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Added reshape, reshape2, squeeze and squeeze2 BF16/FP32 FWD/BWD kerne…
…ls (#34219) * test version of matmul_v2 * added matmul_v2 grad kernel * minor changes * minor changes * minor change for CI approval * CI fix * CI fix * added squeeze and squeeze2 kernels * CI fix * CI fix * CI fix * disabled tests when compiled with cuda * added setting format_tag by strides * added sigmoid BF16 FWD/BWD and gelu BF16 BWD * changes after review * Revert "added sigmoid BF16 FWD/BWD and gelu BF16 BWD" This reverts commit 6e3f767. * Revert "Merge branch 'matmul_v2_grad' into squeeze2_op" This reverts commit 06fcf67, reversing changes made to 6e3f767. * minor change * added reshape1/2 kernels * moved some functions into private block * CI fix * CI fix * CI fix
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| /* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. */ | ||
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| #include "paddle/fluid/operators/squeeze_op.h" | ||
| #include "paddle/fluid/platform/mkldnn_reuse.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| using paddle::framework::LoDTensor; | ||
| using platform::to_void_cast; | ||
| using platform::GetMKLDNNFormat; | ||
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| template <typename T> | ||
| class ReshapeMKLDNNKernel : public framework::OpKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& ctx) const override { | ||
| RunKernel(ctx); | ||
| } | ||
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| private: | ||
| void RunKernel(const framework::ExecutionContext& ctx) const { | ||
| const auto& dev_ctx = | ||
| ctx.template device_context<platform::MKLDNNDeviceContext>(); | ||
| const auto& onednn_engine = dev_ctx.GetEngine(); | ||
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| auto* x = ctx.Input<LoDTensor>("X"); | ||
| auto* xshape = ctx.Output<LoDTensor>("XShape"); | ||
| auto* out = ctx.Output<LoDTensor>("Out"); | ||
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| framework::DDim x_dims; | ||
| // if reshape or squeeze | ||
| if (ctx.Type().find("2") == std::string::npos) { | ||
| x_dims = x->dims(); | ||
| } else { | ||
| auto xshape_dims = xshape->dims(); | ||
| x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size()); | ||
| } | ||
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| auto x_vec_dims = framework::vectorize(x_dims); | ||
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| framework::DDim out_dims; | ||
| if (ctx.Type() == "squeeze") { | ||
| auto& axes = ctx.Attr<std::vector<int>>("axes"); | ||
| out_dims = GetOutputShape(axes, x_dims, true); | ||
| } else { | ||
| out_dims = out->dims(); | ||
| } | ||
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| if (ctx.Type().find("reshape") != std::string::npos) { | ||
| if (ctx.HasInput("Shape")) { | ||
| auto* shape_tensor = ctx.Input<framework::LoDTensor>("Shape"); | ||
| auto* shape_data = shape_tensor->data<int>(); | ||
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| auto shape = | ||
| std::vector<int>(shape_data, shape_data + shape_tensor->numel()); | ||
| out_dims = ValidateShape(shape, x_dims); | ||
| } | ||
| } | ||
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| mkldnn::memory::data_type x_type = framework::ToMKLDNNDataType(x->type()); | ||
| std::string key = | ||
| platform::CreateKey(dev_ctx, x_vec_dims, x->format(), x_type); | ||
| platform::ReorderMKLDNNHandler reorder_handler( | ||
| x_vec_dims, x->type(), x_type, dev_ctx, onednn_engine, key); | ||
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| auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory( | ||
| x->format(), platform::to_void_cast(x->data<T>())); | ||
| out->Resize(x_dims); // to match x numel, format is changed later | ||
| // reorder is done into a plain tag to allow usage with blocked formats | ||
| auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory( | ||
| out, getPlainFormatTag(x), ctx.GetPlace()); | ||
| auto reorder_p = reorder_handler.AcquireReorder(reorder_src_memory_p, | ||
| reorder_dst_memory_p); | ||
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| auto& astream = platform::MKLDNNDeviceContext::tls().get_stream(); | ||
| reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p); | ||
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| astream.wait(); | ||
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| out->Resize(out_dims); | ||
| out->set_layout(framework::DataLayout::kMKLDNN); | ||
| out->set_format(GetMKLDNNFormat(reorder_dst_memory_p->get_desc().reshape( | ||
| framework::vectorize(out_dims)))); | ||
| } | ||
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| protected: | ||
| static mkldnn::memory::format_tag getPlainFormatTag(const Tensor* tensor) { | ||
| auto tensor_dims_size = tensor->dims().size(); | ||
| PADDLE_ENFORCE_EQ( | ||
| tensor_dims_size <= 6 && tensor_dims_size >= 1, true, | ||
| platform::errors::InvalidArgument( | ||
| "Dims for squeeze_grad oneDNN op must be in range <1, 6>")); | ||
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| switch (tensor_dims_size) { | ||
| case 1: | ||
| return mkldnn::memory::format_tag::a; | ||
| case 2: | ||
| return mkldnn::memory::format_tag::ab; | ||
| case 3: | ||
| return mkldnn::memory::format_tag::abc; | ||
| case 4: | ||
| return mkldnn::memory::format_tag::abcd; | ||
| case 5: | ||
| return mkldnn::memory::format_tag::abcde; | ||
| default: | ||
| return mkldnn::memory::format_tag::abcdef; | ||
| } | ||
| } | ||
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| static framework::DDim ValidateShape(const std::vector<int>& shape, | ||
| const framework::DDim& in_dims) { | ||
| const int64_t in_size = framework::product(in_dims); | ||
| auto in_dims_vec = framework::vectorize(in_dims); | ||
| bool all_positive = std::all_of(in_dims_vec.cbegin(), in_dims_vec.cend(), | ||
| [](int64_t i) { return i > 0; }); | ||
| // only one dimension can be set to -1, whose size will be automatically | ||
| // infered | ||
| const int64_t unk_dim_val = -1; | ||
| const int64_t copy_dim_val = 0; | ||
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| std::vector<int64_t> output_shape(shape.size(), 0); | ||
| int64_t capacity = 1; | ||
| int unk_dim_idx = -1; | ||
| for (size_t i = 0; i < shape.size(); ++i) { | ||
| if (shape[i] == unk_dim_val) { | ||
| PADDLE_ENFORCE_EQ( | ||
| unk_dim_idx, -1, | ||
| platform::errors::InvalidArgument( | ||
| "Only one dimension value of 'shape' in ReshapeOp can " | ||
| "be -1. But received shape = [%s], shape[%d] is also -1.", | ||
| framework::make_ddim(shape), i)); | ||
| unk_dim_idx = i; | ||
| } else if (shape[i] == copy_dim_val) { | ||
| PADDLE_ENFORCE_LT( | ||
| static_cast<int>(i), in_dims.size(), | ||
| platform::errors::InvalidArgument( | ||
| "The index of 0 in `shape` must be less than " | ||
| "the input tensor X's dimensions. " | ||
| "But received shape = [%s], shape[%d] = 0, X's shape = [%s], " | ||
| "X's dimensions = %d.", | ||
| framework::make_ddim(shape), i, in_dims, in_dims.size())); | ||
| } else { | ||
| PADDLE_ENFORCE_GT( | ||
| shape[i], 0, | ||
| platform::errors::InvalidArgument( | ||
| "Each dimension value of 'shape' in ReshapeOp must not " | ||
| "be negative except one unknown dimension. " | ||
| "But received shape = [%s], shape[%d] = %d.", | ||
| framework::make_ddim(shape), i, shape[i])); | ||
| } | ||
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| capacity *= (shape[i] ? shape[i] : in_dims[i]); | ||
| output_shape[i] = | ||
| (shape[i] ? static_cast<int64_t>(shape[i]) : in_dims[i]); | ||
| } | ||
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| if (unk_dim_idx != -1) { | ||
| if (all_positive) { | ||
| // in_size < 0 and is un-determinate in compile time, skip the check, | ||
| // for example, in_dims = [-1, 8, 1, 1], shape = [-1, 3, 8], | ||
| // capacity = -24, in_size = -8, output_shape[0] = 0 | ||
| // the following check will fail. | ||
| output_shape[unk_dim_idx] = -in_size / capacity; | ||
| PADDLE_ENFORCE_EQ( | ||
| output_shape[unk_dim_idx] * capacity, -in_size, | ||
| platform::errors::InvalidArgument( | ||
| "The 'shape' attribute in ReshapeOp is invalid. " | ||
| "The input tensor X'size must be divisible by known " | ||
| "capacity of 'shape'. " | ||
| "But received X's shape = [%s], X's size = %d, " | ||
| "'shape' is [%s], known capacity of 'shape' is %d.", | ||
| in_dims, in_size, framework::make_ddim(shape), capacity)); | ||
| } else { | ||
| output_shape[unk_dim_idx] = -1; | ||
| } | ||
| } else { | ||
| if (all_positive) { | ||
| PADDLE_ENFORCE_EQ( | ||
| capacity, in_size, | ||
| platform::errors::InvalidArgument( | ||
| "The 'shape' in ReshapeOp is invalid. " | ||
| "The input tensor X'size must be equal to the capacity of " | ||
| "'shape'. " | ||
| "But received X's shape = [%s], X's size = %d, 'shape' is " | ||
| "[%s], the capacity of 'shape' is %d.", | ||
| in_dims, in_size, framework::make_ddim(shape), capacity)); | ||
| } | ||
| } | ||
| return framework::make_ddim(output_shape); | ||
| } | ||
| }; | ||
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| template <typename T> | ||
| class ReshapeGradMKLDNNKernel : public ReshapeMKLDNNKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& ctx) const override { | ||
| RunKernel(ctx); | ||
| } | ||
|
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| private: | ||
| void RunKernel(const framework::ExecutionContext& ctx) const { | ||
| const auto& dev_ctx = | ||
| ctx.template device_context<platform::MKLDNNDeviceContext>(); | ||
| const auto& onednn_engine = dev_ctx.GetEngine(); | ||
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| auto* dout = ctx.Input<LoDTensor>(framework::GradVarName("Out")); | ||
| auto* dx = ctx.Output<LoDTensor>(framework::GradVarName("X")); | ||
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| framework::DDim x_dims; | ||
| // if reshape or squeeze | ||
| if (ctx.Type().find("2") == std::string::npos) { | ||
| x_dims = dx->dims(); | ||
| } else { | ||
| auto xshape_dims = ctx.Input<framework::LoDTensor>("XShape")->dims(); | ||
| x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size()); | ||
| } | ||
| auto dout_vec_dims = framework::vectorize(dout->dims()); | ||
|
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| mkldnn::memory::data_type dout_type = | ||
| framework::ToMKLDNNDataType(dout->type()); | ||
| std::string key = | ||
| platform::CreateKey(dev_ctx, dout_vec_dims, this->getPlainFormatTag(dx), | ||
| dx->format(), dout_type); | ||
| platform::ReorderMKLDNNHandler reorder_handler( | ||
| dout_vec_dims, dout->type(), dout_type, dev_ctx, onednn_engine, key); | ||
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| auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory( | ||
| dout->format(), platform::to_void_cast(dout->data<T>())); | ||
| auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory( | ||
| dx, this->getPlainFormatTag(dout), ctx.GetPlace()); | ||
| auto reorder_p = reorder_handler.AcquireReorder(reorder_src_memory_p, | ||
| reorder_dst_memory_p); | ||
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| auto& astream = platform::MKLDNNDeviceContext::tls().get_stream(); | ||
| reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p); | ||
| astream.wait(); | ||
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| dx->Resize(x_dims); | ||
| dx->set_layout(framework::DataLayout::kMKLDNN); | ||
| dx->set_format(GetMKLDNNFormat(reorder_dst_memory_p->get_desc().reshape( | ||
| framework::vectorize(x_dims)))); | ||
| } | ||
| }; | ||
| } // namespace operators | ||
| } // namespace paddle | ||
|
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| namespace ops = paddle::operators; | ||
| REGISTER_OP_KERNEL(squeeze, MKLDNN, paddle::platform::CPUPlace, | ||
| ops::ReshapeMKLDNNKernel<float>, | ||
| ops::ReshapeMKLDNNKernel<paddle::platform::bfloat16>); | ||
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| REGISTER_OP_KERNEL(squeeze_grad, MKLDNN, paddle::platform::CPUPlace, | ||
| ops::ReshapeGradMKLDNNKernel<float>, | ||
| ops::ReshapeGradMKLDNNKernel<paddle::platform::bfloat16>); | ||
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| REGISTER_OP_KERNEL(squeeze2, MKLDNN, paddle::platform::CPUPlace, | ||
| ops::ReshapeMKLDNNKernel<float>, | ||
| ops::ReshapeMKLDNNKernel<paddle::platform::bfloat16>); | ||
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| REGISTER_OP_KERNEL(squeeze2_grad, MKLDNN, paddle::platform::CPUPlace, | ||
| ops::ReshapeGradMKLDNNKernel<float>, | ||
| ops::ReshapeGradMKLDNNKernel<paddle::platform::bfloat16>); | ||
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| REGISTER_OP_KERNEL(reshape, MKLDNN, paddle::platform::CPUPlace, | ||
| ops::ReshapeMKLDNNKernel<float>, | ||
| ops::ReshapeMKLDNNKernel<paddle::platform::bfloat16>); | ||
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| REGISTER_OP_KERNEL(reshape_grad, MKLDNN, paddle::platform::CPUPlace, | ||
| ops::ReshapeGradMKLDNNKernel<float>, | ||
| ops::ReshapeGradMKLDNNKernel<paddle::platform::bfloat16>); | ||
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| REGISTER_OP_KERNEL(reshape2, MKLDNN, paddle::platform::CPUPlace, | ||
| ops::ReshapeMKLDNNKernel<float>, | ||
| ops::ReshapeMKLDNNKernel<paddle::platform::bfloat16>); | ||
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| REGISTER_OP_KERNEL(reshape2_grad, MKLDNN, paddle::platform::CPUPlace, | ||
| ops::ReshapeGradMKLDNNKernel<float>, | ||
| ops::ReshapeGradMKLDNNKernel<paddle::platform::bfloat16>); |
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