Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into optimize-sum-seq-pooling-op

paddle/fluid/inference/analysis/analyzer.cc

@@ -70,7 +70,7 @@ class DfgPassManagerImpl final : public DfgPassManager {
       auto trt_teller = [&](const Node* node) {
         std::unordered_set<std::string> teller_set(
             {"mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid",
-             "depthwise_conv2d", "batch_norm", "concat", "tanh",
+             "depthwise_conv2d", "batch_norm", "concat", "tanh", "pad",
              "elementwise_add", "dropout"});
         if (!node->IsFunction()) return false;
 

paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc

@@ -185,3 +185,4 @@ USE_TRT_CONVERTER(softmax);
 USE_TRT_CONVERTER(batch_norm);
 USE_TRT_CONVERTER(concat);
 USE_TRT_CONVERTER(dropout);
+USE_TRT_CONVERTER(pad);

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

 # 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 dropout_op.cc
+batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc pad_op.cc
   DEPS tensorrt_engine operator scope framework_proto op_registry)
 
 nv_test(test_op_converter SRCS test_op_converter.cc DEPS
@@ -26,6 +26,8 @@ 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)
+
+nv_test(test_trt_pad_op SRCS test_pad_op.cc pad_op.cc
+        DEPS ${FLUID_CORE_MODULES} tensorrt_engine pad_op SERIAL)

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

+/* 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 {
+
+/*
+ * PadOp.
+ */
+class PadOpConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope, bool test_mode) override {
+    VLOG(4) << "convert a fluid transpose op to tensorrt tranpose layer";
+
+    framework::OpDesc op_desc(op, nullptr);
+    // Declare inputs
+    auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
+
+    const std::vector<int> paddings =
+        boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
+    const float pad_value = boost::get<float>(op_desc.GetAttr("pad_value"));
+
+    nvinfer1::Dims input_shape = input->getDimensions();
+    int nbDims = input_shape.nbDims;
+    int pad_size = static_cast<int>(paddings.size());
+    PADDLE_ENFORCE_GE(nbDims, 2);
+    PADDLE_ENFORCE_EQ((nbDims + 1) * 2, pad_size);
+    PADDLE_ENFORCE(pad_value == 0.0, "The pad layer of TRT only support zero.");
+
+    nvinfer1::DimsHW pre_pad(paddings[pad_size - 4], paddings[pad_size - 2]);
+    nvinfer1::DimsHW post_pad(paddings[pad_size - 3], paddings[pad_size - 1]);
+
+    auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Padding,
+                                       *const_cast<nvinfer1::ITensor*>(input),
+                                       pre_pad, post_pad);
+
+    PADDLE_ENFORCE(layer != nullptr);
+    auto output_name = op_desc.Output("Out")[0];
+    engine_->SetITensor(output_name, layer->getOutput(0));
+    layer->setName(("scale (Output: " + output_name + ")").c_str());
+    layer->getOutput(0)->setName(output_name.c_str());
+    if (test_mode) {  // the test framework can not determine which is the
+                      // output, so place the declaration inside.
+      engine_->DeclareOutput(output_name);
+    }
+  }
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+REGISTER_TRT_OP_CONVERTER(pad, PadOpConverter);

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

+/* 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(PadConverter, main) {
+  framework::Scope scope;
+  std::unordered_set<std::string> parameters;
+  TRTConvertValidation validator(10, parameters, scope, 1000);
+  validator.DeclInputVar("pad-X", nvinfer1::Dims3(3, 2, 2));
+  validator.DeclOutputVar("pad-Out", nvinfer1::Dims3(3, 3, 5));
+
+  // Prepare Op description
+  framework::OpDesc desc;
+  desc.SetType("pad");
+  desc.SetInput("X", {"pad-X"});
+  desc.SetOutput("Out", {"pad-Out"});
+
+  std::vector<int> paddings = {0, 0, 0, 0, 0, 1, 1, 2};
+  float pad_value = 0.0;
+  desc.SetAttr("paddings", paddings);
+  desc.SetAttr("pad_value", pad_value);
+
+  LOG(INFO) << "set OP";
+  validator.SetOp(*desc.Proto());
+  LOG(INFO) << "execute";
+
+  validator.Execute(2);
+}
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+USE_OP(pad);

paddle/fluid/operators/math/CMakeLists.txt

@@ -53,7 +53,7 @@ cc_library(blas SRCS blas.cc DEPS cblas framework_proto device_context)
 math_library(math_function DEPS blas)
 math_library(maxouting)
 math_library(pooling)
-math_library(selected_rows_functor DEPS selected_rows math_function)
+math_library(selected_rows_functor DEPS selected_rows math_function blas)
 math_library(sequence2batch)
 math_library(sequence_padding)
 math_library(sequence_pooling DEPS math_function)

paddle/fluid/operators/math/selected_rows_functor.cc

@@ -15,7 +15,6 @@ limitations under the License. */
 #include <set>
 #include <unordered_map>
 
-#include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/operators/math/selected_rows_functor.h"
 
 namespace paddle {
@@ -150,6 +149,45 @@ template struct SelectedRowsAddTo<platform::CPUDeviceContext, double>;
 template struct SelectedRowsAddTo<platform::CPUDeviceContext, int>;
 template struct SelectedRowsAddTo<platform::CPUDeviceContext, int64_t>;
 
+template <typename T>
+struct SelectedRowsSumTo<platform::CPUDeviceContext, T> {
+  void operator()(const platform::CPUDeviceContext& context,
+                  const std::vector<framework::SelectedRows*>& input1,
+                  const std::vector<int64_t>& input2_offsets,
+                  framework::SelectedRows* input2) {
+    // Ensure all selected rows have the same height
+    size_t size = 0u;
+    for (auto iter = input1.begin(); iter != input1.end(); ++iter) {
+      auto& in_rows = (*iter)->rows();
+      size += in_rows.end() - in_rows.begin();
+      auto in1_height = (*iter)->height();
+      PADDLE_ENFORCE_EQ(in1_height, input2->height());
+    }
+    // concat rows
+    std::vector<int64_t> in2_rows;
+    in2_rows.reserve(in2_rows.size() + size);
+    for (auto iter = input1.begin(); iter != input1.end(); ++iter) {
+      const framework::Vector<int64_t>& in_rows = (*iter)->rows();
+      in2_rows.insert(in2_rows.end(), in_rows.begin(), in_rows.end());
+    }
+    input2->set_rows(in2_rows);
+
+    auto* in2_value = input2->mutable_value();
+    auto* in2_data = in2_value->data<T>();
+    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
+    size_t offset = 0u;
+    for (size_t i = 0u; i != input1.size(); ++i) {
+      auto& in_value = input1[i]->value();
+      const auto* in_data = in_value.data<T>();
+      offset += input2_offsets[i];
+      blas.VCOPY(in_value.numel(), in_data, in2_data + offset);
+    }
+  }
+};
+
+template struct SelectedRowsSumTo<platform::CPUDeviceContext, float>;
+template struct SelectedRowsSumTo<platform::CPUDeviceContext, double>;
+
 template <typename T>
 struct SelectedRowsAddToTensor<platform::CPUDeviceContext, T> {
   void operator()(const platform::CPUDeviceContext& context,
@@ -260,8 +298,6 @@ struct MergeAdd<platform::CPUDeviceContext, T> {
   }
 };
 
-template struct MergeAdd<platform::CPUDeviceContext, float>;
-template struct MergeAdd<platform::CPUDeviceContext, double>;
 template struct MergeAdd<platform::CPUDeviceContext, int>;
 template struct MergeAdd<platform::CPUDeviceContext, int64_t>;
 

paddle/fluid/operators/math/selected_rows_functor.h

@@ -12,10 +12,13 @@ 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. */
 #pragma once
+
 #include <vector>
 
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/platform/device_context.h"
 
 #define INLINE_FOR2(sizei, sizej)     \
@@ -51,6 +54,15 @@ struct SelectedRowsAddTo {
                   const int64_t input2_offset, framework::SelectedRows* input2);
 };
 
+// input2 = [all input in input1] + input2
+template <typename DeviceContext, typename T>
+struct SelectedRowsSumTo {
+  void operator()(const DeviceContext& context,
+                  const std::vector<framework::SelectedRows*>& input1,
+                  const std::vector<int64_t>& input2_offsets,
+                  framework::SelectedRows* input2);
+};
+
 // input2 = input1 + input2
 template <typename DeviceContext, typename T>
 struct SelectedRowsAddToTensor {
@@ -75,6 +87,108 @@ struct MergeAdd {
                   framework::SelectedRows* output);
 };
 
+template <>
+struct MergeAdd<platform::CPUDeviceContext, float> {
+  framework::SelectedRows operator()(const platform::CPUDeviceContext& context,
+                                     const framework::SelectedRows& input) {
+    framework::SelectedRows out;
+    (*this)(context, input, &out);
+    return out;
+  }
+
+  void operator()(const platform::CPUDeviceContext& context,
+                  const framework::SelectedRows& input,
+                  framework::SelectedRows* output) {
+    framework::SelectedRows& out = *output;
+    auto input_rows = input.rows();
+    std::vector<int64_t> merge_rows;
+    merge_rows.reserve(input_rows.size());
+    std::unordered_map<int64_t, size_t> rows_pos_map;
+    rows_pos_map.reserve(input_rows.size());
+    size_t idx = 0u;
+    for (std::vector<int64_t>::iterator iter = input_rows.begin();
+         iter != input_rows.end(); ++iter) {
+      if (rows_pos_map.find(*iter) == rows_pos_map.end()) {
+        rows_pos_map[*iter] = idx++;
+        merge_rows.emplace_back(*iter);
+      }
+    }
+
+    auto input_width = input.value().dims()[1];
+    out.set_rows(merge_rows);
+    out.set_height(input.height());
+    out.mutable_value()->mutable_data<float>(
+        framework::make_ddim(
+            {static_cast<int64_t>(merge_rows.size()), input_width}),
+        context.GetPlace());
+
+    math::SetConstant<platform::CPUDeviceContext, float> constant_functor;
+    constant_functor(context, out.mutable_value(), 0.0);
+
+    auto* out_data = out.mutable_value()->data<float>();
+    auto* input_data = input.value().data<float>();
+
+    auto blas = GetBlas<platform::CPUDeviceContext, float>(context);
+    for (size_t i = 0; i < input_rows.size(); i++) {
+      size_t out_i = rows_pos_map[input_rows[i]];
+      float* y = out_data + out_i * input_width;
+      const float* x = input_data + i * input_width;
+      blas.AXPY(input_width, 1., x, y);
+    }
+  }
+};
+
+template <>
+struct MergeAdd<platform::CPUDeviceContext, double> {
+  framework::SelectedRows operator()(const platform::CPUDeviceContext& context,
+                                     const framework::SelectedRows& input) {
+    framework::SelectedRows out;
+    (*this)(context, input, &out);
+    return out;
+  }
+
+  void operator()(const platform::CPUDeviceContext& context,
+                  const framework::SelectedRows& input,
+                  framework::SelectedRows* output) {
+    framework::SelectedRows& out = *output;
+    auto input_rows = input.rows();
+    std::vector<int64_t> merge_rows;
+    merge_rows.reserve(input_rows.size());
+    std::unordered_map<int64_t, size_t> rows_pos_map;
+    rows_pos_map.reserve(input_rows.size());
+    size_t idx = 0u;
+    for (std::vector<int64_t>::iterator iter = input_rows.begin();
+         iter != input_rows.end(); ++iter) {
+      if (rows_pos_map.find(*iter) == rows_pos_map.end()) {
+        rows_pos_map[*iter] = idx++;
+        merge_rows.emplace_back(*iter);
+      }
+    }
+
+    auto input_width = input.value().dims()[1];
+    out.set_rows(merge_rows);
+    out.set_height(input.height());
+    out.mutable_value()->mutable_data<double>(
+        framework::make_ddim(
+            {static_cast<int64_t>(merge_rows.size()), input_width}),
+        context.GetPlace());
+
+    math::SetConstant<platform::CPUDeviceContext, double> constant_functor;
+    constant_functor(context, out.mutable_value(), 0.0);
+
+    auto* out_data = out.mutable_value()->data<double>();
+    auto* input_data = input.value().data<double>();
+
+    auto blas = GetBlas<platform::CPUDeviceContext, double>(context);
+    for (size_t i = 0; i < input_rows.size(); i++) {
+      size_t out_i = rows_pos_map[input_rows[i]];
+      double* y = out_data + out_i * input_width;
+      const double* x = input_data + i * input_width;
+      blas.AXPY(input_width, 1., x, y);
+    }
+  }
+};
+
 template <typename DeviceContext, typename T>
 struct Add {
   framework::SelectedRows operator()(const DeviceContext& context,

paddle/fluid/operators/math/selected_rows_functor_test.cc

@@ -220,17 +220,98 @@ TEST(selected_rows_functor, cpu_add_to) {
   EXPECT_EQ(tensor1_data[9 * row_numel + 6], 5.0);
 }
 
-TEST(selected_rows_functor, cpu_merge_add) {
+TEST(selected_rows_functor, cpu_merge_add_float) {
   paddle::platform::CPUPlace cpu_place;
   paddle::platform::CPUDeviceContext ctx(cpu_place);
   paddle::operators::math::SetConstant<paddle::platform::CPUDeviceContext,
                                        float>
-      set_const;
+      functor;
+  int64_t height = 10;
+  int64_t row_numel = 10;
+
+  std::vector<int64_t> rows{0, 4, 4, 7};
+  std::unique_ptr<paddle::framework::SelectedRows> selected_rows{
+      new paddle::framework::SelectedRows(rows, height)};
+  auto* in_value = selected_rows->mutable_value();
+  in_value->mutable_data<float>(
+      paddle::framework::make_ddim(
+          {static_cast<int64_t>(rows.size()), row_numel}),
+      cpu_place);
+  functor(ctx, in_value, 1.0);
+
+  std::unique_ptr<paddle::framework::SelectedRows> output{
+      new paddle::framework::SelectedRows()};
+
+  paddle::operators::math::scatter::MergeAdd<paddle::platform::CPUDeviceContext,
+                                             float>
+      merge_add_functor;
+  merge_add_functor(ctx, *selected_rows, output.get());
+
+  auto out_height = output->height();
+  EXPECT_EQ(out_height, height);
+
+  auto& out_rows = output->rows();
+  EXPECT_EQ(out_rows[0], 0);
+  EXPECT_EQ(out_rows[1], 4);
+  EXPECT_EQ(out_rows[2], 7);
+
+  auto* out_data = output->value().data<float>();
 
+  EXPECT_EQ(out_data[0 * row_numel], 1.0);
+  EXPECT_EQ(out_data[1 * row_numel], 2.0);
+  EXPECT_EQ(out_data[2 * row_numel], 1.0);
+}
+
+TEST(selected_rows_functor, cpu_merge_add_int) {
+  paddle::platform::CPUPlace cpu_place;
+  paddle::platform::CPUDeviceContext ctx(cpu_place);
+  paddle::operators::math::SetConstant<paddle::platform::CPUDeviceContext, int>
+      functor;
   int64_t height = 10;
-  int64_t row_numel = 8;
+  int64_t row_numel = 10;
 
-  std::vector<int64_t> rows1{5, 2, 5, 3, 5};
+  std::vector<int64_t> rows{0, 4, 4, 7};
+  std::unique_ptr<paddle::framework::SelectedRows> selected_rows{
+      new paddle::framework::SelectedRows(rows, height)};
+  auto* in_value = selected_rows->mutable_value();
+  in_value->mutable_data<int>(
+      paddle::framework::make_ddim(
+          {static_cast<int64_t>(rows.size()), row_numel}),
+      cpu_place);
+  functor(ctx, in_value, 1);
+
+  std::unique_ptr<paddle::framework::SelectedRows> output{
+      new paddle::framework::SelectedRows()};
+
+  paddle::operators::math::scatter::MergeAdd<paddle::platform::CPUDeviceContext,
+                                             int>
+      merge_add_functor;
+  merge_add_functor(ctx, *selected_rows, output.get());
+
+  auto out_height = output->height();
+  EXPECT_EQ(out_height, height);
+
+  auto& out_rows = output->rows();
+  EXPECT_EQ(out_rows[0], 0);
+  EXPECT_EQ(out_rows[1], 4);
+  EXPECT_EQ(out_rows[2], 7);
+
+  auto* out_data = output->value().data<int>();
+
+  EXPECT_EQ(out_data[0 * row_numel], 1);
+  EXPECT_EQ(out_data[1 * row_numel], 2);
+  EXPECT_EQ(out_data[2 * row_numel], 1);
+}
+
+TEST(selected_rows_functor, cpu_sum_to) {
+  paddle::platform::CPUPlace cpu_place;
+  paddle::platform::CPUDeviceContext ctx(cpu_place);
+  paddle::operators::math::SetConstant<paddle::platform::CPUDeviceContext,
+                                       float>
+      functor;
+  int64_t height = 10;
+  int64_t row_numel = 10;
+  std::vector<int64_t> rows1{0, 4, 7};
   std::unique_ptr<paddle::framework::SelectedRows> selected_rows1{
       new paddle::framework::SelectedRows(rows1, height)};
   auto* in1_value = selected_rows1->mutable_value();
@@ -238,9 +319,9 @@ TEST(selected_rows_functor, cpu_merge_add) {
       paddle::framework::make_ddim(
           {static_cast<int64_t>(rows1.size()), row_numel}),
       cpu_place);
-  set_const(ctx, in1_value, 1.0);
 
-  std::vector<int64_t> rows2{2, 5, 3, 5, 3};
+  functor(ctx, in1_value, 1.0);
+  std::vector<int64_t> rows2{0, 5, 7, 9};
   std::unique_ptr<paddle::framework::SelectedRows> selected_rows2{
       new paddle::framework::SelectedRows(rows2, height)};
   auto* in2_value = selected_rows2->mutable_value();
@@ -248,33 +329,67 @@ TEST(selected_rows_functor, cpu_merge_add) {
       paddle::framework::make_ddim(
           {static_cast<int64_t>(rows2.size()), row_numel}),
       cpu_place);
-  set_const(ctx, in2_value, 1.0);
 
+  functor(ctx, in2_value, 2.0);
   std::unique_ptr<paddle::framework::SelectedRows> output{
       new paddle::framework::SelectedRows()};
   output->set_height(height);
-  paddle::operators::math::scatter::MergeAdd<paddle::platform::CPUDeviceContext,
+  auto* out_value = output->mutable_value();
+  // simplely concat two SelectedRows
+  out_value->mutable_data<float>(paddle::framework::make_ddim({7, 10}),
+                                 cpu_place);
+  paddle::operators::math::SelectedRowsSumTo<paddle::platform::CPUDeviceContext,
                                              float>
-      merge_add_functor;
-
-  std::vector<const paddle::framework::SelectedRows*> inputs;
-  inputs.push_back(selected_rows1.get());
-  inputs.push_back(selected_rows2.get());
-  merge_add_functor(ctx, inputs, output.get());
-
-  EXPECT_EQ(output->height(), height);
-  EXPECT_EQ(output->value().dims(),
-            paddle::framework::make_ddim({3, row_numel}));
-
-  std::vector<int64_t> ret_rows{2, 3, 5};
-  EXPECT_EQ(output->rows(), ret_rows);
-
+      sum_to_functor;
+  sum_to_functor(ctx, std::vector<paddle::framework::SelectedRows*>(
+                          {selected_rows1.get(), selected_rows2.get()}),
+                 std::vector<int64_t>({0, in1_value->numel()}), output.get());
+  auto out_height = output->height();
+  EXPECT_EQ(out_height, height);
+  auto& out_rows = output->rows();
+  // input1 rows
+  EXPECT_EQ(out_rows[0], 0);
+  EXPECT_EQ(out_rows[1], 4);
+  EXPECT_EQ(out_rows[2], 7);
+  // input2 rows
+  EXPECT_EQ(out_rows[3], 0);
+  EXPECT_EQ(out_rows[4], 5);
+  EXPECT_EQ(out_rows[5], 7);
+  EXPECT_EQ(out_rows[6], 9);
   auto* out_data = output->value().data<float>();
-  for (size_t i = 0; i < ret_rows.size(); ++i) {
-    for (size_t j = 0; j < row_numel; ++j) {
-      EXPECT_EQ(out_data[i * row_numel + j], ret_rows[i]);
-      std::cout << out_data[i * row_numel + j] << " ";
-    }
-    std::cout << "\n";
-  }
+  // input1 value
+  EXPECT_EQ(out_data[0 * row_numel + 0], 1.0);
+  EXPECT_EQ(out_data[0 * row_numel + 8], 1.0);
+  EXPECT_EQ(out_data[1 * row_numel + 1], 1.0);
+  EXPECT_EQ(out_data[2 * row_numel + 6], 1.0);
+  // input2 value
+  EXPECT_EQ(out_data[3 * row_numel + 3], 2.0);
+  EXPECT_EQ(out_data[3 * row_numel + 8], 2.0);
+  EXPECT_EQ(out_data[4 * row_numel + 4], 2.0);
+  EXPECT_EQ(out_data[5 * row_numel + 7], 2.0);
+  EXPECT_EQ(out_data[6 * row_numel + 9], 2.0);
+  std::unique_ptr<paddle::framework::Tensor> tensor1{
+      new paddle::framework::Tensor()};
+  tensor1->mutable_data<float>(
+      paddle::framework::make_ddim({height, row_numel}), cpu_place);
+  functor(ctx, tensor1.get(), 3.0);
+  paddle::operators::math::SelectedRowsAddToTensor<
+      paddle::platform::CPUDeviceContext, float>
+      add_to_tensor_functor;
+  add_to_tensor_functor(ctx, *output, tensor1.get());
+  auto* tensor1_data = tensor1->data<float>();
+  // row0: 1.0 + 2.0 + 3.0
+  EXPECT_EQ(tensor1_data[0 * row_numel + 0], 6.0);
+  // row1: 3.0
+  EXPECT_EQ(tensor1_data[1 * row_numel + 1], 3.0);
+  // row4 : 1.0 + 3.0
+  EXPECT_EQ(tensor1_data[4 * row_numel + 6], 4.0);
+  // row5: 2.0 + 3.0
+  EXPECT_EQ(tensor1_data[5 * row_numel + 7], 5.0);
+  // row6: 3.0
+  EXPECT_EQ(tensor1_data[6 * row_numel + 1], 3.0);
+  // row7: 1.0 + 2.0 + 3.0
+  EXPECT_EQ(tensor1_data[7 * row_numel + 3], 6.0);
+  // row9: 2.0 + 3.0
+  EXPECT_EQ(tensor1_data[9 * row_numel + 6], 5.0);
 }

paddle/fluid/pybind/pybind.cc

@@ -667,16 +667,17 @@ All parameter, weight, gradient are variables in Paddle.
     ExecutionStrategy allows the user to more preciously control how to run
     the program in ParallelExecutor by setting the property.
 
-    The available properties include:
-        use_cuda (bool): Whether to use CUDA or not. Default True.
-        num_threads (int): The number of threads that used to run the
-            operators in ParallelExecutor. If it is not set, it will be
-            set in ParallelExecutor according to the device count.
-            Default 0.
-        allow_op_delay (bool): Whether to delay the communication operators
-            to run. Default False.
-        num_iteration_per_drop_scope (int): how many iterations between
-            the two dropping local scopes. Default 100.
+    Examples:
+        .. code-block:: python
+
+          exec_strategy = fluid.ExecutionStrategy()
+          exec_strategy.num_threads = 4
+
+          train_exe = fluid.ParallelExecutor(use_cuda=True,
+                                             loss_name=loss.name,
+                                             exec_strategy=exec_strategy)
+
+          train_loss, = train_exe.run([loss.name], feed=feed_dict)
 
         )DOC");
 
@@ -686,19 +687,34 @@ All parameter, weight, gradient are variables in Paddle.
           [](const ExecutionStrategy &self) { return self.num_threads_; },
           [](ExecutionStrategy &self, size_t num_threads) {
             self.num_threads_ = num_threads;
-          })
+          },
+          R"DOC(The type is INT, num_threads represents the size of thread pool that
+            used to run the operators of the current program in ParallelExecutor.
+            If :math:`num\_threads=1`, all the operators will execute one by one,
+            but the order maybe difference between iterations.
+            If it is not set, it will be set in ParallelExecutor according to the
+            device type and device count, for GPU, :math:`num\_threads=device\_count*4`, for CPU,
+            :math:`num\_threads=CPU\_NUM*4`, the explanation of:math:`CPU\_NUM` is in ParallelExecutor.
+            if it is not set, ParallelExecutor will get the cpu count by calling
+            `multiprocessing.cpu_count()`. Default 0.)DOC")
       .def_property(
           "use_cuda",
           [](const ExecutionStrategy &self) { return self.use_cuda_; },
           [](ExecutionStrategy &self, bool use_cuda) {
             self.use_cuda_ = use_cuda;
-          })
+          })  // FIXME(chengduo): Doesn't add doc for 'use_cuda', use_cuda may
+      // make user confuse, because ParallelExecutor has a parameter named
+      // 'use_cuda' too, in current implementation, ParallelExecutor's
+      // 'use_cuda' will rewrite ExecutionStrategy's 'use_cuda'.
       .def_property(
           "allow_op_delay",
           [](const ExecutionStrategy &self) { return self.allow_op_delay_; },
           [](ExecutionStrategy &self, bool allow_op_delay) {
             self.allow_op_delay_ = allow_op_delay;
-          })
+          },
+          R"DOC(The type is BOOL, allow_op_delay represents whether to delay the
+                communication operators to run, it may make the execution faster.
+                Note that in some models, allow_op_delay may cause program hang. Default False.)DOC")
       .def_property(
           "num_iteration_per_drop_scope",
           [](const ExecutionStrategy &self) {
@@ -706,7 +722,19 @@ All parameter, weight, gradient are variables in Paddle.
           },
           [](ExecutionStrategy &self, size_t num_iteration_per_drop_scope) {
             self.num_iteration_per_drop_scope_ = num_iteration_per_drop_scope;
-          });
+          },
+          R"DOC(The type is INT, num_iteration_per_drop_scope indicates how
+                many iterations to clean up the temp variables which
+                is generated during execution. It may make the execution faster,
+                because the temp variable's shape maybe the same between two iterations. Default 100.
+
+                NOTES:
+                    1. If you fetch data when calling the 'run', the ParallelExecutor
+                       will clean up the temp variables at the end of the current iteration.
+                    2. In some NLP model, it may cause the GPU memory is insufficient,
+                       in this case, you should reduce `num_iteration_per_drop_scope`.
+              )DOC");
+
   exec_strategy.def_property(
       "use_experimental_executor",
       [](const ExecutionStrategy &self) {
@@ -721,20 +749,17 @@ All parameter, weight, gradient are variables in Paddle.
     BuildStrategy allows the user to more preciously control how to
     build the SSA Graph in ParallelExecutor by setting the property.
 
-    The available properties include:
-        reduce_strategy (str): There are two reduce strategies, 'AllReduce'
-            and 'Reduce'. If you want that all parameters will be optimized
-            on all devices, you can choose 'AllReduce'; if you choose
-            'Reduce', all parameters will be evenly allocated to different
-            devices for optimization, and then broadcast the optimized
-            parameter to other devices. Default 'AllReduce'.
-        gradient_scale_strategy (str): There are two ways of defining loss@grad,
-            'CoeffNumDevice' and 'Customized'. By default, ParallelExecutor
-            sets the loss@grad according to the number of devices. If you want
-            to customize loss@grad, you can choose 'Customized'.
-            Default 'CoeffNumDevice'.
-        debug_graphviz_path (str): Whether to write the SSA Graph to file in the
-            form of graphviz. It is useful for debugging. Default "".
+    Examples:
+        .. code-block:: python
+
+          build_strategy = fluid.BuildStrategy()
+          build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
+
+          train_exe = fluid.ParallelExecutor(use_cuda=True,
+                                             loss_name=loss.name,
+                                             build_strategy=build_strategy)
+
+          train_loss, = train_exe.run([loss.name], feed=feed_dict)
 )DOC");
 
   py::enum_<BuildStrategy::ReduceStrategy>(build_strategy, "ReduceStrategy")
@@ -753,31 +778,51 @@ All parameter, weight, gradient are variables in Paddle.
           [](const BuildStrategy &self) { return self.reduce_; },
           [](BuildStrategy &self, BuildStrategy::ReduceStrategy strategy) {
             self.reduce_ = strategy;
-          })
+          },
+          R"DOC(The type is STR, there are two reduce strategies in ParallelExecutor,
+                  'AllReduce' and 'Reduce'. If you want that all the parameters'
+                  optimization are done on all devices independently, you should choose 'AllReduce';
+                  if you choose 'Reduce', all the parameters' optimization will be evenly distributed
+                  to different devices, and then broadcast the optimized parameter to other devices.
+                  In some models, `Reduce` is faster. Default 'AllReduce'. )DOC")
       .def_property(
           "gradient_scale_strategy",
           [](const BuildStrategy &self) { return self.gradient_scale_; },
           [](BuildStrategy &self,
              BuildStrategy::GradientScaleStrategy strategy) {
             self.gradient_scale_ = strategy;
-          })
+          },
+          R"DOC(The type is STR, there are three ways of defining :math:`loss@grad` in
+                   ParallelExecutor, 'CoeffNumDevice', 'One' and 'Customized'. By default,
+                   ParallelExecutor sets the :math:`loss@grad` according to the number of devices.
+                   If you want to customize :math:`loss@grad`, you can choose 'Customized'.
+                   Default 'CoeffNumDevice'.)DOC")
       .def_property(
           "debug_graphviz_path",
           [](const BuildStrategy &self) { return self.debug_graphviz_path_; },
           [](BuildStrategy &self, const std::string &path) {
             self.debug_graphviz_path_ = path;
-          })
+          },
+          R"DOC(The type is STR, debug_graphviz_path indicate the path that
+                    writing the SSA Graph to file in the form of graphviz, you.
+                    It is useful for debugging. Default "")DOC")
       .def_property(
           "enable_data_balance",
           [](const BuildStrategy &self) { return self.enable_data_balance_; },
-          [](BuildStrategy &self, bool b) { self.enable_data_balance_ = b; })
-      .def_property("fuse_elewise_add_act_ops",
+          [](BuildStrategy &self, bool b) {
+            self.enable_data_balance_ = b;
+          })  // FIXME(chengudo): enable_data_balance seems not important
+      .def_property(
+          "fuse_elewise_add_act_ops",
           [](const BuildStrategy &self) {
             return self.fuse_elewise_add_act_ops_;
           },
           [](BuildStrategy &self, bool b) {
             self.fuse_elewise_add_act_ops_ = b;
-                    })
+          },
+          R"DOC(The type is BOOL, fuse_elewise_add_act_ops indicate whether
+                     to fuse elementwise_add_op and activation_op,
+                     it may make the execution faster. Default False)DOC")
       .def("_create_passes_from_strategy",
            [](BuildStrategy &self) -> std::shared_ptr<ir::PassBuilder> {
              return self.CreatePassesFromStrategy();

python/paddle/fluid/parallel_executor.py

@@ -31,15 +31,32 @@ BuildStrategy = core.ParallelExecutor.BuildStrategy
 
 class ParallelExecutor(object):
     """
-    ParallelExecutor can run program in parallel.
+    ParallelExecutor is designed for data parallelism, which focuses on distributing
+    the data across different nodes and every node operates on the data in parallel.
+    If you use ParallelExecutor to run the current program on GPU, the node means GPU
+    device, and ParallelExecutor will get the available GPU device automatically on
+    the current machine. If you use ParallelExecutor to run the current program on CPU,
+    the node means the CPU device, and you can specify the CPU device number by adding
+    'CPU_NUM' environment variable, for example 'CPU_NUM=4', if the environment variable
+    is not found, ParallelExecutor will call `multiprocessing.cpu_count` to get the number
+    of CPUs in the system.
 
     Args:
         use_cuda (bool): Whether to use CUDA or not.
         loss_name (str): The loss name must set in training. Default None.
         main_program (Program): The program that need to run, if not provided,
             then default_main_program will be used. Default None.
-        share_vars_from(ParallelExecutor): If provied, it will share variables
+        share_vars_from(ParallelExecutor): If provide, it will share variables
             from the specified ParallelExecutor. Default None.
+        exec_strategy(ExecutionStrategy): exec_strategy is used to control how to run
+            the program in ParallelExecutor, for example how many threads are used to
+            execute the program, how many iterations to clean up the temp variables
+            which is generated during execution. For more information, please refer
+            to fluid.ExecutionStrategy. Default None.
+        build_strategy(BuildStrategy): build_strategy is used to control how to
+            build the SSA Graph in ParallelExecutor by setting the property,
+            for example reduce_strategy, gradient_scale_strategy. For more information,
+            please refer to fluid.BuildStrategy. Default None.
         num_trainers(int): If greater than 1, NCCL will be initialized with
             multiple rank of nodes, each node should have same number of GPUs.
             Distributed training will be enabled then. Default 1.