSequenceEqual(self._read_vars(est.model_dir), (2, 1))
+    est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
+    self.assertSequenceEqual(self._read_vars(est.model_dir), (4, 3))
+
+  def testDoNotRestore(self):
+
+    def _input_fn():
+      return dataset_ops.Dataset.range(10)
+
+    est = estimator.Estimator(model_fn=self._model_fn)
+
+    est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
+    self.assertSequenceEqual(self._read_vars(est.model_dir), (2, 1))
+    est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
+    self.assertSequenceEqual(self._read_vars(est.model_dir), (4, 3))
+    # Hook not provided, input pipeline was not restored.
+    est.train(_input_fn, steps=2)
+    self.assertSequenceEqual(self._read_vars(est.model_dir), (6, 1))
+
+  def testRaiseErrorIfNoIterator(self):
+
+    def _input_fn():
+      return constant_op.constant(1, dtype=dtypes.int64)
+
+    est = estimator.Estimator(model_fn=self._model_fn)
+
+    with self.assertRaises(ValueError):
+      est.train(
+          _input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/python/data/ops/iterator_ops.py b/tensorflow/python/data/ops/iterator_ops.py
index 0c76afd29d4626..fd164277b6fd75 100644
--- a/tensorflow/python/data/ops/iterator_ops.py
+++ b/tensorflow/python/data/ops/iterator_ops.py
@@ -52,6 +52,9 @@
     "`next_element` as the input to some computation that is invoked inside "
     "the loop.")
 
+# Collection of all IteratorResources in the `Graph`.
+GLOBAL_ITERATORS = "iterators"
+
 
 @tf_export("data.Iterator")
 class Iterator(object):
@@ -75,8 +78,7 @@ def __init__(self, iterator_resource, initializer, output_types,
       output_shapes: A nested structure of `tf.TensorShape` objects
         corresponding to each component of an element of this dataset.
       output_classes: A nested structure of Python `type` object corresponding
-        to each
-        component of an element of this iterator.
+        to each component of an element of this iterator.
     """
     self._iterator_resource = iterator_resource
     self._initializer = initializer
@@ -86,6 +88,7 @@ def __init__(self, iterator_resource, initializer, output_types,
     self._string_handle = gen_dataset_ops.iterator_to_string_handle(
         self._iterator_resource)
     self._get_next_call_count = 0
+    ops.add_to_collection(GLOBAL_ITERATORS, self._iterator_resource)
 
   @staticmethod
   def from_structure(output_types,

From d8f01370b8e126bf4eedb9e07ba690c651204120 Mon Sep 17 00:00:00 2001
From: Jacques Pienaar 
Date: Fri, 11 May 2018 17:32:25 -0700
Subject: [PATCH 0701/1691] Add IsCondMerge.

PiperOrigin-RevId: 196332782
---
 .../kernel_tests/control_flow_util_test.py    | 31 +++++++++++++++++++
 tensorflow/python/ops/control_flow_util.py    | 30 ++++++++++++++++++
 2 files changed, 61 insertions(+)

diff --git a/tensorflow/python/kernel_tests/control_flow_util_test.py b/tensorflow/python/kernel_tests/control_flow_util_test.py
index 5138ad5aba8220..762c445da05008 100644
--- a/tensorflow/python/kernel_tests/control_flow_util_test.py
+++ b/tensorflow/python/kernel_tests/control_flow_util_test.py
@@ -144,6 +144,37 @@ def testIsLoopSwitch(self):
             control_flow_util.IsLoopSwitch(n),
             msg="Mismatch for {}".format(n.name))
 
+  def testIsCondMerge(self):
+    g = self.build_test_graph()
+    cond_merges = [
+        "OuterCond/cond/OuterWhile/while/NestedCond/cond/Merge",
+        "OuterCond/cond/Merge"
+    ]
+    for n in g.get_operations():
+      if n.name in cond_merges:
+        self.assertTrue(control_flow_util.IsMerge(n))
+        self.assertTrue(control_flow_util.IsCondMerge(n))
+        self.assertFalse(control_flow_util.IsLoopMerge(n))
+      else:
+        self.assertFalse(control_flow_util.IsCondMerge(n))
+        self.assertTrue(not control_flow_util.IsMerge(n) or
+                        control_flow_util.IsLoopMerge(n))
+
+  def testIsLoopMerge(self):
+    g = self.build_test_graph()
+    loop_merges = [
+        "OuterCond/cond/OuterWhile/while/Merge",
+    ]
+    for n in g.get_operations():
+      if n.name in loop_merges:
+        self.assertTrue(control_flow_util.IsMerge(n))
+        self.assertFalse(control_flow_util.IsCondMerge(n))
+        self.assertTrue(control_flow_util.IsLoopMerge(n))
+      else:
+        self.assertFalse(control_flow_util.IsLoopMerge(n))
+        self.assertTrue(not control_flow_util.IsMerge(n) or
+                        control_flow_util.IsCondMerge(n))
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/ops/control_flow_util.py b/tensorflow/python/ops/control_flow_util.py
index 41f16acc7dbd61..7a18986c5b0344 100644
--- a/tensorflow/python/ops/control_flow_util.py
+++ b/tensorflow/python/ops/control_flow_util.py
@@ -53,6 +53,11 @@ def IsSwitch(op):
   return op.type == "Switch" or op.type == "RefSwitch"
 
 
+def IsMerge(op):
+  """Return true if `op` is a Merge."""
+  return op.type == "Merge" or op.type == "RefMerge"
+
+
 def IsLoopEnter(op):
   """Returns true if `op` is an Enter."""
   return op.type == "Enter" or op.type == "RefEnter"
@@ -84,6 +89,23 @@ def IsCondSwitch(op):
   return is_cond_switch
 
 
+def IsCondMerge(op):
+  """Return true if `op` is the Merge for a conditional."""
+  if not IsMerge(op):
+    return False
+  if not op.inputs:
+    return False
+  # Merge nodes are not part of the cond control flow context that they
+  # represent, so consider the inputs to the merge of to determine if it is
+  # cond merge or not: A merge is a cond merge iff all its inputs are in
+  # cond contexts.
+  is_cond_merge = True
+  for i in op.inputs:
+    ctxt = GetOutputContext(i.op)
+    is_cond_merge = is_cond_merge and ctxt is not None and ctxt.IsCondContext()
+  return is_cond_merge
+
+
 def IsLoopSwitch(op):
   """Return true if `op` is the Switch for a while loop."""
   if IsSwitch(op):
@@ -92,6 +114,14 @@ def IsLoopSwitch(op):
   return False
 
 
+def IsLoopMerge(op):
+  """Return true if `op` is the Merge for a while loop."""
+  if IsMerge(op):
+    ctxt = op._get_control_flow_context()  # pylint: disable=protected-access
+    return ctxt is not None and ctxt.IsWhileContext() and not IsCondMerge(op)
+  return False
+
+
 def IsLoopConstantEnter(op):
   """Return true iff op is a loop invariant."""
   return IsLoopEnter(op) and op.get_attr("is_constant")

From 5ec03a85e6cb6ee360fcf2a99611dc7e678dc09c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 17:53:06 -0700
Subject: [PATCH 0702/1691] Implement additional options to control the string
 output of HloInstruction and HloComputation.

PiperOrigin-RevId: 196334340
---
 .../compiler/xla/service/hlo_computation.cc   |  39 +++--
 .../xla/service/hlo_computation_test.cc       | 102 +++++++++++
 .../compiler/xla/service/hlo_graph_dumper.cc  |   3 +-
 .../compiler/xla/service/hlo_instruction.cc   |  95 ++++++++++-
 .../compiler/xla/service/hlo_instruction.h    | 109 ++++++++++--
 .../xla/service/hlo_instruction_test.cc       | 158 ++++++++++++++++++
 6 files changed, 470 insertions(+), 36 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc
index 05dceb1dc0cb4a..63c3dc4a5932f7 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation.cc
@@ -365,25 +365,38 @@ std::list HloComputation::MakeEmbeddedComputationsList()
 string HloComputation::ToString(const HloPrintOptions& options) const {
   std::ostringstream s;
   for (int i = 0; i < options.indent_amount(); i++) {
-    s << "    ";
+    s << "  ";
   }
-  if (options.print_percent()) {
-    s << "%";
+
+  if (!options.is_in_nested_computation()) {
+    if (options.print_percent()) {
+      s << "%";
+    }
+    s << name() << " ";
   }
-  s << name();
+
   if (options.print_program_shape()) {
-    s << " " << ShapeUtil::HumanString(ComputeProgramShape());
-  }
-  s << " {\n";
-  for (const HloInstruction* instruction : MakeInstructionPostOrder()) {
-    for (int i = 0; i < options.indent_amount(); i++) {
-      s << "    ";
+    s << ShapeUtil::HumanString(ComputeProgramShape()) << " ";
+  }
+  s << "{\n";
+  {
+    // Print the instructions in this computation.
+    HloPrintOptions new_options = options;
+    new_options.set_indent_amount(options.indent_amount() + 1)
+        .set_is_in_nested_computation(true);
+    CanonicalNameMap name_map;
+    for (const HloInstruction* instruction : MakeInstructionPostOrder()) {
+      for (int i = 0; i < new_options.indent_amount(); i++) {
+        s << "  ";
+      }
+      s << (instruction == root_instruction_ ? "ROOT " : "")
+        << instruction->ToStringWithCanonicalNameMap(new_options, &name_map)
+        << "\n";
     }
-    s << "  " << (instruction == root_instruction_ ? "ROOT " : "")
-      << instruction->ToString(options) << "\n";
   }
+
   for (int i = 0; i < options.indent_amount(); i++) {
-    s << "    ";
+    s << "  ";
   }
   s << "}";
   return s.str();
diff --git a/tensorflow/compiler/xla/service/hlo_computation_test.cc b/tensorflow/compiler/xla/service/hlo_computation_test.cc
index 7b7588f4ba9aa6..25469a54c48f4f 100644
--- a/tensorflow/compiler/xla/service/hlo_computation_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation_test.cc
@@ -550,6 +550,108 @@ TEST_F(HloComputationTest, Reachability) {
   EXPECT_FALSE(reachability->IsReachable(constant2, copy));
 }
 
+TEST_F(HloComputationTest, Stringification) {
+  const Shape s1 = ShapeUtil::MakeShape(F32, {5, 10});
+  const Shape s2 = ShapeUtil::MakeShape(F32, {20, 10});
+  const Shape s2t = ShapeUtil::MakeShape(F32, {10, 20});
+  const Shape sout = ShapeUtil::MakeShape(F32, {5, 20});
+
+  HloComputation::Builder builder("TransposeDot");
+  HloInstruction* x =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, s1, "x"));
+  HloInstruction* y =
+      builder.AddInstruction(HloInstruction::CreateParameter(1, s2, "y"));
+  HloInstruction* reshape =
+      builder.AddInstruction(HloInstruction::CreateTranspose(s2t, y, {1, 0}));
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(1);
+  dot_dnums.add_rhs_contracting_dimensions(0);
+  builder.AddInstruction(
+      HloInstruction::CreateDot(sout, x, reshape, dot_dnums));
+  auto module = CreateNewModule();
+  auto* computation = module->AddEntryComputation(builder.Build());
+
+  auto options = HloPrintOptions().set_print_metadata(false);
+  EXPECT_EQ(computation->ToString(options),
+            R"(%TransposeDot (x: f32[5,10], y: f32[20,10]) -> f32[5,20] {
+  %x = f32[5,10]{1,0} parameter(0)
+  %y = f32[20,10]{1,0} parameter(1)
+  %transpose = f32[10,20]{1,0} transpose(f32[20,10]{1,0} %y), dimensions={1,0}
+  ROOT %dot = f32[5,20]{1,0} dot(f32[5,10]{1,0} %x, f32[10,20]{1,0} %transpose), lhs_contracting_dims={1}, rhs_contracting_dims={0}
+})");
+}
+
+TEST_F(HloComputationTest, StringificationIndent) {
+  const Shape s1 = ShapeUtil::MakeShape(F32, {5, 10});
+  const Shape s2 = ShapeUtil::MakeShape(F32, {20, 10});
+  const Shape s2t = ShapeUtil::MakeShape(F32, {10, 20});
+  const Shape sout = ShapeUtil::MakeShape(F32, {5, 20});
+
+  HloComputation::Builder builder("TransposeDot");
+  HloInstruction* x =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, s1, "x"));
+  HloInstruction* y =
+      builder.AddInstruction(HloInstruction::CreateParameter(1, s2, "y"));
+  HloInstruction* reshape =
+      builder.AddInstruction(HloInstruction::CreateTranspose(s2t, y, {1, 0}));
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(1);
+  dot_dnums.add_rhs_contracting_dimensions(0);
+  builder.AddInstruction(
+      HloInstruction::CreateDot(sout, x, reshape, dot_dnums));
+  auto module = CreateNewModule();
+  auto* computation = module->AddEntryComputation(builder.Build());
+
+  auto options =
+      HloPrintOptions().set_print_metadata(false).set_indent_amount(2);
+  EXPECT_EQ(computation->ToString(options),
+            R"(    %TransposeDot (x: f32[5,10], y: f32[20,10]) -> f32[5,20] {
+      %x = f32[5,10]{1,0} parameter(0)
+      %y = f32[20,10]{1,0} parameter(1)
+      %transpose = f32[10,20]{1,0} transpose(f32[20,10]{1,0} %y), dimensions={1,0}
+      ROOT %dot = f32[5,20]{1,0} dot(f32[5,10]{1,0} %x, f32[10,20]{1,0} %transpose), lhs_contracting_dims={1}, rhs_contracting_dims={0}
+    })");
+}
+
+TEST_F(HloComputationTest, StringificationCanonical) {
+  const Shape s1 = ShapeUtil::MakeShape(F32, {5, 10});
+  const Shape s2 = ShapeUtil::MakeShape(F32, {20, 10});
+  const Shape s2t = ShapeUtil::MakeShape(F32, {10, 20});
+  const Shape sout = ShapeUtil::MakeShape(F32, {5, 20});
+
+  HloComputation::Builder builder("TransposeDot");
+  HloInstruction* x =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, s1, "x"));
+  HloInstruction* y =
+      builder.AddInstruction(HloInstruction::CreateParameter(1, s2, "y"));
+  HloInstruction* reshape =
+      builder.AddInstruction(HloInstruction::CreateTranspose(s2t, y, {1, 0}));
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(1);
+  dot_dnums.add_rhs_contracting_dimensions(0);
+  builder.AddInstruction(
+      HloInstruction::CreateDot(sout, x, reshape, dot_dnums));
+  auto module = CreateNewModule();
+  auto* computation = module->AddEntryComputation(builder.Build());
+
+  auto options = HloPrintOptions().set_print_metadata(false);
+  EXPECT_EQ(computation->ToString(options),
+            R"(%TransposeDot (x: f32[5,10], y: f32[20,10]) -> f32[5,20] {
+  %x = f32[5,10]{1,0} parameter(0)
+  %y = f32[20,10]{1,0} parameter(1)
+  %transpose = f32[10,20]{1,0} transpose(f32[20,10]{1,0} %y), dimensions={1,0}
+  ROOT %dot = f32[5,20]{1,0} dot(f32[5,10]{1,0} %x, f32[10,20]{1,0} %transpose), lhs_contracting_dims={1}, rhs_contracting_dims={0}
+})");
+
+  options = HloPrintOptions().Canonical();
+  EXPECT_EQ(computation->ToString(options), R"(TransposeDot {
+  tmp_0 = f32[5,10]{1,0} parameter(0)
+  tmp_1 = f32[20,10]{1,0} parameter(1)
+  tmp_2 = f32[10,20]{1,0} transpose(f32[20,10]{1,0} tmp_1), dimensions={1,0}
+  ROOT tmp_3 = f32[5,20]{1,0} dot(f32[5,10]{1,0} tmp_0, f32[10,20]{1,0} tmp_2), lhs_contracting_dims={1}, rhs_contracting_dims={0}
+})");
+}
+
 }  // namespace
 
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 8dc3b83eee27c7..17e3c405f1e526 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -1104,7 +1104,8 @@ string HloDotDumper::GetInstructionNodeExtraInfo(const HloInstruction* instr) {
   // Get the instruction's extra attributes excluding the names of its
   // subcomputations, since those are drawn explicitly in the graph.
   for (const auto& line : instr->ExtraAttributesToString(
-           HloPrintOptions().set_print_subcomputation_references(false))) {
+           HloPrintOptions().set_print_subcomputation_mode(
+               HloPrintOptions::PrintSubcomputationMode::kOff))) {
     lines.push_back(HtmlLikeStringSanitize(line));
   }
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 8d0fd65eb983a3..a269034be37789 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -2106,13 +2106,40 @@ string PrintName(const string& name, const HloPrintOptions& options) {
 }  // namespace
 
 string HloInstruction::ToString(const HloPrintOptions& options) const {
-  string result =
-      StrCat(PrintName(name(), options), " = ",
-             ShapeUtil::HumanStringWithLayout(shape()), " ",
-             HloOpcodeString(opcode()), "(", OperandsToString(options), ")");
+  CanonicalNameMap new_map;
+  return ToStringWithCanonicalNameMap(options, &new_map);
+}
+
+string HloInstruction::ToStringWithCanonicalNameMap(
+    const HloPrintOptions& options,
+    CanonicalNameMap* canonical_name_map) const {
+  string result = "";
+
+  // Logic to print the instruction name (e.g. "%foo = ").
+  if (options.canonicalize_instruction_names()) {
+    if (options.is_in_nested_computation()) {
+      // If we are canonicalizing instruction names and this is a top-level
+      // HloInstruction::ToString() call, don't print an instruction name.
+      StrAppend(&result,
+                PrintName(canonical_name_map->LookupOrInsert(name()), options),
+                " = ");
+    }
+  } else {
+    StrAppend(&result, PrintName(name(), options), " = ");
+  }
+
+  // Print opcode, operand(s) and shape.
+  StrAppend(&result, ShapeUtil::HumanStringWithLayout(shape()), " ",
+            HloOpcodeString(opcode()), "(",
+            OperandsToStringWithCanonicalNameMap(options, canonical_name_map),
+            ")");
+
+  // Print additional attributes. If an instruction contains a subcomputation,
+  // the subcomputation is also printed here.
   for (const string& extra : ExtraAttributesToString(options)) {
     StrAppend(&result, ", ", extra);
   }
+
   if (options.print_metadata() &&
       (!metadata_.op_type().empty() || !metadata_.op_name().empty() ||
        !metadata_.source_file().empty())) {
@@ -2125,6 +2152,13 @@ string HloInstruction::ToString(const HloPrintOptions& options) const {
 }
 
 string HloInstruction::OperandsToString(const HloPrintOptions& options) const {
+  CanonicalNameMap new_map;
+  return OperandsToStringWithCanonicalNameMap(options, &new_map);
+}
+
+string HloInstruction::OperandsToStringWithCanonicalNameMap(
+    const HloPrintOptions& options,
+    CanonicalNameMap* canonical_name_map) const {
   string operands;
   if (opcode() == HloOpcode::kConstant) {
     // For constants, show the actual value in place of an empty operand list.
@@ -2164,7 +2198,14 @@ string HloInstruction::OperandsToString(const HloPrintOptions& options) const {
       if (options.print_operand_shape()) {
         str.push_back(ShapeUtil::HumanStringWithLayout(operand->shape()));
       }
-      if (!options.compact_operands()) {
+
+      // In a top-level HloInstruction::ToString() call, the operand name is not
+      // part of the canonical string.
+      if (options.canonicalize_instruction_names() &&
+          options.is_in_nested_computation()) {
+        str.push_back(PrintName(
+            canonical_name_map->LookupOrInsert(operand->name()), options));
+      } else if (!options.compact_operands()) {
         str.push_back(PrintName(operand->name(), options));
       }
       StrAppend(out, Join(str, " "));
@@ -2233,7 +2274,8 @@ std::vector HloInstruction::ExtraAttributesToString(
     extra.push_back(StrCat("fft_length={", Join(fft_length(), ","), "}"));
   }
 
-  if (options.print_subcomputation_references()) {
+  if (options.print_subcomputation_mode() ==
+      HloPrintOptions::PrintSubcomputationMode::kNameOnly) {
     if (opcode() == HloOpcode::kWhile) {
       extra.push_back(
           StrCat("condition=", PrintName(while_condition()->name(), options)));
@@ -2261,8 +2303,45 @@ std::vector HloInstruction::ExtraAttributesToString(
                                      PrintName(computation->name(), options));
                          })));
     }
+  } else if (options.print_subcomputation_mode() ==
+             HloPrintOptions::PrintSubcomputationMode::kFullBodies) {
+    HloPrintOptions new_options = options;
+    new_options.set_is_in_nested_computation(true);
+    switch (opcode()) {
+      case HloOpcode::kWhile:
+        extra.push_back(
+            StrCat("condition=\n", while_condition()->ToString(new_options)));
+        extra.push_back(StrCat("body=\n", while_body()->ToString(new_options)));
+        break;
+      case HloOpcode::kSelectAndScatter:
+        extra.push_back(StrCat("select=\n", select()->ToString(new_options)));
+        extra.push_back(StrCat("scatter=\n", scatter()->ToString(new_options)));
+        break;
+      case HloOpcode::kConditional:
+        extra.push_back(StrCat("true_computation=\n",
+                               true_computation()->ToString(new_options)));
+        extra.push_back(StrCat("false_computation=\n",
+                               false_computation()->ToString(new_options)));
+        break;
+      case HloOpcode::kCall:
+      case HloOpcode::kMap:
+      case HloOpcode::kReduceWindow:
+      case HloOpcode::kReduce:
+        extra.push_back(
+            StrCat("to_apply=\n", to_apply()->ToString(new_options)));
+        break;
+      default:
+        if (!called_computations().empty()) {
+          extra.push_back(
+              StrCat("calls=\n",
+                     Join(called_computations(), ", ",
+                          [&](string* out, const HloComputation* computation) {
+                            StrAppend(out, computation->ToString(new_options));
+                          })));
+        }
+        break;
+    }
   }
-
   if (opcode() == HloOpcode::kSend || opcode() == HloOpcode::kRecv ||
       opcode() == HloOpcode::kSendDone || opcode() == HloOpcode::kRecvDone) {
     extra.push_back(StrCat("channel_id=", channel_id_));
@@ -2300,7 +2379,7 @@ std::vector HloInstruction::ExtraAttributesToString(
   }
 
   // By contract, we print the custom call target even if
-  // !options.print_subcomputation_references(), because the call target is not
+  // options.print_subcomputation_mode() == kOff, because the call target is not
   // an HloComputation.
   if (opcode() == HloOpcode::kCustomCall) {
     extra.push_back(
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 2e5895efce0760..0089cae51a95ec 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -60,23 +60,31 @@ class HloModule;
 // A bunch of switches that control how the hlo text should be printed.
 class HloPrintOptions {
  public:
+  enum class PrintSubcomputationMode {
+    kOff,         // Do not print anything about subcomputations.
+    kNameOnly,    // Only print the name of subcomputations.
+    kFullBodies,  // Print the full bodies of subcomputations.
+  };
+
   // Constructs the default print options: don't print large constants, don't
   // compact operands, no indentation.
   HloPrintOptions()
       : print_large_constants_(false),
-        print_subcomputation_references_(true),
+        print_subcomputation_mode_(PrintSubcomputationMode::kNameOnly),
         print_metadata_(true),
         print_backend_config_(true),
         compact_operands_(false),
         print_operand_shape_(true),
         print_program_shape_(true),
         print_percent_(true),
-        indent_amount_(0) {}
+        canonicalize_instruction_names_(false),
+        indent_amount_(0),
+        is_in_nested_computation_(false) {}
 
   static HloPrintOptions ShortParsable() {
     return HloPrintOptions()
         .set_print_large_constants(true)
-        .set_print_subcomputation_references(true)
+        .set_print_subcomputation_mode(PrintSubcomputationMode::kNameOnly)
         .set_print_metadata(false)
         .set_print_backend_config(false)
         .set_print_operand_shape(false)
@@ -84,20 +92,28 @@ class HloPrintOptions {
         .set_print_percent(false);
   }
 
+  // Options to produce the canonical string representing an isomorphic
+  // computation graph.
+  static HloPrintOptions Canonical() {
+    return HloPrintOptions()
+        .set_print_subcomputation_mode(PrintSubcomputationMode::kFullBodies)
+        .set_print_metadata(false)
+        .set_compact_operands(true)
+        .set_print_operand_shape(true)
+        .set_print_program_shape(false)
+        .set_print_percent(false)
+        .set_canonicalize_instruction_names(true);
+  }
+
   // If true, large constants will be printed out.
   HloPrintOptions& set_print_large_constants(bool value) {
     print_large_constants_ = value;
     return *this;
   }
 
-  // If true, the names of subcomputations (e.g. a fusion node's fused
-  // computation) won't be printed.  This makes the resulting text not parsable.
-  //
-  // A CustomCall's call target is printed even if
-  // print_subcomputation_references is false, because the call target isn't an
-  // HloComputation.
-  HloPrintOptions& set_print_subcomputation_references(bool value) {
-    print_subcomputation_references_ = value;
+  HloPrintOptions& set_print_subcomputation_mode(
+      PrintSubcomputationMode value) {
+    print_subcomputation_mode_ = value;
     return *this;
   }
 
@@ -138,15 +154,29 @@ class HloPrintOptions {
     return *this;
   }
 
+  // If true, canonicalizes instructions' name. Instead of using "%foo.1" as
+  // the name of an instruction, we use "%tmp_1", "%tmp_2" etc.
+  HloPrintOptions& set_canonicalize_instruction_names(bool value) {
+    canonicalize_instruction_names_ = value;
+    return *this;
+  }
+
   // The indent of the hlo text block.
   HloPrintOptions& set_indent_amount(int value) {
     indent_amount_ = value;
     return *this;
   }
 
+  // If true, indicates the instruction being printed is inside a nested
+  // computation.
+  HloPrintOptions& set_is_in_nested_computation(bool value) {
+    is_in_nested_computation_ = value;
+    return *this;
+  }
+
   bool print_large_constants() const { return print_large_constants_; }
-  bool print_subcomputation_references() const {
-    return print_subcomputation_references_;
+  PrintSubcomputationMode print_subcomputation_mode() const {
+    return print_subcomputation_mode_;
   }
   bool print_metadata() const { return print_metadata_; }
   bool print_backend_config() const { return print_metadata_; }
@@ -154,18 +184,51 @@ class HloPrintOptions {
   bool print_operand_shape() const { return print_operand_shape_; }
   bool print_program_shape() const { return print_program_shape_; }
   bool print_percent() const { return print_percent_; }
+  bool canonicalize_instruction_names() const {
+    return canonicalize_instruction_names_;
+  }
   int indent_amount() const { return indent_amount_; }
+  int is_in_nested_computation() const { return is_in_nested_computation_; }
 
  private:
   bool print_large_constants_;
-  bool print_subcomputation_references_;
+  PrintSubcomputationMode print_subcomputation_mode_;
   bool print_metadata_;
   bool print_backend_config_;
   bool compact_operands_;
   bool print_operand_shape_;
   bool print_program_shape_;
   bool print_percent_;
+  bool canonicalize_instruction_names_;
   int indent_amount_;
+  bool is_in_nested_computation_;
+};
+
+// For canonical string output, we need to have a canonical way to rename
+// each instruction and its operands. Each operand is renamed as "tmp_",
+// where  is an index starting from 0.
+class CanonicalNameMap {
+ public:
+  CanonicalNameMap() : index(0) {}
+
+  string LookupOrInsert(const string& old_name) {
+    auto iter = canonical_name_map.find(old_name);
+    if (iter != canonical_name_map.end()) {
+      return iter->second;
+    }
+
+    string new_name = tensorflow::strings::StrCat("tmp_", index++);
+    canonical_name_map[old_name] = new_name;
+    return new_name;
+  }
+  void Clear() {
+    canonical_name_map.clear();
+    index = 0;
+  }
+
+ private:
+  int64 index;
+  tensorflow::gtl::FlatMap canonical_name_map;
 };
 
 // HLO instructions are the IR used by the high-level compiler.
@@ -1331,6 +1394,24 @@ class HloInstruction {
                         const ShapeIndex& shape_index = {});
 
  private:
+  // Prints an instruction to a string.
+  //
+  // The canonical string representation needs to name operands and instruction
+  // names in a consistent way. This is implemented through the
+  // canonical_name_map.
+  string ToStringWithCanonicalNameMap(
+      const HloPrintOptions& options,
+      CanonicalNameMap* canonical_name_map) const;
+
+  // Prints an operand to a string.
+  string OperandsToStringWithCanonicalNameMap(
+      const HloPrintOptions& options,
+      CanonicalNameMap* canonical_name_map) const;
+
+  // Allow HloInstruction to access the ToStringWithCanonicalNameMap() and
+  // OperandsToStringWithCanonicalNameMap() functions.
+  friend class HloComputation;
+
   enum class UseKind { kNoUse, kReuse, kUsePermutingElements, kUse };
 
   // Helper class for computing OperandElementUse for kFusion.
diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
index 909cdc0b6269ed..a61c472c72804b 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
@@ -1336,5 +1336,163 @@ TEST_F(HloInstructionTest, StringifyGather_1) {
             "index_vector_dim=2, window_bounds={30,29,28,27,26}");
 }
 
+TEST_F(HloInstructionTest, CanonnicalStringificationFusion) {
+  // Tests stringification of a simple op, fusion, while, and conditional.
+  const Shape s1 = ShapeUtil::MakeShape(F32, {5, 10});
+  const Shape s2 = ShapeUtil::MakeShape(F32, {20, 10});
+  const Shape s2t = ShapeUtil::MakeShape(F32, {10, 20});
+  const Shape sout = ShapeUtil::MakeShape(F32, {5, 20});
+
+  HloComputation::Builder builder("TransposeDot");
+  HloInstruction* x =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, s1, "x"));
+  HloInstruction* y =
+      builder.AddInstruction(HloInstruction::CreateParameter(1, s2, "y"));
+  HloInstruction* reshape =
+      builder.AddInstruction(HloInstruction::CreateTranspose(s2t, y, {1, 0}));
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(1);
+  dot_dnums.add_rhs_contracting_dimensions(0);
+  HloInstruction* dot = builder.AddInstruction(
+      HloInstruction::CreateDot(sout, x, reshape, dot_dnums));
+
+  auto options = HloPrintOptions().Canonical();
+
+  EXPECT_EQ(dot->ToString(options),
+            "f32[5,20]{1,0} dot(f32[5,10]{1,0}, f32[10,20]{1,0}), "
+            "lhs_contracting_dims={1}, rhs_contracting_dims={0}");
+
+  auto module = CreateNewModule();
+  auto* computation = module->AddEntryComputation(builder.Build());
+  HloInstruction* fusion = computation->CreateFusionInstruction(
+      {dot, reshape}, HloInstruction::FusionKind::kLoop);
+
+  EXPECT_EQ(
+      fusion->ToString(options),
+      R"(f32[5,20]{1,0} fusion(f32[5,10]{1,0}, f32[20,10]{1,0}), kind=kLoop, calls=
+{
+  tmp_0 = f32[5,10]{1,0} parameter(0)
+  tmp_1 = f32[20,10]{1,0} parameter(1)
+  tmp_2 = f32[10,20]{1,0} transpose(f32[20,10]{1,0} tmp_1), dimensions={1,0}
+  ROOT tmp_3 = f32[5,20]{1,0} dot(f32[5,10]{1,0} tmp_0, f32[10,20]{1,0} tmp_2), lhs_contracting_dims={1}, rhs_contracting_dims={0}
+})");
+}
+
+TEST_F(HloInstructionTest, CanonnicalStringificationWhile) {
+  // Tests stringification of a simple op, fusion, while, and conditional.
+  const Shape s1 = ShapeUtil::MakeShape(F32, {5, 10});
+  const Shape s2 = ShapeUtil::MakeShape(F32, {20, 10});
+  const Shape s2t = ShapeUtil::MakeShape(F32, {10, 20});
+  const Shape sout = ShapeUtil::MakeShape(F32, {5, 20});
+
+  HloComputation::Builder builder("TransposeDot");
+  HloInstruction* x =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, s1, "x"));
+  HloInstruction* y =
+      builder.AddInstruction(HloInstruction::CreateParameter(1, s2, "y"));
+  HloInstruction* reshape =
+      builder.AddInstruction(HloInstruction::CreateTranspose(s2t, y, {1, 0}));
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(1);
+  dot_dnums.add_rhs_contracting_dimensions(0);
+  HloInstruction* dot = builder.AddInstruction(
+      HloInstruction::CreateDot(sout, x, reshape, dot_dnums));
+
+  auto module = CreateNewModule();
+  auto* computation = module->AddEntryComputation(builder.Build());
+  computation->CreateFusionInstruction({dot, reshape},
+                                       HloInstruction::FusionKind::kLoop);
+
+  HloInstruction* loop = builder.AddInstruction(
+      HloInstruction::CreateWhile(sout, computation, computation, x));
+
+  auto options = HloPrintOptions().Canonical();
+  EXPECT_EQ(loop->ToString(options),
+            R"(f32[5,20]{1,0} while(f32[5,10]{1,0}), condition=
+{
+  tmp_0 = f32[5,10]{1,0} parameter(0)
+  tmp_1 = f32[20,10]{1,0} parameter(1)
+  ROOT tmp_2 = f32[5,20]{1,0} fusion(f32[5,10]{1,0} tmp_0, f32[20,10]{1,0} tmp_1), kind=kLoop, calls=
+  {
+    tmp_0 = f32[5,10]{1,0} parameter(0)
+    tmp_1 = f32[20,10]{1,0} parameter(1)
+    tmp_2 = f32[10,20]{1,0} transpose(f32[20,10]{1,0} tmp_1), dimensions={1,0}
+    ROOT tmp_3 = f32[5,20]{1,0} dot(f32[5,10]{1,0} tmp_0, f32[10,20]{1,0} tmp_2), lhs_contracting_dims={1}, rhs_contracting_dims={0}
+  }
+}, body=
+{
+  tmp_0 = f32[5,10]{1,0} parameter(0)
+  tmp_1 = f32[20,10]{1,0} parameter(1)
+  ROOT tmp_2 = f32[5,20]{1,0} fusion(f32[5,10]{1,0} tmp_0, f32[20,10]{1,0} tmp_1), kind=kLoop, calls=
+  {
+    tmp_0 = f32[5,10]{1,0} parameter(0)
+    tmp_1 = f32[20,10]{1,0} parameter(1)
+    tmp_2 = f32[10,20]{1,0} transpose(f32[20,10]{1,0} tmp_1), dimensions={1,0}
+    ROOT tmp_3 = f32[5,20]{1,0} dot(f32[5,10]{1,0} tmp_0, f32[10,20]{1,0} tmp_2), lhs_contracting_dims={1}, rhs_contracting_dims={0}
+  }
+})");
+}
+
+TEST_F(HloInstructionTest, CanonnicalStringificationConditional) {
+  // Tests stringification of a simple op, fusion, while, and conditional.
+  const Shape s1 = ShapeUtil::MakeShape(F32, {5, 10});
+  const Shape s2 = ShapeUtil::MakeShape(F32, {20, 10});
+  const Shape s2t = ShapeUtil::MakeShape(F32, {10, 20});
+  const Shape sout = ShapeUtil::MakeShape(F32, {5, 20});
+
+  HloComputation::Builder builder("TransposeDot");
+  HloInstruction* x =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, s1, "x"));
+  HloInstruction* y =
+      builder.AddInstruction(HloInstruction::CreateParameter(1, s2, "y"));
+  HloInstruction* reshape =
+      builder.AddInstruction(HloInstruction::CreateTranspose(s2t, y, {1, 0}));
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(1);
+  dot_dnums.add_rhs_contracting_dimensions(0);
+  HloInstruction* dot = builder.AddInstruction(
+      HloInstruction::CreateDot(sout, x, reshape, dot_dnums));
+
+  auto module = CreateNewModule();
+  auto* computation = module->AddEntryComputation(builder.Build());
+  computation->CreateFusionInstruction({dot, reshape},
+                                       HloInstruction::FusionKind::kLoop);
+
+  builder.AddInstruction(
+      HloInstruction::CreateWhile(sout, computation, computation, x));
+
+  auto pred = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0(true)));
+  HloInstruction* conditional =
+      builder.AddInstruction(HloInstruction::CreateConditional(
+          sout, pred, x, computation, x, computation));
+  auto options = HloPrintOptions().Canonical();
+  EXPECT_EQ(
+      conditional->ToString(options),
+      R"(f32[5,20]{1,0} conditional(pred[], f32[5,10]{1,0}, f32[5,10]{1,0}), true_computation=
+{
+  tmp_0 = f32[5,10]{1,0} parameter(0)
+  tmp_1 = f32[20,10]{1,0} parameter(1)
+  ROOT tmp_2 = f32[5,20]{1,0} fusion(f32[5,10]{1,0} tmp_0, f32[20,10]{1,0} tmp_1), kind=kLoop, calls=
+  {
+    tmp_0 = f32[5,10]{1,0} parameter(0)
+    tmp_1 = f32[20,10]{1,0} parameter(1)
+    tmp_2 = f32[10,20]{1,0} transpose(f32[20,10]{1,0} tmp_1), dimensions={1,0}
+    ROOT tmp_3 = f32[5,20]{1,0} dot(f32[5,10]{1,0} tmp_0, f32[10,20]{1,0} tmp_2), lhs_contracting_dims={1}, rhs_contracting_dims={0}
+  }
+}, false_computation=
+{
+  tmp_0 = f32[5,10]{1,0} parameter(0)
+  tmp_1 = f32[20,10]{1,0} parameter(1)
+  ROOT tmp_2 = f32[5,20]{1,0} fusion(f32[5,10]{1,0} tmp_0, f32[20,10]{1,0} tmp_1), kind=kLoop, calls=
+  {
+    tmp_0 = f32[5,10]{1,0} parameter(0)
+    tmp_1 = f32[20,10]{1,0} parameter(1)
+    tmp_2 = f32[10,20]{1,0} transpose(f32[20,10]{1,0} tmp_1), dimensions={1,0}
+    ROOT tmp_3 = f32[5,20]{1,0} dot(f32[5,10]{1,0} tmp_0, f32[10,20]{1,0} tmp_2), lhs_contracting_dims={1}, rhs_contracting_dims={0}
+  }
+})");
+}
+
 }  // namespace
 }  // namespace xla

From 84b5938aaee991d6909e16e56c66bf88e8843fbb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 19:31:37 -0700
Subject: [PATCH 0703/1691] Add bool conversion in toco for tflite since bool
 is supported by tflite.

PiperOrigin-RevId: 196339883
---
 tensorflow/contrib/lite/toco/tflite/types.cc   | 18 ++++++++++++++++++
 .../contrib/lite/toco/tflite/types_test.cc     | 15 +++++++++++----
 2 files changed, 29 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/tflite/types.cc b/tensorflow/contrib/lite/toco/tflite/types.cc
index c9c2e9ba0184ef..4867c3a62e6840 100644
--- a/tensorflow/contrib/lite/toco/tflite/types.cc
+++ b/tensorflow/contrib/lite/toco/tflite/types.cc
@@ -36,6 +36,16 @@ DataBuffer::FlatBufferOffset CopyStringToBuffer(
   return builder->CreateVector(dst_data.data(), bytes);
 }
 
+// vector may be implemented using a bit-set, so we can't just
+// reinterpret_cast, accesing it data as vector and let flatbuffer
+// CreateVector handle it.
+// Background: https://isocpp.org/blog/2012/11/on-vectorbool
+DataBuffer::FlatBufferOffset CopyBoolToBuffer(
+    const Array& array, flatbuffers::FlatBufferBuilder* builder) {
+  const auto& src_data = array.GetBuffer().data;
+  return builder->CreateVector(src_data);
+}
+
 template 
 DataBuffer::FlatBufferOffset CopyBuffer(
     const Array& array, flatbuffers::FlatBufferBuilder* builder) {
@@ -86,6 +96,8 @@ ::tflite::TensorType DataType::Serialize(ArrayDataType array_data_type) {
       return ::tflite::TensorType_UINT8;
     case ArrayDataType::kString:
       return ::tflite::TensorType_STRING;
+    case ArrayDataType::kBool:
+      return ::tflite::TensorType_BOOL;
     default:
       // FLOAT32 is filled for unknown data types.
       // TODO(ycling): Implement type inference in TF Lite interpreter.
@@ -105,6 +117,8 @@ ArrayDataType DataType::Deserialize(int tensor_type) {
       return ArrayDataType::kString;
     case ::tflite::TensorType_UINT8:
       return ArrayDataType::kUint8;
+    case ::tflite::TensorType_BOOL:
+      return ArrayDataType::kBool;
     default:
       LOG(FATAL) << "Unhandled tensor type '" << tensor_type << "'.";
   }
@@ -125,6 +139,8 @@ flatbuffers::Offset> DataBuffer::Serialize(
       return CopyStringToBuffer(array, builder);
     case ArrayDataType::kUint8:
       return CopyBuffer(array, builder);
+    case ArrayDataType::kBool:
+      return CopyBoolToBuffer(array, builder);
     default:
       LOG(FATAL) << "Unhandled array data type.";
   }
@@ -146,6 +162,8 @@ void DataBuffer::Deserialize(const ::tflite::Tensor& tensor,
       return CopyStringFromBuffer(buffer, array);
     case ::tflite::TensorType_UINT8:
       return CopyBuffer(buffer, array);
+    case ::tflite::TensorType_BOOL:
+      return CopyBuffer(buffer, array);
     default:
       LOG(FATAL) << "Unhandled tensor type.";
   }
diff --git a/tensorflow/contrib/lite/toco/tflite/types_test.cc b/tensorflow/contrib/lite/toco/tflite/types_test.cc
index 29fb0b2af22ef1..564f303b9bb41a 100644
--- a/tensorflow/contrib/lite/toco/tflite/types_test.cc
+++ b/tensorflow/contrib/lite/toco/tflite/types_test.cc
@@ -28,8 +28,7 @@ using flatbuffers::Vector;
 
 // These are types that exist in TF Mini but don't have a correspondence
 // in TF Lite.
-static const ArrayDataType kUnsupportedTocoTypes[] = {ArrayDataType::kNone,
-                                                      ArrayDataType::kBool};
+static const ArrayDataType kUnsupportedTocoTypes[] = {ArrayDataType::kNone};
 
 // These are TF Lite types for which there is no correspondence in TF Mini.
 static const ::tflite::TensorType kUnsupportedTfLiteTypes[] = {
@@ -44,7 +43,7 @@ template 
 Array ToFlatBufferAndBack(std::initializer_list<::toco::DataType> items) {
   // NOTE: This test does not construct the full buffers list. Since
   // Deserialize normally takes a buffer, we need to synthesize one and provide
-  // an index that is non-zero so the buffer is not assumed to be emtpy.
+  // an index that is non-zero so the buffer is not assumed to be empty.
   Array src;
   src.data_type = T;
   src.GetMutableBuffer().data = items;
@@ -71,7 +70,8 @@ TEST(DataType, SupportedTypes) {
       {ArrayDataType::kUint8, ::tflite::TensorType_UINT8},
       {ArrayDataType::kInt32, ::tflite::TensorType_INT32},
       {ArrayDataType::kInt64, ::tflite::TensorType_INT64},
-      {ArrayDataType::kFloat, ::tflite::TensorType_FLOAT32}};
+      {ArrayDataType::kFloat, ::tflite::TensorType_FLOAT32},
+      {ArrayDataType::kBool, ::tflite::TensorType_BOOL}};
   for (auto x : testdata) {
     EXPECT_EQ(x.second, DataType::Serialize(x.first));
     EXPECT_EQ(x.first, DataType::Deserialize(x.second));
@@ -158,6 +158,13 @@ TEST(DataBuffer, String) {
               ::testing::ElementsAre("AA", "BBB", "Best. String. Ever."));
 }
 
+TEST(DataBuffer, Bool) {
+  Array recovered =
+      ToFlatBufferAndBack({true, false, true});
+  EXPECT_THAT(recovered.GetBuffer().data,
+              ::testing::ElementsAre(true, false, true));
+}
+
 TEST(Padding, All) {
   EXPECT_EQ(::tflite::Padding_SAME, Padding::Serialize(PaddingType::kSame));
   EXPECT_EQ(PaddingType::kSame, Padding::Deserialize(::tflite::Padding_SAME));

From 52e2698ac969a0f82c6ce901f80f04818ca8ac4e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 19:38:48 -0700
Subject: [PATCH 0704/1691] Making GetInput from kernel_util.h return a pointer
 to const data.

PiperOrigin-RevId: 196340200
---
 .../contrib/lite/g3doc/custom_operators.md    |  4 +-
 .../contrib/lite/kernels/activations.cc       | 40 ++++++------
 tensorflow/contrib/lite/kernels/add.cc        | 12 ++--
 tensorflow/contrib/lite/kernels/arg_max.cc    |  8 +--
 .../contrib/lite/kernels/audio_spectrogram.cc |  4 +-
 tensorflow/contrib/lite/kernels/basic_rnn.cc  | 16 ++---
 .../contrib/lite/kernels/batch_to_space_nd.cc |  6 +-
 .../kernels/bidirectional_sequence_lstm.cc    | 65 ++++++++++---------
 tensorflow/contrib/lite/kernels/cast.cc       |  4 +-
 .../contrib/lite/kernels/comparisons.cc       | 20 +++---
 .../contrib/lite/kernels/depthwise_conv.cc    | 20 +++---
 tensorflow/contrib/lite/kernels/dequantize.cc |  2 +-
 tensorflow/contrib/lite/kernels/div.cc        | 12 ++--
 .../contrib/lite/kernels/elementwise.cc       |  8 +--
 .../contrib/lite/kernels/embedding_lookup.cc  |  8 +--
 .../lite/kernels/embedding_lookup_sparse.cc   | 20 +++---
 tensorflow/contrib/lite/kernels/exp.cc        |  2 +-
 tensorflow/contrib/lite/kernels/floor.cc      |  4 +-
 .../contrib/lite/kernels/fully_connected.cc   | 27 ++++----
 tensorflow/contrib/lite/kernels/gather.cc     |  8 +--
 .../contrib/lite/kernels/hashtable_lookup.cc  | 12 ++--
 .../internal/reference/reference_ops.h        | 10 +--
 .../contrib/lite/kernels/internal/tensor.h    | 28 ++++++++
 .../contrib/lite/kernels/kernel_util.cc       | 15 +++--
 tensorflow/contrib/lite/kernels/kernel_util.h | 19 +++---
 tensorflow/contrib/lite/kernels/l2norm.cc     |  4 +-
 .../lite/kernels/local_response_norm.cc       |  4 +-
 .../contrib/lite/kernels/lsh_projection.cc    | 12 ++--
 tensorflow/contrib/lite/kernels/lstm.cc       | 40 ++++++------
 .../contrib/lite/kernels/maximum_minimum.cc   |  4 +-
 tensorflow/contrib/lite/kernels/mean.cc       |  4 +-
 tensorflow/contrib/lite/kernels/mfcc.cc       |  8 +--
 tensorflow/contrib/lite/kernels/mul.cc        | 12 ++--
 tensorflow/contrib/lite/kernels/neg.cc        |  4 +-
 tensorflow/contrib/lite/kernels/pad.cc        |  4 +-
 tensorflow/contrib/lite/kernels/pooling.cc    | 22 +++----
 tensorflow/contrib/lite/kernels/reshape.cc    |  4 +-
 .../contrib/lite/kernels/resize_bilinear.cc   | 14 ++--
 tensorflow/contrib/lite/kernels/select.cc     | 12 ++--
 tensorflow/contrib/lite/kernels/slice.cc      | 28 ++++----
 .../contrib/lite/kernels/space_to_batch_nd.cc |  6 +-
 .../contrib/lite/kernels/space_to_depth.cc    |  4 +-
 tensorflow/contrib/lite/kernels/split.cc      |  8 +--
 tensorflow/contrib/lite/kernels/squeeze.cc    | 11 ++--
 .../contrib/lite/kernels/strided_slice.cc     |  8 +--
 tensorflow/contrib/lite/kernels/sub.cc        | 12 ++--
 tensorflow/contrib/lite/kernels/svdf.cc       | 12 ++--
 tensorflow/contrib/lite/kernels/topk_v2.cc    | 12 ++--
 tensorflow/contrib/lite/kernels/transpose.cc  |  4 +-
 .../kernels/unidirectional_sequence_lstm.cc   | 40 ++++++------
 .../kernels/unidirectional_sequence_rnn.cc    | 16 ++---
 .../models/smartreply/ops/extract_feature.cc  |  4 +-
 52 files changed, 365 insertions(+), 322 deletions(-)

diff --git a/tensorflow/contrib/lite/g3doc/custom_operators.md b/tensorflow/contrib/lite/g3doc/custom_operators.md
index d7cc854ebac08e..972e57f73e8296 100644
--- a/tensorflow/contrib/lite/g3doc/custom_operators.md
+++ b/tensorflow/contrib/lite/g3doc/custom_operators.md
@@ -39,7 +39,7 @@ TfLiteStatus SinPrepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
 
   int num_dims = NumDimensions(input);
@@ -54,7 +54,7 @@ TfLiteStatus SinPrepare(TfLiteContext* context, TfLiteNode* node) {
 
 TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) {
   using namespace tflite;
-  TfLiteTensor* input = GetInput(context, node,0);
+  const TfLiteTensor* input = GetInput(context, node,0);
   TfLiteTensor* output = GetOutput(context, node,0);
 
   float* input_data = input->data.f;
diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc
index 39a54c93962b33..4972159a05eb9a 100644
--- a/tensorflow/contrib/lite/kernels/activations.cc
+++ b/tensorflow/contrib/lite/kernels/activations.cc
@@ -55,7 +55,7 @@ void Free(TfLiteContext* context, void* buffer) {
 TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   TF_LITE_ENSURE_EQ(context, input->type, output->type);
 
@@ -68,7 +68,7 @@ TfLiteStatus TanhPrepare(TfLiteContext* context, TfLiteNode* node) {
 
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   TF_LITE_ENSURE_EQ(context, input->type, output->type);
 
@@ -95,7 +95,7 @@ TfLiteStatus SigmoidPrepare(TfLiteContext* context, TfLiteNode* node) {
 
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   TF_LITE_ENSURE_EQ(context, input->type, output->type);
 
@@ -126,7 +126,7 @@ TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node) {
 
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   TF_LITE_ENSURE_EQ(context, input->type, output->type);
 
@@ -153,9 +153,9 @@ TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node) {
 TfLiteStatus PreluPrepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
-  TfLiteTensor* alpha = GetInput(context, node, 1);
+  const TfLiteTensor* alpha = GetInput(context, node, 1);
 
   output->type = input->type;
 
@@ -179,7 +179,7 @@ TfLiteStatus PreluPrepare(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus ReluEval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   switch (input->type) {
     case kTfLiteFloat32: {
@@ -197,7 +197,7 @@ TfLiteStatus ReluEval(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus Relu1Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   switch (input->type) {
     case kTfLiteFloat32: {
@@ -217,7 +217,7 @@ TfLiteStatus Relu1Eval(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   switch (input->type) {
     case kTfLiteFloat32: {
@@ -236,7 +236,7 @@ TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) {
 
 TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) {
   OpData* data = reinterpret_cast(node->user_data);
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   switch (input->type) {
     case kTfLiteFloat32: {
@@ -265,7 +265,7 @@ TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) {
 TfLiteStatus SigmoidEval(TfLiteContext* context, TfLiteNode* node) {
   OpData* data = reinterpret_cast(node->user_data);
 
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   switch (input->type) {
     case kTfLiteFloat32: {
@@ -292,7 +292,7 @@ TfLiteStatus SigmoidEval(TfLiteContext* context, TfLiteNode* node) {
 }
 
 // Takes a 2D tensor and perform softmax along the second dimension.
-void Softmax2DFloat(TfLiteTensor* input, TfLiteTensor* output,
+void Softmax2DFloat(const TfLiteTensor* input, TfLiteTensor* output,
                     TfLiteSoftmaxParams* params) {
   const int batch_size = input->dims->data[0];
   const int input_size = input->dims->data[1];
@@ -327,7 +327,7 @@ void Softmax2DFloat(TfLiteTensor* input, TfLiteTensor* output,
   }
 }
 
-void Softmax2DQuantized(TfLiteTensor* input, TfLiteTensor* output,
+void Softmax2DQuantized(const TfLiteTensor* input, TfLiteTensor* output,
                         TfLiteSoftmaxParams* params, OpData* data) {
   // TODO(ahentz): this is arguably a dirty trick. Since the implementation
   // always traverses the last dimension of a 4D tensor, we will pretend our 2D
@@ -343,14 +343,14 @@ void Softmax2DQuantized(TfLiteTensor* input, TfLiteTensor* output,
 }
 
 // Takes a 4D tensor and perform softmax along the forth dimension.
-void Softmax4DFloat(TfLiteTensor* input, TfLiteTensor* output,
+void Softmax4DFloat(const TfLiteTensor* input, TfLiteTensor* output,
                     TfLiteSoftmaxParams* params) {
   optimized_ops::Softmax(GetTensorData(input), GetTensorDims(input),
                          params->beta, GetTensorData(output),
                          GetTensorDims(output));
 }
 
-void Softmax4DQuantized(TfLiteTensor* input, TfLiteTensor* output,
+void Softmax4DQuantized(const TfLiteTensor* input, TfLiteTensor* output,
                         TfLiteSoftmaxParams* params, OpData* data) {
   optimized_ops::Softmax(GetTensorData(input), GetTensorDims(input),
                          data->input_multiplier, data->input_left_shift,
@@ -362,7 +362,7 @@ TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->builtin_data);
   OpData* data = reinterpret_cast(node->user_data);
 
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
 
   // TODO(ahentz): consider an implementation that works for many (all?)
@@ -402,7 +402,7 @@ TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus LogSoftmaxEval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   switch (input->type) {
     case kTfLiteFloat32:
@@ -417,9 +417,9 @@ TfLiteStatus LogSoftmaxEval(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus PreluEval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, 0);
-  TfLiteTensor* alpha = GetInput(context, node, 1);
-  TfLiteTensor* output = GetOutput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* alpha = GetInput(context, node, 1);
+  const TfLiteTensor* output = GetOutput(context, node, 0);
 
   if (input->type != kTfLiteFloat32) {
     context->ReportError(context, "Only float32 supported currently.");
diff --git a/tensorflow/contrib/lite/kernels/add.cc b/tensorflow/contrib/lite/kernels/add.cc
index e0aa070e2d02ce..7ca1e35489cba3 100644
--- a/tensorflow/contrib/lite/kernels/add.cc
+++ b/tensorflow/contrib/lite/kernels/add.cc
@@ -57,8 +57,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   TF_LITE_ENSURE_EQ(context, input1->type, input2->type);
@@ -80,7 +80,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 template 
 void EvalAddFloat(TfLiteContext* context, TfLiteNode* node,
                   TfLiteAddParams* params, const OpData* data,
-                  TfLiteTensor* input1, TfLiteTensor* input2,
+                  const TfLiteTensor* input1, const TfLiteTensor* input2,
                   TfLiteTensor* output) {
   float output_activation_min, output_activation_max;
   CalculateActivationRangeFloat(params->activation, &output_activation_min,
@@ -109,7 +109,7 @@ void EvalAddFloat(TfLiteContext* context, TfLiteNode* node,
 template 
 void EvalAddQuantized(TfLiteContext* context, TfLiteNode* node,
                       TfLiteAddParams* params, const OpData* data,
-                      TfLiteTensor* input1, TfLiteTensor* input2,
+                      const TfLiteTensor* input1, const TfLiteTensor* input2,
                       TfLiteTensor* output) {
   auto input1_offset = -input1->params.zero_point;
   auto input2_offset = -input2->params.zero_point;
@@ -164,8 +164,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->builtin_data);
   OpData* data = reinterpret_cast(node->user_data);
 
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   if (output->type == kTfLiteFloat32) {
diff --git a/tensorflow/contrib/lite/kernels/arg_max.cc b/tensorflow/contrib/lite/kernels/arg_max.cc
index a2c5e4ceadbc90..566d37047aea34 100644
--- a/tensorflow/contrib/lite/kernels/arg_max.cc
+++ b/tensorflow/contrib/lite/kernels/arg_max.cc
@@ -33,8 +33,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* axis = GetInput(context, node, kAxis);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* axis = GetInput(context, node, kAxis);
   // Make sure the axis is only 1 dimension.
   TF_LITE_ENSURE_EQ(context, NumElements(axis), 1);
 
@@ -79,8 +79,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 // The current impl actually ignores the axis argument.
 // Only determine the index of the maximum value in the last dimension.
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* axis = GetInput(context, node, kAxis);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* axis = GetInput(context, node, kAxis);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
 #define TF_LITE_ARG_MAX(data_type, axis_type, output_type)                     \
diff --git a/tensorflow/contrib/lite/kernels/audio_spectrogram.cc b/tensorflow/contrib/lite/kernels/audio_spectrogram.cc
index 602f3888c10b37..91d8dd3fa71b4f 100644
--- a/tensorflow/contrib/lite/kernels/audio_spectrogram.cc
+++ b/tensorflow/contrib/lite/kernels/audio_spectrogram.cc
@@ -72,7 +72,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   TF_LITE_ENSURE_EQ(context, NumDimensions(input), 2);
@@ -102,7 +102,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params =
       reinterpret_cast(node->user_data);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   TF_LITE_ENSURE(context, params->spectrogram->Initialize(params->window_size,
diff --git a/tensorflow/contrib/lite/kernels/basic_rnn.cc b/tensorflow/contrib/lite/kernels/basic_rnn.cc
index a54ab8d5c30a14..d812cd7bf094bf 100644
--- a/tensorflow/contrib/lite/kernels/basic_rnn.cc
+++ b/tensorflow/contrib/lite/kernels/basic_rnn.cc
@@ -49,11 +49,11 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, node->inputs->size, 4);
   TF_LITE_ENSURE_EQ(context, node->outputs->size, 2);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
-  TfLiteTensor* recurrent_weights =
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
+  const TfLiteTensor* recurrent_weights =
       GetInput(context, node, kRecurrentWeightsTensor);
-  TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
+  const TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
 
   // Check all the parameters of tensor match within themselves and match the
   // input configuration.
@@ -186,11 +186,11 @@ TfLiteStatus EvalQuantized(const TfLiteTensor* input,
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->builtin_data);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
-  TfLiteTensor* recurrent_weights =
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
+  const TfLiteTensor* recurrent_weights =
       GetInput(context, node, kRecurrentWeightsTensor);
-  TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
+  const TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
   TfLiteTensor* hidden_state = GetOutput(context, node, kHiddenStateTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
diff --git a/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc b/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc
index bd4057556c775e..262e1aeab159d2 100644
--- a/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc
+++ b/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc
@@ -40,9 +40,9 @@ struct BatchToSpaceNDContext {
     crops = GetInput(context, node, 2);
     output = GetOutput(context, node, 0);
   }
-  TfLiteTensor* input;
-  TfLiteTensor* block_shape;
-  TfLiteTensor* crops;
+  const TfLiteTensor* input;
+  const TfLiteTensor* block_shape;
+  const TfLiteTensor* crops;
   TfLiteTensor* output;
 };
 
diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc
index a35ba23cedec43..1cd4884696635a 100644
--- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc
+++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc
@@ -143,13 +143,13 @@ TfLiteStatus CheckLstmTensorDimensions(
     TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[1], n_input);
   }
 
-  TfLiteTensor* input_to_forget_weights =
+  const TfLiteTensor* input_to_forget_weights =
       GetInput(context, node, input_to_forget_weights_tensor);
   TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[0], n_cell);
   TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[1], n_input);
 
-  TfLiteTensor* input_to_cell_weights =
+  const TfLiteTensor* input_to_cell_weights =
       GetInput(context, node, input_to_cell_weights_tensor);
   TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[0], n_cell);
@@ -165,7 +165,7 @@ TfLiteStatus CheckLstmTensorDimensions(
                       n_output);
   }
 
-  TfLiteTensor* recurrent_to_forget_weights =
+  const TfLiteTensor* recurrent_to_forget_weights =
       GetInput(context, node, recurrent_to_forget_weights_tensor);
   TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[0],
@@ -173,7 +173,7 @@ TfLiteStatus CheckLstmTensorDimensions(
   TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[1],
                     n_output);
 
-  TfLiteTensor* recurrent_to_cell_weights =
+  const TfLiteTensor* recurrent_to_cell_weights =
       GetInput(context, node, recurrent_to_cell_weights_tensor);
   TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[0], n_cell);
@@ -231,16 +231,17 @@ TfLiteStatus CheckLstmTensorDimensions(
     TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->data[0], n_cell);
   }
 
-  TfLiteTensor* forget_gate_bias =
+  const TfLiteTensor* forget_gate_bias =
       GetInput(context, node, forget_gate_bias_tensor);
   TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->size, 1);
   TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->data[0], n_cell);
 
-  TfLiteTensor* cell_bias = GetInput(context, node, cell_gate_bias_tensor);
+  const TfLiteTensor* cell_bias =
+      GetInput(context, node, cell_gate_bias_tensor);
   TF_LITE_ENSURE_EQ(context, cell_bias->dims->size, 1);
   TF_LITE_ENSURE_EQ(context, cell_bias->dims->data[0], n_cell);
 
-  TfLiteTensor* output_gate_bias =
+  const TfLiteTensor* output_gate_bias =
       GetInput(context, node, output_gate_bias_tensor);
   TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->size, 1);
   TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->data[0], n_cell);
@@ -312,20 +313,20 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   // Inferring batch size, number of outputs and sequence length and
   // number of cells from the input tensors.
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TF_LITE_ENSURE(context, input->dims->size > 1);
   const int max_time = input->dims->data[0];
   const int n_batch = input->dims->data[1];
   const int n_input = input->dims->data[2];
 
-  TfLiteTensor* fw_input_to_output_weights =
+  const TfLiteTensor* fw_input_to_output_weights =
       GetInput(context, node, kFwInputToOutputWeightsTensor);
   const int n_fw_cell = fw_input_to_output_weights->dims->data[0];
   TF_LITE_ENSURE_EQ(context, fw_input_to_output_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, fw_input_to_output_weights->dims->data[1],
                     n_input);
 
-  TfLiteTensor* fw_recurrent_to_output_weights =
+  const TfLiteTensor* fw_recurrent_to_output_weights =
       GetInput(context, node, kFwRecurrentToOutputWeightsTensor);
   TF_LITE_ENSURE_EQ(context, fw_recurrent_to_output_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, fw_recurrent_to_output_weights->dims->data[0],
@@ -388,14 +389,14 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, fw_scratch_buffer,
                                                    fw_scratch_buffer_size));
   // Same for the backward cell.
-  TfLiteTensor* bw_input_to_output_weights =
+  const TfLiteTensor* bw_input_to_output_weights =
       GetInput(context, node, kBwInputToOutputWeightsTensor);
   const int n_bw_cell = bw_input_to_output_weights->dims->data[0];
   TF_LITE_ENSURE_EQ(context, bw_input_to_output_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, bw_input_to_output_weights->dims->data[1],
                     n_input);
 
-  TfLiteTensor* bw_recurrent_to_output_weights =
+  const TfLiteTensor* bw_recurrent_to_output_weights =
       GetInput(context, node, kBwRecurrentToOutputWeightsTensor);
   TF_LITE_ENSURE_EQ(context, bw_recurrent_to_output_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, bw_recurrent_to_output_weights->dims->data[0],
@@ -463,7 +464,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->builtin_data);
 
   // Input tensor.
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   const int max_time = input->dims->data[0];
   const int n_batch = input->dims->data[1];
   const int n_input = input->dims->data[2];
@@ -471,20 +472,20 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   // Tensors for the forward cell.
   TfLiteTensor* fw_input_to_input_weights =
       GetOptionalInputTensor(context, node, kFwInputToInputWeightsTensor);
-  TfLiteTensor* fw_input_to_forget_weights =
+  const TfLiteTensor* fw_input_to_forget_weights =
       GetInput(context, node, kFwInputToForgetWeightsTensor);
-  TfLiteTensor* fw_input_to_cell_weights =
+  const TfLiteTensor* fw_input_to_cell_weights =
       GetInput(context, node, kFwInputToCellWeightsTensor);
-  TfLiteTensor* fw_input_to_output_weights =
+  const TfLiteTensor* fw_input_to_output_weights =
       GetInput(context, node, kFwInputToOutputWeightsTensor);
 
   TfLiteTensor* fw_recurrent_to_input_weights =
       GetOptionalInputTensor(context, node, kFwRecurrentToInputWeightsTensor);
-  TfLiteTensor* fw_recurrent_to_forget_weights =
+  const TfLiteTensor* fw_recurrent_to_forget_weights =
       GetInput(context, node, kFwRecurrentToForgetWeightsTensor);
-  TfLiteTensor* fw_recurrent_to_cell_weights =
+  const TfLiteTensor* fw_recurrent_to_cell_weights =
       GetInput(context, node, kFwRecurrentToCellWeightsTensor);
-  TfLiteTensor* fw_recurrent_to_output_weights =
+  const TfLiteTensor* fw_recurrent_to_output_weights =
       GetInput(context, node, kFwRecurrentToOutputWeightsTensor);
 
   TfLiteTensor* fw_cell_to_input_weights =
@@ -496,10 +497,11 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
 
   TfLiteTensor* fw_input_gate_bias =
       GetOptionalInputTensor(context, node, kFwInputGateBiasTensor);
-  TfLiteTensor* fw_forget_gate_bias =
+  const TfLiteTensor* fw_forget_gate_bias =
       GetInput(context, node, kFwForgetGateBiasTensor);
-  TfLiteTensor* fw_cell_bias = GetInput(context, node, kFwCellGateBiasTensor);
-  TfLiteTensor* fw_output_gate_bias =
+  const TfLiteTensor* fw_cell_bias =
+      GetInput(context, node, kFwCellGateBiasTensor);
+  const TfLiteTensor* fw_output_gate_bias =
       GetInput(context, node, kFwOutputGateBiasTensor);
 
   TfLiteTensor* fw_projection_weights =
@@ -515,20 +517,20 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   // Tensors for the backward cell.
   TfLiteTensor* bw_input_to_input_weights =
       GetOptionalInputTensor(context, node, kBwInputToInputWeightsTensor);
-  TfLiteTensor* bw_input_to_forget_weights =
+  const TfLiteTensor* bw_input_to_forget_weights =
       GetInput(context, node, kBwInputToForgetWeightsTensor);
-  TfLiteTensor* bw_input_to_cell_weights =
+  const TfLiteTensor* bw_input_to_cell_weights =
       GetInput(context, node, kBwInputToCellWeightsTensor);
-  TfLiteTensor* bw_input_to_output_weights =
+  const TfLiteTensor* bw_input_to_output_weights =
       GetInput(context, node, kBwInputToOutputWeightsTensor);
 
   TfLiteTensor* bw_recurrent_to_input_weights =
       GetOptionalInputTensor(context, node, kBwRecurrentToInputWeightsTensor);
-  TfLiteTensor* bw_recurrent_to_forget_weights =
+  const TfLiteTensor* bw_recurrent_to_forget_weights =
       GetInput(context, node, kBwRecurrentToForgetWeightsTensor);
-  TfLiteTensor* bw_recurrent_to_cell_weights =
+  const TfLiteTensor* bw_recurrent_to_cell_weights =
       GetInput(context, node, kBwRecurrentToCellWeightsTensor);
-  TfLiteTensor* bw_recurrent_to_output_weights =
+  const TfLiteTensor* bw_recurrent_to_output_weights =
       GetInput(context, node, kBwRecurrentToOutputWeightsTensor);
 
   TfLiteTensor* bw_cell_to_input_weights =
@@ -540,10 +542,11 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
 
   TfLiteTensor* bw_input_gate_bias =
       GetOptionalInputTensor(context, node, kBwInputGateBiasTensor);
-  TfLiteTensor* bw_forget_gate_bias =
+  const TfLiteTensor* bw_forget_gate_bias =
       GetInput(context, node, kBwForgetGateBiasTensor);
-  TfLiteTensor* bw_cell_bias = GetInput(context, node, kBwCellGateBiasTensor);
-  TfLiteTensor* bw_output_gate_bias =
+  const TfLiteTensor* bw_cell_bias =
+      GetInput(context, node, kBwCellGateBiasTensor);
+  const TfLiteTensor* bw_output_gate_bias =
       GetInput(context, node, kBwOutputGateBiasTensor);
 
   TfLiteTensor* bw_projection_weights =
diff --git a/tensorflow/contrib/lite/kernels/cast.cc b/tensorflow/contrib/lite/kernels/cast.cc
index 17ef2c572ebbfa..673eedc2e948ba 100644
--- a/tensorflow/contrib/lite/kernels/cast.cc
+++ b/tensorflow/contrib/lite/kernels/cast.cc
@@ -32,7 +32,7 @@ constexpr int kOutputTensor = 0;
 TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   // TODO(ahentz): these two checks would make the new implementation
@@ -77,7 +77,7 @@ TfLiteStatus copyToTensor(const FromT* in, TfLiteTensor* out,
 }
 
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
   const int num_elements = NumElements(input);
   TF_LITE_ENSURE_EQ(context, num_elements, NumElements(output));
diff --git a/tensorflow/contrib/lite/kernels/comparisons.cc b/tensorflow/contrib/lite/kernels/comparisons.cc
index 2885ce032b4b6a..b948334b6d82ae 100644
--- a/tensorflow/contrib/lite/kernels/comparisons.cc
+++ b/tensorflow/contrib/lite/kernels/comparisons.cc
@@ -32,8 +32,8 @@ TfLiteStatus ComparisonPrepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   // Don't support string and bool.
@@ -68,8 +68,8 @@ TfLiteStatus ComparisonPrepare(TfLiteContext* context, TfLiteNode* node) {
             GetTensorData(output), GetTensorDims(output));
 
 TfLiteStatus GreaterEval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
   bool requires_broadcast = !HaveSameShapes(input1, input2);
   // TODO(renjieliu): Support quantized data.
@@ -92,8 +92,8 @@ TfLiteStatus GreaterEval(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus GreaterEqualEval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
   bool requires_broadcast = !HaveSameShapes(input1, input2);
   // TODO(renjieliu): Support quantized data.
@@ -116,8 +116,8 @@ TfLiteStatus GreaterEqualEval(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus LessEval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
   bool requires_broadcast = !HaveSameShapes(input1, input2);
   // TODO(renjieliu): Support quantized data.
@@ -140,8 +140,8 @@ TfLiteStatus LessEval(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus LessEqualEval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
   bool requires_broadcast = !HaveSameShapes(input1, input2);
   // TODO(renjieliu): Support quantized data.
diff --git a/tensorflow/contrib/lite/kernels/depthwise_conv.cc b/tensorflow/contrib/lite/kernels/depthwise_conv.cc
index eeda1bc3c5ba2d..3ad8d7d4e10cb8 100644
--- a/tensorflow/contrib/lite/kernels/depthwise_conv.cc
+++ b/tensorflow/contrib/lite/kernels/depthwise_conv.cc
@@ -83,9 +83,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   bool hasBias = NumInputs(node) == 3;
 
   TF_LITE_ENSURE(context, hasBias || NumInputs(node) == 2);
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
-  TfLiteTensor* bias = nullptr;
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
+  const TfLiteTensor* bias = nullptr;
 
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
@@ -169,8 +169,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 template 
 void EvalFloat(TfLiteContext* context, TfLiteNode* node,
                TfLiteDepthwiseConvParams* params, OpData* data,
-               TfLiteTensor* input, TfLiteTensor* filter, TfLiteTensor* bias,
-               TfLiteTensor* output) {
+               const TfLiteTensor* input, const TfLiteTensor* filter,
+               const TfLiteTensor* bias, TfLiteTensor* output) {
   float output_activation_min, output_activation_max;
   CalculateActivationRangeFloat(params->activation, &output_activation_min,
                                 &output_activation_max);
@@ -196,8 +196,8 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node,
 template 
 void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
                    TfLiteDepthwiseConvParams* params, OpData* data,
-                   TfLiteTensor* input, TfLiteTensor* filter,
-                   TfLiteTensor* bias, TfLiteTensor* output) {
+                   const TfLiteTensor* input, const TfLiteTensor* filter,
+                   const TfLiteTensor* bias, TfLiteTensor* output) {
   auto input_offset = -input->params.zero_point;
   auto filter_offset = -filter->params.zero_point;
   auto output_offset = output->params.zero_point;
@@ -230,9 +230,9 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   OpData* data = reinterpret_cast(node->user_data);
 
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
-  TfLiteTensor* bias =
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
+  const TfLiteTensor* bias =
       (NumInputs(node) == 3) ? GetInput(context, node, kBiasTensor) : nullptr;
 
   // TODO(aselle): Consider whether float conv and quantized conv should be
diff --git a/tensorflow/contrib/lite/kernels/dequantize.cc b/tensorflow/contrib/lite/kernels/dequantize.cc
index e685f2465f627c..672b2170e4990f 100644
--- a/tensorflow/contrib/lite/kernels/dequantize.cc
+++ b/tensorflow/contrib/lite/kernels/dequantize.cc
@@ -32,7 +32,7 @@ struct OpContext {
     input = GetInput(context, node, 0);
     output = GetOutput(context, node, 0);
   }
-  TfLiteTensor* input;
+  const TfLiteTensor* input;
   TfLiteTensor* output;
 };
 
diff --git a/tensorflow/contrib/lite/kernels/div.cc b/tensorflow/contrib/lite/kernels/div.cc
index ec380c8e4956e5..e52e4fe535c4e3 100644
--- a/tensorflow/contrib/lite/kernels/div.cc
+++ b/tensorflow/contrib/lite/kernels/div.cc
@@ -57,8 +57,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   TF_LITE_ENSURE_EQ(context, input1->type, input2->type);
@@ -80,7 +80,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 template 
 void EvalFloat(TfLiteContext* context, TfLiteNode* node,
                TfLiteDivParams* params, const OpData* data,
-               TfLiteTensor* input1, TfLiteTensor* input2,
+               const TfLiteTensor* input1, const TfLiteTensor* input2,
                TfLiteTensor* output) {
   float output_activation_min, output_activation_max;
   CalculateActivationRangeFloat(params->activation, &output_activation_min,
@@ -106,15 +106,13 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node,
 #undef TF_LITE_DIV
 }
 
-
-
 template 
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->builtin_data);
   OpData* data = reinterpret_cast(node->user_data);
 
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   if (output->type == kTfLiteFloat32) {
diff --git a/tensorflow/contrib/lite/kernels/elementwise.cc b/tensorflow/contrib/lite/kernels/elementwise.cc
index 6588256df714a0..b719a0839435e3 100644
--- a/tensorflow/contrib/lite/kernels/elementwise.cc
+++ b/tensorflow/contrib/lite/kernels/elementwise.cc
@@ -26,7 +26,7 @@ namespace elementwise {
 TfLiteStatus SinPrepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   TF_LITE_ENSURE_EQ(context, input->type, output->type);
   // Quantized float is not supported yet.
@@ -36,13 +36,13 @@ TfLiteStatus SinPrepare(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   switch (input->type) {
     case kTfLiteFloat32: {
       size_t elements = NumElements(input);
-      float* in = GetTensorData(input);
-      float* in_end = in + elements;
+      const float* in = GetTensorData(input);
+      const float* in_end = in + elements;
       float* out = output->data.f;
       for (; in < in_end; in++, out++) *out = std::sin(*in);
       return kTfLiteOk;
diff --git a/tensorflow/contrib/lite/kernels/embedding_lookup.cc b/tensorflow/contrib/lite/kernels/embedding_lookup.cc
index 4e8cb396d43a58..7539c0b30ded92 100644
--- a/tensorflow/contrib/lite/kernels/embedding_lookup.cc
+++ b/tensorflow/contrib/lite/kernels/embedding_lookup.cc
@@ -51,11 +51,11 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* lookup = GetInput(context, node, 0);
+  const TfLiteTensor* lookup = GetInput(context, node, 0);
   TF_LITE_ENSURE_EQ(context, NumDimensions(lookup), 1);
   TF_LITE_ENSURE_EQ(context, lookup->type, kTfLiteInt32);
 
-  TfLiteTensor* value = GetInput(context, node, 1);
+  const TfLiteTensor* value = GetInput(context, node, 1);
   TF_LITE_ENSURE(context, NumDimensions(value) >= 2);
 
   TfLiteTensor* output = GetOutput(context, node, 0);
@@ -71,8 +71,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   TfLiteTensor* output = GetOutput(context, node, 0);
-  TfLiteTensor* lookup = GetInput(context, node, 0);
-  TfLiteTensor* value = GetInput(context, node, 1);
+  const TfLiteTensor* lookup = GetInput(context, node, 0);
+  const TfLiteTensor* value = GetInput(context, node, 1);
 
   const int row_size = SizeOfDimension(value, 0);
   const int row_bytes = value->bytes / row_size;
diff --git a/tensorflow/contrib/lite/kernels/embedding_lookup_sparse.cc b/tensorflow/contrib/lite/kernels/embedding_lookup_sparse.cc
index 6c770e7f71efe8..d3be36993c3843 100644
--- a/tensorflow/contrib/lite/kernels/embedding_lookup_sparse.cc
+++ b/tensorflow/contrib/lite/kernels/embedding_lookup_sparse.cc
@@ -81,19 +81,19 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 5);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* ids = GetInput(context, node, 0);
+  const TfLiteTensor* ids = GetInput(context, node, 0);
   TF_LITE_ENSURE_EQ(context, NumDimensions(ids), 1);
   TF_LITE_ENSURE_EQ(context, ids->type, kTfLiteInt32);
 
-  TfLiteTensor* indices = GetInput(context, node, 1);
+  const TfLiteTensor* indices = GetInput(context, node, 1);
   TF_LITE_ENSURE_EQ(context, NumDimensions(indices), 2);
   TF_LITE_ENSURE_EQ(context, indices->type, kTfLiteInt32);
 
-  TfLiteTensor* shape = GetInput(context, node, 2);
+  const TfLiteTensor* shape = GetInput(context, node, 2);
   TF_LITE_ENSURE_EQ(context, NumDimensions(shape), 1);
   TF_LITE_ENSURE_EQ(context, shape->type, kTfLiteInt32);
 
-  TfLiteTensor* weights = GetInput(context, node, 3);
+  const TfLiteTensor* weights = GetInput(context, node, 3);
   TF_LITE_ENSURE_EQ(context, NumDimensions(weights), 1);
   TF_LITE_ENSURE_EQ(context, weights->type, kTfLiteFloat32);
 
@@ -102,7 +102,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, SizeOfDimension(indices, 0),
                     SizeOfDimension(weights, 0));
 
-  TfLiteTensor* value = GetInput(context, node, 4);
+  const TfLiteTensor* value = GetInput(context, node, 4);
   TF_LITE_ENSURE(context, NumDimensions(value) >= 2);
 
   // Mark the output as a dynamic tensor.
@@ -139,11 +139,11 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params =
       reinterpret_cast(node->builtin_data);
   TfLiteTensor* output = GetOutput(context, node, 0);
-  TfLiteTensor* ids = GetInput(context, node, 0);
-  TfLiteTensor* indices = GetInput(context, node, 1);
-  TfLiteTensor* dense_shape = GetInput(context, node, 2);
-  TfLiteTensor* weights = GetInput(context, node, 3);
-  TfLiteTensor* value = GetInput(context, node, 4);
+  const TfLiteTensor* ids = GetInput(context, node, 0);
+  const TfLiteTensor* indices = GetInput(context, node, 1);
+  const TfLiteTensor* dense_shape = GetInput(context, node, 2);
+  const TfLiteTensor* weights = GetInput(context, node, 3);
+  const TfLiteTensor* value = GetInput(context, node, 4);
 
   const int lookup_rank = SizeOfDimension(indices, 1);
   const int embedding_rank = NumDimensions(value);
diff --git a/tensorflow/contrib/lite/kernels/exp.cc b/tensorflow/contrib/lite/kernels/exp.cc
index a9e79b742dc2c8..ce03cdfe26cac8 100644
--- a/tensorflow/contrib/lite/kernels/exp.cc
+++ b/tensorflow/contrib/lite/kernels/exp.cc
@@ -36,7 +36,7 @@ struct ExpContext {
     input = GetInput(context, node, 0);
     output = GetOutput(context, node, 0);
   }
-  TfLiteTensor* input;
+  const TfLiteTensor* input;
   TfLiteTensor* output;
 };
 
diff --git a/tensorflow/contrib/lite/kernels/floor.cc b/tensorflow/contrib/lite/kernels/floor.cc
index 4b4395f711614a..697b777693e275 100644
--- a/tensorflow/contrib/lite/kernels/floor.cc
+++ b/tensorflow/contrib/lite/kernels/floor.cc
@@ -27,7 +27,7 @@ constexpr int kInputTensor = 0;
 constexpr int kOutputTensor = 0;
 
 TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -38,7 +38,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   optimized_ops::Floor(GetTensorData(input), GetTensorDims(input),
diff --git a/tensorflow/contrib/lite/kernels/fully_connected.cc b/tensorflow/contrib/lite/kernels/fully_connected.cc
index 470b52b7bc4e65..39b108629ab21d 100644
--- a/tensorflow/contrib/lite/kernels/fully_connected.cc
+++ b/tensorflow/contrib/lite/kernels/fully_connected.cc
@@ -89,8 +89,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, node->inputs->size, 3);
   TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
   TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
@@ -158,8 +158,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
 TfLiteStatus EvalPie(TfLiteContext* context, TfLiteNode* node,
                      TfLiteFullyConnectedParams* params, OpData* data,
-                     TfLiteTensor* input, TfLiteTensor* filter,
-                     TfLiteTensor* bias, TfLiteTensor* output) {
+                     const TfLiteTensor* input, const TfLiteTensor* filter,
+                     const TfLiteTensor* bias, TfLiteTensor* output) {
   int total_input_size = 1;
   for (int i = 0; i < input->dims->size; i++) {
     total_input_size *= input->dims->data[i];
@@ -191,8 +191,10 @@ TfLiteStatus EvalPie(TfLiteContext* context, TfLiteNode* node,
 
 TfLiteStatus EvalPieQuantized(TfLiteContext* context, TfLiteNode* node,
                               TfLiteFullyConnectedParams* params, OpData* data,
-                              TfLiteTensor* input, TfLiteTensor* filter,
-                              TfLiteTensor* bias, TfLiteTensor* input_quantized,
+                              const TfLiteTensor* input,
+                              const TfLiteTensor* filter,
+                              const TfLiteTensor* bias,
+                              TfLiteTensor* input_quantized,
                               TfLiteTensor* output) {
   // Check the types for this hybrid Op.
   TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32);
@@ -271,8 +273,9 @@ TfLiteStatus EvalPieQuantized(TfLiteContext* context, TfLiteNode* node,
 template 
 TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
                            TfLiteFullyConnectedParams* params, OpData* data,
-                           TfLiteTensor* input, TfLiteTensor* filter,
-                           TfLiteTensor* bias, TfLiteTensor* output) {
+                           const TfLiteTensor* input,
+                           const TfLiteTensor* filter, const TfLiteTensor* bias,
+                           TfLiteTensor* output) {
   gemmlowp::GemmContext* gemm_context = gemm_support::GetFromContext(context);
 
   int32_t input_offset = -input->params.zero_point;
@@ -311,8 +314,8 @@ TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
 template 
 TfLiteStatus EvalFloat(TfLiteContext* context, TfLiteNode* node,
                        TfLiteFullyConnectedParams* params, OpData* data,
-                       TfLiteTensor* input, TfLiteTensor* filter,
-                       TfLiteTensor* bias, TfLiteTensor* output) {
+                       const TfLiteTensor* input, const TfLiteTensor* filter,
+                       const TfLiteTensor* bias, TfLiteTensor* output) {
   float output_activation_min, output_activation_max;
   CalculateActivationRangeFloat(params->activation, &output_activation_min,
                                 &output_activation_max);
@@ -342,8 +345,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
       reinterpret_cast(node->builtin_data);
   OpData* data = reinterpret_cast(node->user_data);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
   TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
diff --git a/tensorflow/contrib/lite/kernels/gather.cc b/tensorflow/contrib/lite/kernels/gather.cc
index 0e4187d1eac646..c452d3ebac7d26 100644
--- a/tensorflow/contrib/lite/kernels/gather.cc
+++ b/tensorflow/contrib/lite/kernels/gather.cc
@@ -35,8 +35,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   const auto* params =
       reinterpret_cast(node->builtin_data);
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* positions = GetInput(context, node, kInputPositions);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* positions = GetInput(context, node, kInputPositions);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
   // Only INT32 positions are supported.
   TF_LITE_ENSURE_EQ(context, positions->type, kTfLiteInt32);
@@ -81,8 +81,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* positions = GetInput(context, node, kInputPositions);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* positions = GetInput(context, node, kInputPositions);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
   const int input_rank = NumDimensions(input);
 #define TF_LITE_GATHER(data_type, index_type)                            \
diff --git a/tensorflow/contrib/lite/kernels/hashtable_lookup.cc b/tensorflow/contrib/lite/kernels/hashtable_lookup.cc
index 3b82601d119b2e..41211d41aa85a5 100644
--- a/tensorflow/contrib/lite/kernels/hashtable_lookup.cc
+++ b/tensorflow/contrib/lite/kernels/hashtable_lookup.cc
@@ -60,15 +60,15 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
 
-  TfLiteTensor* lookup = GetInput(context, node, 0);
+  const TfLiteTensor* lookup = GetInput(context, node, 0);
   TF_LITE_ENSURE_EQ(context, NumDimensions(lookup), 1);
   TF_LITE_ENSURE_EQ(context, lookup->type, kTfLiteInt32);
 
-  TfLiteTensor* key = GetInput(context, node, 1);
+  const TfLiteTensor* key = GetInput(context, node, 1);
   TF_LITE_ENSURE_EQ(context, NumDimensions(key), 1);
   TF_LITE_ENSURE_EQ(context, key->type, kTfLiteInt32);
 
-  TfLiteTensor* value = GetInput(context, node, 2);
+  const TfLiteTensor* value = GetInput(context, node, 2);
   TF_LITE_ENSURE(context, NumDimensions(value) >= 1);
   TF_LITE_ENSURE_EQ(context, SizeOfDimension(key, 0),
                     SizeOfDimension(value, 0));
@@ -102,9 +102,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   TfLiteTensor* output = GetOutput(context, node, 0);
   TfLiteTensor* hits = GetOutput(context, node, 1);
-  TfLiteTensor* lookup = GetInput(context, node, 0);
-  TfLiteTensor* key = GetInput(context, node, 1);
-  TfLiteTensor* value = GetInput(context, node, 2);
+  const TfLiteTensor* lookup = GetInput(context, node, 0);
+  const TfLiteTensor* key = GetInput(context, node, 1);
+  const TfLiteTensor* value = GetInput(context, node, 2);
 
   const int num_rows = SizeOfDimension(value, 0);
   const int row_bytes = value->bytes / num_rows;
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index 273b57414795dd..26a7c160f652db 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -3270,11 +3270,11 @@ inline void Exp(const T* input_data, const size_t num_elements,
 }
 
 template 
-inline bool Mean(T* input_data, const int* input_dims, const int input_num_dims,
-                 T* output_data, const int* output_dims,
-                 const int output_num_dims, const int* axis,
-                 const int num_axis_dimensions, bool keep_dims, int* temp_index,
-                 int* resolved_axis, U* temp_sum) {
+inline bool Mean(const T* input_data, const int* input_dims,
+                 const int input_num_dims, T* output_data,
+                 const int* output_dims, const int output_num_dims,
+                 const int* axis, const int num_axis_dimensions, bool keep_dims,
+                 int* temp_index, int* resolved_axis, U* temp_sum) {
   // resets output data.
   size_t num_outputs = 1;
   for (int idx = 0; idx < output_num_dims; ++idx) {
diff --git a/tensorflow/contrib/lite/kernels/internal/tensor.h b/tensorflow/contrib/lite/kernels/internal/tensor.h
index 62cea143e6afc0..ce887cea8b794b 100644
--- a/tensorflow/contrib/lite/kernels/internal/tensor.h
+++ b/tensorflow/contrib/lite/kernels/internal/tensor.h
@@ -49,6 +49,34 @@ inline bool* GetTensorData(TfLiteTensor* tensor) {
   return tensor != nullptr ? tensor->data.b : nullptr;
 }
 
+template 
+inline const T* GetTensorData(const TfLiteTensor* tensor);
+
+template <>
+inline const float* GetTensorData(const TfLiteTensor* tensor) {
+  return tensor != nullptr ? tensor->data.f : nullptr;
+}
+
+template <>
+inline const uint8_t* GetTensorData(const TfLiteTensor* tensor) {
+  return tensor != nullptr ? tensor->data.uint8 : nullptr;
+}
+
+template <>
+inline const int32_t* GetTensorData(const TfLiteTensor* tensor) {
+  return tensor != nullptr ? tensor->data.i32 : nullptr;
+}
+
+template <>
+inline const int64_t* GetTensorData(const TfLiteTensor* tensor) {
+  return tensor != nullptr ? tensor->data.i64 : nullptr;
+}
+
+template <>
+inline const bool* GetTensorData(const TfLiteTensor* tensor) {
+  return tensor != nullptr ? tensor->data.b : nullptr;
+}
+
 inline int RemapDim(int max_dimensions, int d) {
   return max_dimensions - d - 1;
 }
diff --git a/tensorflow/contrib/lite/kernels/kernel_util.cc b/tensorflow/contrib/lite/kernels/kernel_util.cc
index 955e8c5764c6ad..239b533a17efaa 100644
--- a/tensorflow/contrib/lite/kernels/kernel_util.cc
+++ b/tensorflow/contrib/lite/kernels/kernel_util.cc
@@ -22,9 +22,12 @@ limitations under the License.
 
 namespace tflite {
 
-TfLiteStatus GetQuantizedConvolutionMultipler(
-    TfLiteContext* context, TfLiteTensor* input, TfLiteTensor* filter,
-    TfLiteTensor* bias, TfLiteTensor* output, double* multiplier) {
+TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context,
+                                              const TfLiteTensor* input,
+                                              const TfLiteTensor* filter,
+                                              const TfLiteTensor* bias,
+                                              TfLiteTensor* output,
+                                              double* multiplier) {
   const double input_product_scale = input->params.scale * filter->params.scale;
   const double bias_scale = bias->params.scale;
   const double output_scale = output->params.scale;
@@ -87,13 +90,13 @@ void CalculateActivationRangeFloat(TfLiteFusedActivation activation,
   }
 }
 
-bool HaveSameShapes(TfLiteTensor* input1, TfLiteTensor* input2) {
+bool HaveSameShapes(const TfLiteTensor* input1, const TfLiteTensor* input2) {
   return TfLiteIntArrayEqual(input1->dims, input2->dims);
 }
 
 TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context,
-                                        TfLiteTensor* input1,
-                                        TfLiteTensor* input2,
+                                        const TfLiteTensor* input1,
+                                        const TfLiteTensor* input2,
                                         TfLiteIntArray** output_shape) {
   int64_t dims1 = NumDimensions(input1);
   int64_t dims2 = NumDimensions(input2);
diff --git a/tensorflow/contrib/lite/kernels/kernel_util.h b/tensorflow/contrib/lite/kernels/kernel_util.h
index e225443a67b2ac..de0e3688915ccf 100644
--- a/tensorflow/contrib/lite/kernels/kernel_util.h
+++ b/tensorflow/contrib/lite/kernels/kernel_util.h
@@ -24,8 +24,8 @@ inline int NumDimensions(const TfLiteTensor* t) { return t->dims->size; }
 inline int SizeOfDimension(const TfLiteTensor* t, int dim) {
   return t->dims->data[dim];
 }
-inline TfLiteTensor* GetInput(TfLiteContext* context, TfLiteNode* node,
-                              int index) {
+inline const TfLiteTensor* GetInput(TfLiteContext* context, TfLiteNode* node,
+                                    int index) {
   return &context->tensors[node->inputs->data[index]];
 }
 inline TfLiteTensor* GetOutput(TfLiteContext* context, TfLiteNode* node,
@@ -78,9 +78,12 @@ inline void SetTensorToDynamic(TfLiteTensor* tensor) {
 // Calculates the multiplication factor for a quantized convolution (or
 // quantized depthwise convolution) involving the given tensors. Returns an
 // error if the scales of the tensors are not compatible.
-TfLiteStatus GetQuantizedConvolutionMultipler(
-    TfLiteContext* context, TfLiteTensor* input, TfLiteTensor* filter,
-    TfLiteTensor* bias, TfLiteTensor* output, double* multiplier);
+TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context,
+                                              const TfLiteTensor* input,
+                                              const TfLiteTensor* filter,
+                                              const TfLiteTensor* bias,
+                                              TfLiteTensor* output,
+                                              double* multiplier);
 
 // Calculates the useful range of an activation layer given its activation
 // tensor.
@@ -92,13 +95,13 @@ void CalculateActivationRangeFloat(TfLiteFusedActivation activation,
                                    float* activation_max);
 
 // Return true if the given tensors have the same shape.
-bool HaveSameShapes(TfLiteTensor* input1, TfLiteTensor* input2);
+bool HaveSameShapes(const TfLiteTensor* input1, const TfLiteTensor* input2);
 
 // Calculate the output_shape that is necessary for element-wise operations
 // with broadcasting involving the two input tensors.
 TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context,
-                                        TfLiteTensor* input1,
-                                        TfLiteTensor* input2,
+                                        const TfLiteTensor* input1,
+                                        const TfLiteTensor* input2,
                                         TfLiteIntArray** output_shape);
 }  // namespace tflite
 
diff --git a/tensorflow/contrib/lite/kernels/l2norm.cc b/tensorflow/contrib/lite/kernels/l2norm.cc
index e67f4e06f3680f..7cea63da871219 100644
--- a/tensorflow/contrib/lite/kernels/l2norm.cc
+++ b/tensorflow/contrib/lite/kernels/l2norm.cc
@@ -40,7 +40,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   TF_LITE_ENSURE(context, NumDimensions(input) <= 4);
@@ -64,7 +64,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
 template 
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   if (output->type == kTfLiteFloat32) {
diff --git a/tensorflow/contrib/lite/kernels/local_response_norm.cc b/tensorflow/contrib/lite/kernels/local_response_norm.cc
index c1c70d0dfa0050..c15a5170b85e18 100644
--- a/tensorflow/contrib/lite/kernels/local_response_norm.cc
+++ b/tensorflow/contrib/lite/kernels/local_response_norm.cc
@@ -38,7 +38,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
@@ -60,7 +60,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params =
       reinterpret_cast(node->builtin_data);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   if (output->type == kTfLiteFloat32) {
diff --git a/tensorflow/contrib/lite/kernels/lsh_projection.cc b/tensorflow/contrib/lite/kernels/lsh_projection.cc
index 0ee35775d50b87..25d2dc2cdd699b 100644
--- a/tensorflow/contrib/lite/kernels/lsh_projection.cc
+++ b/tensorflow/contrib/lite/kernels/lsh_projection.cc
@@ -77,16 +77,16 @@ TfLiteStatus Resize(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE(context, NumInputs(node) == 2 || NumInputs(node) == 3);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* hash = GetInput(context, node, 0);
+  const TfLiteTensor* hash = GetInput(context, node, 0);
   TF_LITE_ENSURE_EQ(context, NumDimensions(hash), 2);
   // Support up to 32 bits.
   TF_LITE_ENSURE(context, SizeOfDimension(hash, 1) <= 32);
 
-  TfLiteTensor* input = GetInput(context, node, 1);
+  const TfLiteTensor* input = GetInput(context, node, 1);
   TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
 
   if (NumInputs(node) == 3) {
-    TfLiteTensor* weight = GetInput(context, node, 2);
+    const TfLiteTensor* weight = GetInput(context, node, 2);
     TF_LITE_ENSURE_EQ(context, NumDimensions(weight), 1);
     TF_LITE_ENSURE_EQ(context, SizeOfDimension(weight, 0),
                       SizeOfDimension(input, 0));
@@ -173,9 +173,9 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
       reinterpret_cast(node->builtin_data);
 
   int32_t* out_buf = GetOutput(context, node, 0)->data.i32;
-  TfLiteTensor* hash = GetInput(context, node, 0);
-  TfLiteTensor* input = GetInput(context, node, 1);
-  TfLiteTensor* weight =
+  const TfLiteTensor* hash = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 1);
+  const TfLiteTensor* weight =
       NumInputs(node) == 2 ? nullptr : GetInput(context, node, 2);
 
   switch (params->type) {
diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc
index a1521efbb4e2df..8d447a2dcfcd01 100644
--- a/tensorflow/contrib/lite/kernels/lstm.cc
+++ b/tensorflow/contrib/lite/kernels/lstm.cc
@@ -100,13 +100,13 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
     TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[1], n_input);
   }
 
-  TfLiteTensor* input_to_forget_weights =
+  const TfLiteTensor* input_to_forget_weights =
       GetInput(context, node, kInputToForgetWeightsTensor);
   TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[0], n_cell);
   TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[1], n_input);
 
-  TfLiteTensor* input_to_cell_weights =
+  const TfLiteTensor* input_to_cell_weights =
       GetInput(context, node, kInputToCellWeightsTensor);
   TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[0], n_cell);
@@ -122,7 +122,7 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
                       n_output);
   }
 
-  TfLiteTensor* recurrent_to_forget_weights =
+  const TfLiteTensor* recurrent_to_forget_weights =
       GetInput(context, node, kRecurrentToForgetWeightsTensor);
   TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[0],
@@ -130,7 +130,7 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
   TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[1],
                     n_output);
 
-  TfLiteTensor* recurrent_to_cell_weights =
+  const TfLiteTensor* recurrent_to_cell_weights =
       GetInput(context, node, kRecurrentToCellWeightsTensor);
   TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[0], n_cell);
@@ -188,16 +188,16 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
     TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->data[0], n_cell);
   }
 
-  TfLiteTensor* forget_gate_bias =
+  const TfLiteTensor* forget_gate_bias =
       GetInput(context, node, kForgetGateBiasTensor);
   TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->size, 1);
   TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->data[0], n_cell);
 
-  TfLiteTensor* cell_bias = GetInput(context, node, kCellGateBiasTensor);
+  const TfLiteTensor* cell_bias = GetInput(context, node, kCellGateBiasTensor);
   TF_LITE_ENSURE_EQ(context, cell_bias->dims->size, 1);
   TF_LITE_ENSURE_EQ(context, cell_bias->dims->data[0], n_cell);
 
-  TfLiteTensor* output_gate_bias =
+  const TfLiteTensor* output_gate_bias =
       GetInput(context, node, kOutputGateBiasTensor);
   TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->size, 1);
   TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->data[0], n_cell);
@@ -241,18 +241,18 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   // Inferring batch size, number of outputs and number of cells from the
   // input tensors.
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TF_LITE_ENSURE(context, input->dims->size > 1);
   const int n_batch = input->dims->data[0];
   const int n_input = input->dims->data[1];
 
-  TfLiteTensor* input_to_output_weights =
+  const TfLiteTensor* input_to_output_weights =
       GetInput(context, node, kInputToOutputWeightsTensor);
   const int n_cell = input_to_output_weights->dims->data[0];
   TF_LITE_ENSURE_EQ(context, input_to_output_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, input_to_output_weights->dims->data[1], n_input);
 
-  TfLiteTensor* recurrent_to_output_weights =
+  const TfLiteTensor* recurrent_to_output_weights =
       GetInput(context, node, kRecurrentToOutputWeightsTensor);
   TF_LITE_ENSURE_EQ(context, recurrent_to_output_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, recurrent_to_output_weights->dims->data[0],
@@ -322,24 +322,24 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 // The LSTM Op engine.
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->builtin_data);
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
 
   TfLiteTensor* input_to_input_weights =
       GetOptionalInputTensor(context, node, kInputToInputWeightsTensor);
-  TfLiteTensor* input_to_forget_weights =
+  const TfLiteTensor* input_to_forget_weights =
       GetInput(context, node, kInputToForgetWeightsTensor);
-  TfLiteTensor* input_to_cell_weights =
+  const TfLiteTensor* input_to_cell_weights =
       GetInput(context, node, kInputToCellWeightsTensor);
-  TfLiteTensor* input_to_output_weights =
+  const TfLiteTensor* input_to_output_weights =
       GetInput(context, node, kInputToOutputWeightsTensor);
 
   TfLiteTensor* recurrent_to_input_weights =
       GetOptionalInputTensor(context, node, kRecurrentToInputWeightsTensor);
-  TfLiteTensor* recurrent_to_forget_weights =
+  const TfLiteTensor* recurrent_to_forget_weights =
       GetInput(context, node, kRecurrentToForgetWeightsTensor);
-  TfLiteTensor* recurrent_to_cell_weights =
+  const TfLiteTensor* recurrent_to_cell_weights =
       GetInput(context, node, kRecurrentToCellWeightsTensor);
-  TfLiteTensor* recurrent_to_output_weights =
+  const TfLiteTensor* recurrent_to_output_weights =
       GetInput(context, node, kRecurrentToOutputWeightsTensor);
 
   TfLiteTensor* cell_to_input_weights =
@@ -351,10 +351,10 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
 
   TfLiteTensor* input_gate_bias =
       GetOptionalInputTensor(context, node, kInputGateBiasTensor);
-  TfLiteTensor* forget_gate_bias =
+  const TfLiteTensor* forget_gate_bias =
       GetInput(context, node, kForgetGateBiasTensor);
-  TfLiteTensor* cell_bias = GetInput(context, node, kCellGateBiasTensor);
-  TfLiteTensor* output_gate_bias =
+  const TfLiteTensor* cell_bias = GetInput(context, node, kCellGateBiasTensor);
+  const TfLiteTensor* output_gate_bias =
       GetInput(context, node, kOutputGateBiasTensor);
 
   TfLiteTensor* projection_weights =
diff --git a/tensorflow/contrib/lite/kernels/maximum_minimum.cc b/tensorflow/contrib/lite/kernels/maximum_minimum.cc
index 5a28d663c9e756..8d676218bdcf71 100644
--- a/tensorflow/contrib/lite/kernels/maximum_minimum.cc
+++ b/tensorflow/contrib/lite/kernels/maximum_minimum.cc
@@ -41,8 +41,8 @@ struct OpContext {
     input2 = GetInput(context, node, kInputTensor2);
     output = GetOutput(context, node, kOutputTensor);
   }
-  TfLiteTensor* input1;
-  TfLiteTensor* input2;
+  const TfLiteTensor* input1;
+  const TfLiteTensor* input2;
   TfLiteTensor* output;
 };
 
diff --git a/tensorflow/contrib/lite/kernels/mean.cc b/tensorflow/contrib/lite/kernels/mean.cc
index 98f80e32d95b47..03e5db24de3f3c 100644
--- a/tensorflow/contrib/lite/kernels/mean.cc
+++ b/tensorflow/contrib/lite/kernels/mean.cc
@@ -40,8 +40,8 @@ struct MeanContext {
     output = GetOutput(context, node, 0);
   }
   TfLiteMeanParams* params;
-  TfLiteTensor* input;
-  TfLiteTensor* axis;
+  const TfLiteTensor* input;
+  const TfLiteTensor* axis;
   TfLiteTensor* output;
 };
 
diff --git a/tensorflow/contrib/lite/kernels/mfcc.cc b/tensorflow/contrib/lite/kernels/mfcc.cc
index 018db0dc54c5d2..3f5bc4d68a57da 100644
--- a/tensorflow/contrib/lite/kernels/mfcc.cc
+++ b/tensorflow/contrib/lite/kernels/mfcc.cc
@@ -67,8 +67,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* inputWav = GetInput(context, node, kInputTensorWav);
-  TfLiteTensor* inputRate = GetInput(context, node, kInputTensorRate);
+  const TfLiteTensor* inputWav = GetInput(context, node, kInputTensorWav);
+  const TfLiteTensor* inputRate = GetInput(context, node, kInputTensorRate);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   TF_LITE_ENSURE_EQ(context, NumDimensions(inputWav), 3);
@@ -94,8 +94,8 @@ template 
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->user_data);
 
-  TfLiteTensor* inputWav = GetInput(context, node, kInputTensorWav);
-  TfLiteTensor* inputRate = GetInput(context, node, kInputTensorRate);
+  const TfLiteTensor* inputWav = GetInput(context, node, kInputTensorWav);
+  const TfLiteTensor* inputRate = GetInput(context, node, kInputTensorRate);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   const int32 sample_rate = *GetTensorData(inputRate);
diff --git a/tensorflow/contrib/lite/kernels/mul.cc b/tensorflow/contrib/lite/kernels/mul.cc
index 54575019de4c67..6c4c3a1edc43af 100644
--- a/tensorflow/contrib/lite/kernels/mul.cc
+++ b/tensorflow/contrib/lite/kernels/mul.cc
@@ -57,8 +57,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   TF_LITE_ENSURE_EQ(context, input1->type, input2->type);
@@ -80,7 +80,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 template 
 void EvalFloat(TfLiteContext* context, TfLiteNode* node,
                TfLiteMulParams* params, const OpData* data,
-               TfLiteTensor* input1, TfLiteTensor* input2,
+               const TfLiteTensor* input1, const TfLiteTensor* input2,
                TfLiteTensor* output) {
   float output_activation_min, output_activation_max;
   CalculateActivationRangeFloat(params->activation, &output_activation_min,
@@ -109,7 +109,7 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node,
 template 
 void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
                    TfLiteMulParams* params, const OpData* data,
-                   TfLiteTensor* input1, TfLiteTensor* input2,
+                   const TfLiteTensor* input1, const TfLiteTensor* input2,
                    TfLiteTensor* output) {
   auto input1_offset = -input1->params.zero_point;
   auto input2_offset = -input2->params.zero_point;
@@ -149,8 +149,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->builtin_data);
   OpData* data = reinterpret_cast(node->user_data);
 
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   if (output->type == kTfLiteFloat32) {
diff --git a/tensorflow/contrib/lite/kernels/neg.cc b/tensorflow/contrib/lite/kernels/neg.cc
index 692da817272958..b8b53f340234a2 100644
--- a/tensorflow/contrib/lite/kernels/neg.cc
+++ b/tensorflow/contrib/lite/kernels/neg.cc
@@ -27,7 +27,7 @@ constexpr int kOutputTensor = 0;
 TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   output->type = input->type;
@@ -44,7 +44,7 @@ void Negate(const T* in_data, int num_elements, T* out_data) {
 }
 
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
   const int num_elements = NumElements(input);
   switch (input->type) {
diff --git a/tensorflow/contrib/lite/kernels/pad.cc b/tensorflow/contrib/lite/kernels/pad.cc
index 9e1e4658e971ea..b1eb6f76a435ec 100644
--- a/tensorflow/contrib/lite/kernels/pad.cc
+++ b/tensorflow/contrib/lite/kernels/pad.cc
@@ -46,8 +46,8 @@ struct PadContext {
     dims = NumDimensions(input);
   }
   TfLiteTensor* constant_values;
-  TfLiteTensor* input;
-  TfLiteTensor* paddings;
+  const TfLiteTensor* input;
+  const TfLiteTensor* paddings;
   TfLiteTensor* output;
   int dims;
 };
diff --git a/tensorflow/contrib/lite/kernels/pooling.cc b/tensorflow/contrib/lite/kernels/pooling.cc
index 0bf27c34c1337b..645d9f40086002 100644
--- a/tensorflow/contrib/lite/kernels/pooling.cc
+++ b/tensorflow/contrib/lite/kernels/pooling.cc
@@ -69,7 +69,7 @@ TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
   TfLiteTensor* output = GetOutput(context, node, 0);
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
   TF_LITE_ENSURE_EQ(context, input->type, output->type);
 
@@ -122,7 +122,7 @@ TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) {
 template 
 void AverageEvalFloat(TfLiteContext* context, TfLiteNode* node,
                       TfLitePoolParams* params, OpData* data,
-                      TfLiteTensor* input, TfLiteTensor* output) {
+                      const TfLiteTensor* input, TfLiteTensor* output) {
   float activation_min, activation_max;
   CalculateActivationRangeFloat(params->activation, &activation_min,
                                 &activation_max);
@@ -143,7 +143,7 @@ void AverageEvalFloat(TfLiteContext* context, TfLiteNode* node,
 template 
 void AverageEvalQuantized(TfLiteContext* context, TfLiteNode* node,
                           TfLitePoolParams* params, OpData* data,
-                          TfLiteTensor* input, TfLiteTensor* output) {
+                          const TfLiteTensor* input, TfLiteTensor* output) {
   int32_t activation_min;
   int32_t activation_max;
   CalculateActivationRangeUint8(params->activation, output, &activation_min,
@@ -165,8 +165,8 @@ void AverageEvalQuantized(TfLiteContext* context, TfLiteNode* node,
 
 template 
 void MaxEvalFloat(TfLiteContext* context, TfLiteNode* node,
-                  TfLitePoolParams* params, OpData* data, TfLiteTensor* input,
-                  TfLiteTensor* output) {
+                  TfLitePoolParams* params, OpData* data,
+                  const TfLiteTensor* input, TfLiteTensor* output) {
   float activation_min, activation_max;
   CalculateActivationRangeFloat(params->activation, &activation_min,
                                 &activation_max);
@@ -187,7 +187,7 @@ void MaxEvalFloat(TfLiteContext* context, TfLiteNode* node,
 template 
 void MaxEvalQuantized(TfLiteContext* context, TfLiteNode* node,
                       TfLitePoolParams* params, OpData* data,
-                      TfLiteTensor* input, TfLiteTensor* output) {
+                      const TfLiteTensor* input, TfLiteTensor* output) {
   int32_t activation_min;
   int32_t activation_max;
   CalculateActivationRangeUint8(params->activation, output, &activation_min,
@@ -209,8 +209,8 @@ void MaxEvalQuantized(TfLiteContext* context, TfLiteNode* node,
 
 template 
 void L2EvalFloat(TfLiteContext* context, TfLiteNode* node,
-                 TfLitePoolParams* params, OpData* data, TfLiteTensor* input,
-                 TfLiteTensor* output) {
+                 TfLitePoolParams* params, OpData* data,
+                 const TfLiteTensor* input, TfLiteTensor* output) {
   float activation_min, activation_max;
   CalculateActivationRangeFloat(params->activation, &activation_min,
                                 &activation_max);
@@ -236,7 +236,7 @@ TfLiteStatus AverageEval(TfLiteContext* context, TfLiteNode* node) {
   OpData* data = reinterpret_cast(node->user_data);
 
   TfLiteTensor* output = GetOutput(context, node, 0);
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   switch (input->type) {  // Already know in/out types are same.
     case kTfLiteFloat32:
       AverageEvalFloat(context, node, params, data, input, output);
@@ -258,7 +258,7 @@ TfLiteStatus MaxEval(TfLiteContext* context, TfLiteNode* node) {
   OpData* data = reinterpret_cast(node->user_data);
 
   TfLiteTensor* output = GetOutput(context, node, 0);
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   switch (input->type) {  // Already know in/out types are same.
     case kTfLiteFloat32:
       MaxEvalFloat(context, node, params, data, input, output);
@@ -279,7 +279,7 @@ TfLiteStatus L2Eval(TfLiteContext* context, TfLiteNode* node) {
   OpData* data = reinterpret_cast(node->user_data);
 
   TfLiteTensor* output = GetOutput(context, node, 0);
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   switch (input->type) {  // Already know in/out types are same.
     case kTfLiteFloat32:
       L2EvalFloat(context, node, params, data, input, output);
diff --git a/tensorflow/contrib/lite/kernels/reshape.cc b/tensorflow/contrib/lite/kernels/reshape.cc
index 438f70d3115130..3287040695140e 100644
--- a/tensorflow/contrib/lite/kernels/reshape.cc
+++ b/tensorflow/contrib/lite/kernels/reshape.cc
@@ -35,7 +35,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE(context, NumInputs(node) == 1 || NumInputs(node) == 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   // Tensorflow's Reshape allows one of the shape components to have the
@@ -70,7 +70,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   memcpy(output->data.raw, input->data.raw, input->bytes);
diff --git a/tensorflow/contrib/lite/kernels/resize_bilinear.cc b/tensorflow/contrib/lite/kernels/resize_bilinear.cc
index 9e3e19c09a4012..e4bd0f5b85d50c 100644
--- a/tensorflow/contrib/lite/kernels/resize_bilinear.cc
+++ b/tensorflow/contrib/lite/kernels/resize_bilinear.cc
@@ -36,8 +36,10 @@ constexpr int kInputTensor = 0;
 constexpr int kSizeTensor = 1;
 constexpr int kOutputTensor = 0;
 
-TfLiteStatus ResizeOutputTensor(TfLiteContext* context, TfLiteTensor* input,
-                                TfLiteTensor* size, TfLiteTensor* output) {
+TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
+                                const TfLiteTensor* input,
+                                const TfLiteTensor* size,
+                                TfLiteTensor* output) {
   TfLiteIntArray* output_size = TfLiteIntArrayCreate(4);
   output_size->data[0] = input->dims->data[0];
   const int32* size_data = GetTensorData(size);
@@ -51,8 +53,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* size = GetInput(context, node, kSizeTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* size = GetInput(context, node, kSizeTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   // TODO(ahentz): Our current implementations rely on the inputs being 4D.
@@ -78,9 +80,9 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params =
       reinterpret_cast(node->builtin_data);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
-  TfLiteTensor* size = GetInput(context, node, kSizeTensor);
+  const TfLiteTensor* size = GetInput(context, node, kSizeTensor);
 
   if (IsDynamicTensor(output)) {
     TF_LITE_ENSURE_OK(context,
diff --git a/tensorflow/contrib/lite/kernels/select.cc b/tensorflow/contrib/lite/kernels/select.cc
index 029ad9a709c514..9bc8a1a34a0fc5 100644
--- a/tensorflow/contrib/lite/kernels/select.cc
+++ b/tensorflow/contrib/lite/kernels/select.cc
@@ -33,10 +33,10 @@ TfLiteStatus SelectPrepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input_condition =
+  const TfLiteTensor* input_condition =
       GetInput(context, node, kInputTensorCondition);
-  TfLiteTensor* input_x = GetInput(context, node, kInputTensorX);
-  TfLiteTensor* input_y = GetInput(context, node, kInputTensorY);
+  const TfLiteTensor* input_x = GetInput(context, node, kInputTensorX);
+  const TfLiteTensor* input_y = GetInput(context, node, kInputTensorY);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   // Input must be bool.
@@ -62,10 +62,10 @@ TfLiteStatus SelectPrepare(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus SelectEval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input_condition =
+  const TfLiteTensor* input_condition =
       GetInput(context, node, kInputTensorCondition);
-  TfLiteTensor* input_x = GetInput(context, node, kInputTensorX);
-  TfLiteTensor* input_y = GetInput(context, node, kInputTensorY);
+  const TfLiteTensor* input_x = GetInput(context, node, kInputTensorX);
+  const TfLiteTensor* input_y = GetInput(context, node, kInputTensorY);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   bool is_rank_one = !HaveSameShapes(input_condition, input_x);
diff --git a/tensorflow/contrib/lite/kernels/slice.cc b/tensorflow/contrib/lite/kernels/slice.cc
index 82baf53e1d8543..b28934e2f74181 100644
--- a/tensorflow/contrib/lite/kernels/slice.cc
+++ b/tensorflow/contrib/lite/kernels/slice.cc
@@ -39,8 +39,9 @@ const int kMaxDim = 4;
 
 template 
 TfLiteStatus CalculateOutputShapeVector(
-    TfLiteContext* context, TfLiteTensor* input, TfLiteTensor* begin,
-    TfLiteTensor* size, std::vector* output_shape_vector) {
+    TfLiteContext* context, const TfLiteTensor* input,
+    const TfLiteTensor* begin, const TfLiteTensor* size,
+    std::vector* output_shape_vector) {
   for (int idx = 0; idx < NumDimensions(input); ++idx) {
     T size_value = GetTensorData(size)[idx];
     if (size_value < 0) {
@@ -62,8 +63,8 @@ TfLiteStatus CalculateOutputShapeVector(
 }
 
 template 
-void GetBeginAndSizeVectors(int dimensions, TfLiteTensor* begin,
-                            TfLiteTensor* size, std::vector* begins,
+void GetBeginAndSizeVectors(int dimensions, const TfLiteTensor* begin,
+                            const TfLiteTensor* size, std::vector* begins,
                             std::vector* sizes) {
   for (int idx = dimensions - 1; idx >= 0; --idx) {
     begins->push_back(GetTensorData(begin)[idx]);
@@ -71,9 +72,10 @@ void GetBeginAndSizeVectors(int dimensions, TfLiteTensor* begin,
   }
 }
 
-TfLiteStatus ResizeOutputShape(TfLiteContext* context, TfLiteTensor* input,
-                               TfLiteTensor* begin, TfLiteTensor* size,
-                               TfLiteTensor* output) {
+TfLiteStatus ResizeOutputShape(TfLiteContext* context,
+                               const TfLiteTensor* input,
+                               const TfLiteTensor* begin,
+                               const TfLiteTensor* size, TfLiteTensor* output) {
   std::vector output_shape_vector;
 
   if (begin->type == kTfLiteInt32) {
@@ -98,9 +100,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* begin = GetInput(context, node, kBeginTensor);
-  TfLiteTensor* size = GetInput(context, node, kSizeTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* begin = GetInput(context, node, kBeginTensor);
+  const TfLiteTensor* size = GetInput(context, node, kSizeTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   // Ensure validity of input tensor and its dimension.
@@ -124,9 +126,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* begin = GetInput(context, node, kBeginTensor);
-  TfLiteTensor* size = GetInput(context, node, kSizeTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* begin = GetInput(context, node, kBeginTensor);
+  const TfLiteTensor* size = GetInput(context, node, kSizeTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   if (IsDynamicTensor(output)) {
diff --git a/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc b/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc
index d8c9e352f00627..1e35869958a779 100644
--- a/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc
+++ b/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc
@@ -40,9 +40,9 @@ struct SpaceToBatchNDContext {
     paddings = GetInput(context, node, 2);
     output = GetOutput(context, node, 0);
   }
-  TfLiteTensor* input;
-  TfLiteTensor* block_shape;
-  TfLiteTensor* paddings;
+  const TfLiteTensor* input;
+  const TfLiteTensor* block_shape;
+  const TfLiteTensor* paddings;
   TfLiteTensor* output;
 };
 
diff --git a/tensorflow/contrib/lite/kernels/space_to_depth.cc b/tensorflow/contrib/lite/kernels/space_to_depth.cc
index cb2e509c9811b1..aafce895123cc2 100644
--- a/tensorflow/contrib/lite/kernels/space_to_depth.cc
+++ b/tensorflow/contrib/lite/kernels/space_to_depth.cc
@@ -42,7 +42,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
@@ -76,7 +76,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params =
       reinterpret_cast(node->builtin_data);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
 #define TF_LITE_SPACE_TO_DEPTH(type, scalar)                                  \
diff --git a/tensorflow/contrib/lite/kernels/split.cc b/tensorflow/contrib/lite/kernels/split.cc
index b524c79f8779b0..c6b94c25be3a4e 100644
--- a/tensorflow/contrib/lite/kernels/split.cc
+++ b/tensorflow/contrib/lite/kernels/split.cc
@@ -34,8 +34,8 @@ struct OpContext {
     input = GetInput(context, node, 1);
   }
   TfLiteSplitParams* params;
-  TfLiteTensor* axis;
-  TfLiteTensor* input;
+  const TfLiteTensor* axis;
+  const TfLiteTensor* input;
 };
 
 TfLiteStatus UseDynamicOutputTensors(TfLiteContext* context, TfLiteNode* node) {
@@ -46,8 +46,8 @@ TfLiteStatus UseDynamicOutputTensors(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus ResizeOutputTensors(TfLiteContext* context, TfLiteNode* node,
-                                 TfLiteTensor* axis, TfLiteTensor* input,
-                                 int num_splits) {
+                                 const TfLiteTensor* axis,
+                                 const TfLiteTensor* input, int num_splits) {
   int axis_value = GetTensorData(axis)[0];
   if (axis_value < 0) {
     axis_value += NumDimensions(input);
diff --git a/tensorflow/contrib/lite/kernels/squeeze.cc b/tensorflow/contrib/lite/kernels/squeeze.cc
index 29447ab021c7b6..09a5662fd9e70d 100644
--- a/tensorflow/contrib/lite/kernels/squeeze.cc
+++ b/tensorflow/contrib/lite/kernels/squeeze.cc
@@ -26,13 +26,12 @@ namespace builtin {
 namespace squeeze {
 
 struct SqueezeContext {
-  SqueezeContext(TfLiteContext* context, TfLiteNode* node) {
-    params = reinterpret_cast(node->builtin_data);
-    input = GetInput(context, node, 0);
-    output = GetOutput(context, node, 0);
-  }
+  SqueezeContext(TfLiteContext* context, TfLiteNode* node)
+      : params(reinterpret_cast(node->builtin_data)),
+        input(GetInput(context, node, 0)),
+        output(GetOutput(context, node, 0)) {}
   TfLiteSqueezeParams* params;
-  TfLiteTensor* input;
+  const TfLiteTensor* const input;
   TfLiteTensor* output;
 };
 
diff --git a/tensorflow/contrib/lite/kernels/strided_slice.cc b/tensorflow/contrib/lite/kernels/strided_slice.cc
index 40ac436b7dcabe..9417be32b3b83e 100644
--- a/tensorflow/contrib/lite/kernels/strided_slice.cc
+++ b/tensorflow/contrib/lite/kernels/strided_slice.cc
@@ -49,10 +49,10 @@ struct StridedSliceContext {
     dims = NumDimensions(input);
   }
   const TfLiteStridedSliceParams* params;
-  TfLiteTensor* input;
-  TfLiteTensor* begin;
-  TfLiteTensor* end;
-  TfLiteTensor* strides;
+  const TfLiteTensor* input;
+  const TfLiteTensor* begin;
+  const TfLiteTensor* end;
+  const TfLiteTensor* strides;
   TfLiteTensor* output;
   int dims;
 };
diff --git a/tensorflow/contrib/lite/kernels/sub.cc b/tensorflow/contrib/lite/kernels/sub.cc
index 7c60a4fdbffdc9..9531ecba98991a 100644
--- a/tensorflow/contrib/lite/kernels/sub.cc
+++ b/tensorflow/contrib/lite/kernels/sub.cc
@@ -57,8 +57,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
 
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   TF_LITE_ENSURE_EQ(context, input1->type, input2->type);
@@ -80,7 +80,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 template 
 void EvalFloat(TfLiteContext* context, TfLiteNode* node,
                TfLiteSubParams* params, const OpData* data,
-               TfLiteTensor* input1, TfLiteTensor* input2,
+               const TfLiteTensor* input1, const TfLiteTensor* input2,
                TfLiteTensor* output) {
   float output_activation_min, output_activation_max;
   CalculateActivationRangeFloat(params->activation, &output_activation_min,
@@ -109,7 +109,7 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node,
 template 
 void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
                    TfLiteSubParams* params, const OpData* data,
-                   TfLiteTensor* input1, TfLiteTensor* input2,
+                   const TfLiteTensor* input1, const TfLiteTensor* input2,
                    TfLiteTensor* output) {
   auto input1_offset = -input1->params.zero_point;
   auto input2_offset = -input2->params.zero_point;
@@ -164,8 +164,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->builtin_data);
   OpData* data = reinterpret_cast(node->user_data);
 
-  TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
-  TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
+  const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
+  const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
   if (output->type == kTfLiteFloat32) {
diff --git a/tensorflow/contrib/lite/kernels/svdf.cc b/tensorflow/contrib/lite/kernels/svdf.cc
index 13da51c7a78c36..788812755eedbd 100644
--- a/tensorflow/contrib/lite/kernels/svdf.cc
+++ b/tensorflow/contrib/lite/kernels/svdf.cc
@@ -58,9 +58,10 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, node->outputs->size, 2);
 
   TfLiteTensor* input = &context->tensors[node->inputs->data[kInputTensor]];
-  TfLiteTensor* weights_feature =
+  const TfLiteTensor* weights_feature =
       GetInput(context, node, kWeightsFeatureTensor);
-  TfLiteTensor* weights_time = GetInput(context, node, kWeightsTimeTensor);
+  const TfLiteTensor* weights_time =
+      GetInput(context, node, kWeightsTimeTensor);
 
   // Check all the parameters of tensor match within themselves and match the
   // input configuration.
@@ -123,10 +124,11 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->builtin_data);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* weights_feature =
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* weights_feature =
       GetInput(context, node, kWeightsFeatureTensor);
-  TfLiteTensor* weights_time = GetInput(context, node, kWeightsTimeTensor);
+  const TfLiteTensor* weights_time =
+      GetInput(context, node, kWeightsTimeTensor);
 
   TfLiteTensor* state = GetOutput(context, node, kStateTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
diff --git a/tensorflow/contrib/lite/kernels/topk_v2.cc b/tensorflow/contrib/lite/kernels/topk_v2.cc
index ad9b744f1af271..b331fc8482c79d 100644
--- a/tensorflow/contrib/lite/kernels/topk_v2.cc
+++ b/tensorflow/contrib/lite/kernels/topk_v2.cc
@@ -30,7 +30,7 @@ constexpr int kOutputIndexes = 1;
 
 namespace {
 TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
+  const TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
   // INT32 number of top results is supported.
   TF_LITE_ENSURE_EQ(context, top_k->type, kTfLiteInt32);
   // Check that the tensor contains only one value.
@@ -38,7 +38,7 @@ TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumElements(top_k), 1);
   const int32 k = top_k->data.i32[0];
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   const int num_dimensions = NumDimensions(input);
   // Check that input has one or more dimensions.
   TF_LITE_ENSURE_MSG(context, input->dims->size >= 1,
@@ -162,11 +162,11 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TfLiteTensor* output_values = GetOutput(context, node, kOutputValues);
   TF_LITE_ENSURE_EQ(context, input->type, output_values->type);
 
-  TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
+  const TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
   TF_LITE_ENSURE_EQ(context, top_k->type, kTfLiteInt32);
 
   // Set output dynamic if the input is not const.
@@ -187,11 +187,11 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   if (IsDynamicTensor(output_values)) {
     TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
   }
-  TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
+  const TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
   const int32 k = top_k->data.i32[0];
   // The tensor can have more than 2 dimensions or even be a vector, the code
   // anyway calls the internal dimension as row;
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   const int32 row_size = input->dims->data[input->dims->size - 1];
   int32 num_rows = 1;
   for (int i = 0; i < input->dims->size - 1; ++i) {
diff --git a/tensorflow/contrib/lite/kernels/transpose.cc b/tensorflow/contrib/lite/kernels/transpose.cc
index d3c10a9bb7b074..8316a23c18dea4 100644
--- a/tensorflow/contrib/lite/kernels/transpose.cc
+++ b/tensorflow/contrib/lite/kernels/transpose.cc
@@ -37,8 +37,8 @@ struct TransposeContext {
     perm = GetInput(context, node, 1);
     output = GetOutput(context, node, 0);
   }
-  TfLiteTensor* input;
-  TfLiteTensor* perm;
+  const TfLiteTensor* input;
+  const TfLiteTensor* perm;
   TfLiteTensor* output;
 };
 
diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc
index 5987bf68b5a73e..46d65ca8f8f3e2 100644
--- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc
+++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc
@@ -100,13 +100,13 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
     TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[1], n_input);
   }
 
-  TfLiteTensor* input_to_forget_weights =
+  const TfLiteTensor* input_to_forget_weights =
       GetInput(context, node, kInputToForgetWeightsTensor);
   TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[0], n_cell);
   TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[1], n_input);
 
-  TfLiteTensor* input_to_cell_weights =
+  const TfLiteTensor* input_to_cell_weights =
       GetInput(context, node, kInputToCellWeightsTensor);
   TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[0], n_cell);
@@ -122,7 +122,7 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
                       n_output);
   }
 
-  TfLiteTensor* recurrent_to_forget_weights =
+  const TfLiteTensor* recurrent_to_forget_weights =
       GetInput(context, node, kRecurrentToForgetWeightsTensor);
   TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[0],
@@ -130,7 +130,7 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
   TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[1],
                     n_output);
 
-  TfLiteTensor* recurrent_to_cell_weights =
+  const TfLiteTensor* recurrent_to_cell_weights =
       GetInput(context, node, kRecurrentToCellWeightsTensor);
   TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[0], n_cell);
@@ -188,16 +188,16 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
     TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->data[0], n_cell);
   }
 
-  TfLiteTensor* forget_gate_bias =
+  const TfLiteTensor* forget_gate_bias =
       GetInput(context, node, kForgetGateBiasTensor);
   TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->size, 1);
   TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->data[0], n_cell);
 
-  TfLiteTensor* cell_bias = GetInput(context, node, kCellGateBiasTensor);
+  const TfLiteTensor* cell_bias = GetInput(context, node, kCellGateBiasTensor);
   TF_LITE_ENSURE_EQ(context, cell_bias->dims->size, 1);
   TF_LITE_ENSURE_EQ(context, cell_bias->dims->data[0], n_cell);
 
-  TfLiteTensor* output_gate_bias =
+  const TfLiteTensor* output_gate_bias =
       GetInput(context, node, kOutputGateBiasTensor);
   TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->size, 1);
   TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->data[0], n_cell);
@@ -241,19 +241,19 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   // Inferring batch size, number of outputs and sequence length and
   // number of cells from the input tensors.
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   TF_LITE_ENSURE(context, input->dims->size > 1);
   const int max_time = input->dims->data[0];
   const int n_batch = input->dims->data[1];
   const int n_input = input->dims->data[2];
 
-  TfLiteTensor* input_to_output_weights =
+  const TfLiteTensor* input_to_output_weights =
       GetInput(context, node, kInputToOutputWeightsTensor);
   const int n_cell = input_to_output_weights->dims->data[0];
   TF_LITE_ENSURE_EQ(context, input_to_output_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, input_to_output_weights->dims->data[1], n_input);
 
-  TfLiteTensor* recurrent_to_output_weights =
+  const TfLiteTensor* recurrent_to_output_weights =
       GetInput(context, node, kRecurrentToOutputWeightsTensor);
   TF_LITE_ENSURE_EQ(context, recurrent_to_output_weights->dims->size, 2);
   TF_LITE_ENSURE_EQ(context, recurrent_to_output_weights->dims->data[0],
@@ -324,24 +324,24 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 // The LSTM Op engine.
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->builtin_data);
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
 
   TfLiteTensor* input_to_input_weights =
       GetOptionalInputTensor(context, node, kInputToInputWeightsTensor);
-  TfLiteTensor* input_to_forget_weights =
+  const TfLiteTensor* input_to_forget_weights =
       GetInput(context, node, kInputToForgetWeightsTensor);
-  TfLiteTensor* input_to_cell_weights =
+  const TfLiteTensor* input_to_cell_weights =
       GetInput(context, node, kInputToCellWeightsTensor);
-  TfLiteTensor* input_to_output_weights =
+  const TfLiteTensor* input_to_output_weights =
       GetInput(context, node, kInputToOutputWeightsTensor);
 
   TfLiteTensor* recurrent_to_input_weights =
       GetOptionalInputTensor(context, node, kRecurrentToInputWeightsTensor);
-  TfLiteTensor* recurrent_to_forget_weights =
+  const TfLiteTensor* recurrent_to_forget_weights =
       GetInput(context, node, kRecurrentToForgetWeightsTensor);
-  TfLiteTensor* recurrent_to_cell_weights =
+  const TfLiteTensor* recurrent_to_cell_weights =
       GetInput(context, node, kRecurrentToCellWeightsTensor);
-  TfLiteTensor* recurrent_to_output_weights =
+  const TfLiteTensor* recurrent_to_output_weights =
       GetInput(context, node, kRecurrentToOutputWeightsTensor);
 
   TfLiteTensor* cell_to_input_weights =
@@ -353,10 +353,10 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
 
   TfLiteTensor* input_gate_bias =
       GetOptionalInputTensor(context, node, kInputGateBiasTensor);
-  TfLiteTensor* forget_gate_bias =
+  const TfLiteTensor* forget_gate_bias =
       GetInput(context, node, kForgetGateBiasTensor);
-  TfLiteTensor* cell_bias = GetInput(context, node, kCellGateBiasTensor);
-  TfLiteTensor* output_gate_bias =
+  const TfLiteTensor* cell_bias = GetInput(context, node, kCellGateBiasTensor);
+  const TfLiteTensor* output_gate_bias =
       GetInput(context, node, kOutputGateBiasTensor);
 
   TfLiteTensor* projection_weights =
diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc
index 5ae635bfdab3e2..3eb28107c25b0c 100644
--- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc
+++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc
@@ -54,11 +54,11 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TF_LITE_ENSURE_EQ(context, node->inputs->size, 4);
   TF_LITE_ENSURE_EQ(context, node->outputs->size, 2);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
-  TfLiteTensor* recurrent_weights =
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
+  const TfLiteTensor* recurrent_weights =
       GetInput(context, node, kRecurrentWeightsTensor);
-  TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
+  const TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
 
   // Check all the parameters of tensor match within themselves and match the
   // input configuration.
@@ -260,11 +260,11 @@ TfLiteStatus EvalQuantized(const TfLiteTensor* input,
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast(node->builtin_data);
 
-  TfLiteTensor* input = GetInput(context, node, kInputTensor);
-  TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
-  TfLiteTensor* recurrent_weights =
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* input_weights = GetInput(context, node, kWeightsTensor);
+  const TfLiteTensor* recurrent_weights =
       GetInput(context, node, kRecurrentWeightsTensor);
-  TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
+  const TfLiteTensor* bias = GetInput(context, node, kBiasTensor);
   TfLiteTensor* hidden_state = GetOutput(context, node, kHiddenStateTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
 
diff --git a/tensorflow/contrib/lite/models/smartreply/ops/extract_feature.cc b/tensorflow/contrib/lite/models/smartreply/ops/extract_feature.cc
index f97a6486d6c11c..29c8ad2286d705 100644
--- a/tensorflow/contrib/lite/models/smartreply/ops/extract_feature.cc
+++ b/tensorflow/contrib/lite/models/smartreply/ops/extract_feature.cc
@@ -61,7 +61,7 @@ bool IsValidNgram(const tflite::StringRef& strref) {
 TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TfLiteIntArray* outputSize1 = TfLiteIntArrayCreate(1);
   TfLiteIntArray* outputSize2 = TfLiteIntArrayCreate(1);
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   int dim = input->dims->data[0];
   if (dim == 0) {
     // TFLite non-string output should have size greater than 0.
@@ -76,7 +76,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteTensor* input = GetInput(context, node, 0);
+  const TfLiteTensor* input = GetInput(context, node, 0);
   int num_strings = tflite::GetStringCount(input);
   TfLiteTensor* label = GetOutput(context, node, 0);
   TfLiteTensor* weight = GetOutput(context, node, 1);

From fc5250f97188e9b247845e32692d1c4ffad170c4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 19:41:09 -0700
Subject: [PATCH 0705/1691] Automated g4 rollback of changelist 196166118

PiperOrigin-RevId: 196340289
---
 .../depthwiseconv_uint8_3x3_filter.h          | 6033 ++++++++++++-----
 1 file changed, 4380 insertions(+), 1653 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h
index 4834103241840e..55e0d5c3aa9ebb 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h
@@ -25,1631 +25,4386 @@ namespace optimized_ops {
 
 #ifdef __aarch64__
 
-#define DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE 10 * 10 * 64
+inline void preload_l1_keep(const uint8* ptr) {
+#ifdef GEMMLOWP_ARM_64
+  asm volatile("prfm pldl1keep, [%[ptr]]\n" ::[ptr] "r"(ptr) :);
+#else
+  gemmlowp::Prefetch(ptr);
+#endif
+}
+
+// Implementation of quantized DepthwiseConv for 3x3 filters.
+
+// Below are helper structs to remove the use of arrays.
+// There is an llvm bug that causes significant slowdown when using arrays for
+// NEON intrinsics vector data types.
+// See: https://bugs.llvm.org/show_bug.cgi?id=34945
+
+struct Int32x8 {
+  int32x4_t low, high;
+};
+
+struct Filter3x3x8 {
+  int16x8_t f0, f1, f2, f3, f4, f5, f6, f7, f8;
+};
+
+// Loads 3x3 filter of depth 8 and adds filter offsets.
+inline Filter3x3x8 Load3x3Filter(const uint8* filter_ptr, int32 filter_offset,
+                                 int output_depth) {
+  Filter3x3x8 filter;
+
+  uint8x8_t temp_u8_0, temp_u8_1, temp_u8_2, temp_u8_3, temp_u8_4, temp_u8_5,
+      temp_u8_6, temp_u8_7, temp_u8_8;
+  int16x8_t filter_offset_vec = vdupq_n_s16(filter_offset);
+
+  temp_u8_0 = vld1_u8(filter_ptr + 0 * output_depth);
+  temp_u8_1 = vld1_u8(filter_ptr + 1 * output_depth);
+  temp_u8_2 = vld1_u8(filter_ptr + 2 * output_depth);
+  temp_u8_3 = vld1_u8(filter_ptr + 3 * output_depth);
+  temp_u8_4 = vld1_u8(filter_ptr + 4 * output_depth);
+  temp_u8_5 = vld1_u8(filter_ptr + 5 * output_depth);
+  temp_u8_6 = vld1_u8(filter_ptr + 6 * output_depth);
+  temp_u8_7 = vld1_u8(filter_ptr + 7 * output_depth);
+  temp_u8_8 = vld1_u8(filter_ptr + 8 * output_depth);
+
+  filter.f0 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_0));
+  filter.f1 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_1));
+  filter.f2 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_2));
+  filter.f3 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_3));
+  filter.f4 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_4));
+  filter.f5 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_5));
+  filter.f6 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_6));
+  filter.f7 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_7));
+  filter.f8 = vreinterpretq_s16_u16(vmovl_u8(temp_u8_8));
+
+  filter.f0 = vaddq_s16(filter.f0, filter_offset_vec);
+  filter.f1 = vaddq_s16(filter.f1, filter_offset_vec);
+  filter.f2 = vaddq_s16(filter.f2, filter_offset_vec);
+  filter.f3 = vaddq_s16(filter.f3, filter_offset_vec);
+  filter.f4 = vaddq_s16(filter.f4, filter_offset_vec);
+  filter.f5 = vaddq_s16(filter.f5, filter_offset_vec);
+  filter.f6 = vaddq_s16(filter.f6, filter_offset_vec);
+  filter.f7 = vaddq_s16(filter.f7, filter_offset_vec);
+  filter.f8 = vaddq_s16(filter.f8, filter_offset_vec);
+
+  return filter;
+}
+
+// Applies activation, offset and downquantize on a set of accumulator
+// registers that correspond to a 2x2 output of depth 8.
+// Stores results to output.
+inline void DownquantizeAndStore2x2Output(
+    Int32x8 acc_0, Int32x8 acc_1, Int32x8 acc_2, Int32x8 acc_3,
+    int32 output_offset, int32 output_multiplier, int output_shift,
+    int32 output_activation_min, int32 output_activation_max, uint8* output_ptr,
+    int output_depth, int output_width) {
+  using gemmlowp::RoundingDivideByPOT;
+  const int32x4_t output_offset_vec = vdupq_n_s32(output_offset);
+  const int32x4_t output_activation_min_vec =
+      vdupq_n_s32(output_activation_min);
+  const int32x4_t output_activation_max_vec =
+      vdupq_n_s32(output_activation_max);
+
+  // Fixed-point multiplication.
+  acc_0.low = vqrdmulhq_n_s32(acc_0.low, output_multiplier);
+  acc_0.high = vqrdmulhq_n_s32(acc_0.high, output_multiplier);
+  acc_1.low = vqrdmulhq_n_s32(acc_1.low, output_multiplier);
+  acc_1.high = vqrdmulhq_n_s32(acc_1.high, output_multiplier);
+  acc_2.low = vqrdmulhq_n_s32(acc_2.low, output_multiplier);
+  acc_2.high = vqrdmulhq_n_s32(acc_2.high, output_multiplier);
+  acc_3.low = vqrdmulhq_n_s32(acc_3.low, output_multiplier);
+  acc_3.high = vqrdmulhq_n_s32(acc_3.high, output_multiplier);
+
+  acc_0.low = RoundingDivideByPOT(acc_0.low, output_shift);
+  acc_0.high = RoundingDivideByPOT(acc_0.high, output_shift);
+  acc_1.low = RoundingDivideByPOT(acc_1.low, output_shift);
+  acc_1.high = RoundingDivideByPOT(acc_1.high, output_shift);
+  acc_2.low = RoundingDivideByPOT(acc_2.low, output_shift);
+  acc_2.high = RoundingDivideByPOT(acc_2.high, output_shift);
+  acc_3.low = RoundingDivideByPOT(acc_3.low, output_shift);
+  acc_3.high = RoundingDivideByPOT(acc_3.high, output_shift);
+
+  // Add the output offset.
+  acc_0.low = vaddq_s32(acc_0.low, output_offset_vec);
+  acc_0.high = vaddq_s32(acc_0.high, output_offset_vec);
+  acc_1.low = vaddq_s32(acc_1.low, output_offset_vec);
+  acc_1.high = vaddq_s32(acc_1.high, output_offset_vec);
+  acc_2.low = vaddq_s32(acc_2.low, output_offset_vec);
+  acc_2.high = vaddq_s32(acc_2.high, output_offset_vec);
+  acc_3.low = vaddq_s32(acc_3.low, output_offset_vec);
+  acc_3.high = vaddq_s32(acc_3.high, output_offset_vec);
+
+  // Apply the activation function.
+  acc_0.low = vmaxq_s32(acc_0.low, output_activation_min_vec);
+  acc_0.high = vmaxq_s32(acc_0.high, output_activation_min_vec);
+  acc_1.low = vmaxq_s32(acc_1.low, output_activation_min_vec);
+  acc_1.high = vmaxq_s32(acc_1.high, output_activation_min_vec);
+  acc_2.low = vmaxq_s32(acc_2.low, output_activation_min_vec);
+  acc_2.high = vmaxq_s32(acc_2.high, output_activation_min_vec);
+  acc_3.low = vmaxq_s32(acc_3.low, output_activation_min_vec);
+  acc_3.high = vmaxq_s32(acc_3.high, output_activation_min_vec);
+
+  acc_0.low = vminq_s32(acc_0.low, output_activation_max_vec);
+  acc_0.high = vminq_s32(acc_0.high, output_activation_max_vec);
+  acc_1.low = vminq_s32(acc_1.low, output_activation_max_vec);
+  acc_1.high = vminq_s32(acc_1.high, output_activation_max_vec);
+  acc_2.low = vminq_s32(acc_2.low, output_activation_max_vec);
+  acc_2.high = vminq_s32(acc_2.high, output_activation_max_vec);
+  acc_3.low = vminq_s32(acc_3.low, output_activation_max_vec);
+  acc_3.high = vminq_s32(acc_3.high, output_activation_max_vec);
+
+  // Saturating cast to uint8 and store to destination.
+  int16x4_t acc_0_low_s16 = vqmovn_s32(acc_0.low);
+  int16x4_t acc_0_high_s16 = vqmovn_s32(acc_0.high);
+  int16x4_t acc_1_low_s16 = vqmovn_s32(acc_1.low);
+  int16x4_t acc_1_high_s16 = vqmovn_s32(acc_1.high);
+  int16x4_t acc_2_low_s16 = vqmovn_s32(acc_2.low);
+  int16x4_t acc_2_high_s16 = vqmovn_s32(acc_2.high);
+  int16x4_t acc_3_low_s16 = vqmovn_s32(acc_3.low);
+  int16x4_t acc_3_high_s16 = vqmovn_s32(acc_3.high);
+
+  int16x8_t res_0_s16 = vcombine_s16(acc_0_low_s16, acc_0_high_s16);
+  int16x8_t res_1_s16 = vcombine_s16(acc_1_low_s16, acc_1_high_s16);
+  int16x8_t res_2_s16 = vcombine_s16(acc_2_low_s16, acc_2_high_s16);
+  int16x8_t res_3_s16 = vcombine_s16(acc_3_low_s16, acc_3_high_s16);
+
+  uint8x8_t res_0_u8 = vqmovun_s16(res_0_s16);
+  uint8x8_t res_1_u8 = vqmovun_s16(res_1_s16);
+  uint8x8_t res_2_u8 = vqmovun_s16(res_2_s16);
+  uint8x8_t res_3_u8 = vqmovun_s16(res_3_s16);
+
+  vst1_u8(output_ptr, res_0_u8);
+  vst1_u8(output_ptr + output_depth, res_1_u8);
+  vst1_u8(output_ptr + output_depth * output_width, res_2_u8);
+  vst1_u8(output_ptr + output_depth * output_width + output_depth, res_3_u8);
+}
+
+inline void DownquantizeAndStore(Int32x8 acc, int32 output_offset,
+                                 int32 output_multiplier, int output_shift,
+                                 int32 output_activation_min,
+                                 int32 output_activation_max,
+                                 uint8* output_ptr) {
+  using gemmlowp::RoundingDivideByPOT;
+  const int32x4_t output_offset_vec = vdupq_n_s32(output_offset);
+  const int32x4_t output_activation_min_vec =
+      vdupq_n_s32(output_activation_min);
+  const int32x4_t output_activation_max_vec =
+      vdupq_n_s32(output_activation_max);
+
+  acc.low = vqrdmulhq_n_s32(acc.low, output_multiplier);
+  acc.high = vqrdmulhq_n_s32(acc.high, output_multiplier);
+
+  acc.low = RoundingDivideByPOT(acc.low, output_shift);
+  acc.high = RoundingDivideByPOT(acc.high, output_shift);
+
+  acc.low = vaddq_s32(acc.low, output_offset_vec);
+  acc.high = vaddq_s32(acc.high, output_offset_vec);
+
+  acc.low = vmaxq_s32(acc.low, output_activation_min_vec);
+  acc.high = vmaxq_s32(acc.high, output_activation_min_vec);
+
+  acc.low = vminq_s32(acc.low, output_activation_max_vec);
+  acc.high = vminq_s32(acc.high, output_activation_max_vec);
+
+  int16x4_t acc_low_s16 = vqmovn_s32(acc.low);
+  int16x4_t acc_high_s16 = vqmovn_s32(acc.high);
+
+  int16x8_t res_s16 = vcombine_s16(acc_low_s16, acc_high_s16);
+  uint8x8_t res_u8 = vqmovun_s16(res_s16);
+  vst1_u8(output_ptr, res_u8);
+}
+
+inline void DownquantizeAndStore2Output(
+    Int32x8 acc_0, Int32x8 acc_1, int32 output_offset, int32 output_multiplier,
+    int output_shift, int32 output_activation_min, int32 output_activation_max,
+    uint8* output_ptr, int output_ptr_offset) {
+  {
+    using gemmlowp::RoundingDivideByPOT;
+    const int32x4_t output_offset_vec = vdupq_n_s32(output_offset);
+    const int32x4_t output_activation_min_vec =
+        vdupq_n_s32(output_activation_min);
+    const int32x4_t output_activation_max_vec =
+        vdupq_n_s32(output_activation_max);
+
+    // Fixed-point multiplication.
+    acc_0.low = vqrdmulhq_n_s32(acc_0.low, output_multiplier);
+    acc_0.high = vqrdmulhq_n_s32(acc_0.high, output_multiplier);
+    acc_1.low = vqrdmulhq_n_s32(acc_1.low, output_multiplier);
+    acc_1.high = vqrdmulhq_n_s32(acc_1.high, output_multiplier);
+
+    acc_0.low = RoundingDivideByPOT(acc_0.low, output_shift);
+    acc_0.high = RoundingDivideByPOT(acc_0.high, output_shift);
+    acc_1.low = RoundingDivideByPOT(acc_1.low, output_shift);
+    acc_1.high = RoundingDivideByPOT(acc_1.high, output_shift);
+
+    // Add the output offset.
+    acc_0.low = vaddq_s32(acc_0.low, output_offset_vec);
+    acc_0.high = vaddq_s32(acc_0.high, output_offset_vec);
+    acc_1.low = vaddq_s32(acc_1.low, output_offset_vec);
+    acc_1.high = vaddq_s32(acc_1.high, output_offset_vec);
+
+    // Apply the activation function.
+    acc_0.low = vmaxq_s32(acc_0.low, output_activation_min_vec);
+    acc_0.high = vmaxq_s32(acc_0.high, output_activation_min_vec);
+    acc_1.low = vmaxq_s32(acc_1.low, output_activation_min_vec);
+    acc_1.high = vmaxq_s32(acc_1.high, output_activation_min_vec);
+
+    acc_0.low = vminq_s32(acc_0.low, output_activation_max_vec);
+    acc_0.high = vminq_s32(acc_0.high, output_activation_max_vec);
+    acc_1.low = vminq_s32(acc_1.low, output_activation_max_vec);
+    acc_1.high = vminq_s32(acc_1.high, output_activation_max_vec);
+  }
+
+  // Saturating cast to uint8 and store to destination.
+  int16x8_t res_0_s16;
+  {
+    int16x4_t acc_0_low_s16 = vqmovn_s32(acc_0.low);
+    int16x4_t acc_0_high_s16 = vqmovn_s32(acc_0.high);
+    res_0_s16 = vcombine_s16(acc_0_low_s16, acc_0_high_s16);
+  }
+
+  int16x8_t res_1_s16;
+  {
+    int16x4_t acc_1_low_s16 = vqmovn_s32(acc_1.low);
+    int16x4_t acc_1_high_s16 = vqmovn_s32(acc_1.high);
+    res_1_s16 = vcombine_s16(acc_1_low_s16, acc_1_high_s16);
+  }
+
+  uint8x8_t res_0_u8 = vqmovun_s16(res_0_s16);
+  uint8x8_t res_1_u8 = vqmovun_s16(res_1_s16);
+  vst1_u8(output_ptr, res_0_u8);
+  vst1_u8(output_ptr + output_ptr_offset, res_1_u8);
+}
+
+// Performs multiply accumulate on 3 inputs of depth 8.
+inline Int32x8 MultiplyAccumulateRow(Int32x8 accum, int16x8_t f0, int16x8_t f1,
+                                     int16x8_t f2, int16x8_t i0, int16x8_t i1,
+                                     int16x8_t i2) {
+  accum.low = vmlal_s16(accum.low, vget_low_s16(f0), vget_low_s16(i0));
+  accum.high = vmlal_s16(accum.high, vget_high_s16(f0), vget_high_s16(i0));
+  accum.low = vmlal_s16(accum.low, vget_low_s16(f1), vget_low_s16(i1));
+  accum.high = vmlal_s16(accum.high, vget_high_s16(f1), vget_high_s16(i1));
+  accum.low = vmlal_s16(accum.low, vget_low_s16(f2), vget_low_s16(i2));
+  accum.high = vmlal_s16(accum.high, vget_high_s16(f2), vget_high_s16(i2));
+  return accum;
+}
+
+// Performs multiply accumulate on 3 inputs of depth 8.
+inline Int32x8 MultiplyAccumulate3x3Filter(const Filter3x3x8& f, int16x8_t i0,
+                                           int16x8_t i1, int16x8_t i2,
+                                           int16x8_t i3, int16x8_t i4,
+                                           int16x8_t i5, int16x8_t i6,
+                                           int16x8_t i7, int16x8_t i8,
+                                           Int32x8 accum) {
+  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f0), vget_low_s16(i0));
+  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f0), vget_high_s16(i0));
+  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f1), vget_low_s16(i1));
+  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f1), vget_high_s16(i1));
+  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f2), vget_low_s16(i2));
+  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f2), vget_high_s16(i2));
+  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f3), vget_low_s16(i3));
+  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f3), vget_high_s16(i3));
+  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f4), vget_low_s16(i4));
+  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f4), vget_high_s16(i4));
+  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f5), vget_low_s16(i5));
+  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f5), vget_high_s16(i5));
+  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f6), vget_low_s16(i6));
+  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f6), vget_high_s16(i6));
+  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f7), vget_low_s16(i7));
+  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f7), vget_high_s16(i7));
+  accum.low = vmlal_s16(accum.low, vget_low_s16(f.f8), vget_low_s16(i8));
+  accum.high = vmlal_s16(accum.high, vget_high_s16(f.f8), vget_high_s16(i8));
+  return accum;
+}
+
+inline void DotProductAndStore(const Filter3x3x8& filter, int16x8_t i0,
+                               int16x8_t i1, int16x8_t i2, int16x8_t i3,
+                               int16x8_t i4, int16x8_t i5, int16x8_t i6,
+                               int16x8_t i7, int16x8_t i8,
+                               const int32* bias_ptr, int32 output_offset,
+                               int32 output_multiplier, int output_shift,
+                               int32 output_activation_min,
+                               int32 output_activation_max, uint8* output_ptr) {
+  Int32x8 acc;
+  acc.low = vld1q_s32(bias_ptr);
+  acc.high = vld1q_s32(bias_ptr + 4);
+
+  acc = MultiplyAccumulate3x3Filter(filter, i0, i1, i2, i3, i4, i5, i6, i7, i8,
+                                    acc);
+
+  DownquantizeAndStore(acc, output_offset, output_multiplier, output_shift,
+                       output_activation_min, output_activation_max,
+                       output_ptr);
+}
+
+// Performs multiply-accumulate on a 3x4 input for 2 horizontal outputs.
+inline void DotProductAndStore2xStride1(
+    const Filter3x3x8& filter, int16x8_t i0, int16x8_t i1, int16x8_t i2,
+    int16x8_t i3, int16x8_t i4, int16x8_t i5, int16x8_t i6, int16x8_t i7,
+    int16x8_t i8, int16x8_t i9, int16x8_t i10, int16x8_t i11,
+    const int32* bias_ptr, int32 output_offset, int32 output_multiplier,
+    int output_shift, int32 output_activation_min, int32 output_activation_max,
+    uint8* output_ptr, int output_ptr_offset) {
+  Int32x8 acc_0, acc_1;
+  acc_0.low = vld1q_s32(bias_ptr);
+  acc_1.low = vld1q_s32(bias_ptr);
+  acc_0.high = vld1q_s32(bias_ptr + 4);
+  acc_1.high = vld1q_s32(bias_ptr + 4);
+
+  acc_0 = MultiplyAccumulate3x3Filter(filter, i0, i1, i2, i4, i5, i6, i8, i9,
+                                      i10, acc_0);
+  acc_1 = MultiplyAccumulate3x3Filter(filter, i1, i2, i3, i5, i6, i7, i9, i10,
+                                      i11, acc_1);
+  DownquantizeAndStore2Output(acc_0, acc_1, output_offset, output_multiplier,
+                              output_shift, output_activation_min,
+                              output_activation_max, output_ptr,
+                              output_ptr_offset);
+}
+
+// Performs multiply-accumulate on a 4x3 input for 2 vertical outputs.
+inline void DotProductAndStore2yStride1(
+    const Filter3x3x8& filter, int16x8_t i0, int16x8_t i1, int16x8_t i2,
+    int16x8_t i3, int16x8_t i4, int16x8_t i5, int16x8_t i6, int16x8_t i7,
+    int16x8_t i8, int16x8_t i9, int16x8_t i10, int16x8_t i11,
+    const int32* bias_ptr, int32 output_offset, int32 output_multiplier,
+    int output_shift, int32 output_activation_min, int32 output_activation_max,
+    uint8* output_ptr, int output_ptr_offset) {
+  Int32x8 acc_0, acc_1;
+  acc_0.low = vld1q_s32(bias_ptr);
+  acc_1.low = vld1q_s32(bias_ptr);
+  acc_0.high = vld1q_s32(bias_ptr + 4);
+  acc_1.high = vld1q_s32(bias_ptr + 4);
+
+  acc_0 = MultiplyAccumulate3x3Filter(filter, i0, i1, i2, i3, i4, i5, i6, i7,
+                                      i8, acc_0);
+  acc_1 = MultiplyAccumulate3x3Filter(filter, i3, i4, i5, i6, i7, i8, i9, i10,
+                                      i11, acc_1);
+  DownquantizeAndStore2Output(acc_0, acc_1, output_offset, output_multiplier,
+                              output_shift, output_activation_min,
+                              output_activation_max, output_ptr,
+                              output_ptr_offset);
+}
+
+// A kernel that is optimized on the number of output cells in the x and y
+// direction, and the stride. Assumes 3x3 filters of 8 depth.
+template 
+struct ConvKernel3x3FilterDepth8 {};
+
+template <>
+struct ConvKernel3x3FilterDepth8<8, 8, 1, 1> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+    const int output_row_size = output_depth * output_width;
+
+    // To process 8x8 outputs using a 3x3 filter, we require 10x10 inputs.
+    // Load inputs for the first 2 filters on the top left, then slide to
+    // the right, down, left, down, right, etc. in a snake-like path. This
+    // minimizes the total number of loads.
+    //
+    //        INPUT                          OUTPUT
+    //   |\----------------\               |\------------\
+    //   | \                \              | \            \
+    //   |  \----------------\             |  \------------\
+    //   |  | 0    ...     9 |             |  | 0  ...   7 |
+    //   |  | 10   ...    19 |     --->    |  | 8  ...  15 |
+    //   |  | 20   ...    29 |              \ | .. ...  .. |
+    //    \ | ..   ...    .. |               \| 56 ...  63 |
+    //     \| 90   ...   109 |                |------------|
+    //      |----------------|
+    //
+    // The first set of loads corresponds to:
+    //
+    //        INPUT                          OUTPUT
+    //   |\-----------------                |\-----------
+    //   | \                                | \
+    //   |  \-----------------              |  \----------
+    //   |  | 0  1   2  3 ...               |  | 0  1 ...
+    //   |  | 10 11 12 13 ...     --->      |  | ..   ...
+    //   |  | 20 21 22 23 ...                  | ..   ...
+    //   |  | ..   ...    ...
+    //
+    // The next set of loads correspond to a sliding window to the right.
+    // It loads inputs 4, 5, 14, 15, 23, 24 and keeps 2, 3, 12, 13, and 22:
+    //
+    //        INPUT                          OUTPUT
+    //   |\-------------------                |\-------------
+    //   | \                                  | \
+    //   |  \-------------------              |  \------------
+    //   |  | .. 2  3   4  5 ...              |  | .. 2  3 ...
+    //   |  | .. 12 13 14 15 ...     --->     |  | ..      ...
+    //   |  | .. 21 22 23 24 ...                 | ..      ...
+    //   |  | ..    ...      ...
+    //
+    // And so on...
+
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11;
+
+    // Load inputs for 1x2 outputs starting from the top left. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (0) and (1).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+
+      ptr += input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+
+      ptr += input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr, output_depth);
+
+    // Slide to the right for outputs x = [2, 3], y = 0. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (2) and (3).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 4 * input_depth;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
+        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 2 * output_depth, output_depth);
+
+    // Slide to the right again for outputs x = [4, 5], y = 0. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (4) and (5).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 6 * input_depth;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 4 * output_depth, output_depth);
+
+    // Slide to the right one last time for outputs x = [6, 7], y = 0.
+    // Referring to the indexes in the diagram above, this corresponds to
+    // outputs (6) and (7).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 8 * input_depth;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
+        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 6 * output_depth, output_depth);
+
+    // Slide to down for outputs x = [6, 7], y = 1. Referring to the indexes in
+    // the diagram above, this corresponds to outputs (14) and (15).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 6 * input_depth + 3 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
+        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 6 * output_depth + output_row_size,
+        output_depth);
+
+    // Slide left for outputs x = [4, 5], y = 1. Referring to the indexes in
+    // the diagram above, this corresponds to outputs (12) and (13).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 4 * input_depth + input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
+        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 4 * output_depth + output_row_size,
+        output_depth);
+
+    // Slide left again for outputs x = [2, 3], y = 1. Referring to the indexes
+    // in the diagram above, this corresponds to outputs (10) and (11).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 2 * input_depth + input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
+        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 2 * output_depth + output_row_size,
+        output_depth);
+
+    // Slide left one more time for outputs x = [0, 1], y = 1. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (8) and (9).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
+        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + output_row_size, output_depth);
+
+    // Slide down for outputs x = [0, 1], y = 2. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (16) and (17).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 4 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_8, input_9, input_10, input_11, input_0, input_1, input_2,
+        input_3, input_4, input_5, input_6, input_7, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 2 * output_row_size, output_depth);
+
+    // Slide right for outputs x = [2, 3], y = 2. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (18) and (19).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 4 * input_depth + 2 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_10, input_11, input_8, input_9, input_2, input_3, input_0,
+        input_1, input_6, input_7, input_4, input_5, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 2 * output_depth + 2 * output_row_size, output_depth);
+
+    // Slide right for outputs x = [4, 5], y = 2. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (20) and (21).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 6 * input_depth + 2 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_8, input_9, input_10, input_11, input_0, input_1, input_2,
+        input_3, input_4, input_5, input_6, input_7, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 4 * output_depth + 2 * output_row_size, output_depth);
+
+    // Slide right one more time for outputs x = [6, 7], y = 2. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (22) and (23).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 8 * input_depth + 2 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_10, input_11, input_8, input_9, input_2, input_3, input_0,
+        input_1, input_6, input_7, input_4, input_5, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 6 * output_depth + 2 * output_row_size, output_depth);
+
+    // Slide down for outputs x = [6, 7], y = 3. Referring to the indexes in
+    // the diagram above, this corresponds to outputs (30) and (31).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 6 * input_depth + 5 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
+        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 6 * output_depth + 3 * output_row_size, output_depth);
+
+    // Slide left for outputs x = [4, 5], y = 3. Referring to the indexes in
+    // the diagram above, this corresponds to outputs (28) and (29).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 4 * input_depth + 3 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 4 * output_depth + 3 * output_row_size, output_depth);
+
+    // Slide left for outputs x = [2, 3], y = 3. Referring to the indexes in
+    // the diagram above, this corresponds to outputs (26) and (27).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 2 * input_depth + 3 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
+        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 2 * output_depth + 3 * output_row_size, output_depth);
+
+    // Slide left one more time for outputs x = [0, 1], y = 3. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (24) and (25).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 3 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 3 * output_row_size, output_depth);
+
+    // Slide down for outputs x = [0, 1], y = 4. Referring to the indexes in
+    // the diagram above, this corresponds to outputs (32) and (33).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 6 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
+        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 4 * output_row_size, output_depth);
+
+    // Slide right for outputs x = [2, 3], y = 4. Referring to the indexes in
+    // the diagram above, this corresponds to outputs (34) and (35).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 4 * input_depth + 4 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
+        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 2 * output_depth + 4 * output_row_size, output_depth);
+
+    // Slide right for outputs x = [4, 5], y = 4. Referring to the indexes in
+    // the diagram above, this corresponds to outputs (36) and (37).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 6 * input_depth + 4 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
+        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 4 * output_depth + 4 * output_row_size, output_depth);
+
+    // Slide right one more time for outputs x = [6, 7], y = 4. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (38) and (39).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 8 * input_depth + 4 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
+        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 6 * output_depth + 4 * output_row_size, output_depth);
+
+    // Slide down for outputs x = [6, 7], y = 5. Referring to the  indexes in
+    // the diagram above, this corresponds to outputs (46) and (47).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 6 * input_depth + 7 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_10, input_11, input_8, input_9, input_2, input_3, input_0,
+        input_1, input_6, input_7, input_4, input_5, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 6 * output_depth + 5 * output_row_size, output_depth);
+
+    // Slide left for outputs x = [4, 5], y = 5. Referring to the  indexes in
+    // the diagram above, this corresponds to outputs (44) and (45).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 4 * input_depth + 5 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_8, input_9, input_10, input_11, input_0, input_1, input_2,
+        input_3, input_4, input_5, input_6, input_7, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 4 * output_depth + 5 * output_row_size, output_depth);
+
+    // Slide left for outputs x = [2, 3], y = 5. Referring to the  indexes in
+    // the diagram above, this corresponds to outputs (42) and (43).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 2 * input_depth + 5 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_10, input_11, input_8, input_9, input_2, input_3, input_0,
+        input_1, input_6, input_7, input_4, input_5, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 2 * output_depth + 5 * output_row_size, output_depth);
+
+    // Slide left one more time for outputs x = [0, 1], y = 5. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (40) and (41).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 5 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_8, input_9, input_10, input_11, input_0, input_1, input_2,
+        input_3, input_4, input_5, input_6, input_7, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 5 * output_row_size, output_depth);
+
+    // Slide down for outputs x = [0, 1], y = 6. Referring to the  indexes in
+    // the diagram above, this corresponds to outputs (48) and (49).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 8 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 6 * output_row_size, output_depth);
+
+    // Slide right for outputs x = [2, 3], y = 6. Referring to the  indexes in
+    // the diagram above, this corresponds to outputs (50) and (51).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 4 * input_depth + 6 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
+        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 2 * output_depth + 6 * output_row_size, output_depth);
+
+    // Slide right for outputs x = [4, 5], y = 6. Referring to the  indexes in
+    // the diagram above, this corresponds to outputs (52) and (53).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 6 * input_depth + 6 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 4 * output_depth + 6 * output_row_size, output_depth);
+
+    // Slide right one more time for outputs x = [6, 7], y = 6. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (54) and (55).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 8 * input_depth + 6 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
+        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 6 * output_depth + 6 * output_row_size, output_depth);
+
+    // Slide down for outputs x = [6, 7], y = 7. Referring to the indexes in the
+    // diagram above, this corresponds to outputs (62) and (63).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 6 * input_depth + 9 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
+        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 6 * output_depth + 7 * output_row_size, output_depth);
+
+    // Slide left for outputs x = [4, 5], y = 7. Referring to the indexes in the
+    // diagram above, this corresponds to outputs (60) and (61).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 4 * input_depth + 7 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
+        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 4 * output_depth + 7 * output_row_size, output_depth);
+
+    // Slide left for outputs x = [2, 3], y = 7. Referring to the indexes in the
+    // diagram above, this corresponds to outputs (58) and (59).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 2 * input_depth + 7 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
+        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 2 * output_depth + 7 * output_row_size, output_depth);
+
+    // Slide left one more time for outputs x = [0, 1], y = 7. Referring to the
+    // indexes in the diagram above, this corresponds to outputs (56) and (57).
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 7 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
+        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 7 * output_row_size, output_depth);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<4, 4, 1, 1> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+    const int output_row_size = output_depth * output_width;
+
+    // To process 4x4 outputs using a 3x3 filter, we require 6x6 inputs.
+    // Load inputs for the first 2 filters on the top left, then slide to
+    // the right, down, left, down, right, etc. in a snake-like path. This
+    // minimizes the total number of loads.
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11;
+
+    // Load inputs for 1x2 outputs starting from the top left.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+
+      ptr += input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+
+      ptr += input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr, output_depth);
+
+    // Now load 1x2 inputs on the top right.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 4 * input_depth;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
+        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 2 * output_depth, output_depth);
+
+    // Now load next inputs when sliding window down.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 2 * input_depth + 3 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
+        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 2 * output_depth + output_row_size,
+        output_depth);
+
+    // Now load next inputs when sliding window left.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
+        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + output_row_size, output_depth);
+
+    // Now load next inputs when sliding window down.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 4 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_8, input_9, input_10, input_11, input_0, input_1, input_2,
+        input_3, input_4, input_5, input_6, input_7, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 2 * output_row_size, output_depth);
+
+    // Now load next inputs when sliding window right.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 4 * input_depth + 2 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_10, input_11, input_8, input_9, input_2, input_3, input_0,
+        input_1, input_6, input_7, input_4, input_5, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 2 * output_depth + 2 * output_row_size, output_depth);
+
+    // Now load next inputs when sliding window down.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 2 * input_depth + 5 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
+        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max,
+        output_ptr + 2 * output_depth + 3 * output_row_size, output_depth);
+
+    // Now load next inputs when sliding window left.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 3 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 3 * output_row_size, output_depth);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<4, 2, 1, 1> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+    const int output_row_size = output_depth * output_width;
+
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11;
+
+    // Load inputs for 1x2 outputs starting from the top.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+
+      ptr += input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+
+      ptr += input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr, output_depth);
+
+    output_ptr += output_row_size;
+
+    // Now load next inputs one row down.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 3 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
+        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr, output_depth);
+
+    output_ptr += output_row_size;
+
+    // Now load next row.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 4 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_8, input_9, input_10, input_11, input_0, input_1, input_2,
+        input_3, input_4, input_5, input_6, input_7, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr, output_depth);
+
+    output_ptr += output_row_size;
+
+    // Now load last row.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 5 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr, output_depth);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<4, 1, 1, 1> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+    const int output_row_size = output_depth * output_width;
+
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11;
+
+    // Load inputs for 2x1 outputs starting from the top.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      ptr += input_row_size;
+      temp_3 = vld1_u8(ptr);
+      temp_4 = vld1_u8(ptr + input_depth);
+      temp_5 = vld1_u8(ptr + 2 * input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+
+      ptr += input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      ptr += input_row_size;
+      temp_3 = vld1_u8(ptr);
+      temp_4 = vld1_u8(ptr + input_depth);
+      temp_5 = vld1_u8(ptr + 2 * input_depth);
+
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+    }
+
+    DotProductAndStore2yStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr, output_row_size);
+
+    // Load inputs for bottom 2 rows.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 4 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      ptr += input_row_size;
+      temp_3 = vld1_u8(ptr);
+      temp_4 = vld1_u8(ptr + input_depth);
+      temp_5 = vld1_u8(ptr + 2 * input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+    }
+
+    DotProductAndStore2yStride1(
+        filter, input_6, input_7, input_8, input_9, input_10, input_11, input_0,
+        input_1, input_2, input_3, input_4, input_5, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 2 * output_row_size,
+        output_row_size);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<2, 2, 1, 1> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    Int32x8 acc_0, acc_1, acc_2, acc_3;
+
+    acc_0.low = vld1q_s32(bias_ptr);
+    acc_1.low = vld1q_s32(bias_ptr);
+    acc_2.low = vld1q_s32(bias_ptr);
+    acc_3.low = vld1q_s32(bias_ptr);
+
+    bias_ptr += 4;
+    acc_0.high = vld1q_s32(bias_ptr);
+    acc_1.high = vld1q_s32(bias_ptr);
+    acc_2.high = vld1q_s32(bias_ptr);
+    acc_3.high = vld1q_s32(bias_ptr);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+
+    // Add scope for input registers to help the compiler know that it is
+    // not needed.
+    {
+      // To process 2x2 outputs using a 3x3 filter, we require 4x4 inputs.
+      // Load inputs for the top two filters first.
+      int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+          input_7, input_8, input_9, input_10, input_11;
+
+      const uint8* ptr = input_ptr;
+
+      // Load top 3 rows.
+      {
+        uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+        temp_0 = vld1_u8(ptr);
+        temp_1 = vld1_u8(ptr + input_depth);
+        temp_2 = vld1_u8(ptr + 2 * input_depth);
+        temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+        input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+        input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+        input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+        input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+        input_0 = vaddq_s16(input_0, input_offset_vec);
+        input_1 = vaddq_s16(input_1, input_offset_vec);
+        input_2 = vaddq_s16(input_2, input_offset_vec);
+        input_3 = vaddq_s16(input_3, input_offset_vec);
+
+        ptr += input_row_size;
+        temp_0 = vld1_u8(ptr);
+        temp_1 = vld1_u8(ptr + input_depth);
+        temp_2 = vld1_u8(ptr + 2 * input_depth);
+        temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+        input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+        input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+        input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+        input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+        input_4 = vaddq_s16(input_4, input_offset_vec);
+        input_5 = vaddq_s16(input_5, input_offset_vec);
+        input_6 = vaddq_s16(input_6, input_offset_vec);
+        input_7 = vaddq_s16(input_7, input_offset_vec);
+
+        ptr += input_row_size;
+        temp_0 = vld1_u8(ptr);
+        temp_1 = vld1_u8(ptr + input_depth);
+        temp_2 = vld1_u8(ptr + 2 * input_depth);
+        temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+        input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+        input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+        input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+        input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+        input_8 = vaddq_s16(input_8, input_offset_vec);
+        input_9 = vaddq_s16(input_9, input_offset_vec);
+        input_10 = vaddq_s16(input_10, input_offset_vec);
+        input_11 = vaddq_s16(input_11, input_offset_vec);
+      }
+
+      // Multiply-accum for top-left output.
+      acc_0 = MultiplyAccumulate3x3Filter(filter, input_0, input_1, input_2,
+                                          input_4, input_5, input_6, input_8,
+                                          input_9, input_10, acc_0);
+
+      // Multiply-accum for top-right output.
+      acc_1 = MultiplyAccumulate3x3Filter(filter, input_1, input_2, input_3,
+                                          input_5, input_6, input_7, input_9,
+                                          input_10, input_11, acc_1);
+
+      // Now load the bottom row.
+      {
+        uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+        ptr += input_row_size;
+        temp_0 = vld1_u8(ptr);
+        temp_1 = vld1_u8(ptr + input_depth);
+        temp_2 = vld1_u8(ptr + 2 * input_depth);
+        temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+        input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+        input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+        input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+        input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+        input_0 = vaddq_s16(input_0, input_offset_vec);
+        input_1 = vaddq_s16(input_1, input_offset_vec);
+        input_2 = vaddq_s16(input_2, input_offset_vec);
+        input_3 = vaddq_s16(input_3, input_offset_vec);
+      }
+
+      // Multiply-accum for bottom-left output.
+      acc_2 = MultiplyAccumulate3x3Filter(filter, input_4, input_5, input_6,
+                                          input_8, input_9, input_10, input_0,
+                                          input_1, input_2, acc_2);
+
+      // Multiply-accum for bottom-right output.
+      acc_3 = MultiplyAccumulate3x3Filter(filter, input_5, input_6, input_7,
+                                          input_9, input_10, input_11, input_1,
+                                          input_2, input_3, acc_3);
+    }
+
+    DownquantizeAndStore2x2Output(acc_0, acc_1, acc_2, acc_3, output_offset,
+                                  output_multiplier, output_shift,
+                                  output_activation_min, output_activation_max,
+                                  output_ptr, output_depth, output_width);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<2, 4, 1, 1> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+    const int output_row_size = output_depth * output_width;
+
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11;
+
+    // Load inputs for 1x2 outputs starting from the top left.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+
+      ptr += input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+
+      ptr += input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr, output_depth);
+
+    // Now load 1x2 inputs on the top right.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + 4 * input_depth;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
+        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 2 * output_depth, output_depth);
+
+    // Now load next inputs when sliding window down.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr + 2 * input_depth + 3 * input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_6, input_7, input_4, input_5, input_10, input_11, input_8,
+        input_9, input_2, input_3, input_0, input_1, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 2 * output_depth + output_row_size,
+        output_depth);
+
+    // Now load next inputs when sliding window left.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_4, input_5, input_6, input_7, input_8, input_9, input_10,
+        input_11, input_0, input_1, input_2, input_3, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + output_row_size, output_depth);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<1, 4, 1, 1> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11;
+
+    // Load inputs for 1x2 outputs starting from the left.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3;
+
+      const uint8* ptr = input_ptr;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+
+      ptr += input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+
+      ptr += input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      temp_3 = vld1_u8(ptr + 3 * input_depth);
+
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr, output_depth);
+
+    // Now load 1x2 inputs on the right.
+    {
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr + input_depth * 4;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_2 = vld1_u8(ptr);
+      temp_3 = vld1_u8(ptr + input_depth);
+
+      ptr += input_row_size;
+      temp_4 = vld1_u8(ptr);
+      temp_5 = vld1_u8(ptr + input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+    }
+
+    DotProductAndStore2xStride1(
+        filter, input_2, input_3, input_0, input_1, input_6, input_7, input_4,
+        input_5, input_10, input_11, input_8, input_9, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr + 2 * output_depth, output_depth);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<2, 1, 1, 1> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    // To process 2x1 outputs using a 3x3 filter, we require 4x3 inputs.
+    // Load all inputs at the beginning.
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11;
+
+    // Load inputs for 1x2 outputs starting from the top left.
+    {
+      const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+      uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5;
+
+      const uint8* ptr = input_ptr;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      ptr += input_row_size;
+      temp_3 = vld1_u8(ptr);
+      temp_4 = vld1_u8(ptr + input_depth);
+      temp_5 = vld1_u8(ptr + 2 * input_depth);
+
+      input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_0 = vaddq_s16(input_0, input_offset_vec);
+      input_1 = vaddq_s16(input_1, input_offset_vec);
+      input_2 = vaddq_s16(input_2, input_offset_vec);
+      input_3 = vaddq_s16(input_3, input_offset_vec);
+      input_4 = vaddq_s16(input_4, input_offset_vec);
+      input_5 = vaddq_s16(input_5, input_offset_vec);
+
+      ptr += input_row_size;
+      temp_0 = vld1_u8(ptr);
+      temp_1 = vld1_u8(ptr + input_depth);
+      temp_2 = vld1_u8(ptr + 2 * input_depth);
+      ptr += input_row_size;
+      temp_3 = vld1_u8(ptr);
+      temp_4 = vld1_u8(ptr + input_depth);
+      temp_5 = vld1_u8(ptr + 2 * input_depth);
+
+      input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+      input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+      input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+      input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+      input_10 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+      input_11 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+      input_6 = vaddq_s16(input_6, input_offset_vec);
+      input_7 = vaddq_s16(input_7, input_offset_vec);
+      input_8 = vaddq_s16(input_8, input_offset_vec);
+      input_9 = vaddq_s16(input_9, input_offset_vec);
+      input_10 = vaddq_s16(input_10, input_offset_vec);
+      input_11 = vaddq_s16(input_11, input_offset_vec);
+    }
+
+    DotProductAndStore2yStride1(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9, input_10, input_11, bias_ptr, output_offset,
+        output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_ptr, output_depth * output_width);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<4, 2, 2, 2> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    const int output_row_size = output_depth * output_width;
+
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    Int32x8 acc_0, acc_1;
+    acc_0.low = vld1q_s32(bias_ptr);
+    acc_1.low = vld1q_s32(bias_ptr);
+    acc_0.high = vld1q_s32(bias_ptr + 4);
+    acc_1.high = vld1q_s32(bias_ptr + 4);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, input_9;
+
+    const uint8* ptr = input_ptr;
+    uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4;
+
+    // Load first 2 rows.
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    temp_3 = vld1_u8(ptr + 3 * input_depth);
+    temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+
+    ptr += input_row_size;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    temp_3 = vld1_u8(ptr + 3 * input_depth);
+    temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+    input_9 = vaddq_s16(input_9, input_offset_vec);
+
+    acc_0 = MultiplyAccumulateRow(acc_0, filter.f0, filter.f1, filter.f2,
+                                  input_0, input_1, input_2);
+
+    acc_1 = MultiplyAccumulateRow(acc_1, filter.f0, filter.f1, filter.f2,
+                                  input_2, input_3, input_4);
+
+    acc_0 = MultiplyAccumulateRow(acc_0, filter.f3, filter.f4, filter.f5,
+                                  input_5, input_6, input_7);
+
+    acc_1 = MultiplyAccumulateRow(acc_1, filter.f3, filter.f4, filter.f5,
+                                  input_7, input_8, input_9);
+
+    // Load next 2 rows.
+    ptr += input_row_size;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    temp_3 = vld1_u8(ptr + 3 * input_depth);
+    temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+
+    ptr += input_row_size;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    temp_3 = vld1_u8(ptr + 3 * input_depth);
+    temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+    input_9 = vaddq_s16(input_9, input_offset_vec);
+
+    acc_0 = MultiplyAccumulateRow(acc_0, filter.f6, filter.f7, filter.f8,
+                                  input_0, input_1, input_2);
+
+    acc_1 = MultiplyAccumulateRow(acc_1, filter.f6, filter.f7, filter.f8,
+                                  input_2, input_3, input_4);
+
+    DownquantizeAndStore2Output(
+        acc_0, acc_1, output_offset, output_multiplier, output_shift,
+        output_activation_min, output_activation_max, output_ptr, output_depth);
+
+    output_ptr += output_row_size;
+
+    // Moving onto the next row of outputs.
+    acc_0.low = vld1q_s32(bias_ptr);
+    acc_1.low = vld1q_s32(bias_ptr);
+    acc_0.high = vld1q_s32(bias_ptr + 4);
+    acc_1.high = vld1q_s32(bias_ptr + 4);
+
+    acc_0 = MultiplyAccumulateRow(acc_0, filter.f0, filter.f1, filter.f2,
+                                  input_0, input_1, input_2);
+
+    acc_1 = MultiplyAccumulateRow(acc_1, filter.f0, filter.f1, filter.f2,
+                                  input_2, input_3, input_4);
+
+    acc_0 = MultiplyAccumulateRow(acc_0, filter.f3, filter.f4, filter.f5,
+                                  input_5, input_6, input_7);
+
+    acc_1 = MultiplyAccumulateRow(acc_1, filter.f3, filter.f4, filter.f5,
+                                  input_7, input_8, input_9);
+
+    // Load next 2 rows.
+    ptr += input_row_size;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    temp_3 = vld1_u8(ptr + 3 * input_depth);
+    temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+
+    ptr += input_row_size;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    temp_3 = vld1_u8(ptr + 3 * input_depth);
+    temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+    input_9 = vaddq_s16(input_9, input_offset_vec);
+
+    acc_0 = MultiplyAccumulateRow(acc_0, filter.f6, filter.f7, filter.f8,
+                                  input_0, input_1, input_2);
+
+    acc_1 = MultiplyAccumulateRow(acc_1, filter.f6, filter.f7, filter.f8,
+                                  input_2, input_3, input_4);
+
+    DownquantizeAndStore2Output(
+        acc_0, acc_1, output_offset, output_multiplier, output_shift,
+        output_activation_min, output_activation_max, output_ptr, output_depth);
+
+    output_ptr += output_row_size;
+
+    // Moving onto the next row of outputs.
+    acc_0.low = vld1q_s32(bias_ptr);
+    acc_1.low = vld1q_s32(bias_ptr);
+    acc_0.high = vld1q_s32(bias_ptr + 4);
+    acc_1.high = vld1q_s32(bias_ptr + 4);
+
+    acc_0 = MultiplyAccumulateRow(acc_0, filter.f0, filter.f1, filter.f2,
+                                  input_0, input_1, input_2);
+
+    acc_1 = MultiplyAccumulateRow(acc_1, filter.f0, filter.f1, filter.f2,
+                                  input_2, input_3, input_4);
+
+    acc_0 = MultiplyAccumulateRow(acc_0, filter.f3, filter.f4, filter.f5,
+                                  input_5, input_6, input_7);
+
+    acc_1 = MultiplyAccumulateRow(acc_1, filter.f3, filter.f4, filter.f5,
+                                  input_7, input_8, input_9);
+
+    // Load next 2 rows.
+    ptr += input_row_size;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    temp_3 = vld1_u8(ptr + 3 * input_depth);
+    temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+
+    ptr += input_row_size;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    temp_3 = vld1_u8(ptr + 3 * input_depth);
+    temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+    input_9 = vaddq_s16(input_9, input_offset_vec);
+
+    acc_0 = MultiplyAccumulateRow(acc_0, filter.f6, filter.f7, filter.f8,
+                                  input_0, input_1, input_2);
+
+    acc_1 = MultiplyAccumulateRow(acc_1, filter.f6, filter.f7, filter.f8,
+                                  input_2, input_3, input_4);
+
+    DownquantizeAndStore2Output(
+        acc_0, acc_1, output_offset, output_multiplier, output_shift,
+        output_activation_min, output_activation_max, output_ptr, output_depth);
+
+    output_ptr += output_row_size;
+
+    // Moving onto the next row of outputs.
+    acc_0.low = vld1q_s32(bias_ptr);
+    acc_1.low = vld1q_s32(bias_ptr);
+    acc_0.high = vld1q_s32(bias_ptr + 4);
+    acc_1.high = vld1q_s32(bias_ptr + 4);
+
+    acc_0 = MultiplyAccumulateRow(acc_0, filter.f0, filter.f1, filter.f2,
+                                  input_0, input_1, input_2);
+
+    acc_1 = MultiplyAccumulateRow(acc_1, filter.f0, filter.f1, filter.f2,
+                                  input_2, input_3, input_4);
+
+    acc_0 = MultiplyAccumulateRow(acc_0, filter.f3, filter.f4, filter.f5,
+                                  input_5, input_6, input_7);
+
+    acc_1 = MultiplyAccumulateRow(acc_1, filter.f3, filter.f4, filter.f5,
+                                  input_7, input_8, input_9);
+
+    // Load last row.
+    ptr += input_row_size;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    temp_3 = vld1_u8(ptr + 3 * input_depth);
+    temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+
+    acc_0 = MultiplyAccumulateRow(acc_0, filter.f6, filter.f7, filter.f8,
+                                  input_0, input_1, input_2);
+
+    acc_1 = MultiplyAccumulateRow(acc_1, filter.f6, filter.f7, filter.f8,
+                                  input_2, input_3, input_4);
+
+    DownquantizeAndStore2Output(
+        acc_0, acc_1, output_offset, output_multiplier, output_shift,
+        output_activation_min, output_activation_max, output_ptr, output_depth);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<4, 4, 2, 2> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    // Reuse 4x2 kernel twice.
+    ConvKernel3x3FilterDepth8<4, 2, 2, 2>::Run(
+        input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
+        filter_offset, bias_ptr, output_offset, output_multiplier, output_shift,
+        output_activation_min, output_activation_max, output_ptr, output_depth,
+        output_width);
+
+    ConvKernel3x3FilterDepth8<4, 2, 2, 2>::Run(
+        input_ptr + 4 * input_depth, input_depth, input_offset, input_row_size,
+        filter_ptr, filter_offset, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max,
+        output_ptr + 2 * output_depth, output_depth, output_width);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<4, 1, 2, 2> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    const int output_row_size = output_depth * output_width;
+
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8;
+    uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5, temp_6, temp_7,
+        temp_8;
+
+    const uint8* ptr = input_ptr;
+
+    // Load all inputs for top output.
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    ptr += input_row_size;
+    temp_3 = vld1_u8(ptr);
+    temp_4 = vld1_u8(ptr + input_depth);
+    temp_5 = vld1_u8(ptr + 2 * input_depth);
+    ptr += input_row_size;
+    temp_6 = vld1_u8(ptr);
+    temp_7 = vld1_u8(ptr + input_depth);
+    temp_8 = vld1_u8(ptr + 2 * input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+
+    // Second output.
+    output_ptr += output_row_size;
+
+    ptr += input_row_size;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    ptr += input_row_size;
+    temp_3 = vld1_u8(ptr);
+    temp_4 = vld1_u8(ptr + input_depth);
+    temp_5 = vld1_u8(ptr + 2 * input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_6, input_7, input_8, input_0, input_1, input_2, input_3,
+        input_4, input_5, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+
+    // Third output.
+    output_ptr += output_row_size;
+
+    ptr += input_row_size;
+    temp_6 = vld1_u8(ptr);
+    temp_7 = vld1_u8(ptr + input_depth);
+    temp_8 = vld1_u8(ptr + 2 * input_depth);
+    ptr += input_row_size;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_3, input_4, input_5, input_6, input_7, input_8, input_0,
+        input_1, input_2, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+
+    // Fourth output.
+    output_ptr += output_row_size;
+
+    ptr += input_row_size;
+    temp_3 = vld1_u8(ptr);
+    temp_4 = vld1_u8(ptr + input_depth);
+    temp_5 = vld1_u8(ptr + 2 * input_depth);
+    ptr += input_row_size;
+    temp_6 = vld1_u8(ptr);
+    temp_7 = vld1_u8(ptr + input_depth);
+    temp_8 = vld1_u8(ptr + 2 * input_depth);
+
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
+
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<2, 2, 2, 2> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    Int32x8 acc_0, acc_1, acc_2, acc_3;
+    acc_0.low = vld1q_s32(bias_ptr);
+    acc_1.low = vld1q_s32(bias_ptr);
+    acc_2.low = vld1q_s32(bias_ptr);
+    acc_3.low = vld1q_s32(bias_ptr);
+
+    bias_ptr += 4;
+    acc_0.high = vld1q_s32(bias_ptr);
+    acc_1.high = vld1q_s32(bias_ptr);
+    acc_2.high = vld1q_s32(bias_ptr);
+    acc_3.high = vld1q_s32(bias_ptr);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+
+    // Add scope for input registers to help the compiler know that it is
+    // not needed.
+    {
+      // To process 2x2 outputs using a 3x3 filter at stride 2, we require
+      // 5x5 inputs. We load the first 5x2 inputs at a time.
+      int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+          input_7, input_8, input_9;
+
+      const uint8* ptr = input_ptr;
+
+      // Load inputs.
+      {
+        uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4;
+
+        temp_0 = vld1_u8(ptr);
+        temp_1 = vld1_u8(ptr + input_depth);
+        temp_2 = vld1_u8(ptr + 2 * input_depth);
+        temp_3 = vld1_u8(ptr + 3 * input_depth);
+        temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+        input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+        input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+        input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+        input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+        input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+        input_0 = vaddq_s16(input_0, input_offset_vec);
+        input_1 = vaddq_s16(input_1, input_offset_vec);
+        input_2 = vaddq_s16(input_2, input_offset_vec);
+        input_3 = vaddq_s16(input_3, input_offset_vec);
+        input_4 = vaddq_s16(input_4, input_offset_vec);
+
+        ptr += input_row_size;
+        temp_0 = vld1_u8(ptr);
+        temp_1 = vld1_u8(ptr + input_depth);
+        temp_2 = vld1_u8(ptr + 2 * input_depth);
+        temp_3 = vld1_u8(ptr + 3 * input_depth);
+        temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+        input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+        input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+        input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+        input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+        input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+        input_5 = vaddq_s16(input_5, input_offset_vec);
+        input_6 = vaddq_s16(input_6, input_offset_vec);
+        input_7 = vaddq_s16(input_7, input_offset_vec);
+        input_8 = vaddq_s16(input_8, input_offset_vec);
+        input_9 = vaddq_s16(input_9, input_offset_vec);
+      }
+
+      acc_0 = MultiplyAccumulateRow(acc_0, filter.f0, filter.f1, filter.f2,
+                                    input_0, input_1, input_2);
+
+      acc_1 = MultiplyAccumulateRow(acc_1, filter.f0, filter.f1, filter.f2,
+                                    input_2, input_3, input_4);
+
+      acc_0 = MultiplyAccumulateRow(acc_0, filter.f3, filter.f4, filter.f5,
+                                    input_5, input_6, input_7);
+
+      acc_1 = MultiplyAccumulateRow(acc_1, filter.f3, filter.f4, filter.f5,
+                                    input_7, input_8, input_9);
+
+      // Load next inputs.
+      {
+        uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4;
+
+        ptr += input_row_size;
+        temp_0 = vld1_u8(ptr);
+        temp_1 = vld1_u8(ptr + input_depth);
+        temp_2 = vld1_u8(ptr + 2 * input_depth);
+        temp_3 = vld1_u8(ptr + 3 * input_depth);
+        temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+        input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+        input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+        input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+        input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+        input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+        input_0 = vaddq_s16(input_0, input_offset_vec);
+        input_1 = vaddq_s16(input_1, input_offset_vec);
+        input_2 = vaddq_s16(input_2, input_offset_vec);
+        input_3 = vaddq_s16(input_3, input_offset_vec);
+        input_4 = vaddq_s16(input_4, input_offset_vec);
+
+        ptr += input_row_size;
+        temp_0 = vld1_u8(ptr);
+        temp_1 = vld1_u8(ptr + input_depth);
+        temp_2 = vld1_u8(ptr + 2 * input_depth);
+        temp_3 = vld1_u8(ptr + 3 * input_depth);
+        temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+        input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+        input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+        input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+        input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+        input_9 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+        input_5 = vaddq_s16(input_5, input_offset_vec);
+        input_6 = vaddq_s16(input_6, input_offset_vec);
+        input_7 = vaddq_s16(input_7, input_offset_vec);
+        input_8 = vaddq_s16(input_8, input_offset_vec);
+        input_9 = vaddq_s16(input_9, input_offset_vec);
+      }
+
+      acc_0 = MultiplyAccumulateRow(acc_0, filter.f6, filter.f7, filter.f8,
+                                    input_0, input_1, input_2);
+
+      acc_1 = MultiplyAccumulateRow(acc_1, filter.f6, filter.f7, filter.f8,
+                                    input_2, input_3, input_4);
+
+      // Moving onto the two bottom outputs.
+      acc_2 = MultiplyAccumulateRow(acc_2, filter.f0, filter.f1, filter.f2,
+                                    input_0, input_1, input_2);
+
+      acc_3 = MultiplyAccumulateRow(acc_3, filter.f0, filter.f1, filter.f2,
+                                    input_2, input_3, input_4);
+
+      acc_2 = MultiplyAccumulateRow(acc_2, filter.f3, filter.f4, filter.f5,
+                                    input_5, input_6, input_7);
+
+      acc_3 = MultiplyAccumulateRow(acc_3, filter.f3, filter.f4, filter.f5,
+                                    input_7, input_8, input_9);
+
+      // Load last input row.
+      {
+        uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4;
+
+        ptr += input_row_size;
+        temp_0 = vld1_u8(ptr);
+        temp_1 = vld1_u8(ptr + input_depth);
+        temp_2 = vld1_u8(ptr + 2 * input_depth);
+        temp_3 = vld1_u8(ptr + 3 * input_depth);
+        temp_4 = vld1_u8(ptr + 4 * input_depth);
+
+        input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+        input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+        input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+        input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+        input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+
+        input_0 = vaddq_s16(input_0, input_offset_vec);
+        input_1 = vaddq_s16(input_1, input_offset_vec);
+        input_2 = vaddq_s16(input_2, input_offset_vec);
+        input_3 = vaddq_s16(input_3, input_offset_vec);
+        input_4 = vaddq_s16(input_4, input_offset_vec);
+      }
+
+      acc_2 = MultiplyAccumulateRow(acc_2, filter.f6, filter.f7, filter.f8,
+                                    input_0, input_1, input_2);
+
+      acc_3 = MultiplyAccumulateRow(acc_3, filter.f6, filter.f7, filter.f8,
+                                    input_2, input_3, input_4);
+    }
+
+    DownquantizeAndStore2x2Output(acc_0, acc_1, acc_2, acc_3, output_offset,
+                                  output_multiplier, output_shift,
+                                  output_activation_min, output_activation_max,
+                                  output_ptr, output_depth, output_width);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<2, 4, 2, 2> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    // Reuse 2x2 kernel twice.
+    ConvKernel3x3FilterDepth8<2, 2, 2, 2>::Run(
+        input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
+        filter_offset, bias_ptr, output_offset, output_multiplier, output_shift,
+        output_activation_min, output_activation_max, output_ptr, output_depth,
+        output_width);
+
+    ConvKernel3x3FilterDepth8<2, 2, 2, 2>::Run(
+        input_ptr + 4 * input_depth, input_depth, input_offset, input_row_size,
+        filter_ptr, filter_offset, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max,
+        output_ptr + 2 * output_depth, output_depth, output_width);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<2, 1, 2, 2> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    const int output_row_size = output_depth * output_width;
+
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8;
+    uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5, temp_6, temp_7,
+        temp_8;
+
+    const uint8* ptr = input_ptr;
+
+    // Load all inputs for top output.
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    ptr += input_row_size;
+    temp_3 = vld1_u8(ptr);
+    temp_4 = vld1_u8(ptr + input_depth);
+    temp_5 = vld1_u8(ptr + 2 * input_depth);
+    ptr += input_row_size;
+    temp_6 = vld1_u8(ptr);
+    temp_7 = vld1_u8(ptr + input_depth);
+    temp_8 = vld1_u8(ptr + 2 * input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+
+    // Second output.
+    output_ptr += output_row_size;
+
+    ptr += input_row_size;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    ptr += input_row_size;
+    temp_3 = vld1_u8(ptr);
+    temp_4 = vld1_u8(ptr + input_depth);
+    temp_5 = vld1_u8(ptr + 2 * input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_6, input_7, input_8, input_0, input_1, input_2, input_3,
+        input_4, input_5, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<1, 2, 2, 2> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8;
+    uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5, temp_6, temp_7,
+        temp_8;
+
+    const uint8* ptr = input_ptr;
+
+    // Load all inputs for top output.
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    ptr += input_row_size;
+    temp_3 = vld1_u8(ptr);
+    temp_4 = vld1_u8(ptr + input_depth);
+    temp_5 = vld1_u8(ptr + 2 * input_depth);
+    ptr += input_row_size;
+    temp_6 = vld1_u8(ptr);
+    temp_7 = vld1_u8(ptr + input_depth);
+    temp_8 = vld1_u8(ptr + 2 * input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+
+    // Second output.
+    output_ptr += output_depth;
+
+    ptr = input_ptr + 3 * input_depth;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    ptr += input_row_size;
+    temp_3 = vld1_u8(ptr);
+    temp_4 = vld1_u8(ptr + input_depth);
+    ptr += input_row_size;
+    temp_6 = vld1_u8(ptr);
+    temp_7 = vld1_u8(ptr + input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_2, input_0, input_1, input_5, input_3, input_4, input_8,
+        input_6, input_7, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+  }
+};
+
+template <>
+struct ConvKernel3x3FilterDepth8<1, 4, 2, 2> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8;
+    uint8x8_t temp_0, temp_1, temp_2, temp_3, temp_4, temp_5, temp_6, temp_7,
+        temp_8;
+
+    const uint8* ptr = input_ptr;
+
+    // Load all inputs for top output.
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    temp_2 = vld1_u8(ptr + 2 * input_depth);
+    ptr += input_row_size;
+    temp_3 = vld1_u8(ptr);
+    temp_4 = vld1_u8(ptr + input_depth);
+    temp_5 = vld1_u8(ptr + 2 * input_depth);
+    ptr += input_row_size;
+    temp_6 = vld1_u8(ptr);
+    temp_7 = vld1_u8(ptr + input_depth);
+    temp_8 = vld1_u8(ptr + 2 * input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+
+    // Second output.
+    output_ptr += output_depth;
+
+    ptr = input_ptr + 3 * input_depth;
+    temp_0 = vld1_u8(ptr);
+    temp_1 = vld1_u8(ptr + input_depth);
+    ptr += input_row_size;
+    temp_3 = vld1_u8(ptr);
+    temp_4 = vld1_u8(ptr + input_depth);
+    ptr += input_row_size;
+    temp_6 = vld1_u8(ptr);
+    temp_7 = vld1_u8(ptr + input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
+
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_2, input_0, input_1, input_5, input_3, input_4, input_8,
+        input_6, input_7, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+
+    // Third output.
+    output_ptr += output_depth;
+
+    ptr = input_ptr + 5 * input_depth;
+    temp_2 = vld1_u8(ptr);
+    temp_0 = vld1_u8(ptr + input_depth);
+    ptr += input_row_size;
+    temp_5 = vld1_u8(ptr);
+    temp_3 = vld1_u8(ptr + input_depth);
+    ptr += input_row_size;
+    temp_8 = vld1_u8(ptr);
+    temp_6 = vld1_u8(ptr + input_depth);
+
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
+
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_1, input_2, input_0, input_4, input_5, input_3, input_7,
+        input_8, input_6, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+
+    // Fourth output.
+    output_ptr += output_depth;
+
+    ptr = input_ptr + 7 * input_depth;
+    temp_1 = vld1_u8(ptr);
+    temp_2 = vld1_u8(ptr + input_depth);
+    ptr += input_row_size;
+    temp_4 = vld1_u8(ptr);
+    temp_5 = vld1_u8(ptr + input_depth);
+    ptr += input_row_size;
+    temp_7 = vld1_u8(ptr);
+    temp_8 = vld1_u8(ptr + input_depth);
+
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
+
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+  }
+};
+
+template 
+struct ConvKernel3x3FilterDepth8<1, 1, kFixedStrideWidth, kFixedStrideHeight> {
+  static inline void Run(const uint8* input_ptr, int input_depth,
+                         int32 input_offset, int input_row_size,
+                         const uint8* filter_ptr, int32 filter_offset,
+                         const int32* bias_ptr, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_ptr,
+                         int output_depth, int output_width) {
+    Filter3x3x8 filter = Load3x3Filter(filter_ptr, filter_offset, output_depth);
+
+    int16x8_t input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8;
+
+    uint8x8_t temp_0 = vld1_u8(input_ptr);
+    uint8x8_t temp_1 = vld1_u8(input_ptr + input_depth);
+    uint8x8_t temp_2 = vld1_u8(input_ptr + 2 * input_depth);
+
+    input_ptr += input_row_size;
+    uint8x8_t temp_3 = vld1_u8(input_ptr);
+    uint8x8_t temp_4 = vld1_u8(input_ptr + input_depth);
+    uint8x8_t temp_5 = vld1_u8(input_ptr + 2 * input_depth);
+
+    input_ptr += input_row_size;
+    uint8x8_t temp_6 = vld1_u8(input_ptr);
+    uint8x8_t temp_7 = vld1_u8(input_ptr + input_depth);
+    uint8x8_t temp_8 = vld1_u8(input_ptr + 2 * input_depth);
+
+    input_0 = vreinterpretq_s16_u16(vmovl_u8(temp_0));
+    input_1 = vreinterpretq_s16_u16(vmovl_u8(temp_1));
+    input_2 = vreinterpretq_s16_u16(vmovl_u8(temp_2));
+    input_3 = vreinterpretq_s16_u16(vmovl_u8(temp_3));
+    input_4 = vreinterpretq_s16_u16(vmovl_u8(temp_4));
+    input_5 = vreinterpretq_s16_u16(vmovl_u8(temp_5));
+    input_6 = vreinterpretq_s16_u16(vmovl_u8(temp_6));
+    input_7 = vreinterpretq_s16_u16(vmovl_u8(temp_7));
+    input_8 = vreinterpretq_s16_u16(vmovl_u8(temp_8));
+
+    const int16x8_t input_offset_vec = vdupq_n_s16(input_offset);
+    input_0 = vaddq_s16(input_0, input_offset_vec);
+    input_1 = vaddq_s16(input_1, input_offset_vec);
+    input_2 = vaddq_s16(input_2, input_offset_vec);
+    input_3 = vaddq_s16(input_3, input_offset_vec);
+    input_4 = vaddq_s16(input_4, input_offset_vec);
+    input_5 = vaddq_s16(input_5, input_offset_vec);
+    input_6 = vaddq_s16(input_6, input_offset_vec);
+    input_7 = vaddq_s16(input_7, input_offset_vec);
+    input_8 = vaddq_s16(input_8, input_offset_vec);
+
+    DotProductAndStore(
+        filter, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
+        input_7, input_8, bias_ptr, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max, output_ptr);
+  }
+};
+
+inline void ShuffleInput(const uint8* input_ptr, int input_depth,
+                         int input_width, int input_height, int output_depth,
+                         int output_width, int output_height,
+                         uint8* output_ptr) {
+  const int input_row_size = input_depth * input_width;
+
+  for (int y = 0; y < output_height; y++) {
+    const uint8* ptr = input_ptr;
+    for (int x = 0; x < output_width; x++) {
+      memcpy(output_ptr, ptr, output_depth);
+      output_ptr += output_depth;
+      ptr += input_depth;
+    }
+    input_ptr += input_row_size;
+  }
+}
+
+template 
+struct ConvRow3x3FilterDepth8 {};
+
+template 
+struct ConvRow3x3FilterDepth8<1, kFixedStrideWidth, kFixedStrideHeight> {
+  static inline void Run(const uint8* input_data, int start_x, int start_y,
+                         int input_depth, int input_width, int input_height,
+                         int input_row_size, int32 input_offset,
+                         const uint8* filter_data, int32 filter_offset,
+                         const int32* bias_data, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_data,
+                         int output_depth, int output_width,
+                         uint8* shuffle_workspace) {
+    int out_x = start_x;
+
+    // 1x4 at a time.
+    for (; out_x <= output_width - 4; out_x += 4) {
+      const int32* bias_ptr = bias_data;
+      const uint8* filter_ptr = filter_data;
+
+      const uint8* input_ptr = input_data;
+      uint8* output_ptr = output_data;
+
+      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
+        ConvKernel3x3FilterDepth8<1, 4, kFixedStrideWidth, kFixedStrideHeight>::
+            Run(input_ptr, input_depth, input_offset, input_row_size,
+                filter_ptr, filter_offset, bias_ptr, output_offset,
+                output_multiplier, output_shift, output_activation_min,
+                output_activation_max, output_ptr, output_depth, output_width);
+
+        input_ptr += 8;
+        output_ptr += 8;
+        filter_ptr += 8;
+        bias_ptr += 8;
+      }
+
+      input_data += 4 * kFixedStrideWidth * input_depth;
+      output_data += 4 * output_depth;
+    }
+
+    // 1x1 at a time.
+    for (; out_x < output_width; out_x++) {
+      const int32* bias_ptr = bias_data;
+      const uint8* filter_ptr = filter_data;
+
+      const uint8* input_ptr = input_data;
+      uint8* output_ptr = output_data;
+
+      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
+        ConvKernel3x3FilterDepth8<1, 1, kFixedStrideWidth, kFixedStrideHeight>::
+            Run(input_ptr, input_depth, input_offset, input_row_size,
+                filter_ptr, filter_offset, bias_ptr, output_offset,
+                output_multiplier, output_shift, output_activation_min,
+                output_activation_max, output_ptr, output_depth, output_width);
 
-template 
-struct DepthwiseConvWindow {};
+        input_ptr += 8;
+        output_ptr += 8;
+        filter_ptr += 8;
+        bias_ptr += 8;
+      }
 
-// clang-format gets confused with this file and ends up formatting lines to
-// be larger than 80 characters. Turn off here and back on at the end of the
-// file.
+      input_data += kFixedStrideWidth * input_depth;
+      output_data += output_depth;
+    }
+  }
+};
 
-// clang-format off
-template <>
-struct DepthwiseConvWindow<8, 1, 1> {
- public:
-  static inline void Run(const uint8* input_ptr, int64_t input_depth,
-                         int32 input_offset, int64_t input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
+template 
+struct ConvRow3x3FilterDepth8<2, kFixedStrideWidth, kFixedStrideHeight> {
+  static inline void Run(const uint8* input_data, int start_x, int start_y,
+                         int input_depth, int input_width, int input_height,
+                         int input_row_size, int32 input_offset,
+                         const uint8* filter_data, int32 filter_offset,
+                         const int32* bias_data, int32 output_offset,
                          int32 output_multiplier, int output_shift,
                          int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int64_t output_depth, int output_width,
-                         int output_window_height,
-                         int output_window_width) {
-    const int64_t output_row_size = output_depth * output_width;
-    const int64_t input_width_increment = 2 * input_depth;
-    const int64_t input_height_increment = 2 * input_row_size;
-    const int64_t output_height_increment = 2 * output_row_size;
-
-#define DEPTHWISECONV_LABEL_HEIGHT_2_LOOP "1"
-#define DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP "2"
-#define DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1 "3"
-#define DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP "4"
-#define DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP "5"
-#define DEPTHWISECONV_LABEL_HEIGHT_1 "6"
-#define DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP "7"
-#define DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1 "8"
-#define DEPTHWISECONV_LABEL_HEIGHT_1_END "9"
-
-    asm volatile(
-        // Performs depthwise convolutions for a window specified by
-        // |output_window_height| and |output_window_width|. The inner-most loop
-        // processes 2x2 outputs, and any leftovers at the end.
-        //
-        // Algorithm works as follows:
-        //
-        //   1. Load filters of 8 depth (8x3x3). Registers v0--v8 hold filter
-        //      values.
-        //   2. For 2 output heights at a time:
-        //        i.  For 2 output widths at a time, load inputs for a 2x1 (2
-        //            height, 1 width) output window (4x3 input window).
-        //            Registers v9--v20 hold input values. Mul-add with
-        //            accumulators v21--v24. Then run activation, downquantize
-        //            and store. Repeat for the next 2x1 output window,
-        //            leveraging overlapping inputs.
-        //        ii. Handle single leftover width if exists.
-        //   3. Handle single leftover height if exists.
-        //        i.  For 2 output widths at a time, load inputs for a 1x2 (1
-        //            height, 2 width) output window (3x4 input window).
-        //            Registers v9--v20 hold input values. Mul-add with
-        //            accumulators v21--v24. Then run activation, downquantize
-        //            and store. Repeat for the next 1x2 output window,
-        //            leveraging overlapping inputs.
-        //        ii. Handle single leftover width if exists.
-        //
-        // Loads are placed as soon as the register is no longer needed and
-        // interleaved with arithmetic operations to take advantage of
-        // dual-issue pipelines. We also add input offsets as far from the loads
-        // as possible to give loads enough cycles to fetch data from memory.
-
-        // Set "constant" registers. These registers may be replaced with temp
-        // values from time to time when there are not enough NEON registers.
-        "dup v26.8h, %w[input_offset]\n"
-        "cmp %w[output_window_height], #2\n"
-        "dup v27.4s, %w[output_multiplier]\n"
-
-        "neg w5, %w[output_shift]\n"
-        "dup v28.4s, w5\n"
-
-        "dup v29.4s, %w[output_offset]\n"
-        "dup v30.4s, %w[output_activation_min]\n"
-        "dup v31.4s, %w[output_activation_max]\n"
-
-        "add x5, %[bias_ptr], #16\n"
-        "dup v9.8h, %w[filter_offset]\n"
-
-        // Load filters and add offsets.
-        "ld1 {v0.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "ld1 {v1.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v0.8h, v9.8h, v0.8b\n"
-        "ld1 {v2.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v1.8h, v9.8h, v1.8b\n"
-        "ld1 {v3.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v2.8h, v9.8h, v2.8b\n"
-        "ld1 {v4.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v3.8h, v9.8h, v3.8b\n"
-        "ld1 {v5.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v4.8h, v9.8h, v4.8b\n"
-        "ld1 {v6.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v5.8h, v9.8h, v5.8b\n"
-        "ld1 {v7.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v6.8h, v9.8h, v6.8b\n"
-        "ld1 {v8.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v7.8h, v9.8h, v7.8b\n"
-        "uaddw v8.8h, v9.8h, v8.8b\n"
-
-        "blt " DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP "f\n"
-
-        //"loop_%=:\n"
-        DEPTHWISECONV_LABEL_HEIGHT_2_LOOP ":\n"
-          // This loop processes 2x2 outputs. To avoid register exhaustion,
-          // inputs for the left 2 outputs are loaded first, then the right
-          // two outputs.
-          "mov x6, %[input_ptr]\n"
-          "mov x4, x6\n"
-          "ld1 {v9.8b}, [x4], %[input_depth]\n"
-          "add x0, x6, %[input_row_size]\n"
-          "ld1 {v10.8b}, [x4], %[input_depth]\n"
-          "add x1, x0, %[input_row_size]\n"
-          "ld1 {v11.8b}, [x4], %[input_depth]\n"
-          "add x7, x1, %[input_row_size]\n"
-          "ld1 {v12.8b}, [x0], %[input_depth]\n"
-          "mov w8, %w[output_window_width]\n"
-          "ld1 {v13.8b}, [x0], %[input_depth]\n"
-          "mov x2, %[output_ptr]\n"
-          "ld1 {v14.8b}, [x0], %[input_depth]\n"
-          "add x3, %[output_ptr], %[output_row_size]\n"
-          "ld1 {v15.8b}, [x1], %[input_depth]\n"
-          "cmp w8, #2\n"
-          "ld1 {v16.8b}, [x1], %[input_depth]\n"
-          "ld1 {v17.8b}, [x1], %[input_depth]\n"
-          "ld1 {v18.8b}, [x7], %[input_depth]\n"
-          "ld1 {v19.8b}, [x7], %[input_depth]\n"
-          "ld1 {v20.8b}, [x7], %[input_depth]\n"
-          "ld1 {v21.4s}, [%[bias_ptr]]\n"
-          "ld1 {v22.4s}, [x5]\n"
-          "ld1 {v23.4s}, [%[bias_ptr]]\n"
-          "ld1 {v24.4s}, [x5]\n"
-
-          "uaddw v9.8h, v26.8h, v9.8b\n"
-          "uaddw v10.8h, v26.8h, v10.8b\n"
-          "uaddw v11.8h, v26.8h, v11.8b\n"
-          "uaddw v12.8h, v26.8h, v12.8b\n"
-          "uaddw v13.8h, v26.8h, v13.8b\n"
-          "uaddw v14.8h, v26.8h, v14.8b\n"
-          "uaddw v15.8h, v26.8h, v15.8b\n"
-          "uaddw v16.8h, v26.8h, v16.8b\n"
-          "uaddw v17.8h, v26.8h, v17.8b\n"
-          "uaddw v18.8h, v26.8h, v18.8b\n"
-          "uaddw v19.8h, v26.8h, v19.8b\n"
-          "uaddw v20.8h, v26.8h, v20.8b\n"
-
-          "blt " DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1 "f\n"
-
-          //"loop_%=:\n"
-          DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP ":\n"
-            // Mul-add left outputs.
-            "smlal v21.4s, v0.4h, v9.4h\n"
-            "subs w8, w8, #2\n"
-            "smlal2 v22.4s, v0.8h, v9.8h\n"
-            "cmp w8, #2\n"
-            "smlal v23.4s, v0.4h, v12.4h\n"
-            "ld1 {v9.8b}, [x4]\n"
-            "smlal2 v24.4s, v0.8h, v12.8h\n"
-            "smlal v21.4s, v1.4h, v10.4h\n"
-            "smlal2 v22.4s, v1.8h, v10.8h\n"
-            "smlal v23.4s, v1.4h, v13.4h\n"
-            "smlal2 v24.4s, v1.8h, v13.8h\n"
-            "smlal v21.4s, v2.4h, v11.4h\n"
-            "smlal2 v22.4s, v2.8h, v11.8h\n"
-            "smlal v23.4s, v2.4h, v14.4h\n"
-            "smlal2 v24.4s, v2.8h, v14.8h\n"
-            "smlal v21.4s, v3.4h, v12.4h\n"
-            "smlal2 v22.4s, v3.8h, v12.8h\n"
-            "ld1 {v12.8b}, [x0]\n"
-            "smlal v23.4s, v3.4h, v15.4h\n"
-            "smlal2 v24.4s, v3.8h, v15.8h\n"
-            "smlal v21.4s, v4.4h, v13.4h\n"
-            "smlal2 v22.4s, v4.8h, v13.8h\n"
-            "smlal v23.4s, v4.4h, v16.4h\n"
-            "smlal2 v24.4s, v4.8h, v16.8h\n"
-            "smlal v21.4s, v5.4h, v14.4h\n"
-            "smlal2 v22.4s, v5.8h, v14.8h\n"
-            "smlal v23.4s, v5.4h, v17.4h\n"
-            "smlal2 v24.4s, v5.8h, v17.8h\n"
-            "smlal v21.4s, v6.4h, v15.4h\n"
-            "smlal2 v22.4s, v6.8h, v15.8h\n"
-            "ld1 {v15.8b}, [x1]\n"
-            "smlal v23.4s, v6.4h, v18.4h\n"
-            "smlal2 v24.4s, v6.8h, v18.8h\n"
-            "ld1 {v18.8b}, [x7]\n"
-            "smlal v21.4s, v7.4h, v16.4h\n"
-            "smlal2 v22.4s, v7.8h, v16.8h\n"
-            "smlal v23.4s, v7.4h, v19.4h\n"
-            "smlal2 v24.4s, v7.8h, v19.8h\n"
-            "smlal v21.4s, v8.4h, v17.4h\n"
-            "smlal2 v22.4s, v8.8h, v17.8h\n"
-            "smlal v23.4s, v8.4h, v20.4h\n"
-            "smlal2 v24.4s, v8.8h, v20.8h\n"
-
-            "sqrdmulh v21.4s, v21.4s, v27.4s\n"
-            "sqrdmulh v22.4s, v22.4s, v27.4s\n"
-            "sqrdmulh v23.4s, v23.4s, v27.4s\n"
-            "sqrdmulh v24.4s, v24.4s, v27.4s\n"
-            "and v25.16b, v21.16b, v28.16b\n"
-            "and v29.16b, v22.16b, v28.16b\n"
-            "and v30.16b, v23.16b, v28.16b\n"
-            "and v31.16b, v24.16b, v28.16b\n"
-            "sshr v25.4s, v25.4s, #31\n"
-            "sshr v29.4s, v29.4s, #31\n"
-            "sshr v30.4s, v30.4s, #31\n"
-            "sshr v31.4s, v31.4s, #31\n"
-            "sqadd v21.4s, v21.4s, v25.4s\n"
-            "sqadd v22.4s, v22.4s, v29.4s\n"
-            "dup v29.4s, %w[output_offset]\n"
-            "sqadd v23.4s, v23.4s, v30.4s\n"
-            "dup v30.4s, %w[output_activation_min]\n"
-            "sqadd v24.4s, v24.4s, v31.4s\n"
-            "dup v31.4s, %w[output_activation_max]\n"
-            "srshl v21.4s, v21.4s, v28.4s\n"
-            "srshl v22.4s, v22.4s, v28.4s\n"
-            "srshl v23.4s, v23.4s, v28.4s\n"
-            "srshl v24.4s, v24.4s, v28.4s\n"
-            "add v21.4s, v21.4s, v29.4s\n"
-            "add v22.4s, v22.4s, v29.4s\n"
-            "add v23.4s, v23.4s, v29.4s\n"
-            "add v24.4s, v24.4s, v29.4s\n"
-            "smax v21.4s, v21.4s, v30.4s\n"
-            "smax v22.4s, v22.4s, v30.4s\n"
-            "smax v23.4s, v23.4s, v30.4s\n"
-            "smax v24.4s, v24.4s, v30.4s\n"
-            "smin v21.4s, v21.4s, v31.4s\n"
-            "smin v22.4s, v22.4s, v31.4s\n"
-            "smin v23.4s, v23.4s, v31.4s\n"
-            "smin v24.4s, v24.4s, v31.4s\n"
-            "sqxtn v21.4h, v21.4s\n"
-            "sqxtn v23.4h, v23.4s\n"
-            "sqxtn2 v21.8h, v22.4s\n"
-            "ld1 {v22.4s}, [x5]\n"
-            "sqxtn2 v23.8h, v24.4s\n"
-            "ld1 {v24.4s}, [x5]\n"
-            "sqxtun v21.8b, v21.8h\n"
-            "sqxtun v23.8b, v23.8h\n"
-            "uaddw v9.8h, v26.8h, v9.8b\n"
-            "st1 {v21.8b}, [x2], %[output_depth]\n"
-            "uaddw v12.8h, v26.8h, v12.8b\n"
-            "st1 {v23.8b}, [x3], %[output_depth]\n"
-            "uaddw v15.8h, v26.8h, v15.8b\n"
-            "ld1 {v21.4s}, [%[bias_ptr]]\n"
-            "uaddw v18.8h, v26.8h, v18.8b\n"
-            "ld1 {v23.4s}, [%[bias_ptr]]\n"
-
-            // Mul-add right outputs.
-            "smlal v21.4s, v0.4h, v10.4h\n"
-            "add x6, x6, %[input_width_increment]\n"
-            "smlal2 v22.4s, v0.8h, v10.8h\n"
-            "mov x4, x6\n"
-            "smlal v23.4s, v0.4h, v13.4h\n"
-            "add x0, x6, %[input_row_size]\n"
-            "smlal2 v24.4s, v0.8h, v13.8h\n"
-            "add x1, x0, %[input_row_size]\n"
-            "smlal v21.4s, v1.4h, v11.4h\n"
-            "add x7, x1, %[input_row_size]\n"
-            "smlal2 v22.4s, v1.8h, v11.8h\n"
-            "smlal v23.4s, v1.4h, v14.4h\n"
-            "smlal2 v24.4s, v1.8h, v14.8h\n"
-            "smlal v21.4s, v2.4h, v9.4h\n"
-            "smlal2 v22.4s, v2.8h, v9.8h\n"
-            "ld1 {v9.8b}, [x4], %[input_depth]\n"
-            "smlal v23.4s, v2.4h, v12.4h\n"
-            "ld1 {v10.8b}, [x4], %[input_depth]\n"
-            "smlal2 v24.4s, v2.8h, v12.8h\n"
-            "ld1 {v11.8b}, [x4], %[input_depth]\n"
-            "smlal v21.4s, v3.4h, v13.4h\n"
-            "smlal2 v22.4s, v3.8h, v13.8h\n"
-            "smlal v23.4s, v3.4h, v16.4h\n"
-            "smlal2 v24.4s, v3.8h, v16.8h\n"
-            "smlal v21.4s, v4.4h, v14.4h\n"
-            "smlal2 v22.4s, v4.8h, v14.8h\n"
-            "smlal v23.4s, v4.4h, v17.4h\n"
-            "smlal2 v24.4s, v4.8h, v17.8h\n"
-            "smlal v21.4s, v5.4h, v12.4h\n"
-            "smlal2 v22.4s, v5.8h, v12.8h\n"
-            "ld1 {v12.8b}, [x0], %[input_depth]\n"
-            "smlal v23.4s, v5.4h, v15.4h\n"
-            "ld1 {v13.8b}, [x0], %[input_depth]\n"
-            "smlal2 v24.4s, v5.8h, v15.8h\n"
-            "ld1 {v14.8b}, [x0], %[input_depth]\n"
-            "smlal v21.4s, v6.4h, v16.4h\n"
-            "smlal2 v22.4s, v6.8h, v16.8h\n"
-            "smlal v23.4s, v6.4h, v19.4h\n"
-            "smlal2 v24.4s, v6.8h, v19.8h\n"
-            "smlal v21.4s, v7.4h, v17.4h\n"
-            "smlal2 v22.4s, v7.8h, v17.8h\n"
-            "smlal v23.4s, v7.4h, v20.4h\n"
-            "smlal2 v24.4s, v7.8h, v20.8h\n"
-            "smlal v21.4s, v8.4h, v15.4h\n"
-            "smlal2 v22.4s, v8.8h, v15.8h\n"
-            "ld1 {v15.8b}, [x1], %[input_depth]\n"
-            "smlal v23.4s, v8.4h, v18.4h\n"
-            "ld1 {v16.8b}, [x1], %[input_depth]\n"
-            "smlal2 v24.4s, v8.8h, v18.8h\n"
-            "ld1 {v17.8b}, [x1], %[input_depth]\n"
-
-            "sqrdmulh v21.4s, v21.4s, v27.4s\n"
-            "ld1 {v18.8b}, [x7], %[input_depth]\n"
-            "sqrdmulh v22.4s, v22.4s, v27.4s\n"
-            "ld1 {v19.8b}, [x7], %[input_depth]\n"
-            "sqrdmulh v23.4s, v23.4s, v27.4s\n"
-            "ld1 {v20.8b}, [x7], %[input_depth]\n"
-            "sqrdmulh v24.4s, v24.4s, v27.4s\n"
-            "and v25.16b, v21.16b, v28.16b\n"
-            "and v29.16b, v22.16b, v28.16b\n"
-            "and v30.16b, v23.16b, v28.16b\n"
-            "and v31.16b, v24.16b, v28.16b\n"
-            "sshr v25.4s, v25.4s, #31\n"
-            "sshr v29.4s, v29.4s, #31\n"
-            "sshr v30.4s, v30.4s, #31\n"
-            "sshr v31.4s, v31.4s, #31\n"
-            "sqadd v21.4s, v21.4s, v25.4s\n"
-            "sqadd v22.4s, v22.4s, v29.4s\n"
-            "dup v29.4s, %w[output_offset]\n"
-            "sqadd v23.4s, v23.4s, v30.4s\n"
-            "dup v30.4s, %w[output_activation_min]\n"
-            "sqadd v24.4s, v24.4s, v31.4s\n"
-            "dup v31.4s, %w[output_activation_max]\n"
-            "srshl v21.4s, v21.4s, v28.4s\n"
-            "srshl v22.4s, v22.4s, v28.4s\n"
-            "srshl v23.4s, v23.4s, v28.4s\n"
-            "srshl v24.4s, v24.4s, v28.4s\n"
-            "add v21.4s, v21.4s, v29.4s\n"
-            "add v22.4s, v22.4s, v29.4s\n"
-            "add v23.4s, v23.4s, v29.4s\n"
-            "add v24.4s, v24.4s, v29.4s\n"
-            "smax v21.4s, v21.4s, v30.4s\n"
-            "smax v22.4s, v22.4s, v30.4s\n"
-            "smax v23.4s, v23.4s, v30.4s\n"
-            "smax v24.4s, v24.4s, v30.4s\n"
-            "smin v21.4s, v21.4s, v31.4s\n"
-            "smin v22.4s, v22.4s, v31.4s\n"
-            "smin v23.4s, v23.4s, v31.4s\n"
-            "smin v24.4s, v24.4s, v31.4s\n"
-            "sqxtn v21.4h, v21.4s\n"
-            "sqxtn v23.4h, v23.4s\n"
-            "sqxtn2 v21.8h, v22.4s\n"
-            "ld1 {v22.4s}, [x5]\n"
-            "sqxtn2 v23.8h, v24.4s\n"
-            "ld1 {v24.4s}, [x5]\n"
-            "sqxtun v21.8b, v21.8h\n"
-            "sqxtun v23.8b, v23.8h\n"
-            "uaddw v9.8h, v26.8h, v9.8b\n"
-            "st1 {v21.8b}, [x2], %[output_depth]\n"
-            "uaddw v10.8h, v26.8h, v10.8b\n"
-            "st1 {v23.8b}, [x3], %[output_depth]\n"
-            "uaddw v11.8h, v26.8h, v11.8b\n"
-            "uaddw v12.8h, v26.8h, v12.8b\n"
-            "uaddw v13.8h, v26.8h, v13.8b\n"
-            "uaddw v14.8h, v26.8h, v14.8b\n"
-            "uaddw v15.8h, v26.8h, v15.8b\n"
-            "ld1 {v21.4s}, [%[bias_ptr]]\n"
-            "uaddw v16.8h, v26.8h, v16.8b\n"
-            "ld1 {v23.4s}, [%[bias_ptr]]\n"
-            "uaddw v17.8h, v26.8h, v17.8b\n"
-            "uaddw v18.8h, v26.8h, v18.8b\n"
-            "uaddw v19.8h, v26.8h, v19.8b\n"
-            "uaddw v20.8h, v26.8h, v20.8b\n"
-
-            "bge " DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP "b\n"
-
-          // Do last width column if exists.
-          "cmp w8, #1\n"
-          "blt " DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP "f\n"
-
-          DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1 ":\n"
-          "smlal v21.4s, v0.4h, v9.4h\n"
-          "smlal2 v22.4s, v0.8h, v9.8h\n"
-          "smlal v23.4s, v0.4h, v12.4h\n"
-          "smlal2 v24.4s, v0.8h, v12.8h\n"
-          "smlal v21.4s, v1.4h, v10.4h\n"
-          "smlal2 v22.4s, v1.8h, v10.8h\n"
-          "smlal v23.4s, v1.4h, v13.4h\n"
-          "smlal2 v24.4s, v1.8h, v13.8h\n"
-          "smlal v21.4s, v2.4h, v11.4h\n"
-          "smlal2 v22.4s, v2.8h, v11.8h\n"
-          "smlal v23.4s, v2.4h, v14.4h\n"
-          "smlal2 v24.4s, v2.8h, v14.8h\n"
-          "smlal v21.4s, v3.4h, v12.4h\n"
-          "smlal2 v22.4s, v3.8h, v12.8h\n"
-          "smlal v23.4s, v3.4h, v15.4h\n"
-          "smlal2 v24.4s, v3.8h, v15.8h\n"
-          "smlal v21.4s, v4.4h, v13.4h\n"
-          "smlal2 v22.4s, v4.8h, v13.8h\n"
-          "smlal v23.4s, v4.4h, v16.4h\n"
-          "smlal2 v24.4s, v4.8h, v16.8h\n"
-          "smlal v21.4s, v5.4h, v14.4h\n"
-          "smlal2 v22.4s, v5.8h, v14.8h\n"
-          "smlal v23.4s, v5.4h, v17.4h\n"
-          "smlal2 v24.4s, v5.8h, v17.8h\n"
-          "smlal v21.4s, v6.4h, v15.4h\n"
-          "smlal2 v22.4s, v6.8h, v15.8h\n"
-          "smlal v23.4s, v6.4h, v18.4h\n"
-          "smlal2 v24.4s, v6.8h, v18.8h\n"
-          "smlal v21.4s, v7.4h, v16.4h\n"
-          "smlal2 v22.4s, v7.8h, v16.8h\n"
-          "smlal v23.4s, v7.4h, v19.4h\n"
-          "smlal2 v24.4s, v7.8h, v19.8h\n"
-          "smlal v21.4s, v8.4h, v17.4h\n"
-          "smlal2 v22.4s, v8.8h, v17.8h\n"
-          "smlal v23.4s, v8.4h, v20.4h\n"
-          "smlal2 v24.4s, v8.8h, v20.8h\n"
-
-          "sqrdmulh v21.4s, v21.4s, v27.4s\n"
-          "sqrdmulh v22.4s, v22.4s, v27.4s\n"
-          "sqrdmulh v23.4s, v23.4s, v27.4s\n"
-          "sqrdmulh v24.4s, v24.4s, v27.4s\n"
-          "and v9.16b, v21.16b, v28.16b\n"
-          "and v12.16b, v22.16b, v28.16b\n"
-          "and v15.16b, v23.16b, v28.16b\n"
-          "and v18.16b, v24.16b, v28.16b\n"
-          "sshr v9.4s, v9.4s, #31\n"
-          "sshr v12.4s, v12.4s, #31\n"
-          "sshr v15.4s, v15.4s, #31\n"
-          "sshr v18.4s, v18.4s, #31\n"
-          "sqadd v21.4s, v21.4s, v9.4s\n"
-          "sqadd v22.4s, v22.4s, v12.4s\n"
-          "sqadd v23.4s, v23.4s, v15.4s\n"
-          "sqadd v24.4s, v24.4s, v18.4s\n"
-          "srshl v21.4s, v21.4s, v28.4s\n"
-          "srshl v22.4s, v22.4s, v28.4s\n"
-          "srshl v23.4s, v23.4s, v28.4s\n"
-          "srshl v24.4s, v24.4s, v28.4s\n"
-          "add v21.4s, v21.4s, v29.4s\n"
-          "add v22.4s, v22.4s, v29.4s\n"
-          "add v23.4s, v23.4s, v29.4s\n"
-          "add v24.4s, v24.4s, v29.4s\n"
-          "smax v21.4s, v21.4s, v30.4s\n"
-          "smax v22.4s, v22.4s, v30.4s\n"
-          "smax v23.4s, v23.4s, v30.4s\n"
-          "smax v24.4s, v24.4s, v30.4s\n"
-          "smin v21.4s, v21.4s, v31.4s\n"
-          "smin v22.4s, v22.4s, v31.4s\n"
-          "smin v23.4s, v23.4s, v31.4s\n"
-          "smin v24.4s, v24.4s, v31.4s\n"
-          "sqxtn v21.4h, v21.4s\n"
-          "sqxtn v23.4h, v23.4s\n"
-          "sqxtn2 v21.8h, v22.4s\n"
-          "sqxtn2 v23.8h, v24.4s\n"
-          "sqxtun v21.8b, v21.8h\n"
-          "sqxtun v23.8b, v23.8h\n"
-          "st1 {v21.8b}, [x2], %[output_depth]\n"
-          "st1 {v23.8b}, [x3], %[output_depth]\n"
-
-          DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP ":\n"
-          "subs %w[output_window_height], %w[output_window_height], #2\n"
-          "add %[input_ptr], %[input_ptr], %[input_height_increment]\n"
-          "cmp %w[output_window_height], #2\n"
-          "add %[output_ptr], %[output_ptr], %[output_height_increment]\n"
-          "bge " DEPTHWISECONV_LABEL_HEIGHT_2_LOOP "b\n"
-
-        DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP ":\n"
-        "cmp %w[output_window_height], #1\n"
-        "blt " DEPTHWISECONV_LABEL_HEIGHT_1_END "f\n"
-
-        DEPTHWISECONV_LABEL_HEIGHT_1 ":\n"
-        // Load inputs for 3x4 input window which corresponds to a 1x2 output
-        // window.
-        "mov x4, %[input_ptr]\n"
-        "ld1 {v9.8b}, [x4], %[input_depth]\n"
-        "add x0, %[input_ptr], %[input_row_size]\n"
-        "ld1 {v10.8b}, [x4], %[input_depth]\n"
-        "add x1, x0, %[input_row_size]\n"
-        "ld1 {v11.8b}, [x4], %[input_depth]\n"
-        "add x7, x1, %[input_row_size]\n"
-        "ld1 {v12.8b}, [x4], %[input_depth]\n"
-        "mov w8, %w[output_window_width]\n"
-        "ld1 {v13.8b}, [x0], %[input_depth]\n"
-        "mov x2, %[output_ptr]\n"
-        "ld1 {v14.8b}, [x0], %[input_depth]\n"
-        "add x3, %[output_ptr], %[output_row_size]\n"
-        "ld1 {v15.8b}, [x0], %[input_depth]\n"
-        "cmp w8, #2\n"
-        "ld1 {v16.8b}, [x0], %[input_depth]\n"
-        "ld1 {v17.8b}, [x1], %[input_depth]\n"
-        "ld1 {v18.8b}, [x1], %[input_depth]\n"
-        "ld1 {v19.8b}, [x1], %[input_depth]\n"
-        "ld1 {v20.8b}, [x1], %[input_depth]\n"
-        "ld1 {v21.4s}, [%[bias_ptr]]\n"
-        "ld1 {v22.4s}, [x5]\n"
-        "ld1 {v23.4s}, [%[bias_ptr]]\n"
-        "ld1 {v24.4s}, [x5]\n"
-
-        "uaddw v9.8h, v26.8h, v9.8b\n"
-        "uaddw v10.8h, v26.8h, v10.8b\n"
-        "uaddw v11.8h, v26.8h, v11.8b\n"
-        "uaddw v12.8h, v26.8h, v12.8b\n"
-        "uaddw v13.8h, v26.8h, v13.8b\n"
-        "uaddw v14.8h, v26.8h, v14.8b\n"
-        "uaddw v15.8h, v26.8h, v15.8b\n"
-        "uaddw v16.8h, v26.8h, v16.8b\n"
-        "uaddw v17.8h, v26.8h, v17.8b\n"
-        "uaddw v18.8h, v26.8h, v18.8b\n"
-        "uaddw v19.8h, v26.8h, v19.8b\n"
-        "uaddw v20.8h, v26.8h, v20.8b\n"
-
-        "blt " DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1 "f\n"
-
-        //"loop_%=:\n"
-        DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP ":\n"
-          "smlal v21.4s, v0.4h, v9.4h\n"
-          "subs w8, w8, #2\n"
-          "smlal2 v22.4s, v0.8h, v9.8h\n"
-          "cmp w8, #2\n"
-          "smlal v23.4s, v0.4h, v10.4h\n"
-          "add %[input_ptr], %[input_ptr], %[input_width_increment]\n"
-          "smlal2 v24.4s, v0.8h, v10.8h\n"
-          "mov x4, %[input_ptr]\n"
-          "smlal v21.4s, v1.4h, v10.4h\n"
-          "ld1 {v9.8b}, [x4], %[input_depth]\n"
-          "smlal2 v22.4s, v1.8h, v10.8h\n"
-          "ld1 {v10.8b}, [x4], %[input_depth]\n"
-          "smlal v23.4s, v1.4h, v11.4h\n"
-          "add x0, %[input_ptr], %[input_row_size]\n"
-          "smlal2 v24.4s, v1.8h, v11.8h\n"
-          "add x1, x0, %[input_row_size]\n"
-          "smlal v21.4s, v2.4h, v11.4h\n"
-          "add x7, x1, %[input_row_size]\n"
-          "smlal2 v22.4s, v2.8h, v11.8h\n"
-          "ld1 {v11.8b}, [x4], %[input_depth]\n"
-          "smlal v23.4s, v2.4h, v12.4h\n"
-          "smlal2 v24.4s, v2.8h, v12.8h\n"
-          "ld1 {v12.8b}, [x4], %[input_depth]\n"
-          "smlal v21.4s, v3.4h, v13.4h\n"
-          "smlal2 v22.4s, v3.8h, v13.8h\n"
-          "ld1 {v13.8b}, [x0], %[input_depth]\n"
-          "smlal v23.4s, v3.4h, v14.4h\n"
-          "smlal2 v24.4s, v3.8h, v14.8h\n"
-          "smlal v21.4s, v4.4h, v14.4h\n"
-          "smlal2 v22.4s, v4.8h, v14.8h\n"
-          "ld1 {v14.8b}, [x0], %[input_depth]\n"
-          "smlal v23.4s, v4.4h, v15.4h\n"
-          "smlal2 v24.4s, v4.8h, v15.8h\n"
-          "smlal v21.4s, v5.4h, v15.4h\n"
-          "smlal2 v22.4s, v5.8h, v15.8h\n"
-          "ld1 {v15.8b}, [x0], %[input_depth]\n"
-          "smlal v23.4s, v5.4h, v16.4h\n"
-          "smlal2 v24.4s, v5.8h, v16.8h\n"
-          "ld1 {v16.8b}, [x0], %[input_depth]\n"
-          "smlal v21.4s, v6.4h, v17.4h\n"
-          "smlal2 v22.4s, v6.8h, v17.8h\n"
-          "ld1 {v17.8b}, [x1], %[input_depth]\n"
-          "smlal v23.4s, v6.4h, v18.4h\n"
-          "smlal2 v24.4s, v6.8h, v18.8h\n"
-          "smlal v21.4s, v7.4h, v18.4h\n"
-          "smlal2 v22.4s, v7.8h, v18.8h\n"
-          "ld1 {v18.8b}, [x1], %[input_depth]\n"
-          "smlal v23.4s, v7.4h, v19.4h\n"
-          "smlal2 v24.4s, v7.8h, v19.8h\n"
-          "smlal v21.4s, v8.4h, v19.4h\n"
-          "smlal2 v22.4s, v8.8h, v19.8h\n"
-          "ld1 {v19.8b}, [x1], %[input_depth]\n"
-          "smlal v23.4s, v8.4h, v20.4h\n"
-          "smlal2 v24.4s, v8.8h, v20.8h\n"
-          "ld1 {v20.8b}, [x1], %[input_depth]\n"
-
-          "sqrdmulh v21.4s, v21.4s, v27.4s\n"
-          "sqrdmulh v22.4s, v22.4s, v27.4s\n"
-          "sqrdmulh v23.4s, v23.4s, v27.4s\n"
-          "sqrdmulh v24.4s, v24.4s, v27.4s\n"
-          "and v25.16b, v21.16b, v28.16b\n"
-          "and v29.16b, v22.16b, v28.16b\n"
-          "and v30.16b, v23.16b, v28.16b\n"
-          "and v31.16b, v24.16b, v28.16b\n"
-          "sshr v25.4s, v25.4s, #31\n"
-          "sshr v29.4s, v29.4s, #31\n"
-          "sshr v30.4s, v30.4s, #31\n"
-          "sshr v31.4s, v31.4s, #31\n"
-          "sqadd v21.4s, v21.4s, v25.4s\n"
-          "sqadd v22.4s, v22.4s, v29.4s\n"
-          "dup v29.4s, %w[output_offset]\n"
-          "sqadd v23.4s, v23.4s, v30.4s\n"
-          "dup v30.4s, %w[output_activation_min]\n"
-          "sqadd v24.4s, v24.4s, v31.4s\n"
-          "dup v31.4s, %w[output_activation_max]\n"
-          "srshl v21.4s, v21.4s, v28.4s\n"
-          "srshl v22.4s, v22.4s, v28.4s\n"
-          "srshl v23.4s, v23.4s, v28.4s\n"
-          "srshl v24.4s, v24.4s, v28.4s\n"
-          "add v21.4s, v21.4s, v29.4s\n"
-          "add v22.4s, v22.4s, v29.4s\n"
-          "add v23.4s, v23.4s, v29.4s\n"
-          "add v24.4s, v24.4s, v29.4s\n"
-          "smax v21.4s, v21.4s, v30.4s\n"
-          "smax v22.4s, v22.4s, v30.4s\n"
-          "smax v23.4s, v23.4s, v30.4s\n"
-          "smax v24.4s, v24.4s, v30.4s\n"
-          "smin v21.4s, v21.4s, v31.4s\n"
-          "smin v22.4s, v22.4s, v31.4s\n"
-          "smin v23.4s, v23.4s, v31.4s\n"
-          "smin v24.4s, v24.4s, v31.4s\n"
-          "sqxtn v21.4h, v21.4s\n"
-          "sqxtn v23.4h, v23.4s\n"
-          "sqxtn2 v21.8h, v22.4s\n"
-          "ld1 {v22.4s}, [x5]\n"
-          "sqxtn2 v23.8h, v24.4s\n"
-          "ld1 {v24.4s}, [x5]\n"
-          "sqxtun v21.8b, v21.8h\n"
-          "sqxtun v23.8b, v23.8h\n"
-          "uaddw v9.8h, v26.8h, v9.8b\n"
-          "st1 {v21.8b}, [%[output_ptr]], %[output_depth]\n"
-          "uaddw v10.8h, v26.8h, v10.8b\n"
-          "st1 {v23.8b}, [%[output_ptr]], %[output_depth]\n"
-          "uaddw v11.8h, v26.8h, v11.8b\n"
-          "uaddw v12.8h, v26.8h, v12.8b\n"
-          "uaddw v13.8h, v26.8h, v13.8b\n"
-          "uaddw v14.8h, v26.8h, v14.8b\n"
-          "uaddw v15.8h, v26.8h, v15.8b\n"
-          "ld1 {v21.4s}, [%[bias_ptr]]\n"
-          "uaddw v16.8h, v26.8h, v16.8b\n"
-          "ld1 {v23.4s}, [%[bias_ptr]]\n"
-          "uaddw v17.8h, v26.8h, v17.8b\n"
-          "uaddw v18.8h, v26.8h, v18.8b\n"
-          "uaddw v19.8h, v26.8h, v19.8b\n"
-          "uaddw v20.8h, v26.8h, v20.8b\n"
-
-          "bge " DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP "b\n"
-
-        "cmp w8, #1\n"
-        "blt " DEPTHWISECONV_LABEL_HEIGHT_1_END "f\n"
-
-        // Do bottom right output if exists.
-        DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1 ":\n"
-        "smlal v21.4s, v0.4h, v9.4h\n"
-        "smlal2 v22.4s, v0.8h, v9.8h\n"
-        "smlal v21.4s, v1.4h, v10.4h\n"
-        "smlal2 v22.4s, v1.8h, v10.8h\n"
-        "smlal v21.4s, v2.4h, v11.4h\n"
-        "smlal2 v22.4s, v2.8h, v11.8h\n"
-        "smlal v21.4s, v3.4h, v13.4h\n"
-        "smlal2 v22.4s, v3.8h, v13.8h\n"
-        "smlal v21.4s, v4.4h, v14.4h\n"
-        "smlal2 v22.4s, v4.8h, v14.8h\n"
-        "smlal v21.4s, v5.4h, v15.4h\n"
-        "smlal2 v22.4s, v5.8h, v15.8h\n"
-        "smlal v21.4s, v6.4h, v17.4h\n"
-        "smlal2 v22.4s, v6.8h, v17.8h\n"
-        "smlal v21.4s, v7.4h, v18.4h\n"
-        "smlal2 v22.4s, v7.8h, v18.8h\n"
-        "smlal v21.4s, v8.4h, v19.4h\n"
-        "smlal2 v22.4s, v8.8h, v19.8h\n"
-
-        "sqrdmulh v21.4s, v21.4s, v27.4s\n"
-        "sqrdmulh v22.4s, v22.4s, v27.4s\n"
-        "and v9.16b, v21.16b, v28.16b\n"
-        "and v12.16b, v22.16b, v28.16b\n"
-        "sshr v9.4s, v9.4s, #31\n"
-        "sshr v12.4s, v12.4s, #31\n"
-        "sqadd v21.4s, v21.4s, v9.4s\n"
-        "sqadd v22.4s, v22.4s, v12.4s\n"
-        "srshl v21.4s, v21.4s, v28.4s\n"
-        "srshl v22.4s, v22.4s, v28.4s\n"
-        "add v21.4s, v21.4s, v29.4s\n"
-        "add v22.4s, v22.4s, v29.4s\n"
-        "smax v21.4s, v21.4s, v30.4s\n"
-        "smax v22.4s, v22.4s, v30.4s\n"
-        "smin v21.4s, v21.4s, v31.4s\n"
-        "smin v22.4s, v22.4s, v31.4s\n"
-        "sqxtn v21.4h, v21.4s\n"
-        "sqxtn2 v21.8h, v22.4s\n"
-        "sqxtun v21.8b, v21.8h\n"
-        "st1 {v21.8b}, [%[output_ptr]]\n"
-
-        DEPTHWISECONV_LABEL_HEIGHT_1_END ":\n"
-
-    :
-    // Outputs.
-    [filter_ptr] "+r"(filter_ptr), [input_ptr] "+r"(input_ptr),
-    [output_ptr] "+r"(output_ptr),
-    [output_window_height] "+r"(output_window_height)
-    :
-    // Inputs.
-    [bias_ptr] "r"(bias_ptr), [output_depth] "r"(output_depth),
-    [filter_offset] "r"(filter_offset), [input_row_size] "r"(input_row_size),
-    [input_depth] "r"(input_depth), [input_offset] "r"(input_offset),
-    [output_multiplier] "r"(output_multiplier),
-    [output_shift] "r"(output_shift), [output_offset] "r"(output_offset),
-    [output_activation_min] "r"(output_activation_min),
-    [output_activation_max] "r"(output_activation_max),
-    [output_row_size] "r"(output_row_size),
-    [output_window_width] "r"(output_window_width),
-    [input_width_increment] "r"(input_width_increment),
-    [input_height_increment] "r"(input_height_increment),
-    [output_height_increment] "r"(output_height_increment)
-    :
-    // Clobbers.
-    // We use these NEON registers.
-    "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11",
-    "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21",
-    "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31",
-    // We use these general-purpose registers.
-    "x0", "x1", "x2", "x3", "x4", "x5", "x6", "x7", "w8");
-
-#undef DEPTHWISECONV_LABEL_HEIGHT_1_END
-#undef DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1
-#undef DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP
-#undef DEPTHWISECONV_LABEL_HEIGHT_1
-#undef DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP
-#undef DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP
-#undef DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1
-#undef DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP
-#undef DEPTHWISECONV_LABEL_HEIGHT_2_LOOP
+                         int32 output_activation_max, uint8* output_data,
+                         int output_depth, int output_width,
+                         uint8* shuffle_workspace) {
+    int out_x = start_x;
+
+    // 2x4 at a time.
+    for (; out_x <= output_width - 4; out_x += 4) {
+      const int32* bias_ptr = bias_data;
+      const uint8* filter_ptr = filter_data;
+
+      const uint8* input_ptr = input_data;
+      uint8* output_ptr = output_data;
+
+      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
+        ConvKernel3x3FilterDepth8<2, 4, kFixedStrideWidth, kFixedStrideHeight>::
+            Run(input_ptr, input_depth, input_offset, input_row_size,
+                filter_ptr, filter_offset, bias_ptr, output_offset,
+                output_multiplier, output_shift, output_activation_min,
+                output_activation_max, output_ptr, output_depth, output_width);
+
+        input_ptr += 8;
+        output_ptr += 8;
+        filter_ptr += 8;
+        bias_ptr += 8;
+      }
+
+      input_data += 4 * kFixedStrideWidth * input_depth;
+      output_data += 4 * output_depth;
+    }
+
+    // 2x2 at a time.
+    for (; out_x <= output_width - 2; out_x += 2) {
+      const int32* bias_ptr = bias_data;
+      const uint8* filter_ptr = filter_data;
+
+      const uint8* input_ptr = input_data;
+      uint8* output_ptr = output_data;
+
+      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
+        ConvKernel3x3FilterDepth8<2, 2, kFixedStrideWidth, kFixedStrideHeight>::
+            Run(input_ptr, input_depth, input_offset, input_row_size,
+                filter_ptr, filter_offset, bias_ptr, output_offset,
+                output_multiplier, output_shift, output_activation_min,
+                output_activation_max, output_ptr, output_depth, output_width);
+
+        input_ptr += 8;
+        output_ptr += 8;
+        filter_ptr += 8;
+        bias_ptr += 8;
+      }
+
+      input_data += 2 * kFixedStrideWidth * input_depth;
+      output_data += 2 * output_depth;
+    }
+
+    // 2x1 at a time.
+    for (; out_x < output_width; out_x++) {
+      const int32* bias_ptr = bias_data;
+      const uint8* filter_ptr = filter_data;
+
+      const uint8* input_ptr = input_data;
+      uint8* output_ptr = output_data;
+
+      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
+        ConvKernel3x3FilterDepth8<2, 1, kFixedStrideWidth, kFixedStrideHeight>::
+            Run(input_ptr, input_depth, input_offset, input_row_size,
+                filter_ptr, filter_offset, bias_ptr, output_offset,
+                output_multiplier, output_shift, output_activation_min,
+                output_activation_max, output_ptr, output_depth, output_width);
+
+        input_ptr += 8;
+        output_ptr += 8;
+        filter_ptr += 8;
+        bias_ptr += 8;
+      }
+
+      input_data += kFixedStrideWidth * input_depth;
+      output_data += output_depth;
+    }
   }
 };
 
 template <>
-struct DepthwiseConvWindow<8, 2, 2> {
-  static inline void Run(const uint8* input_ptr, int64_t input_depth,
-                         int32 input_offset, int64_t input_row_size,
-                         const uint8* filter_ptr, int32 filter_offset,
-                         const int32* bias_ptr, int32 output_offset,
+struct ConvRow3x3FilterDepth8<4, 1, 1> {
+  static inline void Run(const uint8* input_data, int start_x, int start_y,
+                         int input_depth, int input_width, int input_height,
+                         int input_row_size, int32 input_offset,
+                         const uint8* filter_data, int32 filter_offset,
+                         const int32* bias_data, int32 output_offset,
                          int32 output_multiplier, int output_shift,
                          int32 output_activation_min,
-                         int32 output_activation_max, uint8* output_ptr,
-                         int64_t output_depth, int output_width,
-                         int output_window_height, int output_window_width) {
-    const int64_t output_row_size = output_depth * output_width;
-    const int64_t input_width_increment = 4 * input_depth;
-    const int64_t input_height_increment = 4 * input_row_size;
-    const int64_t output_height_increment = 2 * output_row_size;
-
-#define DEPTHWISECONV_LABEL_HEIGHT_2_LOOP "1"
-#define DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP "2"
-#define DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1 "3"
-#define DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP "4"
-#define DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP "5"
-#define DEPTHWISECONV_LABEL_HEIGHT_1 "6"
-#define DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP "7"
-#define DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1 "8"
-#define DEPTHWISECONV_LABEL_HEIGHT_1_END "9"
-
-    asm volatile(
-        // Performs depthwise convolutions for a window specified by
-        // |output_window_height| and |output_window_width|. The inner-most loop
-        // processes 2x2 outputs, and any leftovers at the end.
-        //
-        // Algorithm works as follows:
-        //
-        //   1. Load filters of 8 depth (8x3x3). Registers v0--v8 hold filter
-        //      values.
-        //   2. For 2 output heights at a time:
-        //        i.  For 2 output widths at a time at stride 2, a 5x5 input
-        //            window is required. To avoid register exhaustion, we load
-        //            the first 2 rows of the 5x5 input window into registers
-        //            v9--v18, and use the same registers to load the next 2
-        //            rows, and finally v9--v13 to load the last row.
-        //            Accumulators for all 2x2 outputs are reserved by registers
-        //            v21-v22 (top left output), v23-v24 (top right output),
-        //            v19-v20 (bottom left output), v25-v26 (bottom right
-        //            output).
-        //        ii. Handle single leftover width if exists.
-        //   3. Handle single leftover height if exists.
-        //        i.  For 2 output widths at a time at stride 2, load inputs for
-        //            a 1x2 (1 height, 2 width) output window (3x5 input
-        //            window). Registers v9--v24 hold input values. Mul-add with
-        //            accumulators v24--v27.
-        //        ii. Handle single leftover width if exists.
-        //
-        // Loads are placed as soon as the register is no longer needed and
-        // interleaved with arithmetic operations to take advantage of
-        // dual-issue pipelines. We also add input offsets as far from the loads
-        // as possible to give loads enough cycles to fetch data from memory.
-
-        // Set "constant" registers. These registers may be replaced with temp
-        // values from time to time when there are not enough NEON registers.
-        "neg w7, %w[output_shift]\n"
-        "dup v26.4s, w7\n"
-        "cmp %w[output_window_height], #2\n"
-        "dup v27.4s, %w[output_multiplier]\n"
-        "dup v28.8h, %w[input_offset]\n"
-        "dup v29.4s, %w[output_offset]\n"
-        "dup v30.4s, %w[output_activation_min]\n"
-        "dup v31.4s, %w[output_activation_max]\n"
-
-        // Load filters and add offsets.
-        "add x5, %[bias_ptr], #16\n"
-        "ld1 {v0.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "dup v9.8h, %w[filter_offset]\n"
-        "ld1 {v1.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v0.8h, v9.8h, v0.8b\n"
-        "ld1 {v2.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v1.8h, v9.8h, v1.8b\n"
-        "ld1 {v3.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v2.8h, v9.8h, v2.8b\n"
-        "ld1 {v4.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v3.8h, v9.8h, v3.8b\n"
-        "ld1 {v5.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v4.8h, v9.8h, v4.8b\n"
-        "ld1 {v6.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v5.8h, v9.8h, v5.8b\n"
-        "ld1 {v7.8b}, [%[filter_ptr]], %[output_depth]\n"
-        "uaddw v6.8h, v9.8h, v6.8b\n"
-        "ld1 {v8.8b}, [%[filter_ptr]]\n"
-        "uaddw v7.8h, v9.8h, v7.8b\n"
-        "uaddw v8.8h, v9.8h, v8.8b\n"
-
-        "blt " DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP "f\n"
-
-        //"loop_%=:\n"
-        DEPTHWISECONV_LABEL_HEIGHT_2_LOOP ":\n"
-          // Load the first two rows of the 5x5 input window, then reuse the
-          // same registers to load subsequent rows as they become available.
-          "mov x6, %[input_ptr]\n"
-          "mov x0, x6\n"
-          "add x1, x0, %[input_row_size]\n"
-          "ld1 {v9.8b}, [x0], %[input_depth]\n"
-          "mov w4, %w[output_window_width]\n"
-          "ld1 {v10.8b}, [x0], %[input_depth]\n"
-          "cmp w4, #2\n"
-          "ld1 {v11.8b}, [x0], %[input_depth]\n"
-          "add x2, x1, %[input_row_size]\n"
-          "ld1 {v12.8b}, [x0], %[input_depth]\n"
-          "ld1 {v13.8b}, [x0]\n"
-          "add x0, x2, %[input_row_size]\n"
-          "ld1 {v14.8b}, [x1], %[input_depth]\n"
-          "mov x3, %[output_ptr]\n"
-          "ld1 {v15.8b}, [x1], %[input_depth]\n"
-          "add x10, %[output_ptr], %[output_row_size]\n"
-          "ld1 {v16.8b}, [x1], %[input_depth]\n"
-          "ld1 {v17.8b}, [x1], %[input_depth]\n"
-          "ld1 {v18.8b}, [x1]\n"
-          "add x1, x0, %[input_row_size]\n"
-
-          "uaddw v9.8h, v28.8h, v9.8b\n"
-          "uaddw v10.8h, v28.8h, v10.8b\n"
-          "uaddw v11.8h, v28.8h, v11.8b\n"
-          "ld1 {v21.4s}, [%[bias_ptr]]\n"
-          "uaddw v12.8h, v28.8h, v12.8b\n"
-          "ld1 {v22.4s}, [x5]\n"
-          "uaddw v13.8h, v28.8h, v13.8b\n"
-          "ld1 {v23.4s}, [%[bias_ptr]]\n"
-          "uaddw v14.8h, v28.8h, v14.8b\n"
-          "ld1 {v24.4s}, [x5]\n"
-          "uaddw v15.8h, v28.8h, v15.8b\n"
-          "ld1 {v19.4s}, [%[bias_ptr]]\n"
-          "uaddw v16.8h, v28.8h, v16.8b\n"
-          "ld1 {v20.4s}, [x5]\n"
-          "uaddw v17.8h, v28.8h, v17.8b\n"
-          "ld1 {v25.4s}, [%[bias_ptr]]\n"
-          "uaddw v18.8h, v28.8h, v18.8b\n"
-          "ld1 {v26.4s}, [x5]\n"
-
-          "blt " DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1 "f\n"
-
-          //"loop_%=:\n"
-          DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP ":\n"
-            "smlal v21.4s, v0.4h, v9.4h\n"
-            "subs w4, w4, #2\n"
-            "smlal2 v22.4s, v0.8h, v9.8h\n"
-            "ld1 {v9.8b}, [x2], %[input_depth]\n"
-            "smlal v23.4s, v0.4h, v11.4h\n"
-            "cmp w4, #2\n"
-            "smlal2 v24.4s, v0.8h, v11.8h\n"
-            "smlal v21.4s, v1.4h, v10.4h\n"
-            "smlal2 v22.4s, v1.8h, v10.8h\n"
-            "ld1 {v10.8b}, [x2], %[input_depth]\n"
-            "smlal v23.4s, v1.4h, v12.4h\n"
-            "smlal2 v24.4s, v1.8h, v12.8h\n"
-            "smlal v21.4s, v2.4h, v11.4h\n"
-            "smlal2 v22.4s, v2.8h, v11.8h\n"
-            "ld1 {v11.8b}, [x2], %[input_depth]\n"
-            "smlal v23.4s, v2.4h, v13.4h\n"
-            "ld1 {v12.8b}, [x2], %[input_depth]\n"
-            "smlal2 v24.4s, v2.8h, v13.8h\n"
-            "ld1 {v13.8b}, [x2]\n"
-
-            "smlal v21.4s, v3.4h, v14.4h\n"
-            "smlal2 v22.4s, v3.8h, v14.8h\n"
-            "ld1 {v14.8b}, [x0], %[input_depth]\n"
-            "smlal v23.4s, v3.4h, v16.4h\n"
-            "smlal2 v24.4s, v3.8h, v16.8h\n"
-            "smlal v21.4s, v4.4h, v15.4h\n"
-            "smlal2 v22.4s, v4.8h, v15.8h\n"
-            "ld1 {v15.8b}, [x0], %[input_depth]\n"
-            "smlal v23.4s, v4.4h, v17.4h\n"
-            "smlal2 v24.4s, v4.8h, v17.8h\n"
-            "smlal v21.4s, v5.4h, v16.4h\n"
-            "uaddw v9.8h, v28.8h, v9.8b\n"
-            "smlal2 v22.4s, v5.8h, v16.8h\n"
-            "ld1 {v16.8b}, [x0], %[input_depth]\n"
-            "smlal v23.4s, v5.4h, v18.4h\n"
-            "ld1 {v17.8b}, [x0], %[input_depth]\n"
-            "smlal2 v24.4s, v5.8h, v18.8h\n"
-            "ld1 {v18.8b}, [x0]\n"
-
-            "smlal v21.4s, v6.4h, v9.4h\n"
-            "uaddw v10.8h, v28.8h, v10.8b\n"
-            "smlal2 v22.4s, v6.8h, v9.8h\n"
-            "uaddw v11.8h, v28.8h, v11.8b\n"
-            "smlal v19.4s, v0.4h, v9.4h\n"
-            "uaddw v12.8h, v28.8h, v12.8b\n"
-            "smlal2 v20.4s, v0.8h, v9.8h\n"
-            "ld1 {v9.8b}, [x1], %[input_depth]\n"
-            "smlal v23.4s, v6.4h, v11.4h\n"
-            "uaddw v13.8h, v28.8h, v13.8b\n"
-            "smlal2 v24.4s, v6.8h, v11.8h\n"
-            "smlal v21.4s, v7.4h, v10.4h\n"
-            "smlal2 v22.4s, v7.8h, v10.8h\n"
-            "smlal v19.4s, v1.4h, v10.4h\n"
-            "smlal2 v20.4s, v1.8h, v10.8h\n"
-            "ld1 {v10.8b}, [x1], %[input_depth]\n"
-            "smlal v23.4s, v7.4h, v12.4h\n"
-            "smlal2 v24.4s, v7.8h, v12.8h\n"
-            "smlal v25.4s, v1.4h, v12.4h\n"
-            "smlal2 v26.4s, v1.8h, v12.8h\n"
-            "smlal v21.4s, v8.4h, v11.4h\n"
-            "smlal2 v22.4s, v8.8h, v11.8h\n"
-            "smlal v19.4s, v2.4h, v11.4h\n"
-            "add x6, x6, %[input_width_increment]\n"
-            "smlal2 v20.4s, v2.8h, v11.8h\n"
-            "mov x0, x6\n"
-
-            "smlal v25.4s, v0.4h, v11.4h\n"
-            "smlal2 v26.4s, v0.8h, v11.8h\n"
-            "ld1 {v11.8b}, [x1], %[input_depth]\n"
-            "smlal v23.4s, v8.4h, v13.4h\n"
-            "ld1 {v12.8b}, [x1], %[input_depth]\n"
-            "smlal2 v24.4s, v8.8h, v13.8h\n"
-            "smlal v25.4s, v2.4h, v13.4h\n"
-            "smlal2 v26.4s, v2.8h, v13.8h\n"
-            "ld1 {v13.8b}, [x1]\n"
-            "add x1, x0, %[input_row_size]\n"
-
-            "dup v28.4s, w7\n"
-            "add x2, x1, %[input_row_size]\n"
-            "sqrdmulh v21.4s, v21.4s, v27.4s\n"
-            "sqrdmulh v22.4s, v22.4s, v27.4s\n"
-            "sqrdmulh v23.4s, v23.4s, v27.4s\n"
-            "sqrdmulh v24.4s, v24.4s, v27.4s\n"
-            "and v27.16b, v21.16b, v28.16b\n"
-            "and v29.16b, v22.16b, v28.16b\n"
-            "and v30.16b, v23.16b, v28.16b\n"
-            "and v31.16b, v24.16b, v28.16b\n"
-            "sshr v27.4s, v27.4s, #31\n"
-            "sshr v29.4s, v29.4s, #31\n"
-            "sshr v30.4s, v30.4s, #31\n"
-            "sshr v31.4s, v31.4s, #31\n"
-            "sqadd v21.4s, v21.4s, v27.4s\n"
-            "dup v27.4s, %w[output_multiplier]\n"
-            "sqadd v22.4s, v22.4s, v29.4s\n"
-            "dup v29.4s, %w[output_offset]\n"
-            "sqadd v23.4s, v23.4s, v30.4s\n"
-            "dup v30.4s, %w[output_activation_min]\n"
-            "sqadd v24.4s, v24.4s, v31.4s\n"
-            "dup v31.4s, %w[output_activation_max]\n"
-            "srshl v21.4s, v21.4s, v28.4s\n"
-            "srshl v22.4s, v22.4s, v28.4s\n"
-            "srshl v23.4s, v23.4s, v28.4s\n"
-            "srshl v24.4s, v24.4s, v28.4s\n"
-            "dup v28.8h, %w[input_offset]\n"
-            "add v21.4s, v21.4s, v29.4s\n"
-            "add v22.4s, v22.4s, v29.4s\n"
-            "add v23.4s, v23.4s, v29.4s\n"
-            "add v24.4s, v24.4s, v29.4s\n"
-            "smax v21.4s, v21.4s, v30.4s\n"
-            "smax v22.4s, v22.4s, v30.4s\n"
-            "smax v23.4s, v23.4s, v30.4s\n"
-            "smax v24.4s, v24.4s, v30.4s\n"
-            "smin v21.4s, v21.4s, v31.4s\n"
-            "smin v22.4s, v22.4s, v31.4s\n"
-            "smin v23.4s, v23.4s, v31.4s\n"
-            "smin v24.4s, v24.4s, v31.4s\n"
-            "sqxtn v21.4h, v21.4s\n"
-            "sqxtn v23.4h, v23.4s\n"
-            "sqxtn2 v21.8h, v22.4s\n"
-            "ld1 {v22.4s}, [x5]\n"
-            "sqxtn2 v23.8h, v24.4s\n"
-            "ld1 {v24.4s}, [x5]\n"
-            "sqxtun v21.8b, v21.8h\n"
-            "sqxtun v23.8b, v23.8h\n"
-            "uaddw v9.8h, v28.8h, v9.8b\n"
-            "st1 {v21.8b}, [x3], %[output_depth]\n"
-            "uaddw v10.8h, v28.8h, v10.8b\n"
-            "st1 {v23.8b}, [x3], %[output_depth]\n"
-            "uaddw v11.8h, v28.8h, v11.8b\n"
-
-            "smlal v19.4s, v6.4h, v9.4h\n"
-            "uaddw v12.8h, v28.8h, v12.8b\n"
-            "smlal2 v20.4s, v6.8h, v9.8h\n"
-            "ld1 {v9.8b}, [x0], %[input_depth]\n"
-            "smlal v25.4s, v6.4h, v11.4h\n"
-            "uaddw v13.8h, v28.8h, v13.8b\n"
-            "smlal2 v26.4s, v6.8h, v11.8h\n"
-            "uaddw v14.8h, v28.8h, v14.8b\n"
-            "smlal v19.4s, v7.4h, v10.4h\n"
-            "uaddw v15.8h, v28.8h, v15.8b\n"
-            "smlal2 v20.4s, v7.8h, v10.8h\n"
-            "ld1 {v10.8b}, [x0], %[input_depth]\n"
-            "smlal v25.4s, v7.4h, v12.4h\n"
-            "uaddw v16.8h, v28.8h, v16.8b\n"
-            "smlal2 v26.4s, v7.8h, v12.8h\n"
-            "uaddw v17.8h, v28.8h, v17.8b\n"
-            "smlal v19.4s, v8.4h, v11.4h\n"
-            "uaddw v18.8h, v28.8h, v18.8b\n"
-            "smlal2 v20.4s, v8.8h, v11.8h\n"
-            "ld1 {v11.8b}, [x0], %[input_depth]\n"
-            "smlal v25.4s, v8.4h, v13.4h\n"
-            "ld1 {v12.8b}, [x0], %[input_depth]\n"
-            "smlal2 v26.4s, v8.8h, v13.8h\n"
-            "ld1 {v13.8b}, [x0]\n"
-            "add x0, x2, %[input_row_size]\n"
-
-            "smlal v19.4s, v3.4h, v14.4h\n"
-            "smlal2 v20.4s, v3.8h, v14.8h\n"
-            "ld1 {v14.8b}, [x1], %[input_depth]\n"
-            "smlal v25.4s, v3.4h, v16.4h\n"
-            "ld1 {v21.4s}, [%[bias_ptr]]\n"
-            "smlal2 v26.4s, v3.8h, v16.8h\n"
-            "ld1 {v23.4s}, [%[bias_ptr]]\n"
-            "smlal v19.4s, v4.4h, v15.4h\n"
-            "uaddw v9.8h, v28.8h, v9.8b\n"
-            "smlal2 v20.4s, v4.8h, v15.8h\n"
-            "ld1 {v15.8b}, [x1], %[input_depth]\n"
-            "smlal v25.4s, v4.4h, v17.4h\n"
-            "uaddw v10.8h, v28.8h, v10.8b\n"
-            "smlal2 v26.4s, v4.8h, v17.8h\n"
-            "uaddw v11.8h, v28.8h, v11.8b\n"
-            "smlal v19.4s, v5.4h, v16.4h\n"
-            "uaddw v12.8h, v28.8h, v12.8b\n"
-            "smlal2 v20.4s, v5.8h, v16.8h\n"
-            "ld1 {v16.8b}, [x1], %[input_depth]\n"
-            "smlal v25.4s, v5.4h, v18.4h\n"
-            "ld1 {v17.8b}, [x1], %[input_depth]\n"
-            "smlal2 v26.4s, v5.8h, v18.8h\n"
-            "ld1 {v18.8b}, [x1]\n"
-            "add x1, x0, %[input_row_size]\n"
-            "uaddw v13.8h, v28.8h, v13.8b\n"
-
-            "dup v28.4s, w7\n"
-            "sqrdmulh v19.4s, v19.4s, v27.4s\n"
-            "sqrdmulh v20.4s, v20.4s, v27.4s\n"
-            "sqrdmulh v25.4s, v25.4s, v27.4s\n"
-            "sqrdmulh v26.4s, v26.4s, v27.4s\n"
-            "and v27.16b, v19.16b, v28.16b\n"
-            "and v29.16b, v20.16b, v28.16b\n"
-            "and v30.16b, v25.16b, v28.16b\n"
-            "and v31.16b, v26.16b, v28.16b\n"
-            "sshr v27.4s, v27.4s, #31\n"
-            "sshr v29.4s, v29.4s, #31\n"
-            "sshr v30.4s, v30.4s, #31\n"
-            "sshr v31.4s, v31.4s, #31\n"
-            "sqadd v19.4s, v19.4s, v27.4s\n"
-            "dup v27.4s, %w[output_multiplier]\n"
-            "sqadd v20.4s, v20.4s, v29.4s\n"
-            "dup v29.4s, %w[output_offset]\n"
-            "sqadd v25.4s, v25.4s, v30.4s\n"
-            "dup v30.4s, %w[output_activation_min]\n"
-            "sqadd v26.4s, v26.4s, v31.4s\n"
-            "dup v31.4s, %w[output_activation_max]\n"
-            "srshl v19.4s, v19.4s, v28.4s\n"
-            "srshl v20.4s, v20.4s, v28.4s\n"
-            "srshl v25.4s, v25.4s, v28.4s\n"
-            "srshl v26.4s, v26.4s, v28.4s\n"
-            "dup v28.8h, %w[input_offset]\n"
-            "add v19.4s, v19.4s, v29.4s\n"
-            "add v20.4s, v20.4s, v29.4s\n"
-            "add v25.4s, v25.4s, v29.4s\n"
-            "add v26.4s, v26.4s, v29.4s\n"
-            "smax v19.4s, v19.4s, v30.4s\n"
-            "smax v20.4s, v20.4s, v30.4s\n"
-            "smax v25.4s, v25.4s, v30.4s\n"
-            "smax v26.4s, v26.4s, v30.4s\n"
-            "smin v19.4s, v19.4s, v31.4s\n"
-            "smin v20.4s, v20.4s, v31.4s\n"
-            "smin v25.4s, v25.4s, v31.4s\n"
-            "smin v26.4s, v26.4s, v31.4s\n"
-            "sqxtn v19.4h, v19.4s\n"
-            "sqxtn v25.4h, v25.4s\n"
-            "sqxtn2 v19.8h, v20.4s\n"
-            "ld1 {v20.4s}, [x5]\n"
-            "sqxtn2 v25.8h, v26.4s\n"
-            "ld1 {v26.4s}, [x5]\n"
-            "sqxtun v19.8b, v19.8h\n"
-            "sqxtun v25.8b, v25.8h\n"
-            "uaddw v14.8h, v28.8h, v14.8b\n"
-            "st1 {v19.8b}, [x10], %[output_depth]\n"
-            "uaddw v15.8h, v28.8h, v15.8b\n"
-            "st1 {v25.8b}, [x10], %[output_depth]\n"
-            "uaddw v16.8h, v28.8h, v16.8b\n"
-            "uaddw v17.8h, v28.8h, v17.8b\n"
-            "ld1 {v19.4s}, [%[bias_ptr]]\n"
-            "uaddw v18.8h, v28.8h, v18.8b\n"
-            "ld1 {v25.4s}, [%[bias_ptr]]\n"
-
-            "bge " DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP "b\n"
-
-          "cmp w4, #1\n"
-          "blt " DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP "f\n"
-
-          DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1 ":\n"
-          // Registers v9, v10, v11, v14, v15, and v16 have already been loaded
-          // with the correct values at this point. This corresponds to the
-          // first two input rows of the top left output. Now load the last
-          // input row for this output. Once these inputs are no longer needed,
-          // load the input rows for the bottom left output.
-          "ld1 {v12.8b}, [x2], %[input_depth]\n"
-          "smlal v21.4s, v0.4h, v9.4h\n"
-          "ld1 {v13.8b}, [x2], %[input_depth]\n"
-          "smlal2 v22.4s, v0.8h, v9.8h\n"
-          "ld1 {v17.8b}, [x2]\n"
-          "smlal v21.4s, v1.4h, v10.4h\n"
-          "ld1 {v9.8b}, [x0], %[input_depth]\n"
-          "smlal2 v22.4s, v1.8h, v10.8h\n"
-          "ld1 {v10.8b}, [x0], %[input_depth]\n"
-          "smlal v21.4s, v2.4h, v11.4h\n"
-          "smlal2 v22.4s, v2.8h, v11.8h\n"
-          "ld1 {v11.8b}, [x0]\n"
-          "smlal v21.4s, v3.4h, v14.4h\n"
-          "smlal2 v22.4s, v3.8h, v14.8h\n"
-          "ld1 {v14.8b}, [x1], %[input_depth]\n"
-          "smlal v21.4s, v4.4h, v15.4h\n"
-          "smlal2 v22.4s, v4.8h, v15.8h\n"
-          "ld1 {v15.8b}, [x1], %[input_depth]\n"
-          "smlal v21.4s, v5.4h, v16.4h\n"
-          "uaddw v12.8h, v28.8h, v12.8b\n"
-          "smlal2 v22.4s, v5.8h, v16.8h\n"
-          "uaddw v13.8h, v28.8h, v13.8b\n"
-          "ld1 {v16.8b}, [x1]\n"
-
-          "smlal v21.4s, v6.4h, v12.4h\n"
-          "smlal2 v22.4s, v6.8h, v12.8h\n"
-          "smlal v23.4s, v0.4h, v12.4h\n"
-          "uaddw v17.8h, v28.8h, v17.8b\n"
-          "smlal2 v24.4s, v0.8h, v12.8h\n"
-          "smlal v21.4s, v7.4h, v13.4h\n"
-          "smlal2 v22.4s, v7.8h, v13.8h\n"
-          "smlal v23.4s, v1.4h, v13.4h\n"
-          "smlal2 v24.4s, v1.8h, v13.8h\n"
-          "smlal v21.4s, v8.4h, v17.4h\n"
-          "smlal2 v22.4s, v8.8h, v17.8h\n"
-          "smlal v23.4s, v2.4h, v17.4h\n"
-          "smlal2 v24.4s, v2.8h, v17.8h\n"
-
-          "dup v26.4s, w7\n"
-          "sqrdmulh v21.4s, v21.4s, v27.4s\n"
-          "sqrdmulh v22.4s, v22.4s, v27.4s\n"
-          "and v18.16b, v21.16b, v26.16b\n"
-          "and v19.16b, v22.16b, v26.16b\n"
-          "sshr v18.4s, v18.4s, #31\n"
-          "sshr v19.4s, v19.4s, #31\n"
-          "sqadd v21.4s, v21.4s, v18.4s\n"
-          "sqadd v22.4s, v22.4s, v19.4s\n"
-          "srshl v21.4s, v21.4s, v26.4s\n"
-          "srshl v22.4s, v22.4s, v26.4s\n"
-          "add v21.4s, v21.4s, v29.4s\n"
-          "add v22.4s, v22.4s, v29.4s\n"
-          "smax v21.4s, v21.4s, v30.4s\n"
-          "smax v22.4s, v22.4s, v30.4s\n"
-          "smin v21.4s, v21.4s, v31.4s\n"
-          "smin v22.4s, v22.4s, v31.4s\n"
-          "sqxtn v21.4h, v21.4s\n"
-          "sqxtn2 v21.8h, v22.4s\n"
-          "sqxtun v21.8b, v21.8h\n"
-          "uaddw v9.8h, v28.8h, v9.8b\n"
-          "st1 {v21.8b}, [x3]\n"
-          "uaddw v10.8h, v28.8h, v10.8b\n"
-
-          "smlal v23.4s, v3.4h, v9.4h\n"
-          "uaddw v11.8h, v28.8h, v11.8b\n"
-          "smlal2 v24.4s, v3.8h, v9.8h\n"
-          "uaddw v14.8h, v28.8h, v14.8b\n"
-          "smlal v23.4s, v4.4h, v10.4h\n"
-          "uaddw v15.8h, v28.8h, v15.8b\n"
-          "smlal2 v24.4s, v4.8h, v10.8h\n"
-          "uaddw v16.8h, v28.8h, v16.8b\n"
-          "smlal v23.4s, v5.4h, v11.4h\n"
-          "smlal2 v24.4s, v5.8h, v11.8h\n"
-
-          "smlal v23.4s, v6.4h, v14.4h\n"
-          "smlal2 v24.4s, v6.8h, v14.8h\n"
-          "smlal v23.4s, v7.4h, v15.4h\n"
-          "smlal2 v24.4s, v7.8h, v15.8h\n"
-          "smlal v23.4s, v8.4h, v16.4h\n"
-          "smlal2 v24.4s, v8.8h, v16.8h\n"
-
-          "sqrdmulh v23.4s, v23.4s, v27.4s\n"
-          "sqrdmulh v24.4s, v24.4s, v27.4s\n"
-          "and v18.16b, v23.16b, v26.16b\n"
-          "and v19.16b, v24.16b, v26.16b\n"
-          "sshr v18.4s, v18.4s, #31\n"
-          "sshr v19.4s, v19.4s, #31\n"
-          "sqadd v23.4s, v23.4s, v18.4s\n"
-          "sqadd v24.4s, v24.4s, v19.4s\n"
-          "srshl v23.4s, v23.4s, v26.4s\n"
-          "srshl v24.4s, v24.4s, v26.4s\n"
-          "add v23.4s, v23.4s, v29.4s\n"
-          "add v24.4s, v24.4s, v29.4s\n"
-          "smax v23.4s, v23.4s, v30.4s\n"
-          "smax v24.4s, v24.4s, v30.4s\n"
-          "smin v23.4s, v23.4s, v31.4s\n"
-          "smin v24.4s, v24.4s, v31.4s\n"
-          "sqxtn v23.4h, v23.4s\n"
-          "sqxtn2 v23.8h, v24.4s\n"
-          "sqxtun v23.8b, v23.8h\n"
-          "st1 {v23.8b}, [x10]\n"
-
-          DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP ":\n"
-          "subs %w[output_window_height], %w[output_window_height], #2\n"
-          "add %[input_ptr], %[input_ptr], %[input_height_increment]\n"
-          "cmp %w[output_window_height], #2\n"
-          "add %[output_ptr], %[output_ptr], %[output_height_increment]\n"
-          "bge " DEPTHWISECONV_LABEL_HEIGHT_2_LOOP "b\n"
-
-        DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP ":\n"
-        "cmp %w[output_window_height], #1\n"
-        "blt " DEPTHWISECONV_LABEL_HEIGHT_1_END "f\n"
-
-        DEPTHWISECONV_LABEL_HEIGHT_1 ":\n"
-        "mov x6, %[input_ptr]\n"
-        "mov x0, x6\n"
-        "add x1, x0, %[input_row_size]\n"
-        "ld1 {v9.8b}, [x0], %[input_depth]\n"
-        "add x2, x1, %[input_row_size]\n"
-        "ld1 {v10.8b}, [x0], %[input_depth]\n"
-        "mov x3, %[output_ptr]\n"
-        "ld1 {v11.8b}, [x0], %[input_depth]\n"
-        "mov w4, %w[output_window_width]\n"
-        "ld1 {v18.8b}, [x0], %[input_depth]\n"
-        "cmp w4, #2\n"
-        "ld1 {v19.8b}, [x0]\n"
-        "ld1 {v12.8b}, [x1], %[input_depth]\n"
-        "ld1 {v13.8b}, [x1], %[input_depth]\n"
-        "ld1 {v14.8b}, [x1], %[input_depth]\n"
-        "ld1 {v20.8b}, [x1], %[input_depth]\n"
-        "ld1 {v21.8b}, [x1]\n"
-        "ld1 {v15.8b}, [x2], %[input_depth]\n"
-        "ld1 {v16.8b}, [x2], %[input_depth]\n"
-        "ld1 {v17.8b}, [x2], %[input_depth]\n"
-        "ld1 {v22.8b}, [x2], %[input_depth]\n"
-        "ld1 {v23.8b}, [x2]\n"
-
-        "uaddw v9.8h, v28.8h, v9.8b\n"
-        "ld1 {v24.4s}, [%[bias_ptr]]\n"
-        "uaddw v10.8h, v28.8h, v10.8b\n"
-        "ld1 {v25.4s}, [x5]\n"
-        "uaddw v11.8h, v28.8h, v11.8b\n"
-        "ld1 {v26.4s}, [%[bias_ptr]]\n"
-        "uaddw v18.8h, v28.8h, v18.8b\n"
-        "ld1 {v27.4s}, [x5]\n"
-        "uaddw v19.8h, v28.8h, v19.8b\n"
-        "uaddw v12.8h, v28.8h, v12.8b\n"
-        "uaddw v13.8h, v28.8h, v13.8b\n"
-        "uaddw v14.8h, v28.8h, v14.8b\n"
-        "uaddw v20.8h, v28.8h, v20.8b\n"
-        "uaddw v21.8h, v28.8h, v21.8b\n"
-        "uaddw v15.8h, v28.8h, v15.8b\n"
-        "uaddw v16.8h, v28.8h, v16.8b\n"
-        "uaddw v17.8h, v28.8h, v17.8b\n"
-        "uaddw v22.8h, v28.8h, v22.8b\n"
-        "uaddw v23.8h, v28.8h, v23.8b\n"
-
-        "blt " DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1 "f\n"
-
-        //"loop_%=:\n"
-        DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP ":\n"
-          "add x6, x6, %[input_width_increment]\n"
-          "smlal v24.4s, v0.4h, v9.4h\n"
-          "mov x0, x6\n"
-          "add x1, x0, %[input_row_size]\n"
-          "smlal2 v25.4s, v0.8h, v9.8h\n"
-          "ld1 {v9.8b}, [x0], %[input_depth]\n"
-          "smlal v26.4s, v0.4h, v11.4h\n"
-          "add x2, x1, %[input_row_size]\n"
-          "smlal2 v27.4s, v0.8h, v11.8h\n"
-          "subs w4, w4, #2\n"
-          "smlal v24.4s, v1.4h, v10.4h\n"
-          "cmp w4, #2\n"
-          "smlal2 v25.4s, v1.8h, v10.8h\n"
-          "ld1 {v10.8b}, [x0], %[input_depth]\n"
-          "smlal v26.4s, v1.4h, v18.4h\n"
-          "smlal2 v27.4s, v1.8h, v18.8h\n"
-          "smlal v24.4s, v2.4h, v11.4h\n"
-          "smlal2 v25.4s, v2.8h, v11.8h\n"
-          "ld1 {v11.8b}, [x0], %[input_depth]\n"
-          "smlal v26.4s, v2.4h, v19.4h\n"
-          "ld1 {v18.8b}, [x0], %[input_depth]\n"
-          "smlal2 v27.4s, v2.8h, v19.8h\n"
-          "ld1 {v19.8b}, [x0], %[input_depth]\n"
-          "smlal v24.4s, v3.4h, v12.4h\n"
-          "smlal2 v25.4s, v3.8h, v12.8h\n"
-          "ld1 {v12.8b}, [x1], %[input_depth]\n"
-          "smlal v26.4s, v3.4h, v14.4h\n"
-          "smlal2 v27.4s, v3.8h, v14.8h\n"
-          "smlal v24.4s, v4.4h, v13.4h\n"
-          "smlal2 v25.4s, v4.8h, v13.8h\n"
-          "ld1 {v13.8b}, [x1], %[input_depth]\n"
-          "smlal v26.4s, v4.4h, v20.4h\n"
-          "smlal2 v27.4s, v4.8h, v20.8h\n"
-          "smlal v24.4s, v5.4h, v14.4h\n"
-          "smlal2 v25.4s, v5.8h, v14.8h\n"
-          "ld1 {v14.8b}, [x1], %[input_depth]\n"
-          "smlal v26.4s, v5.4h, v21.4h\n"
-          "ld1 {v20.8b}, [x1], %[input_depth]\n"
-          "smlal2 v27.4s, v5.8h, v21.8h\n"
-          "ld1 {v21.8b}, [x1], %[input_depth]\n"
-          "smlal v24.4s, v6.4h, v15.4h\n"
-          "smlal2 v25.4s, v6.8h, v15.8h\n"
-          "ld1 {v15.8b}, [x2], %[input_depth]\n"
-          "smlal v26.4s, v6.4h, v17.4h\n"
-          "smlal2 v27.4s, v6.8h, v17.8h\n"
-          "smlal v24.4s, v7.4h, v16.4h\n"
-          "smlal2 v25.4s, v7.8h, v16.8h\n"
-          "ld1 {v16.8b}, [x2], %[input_depth]\n"
-          "smlal v26.4s, v7.4h, v22.4h\n"
-          "smlal2 v27.4s, v7.8h, v22.8h\n"
-          "smlal v24.4s, v8.4h, v17.4h\n"
-          "smlal2 v25.4s, v8.8h, v17.8h\n"
-          "ld1 {v17.8b}, [x2], %[input_depth]\n"
-          "smlal v26.4s, v8.4h, v23.4h\n"
-          "ld1 {v22.8b}, [x2], %[input_depth]\n"
-          "smlal2 v27.4s, v8.8h, v23.8h\n"
-          "ld1 {v23.8b}, [x2], %[input_depth]\n"
-
-          "dup v28.4s, %w[output_multiplier]\n"
-          "dup v29.4s, w7\n"
-          "sqrdmulh v24.4s, v24.4s, v28.4s\n"
-          "sqrdmulh v25.4s, v25.4s, v28.4s\n"
-          "sqrdmulh v26.4s, v26.4s, v28.4s\n"
-          "sqrdmulh v27.4s, v27.4s, v28.4s\n"
-          "dup v28.4s, %w[output_offset]\n"
-          "and v30.16b, v24.16b, v29.16b\n"
-          "and v31.16b, v25.16b, v29.16b\n"
-          "sshr v30.4s, v30.4s, #31\n"
-          "sshr v31.4s, v31.4s, #31\n"
-          "sqadd v24.4s, v24.4s, v30.4s\n"
-          "sqadd v25.4s, v25.4s, v31.4s\n"
-          "and v30.16b, v26.16b, v29.16b\n"
-          "and v31.16b, v27.16b, v29.16b\n"
-          "sshr v30.4s, v30.4s, #31\n"
-          "sshr v31.4s, v31.4s, #31\n"
-          "sqadd v26.4s, v26.4s, v30.4s\n"
-          "dup v30.4s, %w[output_activation_min]\n"
-          "sqadd v27.4s, v27.4s, v31.4s\n"
-          "dup v31.4s, %w[output_activation_max]\n"
-          "srshl v24.4s, v24.4s, v29.4s\n"
-          "srshl v25.4s, v25.4s, v29.4s\n"
-          "srshl v26.4s, v26.4s, v29.4s\n"
-          "srshl v27.4s, v27.4s, v29.4s\n"
-          "add v24.4s, v24.4s, v28.4s\n"
-          "add v25.4s, v25.4s, v28.4s\n"
-          "add v26.4s, v26.4s, v28.4s\n"
-          "add v27.4s, v27.4s, v28.4s\n"
-          "dup v28.8h, %w[input_offset]\n"
-          "smax v24.4s, v24.4s, v30.4s\n"
-          "smax v25.4s, v25.4s, v30.4s\n"
-          "smax v26.4s, v26.4s, v30.4s\n"
-          "smax v27.4s, v27.4s, v30.4s\n"
-          "smin v24.4s, v24.4s, v31.4s\n"
-          "smin v25.4s, v25.4s, v31.4s\n"
-          "smin v26.4s, v26.4s, v31.4s\n"
-          "smin v27.4s, v27.4s, v31.4s\n"
-          "sqxtn v24.4h, v24.4s\n"
-          "sqxtn v26.4h, v26.4s\n"
-          "sqxtn2 v24.8h, v25.4s\n"
-          "ld1 {v25.4s}, [x5]\n"
-          "sqxtn2 v26.8h, v27.4s\n"
-          "ld1 {v27.4s}, [x5]\n"
-          "sqxtun v24.8b, v24.8h\n"
-          "sqxtun v26.8b, v26.8h\n"
-          "uaddw v9.8h, v28.8h, v9.8b\n"
-          "st1 {v24.8b}, [x3], %[output_depth]\n"
-          "uaddw v10.8h, v28.8h, v10.8b\n"
-          "st1 {v26.8b}, [x3], %[output_depth]\n"
-          "uaddw v11.8h, v28.8h, v11.8b\n"
-          "uaddw v18.8h, v28.8h, v18.8b\n"
-          "uaddw v19.8h, v28.8h, v19.8b\n"
-          "uaddw v12.8h, v28.8h, v12.8b\n"
-          "uaddw v13.8h, v28.8h, v13.8b\n"
-          "uaddw v14.8h, v28.8h, v14.8b\n"
-          "uaddw v20.8h, v28.8h, v20.8b\n"
-          "uaddw v21.8h, v28.8h, v21.8b\n"
-          "ld1 {v24.4s}, [%[bias_ptr]]\n"
-          "uaddw v15.8h, v28.8h, v15.8b\n"
-          "ld1 {v26.4s}, [%[bias_ptr]]\n"
-          "uaddw v16.8h, v28.8h, v16.8b\n"
-          "uaddw v17.8h, v28.8h, v17.8b\n"
-          "uaddw v22.8h, v28.8h, v22.8b\n"
-          "uaddw v23.8h, v28.8h, v23.8b\n"
-
-          "bge " DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP "b\n"
-
-        "cmp w4, #1\n"
-        "blt " DEPTHWISECONV_LABEL_HEIGHT_1_END "f\n"
-
-        DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1 ":\n"
-        "dup v26.4s, w7\n"
-        "dup v27.4s, %w[output_multiplier]\n"
-        "dup v29.4s, %w[output_offset]\n"
-
-        "smlal v24.4s, v0.4h, v9.4h\n"
-        "smlal2 v25.4s, v0.8h, v9.8h\n"
-        "smlal v24.4s, v1.4h, v10.4h\n"
-        "smlal2 v25.4s, v1.8h, v10.8h\n"
-        "smlal v24.4s, v2.4h, v11.4h\n"
-        "smlal2 v25.4s, v2.8h, v11.8h\n"
-        "smlal v24.4s, v3.4h, v12.4h\n"
-        "smlal2 v25.4s, v3.8h, v12.8h\n"
-        "smlal v24.4s, v4.4h, v13.4h\n"
-        "smlal2 v25.4s, v4.8h, v13.8h\n"
-        "smlal v24.4s, v5.4h, v14.4h\n"
-        "smlal2 v25.4s, v5.8h, v14.8h\n"
-        "smlal v24.4s, v6.4h, v15.4h\n"
-        "smlal2 v25.4s, v6.8h, v15.8h\n"
-        "smlal v24.4s, v7.4h, v16.4h\n"
-        "smlal2 v25.4s, v7.8h, v16.8h\n"
-        "smlal v24.4s, v8.4h, v17.4h\n"
-        "smlal2 v25.4s, v8.8h, v17.8h\n"
-
-        "sqrdmulh v24.4s, v24.4s, v27.4s\n"
-        "sqrdmulh v25.4s, v25.4s, v27.4s\n"
-        "and v18.16b, v24.16b, v26.16b\n"
-        "and v19.16b, v25.16b, v26.16b\n"
-        "sshr v18.4s, v18.4s, #31\n"
-        "sshr v19.4s, v19.4s, #31\n"
-        "sqadd v24.4s, v24.4s, v18.4s\n"
-        "sqadd v25.4s, v25.4s, v19.4s\n"
-        "srshl v24.4s, v24.4s, v26.4s\n"
-        "srshl v25.4s, v25.4s, v26.4s\n"
-        "add v24.4s, v24.4s, v29.4s\n"
-        "add v25.4s, v25.4s, v29.4s\n"
-        "smax v24.4s, v24.4s, v30.4s\n"
-        "smax v25.4s, v25.4s, v30.4s\n"
-        "smin v24.4s, v24.4s, v31.4s\n"
-        "smin v25.4s, v25.4s, v31.4s\n"
-        "sqxtn v24.4h, v24.4s\n"
-        "sqxtn2 v24.8h, v25.4s\n"
-        "sqxtun v24.8b, v24.8h\n"
-        "st1 {v24.8b}, [x3]\n"
-
-        DEPTHWISECONV_LABEL_HEIGHT_1_END ":\n"
-    :
-    // Outputs.
-    [filter_ptr] "+r"(filter_ptr), [input_ptr] "+r"(input_ptr),
-    [output_ptr] "+r"(output_ptr),
-    [output_window_height] "+r"(output_window_height)
-    :
-    // Inputs.
-    [bias_ptr] "r"(bias_ptr), [output_depth] "r"(output_depth),
-    [filter_offset] "r"(filter_offset), [input_row_size] "r"(input_row_size),
-    [input_depth] "r"(input_depth), [input_offset] "r"(input_offset),
-    [output_multiplier] "r"(output_multiplier),
-    [output_shift] "r"(output_shift), [output_offset] "r"(output_offset),
-    [output_activation_min] "r"(output_activation_min),
-    [output_activation_max] "r"(output_activation_max),
-    [output_window_width] "r"(output_window_width),
-    [input_width_increment] "r"(input_width_increment),
-    [input_height_increment] "r"(input_height_increment),
-    [output_height_increment] "r"(output_height_increment),
-    [output_row_size] "r"(output_row_size)
-    :
-    // Clobbers.
-    // We use these NEON registers.
-    "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11",
-    "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21",
-    "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31",
-    // We use these general-purpose registers.
-    "x0", "x1", "x2", "x3", "w4", "x5", "x6", "w7", "x10");
-#undef DEPTHWISECONV_LABEL_HEIGHT_1_END
-#undef DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_1
-#undef DEPTHWISECONV_LABEL_HEIGHT_1_WIDTH_2_LOOP
-#undef DEPTHWISECONV_LABEL_HEIGHT_1
-#undef DEPTHWISECONV_LABEL_HEIGHT_2_AFTER_LOOP
-#undef DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_AFTER_LOOP
-#undef DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_1
-#undef DEPTHWISECONV_LABEL_HEIGHT_2_WIDTH_2_LOOP
-#undef DEPTHWISECONV_LABEL_HEIGHT_2_LOOP
-  }
-};
+                         int32 output_activation_max, uint8* output_data,
+                         int output_depth, int output_width,
+                         uint8* shuffle_workspace) {
+    int out_x = start_x;
 
-// Copies a subset of the input designated by |input_ptr| into |output_ptr|
-// with the specified output dimensions. Supports output depths of 64 only as
-// this is the cache line size.
-inline void ShuffleInput(const uint8* input_ptr, int64_t input_depth,
-                         int input_width, int input_height,
-                         int64_t output_depth, int output_width,
-                         int output_height, uint8* output_ptr) {
-  const int64_t input_row_size = input_depth * input_width;
-  for (int y = 0; y < output_height; y++) {
-    const uint8* ptr = input_ptr;
-    for (int x = 0; x < output_width; x++) {
-      memcpy(output_ptr, ptr, output_depth);
-      output_ptr += output_depth;
-      ptr += input_depth;
+    // 4x4 at a time.
+    for (; out_x <= output_width - 4; out_x += 4) {
+      const int32* bias_ptr = bias_data;
+      const uint8* filter_ptr = filter_data;
+
+      const uint8* input_ptr = input_data;
+      uint8* output_ptr = output_data;
+
+      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
+        ConvKernel3x3FilterDepth8<4, 4, 1, 1>::Run(
+            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
+            filter_offset, bias_ptr, output_offset, output_multiplier,
+            output_shift, output_activation_min, output_activation_max,
+            output_ptr, output_depth, output_width);
+
+        input_ptr += 8;
+        output_ptr += 8;
+        filter_ptr += 8;
+        bias_ptr += 8;
+      }
+
+      input_data += 4 * input_depth;
+      output_data += 4 * output_depth;
+    }
+
+    // Handle the rest of the right side.
+    // 4x2 at a time.
+    for (; out_x <= output_width - 2; out_x += 2) {
+      const int32* bias_ptr = bias_data;
+      const uint8* filter_ptr = filter_data;
+
+      const uint8* input_ptr = input_data;
+      uint8* output_ptr = output_data;
+
+      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
+        ConvKernel3x3FilterDepth8<4, 2, 1, 1>::Run(
+            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
+            filter_offset, bias_ptr, output_offset, output_multiplier,
+            output_shift, output_activation_min, output_activation_max,
+            output_ptr, output_depth, output_width);
+
+        input_ptr += 8;
+        output_ptr += 8;
+        filter_ptr += 8;
+        bias_ptr += 8;
+      }
+
+      input_data += 2 * input_depth;
+      output_data += 2 * output_depth;
+    }
+
+    // 4x1 at a time.
+    for (; out_x < output_width; out_x++) {
+      const int32* bias_ptr = bias_data;
+      const uint8* filter_ptr = filter_data;
+
+      const uint8* input_ptr = input_data;
+      uint8* output_ptr = output_data;
+
+      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
+        ConvKernel3x3FilterDepth8<4, 1, 1, 1>::Run(
+            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
+            filter_offset, bias_ptr, output_offset, output_multiplier,
+            output_shift, output_activation_min, output_activation_max,
+            output_ptr, output_depth, output_width);
+
+        input_ptr += 8;
+        output_ptr += 8;
+        filter_ptr += 8;
+        bias_ptr += 8;
+      }
+
+      input_data += input_depth;
+      output_data += output_depth;
     }
-    input_ptr += input_row_size;
   }
-}
+};
 
-template 
-struct DepthwiseConvMultiRow {
- public:
-  constexpr static int kShuffleInputHeight =
-      kStrideHeight * (kShuffleOutputHeight - 1) + 3;
-  constexpr static int kShuffleInputWidth =
-      kStrideWidth * (kShuffleOutputWidth - 1) + 3;
+template <>
+struct ConvRow3x3FilterDepth8<4, 2, 2> {
+  // The buffer size of the shuffled input.
+  static inline constexpr int ShuffleWorkspaceSize() { return 64 * 9 * 9; }
 
   static inline void Run(const uint8* input_data, int start_x, int start_y,
-                         int64_t input_depth, int input_width, int input_height,
-                         int64_t input_row_size, int32 input_offset,
+                         int input_depth, int input_width, int input_height,
+                         int input_row_size, int32 input_offset,
                          const uint8* filter_data, int32 filter_offset,
                          const int32* bias_data, int32 output_offset,
                          int32 output_multiplier, int output_shift,
                          int32 output_activation_min,
                          int32 output_activation_max, uint8* output_data,
-                         int64_t output_depth, int output_width,
+                         int output_depth, int output_width,
                          uint8* shuffle_workspace) {
-    // Make sure shuffle parameters fall within the allowed workspace size.
-    static_assert(64 * kShuffleInputWidth * kShuffleInputHeight <=
-                  DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE,
-                  "Shuffle workspace size is too large.");
-
-    // Although it is possible to have kOutputRows != kShuffleOutputHeight, the
-    // below code assumes that they are the same.
-    static_assert(kOutputRows == kShuffleOutputHeight,
-                  "Output heights that are not equal to the shuffle output "
-                  "height are not supported.");
+    // Branch and cache misses increase substantially with stride 2 kernels.
+    // Adding prefetching reduces latency by as much as 2x.
+    const int i0 = 0;
+    const int i1 = input_depth;
+    const int i2 = 2 * input_depth;
+    const int i3 = 3 * input_depth;
+    const int i4 = 4 * input_depth;
+    const int i5 = 5 * input_depth;
+    const int i6 = 6 * input_depth;
+    const int i7 = 7 * input_depth;
+    const int i8 = 8 * input_depth;
+
+#define DEPTHWISECONV_PRELOAD_ROW(input_ptr, i)         \
+  preload_l1_keep(input_ptr + i * input_row_size + i0); \
+  preload_l1_keep(input_ptr + i * input_row_size + i1); \
+  preload_l1_keep(input_ptr + i * input_row_size + i2); \
+  preload_l1_keep(input_ptr + i * input_row_size + i3); \
+  preload_l1_keep(input_ptr + i * input_row_size + i4); \
+  preload_l1_keep(input_ptr + i * input_row_size + i5); \
+  preload_l1_keep(input_ptr + i * input_row_size + i6); \
+  preload_l1_keep(input_ptr + i * input_row_size + i7); \
+  preload_l1_keep(input_ptr + i * input_row_size + i8);
 
     int out_x = start_x;
-    // Run shuffling on inputs with sufficiently large depth and width. When
-    // these parameters are large enough, more time is taken to load inputs from
-    // memory. At this point, it becomes useful to prefetch and preshuffle the
-    // input data to maximize locality.
-    if (output_depth > 64 || (output_depth <= 64 && input_width > 150)) {
-      for (; out_x <= output_width - kShuffleOutputWidth;
-             out_x += kShuffleOutputWidth) {
-        const uint8* input_ptr = input_data;
-        const int32* bias_ptr = bias_data;
-        const uint8* filter_ptr = filter_data;
-        uint8* output_ptr = output_data;
-        int64_t depth = 0;
-        for (; depth <= output_depth - 64; depth += 64) {
-          // Preload.
-          const uint8* h_ptr = input_ptr;
-          for (int i = 0; i < kShuffleInputHeight; i++) {
-            const uint8* ptr = h_ptr;
-            for (int j = 0; j < kShuffleInputWidth; j++) {
-              asm volatile("prfm pldl1keep, [%[ptr]]\n" ::[ptr] "r"(ptr) :);
-              ptr += input_depth;
-            }
-            h_ptr += input_row_size;
-          }
-
-          // For a large enough input, shuffle into 64 x kShuffleInputWidth x
-          // kShuffleInputHeight buckets.
-          ShuffleInput(input_ptr, input_depth, input_width, input_height, 64,
-                       kShuffleInputWidth, kShuffleInputHeight,
-                       shuffle_workspace);
-          const uint8* shuffled_ptr = shuffle_workspace;
-
-          for (int micro_depth = 0; micro_depth <= 64 - 8; micro_depth += 8) {
-            DepthwiseConvWindow<8, kStrideWidth, kStrideHeight>::Run(
-                shuffled_ptr, 64, input_offset, 64 * kShuffleInputWidth,
-                filter_ptr, filter_offset, bias_ptr, output_offset,
-                output_multiplier, output_shift, output_activation_min,
-                output_activation_max, output_ptr, output_depth, output_width,
-               kShuffleOutputHeight, kShuffleOutputWidth);
-
-            shuffled_ptr += 8;
-            output_ptr += 8;
-            filter_ptr += 8;
-            bias_ptr += 8;
-          }
-          input_ptr += 64;
-        }
-
-        // Preload.
-        const uint8* h_ptr = input_ptr;
-        for (int i = 0; i < kShuffleInputHeight; i++) {
-          const uint8* ptr = h_ptr;
-          for (int j = 0; j < kShuffleInputWidth; j++) {
-            asm volatile("prfm pldl1keep, [%[ptr]]\n" ::[ptr] "r"(ptr) :);
-            ptr += input_depth;
-          }
-          h_ptr += input_row_size;
-        }
+    // 4x4 at a time.
+    for (; out_x <= output_width - 4; out_x += 4) {
+      const int32* bias_ptr = bias_data;
+      const uint8* filter_ptr = filter_data;
 
-        // Handle leftover depth.
-        for (; depth <= output_depth - 8; depth += 8) {
-          DepthwiseConvWindow<8, kStrideWidth, kStrideHeight>::Run(input_ptr,
-              input_depth, input_offset, input_row_size, filter_ptr,
-              filter_offset, bias_ptr, output_offset, output_multiplier,
-              output_shift, output_activation_min, output_activation_max,
-              output_ptr, output_depth, output_width, kShuffleOutputHeight,
-              kShuffleOutputWidth);
+      const uint8* input_ptr = input_data;
+      uint8* output_ptr = output_data;
 
-          input_ptr += 8;
+      int depth = 0;
+      for (; depth <= output_depth - 64; depth += 64) {
+        // Preload 9x9 input.
+        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 0);
+        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 1);
+        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 2);
+        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 3);
+        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 4);
+        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 5);
+        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 6);
+        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 7);
+        DEPTHWISECONV_PRELOAD_ROW(input_ptr, 8);
+
+        // For a large input window (64x9x9) that is small enough to fit in L1
+        // cache, copy the input into a separate buffer and run the kernel on
+        // this new buffer. This reduces the likelihood of cache misses when
+        // the kernel is loading input data. If this size is ever changed,
+        // update the ShuffleWorkspaceSize() function to return the new size.
+        ShuffleInput(input_ptr, input_depth, input_width, input_height, 64, 9,
+                     9, shuffle_workspace);
+        const uint8* shuffled_ptr = &shuffle_workspace[0];
+
+        for (int micro_depth = 0; micro_depth <= 64 - 8; micro_depth += 8) {
+          ConvKernel3x3FilterDepth8<4, 4, 2, 2>::Run(
+              shuffled_ptr, 64, input_offset, 64 * 9, filter_ptr, filter_offset,
+              bias_ptr, output_offset, output_multiplier, output_shift,
+              output_activation_min, output_activation_max, output_ptr,
+              output_depth, output_width);
+
+          shuffled_ptr += 8;
           output_ptr += 8;
           filter_ptr += 8;
           bias_ptr += 8;
         }
+        input_ptr += 64;
+      }
+
+      // Preload 9x9 input one more time for the rest of the depth.
+      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 0);
+      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 1);
+      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 2);
+      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 3);
+      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 4);
+      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 5);
+      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 6);
+      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 7);
+      DEPTHWISECONV_PRELOAD_ROW(input_ptr, 8);
+
+      for (; depth <= output_depth - 8; depth += 8) {
+        ConvKernel3x3FilterDepth8<4, 4, 2, 2>::Run(
+            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
+            filter_offset, bias_ptr, output_offset, output_multiplier,
+            output_shift, output_activation_min, output_activation_max,
+            output_ptr, output_depth, output_width);
+
+        input_ptr += 8;
+        output_ptr += 8;
+        filter_ptr += 8;
+        bias_ptr += 8;
+      }
+
+      input_data += 4 * 2 * input_depth;
+      output_data += 4 * output_depth;
+    }
+
+#undef DEPTHWISECONV_PRELOAD_ROW
+
+    // Handle the rest of the right side.
+    // 4x2 at a time.
+    for (; out_x <= output_width - 2; out_x += 2) {
+      const int32* bias_ptr = bias_data;
+      const uint8* filter_ptr = filter_data;
+
+      const uint8* input_ptr = input_data;
+      uint8* output_ptr = output_data;
+
+      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
+        ConvKernel3x3FilterDepth8<4, 2, 2, 2>::Run(
+            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
+            filter_offset, bias_ptr, output_offset, output_multiplier,
+            output_shift, output_activation_min, output_activation_max,
+            output_ptr, output_depth, output_width);
+
+        input_ptr += 8;
+        output_ptr += 8;
+        filter_ptr += 8;
+        bias_ptr += 8;
+      }
+
+      input_data += 2 * 2 * input_depth;
+      output_data += 2 * output_depth;
+    }
+
+    // 4x1 at a time.
+    for (; out_x < output_width; out_x++) {
+      const int32* bias_ptr = bias_data;
+      const uint8* filter_ptr = filter_data;
+
+      const uint8* input_ptr = input_data;
+      uint8* output_ptr = output_data;
 
-        input_data += kShuffleOutputWidth * kStrideWidth * input_depth;
-        output_data += kShuffleOutputWidth * output_depth;
+      for (int depth = 0; depth <= output_depth - 8; depth += 8) {
+        ConvKernel3x3FilterDepth8<4, 1, 2, 2>::Run(
+            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
+            filter_offset, bias_ptr, output_offset, output_multiplier,
+            output_shift, output_activation_min, output_activation_max,
+            output_ptr, output_depth, output_width);
+
+        input_ptr += 8;
+        output_ptr += 8;
+        filter_ptr += 8;
+        bias_ptr += 8;
       }
+
+      input_data += 2 * input_depth;
+      output_data += output_depth;
     }
+  }
+};
+
+template <>
+struct ConvRow3x3FilterDepth8<8, 2, 2> {
+  static inline void Run(const uint8* input_data, int start_x, int start_y,
+                         int input_depth, int input_width, int input_height,
+                         int input_row_size, int32 input_offset,
+                         const uint8* filter_data, int32 filter_offset,
+                         const int32* bias_data, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_data,
+                         int output_depth, int output_width,
+                         uint8* shuffle_workspace) {
+    // Reuse 4 row kernels twice.
+    ConvRow3x3FilterDepth8<4, 2, 2>::Run(
+        input_data, start_x, start_y, input_depth, input_width, input_height,
+        input_row_size, input_offset, filter_data, filter_offset, bias_data,
+        output_offset, output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_data, output_depth, output_width,
+        shuffle_workspace);
+
+    ConvRow3x3FilterDepth8<4, 2, 2>::Run(
+        input_data + 2 * 4 * input_row_size, start_x, start_y + 4, input_depth,
+        input_width, input_height, input_row_size, input_offset, filter_data,
+        filter_offset, bias_data, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max,
+        output_data + 4 * output_depth * output_width, output_depth,
+        output_width, shuffle_workspace);
+  }
+};
+
+template <>
+struct ConvRow3x3FilterDepth8<8, 1, 1> {
+  // The buffer size of the shuffled input.
+  static inline constexpr int ShuffleWorkspaceSize() { return 64 * 10 * 10; }
 
-    const int output_leftover_width = output_width - out_x;
-    if (output_leftover_width > 0) {
+  static inline void Run(const uint8* input_data, int start_x, int start_y,
+                         int input_depth, int input_width, int input_height,
+                         int input_row_size, int32 input_offset,
+                         const uint8* filter_data, int32 filter_offset,
+                         const int32* bias_data, int32 output_offset,
+                         int32 output_multiplier, int output_shift,
+                         int32 output_activation_min,
+                         int32 output_activation_max, uint8* output_data,
+                         int output_depth, int output_width,
+                         uint8* shuffle_workspace) {
+    int out_x = start_x;
+    // 8x8 at a time.
+    for (; out_x <= output_width - 8; out_x += 8) {
       const int32* bias_ptr = bias_data;
       const uint8* filter_ptr = filter_data;
+
       const uint8* input_ptr = input_data;
       uint8* output_ptr = output_data;
 
-      for (int64_t depth = 0; depth <= output_depth - 8; depth += 8) {
-        DepthwiseConvWindow<8, kStrideWidth, kStrideHeight>::Run(input_ptr,
-            input_depth, input_offset, input_row_size, filter_ptr,
+      int depth = 0;
+      for (; depth <= output_depth - 64; depth += 64) {
+        // For a large input window (64x10x10) that is small enough to fit in L1
+        // cache, copy the input into a separate buffer and run the kernel on
+        // this new buffer. This reduces the likelihood of cache misses when
+        // the kernel is loading input data. If the size of the input window
+        // changes, update the function ShuffleWorkspaceSize() with the new
+        // size.
+        ShuffleInput(input_ptr, input_depth, input_width, input_height, 64, 10,
+                     10, shuffle_workspace);
+        const uint8* shuffled_ptr = shuffle_workspace;
+
+        for (int micro_depth = 0; micro_depth <= 64 - 8; micro_depth += 8) {
+          ConvKernel3x3FilterDepth8<8, 8, 1, 1>::Run(
+              shuffled_ptr, 64, input_offset, 64 * 10, filter_ptr,
+              filter_offset, bias_ptr, output_offset, output_multiplier,
+              output_shift, output_activation_min, output_activation_max,
+              output_ptr, output_depth, output_width);
+
+          shuffled_ptr += 8;
+          output_ptr += 8;
+          filter_ptr += 8;
+          bias_ptr += 8;
+        }
+        input_ptr += 64;
+      }
+
+      for (; depth <= output_depth - 8; depth += 8) {
+        ConvKernel3x3FilterDepth8<8, 8, 1, 1>::Run(
+            input_ptr, input_depth, input_offset, input_row_size, filter_ptr,
             filter_offset, bias_ptr, output_offset, output_multiplier,
             output_shift, output_activation_min, output_activation_max,
-            output_ptr, output_depth, output_width, kShuffleOutputHeight,
-            output_leftover_width);
+            output_ptr, output_depth, output_width);
 
         input_ptr += 8;
         output_ptr += 8;
         filter_ptr += 8;
         bias_ptr += 8;
       }
+
+      input_data += 8 * input_depth;
+      output_data += 8 * output_depth;
     }
+
+    // Handle the rest of the right side by re-using 4 row kernels twice.
+    ConvRow3x3FilterDepth8<4, 1, 1>::Run(
+        input_data, out_x, start_y, input_depth, input_width, input_height,
+        input_row_size, input_offset, filter_data, filter_offset, bias_data,
+        output_offset, output_multiplier, output_shift, output_activation_min,
+        output_activation_max, output_data, output_depth, output_width,
+        shuffle_workspace);
+
+    ConvRow3x3FilterDepth8<4, 1, 1>::Run(
+        input_data + 4 * input_row_size, out_x, start_y + 4, input_depth,
+        input_width, input_height, input_row_size, input_offset, filter_data,
+        filter_offset, bias_data, output_offset, output_multiplier,
+        output_shift, output_activation_min, output_activation_max,
+        output_data + 4 * output_depth * output_width, output_depth,
+        output_width, shuffle_workspace);
   }
 };
 
@@ -1703,13 +4458,11 @@ inline void DepthwiseConv3x3Filter(
     int32 output_offset, int32 output_multiplier, int output_shift,
     int32 output_activation_min, int32 output_activation_max,
     uint8* output_data, const Dims<4>& output_dims) {
-  // 64-bit is used for types that will be added to 64-bit addresses in asm.
   const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
-  const int64_t output_depth =
-      MatchingArraySize(filter_dims, 0, output_dims, 0);
+  const int output_depth = MatchingArraySize(filter_dims, 0, output_dims, 0);
   const int input_height = ArraySize(input_dims, 2);
   const int input_width = ArraySize(input_dims, 1);
-  const int64_t input_depth = ArraySize(input_dims, 0);
+  const int input_depth = ArraySize(input_dims, 0);
   const int filter_height = ArraySize(filter_dims, 2);
   const int filter_width = ArraySize(filter_dims, 1);
   const int output_height = ArraySize(output_dims, 2);
@@ -1727,40 +4480,22 @@ inline void DepthwiseConv3x3Filter(
   TFLITE_DCHECK(stride_width == 1 || stride_width == 2);
   TFLITE_DCHECK(stride_width == stride_height);
 
-  const int64_t input_row_size = input_depth * (input_width + 2 * pad_width);
-  const int64_t output_row_size = output_depth * output_width;
-  const int64_t input_batch_size =
-      input_row_size * (input_height + 2 * pad_height);
-  const int64_t output_batch_size = output_depth * output_width * output_height;
-
-  using conv_row_func_t = decltype(&DepthwiseConvMultiRow<1, 1, 1, 1, 1>::Run);
-  conv_row_func_t conv_1_output_row, conv_2_output_rows, conv_4_output_rows,
-      conv_8_output_rows;
-
-  int conv_2_shuffle_input_width = 0;
-  int conv_4_shuffle_input_width = 0;
-
-  if (stride_width == 1) {
-    conv_1_output_row = DepthwiseConvMultiRow<1, 1, 30, 1, 1>::Run;
-    conv_2_output_rows = DepthwiseConvMultiRow<2, 2, 22, 1, 1>::Run;
-    conv_4_output_rows = DepthwiseConvMultiRow<4, 4, 14, 1, 1>::Run;
-    conv_8_output_rows = DepthwiseConvMultiRow<8, 8, 8, 1, 1>::Run;
-
-    conv_2_shuffle_input_width =
-        DepthwiseConvMultiRow<2, 2, 22, 1, 1>::kShuffleInputWidth;
-    conv_4_shuffle_input_width =
-        DepthwiseConvMultiRow<4, 4, 14, 1, 1>::kShuffleInputWidth;
-
-  } else {
-    conv_1_output_row = DepthwiseConvMultiRow<1, 1, 14, 2, 2>::Run;
-    conv_2_output_rows = DepthwiseConvMultiRow<2, 2, 8, 2, 2>::Run;
-    conv_4_output_rows = DepthwiseConvMultiRow<4, 4, 4, 2, 2>::Run;
-    conv_8_output_rows = DepthwiseConvMultiRow<8, 8, 2, 2, 2>::Run;
-
-    conv_2_shuffle_input_width =
-        DepthwiseConvMultiRow<2, 2, 8, 2, 2>::kShuffleInputWidth;
-    conv_4_shuffle_input_width =
-        DepthwiseConvMultiRow<4, 4, 4, 2, 2>::kShuffleInputWidth;
+  const int input_row_size = input_depth * (input_width + 2 * pad_width);
+  const int output_row_size = output_depth * output_width;
+  const int input_batch_size = input_row_size * (input_height + 2 * pad_height);
+  const int output_batch_size = output_depth * output_width * output_height;
+
+  using conv_row_func_t = decltype(&ConvRow3x3FilterDepth8<1, 1, 1>::Run);
+  conv_row_func_t conv_1_output_row = ConvRow3x3FilterDepth8<1, 1, 1>::Run;
+  conv_row_func_t conv_2_output_rows = ConvRow3x3FilterDepth8<2, 1, 1>::Run;
+  conv_row_func_t conv_4_output_rows = ConvRow3x3FilterDepth8<4, 1, 1>::Run;
+  conv_row_func_t conv_8_output_rows = ConvRow3x3FilterDepth8<8, 1, 1>::Run;
+
+  if (stride_width == 2) {
+    conv_1_output_row = ConvRow3x3FilterDepth8<1, 2, 2>::Run;
+    conv_2_output_rows = ConvRow3x3FilterDepth8<2, 2, 2>::Run;
+    conv_4_output_rows = ConvRow3x3FilterDepth8<4, 2, 2>::Run;
+    conv_8_output_rows = ConvRow3x3FilterDepth8<8, 2, 2>::Run;
   }
 
   // Allocate maximum memory needed for shuffled input.
@@ -1768,56 +4503,49 @@ inline void DepthwiseConv3x3Filter(
   // allocated on the stack. Eventually we will want to move it to the heap
   // and have it allocated outside of this function, like the im2col_array used
   // in gemmlowp.
+#define DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE 10 * 10 * 64
   uint8 shuffle_workspace[DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE];
 
+  // Make sure the kernels using this buffer will not run out of bounds.
+  static_assert(ConvRow3x3FilterDepth8<8, 1, 1>::ShuffleWorkspaceSize() <=
+                    DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE,
+                "Shuffle workspace size is too small.");
+  static_assert(ConvRow3x3FilterDepth8<4, 2, 2>::ShuffleWorkspaceSize() <=
+                    DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE,
+                "Shuffle workspace size is too small.");
+
+#undef DEPTHWISECONV_SHUFFLE_WORKSPACE_SIZE
+
   for (int b = 0; b < batches; ++b) {
     const uint8* input_ptr = input_data + b * input_batch_size;
     uint8* output_ptr = output_data + b * output_batch_size;
 
     int out_y = 0;
 
-    // Shuffling shapes that maximize width over the shuffle workspace size
-    // perform better since the inputs are closer together, minimizing shuffling
-    // time.
-    //
-    // If the input shape has width large enough for the 2 height kernels
-    // |conv_2_output_rows|, we prefer to use this. The innermost loop of the
-    // kernels handle 2 height x 2 width so this is the fastest path.
-    //
-    // If the input shape has smaller width but larger height, shuffling is
-    // still useful and can benefit from kernels |conv_4_output_rows| and
-    // |conv_8_output_rows|.
-
     // Handle 8 rows at a time.
-    if (input_width < conv_4_shuffle_input_width) {
-      for (; out_y <= output_height - 8; out_y += 8) {
-        conv_8_output_rows(input_ptr, 0, out_y, input_depth, input_width,
-                           input_height, input_row_size, input_offset,
-                           filter_data, filter_offset, bias_data,
-                           output_offset, output_multiplier, output_shift,
-                           output_activation_min, output_activation_max,
-                           output_ptr, output_depth, output_width,
-                           shuffle_workspace);
-
-        input_ptr += 8 * stride_height * input_row_size;
-        output_ptr += 8 * output_row_size;
-      }
+    for (; out_y <= output_height - 8; out_y += 8) {
+      conv_8_output_rows(input_ptr, 0, out_y, input_depth, input_width,
+                         input_height, input_row_size, input_offset,
+                         filter_data, filter_offset, bias_data, output_offset,
+                         output_multiplier, output_shift, output_activation_min,
+                         output_activation_max, output_ptr, output_depth,
+                         output_width, shuffle_workspace);
+
+      input_ptr += 8 * stride_height * input_row_size;
+      output_ptr += 8 * output_row_size;
     }
 
     // Handle 4 rows at a time.
-    if (input_width < conv_2_shuffle_input_width) {
-      for (; out_y <= output_height - 4; out_y += 4) {
-        conv_4_output_rows(input_ptr, 0, out_y, input_depth, input_width,
-                           input_height, input_row_size, input_offset,
-                           filter_data, filter_offset, bias_data,
-                           output_offset, output_multiplier, output_shift,
-                           output_activation_min, output_activation_max,
-                           output_ptr, output_depth, output_width,
-                           shuffle_workspace);
-
-        input_ptr += 4 * stride_height * input_row_size;
-        output_ptr += 4 * output_row_size;
-      }
+    for (; out_y <= output_height - 4; out_y += 4) {
+      conv_4_output_rows(input_ptr, 0, out_y, input_depth, input_width,
+                         input_height, input_row_size, input_offset,
+                         filter_data, filter_offset, bias_data, output_offset,
+                         output_multiplier, output_shift, output_activation_min,
+                         output_activation_max, output_ptr, output_depth,
+                         output_width, shuffle_workspace);
+
+      input_ptr += 4 * stride_height * input_row_size;
+      output_ptr += 4 * output_row_size;
     }
 
     // Handle 2 rows at a time.
@@ -1847,7 +4575,6 @@ inline void DepthwiseConv3x3Filter(
     }
   }
 }
-// clang-format on
 
 #endif  // __aarch64__
 

From a436cf493d3a590572aec9fe574f0e9028e8b61e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Fri, 11 May 2018 23:48:06 -0700
Subject: [PATCH 0706/1691] Adding cuDNN header dependency to targets that
 include the cuDNN header file.

PiperOrigin-RevId: 196349902
---
 tensorflow/contrib/fused_conv/BUILD     | 2 ++
 tensorflow/core/grappler/clusters/BUILD | 3 +++
 tensorflow/core/grappler/costs/BUILD    | 3 +++
 tensorflow/core/kernels/BUILD           | 4 ++--
 third_party/gpus/cuda/BUILD.tpl         | 9 +++++++++
 5 files changed, 19 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/fused_conv/BUILD b/tensorflow/contrib/fused_conv/BUILD
index 0eb6889db1fae1..0f0813c07f8bd3 100644
--- a/tensorflow/contrib/fused_conv/BUILD
+++ b/tensorflow/contrib/fused_conv/BUILD
@@ -75,6 +75,7 @@ tf_kernel_library(
         "//tensorflow/core/kernels:gpu_util_hdrs",
         "//tensorflow/core/kernels:ops_util_hdrs",
         "//third_party/eigen3",
+        "@local_config_cuda//cuda:cudnn_header",
     ],
     alwayslink = 1,
 )
@@ -94,6 +95,7 @@ tf_custom_op_library(
         "//tensorflow/core/kernels:conv_ops_gpu_hdrs",
         "//tensorflow/core/kernels:gpu_util_hdrs",
         "//tensorflow/core/kernels:ops_util_hdrs",
+        "@local_config_cuda//cuda:cudnn_header",
     ],
 )
 
diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD
index 30c6126fbb58c1..d0b2cf01be221d 100644
--- a/tensorflow/core/grappler/clusters/BUILD
+++ b/tensorflow/core/grappler/clusters/BUILD
@@ -20,6 +20,9 @@ tf_cuda_library(
     name = "utils",
     srcs = ["utils.cc"],
     hdrs = ["utils.h"],
+    cuda_deps = [
+        "@local_config_cuda//cuda:cudnn_header",
+    ],
     visibility = ["//visibility:public"],
     deps = [
         "//third_party/eigen3",
diff --git a/tensorflow/core/grappler/costs/BUILD b/tensorflow/core/grappler/costs/BUILD
index 35f11eac2955e5..b05406829915ae 100644
--- a/tensorflow/core/grappler/costs/BUILD
+++ b/tensorflow/core/grappler/costs/BUILD
@@ -129,6 +129,9 @@ tf_cuda_library(
     name = "utils",
     srcs = ["utils.cc"],
     hdrs = ["utils.h"],
+    cuda_deps = [
+        "@local_config_cuda//cuda:cudnn_header",
+    ],
     visibility = ["//visibility:public"],
     deps = [
         "//third_party/eigen3",
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 3fb03cd5bd3a8d..02639670567e20 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -3301,7 +3301,7 @@ tf_kernel_library(
         "//tensorflow/core:nn_ops_op_lib",
     ] + if_cuda([
         "@cub_archive//:cub",
-        "@local_config_cuda//cuda:cudnn",
+        "@local_config_cuda//cuda:cudnn_header",
     ]),
 )
 
@@ -3320,7 +3320,7 @@ tf_kernel_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:nn_ops_op_lib",
     ] + if_cuda([
-        "@local_config_cuda//cuda:cudnn",
+        "@local_config_cuda//cuda:cudnn_header",
     ]),
 )
 
diff --git a/third_party/gpus/cuda/BUILD.tpl b/third_party/gpus/cuda/BUILD.tpl
index 2a37c65bc74a0e..f6b497f813185f 100644
--- a/third_party/gpus/cuda/BUILD.tpl
+++ b/third_party/gpus/cuda/BUILD.tpl
@@ -127,6 +127,15 @@ cc_library(
     visibility = ["//visibility:public"],
 )
 
+cc_library(
+    name = "cudnn_header",
+    includes = [
+        ".",
+        "cuda/include",
+    ],
+    visibility = ["//visibility:public"],
+)
+
 cc_library(
     name = "cufft",
     srcs = ["cuda/lib/%{cufft_lib}"],

From 9a1f684b15d3c6011505425bdcc71fe9f986f388 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Sat, 12 May 2018 07:13:06 -0700
Subject: [PATCH 0707/1691] Check that the module group metadata builder
 correctly detects whether there are more than one companion instruction per
 device/module.

PiperOrigin-RevId: 196369766
---
 .../xla/service/hlo_module_group_metadata.cc  | 26 +++++++++++++++++++
 .../xla/service/hlo_module_group_metadata.h   |  5 ++++
 2 files changed, 31 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc
index 67f4c37413f47b..a41cfa75917aac 100644
--- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc
+++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/hlo_module_group_metadata.h"
 
+#include 
 #include 
 #include 
 
@@ -110,6 +111,31 @@ Status HloModuleGroupMetadata::Build() {
       TF_RETURN_IF_ERROR(computation->Accept(visitor));
     }
   }
+  TF_RETURN_IF_ERROR(VerifyCompanionSets());
+  return Status::OK();
+}
+
+Status HloModuleGroupMetadata::VerifyCompanionSets() const {
+  // TODO(dlibenzi): Migrate this to use the device instead of module ID, once
+  // the kDomain CL goes in.
+  for (const auto& companions : companion_sets_) {
+    // A companion set must be composed at most of an instruction per
+    // device/module.
+    std::unordered_set devices;
+    for (HloInstruction* instruction : *companions) {
+      int64 device = GetModuleId(instruction->parent()->parent());
+      if (!devices.insert(device).second) {
+        std::stringstream ss;
+        ss << "Companion set:" << std::endl;
+        for (HloInstruction* hlo : *companions) {
+          ss << "  " << hlo->name() << " ("
+             << GetModuleId(hlo->parent()->parent()) << ")" << std::endl;
+        }
+        ss << "has multiple instructions on the same device";
+        return FailedPrecondition("%s", ss.str().c_str());
+      }
+    }
+  }
   return Status::OK();
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h
index 88ed9a2ecc70aa..3ef4542f912963 100644
--- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h
+++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h
@@ -207,6 +207,11 @@ class HloModuleGroupMetadata {
   // within the graph.
   Status CheckCommunicatingInstruction(HloInstruction* instruction) const;
 
+  // Performs a consistency check on the companion sets built for the input
+  // modules. Check that a companion set does not include instructions from the
+  // same module/device.
+  Status VerifyCompanionSets() const;
+
   // Retrieves a pointer to the stored TrackedInstruction associated with a
   // tracked computation, or nullptr in case such computation is not tracked.
   const TrackedInstruction* GetTrackedInstruction(

From c03bd90c5c89856e53a33f9bae9130237abd3914 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Sat, 12 May 2018 15:40:29 -0700
Subject: [PATCH 0708/1691] Automated g4 rollback of changelist 196349902

PiperOrigin-RevId: 196387391
---
 tensorflow/contrib/fused_conv/BUILD     | 2 --
 tensorflow/core/grappler/clusters/BUILD | 3 ---
 tensorflow/core/grappler/costs/BUILD    | 3 ---
 tensorflow/core/kernels/BUILD           | 4 ++--
 third_party/gpus/cuda/BUILD.tpl         | 9 ---------
 5 files changed, 2 insertions(+), 19 deletions(-)

diff --git a/tensorflow/contrib/fused_conv/BUILD b/tensorflow/contrib/fused_conv/BUILD
index 0f0813c07f8bd3..0eb6889db1fae1 100644
--- a/tensorflow/contrib/fused_conv/BUILD
+++ b/tensorflow/contrib/fused_conv/BUILD
@@ -75,7 +75,6 @@ tf_kernel_library(
         "//tensorflow/core/kernels:gpu_util_hdrs",
         "//tensorflow/core/kernels:ops_util_hdrs",
         "//third_party/eigen3",
-        "@local_config_cuda//cuda:cudnn_header",
     ],
     alwayslink = 1,
 )
@@ -95,7 +94,6 @@ tf_custom_op_library(
         "//tensorflow/core/kernels:conv_ops_gpu_hdrs",
         "//tensorflow/core/kernels:gpu_util_hdrs",
         "//tensorflow/core/kernels:ops_util_hdrs",
-        "@local_config_cuda//cuda:cudnn_header",
     ],
 )
 
diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD
index d0b2cf01be221d..30c6126fbb58c1 100644
--- a/tensorflow/core/grappler/clusters/BUILD
+++ b/tensorflow/core/grappler/clusters/BUILD
@@ -20,9 +20,6 @@ tf_cuda_library(
     name = "utils",
     srcs = ["utils.cc"],
     hdrs = ["utils.h"],
-    cuda_deps = [
-        "@local_config_cuda//cuda:cudnn_header",
-    ],
     visibility = ["//visibility:public"],
     deps = [
         "//third_party/eigen3",
diff --git a/tensorflow/core/grappler/costs/BUILD b/tensorflow/core/grappler/costs/BUILD
index b05406829915ae..35f11eac2955e5 100644
--- a/tensorflow/core/grappler/costs/BUILD
+++ b/tensorflow/core/grappler/costs/BUILD
@@ -129,9 +129,6 @@ tf_cuda_library(
     name = "utils",
     srcs = ["utils.cc"],
     hdrs = ["utils.h"],
-    cuda_deps = [
-        "@local_config_cuda//cuda:cudnn_header",
-    ],
     visibility = ["//visibility:public"],
     deps = [
         "//third_party/eigen3",
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 02639670567e20..3fb03cd5bd3a8d 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -3301,7 +3301,7 @@ tf_kernel_library(
         "//tensorflow/core:nn_ops_op_lib",
     ] + if_cuda([
         "@cub_archive//:cub",
-        "@local_config_cuda//cuda:cudnn_header",
+        "@local_config_cuda//cuda:cudnn",
     ]),
 )
 
@@ -3320,7 +3320,7 @@ tf_kernel_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:nn_ops_op_lib",
     ] + if_cuda([
-        "@local_config_cuda//cuda:cudnn_header",
+        "@local_config_cuda//cuda:cudnn",
     ]),
 )
 
diff --git a/third_party/gpus/cuda/BUILD.tpl b/third_party/gpus/cuda/BUILD.tpl
index f6b497f813185f..2a37c65bc74a0e 100644
--- a/third_party/gpus/cuda/BUILD.tpl
+++ b/third_party/gpus/cuda/BUILD.tpl
@@ -127,15 +127,6 @@ cc_library(
     visibility = ["//visibility:public"],
 )
 
-cc_library(
-    name = "cudnn_header",
-    includes = [
-        ".",
-        "cuda/include",
-    ],
-    visibility = ["//visibility:public"],
-)
-
 cc_library(
     name = "cufft",
     srcs = ["cuda/lib/%{cufft_lib}"],

From 22d5f0b6a94a9f5b05444b4141f39f4703c23515 Mon Sep 17 00:00:00 2001
From: AG Ramesh 
Date: Sat, 12 May 2018 18:35:11 -0700
Subject: [PATCH 0709/1691] Fix for crash in mkl_layout_pass_test (#19107)

---
 tensorflow/core/graph/mkl_layout_pass_test.cc | 27 +++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc
index 5e2a465e22c7cb..029cdcf94af01d 100644
--- a/tensorflow/core/graph/mkl_layout_pass_test.cc
+++ b/tensorflow/core/graph/mkl_layout_pass_test.cc
@@ -2022,6 +2022,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Positive) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'D' op: 'Input'}"
       "node { name: 'E' op: 'BiasAdd'"
@@ -2051,6 +2052,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_NoAddBias) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}");
   EXPECT_EQ(DoMklLayoutOptimizationPass(),
             "A(Input);B(Input);C(_MklConv2D);DMT/_0(Const);DMT/_1(Const)|"
@@ -2069,6 +2071,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_Dataflow1) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'D' op: 'Input'}"
       "node { name: 'E' op: 'Input'}"
@@ -2095,6 +2098,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_Dataflow2) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'D' op: 'Input'}"
       "node { name: 'E' op: 'Input'}"
@@ -2125,6 +2129,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_AttrMismatch) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'D' op: 'Input'}"
       "node { name: 'E' op: 'BiasAdd'"
@@ -2151,6 +2156,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackpropFilterFusion_Positive) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B', 'C'] }"
       "node { name: 'E' op: 'BiasAddGrad'"
       " attr { key: 'T'                value { type: DT_FLOAT } }"
@@ -2178,6 +2184,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackpropFilterFusion_Negative1) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B', 'C'] }"
       "node { name: 'E' op: 'BiasAddGrad'"
       " attr { key: 'T'                value { type: DT_FLOAT } }"
@@ -2204,6 +2211,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackpropFilterFusion_Negative2) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B', 'C'] }"
       "node { name: 'E' op: 'BiasAddGrad'"
       " attr { key: 'T'                value { type: DT_FLOAT } }"
@@ -2233,6 +2241,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackpropFilterFusion_Negative3) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
       "node { name: 'E' op: 'Zeta'"
       " attr {key: 'T'                 value { type: DT_FLOAT } }"
@@ -2272,6 +2281,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_ConvBpropInput_FilterFwd) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'D' op: 'Input'}"
       "node { name: 'E' op: 'BiasAdd'"
@@ -2289,6 +2299,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_ConvBpropInput_FilterFwd) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['F', 'B', 'E']}"
       "node { name: 'Z' op: 'Zeta'"
       " attr {key: 'T'                 value { type: DT_FLOAT } }"
@@ -2319,6 +2330,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_Basic) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'D' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }"
       " input: ['B', 'C'] }");
@@ -2341,6 +2353,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_Positive1) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'D' op: 'Conv2D'"
       " attr { key: 'T'                value { type: DT_FLOAT } }"
@@ -2348,6 +2361,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_Positive1) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'C']}"
       "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }"
       " input: ['C', 'D'] }");
@@ -2370,6 +2384,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_Negative_UnsupportedType) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'D' op: 'Zeta' attr { key: 'T' value { type: DT_HALF } }"
       " input: ['B', 'C'] }");
@@ -2389,6 +2404,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2DGradFilter_Positive) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B', 'C']}"
       "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }"
       " input: ['A', 'D'] }");
@@ -2411,6 +2427,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2DGradInput_Positive) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['B', 'A', 'C']}"
       "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }"
       " input: ['A', 'D'] }");
@@ -2477,6 +2494,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_BiasAddGrad_Positive2) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B', 'M', 'N']}"
       "node { name: 'D' op: 'Zeta'"
       " attr {key: 'T'                 value { type: DT_FLOAT } }"
@@ -2529,6 +2547,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Concat_Input_Mkl) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'F' op: 'Conv2D'"
       " attr { key: 'T'                value { type: DT_FLOAT } }"
@@ -2536,6 +2555,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Concat_Input_Mkl) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['C', 'D']}"
       "node { name: 'G' op: 'Const' "
       " attr { key: 'dtype' value { type: DT_INT32 } }"
@@ -2572,6 +2592,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Concat_Input_MixedMkl) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'F' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }"
       " input: ['C', 'D']}"
@@ -2634,6 +2655,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_ConcatV2_Input_Mkl) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'F' op: 'Conv2D'"
       " attr { key: 'T'                value { type: DT_FLOAT } }"
@@ -2641,6 +2663,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_ConcatV2_Input_Mkl) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['C', 'D']}"
       "node { name: 'G' op: 'Const' "
       " attr { key: 'dtype' value { type: DT_INT32 } }"
@@ -2678,6 +2701,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_ConcatV2_Input_MixedMkl) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'F' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }"
       " input: ['C', 'D']}"
@@ -3274,6 +3298,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_DeviceTest) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B']}"
       "node { name: 'D' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }"
       " input: ['B', 'C'] }",
@@ -3296,6 +3321,7 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_DeviceTest) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B', 'C', 'M', 'N', 'O']}"
       "node { name: 'E' op: 'Zeta'"
       " attr {key: 'T'                 value { type: DT_FLOAT } }"
@@ -3323,6 +3349,7 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2DGradFilter_DeviceTest) {
       " attr { key: 'use_cudnn_on_gpu' value { b: false } }"
       " attr { key: 'strides'          value { list: {i: 1, i:1, i:1, i:1} } }"
       " attr { key: 'padding'          value { s: 'SAME' } }"
+      " attr { key: 'dilations'        value { list: {i: 1, i:1, i:1, i:1} } }"
       " input: ['A', 'B', 'C']}"
       "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }"
       " input: ['A', 'D'] }",

From f27033fb1212d7031a359c913d0f59e976b14c14 Mon Sep 17 00:00:00 2001
From: David Norman 
Date: Sat, 12 May 2018 19:11:23 -0700
Subject: [PATCH 0710/1691] Allow for disabling of 2 tests (#18208)

---
 tensorflow/compiler/xla/tests/dot_operation_test.cc | 2 +-
 tensorflow/compiler/xla/tests/tuple_test.cc         | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index b236cf00a8053a..0fd846cef8095a 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -61,7 +61,7 @@ using TypesF16F32F64CF64 = ::testing::Types;
 #endif
 
 // Check that we can safely pass an input tuple's elements to a dot operation.
-TEST_F(DotOperationTest, DotOfInputTupleElem) {
+XLA_TEST_F(DotOperationTest, DotOfInputTupleElem) {
   XlaBuilder builder(TestName());
 
   XlaOp param;
diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc
index 5c287bac6a7cab..aac82cfa4a0faf 100644
--- a/tensorflow/compiler/xla/tests/tuple_test.cc
+++ b/tensorflow/compiler/xla/tests/tuple_test.cc
@@ -515,7 +515,7 @@ XLA_TEST_F(TupleTest, ComplexTuples) {
 class TupleHloTest : public HloTestBase {};
 
 // Disabled on the interpreter because bitcast doesn't exist on the interpreter.
-TEST_F(TupleHloTest, DISABLED_ON_INTERPRETER(BitcastAfterGTE)) {
+XLA_TEST_F(TupleHloTest, DISABLED_ON_INTERPRETER(BitcastAfterGTE)) {
   const char* testcase = R"(
     HloModule m
 

From 0bde48e75d2e9f7c4d8af487476948d0180b4bdb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Sun, 13 May 2018 10:09:58 -0700
Subject: [PATCH 0711/1691] Make CPython implementation function type-correct,
 which removes UB from calling a function through a pointer of the wrong type,
 and also removes a C-style cast.

PiperOrigin-RevId: 196428430
---
 .../python/lib/core/ndarray_tensor_bridge.cc  | 45 ++++++++++---------
 1 file changed, 23 insertions(+), 22 deletions(-)

diff --git a/tensorflow/python/lib/core/ndarray_tensor_bridge.cc b/tensorflow/python/lib/core/ndarray_tensor_bridge.cc
index 65e2178cda4982..0d5838505f2553 100644
--- a/tensorflow/python/lib/core/ndarray_tensor_bridge.cc
+++ b/tensorflow/python/lib/core/ndarray_tensor_bridge.cc
@@ -72,10 +72,11 @@ struct TensorReleaser {
 
 extern PyTypeObject TensorReleaserType;
 
-static void TensorReleaser_dealloc(TensorReleaser* self) {
+static void TensorReleaser_dealloc(PyObject* pself) {
+  TensorReleaser* self = reinterpret_cast(pself);
   (*self->destructor)();
   delete self->destructor;
-  TensorReleaserType.tp_free(self);
+  TensorReleaserType.tp_free(pself);
 }
 
 PyTypeObject TensorReleaserType = {
@@ -84,26 +85,26 @@ PyTypeObject TensorReleaserType = {
     sizeof(TensorReleaser),           /* tp_basicsize */
     0,                                /* tp_itemsize */
     /* methods */
-    (destructor)TensorReleaser_dealloc, /* tp_dealloc */
-    nullptr,                            /* tp_print */
-    nullptr,                            /* tp_getattr */
-    nullptr,                            /* tp_setattr */
-    nullptr,                            /* tp_compare */
-    nullptr,                            /* tp_repr */
-    nullptr,                            /* tp_as_number */
-    nullptr,                            /* tp_as_sequence */
-    nullptr,                            /* tp_as_mapping */
-    nullptr,                            /* tp_hash */
-    nullptr,                            /* tp_call */
-    nullptr,                            /* tp_str */
-    nullptr,                            /* tp_getattro */
-    nullptr,                            /* tp_setattro */
-    nullptr,                            /* tp_as_buffer */
-    Py_TPFLAGS_DEFAULT,                 /* tp_flags */
-    "Wrapped TensorFlow Tensor",        /* tp_doc */
-    nullptr,                            /* tp_traverse */
-    nullptr,                            /* tp_clear */
-    nullptr,                            /* tp_richcompare */
+    TensorReleaser_dealloc,      /* tp_dealloc */
+    nullptr,                     /* tp_print */
+    nullptr,                     /* tp_getattr */
+    nullptr,                     /* tp_setattr */
+    nullptr,                     /* tp_compare */
+    nullptr,                     /* tp_repr */
+    nullptr,                     /* tp_as_number */
+    nullptr,                     /* tp_as_sequence */
+    nullptr,                     /* tp_as_mapping */
+    nullptr,                     /* tp_hash */
+    nullptr,                     /* tp_call */
+    nullptr,                     /* tp_str */
+    nullptr,                     /* tp_getattro */
+    nullptr,                     /* tp_setattro */
+    nullptr,                     /* tp_as_buffer */
+    Py_TPFLAGS_DEFAULT,          /* tp_flags */
+    "Wrapped TensorFlow Tensor", /* tp_doc */
+    nullptr,                     /* tp_traverse */
+    nullptr,                     /* tp_clear */
+    nullptr,                     /* tp_richcompare */
 };
 
 Status TF_DataType_to_PyArray_TYPE(TF_DataType tf_datatype,

From db62ba7618195c4b6584d90b4c8ee4d6ee82bc13 Mon Sep 17 00:00:00 2001
From: Robin Richtsfeld 
Date: Sun, 13 May 2018 21:32:04 +0200
Subject: [PATCH 0712/1691] Update TFLite Docs on tf.gather

Support was added in ea703f4e0e72d1e016f8157e206dcc9e80602862
---
 .../contrib/lite/g3doc/tf_ops_compatibility.md     | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
index f45fcceb2e6152..1259ae8c0c2e3c 100644
--- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
+++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md
@@ -132,7 +132,6 @@ TensorFlow operation not listed above are likely unsupported. Notably, the
 following common ops are not supported at the moment:
 
 *   [tf.depth_to_space](https://www.tensorflow.org/api_docs/python/tf/depth_to_space)
-*   [tf.gather](https://www.tensorflow.org/api_docs/python/tf/gather)
 *   [tf.image.resize_bilinear](https://www.tensorflow.org/api_docs/python/tf/image/resize_bilinear)
 *   [tf.slice](https://www.tensorflow.org/api_docs/python/tf/slice)
 *   [tf.tanh](https://www.tensorflow.org/api_docs/python/tf/tanh)
@@ -281,6 +280,19 @@ Options {
 }
 ```
 
+**GATHER**
+
+```
+Inputs {
+  0: params tensor
+  1: indices tensor
+  2: axis tensor (optional)
+}
+Outputs {
+  0: a tensor with same type as the params tensor.
+}
+```
+
 **GREATER**
 
 ```

From 13980cc155d514eaa0a620b39d1396616a392775 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Sun, 13 May 2018 13:53:35 -0700
Subject: [PATCH 0713/1691] Fix logic bug: should use logical-AND, not
 bitwise-AND.

PiperOrigin-RevId: 196435466
---
 tensorflow/core/distributed_runtime/session_mgr.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/distributed_runtime/session_mgr.cc b/tensorflow/core/distributed_runtime/session_mgr.cc
index 7ef4206c780d13..a312017b54a937 100644
--- a/tensorflow/core/distributed_runtime/session_mgr.cc
+++ b/tensorflow/core/distributed_runtime/session_mgr.cc
@@ -67,7 +67,7 @@ Status SessionMgr::CreateSession(const string& session,
     worker_name = WorkerNameFromServerDef(server_def);
   }
 
-  if (worker_cache != nullptr & default_worker_cache_.get() != nullptr) {
+  if (worker_cache != nullptr && default_worker_cache_.get() != nullptr) {
     worker_cache->SetLogging(this->is_logging_active_);
   }
 

From 8eb34c50b997ff74e8b4bfb27abcbd03910c81b3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Sun, 13 May 2018 16:52:14 -0700
Subject: [PATCH 0714/1691] ClangTidy - Legacy cleanup:

  * use nullptr
  * converting integer literal to bool, use bool literal instead
  * annotate this function with 'override' or (rarely) 'final'
  * prefer using 'override' or (rarely) 'final' instead of 'virtual'

PiperOrigin-RevId: 196441181
---
 tensorflow/core/common_runtime/gpu/gpu_device_test.cc         | 2 +-
 .../common_runtime/process_function_library_runtime_test.cc   | 4 ++--
 tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc   | 2 +-
 tensorflow/core/kernels/cudnn_rnn_ops.cc                      | 2 +-
 tensorflow/core/kernels/roll_op.cc                            | 2 +-
 tensorflow/tools/graph_transforms/transform_graph.cc          | 2 +-
 6 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/common_runtime/gpu/gpu_device_test.cc b/tensorflow/core/common_runtime/gpu/gpu_device_test.cc
index f3935f6ba26c49..bb00173d1ecb87 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device_test.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device_test.cc
@@ -29,7 +29,7 @@ const char* kDeviceNamePrefix = "/job:localhost/replica:0/task:0";
 
 class GPUDeviceTest : public ::testing::Test {
  public:
-  void TearDown() { ProcessState::singleton()->TestOnlyReset(); }
+  void TearDown() override { ProcessState::singleton()->TestOnlyReset(); }
 
  protected:
   static SessionOptions MakeSessionOptions(
diff --git a/tensorflow/core/common_runtime/process_function_library_runtime_test.cc b/tensorflow/core/common_runtime/process_function_library_runtime_test.cc
index 4fbf2abc6714bb..cce23080118359 100644
--- a/tensorflow/core/common_runtime/process_function_library_runtime_test.cc
+++ b/tensorflow/core/common_runtime/process_function_library_runtime_test.cc
@@ -39,7 +39,7 @@ class TestClusterFLR : public DistributedFunctionLibraryRuntime {
   Status Instantiate(const string& function_name,
                      const FunctionLibraryDefinition& lib_def, AttrSlice attrs,
                      const FunctionLibraryRuntime::InstantiateOptions& options,
-                     FunctionLibraryRuntime::LocalHandle* handle) {
+                     FunctionLibraryRuntime::LocalHandle* handle) override {
     mutex_lock l(mu_);
     *handle = next_handle_;
     next_handle_++;
@@ -49,7 +49,7 @@ class TestClusterFLR : public DistributedFunctionLibraryRuntime {
   void Run(const FunctionLibraryRuntime::Options& opts,
            FunctionLibraryRuntime::LocalHandle handle,
            gtl::ArraySlice args, std::vector* rets,
-           FunctionLibraryRuntime::DoneCallback done) {}
+           FunctionLibraryRuntime::DoneCallback done) override {}
 
  private:
   mutex mu_;
diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
index 30da23d212b0ff..cd7e742e5c0e12 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -281,7 +281,7 @@ class ArithmeticOptimizerStage : public GraphOptimizerStage {
                                     const ArithmeticOptimizerContext ctx_ext)
       : GraphOptimizerStage("ArithmeticOptimizer", name, ctx),
         ctx_ext_(ctx_ext) {}
-  virtual ~ArithmeticOptimizerStage() = default;
+  ~ArithmeticOptimizerStage() override = default;
 
  protected:
   // Simplification graph rewrite can create additional nodes that are inputs
diff --git a/tensorflow/core/kernels/cudnn_rnn_ops.cc b/tensorflow/core/kernels/cudnn_rnn_ops.cc
index 02d4fc89c87378..00ae32eb0824cc 100644
--- a/tensorflow/core/kernels/cudnn_rnn_ops.cc
+++ b/tensorflow/core/kernels/cudnn_rnn_ops.cc
@@ -352,7 +352,7 @@ struct ToTFDataType : std::integral_constant {};
 template 
 class CudnnRnnAllocatorInTemp : public ScratchAllocator {
  public:
-  ~CudnnRnnAllocatorInTemp() = default;
+  ~CudnnRnnAllocatorInTemp() override = default;
 
   explicit CudnnRnnAllocatorInTemp(OpKernelContext* context)
       : context_(context) {}
diff --git a/tensorflow/core/kernels/roll_op.cc b/tensorflow/core/kernels/roll_op.cc
index 4b630809c5a854..96f94d80df9422 100644
--- a/tensorflow/core/kernels/roll_op.cc
+++ b/tensorflow/core/kernels/roll_op.cc
@@ -285,7 +285,7 @@ class RollOp : public OpKernel {
       dim_range[i] = dim_size_prod;
     }
 
-    Tensor* output = NULL;
+    Tensor* output = nullptr;
     OP_REQUIRES_OK(context,
                    context->allocate_output(0, input.shape(), &output));
     auto input_flat = input.flat().data();
diff --git a/tensorflow/tools/graph_transforms/transform_graph.cc b/tensorflow/tools/graph_transforms/transform_graph.cc
index 3b9dd3dd2d4e24..5cae8f8d8f32ef 100644
--- a/tensorflow/tools/graph_transforms/transform_graph.cc
+++ b/tensorflow/tools/graph_transforms/transform_graph.cc
@@ -141,7 +141,7 @@ std::string ExpandPath(const std::string& path_string) {
     return path_string;
   }
 
-  const char* home = NULL;
+  const char* home = nullptr;
   std::string::size_type prefix = path_string.find_first_of('/');
   if (path_string.length() == 1 || prefix == 1) {
     // The value of $HOME, e.g., ~/foo

From 7c88788e63f3a747d2794175076db551d768734e Mon Sep 17 00:00:00 2001
From: Yong Tang 
Date: Sun, 13 May 2018 14:26:06 +0000
Subject: [PATCH 0715/1691] Shape validation of `max_features` in
 `QuantizedReluX`

In shape function of QuantizedReluX, `max_value` and
`min_features` have shape validation but not `max_features`.
This fix add restriction to `max_features` as well.

Signed-off-by: Yong Tang 
---
 tensorflow/core/ops/nn_ops.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc
index bb46dafd424fe6..7c579db267f4c6 100644
--- a/tensorflow/core/ops/nn_ops.cc
+++ b/tensorflow/core/ops/nn_ops.cc
@@ -1452,6 +1452,7 @@ REGISTER_OP("QuantizedReluX")
       ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused));
       c->set_output(1, c->Scalar());
       c->set_output(2, c->Scalar());
       return Status::OK();

From 356f360e8772a2697ec0d30036237342549803f5 Mon Sep 17 00:00:00 2001
From: Yong Tang 
Date: Sun, 13 May 2018 13:55:53 +0000
Subject: [PATCH 0716/1691] Add additional shape validation to
 `compute_accidental_hits`

In `compute_accidental_hits`, the `sampled_candidates` must
be a vector, as is shown in the kernel implementation in
`tensorflow/core/kernels/candidate_sampler_ops.cc`.

This fix adds shape validation of `sampled_candidates`
in the shape function whenever possible.

Signed-off-by: Yong Tang 
---
 tensorflow/core/ops/candidate_sampling_ops.cc | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/ops/candidate_sampling_ops.cc b/tensorflow/core/ops/candidate_sampling_ops.cc
index 6e4d100b04fba2..6e589c8d1c5fea 100644
--- a/tensorflow/core/ops/candidate_sampling_ops.cc
+++ b/tensorflow/core/ops/candidate_sampling_ops.cc
@@ -145,12 +145,15 @@ REGISTER_OP("ComputeAccidentalHits")
       int64 num_true;
       TF_RETURN_IF_ERROR(c->GetAttr("num_true", &num_true));
 
-      // Validate true_classes.
+      // Validate true_classes, must be a matrix.
       ShapeHandle true_classes;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &true_classes));
       DimensionHandle unused;
       TF_RETURN_IF_ERROR(
           c->WithValue(c->Dim(true_classes, 1), num_true, &unused));
+      // Validate sampled_candidates, must be a vector.
+      ShapeHandle sampled_candidates;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &sampled_candidates));
 
       // All three outputs are the same shape.
       ShapeHandle v = c->Vector(InferenceContext::kUnknownDim);

From 2fbc0c5a45955c877e0a165bb561fc2f01518321 Mon Sep 17 00:00:00 2001
From: Shashi Shekhar 
Date: Sun, 13 May 2018 18:21:21 -0700
Subject: [PATCH 0717/1691] Update UI for Camera example.

PiperOrigin-RevId: 196444970
---
 .../demo/app/src/main/AndroidManifest.xml     |  1 +
 .../res/layout-v26/fragment_camera2_basic.xml | 47 +++++++++++--------
 .../res/layout/fragment_camera2_basic.xml     | 46 ++++++++++--------
 .../app/src/main/res/values/base-strings.xml  |  3 +-
 .../demo/app/src/main/res/values/styles.xml   |  7 ++-
 5 files changed, 62 insertions(+), 42 deletions(-)

diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/AndroidManifest.xml b/tensorflow/contrib/lite/java/demo/app/src/main/AndroidManifest.xml
index ba63dce5d9a719..95b6b7016f2818 100644
--- a/tensorflow/contrib/lite/java/demo/app/src/main/AndroidManifest.xml
+++ b/tensorflow/contrib/lite/java/demo/app/src/main/AndroidManifest.xml
@@ -31,6 +31,7 @@
         android:theme="@style/MaterialTheme">
 
         
             
                 
diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/res/layout-v26/fragment_camera2_basic.xml b/tensorflow/contrib/lite/java/demo/app/src/main/res/layout-v26/fragment_camera2_basic.xml
index 72a229ecdb19f5..ddb099a950c2f8 100644
--- a/tensorflow/contrib/lite/java/demo/app/src/main/res/layout-v26/fragment_camera2_basic.xml
+++ b/tensorflow/contrib/lite/java/demo/app/src/main/res/layout-v26/fragment_camera2_basic.xml
@@ -28,7 +28,7 @@
     
 
     
+    
-
+        android:id="@+id/bottom_info_view"
+        android:layout_marginBottom="10dp"
+        android:layout_height="50dp">
+        
+        
         
+            android:layout_marginLeft="10dp"
+            android:background="#0000000f"
+            android:textColor="@android:color/white" />
+    
+
 
-        
-    
 
diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml b/tensorflow/contrib/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml
index 72a229ecdb19f5..e567009a424ed7 100644
--- a/tensorflow/contrib/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml
+++ b/tensorflow/contrib/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml
@@ -28,7 +28,7 @@
     
 
     
+    
-
+        android:id="@+id/bottom_info_view"
+        android:layout_marginBottom="10dp"
+        android:layout_height="50dp">
+        
+        
         
+            android:layout_marginLeft="10dp"
+            android:background="#0000000f"
+            android:textColor="@android:color/white" />
 
-        
-    
+    
 
diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/res/values/base-strings.xml b/tensorflow/contrib/lite/java/demo/app/src/main/res/values/base-strings.xml
index 0a71dbd0e8010f..7af8f3a98c6319 100644
--- a/tensorflow/contrib/lite/java/demo/app/src/main/res/values/base-strings.xml
+++ b/tensorflow/contrib/lite/java/demo/app/src/main/res/values/base-strings.xml
@@ -16,7 +16,7 @@
 -->
 
 
-    TfLiteCameraDemo
+    TfLite Camera Demo
     
         
     
+    Threads:
 
diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/res/values/styles.xml b/tensorflow/contrib/lite/java/demo/app/src/main/res/values/styles.xml
index 3f3bdfb49480e7..1752b3b5f97e28 100644
--- a/tensorflow/contrib/lite/java/demo/app/src/main/res/values/styles.xml
+++ b/tensorflow/contrib/lite/java/demo/app/src/main/res/values/styles.xml
@@ -14,5 +14,10 @@
  limitations under the License.
 -->
 
-    
+
 

From 699b217cd6c5ddc0832be8471dde47999829e435 Mon Sep 17 00:00:00 2001
From: Yu-Cheng Ling 
Date: Sun, 13 May 2018 19:52:18 -0700
Subject: [PATCH 0718/1691] Introduce op version into TFLite

PiperOrigin-RevId: 196448769
---
 tensorflow/contrib/lite/BUILD                 |  14 ++
 tensorflow/contrib/lite/context.h             |  12 +-
 .../label_image/bitmap_helpers_impl.h         |   2 +-
 tensorflow/contrib/lite/kernels/register.cc   |  23 ----
 tensorflow/contrib/lite/kernels/register.h    |  17 +--
 tensorflow/contrib/lite/kernels/test_util.cc  |   2 +-
 tensorflow/contrib/lite/kernels/test_util.h   |  18 ++-
 tensorflow/contrib/lite/model.cc              |  27 ++--
 tensorflow/contrib/lite/model.h               |  13 +-
 tensorflow/contrib/lite/model_test.cc         |   5 +-
 tensorflow/contrib/lite/op_resolver.cc        |  86 ++++++++++++
 tensorflow/contrib/lite/op_resolver.h         |  95 +++++++++++++
 tensorflow/contrib/lite/op_resolver_test.cc   | 128 ++++++++++++++++++
 tensorflow/contrib/lite/schema/schema.fbs     |   4 +
 .../contrib/lite/schema/schema_generated.h    |  29 +++-
 .../contrib/lite/tools/mutable_op_resolver.cc |  28 +---
 .../contrib/lite/tools/mutable_op_resolver.h  |  39 +-----
 tensorflow/contrib/lite/tools/verifier.cc     |  13 +-
 tensorflow/contrib/lite/tools/verifier.h      |   5 +-
 19 files changed, 411 insertions(+), 149 deletions(-)
 create mode 100644 tensorflow/contrib/lite/op_resolver.cc
 create mode 100644 tensorflow/contrib/lite/op_resolver.h
 create mode 100644 tensorflow/contrib/lite/op_resolver_test.cc

diff --git a/tensorflow/contrib/lite/BUILD b/tensorflow/contrib/lite/BUILD
index 10065e894c48d4..01c76b7a66e93a 100644
--- a/tensorflow/contrib/lite/BUILD
+++ b/tensorflow/contrib/lite/BUILD
@@ -114,6 +114,7 @@ cc_library(
         "interpreter.cc",
         "model.cc",
         "nnapi_delegate.cc",
+        "op_resolver.cc",
         "optional_debug_tools.cc",
     ],
     hdrs = [
@@ -124,6 +125,7 @@ cc_library(
         "interpreter.h",
         "model.h",
         "nnapi_delegate.h",
+        "op_resolver.h",
         "optional_debug_tools.h",
     ],
     copts = tflite_copts(),
@@ -226,6 +228,18 @@ cc_test(
     ],
 )
 
+# Test OpResolver.
+cc_test(
+    name = "op_resolver_test",
+    size = "small",
+    srcs = ["op_resolver_test.cc"],
+    deps = [
+        ":framework",
+        "//tensorflow/contrib/lite/testing:util",
+        "@com_google_googletest//:gtest",
+    ],
+)
+
 # Test the C extension API code.
 cc_test(
     name = "context_test",
diff --git a/tensorflow/contrib/lite/context.h b/tensorflow/contrib/lite/context.h
index 12841d233cc1d3..4eb66cc225eb04 100644
--- a/tensorflow/contrib/lite/context.h
+++ b/tensorflow/contrib/lite/context.h
@@ -370,13 +370,21 @@ typedef struct _TfLiteRegistration {
 
   // Builtin codes. If this kernel refers to a builtin this is the code
   // of the builtin. This is so we can do marshaling to other frameworks like
-  // NN API. Note, it is the responsibility of the registration binder to
-  // set this properly.
+  // NN API.
+  // Note: It is the responsibility of the registration binder to set this
+  // properly.
   int32_t builtin_code;
 
   // Custom op name. If the op is a builtin, this will be null.
+  // Note: It is the responsibility of the registration binder to set this
+  // properly.
   // WARNING: This is an experimental interface that is subject to change.
   const char* custom_name;
+
+  // The version of the op.
+  // Note: It is the responsibility of the registration binder to set this
+  // properly.
+  int version;
 } TfLiteRegistration;
 
 // WARNING: This is an experimental interface that is subject to change.
diff --git a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h
index 2a64c1de725b60..b36933d5ade4fc 100644
--- a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h
+++ b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h
@@ -63,7 +63,7 @@ void resize(T* out, uint8_t* in, int image_height, int image_width,
 
   ops::builtin::BuiltinOpResolver resolver;
   TfLiteRegistration* resize_op =
-      resolver.FindOp(BuiltinOperator_RESIZE_BILINEAR);
+      resolver.FindOp(BuiltinOperator_RESIZE_BILINEAR, 1);
   auto* params = reinterpret_cast(
       malloc(sizeof(TfLiteResizeBilinearParams)));
   params->align_corners = false;
diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc
index d7eed96db0193c..0c7cfcaf10c1c2 100644
--- a/tensorflow/contrib/lite/kernels/register.cc
+++ b/tensorflow/contrib/lite/kernels/register.cc
@@ -167,29 +167,6 @@ BuiltinOpResolver::BuiltinOpResolver() {
             tflite::ops::custom::Register_AUDIO_SPECTROGRAM());
 }
 
-TfLiteRegistration* BuiltinOpResolver::FindOp(
-    tflite::BuiltinOperator op) const {
-  auto it = builtins_.find(op);
-  return it != builtins_.end() ? it->second : nullptr;
-}
-
-TfLiteRegistration* BuiltinOpResolver::FindOp(const char* op) const {
-  auto it = custom_ops_.find(op);
-  return it != custom_ops_.end() ? it->second : nullptr;
-}
-
-void BuiltinOpResolver::AddBuiltin(tflite::BuiltinOperator op,
-                                   TfLiteRegistration* registration) {
-  registration->builtin_code = op;
-  builtins_.insert(std::make_pair(op, registration));
-}
-
-void BuiltinOpResolver::AddCustom(const char* name,
-                                  TfLiteRegistration* registration) {
-  registration->builtin_code = BuiltinOperator_CUSTOM;
-  custom_ops_.insert(std::make_pair(std::string(name), registration));
-}
-
 }  // namespace builtin
 }  // namespace ops
 }  // namespace tflite
diff --git a/tensorflow/contrib/lite/kernels/register.h b/tensorflow/contrib/lite/kernels/register.h
index b9cff0ae21086b..b928f1b302580d 100644
--- a/tensorflow/contrib/lite/kernels/register.h
+++ b/tensorflow/contrib/lite/kernels/register.h
@@ -23,24 +23,9 @@ namespace tflite {
 namespace ops {
 namespace builtin {
 
-class BuiltinOpResolver : public OpResolver {
+class BuiltinOpResolver : public MutableOpResolver {
  public:
   BuiltinOpResolver();
-  TfLiteRegistration* FindOp(tflite::BuiltinOperator op) const override;
-  TfLiteRegistration* FindOp(const char* op) const override;
-  void AddBuiltin(tflite::BuiltinOperator op, TfLiteRegistration* registration);
-  void AddCustom(const char* name, TfLiteRegistration* registration);
-
- private:
-  struct BuiltinOperatorHasher {
-    size_t operator()(const tflite::BuiltinOperator& x) const {
-      return std::hash()(static_cast(x));
-    }
-  };
-  std::unordered_map
-      builtins_;
-  std::unordered_map custom_ops_;
 };
 
 }  // namespace builtin
diff --git a/tensorflow/contrib/lite/kernels/test_util.cc b/tensorflow/contrib/lite/kernels/test_util.cc
index 5a6c85e97ef5f0..1a01ee093626c0 100644
--- a/tensorflow/contrib/lite/kernels/test_util.cc
+++ b/tensorflow/contrib/lite/kernels/test_util.cc
@@ -101,7 +101,7 @@ void SingleOpModel::BuildInterpreter(
     }
     resolver_ = std::unique_ptr(resolver);
   }
-  InterpreterBuilder(model, *resolver_)(&interpreter_);
+  CHECK(InterpreterBuilder(model, *resolver_)(&interpreter_) == kTfLiteOk);
 
   CHECK(interpreter_ != nullptr);
 
diff --git a/tensorflow/contrib/lite/kernels/test_util.h b/tensorflow/contrib/lite/kernels/test_util.h
index 6a9fdf11122da5..32529b6d940668 100644
--- a/tensorflow/contrib/lite/kernels/test_util.h
+++ b/tensorflow/contrib/lite/kernels/test_util.h
@@ -89,18 +89,26 @@ struct TensorData {
 class SingleOpResolver : public OpResolver {
  public:
   SingleOpResolver(const BuiltinOperator op, TfLiteRegistration* registration)
-      : op_(op), registration_(registration) {}
-  TfLiteRegistration* FindOp(BuiltinOperator op) const override {
+      : op_(op), registration_(*registration) {
+    registration_.builtin_code = static_cast(op);
+    registration_.version = 1;
+  }
+  TfLiteRegistration* FindOp(BuiltinOperator op, int version) const override {
     if (op == op_) {
-      return registration_;
+      // The current interface requires to return a mutable pointer, but the
+      // caller never changes the structure.
+      // TODO(ycling): Consider refactoring and return constant pointers.
+      return const_cast(®istration_);
     }
     return nullptr;
   }
-  TfLiteRegistration* FindOp(const char* op) const override { return nullptr; }
+  TfLiteRegistration* FindOp(const char* op, int version) const override {
+    return nullptr;
+  }
 
  private:
   const BuiltinOperator op_;
-  TfLiteRegistration* registration_;
+  TfLiteRegistration registration_;
 };
 
 class SingleOpModel {
diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc
index 1fbf9650044685..5d0fe3839e7929 100644
--- a/tensorflow/contrib/lite/model.cc
+++ b/tensorflow/contrib/lite/model.cc
@@ -186,6 +186,8 @@ TfLiteStatus InterpreterBuilder::BuildLocalIndexToRegistrationMapping() {
   for (const OperatorCode* opcode : *opcodes) {
     TfLiteRegistration* registration = nullptr;
     auto builtin_code = opcode->builtin_code();
+    int version = opcode->version();
+
     if (builtin_code > BuiltinOperator_MAX ||
         builtin_code < BuiltinOperator_MIN) {
       error_reporter_->Report(
@@ -194,8 +196,7 @@ TfLiteStatus InterpreterBuilder::BuildLocalIndexToRegistrationMapping() {
           builtin_code);
       status = kTfLiteError;
     } else if (builtin_code != BuiltinOperator_CUSTOM) {
-      flatbuffer_op_index_to_registration_types_.push_back(builtin_code);
-      registration = op_resolver_.FindOp(builtin_code);
+      registration = op_resolver_.FindOp(builtin_code, version);
       if (registration == nullptr) {
         error_reporter_->Report("Didn't find op for builtin opcode '%s'\n",
                                 EnumNameBuiltinOperator(builtin_code));
@@ -207,11 +208,13 @@ TfLiteStatus InterpreterBuilder::BuildLocalIndexToRegistrationMapping() {
       status = kTfLiteError;
     } else {
       const char* name = opcode->custom_code()->c_str();
-      registration = op_resolver_.FindOp(name);
+      registration = op_resolver_.FindOp(name, version);
       flatbuffer_op_index_to_registration_types_.push_back(
           BuiltinOperator_CUSTOM);
       if (registration == nullptr) {
-        error_reporter_->Report("Didn't find custom op for name '%s'\n", name);
+        error_reporter_->Report(
+            "Didn't find custom op for name '%s' with version %d\n", name,
+            version);
         status = kTfLiteError;
       }
     }
@@ -333,6 +336,7 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type,
         params->stride_height = conv_params->stride_h();
         params->activation =
             parse_activation(conv_params->fused_activation_function());
+
         params->dilation_width_factor = conv_params->dilation_w_factor();
         params->dilation_height_factor = conv_params->dilation_h_factor();
       }
@@ -707,27 +711,30 @@ TfLiteStatus InterpreterBuilder::ParseNodes(
       status = kTfLiteError;
       continue;
     }
-    const TfLiteRegistration* reg =
+
+    TfLiteRegistration* registration =
         flatbuffer_op_index_to_registration_[op->opcode_index()];
-    if (reg == nullptr) {
+    if (registration == nullptr) {
       error_reporter_->Report("Skipping op for opcode_index %d\n", index);
       status = kTfLiteError;
       continue;
     }
 
-    auto op_type =
-        flatbuffer_op_index_to_registration_types_[op->opcode_index()];
+    BuiltinOperator op_type =
+        static_cast(registration->builtin_code);
+
     if (op_type != BuiltinOperator_CUSTOM && op->custom_options()) {
       error_reporter_->Report(
           "Found builtin operator %s with custom options.\n",
           EnumNameBuiltinOperator(op_type));
     }
+
     if (op->custom_options()) {
       interpreter->AddNodeWithParameters(
           FlatBufferIntArrayToVector(op->inputs()),
           FlatBufferIntArrayToVector(op->outputs()),
           reinterpret_cast(op->custom_options()->data()),
-          op->custom_options()->size(), nullptr, reg);
+          op->custom_options()->size(), nullptr, registration);
     } else {
       void* builtin_data = nullptr;
       TF_LITE_ENSURE_STATUS(
@@ -735,7 +742,7 @@ TfLiteStatus InterpreterBuilder::ParseNodes(
       interpreter->AddNodeWithParameters(
           FlatBufferIntArrayToVector(op->inputs()),
           FlatBufferIntArrayToVector(op->outputs()), nullptr, 0, builtin_data,
-          reg);
+          registration);
     }
   }
 
diff --git a/tensorflow/contrib/lite/model.h b/tensorflow/contrib/lite/model.h
index 5a55b031a8c280..366bdb52c65a92 100644
--- a/tensorflow/contrib/lite/model.h
+++ b/tensorflow/contrib/lite/model.h
@@ -37,6 +37,7 @@ limitations under the License.
 #include 
 #include "tensorflow/contrib/lite/error_reporter.h"
 #include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/op_resolver.h"
 #include "tensorflow/contrib/lite/schema/schema_generated.h"
 
 namespace tflite {
@@ -131,18 +132,6 @@ class FlatBufferModel {
   Allocation* allocation_ = nullptr;
 };
 
-// Abstract interface that returns TfLiteRegistrations given op codes or custom
-// op names. This is the mechanism that ops being referenced in the flatbuffer
-// model are mapped to executable function pointers (TfLiteRegistrations).
-class OpResolver {
- public:
-  // Finds the op registration for a builtin operator by enum code.
-  virtual TfLiteRegistration* FindOp(tflite::BuiltinOperator op) const = 0;
-  // Finds the op registration of a custom operator by op name.
-  virtual TfLiteRegistration* FindOp(const char* op) const = 0;
-  virtual ~OpResolver() {}
-};
-
 // Build an interpreter capable of interpreting `model`.
 //
 // model: a scoped model whose lifetime must be at least as long as
diff --git a/tensorflow/contrib/lite/model_test.cc b/tensorflow/contrib/lite/model_test.cc
index ae6c1ece18963f..55604ff3e931db 100644
--- a/tensorflow/contrib/lite/model_test.cc
+++ b/tensorflow/contrib/lite/model_test.cc
@@ -55,11 +55,12 @@ class TrivialResolver : public OpResolver {
   explicit TrivialResolver(TfLiteRegistration* constant_return = nullptr)
       : constant_return_(constant_return) {}
   // Find the op registration of a custom operator by op name.
-  TfLiteRegistration* FindOp(tflite::BuiltinOperator op) const override {
+  TfLiteRegistration* FindOp(tflite::BuiltinOperator op,
+                             int version) const override {
     return constant_return_;
   }
   // Find the op registration of a custom operator by op name.
-  TfLiteRegistration* FindOp(const char* op) const override {
+  TfLiteRegistration* FindOp(const char* op, int version) const override {
     return constant_return_;
   }
 
diff --git a/tensorflow/contrib/lite/op_resolver.cc b/tensorflow/contrib/lite/op_resolver.cc
new file mode 100644
index 00000000000000..fddaef12a9cb4c
--- /dev/null
+++ b/tensorflow/contrib/lite/op_resolver.cc
@@ -0,0 +1,86 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/lite/op_resolver.h"
+#include "tensorflow/contrib/lite/context.h"
+
+namespace tflite {
+
+MutableOpResolver::~MutableOpResolver() {
+  for (auto it : builtins_) {
+    free(it.second);
+  }
+  for (auto it : custom_ops_) {
+    free(it.second);
+  }
+}
+
+TfLiteRegistration* MutableOpResolver::FindOp(tflite::BuiltinOperator op,
+                                              int version) const {
+  auto it = builtins_.find(std::make_pair(op, version));
+  return it != builtins_.end() ? it->second : nullptr;
+}
+
+TfLiteRegistration* MutableOpResolver::FindOp(const char* op,
+                                              int version) const {
+  auto it = custom_ops_.find(std::make_pair(op, version));
+  return it != custom_ops_.end() ? it->second : nullptr;
+}
+
+void MutableOpResolver::AddBuiltin(tflite::BuiltinOperator op,
+                                   TfLiteRegistration* registration,
+                                   int min_version, int max_version) {
+  for (int version = min_version; version <= max_version; ++version) {
+    TfLiteRegistration* new_registration =
+        reinterpret_cast(
+            malloc(sizeof(TfLiteRegistration)));
+    memcpy(new_registration, registration, sizeof(TfLiteRegistration));
+    new_registration->builtin_code = op;
+    new_registration->version = version;
+
+    auto op_key = std::make_pair(op, version);
+    auto it = builtins_.find(op_key);
+    if (it == builtins_.end()) {
+      builtins_.insert(std::make_pair(op_key, new_registration));
+    } else {
+      free(it->second);
+      it->second = new_registration;
+    }
+  }
+}
+
+void MutableOpResolver::AddCustom(const char* name,
+                                  TfLiteRegistration* registration,
+                                  int min_version, int max_version) {
+  for (int version = min_version; version <= max_version; ++version) {
+    TfLiteRegistration* new_registration =
+        reinterpret_cast(
+            malloc(sizeof(TfLiteRegistration)));
+    memcpy(new_registration, registration, sizeof(TfLiteRegistration));
+    new_registration->builtin_code = BuiltinOperator_CUSTOM;
+    new_registration->version = version;
+
+    auto op_key = std::make_pair(name, version);
+    auto it = custom_ops_.find(op_key);
+    if (it == custom_ops_.end()) {
+      custom_ops_.insert(std::make_pair(op_key, new_registration));
+    } else {
+      free(it->second);
+      it->second = new_registration;
+    }
+  }
+}
+
+}  // namespace tflite
diff --git a/tensorflow/contrib/lite/op_resolver.h b/tensorflow/contrib/lite/op_resolver.h
new file mode 100644
index 00000000000000..6718ca90e5564a
--- /dev/null
+++ b/tensorflow/contrib/lite/op_resolver.h
@@ -0,0 +1,95 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_CONTRIB_LITE_OP_RESOLVER_H_
+#define TENSORFLOW_CONTRIB_LITE_OP_RESOLVER_H_
+
+#include 
+#include "tensorflow/contrib/lite/context.h"
+#include "tensorflow/contrib/lite/schema/schema_generated.h"
+
+namespace tflite {
+
+// Abstract interface that returns TfLiteRegistrations given op codes or custom
+// op names. This is the mechanism that ops being referenced in the flatbuffer
+// model are mapped to executable function pointers (TfLiteRegistrations).
+class OpResolver {
+ public:
+  // Finds the op registration for a builtin operator by enum code.
+  virtual TfLiteRegistration* FindOp(tflite::BuiltinOperator op,
+                                     int version) const = 0;
+  // Finds the op registration of a custom operator by op name.
+  virtual TfLiteRegistration* FindOp(const char* op, int version) const = 0;
+  virtual ~OpResolver() {}
+};
+
+// Some versions of gcc doesn't support partial specialization in class scope,
+// so these are defined in a namescope.
+namespace op_resolver_hasher {
+template 
+struct ValueHasher {
+  size_t operator()(const V& v) const { return std::hash()(v); }
+};
+
+template <>
+struct ValueHasher {
+  size_t operator()(const tflite::BuiltinOperator& v) const {
+    return std::hash()(static_cast(v));
+  }
+};
+
+template 
+struct OperatorKeyHasher {
+  size_t operator()(const T& x) const {
+    size_t a = ValueHasher()(x.first);
+    size_t b = ValueHasher()(x.second);
+    // Hash combinator used by TensorFlow core.
+    return a ^ (b + 0x9e3779b97f4a7800ULL + (a << 10) + (a >> 4));
+  }
+};
+}  // namespace op_resolver_hasher
+
+// An OpResolver that is mutable, also used as the op in gen_op_registration.
+// A typical usage:
+//   MutableOpResolver resolver;
+//   resolver.AddBuiltin(BuiltinOperator_ADD, Register_ADD());
+//   resolver.AddCustom("CustomOp", Register_CUSTOM_OP());
+//   InterpreterBuilder(model, resolver)(&interpreter);
+class MutableOpResolver : public OpResolver {
+ public:
+  ~MutableOpResolver() override;
+
+  TfLiteRegistration* FindOp(tflite::BuiltinOperator op,
+                             int version) const override;
+  TfLiteRegistration* FindOp(const char* op, int version) const override;
+  void AddBuiltin(tflite::BuiltinOperator op, TfLiteRegistration* registration,
+                  int min_version = 1, int max_version = 1);
+  void AddCustom(const char* name, TfLiteRegistration* registration,
+                 int min_version = 1, int max_version = 1);
+
+ private:
+  typedef std::pair BuiltinOperatorKey;
+  typedef std::pair CustomOperatorKey;
+
+  std::unordered_map >
+      builtins_;
+  std::unordered_map >
+      custom_ops_;
+};
+
+}  // namespace tflite
+
+#endif  // TENSORFLOW_CONTRIB_LITE_OP_RESOLVER_H_
diff --git a/tensorflow/contrib/lite/op_resolver_test.cc b/tensorflow/contrib/lite/op_resolver_test.cc
new file mode 100644
index 00000000000000..173d4099410460
--- /dev/null
+++ b/tensorflow/contrib/lite/op_resolver_test.cc
@@ -0,0 +1,128 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/lite/op_resolver.h"
+
+#include 
+#include "tensorflow/contrib/lite/testing/util.h"
+
+namespace tflite {
+namespace {
+
+// We need some dummy functions to identify the registrations.
+TfLiteStatus DummyInvoke(TfLiteContext* context, TfLiteNode* node) {
+  return kTfLiteOk;
+}
+
+TfLiteRegistration* GetDummyRegistration() {
+  static TfLiteRegistration registration = {
+      .init = nullptr,
+      .free = nullptr,
+      .prepare = nullptr,
+      .invoke = DummyInvoke,
+  };
+  return ®istration;
+}
+
+TEST(MutableOpResolverTest, FinOp) {
+  MutableOpResolver resolver;
+  resolver.AddBuiltin(BuiltinOperator_ADD, GetDummyRegistration());
+
+  TfLiteRegistration* found_registration =
+      resolver.FindOp(BuiltinOperator_ADD, 1);
+  ASSERT_NE(found_registration, nullptr);
+  EXPECT_TRUE(found_registration->invoke == DummyInvoke);
+  EXPECT_EQ(found_registration->builtin_code, BuiltinOperator_ADD);
+  EXPECT_EQ(found_registration->version, 1);
+}
+
+TEST(MutableOpResolverTest, FindMissingOp) {
+  MutableOpResolver resolver;
+  resolver.AddBuiltin(BuiltinOperator_ADD, GetDummyRegistration());
+
+  TfLiteRegistration* found_registration =
+      resolver.FindOp(BuiltinOperator_CONV_2D, 1);
+  EXPECT_EQ(found_registration, nullptr);
+}
+
+TEST(MutableOpResolverTest, RegisterOpWithMultipleVersions) {
+  MutableOpResolver resolver;
+  // The kernel supports version 2 and 3
+  resolver.AddBuiltin(BuiltinOperator_ADD, GetDummyRegistration(), 2, 3);
+
+  TfLiteRegistration* found_registration;
+
+  found_registration = resolver.FindOp(BuiltinOperator_ADD, 2);
+  ASSERT_NE(found_registration, nullptr);
+  EXPECT_TRUE(found_registration->invoke == DummyInvoke);
+  EXPECT_EQ(found_registration->version, 2);
+
+  found_registration = resolver.FindOp(BuiltinOperator_ADD, 3);
+  ASSERT_NE(found_registration, nullptr);
+  EXPECT_TRUE(found_registration->invoke == DummyInvoke);
+  EXPECT_EQ(found_registration->version, 3);
+}
+
+TEST(MutableOpResolverTest, FindOpWithUnsupportedVersions) {
+  MutableOpResolver resolver;
+  // The kernel supports version 2 and 3
+  resolver.AddBuiltin(BuiltinOperator_ADD, GetDummyRegistration(), 2, 3);
+
+  TfLiteRegistration* found_registration;
+
+  found_registration = resolver.FindOp(BuiltinOperator_ADD, 1);
+  EXPECT_EQ(found_registration, nullptr);
+
+  found_registration = resolver.FindOp(BuiltinOperator_ADD, 4);
+  EXPECT_EQ(found_registration, nullptr);
+}
+
+TEST(MutableOpResolverTest, FindCustomOp) {
+  MutableOpResolver resolver;
+  resolver.AddCustom("AWESOME", GetDummyRegistration());
+
+  TfLiteRegistration* found_registration = resolver.FindOp("AWESOME", 1);
+  ASSERT_NE(found_registration, nullptr);
+  EXPECT_EQ(found_registration->builtin_code, BuiltinOperator_CUSTOM);
+  EXPECT_TRUE(found_registration->invoke == DummyInvoke);
+  EXPECT_EQ(found_registration->version, 1);
+  // TODO(ycling): The `custom_name` in TfLiteRegistration isn't properly
+  // filled yet. Fix this and add tests.
+}
+
+TEST(MutableOpResolverTest, FindMissingCustomOp) {
+  MutableOpResolver resolver;
+  resolver.AddCustom("AWESOME", GetDummyRegistration());
+
+  TfLiteRegistration* found_registration = resolver.FindOp("EXCELLENT", 1);
+  EXPECT_EQ(found_registration, nullptr);
+}
+
+TEST(MutableOpResolverTest, FindCustomOpWithUnsupportedVersion) {
+  MutableOpResolver resolver;
+  resolver.AddCustom("AWESOME", GetDummyRegistration());
+
+  TfLiteRegistration* found_registration = resolver.FindOp("AWESOME", 2);
+  EXPECT_EQ(found_registration, nullptr);
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}
diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs
index f310a0585fe9ce..481659d458cce8 100644
--- a/tensorflow/contrib/lite/schema/schema.fbs
+++ b/tensorflow/contrib/lite/schema/schema.fbs
@@ -447,6 +447,10 @@ table SliceOptions {
 table OperatorCode {
   builtin_code:BuiltinOperator;
   custom_code:string;
+
+  // The version of the operator. The version need to be bumped whenever new
+  // parameters are introduced into an op.
+  version:int = 1;
 }
 
 enum CustomOptionsFormat : byte {
diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h
index e31481c18bc922..3f6bbf05662176 100755
--- a/tensorflow/contrib/lite/schema/schema_generated.h
+++ b/tensorflow/contrib/lite/schema/schema_generated.h
@@ -4448,8 +4448,10 @@ struct OperatorCodeT : public flatbuffers::NativeTable {
   typedef OperatorCode TableType;
   BuiltinOperator builtin_code;
   std::string custom_code;
+  int32_t version;
   OperatorCodeT()
-      : builtin_code(BuiltinOperator_ADD) {
+      : builtin_code(BuiltinOperator_ADD),
+        version(1) {
   }
 };
 
@@ -4457,7 +4459,8 @@ struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
   typedef OperatorCodeT NativeTableType;
   enum {
     VT_BUILTIN_CODE = 4,
-    VT_CUSTOM_CODE = 6
+    VT_CUSTOM_CODE = 6,
+    VT_VERSION = 8
   };
   BuiltinOperator builtin_code() const {
     return static_cast(GetField(VT_BUILTIN_CODE, 0));
@@ -4465,11 +4468,15 @@ struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
   const flatbuffers::String *custom_code() const {
     return GetPointer(VT_CUSTOM_CODE);
   }
+  int32_t version() const {
+    return GetField(VT_VERSION, 1);
+  }
   bool Verify(flatbuffers::Verifier &verifier) const {
     return VerifyTableStart(verifier) &&
            VerifyField(verifier, VT_BUILTIN_CODE) &&
            VerifyOffset(verifier, VT_CUSTOM_CODE) &&
            verifier.Verify(custom_code()) &&
+           VerifyField(verifier, VT_VERSION) &&
            verifier.EndTable();
   }
   OperatorCodeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
@@ -4486,6 +4493,9 @@ struct OperatorCodeBuilder {
   void add_custom_code(flatbuffers::Offset custom_code) {
     fbb_.AddOffset(OperatorCode::VT_CUSTOM_CODE, custom_code);
   }
+  void add_version(int32_t version) {
+    fbb_.AddElement(OperatorCode::VT_VERSION, version, 1);
+  }
   explicit OperatorCodeBuilder(flatbuffers::FlatBufferBuilder &_fbb)
         : fbb_(_fbb) {
     start_ = fbb_.StartTable();
@@ -4501,8 +4511,10 @@ struct OperatorCodeBuilder {
 inline flatbuffers::Offset CreateOperatorCode(
     flatbuffers::FlatBufferBuilder &_fbb,
     BuiltinOperator builtin_code = BuiltinOperator_ADD,
-    flatbuffers::Offset custom_code = 0) {
+    flatbuffers::Offset custom_code = 0,
+    int32_t version = 1) {
   OperatorCodeBuilder builder_(_fbb);
+  builder_.add_version(version);
   builder_.add_custom_code(custom_code);
   builder_.add_builtin_code(builtin_code);
   return builder_.Finish();
@@ -4511,11 +4523,13 @@ inline flatbuffers::Offset CreateOperatorCode(
 inline flatbuffers::Offset CreateOperatorCodeDirect(
     flatbuffers::FlatBufferBuilder &_fbb,
     BuiltinOperator builtin_code = BuiltinOperator_ADD,
-    const char *custom_code = nullptr) {
+    const char *custom_code = nullptr,
+    int32_t version = 1) {
   return tflite::CreateOperatorCode(
       _fbb,
       builtin_code,
-      custom_code ? _fbb.CreateString(custom_code) : 0);
+      custom_code ? _fbb.CreateString(custom_code) : 0,
+      version);
 }
 
 flatbuffers::Offset CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
@@ -6721,6 +6735,7 @@ inline void OperatorCode::UnPackTo(OperatorCodeT *_o, const flatbuffers::resolve
   (void)_resolver;
   { auto _e = builtin_code(); _o->builtin_code = _e; };
   { auto _e = custom_code(); if (_e) _o->custom_code = _e->str(); };
+  { auto _e = version(); _o->version = _e; };
 }
 
 inline flatbuffers::Offset OperatorCode::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
@@ -6733,10 +6748,12 @@ inline flatbuffers::Offset CreateOperatorCode(flatbuffers::FlatBuf
   struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorCodeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
   auto _builtin_code = _o->builtin_code;
   auto _custom_code = _o->custom_code.empty() ? 0 : _fbb.CreateString(_o->custom_code);
+  auto _version = _o->version;
   return tflite::CreateOperatorCode(
       _fbb,
       _builtin_code,
-      _custom_code);
+      _custom_code,
+      _version);
 }
 
 inline OperatorT *Operator::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
diff --git a/tensorflow/contrib/lite/tools/mutable_op_resolver.cc b/tensorflow/contrib/lite/tools/mutable_op_resolver.cc
index 8a921d7c5aa20c..dc9080fd964863 100644
--- a/tensorflow/contrib/lite/tools/mutable_op_resolver.cc
+++ b/tensorflow/contrib/lite/tools/mutable_op_resolver.cc
@@ -14,30 +14,4 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/contrib/lite/tools/mutable_op_resolver.h"
-
-namespace tflite {
-
-TfLiteRegistration* MutableOpResolver::FindOp(
-    tflite::BuiltinOperator op) const {
-  auto it = builtins_.find(op);
-  return it != builtins_.end() ? it->second : nullptr;
-}
-
-TfLiteRegistration* MutableOpResolver::FindOp(const char* op) const {
-  auto it = custom_ops_.find(op);
-  return it != custom_ops_.end() ? it->second : nullptr;
-}
-
-void MutableOpResolver::AddBuiltin(tflite::BuiltinOperator op,
-                                   TfLiteRegistration* registration) {
-  registration->builtin_code = op;
-  builtins_.insert(std::make_pair(op, registration));
-}
-
-void MutableOpResolver::AddCustom(const char* name,
-                                  TfLiteRegistration* registration) {
-  registration->builtin_code = BuiltinOperator_CUSTOM;
-  custom_ops_.insert(std::make_pair(std::string(name), registration));
-}
-
-}  // namespace tflite
+// TODO(ycling): Remove this file after removing other dependencies.
diff --git a/tensorflow/contrib/lite/tools/mutable_op_resolver.h b/tensorflow/contrib/lite/tools/mutable_op_resolver.h
index 573a359c458acb..c0f2583cdd9179 100644
--- a/tensorflow/contrib/lite/tools/mutable_op_resolver.h
+++ b/tensorflow/contrib/lite/tools/mutable_op_resolver.h
@@ -15,41 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_CONTRIB_LITE_TOOLS_MUTABLE_OP_RESOLVER_H_
 #define TENSORFLOW_CONTRIB_LITE_TOOLS_MUTABLE_OP_RESOLVER_H_
 
-#include 
-#include "tensorflow/contrib/lite/context.h"
-#include "tensorflow/contrib/lite/model.h"
-
-// Needed to resolve unordered_set hash on older compilers.
-namespace std {
-template <>
-struct hash {
-  size_t operator()(const tflite::BuiltinOperator& op) const {
-    return std::hash()(op);
-  }
-};
-}  // namespace std
-
-namespace tflite {
-
-// An OpResolver that is mutable, also used as the op in gen_op_registration.
-// A typical usage:
-//   MutableOpResolver resolver;
-//   resolver.AddBuiltin(BuiltinOperator_ADD, Register_ADD());
-//   resolver.AddCustom("CustomOp", Register_CUSTOM_OP());
-//   InterpreterBuilder(model, resolver)(&interpreter);
-class MutableOpResolver : public OpResolver {
- public:
-  MutableOpResolver() {}
-  TfLiteRegistration* FindOp(tflite::BuiltinOperator op) const override;
-  TfLiteRegistration* FindOp(const char* op) const override;
-  void AddBuiltin(tflite::BuiltinOperator op, TfLiteRegistration* registration);
-  void AddCustom(const char* name, TfLiteRegistration* registration);
-
- private:
-  std::map builtins_;
-  std::map custom_ops_;
-};
-
-}  // namespace tflite
+#include "tensorflow/contrib/lite/op_resolver.h"
+// MutableOpResolverr is moved into `lite/op_resolver.h`.`
+// TODO(ycling): Remove this file after removing other dependencies.
 
 #endif  // TENSORFLOW_CONTRIB_LITE_TOOLS_MUTABLE_OP_RESOLVER_H_
diff --git a/tensorflow/contrib/lite/tools/verifier.cc b/tensorflow/contrib/lite/tools/verifier.cc
index 8818a7dc85d9ff..8d3a7a624265ca 100644
--- a/tensorflow/contrib/lite/tools/verifier.cc
+++ b/tensorflow/contrib/lite/tools/verifier.cc
@@ -246,15 +246,16 @@ bool VerifyOps(const Model& model, const OpResolver& resolver,
     }
 
     if (opcode->builtin_code() == BuiltinOperator_CUSTOM) {
-      if (!resolver.FindOp(opcode->custom_code()->c_str())) {
-        ReportError(error_reporter, "Unsupported custom op: %s",
-                    opcode->custom_code()->c_str());
+      if (!resolver.FindOp(opcode->custom_code()->c_str(), opcode->version())) {
+        ReportError(error_reporter, "Unsupported custom op: %s, version: %d",
+                    opcode->custom_code()->c_str(), opcode->version());
         return false;
       }
     } else {
-      if (!resolver.FindOp(opcode->builtin_code())) {
-        ReportError(error_reporter, "Unsupported builtin op: %s",
-                    EnumNameBuiltinOperator(opcode->builtin_code()));
+      if (!resolver.FindOp(opcode->builtin_code(), opcode->version())) {
+        ReportError(error_reporter, "Unsupported builtin op: %s, version: %d",
+                    EnumNameBuiltinOperator(opcode->builtin_code()),
+                    opcode->version());
         return false;
       }
     }
diff --git a/tensorflow/contrib/lite/tools/verifier.h b/tensorflow/contrib/lite/tools/verifier.h
index b7ce4e830576af..b64b5d473fdf1e 100644
--- a/tensorflow/contrib/lite/tools/verifier.h
+++ b/tensorflow/contrib/lite/tools/verifier.h
@@ -26,12 +26,13 @@ namespace tflite {
 class AlwaysTrueResolver : public OpResolver {
  public:
   AlwaysTrueResolver() {}
-  TfLiteRegistration* FindOp(tflite::BuiltinOperator op) const override {
+  TfLiteRegistration* FindOp(tflite::BuiltinOperator op,
+                             int version) const override {
     static TfLiteRegistration null_registration = {nullptr, nullptr, nullptr,
                                                    nullptr};
     return &null_registration;
   }
-  TfLiteRegistration* FindOp(const char* op) const override {
+  TfLiteRegistration* FindOp(const char* op, int version) const override {
     static TfLiteRegistration null_registration = {nullptr, nullptr, nullptr,
                                                    nullptr};
     return &null_registration;

From a790d616a249ce35bc299ebdbba4750a8277b63b Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy 
Date: Sun, 13 May 2018 22:30:21 -0700
Subject: [PATCH 0719/1691] Bump protobuf dependency to fix windows build
 issues.

PiperOrigin-RevId: 196456687
---
 .../contrib/cmake/external/protobuf.cmake     |  2 +-
 tensorflow/contrib/cmake/tf_tests.cmake       |  8 +++++-
 tensorflow/workspace.bzl                      | 26 +++++++++----------
 3 files changed, 21 insertions(+), 15 deletions(-)

diff --git a/tensorflow/contrib/cmake/external/protobuf.cmake b/tensorflow/contrib/cmake/external/protobuf.cmake
index ab464bc99a4313..d6f5395344649e 100644
--- a/tensorflow/contrib/cmake/external/protobuf.cmake
+++ b/tensorflow/contrib/cmake/external/protobuf.cmake
@@ -16,7 +16,7 @@ include (ExternalProject)
 
 set(PROTOBUF_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/src)
 set(PROTOBUF_URL https://github.com/google/protobuf.git)
-set(PROTOBUF_TAG b04e5cba356212e4e8c66c61bbe0c3a20537c5b9)
+set(PROTOBUF_TAG 25625b956a2f0d432582009c16553a9fd21c3cea)
 
 if(WIN32)
   if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index 92f2ab6dea8e7d..8ee7ffc114e5dc 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -212,7 +212,13 @@ if (tensorflow_BUILD_PYTHON_TESTS)
     "${tensorflow_source_dir}/tensorflow/contrib/factorization/python/ops/gmm_test.py"
     # Disable following manual tag in BUILD.
     "${tensorflow_source_dir}/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py"
-
+    # Avoid large sharded tests, as they take a long time without sharding in cmake and time out.
+    "${tensorflow_source_dir}/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py"
+    "${tensorflow_source_dir}/tensorflow/python/kernel_tests/linalg/linear_operator_kronecker_test.py"
+    "${tensorflow_source_dir}/tensorflow/python/kernel_tests/linalg/linear_operator_circulant_test.py"
+    "${tensorflow_source_dir}/tensorflow/python/kernel_tests/linalg/linear_operator_block_diag_test.py"
+    "${tensorflow_source_dir}/tensorflow/python/kernel_tests/linalg/linear_operator_composition_test.py"
+    "${tensorflow_source_dir}/tensorflow/python/kernel_tests/conv_ops_test.py"
   )
   if (WIN32)
     set(tf_test_src_py_exclude
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index ea31df0e06df04..02177998b885ad 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -317,7 +317,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       strip_prefix = "backports.weakref-1.0rc1/src",
       build_file = clean_dep("//third_party:backports_weakref.BUILD"),
   )
-  
+
   filegroup_external(
       name = "org_python_license",
       licenses = ["notice"],  # Python 2.0
@@ -332,11 +332,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "protobuf_archive",
       urls = [
-          "https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz",
-          "https://github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz",
+          "https://mirror.bazel.build/github.com/google/protobuf/archive/25625b956a2f0d432582009c16553a9fd21c3cea.tar.gz",
+          "https://github.com/google/protobuf/archive/25625b956a2f0d432582009c16553a9fd21c3cea.tar.gz",
       ],
-      sha256 = "846d907acf472ae233ec0882ef3a2d24edbbe834b80c305e867ac65a1f2c59e3",
-      strip_prefix = "protobuf-396336eb961b75f03b25824fe86cf6490fb75e3a",
+      sha256 = "90f8f29184330b27aa20387c42fffe3a6fa87b3445874b8736ed82afc080e134",
+      strip_prefix = "protobuf-25625b956a2f0d432582009c16553a9fd21c3cea",
   )
 
   # We need to import the protobuf library under the names com_google_protobuf
@@ -345,21 +345,21 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "com_google_protobuf",
       urls = [
-          "https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz",
-          "https://github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz",
+          "https://mirror.bazel.build/github.com/google/protobuf/archive/25625b956a2f0d432582009c16553a9fd21c3cea.tar.gz",
+          "https://github.com/google/protobuf/archive/25625b956a2f0d432582009c16553a9fd21c3cea.tar.gz",
       ],
-      sha256 = "846d907acf472ae233ec0882ef3a2d24edbbe834b80c305e867ac65a1f2c59e3",
-      strip_prefix = "protobuf-396336eb961b75f03b25824fe86cf6490fb75e3a",
+      sha256 = "90f8f29184330b27aa20387c42fffe3a6fa87b3445874b8736ed82afc080e134",
+      strip_prefix = "protobuf-25625b956a2f0d432582009c16553a9fd21c3cea",
   )
 
   tf_http_archive(
       name = "com_google_protobuf_cc",
       urls = [
-          "https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz",
-          "https://github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz",
+          "https://mirror.bazel.build/github.com/google/protobuf/archive/25625b956a2f0d432582009c16553a9fd21c3cea.tar.gz",
+          "https://github.com/google/protobuf/archive/25625b956a2f0d432582009c16553a9fd21c3cea.tar.gz",
       ],
-      sha256 = "846d907acf472ae233ec0882ef3a2d24edbbe834b80c305e867ac65a1f2c59e3",
-      strip_prefix = "protobuf-396336eb961b75f03b25824fe86cf6490fb75e3a",
+      sha256 = "90f8f29184330b27aa20387c42fffe3a6fa87b3445874b8736ed82afc080e134",
+      strip_prefix = "protobuf-25625b956a2f0d432582009c16553a9fd21c3cea",
   )
 
   tf_http_archive(

From 4b1fa0ccdcada19035fe9e685f2b63a5c7a78f21 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 14 May 2018 07:53:04 -0700
Subject: [PATCH 0720/1691] Prevent removal of constant inputs to passthrough
 ops.

PiperOrigin-RevId: 196505061
---
 .../graph_transformations/remove_trivial_passthrough.cc     | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc
index 971e4ff8e6de52..a950fe6442bc65 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc
@@ -85,9 +85,11 @@ bool RemoveTrivialPassthroughOp(GraphTransformation* transformation,
         "Removing %s, keeping its non-constant input array %s and removing %s",
         LogName(*passthru_op), main_input_name, output_name);
     RerouteEdges(output_name, main_input_name, model);
-  } else if (IsDiscardableArray(*model, main_input_name)) {
+  } else if (IsDiscardableArray(*model, main_input_name) &&
+             !IsConstantParameterArray(*model, main_input_name)) {
     transformation->AddMessageF(
-        "Removing %s, keeping its output array %s and removing input %s",
+        "Removing %s, keeping its output array %s and removing non-constant "
+        "input %s",
         LogName(*passthru_op), output_name, main_input_name);
     RerouteEdges(main_input_name, output_name, model);
   } else {

From a5f12aadacfdf690c8f2192d612bf575b8e11cbe Mon Sep 17 00:00:00 2001
From: Paul Van Eck 
Date: Mon, 14 May 2018 08:27:42 -0700
Subject: [PATCH 0721/1691] Make op unique name generation case insensitive
 (#18413)

* Make op unique name generation case insensitive

Unique name generation for operations depends on checking a dict for
names currently in use. This commit makes it so that the names stored in
this dict are always lowercase so that we can check if a name already
exists regardless of the capitalization.

This helps in filesystems where file paths are case insensitive and
tensor dumps (like with tfdbg) try to follow directory structures that
correspond to the tensor names. If two tensors have names with the same
spelling, but different capitalizations, then this can lead to unintended
side effects/errors on these case-insensitive file systems.

* Change variable name to match unique_name

* Adjust op names to fix tests
---
 .../python/losses/python/losses_impl_test.py  |  2 +-
 .../layers/python/layers/layers_test.py       |  2 +-
 .../quantize/python/fold_batch_norms.py       | 14 ++++-----
 .../quantize/python/fold_batch_norms_test.py  |  6 ++--
 .../python/util/receptive_field_test.py       |  2 +-
 tensorflow/python/framework/ops.py            | 30 ++++++++++++-------
 tensorflow/python/framework/ops_test.py       |  9 ++++++
 7 files changed, 41 insertions(+), 24 deletions(-)

diff --git a/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py b/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py
index 2889e937436d2f..9f5fee45422e0b 100644
--- a/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py
+++ b/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py
@@ -570,7 +570,7 @@ def setUp(self):
         'predicted_distributions': self._predicted_distributions,
     }
     self._expected_loss = 1.61610
-    self._expected_op_name = 'mutual_information_loss/mul'
+    self._expected_op_name = 'mutual_information_loss/mul_1'
     self._batch_size = 2
 
 
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index b01fd5d5c95ac1..56e9194cebbe46 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -1333,7 +1333,7 @@ def testCreateDropout(self):
     with self.test_session():
       images = np.random.uniform(size=(5, height, width, 3))
       output = _layers.dropout(images)
-      self.assertEqual(output.op.name, 'Dropout/dropout/mul')
+      self.assertEqual(output.op.name, 'Dropout/dropout_1/mul')
       output.get_shape().assert_is_compatible_with(
           ops.convert_to_tensor(images).get_shape())
 
diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms.py b/tensorflow/contrib/quantize/python/fold_batch_norms.py
index 76f695dce0d1c4..55479bf5f74299 100644
--- a/tensorflow/contrib/quantize/python/fold_batch_norms.py
+++ b/tensorflow/contrib/quantize/python/fold_batch_norms.py
@@ -475,7 +475,7 @@ def _FoldUnfusedBatchNorms(graph, is_training, freeze_batch_norm_delay):
 def _IsValidUnfusedBatchNorm(graph, context):
   """Checks that the output of the unfused batch norm has consumers."""
   add_shift = graph.get_operation_by_name(
-      context + '/BatchNorm/batchnorm/add_1')
+      context + '/BatchNorm/batchnorm_1/add_1')
   # Ensure that the output tensor of batch norm has consumers, otherwise this
   # is a dangling node and not a match.
   return bool(add_shift.outputs[0].consumers())
@@ -568,7 +568,7 @@ def _GetBatchNormParams(graph, context, has_scaling):
 
   op_suffix_mean = '/BatchNorm/moments/Squeeze'
   op_suffix_variance = '/BatchNorm/moments/Squeeze_1'
-  op_suffix_epsilon = '/BatchNorm/batchnorm/add/y'
+  op_suffix_epsilon = '/BatchNorm/batchnorm_1/add/y'
   op_suffix_bn_decay_mean = '/BatchNorm/AssignMovingAvg/decay'
   op_suffix_bn_decay_var = '/BatchNorm/AssignMovingAvg_1/decay'
 
@@ -643,12 +643,12 @@ def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay,
 
   Returns:
     A pair of Operations, the first is the original consumer node of the batch
-      norm (../BatchNorm/batchnorm/add_1), the second is the consumer node of
+      norm (../BatchNorm/batchnorm_1/add_1), the second is the consumer node of
       the folded graph (add_fold).
   """
   mul_scale_name = 'mul_1' if has_scaling else 'mul'
   mul_scale = graph.get_operation_by_name(context +
-                                          '/BatchNorm/batchnorm/' +
+                                          '/BatchNorm/batchnorm_1/' +
                                           mul_scale_name)
   op_below = mul_scale.inputs[0].op
   weights = op_below.inputs[1]
@@ -670,7 +670,7 @@ def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay,
     ]
     scale_name = 'mul' if has_scaling else 'Rsqrt'
     scale = graph.get_operation_by_name(
-        context + '/BatchNorm/batchnorm/' + scale_name)
+        context + '/BatchNorm/batchnorm_1/' + scale_name)
     scale = array_ops.reshape(scale.outputs[0], new_shape,
                               context + '/scale_reshape')
 
@@ -698,7 +698,7 @@ def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay,
                                [(1, mul_fold.outputs[0])])
 
   add_shift = graph.get_operation_by_name(
-      context + '/BatchNorm/batchnorm/add_1')
+      context + '/BatchNorm/batchnorm_1/add_1')
 
   corrected_output = conv_or_fc_folded.outputs[0]
   if correction_offset is not None:
@@ -886,7 +886,7 @@ def _HasScaling(graph, input_to_ops_map, bn):
   Returns:
     A boolean indicating whether this batch norm layer has scaling enabled.
   """
-  rsqrt_op = graph.get_operation_by_name(bn + '/BatchNorm/batchnorm/Rsqrt')
+  rsqrt_op = graph.get_operation_by_name(bn + '/BatchNorm/batchnorm_1/Rsqrt')
   rsqrt_consumers = input_to_ops_map.ConsumerOperations(rsqrt_op)
 
   return sum(1 for op in rsqrt_consumers if op.type == 'Mul') == 1
diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms_test.py b/tensorflow/contrib/quantize/python/fold_batch_norms_test.py
index fa5e11b4708402..bfa9d3bf705e32 100644
--- a/tensorflow/contrib/quantize/python/fold_batch_norms_test.py
+++ b/tensorflow/contrib/quantize/python/fold_batch_norms_test.py
@@ -516,13 +516,13 @@ def _BatchNormMultiplierName(self, scope, has_scaling, fused):
     if has_scaling:
       if fused:
         return scope + '/BatchNorm_Fold/mul'
-      return scope + '/BatchNorm/batchnorm/mul'
-    return scope + '/BatchNorm/batchnorm/Rsqrt'
+      return scope + '/BatchNorm/batchnorm_1/mul'
+    return scope + '/BatchNorm/batchnorm_1/Rsqrt'
 
   def _BathNormBiasName(self, scope, fused):
     if fused:
       return scope + '/BatchNorm_Fold/bias'
-    return scope + '/BatchNorm/batchnorm/sub'
+    return scope + '/BatchNorm/batchnorm_1/sub'
 
   def _WeightInit(self, stddev):
     """Returns a truncated normal variable initializer.
diff --git a/tensorflow/contrib/receptive_field/python/util/receptive_field_test.py b/tensorflow/contrib/receptive_field/python/util/receptive_field_test.py
index cf55da27236d17..a42bbca61135a5 100644
--- a/tensorflow/contrib/receptive_field/python/util/receptive_field_test.py
+++ b/tensorflow/contrib/receptive_field/python/util/receptive_field_test.py
@@ -385,7 +385,7 @@ def testComputeRFFromGraphDefStopPropagation(self):
      effective_stride_y, effective_padding_x, effective_padding_y) = (
          receptive_field.compute_receptive_field_from_graph_def(
              graph_def, input_node, output_node,
-             ['Dropout/dropout/random_uniform']))
+             ['Dropout/dropout_1/random_uniform']))
     self.assertEqual(receptive_field_x, 3)
     self.assertEqual(receptive_field_y, 3)
     self.assertEqual(effective_stride_x, 4)
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index de3bf0032b9efe..71825e4a50dffb 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -3455,8 +3455,9 @@ def _create_op_from_tf_operation(self, c_op, compute_device=True):
     # the name will still appear in _names_in_use even though the name hasn't
     # been used. This is ok, just leave _names_in_use as-is in this case.
     # TODO(skyewm): make the C API guarantee no name conflicts.
-    if ret.name not in self._names_in_use:
-      self._names_in_use[ret.name] = 1
+    name_key = ret.name.lower()
+    if name_key not in self._names_in_use:
+      self._names_in_use[name_key] = 1
     self._create_op_helper(ret, compute_device=compute_device)
     return ret
 
@@ -4172,20 +4173,27 @@ def unique_name(self, name, mark_as_used=True):
     """
     if self._name_stack:
       name = self._name_stack + "/" + name
-    i = self._names_in_use.get(name, 0)
-    # Increment the number for "name".
+
+    # For the sake of checking for names in use, we treat names as case
+    # insensitive (e.g. foo = Foo).
+    name_key = name.lower()
+    i = self._names_in_use.get(name_key, 0)
+    # Increment the number for "name_key".
     if mark_as_used:
-      self._names_in_use[name] = i + 1
+      self._names_in_use[name_key] = i + 1
     if i > 0:
-      base_name = name
-      # Make sure the composed name is not already used.
-      while name in self._names_in_use:
-        name = "%s_%d" % (base_name, i)
+      base_name_key = name_key
+      # Make sure the composed name key is not already used.
+      while name_key in self._names_in_use:
+        name_key = "%s_%d" % (base_name_key, i)
         i += 1
-      # Mark the composed name as used in case someone wants
+      # Mark the composed name_key as used in case someone wants
       # to call unique_name("name_1").
       if mark_as_used:
-        self._names_in_use[name] = 1
+        self._names_in_use[name_key] = 1
+
+      # Return the new name with the original capitalization of the given name.
+      name = "%s_%d" % (name, i-1)
     return name
 
   def get_name_scope(self):
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index c9c1a3d66be105..7d6e3bab7959ef 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -1063,6 +1063,15 @@ def testOutOfOrderUniqueName(self):
     self.assertEqual("foo_1", g.unique_name("foo"))
     self.assertEqual("foo_3", g.unique_name("foo"))
 
+  def testUniqueNameCaseInsensitivity(self):
+    g = ops.Graph()
+    self.assertEqual("foo", g.unique_name("foo"))
+    self.assertEqual("Foo_1", g.unique_name("Foo"))
+    with g.name_scope("bar"):
+      self.assertEqual("bar/foo", g.unique_name("foo"))
+    with g.name_scope("Bar"):
+      self.assertEqual("Bar_1/foo", g.unique_name("foo"))
+
   def testInvalidNameRaisesError(self):
     g = ops.Graph()
     with g.name_scope(""):  # Should not raise

From 7e3e661d35a80afd075db80d0dc7ba5c5f9911a1 Mon Sep 17 00:00:00 2001
From: gracehoney <31743510+aaroey@users.noreply.github.com>
Date: Mon, 14 May 2018 08:27:42 -0700
Subject: [PATCH 0722/1691] Fix various formatting and build issues.

---
 tensorflow/contrib/tensorrt/BUILD             |   2 +
 .../contrib/tensorrt/convert/convert_nodes.cc |   4 +-
 .../tensorrt/custom_plugin_examples/BUILD     |  12 ++-
 .../custom_plugin_examples/__init__.py        |   2 +-
 .../tensorrt/custom_plugin_examples/inc_op.py |   1 +
 .../inc_op_kernel.cu.cc                       |   3 +-
 .../custom_plugin_examples/inc_op_kernel.h    |   6 +-
 .../custom_plugin_examples/inc_op_plugin.cc   |   3 +-
 .../custom_plugin_examples/inc_op_plugin.h    |   6 +-
 .../custom_plugin_examples/ops/inc_op.cc      |   2 +-
 .../custom_plugin_examples/plugin_test.py     | 102 +++++++++---------
 .../contrib/tensorrt/kernels/trt_engine_op.cc |   3 +-
 .../contrib/tensorrt/plugin/trt_plugin.h      |  10 +-
 .../tensorrt/plugin/trt_plugin_factory.cc     |   8 +-
 .../tensorrt/plugin/trt_plugin_factory.h      |  32 +++---
 .../plugin/trt_plugin_factory_test.cc         |  17 +--
 .../tensorrt/plugin/trt_plugin_utils.h        |   6 +-
 17 files changed, 115 insertions(+), 104 deletions(-)

diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD
index 467c96261d7bce..7a8a71ac7f491e 100644
--- a/tensorflow/contrib/tensorrt/BUILD
+++ b/tensorflow/contrib/tensorrt/BUILD
@@ -302,6 +302,7 @@ tf_cuda_library(
         "plugin/trt_plugin_utils.h",
     ],
     deps = [
+        "//tensorflow/core:framework_lite",
         "//tensorflow/core:platform_base",
     ] + if_tensorrt([
         "@local_config_tensorrt//:nv_infer",
@@ -318,6 +319,7 @@ tf_cuda_cc_test(
     ],
     deps = [
         ":trt_plugins",
+        "//tensorflow/core:lib",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
     ] + if_tensorrt([
diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
index f043237ebd07d3..32b211dcd1e282 100644
--- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
+++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
@@ -1223,8 +1223,8 @@ tensorflow::Status ConvertPlugin(Converter& ctx,
     }
   }
 
-  nvinfer1::IPluginLayer* layer =
-      ctx.network()->addPlugin(&all_inputs[0], int(inputs.size()), *plugin);
+  nvinfer1::IPluginLayer* layer = ctx.network()->addPlugin(
+      &all_inputs[0], static_cast(inputs.size()), *plugin);
 
   for (int i = 0; i < layer->getNbOutputs(); i++) {
     nvinfer1::ITensor* output_tensor = layer->getOutput(i);
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
index 6f81ac2b444501..a89cf3ab8bfaec 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD
@@ -6,18 +6,18 @@
 
 package(default_visibility = ["//tensorflow:__subpackages__"])
 
+licenses(["notice"])  # Apache 2.0
+
 load(
     "//tensorflow:tensorflow.bzl",
-    "tf_copts",
     "tf_custom_op_library",
     "tf_custom_op_library_additional_deps",
     "tf_gen_op_libs",
     "tf_gen_op_wrapper_py",
     "tf_kernel_library",
 )
+load("//tensorflow:tensorflow.bzl", "cuda_py_test")
 load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library")
-load("//tensorflow:tensorflow.bzl", "tf_py_test")
-load("//tensorflow:tensorflow.bzl", "tf_py_wrap_cc")
 load(
     "@local_config_tensorrt//:build_defs.bzl",
     "if_tensorrt",
@@ -46,6 +46,7 @@ tf_custom_op_library(
     ],
     deps = [
         "//tensorflow/contrib/tensorrt:trt_plugins",
+        "//tensorflow/core:framework_lite",
     ] + if_tensorrt([
         "@local_config_tensorrt//:nv_infer",
     ]),
@@ -55,6 +56,7 @@ tf_kernel_library(
     name = "inc_op_plugin_kernel",
     srcs = ["inc_op_plugin.cc"],
     hdrs = [
+        "inc_op_kernel.h",
         "inc_op_plugin.h",
     ],
     gpu_srcs = [
@@ -63,6 +65,7 @@ tf_kernel_library(
     ],
     deps = [
         "//tensorflow/contrib/tensorrt:trt_plugins",
+        "//tensorflow/core:stream_executor_headers_lib",
     ] + if_tensorrt([
         "@local_config_tensorrt//:nv_infer",
     ]) + tf_custom_op_library_additional_deps(),
@@ -95,7 +98,7 @@ py_library(
     ],
 )
 
-tf_py_test(
+cuda_py_test(
     name = "plugin_test",
     size = "small",
     srcs = ["plugin_test.py"],
@@ -109,6 +112,7 @@ tf_py_test(
     ],
     tags = [
         "manual",
+        "noguitar",
         "notap",
     ],
 )
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py
index e06904ab564d90..363edab2e80ada 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/__init__.py
@@ -18,7 +18,7 @@
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.tensorrt.custom_plugin_examples.ops import gen_inc_op
 from tensorflow.contrib.tensorrt.custom_plugin_examples import inc_op as import_inc_op_so
+from tensorflow.contrib.tensorrt.custom_plugin_examples.ops import gen_inc_op
 
 inc_op = gen_inc_op.inc_plugin_trt
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op.py
index 47fd55e2f6753a..a007c3f54e208b 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op.py
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op.py
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # =============================================================================
+"""Loader for the custom inc_op."""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc
index abbc0c5680a85d..988b35f74f3989 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc
@@ -18,12 +18,11 @@ limitations under the License.
 #include 
 
 #include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/platform/stream_executor.h"
 
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
 #include "cuda/include/cuda_runtime_api.h"
-
+#include "tensorflow/core/platform/stream_executor.h"
 
 namespace tensorflow {
 namespace tensorrt {
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h
index 1d0ec0b6b083ad..c35955e105798b 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_CONTRIB_TENSORRT_INC_OP
-#define TENSORFLOW_CONTRIB_TENSORRT_INC_OP
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_CUSTOM_PLUGIN_EXAMPLES_INC_OP_KERNEL_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_CUSTOM_PLUGIN_EXAMPLES_INC_OP_KERNEL_H_
 
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
@@ -32,4 +32,4 @@ void IncrementKernel(const float* d_input, float inc, float* d_output,
 #endif  // GOOGLE_TENSORRT
 #endif  // GOOGLE_CUDA
 
-#endif  // TENSORFLOW_CONTRIB_TENSORRT_INC_OP
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_CUSTOM_PLUGIN_EXAMPLES_INC_OP_KERNEL_H_
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc
index d56aedc6d40d7e..8d4c893af56689 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.cc
@@ -13,8 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h"
 #include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h"
+
+#include "tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.h"
 #include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h"
 
 #if GOOGLE_CUDA
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h
index 60153546d2e303..189e9c939b9ffd 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_plugin.h
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_CONTRIB_TENSORRT_INC_OP_PLUGIN
-#define TENSORFLOW_CONTRIB_TENSORRT_INC_OP_PLUGIN
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_CUSTOM_PLUGIN_EXAMPLES_INC_OP_PLUGIN_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_CUSTOM_PLUGIN_EXAMPLES_INC_OP_PLUGIN_H_
 
 #include 
 #include 
@@ -99,4 +99,4 @@ class IncOpPlugin : public PluginTensorRT {
 #endif  // GOOGLE_TENSORRT
 #endif  // GOOGLE_CUDA
 
-#endif  // TENSORFLOW_CONTRIB_TENSORRT_INC_OP_PLUGIN
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_CUSTOM_PLUGIN_EXAMPLES_INC_OP_PLUGIN_H_
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/ops/inc_op.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/ops/inc_op.cc
index 7466e590901600..d0eb0d299dd61d 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/ops/inc_op.cc
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/ops/inc_op.cc
@@ -30,7 +30,7 @@ REGISTER_OP("IncPluginTRT")
       return Status::OK();
     });
 
-} // namespace tensorflow
+}  // namespace tensorflow
 
 #endif  // GOOGLE_CUDA
 #endif  // GOOGLE_TENSORRT
diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py
index aedfb162113d38..bc4d270bec4fb8 100644
--- a/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py
+++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/plugin_test.py
@@ -27,65 +27,69 @@
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import importer
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.platform import test
 
 
-def get_plugin_graph_def():
-  """Create a simple graph and return its graph_def."""
-  g = ops.Graph()
-  with g.as_default():
-    a = array_ops.placeholder(
-        dtype=dtypes.float32, shape=(None, 24, 24, 2), name="input")
-    relu = nn.relu(a, "relu")
-    v = nn_ops.max_pool(
-        relu, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool")
+class TrtPluginTest(test_util.TensorFlowTestCase):
 
-    # insert custom_op in the graph
-    v = custom_plugin_examples.inc_op(v, inc=[16.5], name="plugin_test")
+  def _get_plugin_graph_def(self):
+    """Create a simple graph and return its graph_def."""
+    g = ops.Graph()
+    with g.as_default():
+      a = array_ops.placeholder(
+          dtype=dtypes.float32, shape=(None, 24, 24, 2), name="input")
+      relu = nn.relu(a, "relu")
+      v = nn_ops.max_pool(
+          relu, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool")
 
-    v = v * 2.0
-    v = nn.relu(v)
-    v = nn.relu(v)
-    array_ops.squeeze(v, name="output")
-  return g.as_graph_def()
+      # insert custom_op in the graph
+      v = custom_plugin_examples.inc_op(v, inc=[16.5], name="plugin_test")
 
+      v *= 2.0
+      v = nn.relu(v)
+      v = nn.relu(v)
+      array_ops.squeeze(v, name="output")
+    return g.as_graph_def()
 
-def run_graph(gdef, dumm_inp):
-  """Run given graphdef once."""
-  gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50)
-  ops.reset_default_graph()
-  g = ops.Graph()
-  with g.as_default():
-    inp, out = importer.import_graph_def(
-        graph_def=gdef, return_elements=["input", "output"])
-    inp = inp.outputs[0]
-    out = out.outputs[0]
+  def _run_graph(self, gdef, dumm_inp):
+    """Run given graphdef once."""
+    gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50)
+    ops.reset_default_graph()
+    g = ops.Graph()
+    with g.as_default():
+      inp, out = importer.import_graph_def(
+          graph_def=gdef, return_elements=["input", "output"])
+      inp = inp.outputs[0]
+      out = out.outputs[0]
 
-  with session.Session(
-      config=config_pb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess:
-    val = sess.run(out, {inp: dumm_inp})
-  return val
+    with session.Session(
+        config=config_pb2.ConfigProto(gpu_options=gpu_options),
+        graph=g) as sess:
+      val = sess.run(out, {inp: dumm_inp})
+    return val
 
+  def testIncOpPlugin(self):
+    inp_dims = (5, 24, 24, 2)
+    dummy_input = numpy.ones(inp_dims).astype(numpy.float32)
+    orig_graph = self._get_plugin_graph_def()  # graph with plugin node
 
-if "__main__" in __name__:
-  inp_dims = (5, 24, 24, 2)
-  dummy_input = numpy.ones(inp_dims).astype(numpy.float32)
-  orig_graph = get_plugin_graph_def()  # graph with plugin node
+    # trigger conversion.
+    # plugin nodes have been registered during import, converter will be able to
+    # create corresponding plugin layer during conversion.
+    trt_graph = tensorrt.create_inference_graph(
+        input_graph_def=orig_graph,
+        outputs=["output"],
+        max_batch_size=inp_dims[0],
+        max_workspace_size_bytes=1 << 25,
+        precision_mode="FP32",
+        minimum_segment_size=2)
+    o2 = self._run_graph(trt_graph, dummy_input)
+    self.assertEqual(35, o2.reshape([-1])[0])
 
-  # trigger conversion.
-  # plugin nodes have been registered during import, converter will be able to
-  # create corresponding plugin layer during conversion.
-  trt_graph = tensorrt.create_inference_graph(
-      input_graph_def=orig_graph,
-      outputs=["output"],
-      max_batch_size=inp_dims[0],
-      max_workspace_size_bytes=1 << 25,
-      precision_mode="FP32",
-      minimum_segment_size=2)
-  o2 = run_graph(trt_graph, dummy_input)
-  if o2.reshape([-1])[0] == 35:
-    print("pass")
-  else:
-    raise RuntimeError("contrib/tensorrt/custom_plugin_examples wrong result")
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc
index d84fc8a60e9212..9ac80479448741 100644
--- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc
+++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc
@@ -60,7 +60,8 @@ void TRTEngineOp::Compute(OpKernelContext* context) {
     infer->setGpuAllocator(allocator_.get());
 #endif
     trt_engine_ptr_.reset(infer->deserializeCudaEngine(
-        serialized_engine_.c_str(), serialized_engine_.size(), PluginFactoryTensorRT::GetInstance()));
+        serialized_engine_.c_str(), serialized_engine_.size(),
+        PluginFactoryTensorRT::GetInstance()));
     trt_execution_context_ptr_.reset(trt_engine_ptr_->createExecutionContext());
     // Runtime is safe to delete after engine creation
     infer->destroy();
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin.h
index d80ec44372af54..754920b60ca743 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin.h
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin.h
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN
-#define TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_H_
 
 #include 
 #include 
@@ -55,9 +55,9 @@ class PluginTensorRT : public nvinfer1::IPlugin {
   virtual bool StoreAttribute(const string& key, const void* ptr,
                               const size_t size);
 
-  virtual size_t getSerializationSize() override;
+  size_t getSerializationSize() override;
 
-  virtual void serialize(void* buffer) override;
+  void serialize(void* buffer) override;
 
  protected:
   std::unordered_map > attr_map_;
@@ -71,4 +71,4 @@ class PluginTensorRT : public nvinfer1::IPlugin {
 #endif  // GOOGLE_TENSORRT
 #endif  // GOOGLE_CUDA
 
-#endif  // TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_H_
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc
index 736a1321fe7215..2bc591484dcaf5 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc
@@ -33,7 +33,7 @@ PluginTensorRT* PluginFactoryTensorRT::createPlugin(const char* layer_name,
     return nullptr;
   }
 
-  std::lock_guard lock(instance_m_);
+  tensorflow::mutex_lock lock(instance_m_);
   auto plugin_ptr =
       plugin_registry_[encoded_op_name].first(serial_data, serial_length);
   owned_plugins_.emplace_back(plugin_ptr);
@@ -44,7 +44,7 @@ PluginTensorRT* PluginFactoryTensorRT::createPlugin(const char* layer_name,
 PluginTensorRT* PluginFactoryTensorRT::CreatePlugin(const string& op_name) {
   if (!IsPlugin(op_name)) return nullptr;
 
-  std::lock_guard lock(instance_m_);
+  tensorflow::mutex_lock lock(instance_m_);
   auto plugin_ptr = plugin_registry_[op_name].second();
   owned_plugins_.emplace_back(plugin_ptr);
 
@@ -56,7 +56,7 @@ bool PluginFactoryTensorRT::RegisterPlugin(
     PluginConstructFunc construct_func) {
   if (IsPlugin(op_name)) return false;
 
-  std::lock_guard lock(instance_m_);
+  tensorflow::mutex_lock lock(instance_m_);
   auto ret = plugin_registry_.emplace(
       op_name, std::make_pair(deserialize_func, construct_func));
 
@@ -64,7 +64,7 @@ bool PluginFactoryTensorRT::RegisterPlugin(
 }
 
 void PluginFactoryTensorRT::DestroyPlugins() {
-  std::lock_guard lock(instance_m_);
+  tensorflow::mutex_lock lock(instance_m_);
   for (auto& owned_plugin_ptr : owned_plugins_) {
     owned_plugin_ptr.release();
   }
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
index 0eee705fb985eb..bbae9fb65c22cf 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h
@@ -13,17 +13,17 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_FACTORY
-#define TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_FACTORY
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_FACTORY_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_FACTORY_H_
 
 #include 
-#include 
 #include 
 
 #include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h"
 #include "tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/mutex.h"
 
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
@@ -69,13 +69,12 @@ class PluginFactoryTensorRT : public nvinfer1::IPluginFactory {
   // TODO(jie): Owned plugin should be associated with different sessions;
   //            should really hand ownership of plugins to resource management;
   std::vector> owned_plugins_;
-  std::mutex instance_m_;
+  tensorflow::mutex instance_m_;
 };
 
 class TrtPluginRegistrar {
  public:
-  TrtPluginRegistrar(const string& name,
-                     PluginDeserializeFunc deserialize_func,
+  TrtPluginRegistrar(const string& name, PluginDeserializeFunc deserialize_func,
                      PluginConstructFunc construct_func) {
     auto factory = PluginFactoryTensorRT::GetInstance();
     QCHECK(factory->RegisterPlugin(name, deserialize_func, construct_func))
@@ -83,17 +82,16 @@ class TrtPluginRegistrar {
   }
 };
 
-#define REGISTER_TRT_PLUGIN(name, deserialize_func, construct_func) \
-    REGISTER_TRT_PLUGIN_UNIQ_HELPER(                                \
-        __COUNTER__, name, deserialize_func, construct_func)
-#define REGISTER_TRT_PLUGIN_UNIQ_HELPER(         \
-    ctr, name, deserialize_func, construct_func) \
-    REGISTER_TRT_PLUGIN_UNIQ(ctr, name, deserialize_func, construct_func)
+#define REGISTER_TRT_PLUGIN(name, deserialize_func, construct_func)    \
+  REGISTER_TRT_PLUGIN_UNIQ_HELPER(__COUNTER__, name, deserialize_func, \
+                                  construct_func)
+#define REGISTER_TRT_PLUGIN_UNIQ_HELPER(ctr, name, deserialize_func, \
+                                        construct_func)              \
+  REGISTER_TRT_PLUGIN_UNIQ(ctr, name, deserialize_func, construct_func)
 #define REGISTER_TRT_PLUGIN_UNIQ(ctr, name, deserialize_func, construct_func) \
-    static ::tensorflow::tensorrt::TrtPluginRegistrar                         \
-        trt_plugin_registrar##ctr TF_ATTRIBUTE_UNUSED =                       \
-            ::tensorflow::tensorrt::TrtPluginRegistrar(                       \
-                name, deserialize_func, construct_func)
+  static ::tensorflow::tensorrt::TrtPluginRegistrar trt_plugin_registrar##ctr \
+      TF_ATTRIBUTE_UNUSED = ::tensorflow::tensorrt::TrtPluginRegistrar(       \
+          name, deserialize_func, construct_func)
 
 }  // namespace tensorrt
 }  // namespace tensorflow
@@ -101,4 +99,4 @@ class TrtPluginRegistrar {
 #endif  // GOOGLE_TENSORRT
 #endif  // GOOGLE_CUDA
 
-#endif  // TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_FACTORY
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_FACTORY_H_
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory_test.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory_test.cc
index c5b0e75eb1d146..129bdcdbc2f8d9 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory_test.cc
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory_test.cc
@@ -13,8 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h"
 #include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h"
+
+#include "tensorflow/contrib/tensorrt/plugin/trt_plugin.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/test.h"
@@ -37,16 +38,17 @@ class StubPlugin : public PluginTensorRT {
   StubPlugin(const void* serialized_data, size_t length)
       : PluginTensorRT(serialized_data, length) {}
 
-  const string& GetPluginName() override { return plugin_name_; }
+  const string& GetPluginName() const override { return plugin_name_; }
 
-  virtual bool Finalize() { return true; }
+  bool Finalize() override { return true; }
 
-  virtual bool SetAttribute(const string& key, const void* ptr,
-                            const size_t size) {
+  bool SetAttribute(const string& key, const void* ptr,
+                    const size_t size) override {
     return true;
   }
 
-  virtual bool GetAttribute(const string& key, const void* ptr, size_t& size) {
+  bool GetAttribute(const string& key, const void** ptr,
+                    size_t* size) const override {
     return true;
   }
 
@@ -89,8 +91,7 @@ class TrtPluginFactoryTest : public ::testing::Test {
       return true;
     }
     return PluginFactoryTensorRT::GetInstance()->RegisterPlugin(
-        StubPlugin::kPluginName, CreateStubPluginDeserialize,
-        CreateStubPlugin);
+        StubPlugin::kPluginName, CreateStubPluginDeserialize, CreateStubPlugin);
   }
 };
 
diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h
index 4ff6fbedb4e6e8..274ce42fec9283 100644
--- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h
+++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_utils.h
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_UTILS
-#define TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_UTILS
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_UTILS_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_UTILS_H_
 
 #include 
 
@@ -43,4 +43,4 @@ string ExtractOpName(const void* serial_data, size_t serial_length,
 #endif  // GOOGLE_TENSORRT
 #endif  // GOOGLE_CUDA
 
-#endif  // TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_UTILS
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_PLUGIN_TRT_PLUGIN_UTILS_H_

From bcc9c398eafeaf2b1ae4b02c67e1f6b4260f9355 Mon Sep 17 00:00:00 2001
From: Jan Zikes 
Date: Mon, 14 May 2018 18:03:34 +0200
Subject: [PATCH 0723/1691] Enable OrderedEnqueuer from keras in tf.keras.
 (#19183)

---
 tensorflow/python/keras/utils/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/keras/utils/__init__.py b/tensorflow/python/keras/utils/__init__.py
index 2f74cf031d0520..9d924c8c905d69 100644
--- a/tensorflow/python/keras/utils/__init__.py
+++ b/tensorflow/python/keras/utils/__init__.py
@@ -20,6 +20,7 @@
 
 from tensorflow.python.keras._impl.keras.utils.data_utils import GeneratorEnqueuer
 from tensorflow.python.keras._impl.keras.utils.data_utils import get_file
+from tensorflow.python.keras._impl.keras.utils.data_utils import OrderedEnqueuer
 from tensorflow.python.keras._impl.keras.utils.data_utils import Sequence
 from tensorflow.python.keras._impl.keras.utils.data_utils import SequenceEnqueuer
 from tensorflow.python.keras._impl.keras.utils.generic_utils import custom_object_scope

From 0c59fdb9497dba218857dbfab5616ee77fdb70b7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 14 May 2018 09:06:25 -0700
Subject: [PATCH 0724/1691] Pre-factoring: Fix overly specific test
 expectations to prepare for multi-output fusion. PiperOrigin-RevId: 196514026

---
 .../xla/service/instruction_fusion_test.cc       | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/instruction_fusion_test.cc
index 6dd8fa1ab08737..cf9673a38ad645 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion_test.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion_test.cc
@@ -92,7 +92,8 @@ TEST_F(InstructionFusionTest, AvoidDuplicationIfNotAllFusable) {
   EXPECT_FALSE(
       InstructionFusion(InstructionFusion::IsExpensive, /*may_duplicate=*/true)
           .Run(module.get())
-          .ValueOrDie());
+          .ValueOrDie())
+      << module->ToString();
 }
 
 // Counts the number of HLO ops with a given op code in the specified module.
@@ -151,7 +152,11 @@ TEST_F(InstructionFusionTest, AvoidDuplicationIfNotAllFusableRecursively) {
           .Run(module.get())
           .ValueOrDie())
       << module->ToString();
-  EXPECT_EQ(Count(*module, HloOpcode::kFusion), 1) << module->ToString();
+  HloInstruction* root = module->entry_computation()->root_instruction();
+  EXPECT_THAT(root, op::Fusion());
+  EXPECT_THAT(root->fused_expression_root(),
+              op::Subtract(op::Abs(op::Parameter()), op::Parameter()))
+      << module->ToString();
 
   // Make sure the add hasn't been duplicated.
   EXPECT_EQ(Count(*module, HloOpcode::kAdd), 1) << module->ToString();
@@ -244,7 +249,12 @@ TEST_F(InstructionFusionTest, AvoidDuplicationIfNotAllFusableRecursively) {
           .Run(module.get())
           .ValueOrDie())
       << module->ToString();
-  EXPECT_EQ(Count(*module, HloOpcode::kFusion), 1) << module->ToString();
+  root = module->entry_computation()->root_instruction();
+  EXPECT_THAT(root, op::Fusion());
+  EXPECT_THAT(root->fused_expression_root(),
+              op::Tuple(op::Subtract(op::Parameter(), op::Parameter()),
+                        op::Subtract(op::Parameter(), op::Parameter())))
+      << module->ToString();
 
   // Make sure we didn't duplicate any adds.
   EXPECT_EQ(Count(*module, HloOpcode::kAdd), 2) << module->ToString();

From 4d0a5d1d3f3ae303a123b97528fbf846877ae27e Mon Sep 17 00:00:00 2001
From: Aurelien Geron 
Date: Mon, 14 May 2018 18:24:39 +0200
Subject: [PATCH 0725/1691] Fix errors and typos in the Estimators programmer's
 guide

---
 .../docs_src/programmers_guide/estimators.md  | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/estimators.md b/tensorflow/docs_src/programmers_guide/estimators.md
index ffadf29ad7710d..de830112e001ee 100644
--- a/tensorflow/docs_src/programmers_guide/estimators.md
+++ b/tensorflow/docs_src/programmers_guide/estimators.md
@@ -21,18 +21,17 @@ Note: TensorFlow also includes a deprecated `Estimator` class at
 
 Estimators provide the following benefits:
 
-*   You can run Estimators-based models on a local host or on a
+*   You can run Estimator-based models on a local host or on a
     distributed multi-server environment without changing your model.
-    Furthermore, you can run Estimators-based models on CPUs, GPUs,
+    Furthermore, you can run Estimator-based models on CPUs, GPUs,
     or TPUs without recoding your model.
 *   Estimators simplify sharing implementations between model developers.
-*   You can develop a state of the art model with high-level intuitive code,
+*   You can develop a state of the art model with high-level intuitive code.
     In short, it is generally much easier to create models with Estimators
     than with the low-level TensorFlow APIs.
-*   Estimators are themselves built on tf.layers, which
+*   Estimators are themselves built on @{tf.layers}, which
     simplifies customization.
-*   Estimators build the graph for you.  In other words, you don't have to
-    build the graph.
+*   Estimators build the graph for you.
 *   Estimators provide a safe distributed training loop that controls how and
     when to:
     *   build the graph
@@ -57,7 +56,7 @@ the "plumbing" for you.  That is, pre-made Estimators create and manage
 pre-made Estimators let you experiment with different model architectures by
 making only minimal code changes.  @{tf.estimator.DNNClassifier$`DNNClassifier`},
 for example, is a pre-made Estimator class that trains classification models
-through dense, feed-forward neural networks.
+based on dense, feed-forward neural networks.
 
 
 ### Structure of a pre-made Estimators program
@@ -79,7 +78,7 @@ of the following four steps:
     an input function:
 
         def input_fn(dataset):
-           ...  # manipulate dataset, extracting feature names and the label
+           ...  # manipulate dataset, extracting the feature dict and the label
            return feature_dict, label
 
     (See @{$programmers_guide/datasets} for full details.)
@@ -96,13 +95,13 @@ of the following four steps:
         population = tf.feature_column.numeric_column('population')
         crime_rate = tf.feature_column.numeric_column('crime_rate')
         median_education = tf.feature_column.numeric_column('median_education',
-                            normalizer_fn='lambda x: x - global_education_mean')
+                            normalizer_fn=lambda x: x - global_education_mean)
 
 3.  **Instantiate the relevant pre-made Estimator.**  For example, here's
     a sample instantiation of a pre-made Estimator named `LinearClassifier`:
 
         # Instantiate an estimator, passing the feature columns.
-        estimator = tf.estimator.Estimator.LinearClassifier(
+        estimator = tf.estimator.LinearClassifier(
             feature_columns=[population, crime_rate, median_education],
             )
 

From 6d41d9fb0ca1b3f25d24242ca9e45364828baca8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 14 May 2018 09:45:42 -0700
Subject: [PATCH 0726/1691] Extracts the following optimizations into methods:

PartialConstPropThroughIdentityN
ConstantPushDown

PiperOrigin-RevId: 196520167
---
 .../grappler/optimizers/constant_folding.cc   | 58 ++++++++++++-------
 .../grappler/optimizers/constant_folding.h    |  8 +++
 2 files changed, 44 insertions(+), 22 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 171d4923bc55b5..b2dcbf9df5f4bf 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -2157,6 +2157,30 @@ Status ConstantFolding::SimplifyNode(NodeDef* node, GraphDef* optimized_graph,
     return Status::OK();
   }
 
+  if (ConstantPushDown(node)) {
+    graph_modified_ = true;
+    return Status::OK();
+  }
+
+  if (PartialConstPropThroughIdentityN(node)) {
+    graph_modified_ = true;
+    return Status::OK();
+  }
+
+  if (PartialAssocOpConstFolding(optimized_graph, properties, node)) {
+    graph_modified_ = true;
+    return Status::OK();
+  }
+
+  if (PartialConcatConstFolding(optimized_graph, properties, node)) {
+    graph_modified_ = true;
+    return Status::OK();
+  }
+
+  return Status::OK();
+}
+
+bool ConstantFolding::ConstantPushDown(NodeDef* node) {
   // Consider the transformation
   //
   //                      +                +       = parent
@@ -2178,22 +2202,22 @@ Status ConstantFolding::SimplifyNode(NodeDef* node, GraphDef* optimized_graph,
   // division/multiplication.
   // Don't touch BiasAdd since they can't handle vectors as their first
   // inputs.
-  if (has_fetch_ && (IsAdd(*node) || is_mul) &&
+  if (has_fetch_ && (IsAdd(*node) || IsMul(*node)) &&
       NumNonControlInputs(*node) == 2) {
     NodeDef* left_child = node_map_->GetNode(node->input(0));
     NodeDef* right_child = node_map_->GetNode(node->input(1));
     // One child must be constant, and the other the same op as the parent.
     if (node->op() != left_child->op() && node->op() != right_child->op()) {
-      return Status::OK();
+      return false;
     }
     const bool left_child_is_constant = IsReallyConstant(*left_child);
     const bool right_child_is_constant = IsReallyConstant(*right_child);
     if (!left_child_is_constant && !right_child_is_constant) {
-      return Status::OK();
+      return false;
     }
     if (node->device() != left_child->device() ||
         node->device() != right_child->device()) {
-      return Status::OK();
+      return false;
     }
     NodeDef* op_child_node = left_child_is_constant ? right_child : left_child;
     NodeDef* const_child_node =
@@ -2203,7 +2227,7 @@ Status ConstantFolding::SimplifyNode(NodeDef* node, GraphDef* optimized_graph,
         nodes_to_preserve_.find(op_child_node->name()) !=
             nodes_to_preserve_.end() ||
         NumNonControlOutputs(*op_child_node, *node_map_) > 1) {
-      return Status::OK();
+      return false;
     }
 
     // Identify the nodes to swap.
@@ -2213,7 +2237,7 @@ Status ConstantFolding::SimplifyNode(NodeDef* node, GraphDef* optimized_graph,
     const bool right_leaf_is_constant = IsReallyConstant(*right_leaf);
     if (left_leaf_is_constant && right_leaf_is_constant) {
       // Child is already foldable, leave it alone.
-      return Status::OK();
+      return false;
     }
     const int non_const_leaf_input = left_leaf_is_constant ? 1 : 0;
     const int parent_const_input = left_child_is_constant ? 0 : 1;
@@ -2238,10 +2262,12 @@ Status ConstantFolding::SimplifyNode(NodeDef* node, GraphDef* optimized_graph,
                            node->input(parent_const_input));
     std::swap(*node->mutable_input(parent_const_input),
               *op_child_node->mutable_input(non_const_leaf_input));
-    graph_modified_ = true;
-    return Status::OK();
+    return true;
   }
+  return false;
+}
 
+bool ConstantFolding::PartialConstPropThroughIdentityN(NodeDef* node) {
   // Partial constant propagation through IdentityN.
   if (IsIdentityN(*node) && NumNonControlInputs(*node) > 0) {
     const std::set& tmp = node_map_->GetOutputs(node->name());
@@ -2294,22 +2320,10 @@ Status ConstantFolding::SimplifyNode(NodeDef* node, GraphDef* optimized_graph,
       for (NodeDef* consumer : consumers) {
         DedupControlInputs(consumer);
       }
-      graph_modified_ = true;
-      return Status::OK();
+      return true;
     }
   }
-
-  if (PartialAssocOpConstFolding(optimized_graph, properties, node)) {
-    graph_modified_ = true;
-    return Status::OK();
-  }
-
-  if (PartialConcatConstFolding(optimized_graph, properties, node)) {
-    graph_modified_ = true;
-    return Status::OK();
-  }
-
-  return Status::OK();
+  return false;
 }
 
 bool ConstantFolding::PartialAssocOpConstFolding(GraphDef* optimized_graph,
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index f92f755d8915d5..227caba7ee3041 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -113,6 +113,14 @@ class ConstantFolding : public GraphOptimizer {
   bool PartialAssocOpConstFolding(GraphDef* optimized_graph,
                                   GraphProperties* properties, NodeDef* node);
 
+  // Applies partial constant propagation through IdentityN operator.
+  // Returns true if the transformation applied successfully.
+  bool PartialConstPropThroughIdentityN(NodeDef* node);
+
+  // Pushes down constants on '+' and '*' operators if applicable. Returns true
+  // the transformation applied successfully.
+  bool ConstantPushDown(NodeDef* node);
+
   // Points to an externally provided device or to owned_device_;
   RewriterConfig::Toggle opt_level_;
   DeviceBase* cpu_device_;

From 157c347f832413c29265e467cc733366b4b215a6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 14 May 2018 09:51:52 -0700
Subject: [PATCH 0727/1691] avoid having stream_executor depend on
 tensorflow/core

PiperOrigin-RevId: 196521381
---
 tensorflow/stream_executor/host_or_device_scalar.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/stream_executor/host_or_device_scalar.h b/tensorflow/stream_executor/host_or_device_scalar.h
index c9e3e14778384a..1f5d4b9260ce21 100644
--- a/tensorflow/stream_executor/host_or_device_scalar.h
+++ b/tensorflow/stream_executor/host_or_device_scalar.h
@@ -16,8 +16,8 @@ limitations under the License.
 #ifndef TENSORFLOW_STREAM_EXECUTOR_HOST_OR_DEVICE_SCALAR_H_
 #define TENSORFLOW_STREAM_EXECUTOR_HOST_OR_DEVICE_SCALAR_H_
 
-#include "tensorflow/core/platform/logging.h"
 #include "tensorflow/stream_executor/device_memory.h"
+#include "tensorflow/stream_executor/platform/logging.h"
 
 namespace stream_executor {
 

From 5fb7401959391f7583087f404a48353ab21ef1ca Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 14 May 2018 10:43:08 -0700
Subject: [PATCH 0728/1691] Use utility methods to compute AttrValue hash code
 and check for equality.

PiperOrigin-RevId: 196531355
---
 ...direct_session_with_tracking_alloc_test.cc |   4 +-
 tensorflow/core/framework/attr_value_util.cc  | 236 ++++++++++++------
 tensorflow/core/framework/attr_value_util.h   |  13 +
 .../optimizers/arithmetic_optimizer.cc        |  38 ++-
 .../grappler/optimizers/function_optimizer.cc |   4 +-
 tensorflow/core/lib/hash/hash.h               |   6 +
 6 files changed, 195 insertions(+), 106 deletions(-)

diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
index 695423b2cb1993..95093beced47e2 100644
--- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
@@ -102,9 +102,9 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) {
         EXPECT_EQ(2, shape.dim(0).size());
         EXPECT_EQ(1, shape.dim(1).size());
         if (node->name() == y->name()) {
-          EXPECT_EQ(9, cm->AllocationId(node, 0));
+          EXPECT_EQ(13, cm->AllocationId(node, 0));
         } else {
-          EXPECT_EQ(10, cm->AllocationId(node, 0));
+          EXPECT_EQ(14, cm->AllocationId(node, 0));
         }
       }
       EXPECT_LE(0, cm->MaxExecutionTime(node));
diff --git a/tensorflow/core/framework/attr_value_util.cc b/tensorflow/core/framework/attr_value_util.cc
index 87c1ddd15df4f8..79966f06922a62 100644
--- a/tensorflow/core/framework/attr_value_util.cc
+++ b/tensorflow/core/framework/attr_value_util.cc
@@ -33,6 +33,154 @@ limitations under the License.
 namespace tensorflow {
 namespace {
 
+// Do not construct large tensors to compute their hash or compare for equality.
+constexpr int kMaxAttrValueTensorByteSize = 32 * 1024 * 1024;  // 32mb
+
+// Return the size of the tensor represented by this TensorProto. If shape is
+// not fully defined return -1.
+int64 TensorByteSize(const TensorProto& t) {
+  // num_elements returns -1 if shape is not fully defined.
+  int64 num_elems = TensorShape(t.tensor_shape()).num_elements();
+  return num_elems < 0 ? -1 : num_elems * DataTypeSize(t.dtype());
+}
+
+// Compute TensorProto hash by creating a Tensor, serializing it as tensor
+// content, and computing a hash of it's string representation. This is unsafe
+// operation, because large tensors can be represented as TensorProto, but can't
+// be serialized to tensor content.
+uint64 TensorProtoHash(const TensorProto& tp) {
+  Tensor tensor(tp.dtype());
+  bool success = tensor.FromProto(tp);
+  DCHECK(success);
+  TensorProto p;
+  tensor.AsProtoTensorContent(&p);
+  string s;
+  SerializeToStringDeterministic(p, &s);
+  return Hash64(s);
+}
+
+// Do not create large tensors in memory, compute hash based on TensorProto
+// string representation. Tensors with identical content potentially can have a
+// different hash code if they are defined with different TensorProto
+// representations.
+uint64 FastTensorProtoHash(const TensorProto& tp) {
+  string s;
+  if (TensorByteSize(tp) > kMaxAttrValueTensorByteSize) {
+    string s;
+    bool success = SerializeToStringDeterministic(tp, &s);
+    DCHECK(success);
+    return Hash64(s);
+  } else {
+    return TensorProtoHash(tp);
+  }
+}
+
+// There are multiple equivalent representations of attr values containing
+// TensorProtos. Compare them by constructing Tensors and serializing them
+// back. Comparing Tensor objects is pretty tricky. This is unsafe operation,
+// because large tensors can be represented as TensorProto, but can't be
+// serialized to tensor content.
+bool AreTensorProtosEqual(const TensorProto& lhs, const TensorProto& rhs) {
+  Tensor lhs_t(lhs.dtype());
+  bool success = lhs_t.FromProto(lhs);
+  DCHECK(success);
+
+  Tensor rhs_t(rhs.dtype());
+  success = rhs_t.FromProto(rhs);
+  DCHECK(success);
+
+  TensorProto lhs_tp;
+  lhs_t.AsProtoTensorContent(&lhs_tp);
+
+  TensorProto rhs_tp;
+  rhs_t.AsProtoTensorContent(&rhs_tp);
+
+  string lhs_str, rhs_str;
+  SerializeToStringDeterministic(lhs_tp, &lhs_str);
+  SerializeToStringDeterministic(rhs_tp, &rhs_str);
+
+  return lhs_str == rhs_str;
+}
+
+// Do not construct large tensors in memory, compare equality using TensorProto
+// string representation. Tensors with identical content potentially can have
+// different tensor proto representation.
+bool FastAreTensorProtosEqual(const TensorProto& lhs, const TensorProto& rhs) {
+  if (TensorByteSize(lhs) > kMaxAttrValueTensorByteSize ||
+      TensorByteSize(rhs) > kMaxAttrValueTensorByteSize) {
+    string lhs_str, rhs_str;
+    bool success = lhs.AppendToString(&lhs_str);
+    DCHECK(success);
+    success = rhs.AppendToString(&rhs_str);
+    DCHECK(success);
+
+    return lhs_str == rhs_str;
+  } else {
+    return AreTensorProtosEqual(lhs, rhs);
+  }
+}
+
+using TensorProtoHasher = std::function;
+using TensorProtosEquality =
+    std::function;
+
+uint64 AttrValueHash(const AttrValue& a, const TensorProtoHasher& tensor_hash) {
+  if (a.has_tensor()) return tensor_hash(a.tensor());
+
+  if (a.has_func()) {
+    const NameAttrList& func = a.func();
+    uint64 h = Hash64(func.name());
+    std::map map(func.attr().begin(), func.attr().end());
+    for (const auto& pair : map) {
+      h = Hash64(pair.first.data(), pair.first.size(), h);
+      h = Hash64Combine(AttrValueHash(pair.second, tensor_hash), h);
+    }
+    return h;
+  }
+
+  // If `a` is not a tensor or func, get a hash of serialized string.
+  string s;
+  SerializeToStringDeterministic(a, &s);
+  return Hash64(s);
+}
+
+bool AreAttrValuesEqual(const AttrValue& a, const AttrValue& b,
+                        const TensorProtosEquality& tensor_equality) {
+  if (a.has_tensor() != b.has_tensor()) {
+    return false;
+  } else if (a.has_tensor() && b.has_tensor()) {
+    return tensor_equality(a.tensor(), b.tensor());
+  }
+
+  // `func` field contains a nested AttrValue. Compare such AttrValues
+  // recursively.
+  if (a.has_func() != b.has_func()) {
+    return false;
+  } else if (a.has_func() && b.has_func()) {
+    const NameAttrList& af = a.func();
+    const NameAttrList& bf = b.func();
+    if (af.name() != bf.name()) return false;
+    std::unordered_map am(af.attr().begin(),
+                                             af.attr().end());
+    for (const auto& bm_pair : bf.attr()) {
+      const auto& iter = am.find(bm_pair.first);
+      if (iter == am.end()) return false;
+      if (!AreAttrValuesEqual(iter->second, bm_pair.second, tensor_equality))
+        return false;
+      am.erase(iter);
+    }
+    if (!am.empty()) return false;
+    return true;
+  }
+
+  // All other fields in AttrValue have deterministic representations.
+  // It is safe to compare their serialized strings.
+  string a_str, b_str;
+  SerializeToStringDeterministic(a, &a_str);
+  SerializeToStringDeterministic(b, &b_str);
+  return a_str == b_str;
+}
+
 string SummarizeString(const string& str) {
   string escaped = str_util::CEscape(str);
 
@@ -412,89 +560,19 @@ void SetAttrValue(gtl::ArraySlice value, AttrValue* out) {
 }
 
 bool AreAttrValuesEqual(const AttrValue& a, const AttrValue& b) {
-  // There are multiple equivalent representations of attr values containing
-  // TensorProtos. Compare them by constructing Tensors and serializing them
-  // back. Comparing Tensor objects is pretty tricky.
-  if (a.has_tensor() != b.has_tensor()) {
-    return false;
-  } else if (a.has_tensor() && b.has_tensor()) {
-    Tensor at(a.tensor().dtype());
-    bool success = at.FromProto(a.tensor());
-    DCHECK(success);
-
-    Tensor bt(b.tensor().dtype());
-    success = bt.FromProto(b.tensor());
-    DCHECK(success);
-
-    TensorProto ap;
-    at.AsProtoTensorContent(&ap);
-
-    TensorProto bp;
-    bt.AsProtoTensorContent(&bp);
-
-    string a_str, b_str;
-    SerializeToStringDeterministic(ap, &a_str);
-    SerializeToStringDeterministic(bp, &b_str);
-    return a_str == b_str;
-  }
-
-  // `func` field contains a nested AttrValue. Compare such AttrValues
-  // recursively.
-  if (a.has_func() != b.has_func()) {
-    return false;
-  } else if (a.has_func() && b.has_func()) {
-    const NameAttrList& af = a.func();
-    const NameAttrList& bf = b.func();
-    if (af.name() != bf.name()) return false;
-    std::unordered_map am(af.attr().begin(),
-                                             af.attr().end());
-    for (const auto& bm_pair : bf.attr()) {
-      const auto& iter = am.find(bm_pair.first);
-      if (iter == am.end()) return false;
-      if (!AreAttrValuesEqual(iter->second, bm_pair.second)) return false;
-      am.erase(iter);
-    }
-    if (!am.empty()) return false;
-    return true;
-  }
-
-  // All other fields in AttrValue have deterministic representations.
-  // It is safe to compare their serialized strings.
-  string a_str, b_str;
-  SerializeToStringDeterministic(a, &a_str);
-  SerializeToStringDeterministic(b, &b_str);
-  return a_str == b_str;
+  return AreAttrValuesEqual(a, b, AreTensorProtosEqual);
 }
 
 uint64 AttrValueHash(const AttrValue& a) {
-  if (a.has_tensor()) {
-    // Deal with multiple representations by parsing TensorProto to
-    // Tensor and serializing it back. This is slow, but current use case
-    // don't need high efficiency.
-    Tensor tensor(a.tensor().dtype());
-    bool success = tensor.FromProto(a.tensor());
-    DCHECK(success);
-    TensorProto p;
-    tensor.AsProtoTensorContent(&p);
-    string s;
-    SerializeToStringDeterministic(p, &s);
-    return Hash64(s);
-  }
-  if (a.has_func()) {
-    const NameAttrList& func = a.func();
-    uint64 h = Hash64(func.name());
-    std::map map(func.attr().begin(), func.attr().end());
-    for (const auto& pair : map) {
-      h = Hash64(pair.first.data(), pair.first.size(), h);
-      h = Hash64Combine(AttrValueHash(pair.second), h);
-    }
-    return h;
-  }
+  return AttrValueHash(a, TensorProtoHash);
+}
 
-  // If `a` is not a tensor or func, get a hash of serialized string.
-  string s;
-  SerializeToStringDeterministic(a, &s);
-  return Hash64(s);
+bool FastAreAttrValuesEqual(const AttrValue& a, const AttrValue& b) {
+  return AreAttrValuesEqual(a, b, FastAreTensorProtosEqual);
+}
+
+uint64 FastAttrValueHash(const AttrValue& a) {
+  return AttrValueHash(a, FastTensorProtoHash);
 }
 
 bool HasPlaceHolder(const AttrValue& val) {
diff --git a/tensorflow/core/framework/attr_value_util.h b/tensorflow/core/framework/attr_value_util.h
index 29e34c5090ea91..0da9b1081bdf0b 100644
--- a/tensorflow/core/framework/attr_value_util.h
+++ b/tensorflow/core/framework/attr_value_util.h
@@ -98,6 +98,19 @@ bool AreAttrValuesEqual(const AttrValue& a, const AttrValue& b);
 // probably not persist the returned value.
 uint64 AttrValueHash(const AttrValue& a);
 
+// WARNING: Equality check might return false-negative for large (> 32mb)
+// tensors defined with different TensorProto representations.
+//
+// A pair of consistent hash and equals functions that are guaranteed to be fast
+// with AttrValues that potentially can have very large Tensors (larger than
+// 32mb) defined by TensorProto. If large identical Tensors are defined using
+// different representations (e.g. one with tensor content, and second with
+// bool_val), they will have different hash code and equals will return false.
+// Small (less than 32mb) tensors with different TensorProto representations
+// hashed/compared by their tensor content.
+uint64 FastAttrValueHash(const AttrValue& a);
+bool FastAreAttrValuesEqual(const AttrValue& a, const AttrValue& b);
+
 // Returns true if "val" has a placeholder.
 bool HasPlaceHolder(const AttrValue& val);
 
diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
index cd7e742e5c0e12..adef75f63eb506 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -23,6 +23,7 @@ limitations under the License.
 #include 
 
 #include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/attr_value_util.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
@@ -38,6 +39,7 @@ limitations under the License.
 #include "tensorflow/core/grappler/utils/topological_sort.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/lib/hash/hash.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/tensor_coding.h"
@@ -1784,7 +1786,7 @@ class SqrtDivToRsqrtMulStage : public ArithmeticOptimizerStage {
 class UniqueNodes {
  public:
   NodeDef* FindOrAddRepresentative(NodeDef* node) {
-    std::size_t sig = ComputeSignature(*node);
+    uint64 sig = ComputeSignature(*node);
     std::vector& candidates = rep_[sig];
     for (auto& candidate : candidates) {
       if (SameNode(*candidate, *node)) {
@@ -1796,26 +1798,25 @@ class UniqueNodes {
   }
 
  private:
-  std::size_t ComputeSignature(const NodeDef& node) const;
+  uint64 ComputeSignature(const NodeDef& node) const;
   bool SameNode(const NodeDef& node1, const NodeDef& node2) const;
 
-  std::unordered_map> rep_;
+  std::unordered_map> rep_;
 };
 
-std::size_t UniqueNodes::ComputeSignature(const NodeDef& node) const {
-  std::size_t h = std::hash{}(node.op());
-  h ^= std::hash{}(node.device());
+uint64 UniqueNodes::ComputeSignature(const NodeDef& node) const {
+  uint64 h = Hash64(node.op());
+  h = Hash64Combine(Hash64(node.device()), h);
+
   for (const auto& input : node.input()) {
     int pos;
     string node_name = ParseNodeName(input, &pos);
-    h ^= std::hash{}(node_name);
-    h ^= static_cast(pos);
+    h = Hash64CombineUnordered(Hash64(node_name), h);
+    h = Hash64CombineUnordered(std::hash()(pos), h);
   }
   for (const auto& attr : node.attr()) {
-    h ^= std::hash{}(attr.first);
-    string tmp;
-    attr.second.AppendToString(&tmp);
-    h ^= std::hash{}(tmp);
+    h = Hash64CombineUnordered(Hash64(attr.first), h);
+    h = Hash64CombineUnordered(FastAttrValueHash(attr.second), h);
   }
   return h;
 }
@@ -1871,17 +1872,8 @@ bool UniqueNodes::SameNode(const NodeDef& node1, const NodeDef& node2) const {
   }
   for (const auto& attr1 : node1.attr()) {
     auto it = node2.attr().find(attr1.first);
-    if (it == node2.attr().end()) {
-      return false;
-    }
-    const auto& attr2 = *it;
-    string val1;
-    attr1.second.AppendToString(&val1);
-    string val2;
-    attr2.second.AppendToString(&val2);
-    if (val1 != val2) {
-      return false;
-    }
+    if (it == node2.attr().end()) return false;
+    if (!FastAreAttrValuesEqual(attr1.second, it->second)) return false;
   }
 
   return true;
diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc
index 2864d739f0ad90..5be89369b18d67 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc
@@ -98,7 +98,7 @@ struct FunctionSpecializationSignature {
     for (const auto& lhs : body_parameters) {
       auto it = other.body_parameters.find(lhs.first);
       if (it == other.body_parameters.end()) return false;
-      if (!AreAttrValuesEqual(lhs.second, (*it).second)) return false;
+      if (!FastAreAttrValuesEqual(lhs.second, (*it).second)) return false;
     }
 
     return true;
@@ -123,7 +123,7 @@ struct FunctionSpecializationSignature {
                                        s.body_parameters.end());
       for (const auto& pair : body) {
         h = Hash64Combine(Hash64(pair.first), h);
-        h = Hash64Combine(AttrValueHash(pair.second), h);
+        h = Hash64Combine(FastAttrValueHash(pair.second), h);
       }
 
       std::map inputs(s.const_inputs.begin(),
diff --git a/tensorflow/core/lib/hash/hash.h b/tensorflow/core/lib/hash/hash.h
index 3f85303c0f6573..737d23f6994fe2 100644
--- a/tensorflow/core/lib/hash/hash.h
+++ b/tensorflow/core/lib/hash/hash.h
@@ -44,6 +44,12 @@ inline uint64 Hash64Combine(uint64 a, uint64 b) {
   return a ^ (b + 0x9e3779b97f4a7800ULL + (a << 10) + (a >> 4));
 }
 
+// Combine two hashes in an order-independent way. This operation should be
+// associative and compute the same hash for a collection of elements
+// independent of traversal order. Note that it is better to combine hashes
+// symmetrically with addition rather than XOR, since (x^x) == 0 but (x+x) != 0.
+inline uint64 Hash64CombineUnordered(uint64 a, uint64 b) { return a + b; }
+
 // Hash functor suitable for use with power-of-two sized hashtables.  Use
 // instead of std::hash.
 //

From 39ba73897cd3a5e14d3e78624f0b5942479f533a Mon Sep 17 00:00:00 2001
From: Yong Tang 
Date: Mon, 14 May 2018 11:09:30 -0700
Subject: [PATCH 0729/1691] Fix misleading cupti.h error message (#19224)

This fix tries to address the issue raised in 19223
where the cupti.h eror message was misleading. The following error:
```
Cuda Configuration Error: Cannot find cupti.h under /usr/local/cuda-9.0
```
is not the true patch searched.

This fix updates the bzl file to print out the
complete searched paths when error occurs:
```
Cuda Configuration Error: Cannot find cupti.h under /usr/local/cuda-9.0/extras/CUPTI/include/, /usr/local/cuda-9.0/include/cuda/CUPTI/
```

This fix fixes 19223.

Signed-off-by: Yong Tang 
---
 third_party/gpus/cuda_configure.bzl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index ede7e318976527..f3a80d3dd35a1b 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -604,7 +604,7 @@ def _find_cupti_header_dir(repository_ctx, cuda_config):
   for relative_path in CUPTI_HEADER_PATHS:
     if repository_ctx.path("%s/%scupti.h" % (cuda_toolkit_path, relative_path)).exists:
         return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1]
-  auto_configure_fail("Cannot find cupti.h under %s" % cuda_toolkit_path)
+  auto_configure_fail("Cannot find cupti.h under %s" % ", ".join([cuda_toolkit_path + "/" + s for s in CUPTI_HEADER_PATHS]))
 
 
 def _find_cupti_lib(repository_ctx, cuda_config):

From 0bb7a191a33222c44ff50a3c74b550ee72f8b0e4 Mon Sep 17 00:00:00 2001
From: Yong Tang 
Date: Mon, 14 May 2018 11:10:27 -0700
Subject: [PATCH 0730/1691] Add complex support for tf.segment_mean (#19225)

* Add complex support for tf.segment_mean

While using tf.segment_mean I noticed that it does not
have the complex support like tf.segment_sum. I think it
makes sense to support complex for it. This fix adds the
complex support for tf.segment_mean.

Signed-off-by: Yong Tang 

* Add test cases for complex support with tf.segment_mean

Signed-off-by: Yong Tang 
---
 tensorflow/core/kernels/segment_reduction_ops.cc       |  4 +++-
 tensorflow/core/ops/math_ops.cc                        |  2 +-
 .../python/kernel_tests/segment_reduction_ops_test.py  | 10 ++++++----
 3 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/kernels/segment_reduction_ops.cc b/tensorflow/core/kernels/segment_reduction_ops.cc
index c87ce78e051a45..2328fc6afd8e7b 100644
--- a/tensorflow/core/kernels/segment_reduction_ops.cc
+++ b/tensorflow/core/kernels/segment_reduction_ops.cc
@@ -320,7 +320,9 @@ class SegmentSumGPUOp : public AsyncOpKernel {
   REGISTER_CPU_KERNEL_SEGMENT("SegmentSum", Eigen::internal::SumReducer, \
                               type, index_type, 0);                            \
   REGISTER_CPU_KERNEL_SEGMENT(                                                 \
-      "SegmentProd", Eigen::internal::ProdReducer, type, index_type, 1)
+      "SegmentMean", Eigen::internal::MeanReducer, type, index_type, 0); \
+  REGISTER_CPU_KERNEL_SEGMENT(                                                 \
+      "SegmentProd", Eigen::internal::ProdReducer, type, index_type, 1);
 
 #define REGISTER_REAL_CPU_KERNELS_ALL(type) \
   REGISTER_REAL_CPU_KERNELS(type, int32);   \
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index 8f8443a46cfa68..8c0b073ce46468 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -1017,7 +1017,7 @@ REGISTER_OP("SegmentMean")
     .Input("data: T")
     .Input("segment_ids: Tindices")
     .Output("output: T")
-    .Attr("T: realnumbertype")
+    .Attr("T: numbertype")
     .Attr("Tindices: {int32,int64}")
     .SetShapeFn(SegmentReductionShapeFn);
 
diff --git a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py
index 3bca5fadc42693..794be096b7309a 100644
--- a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py
+++ b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py
@@ -91,16 +91,18 @@ def testValues(self):
     ]
 
     # Each item is np_op1, np_op2, tf_op
-    ops_list = [(np.add, None, math_ops.segment_sum), (self._mean_cum_op,
-                                                       self._mean_reduce_op,
-                                                       math_ops.segment_mean),
+    ops_list = [(np.add, None, math_ops.segment_sum),
+                (self._mean_cum_op, self._mean_reduce_op,
+                 math_ops.segment_mean),
                 (np.ndarray.__mul__, None, math_ops.segment_prod),
                 (np.minimum, None, math_ops.segment_min),
                 (np.maximum, None, math_ops.segment_max)]
 
     # A subset of ops has been enabled for complex numbers
     complex_ops_list = [(np.add, None, math_ops.segment_sum),
-                        (np.ndarray.__mul__, None, math_ops.segment_prod)]
+                        (np.ndarray.__mul__, None, math_ops.segment_prod),
+                        (self._mean_cum_op, self._mean_reduce_op,
+                         math_ops.segment_mean)]
 
     n = 10
     shape = [n, 2]

From 7a2ef3d93358fbf0b006d00acb25cbf451ff1bee Mon Sep 17 00:00:00 2001
From: Yong Tang 
Date: Mon, 14 May 2018 11:12:32 -0700
Subject: [PATCH 0731/1691] Fix warning caused by squeeze_dims (#19227)

The `squeeze_dims` in `tf.squeeze` has been deprecated
in favor of `axis`. This fix fixes the `squeeze_dims`
in text_classification_cnn.py so that the warning
could be removed.

Signed-off-by: Yong Tang 
---
 tensorflow/examples/learn/text_classification_cnn.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/examples/learn/text_classification_cnn.py b/tensorflow/examples/learn/text_classification_cnn.py
index 9e21aee87f6298..a40a9eaecbd9bb 100644
--- a/tensorflow/examples/learn/text_classification_cnn.py
+++ b/tensorflow/examples/learn/text_classification_cnn.py
@@ -73,7 +73,7 @@ def cnn_model(features, labels, mode):
         kernel_size=FILTER_SHAPE2,
         padding='VALID')
     # Max across each filter to get useful features for classification.
-    pool2 = tf.squeeze(tf.reduce_max(conv2, 1), squeeze_dims=[1])
+    pool2 = tf.squeeze(tf.reduce_max(conv2, 1), axis=[1])
 
   # Apply regular WX + B and classification.
   logits = tf.layers.dense(pool2, MAX_LABEL, activation=None)

From c0dd7852bfa216e0c9bc9eeb57d2e613f7996116 Mon Sep 17 00:00:00 2001
From: "Yilei (Dolee) Yang" 
Date: Mon, 14 May 2018 11:34:54 -0700
Subject: [PATCH 0732/1691] Fix links on the community/swift page. (#19230)

They were broken rendered on https://www.tensorflow.org/community/swift.
---
 tensorflow/docs_src/community/swift.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/docs_src/community/swift.md b/tensorflow/docs_src/community/swift.md
index e5a0f02a8c3633..15e5abb655c07b 100644
--- a/tensorflow/docs_src/community/swift.md
+++ b/tensorflow/docs_src/community/swift.md
@@ -8,7 +8,7 @@ Welcome to the Swift for TensorFlow development community!
 
 Swift for TensorFlow is a new way to develop machine learning models. It
 gives you the power of
-[TensorFlow](programmers_guide/eager) directly
+[TensorFlow](https://www.tensorflow.org) directly
 integrated into the [Swift programming language](https://swift.org/about).
 With Swift, you can write the following imperative code, and Swift
 automatically turns it into **a single TensorFlow Graph** and runs it
@@ -28,8 +28,8 @@ print(x)
 ```
 
 Swift combines the flexibility of
-[Eager Execution](programmers_guide/eager) with the
-high performance of [Graphs and Sessions](programmers_guide/graphs).
+[Eager Execution](https://www.tensorflow.org/programmers_guide/eager) with the
+high performance of [Graphs and Sessions](https://www.tensorflow.org/programmers_guide/graphs).
 Behind the scenes, Swift analyzes your Tensor code and automatically builds
 graphs for you. Swift also catches type errors and shape mismatches before
 running your code, and has [Automatic Differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation)

From 69c74f1e74eb5da964638533d594475ee9e54a66 Mon Sep 17 00:00:00 2001
From: Yong Tang 
Date: Mon, 14 May 2018 11:35:40 -0700
Subject: [PATCH 0733/1691] Add int64 support for output_shape of
 tf.nn.conv3d_transpose (#19248)

* Add int64 support for output_shape of tf.nn.conv3d_transpose

This fix tries to address the issue raised in 18887 where
the output_shape of tf.nn.conv3d_transpose only support
int32 data types. The support of int64 has been added in this PR
with test case covered.

This fix fixes 18887.

Signed-off-by: Yong Tang 

* Update op registration for Conv3DBackpropInputV2

Signed-off-by: Yong Tang 

* Add test case for int64 support of output_shape with tf.nn.conv3d_transpose

Signed-off-by: Yong Tang 

* Update test case with both int32 and int64

Signed-off-by: Yong Tang 

* Fix pylint issue

Signed-off-by: Yong Tang 
---
 tensorflow/core/kernels/conv_grad_ops_3d.cc     |  4 ++--
 tensorflow/core/ops/nn_ops.cc                   |  3 ++-
 .../kernel_tests/conv3d_transpose_test.py       | 17 +++++++++++++++++
 3 files changed, 21 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/kernels/conv_grad_ops_3d.cc b/tensorflow/core/kernels/conv_grad_ops_3d.cc
index 9edc6d416e33d6..980b1063de9997 100644
--- a/tensorflow/core/kernels/conv_grad_ops_3d.cc
+++ b/tensorflow/core/kernels/conv_grad_ops_3d.cc
@@ -195,8 +195,8 @@ class Conv3DBackpropInputOp : public OpKernel {
     TensorShape input_shape;
     if (takes_shape_) {
       const Tensor& input_sizes = context->input(0);
-      OP_REQUIRES_OK(context, TensorShapeUtils::MakeShape(
-                                  input_sizes.vec(), &input_shape));
+      // MakeShape is able to handle both DT_INT32 and DT_INT64 for input_sizes.
+      OP_REQUIRES_OK(context, MakeShape(input_sizes, &input_shape));
     } else {
       input_shape = context->input(0).shape();
     }
diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc
index bb46dafd424fe6..fc60e807b90b01 100644
--- a/tensorflow/core/ops/nn_ops.cc
+++ b/tensorflow/core/ops/nn_ops.cc
@@ -547,7 +547,7 @@ REGISTER_OP("Conv3DBackpropFilter")
     });
 
 REGISTER_OP("Conv3DBackpropInputV2")
-    .Input("input_sizes: int32")
+    .Input("input_sizes: Tshape")
     .Input("filter: T")
     .Input("out_backprop: T")
     .Output("output: T")
@@ -556,6 +556,7 @@ REGISTER_OP("Conv3DBackpropInputV2")
     .Attr(GetPaddingAttrString())
     .Attr(GetConvnet3dDataFormatAttrString())
     .Attr("dilations: list(int) = [1, 1, 1, 1, 1]")
+    .Attr("Tshape: {int32, int64} = DT_INT32")
     .SetShapeFn([](InferenceContext* c) {
       ShapeHandle s;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(0, &s));
diff --git a/tensorflow/python/kernel_tests/conv3d_transpose_test.py b/tensorflow/python/kernel_tests/conv3d_transpose_test.py
index 8973a450fa246e..289ae29fcec724 100644
--- a/tensorflow/python/kernel_tests/conv3d_transpose_test.py
+++ b/tensorflow/python/kernel_tests/conv3d_transpose_test.py
@@ -131,6 +131,23 @@ def testConv3DTransposeShapeMismatch(self):
     nn_ops.conv3d_transpose(
         x_value, f_value, y_shape, strides, data_format='NCDHW')
 
+  def testConv3DTransposeOutputShapeType(self):
+    # Test case for GitHub issue 18887
+    for dtype in [dtypes.int32, dtypes.int64]:
+      with self.test_session():
+        x_shape = [2, 5, 6, 4, 3]
+        y_shape = [2, 5, 6, 4, 2]
+        f_shape = [3, 3, 3, 2, 3]
+        strides = [1, 1, 1, 1, 1]
+        x_value = constant_op.constant(
+            1.0, shape=x_shape, name="x", dtype=dtypes.float32)
+        f_value = constant_op.constant(
+            1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
+        output = nn_ops.conv3d_transpose(
+            x_value, f_value, constant_op.constant(y_shape, dtype=dtype),
+            strides=strides, padding="SAME")
+        output.eval()
+
   def testConv3DTransposeValid(self):
     with self.test_session():
       strides = [1, 2, 2, 2, 1]

From 040aaf39aebda57921991d05d29be5123e908d7c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 14 May 2018 11:40:50 -0700
Subject: [PATCH 0734/1691] Don't check that bool arrays are quantized.

PiperOrigin-RevId: 196541955
---
 tensorflow/contrib/lite/toco/tooling_util.cc | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index 7a048f5eef6cae..a789b5c95bc15d 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -2074,15 +2074,21 @@ bool ReshapeIsEquivalentToTranspose(const Model& model,
 void CheckFinalDataTypesSatisfied(const Model& model) {
   for (const auto& array_entry : model.GetArrayMap()) {
     const auto& array = *array_entry.second;
+    if (array.data_type == ArrayDataType::kBool) {
+      // Boolean values are never quantized.
+      continue;
+    }
+
     // If the final data type is int16, the data type may be float, for example
     // after dequantization.
     if (array.final_data_type != ArrayDataType::kNone &&
         array.final_data_type != ArrayDataType::kInt16) {
-      CHECK(array.final_data_type == array.data_type)
+      CHECK(array.data_type == array.final_data_type)
           << "Array \"" << array_entry.first
-          << "\" has mis-matching actual and final data types ("
-          << ArrayDataTypeName(array.data_type) << ","
-          << ArrayDataTypeName(array.final_data_type) << ").";
+          << "\" has mis-matching actual and final data types (data_type="
+          << ArrayDataTypeName(array.data_type)
+          << ", final_data_type=" << ArrayDataTypeName(array.final_data_type)
+          << ").";
     }
   }
 }

From 9e3f097ad0354c3d69ae986357e9bf30c2f83b69 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 14 May 2018 12:03:50 -0700
Subject: [PATCH 0735/1691] Deletes an unused private method in head.py

PiperOrigin-RevId: 196545696
---
 tensorflow/python/estimator/canned/head.py | 20 --------------------
 1 file changed, 20 deletions(-)

diff --git a/tensorflow/python/estimator/canned/head.py b/tensorflow/python/estimator/canned/head.py
index dcf8b15dad5b66..04fe4d97e40d60 100644
--- a/tensorflow/python/estimator/canned/head.py
+++ b/tensorflow/python/estimator/canned/head.py
@@ -1545,26 +1545,6 @@ def _assert_range(labels, n_classes, message=None):
       return array_ops.identity(labels)
 
 
-# TODO(b/69000400): Delete this method.
-def _weights(features, weight_column):
-  """Fetches weights from features."""
-  with ops.name_scope(None, 'weights', values=features.values()):
-    if weight_column is None:
-      return 1.
-    if isinstance(weight_column, six.string_types):
-      weight_column = feature_column_lib.numeric_column(
-          key=weight_column, shape=(1,))
-    if not isinstance(weight_column, feature_column_lib._NumericColumn):  # pylint: disable=protected-access
-      raise TypeError('Weight column must be either a string or _NumericColumn.'
-                      ' Given type: {}.'.format(type(weight_column)))
-    weights = weight_column._get_dense_tensor(  # pylint: disable=protected-access
-        feature_column_lib._LazyBuilder(features))  # pylint: disable=protected-access
-    if not (weights.dtype.is_floating or weights.dtype.is_integer):
-      raise ValueError('Weight column should be castable to float. '
-                       'Given dtype: {}'.format(weights.dtype))
-    return math_ops.to_float(weights, name='weights')
-
-
 def _binary_logistic_or_multi_class_head(
     n_classes, weight_column, label_vocabulary, loss_reduction):
   """Creates either binary or multi-class head.

From 8f4618d7fc30e04a97664b87bc73d97af6389e34 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 14 May 2018 13:00:26 -0700
Subject: [PATCH 0736/1691]   add memory utilization estimate for HLO op
 profile.

PiperOrigin-RevId: 196553696
---
 tensorflow/contrib/tpu/profiler/op_profile.proto | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tensorflow/contrib/tpu/profiler/op_profile.proto b/tensorflow/contrib/tpu/profiler/op_profile.proto
index 840a43913ba0f1..1f249de314a540 100644
--- a/tensorflow/contrib/tpu/profiler/op_profile.proto
+++ b/tensorflow/contrib/tpu/profiler/op_profile.proto
@@ -60,6 +60,11 @@ message Metrics {
   //  - it does not reveal the peak core FLOPS of the hardware
   double flops = 2;
 
+  // The VMEM bandwidth used to load operands from HBM, as a fraction of
+  // thereotical VMEM bandwidth on the specific hardware.
+  double memory_bandwidth = 3;
+
   double raw_time = 11;   // Elapsed core-time in picoseconds.
   double raw_flops = 12;  // Total floating-point operations performed.
+  double raw_bytes_accessed = 13;  // Total bytes accessed (include read/write).
 }

From e528e5ab82fafe1cf8f5d69f9b18426af1b51d09 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 14 May 2018 13:22:09 -0700
Subject: [PATCH 0737/1691] Various ClangTidy-inspired fixes.

PiperOrigin-RevId: 196556727
---
 .../lite/examples/label_image/label_image.cc  | 50 +++++++++----------
 .../contrib/lite/kernels/kernel_util_test.cc  |  2 +-
 tensorflow/contrib/lite/toco/tooling_util.cc  |  2 +-
 .../contrib/lite/tools/verifier_test.cc       |  1 -
 4 files changed, 26 insertions(+), 29 deletions(-)

diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.cc b/tensorflow/contrib/lite/examples/label_image/label_image.cc
index 456c5c6dc782f4..966fcd2a31fd4d 100644
--- a/tensorflow/contrib/lite/examples/label_image/label_image.cc
+++ b/tensorflow/contrib/lite/examples/label_image/label_image.cc
@@ -77,14 +77,13 @@ void PrintProfilingInfo(const profiling::ProfileEvent* e, uint32_t op_index,
   // time (ms) , Node xxx, OpCode xxx, symblic name
   //      5.352, Node   5, OpCode   4, DEPTHWISE_CONV_2D
 
-
   LOG(INFO) << std::fixed << std::setw(10) << std::setprecision(3)
             << (e->end_timestamp_us - e->begin_timestamp_us) / 1000.0
             << ", Node " << std::setw(3) << std::setprecision(3) << op_index
             << ", OpCode " << std::setw(3) << std::setprecision(3)
             << registration.builtin_code << ", "
             << EnumNameBuiltinOperator(
-                   (BuiltinOperator)registration.builtin_code)
+                   static_cast(registration.builtin_code))
             << "\n";
 }
 
@@ -190,13 +189,13 @@ void RunInference(Settings* s) {
   if (s->profiling) profiler->StartProfiling();
 
   struct timeval start_time, stop_time;
-  gettimeofday(&start_time, NULL);
+  gettimeofday(&start_time, nullptr);
   for (int i = 0; i < s->loop_count; i++) {
     if (interpreter->Invoke() != kTfLiteOk) {
       LOG(FATAL) << "Failed to invoke tflite!\n";
     }
   }
-  gettimeofday(&stop_time, NULL);
+  gettimeofday(&stop_time, nullptr);
   LOG(INFO) << "invoked \n";
   LOG(INFO) << "average time: "
             << (get_us(stop_time) - get_us(start_time)) / (s->loop_count * 1000)
@@ -271,17 +270,17 @@ int Main(int argc, char** argv) {
   int c;
   while (1) {
     static struct option long_options[] = {
-        {"accelerated", required_argument, 0, 'a'},
-        {"count", required_argument, 0, 'c'},
-        {"verbose", required_argument, 0, 'v'},
-        {"image", required_argument, 0, 'i'},
-        {"labels", required_argument, 0, 'l'},
-        {"tflite_model", required_argument, 0, 'm'},
-        {"profiling", required_argument, 0, 'p'},
-        {"threads", required_argument, 0, 't'},
-        {"input_mean", required_argument, 0, 'b'},
-        {"input_std", required_argument, 0, 's'},
-        {0, 0, 0, 0}};
+        {"accelerated", required_argument, nullptr, 'a'},
+        {"count", required_argument, nullptr, 'c'},
+        {"verbose", required_argument, nullptr, 'v'},
+        {"image", required_argument, nullptr, 'i'},
+        {"labels", required_argument, nullptr, 'l'},
+        {"tflite_model", required_argument, nullptr, 'm'},
+        {"profiling", required_argument, nullptr, 'p'},
+        {"threads", required_argument, nullptr, 't'},
+        {"input_mean", required_argument, nullptr, 'b'},
+        {"input_std", required_argument, nullptr, 's'},
+        {nullptr, 0, nullptr, 0}};
 
     /* getopt_long stores the option index here. */
     int option_index = 0;
@@ -294,15 +293,14 @@ int Main(int argc, char** argv) {
 
     switch (c) {
       case 'a':
-        s.accel = strtol(  // NOLINT(runtime/deprecated_fn)
-            optarg, (char**)NULL, 10);
+        s.accel = strtol(optarg, nullptr, 10);  // NOLINT(runtime/deprecated_fn)
         break;
       case 'b':
-        s.input_mean = strtod(optarg, NULL);
+        s.input_mean = strtod(optarg, nullptr);
         break;
       case 'c':
-        s.loop_count = strtol(  // NOLINT(runtime/deprecated_fn)
-            optarg, (char**)NULL, 10);
+        s.loop_count =
+            strtol(optarg, nullptr, 10);  // NOLINT(runtime/deprecated_fn)
         break;
       case 'i':
         s.input_bmp_name = optarg;
@@ -314,19 +312,19 @@ int Main(int argc, char** argv) {
         s.model_name = optarg;
         break;
       case 'p':
-        s.profiling = strtol(  // NOLINT(runtime/deprecated_fn)
-            optarg, (char**)NULL, 10);
+        s.profiling =
+            strtol(optarg, nullptr, 10);  // NOLINT(runtime/deprecated_fn)
         break;
       case 's':
-        s.input_std = strtod(optarg, NULL);
+        s.input_std = strtod(optarg, nullptr);
         break;
       case 't':
         s.number_of_threads = strtol(  // NOLINT(runtime/deprecated_fn)
-            optarg, (char**)NULL, 10);
+            optarg, nullptr, 10);
         break;
       case 'v':
-        s.verbose = strtol(  // NOLINT(runtime/deprecated_fn)
-            optarg, (char**)NULL, 10);
+        s.verbose =
+            strtol(optarg, nullptr, 10);  // NOLINT(runtime/deprecated_fn)
         break;
       case 'h':
       case '?':
diff --git a/tensorflow/contrib/lite/kernels/kernel_util_test.cc b/tensorflow/contrib/lite/kernels/kernel_util_test.cc
index c65b68970f6853..bf6f249acc85ee 100644
--- a/tensorflow/contrib/lite/kernels/kernel_util_test.cc
+++ b/tensorflow/contrib/lite/kernels/kernel_util_test.cc
@@ -33,7 +33,7 @@ class KernelUtilTest : public ::testing::Test {
     tensor1_.allocation_type = kTfLiteMmapRo;
     tensor2_.allocation_type = kTfLiteMmapRo;
   }
-  ~KernelUtilTest() {
+  ~KernelUtilTest() override {
     TfLiteTensorFree(&tensor1_);
     TfLiteTensorFree(&tensor2_);
   }
diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index a789b5c95bc15d..1e6314f2dc7829 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -987,7 +987,7 @@ void FixOperatorOrdering(Model* model) {
     for (auto i : remaining) {
       bool can_insert = true;
       auto& op = old_operators[i];
-      CHECK(op.get());
+      CHECK(op);
       for (const auto& input : op->inputs) {
         if (!IsConstantParameterArray(*model, input) &&
             !arrays_behind_us.count(input)) {
diff --git a/tensorflow/contrib/lite/tools/verifier_test.cc b/tensorflow/contrib/lite/tools/verifier_test.cc
index 03b93afe3ed04b..8a10e6848a574a 100644
--- a/tensorflow/contrib/lite/tools/verifier_test.cc
+++ b/tensorflow/contrib/lite/tools/verifier_test.cc
@@ -31,7 +31,6 @@ namespace tflite {
 
 using flatbuffers::FlatBufferBuilder;
 using flatbuffers::Offset;
-using flatbuffers::Vector;
 
 // Build single subgraph model.
 class TfLiteFlatbufferModelBuilder {

From 52cb1594172691bd6ea9048358652585f0ea1920 Mon Sep 17 00:00:00 2001
From: Pete Warden 
Date: Mon, 14 May 2018 13:24:58 -0700
Subject: [PATCH 0738/1691] Updated speech commands example to use new dataset

PiperOrigin-RevId: 196557132
---
 .../docs_src/tutorials/audio_recognition.md      | 16 +++++++++-------
 tensorflow/examples/speech_commands/train.py     |  2 +-
 2 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/tensorflow/docs_src/tutorials/audio_recognition.md b/tensorflow/docs_src/tutorials/audio_recognition.md
index 372ab47df7df30..d7a8da6f96194a 100644
--- a/tensorflow/docs_src/tutorials/audio_recognition.md
+++ b/tensorflow/docs_src/tutorials/audio_recognition.md
@@ -25,13 +25,15 @@ python tensorflow/examples/speech_commands/train.py
 ```
 
 The script will start off by downloading the [Speech Commands
-dataset](https://storage.cloud.google.com/download.tensorflow.org/data/speech_commands_v0.01.tar.gz),
-which consists of 65,000 WAVE audio files of people saying thirty different
-words. This data was collected by Google and released under a CC BY license, and
-you can help improve it by [contributing five minutes of your own
+dataset](https://storage.cloud.google.com/download.tensorflow.org/data/speech_commands_v0.02.tar.gz),
+which consists of over 105,000 WAVE audio files of people saying thirty
+different words. This data was collected by Google and released under a CC BY
+license, and you can help improve it by [contributing five minutes of your own
 voice](https://aiyprojects.withgoogle.com/open_speech_recording). The archive is
-over 1GB, so this part may take a while, but you should see progress logs, and
-once it's been downloaded once you won't need to do this step again.
+over 2GB, so this part may take a while, but you should see progress logs, and
+once it's been downloaded once you won't need to do this step again. You can
+find more information about this dataset in this
+[Speech Commands paper](https://arxiv.org/abs/1804.03209).
 
 Once the downloading has completed, you'll see logging information that looks
 like this:
@@ -229,7 +231,7 @@ You can also build this application yourself, since it's open source and
 [available as part of the TensorFlow repository on
 github](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#building-in-android-studio-using-the-tensorflow-aar-from-jcenter).
 By default it downloads [a pretrained model from
-tensorflow.org](http://download.tensorflow.org/models/speech_commands_v0.01.zip),
+tensorflow.org](http://download.tensorflow.org/models/speech_commands_v0.02.zip),
 but you can easily [replace it with a model you've trained
 yourself](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#install-model-files-optional).
 If you do this, you'll need to make sure that the constants in [the main
diff --git a/tensorflow/examples/speech_commands/train.py b/tensorflow/examples/speech_commands/train.py
index f084931215261f..fc28eb0631dc5e 100644
--- a/tensorflow/examples/speech_commands/train.py
+++ b/tensorflow/examples/speech_commands/train.py
@@ -288,7 +288,7 @@ def main(_):
       '--data_url',
       type=str,
       # pylint: disable=line-too-long
-      default='http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz',
+      default='http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz',
       # pylint: enable=line-too-long
       help='Location of speech training data archive on the web.')
   parser.add_argument(

From c9777417f193509ad434805e53efa212e05eb6c3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 14 May 2018 13:30:53 -0700
Subject: [PATCH 0739/1691] Resolve inlined function input/output types from
 GrapplerFunctionItem.

Remove duplicated code to resolve type from attributes.

PiperOrigin-RevId: 196558061
---
 .../grappler/optimizers/function_optimizer.cc | 127 ++++++++----------
 tensorflow/core/grappler/utils/functions.cc   |  10 --
 tensorflow/core/grappler/utils/functions.h    |   3 -
 3 files changed, 54 insertions(+), 86 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc
index 5be89369b18d67..611d871eeabb87 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc
@@ -532,63 +532,46 @@ Status SpecializeFunction(const NodeDef& func_node, const FunctionDef& func,
   return Status::OK();
 }
 
-// Copy input/output argument type to the type_list. Return error if argument
-// type is not explicitly defined, and not specified in function attributes.
-Status CopyArgType(const NodeDef& func_node,
-                   const std::unordered_map& func_attr,
-                   const string& arg_kind, const OpDef::ArgDef& arg,
-                   AttrValue::ListValue* type_list) {
-  if (arg.type() != DT_INVALID) {
-    type_list->add_type(arg.type());
-  } else {
-    auto it = func_attr.find(arg.type_attr());
-    if (it == func_attr.end() || it->second.type() == DT_INVALID) {
-      return errors::InvalidArgument(
-          "Invalid ", arg_kind, " argument ", arg.name(), " for function ",
-          func_node.op(), " instantiated by ", func_node.name());
-    }
-    type_list->add_type(it->second.type());
-  }
-  return Status::OK();
-}
-
-// Add an IdentityN op to hook the function inputs to: this ensures that
+// Create an IdentityN node to hook the function inputs to: this ensures that
 // they're all evaluated before the evaluation of the function body starts.
-Status HookInlinedFunctionInputs(
-    const NodeDef& func_node, const FunctionDef& func,
-    const std::unordered_map& func_attr, NodeDef* inputs) {
-  inputs->set_name(strings::StrCat(func_node.name(), "/", "inlined_inputs"));
-  inputs->set_op("IdentityN");
-  inputs->set_device(func_node.device());
-  *inputs->mutable_input() = func_node.input();
+NodeDef InlinedFunctionInputsNode(const NodeDef& func_node,
+                                  const GrapplerFunctionItem& item) {
+  NodeDef inputs;
+  inputs.set_name(strings::StrCat(func_node.name(), "/", "inlined_inputs"));
+  inputs.set_op("IdentityN");
+  inputs.set_device(func_node.device());
+  *inputs.mutable_input() = func_node.input();
   AttrValue::ListValue* type_list =
-      (*inputs->mutable_attr())["T"].mutable_list();
-  for (const OpDef::ArgDef& arg : func.signature().input_arg()) {
-    TF_RETURN_IF_ERROR(
-        CopyArgType(func_node, func_attr, "input", arg, type_list));
+      (*inputs.mutable_attr())["T"].mutable_list();
+
+  for (const InputArgExpansion& input_arg : item.inputs()) {
+    for (int i = 0; i < input_arg.placeholders.size(); ++i) {
+      type_list->add_type(input_arg.data_type);
+    }
   }
-  return Status::OK();
+
+  return inputs;
 }
 
-// Add an IdentityN op to hook the function outputs to: this ensures that the
-// function body is fully evaluated before its fanout gets scheduled.
-Status HookInlinedFunctionOutputs(
-    const NodeDef& func_node, const FunctionDef& func,
-    const std::unordered_map& func_attr,
-    const gtl::ArraySlice fetch, NodeDef* outputs) {
-  outputs->set_name(func_node.name());
-  outputs->set_op("IdentityN");
-  outputs->set_device(func_node.device());
+// Create an IdentityN node to hook the function outputs to: this ensures that
+// the function body is fully evaluated before its fanout gets scheduled.
+NodeDef InlinedFunctionOutputsNode(const NodeDef& func_node,
+                                   const GrapplerFunctionItem& item) {
+  NodeDef outputs;
+  outputs.set_name(func_node.name());
+  outputs.set_op("IdentityN");
+  outputs.set_device(func_node.device());
   AttrValue::ListValue* type_list =
-      (*outputs->mutable_attr())["T"].mutable_list();
-  for (int i = 0; i < func.signature().output_arg_size(); ++i) {
-    const OpDef::ArgDef& arg = func.signature().output_arg(i);
-    TF_RETURN_IF_ERROR(
-        CopyArgType(func_node, func_attr, "output", arg, type_list));
-    // Use the fetch names since they take into account the output mapping.
-    outputs->add_input(strings::StrCat(func_node.name(), "/", fetch[i]));
+      (*outputs.mutable_attr())["T"].mutable_list();
+
+  for (const OutputArgExpansion& output_arg : item.outputs()) {
+    for (const string& output_tensor : output_arg.output_tensors) {
+      type_list->add_type(output_arg.data_type);
+      outputs.add_input(strings::StrCat(func_node.name(), "/", output_tensor));
+    }
   }
-  return Status::OK();
+
+  return outputs;
 }
 
 Status InlineFunction(const NodeDef& func_node, const FunctionDef& func,
@@ -609,27 +592,27 @@ Status InlineFunction(const NodeDef& func_node, const FunctionDef& func,
                                    ". Error: ", item_status.error_message());
   }
 
-  std::unordered_map input_nodes;
-  for (int i = 0; i < func.signature().input_arg_size(); ++i) {
-    const OpDef::ArgDef& arg = func.signature().input_arg(i);
-    input_nodes[arg.name()] = i;
+  // Mapping from input placeholder name to function input position.
+  int idx = 0;
+  std::unordered_map input_placeholders_idx;
+  for (const InputArgExpansion& input_arg : item.inputs()) {
+    for (const string& placeholder : input_arg.placeholders) {
+      input_placeholders_idx[placeholder] = idx++;
+    }
   }
 
-  // Hook inlined function inputs to IdentityN node
+  // Hook inlined function inputs to IdentityN node.
   NodeDef* func_inputs = optimized_graph->add_node();
-  TF_RETURN_IF_ERROR(
-      HookInlinedFunctionInputs(func_node, func, func_attr, func_inputs));
+  *func_inputs = InlinedFunctionInputsNode(func_node, item);
 
   for (NodeDef& func_body_node : *item.mutable_function_body().mutable_node()) {
-    if (input_nodes.find(func_body_node.name()) != input_nodes.end()) {
+    if (item.IsInputPlaceholder(func_body_node.name())) {
+      // Turn input placeholders into identity nodes.
       CHECK_EQ(0, func_body_node.input_size());
-      // Turn input placeholders into identity nodes
-      if (IsPlaceholder(func_body_node)) {
-        func_body_node.set_op("Identity");
-      }
-      int input_id = input_nodes[func_body_node.name()];
+      func_body_node.set_op("Identity");
+      int input_idx = input_placeholders_idx[func_body_node.name()];
       func_body_node.add_input(
-          strings::StrCat(func_inputs->name(), ":", input_id));
+          strings::StrCat(func_inputs->name(), ":", input_idx));
     } else {
       // Update the input names if any.
       for (string& input : *func_body_node.mutable_input()) {
@@ -643,18 +626,18 @@ Status InlineFunction(const NodeDef& func_node, const FunctionDef& func,
       }
     }
 
-    // Add the node name as a prefix to avoid collisions after inlining
+    // Add the node name as a prefix to avoid collisions after inlining.
     func_body_node.set_name(
         strings::StrCat(func_node.name(), "/", func_body_node.name()));
 
-    // Make sure the node is placed
+    // Make sure the node is placed.
     func_body_node.set_device(func_node.device());
 
-    // Check if a body node is itself a function
+    // Check if a body node is itself a function.
     const FunctionDef* func_body_node_func =
         ctx.FindInlinedFunction(func_body_node.op());
     if (func_body_node_func != nullptr) {
-      // Recursively inline function calls
+      // Recursively inline function calls.
       TF_RETURN_IF_ERROR(InlineFunction(func_body_node, *func_body_node_func,
                                         ctx, optimized_graph));
     } else {
@@ -662,16 +645,14 @@ Status InlineFunction(const NodeDef& func_node, const FunctionDef& func,
       for (const auto& attr : func.attr()) {
         func_body_node.mutable_attr()->insert(attr);
       }
-      // Move the node to the main graph
+      // Move the node to the main graph.
       optimized_graph->add_node()->Swap(&func_body_node);
     }
   }
 
-  // Hook inlined function outputs to IdentityN node
+  // Hook inlined function outputs to IdentityN node.
   NodeDef* func_outputs = optimized_graph->add_node();
-  std::vector fetch = OutputTensors(item);
-  TF_RETURN_IF_ERROR(HookInlinedFunctionOutputs(func_node, func, func_attr,
-                                                fetch, func_outputs));
+  *func_outputs = InlinedFunctionOutputsNode(func_node, item);
 
   return Status::OK();
 }
diff --git a/tensorflow/core/grappler/utils/functions.cc b/tensorflow/core/grappler/utils/functions.cc
index 34603f98693b07..5a5dc47fa06626 100644
--- a/tensorflow/core/grappler/utils/functions.cc
+++ b/tensorflow/core/grappler/utils/functions.cc
@@ -380,16 +380,6 @@ GrapplerFunctionItem& GrapplerFunctionItem::SwapFunctionBody(GraphDef&& other) {
   return *this;
 }
 
-std::vector OutputTensors(const GrapplerFunctionItem& item) {
-  std::vector output_tensors;
-  for (const OutputArgExpansion& output : item.outputs()) {
-    for (const string& tensor : output.output_tensors) {
-      output_tensors.push_back(tensor);
-    }
-  }
-  return output_tensors;
-}
-
 bool HasParametrizedType(const FunctionDef& func) {
   const auto is_type_parametrized = [](const OpDef::ArgDef& arg) {
     return !arg.type_attr().empty() || !arg.number_attr().empty() ||
diff --git a/tensorflow/core/grappler/utils/functions.h b/tensorflow/core/grappler/utils/functions.h
index 4641bf5252f67d..6227daa71b57f5 100644
--- a/tensorflow/core/grappler/utils/functions.h
+++ b/tensorflow/core/grappler/utils/functions.h
@@ -176,9 +176,6 @@ class GrapplerFunctionItem : public GrapplerItem {
   bool is_stateful_;
 };
 
-// Return all output tensors referenced by item output args.
-std::vector OutputTensors(const GrapplerFunctionItem& item);
-
 // Check if function input/output types are fully defined only at instantiation
 // time (parametrized by it's instantiation node).
 bool HasParametrizedType(const FunctionDef& func);

From d44cb5bee0a3d9a636123403053dd830fcafbd9c Mon Sep 17 00:00:00 2001
From: Benoit Steiner 
Date: Mon, 14 May 2018 13:33:46 -0700
Subject: [PATCH 0740/1691] Added support for strided slicing of symbolic
 shapes

PiperOrigin-RevId: 196558466
---
 tensorflow/core/framework/shape_inference.cc  |  6 +--
 .../core/grappler/costs/graph_properties.cc   | 54 +++++++++++++++++++
 .../grappler/costs/graph_properties_test.cc   | 33 ++++++++++++
 3 files changed, 90 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/framework/shape_inference.cc b/tensorflow/core/framework/shape_inference.cc
index 3185875e3bcb3b..b02bc3adbed9e7 100644
--- a/tensorflow/core/framework/shape_inference.cc
+++ b/tensorflow/core/framework/shape_inference.cc
@@ -616,8 +616,9 @@ Status InferenceContext::Subshape(ShapeHandle s, int64 start, int64 end,
   int64 end_in = end;
 
   const int32 rank = Rank(s);
-  if (start == 0 && ((RankKnown(s) && end >= rank) ||
-                     end == std::numeric_limits::max())) {
+  if (start == 0 && stride == 1 &&
+      ((RankKnown(s) && end >= rank) ||
+       end == std::numeric_limits::max())) {
     *out = s;
     return Status::OK();
   }
@@ -663,7 +664,6 @@ Status InferenceContext::Subshape(ShapeHandle s, int64 start, int64 end,
   }
 
   std::vector dims;
-  dims.reserve((end - start) / stride);
   for (int i = start; stride > 0 ? i < end : i > end; i += stride) {
     dims.push_back(Dim(s, i));
   }
diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc
index eaf7634daa31d1..4941fb2b38b8fa 100644
--- a/tensorflow/core/grappler/costs/graph_properties.cc
+++ b/tensorflow/core/grappler/costs/graph_properties.cc
@@ -817,6 +817,60 @@ class SymbolicShapeRefiner {
           c->output_tensors_as_shapes.resize(1);
           c->output_tensors_as_shapes[0] = result;
         }
+      } else if (IsStridedSlice(node)) {
+        ShapeHandle input = ic->input_tensors_as_shapes()[0];
+        bool valid = ic->RankKnown(input);
+        const Tensor* slice_begin = ic->input_tensor(1);
+        valid &= slice_begin != nullptr && slice_begin->NumElements() == 1;
+        const Tensor* slice_end = ic->input_tensor(2);
+        valid &= slice_end != nullptr && slice_end->NumElements() == 1;
+        const Tensor* slice_stride = ic->input_tensor(3);
+        valid &= slice_stride != nullptr && slice_stride->NumElements() == 1;
+
+        if (node.attr().count("ellipsis_mask") > 0 &&
+            node.attr().at("ellipsis_mask").i() != 0) {
+          valid = false;
+        }
+        if (node.attr().count("new_axis_mask") > 0 &&
+            node.attr().at("new_axis_mask").i() != 0) {
+          valid = false;
+        }
+        if (node.attr().count("shrink_axis_mask") > 0 &&
+            node.attr().at("shrink_axis_mask").i() != 0) {
+          valid = false;
+        }
+        int begin_mask = 0;
+        if (node.attr().count("begin_mask") > 0) {
+          begin_mask = node.attr().at("begin_mask").i();
+        }
+        int end_mask = 0;
+        if (node.attr().count("end_mask") > 0) {
+          end_mask = node.attr().at("end_mask").i();
+        }
+        if (begin_mask < 0 || begin_mask > 1 || end_mask < 0 || end_mask > 1) {
+          valid = false;
+        }
+        if (valid) {
+          int64 begin = 0;
+          if (begin_mask == 0) {
+            begin = slice_begin->dtype() == DT_INT32
+                        ? slice_begin->flat()(0)
+                        : slice_begin->flat()(0);
+          }
+          int64 end = std::numeric_limits::max();
+          if (end_mask == 0) {
+            end =
+                (slice_end->dtype() == DT_INT32 ? slice_end->flat()(0)
+                                                : slice_end->flat()(0));
+          }
+          int64 stride = slice_stride->dtype() == DT_INT32
+                             ? slice_stride->flat()(0)
+                             : slice_stride->flat()(0);
+          ShapeHandle result;
+          TF_RETURN_IF_ERROR(ic->Subshape(input, begin, end, stride, &result));
+          c->output_tensors_as_shapes.resize(1);
+          c->output_tensors_as_shapes[0] = result;
+        }
       }
     }
 
diff --git a/tensorflow/core/grappler/costs/graph_properties_test.cc b/tensorflow/core/grappler/costs/graph_properties_test.cc
index a53f6414c307e0..3e44b222fdb99b 100644
--- a/tensorflow/core/grappler/costs/graph_properties_test.cc
+++ b/tensorflow/core/grappler/costs/graph_properties_test.cc
@@ -952,6 +952,39 @@ TEST_F(GraphPropertiesTest, Performance) {
   TF_CHECK_OK(properties.InferStatically(false));
 }
 
+TEST_F(GraphPropertiesTest, StridedSlicesOfShapes) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+  Output a =
+      ops::Placeholder(s.WithOpName("a"), DT_FLOAT,
+                       ops::Placeholder::Shape(PartialTensorShape({-1, -1})));
+  auto shp = ops::Shape(s.WithOpName("shape"), {a});
+
+  Output index1 = ops::Const(s.WithOpName("index1"), 0, {1});
+  Output index2 = ops::Const(s.WithOpName("index2"), 1, {1});
+  Output index3 = ops::Const(s.WithOpName("index3"), 2, {1});
+
+  Output b = ops::StridedSlice(s.WithOpName("b"), shp, index1, index2, index2);
+  Output c = ops::StridedSlice(s.WithOpName("c"), shp, index2, index3, index2);
+
+  Output zero = ops::Const(s.WithOpName("zero"), 0.0f, {});
+  Output o1 = ops::Fill(s.WithOpName("o1"), b, zero);
+  Output o2 = ops::Fill(s.WithOpName("o2"), c, zero);
+
+  GrapplerItem item;
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+
+  GraphProperties properties(item);
+  TF_CHECK_OK(properties.InferStatically(false));
+  const auto shape_a = properties.GetOutputProperties("a").at(0).shape();
+  const auto shape_o1 = properties.GetOutputProperties("o1").at(0).shape();
+  const auto shape_o2 = properties.GetOutputProperties("o2").at(0).shape();
+  EXPECT_EQ(2, shape_a.dim_size());
+  EXPECT_EQ(1, shape_o1.dim_size());
+  EXPECT_EQ(1, shape_o2.dim_size());
+  EXPECT_EQ(shape_a.dim(0).size(), shape_o1.dim(0).size());
+  EXPECT_EQ(shape_a.dim(1).size(), shape_o2.dim(0).size());
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow

From 14113ead276f82ae205450dc0b6ea23cd918bc0c Mon Sep 17 00:00:00 2001
From: Saurabh Saxena 
Date: Mon, 14 May 2018 13:44:52 -0700
Subject: [PATCH 0741/1691] Add CheckpointInputPipelineHook to the API docs.

PiperOrigin-RevId: 196560221
---
 tensorflow/contrib/data/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py
index 4f2c72b6606ccd..2af61881a94058 100644
--- a/tensorflow/contrib/data/__init__.py
+++ b/tensorflow/contrib/data/__init__.py
@@ -23,6 +23,7 @@
 See the @{$datasets$Importing Data} Programmer's Guide for an overview.
 
 @@Counter
+@@CheckpointInputPipelineHook
 @@SqlDataset
 
 @@assert_element_shape

From 22a5e527e99124b57f05e281f5a07e894a9000ff Mon Sep 17 00:00:00 2001
From: Jianwei Xie 
Date: Mon, 14 May 2018 13:53:00 -0700
Subject: [PATCH 0742/1691] Reserves 'context' key in TPUEstimator params dict.

PiperOrigin-RevId: 196561620
---
 tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 1bf2fc5dea7af7..998e28b817dab0 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -76,12 +76,13 @@
 _TPU_ESTIMATOR = 'tpu_estimator'
 _ITERATIONS_PER_LOOP_VAR = 'iterations_per_loop'
 _BATCH_SIZE_KEY = 'batch_size'
+_CTX_KEY = 'context'
 _CROSS_REPLICA_SUM_OP = 'CrossReplicaSum'
 _ONE_GIGABYTE = 1024 * 1024 * 1024
 _TPU_ENQUEUE_OPS = '_tpu_enqueue_ops'
 _TPU_TRAIN_OP = '_tpu_train_op'
 
-_RESERVED_PARAMS_KEYS = [_BATCH_SIZE_KEY]
+_RESERVED_PARAMS_KEYS = [_BATCH_SIZE_KEY, _CTX_KEY]
 
 
 # TODO(b/65703635): Flip the value and remove all dead code. Currently, this is

From 321d69b55a61a623360b70fc96dac2c7e1f71ad3 Mon Sep 17 00:00:00 2001
From: Jacques Pienaar 
Date: Mon, 14 May 2018 14:04:05 -0700
Subject: [PATCH 0743/1691] Add If op rewriter.

* Add attribute to If op to indicate if lowering to switch-merge form is
  needed;
* Add initial version of If op rewriter than transforms a If op into
  switch/merge nodes (as would have been constructed via tf.cond) if the If op
  has the lowering attribute set.
  - The pass is not ready for general use and, for example, does not support
    reference data types.

PiperOrigin-RevId: 196563421
---
 tensorflow/core/BUILD                         |  25 ++
 tensorflow/core/common_runtime/lower_if_op.cc | 283 ++++++++++++++++++
 tensorflow/core/common_runtime/lower_if_op.h  |  38 +++
 .../core/common_runtime/lower_if_op_test.cc   | 140 +++++++++
 4 files changed, 486 insertions(+)
 create mode 100644 tensorflow/core/common_runtime/lower_if_op.cc
 create mode 100644 tensorflow/core/common_runtime/lower_if_op.h
 create mode 100644 tensorflow/core/common_runtime/lower_if_op_test.cc

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 8be43aade74a76..d20c7fd4b7b030 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -2360,6 +2360,7 @@ CORE_CPU_LIB_HEADERS = CORE_CPU_BASE_HDRS + [
     "common_runtime/executor.h",
     "common_runtime/graph_optimizer.h",
     "common_runtime/local_device.h",
+    "common_runtime/lower_if_op.h",
     "common_runtime/memory_types.h",
     "common_runtime/mkl_cpu_allocator.h",
     "common_runtime/optimization_registry.h",
@@ -2410,6 +2411,7 @@ tf_cuda_library(
         "common_runtime/graph_optimizer.cc",
         "common_runtime/graph_runner.cc",
         "common_runtime/local_device.cc",
+        "common_runtime/lower_if_op.cc",
         "common_runtime/memory_types.cc",
         "common_runtime/mkl_cpu_allocator.cc",
         "common_runtime/optimization_registry.cc",
@@ -4070,6 +4072,29 @@ tf_cc_test_gpu(
     ],
 )
 
+tf_cc_tests(
+    name = "common_runtime_lower_if_op_test",
+    size = "small",
+    srcs = ["common_runtime/lower_if_op_test.cc"],
+    deps = [
+        ":all_kernels",
+        ":core_cpu",
+        ":core_cpu_internal",
+        ":direct_session",
+        ":framework",
+        ":framework_internal",
+        ":lib",
+        ":test",
+        ":test_main",
+        ":testlib",
+        "//tensorflow/cc:cc_ops",
+        "//tensorflow/cc:cc_ops_internal",
+        "//tensorflow/cc:client_session",
+        "//tensorflow/cc:function_ops",
+        "//tensorflow/cc:ops",
+    ],
+)
+
 # Test data
 filegroup(
     name = "image_testdata",
diff --git a/tensorflow/core/common_runtime/lower_if_op.cc b/tensorflow/core/common_runtime/lower_if_op.cc
new file mode 100644
index 00000000000000..b5fee36ff43e08
--- /dev/null
+++ b/tensorflow/core/common_runtime/lower_if_op.cc
@@ -0,0 +1,283 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/common_runtime/lower_if_op.h"
+
+#include "tensorflow/core/common_runtime/function.h"
+#include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/graph/graph.h"
+#include "tensorflow/core/graph/node_builder.h"
+
+namespace tensorflow {
+
+// TODO(jpienaar): Consider making it a public attribute.
+const char* const LowerIfOpPass::kLowerUsingSwitchMergeAttr =
+    "_lower_using_switch_merge";
+
+namespace {
+
+using NodeOut = NodeBuilder::NodeOut;
+
+// Convenience builder to make it easy to construct a conditional with a single
+// function call in the then and else branch. This first converts the if node
+// into switches (for inputs) and merges (for outputs) around a function call
+// per branch, then inlines the function calls.
+class CondBuilder {
+ public:
+  enum Branch { kElseBranch = 0, kThenBranch = 1 };
+
+  // Create a CondBuilder to create the lowering of If op.  that has then and
+  // else functions named `then_fn_name` and `else_fn_name` respectively in the
+  // given graph.
+  CondBuilder(Node* if_op, const string& then_fn_name,
+              const string& else_fn_name, Graph* graph);
+
+  // Constructs the basic conditional control flow using switch and merge nodes.
+  Status CreatePivotNodes();
+
+  // Adds the inputs from the if node to the merge nodes of the lowered if.
+  Status AddInputs();
+
+  // Adds the outputs from the if node to the merge nodes of the lowered if.
+  // Note: no inputs can be added once outputs are added as the then and else
+  // nodes are finalized while adding outputs.
+  Status AddOutputs();
+
+  // Builds an identity node with the same outputs as If.
+  Status BuildLoweredIfOutput();
+
+  // Inline call nodes for then and else.
+  Status InlineCallNodes();
+
+ private:
+  // Returns unique name containing the name of the If op being rewritten
+  // (name_), infix and a suffix to ensure it is unique within the graph.
+  string NewName(const string& infix);
+
+  // Adds input to both the then and else nodes from src:src_output.
+  Status AddInput(Node* src, int src_output);
+
+  // The merged outputs of the then and else nodes.
+  std::vector outputs_;
+
+  // The node that dominates all execution of the then and else body nodes.
+  Node* control_predecessor_;
+  // The original If op.
+  Node* if_op_;
+  // The identity node with the same outputs as the original If op.
+  Node* lowered_if_output_;
+  // The predicate of the conditional.
+  Node* pred_;
+  // Node corresponding to pivot_f branch of predicate switch which is
+  // the pivot node that dominates all nodes in the false/else branch.
+  Node* pivot_f_;
+  // Node corresponding to pivot_t branch of predicate switch which is
+  // the pivot node that dominates all nodes in the true/then branch.
+  Node* pivot_t_;
+  Node* then_call_node_;
+  Node* else_call_node_;
+  Graph* graph_;
+  string name_;
+
+  NodeBuilder then_call_builder_;
+  NodeBuilder else_call_builder_;
+};
+
+CondBuilder::CondBuilder(Node* if_op, const string& then_fn_name,
+                         const string& else_fn_name, Graph* graph)
+    : if_op_(if_op),
+      graph_(graph),
+      name_(if_op->name()),
+      then_call_builder_(NewName("then"), then_fn_name, graph->op_registry()),
+      else_call_builder_(NewName("else"), else_fn_name, graph->op_registry()) {
+  TF_CHECK_OK(if_op_->input_node(0, &pred_));
+}
+
+Status CondBuilder::CreatePivotNodes() {
+  // Construct the basic cond body (consisting of feeding in the predicate to
+  // create pivot nodes).
+  Node* switch_pred;
+  TF_RETURN_IF_ERROR(
+      NodeBuilder(NewName("switch_pred"), "Switch", graph_->op_registry())
+          .Input(NodeOut(pred_, 0))
+          .Input(NodeOut(pred_, 0))
+          .Finalize(graph_, &switch_pred));
+  control_predecessor_ = switch_pred;
+  TF_RETURN_IF_ERROR(
+      NodeBuilder(NewName("pivot_f"), "Identity", graph_->op_registry())
+          .Input(switch_pred, kElseBranch)
+          .Finalize(graph_, &pivot_f_));
+  TF_RETURN_IF_ERROR(
+      NodeBuilder(NewName("pivot_t"), "Identity", graph_->op_registry())
+          .Input(switch_pred, kThenBranch)
+          .Finalize(graph_, &pivot_t_));
+  return Status::OK();
+}
+
+string CondBuilder::NewName(const string& infix) {
+  return graph_->NewName(strings::StrCat(name_, "/", infix));
+}
+
+Status CondBuilder::AddInput(Node* src, int src_output) {
+  Node* input;
+  TF_RETURN_IF_ERROR(
+      NodeBuilder(NewName(src->name()), "Switch", graph_->op_registry())
+          .Input(src, src_output)
+          .Input(pred_, 0)
+          .Finalize(graph_, &input));
+  then_call_builder_.Input(input, kThenBranch);
+  else_call_builder_.Input(input, kElseBranch);
+  return Status::OK();
+}
+
+Status CondBuilder::AddInputs() {
+  // Add input data edges.
+  std::vector edges;
+  TF_RETURN_IF_ERROR(if_op_->input_edges(&edges));
+  // Start at index 1 as the first input is the predicate.
+  for (int i = 1; i < edges.size(); ++i) {
+    const Edge* e = edges[i];
+    TF_RETURN_IF_ERROR(AddInput(e->src(), e->src_output()));
+  }
+  // Add input control edges.
+  for (const Edge* e : if_op_->in_edges()) {
+    if (e->IsControlEdge()) {
+      graph_->AddControlEdge(e->src(), control_predecessor_);
+    }
+  }
+  return Status::OK();
+}
+
+Status CondBuilder::AddOutputs() {
+  // Construct the then and else nodes.
+  TF_RETURN_IF_ERROR(then_call_builder_.Finalize(graph_, &then_call_node_));
+  graph_->AddControlEdge(pivot_t_, then_call_node_);
+  TF_RETURN_IF_ERROR(else_call_builder_.Finalize(graph_, &else_call_node_));
+  graph_->AddControlEdge(pivot_f_, else_call_node_);
+
+  // Merge the outputs from the two branches.
+  std::vector merges(then_call_node_->num_outputs());
+  outputs_.resize(merges.size());
+  for (int i = 0; i < then_call_node_->num_outputs(); ++i) {
+    TF_RETURN_IF_ERROR(
+        NodeBuilder(graph_->NewName("merge"), "Merge", graph_->op_registry())
+            .Input({NodeOut(then_call_node_, i), NodeOut(else_call_node_, i)})
+            .Finalize(graph_, &merges[i]));
+    outputs_[i] = NodeOut(merges[i], 0);
+  }
+
+  TF_RETURN_IF_ERROR(BuildLoweredIfOutput());
+
+  // Add outputs.
+  for (const Edge* e : if_op_->out_edges()) {
+    if (e->IsControlEdge()) {
+      graph_->AddControlEdge(lowered_if_output_, e->dst());
+    } else {
+      // Feed the outputs directly from the merge nodes so that downstream ops
+      // can start before all the outputs have been computed.
+      graph_->AddEdge(merges[e->src_output()], e->src_output(), e->dst(),
+                      e->dst_input());
+    }
+  }
+  return Status::OK();
+}
+
+Status InlineCallInGraph(Node* n, Graph* g) {
+  const auto& lib = g->flib_def();
+  const FunctionDef* fdef = lib.Find(n->type_string());
+  CHECK(fdef != nullptr);
+  FunctionBody* fbody;
+  TF_RETURN_IF_ERROR(
+      FunctionDefToBodyHelper(*fdef, n->attrs(), &lib,
+                              [&lib](const string& op, const OpDef** sig) {
+                                return lib.LookUpOpDef(op, sig);
+                              },
+                              &fbody));
+  // TODO(jpienaar): Improve this interface to make the need to delete it
+  // explicit.
+  InlineFunctionBody(g->flib_def(), g, n, fbody);
+  delete fbody;
+  return Status::OK();
+}
+
+Status CondBuilder::BuildLoweredIfOutput() {
+  // Build the identity node output.
+  NodeBuilder ib(name_, "IdentityN");
+  ib.Input(outputs_);
+  return ib.Finalize(graph_, &lowered_if_output_);
+}
+
+Status CondBuilder::InlineCallNodes() {
+  TF_RETURN_IF_ERROR(InlineCallInGraph(then_call_node_, graph_));
+  TF_RETURN_IF_ERROR(InlineCallInGraph(else_call_node_, graph_));
+  return Status::OK();
+}
+
+}  // namespace
+
+Status LowerIfOpPass::Run(const GraphOptimizationPassOptions& options) {
+  if (options.partition_graphs != nullptr) {
+    return errors::Internal(
+        "Lowering If op should happen before partitioning.");
+  }
+  if (options.graph == nullptr) {
+    return Status::OK();
+  }
+
+  Graph* g = options.graph->get();
+  if (g == nullptr) {
+    return errors::Internal("Lowering If op requires a graph to be available.");
+  }
+
+  // Match all the nodes that need to be rewritten.
+  gtl::InlinedVector matches;
+  for (Node* n : g->op_nodes()) {
+    if (n->type_string() == "If") {
+      // Only rewrite if the If op is marked as needing to be lowered.
+      bool match;
+      Status s = GetNodeAttr(n->attrs(), kLowerUsingSwitchMergeAttr, &match);
+      if (s.ok() && match) matches.push_back(n);
+    }
+  }
+  for (Node* n : matches) {
+    TF_RETURN_IF_ERROR(RewriteNode(n, g));
+  }
+  return Status::OK();
+}
+
+Status LowerIfOpPass::RewriteNode(Node* n, Graph* g) {
+  const AttrValue* then_attr = n->attrs().Find("then_branch");
+  if (then_attr == nullptr) {
+    return errors::InvalidArgument("Then branch function missing");
+  }
+  const AttrValue* else_attr = n->attrs().Find("else_branch");
+  if (else_attr == nullptr) {
+    return errors::InvalidArgument("Else branch function missing");
+  }
+
+  CondBuilder cb(n, then_attr->func().name(), else_attr->func().name(), g);
+  TF_RETURN_IF_ERROR(cb.CreatePivotNodes());
+  TF_RETURN_IF_ERROR(cb.AddInputs());
+  TF_RETURN_IF_ERROR(cb.AddOutputs());
+  TF_RETURN_IF_ERROR(cb.InlineCallNodes());
+  g->RemoveNode(n);
+
+  return Status::OK();
+}
+
+REGISTER_OPTIMIZATION(OptimizationPassRegistry::PRE_PLACEMENT, 0,
+                      LowerIfOpPass);
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/common_runtime/lower_if_op.h b/tensorflow/core/common_runtime/lower_if_op.h
new file mode 100644
index 00000000000000..a9ef39ae5c828d
--- /dev/null
+++ b/tensorflow/core/common_runtime/lower_if_op.h
@@ -0,0 +1,38 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CORE_COMMON_RUNTIME_LOWER_IF_OP_H_
+#define TENSORFLOW_CORE_COMMON_RUNTIME_LOWER_IF_OP_H_
+
+#include "tensorflow/core/common_runtime/optimization_registry.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+
+// Rewrite If ops to use switch and merge nodes instead.
+class LowerIfOpPass : public GraphOptimizationPass {
+ public:
+  static const char* const kLowerUsingSwitchMergeAttr;
+
+  Status Run(const GraphOptimizationPassOptions& options) override;
+
+ private:
+  // Rewrite the given If node `n` in graph `g` to use the switch-merge form.
+  Status RewriteNode(Node* n, Graph* g);
+};
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_CORE_COMMON_RUNTIME_LOWER_IF_OP_H_
diff --git a/tensorflow/core/common_runtime/lower_if_op_test.cc b/tensorflow/core/common_runtime/lower_if_op_test.cc
new file mode 100644
index 00000000000000..319a617b322591
--- /dev/null
+++ b/tensorflow/core/common_runtime/lower_if_op_test.cc
@@ -0,0 +1,140 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/common_runtime/lower_if_op.h"
+
+#include "tensorflow/cc/client/client_session.h"
+#include "tensorflow/cc/framework/ops.h"
+#include "tensorflow/cc/ops/array_ops.h"
+#include "tensorflow/cc/ops/control_flow_ops_internal.h"
+#include "tensorflow/cc/ops/function_ops.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/core/common_runtime/graph_runner.h"
+#include "tensorflow/core/framework/function_testlib.h"
+#include "tensorflow/core/framework/node_def_util.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/graph/graph_constructor.h"
+#include "tensorflow/core/graph/graph_def_builder.h"
+#include "tensorflow/core/graph/graph_def_builder_util.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace {
+
+Status Rewrite(std::unique_ptr* graph) {
+  FunctionDefLibrary flib;
+  FunctionLibraryDefinition flib_def((*graph)->op_registry(), flib);
+
+  GraphOptimizationPassOptions opt_options;
+  opt_options.graph = graph;
+  opt_options.flib_def = &flib_def;
+  LowerIfOpPass pass;
+  return pass.Run(opt_options);
+}
+
+TEST(LowerIfOpTest, Simple) {
+  std::unique_ptr graph(new Graph(OpRegistry::Global()));
+
+  // Add test functions for then and else branch.
+  FunctionDefLibrary f_lib_proto;
+  *(f_lib_proto.add_function()) = test::function::XTimesTwo();
+  *(f_lib_proto.add_function()) = test::function::XTimesFour();
+  FunctionLibraryDefinition f_lib(OpRegistry::Global(), f_lib_proto);
+
+  // Construct simple conditional that switches on `pred` and operates only on
+  // single input `A`.
+  Scope root = Scope::NewRootScope().ExitOnError();
+  TF_ASSERT_OK(root.graph()->AddFunctionLibrary(f_lib_proto));
+  auto a = ops::_Arg(root.WithOpName("A"), DT_INT32, 0);
+  auto pred = ops::_Arg(root.WithOpName("pred"), DT_BOOL, 1);
+  Node* written_if;
+  std::vector inputs({NodeBuilder::NodeOut(a.node())});
+  AttrValue tb;
+  tb.mutable_func()->set_name("XTimesTwo");
+  AttrValue eb;
+  eb.mutable_func()->set_name("XTimesFour");
+  TF_ASSERT_OK(NodeBuilder("if", "If", &f_lib)
+                   .Input(pred.node())
+                   .Input(inputs)
+                   .Attr("then_branch", tb)
+                   .Attr("else_branch", eb)
+                   .Attr(LowerIfOpPass::kLowerUsingSwitchMergeAttr, true)
+                   .Attr("Tout", {DT_INT32})
+                   .Finalize(root.graph(), &written_if));
+  TF_ASSERT_OK(root.DoShapeInference(written_if));
+  TF_ASSERT_OK(root.ToGraph(graph.get()));
+
+  // The input graph has no switch or merge nodes.
+  int node_called_if_count = 0;
+  for (const auto* op : graph->op_nodes()) {
+    ASSERT_FALSE(op->IsSwitch());
+    ASSERT_FALSE(op->IsMerge());
+    if (op->name() == "if") {
+      ++node_called_if_count;
+    }
+  }
+  ASSERT_EQ(node_called_if_count, 1);
+
+  TF_ASSERT_OK(Rewrite(&graph));
+
+  // Verify the resultant graph has switch and merge nodes, and a node called
+  // `if` (but not If nodes).
+  int switch_count = 0;
+  int merge_count = 0;
+  node_called_if_count = 0;
+  for (const auto* op : graph->op_nodes()) {
+    if (op->IsSwitch()) {
+      ++switch_count;
+    }
+    if (op->IsMerge()) {
+      ++merge_count;
+    }
+    ASSERT_NE(op->type_string(), "If");
+    if (op->name() == "if") {
+      ++node_called_if_count;
+    }
+  }
+  // One switch for predicate and one for input (A).
+  ASSERT_EQ(switch_count, 2);
+  // One merge for the single output values of then and else.
+  ASSERT_EQ(merge_count, 1);
+  ASSERT_EQ(node_called_if_count, 1);
+
+  // Verify execution.
+  ClientSession session(root);
+  {
+    ClientSession::FeedType feeds;
+    feeds.emplace(Output(pred.node()), Input::Initializer(false));
+    feeds.emplace(Output(a.node()), Input::Initializer(10));
+    std::vector out_tensors;
+    TF_ASSERT_OK(session.Run(feeds, {Output(written_if)}, &out_tensors));
+    EXPECT_EQ(out_tensors.size(), 1);
+    EXPECT_EQ(out_tensors[0].scalar()(), 40);
+  }
+  {
+    ClientSession::FeedType feeds;
+    feeds.emplace(Output(pred.node()), Input::Initializer(true));
+    feeds.emplace(Output(a.node()), Input::Initializer(10));
+    std::vector out_tensors;
+    TF_ASSERT_OK(session.Run(feeds, {Output(written_if)}, &out_tensors));
+    EXPECT_EQ(out_tensors.size(), 1);
+    EXPECT_EQ(out_tensors[0].scalar()(), 20);
+  }
+}
+
+}  // namespace
+}  // namespace tensorflow

From d0230156b60c1e11ed4ac2fdf888409ae52051f4 Mon Sep 17 00:00:00 2001
From: Justin Lebar 
Date: Mon, 14 May 2018 14:09:01 -0700
Subject: [PATCH 0744/1691] [XLA] Ergonomic improvements to --xla_hlo_profile.

- Don't display ops with 0 optimal seconds and 0 actual cycles.  These
  are ops that were expected to be free and were actually free.

- Fix HloCostAnalysis to mark parameters, constants, and
  get-tuple-element as expected-to-be-free per the definition above.

- Allow optimal-seconds < 0 to indicate "I don't know".  Use this for
  custom calls, and then hide such ops from the "seconds above the
  optimum" table.

- Don't display "" and "" -- instead, just display the
  empty string.  Less visual noise.

- Instead of showing ~5 ops per category in the categories tables, show
  everything.  This isn't so noisy now that we're hiding "free" ops, and
  it makes finding optimization opportunities much easier.

PiperOrigin-RevId: 196564177
---
 .../compiler/aot/tests/tfcompile_test.cc      | 15 +---
 tensorflow/compiler/xla/service/BUILD         |  1 +
 .../compiler/xla/service/hlo_cost_analysis.cc | 19 +++--
 .../xla/service/hlo_execution_profile_test.cc | 48 +++--------
 .../service/human_readable_profile_builder.cc | 80 +++++++++++++------
 .../service/human_readable_profile_builder.h  |  9 ++-
 .../xla/tests/xla_hlo_profile_test.cc         |  4 +-
 7 files changed, 92 insertions(+), 84 deletions(-)

diff --git a/tensorflow/compiler/aot/tests/tfcompile_test.cc b/tensorflow/compiler/aot/tests/tfcompile_test.cc
index 309a991fc11ab7..868d752927bb08 100644
--- a/tensorflow/compiler/aot/tests/tfcompile_test.cc
+++ b/tensorflow/compiler/aot/tests/tfcompile_test.cc
@@ -40,7 +40,7 @@ namespace tfcompile {
 namespace {
 
 using ::testing::HasSubstr;
-using ::testing::UnorderedElementsAre;
+using ::testing::IsSupersetOf;
 
 TEST(TFCompileTest, Add) {
   AddComp add;
@@ -559,17 +559,10 @@ TEST(TFCompileTest, HloProfiling) {
   auto tuple_profile_line = HasSubstr(
       "%tuple.0.8 = (f32[2,2]{1,0}, f32[2,2]{1,0}) tuple(f32[2,2]{1,0} "
       "%dot.0.2, f32[2,2]{1,0} %add.0.5)");
-  auto arg0_profile_line = HasSubstr("%arg0.0.0 = f32[2,2]{1,0} parameter(0)");
-  auto arg1_profile_line = HasSubstr("%arg1.0.1 = f32[2,2]{1,0} parameter(1)");
 
-  hlo_profile_lines.erase(hlo_profile_lines.begin() + 7,
-                          hlo_profile_lines.end());
-
-  EXPECT_THAT(
-      hlo_profile_lines,
-      UnorderedElementsAre(header, total_cycles_profile_line, dot_profile_line,
-                           add_profile_line, tuple_profile_line,
-                           arg0_profile_line, arg1_profile_line));
+  EXPECT_THAT(hlo_profile_lines,
+              IsSupersetOf({header, total_cycles_profile_line, dot_profile_line,
+                            add_profile_line, tuple_profile_line}));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 5b70bf31957775..1049083b2b8dbc 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1766,6 +1766,7 @@ tf_cc_test(
         ":hlo_execution_profile",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
+        "//tensorflow/core:lib",
     ],
 )
 
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
index 44e4f75f75b275..94c9c7eabcc99d 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
@@ -142,19 +142,25 @@ Status HloCostAnalysis::HandleReducePrecision(const HloInstruction* hlo) {
 }
 
 Status HloCostAnalysis::HandleParameter(const HloInstruction*) {
+  current_should_compute_bottleneck_time_ = false;
   current_properties_[kBytesAccessedKey] = 0;
+  current_properties_[kOptimalSecondsKey] = 0;
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleConstant(const HloInstruction*) {
+  current_should_compute_bottleneck_time_ = false;
   current_properties_[kBytesAccessedKey] = 0;
+  current_properties_[kOptimalSecondsKey] = 0;
   return Status::OK();
 }
 
 Status HloCostAnalysis::HandleGetTupleElement(const HloInstruction*) {
   // GetTupleElement forwards a pointer and does not touch each element in the
   // output.
+  current_should_compute_bottleneck_time_ = false;
   current_properties_[kBytesAccessedKey] = 0;
+  current_properties_[kOptimalSecondsKey] = 0;
   return Status::OK();
 }
 
@@ -329,6 +335,7 @@ Status HloCostAnalysis::HandleSelectAndScatter(
 Status HloCostAnalysis::HandleBitcast(const HloInstruction*) {
   // A bitcast does no computation and touches no memory.
   current_properties_[kBytesAccessedKey] = 0;
+  current_properties_[kOptimalSecondsKey] = 0;
   return Status::OK();
 }
 
@@ -555,11 +562,13 @@ Status HloCostAnalysis::HandleCall(const HloInstruction* call) {
 }
 
 Status HloCostAnalysis::HandleCustomCall(const HloInstruction*) {
-  // We can't do anything sane with CustomCalls, since we don't know what they
-  // do, and returning an error status will stop iteration over this
-  // computation, which is probably also not what we want.  So just punt and
-  // return OK.  This will cause all of the properties to be reported as 0,
-  // which is fine.
+  // Mark applicable fields as "unknown", since we don't know what CustomCall
+  // does.  This is better than returning an error, which would stop iteration,
+  // and therefore would prevent us from getting *any* stats for a computation
+  // which contains a CustomCall.
+  current_properties_[kOptimalSecondsKey] = -1;
+  current_properties_[kBytesAccessedKey] = -1;
+  current_properties_[kFlopsKey] = -1;
   current_should_compute_bottleneck_time_ = false;
   return Status::OK();
 }
diff --git a/tensorflow/compiler/xla/service/hlo_execution_profile_test.cc b/tensorflow/compiler/xla/service/hlo_execution_profile_test.cc
index a0cb28246d3be5..dcc45831651c68 100644
--- a/tensorflow/compiler/xla/service/hlo_execution_profile_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_execution_profile_test.cc
@@ -16,34 +16,16 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_execution_profile.h"
 #include "tensorflow/compiler/xla/service/hlo_cost_analysis.h"
 #include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+#include "tensorflow/core/lib/strings/strcat.h"
 
 namespace xla {
 namespace {
 
-class HloExecutionProfileTest : public HloTestBase {
- protected:
-  static constexpr int64 kInstructionCyclesIndex = 0;
-  static constexpr int64 kInstructionNameIndex = 19;
-};
+using tensorflow::strings::StrCat;
+using ::testing::AllOf;
+using ::testing::ContainsRegex;
 
-// Splits `lines` into a sequence of lines delimited by newlines and then split
-// each of those lines into a sequence of words delimited by spaces.  Filter out
-// empty words.
-std::vector> SplitIntoLinesAndWords(
-    tensorflow::StringPiece lines) {
-  std::vector> result;
-  for (const string& line : tensorflow::str_util::Split(lines, '\n')) {
-    std::vector words;
-    for (const string& word : tensorflow::str_util::Split(line, ' ')) {
-      if (!word.empty()) {
-        words.push_back(word);
-      }
-    }
-    result.push_back(std::move(words));
-  }
-
-  return result;
-}
+class HloExecutionProfileTest : public HloTestBase {};
 
 TEST_F(HloExecutionProfileTest, Basic) {
   std::unique_ptr hlo_module = CreateNewModule();
@@ -84,20 +66,12 @@ TEST_F(HloExecutionProfileTest, Basic) {
   execution_profile.SetCyclesTakenBy(add_instruction, add_cycles);
   execution_profile.SetCyclesTakenBy(dot_instruction, dot_cycles);
 
-  string rendered_profile = execution_profile.ToString(
-      backend().default_stream_executor()->GetDeviceDescription());
-  std::vector> lines_and_words =
-      SplitIntoLinesAndWords(rendered_profile);
-  ASSERT_EQ(lines_and_words.size(), 8);
-
-  const std::vector& line_2 = lines_and_words[2];
-  const std::vector& line_3 = lines_and_words[3];
-
-  EXPECT_EQ(line_2[kInstructionCyclesIndex], std::to_string(dot_cycles));
-  EXPECT_EQ(line_2[kInstructionNameIndex], '%' + dot_instruction->name());
-
-  EXPECT_EQ(line_3[kInstructionCyclesIndex], std::to_string(add_cycles));
-  EXPECT_EQ(line_3[kInstructionNameIndex], '%' + add_instruction->name());
+  EXPECT_THAT(execution_profile.ToString(
+                  backend().default_stream_executor()->GetDeviceDescription()),
+              AllOf(ContainsRegex(StrCat(dot_cycles, R"(\b.*%)",
+                                         dot_instruction->name())),
+                    ContainsRegex(StrCat(add_cycles, R"(\b.*%)",
+                                         add_instruction->name()))));
 }
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/human_readable_profile_builder.cc b/tensorflow/compiler/xla/service/human_readable_profile_builder.cc
index 13e4557317f74b..dc3bfce0c495bc 100644
--- a/tensorflow/compiler/xla/service/human_readable_profile_builder.cc
+++ b/tensorflow/compiler/xla/service/human_readable_profile_builder.cc
@@ -27,6 +27,7 @@ using tensorflow::strings::HumanReadableElapsedTime;
 using tensorflow::strings::HumanReadableNumBytes;
 using tensorflow::strings::Printf;
 using tensorflow::strings::StrAppend;
+using tensorflow::strings::StrCat;
 
 string HumanReadableProfileBuilder::ToString() const {
   string s;
@@ -35,20 +36,26 @@ string HumanReadableProfileBuilder::ToString() const {
           computation_name_.c_str(),
           HumanReadableElapsedTime(CyclesToSeconds(total_cycles_)).c_str());
 
-  auto append_op = [&](const OpInfo& op) {
+  auto print_op = [&](const OpInfo& op) {
+    // Skip ops with 0 optimal seconds and 0 actual cycles.  These are ops that
+    // were expected to be free and are actually free -- things like (on most
+    // backends) kParameter or kConstant HLOs.  There's no need to clutter the
+    // profile with these.
+    if (op.optimal_seconds == 0 && op.cycles == 0) {
+      return;
+    }
+
     string bytes_per_sec;
     string bytes_per_cycle;
-    if (op.cycles <= 0 || op.bytes_accessed < 0) {
-      bytes_per_sec = "";
-      bytes_per_cycle = "";
-    } else {
-      bytes_per_sec =
-          HumanReadableNumBytes(op.bytes_accessed / CyclesToSeconds(op.cycles));
+    if (op.cycles > 0 && op.bytes_accessed >= 0) {
+      bytes_per_sec = StrCat(
+          HumanReadableNumBytes(op.bytes_accessed / CyclesToSeconds(op.cycles)),
+          "/s");
+      double bpc = static_cast(op.bytes_accessed) / op.cycles;
       if (op.bytes_accessed > op.cycles) {
-        bytes_per_cycle = HumanReadableNumBytes(op.bytes_accessed / op.cycles);
+        bytes_per_cycle = StrCat(HumanReadableNumBytes(bpc), "/cycle");
       } else {
-        bytes_per_cycle =
-            Printf("%.3fB", static_cast(op.bytes_accessed) / op.cycles);
+        bytes_per_cycle = Printf("%.3fB/cycle", bpc);
       }
     }
 
@@ -59,14 +66,16 @@ string HumanReadableProfileBuilder::ToString() const {
 
     double nsecs = op.cycles / clock_rate_ghz_;
     Appendf(&s,
-            "%15lld cycles (%6.2f%%) :: %12.1f usec (%12.1f optimal) :: %18s "
-            ":: %18s :: %12s/s :: %12s/cycle :: %s\n",
+            "%15lld cycles (%6.2f%%) :: %12.1f usec %22s :: %18s "
+            ":: %18s :: %14s :: %16s :: %s\n",
             op.cycles, cycles_percent, CyclesToMicroseconds(op.cycles),
-            op.optimal_seconds * 1e6,
+            op.optimal_seconds < 0
+                ? ""
+                : Printf("(%12.1f optimal)", op.optimal_seconds * 1e6).c_str(),
             op.flop_count <= 0
-                ? ""
+                ? ""
                 : HumanReadableNumFlops(op.flop_count, nsecs).c_str(),
-            op.transcendental_count <= 0 ? ""
+            op.transcendental_count <= 0 ? ""
                                          : HumanReadableNumTranscendentalOps(
                                                op.transcendental_count, nsecs)
                                                .c_str(),
@@ -78,24 +87,26 @@ string HumanReadableProfileBuilder::ToString() const {
   int64 total_transcendentals = 0.;
   int64 total_bytes = 0;
   for (const auto& op : op_infos_) {
-    optimal_seconds_sum += op.optimal_seconds;
-    total_flops += op.flop_count;
-    total_transcendentals += op.transcendental_count;
-    total_bytes += op.bytes_accessed;
+    if (op.optimal_seconds > 0) {
+      optimal_seconds_sum += op.optimal_seconds;
+    }
+    total_flops += std::max(op.flop_count, int64{0});
+    total_transcendentals += std::max(op.transcendental_count, int64{0});
+    total_bytes += std::max(op.bytes_accessed, int64{0});
   }
 
   VLOG(1) << "Total floating point ops: " << total_flops;
 
-  append_op({"[total]", "[total]", /*category=*/"", total_cycles_, total_flops,
-             total_transcendentals, total_bytes, optimal_seconds_sum});
+  print_op({"[total]", "[total]", /*category=*/"", total_cycles_, total_flops,
+            total_transcendentals, total_bytes, optimal_seconds_sum});
 
-  // Sort ops in decreasing order of cycles.
+  // Sort ops in decreasing order of cycles, and print them.
   std::vector sorted_ops(op_infos_);
   std::sort(
       sorted_ops.begin(), sorted_ops.end(),
       [](const OpInfo& a, const OpInfo& b) { return a.cycles > b.cycles; });
   for (const auto& op : sorted_ops) {
-    append_op(op);
+    print_op(op);
   }
 
   if (total_cycles_ <= 0) {
@@ -109,8 +120,20 @@ string HumanReadableProfileBuilder::ToString() const {
       table.SetMetricName("microseconds above estimated optimum");
       table.SetEntryName("ops");
       table.SetShowCategoryTable();
+      table.SetShowAllEntries();
       float total_discrepancy_in_microseconds = 0.0f;
-      for (const auto& op : sorted_ops) {
+      for (const auto& op : op_infos_) {
+        // Skip ops with < 0 optimal seconds.  These are ops for which we don't
+        // know the optimal time.
+        if (op.optimal_seconds < 0) {
+          continue;
+        }
+        // Also skip ops with 0 actual cycles.  These ops were free; there's no
+        // need to clutter the "above estimated optimum" table with them,
+        // because they can't be optimized further.
+        if (op.cycles == 0) {
+          continue;
+        }
         MetricTableReport::Entry entry;
         entry.text = op.name;
         entry.short_text = op.short_name;
@@ -128,7 +151,14 @@ string HumanReadableProfileBuilder::ToString() const {
       table.SetMetricName("microseconds");
       table.SetEntryName("ops");
       table.SetShowCategoryTable();
-      for (const auto& op : sorted_ops) {
+      table.SetShowAllEntries();
+      for (const auto& op : op_infos_) {
+        // Skip ops with 0 optimal seconds and 0 actual cycles.  As in
+        // print_op(), these are uninteresting because they're expected to be
+        // free, and they were actually free.
+        if (op.cycles == 0 && op.optimal_seconds == 0) {
+          continue;
+        }
         MetricTableReport::Entry entry;
         entry.text = op.name;
         entry.short_text = op.short_name;
diff --git a/tensorflow/compiler/xla/service/human_readable_profile_builder.h b/tensorflow/compiler/xla/service/human_readable_profile_builder.h
index fb36d3a0d6532b..6f56c3aa82e9d1 100644
--- a/tensorflow/compiler/xla/service/human_readable_profile_builder.h
+++ b/tensorflow/compiler/xla/service/human_readable_profile_builder.h
@@ -41,7 +41,8 @@ class HumanReadableProfileBuilder {
   int64 total_cycles() const { return total_cycles_; }
 
   // Adds an operation to the profile.  If you don't know the number of
-  // floating-point ops or bytes touched by the op, pass -1 for that param.
+  // floating-point ops or bytes touched by the op, or if you don't know how
+  // fast it would run optimally, pass -1 for that param.
   void AddOp(tensorflow::StringPiece op_name,
              tensorflow::StringPiece short_name,
              tensorflow::StringPiece category, int64 cycles, int64 flop_count,
@@ -62,10 +63,10 @@ class HumanReadableProfileBuilder {
     string short_name;
     string category;
     int64 cycles;
-    int64 flop_count;
+    int64 flop_count;  // -1 if unknown
     int64 transcendental_count;
-    int64 bytes_accessed;
-    float optimal_seconds;
+    int64 bytes_accessed;   // -1 if unknown
+    float optimal_seconds;  // -1 if unknown
   };
 
   double CyclesToSeconds(int64 cycles) const {
diff --git a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc
index 7944b5132f3d11..3c9a01653c6720 100644
--- a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc
+++ b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc
@@ -84,8 +84,8 @@ Status ParseOneProfileOutputLine(
   string match_percentage = "\\d+\\.\\d\\d%";
   string match_cycles = "(\\d+) cycles +\\( *(" + match_percentage + ")\\)";
   string match_usecs = "([0-9.]+) usec";
-  string match_flops = "([^ ]+)";
-  string match_trops = "([^ ]+)";
+  string match_flops = "([^ ]*)";
+  string match_trops = "([^ ]*)";
   string match_bytes_per_sec = "([0-9.TGMKi]+)B/s";
   string match_bytes_per_cycle = "([0-9.TGMKi]+)B/cycle";
 

From d75c70bc2d6b9f7ae6d6b58f65cfe1b7aa21e84f Mon Sep 17 00:00:00 2001
From: Guangda Lai 
Date: Mon, 14 May 2018 14:15:14 -0700
Subject: [PATCH 0745/1691] Reenable virtual gpu test, and decrease the number
 of testing rounds.

PiperOrigin-RevId: 196565153
---
 tensorflow/python/BUILD                      | 1 -
 tensorflow/python/client/virtual_gpu_test.py | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index ea11b701ba16c9..d80457807029eb 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3969,7 +3969,6 @@ cuda_py_test(
         ":math_ops",
         "//tensorflow/core:protos_all_py",
     ],
-    tags = ["noguitar"],
 )
 
 py_test(
diff --git a/tensorflow/python/client/virtual_gpu_test.py b/tensorflow/python/client/virtual_gpu_test.py
index addf63474c9ba2..ae653e03dda6e0 100644
--- a/tensorflow/python/client/virtual_gpu_test.py
+++ b/tensorflow/python/client/virtual_gpu_test.py
@@ -236,7 +236,7 @@ def testLargeRandomGraph(self):
     with self.test_session(config=self._util.config) as sess:
       if not test.is_gpu_available(cuda_only=True):
         self.skipTest('No GPU available')
-      for _ in range(10):
+      for _ in range(5):
         if not self._util.TestRandomGraph(sess):
           return
 

From 9bde1e0f9edf643e6ec322c79e649b672a86d54e Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy 
Date: Mon, 14 May 2018 14:16:09 -0700
Subject: [PATCH 0746/1691] Disable flaky cudnn_recurrent test

PiperOrigin-RevId: 196565296
---
 tensorflow/python/keras/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD
index 295f23108b41da..bcdcf104583a31 100755
--- a/tensorflow/python/keras/BUILD
+++ b/tensorflow/python/keras/BUILD
@@ -490,6 +490,7 @@ cuda_py_test(
         "//tensorflow/python:client_testlib",
     ],
     shard_count = 2,
+    tags = ["no_oss"],
 )
 
 py_test(

From f0d49110fe413ef20ee358cd5f6e735de69cfdfc Mon Sep 17 00:00:00 2001
From: Yunxing Dai 
Date: Mon, 14 May 2018 14:18:11 -0700
Subject: [PATCH 0747/1691] ReverseDFS scheduler reverses the heuristics used
 in DFSScheduler.

Also fixes hlo_schedule_test to remove the expected order on unrelated operations.

PiperOrigin-RevId: 196565651
---
 .../compiler/xla/service/hlo_scheduling.cc    | 100 ++++++++++++++----
 .../compiler/xla/service/hlo_scheduling.h     |   7 ++
 2 files changed, 88 insertions(+), 19 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc
index 23ace5afeab30d..36ee7bcf84edef 100644
--- a/tensorflow/compiler/xla/service/hlo_scheduling.cc
+++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc
@@ -1,3 +1,5 @@
+
+
 /* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
@@ -62,7 +64,35 @@ StatusOr MinimumMemoryForSequence(
 namespace {
 
 // Class implementing a list scheduler of HLO instructions which produces a
-// sequence which minimizes memory usage.
+// sequence which minimizes memory usage by preferring to schedule the node that
+// frees bigger buffer and defines smaller outputs.
+//
+// Note that list scheduler is a greedy algorithm which cannot guarantee a
+// global optimal solution. As a counterexample, considering the following
+// graph:
+//
+//      +--> B ===> C -------+
+// A -> |                    |
+//      |                    v
+//      +--> D ---> F=======>G
+//      |           ^
+//      |           |
+//      +--> E -----+
+//
+//  --> : Buffer with size 1
+//  ==> : Buffer with size 2
+//
+// The list scheduler will always try to defer scheduling B in a greedy way
+// since its output buffer is bigger than input. The sequence it creates will
+// be:
+//   A D E F B C G
+// , which has a maximum memory usage of 5 (at one point, B and F will be alive
+// together).
+//
+// An optimal to shedule the previous graph will be:
+//   A B C D E F G
+// , which has a maximum memory usage of 4.
+//
 class ListScheduler {
  public:
   // Construct and return a memory-minimizing sequence of HLO instructions
@@ -366,10 +396,10 @@ StatusOr> CreateMemoryMinimizingSequence(
 
 }  // namespace
 
-StatusOr> DFSMemoryScheduler(
+StatusOr> DFSMemorySchedulerImpl(
     const HloComputation& computation,
     const TuplePointsToAnalysis& points_to_analysis,
-    const LogicalBuffer::SizeFunction& size_function) {
+    const LogicalBuffer::SizeFunction& size_function, bool reverse_heuristics) {
   // This ordering is based on DFS post-order, with a heuristic to decide which
   // operand to visit first.  The heuristic is based on 'extra_users', which is
   // simply users-1 for each instruction.  By subtracting 1, we're saying that
@@ -409,19 +439,20 @@ StatusOr> DFSMemoryScheduler(
     return Status::OK();
   });
   TF_RETURN_IF_ERROR(computation.AcceptWithOperandOrder(
-      &visitor, [&extra_users, &total_sizes](const HloInstruction* a,
-                                             const HloInstruction* b) {
-        if (extra_users[a] != extra_users[b]) {
-          return extra_users[a] > extra_users[b];
-        }
-        if (total_sizes[a] != total_sizes[b]) {
-          return total_sizes[a] > total_sizes[b];
-        }
-        return a->name() < b->name();
+      &visitor, [&extra_users, &total_sizes, reverse_heuristics](
+                    const HloInstruction* a, const HloInstruction* b) {
+        auto lhs = std::tuple(extra_users[a],
+                                                    total_sizes[a], b->name());
+        auto rhs = std::tuple(extra_users[b],
+                                                    total_sizes[b], a->name());
+
+        // Reverse heuristics. This helps some cases as a different starting
+        // point of gradient descent, see b/78906799 for more context.
+        return reverse_heuristics ? rhs > lhs : lhs > rhs;
       }));
   CHECK_EQ(sequence.size(), computation.instruction_count());
   return sequence;
-}
+}  // namespace xla
 
 StatusOr> ListMemoryScheduler(
     const HloComputation& computation,
@@ -439,6 +470,22 @@ StatusOr> PostOrderMemoryScheduler(
                                             post_order.end()};
 }
 
+StatusOr> DFSMemoryScheduler(
+    const HloComputation& computation,
+    const TuplePointsToAnalysis& points_to_analysis,
+    const LogicalBuffer::SizeFunction& size_function) {
+  return DFSMemorySchedulerImpl(computation, points_to_analysis, size_function,
+                                /*reverse_heuristics=*/false);
+}
+
+StatusOr> DFSMemorySchedulerReverse(
+    const HloComputation& computation,
+    const TuplePointsToAnalysis& points_to_analysis,
+    const LogicalBuffer::SizeFunction& size_function) {
+  return DFSMemorySchedulerImpl(computation, points_to_analysis, size_function,
+                                /*reverse_heuristics=*/true);
+}
+
 StatusOr> DefaultMemoryScheduler(
     const HloComputation& computation,
     const TuplePointsToAnalysis& points_to_analysis,
@@ -478,19 +525,34 @@ StatusOr> DefaultMemoryScheduler(
   VLOG(2) << "Min-memory post order sequence: "
           << HumanReadableNumBytes(post_order_memory);
 
-  if (post_order_memory < std::min(list_memory, dfs_memory)) {
-    VLOG(2) << "Chose min-memory post_order sequence: "
-            << HumanReadableNumBytes(post_order_memory);
-    return post_order_sequence;
+  TF_ASSIGN_OR_RETURN(std::vector reverse_dfs,
+                      DFSMemorySchedulerReverse(computation, points_to_analysis,
+                                                size_function));
+  TF_ASSIGN_OR_RETURN(
+      const int64 reverse_dfs_memory,
+      MinimumMemoryForComputation(computation, reverse_dfs, points_to_analysis,
+                                  size_function));
+  VLOG(2) << "Min-memory reverse_dfs sequence: "
+          << HumanReadableNumBytes(reverse_dfs_memory);
+  auto min_memory = std::min(
+      {dfs_memory, post_order_memory, reverse_dfs_memory, list_memory});
 
-  } else if (list_memory <= dfs_memory) {
+  if (min_memory == list_memory) {
     VLOG(2) << "Chose min-memory list sequence: "
             << HumanReadableNumBytes(list_memory);
     return list_sequence;
-  } else {
+  } else if (min_memory == dfs_memory) {
     VLOG(2) << "Chose min-memory dfs sequence: "
             << HumanReadableNumBytes(dfs_memory);
     return dfs_sequence;
+  } else if (min_memory == reverse_dfs_memory) {
+    VLOG(2) << "Chose min-memory reverse_dfs memory: "
+            << HumanReadableNumBytes(reverse_dfs_memory);
+    return reverse_dfs;
+  } else {
+    VLOG(2) << "Chose min-memory post_order sequence: "
+            << HumanReadableNumBytes(post_order_memory);
+    return post_order_sequence;
   }
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.h b/tensorflow/compiler/xla/service/hlo_scheduling.h
index fcb006f818fd1d..ef612414aa175c 100644
--- a/tensorflow/compiler/xla/service/hlo_scheduling.h
+++ b/tensorflow/compiler/xla/service/hlo_scheduling.h
@@ -61,6 +61,13 @@ StatusOr> PostOrderMemoryScheduler(
     const TuplePointsToAnalysis& points_to_analysis,
     const LogicalBuffer::SizeFunction& size_function);
 
+// DFS-order scheduler with reversed heuristics. This helps some cases (see
+// b/78906799).
+StatusOr> DFSMemorySchedulerReverse(
+    const HloComputation& computation,
+    const TuplePointsToAnalysis& points_to_analysis,
+    const LogicalBuffer::SizeFunction& size_function);
+
 // The default scheduling algorithm. Runs both the list scheduler
 // and the DFS scheduler, and chooses whichever returns a lower min-memory,
 // not accounting for fragmentation.

From 55bb032ebbae52d6c46ebf111903e8d2d615ba6a Mon Sep 17 00:00:00 2001
From: Akshay Agrawal 
Date: Mon, 14 May 2018 14:25:55 -0700
Subject: [PATCH 0748/1691] Update the eager programmer's guide to reflect the
 fact that "==" is not implemented in the natural way for the Tensor class.

PiperOrigin-RevId: 196566940
---
 tensorflow/docs_src/programmers_guide/eager.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/eager.md b/tensorflow/docs_src/programmers_guide/eager.md
index 5926e9f7f4cef9..9719858e88f786 100644
--- a/tensorflow/docs_src/programmers_guide/eager.md
+++ b/tensorflow/docs_src/programmers_guide/eager.md
@@ -120,11 +120,11 @@ def fizzbuzz(max_num):
   counter = tf.constant(0)
   for num in range(max_num):
     num = tf.constant(num)
-    if num % 3 == 0 and num % 5 == 0:
+    if int(num % 3) == 0 and int(num % 5) == 0:
       print('FizzBuzz')
-    elif num % 3 == 0:
+    elif int(num % 3) == 0:
       print('Fizz')
-    elif num % 5 == 0:
+    elif int(num % 5) == 0:
       print('Buzz')
     else:
       print(num)

From 1a300437cecfae36f7584694dac523851f1cd931 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Mon, 14 May 2018 14:32:03 -0700
Subject: [PATCH 0749/1691] Add score filtering to
 tf.image.non_max_suppression.

PiperOrigin-RevId: 196567928
---
 .../api_def_NonMaxSuppressionV3.pbtxt         |  64 ++++++
 .../api_def_NonMaxSuppressionV3.pbtxt         |   4 +
 .../core/kernels/non_max_suppression_op.cc    | 139 +++++++++----
 .../core/kernels/non_max_suppression_op.h     |   3 +-
 .../kernels/non_max_suppression_op_test.cc    | 191 ++++++++++++++++++
 tensorflow/core/ops/image_ops.cc              |  31 +++
 tensorflow/python/ops/image_ops_impl.py       |   9 +-
 .../tools/api/golden/tensorflow.image.pbtxt   |   2 +-
 8 files changed, 397 insertions(+), 46 deletions(-)
 create mode 100644 tensorflow/core/api_def/base_api/api_def_NonMaxSuppressionV3.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_NonMaxSuppressionV3.pbtxt

diff --git a/tensorflow/core/api_def/base_api/api_def_NonMaxSuppressionV3.pbtxt b/tensorflow/core/api_def/base_api/api_def_NonMaxSuppressionV3.pbtxt
new file mode 100644
index 00000000000000..25ec87eeca27c7
--- /dev/null
+++ b/tensorflow/core/api_def/base_api/api_def_NonMaxSuppressionV3.pbtxt
@@ -0,0 +1,64 @@
+op {
+  graph_op_name: "NonMaxSuppressionV3"
+  in_arg {
+    name: "boxes"
+    description: <