[Paddle-TRT] support group_norm (PaddlePaddle#31040)

* add group norm plugin

* fix compile problems

* move concat axis check to trt op teller

* add nbDims for scale and bias nv dims

* add group norm unit test

* fix unittest

* add trt version restriction for group norm op teller

* fix unittest

paddle/fluid/inference/api/analysis_predictor.cc

@@ -1173,6 +1173,7 @@ USE_TRT_CONVERTER(conv2d_transpose);
 USE_TRT_CONVERTER(leaky_relu);
 USE_TRT_CONVERTER(shuffle_channel);
 USE_TRT_CONVERTER(swish);
+USE_TRT_CONVERTER(group_norm);
 USE_TRT_CONVERTER(instance_norm);
 USE_TRT_CONVERTER(layer_norm);
 USE_TRT_CONVERTER(gelu);

paddle/fluid/inference/tensorrt/convert/CMakeLists.txt

@@ -1,7 +1,7 @@
 # Add TRT tests
 nv_library(tensorrt_converter
            SRCS matmul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc
-                batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc
+                batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc group_norm_op.cc
                 pad_op.cc split_op.cc prelu_op.cc leaky_relu_op.cc gelu_op.cc layer_norm_op.cc multihead_matmul_op.cc
                 shuffle_channel_op.cc swish_op.cc instance_norm_op.cc stack_op.cc transpose_op.cc flatten_op.cc
                 emb_eltwise_layernorm.cc skip_layernorm.cc scale_op.cc slice_op.cc hard_sigmoid_op.cc hard_swish_op.cc clip_op.cc

paddle/fluid/inference/tensorrt/convert/concat_op.cc

@@ -34,7 +34,7 @@ class ConcatOpConverter : public OpConverter {
  public:
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
-    VLOG(3) << "convert a fluid mul op to tensorrt mul layer without bias";
+    VLOG(3) << "convert a paddle concat op to tensorrt concat layer";
 
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs
@@ -43,11 +43,6 @@ class ConcatOpConverter : public OpConverter {
       itensors.push_back(engine_->GetITensor(input_name));
     }
     int axis = BOOST_GET_CONST(int, op_desc.GetAttr("axis"));
-    PADDLE_ENFORCE_GT(axis, 0, platform::errors::InvalidArgument(
-                                   "The axis attr of Concat"
-                                   " op should be larger than 0 for trt. "
-                                   "But received %d.",
-                                   axis));
 
     auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Concatenation, itensors.data(),
                                        itensors.size());

paddle/fluid/inference/tensorrt/convert/group_norm_op.cc

@@ -0,0 +1,122 @@
+/* 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. */
+
+#include <vector>
+#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+
+namespace paddle {
+namespace framework {
+class Scope;
+namespace proto {
+class OpDesc;
+}  // namespace proto
+}  // namespace framework
+}  // namespace paddle
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+class GroupNormOpConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope, bool test_mode) override {
+    VLOG(3) << "convert a fluid group_norm op";
+
+    framework::OpDesc op_desc(op, nullptr);
+
+    auto* input_itensor = engine_->GetITensor(op_desc.Input("X").front());
+
+    int groups = BOOST_GET_CONST(int, op_desc.GetAttr("groups"));
+    float epsilon = BOOST_GET_CONST(float, op_desc.GetAttr("epsilon"));
+
+    std::string scale_name = op_desc.Input("Scale").front();
+    std::string bias_name = op_desc.Input("Bias").front();
+
+    // get the presistable var's data
+    auto get_persistable_data = [&](const std::string& var_name,
+                                    framework::DDim* dims) -> float* {
+      auto* temp_var = scope.FindVar(var_name);
+      auto* temp_tensor = temp_var->GetMutable<framework::LoDTensor>();
+      (*dims) = temp_tensor->dims();
+
+      auto* temp_data = engine_->GetWeightCPUData(var_name, temp_tensor, false);
+      return temp_data;
+    };
+
+    framework::DDim scale_dims;
+    framework::DDim bias_dims;
+    float* scale_data = get_persistable_data(scale_name, &scale_dims);
+    float* bias_data = get_persistable_data(bias_name, &bias_dims);
+
+    int64_t scale_numel = framework::product(scale_dims);
+    int64_t bias_numel = framework::product(bias_dims);
+
+    TensorRTEngine::Weight scale_weights{nvinfer1::DataType::kFLOAT,
+                                         static_cast<void*>(scale_data),
+                                         static_cast<size_t>(scale_numel)};
+    TensorRTEngine::Weight bias_weights{nvinfer1::DataType::kFLOAT,
+                                        static_cast<void*>(bias_data),
+                                        static_cast<size_t>(bias_numel)};
+
+    nvinfer1::Dims scale_nv_dims;
+    nvinfer1::Dims bias_nv_dims;
+    scale_nv_dims.nbDims = scale_dims.size();
+    bias_nv_dims.nbDims = bias_dims.size();
+    for (int i = 0; i < scale_dims.size(); i++) {
+      scale_nv_dims.d[i] = scale_dims.at(i);
+    }
+    for (int i = 0; i < bias_dims.size(); i++) {
+      bias_nv_dims.d[i] = bias_dims.at(i);
+    }
+
+    auto* scale_layer = TRT_ENGINE_ADD_LAYER(engine_, Constant, scale_nv_dims,
+                                             scale_weights.get());
+    auto* bias_layer = TRT_ENGINE_ADD_LAYER(engine_, Constant, bias_nv_dims,
+                                            bias_weights.get());
+
+    std::vector<nvinfer1::ITensor*> plugin_inputs;
+    plugin_inputs.emplace_back(input_itensor);
+    plugin_inputs.emplace_back(scale_layer->getOutput(0));
+    plugin_inputs.emplace_back(bias_layer->getOutput(0));
+
+    const std::vector<nvinfer1::PluginField> fields{
+        {"eps", &epsilon, nvinfer1::PluginFieldType::kFLOAT32, 1},
+        {"num_groups", &groups, nvinfer1::PluginFieldType::kINT32, 1},
+    };
+
+    nvinfer1::PluginFieldCollection* plugin_collections =
+        static_cast<nvinfer1::PluginFieldCollection*>(
+            malloc(sizeof(*plugin_collections) +
+                   fields.size() * sizeof(nvinfer1::PluginField)));
+    plugin_collections->nbFields = static_cast<int>(fields.size());
+    plugin_collections->fields = fields.data();
+
+    auto creator =
+        GetPluginRegistry()->getPluginCreator("GroupNormalizationPlugin", "1");
+    auto group_norm_plugin =
+        creator->createPlugin("GroupNormalizationPlugin", plugin_collections);
+    free(plugin_collections);
+
+    auto group_norm_plugin_layer = engine_->network()->addPluginV2(
+        plugin_inputs.data(), plugin_inputs.size(), *group_norm_plugin);
+
+    auto output_name = op_desc.Output("Y")[0];
+    RreplenishLayerAndOutput(group_norm_plugin_layer, "group_norm",
+                             {output_name}, test_mode);
+  }
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+REGISTER_TRT_OP_CONVERTER(group_norm, GroupNormOpConverter);

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -42,6 +42,9 @@ struct SimpleOpTypeSetTeller : public Teller {
     teller_set.insert("multihead_matmul");
     teller_set.insert("skip_layernorm");
     teller_set.insert("slice");
+#endif
+#if IS_TRT_VERSION_GE(7130)
+    teller_set.insert("group_norm");
 #endif
   }
 
@@ -150,6 +153,21 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
         }
       }
     }
+    if (op_type == "group_norm") {
+      bool has_attrs = (desc.HasAttr("epsilon") && desc.HasAttr("groups"));
+      if (has_attrs == false) return false;
+
+      auto registry = GetPluginRegistry();
+      if (registry == nullptr) return false;
+    }
+    if (op_type == "concat") {
+      if (!desc.HasAttr("axis")) {
+        return false;
+      } else {
+        int axis = BOOST_GET_CONST(int, desc.GetAttr("axis"));
+        if (axis <= 0) return false;
+      }
+    }
     if (op_type == "transpose2" || op_type == "transpose") {
       if (!desc.HasAttr("axis")) {
         return false;

python/paddle/fluid/tests/unittests/ir/inference/test_trt_group_norm_op.py

@@ -0,0 +1,78 @@
+# Copyright (c) 2020 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from inference_pass_test import InferencePassTest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.core import PassVersionChecker
+from paddle.fluid.core import AnalysisConfig
+
+
+class TRTGroupNormTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 512, 12, 12], dtype="float32")
+            relu_out = fluid.layers.relu(data)
+            relu6_out = fluid.layers.relu6(relu_out)
+            tanh_out = fluid.layers.tanh(relu6_out)
+            conv_out = fluid.layers.conv2d(
+                input=tanh_out,
+                num_filters=512,
+                filter_size=3,
+                groups=1,
+                padding=[1, 1],
+                bias_attr=False,
+                act=None)
+            out = self.append_group_norm(conv_out)
+
+        self.feeds = {
+            "data": np.random.random([1, 512, 12, 12]).astype("float32"),
+        }
+        self.enable_trt = True
+        self.trt_parameters = TRTGroupNormTest.TensorRTParam(
+            1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False)
+        self.dynamic_shape_params = TRTGroupNormTest.DynamicShapeParam({
+            'data': [1, 512, 12, 12]
+        }, {'data': [1, 512, 12, 12]}, {'data': [1, 512, 12, 12]}, False)
+        self.fetch_list = [out]
+
+    def append_group_norm(self, data):
+        param_attr = fluid.ParamAttr(
+            name='group_norm_scale',
+            initializer=fluid.initializer.Constant(value=1.0))
+        bias_attr = fluid.ParamAttr(
+            name='group_norm_bias',
+            initializer=fluid.initializer.Constant(value=0.0))
+        return fluid.layers.group_norm(
+            data,
+            groups=32,
+            epsilon=0.000009999999747378752,
+            param_attr=param_attr,
+            bias_attr=bias_attr)
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            use_gpu = True
+            self.check_output_with_option(use_gpu)
+            self.assertTrue(
+                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))
+
+
+if __name__ == "__main__":
+    unittest.main()