Merge pull request #13422 from NHZlX/add_dropout_simoid_trt

Add dropout sigmoid op converter for trt

paddle/fluid/inference/analysis/analyzer.cc

@@ -69,8 +69,9 @@ class DfgPassManagerImpl final : public DfgPassManager {
     if (FLAGS_IA_enable_tensorrt_subgraph_engine) {
       auto trt_teller = [&](const Node* node) {
         std::unordered_set<std::string> teller_set(
-            {"elementwise_add", "mul", "conv2d", "pool2d", "relu", "softmax",
-             "depthwise_conv2d", "batch_norm", "concat"});
+            {"mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid",
+             "depthwise_conv2d", "batch_norm", "concat", "tanh",
+             "elementwise_add", "dropout"});
         if (!node->IsFunction()) return false;
 
         const auto* func = static_cast<const Function*>(node);

paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc

@@ -153,11 +153,21 @@ CreatePaddlePredictor<TensorRTConfig, PaddleEngineKind::kAutoMixedTensorRT>(
 }  // namespace paddle
 
 USE_TRT_CONVERTER(elementwise_add_weight);
+USE_TRT_CONVERTER(elementwise_add_tensor);
+USE_TRT_CONVERTER(elementwise_sub_tensor);
+USE_TRT_CONVERTER(elementwise_div_tensor);
+USE_TRT_CONVERTER(elementwise_mul_tensor);
+USE_TRT_CONVERTER(elementwise_max_tensor);
+USE_TRT_CONVERTER(elementwise_min_tensor);
+USE_TRT_CONVERTER(elementwise_pow_tensor);
 USE_TRT_CONVERTER(mul);
 USE_TRT_CONVERTER(conv2d);
 USE_TRT_CONVERTER(relu);
+USE_TRT_CONVERTER(sigmoid);
+USE_TRT_CONVERTER(tanh);
 USE_TRT_CONVERTER(fc);
 USE_TRT_CONVERTER(pool2d);
 USE_TRT_CONVERTER(softmax);
 USE_TRT_CONVERTER(batch_norm);
 USE_TRT_CONVERTER(concat);
+USE_TRT_CONVERTER(dropout);

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

@@ -1,7 +1,7 @@
 # Add TRT tests
 nv_library(tensorrt_converter
   SRCS mul_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
+batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc
   DEPS tensorrt_engine operator scope framework_proto op_registry)
 
 nv_test(test_op_converter SRCS test_op_converter.cc DEPS
@@ -24,6 +24,8 @@ nv_test(test_trt_softmax_op SRCS test_softmax_op.cc softmax_op.cc
         DEPS ${FLUID_CORE_MODULES} tensorrt_engine softmax_op SERIAL)
 nv_test(test_trt_batch_norm_op SRCS test_batch_norm_op.cc batch_norm_op.cc
         DEPS ${FLUID_CORE_MODULES} tensorrt_engine batch_norm_op SERIAL)
-
 nv_test(test_trt_concat_op SRCS test_concat_op.cc concat_op.cc
         DEPS ${FLUID_CORE_MODULES} tensorrt_engine concat_op SERIAL)
+
+nv_test(test_trt_dropout_op SRCS test_dropout_op.cc dropout_op.cc
+        DEPS ${FLUID_CORE_MODULES} tensorrt_engine dropout_op SERIAL)

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

@@ -19,23 +19,31 @@ namespace paddle {
 namespace inference {
 namespace tensorrt {
 
-class ReluOpConverter : public OpConverter {
+class ActivationOpConverter : public OpConverter {
  public:
-  ReluOpConverter() {}
+  ActivationOpConverter() {}
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
     // Here the two nullptr looks strange, that's because the
     // framework::OpDesc's constructor is strange.
     framework::OpDesc op_desc(op, nullptr);
-    LOG(INFO) << "convert a fluid relu op to tensorrt activation layer whose "
-                 "type is Relu";
+    LOG(INFO)
+        << "convert a fluid Activation op to tensorrt activation layer whose "
+           "type is "
+        << op_type_;
     const nvinfer1::ITensor* input_tensor =
         engine_->GetITensor(op_desc.Input("X")[0]);
+
+    auto op_pair = ops.find(op_type_);
+    if (op_pair == ops.end()) {
+      PADDLE_THROW("Wrong activation op type!");
+    }
+
     nvinfer1::IActivationLayer* layer = TRT_ENGINE_ADD_LAYER(
         engine_, Activation, *const_cast<nvinfer1::ITensor*>(input_tensor),
-        nvinfer1::ActivationType::kRELU);
+        op_pair->second);
     auto output_name = op_desc.Output("Out")[0];
-    layer->setName(("relu (Output: " + output_name + ")").c_str());
+    layer->setName((op_type_ + " (Output: " + output_name + ")").c_str());
     layer->getOutput(0)->setName(output_name.c_str());
     engine_->SetITensor(output_name, layer->getOutput(0));
     if (test_mode) {  // the test framework can not determine which is the
@@ -43,6 +51,32 @@ class ReluOpConverter : public OpConverter {
       engine_->DeclareOutput(output_name);
     }
   }
+
+ protected:
+  std::string op_type_;
+  static const std::unordered_map<std::string, nvinfer1::ActivationType> ops;
+};
+
+const std::unordered_map<std::string, nvinfer1::ActivationType>
+    ActivationOpConverter::ops = {
+        {"relu", nvinfer1::ActivationType::kRELU},
+        {"sigmoid", nvinfer1::ActivationType::kSIGMOID},
+        {"tanh", nvinfer1::ActivationType::kTANH},
+};
+
+class ReluOpConverter : public ActivationOpConverter {
+ public:
+  ReluOpConverter() { op_type_ = "relu"; }
+};
+
+class SigmoidOpConverter : public ActivationOpConverter {
+ public:
+  SigmoidOpConverter() { op_type_ = "sigmoid"; }
+};
+
+class TanhOpConverter : public ActivationOpConverter {
+ public:
+  TanhOpConverter() { op_type_ = "tanh"; }
 };
 
 }  // namespace tensorrt
@@ -50,3 +84,5 @@ class ReluOpConverter : public OpConverter {
 }  // namespace paddle
 
 REGISTER_TRT_OP_CONVERTER(relu, ReluOpConverter);
+REGISTER_TRT_OP_CONVERTER(sigmoid, SigmoidOpConverter);
+REGISTER_TRT_OP_CONVERTER(tanh, TanhOpConverter);

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

@@ -0,0 +1,71 @@
+/* Copyright (c) 2018 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 "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+/*
+ * DropoutOp. This Layer doesn't has weights.
+ */
+class DropoutOpConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope, bool test_mode) override {
+    VLOG(4) << "convert a fluid dropout op to tensorrt dropout layer";
+    framework::OpDesc op_desc(op, nullptr);
+    // Declare inputs
+    auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
+    float dropout_prob = boost::get<float>(op_desc.GetAttr("dropout_prob"));
+
+    platform::CPUPlace cpu_place;
+    std::unique_ptr<framework::LoDTensor> weight_tensor(
+        new framework::LoDTensor());
+    weight_tensor->Resize(framework::make_ddim({1}));
+    auto* weight_data =
+        weight_tensor->mutable_data<float>(platform::CPUPlace());
+    weight_data[0] = 1 - dropout_prob;
+
+    TensorRTEngine::Weight scale_weights{
+        nvinfer1::DataType::kFLOAT, static_cast<void*>(weight_data),
+        weight_tensor->memory_size() / sizeof(float)};
+    TensorRTEngine::Weight shift_weights{nvinfer1::DataType::kFLOAT, nullptr,
+                                         0};
+    TensorRTEngine::Weight power_weights{nvinfer1::DataType::kFLOAT, nullptr,
+                                         0};
+
+    auto* layer = TRT_ENGINE_ADD_LAYER(
+        engine_, Scale, *const_cast<nvinfer1::ITensor*>(input1),
+        nvinfer1::ScaleMode::kUNIFORM, shift_weights.get(), scale_weights.get(),
+        power_weights.get());
+
+    engine_->weight_map[op_desc.Output("Out").front() + "_dropout"] =
+        std::move(weight_tensor);
+    auto output_name = op_desc.Output("Out")[0];
+    layer->setName(("dropout (Output: " + output_name + ")").c_str());
+    engine_->SetITensor(output_name, layer->getOutput(0));
+    if (test_mode) {
+      engine_->DeclareOutput(output_name);
+    }
+  }
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+USE_OP(dropout);
+REGISTER_TRT_OP_CONVERTER(dropout, DropoutOpConverter);

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

@@ -20,18 +20,18 @@ namespace paddle {
 namespace inference {
 namespace tensorrt {
 
-TEST(ReluOpConverter, main) {
+void test_activation(std::string act_type) {
   framework::Scope scope;
   std::unordered_set<std::string> parameters;
   TRTConvertValidation validator(10, parameters, scope, 1000);
-  validator.DeclInputVar("relu-X", nvinfer1::Dims2(10, 6));
-  validator.DeclOutputVar("relu-Out", nvinfer1::Dims2(10, 6));
+  validator.DeclInputVar("act-X", nvinfer1::Dims2(10, 6));
+  validator.DeclOutputVar("act-Out", nvinfer1::Dims2(10, 6));
 
   // Prepare Op description
   framework::OpDesc desc;
-  desc.SetType("relu");
-  desc.SetInput("X", {"relu-X"});
-  desc.SetOutput("Out", {"relu-Out"});
+  desc.SetType(act_type);
+  desc.SetInput("X", {"act-X"});
+  desc.SetOutput("Out", {"act-Out"});
 
   LOG(INFO) << "set OP";
   validator.SetOp(*desc.Proto());
@@ -40,8 +40,16 @@ TEST(ReluOpConverter, main) {
   validator.Execute(5);
 }
 
+TEST(ReluOpConverter, main) { test_activation("relu"); }
+
+TEST(SigmoidOpConverter, main) { test_activation("sigmoid"); }
+
+TEST(TanhOpConverter, main) { test_activation("tanh"); }
+
 }  // namespace tensorrt
 }  // namespace inference
 }  // namespace paddle
 
 USE_OP(relu);
+USE_OP(sigmoid);
+USE_OP(tanh);

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

@@ -0,0 +1,58 @@
+/* Copyright (c) 2018 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 <gtest/gtest.h>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+TEST(DropoutOpConverter, main) {
+  framework::Scope scope;
+  std::unordered_set<std::string> parameters;
+  TRTConvertValidation validator(8, parameters, scope, 1000);
+
+  std::vector<int> tensor_shape{8, 10};
+  validator.DeclInputVar("dropout-X", tensor_shape,
+                         nvinfer1::DimsCHW(10, 1, 1));
+  validator.DeclOutputVar("dropout-Out", nvinfer1::DimsCHW(10, 1, 1));
+  validator.DeclOutputVar("mask-Out", nvinfer1::DimsCHW(10, 1, 1));
+
+  // Prepare Op description
+  framework::OpDesc desc;
+  int is_test = 1;
+  float dropout_prob = 0.4;
+
+  desc.SetType("dropout");
+  desc.SetInput("X", {"dropout-X"});
+  desc.SetOutput("Mask", {"mask-Out"});
+  desc.SetOutput("Out", {"dropout-Out"});
+  desc.SetAttr("is_test", is_test);
+  desc.SetAttr("dropout_prob", dropout_prob);
+
+  LOG(INFO) << "set OP";
+  validator.SetOp(*desc.Proto());
+  LOG(INFO) << "execute";
+
+  std::unordered_set<std::string> neglected_output = {"mask-Out"};
+
+  validator.Execute(8, neglected_output);
+}
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+USE_OP(dropout);